[{"data":1,"prerenderedAt":8731},["ShallowReactive",2],{"lesson:\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots":3,"course-wordcounts":2995,"ref-card-index":3907,"nav:natural-language-processing":8588,"tikz:ed02853a3bedcb55ad7f3d959552eb3712f37413b7546c80bc7579a3b44b09a0":8726,"tikz:9a4113ef1e808d87ae7d6f586f047d3bfde36194667ff4e2ea423d07b1a3e89c":8727,"tikz:8e62a10ce1d0371b7585e33f4dc0c6f8e8f9e8dee9e0a46d16ebd68d4e322067":8728,"tikz:f7e80ba191acc5431d8432f20bb8c01fed9876ec2e55ad35c25422a15091c51c":8729,"tikz:9271499874b57163765a17654d6647d60ceee40608fa3c4ffcb5d0d6de33b782":8730},{"id":4,"title":5,"blurb":6,"body":7,"brief":2965,"category":2966,"description":2967,"draft":2968,"extension":2969,"meta":2970,"module":2972,"navigation":2973,"path":2974,"practice":2975,"rawbody":2976,"readingTime":2977,"seo":2982,"sources":2983,"status":2990,"stem":2991,"summary":2992,"topics":2993,"__hash__":2994},"course\u002F09.natural-language-processing\u002F07.applications\u002F05.dialogue-and-chatbots.md","Dialogue and Chatbots","",{"type":8,"value":9,"toc":2950},"minimark",[10,48,51,56,67,89,92,97,113,134,138,153,257,276,280,296,326,330,358,372,376,379,382,404,408,424,432,442,494,593,607,611,626,704,1216,1300,1633,1636,1663,1667,1694,1732,1735,1742,1747,1754,1781,1787,1794,1798,1814,1817,1933,1944,1963,1986,1989,2085,2120,2123,2234,2691,2719,2745,2753,2946],[11,12,13,14,18,19,22,23,34,35,38,39,42,43,47],"p",{},"Conversation is the first kind of language a child learns and the kind adults use\nmost: ordering lunch, booking a flight, arguing about the weather. For a machine it\nis one of the hardest interfaces to build. A ",[15,16,17],"strong",{},"dialogue\nsystem"," — a ",[15,20,21],{},"conversational agent"," — communicates with a person in natural\nlanguage, and the field splits its programs into two kinds by what they are trying\nto do.",[24,25,26],"sup",{},[27,28,33],"a",{"href":29,"ariaDescribedBy":30,"dataFootnoteRef":6,"id":32},"#user-content-fn-jm-open",[31],"footnote-label","user-content-fnref-jm-open","1"," ",[15,36,37],{},"Task-oriented"," agents use conversation to complete something:\nthe assistants in Siri, Alexa, and Google Assistant that set alarms, find\nrestaurants, and book flights. ",[15,40,41],{},"Chatbots"," are built for the open-ended talk\nitself — mimicking the unstructured ",[44,45,46],"q",{},"chat"," of informal human interaction, mostly\nfor entertainment but also to make task agents feel less mechanical. The two goals\nrequire different machinery; the modern LLM assistant collapses the split.",[11,49,50],{},"Building either kind of system requires understanding what is being imitated.\nHuman conversation is a joint activity with more structure than it\nappears to have, and every property of it is a design constraint on the machine.",[52,53,55],"h2",{"id":54},"properties-of-human-conversation","Properties of human conversation",[11,57,58,59],{},"Consider a fragment of an actual phone call between a human travel agent (A) and a\nhuman client (C), the running example throughout Jurafsky and Martin's\ntreatment:",[24,60,61],{},[27,62,66],{"href":63,"ariaDescribedBy":64,"dataFootnoteRef":6,"id":65},"#user-content-fn-jm-props",[31],"user-content-fnref-jm-props","2",[68,69,70],"blockquote",{},[11,71,72,73,76,77,79,80,82,83,85,86,88],{},"C₁: … I need to travel in May.",[74,75],"br",{},"\nA₂: And, what day in May did you want to travel?",[74,78],{},"\nC₃: OK, uh, I need to be there for a meeting that's from the 12th to the 15th.",[74,81],{},"\nA₄: And you're flying into what city?",[74,84],{},"\nC₅: Seattle.",[74,87],{},"\nA₆: And what time would you like to leave Pittsburgh?",[11,90,91],{},"Every feature of this ordinary exchange is a problem for a machine. Four\nproperties matter most.",[93,94,96],"h3",{"id":95},"turns-and-turn-taking","Turns and turn-taking",[11,98,99,100,103,104,107,108,112],{},"A dialogue is a sequence of ",[15,101,102],{},"turns"," — C₁, A₂, C₃, and so on — each a single\ncontribution from one speaker. A turn can be a word (",[44,105,106],{},"Seattle",") or several\nsentences. The ",[109,110,111],"em",{},"timing"," is precise: speakers hand the floor back and\nforth with almost no gap and almost no overlap, because a listener can predict\nwhen the current speaker is about to finish.",[11,114,115,116,119,120,123,124,127,128],{},"For a spoken system this predictive handoff becomes two hard problems. The machine\nmust know when to ",[109,117,118],{},"stop"," talking — the user may interrupt, or begin a correction\nmid-sentence — and it must know when to ",[109,121,122],{},"start",", detecting that the user has\nfinished so it can process and reply. That second problem, ",[15,125,126],{},"endpointing"," or\nendpoint detection, is genuinely difficult: people pause in the middle of turns,\nand background noise blurs the boundary between a pause and an ending.",[24,129,130],{},[27,131,66],{"href":63,"ariaDescribedBy":132,"dataFootnoteRef":6,"id":133},[31],"user-content-fnref-jm-props-2",[93,135,137],{"id":136},"speech-acts","Speech acts",[11,139,140,141,144,145,148,149,152],{},"The central observation, due originally to Wittgenstein and worked out by Austin,\nis that each utterance in a dialogue is an ",[15,142,143],{},"action"," performed by the speaker — a\n",[15,146,147],{},"speech act"," (or ",[15,150,151],{},"dialogue act","). One common taxonomy has four classes:",[154,155,156,175],"table",{},[157,158,159],"thead",{},[160,161,162,166,169,172],"tr",{},[163,164,165],"th",{},"Speech act",[163,167,168],{},"What the speaker does",[163,170,171],{},"Verbs",[163,173,174],{},"In the example",[176,177,178,198,217,235],"tbody",{},[160,179,180,186,189,192],{},[181,182,183],"td",{},[15,184,185],{},"Constative",[181,187,188],{},"commits to something being the case",[181,190,191],{},"answering, claiming, confirming, stating",[181,193,194,195],{},"C₁ ",[44,196,197],{},"I need to travel in May",[160,199,200,205,208,211],{},[181,201,202],{},[15,203,204],{},"Directive",[181,206,207],{},"attempts to get the hearer to do something",[181,209,210],{},"asking, requesting, ordering, inviting",[181,212,213,214],{},"A₂ ",[44,215,216],{},"what day did you want to travel?",[160,218,219,224,227,230],{},[181,220,221],{},[15,222,223],{},"Commissive",[181,225,226],{},"commits the speaker to a future course of action",[181,228,229],{},"promising, planning, betting, vowing",[181,231,232],{},[44,233,234],{},"I'll take the first one",[160,236,237,242,245,248],{},[181,238,239],{},[15,240,241],{},"Acknowledgment",[181,243,244],{},"expresses an attitude toward the hearer",[181,246,247],{},"greeting, thanking, apologizing",[181,249,250,253,254],{},[44,251,252],{},"OK",", ",[44,255,256],{},"thank you",[11,258,259,260,263,264,267,268,271,272,275],{},"A user asking a machine to do something (",[44,261,262],{},"Turn up the music",") issues a\n",[15,265,266],{},"directive","; a question that expects an answer (A₂) is also a directive, a very\npolite command to answer. Stating a constraint (C₁) is a ",[15,269,270],{},"constative",". The\nspeech act names the ",[109,273,274],{},"intention"," behind the words, and recovering it — not just\nthe surface string — is what a dialogue system must do.",[93,277,279],{"id":278},"grounding","Grounding",[11,281,282,283,286,287,289,290],{},"A dialogue is not a stack of independent speech acts but a collective act, and\npartners in a collective act must keep track of what they mutually agree on, the\n",[15,284,285],{},"common ground",". They build it by ",[15,288,278],{},": acknowledging that the hearer\nhas understood the speaker, the conversational equivalent of an ACK confirming\nreceipt on a data channel.",[24,291,292],{},[27,293,66],{"href":63,"ariaDescribedBy":294,"dataFootnoteRef":6,"id":295},[31],"user-content-fnref-jm-props-3",[11,297,298,299,306,307,309,310,313,314,317,318,321,322,325],{},"Humans ground constantly. A₆ (",[44,300,301,302,305],{},"And what time would you like to leave ",[109,303,304],{},"Pittsburgh","?",") repeats ",[44,308,304],{}," to show it was heard; the ",[44,311,312],{},"And"," that opens\nso many of the agent's turns signals that the new question is ",[109,315,316],{},"in addition"," to\nthe last, implying the previous answer was understood. A dialogue system that never\ngrounds — that answers questions without any signal of understanding — feels\nbroken even when it is technically correct, which is why the choice between\n",[109,319,320],{},"explicit"," and ",[109,323,324],{},"implicit"," confirmation, later in this lesson, matters.",[93,327,329],{"id":328},"dialogue-structure-adjacency-pairs","Dialogue structure: adjacency pairs",[11,331,332,333,336,337,340,341,344,345,353,354,357],{},"Conversation has local structure. Certain speech acts set up an expectation for a\nparticular next act: a QUESTION sets up an expectation for an ANSWER, a PROPOSAL\nfor an ACCEPTANCE or REJECTION, a COMPLIMENT for a DOWNPLAYER. These two-part\ntemplates are ",[15,334,335],{},"adjacency pairs",", a ",[15,338,339],{},"first pair part"," raising the expectation\nand a ",[15,342,343],{},"second pair part"," satisfying it.",[24,346,347],{},[27,348,352],{"href":349,"ariaDescribedBy":350,"dataFootnoteRef":6,"id":351},"#user-content-fn-jm-adj",[31],"user-content-fnref-jm-adj","3"," The expectations help a system\ndecide what to do: after it asks a question, the next user turn is ",[109,355,356],{},"supposed"," to\nbe an answer.",[11,359,360,361,18,364,367,368,371],{},"The two halves need not be adjacent. A ",[15,362,363],{},"side sequence",[15,365,366],{},"subdialogue"," — can\nopen between them; the common case is a ",[15,369,370],{},"clarification question"," when the machine\nmis-hears part of the user's turn.",[373,374],"tikz-figure",{"hash":375},"ed02853a3bedcb55ad7f3d959552eb3712f37413b7546c80bc7579a3b44b09a0",[11,377,378],{},"These properties — turns, speech acts, grounding, dialogue structure, and the\ninferences a speaker expects the hearer to draw — are what make natural\nconversation hard to build, and most of them are still open research problems. The\ntwo traditions below each attack a different subset.",[52,380,41],{"id":381},"chatbots",[11,383,384,385,387,388,391,392,395,396],{},"The simplest dialogue systems are ",[15,386,381],{},": systems that carry on extended\nconversations meant to mimic the unstructured chat of informal human interaction.\nTheir goals are entertainment and engagement rather than task completion, and their\narchitectures fall into two classes — ",[15,389,390],{},"rule-based"," systems, the early influential\nELIZA and PARRY, and ",[15,393,394],{},"corpus-based"," systems, which mine large datasets of\nhuman-human conversation.",[24,397,398],{},[27,399,403],{"href":400,"ariaDescribedBy":401,"dataFootnoteRef":6,"id":402},"#user-content-fn-jm-chat",[31],"user-content-fnref-jm-chat","4",[93,405,407],{"id":406},"the-rule-based-tradition-eliza","The rule-based tradition: ELIZA",[11,409,410,411,419,420,423],{},"ELIZA, written by Joseph Weizenbaum in 1966, is the most important chatbot in the\nhistory of the field, and it works by nothing more than pattern matching.",[24,412,413],{},[27,414,418],{"href":415,"ariaDescribedBy":416,"dataFootnoteRef":6,"id":417},"#user-content-fn-jm-eliza",[31],"user-content-fnref-jm-eliza","5","\nIt simulates a Rogerian psychologist — a style of therapy in which the therapist\nreflects the patient's statements back as questions, and can, as Weizenbaum noted,\n",[44,421,422],{},"assume the pose of knowing almost nothing of the real world."," That pose lets a\nmachine with no world model sustain a conversation.",[11,425,426,427,431],{},"ELIZA is built from pattern\u002Ftransform rules, the same\n",[27,428,430],{"href":429},"\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization","regular-expression","\nmachinery introduced at the start of the course. A rule pairs a pattern with a\ntransform:",[433,434,439],"pre",{"className":435,"code":437,"language":438},[436],"language-text","(0 YOU 0 ME)                       [pattern]\n->\n(WHAT MAKES YOU THINK I 3 YOU)     [transform]\n","text",[440,441,437],"code",{"__ignoreMap":6},[11,443,444,445,448,449,451,452,454,455,458,459,462,463,466,467,470,471,474,475,478,479,481,482,485,486,489,490,493],{},"Here ",[440,446,447],{},"0"," is a Kleene star (match anything) and the digits in the transform index\nthe matched constituents, so ",[440,450,352],{}," refers to the material captured by the second ",[440,453,447],{},".\nThe rule turns ",[109,456,457],{},"You hate me"," into ",[109,460,461],{},"WHAT MAKES YOU THINK I HATE YOU",". Each rule is\nattached to a ",[15,464,465],{},"keyword"," that might appear in the user's sentence, and keywords\ncarry a ",[15,468,469],{},"rank",": specific words (",[109,472,473],{},"everybody",") outrank general ones (",[109,476,477],{},"I","), because\nsomeone using a universal like ",[44,480,473],{}," is usually referring to something quite\nspecific worth probing. The algorithm finds the highest-ranked keyword in the\ninput and fires its best-matching rule; if nothing matches, it falls back to a\nnon-committal ",[44,483,484],{},"PLEASE GO ON"," or ",[44,487,488],{},"THAT'S VERY INTERESTING",". One trick gives\nthe illusion of memory: whenever ",[44,491,492],{},"my"," is the top keyword, ELIZA stores a transform\nof the sentence on a queue and, later, if nothing else matches, returns the oldest\nstored entry — so it seems to circle back to something the user said turns ago.",[433,495,499],{"className":496,"code":497,"language":498,"meta":6,"style":6},"language-algorithm shiki shiki-themes Vesper Light - Orange Boost (Quick Open Adjusted) vesper","caption: $\\textsc{Eliza-Generator}(sentence)$ — pattern\u002Ftransform response, per Weizenbaum\ninput: user $sentence$, keyword-ranked rule set, a memory queue\nfind the word $w$ in $sentence$ with the highest keyword rank\nif $w$ exists then\n  choose the highest-ranked rule $r$ for $w$ that matches $sentence$\n  $response \\gets$ apply the transform in $r$ to $sentence$\n  if $w$ is \"my\" then\n    $future \\gets$ apply a memory-list transform to $sentence$\n    push $future$ onto the memory queue\nelse\n  if the memory queue is non-empty then\n    $response \\gets$ pop the oldest entry from the memory queue\n  else\n    $response \\gets$ apply the NONE-keyword transform to $sentence$\nreturn $response$\n","algorithm",[440,500,501,509,515,521,527,533,539,545,551,557,563,569,575,581,587],{"__ignoreMap":6},[502,503,506],"span",{"class":504,"line":505},"line",1,[502,507,508],{},"caption: $\\textsc{Eliza-Generator}(sentence)$ — pattern\u002Ftransform response, per Weizenbaum\n",[502,510,512],{"class":504,"line":511},2,[502,513,514],{},"input: user $sentence$, keyword-ranked rule set, a memory queue\n",[502,516,518],{"class":504,"line":517},3,[502,519,520],{},"find the word $w$ in $sentence$ with the highest keyword rank\n",[502,522,524],{"class":504,"line":523},4,[502,525,526],{},"if $w$ exists then\n",[502,528,530],{"class":504,"line":529},5,[502,531,532],{},"  choose the highest-ranked rule $r$ for $w$ that matches $sentence$\n",[502,534,536],{"class":504,"line":535},6,[502,537,538],{},"  $response \\gets$ apply the transform in $r$ to $sentence$\n",[502,540,542],{"class":504,"line":541},7,[502,543,544],{},"  if $w$ is \"my\" then\n",[502,546,548],{"class":504,"line":547},8,[502,549,550],{},"    $future \\gets$ apply a memory-list transform to $sentence$\n",[502,552,554],{"class":504,"line":553},9,[502,555,556],{},"    push $future$ onto the memory queue\n",[502,558,560],{"class":504,"line":559},10,[502,561,562],{},"else\n",[502,564,566],{"class":504,"line":565},11,[502,567,568],{},"  if the memory queue is non-empty then\n",[502,570,572],{"class":504,"line":571},12,[502,573,574],{},"    $response \\gets$ pop the oldest entry from the memory queue\n",[502,576,578],{"class":504,"line":577},13,[502,579,580],{},"  else\n",[502,582,584],{"class":504,"line":583},14,[502,585,586],{},"    $response \\gets$ apply the NONE-keyword transform to $sentence$\n",[502,588,590],{"class":504,"line":589},15,[502,591,592],{},"return $response$\n",[11,594,595,596,602,603,606],{},"ELIZA understands nothing, yet it fooled people — the enduring lesson.",[24,597,598],{},[27,599,418],{"href":415,"ariaDescribedBy":600,"dataFootnoteRef":6,"id":601},[31],"user-content-fnref-jm-eliza-2","\nUsers confided in it, Weizenbaum's own secretary asked him to leave\nthe room while she ",[44,604,605],{},"talked"," to it, and some kept believing it understood them after\nbeing shown the rules. A few years later PARRY added a model of mental state —\naffect variables for fear and anger — and became the first program to pass a\nTuring test, when psychiatrists could not distinguish its transcripts from those of\nreal patients. ELIZA's pattern\u002Faction framework survives today in tools like ALICE,\nand it is the baseline for everything that follows.",[93,608,610],{"id":609},"corpus-based-chatbots","Corpus-based chatbots",[11,612,613,614,616,617,625],{},"Instead of hand-built rules, ",[15,615,394],{}," chatbots mine conversations — hundreds\nof millions or billions of words of them.",[24,618,619],{},[27,620,624],{"href":621,"ariaDescribedBy":622,"dataFootnoteRef":6,"id":623},"#user-content-fn-jm-corpus",[31],"user-content-fnref-jm-corpus","6"," Sources include transcribed\ntelephone speech (the Switchboard corpus), movie dialogue, and crowdsourced\nconversation sets, supplemented by pseudo-conversations scraped from Twitter,\nReddit, and Weibo. Given the user's turn in context, these systems produce a\nresponse in one of two ways.",[11,627,628,631,632,656,657,675,676,694,695,698,699,703],{},[15,629,630],{},"Response by retrieval"," treats the user's turn as a query ",[502,633,636],{"className":634},[635],"katex",[502,637,641],{"className":638,"ariaHidden":640},[639],"katex-html","true",[502,642,645,650],{"className":643},[644],"base",[502,646],{"className":647,"style":649},[648],"strut","height:0.625em;vertical-align:-0.1944em;",[502,651,44],{"className":652,"style":655},[653,654],"mord","mathnormal","margin-right:0.0359em;"," and returns the\nmost appropriate turn ",[502,658,660],{"className":659},[635],[502,661,663],{"className":662,"ariaHidden":640},[639],[502,664,666,670],{"className":665},[644],[502,667],{"className":668,"style":669},[648],"height:0.4306em;",[502,671,674],{"className":672,"style":673},[653,654],"margin-right:0.0278em;","r"," from a corpus ",[502,677,679],{"className":678},[635],[502,680,682],{"className":681,"ariaHidden":640},[639],[502,683,685,689],{"className":684},[644],[502,686],{"className":687,"style":688},[648],"height:0.6833em;",[502,690,693],{"className":691,"style":692},[653,654],"margin-right:0.0715em;","C"," of conversations, scoring each candidate\nby similarity and picking the best. The classic form uses tf-idf cosine; the neural\nform is a ",[15,696,697],{},"bi-encoder",", two separately trained encoders — one for the query, one\nfor the candidate — whose dot product is the score, the same\n",[27,700,702],{"href":701},"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering","dense-retrieval","\nidea question answering uses:",[502,705,708],{"className":706},[707],"katex-display",[502,709,711],{"className":710},[635],[502,712,714,768,921,1028,1152],{"className":713,"ariaHidden":640},[639],[502,715,717,721,730,735,738,743,748,751,756,760,765],{"className":716},[644],[502,718],{"className":719,"style":720},[648],"height:1em;vertical-align:-0.25em;",[502,722,725],{"className":723},[724],"mop",[502,726,729],{"className":727},[653,728],"mathrm","response",[502,731,734],{"className":732},[733],"mopen","(",[502,736,44],{"className":737,"style":655},[653,654],[502,739,742],{"className":740},[741],"mpunct",",",[502,744],{"className":745,"style":747},[746],"mspace","margin-right:0.1667em;",[502,749,693],{"className":750,"style":692},[653,654],[502,752,755],{"className":753},[754],"mclose",")",[502,757],{"className":758,"style":759},[746],"margin-right:0.2778em;",[502,761,764],{"className":762},[763],"mrel","=",[502,766],{"className":767,"style":759},[746],[502,769,771,775,857,860,863,909,913,918],{"className":770},[644],[502,772],{"className":773,"style":774},[648],"height:1.6606em;vertical-align:-0.9661em;",[502,776,779],{"className":777},[724,778],"op-limits",[502,780,784,848],{"className":781},[782,783],"vlist-t","vlist-t2",[502,785,788,843],{"className":786},[787],"vlist-r",[502,789,792,820],{"className":790,"style":669},[791],"vlist",[502,793,795,800],{"style":794},"top:-2.1612em;margin-left:0em;",[502,796],{"className":797,"style":799},[798],"pstrut","height:3em;",[502,801,807],{"className":802},[803,804,805,806],"sizing","reset-size6","size3","mtight",[502,808,810,813,817],{"className":809},[653,806],[502,811,674],{"className":812,"style":673},[653,654,806],[502,814,816],{"className":815},[763,806],"∈",[502,818,693],{"className":819,"style":692},[653,654,806],[502,821,823,826],{"style":822},"top:-3em;",[502,824],{"className":825,"style":799},[798],[502,827,828],{},[502,829,831,836,839],{"className":830},[724],[502,832,835],{"className":833,"style":834},[653,728],"margin-right:0.0139em;","arg",[502,837],{"className":838,"style":747},[746],[502,840,842],{"className":841},[653,728],"max",[502,844,847],{"className":845},[846],"vlist-s","​",[502,849,851],{"className":850},[787],[502,852,855],{"className":853,"style":854},[791],"height:0.9661em;",[502,856],{},[502,858],{"className":859,"style":759},[746],[502,861],{"className":862,"style":747},[746],[502,864,866,870],{"className":865},[653],[502,867,869],{"className":868},[653,654],"h",[502,871,874],{"className":872},[873],"msupsub",[502,875,877,900],{"className":876},[782,783],[502,878,880,897],{"className":879},[787],[502,881,884],{"className":882,"style":883},[791],"height:0.1514em;",[502,885,887,891],{"style":886},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[502,888],{"className":889,"style":890},[798],"height:2.7em;",[502,892,894],{"className":893},[803,804,805,806],[502,895,44],{"className":896,"style":655},[653,654,806],[502,898,847],{"className":899},[846],[502,901,903],{"className":902},[787],[502,904,907],{"className":905,"style":906},[791],"height:0.2861em;",[502,908],{},[502,910],{"className":911,"style":912},[746],"margin-right:0.2222em;",[502,914,917],{"className":915},[916],"mbin","⋅",[502,919],{"className":920,"style":912},[746],[502,922,924,928,969,972,976,979,1019,1022,1025],{"className":923},[644],[502,925],{"className":926,"style":927},[648],"height:0.9805em;vertical-align:-0.2861em;",[502,929,931,934],{"className":930},[653],[502,932,869],{"className":933},[653,654],[502,935,937],{"className":936},[873],[502,938,940,960],{"className":939},[782,783],[502,941,943,957],{"className":942},[787],[502,944,946],{"className":945,"style":883},[791],[502,947,948,951],{"style":886},[502,949],{"className":950,"style":890},[798],[502,952,954],{"className":953},[803,804,805,806],[502,955,674],{"className":956,"style":673},[653,654,806],[502,958,847],{"className":959},[846],[502,961,963],{"className":962},[787],[502,964,967],{"className":965,"style":966},[791],"height:0.15em;",[502,968],{},[502,970,742],{"className":971},[741],[502,973],{"className":974,"style":975},[746],"margin-right:2em;",[502,977],{"className":978,"style":747},[746],[502,980,982,985],{"className":981},[653],[502,983,869],{"className":984},[653,654],[502,986,988],{"className":987},[873],[502,989,991,1011],{"className":990},[782,783],[502,992,994,1008],{"className":993},[787],[502,995,997],{"className":996,"style":883},[791],[502,998,999,1002],{"style":886},[502,1000],{"className":1001,"style":890},[798],[502,1003,1005],{"className":1004},[803,804,805,806],[502,1006,44],{"className":1007,"style":655},[653,654,806],[502,1009,847],{"className":1010},[846],[502,1012,1014],{"className":1013},[787],[502,1015,1017],{"className":1016,"style":906},[791],[502,1018],{},[502,1020],{"className":1021,"style":759},[746],[502,1023,764],{"className":1024},[763],[502,1026],{"className":1027,"style":759},[746],[502,1029,1031,1035,1082,1085,1088,1091,1094,1097,1100,1103,1143,1146,1149],{"className":1030},[644],[502,1032],{"className":1033,"style":1034},[648],"height:1.0361em;vertical-align:-0.2861em;",[502,1036,1038,1045],{"className":1037},[653],[502,1039,1041],{"className":1040},[653],[502,1042,1044],{"className":1043},[653,728],"BERT",[502,1046,1048],{"className":1047},[873],[502,1049,1051,1074],{"className":1050},[782,783],[502,1052,1054,1071],{"className":1053},[787],[502,1055,1058],{"className":1056,"style":1057},[791],"height:0.3283em;",[502,1059,1061,1064],{"style":1060},"top:-2.55em;margin-right:0.05em;",[502,1062],{"className":1063,"style":890},[798],[502,1065,1067],{"className":1066},[803,804,805,806],[502,1068,1070],{"className":1069},[653,654,806],"Q",[502,1072,847],{"className":1073},[846],[502,1075,1077],{"className":1076},[787],[502,1078,1080],{"className":1079,"style":906},[791],[502,1081],{},[502,1083,734],{"className":1084},[733],[502,1086,44],{"className":1087,"style":655},[653,654],[502,1089,755],{"className":1090},[754],[502,1092,742],{"className":1093},[741],[502,1095],{"className":1096,"style":759},[746],[502,1098],{"className":1099,"style":759},[746],[502,1101],{"className":1102,"style":747},[746],[502,1104,1106,1109],{"className":1105},[653],[502,1107,869],{"className":1108},[653,654],[502,1110,1112],{"className":1111},[873],[502,1113,1115,1135],{"className":1114},[782,783],[502,1116,1118,1132],{"className":1117},[787],[502,1119,1121],{"className":1120,"style":883},[791],[502,1122,1123,1126],{"style":886},[502,1124],{"className":1125,"style":890},[798],[502,1127,1129],{"className":1128},[803,804,805,806],[502,1130,674],{"className":1131,"style":673},[653,654,806],[502,1133,847],{"className":1134},[846],[502,1136,1138],{"className":1137},[787],[502,1139,1141],{"className":1140,"style":966},[791],[502,1142],{},[502,1144],{"className":1145,"style":759},[746],[502,1147,764],{"className":1148},[763],[502,1150],{"className":1151,"style":759},[746],[502,1153,1155,1158,1203,1206,1209,1212],{"className":1154},[644],[502,1156],{"className":1157,"style":720},[648],[502,1159,1161,1167],{"className":1160},[653],[502,1162,1164],{"className":1163},[653],[502,1165,1044],{"className":1166},[653,728],[502,1168,1170],{"className":1169},[873],[502,1171,1173,1195],{"className":1172},[782,783],[502,1174,1176,1192],{"className":1175},[787],[502,1177,1179],{"className":1178,"style":1057},[791],[502,1180,1181,1184],{"style":1060},[502,1182],{"className":1183,"style":890},[798],[502,1185,1187],{"className":1186},[803,804,805,806],[502,1188,1191],{"className":1189,"style":1190},[653,654,806],"margin-right:0.0077em;","R",[502,1193,847],{"className":1194},[846],[502,1196,1198],{"className":1197},[787],[502,1199,1201],{"className":1200,"style":966},[791],[502,1202],{},[502,1204,734],{"className":1205},[733],[502,1207,674],{"className":1208,"style":673},[653,654],[502,1210,755],{"className":1211},[754],[502,1213,1215],{"className":1214},[653],".",[11,1217,1218,1221,1222,1226,1227,1283,1284,1299],{},[15,1219,1220],{},"Response by generation"," instead casts response production as an encoder-decoder\ntask — transducing the user's turn (plus context) into the system's turn, a machine\nlearning version of ELIZA. It is the same ",[27,1223,1225],{"href":1224},"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation","sequence-to-sequence\ntransduction"," used\nfor translation, generating each response token ",[502,1228,1230],{"className":1229},[635],[502,1231,1233],{"className":1232,"ariaHidden":640},[639],[502,1234,1236,1240],{"className":1235},[644],[502,1237],{"className":1238,"style":1239},[648],"height:0.5806em;vertical-align:-0.15em;",[502,1241,1243,1246],{"className":1242},[653],[502,1244,674],{"className":1245,"style":673},[653,654],[502,1247,1249],{"className":1248},[873],[502,1250,1252,1275],{"className":1251},[782,783],[502,1253,1255,1272],{"className":1254},[787],[502,1256,1259],{"className":1257,"style":1258},[791],"height:0.2806em;",[502,1260,1262,1265],{"style":1261},"top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;",[502,1263],{"className":1264,"style":890},[798],[502,1266,1268],{"className":1267},[803,804,805,806],[502,1269,1271],{"className":1270},[653,654,806],"t",[502,1273,847],{"className":1274},[846],[502,1276,1278],{"className":1277},[787],[502,1279,1281],{"className":1280,"style":966},[791],[502,1282],{}," by conditioning on the\nencoded query ",[502,1285,1287],{"className":1286},[635],[502,1288,1290],{"className":1289,"ariaHidden":640},[639],[502,1291,1293,1296],{"className":1292},[644],[502,1294],{"className":1295,"style":649},[648],[502,1297,44],{"className":1298,"style":655},[653,654]," and the response so far:",[502,1301,1303],{"className":1302},[707],[502,1304,1306],{"className":1305},[635],[502,1307,1309,1398],{"className":1308,"ariaHidden":640},[639],[502,1310,1312,1316,1389,1392,1395],{"className":1311},[644],[502,1313],{"className":1314,"style":1315},[648],"height:0.8444em;vertical-align:-0.15em;",[502,1317,1319,1355],{"className":1318},[653],[502,1320,1323],{"className":1321},[653,1322],"accent",[502,1324,1326],{"className":1325},[782],[502,1327,1329],{"className":1328},[787],[502,1330,1333,1341],{"className":1331,"style":1332},[791],"height:0.6944em;",[502,1334,1335,1338],{"style":822},[502,1336],{"className":1337,"style":799},[798],[502,1339,674],{"className":1340,"style":673},[653,654],[502,1342,1343,1346],{"style":822},[502,1344],{"className":1345,"style":799},[798],[502,1347,1351],{"className":1348,"style":1350},[1349],"accent-body","left:-0.1944em;",[502,1352,1354],{"className":1353},[653],"^",[502,1356,1358],{"className":1357},[873],[502,1359,1361,1381],{"className":1360},[782,783],[502,1362,1364,1378],{"className":1363},[787],[502,1365,1367],{"className":1366,"style":1258},[791],[502,1368,1369,1372],{"style":1261},[502,1370],{"className":1371,"style":890},[798],[502,1373,1375],{"className":1374},[803,804,805,806],[502,1376,1271],{"className":1377},[653,654,806],[502,1379,847],{"className":1380},[846],[502,1382,1384],{"className":1383},[787],[502,1385,1387],{"className":1386,"style":966},[791],[502,1388],{},[502,1390],{"className":1391,"style":759},[746],[502,1393,764],{"className":1394},[763],[502,1396],{"className":1397,"style":759},[746],[502,1399,1401,1405,1470,1473,1476,1481,1485,1488,1627,1630],{"className":1400},[644],[502,1402],{"className":1403,"style":1404},[648],"height:1.7161em;vertical-align:-0.9661em;",[502,1406,1408],{"className":1407},[724,778],[502,1409,1411,1462],{"className":1410},[782,783],[502,1412,1414,1459],{"className":1413},[787],[502,1415,1417,1440],{"className":1416,"style":669},[791],[502,1418,1419,1422],{"style":794},[502,1420],{"className":1421,"style":799},[798],[502,1423,1425],{"className":1424},[803,804,805,806],[502,1426,1428,1433,1436],{"className":1427},[653,806],[502,1429,1432],{"className":1430,"style":1431},[653,654,806],"margin-right:0.0269em;","w",[502,1434,816],{"className":1435},[763,806],[502,1437,1439],{"className":1438,"style":912},[653,654,806],"V",[502,1441,1442,1445],{"style":822},[502,1443],{"className":1444,"style":799},[798],[502,1446,1447],{},[502,1448,1450,1453,1456],{"className":1449},[724],[502,1451,835],{"className":1452,"style":834},[653,728],[502,1454],{"className":1455,"style":747},[746],[502,1457,842],{"className":1458},[653,728],[502,1460,847],{"className":1461},[846],[502,1463,1465],{"className":1464},[787],[502,1466,1468],{"className":1467,"style":854},[791],[502,1469],{},[502,1471],{"className":1472,"style":759},[746],[502,1474],{"className":1475,"style":747},[746],[502,1477,1480],{"className":1478,"style":1479},[653,654],"margin-right:0.1389em;","P",[502,1482],{"className":1483,"style":1484},[746],"margin-right:-0.1667em;",[502,1486],{"className":1487,"style":747},[746],[502,1489,1492,1497,1500,1503,1507,1510,1513,1516,1519,1522,1563,1566,1570,1573,1624],{"className":1490},[1491],"minner",[502,1493,734],{"className":1494,"style":1496},[733,1495],"delimcenter","top:0em;",[502,1498,1432],{"className":1499,"style":1431},[653,654],[502,1501],{"className":1502,"style":759},[746],[502,1504,1506],{"className":1505},[763],"∣",[502,1508],{"className":1509,"style":759},[746],[502,1511,44],{"className":1512,"style":655},[653,654],[502,1514,742],{"className":1515},[741],[502,1517],{"className":1518,"style":759},[746],[502,1520],{"className":1521,"style":747},[746],[502,1523,1525,1528],{"className":1524},[653],[502,1526,674],{"className":1527,"style":673},[653,654],[502,1529,1531],{"className":1530},[873],[502,1532,1534,1555],{"className":1533},[782,783],[502,1535,1537,1552],{"className":1536},[787],[502,1538,1541],{"className":1539,"style":1540},[791],"height:0.3011em;",[502,1542,1543,1546],{"style":1261},[502,1544],{"className":1545,"style":890},[798],[502,1547,1549],{"className":1548},[803,804,805,806],[502,1550,33],{"className":1551},[653,806],[502,1553,847],{"className":1554},[846],[502,1556,1558],{"className":1557},[787],[502,1559,1561],{"className":1560,"style":966},[791],[502,1562],{},[502,1564],{"className":1565,"style":747},[746],[502,1567,1569],{"className":1568},[1491],"…",[502,1571],{"className":1572,"style":747},[746],[502,1574,1576,1579],{"className":1575},[653],[502,1577,674],{"className":1578,"style":673},[653,654],[502,1580,1582],{"className":1581},[873],[502,1583,1585,1615],{"className":1584},[782,783],[502,1586,1588,1612],{"className":1587},[787],[502,1589,1591],{"className":1590,"style":1540},[791],[502,1592,1593,1596],{"style":1261},[502,1594],{"className":1595,"style":890},[798],[502,1597,1599],{"className":1598},[803,804,805,806],[502,1600,1602,1605,1609],{"className":1601},[653,806],[502,1603,1271],{"className":1604},[653,654,806],[502,1606,1608],{"className":1607},[916,806],"−",[502,1610,33],{"className":1611},[653,806],[502,1613,847],{"className":1614},[846],[502,1616,1618],{"className":1617},[787],[502,1619,1622],{"className":1620,"style":1621},[791],"height:0.2083em;",[502,1623],{},[502,1625,755],{"className":1626,"style":1496},[754,1495],[502,1628],{"className":1629,"style":747},[746],[502,1631,1215],{"className":1632},[653],[373,1634],{"hash":1635},"9a4113ef1e808d87ae7d6f586f047d3bfde36194667ff4e2ea423d07b1a3e89c",[11,1637,1638,1639,1642,1643,253,1646,1649,1650,1658,1659,1662],{},"Generation has a characteristic failure. A basic encoder-decoder, trained to\nmaximize response likelihood, learns that safe, generic replies fit almost any\ncontext, so it converges on ",[15,1640,1641],{},"bland",", repetitive turns — ",[44,1644,1645],{},"I'm OK",[44,1647,1648],{},"I don't know"," —\nthat are fluent but shut the exchange down.",[24,1651,1652],{},[27,1653,1657],{"href":1654,"ariaDescribedBy":1655,"dataFootnoteRef":6,"id":1656},"#user-content-fn-jm-bland",[31],"user-content-fnref-jm-bland","7"," The dullness is a\nconsequence of the objective, not the data: the most probable response is the\nleast informative one. Remedies include diversity-promoting beam search and\ntraining objectives, minimum-length constraints, and, because encoder-decoders\noptimize a single turn at a time and ignore the long-term shape of the\nconversation, reinforcement learning and adversarial training that reward turns\nmaking the ",[109,1660,1661],{},"whole"," dialogue more natural.",[52,1664,1666],{"id":1665},"task-oriented-dialogue-the-gus-architecture","Task-oriented dialogue: the GUS architecture",[11,1668,1669,1670,1215,1673,1681,1682,1685,1686,1689,1690,1693],{},"The other tradition helps a user get something done — reserve a flight, book a\nhotel — and almost all of it, from the 1977 GUS travel planner to today's\ncommercial assistants, is built around a ",[15,1671,1672],{},"frame",[24,1674,1675],{},[27,1676,1680],{"href":1677,"ariaDescribedBy":1678,"dataFootnoteRef":6,"id":1679},"#user-content-fn-jm-gus",[31],"user-content-fnref-jm-gus","8"," A frame is a knowledge\nstructure representing a kind of intention the system can extract from user\nsentences. It is a set of ",[15,1683,1684],{},"slots",", each with a semantic ",[15,1687,1688],{},"type"," and a set of\npossible ",[15,1691,1692],{},"values",", and a question template used to fill it.",[1695,1696,1698],"callout",{"type":1697},"definition",[11,1699,1700,1703,1704,1706,1707,1710,1711,1714,1715,1718,1719,253,1722,253,1725,1728,1729,1215],{},[15,1701,1702],{},"Definition (Frame)."," A collection of ",[15,1705,1684],{}," the system needs filled to\nperform a task, each slot constrained to values of a semantic type (a ",[440,1708,1709],{},"city","\nslot takes values like ",[109,1712,1713],{},"San Francisco","; a ",[440,1716,1717],{},"date"," slot is itself a frame with\n",[440,1720,1721],{},"month",[440,1723,1724],{},"day",[440,1726,1727],{},"weekday"," sub-slots). The set of frames for a domain is a\n",[15,1730,1731],{},"domain ontology",[373,1733],{"hash":1734},"8e62a10ce1d0371b7585e33f4dc0c6f8e8f9e8dee9e0a46d16ebd68d4e322067",[11,1736,1737,1738,1741],{},"The control architecture is ",[15,1739,1740],{},"frame-filling",". The system asks the question\nattached to each empty slot, records whatever the user supplies, and skips\nquestions for slots already filled. Because one user utterance can fill several\nslots at once —",[68,1743,1744],{},[11,1745,1746],{},"I want a flight from San Francisco to Denver one way leaving after five p.m. on\nTuesday.",[11,1748,1749,1750,1753],{},"— the system fills all of them and then asks only for what remains. Condition-action\nrules attached to slots handle defaults (fill the hotel-booking city from the flight\ndestination), and because different inputs must switch control among multiple frames\n(a fares frame, a route-information frame), GUS is at bottom a ",[15,1751,1752],{},"production-rule","\nsystem: different inputs fire different productions that fill different frames.",[11,1755,1756,1757,1760,1761,1764,1765,1768,1769,1772,1773,1776,1777,1780],{},"Filling a slot from a sentence means extracting three things: the ",[15,1758,1759],{},"domain"," (is\nthis about airlines or alarm clocks?), the user's ",[15,1762,1763],{},"intent"," (",[440,1766,1767],{},"SHOW-FLIGHTS",",\n",[440,1770,1771],{},"SET-ALARM","), and the ",[15,1774,1775],{},"slot fillers"," themselves. From ",[109,1778,1779],{},"Show me morning flights\nfrom Boston to San Francisco on Tuesday"," a system builds a representation like",[433,1782,1785],{"className":1783,"code":1784,"language":438},[436],"DOMAIN:      AIR-TRAVEL\nINTENT:      SHOW-FLIGHTS\nORIGIN-CITY: Boston\nDEST-CITY:   San Francisco\nDEPART-DATE: Tuesday\nDEPART-TIME: morning\n",[440,1786,1784],{"__ignoreMap":6},[11,1788,1789,1790,1793],{},"The original GUS did this extraction with handwritten rules — a ",[15,1791,1792],{},"semantic grammar","\nwhose non-terminals are slot names — and rule-based slot-filling, prized for high\nprecision, is still common in industry. It has the usual weaknesses of hand-built\nsystems: the grammars are expensive to write and suffer from recall problems,\nmissing the phrasings the designer did not anticipate.",[52,1795,1797],{"id":1796},"the-dialogue-state-architecture","The dialogue-state architecture",[11,1799,1800,1801,1804,1805,1813],{},"Modern research systems replace the rules with a more sophisticated,\nmachine-learned version of the same frame idea: the ",[15,1802,1803],{},"dialogue-state"," (or\nbelief-state) architecture.",[24,1806,1807],{},[27,1808,1812],{"href":1809,"ariaDescribedBy":1810,"dataFootnoteRef":6,"id":1811},"#user-content-fn-jm-ds",[31],"user-content-fnref-jm-ds","9"," It keeps the frame but wraps it in four\nlearned components, a pipeline that runs on every user turn.",[373,1815],{"hash":1816},"f7e80ba191acc5431d8432f20bb8c01fed9876ec2e55ad35c25422a15091c51c",[11,1818,1819,1822,1823,1825,1826,253,1829,253,1832,1835,1836,1839,1840,1843,1844,1215,1847,1855,1856,1860,1861,1864,1865,1905,1906,1921,1922,321,1925,1928,1929,1932],{},[15,1820,1821],{},"Natural language understanding (NLU)"," extracts the slot fillers and the user's\n",[15,1824,151],{}," from the turn. Dialogue acts fuse the speech-act idea with\ngrounding: a tag like ",[440,1827,1828],{},"inform",[440,1830,1831],{},"request",[440,1833,1834],{},"confirm",", or ",[440,1837,1838],{},"bye",", carried together\nwith its slot-value content. So a user might ",[440,1841,1842],{},"inform(food=Italian, near=museum)",",\nand the system later ",[440,1845,1846],{},"confirm(pricerange=moderate)",[24,1848,1849],{},[27,1850,1854],{"href":1851,"ariaDescribedBy":1852,"dataFootnoteRef":6,"id":1853},"#user-content-fn-jm-acts",[31],"user-content-fnref-jm-acts","10"," Slot-filling itself\nis ",[27,1857,1859],{"href":1858},"\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling","sequence labeling",":\npass the words through a contextual encoder like BERT and tag each with a ",[15,1862,1863],{},"BIO","\nlabel (",[502,1866,1868],{"className":1867},[635],[502,1869,1871,1895],{"className":1870,"ariaHidden":640},[639],[502,1872,1874,1878,1881,1885,1888,1892],{"className":1873},[644],[502,1875],{"className":1876,"style":1877},[648],"height:0.7278em;vertical-align:-0.0833em;",[502,1879,66],{"className":1880},[653],[502,1882,1884],{"className":1883},[653,654],"n",[502,1886],{"className":1887,"style":912},[746],[502,1889,1891],{"className":1890},[916],"+",[502,1893],{"className":1894,"style":912},[746],[502,1896,1898,1902],{"className":1897},[644],[502,1899],{"className":1900,"style":1901},[648],"height:0.6444em;",[502,1903,33],{"className":1904},[653]," tags for ",[502,1907,1909],{"className":1908},[635],[502,1910,1912],{"className":1911,"ariaHidden":640},[639],[502,1913,1915,1918],{"className":1914},[644],[502,1916],{"className":1917,"style":669},[648],[502,1919,1884],{"className":1920},[653,654]," slots — a ",[440,1923,1924],{},"B-",[440,1926,1927],{},"I-"," per slot plus a single ",[440,1930,1931],{},"O",").",[11,1934,1935,1936,1939,1940,1943],{},"The ",[15,1937,1938],{},"dialogue-state tracker"," determines the current state of the frame and the\nuser's most recent act; its main job is accumulation. The state is not\njust the slots named in the latest sentence; it is the ",[109,1941,1942],{},"entire"," frame so far,\nsummarizing every constraint the user has expressed across all turns:",[68,1945,1946],{},[11,1947,1948,1949,1951,1952,1955,1957,1958,1951,1960],{},"User: I'm looking for a cheaper restaurant",[74,1950],{},"\n    ",[440,1953,1954],{},"inform(price = cheap)",[74,1956],{},"\nUser: Thai food, somewhere downtown",[74,1959],{},[440,1961,1962],{},"inform(price = cheap, food = Thai, area = centre)",[11,1964,1965,1966,1969,1970,1973,1974,1977,1978,1981,1982,1985],{},"Notice the tracker carries ",[440,1967,1968],{},"price = cheap"," forward into the second turn even though\nthe user did not repeat it. A subtle special case is the ",[15,1971,1972],{},"user correction act"," —\nwhen a user re-states a mis-heard value (",[44,1975,1976],{},"I said BAL-ti-more, not Boston","), often\nwith exaggerated ",[15,1979,1980],{},"hyperarticulation",". Corrections are ",[109,1983,1984],{},"harder"," to\nrecognize than ordinary turns, and detecting them is its own classification problem.",[11,1987,1988],{},"To see the accumulation and a correction in one place, trace the frame across four\nturns of the air-travel task. The NLU tags each turn with a dialogue act and its\nslot-value content; the tracker folds that into the running frame — adding new slots,\noverwriting on a correction, and carrying the rest forward untouched.",[154,1990,1991,2007],{},[157,1992,1993],{},[160,1994,1995,1998,2001,2004],{},[163,1996,1997],{},"turn",[163,1999,2000],{},"user utterance",[163,2002,2003],{},"NLU act",[163,2005,2006],{},"frame after tracker update",[176,2008,2009,2028,2047,2066],{},[160,2010,2011,2013,2018,2023],{},[181,2012,33],{},[181,2014,2015],{},[109,2016,2017],{},"I want to fly from Boston",[181,2019,2020],{},[440,2021,2022],{},"inform(orig=Boston)",[181,2024,2025],{},[440,2026,2027],{},"orig=Boston",[160,2029,2030,2032,2037,2042],{},[181,2031,66],{},[181,2033,2034],{},[109,2035,2036],{},"to Denver",[181,2038,2039],{},[440,2040,2041],{},"inform(dest=Denver)",[181,2043,2044],{},[440,2045,2046],{},"orig=Boston, dest=Denver",[160,2048,2049,2051,2056,2061],{},[181,2050,352],{},[181,2052,2053],{},[109,2054,2055],{},"no, I said Austin",[181,2057,2058],{},[440,2059,2060],{},"correct(orig=Austin)",[181,2062,2063],{},[440,2064,2065],{},"orig=Austin, dest=Denver",[160,2067,2068,2070,2075,2080],{},[181,2069,403],{},[181,2071,2072],{},[109,2073,2074],{},"on Tuesday morning",[181,2076,2077],{},[440,2078,2079],{},"inform(date=Tue, time=morning)",[181,2081,2082],{},[440,2083,2084],{},"orig=Austin, dest=Denver, date=Tue, time=morning",[11,2086,2087,2088,2091,2092,2095,2096,2099,2100,2102,2103,34,2106,2108,2109,2112,2113,2116,2117,2119],{},"Turn 2 adds ",[440,2089,2090],{},"dest"," without disturbing ",[440,2093,2094],{},"orig",". Turn 3 is the correction case: the act\nis ",[440,2097,2098],{},"correct",", not ",[440,2101,1828],{},", so the tracker ",[109,2104,2105],{},"overwrites",[440,2107,2094],{}," from ",[440,2110,2111],{},"Boston"," to\n",[440,2114,2115],{},"Austin"," rather than opening a second origin slot — the recognized ",[440,2118,2098],{}," act is\nwhat tells it to replace rather than append. Turn 4 fills the last two slots,\nand the frame is now complete enough for the policy to execute the flight query. Every\nslot the user did not mention on a given turn is inherited unchanged; that inheritance\nis the whole job of the tracker.",[373,2121],{"hash":2122},"9271499874b57163765a17654d6647d60ceee40608fa3c4ffcb5d0d6de33b782",[11,2124,1935,2125,2128,2129,2146,2147,2233],{},[15,2126,2127],{},"dialogue policy"," decides what the system should do next — which dialogue act\nto generate. Formally, given the state at turn ",[502,2130,2132],{"className":2131},[635],[502,2133,2135],{"className":2134,"ariaHidden":640},[639],[502,2136,2138,2142],{"className":2137},[644],[502,2139],{"className":2140,"style":2141},[648],"height:0.6595em;",[502,2143,2145],{"className":2144},[653,654],"i",", it predicts the best next act\n",[502,2148,2150],{"className":2149},[635],[502,2151,2153],{"className":2152,"ariaHidden":640},[639],[502,2154,2156,2160],{"className":2155},[644],[502,2157],{"className":2158,"style":2159},[648],"height:1.0968em;vertical-align:-0.15em;",[502,2161,2163,2198],{"className":2162},[653],[502,2164,2166],{"className":2165},[653,1322],[502,2167,2169],{"className":2168},[782],[502,2170,2172],{"className":2171},[787],[502,2173,2176,2185],{"className":2174,"style":2175},[791],"height:0.9468em;",[502,2177,2178,2181],{"style":822},[502,2179],{"className":2180,"style":799},[798],[502,2182,2184],{"className":2183},[653,654],"A",[502,2186,2188,2191],{"style":2187},"top:-3.2523em;",[502,2189],{"className":2190,"style":799},[798],[502,2192,2195],{"className":2193,"style":2194},[1349],"left:-0.1111em;",[502,2196,1354],{"className":2197},[653],[502,2199,2201],{"className":2200},[873],[502,2202,2204,2225],{"className":2203},[782,783],[502,2205,2207,2222],{"className":2206},[787],[502,2208,2211],{"className":2209,"style":2210},[791],"height:0.3117em;",[502,2212,2213,2216],{"style":886},[502,2214],{"className":2215,"style":890},[798],[502,2217,2219],{"className":2218},[803,804,805,806],[502,2220,2145],{"className":2221},[653,654,806],[502,2223,847],{"className":2224},[846],[502,2226,2228],{"className":2227},[787],[502,2229,2231],{"className":2230,"style":966},[791],[502,2232],{},"; a practical policy conditions on the current frame and the last exchange:",[502,2235,2237],{"className":2236},[707],[502,2238,2240],{"className":2239},[635],[502,2241,2243,2326],{"className":2242,"ariaHidden":640},[639],[502,2244,2246,2249,2317,2320,2323],{"className":2245},[644],[502,2247],{"className":2248,"style":2159},[648],[502,2250,2252,2283],{"className":2251},[653],[502,2253,2255],{"className":2254},[653,1322],[502,2256,2258],{"className":2257},[782],[502,2259,2261],{"className":2260},[787],[502,2262,2264,2272],{"className":2263,"style":2175},[791],[502,2265,2266,2269],{"style":822},[502,2267],{"className":2268,"style":799},[798],[502,2270,2184],{"className":2271},[653,654],[502,2273,2274,2277],{"style":2187},[502,2275],{"className":2276,"style":799},[798],[502,2278,2280],{"className":2279,"style":2194},[1349],[502,2281,1354],{"className":2282},[653],[502,2284,2286],{"className":2285},[873],[502,2287,2289,2309],{"className":2288},[782,783],[502,2290,2292,2306],{"className":2291},[787],[502,2293,2295],{"className":2294,"style":2210},[791],[502,2296,2297,2300],{"style":886},[502,2298],{"className":2299,"style":890},[798],[502,2301,2303],{"className":2302},[803,804,805,806],[502,2304,2145],{"className":2305},[653,654,806],[502,2307,847],{"className":2308},[846],[502,2310,2312],{"className":2311},[787],[502,2313,2315],{"className":2314,"style":966},[791],[502,2316],{},[502,2318],{"className":2319,"style":759},[746],[502,2321,764],{"className":2322},[763],[502,2324],{"className":2325,"style":759},[746],[502,2327,2329,2333,2440,2443,2446,2449,2452,2455,2685,2688],{"className":2328},[644],[502,2330],{"className":2331,"style":2332},[648],"height:1.7889em;vertical-align:-1.0389em;",[502,2334,2336],{"className":2335},[724,778],[502,2337,2339,2431],{"className":2338},[782,783],[502,2340,2342,2428],{"className":2341},[787],[502,2343,2345,2409],{"className":2344,"style":669},[791],[502,2346,2347,2350],{"style":794},[502,2348],{"className":2349,"style":799},[798],[502,2351,2353],{"className":2352},[803,804,805,806],[502,2354,2356,2402,2405],{"className":2355},[653,806],[502,2357,2359,2362],{"className":2358},[653,806],[502,2360,2184],{"className":2361},[653,654,806],[502,2363,2365],{"className":2364},[873],[502,2366,2368,2393],{"className":2367},[782,783],[502,2369,2371,2390],{"className":2370},[787],[502,2372,2375],{"className":2373,"style":2374},[791],"height:0.3281em;",[502,2376,2378,2382],{"style":2377},"top:-2.357em;margin-left:0em;margin-right:0.0714em;",[502,2379],{"className":2380,"style":2381},[798],"height:2.5em;",[502,2383,2387],{"className":2384},[803,2385,2386,806],"reset-size3","size1",[502,2388,2145],{"className":2389},[653,654,806],[502,2391,847],{"className":2392},[846],[502,2394,2396],{"className":2395},[787],[502,2397,2400],{"className":2398,"style":2399},[791],"height:0.143em;",[502,2401],{},[502,2403,816],{"className":2404},[763,806],[502,2406,2184],{"className":2407},[653,2408,806],"mathcal",[502,2410,2411,2414],{"style":822},[502,2412],{"className":2413,"style":799},[798],[502,2415,2416],{},[502,2417,2419,2422,2425],{"className":2418},[724],[502,2420,835],{"className":2421,"style":834},[653,728],[502,2423],{"className":2424,"style":747},[746],[502,2426,842],{"className":2427},[653,728],[502,2429,847],{"className":2430},[846],[502,2432,2434],{"className":2433},[787],[502,2435,2438],{"className":2436,"style":2437},[791],"height:1.0389em;",[502,2439],{},[502,2441],{"className":2442,"style":759},[746],[502,2444],{"className":2445,"style":747},[746],[502,2447,1480],{"className":2448,"style":1479},[653,654],[502,2450],{"className":2451,"style":1484},[746],[502,2453],{"className":2454,"style":747},[746],[502,2456,2458,2461,2501,2504,2507,2510,2563,2566,2569,2572,2621,2624,2627,2630,2682],{"className":2457},[1491],[502,2459,734],{"className":2460,"style":1496},[733,1495],[502,2462,2464,2467],{"className":2463},[653],[502,2465,2184],{"className":2466},[653,654],[502,2468,2470],{"className":2469},[873],[502,2471,2473,2493],{"className":2472},[782,783],[502,2474,2476,2490],{"className":2475},[787],[502,2477,2479],{"className":2478,"style":2210},[791],[502,2480,2481,2484],{"style":886},[502,2482],{"className":2483,"style":890},[798],[502,2485,2487],{"className":2486},[803,804,805,806],[502,2488,2145],{"className":2489},[653,654,806],[502,2491,847],{"className":2492},[846],[502,2494,2496],{"className":2495},[787],[502,2497,2499],{"className":2498,"style":966},[791],[502,2500],{},[502,2502],{"className":2503,"style":759},[746],[502,2505,1506],{"className":2506},[763],[502,2508],{"className":2509,"style":759},[746],[502,2511,2513,2520],{"className":2512},[653],[502,2514,2516],{"className":2515},[653],[502,2517,2519],{"className":2518},[653,728],"Frame",[502,2521,2523],{"className":2522},[873],[502,2524,2526,2555],{"className":2525},[782,783],[502,2527,2529,2552],{"className":2528},[787],[502,2530,2532],{"className":2531,"style":2210},[791],[502,2533,2534,2537],{"style":1060},[502,2535],{"className":2536,"style":890},[798],[502,2538,2540],{"className":2539},[803,804,805,806],[502,2541,2543,2546,2549],{"className":2542},[653,806],[502,2544,2145],{"className":2545},[653,654,806],[502,2547,1608],{"className":2548},[916,806],[502,2550,33],{"className":2551},[653,806],[502,2553,847],{"className":2554},[846],[502,2556,2558],{"className":2557},[787],[502,2559,2561],{"className":2560,"style":1621},[791],[502,2562],{},[502,2564,742],{"className":2565},[741],[502,2567],{"className":2568,"style":747},[746],[502,2570],{"className":2571,"style":747},[746],[502,2573,2575,2578],{"className":2574},[653],[502,2576,2184],{"className":2577},[653,654],[502,2579,2581],{"className":2580},[873],[502,2582,2584,2613],{"className":2583},[782,783],[502,2585,2587,2610],{"className":2586},[787],[502,2588,2590],{"className":2589,"style":2210},[791],[502,2591,2592,2595],{"style":886},[502,2593],{"className":2594,"style":890},[798],[502,2596,2598],{"className":2597},[803,804,805,806],[502,2599,2601,2604,2607],{"className":2600},[653,806],[502,2602,2145],{"className":2603},[653,654,806],[502,2605,1608],{"className":2606},[916,806],[502,2608,33],{"className":2609},[653,806],[502,2611,847],{"className":2612},[846],[502,2614,2616],{"className":2615},[787],[502,2617,2619],{"className":2618,"style":1621},[791],[502,2620],{},[502,2622,742],{"className":2623},[741],[502,2625],{"className":2626,"style":747},[746],[502,2628],{"className":2629,"style":747},[746],[502,2631,2633,2638],{"className":2632},[653],[502,2634,2637],{"className":2635,"style":2636},[653,654],"margin-right:0.109em;","U",[502,2639,2641],{"className":2640},[873],[502,2642,2644,2674],{"className":2643},[782,783],[502,2645,2647,2671],{"className":2646},[787],[502,2648,2650],{"className":2649,"style":2210},[791],[502,2651,2653,2656],{"style":2652},"top:-2.55em;margin-left:-0.109em;margin-right:0.05em;",[502,2654],{"className":2655,"style":890},[798],[502,2657,2659],{"className":2658},[803,804,805,806],[502,2660,2662,2665,2668],{"className":2661},[653,806],[502,2663,2145],{"className":2664},[653,654,806],[502,2666,1608],{"className":2667},[916,806],[502,2669,33],{"className":2670},[653,806],[502,2672,847],{"className":2673},[846],[502,2675,2677],{"className":2676},[787],[502,2678,2680],{"className":2679,"style":1621},[791],[502,2681],{},[502,2683,755],{"className":2684,"style":1496},[754,1495],[502,2686],{"className":2687,"style":747},[746],[502,2689,1215],{"className":2690},[653],[11,2692,2693,2694,2697,2698,2701,2702,2705,2706,2709,2710,2713,2714,2718],{},"The GUS policy was trivial — ask questions until the frame is full, then query the\ndatabase. A learned policy is richer: it can decide when to answer, when to ask a\nclarification question, when to make a suggestion. The most consequential decisions\nconcern ",[15,2695,2696],{},"confirmation and rejection",", where grounding becomes an engineering\nchoice. Under ",[15,2699,2700],{},"explicit confirmation"," the system asks a direct yes-no\nquestion (",[44,2703,2704],{},"Do you want to leave from Baltimore?","), which is easy for a user to\ncorrect but stilted and long-winded. Under ",[15,2707,2708],{},"implicit confirmation"," it grounds by\nrepeating understanding inside the next question (",[44,2711,2712],{},"When do you want to travel to Berlin?","), which is far more natural. Sophisticated policies can be trained with\n",[27,2715,2717],{"href":2716},"\u002Fdeep-learning\u002Freinforcement-learning\u002Frl-from-human-feedback","reinforcement learning",":\na small action space (execute, confirm, elicit) earns a large reward for finishing\nwith the correct slots, a large penalty for wrong ones, and a small penalty per\nconfirmation to stop the system re-confirming everything.",[11,2720,2721,2722,2725,2726,1764,2729,2732,2733,2736,2737,2740,2741,2744],{},"Finally, ",[15,2723,2724],{},"natural language generation (NLG)"," turns the chosen act into text. GUS\nused fixed ",[15,2727,2728],{},"templates",[44,2730,2731],{},"Hello, how can I help you?",") with variables filled in;\nthe dialogue-state version models generation as ",[15,2734,2735],{},"content planning"," (what to say,\nhandled by the policy) then ",[15,2738,2739],{},"sentence realization"," (how to say it), often trained\nwith ",[15,2742,2743],{},"delexicalization"," — replacing specific slot values with placeholders so the\nmodel generalizes across restaurants and cities, then relexicalizing the real values\nback in.",[11,2746,2747,2748,2752],{},"This lesson covered two traditions: chatbots that chat and frame systems that\ncomplete tasks. Until recently a system had to choose between them, and each had to\nbe evaluated and designed on its own terms. A large language model removes the choice.\nThis continues in ",[27,2749,2751],{"href":2750},"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants","Dialogue Systems: LLM Assistants, Evaluation, and Design",",\nwhich folds both traditions into one aligned model and takes up evaluation, design,\nand ethics.",[2754,2755,2758,2763],"section",{"className":2756,"dataFootnotes":6},[2757],"footnotes",[52,2759,2762],{"className":2760,"id":31},[2761],"sr-only","Footnotes",[2764,2765,2766,2784,2809,2820,2831,2849,2860,2876,2887,2898],"ol",{},[2767,2768,2770,253,2773,2776,2777],"li",{"id":2769},"user-content-fn-jm-open",[15,2771,2772],{},"Jurafsky & Martin",[109,2774,2775],{},"Speech and Language Processing"," (3rd ed.), Ch. 24 opening — dialogue systems \u002F conversational agents, split into task-oriented agents (digital assistants that complete tasks) and chatbots (extended conversation for entertainment or to make task agents more natural). ",[27,2778,2783],{"href":2779,"ariaLabel":2780,"className":2781,"dataFootnoteBackref":6},"#user-content-fnref-jm-open","Back to reference 1",[2782],"data-footnote-backref","↩",[2767,2785,2787,2789,2790,34,2795,34,2802],{"id":2786},"user-content-fn-jm-props",[15,2788,2772],{},", §24.1 — Properties of Human Conversation: the travel-agent\u002Fclient example; turns and turn-taking with the endpointing problem; grounding, common ground, and the ACK analogy. ",[27,2791,2783],{"href":2792,"ariaLabel":2793,"className":2794,"dataFootnoteBackref":6},"#user-content-fnref-jm-props","Back to reference 2",[2782],[27,2796,2783,2800],{"href":2797,"ariaLabel":2798,"className":2799,"dataFootnoteBackref":6},"#user-content-fnref-jm-props-2","Back to reference 2-2",[2782],[24,2801,66],{},[27,2803,2783,2807],{"href":2804,"ariaLabel":2805,"className":2806,"dataFootnoteBackref":6},"#user-content-fnref-jm-props-3","Back to reference 2-3",[2782],[24,2808,352],{},[2767,2810,2812,2814,2815],{"id":2811},"user-content-fn-jm-adj",[15,2813,2772],{},", §24.1 — Subdialogues and Dialogue Structure: conversational analysis, adjacency pairs (first and second pair parts), and side sequences \u002F subdialogues including the clarification question. ",[27,2816,2783],{"href":2817,"ariaLabel":2818,"className":2819,"dataFootnoteBackref":6},"#user-content-fnref-jm-adj","Back to reference 3",[2782],[2767,2821,2823,2825,2826],{"id":2822},"user-content-fn-jm-chat",[15,2824,2772],{},", §24.2 — Chatbots: systems mimicking unstructured human chat, with architectures split into rule-based (ELIZA, PARRY) and corpus-based (retrieval and generation) classes. ",[27,2827,2783],{"href":2828,"ariaLabel":2829,"className":2830,"dataFootnoteBackref":6},"#user-content-fnref-jm-chat","Back to reference 4",[2782],[2767,2832,2834,2836,2837,34,2842],{"id":2833},"user-content-fn-jm-eliza",[15,2835,2772],{},", §24.2.1 — Rule-based chatbots ELIZA and PARRY: the Rogerian-psychologist design, pattern\u002Ftransform rules keyed to ranked keywords, the memory-queue trick, and the simplified generator algorithm (Weizenbaum, 1966). ",[27,2838,2783],{"href":2839,"ariaLabel":2840,"className":2841,"dataFootnoteBackref":6},"#user-content-fnref-jm-eliza","Back to reference 5",[2782],[27,2843,2783,2847],{"href":2844,"ariaLabel":2845,"className":2846,"dataFootnoteBackref":6},"#user-content-fnref-jm-eliza-2","Back to reference 5-2",[2782],[24,2848,66],{},[2767,2850,2852,2854,2855],{"id":2851},"user-content-fn-jm-corpus",[15,2853,2772],{},", §24.2.2 — Corpus-based chatbots: conversational corpora and social-media pseudo-conversations; response by retrieval (tf-idf and bi-encoder scoring) and response by generation (encoder-decoder transduction of the conversation-so-far). ",[27,2856,2783],{"href":2857,"ariaLabel":2858,"className":2859,"dataFootnoteBackref":6},"#user-content-fnref-jm-corpus","Back to reference 6",[2782],[2767,2861,2863,2865,2866,253,2868,2870,2871],{"id":2862},"user-content-fn-jm-bland",[15,2864,2772],{},", §24.2.2 — encoder-decoder response generators tend to produce predictable, repetitive, dull responses (",[44,2867,1645],{},[44,2869,1648],{},") that shut down the conversation; addressed with diversity-promoting decoding\u002Ftraining, minimum-length constraints, and reinforcement\u002Fadversarial methods for multi-turn coherence. ",[27,2872,2783],{"href":2873,"ariaLabel":2874,"className":2875,"dataFootnoteBackref":6},"#user-content-fnref-jm-bland","Back to reference 7",[2782],[2767,2877,2879,2881,2882],{"id":2878},"user-content-fn-jm-gus",[15,2880,2772],{},", §24.3 — GUS: Simple Frame-based Dialogue Systems: frames, slots, types, and question templates; the frame-filling control architecture as a production-rule system; domain, intent, and slot-filler extraction with semantic grammars. ",[27,2883,2783],{"href":2884,"ariaLabel":2885,"className":2886,"dataFootnoteBackref":6},"#user-content-fnref-jm-gus","Back to reference 8",[2782],[2767,2888,2890,2892,2893],{"id":2889},"user-content-fn-jm-ds",[15,2891,2772],{},", §24.4 — The Dialogue-State Architecture: the NLU \u002F dialogue-state-tracker \u002F dialogue-policy \u002F NLG components augmenting the GUS frame model, wrapped by ASR and TTS for spoken systems. ",[27,2894,2783],{"href":2895,"ariaLabel":2896,"className":2897,"dataFootnoteBackref":6},"#user-content-fnref-jm-ds","Back to reference 9",[2782],[2767,2899,2901,2903,2904,2940,2941],{"id":2900},"user-content-fn-jm-acts",[15,2902,2772],{},", §24.4.1–§24.4.3 — dialogue acts as speech-act-plus-grounding tags with slot-value content; slot filling as BIO sequence labeling (",[502,2905,2907],{"className":2906},[635],[502,2908,2910,2931],{"className":2909,"ariaHidden":640},[639],[502,2911,2913,2916,2919,2922,2925,2928],{"className":2912},[644],[502,2914],{"className":2915,"style":1877},[648],[502,2917,66],{"className":2918},[653],[502,2920,1884],{"className":2921},[653,654],[502,2923],{"className":2924,"style":912},[746],[502,2926,1891],{"className":2927},[916],[502,2929],{"className":2930,"style":912},[746],[502,2932,2934,2937],{"className":2933},[644],[502,2935],{"className":2936,"style":1901},[648],[502,2938,33],{"className":2939},[653]," tags); the dialogue-state tracker accumulating the full frame across turns, including user correction acts and hyperarticulation. ",[27,2942,2783],{"href":2943,"ariaLabel":2944,"className":2945,"dataFootnoteBackref":6},"#user-content-fnref-jm-acts","Back to reference 10",[2782],[2947,2948,2949],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark-mode .shiki span {color: var(--shiki-dark-mode);background: var(--shiki-dark-mode-bg);font-style: var(--shiki-dark-mode-font-style);font-weight: var(--shiki-dark-mode-font-weight);text-decoration: var(--shiki-dark-mode-text-decoration);}html.dark-mode .shiki span {color: var(--shiki-dark-mode);background: var(--shiki-dark-mode-bg);font-style: var(--shiki-dark-mode-font-style);font-weight: var(--shiki-dark-mode-font-weight);text-decoration: var(--shiki-dark-mode-text-decoration);}",{"title":6,"searchDepth":511,"depth":511,"links":2951},[2952,2958,2962,2963,2964],{"id":54,"depth":511,"text":55,"children":2953},[2954,2955,2956,2957],{"id":95,"depth":517,"text":96},{"id":136,"depth":517,"text":137},{"id":278,"depth":517,"text":279},{"id":328,"depth":517,"text":329},{"id":381,"depth":511,"text":41,"children":2959},[2960,2961],{"id":406,"depth":517,"text":407},{"id":609,"depth":517,"text":610},{"id":1665,"depth":511,"text":1666},{"id":1796,"depth":511,"text":1797},{"id":31,"depth":511,"text":2762},[],"computer-science","Conversation is the first kind of language a child learns and the kind adults use\nmost: ordering lunch, booking a flight, arguing about the weather. For a machine it\nis one of the hardest interfaces to build. A dialogue\nsystem — a conversational agent — communicates with a person in natural\nlanguage, and the field splits its programs into two kinds by what they are trying\nto do.1 Task-oriented agents use conversation to complete something:\nthe assistants in Siri, Alexa, and Google Assistant that set alarms, find\nrestaurants, and book flights. Chatbots are built for the open-ended talk\nitself — mimicking the unstructured chat of informal human interaction, mostly\nfor entertainment but also to make task agents feel less mechanical. The two goals\nrequire different machinery; the modern LLM assistant collapses the split.",false,"md",{"moduleNumber":541,"lessonNumber":529,"order":2971},705,"Applications",true,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots",[],"---\ntitle: Dialogue and Chatbots\nmodule: Applications\nmoduleNumber: 7\nlessonNumber: 5\norder: 705\nsummary: >\n  Conversation is the most natural interface to a machine and one of the\n  hardest to build. We set up what makes human dialogue work — turns, speech\n  acts, grounding, and the local structure of adjacency pairs — then trace the\n  two traditions that answer it: chatbots built to chat (ELIZA's pattern-matching,\n  corpus retrieval, and seq2seq generation with its blandness problem) and\n  task-oriented systems built to get something done (the GUS frame-and-slot\n  architecture and the modern NLU \u002F state-tracker \u002F policy \u002F NLG pipeline that\n  accumulates a frame across turns).\ntopics: [Applications]\nsources:\n  - book: Jurafsky & Martin\n    ref: \"Ch. 24 — Chatbots & Dialogue Systems; §24.1 Properties of Human Conversation\"\n  - book: Jurafsky & Martin\n    ref: \"§24.2 Chatbots; §24.3 GUS — Simple Frame-based Dialogue Systems\"\n  - book: Jurafsky & Martin\n    ref: \"§24.4 The Dialogue-State Architecture; §24.5 Evaluating Dialogue Systems; §24.6 Dialogue System Design\"\n---\n\nConversation is the first kind of language a child learns and the kind adults use\nmost: ordering lunch, booking a flight, arguing about the weather. For a machine it\nis one of the hardest interfaces to build. A **dialogue\nsystem** — a **conversational agent** — communicates with a person in natural\nlanguage, and the field splits its programs into two kinds by what they are trying\nto do.[^jm-open] **Task-oriented** agents use conversation to complete something:\nthe assistants in Siri, Alexa, and Google Assistant that set alarms, find\nrestaurants, and book flights. **Chatbots** are built for the open-ended talk\nitself — mimicking the unstructured \"chat\" of informal human interaction, mostly\nfor entertainment but also to make task agents feel less mechanical. The two goals\nrequire different machinery; the modern LLM assistant collapses the split.\n\nBuilding either kind of system requires understanding what is being imitated.\nHuman conversation is a joint activity with more structure than it\nappears to have, and every property of it is a design constraint on the machine.\n\n## Properties of human conversation\n\nConsider a fragment of an actual phone call between a human travel agent (A) and a\nhuman client (C), the running example throughout Jurafsky and Martin's\ntreatment:[^jm-props]\n\n> C₁: … I need to travel in May.\u003Cbr>\n> A₂: And, what day in May did you want to travel?\u003Cbr>\n> C₃: OK, uh, I need to be there for a meeting that's from the 12th to the 15th.\u003Cbr>\n> A₄: And you're flying into what city?\u003Cbr>\n> C₅: Seattle.\u003Cbr>\n> A₆: And what time would you like to leave Pittsburgh?\n\nEvery feature of this ordinary exchange is a problem for a machine. Four\nproperties matter most.\n\n### Turns and turn-taking\n\nA dialogue is a sequence of **turns** — C₁, A₂, C₃, and so on — each a single\ncontribution from one speaker. A turn can be a word (\"Seattle\") or several\nsentences. The _timing_ is precise: speakers hand the floor back and\nforth with almost no gap and almost no overlap, because a listener can predict\nwhen the current speaker is about to finish.\n\nFor a spoken system this predictive handoff becomes two hard problems. The machine\nmust know when to _stop_ talking — the user may interrupt, or begin a correction\nmid-sentence — and it must know when to _start_, detecting that the user has\nfinished so it can process and reply. That second problem, **endpointing** or\nendpoint detection, is genuinely difficult: people pause in the middle of turns,\nand background noise blurs the boundary between a pause and an ending.[^jm-props]\n\n### Speech acts\n\nThe central observation, due originally to Wittgenstein and worked out by Austin,\nis that each utterance in a dialogue is an **action** performed by the speaker — a\n**speech act** (or **dialogue act**). One common taxonomy has four classes:\n\n| Speech act | What the speaker does | Verbs | In the example |\n| --- | --- | --- | --- |\n| **Constative** | commits to something being the case | answering, claiming, confirming, stating | C₁ \"I need to travel in May\" |\n| **Directive** | attempts to get the hearer to do something | asking, requesting, ordering, inviting | A₂ \"what day did you want to travel?\" |\n| **Commissive** | commits the speaker to a future course of action | promising, planning, betting, vowing | \"I'll take the first one\" |\n| **Acknowledgment** | expresses an attitude toward the hearer | greeting, thanking, apologizing | \"OK\", \"thank you\" |\n\nA user asking a machine to do something (\"Turn up the music\") issues a\n**directive**; a question that expects an answer (A₂) is also a directive, a very\npolite command to answer. Stating a constraint (C₁) is a **constative**. The\nspeech act names the _intention_ behind the words, and recovering it — not just\nthe surface string — is what a dialogue system must do.\n\n### Grounding\n\nA dialogue is not a stack of independent speech acts but a collective act, and\npartners in a collective act must keep track of what they mutually agree on, the\n**common ground**. They build it by **grounding**: acknowledging that the hearer\nhas understood the speaker, the conversational equivalent of an ACK confirming\nreceipt on a data channel.[^jm-props]\n\nHumans ground constantly. A₆ (\"And what time would you like to leave\n_Pittsburgh_?\") repeats \"Pittsburgh\" to show it was heard; the \"And\" that opens\nso many of the agent's turns signals that the new question is _in addition_ to\nthe last, implying the previous answer was understood. A dialogue system that never\ngrounds — that answers questions without any signal of understanding — feels\nbroken even when it is technically correct, which is why the choice between\n_explicit_ and _implicit_ confirmation, later in this lesson, matters.\n\n### Dialogue structure: adjacency pairs\n\nConversation has local structure. Certain speech acts set up an expectation for a\nparticular next act: a QUESTION sets up an expectation for an ANSWER, a PROPOSAL\nfor an ACCEPTANCE or REJECTION, a COMPLIMENT for a DOWNPLAYER. These two-part\ntemplates are **adjacency pairs**, a **first pair part** raising the expectation\nand a **second pair part** satisfying it.[^jm-adj] The expectations help a system\ndecide what to do: after it asks a question, the next user turn is _supposed_ to\nbe an answer.\n\nThe two halves need not be adjacent. A **side sequence** — a **subdialogue** — can\nopen between them; the common case is a **clarification question** when the machine\nmis-hears part of the user's turn.\n\n$$\n% caption: A short travel dialogue with each turn's dialogue act labeled. A\n% question sets up an expectation for an answer (an adjacency pair); a\n% clarification subdialogue can open between the two pair parts.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize,\n  turnU\u002F.style={draw, minimum width=52mm, minimum height=8mm, align=left, fill=black!3},\n  turnA\u002F.style={draw, minimum width=52mm, minimum height=8mm, align=left, draw=acc, fill=acc!5},\n  act\u002F.style={font=\\scriptsize, text=black}]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\node[turnU] (u1) at (0,0)     {C: I need to travel in May.};\n  \\node[act, anchor=west] at (2.9,0) {CONSTATIVE};\n  \\node[turnA] (a1) at (0,-1.15)  {A: What day in May?};\n  \\node[act, anchor=west] at (2.9,-1.15) {DIRECTIVE (question)};\n  \\node[turnU] (u2) at (0,-2.3)  {C: A meeting from the 12th to 15th.};\n  \\node[act, anchor=west] at (2.9,-2.3) {CONSTATIVE (answer)};\n  \\node[turnA] (a2) at (0,-3.45) {A: Flying into what city?};\n  \\node[act, anchor=west] at (2.9,-3.45) {DIRECTIVE (question)};\n  \\node[turnU] (u3) at (0,-4.6)  {C: Seattle.};\n  \\node[act, anchor=west] at (2.9,-4.6) {CONSTATIVE (answer)};\n  % adjacency-pair bracket linking a1 (question) to u2 (answer)\n  \\draw[acc, thick] (-2.95,-0.75) -- (-3.25,-0.75) -- (-3.25,-2.7) -- (-2.95,-2.7);\n  \\node[acc, font=\\scriptsize, rotate=90, anchor=south] at (-3.5,-1.7) {adjacency pair};\n\\end{tikzpicture}\n$$\n\nThese properties — turns, speech acts, grounding, dialogue structure, and the\ninferences a speaker expects the hearer to draw — are what make natural\nconversation hard to build, and most of them are still open research problems. The\ntwo traditions below each attack a different subset.\n\n## Chatbots\n\nThe simplest dialogue systems are **chatbots**: systems that carry on extended\nconversations meant to mimic the unstructured chat of informal human interaction.\nTheir goals are entertainment and engagement rather than task completion, and their\narchitectures fall into two classes — **rule-based** systems, the early influential\nELIZA and PARRY, and **corpus-based** systems, which mine large datasets of\nhuman-human conversation.[^jm-chat]\n\n### The rule-based tradition: ELIZA\n\nELIZA, written by Joseph Weizenbaum in 1966, is the most important chatbot in the\nhistory of the field, and it works by nothing more than pattern matching.[^jm-eliza]\nIt simulates a Rogerian psychologist — a style of therapy in which the therapist\nreflects the patient's statements back as questions, and can, as Weizenbaum noted,\n\"assume the pose of knowing almost nothing of the real world.\" That pose lets a\nmachine with no world model sustain a conversation.\n\nELIZA is built from pattern\u002Ftransform rules, the same\n[regular-expression](\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization)\nmachinery introduced at the start of the course. A rule pairs a pattern with a\ntransform:\n\n```\n(0 YOU 0 ME)                       [pattern]\n->\n(WHAT MAKES YOU THINK I 3 YOU)     [transform]\n```\n\nHere `0` is a Kleene star (match anything) and the digits in the transform index\nthe matched constituents, so `3` refers to the material captured by the second `0`.\nThe rule turns _You hate me_ into _WHAT MAKES YOU THINK I HATE YOU_. Each rule is\nattached to a **keyword** that might appear in the user's sentence, and keywords\ncarry a **rank**: specific words (_everybody_) outrank general ones (_I_), because\nsomeone using a universal like \"everybody\" is usually referring to something quite\nspecific worth probing. The algorithm finds the highest-ranked keyword in the\ninput and fires its best-matching rule; if nothing matches, it falls back to a\nnon-committal \"PLEASE GO ON\" or \"THAT'S VERY INTERESTING\". One trick gives\nthe illusion of memory: whenever \"my\" is the top keyword, ELIZA stores a transform\nof the sentence on a queue and, later, if nothing else matches, returns the oldest\nstored entry — so it seems to circle back to something the user said turns ago.\n\n```algorithm\ncaption: $\\textsc{Eliza-Generator}(sentence)$ — pattern\u002Ftransform response, per Weizenbaum\ninput: user $sentence$, keyword-ranked rule set, a memory queue\nfind the word $w$ in $sentence$ with the highest keyword rank\nif $w$ exists then\n  choose the highest-ranked rule $r$ for $w$ that matches $sentence$\n  $response \\gets$ apply the transform in $r$ to $sentence$\n  if $w$ is \"my\" then\n    $future \\gets$ apply a memory-list transform to $sentence$\n    push $future$ onto the memory queue\nelse\n  if the memory queue is non-empty then\n    $response \\gets$ pop the oldest entry from the memory queue\n  else\n    $response \\gets$ apply the NONE-keyword transform to $sentence$\nreturn $response$\n```\n\nELIZA understands nothing, yet it fooled people — the enduring lesson.[^jm-eliza]\nUsers confided in it, Weizenbaum's own secretary asked him to leave\nthe room while she \"talked\" to it, and some kept believing it understood them after\nbeing shown the rules. A few years later PARRY added a model of mental state —\naffect variables for fear and anger — and became the first program to pass a\nTuring test, when psychiatrists could not distinguish its transcripts from those of\nreal patients. ELIZA's pattern\u002Faction framework survives today in tools like ALICE,\nand it is the baseline for everything that follows.\n\n### Corpus-based chatbots\n\nInstead of hand-built rules, **corpus-based** chatbots mine conversations — hundreds\nof millions or billions of words of them.[^jm-corpus] Sources include transcribed\ntelephone speech (the Switchboard corpus), movie dialogue, and crowdsourced\nconversation sets, supplemented by pseudo-conversations scraped from Twitter,\nReddit, and Weibo. Given the user's turn in context, these systems produce a\nresponse in one of two ways.\n\n**Response by retrieval** treats the user's turn as a query $q$ and returns the\nmost appropriate turn $r$ from a corpus $C$ of conversations, scoring each candidate\nby similarity and picking the best. The classic form uses tf-idf cosine; the neural\nform is a **bi-encoder**, two separately trained encoders — one for the query, one\nfor the candidate — whose dot product is the score, the same\n[dense-retrieval](\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering)\nidea question answering uses:\n\n$$\n\\response(q, C) = \\argmax_{r \\in C}\\; h_q \\cdot h_r,\n\\qquad h_q = \\mathrm{BERT}_Q(q),\\;\\; h_r = \\mathrm{BERT}_R(r).\n$$\n\n**Response by generation** instead casts response production as an encoder-decoder\ntask — transducing the user's turn (plus context) into the system's turn, a machine\nlearning version of ELIZA. It is the same [sequence-to-sequence\ntransduction](\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation) used\nfor translation, generating each response token $r_t$ by conditioning on the\nencoded query $q$ and the response so far:\n\n$$\n\\hat r_t = \\argmax_{w \\in V}\\; P\\!\\left(w \\mid q,\\; r_1 \\ldots r_{t-1}\\right).\n$$\n\n$$\n% caption: Two ways a neural chatbot produces a response. Retrieval (left) scores\n% each corpus turn by the dot product of query and candidate encodings and returns\n% the best; generation (right) runs an encoder-decoder over the whole conversation.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize,\n  enc\u002F.style={draw, minimum width=24mm, minimum height=7mm, align=center, fill=acc!6},\n  op\u002F.style={draw, minimum width=17mm, minimum height=6mm, align=center, fill=black!4}]\n  \\definecolor{acc}{HTML}{2348F2}\n  % --- retrieval (left) ---\n  \\node[enc] (eq) at (0,0)   {ENCODER (query)};\n  \\node[enc] (er) at (3.1,0) {ENCODER (cand.)};\n  \\node[op, draw=acc] (dp) at (1.55,1.4) {dot-product};\n  \\draw[->, acc] (eq.north) -- (dp.south west);\n  \\draw[->, acc] (er.north) -- (dp.south east);\n  \\node[font=\\scriptsize, anchor=north] at (0,-0.55)   {user turn q};\n  \\node[font=\\scriptsize, anchor=north] at (3.1,-0.55) {candidate r};\n  \\node[font=\\footnotesize\\bfseries, anchor=north] at (1.55,-1.15) {(a) retrieval};\n  % --- generation (right) ---\n  \\begin{scope}[xshift=7.4cm]\n    \\node[enc] (ge) at (0,0)     {ENCODER};\n    \\node[enc, draw=acc] (gd) at (2.6,1.4) {DECODER};\n    \\draw[->, acc] (ge.north) -- (gd.south west);\n    \\node[font=\\scriptsize, anchor=north] at (0,-0.55)   {conversation so far};\n    \\node[font=\\scriptsize, anchor=south] at (2.6,2.15)  {response r1 r2 ...};\n    \\node[font=\\footnotesize\\bfseries, anchor=north] at (1.3,-1.15) {(b) generation};\n  \\end{scope}\n\\end{tikzpicture}\n$$\n\nGeneration has a characteristic failure. A basic encoder-decoder, trained to\nmaximize response likelihood, learns that safe, generic replies fit almost any\ncontext, so it converges on **bland**, repetitive turns — \"I'm OK\", \"I don't know\" —\nthat are fluent but shut the exchange down.[^jm-bland] The dullness is a\nconsequence of the objective, not the data: the most probable response is the\nleast informative one. Remedies include diversity-promoting beam search and\ntraining objectives, minimum-length constraints, and, because encoder-decoders\noptimize a single turn at a time and ignore the long-term shape of the\nconversation, reinforcement learning and adversarial training that reward turns\nmaking the _whole_ dialogue more natural.\n\n## Task-oriented dialogue: the GUS architecture\n\nThe other tradition helps a user get something done — reserve a flight, book a\nhotel — and almost all of it, from the 1977 GUS travel planner to today's\ncommercial assistants, is built around a **frame**.[^jm-gus] A frame is a knowledge\nstructure representing a kind of intention the system can extract from user\nsentences. It is a set of **slots**, each with a semantic **type** and a set of\npossible **values**, and a question template used to fill it.\n\n> **Definition (Frame).** A collection of **slots** the system needs filled to\n> perform a task, each slot constrained to values of a semantic type (a `city`\n> slot takes values like _San Francisco_; a `date` slot is itself a frame with\n> `month`, `day`, `weekday` sub-slots). The set of frames for a domain is a\n> **domain ontology**.\n\n$$\n% caption: A frame for the air-travel domain. Each slot has a semantic type and a\n% question template the system uses to fill it; the goal of the dialogue is to fill\n% every slot, then perform the action (query the flight database).\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize,\n  hd\u002F.style={font=\\footnotesize\\bfseries},\n  cell\u002F.style={anchor=west, font=\\footnotesize}]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\draw[draw=acc] (-0.2,0.55) rectangle (11.6,-3.85);\n  \\draw[acc] (-0.2,0.05) -- (11.6,0.05);\n  \\node[hd] at (1.4,0.3) {Slot};\n  \\node[hd] at (4.3,0.3) {Type};\n  \\node[hd, anchor=west] at (5.6,0.3) {Question template};\n  \\foreach \\y\u002F\\slot\u002F\\typ\u002F\\q in {\n    -0.4\u002FORIGIN-CITY\u002Fcity\u002F{From what city are you leaving?},\n    -1.0\u002FDEST-CITY\u002Fcity\u002F{Where are you going?},\n    -1.6\u002FDEPART-TIME\u002Ftime\u002F{When would you like to leave?},\n    -2.2\u002FDEPART-DATE\u002Fdate\u002F{What day would you like to leave?},\n    -2.8\u002FARRIVE-TIME\u002Ftime\u002F{When do you want to arrive?},\n    -3.4\u002FARRIVE-DATE\u002Fdate\u002F{What day would you like to arrive?}} {\n    \\node[cell] at (0.2,\\y) {\\texttt{\\slot}};\n    \\node[cell] at (4.0,\\y) {\\texttt{\\typ}};\n    \\node[cell] at (5.6,\\y) {\\q};\n  }\n\\end{tikzpicture}\n$$\n\nThe control architecture is **frame-filling**. The system asks the question\nattached to each empty slot, records whatever the user supplies, and skips\nquestions for slots already filled. Because one user utterance can fill several\nslots at once —\n\n> I want a flight from San Francisco to Denver one way leaving after five p.m. on\n> Tuesday.\n\n— the system fills all of them and then asks only for what remains. Condition-action\nrules attached to slots handle defaults (fill the hotel-booking city from the flight\ndestination), and because different inputs must switch control among multiple frames\n(a fares frame, a route-information frame), GUS is at bottom a **production-rule**\nsystem: different inputs fire different productions that fill different frames.\n\nFilling a slot from a sentence means extracting three things: the **domain** (is\nthis about airlines or alarm clocks?), the user's **intent** (`SHOW-FLIGHTS`,\n`SET-ALARM`), and the **slot fillers** themselves. From _Show me morning flights\nfrom Boston to San Francisco on Tuesday_ a system builds a representation like\n\n```\nDOMAIN:      AIR-TRAVEL\nINTENT:      SHOW-FLIGHTS\nORIGIN-CITY: Boston\nDEST-CITY:   San Francisco\nDEPART-DATE: Tuesday\nDEPART-TIME: morning\n```\n\nThe original GUS did this extraction with handwritten rules — a **semantic grammar**\nwhose non-terminals are slot names — and rule-based slot-filling, prized for high\nprecision, is still common in industry. It has the usual weaknesses of hand-built\nsystems: the grammars are expensive to write and suffer from recall problems,\nmissing the phrasings the designer did not anticipate.\n\n## The dialogue-state architecture\n\nModern research systems replace the rules with a more sophisticated,\nmachine-learned version of the same frame idea: the **dialogue-state** (or\nbelief-state) architecture.[^jm-ds] It keeps the frame but wraps it in four\nlearned components, a pipeline that runs on every user turn.\n\n$$\n% caption: The dialogue-state pipeline. NLU extracts dialogue acts and slot\n% fillers from the user's turn; the state tracker accumulates them into the full\n% frame; the policy chooses the next system act; NLG realizes it as text (and, for\n% spoken systems, ASR and TTS wrap the whole loop).\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize,\n  comp\u002F.style={draw, minimum width=27mm, minimum height=11mm, align=center},\n  core\u002F.style={draw, minimum width=27mm, minimum height=11mm, align=center, draw=acc, fill=acc!6}]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\node[core] (nlu)  at (0,0)     {NLU\\\\(slot f\\\u002Filling +\\\\dialogue acts)};\n  \\node[core] (dst)  at (4.2,0)   {dialogue-state\\\\tracker};\n  \\node[core] (pol)  at (8.4,0)   {dialogue\\\\policy};\n  \\node[core] (nlg)  at (8.4,-2.6) {NLG\\\\(response text)};\n  \\node[comp] (asr)  at (0,2.4)   {ASR\\\\(spoken input)};\n  \\node[comp] (tts)  at (0,-2.6)  {TTS\\\\(spoken output)};\n  \\draw[->, acc, thick] (nlu) -- (dst);\n  \\draw[->, acc, thick] (dst) -- (pol);\n  \\draw[->, acc, thick] (pol) -- (nlg);\n  \\draw[->, thick] (asr) -- (nlu);\n  \\draw[->, thick] (nlg) -- (tts);\n  \\draw[->, thick] (nlg.west) .. controls (3.0,-2.6) and (2.2,-1.6) .. (nlu.south);\n  \\node[font=\\scriptsize, text=black, anchor=south] at (4.5,-2.3) {next turn};\n\\end{tikzpicture}\n$$\n\n**Natural language understanding (NLU)** extracts the slot fillers and the user's\n**dialogue act** from the turn. Dialogue acts fuse the speech-act idea with\ngrounding: a tag like `inform`, `request`, `confirm`, or `bye`, carried together\nwith its slot-value content. So a user might `inform(food=Italian, near=museum)`,\nand the system later `confirm(pricerange=moderate)`.[^jm-acts] Slot-filling itself\nis [sequence labeling](\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling):\npass the words through a contextual encoder like BERT and tag each with a **BIO**\nlabel ($2n+1$ tags for $n$ slots — a `B-` and `I-` per slot plus a single `O`).\n\nThe **dialogue-state tracker** determines the current state of the frame and the\nuser's most recent act; its main job is accumulation. The state is not\njust the slots named in the latest sentence; it is the _entire_ frame so far,\nsummarizing every constraint the user has expressed across all turns:\n\n> User: I'm looking for a cheaper restaurant\u003Cbr>\n>     `inform(price = cheap)`\u003Cbr>\n> User: Thai food, somewhere downtown\u003Cbr>\n>     `inform(price = cheap, food = Thai, area = centre)`\n\nNotice the tracker carries `price = cheap` forward into the second turn even though\nthe user did not repeat it. A subtle special case is the **user correction act** —\nwhen a user re-states a mis-heard value (\"I said BAL-ti-more, not Boston\"), often\nwith exaggerated **hyperarticulation**. Corrections are _harder_ to\nrecognize than ordinary turns, and detecting them is its own classification problem.\n\nTo see the accumulation and a correction in one place, trace the frame across four\nturns of the air-travel task. The NLU tags each turn with a dialogue act and its\nslot-value content; the tracker folds that into the running frame — adding new slots,\noverwriting on a correction, and carrying the rest forward untouched.\n\n| turn | user utterance | NLU act | frame after tracker update |\n| --- | --- | --- | --- |\n| 1 | _I want to fly from Boston_ | `inform(orig=Boston)` | `orig=Boston` |\n| 2 | _to Denver_ | `inform(dest=Denver)` | `orig=Boston, dest=Denver` |\n| 3 | _no, I said Austin_ | `correct(orig=Austin)` | `orig=Austin, dest=Denver` |\n| 4 | _on Tuesday morning_ | `inform(date=Tue, time=morning)` | `orig=Austin, dest=Denver, date=Tue, time=morning` |\n\nTurn 2 adds `dest` without disturbing `orig`. Turn 3 is the correction case: the act\nis `correct`, not `inform`, so the tracker _overwrites_ `orig` from `Boston` to\n`Austin` rather than opening a second origin slot — the recognized `correct` act is\nwhat tells it to replace rather than append. Turn 4 fills the last two slots,\nand the frame is now complete enough for the policy to execute the flight query. Every\nslot the user did not mention on a given turn is inherited unchanged; that inheritance\nis the whole job of the tracker.\n\n$$\n% caption: A dialogue-state tracker accumulating a frame over four turns. Each turn's\n% NLU act updates the running frame: new slots are added (dest at turn 2), a\n% correction act overwrites an existing slot (orig Boston to Austin at turn 3, in\n% red), and unmentioned slots carry forward. The frame is complete after turn 4.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize,\n  frm\u002F.style={draw, minimum width=40mm, minimum height=13mm, align=left, font=\\scriptsize},\n  lbl\u002F.style={font=\\scriptsize, text=black, anchor=west}]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{red}{HTML}{C0392B}\n  \\node[frm] (f1) at (0,0)    {orig = Boston};\n  \\node[frm] (f2) at (0,-1.6) {orig = Boston\\\\dest = Denver};\n  \\node[frm] (f3) at (0,-3.5) {orig = Austin\\\\dest = Denver};\n  \\node[frm] (f4) at (0,-5.7) {orig = Austin\\\\dest = Denver\\\\date = Tue, time = morning};\n  \\node[lbl] at (2.6,0)    {turn 1: inform(orig)};\n  \\node[lbl] at (2.6,-1.6) {turn 2: add dest};\n  \\node[lbl, text=red] at (2.6,-3.5) {turn 3: correct(orig)};\n  \\node[lbl] at (2.6,-5.7) {turn 4: f\\\u002Fill date + time (complete)};\n  \\draw[->, acc] (f1) -- (f2);\n  \\draw[->, red, thick] (f2) -- (f3);\n  \\draw[->, acc] (f3) -- (f4);\n\\end{tikzpicture}\n$$\n\nThe **dialogue policy** decides what the system should do next — which dialogue act\nto generate. Formally, given the state at turn $i$, it predicts the best next act\n$\\hat A_i$; a practical policy conditions on the current frame and the last exchange:\n\n$$\n\\hat A_i = \\argmax_{A_i \\in \\mathcal{A}}\\; P\\!\\left(A_i \\mid \\mathrm{Frame}_{i-1},\\, A_{i-1},\\, U_{i-1}\\right).\n$$\n\nThe GUS policy was trivial — ask questions until the frame is full, then query the\ndatabase. A learned policy is richer: it can decide when to answer, when to ask a\nclarification question, when to make a suggestion. The most consequential decisions\nconcern **confirmation and rejection**, where grounding becomes an engineering\nchoice. Under **explicit confirmation** the system asks a direct yes-no\nquestion (\"Do you want to leave from Baltimore?\"), which is easy for a user to\ncorrect but stilted and long-winded. Under **implicit confirmation** it grounds by\nrepeating understanding inside the next question (\"When do you want to travel to\nBerlin?\"), which is far more natural. Sophisticated policies can be trained with\n[reinforcement learning](\u002Fdeep-learning\u002Freinforcement-learning\u002Frl-from-human-feedback):\na small action space (execute, confirm, elicit) earns a large reward for finishing\nwith the correct slots, a large penalty for wrong ones, and a small penalty per\nconfirmation to stop the system re-confirming everything.\n\nFinally, **natural language generation (NLG)** turns the chosen act into text. GUS\nused fixed **templates** (\"Hello, how can I help you?\") with variables filled in;\nthe dialogue-state version models generation as **content planning** (what to say,\nhandled by the policy) then **sentence realization** (how to say it), often trained\nwith **delexicalization** — replacing specific slot values with placeholders so the\nmodel generalizes across restaurants and cities, then relexicalizing the real values\nback in.\n\nThis lesson covered two traditions: chatbots that chat and frame systems that\ncomplete tasks. Until recently a system had to choose between them, and each had to\nbe evaluated and designed on its own terms. A large language model removes the choice.\nThis continues in [Dialogue Systems: LLM Assistants, Evaluation, and Design](\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants),\nwhich folds both traditions into one aligned model and takes up evaluation, design,\nand ethics.\n\n[^jm-open]: **Jurafsky & Martin**, _Speech and Language Processing_ (3rd ed.), Ch. 24 opening — dialogue systems \u002F conversational agents, split into task-oriented agents (digital assistants that complete tasks) and chatbots (extended conversation for entertainment or to make task agents more natural).\n[^jm-props]: **Jurafsky & Martin**, §24.1 — Properties of Human Conversation: the travel-agent\u002Fclient example; turns and turn-taking with the endpointing problem; grounding, common ground, and the ACK analogy.\n[^jm-adj]: **Jurafsky & Martin**, §24.1 — Subdialogues and Dialogue Structure: conversational analysis, adjacency pairs (first and second pair parts), and side sequences \u002F subdialogues including the clarification question.\n[^jm-chat]: **Jurafsky & Martin**, §24.2 — Chatbots: systems mimicking unstructured human chat, with architectures split into rule-based (ELIZA, PARRY) and corpus-based (retrieval and generation) classes.\n[^jm-eliza]: **Jurafsky & Martin**, §24.2.1 — Rule-based chatbots ELIZA and PARRY: the Rogerian-psychologist design, pattern\u002Ftransform rules keyed to ranked keywords, the memory-queue trick, and the simplified generator algorithm (Weizenbaum, 1966).\n[^jm-corpus]: **Jurafsky & Martin**, §24.2.2 — Corpus-based chatbots: conversational corpora and social-media pseudo-conversations; response by retrieval (tf-idf and bi-encoder scoring) and response by generation (encoder-decoder transduction of the conversation-so-far).\n[^jm-bland]: **Jurafsky & Martin**, §24.2.2 — encoder-decoder response generators tend to produce predictable, repetitive, dull responses (\"I'm OK\", \"I don't know\") that shut down the conversation; addressed with diversity-promoting decoding\u002Ftraining, minimum-length constraints, and reinforcement\u002Fadversarial methods for multi-turn coherence.\n[^jm-gus]: **Jurafsky & Martin**, §24.3 — GUS: Simple Frame-based Dialogue Systems: frames, slots, types, and question templates; the frame-filling control architecture as a production-rule system; domain, intent, and slot-filler extraction with semantic grammars.\n[^jm-ds]: **Jurafsky & Martin**, §24.4 — The Dialogue-State Architecture: the NLU \u002F dialogue-state-tracker \u002F dialogue-policy \u002F NLG components augmenting the GUS frame model, wrapped by ASR and TTS for spoken systems.\n[^jm-acts]: **Jurafsky & Martin**, §24.4.1–§24.4.3 — dialogue acts as speech-act-plus-grounding tags with slot-value content; slot filling as BIO sequence labeling ($2n+1$ tags); the dialogue-state tracker accumulating the full frame across turns, including user correction acts and hyperarticulation.\n",{"text":2978,"minutes":2979,"time":2980,"words":2981},"16 min read",15.37,922200,3074,{"title":5,"description":2967},[2984,2986,2988],{"book":2772,"ref":2985},"Ch. 24 — Chatbots & Dialogue Systems; §24.1 Properties of Human Conversation",{"book":2772,"ref":2987},"§24.2 Chatbots; §24.3 GUS — Simple Frame-based Dialogue Systems",{"book":2772,"ref":2989},"§24.4 The Dialogue-State Architecture; §24.5 Evaluating Dialogue Systems; §24.6 Dialogue System Design","available","09.natural-language-processing\u002F07.applications\u002F05.dialogue-and-chatbots","Conversation is the most natural interface to a machine and one of the hardest to build. We set up what makes human dialogue work — turns, speech acts, grounding, and the local structure of adjacency pairs — then trace the two traditions that answer it: chatbots built to chat (ELIZA's pattern-matching, corpus retrieval, and seq2seq generation with its blandness problem) and task-oriented systems built to get something done (the GUS frame-and-slot architecture and the modern NLU \u002F state-tracker \u002F policy \u002F NLG pipeline that accumulates a frame across turns).\n",[2972],"rEk46vePNGFNNDAIZJzw80WeTQ7pHR-d8e_sWpkMmhI",{"\u002Falgorithms\u002Ffoundations\u002Fwhat-is-an-algorithm":2996,"\u002Falgorithms\u002Ffoundations\u002Fproof-techniques":2997,"\u002Falgorithms\u002Ffoundations\u002Fasymptotic-analysis":2998,"\u002Falgorithms\u002Ffoundations\u002Fgrowth-rates-and-loop-analysis":2999,"\u002Falgorithms\u002Ffoundations\u002Frecurrences":3000,"\u002Falgorithms\u002Ffoundations\u002Famortized-analysis":3001,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fmergesort":3002,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fquicksort":3003,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fselection":3004,"\u002Falgorithms\u002Fdivide-and-conquer\u002Ffast-multiplication":3005,"\u002Falgorithms\u002Fsorting\u002Fheaps-and-heapsort":3006,"\u002Falgorithms\u002Fsorting\u002Fsorting-lower-bounds":3007,"\u002Falgorithms\u002Fsorting\u002Flinear-time-sorting":3008,"\u002Falgorithms\u002Fsorting\u002Fexternal-sorting":3009,"\u002Falgorithms\u002Fdata-structures\u002Felementary-structures":3010,"\u002Falgorithms\u002Fdata-structures\u002Fhash-tables":3011,"\u002Falgorithms\u002Fdata-structures\u002Fbinary-search-trees":3012,"\u002Falgorithms\u002Fdata-structures\u002Favl-trees":3013,"\u002Falgorithms\u002Fdata-structures\u002Fbalanced-trees":3014,"\u002Falgorithms\u002Fdata-structures\u002Funion-find":3015,"\u002Falgorithms\u002Fdata-structures\u002Ffenwick-and-segment-trees":3016,"\u002Falgorithms\u002Fdata-structures\u002Fspatial-data-structures":3017,"\u002Falgorithms\u002Fdata-structures\u002Fskip-lists-and-probabilistic-structures":3018,"\u002Falgorithms\u002Fdata-structures\u002Fb-trees":3019,"\u002Falgorithms\u002Fdata-structures\u002Fdata-stream-algorithms":3020,"\u002Falgorithms\u002Fdata-structures\u002Fstreaming-sketches":3021,"\u002Falgorithms\u002Fsequences\u002Ftwo-pointers-and-windows":3022,"\u002Falgorithms\u002Fsequences\u002Fprefix-sums":3023,"\u002Falgorithms\u002Fsequences\u002Fmonotonic-stacks":3024,"\u002Falgorithms\u002Fsequences\u002Fbinary-search-on-the-answer":3025,"\u002Falgorithms\u002Fsequences\u002Fstring-matching":3026,"\u002Falgorithms\u002Fsequences\u002Fkmp-and-z-function":3027,"\u002Falgorithms\u002Fsequences\u002Ftries":3028,"\u002Falgorithms\u002Fsequences\u002Fsuffix-arrays-and-aho-corasick":3029,"\u002Falgorithms\u002Fgraphs\u002Frepresentations-and-traversal":3030,"\u002Falgorithms\u002Fgraphs\u002Fdepth-first-search":3031,"\u002Falgorithms\u002Fgraphs\u002Ftopological-sort-and-scc":3032,"\u002Falgorithms\u002Fgraphs\u002Fminimum-spanning-trees":3033,"\u002Falgorithms\u002Fgraphs\u002Fkruskal-and-prim":3034,"\u002Falgorithms\u002Fgraphs\u002Fshortest-paths":3035,"\u002Falgorithms\u002Fgraphs\u002Fall-pairs-and-negative-weights":3036,"\u002Falgorithms\u002Fgraphs\u002Fnetwork-flow":3037,"\u002Falgorithms\u002Fgraphs\u002Fmax-flow-min-cut":3038,"\u002Falgorithms\u002Fgraphs\u002Fbridges-and-articulation-points":3039,"\u002Falgorithms\u002Fgraphs\u002Flowest-common-ancestor":3040,"\u002Falgorithms\u002Fgraphs\u002Ftwo-sat":3041,"\u002Falgorithms\u002Fgraphs\u002Feulerian-tours":3042,"\u002Falgorithms\u002Fgraphs\u002Fbipartite-matching":3043,"\u002Falgorithms\u002Fgreedy\u002Fthe-greedy-method":3044,"\u002Falgorithms\u002Fgreedy\u002Fscheduling-and-intervals":3045,"\u002Falgorithms\u002Fgreedy\u002Fhuffman-codes":3046,"\u002Falgorithms\u002Fgreedy\u002Fmatroids":3047,"\u002Falgorithms\u002Fgreedy\u002Fstable-matching":3048,"\u002Falgorithms\u002Fdynamic-programming\u002Fprinciples":3049,"\u002Falgorithms\u002Fdynamic-programming\u002Fsequence-dp":3050,"\u002Falgorithms\u002Fdynamic-programming\u002Flongest-increasing-subsequence":3051,"\u002Falgorithms\u002Fdynamic-programming\u002Fknapsack":3052,"\u002Falgorithms\u002Fdynamic-programming\u002Fcoin-change-and-unbounded":3053,"\u002Falgorithms\u002Fdynamic-programming\u002Finterval-dp":3054,"\u002Falgorithms\u002Fdynamic-programming\u002Ftree-dp":3055,"\u002Falgorithms\u002Fdynamic-programming\u002Fbitmask-dp":3056,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-optimizations":3057,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-on-graphs":3058,"\u002Falgorithms\u002Fdynamic-programming\u002Fdigit-and-probability-dp":3059,"\u002Falgorithms\u002Fbacktracking\u002Fbacktracking-fundamentals":3060,"\u002Falgorithms\u002Fbacktracking\u002Fconstraint-search":3061,"\u002Falgorithms\u002Fbacktracking\u002Fbranch-and-bound":3062,"\u002Falgorithms\u002Fbacktracking\u002Fgraph-backtracking":3063,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fnumber-theory-basics":3064,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmodular-exponentiation-and-primality":3065,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fsieve-and-factorization":3066,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fcombinatorics":3067,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmatrix-exponentiation":3068,"\u002Falgorithms\u002Fmathematical-algorithms\u002Ffast-fourier-transform":3069,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fgradient-descent":3070,"\u002Falgorithms\u002Fcomputational-geometry\u002Fgeometric-primitives":3071,"\u002Falgorithms\u002Fcomputational-geometry\u002Fconvex-hull":3072,"\u002Falgorithms\u002Fcomputational-geometry\u002Fsweep-line":3073,"\u002Falgorithms\u002Fcomputational-geometry\u002Fpolygons-and-proximity":3074,"\u002Falgorithms\u002Fintractability\u002Fp-np-reductions":3075,"\u002Falgorithms\u002Fintractability\u002Fnp-completeness":3076,"\u002Falgorithms\u002Fintractability\u002Fcoping-with-hardness":3077,"\u002Falgorithms\u002Fintractability\u002Fapproximation-algorithms":3078,"\u002Falgorithms":3079,"\u002Fcalculus\u002Flimits-and-continuity\u002Ffunctions-and-models":3080,"\u002Fcalculus\u002Flimits-and-continuity\u002Fthe-limit-of-a-function":3081,"\u002Fcalculus\u002Flimits-and-continuity\u002Flimit-laws-and-the-precise-definition":3082,"\u002Fcalculus\u002Flimits-and-continuity\u002Fcontinuity":3083,"\u002Fcalculus\u002Fderivatives\u002Fthe-derivative-and-rates-of-change":3084,"\u002Fcalculus\u002Fderivatives\u002Fdifferentiation-rules-and-the-chain-rule":3085,"\u002Fcalculus\u002Fderivatives\u002Fimplicit-differentiation-and-related-rates":3086,"\u002Fcalculus\u002Fderivatives\u002Flinear-approximations-and-differentials":3087,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fextrema-and-the-mean-value-theorem":3088,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fhow-derivatives-shape-a-graph":3089,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fcurve-sketching-and-optimization":3090,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fnewtons-method-and-antiderivatives":3091,"\u002Fcalculus\u002Fintegrals\u002Farea-and-the-definite-integral":3092,"\u002Fcalculus\u002Fintegrals\u002Fthe-fundamental-theorem-of-calculus":3093,"\u002Fcalculus\u002Fintegrals\u002Fthe-substitution-rule":3094,"\u002Fcalculus\u002Fapplications-of-integration\u002Fareas-and-volumes":3095,"\u002Fcalculus\u002Fapplications-of-integration\u002Fwork-average-value-and-arc-length":3096,"\u002Fcalculus\u002Fapplications-of-integration\u002Fphysics-economics-and-probability":3097,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Finverse-functions-logarithms-and-exponentials":3098,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Fgrowth-decay-inverse-trig-and-hyperbolic-functions":3099,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Flhospitals-rule":3100,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fintegration-by-parts":3101,"\u002Fcalculus\u002Ftechniques-of-integration\u002Ftrigonometric-integrals-and-substitution":3102,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fpartial-fractions-and-integration-strategy":3103,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fapproximate-and-improper-integrals":3104,"\u002Fcalculus\u002Fparametric-and-polar\u002Fparametric-curves-and-their-calculus":3105,"\u002Fcalculus\u002Fparametric-and-polar\u002Fpolar-coordinates":3106,"\u002Fcalculus\u002Fparametric-and-polar\u002Fconic-sections":3107,"\u002Fcalculus\u002Fsequences-and-series\u002Fsequences":3108,"\u002Fcalculus\u002Fsequences-and-series\u002Fseries-and-the-integral-test":3109,"\u002Fcalculus\u002Fsequences-and-series\u002Fthe-convergence-tests":3110,"\u002Fcalculus\u002Fsequences-and-series\u002Fpower-series":3111,"\u002Fcalculus\u002Fsequences-and-series\u002Ftaylor-and-maclaurin-series":3112,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvectors-and-the-dot-product":3113,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fthe-cross-product-lines-and-planes":3094,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fcylinders-and-quadric-surfaces":3114,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvector-functions-and-space-curves":3115,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Farc-length-curvature-and-motion":3116,"\u002Fcalculus\u002Fpartial-derivatives\u002Ffunctions-of-several-variables":3084,"\u002Fcalculus\u002Fpartial-derivatives\u002Fpartial-derivatives":3117,"\u002Fcalculus\u002Fpartial-derivatives\u002Ftangent-planes-and-the-chain-rule":3118,"\u002Fcalculus\u002Fpartial-derivatives\u002Fdirectional-derivatives-and-the-gradient":3119,"\u002Fcalculus\u002Fpartial-derivatives\u002Foptimization-and-lagrange-multipliers":3120,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fdouble-integrals":3121,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Ftriple-integrals-and-coordinate-systems":3122,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fvector-fields-and-line-integrals":3123,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fgreens-theorem-curl-and-divergence":3124,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fsurface-integrals":3125,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fstokes-and-the-divergence-theorem":3126,"\u002Fcalculus":3127,"\u002Fmechanics\u002Ffoundations\u002Fmeasurement-and-dimensions":3128,"\u002Fmechanics\u002Ffoundations\u002Fvector-algebra":3129,"\u002Fmechanics\u002Fkinematics\u002Fone-dimensional-motion":3130,"\u002Fmechanics\u002Fkinematics\u002Fmotion-graphs":3131,"\u002Fmechanics\u002Fkinematics\u002Fprojectile-motion":3132,"\u002Fmechanics\u002Fkinematics\u002Frelative-motion":3133,"\u002Fmechanics\u002Fkinematics\u002Fcircular-motion":3134,"\u002Fmechanics\u002Fdynamics\u002Fnewtons-laws":3135,"\u002Fmechanics\u002Fdynamics\u002Ffree-body-diagrams":3136,"\u002Fmechanics\u002Fdynamics\u002Ffriction-and-curved-motion":3137,"\u002Fmechanics\u002Fdynamics\u002Fnumerical-dynamics":3138,"\u002Fmechanics\u002Fdynamics\u002Fcenter-of-mass-systems":3139,"\u002Fmechanics\u002Fenergy\u002Fwork-and-kinetic-energy":3140,"\u002Fmechanics\u002Fenergy\u002Fpotential-energy":3141,"\u002Fmechanics\u002Fenergy\u002Fmultiparticle-work":3142,"\u002Fmechanics\u002Fenergy\u002Fmass-energy-and-binding":3143,"\u002Fmechanics\u002Fenergy\u002Fphotons-and-quantization":3144,"\u002Fmechanics\u002Fmomentum\u002Fmomentum-and-collisions":3145,"\u002Fmechanics\u002Fmomentum\u002Fcenter-of-mass-collisions":3146,"\u002Fmechanics\u002Fmomentum\u002Frocket-propulsion":3147,"\u002Fmechanics\u002Frotation\u002Frotational-inertia":3148,"\u002Fmechanics\u002Frotation\u002Frotational-dynamics":3149,"\u002Fmechanics\u002Frotation\u002Frolling-motion":3150,"\u002Fmechanics\u002Frotation\u002Fangular-momentum":3151,"\u002Fmechanics\u002Frotation\u002Frolling-resistance":3152,"\u002Fmechanics\u002Frotation\u002Fgyroscopic-precession":3153,"\u002Fmechanics\u002Fgravity-and-matter\u002Fkeplerian-orbits":3154,"\u002Fmechanics\u002Fgravity-and-matter\u002Fgravitational-fields":3155,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstatic-equilibrium":3156,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-statics":3157,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-flow":3158,"\u002Fmechanics\u002Fgravity-and-matter\u002Forbital-motion":3159,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstress-and-elasticity":3160,"\u002Fmechanics\u002Foscillations-waves\u002Fdamped-oscillators":3161,"\u002Fmechanics\u002Foscillations-waves\u002Ftravelling-waves":3162,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-superposition":3163,"\u002Fmechanics\u002Foscillations-waves\u002Fstanding-waves":3164,"\u002Fmechanics\u002Foscillations-waves\u002Fsound-waves":3165,"\u002Fmechanics\u002Foscillations-waves\u002Fdoppler-effect":3166,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-packets":3167,"\u002Fmechanics\u002Foscillations-waves\u002Fbeats-and-coupling":3168,"\u002Fmechanics\u002Foscillations-waves\u002Fsimple-harmonic-motion":3169,"\u002Fmechanics\u002Foscillations-waves\u002Fpendulum-motion":3170,"\u002Fmechanics\u002Foscillations-waves\u002Fdriven-oscillators":3171,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-boundaries":3172,"\u002Fmechanics\u002Fthermodynamics\u002Fkinetic-theory-of-ideal-gases":3173,"\u002Fmechanics\u002Fthermodynamics\u002Ffirst-law-of-thermodynamics":3174,"\u002Fmechanics\u002Fthermodynamics\u002Fentropy-and-the-second-law":3175,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-processes":3176,"\u002Fmechanics\u002Fthermodynamics\u002Fphase-changes":3177,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-machines":3178,"\u002Fmechanics":3179,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcharge-and-conductors":3180,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcoulombs-law":3181,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-and-force":3182,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-maps":3183,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-dipoles":3184,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fcontinuous-charge-fields":3185,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fgauss-law-and-conductors":3186,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpoint-charge-potential":3187,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpotential-gradients-and-equipotentials":3188,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Felectrostatic-energy-and-pressure":3189,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Flaplace-boundary-problems":3190,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fcontinuous-charge-potentials":3191,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitance-fundamentals":3168,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-networks":3192,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-energy-and-force":3193,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fdielectric-polarization-and-breakdown":3194,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fcurrent-and-resistance":3164,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fkirchhoff-network-analysis":3029,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Frc-transients":3195,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-trajectories":3155,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fhall-effect":3196,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-force-on-conductors":3197,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-dipoles":3198,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmass-spectrometry":3199,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmoving-charge-fields":3200,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fbiot-savart-law":3201,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fcircular-current-loops":3202,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Famperes-law":3203,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fgauss-law-for-magnetism":3204,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmagnetic-materials":3129,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-flux":3205,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Ffaradays-law":3206,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Flenzs-law":3207,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmotional-emf":3208,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Feddy-currents":3209,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fself-inductance":3210,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-energy":3211,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Frl-circuits":3212,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-fundamentals":3147,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Freactance":3146,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Frlc-resonance":3213,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-power":3214,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Ftransformers":3215,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdisplacement-current":3216,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-waves":3217,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-momentum":3218,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdipole-radiation":3219,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fpolarization":3220,"\u002Felectricity-and-magnetism\u002Foptics\u002Freflection-and-refraction":3221,"\u002Felectricity-and-magnetism\u002Foptics\u002Fthin-lenses":3173,"\u002Felectricity-and-magnetism\u002Foptics\u002Fspherical-mirrors":3171,"\u002Felectricity-and-magnetism":3222,"\u002Flinear-algebra\u002Flinear-systems\u002Fsystems-and-echelon-forms":3223,"\u002Flinear-algebra\u002Flinear-systems\u002Fvector-and-matrix-equations":3224,"\u002Flinear-algebra\u002Flinear-systems\u002Fsolution-sets-and-applications":3225,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-independence":3226,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-transformations":3227,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-operations":3228,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-inverse-and-invertibility":3229,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fpartitioned-matrices-and-lu":3230,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fsubspaces-dimension-rank":3231,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fapplications-leontief-and-graphics":3081,"\u002Flinear-algebra\u002Fdeterminants\u002Fdeterminants-and-cofactors":3232,"\u002Flinear-algebra\u002Fdeterminants\u002Fproperties-of-determinants":3233,"\u002Flinear-algebra\u002Fdeterminants\u002Fcramer-volume-and-area":3085,"\u002Flinear-algebra\u002Fvector-spaces\u002Fvector-spaces-and-subspaces":3234,"\u002Flinear-algebra\u002Fvector-spaces\u002Fnull-and-column-spaces":3235,"\u002Flinear-algebra\u002Fvector-spaces\u002Fbases-and-independent-sets":3236,"\u002Flinear-algebra\u002Fvector-spaces\u002Fcoordinate-systems":3237,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdimension-and-rank":3238,"\u002Flinear-algebra\u002Fvector-spaces\u002Fchange-of-basis":3239,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdifference-equations-and-markov":3240,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-eigenvalues":3241,"\u002Flinear-algebra\u002Feigenvalues\u002Fthe-characteristic-equation":3242,"\u002Flinear-algebra\u002Feigenvalues\u002Fdiagonalization":3243,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-linear-transformations":3244,"\u002Flinear-algebra\u002Feigenvalues\u002Fcomplex-eigenvalues":3245,"\u002Flinear-algebra\u002Feigenvalues\u002Fdynamical-systems":3246,"\u002Flinear-algebra\u002Feigenvalues\u002Fpower-method":3247,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-length-orthogonality":3248,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Forthogonal-sets-and-projections":3249,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fgram-schmidt-and-qr":3250,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-problems":3251,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-applications":3252,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-spaces":3253,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fdiagonalizing-symmetric-matrices":3120,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fquadratic-forms":3254,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fconstrained-optimization":3255,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsingular-value-decomposition":3256,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsvd-applications-pca-imaging":3257,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-thinking-and-matrix-computation":3258,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Flu-and-cholesky":3259,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fconditioning-and-floating-point":3260,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fstability-and-error-analysis":3261,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fqr-and-numerical-least-squares":3262,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-eigenvalues-and-svd":3263,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-combinations":3264,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-independence-and-barycentric-coordinates":3265,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fconvex-combinations-and-convex-sets":3266,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fhyperplanes-and-polytopes":3267,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fcurves-and-surfaces":3268,"\u002Flinear-algebra":3269,"\u002Ftheory-of-computation":3270,"\u002Fcomputer-architecture\u002Ffoundations\u002Fbits-bytes-and-words":3271,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-representation":3272,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-arithmetic":3273,"\u002Fcomputer-architecture\u002Ffoundations\u002Ffloating-point":3274,"\u002Fcomputer-architecture\u002Ffoundations\u002Fboolean-algebra-and-bit-manipulation":3275,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fthe-machines-view":3276,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fdata-movement":3277,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farithmetic-and-logic":3278,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fcontrol-flow":3279,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fprocedures":3280,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farrays-structs-and-alignment":3281,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fmemory-layout-and-buffer-overflows":3282,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fwhat-an-isa-is":3283,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Finstruction-formats-and-operands":3284,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Faddressing-modes":3285,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fthe-y86-64-instruction-set":3286,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fy86-64-programming":3287,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Ftransistors-gates-and-boolean-functions":3288,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fcombinational-logic-and-hcl":3289,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmultiplexers-decoders-and-the-alu":3290,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmemory-elements-latches-flip-flops-and-clocking":3291,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fregister-files-and-random-access-memory":3292,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-fetch-decode-execute-cycle":3293,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-seq-stages":3294,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fcontrol-logic-and-sequencing":3295,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fassembling-seq":3296,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Ftracing-a-program":3297,"\u002Fcomputer-architecture\u002Fpipelining\u002Fpipelining-principles":3298,"\u002Fcomputer-architecture\u002Fpipelining\u002Ffrom-seq-to-pipe":3299,"\u002Fcomputer-architecture\u002Fpipelining\u002Fdata-hazards-stalling-and-forwarding":3300,"\u002Fcomputer-architecture\u002Fpipelining\u002Fcontrol-hazards-and-branch-prediction":3301,"\u002Fcomputer-architecture\u002Fpipelining\u002Fthe-complete-pipe-processor":3302,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fstorage-technologies-and-the-latency-gap":3303,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Flocality":3304,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-memories-direct-mapped":3305,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fset-associative-and-write-policies":3306,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-performance-and-cache-friendly-code":3307,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Faddress-spaces-and-translation":3308,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fpage-tables-and-page-faults":3309,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fthe-tlb-and-multi-level-page-tables":3310,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Fexceptional-control-flow":3311,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Finterrupts-and-the-kernel":3312,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fprocesses-threads-and-parallelism":3313,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fhardware-multithreading":3314,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fcache-coherence":3315,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmemory-consistency-and-synchronization":3316,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmulticore-organization":3317,"\u002Fcomputer-architecture\u002Fcapstone\u002Fthe-whole-machine":3318,"\u002Fcomputer-architecture\u002Fcapstone\u002Fassembling-a-complete-cpu":3319,"\u002Fcomputer-architecture":3270,"\u002Fdifferential-equations\u002Ffoundations\u002Fmodels-and-direction-fields":3320,"\u002Fdifferential-equations\u002Ffoundations\u002Fclassification-and-terminology":3321,"\u002Fdifferential-equations\u002Ffirst-order\u002Flinear-first-order-integrating-factors":3322,"\u002Fdifferential-equations\u002Ffirst-order\u002Fseparable-and-exact":3085,"\u002Fdifferential-equations\u002Ffirst-order\u002Fmodeling-first-order":3323,"\u002Fdifferential-equations\u002Ffirst-order\u002Fautonomous-and-population-dynamics":3084,"\u002Fdifferential-equations\u002Ffirst-order\u002Fexistence-uniqueness-euler":3091,"\u002Fdifferential-equations\u002Ffirst-order\u002Ffirst-order-difference-equations":3324,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhomogeneous-constant-coefficients":3325,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fcomplex-and-repeated-roots":3125,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fnonhomogeneous-undetermined-coefficients":3326,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fvariation-of-parameters":3327,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fmechanical-electrical-vibrations":3328,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhigher-order-linear":3329,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fpower-series-ordinary-points":3330,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fregular-singular-frobenius":3331,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fbessel-and-special-functions":3332,"\u002Fdifferential-equations\u002Flaplace\u002Flaplace-definition-ivps":3333,"\u002Fdifferential-equations\u002Flaplace\u002Fstep-impulse-convolution":3334,"\u002Fdifferential-equations\u002Fsystems\u002Fmatrices-eigenvalues-review":3335,"\u002Fdifferential-equations\u002Fsystems\u002Fconstant-coefficient-systems-phase-portraits":3336,"\u002Fdifferential-equations\u002Fsystems\u002Frepeated-eigenvalues-fundamental-matrices":3337,"\u002Fdifferential-equations\u002Fnumerical\u002Feuler-and-runge-kutta":3332,"\u002Fdifferential-equations\u002Fnumerical\u002Fmultistep-systems-stability":3338,"\u002Fdifferential-equations\u002Fnonlinear\u002Fphase-plane-autonomous-stability":3339,"\u002Fdifferential-equations\u002Fnonlinear\u002Flocally-linear-and-liapunov":3340,"\u002Fdifferential-equations\u002Fnonlinear\u002Fcompeting-species-predator-prey-limit-cycles":3341,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Ffourier-series":3342,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fheat-wave-laplace-equations":3343,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fsturm-liouville":3344,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fcalculus-of-variations":3345,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fhistorical-notes":3346,"\u002Fdifferential-equations":3347,"\u002Frelativity\u002Ffoundations\u002Fspecial-relativity-postulates":3348,"\u002Frelativity\u002Ffoundations\u002Florentz-transformation-spacetime":3349,"\u002Frelativity\u002Ffoundations\u002Ftime-dilation-length-contraction":3350,"\u002Frelativity\u002Ffoundations\u002Frelativistic-momentum-energy":3351,"\u002Frelativity\u002Ffoundations\u002Fgeneral-relativity":3230,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fminkowski-spacetime-and-the-interval":3352,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Ffour-vectors-and-index-notation":3353,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fthe-lorentz-group-and-rapidity":3354,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fdoppler-aberration-and-appearance":3355,"\u002Frelativity\u002Frelativistic-dynamics\u002Ffour-momentum-force-and-accelerated-motion":3356,"\u002Frelativity\u002Frelativistic-dynamics\u002Fparticle-decays-and-two-body-kinematics":3357,"\u002Frelativity\u002Frelativistic-dynamics\u002Fcollisions-thresholds-and-the-cm-frame":3100,"\u002Frelativity\u002Frelativistic-dynamics\u002Fmandelstam-variables-and-invariants":3358,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ffour-current-and-the-four-potential":3359,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fthe-electromagnetic-field-tensor":3360,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ftransformation-of-electric-and-magnetic-fields":3361,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fcovariant-maxwell-and-the-stress-energy-tensor":3362,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-equivalence-principle-formalized":3363,"\u002Frelativity\u002Fcurved-spacetime\u002Fmanifolds-vectors-and-the-metric":3364,"\u002Frelativity\u002Fcurved-spacetime\u002Fcovariant-derivative-and-christoffel-symbols":3365,"\u002Frelativity\u002Fcurved-spacetime\u002Fgeodesics-and-the-geodesic-equation":3366,"\u002Frelativity\u002Fcurved-spacetime\u002Fcurvature-riemann-and-geodesic-deviation":3367,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-einstein-field-equations":3322,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fthe-schwarzschild-metric":3368,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fgeodesics-and-orbits-in-schwarzschild":3369,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Flight-bending-and-null-geodesics":3370,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fperihelion-precession-of-mercury":3371,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fdeflection-of-light-and-gravitational-lensing":3372,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fgravitational-redshift-and-shapiro-delay":3251,"\u002Frelativity\u002Ftests-of-general-relativity\u002Frelativity-in-technology-gps":3373,"\u002Frelativity\u002Fblack-holes\u002Fhorizons-and-coordinate-singularities":3374,"\u002Frelativity\u002Fblack-holes\u002Frotating-and-charged-black-holes":3261,"\u002Frelativity\u002Fblack-holes\u002Fblack-hole-thermodynamics":3375,"\u002Frelativity\u002Fgravitational-waves\u002Flinearized-gravity-and-wave-solutions":3376,"\u002Frelativity\u002Fgravitational-waves\u002Fgeneration-and-the-quadrupole-formula":3377,"\u002Frelativity\u002Fgravitational-waves\u002Fdetection-ligo-and-the-first-events":3378,"\u002Frelativity\u002Fcosmological-bridge\u002Fthe-cosmological-principle-and-flrw-metric":3379,"\u002Frelativity\u002Fcosmological-bridge\u002Ffriedmann-equations-and-cosmic-dynamics":3380,"\u002Frelativity":3381,"\u002Fphysical-computing":3270,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fblackbody-radiation-and-the-planck-quantum":3382,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-photoelectric-effect-and-the-photon":3361,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fx-rays-and-the-compton-effect":3383,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-old-quantum-theory-bohr-and-sommerfeld":3384,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fde-broglie-waves-and-electron-diffraction":3385,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fwave-packets-and-the-probability-interpretation":3386,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fthe-uncertainty-principle":3387,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-schrodinger-equation-in-one-dimension":3388,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-free-particle-and-wave-packet-dynamics":3389,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fparticle-in-infinite-and-finite-square-wells":3337,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Foperators-expectation-values-and-the-harmonic-oscillator":3261,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-dirac-delta-potential":3390,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fbarrier-penetration-and-quantum-tunneling":3391,"\u002Fquantum-mechanics\u002Fformalism\u002Fhilbert-space-and-dirac-notation":3392,"\u002Fquantum-mechanics\u002Fformalism\u002Fobservables-hermitian-operators-and-eigenvalues":3393,"\u002Fquantum-mechanics\u002Fformalism\u002Fthe-postulates-and-quantum-measurement":3389,"\u002Fquantum-mechanics\u002Fformalism\u002Fposition-momentum-and-continuous-spectra":3394,"\u002Fquantum-mechanics\u002Fformalism\u002Fcommutators-and-the-generalized-uncertainty-principle":3369,"\u002Fquantum-mechanics\u002Fformalism\u002Ftime-evolution-schrodinger-and-heisenberg-pictures":3122,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fladder-operators-and-the-number-states":3395,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fcoherent-and-squeezed-states":3396,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fsymmetries-generators-and-conservation-laws":3102,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fparity-time-reversal-and-discrete-symmetries":3397,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Forbital-angular-momentum-and-spherical-harmonics":3398,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Fthe-angular-momentum-algebra":3399,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Faddition-of-angular-momenta-and-clebsch-gordan":3400,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-schrodinger-equation-in-three-dimensions":3401,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-hydrogen-atom":3402,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-isotropic-oscillator-and-hidden-symmetry":3403,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-half-pauli-matrices-and-stern-gerlach":3404,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-in-a-magnetic-field-precession-and-resonance":3405,"\u002Fquantum-mechanics\u002Fspin\u002Ftwo-level-systems-and-the-bloch-sphere":3361,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fidentical-particles-and-exchange-symmetry":3406,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fthe-pauli-principle-atoms-and-the-periodic-table":3407,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ftime-independent-perturbation-theory":3408,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ffine-structure-and-the-real-hydrogen-atom":3395,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-zeeman-and-stark-effects":3083,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-variational-method":3409,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-wkb-approximation":3410,"\u002Fquantum-mechanics":3411,"\u002Freal-analysis\u002Ffoundations\u002Fsets-logic-functions":3345,"\u002Freal-analysis\u002Ffoundations\u002Fordered-fields-completeness":3412,"\u002Freal-analysis\u002Ffoundations\u002Fabsolute-value-bounds":3413,"\u002Freal-analysis\u002Ffoundations\u002Fintervals-uncountability":3234,"\u002Freal-analysis\u002Fsequences-series\u002Fsequences-limits":3414,"\u002Freal-analysis\u002Fsequences-series\u002Flimit-laws-monotone":3120,"\u002Freal-analysis\u002Fsequences-series\u002Flimsup-bolzano-weierstrass":3415,"\u002Freal-analysis\u002Fsequences-series\u002Fcauchy-completeness":3416,"\u002Freal-analysis\u002Fsequences-series\u002Fseries-convergence":3257,"\u002Freal-analysis\u002Fsequences-series\u002Fabsolute-conditional-rearrangement":3367,"\u002Freal-analysis\u002Fmetric-spaces\u002Fmetric-spaces-norms":3417,"\u002Freal-analysis\u002Fmetric-spaces\u002Fopen-closed-sets":3418,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconvergence-completeness":3419,"\u002Freal-analysis\u002Fmetric-spaces\u002Fcompactness":3420,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconnectedness":3421,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-of-functions":3394,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuous-functions":3422,"\u002Freal-analysis\u002Fcontinuity\u002Fevt-ivt":3227,"\u002Freal-analysis\u002Fcontinuity\u002Funiform-continuity":3423,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuity-metric-spaces":3424,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-infinity-monotone":3081,"\u002Freal-analysis\u002Fdifferentiation\u002Fthe-derivative":3425,"\u002Freal-analysis\u002Fdifferentiation\u002Fmean-value-theorem":3426,"\u002Freal-analysis\u002Fdifferentiation\u002Ftaylors-theorem":3383,"\u002Freal-analysis\u002Fdifferentiation\u002Finverse-function-1d":3110,"\u002Freal-analysis\u002Friemann-integration\u002Fdarboux-integral":3261,"\u002Freal-analysis\u002Friemann-integration\u002Fintegrability-classes":3427,"\u002Freal-analysis\u002Friemann-integration\u002Fproperties-of-the-integral":3428,"\u002Freal-analysis\u002Friemann-integration\u002Ffundamental-theorem":3246,"\u002Freal-analysis\u002Friemann-integration\u002Flog-exp-improper":3371,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpointwise-uniform-convergence":3429,"\u002Freal-analysis\u002Ffunction-sequences\u002Finterchange-of-limits":3430,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpower-series-weierstrass":3431,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpicard-ode":3266,"\u002Freal-analysis\u002Fseveral-variables\u002Fdifferentiability-rn":3432,"\u002Freal-analysis\u002Fseveral-variables\u002Fgradient-chain-rule":3433,"\u002Freal-analysis\u002Fseveral-variables\u002Fhigher-derivatives-taylor-extrema":3434,"\u002Freal-analysis\u002Fseveral-variables\u002Finverse-implicit-theorems":3434,"\u002Freal-analysis\u002Fseveral-variables\u002Fmultiple-integrals":3435,"\u002Freal-analysis":3436,"\u002Fabstract-algebra\u002Ffoundations\u002Fsets-functions-relations":3437,"\u002Fabstract-algebra\u002Ffoundations\u002Fintegers-and-modular-arithmetic":3438,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fgroup-axioms-and-first-examples":3439,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fdihedral-and-symmetric-groups":3440,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fmatrix-and-quaternion-groups":3441,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fhomomorphisms-and-group-actions":3442,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fsubgroups-and-substructures":3443,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcyclic-groups":3444,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fgeneration-and-subgroup-lattices":3445,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcosets-lagrange-and-normal-subgroups":3446,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fisomorphism-theorems":3410,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcomposition-series-and-the-alternating-group":3447,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Factions-and-cayleys-theorem":3439,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fconjugation-and-the-class-equation":3356,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fsylow-theorems":3448,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fautomorphisms-and-simple-groups":3449,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fdirect-products-and-finite-abelian-groups":3450,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fsemidirect-products":3451,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fnilpotent-and-solvable-groups":3452,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fclassifying-small-groups":3453,"\u002Fabstract-algebra\u002Fring-theory\u002Frings-definitions-and-examples":3454,"\u002Fabstract-algebra\u002Fring-theory\u002Fideals-quotients-and-homomorphisms":3455,"\u002Fabstract-algebra\u002Fring-theory\u002Ffractions-and-the-chinese-remainder-theorem":3449,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Feuclidean-domains-pids-ufds":3456,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fpolynomial-rings-over-fields":3425,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fgauss-lemma-and-unique-factorization":3457,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Firreducibility-criteria-and-groebner":3458,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fintroduction-to-modules":3459,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ffree-modules-and-direct-sums":3460,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ftensor-products-and-exact-sequences":3461,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fvector-spaces-and-linear-maps":3462,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fstructure-theorem-over-pids":3463,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Frational-canonical-form":3464,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fjordan-canonical-form":3465,"\u002Fabstract-algebra\u002Ffield-theory\u002Ffield-extensions-and-algebraic-elements":3466,"\u002Fabstract-algebra\u002Ffield-theory\u002Fstraightedge-and-compass-constructions":3099,"\u002Fabstract-algebra\u002Ffield-theory\u002Fsplitting-fields-and-algebraic-closure":3467,"\u002Fabstract-algebra\u002Ffield-theory\u002Fseparable-and-cyclotomic-extensions":3468,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fthe-galois-correspondence":3341,"\u002Fabstract-algebra\u002Fgalois-theory\u002Ffinite-fields":3469,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fcyclotomic-and-abelian-extensions":3470,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fgalois-groups-of-polynomials":3398,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fsolvability-by-radicals-and-the-quintic":3470,"\u002Fabstract-algebra\u002Fcapstone\u002Fcommutative-algebra-and-algebraic-geometry":3471,"\u002Fabstract-algebra\u002Fcapstone\u002Frepresentation-and-character-theory":3472,"\u002Fabstract-algebra":3473,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fatomic-spectra-rutherford":3474,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-model-hydrogen":3475,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fx-ray-spectra-franck-hertz":3476,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-sommerfeld-old-quantum-theory":3477,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fold-quantum-theory-limits-wkb":3478,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fschrodinger-3d-hydrogen":3390,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fhydrogen-wave-functions":3479,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fradial-equation-in-full":3480,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fsymmetry-degeneracy-runge-lenz":3481,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fexpectation-values-virial":3482,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fquantum-defects-alkali-spectra":3483,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Frydberg-atoms":3484,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Frelativistic-kinetic-correction":3485,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fspin-orbit-thomas-precession":3224,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdarwin-term-fine-structure-formula":3353,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdirac-equation-hydrogen":3099,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Flamb-shift-qed":3486,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fhyperfine-structure-21cm":3083,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fnuclear-effects-isotope-shift":3487,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fperiodic-table-atomic-spectra":3488,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fcentral-field-self-consistent":3121,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fidentical-particles-hartree-fock":3415,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhelium-two-electron-atom":3489,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fls-jj-coupling-term-symbols":3490,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhund-rules-ground-terms":3491,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fzeeman-effect":3492,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fpaschen-back-intermediate":3493,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fstark-effect-polarizability":3494,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Ftime-dependent-perturbation-golden-rule":3495,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fdipole-approximation-einstein-coefficients":3496,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fselection-rules-forbidden-transitions":3497,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Flifetimes-and-line-shapes":3498,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Flaser-principles":3499,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fspectroscopy-techniques":3500,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fline-catalog-nist-asd":3501,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Flaser-cooling-doppler":3502,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fsub-doppler-trapping":3446,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fbose-einstein-condensation":3503,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Foptical-clocks-precision":3504,"\u002Fatomic-physics":3505,"\u002Fdatabases":3270,"\u002Fcategory-theory\u002Ffoundations\u002Fwhat-is-a-category":3506,"\u002Fcategory-theory\u002Ffoundations\u002Fexamples-of-categories":3507,"\u002Fcategory-theory\u002Ffoundations\u002Fspecial-morphisms":3508,"\u002Fcategory-theory\u002Ffoundations\u002Ffunctors":3437,"\u002Fcategory-theory\u002Ffoundations\u002Fnatural-transformations":3509,"\u002Fcategory-theory\u002Ffoundations\u002Fsize-and-set-theory":3510,"\u002Fcategory-theory\u002Funiversal-properties\u002Funiversal-properties":3511,"\u002Fcategory-theory\u002Funiversal-properties\u002Fproducts-and-coproducts":3512,"\u002Fcategory-theory\u002Funiversal-properties\u002Fconstructions-on-categories":3513,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Frepresentable-functors":3514,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-lemma":3515,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-consequences":3516,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits":3517,"\u002Fcategory-theory\u002Flimits-colimits\u002Fproducts-equalizers-pullbacks":3518,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcolimits":3519,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcomputing-limits":3520,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits-and-functors":3521,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions":3522,"\u002Fcategory-theory\u002Fadjunctions\u002Funits-and-counits":3523,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions-via-universal-arrows":3524,"\u002Fcategory-theory\u002Fadjunctions\u002Ffree-forgetful-adjunctions":3525,"\u002Fcategory-theory\u002Fadjoints-limits\u002Flimits-via-adjoints":3526,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fpresheaf-limits-colimits":3527,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoints-preserve-limits":3528,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoint-functor-theorem":3519,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fmonads":3529,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-eilenberg-moore":3530,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fkleisli-and-programming":3531,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-for-endofunctors":3532,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Fcartesian-closed-categories":3533,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Flambda-calculus-correspondence":3478,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Ffixed-points-and-recursion":3534,"\u002Fcategory-theory":3535,"\u002Fdeep-learning\u002Fmathematical-background\u002Flinear-algebra-for-deep-learning":3536,"\u002Fdeep-learning\u002Fmathematical-background\u002Fprobability-and-information-theory":3537,"\u002Fdeep-learning\u002Fmathematical-background\u002Fnumerical-computation":3538,"\u002Fdeep-learning\u002Fmathematical-background\u002Fcalculus":3539,"\u002Fdeep-learning\u002Ffoundations\u002Fwhat-is-deep-learning":3540,"\u002Fdeep-learning\u002Ffoundations\u002Fmachine-learning-refresher":3541,"\u002Fdeep-learning\u002Ffoundations\u002Flinear-models-and-the-perceptron":3500,"\u002Fdeep-learning\u002Fneural-networks\u002Fthe-multilayer-perceptron":3542,"\u002Fdeep-learning\u002Fneural-networks\u002Factivation-functions":3543,"\u002Fdeep-learning\u002Fneural-networks\u002Funiversal-approximation":3544,"\u002Fdeep-learning\u002Fneural-networks\u002Fbackpropagation":3545,"\u002Fdeep-learning\u002Fneural-networks\u002Floss-functions-and-output-units":3546,"\u002Fdeep-learning\u002Foptimization\u002Fgradient-descent-and-sgd":3547,"\u002Fdeep-learning\u002Foptimization\u002Fmomentum-and-adaptive-methods":3548,"\u002Fdeep-learning\u002Foptimization\u002Finitialization":3549,"\u002Fdeep-learning\u002Foptimization\u002Fthe-optimization-landscape":3550,"\u002Fdeep-learning\u002Foptimization\u002Fsecond-order-and-approximate-methods":3551,"\u002Fdeep-learning\u002Fregularization\u002Fregularization-overview":3552,"\u002Fdeep-learning\u002Fregularization\u002Fdropout-and-data-augmentation":3553,"\u002Fdeep-learning\u002Fregularization\u002Fearly-stopping-and-parameter-sharing":3554,"\u002Fdeep-learning\u002Fregularization\u002Fnormalization":3555,"\u002Fdeep-learning\u002Farchitectures\u002Fconvolutional-networks":3556,"\u002Fdeep-learning\u002Farchitectures\u002Fcnn-architectures":3557,"\u002Fdeep-learning\u002Farchitectures\u002Frecurrent-networks":3558,"\u002Fdeep-learning\u002Farchitectures\u002Flstm-and-gru":3559,"\u002Fdeep-learning\u002Farchitectures\u002Fattention-and-transformers":3560,"\u002Fdeep-learning\u002Farchitectures\u002Fthe-transformer-architecture":3561,"\u002Fdeep-learning\u002Farchitectures\u002Ftransformers-in-practice":3562,"\u002Fdeep-learning\u002Farchitectures\u002Fgraph-neural-networks":3563,"\u002Fdeep-learning\u002Farchitectures\u002Fstate-space-models":3564,"\u002Fdeep-learning\u002Ftheory\u002Fgeneralization-theory":3565,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-robustness":3566,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-defenses":3567,"\u002Fdeep-learning\u002Ftheory\u002Fbayesian-and-ensemble-methods":3568,"\u002Fdeep-learning\u002Ftheory\u002Fdeep-equilibrium-models":3512,"\u002Fdeep-learning\u002Fgenerative-models\u002Flinear-factor-models":3569,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoencoders":3570,"\u002Fdeep-learning\u002Fgenerative-models\u002Fvariational-autoencoders":3571,"\u002Fdeep-learning\u002Fgenerative-models\u002Fgenerative-adversarial-networks":3572,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoregressive-and-normalizing-flows":3573,"\u002Fdeep-learning\u002Fgenerative-models\u002Fenergy-based-and-boltzmann-machines":3574,"\u002Fdeep-learning\u002Fgenerative-models\u002Fdiffusion-and-score-based-models":3575,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fstructured-probabilistic-models":3064,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fmonte-carlo-and-mcmc":3576,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fapproximate-inference":3577,"\u002Fdeep-learning\u002Fpractical\u002Fpractical-methodology":3300,"\u002Fdeep-learning\u002Fpractical\u002Fhyperparameters-and-debugging":3578,"\u002Fdeep-learning\u002Fpractical\u002Frepresentation-learning":3579,"\u002Fdeep-learning\u002Fpractical\u002Ftransfer-learning":3580,"\u002Fdeep-learning\u002Fpractical\u002Fapplications":3581,"\u002Fdeep-learning\u002Fpractical\u002Fmodel-compression-and-distillation":3582,"\u002Fdeep-learning\u002Fpractical\u002Fmeta-learning-and-few-shot":3583,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Flarge-language-models":3584,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fscaling-inference-and-alignment":3585,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fseq2seq-pretraining-and-bart":3586,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ftext-to-text-transfer-and-conditional-generation":3587,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fspeech-and-audio-models":3588,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fself-supervised-speech-and-synthesis":3589,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fai-agents":3273,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fagent-memory-retrieval-and-orchestration":3590,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmixture-of-experts":3591,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmultimodal-models":3592,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ffusion-and-vision-language-models":3593,"\u002Fdeep-learning\u002Freinforcement-learning\u002Ffoundations-of-reinforcement-learning":3310,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fmodel-free-prediction-and-control":3594,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fdeep-q-networks":3595,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fpolicy-gradients-and-actor-critic":3596,"\u002Fdeep-learning\u002Freinforcement-learning\u002Frl-from-human-feedback":3597,"\u002Fdeep-learning":3270,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fequilibrium-state-variables-zeroth-law":3598,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Ffirst-law-heat-and-work":3395,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fsecond-law-entropy-and-the-carnot-bound":3599,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fthermodynamic-potentials-and-maxwell-relations":3600,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fstability-response-functions-and-the-third-law":3601,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fclassical-statistics-and-equipartition":3602,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fphase-space-and-liouvilles-theorem":3603,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fensembles-and-the-equal-probability-postulate":3604,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fstatistical-entropy-boltzmann-and-gibbs":3605,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fmicrocanonical-ensemble-and-entropy":3606,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fequilibrium-conditions-temperature-pressure-chemical-potential":3442,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fideal-gas-phase-space-and-the-sackur-tetrode-entropy":3607,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Ftwo-state-systems-paramagnets-and-negative-temperature":3608,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fcanonical-ensemble-and-the-boltzmann-distribution":3609,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fpartition-function-and-the-helmholtz-free-energy":3329,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fenergy-fluctuations-and-ensemble-equivalence":3122,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fthe-einstein-solid-and-harmonic-systems":3610,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fparamagnetism-and-the-schottky-anomaly":3611,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fideal-gas-partition-function-and-the-gibbs-paradox":3612,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fequipartition-and-the-virial-theorem":3256,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fmolecular-gases-rotation-and-vibration":3613,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fgrand-canonical-ensemble-and-the-grand-partition-function":3614,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fchemical-potential-fugacity-and-number-fluctuations":3334,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fensemble-summary-and-the-thermodynamic-web":3261,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fquantum-statistics-bose-einstein-and-fermi-dirac":3615,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fderiving-the-quantum-distributions":3230,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fthe-classical-limit-and-quantum-concentration":3100,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fideal-quantum-gases-general-framework":3116,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-and-the-fermion-gas":3616,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthe-photon-gas-and-plancks-radiation-law":3617,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fblackbody-thermodynamics-and-radiation-pressure":3618,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fphonons-and-the-debye-model":3248,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-derived":3619,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthermodynamics-of-the-bose-gas-and-superfluidity":3110,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fthe-ideal-fermi-gas-at-zero-temperature":3620,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fsommerfeld-expansion-and-electrons-in-metals":3621,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":3622,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fneutron-stars-and-nuclear-matter":3623,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-cluster-expansion-and-virial-coefficients":3444,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-van-der-waals-gas-and-liquid-gas-coexistence":3624,"\u002Fstatistical-mechanics\u002Finteractions\u002Fquantum-gases-with-interactions-and-exchange":3625,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fphases-coexistence-and-classification":3626,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-ising-model-and-exact-solutions":3627,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fmean-field-theory-and-the-weiss-model":3390,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fcritical-exponents-and-landau-theory":3628,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-renormalization-group-idea":3368,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fthermodynamic-fluctuations-and-response":3629,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fbrownian-motion-and-the-langevin-equation":3229,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Flinear-response-and-the-fluctuation-dissipation-theorem":3630,"\u002Fstatistical-mechanics":3631,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fbonding-mechanisms":3632,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fmolecular-orbitals-and-h2-plus":3095,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fhydrogen-molecule-and-exchange":3355,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fvan-der-waals-forces":3633,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Frotational-vibrational-spectra":3634,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Fanharmonicity-and-rovibrational-structure":3635,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Framan-and-electronic-bands":3636,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Flasers-and-masers":3637,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fstructure-of-solids":3638,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fbravais-lattices-and-crystal-systems":3228,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Freciprocal-lattice-and-brillouin-zones":3639,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fdiffraction-and-structure-factors":3640,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonon-dispersion":3641,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonons-quantization-and-dos":3642,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fdebye-einstein-heat-capacity":3371,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fanharmonicity-and-thermal-transport":3643,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ffree-electron-gas-and-conduction":3644,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fsommerfeld-model-and-heat-capacity":3645,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ftransport-and-the-hall-effect":3646,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fscreening-and-plasmons":3647,"\u002Fcondensed-matter\u002Fband-theory\u002Fblochs-theorem-and-energy-bands":3426,"\u002Fcondensed-matter\u002Fband-theory\u002Fnearly-free-electron-model":3370,"\u002Fcondensed-matter\u002Fband-theory\u002Ftight-binding-method":3648,"\u002Fcondensed-matter\u002Fband-theory\u002Ffermi-surfaces-and-semiclassical-dynamics":3649,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fsemiconductor-bands-and-junctions":3650,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fintrinsic-and-extrinsic-semiconductors":3651,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fcarrier-transport-and-recombination":3361,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fthe-pn-junction":3652,"\u002Fcondensed-matter\u002Fsemiconductors\u002Ftransistors-and-optoelectronics":3653,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fdielectrics-and-polarization":3598,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fferroelectrics-and-piezoelectrics":3498,"\u002Fcondensed-matter\u002Fmagnetism\u002Fdiamagnetism-and-paramagnetism":3225,"\u002Fcondensed-matter\u002Fmagnetism\u002Fexchange-and-ferromagnetism":3654,"\u002Fcondensed-matter\u002Fmagnetism\u002Fantiferromagnetism-and-domains":3091,"\u002Fcondensed-matter\u002Fmagnetism\u002Fspin-waves-and-magnons":3655,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fsuperconductivity-phenomenology":3656,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Flondon-theory-and-the-meissner-effect":3236,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fginzburg-landau-theory":3657,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fbcs-theory":3491,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fjosephson-and-high-tc":3658,"\u002Fcondensed-matter\u002Fnanostructures\u002Fquantum-wells-wires-and-dots":3083,"\u002Fcondensed-matter\u002Fnanostructures\u002Finteger-quantum-hall-effect":3659,"\u002Fcondensed-matter\u002Fnanostructures\u002Ffractional-quantum-hall-and-topology":3094,"\u002Fcondensed-matter\u002Fnanostructures\u002Fgraphene-and-dirac-materials":3660,"\u002Fcondensed-matter":3411,"\u002Flogic\u002Ffoundations\u002Flogic-as-a-mathematical-model":3661,"\u002Flogic\u002Fsentential-logic\u002Fformal-languages-and-well-formed-formulas":3662,"\u002Flogic\u002Fsentential-logic\u002Ftruth-assignments-and-tautologies":3663,"\u002Flogic\u002Fsentential-logic\u002Funique-readability-and-parsing":3664,"\u002Flogic\u002Fsentential-logic\u002Finduction-and-recursion":3108,"\u002Flogic\u002Fsentential-logic\u002Fexpressive-completeness-and-normal-forms":3665,"\u002Flogic\u002Fsentential-logic\u002Fboolean-circuits":3666,"\u002Flogic\u002Fsentential-logic\u002Fcompactness-and-effectiveness":3108,"\u002Flogic\u002Ffirst-order-languages\u002Ffirst-order-languages":3667,"\u002Flogic\u002Ffirst-order-languages\u002Fstructures-truth-and-satisfaction":3521,"\u002Flogic\u002Ffirst-order-languages\u002Fdefinability-and-elementary-equivalence":3668,"\u002Flogic\u002Ffirst-order-languages\u002Fterms-substitution-and-parsing":3669,"\u002Flogic\u002Fdeductive-calculus\u002Fa-deductive-calculus":3670,"\u002Flogic\u002Fdeductive-calculus\u002Fdeduction-theorem-and-derived-rules":3668,"\u002Flogic\u002Fdeductive-calculus\u002Fsoundness":3671,"\u002Flogic\u002Fdeductive-calculus\u002Fcompleteness-and-consistency":3672,"\u002Flogic\u002Fmodels-and-theories\u002Fcompactness-and-lowenheim-skolem":3673,"\u002Flogic\u002Fmodels-and-theories\u002Ftheories-elementary-classes-and-categoricity":3674,"\u002Flogic\u002Fmodels-and-theories\u002Finterpretations-between-theories":3675,"\u002Flogic\u002Fmodels-and-theories\u002Fnonstandard-analysis":3676,"\u002Flogic\u002Farithmetic-and-definability\u002Fdefinability-in-arithmetic":3677,"\u002Flogic\u002Farithmetic-and-definability\u002Fnatural-numbers-with-successor":3678,"\u002Flogic\u002Farithmetic-and-definability\u002Fpresburger-and-reducts":3599,"\u002Flogic\u002Farithmetic-and-definability\u002Fa-subtheory-and-representability":3679,"\u002Flogic\u002Fincompleteness\u002Farithmetization-of-syntax":3672,"\u002Flogic\u002Fincompleteness\u002Fincompleteness-and-undecidability":3680,"\u002Flogic\u002Fincompleteness\u002Fsecond-incompleteness-theorem":3681,"\u002Flogic\u002Fcomputability-and-representability\u002Frecursive-functions":3314,"\u002Flogic\u002Fcomputability-and-representability\u002Frepresenting-exponentiation":3682,"\u002Flogic\u002Fsecond-order-logic\u002Fsecond-order-languages":3499,"\u002Flogic\u002Fsecond-order-logic\u002Fskolem-functions-and-many-sorted-logic":3683,"\u002Flogic\u002Fsecond-order-logic\u002Fgeneral-structures":3684,"\u002Flogic":3685,"\u002Freinforcement-learning\u002Ffoundations\u002Fwhat-is-reinforcement-learning":3686,"\u002Freinforcement-learning\u002Ffoundations\u002Fa-brief-history-of-rl":3687,"\u002Freinforcement-learning\u002Ffoundations\u002Fmulti-armed-bandits":3300,"\u002Freinforcement-learning\u002Ffoundations\u002Fbandit-exploration-algorithms":3688,"\u002Freinforcement-learning\u002Ffoundations\u002Fmarkov-decision-processes":3689,"\u002Freinforcement-learning\u002Ffoundations\u002Fvalue-functions-and-optimality":3690,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdynamic-programming":3691,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdp-async-and-gpi":3681,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-methods":3692,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-off-policy":3693,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftemporal-difference-learning":3694,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftd-control-sarsa-and-q-learning":3592,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-bootstrapping":3695,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-off-policy-methods":3696,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-and-learning":3697,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-focusing-and-decision-time":3698,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdecision-time-planning":3699,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-tree-search":3317,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-prediction":3700,"\u002Freinforcement-learning\u002Fapproximation\u002Ffeature-construction-and-nonlinear":3701,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-control":3702,"\u002Freinforcement-learning\u002Fapproximation\u002Faverage-reward-control":3703,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-and-the-deadly-triad":3508,"\u002Freinforcement-learning\u002Fapproximation\u002Fbellman-error-and-gradient-td":3704,"\u002Freinforcement-learning\u002Fapproximation\u002Feligibility-traces":3680,"\u002Freinforcement-learning\u002Fapproximation\u002Ftrue-online-and-sarsa-lambda":3705,"\u002Freinforcement-learning\u002Fapproximation\u002Fpolicy-gradient-methods":3706,"\u002Freinforcement-learning\u002Fapproximation\u002Factor-critic-and-continuous-actions":3707,"\u002Freinforcement-learning\u002Fapproximation\u002Fleast-squares-and-memory-based-methods":3328,"\u002Freinforcement-learning\u002Fapproximation\u002Fmemory-and-kernel-methods":3708,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-eligibility-traces":3458,"\u002Freinforcement-learning\u002Fapproximation\u002Fstable-off-policy-traces":3709,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdeep-q-networks":3710,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdqn-improvements":3290,"\u002Freinforcement-learning\u002Fdeep-rl\u002Factor-critic-and-ppo":3711,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fppo-and-continuous-control":3712,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fcase-studies":2981,"\u002Freinforcement-learning\u002Fdeep-rl\u002Frl-beyond-games":3713,"\u002Freinforcement-learning\u002Fdeep-rl\u002Ffrontiers":3714,"\u002Freinforcement-learning\u002Fdeep-rl\u002Freward-design-and-open-problems":3568,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow":3715,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow-part-2":3716,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control":3717,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control-part-2":3603,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl":3718,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl-part-2":3719,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration":3720,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration-part-2":3317,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl":3384,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl-part-2":3721,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl":3722,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl-part-2":3723,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl":3724,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl-part-2":3725,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl":3726,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl-part-2":3727,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Frlhf-and-language-models":3728,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps":3729,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps-part-2":3730,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl":3731,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl-part-2":3732,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmeta-rl-and-generalization":3733,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fpsychology-of-reinforcement":3734,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Finstrumental-conditioning-and-control":3735,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-and-td-error":3736,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-in-the-brain":3737,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fanimal-learning-and-cognition":3738,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fcognitive-maps-and-planning":3739,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fneuroscience-of-reinforcement":3740,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fseveral-learning-systems":3741,"\u002Freinforcement-learning":3270,"\u002Fartificial-intelligence\u002Ffoundations\u002Fwhat-is-ai":3742,"\u002Fartificial-intelligence\u002Ffoundations\u002Ffoundations-of-ai":3743,"\u002Fartificial-intelligence\u002Ffoundations\u002Fintelligent-agents":3744,"\u002Fartificial-intelligence\u002Ffoundations\u002Fagent-architectures":3745,"\u002Fartificial-intelligence\u002Fsearch\u002Funinformed-search":3746,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-strategies-compared":3747,"\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search":3748,"\u002Fartificial-intelligence\u002Fsearch\u002Fheuristic-functions":3749,"\u002Fartificial-intelligence\u002Fsearch\u002Flocal-search":3750,"\u002Fartificial-intelligence\u002Fsearch\u002Fpopulation-and-continuous-search":3751,"\u002Fartificial-intelligence\u002Fsearch\u002Fadversarial-search":3752,"\u002Fartificial-intelligence\u002Fsearch\u002Fgames-of-chance-and-imperfect-information":3753,"\u002Fartificial-intelligence\u002Fsearch\u002Fconstraint-satisfaction":3754,"\u002Fartificial-intelligence\u002Fsearch\u002Fcsp-search-and-structure":3597,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-under-uncertainty":3452,"\u002Fartificial-intelligence\u002Fsearch\u002Fbelief-state-and-online-search":3755,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic":3756,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-inference":3757,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic":3758,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic-in-use":3759,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Finference-and-resolution":3760,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-resolution":3579,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fclassical-planning":3761,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-graphs-and-graphplan":3762,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-in-the-real-world":3763,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-under-uncertainty":3764,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fknowledge-representation":3765,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Freasoning-systems-and-defaults":3766,"\u002Fartificial-intelligence\u002Funcertainty\u002Fprobability-and-bayes":3767,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayes-rule-and-naive-bayes":3768,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayesian-networks":3769,"\u002Fartificial-intelligence\u002Funcertainty\u002Finference-in-bayesian-networks":3770,"\u002Fartificial-intelligence\u002Funcertainty\u002Freasoning-over-time":3771,"\u002Fartificial-intelligence\u002Funcertainty\u002Ftracking-and-data-association":3772,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions":3511,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmarkov-decision-processes":3762,"\u002Fartificial-intelligence\u002Funcertainty\u002Fdecision-networks-and-game-theory":3773,"\u002Fartificial-intelligence\u002Funcertainty\u002Fgame-theory-and-mechanism-design":3051,"\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples":3774,"\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families":3775,"\u002Fartificial-intelligence\u002Flearning\u002Fprobabilistic-learning":3776,"\u002Fartificial-intelligence\u002Flearning\u002Fexpectation-maximization":3777,"\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning":3778,"\u002Fartificial-intelligence\u002Flearning\u002Fgeneralization-and-policy-search":3560,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-in-learning":3779,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-based-learning-methods":3780,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fvision-and-perception":3781,"\u002Fartificial-intelligence\u002Ffrontiers\u002Freconstructing-the-3d-world":3782,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobotics":3783,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobot-planning-and-control":3784,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnatural-language-in-ai":3785,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnlp-grammar-translation-and-speech":3786,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fphilosophy-and-future":3787,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fai-ethics-and-future":3788,"\u002Fartificial-intelligence":3270,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-constituents-nuclide-chart":3527,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-size-charge-distributions":3789,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-masses-binding-energy":3087,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fsemi-empirical-mass-formula":3085,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-moments-multipoles":3621,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnuclear-force-shell-overview":3790,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fthe-deuteron":3113,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnucleon-nucleon-scattering":3423,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fmeson-theory-isospin":3791,"\u002Fnuclear-physics\u002Fnuclear-models\u002Ffermi-gas-model":3792,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fliquid-drop-collective-coordinates":3793,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fshell-model-single-particle":3483,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fcollective-model-rotations-vibrations":3794,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-law-modes":3795,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-kinetics-equilibrium":3796,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-decay-gamow-theory":3682,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-fine-structure-hindrance":3797,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fbeta-decay-energetics-neutrino":3798,"\u002Fnuclear-physics\u002Fbeta-decay\u002Ffermi-theory-beta-decay":3097,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fweak-interaction-parity-violation":3326,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fdouble-beta-decay-neutrino-mass":3799,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fgamma-multipole-radiation":3427,"\u002Fnuclear-physics\u002Fgamma-decay\u002Finternal-conversion-isomers":3800,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fangular-correlations-mossbauer":3801,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Freaction-kinematics-cross-sections":3322,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fcompound-nucleus-resonances":3410,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fdirect-reactions-optical-model":3802,"\u002Fnuclear-physics\u002Ffission\u002Ffission-barrier-dynamics":3803,"\u002Fnuclear-physics\u002Ffission\u002Fchain-reactions-reactor-physics":3804,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Ffusion-reactions-confinement":3092,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fstellar-nucleosynthesis":3467,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fbig-bang-nucleosynthesis":3329,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fcharged-particle-stopping-power":3805,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fphoton-neutron-interactions":3377,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fradiation-detectors":3441,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fdosimetry-radiation-biology":3806,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fnuclear-applications-dating-medicine":3807,"\u002Fnuclear-physics":3808,"\u002Fnatural-language-processing\u002Ffoundations\u002Fwhat-is-nlp":3809,"\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization":3810,"\u002Fnatural-language-processing\u002Ffoundations\u002Fminimum-edit-distance":3448,"\u002Fnatural-language-processing\u002Ffoundations\u002Fn-gram-language-models":3811,"\u002Fnatural-language-processing\u002Ffoundations\u002Fsmoothing-and-backoff":3812,"\u002Fnatural-language-processing\u002Fclassification\u002Fnaive-bayes-and-sentiment":3813,"\u002Fnatural-language-processing\u002Fclassification\u002Fevaluating-classifiers":3296,"\u002Fnatural-language-processing\u002Fclassification\u002Flogistic-regression":3814,"\u002Fnatural-language-processing\u002Fclassification\u002Fsentiment-and-affect-lexicons":3815,"\u002Fnatural-language-processing\u002Fsemantics\u002Fvector-semantics-and-embeddings":3593,"\u002Fnatural-language-processing\u002Fsemantics\u002Fstatic-word-embeddings":3816,"\u002Fnatural-language-processing\u002Fsemantics\u002Fneural-language-models":3761,"\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling":3817,"\u002Fnatural-language-processing\u002Fsequences\u002Fcrfs-and-neural-taggers":3818,"\u002Fnatural-language-processing\u002Fsequences\u002Frnns-and-lstms":3819,"\u002Fnatural-language-processing\u002Ftransformers\u002Ftransformers-and-attention":3820,"\u002Fnatural-language-processing\u002Ftransformers\u002Fthe-transformer-architecture":3821,"\u002Fnatural-language-processing\u002Ftransformers\u002Flarge-language-models":3822,"\u002Fnatural-language-processing\u002Ftransformers\u002Fllm-pretraining-and-scaling":3823,"\u002Fnatural-language-processing\u002Ftransformers\u002Ffine-tuning-and-prompting":3274,"\u002Fnatural-language-processing\u002Ftransformers\u002Fprompting-and-alignment":3824,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-parsing":3825,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcky-scoring-and-evaluation":3767,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdependency-parsing":3826,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fgraph-based-and-neural-dependency-parsing":3827,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fword-senses-and-wsd":3828,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fwsd-in-practice-and-induction":3829,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-roles-and-information-extraction":3830,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Frelations-events-and-templates":3831,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoreference-and-discourse":3832,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoherence-and-discourse-structure":3833,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Flogical-semantics":3692,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcompositional-semantics-and-description-logics":3834,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-parsing":3835,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fneural-semantic-parsing":3836,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Finformation-extraction":3837,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftimes-events-and-templates":3838,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdiscourse-coherence":3839,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fentity-based-and-global-coherence":3840,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-grammars":3841,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftreebanks-and-lexicalized-grammars":3842,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation":3843,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation-decoding-and-evaluation":3844,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering":3845,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering-knowledge-and-llms":3546,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots":2981,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants":3302,"\u002Fnatural-language-processing\u002Fapplications\u002Ftext-summarization":3846,"\u002Fnatural-language-processing\u002Fapplications\u002Fabstractive-summarization-and-evaluation":3847,"\u002Fnatural-language-processing\u002Fspeech\u002Fphonetics":3848,"\u002Fnatural-language-processing\u002Fspeech\u002Facoustic-phonetics":3849,"\u002Fnatural-language-processing\u002Fspeech\u002Fautomatic-speech-recognition":3560,"\u002Fnatural-language-processing\u002Fspeech\u002Fasr-evaluation-and-applications":3850,"\u002Fnatural-language-processing":3270,"\u002Fparticle-physics\u002Ffoundations\u002Fhistorical-overview-particle-zoo":3851,"\u002Fparticle-physics\u002Ffoundations\u002Fparticle-physics-basic-concepts":3468,"\u002Fparticle-physics\u002Ffoundations\u002Ffundamental-interactions-force-carriers":3852,"\u002Fparticle-physics\u002Funits-kinematics\u002Fnatural-units-and-scales":3613,"\u002Fparticle-physics\u002Funits-kinematics\u002Ffour-vectors-invariant-mass":3853,"\u002Fparticle-physics\u002Funits-kinematics\u002Fdecay-scattering-kinematics-mandelstam":3854,"\u002Fparticle-physics\u002Funits-kinematics\u002Fcross-sections-golden-rule":3802,"\u002Fparticle-physics\u002Fsymmetries\u002Fconservation-laws-symmetries":3855,"\u002Fparticle-physics\u002Fsymmetries\u002Fdiscrete-symmetries-cpt":3856,"\u002Fparticle-physics\u002Fsymmetries\u002Fparity-violation-weak":3418,"\u002Fparticle-physics\u002Fsymmetries\u002Fsu2-su3-flavor-symmetry":3120,"\u002Fparticle-physics\u002Fquark-model\u002Feightfold-way-su3":3857,"\u002Fparticle-physics\u002Fquark-model\u002Fmeson-spectroscopy":3372,"\u002Fparticle-physics\u002Fquark-model\u002Fbaryon-spectroscopy":3858,"\u002Fparticle-physics\u002Fquark-model\u002Fcolor-confinement-exotics":3405,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fklein-gordon-equation":3859,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fdirac-equation-spinors":3659,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fantiparticles-hole-theory":3860,"\u002Fparticle-physics\u002Fqed\u002Ffeynman-rules-qed":3861,"\u002Fparticle-physics\u002Fqed\u002Fqed-tree-processes":3628,"\u002Fparticle-physics\u002Fqed\u002Frenormalization-running-coupling":3862,"\u002Fparticle-physics\u002Fqed\u002Felectron-g-2":3091,"\u002Fparticle-physics\u002Fweak-interaction\u002Fva-structure-weak":3863,"\u002Fparticle-physics\u002Fweak-interaction\u002Fw-z-bosons-decays":3864,"\u002Fparticle-physics\u002Fweak-interaction\u002Fckm-matrix":3865,"\u002Fparticle-physics\u002Fweak-interaction\u002Fcp-violation-kaons-b-mesons":3409,"\u002Fparticle-physics\u002Fqcd\u002Fcolor-su3-gluons":3625,"\u002Fparticle-physics\u002Fqcd\u002Fasymptotic-freedom-confinement":3866,"\u002Fparticle-physics\u002Fqcd\u002Fdeep-inelastic-scattering-partons":3867,"\u002Fparticle-physics\u002Fqcd\u002Fjets-hadronization":3484,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Felectroweak-su2-u1":3868,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fspontaneous-symmetry-breaking":3529,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-mechanism":3869,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-boson-discovery":3870,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fstandard-model":3418,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-oscillations":3451,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-mass-pmns":3871,"\u002Fparticle-physics\u002Fneutrinos\u002Fdirac-majorana-experiments":3872,"\u002Fparticle-physics\u002Fexperiment\u002Faccelerators-luminosity":3873,"\u002Fparticle-physics\u002Fexperiment\u002Fdetectors-subsystems":3507,"\u002Fparticle-physics\u002Fexperiment\u002Fhow-discoveries-are-made":3874,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fbeyond-standard-model":3248,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fgrand-unified-theories":3875,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fsupersymmetry":3351,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fhierarchy-problem-naturalness":3876,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fdark-matter-candidates":3877,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fmatter-antimatter-open-questions":3675,"\u002Fparticle-physics":3878,"\u002Fastrophysics-cosmology\u002Forientation\u002Fthe-sun-and-stars":3469,"\u002Fastrophysics-cosmology\u002Forientation\u002Fstellar-death-final-states":3623,"\u002Fastrophysics-cosmology\u002Forientation\u002Fgalaxies-and-cosmology":3879,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fmagnitudes-fluxes-and-the-distance-modulus":3408,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fstellar-spectra-and-spectral-classification":3880,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Ftelescopes-and-detectors-across-the-spectrum":3468,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fthe-cosmic-distance-ladder":3380,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fblackbody-radiation-and-specific-intensity":3881,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fradiative-transfer-and-the-transfer-equation":3882,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fspectral-line-formation-and-broadening":3883,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fopacity-and-the-rosseland-mean":3884,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fhydrostatic-equilibrium-and-the-virial-theorem":3885,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equations-of-stellar-structure":3674,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equation-of-state-and-polytropes":3605,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-standard-solar-model":3886,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fthermonuclear-reaction-rates-and-the-gamow-peak":3887,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhydrogen-burning-pp-chains-and-cno":3888,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhelium-burning-and-the-triple-alpha-process":3889,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fadvanced-burning-and-neutron-capture-nucleosynthesis":3801,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fphases-of-the-interstellar-medium":3890,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fmolecular-clouds-and-gravitational-collapse":3364,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fprotostars-and-the-pre-main-sequence":3427,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-main-sequence-and-its-structure":3409,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fpost-main-sequence-low-mass-evolution":3891,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-evolution-of-massive-stars":3493,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fstellar-pulsation-and-the-instability-strip":3109,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":3892,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fcore-collapse-supernovae":3893,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fthermonuclear-supernovae-type-ia":3624,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fneutron-stars-and-pulsars":3894,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fblack-holes-schwarzschild-and-kerr":3895,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fbinary-systems-and-mass-transfer":3896,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Faccreting-compact-objects":3897,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fgravitational-waves-from-inspiraling-binaries":3090,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fmultimessenger-astronomy-and-gamma-ray-bursts":3898,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fthe-milky-way":3899,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-morphology-and-classification":3351,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-rotation-curves-and-dark-matter":3900,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Factive-galactic-nuclei-and-supermassive-black-holes":3901,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-clusters-and-large-scale-structure":3617,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-expanding-universe-and-hubbles-law":3272,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-frw-metric-and-cosmological-redshift":3902,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-friedmann-equations-and-cosmic-dynamics":3903,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fcosmological-models-and-distances":3529,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fdark-energy-and-the-accelerating-universe":3515,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fthe-thermal-history-of-the-universe":3527,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fbig-bang-nucleosynthesis":3624,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Frecombination-and-the-cosmic-microwave-background":3904,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcmb-anisotropies-and-cosmological-parameters":3089,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcosmic-inflation":3281,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fstructure-formation-and-the-growth-of-perturbations":3905,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fdark-matter-dark-energy-and-open-questions":3368,"\u002Fastrophysics-cosmology":3473,"\u002Fcolophon":3906,"\u002F":3270},4250,4808,3626,2682,4109,4786,3878,3875,3751,3415,4067,3153,3000,4042,5461,5808,3961,3749,4327,5067,4246,4655,4154,5436,2640,4003,3601,2158,4331,4189,2273,3252,4633,4964,4172,3131,5524,3160,4031,2309,4207,3226,2648,4842,5340,3307,5701,4977,4039,2615,3472,4460,3848,4075,4400,3382,3010,3602,3737,3740,3707,3922,5191,4043,3804,4542,4214,5062,2850,4361,3443,3627,4044,3766,4140,3860,4006,5199,4334,5234,3651,5509,5680,153,1375,1073,1093,1125,1146,1014,1132,876,1541,1189,1173,984,1402,1301,950,1268,1063,1107,1408,1161,925,1012,866,964,1090,1142,1085,1020,1207,973,980,728,764,1225,1329,796,929,801,878,774,1044,1488,1175,1130,890,814,870,154,4073,5140,4961,5127,4870,5382,5195,4955,5369,4501,5576,3824,4132,4289,4307,4570,3403,5084,5105,5201,5116,5341,5175,5368,5188,5211,5499,5155,4981,5125,5415,5255,5304,5130,5167,5552,5164,5094,5239,5036,5190,5004,5099,5035,5159,5088,5026,4937,5023,5264,5244,133,5114,5078,5043,5312,5170,5342,5139,5151,5049,5212,5013,5068,5079,5102,5121,5081,5029,5379,5854,5110,2139,3798,5055,5364,4984,4935,4895,4972,5289,5112,5156,4987,5031,5025,5149,5302,5042,5002,4979,4922,4960,5279,126,1877,1180,1129,907,958,1112,1300,1053,1250,1181,1241,1234,966,1050,734,1190,484,1082,926,733,761,571,607,798,804,952,977,731,784,645,771,1017,742,1004,1000,1562,1254,1288,1101,1011,1486,1061,856,992,1169,988,137,0,2037,1782,2384,2254,2123,2332,1643,1714,2089,1751,1367,1660,2511,1998,1892,1854,1791,2438,2487,1917,2375,2525,2266,1845,2275,1810,1631,2310,2166,2233,2113,2505,2347,2672,2112,2473,2592,2380,3013,2513,3256,3218,2194,2173,2205,2326,2081,3342,3152,1799,1670,1027,960,1095,1291,986,897,1209,1055,1817,1801,1593,1465,1196,1464,1201,1230,1435,1684,1461,1926,1500,1409,1284,1774,1869,162,1487,1122,1188,1001,1351,982,1005,979,1325,1046,943,1279,824,1008,989,1798,1277,1025,987,1043,1211,1074,981,939,1002,739,1139,1108,1013,1070,978,1458,1317,157,1357,1077,2355,1116,1037,1178,1637,1314,1109,1056,1702,1474,1071,1158,832,993,1404,1024,1068,1339,1106,1264,1248,913,1848,1328,1633,1224,1143,135,1378,959,1028,998,911,1527,1203,1266,1483,1165,990,938,965,1257,1418,1099,942,1352,956,1035,1398,1003,1094,1292,138,1721,1827,1449,1354,1148,1184,1285,1281,1213,1290,1271,1252,1274,1778,1591,1503,1437,1571,1584,1957,1117,1781,1648,1342,1667,1510,1965,1607,1365,1849,1259,1303,1356,1238,2208,1564,173,1671,1286,1227,1638,1529,668,1078,918,709,865,880,940,1534,1015,874,922,841,794,1194,822,1105,1658,1359,1296,1438,1921,1844,1570,1429,1324,1400,140,1787,1558,1654,1492,1747,2224,2002,2009,1323,1349,1785,1573,1722,1829,1353,1548,1552,1583,1624,1585,1245,1364,1514,1343,1397,1355,2211,1481,1770,160,2388,2293,2256,2552,2569,2478,2039,2496,2578,2814,2519,2461,2587,2492,2714,3278,2654,3050,2447,2849,2238,2369,2061,2214,2602,2563,2186,2985,2749,3364,2038,2282,2409,2126,2573,2206,2176,2268,2182,2402,2705,2633,2414,2213,2801,3313,3410,3195,1952,2017,1509,2537,2645,2027,2415,2838,2356,1906,3184,2950,2807,2954,1683,1316,1034,1138,1763,1822,1705,1246,1701,1097,1104,1187,1032,1083,1228,916,1489,1033,1652,997,692,837,1023,888,864,1089,1231,1214,1675,1156,1075,1520,1309,139,1205,1051,735,1123,1072,915,567,768,825,1253,983,1007,762,1058,861,862,971,1208,1149,1145,1029,1084,927,810,838,857,807,936,949,2321,1622,1069,1113,1057,854,1958,1528,1618,2049,1432,1679,1796,1685,1346,1275,1476,1505,1610,2018,1599,1215,1838,1909,132,3902,2215,2240,3266,3208,3073,2454,2969,2451,1875,2728,1884,2371,2516,2842,1690,1904,2346,3146,1386,2607,1966,2668,1665,2885,1606,2577,2869,2403,2433,2082,1939,1587,2460,2747,2032,2642,1619,3123,1993,2090,2339,3829,1737,2622,2340,2322,3828,4409,2305,3411,2510,4527,3030,3569,3043,2457,1946,2277,2044,2909,1693,1945,2093,2399,2115,2898,2742,2242,3895,3378,3376,2769,2223,3062,3262,2651,2949,2768,3128,2423,1977,2087,2866,3388,2830,2210,2489,2884,3945,2099,2713,3402,1692,2931,4195,3989,3206,4391,3004,3704,3494,2902,999,881,901,919,748,869,1018,1045,1049,1333,954,1092,1019,976,1771,1480,1396,953,1026,161,3533,2495,1818,3007,2595,3427,3537,2216,1895,2304,3396,1739,2073,1962,2203,1767,2666,2264,2276,2852,1807,3735,1560,4144,1669,1676,1972,2418,3291,1525,2040,2766,2337,2220,2800,3001,2078,1759,2836,1896,2026,1758,1543,1047,896,946,1060,1384,1482,815,1414,1322,1440,1240,1468,1098,1133,847,1009,1381,1052,1191,1258,1370,1712,1441,1199,957,1079,150,1262,1417,1368,1219,1136,1064,1463,1636,1059,931,1115,1736,1174,1376,1363,1411,1247,1746,1313,1299,1617,1102,1076,1495,1265,1193,1263,80,{"\u002Falgorithms\u002Ffoundations\u002Fwhat-is-an-algorithm":3908,"\u002Falgorithms\u002Ffoundations\u002Fproof-techniques":3913,"\u002Falgorithms\u002Ffoundations\u002Fasymptotic-analysis":3917,"\u002Falgorithms\u002Ffoundations\u002Fgrowth-rates-and-loop-analysis":3921,"\u002Falgorithms\u002Ffoundations\u002Frecurrences":3925,"\u002Falgorithms\u002Ffoundations\u002Famortized-analysis":3929,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fmergesort":3933,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fquicksort":3938,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fselection":3942,"\u002Falgorithms\u002Fdivide-and-conquer\u002Ffast-multiplication":3946,"\u002Falgorithms\u002Fsorting\u002Fheaps-and-heapsort":3950,"\u002Falgorithms\u002Fsorting\u002Fsorting-lower-bounds":3955,"\u002Falgorithms\u002Fsorting\u002Flinear-time-sorting":3959,"\u002Falgorithms\u002Fsorting\u002Fexternal-sorting":3963,"\u002Falgorithms\u002Fdata-structures\u002Felementary-structures":3967,"\u002Falgorithms\u002Fdata-structures\u002Fhash-tables":3972,"\u002Falgorithms\u002Fdata-structures\u002Fbinary-search-trees":3976,"\u002Falgorithms\u002Fdata-structures\u002Favl-trees":3980,"\u002Falgorithms\u002Fdata-structures\u002Fbalanced-trees":3984,"\u002Falgorithms\u002Fdata-structures\u002Funion-find":3988,"\u002Falgorithms\u002Fdata-structures\u002Ffenwick-and-segment-trees":3992,"\u002Falgorithms\u002Fdata-structures\u002Fspatial-data-structures":3996,"\u002Falgorithms\u002Fdata-structures\u002Fskip-lists-and-probabilistic-structures":4000,"\u002Falgorithms\u002Fdata-structures\u002Fb-trees":4004,"\u002Falgorithms\u002Fdata-structures\u002Fdata-stream-algorithms":4008,"\u002Falgorithms\u002Fdata-structures\u002Fstreaming-sketches":4012,"\u002Falgorithms\u002Fsequences\u002Ftwo-pointers-and-windows":4016,"\u002Falgorithms\u002Fsequences\u002Fprefix-sums":4021,"\u002Falgorithms\u002Fsequences\u002Fmonotonic-stacks":4025,"\u002Falgorithms\u002Fsequences\u002Fbinary-search-on-the-answer":4029,"\u002Falgorithms\u002Fsequences\u002Fstring-matching":4033,"\u002Falgorithms\u002Fsequences\u002Fkmp-and-z-function":4037,"\u002Falgorithms\u002Fsequences\u002Ftries":4041,"\u002Falgorithms\u002Fsequences\u002Fsuffix-arrays-and-aho-corasick":4045,"\u002Falgorithms\u002Fgraphs\u002Frepresentations-and-traversal":4049,"\u002Falgorithms\u002Fgraphs\u002Fdepth-first-search":4054,"\u002Falgorithms\u002Fgraphs\u002Ftopological-sort-and-scc":4058,"\u002Falgorithms\u002Fgraphs\u002Fminimum-spanning-trees":4062,"\u002Falgorithms\u002Fgraphs\u002Fkruskal-and-prim":4066,"\u002Falgorithms\u002Fgraphs\u002Fshortest-paths":4070,"\u002Falgorithms\u002Fgraphs\u002Fall-pairs-and-negative-weights":4074,"\u002Falgorithms\u002Fgraphs\u002Fnetwork-flow":4078,"\u002Falgorithms\u002Fgraphs\u002Fmax-flow-min-cut":4082,"\u002Falgorithms\u002Fgraphs\u002Fbridges-and-articulation-points":4086,"\u002Falgorithms\u002Fgraphs\u002Flowest-common-ancestor":4090,"\u002Falgorithms\u002Fgraphs\u002Ftwo-sat":4094,"\u002Falgorithms\u002Fgraphs\u002Feulerian-tours":4098,"\u002Falgorithms\u002Fgraphs\u002Fbipartite-matching":4102,"\u002Falgorithms\u002Fgreedy\u002Fthe-greedy-method":4106,"\u002Falgorithms\u002Fgreedy\u002Fscheduling-and-intervals":4111,"\u002Falgorithms\u002Fgreedy\u002Fhuffman-codes":4115,"\u002Falgorithms\u002Fgreedy\u002Fmatroids":4119,"\u002Falgorithms\u002Fgreedy\u002Fstable-matching":4123,"\u002Falgorithms\u002Fdynamic-programming\u002Fprinciples":4127,"\u002Falgorithms\u002Fdynamic-programming\u002Fsequence-dp":4132,"\u002Falgorithms\u002Fdynamic-programming\u002Flongest-increasing-subsequence":4136,"\u002Falgorithms\u002Fdynamic-programming\u002Fknapsack":4140,"\u002Falgorithms\u002Fdynamic-programming\u002Fcoin-change-and-unbounded":4144,"\u002Falgorithms\u002Fdynamic-programming\u002Finterval-dp":4148,"\u002Falgorithms\u002Fdynamic-programming\u002Ftree-dp":4152,"\u002Falgorithms\u002Fdynamic-programming\u002Fbitmask-dp":4156,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-optimizations":4160,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-on-graphs":4164,"\u002Falgorithms\u002Fdynamic-programming\u002Fdigit-and-probability-dp":4168,"\u002Falgorithms\u002Fbacktracking\u002Fbacktracking-fundamentals":4172,"\u002Falgorithms\u002Fbacktracking\u002Fconstraint-search":4177,"\u002Falgorithms\u002Fbacktracking\u002Fbranch-and-bound":4181,"\u002Falgorithms\u002Fbacktracking\u002Fgraph-backtracking":4185,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fnumber-theory-basics":4189,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmodular-exponentiation-and-primality":4194,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fsieve-and-factorization":4198,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fcombinatorics":4202,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmatrix-exponentiation":4206,"\u002Falgorithms\u002Fmathematical-algorithms\u002Ffast-fourier-transform":4210,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fgradient-descent":4214,"\u002Falgorithms\u002Fcomputational-geometry\u002Fgeometric-primitives":4218,"\u002Falgorithms\u002Fcomputational-geometry\u002Fconvex-hull":4223,"\u002Falgorithms\u002Fcomputational-geometry\u002Fsweep-line":4227,"\u002Falgorithms\u002Fcomputational-geometry\u002Fpolygons-and-proximity":4231,"\u002Falgorithms\u002Fintractability\u002Fp-np-reductions":4235,"\u002Falgorithms\u002Fintractability\u002Fnp-completeness":4240,"\u002Falgorithms\u002Fintractability\u002Fcoping-with-hardness":4244,"\u002Falgorithms\u002Fintractability\u002Fapproximation-algorithms":4248,"\u002Falgorithms":4252,"\u002Fcalculus\u002Flimits-and-continuity\u002Ffunctions-and-models":4255,"\u002Fcalculus\u002Flimits-and-continuity\u002Fthe-limit-of-a-function":4260,"\u002Fcalculus\u002Flimits-and-continuity\u002Flimit-laws-and-the-precise-definition":4264,"\u002Fcalculus\u002Flimits-and-continuity\u002Fcontinuity":4268,"\u002Fcalculus\u002Fderivatives\u002Fthe-derivative-and-rates-of-change":4272,"\u002Fcalculus\u002Fderivatives\u002Fdifferentiation-rules-and-the-chain-rule":4277,"\u002Fcalculus\u002Fderivatives\u002Fimplicit-differentiation-and-related-rates":4281,"\u002Fcalculus\u002Fderivatives\u002Flinear-approximations-and-differentials":4285,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fextrema-and-the-mean-value-theorem":4289,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fhow-derivatives-shape-a-graph":4294,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fcurve-sketching-and-optimization":4298,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fnewtons-method-and-antiderivatives":4302,"\u002Fcalculus\u002Fintegrals\u002Farea-and-the-definite-integral":4306,"\u002Fcalculus\u002Fintegrals\u002Fthe-fundamental-theorem-of-calculus":4311,"\u002Fcalculus\u002Fintegrals\u002Fthe-substitution-rule":4315,"\u002Fcalculus\u002Fapplications-of-integration\u002Fareas-and-volumes":4319,"\u002Fcalculus\u002Fapplications-of-integration\u002Fwork-average-value-and-arc-length":4324,"\u002Fcalculus\u002Fapplications-of-integration\u002Fphysics-economics-and-probability":4328,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Finverse-functions-logarithms-and-exponentials":4332,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Fgrowth-decay-inverse-trig-and-hyperbolic-functions":4337,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Flhospitals-rule":4341,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fintegration-by-parts":4345,"\u002Fcalculus\u002Ftechniques-of-integration\u002Ftrigonometric-integrals-and-substitution":4350,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fpartial-fractions-and-integration-strategy":4354,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fapproximate-and-improper-integrals":4358,"\u002Fcalculus\u002Fparametric-and-polar\u002Fparametric-curves-and-their-calculus":4362,"\u002Fcalculus\u002Fparametric-and-polar\u002Fpolar-coordinates":4367,"\u002Fcalculus\u002Fparametric-and-polar\u002Fconic-sections":4371,"\u002Fcalculus\u002Fsequences-and-series\u002Fsequences":4375,"\u002Fcalculus\u002Fsequences-and-series\u002Fseries-and-the-integral-test":4380,"\u002Fcalculus\u002Fsequences-and-series\u002Fthe-convergence-tests":4384,"\u002Fcalculus\u002Fsequences-and-series\u002Fpower-series":4388,"\u002Fcalculus\u002Fsequences-and-series\u002Ftaylor-and-maclaurin-series":4392,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvectors-and-the-dot-product":4396,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fthe-cross-product-lines-and-planes":4401,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fcylinders-and-quadric-surfaces":4405,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvector-functions-and-space-curves":4409,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Farc-length-curvature-and-motion":4413,"\u002Fcalculus\u002Fpartial-derivatives\u002Ffunctions-of-several-variables":4417,"\u002Fcalculus\u002Fpartial-derivatives\u002Fpartial-derivatives":4422,"\u002Fcalculus\u002Fpartial-derivatives\u002Ftangent-planes-and-the-chain-rule":4425,"\u002Fcalculus\u002Fpartial-derivatives\u002Fdirectional-derivatives-and-the-gradient":4429,"\u002Fcalculus\u002Fpartial-derivatives\u002Foptimization-and-lagrange-multipliers":4433,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fdouble-integrals":4437,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Ftriple-integrals-and-coordinate-systems":4442,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fvector-fields-and-line-integrals":4446,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fgreens-theorem-curl-and-divergence":4450,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fsurface-integrals":4454,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fstokes-and-the-divergence-theorem":4458,"\u002Fcalculus":4462,"\u002Fmechanics\u002Ffoundations\u002Fmeasurement-and-dimensions":4465,"\u002Fmechanics\u002Ffoundations\u002Fvector-algebra":4469,"\u002Fmechanics\u002Fkinematics\u002Fone-dimensional-motion":4473,"\u002Fmechanics\u002Fkinematics\u002Fmotion-graphs":4478,"\u002Fmechanics\u002Fkinematics\u002Fprojectile-motion":4482,"\u002Fmechanics\u002Fkinematics\u002Frelative-motion":4486,"\u002Fmechanics\u002Fkinematics\u002Fcircular-motion":4490,"\u002Fmechanics\u002Fdynamics\u002Fnewtons-laws":4494,"\u002Fmechanics\u002Fdynamics\u002Ffree-body-diagrams":4499,"\u002Fmechanics\u002Fdynamics\u002Ffriction-and-curved-motion":4503,"\u002Fmechanics\u002Fdynamics\u002Fnumerical-dynamics":4507,"\u002Fmechanics\u002Fdynamics\u002Fcenter-of-mass-systems":4511,"\u002Fmechanics\u002Fenergy\u002Fwork-and-kinetic-energy":4515,"\u002Fmechanics\u002Fenergy\u002Fpotential-energy":4520,"\u002Fmechanics\u002Fenergy\u002Fmultiparticle-work":4524,"\u002Fmechanics\u002Fenergy\u002Fmass-energy-and-binding":4528,"\u002Fmechanics\u002Fenergy\u002Fphotons-and-quantization":4532,"\u002Fmechanics\u002Fmomentum\u002Fmomentum-and-collisions":4536,"\u002Fmechanics\u002Fmomentum\u002Fcenter-of-mass-collisions":4541,"\u002Fmechanics\u002Fmomentum\u002Frocket-propulsion":4545,"\u002Fmechanics\u002Frotation\u002Frotational-inertia":4549,"\u002Fmechanics\u002Frotation\u002Frotational-dynamics":4554,"\u002Fmechanics\u002Frotation\u002Frolling-motion":4558,"\u002Fmechanics\u002Frotation\u002Fangular-momentum":4562,"\u002Fmechanics\u002Frotation\u002Frolling-resistance":4566,"\u002Fmechanics\u002Frotation\u002Fgyroscopic-precession":4570,"\u002Fmechanics\u002Fgravity-and-matter\u002Fkeplerian-orbits":4574,"\u002Fmechanics\u002Fgravity-and-matter\u002Fgravitational-fields":4579,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstatic-equilibrium":4583,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-statics":4587,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-flow":4591,"\u002Fmechanics\u002Fgravity-and-matter\u002Forbital-motion":4595,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstress-and-elasticity":4599,"\u002Fmechanics\u002Foscillations-waves\u002Fdamped-oscillators":4603,"\u002Fmechanics\u002Foscillations-waves\u002Ftravelling-waves":4608,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-superposition":4612,"\u002Fmechanics\u002Foscillations-waves\u002Fstanding-waves":4616,"\u002Fmechanics\u002Foscillations-waves\u002Fsound-waves":4620,"\u002Fmechanics\u002Foscillations-waves\u002Fdoppler-effect":4624,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-packets":4628,"\u002Fmechanics\u002Foscillations-waves\u002Fbeats-and-coupling":4632,"\u002Fmechanics\u002Foscillations-waves\u002Fsimple-harmonic-motion":4636,"\u002Fmechanics\u002Foscillations-waves\u002Fpendulum-motion":4640,"\u002Fmechanics\u002Foscillations-waves\u002Fdriven-oscillators":4644,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-boundaries":4648,"\u002Fmechanics\u002Fthermodynamics\u002Fkinetic-theory-of-ideal-gases":4652,"\u002Fmechanics\u002Fthermodynamics\u002Ffirst-law-of-thermodynamics":4657,"\u002Fmechanics\u002Fthermodynamics\u002Fentropy-and-the-second-law":4661,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-processes":4665,"\u002Fmechanics\u002Fthermodynamics\u002Fphase-changes":4669,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-machines":4673,"\u002Fmechanics":4677,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcharge-and-conductors":4680,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcoulombs-law":4685,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-and-force":4689,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-maps":4693,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-dipoles":4697,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fcontinuous-charge-fields":4701,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fgauss-law-and-conductors":4706,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpoint-charge-potential":4710,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpotential-gradients-and-equipotentials":4715,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Felectrostatic-energy-and-pressure":4719,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Flaplace-boundary-problems":4723,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fcontinuous-charge-potentials":4727,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitance-fundamentals":4731,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-networks":4736,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-energy-and-force":4740,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fdielectric-polarization-and-breakdown":4744,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fcurrent-and-resistance":4748,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fkirchhoff-network-analysis":4753,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Frc-transients":4757,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-trajectories":4761,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fhall-effect":4766,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-force-on-conductors":4770,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-dipoles":4774,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmass-spectrometry":4778,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmoving-charge-fields":4782,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fbiot-savart-law":4787,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fcircular-current-loops":4791,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Famperes-law":4795,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fgauss-law-for-magnetism":4799,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmagnetic-materials":4803,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-flux":4807,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Ffaradays-law":4812,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Flenzs-law":4816,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmotional-emf":4820,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Feddy-currents":4824,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fself-inductance":4828,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-energy":4832,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Frl-circuits":4836,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-fundamentals":4840,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Freactance":4845,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Frlc-resonance":4849,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-power":4853,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Ftransformers":4857,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdisplacement-current":4861,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-waves":4866,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-momentum":4870,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdipole-radiation":4874,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fpolarization":4878,"\u002Felectricity-and-magnetism\u002Foptics\u002Freflection-and-refraction":4882,"\u002Felectricity-and-magnetism\u002Foptics\u002Fthin-lenses":4887,"\u002Felectricity-and-magnetism\u002Foptics\u002Fspherical-mirrors":4891,"\u002Felectricity-and-magnetism":4895,"\u002Flinear-algebra\u002Flinear-systems\u002Fsystems-and-echelon-forms":4898,"\u002Flinear-algebra\u002Flinear-systems\u002Fvector-and-matrix-equations":4903,"\u002Flinear-algebra\u002Flinear-systems\u002Fsolution-sets-and-applications":4907,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-independence":4911,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-transformations":4915,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-operations":4919,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-inverse-and-invertibility":4924,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fpartitioned-matrices-and-lu":4928,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fsubspaces-dimension-rank":4932,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fapplications-leontief-and-graphics":4936,"\u002Flinear-algebra\u002Fdeterminants\u002Fdeterminants-and-cofactors":4940,"\u002Flinear-algebra\u002Fdeterminants\u002Fproperties-of-determinants":4945,"\u002Flinear-algebra\u002Fdeterminants\u002Fcramer-volume-and-area":4949,"\u002Flinear-algebra\u002Fvector-spaces\u002Fvector-spaces-and-subspaces":4953,"\u002Flinear-algebra\u002Fvector-spaces\u002Fnull-and-column-spaces":4958,"\u002Flinear-algebra\u002Fvector-spaces\u002Fbases-and-independent-sets":4962,"\u002Flinear-algebra\u002Fvector-spaces\u002Fcoordinate-systems":4966,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdimension-and-rank":4970,"\u002Flinear-algebra\u002Fvector-spaces\u002Fchange-of-basis":4974,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdifference-equations-and-markov":4978,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-eigenvalues":4982,"\u002Flinear-algebra\u002Feigenvalues\u002Fthe-characteristic-equation":4987,"\u002Flinear-algebra\u002Feigenvalues\u002Fdiagonalization":4991,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-linear-transformations":4995,"\u002Flinear-algebra\u002Feigenvalues\u002Fcomplex-eigenvalues":4999,"\u002Flinear-algebra\u002Feigenvalues\u002Fdynamical-systems":5003,"\u002Flinear-algebra\u002Feigenvalues\u002Fpower-method":5007,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-length-orthogonality":5011,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Forthogonal-sets-and-projections":5016,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fgram-schmidt-and-qr":5020,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-problems":5024,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-applications":5028,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-spaces":5032,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fdiagonalizing-symmetric-matrices":5036,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fquadratic-forms":5041,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fconstrained-optimization":5045,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsingular-value-decomposition":5049,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsvd-applications-pca-imaging":5053,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-thinking-and-matrix-computation":5057,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Flu-and-cholesky":5062,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fconditioning-and-floating-point":5066,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fstability-and-error-analysis":5070,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fqr-and-numerical-least-squares":5074,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-eigenvalues-and-svd":5078,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-combinations":5082,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-independence-and-barycentric-coordinates":5087,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fconvex-combinations-and-convex-sets":5091,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fhyperplanes-and-polytopes":5095,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fcurves-and-surfaces":5099,"\u002Flinear-algebra":5103,"\u002Ftheory-of-computation":5106,"\u002Fcomputer-architecture\u002Ffoundations\u002Fbits-bytes-and-words":5109,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-representation":5113,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-arithmetic":5117,"\u002Fcomputer-architecture\u002Ffoundations\u002Ffloating-point":5121,"\u002Fcomputer-architecture\u002Ffoundations\u002Fboolean-algebra-and-bit-manipulation":5125,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fthe-machines-view":5129,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fdata-movement":5134,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farithmetic-and-logic":5138,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fcontrol-flow":5142,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fprocedures":5146,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farrays-structs-and-alignment":5150,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fmemory-layout-and-buffer-overflows":5154,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fwhat-an-isa-is":5158,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Finstruction-formats-and-operands":5163,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Faddressing-modes":5167,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fthe-y86-64-instruction-set":5171,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fy86-64-programming":5175,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Ftransistors-gates-and-boolean-functions":5179,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fcombinational-logic-and-hcl":5184,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmultiplexers-decoders-and-the-alu":5188,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmemory-elements-latches-flip-flops-and-clocking":5192,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fregister-files-and-random-access-memory":5196,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-fetch-decode-execute-cycle":5200,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-seq-stages":5205,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fcontrol-logic-and-sequencing":5209,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fassembling-seq":5213,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Ftracing-a-program":5217,"\u002Fcomputer-architecture\u002Fpipelining\u002Fpipelining-principles":5221,"\u002Fcomputer-architecture\u002Fpipelining\u002Ffrom-seq-to-pipe":5226,"\u002Fcomputer-architecture\u002Fpipelining\u002Fdata-hazards-stalling-and-forwarding":5230,"\u002Fcomputer-architecture\u002Fpipelining\u002Fcontrol-hazards-and-branch-prediction":5234,"\u002Fcomputer-architecture\u002Fpipelining\u002Fthe-complete-pipe-processor":5238,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fstorage-technologies-and-the-latency-gap":5242,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Flocality":5247,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-memories-direct-mapped":5251,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fset-associative-and-write-policies":5255,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-performance-and-cache-friendly-code":5259,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Faddress-spaces-and-translation":5263,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fpage-tables-and-page-faults":5268,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fthe-tlb-and-multi-level-page-tables":5272,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Fexceptional-control-flow":5276,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Finterrupts-and-the-kernel":5281,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fprocesses-threads-and-parallelism":5285,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fhardware-multithreading":5290,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fcache-coherence":5294,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmemory-consistency-and-synchronization":5298,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmulticore-organization":5302,"\u002Fcomputer-architecture\u002Fcapstone\u002Fthe-whole-machine":5306,"\u002Fcomputer-architecture\u002Fcapstone\u002Fassembling-a-complete-cpu":5311,"\u002Fcomputer-architecture":5315,"\u002Fdifferential-equations\u002Ffoundations\u002Fmodels-and-direction-fields":5318,"\u002Fdifferential-equations\u002Ffoundations\u002Fclassification-and-terminology":5322,"\u002Fdifferential-equations\u002Ffirst-order\u002Flinear-first-order-integrating-factors":5326,"\u002Fdifferential-equations\u002Ffirst-order\u002Fseparable-and-exact":5331,"\u002Fdifferential-equations\u002Ffirst-order\u002Fmodeling-first-order":5335,"\u002Fdifferential-equations\u002Ffirst-order\u002Fautonomous-and-population-dynamics":5339,"\u002Fdifferential-equations\u002Ffirst-order\u002Fexistence-uniqueness-euler":5343,"\u002Fdifferential-equations\u002Ffirst-order\u002Ffirst-order-difference-equations":5347,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhomogeneous-constant-coefficients":5351,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fcomplex-and-repeated-roots":5356,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fnonhomogeneous-undetermined-coefficients":5360,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fvariation-of-parameters":5364,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fmechanical-electrical-vibrations":5368,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhigher-order-linear":5372,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fpower-series-ordinary-points":5376,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fregular-singular-frobenius":5381,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fbessel-and-special-functions":5385,"\u002Fdifferential-equations\u002Flaplace\u002Flaplace-definition-ivps":5389,"\u002Fdifferential-equations\u002Flaplace\u002Fstep-impulse-convolution":5394,"\u002Fdifferential-equations\u002Fsystems\u002Fmatrices-eigenvalues-review":5398,"\u002Fdifferential-equations\u002Fsystems\u002Fconstant-coefficient-systems-phase-portraits":5403,"\u002Fdifferential-equations\u002Fsystems\u002Frepeated-eigenvalues-fundamental-matrices":5407,"\u002Fdifferential-equations\u002Fnumerical\u002Feuler-and-runge-kutta":5411,"\u002Fdifferential-equations\u002Fnumerical\u002Fmultistep-systems-stability":5416,"\u002Fdifferential-equations\u002Fnonlinear\u002Fphase-plane-autonomous-stability":5420,"\u002Fdifferential-equations\u002Fnonlinear\u002Flocally-linear-and-liapunov":5425,"\u002Fdifferential-equations\u002Fnonlinear\u002Fcompeting-species-predator-prey-limit-cycles":5429,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Ffourier-series":5433,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fheat-wave-laplace-equations":5438,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fsturm-liouville":5442,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fcalculus-of-variations":5446,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fhistorical-notes":5451,"\u002Fdifferential-equations":5455,"\u002Frelativity\u002Ffoundations\u002Fspecial-relativity-postulates":5458,"\u002Frelativity\u002Ffoundations\u002Florentz-transformation-spacetime":5463,"\u002Frelativity\u002Ffoundations\u002Ftime-dilation-length-contraction":5467,"\u002Frelativity\u002Ffoundations\u002Frelativistic-momentum-energy":5471,"\u002Frelativity\u002Ffoundations\u002Fgeneral-relativity":5475,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fminkowski-spacetime-and-the-interval":5479,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Ffour-vectors-and-index-notation":5484,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fthe-lorentz-group-and-rapidity":5488,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fdoppler-aberration-and-appearance":5492,"\u002Frelativity\u002Frelativistic-dynamics\u002Ffour-momentum-force-and-accelerated-motion":5496,"\u002Frelativity\u002Frelativistic-dynamics\u002Fparticle-decays-and-two-body-kinematics":5501,"\u002Frelativity\u002Frelativistic-dynamics\u002Fcollisions-thresholds-and-the-cm-frame":5505,"\u002Frelativity\u002Frelativistic-dynamics\u002Fmandelstam-variables-and-invariants":5509,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ffour-current-and-the-four-potential":5513,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fthe-electromagnetic-field-tensor":5518,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ftransformation-of-electric-and-magnetic-fields":5522,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fcovariant-maxwell-and-the-stress-energy-tensor":5526,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-equivalence-principle-formalized":5530,"\u002Frelativity\u002Fcurved-spacetime\u002Fmanifolds-vectors-and-the-metric":5535,"\u002Frelativity\u002Fcurved-spacetime\u002Fcovariant-derivative-and-christoffel-symbols":5539,"\u002Frelativity\u002Fcurved-spacetime\u002Fgeodesics-and-the-geodesic-equation":5543,"\u002Frelativity\u002Fcurved-spacetime\u002Fcurvature-riemann-and-geodesic-deviation":5547,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-einstein-field-equations":5551,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fthe-schwarzschild-metric":5555,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fgeodesics-and-orbits-in-schwarzschild":5560,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Flight-bending-and-null-geodesics":5564,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fperihelion-precession-of-mercury":5568,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fdeflection-of-light-and-gravitational-lensing":5573,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fgravitational-redshift-and-shapiro-delay":5577,"\u002Frelativity\u002Ftests-of-general-relativity\u002Frelativity-in-technology-gps":5581,"\u002Frelativity\u002Fblack-holes\u002Fhorizons-and-coordinate-singularities":5585,"\u002Frelativity\u002Fblack-holes\u002Frotating-and-charged-black-holes":5590,"\u002Frelativity\u002Fblack-holes\u002Fblack-hole-thermodynamics":5594,"\u002Frelativity\u002Fgravitational-waves\u002Flinearized-gravity-and-wave-solutions":5598,"\u002Frelativity\u002Fgravitational-waves\u002Fgeneration-and-the-quadrupole-formula":5603,"\u002Frelativity\u002Fgravitational-waves\u002Fdetection-ligo-and-the-first-events":5607,"\u002Frelativity\u002Fcosmological-bridge\u002Fthe-cosmological-principle-and-flrw-metric":5611,"\u002Frelativity\u002Fcosmological-bridge\u002Ffriedmann-equations-and-cosmic-dynamics":5616,"\u002Frelativity":5620,"\u002Fphysical-computing":5623,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fblackbody-radiation-and-the-planck-quantum":5626,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-photoelectric-effect-and-the-photon":5631,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fx-rays-and-the-compton-effect":5635,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-old-quantum-theory-bohr-and-sommerfeld":5639,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fde-broglie-waves-and-electron-diffraction":5643,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fwave-packets-and-the-probability-interpretation":5648,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fthe-uncertainty-principle":5652,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-schrodinger-equation-in-one-dimension":5656,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-free-particle-and-wave-packet-dynamics":5661,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fparticle-in-infinite-and-finite-square-wells":5665,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Foperators-expectation-values-and-the-harmonic-oscillator":5669,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-dirac-delta-potential":5673,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fbarrier-penetration-and-quantum-tunneling":5677,"\u002Fquantum-mechanics\u002Fformalism\u002Fhilbert-space-and-dirac-notation":5681,"\u002Fquantum-mechanics\u002Fformalism\u002Fobservables-hermitian-operators-and-eigenvalues":5686,"\u002Fquantum-mechanics\u002Fformalism\u002Fthe-postulates-and-quantum-measurement":5690,"\u002Fquantum-mechanics\u002Fformalism\u002Fposition-momentum-and-continuous-spectra":5694,"\u002Fquantum-mechanics\u002Fformalism\u002Fcommutators-and-the-generalized-uncertainty-principle":5698,"\u002Fquantum-mechanics\u002Fformalism\u002Ftime-evolution-schrodinger-and-heisenberg-pictures":5702,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fladder-operators-and-the-number-states":5706,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fcoherent-and-squeezed-states":5711,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fsymmetries-generators-and-conservation-laws":5715,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fparity-time-reversal-and-discrete-symmetries":5719,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Forbital-angular-momentum-and-spherical-harmonics":5723,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Fthe-angular-momentum-algebra":5727,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Faddition-of-angular-momenta-and-clebsch-gordan":5731,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-schrodinger-equation-in-three-dimensions":5735,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-hydrogen-atom":5740,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-isotropic-oscillator-and-hidden-symmetry":5744,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-half-pauli-matrices-and-stern-gerlach":5748,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-in-a-magnetic-field-precession-and-resonance":5753,"\u002Fquantum-mechanics\u002Fspin\u002Ftwo-level-systems-and-the-bloch-sphere":5757,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fidentical-particles-and-exchange-symmetry":5761,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fthe-pauli-principle-atoms-and-the-periodic-table":5766,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ftime-independent-perturbation-theory":5770,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ffine-structure-and-the-real-hydrogen-atom":5775,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-zeeman-and-stark-effects":5779,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-variational-method":5783,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-wkb-approximation":5787,"\u002Fquantum-mechanics":5791,"\u002Freal-analysis\u002Ffoundations\u002Fsets-logic-functions":5794,"\u002Freal-analysis\u002Ffoundations\u002Fordered-fields-completeness":5799,"\u002Freal-analysis\u002Ffoundations\u002Fabsolute-value-bounds":5803,"\u002Freal-analysis\u002Ffoundations\u002Fintervals-uncountability":5807,"\u002Freal-analysis\u002Fsequences-series\u002Fsequences-limits":5811,"\u002Freal-analysis\u002Fsequences-series\u002Flimit-laws-monotone":5816,"\u002Freal-analysis\u002Fsequences-series\u002Flimsup-bolzano-weierstrass":5820,"\u002Freal-analysis\u002Fsequences-series\u002Fcauchy-completeness":5824,"\u002Freal-analysis\u002Fsequences-series\u002Fseries-convergence":5828,"\u002Freal-analysis\u002Fsequences-series\u002Fabsolute-conditional-rearrangement":5832,"\u002Freal-analysis\u002Fmetric-spaces\u002Fmetric-spaces-norms":5836,"\u002Freal-analysis\u002Fmetric-spaces\u002Fopen-closed-sets":5841,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconvergence-completeness":5845,"\u002Freal-analysis\u002Fmetric-spaces\u002Fcompactness":5849,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconnectedness":5853,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-of-functions":5857,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuous-functions":5861,"\u002Freal-analysis\u002Fcontinuity\u002Fevt-ivt":5865,"\u002Freal-analysis\u002Fcontinuity\u002Funiform-continuity":5869,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuity-metric-spaces":5873,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-infinity-monotone":5877,"\u002Freal-analysis\u002Fdifferentiation\u002Fthe-derivative":5881,"\u002Freal-analysis\u002Fdifferentiation\u002Fmean-value-theorem":5886,"\u002Freal-analysis\u002Fdifferentiation\u002Ftaylors-theorem":5890,"\u002Freal-analysis\u002Fdifferentiation\u002Finverse-function-1d":5894,"\u002Freal-analysis\u002Friemann-integration\u002Fdarboux-integral":5898,"\u002Freal-analysis\u002Friemann-integration\u002Fintegrability-classes":5903,"\u002Freal-analysis\u002Friemann-integration\u002Fproperties-of-the-integral":5907,"\u002Freal-analysis\u002Friemann-integration\u002Ffundamental-theorem":5911,"\u002Freal-analysis\u002Friemann-integration\u002Flog-exp-improper":5914,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpointwise-uniform-convergence":5918,"\u002Freal-analysis\u002Ffunction-sequences\u002Finterchange-of-limits":5923,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpower-series-weierstrass":5927,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpicard-ode":5931,"\u002Freal-analysis\u002Fseveral-variables\u002Fdifferentiability-rn":5935,"\u002Freal-analysis\u002Fseveral-variables\u002Fgradient-chain-rule":5940,"\u002Freal-analysis\u002Fseveral-variables\u002Fhigher-derivatives-taylor-extrema":5944,"\u002Freal-analysis\u002Fseveral-variables\u002Finverse-implicit-theorems":5948,"\u002Freal-analysis\u002Fseveral-variables\u002Fmultiple-integrals":5952,"\u002Freal-analysis":5956,"\u002Fabstract-algebra\u002Ffoundations\u002Fsets-functions-relations":5959,"\u002Fabstract-algebra\u002Ffoundations\u002Fintegers-and-modular-arithmetic":5963,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fgroup-axioms-and-first-examples":5967,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fdihedral-and-symmetric-groups":5972,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fmatrix-and-quaternion-groups":5976,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fhomomorphisms-and-group-actions":5980,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fsubgroups-and-substructures":5984,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcyclic-groups":5989,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fgeneration-and-subgroup-lattices":5993,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcosets-lagrange-and-normal-subgroups":5997,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fisomorphism-theorems":6001,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcomposition-series-and-the-alternating-group":6005,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Factions-and-cayleys-theorem":6009,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fconjugation-and-the-class-equation":6014,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fsylow-theorems":6018,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fautomorphisms-and-simple-groups":6022,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fdirect-products-and-finite-abelian-groups":6026,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fsemidirect-products":6031,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fnilpotent-and-solvable-groups":6035,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fclassifying-small-groups":6039,"\u002Fabstract-algebra\u002Fring-theory\u002Frings-definitions-and-examples":6043,"\u002Fabstract-algebra\u002Fring-theory\u002Fideals-quotients-and-homomorphisms":6048,"\u002Fabstract-algebra\u002Fring-theory\u002Ffractions-and-the-chinese-remainder-theorem":6052,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Feuclidean-domains-pids-ufds":6056,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fpolynomial-rings-over-fields":6061,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fgauss-lemma-and-unique-factorization":6065,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Firreducibility-criteria-and-groebner":6069,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fintroduction-to-modules":6073,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ffree-modules-and-direct-sums":6078,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ftensor-products-and-exact-sequences":6082,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fvector-spaces-and-linear-maps":6086,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fstructure-theorem-over-pids":6090,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Frational-canonical-form":6095,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fjordan-canonical-form":6099,"\u002Fabstract-algebra\u002Ffield-theory\u002Ffield-extensions-and-algebraic-elements":6103,"\u002Fabstract-algebra\u002Ffield-theory\u002Fstraightedge-and-compass-constructions":6108,"\u002Fabstract-algebra\u002Ffield-theory\u002Fsplitting-fields-and-algebraic-closure":6112,"\u002Fabstract-algebra\u002Ffield-theory\u002Fseparable-and-cyclotomic-extensions":6116,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fthe-galois-correspondence":6120,"\u002Fabstract-algebra\u002Fgalois-theory\u002Ffinite-fields":6125,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fcyclotomic-and-abelian-extensions":6129,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fgalois-groups-of-polynomials":6133,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fsolvability-by-radicals-and-the-quintic":6137,"\u002Fabstract-algebra\u002Fcapstone\u002Fcommutative-algebra-and-algebraic-geometry":6141,"\u002Fabstract-algebra\u002Fcapstone\u002Frepresentation-and-character-theory":6146,"\u002Fabstract-algebra":6150,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fatomic-spectra-rutherford":6153,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-model-hydrogen":6158,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fx-ray-spectra-franck-hertz":6162,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-sommerfeld-old-quantum-theory":6166,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fold-quantum-theory-limits-wkb":6170,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fschrodinger-3d-hydrogen":6174,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fhydrogen-wave-functions":6179,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fradial-equation-in-full":6183,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fsymmetry-degeneracy-runge-lenz":6187,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fexpectation-values-virial":6191,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fquantum-defects-alkali-spectra":6195,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Frydberg-atoms":6199,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Frelativistic-kinetic-correction":6203,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fspin-orbit-thomas-precession":6208,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdarwin-term-fine-structure-formula":6212,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdirac-equation-hydrogen":6216,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Flamb-shift-qed":6220,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fhyperfine-structure-21cm":6225,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fnuclear-effects-isotope-shift":6229,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fperiodic-table-atomic-spectra":6233,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fcentral-field-self-consistent":6238,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fidentical-particles-hartree-fock":6242,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhelium-two-electron-atom":6246,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fls-jj-coupling-term-symbols":6250,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhund-rules-ground-terms":6254,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fzeeman-effect":6258,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fpaschen-back-intermediate":6263,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fstark-effect-polarizability":6267,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Ftime-dependent-perturbation-golden-rule":6271,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fdipole-approximation-einstein-coefficients":6276,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fselection-rules-forbidden-transitions":6280,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Flifetimes-and-line-shapes":6284,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Flaser-principles":6288,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fspectroscopy-techniques":6293,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fline-catalog-nist-asd":6297,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Flaser-cooling-doppler":6301,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fsub-doppler-trapping":6306,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fbose-einstein-condensation":6310,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Foptical-clocks-precision":6314,"\u002Fatomic-physics":6318,"\u002Fdatabases":6321,"\u002Fcategory-theory\u002Ffoundations\u002Fwhat-is-a-category":6324,"\u002Fcategory-theory\u002Ffoundations\u002Fexamples-of-categories":6328,"\u002Fcategory-theory\u002Ffoundations\u002Fspecial-morphisms":6332,"\u002Fcategory-theory\u002Ffoundations\u002Ffunctors":6336,"\u002Fcategory-theory\u002Ffoundations\u002Fnatural-transformations":6340,"\u002Fcategory-theory\u002Ffoundations\u002Fsize-and-set-theory":6344,"\u002Fcategory-theory\u002Funiversal-properties\u002Funiversal-properties":6348,"\u002Fcategory-theory\u002Funiversal-properties\u002Fproducts-and-coproducts":6353,"\u002Fcategory-theory\u002Funiversal-properties\u002Fconstructions-on-categories":6357,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Frepresentable-functors":6361,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-lemma":6366,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-consequences":6370,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits":6374,"\u002Fcategory-theory\u002Flimits-colimits\u002Fproducts-equalizers-pullbacks":6379,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcolimits":6383,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcomputing-limits":6387,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits-and-functors":6391,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions":6395,"\u002Fcategory-theory\u002Fadjunctions\u002Funits-and-counits":6400,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions-via-universal-arrows":6404,"\u002Fcategory-theory\u002Fadjunctions\u002Ffree-forgetful-adjunctions":6408,"\u002Fcategory-theory\u002Fadjoints-limits\u002Flimits-via-adjoints":6412,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fpresheaf-limits-colimits":6417,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoints-preserve-limits":6421,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoint-functor-theorem":6425,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fmonads":6429,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-eilenberg-moore":6434,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fkleisli-and-programming":6438,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-for-endofunctors":6442,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Fcartesian-closed-categories":6446,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Flambda-calculus-correspondence":6451,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Ffixed-points-and-recursion":6455,"\u002Fcategory-theory":6459,"\u002Fdeep-learning\u002Fmathematical-background\u002Flinear-algebra-for-deep-learning":6462,"\u002Fdeep-learning\u002Fmathematical-background\u002Fprobability-and-information-theory":6466,"\u002Fdeep-learning\u002Fmathematical-background\u002Fnumerical-computation":6470,"\u002Fdeep-learning\u002Fmathematical-background\u002Fcalculus":6474,"\u002Fdeep-learning\u002Ffoundations\u002Fwhat-is-deep-learning":6477,"\u002Fdeep-learning\u002Ffoundations\u002Fmachine-learning-refresher":6481,"\u002Fdeep-learning\u002Ffoundations\u002Flinear-models-and-the-perceptron":6485,"\u002Fdeep-learning\u002Fneural-networks\u002Fthe-multilayer-perceptron":6489,"\u002Fdeep-learning\u002Fneural-networks\u002Factivation-functions":6494,"\u002Fdeep-learning\u002Fneural-networks\u002Funiversal-approximation":6498,"\u002Fdeep-learning\u002Fneural-networks\u002Fbackpropagation":6502,"\u002Fdeep-learning\u002Fneural-networks\u002Floss-functions-and-output-units":6506,"\u002Fdeep-learning\u002Foptimization\u002Fgradient-descent-and-sgd":6510,"\u002Fdeep-learning\u002Foptimization\u002Fmomentum-and-adaptive-methods":6515,"\u002Fdeep-learning\u002Foptimization\u002Finitialization":6519,"\u002Fdeep-learning\u002Foptimization\u002Fthe-optimization-landscape":6523,"\u002Fdeep-learning\u002Foptimization\u002Fsecond-order-and-approximate-methods":6527,"\u002Fdeep-learning\u002Fregularization\u002Fregularization-overview":6531,"\u002Fdeep-learning\u002Fregularization\u002Fdropout-and-data-augmentation":6536,"\u002Fdeep-learning\u002Fregularization\u002Fearly-stopping-and-parameter-sharing":6540,"\u002Fdeep-learning\u002Fregularization\u002Fnormalization":6544,"\u002Fdeep-learning\u002Farchitectures\u002Fconvolutional-networks":6548,"\u002Fdeep-learning\u002Farchitectures\u002Fcnn-architectures":6553,"\u002Fdeep-learning\u002Farchitectures\u002Frecurrent-networks":6557,"\u002Fdeep-learning\u002Farchitectures\u002Flstm-and-gru":6561,"\u002Fdeep-learning\u002Farchitectures\u002Fattention-and-transformers":6565,"\u002Fdeep-learning\u002Farchitectures\u002Fthe-transformer-architecture":6569,"\u002Fdeep-learning\u002Farchitectures\u002Ftransformers-in-practice":6573,"\u002Fdeep-learning\u002Farchitectures\u002Fgraph-neural-networks":6577,"\u002Fdeep-learning\u002Farchitectures\u002Fstate-space-models":6581,"\u002Fdeep-learning\u002Ftheory\u002Fgeneralization-theory":6585,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-robustness":6590,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-defenses":6594,"\u002Fdeep-learning\u002Ftheory\u002Fbayesian-and-ensemble-methods":6598,"\u002Fdeep-learning\u002Ftheory\u002Fdeep-equilibrium-models":6602,"\u002Fdeep-learning\u002Fgenerative-models\u002Flinear-factor-models":6606,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoencoders":6611,"\u002Fdeep-learning\u002Fgenerative-models\u002Fvariational-autoencoders":6615,"\u002Fdeep-learning\u002Fgenerative-models\u002Fgenerative-adversarial-networks":6619,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoregressive-and-normalizing-flows":6623,"\u002Fdeep-learning\u002Fgenerative-models\u002Fenergy-based-and-boltzmann-machines":6627,"\u002Fdeep-learning\u002Fgenerative-models\u002Fdiffusion-and-score-based-models":6631,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fstructured-probabilistic-models":6635,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fmonte-carlo-and-mcmc":6640,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fapproximate-inference":6644,"\u002Fdeep-learning\u002Fpractical\u002Fpractical-methodology":6648,"\u002Fdeep-learning\u002Fpractical\u002Fhyperparameters-and-debugging":6653,"\u002Fdeep-learning\u002Fpractical\u002Frepresentation-learning":6657,"\u002Fdeep-learning\u002Fpractical\u002Ftransfer-learning":6661,"\u002Fdeep-learning\u002Fpractical\u002Fapplications":6665,"\u002Fdeep-learning\u002Fpractical\u002Fmodel-compression-and-distillation":6668,"\u002Fdeep-learning\u002Fpractical\u002Fmeta-learning-and-few-shot":6672,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Flarge-language-models":6676,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fscaling-inference-and-alignment":6681,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fseq2seq-pretraining-and-bart":6685,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ftext-to-text-transfer-and-conditional-generation":6689,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fspeech-and-audio-models":6693,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fself-supervised-speech-and-synthesis":6697,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fai-agents":6701,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fagent-memory-retrieval-and-orchestration":6705,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmixture-of-experts":6709,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmultimodal-models":6713,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ffusion-and-vision-language-models":6717,"\u002Fdeep-learning\u002Freinforcement-learning\u002Ffoundations-of-reinforcement-learning":6721,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fmodel-free-prediction-and-control":6726,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fdeep-q-networks":6730,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fpolicy-gradients-and-actor-critic":6734,"\u002Fdeep-learning\u002Freinforcement-learning\u002Frl-from-human-feedback":6738,"\u002Fdeep-learning":6741,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fequilibrium-state-variables-zeroth-law":6744,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Ffirst-law-heat-and-work":6748,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fsecond-law-entropy-and-the-carnot-bound":6752,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fthermodynamic-potentials-and-maxwell-relations":6756,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fstability-response-functions-and-the-third-law":6760,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fclassical-statistics-and-equipartition":6764,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fphase-space-and-liouvilles-theorem":6769,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fensembles-and-the-equal-probability-postulate":6773,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fstatistical-entropy-boltzmann-and-gibbs":6777,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fmicrocanonical-ensemble-and-entropy":6781,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fequilibrium-conditions-temperature-pressure-chemical-potential":6786,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fideal-gas-phase-space-and-the-sackur-tetrode-entropy":6790,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Ftwo-state-systems-paramagnets-and-negative-temperature":6794,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fcanonical-ensemble-and-the-boltzmann-distribution":6798,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fpartition-function-and-the-helmholtz-free-energy":6803,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fenergy-fluctuations-and-ensemble-equivalence":6807,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fthe-einstein-solid-and-harmonic-systems":6811,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fparamagnetism-and-the-schottky-anomaly":6815,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fideal-gas-partition-function-and-the-gibbs-paradox":6819,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fequipartition-and-the-virial-theorem":6824,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fmolecular-gases-rotation-and-vibration":6828,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fgrand-canonical-ensemble-and-the-grand-partition-function":6832,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fchemical-potential-fugacity-and-number-fluctuations":6837,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fensemble-summary-and-the-thermodynamic-web":6841,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fquantum-statistics-bose-einstein-and-fermi-dirac":6845,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fderiving-the-quantum-distributions":6850,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fthe-classical-limit-and-quantum-concentration":6854,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fideal-quantum-gases-general-framework":6858,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-and-the-fermion-gas":6862,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthe-photon-gas-and-plancks-radiation-law":6867,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fblackbody-thermodynamics-and-radiation-pressure":6871,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fphonons-and-the-debye-model":6875,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-derived":6879,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthermodynamics-of-the-bose-gas-and-superfluidity":6883,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fthe-ideal-fermi-gas-at-zero-temperature":6887,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fsommerfeld-expansion-and-electrons-in-metals":6892,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":6896,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fneutron-stars-and-nuclear-matter":6900,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-cluster-expansion-and-virial-coefficients":6904,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-van-der-waals-gas-and-liquid-gas-coexistence":6909,"\u002Fstatistical-mechanics\u002Finteractions\u002Fquantum-gases-with-interactions-and-exchange":6913,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fphases-coexistence-and-classification":6917,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-ising-model-and-exact-solutions":6922,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fmean-field-theory-and-the-weiss-model":6926,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fcritical-exponents-and-landau-theory":6930,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-renormalization-group-idea":6934,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fthermodynamic-fluctuations-and-response":6938,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fbrownian-motion-and-the-langevin-equation":6943,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Flinear-response-and-the-fluctuation-dissipation-theorem":6947,"\u002Fstatistical-mechanics":6951,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fbonding-mechanisms":6954,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fmolecular-orbitals-and-h2-plus":6959,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fhydrogen-molecule-and-exchange":6963,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fvan-der-waals-forces":6967,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Frotational-vibrational-spectra":6971,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Fanharmonicity-and-rovibrational-structure":6976,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Framan-and-electronic-bands":6980,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Flasers-and-masers":6984,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fstructure-of-solids":6988,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fbravais-lattices-and-crystal-systems":6993,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Freciprocal-lattice-and-brillouin-zones":6997,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fdiffraction-and-structure-factors":7001,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonon-dispersion":7005,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonons-quantization-and-dos":7010,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fdebye-einstein-heat-capacity":7014,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fanharmonicity-and-thermal-transport":7018,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ffree-electron-gas-and-conduction":7022,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fsommerfeld-model-and-heat-capacity":7027,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ftransport-and-the-hall-effect":7031,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fscreening-and-plasmons":7035,"\u002Fcondensed-matter\u002Fband-theory\u002Fblochs-theorem-and-energy-bands":7039,"\u002Fcondensed-matter\u002Fband-theory\u002Fnearly-free-electron-model":7044,"\u002Fcondensed-matter\u002Fband-theory\u002Ftight-binding-method":7048,"\u002Fcondensed-matter\u002Fband-theory\u002Ffermi-surfaces-and-semiclassical-dynamics":7052,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fsemiconductor-bands-and-junctions":7056,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fintrinsic-and-extrinsic-semiconductors":7061,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fcarrier-transport-and-recombination":7065,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fthe-pn-junction":7069,"\u002Fcondensed-matter\u002Fsemiconductors\u002Ftransistors-and-optoelectronics":7073,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fdielectrics-and-polarization":7077,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fferroelectrics-and-piezoelectrics":7082,"\u002Fcondensed-matter\u002Fmagnetism\u002Fdiamagnetism-and-paramagnetism":7086,"\u002Fcondensed-matter\u002Fmagnetism\u002Fexchange-and-ferromagnetism":7091,"\u002Fcondensed-matter\u002Fmagnetism\u002Fantiferromagnetism-and-domains":7095,"\u002Fcondensed-matter\u002Fmagnetism\u002Fspin-waves-and-magnons":7099,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fsuperconductivity-phenomenology":7103,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Flondon-theory-and-the-meissner-effect":7108,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fginzburg-landau-theory":7112,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fbcs-theory":7116,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fjosephson-and-high-tc":7120,"\u002Fcondensed-matter\u002Fnanostructures\u002Fquantum-wells-wires-and-dots":7124,"\u002Fcondensed-matter\u002Fnanostructures\u002Finteger-quantum-hall-effect":7129,"\u002Fcondensed-matter\u002Fnanostructures\u002Ffractional-quantum-hall-and-topology":7133,"\u002Fcondensed-matter\u002Fnanostructures\u002Fgraphene-and-dirac-materials":7137,"\u002Fcondensed-matter":7141,"\u002Flogic\u002Ffoundations\u002Flogic-as-a-mathematical-model":7144,"\u002Flogic\u002Fsentential-logic\u002Fformal-languages-and-well-formed-formulas":7148,"\u002Flogic\u002Fsentential-logic\u002Ftruth-assignments-and-tautologies":7153,"\u002Flogic\u002Fsentential-logic\u002Funique-readability-and-parsing":7157,"\u002Flogic\u002Fsentential-logic\u002Finduction-and-recursion":7161,"\u002Flogic\u002Fsentential-logic\u002Fexpressive-completeness-and-normal-forms":7165,"\u002Flogic\u002Fsentential-logic\u002Fboolean-circuits":7169,"\u002Flogic\u002Fsentential-logic\u002Fcompactness-and-effectiveness":7173,"\u002Flogic\u002Ffirst-order-languages\u002Ffirst-order-languages":7177,"\u002Flogic\u002Ffirst-order-languages\u002Fstructures-truth-and-satisfaction":7182,"\u002Flogic\u002Ffirst-order-languages\u002Fdefinability-and-elementary-equivalence":7186,"\u002Flogic\u002Ffirst-order-languages\u002Fterms-substitution-and-parsing":7190,"\u002Flogic\u002Fdeductive-calculus\u002Fa-deductive-calculus":7194,"\u002Flogic\u002Fdeductive-calculus\u002Fdeduction-theorem-and-derived-rules":7199,"\u002Flogic\u002Fdeductive-calculus\u002Fsoundness":7203,"\u002Flogic\u002Fdeductive-calculus\u002Fcompleteness-and-consistency":7207,"\u002Flogic\u002Fmodels-and-theories\u002Fcompactness-and-lowenheim-skolem":7211,"\u002Flogic\u002Fmodels-and-theories\u002Ftheories-elementary-classes-and-categoricity":7216,"\u002Flogic\u002Fmodels-and-theories\u002Finterpretations-between-theories":7220,"\u002Flogic\u002Fmodels-and-theories\u002Fnonstandard-analysis":7224,"\u002Flogic\u002Farithmetic-and-definability\u002Fdefinability-in-arithmetic":7228,"\u002Flogic\u002Farithmetic-and-definability\u002Fnatural-numbers-with-successor":7233,"\u002Flogic\u002Farithmetic-and-definability\u002Fpresburger-and-reducts":7237,"\u002Flogic\u002Farithmetic-and-definability\u002Fa-subtheory-and-representability":7241,"\u002Flogic\u002Fincompleteness\u002Farithmetization-of-syntax":7245,"\u002Flogic\u002Fincompleteness\u002Fincompleteness-and-undecidability":7250,"\u002Flogic\u002Fincompleteness\u002Fsecond-incompleteness-theorem":7254,"\u002Flogic\u002Fcomputability-and-representability\u002Frecursive-functions":7258,"\u002Flogic\u002Fcomputability-and-representability\u002Frepresenting-exponentiation":7263,"\u002Flogic\u002Fsecond-order-logic\u002Fsecond-order-languages":7267,"\u002Flogic\u002Fsecond-order-logic\u002Fskolem-functions-and-many-sorted-logic":7272,"\u002Flogic\u002Fsecond-order-logic\u002Fgeneral-structures":7276,"\u002Flogic":7280,"\u002Freinforcement-learning\u002Ffoundations\u002Fwhat-is-reinforcement-learning":7283,"\u002Freinforcement-learning\u002Ffoundations\u002Fa-brief-history-of-rl":7287,"\u002Freinforcement-learning\u002Ffoundations\u002Fmulti-armed-bandits":7291,"\u002Freinforcement-learning\u002Ffoundations\u002Fbandit-exploration-algorithms":7295,"\u002Freinforcement-learning\u002Ffoundations\u002Fmarkov-decision-processes":7299,"\u002Freinforcement-learning\u002Ffoundations\u002Fvalue-functions-and-optimality":7303,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdynamic-programming":7307,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdp-async-and-gpi":7311,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-methods":7315,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-off-policy":7319,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftemporal-difference-learning":7323,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftd-control-sarsa-and-q-learning":7327,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-bootstrapping":7331,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-off-policy-methods":7335,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-and-learning":7339,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-focusing-and-decision-time":7343,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdecision-time-planning":7347,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-tree-search":7351,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-prediction":7355,"\u002Freinforcement-learning\u002Fapproximation\u002Ffeature-construction-and-nonlinear":7360,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-control":7364,"\u002Freinforcement-learning\u002Fapproximation\u002Faverage-reward-control":7368,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-and-the-deadly-triad":7372,"\u002Freinforcement-learning\u002Fapproximation\u002Fbellman-error-and-gradient-td":7376,"\u002Freinforcement-learning\u002Fapproximation\u002Feligibility-traces":7380,"\u002Freinforcement-learning\u002Fapproximation\u002Ftrue-online-and-sarsa-lambda":7384,"\u002Freinforcement-learning\u002Fapproximation\u002Fpolicy-gradient-methods":7388,"\u002Freinforcement-learning\u002Fapproximation\u002Factor-critic-and-continuous-actions":7392,"\u002Freinforcement-learning\u002Fapproximation\u002Fleast-squares-and-memory-based-methods":7396,"\u002Freinforcement-learning\u002Fapproximation\u002Fmemory-and-kernel-methods":7400,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-eligibility-traces":7404,"\u002Freinforcement-learning\u002Fapproximation\u002Fstable-off-policy-traces":7408,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdeep-q-networks":7412,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdqn-improvements":7416,"\u002Freinforcement-learning\u002Fdeep-rl\u002Factor-critic-and-ppo":7420,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fppo-and-continuous-control":7424,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fcase-studies":7428,"\u002Freinforcement-learning\u002Fdeep-rl\u002Frl-beyond-games":7432,"\u002Freinforcement-learning\u002Fdeep-rl\u002Ffrontiers":7436,"\u002Freinforcement-learning\u002Fdeep-rl\u002Freward-design-and-open-problems":7440,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow":7444,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow-part-2":7449,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control":7453,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control-part-2":7457,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl":7461,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl-part-2":7465,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration":7469,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration-part-2":7473,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl":7477,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl-part-2":7481,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl":7485,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl-part-2":7489,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl":7493,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl-part-2":7497,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl":7501,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl-part-2":7505,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Frlhf-and-language-models":7509,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps":7513,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps-part-2":7517,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl":7521,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl-part-2":7525,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmeta-rl-and-generalization":7529,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fpsychology-of-reinforcement":7533,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Finstrumental-conditioning-and-control":7538,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-and-td-error":7542,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-in-the-brain":7546,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fanimal-learning-and-cognition":7550,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fcognitive-maps-and-planning":7554,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fneuroscience-of-reinforcement":7558,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fseveral-learning-systems":7562,"\u002Freinforcement-learning":7566,"\u002Fartificial-intelligence\u002Ffoundations\u002Fwhat-is-ai":7568,"\u002Fartificial-intelligence\u002Ffoundations\u002Ffoundations-of-ai":7572,"\u002Fartificial-intelligence\u002Ffoundations\u002Fintelligent-agents":7576,"\u002Fartificial-intelligence\u002Ffoundations\u002Fagent-architectures":7580,"\u002Fartificial-intelligence\u002Fsearch\u002Funinformed-search":7584,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-strategies-compared":7589,"\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search":7593,"\u002Fartificial-intelligence\u002Fsearch\u002Fheuristic-functions":7597,"\u002Fartificial-intelligence\u002Fsearch\u002Flocal-search":7601,"\u002Fartificial-intelligence\u002Fsearch\u002Fpopulation-and-continuous-search":7605,"\u002Fartificial-intelligence\u002Fsearch\u002Fadversarial-search":7609,"\u002Fartificial-intelligence\u002Fsearch\u002Fgames-of-chance-and-imperfect-information":7613,"\u002Fartificial-intelligence\u002Fsearch\u002Fconstraint-satisfaction":7617,"\u002Fartificial-intelligence\u002Fsearch\u002Fcsp-search-and-structure":7621,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-under-uncertainty":7625,"\u002Fartificial-intelligence\u002Fsearch\u002Fbelief-state-and-online-search":7629,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic":7633,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-inference":7638,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic":7642,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic-in-use":7646,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Finference-and-resolution":7650,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-resolution":7654,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fclassical-planning":7658,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-graphs-and-graphplan":7662,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-in-the-real-world":7666,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-under-uncertainty":7670,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fknowledge-representation":7674,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Freasoning-systems-and-defaults":7678,"\u002Fartificial-intelligence\u002Funcertainty\u002Fprobability-and-bayes":7682,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayes-rule-and-naive-bayes":7687,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayesian-networks":7691,"\u002Fartificial-intelligence\u002Funcertainty\u002Finference-in-bayesian-networks":7695,"\u002Fartificial-intelligence\u002Funcertainty\u002Freasoning-over-time":7699,"\u002Fartificial-intelligence\u002Funcertainty\u002Ftracking-and-data-association":7703,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions":7707,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmarkov-decision-processes":7711,"\u002Fartificial-intelligence\u002Funcertainty\u002Fdecision-networks-and-game-theory":7714,"\u002Fartificial-intelligence\u002Funcertainty\u002Fgame-theory-and-mechanism-design":7718,"\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples":7722,"\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families":7727,"\u002Fartificial-intelligence\u002Flearning\u002Fprobabilistic-learning":7731,"\u002Fartificial-intelligence\u002Flearning\u002Fexpectation-maximization":7735,"\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning":7739,"\u002Fartificial-intelligence\u002Flearning\u002Fgeneralization-and-policy-search":7742,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-in-learning":7746,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-based-learning-methods":7750,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fvision-and-perception":7754,"\u002Fartificial-intelligence\u002Ffrontiers\u002Freconstructing-the-3d-world":7759,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobotics":7763,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobot-planning-and-control":7767,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnatural-language-in-ai":7771,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnlp-grammar-translation-and-speech":7775,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fphilosophy-and-future":7779,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fai-ethics-and-future":7783,"\u002Fartificial-intelligence":7787,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-constituents-nuclide-chart":7790,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-size-charge-distributions":7795,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-masses-binding-energy":7799,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fsemi-empirical-mass-formula":7803,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-moments-multipoles":7807,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnuclear-force-shell-overview":7811,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fthe-deuteron":7816,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnucleon-nucleon-scattering":7820,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fmeson-theory-isospin":7824,"\u002Fnuclear-physics\u002Fnuclear-models\u002Ffermi-gas-model":7828,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fliquid-drop-collective-coordinates":7833,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fshell-model-single-particle":7837,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fcollective-model-rotations-vibrations":7841,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-law-modes":7845,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-kinetics-equilibrium":7850,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-decay-gamow-theory":7854,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-fine-structure-hindrance":7859,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fbeta-decay-energetics-neutrino":7863,"\u002Fnuclear-physics\u002Fbeta-decay\u002Ffermi-theory-beta-decay":7868,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fweak-interaction-parity-violation":7872,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fdouble-beta-decay-neutrino-mass":7876,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fgamma-multipole-radiation":7880,"\u002Fnuclear-physics\u002Fgamma-decay\u002Finternal-conversion-isomers":7885,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fangular-correlations-mossbauer":7889,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Freaction-kinematics-cross-sections":7893,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fcompound-nucleus-resonances":7898,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fdirect-reactions-optical-model":7902,"\u002Fnuclear-physics\u002Ffission\u002Ffission-barrier-dynamics":7906,"\u002Fnuclear-physics\u002Ffission\u002Fchain-reactions-reactor-physics":7911,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Ffusion-reactions-confinement":7915,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fstellar-nucleosynthesis":7920,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fbig-bang-nucleosynthesis":7924,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fcharged-particle-stopping-power":7928,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fphoton-neutron-interactions":7933,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fradiation-detectors":7937,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fdosimetry-radiation-biology":7941,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fnuclear-applications-dating-medicine":7945,"\u002Fnuclear-physics":7949,"\u002Fnatural-language-processing\u002Ffoundations\u002Fwhat-is-nlp":7952,"\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization":7956,"\u002Fnatural-language-processing\u002Ffoundations\u002Fminimum-edit-distance":7959,"\u002Fnatural-language-processing\u002Ffoundations\u002Fn-gram-language-models":7963,"\u002Fnatural-language-processing\u002Ffoundations\u002Fsmoothing-and-backoff":7967,"\u002Fnatural-language-processing\u002Fclassification\u002Fnaive-bayes-and-sentiment":7971,"\u002Fnatural-language-processing\u002Fclassification\u002Fevaluating-classifiers":7976,"\u002Fnatural-language-processing\u002Fclassification\u002Flogistic-regression":7980,"\u002Fnatural-language-processing\u002Fclassification\u002Fsentiment-and-affect-lexicons":7984,"\u002Fnatural-language-processing\u002Fsemantics\u002Fvector-semantics-and-embeddings":7988,"\u002Fnatural-language-processing\u002Fsemantics\u002Fstatic-word-embeddings":7993,"\u002Fnatural-language-processing\u002Fsemantics\u002Fneural-language-models":7997,"\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling":8001,"\u002Fnatural-language-processing\u002Fsequences\u002Fcrfs-and-neural-taggers":8004,"\u002Fnatural-language-processing\u002Fsequences\u002Frnns-and-lstms":8008,"\u002Fnatural-language-processing\u002Ftransformers\u002Ftransformers-and-attention":8012,"\u002Fnatural-language-processing\u002Ftransformers\u002Fthe-transformer-architecture":8016,"\u002Fnatural-language-processing\u002Ftransformers\u002Flarge-language-models":8019,"\u002Fnatural-language-processing\u002Ftransformers\u002Fllm-pretraining-and-scaling":8022,"\u002Fnatural-language-processing\u002Ftransformers\u002Ffine-tuning-and-prompting":8026,"\u002Fnatural-language-processing\u002Ftransformers\u002Fprompting-and-alignment":8030,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-parsing":8034,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcky-scoring-and-evaluation":8039,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdependency-parsing":8043,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fgraph-based-and-neural-dependency-parsing":8047,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fword-senses-and-wsd":8051,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fwsd-in-practice-and-induction":8055,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-roles-and-information-extraction":8059,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Frelations-events-and-templates":8063,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoreference-and-discourse":8067,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoherence-and-discourse-structure":8071,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Flogical-semantics":8075,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcompositional-semantics-and-description-logics":8079,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-parsing":8083,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fneural-semantic-parsing":8087,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Finformation-extraction":8091,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftimes-events-and-templates":8095,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdiscourse-coherence":8099,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fentity-based-and-global-coherence":8103,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-grammars":8107,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftreebanks-and-lexicalized-grammars":8111,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation":8115,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation-decoding-and-evaluation":8118,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering":8122,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering-knowledge-and-llms":8125,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots":8129,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants":8130,"\u002Fnatural-language-processing\u002Fapplications\u002Ftext-summarization":8132,"\u002Fnatural-language-processing\u002Fapplications\u002Fabstractive-summarization-and-evaluation":8136,"\u002Fnatural-language-processing\u002Fspeech\u002Fphonetics":8140,"\u002Fnatural-language-processing\u002Fspeech\u002Facoustic-phonetics":8145,"\u002Fnatural-language-processing\u002Fspeech\u002Fautomatic-speech-recognition":8149,"\u002Fnatural-language-processing\u002Fspeech\u002Fasr-evaluation-and-applications":8153,"\u002Fnatural-language-processing":8157,"\u002Fparticle-physics\u002Ffoundations\u002Fhistorical-overview-particle-zoo":8160,"\u002Fparticle-physics\u002Ffoundations\u002Fparticle-physics-basic-concepts":8164,"\u002Fparticle-physics\u002Ffoundations\u002Ffundamental-interactions-force-carriers":8168,"\u002Fparticle-physics\u002Funits-kinematics\u002Fnatural-units-and-scales":8172,"\u002Fparticle-physics\u002Funits-kinematics\u002Ffour-vectors-invariant-mass":8177,"\u002Fparticle-physics\u002Funits-kinematics\u002Fdecay-scattering-kinematics-mandelstam":8181,"\u002Fparticle-physics\u002Funits-kinematics\u002Fcross-sections-golden-rule":8185,"\u002Fparticle-physics\u002Fsymmetries\u002Fconservation-laws-symmetries":8189,"\u002Fparticle-physics\u002Fsymmetries\u002Fdiscrete-symmetries-cpt":8194,"\u002Fparticle-physics\u002Fsymmetries\u002Fparity-violation-weak":8198,"\u002Fparticle-physics\u002Fsymmetries\u002Fsu2-su3-flavor-symmetry":8202,"\u002Fparticle-physics\u002Fquark-model\u002Feightfold-way-su3":8206,"\u002Fparticle-physics\u002Fquark-model\u002Fmeson-spectroscopy":8211,"\u002Fparticle-physics\u002Fquark-model\u002Fbaryon-spectroscopy":8215,"\u002Fparticle-physics\u002Fquark-model\u002Fcolor-confinement-exotics":8219,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fklein-gordon-equation":8223,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fdirac-equation-spinors":8228,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fantiparticles-hole-theory":8232,"\u002Fparticle-physics\u002Fqed\u002Ffeynman-rules-qed":8236,"\u002Fparticle-physics\u002Fqed\u002Fqed-tree-processes":8241,"\u002Fparticle-physics\u002Fqed\u002Frenormalization-running-coupling":8245,"\u002Fparticle-physics\u002Fqed\u002Felectron-g-2":8249,"\u002Fparticle-physics\u002Fweak-interaction\u002Fva-structure-weak":8253,"\u002Fparticle-physics\u002Fweak-interaction\u002Fw-z-bosons-decays":8258,"\u002Fparticle-physics\u002Fweak-interaction\u002Fckm-matrix":8262,"\u002Fparticle-physics\u002Fweak-interaction\u002Fcp-violation-kaons-b-mesons":8266,"\u002Fparticle-physics\u002Fqcd\u002Fcolor-su3-gluons":8270,"\u002Fparticle-physics\u002Fqcd\u002Fasymptotic-freedom-confinement":8275,"\u002Fparticle-physics\u002Fqcd\u002Fdeep-inelastic-scattering-partons":8279,"\u002Fparticle-physics\u002Fqcd\u002Fjets-hadronization":8283,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Felectroweak-su2-u1":8287,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fspontaneous-symmetry-breaking":8292,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-mechanism":8296,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-boson-discovery":8300,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fstandard-model":8304,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-oscillations":8308,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-mass-pmns":8313,"\u002Fparticle-physics\u002Fneutrinos\u002Fdirac-majorana-experiments":8317,"\u002Fparticle-physics\u002Fexperiment\u002Faccelerators-luminosity":8321,"\u002Fparticle-physics\u002Fexperiment\u002Fdetectors-subsystems":8326,"\u002Fparticle-physics\u002Fexperiment\u002Fhow-discoveries-are-made":8330,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fbeyond-standard-model":8334,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fgrand-unified-theories":8338,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fsupersymmetry":8342,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fhierarchy-problem-naturalness":8346,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fdark-matter-candidates":8350,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fmatter-antimatter-open-questions":8354,"\u002Fparticle-physics":8358,"\u002Fastrophysics-cosmology\u002Forientation\u002Fthe-sun-and-stars":8361,"\u002Fastrophysics-cosmology\u002Forientation\u002Fstellar-death-final-states":8366,"\u002Fastrophysics-cosmology\u002Forientation\u002Fgalaxies-and-cosmology":8370,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fmagnitudes-fluxes-and-the-distance-modulus":8374,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fstellar-spectra-and-spectral-classification":8379,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Ftelescopes-and-detectors-across-the-spectrum":8383,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fthe-cosmic-distance-ladder":8387,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fblackbody-radiation-and-specific-intensity":8391,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fradiative-transfer-and-the-transfer-equation":8396,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fspectral-line-formation-and-broadening":8400,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fopacity-and-the-rosseland-mean":8404,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fhydrostatic-equilibrium-and-the-virial-theorem":8408,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equations-of-stellar-structure":8413,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equation-of-state-and-polytropes":8417,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-standard-solar-model":8421,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fthermonuclear-reaction-rates-and-the-gamow-peak":8425,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhydrogen-burning-pp-chains-and-cno":8430,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhelium-burning-and-the-triple-alpha-process":8434,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fadvanced-burning-and-neutron-capture-nucleosynthesis":8438,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fphases-of-the-interstellar-medium":8442,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fmolecular-clouds-and-gravitational-collapse":8447,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fprotostars-and-the-pre-main-sequence":8451,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-main-sequence-and-its-structure":8455,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fpost-main-sequence-low-mass-evolution":8460,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-evolution-of-massive-stars":8464,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fstellar-pulsation-and-the-instability-strip":8468,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":8472,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fcore-collapse-supernovae":8476,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fthermonuclear-supernovae-type-ia":8480,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fneutron-stars-and-pulsars":8484,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fblack-holes-schwarzschild-and-kerr":8488,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fbinary-systems-and-mass-transfer":8492,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Faccreting-compact-objects":8497,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fgravitational-waves-from-inspiraling-binaries":8501,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fmultimessenger-astronomy-and-gamma-ray-bursts":8505,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fthe-milky-way":8509,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-morphology-and-classification":8514,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-rotation-curves-and-dark-matter":8518,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Factive-galactic-nuclei-and-supermassive-black-holes":8522,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-clusters-and-large-scale-structure":8526,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-expanding-universe-and-hubbles-law":8530,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-frw-metric-and-cosmological-redshift":8535,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-friedmann-equations-and-cosmic-dynamics":8539,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fcosmological-models-and-distances":8542,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fdark-energy-and-the-accelerating-universe":8546,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fthe-thermal-history-of-the-universe":8550,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fbig-bang-nucleosynthesis":8555,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Frecombination-and-the-cosmic-microwave-background":8559,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcmb-anisotropies-and-cosmological-parameters":8563,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcosmic-inflation":8567,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fstructure-formation-and-the-growth-of-perturbations":8571,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fdark-matter-dark-energy-and-open-questions":8575,"\u002Fastrophysics-cosmology":8579,"\u002Fcolophon":8582,"\u002F":8585},{"path":3909,"title":3910,"module":3911,"summary":3912},"\u002Falgorithms\u002Ffoundations\u002Fwhat-is-an-algorithm","What Is an Algorithm?","Foundations","An algorithm is a finite, mechanical recipe that transforms inputs into outputs. We define what counts as an algorithm, how we write one down, and the three things we always ask of it: is it correct, is it fast, and can we prove it.\n",{"path":3914,"title":3915,"module":3911,"summary":3916},"\u002Falgorithms\u002Ffoundations\u002Fproof-techniques","Proof Techniques","An algorithm without a proof is a conjecture. This lesson collects the handful\nof arguments that certify the algorithms in this course — direct proof,\ncontrapositive, contradiction, ordinary and strong induction, construction, and\ndisproof by counterexample — each with a small worked\nexample and a picture. Loop invariants are a form of induction,\nrecursive correctness falls to strong induction, and the classic broken proofs\n(all horses are the same color) show where inductions go wrong.\n",{"path":3918,"title":3919,"module":3911,"summary":3920},"\u002Falgorithms\u002Ffoundations\u002Fasymptotic-analysis","Asymptotic Analysis","We measure an algorithm's running time as a function of its input size, then strip away machine-specific constants and lower-order terms to compare algorithms cleanly. This lesson defines the RAM model and the $O$, $\\Omega$, $\\Theta$, $o$, and $\\omega$ notations, proves the polynomial theorem, and shows how to rank growth rates with the limit test, L'Hôpital, base substitution, and the logarithm identities the arguments lean on.\n",{"path":3922,"title":3923,"module":3911,"summary":3924},"\u002Falgorithms\u002Ffoundations\u002Fgrowth-rates-and-loop-analysis","Growth Rates and Loop Analysis","With the asymptotic notations in hand, we rank the functions that actually arise in running times — from constant to factorial — proving the orderings between rungs, then read the running time of a loop nest straight off the page. Sequential blocks add, nested loops multiply, index scaling gives logarithms; a worked trace and a tour of cache-aware and galactic algorithms close the lesson.\n",{"path":3926,"title":3927,"module":3911,"summary":3928},"\u002Falgorithms\u002Ffoundations\u002Frecurrences","Recurrences and the Master Theorem","Recursive and divide-and-conquer algorithms describe their own running time with a recurrence: $T(n)$ in terms of $T$ on smaller inputs. We solve recurrences three ways — drawing the recursion tree, guessing-and-verifying by induction, and applying the Master Theorem — using merge sort as the running example, then handle unequal splits with Akra–Bazzi.\n",{"path":3930,"title":3931,"module":3911,"summary":3932},"\u002Falgorithms\u002Ffoundations\u002Famortized-analysis","Amortized Analysis","Some operations are occasionally expensive but cheap on average across any\nsequence. Amortized analysis bounds the average cost per operation over a\nworst-case sequence — not an expectation — so a rare costly step is paid for by\nthe many cheap ones around it. This lesson develops the aggregate, accounting,\nand potential methods on dynamic-array doubling, the binary counter, and a\nstack with multipop.\n",{"path":3934,"title":3935,"module":3936,"summary":3937},"\u002Falgorithms\u002Fdivide-and-conquer\u002Fmergesort","Divide and Conquer & Mergesort","Divide & Conquer","Divide and conquer breaks a problem into smaller copies of itself, solves\nthem recursively, and stitches the answers together. We meet the paradigm\nthrough mergesort — its merge step, its loop-invariant proof, and the\nrecursion tree that pins its cost at $\\Theta(n\\log n)$ — then count inversions\nwith the same machinery and distill the whole pattern into the master theorem.\n",{"path":3939,"title":3940,"module":3936,"summary":3941},"\u002Falgorithms\u002Fdivide-and-conquer\u002Fquicksort","Quicksort","Quicksort sorts in place by partitioning around a pivot and recursing on\neach side. We give Lomuto and Hoare partitioning with a correctness\ninvariant, see why a bad pivot costs $\\Theta(n^2)$ while a balanced one gives\n$\\Theta(n\\log n)$, and prove that randomizing the pivot makes the expected\ncost $\\Theta(n\\log n)$ on every input.\n",{"path":3943,"title":3944,"module":3936,"summary":3945},"\u002Falgorithms\u002Fdivide-and-conquer\u002Fselection","Linear-Time Selection","Finding the $k$-th smallest element looks like it should require sorting, but\nit does not. Quickselect adapts quicksort's partition to recurse on just one\nside, achieving expected $O(n)$. The median-of-medians algorithm guarantees a\ngood pivot with the groups-of-five trick, pushing the worst case down to a\nprovable $O(n)$.\n",{"path":3947,"title":3948,"module":3936,"summary":3949},"\u002Falgorithms\u002Fdivide-and-conquer\u002Ffast-multiplication","Fast Multiplication","Grade-school multiplication is $\\Theta(n^2)$, yet divide and conquer beats it.\nKaratsuba multiplies $n$-bit integers with three half-size products instead of\nfour, giving $\\Theta(n^{\\log_2 3})$, and Strassen multiplies matrices with\nseven block products instead of eight, giving $\\Theta(n^{\\log_2 7})$. Both\nspend cheap additions to save an expensive multiplication, and the master\ntheorem quantifies the savings.\n",{"path":3951,"title":3952,"module":3953,"summary":3954},"\u002Falgorithms\u002Fsorting\u002Fheaps-and-heapsort","Heaps and Heapsort","Sorting & Order Statistics","A binary heap is a tree we store flat in an array, with index arithmetic\nstanding in for pointers. We build the max-heap property bottom-up in $O(n)$\ntime, sort in place in $\\Theta(n\\log n)$ by repeatedly extracting the maximum,\nand reuse the same structure to implement a priority queue.\n",{"path":3956,"title":3957,"module":3953,"summary":3958},"\u002Falgorithms\u002Fsorting\u002Fsorting-lower-bounds","Lower Bounds for Comparison Sorting","Every sort we have seen runs in $\\Omega(n\\log n)$, and that is no accident.\nModeling a sort as a decision tree of comparisons, we show any such tree must\nhave $n!$ leaves, forcing height $\\ge \\log_2(n!) = \\Omega(n\\log n)$ — a bound\nno comparison sort beats in the worst case, on average, or with randomness.\n",{"path":3960,"title":3961,"module":3953,"summary":3962},"\u002Falgorithms\u002Fsorting\u002Flinear-time-sorting","Sorting in Linear Time","The $\\Omega(n\\log n)$ barrier only binds algorithms that compare. By instead\nusing keys as array indices we slip past it: counting sort runs in\n$\\Theta(n+k)$ and is stable, radix sort layers it digit by digit, and bucket\nsort averages $\\Theta(n)$ on uniform data. We see exactly when each applies.\n",{"path":3964,"title":3965,"module":3953,"summary":3966},"\u002Falgorithms\u002Fsorting\u002Fexternal-sorting","External Sorting","When the data dwarfs main memory, the cost that matters is no longer\ncomparisons but block transfers to and from disk. External merge sort sorts\nmemory-sized runs, then folds them together with a heap-driven $k$-way merge in\n$\\Theta(\\log_k(N\u002FM))$ passes. Larger fan-out cuts passes; replacement selection\nbuilds longer runs to cut them further.\n",{"path":3968,"title":3969,"module":3970,"summary":3971},"\u002Falgorithms\u002Fdata-structures\u002Felementary-structures","Elementary Data Structures","Data Structures","Every container is built one of two ways: **contiguous** in an array, or\n**linked** through pointers. We trade cache-friendly random access against\n$O(1)$ splicing, derive the **amortized $O(1)$** append of a doubling dynamic\narray, and assemble the two ordered access disciplines — the LIFO **stack** and\nthe FIFO **queue** (with its generalization, the **deque**) — on top of both.\n",{"path":3973,"title":3974,"module":3970,"summary":3975},"\u002Falgorithms\u002Fdata-structures\u002Fhash-tables","Hash Tables","A hash table implements the dictionary — insert, search, delete — in expected\n$O(1)$ time by scattering keys across an array with a hash function. We build\nup from direct addressing, handle collisions by chaining and by open\naddressing, analyze the load factor $\\alpha$, and see how universal hashing\nachieves its expected-time guarantee against every input.\n",{"path":3977,"title":3978,"module":3970,"summary":3979},"\u002Falgorithms\u002Fdata-structures\u002Fbinary-search-trees","Binary Search Trees","A binary search tree keeps keys ordered so that every operation follows a\nsingle root-to-leaf path. We state the BST property, trace search, insert,\nsuccessor, and all three delete cases on concrete trees, prove the inorder\nwalk sorts, and note the drawback — every operation costs $O(h)$, and a\ncarelessly built tree degrades to height $h = \\Theta(n)$, motivating balance.\n",{"path":3981,"title":3982,"module":3970,"summary":3983},"\u002Falgorithms\u002Fdata-structures\u002Favl-trees","AVL Trees","An AVL tree is the first balanced BST: at every node the two subtrees' heights\ndiffer by at most $1$. A Fibonacci-style minimal-node argument forces height\n$h \\le 1.44\\log_2 n = O(\\log n)$, so search, insert, and delete are all\n$O(\\log n)$. Insertion rebalances with at most one of four rotation cases\n(LL, RR, LR, RL); deletion may rotate all the way to the root.\n",{"path":3985,"title":3986,"module":3970,"summary":3987},"\u002Falgorithms\u002Fdata-structures\u002Fbalanced-trees","Balanced Search Trees","An ordinary BST can degrade to height $\\Theta(n)$; balanced search trees\nguarantee $h = O(\\log n)$ by maintaining invariants and repairing them after\nevery update. We meet rotations, the local restructuring primitive, then\nred-black trees, whose color invariants force logarithmic height, and finally\nB-trees, which trade tall-and-thin for short-and-wide to win on disk.\n",{"path":3989,"title":3990,"module":3970,"summary":3991},"\u002Falgorithms\u002Fdata-structures\u002Funion-find","Disjoint Sets (Union-Find)","The disjoint-set data structure tracks a partition of elements into groups,\nanswering \"are these two in the same group?\" and merging groups on demand. A\nforest of parent pointers, sped up by union by rank and path compression,\ndrives every operation to near-constant $O(\\alpha(n))$ amortized time — the\nstructure behind connectivity queries and Kruskal's minimum spanning tree.\n",{"path":3993,"title":3994,"module":3970,"summary":3995},"\u002Falgorithms\u002Fdata-structures\u002Ffenwick-and-segment-trees","Fenwick & Segment Trees","A prefix-sum array answers a range sum in $O(1)$ but pays $O(n)$ per update;\na plain array updates in $O(1)$ but pays $O(n)$ per range sum. Fenwick and\nsegment trees give us _both_ in $O(\\log n)$. The Fenwick (binary indexed) tree\nis a tiny array keyed by the low bit; the segment tree is a general balanced\ntree over canonical ranges that handles any associative aggregate and, with\nlazy propagation, range updates too.\n",{"path":3997,"title":3998,"module":3970,"summary":3999},"\u002Falgorithms\u002Fdata-structures\u002Fspatial-data-structures","Spatial Data Structures","A balanced BST orders keys on a line, but points in the plane have no single\nnatural order. Quadtrees subdivide space recursively into quadrants; k-d trees\nsplit on alternating coordinates at the median. Both make range and\nnearest-neighbour queries fast by carving the plane into boxes a query can\nprune away. Range trees nest a y-tree in an x-tree for fast orthogonal range\nreporting; interval trees index intervals to answer stabbing queries.\n",{"path":4001,"title":4002,"module":3970,"summary":4003},"\u002Falgorithms\u002Fdata-structures\u002Fskip-lists-and-probabilistic-structures","Skip Lists & Probabilistic Structures","Balanced trees achieve $O(\\log n)$ with rotations and invariants; randomization\ngives the same bound far more simply. A skip list is a layered linked list whose\nexpress lanes are chosen by coin flips, giving expected $O(\\log n)$ search and\ninsert with no rebalancing. A Bloom filter trades exactness for space: a bit\narray and a few hashes answer set membership with no false negatives and a\ntunable false-positive rate, but cannot delete.\n",{"path":4005,"title":4006,"module":3970,"summary":4007},"\u002Falgorithms\u002Fdata-structures\u002Fb-trees","B-Trees","When data lives on disk, the cost that dominates is block transfers, not\ncomparisons — and a binary tree of a billion keys is thirty reads deep. A\nB-tree of minimum degree $t$ is short and wide: $t-1$ to $2t-1$ keys per node,\nall leaves at one depth, height $O(\\log_t n)$. Insertion splits a full node on\nthe way down and pushes its median up; deletion borrows or merges to keep nodes\nfull enough. High fan-out is what minimizes disk I\u002FO.\n",{"path":4009,"title":4010,"module":3970,"summary":4011},"\u002Falgorithms\u002Fdata-structures\u002Fdata-stream-algorithms","Data-Stream Algorithms","Most of this course assumes data sits in fast memory, addressable at will.\nExternal sorting relaxed that to a re-readable disk. The streaming model goes\nfurther: items arrive one at a time, are seen once, and must be discarded, with\nonly sublinear, often polylogarithmic, memory. In exchange, the answers are\napproximate and probabilistic. We set up the model, then meet reservoir\nsampling for a uniform sample of an unknown-length stream and Morris counting\nfor an approximate tally in doubly-logarithmic space.\n",{"path":4013,"title":4014,"module":3970,"summary":4015},"\u002Falgorithms\u002Fdata-structures\u002Fstreaming-sketches","Streaming Sketches","Sampling and counting kept a random subset or a single approximate tally.\nSketches go further: fixed, tiny summaries that answer questions about a\nstream's frequencies. We meet the Count–Min sketch for point frequency\nestimation, Misra–Gries for heavy hitters, and HyperLogLog for distinct\ncounts, each trading a controlled error for space that never grows with the\nstream.\n",{"path":4017,"title":4018,"module":4019,"summary":4020},"\u002Falgorithms\u002Fsequences\u002Ftwo-pointers-and-windows","Two Pointers & Sliding Windows","Sequences & Strings","A family of array idioms that collapse an obvious $O(n^2)$ scan into a single\n$O(n)$ pass by maintaining an invariant as indices move. We meet two pointers\n(converging on a sorted array, and a fast\u002Fslow pair for in-place rewriting)\nand the sliding window (fixed and variable size, amortized $O(n)$). The\ncompanion lesson on prefix sums picks up where the window's positivity\nassumption fails.\n",{"path":4022,"title":4023,"module":4019,"summary":4024},"\u002Falgorithms\u002Fsequences\u002Fprefix-sums","Prefix Sums & Difference Arrays","Prefix sums precompute the running total once so that any range-sum query is a\nsingle subtraction, $P[r{+}1]-P[l]$, in $O(1)$. A hash map of prefix\nfrequencies then counts subarrays summing to $k$ in $O(n)$ — even with negative\nentries, where the sliding window fails. The difference-array dual turns $m$\nrange-adds into $O(m+n)$, and the whole idea lifts to 2-D rectangle sums by\ninclusion–exclusion.\n",{"path":4026,"title":4027,"module":4019,"summary":4028},"\u002Falgorithms\u002Fsequences\u002Fmonotonic-stacks","Monotonic Stacks & Queues","A **monotonic stack** keeps its contents sorted by popping every element that\nwould break the order before each push — turning a family of \"previous\u002Fnext\ngreater (or smaller) element\" questions into a single $O(n)$ scan. We trace\nthe next-greater-element routine push by push and prove its amortized bound,\nfuse two such scans to measure the **largest rectangle in a histogram** in\nlinear time, extend the idea to a **monotonic deque** that streams the\n**sliding-window maximum** in $O(n)$, and use asymmetric tie-breaking to\ncount **subarray minimums** without double-counting duplicates.\n",{"path":4030,"title":4031,"module":4019,"summary":4032},"\u002Falgorithms\u002Fsequences\u002Fbinary-search-on-the-answer","Binary Search on the Answer","Binary search locates the boundary of a **monotone predicate** $p(x)$ in\n$O(\\log(\\text{range}))$ probes; sorted arrays are only one instance. We first\nestablish the half-open `while (lo \u003C hi)` template for $\\textsc{lower\\_bound}$\nand $\\textsc{upper\\_bound}$, then generalize to \"binary search on the answer\":\nwhenever feasibility is monotone in a numeric parameter, we binary search the\nparameter itself, calling a feasibility check at each step.\n",{"path":4034,"title":4035,"module":4019,"summary":4036},"\u002Falgorithms\u002Fsequences\u002Fstring-matching","String Matching: Naive & Rabin–Karp","Given a text $T$ of length $n$ and a pattern $P$ of length $m$, find every\noccurrence of $P$ in $T$. The naive scan costs $O(nm)$ and re-reads text it has\nalready seen. Rabin–Karp fixes the first inefficiency with a **rolling hash**:\neach length-$m$ window is summarized by one number, updated in $O(1)$ per slide,\nverified on a hash match to kill collisions, for expected $O(n+m)$. A companion\nlesson removes the re-reading entirely with KMP and the Z-function.\n",{"path":4038,"title":4039,"module":4019,"summary":4040},"\u002Falgorithms\u002Fsequences\u002Fkmp-and-z-function","String Matching: KMP & the Z-Function","Two linear-time matchers that beat Rabin–Karp's expected bound with a\nworst-case guarantee and no randomness. KMP precomputes a **failure function**\n$\\pi$ so a mismatch slides the pattern by $q-\\pi[q-1]$ and the text pointer\nnever backs up, for $O(n+m)$. The **Z-function** computes the longest\nprefix-match at every position via the Z-box, giving the same bound from a\ndifferent angle; the two encodings of a string's self-overlap convert freely.\n",{"path":4042,"title":4043,"module":4019,"summary":4044},"\u002Falgorithms\u002Fsequences\u002Ftries","Tries & Prefix Trees","A **trie** stores a set of strings in a tree keyed by _characters_, so that\ninsert, search, delete, and prefix-test all run in $O(L)$ time — the length\nof the key, _independent of how many keys are stored_. Shared prefixes are\nstored once, which makes tries the natural structure for autocomplete,\nwildcard dictionaries, board word-search, and — over the alphabet $\\{0,1\\}$\n— the maximum-XOR-pair problem. Radix (Patricia) trees compress the chains.\n",{"path":4046,"title":4047,"module":4019,"summary":4048},"\u002Falgorithms\u002Fsequences\u002Fsuffix-arrays-and-aho-corasick","Suffix Arrays, LCP & Aho–Corasick","A **suffix array** sorts all $n$ suffixes of a string, indexing every substring\nat once; built in $O(n\\log n)$, it locates a pattern by binary search in\n$O(m\\log n)$. Its companion **LCP array** (Kasai's $O(n)$ algorithm) counts\ndistinct substrings and finds the longest repeated substring. **Aho–Corasick**\ngeneralises KMP to a whole dictionary: a trie of patterns plus failure links\nscans the text once in $O(\\text{text} + \\text{matches})$ to report every\noccurrence of every pattern. Manacher's algorithm finds all palindromic\nsubstrings in $O(n)$.\n",{"path":4050,"title":4051,"module":4052,"summary":4053},"\u002Falgorithms\u002Fgraphs\u002Frepresentations-and-traversal","Graph Representations and Traversal","Graphs","A graph captures _relationships_ — who connects to whom. We fix the\nvocabulary, weigh the two standard representations (adjacency list versus\nmatrix), then meet the single search skeleton behind everything that follows:\nWhatever-First-Search, and its breadth-first reading, which finds shortest\npaths by number of edges in $O(V + E)$.\n",{"path":4055,"title":4056,"module":4052,"summary":4057},"\u002Falgorithms\u002Fgraphs\u002Fdepth-first-search","Depth-First Search","Swap BFS's queue for a stack and the search plunges instead of fanning out.\nDepth-first search stamps every vertex with discovery and finish times that\nnest like parentheses, classifies each edge as tree, back, forward, or cross,\nand — through the back edge — decides in one pass whether a graph has a cycle.\nThese timestamps underpin topological sort, strong\nconnectivity, and the rest of this module.\n",{"path":4059,"title":4060,"module":4052,"summary":4061},"\u002Falgorithms\u002Fgraphs\u002Ftopological-sort-and-scc","Topological Sort and Strong Connectivity","Directed acyclic graphs model dependencies: tasks that must precede other\ntasks. A _topological order_ lays such a graph out in a line so every edge\npoints forward, and depth-first finish times yield one almost for free.\nWe then ask the harder question for graphs _with_ cycles: which vertices can\nreach each other? The answer is the strongly connected components, found by a\ntwo-pass DFS.\n",{"path":4063,"title":4064,"module":4052,"summary":4065},"\u002Falgorithms\u002Fgraphs\u002Fminimum-spanning-trees","Minimum Spanning Trees","Given a weighted network, how do we connect everything as cheaply as possible?\nThe answer is a minimum spanning tree, and one lemma — the cut property —\njustifies _every_ correct MST algorithm. We prove the cut and cycle\nproperties by exchange arguments, use them to settle uniqueness, and meet the\noldest MST algorithm, Borůvka's, whose parallel component-merging rounds fall\nstraight out of the cut rule.\n",{"path":4067,"title":4068,"module":4052,"summary":4069},"\u002Falgorithms\u002Fgraphs\u002Fkruskal-and-prim","Kruskal and Prim","The two minimum-spanning-tree algorithms you will actually implement.\nKruskal grows a forest edge by edge, cheapest first, using a union-find\nstructure to reject cycle-closing edges; Prim grows one tree outward from a\nroot with a priority queue, exactly Dijkstra rekeyed by attachment cost. Both\ntraced in full on a nine-town graph, with the edge cases, the bottleneck\nproperty, and where each one wins.\n",{"path":4071,"title":4072,"module":4052,"summary":4073},"\u002Falgorithms\u002Fgraphs\u002Fshortest-paths","Shortest Paths","Finding the cheapest route through a weighted network is one of the most-used\nalgorithms in computing, and a single operation — _relaxation_ — underlies\nevery method. We build the primitive, prove the triangle inequality and\noptimal substructure that make it work, then meet Dijkstra's algorithm: the\ngreedy solution for non-negative weights, traced vertex by vertex, with the\ncut argument that proves each extraction is final.\n",{"path":4075,"title":4076,"module":4052,"summary":4077},"\u002Falgorithms\u002Fgraphs\u002Fall-pairs-and-negative-weights","All-Pairs and Negative Weights","Dijkstra's greedy schedule breaks the moment an edge goes negative. We give it\nup for dynamic programming: Bellman-Ford derived as a DP over edge budgets,\nwith its negative-cycle detector, and Floyd-Warshall computing the distance\nbetween _every_ pair of vertices via a DP over which vertices a path may pass\nthrough. We close with Johnson's algorithm and the arbitrage problems that\nnegative cycles encode.\n",{"path":4079,"title":4080,"module":4052,"summary":4081},"\u002Falgorithms\u002Fgraphs\u002Fnetwork-flow","Network Flow","How much can flow through a network from source to sink? We build flow\nnetworks with capacity and conservation constraints, increase a flow by\npushing along augmenting paths in the residual graph, and see how reverse\nedges let the algorithm undo earlier routing. Ford-Fulkerson and its BFS refinement\nEdmonds-Karp find a maximum flow, traced end to end on a worked network.\n",{"path":4083,"title":4084,"module":4052,"summary":4085},"\u002Falgorithms\u002Fgraphs\u002Fmax-flow-min-cut","Max-Flow Min-Cut and Applications","Why is the flow found when no augmenting path remains actually optimal? The\nanswer is a duality theorem: the maximum flow equals the minimum cut. We prove\nit, read the minimum cut off the final residual graph, then derive bipartite\nmatching and a catalog of modeling reductions from the flow\nabstraction — before touching the modern algorithms that supersede\nEdmonds-Karp.\n",{"path":4087,"title":4088,"module":4052,"summary":4089},"\u002Falgorithms\u002Fgraphs\u002Fbridges-and-articulation-points","Bridges & Articulation Points","A **bridge** is an edge whose removal disconnects the graph; an **articulation\npoint** is a vertex whose removal does. Both are single points of failure in a\nnetwork. A single depth-first search computes discovery times and **low-links**,\nand two local criteria — $low[v] > disc[u]$ for bridges, $low[v] \\ge disc[u]$\nfor cut vertices — find them all in $O(V+E)$.\n",{"path":4091,"title":4092,"module":4052,"summary":4093},"\u002Falgorithms\u002Fgraphs\u002Flowest-common-ancestor","Lowest Common Ancestor & Binary Lifting","Given a rooted tree, the lowest common ancestor of $u$ and $v$ is the deepest\nnode that is an ancestor of both. A naive walk answers one query in $O(h)$;\n**binary lifting** precomputes the $2^k$-th ancestor of every node in\n$O(n\\log n)$, then answers $k$-th-ancestor and LCA queries in $O(\\log n)$ each.\nWe derive both jumps, apply them to tree distance, and compare against the\nEuler-tour + RMQ and Tarjan offline alternatives.\n",{"path":4095,"title":4096,"module":4052,"summary":4097},"\u002Falgorithms\u002Fgraphs\u002Ftwo-sat","2-SAT via Implication Graphs","A boolean formula whose every clause has exactly two literals can be solved in\n_linear_ time — even though its three-literal cousin is NP-complete. The idea\nis to read each clause as a pair of implications, build a directed graph on the\n$2n$ literals, and ask a question we already know how to answer: which literals\nshare a strongly connected component? The formula is satisfiable iff no variable\nlands in the same SCC as its own negation, and the SCCs' topological order\nyields a satisfying assignment for free.\n",{"path":4099,"title":4100,"module":4052,"summary":4101},"\u002Falgorithms\u002Fgraphs\u002Feulerian-tours","Eulerian Tours","An **Eulerian tour** uses every _edge_ of a graph exactly once. We give the\nexact parity and balance conditions under which one exists (even degree\nfor undirected graphs, in-degree equal to out-degree for directed) and Hierholzer's\n$O(E)$ algorithm that constructs one by splicing closed sub-tours. We contrast\nthis sharply with the **Hamiltonian** problem (visit every _vertex_ once),\nwhich is NP-complete: visiting edges is easy, visiting vertices is hard.\n",{"path":4103,"title":4104,"module":4052,"summary":4105},"\u002Falgorithms\u002Fgraphs\u002Fbipartite-matching","Bipartite Matching","Pairing applicants to jobs, students to slots, files to disks: all are\n**maximum bipartite matching**. We solve it combinatorially with **augmenting\npaths** (Kuhn's algorithm, $O(VE)$), speed it up to $O(E\\sqrt V)$ with\n**Hopcroft–Karp**, and uncover the structure behind it — **König's theorem**\n(max matching equals min vertex cover) and **Hall's marriage theorem** (a\nperfect matching exists iff every set has enough neighbors).\n",{"path":4107,"title":4108,"module":4109,"summary":4110},"\u002Falgorithms\u002Fgreedy\u002Fthe-greedy-method","The Greedy Method","Greedy Algorithms","A greedy algorithm builds a solution one locally-best choice at a time and\nnever looks back. We isolate the two properties that make this work — the\ngreedy-choice property and optimal substructure — prove the canonical\nactivity-selection algorithm correct with an exchange argument, watch greedy\nfail on the 0\u002F1 knapsack, and glimpse matroids as the theory\nthat says exactly when the greedy method is optimal.\n",{"path":4112,"title":4113,"module":4109,"summary":4114},"\u002Falgorithms\u002Fgreedy\u002Fscheduling-and-intervals","Scheduling & Interval Partitioning","Three classic scheduling problems all yield to greedy algorithms — and all\nthree turn on a single design decision: which key to sort by. Interval\nscheduling sorts by **finish** time to pack the most compatible jobs;\ninterval partitioning sorts by **start** time and proves the rooms needed\nequal the maximum overlap **depth**; minimizing maximum lateness sorts by\n**deadline** and is justified by an adjacent-swap exchange argument.\n",{"path":4116,"title":4117,"module":4109,"summary":4118},"\u002Falgorithms\u002Fgreedy\u002Fhuffman-codes","Huffman Codes","Huffman coding builds a\nprovably optimal prefix-free binary code by repeatedly merging the two least\nfrequent symbols. We develop prefix-free codes as binary trees, give the\nalgorithm with a priority queue, build a Huffman tree from example\nfrequencies, prove optimality with the same greedy-choice-plus-substructure\nargument, and pin the running time at $O(n\\log n)$.\n",{"path":4120,"title":4121,"module":4109,"summary":4122},"\u002Falgorithms\u002Fgreedy\u002Fmatroids","Matroids & Exchange Arguments","The capstone of the greedy module: _why_ and _when_ a greedy algorithm is\nprovably optimal. We recap the two correctness templates — **greedy-stays-ahead**\nand the **exchange argument** — then meet the **matroid** $M=(S,\\mathcal{I})$, an\nabstraction whose **exchange property** is the structure greedy needs.\nThe matroid–greedy theorem says sorting by weight and taking what stays\nindependent yields a maximum-weight basis _if and only if_ the structure is a\nmatroid. Kruskal's MST is the canonical instance; 0\u002F1 knapsack and TSP are the\ncanonical failures.\n",{"path":4124,"title":4125,"module":4109,"summary":4126},"\u002Falgorithms\u002Fgreedy\u002Fstable-matching","Stable Matching (Gale–Shapley)","Two sides each rank the other; we want a matching with no **blocking pair** — no\ntwo participants who both prefer each other to their assigned partners. The\n**Gale–Shapley deferred-acceptance** algorithm has proposers propose in\npreference order while receivers tentatively hold the best offer so far. We prove\nit terminates in $\\O(n^2)$ proposals, returns a **perfect** matching, and that\nthe matching is **stable**. A sharper asymmetry follows: deferred acceptance is\n**proposer-optimal** and **receiver-pessimal**, the structural fact behind the\nresidency match and school-choice systems.\n",{"path":4128,"title":4129,"module":4130,"summary":4131},"\u002Falgorithms\u002Fdynamic-programming\u002Fprinciples","Principles of Dynamic Programming","Dynamic Programming","Dynamic programming is recursion with memory: when a recursive solution\nre-solves the same subproblems again and again, we solve each one once and\nstore the answer. We identify the two structural conditions that make this\nwork — overlapping subproblems and optimal substructure — contrast top-down\nmemoization with bottom-up tabulation, and distil the whole method into a\nfive-step recipe.\n",{"path":4133,"title":4134,"module":4130,"summary":4135},"\u002Falgorithms\u002Fdynamic-programming\u002Fsequence-dp","Sequence Alignment & LCS","Two strings can be compared by how much of one appears inside the\nother. The longest common subsequence (LCS) and edit distance are the two\nclassic measures, and they are the _same_ dynamic program with different\ncosts. We derive the LCS recurrence by examining the last characters, fill a\nworked DP table, reconstruct the subsequence, and then show edit distance as\nthe identical $\\Theta(mn)$ pattern.\n",{"path":4137,"title":4138,"module":4130,"summary":4139},"\u002Falgorithms\u002Fdynamic-programming\u002Flongest-increasing-subsequence","Longest Increasing Subsequence","Given a sequence of numbers, how long is its longest strictly increasing\nsubsequence? A first dynamic program indexes subproblems by the element each\nsubsequence _ends at_, giving an $O(n^2)$ solution with parent-pointer\nreconstruction. A sharper idea, the patience-sorting _tails_ array searched by\nbinary search, drops the time to $O(n\\log n)$. We then fold in the\nvariants: non-decreasing, counting, Russian-doll envelopes, and bitonic.\n",{"path":4141,"title":4142,"module":4130,"summary":4143},"\u002Falgorithms\u002Fdynamic-programming\u002Fknapsack","Knapsack & Subset Problems","We start from $\\textsc{Subset-sum}$ — does some sublist hit a target $t$? — and its\ninclude\u002Fexclude recurrence over a boolean table $A(i, u)$, then bolt on values\nto get 0\u002F1 knapsack as the same machine with $\\lor$ promoted to $\\max$. We fill\nboth tables, recover the chosen items, and confront the surprise that the\n$\\Theta(nt)$ running time is only _pseudo-polynomial_ — exponential in the bit\nlength $b$, and unimprovable unless $\\mathrm{P}=\\mathrm{NP}$ since subset-sum is\n$\\textsc{NP-complete}$. The fractional variant reveals the sharp line between greedy\nand dynamic programming.\n",{"path":4145,"title":4146,"module":4130,"summary":4147},"\u002Falgorithms\u002Fdynamic-programming\u002Fcoin-change-and-unbounded","Coin Change & Unbounded Knapsack","The previous lesson let each item be taken at most once. Drop that cap — items\nmay be reused _any number of times_ — and the 0\u002F1 knapsack collapses from a\ntwo-dimensional table to a one-dimensional one, because there is no longer a\nprefix of \"already-used\" items to track. We meet **unbounded knapsack**, then\nits most famous instance, **coin change**: the minimum-coins recurrence\n$C[a] = 1 + \\min_c C[a-c]$, and the counting variant where the _order of the\nloops_ decides whether you count unordered combinations or ordered sequences —\nthe classic bug. Greed fails in general but works for canonical coin systems.\n",{"path":4149,"title":4150,"module":4130,"summary":4151},"\u002Falgorithms\u002Fdynamic-programming\u002Finterval-dp","Interval DP","Many problems ask for the best way to combine a contiguous range of items, and\nthe answer is a dynamic program over subintervals $[i,j]$ that chooses a split\npoint $k$. We derive the pattern from matrix-chain multiplication —\nparenthesising a product to minimize scalar multiplications in $O(n^3)$ — distil\nit into a reusable template filled by increasing interval length, and then meet\nits sharpest variant: the \"last operation\" trick behind Burst Balloons and\ncutting a stick, where fixing the _last_ move (not the first) makes the two\nsides independent.\n",{"path":4153,"title":4154,"module":4130,"summary":4155},"\u002Falgorithms\u002Fdynamic-programming\u002Ftree-dp","Dynamic Programming on Trees","When the subproblems of a dynamic program are _rooted subtrees_, a single\npost-order DFS solves the whole thing in $O(n)$: each node combines the\nalready-computed answers of its children. We meet the archetype — maximum-weight\nindependent set on a tree — then the \"path through a node\" pattern behind tree\ndiameter and maximum path sum, and finally **rerooting**, which computes a\nper-node answer for _every_ node as root in $O(n)$ with two passes.\n",{"path":4157,"title":4158,"module":4130,"summary":4159},"\u002Falgorithms\u002Fdynamic-programming\u002Fbitmask-dp","Bitmask DP","When a subproblem depends not on an index or a prefix but on _which subset_ of\na small ground set has been used, we can encode that subset as the bits of an\ninteger and index a DP table by it. With $n \\le \\sim 20$ the $2^n$ subsets fit\nin a table, turning $\\Theta(n!)$ brute force into $O(2^n \\cdot \\text{poly}(n))$.\nWe meet the bit tricks, the Held–Karp TSP archetype, assignment by mask,\nsubset-sum partitioning, and submask enumeration with its $3^n$ bound.\n",{"path":4161,"title":4162,"module":4130,"summary":4163},"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-optimizations","DP Optimizations","A correct DP recurrence is only half the battle; its naive evaluation is often\na factor of $n$ slower than necessary. This capstone surveys five techniques,\nmonotonic-queue, the convex hull trick, divide-and-conquer optimization,\nKnuth's optimization, and SOS DP, that each exploit _structure in the\ntransition_ (a sliding window, linear costs, monotone optimal splits, the\nquadrangle inequality, or subset lattices) to shave an $O(n)$, $O(\\log n)$, or\nworse factor off the running time.\n",{"path":4165,"title":4166,"module":4130,"summary":4167},"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-on-graphs","Dynamic Programming on Graphs","Many graph algorithms are dynamic programs: the subproblem is the\n_best value reachable under a restricted resource_ — intermediate vertices\nallowed, edges allowed, or a topological prefix — and edge _relaxation_ is the\nDP transition. We frame Floyd–Warshall as the archetype ($O(V^3)$ all-pairs\nshortest paths), Bellman–Ford as a DP over path length (the at-most-$K$-stops\nvariant), DAG-DP in topological order ($O(V+E)$), and Warshall's transitive\nclosure as the boolean analog.\n",{"path":4169,"title":4170,"module":4130,"summary":4171},"\u002Falgorithms\u002Fdynamic-programming\u002Fdigit-and-probability-dp","Digit & Probability DP","Two DP patterns with unusual state. _Digit DP_ counts the\nintegers in a range $[L, R]$ that satisfy a digit constraint by walking the\ndecimal places of the bound, carrying a _tight_ flag that marks when the prefix\nstill equals the bound's. _Probability\u002FExpectation DP_ replaces \"best value\" with\n\"expected value,\" using linearity of expectation to make each state an\naverage over its weighted transitions — the natural tool for expected step\ncounts and absorbing Markov chains.\n",{"path":4173,"title":4174,"module":4175,"summary":4176},"\u002Falgorithms\u002Fbacktracking\u002Fbacktracking-fundamentals","Backtracking: Subsets, Permutations & Combinations","Backtracking & Search","Backtracking builds a solution one choice at a time and abandons a partial\nsolution the moment it cannot be completed, exploring a state-space tree by\ndepth-first search. We meet the universal choose\u002Fexplore\u002Fun-choose template,\nderive the canonical enumerations — subsets ($2^n$), permutations ($n!$), and\ncombinations ($\\binom{n}{k}$) — handle duplicate elements by skipping equal\nsiblings, and see how pruning turns an exponential search into a tractable one.\n",{"path":4178,"title":4179,"module":4175,"summary":4180},"\u002Falgorithms\u002Fbacktracking\u002Fconstraint-search","Constraint Search: N-Queens & Sudoku","Many hard puzzles are **constraint satisfaction problems**: assign each\nvariable a value from its domain so that every constraint holds. Backtracking\nsolves them by assigning variables one at a time and rejecting a partial\nassignment the instant a constraint breaks. We make the rejection cheap — $O(1)$\nconflict checks for N-Queens via column and diagonal sets — and prune harder\nwith **forward checking**, **MRV** ordering, and **constraint propagation**,\nwhich is what lets an exponential search actually finish.\n",{"path":4182,"title":4183,"module":4175,"summary":4184},"\u002Falgorithms\u002Fbacktracking\u002Fbranch-and-bound","Branch & Bound and Meet in the Middle","Plain backtracking prunes a search tree by _feasibility_; for _optimization_\nproblems we can prune far more aggressively by _value_. **Branch and bound**\nkeeps the best complete solution found so far and discards any partial solution\nwhose optimistic bound cannot beat it. **Meet in the middle** splits the\ninstance in two, enumerates each half, and recombines by binary search — turning\n$2^n$ into $O(2^{n\u002F2}\\,n)$ and pushing exact search out to $n \\approx 40$.\n",{"path":4186,"title":4187,"module":4175,"summary":4188},"\u002Falgorithms\u002Fbacktracking\u002Fgraph-backtracking","Graph Backtracking: m-Coloring & Hamiltonian Paths","Two famous graph problems have no known efficient algorithm, yet yield cleanly\nto backtracking with the right pruning. **Graph $m$-coloring** assigns one of\n$m$ colors to each vertex so no edge is monochromatic; we color vertices in turn\nand reject a color the instant a neighbor already has it. **Hamiltonian\npath\u002Fcycle** asks for a walk visiting every vertex exactly once; we extend a path\ngreedily and backtrack on dead ends. Both are NP-complete, so the worst case is\nexponential — but feasibility pruning and good vertex ordering make real\ninstances tractable, and the contrast with the easy Eulerian condition shows why.\n",{"path":4190,"title":4191,"module":4192,"summary":4193},"\u002Falgorithms\u002Fmathematical-algorithms\u002Fnumber-theory-basics","Number Theory: GCD & Modular Arithmetic","Mathematical Algorithms","This lesson opens the mathematical-algorithms module with the bedrock of\ncomputational number theory. We prove Euclid's recurrence\n$\\gcd(a,b)=\\gcd(b,\\,a\\bmod b)$ and its $O(\\log\\min(a,b))$ running time, extend\nit to recover Bézout coefficients $x,y$ with $ax+by=\\gcd(a,b)$, and build\nmodular arithmetic on residue classes — including when a modular inverse\n$a^{-1}\\bmod m$ exists and how to compute it.\n",{"path":4195,"title":4196,"module":4192,"summary":4197},"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmodular-exponentiation-and-primality","Modular Exponentiation & Primality","Computing $a^n \\bmod m$ naively costs $n$ multiplications; **repeated squaring**\ndoes it in $O(\\log n)$ by reading the bits of the exponent. We use this routine\nto state **Fermat's little theorem** (and the modular inverse it gives), then to\ntest primality — trial division, the probabilistic **Fermat** and **Miller–Rabin**\ntests, and the deterministic witness set that settles primality for every 64-bit\nnumber.\n",{"path":4199,"title":4200,"module":4192,"summary":4201},"\u002Falgorithms\u002Fmathematical-algorithms\u002Fsieve-and-factorization","Sieves & Factorization","The previous lesson tested one number for primality; here we ask for _all_\nprimes up to $n$ at once. The **sieve of Eratosthenes** cross-cuts composites\nin $O(n\\log\\log n)$, and a **linear sieve** does it in $O(n)$ while recording\neach number's **smallest prime factor**, which then factors any $x \\le n$ in\n$O(\\log x)$. From a factorization $x = \\prod p_i^{e_i}$ the multiplicative\nfunctions $\\tau$, $\\sigma$, and Euler's totient $\\varphi$ fall out immediately.\n",{"path":4203,"title":4204,"module":4192,"summary":4205},"\u002Falgorithms\u002Fmathematical-algorithms\u002Fcombinatorics","Combinatorics & Counting","Counting is the arithmetic of finite sets. We build up from permutations\n$n!$ and combinations $\\binom{n}{k}$, prove Pascal's rule by a bijection,\nand count multisets with stars and bars. The practical core is computing\n$\\binom{n}{k}\\bmod p$ in $O(1)$ from precomputed factorials and inverse\nfactorials. We close with inclusion–exclusion and the Chinese Remainder\nTheorem, both of which lean on the modular inverse from the previous lesson.\n",{"path":4207,"title":4208,"module":4192,"summary":4209},"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmatrix-exponentiation","Matrix Exponentiation","A linear recurrence advances by a fixed linear rule, so one step is a\n**matrix–vector** product and $n$ steps are a **matrix power**. Packaging\nFibonacci, and any $k$-term recurrence, into a transition matrix lets us jump\nto the $n$-th term in $O(k^3 \\log n)$ by **exponentiation by squaring** — the\nsame doubling trick from modular exponentiation, now over matrices.\n",{"path":4211,"title":4212,"module":4192,"summary":4213},"\u002Falgorithms\u002Fmathematical-algorithms\u002Ffast-fourier-transform","Fast Fourier Transform","Multiplying two degree-$n$ polynomials by the schoolbook method costs\n$\\Theta(n^2)$. Evaluating them at the **$n$-th roots of unity** turns\nmultiplication into pointwise products, and the **Cooley–Tukey FFT** computes\nall those evaluations in $\\Theta(n\\log n)$ by splitting even and odd\ncoefficients. The inverse FFT interpolates back, giving $\\Theta(n\\log n)$\npolynomial and big-integer multiplication.\n",{"path":4215,"title":4216,"module":4192,"summary":4217},"\u002Falgorithms\u002Fmathematical-algorithms\u002Fgradient-descent","Numerical Optimization and Gradient Descent","Most of this course chases **discrete** optima over finite structures; here the\nsearch space is **continuous** and the objective $f$ is differentiable. The\n**gradient** points uphill, so stepping against it —\n$x_{t+1} = x_t - \\eta\\,\\nabla f(x_t)$ — walks downhill. **Convexity** makes every\nlocal minimum global; for convex $L$-smooth $f$ gradient descent converges at\n$O(1\u002Ft)$, and **geometrically** under strong convexity. **Newton's method** uses\nthe Hessian for local quadratic convergence, and **bisection** is the robust\nbracketing fallback for roots.\n",{"path":4219,"title":4220,"module":4221,"summary":4222},"\u002Falgorithms\u002Fcomputational-geometry\u002Fgeometric-primitives","Geometric Primitives & Orientation","Computational Geometry","Computational geometry is built on a single reliable primitive — the\n**orientation test**, a sign of a cross product that tells whether three points\nturn left, right, or lie collinear. From points-as-vectors and the dot and\ncross products we derive orientation, segment intersection, the shoelace area\nformula, and point-in-polygon tests, keeping all arithmetic **exact and\ninteger** so that no floating-point rounding can corrupt a sign.\n",{"path":4224,"title":4225,"module":4221,"summary":4226},"\u002Falgorithms\u002Fcomputational-geometry\u002Fconvex-hull","Convex Hull","The convex hull is the smallest convex polygon enclosing a point set — the\nrubber band snapped around the nails. We build it with Andrew's monotone chain,\nsorting by $(x,y)$ and sweeping a lower and upper hull while popping any\nnon-left turn via the orientation primitive, in $O(n\\log n)$. A reduction from\nsorting shows that bound is optimal, and the hull yields diameter, smallest\nenclosing rectangle, and more through rotating calipers.\n",{"path":4228,"title":4229,"module":4221,"summary":4230},"\u002Falgorithms\u002Fcomputational-geometry\u002Fsweep-line","Sweep-Line Algorithms","The plane-sweep paradigm turns a static $2$-D geometry problem into a dynamic\n$1$-D ordered-set problem: a vertical line sweeps left to right, stopping at an\n$x$-sorted **event queue** while a balanced-BST **status structure** tracks the\nobjects it currently crosses, ordered by $y$. We derive Bentley–Ottmann segment\nintersection in $O((n+k)\\log n)$, recover closest-pair in $O(n\\log n)$, and\nreduce skyline, rectangle-area, and overlap problems to $\\pm1$ event sweeps.\n",{"path":4232,"title":4233,"module":4221,"summary":4234},"\u002Falgorithms\u002Fcomputational-geometry\u002Fpolygons-and-proximity","Polygons & Proximity","Four classics that live on top of the orientation primitive and the convex\nhull. **Closest pair** falls to divide-and-conquer in $\\Theta(n\\log n)$, where a\npacking argument caps the cross-boundary combine at seven neighbours per point.\n**Point-in-polygon** is the ray-casting parity test or the winding-number count\nthat also handles self-intersecting boundaries, both with their edge caveats. The **shoelace formula**\ngives signed area as a sum of cross products, and **rotating calipers** walk the\nhull to read off diameter and width in $O(n)$.\n",{"path":4236,"title":4237,"module":4238,"summary":4239},"\u002Falgorithms\u002Fintractability\u002Fp-np-reductions","P, NP, and Reductions","Intractability","Most problems we have met so far have fast algorithms. A vast and important\nfamily seemingly does not. This lesson builds the vocabulary for that\ndivide: decision problems, the class $\\mathsf{P}$ of problems we can solve\nquickly, the class $\\mathsf{NP}$ of problems whose solutions we can _check_\nquickly, and polynomial-time reductions, the tool that lets us compare the\ndifficulty of two problems without solving either.\n",{"path":4241,"title":4242,"module":4238,"summary":4243},"\u002Falgorithms\u002Fintractability\u002Fnp-completeness","NP-Completeness","Some problems in $\\mathsf{NP}$ are universally hardest: every other problem\nin $\\mathsf{NP}$ reduces to them. This lesson defines $\\mathsf{NP}$-hard and\n$\\mathsf{NP}$-complete, states the Cook–Levin theorem that anchors the\ntheory on **SAT**, walks the web of reductions that grows from it, and gives\nthe four-step recipe for proving a brand-new problem $\\mathsf{NP}$-complete.\n",{"path":4245,"title":4246,"module":4238,"summary":4247},"\u002Falgorithms\u002Fintractability\u002Fcoping-with-hardness","Coping with NP-Hardness","An $\\mathsf{NP}$-hardness proof rules out an exact polynomial-time algorithm,\nnot the need for answers. This lesson surveys four practical responses to\nhardness: approximation algorithms with a provable ratio (worked through a\n2-approximation for vertex cover), heuristics and local search, exact\nexponential methods like branch and bound, and exploiting special structure\nin the instances you actually face.\n",{"path":4249,"title":4250,"module":4238,"summary":4251},"\u002Falgorithms\u002Fintractability\u002Fapproximation-algorithms","Approximation Algorithms","When a problem is $\\mathsf{NP}$-hard we can still ask for a solution\nprovably close to optimal. This lesson makes the approximation ratio\n$\\rho$ precise, separates absolute from relative guarantees, and proves the\nratios of four classic algorithms: greedy set cover ($H_n \\approx \\ln n$),\nthe MST-doubling $2$-approximation for metric TSP, load balancing, and the\nknapsack FPTAS. It closes with the hierarchy PTAS \u002F FPTAS and the limits of\ninapproximability.\n",{"path":4253,"title":4254,"module":6,"summary":6},"\u002Falgorithms","Algorithms",{"path":4256,"title":4257,"module":4258,"summary":4259},"\u002Fcalculus\u002Flimits-and-continuity\u002Ffunctions-and-models","Functions and Mathematical Models","Limits and Continuity","A function assigns exactly one output to each input and can be presented four ways: verbally, numerically, graphically, or by a formula. The elementary families — linear, polynomial, power, rational, trigonometric, exponential — model most elementary phenomena, and transformation, combination, and composition build every other function from them.\n",{"path":4261,"title":4262,"module":4258,"summary":4263},"\u002Fcalculus\u002Flimits-and-continuity\u002Fthe-limit-of-a-function","The Limit of a Function","The tangent and velocity problems both ask for a value a ratio approaches but never reaches — the limit. Its intuitive two-sided form splits into one-sided limits that must agree; a limit fails to exist when they disagree or when the function grows without bound, the latter producing a vertical asymptote.\n",{"path":4265,"title":4266,"module":4258,"summary":4267},"\u002Fcalculus\u002Flimits-and-continuity\u002Flimit-laws-and-the-precise-definition","Limit Laws and the ε–δ Definition","The Limit Laws reduce a limit to arithmetic on simpler limits, and direct substitution settles polynomials and rational functions outright. The 0\u002F0 forms that resist substitution yield to algebra or the Squeeze Theorem, and the ε–δ definition makes \"arbitrarily close\" precise as a pair of quantified inequalities.\n",{"path":4269,"title":4270,"module":4258,"summary":4271},"\u002Fcalculus\u002Flimits-and-continuity\u002Fcontinuity","Continuity","A function is continuous at a point when its limit there equals its value, so the graph has no break. Continuity fails in three geometric ways; it is closed under arithmetic and composition, so the elementary families and their combinations are continuous; and on a closed interval it forces the Intermediate Value Theorem, which locates roots.\n",{"path":4273,"title":4274,"module":4275,"summary":4276},"\u002Fcalculus\u002Fderivatives\u002Fthe-derivative-and-rates-of-change","The Derivative and Rates of Change","Derivatives","A single limit with three readings: the slope of the tangent line, the instantaneous velocity of a moving object, and the rate of change of one quantity with respect to another. Built from the difference quotient, extended from a value at one point to a function of x, and undefined exactly where a corner, jump, or vertical tangent appears.\n",{"path":4278,"title":4279,"module":4275,"summary":4280},"\u002Fcalculus\u002Fderivatives\u002Fdifferentiation-rules-and-the-chain-rule","Differentiation Rules and the Chain Rule","Computing every derivative from the limit definition is tedious. A short list of rules — power, constant multiple, sum, product, quotient — differentiates any polynomial or rational function by inspection. The trigonometric derivatives follow from one limit, and the chain rule extends everything to composite functions by multiplying rates along the composition.\n",{"path":4282,"title":4283,"module":4275,"summary":4284},"\u002Fcalculus\u002Fderivatives\u002Fimplicit-differentiation-and-related-rates","Implicit Differentiation and Related Rates","Not every curve is the graph of y = f(x). Implicit differentiation finds a slope from an equation in x and y directly, treating y as an unknown function and differentiating both sides. The same chain-rule idea drives related rates, where one measured rate of change forces another through a geometric constraint, and interprets the derivative as a rate across the sciences.\n",{"path":4286,"title":4287,"module":4275,"summary":4288},"\u002Fcalculus\u002Fderivatives\u002Flinear-approximations-and-differentials","Linear Approximations and Differentials","A differentiable curve looks like its tangent line under enough magnification, so the tangent is a usable stand-in for the function near the point of contact. The linear approximation and its linearization, written in the language of differentials dy and dx, estimate both function values and the measurement error propagated into a computed quantity.\n",{"path":4290,"title":4291,"module":4292,"summary":4293},"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fextrema-and-the-mean-value-theorem","Extrema and the Mean Value Theorem","Applications of Derivatives","Absolute and local extrema, the Extreme Value Theorem that guarantees them, and Fermat's Theorem pinning candidates to critical numbers. The Closed Interval Method turns the search for extrema into a finite checklist. Rolle's Theorem and the Mean Value Theorem then connect a function's values to its derivative, giving the tool that most of differential calculus rests on.\n",{"path":4295,"title":4296,"module":4292,"summary":4297},"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fhow-derivatives-shape-a-graph","How Derivatives Shape a Graph","The sign of the first derivative fixes where a function rises and falls, and a sign change identifies each local extremum through the First Derivative Test. The second derivative sets concavity and inflection points and gives a faster Second Derivative Test. Limits at infinity describe end behavior and the horizontal asymptotes a curve settles toward.\n",{"path":4299,"title":4300,"module":4292,"summary":4301},"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fcurve-sketching-and-optimization","Curve Sketching and Optimization","A checklist that synthesizes domain, symmetry, asymptotes, monotonicity, extrema, and concavity into a hand sketch of any function, plus the slant asymptote for rational functions whose degree exceeds the denominator's. The same extremum machinery, applied to a word problem, becomes the optimization template: model one quantity, reduce it to a function of a single variable, and find its absolute extremum.\n",{"path":4303,"title":4304,"module":4292,"summary":4305},"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fnewtons-method-and-antiderivatives","Newton's Method and Antiderivatives","Newton's method solves $f(x) = 0$ by repeatedly replacing the curve with its tangent line and jumping to the tangent's root, converging fast when it works and diverging when the derivative is small. Antiderivatives reverse differentiation: every antiderivative of a function differs from another by a constant, so the general antiderivative is a family of parallel curves, pinned to one by an initial condition.\n",{"path":4307,"title":4308,"module":4309,"summary":4310},"\u002Fcalculus\u002Fintegrals\u002Farea-and-the-definite-integral","Area and the Definite Integral","Integrals","The area under a curve is defined as a limit of sums of rectangle areas. The same limit — a Riemann sum taken as the mesh shrinks to zero — defines the definite integral, a single number measuring signed area, total distance, and every accumulated quantity built the same way. Its properties, comparison bounds, and reading as net area follow directly from the limit.\n",{"path":4312,"title":4313,"module":4309,"summary":4314},"\u002Fcalculus\u002Fintegrals\u002Fthe-fundamental-theorem-of-calculus","The Fundamental Theorem of Calculus","Differentiation and integration are inverse operations. Part 1 says the derivative of an area-accumulation function is the integrand; Part 2 says a definite integral equals the change in any antiderivative across the interval. Together they replace limits of Riemann sums with antiderivative lookups, define the indefinite integral, and give the Net Change Theorem for rates.\n",{"path":4316,"title":4317,"module":4309,"summary":4318},"\u002Fcalculus\u002Fintegrals\u002Fthe-substitution-rule","The Substitution Rule","Substitution runs the Chain Rule backward: spotting an inner function whose derivative also appears in the integrand lets the variable change to $u$ and collapse a composite integral to a simple one. The rule applies to indefinite and definite integrals, with two ways to handle the limits, and it yields the symmetry shortcuts that double even integrands and vanish odd ones.\n",{"path":4320,"title":4321,"module":4322,"summary":4323},"\u002Fcalculus\u002Fapplications-of-integration\u002Fareas-and-volumes","Areas Between Curves and Volumes","Applications of Integration","A definite integral computes any quantity that a limit of Riemann sums approximates. Applied to geometry it gives the area between two curves and the volume of a solid: by cross-sections, by disks and washers when the region is revolved, and by cylindrical shells when inverting the boundary is awkward.\n",{"path":4325,"title":4326,"module":4322,"summary":4327},"\u002Fcalculus\u002Fapplications-of-integration\u002Fwork-average-value-and-arc-length","Work, Average Value, Arc Length, and Surface Area","The work done by a force that varies with position, the average value of a function and the Mean Value Theorem it satisfies, the length of a curve, and the area of a surface swept out by revolving that curve. Each is a limit of Riemann sums, hence a definite integral.\n",{"path":4329,"title":4330,"module":4322,"summary":4331},"\u002Fcalculus\u002Fapplications-of-integration\u002Fphysics-economics-and-probability","Applications to Physics, Economics, and Probability","Definite integrals in physics, economics, and statistics: the force a fluid exerts on a submerged plate, the balance point of a plane region, the money consumers save at a market price, and the probability that a continuous random variable lands in an interval, together with its mean.\n",{"path":4333,"title":4334,"module":4335,"summary":4336},"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Finverse-functions-logarithms-and-exponentials","Inverse Functions, Logarithms, and Exponentials","Exponential, Logarithmic, and Inverse Functions","A one-to-one function has an inverse that reverses it, with a graph mirrored across y = x and a derivative given by the reciprocal-slope rule. The exponential e^x is its own derivative and the natural logarithm has derivative 1\u002Fx; logarithmic differentiation turns products, quotients, and variable powers into sums.\n",{"path":4338,"title":4339,"module":4335,"summary":4340},"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Fgrowth-decay-inverse-trig-and-hyperbolic-functions","Growth, Decay, Inverse Trigonometric, and Hyperbolic Functions","Any quantity whose rate of change is proportional to its size grows or decays exponentially, the single equation y' = ky behind populations, radioactive decay, cooling, and continuously compounded interest. The inverse trigonometric functions have algebraic derivatives, and the hyperbolic functions, built from e^x and e^{-x}, describe the hanging cable.\n",{"path":4342,"title":4343,"module":4335,"summary":4344},"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Flhospitals-rule","Indeterminate Forms and l'Hospital's Rule","When a limit produces 0\u002F0 or infinity over infinity, the value is undetermined by the forms alone. l'Hospital's Rule resolves both by replacing the ratio of functions with the ratio of their derivatives. Products, differences, and powers reduce to a quotient the rule can handle, and repeated use ranks the growth of logarithms, powers, and exponentials.\n",{"path":4346,"title":4347,"module":4348,"summary":4349},"\u002Fcalculus\u002Ftechniques-of-integration\u002Fintegration-by-parts","Integration by Parts","Techniques of Integration","The product rule for derivatives reverses into integration by parts, trading the integral of $u\\,\\d v$ for the integral of $v\\,\\d u$ whenever the second is easier. The LIATE ordering fixes which factor to differentiate. Standard cases: a polynomial against a transcendental factor, repeated parts, cyclic integrals that solve for themselves, and reduction formulas that peel an exponent down by recursion.\n",{"path":4351,"title":4352,"module":4348,"summary":4353},"\u002Fcalculus\u002Ftechniques-of-integration\u002Ftrigonometric-integrals-and-substitution","Trigonometric Integrals and Substitution","Two related techniques. Trigonometric integrals evaluate powers and products of sine, cosine, tangent, and secant by splitting off one factor and converting the rest with a Pythagorean identity, or by dropping even powers with half-angle formulas. Trigonometric substitution runs the idea in reverse: replace x by a sine, tangent, or secant to clear a radical, integrate, then read the answer back off a reference triangle.\n",{"path":4355,"title":4356,"module":4348,"summary":4357},"\u002Fcalculus\u002Ftechniques-of-integration\u002Fpartial-fractions-and-integration-strategy","Partial Fractions and Integration Strategy","Any rational function integrates in closed form: factor the denominator, split the fraction into simple pieces by partial fractions, and integrate each piece as a logarithm or an arctangent. Four denominator cases exhaust the possibilities. A four-step strategy then sorts an arbitrary integrand by its shape to the technique that fits it, and a short catalog records elementary functions whose antiderivatives are not elementary.\n",{"path":4359,"title":4360,"module":4348,"summary":4361},"\u002Fcalculus\u002Ftechniques-of-integration\u002Fapproximate-and-improper-integrals","Approximate and Improper Integrals","Two definite integrals the Fundamental Theorem cannot reach. With no antiderivative available, the Midpoint, Trapezoidal, and Simpson rules approximate the integral from sample values, each carrying a provable error bound. With an infinite interval or an integrand that blows up, the improper integral is defined as a limit that either converges or diverges; the Comparison Test settles which without evaluating it.\n",{"path":4363,"title":4364,"module":4365,"summary":4366},"\u002Fcalculus\u002Fparametric-and-polar\u002Fparametric-curves-and-their-calculus","Parametric Curves and Their Calculus","Parametric Equations and Polar Coordinates","A parametric curve gives x and y separately as functions of a third variable, recording not only a path but the direction and timing with which it is traced. Eliminating the parameter recovers a Cartesian equation; the slope, area, arc-length, and surface-area formulas run directly on the parameter, with the cycloid and astroid as worked examples.\n",{"path":4368,"title":4369,"module":4365,"summary":4370},"\u002Fcalculus\u002Fparametric-and-polar\u002Fpolar-coordinates","Polar Coordinates","Polar coordinates locate a point by a distance from the pole and an angle from the polar axis, giving circles, spirals, and flower-shaped curves short equations. Conversion between the two systems is right-triangle trigonometry, and treating a polar curve as a parametric curve in the angle yields the tangent, area, and arc-length formulas.\n",{"path":4372,"title":4373,"module":4365,"summary":4374},"\u002Fcalculus\u002Fparametric-and-polar\u002Fconic-sections","Conic Sections","Parabolas, ellipses, and hyperbolas are the plane curves cut from a double cone. Each has a focus-based geometric definition and a standard Cartesian equation. A single number, the eccentricity, ties the three together, and placing a focus at the pole gives all of them one polar equation that describes planetary orbits.\n",{"path":4376,"title":4377,"module":4378,"summary":4379},"\u002Fcalculus\u002Fsequences-and-series\u002Fsequences","Sequences","Infinite Sequences and Series","A sequence is a function on the positive integers, and its limit is defined almost exactly like a limit at infinity. The Limit Laws and Squeeze Theorem carry over from functions, monotonic and bounded sequences give a convergence criterion, and the Monotonic Sequence Theorem guarantees a limit exists without naming it.\n",{"path":4381,"title":4382,"module":4378,"summary":4383},"\u002Fcalculus\u002Fsequences-and-series\u002Fseries-and-the-integral-test","Series and the Integral Test","Adding infinitely many terms is made precise as the limit of partial sums. The two series with closed-form partial sums are geometric and telescoping; the harmonic series diverges even as its terms shrink to zero. The Integral Test compares a positive series to an improper integral, settling the p-series and supplying a remainder bound for estimating sums.\n",{"path":4385,"title":4386,"module":4378,"summary":4387},"\u002Fcalculus\u002Fsequences-and-series\u002Fthe-convergence-tests","The Convergence Tests","The comparison, alternating-series, ratio, and root tests decide convergence without a closed-form partial sum. Absolute convergence is stronger than conditional convergence and is preserved under rearrangement; an alternating series errs by less than its first omitted term. A test is chosen from the shape of the general term.\n",{"path":4389,"title":4390,"module":4378,"summary":4391},"\u002Fcalculus\u002Fsequences-and-series\u002Fpower-series","Power Series","A power series is a polynomial of infinite degree whose convergence set is an interval centered at $a$, with a radius the Ratio Test finds and endpoints that must be tested by hand. Inside that interval the series represents a function that can be differentiated and integrated term by term, generating new representations from the geometric series.\n",{"path":4393,"title":4394,"module":4378,"summary":4395},"\u002Fcalculus\u002Fsequences-and-series\u002Ftaylor-and-maclaurin-series","Taylor and Maclaurin Series","If a function equals a power series, its coefficients are forced: the nth is the nth derivative at the center over n factorial. We derive that formula, use Taylor's Inequality to prove the standard series for the exponential, sine, and cosine, record the binomial series and a reference table, and bound the error when a Taylor polynomial replaces a function.\n",{"path":4397,"title":4398,"module":4399,"summary":4400},"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvectors-and-the-dot-product","Three-Dimensional Coordinates, Vectors, and the Dot Product","Vectors and the Geometry of Space","Space needs three coordinates, so we set up the rectangular system, the distance formula, and the equation of a sphere. Vectors then package magnitude and direction into a single algebraic object with its own arithmetic. The dot product turns two vectors into a number that measures the angle between them, gives a clean test for orthogonality, and produces the projection of one vector onto another.\n",{"path":4402,"title":4403,"module":4399,"summary":4404},"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fthe-cross-product-lines-and-planes","The Cross Product, Lines, and Planes","The cross product multiplies two vectors into a third perpendicular to both, with length equal to the area of the parallelogram they span. That one construction supplies the direction of a line, the normal of a plane, and, through the scalar triple product, the volume of a parallelepiped. Lines carry a point and a direction vector; planes carry a point and a normal, which fixes the angle between planes and the distance from a point to a plane.\n",{"path":4406,"title":4407,"module":4399,"summary":4408},"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fcylinders-and-quadric-surfaces","Cylinders and Quadric Surfaces","A surface whose equation omits one variable is a cylinder: the graph of a plane curve swept along the missing axis. A second-degree equation in three variables is a quadric, and translation and rotation reduce every one to a short standard list. Traces — the curves cut by planes parallel to the coordinate planes — sort the six quadrics into ellipsoid, the two paraboloids, the cone, and the two hyperboloids.\n",{"path":4410,"title":4411,"module":4399,"summary":4412},"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvector-functions-and-space-curves","Vector Functions and Space Curves","A vector function assigns a vector to each value of a parameter, and as the parameter runs its tip traces a space curve. Taking limits, derivatives, and integrals component by component carries all of single-variable calculus into three dimensions. The derivative of a vector function is the tangent vector to its curve, and normalizing it gives the unit tangent that points the way along the path.\n",{"path":4414,"title":4415,"module":4399,"summary":4416},"\u002Fcalculus\u002Fvectors-and-space-curves\u002Farc-length-curvature-and-motion","Arc Length, Curvature, and Motion in Space","Integrating the speed of a vector function gives the length of its curve and a natural parameter, arc length, that depends only on the curve's shape. Curvature measures how fast the unit tangent turns, and together with the normal and binormal it builds the moving TNB frame. Reading the same vector function as a trajectory, its first two derivatives are velocity and acceleration, and acceleration splits cleanly into tangential and normal parts.\n",{"path":4418,"title":4419,"module":4420,"summary":4421},"\u002Fcalculus\u002Fpartial-derivatives\u002Ffunctions-of-several-variables","Functions of Several Variables, Limits, and Continuity","Partial Derivatives","A function of several variables assigns one number to each point of a region in the plane or in space. Domain, graph, level curve, and level surface describe it; limits and continuity extend to two variables, where a limit must agree along every path of approach, not just from the left and the right.\n",{"path":4423,"title":4420,"module":4420,"summary":4424},"\u002Fcalculus\u002Fpartial-derivatives\u002Fpartial-derivatives","A partial derivative holds every variable but one fixed and differentiates in the ordinary sense. Geometrically it is the slope of a trace curve cut from the surface by a coordinate plane. The freeze-and-differentiate rule computes the two first partials; the four second partials follow, and the two mixed ones agree under Clairaut's Theorem when they are continuous.\n",{"path":4426,"title":4427,"module":4420,"summary":4428},"\u002Fcalculus\u002Fpartial-derivatives\u002Ftangent-planes-and-the-chain-rule","Tangent Planes, Linear Approximation, and the Chain Rule","Near a point, a smooth surface looks like its tangent plane, and the plane's equation is built from the two partial derivatives. That linearization defines the total differential and the meaning of differentiability in two variables. The chain rule then propagates derivatives through composed functions, tracked by a tree diagram, and yields clean formulas for implicit differentiation.\n",{"path":4430,"title":4431,"module":4420,"summary":4432},"\u002Fcalculus\u002Fpartial-derivatives\u002Fdirectional-derivatives-and-the-gradient","Directional Derivatives and the Gradient","The partial derivatives measure slope along the two axes; the directional derivative measures slope along any chosen direction, and equals the gradient dotted with a unit vector. The gradient points in the direction of steepest increase, its length is the greatest rate, and it stands perpendicular to level curves and surfaces, which fixes the tangent plane to a level surface.\n",{"path":4434,"title":4435,"module":4420,"summary":4436},"\u002Fcalculus\u002Fpartial-derivatives\u002Foptimization-and-lagrange-multipliers","Optimization and Lagrange Multipliers","Extrema of a two-variable function sit at critical points where the gradient vanishes; the Second Derivatives Test sorts them into peaks, valleys, and saddles by the sign of a discriminant. Absolute extrema on a closed region also need the boundary. When the domain is itself a constraint curve, Lagrange multipliers set the two gradients parallel and solve the constrained problem.\n",{"path":4438,"title":4439,"module":4440,"summary":4441},"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fdouble-integrals","Double Integrals","Multiple Integrals and Vector Calculus","The double integral extends the definite integral to functions of two variables: a limit of Riemann sums that measures signed volume under a surface. Fubini's Theorem turns it into two ordinary integrations done one after the other, general regions of type I and type II fix the inner limits, polar coordinates absorb circular symmetry through the factor r, and the same machine computes mass, center of mass, and moments of a lamina.\n",{"path":4443,"title":4444,"module":4440,"summary":4445},"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Ftriple-integrals-and-coordinate-systems","Triple Integrals and Coordinate Systems","The triple integral integrates a function of three variables over a solid, as a limit of Riemann sums evaluated by three nested single integrations. Cylindrical coordinates add the factor r to handle axial symmetry, spherical coordinates add rho-squared sine-phi for radial symmetry, and the general change of variables shows both volume elements are Jacobian determinants of the coordinate map. Surface area for a graph completes the measurement toolkit.\n",{"path":4447,"title":4448,"module":4440,"summary":4449},"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fvector-fields-and-line-integrals","Vector Fields and Line Integrals","A vector field assigns a vector to every point of space; the line integral of a field along a curve accumulates its tangential component, measuring work. Conservative fields are gradients of a potential, and for them the Fundamental Theorem for Line Integrals makes the integral depend only on the endpoints. Path independence, closed-loop integrals of zero, and the component test for a potential are three faces of the same property.\n",{"path":4451,"title":4452,"module":4440,"summary":4453},"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fgreens-theorem-curl-and-divergence","Green's Theorem, Curl, and Divergence","Green's Theorem equates the line integral of a field around a positively oriented closed curve with a double integral over the enclosed region, turning a boundary computation into an area computation and vice versa. Curl measures local circulation and divergence measures local outflow; the two vector forms of Green's Theorem express the boundary integral as the integrated curl or divergence, the planar case of Stokes' and the Divergence Theorem.\n",{"path":4455,"title":4456,"module":4440,"summary":4457},"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fsurface-integrals","Parametric Surfaces and Surface Integrals","A parametric surface is the image of a two-variable vector function; its area element is the magnitude of the cross product of the two tangent vectors. The surface integral of a scalar function sums it over that area, and the flux integral of a vector field sums the field's normal component, measuring flow through the surface. Orientation by a choice of unit normal makes flux well-defined, the integral Stokes' and the Divergence Theorem operate on.\n",{"path":4459,"title":4460,"module":4440,"summary":4461},"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fstokes-and-the-divergence-theorem","Stokes' Theorem and the Divergence Theorem","Stokes' Theorem lifts Green's Theorem into space: the line integral of a field around the boundary of a surface equals the flux of its curl through the surface. The Divergence Theorem relates the outward flux across a closed surface to the triple integral of divergence over the solid it encloses. Together with the Fundamental Theorem of Calculus and its line-integral and Green counterparts, they are one theorem: the integral of a derivative over a region equals the integral of the field over its oriented boundary.\n",{"path":4463,"title":4464,"module":6,"summary":6},"\u002Fcalculus","Calculus",{"path":4466,"title":4467,"module":3911,"summary":4468},"\u002Fmechanics\u002Ffoundations\u002Fmeasurement-and-dimensions","Measurement and Dimensions","Every physical quantity is a number attached to a unit, and that pairing is what lets you check an equation before computing anything, since terms that add together must carry the same dimensions. We build the SI base units and the notion of dimension, then use dimensional analysis to test a proposed relation and form scaling groups — a method that fixes a formula's shape but never its numerical constants. The lesson also sets how precisely a result may be stated, through significant figures, propagated uncertainty, and order-of-magnitude checks that catch errors a raw calculator answer hides.\n",{"path":4470,"title":4471,"module":3911,"summary":4472},"\u002Fmechanics\u002Ffoundations\u002Fvector-algebra","Vector Algebra","Force, velocity, and displacement all carry a direction, so mechanics needs an arithmetic that respects it; adding magnitudes alone gives the wrong answer the moment two arrows point different ways. We set up vectors and their components in a chosen basis, then build the two products that carry most of the physics — the dot product, which extracts the part of one vector along another and yields work and power, and the cross product, which measures oriented area and yields torque and angular momentum. Rotating the axes changes the components while leaving the vector itself untouched, and the same component method resolves a force along whatever directions a constraint picks out.\n",{"path":4474,"title":4475,"module":4476,"summary":4477},"\u002Fmechanics\u002Fkinematics\u002Fone-dimensional-motion","One-Dimensional Motion","Kinematics","Motion along a line already forces the two questions the whole of kinematics repeats: how fast is the object moving now, and where will it be next? Velocity and acceleration answer the first as derivatives of position; integrating them back — the signed area under a graph — answers the second. We derive the constant-acceleration equations, mark exactly where the \"constant\" assumption is load-bearing, and see why sign, not magnitude, is what carries direction.\n",{"path":4479,"title":4480,"module":4476,"summary":4481},"\u002Fmechanics\u002Fkinematics\u002Fmotion-graphs","Motion Graphs","Draw a motion as a graph and its two most useful facts turn geometric: the slope of the position curve is the velocity, and the area under the velocity curve is the displacement. We read motion in both directions — differentiating a graph for the next rate, integrating it back to recover position — and handle the curved, piecewise, and noisy graphs that real measurements produce. Along the way we see why a velocity estimated from two positions belongs to the midpoint of their interval, not its end.\n",{"path":4483,"title":4484,"module":4476,"summary":4485},"\u002Fmechanics\u002Fkinematics\u002Fprojectile-motion","Projectile Motion","Throw an object and it seems to trace one curved path, but the motion is really two independent one-dimensional motions running at once: constant velocity across the ground and free fall in the vertical. Splitting it that way turns every projectile question — how long it stays up, how far it lands, how high it climbs, whether it clears an obstacle — into a pair of equations you already know. We derive the parabolic trajectory, work both the forward and the inverse problems, and show why the familiar $45^\\circ$ range-maximizing angle holds only when launch and landing heights match.\n",{"path":4487,"title":4488,"module":4476,"summary":4489},"\u002Fmechanics\u002Fkinematics\u002Frelative-motion","Relative Motion","A velocity is only ever measured relative to some observer, so a boat's speed through the water, over the ground, and as seen from another boat are three different vectors. Choosing the right frame — and subtracting one motion from another — collapses river crossings, crosswind headings, pursuit, and closest-approach problems into a single vector equation. We build the relative-velocity and relative-position relations for uniformly moving frames, show why acceleration is the one quantity all such observers agree on, and note where rotating frames break the simple subtraction.\n",{"path":4491,"title":4492,"module":4476,"summary":4493},"\u002Fmechanics\u002Fkinematics\u002Fcircular-motion","Circular Motion","An object going around a circle at a steady speed is still accelerating, because its velocity is forever changing direction — the fact that governs everything from a car on a curve to a satellite in orbit. We tie the angular description (angle, angular velocity, angular acceleration) to the linear one through $v=r\\omega$, split the acceleration into an inward part that turns the velocity and a tangential part that changes its speed, and extend the inward $v^2\u002Fr$ result to any curved path through its local radius of curvature. Constant angular acceleration then mirrors straight-line motion equation for equation.\n",{"path":4495,"title":4496,"module":4497,"summary":4498},"\u002Fmechanics\u002Fdynamics\u002Fnewtons-laws","Newton's Laws","Dynamics","What makes a body change its motion, and in which frames does the answer take its simplest form? Newton's three laws settle both: inertial frames are the ones where a force-free body coasts, force is whatever changes momentum, and every interaction pushes back on its source. We write the second law as $\\sum\\vec F=\\d\\vec p\u002F\\d t$, reduce it to $m\\vec a$ at constant mass, and separate what a scale actually reads — the support force — from the weight $m\\vec g$ it is so often mistaken for.\n",{"path":4500,"title":4501,"module":4497,"summary":4502},"\u002Fmechanics\u002Fdynamics\u002Ffree-body-diagrams","Free-Body Diagrams","Once several forces act on a body at once, the reliable way to predict its motion is to isolate that one body and draw every external push and pull on it — nothing more, nothing less. The free-body diagram is that discipline. We fix a system boundary, resolve $\\sum\\vec F=m\\vec a$ into components along axes chosen to fit the geometry, and solve for the unknowns a problem hands us — normal forces, tensions, friction, and the acceleration a constraint permits — seeing why internal forces drop out only when the boundary encloses both bodies that share them.\n",{"path":4504,"title":4505,"module":4497,"summary":4506},"\u002Fmechanics\u002Fdynamics\u002Ffriction-and-curved-motion","Friction and Curved Motion","Real surfaces grip before they slip, fluids push back harder the faster you move through them, and anything rounding a bend must be pulled toward the inside of the curve by something. This lesson supplies the force laws for those three cases. We bound static friction by $|f_s|\\leq\\mu_sN$ and switch to kinetic friction $\\mu_kN$ once sliding starts, model drag as a speed-dependent resistance that levels off at a terminal speed, and show that circular motion demands an inward net force $mv^2\u002Fr$ furnished by real interactions — friction, a banked normal force, tension — never by an invented outward one.\n",{"path":4508,"title":4509,"module":4497,"summary":4510},"\u002Fmechanics\u002Fdynamics\u002Fnumerical-dynamics","Numerical Dynamics","Most force laws — quadratic drag, coupled oscillators, anything nonlinear — admit no closed-form trajectory, so we advance the motion one small time step at a time and let arithmetic do what algebra cannot. This lesson turns $\\d\\vec y\u002F\\d t=f(t,\\vec y)$ into a marching rule. We derive the Euler, Euler--Cromer, midpoint, and Verlet updates, weigh their accuracy and stability, watch a drifting energy expose a bad scheme, and use step-halving and conserved quantities to separate the error of the method from the error of the model.\n",{"path":4512,"title":4513,"module":4497,"summary":4514},"\u002Fmechanics\u002Fdynamics\u002Fcenter-of-mass-systems","Center-of-Mass Systems","A firework bursts into a dozen fragments, yet one point keeps gliding along the original parabola as though nothing had happened. That point is the centre of mass, and following it collapses a many-body tangle into a single equation of motion. We define $\\vec R=\\frac1M\\sum_i m_i\\vec r_i$ and its continuous form, show that internal forces cancel so that only external ones move it, $M\\vec A_{\\rm cm}=\\sum\\vec F_{\\rm ext}$, and put the result to work on recoil, collisions viewed from the centre-of-mass frame, and rocket propulsion, where mass leaving the boundary carries momentum with it.\n",{"path":4516,"title":4517,"module":4518,"summary":4519},"\u002Fmechanics\u002Fenergy\u002Fwork-and-kinetic-energy","Work and Kinetic Energy","Energy","A constant push along a straight path is trivial to score, but real forces vary and bend along curved trajectories, and only the component along the motion transfers any energy. Work captures exactly that transfer as the line integral $W=\\int\\vec F\\cdot\\d\\vec r$, and the work-kinetic-energy theorem turns it into a statement about speed: the net work on a particle equals the change in its $\\tfrac12 mv^2$. We build work up from the dot product to the signed area under a force curve, derive the theorem from Newton's second law, and read power as its instantaneous rate $P=\\vec F\\cdot\\vec v$.\n",{"path":4521,"title":4522,"module":4518,"summary":4523},"\u002Fmechanics\u002Fenergy\u002Fpotential-energy","Potential Energy","When a force does the same work no matter which path a particle takes, that work can be stored as a function of position alone, and solving for the motion becomes bookkeeping instead of integration. We single out the forces that qualify — the conservative ones, for which $\\oint\\vec F\\cdot\\d\\vec r=0$ — define their potential energy through $\\vec F=-\\nabla U$, and use conservation of mechanical energy to read speeds, turning points, and equilibria straight off a potential curve. Friction breaks the shortcut, so we also track where mechanical energy leaks away as heat.\n",{"path":4525,"title":4526,"module":4518,"summary":4527},"\u002Fmechanics\u002Fenergy\u002Fmultiparticle-work","Multiparticle Work","A single particle has one velocity and one kinetic energy; a system of many can spin, deform, explode, and warm up while its centre of mass glides along as if nothing happened. Splitting the motion into a centre-of-mass part and an internal part separates the energy that momentum already fixes from the energy left free for relative motion, $K=\\tfrac12MV_{\\rm cm}^2+K'$. We derive the centre-of-mass work theorem, see why an explosion or a released spring can raise total kinetic energy with no external work at all, and use the reduced-mass and centre-of-mass frames to make collisions and internal transfers clean.\n",{"path":4529,"title":4530,"module":4518,"summary":4531},"\u002Fmechanics\u002Fenergy\u002Fmass-energy-and-binding","Mass-Energy and Binding","Relativity puts rest itself on the energy ledger: a mass $m$ carries energy $mc^2$ even when it sits still, so weighing a system's separated pieces and weighing the assembled whole give different answers, and the gap is binding energy. We convert freely between mass units and MeV, compute the energy that holds a nucleus together, and read the binding-energy-per-nucleon curve that explains why fusing light nuclei and splitting heavy ones both release energy. Reaction $Q$ values, thresholds, and recoil then follow from the same mass-difference accounting, once the frame and mass convention are fixed.\n",{"path":4533,"title":4534,"module":4518,"summary":4535},"\u002Fmechanics\u002Fenergy\u002Fphotons-and-quantization","Photons and Quantization","Light delivers its energy in indivisible lumps: a photon of frequency $f$ carries exactly $hf$, and this one fact explains why a dim blue lamp ejects electrons that an intense red one cannot. We fix a photon's energy and momentum from its wavelength, follow the quanta through emission, absorption, and the photoelectric threshold $K_{\\rm max}=hf-\\phi$, and watch energy and momentum conservation together produce the Compton wavelength shift when a photon scatters from an electron. The recurring discipline is unit and frame care, where a stray factor of $10^9$ or a forgotten rest energy quietly ruins an answer.\n",{"path":4537,"title":4538,"module":4539,"summary":4540},"\u002Fmechanics\u002Fmomentum\u002Fmomentum-and-collisions","Momentum and Collisions","Momentum","When two objects collide, the forces between them are too brief and too tangled to integrate directly, yet the result is fixed by one conserved quantity. Linear momentum $\\vec p=m\\vec v$ turns Newton's second law into the impulse-momentum theorem $\\vec J=\\Delta\\vec p$, and for an isolated system into a conservation law that holds through any internal collision, however dissipative. We use it to separate elastic from inelastic collisions, follow the centre of mass, and read impulse as the signed area under a force-time curve — always tracking which external impulses the chosen system and interval let us drop.\n",{"path":4542,"title":4543,"module":4539,"summary":4544},"\u002Fmechanics\u002Fmomentum\u002Fcenter-of-mass-collisions","Center-of-Mass Collisions","A two-body collision that looks asymmetric in the laboratory becomes almost trivial in the frame that rides along with the centre of mass, where the total momentum is zero and the two momenta stay equal and opposite. We build that frame, reduce the pair to a single relative coordinate carrying the reduced mass $\\mu$, and show that an elastic collision there only rotates one momentum vector while its length holds fixed. Transforming back to the laboratory then handles elastic and inelastic collisions, scattering angles, and reaction thresholds with the same construction — and shows why relative speed, not laboratory kinetic energy, measures what a collision can convert.\n",{"path":4546,"title":4547,"module":4539,"summary":4548},"\u002Fmechanics\u002Fmomentum\u002Frocket-propulsion","Rocket Propulsion","A rocket speeds up by throwing mass backward, so its own mass drops as it flies and $\\vec F=m\\vec a$ no longer applies to a fixed body. Tracking the momentum the exhaust carries across the vehicle boundary gives thrust $T=Ru_e$ and, for a force-free burn, the rocket equation $\\Delta v=u_e\\ln(m_i\u002Fm_f)$ — a logarithm that makes large velocity changes expensive in propellant and forces staging. We then add the forces a real ascent cannot ignore, gravity, drag, and steering, and show how thrust and mass-flow records are cross-checked to infer the exhaust speed.\n",{"path":4550,"title":4551,"module":4552,"summary":4553},"\u002Fmechanics\u002Frotation\u002Frotational-inertia","Rotational Inertia","Rotation","Push a wheel and a merry-go-round with the same force and they speed up at wildly different rates: the same mass resists rotation differently depending on where it sits relative to the axis. That single fact is the moment of inertia, $I=\\int r_\\perp^2\\,\\d m$, and this lesson builds it from the ground up. We tie angular motion to linear through $s=r\\theta$, $v=r\\omega$, and $a_t=r\\alpha$, derive $I$ for rods, disks, and spheres, and use the parallel- and perpendicular-axis theorems to move between axes — always naming the axis, because the same body has as many moments of inertia as it has lines to spin about.\n",{"path":4555,"title":4556,"module":4552,"summary":4557},"\u002Fmechanics\u002Frotation\u002Frotational-dynamics","Rotational Dynamics","A force applied to a wheel does nothing unless it acts off the axis: what turns a rigid body is torque, force times lever arm. This lesson makes that precise and turns it into the rotational Newton's second law, $\\sum\\tau=I\\alpha$ about a fixed axis, the exact analogue of $\\sum F=ma$. From there we get rotational work $W=\\int\\tau\\,\\d\\theta$ and power $P=\\tau\\omega$, size a motor to a load, and solve pulleys and Atwood machines where the pulley's own inertia can no longer be ignored — always insisting that every torque be measured about the same axis.\n",{"path":4559,"title":4560,"module":4552,"summary":4561},"\u002Fmechanics\u002Frotation\u002Frolling-motion","Rolling Motion","A rolling wheel is doing two things at once — translating and spinning — but the no-slip condition $v_{cm}=R\\omega$ locks them together, and that single constraint is what makes rolling tractable. We use it to split the kinetic energy into $\\tfrac12Mv_{cm}^2+\\tfrac12I\\omega^2$, find how fast a cylinder reaches the bottom of an incline, and show why the contact point is instantaneously at rest. The static friction that enforces rolling does no work; we track its direction from the tendency to slip, and mark exactly where the model breaks once the required friction exceeds $\\mu_sN$.\n",{"path":4563,"title":4564,"module":4552,"summary":4565},"\u002Fmechanics\u002Frotation\u002Fangular-momentum","Angular Momentum","A skater pulls in her arms and spins faster, with no torque acting: that is angular momentum conservation, and it lets us answer questions that would be hopeless force by force. We build $\\vec L=\\vec r\\times\\vec p$, show it obeys $\\vec\\tau_{ext}=\\d\\vec L\u002F\\d t$, and use its conservation under zero external torque to link before and after in collisions, reconfigurations, and coupled rotors without ever resolving the internal forces. The catch is bookkeeping: the origin, the system boundary, and the frame must be fixed first, and a change in total $\\vec L$ always points to an external impulse someone forgot.\n",{"path":4567,"title":4568,"module":4552,"summary":4569},"\u002Fmechanics\u002Frotation\u002Frolling-resistance","Rolling Resistance","Ideal rolling should coast forever, yet every real wheel slows down. The reason is that a deformable tire and road do not press through a single point: the contact patch spreads, the normal-force resultant shifts ahead of the axle, and that offset is a resisting moment even with no gross sliding. We package it as an equivalent force $F_{rr}=C_{rr}N$, tie the coefficient to load, surface, speed, and temperature, and use coast-down, towing, and traction tests to separate this contact loss from aerodynamic drag, bearing friction, and the adhesion limit where rolling gives way to skidding.\n",{"path":4571,"title":4572,"module":4552,"summary":4573},"\u002Fmechanics\u002Frotation\u002Fgyroscopic-precession","Gyroscopic Precession","A spinning top leans over but does not fall — it swings its axis in a slow horizontal circle instead. The paradox dissolves once torque is read as the rate of change of a vector: gravity's torque is perpendicular to the spin angular momentum, so it turns $\\vec L$ rather than toppling it. We derive the steady precession rate $\\Omega\\simeq Mgr\u002F(I_s\\omega_s)$ in the fast-top limit, state the assumptions it leans on — dominant spin, slow tilt, negligible bearing torque — and read nutation, support motion, and a decaying spin as the ways real gyroscopes depart from it.\n",{"path":4575,"title":4576,"module":4577,"summary":4578},"\u002Fmechanics\u002Fgravity-and-matter\u002Fkeplerian-orbits","Keplerian Orbits","Gravitation and Matter","Why do the planets trace ellipses rather than any other curve? Newton's inverse-square law collapses the two-body problem onto a single conic section, and the answer falls out of two conserved quantities: a central force can exert no torque, so angular momentum is fixed, and gravity is conservative, so energy is fixed. We read an orbit's size and shape straight off those invariants, recover all three of Kepler's laws, and derive escape speed, the vis-viva relation, and the timing of a pass. We also mark where the ideal ellipse breaks down — drag, oblateness, and a third body slowly move a real orbit.\n",{"path":4580,"title":4581,"module":4577,"summary":4582},"\u002Fmechanics\u002Fgravity-and-matter\u002Fgravitational-fields","Gravitational Fields","Instead of tracking the force between every pair of masses, we attach a field to the source and ask a test mass to read it off locally. That move pays off because gravity is conservative: the field is the gradient of a single scalar potential, and potentials from many sources simply add. We build the field-potential picture, use spherical symmetry and the shell theorem to get the point-mass exterior field and the zero interior field of a shell, and read tides straight out of the field's gradient. Along the way we mark exactly when the constant-$g$ and point-mass shortcuts hold and when a shape correction is needed.\n",{"path":4584,"title":4585,"module":4577,"summary":4586},"\u002Fmechanics\u002Fgravity-and-matter\u002Fstatic-equilibrium","Static Equilibrium","What does it take for a loaded structure to stay put? A body at rest needs its forces to cancel and its turning effects to cancel — $\\sum\\vec F=0$ and $\\sum\\vec\\tau=0$ about any point — and almost all of statics is the craft of turning a physical setup into those equations. We build free-body diagrams, replace supports, cables, friction, couples, and distributed loads with their idealized reactions, and locate the centre of gravity that decides whether a body tips. We also count equations against unknowns to separate a determinate problem from one that needs the material's deformation to resolve, and read every negative or inconsistent reaction as a sign that a contact or a boundary was chosen wrong.\n",{"path":4588,"title":4589,"module":4577,"summary":4590},"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-statics","Fluid Statics","A fluid at rest cannot support a shear, so the only stress it carries is a pressure that must grow with depth to hold up the fluid above it. That single balance, $\\d p\u002F\\d z=-\\rho g$, runs the whole subject: it sets manometer readings, the force on a dam, and — integrated over a submerged boundary — Archimedes' buoyant force $F_B=\\rho g V_{\\rm disp}$. We derive these, use them to decide when a body floats and whether it floats upright, and mark where acceleration, rotation, compressibility, or capillarity forces a richer pressure model.\n",{"path":4592,"title":4593,"module":4577,"summary":4594},"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-flow","Fluid Flow","Two accounting rules carry most of steady flow: mass cannot pile up, so the same volume crosses every section each second, and mechanical energy is conserved along a streamline when the fluid is ideal. From those we get continuity, Bernoulli's relation between pressure, speed, and height, and the results that follow — Torricelli's efflux speed, the Venturi meter, the Pitot tube. We then let go of the ideal assumptions one at a time: viscosity adds wall shear and head loss, Reynolds number decides laminar versus turbulent, and Mach number marks where a gas stops behaving as incompressible.\n",{"path":4596,"title":4597,"module":4577,"summary":4598},"\u002Fmechanics\u002Fgravity-and-matter\u002Forbital-motion","Orbital Motion","A circular orbit is nothing but free fall with enough sideways speed to keep missing the ground, and setting gravity equal to the centripetal requirement fixes that speed and the period once and for all. From the same energy bookkeeping we read off escape speed, sort orbits into bound, parabolic, and hyperbolic by the sign of their specific energy, and see why a tangential burn is the efficient way to change an orbit. We build the Hohmann transfer and its launch window, work the numbers for a geostationary orbit and an escape burn, and mark where finite thrust, perturbations, and an uncertain initial state pull a real trajectory off the ideal.\n",{"path":4600,"title":4601,"module":4577,"summary":4602},"\u002Fmechanics\u002Fgravity-and-matter\u002Fstress-and-elasticity","Stress and Elasticity","Rigid bodies are a fiction; every real material stretches, shears, or squeezes under load, and the useful question is how much. We define stress as force per area and strain as fractional deformation, then find that for small deformations the two are simply proportional — Hooke's law — with Young's, shear, and bulk moduli as the constants for stretch, twist, and volume change. From these we compute extensions, torsional twist, and stored elastic energy, and read a tensile curve for the yield, ultimate, and fracture points where linear elasticity ends. We also mark the practical limits: stress concentrations, fatigue, and the multiaxial states a single uniaxial modulus cannot capture.\n",{"path":4604,"title":4605,"module":4606,"summary":4607},"\u002Fmechanics\u002Foscillations-waves\u002Fdamped-oscillators","Damped Oscillators","Oscillations and Waves","Every real oscillator eventually stops: friction, drag, and internal loss drain its energy, so free motion is a decay rather than a permanent swing. Adding a velocity-proportional resistance to the spring-mass equation produces one dimensionless number, $b\u002F(2\\sqrt{mk})$, that decides whether the mass rings down through many cycles, returns once without overshoot, or crawls back slowly. We solve the three regimes, tie the observed decay to the power balance $b\\dot x^2$, and turn a measured ring-down into the decay rate and quality factor of the apparatus — reading damping off the data instead of assuming it.\n",{"path":4609,"title":4610,"module":4606,"summary":4611},"\u002Fmechanics\u002Foscillations-waves\u002Ftravelling-waves","Travelling Waves","A wave carries a shape, not the material: each element of a rope or air column oscillates in place while the disturbance travels through it. Writing that shape as $f(x\\mp vt)$ turns \"the pattern moves\" into a statement about the cosine's argument, and a local force balance on one string segment fixes the speed at $v=\\sqrt{T\u002F\\mu}$ — restoring stiffness over inertia, with amplitude nowhere in it. We build the sinusoidal wave and its phase, derive the wave equation from Newton's second law, and follow the energy a travelling wave transports, then check speed and power against those predictions.\n",{"path":4613,"title":4614,"module":4606,"summary":4615},"\u002Fmechanics\u002Foscillations-waves\u002Fwave-superposition","Wave Superposition","When two waves cross the same point, what does a probe read? In a linear medium the answer is arithmetic: the displacements add, $y=y_1+y_2$, and the pulses pass through each other unchanged. That one rule produces interference — reinforcement where the signs agree, cancellation where they oppose — and it guards against a common mistake, since displacement can vanish at an instant while the energy sits in transverse motion instead. We work out the signed sum, the phase bookkeeping for equal-frequency components, and why a null in the record is not a null in the wave.\n",{"path":4617,"title":4618,"module":4606,"summary":4619},"\u002Fmechanics\u002Foscillations-waves\u002Fstanding-waves","Standing Waves","Clamp a string at both ends and only certain frequencies survive: the ends must be nodes, and that single geometric demand quantizes the wave into a discrete set of modes $f_n=nv\u002F(2L)$. The travelling wave becomes a fixed pattern of nodes and antinodes — standing, not moving — because equal waves running in opposite directions superpose. We build the standing wave from its counter-propagating pieces, read the harmonic sequence off the boundary conditions (half-wavelengths for a fixed-fixed string, odd quarter-wavelengths for a closed pipe), and test the ideal model against node scans and resonance peaks.\n",{"path":4621,"title":4622,"module":4606,"summary":4623},"\u002Fmechanics\u002Foscillations-waves\u002Fsound-waves","Sound Waves","Sound is a pressure wave so small that a loud tone displaces air molecules by less than the width of an atom, yet a microphone reads it easily — because pressure, not displacement, is what the ear and the instrument sense. The acoustic impedance $Z=\\rho c$ ties pressure, density, and particle velocity together, fixes the intensity a wave carries, and sets the reference for the decibel, a logarithm that tames a $10^{12}$ range in power. We derive the sound speed from the gas's stiffness, convert between pressure and intensity levels, and treat the measurement itself — calibration, geometry, background, averaging — as part of the physics.\n",{"path":4625,"title":4626,"module":4606,"summary":4627},"\u002Fmechanics\u002Foscillations-waves\u002Fdoppler-effect","Doppler Effect","A passing siren drops in pitch not because the source changes but because motion repacks the wavefronts: an approaching source crowds its crests, a receding one stretches them, and a moving listener samples them at a different rate. For mechanical waves every velocity is measured against the medium, and one signed ratio $f_r=f_s(v-u_r)\u002F(v-u_s)$ captures both effects at once. We separate source motion, which sets crest spacing, from receiver motion, which sets arrival rate, invert the shift to recover radial velocity, and mark where the model breaks — supersonic sources, moving air, and reflected paths that carry two shifts, not one.\n",{"path":4629,"title":4630,"module":4606,"summary":4631},"\u002Fmechanics\u002Foscillations-waves\u002Fwave-packets","Wave Packets","No real signal is a single frequency: a disturbance that starts and stops is built from a band of wave numbers, and the width of that band is what makes it local. We ask how such a packet moves — carrier crests at the phase velocity $v_\\mathrm p=\\omega\u002Fk$, the envelope at the group velocity $v_\\mathrm g=\\d\\omega\u002F\\d k$ — and why the two differ once a medium is dispersive. Curvature $\\d^2\\omega\u002F\\d k^2$ spreads and chirps the packet as it travels, and the Fourier reciprocity that ties bandwidth to duration explains why a finite record, aliasing, or a coarse probe can imitate that spreading unless the sampling limits are respected.\n",{"path":4633,"title":4634,"module":4606,"summary":4635},"\u002Fmechanics\u002Foscillations-waves\u002Fbeats-and-coupling","Beats and Coupling","Add two tones a few hertz apart and the sum swells and fades at their difference frequency — a beat — though neither source is changing. We work out that envelope, then ask the mechanical version of the same question: join two oscillators and a single resonance splits into normal modes, with energy sloshing between the coordinates at their frequency difference. The lesson identifies when a slow amplitude envelope signals genuine coupling rather than two independent sources, drift, or deliberate modulation, reading it from envelope timing, spectral sidebands, and the mode shapes.\n",{"path":4637,"title":4638,"module":4606,"summary":4639},"\u002Fmechanics\u002Foscillations-waves\u002Fsimple-harmonic-motion","Simple Harmonic Motion","Any system pushed back toward equilibrium by a force proportional to its displacement obeys one equation, $\\ddot x+\\omega_0^2x=0$, and so moves sinusoidally at $\\omega_0=\\sqrt{k\u002Fm}$ whatever the amplitude. We derive that motion, follow its energy $E=mv^2\u002F2+kx^2\u002F2$ trading between kinetic and potential form at constant total, and read the elliptical phase-space orbit Hooke's law implies. Period, amplitude, velocity, and acceleration then supply redundant checks: an amplitude-dependent period or a curved force residual is the signature that the linear model has failed, and mass-loading and offset tests separate a calibration error from a real frequency shift.\n",{"path":4641,"title":4642,"module":4606,"summary":4643},"\u002Fmechanics\u002Foscillations-waves\u002Fpendulum-motion","Pendulum Motion","A pendulum keeps time only because, for small swings, gravity supplies a restoring torque proportional to the angle — and $T=2\\pi\\sqrt{L\u002Fg}$ then follows without the mass appearing at all. We derive that result, mark exactly which assumptions carry it (small angle, negligible pivot loss, a rigid support), then relax them: finite amplitude lengthens the period through an elliptic integral, and an extended body replaces $L$ with the ratio of its moment of inertia to its center-of-mass distance. How the period drifts with amplitude or pivot position is what diagnoses the geometric, damping, and distributed-mass corrections.\n",{"path":4645,"title":4646,"module":4606,"summary":4647},"\u002Fmechanics\u002Foscillations-waves\u002Fdriven-oscillators","Driven Oscillators","Drive a damped oscillator at a frequency you control and it eventually forgets its own: $m\\ddot x+b\\dot x+kx=F_0\\cos\\omega t$ settles into a steady response whose amplitude and phase depend sharply on how close the drive sits to resonance. We solve for that response, show how damping alone fixes the resonance width, the peak power, and the settling time, and treat base excitation as the same problem with a different input. The steady-state formulas hold only for constant $m$, $b$, and $k$; level-dependent peaks or hysteresis between up- and down-sweeps are how nonlinearity or an extra mode announces itself.\n",{"path":4649,"title":4650,"module":4606,"summary":4651},"\u002Fmechanics\u002Foscillations-waves\u002Fwave-boundaries","Wave Boundaries","A pulse traveling along a string does something abrupt where the string's properties change: part reflects, part transmits, and which is which is set by the impedance mismatch alone. We impose continuity of displacement and transverse force at the join to get the reflection and transmission coefficients in terms of $Z=\\sqrt{T\\mu}$, fix their signs and the polarity flip, and balance the energy. The clean result assumes linear, nondispersive segments meeting at a localized join; pulse polarity, return timing, and energy ratios are the measurements that expose a real connector's mass, loss, or distributed transition.\n",{"path":4653,"title":4654,"module":4655,"summary":4656},"\u002Fmechanics\u002Fthermodynamics\u002Fkinetic-theory-of-ideal-gases","Kinetic Theory of Ideal Gases","Thermodynamics","A gas has no springs and no gears, yet it pushes on its container with a definite pressure and stores energy in a lawful way. Kinetic theory explains both from the motion of the molecules alone: pressure is the accumulated recoil of countless elastic impacts, and temperature is the average translational kinetic energy each molecule carries. We derive $pV=\\tfrac13Nm\\overline{v^2}$ from momentum transfer, read off $\\overline{K}_{\\rm tr}=\\tfrac32kT$, and use the Maxwell–Boltzmann distribution to separate the most probable, mean, and rms speeds — each the right average for a different question — while marking where the dilute, classical assumptions stop holding.\n",{"path":4658,"title":4659,"module":4655,"summary":4660},"\u002Fmechanics\u002Fthermodynamics\u002Ffirst-law-of-thermodynamics","First Law of Thermodynamics","Heat a gas and it may warm, expand, or both; compress it and the same energy can reappear as a temperature rise. The first law settles the bookkeeping: internal energy is a state property whose change equals the heat added plus the work done on the system, $\\Delta E_{\\rm int}=Q_{\\rm in}+W_{\\rm on}$. We fix a system boundary and one sign convention, compute boundary work as $\\int p\\,\\d V$ along a path, and use calorimetry to measure heat and heat capacities. The recurring point is that heat and work are path-dependent transfers while their sum is not, so an energy ledger closes only once every boundary crossing is named.\n",{"path":4662,"title":4663,"module":4655,"summary":4664},"\u002Fmechanics\u002Fthermodynamics\u002Fentropy-and-the-second-law","Entropy and the Second Law","The first law lets energy flow either way; it never says which way heat actually goes. The second law supplies the missing arrow. Entropy, defined through the reversible transfer $\\d S=\\delta Q_{\\rm rev}\u002FT$, can only increase in an isolated system, and that single inequality fixes the direction of heat flow and caps every engine, refrigerator, and heat pump at its Carnot value. We build entropy ledgers for reservoirs and working substances, separate the entropy carried by heat from the entropy generated by irreversibility, and read the sign of the total as a hard check on any proposed thermal machine.\n",{"path":4666,"title":4667,"module":4655,"summary":4668},"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-processes","Thermal Processes","Heat rarely sits still: it stretches solids, pushes real gases off their ideal isotherms, and leaks across walls by conduction, convection, and radiation. Each behavior becomes a number a designer can use. Thermal expansion sets the gaps in a bridge and the stress in a clamped rod; the van der Waals equation and a phase diagram fix when $pV=nRT$ or a latent-heat term applies; Fourier's law, Newton cooling, and Stefan–Boltzmann radiation give the rate of heat flow. We assemble these into thermal-resistance networks and transient time constants, then mark where contact resistance, phase change, or a hidden thermal bridge breaks the simple model.\n",{"path":4670,"title":4671,"module":4655,"summary":4672},"\u002Fmechanics\u002Fthermodynamics\u002Fphase-changes","Phase Changes","Add heat to ice and its temperature climbs — until it reaches $0\\ ^\\circ\\mathrm C$, where the thermometer stalls while the ice melts. That plateau is the whole subject: at a phase boundary the energy rearranges molecules, $Q=mL$, instead of raising temperature, which resumes only once one phase is gone. We stage a heating path into sensible-heat legs ($Q=mc\\Delta T$) and latent plateaus, use the Clausius–Clapeyron relation to track how a boiling point moves with pressure, and solve calorimetry by testing each coexistence endpoint — so a melt fraction that lands outside $[0,1]$ flags a wrong final-state guess rather than a real state.\n",{"path":4674,"title":4675,"module":4655,"summary":4676},"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-machines","Thermal Machines","An engine, a refrigerator, and a heat pump are one machine read three ways: each shuttles heat between a hot and a cold reservoir while trading work at the boundary, and only the flow you call useful separates them. A heat engine turns part of $Q_h$ into work, $W=Q_h-Q_c$; a refrigerator spends work to pull $Q_c$ from the cold side; a heat pump counts the warm-side delivery instead. We measure each with its own ratio — efficiency or coefficient of performance — bound them all by the Carnot limit that reservoir temperatures alone set, and track how finite temperature differences, throttling, and friction generate entropy and pull real machines below that bound.\n",{"path":4678,"title":4679,"module":6,"summary":6},"\u002Fmechanics","Mechanics & Dynamics",{"path":4681,"title":4682,"module":4683,"summary":4684},"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcharge-and-conductors","Charge and Conductors","Electric Fields","Rub two objects together and one pulls electrons from the other; nothing is created, only moved. We define what electric charge is — conserved, additive, and quantized in units of $e$ — and why a conductor's mobile carriers rearrange until its interior field vanishes and its surface sits at one potential. We follow charge through contact, induction, and grounding, treat the field-free cavity that turns a conductor into a shield, and mark where finite conductivity and leakage set the limits of the electrostatic picture.\n",{"path":4686,"title":4687,"module":4683,"summary":4688},"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcoulombs-law","Coulomb's Law","Two charges at rest push or pull along the line joining them, and the whole of electrostatics is assembled by adding up such pairs. We measure that force — its inverse-square falloff, its linear dependence on each charge, the sign that says attract or repel — and write it as a vector so direction survives superposition. We work the magnitude and component forms on real numbers, check them against limiting cases and dimensions, and fix the point-charge approximation to source sizes small against every separation.\n",{"path":4690,"title":4691,"module":4683,"summary":4692},"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-and-force","Electric Field and Force","Rather than ask how one charge reaches across empty space to another, we credit the source with a field that fills the space and let a second charge respond to whatever field sits at its own location. Electric field is force per unit positive test charge, $\\vec E=kq\\hat r\u002Fr^2$ for a point source, and source fields add before any receiving charge is placed. We compute those fields and the force $\\vec F=q\\vec E$ they exert, then follow a charge along its parabolic path through a uniform field and into nonuniform fields where the dynamics turn position-dependent.\n",{"path":4694,"title":4695,"module":4683,"summary":4696},"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-maps","Electric Field Maps","A field is a vector at every point of space, and the quickest way to grasp one is to draw it. We build the two standard pictures — continuous field lines tangent to $\\vec E$, and scaled vector arrows — and read direction, magnitude, and the location of nulls straight off them. We fix what a line drawing can and cannot say: density encodes magnitude only under a stated seeding rule, and integral curves never cross at a regular point. From there we work the topology near sources, sinks, and conductor surfaces, and state the step-size and interpolation checks a numerical map must pass.\n",{"path":4698,"title":4699,"module":4683,"summary":4700},"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-dipoles","Electric Dipoles","Most neutral matter carries no net charge yet still responds to an electric field, because its positive and negative charge sit slightly apart. That separation is a dipole, moment $\\vec p=q\\vec d$ pointing from the negative to the positive charge, and it is the leading term in how any neutral distribution looks from far away. We derive the torque $\\vec p\\times\\vec E$ and energy $-\\vec p\\cdot\\vec E$ a uniform field imposes, the net force a field gradient adds, and the axial and equatorial $1\u002Fr^3$ fields the pair produces, then measure how far out the point-dipole approximation still holds.\n",{"path":4702,"title":4703,"module":4704,"summary":4705},"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fcontinuous-charge-fields","Continuous Charge Fields","Continuous Charge Distributions","A charged rod, ring, or disk is not a point, yet its field is still nothing but Coulomb's law added up over the charge it carries. We replace the discrete sum by an integral, with $\\d q=\\lambda\\d\\ell$, $\\sigma\\d A$, or $\\rho\\d V$, so the real work becomes geometry: writing the vector from each source element to the field point, and letting symmetry cancel the components that must cancel before any integral is attempted. We carry the line, ring, and disk fields through in full, then check each result against its near field, its far field, and its dimensions.\n",{"path":4707,"title":4708,"module":4704,"summary":4709},"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fgauss-law-and-conductors","Gauss's Law and Conductors","Adding up Coulomb's law over a whole distribution is laborious; Gauss's law trades that sum for a single statement, that the flux of $\\vec E$ out of any closed surface counts the charge inside, $\\oint\\vec E\\cdot\\d\\vec A=Q_{\\rm enc}\u002F\\varepsilon_0$. The law is always true, but it hands over the field only when the source is symmetric enough to pull $E$ outside the integral. We apply it to spheres, lines, and sheets, then turn it on conductors, where the zero interior field drives every excess charge to the surface and fixes the normal-field jump $\\sigma\u002F\\varepsilon_0$, the charge induced on a cavity wall, and electrostatic shielding.\n",{"path":4711,"title":4712,"module":4713,"summary":4714},"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpoint-charge-potential","Point-Charge Potential","Electric Potential","The electrostatic force is conservative, so the work it does between two points\ndepends only on the endpoints. That lets us trade the vector field for a single\nscalar attached to each point, the electric potential, the potential energy a unit\ncharge would have there. We build potential from the work integral, fix the usual\nreference at infinity, and add point sources as scalars, $V=k\\sum_i q_i\u002Fr_i$,\navoiding the vector bookkeeping the field demands. Signed charges, the reference\nchoice, equipotential motion, and far-field expansions each give an independent\ncheck on a result.\n",{"path":4716,"title":4717,"module":4713,"summary":4718},"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpotential-gradients-and-equipotentials","Potential Gradients and Equipotentials","Given the potential everywhere, how do we recover the field? The field is the\nnegative gradient, $\\vec E=-\\nabla V$: it points down the steepest local drop in\npotential, and its magnitude is set by how fast $V$ changes, not by the shape of a\ncontour. We read off components with directional derivatives, reconstruct fields\nfrom measured potential grids using centered differences, and use closed-loop\nintegrals and grid refinement to test whether a reconstructed field is physically\nconsistent.\n",{"path":4720,"title":4721,"module":4713,"summary":4722},"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Felectrostatic-energy-and-pressure","Electrostatic Energy and Pressure","Assembling a charge configuration takes work, and that work is stored, but where\nis it kept and how much is there? We total it two ways: as a sum over the charges,\n$U=\\tfrac12\\sum_i q_iV_i$, and as an integral over the field itself,\n$u_E=\\tfrac12\\varepsilon_0E^2$, energy the field carries in every region it fills.\nDifferentiating the stored energy at fixed charge or at fixed voltage recovers the\nmechanical force on a conductor, and at a charged surface the same field scale\nappears as an outward electrostatic pressure. We work the parallel-plate case in\nfull and mark where curvature and fringing make the pressure nonuniform.\n",{"path":4724,"title":4725,"module":4713,"summary":4726},"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Flaplace-boundary-problems","Laplace Boundary Problems","Often the charges are not given, only the conductors and the voltages held on\nthem, and the potential in the empty space between has to be found. There $V$ obeys\nLaplace's equation $\\nabla^2V=0$, and the boundary data alone determine a unique solution.\nWe solve it two ways: separation of variables into boundary-matched modes, whose\nhigher spatial frequencies die away with depth into the domain, and finite-difference\nrelaxation for boundaries no analytic mode fits. Residual norms, boundary error, and\nflux balance tell us when the computed potential and its field can be trusted.\n",{"path":4728,"title":4729,"module":4713,"summary":4730},"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fcontinuous-charge-potentials","Continuous Charge Potentials","When charge is spread over a line, a surface, or a volume, the sum over point\nsources becomes an integral, $V(\\vec r)=k\\int \\d q\u002F|\\vec r-\\vec r'|$. Because\npotential is a scalar, this integral sidesteps the component algebra the field\nwould force, until the field is actually wanted through $\\vec E=-\\nabla V$. We set\nup the right density element for each geometry, choose a workable reference, handle\nthe integrable singularities that arise when the observation point sits on the\ncharge, and check every result against symmetry, dimensions, and the far-field\nmultipole limit.\n",{"path":4732,"title":4733,"module":4734,"summary":4735},"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitance-fundamentals","Capacitance Fundamentals","Capacitance","How much charge must you separate onto two conductors to hold a given voltage between\nthem? That ratio, $C=Q\u002F\\Delta V$, is fixed by the conductor geometry and the medium,\nnot by how much charge is presently stored. We compute it from the field for the\nparallel-plate, isolated-sphere, concentric-sphere, and coaxial geometries, trace how\nsurface charge and boundary conditions set each result, and see where fringing,\nguarding, and stray coupling separate the ideal formula from what a bridge measures.\n",{"path":4737,"title":4738,"module":4734,"summary":4739},"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-networks","Capacitor Networks","Wire several capacitors together and the source sees one equivalent capacitance — but\nwhich? The answer comes not from how the symbols are drawn but from which conductors\nshare a node: parallel branches hold a common voltage and add, $C_{\\rm eq}=\\sum_iC_i$,\nwhile series branches share a common charge and add reciprocally. We derive both rules\nfrom charge conservation on the floating internal node, then extend the node-charge\nmethod to unequal, precharged, and stray-coupled branches and carry a worked reduction\nthrough to the charge and voltage on every element.\n",{"path":4741,"title":4742,"module":4734,"summary":4743},"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-energy-and-force","Capacitor Energy and Force","Charging a capacitor takes work, because every increment of charge is pushed through\nthe voltage the earlier charge already established. We total that work three\nequivalent ways, $U=Q^2\u002F(2C)=Q\\Delta V\u002F2=C(\\Delta V)^2\u002F2$, locate it in the field as\na density $u=\\tfrac12\\epsilon_0E^2$, then let the plates move. Differentiating the\nstored energy at fixed charge, or the coenergy at fixed voltage, gives the mechanical\nforce; the two boundaries differ only by the work the source supplies. We work the\nparallel-plate attraction and its electrostatic pressure in full, and follow the same\ngradient into pull-in, tilt, comb drives, and traceable force calibration.\n",{"path":4745,"title":4746,"module":4734,"summary":4747},"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fdielectric-polarization-and-breakdown","Dielectric Polarization and Breakdown","Slide a dielectric between the plates and the capacitance rises — but why, and how\nhard can you drive it before the insulator fails? Bound charge answers the first:\npolarization $\\vec P$ sets up surface and volume charge that partly cancels the\napplied field, so $\\vec D=\\varepsilon_0\\vec E+\\vec P$ separates what the circuit\ncontrols from what the material contributes. We follow the field across layered\ndielectrics and interfaces, tie permittivity and loss to their frequency dependence,\nand treat dielectric strength as a measured, geometry-dependent limit rather than one\nmaterial number.\n",{"path":4749,"title":4750,"module":4751,"summary":4752},"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fcurrent-and-resistance","Current and Resistance","Direct-Current Circuits","What does it mean, physically, for charge to flow, and what sets how hard a wire resists that flow? Current counts charge crossing a surface, $I=\\int\\vec J\\cdot\\d\\vec A$, and traces back to a slow drift of many carriers, $\\vec J=nq\\vec v_d$. We establish when the linear law $V=IR$ actually holds, how resistivity and geometry combine into bulk resistance, why real sources sag under load through their internal resistance, and how the three power forms $P=IV=I^2R=V^2\u002FR$ tie electrical work to heating and component ratings.\n",{"path":4754,"title":4755,"module":4751,"summary":4756},"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fkirchhoff-network-analysis","Kirchhoff Network Analysis","Once a circuit has more than one loop, no amount of series-parallel folding will reduce it — you need the two conservation laws written as equations. Kirchhoff's junction law is charge conservation at a node; his loop law is energy conservation around a closed path. We turn a labelled network into a linear system in node voltages or mesh currents, fix the sign conventions so a negative answer just means a reversed arrow, and use power balance as an independent check that the algebra describes the circuit that was actually built.\n",{"path":4758,"title":4759,"module":4751,"summary":4760},"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Frc-transients","RC Transients","How does a circuit get from one steady state to the next when a capacitor refuses to change its voltage all at once? Because a jump would demand infinite current, an RC circuit slides between states exponentially, with a single time constant $\\tau=RC$ that sets the whole schedule: charging fills as $1-e^{-t\u002F\\tau}$, discharge empties as $e^{-t\u002F\\tau}$. We solve the first-order loop equation, read the response off three numbers — the switch-instant voltage, the final dc voltage, and the Thevenin resistance the capacitor sees — and mark where source and probe resistance shift $\\tau$ or where a second storage element hides a mode a one-$\\tau$ fit misses.\n",{"path":4762,"title":4763,"module":4764,"summary":4765},"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-trajectories","Magnetic Trajectories","Magnetic Field","A charged particle in a magnetic field never speeds up or slows down, yet its path curves relentlessly. We work out why: the magnetic force is always perpendicular to velocity, so it does no work and bends the transverse motion into a circle of radius $r=mv_\\perp\u002F(|q|B)$ while leaving the parallel motion untouched, producing a helix. We derive the cyclotron frequency, show why it is independent of speed until relativity intervenes, and turn the geometry around: a measured curvature reads back a particle's momentum, which is how tracking detectors weigh what they cannot see.\n",{"path":4767,"title":4768,"module":4764,"summary":4769},"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fhall-effect","Hall Effect","Current tells you charge is moving, but not whether the movers are positive or negative, nor how many there are. A magnetic field settles both questions. Push current through a strip in a transverse field and the carriers pile up on one edge until a transverse electric field just balances the magnetic deflection; the sign of the resulting Hall voltage names the carrier's charge and its size counts the carriers per volume. We derive the balance $q\\vec E+q\\vec v_d\\times\\vec B=0$, read off $V_H=IB\u002F(nqt)$, and see why field-and-current reversal is what separates the real Hall signal from the offsets that mimic it.\n",{"path":4771,"title":4772,"module":4764,"summary":4773},"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-force-on-conductors","Magnetic Force on Conductors","A magnet pushes on a current-carrying wire even though the wire is electrically neutral. The reason is that each moving carrier feels the Lorentz force, and those microscopic pushes add up to a force the wire's supports must hold. We sum them into $\\d\\vec F=I\\,\\d\\vec\\ell\\times\\vec B$, collapse it to $\\vec F=I\\vec L\\times\\vec B$ for a straight segment in a uniform field, and see exactly when that shortcut fails and the full path integral is needed. The same law runs backward as a measurement: a force-versus-current slope weighs a magnetic field against a known length.\n",{"path":4775,"title":4776,"module":4764,"summary":4777},"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-dipoles","Magnetic Dipoles","A compass needle turns to point north; a current loop in a field does the same thing, and for the same reason. Both are magnetic dipoles, and a uniform field cannot push a dipole anywhere, only twist it. We package a loop's response into one vector, the magnetic moment $\\vec\\mu=IA\\hat n$, from which torque $\\vec\\tau=\\vec\\mu\\times\\vec B$ and orientation energy $U=-\\vec\\mu\\cdot\\vec B$ both follow. Stable alignment sits at the energy minimum, a field gradient is what it takes to produce a net force $\\vec F=\\nabla(\\vec\\mu\\cdot\\vec B)$, and the same moment reappears whenever anything from an electron to a planet acts magnetic.\n",{"path":4779,"title":4780,"module":4764,"summary":4781},"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmass-spectrometry","Mass Spectrometry","To weigh a single atom you cannot use a scale, so you use a magnetic field instead. A charged ion of unknown mass bends in a field by an amount that depends on its momentum and charge, so if every ion enters with the same velocity, its landing position reads off its mass-to-charge ratio directly. We build the instrument in two stages: crossed electric and magnetic fields that pass only ions with $v=E\u002FB$, and a magnetic sector that bends the survivors along $r=mv\u002F(|q|B)$. Then we ask what blurs a spectral line and how reference ions turn a position into a calibrated mass.\n",{"path":4783,"title":4784,"module":4785,"summary":4786},"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmoving-charge-fields","Moving-Charge Fields","Magnetic Sources","Every magnetic field comes from charge in motion, and the simplest source is a single point charge drifting past. We work out the field it produces — normal to both the velocity and the line of sight, falling off as the inverse square — and read off why it vanishes straight ahead of the charge and peaks broadside. Summing many such charges is the bridge to steady currents, valid while speeds stay far below $c$ and the motion changes little during the time its field takes to propagate outward.\n",{"path":4788,"title":4789,"module":4785,"summary":4790},"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fbiot-savart-law","Biot–Savart Law","A steady current is a continuous stream of current elements, and the Biot–Savart law hands each one a magnetic contribution — a right-hand cross product that falls off as the inverse square of distance. Summing the contributions along a conductor is a vector line integral, which we carry out for the straight wire to get the endpoint-angle formula. The infinite-wire field $B=\\mu_0 I\u002F2\\pi s$ falls out as the limit where both ends recede, and we mark how fast a finite wire departs from it and when a thin-filament model is safe.\n",{"path":4792,"title":4793,"module":4785,"summary":4794},"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fcircular-current-loops","Circular Current Loops","A ring of current is the simplest source with a well-defined magnetic axis, and it is the building block of every coil and electromagnet. Symmetry kills the transverse Biot–Savart contributions along that axis and leaves a single clean integral; we evaluate it to get $B_z=\\mu_0 I R^2\u002F[2(R^2+z^2)^{3\u002F2}]$, read off the centre field $\\mu_0 I\u002F2R$, and watch it fall into the $1\u002Fz^3$ tail of a magnetic dipole far away. Stacking turns just adds their axial contributions, which is what makes a solenoid out of a pile of loops.\n",{"path":4796,"title":4797,"module":4785,"summary":4798},"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Famperes-law","Ampère’s Law","When a current arrangement is symmetric enough, the Biot–Savart integral is overkill: Ampère's law, $\\oint_C\\vec B\\cdot\\d\\vec\\ell=\\mu_0 I_{\\rm enc}$, gets the field from a single line of reasoning about how much current a loop encloses. We see why the law holds for any steady current, then use cylindrical, planar, and toroidal symmetry to turn the circulation into simple algebra — the field inside and outside a wire, an infinite sheet, a solenoid, and a toroid. We also mark the catch: without symmetry the law still holds but no longer hands you the field pointwise.\n",{"path":4800,"title":4801,"module":4785,"summary":4802},"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fgauss-law-for-magnetism","Gauss’s Law for Magnetism","Electric field lines start and end on charges; magnetic field lines do neither, because no one has ever found an isolated magnetic pole. That single experimental fact is Gauss's law for magnetism: the flux of $\\vec B$ through any closed surface is zero, $\\oint\\vec B\\cdot\\d\\vec A=0$, or in differential form $\\nabla\\cdot\\vec B=0$. We work through what it says — every field line that enters a closed surface must leave it, so field lines close on themselves — and, just as important, what it does not say, since flux through an open surface is generally nonzero.\n",{"path":4804,"title":4805,"module":4785,"summary":4806},"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmagnetic-materials","Magnetic Materials","Put matter in a magnetic field and its atoms respond, each acting as a tiny current loop; the aligned moments per unit volume are the magnetization $\\vec M$, whose bound currents add to the field. Separating what we control (the free current) from what the material supplies leads to $\\vec H$ and the relation $\\vec B=\\mu_0(\\vec H+\\vec M)$. We sort materials into diamagnets, paramagnets, and ferromagnets by how $\\vec M$ answers, follow a ferromagnet around its hysteresis loop, and see why the loop's area is the energy dissipated per cycle and why a sample's shape changes the field it actually feels.\n",{"path":4808,"title":4809,"module":4810,"summary":4811},"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-flux","Magnetic Flux","Electromagnetic Induction","A magnetic field threading a loop collapses to one signed number, the flux, and every induced voltage in this module turns out to be a rate of change of that number — so defining the flux and its sign comes first. We define it as the surface integral of $\\vec B$ over an oriented surface, reduce it to $BA\\cos\\theta$ for a uniform field on a flat loop, and carry the flux linkage $N\\Phi_B$ of a coil. The chosen normal fixes the sign; reversing it flips the sign without touching the field. Nonuniform fields and curved surfaces force the integral, so we also build the numerical estimate and the checks that separate a reliable value from a nominal field-times-area product.\n",{"path":4813,"title":4814,"module":4810,"summary":4815},"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Ffaradays-law","Faraday's Law","Move a magnet toward a coil, or ramp the current in a nearby circuit, and a voltage appears with no battery in sight. Faraday's law names the cause: the emf around a loop equals minus the rate of change of the magnetic flux through it, so any change of field, area, orientation, or position that alters the flux drives an emf. We separate the emf, which lives around the boundary whether or not current can flow, from the current that follows only when the path is closed; fix the single sign convention that ties flux to loop orientation; and read the emf off rotating coils and off flux sampled at discrete times.\n",{"path":4817,"title":4818,"module":4810,"summary":4819},"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Flenzs-law","Lenz's Law","The minus sign in Faraday's law is not decoration: it decides which way the induced current flows, and it always chooses the direction that fights the change that produced it. Lenz's law reads that sign off energy conservation — a current that aided the change would be free energy — and turns it into a repeatable procedure. We fix a surface normal and a positive loop direction so the sign is calculable, then work through approaching magnets, expanding loops, coupled coils, and rotating generators, using mechanical work and Joule heating as an independent check on every direction we draw.\n",{"path":4821,"title":4822,"module":4810,"summary":4823},"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmotional-emf","Motional EMF","Push a wire through a magnetic field and its free charges feel a sideways magnetic force that piles them up at the ends — a battery made of motion. Motional emf is that effect: the work per unit charge a moving conductor supplies is the line integral of $\\vec v\\times\\vec B$ along it, which for a rod moving perpendicular to both its length and the field collapses to $B\\ell v$. We chase where the energy comes from — the hand or motor fighting the magnetic drag, never the magnetic force itself — solve the sliding-rail circuit from both flux and carrier forces, and carry the idea into rotating rods, homopolar disks, generators, and the back emf of a motor.\n",{"path":4825,"title":4826,"module":4810,"summary":4827},"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Feddy-currents","Eddy Currents","A wire carries current along one path; a solid block of metal offers a continuum of them, and any changing flux threading that block sets charge circulating in closed loops it chooses for itself. We ask what those eddy currents do — where they heat, where they drag, and how Lenz's law fixes their direction — and why the same circulation is a feature in an induction furnace and a loss to be suppressed in a transformer core. From a representative-loop estimate we get the scaling (heating grows with the square of frequency and flux rate) and the two design levers, lamination and resistivity, that break the paths a solid conductor would otherwise hand the current.\n",{"path":4829,"title":4830,"module":4810,"summary":4831},"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fself-inductance","Self-Inductance","A coil resists changes to its own current. Drive current through it and the flux it produces threads its own turns; change that current and Faraday's law turns the coil against the source with a back emf $\\mathcal E_L=-L\\,\\d I\u002F\\d t$. We define self-inductance as the flux linkage per ampere fixed by winding and core geometry, derive the long-solenoid value $L=\\mu_0 N^2A\u002F\\ell$, and follow the consequence that dominates circuits: because a finite voltage can only sustain a finite $\\d I\u002F\\d t$, an inductor's current cannot jump — which is why opening a switch on a live coil throws a spark.\n",{"path":4833,"title":4834,"module":4810,"summary":4835},"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-energy","Magnetic Energy","Building current in a coil means working against its back emf, and that work does not vanish — it sits in the magnetic field as recoverable energy $U_B=\\tfrac12LI^2$, spread through space at density $u_B=B^2\u002F(2\\mu_0)$. We derive both forms, show they agree for a solenoid, and read a force out of the same energy: an armature is pulled toward higher inductance, and $B^2\u002F(2\\mu_0)$ doubles as a magnetic pressure. The lesson closes on the accounting a real switching event demands, where recoverable energy, copper heating, core loss, and clamp dissipation must balance a single ledger.\n",{"path":4837,"title":4838,"module":4810,"summary":4839},"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Frl-circuits","RL Circuits","Put a resistor and an inductor in series and the current cannot switch on or off at will: it climbs to $V_0\u002FR$ and falls away exponentially on a single time scale $\\tau=L\u002FR$ set by how much flux the coil hoards against how fast the resistor bleeds it. We solve the turn-on and turn-off, then confront the practical sting — because the coil's current refuses to stop instantly, breaking its path throws up a large voltage, which is why real inductive circuits carry freewheel diodes and clamps that trade voltage stress against how quickly the current dies.\n",{"path":4841,"title":4842,"module":4843,"summary":4844},"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-fundamentals","AC Fundamentals","Alternating Current","A wall socket delivers a voltage that averages to zero over each cycle, yet it still heats a filament and runs a motor. The resolution is that dissipation follows the mean of the square, not the mean, so we define the root-mean-square value that makes an alternating source the equal of a DC one for resistive heating. We show a sinusoid's RMS is its peak divided by $\\sqrt2$, work out the average power an ideal resistor draws when its current stays in phase with the applied voltage, and separate the peak, average, and RMS descriptions that a single number cannot combine.\n",{"path":4846,"title":4847,"module":4843,"summary":4848},"\u002Felectricity-and-magnetism\u002Falternating-current\u002Freactance","Reactance","A resistor obeys Ohm's law instant by instant, but a capacitor responds to how fast its voltage changes and an inductor to how fast its current changes. Under a steady sinusoid that rate-dependence collapses to a fixed quarter-cycle phase shift and a frequency-dependent amplitude ratio, the reactance. We derive $X_C=1\u002F(\\omega C)$ and $X_L=\\omega L$, adopt phasors to turn the defining derivatives into multiplication by $j\\omega$ so a single complex impedance carries amplitude and phase together, and track the energy an ideal reactance stores and returns without dissipating it. Real windings and dielectrics add loss, leakage, and self-resonance that bound where the ideal formulas hold.\n",{"path":4850,"title":4851,"module":4843,"summary":4852},"\u002Felectricity-and-magnetism\u002Falternating-current\u002Frlc-resonance","RLC Resonance","Put a resistor, inductor, and capacitor in one loop and their reactances work against each other: inductive reactance grows with frequency while capacitive reactance shrinks, and at one frequency they cancel exactly. There the branch looks purely resistive, the current peaks, and the inductor and capacitor voltages can swing far above the source. We locate that resonance at $\\omega_0=1\u002F\\sqrt{LC}$, measure how sharp the peak is with the quality factor $Q=\\omega_0L\u002FR$, tie its half-power bandwidth $R\u002FL$ to the ringdown of the unforced circuit, and read the same poles off as bandpass and peaked filters at the R, L, or C terminals.\n",{"path":4854,"title":4855,"module":4843,"summary":4856},"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-power","AC Power","Multiply an AC load's RMS voltage by its RMS current and you get an answer in volt-amperes that the wiring must carry, but not in general the watts the load consumes. The phase between voltage and current splits that product into a part that does net work and a part that merely sloshes energy back and forth. We derive the average power $P=V_{\\rm rms}I_{\\rm rms}\\cos\\phi$, package amplitude and phase into complex power $S=P+jQ$ so that real, reactive, and apparent power form one right triangle, and see why a harmonic-rich current forces the time-domain definition $P=\\langle vi\\rangle$ in place of a single phase angle.\n",{"path":4858,"title":4859,"module":4843,"summary":4860},"\u002Felectricity-and-magnetism\u002Falternating-current\u002Ftransformers","Transformers","Two coils sharing an iron core exchange no charge, yet a changing current in one drives a voltage in the other, and the ratio of their turns sets how voltage and current trade off between the windings. That lets a transformer step a voltage up or down, isolate two circuits, and make a load look larger or smaller to the source by the square of the turns ratio. We build the ideal ratio element from Faraday's law and the dot convention, derive the reflected-impedance rule, then add the winding resistance, leakage, magnetizing current, and core loss that turn the ideal ratios into real regulation, efficiency, and a bounded voltage-frequency range.\n",{"path":4862,"title":4863,"module":4864,"summary":4865},"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdisplacement-current","Displacement Current","Maxwell’s Equations and Electromagnetic Waves","Ampère's law asks for the current through a surface bounded by a loop, but a charging capacitor breaks it: slide the surface off the wire and into the gap and the enclosed conduction current drops to zero, while the magnetic field around the loop plainly does not. Maxwell's repair is to count a changing electric flux as itself a source of magnetic circulation. We derive the displacement-current term $\\varepsilon_0\\,\\d\\Phi_E\u002F\\d t$, show that charge continuity demands it, compute the magnetic field it produces inside a charging capacitor, and see how it closes the Ampère–Maxwell law so that electric and magnetic fields can sustain one another as a wave.\n",{"path":4867,"title":4868,"module":4864,"summary":4869},"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-waves","Electromagnetic Waves","Once a changing electric flux can drive a magnetic field, the two curl laws feed each other: a disturbance in one regenerates the other, and the pair walks off through empty space with no medium holding it up. We take the curl of Faraday's law, land on a wave equation whose speed is fixed entirely by $\\mu_0$ and $\\varepsilon_0$, and find that $c=1\u002F\\sqrt{\\mu_0\\varepsilon_0}$ falls out of purely electric and magnetic constants. The plane-wave solution then fixes the geometry — $\\vec E$, $\\vec B$, and the propagation direction mutually perpendicular, oscillating in phase, with amplitudes locked at $E=cB$ — a set of independent predictions any real measurement must meet at once.\n",{"path":4871,"title":4872,"module":4864,"summary":4873},"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-momentum","Electromagnetic Momentum","A light beam carries no mass, yet it pushes: shine it on a surface and the surface feels a force. We trace that force back to the fields, which store energy with density $\\varepsilon_0E^2$ and carry it along the Poynting vector $\\vec S=\\vec E\\times\\vec B\u002F\\mu_0$. Because that energy also carries momentum $U\u002Fc$, an absorbed beam presses with $I\u002Fc$ and a mirror with $2I\u002Fc$. We derive the Poynting theorem as local energy conservation, tie intensity to field amplitude, and work the momentum balance carefully enough that oblique incidence, partial reflection, and finite beams all drop out of one accounting.\n",{"path":4875,"title":4876,"module":4864,"summary":4877},"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdipole-radiation","Dipole Radiation","Only accelerating charge radiates, and the simplest accelerator is a charge sloshing back and forth: an oscillating electric dipole. We work out the field it throws off, keeping the part that survives to large distance — the $1\u002Fr$ radiation field whose intensity goes as $\\sin^2\\theta\u002Fr^2$, zero along the dipole axis and strongest broadside. From it follow the $\\omega^4$ scaling of total radiated power, radiation resistance as the feed's view of that escaping power, and, through reciprocity, the fact that a good transmitter receives well in the same directions. The near-zone terms that fall off faster carry no net power, and we mark carefully where each description is allowed to be used.\n",{"path":4879,"title":4880,"module":4864,"summary":4881},"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fpolarization","Polarization","A plane wave still leaves one thing free: which way its electric field points as it oscillates. That freedom is polarization, set entirely by the relative amplitude and phase of the two transverse field components — in phase gives a line, equal amplitudes a quarter cycle apart give a circle, everything else an ellipse. We work out how a linear analyzer reads a state through Malus's law $I=I_0\\cos^2\\theta$, why that scan alone cannot tell circular light from unpolarized, and how a quarter-wave plate plus a few analyzer settings recover the full Stokes vector and the degree of polarization.\n",{"path":4883,"title":4884,"module":4885,"summary":4886},"\u002Felectricity-and-magnetism\u002Foptics\u002Freflection-and-refraction","Reflection and Refraction","Geometrical Optics","Light meeting a boundary between two transparent media splits into a reflected ray and a bent transmitted one, and predicting where those rays go is the whole starting point of geometrical optics. Fixing one convention — every angle measured from the surface normal — we get reflection's equal angles and derive Snell's law $n_1\\sin\\theta_1=n_2\\sin\\theta_2$ from wavefront timing. That single relation, applied once or twice, yields the critical angle and total internal reflection, prism deviation, the lateral shift through a window, apparent depth, and a fiber's acceptance cone; a wavelength-dependent index then adds dispersion. We mark throughout where the ray picture is trustworthy: feature sizes large against the wavelength and clean interface geometry.\n",{"path":4888,"title":4889,"module":4885,"summary":4890},"\u002Felectricity-and-magnetism\u002Foptics\u002Fthin-lenses","Thin Lenses","A lens gathers the light spreading from one point back onto another, and a single paraxial relation $1\u002Fs+1\u002Fs'=1\u002Ff$ predicts where that image lands and how large it is. We collapse two refractions into one bending plane, read image position and orientation off the three principal rays, and trace focal length back to glass and curvature through the lensmaker equation. Sign conventions carry the physics here — they separate real from virtual images and upright from inverted — so we drill them before chaining lenses in sequence and in contact. The lesson ends on how focal length is actually measured on a bench, and where finite thickness, aperture, and dispersion break the thin-lens picture.\n",{"path":4892,"title":4893,"module":4885,"summary":4894},"\u002Felectricity-and-magnetism\u002Foptics\u002Fspherical-mirrors","Spherical Mirrors","Curve a mirror and it stops merely reflecting an image and starts forming one: the same $1\u002Fs+1\u002Fs'=1\u002Ff$ that governs lenses reappears, now with $f=R\u002F2$ and reflected rays and object sharing one side of the glass. We derive the mirror equation from the reflection geometry of a single paraxial ray, then let signed distances do the sorting — real inverted images on the near branch, virtual upright ones behind the surface — and check the concave, convex, and plane-mirror limits against each other. The second half turns to how focal length is actually measured on a bench, by finite conjugates, distant targets, return imaging, and sagitta, and to the aperture and off-axis aberrations the single paraxial focus cannot capture.\n",{"path":4896,"title":4897,"module":6,"summary":6},"\u002Felectricity-and-magnetism","Electricity & Magnetism",{"path":4899,"title":4900,"module":4901,"summary":4902},"\u002Flinear-algebra\u002Flinear-systems\u002Fsystems-and-echelon-forms","Systems of Linear Equations and Row Reduction","Linear Equations in Linear Algebra","A linear system is a finite set of linear equations in shared variables. Elementary row operations rewrite it without changing its solution set, and reducing the augmented matrix to echelon form decides both existence and uniqueness. Pivot positions say whether the solution set is empty, a single point, or infinite.\n",{"path":4904,"title":4905,"module":4901,"summary":4906},"\u002Flinear-algebra\u002Flinear-systems\u002Fvector-and-matrix-equations","Vector Equations and the Matrix Equation Ax = b","The same linear system reads three equivalent ways: a system of equations, a vector equation asking whether b is a linear combination of fixed vectors, and a matrix equation Ax = b. Ax is the linear combination of A's columns weighted by x, so consistency for a given b means b lies in the span of the columns, and consistency for every b means the columns span all of R^m.\n",{"path":4908,"title":4909,"module":4901,"summary":4910},"\u002Flinear-algebra\u002Flinear-systems\u002Fsolution-sets-and-applications","Solution Sets and Applied Linear Systems","A homogeneous system Ax = 0 has a solution set that is a span through the origin; a consistent Ax = b has that same span translated by any one particular solution. Parametric vector form writes both explicitly. The structure shows up in applied systems with many solutions: equilibrium prices, balanced chemical reactions, network flows, weight-loss diets, and migration models.\n",{"path":4912,"title":4913,"module":4901,"summary":4914},"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-independence","Linear Independence","A set of vectors is linearly independent when the only linear combination equal to zero is the trivial one; otherwise a dependence relation writes one vector in terms of the others. For the columns of A the question becomes whether Ax = 0 has only the trivial solution — a pivot in every column. Counting pivots settles independence, and any set with more vectors than entries is automatically dependent.\n",{"path":4916,"title":4917,"module":4901,"summary":4918},"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-transformations","Linear Transformations and Their Matrices","Reading A as an action rather than an array, x maps to Ax is a transformation from R^n to R^m. The ones that preserve addition and scalar multiplication are the linear transformations, and every one is x maps to Ax for a unique standard matrix whose columns are the images of the standard basis vectors. Onto and one-to-one translate into the span and independence of those columns.\n",{"path":4920,"title":4921,"module":4922,"summary":4923},"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-operations","Matrix Operations","Matrix Algebra","Matrices add and scale entrywise, but their product is defined so that multiplication corresponds to composition of linear maps: the columns of AB are A applied to the columns of B. From that requirement follow the row-column rule, the algebra of products (associative and distributive but not commutative), powers, and the transpose.\n",{"path":4925,"title":4926,"module":4922,"summary":4927},"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-inverse-and-invertibility","The Inverse and the Invertible Matrix Theorem","The inverse of a square matrix is the matrix analogue of a reciprocal, defined by AA⁻¹ = I. A closed form settles the 2×2 case; the Gauss–Jordan algorithm row reduces [A | I] to [I | A⁻¹] in general; and elementary matrices record single row operations. The Invertible Matrix Theorem collects a dozen equivalent conditions for invertibility into one statement.\n",{"path":4929,"title":4930,"module":4922,"summary":4931},"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fpartitioned-matrices-and-lu","Block Matrices and the LU Factorization","Partitioning a matrix into blocks lets sums, products, and inverses be computed block by block, as if the submatrices were scalars. Block structure also underlies the LU factorization A = LU, which splits solving Ax = b into two fast triangular solves and repays the cost whenever many systems share one coefficient matrix.\n",{"path":4933,"title":4934,"module":4922,"summary":4935},"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fsubspaces-dimension-rank","Subspaces of Rⁿ, Dimension, and Rank","A subspace is a set closed under addition and scalar multiplication. Every matrix carries two: the column space of all attainable outputs Ax, and the null space of all solutions of Ax = 0. A basis measures each with a minimal spanning set, dimension counts it, and the Rank Theorem ties pivots and free variables together as rank + nullity = n.\n",{"path":4937,"title":4938,"module":4922,"summary":4939},"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fapplications-leontief-and-graphics","Applications: Leontief Economics and Computer Graphics","The Leontief input–output model balances an economy through (I − C)x = d and expands the inverse as a geometric series in the consumption matrix. Computer graphics moves figures with matrix products, using homogeneous coordinates so that translation and perspective projection become matrix multiplications too.\n",{"path":4941,"title":4942,"module":4943,"summary":4944},"\u002Flinear-algebra\u002Fdeterminants\u002Fdeterminants-and-cofactors","Introduction to Determinants","Determinants","The determinant of a square matrix is defined recursively by cofactor expansion: an n-by-n determinant is a signed sum of (n-1)-by-(n-1) determinants built from the first row. The expansion can equally run along any row or down any column, and a triangular matrix has determinant equal to the product of its diagonal.\n",{"path":4946,"title":4947,"module":4943,"summary":4948},"\u002Flinear-algebra\u002Fdeterminants\u002Fproperties-of-determinants","Properties of Determinants","Row operations act on the determinant in three predictable ways, and this turns row reduction into a fast algorithm: the determinant is the product of the pivots times a sign for the interchanges. The same properties yield the invertibility test det A is nonzero, the transpose identity, and the multiplicative law det(AB) equals det A times det B.\n",{"path":4950,"title":4951,"module":4943,"summary":4952},"\u002Flinear-algebra\u002Fdeterminants\u002Fcramer-volume-and-area","Cramer's Rule, Volume, and Linear Transformations","Cramer's rule writes each unknown of an invertible system as a ratio of determinants, and the same idea gives a closed formula for the inverse through the adjugate. Geometrically the absolute determinant is the area of the parallelogram or the volume of the parallelepiped spanned by the columns, so a linear map scales every region's measure by that factor.\n",{"path":4954,"title":4955,"module":4956,"summary":4957},"\u002Flinear-algebra\u002Fvector-spaces\u002Fvector-spaces-and-subspaces","Vector Spaces and Subspaces","Vector Spaces","A vector space is any set closed under addition and scalar multiplication that obeys ten algebraic axioms. The same axioms that govern arrows in the plane govern polynomials, functions, matrices, and infinite signals, so one theory covers them all. A subspace is a subset that is a vector space in its own right, tested by three conditions, and the span of any set of vectors is the smallest subspace containing them.\n",{"path":4959,"title":4960,"module":4956,"summary":4961},"\u002Flinear-algebra\u002Fvector-spaces\u002Fnull-and-column-spaces","Null Spaces, Column Spaces, and Linear Transformations","Two subspaces sit inside every matrix. The null space collects all solutions of $Ax = 0$ and lives in the domain; the column space collects every attainable $Ax$ and lives in the codomain. One is defined implicitly by a condition, the other explicitly by a spanning set, and the same pair appears for an abstract linear transformation as its kernel and range.\n",{"path":4963,"title":4964,"module":4956,"summary":4965},"\u002Flinear-algebra\u002Fvector-spaces\u002Fbases-and-independent-sets","Linearly Independent Sets and Bases","A basis is a spanning set with no redundancy: linearly independent and still large enough to reach every vector. The spanning-set theorem shows any spanning set can be trimmed to a basis by discarding dependent vectors, and the pivot columns of a matrix give a basis for its column space. Independence and spanning are defined for abstract spaces exactly as in $\\mathbb{R}^n$.\n",{"path":4967,"title":4968,"module":4956,"summary":4969},"\u002Flinear-algebra\u002Fvector-spaces\u002Fcoordinate-systems","Coordinate Systems","Fixing a basis assigns every vector a unique list of coordinates, turning an abstract space into $\\mathbb{R}^n$. The coordinate mapping is a one-to-one linear transformation onto $\\mathbb{R}^n$ — an isomorphism — so any $n$-dimensional space is indistinguishable from $\\mathbb{R}^n$ as far as vector-space computations go. In $\\mathbb{R}^n$ the change-of-coordinates matrix $P_B$ and its inverse convert between basis coordinates and standard coordinates.\n",{"path":4971,"title":4972,"module":4956,"summary":4973},"\u002Flinear-algebra\u002Fvector-spaces\u002Fdimension-and-rank","The Dimension of a Vector Space and Rank","Every basis of a space has the same number of vectors, and that number is the dimension. Rank is the dimension of the column space, equal to the dimension of the row space and to the number of pivots. The Rank Theorem, rank plus nullity equals the number of columns, ties the four fundamental subspaces of a matrix together and adds six lines to the Invertible Matrix Theorem.\n",{"path":4975,"title":4976,"module":4956,"summary":4977},"\u002Flinear-algebra\u002Fvector-spaces\u002Fchange-of-basis","Change of Basis","Two bases give the same vector two different coordinate vectors, and a single invertible matrix converts between them. Its columns are the coordinate vectors of the old basis expressed in the new one, and its inverse reverses the conversion. In $\\mathbb{R}^n$ the change-of-coordinates matrix between two bases is found by one row reduction.\n",{"path":4979,"title":4980,"module":4956,"summary":4981},"\u002Flinear-algebra\u002Fvector-spaces\u002Fdifference-equations-and-markov","Applications: Difference Equations and Markov Chains","The solutions of an nth-order linear difference equation form an $n$-dimensional vector space, so finding $n$ independent solutions gives them all. A Markov chain evolves a probability distribution by repeated multiplication by a stochastic matrix, and a regular chain converges to a unique steady-state vector fixed by that matrix. Both applications turn a dynamic process into a subspace or a fixed-point question.\n",{"path":4983,"title":4984,"module":4985,"summary":4986},"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-eigenvalues","Eigenvectors and Eigenvalues","Eigenvalues and Eigenvectors","An eigenvector of a square matrix is a nonzero vector the matrix only stretches; its eigenvalue is the stretch factor. The eigenspace of an eigenvalue is the null space of A minus lambda times the identity, the eigenvalues of a triangular matrix are its diagonal entries, and eigenvectors for distinct eigenvalues are linearly independent.\n",{"path":4988,"title":4989,"module":4985,"summary":4990},"\u002Flinear-algebra\u002Feigenvalues\u002Fthe-characteristic-equation","The Characteristic Equation","The eigenvalues of a matrix are the roots of its characteristic polynomial det(A minus lambda I). This degree-n polynomial carries an algebraic multiplicity at each repeated root, a nonzero determinant is equivalent to zero not being an eigenvalue, and similar matrices share a characteristic polynomial and hence the same eigenvalues.\n",{"path":4992,"title":4993,"module":4985,"summary":4994},"\u002Flinear-algebra\u002Feigenvalues\u002Fdiagonalization","Diagonalization","A matrix is diagonalizable when it factors as A equals P D P inverse with D diagonal, which happens exactly when it has n linearly independent eigenvectors. The factorization computes matrix powers cheaply, distinct eigenvalues guarantee it, and a repeated eigenvalue permits it only when its eigenspace dimension equals its multiplicity.\n",{"path":4996,"title":4997,"module":4985,"summary":4998},"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-linear-transformations","Eigenvectors and Linear Transformations","Every linear transformation between finite-dimensional spaces has a matrix relative to chosen bases, built from the coordinate vectors of the images of the basis vectors. For a map from a space to itself, an eigenvector basis makes that matrix diagonal, and that change of basis is diagonalization; the matrices similar to A are the representations of the map in every basis.\n",{"path":5000,"title":5001,"module":4985,"summary":5002},"\u002Flinear-algebra\u002Feigenvalues\u002Fcomplex-eigenvalues","Complex Eigenvalues","A real matrix with no real eigenvalues still has complex ones, occurring in conjugate pairs. A real 2-by-2 matrix with eigenvalue a plus b i is similar to a rotation-scaling matrix, whose rotation angle is the argument of the eigenvalue and whose scale factor is its modulus; the modulus decides whether the trajectories close up, spiral in, or spiral out.\n",{"path":5004,"title":5005,"module":4985,"summary":5006},"\u002Flinear-algebra\u002Feigenvalues\u002Fdynamical-systems","Discrete and Continuous Dynamical Systems","Eigenvalues govern the long-term behavior of a system that evolves by x becomes A x or by x prime equals A x. An eigenvector basis decouples both kinds of system into independent scalar equations; the eigenvalues then classify the origin as attractor, repeller, saddle, or spiral, and the dominant eigenpair fixes the growth rate and limiting direction.\n",{"path":5008,"title":5009,"module":4985,"summary":5010},"\u002Flinear-algebra\u002Feigenvalues\u002Fpower-method","Iterative Estimates for Eigenvalues","When only a numerical eigenvalue is needed, iteration is preferred over the characteristic polynomial. The power method repeatedly multiplies by A to converge on the dominant eigenvalue and its eigenvector; the Rayleigh quotient sharpens the estimate for symmetric matrices; and the inverse power method targets any eigenvalue near a known guess.\n",{"path":5012,"title":5013,"module":5014,"summary":5015},"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-length-orthogonality","Inner Product, Length, and Orthogonality","Orthogonality and Least Squares","The dot product turns the algebra of vectors in R^n into geometry: length, distance, and perpendicularity. The inner product yields the norm, the Pythagorean theorem, and the orthogonal complement, and the null space of a matrix is the orthogonal complement of its row space.\n",{"path":5017,"title":5018,"module":5014,"summary":5019},"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Forthogonal-sets-and-projections","Orthogonal Sets and Orthogonal Projections","An orthogonal basis makes coordinates trivial: each weight is a single dot product, no linear system required. Orthogonal and orthonormal bases give a direct projection formula onto a line and onto a subspace, the orthogonal decomposition and best-approximation theorems, and the matrix form U U-transpose of a projection.\n",{"path":5021,"title":5022,"module":5014,"summary":5023},"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fgram-schmidt-and-qr","The Gram-Schmidt Process and QR Factorization","Gram-Schmidt turns any basis into an orthogonal one by repeatedly subtracting off projections onto the span already built. Normalizing the result and recording the coefficients factors the matrix as A = QR, with Q orthonormal and R upper triangular, the factorization behind stable least-squares and eigenvalue algorithms.\n",{"path":5025,"title":5026,"module":5014,"summary":5027},"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-problems","Least-Squares Problems","When Ax = b has no solution, the least-squares solution makes Ax as close to b as possible. The closest Ax is the projection of b onto the column space, and the vector that produces it solves the normal equations A-transpose A x = A-transpose b. Uniqueness, the residual error, and the stabler QR route follow.\n",{"path":5029,"title":5030,"module":5014,"summary":5031},"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-applications","Applications to Linear Models","Curve fitting is a least-squares problem in statistical notation. The least-squares line, polynomial fits, and multiple regression all reduce to X beta = y with a design matrix X built from the data, solved by the same normal equations.\n",{"path":5033,"title":5034,"module":5014,"summary":5035},"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-spaces","Inner Product Spaces","Promoting the four properties of the dot product to axioms defines an inner product on any vector space, including spaces of functions. Length, distance, orthogonality, Gram-Schmidt, and best approximation all carry over, along with the Cauchy-Schwarz and triangle inequalities and the integral inner product behind Fourier approximation.\n",{"path":5037,"title":5038,"module":5039,"summary":5040},"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fdiagonalizing-symmetric-matrices","Diagonalization of Symmetric Matrices","Symmetric Matrices, Quadratic Forms, and the SVD","A symmetric matrix is one that equals its own transpose. Every such matrix can be diagonalized by an orthogonal change of basis, A = PDPᵀ, with real eigenvalues and perpendicular eigenvectors. This is the Spectral Theorem, and it rewrites A as a weighted sum of rank-one projections onto its eigenvectors.\n",{"path":5042,"title":5043,"module":5039,"summary":5044},"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fquadratic-forms","Quadratic Forms","A quadratic form xᵀAx is the second-degree analogue of a linear map, attached to a symmetric matrix A. Orthogonal diagonalization changes variables to the eigenbasis, removing all cross-terms and rotating the form into standard position. The signs of the eigenvalues then classify it as definite or indefinite.\n",{"path":5046,"title":5047,"module":5039,"summary":5048},"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fconstrained-optimization","Constrained Optimization","Maximizing a quadratic form xᵀAx over the unit sphere has an exact answer: the maximum is the largest eigenvalue of A, attained at its eigenvector, and the minimum is the smallest eigenvalue. Adding orthogonality constraints peels off the eigenvalues in order, characterizing the whole spectrum by optimization.\n",{"path":5050,"title":5051,"module":5039,"summary":5052},"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsingular-value-decomposition","The Singular Value Decomposition","The singular value decomposition factors any m×n matrix as A = UΣVᵀ, with orthogonal U and V and a nonnegative diagonal Σ of singular values. The singular values are the square roots of the eigenvalues of AᵀA, and they describe the matrix geometrically as a rotation, an axiswise stretch, and another rotation, exposing rank, the four fundamental subspaces, and a best low-rank approximation.\n",{"path":5054,"title":5055,"module":5039,"summary":5056},"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsvd-applications-pca-imaging","Applications: Image Processing and Statistics","Principal component analysis diagonalizes the covariance matrix of a data set, producing uncorrelated variables ordered by variance. The leading components capture most of the variation, which reduces dimension, compresses images through low-rank SVD approximation, and connects directly to the singular values of the data matrix.\n",{"path":5058,"title":5059,"module":5060,"summary":5061},"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-thinking-and-matrix-computation","Numerical Thinking and Matrix Computation","Numerical Linear Algebra","Numerical analysis builds efficient discrete algorithms for continuous problems, and its cost is dominated as much by memory traffic as by arithmetic. Block matrix calculus, flop counts, and the BLAS efficiency ratio fix the cost model; triangular and unitary matrices are the two computational building blocks every factorization rests on.\n",{"path":5063,"title":5064,"module":5060,"summary":5065},"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Flu-and-cholesky","LU and Cholesky Factorization in Practice","Gaussian elimination, read as a factorization A = LU, turns a linear system into two triangular solves. A single near-zero pivot wrecks it, so partial pivoting reorders rows to pick the largest available pivot and makes the method work for every invertible matrix. For symmetric positive-definite systems, Cholesky halves the cost and needs no pivoting.\n",{"path":5067,"title":5068,"module":5060,"summary":5069},"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fconditioning-and-floating-point","Conditioning and Floating-Point Arithmetic","A problem's condition number measures how much its answer moves when its data is perturbed, independent of any algorithm. Subtraction is ill-conditioned under cancellation, and for a linear system the amplifier is the matrix condition number κ(A). Floating-point arithmetic supplies the perturbation: every real number is rounded to within a relative machine precision, so even perfect computation inherits an error of order κ times the unit roundoff.\n",{"path":5071,"title":5072,"module":5060,"summary":5073},"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fstability-and-error-analysis","Numerical Stability and Backward Error Analysis","An algorithm is backward stable when its computed answer is the exact answer to a slightly perturbed problem. Combined with the condition number this gives the governing rule of thumb: forward error is at most condition times stability. Three cancellation case studies make the point, then the residual-based backward error applies it to Ax = b and shows why partial pivoting keeps Gaussian elimination stable.\n",{"path":5075,"title":5076,"module":5060,"summary":5077},"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fqr-and-numerical-least-squares","QR, Householder, and Numerical Least Squares","The least-squares problem reduces to the normal equations, but forming AᵀA squares the condition number and can wreck accuracy. The stable route computes a QR factorization directly on A and solves Rx = Qᵀb. Householder reflectors build that QR one column at a time using length-preserving reflections, the unconditionally backward-stable building block behind every serious least-squares solver.\n",{"path":5079,"title":5080,"module":5060,"summary":5081},"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-eigenvalues-and-svd","Numerical Eigenvalue Problems and the SVD","Eigenvalues cannot be found by a formula for large matrices, so they are found by iteration. Power and inverse iteration converge to one eigenvector at a rate set by the eigenvalue gap; the QR algorithm sweeps a matrix to Schur form and, with a good shift and a Hessenberg reduction, computes the whole spectrum in cubic time. Singular values follow from the same machinery applied without ever forming AᵀA.\n",{"path":5083,"title":5084,"module":5085,"summary":5086},"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-combinations","Affine Combinations","Geometry of Vector Spaces","An affine combination is a linear combination whose weights sum to one. The affine hull of a set is the smallest flat containing it: a point, a line, a plane, or a translated subspace. Homogeneous coordinates turn every affine combination into an ordinary linear combination one dimension up.\n",{"path":5088,"title":5089,"module":5085,"summary":5090},"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-independence-and-barycentric-coordinates","Affine Independence and Barycentric Coordinates","Affine independence is linear independence for the translated or lifted points, and it guarantees each point of an affine hull a unique weight vector. Those weights are barycentric coordinates: centers of mass, ratios of triangle areas, and the interpolation rule behind smooth shading in computer graphics.\n",{"path":5092,"title":5093,"module":5085,"summary":5094},"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fconvex-combinations-and-convex-sets","Convex Combinations and Convex Sets","A convex combination is an affine combination with nonnegative weights, and the convex hull of a set is the smallest convex set containing it. Convex sets are closed under intersection, and Carathéodory's theorem bounds how many points a convex combination in $\\mathbb{R}^n$ ever needs: at most $n+1$.\n",{"path":5096,"title":5097,"module":5085,"summary":5098},"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fhyperplanes-and-polytopes","Hyperplanes and Polytopes","A hyperplane is a level set of a linear functional, the set where an inner product equals a constant. Hyperplanes separate disjoint convex sets and support them at their boundaries. Polytopes are convex hulls of finite point sets; their vertices are the extreme points, and a linear functional attains its extremes there.\n",{"path":5100,"title":5101,"module":5085,"summary":5102},"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fcurves-and-surfaces","Curves and Surfaces","Bézier curves are affine combinations of control points with polynomial weights, so they lie in the convex hull of those points and bend toward them. The de Casteljau algorithm evaluates them by repeated interpolation, a matrix form factors them for computation, and matching endpoints and tangents joins segments into smooth curves and surfaces.\n",{"path":5104,"title":5105,"module":6,"summary":6},"\u002Flinear-algebra","Linear Algebra",{"path":5107,"title":5108,"module":6,"summary":6},"\u002Ftheory-of-computation","Theory of Computation",{"path":5110,"title":5111,"module":3911,"summary":5112},"\u002Fcomputer-architecture\u002Ffoundations\u002Fbits-bytes-and-words","Bits, Bytes, and Words","Everything a machine stores is a string of bits grouped into bytes. We set out binary and hexadecimal, the byte as the unit of addressing, the word as the machine's natural integer size, and byte ordering — why the same four bytes read as 0x01234567 on one machine and 0x67452301 on another.\n",{"path":5114,"title":5115,"module":3911,"summary":5116},"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-representation","Integer Representation","A fixed-width byte string is just a pattern; what makes it a number is the rule we read it by. We define unsigned encoding and two's complement — where the top bit carries a negative weight — derive the ranges UMax, TMin, and TMax, and show how the same bits reinterpret between signed and unsigned, how widening sign-extends, and what truncation throws away.\n",{"path":5118,"title":5119,"module":3911,"summary":5120},"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-arithmetic","Integer Arithmetic","Fixed-width integer arithmetic is arithmetic modulo a power of two: add past the top and the result wraps. We work out unsigned and two's-complement addition and the rules that detect their overflow, why negation is a complement-plus-one, how multiplication truncates to the low-order bits and how compilers turn constant multiplies into shifts and adds, why C declares signed overflow undefined, and the bias fix that keeps shift-based signed division rounding toward zero.\n",{"path":5122,"title":5123,"module":3911,"summary":5124},"\u002Fcomputer-architecture\u002Ffoundations\u002Ffloating-point","Floating Point","IEEE-754 trades the exactness of integers for enormous range by storing numbers as sign, exponent, and fraction — scientific notation in binary. We lay out the single and double formats, the bias that encodes the exponent, the three regimes (normalized, denormalized, special), a worked encode\u002Fdecode, the four rounding modes and round-to-even at the bit level, why addition is not associative, the pitfalls of float-int conversion, and why 0.1 has no exact binary representation.\n",{"path":5126,"title":5127,"module":3911,"summary":5128},"\u002Fcomputer-architecture\u002Ffoundations\u002Fboolean-algebra-and-bit-manipulation","Boolean Algebra and Bit Manipulation","Treat a word as a vector of independent bits and the bitwise operators become an algebra. We define AND, OR, NOT, and XOR as bit vectors, build the masking idioms that set, clear, toggle, and test individual bits, extract fields with zero- and sign-extension, count set bits three ways, derive the classic x & (x - 1) family of tricks, and distinguish bitwise operators from C's short-circuiting logical operators.\n",{"path":5130,"title":5131,"module":5132,"summary":5133},"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fthe-machines-view","The Machine's View","Machine-Level Programming","The instruction set architecture is the contract a compiler writes against: the program counter, sixteen integer registers with their sub-register widths, and the condition codes. We follow one C function down through gcc to assembly, learn to read an instruction as operation plus operands, and fix the vocabulary the rest of the module uses.\n",{"path":5135,"title":5136,"module":5132,"summary":5137},"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fdata-movement","Data Movement","Most instructions a program runs simply move data. We cover the mov family and its size suffixes, the three operand forms, the full memory addressing mode D(Rb,Ri,S) and its special cases, lea for address arithmetic, and how push and pop manipulate the stack pointer %rsp on a stack that grows toward lower addresses.\n",{"path":5139,"title":5140,"module":5132,"summary":5141},"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farithmetic-and-logic","Arithmetic and Logic","The ALU instructions that compute on register and memory values: add, sub, and imul; the unary inc\u002Fdec\u002Fneg\u002Fnot; the shifts sal\u002Fshr\u002Fsar; the bitwise and\u002For\u002Fxor; and lea reused as a fast arithmetic trick. Each binary operation also sets the condition-code flags CF, ZF, SF, and OF, which cmp and test compute without keeping a result.\n",{"path":5143,"title":5144,"module":5132,"summary":5145},"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fcontrol-flow","Control Flow","How a flat instruction stream realizes branches and loops. The conditional jumps read the condition-code flags; set instructions turn flags into a 0\u002F1 byte. We translate if\u002Felse into the standard compare-and-branch pattern, while\u002Ffor loops into the guarded-do form, and dense switches into jump tables that index a target directly.\n",{"path":5147,"title":5148,"module":5132,"summary":5149},"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fprocedures","Procedures","How a function call works at the machine level: the run-time stack, call and ret passing control through a saved return address, the System V convention that routes the first six arguments through %rdi..%r9 and the result through %rax, the caller-saved versus callee-saved split, the stack frame, and a recursive factorial traced through its frames.\n",{"path":5151,"title":5152,"module":5132,"summary":5153},"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farrays-structs-and-alignment","Arrays, Structs, and Alignment","How aggregate data lays out in memory. Arrays as base-plus-scaled-index, the row-major ordering of multidimensional arrays, pointer arithmetic in units of the pointed-to type, struct fields at fixed byte offsets, the overlapping storage of unions, and the alignment rules that force padding into a struct.\n",{"path":5155,"title":5156,"module":5132,"summary":5157},"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fmemory-layout-and-buffer-overflows","Memory Layout and Buffer Overflows","The process address space — text, data, heap, and stack — and the classic vulnerability it enables. A stack buffer that is written past its end can overwrite the saved return address and redirect ret, so we sketch the mechanism defensively and then the three standard protections: stack canaries, a non-executable stack, and address-space layout randomization.\n",{"path":5159,"title":5160,"module":5161,"summary":5162},"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fwhat-an-isa-is","What an ISA Is","Instruction Set Architecture","The instruction set architecture is the contract that lets a compiler and a chip be written by people who never meet: the stable interface software targets and hardware implements. We separate architecture from microarchitecture, read RISC and CISC as opposite answers to where complexity should live, price out what each choice costs in decode hardware, code density, and pipeline friendliness, and see how x86-64 endures by translating its instructions into RISC-like operations on the fly.\n",{"path":5164,"title":5165,"module":5161,"summary":5166},"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Finstruction-formats-and-operands","Instruction Formats and Operands","An instruction is an opcode plus a way to name its operands. We count operands — 3-address, 2-address, 1-address accumulator, and 0-address stack machines — by writing the same C = A + B four ways, weigh register operands against memory operands, then lay out the same add byte by byte in x86-64 (REX prefix, opcode, ModRM) and in Y86-64, and what fixed versus variable length costs at fetch time.\n",{"path":5168,"title":5169,"module":5161,"summary":5170},"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Faddressing-modes","Addressing Modes","Once an operand field exists, it needs a rule for turning its bits into the data it names. That rule is the addressing mode. We walk the standard set — immediate, register, direct, register-indirect, displacement, scaled-indexed, and PC-relative — fixing the effective-address computation for each, run every mode against one concrete machine state, and price out what Y86-64 loses by keeping only base plus displacement.\n",{"path":5172,"title":5173,"module":5161,"summary":5174},"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fthe-y86-64-instruction-set","The Y86-64 Instruction Set","Y86-64 is a teaching ISA — a stripped-down x86-64 simple enough to implement by hand yet real enough to compile to. We fix its programmer-visible state (fifteen registers, three condition codes, the PC, memory, and a status code), give the instruction set with exact byte encodings, spell out how the condition codes decide every jXX and cmovXX, and run the encoding both directions: assembly to bytes and raw bytes back to meaning.\n",{"path":5176,"title":5177,"module":5161,"summary":5178},"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fy86-64-programming","Y86-64 Programming","With the encodings fixed, we write real Y86-64 assembly: the .pos, .align, and .quad directives, the calling convention borrowed from x86-64, a stack set up by hand, and complete programs — an array sum and a branch-free max. We watch the assembler turn the listing into the exact byte image the processor will execute, and trace the stack across the call.\n",{"path":5180,"title":5181,"module":5182,"summary":5183},"\u002Fcomputer-architecture\u002Fdigital-logic\u002Ftransistors-gates-and-boolean-functions","Transistors, Gates, and Boolean Functions","Digital Logic","A processor is built from millions of transistor switches. We start at the MOS transistor as a voltage-controlled switch, build the CMOS inverter and NAND transistor by transistor, meet the seven standard gates with their truth tables, show that NAND alone is functionally complete, price each gate in transistors and in time, and turn any truth table into a sum-of-products circuit.\n",{"path":5185,"title":5186,"module":5182,"summary":5187},"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fcombinational-logic-and-hcl","Combinational Logic and HCL","A combinational circuit is a pure Boolean function of its current inputs — no memory, no clock. We draw the line between combinational and sequential logic, do the gate-delay accounting that finds a circuit's critical path and bounds the clock, meet don't-cares, then introduce CS:APP's Hardware Control Language: bit-level operators, word-level signals, equality nets, and the case expression that compiles to a multiplexer tree.\n",{"path":5189,"title":5190,"module":5182,"summary":5191},"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmultiplexers-decoders-and-the-alu","Multiplexers, Decoders, and the ALU","The combinational building blocks that make a datapath. We build the 2:1 and 4:1 multiplexer and tie it back to HCL's case expression, the n-to-2^n decoder, a one-bit full adder (sum is XOR, carry is majority), the ripple-carry adder that chains them, and finally the ALU — a function unit that selects among add, sub, and, and xor under a control input and exposes condition flags.\n",{"path":5193,"title":5194,"module":5182,"summary":5195},"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmemory-elements-latches-flip-flops-and-clocking","Memory Elements: Latches, Flip-Flops, and Clocking","A combinational circuit holds no state; feeding a circuit's output back to its input creates memory. We build the SR latch from cross-coupled gates, the level-sensitive D latch, and the master\u002Fslave edge-triggered D flip-flop, then introduce the clock and the synchronous design discipline, the setup\u002Fhold timing window, clock skew, metastability, and the register as n flip-flops sharing one clock.\n",{"path":5197,"title":5198,"module":5182,"summary":5199},"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fregister-files-and-random-access-memory","Register Files and Random-Access Memory","Storage organized for access by address. We build the register file (a small bank of registers with addressed read ports and clocked write ports, the exact structure Y86-64's decode and write-back stages use), then descend to the SRAM and DRAM cells of main memory, why one is fast and dear and the other dense and slow, and how a row decoder picks a word out of a memory array.\n",{"path":5201,"title":5202,"module":5203,"summary":5204},"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-fetch-decode-execute-cycle","The Fetch-Decode-Execute Cycle","Processor Design","A processor is a machine that repeats one loop forever: read the next instruction from memory, figure out what it asks for, do it, and advance. We fix the stored-program idea, lay out the datapath at a high level — PC, instruction memory, register file, ALU, data memory — and the control unit that sequences them, break the work into the six stages the rest of the module builds in hardware, and work out exactly how fetch parses variable-length instructions and computes the next PC.\n",{"path":5206,"title":5207,"module":5203,"summary":5208},"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-seq-stages","The SEQ Stages","The six SEQ stages, made exact. For every Y86-64 instruction — halt, nop, the moves, OPq, the jumps, call and ret, pushq and popq — we write down what Fetch, Decode, Execute, Memory, Write-back, and PC update each compute, as per-instruction stage tables with every row justified. Once the tables are filled in, the processor is fully specified; the remaining lessons turn them into wires.\n",{"path":5210,"title":5211,"module":5203,"summary":5212},"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fcontrol-logic-and-sequencing","Control Logic and Sequencing","The stage tables say what each instruction needs; the control logic computes it from icode. We write the HCL for the register-port selections (srcA, srcB, dstE, dstM), the ALU function and input selection, the memory read\u002Fwrite and address, the branch condition, and the next-PC mux — each a case expression on icode that compiles to a mux — and see how one blob of combinational logic serves every instruction at once. We close by contrasting hardwired control with the microprogrammed alternative.\n",{"path":5214,"title":5215,"module":5203,"summary":5216},"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fassembling-seq","Assembling SEQ","We wire the whole thing together. The functional units from digital logic and the control signals from the last lesson assemble into the complete SEQ datapath, laid out the way CS:APP draws it — six stages stacked bottom to top, Fetch at the floor and PC update at the ceiling, signals flowing up the margins. Then the timing analysis: why everything must settle in one cycle, the no-reading-back principle that makes single-cycle execution consistent, and the critical path that sets the clock. We close by walking an OPq and a ret through the assembled machine.\n",{"path":5218,"title":5219,"module":5203,"summary":5220},"\u002Fcomputer-architecture\u002Fprocessor-design\u002Ftracing-a-program","Tracing a Program","To close the module, we take a complete Y86-64 program — a loop that sums 1 through 3 — and run it through SEQ one cycle at a time, recording the PC, the fetched instruction, every stage computation, and the registers, condition codes, and memory after each cycle. Then we examine single cycles in detail: every named signal of an OPq in concrete hex, and a second program whose call and ret we trace through the stack. The traces confirm that the assembled datapath and control logic behave as a processor.\n",{"path":5222,"title":5223,"module":5224,"summary":5225},"\u002Fcomputer-architecture\u002Fpipelining\u002Fpipelining-principles","Pipelining Principles","Pipelining","A processor that runs one instruction to completion before starting the next wastes most of its hardware most of the time. Pipelining splits the work into stages separated by registers so several instructions are in flight at once. We separate throughput from latency, work the 300 ps example through one, two, and three stages, and derive the three ceilings on the gain: uneven stages, register overhead, and the dependencies between instructions.\n",{"path":5227,"title":5228,"module":5224,"summary":5229},"\u002Fcomputer-architecture\u002Fpipelining\u002Ffrom-seq-to-pipe","From SEQ to PIPE","We turn the sequential Y86-64 processor into a pipelined one by inserting pipeline registers between its stages so each cycle holds one instruction per stage. Doing it correctly forces a rearrangement: the next-PC computation must move into Fetch as a prediction, because the later stages that used to compute it are now busy with other instructions. We walk SEQ to SEQ+ to PIPE, spell out exactly what each pipeline register carries, and fix the naming discipline (D_stat versus d_stat) that keeps five in-flight instructions straight.\n",{"path":5231,"title":5232,"module":5224,"summary":5233},"\u002Fcomputer-architecture\u002Fpipelining\u002Fdata-hazards-stalling-and-forwarding","Data Hazards: Stalling and Forwarding","Overlapping instructions collide when a later one needs a value an earlier one has not finished computing: a read-after-write data hazard. We map exactly which instruction distances are dangerous, fix hazards the slow way by stalling (three bubbles), then the fast way by forwarding from five distinct sources into Decode, in a priority order that sequential semantics forces. Forwarding handles almost everything; the load-use hazard still needs exactly one stall.\n",{"path":5235,"title":5236,"module":5224,"summary":5237},"\u002Fcomputer-architecture\u002Fpipelining\u002Fcontrol-hazards-and-branch-prediction","Control Hazards and Branch Prediction","A pipeline must fetch an instruction every cycle, but after a conditional jump or a ret the next address is not yet known: a control hazard. We measure the branch penalty, weigh predict-taken against its alternatives with real loop arithmetic, watch PIPE detect a misprediction in Execute and squash the two wrong-path instructions, and meet the ret hazard, which has nothing to predict and stalls three cycles. A 2-bit counter gives a taste of dynamic prediction.\n",{"path":5239,"title":5240,"module":5224,"summary":5241},"\u002Fcomputer-architecture\u002Fpipelining\u002Fthe-complete-pipe-processor","The Complete PIPE Processor","We assemble the full pipelined Y86-64: five stages, five pipeline registers, forwarding paths, and a small control unit that decides, each cycle, whether to stall or bubble each register. The subtle part is when hazards combine: one pairing hides a genuine bug. A fourth control case reads stat and keeps exceptions precise. Performance reduces to CPI = 1 + lp + mp + rp, worked out to 1.27 with realistic frequencies, and PIPE beats SEQ by several times despite every penalty.\n",{"path":5243,"title":5244,"module":5245,"summary":5246},"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fstorage-technologies-and-the-latency-gap","Storage Technologies and the Latency Gap","The Memory Hierarchy","No single memory is both fast and large and cheap. We survey the technologies a machine can store bits in — SRAM, DRAM, flash, and rotating disk — open up a DRAM chip to find the row buffer, work a disk access down to the millisecond, and rank everything by speed, density, and cost per bit. Then we watch the processor outrun memory decade after decade. That widening gap is the whole reason a machine stacks fast small storage on top of slow large storage into a hierarchy.\n",{"path":5248,"title":5249,"module":5245,"summary":5250},"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Flocality","Locality","A hierarchy only pays off because programs do not touch memory at random. They reuse recently-used data (temporal locality) and touch nearby data soon after (spatial locality). We make both precise and then quantitative: miss rates for stride-1 and stride-k traversals against a concrete block size, and the loop-order pair on a 2-D array where the same sum misses 16 times one way and 64 times the other — why row-major versus column-major order can change a program's speed by an order of magnitude.\n",{"path":5252,"title":5253,"module":5245,"summary":5254},"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-memories-direct-mapped","Cache Memories and Direct Mapping","A cache is fast SRAM that holds copies of recently-used blocks of main memory. We fix its organization — S sets, E lines per set, B bytes per block — and the way it dissects an address into tag, set index, and block offset, worked bit by bit on a concrete 16-byte cache. Then we run the direct-mapped (E=1) access algorithm end to end on a seven-access trace: index to a set, compare the tag, hit or miss, evict. Cold and conflict misses fall out of the structure, and a two-array ping-pong shows conflict thrashing and its padding fix.\n",{"path":5256,"title":5257,"module":5245,"summary":5258},"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fset-associative-and-write-policies","Set-Associative Caches and Write Policies","Give each set several lines and a block has a choice of homes — fewer conflict misses, at the cost of comparing E tags in parallel and choosing a victim to evict. We re-run the direct-mapped ping-pong trace on a 2-way cache and watch the conflicts vanish, weigh LRU against random replacement, then turn to writes: write-through versus write-back with a dirty bit on a hit, write-allocate versus no-write-allocate on a miss, and a worked traffic count showing when each pairing wins.\n",{"path":5260,"title":5261,"module":5245,"summary":5262},"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-performance-and-cache-friendly-code","Cache Performance and Cache-Friendly Code","Turn the cache mechanism into a number. Hit time, miss rate, and miss penalty combine into the average memory access time; we compute AMAT for a two-level hierarchy with real numbers, weigh the design knobs against each other, and read the memory mountain. Then we write cache-friendly code — the matrix-multiply loop-order case study (ijk versus kij, misses counted per iteration) and loop blocking, where cache-sized tiles turn evicted reuse back into hits.\n",{"path":5264,"title":5265,"module":5266,"summary":5267},"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Faddress-spaces-and-translation","Address Spaces and Translation","Virtual Memory","Every process runs as if it owns a private, contiguous span of memory — its virtual address space — while the hardware maps those addresses onto a single shared physical memory. We fix virtual memory's three jobs (a cache for disk, a memory manager, a protection boundary), the page as the unit of mapping, and the MMU replacing the virtual page number while the offset passes through untouched — then run one translation end to end at the bit level and trace the control flow of a page hit against a page fault.\n",{"path":5269,"title":5270,"module":5266,"summary":5271},"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fpage-tables-and-page-faults","Page Tables and Page Faults","The page table is an array of page-table entries indexed by virtual page number; each entry's valid bit says whether the page is in DRAM, on disk, or unallocated, and its permission, reference, and dirty bits drive protection and replacement. We walk translation as a table lookup, the page fault and demand paging, the clock algorithm the OS uses to approximate LRU, memory mapping and copy-on-write (why fork is cheap), the taxonomy of bad references, and thrashing.\n",{"path":5273,"title":5274,"module":5266,"summary":5275},"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fthe-tlb-and-multi-level-page-tables","The TLB and Multi-Level Page Tables","A page-table read on every access would double memory traffic; a flat table for a 48-bit space would occupy 512 GB per process. The TLB fixes the first: a small set-associative cache of PTEs inside the MMU whose tag and index come from the VPN. Multi-level page tables fix the second, allocating only the sub-tables a process uses; x86-64 walks four levels with a 9+9+9+9+12 split. We trace one reference end to end through TLB, walk, and cache, and close with the overlap trick that lets the L1 cache start before translation ends.\n",{"path":5277,"title":5278,"module":5279,"summary":5280},"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Fexceptional-control-flow","Exceptional Control Flow","Exceptions & I\u002FO","Beyond the sequential, branch, and call flow a program controls itself, the hardware can divert the processor in response to events. We sort these into four classes — interrupts (asynchronous, from devices), traps (intentional syscalls), faults (recoverable, like a page fault), and aborts (unrecoverable) — then take the mechanism apart: exception numbers and the table dispatch, what the hardware pushes and why it differs from a procedure call, the divide-error \u002F page-fault \u002F general-protection trio on x86-64, the full syscall round trip with a worked write in assembly, and processes and signals as the abstractions ECF makes possible.\n",{"path":5282,"title":5283,"module":5279,"summary":5284},"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Finterrupts-and-the-kernel","Interrupts and the Kernel","An I\u002FO device signals completion by raising an interrupt, crossing the privilege boundary from user mode into the kernel. We fix that boundary, follow an interrupt from device through the interrupt controller to its vectored handler, and use the timer interrupt to drive preemptive scheduling and the context switch. Then the I\u002FO mechanics: polling versus interrupt-driven I\u002FO with a cycle count, device registers and memory-mapped I\u002FO versus port I\u002FO, DMA's full transfer walkthrough and its cache hazard, and a disk read traced end to end, from the read syscall to the completion interrupt.\n",{"path":5286,"title":5287,"module":5288,"summary":5289},"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fprocesses-threads-and-parallelism","Processes, Threads, and Parallelism","Multithreading & Multicore","Around 2004 the single core stopped getting faster, and the industry's answer was to hand programmers more cores instead. This lesson builds the vocabulary that shift demands: process versus thread and exactly which hardware state each one owns, concurrency versus parallelism, the three kinds of parallelism a machine can exploit, why Dennard scaling ended and forced the multicore turn, and Amdahl's law — the arithmetic that bounds the speedup those cores can deliver.\n",{"path":5291,"title":5292,"module":5288,"summary":5293},"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fhardware-multithreading","Hardware Multithreading","A pipeline spends much of its life waiting — on cache misses, on dependences, on branches. Hardware multithreading fills the dead cycles with instructions from another thread. We compare coarse-grained switching (change threads on a long stall), fine-grained interleaving (change every cycle), and simultaneous multithreading (mix threads inside a single cycle), work out exactly which hardware a second thread context duplicates and which it shares, and weigh when SMT pays off and when two threads just fight over one cache.\n",{"path":5295,"title":5296,"module":5288,"summary":5297},"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fcache-coherence","Cache Coherence","Give each core its own cache and the same address can live in two places at once, with copies that disagree. We reproduce the stale-copy bug with a two-core trace, then fix it the way hardware does: snooping caches that watch a shared bus and keep every line in a protocol state. We build MSI in full, upgrade it to MESI, contrast invalidation with updating, add coherence misses as the fourth C, and end with false sharing: the performance bug where cores fight over a line while never touching the same byte.\n",{"path":5299,"title":5300,"module":5288,"summary":5301},"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmemory-consistency-and-synchronization","Memory Consistency and Synchronization","Coherence keeps cores agreeing about one location; consistency is the contract about many. We define sequential consistency, then watch real hardware break it: the store buffer lets a load slip ahead of an older store, and the classic two-thread litmus test ends with both sides reading zero. We state x86-TSO precisely, restore order with mfence, build atomic read-modify-write from the lock prefix, xchg, and cmpxchg, and write a spinlock twice — once naively, once bus-friendly — closing with what lock-free progress actually guarantees.\n",{"path":5303,"title":5304,"module":5288,"summary":5305},"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmulticore-organization","Multicore Organization","Where everything sits on the die. A modern die gives each core private L1 and L2 caches, spreads a shared last-level cache across slices, and wires it all together with a ring or mesh; multi-socket servers add NUMA, where memory is local to one socket and every remote access pays a latency penalty. We walk the floorplan, put numbers on local versus remote latency, meet thread affinity, and account for the two shared resources — coherence traffic and LLC capacity — that decide how far a parallel program scales.\n",{"path":5307,"title":5308,"module":5309,"summary":5310},"\u002Fcomputer-architecture\u002Fcapstone\u002Fthe-whole-machine","The Whole Machine","Capstone","We take one line of C down the whole tower the course built — compiler to assembly, assembly to machine-code bytes, the bytes into the fetch–decode–execute datapath — then trace one load and one add through the pipelined, cached, translated, interruptible machine, each step cross-linked to the lesson that built it. We close with the map of the course as a stack of layers and an accounting of what we simplified: out-of-order execution, superscalar issue, and speculation past the branch predictor.\n",{"path":5312,"title":5313,"module":5309,"summary":5314},"\u002Fcomputer-architecture\u002Fcapstone\u002Fassembling-a-complete-cpu","Assembling a Complete CPU","We bolt the parts the course built — PC, instruction memory and its fetch logic, register file, ALU, condition codes, data memory, and the control unit — into one complete CPU, name the lesson that built each, wire them in a deliberate order, and power the machine on from reset. Then we assemble a real test program (sum a four-element array through a call\u002Fret procedure), give its exact bytes and memory layout, and trace it cycle by cycle to the answer 0xabcdabcdabcd. We close with how to validate such a machine, and what it takes to put two of them on one die.\n",{"path":5316,"title":5317,"module":6,"summary":6},"\u002Fcomputer-architecture","Computer Architecture",{"path":5319,"title":5320,"module":3911,"summary":5321},"\u002Fdifferential-equations\u002Ffoundations\u002Fmodels-and-direction-fields","Models, Direction Fields, and Solution Curves","A differential equation relates an unknown function to its own rates of change. Three first-order models — a falling body, a cooling object, a population under predation — share the form dy\u002Fdt = ay - b; the slope field fixes their equilibria and long-run behavior before any formula is found. Solving the linear case gives the general solution, its integral curves, and the particular solution selected by an initial condition.\n",{"path":5323,"title":5324,"module":3911,"summary":5325},"\u002Fdifferential-equations\u002Ffoundations\u002Fclassification-and-terminology","Classifying Equations: Order, Linearity, ODE vs. PDE","Every solution method targets a specific class of equation, so the first question about any differential equation is which classes it belongs to. Four independent axes sort them: ordinary versus partial, order, linear versus nonlinear, and homogeneous versus nonhomogeneous. Systems, verification of a solution by substitution, and the split between initial and boundary value problems complete the vocabulary.\n",{"path":5327,"title":5328,"module":5329,"summary":5330},"\u002Fdifferential-equations\u002Ffirst-order\u002Flinear-first-order-integrating-factors","Linear Equations and Integrating Factors","First-Order Equations","A first-order linear equation has the unknown and its derivative to the first power only. Multiplying by an integrating factor collapses the left side into a single derivative, and one integration gives the general solution in closed form. The solution exists wherever the coefficients are continuous, and for a constant coefficient it splits into a decaying transient and a steady state set by the forcing.\n",{"path":5332,"title":5333,"module":5329,"summary":5334},"\u002Fdifferential-equations\u002Ffirst-order\u002Fseparable-and-exact","Separable and Exact Equations","Two nonlinear first-order classes solve by direct integration. A separable equation splits so that each variable can be integrated on its own side, giving an implicit relation. An exact equation is the total differential of a hidden potential function, recognized by a symmetry test on its coefficients; when the test fails, an integrating factor can sometimes restore exactness. A change of variable brings homogeneous equations into the separable class.\n",{"path":5336,"title":5337,"module":5329,"summary":5338},"\u002Fdifferential-equations\u002Ffirst-order\u002Fmodeling-first-order","Modeling with First-Order Equations","A rate law is a differential equation. Each first-order model starts from one governing principle: conservation of mass for a mixing tank, proportional change for interest and radioactive decay, Newton's law of cooling, a force balance for a body falling against drag, and Kirchhoff's law for a series circuit. Setting the derivative to zero recovers the steady state, and the transient records how the initial condition relaxes toward it.\n",{"path":5340,"title":5341,"module":5329,"summary":5342},"\u002Fdifferential-equations\u002Ffirst-order\u002Fautonomous-and-population-dynamics","Autonomous Equations, Phase Lines, and Population Dynamics","An autonomous equation y' = f(y) can be analyzed qualitatively without being solved. Its constant solutions are the zeros of f, and the sign of f between them fixes whether nearby solutions rise or fall, which the phase line records as a column of arrows. The logistic and threshold models, constant- and effort-proportional harvesting, and the properties nonlinear equations lose all follow from this reading.\n",{"path":5344,"title":5345,"module":5329,"summary":5346},"\u002Fdifferential-equations\u002Ffirst-order\u002Fexistence-uniqueness-euler","Existence, Uniqueness, and Euler's Method","Existence and uniqueness can be settled before any attempt to solve. The existence-uniqueness theorem gives sufficient conditions on f, and a standard example shows what fails when they do not hold. Picard's successive approximations build the solution as the limit of an iteration, and Euler's method turns the same tangent-line idea into a numerical procedure for the equations no formula reaches.\n",{"path":5348,"title":5349,"module":5329,"summary":5350},"\u002Fdifferential-equations\u002Ffirst-order\u002Ffirst-order-difference-equations","First-Order Difference Equations","A difference equation advances a sequence one index at a time by a rule y_{n+1} = f(y_n). The linear case y_{n+1} = rho*y_n + b solves in closed form and converges to its equilibrium exactly when the ratio has magnitude below one, which underlies compound-interest and loan calculations. The logistic difference equation shows the nonlinear counterpart: an exchange of stability, a cascade of period doublings, and the onset of chaos.\n",{"path":5352,"title":5353,"module":5354,"summary":5355},"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhomogeneous-constant-coefficients","Homogeneous Equations, the Wronskian, and Real Roots","Second-Order Linear Equations","A second-order linear homogeneous equation with constant coefficients is solved by guessing an exponential and reducing to the quadratic characteristic equation. Two solutions span every solution exactly when their Wronskian is nonzero; that condition, superposition, and Abel's formula give the full structure of the general solution for the case of two distinct real roots.\n",{"path":5357,"title":5358,"module":5354,"summary":5359},"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fcomplex-and-repeated-roots","Complex Roots, Repeated Roots, and Reduction of Order","When the characteristic equation has complex conjugate roots, Euler's formula converts the complex exponentials into a real fundamental set of decaying or growing oscillations. When it has a repeated root, one exponential is lost and reduction of order recovers the missing second solution as $t\\,e^{rt}$. The same substitution $y = v(t)y_1(t)$ finds a second solution from any known one.\n",{"path":5361,"title":5362,"module":5354,"summary":5363},"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fnonhomogeneous-undetermined-coefficients","Nonhomogeneous Equations: Undetermined Coefficients","The general solution of a nonhomogeneous linear equation is a complementary solution plus any one particular solution. When the forcing term is a polynomial, exponential, sine, or cosine, a particular solution can be found by assuming a trial form of the same shape with unknown coefficients and solving for them. The one complication is resonance, handled by multiplying the trial by a power of $t$.\n",{"path":5365,"title":5366,"module":5354,"summary":5367},"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fvariation-of-parameters","Variation of Parameters","Variation of parameters finds a particular solution of any nonhomogeneous linear equation from a fundamental set of the homogeneous one. Replacing the constants in the complementary solution by functions and imposing one convenient constraint reduces the problem to a two-by-two linear system whose solution is expressed through the Wronskian, giving an integral formula that works for forcing terms undetermined coefficients cannot touch.\n",{"path":5369,"title":5370,"module":5354,"summary":5371},"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fmechanical-electrical-vibrations","Mechanical and Electrical Vibrations","A spring-mass-damper obeys a second-order linear equation, and so does a series RLC circuit, with the same mathematics governing both. Free undamped motion is a pure sinusoid; damping adds a decaying envelope with three regimes; periodic forcing produces a transient that dies out and a steady-state oscillation whose amplitude peaks sharply near the natural frequency, the phenomenon of resonance.\n",{"path":5373,"title":5374,"module":5354,"summary":5375},"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhigher-order-linear","Higher-Order Linear Equations","The second-order theory extends directly to order $n$: the solution space is $n$-dimensional, spanned by any $n$ solutions with nonzero Wronskian. For constant coefficients the characteristic polynomial has degree $n$, and its roots (counted with multiplicity, real and complex) build the basis by the same rules as before. Coupled oscillators are the natural application that raises the order.\n",{"path":5377,"title":5378,"module":5379,"summary":5380},"\u002Fdifferential-equations\u002Fseries-solutions\u002Fpower-series-ordinary-points","Power Series Solutions Near Ordinary Points","Series Solutions and Special Functions","A linear equation with variable coefficients has no characteristic equation. A power series substituted into the equation matches coefficients to a recurrence relation, which near an ordinary point yields two independent analytic solutions. The radius of convergence is at least the distance from the expansion point to the nearest singular point in the complex plane.\n",{"path":5382,"title":5383,"module":5379,"summary":5384},"\u002Fdifferential-equations\u002Fseries-solutions\u002Fregular-singular-frobenius","Euler Equations, Regular Singular Points, and Frobenius","The Euler equation x^2 y'' + a x y' + b y = 0 is solved outright by y = x^r, and its three root cases fix the behavior at any regular singular point. The Frobenius method multiplies x^r by a power series; the indicial equation chooses the exponents, and equal or integer-separated roots force a logarithm in the second solution. Gauss's hypergeometric equation is the archetype containing most classical functions as special cases.\n",{"path":5386,"title":5387,"module":5379,"summary":5388},"\u002Fdifferential-equations\u002Fseries-solutions\u002Fbessel-and-special-functions","Bessel's Equation, Legendre Polynomials, and Special Functions","Bessel's equation puts the Frobenius machinery through all three of its cases and produces the functions J and Y that govern anything vibrating or diffusing with circular symmetry. The gamma function extends the factorial so that Bessel functions of every order make sense; Legendre's equation, run through the hypergeometric form, yields the polynomials that play the same role in spherical geometry. Orthogonality ties both families to the eigenfunction expansions of Sturm–Liouville theory.\n",{"path":5390,"title":5391,"module":5392,"summary":5393},"\u002Fdifferential-equations\u002Flaplace\u002Flaplace-definition-ivps","The Laplace Transform: Definition, Properties, and Solving IVPs","The Laplace Transform","The Laplace transform sends a function of time to a function of a complex frequency by integrating it against the kernel e^{-st}. Differentiation in t becomes multiplication by s, so a linear constant-coefficient initial value problem turns into an algebraic equation. Existence rests on piecewise continuity and exponential order; the derivative rule folds in the initial data; and inversion runs through a transform table and partial fractions.\n",{"path":5395,"title":5396,"module":5392,"summary":5397},"\u002Fdifferential-equations\u002Flaplace\u002Fstep-impulse-convolution","Step Functions, Discontinuous Forcing, Impulses, and Convolution","The Heaviside step function and the second shifting theorem transform switches and discontinuous forcing into exponential factors on the transform. The Dirac delta idealizes an instantaneous impulse and transforms to a pure exponential. The convolution theorem inverts a product of transforms, writes the forced response as the impulse response convolved with the input, and solves Abel's tautochrone by transform.\n",{"path":5399,"title":5400,"module":5401,"summary":5402},"\u002Fdifferential-equations\u002Fsystems\u002Fmatrices-eigenvalues-review","Matrices, Linear Systems, and the Eigenvalue Toolkit","Systems of First-Order Linear Equations","Any nth-order linear equation, and any coupled collection of them, rewrites as a single first-order system x' = P(t)x + g(t). The matrix and vector algebra behind that form, the eigenvalue problem det(A - λI) = 0 that drives every solution method, and the fundamental theory — superposition, the Wronskian, Abel's theorem — together establish that n independent solutions span all solutions.\n",{"path":5404,"title":5405,"module":5401,"summary":5406},"\u002Fdifferential-equations\u002Fsystems\u002Fconstant-coefficient-systems-phase-portraits","Homogeneous Constant-Coefficient Systems and Phase Portraits","For x' = Ax with A constant, the trial x = ξe^{rt} turns the differential equation into the eigenvalue problem Aξ = rξ. The eigenvalues fix the geometry of the phase plane: real opposite signs give a saddle, real same sign a node, complex a spiral, purely imaginary a center. Worked in the plane, these cases form the eigenvalue-type classification of equilibria.\n",{"path":5408,"title":5409,"module":5401,"summary":5410},"\u002Fdifferential-equations\u002Fsystems\u002Frepeated-eigenvalues-fundamental-matrices","Repeated Eigenvalues, Fundamental Matrices, and Nonhomogeneous Systems","When a repeated eigenvalue supplies too few eigenvectors, a generalized eigenvector supplies the missing solution as ξte^{ρt} + ηe^{ρt}, giving an improper node. A fundamental set packaged as a matrix Φ(t) yields the matrix exponential e^{At}, the propagator mapping initial states to later ones. Variation of parameters solves the nonhomogeneous system x' = Ax + g(t).\n",{"path":5412,"title":5413,"module":5414,"summary":5415},"\u002Fdifferential-equations\u002Fnumerical\u002Feuler-and-runge-kutta","Euler, Improved Euler, and Runge–Kutta","Numerical Methods","Most initial value problems have no closed-form solution, so the solution is approximated on a grid. Euler's method steps along the tangent line, the improved Euler method averages two slopes, and the classical Runge–Kutta method averages four. Each added stage raises the order of accuracy at the cost of more evaluations per step, measured by how the local and global truncation errors scale with the step size.\n",{"path":5417,"title":5418,"module":5414,"summary":5419},"\u002Fdifferential-equations\u002Fnumerical\u002Fmultistep-systems-stability","Multistep Methods, Systems, and Stability","One-step methods discard everything but the last point. Multistep methods fit a polynomial to several past values and integrate it forward: the explicit Adams–Bashforth formulas, the implicit and more accurate Adams–Moulton formulas, and predictor–corrector pairs that combine them. The same rules extend verbatim to systems in vector form. A separate concern is stability: round-off can dominate truncation, and stiff equations force a tiny step for stability even when accuracy would allow a large one.\n",{"path":5421,"title":5422,"module":5423,"summary":5424},"\u002Fdifferential-equations\u002Fnonlinear\u002Fphase-plane-autonomous-stability","The Phase Plane, Critical Points, and Stability","Nonlinear Systems and Stability","Most nonlinear systems cannot be solved in closed form, so they are studied geometrically. The phase plane turns an autonomous planar system into a family of trajectories; the five archetypes of critical point follow from the eigenvalues of the coefficient matrix; the trace-determinant plane reads off type and stability directly; and epsilon-delta definitions make stability, asymptotic stability, and instability precise.\n",{"path":5426,"title":5427,"module":5423,"summary":5428},"\u002Fdifferential-equations\u002Fnonlinear\u002Flocally-linear-and-liapunov","Locally Linear Systems and Liapunov's Method","Near a critical point a nonlinear system looks linear, and the linear part is the Jacobian. The linearization fixes the type and stability of the nonlinear critical point in every case except a center or a repeated eigenvalue. Liapunov's direct method settles those cases and bounds the basin of attraction by constructing an energy-like function, without solving the system.\n",{"path":5430,"title":5431,"module":5423,"summary":5432},"\u002Fdifferential-equations\u002Fnonlinear\u002Fcompeting-species-predator-prey-limit-cycles","Population Models, Limit Cycles, and Chaos","The phase-plane methods apply directly to interacting-population models. Competing species either coexist or drive one another to extinction, decided by a single inequality among the interaction constants; the Lotka-Volterra predator-prey system produces closed population cycles. Limit cycles and the Poincaré-Bendixson theorem, the van der Pol oscillator, and the Lorenz equations with their strange attractor carry the theory into chaos.\n",{"path":5434,"title":5435,"module":5436,"summary":5437},"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Ffourier-series","Fourier Series and Convergence","PDEs, Fourier Series, and Boundary Value Problems","A two-point boundary value problem has nontrivial solutions only at a discrete set of eigenvalues, the same trichotomy that governs a singular linear system. For y'' + lambda y = 0 with zero endpoints the eigenfunctions are sines and cosines, and their orthogonality gives the Euler-Fourier coefficient formulas. The convergence theorem fixes when the series returns the function, the Gibbs phenomenon measures the overshoot at a jump, and even\u002Fodd symmetry produces half-range sine and cosine series.\n",{"path":5439,"title":5440,"module":5436,"summary":5441},"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fheat-wave-laplace-equations","Separation of Variables: Heat, Wave, and Laplace Equations","Separation of variables replaces a partial differential equation by a pair of ordinary ones joined through a shared separation constant. Applied to the heat equation it produces the eigenvalue problem X'' + lambda X = 0, and the solution assembles as a Fourier series in the eigenfunctions. The same steps solve the wave equation, whose modes are standing waves, and Laplace's equation, the steady-state limit posed on a region rather than an interval.\n",{"path":5443,"title":5444,"module":5436,"summary":5445},"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fsturm-liouville","Sturm-Liouville Theory","The eigenvalue problem behind separation of variables generalizes to the self-adjoint Sturm-Liouville form. Lagrange's identity makes the operator symmetric, and from that one fact follow real eigenvalues, orthogonal eigenfunctions, and eigenfunction expansions that behave like Fourier series. Singular problems admit Bessel and Legendre functions, and Sturm's separation and comparison theorems describe how the eigenfunctions oscillate.\n",{"path":5447,"title":5448,"module":5449,"summary":5450},"\u002Fdifferential-equations\u002Fhistory-variations\u002Fcalculus-of-variations","The Calculus of Variations","Historical Notes and the Calculus of Variations","Ordinary calculus finds the point where a function is stationary; the calculus of variations finds the whole curve where an integral is stationary. Euler's differential equation is the necessary condition for an extremal, and it becomes integrable in three cases, solving the shortest-path, minimal-surface, and brachistochrone problems. Lagrange multipliers extend the method to isoperimetric constraints, and Hamilton's principle recovers Newton's law from a single stationary integral.\n",{"path":5452,"title":5453,"module":5449,"summary":5454},"\u002Fdifferential-equations\u002Fhistory-variations\u002Fhistorical-notes","Great Problems and the People Who Solved Them","Differential equations grew out of specific problems, not a plan: the invention of calculus by Newton and Leibniz, the Bernoulli brachistochrone challenge, Euler's flood of methods, Lagrange's analytical mechanics, Gauss and Riemann's rigor, Laplace's celestial mechanics, and Poincaré's qualitative theory. Each method descends from a named problem, and reading the subject forward from those problems explains why its parts fit together.\n",{"path":5456,"title":5457,"module":6,"summary":6},"\u002Fdifferential-equations","Differential Equations",{"path":5459,"title":5460,"module":5461,"summary":5462},"\u002Frelativity\u002Ffoundations\u002Fspecial-relativity-postulates","The Postulates of Special Relativity","Foundations of Relativity","Newton's laws are the same in every inertial frame, but Maxwell's are not: the equations of electromagnetism single out one speed, c, and the nineteenth century read that as the speed of light relative to a medium, the ether. The Michelson-Morley experiment looked for Earth's motion through that medium and found nothing. Einstein's two postulates replace the ether, and their first consequence is that simultaneity is frame-dependent.\n",{"path":5464,"title":5465,"module":5461,"summary":5466},"\u002Frelativity\u002Ffoundations\u002Florentz-transformation-spacetime","The Lorentz Transformation and Spacetime","Requiring that a light sphere stay a light sphere in every inertial frame fixes the coordinate change between frames uniquely: the Lorentz transformation, with its factor gamma. Differentiating it gives relativistic velocity addition, which caps composed speeds at c. Plotting the same events on skewed spacetime axes turns the algebra into geometry, with calibration hyperbolae, an invariant interval, and a light cone that sorts events into past, future, and elsewhere.\n",{"path":5468,"title":5469,"module":5461,"summary":5470},"\u002Frelativity\u002Ffoundations\u002Ftime-dilation-length-contraction","Time Dilation, Length Contraction, and Paradoxes","A light clock and the constancy of c give the two headline effects directly: a moving clock runs slow by gamma, and a moving rod is short by the same factor. Cosmic-ray muons reaching sea level are the standing experimental proof. The relativistic Doppler effect adds the time-dilation factor to the classical shift, and the twin and pole-barn paradoxes dissolve once the relativity of simultaneity is taken seriously.\n",{"path":5472,"title":5473,"module":5461,"summary":5474},"\u002Frelativity\u002Ffoundations\u002Frelativistic-momentum-energy","Relativistic Momentum and Energy","Conserving momentum in every inertial frame forces the redefinition p = gamma m u, which diverges as the speed approaches c. Integrating the corresponding force gives the total energy E = gamma m c-squared, whose rest term m c-squared is Einstein's mass-energy equivalence. Energy and momentum join into a four-vector whose invariant length is the rest energy, giving E-squared = (pc)-squared + (m c-squared)-squared, massless particles, and nuclear binding energy.\n",{"path":5476,"title":5477,"module":5461,"summary":5478},"\u002Frelativity\u002Ffoundations\u002Fgeneral-relativity","A Taste of General Relativity","Einstein's happiest thought was that a freely falling observer feels no gravity: a uniform gravitational field is locally indistinguishable from an accelerating frame. That equivalence principle predicts that light bends near a mass, that clocks run slow deep in a gravitational well, that Mercury's orbit precesses, and that radar echoes are delayed. Every prediction has been confirmed, and pushing the redshift to its limit gives the black hole.\n",{"path":5480,"title":5481,"module":5482,"summary":5483},"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fminkowski-spacetime-and-the-interval","Minkowski Spacetime and the Interval","Spacetime and the Lorentz Group","The Lorentz transformation of the foundations module is repackaged as the geometry of a four-dimensional space whose invariant is not a distance but the spacetime interval. Events, worldlines, and the metric signature define a causal structure that every observer shares. Proper time is the length of a timelike worldline, and the twin paradox becomes the statement that a straight worldline accumulates the most proper time.\n",{"path":5485,"title":5486,"module":5482,"summary":5487},"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Ffour-vectors-and-index-notation","Four-Vectors and Index Notation","The index calculus that the rest of the course runs on. Contravariant and covariant components, the Minkowski metric as the machine that raises and lowers indices, and the Einstein summation convention are assembled into scalar products that are the same in every frame. The four-velocity and four-acceleration follow, together with the identity that the four-velocity has constant invariant length.\n",{"path":5489,"title":5490,"module":5482,"summary":5491},"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fthe-lorentz-group-and-rapidity","The Lorentz Group and Rapidity","The Lorentz transformations are the linear maps that preserve the Minkowski metric, and they form the group O(1,3). Boosts are hyperbolic rotations parametrized by rapidity, which adds along a line where velocity does not. The boost and rotation generators fix the group's local structure; its four disconnected components are set by two signs; and two non-collinear boosts compose into a boost plus a rotation, the Wigner rotation behind Thomas precession.\n",{"path":5493,"title":5494,"module":5482,"summary":5495},"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fdoppler-aberration-and-appearance","Doppler, Aberration, and Appearance","Light carries a null four-momentum, and boosting it produces every optical effect of relativity at once. The covariant Doppler formula follows from the transformation of frequency, aberration from the transformation of direction, and the headlight effect from the resulting concentration of light forward. The Terrell-Penrose result shows that a fast object photographs as rotated, not contracted.\n",{"path":5497,"title":5498,"module":5499,"summary":5500},"\u002Frelativity\u002Frelativistic-dynamics\u002Ffour-momentum-force-and-accelerated-motion","Four-Momentum, Four-Force, and Accelerated Motion","Relativistic Dynamics","The four-momentum packages energy and momentum into a single vector whose invariant length is the rest mass. Its proper-time derivative is the four-force, always orthogonal to the four-velocity, and a constant orthogonal four-force produces hyperbolic motion. Constant proper acceleration gives rapidity linear in proper time, the relativistic rocket equation, and the Rindler horizon behind an eternally accelerating observer.\n",{"path":5502,"title":5503,"module":5499,"summary":5504},"\u002Frelativity\u002Frelativistic-dynamics\u002Fparticle-decays-and-two-body-kinematics","Particle Decays and Two-Body Kinematics","Conservation of four-momentum fixes the kinematics of a decay from the masses alone. In the center-of-momentum frame a parent breaks into two daughters with equal and opposite momenta and energies set by the Kallen triangle function. Boosting to the lab opens the decay into a cone, and the invariant mass built from the daughters reconstructs the parent as a peak. Worked cases: the two-photon decay of the neutral pion and a heavy two-body hadronic decay.\n",{"path":5506,"title":5507,"module":5499,"summary":5508},"\u002Frelativity\u002Frelativistic-dynamics\u002Fcollisions-thresholds-and-the-cm-frame","Relativistic Collisions and Threshold Energies","Two-body collisions run on the same conserved four-momentum as decays. The invariant s sets the total energy available in the center-of-momentum frame and therefore the threshold for producing new particles. Fixed-target energy grows only as the square root of beam energy while a collider grows linearly, which is why colliders reach high energy. Compton scattering follows as a worked photon-electron collision giving the wavelength shift.\n",{"path":5510,"title":5511,"module":5499,"summary":5512},"\u002Frelativity\u002Frelativistic-dynamics\u002Fmandelstam-variables-and-invariants","Mandelstam Variables and Lorentz Invariants","For a two-to-two process the three Mandelstam invariants s, t, and u encode all the kinematics in frame-independent form. They obey a single linear constraint, the sum of the four squared masses, so only two are independent. s is the center-of-momentum energy squared, t and u are momentum transfers tied to the scattering angle, and crossing symmetry relates one amplitude across three channels through these variables.\n",{"path":5514,"title":5515,"module":5516,"summary":5517},"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ffour-current-and-the-four-potential","The Four-Current and Four-Potential","Covariant Electromagnetism","Charge density and current combine into a single four-vector whose divergence is charge conservation. The scalar and vector potentials combine likewise into the four-potential, whose gauge freedom fixes to the Lorenz condition, reducing Maxwell's equations for the potentials to a single wave equation sourced by the four-current.\n",{"path":5519,"title":5520,"module":5516,"summary":5521},"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fthe-electromagnetic-field-tensor","The Electromagnetic Field Tensor","The antisymmetric derivative of the four-potential is the field-strength tensor F, gauge invariant by construction, with the electric and magnetic fields as its components. Its dual exchanges E and B, and its two contractions form the Lorentz invariants that classify a field as electric, magnetic, or radiative in every frame.\n",{"path":5523,"title":5524,"module":5516,"summary":5525},"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ftransformation-of-electric-and-magnetic-fields","How E and B Transform","Transforming the field tensor under a boost gives explicit rules for the electric and magnetic fields: components along the motion are unchanged, transverse components mix and pick up a gamma. The field of a uniformly moving charge compresses transversely, and the force between a current and a moving charge shows that magnetism is the relativistic shadow of electrostatics.\n",{"path":5527,"title":5528,"module":5516,"summary":5529},"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fcovariant-maxwell-and-the-stress-energy-tensor","Covariant Maxwell and the Stress–Energy Tensor","Maxwell's four equations collapse into two tensor equations, one sourced by the four-current and one an identity on the field strength, with charge conservation automatic. The Lorentz force becomes a four-vector law, and the field's energy, momentum, and stress assemble into a symmetric, conserved stress–energy tensor — the object that will source gravity.\n",{"path":5531,"title":5532,"module":5533,"summary":5534},"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-equivalence-principle-formalized","The Equivalence Principle","Curved Spacetime","The equality of gravitational and inertial mass promotes to a physical principle in three graded strengths — weak, Einstein, and strong. A freely falling laboratory is locally indistinguishable from an inertial frame, but the qualifier \"locally\" is essential: the size of the patch over which gravity vanishes is set by the tidal field, which no change of frame can remove. Tidal forces are the true, coordinate-independent signature of gravity, and they are what curvature will measure.\n",{"path":5536,"title":5537,"module":5533,"summary":5538},"\u002Frelativity\u002Fcurved-spacetime\u002Fmanifolds-vectors-and-the-metric","Manifolds, Vectors, and the Metric","A manifold is a space that looks locally like flat space, described by overlapping coordinate charts. Tangent vectors are directional derivatives with the coordinate basis vectors as partial-derivative operators; one-forms live in the dual space; and the metric tensor turns a coordinate line element into an invariant length. The 2-sphere and Rindler metrics serve as worked examples, including the coordinate singularities that are artefacts of the chart, not of the geometry.\n",{"path":5540,"title":5541,"module":5533,"summary":5542},"\u002Frelativity\u002Fcurved-spacetime\u002Fcovariant-derivative-and-christoffel-symbols","Parallel Transport and the Covariant Derivative","The ordinary derivative of a vector field is not a tensor, because it subtracts vectors living in different tangent spaces. A connection supplies the missing comparison: the covariant derivative adds Christoffel-symbol correction terms that cancel the coordinate artefacts. Requiring the connection to be torsion-free and to preserve the metric fixes the Christoffel symbols uniquely in terms of derivatives of the metric, giving the Levi-Civita connection that general relativity uses.\n",{"path":5544,"title":5545,"module":5533,"summary":5546},"\u002Frelativity\u002Fcurved-spacetime\u002Fgeodesics-and-the-geodesic-equation","Geodesics and the Newtonian Limit","Free fall is geodesic motion: a freely falling particle follows the straightest possible worldline, obtained either by parallel-transporting its own tangent vector or by extremizing proper time. Both routes give the geodesic equation. Affine parameters, and conserved quantities from symmetries via Killing vectors, make it solvable. In the weak-field slow-motion limit the geodesic equation reproduces Newton's law of gravity, fixing the time-time metric component as the Newtonian potential.\n",{"path":5548,"title":5549,"module":5533,"summary":5550},"\u002Frelativity\u002Fcurved-spacetime\u002Fcurvature-riemann-and-geodesic-deviation","Curvature and the Riemann Tensor","Curvature is the failure of parallel transport to commute: carrying a vector around an infinitesimal loop returns it rotated, and the rotation per unit area is the Riemann tensor. Its symmetries cut the components to twenty in four dimensions. Geodesic deviation makes it the equation of tidal forces, and its contractions — the Ricci tensor, the Ricci scalar, and the divergence-free Einstein tensor — assemble the objects the field equation is built from.\n",{"path":5552,"title":5553,"module":5533,"summary":5554},"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-einstein-field-equations","The Einstein Field Equations","The field equation is assembled from a short list of requirements: a symmetric, divergence-free, second-order geometric tensor set proportional to the stress–energy tensor, with the coefficient fixed by the Newtonian limit. The cosmological constant is the one extra term the requirements allow. The Einstein–Hilbert action gives the same equation from a variational principle, and the coupled system closes the logic of the module: matter curves spacetime, and spacetime tells matter how to move.\n",{"path":5556,"title":5557,"module":5558,"summary":5559},"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fthe-schwarzschild-metric","The Schwarzschild Metric","The Schwarzschild Solution","The first exact solution of Einstein's equation follows from two assumptions, staticity and spherical symmetry, imposed on the vacuum outside a mass. Solving the vacuum field equations fixes two metric functions and produces the Schwarzschild geometry, whose one length scale is the Schwarzschild radius $r_s = 2GM\u002Fc^2$. Birkhoff's theorem shows this is the only spherical vacuum, and the far field reduces to Newtonian gravity.\n",{"path":5561,"title":5562,"module":5558,"summary":5563},"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fgeodesics-and-orbits-in-schwarzschild","Orbits in the Schwarzschild Geometry","The two Killing symmetries of the Schwarzschild metric give a conserved energy and angular momentum per unit mass, reducing geodesic motion to a one-dimensional problem in an effective potential. The potential carries an extra attractive $1\u002Fr^3$ term absent from Newton's, which caps the centrifugal barrier, produces an innermost stable circular orbit at $6GM\u002Fc^2$, and makes bound orbits precess instead of closing.\n",{"path":5565,"title":5566,"module":5558,"summary":5567},"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Flight-bending-and-null-geodesics","Null Geodesics and the Photon Sphere","Light follows null geodesics, governed by a photon effective potential with a single unstable maximum at $3GM\u002Fc^2$, the photon sphere. The impact parameter sorts rays into those that escape with a deflection and those captured, with the critical value $b_c = 3\\sqrt{3}\\,GM\u002Fc^2$ dividing them. A grazing ray bends by $4GM\u002F(c^2 b)$, twice the naive Newtonian value, and the critical impact parameter sets the edge of a black hole's shadow.\n",{"path":5569,"title":5570,"module":5571,"summary":5572},"\u002Frelativity\u002Ftests-of-general-relativity\u002Fperihelion-precession-of-mercury","The Perihelion Precession of Mercury","Tests of General Relativity","A single extra term in the Schwarzschild orbit equation, cubic in the inverse radius, keeps a bound orbit from closing. The perturbation advances the perihelion by 6πGM\u002F(c²a(1−e²)) per revolution, which for Mercury is 43 arcseconds per century — exactly the anomaly left after Newtonian planetary perturbations are subtracted. A note on frame dragging closes the lesson.\n",{"path":5574,"title":5575,"module":5571,"summary":5576},"\u002Frelativity\u002Ftests-of-general-relativity\u002Fdeflection-of-light-and-gravitational-lensing","Light Deflection and Gravitational Lensing","A light ray grazing the Sun bends by 4GM\u002F(c²b), exactly twice the value a Newtonian corpuscle would give; the extra factor is the curvature of space. The 1919 eclipse confirmed it. The same bending focuses light from distant sources into Einstein rings, multiple images, and microlensing brightenings, making lensing a direct probe of mass, including mass that emits no light.\n",{"path":5578,"title":5579,"module":5571,"summary":5580},"\u002Frelativity\u002Ftests-of-general-relativity\u002Fgravitational-redshift-and-shapiro-delay","Gravitational Redshift and the Shapiro Delay","A clock deeper in a gravitational well ticks slower, and a photon climbing out loses frequency by the ratio of the metric's time-time components. Pound and Rebka measured the 2.5×10⁻¹⁵ shift over a 22.5-metre tower. Radar signals grazing the Sun return late by about 250 microseconds, the Shapiro delay. Both probe the time part of the metric directly.\n",{"path":5582,"title":5583,"module":5571,"summary":5584},"\u002Frelativity\u002Ftests-of-general-relativity\u002Frelativity-in-technology-gps","Relativity and the Global Positioning System","A GPS satellite clock runs slow by 7 microseconds a day from its orbital speed and fast by 46 from its higher gravitational potential, a net gain of about 38 microseconds a day. Left uncorrected, the timing error would grow into kilometres of position error within a day and exceed navigation tolerance within minutes. The satellites carry a pre-launch frequency offset to cancel it.\n",{"path":5586,"title":5587,"module":5588,"summary":5589},"\u002Frelativity\u002Fblack-holes\u002Fhorizons-and-coordinate-singularities","Horizons and Coordinate Singularities","Black Holes","The Schwarzschild radius is a coordinate singularity, not a curvature singularity: the metric blows up there only because the static coordinates fail, while the geometry stays finite. Eddington–Finkelstein and Kruskal– Szekeres coordinates cross the horizon smoothly and show the light cones tipping toward the center. A freely falling observer reaches the true singularity at r=0 in finite proper time, while a distant observer sees the infall freeze and redden at the horizon.\n",{"path":5591,"title":5592,"module":5588,"summary":5593},"\u002Frelativity\u002Fblack-holes\u002Frotating-and-charged-black-holes","Rotating and Charged Black Holes","A stationary black hole is fixed by three numbers: mass, angular momentum, and charge. The Reissner–Nordström metric adds charge and splits the horizon in two; the Kerr metric adds rotation, drags inertial frames, and wraps the horizon in an ergosphere where nothing can stay still. Inside the ergosphere the Penrose process extracts rotational energy, and the no-hair theorem states that no other detail of the collapsed matter survives.\n",{"path":5595,"title":5596,"module":5588,"summary":5597},"\u002Frelativity\u002Fblack-holes\u002Fblack-hole-thermodynamics","Black-Hole Thermodynamics","The four laws of black-hole mechanics mirror the four laws of thermodynamics term for term, with horizon area playing the role of entropy and surface gravity the role of temperature. Hawking's calculation makes the analogy literal: a black hole radiates at a temperature set by its surface gravity, carries a real entropy proportional to its horizon area, and slowly evaporates. The thermal spectrum raises the information paradox.\n",{"path":5599,"title":5600,"module":5601,"summary":5602},"\u002Frelativity\u002Fgravitational-waves\u002Flinearized-gravity-and-wave-solutions","Linearized Gravity and Wave Solutions","Gravitational Waves","Weak gravity is a small perturbation of flat spacetime, and the linearized Einstein equation in the Lorenz gauge is an ordinary wave equation propagating at the speed of light. The trace-reversed perturbation carries the dynamics, residual gauge freedom fixes the transverse-traceless form, and the two physical polarizations deform a ring of freely falling masses into oscillating ellipses whose fractional size change is the strain.\n",{"path":5604,"title":5605,"module":5601,"summary":5606},"\u002Frelativity\u002Fgravitational-waves\u002Fgeneration-and-the-quadrupole-formula","The Quadrupole Formula","The retarded solution of the linearized field equation gives the field of a moving source, and conservation of mass and momentum forbids monopole and dipole radiation, leaving the mass quadrupole as the leading emitter. The quadrupole formula fixes the strain and the radiated luminosity, and applied to a compact binary it predicts the inspiral chirp of rising frequency and amplitude. The Hulse-Taylor pulsar's orbital decay confirmed it to a fraction of a percent.\n",{"path":5608,"title":5609,"module":5601,"summary":5610},"\u002Frelativity\u002Fgravitational-waves\u002Fdetection-ligo-and-the-first-events","LIGO and the First Detections","A gravitational wave is measured as a differential length change of the two arms of a kilometre-scale Michelson interferometer, a strain of order ten to the minus twenty-one that moves the mirrors by a fraction of a proton radius. GW150914 recorded the inspiral, merger, and ringdown of two black holes, fixing their masses and the energy radiated, and GW170817 with its coincident gamma-ray burst and kilonova opened multimessenger astronomy.\n",{"path":5612,"title":5613,"module":5614,"summary":5615},"\u002Frelativity\u002Fcosmological-bridge\u002Fthe-cosmological-principle-and-flrw-metric","The Cosmological Principle and the FLRW Metric","A Bridge to Cosmology","Homogeneity and isotropy restrict the spacetime of the universe to a single family of metrics: a flat cosmic-time slicing of spatial sections of constant curvature, scaled by a time-dependent factor a(t). This lesson builds the Friedmann–Lemaître–Robertson–Walker metric from those symmetries, separates comoving from proper distance, and derives cosmological redshift as the stretching of wavelengths with the scale factor.\n",{"path":5617,"title":5618,"module":5614,"summary":5619},"\u002Frelativity\u002Fcosmological-bridge\u002Ffriedmann-equations-and-cosmic-dynamics","The Friedmann Equations and Cosmic Dynamics","The Einstein equation applied to the FLRW metric with a perfect-fluid source yields the two Friedmann equations and the conservation law that ties them together. This lesson derives them, defines the critical density and the density parameters that fix the spatial geometry, works out how matter, radiation, and a cosmological constant dilute and drive the expansion, and hands off to a dedicated cosmology subject.\n",{"path":5621,"title":5622,"module":6,"summary":6},"\u002Frelativity","Relativity",{"path":5624,"title":5625,"module":6,"summary":6},"\u002Fphysical-computing","Physical Computing",{"path":5627,"title":5628,"module":5629,"summary":5630},"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fblackbody-radiation-and-the-planck-quantum","Blackbody Radiation and the Planck Quantum","Origins of the Quantum","Millikan's oil-drop experiment fixed the electron charge as an indivisible unit, and the spectrum of thermal radiation forced a second, deeper quantum. Classical physics predicts an infinite energy density at short wavelengths; Planck removed the divergence by allowing a cavity oscillator to hold only energies that are integer multiples of hf, the first appearance of the quantum of action.\n",{"path":5632,"title":5633,"module":5629,"summary":5634},"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-photoelectric-effect-and-the-photon","The Photoelectric Effect and the Photon","Light shone on a clean metal ejects electrons, but the details defied the wave theory: the electrons' maximum energy depends on the light's frequency, not its brightness, and there is a sharp threshold frequency below which nothing happens. Einstein resolved every anomaly by treating light as a stream of energy quanta hf, each absorbed whole by one electron, and Millikan's measurement of the stopping-potential slope confirmed h to a decade before anyone expected.\n",{"path":5636,"title":5637,"module":5629,"summary":5638},"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fx-rays-and-the-compton-effect","X-Rays and the Compton Effect","X-rays are short-wavelength electromagnetic waves produced when fast electrons are braked in a target, and their diffraction by crystals lets Bragg's law measure atomic spacings. Compton then scattered X-rays off electrons and found the wavelength shifted by an amount that only a photon carrying momentum hf\u002Fc could explain, closing the case for the particle nature of light.\n",{"path":5640,"title":5641,"module":5629,"summary":5642},"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-old-quantum-theory-bohr-and-sommerfeld","The Old Quantum Theory: Bohr, Sommerfeld, and Correspondence","Between Bohr's 1913 atom and Schrödinger's 1926 equation, physics ran on a provisional recipe: keep classical orbits, but admit only those whose action integral is a whole multiple of Planck's constant. This lesson develops the Wilson-Sommerfeld phase-integral rule, applies it to the oscillator and to the elliptical Kepler orbits of hydrogen, derives Sommerfeld's relativistic fine structure and the quantization of orbit orientation, and shows how the correspondence principle fixed intensities and selection rules. The systematic failures — helium, line intensities, the anomalous Zeeman effect — mark exactly where a theory of orbits had to give way to a theory of waves.\n",{"path":5644,"title":5645,"module":5646,"summary":5647},"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fde-broglie-waves-and-electron-diffraction","De Broglie Waves and Electron Diffraction","The Wave Nature of Matter","In 1924 de Broglie proposed that every particle carries a wave of wavelength h\u002Fp. The hypothesis explains Bohr's quantized orbits as standing waves, and Davisson and Germer, then G. P. Thomson, confirmed it by diffracting electrons from crystals exactly as X-rays diffract. We derive the electron wavelength, work the Bragg analysis of the data, and give the relativistic form.\n",{"path":5649,"title":5650,"module":5646,"summary":5651},"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fwave-packets-and-the-probability-interpretation","Wave Packets and the Probabilistic Wave Function","A single de Broglie wave fills all space, but a particle is localized. Adding many waves of nearby wavelength builds a wave packet that is confined and moves at the group velocity, which equals the particle velocity. Born's rule reads the squared amplitude of the wave function as a probability density, the meaning confirmed by electron interference building up one detection at a time.\n",{"path":5653,"title":5654,"module":5646,"summary":5655},"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fthe-uncertainty-principle","The Uncertainty Principle and Wave-Particle Duality","The packet relations delta-k delta-x about 1 become Heisenberg's principle once momentum is hbar times wave number: position and momentum cannot both be sharp, nor energy and time. The gamma-ray microscope shows the limit is physical, not technical. It fixes the zero-point energy of a confined particle, the size of the hydrogen atom, and the natural width of spectral lines, and it frames the wave-particle duality of all matter and radiation.\n",{"path":5657,"title":5658,"module":5659,"summary":5660},"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-schrodinger-equation-in-one-dimension","The Schrödinger Equation in One Dimension","Wave Mechanics in One Dimension","The wave equation for matter cannot be derived; it is postulated and judged by experiment. We build the time-dependent Schrödinger equation from the de Broglie relations, read Born's probability rule off the complex wave function, and separate the time and space dependence to get the time-independent equation whose bound-state solutions are the stationary states. The five acceptability conditions on the wave function are what force energy to be quantized.\n",{"path":5662,"title":5663,"module":5659,"summary":5664},"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-free-particle-and-wave-packet-dynamics","The Free Particle and Wave-Packet Dynamics","The free particle has no bound states: its stationary solutions are non-normalizable plane waves forming a continuum. Physical states are wave packets built by superposing them, and the superposition is a Fourier transform. We delta-normalize the plane waves, assemble a Gaussian packet, solve for its exact time evolution, and read off the two facts that reconcile the wave picture with mechanics: the packet moves at the group velocity ħk\u002Fm, the classical velocity, and it spreads because its component momenta travel at different speeds.\n",{"path":5666,"title":5667,"module":5659,"summary":5668},"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fparticle-in-infinite-and-finite-square-wells","Particle in Infinite and Finite Square Wells","The infinite square well is the simplest bound-state problem: two boundary conditions quantize the energy into a ladder E_n = n² E_1, and the eigenfunctions are the standing waves of a string fixed at both ends. Relaxing the walls to a finite depth lets the wave function leak into the classically forbidden region, keeps the number of bound states finite, and turns the eigenvalue condition into a transcendental equation solved graphically.\n",{"path":5670,"title":5671,"module":5659,"summary":5672},"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Foperators-expectation-values-and-the-harmonic-oscillator","Operators, Expectation Values, and the Harmonic Oscillator","Measurable quantities are extracted from the wave function as expectation values, and each observable is represented by an operator that acts between Ψ* and Ψ — position by multiplication, momentum by a derivative, energy by the Hamiltonian. Applied to the harmonic oscillator, the machinery yields evenly spaced levels E_n = (n+½)ℏω, Gaussian- times-Hermite eigenfunctions of definite parity, and the selection rule Δn = ±1.\n",{"path":5674,"title":5675,"module":5659,"summary":5676},"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-dirac-delta-potential","The Dirac-Delta Potential: A Single Bound State and Scattering","A potential concentrated at a single point is solvable in closed form and isolates the physics of matching a wave function across a discontinuity. Integrating the Schrödinger equation across the spike gives a jump condition on the derivative; the attractive delta well then supports exactly one bound state, of energy set by the strength alone, while the same spike scatters an incoming beam with a transmission that rises from zero to one. The attractive well and the repulsive barrier scatter identically yet only the well binds.\n",{"path":5678,"title":5679,"module":5659,"summary":5680},"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fbarrier-penetration-and-quantum-tunneling","Barrier Penetration and Quantum Tunneling","Unbound states scatter rather than bind. A particle meeting a step is partly reflected even when it has more than enough energy to pass, and a particle meeting a barrier taller than its energy has a nonzero chance of appearing on the far side. Matching the wave function across the boundaries gives the reflection and transmission coefficients and the exponential tunneling probability that explains alpha decay, the scanning tunneling microscope, and the ammonia clock.\n",{"path":5682,"title":5683,"module":5684,"summary":5685},"\u002Fquantum-mechanics\u002Fformalism\u002Fhilbert-space-and-dirac-notation","Hilbert Space and Dirac Bra–Ket Notation","The Formalism of Quantum Mechanics","Wave mechanics is one representation of a deeper structure: quantum states are vectors in a complex inner-product space, and observables act on them as linear operators. We build that space from the axioms, introduce Dirac's kets and bras as vectors and the linear functionals that measure them, and identify the wavefunction as the components of an abstract state in the position basis. The resolution of the identity is the single algebraic tool that ties every basis, expansion, and matrix element together.\n",{"path":5687,"title":5688,"module":5684,"summary":5689},"\u002Fquantum-mechanics\u002Fformalism\u002Fobservables-hermitian-operators-and-eigenvalues","Observables, Hermitian Operators, and the Spectral Theorem","Every measurable quantity is represented by a Hermitian operator, and the reason is forced: a measurement needs real eigenvalues, orthogonal eigenvectors, and a complete eigenbasis, and Hermiticity delivers precisely those. We derive those properties from self-adjointness, state the spectral theorem, handle degeneracy, and show that two observables share an eigenbasis precisely when they commute — the algebraic condition behind compatible and incompatible measurements.\n",{"path":5691,"title":5692,"module":5684,"summary":5693},"\u002Fquantum-mechanics\u002Fformalism\u002Fthe-postulates-and-quantum-measurement","The Postulates and Quantum Measurement","With states as vectors and observables as Hermitian operators, the physical content of quantum mechanics reduces to a short list of postulates. We state them precisely, derive the Born probability rule for discrete and continuous spectra, work out projective collapse and its idempotence, compute expectation values and their variance, and state the measurement problem cleanly — the one place the postulates split unitary evolution from measurement without explaining the seam.\n",{"path":5695,"title":5696,"module":5684,"summary":5697},"\u002Fquantum-mechanics\u002Fformalism\u002Fposition-momentum-and-continuous-spectra","Position, Momentum, and Continuous Spectra","Position and momentum are the observables with no normalizable eigenstates: their spectra are continuous, their eigenkets are delta-normalized, and the two are Fourier conjugates. We derive the canonical commutator from the momentum operator, build the continuous-basis machinery (Dirac deltas replacing Kronecker deltas), show the position and momentum wavefunctions are a Fourier-transform pair, and compute expectation values in either representation.\n",{"path":5699,"title":5700,"module":5684,"summary":5701},"\u002Fquantum-mechanics\u002Fformalism\u002Fcommutators-and-the-generalized-uncertainty-principle","Commutators and the Generalized Uncertainty Principle","The commutator of two observables measures the obstruction to sharing an eigenbasis, and it bounds how sharply both can be known at once. We derive the generalized uncertainty relation from the Schwarz inequality, recover the position–momentum bound as a special case, characterize the minimum-uncertainty states that saturate it as Gaussians, and give the energy–time relation its correct reading as a lifetime bound rather than a commutator relation.\n",{"path":5703,"title":5704,"module":5684,"summary":5705},"\u002Fquantum-mechanics\u002Fformalism\u002Ftime-evolution-schrodinger-and-heisenberg-pictures","Time Evolution, Propagators, and the Heisenberg Picture","Time evolution is generated by the Hamiltonian and implemented by a unitary operator that preserves probability. We build that operator, expand a state in stationary states to see why probability densities freeze while phases wind, introduce the propagator, transfer the time dependence onto operators in the Heisenberg picture, and derive Ehrenfest's theorem — which recovers classical equations of motion for expectation values and identifies conserved quantities as observables commuting with the Hamiltonian.\n",{"path":5707,"title":5708,"module":5709,"summary":5710},"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fladder-operators-and-the-number-states","Ladder Operators and the Number States","The Oscillator Algebraically, and Symmetry","The harmonic oscillator can be solved without touching a differential equation. Factoring the Hamiltonian into a lowering operator and its adjoint turns the spectrum into pure algebra: the commutator relation fixes the ladder, the vacuum condition fixes the ground state, and the energies fall out as equally spaced rungs. The same operators give the matrix elements of position and momentum for free.\n",{"path":5712,"title":5713,"module":5709,"summary":5714},"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fcoherent-and-squeezed-states","Coherent and Squeezed States","A single number state never moves — its position expectation is pinned at the origin. The superposition that oscillates like a classical particle is the eigenstate of the annihilation operator: the coherent state. It is a displaced vacuum, carries Poissonian photon statistics, saturates the uncertainty bound, and traces a rigid Gaussian orbit in phase space. Squeezing deforms that circle, trading precision in one quadrature for noise in the other.\n",{"path":5716,"title":5717,"module":5709,"summary":5718},"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fsymmetries-generators-and-conservation-laws","Symmetries, Generators, and Conservation Laws","Every continuous symmetry of a quantum system is a unitary operator built by exponentiating a Hermitian generator: momentum generates translations, angular momentum generates rotations, the Hamiltonian generates time evolution. When a generator commutes with the Hamiltonian, the transformation leaves the dynamics unchanged and the generator is conserved — the quantum form of Noether's theorem — and any symmetry that mixes states within a level forces degeneracy.\n",{"path":5720,"title":5721,"module":5709,"summary":5722},"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fparity-time-reversal-and-discrete-symmetries","Parity, Time Reversal, and Discrete Symmetries","Parity and time reversal are symmetries no continuous generator can reach. Parity is a unitary involution whose eigenvalues label states even or odd, fixing the dipole selection rules. Time reversal is antiunitary: it conjugates i, flips momenta and spins, and for half-integer spin squares to minus one, which by Kramers' theorem makes every level of a time-reversal-invariant Hamiltonian at least doubly degenerate.\n",{"path":5724,"title":5725,"module":4564,"summary":5726},"\u002Fquantum-mechanics\u002Fangular-momentum\u002Forbital-angular-momentum-and-spherical-harmonics","Orbital Angular Momentum and Spherical Harmonics","Orbital angular momentum is the operator triple built from position and momentum. Its components fail to commute, so no state carries sharp values of more than one of them, but each commutes with the total square. Solving the common eigenvalue problem in spherical coordinates quantizes both the magnitude and the projection and produces the spherical harmonics, the angular part of every central-force wavefunction.\n",{"path":5728,"title":5729,"module":4564,"summary":5730},"\u002Fquantum-mechanics\u002Fangular-momentum\u002Fthe-angular-momentum-algebra","The Angular-Momentum Algebra and Ladder Operators","The eigenvalues of angular momentum follow from the commutation relations alone, with no reference to coordinates or wavefunctions. Raising and lowering operators built from the components generate finite multiplets, force the total quantum number to be a non-negative integer or half-integer, and fix the matrix elements of every component. The half-integer values excluded by orbital motion appear here, and they are what spin realizes.\n",{"path":5732,"title":5733,"module":4564,"summary":5734},"\u002Fquantum-mechanics\u002Fangular-momentum\u002Faddition-of-angular-momenta-and-clebsch-gordan","Addition of Angular Momenta and Clebsch–Gordan Coefficients","Two angular momenta combine into a total whose allowed magnitudes run from the difference to the sum of the parts in integer steps. The change from the uncoupled product basis to the coupled total-angular-momentum basis is carried out with the lowering operator and orthogonality, and its matrix of overlaps is the table of Clebsch–Gordan coefficients. Two spin-halves split into a triplet and a singlet, the prototype for every composite spin.\n",{"path":5736,"title":5737,"module":5738,"summary":5739},"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-schrodinger-equation-in-three-dimensions","The Schrödinger Equation in Three Dimensions","Central Potentials","A central potential depends only on the distance from a force center, so the three-dimensional Schrödinger equation separates in spherical coordinates. The angular factor is a spherical harmonic; the radial factor obeys a one-dimensional equation with an effective potential whose centrifugal barrier depends on the angular-momentum quantum number. The free particle and the spherical box fix the two limiting cases through the spherical Bessel functions.\n",{"path":5741,"title":5742,"module":5738,"summary":5743},"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-hydrogen-atom","The Hydrogen Atom","The Coulomb potential turns the radial equation into one whose bound states exist only for a discrete set of energies. A power-series solution truncated to keep the wavefunction normalizable forces the principal quantum number, and the energy comes out proportional to minus one over its square, recovering the Rydberg spectrum. The bound states are the associated Laguerre functions times spherical harmonics, and their energy depends on the principal number alone, giving an n-squared degeneracy larger than rotational symmetry can explain.\n",{"path":5745,"title":5746,"module":5738,"summary":5747},"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-isotropic-oscillator-and-hidden-symmetry","The Isotropic Oscillator and Hidden Symmetry","The three-dimensional isotropic harmonic oscillator solves in both Cartesian and spherical bases, and the two solutions must agree on the degeneracy of every level. That agreement, and the accidental degeneracy of hydrogen, both come from a symmetry larger than rotation: the oscillator carries an SU(3) invariance built from a conserved quadrupole tensor, and the Coulomb problem carries an SO(4) invariance built from the conserved Runge–Lenz vector. These hidden symmetries pin the degeneracies that rotational invariance alone leaves unexplained.\n",{"path":5749,"title":5750,"module":5751,"summary":5752},"\u002Fquantum-mechanics\u002Fspin\u002Fspin-half-pauli-matrices-and-stern-gerlach","Spin-½, the Pauli Matrices, and Stern–Gerlach","Spin","A silver atom passing through an inhomogeneous magnetic field splits into two beams, not a smear. That single fact fixes the internal angular momentum of the electron to a two-valued quantity with no spatial wavefunction. We build the two-dimensional spin space, the Pauli matrices and their algebra, the spinor for measurement along an arbitrary axis, and the sequential Stern–Gerlach filters that expose measurement disturbance.\n",{"path":5754,"title":5755,"module":5751,"summary":5756},"\u002Fquantum-mechanics\u002Fspin\u002Fspin-in-a-magnetic-field-precession-and-resonance","Spin in a Magnetic Field: Precession and Resonance","A spin coupled to a magnetic field is the simplest nontrivial quantum dynamics. A static field makes the spin expectation precess on a cone at the Larmor frequency while the energy levels split linearly. Adding a weak oscillating field and passing to the rotating frame produces Rabi oscillations and a resonance lineshape — the physics of NMR and ESR, and the driven qubit.\n",{"path":5758,"title":5759,"module":5751,"summary":5760},"\u002Fquantum-mechanics\u002Fspin\u002Ftwo-level-systems-and-the-bloch-sphere","Two-Level Systems and the Bloch Sphere","Every two-state quantum system is a spin-½ in disguise. Its Hamiltonian is an effective magnetic field, its pure states are points on the Bloch sphere, and its unitary evolution is a rigid rotation of that sphere. The same structure produces avoided level crossings, the ammonia inversion doublet and its maser, and the qubit.\n",{"path":5762,"title":5763,"module":5764,"summary":5765},"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fidentical-particles-and-exchange-symmetry","Identical Particles and Exchange Symmetry","Identical Particles","Two electrons carry no label that distinguishes one from the other, and that bare fact reshapes the state space. The exchange operator that swaps particle labels commutes with any Hamiltonian built from identical particles, so its eigenvalue is conserved, and nature admits only its two extremes: totally symmetric states for bosons and totally antisymmetric states for fermions. The antisymmetry forces a statistical correlation, the exchange \"force,\" that keeps fermions apart and draws bosons together even with no interaction between them.\n",{"path":5767,"title":5768,"module":5764,"summary":5769},"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fthe-pauli-principle-atoms-and-the-periodic-table","The Pauli Principle, Atoms, and the Periodic Table","Antisymmetry packaged as a Slater determinant turns the exclusion principle into an operating rule for building atoms. Helium shows the machinery in full: the electron-electron repulsion splits into a direct Coulomb integral and an exchange integral, and the exchange term alone pushes the spin-triplet (orthohelium) below the spin-singlet (parahelium) with no magnetic interaction in sight. Screening, the aufbau order, and Hund's rules then assemble the whole periodic table from the same antisymmetry.\n",{"path":5771,"title":5772,"module":5773,"summary":5774},"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ftime-independent-perturbation-theory","Time-Independent Perturbation Theory","Approximation Methods for Bound States","Almost no realistic Hamiltonian can be solved exactly. Perturbation theory treats a hard Hamiltonian as a solvable one plus a small correction and expands the eigenvalues and eigenstates in powers of that correction. We derive the first- and second-order energy shifts and the first-order state correction for a nondegenerate level, expose the small-denominator failure that degeneracy forces, and fix it by diagonalizing the perturbation inside the degenerate subspace to find the \"good\" zeroth-order states.\n",{"path":5776,"title":5777,"module":5773,"summary":5778},"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ffine-structure-and-the-real-hydrogen-atom","Fine Structure and the Real Hydrogen Atom","The Bohr spectrum is only the leading term. Two relativistic corrections of order alpha-squared — the relativistic kinetic-energy correction and spin–orbit coupling, joined by the Darwin term for s states — split the hydrogen levels into fine structure that depends on the total angular momentum j. We derive each shift as a first-order perturbation, combine them into a formula depending only on n and j, and continue down the energy ladder to the Lamb shift and the hyperfine 21 cm line.\n",{"path":5780,"title":5781,"module":5773,"summary":5782},"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-zeeman-and-stark-effects","The Zeeman and Stark Effects","An atom in an external field is a perturbation problem whose good basis depends on which interaction wins. A magnetic field competes with the internal spin–orbit coupling: the weak-field limit gives the anomalous Zeeman splitting set by the Landé g-factor, the strong-field limit gives the Paschen–Back pattern in the uncoupled basis, and the intermediate regime is a matrix diagonalization. An electric field gives a quadratic shift for the nondegenerate ground state and a linear splitting for the degenerate n = 2 level.\n",{"path":5784,"title":5785,"module":5773,"summary":5786},"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-variational-method","The Variational Method","The expectation of the Hamiltonian in any trial state is an upper bound on the true ground-state energy. Minimizing that expectation over a parametrized family of trial functions turns the ground-state problem into ordinary calculus and needs no small parameter. We prove the bound, apply it to the helium atom with a screened effective charge, use a two-center trial to predict binding in the hydrogen molecular ion, and extend the method to excited states through orthogonality.\n",{"path":5788,"title":5789,"module":5773,"summary":5790},"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-wkb-approximation","The WKB Approximation","When the potential varies slowly on the scale of the de Broglie wavelength, the wavefunction is locally a plane wave with a position-dependent wavelength. This semiclassical picture builds the wavefunction from the classical momentum, breaks down at the turning points where the momentum vanishes, and is repaired there by connection formulas. The result recovers the Bohr–Sommerfeld quantization rule with its half-integer correction and gives the exponential tunneling rate through a smooth barrier, the Gamow factor.\n",{"path":5792,"title":5793,"module":6,"summary":6},"\u002Fquantum-mechanics","Quantum Mechanics",{"path":5795,"title":5796,"module":5797,"summary":5798},"\u002Freal-analysis\u002Ffoundations\u002Fsets-logic-functions","Sets, Logic, and Functions","Foundations and the Real Number System","The working language of analysis: quantifiers and the proof patterns (contrapositive, contradiction, induction), sets and their operations, relations and equivalence classes, and functions with their images, injections, surjections, and bijections. Cardinality is measured by bijection, and Cantor's theorem that no set surjects onto its power set forces uncountable sets to exist.\n",{"path":5800,"title":5801,"module":5797,"summary":5802},"\u002Freal-analysis\u002Ffoundations\u002Fordered-fields-completeness","Ordered Fields and the Completeness Axiom","The real numbers are the unique ordered field with the least-upper-bound property. The field and order axioms, the exact failure of the rationals (no supremum for the set of rationals below √2), and completeness as the defining axiom of ℝ lead to the first consequences: the existence of √2, the Archimedean property, and the density of ℚ in ℝ.\n",{"path":5804,"title":5805,"module":5797,"summary":5806},"\u002Freal-analysis\u002Ffoundations\u002Fabsolute-value-bounds","Absolute Value, Bounded Sets, and Inequalities","The absolute value turns the order on ℝ into a notion of distance, with the triangle inequality as the estimate underlying most later proofs. Covered: its algebra, the triangle and reverse-triangle inequalities, and the extension of the sup\u002Finf vocabulary from sets to bounded functions.\n",{"path":5808,"title":5809,"module":5797,"summary":5810},"\u002Freal-analysis\u002Ffoundations\u002Fintervals-uncountability","Intervals, Uncountability, and Decimals","Intervals are classified, and ℝ is proved uncountable two ways: a nested-interval construction and the decimal diagonal argument. Decimal expansions are built as suprema of truncations, which pins the source of their non-uniqueness (the 0.4999… equals 0.5000… identity) and the identification of the rationals with the eventually-repeating expansions. The middle-thirds Cantor set is an uncountable set of measure zero.\n",{"path":5812,"title":5813,"module":5814,"summary":5815},"\u002Freal-analysis\u002Fsequences-series\u002Fsequences-limits","Sequences and Their Limits","Sequences and Series","A sequence is a function on the natural numbers; it converges to a limit when its terms eventually stay within any prescribed tolerance of that number. The epsilon-M definition fixes the order of the quantifiers, and from it the limit is unique, every convergent sequence is bounded, and only the tail matters. Divergence to plus or minus infinity records terms that outgrow every bound.\n",{"path":5817,"title":5818,"module":5814,"summary":5819},"\u002Freal-analysis\u002Fsequences-series\u002Flimit-laws-monotone","Limit Laws and Monotone Convergence","Limits commute with sums, products, quotients, roots, and absolute values and preserve non-strict inequalities, so a limit can be assembled from the limits of its parts without returning to epsilon and M. The squeeze lemma transfers a limit through two envelopes; the monotone convergence theorem produces a limit from boundedness alone; and the ratio test settles the geometric and factorial standard limits.\n",{"path":5821,"title":5822,"module":5814,"summary":5823},"\u002Freal-analysis\u002Fsequences-series\u002Flimsup-bolzano-weierstrass","Subsequences, Limit Superior, and Bolzano–Weierstrass","A bounded sequence need not converge, but it always has convergent subsequences, and its terms cluster between two extreme values. The limit superior and inferior are the limits of the tail suprema and infima; they always exist for a bounded sequence, coincide exactly when it converges, and are its largest and smallest subsequential limits. Bolzano–Weierstrass extracts a convergent subsequence from boundedness alone.\n",{"path":5825,"title":5826,"module":5814,"summary":5827},"\u002Freal-analysis\u002Fsequences-series\u002Fcauchy-completeness","Cauchy Sequences and the Completeness of the Reals","The Cauchy criterion tests convergence without knowing the limit: a sequence converges exactly when its terms eventually all lie within any tolerance of one another. Cauchy sequences are bounded, in the reals Cauchy and convergent are equivalent, and this completeness property is interchangeable with the least-upper-bound axiom — the single feature that separates the real line from the rationals.\n",{"path":5829,"title":5830,"module":5814,"summary":5831},"\u002Freal-analysis\u002Fsequences-series\u002Fseries-convergence","Series and Convergence Tests","A series converges when its sequence of partial sums does, so every fact about sequences transfers. Geometric and telescoping series sum in closed form; the n-th term test rejects series whose terms miss zero, though the harmonic series shows the converse fails; and the comparison test against the geometric and p-series benchmarks settles most nonnegative-term series.\n",{"path":5833,"title":5834,"module":5814,"summary":5835},"\u002Freal-analysis\u002Fsequences-series\u002Fabsolute-conditional-rearrangement","Absolute Convergence, the Ratio and Root Tests, and Rearrangements","Absolute convergence is the strong form of convergence that permits free manipulation; conditional convergence is fragile. Absolute convergence implies convergence, and the ratio and root tests detect it by comparison with the geometric series. The alternating series test supplies conditionally convergent series, Riemann's theorem rearranges any of them to any sum, and Mertens' theorem multiplies series when at least one converges absolutely.\n",{"path":5837,"title":5838,"module":5839,"summary":5840},"\u002Freal-analysis\u002Fmetric-spaces\u002Fmetric-spaces-norms","Metric Spaces, Norms, and Examples","Metric Spaces and Topology","A metric is a function $d(x,y)$ obeying four axioms: nonnegativity, identity of indiscernibles, symmetry, and the triangle inequality. The Euclidean, taxicab, sup, discrete, and great-circle metrics all qualify, as does the sup metric on $C[a,b]$. Every norm induces a metric, and strongly equivalent metrics share the same open sets.\n",{"path":5842,"title":5843,"module":5839,"summary":5844},"\u002Freal-analysis\u002Fmetric-spaces\u002Fopen-closed-sets","Open and Closed Sets, Interior, Closure","Open sets are those in which every point has room to move; closed sets are their complements. From the single ball construction come the topology axioms (arbitrary unions, finite intersections), the interior, closure, and boundary of a set, and the fact that openness is always relative to the ambient space.\n",{"path":5846,"title":5847,"module":5839,"summary":5848},"\u002Freal-analysis\u002Fmetric-spaces\u002Fconvergence-completeness","Convergence, Cauchy Sequences, and Completeness","The $\\varepsilon$-$N$ definition of a limit transfers verbatim to any metric space once $|x-y|$ is replaced by $d(x,y)$. Convergent sequences characterize closed sets and closures; Cauchy sequences and completeness capture spaces with no missing limits, with $\\mathbb{R}^n$ and $C[a,b]$ complete and $\\mathbb{Q}$ and $(0,1]$ not.\n",{"path":5850,"title":5851,"module":5839,"summary":5852},"\u002Freal-analysis\u002Fmetric-spaces\u002Fcompactness","Compactness","A set is compact if every open cover has a finite subcover. In a metric space this is equivalent to sequential compactness and to being complete and totally bounded. Compact sets are closed and bounded; the Heine–Borel theorem gives the converse in $\\mathbb{R}^n$ but nowhere else in general.\n",{"path":5854,"title":5855,"module":5839,"summary":5856},"\u002Freal-analysis\u002Fmetric-spaces\u002Fconnectedness","Connectedness","A space is connected when it cannot be split into two nonempty open pieces. The connected subsets of $\\mathbb{R}$ are precisely the intervals, path- connectedness gives a constructive sufficient condition, and connectedness is a topological invariant preserved by continuous maps, the fact behind the intermediate value theorem.\n",{"path":5858,"title":5859,"module":4258,"summary":5860},"\u002Freal-analysis\u002Fcontinuity\u002Flimits-of-functions","Limits of Functions","The limit of a function at a point is an epsilon–delta condition pinning one value L as the target of f(x) as x approaches c, mirroring the sequence definition with distance replacing index. It is stated only at cluster points of the domain, is unique when it exists, and reduces to sequential limits through the Heine criterion. The algebra of limits and one-sided limits follow from that reduction.\n",{"path":5862,"title":5863,"module":4258,"summary":5864},"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuous-functions","Continuous Functions","A function is continuous at c when its limit there equals its own value, lim f(x) = f(c). The epsilon–delta and sequential forms agree; sums, products, quotients, and compositions of continuous functions are continuous; and the failures split into jump, Dirichlet, popcorn, and removable types. The topological reading is that preimages of open sets are open.\n",{"path":5866,"title":5867,"module":4258,"summary":5868},"\u002Freal-analysis\u002Fcontinuity\u002Fevt-ivt","Extreme and Intermediate Value Theorems","On a closed bounded interval a continuous function attains an absolute maximum and minimum (the extreme value theorem, compactness preserved by continuity) and takes every value between its endpoint values (the intermediate value theorem, connectedness preserved). Both proofs run through Bolzano–Weierstrass and bisection, and yield root-finding, existence of k-th roots, and fixed-point theorems.\n",{"path":5870,"title":5871,"module":4258,"summary":5872},"\u002Freal-analysis\u002Fcontinuity\u002Funiform-continuity","Uniform Continuity","Uniform continuity strengthens continuity by demanding one delta that works at every point of the domain, not a delta re-chosen at each point. It separates x^2 on a compact interval from x^2 on the whole line and from 1\u002Fx near zero; continuity on a closed bounded interval is automatically uniform; uniformly continuous functions preserve Cauchy sequences and extend to endpoints; and Lipschitz continuity is the strongest of the three, through its secant-slope bound.\n",{"path":5874,"title":5875,"module":4258,"summary":5876},"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuity-metric-spaces","Continuity on Metric Spaces","The epsilon–delta definition used only distances, so continuity transfers to maps between metric spaces by replacing absolute values with the two metrics. In this generality continuity still admits a sequential form, preserves compactness and connectedness, is uniform on a compact domain, and reads topologically as preimages of open sets being open, the formulation that defines homeomorphisms.\n",{"path":5878,"title":5879,"module":4258,"summary":5880},"\u002Freal-analysis\u002Fcontinuity\u002Flimits-infinity-monotone","Limits at Infinity and Monotone Functions","Treating infinity as a cluster point extends the epsilon–delta limit to x approaching plus or minus infinity, giving horizontal asymptotes and infinite limits. For monotone functions the one-sided limits always exist as suprema and infima, the discontinuities are jumps and at most countably many, the continuity is equivalent to the image being an interval, and a strictly monotone function always has a continuous inverse.\n",{"path":5882,"title":5883,"module":5884,"summary":5885},"\u002Freal-analysis\u002Fdifferentiation\u002Fthe-derivative","The Derivative","Differentiation","The derivative is the limit of the difference quotient, the slope the secant lines approach as the second point slides into the first. Differentiability forces continuity; linearity and the product, quotient, and chain rules follow from the definition; and a continuous function can fail to be differentiable, as the absolute value does at the origin.\n",{"path":5887,"title":5888,"module":5884,"summary":5889},"\u002Freal-analysis\u002Fdifferentiation\u002Fmean-value-theorem","The Mean Value Theorem","A relative extremum in the interior forces the derivative to vanish; Rolle's theorem and the mean value theorem turn that local fact into global control. The sign of the derivative fixes monotonicity, a bounded derivative yields a Lipschitz bound, and Darboux's theorem shows derivatives have the intermediate value property even where they are discontinuous.\n",{"path":5891,"title":5892,"module":5884,"summary":5893},"\u002Freal-analysis\u002Fdifferentiation\u002Ftaylors-theorem","Taylor's Theorem","Taylor's theorem generalizes the mean value theorem: an n-times differentiable function is matched near a point by a degree-n polynomial, with a Lagrange remainder that names the error exactly through one higher derivative. Iterating the mean value theorem proves it; the second-derivative test is the order-one case; and a smooth non-analytic bump separates a Taylor series from the function it fails to represent.\n",{"path":5895,"title":5896,"module":5884,"summary":5897},"\u002Freal-analysis\u002Fdifferentiation\u002Finverse-function-1d","The Inverse Function Theorem in One Variable","A nonzero derivative certifies a local inverse and fixes its slope. A strictly monotone differentiable function has a differentiable inverse whose derivative is the reciprocal of the original; the inverse function theorem removes the monotonicity hypothesis, and the reciprocal formula constructs nth roots and the logarithm's derivative, failing exactly where the derivative vanishes.\n",{"path":5899,"title":5900,"module":5901,"summary":5902},"\u002Freal-analysis\u002Friemann-integration\u002Fdarboux-integral","Partitions, Darboux Sums, and Integrability","The Riemann Integral","The Riemann integral is defined by trapping the area under a bounded function between under- and over-estimates. Partitions cut the domain into strips; lower and upper Darboux sums bracket the area; refining a partition tightens the bracket. A function is integrable exactly when the bracket can be made arbitrarily thin, and the tagged Riemann-sum limit gives the same number.\n",{"path":5904,"title":5905,"module":5901,"summary":5906},"\u002Freal-analysis\u002Friemann-integration\u002Fintegrability-classes","Which Functions Are Integrable","The Cauchy criterion certifies whole classes of functions as integrable. Continuous functions are integrable because uniform continuity makes every oscillation cap small; monotone functions are integrable because their caps telescope to a single total jump; bounded functions with finitely many discontinuities are integrable by isolating the bad points. The Dirichlet function fails, and the Lebesgue criterion names the exact boundary.\n",{"path":5908,"title":5909,"module":5901,"summary":5910},"\u002Freal-analysis\u002Friemann-integration\u002Fproperties-of-the-integral","Properties of the Integral","The integral is a linear, order-preserving, additive operator on the integrable functions. It splits across subintervals, respects inequalities, bounds the size of a function by the integral of its absolute value, and preserves products. The mean value theorem for integrals identifies the integral with an attained average height on a fixed rectangle.\n",{"path":5912,"title":4313,"module":5901,"summary":5913},"\u002Freal-analysis\u002Friemann-integration\u002Ffundamental-theorem","The fundamental theorem ties the integral to the derivative in two forms. The evaluation form computes a definite integral from any antiderivative; the differentiation form shows the area function has derivative equal to the integrand at points of continuity. Together they make differentiation and integration inverse operations, and yield integration by parts and change of variables.\n",{"path":5915,"title":5916,"module":5901,"summary":5917},"\u002Freal-analysis\u002Friemann-integration\u002Flog-exp-improper","The Logarithm, Exponential, and Improper Integrals","The integral defines transcendental functions. The logarithm is the area under 1\u002Ft, the exponential is its inverse, and their calculus properties follow from the fundamental theorem. Improper integrals extend integration to unbounded intervals and unbounded integrands as limits of proper integrals, with a p-test, a comparison test, absolute versus conditional convergence, and the integral test linking integrals to series.\n",{"path":5919,"title":5920,"module":5921,"summary":5922},"\u002Freal-analysis\u002Ffunction-sequences\u002Fpointwise-uniform-convergence","Pointwise and Uniform Convergence","Sequences and Series of Functions","A sequence of functions has two natural notions of limit. Pointwise convergence fixes each input and takes the limit of numbers; uniform convergence demands one rate that works for every input at once. The uniform norm turns the second into a statement about a single sequence of numbers, and the uniform Cauchy criterion and the Weierstrass M-test let us certify it.\n",{"path":5924,"title":5925,"module":5921,"summary":5926},"\u002Freal-analysis\u002Ffunction-sequences\u002Finterchange-of-limits","Interchange of Limits: Continuity, Integration, Differentiation","Passing to a limit inside a continuity statement, an integral, or a derivative is an interchange of two limits, and the two limits do not always commute. Uniform convergence licenses the first two swaps: the uniform limit of continuous functions is continuous, and the limit of the integrals is the integral of the limit. Differentiation needs uniform convergence of the derivatives, and counterexamples show why each hypothesis is required.\n",{"path":5928,"title":5929,"module":5921,"summary":5930},"\u002Freal-analysis\u002Ffunction-sequences\u002Fpower-series-weierstrass","Power Series and the Weierstrass Approximation Theorem","A power series converges uniformly on every closed subinterval inside its radius of convergence, together with all of its derivatives. That makes it continuous, differentiable, and integrable term by term, so a power series defines an infinitely differentiable function. The Weierstrass approximation theorem then shows that polynomials come uniformly close to any continuous function on a closed bounded interval.\n",{"path":5932,"title":5933,"module":5921,"summary":5934},"\u002Freal-analysis\u002Ffunction-sequences\u002Fpicard-ode","Picard's Existence and Uniqueness Theorem","The Banach fixed-point theorem says a contraction of a complete metric space has exactly one fixed point, found by iterating from any start. Applied to the space of continuous functions with the uniform norm, it proves Picard's theorem: a first-order differential equation with a Lipschitz right-hand side has a unique local solution. Picard iteration constructs that solution explicitly, and worked examples show the Lipschitz condition is not optional.\n",{"path":5936,"title":5937,"module":5938,"summary":5939},"\u002Freal-analysis\u002Fseveral-variables\u002Fdifferentiability-rn","The Derivative of a Map ℝⁿ → ℝᵐ","Functions of Several Variables (Introduction)","The derivative of a map between Euclidean spaces is the linear transformation of vanishing relative error, unique when it exists and represented in coordinates by the Jacobian matrix of partial derivatives. Differentiability forces continuity through a local Lipschitz bound. Existence of the partial derivatives alone does not suffice; continuity of the partials does.\n",{"path":5941,"title":5942,"module":5938,"summary":5943},"\u002Freal-analysis\u002Fseveral-variables\u002Fgradient-chain-rule","Directional Derivatives, the Gradient, and the Chain Rule","The directional derivative measures the rate of change of a scalar field along a chosen heading and equals the derivative applied to that direction. The gradient collects these into a vector that points along steepest ascent and sits orthogonal to level sets. The chain rule composes derivatives by multiplying Jacobians, and a mean value theorem holds for scalar fields but fails for vector-valued maps.\n",{"path":5945,"title":5946,"module":5938,"summary":5947},"\u002Freal-analysis\u002Fseveral-variables\u002Fhigher-derivatives-taylor-extrema","Higher Derivatives, Taylor's Theorem, and Extrema","Iterating the derivative gives a symmetric second derivative, the Hessian, whose mixed partials agree when they are continuous. Taylor's theorem expands a smooth map to any order with a Lagrange-type remainder, and at a critical point the definiteness of the Hessian decides between a local minimum, a local maximum, and a saddle.\n",{"path":5949,"title":5950,"module":5938,"summary":5951},"\u002Freal-analysis\u002Fseveral-variables\u002Finverse-implicit-theorems","The Inverse and Implicit Function Theorems","A nonlinear map with a nonsingular Jacobian is locally invertible, with the inverse's derivative given by the inverse matrix. The contraction mapping principle supplies the local inverse; the implicit function theorem then solves a system for some variables in terms of the rest whenever the relevant Jacobian block is invertible. Worked coordinate changes show both theorems in use.\n",{"path":5953,"title":5954,"module":5938,"summary":5955},"\u002Freal-analysis\u002Fseveral-variables\u002Fmultiple-integrals","Multiple Integrals","The Riemann integral of a bounded function over a closed rectangle in Euclidean space is built from Darboux upper and lower sums on a grid of subrectangles, with the same squeeze criterion that governs the one-variable integral. Continuous integrands are integrable, and a set of content zero can be ignored. Fubini's theorem evaluates a multiple integral as an iterated one in either order, and the indicator trick extends the theory to regions bounded by curves.\n",{"path":5957,"title":5958,"module":6,"summary":6},"\u002Freal-analysis","Real Analysis",{"path":5960,"title":5961,"module":3911,"summary":5962},"\u002Fabstract-algebra\u002Ffoundations\u002Fsets-functions-relations","Sets, Functions, and Equivalence Relations","Algebra is built on three prior notions: the set, the map between sets, and the equivalence relation that reorganizes a set into disjoint classes. Sets, maps (injective, surjective, bijective), fibers and preimages, and the correspondence between equivalence relations and partitions — the one structural fact reused in every later quotient construction.\n",{"path":5964,"title":5965,"module":3911,"summary":5966},"\u002Fabstract-algebra\u002Ffoundations\u002Fintegers-and-modular-arithmetic","The Integers and Modular Arithmetic","The integers carry the template every ring later imitates: well-ordering drives induction, induction drives the division algorithm, and division drives the Euclidean algorithm, gcd, Bézout's identity, and unique factorization into primes. Quotienting by congruence mod n builds the first finite arithmetic, Z\u002FnZ, whose invertible elements form the group of units.\n",{"path":5968,"title":5969,"module":5970,"summary":5971},"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fgroup-axioms-and-first-examples","Group Axioms and First Examples","Groups and Symmetry","A group is a set with one associative operation that has an identity and inverses. We state the axioms, prove that the identity, inverses, and cancellation behave as expected, define the order of a group and of an element, and catalogue the running examples: the integers, the additive group of residues mod n, and the multiplicative group of units mod n.\n",{"path":5973,"title":5974,"module":5970,"summary":5975},"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fdihedral-and-symmetric-groups","Dihedral and Symmetric Groups","The dihedral group D_{2n} is the symmetries of a regular n-gon, generated by a rotation r and a reflection s subject to three relations. The symmetric group S_n is all permutations of n objects, written in cycle notation. Orders, generators and relations, cycle decomposition, the order of a permutation from its cycle type, and the parity that splits S_n in half.\n",{"path":5977,"title":5978,"module":5970,"summary":5979},"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fmatrix-and-quaternion-groups","Matrix and Quaternion Groups","Invertible matrices over a field form the general linear group GL_n(F), with the determinant-one matrices as the subgroup SL_n(F). Over a finite field the order of GL_n(F) has a clean product formula. The quaternion group Q_8 is a second small nonabelian group, distinct from the dihedral group of the same order; its multiplication and subgroup structure sharpen the contrast between the two.\n",{"path":5981,"title":5982,"module":5970,"summary":5983},"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fhomomorphisms-and-group-actions","Homomorphisms, Isomorphisms, and Actions","A homomorphism is a map between groups that respects the operation; an isomorphism is a bijective one, making two groups the same up to relabeling. The kernel and image measure how far a homomorphism is from injective and surjective. A group action realizes a group as permutations of a set, and actions correspond exactly to homomorphisms into a symmetric group, with orbits and stabilizers as the first tools for counting.\n",{"path":5985,"title":5986,"module":5987,"summary":5988},"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fsubgroups-and-substructures","Subgroups and Their Substructures","Subgroups and Quotients","A subgroup is a subset that is a group under the inherited operation. One test decides it: nonempty and closed under the map $(x,y) \\mapsto xy^{-1}$. From an arbitrary subset $A$ we build the centralizer, normalizer, and center, and from an action the stabilizer and kernel, all of them subgroups nested in a fixed chain inside $G$.\n",{"path":5990,"title":5991,"module":5987,"summary":5992},"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcyclic-groups","Cyclic Groups","A cyclic group is generated by one element. Two facts organize the whole theory: the order of an element equals the order of the subgroup it generates, and cyclic groups of equal order are isomorphic, so $\\mathbb{Z}$ and $\\mathbb{Z}\u002Fn\\mathbb{Z}$ are the only ones. From there the generators ($\\varphi(n)$ of them), the subgroups (one per divisor of $n$), and a fast exponentiation algorithm all follow.\n",{"path":5994,"title":5995,"module":5987,"summary":5996},"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fgeneration-and-subgroup-lattices","Generation and the Lattice of Subgroups","The subgroup generated by a subset $A$ is the smallest subgroup containing it, described top-down as an intersection and bottom-up as the set of words in $A$ and its inverses. Collecting all subgroups and ordering them by containment produces the subgroup lattice, whose Hasse diagram shows the joins, meets, and containment relations among all subgroups.\n",{"path":5998,"title":5999,"module":5987,"summary":6000},"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcosets-lagrange-and-normal-subgroups","Cosets, Lagrange, and Normal Subgroups","The left cosets of a subgroup partition a group into equal-sized blocks, so the order of a subgroup divides the order of the group: Lagrange's theorem. When the blocks can be multiplied consistently — exactly when the subgroup is normal — they form the quotient group $G\u002FN$. Fermat's and Euler's theorems fall out as index computations.\n",{"path":6002,"title":6003,"module":5987,"summary":6004},"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fisomorphism-theorems","The Isomorphism Theorems","Four theorems relate homomorphisms, quotients, and subgroup lattices. The first identifies the image of a homomorphism with the quotient by its kernel; the second and third compute quotients built from two subgroups and quotients of quotients; the fourth matches the subgroups of $G\u002FN$ with the subgroups of $G$ lying above $N$. Together they make quotient groups computable.\n",{"path":6006,"title":6007,"module":5987,"summary":6008},"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcomposition-series-and-the-alternating-group","Composition Series and the Alternating Group","A composition series breaks a finite group into simple quotient factors, and Jordan-Hölder says those factors are unique up to order. This turns classification into two problems: list the simple groups, and describe how to reassemble them. The sign homomorphism splits $S_n$ into even and odd permutations, defining the alternating group $A_n$, simple for $n \\ge 5$.\n",{"path":6010,"title":6011,"module":6012,"summary":6013},"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Factions-and-cayleys-theorem","Actions, Orbits, and Cayley's Theorem","Group Actions and Sylow Theory","A group action turns abstract elements into permutations of a set. The action splits the set into orbits, and the orbit-stabilizer theorem ties each orbit's size to the index of a stabilizer. Applied to a group acting on itself by left multiplication, this gives Cayley's theorem: every group is a group of permutations.\n",{"path":6015,"title":6016,"module":6012,"summary":6017},"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fconjugation-and-the-class-equation","Conjugation and the Class Equation","A group acts on itself by conjugation, and the orbits are the conjugacy classes. Orbit-stabilizer turns the resulting partition into the class equation, which forces every group of prime-power order to have a nontrivial center. Conjugacy in the symmetric group is cycle type, and Burnside's lemma counts orbits by averaging fixed points.\n",{"path":6019,"title":6020,"module":6012,"summary":6021},"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fsylow-theorems","The Sylow Theorems","Lagrange's theorem forbids subgroups whose order fails to divide the group order; Sylow's theorems supply a partial converse for prime powers. A Sylow p-subgroup always exists, all of them are conjugate, and their count satisfies two congruence-and-divisibility constraints tight enough to prove many groups non-simple from their order alone.\n",{"path":6023,"title":6024,"module":6012,"summary":6025},"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fautomorphisms-and-simple-groups","Automorphisms and Simplicity of Aₙ","Conjugation makes a group act on itself and on its normal subgroups by automorphisms, giving the inner automorphism group G\u002FZ(G) and the embedding of N(H)\u002FC(H) into Aut(H). Characteristic subgroups are those every automorphism fixes, and the automorphism group of a cyclic group is its unit group. The lesson closes by proving the alternating group Aₙ is simple for n ≥ 5.\n",{"path":6027,"title":6028,"module":6029,"summary":6030},"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fdirect-products-and-finite-abelian-groups","Direct Products and Finite Abelian Groups","Products and Group Structure","The direct product assembles a larger group from componentwise copies of smaller ones, and a recognition theorem reverses the process when two normal subgroups meet trivially and span the group. The Fundamental Theorem of Finitely Generated Abelian Groups then classifies every such group by two equivalent invariants, invariant factors and elementary divisors.\n",{"path":6032,"title":6033,"module":6029,"summary":6034},"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fsemidirect-products","Semidirect Products","The semidirect product relaxes the direct product by requiring only one factor to be normal, with the other acting on it through a homomorphism into its automorphism group. This single twisting map lets abelian pieces assemble into non-abelian groups, realizes the dihedral groups as $\\mathbb{Z}_n \\rtimes \\mathbb{Z}_2$, and, with a recognition theorem, classifies groups of several small orders.\n",{"path":6036,"title":6037,"module":6029,"summary":6038},"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fnilpotent-and-solvable-groups","p-Groups, Nilpotent, and Solvable Groups","Finite p-groups have nontrivial center, and iterating the center upward builds the nilpotent groups, which decompose as the direct product of their Sylow subgroups. Iterating the commutator downward builds the solvable groups, whose factors are abelian. The chain cyclic, abelian, nilpotent, solvable orders these classes, and A_5 breaks the last link.\n",{"path":6040,"title":6041,"module":6029,"summary":6042},"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fclassifying-small-groups","Classifying Groups of Small Order","With Sylow's theorem to force normal subgroups, direct and semidirect products to assemble them, and presentations to name the result, every group up to order fifteen can be listed explicitly. Free groups make presentations precise: generators with no relations, from which any group is a quotient by the normal closure of its relations.\n",{"path":6044,"title":6045,"module":6046,"summary":6047},"\u002Fabstract-algebra\u002Fring-theory\u002Frings-definitions-and-examples","Rings: Definitions and Examples","Ring Theory","A ring carries two operations: an abelian group under addition and an associative multiplication linked by the distributive laws. The named special cases — commutative rings, integral domains, division rings, and fields — differ only in how their multiplication behaves. Standard examples include quadratic integer rings, polynomial rings, matrix rings, and group rings.\n",{"path":6049,"title":6050,"module":6046,"summary":6051},"\u002Fabstract-algebra\u002Fring-theory\u002Fideals-quotients-and-homomorphisms","Ideals, Quotient Rings, and Homomorphisms","Ring homomorphisms have kernels that absorb multiplication; such subsets are ideals, and every ideal is the kernel of the projection onto a quotient ring. The quotient construction yields the ring isomorphism theorems and classifies ideals by their quotients: R\u002FI is a field exactly when I is maximal, an integral domain exactly when I is prime.\n",{"path":6053,"title":6054,"module":6046,"summary":6055},"\u002Fabstract-algebra\u002Fring-theory\u002Ffractions-and-the-chinese-remainder-theorem","Fields of Fractions and the CRT","Rings of fractions invert a multiplicatively closed set, enlarging an integral domain into its field of fractions the way Z becomes Q. The Chinese Remainder Theorem splits a quotient by comaximal ideals into a direct product, generalizing Z\u002FmnZ ≅ Z\u002FmZ × Z\u002FnZ and explaining why the Euler function is multiplicative.\n",{"path":6057,"title":6058,"module":6059,"summary":6060},"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Feuclidean-domains-pids-ufds","Euclidean Domains, PIDs, and UFDs","Factorization and Polynomial Rings","Three classes of integral domain, ordered by how much of elementary arithmetic survives: Euclidean domains carry a division algorithm, principal ideal domains make every ideal a single multiple, and unique factorization domains factor every element into irreducibles in one way. We prove the chain ED implies PID implies UFD, the classes are separated by explicit counterexamples, and irreducible and prime coincide exactly in a UFD.\n",{"path":6062,"title":6063,"module":6059,"summary":6064},"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fpolynomial-rings-over-fields","Polynomial Rings over Fields","When the coefficients form a field, polynomial long division works exactly as it does over the rationals, and it works with a unique quotient and remainder. That single fact makes F[x] a Euclidean domain, hence a PID and a UFD: every ideal is the multiples of one polynomial, roots correspond to linear factors, and F[x]\u002F(f) is a field precisely when f is irreducible.\n",{"path":6066,"title":6067,"module":6059,"summary":6068},"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fgauss-lemma-and-unique-factorization","Gauss's Lemma and Unique Factorization","A UFD is not a field, so its polynomial ring is not a PID — yet unique factorization survives the passage from R to R[x]. Gauss's lemma supplies the passage: a polynomial that factors over the fraction field already factors over R, once content is factored out. This gives the theorem that R[x] is a UFD whenever R is, so Z[x] and Q[x,y] factor uniquely even though neither is a PID.\n",{"path":6070,"title":6071,"module":6059,"summary":6072},"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Firreducibility-criteria-and-groebner","Irreducibility Criteria and Gröbner Bases","Deciding whether a given polynomial is irreducible, and computing in multivariate polynomial rings. In one variable: the rational root test, reduction modulo a prime, and Eisenstein's criterion. In several variables, where division fails, a monomial order gives leading terms, a Gröbner basis restores a well-defined remainder, and Buchberger's algorithm computes it.\n",{"path":6074,"title":6075,"module":6076,"summary":6077},"\u002Fabstract-algebra\u002Fmodule-theory\u002Fintroduction-to-modules","Introduction to Modules","Module Theory","A module is an abelian group on which a ring acts, generalizing both vector spaces (when the ring is a field) and abelian groups (when the ring is the integers). Submodules, homomorphisms, quotients, and the isomorphism theorems carry over from groups, and an F[x]-module is the same datum as a vector space with a chosen linear operator — the correspondence behind the canonical forms.\n",{"path":6079,"title":6080,"module":6076,"summary":6081},"\u002Fabstract-algebra\u002Fmodule-theory\u002Ffree-modules-and-direct-sums","Generation, Direct Sums, and Free Modules","A generating set spans a module by R-linear combinations; a direct sum decomposes it into independent pieces; a free module has a basis and the universal property that a homomorphism is determined by arbitrary values on that basis. Rank is well defined over a commutative ring, torsion blocks a basis, and every module is a quotient of a free one — a presentation by generators and relations.\n",{"path":6083,"title":6084,"module":6076,"summary":6085},"\u002Fabstract-algebra\u002Fmodule-theory\u002Ftensor-products-and-exact-sequences","Tensor Products and Exact Sequences","The tensor product builds a module in which elements of two modules can be multiplied, characterized by a universal property turning bilinear maps into linear ones; extension of scalars is its guiding case. Exact sequences track how a module is assembled from a submodule and a quotient, when that assembly splits, and which modules — projective, injective, flat — make the Hom and tensor functors preserve exactness.\n",{"path":6087,"title":6088,"module":6076,"summary":6089},"\u002Fabstract-algebra\u002Fmodule-theory\u002Fvector-spaces-and-linear-maps","Vector Spaces and Linear Maps","A vector space is a module over a field, and the field hypothesis removes every pathology a general module can have: every vector space is free, so it has a basis, a well-defined dimension, and a coordinate isomorphism with F^n. Linear maps become matrices, change of basis becomes similarity, every space pairs with a dual of the same dimension, and the determinant is the unique alternating multilinear normalized form.\n",{"path":6091,"title":6092,"module":6093,"summary":6094},"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fstructure-theorem-over-pids","The Structure Theorem for Modules over a PID","Modules over PIDs and Canonical Forms","Every finitely generated module over a principal ideal domain splits as a free part plus a direct sum of cyclic torsion pieces, in two canonical ways: invariant factors, tied together by a divisibility chain, and elementary divisors, one prime power at a time. Existence follows from the stacked-basis theorem, both lists are unique, and the case $R = \\mathbb{Z}$ is the classification of finitely generated abelian groups.\n",{"path":6096,"title":6097,"module":6093,"summary":6098},"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Frational-canonical-form","Rational Canonical Form","A linear operator turns its vector space into a module over the polynomial ring $F[x]$, with $x$ acting as the operator. The structure theorem's invariant factors then become polynomials, each cyclic summand becomes a companion matrix, and the block-diagonal assembly is the rational canonical form. It is unique, it is computed inside the base field, and two matrices are similar exactly when their rational canonical forms agree.\n",{"path":6100,"title":6101,"module":6093,"summary":6102},"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fjordan-canonical-form","Jordan Canonical Form","When the base field contains all the eigenvalues, the elementary divisors of an operator are powers of linear polynomials, and each cyclic summand becomes a Jordan block: an eigenvalue on the diagonal with ones just above it. Stacking the blocks gives the Jordan canonical form, unique up to reordering, as close to diagonal as the operator allows. Diagonalizability reads off the minimal polynomial, and the block sizes are counted by ranks of powers of the operator minus the eigenvalue.\n",{"path":6104,"title":6105,"module":6106,"summary":6107},"\u002Fabstract-algebra\u002Ffield-theory\u002Ffield-extensions-and-algebraic-elements","Field Extensions and Algebraic Elements","Field Theory","A field extension makes a larger field K into a vector space over a smaller field F, and its degree [K:F] is that dimension. Adjoining a root of an irreducible polynomial builds a simple extension F(α) isomorphic to F[x]\u002F(m), whose degree is the degree of the minimal polynomial. The tower law makes these degrees multiply, which turns algebra over fields into bookkeeping with integers.\n",{"path":6109,"title":6110,"module":6106,"summary":6111},"\u002Fabstract-algebra\u002Ffield-theory\u002Fstraightedge-and-compass-constructions","Straightedge-and-Compass Constructions","The lengths a straightedge and compass can build from a unit form a field closed under square roots, and every constructible number lies in a tower of quadratic extensions. So its degree over the rationals is a power of two. That single obstruction settles three problems the Greeks left open: doubling the cube, trisecting a general angle, and squaring the circle are all impossible.\n",{"path":6113,"title":6114,"module":6106,"summary":6115},"\u002Fabstract-algebra\u002Ffield-theory\u002Fsplitting-fields-and-algebraic-closure","Splitting Fields and Algebraic Closure","The splitting field of a polynomial is the smallest extension in which it factors into linear pieces, obtained by adjoining all its roots. Every polynomial has one, its degree is at most n factorial, and any two splitting fields are isomorphic. Pushing this to all polynomials at once gives the algebraic closure, a field in which every polynomial splits and which is unique up to isomorphism.\n",{"path":6117,"title":6118,"module":6106,"summary":6119},"\u002Fabstract-algebra\u002Ffield-theory\u002Fseparable-and-cyclotomic-extensions","Separable Extensions and Cyclotomic Fields","A polynomial is separable when its roots are distinct, detected by whether it shares a factor with its formal derivative. Over perfect fields — characteristic zero and finite fields — every irreducible is separable, and the existence and uniqueness of the finite fields follow. Cyclotomic polynomials package the roots of unity by order, are irreducible over the rationals, and give the cyclotomic field its degree phi(n).\n",{"path":6121,"title":6122,"module":6123,"summary":6124},"\u002Fabstract-algebra\u002Fgalois-theory\u002Fthe-galois-correspondence","The Galois Correspondence","Galois Theory","Galois theory attaches to a field extension its group of symmetries and shows that, for the right extensions, the subgroups of that group are in exact order-reversing correspondence with the intermediate fields. The automorphism group, Artin's theorem, the characterization of Galois extensions, and the Fundamental Theorem together turn questions about fields into questions about finite groups.\n",{"path":6126,"title":6127,"module":6123,"summary":6128},"\u002Fabstract-algebra\u002Fgalois-theory\u002Ffinite-fields","Finite Fields","Every finite field has prime-power order, is the splitting field of $x^{p^n} - x$, and is unique up to isomorphism. Its extension over the prime field is Galois with cyclic group generated by the Frobenius map $x \\mapsto x^p$, so the Galois correspondence reduces the subfield lattice to the divisor lattice of $n$. Möbius inversion counts the irreducible polynomials of each degree, and cyclic error-correcting codes are one application.\n",{"path":6130,"title":6131,"module":6123,"summary":6132},"\u002Fabstract-algebra\u002Fgalois-theory\u002Fcyclotomic-and-abelian-extensions","Cyclotomic and Abelian Extensions","The Galois group of the $n$th cyclotomic field over $\\mathbb{Q}$ is the unit group $(\\mathbb{Z}\u002Fn\\mathbb{Z})^\\times$, which makes cyclotomic fields the worked catalogue of abelian extensions of $\\mathbb{Q}$. The isomorphism identifies subfields with subgroups, realizes every finite abelian group as a Galois group over $\\mathbb{Q}$, and leads to Kronecker–Weber. Composites of Galois extensions and the primitive element theorem supply the machinery.\n",{"path":6134,"title":6135,"module":6123,"summary":6136},"\u002Fabstract-algebra\u002Fgalois-theory\u002Fgalois-groups-of-polynomials","Galois Groups of Polynomials","Ordering the roots of a separable polynomial embeds its Galois group in the symmetric group $S_n$, and the group is transitive exactly when the polynomial is irreducible. The discriminant decides membership in $A_n$; for cubics and quartics the resolvent cubic pins the group down; and reduction modulo a prime produces elements of prescribed cycle type, the standard tool for computing Galois groups over $\\mathbb{Q}$.\n",{"path":6138,"title":6139,"module":6123,"summary":6140},"\u002Fabstract-algebra\u002Fgalois-theory\u002Fsolvability-by-radicals-and-the-quintic","Solvability by Radicals and the Quintic","A polynomial is solvable by radicals exactly when its Galois group is solvable. Cyclic extensions are radical extensions once roots of unity are present, which turns a radical tower into a solvable subnormal series. Since $S_n$ is solvable only for $n \\le 4$, the general quintic has no radical formula, and an explicit quintic with Galois group $S_5$ has roots provably not expressible in radicals.\n",{"path":6142,"title":6143,"module":6144,"summary":6145},"\u002Fabstract-algebra\u002Fcapstone\u002Fcommutative-algebra-and-algebraic-geometry","A Glimpse of Commutative Algebra and Algebraic Geometry","Capstone: Where Algebra Goes Next","Commutative algebra reads geometry off the ring of polynomial functions. The dictionary runs through Noetherian rings and the ascending chain condition, Hilbert's Basis Theorem, affine algebraic sets and the two maps connecting ideals to zero sets, radicals, the Zariski topology, and Hilbert's Nullstellensatz, which over an algebraically closed field makes radical ideals and algebraic sets the same object.\n",{"path":6147,"title":6148,"module":6144,"summary":6149},"\u002Fabstract-algebra\u002Fcapstone\u002Frepresentation-and-character-theory","A Glimpse of Representation and Character Theory","Representation theory studies a group by the ways it can act linearly on a vector space. Representations are equivalent to modules over the group ring; Maschke's theorem gives complete reducibility, the Wedderburn consequences bound the irreducible degrees, and character theory reduces a representation to a trace invariant governed by the orthogonality relations and displayed in the character table of a small group.\n",{"path":6151,"title":6152,"module":6,"summary":6},"\u002Fabstract-algebra","Abstract Algebra",{"path":6154,"title":6155,"module":6156,"summary":6157},"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fatomic-spectra-rutherford","Atomic Spectra and Rutherford's Nucleus","Early Atomic Models and the Old Quantum Theory","Atoms emit light only at sharp, reproducible wavelengths, and by 1890 those wavelengths were captured by the Rydberg-Ritz formula. Neither empirical regularity had a mechanical explanation. Rutherford's alpha-scattering experiment supplied the missing structure: the atom's positive charge and nearly all its mass sit in a tiny central nucleus, with the electrons far outside.\n",{"path":6159,"title":6160,"module":6156,"summary":6161},"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-model-hydrogen","The Bohr Model of Hydrogen","Bohr grafted three quantum postulates onto Rutherford's nuclear atom: certain orbits do not radiate, radiation accompanies a jump between them, and quantization must match classical physics for large orbits. Quantizing the angular momentum fixes the orbit radii and energies, reproduces the Rydberg-Ritz formula, and predicts the Rydberg constant from fundamental constants alone.\n",{"path":6163,"title":6164,"module":6156,"summary":6165},"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fx-ray-spectra-franck-hertz","X-Ray Spectra and the Franck-Hertz Experiment","Two 1913-14 experiments confirmed the Bohr-Rutherford atom independently of optical spectra. Moseley found that the square root of a characteristic X-ray frequency is linear in atomic number, fixing Z as nuclear charge and ordering the periodic table. Franck and Hertz measured discrete atomic energy levels directly by scattering electrons through a mercury vapor.\n",{"path":6167,"title":6168,"module":6156,"summary":6169},"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-sommerfeld-old-quantum-theory","The Bohr-Sommerfeld Old Quantum Theory","Bohr fixed the hydrogen levels with a single quantum number by quantizing angular momentum. Sommerfeld replaced that ad hoc rule with a general prescription: quantize the action of each separable coordinate. The rule produces elliptical orbits, a second (azimuthal) quantum number, space quantization, and — once the relativistic mass variation is included — a fine-structure splitting that matches experiment to order alpha squared.\n",{"path":6171,"title":6172,"module":6156,"summary":6173},"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fold-quantum-theory-limits-wkb","Limits of the Old Quantum Theory and the WKB Bridge","The old quantum theory works only where the classical motion is separable into independent periodic coordinates. It fails for helium, forbids the correct zero angular momentum of the hydrogen ground state, and misses the half-integer in the oscillator and in molecular spectra. The WKB quantization condition, derived from the Schrodinger equation, is the modern descendant of the Sommerfeld rule and repairs the half-integer through the Maslov correction.\n",{"path":6175,"title":6176,"module":6177,"summary":6178},"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fschrodinger-3d-hydrogen","The Schrödinger Equation in Three Dimensions and Hydrogen","The Quantum Hydrogen Atom","Extending the Schrödinger equation to three dimensions and separating it in spherical coordinates produces three ordinary differential equations, one per coordinate. Their boundary conditions generate the quantum numbers n, ℓ, and mℓ, quantize the angular momentum to √(ℓ(ℓ+1))ℏ with projections mℏ, and fix the bound-state energies of hydrogen at −Z²(13.6 eV)\u002Fn².\n",{"path":6180,"title":6181,"module":6177,"summary":6182},"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fhydrogen-wave-functions","Hydrogen Wave Functions and Orbitals","The hydrogen wave functions factor into a radial part Rₙℓ(r) and an angular spherical harmonic Yℓm(θ,φ). Squaring gives the probability cloud; the radial distribution P(r) = r²|ψ|² peaks at the Bohr radius for the ground state and at the Bohr orbits for excited states. The angular part fixes the s, p, and d orbital shapes that govern chemical bonding.\n",{"path":6184,"title":6185,"module":6177,"summary":6186},"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fradial-equation-in-full","Solving the Radial Equation in Full","The hydrogen radial equation is solved from the differential equation up. The substitution u = rR turns it into a one-dimensional problem with a centrifugal barrier; matching the asymptotic behaviour at the origin and at infinity peels off the factors r^(ℓ+1) and e^(−r\u002Fna₀); a Frobenius series for the remainder must terminate, and that termination condition yields the quantization n ≥ ℓ+1 with E = −Z²Ry\u002Fn². The surviving polynomials are the associated Laguerre functions, whose degree n−ℓ−1 counts the radial nodes.\n",{"path":6188,"title":6189,"module":6177,"summary":6190},"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fsymmetry-degeneracy-runge-lenz","Accidental Degeneracy and the Runge-Lenz Symmetry","Hydrogen energies depend only on n, so states of different ℓ at the same n are degenerate. This is not a coincidence but the mark of a hidden symmetry: the quantum Runge-Lenz vector is conserved for the 1\u002Fr potential alone, and together with angular momentum it generates the group SO(4). The Casimir invariant of that group reproduces E = −Z²Ry\u002Fn² and its representations count the n² states. Any departure from 1\u002Fr breaks the symmetry and lifts the ℓ-degeneracy.\n",{"path":6192,"title":6193,"module":6177,"summary":6194},"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fexpectation-values-virial","Expectation Values, the Virial Theorem, and Scaling","The radial matrix elements ⟨r^k⟩ of hydrogenic states are the raw material of every later correction. This lesson derives ⟨1\u002Fr⟩ from the virial theorem, builds the full family ⟨r⟩, ⟨r²⟩, ⟨1\u002Fr²⟩, ⟨1\u002Fr³⟩ from Kramers' recursion and the Feynman-Hellmann theorem, and reads off their scaling with n, ℓ, and Z. The virial balance ⟨T⟩ = −½⟨V⟩ = −E fixes the energy budget of every bound state.\n",{"path":6196,"title":6197,"module":6177,"summary":6198},"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fquantum-defects-alkali-spectra","Quantum Defects and Alkali Spectra","An alkali atom is one valence electron outside a closed-shell core, and to a good approximation it is hydrogen with a modified quantum number. Core penetration makes low-ℓ states more bound than the Coulomb formula predicts, and the shortfall is captured by a single number per ℓ, the quantum defect δℓ. The spectrum then follows the Rydberg formula with n replaced by the effective n − δℓ, and the sodium D-line doublet is the worked case.\n",{"path":6200,"title":6201,"module":6177,"summary":6202},"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Frydberg-atoms","Rydberg Atoms","A Rydberg atom is an atom excited to a very high principal quantum number, and every hydrogenic property becomes exaggerated by a power of n. Size grows as n², binding falls as n⁻², radiative lifetime lengthens as n³, and the static polarizability explodes as n⁷. The levels crowd toward the ionization limit, and the enormous dipole interaction between two Rydberg atoms produces the blockade that underlies neutral-atom quantum computing.\n",{"path":6204,"title":6205,"module":6206,"summary":6207},"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Frelativistic-kinetic-correction","The Relativistic Kinetic-Energy Correction","Fine Structure and the Dirac Atom","The Bohr energies treat the electron as slowly moving, but its speed is of order αc, so the kinetic energy needs a relativistic correction. Expanding √(p²c²+m²c⁴) to order (v\u002Fc)² produces the perturbation −p⁴\u002F8m³c², whose first-order shift on a hydrogenic state is evaluated with the trick p²=2m(E−V). The result depends on n and ℓ, is smaller than the gross structure by α²≈5×10⁻⁵, and is one of the three pieces that combine into the fine-structure formula.\n",{"path":6209,"title":6210,"module":6206,"summary":6211},"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fspin-orbit-thomas-precession","Spin-Orbit Coupling and Thomas Precession","In the electron's rest frame the nucleus orbits it, and the resulting current produces a magnetic field that couples to the electron's spin moment. The interaction is ξ(r) L·S, with ξ built from the Coulomb potential and the radial expectation ⟨1\u002Fr³⟩. A relativistic subtlety, Thomas precession, halves the naive coefficient because the electron's rest frame is accelerating. The result splits each ℓ≥1 level into a j=ℓ±½ doublet and makes (n, ℓ, j, mⱼ) the good quantum numbers.\n",{"path":6213,"title":6214,"module":6206,"summary":6215},"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdarwin-term-fine-structure-formula","The Darwin Term and the Fine-Structure Formula","The third fine-structure correction, the Darwin term, is a contact interaction proportional to ∇²V that acts only on s-states, physically a smearing of the electron over a Compton wavelength. Adding the relativistic, spin-orbit, and Darwin shifts, the ℓ-dependence cancels and the total collapses to a formula in n and j alone. The n=2 shell splits into 2S₁\u002F₂, 2P₁\u002F₂, 2P₃\u002F₂, with the two j=½ levels exactly degenerate, a coincidence the Dirac theory explains.\n",{"path":6217,"title":6218,"module":6206,"summary":6219},"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdirac-equation-hydrogen","The Dirac Equation for Hydrogen","The fine-structure formula was assembled from three perturbations; the Dirac equation produces it in one stroke and exactly. A first-order relativistic wave equation forces a four-component spinor, from which spin s=½, the g-factor of 2, the spin-orbit term, and antiparticles all emerge automatically. Its exact Coulomb spectrum depends only on n and j, and expanding in Zα reproduces the perturbative result, including the 2S₁\u002F₂–2P₁\u002F₂ degeneracy that sets up the Lamb shift.\n",{"path":6221,"title":6222,"module":6223,"summary":6224},"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Flamb-shift-qed","The Lamb Shift and QED Radiative Corrections","QED Corrections and Hyperfine Structure","The Dirac equation makes the 2S₁\u002F₂ and 2P₁\u002F₂ levels of hydrogen exactly degenerate. Lamb and Retherford measured a splitting of about 1058 MHz that the Dirac theory cannot produce. The gap comes from the electron's coupling to the quantized electromagnetic field: self-energy, vacuum polarization, and the anomalous magnetic moment. Welton's vacuum-fluctuation estimate reproduces the size and shows why the effect lands almost entirely on s-states, and the same radiative corrections make hydrogen the most stringent test of QED.\n",{"path":6226,"title":6227,"module":6223,"summary":6228},"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fhyperfine-structure-21cm","Hyperfine Structure and the 21 cm Line","The proton carries a magnetic moment, and it interacts with the magnetic field the electron produces at the nucleus. For s-states that interaction is the Fermi contact term, proportional to the electron density at the origin and to the dot product of the nuclear and electronic spins. Coupling I and J into F = I + J splits each level by a Landé interval rule; in hydrogen's ground state it produces the F = 0\u002FF = 1 doublet whose 1420 MHz, 21 cm transition maps neutral hydrogen across the galaxy.\n",{"path":6230,"title":6231,"module":6223,"summary":6232},"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fnuclear-effects-isotope-shift","Nuclear Size, Moments, and Isotope Shifts","A real nucleus has a finite size, a mass that changes between isotopes, and, when its spin is at least one, an electric quadrupole moment. Each leaves a fingerprint in the atomic spectrum: the volume shift from s-electrons sampling the charge distribution, the mass and field isotope shifts that separate on a King plot, the quadrupole interaction that breaks the Landé interval rule, and the hyperfine anomaly from the magnetization distribution. Atomic spectroscopy reads nuclear properties out of these shifts.\n",{"path":6234,"title":6235,"module":6236,"summary":6237},"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fperiodic-table-atomic-spectra","The Periodic Table and Atomic Spectra","Many-Electron Atoms","Identical electrons demand antisymmetric wave functions, which is the Pauli exclusion principle: no two electrons share all four quantum numbers. Filling shells in order of increasing energy — shifted by penetration and shielding — builds the periodic table and its recurring ionization pattern. Selection rules govern optical spectra, and an external field splits lines by the Zeeman effect.\n",{"path":6239,"title":6240,"module":6236,"summary":6241},"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fcentral-field-self-consistent","The Central-Field Approximation and the Self-Consistent Field","The N-electron Hamiltonian does not separate because every pair of electrons repels. The central-field approximation replaces that pairwise repulsion with an averaged spherical potential each electron feels, restoring hydrogen-like orbitals labelled by n and ℓ. The Thomas-Fermi statistical model fixes the shape of the screened charge from Fermi-gas thermodynamics; the Hartree self-consistent field determines it exactly by iterating orbitals against the potential they generate until the two agree.\n",{"path":6243,"title":6244,"module":6236,"summary":6245},"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fidentical-particles-hartree-fock","Exchange, Slater Determinants, and Hartree-Fock","A product wave function ignores that electrons are identical fermions. Enforcing antisymmetry writes the state as a Slater determinant, which vanishes whenever two electrons share a spin-orbital — the exclusion principle made algebraic. The energy of a determinant carries a new term with no classical analogue, the exchange integral, nonzero only for parallel spins; it lowers the energy of aligned electrons and carves a Fermi hole around each one. Adding the exchange operator to the mean field gives the Hartree-Fock equations, and what they still miss defines the correlation energy.\n",{"path":6247,"title":6248,"module":6236,"summary":6249},"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhelium-two-electron-atom","Helium: the Prototype Two-Electron Atom","Helium is the smallest atom the Schrödinger equation cannot solve exactly, and the smallest that shows every many-electron effect. Ignoring the electron repulsion overbinds the ground state by 30 eV; first-order perturbation theory and a one-parameter variational calculation with an effective charge close most of the gap. The excited configurations split into para (singlet) and ortho (triplet) states separated by the exchange integral, with the triplet lower — and the absence of a 1s² triplet is the Pauli principle in its plainest form.\n",{"path":6251,"title":6252,"module":6236,"summary":6253},"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fls-jj-coupling-term-symbols","LS and jj Coupling; Term Symbols","A configuration is not a single energy level. The residual electrostatic repulsion and the spin-orbit interaction split it, and which one dominates fixes the coupling scheme. In light atoms the electrostatic term wins: orbital and spin angular momenta couple separately into L and S, then into J, giving Russell- Saunders term symbols. In heavy atoms spin-orbit wins and each electron's j forms first. The Pauli principle prunes the allowed terms of equivalent electrons, the Landé interval rule spaces the fine-structure multiplet, and the scheme crosses over from LS to jj down a column.\n",{"path":6255,"title":6256,"module":6236,"summary":6257},"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhund-rules-ground-terms","Hund's Rules and Ground-State Terms","A configuration allows several terms; Hund's three rules pick the ground one. Maximize the spin S first, then the orbital L, then set J to |L−S| for a less-than-half shell and L+S for a more-than-half shell. The first two rules come from exchange lowering the energy of apart-kept electrons; the third comes from the sign of the spin-orbit coupling, which flips as a shell passes half-filling and turns the multiplet from normal to inverted. Worked ground terms for carbon, nitrogen, oxygen, and iron show the rules in action.\n",{"path":6259,"title":6260,"module":6261,"summary":6262},"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fzeeman-effect","The Zeeman Effect","Atoms in External Fields","A magnetic field couples to the atom through its magnetic moment, splitting each level into equally spaced sublevels labelled by the projection of the total angular momentum. When spin is present the spacing is not the classical one: it carries the Landé g-factor, a projection of the spin and orbital moments onto the total angular momentum. We derive the weak-field Hamiltonian from minimal coupling, evaluate the shift with the projection theorem, and read off the polarization of the emitted components.\n",{"path":6264,"title":6265,"module":6261,"summary":6266},"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fpaschen-back-intermediate","The Paschen-Back and Intermediate-Field Regimes","When the magnetic interaction grows past the fine-structure coupling, spin and orbital angular momentum decouple and precess independently about the field. The anomalous Zeeman pattern reverts to a simple triplet, the Paschen-Back effect. Between the two limits neither coupling dominates and the level positions follow from diagonalizing the combined spin-orbit and Zeeman Hamiltonian. We build the two-by-two problem for a single valence electron, solve it in closed form, and show both limits emerge from one expression.\n",{"path":6268,"title":6269,"module":6261,"summary":6270},"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fstark-effect-polarizability","The Stark Effect and Field Ionization","An electric field shifts atomic levels by coupling to the electron's position. Parity forbids a first-order shift for a non-degenerate state, so most atoms respond only at second order through their polarizability, a quadratic Stark shift. Hydrogen is the exception: its accidental degeneracy admits a permanent dipole and a linear shift, cleanest in parabolic coordinates. At large fields the Coulomb well develops a saddle, and Rydberg states field-ionize at a threshold that falls as the fourth power of the principal quantum number.\n",{"path":6272,"title":6273,"module":6274,"summary":6275},"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Ftime-dependent-perturbation-golden-rule","Time-Dependent Perturbation Theory and the Golden Rule","Radiative Transitions and Spectral Lines","An atom in a weak oscillating field makes transitions between its stationary states. First-order time-dependent perturbation theory gives the transition amplitude as a Fourier component of the perturbation at the Bohr frequency, and the resulting probability is a sinc-squared resonance that sharpens as the field acts longer. For a two-level system the same coupling produces Rabi oscillations; for a transition into a continuum the long-time limit collapses the sinc-squared into a delta function and yields Fermi's golden rule, a constant transition rate set by the coupling strength and the density of final states.\n",{"path":6277,"title":6278,"module":6274,"summary":6279},"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fdipole-approximation-einstein-coefficients","The Dipole Approximation and Einstein Coefficients","The coupling between an atom and light is the interaction of the electron with the electromagnetic field. Because an optical wavelength dwarfs the atom, the spatial variation of the field across the atom can be dropped, leaving the electric-dipole interaction and its matrix element. That matrix element defines the oscillator strength, which obeys the Thomas-Reiche-Kuhn sum rule. Einstein's three rate coefficients (absorption, stimulated emission, spontaneous emission) follow from detailed balance with thermal radiation, fixing the ratio of spontaneous to stimulated rates and its steep growth with frequency.\n",{"path":6281,"title":6282,"module":6274,"summary":6283},"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fselection-rules-forbidden-transitions","Selection Rules and Forbidden Transitions","The dipole matrix element vanishes for most pairs of states, and the pattern of which survive is the set of selection rules. Parity forces the orbital angular momentum to change by one; the angular integral of three spherical harmonics restricts the magnetic quantum number to change by zero or one; the photon's spin restricts the total angular momentum. When the dipole element vanishes, higher multipoles (magnetic dipole and electric quadrupole) can still drive the transition at rates smaller by powers of the fine-structure constant, and states with no allowed decay become metastable.\n",{"path":6285,"title":6286,"module":6274,"summary":6287},"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Flifetimes-and-line-shapes","Lifetimes, Line Widths, and Line Shapes","A spectral line is never infinitely sharp. The finite lifetime of the excited state gives every line a natural Lorentzian width set by the total decay rate, the Fourier transform of an exponentially damped emission. Thermal motion adds a Gaussian Doppler width that usually dominates in a gas; collisions add a further Lorentzian pressure width; the observed profile is the Voigt convolution of the Gaussian and Lorentzian parts. Strong driving fields broaden the line further through saturation. Each mechanism has a distinct dependence on temperature, density, and intensity that lets it be identified and, where possible, removed.\n",{"path":6289,"title":6290,"module":6291,"summary":6292},"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Flaser-principles","Population Inversion, Gain, and the Laser","Lasers and Spectroscopy","A laser is an optical amplifier placed inside a resonant cavity. Amplification requires that stimulated emission outrun absorption, which requires more atoms in the upper level than the lower one — a population inversion that the Einstein relations forbid in thermal equilibrium and that no two-level pump can produce. Three- and four-level schemes reach it by routing atoms through auxiliary states. The gain coefficient sets how strongly a weak beam grows, the cavity fixes the threshold and selects a comb of longitudinal modes, and gain saturation clamps the steady-state inversion at its threshold value.\n",{"path":6294,"title":6295,"module":6291,"summary":6296},"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fspectroscopy-techniques","Spectroscopic Techniques and Frequency Combs","A tunable laser turns spectroscopy from photographing a spectrum into interrogating a single transition, but at room temperature the Doppler width buries the natural linewidth under a thousandfold-broader Gaussian. Saturated absorption and two-photon spectroscopy defeat the first-order Doppler shift by selecting the zero-velocity class or cancelling the shift between counter-propagating photons, recovering natural-width features. Laser-induced fluorescence pushes sensitivity to single atoms, and the optical frequency comb converts an optical frequency into a countable radio-frequency beat, giving absolute frequency measurement across the visible spectrum.\n",{"path":6298,"title":6299,"module":6291,"summary":6300},"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fline-catalog-nist-asd","Reading Real Spectra with the NIST Database","Every quantity computed in this course — energy levels, transition frequencies, oscillator strengths, lifetimes — is tabulated for real atoms in the NIST Atomic Spectra Database. This lesson reads that data as physics: how levels are labelled by term symbols and energies in wavenumbers, how a transition list encodes wavelength, Einstein coefficient, and line strength, how a Grotrian diagram is reconstructed from the tables, and how a measured spectrum is matched to catalog lines. The residual between computed and tabulated positions is the running score of atomic theory.\n",{"path":6302,"title":6303,"module":6304,"summary":6305},"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Flaser-cooling-doppler","Laser Cooling and Optical Molasses","Modern Atomic Physics","A near-resonant laser beam pushes an atom because every absorbed photon delivers one unit of momentum and the subsequent spontaneous emission averages to zero. Two counter-propagating red-detuned beams turn that push into friction: the Doppler shift brings a moving atom closer to resonance with the beam it moves against, so the net force opposes the velocity. Six beams give optical molasses in three dimensions. The random recoil of spontaneous emission heats against the friction, and the balance sets the Doppler cooling limit. Adding a magnetic-field gradient makes the force position-dependent as well, giving the magneto-optical trap.\n",{"path":6307,"title":6308,"module":6304,"summary":6309},"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fsub-doppler-trapping","Sub-Doppler Cooling and Atom Traps","Optical molasses cools multilevel atoms below the Doppler limit. A polarization gradient plus optical pumping makes an atom repeatedly climb a light-shift hill and be pumped to the valley, losing kinetic energy each cycle — Sisyphus cooling. The floor is the recoil limit, one photon momentum of residual motion. Below it, cooling must avoid scattering photons: conservative magnetic and optical-dipole traps hold the atoms while forced evaporation removes the hot tail, driving the phase-space density up toward quantum degeneracy.\n",{"path":6311,"title":6312,"module":6304,"summary":6313},"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fbose-einstein-condensation","Bose-Einstein Condensation of Atomic Gases","Below a critical temperature a gas of identical bosons places a macroscopic fraction of its atoms in the single lowest-energy state. The transition occurs when the thermal de Broglie wavelength grows to the interparticle spacing, so the phase- space density reaches order unity. The critical temperature follows from the Bose-Einstein distribution and the density of states, the condensate fraction grows as one minus (T\u002FTc) to the three-halves, and the condensate reveals itself in time-of-flight as a sharp bimodal peak in the momentum distribution. The 1995 rubidium and sodium experiments realized it in dilute trapped gases.\n",{"path":6315,"title":6316,"module":6304,"summary":6317},"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Foptical-clocks-precision","Optical Atomic Clocks and Precision Measurement","An atomic clock counts the oscillations of a field locked to an atomic transition. The cesium microwave standard defines the second through the 9.19 GHz ground-state hyperfine transition, interrogated by Ramsey's separated-oscillatory-field method whose fringe width is set by the free-precession time. Optical clocks replace the microwave transition with an optical one five orders of magnitude higher in frequency, raising the quality factor and the fractional stability in proportion. Lattice and single-ion clocks reach fractional uncertainties near ten-to-the-minus- eighteen by trapping the atoms at a magic wavelength that cancels the light shift, and at that level they measure the gravitational redshift over centimetres of height.\n",{"path":6319,"title":6320,"module":6,"summary":6},"\u002Fatomic-physics","Atomic Physics",{"path":6322,"title":6323,"module":6,"summary":6},"\u002Fdatabases","Databases",{"path":6325,"title":6326,"module":3911,"summary":6327},"\u002Fcategory-theory\u002Ffoundations\u002Fwhat-is-a-category","Categories, Objects, and Arrows","A category is objects, arrows between them, a rule for composing arrows, and an identity arrow on every object, subject to associativity and the unit laws. The axioms mention no elements: arrows need not be functions, and an object is known only through the arrows into and out of it. Isomorphism, commutative diagrams, duality, and the terminal object are the first consequences.\n",{"path":6329,"title":6330,"module":3911,"summary":6331},"\u002Fcategory-theory\u002Ffoundations\u002Fexamples-of-categories","A Zoo of Categories","The axioms admit two very different kinds of model: large categories of structured sets and their structure-preserving maps (Set, Mon, Grp, Top, Vect), and small categories that are themselves single algebraic objects — a monoid as a one-object category, a poset as a thin category. The awkward cases Rel and Pfn have sets as objects but relations and partial functions as arrows, and a typed programming language presents its types and programs as a category.\n",{"path":6333,"title":6334,"module":3911,"summary":6335},"\u002Fcategory-theory\u002Ffoundations\u002Fspecial-morphisms","Isomorphisms, Monos, and Epis","Injectivity and surjectivity mention elements, so a general category re-expresses them by cancellation: monomorphisms cancel on the left, epimorphisms on the right. Sections and retractions are the split versions with an explicit one-sided inverse. Mono plus epi does not force an isomorphism, and subobjects are equivalence classes of monos into a fixed object.\n",{"path":6337,"title":6338,"module":3911,"summary":6339},"\u002Fcategory-theory\u002Ffoundations\u002Ffunctors","Functors: Maps Between Categories","A functor sends objects to objects and arrows to arrows while preserving composition and identities. Covariant and contravariant functors, the standard stock (forgetful, free, hom, and powerset), and the classification by faithfulness, fullness, and essential surjectivity all follow. Functors compose, so categories and functors form a category themselves.\n",{"path":6341,"title":6342,"module":3911,"summary":6343},"\u002Fcategory-theory\u002Ffoundations\u002Fnatural-transformations","Natural Transformations and Functor Categories","A natural transformation is a map between two parallel functors: one component arrow per object, subject to a commuting square for every arrow of the source. Naturality is verified for the determinant, the double dual, and list operations; functors and natural transformations form the functor category [C, D]; and vertical and horizontal composition satisfy the Godement interchange law.\n",{"path":6345,"title":6346,"module":3911,"summary":6347},"\u002Fcategory-theory\u002Ffoundations\u002Fsize-and-set-theory","Size: Small, Large, Locally Small","The objects of Set do not form a set, and pretending otherwise reproduces the classical paradoxes. Classes make the small\u002Flarge distinction precise, with locally small and essentially small as the intermediate notions. Cantor's theorem shows Set and its algebraic relatives are large, and the function-based axiomatization of sets is the one category theory prefers to ZFC.\n",{"path":6349,"title":6350,"module":6351,"summary":6352},"\u002Fcategory-theory\u002Funiversal-properties\u002Funiversal-properties","Universal Properties, Initial and Terminal Objects","Universal Properties and Basic Constructions","A universal property characterizes an object by a for-all\u002Fexists-unique condition on the arrows into or out of it, and any two objects satisfying the same property are isomorphic by a unique isomorphism. Initial and terminal objects are the simplest cases; the free vector space, the discrete topology, and the ring of integers show the pattern at work.\n",{"path":6354,"title":6355,"module":6351,"summary":6356},"\u002Fcategory-theory\u002Funiversal-properties\u002Fproducts-and-coproducts","Products and Coproducts","The product of two objects is a wedge of projections through which every other wedge factors uniquely; the coproduct is the dual, built from injections. In Set these are the cartesian product and the disjoint union, in a poset the meet and join, and in abelian groups the two coincide. The mediating-arrow discipline established here is the template for all limits.\n",{"path":6358,"title":6359,"module":6351,"summary":6360},"\u002Fcategory-theory\u002Funiversal-properties\u002Fconstructions-on-categories","Opposite, Product, Slice, and Comma Categories","Categories are themselves mathematical structures, and the standard algebraic constructions apply: opposites, products, subcategories, slices, and the comma category that subsumes them. The opposite category yields the duality principle, halving the subject's proofs; slice and comma categories repackage every universal property as an initial or terminal object.\n",{"path":6362,"title":6363,"module":6364,"summary":6365},"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Frepresentable-functors","Hom-Functors and Representables","Representables and the Yoneda Lemma","Fixing an object A of a locally small category produces a set-valued functor, the hom-functor A(A,-), that records every map out of A. A functor is representable when it is naturally isomorphic to such a hom-functor. We define the covariant and contravariant hom-functors, collect the standard representables (identity, forgetful, powerset), and read maps as generalized elements of varying shape.\n",{"path":6367,"title":6368,"module":6364,"summary":6369},"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-lemma","The Yoneda Lemma","The Yoneda lemma computes the natural transformations out of a representable presheaf: they form a set in natural bijection with X(A). The proof fixes a single degree of freedom, the image of the identity arrow, and shows naturality forces everything else. We prove the bijection, verify naturality in both variables, and read off that a natural transformation out of a representable is just one element.\n",{"path":6371,"title":6372,"module":6364,"summary":6373},"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-consequences","The Yoneda Embedding and Its Uses","Three corollaries turn the Yoneda lemma into working machinery. A representation of a presheaf is the same thing as a universal element; the Yoneda embedding of a category into its presheaf category is full and faithful; and two objects are isomorphic exactly when their representables are. Together they justify constructing arrows by constructing natural transformations between hom-functors, and they contain Cayley's theorem as the one-object case.\n",{"path":6375,"title":6376,"module":6377,"summary":6378},"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits","Cones and Limits","Limits and Colimits","A diagram is a functor from a small shape category; a cone over it is an object with compatible legs to every node; and a limit is the terminal cone, the one every other cone factors through uniquely. Products and terminal objects reappear as limits over particular shapes, and the whole construction is unique up to a single isomorphism.\n",{"path":6380,"title":6381,"module":6377,"summary":6382},"\u002Fcategory-theory\u002Flimits-colimits\u002Fproducts-equalizers-pullbacks","Equalizers and Pullbacks","The equalizer of a parallel pair is the universal arrow that makes the two composites agree; the pullback of a cospan is the universal commutative square. In Set they are solution sets and fibered products, every equalizer is monic, monics are stable under pullback, and products plus equalizers together generate all limits.\n",{"path":6384,"title":6385,"module":6377,"summary":6386},"\u002Fcategory-theory\u002Flimits-colimits\u002Fcolimits","Colimits: Coproducts, Coequalizers, Pushouts","Colimits are limits in the opposite category: cocones replace cones, and the universal cocone is initial rather than terminal. Coproducts glue objects side by side, coequalizers impose relations and produce quotients, pushouts glue along a shared part, and in Set every colimit is a quotient of a disjoint union. Directed colimits admit a clean elementwise description.\n",{"path":6388,"title":6389,"module":6377,"summary":6390},"\u002Fcategory-theory\u002Flimits-colimits\u002Fcomputing-limits","Computing Limits in Concrete Categories","In Set the limit of any diagram is the set of threads: choice functions through the nodes that commute with every edge. In Pos, Mon, and Top the recipe is the same limit downstairs plus the unique structure that makes the projections structure-preserving — pointwise order, componentwise operations, the topology generated by the projections. The pattern is what \"the forgetful functor creates limits\" means concretely.\n",{"path":6392,"title":6393,"module":6377,"summary":6394},"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits-and-functors","Preservation, Reflection, and Creation of Limits","A functor preserves limits if it sends limit cones to limit cones, reflects them if it recognizes them, and creates them if limits downstairs lift uniquely upstairs. Representable functors preserve all limits, forgetful functors from algebra create them, and limits in functor categories are computed pointwise, one evaluation at a time.\n",{"path":6396,"title":6397,"module":6398,"summary":6399},"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions","Adjoint Functors via Hom-Set Bijections","Adjunctions","An adjunction is a natural bijection between two hom-sets: maps out of $F(A)$ in one category correspond to maps into $G(B)$ in the other. We give the definition, spell out the naturality axioms that make the correspondence compatible with composition, and work the flagship examples — free vector spaces, free groups, discrete and indiscrete topologies, and currying.\n",{"path":6401,"title":6402,"module":6398,"summary":6403},"\u002Fcategory-theory\u002Fadjunctions\u002Funits-and-counits","Units, Counits, and the Triangle Identities","The whole hom-set bijection of an adjunction is generated by two natural transformations: the unit, obtained by transposing identity maps on one side, and the counit, by transposing them on the other. Two triangle identities are all they must satisfy, and any pair satisfying them determines a unique adjunction. The same correspondence specializes to order-preserving maps between posets and to free constructions.\n",{"path":6405,"title":6406,"module":6398,"summary":6407},"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions-via-universal-arrows","Adjunctions from Universal Arrows","The unit component at a single object is an initial object of a comma category, and this universal property alone rebuilds the whole adjunction. A functor has a left adjoint exactly when every object admits such a universal arrow, and the left adjoint is assembled from them one object at a time. We prove the equivalence of all three formulations of adjointness.\n",{"path":6409,"title":6410,"module":6398,"summary":6411},"\u002Fcategory-theory\u002Fadjunctions\u002Ffree-forgetful-adjunctions","Free Constructions and Free–Forgetful Adjunctions","Free monoids, free groups, and free vector spaces are left adjoints to forgetful functors, and the universal mapping property is all one needs to prove it. Some forgetful functors also have right adjoints (co-free constructions like the indiscrete topology), producing three-functor chains. Contravariant adjunctions, symmetric in their two functors, close the lesson with the pattern behind duality and representation theorems.\n",{"path":6413,"title":6414,"module":6415,"summary":6416},"\u002Fcategory-theory\u002Fadjoints-limits\u002Flimits-via-adjoints","Limits as Adjoints and as Representables","Adjoints, Representables, and Limits Together","A cone on a diagram is a natural transformation from a constant diagram, so a limit is a representation of the cone functor and, equivalently, a value of the right adjoint to the diagonal functor. We prove both rephrasings, derive uniqueness and functoriality of limits from them, and record the dual statement that a colimit is the left adjoint to the diagonal.\n",{"path":6418,"title":6419,"module":6415,"summary":6420},"\u002Fcategory-theory\u002Fadjoints-limits\u002Fpresheaf-limits-colimits","Limits and Colimits of Presheaves","Representables preserve limits, and limits in a functor category are computed one object at a time, so a presheaf category is complete and cocomplete with all its structure inherited pointwise from Set. The Yoneda embedding then preserves limits but not colimits, and the density theorem repairs the colimit side: every presheaf is a canonical colimit of representables.\n",{"path":6422,"title":6423,"module":6415,"summary":6424},"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoints-preserve-limits","Right Adjoints Preserve Limits (RAPL)","A functor with a left adjoint preserves every limit that exists, and dually a functor with a right adjoint preserves colimits. The proof is a four-line chain of natural isomorphisms through the adjunction and the continuity of representables. The theorem yields product-and-exponential arithmetic in Set, another proof that limits commute with limits, and a standard test for proving that a functor has no adjoint.\n",{"path":6426,"title":6427,"module":6415,"summary":6428},"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoint-functor-theorem","The Adjoint Functor Theorem","RAPL makes limit preservation necessary for having a left adjoint; the adjoint functor theorems identify when it is sufficient. For ordered sets no extra hypothesis is needed. In general the candidate adjoint is a limit over a comma category that may be large, and the general adjoint functor theorem tames it with a weakly initial set. We prove GAFT in full and apply it to free groups and, through the special adjoint functor theorem, the Stone–Čech compactification.\n",{"path":6430,"title":6431,"module":6432,"summary":6433},"\u002Fcategory-theory\u002Fmonads-algebras\u002Fmonads","Monads from Adjunctions","Monads and Algebras","A monad on a category is an endofunctor equipped with a unit and a multiplication satisfying associativity and unit laws — the data of a monoid, written internally to the category of endofunctors. Every adjunction induces one, and the list, exception, and state constructions that model computational effects are all monads on Set.\n",{"path":6435,"title":6436,"module":6432,"summary":6437},"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-eilenberg-moore","Algebras for a Monad","An algebra for a monad is an object with a structure map that interacts correctly with the unit and multiplication. The algebras form the Eilenberg–Moore category, whose free–forgetful adjunction induces the monad back; a comparison functor relates any other inducing adjunction to it, and for the list monad the algebras are exactly monoids.\n",{"path":6439,"title":6440,"module":6432,"summary":6441},"\u002Fcategory-theory\u002Fmonads-algebras\u002Fkleisli-and-programming","The Kleisli Category and Monads in Programming","The Kleisli category of a monad has the same objects as the base but takes arrows A to TB, composed by mapping and flattening. These arrows are effectful programs, Kleisli composition is the bind of functional programming, and the Kleisli adjunction is the initial resolution of the monad, with Eilenberg–Moore at the terminal end.\n",{"path":6443,"title":6444,"module":6432,"summary":6445},"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-for-endofunctors","Algebras for an Endofunctor and Recursion","Dropping the monad laws leaves algebras for a bare endofunctor, whose initial objects are the least fixed points of the functor by Lambek's lemma. The natural numbers, lists, and trees are initial algebras; the unique map out of an initial algebra is the fold of functional programming; and the Smyth–Plotkin fixed-point technique builds Scott domains the same way.\n",{"path":6447,"title":6448,"module":6449,"summary":6450},"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Fcartesian-closed-categories","Cartesian Closed Categories","Cartesian Closed Categories and Typed Lambda Calculus","A cartesian closed category has a terminal object, binary products, and for every pair of objects an exponential object that internalizes the hom-set as an object of the category. The defining data is an evaluation arrow and a currying operation, packaged by the adjunction between product-with-A and exponential-by-A. Set, Boolean and Heyting algebras, functor categories, and Cat are all cartesian closed.\n",{"path":6452,"title":6453,"module":6449,"summary":6454},"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Flambda-calculus-correspondence","Typed Lambda Calculus and CCCs","The typed lambda calculus and the cartesian closed category are two presentations of the same theory. Types become objects, terms with one free variable become arrows, product types become products, and function types become exponentials, with abstraction matching currying and application matching evaluation. Building the category of a lambda theory and the internal language of a category are mutually inverse up to equivalence.\n",{"path":6456,"title":6457,"module":6449,"summary":6458},"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Ffixed-points-and-recursion","Fixed Points in Cartesian Closed Categories","The untyped lambda calculus has a fixed-point combinator; the typed calculus cannot, and Lawvere's fixed-point theorem explains why: any point-surjection onto an exponential forces every endomap to have a fixed point, which is the abstract form of Cantor's diagonal argument. Recursion is recovered instead by restricting to omega-complete partially ordered objects, where every continuous endomap has a least fixed point built by iterating from bottom. This gives While loops a semantics.\n",{"path":6460,"title":6461,"module":6,"summary":6},"\u002Fcategory-theory","Category Theory",{"path":6463,"title":5105,"module":6464,"summary":6465},"\u002Fdeep-learning\u002Fmathematical-background\u002Flinear-algebra-for-deep-learning","Mathematical Background","Every quantity a network touches is a tensor, and every layer is a matrix acting on one. This lesson compiles the linear algebra deep learning actually uses: products and norms, the system $Ax=b$ and when it is solvable, the two decompositions (eigen and SVD) that diagonalize a transformation, and the pseudoinverse that solves what cannot be solved exactly. It then derives PCA as the worked example that ties it all together.\n",{"path":6467,"title":6468,"module":6464,"summary":6469},"\u002Fdeep-learning\u002Fmathematical-background\u002Fprobability-and-information-theory","Probability & Information Theory","This lesson assembles the probabilistic vocabulary a network is trained in (random variables, densities, the chain rule, expectation and covariance, the handful of distributions that recur everywhere) and then the information theory that turns a probabilistic model into a loss: self-information, entropy, and the KL divergence whose asymmetry is the cross-entropy objective itself.\n",{"path":6471,"title":6472,"module":6464,"summary":6473},"\u002Fdeep-learning\u002Fmathematical-background\u002Fnumerical-computation","Numerical Computation","Machine learning runs on finite-precision arithmetic, where every number is approximated and every operation rounds. This lesson sets the numerical ground rules: overflow and underflow and the standard stabilizations, the condition number that measures how much a problem amplifies error, and the gradient-based optimization (first and second order, constrained and unconstrained) that every training loop runs.\n",{"path":6475,"title":4464,"module":6464,"summary":6476},"\u002Fdeep-learning\u002Fmathematical-background\u002Fcalculus","This lesson assembles the differential calculus used in training networks: the gradient and directional derivative, the Jacobian and Hessian, and the chain rule in scalar, vector, and matrix form. From the chain rule it derives back-propagation as a single sweep over the computational graph, tabulates the matrix-calculus identities that recur in layer gradients, reads optimization off a second-order Taylor expansion, and ends with why reverse-mode automatic differentiation is the algorithm every framework runs.\n",{"path":6478,"title":6479,"module":3911,"summary":6480},"\u002Fdeep-learning\u002Ffoundations\u002Fwhat-is-deep-learning","What Is Deep Learning?","Deep learning is representation learning by composition: stack simple differentiable layers, define a loss, and let gradient descent discover the features a human would otherwise have to engineer by hand. We set up the whole vocabulary (model, loss, optimizer, data), the training loop that ties them together, and the three reasons the approach became practical.\n",{"path":6482,"title":6483,"module":3911,"summary":6484},"\u002Fdeep-learning\u002Ffoundations\u002Fmachine-learning-refresher","A Machine-Learning Refresher","The statistical framework the networks live in: data drawn from an unknown distribution, a loss to minimize, and the central question of generalization: will it work on data we have not seen? We set up empirical risk, capacity, the bias–variance tradeoff, and maximum likelihood.\n",{"path":6486,"title":6487,"module":3911,"summary":6488},"\u002Fdeep-learning\u002Ffoundations\u002Flinear-models-and-the-perceptron","Linear Models & the Perceptron","The simplest learners (linear regression, logistic regression, the perceptron) already contain the whole template: a weighted sum, a loss, a gradient step. They also fail on the XOR problem, which no linear model can solve — the limitation that motivates deep learning.\n",{"path":6490,"title":6491,"module":6492,"summary":6493},"\u002Fdeep-learning\u002Fneural-networks\u002Fthe-multilayer-perceptron","The Multilayer Perceptron","Neural Networks","Stacking linear layers with a nonlinearity between them removes the limitation that stopped the perceptron. We build the multilayer perceptron in explicit matrix form (the forward pass, its dimensions, a worked XOR network with concrete weights) and prove why the nonlinearity is essential: without it the deepest stack collapses to a single hyperplane.\n",{"path":6495,"title":6496,"module":6492,"summary":6497},"\u002Fdeep-learning\u002Fneural-networks\u002Factivation-functions","Activation Functions","The activation is the only nonlinear part of a layer, and the reason depth adds expressive power. We catalog the standard hidden units (sigmoid, tanh, ReLU and its descendants, plus GELU, softplus, swish and maxout), derive each unit's derivative in full, make the vanishing-gradient problem quantitative with the chain-rule product, work numeric examples, and explain why the saturating units gave way to ReLU and why ReLU's own dead-unit failure gave way to Leaky\u002FPReLU\u002FELU\u002FGELU.\n",{"path":6499,"title":6500,"module":6492,"summary":6501},"\u002Fdeep-learning\u002Fneural-networks\u002Funiversal-approximation","Universal Approximation","One hidden layer with a non-polynomial activation can approximate any continuous function on a compact set to arbitrary accuracy: the universal approximation theorem. We prove it constructively (two sigmoids make a bump; sums of bumps make any curve), then show the limitation: existence is not efficiency. Depth-separation results exhibit functions a deep net represents with $O(n)$ units that a shallow net needs $\\exp(n)$ units to match.\n",{"path":6503,"title":6504,"module":6492,"summary":6505},"\u002Fdeep-learning\u002Fneural-networks\u002Fbackpropagation","Backpropagation","Backpropagation is the chain rule run backward over a computational graph. We formalize the graph, derive the four backprop equations for an MLP, present the forward and backward passes as algorithms, and work a tiny two-layer net by hand with explicit numbers. The result: one scalar loss, reverse-mode autodiff, and a gradient for every parameter at twice the cost of a forward pass.\n",{"path":6507,"title":6508,"module":6492,"summary":6509},"\u002Fdeep-learning\u002Fneural-networks\u002Floss-functions-and-output-units","Loss Functions & Output Units","The last layer is where a network's hidden representation meets the task. Choosing an output unit and a loss is not two independent choices; maximum likelihood fixes the pair. We derive the standard couplings (linear\u002FMSE, sigmoid\u002FBCE, softmax\u002Fcross-entropy), show why softmax and cross-entropy were built to cancel into the residual $\\hat y - y$, and prove why squared error is the wrong loss for a saturating classifier.\n",{"path":6511,"title":6512,"module":6513,"summary":6514},"\u002Fdeep-learning\u002Foptimization\u002Fgradient-descent-and-sgd","Gradient Descent & SGD","Optimization","Training is descent on the empirical risk: step the parameters against the gradient. We derive the minibatch gradient as an unbiased estimator whose variance falls as $1\u002FB$, derive the learning-rate ceiling from the smoothness-stability bound $\\eta \u003C 2\u002FL$, and lay out the schedules (step, exponential, cosine, warmup) that anneal it over training.\n",{"path":6516,"title":6517,"module":6513,"summary":6518},"\u002Fdeep-learning\u002Foptimization\u002Fmomentum-and-adaptive-methods","Momentum & Adaptive Methods","Plain gradient descent zig-zags across ravines and moves slowly along flat valleys, because one global learning rate cannot suit a surface with wildly different curvature in different directions. Two fixes address the two problems: momentum accumulates a velocity that damps the oscillation and accelerates the drift, and adaptive methods give every parameter its own learning rate scaled by the history of its gradients. Adam fuses both, and is the default optimizer of modern deep learning.\n",{"path":6520,"title":6521,"module":6513,"summary":6522},"\u002Fdeep-learning\u002Foptimization\u002Finitialization","Weight Initialization","The initial weights determine whether training can succeed before the first gradient step. Initialize every weight equal and all hidden units compute the same function forever; initialize too small or too large and the signal vanishes or explodes as it crosses depth. A single variance condition, $n_{\\text{in}}\\mathrm{Var}(W)=1$, fixes both, and reading it off the forward and backward passes yields Xavier and He initialization directly.\n",{"path":6524,"title":6525,"module":6513,"summary":6526},"\u002Fdeep-learning\u002Foptimization\u002Fthe-optimization-landscape","The Optimization Landscape","The loss of a deep network is a non-convex surface in millions of dimensions, so local search carries no global guarantee, yet it works. We classify critical points by the eigenvalues of the Hessian, show that in high dimension nearly all of them are saddle points rather than bad local minima, and read off the practical terrain — plateaus, cliffs, ill-conditioning, and the sharp-versus-flat distinction that ties the geometry of a minimum to how well it generalizes.\n",{"path":6528,"title":6529,"module":6513,"summary":6530},"\u002Fdeep-learning\u002Foptimization\u002Fsecond-order-and-approximate-methods","Second-Order & Approximate Methods","Newton's method reads the curvature of the loss off its Hessian and jumps to the minimum of the local quadratic in a single step, rescaling away the ill-conditioning that slows first-order descent. We derive it, then explain the three obstacles that keep it out of deep learning: a $d \\times d$ Hessian for $d$ in the billions, an attraction to saddle points, and minibatch noise. The alternative is approximation (conjugate gradients, BFGS and L-BFGS, the natural gradient and Hessian-free methods), each buying some of Newton's curvature information without ever forming or inverting $H$.\n",{"path":6532,"title":6533,"module":6534,"summary":6535},"\u002Fdeep-learning\u002Fregularization\u002Fregularization-overview","Regularization Overview","Regularization","Regularization is any modification to a learning algorithm meant to lower test error at the possible expense of training error. We derive the bias–variance decomposition that explains why it helps, set up the two parameter-norm penalties, $L^2$ weight decay and $L^1$, derive their update rules and eigenbasis shrinkage, show geometrically why $L^1$ alone produces sparse weights (soft-thresholding), distinguish weight decay from loss-added $L^2$ under AdamW, and read both penalties through the two lenses that recur across the chapter: a norm-ball constraint via KKT, and a prior via MAP estimation.\n",{"path":6537,"title":6538,"module":6534,"summary":6539},"\u002Fdeep-learning\u002Fregularization\u002Fdropout-and-data-augmentation","Dropout & Data Augmentation","Two of the most effective regularizers add no penalty term at all; they perturb the computation instead. Dropout multiplies hidden units by a random Bernoulli mask, training an exponential ensemble of thinned subnetworks that share weights; inverted scaling collapses that ensemble into one cheap forward pass at test time. Data augmentation enlarges the training set with label-preserving transforms, injecting the invariances the task demands, and noise injection (input, weight, label smoothing, Mixup) generalizes the same idea into a continuous family.\n",{"path":6541,"title":6542,"module":6534,"summary":6543},"\u002Fdeep-learning\u002Fregularization\u002Fearly-stopping-and-parameter-sharing","Early Stopping & Parameter Sharing","Two cheap regularizers that cost no extra term in the loss. Early stopping treats training time itself as a hyperparameter (watch the validation curve, halt at its minimum, keep the best checkpoint), and for a quadratic objective it is provably equivalent to $L^2$ weight decay. Parameter sharing goes the other way: it constrains many weights to be _equal_, the prior behind every convolution and every recurrent step, and the reason a CNN has orders of magnitude fewer parameters than the dense net it replaces.\n",{"path":6545,"title":6546,"module":6534,"summary":6547},"\u002Fdeep-learning\u002Fregularization\u002Fnormalization","Normalization","Normalization layers standardize activations to zero mean and unit variance inside the network, then hand the model a learnable scale and shift to undo the constraint when it pays to. Batch normalization does this across the batch and must keep separate train-time and test-time statistics; layer, instance, and group norm change only the axes they average over. The result is faster, better-conditioned optimization and a free dose of regularizing batch noise.\n",{"path":6549,"title":6550,"module":6551,"summary":6552},"\u002Fdeep-learning\u002Farchitectures\u002Fconvolutional-networks","Convolutional Networks","Architectures","A convolutional network replaces the dense layer's all-to-all weight matrix with a small kernel slid across the input. Three structural commitments (sparse connectivity, parameter sharing, and translation equivariance) collapse the parameter count by orders of magnitude and bake the right prior for images directly into the architecture. We derive the convolution arithmetic, the output geometry, pooling, and the receptive field, then assemble the canonical stack.\n",{"path":6554,"title":6555,"module":6551,"summary":6556},"\u002Fdeep-learning\u002Farchitectures\u002Fcnn-architectures","CNN Architectures","Six landmark networks, each contributing exactly one idea: LeNet's conv-pool stack, AlexNet's ReLU-and-dropout scale, VGG's $3\\times3$ uniformity, Inception's multi-scale module, ResNet's residual skip, and DenseNet's dense connectivity. The common thread is the degradation problem (why plain deeper nets train worse, not just overfit) and the residual block that solved it by keeping a $+1$ path open for the gradient.\n",{"path":6558,"title":6559,"module":6551,"summary":6560},"\u002Fdeep-learning\u002Farchitectures\u002Frecurrent-networks","Recurrent Networks","A recurrent network folds a sequence into a fixed-size hidden state, reusing one set of weights at every time step, the architectural prior that the same rule applies wherever it lands in time. Unrolling the recurrence exposes a deep feed-forward graph; backpropagation through it sums gradient contributions across all steps and chains a product of Jacobians, and that product is why long-range gradients vanish or explode. That failure motivates gated architectures.\n",{"path":6562,"title":6563,"module":6551,"summary":6564},"\u002Fdeep-learning\u002Farchitectures\u002Flstm-and-gru","LSTM & GRU","A plain recurrent network propagates its hidden state through a repeated weight-matrix multiply, and the Jacobian product that results vanishes or explodes long before a useful gradient can reach the early steps. Gated RNNs fix this with an additive memory path: a cell state that is carried forward almost unchanged, past which the gradient flows along a near-identity highway. We derive that highway, give the full LSTM and GRU equations, and compare the two.\n",{"path":6566,"title":6567,"module":6551,"summary":6568},"\u002Fdeep-learning\u002Farchitectures\u002Fattention-and-transformers","Attention & Transformers","Attention replaces fixed wiring with content-based routing: every position reads from every other through a soft, learned dot-product lookup. We derive scaled dot-product attention and its $\\sqrt{d_k}$ correction, build it into multi-head self-attention, inject order with positional encodings, and stack the whole thing into the Transformer block that displaced recurrence and convolution alike.\n",{"path":6570,"title":6571,"module":6551,"summary":6572},"\u002Fdeep-learning\u002Farchitectures\u002Fthe-transformer-architecture","The Transformer Architecture","The Transformer is the architecture built around the attention mechanism. This first part assembles the full encoder–decoder of \"Attention Is All You Need\" — embeddings and positional encoding, stacked self-attention and feed-forward sublayers wrapped in residual connections and LayerNorm, masked decoding and cross-attention — works through causal masking and the three modern families (encoder-only, decoder-only, encoder–decoder), and accounts for where the parameters and the $O(n^2)$ compute actually go.\n",{"path":6574,"title":6575,"module":6551,"summary":6576},"\u002Fdeep-learning\u002Farchitectures\u002Ftransformers-in-practice","Transformers in Practice","The Transformer makes no assumption about what a token represents. This part follows the architecture out of language: image patches feed a plain encoder (the Vision Transformer), the decoder-only half scales into the GPT line of large language models, and one substrate covers translation, retrieval, and multimodal grounding. We work the ViT patch arithmetic and a GPT parameter count by hand, then close on the empirical scaling laws — power-law loss, the Chinchilla compute-optimal balance, and emergent behavior — that made scale the dominant lever.\n",{"path":6578,"title":6579,"module":6551,"summary":6580},"\u002Fdeep-learning\u002Farchitectures\u002Fgraph-neural-networks","Graph Neural Networks","A graph neural network learns on data with no grid and no canonical ordering: atoms in a molecule, users in a social network, road segments in a map. The unifying idea is message passing — each node repeatedly aggregates its neighbors' states and updates its own — built to respect the one symmetry graphs demand, permutation equivariance. We derive the message-passing framework, specialize it into GCN, GraphSAGE, GAT, and GIN, read off graph-level outputs, and bound what message passing can and cannot tell apart.\n",{"path":6582,"title":6583,"module":6551,"summary":6584},"\u002Fdeep-learning\u002Farchitectures\u002Fstate-space-models","State-Space Models and Mamba","A state-space model carries a continuous linear hidden state through a sequence, and that linearity buys two equivalent algorithms from one set of weights: a recurrence that runs in linear time with constant memory, and a global convolution that trains in parallel. Long-range memory comes from how the transition matrix is initialized (HiPPO) and parameterized (S4's diagonal-plus-low-rank form). Mamba breaks the convolution on purpose, making the parameters input-dependent so the model can select what to remember, recovered at speed by a hardware-aware parallel scan.\n",{"path":6586,"title":6587,"module":6588,"summary":6589},"\u002Fdeep-learning\u002Ftheory\u002Fgeneralization-theory","Generalization Theory","Theory & Frontiers","Classical learning theory bounds the gap between training and test error by a model's capacity (VC dimension, Rademacher complexity), and predicts that a model with more parameters than data should overfit catastrophically. Modern networks do the opposite: they interpolate, even fit pure noise, and still generalize. We derive the classical bounds, work the bias-variance decomposition, show why the bounds go vacuous, and survey what replaced them: double descent, the interpolation threshold, margin and norm-based bounds, and the implicit bias of the optimizer itself.\n",{"path":6591,"title":6592,"module":6588,"summary":6593},"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-robustness","Adversarial Robustness","A trained network can be fooled by a perturbation too small for a human to see: add a carefully aimed vector of magnitude $\\epsilon$ to a correctly classified image and the prediction flips. We derive the fast gradient sign method as the first-order-optimal step inside an $L_\\infty$ ball, explain the linearity hypothesis that makes high-dimensional models so easy to push around, build up to projected gradient descent, and frame adversarial training as a min-max robust-optimization problem with its own accuracy cost. Defenses beyond training continue in the next lesson.\n",{"path":6595,"title":6596,"module":6588,"summary":6597},"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-defenses","Adversarial Defenses","Defending a network against an adversary is far harder than attacking one. This lesson covers the defense side: certified guarantees via randomized smoothing, the transferability that makes black-box attacks possible, and the recurring failure of gradient masking, where a defense hides the attacker's gradient instead of moving the decision boundary. It ends with the adaptive-attack discipline (BPDA, EOT, transfer) that every robustness claim must be tested against.\n",{"path":6599,"title":6600,"module":6588,"summary":6601},"\u002Fdeep-learning\u002Ftheory\u002Fbayesian-and-ensemble-methods","Bayesian & Ensemble Methods","A trained network returns a single point prediction and, with the softmax, a confidence, but that confidence is usually miscalibrated, collapsing to near- certainty even on inputs the model has never seen. This lesson covers uncertainty estimation for networks: the two kinds of uncertainty, the Bayesian posterior over weights and its tractable stand-ins (MC dropout, deep ensembles), and how to check whether a model's reported confidences match observed frequencies.\n",{"path":6603,"title":6604,"module":6588,"summary":6605},"\u002Fdeep-learning\u002Ftheory\u002Fdeep-equilibrium-models","Deep Equilibrium Models","A deep network need not be a fixed stack of layers; it can be a single weight-tied layer iterated to convergence, its output defined implicitly as the fixed point $z^\\star = f_\\theta(z^\\star, x)$. The forward pass becomes root-finding and the backward pass becomes implicit differentiation, so training costs O(1) memory regardless of effective depth. We derive both passes from the implicit function theorem and close the course on defining a layer by a fixed-point condition rather than an explicit stack.\n",{"path":6607,"title":6608,"module":6609,"summary":6610},"\u002Fdeep-learning\u002Fgenerative-models\u002Flinear-factor-models","Linear Factor Models","Generative Models","The simplest generative models share one template: a latent variable drawn from a fixed prior, run through a linear decoder, plus noise. Probabilistic PCA, factor analysis, independent component analysis, and sparse coding are all this template with a different prior on the latents and a different noise model. We derive each marginal, see why ICA needs non-Gaussianity to identify its sources, and show how sparse coding learns Gabor-like dictionary atoms.\n",{"path":6612,"title":6613,"module":6609,"summary":6614},"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoencoders","Autoencoders","An autoencoder is a network trained to copy its input to its output through a narrow channel; the useful product is the bottleneck representation $h$, not the reconstruction. We derive the undercomplete autoencoder and prove its linear case recovers PCA, then trade the bottleneck for explicit regularization (sparse, denoising, contractive) and show how a denoising autoencoder learns the low-dimensional manifold the data lives on.\n",{"path":6616,"title":6617,"module":6609,"summary":6618},"\u002Fdeep-learning\u002Fgenerative-models\u002Fvariational-autoencoders","Variational Autoencoders","An autoencoder compresses, but its latent space has gaps: sample a point between two encodings and the decoder produces noise. The variational autoencoder fixes this by training a probabilistic encoder against a prior, so the latent space becomes a smooth, samplable density. We derive the evidence lower bound it maximizes, the reparameterization trick that lets gradients flow through a random sample, and the closed-form Gaussian regularizer that pulls the posterior toward the prior.\n",{"path":6620,"title":6621,"module":6609,"summary":6622},"\u002Fdeep-learning\u002Fgenerative-models\u002Fgenerative-adversarial-networks","Generative Adversarial Networks","A generative adversarial network trains two networks against each other: a generator that turns noise into samples, and a discriminator that tries to tell real data from forgeries. The game has a clean theory: the optimal discriminator is a likelihood ratio, and at equilibrium the generator minimizes the Jensen–Shannon divergence to the data, with a global optimum exactly when its distribution matches the data. We derive that result, fix the saturating loss that breaks training, and catalogue the failure modes (mode collapse, instability, vanishing gradients) and the architectural fixes.\n",{"path":6624,"title":6625,"module":6609,"summary":6626},"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoregressive-and-normalizing-flows","Autoregressive Models & Normalizing Flows","Two families that provide exact likelihoods, each at a cost. Autoregressive models factor the joint by the probability chain rule and learn each conditional with a masked network: exact $\\log p(x)$, but sampling proceeds one coordinate at a time. Normalizing flows push a simple base density through an invertible map and read $\\log p(x)$ off the change-of-variables formula, trading architectural freedom for a cheap Jacobian determinant via triangular coupling layers.\n",{"path":6628,"title":6629,"module":6609,"summary":6630},"\u002Fdeep-learning\u002Fgenerative-models\u002Fenergy-based-and-boltzmann-machines","Energy-Based & Boltzmann Machines","Energy-based models replace an explicit density with a scalar energy and a Boltzmann normalization, $p(x) = e^{-E(x)}\u002FZ$: simple to specify, but with an intractable partition function $Z$. The Boltzmann machine and its restricted variant make the energy bilinear so the hidden units factorize, and contrastive divergence sidesteps $Z$ by replacing the model expectation with a few Gibbs steps started at the data. We close on the undirected deep models (DBNs and DBMs) and how they differ from the directed VAE.\n",{"path":6632,"title":6633,"module":6609,"summary":6634},"\u002Fdeep-learning\u002Fgenerative-models\u002Fdiffusion-and-score-based-models","Diffusion and Score-Based Models","Corrupt a data point with Gaussian noise in small steps until only noise remains, then train a network to undo one step at a time. We derive the forward process and its closed-form marginal, reduce the variational bound to the single noise-prediction objective that makes diffusion trainable, and show the score-matching view that unifies it with Langevin sampling and the continuous SDE. The lesson closes with DDIM fast sampling, classifier-free guidance, and the latent diffusion that powers modern text-to-image systems.\n",{"path":6636,"title":6637,"module":6638,"summary":6639},"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fstructured-probabilistic-models","Structured Probabilistic Models","Probabilistic Methods","A joint distribution over $n$ variables is a table with exponentially many entries; nobody can store it, fit it, or sample from it directly. Structure fixes this: a graph whose missing edges encode conditional independencies that factor the joint into small local pieces. We build the two dialects, directed (Bayesian networks) and undirected (Markov random fields), read independence off the graph, and connect the machinery to the latent-variable and energy-based models that power deep generative learning.\n",{"path":6641,"title":6642,"module":6638,"summary":6643},"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fmonte-carlo-and-mcmc","Monte Carlo & MCMC","Most quantities of interest in a probabilistic model are integrals nobody can compute in closed form: expectations, marginals, partition functions. Monte Carlo replaces the integral with an average over samples; importance sampling reweights samples from a tractable proposal; and when even sampling the target is hard, Markov-chain Monte Carlo builds a chain whose stationary distribution _is_ the target. We derive Metropolis–Hastings and Gibbs, analyze mixing, and close on the partition-function gradient that powers energy-based learning.\n",{"path":6645,"title":6646,"module":6638,"summary":6647},"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fapproximate-inference","Approximate Inference","In a latent-variable model the quantity we need, the posterior $p(h\\mid v)$ over hidden causes, is almost never computable, because its normalizer is an intractable sum over configurations. Approximate inference reframes the problem as optimization: maximize the evidence lower bound, a tractable functional whose gap to the true log-evidence equals a KL divergence. From that single bound fall expectation–maximization, mean-field variational inference, MAP, and the learned encoders behind variational autoencoders.\n",{"path":6649,"title":6650,"module":6651,"summary":6652},"\u002Fdeep-learning\u002Fpractical\u002Fpractical-methodology","Practical Methodology","Practical Deep Learning","Knowing the algorithms is half the job; the other half is a disciplined loop. Fix a goal and a metric, stand up an end-to-end baseline, then read the train\u002Fvalidation gap to decide whether the next move is more data or a bigger model. We detail that loop: choosing metrics under class imbalance, default baselines by data type, extrapolating the data a target needs, and guarding the data pipeline against the leaks and label bugs that corrupt every gradient. Hyperparameter tuning, debugging, and deployment continue in the sequel.\n",{"path":6654,"title":6655,"module":6651,"summary":6656},"\u002Fdeep-learning\u002Fpractical\u002Fhyperparameters-and-debugging","Hyperparameters & Debugging","The tuning half of the methodology loop. The learning rate is the one hyperparameter that dominates, so we tune it first, on a log scale, coarse to fine, and prefer random search to grid when only a few dials matter. Then an ordered debugging playbook — overfit one batch, check the loss at initialization against ln C, watch the gradient norm, gradient-check against centered finite differences — and, after launch, monitoring for train-test skew and distribution drift with confidence-based abstention.\n",{"path":6658,"title":6659,"module":6651,"summary":6660},"\u002Fdeep-learning\u002Fpractical\u002Frepresentation-learning","Representation Learning","A good representation makes a hard task easy by changing coordinates: it disentangles the factors of variation, spends its bits as a distributed code, and respects the low-dimensional manifold the data lives on. We make those three properties precise, recover the manifold hypothesis, and close on the first method that turned them into training practice — greedy layer-wise unsupervised pretraining — before the sequel picks up how the field learned to reuse those features.\n",{"path":6662,"title":6663,"module":6651,"summary":6664},"\u002Fdeep-learning\u002Fpractical\u002Ftransfer-learning","Transfer Learning","A representation learned once can be reused everywhere. We cover the main mechanisms of reuse: feature extraction versus fine-tuning, the generic-to-specific gradient of features that sets the freeze boundary, the learning-rate discipline that keeps borrowed weights from being erased, domain adaptation when only the input distribution shifts, and the modern arc from supervised transfer to self-supervised foundation models.\n",{"path":6666,"title":2972,"module":6651,"summary":6667},"\u002Fdeep-learning\u002Fpractical\u002Fapplications","We survey large-scale training (the hardware, the two axes of parallelism, mixed precision, and the compression tricks that shrink a model after it is trained), then specialize the same gradient loop to vision, language, speech, and recommendation. Each domain is a different prior bolted onto one optimizer: convolutional invariance for pixels, distributed word vectors for tokens, sequence transduction for audio, low-rank factorization for the user–item matrix.\n",{"path":6669,"title":6670,"module":6651,"summary":6671},"\u002Fdeep-learning\u002Fpractical\u002Fmodel-compression-and-distillation","Model Compression and Distillation","A trained network and a deployable one are rarely the same object. This lesson is the toolkit for closing that gap: knowledge distillation transfers a large teacher's soft, information-rich logits into a small student; pruning deletes the weights that contribute least; quantization swaps 32-bit floats for 8- or 4-bit integers; and low-rank factorization replaces a fat matrix with two thin ones. We derive each method, show what it costs in accuracy, and lay out which combinations win on which hardware.\n",{"path":6673,"title":6674,"module":6651,"summary":6675},"\u002Fdeep-learning\u002Fpractical\u002Fmeta-learning-and-few-shot","Meta-Learning and Few-Shot Learning","A deep network trained on one example per class overfits. Meta-learning targets this few-shot regime by training across a distribution of tasks so that a new task is learnable from a handful of examples. We formalize the $N$-way $K$-shot episode, then derive the two dominant families: metric methods that learn an embedding where distance classifies (Prototypical Networks), and optimization methods that learn an initialization a few gradient steps can adapt (MAML). We close on the link to transfer learning and to the in-context few-shot behavior of large language models.\n",{"path":6677,"title":6678,"module":6679,"summary":6680},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Flarge-language-models","Large Language Models","Large Models & Agents","A large language model is a decoder-only Transformer trained on one objective, next-token prediction, then scaled until new behavior appears. This first part builds the object itself: the equivalence between next-token prediction and lossless compression, subword tokenization (BPE, WordPiece, Unigram, SentencePiece) worked on a real sentence, the four pretraining objectives and the attention masks that distinguish them, and the three model families (encoder-only, decoder-only, encoder--decoder) with their parameter budgets. Scaling, decoding, the KV cache, and alignment continue in part two.\n",{"path":6682,"title":6683,"module":6679,"summary":6684},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fscaling-inference-and-alignment","Scaling, Inference, and Alignment of Language Models","Once a language model is built, three questions remain: how does it improve as it grows, how is it decoded and served affordably, and how is a raw next-token predictor turned into an assistant. We derive the Kaplan power laws and the Chinchilla compute-optimal balance, trace emergent abilities and in-context learning, catalog the decoding strategies from greedy to nucleus sampling, work the KV cache that makes generation quadratic instead of cubic, cover parameter-efficient adaptation by low-rank updates (LoRA), and close on the alignment stack: instruction tuning, RLHF, and DPO.\n",{"path":6686,"title":6687,"module":6679,"summary":6688},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fseq2seq-pretraining-and-bart","Denoising Sequence-to-Sequence Pretraining: BART","BERT corrupts and reconstructs; GPT predicts the next token. Sequence-to-sequence pretraining unifies both by training a full encoder–decoder as a denoising autoencoder: corrupt the text with a noise function, then reconstruct the original through a bidirectional encoder and an autoregressive decoder. This first part derives the denoising objective, catalogs BART's five noise functions (with a worked Poisson-infilling budget), proves BART specializes to both BERT and GPT, and traces a dimension-annotated forward pass through its encoder--decoder. T5, PEGASUS, fine-tuning, and decoding continue in part two.\n",{"path":6690,"title":6691,"module":6679,"summary":6692},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ftext-to-text-transfer-and-conditional-generation","Text-to-Text Transfer and Conditional Generation","BART reconstructs a corrupted document; T5 pushes the same denoising idea into a single interface where every task is a string-to-string map. This second part covers T5's span corruption with sentinel tokens (with a worked token budget), PEGASUS's summarization-matched gap sentences and the MASS midpoint, supervised fine-tuning and beam-search decoding with a length penalty, the exposure-bias failure modes of autoregressive decoding, and a theorem showing why a bidirectional encoder--decoder strictly dominates a decoder-only model when the output is conditioned on a full input.\n",{"path":6694,"title":6695,"module":6679,"summary":6696},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fspeech-and-audio-models","Speech Recognition: Front-Ends and Alignment","Speech is a long, high-rate sequence whose label is short and unaligned, so the whole subject turns on bridging that mismatch. This first part builds the spectral front-ends that compress a waveform into frames (STFT, mel spectrogram, MFCC, with a worked frame-count), derives CTC's marginalization over alignments and its forward-backward recursion with a two-frame numeric example, and contrasts it with attention-based seq2seq (LAS) and the RNN transducer. Self-supervised and weakly-supervised models, and text-to-speech, continue in part two.\n",{"path":6698,"title":6699,"module":6679,"summary":6700},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fself-supervised-speech-and-synthesis","Self-Supervised Speech Models and Synthesis","The recognition front-ends and alignment losses of part one all need transcribed audio, which is scarce. This second part removes that dependence: wav2vec 2.0 learns speech representations from unlabeled audio by a masked contrastive objective, HuBERT swaps the contrast for masked prediction of clustered units, and Whisper trades curation for scale with weakly-supervised web audio and a multitask token interface. We close with text-to-speech (the same length mismatch run backwards) and a tour of speech foundation models, discrete audio codecs, and neural TTS.\n",{"path":6702,"title":6703,"module":6679,"summary":6704},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fai-agents","AI Agents: Tools and Reasoning","A language model that only emits text is a function from prompt to prompt; an agent closes the loop, letting that model act on an environment, read back the result, and decide again. This first part formalizes the agent as a policy over interaction histories, builds out tool calling and the executor trust boundary, the ReAct interleaving of reasoning and action (with concrete traces), and search over thoughts: chain-of-thought, self-consistency, least-to-most, and Tree of Thoughts. Memory, retrieval, reflection, and multi-agent orchestration continue in part two.\n",{"path":6706,"title":6707,"module":6679,"summary":6708},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fagent-memory-retrieval-and-orchestration","Agent Memory, Retrieval, and Orchestration","An agent's reasoning and tool use only matter if it can remember what it learned and coordinate work larger than one context window. This second part builds the systems around the loop: short-term scratchpad versus long-term vector store, retrieval-augmented generation with a worked softmax over passage scores, reflection (Reflexion, Self-Refine), and multi-agent orchestration. It closes on the failure modes that bound agents — invalid tool calls, horizon-error compounding, context overflow, non-terminating loops — and the benchmarks that score the full loop.\n",{"path":6710,"title":6711,"module":6679,"summary":6712},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmixture-of-experts","Mixture-of-Experts","A mixture-of-experts layer replaces one feed-forward network with many and a router that sends each token to only a few of them, so the parameter count and the per-token compute become separate dials. We derive the gated output, sparse top-$k$ routing softmax, the load-balancing loss that stops the router from collapsing onto a single expert, and expert\u002Ftoken capacity with dropping, then work the dimension-annotated tensor shapes and FLOP arithmetic. We trace the architectures from the sparsely-gated LSTM through GShard, Switch Transformer, and Mixtral, cover distributed expert parallelism, and close on the training dynamics, failure modes, and serving costs of a sparse model.\n",{"path":6714,"title":6715,"module":6679,"summary":6716},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmultimodal-models","Multimodal Contrastive Learning","A multimodal model places images, text, and audio in one representation space, so a picture and its caption land close together. This first part builds the contrastive route: the shared embedding space and its residual modality gap, the Vision Transformer image encoder (patch embedding, CLS token, position embeddings, with shapes), the symmetric InfoNCE loss that trains the CLIP dual encoder from a batch similarity matrix (with a worked numeric step), and zero-shot classification as a softmax over class-prompt embeddings. Fusion and vision-language models continue in part two.\n",{"path":6718,"title":6719,"module":6679,"summary":6720},"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ffusion-and-vision-language-models","Fusion and Vision-Language Models","A contrastive model compares modalities but never lets one read another. This second part builds the fusion route: early, late, and cross-attention fusion, then the three designs that connect a frozen vision encoder to a frozen language model — Flamingo's zero-initialized gated cross-attention, BLIP-2's Q-Former, and LLaVA's linear projector. We work the token-budget arithmetic that separates them, name the object-hallucination and fine-detail failure modes, cover the contrastive-then- instruction-tune recipe and its retrieval\u002Fcaptioning\u002FVQA benchmarks, and close on natively multimodal models.\n",{"path":6722,"title":6723,"module":6724,"summary":6725},"\u002Fdeep-learning\u002Freinforcement-learning\u002Ffoundations-of-reinforcement-learning","Foundations of Reinforcement Learning","Reinforcement Learning","Reinforcement learning is the third paradigm: an agent learns to act by interacting with an environment that returns rewards, not labels. We formalize the interaction as a Markov decision process, define the value functions that rank states and actions, and derive the Bellman expectation and optimality equations that every method downstream solves. Dynamic programming gives the exact answer when the model is known, and its convergence rests on a single fact: the Bellman operator is a contraction.\n",{"path":6727,"title":6728,"module":6724,"summary":6729},"\u002Fdeep-learning\u002Freinforcement-learning\u002Fmodel-free-prediction-and-control","Model-Free Prediction and Control","When the dynamics are unknown, an agent cannot plan against a model; it must learn directly from sampled experience. We build prediction and control from two estimators of the same return: Monte Carlo averages whole episodes, while temporal-difference learning bootstraps from its own next estimate. We trace the bias-variance contrast between them, derive SARSA and Q-learning as the on-policy and off-policy forms of control, unify everything through n-step returns and eligibility traces, and close on the deadly triad that makes off-policy bootstrapping with function approximation diverge.\n",{"path":6731,"title":6732,"module":6724,"summary":6733},"\u002Fdeep-learning\u002Freinforcement-learning\u002Fdeep-q-networks","Deep Q-Networks","A Deep Q-Network replaces the tabular action-value function with a neural approximator $Q(s,a;\\theta)$ and trains it by regression toward a bootstrapped target. Naive online Q-learning with a network diverges, so DQN adds two stabilizers: an experience-replay buffer that decorrelates samples, and a periodically-frozen target network that holds the regression target still. We derive the loss, give the full algorithm and the Atari pipeline, and then layer on Double DQN, the dueling split, prioritized replay, and the Rainbow combination.\n",{"path":6735,"title":6736,"module":6724,"summary":6737},"\u002Fdeep-learning\u002Freinforcement-learning\u002Fpolicy-gradients-and-actor-critic","Policy Gradients and Actor-Critic Methods","Value-based reinforcement learning learns what each state is worth and acts greedily; policy-gradient methods skip the detour and optimize a parameterized policy directly by ascending the gradient of expected return. The policy gradient theorem makes this tractable through the log-derivative trick, turning an intractable gradient of an expectation into an expectation of a gradient. REINFORCE realizes the idea but suffers high variance; baselines, the advantage function, and actor-critic learning reduce it, and trust-region methods (TRPO, PPO) keep each update from destroying the policy it just learned.\n",{"path":2716,"title":6739,"module":6724,"summary":6740},"Reinforcement Learning from Human Feedback","Many objectives we want from a model, that it be helpful and harmless, are hard to write down but easy to judge by comparison. RLHF turns that asymmetry into a training signal: fit a reward model to pairwise human preferences under the Bradley-Terry likelihood, then fine-tune the policy to maximize that reward under a KL penalty toward a reference. We derive the reward loss, the KL-regularized RL objective and its closed-form optimum, then show how DPO inverts that optimum to collapse the whole pipeline into one supervised log-sigmoid loss, and survey IPO, KTO, RLAIF, and GRPO.\n",{"path":6742,"title":6743,"module":6,"summary":6},"\u002Fdeep-learning","Deep Learning",{"path":6745,"title":6746,"module":4655,"summary":6747},"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fequilibrium-state-variables-zeroth-law","Equilibrium, State Variables, and the Zeroth Law","Thermodynamics describes a many-body system by a handful of macroscopic variables and the equilibrium relations among them. This lesson fixes the vocabulary: systems and the walls that separate them, state variables versus path-dependent process quantities, quasi-static and reversible idealizations, and the zeroth law, whose transitivity of thermal equilibrium is what lets temperature exist as a number. The ideal-gas thermometer turns that number into a scale, and an equation of state ties the variables into a surface.\n",{"path":6749,"title":6750,"module":4655,"summary":6751},"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Ffirst-law-heat-and-work","The First Law: Internal Energy, Heat, and Work","The first law is energy conservation for a system that exchanges energy as both heat and work. Internal energy is a state function with an exact differential; heat and work are path-dependent process quantities. This lesson states $\\d U=\\delta Q+\\delta W$, computes compression work as an area on the $P$–$V$ plane, defines the heat capacities $C_V$ and $C_P$ and the enthalpy that makes $C_P$ natural, and works the isothermal and adiabatic processes of an ideal gas, including the adiabat $PV^\\gamma=\\text{const}$.\n",{"path":6753,"title":6754,"module":4655,"summary":6755},"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fsecond-law-entropy-and-the-carnot-bound","The Second Law, Carnot Cycles, and Entropy","The second law forbids the free conversion of heat into work. This lesson states the Kelvin and Clausius forms, proves them equivalent, and analyzes the Carnot cycle to get the efficiency bound $1-T_c\u002FT_h$. Carnot's theorem makes that bound universal and defines the thermodynamic temperature scale. The Clausius inequality $\\oint \\delta Q\u002FT\\le 0$ then constructs entropy as a state function, $\\d S=\\delta Q_{\\rm rev}\u002FT$, whose non-decrease in isolated systems is the arrow of time.\n",{"path":6757,"title":6758,"module":4655,"summary":6759},"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fthermodynamic-potentials-and-maxwell-relations","Thermodynamic Potentials and Maxwell Relations","The fundamental relation $\\d U=T\\,\\d S-P\\,\\d V+\\mu\\,\\d N$ packages the first and second laws into one exact differential. Legendre transforms swap each conjugate pair to produce the Helmholtz, enthalpy, Gibbs, and grand potentials, each minimized under its own natural variables. Equality of mixed second partials of these potentials gives the Maxwell relations, which convert unmeasurable entropy derivatives into measurable ones from the equation of state.\n",{"path":6761,"title":6762,"module":4655,"summary":6763},"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fstability-response-functions-and-the-third-law","Response Functions, Stability, and the Third Law","Response functions — heat capacities, compressibilities, thermal expansion — are the second derivatives of the potentials and the quantities an experiment actually measures. This lesson derives the general relation $C_P-C_V=TV\\alpha^2\u002F\\kappa_T$, shows that convexity of the potentials forces the stability conditions $C_V>0$ and $\\kappa_T>0$, and states the third law: entropy approaches a constant as $T\\to0$, so heat capacities and expansion coefficients vanish there and absolute zero is unattainable.\n",{"path":6765,"title":6766,"module":6767,"summary":6768},"\u002Fstatistical-mechanics\u002Ffoundations\u002Fclassical-statistics-and-equipartition","Classical Statistics and Equipartition","Microstates, Phase Space, and Statistical Entropy","A liter of gas holds on the order of a trillion trillion molecules, far too many to track by their equations of motion. Classical statistical mechanics replaces the trajectories with a single probability law, the Boltzmann distribution, and reads the measurable properties of matter off it: the Maxwell speed distribution, the average energy per degree of freedom, and the heat capacities of gases and solids — together with the low-temperature failures that forced the quantum revision.\n",{"path":6770,"title":6771,"module":6767,"summary":6772},"\u002Fstatistical-mechanics\u002Ffoundations\u002Fphase-space-and-liouvilles-theorem","Phase Space, Trajectories, and Liouville's Theorem","A classical system of N particles is one point in a 6N-dimensional phase space, and its evolution is a single trajectory driven by Hamilton's equations. This lesson builds that geometric picture, introduces the phase-space density of an ensemble, and proves Liouville's theorem: the density is carried by the flow as an incompressible fluid, so phase-space volume is conserved. The stationary densities of equilibrium follow as functions of the conserved quantities alone.\n",{"path":6774,"title":6775,"module":6767,"summary":6776},"\u002Fstatistical-mechanics\u002Ffoundations\u002Fensembles-and-the-equal-probability-postulate","Ensembles and the Postulate of Equal a Priori Probabilities","An ensemble is a probability distribution over the microstates of a system. This lesson states the single postulate on which equilibrium statistical mechanics rests — that an isolated system in equilibrium is equally likely to be in any of its accessible microstates — and works out its consequences: the accessible phase-space volume, the overwhelming dominance of the most probable macrostate as the particle number grows, and the ergodic hypothesis that lets a time average be replaced by an ensemble average.\n",{"path":6778,"title":6779,"module":6767,"summary":6780},"\u002Fstatistical-mechanics\u002Ffoundations\u002Fstatistical-entropy-boltzmann-and-gibbs","Statistical Entropy: Boltzmann and Gibbs","Entropy is the logarithm of the number of accessible microstates. This lesson builds the two statistical entropies — Boltzmann's S = k ln Omega for an isolated system and Gibbs's S = -k sum p ln p for any ensemble — proves they agree for a uniform distribution, and connects both to Shannon's measure of missing information. The second law emerges as the drift toward maximum multiplicity, and maximizing the Gibbs entropy under constraints previews the canonical distribution.\n",{"path":6782,"title":6783,"module":6784,"summary":6785},"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fmicrocanonical-ensemble-and-entropy","The Microcanonical Ensemble and Statistical Entropy","The Microcanonical Ensemble","An isolated system holds its energy, volume, and particle number fixed, and the fundamental postulate assigns equal probability to every microstate on its energy shell. This lesson builds the microcanonical distribution, defines the enclosed phase-space volume $\\Gamma(E)$, the surface density of states $\\omega(E)=\\d\\Gamma\u002F\\d E$, and the shell count $\\Omega(E)$, shows their logarithms agree to $O(\\ln N)$ for large $N$, and reads the Boltzmann entropy $S=k\\ln\\Omega$ off the count. The measure factors $h^{3N}$ and $N!$ enter here and make $S$ extensive.\n",{"path":6787,"title":6788,"module":6784,"summary":6789},"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fequilibrium-conditions-temperature-pressure-chemical-potential","Thermal, Mechanical, and Diffusive Equilibrium","Two isolated subsystems that can exchange energy, volume, or particles reach equilibrium at the partition that maximizes their combined entropy. Setting the derivative of the total entropy to zero identifies the statistical definitions $1\u002FT=(\\partial S\u002F\\partial E)$, $P\u002FT=(\\partial S\u002F\\partial V)$, and $-\\mu\u002FT=(\\partial S\u002F\\partial N)$, shows heat flows from hot to cold as an entropy increase, and recovers the fundamental relation $\\d S=(\\d E+P\\,\\d V-\\mu\\,\\d N)\u002FT$ from pure counting.\n",{"path":6791,"title":6792,"module":6784,"summary":6793},"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fideal-gas-phase-space-and-the-sackur-tetrode-entropy","The Ideal Gas, Phase-Space Volume, and the Sackur–Tetrode Entropy","The monatomic ideal gas is the first system whose microcanonical count can be done in closed form. The momentum integral is the volume of a $3N$-dimensional ball of radius $\\sqrt{2mE}$, the configuration integral is $V^N$, and together they give the Sackur–Tetrode entropy $S=Nk[\\ln(V\u002FN\\lambda^3)+5\u002F2]$ with the thermal wavelength $\\lambda=h\u002F\\sqrt{2\\pi mkT}$. The formula matches the measured entropy of helium, fixes the classical regime $n\\ll n_Q$, and shows why the $N!$ is needed for extensivity.\n",{"path":6795,"title":6796,"module":6784,"summary":6797},"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Ftwo-state-systems-paramagnets-and-negative-temperature","Two-State Systems, Paramagnets, and Negative Temperature","The ideal two-state paramagnet has a multiplicity counted by the binomial coefficient, an entropy that is an inverted dome in the energy, and a temperature read from the slope $1\u002FT=\\partial S\u002F\\partial E$. Because the energy is bounded above, the slope changes sign past the entropy maximum: a population-inverted spin system has a negative absolute temperature, which is hotter than any positive temperature. Nuclear-spin experiments and lasers realize the inverted state.\n",{"path":6799,"title":6800,"module":6801,"summary":6802},"\u002Fstatistical-mechanics\u002Fcanonical\u002Fcanonical-ensemble-and-the-boltzmann-distribution","The Canonical Ensemble and the Boltzmann Distribution","The Canonical Ensemble","A system held at fixed temperature by contact with a heat reservoir is described by the canonical ensemble. Expanding the reservoir entropy to first order in the system energy gives the Boltzmann distribution $p_i\\propto e^{-\\beta E_i}$, and the same law follows from maximizing the Gibbs entropy at fixed mean energy. Both routes identify $\\beta=1\u002Fk_BT$ and fix the probability of every microstate from the temperature alone.\n",{"path":6804,"title":6805,"module":6801,"summary":6806},"\u002Fstatistical-mechanics\u002Fcanonical\u002Fpartition-function-and-the-helmholtz-free-energy","The Partition Function and the Helmholtz Free Energy","The normalizing sum of the Boltzmann distribution, the partition function $Z=\\sum_i e^{-\\beta E_i}$, is a generating function for the thermodynamics. The mean energy is $-\\partial\\ln Z\u002F\\partial\\beta$, and the Gibbs entropy of the canonical distribution collapses to the bridge relation $F=-k_BT\\ln Z$. From $F$ every thermodynamic quantity follows by differentiation, and $Z$ factorizes over independent degrees of freedom.\n",{"path":6808,"title":6809,"module":6801,"summary":6810},"\u002Fstatistical-mechanics\u002Fcanonical\u002Fenergy-fluctuations-and-ensemble-equivalence","Energy Fluctuations and the Equivalence of Ensembles","In the canonical ensemble the energy fluctuates, and the second derivative of $\\ln Z$ gives its variance. The fluctuation–response identity $\\langle\\Delta E^2\\rangle = k_BT^2C_V$ ties the spread of the energy to the heat capacity, and the relative fluctuation falls as $1\u002F\\sqrt{N}$. In the thermodynamic limit the canonical energy distribution is a sharp spike, and the canonical and microcanonical ensembles predict the same thermodynamics.\n",{"path":6812,"title":6813,"module":6801,"summary":6814},"\u002Fstatistical-mechanics\u002Fcanonical\u002Fthe-einstein-solid-and-harmonic-systems","Harmonic Systems: The Einstein Solid and Vibrational Heat Capacity","A quantum harmonic oscillator has a geometric partition function summed in closed form, giving a mean energy $\\hbar\\omega(\\tfrac12+\\langle n\\rangle)$ with the Bose occupation factor. Modeling a solid as $3N$ independent oscillators yields a heat capacity that rises from zero and saturates at the Dulong–Petit value $3Nk_B$. The Einstein temperature sets the crossover, and the model's exponential low-temperature falloff, too steep against the observed $T^3$, motivates the Debye theory.\n",{"path":6816,"title":6817,"module":6801,"summary":6818},"\u002Fstatistical-mechanics\u002Fcanonical\u002Fparamagnetism-and-the-schottky-anomaly","Paramagnetism, Two-Level Systems, and the Schottky Anomaly","A magnetic moment in a field is a two-level system whose partition function is a hyperbolic cosine. The magnetization of a spin-$\\tfrac12$ paramagnet is $N\\mu\\tanh(\\mu B\u002Fk_BT)$, generalizing to the Brillouin function for spin $J$; it gives Curie's law $\\chi\\propto 1\u002FT$ at high temperature and saturates at low temperature. A finite level gap produces the Schottky heat-capacity peak, and the temperature dependence of the entropy on the field is the basis of adiabatic demagnetization cooling.\n",{"path":6820,"title":6821,"module":6822,"summary":6823},"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fideal-gas-partition-function-and-the-gibbs-paradox","The Ideal Gas Partition Function and the Gibbs Paradox","The Classical Ideal Gas","The classical monatomic ideal gas built from the partition function. The single-particle sum is $z_1=V\u002F\\lambda^3$ with the thermal de Broglie wavelength $\\lambda$; the $N$-particle partition function is $z_1^N\u002FN!$, and the $N!$ is forced by indistinguishability. From $Z$ the ideal-gas law, $U=\\tfrac32 Nk_BT$, and the Sackur–Tetrode entropy follow. The $N!$ makes the entropy extensive and resolves the Gibbs paradox: mixing identical gases produces no entropy change.\n",{"path":6825,"title":6826,"module":6822,"summary":6827},"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fequipartition-and-the-virial-theorem","Equipartition and the Virial Theorem","The equipartition theorem derived from the canonical ensemble: every phase-space coordinate that enters the Hamiltonian quadratically carries a mean energy $\\tfrac12 k_BT$. The generalized form $\\langle x_i\\,\\partial H\u002F\\partial x_j\\rangle = k_BT\\,\\delta_{ij}$ contains equipartition and the classical virial theorem as special cases. Equipartition fixes the classical heat capacities, fails by quantum freeze-out when a level gap exceeds $k_BT$, and shifts for a relativistic gas whose energy is linear rather than quadratic in momentum.\n",{"path":6829,"title":6830,"module":6822,"summary":6831},"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fmolecular-gases-rotation-and-vibration","Molecular Gases: Rotational and Vibrational Degrees of Freedom","The internal partition function of a diatomic gas factorizes into translational, rotational, vibrational, and electronic parts. The rigid rotor gives a rotational temperature $\\theta_{\\rm rot}$; the harmonic bond gives a vibrational temperature $\\theta_{\\rm vib}$. Each mode contributes to the heat capacity only above its characteristic temperature, producing the diatomic $C_V$ staircase from $\\tfrac32 R$ to $\\tfrac52 R$ to $\\tfrac72 R$. Homonuclear molecules carry a symmetry number, and hydrogen splits into ortho and para species.\n",{"path":6833,"title":6834,"module":6835,"summary":6836},"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fgrand-canonical-ensemble-and-the-grand-partition-function","The Grand Canonical Ensemble","Grand Canonical Ensemble","When a system exchanges both energy and particles with a reservoir, the reservoir fixes its temperature and its chemical potential. Expanding the reservoir entropy to first order in the exchanged energy and particle number gives the Gibbs factor $e^{-\\beta(E-\\mu N)}$, and summing it over every microstate of every particle number gives the grand partition function $\\Xi$. The grand potential $\\Phi = -k_BT\\ln\\Xi = -PV$ generates the mean particle number, energy, entropy, and pressure by differentiation.\n",{"path":6838,"title":6839,"module":6835,"summary":6840},"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fchemical-potential-fugacity-and-number-fluctuations","Chemical Potential, Fugacity, and Number Fluctuations","The chemical potential is the energy to add one particle at fixed entropy and volume, equal to the slope of the free energy in the particle number. For the classical ideal gas $\\mu=k_BT\\ln(n\\lambda^3)$ is large and negative, and the fugacity $z=n\\lambda^3$ is small. The grand ensemble makes the particle number fluctuate; its variance $\\langle\\Delta N^2\\rangle=k_BT(\\partial N\u002F\\partial\\mu)$ equals $k_BT\\,N^2\\kappa_T\u002FV$, tying density fluctuations to the isothermal compressibility. Equality of $\\mu$ is the condition for diffusive equilibrium and phase coexistence.\n",{"path":6842,"title":6843,"module":6835,"summary":6844},"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fensemble-summary-and-the-thermodynamic-web","The Three Ensembles and the Thermodynamic Web","The microcanonical, canonical, and grand canonical ensembles hold different variables fixed and generate different potentials — the entropy $S$, the Helmholtz free energy $F$, and the grand potential $\\Phi$ — linked by Legendre transforms that trade each fixed variable for its conjugate. Each successive ensemble lets one more quantity fluctuate. In the thermodynamic limit the three agree, the relative fluctuations vanishing as $1\u002F\\sqrt{N}$; the ideal gas gives the same equation of state in all three. The choice of ensemble is a matter of convenience, set by which sum is easiest.\n",{"path":6846,"title":6847,"module":6848,"summary":6849},"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fquantum-statistics-bose-einstein-and-fermi-dirac","Quantum Statistics — Bose-Einstein and Fermi-Dirac","Quantum Statistics","Quantum particles of the same kind are genuinely indistinguishable: no label survives an overlap of their wave functions. Counting states with that constraint replaces the Boltzmann distribution with two quantum laws — the Bose-Einstein distribution for integer-spin particles, which clump into shared states, and the Fermi-Dirac distribution for half-integer-spin particles, which exclude one another. Both reduce to Boltzmann in the dilute, hot limit, and a de Broglie criterion says exactly when.\n",{"path":6851,"title":6852,"module":6848,"summary":6853},"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fderiving-the-quantum-distributions","Deriving the Quantum Distributions from the Grand Ensemble","The Bose-Einstein and Fermi-Dirac distributions follow from one observation: in the occupation-number representation the single-particle modes are independent, so the grand partition function factorizes into one factor per mode. A boson mode sums a geometric series over all occupancies; a fermion mode sums two terms. Differentiating each factor gives the mean occupation $1\u002F(e^{\\beta(\\varepsilon-\\mu)}\\mp 1)$, the Maxwell-Boltzmann limit when occupancies are small, and the occupation fluctuations that distinguish bunching from anti-bunching.\n",{"path":6855,"title":6856,"module":6848,"summary":6857},"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fthe-classical-limit-and-quantum-concentration","The Classical Limit and Quantum Concentration","When every single-particle level is nearly empty, both quantum distributions collapse to the Maxwell-Boltzmann form, and the fugacity equals the ratio of the number density to the quantum concentration $n_Q = 1\u002F\\lambda^3$. The gas is classical when $n \\ll n_Q$, degenerate when $n \\gtrsim n_Q$. The chemical potential is large and negative in the classical regime and rises through zero as the gas degenerates. The leading quantum correction to the ideal-gas law is a second virial term that lowers the pressure for bosons and raises it for fermions — a statistical attraction and repulsion with no interaction behind it.\n",{"path":6859,"title":6860,"module":6848,"summary":6861},"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fideal-quantum-gases-general-framework","Ideal Quantum Gases: The General Framework","Every ideal quantum gas is handled by one calculation. The sum over single-particle modes becomes an energy integral weighted by a density of states $g(\\varepsilon)\\propto\\varepsilon^{1\u002F2}$, and the number and pressure reduce to the Bose and Fermi functions $g_\\nu(z)$ and $f_\\nu(z)$ of the fugacity. An integration by parts fixes $PV=\\tfrac23 U$ for a nonrelativistic gas and $PV=\\tfrac13 U$ for an ultrarelativistic one, independent of statistics. Specializing the density of states and the chemical potential then produces the photon gas, phonons, the Bose gas, and the Fermi gas as four branches of the same framework.\n",{"path":6863,"title":6864,"module":6865,"summary":6866},"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-and-the-fermion-gas","Bose-Einstein Condensation and the Fermion Gas","Bosonic Systems","Below a critical temperature a boson gas drops a macroscopic fraction of its particles into the single ground state — Bose-Einstein condensation, the mechanism behind superfluid helium and the dilute-atom condensates cooled to nanokelvin. The same statistics applied to a photon gas reproduces Planck's blackbody spectrum. Fermions do the opposite: forbidden from sharing states, they fill every level up to the Fermi energy, and that filled sea governs the electrons in metals and the pressure that holds up a white dwarf.\n",{"path":6868,"title":6869,"module":6865,"summary":6870},"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthe-photon-gas-and-plancks-radiation-law","The Photon Gas and Planck's Radiation Law","Electromagnetic radiation in equilibrium with cavity walls is a gas of non-conserved bosons, and non-conservation forces the chemical potential to zero. Counting standing-wave modes with two polarizations and weighting each by the Bose occupation gives the Planck spectral energy density. Its low-frequency tail reproduces the classical Rayleigh-Jeans law and the ultraviolet catastrophe; the Bose factor cuts the divergence off at high frequency and the peak obeys Wien's displacement law.\n",{"path":6872,"title":6873,"module":6865,"summary":6874},"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fblackbody-thermodynamics-and-radiation-pressure","Blackbody Thermodynamics and Radiation Pressure","Integrating the Planck spectrum over all frequencies gives the total energy density proportional to the fourth power of temperature — the Stefan-Boltzmann law — and the isotropy of a relativistic gas fixes the radiation pressure at one third of the energy density. From the free energy follow the entropy and heat capacity, both proportional to T cubed, and the adiabatic law for radiation. The results govern the pressure inside stars and the cooling of the cosmic microwave background as the universe expands.\n",{"path":6876,"title":6877,"module":6865,"summary":6878},"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fphonons-and-the-debye-model","Phonons and the Debye Model","The vibrations of a crystal lattice are quantized into phonons — bosons of zero chemical potential, counted exactly like cavity photons but with three polarizations, a finite sound speed, and a total of 3N modes. The Debye model replaces the true dispersion by a linear one cut off at a frequency that enforces that count. It gives the correct low-temperature T-cubed heat capacity the Einstein model missed and recovers the Dulong-Petit value at high temperature.\n",{"path":6880,"title":6881,"module":6865,"summary":6882},"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-derived","Bose-Einstein Condensation Derived","For a gas of conserved bosons the excited states can hold only a finite number of particles at fixed temperature, set by the Bose function at unit fugacity. When the total exceeds that ceiling the surplus collapses into the single ground state, which the continuum density-of-states integral misses and which must be restored by hand. This fixes the critical temperature, the condensate fraction, and the fact that a uniform gas condenses only in three or more dimensions.\n",{"path":6884,"title":6885,"module":6865,"summary":6886},"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthermodynamics-of-the-bose-gas-and-superfluidity","Thermodynamics of the Bose Gas and Superfluidity","The energy and pressure of the ideal Bose gas follow from the Bose function at the order above the density, and below the critical temperature the pressure depends on temperature alone because the condensate carries none. The heat capacity rises to a cusp at the transition. Real superfluid helium departs from the ideal gas because interactions matter: the Landau criterion ties frictionless flow to the phonon-roton excitation spectrum, and the two-fluid model carries a second sound.\n",{"path":6888,"title":6889,"module":6890,"summary":6891},"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fthe-ideal-fermi-gas-at-zero-temperature","The Ideal Fermi Gas at Zero Temperature","Degenerate Fermi Gas","At absolute zero a gas of non-interacting fermions fills every single-particle state up to the Fermi energy and leaves the rest empty, a filled Fermi sphere in momentum space. This lesson computes the Fermi momentum, energy, and temperature from the density, the density of states, the total ground-state energy, and the degeneracy pressure that grows as $n^{5\u002F3}$. Numerical Fermi energies for metals set the scale: they are electron-volts, so room temperature is deep in the degenerate regime.\n",{"path":6893,"title":6894,"module":6890,"summary":6895},"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fsommerfeld-expansion-and-electrons-in-metals","The Sommerfeld Expansion and Electrons in Metals","Turning on a small temperature blurs the Fermi step over a shell of width $k_BT$ around $\\epsilon_F$. The Sommerfeld expansion turns integrals over the Fermi function into a power series in $(k_BT\u002F\\epsilon_F)^2$, giving the shift of the chemical potential and a heat capacity linear in $T$. This resolves the old puzzle of the missing electronic heat capacity, predicts the combined $C=\\gamma T+AT^3$ of a metal, and gives the temperature-independent Pauli paramagnetism of the electron gas.\n",{"path":6897,"title":6898,"module":6890,"summary":6899},"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fwhite-dwarfs-and-the-chandrasekhar-limit","White Dwarfs and the Chandrasekhar Limit","A white dwarf is held up against its own gravity by the degeneracy pressure of its electrons. Balancing that pressure against gravity gives a mass-radius relation $R\\propto M^{-1\u002F3}$: heavier white dwarfs are smaller and denser. As the density rises the electrons turn relativistic, the pressure softens from $n^{5\u002F3}$ to $n^{4\u002F3}$, and the star can no longer support itself above a critical mass. This lesson derives that Chandrasekhar mass, about $1.4\\,M_\\odot$, and what lies beyond it.\n",{"path":6901,"title":6902,"module":6890,"summary":6903},"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fneutron-stars-and-nuclear-matter","Neutron Stars and Dense Matter","When a collapsing core passes nuclear density, electron capture converts the matter to neutrons and their degeneracy pressure takes over. The same balance that fixes a white dwarf, rescaled by the neutron mass, gives a neutron star of a few solar masses in a ten-kilometre radius. General relativity is no longer a correction: the Tolman-Oppenheimer-Volkoff equation replaces the Newtonian balance and sets a maximum mass around two solar masses. This lesson rescales the Fermi-gas argument, states where it breaks, and places the compact objects in one stability sequence.\n",{"path":6905,"title":6906,"module":6907,"summary":6908},"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-cluster-expansion-and-virial-coefficients","The Cluster Expansion and Virial Coefficients","Interacting Gases","A real gas departs from $PV=Nk_BT$ because its molecules interact. The configuration integral factors through the Mayer function $f_{ij}=e^{-\\beta u_{ij}}-1$, and expanding it in powers of density produces the virial expansion $PV\u002FNk_BT = 1 + B_2(T)n + B_3(T)n^2 + \\cdots$. The second virial coefficient $B_2(T)=-\\tfrac12\\int f\\,\\d^3r$ is a single integral over the pair potential; it is positive for a hard core, negative for an attractive well, and vanishes at the Boyle temperature where the two balance.\n",{"path":6910,"title":6911,"module":6907,"summary":6912},"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-van-der-waals-gas-and-liquid-gas-coexistence","The van der Waals Gas and Liquid-Gas Coexistence","Resumming the second virial coefficient $B_2=b-a\u002Fk_BT$ into an equation of state gives the van der Waals model $(P+a\u002Fv^2)(v-b)=k_BT$, the simplest theory of a fluid that condenses. Below the critical temperature its isotherms develop a mechanically unstable loop; the Maxwell equal-area construction replaces the loop with a coexistence tie line. The critical point sits at $v_c=3b$, $k_BT_c=8a\u002F27b$, $P_c=a\u002F27b^2$, and the model predicts universal but incorrect critical exponents because it ignores fluctuations.\n",{"path":6914,"title":6915,"module":6907,"summary":6916},"\u002Fstatistical-mechanics\u002Finteractions\u002Fquantum-gases-with-interactions-and-exchange","Quantum Gases with Interactions and Statistical Exchange","A quantum gas has a nonzero second virial coefficient even with no forces between the particles: symmetrization alone produces an effective statistical interaction, attractive for bosons and repulsive for fermions, with range the thermal wavelength $\\lambda$. This lesson derives that exchange contribution $B_2=\\mp\\lambda^3\u002F2^{5\u002F2}g$, writes it as a statistical potential $v_s(r)=-k_BT\\ln(1\\pm e^{-2\\pi r^2\u002F\\lambda^2})$, and shows how real interactions add on top through the Beth-Uhlenbeck phase-shift formula, reducing at low temperature to a single scattering length.\n",{"path":6918,"title":6919,"module":6920,"summary":6921},"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fphases-coexistence-and-classification","Phases, Coexistence, and the Classification of Transitions","Phase Transitions","A phase transition is a point where the free energy of a substance loses analyticity, so a small change in temperature or pressure produces a qualitative change of state. This lesson maps the coexistence curves of a pure substance, derives the Clausius-Clapeyron relation between the slope of a coexistence line and its latent heat, and separates first-order transitions (discontinuous entropy and density) from continuous ones (a vanishing order parameter and divergent response). The Ehrenfest scheme, the order parameter, and the triple and critical points fix the vocabulary the rest of the module builds on.\n",{"path":6923,"title":6924,"module":6920,"summary":6925},"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-ising-model-and-exact-solutions","The Ising Model and Exact Results","The Ising model reduces cooperative ordering to spins on a lattice coupled to their neighbors, and the same Hamiltonian describes uniaxial magnets, the liquid-gas critical point through the lattice gas, and binary alloys. This lesson solves the one-dimensional chain exactly with the transfer matrix, shows by a domain-wall argument why one dimension has no ordered phase at any positive temperature, contrasts the survival of order in two dimensions, and quotes Onsager's exact two-dimensional results: the critical temperature, the logarithmically divergent heat capacity, and the magnetization exponent one eighth.\n",{"path":6927,"title":6928,"module":6920,"summary":6929},"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fmean-field-theory-and-the-weiss-model","Mean-Field Theory and Spontaneous Symmetry Breaking","Mean-field theory replaces the neighbors of each spin by their average, turning the interacting Ising model into a single spin in a self-consistent field. The resulting equation m = tanh(beta J z m + beta h) has only the zero solution above a critical temperature and gains a nonzero root below it, giving spontaneous magnetization and a mean-field critical temperature k T_c = J z. The Bragg-Williams free energy turns single-welled above T_c and double-welled below, the picture of spontaneous symmetry breaking. The approximation is exact in high dimension and fails below the upper critical dimension four, quantified by the Ginzburg criterion.\n",{"path":6931,"title":6932,"module":6920,"summary":6933},"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fcritical-exponents-and-landau-theory","Critical Exponents, Scaling, and Landau Theory","Near a continuous transition every singular quantity follows a power law in the reduced temperature, and the exponents alpha, beta, gamma, delta, nu, and eta encode the transition more sharply than T_c itself. Landau theory expands the free energy in the order parameter and delivers the mean-field exponents in a few lines. They disagree with experiment and with the exact two-dimensional Ising values, but the exponents are not independent: the scaling relations of Rushbrooke, Widom, Fisher, and Josephson tie them together, and the correlation length sets the length scale that organizes universality classes.\n",{"path":6935,"title":6936,"module":6920,"summary":6937},"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-renormalization-group-idea","Scaling and the Renormalization-Group Idea","At a critical point fluctuations exist on every length scale, so the system looks the same after coarse-graining. The renormalization group makes this self-similarity a computation: group spins into blocks, integrate out the short scales, and track how the couplings change. The transformation has fixed points, and the flow near a critical fixed point separates relevant couplings that grow from irrelevant ones that shrink, which is why only dimension and symmetry survive to set the exponents. The one-dimensional Ising decimation carries the whole scheme through in closed form and reproduces the absence of a finite-temperature transition.\n",{"path":6939,"title":6940,"module":6941,"summary":6942},"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fthermodynamic-fluctuations-and-response","Thermodynamic Fluctuations and Response Functions","Fluctuations and Response","Thermodynamic variables are sharp only on average; a macroscopic system in equilibrium fluctuates about its mean values. Einstein inverted Boltzmann's $S=k_B\\ln\\Omega$ into a Gaussian probability for a fluctuation, $w\\propto e^{\\Delta S\u002Fk_B}$, and the second moments it predicts reproduce the response functions: $\\langle\\Delta E^2\\rangle=k_BT^2C_V$, $\\langle\\Delta V^2\\rangle=k_BTV\\kappa_T$, $\\langle\\Delta M^2\\rangle=k_BT\\chi_T$. The variances diverge where the responses diverge, at a critical point, producing critical opalescence and the breakdown of the thermodynamic description.\n",{"path":6944,"title":6945,"module":6941,"summary":6946},"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fbrownian-motion-and-the-langevin-equation","Brownian Motion and the Langevin Equation","A pollen grain in water executes a random walk driven by molecular collisions. Einstein tied its diffusion constant to its mobility, $D=\\mu_{\\mathrm{mob}}k_BT$, turning a visible motion into a measurement of Avogadro's number. The Langevin equation splits the collisions into a systematic drag and a random force whose strength is fixed by the drag through $\\langle\\xi(t)\\xi(t')\\rangle=2\\gamma k_BT\\,\\delta(t-t')$ — the first fluctuation–dissipation relation. The mean-square displacement grows ballistically at short times and linearly, $\\langle r^2\\rangle=2dDt$, at long times, and the Stokes–Einstein relation $D=k_BT\u002F6\\pi\\eta a$ closes the loop to Perrin's experiments.\n",{"path":6948,"title":6949,"module":6941,"summary":6950},"\u002Fstatistical-mechanics\u002Ffluctuations\u002Flinear-response-and-the-fluctuation-dissipation-theorem","Linear Response and the Fluctuation-Dissipation Theorem","A system driven by a weak external field responds through a generalized susceptibility $\\chi(\\omega)$ whose imaginary part measures dissipation. The Wiener–Khinchin theorem makes the power spectrum of equilibrium fluctuations the Fourier transform of their correlation function, and the fluctuation–dissipation theorem ties the two together: $S_x(\\omega)=(2k_BT\u002F\\omega)\\,\\chi''(\\omega)$, so the spectrum of spontaneous fluctuations is fixed by the dissipative response. The Johnson–Nyquist noise of a resistor, $\\langle V^2\\rangle=4k_BTR\\,\\Delta f$, is the canonical example, and Onsager reciprocity closes the subject.\n",{"path":6952,"title":6953,"module":6,"summary":6},"\u002Fstatistical-mechanics","Statistical Mechanics",{"path":6955,"title":6956,"module":6957,"summary":6958},"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fbonding-mechanisms","Bonding Mechanisms","Molecules and Chemical Bonding","A molecule forms when the total energy of two atoms drops below the energy of the separated pair. This lesson works through the four mechanisms that produce that minimum: the ionic bond from charge transfer, the covalent bond from shared electron wave functions, the metallic bond, and the weak dipole-dipole and hydrogen bonds, computing bond lengths and dissociation energies for NaCl, H₂, and H₂⁺.\n",{"path":6960,"title":6961,"module":6957,"summary":6962},"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fmolecular-orbitals-and-h2-plus","The Molecular-Orbital Method and H₂⁺","The hydrogen molecule ion is the two-center problem that fixes the language of chemical bonding. This lesson builds the molecular orbital as a linear combination of atomic orbitals, minimizes the energy through the variational secular equation, and reduces the result to three two-center integrals: the overlap, the Coulomb term, and the exchange (resonance) integral. The bonding and antibonding levels, their potential-energy curves, and the charge piled between the nuclei follow from those integrals.\n",{"path":6964,"title":6965,"module":6957,"summary":6966},"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fhydrogen-molecule-and-exchange","The Hydrogen Molecule, Exchange, and Hybridization","Adding the second electron turns the one-electron ion into the two-electron hydrogen molecule, where electron-electron repulsion and the Pauli principle govern the bond. This lesson contrasts the Heitler-London valence-bond and molecular-orbital wave functions, derives the singlet-triplet splitting as an exchange energy, shows why naive molecular orbitals fail at dissociation, and builds the sp, sp², and sp³ hybrids that fix the directed geometry of covalent bonds.\n",{"path":6968,"title":6969,"module":6957,"summary":6970},"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fvan-der-waals-forces","Van der Waals Forces","The bond of last resort acts between all atoms, even closed-shell noble gases with no permanent moment. This lesson separates the three van der Waals contributions — Keesom orientation, Debye induction, and London dispersion — derives the London 1\u002Fr⁶ attraction from the coupled-oscillator and second-order perturbation pictures, and assembles the Lennard-Jones potential to compute the equilibrium spacing and cohesive energy of the noble-gas crystals, argon in particular.\n",{"path":6972,"title":6973,"module":6974,"summary":6975},"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Frotational-vibrational-spectra","Rotational and Vibrational Spectra of Molecules","Molecular Spectra","A diatomic molecule stores energy in three well-separated ledgers: electronic, vibrational, and rotational. Quantizing the rigid rotor gives levels spaced as ℓ(ℓ+1); quantizing the bond as a harmonic oscillator gives equally spaced vibrational levels. Their combination produces the P and R branches of an infrared absorption band, from which the bond length and force constant are read directly.\n",{"path":6977,"title":6978,"module":6974,"summary":6979},"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Fanharmonicity-and-rovibrational-structure","Anharmonicity and Rovibrational Structure","The rigid rotor and harmonic oscillator are first approximations. A real bond follows the Morse potential, whose levels converge toward dissociation; a real rotor stretches centrifugally; and vibration couples to rotation, so the rotational constant depends on the vibrational level. This lesson works out the anharmonic and centrifugal corrections, the Birge-Sponer route to the dissociation energy, the isotope shift, and the thermal band envelope.\n",{"path":6981,"title":6982,"module":6974,"summary":6983},"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Framan-and-electronic-bands","Raman Scattering and Electronic Bands","Not every vibration absorbs in the infrared. Raman scattering reaches modes that modulate the polarizability, giving Stokes and anti-Stokes lines whose intensity ratio measures temperature, and the mutual-exclusion rule pairs it with infrared absorption. Electronic transitions add the vibronic structure of band spectra, governed by the Franck-Condon principle, and the radiative fates of an excited state are sorted by the Jablonski diagram into fluorescence and phosphorescence.\n",{"path":6985,"title":6986,"module":6974,"summary":6987},"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Flasers-and-masers","Lasers, Masers, and Stimulated Emission","Einstein's three radiative processes — absorption, spontaneous emission, and stimulated emission — and the coefficients that relate them. Stimulated emission produces coherent photons, and inverting the level populations turns it into net amplification. We build the ruby three-level laser and the helium-neon four-level laser, and show why the fourth level makes inversion easy.\n",{"path":6989,"title":6990,"module":6991,"summary":6992},"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fstructure-of-solids","The Structure of Solids","Crystal Structure","A crystal is a unit cell repeated in three dimensions. We classify the common cubic lattices, compute the Coulomb energy of an ionic crystal through the Madelung constant, and show how the divergent naive lattice sum is tamed by cubic shells. The cohesive energy that results predicts melting points and connects the diatomic bond of an earlier lesson to the bulk solid.\n",{"path":6994,"title":6995,"module":6991,"summary":6996},"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fbravais-lattices-and-crystal-systems","Bravais Lattices, Bases, and Crystal Structures","A crystal is a Bravais lattice decorated by a basis. This lesson separates the two, builds primitive and Wigner-Seitz cells, enumerates the seven crystal systems and fourteen Bravais lattices, and fixes the language of point and space groups. Miller indices label planes and directions, and the packing fractions of the close-packed, cubic, and diamond structures follow from the geometry.\n",{"path":6998,"title":6999,"module":6991,"summary":7000},"\u002Fcondensed-matter\u002Fcrystal-structure\u002Freciprocal-lattice-and-brillouin-zones","The Reciprocal Lattice and Brillouin Zones","Every periodic crystal has a dual lattice in wavevector space. This lesson defines the reciprocal lattice through the condition b_i dot a_j equals two pi delta, derives its properties, shows the reciprocal of fcc is bcc, links reciprocal vectors to families of lattice planes, and builds the first Brillouin zone as the Wigner-Seitz cell of the reciprocal lattice, including the higher zones.\n",{"path":7002,"title":7003,"module":6991,"summary":7004},"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fdiffraction-and-structure-factors","X-ray and Neutron Diffraction","A crystal diffracts radiation whose wavelength matches its atomic spacing. This lesson derives the Bragg condition, the equivalent Laue condition 2k dot G equals G squared, and the Ewald-sphere construction, then computes the geometric structure factor that produces systematic absences for bcc and fcc, the atomic form factor, and the powder method. It closes on why neutrons and electrons complement X-rays.\n",{"path":7006,"title":7007,"module":7008,"summary":7009},"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonon-dispersion","The Harmonic Crystal and Phonon Dispersion","Lattice Dynamics","Atoms in a crystal vibrate about their equilibrium sites, and expanding the potential to second order turns the whole lattice into a set of coupled harmonic oscillators. This lesson sets up the harmonic approximation and the dynamical matrix, solves the monatomic linear chain for its dispersion omega(k) = 2 sqrt(K\u002FM) times the absolute sine of ka over two, explains why wavevectors outside the first Brillouin zone are redundant, and extends the chain to two atoms per cell to produce acoustic and optical branches with a frequency gap.\n",{"path":7011,"title":7012,"module":7008,"summary":7013},"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonons-quantization-and-dos","Phonons, Density of States, and Crystal Momentum","Quantizing the normal modes of a harmonic crystal turns each vibrational mode into a quantum oscillator whose excitations are phonons. This lesson counts phonons with Bose-Einstein statistics, defines crystal momentum and the normal versus Umklapp distinction in momentum conservation, builds the density of states with its van Hove singularities, and shows how inelastic neutron scattering measures a dispersion curve point by point.\n",{"path":7015,"title":7016,"module":7008,"summary":7017},"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fdebye-einstein-heat-capacity","Thermal Properties — Einstein and Debye Models","The lattice heat capacity follows the classical Dulong-Petit value at high temperature but collapses toward zero as T approaches zero, a purely quantum effect. This lesson derives that behavior from the Einstein model of a single frequency, then the Debye model of a linear phonon spectrum with a cutoff, obtaining the Debye T-cubed law at low temperature and the Debye interpolation across all temperatures, and closes with thermal expansion and the Gruneisen parameter.\n",{"path":7019,"title":7020,"module":7008,"summary":7021},"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fanharmonicity-and-thermal-transport","Anharmonicity, Thermal Expansion, and Heat Conduction","A perfectly harmonic crystal neither expands when heated nor resists heat flow. Both effects come from the cubic and higher terms the harmonic approximation discards. This lesson derives thermal expansion from an asymmetric interatomic potential, treats phonon-phonon scattering as the decay channel these terms open, shows why Umklapp processes are what make lattice thermal conductivity finite, and traces the temperature dependence of the conductivity and the phonon mean free path.\n",{"path":7023,"title":7024,"module":7025,"summary":7026},"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ffree-electron-gas-and-conduction","Conduction and the Free-Electron Gas","Free-Electron Fermi Gas","Drude's classical free-electron model gets Ohm's law right but the resistivity, its temperature dependence, and the heat capacity wrong. Replacing the Maxwell-Boltzmann distribution with the Fermi-Dirac distribution and treating electron-lattice collisions as wave scattering repairs all three: the Fermi energy, Fermi speed, and a mean free path set by thermal lattice vibrations.\n",{"path":7028,"title":7029,"module":7025,"summary":7030},"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fsommerfeld-model-and-heat-capacity","The Sommerfeld Model: Ground State and Heat Capacity","Quantizing the free-electron gas in a box fills a Fermi sphere in k-space. The density of states grows as the square root of energy in three dimensions, and the Fermi energy, temperature, and wavevector follow for real metals. The Sommerfeld expansion shows only a thermal shell of width k_BT near E_F is excited, giving an electronic heat capacity linear in T that sits beneath the phonon T-cubed term.\n",{"path":7032,"title":7033,"module":7025,"summary":7034},"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ftransport-and-the-hall-effect","Transport, Wiedemann–Franz, and the Hall Effect","The relaxation-time picture displaces the Fermi sphere under an applied field and gives the electrical conductivity ne-squared-tau over m. The same electrons carry heat, and their ratio yields the Wiedemann–Franz law with the universal Lorenz number. A magnetic field bends the carriers into cyclotron orbits and produces the Hall voltage, whose sign reveals the charge of the carriers.\n",{"path":7036,"title":7037,"module":7025,"summary":7038},"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fscreening-and-plasmons","Screening, Plasmons, and the Limits of Free Electrons","A mobile electron gas rearranges to screen any foreign charge, turning the bare Coulomb potential into a short-ranged Yukawa form over the Thomas–Fermi length. Displaced collectively, the gas rings at the plasma frequency, whose quantum is the plasmon and whose value sets the reflectivity edge of metals. A ledger of free-electron successes and failures then motivates band theory.\n",{"path":7040,"title":7041,"module":7042,"summary":7043},"\u002Fcondensed-matter\u002Fband-theory\u002Fblochs-theorem-and-energy-bands","Bloch's Theorem and Energy Bands","Band Theory","An electron in a periodic potential has stationary states that are plane waves modulated by a lattice-periodic envelope. This lesson proves Bloch's theorem two ways, defines crystal momentum and the band index, counts the allowed wavevectors from Born–von Kármán boundary conditions, and sets up the extended, reduced, and repeated-zone descriptions of a band.\n",{"path":7045,"title":7046,"module":7042,"summary":7047},"\u002Fcondensed-matter\u002Fband-theory\u002Fnearly-free-electron-model","The Nearly-Free-Electron Model","A weak periodic potential leaves the free-electron parabola almost intact except near Brillouin-zone boundaries, where two nearly degenerate plane waves mix. This lesson solves the resulting two-by-two secular problem, shows the gap of size twice the potential component opening at each boundary, identifies the two standing waves that pile charge on and between the ions, and works the exactly solvable Kronig–Penney model.\n",{"path":7049,"title":7050,"module":7042,"summary":7051},"\u002Fcondensed-matter\u002Fband-theory\u002Ftight-binding-method","The Tight-Binding Method","The opposite limit to nearly-free electrons builds bands from atomic orbitals. A Bloch sum of one orbital per site gives a dispersion set by the hopping integral between neighbours; the band widens from a sharp atomic level as the atoms approach. This lesson derives the s-band cosine dispersion, extends it to p-bands, and introduces Wannier functions as the localized dual of Bloch states.\n",{"path":7053,"title":7054,"module":7042,"summary":7055},"\u002Fcondensed-matter\u002Fband-theory\u002Ffermi-surfaces-and-semiclassical-dynamics","Fermi Surfaces, Effective Mass, and Metals vs Insulators","Filling the bands settles which crystals conduct. A filled band carries no current, so a crystal with filled bands and a gap is an insulator, while a partly filled band makes a metal. This lesson derives the no-current theorem for a filled band, defines the Fermi surface and Harrison's construction, introduces holes and the effective mass from band curvature, and states the semiclassical equations of motion that lead to Bloch oscillations.\n",{"path":7057,"title":7058,"module":7059,"summary":7060},"\u002Fcondensed-matter\u002Fsemiconductors\u002Fsemiconductor-bands-and-junctions","Band Theory and Semiconductors","Semiconductors","The periodic lattice splits atomic levels into allowed energy bands separated by forbidden gaps. Whether the highest occupied band is full or partly full, and how wide the gap above it is, sorts every solid into conductor, insulator, or semiconductor. Doping adds donor or acceptor levels inside the gap, and a p-n junction built from doped regions gives the diode, the solar cell, the LED, and the transistor.\n",{"path":7062,"title":7063,"module":7059,"summary":7064},"\u002Fcondensed-matter\u002Fsemiconductors\u002Fintrinsic-and-extrinsic-semiconductors","Carrier Statistics: Intrinsic and Extrinsic Semiconductors","The number of mobile electrons and holes in a semiconductor follows from the density of states near each band edge and the Fermi-Dirac tail that reaches into it. This lesson derives the effective densities of states, the intrinsic concentration and its exponential gap dependence, the law of mass action, the temperature march of the Fermi level, and the freeze-out, saturation, and intrinsic regimes of a doped crystal.\n",{"path":7066,"title":7067,"module":7059,"summary":7068},"\u002Fcondensed-matter\u002Fsemiconductors\u002Fcarrier-transport-and-recombination","Carrier Transport and Recombination","Carriers move by drift in a field and by diffusion down a concentration gradient, the two tied together by the Einstein relation. This lesson derives mobility and its scattering-limited temperature dependence, the drift and diffusion currents, the continuity equations, band-to-band and trap-assisted recombination, and the minority-carrier lifetime and diffusion length that set the length scale of every junction device.\n",{"path":7070,"title":7071,"module":7059,"summary":7072},"\u002Fcondensed-matter\u002Fsemiconductors\u002Fthe-pn-junction","The p-n Junction in Depth","Joining p-type and n-type silicon aligns their Fermi levels and leaves a depletion region of fixed charge with a built-in potential. This lesson derives the space-charge field and potential from Poisson's equation in the depletion approximation, the built-in voltage from Fermi-level alignment, the Shockley diode equation from minority-carrier diffusion, junction and diffusion capacitance, and the avalanche and Zener breakdown mechanisms.\n",{"path":7074,"title":7075,"module":7059,"summary":7076},"\u002Fcondensed-matter\u002Fsemiconductors\u002Ftransistors-and-optoelectronics","Transistors and Optoelectronic Devices","Two junctions in series make a bipolar transistor whose thin base gives current gain; a gate over an oxide makes a MOSFET whose inversion channel switches digital logic. Run in reverse, a junction converts photons to current. This lesson derives the transistor current gain and the MOSFET channel current, then treats the LED, the diode laser, and the illuminated solar-cell characteristic.\n",{"path":7078,"title":7079,"module":7080,"summary":7081},"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fdielectrics-and-polarization","Dielectrics, Polarization, and the Local Field","Dielectrics and Ferroelectrics","An insulator responds to an electric field by polarizing. This lesson builds the macroscopic polarization and the dielectric constant, sorts the microscopic response into electronic, ionic, and orientational polarizability, and corrects the field an atom actually feels to the Lorentz local field E + P\u002F3 epsilon-0. The Clausius-Mossotti relation links the measured permittivity to the atomic polarizability, and the frequency dependence of each mechanism explains why the static and optical dielectric constants differ.\n",{"path":7083,"title":7084,"module":7080,"summary":7085},"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fferroelectrics-and-piezoelectrics","Ferroelectrics, Piezoelectrics, and Structural Transitions","Some crystals carry a polarization with no applied field and switch it under a reversing field, tracing a hysteresis loop. This lesson develops the ferroelectric transition through the perovskite BaTiO3 displacive instability and its soft transverse-optical mode, builds the Landau free-energy theory of first- and second-order polar transitions, derives the Curie-Weiss divergence of the dielectric constant, and closes with piezoelectricity and pyroelectricity and their devices.\n",{"path":7087,"title":7088,"module":7089,"summary":7090},"\u002Fcondensed-matter\u002Fmagnetism\u002Fdiamagnetism-and-paramagnetism","Diamagnetism and Paramagnetism","Magnetism in Solids","Every solid responds to a magnetic field. Filled shells give a small negative diamagnetic susceptibility from induced Larmor currents; localized moments give a positive Curie paramagnetism described by the Brillouin function, with the ground-state moment fixed by Hund's rules. The conduction electrons add a temperature-independent Pauli paramagnetism from the thermal shell near the Fermi surface, partly cancelled by Landau diamagnetism of their orbital motion.\n",{"path":7092,"title":7093,"module":7089,"summary":7094},"\u002Fcondensed-matter\u002Fmagnetism\u002Fexchange-and-ferromagnetism","Exchange and Ferromagnetism","Magnetic ordering at hundreds of kelvin cannot be dipolar; it is an exchange effect, the Coulomb repulsion sorted by the Pauli principle into a spin-dependent energy captured by the Heisenberg Hamiltonian. Weiss molecular-field theory replaces the exchange field by an average proportional to the magnetization, giving a self-consistent equation whose solution is spontaneous magnetization below a Curie temperature and a Curie–Weiss susceptibility above it. Itinerant ferromagnetism follows from the Stoner criterion on the band density of states.\n",{"path":7096,"title":7097,"module":7089,"summary":7098},"\u002Fcondensed-matter\u002Fmagnetism\u002Fantiferromagnetism-and-domains","Antiferromagnetism, Ferrimagnetism, and Domains","A negative exchange coupling orders neighboring spins antiparallel. Two-sublattice molecular-field theory gives a Néel temperature marked by a cusp in the susceptibility, and unequal sublattices leave a net moment — ferrimagnetism, the magnetism of the ferrites. A ferromagnet breaks into domains to reduce its magnetostatic energy, separated by Bloch walls whose width is set by the competition between exchange and magnetocrystalline anisotropy, and the irreversible motion of those walls produces the hysteresis loop.\n",{"path":7100,"title":7101,"module":7089,"summary":7102},"\u002Fcondensed-matter\u002Fmagnetism\u002Fspin-waves-and-magnons","Spin Waves and Magnons","The lowest excitations of a ferromagnet are not single flipped spins but collective precessions in which every moment tips slightly and its phase advances along the crystal. These spin waves have a quadratic dispersion at long wavelength, quantize into magnons obeying Bose statistics, and their thermal population removes magnetization as the Bloch T-to-the-three-halves law. Antiferromagnetic magnons disperse linearly, and inelastic neutron scattering measures both.\n",{"path":7104,"title":7105,"module":7106,"summary":7107},"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fsuperconductivity-phenomenology","Superconductivity: Phenomenology and BCS","Superconductivity","Below a critical temperature some materials lose all resistance and expel magnetic flux — the Meissner effect that defines the state. The isotope effect points to lattice vibrations, and BCS theory binds electrons into Cooper pairs through phonon exchange. The paired condensate opens an energy gap, quantizes magnetic flux, and drives the Josephson effects.\n",{"path":7109,"title":7110,"module":7106,"summary":7111},"\u002Fcondensed-matter\u002Fsuperconductivity\u002Flondon-theory-and-the-meissner-effect","London Theory and the Meissner Effect","A perfect conductor freezes the field it was cooled in; a superconductor expels it. The distinction needs a constitutive law beyond zero resistance — the two London equations — whose solution is exponential flux decay over the penetration depth. The same rigidity follows from a macroscopic condensate wave function, and the thermodynamics of the critical field fixes the condensation energy, the latent heat, and the specific-heat jump.\n",{"path":7113,"title":7114,"module":7106,"summary":7115},"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fginzburg-landau-theory","Ginzburg–Landau Theory, Vortices, and Type-II","A complex order parameter and a free-energy expansion turn the superconducting transition into a Landau theory. Two lengths emerge — the coherence length and the penetration depth — whose ratio kappa sorts superconductors into type I and type II. Type-II materials admit flux as an Abrikosov lattice of vortices, each threading exactly one quantum h\u002F2e, between a lower and an upper critical field.\n",{"path":7117,"title":7118,"module":7106,"summary":7119},"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fbcs-theory","Microscopic BCS Theory","A phonon-mediated attraction, however weak, binds two electrons above the Fermi sea — the Cooper problem shows the sea is unstable. The BCS variational ground state pairs all electrons near the Fermi surface and, through a self-consistent gap equation, opens an energy gap. Weak-coupling solution gives the exponential T_c and the universal ratios 2 Delta(0) = 3.53 k_B T_c and Delta C \u002F C_n = 1.43.\n",{"path":7121,"title":7122,"module":7106,"summary":7123},"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fjosephson-and-high-tc","Josephson Effects and Unconventional Superconductors","Two superconductors joined by a thin barrier carry a supercurrent set by their phase difference — the dc Josephson effect — and oscillate at 2eV\u002Fh under a voltage. A two-junction loop turns flux quantization into a magnetometer of single-quantum sensitivity. The cuprates superconduct in CuO2 planes with a doping-dependent dome, d-wave pairing, and a pseudogap that lie outside the phonon picture.\n",{"path":7125,"title":7126,"module":7127,"summary":7128},"\u002Fcondensed-matter\u002Fnanostructures\u002Fquantum-wells-wires-and-dots","Quantum Wells, Wires, and Dots","Nanostructures","When a crystal is shrunk until one or more of its dimensions approaches the electron wavelength, the continuous bands of the bulk break into discrete subbands. Confining in one direction gives a quantum well with a step-like density of states, in two directions a quantum wire with inverse-square-root singularities, and in all three a quantum dot whose levels are sharp like an atom's. This lesson derives the density of states in each case and applies it to size-tunable dot emission and the Coulomb blockade of a single-electron transistor.\n",{"path":7130,"title":7131,"module":7127,"summary":7132},"\u002Fcondensed-matter\u002Fnanostructures\u002Finteger-quantum-hall-effect","The 2D Electron Gas and the Integer Quantum Hall Effect","A two-dimensional electron gas in a strong perpendicular magnetic field has its continuous density of states collapse into macroscopically degenerate Landau levels. As the field is swept, the Hall resistance locks onto exact plateaus at h over an integer times e squared, while the longitudinal resistance drops to zero. This lesson derives the Landau levels and their degeneracy, explains the plateaus through disorder-localized states and current-carrying edge channels, and states why the von Klitzing constant is now a resistance standard.\n",{"path":7134,"title":7135,"module":7127,"summary":7136},"\u002Fcondensed-matter\u002Fnanostructures\u002Ffractional-quantum-hall-and-topology","The Fractional Quantum Hall Effect and Topological Order","When the lowest Landau level is only partly filled, the non-interacting theory predicts no gap, yet a plateau appears at filling one-third. It is a many-body effect: Coulomb repulsion selects a correlated ground state, the Laughlin wavefunction, whose excitations carry a fraction of the electron charge. This lesson builds the Laughlin state, introduces composite fermions that map the fractional effect onto an integer one, and explains how the quantum Hall effect brought the Chern number and topology into condensed-matter physics.\n",{"path":7138,"title":7139,"module":7127,"summary":7140},"\u002Fcondensed-matter\u002Fnanostructures\u002Fgraphene-and-dirac-materials","Graphene and Dirac Materials","Graphene is one atomic layer of carbon on a honeycomb lattice. A tight-binding calculation on its two-atom basis gives valence and conduction bands that touch at the corners of the Brillouin zone, where the dispersion is linear and the electrons behave as massless two-dimensional Dirac particles with a fixed speed. This lesson derives the Dirac cones, the Berry phase of pi and the sublattice chirality, the anomalous half-integer quantum Hall effect that follows, and how opening a gap in a Dirac cone points toward topological insulators.\n",{"path":7142,"title":7143,"module":6,"summary":6},"\u002Fcondensed-matter","Condensed Matter Physics",{"path":7145,"title":7146,"module":3911,"summary":7147},"\u002Flogic\u002Ffoundations\u002Flogic-as-a-mathematical-model","Logic as a Mathematical Model of Deduction","Symbolic logic models deductive reasoning the way probability theory models chance: it keeps the form of a correct deduction and discards its content. A deduction is valid when its conclusion follows from the form of the premises alone, independent of what the non-logical words mean. Two models carry the subject — coarse sentential logic and fine first-order logic — and four questions organize it: logical consequence, methods of proof, the gap between provable and true, and the link between logic and computability. Tuples, relations, functions, equivalence classes, and cardinality supply the set-theoretic vocabulary every later chapter uses.\n",{"path":7149,"title":7150,"module":7151,"summary":7152},"\u002Flogic\u002Fsentential-logic\u002Fformal-languages-and-well-formed-formulas","Formal Languages and Well-Formed Formulas","Sentential Logic","The language of sentential logic has an alphabet of sentence symbols, five connectives, and two parentheses, with formation rules that pick out the well-formed formulas. The wffs are the least set of expressions closed under the five formula-building operations, and every such generated set carries an induction principle.\n",{"path":7154,"title":7155,"module":7151,"summary":7156},"\u002Flogic\u002Fsentential-logic\u002Ftruth-assignments-and-tautologies","Truth Assignments, Tautologies, and Consequence","A truth assignment fixes the sentence symbols true or false, and a recursion extends it uniquely to every formula. Satisfaction, tautologies, and tautological implication — one formula following semantically from others — rest on that extension, and the truth-table procedure decides implication for finite premise sets.\n",{"path":7158,"title":7159,"module":7151,"summary":7160},"\u002Flogic\u002Fsentential-logic\u002Funique-readability-and-parsing","Unique Readability and a Parsing Algorithm","Parentheses keep a formula from being read two ways. The parenthesis lemmas and a top-down parsing algorithm recover a formula's structure and yield unique readability: every wff has exactly one formation tree, which is what makes the truth recursion well defined.\n",{"path":7162,"title":7163,"module":7151,"summary":7164},"\u002Flogic\u002Fsentential-logic\u002Finduction-and-recursion","Induction and Recursion on Formulas","Two principles govern any set generated from initial elements by operations: prove a property of all its members by covering the initial elements and the closure steps, and define a function on it by recursion on structure. The recursion theorem needs the set to be freely generated, and unique readability supplies that condition for the well-formed formulas.\n",{"path":7166,"title":7167,"module":7151,"summary":7168},"\u002Flogic\u002Fsentential-logic\u002Fexpressive-completeness-and-normal-forms","Sentential Connectives and Normal Forms","Every formula computes a Boolean function of its atoms, and Post's theorem gives the converse: every Boolean function is realized by a wff in disjunctive normal form, so the five connectives are more than enough. Minimal complete sets follow, down to the single connectives NAND and NOR, together with a method for proving a set of connectives incomplete.\n",{"path":7170,"title":7171,"module":7151,"summary":7172},"\u002Flogic\u002Fsentential-logic\u002Fboolean-circuits","Switching Circuits","A memoryless two-valued circuit computes a Boolean function, so every formula names a gate network and every network a formula. Cost and delay are read off the formula by recursion, and tautological equivalence and normal forms design and simplify circuits realizing a given specification.\n",{"path":7174,"title":7175,"module":7151,"summary":7176},"\u002Flogic\u002Fsentential-logic\u002Fcompactness-and-effectiveness","Compactness and Effectiveness","The compactness theorem reduces satisfiability of an infinite set of formulas to its finite subsets, proved by extension to a maximal finitely satisfiable set and applied to color infinite graphs. Effectiveness fixes what \"decidable\" and \"effectively enumerable\" mean and settles the decidability of tautologyhood.\n",{"path":7178,"title":7179,"module":7180,"summary":7181},"\u002Flogic\u002Ffirst-order-languages\u002Ffirst-order-languages","First-Order Languages","First-Order Languages and Structures","Sentential logic cannot see inside a simple statement, so it misses valid arguments that turn on quantifiers and predicates. A first-order language adds a quantifier, variables, and a chosen vocabulary of predicate, function, and constant symbols. Terms and well-formed formulas are built by recursion over this alphabet, and a variable occurs free or bound according to the quantifiers that reach it.\n",{"path":7183,"title":7184,"module":7180,"summary":7185},"\u002Flogic\u002Ffirst-order-languages\u002Fstructures-truth-and-satisfaction","Structures, Truth, and Satisfaction","A structure interprets a language: a nonempty universe plus a meaning for every predicate, function, and constant symbol. Tarski's recursion defines when a structure satisfies a formula under a variable assignment, and hence when a sentence is true. From satisfaction we recover logical implication, validity, and logical equivalence for first-order logic.\n",{"path":7187,"title":7188,"module":7180,"summary":7189},"\u002Flogic\u002Ffirst-order-languages\u002Fdefinability-and-elementary-equivalence","Definability and Elementary Equivalence","Fix a structure and ask which relations a formula can pick out: the definable ones. A set of sentences picks out a class of structures, the elementary classes. Homomorphisms and isomorphisms compare structures, and the homomorphism theorem shows isomorphic structures satisfy the same sentences. Automorphisms bound what first-order logic can distinguish, giving a tool for proving relations undefinable.\n",{"path":7191,"title":7192,"module":7180,"summary":7193},"\u002Flogic\u002Ffirst-order-languages\u002Fterms-substitution-and-parsing","Parsing, Substitution, and Substitutability","Every recursion on first-order syntax rests on unique readability. A parenthesis-counting function proves that terms and formulas decompose in exactly one way, and a parsing algorithm recovers the decomposition. Substituting a term for a free variable can capture it under a quantifier; the substitutability condition rules that out, and the substitution lemma trades syntactic substitution for a change of assignment.\n",{"path":7195,"title":7196,"module":7197,"summary":7198},"\u002Flogic\u002Fdeductive-calculus\u002Fa-deductive-calculus","A Deductive Calculus for First-Order Logic","The Deductive Calculus and Its Metatheorems","A proof must be finite and mechanically checkable. A Hilbert-style calculus meets both demands: six schemas of logical axioms, a single rule of inference (modus ponens), and the syntactic consequence relation they generate. Substitution and substitutability are defined by recursion, and the bridge theorem reduces deducibility to tautological implication from the axioms.\n",{"path":7200,"title":7201,"module":7197,"summary":7202},"\u002Flogic\u002Fdeductive-calculus\u002Fdeduction-theorem-and-derived-rules","The Deduction Theorem and Derived Rules","Raw deductions from axioms are unusable by hand. The generalization theorem, the deduction theorem, contraposition, reductio ad absurdum, and rule T reduce the calculus to the moves of ordinary mathematics, each proved once to license a block of axiom-level steps. Generalization on constants and alphabetic variants handle the quantifier and substitution bookkeeping.\n",{"path":7204,"title":7205,"module":7197,"summary":7206},"\u002Flogic\u002Fdeductive-calculus\u002Fsoundness","The Soundness Theorem","Soundness is the easy half of the match between proof and truth. Whatever the calculus deduces is logically implied, by an induction on deduction length that rests on one lemma: every logical axiom is valid. The only hard case, quantifier instantiation, needs the substitution lemma. The contrapositive corollary states that every satisfiable set is consistent.\n",{"path":7208,"title":7209,"module":7197,"summary":7210},"\u002Flogic\u002Fdeductive-calculus\u002Fcompleteness-and-consistency","The Completeness Theorem","Gödel's completeness theorem is the deep converse of soundness: whatever is logically implied can be deduced. Equivalently, every consistent set has a model. The Henkin proof manufactures that model out of syntax alone: add witnessing constants, extend to a maximal consistent set, and read a term model off the formulas it contains. Compactness and the enumerability theorem drop out.\n",{"path":7212,"title":7213,"module":7214,"summary":7215},"\u002Flogic\u002Fmodels-and-theories\u002Fcompactness-and-lowenheim-skolem","Compactness and the Löwenheim–Skolem Theorems","Models, Compactness, and Theories","A set of first-order sentences has a model whenever each of its finite subsets does. This compactness theorem follows from completeness and yields the finiteness limitation, the downward and upward Löwenheim–Skolem theorems, models of every infinite cardinality, and nonstandard models of arithmetic.\n",{"path":7217,"title":7218,"module":7214,"summary":7219},"\u002Flogic\u002Fmodels-and-theories\u002Ftheories-elementary-classes-and-categoricity","Theories, Elementary Classes, and Categoricity","A theory is a set of sentences closed under logical consequence. Theories correspond to classes of models; a theory may be complete, axiomatizable, or finitely axiomatizable, and completeness together with axiomatizability yields decidability. The Łoś–Vaught test derives completeness from categoricity in a cardinal, applied to dense linear orders and to algebraically closed fields.\n",{"path":7221,"title":7222,"module":7214,"summary":7223},"\u002Flogic\u002Fmodels-and-theories\u002Finterpretations-between-theories","Interpretations Between Theories","An interpretation translates the vocabulary of one theory into formulas of another, relativizing quantifiers to a definable domain and mapping symbols to defining formulas. Defined function symbols meet a noncreativity criterion; the syntactic translation of formulas carries theoremhood forward, and a faithful interpretation transfers decidability and undecidability between theories.\n",{"path":7225,"title":7226,"module":7214,"summary":7227},"\u002Flogic\u002Fmodels-and-theories\u002Fnonstandard-analysis","Nonstandard Analysis","Compactness builds a model of the real ordered field containing infinite elements and nonzero infinitesimals. The transfer principle carries every first-order truth from the reals to this extension, the standard-part map collapses finite hyperreals back onto the reals, and continuity and the derivative are rederived by working with infinitely small quantities directly.\n",{"path":7229,"title":7230,"module":7231,"summary":7232},"\u002Flogic\u002Farithmetic-and-definability\u002Fdefinability-in-arithmetic","The Structure of Arithmetic and Definability","Number Theory and Definability","Number theory is the theory of one fixed structure, the natural numbers under successor, order, addition, multiplication, and exponentiation. Every number is named by a numeral, and a relation is definable when a single formula picks out exactly its tuples. The central gap separates the sentences true in that structure from those any reasonable set of axioms can prove.\n",{"path":7234,"title":7235,"module":7231,"summary":7236},"\u002Flogic\u002Farithmetic-and-definability\u002Fnatural-numbers-with-successor","Natural Numbers with Successor","The weakest reduct keeps only zero and successor. Its models are a standard chain together with disjoint copies of the integers, which makes the theory categorical in every uncountable power, hence complete and decidable. A quantifier-elimination procedure gives a practical decision method and shows a subset is definable if and only if it is finite or cofinite.\n",{"path":7238,"title":7239,"module":7231,"summary":7240},"\u002Flogic\u002Farithmetic-and-definability\u002Fpresburger-and-reducts","Reducts: Order, Addition, and Multiplication","Adding order to the successor reduct keeps decidability and makes the theory finitely axiomatizable; adding addition gives Presburger arithmetic, still decidable by quantifier elimination once congruence predicates are included, with definable sets exactly the eventually periodic ones. Multiplication is the break point: neither addition nor order can define it, and once it joins addition the theory stops being decidable.\n",{"path":7242,"title":7243,"module":7231,"summary":7244},"\u002Flogic\u002Farithmetic-and-definability\u002Fa-subtheory-and-representability","A Subtheory of Number Theory and Representability","A finite set of eleven axioms, the recursion equations for successor, order, addition, multiplication, and exponentiation, already proves every true quantifier-free and existential sentence. Representability asks a theory to prove the right instances of a formula rather than merely make them true, and a relation is defined to be recursive exactly when some consistent finite theory represents it. Church's thesis identifies that with decidability, and closure under composition, minimization, and primitive recursion builds the catalog the incompleteness proofs need.\n",{"path":7246,"title":7247,"module":7248,"summary":7249},"\u002Flogic\u002Fincompleteness\u002Farithmetization-of-syntax","Arithmetization of Syntax","Arithmetization and the Incompleteness Theorems","Gödel numbering assigns a natural number to every symbol, expression, formula, and deduction, turning statements about syntax into statements about numbers. The syntactic operations — substitution, \"is a wff\", \"is an axiom\", \"d codes a deduction of a\" — come out primitive recursive and hence representable in the subtheory, which lets a formula of arithmetic talk about formulas, including itself.\n",{"path":7251,"title":7252,"module":7248,"summary":7253},"\u002Flogic\u002Fincompleteness\u002Fincompleteness-and-undecidability","Incompleteness, Undecidability, and Church's Theorem","The fixed-point lemma manufactures a sentence that talks about its own Gödel number. Pointed at truth it gives Tarski's theorem — arithmetic truth is not arithmetically definable; pointed at provability it gives Gödel's first incompleteness theorem and the undecidability of the theory of the natural numbers, and, applied to validity, Church's theorem that first-order logic is undecidable. The set of theorems of a recursive theory is only recursively enumerable — the gap between provable and true.\n",{"path":7255,"title":7256,"module":7248,"summary":7257},"\u002Flogic\u002Fincompleteness\u002Fsecond-incompleteness-theorem","The Second Incompleteness Theorem","Consistency of a recursively axiomatized theory is itself an arithmetic sentence, built from a provability predicate. When the theory is strong enough to formalize its own reflection and modus ponens — the Hilbert–Bernays–Löb derivability conditions — it cannot prove that sentence unless it is inconsistent. Löb's theorem is the companion result, and set theory is the case that closes Hilbert's program.\n",{"path":7259,"title":7260,"module":7261,"summary":7262},"\u002Flogic\u002Fcomputability-and-representability\u002Frecursive-functions","Recursive Functions and Church's Thesis","Recursive Functions and Representability","The recursive functions are the formal counterpart of the effectively computable ones: built from three initial functions by composition, primitive recursion, and minimization, and equivalently the functions representable in a finitely axiomatized arithmetic. Church's thesis identifies the class with effective calculability; Kleene's normal form theorem and the unsolvable halting problem place the recursive sets strictly inside the recursively enumerable ones.\n",{"path":7264,"title":7265,"module":7261,"summary":7266},"\u002Flogic\u002Fcomputability-and-representability\u002Frepresenting-exponentiation","Representing Exponentiation and the β-Function","Coding finite sequences by prime-power exponents already assumes exponentiation, so representing exponentiation from addition and multiplication alone needs a different encoder. Gödel's β-function, built from a pairing function and the Chinese remainder theorem, reads back arbitrary finite sequences using only plus and times. This represents exponentiation in the addition-multiplication arithmetic and closes the last gap in the representability of every recursive syntactic operation.\n",{"path":7268,"title":7269,"module":7270,"summary":7271},"\u002Flogic\u002Fsecond-order-logic\u002Fsecond-order-languages","Second-Order Languages","Second-Order Logic and Beyond","Second-order logic quantifies over relations and functions, not just individuals. Second-order Peano arithmetic and the second-order theory of the reals become categorical, and finiteness is definable by a single sentence. Compactness, completeness, and the Löwenheim–Skolem theorems all fail for the standard semantics.\n",{"path":7273,"title":7274,"module":7270,"summary":7275},"\u002Flogic\u002Fsecond-order-logic\u002Fskolem-functions-and-many-sorted-logic","Skolem Functions and Many-Sorted Logic","Skolem functions replace existential quantifiers with named witnesses, putting any first-order formula into a prenex form with all existentials — now over functions — pulled to the front. The Skolemized formula is equisatisfiable with the original, which reduces satisfiability to universal sentences and, through Herbrand expansions, to sentential logic. Many-sorted logic then adds several universes at once and reduces cleanly to ordinary one-sorted logic.\n",{"path":7277,"title":7278,"module":7270,"summary":7279},"\u002Flogic\u002Fsecond-order-logic\u002Fgeneral-structures","General (Henkin) Structures","General semantics reinterprets second-order logic by letting the predicate and function quantifiers range over a designated collection of relations and functions rather than all of them. Recast as many-sorted first-order logic with comprehension axioms, general second-order logic recovers a sound and complete calculus together with compactness and Löwenheim–Skolem, giving up the categoricity of the standard semantics. The ω-models of analysis show the trade.\n",{"path":7281,"title":7282,"module":6,"summary":6},"\u002Flogic","Logic",{"path":7284,"title":7285,"module":3911,"summary":7286},"\u002Freinforcement-learning\u002Ffoundations\u002Fwhat-is-reinforcement-learning","What Is Reinforcement Learning?","Reinforcement learning is learning what to do — how to map situations to actions — so as to maximize a numerical reward signal, discovered by trial and error rather than told. We set up the agent–environment loop, separate it from supervised and unsupervised learning, name the four elements (policy, reward, value, and an optional model), and train a tic-tac-toe player with a temporal-difference value update.\n",{"path":7288,"title":7289,"module":3911,"summary":7290},"\u002Freinforcement-learning\u002Ffoundations\u002Fa-brief-history-of-rl","A Brief History of Reinforcement Learning","The origins of reinforcement learning. Three threads — trial-and-error learning from animal psychology, optimal control and dynamic programming, and temporal-difference learning — ran independently for decades and merged around 1989 into the modern field. Replacing the lookup table with a neural network then produced deep reinforcement learning: DQN, AlphaGo, AlphaZero, MuZero, and RLHF.\n",{"path":7292,"title":7293,"module":3911,"summary":7294},"\u002Freinforcement-learning\u002Ffoundations\u002Fmulti-armed-bandits","Multi-Armed Bandits","A bandit is reinforcement learning stripped to a single decision, repeated: no state, no consequences, only the tension between exploiting the arm that looks best and exploring the ones that might be better. We build up the whole toolkit — sample-average value estimates, the incremental update rule, ε-greedy, optimistic initialization, UCB, and gradient bandits — and use it to study exploration in isolation, the one problem that carries over to the full setting.\n",{"path":7296,"title":7297,"module":3911,"summary":7298},"\u002Freinforcement-learning\u002Ffoundations\u002Fbandit-exploration-algorithms","Bandit Exploration Algorithms","Better ways to explore than picking at random. Upper-confidence-bound selection explores by optimism about what it hasn't measured; gradient bandits learn action preferences by stochastic gradient ascent on reward. We then add context to get the contextual bandit, the bridge to full RL, and measure everything by regret — where UCB1 and Thompson sampling reach the logarithmic optimum that fixed-ε greedy cannot.\n",{"path":7300,"title":7301,"module":3911,"summary":7302},"\u002Freinforcement-learning\u002Ffoundations\u002Fmarkov-decision-processes","Markov Decision Processes","A Markov decision process is the formal interface between an agent and its environment: at each step the agent reads a state, chooses an action, and receives a reward and a next state. We fix that loop, the dynamics function that governs it, and the Markov property that makes the state sufficient; then turn goals into a scalar reward and rewards into a discounted return, with one notation that covers both episodic and continuing tasks.\n",{"path":7304,"title":7305,"module":3911,"summary":7306},"\u002Freinforcement-learning\u002Ffoundations\u002Fvalue-functions-and-optimality","Value Functions and Optimality","A value function scores how good a state (or state–action pair) is under a policy: the expected return from there onward. Its defining property is the Bellman equation, a self-consistency condition linking a state's value to its successors' values, which we derive from the return and the dynamics. Pushing the same idea to the best-achievable value gives the Bellman optimality equations, whose solution yields an optimal policy — and whose intractability is what the rest of the course is about.\n",{"path":7308,"title":4130,"module":7309,"summary":7310},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdynamic-programming","Tabular Solution Methods","Dynamic programming computes optimal policies when a perfect model of the MDP is given, by turning the Bellman equations into assignment statements. We build up iterative policy evaluation (the expected update), the policy improvement theorem, and the two classic algorithms that alternate them — policy iteration and value iteration — worked on the gridworld, a two-state MDP, Jack's car rental, and the gambler's problem.\n",{"path":7312,"title":7313,"module":7309,"summary":7314},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdp-async-and-gpi","Dynamic Programming: Asynchronous DP and Generalized Policy Iteration","Policy and value iteration both sweep the entire state set on every pass, which is impossible once the state space is huge. This lesson loosens the schedule: asynchronous DP updates states in any order, generalized policy iteration names the alternation of evaluation and improvement that underlies nearly every RL method, and a look at efficiency and the curse of dimensionality places DP among the alternatives. We close past Sutton & Barto with prioritized sweeping, neuro-dynamic programming, value-iteration networks, and MuZero.\n",{"path":7316,"title":7317,"module":7309,"summary":7318},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-methods","Monte Carlo Methods","Monte Carlo methods learn value functions and optimal policies from complete sampled episodes, with no model of the environment: they simply average the returns that actually followed each state. We build prediction (first-visit and every-visit averaging), see why estimating action values forces the exploration question, and answer it two ways on-policy — exploring starts and epsilon-soft control. Throughout, Monte Carlo samples one whole trajectory to termination and never bootstraps.\n",{"path":7320,"title":7321,"module":7309,"summary":7322},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-off-policy","Monte Carlo Methods: Off-Policy Learning","On-policy Monte Carlo can only reach the best exploring policy, not the true optimum. Off-policy methods remove that ceiling by learning about a greedy target policy from data generated by a soft behavior policy, corrected with importance sampling. We derive the importance-sampling ratio, weigh ordinary against weighted estimators on real numbers, give the incremental off-policy algorithm, sharpen it with discounting-aware sampling, and close by placing Monte Carlo on the model\u002Fbootstrap map beside DP and temporal-difference learning.\n",{"path":7324,"title":7325,"module":7309,"summary":7326},"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftemporal-difference-learning","Temporal-Difference Learning","Temporal-difference learning is the one idea most central to reinforcement learning: learn a value directly from experience, like Monte Carlo, but update each guess toward the next guess before the episode ends, like dynamic programming. We derive the TD(0) prediction rule and its reward-prediction error, contrast its one-step backup with MC and DP, work the driving-home and random-walk examples, and show the batch-updating optimality that makes TD approximate the certainty-equivalence estimate.\n",{"path":7328,"title":7329,"module":7309,"summary":7330},"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftd-control-sarsa-and-q-learning","TD Control: Sarsa, Q-learning, and Double Learning","With TD prediction in hand, control follows the generalized-policy-iteration pattern with TD as the evaluation step. We build Sarsa (on-policy), Q-learning (off-policy, targeting the optimal policy), and Expected Sarsa that spans the two, then confront the maximization bias every max-based method inherits and fix it with Double Q-learning. We close past Sutton & Barto, following each one-step tabular update into its deep-RL descendant — DQN, Double DQN, and Rainbow.\n",{"path":7332,"title":7333,"module":7309,"summary":7334},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-bootstrapping","n-Step Bootstrapping","Monte Carlo waits for the full return; one-step TD bootstraps after a single reward. Between them lies a whole spectrum, indexed by one integer n: look ahead n real rewards, then bootstrap from the value n steps out. The n-step return unifies the previous two lessons, and — on the random walk — an intermediate n beats both extremes. We build the n-step return, the n-step TD update, the backup-diagram spectrum, and n-step Sarsa for control.\n",{"path":7336,"title":7337,"module":7309,"summary":7338},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-off-policy-methods","n-Step Bootstrapping: Off-Policy Methods","Taking the n-step family off-policy raises the same importance-sampling questions Monte Carlo did, now over a window of exactly n actions. We reweight n-step returns by the policy ratio, watch the ratio product inflate variance on real numbers, then build the tree-backup algorithm that learns off-policy with no ratios at all — and finally n-step Q(sigma), one algorithm whose per-step switch recovers Sarsa, tree backup, and Expected Sarsa as special cases.\n",{"path":7340,"title":7341,"module":7309,"summary":7342},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-and-learning","Planning and Learning","Planning and learning are the same operation run on two kinds of experience. A model turns states and actions into simulated transitions; planning backs up values over that simulated experience exactly as learning backs them up over real experience. We build the Dyna architecture that interleaves acting, model-learning, direct RL, and planning in one loop, trace a single Dyna-Q step by hand, and patch the architecture for when the model goes stale.\n",{"path":7344,"title":7345,"module":7309,"summary":7346},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-focusing-and-decision-time","Planning: Focusing Updates and Decision-Time Search","Dyna plans by replaying remembered transitions, but sampling them uniformly wastes most of the effort. This lesson sharpens planning: prioritized sweeping works backward from states whose value just changed, expected versus sample updates weigh thoroughness against cost, and trajectory sampling and real-time DP focus updates on the states the policy actually visits. We trace Dyna forward to model-based deep RL, then turn to decision-time planning — heuristic search, rollouts, and Monte Carlo Tree Search.\n",{"path":7348,"title":7349,"module":7309,"summary":7350},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdecision-time-planning","Decision-Time Planning","Planning need not build a global policy. Decision-time planning runs a fresh lookahead every time a state arrives and returns just one action, then throws the work away. We start from real-time dynamic programming — asynchronous value iteration on the states the agent actually visits — then move through heuristic search and rollout algorithms, each a one-step policy improvement applied on the fly to the current state.\n",{"path":7352,"title":7353,"module":7309,"summary":7354},"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-tree-search","Monte Carlo Tree Search","Monte Carlo Tree Search is a rollout algorithm with memory: it accumulates value estimates across simulations and steers later ones toward promising branches. We work through the four steps — selection, expansion, simulation, backup — the UCT selection rule computed on real numbers, the asymmetric growing tree, and the full pseudocode. We close past Sutton & Barto with the lineage from UCT to AlphaGo, AlphaZero, and MuZero, where a learned network stands in for the leaf value and the rollout.\n",{"path":7356,"title":7357,"module":7358,"summary":7359},"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-prediction","On-Policy Prediction with Approximation","Approximate Solution Methods","Every tabular method so far stored one number per state, which fails once the state space is large or continuous. We replace the table with a parameterized value function $\\hat v(s,\\mathbf{w})$, define the mean squared value error it should minimize under the on-policy distribution, and derive stochastic- and semi-gradient learning rules — the semi-gradient TD(0) update that bootstraps and so is not a true gradient. Linear methods make the analysis clean and give the TD fixed point; feature construction (polynomials, Fourier basis, coarse and tile coding, RBFs) supplies the vectors $\\mathbf{x}(s)$, and neural networks are the nonlinear bridge to deep RL.\n",{"path":7361,"title":7362,"module":7358,"summary":7363},"\u002Freinforcement-learning\u002Fapproximation\u002Ffeature-construction-and-nonlinear","Feature Construction and Nonlinear Approximation","Linear methods are only as good as the feature vectors $\\mathbf{x}(s)$ fed to them, and this lesson builds those vectors. Polynomials and the Fourier basis turn a state's coordinates into smooth global features; coarse coding, tile coding, and radial basis functions cover a continuous space with overlapping local receptive fields whose size sets the reach of generalization. Then we stop designing features by hand: a neural network learns the representation itself by gradient descent, trading the convergence guarantees of the linear case for expressiveness — the bridge to deep reinforcement learning.\n",{"path":7365,"title":7366,"module":7358,"summary":7367},"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-control","On-Policy Control with Approximation","Prediction learned a value function from features; control learns to act. We carry semi-gradient methods over to action values $\\hat q(s,a,\\mathbf{w})$, giving episodic semi-gradient Sarsa and its n-step form, and solve Mountain Car by descending a cost-to-go surface. In the continuing case, function approximation makes discounting unable to affect which policy is best, so we replace it with the average-reward setting — the differential return, differential value functions, and differential semi-gradient Sarsa.\n",{"path":7369,"title":7370,"module":7358,"summary":7371},"\u002Freinforcement-learning\u002Fapproximation\u002Faverage-reward-control","Average-Reward Control for Continuing Tasks","With function approximation, discounting has no effect on a continuing task: averaged over the on-policy distribution, the discounted objective equals the average reward times a policy-independent constant, so $\\gamma$ cannot change which policy is best. This lesson replaces discounting with the average-reward setting — the long-run reward rate $r(\\pi)$, the differential return that measures each state's transient advantage over that rate, differential value functions and TD error, and differential semi-gradient Sarsa, the control method for continuing tasks that never invokes a discount factor.\n",{"path":7373,"title":7374,"module":7358,"summary":7375},"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-and-the-deadly-triad","Off-Policy Methods and the Deadly Triad","Off-policy learning with function approximation is where the convergence guarantees of reinforcement learning fail. We extend the tabular off-policy updates to semi-gradient form with per-step importance sampling, show Baird's counterexample driving the weights to infinity, and identify the cause: the deadly triad of function approximation, bootstrapping, and off-policy training — any two are safe, all three can diverge. The divergence is not caused by sampling noise: a fully synchronous dynamic-programming update blows up just the same, which is what makes the triad a structural hazard rather than a fluke.\n",{"path":7377,"title":7378,"module":7358,"summary":7379},"\u002Freinforcement-learning\u002Fapproximation\u002Fbellman-error-and-gradient-td","Value-Function Geometry and Gradient-TD Methods","Why does the deadly triad diverge, and how do you stop it? This lesson develops the geometry that explains the failure: value functions as vectors, the projection operator onto the representable subspace, and the split between the Bellman error, the value error, and the projected Bellman error: the three objectives have different minimizers. The projected Bellman error is the learnable one, and Gradient-TD methods (GTD2, TDC) do true stochastic gradient descent on it, staying stable even off-policy at $O(d)$ cost. Emphatic TD reweights states instead, and a survey of variance-reduction techniques closes the gap between stability and usable learning.\n",{"path":7381,"title":7382,"module":7358,"summary":7383},"\u002Freinforcement-learning\u002Fapproximation\u002Feligibility-traces","Eligibility Traces","n-step methods unify TD and Monte Carlo by storing the last n feature vectors; eligibility traces do the same job with a single short-term memory vector. The λ-return averages every n-step return under a geometric weighting; the forward view looks ahead to that average, and the backward view produces nearly the same updates online through a decaying trace vector. We build the λ-return, TD(λ) with its trace, the two ways λ recovers TD(0) and Monte Carlo, a note on the exact equivalence of true online TD(λ), and Sarsa(λ) for control.\n",{"path":7385,"title":7386,"module":7358,"summary":7387},"\u002Freinforcement-learning\u002Fapproximation\u002Ftrue-online-and-sarsa-lambda","True Online TD(λ) and Sarsa(λ)","Plain TD(λ) makes the forward and backward views nearly agree; this lesson closes the gap. True online TD(λ) uses a dutch trace and a small correction term to produce exactly the same weight sequence as the online λ-return algorithm, at the same memory and only a constant factor more compute — the sharpest statement of the forward\u002Fbackward duality. The whole apparatus then lifts to control unchanged: Sarsa(λ) threads a single delayed reward back along an entire trajectory in one sweep, and the λ-weighting reappears in modern deep RL as generalized advantage estimation.\n",{"path":7389,"title":7390,"module":7358,"summary":7391},"\u002Freinforcement-learning\u002Fapproximation\u002Fpolicy-gradient-methods","Policy Gradient Methods","Every method so far learned values and read a policy off them. Policy gradient methods drop the intermediary: parameterize the policy directly and climb the performance gradient. We build the softmax-in-preferences parameterization, prove the policy gradient theorem that makes the gradient computable without the unknown state distribution, and derive REINFORCE and its variance-cutting state-value baseline — the launch point for the bootstrapping actor-critic that follows.\n",{"path":7393,"title":7394,"module":7358,"summary":7395},"\u002Freinforcement-learning\u002Fapproximation\u002Factor-critic-and-continuous-actions","Actor-Critic Methods and Continuous Actions","REINFORCE with a baseline learns a value function but never bootstraps; this lesson adds the bootstrapping critic that completes the actor-critic architecture. The critic scores each transition into a single TD error that steers both the actor's policy step and its own value step, trading a little bias for much lower variance and fully online, continuing-task learning. The policy gradient theorem carries over unchanged to the average-reward setting, a Gaussian policy handles real-valued actions with self-tuning exploration, and the natural policy gradient leads straight to TRPO, PPO, and the deep actor-critic methods that train today's agents.\n",{"path":7397,"title":7398,"module":7358,"summary":7399},"\u002Freinforcement-learning\u002Fapproximation\u002Fleast-squares-and-memory-based-methods","Least-Squares TD","Semi-gradient TD spends one cheap step per example and needs many examples; this lesson makes the opposite tradeoff. Least-Squares TD (LSTD) accumulates the matrices $\\mathbf{A}$ and $\\mathbf{b}$ and solves the TD fixed point $\\mathbf{w} = \\mathbf{A}^{-1}\\mathbf{b}$ directly, using the Sherman-Morrison identity to maintain the inverse in $O(d^2)$ — the most data-efficient linear TD method, at a quadratic cost. We work a solve by hand, weigh the quadratic cost against semi-gradient TD's cheap steps, and note that LSTD never forgets — a problem in control, where least-squares policy iteration is the natural extension.\n",{"path":7401,"title":7402,"module":7358,"summary":7403},"\u002Freinforcement-learning\u002Fapproximation\u002Fmemory-and-kernel-methods","Memory-Based and Kernel Methods","Least-squares TD spent more compute to extract more from each example; this lesson drops the parametric form entirely. Memory-based methods store training examples untouched and answer a query locally at retrieval time — nearest neighbor, weighted average, locally weighted regression — so accuracy grows with the data and effort concentrates where the agent actually goes. Kernel-based methods weight stored examples by a similarity kernel $k(s,s')$, and every linear method turns out to be a kernel method. Interest and emphasis, finally, make the on-policy weighting itself a design choice, aiming scarce approximation capacity at the states that matter.\n",{"path":7405,"title":7406,"module":7358,"summary":7407},"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-eligibility-traces","Off-Policy Eligibility Traces","Eligibility traces meet off-policy learning and function approximation — the corner where stability gets hard. We first let the bootstrapping and discounting parameters vary with state, so a single generalized return covers episodic and continuing tasks and folds termination into the discount. Then we fold the per-decision importance ratio into the trace with a control-variate correction, and build Watkins's Q(λ) and its importance-sampling-free successor Tree-Backup(λ) — all correct in expectation, but still semi-gradient, so the deadly triad and its fixes wait for the next lesson.\n",{"path":7409,"title":7410,"module":7358,"summary":7411},"\u002Freinforcement-learning\u002Fapproximation\u002Fstable-off-policy-traces","Stable Off-Policy Methods with Traces","Off-policy traces get the expected target right, but with $\\lambda \u003C 1$ they bootstrap, so off-policy plus bootstrapping plus function approximation is the deadly triad and the weights can diverge. This lesson carries the two one-step fixes to traces: GTD(λ) and GQ(λ) add a second weight vector and a gradient correction for true gradient descent on the projected Bellman error, while Emphatic TD(λ) reweights updates through a followon trace and interest to recover the on-policy stability. It closes with the implementation reality that traces are cheap because they are sparse, and with Retrace and V-trace — the clipped-ratio descendants that make off-policy traces work at deep-RL scale.\n",{"path":7413,"title":6732,"module":7414,"summary":7415},"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdeep-q-networks","Deep Reinforcement Learning","Deep Q-networks replace the linear value function with a neural network $Q(s,a;\\mathbf{w})$ and confront the fact that a nonlinear approximator, off-policy bootstrapping, and correlated online data — the deadly triad — make naive Q-learning diverge. DQN counters this empirically with two stabilizers: an experience replay buffer that decorrelates and reuses samples, and a periodically-frozen target network that fixes the bootstrap target. We derive the DQN loss and gradient, walk through the Atari convolutional architecture and its results, and then add the three refinements that define modern value-based deep RL — Double DQN, dueling networks, and prioritized experience replay.\n",{"path":7417,"title":7418,"module":7414,"summary":7419},"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdqn-improvements","DQN Improvements: Double, Dueling, and Prioritized Replay","Three refinements that turn plain DQN into the standard modern value-based agent, each touching a different part of the system. Double DQN fixes the maximization bias in the target by splitting action selection from evaluation; dueling networks restructure the network around a state value and per-action advantages; prioritized replay changes which transitions are learned from. We close with Rainbow, which combines them, and the distributional view that predicts the whole return distribution rather than its mean.\n",{"path":7421,"title":7422,"module":7414,"summary":7423},"\u002Freinforcement-learning\u002Fdeep-rl\u002Factor-critic-and-ppo","Actor–Critic and GAE","Make the actor and the critic deep networks and the policy-gradient architecture becomes modern deep RL. We build the neural actor-critic, the advantage estimate that replaces the raw return, and Generalized Advantage Estimation as a λ-blend of n-step advantages, then the parallel-worker methods A3C and A2C that decorrelate on-policy data. The step-size constraints — trust regions, PPO, and the continuous-control family — follow in the next lesson.\n",{"path":7425,"title":7426,"module":7414,"summary":7427},"\u002Freinforcement-learning\u002Fdeep-rl\u002Fppo-and-continuous-control","PPO and Continuous Control","Keeping the policy-gradient step from destroying the policy, and the algorithms that result. Trust-region optimization bounds each update by a KL constraint; PPO keeps that goal but replaces the second-order machinery with a first-order clip on the probability ratio, which is why it is the modern default and the optimizer inside RLHF. We then tour the off-policy continuous-control family — DDPG, TD3, and SAC — and where actor-critic went at scale, from OpenAI Five to language-model alignment.\n",{"path":7429,"title":7430,"module":7414,"summary":7431},"\u002Freinforcement-learning\u002Fdeep-rl\u002Fcase-studies","Case Studies: Learning to Play","The game-playing systems that turned reinforcement learning from a theory into a track record: Samuel's checkers player, TD-Gammon, Watson's Daily-Double wagering, a reinforcement-learning memory controller, DQN, and AlphaGo through AlphaGo Zero. Read as a set they draw one line — a value function, learned by self-play or interaction, refined by search, carried by a deep network — that runs from a 1959 checkers program to superhuman Go.\n",{"path":7433,"title":7434,"module":7414,"summary":7435},"\u002Freinforcement-learning\u002Fdeep-rl\u002Frl-beyond-games","Reinforcement Learning Beyond Games","The same value-and-reward machinery, pointed at problems with no opponent. Web personalization as a contextual bandit and then a full MDP for life-time value; thermal soaring, where a glider learns to climb on turbulent air and reward design does most of the work; and the industrial-scale systems that carried the same design past Sutton & Barto — AlphaStar, OpenAI Five, GT Sophy, and RLHF, where the reward itself is learned from human preference.\n",{"path":7437,"title":7438,"module":7414,"summary":7439},"\u002Freinforcement-learning\u002Fdeep-rl\u002Ffrontiers","Frontiers: Beyond the Standard MDP","The standard MDP fixes three things — state, reward, and single-step actions — and this lesson loosens two of them. We generalize the value function into a general value function that predicts any signal, and use those predictions as auxiliary tasks that shape representations; we extend actions in time with the options framework; and we treat state as a construction the agent builds from a stream of observations. Reward design and the open problems follow in the next lesson.\n",{"path":7441,"title":7442,"module":7414,"summary":7443},"\u002Freinforcement-learning\u002Fdeep-rl\u002Freward-design-and-open-problems","Reward Design and Open Problems","How to design a reward signal that encodes the intended goal — sparse reward, shaping, and reward hacking — and the problems the whole tabular, approximate, and deep arc leaves unsolved. We close with how the frontiers were pushed after Sutton & Barto: auxiliary tasks, learned options, intrinsic-motivation bonuses, learned world models, and offline RL, then the two concerns of reward hacking and safety that any real-world agent must address.\n",{"path":7445,"title":7446,"module":7447,"summary":7448},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow","Sharpening DQN: Improvements and the Distributional Idea","Modern Deep Reinforcement Learning","In the years after the 2015 DQN paper, a stream of focused improvements each fixed one weakness of the baseline without disturbing its frame. This lesson recaps five that keep the scalar $Q$-value — Double DQN, multi-step returns, dueling networks, prioritized replay, and NoisyNets, each changing a different slot of the same Q-learning loop — then develops the sixth, distributional RL, which changes the objective itself: learn the whole return distribution $Z(s,a)$. We build the distributional Bellman equation and the C51 categorical algorithm, projection step and all, worked end to end on real numbers. A companion lesson takes up QR-DQN, Rainbow, and the modern distributional line.\n",{"path":7450,"title":7451,"module":7447,"summary":7452},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow-part-2","Distributional RL and Rainbow","A companion to the DQN improvements lesson. C51 fixed the return atoms and learned their probabilities; QR-DQN does the reverse — fix the probabilities, learn the values — which removes the projection and trains with a quantile loss. We cover why the distribution helps even when you act on the mean, then assemble Rainbow: all six improvements in one Q-learning loop, with the component ablation that shows each one's real weight. The distributional line then runs on through IQN, FQF, and Agent57, the first agent to beat the human baseline on all 57 Atari games.\n",{"path":7454,"title":7455,"module":7447,"summary":7456},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control","Continuous Control: DDPG and TD3","When actions are real-valued, the $\\arg\\max_a Q(s,a)$ in Q-learning becomes an optimization problem on every step. This lesson builds the off-policy actor-critic family that sidesteps it: the deterministic policy gradient and DDPG, which replaces the max with a learned actor, and the three fixes of TD3 that counter the value overestimation DDPG inherits. A companion lesson takes up SAC's maximum-entropy objective and the methods built on this template.\n",{"path":7458,"title":7459,"module":7447,"summary":7460},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control-part-2","Continuous Control: SAC and Beyond","A companion to the DDPG and TD3 lesson. Where those actors are deterministic and explore with bolted-on noise, soft actor-critic (SAC) changes the objective itself: maximize return plus the entropy of the policy, so exploration becomes intrinsic and the agent stays robust. We develop the maximum-entropy objective, the reparameterized squashed-Gaussian actor, and automatic temperature tuning, then survey the methods built on this off-policy template — distributional critics (D4PG), critic ensembles (REDQ), and control from pixels (DrQ, RAD).\n",{"path":7462,"title":7463,"module":7447,"summary":7464},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl","Model-Based Deep RL: Sample Efficiency and PETS","A model turns experience into imagined planning. This lesson makes the sample-efficiency case for learning a dynamics model, works through why a learned model's errors compound over the planning horizon, and builds the most direct model-based method: PETS plans online with a probabilistic ensemble under model-predictive control, distrusting the model exactly where its members disagree. A companion lesson takes up latent world models (Dreamer) and MuZero.\n",{"path":7466,"title":7467,"module":7447,"summary":7468},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl-part-2","Model-Based Deep RL: World Models, Dreamer, and MuZero","A companion to the PETS lesson. PETS plans in the environment's native state space; these methods change what the model represents. World Models and Dreamer learn a compact latent state and do almost all their learning by imagining inside it, with value gradients flowing through the differentiable dynamics. MuZero predicts neither states nor pixels — only the reward, value, and policy that MCTS reads — and plans with search against that learned model, AlphaZero without the rules. We close with MBPO, TD-MPC, and EfficientZero.\n",{"path":7470,"title":7471,"module":7447,"summary":7472},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration","Exploration in Deep RL: Novelty as Reward","When the state space is enormous and reward is rare, ε-greedy amounts to a random walk that almost never reaches the first reward. This lesson scales the bandit's exploration ideas up to deep RL through the dominant approach — manufacture a reward for novelty and let the agent chase it: optimism and pseudo-counts from density models, and intrinsic motivation and curiosity (the Intrinsic Curiosity Module and Random Network Distillation). A companion lesson takes up posterior sampling, Go-Explore, and the modern methods.\n",{"path":7474,"title":7475,"module":7447,"summary":7476},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration-part-2","Exploration in Deep RL: Posterior Sampling and Go-Explore","A companion to the novelty-as-reward lesson. Pseudo-counts and curiosity reward the unfamiliar after the agent stumbles into it; this lesson covers two ideas that go further. Bootstrapped DQN keeps an ensemble that approximates a posterior over value functions and explores by committing to one sampled hypothesis per episode — the deep, directed exploration ε-greedy cannot manage. Go-Explore remembers and returns to the frontier, defeating detachment and derailment to solve Montezuma's Revenge. We close with episodic memory (Never Give Up), Agent57, and model-based exploration.\n",{"path":7478,"title":7479,"module":7447,"summary":7480},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl","Offline RL: The Problem and Value-Based Fixes","Offline reinforcement learning learns a policy from a fixed logged dataset with no further environment interaction — off-policy learning pushed to the extreme, and it breaks for the extreme version of the same reason. Bootstrapping queries the value function at out-of-distribution actions the data never covers, those errors are optimistic, and with no online feedback to correct them they compound through the Bellman backup. This lesson sets up the failure and off-policy evaluation, then builds the first two families of pessimistic fixes: policy constraint (BCQ) and conservative value estimation (CQL). A companion lesson takes up implicit methods, model-based offline RL, and Decision Transformer.\n",{"path":7482,"title":7483,"module":7447,"summary":7484},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl-part-2","Offline RL: Implicit Methods, Sequence Models, and Beyond","A companion to the offline-RL problem lesson. Policy constraint and conservative value estimation both still query a learned value function; implicit methods (IQL) avoid querying it off the data at all, using an in-sample expectile backup. We then build pessimism into a learned model (MOPO, COMBO) and drop bootstrapping entirely with Decision Transformer's return-conditioned sequence modeling, closing with offline-to-online fine-tuning, diffusion planners, and the offline view of RLHF. The one rule throughout: without online correction, be pessimistic about what you cannot verify.\n",{"path":7486,"title":7487,"module":7447,"summary":7488},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl","Imitation Learning: Cloning, DAgger, and Inverse RL","When a reward is hard to specify but an expert is easy to watch, learn from demonstrations instead. Behavioral cloning treats control as supervised learning of the expert's state-to-action map, and fails through compounding error: small mistakes carry the agent off the expert's distribution, where it was never trained. DAgger fixes the mismatch by querying the expert on the learner's own states. Inverse RL instead recovers the reward the expert seems to optimize — an ill-posed problem that maximum-entropy IRL disambiguates. A companion lesson casts imitation as adversarial occupancy matching (GAIL, AIRL).\n",{"path":7490,"title":7491,"module":7447,"summary":7492},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl-part-2","Imitation as Adversarial Matching: GAIL and AIRL","A companion to the imitation-learning lesson. If the point of recovering a reward is only to re-run RL and match the expert, you can skip the reward and match the behavior directly. GAIL casts imitation as a GAN — a discriminator separating expert from learner state-action pairs supplies the reward a policy-gradient method optimizes — matching occupancy measures without ever naming a reward. AIRL reads a transferable reward back out of the discriminator. We compare all four methods and close with reward models in RLHF, scaled cloning, and diffusion policies.\n",{"path":7494,"title":7495,"module":7447,"summary":7496},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl","Multi-Agent RL: Markov Games and Centralized Training","With more than one learning agent in an environment, each agent's world becomes non-stationary because the others are changing too. This lesson builds the Markov-game generalization of the MDP, diagnoses non-stationarity as the central obstacle, shows why the naive baselines fail, and develops the dominant fix — centralized training with decentralized execution (MADDPG, VDN, QMIX). A companion lesson takes up self-play, the landmark game-playing systems, and the equilibrium concepts that define what \"solved\" means.\n",{"path":7498,"title":7499,"module":7447,"summary":7500},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl-part-2","Multi-Agent RL: Self-Play and Solution Concepts","A companion to the Markov-games lesson. In the purely competitive setting, an agent can generate its own training curriculum by playing against copies of itself — self-play, the method behind AlphaGo, OpenAI Five, and AlphaStar. We develop why self-play produces an ever-improving opponent, the systems it built, and then the equilibrium solution concepts (Nash, correlated, coarse-correlated) that define what \"solved\" means once there is an opponent, closing with PSRO, MAPPO, and the language-model-agent frontier.\n",{"path":7502,"title":7503,"module":7447,"summary":7504},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl","Hierarchical RL: Options and the Option-Critic","Flat RL cannot explore a long horizon: reaching reward through hundreds of primitive actions is exponentially unlikely, and every credit-assignment update crawls one step at a time. Hierarchy breaks one hard long-horizon problem into many short ones. This lesson develops temporal abstraction — the options framework and its semi-Markov view, and learning options end to end with the option-critic. A companion lesson takes up goal-conditioned manager\u002Fworker hierarchies (FeUdal Networks and HIRO), hindsight relabeling, and unsupervised skill discovery.\n",{"path":7506,"title":7507,"module":7447,"summary":7508},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl-part-2","Hierarchical RL: Goal-Conditioned Hierarchies and Skills","A companion to the options lesson. Options package a behavior; goal-conditioned hierarchies instead give the top level an explicit language of goals — a manager proposes a target state or a latent direction, and a worker is rewarded for reaching it (FeUdal Networks, HIRO). We develop that architecture, the hindsight relabeling that lets it learn from sparse reward, and unsupervised skill discovery (DIAYN) that learns a repertoire of behaviors with no reward at all. The shared idea throughout: shorten the horizon by inserting a level that decides less often.\n",{"path":7510,"title":7511,"module":7447,"summary":7512},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Frlhf-and-language-models","RLHF and Language Models","A language model trained to predict the next token is fluent but not helpful, honest, or harmless — the objective it was optimized for is not the objective we want. RLHF closes that gap by turning the one thing humans do reliably, comparing two outputs, into a reward. We build the three-stage pipeline: supervised fine-tuning, a Bradley-Terry reward model fit to preference pairs, then PPO against that reward with a KL penalty keeping it near the reference policy. We then cover reward hacking and why the KL penalty matters, Direct Preference Optimization, which folds the reward model into a single classification loss, and the RLAIF and verifiable-reward variants. This pipeline is what makes the largest models usable as assistants.\n",{"path":7514,"title":7515,"module":7447,"summary":7516},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps","Partial Observability: POMDPs and the Belief State","Drop the assumption that the agent sees the state. It sees an observation, a partial and noisy function of a hidden state, and one observation is no longer a Markov signal. This lesson builds the POMDP tuple, shows that the belief state — the posterior over hidden states — is a sufficient statistic that turns a POMDP back into an MDP over beliefs, and works the Bayes-filter belief update step by step. A companion lesson explains why exact planning is intractable and develops the deep-RL answer of recurrent, history-based policies.\n",{"path":7518,"title":7519,"module":7447,"summary":7520},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps-part-2","Partial Observability: Planning and Recurrent Policies","A companion to the belief-state lesson. In principle a POMDP reduces to an MDP over beliefs; in practice two obstacles block that. Exact planning over the belief simplex is intractable — the value function is piecewise-linear-and-convex with a number of pieces that can explode — and computing the belief needs a model the agent rarely has. This lesson develops the intractability, the point-based approximations that address it, and the deep-RL answer: make the policy a function of history with a recurrent network (DRQN, R2D2), with frame-stacking, attention, and world-model latents as learned beliefs.\n",{"path":7522,"title":7523,"module":7447,"summary":7524},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl","Safe and Constrained RL: The CMDP and Policy Methods","Maximizing a scalar reward is not the same as behaving well: a capable optimizer will find and exploit any gap between the reward and what its designer actually meant, a failure called specification gaming or reward hacking. The remedy is to add explicit cost constraints — the constrained MDP — maximizing return subject to an expected-cost budget. This lesson builds the core toolkit: the CMDP itself, Lagrangian primal-dual methods that learn a multiplier on the constraint (RCPO), and constrained policy optimization (CPO) with its trust-region cost bound. A companion lesson covers risk-sensitivity, safe exploration, and the alignment framing.\n",{"path":7526,"title":7527,"module":7447,"summary":7528},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl-part-2","Safe RL: Risk, Safe Exploration, and Alignment","A companion to the constrained-MDP lesson. Constraining the mean cost is not enough: a policy safe on average can be catastrophic in the tail, and a policy safe at convergence can violate its limits wildly while learning. This lesson optimizes the tail with risk-sensitive objectives (CVaR), then makes exploration itself safe with shields, Lyapunov methods, and safety layers that project unsafe actions onto the feasible set — closing with benchmarks, safe RLHF, robustness, and the alignment framing that ties safety back to the problem of incompletely specified reward.\n",{"path":7530,"title":7531,"module":7447,"summary":7532},"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmeta-rl-and-generalization","Meta-RL and Generalization","An agent that masters one task often fails on the next; it has overfit to a single environment. This lesson treats fast adaptation as a meta-problem over a distribution of tasks: meta-train so that a few episodes at meta-test time suffice. We cover the two families — optimization-based (MAML learns an initialization) and context-based (RL-squared and PEARL infer a latent task) — the exploration cost of adaptation, and the parallel problem of generalization: why deep RL memorizes environments and what fixes it (domain randomization, procedural generation, augmentation, regularization). It closes on foundation models and sequence-model agents as the generalist endpoint.\n",{"path":7534,"title":7535,"module":7536,"summary":7537},"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fpsychology-of-reinforcement","The Psychology of Reinforcement","Reinforcement Learning in Minds and Brains","Reinforcement learning is both an engineering method and a theory of how animals learn. The prediction\u002Fcontrol split of the algorithms mirrors the psychologist's split between classical and instrumental conditioning. We trace the correspondence: the Rescorla–Wagner model as a prediction-error rule that explains blocking, its real-time TD extension, Thorndike's Law of Effect behind trial-and-error control, and the habitual\u002Fgoal-directed distinction that maps onto model-free versus model-based learning.\n",{"path":7539,"title":7540,"module":7536,"summary":7541},"\u002Freinforcement-learning\u002Fminds-and-brains\u002Finstrumental-conditioning-and-control","The Psychology of Reinforcement: Instrumental Control","Classical conditioning was prediction; instrumental conditioning is control. Thorndike's Law of Effect is trial-and-error control — selection plus association, search plus memory — and Skinner's shaping and schedules are reward engineering. The habitual\u002Fgoal-directed distinction maps onto model-free versus model-based control, dissociated by outcome devaluation and arbitrated by uncertainty. Delayed reinforcement is the credit-assignment problem, and the stimulus traces and secondary reinforcers of animal-learning theory are eligibility traces and value functions.\n",{"path":7543,"title":7544,"module":7536,"summary":7545},"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-and-td-error","Dopamine and the TD Error","The TD error was invented as an algorithm; a decade later it turned out to closely describe the firing of the brain's dopamine neurons. We follow Schultz's experiments — dopamine fires at an unpredicted reward, shifts to the earliest predictive cue, and dips below baseline when a predicted reward is withheld — and match each result to the TD error term by term. We then read the basal ganglia as a neural actor–critic with dopamine as its shared training signal, and close on addiction as a hijacking of that signal.\n",{"path":7547,"title":7548,"module":7536,"summary":7549},"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-in-the-brain","Dopamine in the Brain: The Neural Actor–Critic","If phasic dopamine is a TD error, where does it go and what does it change? We follow the axons into the basal ganglia, read the corticostriatal synapse as the place where state, action, and error meet, and map the ventral and dorsal striatum onto the critic and the actor of an actor–critic. Addiction becomes a broken cancellation in the same learning signal, and distributional dopamine extends the scalar RPE into a population code.\n",{"path":7551,"title":7552,"module":7536,"summary":7553},"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fanimal-learning-and-cognition","Animal Learning and Cognition","Three classic associative phenomena turn out to be reinforcement-learning mechanisms seen in behavior. Blocking says learning is driven by prediction error, not co-occurrence, and reduces to least-squares regression fitting a collinear feature. Higher-order conditioning and conditioned reinforcement make a value estimate a secondary reinforcer — bootstrapping in an animal. Delayed reinforcement is the credit-assignment problem, and the stimulus traces and goal gradients of Pavlov and Hull are eligibility traces and TD-learned value functions.\n",{"path":7555,"title":7556,"module":7536,"summary":7557},"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fcognitive-maps-and-planning","Cognitive Maps and Model-Based Learning","Tolman's rats learned the layout of a maze with no reward, then used it the moment food appeared — latent learning, a cognitive map, and the behavioral face of model-based reinforcement learning. The map is learned by system identification (stimulus–stimulus associations), which fills in whether or not reward is present, and queried by planning, which re-solves a route from a single changed reward. The successor representation sits between cache and model, and hippocampal predictive maps and scaled-up world models carry the same idea into brain and machine.\n",{"path":7559,"title":7560,"module":7536,"summary":7561},"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fneuroscience-of-reinforcement","The Neuroscience of Reinforcement","The dopamine story is one contact point between reinforcement learning and the brain; this lesson fills in the surrounding neuroscience so the mapping stands on its own. We build a working primer of neurons, synapses, and neuromodulation; separate four signals that casual usage conflates — reward, reinforcement, value, and prediction error; and read the actor and critic as corticostriatal synapses updated by two- and three-factor rules, grounded in spike-timing-dependent and reward-modulated plasticity.\n",{"path":7563,"title":7564,"module":7536,"summary":7565},"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fseveral-learning-systems","The Brain's Several Learning Systems","The actor's three-factor rule has an ancestor in Klopf's hedonistic neuron — a single cell as a reinforcement-seeking agent — and a bacterium's run-and-twiddle shows the Law of Effect with no synapses at all. Teams of such neurons implement policy gradient collectively, the broadcast reward replacing backpropagation. And the brain is not only model-free: outcome devaluation, prefrontal value coding, and hippocampal forward sweeps localize a model-based system. The recurring conclusion is that the brain is several interacting learning systems, not one algorithm.\n",{"path":7567,"title":6724,"module":6,"summary":6},"\u002Freinforcement-learning",{"path":7569,"title":7570,"module":3911,"summary":7571},"\u002Fartificial-intelligence\u002Ffoundations\u002Fwhat-is-ai","What Is Artificial Intelligence?","Eight definitions of AI fall into a two-by-two grid: think versus act, and measure success against human performance versus an ideal standard of rationality. We work through all four schools — the Turing test, cognitive modelling, the laws of thought, and the rational agent — and adopt the last as the frame for the whole course: AI is the study and design of rational agents.\n",{"path":7573,"title":7574,"module":3911,"summary":7575},"\u002Fartificial-intelligence\u002Ffoundations\u002Ffoundations-of-ai","The Foundations of AI","Where the rational-agent idea came from and what surrounds it. AI inherited its core tools from eight older disciplines — philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory, and linguistics. Its history runs in cycles of boom and winter, from the 1956 Dartmouth workshop through expert systems to the statistical turn. And the deep-learning era — AlexNet, the Transformer, GPT-3, AlphaGo — is a new way of computing the agent function at scale, not a new definition of AI.\n",{"path":7577,"title":7578,"module":3911,"summary":7579},"\u002Fartificial-intelligence\u002Ffoundations\u002Fintelligent-agents","Intelligent Agents","An agent perceives an environment through sensors and acts on it through actuators; its behavior is an agent function mapping percept sequences to actions. A rational agent chooses, for each percept sequence, the action that maximizes its expected performance measure given its knowledge. We build the first half of the vocabulary the whole course rests on — the agent function, rationality, PEAS task specifications, and the six axes along which task environments vary.\n",{"path":7581,"title":7582,"module":3911,"summary":7583},"\u002Fartificial-intelligence\u002Ffoundations\u002Fagent-architectures","Agent Architectures","How to build a program that computes a good agent function without storing an astronomically large lookup table. Four skeleton architectures in order of increasing power — simple reflex, model-based, goal-based, and utility-based — plus the learning agent that improves any of them, the scale of world representations (atomic, factored, structured) they rest on, and how a modern language-model agent fits the same frame.\n",{"path":7585,"title":7586,"module":7587,"summary":7588},"\u002Fartificial-intelligence\u002Fsearch\u002Funinformed-search","Uninformed Search","Search","A goal-based agent that cannot see which action is best turns the problem into a state space — an initial state, a set of actions, a transition model, a goal test, and a path cost — and searches for a sequence of actions reaching the goal. We build the state-space formulation on the 8-puzzle and route-finding, give the one TREE-SEARCH \u002F GRAPH-SEARCH skeleton every algorithm specializes, and measure strategies by completeness, optimality, and complexity. This lesson develops the first two frontier disciplines — breadth-first and uniform-cost search; the rest follow in the next lesson.\n",{"path":7590,"title":7591,"module":7587,"summary":7592},"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-strategies-compared","Search Strategies Compared","Breadth-first and uniform-cost search pay for optimality in memory. This lesson develops the strategies that trade memory for depth: depth-first search, which keeps only the current path; depth-limited and iterative-deepening search, which fix DFS's failure on infinite paths; and bidirectional search, which meets in the middle for a square-root saving. It closes by lining up all six uninformed strategies against completeness, optimality, and complexity, and tracing where the algorithms came from and where they went.\n",{"path":7594,"title":7595,"module":7587,"summary":7596},"\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search","Informed Search and A*","An informed search uses a heuristic $h(n)$, an estimate of the cost from a node to the goal, to decide what to expand next. Greedy best-first search follows the heuristic blindly and gives up optimality; A* corrects it by ranking nodes on $f(n) = g(n) + h(n)$, and is optimal when the heuristic is admissible (tree search) or consistent (graph search). This lesson defines the heuristic, builds best-first search, and proves why A* is optimal, with the contour picture that explains its pruning. Where good heuristics come from is the next lesson.\n",{"path":7598,"title":7599,"module":7587,"summary":7600},"\u002Fartificial-intelligence\u002Fsearch\u002Fheuristic-functions","Heuristic Functions and Memory-Bounded Search","A* is only as good as its heuristic, so this lesson answers where good heuristics come from: relaxed problems, whose exact solution cost is an admissible heuristic, and pattern databases, which precompute subproblem costs. It measures heuristic quality with dominance and the effective branching factor, then tackles A*'s memory problem with IDA*, RBFS, and SMA*. It closes with modern heuristic search — weighted A*, learned and disjoint pattern-database heuristics, and bidirectional A*.\n",{"path":7602,"title":7603,"module":7587,"summary":7604},"\u002Fartificial-intelligence\u002Fsearch\u002Flocal-search","Local Search and Optimization","When the path to a goal is irrelevant and only the final state matters, we can discard the search tree entirely and keep just the current state, moving to a better neighbor at each step. This lesson builds the state-space landscape metaphor, works through hill climbing and the three obstacles that defeat it (local maxima, ridges, plateaus), then develops the first escapes: random restarts and simulated annealing with its temperature schedule. The population-based methods and continuous-space calculus follow in the next lesson.\n",{"path":7606,"title":7607,"module":7587,"summary":7608},"\u002Fartificial-intelligence\u002Fsearch\u002Fpopulation-and-continuous-search","Population and Continuous Search","Single-state local search escapes a trap by restarting or tolerating downhill moves. This lesson develops the alternatives that keep several states at once — local beam search, which shares successors across parallel threads, and genetic algorithms, which recombine two parents through crossover and mutation — then crosses into continuous spaces, where calculus replaces the finite neighbor set: gradient ascent, line search, and Newton's method. It closes with the industrial descendants of these methods and the loop they all share.\n",{"path":7610,"title":7611,"module":7587,"summary":7612},"\u002Fartificial-intelligence\u002Fsearch\u002Fadversarial-search","Adversarial Search and Games","When another agent plans against you, search becomes a game. We formalize two-player, zero-sum, perfect-information games as search problems, define the minimax value that optimal play backs up through the game tree, and give the MINIMAX algorithm that computes it. Alpha–beta pruning then cuts the cost of that search roughly in half in the exponent without changing the answer, and a heuristic evaluation function plus a cutoff test turns the exact algorithm into a real-time player that copes with the horizon effect.\n",{"path":7614,"title":7615,"module":7587,"summary":7616},"\u002Fartificial-intelligence\u002Fsearch\u002Fgames-of-chance-and-imperfect-information","Games of Chance and Imperfect Information","Minimax and alpha–beta assume a deterministic game both players can see in full. Drop either assumption and search must change. This lesson adds chance nodes and the expectiminimax value for games with dice, then belief-state reasoning for partially observable games — Kriegspiel and card games — where averaging over clairvoyance both helps and misleads. It closes with the line from Deep Blue's alpha–beta to AlphaGo's learned evaluation and Monte Carlo tree search, and the provable-pruning and self-play research around each end of that story.\n",{"path":7618,"title":7619,"module":7587,"summary":7620},"\u002Fartificial-intelligence\u002Fsearch\u002Fconstraint-satisfaction","Constraint Satisfaction Problems","A constraint satisfaction problem replaces the black-box state with a factored one: variables, domains, and constraints. That structure supports inference before any search runs. This lesson defines the CSP on map coloring, Sudoku, and scheduling, then develops constraint propagation: node and arc consistency, the AC-3 algorithm that makes a whole network arc-consistent, and the way one deleted value cascades across the graph to prune impossible options ahead of search.\n",{"path":7622,"title":7623,"module":7587,"summary":7624},"\u002Fartificial-intelligence\u002Fsearch\u002Fcsp-search-and-structure","CSP Search and Structure","Propagation prunes a CSP but rarely finishes it, so we search. This lesson builds backtracking search over partial assignments and the general-purpose heuristics that make it fast — MRV, degree, least-constraining-value, forward checking, MAC, and intelligent backtracking. It then shows how the shape of the constraint graph controls difficulty: tree-structured problems fall in linear time, cutset conditioning handles the rest, and min-conflicts local search solves a million queens in a constant number of steps.\n",{"path":7626,"title":7627,"module":7587,"summary":7628},"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-under-uncertainty","Search Under Uncertainty","Classical search assumes the agent knows the state it is in and exactly what each action does. Drop the second assumption and a plan can no longer be a fixed sequence of actions. This lesson develops the first response: AND-OR search over nondeterministic actions, which returns a branching contingency plan rather than a straight line. We build it on the erratic vacuum world, show how OR nodes (the agent's choices) alternate with AND nodes (nature's outcomes), trace the recursion that finds a plan, and handle the case where the only solution is a cyclic \"try, try again.\"\n",{"path":7630,"title":7631,"module":7587,"summary":7632},"\u002Fartificial-intelligence\u002Fsearch\u002Fbelief-state-and-online-search","Belief-State and Online Search","When the agent cannot see the full state, a plan can no longer test where it actually is — it must reason over the set of states it might be in. This lesson develops belief-state search, from sensorless (conformant) planning that coerces an unknown world into a goal, through the predict-observe-update cycle of contingent planning with percepts, to online search in unknown environments, where the agent must act in order to learn. It closes with LRTA*, which refines its own heuristic as it explores, one step from reinforcement learning.\n",{"path":7634,"title":7635,"module":7636,"summary":7637},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic","Logical Agents and Propositional Logic","Logic and Planning","A knowledge-based agent keeps a store of sentences and acts by asking it what to do. To make \"asking\" mean something we need entailment — the relation $KB \\models \\alpha$ that holds when every model of the knowledge base is a model of the query. Propositional logic gives a syntax and a truth-table semantics for which entailment is decidable. This first part builds the foundations: the agent loop, the Wumpus World, models and entailment, the connectives and truth tables, theorem proving by refutation, and the resolution rule with its CNF conversion — a single complete inference procedure for all of propositional logic.\n",{"path":7639,"title":7640,"module":7636,"summary":7641},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-inference","Propositional Inference and Logical Agents","Model checking and resolution decide entailment, but both can blow up. This part turns propositional logic into a practical engine and a working agent. Horn clauses give linear-time forward and backward chaining — the basis of logic programming. DPLL and WalkSAT make satisfiability testing fast in the common case. Then we make the agent situated: time-indexed fluents, the frame problem and its solution by successor-state axioms, a hybrid agent that deduces a safe map and plans a route through it, and SATPlan, which finds a plan by asking a SAT solver for a satisfying model.\n",{"path":7643,"title":7644,"module":7636,"summary":7645},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic","First-Order Logic","Propositional logic can only say that facts hold; it cannot talk about the objects a fact is about, or state a rule once and have it cover every object. First-order logic fixes this by committing to a world of objects, relations, and functions. This first part builds the language from the ground up: the ontology it commits to, the model that gives a sentence a truth value, the syntax of terms and sentences, the two quantifiers with their standard mistakes, and equality.\n",{"path":7647,"title":7648,"module":7636,"summary":7649},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic-in-use","First-Order Logic in Use","With the language of first-order logic in hand, this part is about using it well. Database semantics trades expressive power for the convenience of a single intended model; higher-order logic shows what first-order logic gives up for decidability. Then we put the language to work: the Tell\u002FAsk interface, the kinship domain axiomatized from scratch, and the seven-step knowledge-engineering process applied to a digital circuit.\n",{"path":7651,"title":7652,"module":7636,"summary":7653},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Finference-and-resolution","Inference in First-Order Logic","Propositional inference lifts to first-order logic once we can make terms match. Unification is that machinery: the algorithm that finds the substitution making two expressions identical, and the basis of generalized modus ponens. This first part builds the lifted inference rules and the two chaining algorithms they drive — forward chaining, the data-driven procedure behind production systems and Datalog, and backward chaining, the goal-driven procedure behind Prolog.\n",{"path":7655,"title":7656,"module":7636,"summary":7657},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-resolution","First-Order Resolution","Chaining is complete only for Horn knowledge bases. General first-order sentences — with disjunctive conclusions and negations — need a single sound and complete rule: resolution. This part converts arbitrary sentences to CNF by skolemizing away the existentials, lifts the resolution rule with unification, and proves entailment by refuting the negated goal. The result is the proof procedure Gödel's completeness theorem guarantees will find any entailment, together with the search strategies that make it usable.\n",{"path":7659,"title":7660,"module":7636,"summary":7661},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fclassical-planning","Classical Planning","Classical planning represents a problem in a factored language, PDDL: states are sets of ground fluents, and actions are lifted schemas with a precondition and an effect. That structure turns planning into search — forward through states or backward through goals — and lets a program read heuristics straight off the schemas by relaxing the problem. This first part develops the representation, the two search directions, and the domain-independent heuristics that come from ignoring preconditions or delete lists.\n",{"path":7663,"title":7664,"module":7636,"summary":7665},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-graphs-and-graphplan","Planning Heuristics and GraphPlan","Every relaxation heuristic can be inaccurate, and none can tell how far apart subgoals sit. The planning graph is a polynomial-size structure that does better: leveled off the problem, it yields admissible distance estimates and a record of which actions and fluents cannot coexist. This part builds the graph, reads heuristics from it, extracts plans with GraphPlan, and closes with the other classical approaches — SATPlan and partial-order planning — and the representational trade that makes all of it work.\n",{"path":7667,"title":7668,"module":7636,"summary":7669},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-in-the-real-world","Planning and Acting in the Real World","Classical planning's clean theory rests on four assumptions: time is ignored, actions are atomic, the world is deterministic and fully observable, and the agent is alone. This first part drops the first two. We add durations and resource constraints — turning a plan into a schedule, solved by the critical-path method and, once resources contend, by NP-hard job-shop scheduling — and let a planner reason at multiple levels of abstraction through high-level actions and their angelic reachable sets.\n",{"path":7671,"title":7672,"module":7636,"summary":7673},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-under-uncertainty","Planning Under Uncertainty","Classical planning assumed the world was deterministic, fully observable, and the agent alone. This part drops the last two assumptions. When the agent cannot see or predict the world, planning moves into belief-state space: sensorless plans that coerce the world into the goal without sensing, contingent plans that branch on what is sensed, and online agents that monitor and replan when execution diverges. Then we add other agents — joint plans, the coordination problem, and the conventions that let a team act without constant negotiation.\n",{"path":7675,"title":7676,"module":7636,"summary":7677},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fknowledge-representation","Knowledge Representation","First-order logic gives you the language; this lesson is about what to say in it. This first part builds the content: a general upper ontology from the top down, categories as first-class objects with taxonomies and inheritance, physical composition and the count-noun\u002Fmass-noun split, events and time reified through the event calculus, and belief modeled with modal logic — the machinery for representing the world an agent reasons about.\n",{"path":7679,"title":7680,"module":7636,"summary":7681},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Freasoning-systems-and-defaults","Reasoning Systems and Default Logic","Having represented the world, this part is about reasoning with it at scale. Semantic networks give a graphical notation with fast inheritance; description logics keep subsumption and classification tractable by design. Then we confront the fact that most useful rules hold only by default: circumscription and default logic give a logical account of nonmonotonic reasoning, and truth maintenance systems retract conclusions cleanly when the beliefs beneath them change.\n",{"path":7683,"title":7684,"module":7685,"summary":7686},"\u002Fartificial-intelligence\u002Funcertainty\u002Fprobability-and-bayes","Quantifying Uncertainty","Uncertainty","Logic breaks down in any domain where the rules have exceptions you cannot enumerate — the qualification problem. Probability replaces truth values with degrees of belief that obey Kolmogorov's axioms, and the full joint distribution becomes a knowledge base from which any query is answered by summing entries: marginalization, conditioning, and normalization. Independence factors that joint into smaller pieces — the first step toward a calculus of rational belief that an agent can actually compute with.\n",{"path":7688,"title":7689,"module":7685,"summary":7690},"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayes-rule-and-naive-bayes","Bayes' Rule and Naive Bayes","Bayes' rule inverts a causal model into a diagnostic one, turning \"how a cause produces its symptoms\" into \"which cause explains what I observed.\" Ignoring the prior is the base-rate fallacy behind overconfident test results. Conditional independence then lets several pieces of evidence combine by multiplying likelihood ratios instead of building an exponential joint, giving the naive Bayes model and pointing directly at Bayesian networks.\n",{"path":7692,"title":7693,"module":7685,"summary":7694},"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayesian-networks","Bayesian Networks","A Bayesian network is a directed acyclic graph of random variables in which each node carries a conditional probability table for itself given its parents. That structure factors the full joint distribution into a product of local terms, turning an exponential table into a linear one, and it makes the conditional independences of the domain explicit. We build the canonical burglary–alarm network, read compactness and d-separation off the graph, run exact inference by variable elimination, and, where that is intractable, estimate answers by sampling.\n",{"path":7696,"title":7697,"module":7685,"summary":7698},"\u002Fartificial-intelligence\u002Funcertainty\u002Finference-in-bayesian-networks","Bayesian Networks: Inference and Relational Models","When exact inference is intractable, sampling estimates the posterior instead: prior and rejection sampling, likelihood weighting, and Gibbs\u002FMCMC, whose error shrinks as one over the square root of the sample count. The same graphical idea then lifts from a fixed set of variables to whole populations — relational and open-universe probability models write dependencies once and unroll them over objects — and we close by placing probability against the rule-based, Dempster–Shafer, and fuzzy alternatives it displaced.\n",{"path":7700,"title":7701,"module":7685,"summary":7702},"\u002Fartificial-intelligence\u002Funcertainty\u002Freasoning-over-time","Probabilistic Reasoning over Time","A world that changes needs a state variable at every point in time. The Markov assumption cuts the dependence on history down to the previous slice, leaving a transition model and a sensor model that define a temporal Bayesian network. Four recursive tasks fall out — filtering, prediction, smoothing, and the most likely explanation — each a message passed along the sequence. We ground them in hidden Markov models and their matrix form, sketch the Kalman filter for continuous state, and reach dynamic Bayesian networks with particle filtering as the general approximate method.\n",{"path":7704,"title":7705,"module":7685,"summary":7706},"\u002Fartificial-intelligence\u002Funcertainty\u002Ftracking-and-data-association","Reasoning over Time: Tracking and Data Association","Dynamic Bayesian networks generalize HMMs and Kalman filters to arbitrarily many state variables per slice, and when exact inference blows up, particle filtering approximates the belief state with a population of weighted samples that propagate, reweight, and resample. Tracking several objects at once adds the data-association problem — which observation came from which object — whose combinatorics defeat any exact filter, so particle filters and MCMC keep many hypotheses alive. We close with SLAM and learned state-space models.\n",{"path":7708,"title":7709,"module":7685,"summary":7710},"\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions","Making Decisions: Utility Theory","A rational agent chooses the action that maximizes expected utility — the probability of each outcome weighted by how much the agent wants it. We derive the utility function from six axioms on preferences, so maximizing expected utility is forced by consistency rather than assumed; look at risk aversion in the utility-of-money curve; package one-shot choices into decision networks; and quantify what an observation is worth with the value of information.\n",{"path":7712,"title":7301,"module":7685,"summary":7713},"\u002Fartificial-intelligence\u002Funcertainty\u002Fmarkov-decision-processes","When an agent must act repeatedly in a stochastic world, a fixed plan is useless — it needs a policy, an action for every state. The Markov decision process makes this precise with a transition model, a reward, and a discount factor; the Bellman equation characterizes the optimal state utilities, and value iteration and policy iteration solve it. Partial observability lifts the problem to belief states, and bandits, Monte-Carlo tree search, and scalable POMDP solvers extend it — this is the model-known half of reinforcement learning.\n",{"path":7715,"title":7716,"module":7685,"summary":7717},"\u002Fartificial-intelligence\u002Funcertainty\u002Fdecision-networks-and-game-theory","Decision Analysis: Multi-Attribute Utility and Decision Networks","Decision analysis takes the single-agent utility framework and makes it practical: utility over several attributes, dominance and additive value functions, influence diagrams that fold Bayesian networks together with decision and utility nodes, and the value of information that tells an agent which questions are worth asking. Structure in an agent's preferences — dominance, preferential and utility independence — collapses an exponential utility table into a few one-dimensional functions, the same move that made Bayesian networks compact.\n",{"path":7719,"title":7720,"module":7685,"summary":7721},"\u002Fartificial-intelligence\u002Funcertainty\u002Fgame-theory-and-mechanism-design","Game Theory and Mechanism Design","When outcomes depend on other rational agents, single-agent utility maximization no longer suffices. Game theory studies decisions among agents — normal-form games, dominant strategies, Nash and maximin equilibria, and repeated games — and mechanism design runs the logic backwards, engineering rules (auctions, VCG) so that self-interested play produces a good collective outcome. Algorithmic game theory then asks whether equilibria can be computed, what selfishness costs society, and how the mechanisms deployed at internet scale actually behave.\n",{"path":7723,"title":7724,"module":7725,"summary":7726},"\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples","Learning from Examples","Learning","An agent that improves with experience does not need its designer to anticipate every situation. Inductive learning takes that ambition and narrows it to one tractable problem: from labelled input-output pairs, recover a function that predicts the output for inputs never seen. This first part builds the foundation around a single organizing question — generalization — through decision trees and information gain, and the training\u002Fvalidation\u002Ftest discipline for evaluating and choosing hypotheses. A second part takes up the theory of learning and the main model families.\n",{"path":7728,"title":7729,"module":7725,"summary":7730},"\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families","The Theory of Learning and Model Families","Cross-validation measures generalization but does not explain it. This part supplies the theory — PAC learning, sample complexity, and the VC dimension — that says when a hypothesis consistent with enough data is probably approximately correct, and why an unrestricted hypothesis space can never generalize. It then surveys the model families a practitioner reaches for: linear regression and gradient descent, the perceptron and logistic regression, support vector machines and the kernel trick, and ensembles by bagging and boosting — closing with what deep learning changed about the classical picture.\n",{"path":7732,"title":7733,"module":7725,"summary":7734},"\u002Fartificial-intelligence\u002Flearning\u002Fprobabilistic-learning","Learning Probabilistic Models","A [Bayesian network](\u002Fartificial-intelligence\u002Funcertainty\u002Fbayesian-networks) is useless until its numbers are filled in, and those numbers come from data. This first part casts learning itself as probabilistic inference: hypotheses carry a prior, data update it to a posterior, and predictions average over what remains. From that frame fall the standard estimators — maximum likelihood by counting, MAP with a conjugate prior, full Bayesian updating — for the case where every variable is observed. A second part takes up the harder case of hidden variables and the EM algorithm.\n",{"path":7736,"title":7737,"module":7725,"summary":7738},"\u002Fartificial-intelligence\u002Flearning\u002Fexpectation-maximization","Learning with Hidden Variables: The EM Algorithm","Complete data can be learned by counting; real data usually hide some variables — the disease behind the symptoms, the cluster behind the points. This part develops the expectation-maximization algorithm, which learns those models by alternating an expected completion of the missing data with a re-estimation of the parameters. It works the idea through mixtures of Gaussians, Bayesian networks, and hidden Markov models, proves the monotone-likelihood guarantee from the evidence lower bound, and traces the line from EM to variational inference and the variational autoencoder.\n",{"path":7740,"title":6724,"module":7725,"summary":7741},"\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning","Reinforcement learning is an MDP with the model unknown: the agent knows neither how its actions move the world nor which states are rewarded, and must recover good behaviour from experienced transitions and rewards alone. This first part builds the classical tabular theory — passive learning (fix a policy, learn its value, by direct estimation, adaptive dynamic programming, and temporal differences) and active learning (choose actions, trade exploration against exploitation, and learn control with Q-learning and SARSA). A second part lifts it off the lookup table with function approximation and policy search.\n",{"path":7743,"title":7744,"module":7725,"summary":7745},"\u002Fartificial-intelligence\u002Flearning\u002Fgeneralization-and-policy-search","Reinforcement Learning: Generalization and Policy Search","Tabular reinforcement learning stores one number per state, which is hopeless for backgammon or chess. This part lifts RL off the lookup table with function approximation, so that updating one state generalizes to related ones, then turns to policy search — representing and optimizing the policy directly, up to the REINFORCE policy gradient and correlated sampling. It closes with the bridge to deep reinforcement learning (deep Q-networks, actor-critic, PPO), the classic applications, and the hand-off to the dedicated RL subject.\n",{"path":7747,"title":7748,"module":7725,"summary":7749},"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-in-learning","Knowledge in Learning","Pure induction learns a function from labelled examples while knowing almost nothing to begin with. This first part brings prior knowledge into the loop by recasting learning as logical inference — hypotheses, examples, and classifications as sentences. It develops current-best-hypothesis search, the version space and its general\u002Fspecific boundary maintained by candidate elimination, and states the three entailment constraints that fix how background knowledge enters. A second part builds the three knowledge-based methods those constraints define.\n",{"path":7751,"title":7752,"module":7725,"summary":7753},"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-based-learning-methods","Knowledge-Based Learning: EBL, Relevance, and ILP","Once learning is cast as logical inference, three methods follow from the three ways prior knowledge can enter. Explanation-based learning generalizes a single example by explaining it with the domain theory, gaining speed but nothing new. Relevance-based learning uses determinations to shrink the hypothesis space and converge from fewer examples. Inductive logic programming learns genuinely new first-order rules — top-down with FOIL, bottom-up by inverting resolution, even inventing new predicates — and connects to modern statistical relational and neuro-symbolic learning.\n",{"path":7755,"title":7756,"module":7757,"summary":7758},"\u002Fartificial-intelligence\u002Ffrontiers\u002Fvision-and-perception","Vision and Perception","Frontiers","Perception connects an agent to the physical world. We follow one modality — vision — from the physics of image formation (the pinhole camera, perspective projection, lenses, shading, color) through the early operations that turn a pixel array into edges, texture, and motion, and into recognition by appearance. The recurring problem is inversion: a camera collapses a 3-D world onto a 2-D grid, and an agent that wants to act must build the scene back up. Rebuilding the scene is the subject of the companion lesson.\n",{"path":7760,"title":7761,"module":7757,"summary":7762},"\u002Fartificial-intelligence\u002Ffrontiers\u002Freconstructing-the-3d-world","Vision: Reconstructing the 3D World","A camera collapses a three-dimensional world onto a flat grid; this lesson inverts that collapse. We build the camera projection matrix (intrinsics and extrinsics), triangulate a point from two views, then work through the toolbox of depth cues — motion parallax, binocular stereopsis, multiple views, texture, shading, and contour — that turn an ambiguous image back into a scene. We add structural recognition (pictorial-structure \"cardboard people\"), the task-driven use of vision in cars and robots, and the shift from hand-built pipelines to learned deep-vision networks.\n",{"path":7764,"title":7765,"module":7757,"summary":7766},"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobotics","Robotics","A robot is an agent with a body: sensors that read the physical world and effectors that push back on it. This lesson grounds the abstract AI machinery in that body. We build up the hardware (range finders, proprioception, degrees of freedom), then cast perception as probabilistic filtering — the kinematic motion and sensor models, Monte Carlo localization, the extended Kalman filter, and simultaneous localization and mapping (SLAM). The companion lesson takes the estimated pose forward into planning and control.\n",{"path":7768,"title":7769,"module":7757,"summary":7770},"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobot-planning-and-control","Robotics: Planning and Control","A robot that knows where it is still has to decide how to move, and then make a slipping, sensing-imperfect body actually go there. This lesson takes the pose estimate forward: planning motion in configuration space with cell decomposition and sampling-based roadmaps (PRMs and RRTs), planning under uncertainty with most-likely-state and online replanning, closing the loop with P\u002FPD\u002FPID control and potential fields, and finally the software architectures — subsumption, three-layer, and pipeline — that assemble it all, plus the learning-based turn in modern robotics.\n",{"path":7772,"title":7773,"module":7757,"summary":7774},"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnatural-language-in-ai","Natural Language for AI Agents","Language is how agents acquire the knowledge already written down and how they communicate with the humans they serve. This lesson gives the classical AI account of language as a source of information: n-gram language models and the information-seeking tasks built on them — text classification, information retrieval (BM25, the inverted index, PageRank), and information extraction with finite-state templates and hidden Markov models. Throughout, we point to the dedicated NLP subject for the modern deep-learning treatment; the companion lesson takes up grammar, translation, and speech.\n",{"path":7776,"title":7777,"module":7757,"summary":7778},"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnlp-grammar-translation-and-speech","Language for AI Agents: Grammar, Translation, and Speech","N-gram models see only a local window; they cannot say why \"black dog\" is well-formed English and \"dog black\" is not, because that is a fact about structure. This lesson takes up structure: phrase-structure and probabilistic context-free grammars, syntactic analysis by chart parsing and CYK, augmented grammars and compositional semantics, then the two major statistical successes — machine translation and speech recognition — cast as noisy-channel problems. It closes with the bridge from n-grams to transformers and where the classical account sits relative to modern NLP.\n",{"path":7780,"title":7781,"module":7757,"summary":7782},"\u002Fartificial-intelligence\u002Ffrontiers\u002Fphilosophy-and-future","Philosophy, Ethics, and the Future of AI","Two questions have shadowed the field since its founding: can machines act intelligently (weak AI), and can they really think (strong AI)? We work through Turing's objections and their rebuttals — the arguments from disability, mathematics, and informality — then the strong-AI debate: the mind-body problem, functionalism and the brain prosthesis, Searle's Chinese Room and the systems reply, and consciousness and qualia. The companion lesson turns from what AI can do to what it should, and closes the course.\n",{"path":7784,"title":7785,"module":7757,"summary":7786},"\u002Fartificial-intelligence\u002Ffrontiers\u002Fai-ethics-and-future","The Ethics and Future of AI","Having asked whether machines can act intelligently and really think, we turn to whether we should build them at all. This lesson works through the six ethical risks — lost jobs, autonomous weapons, surveillance and privacy, biased decisions, the safety of superintelligence, and the erosion of accountability — then the value-alignment problem in the LLM era, and where the classical agent components could go next. It closes the course by tying search, logic, probability, and learning into a single picture of intelligence as rational agency.\n",{"path":7788,"title":7789,"module":6,"summary":6},"\u002Fartificial-intelligence","Artificial Intelligence",{"path":7791,"title":7792,"module":7793,"summary":7794},"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-constituents-nuclide-chart","Nuclear Composition and Ground-State Properties","Nuclear Properties","The nucleus is a bound assembly of Z protons and N neutrons packed to a radius R = R0 A^(1\u002F3) at a nearly constant density of about 10^17 kg\u002Fm^3. We fix the vocabulary of nuclides, derive nuclear size from mirror-nuclide and electron-scattering data, read the binding-energy-per-nucleon curve, and model it with the liquid-drop semiempirical mass formula.\n",{"path":7796,"title":7797,"module":7793,"summary":7798},"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-size-charge-distributions","Nuclear Size, Shape, and Charge Distributions","Elastic electron scattering resolves the nucleus by its de Broglie wavelength. The measured cross section is the Mott point-charge cross section modulated by a form factor, and that form factor is the Fourier transform of the charge density. Diffraction minima fix the radius, the small-angle slope fixes the mean-square radius, and the fitted Woods-Saxon profile gives a central density and a skin thickness. Mirror-nucleus Coulomb energies, muonic-atom X-rays, and optical isotope shifts give independent radii that all track R = R0 A^(1\u002F3).\n",{"path":7800,"title":7801,"module":7793,"summary":7802},"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-masses-binding-energy","Nuclear Masses, Mass Excess, and Separation Energies","The atomic mass unit fixes the scale, and the mass excess collects the small binding-driven deviation from the integer mass number. Penning-trap cyclotron frequencies now measure masses to parts in a billion, and every decay and reaction Q-value is a difference of these masses. One- and two-nucleon separation energies read the binding difference between neighbouring nuclides directly, showing the even-odd pairing stagger and the sharp drops at magic numbers, and their vanishing marks the neutron and proton drip lines that bound the chart of the nuclides.\n",{"path":7804,"title":7805,"module":7793,"summary":7806},"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fsemi-empirical-mass-formula","The Semi-Empirical Mass Formula and the Valley of Stability","Five physical terms reproduce nuclear binding across the chart: a volume term from saturation, a surface term from the deficit of edge neighbours, a Coulomb term from the electrostatic self-energy of a charged sphere, an asymmetry term from the Pauli cost of unequal proton and neutron filling, and a pairing term. The formula is quadratic in Z at fixed A, so isobars lie on a mass parabola whose minimum sets the most stable charge and whose slope dictates the direction of beta decay. The same competition between surface and Coulomb energy defines the fissility parameter and the onset of fission.\n",{"path":7808,"title":7809,"module":7793,"summary":7810},"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-moments-multipoles","Nuclear Spin, Magnetic Dipole, and Electric Quadrupole Moments","The ground state of a nucleus carries a definite spin and parity, a magnetic dipole moment of order the nuclear magneton, and, when its spin exceeds one-half, an electric quadrupole moment that measures its shape. The single-particle Schmidt lines predict the magnetic moment of an odd-A nucleus from the last unpaired nucleon, and the measured moments fall between them. The quadrupole moment distinguishes prolate from oblate deformation, and hyperfine structure is the experimental handle that fixes the spin and the moments from an atomic spectrum.\n",{"path":7812,"title":7813,"module":7814,"summary":7815},"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnuclear-force-shell-overview","The Nuclear Force and the Shell Model","The Nuclear Force","The strong force between nucleons is short-range, charge-independent, saturated, and repulsive at its core, about a hundred times stronger than Coulomb. Yukawa explained it as an exchange of massive mesons, tying the force's range to the meson mass through the uncertainty principle. Layered on top, an independent-particle shell model with strong spin-orbit coupling reproduces the magic numbers 2, 8, 20, 28, 50, 82, 126.\n",{"path":7817,"title":7818,"module":7814,"summary":7819},"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fthe-deuteron","The Deuteron and the Tensor Force","The deuteron is the only bound two-nucleon state: one shallow level at 2.22 MeV, no excited states. A square-well fit fixes a depth near 35 MeV over a 2 fm range, yet the wavefunction leaks so far past the edge that most of the probability lies outside the force. Its spin-1 ground state, magnetic moment close to the sum of the free-nucleon moments, and small but nonzero electric quadrupole moment together force a D-state admixture and a non-central tensor force.\n",{"path":7821,"title":7822,"module":7814,"summary":7823},"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnucleon-nucleon-scattering","Nucleon-Nucleon Scattering and the Interaction's Structure","Scattering probes the nuclear force above threshold. Partial-wave analysis reduces low-energy data to a single s-wave phase shift, and the effective-range expansion packages that into a scattering length and an effective range. The triplet channel binds (the deuteron) while the singlet is only virtual, which together explain the anomalously large free neutron-proton cross section. Comparing pp, nn, and np results establishes charge symmetry and charge independence, and polarization experiments expose the spin-orbit and tensor pieces.\n",{"path":7825,"title":7826,"module":7814,"summary":7827},"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fmeson-theory-isospin","Meson Exchange, the Yukawa Potential, and Isospin","Yukawa's massive-field propagator turns the range of the nuclear force into a meson mass: the exchanged quantum's Compton wavelength is the range. One-pion exchange fixes the long-range tail, complete with the tensor structure the deuteron demanded, while heavier mesons build the intermediate attraction and the repulsive core. Charge independence becomes an isospin symmetry, the force is diagonalized by the total isospin through a tau-dot-tau interaction, and the whole picture sits inside QCD as a residual color force between color-neutral nucleons.\n",{"path":7829,"title":7830,"module":7831,"summary":7832},"\u002Fnuclear-physics\u002Fnuclear-models\u002Ffermi-gas-model","The Fermi Gas Model","Nuclear Models","Treating the nucleus as two degenerate Fermi gases of protons and neutrons confined in a common well fixes the Fermi momentum near 250 MeV\u002Fc and the Fermi energy near 33 MeV from the nuclear density alone. The average kinetic energy per nucleon is about 20 MeV, the well depth is the Fermi energy plus the separation energy, and unequal proton and neutron Fermi levels reproduce the asymmetry term of the mass formula.\n",{"path":7834,"title":7835,"module":7831,"summary":7836},"\u002Fnuclear-physics\u002Fnuclear-models\u002Fliquid-drop-collective-coordinates","The Liquid-Drop Model and Collective Deformation","Deforming a charged liquid drop into a spheroid raises its surface energy and lowers its Coulomb energy; the two effects compete through the deformation parameter to set a stability minimum and a fission barrier. The ratio of Coulomb to twice the surface energy is the fissility Z-squared over A, which crosses one near 49 and marks the point where the sphere is unstable. The same surface tension that restores small deformations quantizes into collective vibrations, carrying the static mass formula into dynamic collective motion.\n",{"path":7838,"title":7839,"module":7831,"summary":7840},"\u002Fnuclear-physics\u002Fnuclear-models\u002Fshell-model-single-particle","The Shell Model: Single-Particle States and Spin-Orbit Coupling","A harmonic-oscillator well reproduces the first three magic numbers but fails above twenty; adding a strong inverted spin-orbit term that drives the stretched j equals l plus one-half level down closes the gaps at 28, 50, 82, and 126. The filled shells couple to zero, so the last unpaired nucleon fixes the ground-state spin and parity, and its single-particle magnetic moment falls on the Schmidt lines. Configuration mixing sets the limits of the extreme single-particle model.\n",{"path":7842,"title":7843,"module":7831,"summary":7844},"\u002Fnuclear-physics\u002Fnuclear-models\u002Fcollective-model-rotations-vibrations","The Collective Model: Rotations, Vibrations, and Deformed Nuclei","Deformed nuclei rotate with energies proportional to I times I plus one, giving the ground-state band its characteristic level ratios, while near-spherical nuclei vibrate in quantized surface phonons that build one- and two-phonon multiplets. The Nilsson model tracks single-particle levels as the well deforms, moments of inertia fall between the rigid and irrotational limits, backbending marks the sudden alignment of a broken pair, and giant resonances are the bulk dipole and quadrupole modes of the whole nucleus.\n",{"path":7846,"title":7847,"module":7848,"summary":7849},"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-law-modes","Radioactivity and Decay Modes","Radioactive Decay","Unstable nuclei decay at a rate proportional to how many remain, giving the exponential law N(t) = N0 e^(-lambda t) with half-life t = 0.693\u002Flambda. We work through the three common modes: alpha decay as Coulomb-barrier tunneling with the Geiger-Nuttall rule, beta decay whose continuous spectrum demands the neutrino, and gamma de-excitation, and follow a decay chain across the chart of nuclides.\n",{"path":7851,"title":7852,"module":7848,"summary":7853},"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-kinetics-equilibrium","Serial Decay, the Bateman Equations, and Radioactive Equilibrium","A radioactive parent that decays into a radioactive daughter obeys a coupled pair of rate equations whose solution is the Bateman formula. Depending on the half-life ordering the chain settles into secular equilibrium (equal activities), transient equilibrium (a fixed activity ratio), or no equilibrium. Constant production under irradiation drives the activity toward a saturation value equal to the production rate, competing decay modes split the total decay constant into partial constants, and the natural decay series in secular equilibrium underpin radiometric dating.\n",{"path":7855,"title":7856,"module":7857,"summary":7858},"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-decay-gamow-theory","Alpha Decay and the Gamow Theory of Tunneling","Alpha Decay","The alpha Q-value turns positive above mass number 150 because the emitted helium-4 is exceptionally tightly bound. Emission proceeds by quantum tunneling through the Coulomb barrier: a WKB integral from the nuclear surface to the outer turning point gives the Gamow factor, and multiplying its penetrability by the assault frequency yields half-lives spanning more than twenty orders of magnitude. The leading term reproduces the Geiger-Nuttall relation, log t½ proportional to the daughter charge over the square root of Q.\n",{"path":7860,"title":7861,"module":7857,"summary":7862},"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-fine-structure-hindrance","Fine Structure, Angular Momentum, and Hindrance Factors","A single parent emits several alpha groups of slightly different energy, each feeding a distinct level of the daughter, so the alpha spectrum maps the daughter's low-lying states. Emission with orbital angular momentum L raises the barrier by a centrifugal term and is allowed only when angular-momentum and parity selection rules permit. Comparing the measured partial half-life to the Gamow estimate defines a hindrance factor near unity for even-even ground-state transitions and large for odd-A decays that must rearrange the unpaired nucleon.\n",{"path":7864,"title":7865,"module":7866,"summary":7867},"\u002Fnuclear-physics\u002Fbeta-decay\u002Fbeta-decay-energetics-neutrino","Beta Decay Energetics and the Neutrino","Beta Decay and the Weak Interaction","Beta decay converts a neutron into a proton or the reverse, adjusting Z at fixed A along an isobaric mass parabola. We write the three processes (beta-minus, beta-plus, electron capture), reduce every Q-value to a difference of neutral atomic masses, and read the continuous electron spectrum as the fingerprint of a third, nearly massless particle. Pauli's neutrino, its detection by Reines and Cowan, and the endpoint bound on its mass close the lesson.\n",{"path":7869,"title":7870,"module":7866,"summary":7871},"\u002Fnuclear-physics\u002Fbeta-decay\u002Ffermi-theory-beta-decay","Fermi's Theory: Kurie Plots and ft Values","Fermi treated beta decay as a point-contact weak transition and read its rate from the golden rule. The electron spectrum then follows from phase space and the Coulomb Fermi function; the Kurie plot straightens it to a line whose intercept is the endpoint. Integrating the spectrum gives the comparative half-life ft, whose logarithm sorts transitions into superallowed, allowed, and forbidden classes governed by the Fermi and Gamow-Teller selection rules.\n",{"path":7873,"title":7874,"module":7866,"summary":7875},"\u002Fnuclear-physics\u002Fbeta-decay\u002Fweak-interaction-parity-violation","The Weak Interaction and Parity Violation","Beta decay violates mirror symmetry. The Wu experiment on polarized cobalt-60 showed electrons emitted preferentially against the nuclear spin, a pseudoscalar correlation forbidden if parity were conserved. The result fixes the weak charged current as left-handed V minus A, forces neutrinos to be left-handed and antineutrinos right-handed (measured by Goldhaber), and places beta decay within the electroweak theory as W-boson exchange turning a down quark into an up quark.\n",{"path":7877,"title":7878,"module":7866,"summary":7879},"\u002Fnuclear-physics\u002Fbeta-decay\u002Fdouble-beta-decay-neutrino-mass","Double Beta Decay and Neutrino Mass","For even-A isobars the pairing term splits the mass parabola into two curves, and a handful of even-even nuclides sit below their odd-odd neighbor yet above the next even-even one: single beta decay is forbidden but second-order double beta decay is allowed. The two-neutrino mode is a standard-model process with the longest measured lifetimes in nature; the neutrinoless mode would require the neutrino to be its own antiparticle and its rate measures the effective Majorana mass, the sharpest probe of the absolute neutrino mass scale.\n",{"path":7881,"title":7882,"module":7883,"summary":7884},"\u002Fnuclear-physics\u002Fgamma-decay\u002Fgamma-multipole-radiation","Multipole Radiation and Selection Rules","Gamma Decay","Gamma decay carries a nucleus from an excited state to a lower one by emitting a photon of definite angular momentum and parity. We correct the photon energy for nuclear recoil, expand the radiation field into electric and magnetic multipoles, and read off how the transition rate collapses with each increase in multipole order. The Weisskopf single-particle estimates set the scale, and angular-momentum and parity conservation fix which multipole dominates.\n",{"path":7886,"title":7887,"module":7883,"summary":7888},"\u002Fnuclear-physics\u002Fgamma-decay\u002Finternal-conversion-isomers","Internal Conversion and Isomers","A nucleus can shed excitation energy without emitting a photon by handing it directly to an atomic electron. We define the internal-conversion coefficient, trace its growth with atomic number, multipole order, and decreasing energy, and treat the electron-only E0 transitions and internal pair formation. When the lowest allowed multipole is high and the energy low, the gamma rate falls so far that the excited state survives as a metastable isomer.\n",{"path":7890,"title":7891,"module":7883,"summary":7892},"\u002Fnuclear-physics\u002Fgamma-decay\u002Fangular-correlations-mossbauer","Angular Correlations and the Mössbauer Effect","Two gammas emitted in cascade are not independent in direction: detecting the first selects magnetic substates of the intermediate level and makes the second anisotropic, so the correlation function fixes the intermediate spin. The same nuclear resonance that recoil normally destroys is recovered when the emitter is locked in a lattice, giving the Mössbauer effect and its part-in-a-trillion resolution of isomer shifts and hyperfine fields.\n",{"path":7894,"title":7895,"module":7896,"summary":7897},"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Freaction-kinematics-cross-sections","Nuclear Reactions, Fission, and Fusion","Nuclear Reactions","A nuclear reaction X(x, y)Y is governed by its Q value and its cross section, the effective target area for a given process. Splitting the curve of binding energy near iron in either direction releases energy: fission of heavy nuclei by neutron capture and a chain reaction, and fusion of light nuclei that powers the Sun and needs Lawson's density-confinement criterion to be practical.\n",{"path":7899,"title":7900,"module":7896,"summary":7901},"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fcompound-nucleus-resonances","The Compound Nucleus and Resonance Reactions","Low-energy reactions proceed through a long-lived intermediate state whose decay forgets how it formed. Bohr's independence hypothesis factorizes the cross section into a formation step and a branching ratio, an isolated level gives the single-level Breit-Wigner line shape with total width Γ tied to the lifetime by Γτ = ħ, and at high excitation overlapping levels merge into a statistical continuum described by evaporation spectra and the Hauser-Feshbach average.\n",{"path":7903,"title":7904,"module":7896,"summary":7905},"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fdirect-reactions-optical-model","Direct Reactions and the Optical Model","A complex optical potential replaces the many-body target by a single particle moving in an average field whose imaginary part removes flux into non-elastic channels, reproducing the diffraction pattern of elastic scattering. Direct reactions bypass the compound nucleus, transferring a nucleon in one step: stripping and pickup deposit or remove a single nucleon, the angle of the first peak in the distorted-wave angular distribution fixes the transferred orbital angular momentum, and its magnitude gives the spectroscopic factor.\n",{"path":7907,"title":7908,"module":7909,"summary":7910},"\u002Fnuclear-physics\u002Ffission\u002Ffission-barrier-dynamics","The Fission Barrier and Fragment Energetics","Nuclear Fission","Fission is the large-amplitude collective deformation of a heavy nucleus into two fragments. The liquid-drop model sets a barrier from the competition between rising surface energy and falling Coulomb energy under quadrupole deformation, with the fissility parameter Z²\u002FA measuring how close a nucleus is to instability. Bohr-Wheeler theory separates spontaneous from neutron-induced fission, the fragment mass yield is double-humped and asymmetric, about 200 MeV is released per event, and shell corrections add a second minimum that produces fission isomers.\n",{"path":7912,"title":7913,"module":7909,"summary":7914},"\u002Fnuclear-physics\u002Ffission\u002Fchain-reactions-reactor-physics","Chain Reactions and Reactor Physics","A self-sustaining chain reaction is a fixed point of neutron bookkeeping: the multiplication factor k counts the neutrons in one generation per neutron in the last, and criticality is k = 1. The four-factor formula tracks a neutron through fast fission, resonance escape, thermal utilization, and reproduction; moderation slows fission neutrons to the thermal energies where the fission cross section is largest; and the small delayed-neutron fraction sets the timescale that makes a reactor controllable. Breeding converts fertile U-238 and Th-232 into new fissile fuel.\n",{"path":7916,"title":7917,"module":7918,"summary":7919},"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Ffusion-reactions-confinement","Fusion Reactions and Confinement","Fusion and Nucleosynthesis","Light nuclei release energy when they fuse because binding per nucleon rises steeply toward the iron peak, but the Coulomb barrier suppresses the rate at reactor temperatures. The thermonuclear rate is a convolution of the Maxwell distribution with the tunneling probability, sharply peaked at the Gamow energy. The deuterium-tritium reaction has the lowest barrier and largest cross section; sustained energy gain requires the Lawson triple product of density, temperature, and confinement time, reached by magnetic or inertial confinement.\n",{"path":7921,"title":7922,"module":7918,"summary":7923},"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fstellar-nucleosynthesis","Stellar Nucleosynthesis","Main-sequence stars burn hydrogen to helium through the proton-proton chain and the CNO cycle, both releasing 26.7 MeV per helium nucleus. Helium burning bridges the mass-5 and mass-8 gaps by the triple-alpha process through the Beryllium-8 and Hoyle resonances, and successive carbon-to-silicon burning stages climb to the iron peak, where fusion stops. The elements beyond iron are built by slow and rapid neutron capture, and the solar neutrino flux confirms the reactions directly.\n",{"path":7925,"title":7926,"module":7918,"summary":7927},"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fbig-bang-nucleosynthesis","Big-Bang Nucleosynthesis","In the first three minutes the expanding universe forged the light elements. The weak interaction froze the neutron-to-proton ratio near one in six when the reaction rate fell below the expansion rate, and free-neutron decay lowered it to about one in seven before the deuterium bottleneck broke. Almost every surviving neutron ended in helium-4, fixing the primordial helium mass fraction near 0.25, with trace deuterium, helium-3, and lithium-7. The deuterium abundance measures the cosmic baryon density.\n",{"path":7929,"title":7930,"module":7931,"summary":7932},"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fcharged-particle-stopping-power","Stopping Power and the Range of Charged Particles","Radiation and Applications","A heavy charged particle loses energy in a dense sequence of small Coulomb collisions with atomic electrons, at a rate the Bethe-Bloch formula fixes from the particle's charge and speed and the medium's electron density and mean excitation energy. The rate scales as the inverse square of the speed, so most energy is deposited at the end of the track in the Bragg peak, and integrating the reciprocal rate gives a sharp range. Electrons differ: they also radiate, and above a critical energy bremsstrahlung dominates. Fast particles above the phase velocity of light in the medium emit Cherenkov radiation.\n",{"path":7934,"title":7935,"module":7931,"summary":7936},"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fphoton-neutron-interactions","Interactions of Photons and Neutrons","Photons are removed from a beam in single events, so their intensity falls exponentially with a linear attenuation coefficient built from three processes: the photoelectric effect at low energy, Compton scattering at intermediate energy, and pair production above twice the electron rest energy, each with its own atomic-number and energy dependence. Neutrons carry no charge and interact only with nuclei, moderating by elastic scattering and being captured with a cross section that rises as one over speed away from resonances.\n",{"path":7938,"title":7939,"module":7931,"summary":7940},"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fradiation-detectors","Radiation Detectors and Nuclear Spectroscopy","Every detector converts the energy a radiation deposits into a measurable electrical signal. Gas counters read the ionization directly, in three operating regions set by the applied voltage; scintillators convert the energy to light read out by a photomultiplier; semiconductor detectors collect electron-hole pairs and give the best energy resolution because so many carriers are made per event. The resolution is governed by the number of independent charge carriers, and the pulse-height spectrum of a gamma line shows a full-energy photopeak, a Compton continuum with its edge, and escape peaks.\n",{"path":7942,"title":7943,"module":7931,"summary":7944},"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fdosimetry-radiation-biology","Dosimetry, Radiation Biology, and Protection","Absorbed dose is the energy deposited per unit mass, measured in gray. Equal absorbed doses do unequal biological damage because densely ionizing radiation deposits its energy along short tracks: weighting the dose by a radiation factor gives the equivalent dose, and weighting by tissue sensitivity gives the effective dose, both in sieverts. Deterministic effects have a threshold and a severity that grows with dose; stochastic effects are assumed to follow a linear-no-threshold probability. Natural background dominates the dose to the population, and protection rests on time, distance, and shielding.\n",{"path":7946,"title":7947,"module":7931,"summary":7948},"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fnuclear-applications-dating-medicine","Applications — Dating, Analysis, and Nuclear Medicine","Charged particles lose energy continuously and stop at a well-defined range with a Bragg peak, while gamma rays are attenuated exponentially. These interactions define radiation detectors and dosimetry (gray and sievert) and drive the applications: neutron activation analysis, magnetic resonance imaging, PET, and radiometric dating with carbon-14 and long-lived rock clocks.\n",{"path":7950,"title":7951,"module":6,"summary":6},"\u002Fnuclear-physics","Nuclear Physics",{"path":7953,"title":7954,"module":3911,"summary":7955},"\u002Fnatural-language-processing\u002Ffoundations\u002Fwhat-is-nlp","What Is Natural Language Processing?","Natural language processing is the computational treatment of human language: reading it, representing it, and generating it. We set up why the problem is hard — ambiguity at every level, from sound to intent — trace the field from ELIZA's pattern-matching through statistical methods to today's neural models, lay out the linguistic levels and task families the course covers, and fix the vocabulary of tokens, types, and corpora the rest of the notes rely on.\n",{"path":429,"title":7957,"module":3911,"summary":7958},"Regular Expressions and Text Normalization","Before any model touches text, the text has to be found and cleaned. Regular expressions give an algebra for describing string patterns; tokenization, case folding, and stemming turn raw characters into the units a model counts; and byte-pair encoding builds a subword vocabulary that spells out any word. Measuring how far apart two strings are — minimum edit distance — is the next lesson.\n",{"path":7960,"title":7961,"module":3911,"summary":7962},"\u002Fnatural-language-processing\u002Ffoundations\u002Fminimum-edit-distance","Minimum Edit Distance","Much of language processing needs to measure how similar two strings are — a speller ranking corrections, a diff tool, a coreference resolver. Minimum edit distance counts the insertions, deletions, and substitutions that turn one string into another, computed by a dynamic-programming table. We fill the table for intention to execution, backtrace to recover the alignment, and see how the same machinery generalizes to weighted edits, Viterbi, and biological sequence alignment.\n",{"path":7964,"title":7965,"module":3911,"summary":7966},"\u002Fnatural-language-processing\u002Ffoundations\u002Fn-gram-language-models","N-Gram Language Models","A language model assigns a probability to a sequence of words and, equivalently, predicts the next word from its history. The n-gram model makes this tractable by truncating the history to the last few words, estimates the resulting conditional probabilities by counting, and is scored by perplexity. We build the model from the chain rule, work a bigram example on a small corpus, and read perplexity as a branching factor. The next lesson covers the zero counts that break this model and the smoothing that repairs them.\n",{"path":7968,"title":7969,"module":3911,"summary":7970},"\u002Fnatural-language-processing\u002Ffoundations\u002Fsmoothing-and-backoff","Smoothing and Backoff","Every finite corpus is missing good word sequences it simply never saw, so a raw n-gram model assigns them probability zero and breaks. Smoothing repairs the zeros: add-one and add-k shave mass off seen events, backoff and interpolation fall back on shorter contexts, and Kneser-Ney — worked here by hand — replaces raw frequency with how many contexts a word completes. We close on web-scale stupid backoff and the neural models that dissolve the zero problem rather than patch it.\n",{"path":7972,"title":7973,"module":7974,"summary":7975},"\u002Fnatural-language-processing\u002Fclassification\u002Fnaive-bayes-and-sentiment","Naive Bayes and Sentiment Classification","Text Classification","Text classification assigns a category to a document — positive or negative, spam or not, one topic among many. Naive Bayes is a generative solution: apply Bayes' rule, assume the words are conditionally independent given the class, and the winning class is the one maximizing the product of a prior and per-word likelihoods. We train it by counting with add-one smoothing, work a full sentiment example by hand, sharpen it for sentiment (binary counts, negation, lexicons), and place it among the transformer classifiers that came after.\n",{"path":7977,"title":7978,"module":7974,"summary":7979},"\u002Fnatural-language-processing\u002Fclassification\u002Fevaluating-classifiers","Evaluating Classifiers","A trained classifier is only useful once we can measure how good it is. We build the confusion matrix, see why accuracy misleads on unbalanced data, and define precision, recall, and the F-measure that balances them. Multi-class tasks need macro- versus micro-averaging; reliable estimates need cross-validation. We close on statistical significance — the paired bootstrap test for whether one system's lead over another is significant.\n",{"path":7981,"title":7982,"module":7974,"summary":7983},"\u002Fnatural-language-processing\u002Fclassification\u002Flogistic-regression","Logistic Regression","Logistic regression is the discriminative counterpart to naive Bayes: instead of modelling how a document is generated, it learns weights that directly separate the classes. We build it from the sigmoid, derive the cross-entropy loss from maximum likelihood, learn the weights by stochastic gradient descent, regularize to curb overfitting, and generalize to many classes with the softmax. The two-class model is already a one-neuron network, so this is the bridge to neural language models.\n",{"path":7985,"title":7986,"module":7974,"summary":7987},"\u002Fnatural-language-processing\u002Fclassification\u002Fsentiment-and-affect-lexicons","Sentiment and Affect Lexicons","A sentiment lexicon is a list of words annotated with the affective meaning they carry — positive or negative, or scores along valence, arousal, and dominance. We fix what \"emotion\" means (basic-emotion versus dimensional models), survey the standard lexicons, and then build lexicons three ways: by human labeling with best-worst scaling, by semi-supervised induction from seed words over an embedding space, and by supervised learning from starred reviews. We close on connotation frames, which record the sentiment a verb implies about each of its arguments.\n",{"path":7989,"title":7990,"module":7991,"summary":7992},"\u002Fnatural-language-processing\u002Fsemantics\u002Fvector-semantics-and-embeddings","Vector Semantics and Embeddings","Semantics","Vector semantics represents a word's meaning as a point in space, derived from the company the word keeps. This first part builds the count-based side: the distributional hypothesis, co-occurrence matrices in their term-document and word-word forms, cosine as the similarity measure, and the two weightings — tf-idf and PPMI — that fix what raw counts get wrong. The result is a sparse, interpretable vector for every word, and the setup for the dense embeddings of the next lesson.\n",{"path":7994,"title":7995,"module":7991,"summary":7996},"\u002Fnatural-language-processing\u002Fsemantics\u002Fstatic-word-embeddings","Static Word Embeddings: word2vec and After","Count-based vectors are long and sparse; embeddings are the short, dense alternative. This lesson builds them with word2vec's skip-gram and negative sampling — a classifier whose learned weights are the vectors — derives its gradient, and works one update by hand. It then reads relations off the analogy parallelogram, surveys the papers that framed the static-embedding era (word2vec, GloVe, the SGNS-as-PPMI equivalence, fastText, ELMo), and closes on the biases embeddings inherit and the single-vector-per-word ceiling that contextual models break.\n",{"path":7998,"title":7999,"module":7991,"summary":8000},"\u002Fnatural-language-processing\u002Fsemantics\u002Fneural-language-models","Neural Networks and Neural Language Models","A neural network is a stack of units, each a weighted sum passed through a non-linearity — a single unit on its own is logistic regression. We build the network up from that unit: the activation functions that give it power, the XOR problem that forces a hidden layer, the feedforward forward pass in matrix form, and the Bengio-style feedforward neural language model that concatenates word embeddings and predicts the next word with a softmax. Training is cross-entropy minimized by gradient descent, with backpropagation supplying the gradient. Embeddings let the model share statistical strength across similar words, avoiding the sparsity that limits n-gram models.\n",{"path":1858,"title":8002,"module":4377,"summary":8003},"Sequence Labeling: POS and NER","Sequence labeling assigns one tag to every token in a sentence. This first part sets up the task through its two canonical cases — part-of-speech tagging over the Penn Treebank tagset, and named-entity recognition reframed as token labeling with the BIO scheme — then builds the hidden Markov model, the classic probabilistic tagger. The HMM tags by Bayesian inference: transition and emission probabilities under two Markov assumptions, reducing tagging to an argmax over tag sequences. That argmax is exponential to enumerate, which sets up the Viterbi decoder, the CRF, and neural taggers of the next lesson.\n",{"path":8005,"title":8006,"module":4377,"summary":8007},"\u002Fnatural-language-processing\u002Fsequences\u002Fcrfs-and-neural-taggers","Viterbi Decoding, CRFs, and Neural Taggers","The HMM reduced tagging to an argmax over exponentially many tag sequences. This lesson builds the decoder that makes it tractable — the Viterbi dynamic program, worked through a full numeric trace on real WSJ probabilities — then keeps that same decoder while replacing the HMM's rigid tables. The linear-chain conditional random field is a discriminative log-linear model whose global feature functions can inspect any part of the input, which is why CRFs win for NER. Finally it traces the shift to neural taggers (biLSTM-CRF, character-aware NER, ELMo), where hand-built features become learned representations while the Viterbi decoder carries over unchanged.\n",{"path":8009,"title":8010,"module":4377,"summary":8011},"\u002Fnatural-language-processing\u002Fsequences\u002Frnns-and-lstms","RNNs and LSTMs","A feedforward neural language model sees a fixed window of words and can look no further back. The recurrent neural network removes that limit: it carries a hidden state across time, so each word is read in the context of everything before it. We build the RNN from its one recurrent equation, use it as a language model, train it by backpropagation through time, and diagnose the vanishing-gradient problem that makes plain RNNs forget. The LSTM fixes the forgetting with a cell state and three gates, and the encoder-decoder stacks two RNNs into a sequence-to-sequence model — and its single-vector bottleneck is the problem attention was invented to remove.\n",{"path":8013,"title":8014,"module":4859,"summary":8015},"\u002Fnatural-language-processing\u002Ftransformers\u002Ftransformers-and-attention","Transformers and Self-Attention","Recurrence forced language models to read one word at a time and to squeeze every dependency through a chain of hidden states. Self-attention removes the recurrence: at every layer each position compares itself to every other and reads a weighted mixture of them, in a single parallel step. This first part builds the attention operation from the ground up — the soft lookup, queries and keys and values, the scaled dot-product, the numeric trace, the matrix form, and the causal mask — and sets up the full transformer architecture that follows.\n",{"path":8017,"title":6571,"module":4859,"summary":8018},"\u002Fnatural-language-processing\u002Ftransformers\u002Fthe-transformer-architecture","This part takes the scaled dot-product attention of the previous lesson and assembles the full transformer architecture around it: multi-head attention so several relations can be read at once, the transformer block of residual connections and layer norm that makes deep stacks trainable, positional embeddings that restore word order, the decoder-only language model, and the encoder, decoder, and encoder-decoder shapes — closing with the 2017 paper and the pre-norm, FlashAttention, and RoPE refinements that scaled it up.\n",{"path":8020,"title":6678,"module":4859,"summary":8021},"\u002Fnatural-language-processing\u002Ftransformers\u002Flarge-language-models","A large language model is a decoder-only transformer trained on one objective — predict the next token. This first part assembles the inference side: the language-modeling head that turns a hidden state into a distribution over the vocabulary, autoregressive generation, and the decoding strategies — greedy, beam, and sampling with temperature, top-k, and nucleus — that read text back out of that distribution. Training the distribution at web scale comes next.\n",{"path":8023,"title":8024,"module":4859,"summary":8025},"\u002Fnatural-language-processing\u002Ftransformers\u002Fllm-pretraining-and-scaling","Large Language Models: Pretraining and Scaling","A language model's next-token distribution is only as good as the parameters behind it. This part is where those parameters come from: self-supervised pretraining on web-scale text with teacher forcing and cross-entropy, the scaling laws that make test loss a predictable power law in parameters, data, and compute, the KV cache that keeps long-context inference affordable, and how a finished model is evaluated by perplexity and benchmarks — closing with the Kaplan, Chinchilla, GPT-3, and emergence papers behind the scaling story.\n",{"path":8027,"title":8028,"module":4859,"summary":8029},"\u002Fnatural-language-processing\u002Ftransformers\u002Ffine-tuning-and-prompting","Fine-Tuning and Prompting","A pretrained transformer is a general-purpose knowledge source; a task is what you do with it. There are two ways to adapt one, and this first part covers the one that updates the weights: fine-tuning. A bidirectional encoder like BERT is pretrained by masked language modeling, then a small task head is bolted on and the whole thing is trained on labelled data for classification, sequence labeling, or span-based question answering — with parameter-efficient variants (adapters, LoRA) that touch only a sliver of the weights. Prompting, the family that leaves the weights frozen, comes next.\n",{"path":8031,"title":8032,"module":4859,"summary":8033},"\u002Fnatural-language-processing\u002Ftransformers\u002Fprompting-and-alignment","Prompting and Alignment","Fine-tuning adapts a model by changing its weights. The second family of adaptation changes nothing: a large frozen model performs a task from an instruction and a few examples placed in its context. This part covers prompting and in-context learning, chain-of-thought that elicits reasoning, and the two training stages — instruction tuning and RLHF — that turn a fluent base predictor into an aligned assistant, closing with the BERT, LoRA, chain-of-thought, InstructGPT, and retrieval-augmentation papers behind the modern adaptation pipeline.\n",{"path":8035,"title":8036,"module":8037,"summary":8038},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-parsing","Constituency Parsing","Linguistic Structure","A constituency parse groups a sentence into nested phrases described by a context-free grammar. We build the CFG formalism, read the phrase structure of English off a treebank, confront the structural ambiguity that makes parsing hard, convert to Chomsky normal form, and then solve it with CKY — the dynamic-programming chart that fills a triangular table bottom-up. Probabilistic and neural span parsers, evaluation, and shallow parsing follow in the companion lesson.\n",{"path":8040,"title":8041,"module":8037,"summary":8042},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcky-scoring-and-evaluation","CKY Scoring, Evaluation, and Shallow Parsing","The CKY chart returns every parse but does not say which is correct. Disambiguation needs a score on trees. This lesson attaches probabilities to a grammar (the PCFG and lexicalization), replaces the grammar with a neural span scorer over a pretrained encoder, states the self-attentive results that made it the state of the art, evaluates parsers against a treebank with PARSEVAL, and closes with chunking and shallow parsing for tasks that need only the flat phrases.\n",{"path":8044,"title":8045,"module":8037,"summary":8046},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdependency-parsing","Dependency Parsing","A dependency parse throws away phrases and keeps only directed, labeled arcs from heads to their dependents, so the subject and object of a verb hang off the verb directly. We fix the formalism (rooted trees, typed Universal-Dependency relations, projectivity), then build the first parser family: transition-based arc-standard and arc-eager parsing, a greedy stack-and-buffer machine trained from an oracle. Graph-based and neural dependency parsing follow in the companion lesson.\n",{"path":8048,"title":8049,"module":8037,"summary":8050},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fgraph-based-and-neural-dependency-parsing","Graph-Based and Neural Dependency Parsing","Greedy transition parsing commits locally; the graph-based family scores whole trees instead. This lesson scores every candidate head-dependent edge and extracts the maximum spanning tree with Chu-Liu\u002FEdmonds, develops the biaffine neural scorer that made graph-based parsing the accuracy leader, evaluates parsers with the unlabeled and labeled attachment scores (UAS and LAS), and closes on where the two parser families sit and what they feed downstream.\n",{"path":8052,"title":8053,"module":8037,"summary":8054},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fword-senses-and-wsd","Word Senses and Disambiguation","A word is not an atom of meaning: \"bass\" names a fish, a voice, and an instrument, and one static embedding blurs them into a single point. This lesson pulls those senses apart. We define polysemy and the relations that organize senses — synonymy, antonymy, hyponymy, meronymy — build them into WordNet's synset graph, measure similarity along that graph, and then solve the core of word sense disambiguation: the most-frequent-sense baseline, the Lesk gloss-overlap algorithm, feature-based classifiers, and the nearest-neighbor method over BERT embeddings. WSD variants, embeddings, and evaluation follow in the companion lesson.\n",{"path":8056,"title":8057,"module":8037,"summary":8058},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fwsd-in-practice-and-induction","WSD in Practice and Word Sense Induction","Beyond core word sense disambiguation lie the variants and loose ends: the sense-inventory-free Word-in-Context task, retrofitting static embeddings to a thesaurus, discovering senses without a fixed inventory (word sense induction), the gloss-aware and bi-encoder neural systems that hold the state of the art, and how WSD and its cousins are evaluated. Together they connect one-vector-per-word embeddings to sense-aware contextual representations.\n",{"path":8060,"title":8061,"module":8037,"summary":8062},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-roles-and-information-extraction","Semantic Roles and Information Extraction","Semantic roles answer \"who did what to whom\" for a single event, abstracting away the syntax that expresses it. We show why syntax alone is not enough, generalize over diathesis alternations with thematic roles, number a predicate's arguments with PropBank and group predicates into frames with FrameNet, tag each argument automatically with semantic role labeling, and factor predicates into primitives. Information extraction scales the idea to a corpus in the companion lesson.\n",{"path":8064,"title":8065,"module":8037,"summary":8066},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Frelations-events-and-templates","Relations, Events, and Templates","Semantic roles answer \"who did what\" for one predicate; information extraction scales the idea to a whole corpus. This lesson turns unstructured text into structured data: relation extraction pulls entity-relation-entity triples out of sentences by patterns, supervision, and distant supervision; event and temporal extraction place those facts on a timeline; and template filling and knowledge-base population assemble them into a database a downstream system can query.\n",{"path":8068,"title":8069,"module":8037,"summary":8070},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoreference-and-discourse","Coreference and Discourse","A text is more than a bag of sentences: entities recur under different names. Coreference resolution links every mention to the discourse entity it evokes — the linguistic background of pronouns, definite NPs, and names; mention detection; the mention-pair, mention-ranking, and entity-based architectures; a neural end-to-end span model that scores candidate antecedents; features, evaluation by the CoNLL F1, gender bias, and the neural coreference lineage. Discourse coherence follows in the companion lesson.\n",{"path":8072,"title":8073,"module":8037,"summary":8074},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoherence-and-discourse-structure","Coherence and Discourse Structure","Coherence is what makes a run of sentences a discourse rather than an arbitrary collection. This lesson develops coherence relations and Rhetorical Structure Theory trees, discourse-structure parsing, Centering and the entity grid for entity-based coherence, and representation-learning models of local coherence, measured in part over the coreference chains recovered in the companion lesson.\n",{"path":8076,"title":8077,"module":8037,"summary":8078},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Flogical-semantics","Logical Representations of Meaning","A meaning representation turns a sentence into a formal structure a machine can check against a world and reason over. We set the desiderata a good representation must meet, ground truth in a model, build up first-order logic for sentences with its connectives, quantifiers, and inference, and reify events with the neo-Davidsonian event variable to escape fixed predicate arity. The compositional lambda calculus, quantifier scope, and description logics follow in the companion lesson.\n",{"path":8080,"title":8081,"module":8037,"summary":8082},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcompositional-semantics-and-description-logics","Compositional Semantics and Description Logics","How do you compute a logical form from a sentence automatically? This lesson builds the compositional machinery: the lambda calculus that assembles a formula from a parse tree one beta-reduction at a time, the quantifier-scope ambiguity a single syntax tree leaves open, and the decidable description logics — TBox, ABox, subsumption, role restrictions — behind the Web Ontology Language, closing with how the map from string to logical form can be learned.\n",{"path":8084,"title":8085,"module":8037,"summary":8086},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-parsing","Semantic Parsing","Turning a sentence into a structured, executable meaning, the grammar-based way. We take the logical forms defined earlier and build them compositionally: a rule-based parser that walks a syntax tree applying lambda terms, then Combinatory Categorial Grammar (CCG), which fuses syntax and semantics so one lexicalized derivation produces both — including supertagging and A* parsing. Learned and neural semantic parsers follow in the companion lesson.\n",{"path":8088,"title":8089,"module":8037,"summary":8090},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fneural-semantic-parsing","Learned and Neural Semantic Parsing","Hand-writing a lexicon of lambda terms does not scale, so this lesson learns the parser instead. We cover the two supervision regimes (from logical forms and from denotations), Abstract Meaning Representation as a rooted concept graph, neural sequence-to-sequence parsing with constrained decoding and copy mechanisms, executable text-to-SQL and knowledge-based question answering, the practical systems that made learned parsers accurate, and how the task is evaluated.\n",{"path":8092,"title":8093,"module":8037,"summary":8094},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Finformation-extraction","Information Extraction","Information extraction turns free text into a database, and the first step is relation extraction: pulling entity-relation-entity triples out of sentences. We cover all five families — hand-built patterns, supervised classifiers, semi-supervised bootstrapping, distant supervision, and unsupervised Open IE — with worked bootstrapping and distant-supervision traces, then the neural and LLM systems that extended them. Times, events, and templates follow in the companion lesson.\n",{"path":8096,"title":8097,"module":8037,"summary":8098},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftimes-events-and-templates","Extracting Times, Events, and Templates","Once relation extraction has produced typed triples, the information-extraction pipeline still has to place facts in time and assemble them into records. This lesson detects and normalizes temporal expressions to ISO 8601 values, detects events and orders them on a timeline with the 13 Allen relations, and fills slot-and-filler templates — flat and hierarchical — for stereotyped situations, closing the loop from text to a queryable database.\n",{"path":8100,"title":8101,"module":8037,"summary":8102},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdiscourse-coherence","Discourse Coherence","A text is more than a set of sentences. What binds a run of sentences into a discourse is coherence, and one of its sources is structured relations between clauses. This lesson develops relational coherence — RST and the PDTB models of coherence relations — and discourse-structure parsing: EDU segmentation and shift-reduce RST parsing, then PDTB relation classification. Entity-based and global coherence follow in the companion lesson.\n",{"path":8104,"title":8105,"module":8037,"summary":8106},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fentity-based-and-global-coherence","Entity-Based and Global Coherence","A text coheres not only through relations between clauses but by staying about the same entities and the same topic, and by obeying the macro-structure of its genre. This lesson develops Centering Theory and the entity grid for entity-based coherence, representation-learning models of local coherence, and global coherence — topic segmentation, narrative and argumentation structure, and scientific discourse — then the neural models that learn each.\n",{"path":8108,"title":8109,"module":8037,"summary":8110},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-grammars","Constituency Grammars","A constituency grammar is the declarative theory of sentence structure that a parser operates on. We build the context-free grammar formalism from its four parts, show how derivations become parse trees, and work through the phrase structure of English — noun phrases, verb phrases and their subcategorization frames, agreement, coordination, and long-distance dependencies. The treebank, normal-form, and lexicalized views follow in the companion lesson.\n",{"path":8112,"title":8113,"module":8037,"summary":8114},"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftreebanks-and-lexicalized-grammars","Treebanks and Lexicalized Grammars","Where does a grammar come from, and how is it prepared for a parser? We read a context-free grammar off the Penn Treebank, normalize it to Chomsky Normal Form for the CKY chart, then invert the phrase-structure emphasis with lexicalized grammars — Combinatory Categorial Grammar and its slash categories — and close with the grammar's fate in the neural era: span scoring, self-attention, and grammar induction.\n",{"path":1224,"title":8116,"module":2972,"summary":8117},"Machine Translation","Machine translation is the task that built the modern toolkit: the encoder-decoder was invented for it, attention was invented to fix its fixed-context bottleneck, and both were later folded into the general transformer. We work through why translation is hard (word order, morphology, lexical and structural divergences), the sequence-to-sequence model and its attention mechanism, transformer-based NMT with cross-attention, subword tokenization with a shared vocabulary, beam-search decoding, and evaluation by BLEU and its successors chrF, BERTScore, and COMET — closing on multilingual and low-resource translation and backtranslation.\n",{"path":8119,"title":8120,"module":2972,"summary":8121},"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation-decoding-and-evaluation","Machine Translation: Decoding, Evaluation, and Scale","Having built the transformer translation model, we now decode from it and measure the output. Beam search turns the decoder's per-step distributions into a single output string; length normalization keeps it from favoring short translations. We then score translations automatically — BLEU with its n-gram precision, clipping, and brevity penalty, worked through by hand, then its successors chrF, BERTScore, and COMET — and close on the parts of MT that scale beyond one language pair: multilingual and low-resource translation, backtranslation, gender bias, and the lineage from the Transformer to massively multilingual models like NLLB-200.\n",{"path":701,"title":8123,"module":2972,"summary":8124},"Question Answering","A question-answering system takes a natural-language question and returns an answer, not a ranked list of documents. Almost every modern system is built on one pattern: retrieve then read. We start with the information-retrieval machinery that finds candidate text — tf-idf and BM25 term weighting, a worked ranking example, the inverted index, and dense embedding retrieval — then build the retriever-reader pipeline that extracts an answer span with BERT and trace a full retrieve-and-read example end to end.\n",{"path":8126,"title":8127,"module":2972,"summary":8128},"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering-knowledge-and-llms","Question Answering: Knowledge Bases and Language Models","The retrieve-and-read pipeline extracts an answer span from prose, but not all knowledge lives in prose. This part covers the rest of the QA stack: entity linking (Wikification) that grounds a question's entities to a knowledge base, knowledge-based QA by semantic parsing a question into an executable query, and the modern default — closed-book QA and retrieval-augmented generation with a large language model — closing on the DPR\u002FRAG\u002Ffusion-in-decoder lineage and how factoid answers are scored by exact match and F1.\n",{"path":2974,"title":5,"module":2972,"summary":2992},{"path":2750,"title":2751,"module":2972,"summary":8131},"Two dialogue traditions — chatbots built to chat and task-oriented frame systems built to get something done — met in the aligned LLM assistant. Instruction tuning plus RLHF fold chit-chat and task dialogue into one model; the LaMDA \u002F InstructGPT \u002F ChatGPT lineage fills in how. The lesson then turns to evaluation (human ratings and acute-eval for chatbots, task success and slot error rate for task systems), user-centered design with Wizard-of-Oz prototyping, and the ethical stakes of building agents people talk to.\n",{"path":8133,"title":8134,"module":2972,"summary":8135},"\u002Fnatural-language-processing\u002Fapplications\u002Ftext-summarization","Text Summarization","Summarization compresses a document to its essential meaning, by either selecting sentences to keep (extractive) or writing new ones (abstractive). This part fixes the task and its flavors — single vs. multi-document, generic vs. query-focused, extractive vs. abstractive — then works through extractive summarization in full: scoring by position and centrality, the TextRank\u002FLexRank graph algorithm run as PageRank over a sentence-similarity graph with a worked iteration, and supervised sentence selection.\n",{"path":8137,"title":8138,"module":2972,"summary":8139},"\u002Fnatural-language-processing\u002Fapplications\u002Fabstractive-summarization-and-evaluation","Abstractive Summarization and Evaluation","Extractive methods can only reuse the source's own sentences; to compress within a sentence or paraphrase, a summarizer has to generate. This part covers abstractive summarization: the sequence-to-sequence approach, the pointer-generator's copy switch and coverage mechanism, pretrained summarizers (BART, PEGASUS) and zero-shot LLM prompting, the long-document and factuality problems, and ROUGE evaluation with a worked example and its limits — closing on the abstractive lineage from See 2017 through faithfulness metrics.\n",{"path":8141,"title":8142,"module":8143,"summary":8144},"\u002Fnatural-language-processing\u002Fspeech\u002Fphonetics","Phonetics","Speech","Before a recognizer can read speech it has to know what speech is. This first part covers the linguistic substrate: phones and their transcription in the IPA and ARPAbet; articulatory phonetics — how the vocal tract shapes airflow into consonants and vowels; and prosody — stress, tune, and the F0 contour. The acoustic side — the waveform, its spectrum, formants, and the spectrogram — is the second part.\n",{"path":8146,"title":8147,"module":8143,"summary":8148},"\u002Fnatural-language-processing\u002Fspeech\u002Facoustic-phonetics","Acoustic Phonetics","Articulation is the cause; the acoustic signal is the effect, and the effect is all a microphone ever gets. This part follows the sound out of the mouth: waves, sampling and the Nyquist limit, F0 and the pitch track, the mel scale, the spectrum and Fourier analysis, the source-filter model that explains why each vowel carries its own formants, and the spectrogram the log-mel front end of every ASR system sits directly on top of — closing with neural TTS, wav2vec, HuBERT, and Whisper, where phonetics went in neural speech.\n",{"path":8150,"title":8151,"module":8143,"summary":8152},"\u002Fnatural-language-processing\u002Fspeech\u002Fautomatic-speech-recognition","Automatic Speech Recognition","Speech recognition maps an acoustic waveform to a string of words, and once the waveform is turned into a sequence of log-mel spectrogram frames the problem is the same sequence-to-sequence transduction the rest of the course already solved. This first part builds the feature front end (framing, the DFT, the mel filterbank, the log), then the modern architectures: the attention-based encoder-decoder, the CTC alignment trick that collapses repeated and blank frames, and RNN-T for streaming. Training-data advances, evaluation, TTS, and the other speech tasks come next.\n",{"path":8154,"title":8155,"module":8143,"summary":8156},"\u002Fnatural-language-processing\u002Fspeech\u002Fasr-evaluation-and-applications","ASR Evaluation and Speech Applications","A recognizer turns a waveform into text; this part scores that text and puts the same machinery to other uses. It opens with the self-supervised and weakly- supervised systems (wav2vec 2.0, HuBERT, Whisper) that made ASR error rates fall. Word error rate reuses the edit distance from the first module, run over words. Text-to-speech runs the whole pipeline in reverse — text to mel spectrogram to waveform. And a family of smaller tasks — wake-word detection, speaker recognition and diarization, language identification — reuse the same log-mel front end without the decoder.\n",{"path":8158,"title":8159,"module":6,"summary":6},"\u002Fnatural-language-processing","Natural Language Processing",{"path":8161,"title":8162,"module":3911,"summary":8163},"\u002Fparticle-physics\u002Ffoundations\u002Fhistorical-overview-particle-zoo","From the Electron to the Particle Zoo","A timeline of the subject, from J. J. Thomson's electron in 1897 to the Higgs boson in 2012. The electron, photon, nucleus, proton, and neutron gave a tidy picture that Yukawa's meson prediction and the muon–pion confusion complicated; strange particles in cosmic rays and the accelerator-era flood of hadrons then produced a \"particle zoo\" that only the quark model organized.\n",{"path":8165,"title":8166,"module":3911,"summary":8167},"\u002Fparticle-physics\u002Ffoundations\u002Fparticle-physics-basic-concepts","Basic Concepts and Particle Classification","Every particle has an antiparticle of equal mass and opposite charge, a consequence of the Dirac equation confirmed by the positron. Feynman diagrams track interactions in spacetime; the material particles sort into leptons and the composite hadrons built from quarks, with baryons carrying three quarks and mesons a quark-antiquark pair.\n",{"path":8169,"title":8170,"module":3911,"summary":8171},"\u002Fparticle-physics\u002Ffoundations\u002Ffundamental-interactions-force-carriers","Fundamental Interactions and Force Carriers","Four interactions account for every force in nature: strong, electromagnetic, weak, and gravitational, in decreasing strength. Each is carried by a boson — the gluon, photon, W and Z, and the graviton — with a range fixed by the carrier's mass through the Yukawa relation, and a coupling constant that itself varies with distance.\n",{"path":8173,"title":8174,"module":8175,"summary":8176},"\u002Fparticle-physics\u002Funits-kinematics\u002Fnatural-units-and-scales","Natural Units and Scales","Units and Kinematics","Setting $\\hbar = c = 1$ collapses mass, momentum, and energy into a single unit, the GeV, and turns lengths and times into inverse energies through the conversion $\\hbar c = 197.3$ MeV·fm. This lesson fixes the natural-unit conventions used for the rest of the course, converts cross sections between barns and GeV$^{-2}$, and shows how to restore factors of $\\hbar$ and $c$ by dimensional analysis.\n",{"path":8178,"title":8179,"module":8175,"summary":8180},"\u002Fparticle-physics\u002Funits-kinematics\u002Ffour-vectors-invariant-mass","Four-Vectors and Invariant Mass","The energy and momentum of a particle form a four-vector whose square is the frame-independent quantity $p^2 = m^2$. This lesson develops the metric and four-vector products, the invariant mass of a multiparticle system, the center-of-momentum and laboratory frames, and the description of collinear boosts by rapidity, whose additivity replaces the awkward velocity-addition law.\n",{"path":8182,"title":8183,"module":8175,"summary":8184},"\u002Fparticle-physics\u002Funits-kinematics\u002Fdecay-scattering-kinematics-mandelstam","Decay, Scattering, and Mandelstam Variables","Two-body decay in the rest frame fixes the daughter momenta from the three masses alone; production thresholds follow from the minimum invariant mass. This lesson works both, then introduces the Mandelstam invariants $s$, $t$, $u$ for $2\\to2$ scattering, proves the identity $s+t+u=\\sum m_i^2$, and maps the physical regions and the crossing that relates channels.\n",{"path":8186,"title":8187,"module":8175,"summary":8188},"\u002Fparticle-physics\u002Funits-kinematics\u002Fcross-sections-golden-rule","Cross Sections and the Golden Rule","The cross section measures how often a scattering happens and the decay width how fast a particle disintegrates. This lesson defines both, relates event rate to luminosity through $R=\\mathcal L\\,\\sigma$ and lifetime to width through $\\tau=\\hbar\u002F\\Gamma$, and states Fermi's golden rule with Lorentz-invariant phase space, giving the master formulas that turn an amplitude $\\mathcal M$ into a measurable rate for $1\\to2$ decay and $2\\to2$ scattering.\n",{"path":8190,"title":8191,"module":8192,"summary":8193},"\u002Fparticle-physics\u002Fsymmetries\u002Fconservation-laws-symmetries","Conservation Laws and Symmetries","Symmetries and Conservation Laws","Which decays occur is decided by conservation laws, each tied by Noether's theorem to a symmetry of physical law. Energy, charge, baryon number, and lepton number are conserved universally; strangeness, isospin, and parity hold in the strong and electromagnetic interactions but break in the weak one, whose parity and CP violation distinguish matter from antimatter.\n",{"path":8195,"title":8196,"module":8192,"summary":8197},"\u002Fparticle-physics\u002Fsymmetries\u002Fdiscrete-symmetries-cpt","Discrete Symmetries — C, P, T, and CPT","Parity reflects space, charge conjugation swaps particle for antiparticle, and time reversal runs the clock backward. Each assigns multiplicative quantum numbers that act as selection rules — intrinsic parities, the photon's C = −1, the C-parity argument fixing the pion's two-photon decay. Their product CPT is a theorem of any local relativistic field theory, forcing particle and antiparticle to share mass and lifetime.\n",{"path":8199,"title":8200,"module":8192,"summary":8201},"\u002Fparticle-physics\u002Fsymmetries\u002Fparity-violation-weak","Parity Violation and the Weak Force","The tau–theta puzzle forced a choice: two particles with identical mass but opposite parity, or one particle whose decay violates parity. Lee and Yang proposed the latter, Wu's polarized cobalt-60 confirmed it, and the violation proved maximal. The charged weak force couples only to left-handed chirality — the Goldhaber experiment showed the neutrino is left-handed — which is why the mirror image of a weak decay is something nature never produces.\n",{"path":8203,"title":8204,"module":8192,"summary":8205},"\u002Fparticle-physics\u002Fsymmetries\u002Fsu2-su3-flavor-symmetry","Isospin, SU(2), and Flavor SU(3)","The near-equal masses of the proton and neutron, and of the three pions, signal a continuous internal symmetry of the strong force: isospin, an SU(2) whose ladder operators move between the members of a multiplet. Adding strangeness enlarges it to an approximate SU(3) flavor symmetry, and the Gell-Mann–Nishijima relation Q = I3 + Y\u002F2 places every hadron on a weight diagram in the isospin–hypercharge plane — the language in which the quark model is written.\n",{"path":8207,"title":8208,"module":8209,"summary":8210},"\u002Fparticle-physics\u002Fquark-model\u002Feightfold-way-su3","The Eightfold Way and SU(3) Flavor","The Quark Model","Gell-Mann and Ne'eman's classification of the hadrons into geometric multiplets, read as representations of an approximate flavor SU(3). The fundamental triplet (u, d, s) and its antitriplet combine into the meson nonet from 3⊗3̄ = 8⊕1 and the baryon octet and decuplet from 3⊗3⊗3, and the empty corner of the decuplet forecast the Ω⁻.\n",{"path":8212,"title":8213,"module":8209,"summary":8214},"\u002Fparticle-physics\u002Fquark-model\u002Fmeson-spectroscopy","Meson Multiplets and Quantum Numbers","Mesons as quark–antiquark bound states. The spin singlet and triplet, orbital excitations, and the assignment of J^PC from the quark spins and orbital angular momentum, giving the pseudoscalar and vector nonets. The η–η' and ω–φ mixing problems, and the charmonium and bottomonium spectra read as heavy-quark positronium.\n",{"path":8216,"title":8217,"module":8209,"summary":8218},"\u002Fparticle-physics\u002Fquark-model\u002Fbaryon-spectroscopy","Baryon Multiplets, Spin, and the Color Puzzle","Baryons as three-quark states, with a wavefunction factored into space, spin, flavor, and color. The spin-3\u002F2 Δ⁺⁺ = uuu forces a totally symmetric state that the Pauli principle forbids, and the resolution is an antisymmetric color factor — the first evidence for color. The octet and decuplet spin content, and baryon magnetic moments as a quantitative test of the model.\n",{"path":8220,"title":8221,"module":8209,"summary":8222},"\u002Fparticle-physics\u002Fquark-model\u002Fcolor-confinement-exotics","Color, Confinement, and Exotic Hadrons","Color as the gauged SU(3) charge, and the requirement that every physical hadron be a color singlet — which selects q-qbar mesons and qqq baryons as the simplest states. The R-ratio of e⁺e⁻ annihilation measures three colors directly. Beyond the simplest singlets lie glueballs, tetraquarks, and pentaquarks, and the recent XYZ states, read as either compact multiquarks or loose hadronic molecules.\n",{"path":8224,"title":8225,"module":8226,"summary":8227},"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fklein-gordon-equation","The Klein-Gordon Equation","Relativistic Wave Equations","Quantizing the relativistic energy relation $E^2 = p^2 + m^2$ produces the Klein-Gordon equation for a scalar field. Its plane-wave solutions come in positive- and negative-energy branches, and the conserved density it supplies is not positive-definite — the two difficulties that first drove physicists to seek a first-order equation. The static Klein-Gordon equation with a point source gives the Yukawa potential, and the free equation gives the scalar propagator that later modules attach to exchanged lines.\n",{"path":8229,"title":8230,"module":8226,"summary":8231},"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fdirac-equation-spinors","The Dirac Equation and Spinors","Dirac demanded a wave equation first order in time to fix the Klein-Gordon density problem. Factorizing $E^2 = p^2 + m^2$ into a linear form forces the coefficients to be anticommuting matrices — the gamma matrices of the Clifford algebra — so the wavefunction becomes a four-component spinor. The plane-wave solutions split into two particle and two antiparticle states, spin appears automatically with the correct $g = 2$ magnetic moment, and the chirality projectors that the weak interaction later needs fall straight out of the fifth gamma matrix.\n",{"path":8233,"title":8234,"module":8226,"summary":8235},"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fantiparticles-hole-theory","Antiparticles and Hole Theory","The negative-energy solutions of the Dirac equation refuse to go away, so they must mean something. Dirac read them as a filled sea of occupied negative-energy states whose holes are positive-energy antiparticles, predicting the positron before its discovery. The picture works for fermions but not bosons, and the Feynman-Stückelberg interpretation replaces it: an antiparticle is a negative-energy solution propagating backward in time, equivalent to a positive-energy antiparticle going forward. Crossing symmetry ties incoming particles to outgoing antiparticles in a single amplitude.\n",{"path":8237,"title":8238,"module":8239,"summary":8240},"\u002Fparticle-physics\u002Fqed\u002Ffeynman-rules-qed","Feynman Rules for QED","Quantum Electrodynamics","Quantum electrodynamics computes a process by summing diagrams, each a term in a power series in the coupling. Every diagram translates into an amplitude by a fixed dictionary: spinors and polarization vectors for external lines, propagators for internal lines, and the vertex factor $ie\\gamma^\\mu$ for each photon-fermion junction. Squaring the amplitude and feeding it to Fermi's golden rule produces a cross section or decay rate, with each extra vertex costing one power of $\\alpha$.\n",{"path":8242,"title":8243,"module":8239,"summary":8244},"\u002Fparticle-physics\u002Fqed\u002Fqed-tree-processes","Tree-Level QED Processes","The Feynman rules become numbers on the reference reactions of QED. Muon pair production $e^+e^-\\to\\mu^+\\mu^-$ sets the scale with its $1+\\cos^2\\theta$ distribution and $4\\pi\\alpha^2\u002F3s$ total cross section, and its ratio to hadron production counts colors. Compton scattering gives the Klein-Nishina formula and the Thomson limit; Bhabha scattering shows $s$- and $t$-channel interference. Casimir's trick turns every spin-averaged square into a trace of gamma matrices.\n",{"path":8246,"title":8247,"module":8239,"summary":8248},"\u002Fparticle-physics\u002Fqed\u002Frenormalization-running-coupling","Renormalization and the Running Coupling","Beyond tree level, QED loops diverge. The three primitive one-loop diagrams — vacuum polarization, electron self-energy, and vertex correction — carry ultraviolet divergences that regularization exposes as logarithms of a cutoff. Renormalization absorbs them into the measured mass, charge, and field normalization, leaving finite predictions. The surviving physical content is that the coupling runs: vacuum polarization screens charge, so $\\alpha$ grows from $1\u002F137$ at low energy to about $1\u002F128$ at the $Z$ mass.\n",{"path":8250,"title":8251,"module":8239,"summary":8252},"\u002Fparticle-physics\u002Fqed\u002Felectron-g-2","The Anomalous Magnetic Moment","The Dirac equation predicts $g=2$; loops shift it. Schwinger's one-loop vertex correction gives the anomaly $a=(g-2)\u002F2=\\alpha\u002F2\\pi$, and the QED series continues to five loops. The electron $a_e$ agrees with theory to better than a part in a billion, the most precise confrontation of theory and experiment in physics. The muon $a_\\mu$, heavier and so more sensitive to virtual heavy states, is dominated by hadronic uncertainty and sits at the center of a long-running comparison with the Standard Model prediction.\n",{"path":8254,"title":8255,"module":8256,"summary":8257},"\u002Fparticle-physics\u002Fweak-interaction\u002Fva-structure-weak","The V–A Charged Weak Current","The Weak Interaction","Fermi modelled beta decay as a four-fermion contact interaction, but a coupling with dimensions of inverse mass squared makes cross sections grow without bound and the theory fails near 300 GeV. The cure is a heavy mediator: the $W$ boson, whose propagator collapses to Fermi's contact term at low energy and fixes $G_F\u002F\\sqrt2 = g^2\u002F8M_W^2$. Parity violation dictates the current's form — vector minus axial-vector, coupling only to left-chiral fields — and universality of the coupling ties muon decay, beta decay, and pion decay to one constant. Pion decay's helicity suppression of the electron channel is the sharpest test.\n",{"path":8259,"title":8260,"module":8256,"summary":8261},"\u002Fparticle-physics\u002Fweak-interaction\u002Fw-z-bosons-decays","The W and Z Bosons","The contact theory hides a massive mediator. The charged $W^\\pm$ carries the current that changes flavour; the neutral $Z^0$ carries a current that does not. Both were found at CERN's proton–antiproton collider in 1983 at the masses the electroweak theory demanded. Their decay widths partition into leptonic and hadronic channels, and the $Z$ carries a decisive extra: an invisible width from decays to neutrinos that counts the number of light generations at exactly three. Beta decay and muon decay are re-read at the parton level as $W$ exchange.\n",{"path":8263,"title":8264,"module":8256,"summary":8265},"\u002Fparticle-physics\u002Fweak-interaction\u002Fckm-matrix","Quark Mixing and the CKM Matrix","The quark eigenstates the weak force acts on are not the mass eigenstates. Cabibbo captured this with one rotation angle; the GIM mechanism added a fourth quark to cancel dangerous flavour-changing neutral currents and predicted charm before its discovery. Three generations promote the rotation to the unitary Cabibbo–Kobayashi–Maskawa matrix — three angles and one irreducible complex phase, the sole source of Standard-Model CP violation. The Wolfenstein parametrization exposes its steep hierarchy, and unitarity closes into a triangle whose area measures the phase.\n",{"path":8267,"title":8268,"module":8256,"summary":8269},"\u002Fparticle-physics\u002Fweak-interaction\u002Fcp-violation-kaons-b-mesons","CP Violation in Kaons and B Mesons","The neutral kaon is its own laboratory for CP. Weak box diagrams mix $K^0$ and its antiparticle into short- and long-lived states that should be pure CP eigenstates decaying to two and three pions. In 1964 Cronin and Fitch caught the long-lived kaon decaying to two pions — CP is violated, at the two-per-mille level of $\\epsilon$. Direct violation ($\\epsilon'$) followed, and the $B$ factories turned the CKM phase into a large, clean time-dependent asymmetry measuring $\\sin 2\\beta$. The effect is real but far too small to explain why the universe is made of matter.\n",{"path":8271,"title":8272,"module":8273,"summary":8274},"\u002Fparticle-physics\u002Fqcd\u002Fcolor-su3-gluons","Color SU(3), Gluons, and the QCD Lagrangian","Quantum Chromodynamics","Color is the exact gauged SU(3) charge of the strong force. Gauging it forces eight massless gluons in the adjoint representation and, because the gauge group is non-abelian, three- and four-gluon self-couplings absent from QED. This lesson builds the QCD Lagrangian from the covariant derivative and the non-abelian field strength, states the Feynman rules with their color factors, and computes the Casimir invariants that set the strength of quark-gluon and gluon-gluon coupling.\n",{"path":8276,"title":8277,"module":8273,"summary":8278},"\u002Fparticle-physics\u002Fqcd\u002Fasymptotic-freedom-confinement","Asymptotic Freedom and Confinement","The QCD beta function is negative: gluon self-interaction antiscreens color, so the coupling weakens at short distance (asymptotic freedom) and strengthens at long distance (confinement). This lesson computes the one-loop beta coefficient, solves for the running of alpha_s and the emergent scale Lambda_QCD, and reads the strong-coupling regime as the linear quark-antiquark potential of a color flux tube that breaks by pair creation.\n",{"path":8280,"title":8281,"module":8273,"summary":8282},"\u002Fparticle-physics\u002Fqcd\u002Fdeep-inelastic-scattering-partons","Deep Inelastic Scattering and the Parton Model","Scattering electrons hard off a proton resolves pointlike constituents. This lesson sets up the deep-inelastic kinematics, defines the structure functions F1 and F2, and reads Bjorken scaling as the signature of free spin-half partons. The Callan-Gross relation fixes the parton spin, the structure function becomes a charge-weighted sum of parton distributions, and the slow logarithmic scaling violations expose the gluon through DGLAP evolution.\n",{"path":8284,"title":8285,"module":8273,"summary":8286},"\u002Fparticle-physics\u002Fqcd\u002Fjets-hadronization","Jets, Hadronization, and Testing QCD","Quarks and gluons produced in a collision fragment into collimated sprays of hadrons — jets — whose directions track the underlying partons. This lesson reads two-jet events as the quark and antiquark of electron-positron annihilation, three-jet events as direct evidence of the radiated gluon, and the hadronization step as the flux tube breaking into color singlets. Jet algorithms and event-shape variables turn the pattern into precision measurements of alpha_s.\n",{"path":8288,"title":8289,"module":8290,"summary":8291},"\u002Fparticle-physics\u002Felectroweak-higgs\u002Felectroweak-su2-u1","The Electroweak Theory","Electroweak Unification and the Higgs","The electromagnetic and weak interactions are two faces of a single gauge theory built on $SU(2)_L \\times U(1)_Y$. Left-handed fermions sit in weak-isospin doublets and right-handed fermions in singlets, each carrying a hypercharge fixed by the Gell-Mann–Nishijima relation $Q = T_3 + Y\u002F2$. The four gauge fields $W^{1,2,3}$ and $B$ mix: the charged combinations $W^\\pm$ mediate the charged current, while $W^3$ and $B$ rotate through the Weinberg angle into the massless photon and the massive $Z$. The single angle $\\theta_W$ ties the couplings, the boson masses, and the neutral-current strengths together.\n",{"path":8293,"title":8294,"module":8290,"summary":8295},"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fspontaneous-symmetry-breaking","Spontaneous Symmetry Breaking","A symmetry of the Lagrangian need not be a symmetry of the ground state. When the lowest-energy configuration sits away from the symmetric point, the symmetry is spontaneously broken and the vacuum is one of a degenerate family. Breaking a continuous global symmetry produces one massless scalar — a Goldstone boson — for every broken generator, the flat direction along the vacuum manifold. The Mexican-hat potential and the ferromagnet below its Curie point are the working pictures.\n",{"path":8297,"title":8298,"module":8290,"summary":8299},"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-mechanism","The Higgs Mechanism","Gauging a spontaneously broken symmetry converts the would-be Goldstone bosons into the longitudinal polarizations of the gauge fields, which thereby acquire mass. Applied to $SU(2)_L \\times U(1)_Y$ with a single Higgs doublet, three of the four scalar degrees of freedom are eaten by the $W^\\pm$ and $Z$; the fourth survives as the physical Higgs boson, and the photon stays massless. Fermion masses come from Yukawa couplings to the same field, each mass proportional to its coupling times the vacuum expectation value $v \\approx 246$ GeV.\n",{"path":8301,"title":8302,"module":8290,"summary":8303},"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-boson-discovery","The Higgs Boson","The Higgs boson is produced at the LHC chiefly through gluon fusion, with vector-boson fusion and associated production as cleaner but rarer channels. It decays most often to $b\\bar b$ and $WW^\\ast$, but the discovery rested on two rare clean modes, $H \\to \\gamma\\gamma$ and $H \\to ZZ^\\ast \\to 4\\ell$, whose narrow invariant-mass peaks emerged over smooth backgrounds. ATLAS and CMS announced a boson near 125 GeV in 2012; its measured spin-parity $0^+$ and its couplings, which scale with particle mass, identify it as the Standard Model Higgs.\n",{"path":8305,"title":8306,"module":8290,"summary":8307},"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fstandard-model","The Standard Model","The Standard Model combines the quark model, quantum chromodynamics, and the electroweak theory. SU(3) symmetry sorts the hadrons and predicted the omega; color explains why only colorless quark combinations exist; QCD gives asymptotic freedom and confinement; and spontaneous symmetry breaking through the Higgs field gives the weak bosons their mass.\n",{"path":8309,"title":8310,"module":8311,"summary":8312},"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-oscillations","Neutrino Oscillations","Neutrino Physics","Neutrinos are produced and detected in flavour states, but they propagate as mass states, and the two bases are misaligned. A flavour therefore evolves coherently into a superposition of other flavours with a probability set by the mass-squared splitting and the ratio L\u002FE. This lesson derives the two-flavour oscillation formula, applies it to the solar and atmospheric neutrino deficits, shows how the SNO neutral-current measurement resolved the solar problem, and works out the MSW resonance that amplifies mixing inside the Sun.\n",{"path":8314,"title":8315,"module":8311,"summary":8316},"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-mass-pmns","Neutrino Mass and the PMNS Matrix","Three-flavour mixing promotes the single oscillation angle to the unitary Pontecorvo–Maki–Nakagawa–Sakata matrix, parametrised by three angles and a Dirac CP phase. This lesson decomposes the PMNS matrix into three rotations, records the measured angles and mass-squared splittings, lays out the normal and inverted mass orderings, contrasts the large leptonic mixing with the near-diagonal CKM matrix, and collects the absolute-mass bounds from beta decay and cosmology.\n",{"path":8318,"title":8319,"module":8311,"summary":8320},"\u002Fparticle-physics\u002Fneutrinos\u002Fdirac-majorana-experiments","Dirac, Majorana, and Neutrino Experiments","A neutral fermion can carry a mass term forbidden to every charged particle, so the neutrino may be its own antiparticle. This lesson contrasts the Dirac and Majorana mass terms and their state content, derives the seesaw mechanism that ties a tiny light mass to a heavy right-handed partner, presents neutrinoless double-beta decay as the decisive lepton-number test, surveys the reactor, accelerator, solar, and atmospheric sources on a baseline–energy map, and explains why neutrino mass is physics beyond the original Standard Model.\n",{"path":8322,"title":8323,"module":8324,"summary":8325},"\u002Fparticle-physics\u002Fexperiment\u002Faccelerators-luminosity","Accelerators, Colliders, and Luminosity","Accelerators and Detectors","Fixed-target machines waste energy in the center-of-mass motion of the whole system, so the reachable $\\sqrt s$ grows only as the square root of the beam energy, while colliders put the full beam energy into the collision. Circular electron machines are limited by synchrotron radiation scaling as $E^4\u002Fm^4R$; proton machines are limited by bending fields. Luminosity, set by beam current and focusing, converts a cross section into an event rate through $R=\\mathcal L\\,\\sigma$, and integrated luminosity sets the total event count.\n",{"path":8327,"title":8328,"module":8324,"summary":8329},"\u002Fparticle-physics\u002Fexperiment\u002Fdetectors-subsystems","Particle Detectors and Subsystems","A detector reads a collision by the energy particles deposit as they cross matter. Charged particles ionize at the Bethe-Bloch rate, radiate in the field of nuclei above a critical energy, and emit Cherenkov light above a velocity threshold; electrons and photons build electromagnetic showers over a radiation length, and hadrons build wider showers over a nuclear interaction length. The onion of tracker, electromagnetic and hadronic calorimeters, and outer muon chambers turns these processes into momentum, energy, and identity, with neutrinos inferred from missing transverse momentum.\n",{"path":8331,"title":8332,"module":8324,"summary":8333},"\u002Fparticle-physics\u002Fexperiment\u002Fhow-discoveries-are-made","From Collisions to Discoveries","A discovery is a peak that survives statistics. Events are reconstructed into invariant masses, a signal accumulates as a bump over a smooth background, and its significance is judged by a p-value; the field's threshold is five sigma. The expected yield is a product — luminosity times cross section times branching ratio times acceptance and efficiency — that must be balanced by a trigger and data-reduction chain against an overwhelming rate. Worked reconstructions of $Z\\to\\ell\\ell$, the $J\u002F\\psi$, and the Higgs show the same peak-over-background logic at three scales.\n",{"path":8335,"title":8336,"module":8336,"summary":8337},"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fbeyond-standard-model","Beyond the Standard Model","The Standard Model leaves the four interactions ununified and the neutrinos massless, both now known to be wrong. Grand unification predicts the couplings merge near ten-to-the-sixteen GeV and the proton decays; supersymmetry pairs each particle with a superpartner; and the confirmed oscillation of neutrinos proves they carry mass, the first crack in the model.\n",{"path":8339,"title":8340,"module":8336,"summary":8341},"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fgrand-unified-theories","Grand Unified Theories and Proton Decay","The Standard Model gauge group is a product of three factors with three independent couplings. A grand unified theory embeds them in a single simple group — SU(5) is the minimal choice — so that one coupling runs into all three and the fractional quark charges follow from a tracelessness condition. The same embedding places quarks and leptons in shared multiplets, mediates baryon-number violation through superheavy gauge bosons, and predicts the proton decays with a lifetime that Super-Kamiokande has pushed past ten-to-the-thirty-four years.\n",{"path":8343,"title":8344,"module":8336,"summary":8345},"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fsupersymmetry","Supersymmetry","Supersymmetry relates fermions and bosons, pairing every Standard Model particle with a superpartner whose spin differs by one half. The pairing makes the scalar and fermion loop corrections to the Higgs mass cancel, removing the quadratic sensitivity to high scales; it sharpens the meeting of the three gauge couplings; and, when R-parity is conserved, it leaves the lightest superpartner stable and neutral, a natural dark-matter candidate. The LHC has excluded gluinos and light squarks below roughly two TeV.\n",{"path":8347,"title":8348,"module":8336,"summary":8349},"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fhierarchy-problem-naturalness","The Hierarchy Problem and Naturalness","The electroweak scale sits sixteen orders of magnitude below the Planck scale, and nothing in the Standard Model protects that gap. The Higgs mass squared picks up quadratic corrections proportional to the highest scale in the theory, so keeping it at the observed value requires the bare mass and its counterterm to cancel to some thirty significant figures. Naturalness treats that cancellation as a symptom of missing physics. Supersymmetry, compositeness, and extra dimensions each remove the quadratic sensitivity, but the LHC has found none of them at the predicted scale.\n",{"path":8351,"title":8352,"module":8336,"summary":8353},"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fdark-matter-candidates","Dark Matter and Particle Candidates","Flat galactic rotation curves, gravitational lensing, the cosmic microwave background, and structure formation all require about five times more matter than the visible baryons, none of it interacting electromagnetically. A stable weakly interacting particle of roughly weak-scale mass freezes out of the early universe with close to the observed abundance — the WIMP miracle — and is the leading candidate, with axions and sterile neutrinos as alternatives. Direct, indirect, and collider searches have so far only tightened the limits.\n",{"path":8355,"title":8356,"module":8336,"summary":8357},"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fmatter-antimatter-open-questions","Matter-Antimatter Asymmetry and Open Questions","The universe is made of matter, with about one extra baryon for every billion photons and no antimatter regions. Sakharov identified the three conditions any dynamical explanation must meet: baryon-number violation, C and CP violation, and a departure from thermal equilibrium. The Standard Model contains all three in principle, but its CP violation falls short by some ten orders of magnitude, so baryogenesis requires new physics — leptogenesis being the leading route. A closing survey collects the open questions and the experiments aimed at them.\n",{"path":8359,"title":8360,"module":6,"summary":6},"\u002Fparticle-physics","Particle Physics",{"path":8362,"title":8363,"module":8364,"summary":8365},"\u002Fastrophysics-cosmology\u002Forientation\u002Fthe-sun-and-stars","The Sun and the Life of Stars","Orientation","The Sun is the one star close enough to study in detail: its luminosity fixes a surface temperature of 5780 K, and the proton-proton fusion cycle in its 1.5-million-kelvin core supplies its power. Measuring other stars needs the magnitude scale, parallax, and the distance ladder; plotting luminosity against temperature builds the Hertzsprung-Russell diagram, on which a star's mass sets its lifetime and its evolutionary track off the main sequence.\n",{"path":8367,"title":8368,"module":8364,"summary":8369},"\u002Fastrophysics-cosmology\u002Forientation\u002Fstellar-death-final-states","Cataclysmic Events and the Final States of Stars","A star's death is set by its mass. In close binaries, matter poured across the Roche lobe onto a white dwarf produces novae and, at the Chandrasekhar limit of 1.4 solar masses, a Type Ia supernova; a massive star fusing to an iron core collapses into a Type II supernova. The remnant is a white dwarf held by electron degeneracy, a neutron star held by neutron degeneracy, or, above the neutron-star limit, a black hole inside its Schwarzschild radius.\n",{"path":8371,"title":8372,"module":8364,"summary":8373},"\u002Fastrophysics-cosmology\u002Forientation\u002Fgalaxies-and-cosmology","Galaxies, Cosmology, and the Evolving Universe","Galaxies come in elliptical, spiral, and irregular forms, and their redshifts obey Hubble's law, evidence that space itself is expanding. The critical density and the density parameter decide whether the universe is open, flat, or closed; baryons, dark matter, and dark energy each contribute. The cosmic microwave background and primordial helium anchor the Big Bang, whose thermal history runs from inflation through nucleosynthesis to the atoms of today.\n",{"path":8375,"title":8376,"module":8377,"summary":8378},"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fmagnitudes-fluxes-and-the-distance-modulus","Magnitudes, Fluxes, and the Distance Modulus","Observational Foundations","The brightness of a star reaches us as a radiant flux that falls off as the inverse square of distance. The magnitude scale encodes flux logarithmically through the Pogson ratio; the apparent and absolute magnitudes differ by the distance modulus, which converts a measured brightness into a distance. The bolometric correction folds a filtered magnitude into a total luminosity, and the difference of two magnitudes in different bands, the color index, measures surface temperature.\n",{"path":8380,"title":8381,"module":8377,"summary":8382},"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fstellar-spectra-and-spectral-classification","Stellar Spectra and Spectral Classification","A stellar spectrum is a continuum crossed by absorption lines whose strengths are set by the temperature of the atmosphere. The Boltzmann factor governs how atoms populate excited states, and the Saha equation governs how they ionize; their product explains why each line, such as the hydrogen Balmer series, peaks in strength at a characteristic temperature. This behavior orders stars into the OBAFGKM sequence, and the luminosity classes of the MK system add a second dimension for surface gravity.\n",{"path":8384,"title":8385,"module":8377,"summary":8386},"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Ftelescopes-and-detectors-across-the-spectrum","Telescopes and Detectors Across the Spectrum","A telescope collects light in proportion to its collecting area and resolves detail down to the diffraction limit set by its aperture and the observing wavelength. The atmosphere blurs and blocks large parts of the spectrum, which drives the choice between ground and space and between refractors, reflectors, and radio dishes. CCDs record the light with high quantum efficiency, and interferometry synthesizes an aperture as large as the separation of two telescopes.\n",{"path":8388,"title":8389,"module":8377,"summary":8390},"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fthe-cosmic-distance-ladder","The Cosmic Distance Ladder","No single method measures distances from the nearest stars to the far reaches of the universe. Instead a ladder of overlapping techniques, each calibrated by the one below it, extends the scale rung by rung: trigonometric parallax, main-sequence fitting, pulsating variables, the tip of the red-giant branch, the Tully-Fisher relation, and Type Ia supernovae. Each rung inherits the uncertainty of every rung beneath it, so the whole chain sets the accuracy of the Hubble constant.\n",{"path":8392,"title":8393,"module":8394,"summary":8395},"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fblackbody-radiation-and-specific-intensity","Blackbody Radiation and Specific Intensity","Radiation and Matter","Specific intensity is the fundamental measure of a radiation field: energy per unit area, time, frequency, and solid angle. It is conserved along a ray in empty space, and its angular moments give the mean intensity, flux, and radiation pressure. In thermal equilibrium the intensity equals the Planck function, whose limits and integrals reproduce the Rayleigh-Jeans law, the Wien law, Stefan-Boltzmann, and Wien's displacement law.\n",{"path":8397,"title":8398,"module":8394,"summary":8399},"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fradiative-transfer-and-the-transfer-equation","Radiative Transfer and the Transfer Equation","Along a ray, matter adds intensity through emission and removes it through absorption. Measuring path length in optical depth turns this into the transfer equation, whose formal solution superposes an attenuated background on the source function integrated along the line of sight. In local thermodynamic equilibrium the source function is the Planck function, and the Eddington-Barbier relation shows that the emergent intensity samples the source function at optical depth of order unity, explaining absorption lines and solar limb darkening.\n",{"path":8401,"title":8402,"module":8394,"summary":8403},"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fspectral-line-formation-and-broadening","Spectral-Line Formation and Broadening","A spectral line is a bound-bound transition whose strength is set by an oscillator strength and whose shape is set by three broadening mechanisms: the Lorentzian natural and collisional wings, the Gaussian thermal Doppler core, and their Voigt convolution. Equivalent width measures the total absorption, and the curve of growth relates it to the number of absorbers through a linear, saturated, and damping regime, turning line strengths into abundances.\n",{"path":8405,"title":8406,"module":8394,"summary":8407},"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fopacity-and-the-rosseland-mean","Opacity Sources and the Rosseland Mean","Stellar opacity comes from four processes: bound-bound line absorption, bound-free photoionization, free-free absorption, and electron scattering. The bound-free and free-free terms follow a Kramers law, electron scattering sets a frequency-flat floor, and the negative hydrogen ion dominates cool photospheres. The Rosseland mean averages these harmonically, weighting transparent frequencies because they carry the flux, and its value fixes the radiative temperature gradient and decides where a star becomes convective.\n",{"path":8409,"title":8410,"module":8411,"summary":8412},"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fhydrostatic-equilibrium-and-the-virial-theorem","Hydrostatic Equilibrium and the Virial Theorem","Stellar Structure","A star holds itself up by balancing the inward pull of gravity against an outward pressure gradient. This balance, hydrostatic equilibrium, fixes a lower bound on the central pressure and, combined with the gravitational potential energy, yields the virial theorem. The virial relation gives a star a negative heat capacity, so that losing energy makes it hotter, and sets the Kelvin-Helmholtz timescale over which contraction alone can power the Sun.\n",{"path":8414,"title":8415,"module":8411,"summary":8416},"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equations-of-stellar-structure","The Equations of Stellar Structure","A static star is described by four coupled first-order differential equations in the interior mass or radius: mass conservation, hydrostatic equilibrium, energy generation, and energy transport. Closed with an equation of state, opacity, and reaction rates, and subject to central and surface boundary conditions, they determine the structure uniquely from mass and composition, the Vogt-Russell theorem. Energy moves by radiation until the temperature gradient exceeds the Schwarzschild limit, where convection takes over.\n",{"path":8418,"title":8419,"module":8411,"summary":8420},"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equation-of-state-and-polytropes","The Equation of State and Polytropes","Stellar pressure comes from gas, radiation, and, at high density, degenerate electrons. When pressure depends on density as a power law, hydrostatic equilibrium reduces to the Lane-Emden equation, whose solutions describe polytropes of index n. The relativistic degenerate case, n equal to three, gives a mass independent of radius, the Chandrasekhar mass. Eddington's standard model treats a radiation-supported star as an n equal to three polytrope and yields the quartic relating radiation fraction to mass.\n",{"path":8422,"title":8423,"module":8411,"summary":8424},"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-standard-solar-model","The Standard Solar Model","The standard solar model integrates the structure equations for one solar mass and calibrates the composition and convection parameter to reproduce the Sun's observed luminosity, radius, and age. Helioseismology tests the model's sound speed through the Sun's acoustic p-mode oscillations, and the model predicts a neutrino flux by production channel. The measured deficit, the solar-neutrino problem, is resolved by matter-enhanced flavor oscillation, confirmed when SNO measured the total flux across all flavors.\n",{"path":8426,"title":8427,"module":8428,"summary":8429},"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fthermonuclear-reaction-rates-and-the-gamow-peak","Thermonuclear Reaction Rates and the Gamow Peak","Nuclear Astrophysics","Stellar fusion proceeds only by quantum tunneling through the Coulomb barrier, because thermal energies are a thousand times smaller than the barrier height. The reaction rate is an integral over the Maxwell–Boltzmann distribution and the tunneling probability, whose product is sharply peaked at the Gamow energy. The astrophysical S-factor isolates the nuclear physics from the barrier penetration, and the steep temperature dependence follows from the width and position of the Gamow peak.\n",{"path":8431,"title":8432,"module":8428,"summary":8433},"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhydrogen-burning-pp-chains-and-cno","Hydrogen Burning: pp Chains and the CNO Cycle","Four protons fuse into one helium-4 nucleus, releasing 26.7 MeV, through two competing networks. The pp chain begins with a weak-interaction bottleneck and branches three ways; the CNO cycle uses carbon, nitrogen, and oxygen as catalysts and is limited by nitrogen-14 proton capture. Their steep and gentle temperature dependences cross near 1.8e7 K, which divides pp-powered lower-main-sequence stars from CNO-powered upper-main-sequence stars.\n",{"path":8435,"title":8436,"module":8428,"summary":8437},"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhelium-burning-and-the-triple-alpha-process","Helium Burning and the Triple-Alpha Process","Helium fuses to carbon in two steps through the unbound beryllium-8 nucleus and a resonant excited state of carbon-12, the Hoyle state, whose existence was predicted from the observed carbon abundance. The rate scales as roughly the fortieth power of temperature, and in a degenerate low-mass core this drives the runaway helium flash. A competing alpha capture on carbon-12 sets the carbon-to-oxygen ratio and the composition of the resulting white dwarf.\n",{"path":8439,"title":8440,"module":8428,"summary":8441},"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fadvanced-burning-and-neutron-capture-nucleosynthesis","Advanced Burning, the Iron Peak, and the s\u002Fr Processes","Massive stars burn carbon, neon, oxygen, and silicon in ever-shorter stages, building an onion-shell interior and reaching nuclear statistical equilibrium at the iron peak, where the binding-energy-per-nucleon curve turns over and fusion can release no more energy. Elements beyond iron form by neutron capture: the slow s-process in AGB stars tracks the valley of stability, while the rapid r-process in supernovae and neutron-star mergers builds the heaviest nuclei far from it.\n",{"path":8443,"title":8444,"module":8445,"summary":8446},"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fphases-of-the-interstellar-medium","The Phases of the Interstellar Medium","The Interstellar Medium","The gas between the stars separates into distinct thermal phases, from cold molecular clouds at 10 K to a diffuse million-degree corona, held near a common pressure by a balance of photoelectric heating and radiative cooling. Neutral hydrogen is traced by the 21-cm hyperfine line, dust reddens and extinguishes starlight along a characteristic wavelength law, and the ultraviolet output of hot stars carves ionized Strömgren spheres out of the surrounding gas.\n",{"path":8448,"title":8449,"module":8445,"summary":8450},"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fmolecular-clouds-and-gravitational-collapse","Molecular Clouds and Gravitational Collapse","Stars form in cold, dense molecular clouds when self-gravity overcomes thermal and magnetic support. The virial theorem fixes the Jeans mass and length at which a clump becomes unstable, the free-fall time sets how fast it collapses, and a fragmentation cascade — cut off at a minimum mass by the onset of opacity — turns one cloud into a whole cluster, imprinting the stellar initial mass function.\n",{"path":8452,"title":8453,"module":8445,"summary":8454},"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fprotostars-and-the-pre-main-sequence","Protostars and Pre-Main-Sequence Evolution","A collapsing core becomes optically thick and forms a protostar that grows by accretion through a disk while driving bipolar outflows. The newborn star appears on the birthline and contracts down the fully convective Hayashi track, then crosses the radiative Henyey track to the zero-age main sequence, powered by gravitational contraction until hydrogen ignites. Below about 0.08 solar masses degeneracy halts contraction before ignition, dividing stars from brown dwarfs.\n",{"path":8456,"title":8457,"module":8458,"summary":8459},"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-main-sequence-and-its-structure","The Main Sequence and Its Structure","Stellar Evolution","A star settles onto the zero-age main sequence when core hydrogen ignition halts contraction. Homology scaling of the structure equations reproduces the mass–luminosity relation, and the burning mode splits the sequence into an upper branch with a convective core and a lower branch with a convective envelope. The main-sequence lifetime falls steeply with mass, and the turnoff of a coeval cluster serves as a clock.\n",{"path":8461,"title":8462,"module":8458,"summary":8463},"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fpost-main-sequence-low-mass-evolution","Post-Main-Sequence Evolution of Low-Mass Stars","When a low-mass star exhausts core hydrogen, burning moves to a shell, the core contracts, and the envelope swells into a red giant. A degenerate helium core ignites in a flash, settles onto the horizontal branch, and after a second contraction the star climbs the asymptotic giant branch with two burning shells. Thermal pulses and dredge-up enrich the surface, and mass loss ejects a planetary nebula, leaving a carbon–oxygen white dwarf.\n",{"path":8465,"title":8466,"module":8458,"summary":8467},"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-evolution-of-massive-stars","The Evolution of Massive Stars","Stars above about eight solar masses burn through hydrogen, helium, carbon, neon, oxygen, and silicon in stages that grow shorter as neutrino losses accelerate contraction. The interior becomes an onion of concentric burning shells around an inert iron core. Radiation pressure near the Eddington limit drives fierce winds that can strip the hydrogen envelope entirely, and silicon burning builds an iron core toward the threshold of collapse.\n",{"path":8469,"title":8470,"module":8458,"summary":8471},"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fstellar-pulsation-and-the-instability-strip","Stellar Pulsation and the Instability Strip","Radial pulsation is a standing sound wave whose period scales inversely with the square root of the mean density. The kappa mechanism, an opacity valve seated in the helium partial-ionization zone, turns a star into a heat engine that pumps the oscillation. Stars in the instability strip pulsate as Cepheids, RR Lyrae, and Mira variables, and the Cepheid period–luminosity relation calibrates the distance ladder.\n",{"path":8473,"title":6898,"module":8474,"summary":8475},"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fwhite-dwarfs-and-the-chandrasekhar-limit","Stellar Death and Compact Remnants","A white dwarf is held up by the degeneracy pressure of its electrons, a quantum-mechanical stiffness that survives to zero temperature. Filling the Fermi sea sets a pressure that scales as density to the five-thirds power when the electrons are slow and only four-thirds when they are relativistic. The softer relativistic law produces the inverted mass-radius relation and a maximum mass, the Chandrasekhar limit near 1.4 solar masses, above which no cold equilibrium exists. Cooling and crystallization then turn the white-dwarf population into a clock for the Galactic disk.\n",{"path":8477,"title":8478,"module":8474,"summary":8479},"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fcore-collapse-supernovae","Core-Collapse Supernovae","When a massive star builds an iron core past the Chandrasekhar mass, degeneracy fails and the core collapses in less than a second. Photodisintegration and electron capture remove pressure support and neutronize the matter; the collapse halts abruptly at nuclear density, launching a shock that stalls and is revived by neutrino heating. The event is a Type II or stripped-envelope Ib\u002FIc supernova, and the neutrinos from SN 1987A confirmed the picture directly.\n",{"path":8481,"title":8482,"module":8474,"summary":8483},"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fthermonuclear-supernovae-type-ia","Thermonuclear Supernovae","A carbon-oxygen white dwarf driven toward the Chandrasekhar mass ignites its degenerate fuel and unbinds itself in a thermonuclear runaway, the Type Ia supernova. The light curve is powered by the radioactive decay of nickel-56 to cobalt-56 to iron-56, and the Phillips relation between peak brightness and decline rate makes these events standardizable candles. Their near-uniform luminosity turns them into the distance indicators that revealed cosmic acceleration.\n",{"path":8485,"title":8486,"module":8474,"summary":8487},"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fneutron-stars-and-pulsars","Neutron Stars and Pulsars","A neutron star is held up by neutron degeneracy and the repulsive nuclear force, with a maximum mass, the Tolman-Oppenheimer-Volkoff limit, set by an uncertain dense-matter equation of state. Its rotating magnetic dipole sweeps a beam past Earth as a pulsar, and magnetic braking traces a track across the period-period- derivative diagram. Millisecond pulsars, magnetars, glitches, and the orbital decay of the Hulse-Taylor binary follow from the same structure.\n",{"path":8489,"title":8490,"module":8474,"summary":8491},"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fblack-holes-schwarzschild-and-kerr","Black Holes, Schwarzschild and Kerr","Above the neutron-star mass limit gravity wins completely and the remnant is a black hole. The Schwarzschild solution gives the event horizon, gravitational redshift, and time dilation; the innermost stable circular orbit sets the efficiency of accretion. Rotating Kerr black holes drag spacetime and carry an ergosphere. Stellar-mass black holes are found in X-ray binaries, and the Event Horizon Telescope has imaged the shadow of a supermassive one.\n",{"path":8493,"title":8494,"module":8495,"summary":8496},"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fbinary-systems-and-mass-transfer","Binary Systems and Mass Transfer","Binaries and Gravitational Waves","Most stars are born in pairs, and a binary is the only setting where a stellar mass can be measured directly. Visual, spectroscopic, and eclipsing binaries each expose a different combination of the orbital elements, and together they calibrate the mass-luminosity relation. When one star swells to fill its Roche lobe, gas streams through the inner Lagrange point onto its companion. Conservative transfer widens or shrinks the orbit depending on the mass ratio, and the sign of that response explains the Algol paradox.\n",{"path":8498,"title":8499,"module":8495,"summary":8500},"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Faccreting-compact-objects","Accreting Compact Objects","Gas falling onto a compact object converts gravitational binding energy into radiation with an efficiency set by the depth of the potential well, up to tens of percent of the rest mass for a neutron star or black hole. Angular momentum forces the flow into a disk, and viscous dissipation gives a temperature profile that falls as radius to the minus three-quarters, producing a multicolor blackbody spectrum. Radiation pressure caps the steady luminosity at the Eddington limit. Unstable nuclear burning of the accreted fuel powers classical novae on white dwarfs and Type I X-ray bursts on neutron stars.\n",{"path":8502,"title":8503,"module":8495,"summary":8504},"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fgravitational-waves-from-inspiraling-binaries","Gravitational Waves from Inspiraling Binaries","A time-varying mass quadrupole radiates gravitational waves, ripples in spacetime that stretch and squeeze a ring of free masses along two polarizations. The radiated power drains a binary's orbital energy, shrinking the orbit and sweeping the wave frequency upward in a chirp whose rate fixes the chirp mass. Laser interferometers with kilometre arms measure the resulting strain of order ten to the minus twenty-one. The first detection, GW150914, matched a template for two merging black holes near thirty solar masses each.\n",{"path":8506,"title":8507,"module":8495,"summary":8508},"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fmultimessenger-astronomy-and-gamma-ray-bursts","Multimessenger Astronomy and Gamma-Ray Bursts","Gamma-ray bursts split into two populations: long bursts from the collapse of massive stars and short bursts from merging compact objects. The compactness problem forces the emitting plasma to move at ultra-relativistic speed, beaming the radiation into a narrow jet. The neutron-star merger GW170817 tied a gravitational chirp to a short gamma-ray burst, a radioactive kilonova, and a broadband afterglow, confirming that mergers forge r-process elements. A merger with a measured redshift is a standard siren that reads the Hubble constant from gravitational data alone.\n",{"path":8510,"title":8511,"module":8512,"summary":8513},"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fthe-milky-way","The Milky Way Galaxy","Galaxies and Dark Matter","The Galaxy resolves into a thin disk of gas and young stars, a central bar and bulge, and a diffuse old halo studded with globular clusters. Star counts and the reddening of distant light map these components, while the differential rotation of the disk — encoded in the Oort constants and the flat rotation curve — measures the enclosed mass and reveals more than the stars can account for. Spiral arms are density waves, not material structures, and the innermost stellar orbits around Sgr A* weigh a four-million-solar-mass black hole.\n",{"path":8515,"title":8516,"module":8512,"summary":8517},"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-morphology-and-classification","Galaxy Morphology and Classification","Galaxies sort along the Hubble tuning fork from smooth ellipticals through lenticulars to grand-design and barred spirals, with irregulars off the end. The light of a spheroid follows the de Vaucouleurs quarter-power law while a disk fades exponentially, and the general Sérsic profile interpolates between them. Virial scaling relations — Tully–Fisher for disks, Faber–Jackson and the fundamental plane for spheroids — tie luminosity to internal motions, and the Schechter function fixes the abundance of galaxies as a function of luminosity.\n",{"path":8519,"title":8520,"module":8512,"summary":8521},"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-rotation-curves-and-dark-matter","Galaxy Rotation Curves and Dark Matter","The rotation curves of disk galaxies stay flat far beyond the light, demanding an extended halo whose density falls as the inverse square of radius. Decomposing the curve into disk, bulge, and halo, and fitting isothermal or NFW profiles, quantifies the missing mass. Gravitational lensing weighs the same mass without dynamics, the mass-to-light ratio climbs from stars to clusters, and the Bullet Cluster separates the collisionless dark matter from the colliding gas — evidence that MOND strains to match.\n",{"path":8523,"title":8524,"module":8512,"summary":8525},"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Factive-galactic-nuclei-and-supermassive-black-holes","Active Galactic Nuclei","A small fraction of galaxies pour out enormous luminosity from a region smaller than the solar system. Accretion onto a supermassive black hole, limited by the Eddington balance of radiation pressure and gravity, powers the Seyferts, quasars, radio galaxies, and blazars — one engine seen from different angles through an obscuring torus. Relativistic jets produce apparent superluminal motion, reverberation mapping and stellar dynamics weigh the central mass, and the M–sigma relation ties that mass to the host bulge.\n",{"path":8527,"title":8528,"module":8512,"summary":8529},"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-clusters-and-large-scale-structure","Galaxy Clusters and Large-Scale Structure","Galaxies gather into groups and rich clusters bound by a common dark halo and filled with hot X-ray gas. Three independent probes — the virial theorem, the hydrostatic X-ray temperature, and gravitational lensing — agree on a mass that dwarfs the stars. On the largest scales galaxies trace a cosmic web of filaments, walls, and voids, quantified by the two-point correlation function, whose baryon acoustic oscillation bump provides a standard ruler for cosmology.\n",{"path":8531,"title":8532,"module":8533,"summary":8534},"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-expanding-universe-and-hubbles-law","The Expanding Universe and Hubble's Law","Cosmic Expansion and Dynamics","The universe is homogeneous and isotropic on large scales, so its expansion is captured by a single function of time, the scale factor. Comoving coordinates stay fixed while proper distances grow in proportion to the scale factor, producing Hubble's law and a cosmological redshift that measures stretched space rather than a Doppler shift. A Newtonian energy argument reproduces the dynamics, and the same finite, expanding cosmos resolves Olbers' paradox.\n",{"path":8536,"title":8537,"module":8533,"summary":8538},"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-frw-metric-and-cosmological-redshift","The FRW Metric and Cosmological Redshift","The geometry of a homogeneous, isotropic universe is fixed by symmetry to the Robertson-Walker metric, with the entire freedom reduced to a scale factor and a single curvature constant selecting an open, flat, or closed space. From the metric the null geodesic of light gives comoving distance, the exact cosmological redshift, and the distinction between the proper distance we cannot measure and the redshift we can.\n",{"path":8540,"title":5618,"module":8533,"summary":8541},"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-friedmann-equations-and-cosmic-dynamics","The scale factor obeys the Friedmann equation, the acceleration equation, and the fluid equation, only two of which are independent. An equation of state fixes how each component behaves under expansion, so radiation dilutes as the inverse fourth power of the scale factor, matter as the inverse cube, and vacuum energy not at all. The critical density defines the density parameters, and the deceleration parameter encodes whether gravity or dark energy is winning.\n",{"path":8543,"title":8544,"module":8533,"summary":8545},"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fcosmological-models-and-distances","Cosmological Models and Distances","Integrating the Friedmann equation for particular mixtures gives the benchmark models, from the matter-only Einstein-de Sitter universe to the concordance Lambda-CDM, each with its own scale-factor history and age. Because the redshift is the only direct observable, several distance measures diverge at high redshift, and the angular-diameter distance even turns over so that the most distant objects look larger. The horizon and lookback time set what is causally and observationally reachable.\n",{"path":8547,"title":8548,"module":8533,"summary":8549},"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fdark-energy-and-the-accelerating-universe","Dark Energy and the Accelerating Universe","In 1998 two teams found that distant Type Ia supernovae are fainter than a decelerating universe predicts, revealing that the expansion is accelerating and that a component with negative pressure dominates the energy budget. The simplest candidate is the cosmological constant, or vacuum energy, with an equation of state near minus one. It works observationally but leaves two deep puzzles: why the vacuum energy is a hundred and twenty orders of magnitude smaller than expected, and why it is comparable to the matter density just now.\n",{"path":8551,"title":8552,"module":8553,"summary":8554},"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fthe-thermal-history-of-the-universe","The Thermal History of the Universe","The Hot Big Bang","Running the expansion backward compresses and heats the universe, so its past is a sequence of thermal epochs set by temperature. Temperature scales as the inverse scale factor; species stay in equilibrium while their interaction rate exceeds the expansion rate and freeze out when it drops below. The effective degrees of freedom count the relativistic species and step down through mass thresholds, and neutrino decoupling just before electron-positron annihilation leaves a relic neutrino background slightly cooler than the photons.\n",{"path":8556,"title":8557,"module":8553,"summary":8558},"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fbig-bang-nucleosynthesis","Big Bang Nucleosynthesis","In the first three minutes the weak interactions freeze out the neutron-to-proton ratio, and once deuterium survives photodissociation a fast reaction network converts nearly all free neutrons into helium-4. The primordial abundances of deuterium, helium-3, helium-4, and lithium-7 depend on a single free parameter, the baryon-to-photon ratio, so measuring them fixes the baryon density. The predictions match observation across nine decades of abundance, with a persistent discrepancy in lithium-7.\n",{"path":8560,"title":8561,"module":8553,"summary":8562},"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Frecombination-and-the-cosmic-microwave-background","Recombination and the Cosmic Microwave Background","As the universe cooled through a few thousand kelvin the free electrons bound to protons, and the Saha equation tracks the falling ionization fraction. Once the plasma neutralized, photons stopped scattering and streamed freely from a spherical surface of last scattering at redshift about 1100. Those photons are the cosmic microwave background, an almost perfect blackbody at 2.725 kelvin with a dipole from our motion through it.\n",{"path":8564,"title":8565,"module":8553,"summary":8566},"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcmb-anisotropies-and-cosmological-parameters","CMB Anisotropies and Cosmological Parameters","The cosmic microwave background carries temperature fluctuations at the ten-parts-per-million level, imprinted by sound waves in the photon-baryon plasma before recombination. Decomposed into spherical harmonics, the fluctuations form an angular power spectrum whose acoustic peaks encode the geometry and contents of the universe: the first peak fixes spatial flatness, the odd-even peak ratio the baryon density, and the third peak the dark-matter density. Polarization adds an independent channel, and the Planck measurements pin the concordance parameters.\n",{"path":8568,"title":8569,"module":8553,"summary":8570},"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcosmic-inflation","Cosmic Inflation","The hot Big Bang leaves three initial-condition puzzles unexplained: why causally disconnected patches share a temperature, why the geometry is so nearly flat, and why no magnetic monopoles are seen. A brief epoch of accelerated expansion driven by a slowly rolling scalar field solves all three by stretching a small causal patch across the observable universe. The same accelerated expansion freezes quantum fluctuations into a near-scale-invariant spectrum of density perturbations, seeding all later structure.\n",{"path":8572,"title":8573,"module":8553,"summary":8574},"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fstructure-formation-and-the-growth-of-perturbations","Structure Formation and the Growth of Perturbations","The near-uniform early universe grew its galaxies and clusters by gravitational instability acting on the tiny inflationary perturbations. In an expanding background the growth is slowed to a power law rather than the exponential of a static medium; perturbations stall during radiation domination and grow with the scale factor once matter dominates. The transfer function turns the primordial spectrum into the processed matter power spectrum, and cold dark matter builds structure from the bottom up.\n",{"path":8576,"title":8577,"module":8553,"summary":8578},"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fdark-matter-dark-energy-and-open-questions","Dark Matter, Dark Energy, and Open Questions","Five independent lines of evidence converge on a universe whose energy budget is dominated by dark energy and dark matter, with ordinary baryons a small remainder. The candidate particles for dark matter range from WIMPs to axions to sterile neutrinos, each with its own detection strategy. The concordance model fits the data with six parameters but leaves the nature of dark energy, the Hubble tension, small-scale structure, and the matter-antimatter asymmetry unexplained.\n",{"path":8580,"title":8581,"module":6,"summary":6},"\u002Fastrophysics-cosmology","Astrophysics & Cosmology",{"path":8583,"title":8584,"module":6,"summary":6},"\u002Fcolophon","Colophon",{"path":8586,"title":8587,"module":6,"summary":6},"\u002F","Study Notes",[8589,8602,8614,8623,8632,8647,8696,8715],{"module":3911,"moduleNumber":505,"slug":8590,"lessons":8591},"foundations",[8592,8594,8596,8598,8600],{"title":7954,"path":7953,"lessonNumber":505,"topics":8593,"summary":7955},[3911],{"title":7957,"path":429,"lessonNumber":511,"topics":8595,"summary":7958},[3911],{"title":7961,"path":7960,"lessonNumber":517,"topics":8597,"summary":7962},[3911],{"title":7965,"path":7964,"lessonNumber":523,"topics":8599,"summary":7966},[3911],{"title":7969,"path":7968,"lessonNumber":529,"topics":8601,"summary":7970},[3911],{"module":7974,"moduleNumber":511,"slug":8603,"lessons":8604},"classification",[8605,8608,8610,8612],{"title":7973,"path":7972,"lessonNumber":505,"topics":8606,"summary":7975},[8607],"Classification",{"title":7978,"path":7977,"lessonNumber":511,"topics":8609,"summary":7979},[8607],{"title":7982,"path":7981,"lessonNumber":517,"topics":8611,"summary":7983},[8607],{"title":7986,"path":7985,"lessonNumber":523,"topics":8613,"summary":7987},[8607],{"module":7991,"moduleNumber":517,"slug":8615,"lessons":8616},"semantics",[8617,8619,8621],{"title":7990,"path":7989,"lessonNumber":505,"topics":8618,"summary":7992},[7991],{"title":7995,"path":7994,"lessonNumber":511,"topics":8620,"summary":7996},[7991],{"title":7999,"path":7998,"lessonNumber":517,"topics":8622,"summary":8000},[7991],{"module":4377,"moduleNumber":523,"slug":8624,"lessons":8625},"sequences",[8626,8628,8630],{"title":8002,"path":1858,"lessonNumber":505,"topics":8627,"summary":8003},[4377],{"title":8006,"path":8005,"lessonNumber":511,"topics":8629,"summary":8007},[4377],{"title":8010,"path":8009,"lessonNumber":517,"topics":8631,"summary":8011},[4377],{"module":4859,"moduleNumber":529,"slug":8633,"lessons":8634},"transformers",[8635,8637,8639,8641,8643,8645],{"title":8014,"path":8013,"lessonNumber":505,"topics":8636,"summary":8015},[4859],{"title":6571,"path":8017,"lessonNumber":511,"topics":8638,"summary":8018},[4859],{"title":6678,"path":8020,"lessonNumber":517,"topics":8640,"summary":8021},[4859],{"title":8024,"path":8023,"lessonNumber":523,"topics":8642,"summary":8025},[4859],{"title":8028,"path":8027,"lessonNumber":529,"topics":8644,"summary":8029},[4859],{"title":8032,"path":8031,"lessonNumber":535,"topics":8646,"summary":8033},[4859],{"module":8037,"moduleNumber":535,"slug":8648,"lessons":8649},"linguistic-structure",[8650,8653,8655,8657,8659,8661,8663,8665,8667,8669,8671,8673,8675,8677,8679,8681,8684,8687,8690,8693],{"title":8036,"path":8035,"lessonNumber":505,"topics":8651,"summary":8038},[8652],"Structure",{"title":8041,"path":8040,"lessonNumber":511,"topics":8654,"summary":8042},[8652],{"title":8045,"path":8044,"lessonNumber":517,"topics":8656,"summary":8046},[8652],{"title":8049,"path":8048,"lessonNumber":523,"topics":8658,"summary":8050},[8652],{"title":8053,"path":8052,"lessonNumber":529,"topics":8660,"summary":8054},[8652],{"title":8057,"path":8056,"lessonNumber":535,"topics":8662,"summary":8058},[8652],{"title":8061,"path":8060,"lessonNumber":541,"topics":8664,"summary":8062},[8652],{"title":8065,"path":8064,"lessonNumber":547,"topics":8666,"summary":8066},[8652],{"title":8069,"path":8068,"lessonNumber":553,"topics":8668,"summary":8070},[8652],{"title":8073,"path":8072,"lessonNumber":559,"topics":8670,"summary":8074},[8652],{"title":8077,"path":8076,"lessonNumber":565,"topics":8672,"summary":8078},[8652],{"title":8081,"path":8080,"lessonNumber":571,"topics":8674,"summary":8082},[8652],{"title":8085,"path":8084,"lessonNumber":577,"topics":8676,"summary":8086},[8652],{"title":8089,"path":8088,"lessonNumber":583,"topics":8678,"summary":8090},[8652],{"title":8093,"path":8092,"lessonNumber":589,"topics":8680,"summary":8094},[8652],{"title":8097,"path":8096,"lessonNumber":8682,"topics":8683,"summary":8098},16,[8652],{"title":8101,"path":8100,"lessonNumber":8685,"topics":8686,"summary":8102},17,[8652],{"title":8105,"path":8104,"lessonNumber":8688,"topics":8689,"summary":8106},18,[8652],{"title":8109,"path":8108,"lessonNumber":8691,"topics":8692,"summary":8110},19,[8652],{"title":8113,"path":8112,"lessonNumber":8694,"topics":8695,"summary":8114},20,[8652],{"module":2972,"moduleNumber":541,"slug":8697,"lessons":8698},"applications",[8699,8701,8703,8705,8707,8709,8711,8713],{"title":8116,"path":1224,"lessonNumber":505,"topics":8700,"summary":8117},[2972],{"title":8120,"path":8119,"lessonNumber":511,"topics":8702,"summary":8121},[2972],{"title":8123,"path":701,"lessonNumber":517,"topics":8704,"summary":8124},[2972],{"title":8127,"path":8126,"lessonNumber":523,"topics":8706,"summary":8128},[2972],{"title":5,"path":2974,"lessonNumber":529,"topics":8708,"summary":2992},[2972],{"title":2751,"path":2750,"lessonNumber":535,"topics":8710,"summary":8131},[2972],{"title":8134,"path":8133,"lessonNumber":541,"topics":8712,"summary":8135},[2972],{"title":8138,"path":8137,"lessonNumber":547,"topics":8714,"summary":8139},[2972],{"module":8143,"moduleNumber":547,"slug":8716,"lessons":8717},"speech",[8718,8720,8722,8724],{"title":8142,"path":8141,"lessonNumber":505,"topics":8719,"summary":8144},[8143],{"title":8147,"path":8146,"lessonNumber":511,"topics":8721,"summary":8148},[8143],{"title":8151,"path":8150,"lessonNumber":517,"topics":8723,"summary":8152},[8143],{"title":8155,"path":8154,"lessonNumber":523,"topics":8725,"summary":8156},[8143],"\u003Csvg style=\"width:100%;max-width:395.247px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 296.435 160.045\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg fill=\"var(--tk-bg)\">\u003Cpath d=\"M-30.241-49.308h147.954V-72.07H-30.24Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M44.209-63.423Q44.209-64.017 44.441-64.548Q44.673-65.080 45.090-65.480Q45.506-65.880 46.039-66.101Q46.572-66.322 47.177-66.322Q47.615-66.322 48.013-66.140Q48.412-65.959 48.720-65.626L49.193-66.291Q49.224-66.322 49.256-66.322L49.302-66.322Q49.330-66.322 49.361-66.291Q49.392-66.259 49.392-66.232L49.392-64.095Q49.392-64.072 49.361-64.041Q49.330-64.009 49.302-64.009L49.185-64.009Q49.158-64.009 49.127-64.041Q49.095-64.072 49.095-64.095Q49.095-64.361 48.953-64.714Q48.810-65.068 48.631-65.306Q48.377-65.638 48.027-65.832Q47.677-66.025 47.271-66.025Q46.767-66.025 46.314-65.806Q45.861-65.587 45.568-65.193Q45.072-64.525 45.072-63.423Q45.072-62.892 45.209-62.425Q45.345-61.959 45.621-61.593Q45.896-61.228 46.316-61.023Q46.736-60.818 47.279-60.818Q47.767-60.818 48.191-61.062Q48.615-61.306 48.863-61.726Q49.111-62.146 49.111-62.642Q49.111-62.677 49.140-62.703Q49.170-62.728 49.201-62.728L49.302-62.728Q49.345-62.728 49.369-62.699Q49.392-62.669 49.392-62.627Q49.392-62.189 49.216-61.800Q49.041-61.412 48.730-61.128Q48.420-60.845 48.009-60.683Q47.599-60.521 47.177-60.521Q46.588-60.521 46.047-60.742Q45.506-60.962 45.090-61.367Q44.673-61.771 44.441-62.300Q44.209-62.830 44.209-63.423M50.591-61.154Q50.591-61.337 50.728-61.474Q50.865-61.611 51.056-61.611Q51.248-61.611 51.381-61.478Q51.513-61.345 51.513-61.154Q51.513-60.955 51.381-60.822Q51.248-60.689 51.056-60.689Q50.865-60.689 50.728-60.826Q50.591-60.962 50.591-61.154M50.591-63.681Q50.591-63.865 50.728-64.001Q50.865-64.138 51.056-64.138Q51.248-64.138 51.381-64.005Q51.513-63.873 51.513-63.681Q51.513-63.482 51.381-63.349Q51.248-63.216 51.056-63.216Q50.865-63.216 50.728-63.353Q50.591-63.490 50.591-63.681\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M57.832-60.689L55.367-60.689L55.367-60.986Q55.699-60.986 55.957-61.033Q56.215-61.080 56.215-61.248L56.215-65.591Q56.215-65.857 55.367-65.857L55.367-66.154L57.832-66.154L57.832-65.857Q56.980-65.857 56.980-65.591L56.980-61.248Q56.980-60.986 57.832-60.986\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M63.132-60.689L61.276-60.689L61.276-60.986Q61.550-60.986 61.718-61.033Q61.886-61.080 61.886-61.248L61.886-63.384Q61.886-63.599 61.823-63.695Q61.761-63.791 61.642-63.812Q61.523-63.834 61.276-63.834L61.276-64.130L62.468-64.216L62.468-63.482Q62.581-63.697 62.775-63.865Q62.968-64.033 63.206-64.125Q63.444-64.216 63.698-64.216Q64.866-64.216 64.866-63.138L64.866-61.248Q64.866-61.080 65.036-61.033Q65.206-60.986 65.476-60.986L65.476-60.689L63.620-60.689L63.620-60.986Q63.894-60.986 64.062-61.033Q64.230-61.080 64.230-61.248L64.230-63.123Q64.230-63.505 64.109-63.734Q63.987-63.962 63.636-63.962Q63.323-63.962 63.069-63.800Q62.816-63.638 62.669-63.369Q62.523-63.099 62.523-62.802L62.523-61.248Q62.523-61.080 62.693-61.033Q62.862-60.986 63.132-60.986L63.132-60.689M65.921-62.443Q65.921-62.923 66.153-63.339Q66.386-63.755 66.796-64.005Q67.206-64.255 67.683-64.255Q68.413-64.255 68.812-63.814Q69.210-63.373 69.210-62.642Q69.210-62.537 69.116-62.513L66.667-62.513L66.667-62.443Q66.667-62.033 66.788-61.677Q66.909-61.322 67.181-61.105Q67.452-60.888 67.882-60.888Q68.245-60.888 68.542-61.117Q68.839-61.345 68.941-61.697Q68.948-61.744 69.034-61.759L69.116-61.759Q69.210-61.732 69.210-61.650Q69.210-61.642 69.202-61.611Q69.140-61.384 69.001-61.201Q68.862-61.017 68.671-60.884Q68.480-60.752 68.261-60.681Q68.042-60.611 67.804-60.611Q67.433-60.611 67.095-60.748Q66.757-60.884 66.489-61.136Q66.222-61.388 66.071-61.728Q65.921-62.068 65.921-62.443M66.675-62.752L68.636-62.752Q68.636-63.056 68.534-63.347Q68.433-63.638 68.216-63.820Q67.999-64.001 67.683-64.001Q67.382-64.001 67.151-63.814Q66.921-63.627 66.798-63.335Q66.675-63.044 66.675-62.752M69.698-62.443Q69.698-62.923 69.931-63.339Q70.163-63.755 70.573-64.005Q70.984-64.255 71.460-64.255Q72.191-64.255 72.589-63.814Q72.987-63.373 72.987-62.642Q72.987-62.537 72.894-62.513L70.444-62.513L70.444-62.443Q70.444-62.033 70.566-61.677Q70.687-61.322 70.958-61.105Q71.230-60.888 71.659-60.888Q72.023-60.888 72.319-61.117Q72.616-61.345 72.718-61.697Q72.726-61.744 72.812-61.759L72.894-61.759Q72.987-61.732 72.987-61.650Q72.987-61.642 72.980-61.611Q72.917-61.384 72.778-61.201Q72.640-61.017 72.448-60.884Q72.257-60.752 72.038-60.681Q71.819-60.611 71.581-60.611Q71.210-60.611 70.872-60.748Q70.534-60.884 70.267-61.136Q69.999-61.388 69.849-61.728Q69.698-62.068 69.698-62.443M70.452-62.752L72.413-62.752Q72.413-63.056 72.312-63.347Q72.210-63.638 71.993-63.820Q71.776-64.001 71.460-64.001Q71.159-64.001 70.929-63.814Q70.698-63.627 70.575-63.335Q70.452-63.044 70.452-62.752M75.292-60.611Q74.812-60.611 74.403-60.855Q73.995-61.099 73.757-61.513Q73.519-61.927 73.519-62.416Q73.519-62.908 73.776-63.324Q74.034-63.740 74.466-63.978Q74.898-64.216 75.390-64.216Q76.011-64.216 76.460-63.779L76.460-65.408Q76.460-65.623 76.398-65.718Q76.335-65.814 76.218-65.835Q76.101-65.857 75.855-65.857L75.855-66.154L77.077-66.240L77.077-61.431Q77.077-61.220 77.140-61.125Q77.202-61.029 77.319-61.007Q77.437-60.986 77.687-60.986L77.687-60.689L76.437-60.611L76.437-61.095Q75.972-60.611 75.292-60.611M75.359-60.865Q75.698-60.865 75.991-61.056Q76.284-61.248 76.437-61.544L76.437-63.377Q76.288-63.650 76.026-63.806Q75.765-63.962 75.452-63.962Q74.827-63.962 74.544-63.515Q74.261-63.068 74.261-62.408Q74.261-61.763 74.513-61.314Q74.765-60.865 75.359-60.865\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M81.661-61.650L81.661-63.841L80.958-63.841L80.958-64.095Q81.314-64.095 81.556-64.328Q81.798-64.560 81.909-64.908Q82.021-65.255 82.021-65.611L82.302-65.611L82.302-64.138L83.478-64.138L83.478-63.841L82.302-63.841L82.302-61.666Q82.302-61.345 82.421-61.117Q82.540-60.888 82.821-60.888Q83.001-60.888 83.118-61.011Q83.236-61.134 83.288-61.314Q83.341-61.494 83.341-61.666L83.341-62.138L83.622-62.138L83.622-61.650Q83.622-61.396 83.517-61.156Q83.411-60.916 83.214-60.763Q83.017-60.611 82.759-60.611Q82.443-60.611 82.191-60.734Q81.939-60.857 81.800-61.091Q81.661-61.326 81.661-61.650M84.341-62.384Q84.341-62.888 84.597-63.320Q84.853-63.752 85.288-64.003Q85.724-64.255 86.224-64.255Q86.611-64.255 86.952-64.111Q87.294-63.966 87.556-63.705Q87.818-63.443 87.960-63.107Q88.103-62.771 88.103-62.384Q88.103-61.892 87.839-61.482Q87.575-61.072 87.146-60.841Q86.716-60.611 86.224-60.611Q85.732-60.611 85.298-60.843Q84.864-61.076 84.603-61.484Q84.341-61.892 84.341-62.384M86.224-60.888Q86.681-60.888 86.933-61.111Q87.185-61.334 87.273-61.685Q87.361-62.037 87.361-62.482Q87.361-62.912 87.267-63.250Q87.173-63.587 86.919-63.794Q86.665-64.001 86.224-64.001Q85.575-64.001 85.331-63.585Q85.087-63.169 85.087-62.482Q85.087-62.037 85.175-61.685Q85.263-61.334 85.515-61.111Q85.767-60.888 86.224-60.888\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M92.050-61.650L92.050-63.841L91.347-63.841L91.347-64.095Q91.703-64.095 91.945-64.328Q92.187-64.560 92.298-64.908Q92.410-65.255 92.410-65.611L92.691-65.611L92.691-64.138L93.867-64.138L93.867-63.841L92.691-63.841L92.691-61.666Q92.691-61.345 92.810-61.117Q92.929-60.888 93.210-60.888Q93.390-60.888 93.507-61.011Q93.624-61.134 93.677-61.314Q93.730-61.494 93.730-61.666L93.730-62.138L94.011-62.138L94.011-61.650Q94.011-61.396 93.906-61.156Q93.800-60.916 93.603-60.763Q93.406-60.611 93.148-60.611Q92.832-60.611 92.580-60.734Q92.328-60.857 92.189-61.091Q92.050-61.326 92.050-61.650M96.738-60.689L94.757-60.689L94.757-60.986Q95.027-60.986 95.195-61.031Q95.363-61.076 95.363-61.248L95.363-63.384Q95.363-63.599 95.300-63.695Q95.238-63.791 95.121-63.812Q95.003-63.834 94.757-63.834L94.757-64.130L95.925-64.216L95.925-63.431Q96.003-63.642 96.156-63.828Q96.308-64.013 96.507-64.115Q96.707-64.216 96.933-64.216Q97.179-64.216 97.371-64.072Q97.562-63.927 97.562-63.697Q97.562-63.541 97.457-63.431Q97.351-63.322 97.195-63.322Q97.039-63.322 96.929-63.431Q96.820-63.541 96.820-63.697Q96.820-63.857 96.925-63.962Q96.601-63.962 96.386-63.734Q96.171-63.505 96.076-63.166Q95.980-62.826 95.980-62.521L95.980-61.248Q95.980-61.080 96.207-61.033Q96.433-60.986 96.738-60.986L96.738-60.689M98.140-61.521Q98.140-62.005 98.542-62.300Q98.945-62.595 99.496-62.714Q100.046-62.834 100.539-62.834L100.539-63.123Q100.539-63.349 100.423-63.556Q100.308-63.763 100.111-63.882Q99.914-64.001 99.683-64.001Q99.257-64.001 98.972-63.896Q99.042-63.869 99.089-63.814Q99.136-63.759 99.162-63.689Q99.187-63.619 99.187-63.544Q99.187-63.439 99.136-63.347Q99.085-63.255 98.994-63.205Q98.902-63.154 98.796-63.154Q98.691-63.154 98.599-63.205Q98.507-63.255 98.457-63.347Q98.406-63.439 98.406-63.544Q98.406-63.962 98.794-64.109Q99.183-64.255 99.683-64.255Q100.015-64.255 100.369-64.125Q100.722-63.994 100.951-63.740Q101.179-63.486 101.179-63.138L101.179-61.337Q101.179-61.205 101.251-61.095Q101.324-60.986 101.453-60.986Q101.578-60.986 101.646-61.091Q101.714-61.197 101.714-61.337L101.714-61.849L101.996-61.849L101.996-61.337Q101.996-61.134 101.878-60.976Q101.761-60.818 101.580-60.734Q101.398-60.650 101.195-60.650Q100.964-60.650 100.812-60.822Q100.660-60.994 100.628-61.224Q100.468-60.943 100.160-60.777Q99.851-60.611 99.499-60.611Q98.988-60.611 98.564-60.834Q98.140-61.056 98.140-61.521M98.828-61.521Q98.828-61.236 99.054-61.050Q99.281-60.865 99.574-60.865Q99.820-60.865 100.044-60.982Q100.269-61.099 100.404-61.302Q100.539-61.505 100.539-61.759L100.539-62.591Q100.273-62.591 99.988-62.537Q99.703-62.482 99.431-62.353Q99.160-62.224 98.994-62.017Q98.828-61.810 98.828-61.521\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M103.858-60.720L102.635-63.576Q102.553-63.752 102.409-63.796Q102.264-63.841 101.995-63.841L101.995-64.138L103.706-64.138L103.706-63.841Q103.284-63.841 103.284-63.658Q103.284-63.623 103.299-63.576L104.245-61.384L105.085-63.361Q105.124-63.439 105.124-63.529Q105.124-63.669 105.018-63.755Q104.913-63.841 104.772-63.841L104.772-64.138L106.124-64.138L106.124-63.841Q105.600-63.841 105.385-63.361L104.260-60.720Q104.198-60.611 104.092-60.611L104.026-60.611Q103.913-60.611 103.858-60.720\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M106.307-62.443Q106.307-62.923 106.540-63.339Q106.772-63.755 107.182-64.005Q107.592-64.255 108.069-64.255Q108.799-64.255 109.198-63.814Q109.596-63.373 109.596-62.642Q109.596-62.537 109.503-62.513L107.053-62.513L107.053-62.443Q107.053-62.033 107.174-61.677Q107.296-61.322 107.567-61.105Q107.839-60.888 108.268-60.888Q108.632-60.888 108.928-61.117Q109.225-61.345 109.327-61.697Q109.335-61.744 109.421-61.759L109.503-61.759Q109.596-61.732 109.596-61.650Q109.596-61.642 109.589-61.611Q109.526-61.384 109.387-61.201Q109.249-61.017 109.057-60.884Q108.866-60.752 108.647-60.681Q108.428-60.611 108.190-60.611Q107.819-60.611 107.481-60.748Q107.143-60.884 106.876-61.136Q106.608-61.388 106.458-61.728Q106.307-62.068 106.307-62.443M107.061-62.752L109.022-62.752Q109.022-63.056 108.921-63.347Q108.819-63.638 108.602-63.820Q108.385-64.001 108.069-64.001Q107.768-64.001 107.538-63.814Q107.307-63.627 107.184-63.335Q107.061-63.044 107.061-62.752M111.999-60.689L110.167-60.689L110.167-60.986Q110.440-60.986 110.608-61.033Q110.776-61.080 110.776-61.248L110.776-65.408Q110.776-65.623 110.714-65.718Q110.651-65.814 110.532-65.835Q110.413-65.857 110.167-65.857L110.167-66.154L111.389-66.240L111.389-61.248Q111.389-61.080 111.557-61.033Q111.725-60.986 111.999-60.986\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M117.139-60.689L115.361-60.689L115.361-60.986Q115.635-60.986 115.803-61.033Q115.971-61.080 115.971-61.248L115.971-63.384Q115.971-63.599 115.914-63.695Q115.857-63.791 115.744-63.812Q115.631-63.834 115.385-63.834L115.385-64.130L116.584-64.216L116.584-61.248Q116.584-61.080 116.730-61.033Q116.877-60.986 117.139-60.986L117.139-60.689M115.697-65.611Q115.697-65.802 115.832-65.933Q115.967-66.064 116.162-66.064Q116.283-66.064 116.387-66.001Q116.490-65.939 116.553-65.835Q116.615-65.732 116.615-65.611Q116.615-65.416 116.484-65.281Q116.353-65.146 116.162-65.146Q115.963-65.146 115.830-65.279Q115.697-65.412 115.697-65.611M119.568-60.689L117.713-60.689L117.713-60.986Q117.986-60.986 118.154-61.033Q118.322-61.080 118.322-61.248L118.322-63.384Q118.322-63.599 118.260-63.695Q118.197-63.791 118.078-63.812Q117.959-63.834 117.713-63.834L117.713-64.130L118.904-64.216L118.904-63.482Q119.018-63.697 119.211-63.865Q119.404-64.033 119.643-64.125Q119.881-64.216 120.135-64.216Q121.303-64.216 121.303-63.138L121.303-61.248Q121.303-61.080 121.473-61.033Q121.643-60.986 121.912-60.986L121.912-60.689L120.057-60.689L120.057-60.986Q120.330-60.986 120.498-61.033Q120.666-61.080 120.666-61.248L120.666-63.123Q120.666-63.505 120.545-63.734Q120.424-63.962 120.072-63.962Q119.760-63.962 119.506-63.800Q119.252-63.638 119.105-63.369Q118.959-63.099 118.959-62.802L118.959-61.248Q118.959-61.080 119.129-61.033Q119.299-60.986 119.568-60.986\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M127.255-60.689L125.333-60.689L125.333-60.986Q126.142-60.986 126.142-61.466L126.142-65.591Q126.142-65.763 125.899-65.810Q125.657-65.857 125.333-65.857L125.333-66.154L126.829-66.154Q126.938-66.154 126.997-66.041L128.845-61.498L130.685-66.041Q130.747-66.154 130.853-66.154L132.356-66.154L132.356-65.857Q132.036-65.857 131.794-65.810Q131.552-65.763 131.552-65.591L131.552-61.248Q131.552-61.080 131.794-61.033Q132.036-60.986 132.356-60.986L132.356-60.689L130.056-60.689L130.056-60.986Q130.860-60.986 130.860-61.248L130.860-65.841L128.813-60.802Q128.778-60.689 128.645-60.689Q128.505-60.689 128.470-60.802L126.446-65.779L126.446-61.466Q126.446-60.986 127.255-60.986L127.255-60.689M133.071-61.521Q133.071-62.005 133.474-62.300Q133.876-62.595 134.427-62.714Q134.978-62.834 135.470-62.834L135.470-63.123Q135.470-63.349 135.354-63.556Q135.239-63.763 135.042-63.882Q134.845-64.001 134.614-64.001Q134.188-64.001 133.903-63.896Q133.974-63.869 134.020-63.814Q134.067-63.759 134.093-63.689Q134.118-63.619 134.118-63.544Q134.118-63.439 134.067-63.347Q134.017-63.255 133.925-63.205Q133.833-63.154 133.728-63.154Q133.622-63.154 133.530-63.205Q133.438-63.255 133.388-63.347Q133.337-63.439 133.337-63.544Q133.337-63.962 133.726-64.109Q134.114-64.255 134.614-64.255Q134.946-64.255 135.300-64.125Q135.653-63.994 135.882-63.740Q136.110-63.486 136.110-63.138L136.110-61.337Q136.110-61.205 136.183-61.095Q136.255-60.986 136.384-60.986Q136.509-60.986 136.577-61.091Q136.645-61.197 136.645-61.337L136.645-61.849L136.927-61.849L136.927-61.337Q136.927-61.134 136.810-60.976Q136.692-60.818 136.511-60.734Q136.329-60.650 136.126-60.650Q135.895-60.650 135.743-60.822Q135.591-60.994 135.560-61.224Q135.399-60.943 135.091-60.777Q134.782-60.611 134.431-60.611Q133.919-60.611 133.495-60.834Q133.071-61.056 133.071-61.521M133.759-61.521Q133.759-61.236 133.985-61.050Q134.212-60.865 134.505-60.865Q134.751-60.865 134.976-60.982Q135.200-61.099 135.335-61.302Q135.470-61.505 135.470-61.759L135.470-62.591Q135.204-62.591 134.919-62.537Q134.634-62.482 134.362-62.353Q134.091-62.224 133.925-62.017Q133.759-61.810 133.759-61.521\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M137.411-59.392Q137.525-59.314 137.700-59.314Q137.989-59.314 138.210-59.527Q138.431-59.740 138.556-60.041L138.845-60.689L137.571-63.576Q137.489-63.752 137.345-63.796Q137.200-63.841 136.931-63.841L136.931-64.138L138.650-64.138L138.650-63.841Q138.228-63.841 138.228-63.658Q138.228-63.646 138.243-63.576L139.181-61.451L140.013-63.361Q140.052-63.451 140.052-63.529Q140.052-63.669 139.950-63.755Q139.849-63.841 139.708-63.841L139.708-64.138L141.060-64.138L141.060-63.841Q140.806-63.841 140.612-63.716Q140.419-63.591 140.314-63.361L138.868-60.041Q138.755-59.787 138.589-59.564Q138.423-59.341 138.194-59.199Q137.966-59.056 137.700-59.056Q137.403-59.056 137.163-59.248Q136.923-59.439 136.923-59.728Q136.923-59.884 137.028-59.986Q137.134-60.087 137.282-60.087Q137.388-60.087 137.468-60.041Q137.548-59.994 137.595-59.916Q137.642-59.837 137.642-59.728Q137.642-59.607 137.581-59.519Q137.521-59.431 137.411-59.392\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-49.58 2)\">\u003Cpath d=\"M141.252-61.154Q141.252-61.337 141.388-61.474Q141.525-61.611 141.717-61.611Q141.908-61.611 142.041-61.478Q142.174-61.345 142.174-61.154Q142.174-60.955 142.041-60.822Q141.908-60.689 141.717-60.689Q141.525-60.689 141.388-60.826Q141.252-60.962 141.252-61.154\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(86.046 2.392)\">\u003Cpath d=\"M44.225-63.082Q44.225-63.608 44.442-64.076Q44.659-64.544 45.042-64.890Q45.424-65.235 45.908-65.423Q46.392-65.611 46.922-65.611Q47.325-65.611 47.689-65.454Q48.053-65.296 48.337-65.002L48.760-65.584Q48.795-65.611 48.819-65.611L48.866-65.611Q48.897-65.611 48.921-65.587Q48.945-65.563 48.945-65.532L48.945-63.669Q48.945-63.646 48.919-63.620Q48.894-63.594 48.866-63.594L48.740-63.594Q48.678-63.594 48.665-63.669Q48.634-63.984 48.499-64.288Q48.364-64.592 48.149-64.826Q47.933-65.061 47.644-65.196Q47.356-65.331 47.028-65.331Q46.385-65.331 45.927-65.037Q45.469-64.743 45.236-64.230Q45.004-63.717 45.004-63.082Q45.004-62.610 45.134-62.201Q45.264-61.793 45.524-61.480Q45.783-61.168 46.163-60.998Q46.542-60.829 47.034-60.829Q47.362-60.829 47.656-60.945Q47.950-61.062 48.184-61.277Q48.419-61.492 48.548-61.781Q48.678-62.070 48.678-62.398Q48.678-62.425 48.706-62.449Q48.733-62.473 48.754-62.473L48.866-62.473Q48.904-62.473 48.924-62.448Q48.945-62.422 48.945-62.384Q48.945-61.988 48.779-61.651Q48.613-61.314 48.326-61.067Q48.039-60.819 47.670-60.684Q47.301-60.549 46.922-60.549Q46.402-60.549 45.910-60.739Q45.418-60.928 45.038-61.272Q44.659-61.615 44.442-62.084Q44.225-62.552 44.225-63.082M49.929-63.054Q49.929-63.741 50.278-64.331Q50.627-64.920 51.221-65.266Q51.816-65.611 52.507-65.611Q53.197-65.611 53.792-65.266Q54.386-64.920 54.735-64.331Q55.084-63.741 55.084-63.054Q55.084-62.367 54.732-61.793Q54.380-61.219 53.787-60.884Q53.194-60.549 52.507-60.549Q51.994-60.549 51.526-60.742Q51.057-60.935 50.698-61.277Q50.340-61.619 50.134-62.080Q49.929-62.542 49.929-63.054M52.507-60.809Q53.111-60.809 53.518-61.154Q53.925-61.499 54.115-62.037Q54.304-62.576 54.304-63.174Q54.304-63.608 54.193-63.996Q54.082-64.384 53.857-64.691Q53.631-64.999 53.289-65.179Q52.947-65.358 52.507-65.358Q52.069-65.358 51.731-65.182Q51.392-65.006 51.163-64.691Q50.934-64.377 50.821-63.984Q50.709-63.591 50.709-63.174Q50.709-62.586 50.900-62.043Q51.091-61.499 51.498-61.154Q51.905-60.809 52.507-60.809M57.692-60.689L55.959-60.689L55.959-60.969Q56.680-60.969 56.680-61.369L56.680-65.156Q56.447-65.190 55.959-65.190L55.959-65.471L57.316-65.471Q57.357-65.457 57.377-65.443L60.081-61.796L60.081-64.791Q60.081-65.190 59.360-65.190L59.360-65.471L61.096-65.471L61.096-65.190Q60.375-65.190 60.375-64.791L60.375-60.781Q60.358-60.706 60.289-60.689L60.170-60.689Q60.122-60.699 60.108-60.723L56.974-64.944L56.974-61.369Q56.974-60.969 57.692-60.969L57.692-60.689M61.967-60.627L61.967-62.200Q61.967-62.227 61.993-62.253Q62.019-62.278 62.046-62.278L62.159-62.278Q62.186-62.278 62.210-62.251Q62.234-62.224 62.234-62.200Q62.234-61.855 62.366-61.591Q62.497-61.328 62.726-61.159Q62.955-60.990 63.258-60.909Q63.560-60.829 63.902-60.829Q64.169-60.829 64.404-60.957Q64.640-61.085 64.786-61.308Q64.931-61.530 64.931-61.796Q64.931-62.019 64.825-62.215Q64.719-62.412 64.538-62.547Q64.357-62.682 64.131-62.733L63.102-62.965Q62.791-63.037 62.531-63.223Q62.272-63.410 62.120-63.681Q61.967-63.953 61.967-64.268Q61.967-64.654 62.181-64.961Q62.395-65.269 62.742-65.440Q63.089-65.611 63.468-65.611Q63.697-65.611 63.926-65.558Q64.155-65.505 64.353-65.397Q64.551-65.290 64.705-65.126L64.999-65.566Q65.023-65.611 65.064-65.611L65.112-65.611Q65.143-65.611 65.165-65.585Q65.187-65.560 65.187-65.532L65.187-63.957Q65.187-63.936 65.163-63.909Q65.139-63.881 65.112-63.881L64.999-63.881Q64.938-63.881 64.924-63.957Q64.883-64.370 64.702-64.690Q64.521-65.009 64.210-65.184Q63.899-65.358 63.468-65.358Q63.218-65.358 62.979-65.247Q62.740-65.136 62.590-64.938Q62.439-64.739 62.439-64.476Q62.439-64.264 62.547-64.083Q62.654-63.902 62.830-63.782Q63.007-63.663 63.215-63.622L64.244-63.393Q64.562-63.321 64.828-63.116Q65.095-62.911 65.247-62.617Q65.399-62.323 65.399-61.991Q65.399-61.598 65.194-61.263Q64.989-60.928 64.644-60.739Q64.298-60.549 63.902-60.549Q63.482-60.549 63.102-60.662Q62.723-60.774 62.453-61.024L62.159-60.590Q62.132-60.549 62.094-60.549L62.046-60.549Q62.019-60.549 61.993-60.574Q61.967-60.600 61.967-60.627M70.116-60.689L67.378-60.689L67.378-60.969Q67.727-60.969 68.063-61.005Q68.400-61.041 68.400-61.178L68.400-64.979Q68.400-65.122 68.311-65.156Q68.222-65.190 68.038-65.190L67.679-65.190Q67.378-65.190 67.163-65.143Q66.947-65.095 66.790-64.938Q66.653-64.804 66.594-64.526Q66.534-64.247 66.496-63.834L66.230-63.834L66.377-65.471L71.111-65.471L71.257-63.834L70.991-63.834Q70.953-64.247 70.897-64.524Q70.840-64.801 70.697-64.938Q70.536-65.098 70.324-65.144Q70.112-65.190 69.808-65.190L69.456-65.190Q69.272-65.190 69.183-65.156Q69.094-65.122 69.094-64.979L69.094-61.178Q69.094-61.041 69.431-61.005Q69.767-60.969 70.116-60.969\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 2.392)\">\u003Cpath d=\"M72.869-60.689L71.279-60.689L71.279-60.969Q71.922-60.969 72.079-61.369L73.723-65.584Q73.757-65.679 73.870-65.679L73.952-65.679Q74.062-65.679 74.103-65.584L75.822-61.178Q75.890-61.038 76.080-61.003Q76.270-60.969 76.543-60.969L76.543-60.689L74.544-60.689L74.544-60.969Q75.108-60.969 75.108-61.144Q75.108-61.161 75.106-61.168Q75.104-61.174 75.101-61.178L74.680-62.244L72.722-62.244L72.380-61.369Q72.366-61.369 72.366-61.291Q72.366-61.130 72.529-61.050Q72.691-60.969 72.869-60.969L72.869-60.689M73.703-64.763L72.835-62.524L74.578-62.524\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 2.392)\">\u003Cpath d=\"M80.443-60.689L77.705-60.689L77.705-60.969Q78.054-60.969 78.391-61.005Q78.727-61.041 78.727-61.178L78.727-64.979Q78.727-65.122 78.639-65.156Q78.550-65.190 78.365-65.190L78.006-65.190Q77.705-65.190 77.490-65.143Q77.275-65.095 77.118-64.938Q76.981-64.804 76.921-64.526Q76.861-64.247 76.824-63.834L76.557-63.834L76.704-65.471L81.438-65.471L81.585-63.834L81.318-63.834Q81.281-64.247 81.224-64.524Q81.168-64.801 81.024-64.938Q80.864-65.098 80.652-65.144Q80.440-65.190 80.136-65.190L79.784-65.190Q79.599-65.190 79.510-65.156Q79.421-65.122 79.421-64.979L79.421-61.178Q79.421-61.041 79.758-61.005Q80.095-60.969 80.443-60.969L80.443-60.689M84.473-60.689L82.268-60.689L82.268-60.969Q83.027-60.969 83.027-61.178L83.027-64.979Q83.027-65.190 82.268-65.190L82.268-65.471L84.473-65.471L84.473-65.190Q83.718-65.190 83.718-64.979L83.718-61.178Q83.718-60.969 84.473-60.969L84.473-60.689M87.563-60.648L85.721-64.979Q85.652-65.122 85.471-65.156Q85.290-65.190 85.027-65.190L85.027-65.471L86.995-65.471L86.995-65.190Q86.442-65.190 86.442-65.016Q86.445-65.006 86.447-64.996Q86.449-64.985 86.449-64.979L87.970-61.403L89.402-64.791Q89.426-64.828 89.426-64.883Q89.426-65.043 89.275-65.117Q89.125-65.190 88.940-65.190L88.940-65.471L90.489-65.471L90.489-65.190Q90.229-65.190 90.019-65.096Q89.808-65.002 89.713-64.791L87.956-60.648Q87.905-60.549 87.809-60.549L87.710-60.549Q87.611-60.549 87.563-60.648M95.493-60.689L91.090-60.689L91.090-60.969Q91.811-60.969 91.811-61.178L91.811-64.979Q91.811-65.190 91.090-65.190L91.090-65.471L95.380-65.471L95.588-63.834L95.325-63.834Q95.267-64.305 95.164-64.570Q95.062-64.835 94.877-64.968Q94.693-65.102 94.421-65.146Q94.149-65.190 93.650-65.190L92.868-65.190Q92.680-65.190 92.591-65.156Q92.502-65.122 92.502-64.979L92.502-63.314L93.076-63.314Q93.466-63.314 93.649-63.365Q93.831-63.417 93.913-63.589Q93.995-63.762 93.995-64.134L94.259-64.134L94.259-62.213L93.995-62.213Q93.995-62.586 93.913-62.759Q93.831-62.931 93.649-62.982Q93.466-63.034 93.076-63.034L92.502-63.034L92.502-61.178Q92.502-61.038 92.591-61.003Q92.680-60.969 92.868-60.969L93.715-60.969Q94.245-60.969 94.554-61.038Q94.864-61.106 95.052-61.273Q95.240-61.441 95.347-61.743Q95.455-62.046 95.540-62.559L95.807-62.559\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-soft-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath d=\"M-30.241-16.587h147.954V-39.35H-30.24Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-40.835 34.72)\">\u003Cpath d=\"M45.775-60.689L44.025-60.689L44.025-60.986Q44.724-60.986 44.912-61.466L46.713-66.291Q46.767-66.400 46.881-66.400L46.951-66.400Q47.064-66.400 47.119-66.291L49.009-61.248Q49.088-61.080 49.291-61.033Q49.494-60.986 49.806-60.986L49.806-60.689L47.584-60.689L47.584-60.986Q48.224-60.986 48.224-61.201Q48.224-61.220 48.222-61.230Q48.220-61.240 48.216-61.248L47.752-62.482L45.607-62.482L45.224-61.466Q45.220-61.451 45.215-61.421Q45.209-61.392 45.209-61.369Q45.209-61.228 45.298-61.144Q45.388-61.060 45.521-61.023Q45.654-60.986 45.775-60.986L45.775-60.689M46.681-65.345L45.713-62.779L47.638-62.779L46.681-65.345M50.814-61.154Q50.814-61.337 50.951-61.474Q51.088-61.611 51.279-61.611Q51.470-61.611 51.603-61.478Q51.736-61.345 51.736-61.154Q51.736-60.955 51.603-60.822Q51.470-60.689 51.279-60.689Q51.088-60.689 50.951-60.826Q50.814-60.962 50.814-61.154M50.814-63.681Q50.814-63.865 50.951-64.001Q51.088-64.138 51.279-64.138Q51.470-64.138 51.603-64.005Q51.736-63.873 51.736-63.681Q51.736-63.482 51.603-63.349Q51.470-63.216 51.279-63.216Q51.088-63.216 50.951-63.353Q50.814-63.490 50.814-63.681\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-40.835 34.72)\">\u003Cpath d=\"M57.871-60.634L56.168-65.591Q56.102-65.767 55.926-65.812Q55.750-65.857 55.457-65.857L55.457-66.154L57.602-66.154L57.602-65.857Q56.945-65.857 56.945-65.642Q56.949-65.630 56.951-65.621Q56.953-65.611 56.953-65.591L58.305-61.658L59.504-65.162L59.359-65.591Q59.289-65.767 59.115-65.812Q58.942-65.857 58.649-65.857L58.649-66.154L60.785-66.154L60.785-65.857Q60.137-65.857 60.137-65.642Q60.137-65.599 60.145-65.591L61.496-61.658L62.770-65.376Q62.785-65.423 62.785-65.459Q62.785-65.599 62.676-65.691Q62.567-65.783 62.410-65.820Q62.254-65.857 62.121-65.857L62.121-66.154L63.856-66.154L63.856-65.857Q63.238-65.857 63.067-65.376L61.442-60.634Q61.422-60.584 61.379-60.552Q61.336-60.521 61.281-60.521L61.227-60.521Q61.106-60.521 61.067-60.634L59.656-64.728L58.250-60.634Q58.231-60.584 58.188-60.552Q58.145-60.521 58.090-60.521L58.031-60.521Q57.918-60.521 57.871-60.634M66.192-60.689L64.336-60.689L64.336-60.986Q64.609-60.986 64.777-61.033Q64.945-61.080 64.945-61.248L64.945-65.408Q64.945-65.623 64.883-65.718Q64.820-65.814 64.701-65.835Q64.582-65.857 64.336-65.857L64.336-66.154L65.559-66.240L65.559-63.537Q65.684-63.748 65.871-63.898Q66.059-64.048 66.285-64.132Q66.512-64.216 66.758-64.216Q67.926-64.216 67.926-63.138L67.926-61.248Q67.926-61.080 68.096-61.033Q68.266-60.986 68.535-60.986L68.535-60.689L66.680-60.689L66.680-60.986Q66.953-60.986 67.121-61.033Q67.289-61.080 67.289-61.248L67.289-63.123Q67.289-63.505 67.168-63.734Q67.047-63.962 66.695-63.962Q66.383-63.962 66.129-63.800Q65.875-63.638 65.729-63.369Q65.582-63.099 65.582-62.802L65.582-61.248Q65.582-61.080 65.752-61.033Q65.922-60.986 66.192-60.986L66.192-60.689M69.078-61.521Q69.078-62.005 69.481-62.300Q69.883-62.595 70.434-62.714Q70.984-62.834 71.477-62.834L71.477-63.123Q71.477-63.349 71.361-63.556Q71.246-63.763 71.049-63.882Q70.852-64.001 70.621-64.001Q70.195-64.001 69.910-63.896Q69.981-63.869 70.027-63.814Q70.074-63.759 70.100-63.689Q70.125-63.619 70.125-63.544Q70.125-63.439 70.074-63.347Q70.024-63.255 69.932-63.205Q69.840-63.154 69.734-63.154Q69.629-63.154 69.537-63.205Q69.445-63.255 69.395-63.347Q69.344-63.439 69.344-63.544Q69.344-63.962 69.733-64.109Q70.121-64.255 70.621-64.255Q70.953-64.255 71.307-64.125Q71.660-63.994 71.889-63.740Q72.117-63.486 72.117-63.138L72.117-61.337Q72.117-61.205 72.190-61.095Q72.262-60.986 72.391-60.986Q72.516-60.986 72.584-61.091Q72.652-61.197 72.652-61.337L72.652-61.849L72.934-61.849L72.934-61.337Q72.934-61.134 72.817-60.976Q72.699-60.818 72.518-60.734Q72.336-60.650 72.133-60.650Q71.902-60.650 71.750-60.822Q71.598-60.994 71.567-61.224Q71.406-60.943 71.098-60.777Q70.789-60.611 70.438-60.611Q69.926-60.611 69.502-60.834Q69.078-61.056 69.078-61.521M69.766-61.521Q69.766-61.236 69.992-61.050Q70.219-60.865 70.512-60.865Q70.758-60.865 70.983-60.982Q71.207-61.099 71.342-61.302Q71.477-61.505 71.477-61.759L71.477-62.591Q71.211-62.591 70.926-62.537Q70.641-62.482 70.369-62.353Q70.098-62.224 69.932-62.017Q69.766-61.810 69.766-61.521M73.852-61.650L73.852-63.841L73.149-63.841L73.149-64.095Q73.504-64.095 73.746-64.328Q73.988-64.560 74.100-64.908Q74.211-65.255 74.211-65.611L74.492-65.611L74.492-64.138L75.668-64.138L75.668-63.841L74.492-63.841L74.492-61.666Q74.492-61.345 74.611-61.117Q74.731-60.888 75.012-60.888Q75.192-60.888 75.309-61.011Q75.426-61.134 75.479-61.314Q75.531-61.494 75.531-61.666L75.531-62.138L75.813-62.138L75.813-61.650Q75.813-61.396 75.707-61.156Q75.602-60.916 75.404-60.763Q75.207-60.611 74.949-60.611Q74.633-60.611 74.381-60.734Q74.129-60.857 73.990-61.091Q73.852-61.326 73.852-61.650\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-40.835 34.72)\">\u003Cpath d=\"M81.192-60.611Q80.711-60.611 80.303-60.855Q79.895-61.099 79.657-61.513Q79.418-61.927 79.418-62.416Q79.418-62.908 79.676-63.324Q79.934-63.740 80.366-63.978Q80.797-64.216 81.289-64.216Q81.910-64.216 82.360-63.779L82.360-65.408Q82.360-65.623 82.297-65.718Q82.235-65.814 82.117-65.835Q82-65.857 81.754-65.857L81.754-66.154L82.977-66.240L82.977-61.431Q82.977-61.220 83.039-61.125Q83.102-61.029 83.219-61.007Q83.336-60.986 83.586-60.986L83.586-60.689L82.336-60.611L82.336-61.095Q81.871-60.611 81.192-60.611M81.258-60.865Q81.598-60.865 81.891-61.056Q82.184-61.248 82.336-61.544L82.336-63.377Q82.188-63.650 81.926-63.806Q81.664-63.962 81.352-63.962Q80.727-63.962 80.444-63.515Q80.160-63.068 80.160-62.408Q80.160-61.763 80.412-61.314Q80.664-60.865 81.258-60.865M84.192-61.521Q84.192-62.005 84.594-62.300Q84.996-62.595 85.547-62.714Q86.098-62.834 86.590-62.834L86.590-63.123Q86.590-63.349 86.475-63.556Q86.360-63.763 86.162-63.882Q85.965-64.001 85.735-64.001Q85.309-64.001 85.024-63.896Q85.094-63.869 85.141-63.814Q85.188-63.759 85.213-63.689Q85.239-63.619 85.239-63.544Q85.239-63.439 85.188-63.347Q85.137-63.255 85.045-63.205Q84.953-63.154 84.848-63.154Q84.742-63.154 84.651-63.205Q84.559-63.255 84.508-63.347Q84.457-63.439 84.457-63.544Q84.457-63.962 84.846-64.109Q85.235-64.255 85.735-64.255Q86.067-64.255 86.420-64.125Q86.774-63.994 87.002-63.740Q87.231-63.486 87.231-63.138L87.231-61.337Q87.231-61.205 87.303-61.095Q87.375-60.986 87.504-60.986Q87.629-60.986 87.698-61.091Q87.766-61.197 87.766-61.337L87.766-61.849L88.047-61.849L88.047-61.337Q88.047-61.134 87.930-60.976Q87.813-60.818 87.631-60.734Q87.450-60.650 87.246-60.650Q87.016-60.650 86.864-60.822Q86.711-60.994 86.680-61.224Q86.520-60.943 86.211-60.777Q85.903-60.611 85.551-60.611Q85.039-60.611 84.616-60.834Q84.192-61.056 84.192-61.521M84.879-61.521Q84.879-61.236 85.106-61.050Q85.332-60.865 85.625-60.865Q85.871-60.865 86.096-60.982Q86.321-61.099 86.455-61.302Q86.590-61.505 86.590-61.759L86.590-62.591Q86.325-62.591 86.039-62.537Q85.754-62.482 85.483-62.353Q85.211-62.224 85.045-62.017Q84.879-61.810 84.879-61.521\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-40.835 34.72)\">\u003Cpath d=\"M88.529-59.392Q88.643-59.314 88.818-59.314Q89.107-59.314 89.328-59.527Q89.549-59.740 89.674-60.041L89.963-60.689L88.689-63.576Q88.607-63.752 88.463-63.796Q88.318-63.841 88.049-63.841L88.049-64.138L89.768-64.138L89.768-63.841Q89.346-63.841 89.346-63.658Q89.346-63.646 89.361-63.576L90.299-61.451L91.131-63.361Q91.170-63.451 91.170-63.529Q91.170-63.669 91.068-63.755Q90.967-63.841 90.826-63.841L90.826-64.138L92.178-64.138L92.178-63.841Q91.924-63.841 91.730-63.716Q91.537-63.591 91.432-63.361L89.986-60.041Q89.873-59.787 89.707-59.564Q89.541-59.341 89.312-59.199Q89.084-59.056 88.818-59.056Q88.521-59.056 88.281-59.248Q88.041-59.439 88.041-59.728Q88.041-59.884 88.146-59.986Q88.252-60.087 88.400-60.087Q88.506-60.087 88.586-60.041Q88.666-59.994 88.713-59.916Q88.760-59.837 88.760-59.728Q88.760-59.607 88.699-59.519Q88.639-59.431 88.529-59.392\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-40.835 34.72)\">\u003Cpath d=\"M97.290-60.689L95.512-60.689L95.512-60.986Q95.786-60.986 95.954-61.033Q96.122-61.080 96.122-61.248L96.122-63.384Q96.122-63.599 96.065-63.695Q96.008-63.791 95.895-63.812Q95.782-63.834 95.536-63.834L95.536-64.130L96.735-64.216L96.735-61.248Q96.735-61.080 96.881-61.033Q97.028-60.986 97.290-60.986L97.290-60.689M95.848-65.611Q95.848-65.802 95.983-65.933Q96.118-66.064 96.313-66.064Q96.434-66.064 96.538-66.001Q96.641-65.939 96.704-65.835Q96.766-65.732 96.766-65.611Q96.766-65.416 96.635-65.281Q96.504-65.146 96.313-65.146Q96.114-65.146 95.981-65.279Q95.848-65.412 95.848-65.611M99.719-60.689L97.864-60.689L97.864-60.986Q98.137-60.986 98.305-61.033Q98.473-61.080 98.473-61.248L98.473-63.384Q98.473-63.599 98.411-63.695Q98.348-63.791 98.229-63.812Q98.110-63.834 97.864-63.834L97.864-64.130L99.055-64.216L99.055-63.482Q99.169-63.697 99.362-63.865Q99.555-64.033 99.794-64.125Q100.032-64.216 100.286-64.216Q101.454-64.216 101.454-63.138L101.454-61.248Q101.454-61.080 101.624-61.033Q101.794-60.986 102.063-60.986L102.063-60.689L100.208-60.689L100.208-60.986Q100.481-60.986 100.649-61.033Q100.817-61.080 100.817-61.248L100.817-63.123Q100.817-63.505 100.696-63.734Q100.575-63.962 100.223-63.962Q99.911-63.962 99.657-63.800Q99.403-63.638 99.256-63.369Q99.110-63.099 99.110-62.802L99.110-61.248Q99.110-61.080 99.280-61.033Q99.450-60.986 99.719-60.986\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-40.835 34.72)\">\u003Cpath d=\"M107.406-60.689L105.484-60.689L105.484-60.986Q106.293-60.986 106.293-61.466L106.293-65.591Q106.293-65.763 106.050-65.810Q105.808-65.857 105.484-65.857L105.484-66.154L106.980-66.154Q107.089-66.154 107.148-66.041L108.996-61.498L110.836-66.041Q110.898-66.154 111.004-66.154L112.507-66.154L112.507-65.857Q112.187-65.857 111.945-65.810Q111.703-65.763 111.703-65.591L111.703-61.248Q111.703-61.080 111.945-61.033Q112.187-60.986 112.507-60.986L112.507-60.689L110.207-60.689L110.207-60.986Q111.011-60.986 111.011-61.248L111.011-65.841L108.964-60.802Q108.929-60.689 108.796-60.689Q108.656-60.689 108.621-60.802L106.597-65.779L106.597-61.466Q106.597-60.986 107.406-60.986L107.406-60.689M113.222-61.521Q113.222-62.005 113.625-62.300Q114.027-62.595 114.578-62.714Q115.129-62.834 115.621-62.834L115.621-63.123Q115.621-63.349 115.505-63.556Q115.390-63.763 115.193-63.882Q114.996-64.001 114.765-64.001Q114.339-64.001 114.054-63.896Q114.125-63.869 114.171-63.814Q114.218-63.759 114.244-63.689Q114.269-63.619 114.269-63.544Q114.269-63.439 114.218-63.347Q114.168-63.255 114.076-63.205Q113.984-63.154 113.879-63.154Q113.773-63.154 113.681-63.205Q113.589-63.255 113.539-63.347Q113.488-63.439 113.488-63.544Q113.488-63.962 113.877-64.109Q114.265-64.255 114.765-64.255Q115.097-64.255 115.451-64.125Q115.804-63.994 116.033-63.740Q116.261-63.486 116.261-63.138L116.261-61.337Q116.261-61.205 116.334-61.095Q116.406-60.986 116.535-60.986Q116.660-60.986 116.728-61.091Q116.796-61.197 116.796-61.337L116.796-61.849L117.078-61.849L117.078-61.337Q117.078-61.134 116.961-60.976Q116.843-60.818 116.662-60.734Q116.480-60.650 116.277-60.650Q116.046-60.650 115.894-60.822Q115.742-60.994 115.711-61.224Q115.550-60.943 115.242-60.777Q114.933-60.611 114.582-60.611Q114.070-60.611 113.646-60.834Q113.222-61.056 113.222-61.521M113.910-61.521Q113.910-61.236 114.136-61.050Q114.363-60.865 114.656-60.865Q114.902-60.865 115.127-60.982Q115.351-61.099 115.486-61.302Q115.621-61.505 115.621-61.759L115.621-62.591Q115.355-62.591 115.070-62.537Q114.785-62.482 114.513-62.353Q114.242-62.224 114.076-62.017Q113.910-61.810 113.910-61.521\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-40.835 34.72)\">\u003Cpath d=\"M117.563-59.392Q117.677-59.314 117.852-59.314Q118.141-59.314 118.362-59.527Q118.583-59.740 118.708-60.041L118.997-60.689L117.723-63.576Q117.641-63.752 117.497-63.796Q117.352-63.841 117.083-63.841L117.083-64.138L118.802-64.138L118.802-63.841Q118.380-63.841 118.380-63.658Q118.380-63.646 118.395-63.576L119.333-61.451L120.165-63.361Q120.204-63.451 120.204-63.529Q120.204-63.669 120.102-63.755Q120.001-63.841 119.860-63.841L119.860-64.138L121.212-64.138L121.212-63.841Q120.958-63.841 120.764-63.716Q120.571-63.591 120.466-63.361L119.020-60.041Q118.907-59.787 118.741-59.564Q118.575-59.341 118.346-59.199Q118.118-59.056 117.852-59.056Q117.555-59.056 117.315-59.248Q117.075-59.439 117.075-59.728Q117.075-59.884 117.180-59.986Q117.286-60.087 117.434-60.087Q117.540-60.087 117.620-60.041Q117.700-59.994 117.747-59.916Q117.794-59.837 117.794-59.728Q117.794-59.607 117.733-59.519Q117.673-59.431 117.563-59.392M122.813-61.154Q122.813-61.345 122.946-61.478Q123.079-61.611 123.274-61.611Q123.466-61.611 123.598-61.478Q123.731-61.345 123.731-61.154Q123.731-60.955 123.598-60.822Q123.466-60.689 123.274-60.689Q123.079-60.689 122.946-60.822Q122.813-60.955 122.813-61.154M123.130-62.330L123.130-62.728Q123.130-63.127 123.259-63.527Q123.387-63.927 123.634-64.263Q123.712-64.369 123.878-64.544Q124.044-64.720 124.112-64.857Q124.180-64.994 124.180-65.232Q124.180-65.720 123.977-65.892Q123.774-66.064 123.274-66.064Q122.966-66.064 122.688-65.953Q122.411-65.841 122.259-65.619Q122.430-65.619 122.538-65.505Q122.645-65.392 122.645-65.224Q122.645-65.123 122.594-65.031Q122.544-64.939 122.454-64.886Q122.364-64.834 122.251-64.834Q122.141-64.834 122.052-64.886Q121.962-64.939 121.911-65.031Q121.860-65.123 121.860-65.224Q121.860-65.572 122.071-65.822Q122.282-66.072 122.606-66.197Q122.930-66.322 123.274-66.322Q123.934-66.322 124.428-66.072Q124.923-65.822 124.923-65.224Q124.923-64.974 124.800-64.759Q124.677-64.544 124.466-64.408Q124.153-64.205 123.921-63.949Q123.688-63.693 123.550-63.377Q123.411-63.060 123.411-62.712L123.411-62.330Q123.411-62.294 123.384-62.267Q123.356-62.240 123.325-62.240L123.219-62.240Q123.188-62.240 123.159-62.265Q123.130-62.291 123.130-62.330\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(86.046 34.47)\">\u003Cpath d=\"M46.901-60.689L44.129-60.689L44.129-60.969Q44.850-60.969 44.850-61.178L44.850-64.979Q44.850-65.190 44.129-65.190L44.129-65.471L46.901-65.471Q47.386-65.471 47.822-65.276Q48.258-65.081 48.581-64.739Q48.904-64.397 49.082-63.957Q49.259-63.516 49.259-63.034Q49.259-62.548 49.075-62.125Q48.890-61.701 48.567-61.379Q48.244-61.058 47.812-60.874Q47.380-60.689 46.901-60.689M45.513-64.979L45.513-61.178Q45.513-61.038 45.602-61.003Q45.691-60.969 45.879-60.969L46.703-60.969Q47.328-60.969 47.732-61.231Q48.135-61.492 48.323-61.959Q48.511-62.425 48.511-63.034Q48.511-63.512 48.419-63.905Q48.326-64.298 48.077-64.596Q47.838-64.883 47.474-65.037Q47.110-65.190 46.703-65.190L45.879-65.190Q45.691-65.190 45.602-65.156Q45.513-65.122 45.513-64.979M52.301-60.689L50.097-60.689L50.097-60.969Q50.856-60.969 50.856-61.178L50.856-64.979Q50.856-65.190 50.097-65.190L50.097-65.471L52.301-65.471L52.301-65.190Q51.546-65.190 51.546-64.979L51.546-61.178Q51.546-60.969 52.301-60.969L52.301-60.689M55.138-60.689L53.033-60.689L53.033-60.969Q53.754-60.969 53.754-61.178L53.754-64.979Q53.754-65.190 53.033-65.190L53.033-65.471L55.371-65.471Q55.682-65.471 56.025-65.397Q56.369-65.324 56.690-65.168Q57.011-65.013 57.213-64.767Q57.415-64.521 57.415-64.196Q57.415-63.909 57.227-63.678Q57.039-63.447 56.762-63.299Q56.485-63.150 56.194-63.068Q56.529-62.965 56.764-62.733Q56.998-62.501 57.042-62.172L57.128-61.564Q57.155-61.352 57.201-61.183Q57.247-61.014 57.351-60.894Q57.456-60.774 57.644-60.774Q57.859-60.774 57.975-60.959Q58.091-61.144 58.091-61.376Q58.109-61.441 58.184-61.458L58.276-61.458Q58.358-61.438 58.358-61.356Q58.358-61.150 58.267-60.964Q58.177-60.778 58.011-60.663Q57.845-60.549 57.644-60.549Q57.305-60.549 57.011-60.650Q56.717-60.751 56.533-60.973Q56.348-61.195 56.348-61.543L56.348-62.152Q56.348-62.401 56.205-62.588Q56.061-62.774 55.837-62.873Q55.613-62.972 55.364-62.972L54.417-62.972L54.417-61.178Q54.417-60.969 55.138-60.969L55.138-60.689M54.417-64.979L54.417-63.194L55.272-63.194Q55.559-63.194 55.798-63.237Q56.037-63.280 56.225-63.389Q56.413-63.499 56.524-63.697Q56.635-63.895 56.635-64.196Q56.635-64.773 56.268-64.982Q55.901-65.190 55.272-65.190L54.783-65.190Q54.595-65.190 54.506-65.156Q54.417-65.122 54.417-64.979M63.225-60.689L58.823-60.689L58.823-60.969Q59.544-60.969 59.544-61.178L59.544-64.979Q59.544-65.190 58.823-65.190L58.823-65.471L63.112-65.471L63.321-63.834L63.058-63.834Q63-64.305 62.897-64.570Q62.795-64.835 62.610-64.968Q62.425-65.102 62.154-65.146Q61.882-65.190 61.383-65.190L60.600-65.190Q60.412-65.190 60.323-65.156Q60.235-65.122 60.235-64.979L60.235-63.314L60.809-63.314Q61.198-63.314 61.381-63.365Q61.564-63.417 61.646-63.589Q61.728-63.762 61.728-64.134L61.991-64.134L61.991-62.213L61.728-62.213Q61.728-62.586 61.646-62.759Q61.564-62.931 61.381-62.982Q61.198-63.034 60.809-63.034L60.235-63.034L60.235-61.178Q60.235-61.038 60.323-61.003Q60.412-60.969 60.600-60.969L61.448-60.969Q61.978-60.969 62.287-61.038Q62.596-61.106 62.784-61.273Q62.972-61.441 63.080-61.743Q63.188-62.046 63.273-62.559L63.540-62.559L63.225-60.689M64.302-63.082Q64.302-63.608 64.519-64.076Q64.736-64.544 65.119-64.890Q65.502-65.235 65.985-65.423Q66.469-65.611 66.999-65.611Q67.402-65.611 67.766-65.454Q68.130-65.296 68.414-65.002L68.838-65.584Q68.872-65.611 68.896-65.611L68.944-65.611Q68.974-65.611 68.998-65.587Q69.022-65.563 69.022-65.532L69.022-63.669Q69.022-63.646 68.996-63.620Q68.971-63.594 68.944-63.594L68.817-63.594Q68.756-63.594 68.742-63.669Q68.711-63.984 68.576-64.288Q68.441-64.592 68.226-64.826Q68.010-65.061 67.722-65.196Q67.433-65.331 67.105-65.331Q66.462-65.331 66.004-65.037Q65.546-64.743 65.314-64.230Q65.081-63.717 65.081-63.082Q65.081-62.610 65.211-62.201Q65.341-61.793 65.601-61.480Q65.861-61.168 66.240-60.998Q66.619-60.829 67.111-60.829Q67.440-60.829 67.734-60.945Q68.028-61.062 68.262-61.277Q68.496-61.492 68.626-61.781Q68.756-62.070 68.756-62.398Q68.756-62.425 68.783-62.449Q68.810-62.473 68.831-62.473L68.944-62.473Q68.981-62.473 69.002-62.448Q69.022-62.422 69.022-62.384Q69.022-61.988 68.856-61.651Q68.691-61.314 68.403-61.067Q68.116-60.819 67.747-60.684Q67.378-60.549 66.999-60.549Q66.479-60.549 65.987-60.739Q65.495-60.928 65.115-61.272Q64.736-61.615 64.519-62.084Q64.302-62.552 64.302-63.082M73.739-60.689L71.001-60.689L71.001-60.969Q71.350-60.969 71.686-61.005Q72.023-61.041 72.023-61.178L72.023-64.979Q72.023-65.122 71.934-65.156Q71.845-65.190 71.661-65.190L71.302-65.190Q71.001-65.190 70.786-65.143Q70.570-65.095 70.413-64.938Q70.277-64.804 70.217-64.526Q70.157-64.247 70.119-63.834L69.853-63.834L70-65.471L74.734-65.471L74.881-63.834L74.614-63.834Q74.576-64.247 74.520-64.524Q74.464-64.801 74.320-64.938Q74.159-65.098 73.947-65.144Q73.736-65.190 73.431-65.190L73.079-65.190Q72.895-65.190 72.806-65.156Q72.717-65.122 72.717-64.979L72.717-61.178Q72.717-61.041 73.054-61.005Q73.390-60.969 73.739-60.969L73.739-60.689M77.769-60.689L75.564-60.689L75.564-60.969Q76.323-60.969 76.323-61.178L76.323-64.979Q76.323-65.190 75.564-65.190L75.564-65.471L77.769-65.471L77.769-65.190Q77.013-65.190 77.013-64.979L77.013-61.178Q77.013-60.969 77.769-60.969L77.769-60.689M80.859-60.648L79.016-64.979Q78.948-65.122 78.767-65.156Q78.586-65.190 78.322-65.190L78.322-65.471L80.291-65.471L80.291-65.190Q79.737-65.190 79.737-65.016Q79.741-65.006 79.743-64.996Q79.744-64.985 79.744-64.979L81.265-61.403L82.697-64.791Q82.721-64.828 82.721-64.883Q82.721-65.043 82.571-65.117Q82.421-65.190 82.236-65.190L82.236-65.471L83.784-65.471L83.784-65.190Q83.525-65.190 83.314-65.096Q83.104-65.002 83.008-64.791L81.252-60.648Q81.200-60.549 81.105-60.549L81.006-60.549Q80.906-60.549 80.859-60.648M88.788-60.689L84.386-60.689L84.386-60.969Q85.107-60.969 85.107-61.178L85.107-64.979Q85.107-65.190 84.386-65.190L84.386-65.471L88.675-65.471L88.884-63.834L88.621-63.834Q88.563-64.305 88.460-64.570Q88.358-64.835 88.173-64.968Q87.988-65.102 87.717-65.146Q87.445-65.190 86.946-65.190L86.163-65.190Q85.975-65.190 85.886-65.156Q85.798-65.122 85.798-64.979L85.798-63.314L86.372-63.314Q86.761-63.314 86.944-63.365Q87.127-63.417 87.209-63.589Q87.291-63.762 87.291-64.134L87.554-64.134L87.554-62.213L87.291-62.213Q87.291-62.586 87.209-62.759Q87.127-62.931 86.944-62.982Q86.761-63.034 86.372-63.034L85.798-63.034L85.798-61.178Q85.798-61.038 85.886-61.003Q85.975-60.969 86.163-60.969L87.011-60.969Q87.541-60.969 87.850-61.038Q88.159-61.106 88.347-61.273Q88.535-61.441 88.643-61.743Q88.751-62.046 88.836-62.559L89.103-62.559\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 34.47)\">\u003Cpath d=\"M94.548-58.939Q93.998-59.339 93.627-59.894Q93.256-60.450 93.075-61.096Q92.894-61.742 92.894-62.439Q92.894-62.952 92.994-63.447Q93.095-63.943 93.300-64.394Q93.505-64.845 93.818-65.237Q94.131-65.628 94.548-65.932Q94.558-65.936 94.565-65.937Q94.572-65.939 94.582-65.939L94.650-65.939Q94.685-65.939 94.707-65.915Q94.729-65.891 94.729-65.854Q94.729-65.809 94.702-65.792Q94.353-65.491 94.100-65.107Q93.847-64.722 93.695-64.281Q93.543-63.840 93.471-63.384Q93.399-62.928 93.399-62.439Q93.399-61.438 93.709-60.551Q94.018-59.664 94.702-59.079Q94.729-59.062 94.729-59.018Q94.729-58.980 94.707-58.956Q94.685-58.932 94.650-58.932L94.582-58.932Q94.575-58.936 94.567-58.937Q94.558-58.939 94.548-58.939M97.163-60.621Q96.732-60.621 96.356-60.833Q95.980-61.044 95.760-61.405Q95.539-61.766 95.539-62.200Q95.539-62.644 95.777-63.005Q96.014-63.365 96.400-63.570Q96.787-63.775 97.228-63.775Q97.545-63.775 97.829-63.620Q98.113-63.464 98.297-63.188L98.543-63.775L98.779-63.775L98.779-59.821Q98.779-59.684 98.930-59.648Q99.080-59.612 99.306-59.612L99.306-59.332L97.675-59.332L97.675-59.612Q97.901-59.612 98.050-59.647Q98.198-59.681 98.198-59.821L98.198-61.082Q98-60.863 97.728-60.742Q97.457-60.621 97.163-60.621M97.214-60.843Q97.545-60.843 97.817-61.048Q98.089-61.253 98.229-61.578L98.229-62.712Q98.171-62.935 98.039-63.117Q97.908-63.300 97.718-63.411Q97.528-63.522 97.303-63.522Q96.975-63.522 96.723-63.326Q96.472-63.129 96.339-62.822Q96.206-62.514 96.206-62.193Q96.206-61.892 96.322-61.576Q96.438-61.260 96.667-61.051Q96.896-60.843 97.214-60.843M100.314-61.523L100.314-63.027Q100.314-63.297 100.206-63.358Q100.099-63.420 99.788-63.420L99.788-63.700L100.895-63.775L100.895-61.543L100.895-61.523Q100.895-61.243 100.946-61.099Q100.998-60.956 101.139-60.899Q101.281-60.843 101.568-60.843Q101.821-60.843 102.026-60.983Q102.231-61.123 102.348-61.349Q102.464-61.574 102.464-61.824L102.464-63.027Q102.464-63.297 102.356-63.358Q102.249-63.420 101.937-63.420L101.937-63.700L103.045-63.775L103.045-61.362Q103.045-61.171 103.098-61.089Q103.151-61.007 103.252-60.988Q103.353-60.969 103.568-60.969L103.568-60.689L102.491-60.621L102.491-61.185Q102.382-61.003 102.237-60.880Q102.091-60.757 101.905-60.689Q101.719-60.621 101.517-60.621Q100.314-60.621 100.314-61.523M104.115-62.224Q104.115-62.545 104.239-62.834Q104.364-63.123 104.590-63.346Q104.815-63.570 105.111-63.690Q105.407-63.810 105.725-63.810Q106.053-63.810 106.314-63.710Q106.576-63.611 106.752-63.429Q106.928-63.246 107.022-62.988Q107.116-62.730 107.116-62.398Q107.116-62.306 107.034-62.285L104.778-62.285L104.778-62.224Q104.778-61.636 105.062-61.253Q105.345-60.870 105.913-60.870Q106.234-60.870 106.502-61.063Q106.771-61.256 106.859-61.571Q106.866-61.612 106.941-61.626L107.034-61.626Q107.116-61.602 107.116-61.530Q107.116-61.523 107.109-61.496Q106.996-61.099 106.625-60.860Q106.254-60.621 105.831-60.621Q105.393-60.621 104.993-60.829Q104.593-61.038 104.354-61.405Q104.115-61.772 104.115-62.224M104.785-62.494L106.600-62.494Q106.600-62.771 106.502-63.023Q106.405-63.276 106.207-63.432Q106.008-63.587 105.725-63.587Q105.448-63.587 105.234-63.429Q105.021-63.270 104.903-63.015Q104.785-62.760 104.785-62.494M107.704-60.696L107.704-61.759Q107.704-61.783 107.731-61.810Q107.758-61.837 107.782-61.837L107.892-61.837Q107.957-61.837 107.970-61.779Q108.066-61.345 108.312-61.094Q108.558-60.843 108.972-60.843Q109.313-60.843 109.566-60.976Q109.819-61.109 109.819-61.417Q109.819-61.574 109.725-61.689Q109.631-61.803 109.493-61.872Q109.354-61.940 109.187-61.978L108.606-62.077Q108.250-62.145 107.977-62.366Q107.704-62.586 107.704-62.928Q107.704-63.177 107.815-63.352Q107.926-63.526 108.112-63.625Q108.298-63.724 108.514-63.767Q108.729-63.810 108.972-63.810Q109.385-63.810 109.666-63.628L109.881-63.803Q109.891-63.806 109.898-63.808Q109.905-63.810 109.915-63.810L109.966-63.810Q109.994-63.810 110.018-63.786Q110.041-63.762 110.041-63.734L110.041-62.887Q110.041-62.866 110.018-62.839Q109.994-62.812 109.966-62.812L109.854-62.812Q109.826-62.812 109.801-62.837Q109.775-62.863 109.775-62.887Q109.775-63.123 109.669-63.287Q109.563-63.451 109.380-63.533Q109.197-63.615 108.965-63.615Q108.637-63.615 108.380-63.512Q108.124-63.410 108.124-63.133Q108.124-62.938 108.307-62.829Q108.490-62.719 108.719-62.678L109.293-62.572Q109.539-62.524 109.753-62.396Q109.966-62.268 110.103-62.065Q110.240-61.861 110.240-61.612Q110.240-61.099 109.874-60.860Q109.508-60.621 108.972-60.621Q108.476-60.621 108.145-60.915L107.878-60.641Q107.857-60.621 107.830-60.621L107.782-60.621Q107.758-60.621 107.731-60.648Q107.704-60.675 107.704-60.696M111.395-61.530L111.395-63.427L110.756-63.427L110.756-63.649Q111.074-63.649 111.291-63.859Q111.508-64.069 111.609-64.379Q111.709-64.688 111.709-64.996L111.976-64.996L111.976-63.707L113.053-63.707L113.053-63.427L111.976-63.427L111.976-61.543Q111.976-61.267 112.080-61.068Q112.185-60.870 112.444-60.870Q112.602-60.870 112.708-60.974Q112.813-61.079 112.863-61.232Q112.913-61.386 112.913-61.543L112.913-61.957L113.179-61.957L113.179-61.530Q113.179-61.304 113.080-61.094Q112.981-60.884 112.796-60.752Q112.612-60.621 112.383-60.621Q111.945-60.621 111.670-60.858Q111.395-61.096 111.395-61.530M115.606-60.689L114.054-60.689L114.054-60.969Q114.280-60.969 114.428-61.003Q114.577-61.038 114.577-61.178L114.577-63.027Q114.577-63.215 114.529-63.299Q114.481-63.382 114.384-63.401Q114.287-63.420 114.075-63.420L114.075-63.700L115.131-63.775L115.131-61.178Q115.131-61.038 115.262-61.003Q115.394-60.969 115.606-60.969L115.606-60.689M114.334-64.996Q114.334-65.167 114.458-65.286Q114.581-65.406 114.751-65.406Q114.919-65.406 115.042-65.286Q115.165-65.167 115.165-64.996Q115.165-64.821 115.042-64.698Q114.919-64.575 114.751-64.575Q114.581-64.575 114.458-64.698Q114.334-64.821 114.334-64.996M116.211-62.172Q116.211-62.514 116.346-62.813Q116.481-63.112 116.720-63.336Q116.959-63.560 117.277-63.685Q117.595-63.810 117.927-63.810Q118.371-63.810 118.771-63.594Q119.171-63.379 119.405-63.001Q119.639-62.624 119.639-62.172Q119.639-61.831 119.497-61.547Q119.355-61.263 119.111-61.056Q118.867-60.850 118.557-60.735Q118.248-60.621 117.927-60.621Q117.496-60.621 117.094-60.822Q116.693-61.024 116.452-61.376Q116.211-61.728 116.211-62.172M117.927-60.870Q118.528-60.870 118.752-61.248Q118.976-61.626 118.976-62.258Q118.976-62.870 118.742-63.229Q118.508-63.587 117.927-63.587Q116.874-63.587 116.874-62.258Q116.874-61.626 117.100-61.248Q117.325-60.870 117.927-60.870M121.916-60.689L120.282-60.689L120.282-60.969Q120.511-60.969 120.659-61.003Q120.808-61.038 120.808-61.178L120.808-63.027Q120.808-63.297 120.700-63.358Q120.593-63.420 120.282-63.420L120.282-63.700L121.341-63.775L121.341-63.126Q121.512-63.434 121.816-63.605Q122.121-63.775 122.466-63.775Q122.972-63.775 123.255-63.552Q123.539-63.328 123.539-62.832L123.539-61.178Q123.539-61.041 123.688-61.005Q123.836-60.969 124.062-60.969L124.062-60.689L122.432-60.689L122.432-60.969Q122.661-60.969 122.809-61.003Q122.958-61.038 122.958-61.178L122.958-62.818Q122.958-63.153 122.838-63.353Q122.719-63.553 122.404-63.553Q122.134-63.553 121.900-63.417Q121.666-63.280 121.528-63.046Q121.389-62.812 121.389-62.538L121.389-61.178Q121.389-61.041 121.540-61.005Q121.690-60.969 121.916-60.969L121.916-60.689M124.971-58.932L124.903-58.932Q124.869-58.932 124.846-58.958Q124.824-58.983 124.824-59.018Q124.824-59.062 124.855-59.079Q125.210-59.383 125.460-59.773Q125.709-60.163 125.862-60.595Q126.014-61.027 126.084-61.496Q126.154-61.964 126.154-62.439Q126.154-62.918 126.084-63.384Q126.014-63.851 125.860-64.286Q125.706-64.722 125.455-65.110Q125.204-65.498 124.855-65.792Q124.824-65.809 124.824-65.854Q124.824-65.888 124.846-65.913Q124.869-65.939 124.903-65.939L124.971-65.939Q124.981-65.939 124.990-65.937Q124.999-65.936 125.009-65.932Q125.552-65.532 125.925-64.979Q126.297-64.425 126.479-63.779Q126.660-63.133 126.660-62.439Q126.660-61.738 126.479-61.091Q126.297-60.443 125.923-59.889Q125.549-59.335 125.009-58.939Q124.999-58.939 124.990-58.937Q124.981-58.936 124.971-58.932\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-bg)\">\u003Cpath d=\"M-30.241 16.134h147.954V-6.63H-30.24Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-68 67.441)\">\u003Cpath d=\"M44.209-63.423Q44.209-64.017 44.441-64.548Q44.673-65.080 45.090-65.480Q45.506-65.880 46.039-66.101Q46.572-66.322 47.177-66.322Q47.615-66.322 48.013-66.140Q48.412-65.959 48.720-65.626L49.193-66.291Q49.224-66.322 49.256-66.322L49.302-66.322Q49.330-66.322 49.361-66.291Q49.392-66.259 49.392-66.232L49.392-64.095Q49.392-64.072 49.361-64.041Q49.330-64.009 49.302-64.009L49.185-64.009Q49.158-64.009 49.127-64.041Q49.095-64.072 49.095-64.095Q49.095-64.361 48.953-64.714Q48.810-65.068 48.631-65.306Q48.377-65.638 48.027-65.832Q47.677-66.025 47.271-66.025Q46.767-66.025 46.314-65.806Q45.861-65.587 45.568-65.193Q45.072-64.525 45.072-63.423Q45.072-62.892 45.209-62.425Q45.345-61.959 45.621-61.593Q45.896-61.228 46.316-61.023Q46.736-60.818 47.279-60.818Q47.767-60.818 48.191-61.062Q48.615-61.306 48.863-61.726Q49.111-62.146 49.111-62.642Q49.111-62.677 49.140-62.703Q49.170-62.728 49.201-62.728L49.302-62.728Q49.345-62.728 49.369-62.699Q49.392-62.669 49.392-62.627Q49.392-62.189 49.216-61.800Q49.041-61.412 48.730-61.128Q48.420-60.845 48.009-60.683Q47.599-60.521 47.177-60.521Q46.588-60.521 46.047-60.742Q45.506-60.962 45.090-61.367Q44.673-61.771 44.441-62.300Q44.209-62.830 44.209-63.423M50.591-61.154Q50.591-61.337 50.728-61.474Q50.865-61.611 51.056-61.611Q51.248-61.611 51.381-61.478Q51.513-61.345 51.513-61.154Q51.513-60.955 51.381-60.822Q51.248-60.689 51.056-60.689Q50.865-60.689 50.728-60.826Q50.591-60.962 50.591-61.154M50.591-63.681Q50.591-63.865 50.728-64.001Q50.865-64.138 51.056-64.138Q51.248-64.138 51.381-64.005Q51.513-63.873 51.513-63.681Q51.513-63.482 51.381-63.349Q51.248-63.216 51.056-63.216Q50.865-63.216 50.728-63.353Q50.591-63.490 50.591-63.681\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-68 67.441)\">\u003Cpath d=\"M57.109-60.689L55.359-60.689L55.359-60.986Q56.058-60.986 56.246-61.466L58.047-66.291Q58.101-66.400 58.215-66.400L58.285-66.400Q58.398-66.400 58.453-66.291L60.343-61.248Q60.422-61.080 60.625-61.033Q60.828-60.986 61.140-60.986L61.140-60.689L58.918-60.689L58.918-60.986Q59.558-60.986 59.558-61.201Q59.558-61.220 59.556-61.230Q59.554-61.240 59.550-61.248L59.086-62.482L56.941-62.482L56.558-61.466Q56.554-61.451 56.549-61.421Q56.543-61.392 56.543-61.369Q56.543-61.228 56.633-61.144Q56.722-61.060 56.855-61.023Q56.988-60.986 57.109-60.986L57.109-60.689M58.015-65.345L57.047-62.779L58.972-62.779\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-68 67.441)\">\u003Cpath d=\"M66.438-60.689L64.582-60.689L64.582-60.986Q64.856-60.986 65.024-61.033Q65.192-61.080 65.192-61.248L65.192-63.384Q65.192-63.599 65.129-63.695Q65.067-63.791 64.948-63.812Q64.829-63.834 64.582-63.834L64.582-64.130L65.774-64.216L65.774-63.482Q65.887-63.697 66.081-63.865Q66.274-64.033 66.512-64.125Q66.750-64.216 67.004-64.216Q67.965-64.216 68.141-63.505Q68.325-63.834 68.653-64.025Q68.981-64.216 69.360-64.216Q70.536-64.216 70.536-63.138L70.536-61.248Q70.536-61.080 70.704-61.033Q70.872-60.986 71.141-60.986L71.141-60.689L69.286-60.689L69.286-60.986Q69.559-60.986 69.727-61.031Q69.895-61.076 69.895-61.248L69.895-63.123Q69.895-63.509 69.770-63.736Q69.645-63.962 69.293-63.962Q68.989-63.962 68.733-63.800Q68.477-63.638 68.329-63.369Q68.180-63.099 68.180-62.802L68.180-61.248Q68.180-61.080 68.350-61.033Q68.520-60.986 68.790-60.986L68.790-60.689L66.934-60.689L66.934-60.986Q67.207-60.986 67.375-61.033Q67.543-61.080 67.543-61.248L67.543-63.123Q67.543-63.509 67.418-63.736Q67.293-63.962 66.942-63.962Q66.637-63.962 66.381-63.800Q66.125-63.638 65.977-63.369Q65.829-63.099 65.829-62.802L65.829-61.248Q65.829-61.080 65.999-61.033Q66.168-60.986 66.438-60.986L66.438-60.689M71.586-62.443Q71.586-62.923 71.819-63.339Q72.051-63.755 72.461-64.005Q72.872-64.255 73.348-64.255Q74.079-64.255 74.477-63.814Q74.875-63.373 74.875-62.642Q74.875-62.537 74.782-62.513L72.332-62.513L72.332-62.443Q72.332-62.033 72.454-61.677Q72.575-61.322 72.846-61.105Q73.118-60.888 73.547-60.888Q73.911-60.888 74.207-61.117Q74.504-61.345 74.606-61.697Q74.614-61.744 74.700-61.759L74.782-61.759Q74.875-61.732 74.875-61.650Q74.875-61.642 74.868-61.611Q74.805-61.384 74.666-61.201Q74.528-61.017 74.336-60.884Q74.145-60.752 73.926-60.681Q73.707-60.611 73.469-60.611Q73.098-60.611 72.760-60.748Q72.422-60.884 72.155-61.136Q71.887-61.388 71.737-61.728Q71.586-62.068 71.586-62.443M72.340-62.752L74.301-62.752Q74.301-63.056 74.200-63.347Q74.098-63.638 73.881-63.820Q73.665-64.001 73.348-64.001Q73.047-64.001 72.817-63.814Q72.586-63.627 72.463-63.335Q72.340-63.044 72.340-62.752M75.364-62.443Q75.364-62.923 75.596-63.339Q75.829-63.755 76.239-64.005Q76.649-64.255 77.125-64.255Q77.856-64.255 78.254-63.814Q78.653-63.373 78.653-62.642Q78.653-62.537 78.559-62.513L76.110-62.513L76.110-62.443Q76.110-62.033 76.231-61.677Q76.352-61.322 76.624-61.105Q76.895-60.888 77.325-60.888Q77.688-60.888 77.985-61.117Q78.282-61.345 78.383-61.697Q78.391-61.744 78.477-61.759L78.559-61.759Q78.653-61.732 78.653-61.650Q78.653-61.642 78.645-61.611Q78.582-61.384 78.444-61.201Q78.305-61.017 78.114-60.884Q77.922-60.752 77.704-60.681Q77.485-60.611 77.247-60.611Q76.875-60.611 76.538-60.748Q76.200-60.884 75.932-61.136Q75.665-61.388 75.514-61.728Q75.364-62.068 75.364-62.443M76.118-62.752L78.079-62.752Q78.079-63.056 77.977-63.347Q77.875-63.638 77.659-63.820Q77.442-64.001 77.125-64.001Q76.825-64.001 76.594-63.814Q76.364-63.627 76.241-63.335Q76.118-63.044 76.118-62.752M79.766-61.650L79.766-63.841L79.063-63.841L79.063-64.095Q79.418-64.095 79.661-64.328Q79.903-64.560 80.014-64.908Q80.125-65.255 80.125-65.611L80.407-65.611L80.407-64.138L81.582-64.138L81.582-63.841L80.407-63.841L80.407-61.666Q80.407-61.345 80.526-61.117Q80.645-60.888 80.926-60.888Q81.106-60.888 81.223-61.011Q81.340-61.134 81.393-61.314Q81.446-61.494 81.446-61.666L81.446-62.138L81.727-62.138L81.727-61.650Q81.727-61.396 81.622-61.156Q81.516-60.916 81.319-60.763Q81.122-60.611 80.864-60.611Q80.547-60.611 80.295-60.734Q80.043-60.857 79.905-61.091Q79.766-61.326 79.766-61.650M84.305-60.689L82.528-60.689L82.528-60.986Q82.801-60.986 82.969-61.033Q83.137-61.080 83.137-61.248L83.137-63.384Q83.137-63.599 83.081-63.695Q83.024-63.791 82.911-63.812Q82.797-63.834 82.551-63.834L82.551-64.130L83.750-64.216L83.750-61.248Q83.750-61.080 83.897-61.033Q84.043-60.986 84.305-60.986L84.305-60.689M82.864-65.611Q82.864-65.802 82.999-65.933Q83.133-66.064 83.329-66.064Q83.450-66.064 83.553-66.001Q83.657-65.939 83.719-65.835Q83.782-65.732 83.782-65.611Q83.782-65.416 83.651-65.281Q83.520-65.146 83.329-65.146Q83.129-65.146 82.997-65.279Q82.864-65.412 82.864-65.611M86.735-60.689L84.879-60.689L84.879-60.986Q85.153-60.986 85.321-61.033Q85.489-61.080 85.489-61.248L85.489-63.384Q85.489-63.599 85.426-63.695Q85.364-63.791 85.245-63.812Q85.125-63.834 84.879-63.834L84.879-64.130L86.071-64.216L86.071-63.482Q86.184-63.697 86.377-63.865Q86.571-64.033 86.809-64.125Q87.047-64.216 87.301-64.216Q88.469-64.216 88.469-63.138L88.469-61.248Q88.469-61.080 88.639-61.033Q88.809-60.986 89.079-60.986L89.079-60.689L87.223-60.689L87.223-60.986Q87.497-60.986 87.665-61.033Q87.832-61.080 87.832-61.248L87.832-63.123Q87.832-63.505 87.711-63.734Q87.590-63.962 87.239-63.962Q86.926-63.962 86.672-63.800Q86.418-63.638 86.272-63.369Q86.125-63.099 86.125-62.802L86.125-61.248Q86.125-61.080 86.295-61.033Q86.465-60.986 86.735-60.986L86.735-60.689M89.524-60.080Q89.524-60.361 89.735-60.572Q89.946-60.783 90.231-60.873Q90.075-60.998 89.997-61.187Q89.918-61.377 89.918-61.576Q89.918-61.931 90.149-62.224Q89.782-62.564 89.782-63.033Q89.782-63.384 89.985-63.654Q90.188-63.923 90.508-64.070Q90.829-64.216 91.172-64.216Q91.692-64.216 92.063-63.935Q92.426-64.306 92.973-64.306Q93.153-64.306 93.280-64.179Q93.407-64.052 93.407-63.873Q93.407-63.767 93.329-63.689Q93.250-63.611 93.141-63.611Q93.032-63.611 92.956-63.687Q92.879-63.763 92.879-63.873Q92.879-63.974 92.918-64.025Q92.926-64.033 92.930-64.039Q92.934-64.044 92.934-64.048Q92.559-64.048 92.239-63.794Q92.559-63.455 92.559-63.033Q92.559-62.763 92.442-62.546Q92.325-62.330 92.120-62.171Q91.915-62.013 91.672-61.931Q91.430-61.849 91.172-61.849Q90.954-61.849 90.741-61.908Q90.528-61.966 90.332-62.087Q90.239-61.947 90.239-61.767Q90.239-61.560 90.375-61.408Q90.512-61.255 90.719-61.255L91.415-61.255Q91.903-61.255 92.315-61.171Q92.727-61.087 93.006-60.830Q93.286-60.572 93.286-60.080Q93.286-59.716 92.965-59.484Q92.645-59.252 92.204-59.150Q91.762-59.048 91.407-59.048Q91.051-59.048 90.608-59.150Q90.165-59.252 89.844-59.484Q89.524-59.716 89.524-60.080M90.028-60.080Q90.028-59.884 90.172-59.736Q90.317-59.587 90.530-59.498Q90.743-59.408 90.983-59.361Q91.223-59.314 91.407-59.314Q91.649-59.314 91.979-59.392Q92.309-59.470 92.545-59.644Q92.782-59.818 92.782-60.080Q92.782-60.486 92.372-60.595Q91.961-60.705 91.399-60.705L90.719-60.705Q90.450-60.705 90.239-60.527Q90.028-60.349 90.028-60.080M91.172-62.115Q91.895-62.115 91.895-63.033Q91.895-63.955 91.172-63.955Q90.446-63.955 90.446-63.033Q90.446-62.115 91.172-62.115\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-68 67.441)\">\u003Cpath d=\"M98.686-60.689L96.701-60.689L96.701-60.986Q96.975-60.986 97.143-61.033Q97.311-61.080 97.311-61.248L97.311-63.841L96.670-63.841L96.670-64.138L97.311-64.138L97.311-65.072Q97.311-65.337 97.428-65.574Q97.545-65.810 97.738-65.974Q97.932-66.138 98.180-66.230Q98.428-66.322 98.694-66.322Q98.979-66.322 99.203-66.164Q99.428-66.005 99.428-65.728Q99.428-65.572 99.322-65.462Q99.217-65.353 99.053-65.353Q98.897-65.353 98.787-65.462Q98.678-65.572 98.678-65.728Q98.678-65.935 98.838-66.041Q98.740-66.064 98.647-66.064Q98.416-66.064 98.244-65.908Q98.072-65.751 97.986-65.515Q97.901-65.279 97.901-65.056L97.901-64.138L98.869-64.138L98.869-63.841L97.924-63.841L97.924-61.248Q97.924-61.080 98.151-61.033Q98.377-60.986 98.686-60.986L98.686-60.689M101.221-60.689L99.240-60.689L99.240-60.986Q99.510-60.986 99.678-61.031Q99.846-61.076 99.846-61.248L99.846-63.384Q99.846-63.599 99.783-63.695Q99.721-63.791 99.604-63.812Q99.486-63.834 99.240-63.834L99.240-64.130L100.408-64.216L100.408-63.431Q100.486-63.642 100.639-63.828Q100.791-64.013 100.990-64.115Q101.190-64.216 101.416-64.216Q101.662-64.216 101.854-64.072Q102.045-63.927 102.045-63.697Q102.045-63.541 101.940-63.431Q101.834-63.322 101.678-63.322Q101.522-63.322 101.412-63.431Q101.303-63.541 101.303-63.697Q101.303-63.857 101.408-63.962Q101.084-63.962 100.869-63.734Q100.654-63.505 100.559-63.166Q100.463-62.826 100.463-62.521L100.463-61.248Q100.463-61.080 100.690-61.033Q100.916-60.986 101.221-60.986L101.221-60.689M102.526-62.384Q102.526-62.888 102.781-63.320Q103.037-63.752 103.473-64.003Q103.908-64.255 104.408-64.255Q104.795-64.255 105.137-64.111Q105.479-63.966 105.740-63.705Q106.002-63.443 106.145-63.107Q106.287-62.771 106.287-62.384Q106.287-61.892 106.024-61.482Q105.760-61.072 105.330-60.841Q104.901-60.611 104.408-60.611Q103.916-60.611 103.483-60.843Q103.049-61.076 102.787-61.484Q102.526-61.892 102.526-62.384M104.408-60.888Q104.865-60.888 105.117-61.111Q105.369-61.334 105.457-61.685Q105.545-62.037 105.545-62.482Q105.545-62.912 105.451-63.250Q105.358-63.587 105.104-63.794Q104.850-64.001 104.408-64.001Q103.760-64.001 103.516-63.585Q103.272-63.169 103.272-62.482Q103.272-62.037 103.360-61.685Q103.447-61.334 103.699-61.111Q103.951-60.888 104.408-60.888M108.701-60.689L106.846-60.689L106.846-60.986Q107.119-60.986 107.287-61.033Q107.455-61.080 107.455-61.248L107.455-63.384Q107.455-63.599 107.393-63.695Q107.330-63.791 107.211-63.812Q107.092-63.834 106.846-63.834L106.846-64.130L108.037-64.216L108.037-63.482Q108.151-63.697 108.344-63.865Q108.537-64.033 108.776-64.125Q109.014-64.216 109.268-64.216Q110.229-64.216 110.404-63.505Q110.588-63.834 110.916-64.025Q111.244-64.216 111.623-64.216Q112.799-64.216 112.799-63.138L112.799-61.248Q112.799-61.080 112.967-61.033Q113.135-60.986 113.404-60.986L113.404-60.689L111.549-60.689L111.549-60.986Q111.822-60.986 111.990-61.031Q112.158-61.076 112.158-61.248L112.158-63.123Q112.158-63.509 112.033-63.736Q111.908-63.962 111.557-63.962Q111.252-63.962 110.996-63.800Q110.740-63.638 110.592-63.369Q110.444-63.099 110.444-62.802L110.444-61.248Q110.444-61.080 110.613-61.033Q110.783-60.986 111.053-60.986L111.053-60.689L109.197-60.689L109.197-60.986Q109.471-60.986 109.639-61.033Q109.807-61.080 109.807-61.248L109.807-63.123Q109.807-63.509 109.682-63.736Q109.557-63.962 109.205-63.962Q108.901-63.962 108.645-63.800Q108.389-63.638 108.240-63.369Q108.092-63.099 108.092-62.802L108.092-61.248Q108.092-61.080 108.262-61.033Q108.432-60.986 108.701-60.986\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-68 67.441)\">\u003Cpath d=\"M117.321-61.650L117.321-63.841L116.618-63.841L116.618-64.095Q116.974-64.095 117.216-64.328Q117.458-64.560 117.569-64.908Q117.681-65.255 117.681-65.611L117.962-65.611L117.962-64.138L119.138-64.138L119.138-63.841L117.962-63.841L117.962-61.666Q117.962-61.345 118.081-61.117Q118.200-60.888 118.481-60.888Q118.661-60.888 118.778-61.011Q118.895-61.134 118.948-61.314Q119.001-61.494 119.001-61.666L119.001-62.138L119.282-62.138L119.282-61.650Q119.282-61.396 119.177-61.156Q119.071-60.916 118.874-60.763Q118.677-60.611 118.419-60.611Q118.103-60.611 117.851-60.734Q117.599-60.857 117.460-61.091Q117.321-61.326 117.321-61.650M121.931-60.689L120.075-60.689L120.075-60.986Q120.349-60.986 120.517-61.033Q120.685-61.080 120.685-61.248L120.685-65.408Q120.685-65.623 120.622-65.718Q120.560-65.814 120.440-65.835Q120.321-65.857 120.075-65.857L120.075-66.154L121.298-66.240L121.298-63.537Q121.423-63.748 121.610-63.898Q121.798-64.048 122.024-64.132Q122.251-64.216 122.497-64.216Q123.665-64.216 123.665-63.138L123.665-61.248Q123.665-61.080 123.835-61.033Q124.005-60.986 124.274-60.986L124.274-60.689L122.419-60.689L122.419-60.986Q122.692-60.986 122.860-61.033Q123.028-61.080 123.028-61.248L123.028-63.123Q123.028-63.505 122.907-63.734Q122.786-63.962 122.435-63.962Q122.122-63.962 121.868-63.800Q121.614-63.638 121.468-63.369Q121.321-63.099 121.321-62.802L121.321-61.248Q121.321-61.080 121.491-61.033Q121.661-60.986 121.931-60.986L121.931-60.689M124.720-62.443Q124.720-62.923 124.952-63.339Q125.185-63.755 125.595-64.005Q126.005-64.255 126.481-64.255Q127.212-64.255 127.610-63.814Q128.009-63.373 128.009-62.642Q128.009-62.537 127.915-62.513L125.466-62.513L125.466-62.443Q125.466-62.033 125.587-61.677Q125.708-61.322 125.979-61.105Q126.251-60.888 126.681-60.888Q127.044-60.888 127.341-61.117Q127.638-61.345 127.739-61.697Q127.747-61.744 127.833-61.759L127.915-61.759Q128.009-61.732 128.009-61.650Q128.009-61.642 128.001-61.611Q127.938-61.384 127.800-61.201Q127.661-61.017 127.470-60.884Q127.278-60.752 127.060-60.681Q126.841-60.611 126.603-60.611Q126.231-60.611 125.894-60.748Q125.556-60.884 125.288-61.136Q125.020-61.388 124.870-61.728Q124.720-62.068 124.720-62.443M125.474-62.752L127.435-62.752Q127.435-63.056 127.333-63.347Q127.231-63.638 127.015-63.820Q126.798-64.001 126.481-64.001Q126.181-64.001 125.950-63.814Q125.720-63.627 125.597-63.335Q125.474-63.044 125.474-62.752\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-68 67.441)\">\u003Cpath d=\"M134.691-60.689L131.898-60.689L131.898-60.986Q132.960-60.986 132.960-61.248L132.960-65.416Q132.531-65.201 131.851-65.201L131.851-65.498Q132.870-65.498 133.386-66.009L133.531-66.009Q133.605-65.990 133.624-65.912L133.624-61.248Q133.624-60.986 134.691-60.986L134.691-60.689M138.929-60.689L135.769-60.689L135.769-60.896Q135.769-60.923 135.792-60.955L137.144-62.353Q137.523-62.740 137.771-63.029Q138.019-63.318 138.193-63.675Q138.367-64.033 138.367-64.423Q138.367-64.771 138.234-65.064Q138.101-65.357 137.847-65.535Q137.593-65.712 137.238-65.712Q136.878-65.712 136.587-65.517Q136.296-65.322 136.152-64.994L136.206-64.994Q136.390-64.994 136.515-64.873Q136.640-64.751 136.640-64.560Q136.640-64.380 136.515-64.251Q136.390-64.123 136.206-64.123Q136.027-64.123 135.898-64.251Q135.769-64.380 135.769-64.560Q135.769-64.962 135.990-65.298Q136.210-65.634 136.576-65.822Q136.941-66.009 137.343-66.009Q137.824-66.009 138.240-65.822Q138.656-65.634 138.908-65.273Q139.160-64.912 139.160-64.423Q139.160-64.064 139.005-63.761Q138.851-63.459 138.599-63.199Q138.347-62.939 137.997-62.654Q137.648-62.369 137.480-62.216L136.550-61.377L137.265-61.377Q138.640-61.377 138.679-61.416Q138.749-61.494 138.792-61.679Q138.835-61.865 138.878-62.154L139.160-62.154L138.929-60.689M140.452-61.650L140.452-63.841L139.749-63.841L139.749-64.095Q140.105-64.095 140.347-64.328Q140.589-64.560 140.701-64.908Q140.812-65.255 140.812-65.611L141.093-65.611L141.093-64.138L142.269-64.138L142.269-63.841L141.093-63.841L141.093-61.666Q141.093-61.345 141.212-61.117Q141.331-60.888 141.613-60.888Q141.792-60.888 141.910-61.011Q142.027-61.134 142.079-61.314Q142.132-61.494 142.132-61.666L142.132-62.138L142.413-62.138L142.413-61.650Q142.413-61.396 142.308-61.156Q142.202-60.916 142.005-60.763Q141.808-60.611 141.550-60.611Q141.234-60.611 140.982-60.734Q140.730-60.857 140.591-61.091Q140.452-61.326 140.452-61.650M145.062-60.689L143.206-60.689L143.206-60.986Q143.480-60.986 143.648-61.033Q143.816-61.080 143.816-61.248L143.816-65.408Q143.816-65.623 143.753-65.718Q143.691-65.814 143.572-65.835Q143.452-65.857 143.206-65.857L143.206-66.154L144.429-66.240L144.429-63.537Q144.554-63.748 144.742-63.898Q144.929-64.048 145.156-64.132Q145.382-64.216 145.628-64.216Q146.796-64.216 146.796-63.138L146.796-61.248Q146.796-61.080 146.966-61.033Q147.136-60.986 147.406-60.986L147.406-60.689L145.550-60.689L145.550-60.986Q145.824-60.986 145.992-61.033Q146.160-61.080 146.160-61.248L146.160-63.123Q146.160-63.505 146.038-63.734Q145.917-63.962 145.566-63.962Q145.253-63.962 144.999-63.800Q144.745-63.638 144.599-63.369Q144.452-63.099 144.452-62.802L144.452-61.248Q144.452-61.080 144.622-61.033Q144.792-60.986 145.062-60.986\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-68 67.441)\">\u003Cpath d=\"M151.321-61.650L151.321-63.841L150.618-63.841L150.618-64.095Q150.974-64.095 151.216-64.328Q151.458-64.560 151.569-64.908Q151.681-65.255 151.681-65.611L151.962-65.611L151.962-64.138L153.138-64.138L153.138-63.841L151.962-63.841L151.962-61.666Q151.962-61.345 152.081-61.117Q152.200-60.888 152.481-60.888Q152.661-60.888 152.778-61.011Q152.895-61.134 152.948-61.314Q153.001-61.494 153.001-61.666L153.001-62.138L153.282-62.138L153.282-61.650Q153.282-61.396 153.177-61.156Q153.071-60.916 152.874-60.763Q152.677-60.611 152.419-60.611Q152.103-60.611 151.851-60.734Q151.599-60.857 151.460-61.091Q151.321-61.326 151.321-61.650M154.001-62.384Q154.001-62.888 154.257-63.320Q154.513-63.752 154.948-64.003Q155.384-64.255 155.884-64.255Q156.270-64.255 156.612-64.111Q156.954-63.966 157.216-63.705Q157.478-63.443 157.620-63.107Q157.763-62.771 157.763-62.384Q157.763-61.892 157.499-61.482Q157.235-61.072 156.806-60.841Q156.376-60.611 155.884-60.611Q155.392-60.611 154.958-60.843Q154.524-61.076 154.263-61.484Q154.001-61.892 154.001-62.384M155.884-60.888Q156.341-60.888 156.593-61.111Q156.845-61.334 156.933-61.685Q157.020-62.037 157.020-62.482Q157.020-62.912 156.927-63.250Q156.833-63.587 156.579-63.794Q156.325-64.001 155.884-64.001Q155.235-64.001 154.991-63.585Q154.747-63.169 154.747-62.482Q154.747-62.037 154.835-61.685Q154.923-61.334 155.175-61.111Q155.427-60.888 155.884-60.888\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-68 67.441)\">\u003Cpath d=\"M164.441-60.689L161.648-60.689L161.648-60.986Q162.710-60.986 162.710-61.248L162.710-65.416Q162.281-65.201 161.601-65.201L161.601-65.498Q162.620-65.498 163.136-66.009L163.281-66.009Q163.355-65.990 163.374-65.912L163.374-61.248Q163.374-60.986 164.441-60.986L164.441-60.689M165.933-61.568L165.870-61.568Q166.011-61.216 166.335-61.005Q166.660-60.794 167.046-60.794Q167.640-60.794 167.890-61.228Q168.140-61.662 168.140-62.298Q168.140-62.892 167.970-63.339Q167.800-63.787 167.300-63.787Q167.003-63.787 166.798-63.707Q166.593-63.627 166.492-63.535Q166.390-63.443 166.275-63.310Q166.160-63.177 166.109-63.162L166.038-63.162Q165.952-63.185 165.933-63.263L165.933-65.912Q165.964-66.009 166.038-66.009Q166.054-66.009 166.062-66.007Q166.070-66.005 166.077-66.001Q166.663-65.751 167.261-65.751Q167.843-65.751 168.460-66.009L168.484-66.009Q168.527-66.009 168.554-65.984Q168.581-65.959 168.581-65.919L168.581-65.841Q168.581-65.810 168.558-65.787Q168.261-65.435 167.839-65.238Q167.417-65.041 166.956-65.041Q166.609-65.041 166.230-65.146L166.230-63.650Q166.449-63.845 166.724-63.943Q166.999-64.041 167.300-64.041Q167.757-64.041 168.126-63.793Q168.495-63.544 168.702-63.140Q168.910-62.736 168.910-62.291Q168.910-61.802 168.654-61.394Q168.398-60.986 167.966-60.753Q167.535-60.521 167.046-60.521Q166.652-60.521 166.296-60.712Q165.941-60.904 165.730-61.238Q165.519-61.572 165.519-61.986Q165.519-62.166 165.636-62.279Q165.753-62.392 165.933-62.392Q166.050-62.392 166.142-62.339Q166.234-62.287 166.286-62.195Q166.339-62.103 166.339-61.986Q166.339-61.802 166.226-61.685Q166.113-61.568 165.933-61.568M170.202-61.650L170.202-63.841L169.499-63.841L169.499-64.095Q169.855-64.095 170.097-64.328Q170.339-64.560 170.451-64.908Q170.562-65.255 170.562-65.611L170.843-65.611L170.843-64.138L172.019-64.138L172.019-63.841L170.843-63.841L170.843-61.666Q170.843-61.345 170.962-61.117Q171.081-60.888 171.363-60.888Q171.542-60.888 171.660-61.011Q171.777-61.134 171.829-61.314Q171.882-61.494 171.882-61.666L171.882-62.138L172.163-62.138L172.163-61.650Q172.163-61.396 172.058-61.156Q171.952-60.916 171.755-60.763Q171.558-60.611 171.300-60.611Q170.984-60.611 170.732-60.734Q170.480-60.857 170.341-61.091Q170.202-61.326 170.202-61.650M174.812-60.689L172.956-60.689L172.956-60.986Q173.230-60.986 173.398-61.033Q173.566-61.080 173.566-61.248L173.566-65.408Q173.566-65.623 173.503-65.718Q173.441-65.814 173.322-65.835Q173.202-65.857 172.956-65.857L172.956-66.154L174.179-66.240L174.179-63.537Q174.304-63.748 174.492-63.898Q174.679-64.048 174.906-64.132Q175.132-64.216 175.378-64.216Q176.546-64.216 176.546-63.138L176.546-61.248Q176.546-61.080 176.716-61.033Q176.886-60.986 177.156-60.986L177.156-60.689L175.300-60.689L175.300-60.986Q175.574-60.986 175.742-61.033Q175.910-61.080 175.910-61.248L175.910-63.123Q175.910-63.505 175.788-63.734Q175.667-63.962 175.316-63.962Q175.003-63.962 174.749-63.800Q174.495-63.638 174.349-63.369Q174.202-63.099 174.202-62.802L174.202-61.248Q174.202-61.080 174.372-61.033Q174.542-60.986 174.812-60.986L174.812-60.689M178.081-61.154Q178.081-61.337 178.218-61.474Q178.355-61.611 178.546-61.611Q178.738-61.611 178.870-61.478Q179.003-61.345 179.003-61.154Q179.003-60.955 178.870-60.822Q178.738-60.689 178.546-60.689Q178.355-60.689 178.218-60.826Q178.081-60.962 178.081-61.154\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(86.046 67.191)\">\u003Cpath d=\"M44.225-63.082Q44.225-63.608 44.442-64.076Q44.659-64.544 45.042-64.890Q45.424-65.235 45.908-65.423Q46.392-65.611 46.922-65.611Q47.325-65.611 47.689-65.454Q48.053-65.296 48.337-65.002L48.760-65.584Q48.795-65.611 48.819-65.611L48.866-65.611Q48.897-65.611 48.921-65.587Q48.945-65.563 48.945-65.532L48.945-63.669Q48.945-63.646 48.919-63.620Q48.894-63.594 48.866-63.594L48.740-63.594Q48.678-63.594 48.665-63.669Q48.634-63.984 48.499-64.288Q48.364-64.592 48.149-64.826Q47.933-65.061 47.644-65.196Q47.356-65.331 47.028-65.331Q46.385-65.331 45.927-65.037Q45.469-64.743 45.236-64.230Q45.004-63.717 45.004-63.082Q45.004-62.610 45.134-62.201Q45.264-61.793 45.524-61.480Q45.783-61.168 46.163-60.998Q46.542-60.829 47.034-60.829Q47.362-60.829 47.656-60.945Q47.950-61.062 48.184-61.277Q48.419-61.492 48.548-61.781Q48.678-62.070 48.678-62.398Q48.678-62.425 48.706-62.449Q48.733-62.473 48.754-62.473L48.866-62.473Q48.904-62.473 48.924-62.448Q48.945-62.422 48.945-62.384Q48.945-61.988 48.779-61.651Q48.613-61.314 48.326-61.067Q48.039-60.819 47.670-60.684Q47.301-60.549 46.922-60.549Q46.402-60.549 45.910-60.739Q45.418-60.928 45.038-61.272Q44.659-61.615 44.442-62.084Q44.225-62.552 44.225-63.082M49.929-63.054Q49.929-63.741 50.278-64.331Q50.627-64.920 51.221-65.266Q51.816-65.611 52.507-65.611Q53.197-65.611 53.792-65.266Q54.386-64.920 54.735-64.331Q55.084-63.741 55.084-63.054Q55.084-62.367 54.732-61.793Q54.380-61.219 53.787-60.884Q53.194-60.549 52.507-60.549Q51.994-60.549 51.526-60.742Q51.057-60.935 50.698-61.277Q50.340-61.619 50.134-62.080Q49.929-62.542 49.929-63.054M52.507-60.809Q53.111-60.809 53.518-61.154Q53.925-61.499 54.115-62.037Q54.304-62.576 54.304-63.174Q54.304-63.608 54.193-63.996Q54.082-64.384 53.857-64.691Q53.631-64.999 53.289-65.179Q52.947-65.358 52.507-65.358Q52.069-65.358 51.731-65.182Q51.392-65.006 51.163-64.691Q50.934-64.377 50.821-63.984Q50.709-63.591 50.709-63.174Q50.709-62.586 50.900-62.043Q51.091-61.499 51.498-61.154Q51.905-60.809 52.507-60.809M57.692-60.689L55.959-60.689L55.959-60.969Q56.680-60.969 56.680-61.369L56.680-65.156Q56.447-65.190 55.959-65.190L55.959-65.471L57.316-65.471Q57.357-65.457 57.377-65.443L60.081-61.796L60.081-64.791Q60.081-65.190 59.360-65.190L59.360-65.471L61.096-65.471L61.096-65.190Q60.375-65.190 60.375-64.791L60.375-60.781Q60.358-60.706 60.289-60.689L60.170-60.689Q60.122-60.699 60.108-60.723L56.974-64.944L56.974-61.369Q56.974-60.969 57.692-60.969L57.692-60.689M61.967-60.627L61.967-62.200Q61.967-62.227 61.993-62.253Q62.019-62.278 62.046-62.278L62.159-62.278Q62.186-62.278 62.210-62.251Q62.234-62.224 62.234-62.200Q62.234-61.855 62.366-61.591Q62.497-61.328 62.726-61.159Q62.955-60.990 63.258-60.909Q63.560-60.829 63.902-60.829Q64.169-60.829 64.404-60.957Q64.640-61.085 64.786-61.308Q64.931-61.530 64.931-61.796Q64.931-62.019 64.825-62.215Q64.719-62.412 64.538-62.547Q64.357-62.682 64.131-62.733L63.102-62.965Q62.791-63.037 62.531-63.223Q62.272-63.410 62.120-63.681Q61.967-63.953 61.967-64.268Q61.967-64.654 62.181-64.961Q62.395-65.269 62.742-65.440Q63.089-65.611 63.468-65.611Q63.697-65.611 63.926-65.558Q64.155-65.505 64.353-65.397Q64.551-65.290 64.705-65.126L64.999-65.566Q65.023-65.611 65.064-65.611L65.112-65.611Q65.143-65.611 65.165-65.585Q65.187-65.560 65.187-65.532L65.187-63.957Q65.187-63.936 65.163-63.909Q65.139-63.881 65.112-63.881L64.999-63.881Q64.938-63.881 64.924-63.957Q64.883-64.370 64.702-64.690Q64.521-65.009 64.210-65.184Q63.899-65.358 63.468-65.358Q63.218-65.358 62.979-65.247Q62.740-65.136 62.590-64.938Q62.439-64.739 62.439-64.476Q62.439-64.264 62.547-64.083Q62.654-63.902 62.830-63.782Q63.007-63.663 63.215-63.622L64.244-63.393Q64.562-63.321 64.828-63.116Q65.095-62.911 65.247-62.617Q65.399-62.323 65.399-61.991Q65.399-61.598 65.194-61.263Q64.989-60.928 64.644-60.739Q64.298-60.549 63.902-60.549Q63.482-60.549 63.102-60.662Q62.723-60.774 62.453-61.024L62.159-60.590Q62.132-60.549 62.094-60.549L62.046-60.549Q62.019-60.549 61.993-60.574Q61.967-60.600 61.967-60.627M70.116-60.689L67.378-60.689L67.378-60.969Q67.727-60.969 68.063-61.005Q68.400-61.041 68.400-61.178L68.400-64.979Q68.400-65.122 68.311-65.156Q68.222-65.190 68.038-65.190L67.679-65.190Q67.378-65.190 67.163-65.143Q66.947-65.095 66.790-64.938Q66.653-64.804 66.594-64.526Q66.534-64.247 66.496-63.834L66.230-63.834L66.377-65.471L71.111-65.471L71.257-63.834L70.991-63.834Q70.953-64.247 70.897-64.524Q70.840-64.801 70.697-64.938Q70.536-65.098 70.324-65.144Q70.112-65.190 69.808-65.190L69.456-65.190Q69.272-65.190 69.183-65.156Q69.094-65.122 69.094-64.979L69.094-61.178Q69.094-61.041 69.431-61.005Q69.767-60.969 70.116-60.969\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 67.191)\">\u003Cpath d=\"M72.869-60.689L71.279-60.689L71.279-60.969Q71.922-60.969 72.079-61.369L73.723-65.584Q73.757-65.679 73.870-65.679L73.952-65.679Q74.062-65.679 74.103-65.584L75.822-61.178Q75.890-61.038 76.080-61.003Q76.270-60.969 76.543-60.969L76.543-60.689L74.544-60.689L74.544-60.969Q75.108-60.969 75.108-61.144Q75.108-61.161 75.106-61.168Q75.104-61.174 75.101-61.178L74.680-62.244L72.722-62.244L72.380-61.369Q72.366-61.369 72.366-61.291Q72.366-61.130 72.529-61.050Q72.691-60.969 72.869-60.969L72.869-60.689M73.703-64.763L72.835-62.524L74.578-62.524\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 67.191)\">\u003Cpath d=\"M80.443-60.689L77.705-60.689L77.705-60.969Q78.054-60.969 78.391-61.005Q78.727-61.041 78.727-61.178L78.727-64.979Q78.727-65.122 78.639-65.156Q78.550-65.190 78.365-65.190L78.006-65.190Q77.705-65.190 77.490-65.143Q77.275-65.095 77.118-64.938Q76.981-64.804 76.921-64.526Q76.861-64.247 76.824-63.834L76.557-63.834L76.704-65.471L81.438-65.471L81.585-63.834L81.318-63.834Q81.281-64.247 81.224-64.524Q81.168-64.801 81.024-64.938Q80.864-65.098 80.652-65.144Q80.440-65.190 80.136-65.190L79.784-65.190Q79.599-65.190 79.510-65.156Q79.421-65.122 79.421-64.979L79.421-61.178Q79.421-61.041 79.758-61.005Q80.095-60.969 80.443-60.969L80.443-60.689M84.473-60.689L82.268-60.689L82.268-60.969Q83.027-60.969 83.027-61.178L83.027-64.979Q83.027-65.190 82.268-65.190L82.268-65.471L84.473-65.471L84.473-65.190Q83.718-65.190 83.718-64.979L83.718-61.178Q83.718-60.969 84.473-60.969L84.473-60.689M87.563-60.648L85.721-64.979Q85.652-65.122 85.471-65.156Q85.290-65.190 85.027-65.190L85.027-65.471L86.995-65.471L86.995-65.190Q86.442-65.190 86.442-65.016Q86.445-65.006 86.447-64.996Q86.449-64.985 86.449-64.979L87.970-61.403L89.402-64.791Q89.426-64.828 89.426-64.883Q89.426-65.043 89.275-65.117Q89.125-65.190 88.940-65.190L88.940-65.471L90.489-65.471L90.489-65.190Q90.229-65.190 90.019-65.096Q89.808-65.002 89.713-64.791L87.956-60.648Q87.905-60.549 87.809-60.549L87.710-60.549Q87.611-60.549 87.563-60.648M95.493-60.689L91.090-60.689L91.090-60.969Q91.811-60.969 91.811-61.178L91.811-64.979Q91.811-65.190 91.090-65.190L91.090-65.471L95.380-65.471L95.588-63.834L95.325-63.834Q95.267-64.305 95.164-64.570Q95.062-64.835 94.877-64.968Q94.693-65.102 94.421-65.146Q94.149-65.190 93.650-65.190L92.868-65.190Q92.680-65.190 92.591-65.156Q92.502-65.122 92.502-64.979L92.502-63.314L93.076-63.314Q93.466-63.314 93.649-63.365Q93.831-63.417 93.913-63.589Q93.995-63.762 93.995-64.134L94.259-64.134L94.259-62.213L93.995-62.213Q93.995-62.586 93.913-62.759Q93.831-62.931 93.649-62.982Q93.466-63.034 93.076-63.034L92.502-63.034L92.502-61.178Q92.502-61.038 92.591-61.003Q92.680-60.969 92.868-60.969L93.715-60.969Q94.245-60.969 94.554-61.038Q94.864-61.106 95.052-61.273Q95.240-61.441 95.347-61.743Q95.455-62.046 95.540-62.559L95.807-62.559\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 67.191)\">\u003Cpath d=\"M101.236-58.939Q100.686-59.339 100.315-59.894Q99.944-60.450 99.763-61.096Q99.582-61.742 99.582-62.439Q99.582-62.952 99.682-63.447Q99.783-63.943 99.988-64.394Q100.193-64.845 100.506-65.237Q100.819-65.628 101.236-65.932Q101.246-65.936 101.253-65.937Q101.260-65.939 101.270-65.939L101.338-65.939Q101.373-65.939 101.395-65.915Q101.417-65.891 101.417-65.854Q101.417-65.809 101.390-65.792Q101.041-65.491 100.788-65.107Q100.535-64.722 100.383-64.281Q100.231-63.840 100.159-63.384Q100.087-62.928 100.087-62.439Q100.087-61.438 100.397-60.551Q100.706-59.664 101.390-59.079Q101.417-59.062 101.417-59.018Q101.417-58.980 101.395-58.956Q101.373-58.932 101.338-58.932L101.270-58.932Q101.263-58.936 101.255-58.937Q101.246-58.939 101.236-58.939M102.285-61.417Q102.285-61.749 102.509-61.976Q102.733-62.203 103.076-62.331Q103.420-62.460 103.792-62.512Q104.165-62.565 104.469-62.565L104.469-62.818Q104.469-63.023 104.362-63.203Q104.254-63.382 104.073-63.485Q103.892-63.587 103.683-63.587Q103.276-63.587 103.041-63.495Q103.129-63.458 103.176-63.374Q103.222-63.290 103.222-63.188Q103.222-63.092 103.176-63.013Q103.129-62.935 103.049-62.890Q102.969-62.846 102.880-62.846Q102.730-62.846 102.629-62.943Q102.528-63.041 102.528-63.188Q102.528-63.810 103.683-63.810Q103.895-63.810 104.145-63.746Q104.394-63.683 104.596-63.564Q104.797-63.444 104.924-63.259Q105.050-63.075 105.050-62.832L105.050-61.256Q105.050-61.140 105.112-61.044Q105.173-60.949 105.286-60.949Q105.396-60.949 105.460-61.043Q105.525-61.137 105.525-61.256L105.525-61.704L105.792-61.704L105.792-61.256Q105.792-60.986 105.565-60.821Q105.337-60.655 105.057-60.655Q104.849-60.655 104.712-60.809Q104.575-60.962 104.551-61.178Q104.404-60.911 104.122-60.766Q103.840-60.621 103.516-60.621Q103.239-60.621 102.955-60.696Q102.671-60.771 102.478-60.950Q102.285-61.130 102.285-61.417M102.900-61.417Q102.900-61.243 103.001-61.113Q103.102-60.983 103.258-60.913Q103.413-60.843 103.577-60.843Q103.796-60.843 104.004-60.940Q104.213-61.038 104.341-61.219Q104.469-61.400 104.469-61.626L104.469-62.354Q104.145-62.354 103.779-62.263Q103.413-62.172 103.157-61.960Q102.900-61.749 102.900-61.417M107.891-60.689L106.257-60.689L106.257-60.969Q106.486-60.969 106.635-61.003Q106.783-61.038 106.783-61.178L106.783-63.027Q106.783-63.297 106.676-63.358Q106.568-63.420 106.257-63.420L106.257-63.700L107.316-63.775L107.316-63.126Q107.487-63.434 107.792-63.605Q108.096-63.775 108.441-63.775Q108.947-63.775 109.230-63.552Q109.514-63.328 109.514-62.832L109.514-61.178Q109.514-61.041 109.663-61.005Q109.812-60.969 110.037-60.969L110.037-60.689L108.407-60.689L108.407-60.969Q108.636-60.969 108.784-61.003Q108.933-61.038 108.933-61.178L108.933-62.818Q108.933-63.153 108.813-63.353Q108.694-63.553 108.379-63.553Q108.109-63.553 107.875-63.417Q107.641-63.280 107.503-63.046Q107.364-62.812 107.364-62.538L107.364-61.178Q107.364-61.041 107.515-61.005Q107.665-60.969 107.891-60.969L107.891-60.689M110.625-60.696L110.625-61.759Q110.625-61.783 110.652-61.810Q110.680-61.837 110.704-61.837L110.813-61.837Q110.878-61.837 110.892-61.779Q110.987-61.345 111.233-61.094Q111.480-60.843 111.893-60.843Q112.235-60.843 112.488-60.976Q112.741-61.109 112.741-61.417Q112.741-61.574 112.647-61.689Q112.553-61.803 112.414-61.872Q112.276-61.940 112.108-61.978L111.527-62.077Q111.172-62.145 110.898-62.366Q110.625-62.586 110.625-62.928Q110.625-63.177 110.736-63.352Q110.847-63.526 111.033-63.625Q111.220-63.724 111.435-63.767Q111.650-63.810 111.893-63.810Q112.307-63.810 112.587-63.628L112.802-63.803Q112.813-63.806 112.819-63.808Q112.826-63.810 112.836-63.810L112.888-63.810Q112.915-63.810 112.939-63.786Q112.963-63.762 112.963-63.734L112.963-62.887Q112.963-62.866 112.939-62.839Q112.915-62.812 112.888-62.812L112.775-62.812Q112.748-62.812 112.722-62.837Q112.696-62.863 112.696-62.887Q112.696-63.123 112.590-63.287Q112.484-63.451 112.302-63.533Q112.119-63.615 111.886-63.615Q111.558-63.615 111.302-63.512Q111.045-63.410 111.045-63.133Q111.045-62.938 111.228-62.829Q111.411-62.719 111.640-62.678L112.214-62.572Q112.460-62.524 112.674-62.396Q112.888-62.268 113.024-62.065Q113.161-61.861 113.161-61.612Q113.161-61.099 112.795-60.860Q112.430-60.621 111.893-60.621Q111.397-60.621 111.066-60.915L110.799-60.641Q110.779-60.621 110.751-60.621L110.704-60.621Q110.680-60.621 110.652-60.648Q110.625-60.675 110.625-60.696M115.178-60.716L114.197-63.215Q114.135-63.358 114.017-63.393Q113.899-63.427 113.684-63.427L113.684-63.707L115.164-63.707L115.164-63.427Q114.785-63.427 114.785-63.266Q114.785-63.256 114.798-63.215L115.513-61.383L116.186-63.088Q116.155-63.160 116.155-63.188Q116.155-63.215 116.128-63.215Q116.066-63.362 115.948-63.394Q115.831-63.427 115.619-63.427L115.619-63.707L117.017-63.707L117.017-63.427Q116.641-63.427 116.641-63.266Q116.641-63.235 116.647-63.215L117.403-61.277L118.090-63.027Q118.110-63.078 118.110-63.133Q118.110-63.273 117.998-63.350Q117.885-63.427 117.745-63.427L117.745-63.707L118.965-63.707L118.965-63.427Q118.760-63.427 118.604-63.321Q118.449-63.215 118.377-63.027L117.471-60.716Q117.437-60.621 117.324-60.621L117.256-60.621Q117.146-60.621 117.109-60.716L116.326-62.719L115.540-60.716Q115.506-60.621 115.393-60.621L115.325-60.621Q115.215-60.621 115.178-60.716\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 67.191)\">\u003Cpath d=\"M119.254-62.224Q119.254-62.545 119.379-62.834Q119.504-63.123 119.730-63.346Q119.955-63.570 120.251-63.690Q120.546-63.810 120.864-63.810Q121.192-63.810 121.454-63.710Q121.715-63.611 121.891-63.429Q122.067-63.246 122.161-62.988Q122.255-62.730 122.255-62.398Q122.255-62.306 122.173-62.285L119.918-62.285L119.918-62.224Q119.918-61.636 120.201-61.253Q120.485-60.870 121.052-60.870Q121.374-60.870 121.642-61.063Q121.910-61.256 121.999-61.571Q122.006-61.612 122.081-61.626L122.173-61.626Q122.255-61.602 122.255-61.530Q122.255-61.523 122.249-61.496Q122.136-61.099 121.765-60.860Q121.394-60.621 120.970-60.621Q120.533-60.621 120.133-60.829Q119.733-61.038 119.494-61.405Q119.254-61.772 119.254-62.224M119.924-62.494L121.739-62.494Q121.739-62.771 121.642-63.023Q121.544-63.276 121.346-63.432Q121.148-63.587 120.864-63.587Q120.587-63.587 120.374-63.429Q120.160-63.270 120.042-63.015Q119.924-62.760 119.924-62.494M124.593-60.689L122.857-60.689L122.857-60.969Q123.086-60.969 123.235-61.003Q123.383-61.038 123.383-61.178L123.383-63.027Q123.383-63.297 123.276-63.358Q123.168-63.420 122.857-63.420L122.857-63.700L123.886-63.775L123.886-63.068Q124.016-63.376 124.258-63.575Q124.501-63.775 124.819-63.775Q125.038-63.775 125.209-63.651Q125.379-63.526 125.379-63.314Q125.379-63.177 125.280-63.078Q125.181-62.979 125.048-62.979Q124.911-62.979 124.812-63.078Q124.713-63.177 124.713-63.314Q124.713-63.454 124.812-63.553Q124.522-63.553 124.322-63.357Q124.122-63.160 124.029-62.866Q123.937-62.572 123.937-62.292L123.937-61.178Q123.937-60.969 124.593-60.969L124.593-60.689M126.285-58.932L126.217-58.932Q126.183-58.932 126.160-58.958Q126.138-58.983 126.138-59.018Q126.138-59.062 126.169-59.079Q126.524-59.383 126.774-59.773Q127.023-60.163 127.176-60.595Q127.328-61.027 127.398-61.496Q127.468-61.964 127.468-62.439Q127.468-62.918 127.398-63.384Q127.328-63.851 127.174-64.286Q127.020-64.722 126.769-65.110Q126.518-65.498 126.169-65.792Q126.138-65.809 126.138-65.854Q126.138-65.888 126.160-65.913Q126.183-65.939 126.217-65.939L126.285-65.939Q126.295-65.939 126.304-65.937Q126.313-65.936 126.323-65.932Q126.866-65.532 127.239-64.979Q127.611-64.425 127.793-63.779Q127.974-63.133 127.974-62.439Q127.974-61.738 127.793-61.091Q127.611-60.443 127.237-59.889Q126.863-59.335 126.323-58.939Q126.313-58.939 126.304-58.937Q126.295-58.936 126.285-58.932\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-soft-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath d=\"M-30.241 48.854h147.954V26.092H-30.24Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-47.158 100.162)\">\u003Cpath d=\"M45.775-60.689L44.025-60.689L44.025-60.986Q44.724-60.986 44.912-61.466L46.713-66.291Q46.767-66.400 46.881-66.400L46.951-66.400Q47.064-66.400 47.119-66.291L49.009-61.248Q49.088-61.080 49.291-61.033Q49.494-60.986 49.806-60.986L49.806-60.689L47.584-60.689L47.584-60.986Q48.224-60.986 48.224-61.201Q48.224-61.220 48.222-61.230Q48.220-61.240 48.216-61.248L47.752-62.482L45.607-62.482L45.224-61.466Q45.220-61.451 45.215-61.421Q45.209-61.392 45.209-61.369Q45.209-61.228 45.298-61.144Q45.388-61.060 45.521-61.023Q45.654-60.986 45.775-60.986L45.775-60.689M46.681-65.345L45.713-62.779L47.638-62.779L46.681-65.345M50.814-61.154Q50.814-61.337 50.951-61.474Q51.088-61.611 51.279-61.611Q51.470-61.611 51.603-61.478Q51.736-61.345 51.736-61.154Q51.736-60.955 51.603-60.822Q51.470-60.689 51.279-60.689Q51.088-60.689 50.951-60.826Q50.814-60.962 50.814-61.154M50.814-63.681Q50.814-63.865 50.951-64.001Q51.088-64.138 51.279-64.138Q51.470-64.138 51.603-64.005Q51.736-63.873 51.736-63.681Q51.736-63.482 51.603-63.349Q51.470-63.216 51.279-63.216Q51.088-63.216 50.951-63.353Q50.814-63.490 50.814-63.681\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-47.158 100.162)\">\u003Cpath d=\"M58.219-60.689L55.633-60.689L55.633-60.986Q55.953-60.986 56.197-61.033Q56.442-61.080 56.442-61.248L56.442-65.591Q56.442-65.763 56.197-65.810Q55.953-65.857 55.633-65.857L55.633-66.154L60.250-66.154L60.481-64.306L60.199-64.306Q60.113-64.990 59.951-65.310Q59.789-65.630 59.449-65.744Q59.109-65.857 58.418-65.857L57.609-65.857Q57.391-65.857 57.299-65.814Q57.207-65.771 57.207-65.591L57.207-63.568L57.817-63.568Q58.242-63.568 58.440-63.634Q58.637-63.701 58.719-63.894Q58.801-64.087 58.801-64.505L59.082-64.505L59.082-62.337L58.801-62.337Q58.801-62.755 58.719-62.949Q58.637-63.142 58.440-63.209Q58.242-63.275 57.817-63.275L57.207-63.275L57.207-61.248Q57.207-61.084 57.526-61.035Q57.844-60.986 58.219-60.986L58.219-60.689M62.992-60.689L61.160-60.689L61.160-60.986Q61.434-60.986 61.602-61.033Q61.770-61.080 61.770-61.248L61.770-65.408Q61.770-65.623 61.707-65.718Q61.645-65.814 61.526-65.835Q61.406-65.857 61.160-65.857L61.160-66.154L62.383-66.240L62.383-61.248Q62.383-61.080 62.551-61.033Q62.719-60.986 62.992-60.986L62.992-60.689M63.856-59.392Q63.969-59.314 64.145-59.314Q64.434-59.314 64.654-59.527Q64.875-59.740 65-60.041L65.289-60.689L64.016-63.576Q63.934-63.752 63.789-63.796Q63.645-63.841 63.375-63.841L63.375-64.138L65.094-64.138L65.094-63.841Q64.672-63.841 64.672-63.658Q64.672-63.646 64.688-63.576L65.625-61.451L66.457-63.361Q66.496-63.451 66.496-63.529Q66.496-63.669 66.395-63.755Q66.293-63.841 66.152-63.841L66.152-64.138L67.504-64.138L67.504-63.841Q67.250-63.841 67.057-63.716Q66.863-63.591 66.758-63.361L65.313-60.041Q65.199-59.787 65.033-59.564Q64.867-59.341 64.639-59.199Q64.410-59.056 64.145-59.056Q63.848-59.056 63.608-59.248Q63.367-59.439 63.367-59.728Q63.367-59.884 63.473-59.986Q63.578-60.087 63.727-60.087Q63.832-60.087 63.912-60.041Q63.992-59.994 64.039-59.916Q64.086-59.837 64.086-59.728Q64.086-59.607 64.026-59.519Q63.965-59.431 63.856-59.392M69.777-60.689L68-60.689L68-60.986Q68.274-60.986 68.442-61.033Q68.609-61.080 68.609-61.248L68.609-63.384Q68.609-63.599 68.553-63.695Q68.496-63.791 68.383-63.812Q68.270-63.834 68.024-63.834L68.024-64.130L69.223-64.216L69.223-61.248Q69.223-61.080 69.369-61.033Q69.516-60.986 69.777-60.986L69.777-60.689M68.336-65.611Q68.336-65.802 68.471-65.933Q68.606-66.064 68.801-66.064Q68.922-66.064 69.026-66.001Q69.129-65.939 69.192-65.835Q69.254-65.732 69.254-65.611Q69.254-65.416 69.123-65.281Q68.992-65.146 68.801-65.146Q68.602-65.146 68.469-65.279Q68.336-65.412 68.336-65.611M72.207-60.689L70.352-60.689L70.352-60.986Q70.625-60.986 70.793-61.033Q70.961-61.080 70.961-61.248L70.961-63.384Q70.961-63.599 70.899-63.695Q70.836-63.791 70.717-63.812Q70.598-63.834 70.352-63.834L70.352-64.130L71.543-64.216L71.543-63.482Q71.656-63.697 71.850-63.865Q72.043-64.033 72.281-64.125Q72.520-64.216 72.774-64.216Q73.942-64.216 73.942-63.138L73.942-61.248Q73.942-61.080 74.111-61.033Q74.281-60.986 74.551-60.986L74.551-60.689L72.695-60.689L72.695-60.986Q72.969-60.986 73.137-61.033Q73.305-61.080 73.305-61.248L73.305-63.123Q73.305-63.505 73.184-63.734Q73.063-63.962 72.711-63.962Q72.399-63.962 72.145-63.800Q71.891-63.638 71.744-63.369Q71.598-63.099 71.598-62.802L71.598-61.248Q71.598-61.080 71.768-61.033Q71.938-60.986 72.207-60.986L72.207-60.689M74.996-60.080Q74.996-60.361 75.207-60.572Q75.418-60.783 75.703-60.873Q75.547-60.998 75.469-61.187Q75.391-61.377 75.391-61.576Q75.391-61.931 75.621-62.224Q75.254-62.564 75.254-63.033Q75.254-63.384 75.457-63.654Q75.660-63.923 75.981-64.070Q76.301-64.216 76.645-64.216Q77.164-64.216 77.535-63.935Q77.899-64.306 78.445-64.306Q78.625-64.306 78.752-64.179Q78.879-64.052 78.879-63.873Q78.879-63.767 78.801-63.689Q78.723-63.611 78.613-63.611Q78.504-63.611 78.428-63.687Q78.352-63.763 78.352-63.873Q78.352-63.974 78.391-64.025Q78.399-64.033 78.402-64.039Q78.406-64.044 78.406-64.048Q78.031-64.048 77.711-63.794Q78.031-63.455 78.031-63.033Q78.031-62.763 77.914-62.546Q77.797-62.330 77.592-62.171Q77.387-62.013 77.145-61.931Q76.902-61.849 76.645-61.849Q76.426-61.849 76.213-61.908Q76-61.966 75.805-62.087Q75.711-61.947 75.711-61.767Q75.711-61.560 75.848-61.408Q75.984-61.255 76.192-61.255L76.887-61.255Q77.375-61.255 77.787-61.171Q78.199-61.087 78.479-60.830Q78.758-60.572 78.758-60.080Q78.758-59.716 78.438-59.484Q78.117-59.252 77.676-59.150Q77.234-59.048 76.879-59.048Q76.524-59.048 76.080-59.150Q75.637-59.252 75.317-59.484Q74.996-59.716 74.996-60.080M75.500-60.080Q75.500-59.884 75.645-59.736Q75.789-59.587 76.002-59.498Q76.215-59.408 76.455-59.361Q76.695-59.314 76.879-59.314Q77.121-59.314 77.451-59.392Q77.781-59.470 78.018-59.644Q78.254-59.818 78.254-60.080Q78.254-60.486 77.844-60.595Q77.434-60.705 76.871-60.705L76.192-60.705Q75.922-60.705 75.711-60.527Q75.500-60.349 75.500-60.080M76.645-62.115Q77.367-62.115 77.367-63.033Q77.367-63.955 76.645-63.955Q75.918-63.955 75.918-63.033Q75.918-62.115 76.645-62.115\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-47.158 100.162)\">\u003Cpath d=\"M83.954-60.689L82.176-60.689L82.176-60.986Q82.450-60.986 82.618-61.033Q82.786-61.080 82.786-61.248L82.786-63.384Q82.786-63.599 82.729-63.695Q82.672-63.791 82.559-63.812Q82.446-63.834 82.200-63.834L82.200-64.130L83.399-64.216L83.399-61.248Q83.399-61.080 83.545-61.033Q83.692-60.986 83.954-60.986L83.954-60.689M82.512-65.611Q82.512-65.802 82.647-65.933Q82.782-66.064 82.977-66.064Q83.098-66.064 83.202-66.001Q83.305-65.939 83.368-65.835Q83.430-65.732 83.430-65.611Q83.430-65.416 83.299-65.281Q83.168-65.146 82.977-65.146Q82.778-65.146 82.645-65.279Q82.512-65.412 82.512-65.611M86.383-60.689L84.528-60.689L84.528-60.986Q84.801-60.986 84.969-61.033Q85.137-61.080 85.137-61.248L85.137-63.384Q85.137-63.599 85.075-63.695Q85.012-63.791 84.893-63.812Q84.774-63.834 84.528-63.834L84.528-64.130L85.719-64.216L85.719-63.482Q85.833-63.697 86.026-63.865Q86.219-64.033 86.458-64.125Q86.696-64.216 86.950-64.216Q88.118-64.216 88.118-63.138L88.118-61.248Q88.118-61.080 88.288-61.033Q88.458-60.986 88.727-60.986L88.727-60.689L86.872-60.689L86.872-60.986Q87.145-60.986 87.313-61.033Q87.481-61.080 87.481-61.248L87.481-63.123Q87.481-63.505 87.360-63.734Q87.239-63.962 86.887-63.962Q86.575-63.962 86.321-63.800Q86.067-63.638 85.920-63.369Q85.774-63.099 85.774-62.802L85.774-61.248Q85.774-61.080 85.944-61.033Q86.114-60.986 86.383-60.986\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-47.158 100.162)\">\u003Cpath d=\"M89.566-61.650L89.566-63.841L88.863-63.841L88.863-64.095Q89.219-64.095 89.461-64.328Q89.703-64.560 89.814-64.908Q89.926-65.255 89.926-65.611L90.207-65.611L90.207-64.138L91.383-64.138L91.383-63.841L90.207-63.841L90.207-61.666Q90.207-61.345 90.326-61.117Q90.445-60.888 90.726-60.888Q90.906-60.888 91.023-61.011Q91.141-61.134 91.193-61.314Q91.246-61.494 91.246-61.666L91.246-62.138L91.527-62.138L91.527-61.650Q91.527-61.396 91.422-61.156Q91.316-60.916 91.119-60.763Q90.922-60.611 90.664-60.611Q90.348-60.611 90.096-60.734Q89.844-60.857 89.705-61.091Q89.566-61.326 89.566-61.650M92.246-62.384Q92.246-62.888 92.502-63.320Q92.758-63.752 93.193-64.003Q93.629-64.255 94.129-64.255Q94.516-64.255 94.857-64.111Q95.199-63.966 95.461-63.705Q95.723-63.443 95.865-63.107Q96.008-62.771 96.008-62.384Q96.008-61.892 95.744-61.482Q95.480-61.072 95.051-60.841Q94.621-60.611 94.129-60.611Q93.637-60.611 93.203-60.843Q92.769-61.076 92.508-61.484Q92.246-61.892 92.246-62.384M94.129-60.888Q94.586-60.888 94.838-61.111Q95.090-61.334 95.178-61.685Q95.266-62.037 95.266-62.482Q95.266-62.912 95.172-63.250Q95.078-63.587 94.824-63.794Q94.570-64.001 94.129-64.001Q93.480-64.001 93.236-63.585Q92.992-63.169 92.992-62.482Q92.992-62.037 93.080-61.685Q93.168-61.334 93.420-61.111Q93.672-60.888 94.129-60.888\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-47.158 100.162)\">\u003Cpath d=\"M100.916-60.720L99.846-63.576Q99.779-63.755 99.649-63.798Q99.518-63.841 99.260-63.841L99.260-64.138L100.940-64.138L100.940-63.841Q100.490-63.841 100.490-63.642Q100.494-63.627 100.496-63.609Q100.498-63.591 100.498-63.576L101.291-61.482L102.002-63.392Q101.967-63.486 101.967-63.531Q101.967-63.576 101.932-63.576Q101.865-63.755 101.735-63.798Q101.604-63.841 101.350-63.841L101.350-64.138L102.940-64.138L102.940-63.841Q102.490-63.841 102.490-63.642Q102.494-63.623 102.496-63.605Q102.498-63.587 102.498-63.576L103.330-61.361L104.084-63.361Q104.108-63.419 104.108-63.490Q104.108-63.650 103.971-63.746Q103.834-63.841 103.666-63.841L103.666-64.138L105.053-64.138L105.053-63.841Q104.819-63.841 104.641-63.714Q104.463-63.587 104.381-63.361L103.397-60.720Q103.342-60.611 103.229-60.611L103.170-60.611Q103.057-60.611 103.014-60.720L102.154-62.994L101.299-60.720Q101.260-60.611 101.139-60.611L101.084-60.611Q100.971-60.611 100.916-60.720M107.397-60.689L105.541-60.689L105.541-60.986Q105.815-60.986 105.983-61.033Q106.151-61.080 106.151-61.248L106.151-65.408Q106.151-65.623 106.088-65.718Q106.026-65.814 105.906-65.835Q105.787-65.857 105.541-65.857L105.541-66.154L106.764-66.240L106.764-63.537Q106.889-63.748 107.076-63.898Q107.264-64.048 107.490-64.132Q107.717-64.216 107.963-64.216Q109.131-64.216 109.131-63.138L109.131-61.248Q109.131-61.080 109.301-61.033Q109.471-60.986 109.740-60.986L109.740-60.689L107.885-60.689L107.885-60.986Q108.158-60.986 108.326-61.033Q108.494-61.080 108.494-61.248L108.494-63.123Q108.494-63.505 108.373-63.734Q108.252-63.962 107.901-63.962Q107.588-63.962 107.334-63.800Q107.080-63.638 106.934-63.369Q106.787-63.099 106.787-62.802L106.787-61.248Q106.787-61.080 106.957-61.033Q107.127-60.986 107.397-60.986L107.397-60.689M110.283-61.521Q110.283-62.005 110.686-62.300Q111.088-62.595 111.639-62.714Q112.190-62.834 112.682-62.834L112.682-63.123Q112.682-63.349 112.567-63.556Q112.451-63.763 112.254-63.882Q112.057-64.001 111.826-64.001Q111.401-64.001 111.115-63.896Q111.186-63.869 111.233-63.814Q111.279-63.759 111.305-63.689Q111.330-63.619 111.330-63.544Q111.330-63.439 111.279-63.347Q111.229-63.255 111.137-63.205Q111.045-63.154 110.940-63.154Q110.834-63.154 110.742-63.205Q110.651-63.255 110.600-63.347Q110.549-63.439 110.549-63.544Q110.549-63.962 110.938-64.109Q111.326-64.255 111.826-64.255Q112.158-64.255 112.512-64.125Q112.865-63.994 113.094-63.740Q113.322-63.486 113.322-63.138L113.322-61.337Q113.322-61.205 113.395-61.095Q113.467-60.986 113.596-60.986Q113.721-60.986 113.789-61.091Q113.858-61.197 113.858-61.337L113.858-61.849L114.139-61.849L114.139-61.337Q114.139-61.134 114.022-60.976Q113.904-60.818 113.723-60.734Q113.541-60.650 113.338-60.650Q113.108-60.650 112.955-60.822Q112.803-60.994 112.772-61.224Q112.612-60.943 112.303-60.777Q111.994-60.611 111.643-60.611Q111.131-60.611 110.707-60.834Q110.283-61.056 110.283-61.521M110.971-61.521Q110.971-61.236 111.197-61.050Q111.424-60.865 111.717-60.865Q111.963-60.865 112.188-60.982Q112.412-61.099 112.547-61.302Q112.682-61.505 112.682-61.759L112.682-62.591Q112.416-62.591 112.131-62.537Q111.846-62.482 111.574-62.353Q111.303-62.224 111.137-62.017Q110.971-61.810 110.971-61.521M115.057-61.650L115.057-63.841L114.354-63.841L114.354-64.095Q114.709-64.095 114.951-64.328Q115.194-64.560 115.305-64.908Q115.416-65.255 115.416-65.611L115.697-65.611L115.697-64.138L116.873-64.138L116.873-63.841L115.697-63.841L115.697-61.666Q115.697-61.345 115.817-61.117Q115.936-60.888 116.217-60.888Q116.397-60.888 116.514-61.011Q116.631-61.134 116.684-61.314Q116.737-61.494 116.737-61.666L116.737-62.138L117.018-62.138L117.018-61.650Q117.018-61.396 116.912-61.156Q116.807-60.916 116.610-60.763Q116.412-60.611 116.154-60.611Q115.838-60.611 115.586-60.734Q115.334-60.857 115.196-61.091Q115.057-61.326 115.057-61.650\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-47.158 100.162)\">\u003Cpath d=\"M120.624-62.416Q120.624-62.912 120.874-63.337Q121.124-63.763 121.544-64.009Q121.964-64.255 122.464-64.255Q123.003-64.255 123.394-64.130Q123.784-64.005 123.784-63.591Q123.784-63.486 123.734-63.394Q123.683-63.302 123.591-63.252Q123.499-63.201 123.390-63.201Q123.284-63.201 123.193-63.252Q123.101-63.302 123.050-63.394Q122.999-63.486 122.999-63.591Q122.999-63.814 123.167-63.919Q122.945-63.978 122.472-63.978Q122.175-63.978 121.960-63.839Q121.745-63.701 121.614-63.470Q121.484-63.240 121.425-62.970Q121.366-62.701 121.366-62.416Q121.366-62.021 121.499-61.671Q121.632-61.322 121.904-61.105Q122.175-60.888 122.573-60.888Q122.948-60.888 123.224-61.105Q123.499-61.322 123.601-61.681Q123.616-61.744 123.679-61.744L123.784-61.744Q123.820-61.744 123.845-61.716Q123.870-61.689 123.870-61.650L123.870-61.627Q123.738-61.146 123.353-60.878Q122.968-60.611 122.464-60.611Q122.101-60.611 121.767-60.748Q121.433-60.884 121.173-61.134Q120.913-61.384 120.769-61.720Q120.624-62.056 120.624-62.416M126.218-60.689L124.441-60.689L124.441-60.986Q124.714-60.986 124.882-61.033Q125.050-61.080 125.050-61.248L125.050-63.384Q125.050-63.599 124.993-63.695Q124.937-63.791 124.823-63.812Q124.710-63.834 124.464-63.834L124.464-64.130L125.663-64.216L125.663-61.248Q125.663-61.080 125.810-61.033Q125.956-60.986 126.218-60.986L126.218-60.689M124.777-65.611Q124.777-65.802 124.911-65.933Q125.046-66.064 125.241-66.064Q125.363-66.064 125.466-66.001Q125.570-65.939 125.632-65.835Q125.695-65.732 125.695-65.611Q125.695-65.416 125.564-65.281Q125.433-65.146 125.241-65.146Q125.042-65.146 124.909-65.279Q124.777-65.412 124.777-65.611M127.343-61.650L127.343-63.841L126.640-63.841L126.640-64.095Q126.995-64.095 127.238-64.328Q127.480-64.560 127.591-64.908Q127.702-65.255 127.702-65.611L127.984-65.611L127.984-64.138L129.159-64.138L129.159-63.841L127.984-63.841L127.984-61.666Q127.984-61.345 128.103-61.117Q128.222-60.888 128.503-60.888Q128.683-60.888 128.800-61.011Q128.917-61.134 128.970-61.314Q129.023-61.494 129.023-61.666L129.023-62.138L129.304-62.138L129.304-61.650Q129.304-61.396 129.198-61.156Q129.093-60.916 128.896-60.763Q128.698-60.611 128.441-60.611Q128.124-60.611 127.872-60.734Q127.620-60.857 127.482-61.091Q127.343-61.326 127.343-61.650\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-47.158 100.162)\">\u003Cpath d=\"M130.207-59.392Q130.321-59.314 130.496-59.314Q130.785-59.314 131.006-59.527Q131.227-59.740 131.352-60.041L131.641-60.689L130.367-63.576Q130.285-63.752 130.141-63.796Q129.996-63.841 129.727-63.841L129.727-64.138L131.446-64.138L131.446-63.841Q131.024-63.841 131.024-63.658Q131.024-63.646 131.039-63.576L131.977-61.451L132.809-63.361Q132.848-63.451 132.848-63.529Q132.848-63.669 132.746-63.755Q132.645-63.841 132.504-63.841L132.504-64.138L133.856-64.138L133.856-63.841Q133.602-63.841 133.408-63.716Q133.215-63.591 133.110-63.361L131.664-60.041Q131.551-59.787 131.385-59.564Q131.219-59.341 130.990-59.199Q130.762-59.056 130.496-59.056Q130.199-59.056 129.959-59.248Q129.719-59.439 129.719-59.728Q129.719-59.884 129.824-59.986Q129.930-60.087 130.078-60.087Q130.184-60.087 130.264-60.041Q130.344-59.994 130.391-59.916Q130.438-59.837 130.438-59.728Q130.438-59.607 130.377-59.519Q130.317-59.431 130.207-59.392M135.457-61.154Q135.457-61.345 135.590-61.478Q135.723-61.611 135.918-61.611Q136.110-61.611 136.242-61.478Q136.375-61.345 136.375-61.154Q136.375-60.955 136.242-60.822Q136.110-60.689 135.918-60.689Q135.723-60.689 135.590-60.822Q135.457-60.955 135.457-61.154M135.774-62.330L135.774-62.728Q135.774-63.127 135.903-63.527Q136.031-63.927 136.278-64.263Q136.356-64.369 136.522-64.544Q136.688-64.720 136.756-64.857Q136.824-64.994 136.824-65.232Q136.824-65.720 136.621-65.892Q136.418-66.064 135.918-66.064Q135.610-66.064 135.332-65.953Q135.055-65.841 134.903-65.619Q135.074-65.619 135.182-65.505Q135.289-65.392 135.289-65.224Q135.289-65.123 135.238-65.031Q135.188-64.939 135.098-64.886Q135.008-64.834 134.895-64.834Q134.785-64.834 134.696-64.886Q134.606-64.939 134.555-65.031Q134.504-65.123 134.504-65.224Q134.504-65.572 134.715-65.822Q134.926-66.072 135.250-66.197Q135.574-66.322 135.918-66.322Q136.578-66.322 137.072-66.072Q137.567-65.822 137.567-65.224Q137.567-64.974 137.444-64.759Q137.321-64.544 137.110-64.408Q136.797-64.205 136.565-63.949Q136.332-63.693 136.194-63.377Q136.055-63.060 136.055-62.712L136.055-62.330Q136.055-62.294 136.028-62.267Q136-62.240 135.969-62.240L135.863-62.240Q135.832-62.240 135.803-62.265Q135.774-62.291 135.774-62.330\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(86.046 99.912)\">\u003Cpath d=\"M46.901-60.689L44.129-60.689L44.129-60.969Q44.850-60.969 44.850-61.178L44.850-64.979Q44.850-65.190 44.129-65.190L44.129-65.471L46.901-65.471Q47.386-65.471 47.822-65.276Q48.258-65.081 48.581-64.739Q48.904-64.397 49.082-63.957Q49.259-63.516 49.259-63.034Q49.259-62.548 49.075-62.125Q48.890-61.701 48.567-61.379Q48.244-61.058 47.812-60.874Q47.380-60.689 46.901-60.689M45.513-64.979L45.513-61.178Q45.513-61.038 45.602-61.003Q45.691-60.969 45.879-60.969L46.703-60.969Q47.328-60.969 47.732-61.231Q48.135-61.492 48.323-61.959Q48.511-62.425 48.511-63.034Q48.511-63.512 48.419-63.905Q48.326-64.298 48.077-64.596Q47.838-64.883 47.474-65.037Q47.110-65.190 46.703-65.190L45.879-65.190Q45.691-65.190 45.602-65.156Q45.513-65.122 45.513-64.979M52.301-60.689L50.097-60.689L50.097-60.969Q50.856-60.969 50.856-61.178L50.856-64.979Q50.856-65.190 50.097-65.190L50.097-65.471L52.301-65.471L52.301-65.190Q51.546-65.190 51.546-64.979L51.546-61.178Q51.546-60.969 52.301-60.969L52.301-60.689M55.138-60.689L53.033-60.689L53.033-60.969Q53.754-60.969 53.754-61.178L53.754-64.979Q53.754-65.190 53.033-65.190L53.033-65.471L55.371-65.471Q55.682-65.471 56.025-65.397Q56.369-65.324 56.690-65.168Q57.011-65.013 57.213-64.767Q57.415-64.521 57.415-64.196Q57.415-63.909 57.227-63.678Q57.039-63.447 56.762-63.299Q56.485-63.150 56.194-63.068Q56.529-62.965 56.764-62.733Q56.998-62.501 57.042-62.172L57.128-61.564Q57.155-61.352 57.201-61.183Q57.247-61.014 57.351-60.894Q57.456-60.774 57.644-60.774Q57.859-60.774 57.975-60.959Q58.091-61.144 58.091-61.376Q58.109-61.441 58.184-61.458L58.276-61.458Q58.358-61.438 58.358-61.356Q58.358-61.150 58.267-60.964Q58.177-60.778 58.011-60.663Q57.845-60.549 57.644-60.549Q57.305-60.549 57.011-60.650Q56.717-60.751 56.533-60.973Q56.348-61.195 56.348-61.543L56.348-62.152Q56.348-62.401 56.205-62.588Q56.061-62.774 55.837-62.873Q55.613-62.972 55.364-62.972L54.417-62.972L54.417-61.178Q54.417-60.969 55.138-60.969L55.138-60.689M54.417-64.979L54.417-63.194L55.272-63.194Q55.559-63.194 55.798-63.237Q56.037-63.280 56.225-63.389Q56.413-63.499 56.524-63.697Q56.635-63.895 56.635-64.196Q56.635-64.773 56.268-64.982Q55.901-65.190 55.272-65.190L54.783-65.190Q54.595-65.190 54.506-65.156Q54.417-65.122 54.417-64.979M63.225-60.689L58.823-60.689L58.823-60.969Q59.544-60.969 59.544-61.178L59.544-64.979Q59.544-65.190 58.823-65.190L58.823-65.471L63.112-65.471L63.321-63.834L63.058-63.834Q63-64.305 62.897-64.570Q62.795-64.835 62.610-64.968Q62.425-65.102 62.154-65.146Q61.882-65.190 61.383-65.190L60.600-65.190Q60.412-65.190 60.323-65.156Q60.235-65.122 60.235-64.979L60.235-63.314L60.809-63.314Q61.198-63.314 61.381-63.365Q61.564-63.417 61.646-63.589Q61.728-63.762 61.728-64.134L61.991-64.134L61.991-62.213L61.728-62.213Q61.728-62.586 61.646-62.759Q61.564-62.931 61.381-62.982Q61.198-63.034 60.809-63.034L60.235-63.034L60.235-61.178Q60.235-61.038 60.323-61.003Q60.412-60.969 60.600-60.969L61.448-60.969Q61.978-60.969 62.287-61.038Q62.596-61.106 62.784-61.273Q62.972-61.441 63.080-61.743Q63.188-62.046 63.273-62.559L63.540-62.559L63.225-60.689M64.302-63.082Q64.302-63.608 64.519-64.076Q64.736-64.544 65.119-64.890Q65.502-65.235 65.985-65.423Q66.469-65.611 66.999-65.611Q67.402-65.611 67.766-65.454Q68.130-65.296 68.414-65.002L68.838-65.584Q68.872-65.611 68.896-65.611L68.944-65.611Q68.974-65.611 68.998-65.587Q69.022-65.563 69.022-65.532L69.022-63.669Q69.022-63.646 68.996-63.620Q68.971-63.594 68.944-63.594L68.817-63.594Q68.756-63.594 68.742-63.669Q68.711-63.984 68.576-64.288Q68.441-64.592 68.226-64.826Q68.010-65.061 67.722-65.196Q67.433-65.331 67.105-65.331Q66.462-65.331 66.004-65.037Q65.546-64.743 65.314-64.230Q65.081-63.717 65.081-63.082Q65.081-62.610 65.211-62.201Q65.341-61.793 65.601-61.480Q65.861-61.168 66.240-60.998Q66.619-60.829 67.111-60.829Q67.440-60.829 67.734-60.945Q68.028-61.062 68.262-61.277Q68.496-61.492 68.626-61.781Q68.756-62.070 68.756-62.398Q68.756-62.425 68.783-62.449Q68.810-62.473 68.831-62.473L68.944-62.473Q68.981-62.473 69.002-62.448Q69.022-62.422 69.022-62.384Q69.022-61.988 68.856-61.651Q68.691-61.314 68.403-61.067Q68.116-60.819 67.747-60.684Q67.378-60.549 66.999-60.549Q66.479-60.549 65.987-60.739Q65.495-60.928 65.115-61.272Q64.736-61.615 64.519-62.084Q64.302-62.552 64.302-63.082M73.739-60.689L71.001-60.689L71.001-60.969Q71.350-60.969 71.686-61.005Q72.023-61.041 72.023-61.178L72.023-64.979Q72.023-65.122 71.934-65.156Q71.845-65.190 71.661-65.190L71.302-65.190Q71.001-65.190 70.786-65.143Q70.570-65.095 70.413-64.938Q70.277-64.804 70.217-64.526Q70.157-64.247 70.119-63.834L69.853-63.834L70-65.471L74.734-65.471L74.881-63.834L74.614-63.834Q74.576-64.247 74.520-64.524Q74.464-64.801 74.320-64.938Q74.159-65.098 73.947-65.144Q73.736-65.190 73.431-65.190L73.079-65.190Q72.895-65.190 72.806-65.156Q72.717-65.122 72.717-64.979L72.717-61.178Q72.717-61.041 73.054-61.005Q73.390-60.969 73.739-60.969L73.739-60.689M77.769-60.689L75.564-60.689L75.564-60.969Q76.323-60.969 76.323-61.178L76.323-64.979Q76.323-65.190 75.564-65.190L75.564-65.471L77.769-65.471L77.769-65.190Q77.013-65.190 77.013-64.979L77.013-61.178Q77.013-60.969 77.769-60.969L77.769-60.689M80.859-60.648L79.016-64.979Q78.948-65.122 78.767-65.156Q78.586-65.190 78.322-65.190L78.322-65.471L80.291-65.471L80.291-65.190Q79.737-65.190 79.737-65.016Q79.741-65.006 79.743-64.996Q79.744-64.985 79.744-64.979L81.265-61.403L82.697-64.791Q82.721-64.828 82.721-64.883Q82.721-65.043 82.571-65.117Q82.421-65.190 82.236-65.190L82.236-65.471L83.784-65.471L83.784-65.190Q83.525-65.190 83.314-65.096Q83.104-65.002 83.008-64.791L81.252-60.648Q81.200-60.549 81.105-60.549L81.006-60.549Q80.906-60.549 80.859-60.648M88.788-60.689L84.386-60.689L84.386-60.969Q85.107-60.969 85.107-61.178L85.107-64.979Q85.107-65.190 84.386-65.190L84.386-65.471L88.675-65.471L88.884-63.834L88.621-63.834Q88.563-64.305 88.460-64.570Q88.358-64.835 88.173-64.968Q87.988-65.102 87.717-65.146Q87.445-65.190 86.946-65.190L86.163-65.190Q85.975-65.190 85.886-65.156Q85.798-65.122 85.798-64.979L85.798-63.314L86.372-63.314Q86.761-63.314 86.944-63.365Q87.127-63.417 87.209-63.589Q87.291-63.762 87.291-64.134L87.554-64.134L87.554-62.213L87.291-62.213Q87.291-62.586 87.209-62.759Q87.127-62.931 86.944-62.982Q86.761-63.034 86.372-63.034L85.798-63.034L85.798-61.178Q85.798-61.038 85.886-61.003Q85.975-60.969 86.163-60.969L87.011-60.969Q87.541-60.969 87.850-61.038Q88.159-61.106 88.347-61.273Q88.535-61.441 88.643-61.743Q88.751-62.046 88.836-62.559L89.103-62.559\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 99.912)\">\u003Cpath d=\"M94.548-58.939Q93.998-59.339 93.627-59.894Q93.256-60.450 93.075-61.096Q92.894-61.742 92.894-62.439Q92.894-62.952 92.994-63.447Q93.095-63.943 93.300-64.394Q93.505-64.845 93.818-65.237Q94.131-65.628 94.548-65.932Q94.558-65.936 94.565-65.937Q94.572-65.939 94.582-65.939L94.650-65.939Q94.685-65.939 94.707-65.915Q94.729-65.891 94.729-65.854Q94.729-65.809 94.702-65.792Q94.353-65.491 94.100-65.107Q93.847-64.722 93.695-64.281Q93.543-63.840 93.471-63.384Q93.399-62.928 93.399-62.439Q93.399-61.438 93.709-60.551Q94.018-59.664 94.702-59.079Q94.729-59.062 94.729-59.018Q94.729-58.980 94.707-58.956Q94.685-58.932 94.650-58.932L94.582-58.932Q94.575-58.936 94.567-58.937Q94.558-58.939 94.548-58.939M97.163-60.621Q96.732-60.621 96.356-60.833Q95.980-61.044 95.760-61.405Q95.539-61.766 95.539-62.200Q95.539-62.644 95.777-63.005Q96.014-63.365 96.400-63.570Q96.787-63.775 97.228-63.775Q97.545-63.775 97.829-63.620Q98.113-63.464 98.297-63.188L98.543-63.775L98.779-63.775L98.779-59.821Q98.779-59.684 98.930-59.648Q99.080-59.612 99.306-59.612L99.306-59.332L97.675-59.332L97.675-59.612Q97.901-59.612 98.050-59.647Q98.198-59.681 98.198-59.821L98.198-61.082Q98-60.863 97.728-60.742Q97.457-60.621 97.163-60.621M97.214-60.843Q97.545-60.843 97.817-61.048Q98.089-61.253 98.229-61.578L98.229-62.712Q98.171-62.935 98.039-63.117Q97.908-63.300 97.718-63.411Q97.528-63.522 97.303-63.522Q96.975-63.522 96.723-63.326Q96.472-63.129 96.339-62.822Q96.206-62.514 96.206-62.193Q96.206-61.892 96.322-61.576Q96.438-61.260 96.667-61.051Q96.896-60.843 97.214-60.843M100.314-61.523L100.314-63.027Q100.314-63.297 100.206-63.358Q100.099-63.420 99.788-63.420L99.788-63.700L100.895-63.775L100.895-61.543L100.895-61.523Q100.895-61.243 100.946-61.099Q100.998-60.956 101.139-60.899Q101.281-60.843 101.568-60.843Q101.821-60.843 102.026-60.983Q102.231-61.123 102.348-61.349Q102.464-61.574 102.464-61.824L102.464-63.027Q102.464-63.297 102.356-63.358Q102.249-63.420 101.937-63.420L101.937-63.700L103.045-63.775L103.045-61.362Q103.045-61.171 103.098-61.089Q103.151-61.007 103.252-60.988Q103.353-60.969 103.568-60.969L103.568-60.689L102.491-60.621L102.491-61.185Q102.382-61.003 102.237-60.880Q102.091-60.757 101.905-60.689Q101.719-60.621 101.517-60.621Q100.314-60.621 100.314-61.523M104.115-62.224Q104.115-62.545 104.239-62.834Q104.364-63.123 104.590-63.346Q104.815-63.570 105.111-63.690Q105.407-63.810 105.725-63.810Q106.053-63.810 106.314-63.710Q106.576-63.611 106.752-63.429Q106.928-63.246 107.022-62.988Q107.116-62.730 107.116-62.398Q107.116-62.306 107.034-62.285L104.778-62.285L104.778-62.224Q104.778-61.636 105.062-61.253Q105.345-60.870 105.913-60.870Q106.234-60.870 106.502-61.063Q106.771-61.256 106.859-61.571Q106.866-61.612 106.941-61.626L107.034-61.626Q107.116-61.602 107.116-61.530Q107.116-61.523 107.109-61.496Q106.996-61.099 106.625-60.860Q106.254-60.621 105.831-60.621Q105.393-60.621 104.993-60.829Q104.593-61.038 104.354-61.405Q104.115-61.772 104.115-62.224M104.785-62.494L106.600-62.494Q106.600-62.771 106.502-63.023Q106.405-63.276 106.207-63.432Q106.008-63.587 105.725-63.587Q105.448-63.587 105.234-63.429Q105.021-63.270 104.903-63.015Q104.785-62.760 104.785-62.494M107.704-60.696L107.704-61.759Q107.704-61.783 107.731-61.810Q107.758-61.837 107.782-61.837L107.892-61.837Q107.957-61.837 107.970-61.779Q108.066-61.345 108.312-61.094Q108.558-60.843 108.972-60.843Q109.313-60.843 109.566-60.976Q109.819-61.109 109.819-61.417Q109.819-61.574 109.725-61.689Q109.631-61.803 109.493-61.872Q109.354-61.940 109.187-61.978L108.606-62.077Q108.250-62.145 107.977-62.366Q107.704-62.586 107.704-62.928Q107.704-63.177 107.815-63.352Q107.926-63.526 108.112-63.625Q108.298-63.724 108.514-63.767Q108.729-63.810 108.972-63.810Q109.385-63.810 109.666-63.628L109.881-63.803Q109.891-63.806 109.898-63.808Q109.905-63.810 109.915-63.810L109.966-63.810Q109.994-63.810 110.018-63.786Q110.041-63.762 110.041-63.734L110.041-62.887Q110.041-62.866 110.018-62.839Q109.994-62.812 109.966-62.812L109.854-62.812Q109.826-62.812 109.801-62.837Q109.775-62.863 109.775-62.887Q109.775-63.123 109.669-63.287Q109.563-63.451 109.380-63.533Q109.197-63.615 108.965-63.615Q108.637-63.615 108.380-63.512Q108.124-63.410 108.124-63.133Q108.124-62.938 108.307-62.829Q108.490-62.719 108.719-62.678L109.293-62.572Q109.539-62.524 109.753-62.396Q109.966-62.268 110.103-62.065Q110.240-61.861 110.240-61.612Q110.240-61.099 109.874-60.860Q109.508-60.621 108.972-60.621Q108.476-60.621 108.145-60.915L107.878-60.641Q107.857-60.621 107.830-60.621L107.782-60.621Q107.758-60.621 107.731-60.648Q107.704-60.675 107.704-60.696M111.395-61.530L111.395-63.427L110.756-63.427L110.756-63.649Q111.074-63.649 111.291-63.859Q111.508-64.069 111.609-64.379Q111.709-64.688 111.709-64.996L111.976-64.996L111.976-63.707L113.053-63.707L113.053-63.427L111.976-63.427L111.976-61.543Q111.976-61.267 112.080-61.068Q112.185-60.870 112.444-60.870Q112.602-60.870 112.708-60.974Q112.813-61.079 112.863-61.232Q112.913-61.386 112.913-61.543L112.913-61.957L113.179-61.957L113.179-61.530Q113.179-61.304 113.080-61.094Q112.981-60.884 112.796-60.752Q112.612-60.621 112.383-60.621Q111.945-60.621 111.670-60.858Q111.395-61.096 111.395-61.530M115.606-60.689L114.054-60.689L114.054-60.969Q114.280-60.969 114.428-61.003Q114.577-61.038 114.577-61.178L114.577-63.027Q114.577-63.215 114.529-63.299Q114.481-63.382 114.384-63.401Q114.287-63.420 114.075-63.420L114.075-63.700L115.131-63.775L115.131-61.178Q115.131-61.038 115.262-61.003Q115.394-60.969 115.606-60.969L115.606-60.689M114.334-64.996Q114.334-65.167 114.458-65.286Q114.581-65.406 114.751-65.406Q114.919-65.406 115.042-65.286Q115.165-65.167 115.165-64.996Q115.165-64.821 115.042-64.698Q114.919-64.575 114.751-64.575Q114.581-64.575 114.458-64.698Q114.334-64.821 114.334-64.996M116.211-62.172Q116.211-62.514 116.346-62.813Q116.481-63.112 116.720-63.336Q116.959-63.560 117.277-63.685Q117.595-63.810 117.927-63.810Q118.371-63.810 118.771-63.594Q119.171-63.379 119.405-63.001Q119.639-62.624 119.639-62.172Q119.639-61.831 119.497-61.547Q119.355-61.263 119.111-61.056Q118.867-60.850 118.557-60.735Q118.248-60.621 117.927-60.621Q117.496-60.621 117.094-60.822Q116.693-61.024 116.452-61.376Q116.211-61.728 116.211-62.172M117.927-60.870Q118.528-60.870 118.752-61.248Q118.976-61.626 118.976-62.258Q118.976-62.870 118.742-63.229Q118.508-63.587 117.927-63.587Q116.874-63.587 116.874-62.258Q116.874-61.626 117.100-61.248Q117.325-60.870 117.927-60.870M121.916-60.689L120.282-60.689L120.282-60.969Q120.511-60.969 120.659-61.003Q120.808-61.038 120.808-61.178L120.808-63.027Q120.808-63.297 120.700-63.358Q120.593-63.420 120.282-63.420L120.282-63.700L121.341-63.775L121.341-63.126Q121.512-63.434 121.816-63.605Q122.121-63.775 122.466-63.775Q122.972-63.775 123.255-63.552Q123.539-63.328 123.539-62.832L123.539-61.178Q123.539-61.041 123.688-61.005Q123.836-60.969 124.062-60.969L124.062-60.689L122.432-60.689L122.432-60.969Q122.661-60.969 122.809-61.003Q122.958-61.038 122.958-61.178L122.958-62.818Q122.958-63.153 122.838-63.353Q122.719-63.553 122.404-63.553Q122.134-63.553 121.900-63.417Q121.666-63.280 121.528-63.046Q121.389-62.812 121.389-62.538L121.389-61.178Q121.389-61.041 121.540-61.005Q121.690-60.969 121.916-60.969L121.916-60.689M124.971-58.932L124.903-58.932Q124.869-58.932 124.846-58.958Q124.824-58.983 124.824-59.018Q124.824-59.062 124.855-59.079Q125.210-59.383 125.460-59.773Q125.709-60.163 125.862-60.595Q126.014-61.027 126.084-61.496Q126.154-61.964 126.154-62.439Q126.154-62.918 126.084-63.384Q126.014-63.851 125.860-64.286Q125.706-64.722 125.455-65.110Q125.204-65.498 124.855-65.792Q124.824-65.809 124.824-65.854Q124.824-65.888 124.846-65.913Q124.869-65.939 124.903-65.939L124.971-65.939Q124.981-65.939 124.990-65.937Q124.999-65.936 125.009-65.932Q125.552-65.532 125.925-64.979Q126.297-64.425 126.479-63.779Q126.660-63.133 126.660-62.439Q126.660-61.738 126.479-61.091Q126.297-60.443 125.923-59.889Q125.549-59.335 125.009-58.939Q124.999-58.939 124.990-58.937Q124.981-58.936 124.971-58.932\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-bg)\">\u003Cpath d=\"M-30.241 81.575h147.954V58.813H-30.24Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-19.598 133.66)\">\u003Cpath d=\"M44.209-63.423Q44.209-64.017 44.441-64.548Q44.673-65.080 45.090-65.480Q45.506-65.880 46.039-66.101Q46.572-66.322 47.177-66.322Q47.615-66.322 48.013-66.140Q48.412-65.959 48.720-65.626L49.193-66.291Q49.224-66.322 49.256-66.322L49.302-66.322Q49.330-66.322 49.361-66.291Q49.392-66.259 49.392-66.232L49.392-64.095Q49.392-64.072 49.361-64.041Q49.330-64.009 49.302-64.009L49.185-64.009Q49.158-64.009 49.127-64.041Q49.095-64.072 49.095-64.095Q49.095-64.361 48.953-64.714Q48.810-65.068 48.631-65.306Q48.377-65.638 48.027-65.832Q47.677-66.025 47.271-66.025Q46.767-66.025 46.314-65.806Q45.861-65.587 45.568-65.193Q45.072-64.525 45.072-63.423Q45.072-62.892 45.209-62.425Q45.345-61.959 45.621-61.593Q45.896-61.228 46.316-61.023Q46.736-60.818 47.279-60.818Q47.767-60.818 48.191-61.062Q48.615-61.306 48.863-61.726Q49.111-62.146 49.111-62.642Q49.111-62.677 49.140-62.703Q49.170-62.728 49.201-62.728L49.302-62.728Q49.345-62.728 49.369-62.699Q49.392-62.669 49.392-62.627Q49.392-62.189 49.216-61.800Q49.041-61.412 48.730-61.128Q48.420-60.845 48.009-60.683Q47.599-60.521 47.177-60.521Q46.588-60.521 46.047-60.742Q45.506-60.962 45.090-61.367Q44.673-61.771 44.441-62.300Q44.209-62.830 44.209-63.423M50.591-61.154Q50.591-61.337 50.728-61.474Q50.865-61.611 51.056-61.611Q51.248-61.611 51.381-61.478Q51.513-61.345 51.513-61.154Q51.513-60.955 51.381-60.822Q51.248-60.689 51.056-60.689Q50.865-60.689 50.728-60.826Q50.591-60.962 50.591-61.154M50.591-63.681Q50.591-63.865 50.728-64.001Q50.865-64.138 51.056-64.138Q51.248-64.138 51.381-64.005Q51.513-63.873 51.513-63.681Q51.513-63.482 51.381-63.349Q51.248-63.216 51.056-63.216Q50.865-63.216 50.728-63.353Q50.591-63.490 50.591-63.681\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-19.598 133.66)\">\u003Cpath d=\"M55.543-60.611L55.543-62.416Q55.543-62.443 55.574-62.474Q55.605-62.505 55.629-62.505L55.734-62.505Q55.765-62.505 55.795-62.476Q55.824-62.447 55.824-62.416Q55.824-61.634 56.340-61.226Q56.855-60.818 57.664-60.818Q57.961-60.818 58.216-60.968Q58.472-61.119 58.623-61.375Q58.773-61.630 58.773-61.927Q58.773-62.326 58.527-62.630Q58.281-62.935 57.910-63.017L56.789-63.275Q56.449-63.349 56.162-63.570Q55.875-63.791 55.709-64.109Q55.543-64.427 55.543-64.779Q55.543-65.209 55.773-65.564Q56.004-65.919 56.384-66.121Q56.765-66.322 57.191-66.322Q57.441-66.322 57.687-66.263Q57.933-66.205 58.152-66.082Q58.371-65.959 58.535-65.779L58.863-66.275Q58.894-66.322 58.933-66.322L58.980-66.322Q59.008-66.322 59.039-66.291Q59.070-66.259 59.070-66.232L59.070-64.423Q59.070-64.400 59.039-64.369Q59.008-64.337 58.980-64.337L58.879-64.337Q58.847-64.337 58.818-64.367Q58.789-64.396 58.789-64.423Q58.789-64.556 58.746-64.742Q58.703-64.927 58.638-65.082Q58.574-65.236 58.474-65.394Q58.375-65.552 58.285-65.642Q57.855-66.048 57.191-66.048Q56.914-66.048 56.654-65.916Q56.394-65.783 56.236-65.548Q56.078-65.314 56.078-65.033Q56.078-64.677 56.318-64.406Q56.558-64.134 56.925-64.048L58.039-63.794Q58.316-63.728 58.549-63.574Q58.781-63.419 58.951-63.201Q59.121-62.982 59.215-62.724Q59.308-62.466 59.308-62.177Q59.308-61.849 59.183-61.546Q59.058-61.244 58.824-61.007Q58.590-60.771 58.297-60.646Q58.004-60.521 57.664-60.521Q56.648-60.521 56.078-61.064L55.750-60.568Q55.718-60.521 55.679-60.521L55.629-60.521Q55.605-60.521 55.574-60.552Q55.543-60.584 55.543-60.611M60.027-62.443Q60.027-62.923 60.259-63.339Q60.492-63.755 60.902-64.005Q61.312-64.255 61.789-64.255Q62.519-64.255 62.918-63.814Q63.316-63.373 63.316-62.642Q63.316-62.537 63.222-62.513L60.773-62.513L60.773-62.443Q60.773-62.033 60.894-61.677Q61.015-61.322 61.287-61.105Q61.558-60.888 61.988-60.888Q62.351-60.888 62.648-61.117Q62.945-61.345 63.047-61.697Q63.054-61.744 63.140-61.759L63.222-61.759Q63.316-61.732 63.316-61.650Q63.316-61.642 63.308-61.611Q63.246-61.384 63.107-61.201Q62.968-61.017 62.777-60.884Q62.586-60.752 62.367-60.681Q62.148-60.611 61.910-60.611Q61.539-60.611 61.201-60.748Q60.863-60.884 60.595-61.136Q60.328-61.388 60.177-61.728Q60.027-62.068 60.027-62.443M60.781-62.752L62.742-62.752Q62.742-63.056 62.640-63.347Q62.539-63.638 62.322-63.820Q62.105-64.001 61.789-64.001Q61.488-64.001 61.258-63.814Q61.027-63.627 60.904-63.335Q60.781-63.044 60.781-62.752M63.902-61.521Q63.902-62.005 64.304-62.300Q64.707-62.595 65.257-62.714Q65.808-62.834 66.300-62.834L66.300-63.123Q66.300-63.349 66.185-63.556Q66.070-63.763 65.873-63.882Q65.675-64.001 65.445-64.001Q65.019-64.001 64.734-63.896Q64.804-63.869 64.851-63.814Q64.898-63.759 64.924-63.689Q64.949-63.619 64.949-63.544Q64.949-63.439 64.898-63.347Q64.847-63.255 64.756-63.205Q64.664-63.154 64.558-63.154Q64.453-63.154 64.361-63.205Q64.269-63.255 64.218-63.347Q64.168-63.439 64.168-63.544Q64.168-63.962 64.556-64.109Q64.945-64.255 65.445-64.255Q65.777-64.255 66.131-64.125Q66.484-63.994 66.713-63.740Q66.941-63.486 66.941-63.138L66.941-61.337Q66.941-61.205 67.013-61.095Q67.086-60.986 67.215-60.986Q67.340-60.986 67.408-61.091Q67.476-61.197 67.476-61.337L67.476-61.849L67.757-61.849L67.757-61.337Q67.757-61.134 67.640-60.976Q67.523-60.818 67.341-60.734Q67.160-60.650 66.957-60.650Q66.726-60.650 66.574-60.822Q66.422-60.994 66.390-61.224Q66.230-60.943 65.922-60.777Q65.613-60.611 65.261-60.611Q64.750-60.611 64.326-60.834Q63.902-61.056 63.902-61.521M64.590-61.521Q64.590-61.236 64.816-61.050Q65.043-60.865 65.336-60.865Q65.582-60.865 65.806-60.982Q66.031-61.099 66.166-61.302Q66.300-61.505 66.300-61.759L66.300-62.591Q66.035-62.591 65.750-62.537Q65.465-62.482 65.193-62.353Q64.922-62.224 64.756-62.017Q64.590-61.810 64.590-61.521M68.675-61.650L68.675-63.841L67.972-63.841L67.972-64.095Q68.328-64.095 68.570-64.328Q68.812-64.560 68.924-64.908Q69.035-65.255 69.035-65.611L69.316-65.611L69.316-64.138L70.492-64.138L70.492-63.841L69.316-63.841L69.316-61.666Q69.316-61.345 69.435-61.117Q69.554-60.888 69.836-60.888Q70.015-60.888 70.132-61.011Q70.250-61.134 70.302-61.314Q70.355-61.494 70.355-61.666L70.355-62.138L70.636-62.138L70.636-61.650Q70.636-61.396 70.531-61.156Q70.425-60.916 70.228-60.763Q70.031-60.611 69.773-60.611Q69.457-60.611 69.205-60.734Q68.953-60.857 68.814-61.091Q68.675-61.326 68.675-61.650M71.980-61.650L71.980-63.841L71.277-63.841L71.277-64.095Q71.632-64.095 71.875-64.328Q72.117-64.560 72.228-64.908Q72.340-65.255 72.340-65.611L72.621-65.611L72.621-64.138L73.797-64.138L73.797-63.841L72.621-63.841L72.621-61.666Q72.621-61.345 72.740-61.117Q72.859-60.888 73.140-60.888Q73.320-60.888 73.437-61.011Q73.554-61.134 73.607-61.314Q73.660-61.494 73.660-61.666L73.660-62.138L73.941-62.138L73.941-61.650Q73.941-61.396 73.836-61.156Q73.730-60.916 73.533-60.763Q73.336-60.611 73.078-60.611Q72.761-60.611 72.509-60.734Q72.257-60.857 72.119-61.091Q71.980-61.326 71.980-61.650M76.574-60.689L74.742-60.689L74.742-60.986Q75.015-60.986 75.183-61.033Q75.351-61.080 75.351-61.248L75.351-65.408Q75.351-65.623 75.289-65.718Q75.226-65.814 75.107-65.835Q74.988-65.857 74.742-65.857L74.742-66.154L75.965-66.240L75.965-61.248Q75.965-61.080 76.132-61.033Q76.300-60.986 76.574-60.986L76.574-60.689M77.019-62.443Q77.019-62.923 77.252-63.339Q77.484-63.755 77.894-64.005Q78.304-64.255 78.781-64.255Q79.511-64.255 79.910-63.814Q80.308-63.373 80.308-62.642Q80.308-62.537 80.215-62.513L77.765-62.513L77.765-62.443Q77.765-62.033 77.886-61.677Q78.007-61.322 78.279-61.105Q78.550-60.888 78.980-60.888Q79.343-60.888 79.640-61.117Q79.937-61.345 80.039-61.697Q80.047-61.744 80.132-61.759L80.215-61.759Q80.308-61.732 80.308-61.650Q80.308-61.642 80.300-61.611Q80.238-61.384 80.099-61.201Q79.961-61.017 79.769-60.884Q79.578-60.752 79.359-60.681Q79.140-60.611 78.902-60.611Q78.531-60.611 78.193-60.748Q77.855-60.884 77.588-61.136Q77.320-61.388 77.170-61.728Q77.019-62.068 77.019-62.443M77.773-62.752L79.734-62.752Q79.734-63.056 79.632-63.347Q79.531-63.638 79.314-63.820Q79.097-64.001 78.781-64.001Q78.480-64.001 78.250-63.814Q78.019-63.627 77.896-63.335Q77.773-63.044 77.773-62.752M81.277-61.154Q81.277-61.337 81.414-61.474Q81.550-61.611 81.742-61.611Q81.933-61.611 82.066-61.478Q82.199-61.345 82.199-61.154Q82.199-60.955 82.066-60.822Q81.933-60.689 81.742-60.689Q81.550-60.689 81.414-60.826Q81.277-60.962 81.277-61.154\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(86.046 132.633)\">\u003Cpath d=\"M44.225-63.082Q44.225-63.608 44.442-64.076Q44.659-64.544 45.042-64.890Q45.424-65.235 45.908-65.423Q46.392-65.611 46.922-65.611Q47.325-65.611 47.689-65.454Q48.053-65.296 48.337-65.002L48.760-65.584Q48.795-65.611 48.819-65.611L48.866-65.611Q48.897-65.611 48.921-65.587Q48.945-65.563 48.945-65.532L48.945-63.669Q48.945-63.646 48.919-63.620Q48.894-63.594 48.866-63.594L48.740-63.594Q48.678-63.594 48.665-63.669Q48.634-63.984 48.499-64.288Q48.364-64.592 48.149-64.826Q47.933-65.061 47.644-65.196Q47.356-65.331 47.028-65.331Q46.385-65.331 45.927-65.037Q45.469-64.743 45.236-64.230Q45.004-63.717 45.004-63.082Q45.004-62.610 45.134-62.201Q45.264-61.793 45.524-61.480Q45.783-61.168 46.163-60.998Q46.542-60.829 47.034-60.829Q47.362-60.829 47.656-60.945Q47.950-61.062 48.184-61.277Q48.419-61.492 48.548-61.781Q48.678-62.070 48.678-62.398Q48.678-62.425 48.706-62.449Q48.733-62.473 48.754-62.473L48.866-62.473Q48.904-62.473 48.924-62.448Q48.945-62.422 48.945-62.384Q48.945-61.988 48.779-61.651Q48.613-61.314 48.326-61.067Q48.039-60.819 47.670-60.684Q47.301-60.549 46.922-60.549Q46.402-60.549 45.910-60.739Q45.418-60.928 45.038-61.272Q44.659-61.615 44.442-62.084Q44.225-62.552 44.225-63.082M49.929-63.054Q49.929-63.741 50.278-64.331Q50.627-64.920 51.221-65.266Q51.816-65.611 52.507-65.611Q53.197-65.611 53.792-65.266Q54.386-64.920 54.735-64.331Q55.084-63.741 55.084-63.054Q55.084-62.367 54.732-61.793Q54.380-61.219 53.787-60.884Q53.194-60.549 52.507-60.549Q51.994-60.549 51.526-60.742Q51.057-60.935 50.698-61.277Q50.340-61.619 50.134-62.080Q49.929-62.542 49.929-63.054M52.507-60.809Q53.111-60.809 53.518-61.154Q53.925-61.499 54.115-62.037Q54.304-62.576 54.304-63.174Q54.304-63.608 54.193-63.996Q54.082-64.384 53.857-64.691Q53.631-64.999 53.289-65.179Q52.947-65.358 52.507-65.358Q52.069-65.358 51.731-65.182Q51.392-65.006 51.163-64.691Q50.934-64.377 50.821-63.984Q50.709-63.591 50.709-63.174Q50.709-62.586 50.900-62.043Q51.091-61.499 51.498-61.154Q51.905-60.809 52.507-60.809M57.692-60.689L55.959-60.689L55.959-60.969Q56.680-60.969 56.680-61.369L56.680-65.156Q56.447-65.190 55.959-65.190L55.959-65.471L57.316-65.471Q57.357-65.457 57.377-65.443L60.081-61.796L60.081-64.791Q60.081-65.190 59.360-65.190L59.360-65.471L61.096-65.471L61.096-65.190Q60.375-65.190 60.375-64.791L60.375-60.781Q60.358-60.706 60.289-60.689L60.170-60.689Q60.122-60.699 60.108-60.723L56.974-64.944L56.974-61.369Q56.974-60.969 57.692-60.969L57.692-60.689M61.967-60.627L61.967-62.200Q61.967-62.227 61.993-62.253Q62.019-62.278 62.046-62.278L62.159-62.278Q62.186-62.278 62.210-62.251Q62.234-62.224 62.234-62.200Q62.234-61.855 62.366-61.591Q62.497-61.328 62.726-61.159Q62.955-60.990 63.258-60.909Q63.560-60.829 63.902-60.829Q64.169-60.829 64.404-60.957Q64.640-61.085 64.786-61.308Q64.931-61.530 64.931-61.796Q64.931-62.019 64.825-62.215Q64.719-62.412 64.538-62.547Q64.357-62.682 64.131-62.733L63.102-62.965Q62.791-63.037 62.531-63.223Q62.272-63.410 62.120-63.681Q61.967-63.953 61.967-64.268Q61.967-64.654 62.181-64.961Q62.395-65.269 62.742-65.440Q63.089-65.611 63.468-65.611Q63.697-65.611 63.926-65.558Q64.155-65.505 64.353-65.397Q64.551-65.290 64.705-65.126L64.999-65.566Q65.023-65.611 65.064-65.611L65.112-65.611Q65.143-65.611 65.165-65.585Q65.187-65.560 65.187-65.532L65.187-63.957Q65.187-63.936 65.163-63.909Q65.139-63.881 65.112-63.881L64.999-63.881Q64.938-63.881 64.924-63.957Q64.883-64.370 64.702-64.690Q64.521-65.009 64.210-65.184Q63.899-65.358 63.468-65.358Q63.218-65.358 62.979-65.247Q62.740-65.136 62.590-64.938Q62.439-64.739 62.439-64.476Q62.439-64.264 62.547-64.083Q62.654-63.902 62.830-63.782Q63.007-63.663 63.215-63.622L64.244-63.393Q64.562-63.321 64.828-63.116Q65.095-62.911 65.247-62.617Q65.399-62.323 65.399-61.991Q65.399-61.598 65.194-61.263Q64.989-60.928 64.644-60.739Q64.298-60.549 63.902-60.549Q63.482-60.549 63.102-60.662Q62.723-60.774 62.453-61.024L62.159-60.590Q62.132-60.549 62.094-60.549L62.046-60.549Q62.019-60.549 61.993-60.574Q61.967-60.600 61.967-60.627M70.116-60.689L67.378-60.689L67.378-60.969Q67.727-60.969 68.063-61.005Q68.400-61.041 68.400-61.178L68.400-64.979Q68.400-65.122 68.311-65.156Q68.222-65.190 68.038-65.190L67.679-65.190Q67.378-65.190 67.163-65.143Q66.947-65.095 66.790-64.938Q66.653-64.804 66.594-64.526Q66.534-64.247 66.496-63.834L66.230-63.834L66.377-65.471L71.111-65.471L71.257-63.834L70.991-63.834Q70.953-64.247 70.897-64.524Q70.840-64.801 70.697-64.938Q70.536-65.098 70.324-65.144Q70.112-65.190 69.808-65.190L69.456-65.190Q69.272-65.190 69.183-65.156Q69.094-65.122 69.094-64.979L69.094-61.178Q69.094-61.041 69.431-61.005Q69.767-60.969 70.116-60.969\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 132.633)\">\u003Cpath d=\"M72.869-60.689L71.279-60.689L71.279-60.969Q71.922-60.969 72.079-61.369L73.723-65.584Q73.757-65.679 73.870-65.679L73.952-65.679Q74.062-65.679 74.103-65.584L75.822-61.178Q75.890-61.038 76.080-61.003Q76.270-60.969 76.543-60.969L76.543-60.689L74.544-60.689L74.544-60.969Q75.108-60.969 75.108-61.144Q75.108-61.161 75.106-61.168Q75.104-61.174 75.101-61.178L74.680-62.244L72.722-62.244L72.380-61.369Q72.366-61.369 72.366-61.291Q72.366-61.130 72.529-61.050Q72.691-60.969 72.869-60.969L72.869-60.689M73.703-64.763L72.835-62.524L74.578-62.524\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 132.633)\">\u003Cpath d=\"M80.443-60.689L77.705-60.689L77.705-60.969Q78.054-60.969 78.391-61.005Q78.727-61.041 78.727-61.178L78.727-64.979Q78.727-65.122 78.639-65.156Q78.550-65.190 78.365-65.190L78.006-65.190Q77.705-65.190 77.490-65.143Q77.275-65.095 77.118-64.938Q76.981-64.804 76.921-64.526Q76.861-64.247 76.824-63.834L76.557-63.834L76.704-65.471L81.438-65.471L81.585-63.834L81.318-63.834Q81.281-64.247 81.224-64.524Q81.168-64.801 81.024-64.938Q80.864-65.098 80.652-65.144Q80.440-65.190 80.136-65.190L79.784-65.190Q79.599-65.190 79.510-65.156Q79.421-65.122 79.421-64.979L79.421-61.178Q79.421-61.041 79.758-61.005Q80.095-60.969 80.443-60.969L80.443-60.689M84.473-60.689L82.268-60.689L82.268-60.969Q83.027-60.969 83.027-61.178L83.027-64.979Q83.027-65.190 82.268-65.190L82.268-65.471L84.473-65.471L84.473-65.190Q83.718-65.190 83.718-64.979L83.718-61.178Q83.718-60.969 84.473-60.969L84.473-60.689M87.563-60.648L85.721-64.979Q85.652-65.122 85.471-65.156Q85.290-65.190 85.027-65.190L85.027-65.471L86.995-65.471L86.995-65.190Q86.442-65.190 86.442-65.016Q86.445-65.006 86.447-64.996Q86.449-64.985 86.449-64.979L87.970-61.403L89.402-64.791Q89.426-64.828 89.426-64.883Q89.426-65.043 89.275-65.117Q89.125-65.190 88.940-65.190L88.940-65.471L90.489-65.471L90.489-65.190Q90.229-65.190 90.019-65.096Q89.808-65.002 89.713-64.791L87.956-60.648Q87.905-60.549 87.809-60.549L87.710-60.549Q87.611-60.549 87.563-60.648M95.493-60.689L91.090-60.689L91.090-60.969Q91.811-60.969 91.811-61.178L91.811-64.979Q91.811-65.190 91.090-65.190L91.090-65.471L95.380-65.471L95.588-63.834L95.325-63.834Q95.267-64.305 95.164-64.570Q95.062-64.835 94.877-64.968Q94.693-65.102 94.421-65.146Q94.149-65.190 93.650-65.190L92.868-65.190Q92.680-65.190 92.591-65.156Q92.502-65.122 92.502-64.979L92.502-63.314L93.076-63.314Q93.466-63.314 93.649-63.365Q93.831-63.417 93.913-63.589Q93.995-63.762 93.995-64.134L94.259-64.134L94.259-62.213L93.995-62.213Q93.995-62.586 93.913-62.759Q93.831-62.931 93.649-62.982Q93.466-63.034 93.076-63.034L92.502-63.034L92.502-61.178Q92.502-61.038 92.591-61.003Q92.680-60.969 92.868-60.969L93.715-60.969Q94.245-60.969 94.554-61.038Q94.864-61.106 95.052-61.273Q95.240-61.441 95.347-61.743Q95.455-62.046 95.540-62.559L95.807-62.559\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 132.633)\">\u003Cpath d=\"M101.236-58.939Q100.686-59.339 100.315-59.894Q99.944-60.450 99.763-61.096Q99.582-61.742 99.582-62.439Q99.582-62.952 99.682-63.447Q99.783-63.943 99.988-64.394Q100.193-64.845 100.506-65.237Q100.819-65.628 101.236-65.932Q101.246-65.936 101.253-65.937Q101.260-65.939 101.270-65.939L101.338-65.939Q101.373-65.939 101.395-65.915Q101.417-65.891 101.417-65.854Q101.417-65.809 101.390-65.792Q101.041-65.491 100.788-65.107Q100.535-64.722 100.383-64.281Q100.231-63.840 100.159-63.384Q100.087-62.928 100.087-62.439Q100.087-61.438 100.397-60.551Q100.706-59.664 101.390-59.079Q101.417-59.062 101.417-59.018Q101.417-58.980 101.395-58.956Q101.373-58.932 101.338-58.932L101.270-58.932Q101.263-58.936 101.255-58.937Q101.246-58.939 101.236-58.939M102.285-61.417Q102.285-61.749 102.509-61.976Q102.733-62.203 103.076-62.331Q103.420-62.460 103.792-62.512Q104.165-62.565 104.469-62.565L104.469-62.818Q104.469-63.023 104.362-63.203Q104.254-63.382 104.073-63.485Q103.892-63.587 103.683-63.587Q103.276-63.587 103.041-63.495Q103.129-63.458 103.176-63.374Q103.222-63.290 103.222-63.188Q103.222-63.092 103.176-63.013Q103.129-62.935 103.049-62.890Q102.969-62.846 102.880-62.846Q102.730-62.846 102.629-62.943Q102.528-63.041 102.528-63.188Q102.528-63.810 103.683-63.810Q103.895-63.810 104.145-63.746Q104.394-63.683 104.596-63.564Q104.797-63.444 104.924-63.259Q105.050-63.075 105.050-62.832L105.050-61.256Q105.050-61.140 105.112-61.044Q105.173-60.949 105.286-60.949Q105.396-60.949 105.460-61.043Q105.525-61.137 105.525-61.256L105.525-61.704L105.792-61.704L105.792-61.256Q105.792-60.986 105.565-60.821Q105.337-60.655 105.057-60.655Q104.849-60.655 104.712-60.809Q104.575-60.962 104.551-61.178Q104.404-60.911 104.122-60.766Q103.840-60.621 103.516-60.621Q103.239-60.621 102.955-60.696Q102.671-60.771 102.478-60.950Q102.285-61.130 102.285-61.417M102.900-61.417Q102.900-61.243 103.001-61.113Q103.102-60.983 103.258-60.913Q103.413-60.843 103.577-60.843Q103.796-60.843 104.004-60.940Q104.213-61.038 104.341-61.219Q104.469-61.400 104.469-61.626L104.469-62.354Q104.145-62.354 103.779-62.263Q103.413-62.172 103.157-61.960Q102.900-61.749 102.900-61.417M107.891-60.689L106.257-60.689L106.257-60.969Q106.486-60.969 106.635-61.003Q106.783-61.038 106.783-61.178L106.783-63.027Q106.783-63.297 106.676-63.358Q106.568-63.420 106.257-63.420L106.257-63.700L107.316-63.775L107.316-63.126Q107.487-63.434 107.792-63.605Q108.096-63.775 108.441-63.775Q108.947-63.775 109.230-63.552Q109.514-63.328 109.514-62.832L109.514-61.178Q109.514-61.041 109.663-61.005Q109.812-60.969 110.037-60.969L110.037-60.689L108.407-60.689L108.407-60.969Q108.636-60.969 108.784-61.003Q108.933-61.038 108.933-61.178L108.933-62.818Q108.933-63.153 108.813-63.353Q108.694-63.553 108.379-63.553Q108.109-63.553 107.875-63.417Q107.641-63.280 107.503-63.046Q107.364-62.812 107.364-62.538L107.364-61.178Q107.364-61.041 107.515-61.005Q107.665-60.969 107.891-60.969L107.891-60.689M110.625-60.696L110.625-61.759Q110.625-61.783 110.652-61.810Q110.680-61.837 110.704-61.837L110.813-61.837Q110.878-61.837 110.892-61.779Q110.987-61.345 111.233-61.094Q111.480-60.843 111.893-60.843Q112.235-60.843 112.488-60.976Q112.741-61.109 112.741-61.417Q112.741-61.574 112.647-61.689Q112.553-61.803 112.414-61.872Q112.276-61.940 112.108-61.978L111.527-62.077Q111.172-62.145 110.898-62.366Q110.625-62.586 110.625-62.928Q110.625-63.177 110.736-63.352Q110.847-63.526 111.033-63.625Q111.220-63.724 111.435-63.767Q111.650-63.810 111.893-63.810Q112.307-63.810 112.587-63.628L112.802-63.803Q112.813-63.806 112.819-63.808Q112.826-63.810 112.836-63.810L112.888-63.810Q112.915-63.810 112.939-63.786Q112.963-63.762 112.963-63.734L112.963-62.887Q112.963-62.866 112.939-62.839Q112.915-62.812 112.888-62.812L112.775-62.812Q112.748-62.812 112.722-62.837Q112.696-62.863 112.696-62.887Q112.696-63.123 112.590-63.287Q112.484-63.451 112.302-63.533Q112.119-63.615 111.886-63.615Q111.558-63.615 111.302-63.512Q111.045-63.410 111.045-63.133Q111.045-62.938 111.228-62.829Q111.411-62.719 111.640-62.678L112.214-62.572Q112.460-62.524 112.674-62.396Q112.888-62.268 113.024-62.065Q113.161-61.861 113.161-61.612Q113.161-61.099 112.795-60.860Q112.430-60.621 111.893-60.621Q111.397-60.621 111.066-60.915L110.799-60.641Q110.779-60.621 110.751-60.621L110.704-60.621Q110.680-60.621 110.652-60.648Q110.625-60.675 110.625-60.696M115.178-60.716L114.197-63.215Q114.135-63.358 114.017-63.393Q113.899-63.427 113.684-63.427L113.684-63.707L115.164-63.707L115.164-63.427Q114.785-63.427 114.785-63.266Q114.785-63.256 114.798-63.215L115.513-61.383L116.186-63.088Q116.155-63.160 116.155-63.188Q116.155-63.215 116.128-63.215Q116.066-63.362 115.948-63.394Q115.831-63.427 115.619-63.427L115.619-63.707L117.017-63.707L117.017-63.427Q116.641-63.427 116.641-63.266Q116.641-63.235 116.647-63.215L117.403-61.277L118.090-63.027Q118.110-63.078 118.110-63.133Q118.110-63.273 117.998-63.350Q117.885-63.427 117.745-63.427L117.745-63.707L118.965-63.707L118.965-63.427Q118.760-63.427 118.604-63.321Q118.449-63.215 118.377-63.027L117.471-60.716Q117.437-60.621 117.324-60.621L117.256-60.621Q117.146-60.621 117.109-60.716L116.326-62.719L115.540-60.716Q115.506-60.621 115.393-60.621L115.325-60.621Q115.215-60.621 115.178-60.716\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(86.046 132.633)\">\u003Cpath d=\"M119.254-62.224Q119.254-62.545 119.379-62.834Q119.504-63.123 119.730-63.346Q119.955-63.570 120.251-63.690Q120.546-63.810 120.864-63.810Q121.192-63.810 121.454-63.710Q121.715-63.611 121.891-63.429Q122.067-63.246 122.161-62.988Q122.255-62.730 122.255-62.398Q122.255-62.306 122.173-62.285L119.918-62.285L119.918-62.224Q119.918-61.636 120.201-61.253Q120.485-60.870 121.052-60.870Q121.374-60.870 121.642-61.063Q121.910-61.256 121.999-61.571Q122.006-61.612 122.081-61.626L122.173-61.626Q122.255-61.602 122.255-61.530Q122.255-61.523 122.249-61.496Q122.136-61.099 121.765-60.860Q121.394-60.621 120.970-60.621Q120.533-60.621 120.133-60.829Q119.733-61.038 119.494-61.405Q119.254-61.772 119.254-62.224M119.924-62.494L121.739-62.494Q121.739-62.771 121.642-63.023Q121.544-63.276 121.346-63.432Q121.148-63.587 120.864-63.587Q120.587-63.587 120.374-63.429Q120.160-63.270 120.042-63.015Q119.924-62.760 119.924-62.494M124.593-60.689L122.857-60.689L122.857-60.969Q123.086-60.969 123.235-61.003Q123.383-61.038 123.383-61.178L123.383-63.027Q123.383-63.297 123.276-63.358Q123.168-63.420 122.857-63.420L122.857-63.700L123.886-63.775L123.886-63.068Q124.016-63.376 124.258-63.575Q124.501-63.775 124.819-63.775Q125.038-63.775 125.209-63.651Q125.379-63.526 125.379-63.314Q125.379-63.177 125.280-63.078Q125.181-62.979 125.048-62.979Q124.911-62.979 124.812-63.078Q124.713-63.177 124.713-63.314Q124.713-63.454 124.812-63.553Q124.522-63.553 124.322-63.357Q124.122-63.160 124.029-62.866Q123.937-62.572 123.937-62.292L123.937-61.178Q123.937-60.969 124.593-60.969L124.593-60.689M126.285-58.932L126.217-58.932Q126.183-58.932 126.160-58.958Q126.138-58.983 126.138-59.018Q126.138-59.062 126.169-59.079Q126.524-59.383 126.774-59.773Q127.023-60.163 127.176-60.595Q127.328-61.027 127.398-61.496Q127.468-61.964 127.468-62.439Q127.468-62.918 127.398-63.384Q127.328-63.851 127.174-64.286Q127.020-64.722 126.769-65.110Q126.518-65.498 126.169-65.792Q126.138-65.809 126.138-65.854Q126.138-65.888 126.160-65.913Q126.183-65.939 126.217-65.939L126.285-65.939Q126.295-65.939 126.304-65.937Q126.313-65.936 126.323-65.932Q126.866-65.532 127.239-64.979Q127.611-64.425 127.793-63.779Q127.974-63.133 127.974-62.439Q127.974-61.738 127.793-61.091Q127.611-60.443 127.237-59.889Q126.863-59.335 126.323-58.939Q126.313-58.939 126.304-58.937Q126.295-58.936 126.285-58.932\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-40.2-39.35h-8.535v55.483h8.536\" style=\"stroke-width:.8\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"rotate(-90 28.341 28.395)\">\u003Cpath d=\"M44.109-61.417Q44.109-61.749 44.332-61.976Q44.556-62.203 44.900-62.331Q45.243-62.460 45.616-62.512Q45.988-62.565 46.293-62.565L46.293-62.818Q46.293-63.023 46.185-63.203Q46.077-63.382 45.896-63.485Q45.715-63.587 45.507-63.587Q45.100-63.587 44.864-63.495Q44.953-63.458 44.999-63.374Q45.045-63.290 45.045-63.188Q45.045-63.092 44.999-63.013Q44.953-62.935 44.872-62.890Q44.792-62.846 44.703-62.846Q44.553-62.846 44.452-62.943Q44.351-63.041 44.351-63.188Q44.351-63.810 45.507-63.810Q45.718-63.810 45.968-63.746Q46.217-63.683 46.419-63.564Q46.621-63.444 46.747-63.259Q46.874-63.075 46.874-62.832L46.874-61.256Q46.874-61.140 46.935-61.044Q46.997-60.949 47.110-60.949Q47.219-60.949 47.284-61.043Q47.349-61.137 47.349-61.256L47.349-61.704L47.615-61.704L47.615-61.256Q47.615-60.986 47.388-60.821Q47.161-60.655 46.881-60.655Q46.672-60.655 46.535-60.809Q46.399-60.962 46.375-61.178Q46.228-60.911 45.946-60.766Q45.664-60.621 45.339-60.621Q45.062-60.621 44.778-60.696Q44.495-60.771 44.302-60.950Q44.109-61.130 44.109-61.417M44.724-61.417Q44.724-61.243 44.825-61.113Q44.925-60.983 45.081-60.913Q45.236-60.843 45.401-60.843Q45.619-60.843 45.828-60.940Q46.036-61.038 46.164-61.219Q46.293-61.400 46.293-61.626L46.293-62.354Q45.968-62.354 45.602-62.263Q45.236-62.172 44.980-61.960Q44.724-61.749 44.724-61.417M48.032-62.200Q48.032-62.538 48.173-62.829Q48.313-63.119 48.557-63.333Q48.801-63.546 49.106-63.661Q49.410-63.775 49.735-63.775Q50.005-63.775 50.268-63.676Q50.531-63.577 50.722-63.399L50.722-64.797Q50.722-65.067 50.615-65.129Q50.507-65.190 50.196-65.190L50.196-65.471L51.273-65.546L51.273-61.362Q51.273-61.174 51.327-61.091Q51.382-61.007 51.483-60.988Q51.584-60.969 51.799-60.969L51.799-60.689L50.692-60.621L50.692-61.038Q50.275-60.621 49.649-60.621Q49.218-60.621 48.846-60.833Q48.473-61.044 48.253-61.405Q48.032-61.766 48.032-62.200M49.707-60.843Q49.916-60.843 50.102-60.915Q50.288-60.986 50.442-61.123Q50.596-61.260 50.692-61.438L50.692-63.047Q50.606-63.194 50.461-63.314Q50.316-63.434 50.146-63.493Q49.977-63.553 49.796-63.553Q49.236-63.553 48.967-63.164Q48.699-62.774 48.699-62.193Q48.699-61.622 48.933-61.232Q49.167-60.843 49.707-60.843M51.919-59.841Q51.919-59.992 52.019-60.089Q52.120-60.187 52.267-60.187Q52.356-60.187 52.436-60.142Q52.517-60.098 52.563-60.019Q52.609-59.940 52.609-59.841Q52.609-59.640 52.448-59.541Q52.589-59.486 52.794-59.486Q53.064-59.486 53.180-59.759Q53.296-60.033 53.296-60.347L53.296-63.027Q53.296-63.293 53.170-63.357Q53.043-63.420 52.715-63.420L52.715-63.700L53.850-63.775L53.850-60.327Q53.850-60.040 53.703-59.795Q53.556-59.551 53.305-59.406Q53.053-59.260 52.777-59.260Q52.448-59.260 52.184-59.402Q51.919-59.544 51.919-59.841M53.016-64.996Q53.016-65.167 53.139-65.286Q53.262-65.406 53.436-65.406Q53.604-65.406 53.727-65.286Q53.850-65.167 53.850-64.996Q53.850-64.821 53.727-64.698Q53.604-64.575 53.436-64.575Q53.262-64.575 53.139-64.698Q53.016-64.821 53.016-64.996M54.985-61.417Q54.985-61.749 55.208-61.976Q55.432-62.203 55.776-62.331Q56.119-62.460 56.492-62.512Q56.864-62.565 57.169-62.565L57.169-62.818Q57.169-63.023 57.061-63.203Q56.953-63.382 56.772-63.485Q56.591-63.587 56.382-63.587Q55.976-63.587 55.740-63.495Q55.829-63.458 55.875-63.374Q55.921-63.290 55.921-63.188Q55.921-63.092 55.875-63.013Q55.829-62.935 55.748-62.890Q55.668-62.846 55.579-62.846Q55.429-62.846 55.328-62.943Q55.227-63.041 55.227-63.188Q55.227-63.810 56.382-63.810Q56.594-63.810 56.844-63.746Q57.093-63.683 57.295-63.564Q57.497-63.444 57.623-63.259Q57.750-63.075 57.750-62.832L57.750-61.256Q57.750-61.140 57.811-61.044Q57.873-60.949 57.986-60.949Q58.095-60.949 58.160-61.043Q58.225-61.137 58.225-61.256L58.225-61.704L58.491-61.704L58.491-61.256Q58.491-60.986 58.264-60.821Q58.037-60.655 57.757-60.655Q57.548-60.655 57.411-60.809Q57.275-60.962 57.251-61.178Q57.104-60.911 56.822-60.766Q56.540-60.621 56.215-60.621Q55.938-60.621 55.654-60.696Q55.371-60.771 55.178-60.950Q54.985-61.130 54.985-61.417M55.600-61.417Q55.600-61.243 55.701-61.113Q55.801-60.983 55.957-60.913Q56.112-60.843 56.277-60.843Q56.495-60.843 56.704-60.940Q56.912-61.038 57.040-61.219Q57.169-61.400 57.169-61.626L57.169-62.354Q56.844-62.354 56.478-62.263Q56.112-62.172 55.856-61.960Q55.600-61.749 55.600-61.417M58.908-62.200Q58.908-62.528 59.043-62.829Q59.178-63.129 59.414-63.350Q59.650-63.570 59.954-63.690Q60.258-63.810 60.583-63.810Q61.089-63.810 61.438-63.707Q61.786-63.605 61.786-63.229Q61.786-63.082 61.689-62.981Q61.591-62.880 61.444-62.880Q61.291-62.880 61.192-62.979Q61.092-63.078 61.092-63.229Q61.092-63.417 61.233-63.509Q61.031-63.560 60.590-63.560Q60.235-63.560 60.006-63.364Q59.777-63.167 59.676-62.858Q59.575-62.548 59.575-62.200Q59.575-61.851 59.701-61.545Q59.828-61.239 60.082-61.055Q60.337-60.870 60.693-60.870Q60.915-60.870 61.099-60.954Q61.284-61.038 61.419-61.193Q61.554-61.349 61.612-61.557Q61.626-61.612 61.680-61.612L61.793-61.612Q61.824-61.612 61.846-61.588Q61.868-61.564 61.868-61.530L61.868-61.509Q61.783-61.222 61.595-61.024Q61.407-60.826 61.142-60.723Q60.877-60.621 60.583-60.621Q60.153-60.621 59.765-60.827Q59.377-61.034 59.142-61.397Q58.908-61.759 58.908-62.200M62.415-62.224Q62.415-62.545 62.540-62.834Q62.665-63.123 62.890-63.346Q63.116-63.570 63.412-63.690Q63.707-63.810 64.025-63.810Q64.353-63.810 64.615-63.710Q64.876-63.611 65.052-63.429Q65.228-63.246 65.322-62.988Q65.416-62.730 65.416-62.398Q65.416-62.306 65.334-62.285L63.078-62.285L63.078-62.224Q63.078-61.636 63.362-61.253Q63.646-60.870 64.213-60.870Q64.534-60.870 64.803-61.063Q65.071-61.256 65.160-61.571Q65.167-61.612 65.242-61.626L65.334-61.626Q65.416-61.602 65.416-61.530Q65.416-61.523 65.409-61.496Q65.297-61.099 64.926-60.860Q64.555-60.621 64.131-60.621Q63.694-60.621 63.294-60.829Q62.894-61.038 62.654-61.405Q62.415-61.772 62.415-62.224M63.085-62.494L64.900-62.494Q64.900-62.771 64.803-63.023Q64.705-63.276 64.507-63.432Q64.309-63.587 64.025-63.587Q63.748-63.587 63.535-63.429Q63.321-63.270 63.203-63.015Q63.085-62.760 63.085-62.494M67.686-60.689L66.052-60.689L66.052-60.969Q66.281-60.969 66.430-61.003Q66.578-61.038 66.578-61.178L66.578-63.027Q66.578-63.297 66.471-63.358Q66.363-63.420 66.052-63.420L66.052-63.700L67.111-63.775L67.111-63.126Q67.282-63.434 67.587-63.605Q67.891-63.775 68.236-63.775Q68.742-63.775 69.026-63.552Q69.309-63.328 69.309-62.832L69.309-61.178Q69.309-61.041 69.458-61.005Q69.607-60.969 69.832-60.969L69.832-60.689L68.202-60.689L68.202-60.969Q68.431-60.969 68.580-61.003Q68.728-61.038 68.728-61.178L68.728-62.818Q68.728-63.153 68.609-63.353Q68.489-63.553 68.174-63.553Q67.904-63.553 67.670-63.417Q67.436-63.280 67.298-63.046Q67.159-62.812 67.159-62.538L67.159-61.178Q67.159-61.041 67.310-61.005Q67.460-60.969 67.686-60.969L67.686-60.689M70.420-62.200Q70.420-62.528 70.555-62.829Q70.690-63.129 70.926-63.350Q71.162-63.570 71.466-63.690Q71.770-63.810 72.095-63.810Q72.601-63.810 72.949-63.707Q73.298-63.605 73.298-63.229Q73.298-63.082 73.201-62.981Q73.103-62.880 72.956-62.880Q72.802-62.880 72.703-62.979Q72.604-63.078 72.604-63.229Q72.604-63.417 72.744-63.509Q72.543-63.560 72.102-63.560Q71.746-63.560 71.517-63.364Q71.288-63.167 71.187-62.858Q71.087-62.548 71.087-62.200Q71.087-61.851 71.213-61.545Q71.340-61.239 71.594-61.055Q71.849-60.870 72.204-60.870Q72.426-60.870 72.611-60.954Q72.796-61.038 72.931-61.193Q73.066-61.349 73.124-61.557Q73.137-61.612 73.192-61.612L73.305-61.612Q73.336-61.612 73.358-61.588Q73.380-61.564 73.380-61.530L73.380-61.509Q73.295-61.222 73.107-61.024Q72.919-60.826 72.654-60.723Q72.389-60.621 72.095-60.621Q71.664-60.621 71.276-60.827Q70.888-61.034 70.654-61.397Q70.420-61.759 70.420-62.200M74.303-59.554Q74.433-59.486 74.569-59.486Q74.740-59.486 74.891-59.575Q75.041-59.664 75.152-59.809Q75.263-59.954 75.342-60.122L75.605-60.689L74.436-63.215Q74.361-63.362 74.231-63.394Q74.101-63.427 73.869-63.427L73.869-63.707L75.390-63.707L75.390-63.427Q75.041-63.427 75.041-63.280Q75.045-63.259 75.046-63.242Q75.048-63.225 75.048-63.215L75.906-61.356L76.678-63.027Q76.713-63.095 76.713-63.174Q76.713-63.287 76.629-63.357Q76.545-63.427 76.432-63.427L76.432-63.707L77.629-63.707L77.629-63.427Q77.410-63.427 77.237-63.323Q77.065-63.218 76.972-63.027L75.636-60.122Q75.465-59.752 75.195-59.506Q74.925-59.260 74.569-59.260Q74.299-59.260 74.081-59.426Q73.862-59.592 73.862-59.855Q73.862-59.992 73.954-60.081Q74.047-60.169 74.187-60.169Q74.323-60.169 74.412-60.081Q74.501-59.992 74.501-59.855Q74.501-59.752 74.448-59.674Q74.395-59.595 74.303-59.554\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"rotate(-90 28.341 28.395)\">\u003Cpath d=\"M82.542-59.332L80.912-59.332L80.912-59.612Q81.141-59.612 81.290-59.647Q81.438-59.681 81.438-59.821L81.438-63.167Q81.438-63.338 81.302-63.379Q81.165-63.420 80.912-63.420L80.912-63.700L81.992-63.775L81.992-63.369Q82.214-63.570 82.501-63.673Q82.789-63.775 83.096-63.775Q83.523-63.775 83.887-63.562Q84.251-63.348 84.465-62.984Q84.679-62.620 84.679-62.200Q84.679-61.755 84.439-61.391Q84.200-61.027 83.807-60.824Q83.414-60.621 82.970-60.621Q82.703-60.621 82.455-60.721Q82.208-60.822 82.020-61.003L82.020-59.821Q82.020-59.684 82.168-59.648Q82.317-59.612 82.542-59.612L82.542-59.332M82.020-63.020L82.020-61.410Q82.153-61.157 82.396-61Q82.638-60.843 82.915-60.843Q83.243-60.843 83.496-61.044Q83.749-61.246 83.882-61.564Q84.016-61.882 84.016-62.200Q84.016-62.429 83.951-62.658Q83.886-62.887 83.758-63.085Q83.629-63.283 83.435-63.403Q83.240-63.522 83.007-63.522Q82.713-63.522 82.445-63.393Q82.177-63.263 82.020-63.020M85.373-61.417Q85.373-61.749 85.596-61.976Q85.820-62.203 86.164-62.331Q86.507-62.460 86.880-62.512Q87.252-62.565 87.557-62.565L87.557-62.818Q87.557-63.023 87.449-63.203Q87.341-63.382 87.160-63.485Q86.979-63.587 86.771-63.587Q86.364-63.587 86.128-63.495Q86.217-63.458 86.263-63.374Q86.309-63.290 86.309-63.188Q86.309-63.092 86.263-63.013Q86.217-62.935 86.136-62.890Q86.056-62.846 85.967-62.846Q85.817-62.846 85.716-62.943Q85.615-63.041 85.615-63.188Q85.615-63.810 86.771-63.810Q86.982-63.810 87.232-63.746Q87.481-63.683 87.683-63.564Q87.885-63.444 88.011-63.259Q88.138-63.075 88.138-62.832L88.138-61.256Q88.138-61.140 88.199-61.044Q88.261-60.949 88.374-60.949Q88.483-60.949 88.548-61.043Q88.613-61.137 88.613-61.256L88.613-61.704L88.879-61.704L88.879-61.256Q88.879-60.986 88.652-60.821Q88.425-60.655 88.145-60.655Q87.936-60.655 87.799-60.809Q87.663-60.962 87.639-61.178Q87.492-60.911 87.210-60.766Q86.928-60.621 86.603-60.621Q86.326-60.621 86.042-60.696Q85.759-60.771 85.566-60.950Q85.373-61.130 85.373-61.417M85.988-61.417Q85.988-61.243 86.089-61.113Q86.189-60.983 86.345-60.913Q86.501-60.843 86.665-60.843Q86.883-60.843 87.092-60.940Q87.300-61.038 87.428-61.219Q87.557-61.400 87.557-61.626L87.557-62.354Q87.232-62.354 86.866-62.263Q86.501-62.172 86.244-61.960Q85.988-61.749 85.988-61.417M90.913-60.689L89.361-60.689L89.361-60.969Q89.587-60.969 89.736-61.003Q89.884-61.038 89.884-61.178L89.884-63.027Q89.884-63.215 89.836-63.299Q89.789-63.382 89.691-63.401Q89.594-63.420 89.382-63.420L89.382-63.700L90.438-63.775L90.438-61.178Q90.438-61.038 90.570-61.003Q90.701-60.969 90.913-60.969L90.913-60.689M89.642-64.996Q89.642-65.167 89.765-65.286Q89.888-65.406 90.059-65.406Q90.226-65.406 90.349-65.286Q90.472-65.167 90.472-64.996Q90.472-64.821 90.349-64.698Q90.226-64.575 90.059-64.575Q89.888-64.575 89.765-64.698Q89.642-64.821 89.642-64.996M93.309-60.689L91.573-60.689L91.573-60.969Q91.802-60.969 91.950-61.003Q92.099-61.038 92.099-61.178L92.099-63.027Q92.099-63.297 91.991-63.358Q91.884-63.420 91.573-63.420L91.573-63.700L92.602-63.775L92.602-63.068Q92.731-63.376 92.974-63.575Q93.217-63.775 93.535-63.775Q93.753-63.775 93.924-63.651Q94.095-63.526 94.095-63.314Q94.095-63.177 93.996-63.078Q93.897-62.979 93.764-62.979Q93.627-62.979 93.528-63.078Q93.429-63.177 93.429-63.314Q93.429-63.454 93.528-63.553Q93.237-63.553 93.037-63.357Q92.837-63.160 92.745-62.866Q92.653-62.572 92.653-62.292L92.653-61.178Q92.653-60.969 93.309-60.969\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">A short travel dialogue with each turn's dialogue act labeled. A question sets up an expectation for an answer (an adjacency pair); a clarification subdialogue can open between the two pair parts.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:491.416px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 368.562 127.498\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg fill=\"var(--tk-soft-accent)\">\u003Cpath d=\"M-68.737 11.6H11.58V-8.317h-80.316Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-36.825 2)\">\u003Cpath d=\"M-23.395 1.641L-28.243 1.641L-28.243 1.344Q-27.923 1.344-27.679 1.297Q-27.434 1.250-27.434 1.082L-27.434-3.261Q-27.434-3.433-27.679-3.480Q-27.923-3.527-28.243-3.527L-28.243-3.824L-23.509-3.824L-23.274-1.976L-23.556-1.976Q-23.638-2.671-23.813-2.992Q-23.989-3.312-24.337-3.420Q-24.684-3.527-25.403-3.527L-26.267-3.527Q-26.485-3.527-26.577-3.484Q-26.669-3.441-26.669-3.261L-26.669-1.343L-26.036-1.343Q-25.610-1.343-25.403-1.406Q-25.196-1.468-25.108-1.664Q-25.020-1.859-25.020-2.281L-24.739-2.281L-24.739-0.113L-25.020-0.113Q-25.020-0.535-25.108-0.728Q-25.196-0.921-25.403-0.984Q-25.610-1.046-26.036-1.046L-26.669-1.046L-26.669 1.082Q-26.669 1.254-26.577 1.299Q-26.485 1.344-26.267 1.344L-25.341 1.344Q-24.759 1.344-24.405 1.262Q-24.052 1.180-23.847 0.983Q-23.642 0.786-23.528 0.448Q-23.415 0.110-23.321-0.472L-23.044-0.472L-23.395 1.641M-20.544 1.641L-22.466 1.641L-22.466 1.344Q-21.657 1.344-21.657 0.864L-21.657-3.480Q-21.915-3.527-22.466-3.527L-22.466-3.824L-20.970-3.824Q-20.911-3.804-20.899-3.793L-17.892 0.395L-17.892-3.046Q-17.892-3.527-18.696-3.527L-18.696-3.824L-16.778-3.824L-16.778-3.527Q-17.587-3.527-17.587-3.046L-17.587 1.536Q-17.606 1.621-17.681 1.641L-17.786 1.641Q-17.841 1.629-17.856 1.602L-21.352-3.254L-21.352 0.864Q-21.352 1.344-20.544 1.344L-20.544 1.641M-15.970-1.093Q-15.970-1.687-15.737-2.218Q-15.505-2.750-15.089-3.150Q-14.673-3.550-14.140-3.771Q-13.606-3.992-13.001-3.992Q-12.563-3.992-12.165-3.810Q-11.767-3.629-11.458-3.296L-10.985-3.961Q-10.954-3.992-10.923-3.992L-10.876-3.992Q-10.849-3.992-10.817-3.961Q-10.786-3.929-10.786-3.902L-10.786-1.765Q-10.786-1.742-10.817-1.711Q-10.849-1.679-10.876-1.679L-10.993-1.679Q-11.020-1.679-11.052-1.711Q-11.083-1.742-11.083-1.765Q-11.083-2.031-11.225-2.384Q-11.368-2.738-11.548-2.976Q-11.802-3.308-12.151-3.502Q-12.501-3.695-12.907-3.695Q-13.411-3.695-13.864-3.476Q-14.317-3.257-14.610-2.863Q-15.106-2.195-15.106-1.093Q-15.106-0.562-14.970-0.095Q-14.833 0.371-14.558 0.737Q-14.282 1.102-13.862 1.307Q-13.442 1.512-12.899 1.512Q-12.411 1.512-11.987 1.268Q-11.563 1.024-11.315 0.604Q-11.067 0.184-11.067-0.312Q-11.067-0.347-11.038-0.373Q-11.009-0.398-10.977-0.398L-10.876-0.398Q-10.833-0.398-10.809-0.369Q-10.786-0.339-10.786-0.296Q-10.786 0.141-10.962 0.530Q-11.138 0.918-11.448 1.202Q-11.759 1.485-12.169 1.647Q-12.579 1.809-13.001 1.809Q-13.591 1.809-14.132 1.588Q-14.673 1.368-15.089 0.963Q-15.505 0.559-15.737 0.030Q-15.970-0.500-15.970-1.093M-7.001 1.809Q-7.583 1.809-8.100 1.582Q-8.618 1.356-9.007 0.957Q-9.395 0.559-9.614 0.034Q-9.833-0.492-9.833-1.062Q-9.833-1.832-9.458-2.509Q-9.083-3.187-8.433-3.589Q-7.782-3.992-7.001-3.992Q-6.227-3.992-5.577-3.589Q-4.927-3.187-4.552-2.509Q-4.177-1.832-4.177-1.062Q-4.177-0.492-4.397 0.039Q-4.618 0.571-5.003 0.963Q-5.388 1.356-5.905 1.582Q-6.423 1.809-7.001 1.809M-8.442 0.739Q-8.278 0.969-8.048 1.145Q-7.817 1.321-7.550 1.416Q-7.282 1.512-7.001 1.512Q-6.583 1.512-6.202 1.301Q-5.821 1.090-5.571 0.739Q-5.040 0.020-5.040-1.199Q-5.040-1.828-5.255-2.406Q-5.470-2.984-5.911-3.347Q-6.352-3.711-7.001-3.711Q-7.653-3.711-8.097-3.347Q-8.540-2.984-8.755-2.406Q-8.970-1.828-8.970-1.199Q-8.970 0.020-8.442 0.739M-0.282 1.641L-3.345 1.641L-3.345 1.344Q-3.020 1.344-2.778 1.297Q-2.536 1.250-2.536 1.082L-2.536-3.261Q-2.536-3.433-2.778-3.480Q-3.020-3.527-3.345-3.527L-3.345-3.824L-0.282-3.824Q0.269-3.824 0.747-3.597Q1.226-3.371 1.579-2.976Q1.933-2.582 2.122-2.082Q2.312-1.582 2.312-1.039Q2.312-0.332 1.968 0.286Q1.624 0.903 1.028 1.272Q0.433 1.641-0.282 1.641M-1.794-3.261L-1.794 1.082Q-1.794 1.254-1.702 1.299Q-1.610 1.344-1.392 1.344L-0.497 1.344Q-0.052 1.344 0.341 1.174Q0.733 1.004 1.005 0.680Q1.276 0.356 1.374-0.064Q1.472-0.484 1.472-1.039Q1.472-1.394 1.435-1.707Q1.398-2.019 1.292-2.314Q1.187-2.609 1.007-2.832Q0.823-3.066 0.585-3.216Q0.347-3.367 0.066-3.447Q-0.216-3.527-0.497-3.527L-1.392-3.527Q-1.610-3.527-1.702-3.484Q-1.794-3.441-1.794-3.261M7.976 1.641L3.128 1.641L3.128 1.344Q3.448 1.344 3.692 1.297Q3.937 1.250 3.937 1.082L3.937-3.261Q3.937-3.433 3.692-3.480Q3.448-3.527 3.128-3.527L3.128-3.824L7.862-3.824L8.097-1.976L7.816-1.976Q7.733-2.671 7.558-2.992Q7.382-3.312 7.034-3.420Q6.687-3.527 5.968-3.527L5.105-3.527Q4.886-3.527 4.794-3.484Q4.702-3.441 4.702-3.261L4.702-1.343L5.335-1.343Q5.761-1.343 5.968-1.406Q6.175-1.468 6.263-1.664Q6.351-1.859 6.351-2.281L6.632-2.281L6.632-0.113L6.351-0.113Q6.351-0.535 6.263-0.728Q6.175-0.921 5.968-0.984Q5.761-1.046 5.335-1.046L4.702-1.046L4.702 1.082Q4.702 1.254 4.794 1.299Q4.886 1.344 5.105 1.344L6.030 1.344Q6.612 1.344 6.966 1.262Q7.319 1.180 7.525 0.983Q7.730 0.786 7.843 0.448Q7.956 0.110 8.050-0.472L8.327-0.472L7.976 1.641M11.280 1.641L8.921 1.641L8.921 1.344Q9.245 1.344 9.487 1.297Q9.730 1.250 9.730 1.082L9.730-3.261Q9.730-3.433 9.487-3.480Q9.245-3.527 8.921-3.527L8.921-3.824L11.515-3.824Q11.847-3.824 12.233-3.738Q12.620-3.652 12.968-3.478Q13.316-3.304 13.534-3.023Q13.753-2.742 13.753-2.375Q13.753-2.050 13.552-1.785Q13.351-1.519 13.044-1.343Q12.737-1.168 12.409-1.078Q12.776-0.957 13.036-0.687Q13.296-0.418 13.347-0.054L13.441 0.641Q13.511 1.090 13.608 1.321Q13.706 1.551 14.003 1.551Q14.249 1.551 14.382 1.334Q14.515 1.118 14.515 0.856Q14.534 0.782 14.616 0.762L14.698 0.762Q14.792 0.786 14.792 0.879Q14.792 1.110 14.694 1.325Q14.597 1.539 14.419 1.674Q14.241 1.809 14.011 1.809Q13.413 1.809 12.995 1.522Q12.577 1.235 12.577 0.664L12.577-0.031Q12.577-0.312 12.425-0.527Q12.273-0.742 12.023-0.855Q11.773-0.968 11.499-0.968L10.472-0.968L10.472 1.082Q10.472 1.246 10.716 1.295Q10.960 1.344 11.280 1.344L11.280 1.641M10.472-3.261L10.472-1.222L11.401-1.222Q11.722-1.222 11.989-1.275Q12.257-1.328 12.456-1.455Q12.655-1.582 12.773-1.814Q12.890-2.046 12.890-2.375Q12.890-3.027 12.493-3.277Q12.097-3.527 11.401-3.527L10.874-3.527Q10.655-3.527 10.564-3.484Q10.472-3.441 10.472-3.261\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-36.825 2)\">\u003Cpath d=\"M20.292 3.633Q19.679 3.176 19.277 2.541Q18.874 1.907 18.679 1.161Q18.484 0.414 18.484-0.359Q18.484-1.132 18.679-1.879Q18.874-2.625 19.277-3.259Q19.679-3.894 20.292-4.351Q20.304-4.355 20.312-4.357Q20.320-4.359 20.331-4.359L20.409-4.359Q20.448-4.359 20.474-4.332Q20.499-4.304 20.499-4.261Q20.499-4.211 20.468-4.191Q19.960-3.738 19.638-3.115Q19.316-2.492 19.175-1.796Q19.034-1.101 19.034-0.359Q19.034 0.375 19.173 1.075Q19.312 1.774 19.636 2.399Q19.960 3.024 20.468 3.473Q20.499 3.493 20.499 3.543Q20.499 3.586 20.474 3.614Q20.448 3.641 20.409 3.641L20.331 3.641Q20.323 3.637 20.314 3.635Q20.304 3.633 20.292 3.633M25.437 3.192L23.581 3.192L23.581 2.899Q23.851 2.899 24.019 2.854Q24.187 2.809 24.187 2.633L24.187 1.184Q23.984 1.430 23.683 1.575Q23.382 1.719 23.050 1.719Q22.566 1.719 22.154 1.477Q21.741 1.235 21.501 0.823Q21.261 0.411 21.261-0.086Q21.261-0.582 21.517-0.996Q21.773-1.410 22.202-1.648Q22.632-1.886 23.124-1.886Q23.480-1.886 23.786-1.707Q24.093-1.527 24.284-1.214L24.573-1.886L24.827-1.886L24.827 2.633Q24.827 2.809 24.995 2.854Q25.163 2.899 25.437 2.899L25.437 3.192M23.109 1.465Q23.476 1.465 23.769 1.233Q24.062 1 24.210 0.641L24.210-0.695Q24.116-1.078 23.843-1.341Q23.570-1.605 23.195-1.605Q22.835-1.605 22.562-1.375Q22.288-1.144 22.146-0.787Q22.003-0.429 22.003-0.078Q22.003 0.258 22.128 0.620Q22.253 0.981 22.505 1.223Q22.757 1.465 23.109 1.465M26.382 0.688L26.382-1.054Q26.382-1.269 26.320-1.365Q26.257-1.461 26.138-1.482Q26.019-1.504 25.773-1.504L25.773-1.800L27.019-1.886L27.019 0.664L27.019 0.688Q27.019 1 27.073 1.162Q27.128 1.325 27.279 1.395Q27.429 1.465 27.749 1.465Q28.179 1.465 28.452 1.127Q28.726 0.789 28.726 0.344L28.726-1.054Q28.726-1.269 28.663-1.365Q28.601-1.461 28.482-1.482Q28.363-1.504 28.116-1.504L28.116-1.800L29.363-1.886L29.363 0.899Q29.363 1.110 29.425 1.205Q29.488 1.301 29.607 1.323Q29.726 1.344 29.972 1.344L29.972 1.641L28.749 1.719L28.749 1.098Q28.581 1.387 28.300 1.553Q28.019 1.719 27.698 1.719Q26.382 1.719 26.382 0.688M30.417-0.113Q30.417-0.593 30.650-1.009Q30.882-1.425 31.292-1.675Q31.702-1.925 32.179-1.925Q32.909-1.925 33.308-1.484Q33.706-1.043 33.706-0.312Q33.706-0.207 33.612-0.183L31.163-0.183L31.163-0.113Q31.163 0.297 31.284 0.653Q31.405 1.008 31.677 1.225Q31.948 1.442 32.378 1.442Q32.741 1.442 33.038 1.213Q33.335 0.985 33.437 0.633Q33.445 0.586 33.530 0.571L33.612 0.571Q33.706 0.598 33.706 0.680Q33.706 0.688 33.698 0.719Q33.636 0.946 33.497 1.129Q33.359 1.313 33.167 1.446Q32.976 1.579 32.757 1.649Q32.538 1.719 32.300 1.719Q31.929 1.719 31.591 1.582Q31.253 1.446 30.986 1.194Q30.718 0.942 30.568 0.602Q30.417 0.262 30.417-0.113M31.171-0.421L33.132-0.421Q33.132-0.726 33.030-1.017Q32.929-1.308 32.712-1.490Q32.495-1.671 32.179-1.671Q31.878-1.671 31.648-1.484Q31.417-1.296 31.294-1.005Q31.171-0.714 31.171-0.421M36.202 1.641L34.222 1.641L34.222 1.344Q34.491 1.344 34.659 1.299Q34.827 1.254 34.827 1.082L34.827-1.054Q34.827-1.269 34.765-1.365Q34.702-1.461 34.585-1.482Q34.468-1.504 34.222-1.504L34.222-1.800L35.390-1.886L35.390-1.101Q35.468-1.312 35.620-1.498Q35.773-1.683 35.972-1.785Q36.171-1.886 36.398-1.886Q36.644-1.886 36.835-1.742Q37.027-1.597 37.027-1.367Q37.027-1.211 36.921-1.101Q36.816-0.992 36.659-0.992Q36.503-0.992 36.394-1.101Q36.284-1.211 36.284-1.367Q36.284-1.527 36.390-1.632Q36.066-1.632 35.851-1.404Q35.636-1.175 35.540-0.836Q35.445-0.496 35.445-0.191L35.445 1.082Q35.445 1.250 35.671 1.297Q35.898 1.344 36.202 1.344L36.202 1.641M37.925 2.938Q38.038 3.016 38.214 3.016Q38.503 3.016 38.724 2.803Q38.945 2.590 39.070 2.289L39.359 1.641L38.085-1.246Q38.003-1.421 37.859-1.466Q37.714-1.511 37.445-1.511L37.445-1.808L39.163-1.808L39.163-1.511Q38.741-1.511 38.741-1.328Q38.741-1.316 38.757-1.246L39.695 0.879L40.527-1.031Q40.566-1.121 40.566-1.199Q40.566-1.339 40.464-1.425Q40.362-1.511 40.222-1.511L40.222-1.808L41.573-1.808L41.573-1.511Q41.320-1.511 41.126-1.386Q40.933-1.261 40.827-1.031L39.382 2.289Q39.269 2.543 39.103 2.766Q38.937 2.989 38.708 3.131Q38.480 3.274 38.214 3.274Q37.917 3.274 37.677 3.082Q37.437 2.891 37.437 2.602Q37.437 2.446 37.542 2.344Q37.648 2.243 37.796 2.243Q37.902 2.243 37.982 2.289Q38.062 2.336 38.109 2.414Q38.155 2.493 38.155 2.602Q38.155 2.723 38.095 2.811Q38.034 2.899 37.925 2.938M42.390 3.641L42.308 3.641Q42.273 3.641 42.247 3.612Q42.222 3.582 42.222 3.543Q42.222 3.493 42.253 3.473Q42.640 3.137 42.923 2.688Q43.206 2.239 43.372 1.739Q43.538 1.239 43.612 0.721Q43.687 0.204 43.687-0.359Q43.687-0.929 43.612-1.445Q43.538-1.961 43.372-2.457Q43.206-2.953 42.927-3.400Q42.648-3.847 42.253-4.191Q42.222-4.211 42.222-4.261Q42.222-4.300 42.247-4.330Q42.273-4.359 42.308-4.359L42.390-4.359Q42.402-4.359 42.411-4.357Q42.421-4.355 42.429-4.351Q43.042-3.894 43.445-3.259Q43.847-2.625 44.042-1.879Q44.237-1.132 44.237-0.359Q44.237 0.414 44.042 1.161Q43.847 1.907 43.445 2.541Q43.042 3.176 42.429 3.633Q42.417 3.633 42.409 3.635Q42.402 3.637 42.390 3.641\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-soft-accent)\">\u003Cpath d=\"M19.943 11.6h79.364V-8.317H19.943Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(51.855 2)\">\u003Cpath d=\"M-23.395 1.641L-28.243 1.641L-28.243 1.344Q-27.923 1.344-27.679 1.297Q-27.434 1.250-27.434 1.082L-27.434-3.261Q-27.434-3.433-27.679-3.480Q-27.923-3.527-28.243-3.527L-28.243-3.824L-23.509-3.824L-23.274-1.976L-23.556-1.976Q-23.638-2.671-23.813-2.992Q-23.989-3.312-24.337-3.420Q-24.684-3.527-25.403-3.527L-26.267-3.527Q-26.485-3.527-26.577-3.484Q-26.669-3.441-26.669-3.261L-26.669-1.343L-26.036-1.343Q-25.610-1.343-25.403-1.406Q-25.196-1.468-25.108-1.664Q-25.020-1.859-25.020-2.281L-24.739-2.281L-24.739-0.113L-25.020-0.113Q-25.020-0.535-25.108-0.728Q-25.196-0.921-25.403-0.984Q-25.610-1.046-26.036-1.046L-26.669-1.046L-26.669 1.082Q-26.669 1.254-26.577 1.299Q-26.485 1.344-26.267 1.344L-25.341 1.344Q-24.759 1.344-24.405 1.262Q-24.052 1.180-23.847 0.983Q-23.642 0.786-23.528 0.448Q-23.415 0.110-23.321-0.472L-23.044-0.472L-23.395 1.641M-20.544 1.641L-22.466 1.641L-22.466 1.344Q-21.657 1.344-21.657 0.864L-21.657-3.480Q-21.915-3.527-22.466-3.527L-22.466-3.824L-20.970-3.824Q-20.911-3.804-20.899-3.793L-17.892 0.395L-17.892-3.046Q-17.892-3.527-18.696-3.527L-18.696-3.824L-16.778-3.824L-16.778-3.527Q-17.587-3.527-17.587-3.046L-17.587 1.536Q-17.606 1.621-17.681 1.641L-17.786 1.641Q-17.841 1.629-17.856 1.602L-21.352-3.254L-21.352 0.864Q-21.352 1.344-20.544 1.344L-20.544 1.641M-15.970-1.093Q-15.970-1.687-15.737-2.218Q-15.505-2.750-15.089-3.150Q-14.673-3.550-14.140-3.771Q-13.606-3.992-13.001-3.992Q-12.563-3.992-12.165-3.810Q-11.767-3.629-11.458-3.296L-10.985-3.961Q-10.954-3.992-10.923-3.992L-10.876-3.992Q-10.849-3.992-10.817-3.961Q-10.786-3.929-10.786-3.902L-10.786-1.765Q-10.786-1.742-10.817-1.711Q-10.849-1.679-10.876-1.679L-10.993-1.679Q-11.020-1.679-11.052-1.711Q-11.083-1.742-11.083-1.765Q-11.083-2.031-11.225-2.384Q-11.368-2.738-11.548-2.976Q-11.802-3.308-12.151-3.502Q-12.501-3.695-12.907-3.695Q-13.411-3.695-13.864-3.476Q-14.317-3.257-14.610-2.863Q-15.106-2.195-15.106-1.093Q-15.106-0.562-14.970-0.095Q-14.833 0.371-14.558 0.737Q-14.282 1.102-13.862 1.307Q-13.442 1.512-12.899 1.512Q-12.411 1.512-11.987 1.268Q-11.563 1.024-11.315 0.604Q-11.067 0.184-11.067-0.312Q-11.067-0.347-11.038-0.373Q-11.009-0.398-10.977-0.398L-10.876-0.398Q-10.833-0.398-10.809-0.369Q-10.786-0.339-10.786-0.296Q-10.786 0.141-10.962 0.530Q-11.138 0.918-11.448 1.202Q-11.759 1.485-12.169 1.647Q-12.579 1.809-13.001 1.809Q-13.591 1.809-14.132 1.588Q-14.673 1.368-15.089 0.963Q-15.505 0.559-15.737 0.030Q-15.970-0.500-15.970-1.093M-7.001 1.809Q-7.583 1.809-8.100 1.582Q-8.618 1.356-9.007 0.957Q-9.395 0.559-9.614 0.034Q-9.833-0.492-9.833-1.062Q-9.833-1.832-9.458-2.509Q-9.083-3.187-8.433-3.589Q-7.782-3.992-7.001-3.992Q-6.227-3.992-5.577-3.589Q-4.927-3.187-4.552-2.509Q-4.177-1.832-4.177-1.062Q-4.177-0.492-4.397 0.039Q-4.618 0.571-5.003 0.963Q-5.388 1.356-5.905 1.582Q-6.423 1.809-7.001 1.809M-8.442 0.739Q-8.278 0.969-8.048 1.145Q-7.817 1.321-7.550 1.416Q-7.282 1.512-7.001 1.512Q-6.583 1.512-6.202 1.301Q-5.821 1.090-5.571 0.739Q-5.040 0.020-5.040-1.199Q-5.040-1.828-5.255-2.406Q-5.470-2.984-5.911-3.347Q-6.352-3.711-7.001-3.711Q-7.653-3.711-8.097-3.347Q-8.540-2.984-8.755-2.406Q-8.970-1.828-8.970-1.199Q-8.970 0.020-8.442 0.739M-0.282 1.641L-3.345 1.641L-3.345 1.344Q-3.020 1.344-2.778 1.297Q-2.536 1.250-2.536 1.082L-2.536-3.261Q-2.536-3.433-2.778-3.480Q-3.020-3.527-3.345-3.527L-3.345-3.824L-0.282-3.824Q0.269-3.824 0.747-3.597Q1.226-3.371 1.579-2.976Q1.933-2.582 2.122-2.082Q2.312-1.582 2.312-1.039Q2.312-0.332 1.968 0.286Q1.624 0.903 1.028 1.272Q0.433 1.641-0.282 1.641M-1.794-3.261L-1.794 1.082Q-1.794 1.254-1.702 1.299Q-1.610 1.344-1.392 1.344L-0.497 1.344Q-0.052 1.344 0.341 1.174Q0.733 1.004 1.005 0.680Q1.276 0.356 1.374-0.064Q1.472-0.484 1.472-1.039Q1.472-1.394 1.435-1.707Q1.398-2.019 1.292-2.314Q1.187-2.609 1.007-2.832Q0.823-3.066 0.585-3.216Q0.347-3.367 0.066-3.447Q-0.216-3.527-0.497-3.527L-1.392-3.527Q-1.610-3.527-1.702-3.484Q-1.794-3.441-1.794-3.261M7.976 1.641L3.128 1.641L3.128 1.344Q3.448 1.344 3.692 1.297Q3.937 1.250 3.937 1.082L3.937-3.261Q3.937-3.433 3.692-3.480Q3.448-3.527 3.128-3.527L3.128-3.824L7.862-3.824L8.097-1.976L7.816-1.976Q7.733-2.671 7.558-2.992Q7.382-3.312 7.034-3.420Q6.687-3.527 5.968-3.527L5.105-3.527Q4.886-3.527 4.794-3.484Q4.702-3.441 4.702-3.261L4.702-1.343L5.335-1.343Q5.761-1.343 5.968-1.406Q6.175-1.468 6.263-1.664Q6.351-1.859 6.351-2.281L6.632-2.281L6.632-0.113L6.351-0.113Q6.351-0.535 6.263-0.728Q6.175-0.921 5.968-0.984Q5.761-1.046 5.335-1.046L4.702-1.046L4.702 1.082Q4.702 1.254 4.794 1.299Q4.886 1.344 5.105 1.344L6.030 1.344Q6.612 1.344 6.966 1.262Q7.319 1.180 7.525 0.983Q7.730 0.786 7.843 0.448Q7.956 0.110 8.050-0.472L8.327-0.472L7.976 1.641M11.280 1.641L8.921 1.641L8.921 1.344Q9.245 1.344 9.487 1.297Q9.730 1.250 9.730 1.082L9.730-3.261Q9.730-3.433 9.487-3.480Q9.245-3.527 8.921-3.527L8.921-3.824L11.515-3.824Q11.847-3.824 12.233-3.738Q12.620-3.652 12.968-3.478Q13.316-3.304 13.534-3.023Q13.753-2.742 13.753-2.375Q13.753-2.050 13.552-1.785Q13.351-1.519 13.044-1.343Q12.737-1.168 12.409-1.078Q12.776-0.957 13.036-0.687Q13.296-0.418 13.347-0.054L13.441 0.641Q13.511 1.090 13.608 1.321Q13.706 1.551 14.003 1.551Q14.249 1.551 14.382 1.334Q14.515 1.118 14.515 0.856Q14.534 0.782 14.616 0.762L14.698 0.762Q14.792 0.786 14.792 0.879Q14.792 1.110 14.694 1.325Q14.597 1.539 14.419 1.674Q14.241 1.809 14.011 1.809Q13.413 1.809 12.995 1.522Q12.577 1.235 12.577 0.664L12.577-0.031Q12.577-0.312 12.425-0.527Q12.273-0.742 12.023-0.855Q11.773-0.968 11.499-0.968L10.472-0.968L10.472 1.082Q10.472 1.246 10.716 1.295Q10.960 1.344 11.280 1.344L11.280 1.641M10.472-3.261L10.472-1.222L11.401-1.222Q11.722-1.222 11.989-1.275Q12.257-1.328 12.456-1.455Q12.655-1.582 12.773-1.814Q12.890-2.046 12.890-2.375Q12.890-3.027 12.493-3.277Q12.097-3.527 11.401-3.527L10.874-3.527Q10.655-3.527 10.564-3.484Q10.472-3.441 10.472-3.261\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(51.855 2)\">\u003Cpath d=\"M20.292 3.633Q19.679 3.176 19.277 2.541Q18.874 1.907 18.679 1.161Q18.484 0.414 18.484-0.359Q18.484-1.132 18.679-1.879Q18.874-2.625 19.277-3.259Q19.679-3.894 20.292-4.351Q20.304-4.355 20.312-4.357Q20.320-4.359 20.331-4.359L20.409-4.359Q20.448-4.359 20.474-4.332Q20.499-4.304 20.499-4.261Q20.499-4.211 20.468-4.191Q19.960-3.738 19.638-3.115Q19.316-2.492 19.175-1.796Q19.034-1.101 19.034-0.359Q19.034 0.375 19.173 1.075Q19.312 1.774 19.636 2.399Q19.960 3.024 20.468 3.473Q20.499 3.493 20.499 3.543Q20.499 3.586 20.474 3.614Q20.448 3.641 20.409 3.641L20.331 3.641Q20.323 3.637 20.314 3.635Q20.304 3.633 20.292 3.633M21.261-0.086Q21.261-0.582 21.511-1.007Q21.761-1.433 22.181-1.679Q22.601-1.925 23.101-1.925Q23.640-1.925 24.030-1.800Q24.421-1.675 24.421-1.261Q24.421-1.156 24.370-1.064Q24.320-0.972 24.228-0.921Q24.136-0.871 24.027-0.871Q23.921-0.871 23.829-0.921Q23.738-0.972 23.687-1.064Q23.636-1.156 23.636-1.261Q23.636-1.484 23.804-1.589Q23.581-1.648 23.109-1.648Q22.812-1.648 22.597-1.509Q22.382-1.371 22.251-1.140Q22.120-0.910 22.062-0.640Q22.003-0.371 22.003-0.086Q22.003 0.309 22.136 0.659Q22.269 1.008 22.540 1.225Q22.812 1.442 23.210 1.442Q23.585 1.442 23.861 1.225Q24.136 1.008 24.238 0.649Q24.253 0.586 24.316 0.586L24.421 0.586Q24.456 0.586 24.482 0.614Q24.507 0.641 24.507 0.680L24.507 0.704Q24.374 1.184 23.989 1.452Q23.605 1.719 23.101 1.719Q22.738 1.719 22.404 1.582Q22.070 1.446 21.810 1.196Q21.550 0.946 21.405 0.610Q21.261 0.274 21.261-0.086M25.093 0.809Q25.093 0.325 25.495 0.030Q25.898-0.265 26.448-0.384Q26.999-0.504 27.491-0.504L27.491-0.793Q27.491-1.019 27.376-1.226Q27.261-1.433 27.064-1.552Q26.866-1.671 26.636-1.671Q26.210-1.671 25.925-1.566Q25.995-1.539 26.042-1.484Q26.089-1.429 26.114-1.359Q26.140-1.289 26.140-1.214Q26.140-1.109 26.089-1.017Q26.038-0.925 25.946-0.875Q25.855-0.824 25.749-0.824Q25.644-0.824 25.552-0.875Q25.460-0.925 25.409-1.017Q25.359-1.109 25.359-1.214Q25.359-1.632 25.747-1.779Q26.136-1.925 26.636-1.925Q26.968-1.925 27.321-1.795Q27.675-1.664 27.904-1.410Q28.132-1.156 28.132-0.808L28.132 0.993Q28.132 1.125 28.204 1.235Q28.277 1.344 28.405 1.344Q28.530 1.344 28.599 1.239Q28.667 1.133 28.667 0.993L28.667 0.481L28.948 0.481L28.948 0.993Q28.948 1.196 28.831 1.354Q28.714 1.512 28.532 1.596Q28.351 1.680 28.148 1.680Q27.917 1.680 27.765 1.508Q27.613 1.336 27.581 1.106Q27.421 1.387 27.113 1.553Q26.804 1.719 26.452 1.719Q25.941 1.719 25.517 1.496Q25.093 1.274 25.093 0.809M25.780 0.809Q25.780 1.094 26.007 1.280Q26.234 1.465 26.527 1.465Q26.773 1.465 26.997 1.348Q27.222 1.231 27.357 1.028Q27.491 0.825 27.491 0.571L27.491-0.261Q27.226-0.261 26.941-0.207Q26.655-0.152 26.384-0.023Q26.113 0.106 25.946 0.313Q25.780 0.520 25.780 0.809M31.171 1.641L29.316 1.641L29.316 1.344Q29.589 1.344 29.757 1.297Q29.925 1.250 29.925 1.082L29.925-1.054Q29.925-1.269 29.863-1.365Q29.800-1.461 29.681-1.482Q29.562-1.504 29.316-1.504L29.316-1.800L30.507-1.886L30.507-1.152Q30.620-1.367 30.814-1.535Q31.007-1.703 31.245-1.795Q31.484-1.886 31.738-1.886Q32.905-1.886 32.905-0.808L32.905 1.082Q32.905 1.250 33.075 1.297Q33.245 1.344 33.515 1.344L33.515 1.641L31.659 1.641L31.659 1.344Q31.933 1.344 32.101 1.297Q32.269 1.250 32.269 1.082L32.269-0.793Q32.269-1.175 32.148-1.404Q32.027-1.632 31.675-1.632Q31.363-1.632 31.109-1.470Q30.855-1.308 30.708-1.039Q30.562-0.769 30.562-0.472L30.562 1.082Q30.562 1.250 30.732 1.297Q30.902 1.344 31.171 1.344L31.171 1.641M35.777 1.719Q35.296 1.719 34.888 1.475Q34.480 1.231 34.241 0.817Q34.003 0.403 34.003-0.086Q34.003-0.578 34.261-0.994Q34.519-1.410 34.950-1.648Q35.382-1.886 35.874-1.886Q36.495-1.886 36.945-1.449L36.945-3.078Q36.945-3.293 36.882-3.388Q36.820-3.484 36.702-3.505Q36.585-3.527 36.339-3.527L36.339-3.824L37.562-3.910L37.562 0.899Q37.562 1.110 37.624 1.205Q37.687 1.301 37.804 1.323Q37.921 1.344 38.171 1.344L38.171 1.641L36.921 1.719L36.921 1.235Q36.456 1.719 35.777 1.719M35.843 1.465Q36.183 1.465 36.476 1.274Q36.769 1.082 36.921 0.786L36.921-1.046Q36.773-1.320 36.511-1.476Q36.249-1.632 35.937-1.632Q35.312-1.632 35.029-1.185Q34.745-0.738 34.745-0.078Q34.745 0.567 34.997 1.016Q35.249 1.465 35.843 1.465M39.159 1.176Q39.159 0.993 39.296 0.856Q39.433 0.719 39.624 0.719Q39.816 0.719 39.948 0.852Q40.081 0.985 40.081 1.176Q40.081 1.375 39.948 1.508Q39.816 1.641 39.624 1.641Q39.433 1.641 39.296 1.504Q39.159 1.368 39.159 1.176M41.441 3.641L41.359 3.641Q41.323 3.641 41.298 3.612Q41.273 3.582 41.273 3.543Q41.273 3.493 41.304 3.473Q41.691 3.137 41.974 2.688Q42.257 2.239 42.423 1.739Q42.589 1.239 42.663 0.721Q42.737 0.204 42.737-0.359Q42.737-0.929 42.663-1.445Q42.589-1.961 42.423-2.457Q42.257-2.953 41.978-3.400Q41.698-3.847 41.304-4.191Q41.273-4.211 41.273-4.261Q41.273-4.300 41.298-4.330Q41.323-4.359 41.359-4.359L41.441-4.359Q41.452-4.359 41.462-4.357Q41.472-4.355 41.480-4.351Q42.093-3.894 42.495-3.259Q42.898-2.625 43.093-1.879Q43.288-1.132 43.288-0.359Q43.288 0.414 43.093 1.161Q42.898 1.907 42.495 2.541Q42.093 3.176 41.480 3.633Q41.468 3.633 41.460 3.635Q41.452 3.637 41.441 3.641\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-soft-neutral)\" stroke=\"var(--tk-accent)\">\u003Cpath d=\"M-9.89-29.656h50.826v-17.072H-9.89Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(22.022 -37.834)\">\u003Cpath d=\"M-26.524 1.719Q-27.005 1.719-27.413 1.475Q-27.821 1.231-28.059 0.817Q-28.298 0.403-28.298-0.086Q-28.298-0.578-28.040-0.994Q-27.782-1.410-27.350-1.648Q-26.919-1.886-26.427-1.886Q-25.806-1.886-25.356-1.449L-25.356-3.078Q-25.356-3.293-25.419-3.388Q-25.481-3.484-25.599-3.505Q-25.716-3.527-25.962-3.527L-25.962-3.824L-24.739-3.910L-24.739 0.899Q-24.739 1.110-24.677 1.205Q-24.614 1.301-24.497 1.323Q-24.380 1.344-24.130 1.344L-24.130 1.641L-25.380 1.719L-25.380 1.235Q-25.845 1.719-26.524 1.719M-26.458 1.465Q-26.118 1.465-25.825 1.274Q-25.532 1.082-25.380 0.786L-25.380-1.046Q-25.528-1.320-25.790-1.476Q-26.052-1.632-26.364-1.632Q-26.989-1.632-27.272-1.185Q-27.556-0.738-27.556-0.078Q-27.556 0.567-27.304 1.016Q-27.052 1.465-26.458 1.465M-23.622-0.054Q-23.622-0.558-23.366-0.990Q-23.110-1.421-22.675-1.673Q-22.239-1.925-21.739-1.925Q-21.352-1.925-21.011-1.781Q-20.669-1.636-20.407-1.375Q-20.145-1.113-20.003-0.777Q-19.860-0.441-19.860-0.054Q-19.860 0.438-20.124 0.848Q-20.388 1.258-20.817 1.489Q-21.247 1.719-21.739 1.719Q-22.231 1.719-22.665 1.487Q-23.099 1.254-23.360 0.846Q-23.622 0.438-23.622-0.054M-21.739 1.442Q-21.282 1.442-21.030 1.219Q-20.778 0.996-20.690 0.645Q-20.602 0.293-20.602-0.152Q-20.602-0.582-20.696-0.920Q-20.790-1.257-21.044-1.464Q-21.298-1.671-21.739-1.671Q-22.388-1.671-22.632-1.255Q-22.876-0.839-22.876-0.152Q-22.876 0.293-22.788 0.645Q-22.700 0.996-22.448 1.219Q-22.196 1.442-21.739 1.442M-18.751 0.680L-18.751-1.511L-19.454-1.511L-19.454-1.765Q-19.099-1.765-18.856-1.998Q-18.614-2.230-18.503-2.578Q-18.392-2.925-18.392-3.281L-18.110-3.281L-18.110-1.808L-16.934-1.808L-16.934-1.511L-18.110-1.511L-18.110 0.664Q-18.110 0.985-17.991 1.213Q-17.872 1.442-17.591 1.442Q-17.411 1.442-17.294 1.319Q-17.177 1.196-17.124 1.016Q-17.071 0.836-17.071 0.664L-17.071 0.192L-16.790 0.192L-16.790 0.680Q-16.790 0.934-16.895 1.174Q-17.001 1.414-17.198 1.567Q-17.395 1.719-17.653 1.719Q-17.970 1.719-18.222 1.596Q-18.474 1.473-18.612 1.239Q-18.751 1.004-18.751 0.680M-13.958 0.192L-16.212 0.192L-16.212-0.359L-13.958-0.359L-13.958 0.192M-11.356 3.192L-13.212 3.192L-13.212 2.899Q-12.942 2.899-12.774 2.854Q-12.606 2.809-12.606 2.633L-12.606-1.191Q-12.606-1.398-12.763-1.451Q-12.919-1.504-13.212-1.504L-13.212-1.800L-11.989-1.886L-11.989-1.421Q-11.759-1.644-11.444-1.765Q-11.130-1.886-10.790-1.886Q-10.317-1.886-9.913-1.640Q-9.509-1.394-9.276-0.978Q-9.044-0.562-9.044-0.086Q-9.044 0.289-9.192 0.618Q-9.341 0.946-9.610 1.198Q-9.880 1.450-10.224 1.584Q-10.567 1.719-10.927 1.719Q-11.216 1.719-11.487 1.598Q-11.759 1.477-11.966 1.266L-11.966 2.633Q-11.966 2.809-11.798 2.854Q-11.630 2.899-11.356 2.899L-11.356 3.192M-11.966-1.023L-11.966 0.817Q-11.813 1.106-11.552 1.286Q-11.290 1.465-10.981 1.465Q-10.696 1.465-10.474 1.327Q-10.251 1.188-10.099 0.957Q-9.946 0.727-9.868 0.455Q-9.790 0.184-9.790-0.086Q-9.790-0.418-9.915-0.775Q-10.040-1.132-10.288-1.369Q-10.536-1.605-10.884-1.605Q-11.208-1.605-11.503-1.449Q-11.798-1.293-11.966-1.023M-6.513 1.641L-8.493 1.641L-8.493 1.344Q-8.224 1.344-8.056 1.299Q-7.888 1.254-7.888 1.082L-7.888-1.054Q-7.888-1.269-7.950-1.365Q-8.013-1.461-8.130-1.482Q-8.247-1.504-8.493-1.504L-8.493-1.800L-7.325-1.886L-7.325-1.101Q-7.247-1.312-7.095-1.498Q-6.942-1.683-6.743-1.785Q-6.544-1.886-6.317-1.886Q-6.071-1.886-5.880-1.742Q-5.688-1.597-5.688-1.367Q-5.688-1.211-5.794-1.101Q-5.899-0.992-6.056-0.992Q-6.212-0.992-6.321-1.101Q-6.431-1.211-6.431-1.367Q-6.431-1.527-6.325-1.632Q-6.649-1.632-6.864-1.404Q-7.079-1.175-7.175-0.836Q-7.270-0.496-7.270-0.191L-7.270 1.082Q-7.270 1.250-7.044 1.297Q-6.817 1.344-6.513 1.344L-6.513 1.641M-5.208-0.054Q-5.208-0.558-4.952-0.990Q-4.696-1.421-4.261-1.673Q-3.825-1.925-3.325-1.925Q-2.938-1.925-2.597-1.781Q-2.255-1.636-1.993-1.375Q-1.731-1.113-1.589-0.777Q-1.446-0.441-1.446-0.054Q-1.446 0.438-1.710 0.848Q-1.974 1.258-2.403 1.489Q-2.833 1.719-3.325 1.719Q-3.817 1.719-4.251 1.487Q-4.684 1.254-4.946 0.846Q-5.208 0.438-5.208-0.054M-3.325 1.442Q-2.868 1.442-2.616 1.219Q-2.364 0.996-2.276 0.645Q-2.188 0.293-2.188-0.152Q-2.188-0.582-2.282-0.920Q-2.376-1.257-2.630-1.464Q-2.884-1.671-3.325-1.671Q-3.974-1.671-4.218-1.255Q-4.462-0.839-4.462-0.152Q-4.462 0.293-4.374 0.645Q-4.286 0.996-4.034 1.219Q-3.782 1.442-3.325 1.442\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(22.022 -37.834)\">\u003Cpath d=\"M1.108 1.719Q0.627 1.719 0.219 1.475Q-0.189 1.231-0.427 0.817Q-0.666 0.403-0.666-0.086Q-0.666-0.578-0.408-0.994Q-0.150-1.410 0.282-1.648Q0.713-1.886 1.205-1.886Q1.826-1.886 2.276-1.449L2.276-3.078Q2.276-3.293 2.213-3.388Q2.151-3.484 2.033-3.505Q1.916-3.527 1.670-3.527L1.670-3.824L2.893-3.910L2.893 0.899Q2.893 1.110 2.955 1.205Q3.018 1.301 3.135 1.323Q3.252 1.344 3.502 1.344L3.502 1.641L2.252 1.719L2.252 1.235Q1.787 1.719 1.108 1.719M1.174 1.465Q1.514 1.465 1.807 1.274Q2.100 1.082 2.252 0.786L2.252-1.046Q2.104-1.320 1.842-1.476Q1.580-1.632 1.268-1.632Q0.643-1.632 0.360-1.185Q0.076-0.738 0.076-0.078Q0.076 0.567 0.328 1.016Q0.580 1.465 1.174 1.465M4.694 0.688L4.694-1.054Q4.694-1.269 4.631-1.365Q4.569-1.461 4.449-1.482Q4.330-1.504 4.084-1.504L4.084-1.800L5.330-1.886L5.330 0.664L5.330 0.688Q5.330 1 5.385 1.162Q5.440 1.325 5.590 1.395Q5.740 1.465 6.061 1.465Q6.490 1.465 6.764 1.127Q7.037 0.789 7.037 0.344L7.037-1.054Q7.037-1.269 6.975-1.365Q6.912-1.461 6.793-1.482Q6.674-1.504 6.428-1.504L6.428-1.800L7.674-1.886L7.674 0.899Q7.674 1.110 7.737 1.205Q7.799 1.301 7.918 1.323Q8.037 1.344 8.283 1.344L8.283 1.641L7.061 1.719L7.061 1.098Q6.893 1.387 6.612 1.553Q6.330 1.719 6.010 1.719Q4.694 1.719 4.694 0.688M8.772-0.086Q8.772-0.582 9.022-1.007Q9.272-1.433 9.692-1.679Q10.112-1.925 10.612-1.925Q11.151-1.925 11.541-1.800Q11.932-1.675 11.932-1.261Q11.932-1.156 11.881-1.064Q11.830-0.972 11.739-0.921Q11.647-0.871 11.537-0.871Q11.432-0.871 11.340-0.921Q11.248-0.972 11.198-1.064Q11.147-1.156 11.147-1.261Q11.147-1.484 11.315-1.589Q11.092-1.648 10.619-1.648Q10.323-1.648 10.108-1.509Q9.893-1.371 9.762-1.140Q9.631-0.910 9.573-0.640Q9.514-0.371 9.514-0.086Q9.514 0.309 9.647 0.659Q9.780 1.008 10.051 1.225Q10.323 1.442 10.721 1.442Q11.096 1.442 11.371 1.225Q11.647 1.008 11.748 0.649Q11.764 0.586 11.826 0.586L11.932 0.586Q11.967 0.586 11.992 0.614Q12.018 0.641 12.018 0.680L12.018 0.704Q11.885 1.184 11.500 1.452Q11.116 1.719 10.612 1.719Q10.248 1.719 9.914 1.582Q9.580 1.446 9.321 1.196Q9.061 0.946 8.916 0.610Q8.772 0.274 8.772-0.086M13.131 0.680L13.131-1.511L12.428-1.511L12.428-1.765Q12.783-1.765 13.026-1.998Q13.268-2.230 13.379-2.578Q13.491-2.925 13.491-3.281L13.772-3.281L13.772-1.808L14.948-1.808L14.948-1.511L13.772-1.511L13.772 0.664Q13.772 0.985 13.891 1.213Q14.010 1.442 14.291 1.442Q14.471 1.442 14.588 1.319Q14.705 1.196 14.758 1.016Q14.811 0.836 14.811 0.664L14.811 0.192L15.092 0.192L15.092 0.680Q15.092 0.934 14.987 1.174Q14.881 1.414 14.684 1.567Q14.487 1.719 14.229 1.719Q13.912 1.719 13.660 1.596Q13.408 1.473 13.270 1.239Q13.131 1.004 13.131 0.680\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath fill=\"none\" d=\"m-28.579-8.517 17.165-19.44\"\u002F>\u003Cpath stroke=\"none\" d=\"m-10.09-29.456-3.317 1.34 1.993.159.405 1.958\"\u002F>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath fill=\"none\" d=\"M59.625-8.517 42.46-27.957\"\u002F>\u003Cpath stroke=\"none\" d=\"M41.136-29.456 42.055-26l.405-1.958 1.993-.16\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-19.47 23.488)\">\u003Cpath d=\"M-27.690 0.807L-27.690-0.697Q-27.690-0.967-27.798-1.028Q-27.906-1.090-28.217-1.090L-28.217-1.370L-27.109-1.445L-27.109 0.787L-27.109 0.807Q-27.109 1.087-27.058 1.231Q-27.007 1.374-26.865 1.431Q-26.723 1.487-26.436 1.487Q-26.183 1.487-25.978 1.347Q-25.773 1.207-25.657 0.981Q-25.540 0.756-25.540 0.506L-25.540-0.697Q-25.540-0.967-25.648-1.028Q-25.756-1.090-26.067-1.090L-26.067-1.370L-24.959-1.445L-24.959 0.968Q-24.959 1.159-24.906 1.241Q-24.853 1.323-24.753 1.342Q-24.652 1.361-24.436 1.361L-24.436 1.641L-25.513 1.709L-25.513 1.145Q-25.622 1.327-25.768 1.450Q-25.913 1.573-26.099 1.641Q-26.286 1.709-26.487 1.709Q-27.690 1.709-27.690 0.807M-23.849 1.634L-23.849 0.571Q-23.849 0.547-23.821 0.520Q-23.794 0.493-23.770 0.493L-23.661 0.493Q-23.596 0.493-23.582 0.551Q-23.486 0.985-23.240 1.236Q-22.994 1.487-22.580 1.487Q-22.239 1.487-21.986 1.354Q-21.733 1.221-21.733 0.913Q-21.733 0.756-21.827 0.641Q-21.921 0.527-22.059 0.458Q-22.198 0.390-22.365 0.352L-22.946 0.253Q-23.302 0.185-23.575-0.036Q-23.849-0.256-23.849-0.598Q-23.849-0.847-23.737-1.022Q-23.626-1.196-23.440-1.295Q-23.254-1.394-23.038-1.437Q-22.823-1.480-22.580-1.480Q-22.167-1.480-21.887-1.298L-21.671-1.473Q-21.661-1.476-21.654-1.478Q-21.647-1.480-21.637-1.480L-21.586-1.480Q-21.558-1.480-21.535-1.456Q-21.511-1.432-21.511-1.404L-21.511-0.557Q-21.511-0.536-21.535-0.509Q-21.558-0.482-21.586-0.482L-21.699-0.482Q-21.726-0.482-21.752-0.507Q-21.777-0.533-21.777-0.557Q-21.777-0.793-21.883-0.957Q-21.989-1.121-22.172-1.203Q-22.355-1.285-22.587-1.285Q-22.915-1.285-23.172-1.182Q-23.428-1.080-23.428-0.803Q-23.428-0.608-23.245-0.499Q-23.062-0.389-22.833-0.348L-22.259-0.242Q-22.013-0.194-21.799-0.066Q-21.586 0.062-21.449 0.265Q-21.312 0.469-21.312 0.718Q-21.312 1.231-21.678 1.470Q-22.044 1.709-22.580 1.709Q-23.076 1.709-23.408 1.415L-23.674 1.689Q-23.695 1.709-23.722 1.709L-23.770 1.709Q-23.794 1.709-23.821 1.682Q-23.849 1.655-23.849 1.634M-20.725 0.106Q-20.725-0.215-20.600-0.504Q-20.475-0.793-20.249-1.016Q-20.024-1.240-19.728-1.360Q-19.433-1.480-19.115-1.480Q-18.787-1.480-18.525-1.380Q-18.264-1.281-18.088-1.099Q-17.912-0.916-17.818-0.658Q-17.724-0.400-17.724-0.068Q-17.724 0.024-17.806 0.045L-20.061 0.045L-20.061 0.106Q-20.061 0.694-19.778 1.077Q-19.494 1.460-18.927 1.460Q-18.605 1.460-18.337 1.267Q-18.069 1.074-17.980 0.759Q-17.973 0.718-17.898 0.704L-17.806 0.704Q-17.724 0.728-17.724 0.800Q-17.724 0.807-17.730 0.834Q-17.843 1.231-18.214 1.470Q-18.585 1.709-19.009 1.709Q-19.446 1.709-19.846 1.501Q-20.246 1.292-20.485 0.925Q-20.725 0.558-20.725 0.106M-20.055-0.164L-18.240-0.164Q-18.240-0.441-18.337-0.693Q-18.434-0.946-18.633-1.102Q-18.831-1.257-19.115-1.257Q-19.392-1.257-19.605-1.099Q-19.819-0.940-19.937-0.685Q-20.055-0.430-20.055-0.164M-15.386 1.641L-17.122 1.641L-17.122 1.361Q-16.893 1.361-16.744 1.327Q-16.596 1.292-16.596 1.152L-16.596-0.697Q-16.596-0.967-16.703-1.028Q-16.811-1.090-17.122-1.090L-17.122-1.370L-16.093-1.445L-16.093-0.738Q-15.963-1.046-15.721-1.245Q-15.478-1.445-15.160-1.445Q-14.941-1.445-14.770-1.321Q-14.600-1.196-14.600-0.984Q-14.600-0.847-14.699-0.748Q-14.798-0.649-14.931-0.649Q-15.068-0.649-15.167-0.748Q-15.266-0.847-15.266-0.984Q-15.266-1.124-15.167-1.223Q-15.457-1.223-15.657-1.027Q-15.857-0.830-15.950-0.536Q-16.042-0.242-16.042 0.038L-16.042 1.152Q-16.042 1.361-15.386 1.361\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-19.47 23.488)\">\u003Cpath d=\"M-10.778 0.800L-10.778-1.097L-11.417-1.097L-11.417-1.319Q-11.099-1.319-10.882-1.529Q-10.665-1.739-10.565-2.049Q-10.464-2.358-10.464-2.666L-10.197-2.666L-10.197-1.377L-9.120-1.377L-9.120-1.097L-10.197-1.097L-10.197 0.787Q-10.197 1.063-10.093 1.262Q-9.989 1.460-9.729 1.460Q-9.572 1.460-9.466 1.356Q-9.360 1.251-9.310 1.098Q-9.261 0.944-9.261 0.787L-9.261 0.373L-8.994 0.373L-8.994 0.800Q-8.994 1.026-9.093 1.236Q-9.192 1.446-9.377 1.578Q-9.561 1.709-9.790 1.709Q-10.228 1.709-10.503 1.472Q-10.778 1.234-10.778 0.800M-7.610 0.807L-7.610-0.697Q-7.610-0.967-7.717-1.028Q-7.825-1.090-8.136-1.090L-8.136-1.370L-7.029-1.445L-7.029 0.787L-7.029 0.807Q-7.029 1.087-6.977 1.231Q-6.926 1.374-6.784 1.431Q-6.642 1.487-6.355 1.487Q-6.102 1.487-5.897 1.347Q-5.692 1.207-5.576 0.981Q-5.460 0.756-5.460 0.506L-5.460-0.697Q-5.460-0.967-5.567-1.028Q-5.675-1.090-5.986-1.090L-5.986-1.370L-4.879-1.445L-4.879 0.968Q-4.879 1.159-4.826 1.241Q-4.773 1.323-4.672 1.342Q-4.571 1.361-4.356 1.361L-4.356 1.641L-5.432 1.709L-5.432 1.145Q-5.542 1.327-5.687 1.450Q-5.832 1.573-6.019 1.641Q-6.205 1.709-6.407 1.709Q-7.610 1.709-7.610 0.807M-2.018 1.641L-3.754 1.641L-3.754 1.361Q-3.525 1.361-3.377 1.327Q-3.228 1.292-3.228 1.152L-3.228-0.697Q-3.228-0.967-3.336-1.028Q-3.443-1.090-3.754-1.090L-3.754-1.370L-2.725-1.445L-2.725-0.738Q-2.596-1.046-2.353-1.245Q-2.110-1.445-1.792-1.445Q-1.574-1.445-1.403-1.321Q-1.232-1.196-1.232-0.984Q-1.232-0.847-1.331-0.748Q-1.430-0.649-1.563-0.649Q-1.700-0.649-1.799-0.748Q-1.898-0.847-1.898-0.984Q-1.898-1.124-1.799-1.223Q-2.090-1.223-2.290-1.027Q-2.490-0.830-2.582-0.536Q-2.674-0.242-2.674 0.038L-2.674 1.152Q-2.674 1.361-2.018 1.361L-2.018 1.641M1.034 1.641L-0.599 1.641L-0.599 1.361Q-0.370 1.361-0.222 1.327Q-0.073 1.292-0.073 1.152L-0.073-0.697Q-0.073-0.967-0.181-1.028Q-0.288-1.090-0.599-1.090L-0.599-1.370L0.460-1.445L0.460-0.796Q0.631-1.104 0.935-1.275Q1.239-1.445 1.585-1.445Q2.090-1.445 2.374-1.222Q2.658-0.998 2.658-0.502L2.658 1.152Q2.658 1.289 2.807 1.325Q2.955 1.361 3.181 1.361L3.181 1.641L1.550 1.641L1.550 1.361Q1.779 1.361 1.928 1.327Q2.077 1.292 2.077 1.152L2.077-0.488Q2.077-0.823 1.957-1.023Q1.838-1.223 1.523-1.223Q1.253-1.223 1.019-1.087Q0.785-0.950 0.646-0.716Q0.508-0.482 0.508-0.208L0.508 1.152Q0.508 1.289 0.658 1.325Q0.809 1.361 1.034 1.361\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-19.47 23.488)\">\u003Cpath d=\"M8.097 1.709Q7.666 1.709 7.290 1.497Q6.914 1.286 6.694 0.925Q6.473 0.564 6.473 0.130Q6.473-0.314 6.711-0.675Q6.949-1.035 7.335-1.240Q7.721-1.445 8.162-1.445Q8.480-1.445 8.763-1.290Q9.047-1.134 9.232-0.858L9.478-1.445L9.714-1.445L9.714 2.509Q9.714 2.646 9.864 2.682Q10.014 2.718 10.240 2.718L10.240 2.998L8.610 2.998L8.610 2.718Q8.835 2.718 8.984 2.683Q9.133 2.649 9.133 2.509L9.133 1.248Q8.934 1.467 8.663 1.588Q8.391 1.709 8.097 1.709M8.148 1.487Q8.480 1.487 8.752 1.282Q9.023 1.077 9.163 0.752L9.163-0.382Q9.105-0.605 8.974-0.787Q8.842-0.970 8.652-1.081Q8.463-1.192 8.237-1.192Q7.909-1.192 7.658-0.996Q7.407-0.799 7.273-0.492Q7.140-0.184 7.140 0.137Q7.140 0.438 7.256 0.754Q7.372 1.070 7.601 1.279Q7.830 1.487 8.148 1.487\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(68.433 24.043)\">\u003Cpath d=\"M-28.265 0.130Q-28.265-0.198-28.130-0.499Q-27.995-0.799-27.759-1.020Q-27.523-1.240-27.219-1.360Q-26.914-1.480-26.590-1.480Q-26.084-1.480-25.735-1.377Q-25.387-1.275-25.387-0.899Q-25.387-0.752-25.484-0.651Q-25.581-0.550-25.728-0.550Q-25.882-0.550-25.981-0.649Q-26.080-0.748-26.080-0.899Q-26.080-1.087-25.940-1.179Q-26.142-1.230-26.583-1.230Q-26.938-1.230-27.167-1.034Q-27.396-0.837-27.497-0.528Q-27.598-0.218-27.598 0.130Q-27.598 0.479-27.472 0.785Q-27.345 1.091-27.090 1.275Q-26.836 1.460-26.480 1.460Q-26.258 1.460-26.074 1.376Q-25.889 1.292-25.754 1.137Q-25.619 0.981-25.561 0.773Q-25.547 0.718-25.493 0.718L-25.380 0.718Q-25.349 0.718-25.327 0.742Q-25.305 0.766-25.305 0.800L-25.305 0.821Q-25.390 1.108-25.578 1.306Q-25.766 1.504-26.031 1.607Q-26.296 1.709-26.590 1.709Q-27.020 1.709-27.408 1.503Q-27.796 1.296-28.030 0.933Q-28.265 0.571-28.265 0.130M-24.659 0.913Q-24.659 0.581-24.435 0.354Q-24.211 0.127-23.867-0.001Q-23.524-0.130-23.151-0.182Q-22.779-0.235-22.475-0.235L-22.475-0.488Q-22.475-0.693-22.582-0.873Q-22.690-1.052-22.871-1.155Q-23.052-1.257-23.261-1.257Q-23.667-1.257-23.903-1.165Q-23.814-1.128-23.768-1.044Q-23.722-0.960-23.722-0.858Q-23.722-0.762-23.768-0.683Q-23.814-0.605-23.895-0.560Q-23.975-0.516-24.064-0.516Q-24.214-0.516-24.315-0.613Q-24.416-0.711-24.416-0.858Q-24.416-1.480-23.261-1.480Q-23.049-1.480-22.799-1.416Q-22.550-1.353-22.348-1.234Q-22.146-1.114-22.020-0.929Q-21.893-0.745-21.893-0.502L-21.893 1.074Q-21.893 1.190-21.832 1.286Q-21.770 1.381-21.658 1.381Q-21.548 1.381-21.483 1.287Q-21.418 1.193-21.418 1.074L-21.418 0.626L-21.152 0.626L-21.152 1.074Q-21.152 1.344-21.379 1.509Q-21.606 1.675-21.887 1.675Q-22.095 1.675-22.232 1.521Q-22.369 1.368-22.392 1.152Q-22.539 1.419-22.821 1.564Q-23.103 1.709-23.428 1.709Q-23.705 1.709-23.989 1.634Q-24.272 1.559-24.465 1.380Q-24.659 1.200-24.659 0.913M-24.043 0.913Q-24.043 1.087-23.943 1.217Q-23.842 1.347-23.686 1.417Q-23.531 1.487-23.367 1.487Q-23.148 1.487-22.939 1.390Q-22.731 1.292-22.603 1.111Q-22.475 0.930-22.475 0.704L-22.475-0.024Q-22.799-0.024-23.165 0.067Q-23.531 0.158-23.787 0.370Q-24.043 0.581-24.043 0.913M-19.053 1.641L-20.687 1.641L-20.687 1.361Q-20.458 1.361-20.309 1.327Q-20.161 1.292-20.161 1.152L-20.161-0.697Q-20.161-0.967-20.268-1.028Q-20.376-1.090-20.687-1.090L-20.687-1.370L-19.627-1.445L-19.627-0.796Q-19.456-1.104-19.152-1.275Q-18.848-1.445-18.503-1.445Q-17.997-1.445-17.713-1.222Q-17.430-0.998-17.430-0.502L-17.430 1.152Q-17.430 1.289-17.281 1.325Q-17.132 1.361-16.907 1.361L-16.907 1.641L-18.537 1.641L-18.537 1.361Q-18.308 1.361-18.159 1.327Q-18.011 1.292-18.011 1.152L-18.011-0.488Q-18.011-0.823-18.130-1.023Q-18.250-1.223-18.564-1.223Q-18.834-1.223-19.069-1.087Q-19.303-0.950-19.441-0.716Q-19.579-0.482-19.579-0.208L-19.579 1.152Q-19.579 1.289-19.429 1.325Q-19.279 1.361-19.053 1.361L-19.053 1.641M-16.319 0.130Q-16.319-0.208-16.179-0.499Q-16.038-0.789-15.794-1.003Q-15.550-1.216-15.246-1.331Q-14.941-1.445-14.617-1.445Q-14.347-1.445-14.083-1.346Q-13.820-1.247-13.629-1.069L-13.629-2.467Q-13.629-2.737-13.736-2.799Q-13.844-2.860-14.155-2.860L-14.155-3.141L-13.079-3.216L-13.079 0.968Q-13.079 1.156-13.024 1.239Q-12.969 1.323-12.868 1.342Q-12.767 1.361-12.552 1.361L-12.552 1.641L-13.660 1.709L-13.660 1.292Q-14.077 1.709-14.702 1.709Q-15.133 1.709-15.505 1.497Q-15.878 1.286-16.098 0.925Q-16.319 0.564-16.319 0.130M-14.644 1.487Q-14.435 1.487-14.249 1.415Q-14.063 1.344-13.909 1.207Q-13.755 1.070-13.660 0.892L-13.660-0.717Q-13.745-0.864-13.890-0.984Q-14.036-1.104-14.205-1.163Q-14.374-1.223-14.555-1.223Q-15.116-1.223-15.384-0.834Q-15.652-0.444-15.652 0.137Q-15.652 0.708-15.418 1.098Q-15.184 1.487-14.644 1.487M-10.286 1.641L-11.838 1.641L-11.838 1.361Q-11.612 1.361-11.464 1.327Q-11.315 1.292-11.315 1.152L-11.315-0.697Q-11.315-0.885-11.363-0.969Q-11.411-1.052-11.508-1.071Q-11.605-1.090-11.817-1.090L-11.817-1.370L-10.761-1.445L-10.761 1.152Q-10.761 1.292-10.630 1.327Q-10.498 1.361-10.286 1.361L-10.286 1.641M-11.558-2.666Q-11.558-2.837-11.434-2.956Q-11.311-3.076-11.141-3.076Q-10.973-3.076-10.850-2.956Q-10.727-2.837-10.727-2.666Q-10.727-2.491-10.850-2.368Q-10.973-2.245-11.141-2.245Q-11.311-2.245-11.434-2.368Q-11.558-2.491-11.558-2.666M-9.640 0.130Q-9.640-0.208-9.500-0.499Q-9.360-0.789-9.115-1.003Q-8.871-1.216-8.567-1.331Q-8.263-1.445-7.938-1.445Q-7.668-1.445-7.405-1.346Q-7.142-1.247-6.950-1.069L-6.950-2.467Q-6.950-2.737-7.058-2.799Q-7.165-2.860-7.476-2.860L-7.476-3.141L-6.400-3.216L-6.400 0.968Q-6.400 1.156-6.345 1.239Q-6.290 1.323-6.190 1.342Q-6.089 1.361-5.873 1.361L-5.873 1.641L-6.981 1.709L-6.981 1.292Q-7.398 1.709-8.023 1.709Q-8.454 1.709-8.827 1.497Q-9.199 1.286-9.420 0.925Q-9.640 0.564-9.640 0.130M-7.965 1.487Q-7.757 1.487-7.570 1.415Q-7.384 1.344-7.230 1.207Q-7.077 1.070-6.981 0.892L-6.981-0.717Q-7.066-0.864-7.212-0.984Q-7.357-1.104-7.526-1.163Q-7.695-1.223-7.876-1.223Q-8.437-1.223-8.705-0.834Q-8.974-0.444-8.974 0.137Q-8.974 0.708-8.739 1.098Q-8.505 1.487-7.965 1.487M-5.166 0.913Q-5.166 0.581-4.942 0.354Q-4.718 0.127-4.375-0.001Q-4.031-0.130-3.659-0.182Q-3.286-0.235-2.982-0.235L-2.982-0.488Q-2.982-0.693-3.089-0.873Q-3.197-1.052-3.378-1.155Q-3.559-1.257-3.768-1.257Q-4.175-1.257-4.411-1.165Q-4.322-1.128-4.276-1.044Q-4.229-0.960-4.229-0.858Q-4.229-0.762-4.276-0.683Q-4.322-0.605-4.402-0.560Q-4.482-0.516-4.571-0.516Q-4.722-0.516-4.822-0.613Q-4.923-0.711-4.923-0.858Q-4.923-1.480-3.768-1.480Q-3.556-1.480-3.307-1.416Q-3.057-1.353-2.855-1.234Q-2.654-1.114-2.527-0.929Q-2.401-0.745-2.401-0.502L-2.401 1.074Q-2.401 1.190-2.339 1.286Q-2.278 1.381-2.165 1.381Q-2.056 1.381-1.991 1.287Q-1.926 1.193-1.926 1.074L-1.926 0.626L-1.659 0.626L-1.659 1.074Q-1.659 1.344-1.886 1.509Q-2.114 1.675-2.394 1.675Q-2.602 1.675-2.739 1.521Q-2.876 1.368-2.900 1.152Q-3.047 1.419-3.329 1.564Q-3.611 1.709-3.935 1.709Q-4.212 1.709-4.496 1.634Q-4.780 1.559-4.973 1.380Q-5.166 1.200-5.166 0.913M-4.551 0.913Q-4.551 1.087-4.450 1.217Q-4.349 1.347-4.194 1.417Q-4.038 1.487-3.874 1.487Q-3.655 1.487-3.447 1.390Q-3.238 1.292-3.110 1.111Q-2.982 0.930-2.982 0.704L-2.982-0.024Q-3.307-0.024-3.672 0.067Q-4.038 0.158-4.294 0.370Q-4.551 0.581-4.551 0.913M-0.716 0.800L-0.716-1.097L-1.355-1.097L-1.355-1.319Q-1.037-1.319-0.820-1.529Q-0.603-1.739-0.502-2.049Q-0.401-2.358-0.401-2.666L-0.135-2.666L-0.135-1.377L0.942-1.377L0.942-1.097L-0.135-1.097L-0.135 0.787Q-0.135 1.063-0.030 1.262Q0.074 1.460 0.334 1.460Q0.491 1.460 0.597 1.356Q0.703 1.251 0.752 1.098Q0.802 0.944 0.802 0.787L0.802 0.373L1.068 0.373L1.068 0.800Q1.068 1.026 0.969 1.236Q0.870 1.446 0.686 1.578Q0.501 1.709 0.272 1.709Q-0.165 1.709-0.441 1.472Q-0.716 1.234-0.716 0.800M1.838 0.106Q1.838-0.215 1.962-0.504Q2.087-0.793 2.313-1.016Q2.538-1.240 2.834-1.360Q3.129-1.480 3.447-1.480Q3.775-1.480 4.037-1.380Q4.298-1.281 4.474-1.099Q4.650-0.916 4.744-0.658Q4.838-0.400 4.838-0.068Q4.838 0.024 4.756 0.045L2.501 0.045L2.501 0.106Q2.501 0.694 2.784 1.077Q3.068 1.460 3.635 1.460Q3.957 1.460 4.225 1.267Q4.493 1.074 4.582 0.759Q4.589 0.718 4.664 0.704L4.756 0.704Q4.838 0.728 4.838 0.800Q4.838 0.807 4.832 0.834Q4.719 1.231 4.348 1.470Q3.977 1.709 3.553 1.709Q3.116 1.709 2.716 1.501Q2.316 1.292 2.077 0.925Q1.838 0.558 1.838 0.106M2.507-0.164L4.322-0.164Q4.322-0.441 4.225-0.693Q4.128-0.946 3.929-1.102Q3.731-1.257 3.447-1.257Q3.171-1.257 2.957-1.099Q2.743-0.940 2.625-0.685Q2.507-0.430 2.507-0.164\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(68.433 24.043)\">\u003Cpath d=\"M9.902 1.641L8.166 1.641L8.166 1.361Q8.395 1.361 8.544 1.327Q8.692 1.292 8.692 1.152L8.692-0.697Q8.692-0.967 8.585-1.028Q8.477-1.090 8.166-1.090L8.166-1.370L9.195-1.445L9.195-0.738Q9.325-1.046 9.567-1.245Q9.810-1.445 10.128-1.445Q10.347-1.445 10.518-1.321Q10.689-1.196 10.689-0.984Q10.689-0.847 10.589-0.748Q10.490-0.649 10.357-0.649Q10.220-0.649 10.121-0.748Q10.022-0.847 10.022-0.984Q10.022-1.124 10.121-1.223Q9.831-1.223 9.631-1.027Q9.431-0.830 9.338-0.536Q9.246-0.242 9.246 0.038L9.246 1.152Q9.246 1.361 9.902 1.361\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmbx8\" font-size=\"8\">\u003Cg transform=\"translate(18.413 42.253)\">\u003Cpath d=\"M-25.618 3.625Q-26.606 2.969-27.145 1.912Q-27.684 0.856-27.684-0.359Q-27.684-1.574-27.145-2.634Q-26.606-3.695-25.618-4.343Q-25.602-4.351-25.591-4.353Q-25.579-4.355-25.563-4.359L-25.450-4.359Q-25.395-4.359-25.358-4.314Q-25.321-4.269-25.321-4.222Q-25.321-4.171-25.380-4.113Q-25.767-3.777-26.040-3.341Q-26.313-2.906-26.481-2.416Q-26.649-1.925-26.725-1.406Q-26.802-0.886-26.802-0.359Q-26.802 0.368-26.661 1.051Q-26.520 1.735-26.204 2.342Q-25.888 2.950-25.380 3.395Q-25.321 3.454-25.321 3.504Q-25.321 3.614-25.450 3.641L-25.563 3.641Q-25.602 3.641-25.618 3.625M-24.497 0.711Q-24.497 0.340-24.202 0.088Q-23.907-0.164-23.456-0.295Q-23.005-0.425-22.569-0.472Q-22.134-0.519-21.747-0.519L-21.747-0.773Q-21.747-1.171-21.977-1.402Q-22.208-1.632-22.602-1.632Q-23.028-1.632-23.235-1.582Q-23.075-1.437-23.075-1.183Q-23.075-0.949-23.233-0.791Q-23.392-0.632-23.626-0.632Q-23.868-0.632-24.026-0.791Q-24.184-0.949-24.184-1.183Q-24.184-1.695-23.720-1.847Q-23.255-2-22.602-2Q-22.181-2-21.751-1.884Q-21.321-1.769-21.030-1.498Q-20.739-1.226-20.739-0.793L-20.739 1.067Q-20.708 1.192-20.169 1.192Q-20.110 1.192-20.063 1.239Q-20.017 1.286-20.017 1.344L-20.017 1.481Q-20.017 1.547-20.063 1.594Q-20.110 1.641-20.169 1.641L-20.716 1.641Q-21.595 1.641-21.595 1.137Q-21.782 1.414-22.128 1.555Q-22.474 1.696-22.841 1.696Q-23.216 1.696-23.597 1.612Q-23.977 1.528-24.237 1.305Q-24.497 1.082-24.497 0.711M-23.489 0.711Q-23.489 0.899-23.378 1.039Q-23.267 1.180-23.091 1.254Q-22.915 1.329-22.731 1.329Q-22.501 1.329-22.272 1.248Q-22.044 1.168-21.895 1.008Q-21.747 0.848-21.747 0.610L-21.747-0.183Q-22.083-0.183-22.485-0.099Q-22.888-0.015-23.188 0.186Q-23.489 0.387-23.489 0.711M-19.224 3.641L-19.337 3.641Q-19.384 3.641-19.425 3.596Q-19.466 3.551-19.466 3.504Q-19.466 3.446-19.407 3.395Q-19.020 3.051-18.741 2.612Q-18.462 2.172-18.298 1.692Q-18.134 1.211-18.056 0.690Q-17.977 0.168-17.977-0.359Q-17.977-0.886-18.056-1.412Q-18.134-1.937-18.296-2.414Q-18.458-2.890-18.739-3.334Q-19.020-3.777-19.407-4.113Q-19.466-4.164-19.466-4.222Q-19.466-4.332-19.337-4.359L-19.224-4.359Q-19.192-4.359-19.161-4.343Q-18.169-3.687-17.638-2.630Q-17.106-1.574-17.106-0.359Q-17.106 0.250-17.243 0.827Q-17.380 1.403-17.638 1.909Q-17.895 2.414-18.288 2.860Q-18.681 3.305-19.161 3.625Q-19.177 3.629-19.188 3.631Q-19.200 3.633-19.224 3.641\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(18.413 42.253)\">\u003Cpath d=\"M-10.430 1.641L-12.574 1.641L-12.574 1.192L-12.047 1.192L-12.047-1.230Q-12.047-1.382-12.194-1.420Q-12.340-1.457-12.574-1.457L-12.574-1.902L-11.192-1.968L-11.192-1.160Q-11.035-1.515-10.756-1.742Q-10.477-1.968-10.110-1.968Q-9.746-1.968-9.451-1.781Q-9.156-1.593-9.156-1.254Q-9.156-1.109-9.229-0.982Q-9.301-0.855-9.428-0.783Q-9.555-0.711-9.703-0.711Q-9.848-0.711-9.975-0.783Q-10.102-0.855-10.174-0.982Q-10.246-1.109-10.246-1.254Q-10.246-1.468-10.133-1.597Q-10.465-1.597-10.682-1.351Q-10.899-1.105-10.992-0.746Q-11.086-0.386-11.086-0.070L-11.086 1.192L-10.430 1.192L-10.430 1.641M-8.606-0.160Q-8.606-0.738-8.322-1.158Q-8.039-1.578-7.555-1.789Q-7.071-2-6.504-2Q-6.063-2-5.727-1.888Q-5.391-1.777-5.156-1.556Q-4.922-1.336-4.797-1.005Q-4.672-0.675-4.672-0.238Q-4.672-0.082-4.824-0.054L-7.496-0.054Q-7.496 1.289-6.239 1.289Q-5.887 1.289-5.580 1.133Q-5.274 0.977-5.153 0.680Q-5.078 0.551-4.992 0.551L-4.824 0.551Q-4.672 0.586-4.672 0.719Q-4.672 0.754-4.680 0.770Q-4.864 1.246-5.328 1.471Q-5.793 1.696-6.367 1.696Q-6.965 1.696-7.475 1.493Q-7.985 1.289-8.295 0.868Q-8.606 0.446-8.606-0.160M-7.496-0.398L-5.489-0.398Q-5.489-0.937-5.737-1.285Q-5.985-1.632-6.504-1.632Q-6.848-1.632-7.072-1.466Q-7.297-1.300-7.397-1.023Q-7.496-0.746-7.496-0.398M-3.535 0.719L-3.535-1.464L-4.207-1.464L-4.207-1.832Q-3.821-1.832-3.547-2.078Q-3.274-2.324-3.141-2.699Q-3.008-3.074-3.008-3.437L-2.528-3.437L-2.528-1.910L-1.293-1.910L-1.293-1.464L-2.528-1.464L-2.528 0.704Q-2.528 1.289-2.071 1.289Q-1.860 1.289-1.737 1.106Q-1.614 0.922-1.614 0.704L-1.614 0.250L-1.133 0.250L-1.133 0.719Q-1.133 0.993-1.280 1.215Q-1.426 1.438-1.666 1.567Q-1.906 1.696-2.176 1.696Q-2.750 1.696-3.143 1.473Q-3.535 1.250-3.535 0.719M1.914 1.641L-0.231 1.641L-0.231 1.192L0.297 1.192L0.297-1.230Q0.297-1.382 0.150-1.420Q0.004-1.457-0.231-1.457L-0.231-1.902L1.152-1.968L1.152-1.160Q1.308-1.515 1.588-1.742Q1.867-1.968 2.234-1.968Q2.597-1.968 2.892-1.781Q3.187-1.593 3.187-1.254Q3.187-1.109 3.115-0.982Q3.043-0.855 2.916-0.783Q2.789-0.711 2.640-0.711Q2.496-0.711 2.369-0.783Q2.242-0.855 2.170-0.982Q2.097-1.109 2.097-1.254Q2.097-1.468 2.211-1.597Q1.879-1.597 1.662-1.351Q1.445-1.105 1.351-0.746Q1.258-0.386 1.258-0.070L1.258 1.192L1.914 1.192L1.914 1.641M5.879 1.641L3.922 1.641L3.922 1.192L4.449 1.192L4.449-1.230Q4.449-1.382 4.312-1.420Q4.176-1.457 3.945-1.457L3.945-1.902L5.410-1.968L5.410 1.192L5.879 1.192L5.879 1.641M4.168-3.261Q4.168-3.539 4.361-3.728Q4.554-3.918 4.824-3.918Q5.004-3.918 5.154-3.828Q5.304-3.738 5.392-3.591Q5.480-3.445 5.480-3.261Q5.480-2.992 5.287-2.798Q5.094-2.605 4.824-2.605Q4.551-2.605 4.359-2.796Q4.168-2.988 4.168-3.261M6.457-0.160Q6.457-0.738 6.740-1.158Q7.023-1.578 7.508-1.789Q7.992-2 8.558-2Q9-2 9.336-1.888Q9.672-1.777 9.906-1.556Q10.140-1.336 10.265-1.005Q10.390-0.675 10.390-0.238Q10.390-0.082 10.238-0.054L7.566-0.054Q7.566 1.289 8.824 1.289Q9.176 1.289 9.482 1.133Q9.789 0.977 9.910 0.680Q9.984 0.551 10.070 0.551L10.238 0.551Q10.390 0.586 10.390 0.719Q10.390 0.754 10.383 0.770Q10.199 1.246 9.734 1.471Q9.269 1.696 8.695 1.696Q8.097 1.696 7.588 1.493Q7.078 1.289 6.767 0.868Q6.457 0.446 6.457-0.160M7.566-0.398L9.574-0.398Q9.574-0.937 9.326-1.285Q9.078-1.632 8.558-1.632Q8.215-1.632 7.990-1.466Q7.765-1.300 7.666-1.023Q7.566-0.746 7.566-0.398M12.902 1.512L11.426-1.464L10.910-1.464L10.910-1.910L12.863-1.910L12.863-1.464L12.496-1.464L13.519 0.602L14.527-1.414Q14.527-1.464 14.183-1.464L14.183-1.910L15.601-1.910L15.601-1.464Q15.105-1.464 15.054-1.406L13.609 1.512Q13.527 1.680 13.336 1.680L13.176 1.680Q12.984 1.680 12.902 1.512\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(18.413 42.253)\">\u003Cpath d=\"M15.586 0.711Q15.586 0.340 15.881 0.088Q16.176-0.164 16.627-0.295Q17.079-0.425 17.514-0.472Q17.950-0.519 18.336-0.519L18.336-0.773Q18.336-1.171 18.106-1.402Q17.876-1.632 17.481-1.632Q17.055-1.632 16.848-1.582Q17.008-1.437 17.008-1.183Q17.008-0.949 16.850-0.791Q16.692-0.632 16.458-0.632Q16.215-0.632 16.057-0.791Q15.899-0.949 15.899-1.183Q15.899-1.695 16.364-1.847Q16.829-2 17.481-2Q17.903-2 18.333-1.884Q18.762-1.769 19.053-1.498Q19.344-1.226 19.344-0.793L19.344 1.067Q19.376 1.192 19.915 1.192Q19.973 1.192 20.020 1.239Q20.067 1.286 20.067 1.344L20.067 1.481Q20.067 1.547 20.020 1.594Q19.973 1.641 19.915 1.641L19.368 1.641Q18.489 1.641 18.489 1.137Q18.301 1.414 17.956 1.555Q17.610 1.696 17.243 1.696Q16.868 1.696 16.487 1.612Q16.106 1.528 15.846 1.305Q15.586 1.082 15.586 0.711M16.594 0.711Q16.594 0.899 16.706 1.039Q16.817 1.180 16.993 1.254Q17.168 1.329 17.352 1.329Q17.583 1.329 17.811 1.248Q18.040 1.168 18.188 1.008Q18.336 0.848 18.336 0.610L18.336-0.183Q18.001-0.183 17.598-0.099Q17.196-0.015 16.895 0.186Q16.594 0.387 16.594 0.711M22.547 1.641L20.532 1.641L20.532 1.192L21.059 1.192L21.059-3.175Q21.059-3.324 20.913-3.361Q20.766-3.398 20.532-3.398L20.532-3.847L22.020-3.910L22.020 1.192L22.547 1.192\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-soft-accent)\">\u003Cpath d=\"M147.828 11.6h68.287V-8.317h-68.287Z\"\u002F>\u003Cg transform=\"translate(188.84 2.733)\">\u003Cpath d=\"M-23.395 1.641L-28.243 1.641L-28.243 1.344Q-27.923 1.344-27.679 1.297Q-27.434 1.250-27.434 1.082L-27.434-3.261Q-27.434-3.433-27.679-3.480Q-27.923-3.527-28.243-3.527L-28.243-3.824L-23.509-3.824L-23.274-1.976L-23.556-1.976Q-23.638-2.671-23.813-2.992Q-23.989-3.312-24.337-3.420Q-24.684-3.527-25.403-3.527L-26.267-3.527Q-26.485-3.527-26.577-3.484Q-26.669-3.441-26.669-3.261L-26.669-1.343L-26.036-1.343Q-25.610-1.343-25.403-1.406Q-25.196-1.468-25.108-1.664Q-25.020-1.859-25.020-2.281L-24.739-2.281L-24.739-0.113L-25.020-0.113Q-25.020-0.535-25.108-0.728Q-25.196-0.921-25.403-0.984Q-25.610-1.046-26.036-1.046L-26.669-1.046L-26.669 1.082Q-26.669 1.254-26.577 1.299Q-26.485 1.344-26.267 1.344L-25.341 1.344Q-24.759 1.344-24.405 1.262Q-24.052 1.180-23.847 0.983Q-23.642 0.786-23.528 0.448Q-23.415 0.110-23.321-0.472L-23.044-0.472L-23.395 1.641M-20.544 1.641L-22.466 1.641L-22.466 1.344Q-21.657 1.344-21.657 0.864L-21.657-3.480Q-21.915-3.527-22.466-3.527L-22.466-3.824L-20.970-3.824Q-20.911-3.804-20.899-3.793L-17.892 0.395L-17.892-3.046Q-17.892-3.527-18.696-3.527L-18.696-3.824L-16.778-3.824L-16.778-3.527Q-17.587-3.527-17.587-3.046L-17.587 1.536Q-17.606 1.621-17.681 1.641L-17.786 1.641Q-17.841 1.629-17.856 1.602L-21.352-3.254L-21.352 0.864Q-21.352 1.344-20.544 1.344L-20.544 1.641M-15.970-1.093Q-15.970-1.687-15.737-2.218Q-15.505-2.750-15.089-3.150Q-14.673-3.550-14.140-3.771Q-13.606-3.992-13.001-3.992Q-12.563-3.992-12.165-3.810Q-11.767-3.629-11.458-3.296L-10.985-3.961Q-10.954-3.992-10.923-3.992L-10.876-3.992Q-10.849-3.992-10.817-3.961Q-10.786-3.929-10.786-3.902L-10.786-1.765Q-10.786-1.742-10.817-1.711Q-10.849-1.679-10.876-1.679L-10.993-1.679Q-11.020-1.679-11.052-1.711Q-11.083-1.742-11.083-1.765Q-11.083-2.031-11.225-2.384Q-11.368-2.738-11.548-2.976Q-11.802-3.308-12.151-3.502Q-12.501-3.695-12.907-3.695Q-13.411-3.695-13.864-3.476Q-14.317-3.257-14.610-2.863Q-15.106-2.195-15.106-1.093Q-15.106-0.562-14.970-0.095Q-14.833 0.371-14.558 0.737Q-14.282 1.102-13.862 1.307Q-13.442 1.512-12.899 1.512Q-12.411 1.512-11.987 1.268Q-11.563 1.024-11.315 0.604Q-11.067 0.184-11.067-0.312Q-11.067-0.347-11.038-0.373Q-11.009-0.398-10.977-0.398L-10.876-0.398Q-10.833-0.398-10.809-0.369Q-10.786-0.339-10.786-0.296Q-10.786 0.141-10.962 0.530Q-11.138 0.918-11.448 1.202Q-11.759 1.485-12.169 1.647Q-12.579 1.809-13.001 1.809Q-13.591 1.809-14.132 1.588Q-14.673 1.368-15.089 0.963Q-15.505 0.559-15.737 0.030Q-15.970-0.500-15.970-1.093M-7.001 1.809Q-7.583 1.809-8.100 1.582Q-8.618 1.356-9.007 0.957Q-9.395 0.559-9.614 0.034Q-9.833-0.492-9.833-1.062Q-9.833-1.832-9.458-2.509Q-9.083-3.187-8.433-3.589Q-7.782-3.992-7.001-3.992Q-6.227-3.992-5.577-3.589Q-4.927-3.187-4.552-2.509Q-4.177-1.832-4.177-1.062Q-4.177-0.492-4.397 0.039Q-4.618 0.571-5.003 0.963Q-5.388 1.356-5.905 1.582Q-6.423 1.809-7.001 1.809M-8.442 0.739Q-8.278 0.969-8.048 1.145Q-7.817 1.321-7.550 1.416Q-7.282 1.512-7.001 1.512Q-6.583 1.512-6.202 1.301Q-5.821 1.090-5.571 0.739Q-5.040 0.020-5.040-1.199Q-5.040-1.828-5.255-2.406Q-5.470-2.984-5.911-3.347Q-6.352-3.711-7.001-3.711Q-7.653-3.711-8.097-3.347Q-8.540-2.984-8.755-2.406Q-8.970-1.828-8.970-1.199Q-8.970 0.020-8.442 0.739M-0.282 1.641L-3.345 1.641L-3.345 1.344Q-3.020 1.344-2.778 1.297Q-2.536 1.250-2.536 1.082L-2.536-3.261Q-2.536-3.433-2.778-3.480Q-3.020-3.527-3.345-3.527L-3.345-3.824L-0.282-3.824Q0.269-3.824 0.747-3.597Q1.226-3.371 1.579-2.976Q1.933-2.582 2.122-2.082Q2.312-1.582 2.312-1.039Q2.312-0.332 1.968 0.286Q1.624 0.903 1.028 1.272Q0.433 1.641-0.282 1.641M-1.794-3.261L-1.794 1.082Q-1.794 1.254-1.702 1.299Q-1.610 1.344-1.392 1.344L-0.497 1.344Q-0.052 1.344 0.341 1.174Q0.733 1.004 1.005 0.680Q1.276 0.356 1.374-0.064Q1.472-0.484 1.472-1.039Q1.472-1.394 1.435-1.707Q1.398-2.019 1.292-2.314Q1.187-2.609 1.007-2.832Q0.823-3.066 0.585-3.216Q0.347-3.367 0.066-3.447Q-0.216-3.527-0.497-3.527L-1.392-3.527Q-1.610-3.527-1.702-3.484Q-1.794-3.441-1.794-3.261M7.976 1.641L3.128 1.641L3.128 1.344Q3.448 1.344 3.692 1.297Q3.937 1.250 3.937 1.082L3.937-3.261Q3.937-3.433 3.692-3.480Q3.448-3.527 3.128-3.527L3.128-3.824L7.862-3.824L8.097-1.976L7.816-1.976Q7.733-2.671 7.558-2.992Q7.382-3.312 7.034-3.420Q6.687-3.527 5.968-3.527L5.105-3.527Q4.886-3.527 4.794-3.484Q4.702-3.441 4.702-3.261L4.702-1.343L5.335-1.343Q5.761-1.343 5.968-1.406Q6.175-1.468 6.263-1.664Q6.351-1.859 6.351-2.281L6.632-2.281L6.632-0.113L6.351-0.113Q6.351-0.535 6.263-0.728Q6.175-0.921 5.968-0.984Q5.761-1.046 5.335-1.046L4.702-1.046L4.702 1.082Q4.702 1.254 4.794 1.299Q4.886 1.344 5.105 1.344L6.030 1.344Q6.612 1.344 6.966 1.262Q7.319 1.180 7.525 0.983Q7.730 0.786 7.843 0.448Q7.956 0.110 8.050-0.472L8.327-0.472L7.976 1.641M11.280 1.641L8.921 1.641L8.921 1.344Q9.245 1.344 9.487 1.297Q9.730 1.250 9.730 1.082L9.730-3.261Q9.730-3.433 9.487-3.480Q9.245-3.527 8.921-3.527L8.921-3.824L11.515-3.824Q11.847-3.824 12.233-3.738Q12.620-3.652 12.968-3.478Q13.316-3.304 13.534-3.023Q13.753-2.742 13.753-2.375Q13.753-2.050 13.552-1.785Q13.351-1.519 13.044-1.343Q12.737-1.168 12.409-1.078Q12.776-0.957 13.036-0.687Q13.296-0.418 13.347-0.054L13.441 0.641Q13.511 1.090 13.608 1.321Q13.706 1.551 14.003 1.551Q14.249 1.551 14.382 1.334Q14.515 1.118 14.515 0.856Q14.534 0.782 14.616 0.762L14.698 0.762Q14.792 0.786 14.792 0.879Q14.792 1.110 14.694 1.325Q14.597 1.539 14.419 1.674Q14.241 1.809 14.011 1.809Q13.413 1.809 12.995 1.522Q12.577 1.235 12.577 0.664L12.577-0.031Q12.577-0.312 12.425-0.527Q12.273-0.742 12.023-0.855Q11.773-0.968 11.499-0.968L10.472-0.968L10.472 1.082Q10.472 1.246 10.716 1.295Q10.960 1.344 11.280 1.344L11.280 1.641M10.472-3.261L10.472-1.222L11.401-1.222Q11.722-1.222 11.989-1.275Q12.257-1.328 12.456-1.455Q12.655-1.582 12.773-1.814Q12.890-2.046 12.890-2.375Q12.890-3.027 12.493-3.277Q12.097-3.527 11.401-3.527L10.874-3.527Q10.655-3.527 10.564-3.484Q10.472-3.441 10.472-3.261\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-soft-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath d=\"M221.805-28.234h68.287V-48.15h-68.287Z\"\u002F>\u003Cg transform=\"translate(262.756 -37.1)\">\u003Cpath d=\"M-25.165 1.641L-28.227 1.641L-28.227 1.344Q-27.903 1.344-27.661 1.297Q-27.419 1.250-27.419 1.082L-27.419-3.261Q-27.419-3.433-27.661-3.480Q-27.903-3.527-28.227-3.527L-28.227-3.824L-25.165-3.824Q-24.614-3.824-24.136-3.597Q-23.657-3.371-23.304-2.976Q-22.950-2.582-22.761-2.082Q-22.571-1.582-22.571-1.039Q-22.571-0.332-22.915 0.286Q-23.259 0.903-23.854 1.272Q-24.450 1.641-25.165 1.641M-26.677-3.261L-26.677 1.082Q-26.677 1.254-26.585 1.299Q-26.493 1.344-26.274 1.344L-25.380 1.344Q-24.934 1.344-24.542 1.174Q-24.149 1.004-23.878 0.680Q-23.606 0.356-23.509-0.064Q-23.411-0.484-23.411-1.039Q-23.411-1.394-23.448-1.707Q-23.485-2.019-23.591-2.314Q-23.696-2.609-23.876-2.832Q-24.059-3.066-24.298-3.216Q-24.536-3.367-24.817-3.447Q-25.099-3.527-25.380-3.527L-26.274-3.527Q-26.493-3.527-26.585-3.484Q-26.677-3.441-26.677-3.261M-16.907 1.641L-21.755 1.641L-21.755 1.344Q-21.434 1.344-21.190 1.297Q-20.946 1.250-20.946 1.082L-20.946-3.261Q-20.946-3.433-21.190-3.480Q-21.434-3.527-21.755-3.527L-21.755-3.824L-17.020-3.824L-16.786-1.976L-17.067-1.976Q-17.149-2.671-17.325-2.992Q-17.501-3.312-17.849-3.420Q-18.196-3.527-18.915-3.527L-19.778-3.527Q-19.997-3.527-20.089-3.484Q-20.181-3.441-20.181-3.261L-20.181-1.343L-19.548-1.343Q-19.122-1.343-18.915-1.406Q-18.708-1.468-18.620-1.664Q-18.532-1.859-18.532-2.281L-18.251-2.281L-18.251-0.113L-18.532-0.113Q-18.532-0.535-18.620-0.728Q-18.708-0.921-18.915-0.984Q-19.122-1.046-19.548-1.046L-20.181-1.046L-20.181 1.082Q-20.181 1.254-20.089 1.299Q-19.997 1.344-19.778 1.344L-18.852 1.344Q-18.270 1.344-17.917 1.262Q-17.563 1.180-17.358 0.983Q-17.153 0.786-17.040 0.448Q-16.927 0.110-16.833-0.472L-16.556-0.472L-16.907 1.641M-15.841-1.093Q-15.841-1.687-15.608-2.218Q-15.376-2.750-14.960-3.150Q-14.544-3.550-14.011-3.771Q-13.477-3.992-12.872-3.992Q-12.434-3.992-12.036-3.810Q-11.638-3.629-11.329-3.296L-10.856-3.961Q-10.825-3.992-10.794-3.992L-10.747-3.992Q-10.720-3.992-10.688-3.961Q-10.657-3.929-10.657-3.902L-10.657-1.765Q-10.657-1.742-10.688-1.711Q-10.720-1.679-10.747-1.679L-10.864-1.679Q-10.892-1.679-10.923-1.711Q-10.954-1.742-10.954-1.765Q-10.954-2.031-11.097-2.384Q-11.239-2.738-11.419-2.976Q-11.673-3.308-12.022-3.502Q-12.372-3.695-12.778-3.695Q-13.282-3.695-13.735-3.476Q-14.188-3.257-14.481-2.863Q-14.977-2.195-14.977-1.093Q-14.977-0.562-14.841-0.095Q-14.704 0.371-14.429 0.737Q-14.153 1.102-13.733 1.307Q-13.313 1.512-12.770 1.512Q-12.282 1.512-11.858 1.268Q-11.434 1.024-11.186 0.604Q-10.938 0.184-10.938-0.312Q-10.938-0.347-10.909-0.373Q-10.880-0.398-10.849-0.398L-10.747-0.398Q-10.704-0.398-10.681-0.369Q-10.657-0.339-10.657-0.296Q-10.657 0.141-10.833 0.530Q-11.009 0.918-11.319 1.202Q-11.630 1.485-12.040 1.647Q-12.450 1.809-12.872 1.809Q-13.462 1.809-14.003 1.588Q-14.544 1.368-14.960 0.963Q-15.376 0.559-15.608 0.030Q-15.841-0.500-15.841-1.093M-6.872 1.809Q-7.454 1.809-7.972 1.582Q-8.489 1.356-8.878 0.957Q-9.267 0.559-9.485 0.034Q-9.704-0.492-9.704-1.062Q-9.704-1.832-9.329-2.509Q-8.954-3.187-8.304-3.589Q-7.653-3.992-6.872-3.992Q-6.099-3.992-5.448-3.589Q-4.798-3.187-4.423-2.509Q-4.048-1.832-4.048-1.062Q-4.048-0.492-4.268 0.039Q-4.489 0.571-4.874 0.963Q-5.259 1.356-5.776 1.582Q-6.294 1.809-6.872 1.809M-8.313 0.739Q-8.149 0.969-7.919 1.145Q-7.688 1.321-7.421 1.416Q-7.153 1.512-6.872 1.512Q-6.454 1.512-6.073 1.301Q-5.692 1.090-5.442 0.739Q-4.911 0.020-4.911-1.199Q-4.911-1.828-5.126-2.406Q-5.341-2.984-5.782-3.347Q-6.224-3.711-6.872-3.711Q-7.524-3.711-7.968-3.347Q-8.411-2.984-8.626-2.406Q-8.841-1.828-8.841-1.199Q-8.841 0.020-8.313 0.739M-0.153 1.641L-3.216 1.641L-3.216 1.344Q-2.892 1.344-2.649 1.297Q-2.407 1.250-2.407 1.082L-2.407-3.261Q-2.407-3.433-2.649-3.480Q-2.892-3.527-3.216-3.527L-3.216-3.824L-0.153-3.824Q0.398-3.824 0.876-3.597Q1.355-3.371 1.708-2.976Q2.062-2.582 2.251-2.082Q2.441-1.582 2.441-1.039Q2.441-0.332 2.097 0.286Q1.753 0.903 1.157 1.272Q0.562 1.641-0.153 1.641M-1.665-3.261L-1.665 1.082Q-1.665 1.254-1.573 1.299Q-1.481 1.344-1.263 1.344L-0.368 1.344Q0.077 1.344 0.470 1.174Q0.862 1.004 1.134 0.680Q1.405 0.356 1.503-0.064Q1.601-0.484 1.601-1.039Q1.601-1.394 1.564-1.707Q1.526-2.019 1.421-2.314Q1.316-2.609 1.136-2.832Q0.952-3.066 0.714-3.216Q0.476-3.367 0.194-3.447Q-0.087-3.527-0.368-3.527L-1.263-3.527Q-1.481-3.527-1.573-3.484Q-1.665-3.441-1.665-3.261M8.105 1.641L3.257 1.641L3.257 1.344Q3.577 1.344 3.821 1.297Q4.066 1.250 4.066 1.082L4.066-3.261Q4.066-3.433 3.821-3.480Q3.577-3.527 3.257-3.527L3.257-3.824L7.991-3.824L8.226-1.976L7.944-1.976Q7.862-2.671 7.687-2.992Q7.511-3.312 7.163-3.420Q6.816-3.527 6.097-3.527L5.233-3.527Q5.015-3.527 4.923-3.484Q4.831-3.441 4.831-3.261L4.831-1.343L5.464-1.343Q5.890-1.343 6.097-1.406Q6.304-1.468 6.392-1.664Q6.480-1.859 6.480-2.281L6.761-2.281L6.761-0.113L6.480-0.113Q6.480-0.535 6.392-0.728Q6.304-0.921 6.097-0.984Q5.890-1.046 5.464-1.046L4.831-1.046L4.831 1.082Q4.831 1.254 4.923 1.299Q5.015 1.344 5.233 1.344L6.159 1.344Q6.741 1.344 7.095 1.262Q7.448 1.180 7.653 0.983Q7.858 0.786 7.972 0.448Q8.085 0.110 8.179-0.472L8.456-0.472L8.105 1.641M11.409 1.641L9.050 1.641L9.050 1.344Q9.374 1.344 9.616 1.297Q9.858 1.250 9.858 1.082L9.858-3.261Q9.858-3.433 9.616-3.480Q9.374-3.527 9.050-3.527L9.050-3.824L11.644-3.824Q11.976-3.824 12.362-3.738Q12.749-3.652 13.097-3.478Q13.444-3.304 13.663-3.023Q13.882-2.742 13.882-2.375Q13.882-2.050 13.681-1.785Q13.480-1.519 13.173-1.343Q12.866-1.168 12.538-1.078Q12.905-0.957 13.165-0.687Q13.425-0.418 13.476-0.054L13.569 0.641Q13.640 1.090 13.737 1.321Q13.835 1.551 14.132 1.551Q14.378 1.551 14.511 1.334Q14.644 1.118 14.644 0.856Q14.663 0.782 14.745 0.762L14.827 0.762Q14.921 0.786 14.921 0.879Q14.921 1.110 14.823 1.325Q14.726 1.539 14.548 1.674Q14.370 1.809 14.140 1.809Q13.542 1.809 13.124 1.522Q12.706 1.235 12.706 0.664L12.706-0.031Q12.706-0.312 12.554-0.527Q12.401-0.742 12.151-0.855Q11.901-0.968 11.628-0.968L10.601-0.968L10.601 1.082Q10.601 1.246 10.845 1.295Q11.089 1.344 11.409 1.344L11.409 1.641M10.601-3.261L10.601-1.222L11.530-1.222Q11.851-1.222 12.118-1.275Q12.386-1.328 12.585-1.455Q12.784-1.582 12.901-1.814Q13.019-2.046 13.019-2.375Q13.019-3.027 12.622-3.277Q12.226-3.527 11.530-3.527L11.003-3.527Q10.784-3.527 10.692-3.484Q10.601-3.441 10.601-3.261\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath fill=\"none\" d=\"m181.971-8.517 37.84-18.633\"\u002F>\u003Cpath stroke=\"none\" d=\"m221.605-28.034-3.577-.022 1.783.906-.37 1.965\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(177.806 24.043)\">\u003Cpath d=\"M-28.265 0.130Q-28.265-0.198-28.130-0.499Q-27.995-0.799-27.759-1.020Q-27.523-1.240-27.219-1.360Q-26.914-1.480-26.590-1.480Q-26.084-1.480-25.735-1.377Q-25.387-1.275-25.387-0.899Q-25.387-0.752-25.484-0.651Q-25.581-0.550-25.728-0.550Q-25.882-0.550-25.981-0.649Q-26.080-0.748-26.080-0.899Q-26.080-1.087-25.940-1.179Q-26.142-1.230-26.583-1.230Q-26.938-1.230-27.167-1.034Q-27.396-0.837-27.497-0.528Q-27.598-0.218-27.598 0.130Q-27.598 0.479-27.472 0.785Q-27.345 1.091-27.090 1.275Q-26.836 1.460-26.480 1.460Q-26.258 1.460-26.074 1.376Q-25.889 1.292-25.754 1.137Q-25.619 0.981-25.561 0.773Q-25.547 0.718-25.493 0.718L-25.380 0.718Q-25.349 0.718-25.327 0.742Q-25.305 0.766-25.305 0.800L-25.305 0.821Q-25.390 1.108-25.578 1.306Q-25.766 1.504-26.031 1.607Q-26.296 1.709-26.590 1.709Q-27.020 1.709-27.408 1.503Q-27.796 1.296-28.030 0.933Q-28.265 0.571-28.265 0.130M-24.758 0.158Q-24.758-0.184-24.623-0.483Q-24.488-0.782-24.248-1.006Q-24.009-1.230-23.691-1.355Q-23.373-1.480-23.042-1.480Q-22.598-1.480-22.198-1.264Q-21.798-1.049-21.564-0.671Q-21.329-0.294-21.329 0.158Q-21.329 0.499-21.471 0.783Q-21.613 1.067-21.858 1.274Q-22.102 1.480-22.411 1.595Q-22.721 1.709-23.042 1.709Q-23.473 1.709-23.874 1.508Q-24.276 1.306-24.517 0.954Q-24.758 0.602-24.758 0.158M-23.042 1.460Q-22.440 1.460-22.216 1.082Q-21.993 0.704-21.993 0.072Q-21.993-0.540-22.227-0.899Q-22.461-1.257-23.042-1.257Q-24.095-1.257-24.095 0.072Q-24.095 0.704-23.869 1.082Q-23.643 1.460-23.042 1.460M-19.053 1.641L-20.687 1.641L-20.687 1.361Q-20.458 1.361-20.309 1.327Q-20.161 1.292-20.161 1.152L-20.161-0.697Q-20.161-0.967-20.268-1.028Q-20.376-1.090-20.687-1.090L-20.687-1.370L-19.627-1.445L-19.627-0.796Q-19.456-1.104-19.152-1.275Q-18.848-1.445-18.503-1.445Q-17.997-1.445-17.713-1.222Q-17.430-0.998-17.430-0.502L-17.430 1.152Q-17.430 1.289-17.281 1.325Q-17.132 1.361-16.907 1.361L-16.907 1.641L-18.537 1.641L-18.537 1.361Q-18.308 1.361-18.159 1.327Q-18.011 1.292-18.011 1.152L-18.011-0.488Q-18.011-0.823-18.130-1.023Q-18.250-1.223-18.564-1.223Q-18.834-1.223-19.069-1.087Q-19.303-0.950-19.441-0.716Q-19.579-0.482-19.579-0.208L-19.579 1.152Q-19.579 1.289-19.429 1.325Q-19.279 1.361-19.053 1.361\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(177.806 24.043)\">\u003Cpath d=\"M-14.932 1.614L-16.060-0.885Q-16.132-1.032-16.262-1.064Q-16.392-1.097-16.621-1.097L-16.621-1.377L-15.107-1.377L-15.107-1.097Q-15.459-1.097-15.459-0.950Q-15.459-0.905-15.448-0.885L-14.584 1.033L-13.804-0.697Q-13.770-0.765-13.770-0.844Q-13.770-0.957-13.854-1.027Q-13.938-1.097-14.057-1.097L-14.057-1.377L-12.861-1.377L-12.861-1.097Q-13.080-1.097-13.251-0.994Q-13.421-0.892-13.510-0.697L-14.546 1.614Q-14.594 1.709-14.700 1.709L-14.778 1.709Q-14.884 1.709-14.932 1.614\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(177.806 24.043)\">\u003Cpath d=\"M-12.577 0.106Q-12.577-0.215-12.452-0.504Q-12.327-0.793-12.101-1.016Q-11.876-1.240-11.580-1.360Q-11.285-1.480-10.967-1.480Q-10.639-1.480-10.377-1.380Q-10.116-1.281-9.940-1.099Q-9.764-0.916-9.670-0.658Q-9.576-0.400-9.576-0.068Q-9.576 0.024-9.658 0.045L-11.913 0.045L-11.913 0.106Q-11.913 0.694-11.630 1.077Q-11.346 1.460-10.779 1.460Q-10.457 1.460-10.189 1.267Q-9.921 1.074-9.832 0.759Q-9.825 0.718-9.750 0.704L-9.658 0.704Q-9.576 0.728-9.576 0.800Q-9.576 0.807-9.582 0.834Q-9.695 1.231-10.066 1.470Q-10.437 1.709-10.861 1.709Q-11.298 1.709-11.698 1.501Q-12.098 1.292-12.337 0.925Q-12.577 0.558-12.577 0.106M-11.907-0.164L-10.092-0.164Q-10.092-0.441-10.189-0.693Q-10.287-0.946-10.485-1.102Q-10.683-1.257-10.967-1.257Q-11.244-1.257-11.457-1.099Q-11.671-0.940-11.789-0.685Q-11.907-0.430-11.907-0.164M-7.238 1.641L-8.974 1.641L-8.974 1.361Q-8.745 1.361-8.596 1.327Q-8.448 1.292-8.448 1.152L-8.448-0.697Q-8.448-0.967-8.555-1.028Q-8.663-1.090-8.974-1.090L-8.974-1.370L-7.945-1.445L-7.945-0.738Q-7.815-1.046-7.573-1.245Q-7.330-1.445-7.012-1.445Q-6.793-1.445-6.622-1.321Q-6.452-1.196-6.452-0.984Q-6.452-0.847-6.551-0.748Q-6.650-0.649-6.783-0.649Q-6.920-0.649-7.019-0.748Q-7.118-0.847-7.118-0.984Q-7.118-1.124-7.019-1.223Q-7.309-1.223-7.509-1.027Q-7.709-0.830-7.802-0.536Q-7.894-0.242-7.894 0.038L-7.894 1.152Q-7.894 1.361-7.238 1.361L-7.238 1.641M-5.867 1.634L-5.867 0.571Q-5.867 0.547-5.840 0.520Q-5.812 0.493-5.788 0.493L-5.679 0.493Q-5.614 0.493-5.600 0.551Q-5.505 0.985-5.259 1.236Q-5.013 1.487-4.599 1.487Q-4.257 1.487-4.004 1.354Q-3.751 1.221-3.751 0.913Q-3.751 0.756-3.845 0.641Q-3.939 0.527-4.078 0.458Q-4.216 0.390-4.384 0.352L-4.965 0.253Q-5.320 0.185-5.594-0.036Q-5.867-0.256-5.867-0.598Q-5.867-0.847-5.756-1.022Q-5.645-1.196-5.459-1.295Q-5.272-1.394-5.057-1.437Q-4.842-1.480-4.599-1.480Q-4.185-1.480-3.905-1.298L-3.690-1.473Q-3.680-1.476-3.673-1.478Q-3.666-1.480-3.656-1.480L-3.604-1.480Q-3.577-1.480-3.553-1.456Q-3.529-1.432-3.529-1.404L-3.529-0.557Q-3.529-0.536-3.553-0.509Q-3.577-0.482-3.604-0.482L-3.717-0.482Q-3.745-0.482-3.770-0.507Q-3.796-0.533-3.796-0.557Q-3.796-0.793-3.902-0.957Q-4.008-1.121-4.191-1.203Q-4.373-1.285-4.606-1.285Q-4.934-1.285-5.190-1.182Q-5.447-1.080-5.447-0.803Q-5.447-0.608-5.264-0.499Q-5.081-0.389-4.852-0.348L-4.278-0.242Q-4.032-0.194-3.818-0.066Q-3.604 0.062-3.468 0.265Q-3.331 0.469-3.331 0.718Q-3.331 1.231-3.697 1.470Q-4.062 1.709-4.599 1.709Q-5.095 1.709-5.426 1.415L-5.693 1.689Q-5.713 1.709-5.741 1.709L-5.788 1.709Q-5.812 1.709-5.840 1.682Q-5.867 1.655-5.867 1.634M-2.644 0.913Q-2.644 0.581-2.420 0.354Q-2.196 0.127-1.853-0.001Q-1.509-0.130-1.137-0.182Q-0.764-0.235-0.460-0.235L-0.460-0.488Q-0.460-0.693-0.568-0.873Q-0.675-1.052-0.856-1.155Q-1.037-1.257-1.246-1.257Q-1.653-1.257-1.889-1.165Q-1.800-1.128-1.754-1.044Q-1.707-0.960-1.707-0.858Q-1.707-0.762-1.754-0.683Q-1.800-0.605-1.880-0.560Q-1.960-0.516-2.049-0.516Q-2.200-0.516-2.300-0.613Q-2.401-0.711-2.401-0.858Q-2.401-1.480-1.246-1.480Q-1.034-1.480-0.785-1.416Q-0.535-1.353-0.333-1.234Q-0.132-1.114-0.005-0.929Q0.121-0.745 0.121-0.502L0.121 1.074Q0.121 1.190 0.183 1.286Q0.244 1.381 0.357 1.381Q0.466 1.381 0.531 1.287Q0.596 1.193 0.596 1.074L0.596 0.626L0.863 0.626L0.863 1.074Q0.863 1.344 0.636 1.509Q0.408 1.675 0.128 1.675Q-0.080 1.675-0.217 1.521Q-0.354 1.368-0.378 1.152Q-0.525 1.419-0.807 1.564Q-1.089 1.709-1.413 1.709Q-1.690 1.709-1.974 1.634Q-2.258 1.559-2.451 1.380Q-2.644 1.200-2.644 0.913M-2.029 0.913Q-2.029 1.087-1.928 1.217Q-1.827 1.347-1.672 1.417Q-1.516 1.487-1.352 1.487Q-1.133 1.487-0.925 1.390Q-0.716 1.292-0.588 1.111Q-0.460 0.930-0.460 0.704L-0.460-0.024Q-0.785-0.024-1.150 0.067Q-1.516 0.158-1.772 0.370Q-2.029 0.581-2.029 0.913M1.806 0.800L1.806-1.097L1.167-1.097L1.167-1.319Q1.485-1.319 1.702-1.529Q1.919-1.739 2.020-2.049Q2.121-2.358 2.121-2.666L2.387-2.666L2.387-1.377L3.464-1.377L3.464-1.097L2.387-1.097L2.387 0.787Q2.387 1.063 2.492 1.262Q2.596 1.460 2.856 1.460Q3.013 1.460 3.119 1.356Q3.225 1.251 3.274 1.098Q3.324 0.944 3.324 0.787L3.324 0.373L3.590 0.373L3.590 0.800Q3.590 1.026 3.491 1.236Q3.392 1.446 3.208 1.578Q3.023 1.709 2.794 1.709Q2.357 1.709 2.081 1.472Q1.806 1.234 1.806 0.800M6.017 1.641L4.465 1.641L4.465 1.361Q4.691 1.361 4.840 1.327Q4.988 1.292 4.988 1.152L4.988-0.697Q4.988-0.885 4.941-0.969Q4.893-1.052 4.795-1.071Q4.698-1.090 4.486-1.090L4.486-1.370L5.542-1.445L5.542 1.152Q5.542 1.292 5.674 1.327Q5.805 1.361 6.017 1.361L6.017 1.641M4.746-2.666Q4.746-2.837 4.869-2.956Q4.992-3.076 5.163-3.076Q5.330-3.076 5.453-2.956Q5.576-2.837 5.576-2.666Q5.576-2.491 5.453-2.368Q5.330-2.245 5.163-2.245Q4.992-2.245 4.869-2.368Q4.746-2.491 4.746-2.666M6.622 0.158Q6.622-0.184 6.757-0.483Q6.892-0.782 7.131-1.006Q7.371-1.230 7.689-1.355Q8.006-1.480 8.338-1.480Q8.782-1.480 9.182-1.264Q9.582-1.049 9.816-0.671Q10.050-0.294 10.050 0.158Q10.050 0.499 9.909 0.783Q9.767 1.067 9.522 1.274Q9.278 1.480 8.969 1.595Q8.659 1.709 8.338 1.709Q7.907 1.709 7.506 1.508Q7.104 1.306 6.863 0.954Q6.622 0.602 6.622 0.158M8.338 1.460Q8.940 1.460 9.163 1.082Q9.387 0.704 9.387 0.072Q9.387-0.540 9.153-0.899Q8.919-1.257 8.338-1.257Q7.285-1.257 7.285 0.072Q7.285 0.704 7.511 1.082Q7.736 1.460 8.338 1.460M12.327 1.641L10.693 1.641L10.693 1.361Q10.922 1.361 11.071 1.327Q11.219 1.292 11.219 1.152L11.219-0.697Q11.219-0.967 11.112-1.028Q11.004-1.090 10.693-1.090L10.693-1.370L11.753-1.445L11.753-0.796Q11.923-1.104 12.228-1.275Q12.532-1.445 12.877-1.445Q13.383-1.445 13.667-1.222Q13.950-0.998 13.950-0.502L13.950 1.152Q13.950 1.289 14.099 1.325Q14.248 1.361 14.473 1.361L14.473 1.641L12.843 1.641L12.843 1.361Q13.072 1.361 13.221 1.327Q13.369 1.292 13.369 1.152L13.369-0.488Q13.369-0.823 13.250-1.023Q13.130-1.223 12.816-1.223Q12.546-1.223 12.311-1.087Q12.077-0.950 11.939-0.716Q11.800-0.482 11.800-0.208L11.800 1.152Q11.800 1.289 11.951 1.325Q12.101 1.361 12.327 1.361\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(177.806 24.043)\">\u003Cpath d=\"M17.785 1.634L17.785 0.571Q17.785 0.547 17.813 0.520Q17.840 0.493 17.864 0.493L17.973 0.493Q18.038 0.493 18.052 0.551Q18.148 0.985 18.394 1.236Q18.640 1.487 19.054 1.487Q19.395 1.487 19.648 1.354Q19.901 1.221 19.901 0.913Q19.901 0.756 19.807 0.641Q19.713 0.527 19.575 0.458Q19.436 0.390 19.269 0.352L18.688 0.253Q18.332 0.185 18.059-0.036Q17.785-0.256 17.785-0.598Q17.785-0.847 17.897-1.022Q18.008-1.196 18.194-1.295Q18.380-1.394 18.596-1.437Q18.811-1.480 19.054-1.480Q19.467-1.480 19.747-1.298L19.963-1.473Q19.973-1.476 19.980-1.478Q19.987-1.480 19.997-1.480L20.048-1.480Q20.075-1.480 20.099-1.456Q20.123-1.432 20.123-1.404L20.123-0.557Q20.123-0.536 20.099-0.509Q20.075-0.482 20.048-0.482L19.935-0.482Q19.908-0.482 19.882-0.507Q19.857-0.533 19.857-0.557Q19.857-0.793 19.751-0.957Q19.645-1.121 19.462-1.203Q19.279-1.285 19.047-1.285Q18.719-1.285 18.462-1.182Q18.206-1.080 18.206-0.803Q18.206-0.608 18.389-0.499Q18.572-0.389 18.801-0.348L19.375-0.242Q19.621-0.194 19.835-0.066Q20.048 0.062 20.185 0.265Q20.322 0.469 20.322 0.718Q20.322 1.231 19.956 1.470Q19.590 1.709 19.054 1.709Q18.558 1.709 18.226 1.415L17.960 1.689Q17.939 1.709 17.912 1.709L17.864 1.709Q17.840 1.709 17.813 1.682Q17.785 1.655 17.785 1.634M20.909 0.158Q20.909-0.184 21.044-0.483Q21.179-0.782 21.419-1.006Q21.658-1.230 21.976-1.355Q22.294-1.480 22.625-1.480Q23.070-1.480 23.470-1.264Q23.869-1.049 24.104-0.671Q24.338-0.294 24.338 0.158Q24.338 0.499 24.196 0.783Q24.054 1.067 23.810 1.274Q23.565 1.480 23.256 1.595Q22.947 1.709 22.625 1.709Q22.195 1.709 21.793 1.508Q21.391 1.306 21.150 0.954Q20.909 0.602 20.909 0.158M22.625 1.460Q23.227 1.460 23.451 1.082Q23.675 0.704 23.675 0.072Q23.675-0.540 23.440-0.899Q23.206-1.257 22.625-1.257Q21.573-1.257 21.573 0.072Q21.573 0.704 21.798 1.082Q22.024 1.460 22.625 1.460\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(177.806 24.043)\">\u003Cpath d=\"M29.432 1.641L27.699 1.641L27.699 1.361Q27.925 1.361 28.074 1.327Q28.222 1.292 28.222 1.152L28.222-1.097L27.634-1.097L27.634-1.377L28.222-1.377L28.222-2.194Q28.222-2.512 28.400-2.760Q28.578-3.007 28.868-3.148Q29.159-3.288 29.470-3.288Q29.726-3.288 29.930-3.146Q30.133-3.004 30.133-2.761Q30.133-2.625 30.034-2.526Q29.935-2.426 29.798-2.426Q29.661-2.426 29.562-2.526Q29.463-2.625 29.463-2.761Q29.463-2.942 29.603-3.035Q29.525-3.062 29.425-3.062Q29.217-3.062 29.063-2.929Q28.909-2.796 28.829-2.592Q28.749-2.389 28.749-2.180L28.749-1.377L29.637-1.377L29.637-1.097L28.776-1.097L28.776 1.152Q28.776 1.361 29.432 1.361L29.432 1.641M30.171 0.913Q30.171 0.581 30.394 0.354Q30.618 0.127 30.962-0.001Q31.305-0.130 31.678-0.182Q32.050-0.235 32.355-0.235L32.355-0.488Q32.355-0.693 32.247-0.873Q32.139-1.052 31.958-1.155Q31.777-1.257 31.569-1.257Q31.162-1.257 30.926-1.165Q31.015-1.128 31.061-1.044Q31.107-0.960 31.107-0.858Q31.107-0.762 31.061-0.683Q31.015-0.605 30.935-0.560Q30.854-0.516 30.765-0.516Q30.615-0.516 30.514-0.613Q30.413-0.711 30.413-0.858Q30.413-1.480 31.569-1.480Q31.780-1.480 32.030-1.416Q32.279-1.353 32.481-1.234Q32.683-1.114 32.809-0.929Q32.936-0.745 32.936-0.502L32.936 1.074Q32.936 1.190 32.997 1.286Q33.059 1.381 33.172 1.381Q33.281 1.381 33.346 1.287Q33.411 1.193 33.411 1.074L33.411 0.626L33.677 0.626L33.677 1.074Q33.677 1.344 33.450 1.509Q33.223 1.675 32.943 1.675Q32.734 1.675 32.597 1.521Q32.461 1.368 32.437 1.152Q32.290 1.419 32.008 1.564Q31.726 1.709 31.401 1.709Q31.124 1.709 30.841 1.634Q30.557 1.559 30.364 1.380Q30.171 1.200 30.171 0.913M30.786 0.913Q30.786 1.087 30.887 1.217Q30.987 1.347 31.143 1.417Q31.299 1.487 31.463 1.487Q31.681 1.487 31.890 1.390Q32.098 1.292 32.226 1.111Q32.355 0.930 32.355 0.704L32.355-0.024Q32.030-0.024 31.664 0.067Q31.299 0.158 31.042 0.370Q30.786 0.581 30.786 0.913M35.844 1.641L34.108 1.641L34.108 1.361Q34.337 1.361 34.486 1.327Q34.634 1.292 34.634 1.152L34.634-0.697Q34.634-0.967 34.527-1.028Q34.419-1.090 34.108-1.090L34.108-1.370L35.137-1.445L35.137-0.738Q35.267-1.046 35.509-1.245Q35.752-1.445 36.070-1.445Q36.289-1.445 36.460-1.321Q36.631-1.196 36.631-0.984Q36.631-0.847 36.531-0.748Q36.432-0.649 36.299-0.649Q36.162-0.649 36.063-0.748Q35.964-0.847 35.964-0.984Q35.964-1.124 36.063-1.223Q35.773-1.223 35.573-1.027Q35.373-0.830 35.280-0.536Q35.188-0.242 35.188 0.038L35.188 1.152Q35.188 1.361 35.844 1.361\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(255.175 -66.067)\">\u003Cpath d=\"M-26.515 1.641L-28.251 1.641L-28.251 1.361Q-28.022 1.361-27.873 1.327Q-27.725 1.292-27.725 1.152L-27.725-0.697Q-27.725-0.967-27.832-1.028Q-27.940-1.090-28.251-1.090L-28.251-1.370L-27.222-1.445L-27.222-0.738Q-27.092-1.046-26.850-1.245Q-26.607-1.445-26.289-1.445Q-26.070-1.445-25.899-1.321Q-25.728-1.196-25.728-0.984Q-25.728-0.847-25.828-0.748Q-25.927-0.649-26.060-0.649Q-26.197-0.649-26.296-0.748Q-26.395-0.847-26.395-0.984Q-26.395-1.124-26.296-1.223Q-26.586-1.223-26.786-1.027Q-26.986-0.830-27.079-0.536Q-27.171-0.242-27.171 0.038L-27.171 1.152Q-27.171 1.361-26.515 1.361L-26.515 1.641M-25.185 0.106Q-25.185-0.215-25.060-0.504Q-24.935-0.793-24.710-1.016Q-24.484-1.240-24.189-1.360Q-23.893-1.480-23.575-1.480Q-23.247-1.480-22.985-1.380Q-22.724-1.281-22.548-1.099Q-22.372-0.916-22.278-0.658Q-22.184-0.400-22.184-0.068Q-22.184 0.024-22.266 0.045L-24.522 0.045L-24.522 0.106Q-24.522 0.694-24.238 1.077Q-23.954 1.460-23.387 1.460Q-23.066 1.460-22.798 1.267Q-22.529 1.074-22.440 0.759Q-22.433 0.718-22.358 0.704L-22.266 0.704Q-22.184 0.728-22.184 0.800Q-22.184 0.807-22.191 0.834Q-22.304 1.231-22.674 1.470Q-23.045 1.709-23.469 1.709Q-23.907 1.709-24.307 1.501Q-24.706 1.292-24.946 0.925Q-25.185 0.558-25.185 0.106M-24.515-0.164L-22.700-0.164Q-22.700-0.441-22.798-0.693Q-22.895-0.946-23.093-1.102Q-23.291-1.257-23.575-1.257Q-23.852-1.257-24.066-1.099Q-24.279-0.940-24.397-0.685Q-24.515-0.430-24.515-0.164M-21.596 1.634L-21.596 0.571Q-21.596 0.547-21.569 0.520Q-21.541 0.493-21.517 0.493L-21.408 0.493Q-21.343 0.493-21.329 0.551Q-21.234 0.985-20.988 1.236Q-20.742 1.487-20.328 1.487Q-19.986 1.487-19.733 1.354Q-19.480 1.221-19.480 0.913Q-19.480 0.756-19.574 0.641Q-19.668 0.527-19.807 0.458Q-19.945 0.390-20.113 0.352L-20.694 0.253Q-21.049 0.185-21.323-0.036Q-21.596-0.256-21.596-0.598Q-21.596-0.847-21.485-1.022Q-21.374-1.196-21.188-1.295Q-21.001-1.394-20.786-1.437Q-20.571-1.480-20.328-1.480Q-19.914-1.480-19.634-1.298L-19.419-1.473Q-19.409-1.476-19.402-1.478Q-19.395-1.480-19.385-1.480L-19.333-1.480Q-19.306-1.480-19.282-1.456Q-19.258-1.432-19.258-1.404L-19.258-0.557Q-19.258-0.536-19.282-0.509Q-19.306-0.482-19.333-0.482L-19.446-0.482Q-19.474-0.482-19.499-0.507Q-19.525-0.533-19.525-0.557Q-19.525-0.793-19.631-0.957Q-19.737-1.121-19.920-1.203Q-20.102-1.285-20.335-1.285Q-20.663-1.285-20.919-1.182Q-21.176-1.080-21.176-0.803Q-21.176-0.608-20.993-0.499Q-20.810-0.389-20.581-0.348L-20.007-0.242Q-19.761-0.194-19.547-0.066Q-19.333 0.062-19.197 0.265Q-19.060 0.469-19.060 0.718Q-19.060 1.231-19.426 1.470Q-19.791 1.709-20.328 1.709Q-20.824 1.709-21.155 1.415L-21.422 1.689Q-21.442 1.709-21.470 1.709L-21.517 1.709Q-21.541 1.709-21.569 1.682Q-21.596 1.655-21.596 1.634M-16.787 2.998L-18.417 2.998L-18.417 2.718Q-18.188 2.718-18.040 2.683Q-17.891 2.649-17.891 2.509L-17.891-0.837Q-17.891-1.008-18.028-1.049Q-18.164-1.090-18.417-1.090L-18.417-1.370L-17.337-1.445L-17.337-1.039Q-17.115-1.240-16.828-1.343Q-16.541-1.445-16.233-1.445Q-15.806-1.445-15.442-1.232Q-15.078-1.018-14.864-0.654Q-14.651-0.290-14.651 0.130Q-14.651 0.575-14.890 0.939Q-15.129 1.303-15.522 1.506Q-15.915 1.709-16.360 1.709Q-16.626 1.709-16.874 1.609Q-17.122 1.508-17.310 1.327L-17.310 2.509Q-17.310 2.646-17.161 2.682Q-17.013 2.718-16.787 2.718L-16.787 2.998M-17.310-0.690L-17.310 0.920Q-17.177 1.173-16.934 1.330Q-16.691 1.487-16.414 1.487Q-16.086 1.487-15.833 1.286Q-15.580 1.084-15.447 0.766Q-15.314 0.448-15.314 0.130Q-15.314-0.099-15.379-0.328Q-15.444-0.557-15.572-0.755Q-15.700-0.953-15.895-1.073Q-16.090-1.192-16.322-1.192Q-16.616-1.192-16.884-1.063Q-17.153-0.933-17.310-0.690\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(255.175 -66.067)\">\u003Cpath d=\"M-13.825 0.158Q-13.825-0.184-13.690-0.483Q-13.555-0.782-13.315-1.006Q-13.076-1.230-12.758-1.355Q-12.440-1.480-12.109-1.480Q-11.664-1.480-11.265-1.264Q-10.865-1.049-10.630-0.671Q-10.396-0.294-10.396 0.158Q-10.396 0.499-10.538 0.783Q-10.680 1.067-10.924 1.274Q-11.169 1.480-11.478 1.595Q-11.787 1.709-12.109 1.709Q-12.539 1.709-12.941 1.508Q-13.343 1.306-13.584 0.954Q-13.825 0.602-13.825 0.158M-12.109 1.460Q-11.507 1.460-11.283 1.082Q-11.059 0.704-11.059 0.072Q-11.059-0.540-11.294-0.899Q-11.528-1.257-12.109-1.257Q-13.161-1.257-13.161 0.072Q-13.161 0.704-12.936 1.082Q-12.710 1.460-12.109 1.460M-8.120 1.641L-9.754 1.641L-9.754 1.361Q-9.525 1.361-9.376 1.327Q-9.227 1.292-9.227 1.152L-9.227-0.697Q-9.227-0.967-9.335-1.028Q-9.443-1.090-9.754-1.090L-9.754-1.370L-8.694-1.445L-8.694-0.796Q-8.523-1.104-8.219-1.275Q-7.915-1.445-7.570-1.445Q-7.064-1.445-6.780-1.222Q-6.496-0.998-6.496-0.502L-6.496 1.152Q-6.496 1.289-6.348 1.325Q-6.199 1.361-5.973 1.361L-5.973 1.641L-7.604 1.641L-7.604 1.361Q-7.375 1.361-7.226 1.327Q-7.077 1.292-7.077 1.152L-7.077-0.488Q-7.077-0.823-7.197-1.023Q-7.317-1.223-7.631-1.223Q-7.901-1.223-8.135-1.087Q-8.369-0.950-8.508-0.716Q-8.646-0.482-8.646-0.208L-8.646 1.152Q-8.646 1.289-8.496 1.325Q-8.346 1.361-8.120 1.361L-8.120 1.641M-5.386 1.634L-5.386 0.571Q-5.386 0.547-5.358 0.520Q-5.331 0.493-5.307 0.493L-5.198 0.493Q-5.133 0.493-5.119 0.551Q-5.023 0.985-4.777 1.236Q-4.531 1.487-4.118 1.487Q-3.776 1.487-3.523 1.354Q-3.270 1.221-3.270 0.913Q-3.270 0.756-3.364 0.641Q-3.458 0.527-3.596 0.458Q-3.735 0.390-3.902 0.352L-4.483 0.253Q-4.839 0.185-5.112-0.036Q-5.386-0.256-5.386-0.598Q-5.386-0.847-5.275-1.022Q-5.163-1.196-4.977-1.295Q-4.791-1.394-4.576-1.437Q-4.360-1.480-4.118-1.480Q-3.704-1.480-3.424-1.298L-3.208-1.473Q-3.198-1.476-3.191-1.478Q-3.184-1.480-3.174-1.480L-3.123-1.480Q-3.096-1.480-3.072-1.456Q-3.048-1.432-3.048-1.404L-3.048-0.557Q-3.048-0.536-3.072-0.509Q-3.096-0.482-3.123-0.482L-3.236-0.482Q-3.263-0.482-3.289-0.507Q-3.314-0.533-3.314-0.557Q-3.314-0.793-3.420-0.957Q-3.526-1.121-3.709-1.203Q-3.892-1.285-4.124-1.285Q-4.452-1.285-4.709-1.182Q-4.965-1.080-4.965-0.803Q-4.965-0.608-4.782-0.499Q-4.599-0.389-4.370-0.348L-3.796-0.242Q-3.550-0.194-3.337-0.066Q-3.123 0.062-2.986 0.265Q-2.849 0.469-2.849 0.718Q-2.849 1.231-3.215 1.470Q-3.581 1.709-4.118 1.709Q-4.613 1.709-4.945 1.415L-5.211 1.689Q-5.232 1.709-5.259 1.709L-5.307 1.709Q-5.331 1.709-5.358 1.682Q-5.386 1.655-5.386 1.634M-2.262 0.106Q-2.262-0.215-2.137-0.504Q-2.012-0.793-1.786-1.016Q-1.561-1.240-1.265-1.360Q-0.970-1.480-0.652-1.480Q-0.324-1.480-0.062-1.380Q0.199-1.281 0.375-1.099Q0.551-0.916 0.645-0.658Q0.739-0.400 0.739-0.068Q0.739 0.024 0.657 0.045L-1.598 0.045L-1.598 0.106Q-1.598 0.694-1.315 1.077Q-1.031 1.460-0.464 1.460Q-0.142 1.460 0.126 1.267Q0.394 1.074 0.483 0.759Q0.490 0.718 0.565 0.704L0.657 0.704Q0.739 0.728 0.739 0.800Q0.739 0.807 0.733 0.834Q0.620 1.231 0.249 1.470Q-0.122 1.709-0.546 1.709Q-0.983 1.709-1.383 1.501Q-1.783 1.292-2.022 0.925Q-2.262 0.558-2.262 0.106M-1.592-0.164L0.223-0.164Q0.223-0.441 0.126-0.693Q0.028-0.946-0.170-1.102Q-0.368-1.257-0.652-1.257Q-0.929-1.257-1.142-1.099Q-1.356-0.940-1.474-0.685Q-1.592-0.430-1.592-0.164\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(255.175 -66.067)\">\u003Cpath d=\"M5.787 1.641L4.051 1.641L4.051 1.361Q4.280 1.361 4.429 1.327Q4.577 1.292 4.577 1.152L4.577-0.697Q4.577-0.967 4.470-1.028Q4.362-1.090 4.051-1.090L4.051-1.370L5.080-1.445L5.080-0.738Q5.210-1.046 5.452-1.245Q5.695-1.445 6.013-1.445Q6.232-1.445 6.403-1.321Q6.574-1.196 6.574-0.984Q6.574-0.847 6.474-0.748Q6.375-0.649 6.242-0.649Q6.105-0.649 6.006-0.748Q5.907-0.847 5.907-0.984Q5.907-1.124 6.006-1.223Q5.716-1.223 5.516-1.027Q5.316-0.830 5.223-0.536Q5.131-0.242 5.131 0.038L5.131 1.152Q5.131 1.361 5.787 1.361L5.787 1.641M10.169 1.641L7.640 1.641L7.640 1.361Q8.607 1.361 8.607 1.152L8.607-2.467Q8.214-2.279 7.592-2.279L7.592-2.560Q8.009-2.560 8.373-2.661Q8.737-2.761 8.994-3.007L9.120-3.007Q9.185-2.990 9.202-2.922L9.202 1.152Q9.202 1.361 10.169 1.361\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(255.175 -66.067)\">\u003Cpath d=\"M15.592 1.641L13.856 1.641L13.856 1.361Q14.085 1.361 14.234 1.327Q14.382 1.292 14.382 1.152L14.382-0.697Q14.382-0.967 14.275-1.028Q14.167-1.090 13.856-1.090L13.856-1.370L14.885-1.445L14.885-0.738Q15.015-1.046 15.257-1.245Q15.500-1.445 15.818-1.445Q16.037-1.445 16.208-1.321Q16.379-1.196 16.379-0.984Q16.379-0.847 16.279-0.748Q16.180-0.649 16.047-0.649Q15.910-0.649 15.811-0.748Q15.712-0.847 15.712-0.984Q15.712-1.124 15.811-1.223Q15.521-1.223 15.321-1.027Q15.121-0.830 15.028-0.536Q14.936-0.242 14.936 0.038L14.936 1.152Q14.936 1.361 15.592 1.361L15.592 1.641M19.974 1.641L17.090 1.641L17.090 1.439Q17.090 1.409 17.117 1.381L18.364 0.164Q18.436 0.089 18.479 0.047Q18.522 0.004 18.600-0.075Q19.014-0.488 19.245-0.846Q19.475-1.203 19.475-1.627Q19.475-1.859 19.397-2.062Q19.318-2.266 19.176-2.416Q19.034-2.567 18.840-2.647Q18.645-2.727 18.412-2.727Q18.101-2.727 17.843-2.568Q17.585-2.409 17.455-2.132L17.476-2.132Q17.643-2.132 17.751-2.021Q17.859-1.910 17.859-1.746Q17.859-1.589 17.749-1.476Q17.640-1.363 17.476-1.363Q17.315-1.363 17.202-1.476Q17.090-1.589 17.090-1.746Q17.090-2.122 17.298-2.409Q17.507-2.696 17.841-2.852Q18.176-3.007 18.532-3.007Q18.956-3.007 19.335-2.849Q19.715-2.690 19.949-2.373Q20.183-2.057 20.183-1.627Q20.183-1.316 20.043-1.047Q19.903-0.779 19.697-0.574Q19.492-0.369 19.130-0.087Q18.768 0.195 18.658 0.291L17.804 1.019L18.446 1.019Q18.710 1.019 18.998 1.017Q19.287 1.016 19.506 1.007Q19.725 0.998 19.742 0.981Q19.803 0.916 19.841 0.749Q19.879 0.581 19.916 0.339L20.183 0.339\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(255.175 -66.067)\">\u003Cpath d=\"M24.048 1.221Q24.048 1.053 24.171 0.930Q24.294 0.807 24.469 0.807Q24.636 0.807 24.759 0.930Q24.882 1.053 24.882 1.221Q24.882 1.395 24.759 1.518Q24.636 1.641 24.469 1.641Q24.294 1.641 24.171 1.518Q24.048 1.395 24.048 1.221M26.311 1.221Q26.311 1.053 26.434 0.930Q26.557 0.807 26.731 0.807Q26.899 0.807 27.022 0.930Q27.145 1.053 27.145 1.221Q27.145 1.395 27.022 1.518Q26.899 1.641 26.731 1.641Q26.557 1.641 26.434 1.518Q26.311 1.395 26.311 1.221M28.574 1.221Q28.574 1.053 28.697 0.930Q28.820 0.807 28.994 0.807Q29.162 0.807 29.285 0.930Q29.408 1.053 29.408 1.221Q29.408 1.395 29.285 1.518Q29.162 1.641 28.994 1.641Q28.820 1.641 28.697 1.518Q28.574 1.395 28.574 1.221\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmbx8\" font-size=\"8\">\u003Cg transform=\"translate(216.865 42.253)\">\u003Cpath d=\"M-25.618 3.625Q-26.606 2.969-27.145 1.912Q-27.684 0.856-27.684-0.359Q-27.684-1.574-27.145-2.634Q-26.606-3.695-25.618-4.343Q-25.602-4.351-25.591-4.353Q-25.579-4.355-25.563-4.359L-25.450-4.359Q-25.395-4.359-25.358-4.314Q-25.321-4.269-25.321-4.222Q-25.321-4.171-25.380-4.113Q-25.767-3.777-26.040-3.341Q-26.313-2.906-26.481-2.416Q-26.649-1.925-26.725-1.406Q-26.802-0.886-26.802-0.359Q-26.802 0.368-26.661 1.051Q-26.520 1.735-26.204 2.342Q-25.888 2.950-25.380 3.395Q-25.321 3.454-25.321 3.504Q-25.321 3.614-25.450 3.641L-25.563 3.641Q-25.602 3.641-25.618 3.625M-23.411 1.641L-23.892 1.641L-23.892-3.175Q-23.892-3.324-24.038-3.361Q-24.184-3.398-24.419-3.398L-24.419-3.847L-22.931-3.910L-22.931-1.605Q-22.395-1.968-21.673-1.968Q-21.106-1.968-20.649-1.754Q-20.192-1.539-19.925-1.121Q-19.657-0.703-19.657-0.136Q-19.657 0.321-19.831 0.662Q-20.005 1.004-20.306 1.237Q-20.606 1.469-20.997 1.582Q-21.388 1.696-21.809 1.696Q-22.157 1.696-22.483 1.567Q-22.809 1.438-23.052 1.207L-23.411 1.641M-22.884-1.101L-22.884 0.770Q-22.716 1.028-22.450 1.178Q-22.184 1.329-21.884 1.329Q-21.227 1.329-20.999 0.946Q-20.770 0.563-20.770-0.136Q-20.770-0.566-20.839-0.882Q-20.907-1.199-21.128-1.398Q-21.349-1.597-21.778-1.597Q-22.095-1.597-22.392-1.470Q-22.688-1.343-22.884-1.101M-18.544 3.641L-18.657 3.641Q-18.704 3.641-18.745 3.596Q-18.786 3.551-18.786 3.504Q-18.786 3.446-18.727 3.395Q-18.341 3.051-18.061 2.612Q-17.782 2.172-17.618 1.692Q-17.454 1.211-17.376 0.690Q-17.298 0.168-17.298-0.359Q-17.298-0.886-17.376-1.412Q-17.454-1.937-17.616-2.414Q-17.778-2.890-18.059-3.334Q-18.341-3.777-18.727-4.113Q-18.786-4.164-18.786-4.222Q-18.786-4.332-18.657-4.359L-18.544-4.359Q-18.513-4.359-18.481-4.343Q-17.489-3.687-16.958-2.630Q-16.427-1.574-16.427-0.359Q-16.427 0.250-16.563 0.827Q-16.700 1.403-16.958 1.909Q-17.216 2.414-17.608 2.860Q-18.001 3.305-18.481 3.625Q-18.497 3.629-18.509 3.631Q-18.520 3.633-18.544 3.641\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(216.865 42.253)\">\u003Cpath d=\"M-11.972 2.215Q-11.972 1.969-11.768 1.784Q-11.565 1.598-11.300 1.528Q-11.628 1.219-11.628 0.762Q-11.628 0.371-11.366 0.055Q-11.550-0.101-11.661-0.316Q-11.772-0.531-11.772-0.765Q-11.772-1.191-11.516-1.461Q-11.261-1.730-10.872-1.849Q-10.483-1.968-10.069-1.968Q-9.393-1.968-8.956-1.718Q-8.753-1.863-8.516-1.943Q-8.280-2.023-8.030-2.023Q-7.807-2.023-7.645-1.865Q-7.483-1.707-7.483-1.488Q-7.483-1.312-7.597-1.199Q-7.710-1.086-7.886-1.086Q-8.054-1.086-8.165-1.201Q-8.276-1.316-8.276-1.488L-8.276-1.566Q-8.276-1.566-8.266-1.597Q-8.257-1.629-8.253-1.640Q-8.448-1.609-8.679-1.504Q-8.366-1.199-8.366-0.765Q-8.366-0.347-8.622-0.076Q-8.878 0.196-9.265 0.315Q-9.651 0.434-10.069 0.434Q-10.667 0.434-11.085 0.235Q-11.081 0.235-11.108 0.289Q-11.140 0.399-11.140 0.450Q-11.140 0.657-10.987 0.793Q-10.835 0.930-10.636 0.930L-9.780 0.930Q-9.343 0.930-8.991 0.967Q-8.640 1.004-8.327 1.127Q-8.015 1.250-7.817 1.514Q-7.620 1.778-7.620 2.215Q-7.620 2.809-8.329 3.034Q-9.038 3.258-9.796 3.258Q-10.554 3.258-11.263 3.034Q-11.972 2.809-11.972 2.215M-11.198 2.215Q-11.198 2.477-10.954 2.625Q-10.710 2.774-10.386 2.827Q-10.061 2.879-9.796 2.879Q-9.315 2.879-8.856 2.733Q-8.397 2.586-8.397 2.215Q-8.397 1.793-9.780 1.793L-10.636 1.793Q-10.866 1.793-11.032 1.893Q-11.198 1.993-11.198 2.215M-10.069 0.055Q-9.636 0.055-9.479-0.136Q-9.323-0.328-9.323-0.765Q-9.323-1.058-9.386-1.234Q-9.448-1.410-9.614-1.500Q-9.780-1.589-10.069-1.589Q-10.358-1.589-10.522-1.502Q-10.686-1.414-10.749-1.236Q-10.811-1.058-10.811-0.765Q-10.811-0.328-10.655-0.136Q-10.499 0.055-10.069 0.055M-7.077-0.160Q-7.077-0.738-6.794-1.158Q-6.511-1.578-6.026-1.789Q-5.542-2-4.975-2Q-4.534-2-4.198-1.888Q-3.862-1.777-3.628-1.556Q-3.393-1.336-3.268-1.005Q-3.143-0.675-3.143-0.238Q-3.143-0.082-3.296-0.054L-5.968-0.054Q-5.968 1.289-4.710 1.289Q-4.358 1.289-4.052 1.133Q-3.745 0.977-3.624 0.680Q-3.550 0.551-3.464 0.551L-3.296 0.551Q-3.143 0.586-3.143 0.719Q-3.143 0.754-3.151 0.770Q-3.335 1.246-3.800 1.471Q-4.265 1.696-4.839 1.696Q-5.436 1.696-5.946 1.493Q-6.456 1.289-6.766 0.868Q-7.077 0.446-7.077-0.160M-5.968-0.398L-3.960-0.398Q-3.960-0.937-4.208-1.285Q-4.456-1.632-4.975-1.632Q-5.319-1.632-5.544-1.466Q-5.768-1.300-5.868-1.023Q-5.968-0.746-5.968-0.398M-0.366 1.641L-2.429 1.641L-2.429 1.192L-1.901 1.192L-1.901-1.230Q-1.901-1.382-2.048-1.420Q-2.194-1.457-2.429-1.457L-2.429-1.902L-0.999-1.968L-0.999-1.175Q-0.850-1.421-0.616-1.601Q-0.382-1.781-0.104-1.875Q0.173-1.968 0.466-1.968Q0.907-1.968 1.204-1.861Q1.501-1.754 1.663-1.494Q1.825-1.234 1.825-0.800L1.825 1.192L2.353 1.192L2.353 1.641L0.290 1.641L0.290 1.192L0.818 1.192L0.818-0.773Q0.818-1.148 0.739-1.373Q0.661-1.597 0.368-1.597Q-0.143-1.597-0.518-1.263Q-0.893-0.929-0.893-0.421L-0.893 1.192L-0.366 1.192L-0.366 1.641M2.853-0.160Q2.853-0.738 3.136-1.158Q3.419-1.578 3.903-1.789Q4.388-2 4.954-2Q5.396-2 5.732-1.888Q6.068-1.777 6.302-1.556Q6.536-1.336 6.661-1.005Q6.786-0.675 6.786-0.238Q6.786-0.082 6.634-0.054L3.962-0.054Q3.962 1.289 5.220 1.289Q5.571 1.289 5.878 1.133Q6.185 0.977 6.306 0.680Q6.380 0.551 6.466 0.551L6.634 0.551Q6.786 0.586 6.786 0.719Q6.786 0.754 6.778 0.770Q6.595 1.246 6.130 1.471Q5.665 1.696 5.091 1.696Q4.493 1.696 3.984 1.493Q3.474 1.289 3.163 0.868Q2.853 0.446 2.853-0.160M3.962-0.398L5.970-0.398Q5.970-0.937 5.722-1.285Q5.474-1.632 4.954-1.632Q4.610-1.632 4.386-1.466Q4.161-1.300 4.062-1.023Q3.962-0.746 3.962-0.398M9.564 1.641L7.419 1.641L7.419 1.192L7.946 1.192L7.946-1.230Q7.946-1.382 7.800-1.420Q7.653-1.457 7.419-1.457L7.419-1.902L8.802-1.968L8.802-1.160Q8.958-1.515 9.237-1.742Q9.517-1.968 9.884-1.968Q10.247-1.968 10.542-1.781Q10.837-1.593 10.837-1.254Q10.837-1.109 10.765-0.982Q10.693-0.855 10.566-0.783Q10.439-0.711 10.290-0.711Q10.146-0.711 10.019-0.783Q9.892-0.855 9.819-0.982Q9.747-1.109 9.747-1.254Q9.747-1.468 9.860-1.597Q9.528-1.597 9.312-1.351Q9.095-1.105 9.001-0.746Q8.907-0.386 8.907-0.070L8.907 1.192L9.564 1.192L9.564 1.641M11.388 0.711Q11.388 0.340 11.683 0.088Q11.978-0.164 12.429-0.295Q12.880-0.425 13.316-0.472Q13.751-0.519 14.138-0.519L14.138-0.773Q14.138-1.171 13.907-1.402Q13.677-1.632 13.282-1.632Q12.857-1.632 12.650-1.582Q12.810-1.437 12.810-1.183Q12.810-0.949 12.651-0.791Q12.493-0.632 12.259-0.632Q12.017-0.632 11.859-0.791Q11.700-0.949 11.700-1.183Q11.700-1.695 12.165-1.847Q12.630-2 13.282-2Q13.704-2 14.134-1.884Q14.564-1.769 14.855-1.498Q15.146-1.226 15.146-0.793L15.146 1.067Q15.177 1.192 15.716 1.192Q15.775 1.192 15.821 1.239Q15.868 1.286 15.868 1.344L15.868 1.481Q15.868 1.547 15.821 1.594Q15.775 1.641 15.716 1.641L15.169 1.641Q14.290 1.641 14.290 1.137Q14.103 1.414 13.757 1.555Q13.411 1.696 13.044 1.696Q12.669 1.696 12.288 1.612Q11.907 1.528 11.648 1.305Q11.388 1.082 11.388 0.711M12.396 0.711Q12.396 0.899 12.507 1.039Q12.618 1.180 12.794 1.254Q12.970 1.329 13.153 1.329Q13.384 1.329 13.612 1.248Q13.841 1.168 13.989 1.008Q14.138 0.848 14.138 0.610L14.138-0.183Q13.802-0.183 13.400-0.099Q12.997-0.015 12.696 0.186Q12.396 0.387 12.396 0.711M16.732 0.719L16.732-1.464L16.060-1.464L16.060-1.832Q16.446-1.832 16.720-2.078Q16.993-2.324 17.126-2.699Q17.259-3.074 17.259-3.437L17.739-3.437L17.739-1.910L18.974-1.910L18.974-1.464L17.739-1.464L17.739 0.704Q17.739 1.289 18.196 1.289Q18.407 1.289 18.530 1.106Q18.653 0.922 18.653 0.704L18.653 0.250L19.134 0.250L19.134 0.719Q19.134 0.993 18.987 1.215Q18.841 1.438 18.601 1.567Q18.360 1.696 18.091 1.696Q17.517 1.696 17.124 1.473Q16.732 1.250 16.732 0.719M22.099 1.641L20.142 1.641L20.142 1.192L20.669 1.192L20.669-1.230Q20.669-1.382 20.532-1.420Q20.396-1.457 20.165-1.457L20.165-1.902L21.630-1.968L21.630 1.192L22.099 1.192L22.099 1.641M20.388-3.261Q20.388-3.539 20.581-3.728Q20.775-3.918 21.044-3.918Q21.224-3.918 21.374-3.828Q21.525-3.738 21.612-3.591Q21.700-3.445 21.700-3.261Q21.700-2.992 21.507-2.798Q21.314-2.605 21.044-2.605Q20.771-2.605 20.579-2.796Q20.388-2.988 20.388-3.261M22.677-0.093Q22.677-0.558 22.847-0.918Q23.017-1.277 23.319-1.519Q23.622-1.761 24.015-1.880Q24.407-2 24.853-2Q25.298-2 25.689-1.882Q26.079-1.765 26.386-1.521Q26.693-1.277 26.860-0.918Q27.028-0.558 27.028-0.093Q27.028 0.356 26.851 0.694Q26.673 1.032 26.360 1.256Q26.048 1.481 25.663 1.588Q25.278 1.696 24.853 1.696Q24.431 1.696 24.042 1.588Q23.653 1.481 23.343 1.256Q23.032 1.032 22.855 0.692Q22.677 0.352 22.677-0.093M24.853 1.289Q25.333 1.289 25.566 1.098Q25.798 0.907 25.857 0.604Q25.915 0.301 25.915-0.207Q25.915-0.937 25.722-1.285Q25.528-1.632 24.853-1.632Q24.497-1.632 24.280-1.529Q24.064-1.425 23.958-1.238Q23.853-1.050 23.819-0.810Q23.786-0.570 23.786-0.207Q23.786 0.301 23.847 0.606Q23.907 0.911 24.142 1.100Q24.376 1.289 24.853 1.289M29.794 1.641L27.732 1.641L27.732 1.192L28.259 1.192L28.259-1.230Q28.259-1.382 28.112-1.420Q27.966-1.457 27.732-1.457L27.732-1.902L29.161-1.968L29.161-1.175Q29.310-1.421 29.544-1.601Q29.778-1.781 30.056-1.875Q30.333-1.968 30.626-1.968Q31.068-1.968 31.364-1.861Q31.661-1.754 31.823-1.494Q31.985-1.234 31.985-0.800L31.985 1.192L32.513 1.192L32.513 1.641L30.450 1.641L30.450 1.192L30.978 1.192L30.978-0.773Q30.978-1.148 30.900-1.373Q30.821-1.597 30.528-1.597Q30.017-1.597 29.642-1.263Q29.267-0.929 29.267-0.421L29.267 1.192L29.794 1.192\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">Two ways a neural chatbot produces a response. Retrieval (left) scores each corpus turn by the dot product of query and candidate encodings and returns the best; generation (right) runs an encoder-decoder over the whole conversation.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:465.079px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 348.809 131.592\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-65.403-72.07V53.122h335.742V-72.07ZM-65.403-57.844h335.742\"\u002F>\u003Cg transform=\"translate(31.395 -5.758)\">\u003Cpath d=\"M-58.928-56.308L-59.041-56.308Q-59.170-56.339-59.170-56.444L-59.170-58.116Q-59.170-58.163-59.129-58.204Q-59.088-58.245-59.041-58.245L-58.818-58.245Q-58.690-58.214-58.690-58.116Q-58.690-57.366-58.111-57.062Q-57.533-56.757-56.721-56.757Q-56.514-56.757-56.317-56.814Q-56.119-56.870-55.971-56.983Q-55.822-57.097-55.740-57.269Q-55.658-57.441-55.658-57.659Q-55.658-57.999-55.901-58.237Q-56.143-58.476-56.483-58.550L-57.705-58.796Q-58.104-58.890-58.438-59.107Q-58.772-59.323-58.971-59.657Q-59.170-59.991-59.170-60.390Q-59.170-60.780-59.016-61.095Q-58.861-61.409-58.600-61.610Q-58.338-61.812-57.986-61.917Q-57.635-62.023-57.264-62.023Q-56.381-62.023-55.818-61.597L-55.393-61.995Q-55.358-62.023-55.322-62.023L-55.217-62.023Q-55.088-61.991-55.088-61.894L-55.088-60.222Q-55.088-60.171-55.131-60.128Q-55.174-60.085-55.217-60.085L-55.440-60.085Q-55.549-60.101-55.561-60.191Q-55.608-60.534-55.746-60.806Q-55.885-61.077-56.102-61.255Q-56.318-61.433-56.604-61.523Q-56.889-61.612-57.264-61.612Q-57.701-61.612-58.016-61.409Q-58.330-61.206-58.330-60.796Q-58.330-60.495-58.088-60.286Q-57.846-60.077-57.529-60.007L-56.307-59.757Q-55.885-59.659-55.549-59.423Q-55.213-59.187-55.016-58.833Q-54.818-58.480-54.818-58.054Q-54.818-57.519-55.068-57.120Q-55.318-56.722-55.754-56.515Q-56.190-56.308-56.721-56.308Q-57.217-56.308-57.654-56.401Q-58.092-56.495-58.447-56.726L-58.858-56.331Q-58.928-56.308-58.928-56.308M-51.799-56.421L-53.815-56.421L-53.815-56.870L-53.287-56.870L-53.287-61.237Q-53.287-61.386-53.434-61.423Q-53.580-61.460-53.815-61.460L-53.815-61.909L-52.326-61.972L-52.326-56.870L-51.799-56.870L-51.799-56.421M-51.279-58.155Q-51.279-58.620-51.109-58.980Q-50.940-59.339-50.637-59.581Q-50.334-59.823-49.942-59.942Q-49.549-60.062-49.104-60.062Q-48.658-60.062-48.268-59.944Q-47.877-59.827-47.570-59.583Q-47.264-59.339-47.096-58.980Q-46.928-58.620-46.928-58.155Q-46.928-57.706-47.106-57.368Q-47.283-57.030-47.596-56.806Q-47.908-56.581-48.293-56.474Q-48.678-56.366-49.104-56.366Q-49.526-56.366-49.914-56.474Q-50.303-56.581-50.613-56.806Q-50.924-57.030-51.102-57.370Q-51.279-57.710-51.279-58.155M-49.104-56.773Q-48.623-56.773-48.391-56.964Q-48.158-57.155-48.100-57.458Q-48.041-57.761-48.041-58.269Q-48.041-58.999-48.234-59.347Q-48.428-59.694-49.104-59.694Q-49.459-59.694-49.676-59.591Q-49.893-59.487-49.998-59.300Q-50.104-59.112-50.137-58.872Q-50.170-58.632-50.170-58.269Q-50.170-57.761-50.109-57.456Q-50.049-57.151-49.815-56.962Q-49.580-56.773-49.104-56.773M-45.803-57.343L-45.803-59.526L-46.475-59.526L-46.475-59.894Q-46.088-59.894-45.815-60.140Q-45.541-60.386-45.408-60.761Q-45.276-61.136-45.276-61.499L-44.795-61.499L-44.795-59.972L-43.561-59.972L-43.561-59.526L-44.795-59.526L-44.795-57.358Q-44.795-56.773-44.338-56.773Q-44.127-56.773-44.004-56.956Q-43.881-57.140-43.881-57.358L-43.881-57.812L-43.401-57.812L-43.401-57.343Q-43.401-57.069-43.547-56.847Q-43.693-56.624-43.934-56.495Q-44.174-56.366-44.443-56.366Q-45.018-56.366-45.410-56.589Q-45.803-56.812-45.803-57.343\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmbx8\" font-size=\"8\">\u003Cg transform=\"translate(111.386 -6.57)\">\u003Cpath d=\"M-54.483-56.421L-58.139-56.421L-58.139-56.870L-56.920-56.870L-56.920-61.460L-57.522-61.460Q-58.217-61.460-58.529-61.155Q-58.701-60.980-58.772-60.679Q-58.842-60.378-58.881-59.933L-59.361-59.933L-59.178-61.909L-53.440-61.909L-53.256-59.933L-53.736-59.933Q-53.776-60.378-53.842-60.677Q-53.908-60.976-54.088-61.155Q-54.401-61.460-55.096-61.460L-55.697-61.460L-55.697-56.870L-54.483-56.870\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(111.386 -6.57)\">\u003Cpath d=\"M-52.977-55.620Q-52.977-55.843-52.828-55.987Q-52.680-56.132-52.457-56.132Q-52.316-56.132-52.197-56.066Q-52.078-55.999-52.012-55.880Q-51.945-55.761-51.945-55.620Q-51.945-55.331-52.160-55.191Q-52.137-55.183-52.078-55.183Q-51.781-55.183-51.535-55.366Q-51.289-55.550-51.152-55.827L-50.855-56.421L-52.406-59.526L-52.930-59.526L-52.930-59.972L-50.969-59.972L-50.969-59.526L-51.336-59.526L-50.320-57.483L-49.320-59.476Q-49.320-59.526-49.664-59.526L-49.664-59.972L-48.238-59.972L-48.238-59.526Q-48.734-59.526-48.785-59.468L-50.617-55.827Q-50.832-55.394-51.223-55.103Q-51.613-54.812-52.078-54.812Q-52.305-54.812-52.514-54.915Q-52.723-55.019-52.850-55.206Q-52.977-55.394-52.977-55.620M-45.586-54.870L-47.648-54.870L-47.648-55.316L-47.121-55.316L-47.121-59.351Q-47.121-59.519-47.648-59.519L-47.648-59.964L-46.160-60.030L-46.160-59.659Q-45.609-60.030-44.855-60.030Q-44.301-60.030-43.852-59.812Q-43.402-59.593-43.145-59.177Q-42.887-58.761-42.887-58.198Q-42.887-57.749-43.059-57.403Q-43.230-57.058-43.531-56.825Q-43.832-56.593-44.223-56.480Q-44.613-56.366-45.039-56.366Q-45.641-56.366-46.113-56.710L-46.113-55.316L-45.586-55.316L-45.586-54.870M-46.113-59.148L-46.113-57.284Q-45.941-57.034-45.680-56.884Q-45.418-56.733-45.121-56.733Q-44.559-56.733-44.279-57.167Q-44-57.601-44-58.198Q-44-58.546-44.100-58.870Q-44.199-59.194-44.424-59.407Q-44.648-59.620-45.008-59.620Q-45.332-59.620-45.627-59.497Q-45.922-59.374-46.113-59.148\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(111.386 -6.57)\">\u003Cpath d=\"M-42.006-58.222Q-42.006-58.800-41.722-59.220Q-41.439-59.640-40.955-59.851Q-40.470-60.062-39.904-60.062Q-39.463-60.062-39.127-59.950Q-38.791-59.839-38.556-59.618Q-38.322-59.398-38.197-59.067Q-38.072-58.737-38.072-58.300Q-38.072-58.144-38.224-58.116L-40.896-58.116Q-40.896-56.773-39.638-56.773Q-39.287-56.773-38.980-56.929Q-38.674-57.085-38.552-57.382Q-38.478-57.511-38.392-57.511L-38.224-57.511Q-38.072-57.476-38.072-57.343Q-38.072-57.308-38.080-57.292Q-38.263-56.816-38.728-56.591Q-39.193-56.366-39.767-56.366Q-40.365-56.366-40.875-56.569Q-41.384-56.773-41.695-57.194Q-42.006-57.616-42.006-58.222M-40.896-58.460L-38.888-58.460Q-38.888-58.999-39.136-59.347Q-39.384-59.694-39.904-59.694Q-40.248-59.694-40.472-59.528Q-40.697-59.362-40.797-59.085Q-40.896-58.808-40.896-58.460\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmbx8\" font-size=\"8\">\u003Cg transform=\"translate(162.869 -6.536)\">\u003Cpath d=\"M-56.033-56.308Q-56.686-56.308-57.254-56.491Q-57.822-56.675-58.256-57.036Q-58.690-57.398-58.930-57.931Q-59.170-58.464-59.170-59.124Q-59.170-59.804-58.936-60.347Q-58.701-60.890-58.276-61.263Q-57.850-61.636-57.270-61.829Q-56.690-62.023-56.033-62.023Q-55.373-62.023-54.795-61.827Q-54.217-61.632-53.793-61.259Q-53.369-60.886-53.137-60.347Q-52.904-59.808-52.904-59.124Q-52.904-58.245-53.352-57.560Q-53.799-56.874-54.608-56.558Q-54.276-56.183-53.928-56.183Q-53.186-56.183-53.186-56.437Q-53.174-56.499-53.131-56.544Q-53.088-56.589-53.018-56.589Q-52.943-56.589-52.897-56.534Q-52.850-56.480-52.850-56.405Q-52.850-55.823-53.158-55.347Q-53.467-54.870-54.026-54.870Q-54.404-54.870-54.641-55.095Q-54.877-55.319-54.979-55.577Q-55.080-55.835-55.240-56.382Q-55.615-56.308-56.033-56.308M-56.537-57.015Q-56.537-56.718-56.033-56.718Q-55.682-56.718-55.393-56.819Q-55.498-57.062-55.647-57.196Q-55.795-57.331-56.033-57.331Q-56.217-57.331-56.377-57.255Q-56.537-57.179-56.537-57.015M-56.904-57.015Q-56.904-57.233-56.776-57.388Q-56.647-57.542-56.442-57.622Q-56.236-57.702-56.033-57.702Q-55.815-57.702-55.625-57.612Q-55.436-57.523-55.287-57.384Q-55.139-57.245-54.971-57.030Q-54.506-57.343-54.338-57.884Q-54.170-58.425-54.170-59.124Q-54.170-60.214-54.590-60.913Q-55.010-61.612-56.033-61.612Q-56.733-61.612-57.152-61.267Q-57.572-60.921-57.738-60.364Q-57.904-59.808-57.904-59.124Q-57.904-58.784-57.863-58.462Q-57.822-58.140-57.711-57.835Q-57.600-57.530-57.404-57.298Q-57.209-57.066-56.897-56.909L-56.897-56.933Q-56.904-56.956-56.904-57.015M-51.393-57.343L-51.393-59.292Q-51.393-59.444-51.539-59.482Q-51.686-59.519-51.920-59.519L-51.920-59.964L-50.385-60.030L-50.385-57.358L-50.385-57.343Q-50.385-57.108-50.352-56.991Q-50.318-56.874-50.244-56.819Q-50.170-56.765-50.051-56.749Q-49.932-56.733-49.674-56.733Q-49.416-56.733-49.186-56.855Q-48.955-56.976-48.815-57.191Q-48.674-57.405-48.674-57.667L-48.674-59.292Q-48.674-59.444-48.820-59.482Q-48.967-59.519-49.201-59.519L-49.201-59.964L-47.666-60.030L-47.666-57.093Q-47.666-56.944-47.520-56.907Q-47.373-56.870-47.139-56.870L-47.139-56.421L-48.627-56.366L-48.627-56.901Q-48.818-56.651-49.123-56.509Q-49.428-56.366-49.760-56.366Q-50.201-56.366-50.551-56.429Q-50.901-56.491-51.147-56.708Q-51.393-56.925-51.393-57.343M-46.639-58.222Q-46.639-58.800-46.356-59.220Q-46.072-59.640-45.588-59.851Q-45.104-60.062-44.537-60.062Q-44.096-60.062-43.760-59.950Q-43.424-59.839-43.190-59.618Q-42.955-59.398-42.830-59.067Q-42.705-58.737-42.705-58.300Q-42.705-58.144-42.858-58.116L-45.529-58.116Q-45.529-56.773-44.272-56.773Q-43.920-56.773-43.613-56.929Q-43.307-57.085-43.186-57.382Q-43.111-57.511-43.026-57.511L-42.858-57.511Q-42.705-57.476-42.705-57.343Q-42.705-57.308-42.713-57.292Q-42.897-56.816-43.361-56.591Q-43.826-56.366-44.401-56.366Q-44.998-56.366-45.508-56.569Q-46.018-56.773-46.328-57.194Q-46.639-57.616-46.639-58.222M-45.529-58.460L-43.522-58.460Q-43.522-58.999-43.770-59.347Q-44.018-59.694-44.537-59.694Q-44.881-59.694-45.106-59.528Q-45.330-59.362-45.430-59.085Q-45.529-58.808-45.529-58.460M-41.858-56.366L-41.967-56.366Q-42.096-56.398-42.096-56.499L-42.096-57.542Q-42.096-57.585-42.059-57.626Q-42.022-57.667-41.967-57.667L-41.744-57.667Q-41.643-57.640-41.615-57.566Q-41.498-57.151-41.213-56.942Q-40.928-56.733-40.494-56.733Q-40.108-56.733-39.826-56.843Q-39.545-56.952-39.545-57.284Q-39.545-57.519-39.748-57.642Q-39.951-57.765-40.240-57.819L-40.865-57.917Q-41.084-57.960-41.301-58.042Q-41.518-58.124-41.701-58.253Q-41.885-58.382-41.990-58.560Q-42.096-58.737-42.096-58.972Q-42.096-59.394-41.861-59.636Q-41.627-59.878-41.272-59.970Q-40.916-60.062-40.494-60.062Q-39.986-60.062-39.682-59.925L-39.408-60.054Q-39.401-60.058-39.391-60.060Q-39.381-60.062-39.369-60.062L-39.264-60.062Q-39.135-60.030-39.135-59.933L-39.135-59.124Q-39.135-59.073-39.178-59.030Q-39.221-58.987-39.264-58.987L-39.486-58.987Q-39.615-59.026-39.615-59.124Q-39.615-59.355-39.744-59.491Q-39.873-59.628-40.070-59.681Q-40.268-59.733-40.502-59.733Q-41.447-59.733-41.447-59.276Q-41.447-58.960-40.799-58.847L-40.166-58.741Q-39.651-58.648-39.274-58.351Q-38.897-58.054-38.897-57.566Q-38.897-56.909-39.348-56.638Q-39.799-56.366-40.494-56.366Q-41.053-56.366-41.440-56.597L-41.799-56.382Q-41.830-56.366-41.858-56.366M-37.705-57.343L-37.705-59.526L-38.377-59.526L-38.377-59.894Q-37.990-59.894-37.717-60.140Q-37.443-60.386-37.311-60.761Q-37.178-61.136-37.178-61.499L-36.697-61.499L-36.697-59.972L-35.463-59.972L-35.463-59.526L-36.697-59.526L-36.697-57.358Q-36.697-56.773-36.240-56.773Q-36.029-56.773-35.906-56.956Q-35.783-57.140-35.783-57.358L-35.783-57.812L-35.303-57.812L-35.303-57.343Q-35.303-57.069-35.449-56.847Q-35.596-56.624-35.836-56.495Q-36.076-56.366-36.346-56.366Q-36.920-56.366-37.313-56.589Q-37.705-56.812-37.705-57.343M-32.338-56.421L-34.295-56.421L-34.295-56.870L-33.768-56.870L-33.768-59.292Q-33.768-59.444-33.904-59.482Q-34.041-59.519-34.272-59.519L-34.272-59.964L-32.807-60.030L-32.807-56.870L-32.338-56.870L-32.338-56.421M-34.049-61.323Q-34.049-61.601-33.856-61.790Q-33.662-61.980-33.393-61.980Q-33.213-61.980-33.063-61.890Q-32.912-61.800-32.824-61.653Q-32.736-61.507-32.736-61.323Q-32.736-61.054-32.930-60.860Q-33.123-60.667-33.393-60.667Q-33.666-60.667-33.858-60.858Q-34.049-61.050-34.049-61.323M-31.760-58.155Q-31.760-58.620-31.590-58.980Q-31.420-59.339-31.117-59.581Q-30.815-59.823-30.422-59.942Q-30.029-60.062-29.584-60.062Q-29.139-60.062-28.748-59.944Q-28.358-59.827-28.051-59.583Q-27.744-59.339-27.576-58.980Q-27.408-58.620-27.408-58.155Q-27.408-57.706-27.586-57.368Q-27.764-57.030-28.076-56.806Q-28.389-56.581-28.774-56.474Q-29.158-56.366-29.584-56.366Q-30.006-56.366-30.395-56.474Q-30.783-56.581-31.094-56.806Q-31.404-57.030-31.582-57.370Q-31.760-57.710-31.760-58.155M-29.584-56.773Q-29.104-56.773-28.871-56.964Q-28.639-57.155-28.580-57.458Q-28.522-57.761-28.522-58.269Q-28.522-58.999-28.715-59.347Q-28.908-59.694-29.584-59.694Q-29.940-59.694-30.156-59.591Q-30.373-59.487-30.479-59.300Q-30.584-59.112-30.617-58.872Q-30.651-58.632-30.651-58.269Q-30.651-57.761-30.590-57.456Q-30.529-57.151-30.295-56.962Q-30.061-56.773-29.584-56.773M-24.643-56.421L-26.705-56.421L-26.705-56.870L-26.178-56.870L-26.178-59.292Q-26.178-59.444-26.324-59.482Q-26.471-59.519-26.705-59.519L-26.705-59.964L-25.276-60.030L-25.276-59.237Q-25.127-59.483-24.893-59.663Q-24.658-59.843-24.381-59.937Q-24.104-60.030-23.811-60.030Q-23.369-60.030-23.072-59.923Q-22.776-59.816-22.613-59.556Q-22.451-59.296-22.451-58.862L-22.451-56.870L-21.924-56.870L-21.924-56.421L-23.986-56.421L-23.986-56.870L-23.459-56.870L-23.459-58.835Q-23.459-59.210-23.537-59.435Q-23.615-59.659-23.908-59.659Q-24.420-59.659-24.795-59.325Q-25.170-58.991-25.170-58.483L-25.170-56.870L-24.643-56.870\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 -6.536)\">\u003Cpath d=\"M-17.553-57.343L-17.553-59.526L-18.224-59.526L-18.224-59.894Q-17.838-59.894-17.564-60.140Q-17.291-60.386-17.158-60.761Q-17.025-61.136-17.025-61.499L-16.545-61.499L-16.545-59.972L-15.310-59.972L-15.310-59.526L-16.545-59.526L-16.545-57.358Q-16.545-56.773-16.088-56.773Q-15.877-56.773-15.754-56.956Q-15.631-57.140-15.631-57.358L-15.631-57.812L-15.150-57.812L-15.150-57.343Q-15.150-57.069-15.297-56.847Q-15.443-56.624-15.683-56.495Q-15.924-56.366-16.193-56.366Q-16.767-56.366-17.160-56.589Q-17.553-56.812-17.553-57.343M-14.326-58.222Q-14.326-58.800-14.043-59.220Q-13.760-59.640-13.275-59.851Q-12.791-60.062-12.224-60.062Q-11.783-60.062-11.447-59.950Q-11.111-59.839-10.877-59.618Q-10.642-59.398-10.517-59.067Q-10.392-58.737-10.392-58.300Q-10.392-58.144-10.545-58.116L-13.217-58.116Q-13.217-56.773-11.959-56.773Q-11.607-56.773-11.301-56.929Q-10.994-57.085-10.873-57.382Q-10.799-57.511-10.713-57.511L-10.545-57.511Q-10.392-57.476-10.392-57.343Q-10.392-57.308-10.400-57.292Q-10.584-56.816-11.049-56.591Q-11.513-56.366-12.088-56.366Q-12.685-56.366-13.195-56.569Q-13.705-56.773-14.015-57.194Q-14.326-57.616-14.326-58.222M-13.217-58.460L-11.209-58.460Q-11.209-58.999-11.457-59.347Q-11.705-59.694-12.224-59.694Q-12.568-59.694-12.793-59.528Q-13.017-59.362-13.117-59.085Q-13.217-58.808-13.217-58.460M-7.615-56.421L-9.678-56.421L-9.678-56.870L-9.150-56.870L-9.150-59.292Q-9.150-59.444-9.297-59.482Q-9.443-59.519-9.678-59.519L-9.678-59.964L-8.248-60.030L-8.248-59.237Q-8.099-59.483-7.865-59.663Q-7.631-59.843-7.353-59.937Q-7.076-60.030-6.783-60.030Q-6.287-60.030-5.941-59.874Q-5.596-59.718-5.478-59.308Q-5.248-59.651-4.871-59.841Q-4.494-60.030-4.064-60.030Q-3.623-60.030-3.326-59.923Q-3.029-59.816-2.867-59.556Q-2.705-59.296-2.705-58.862L-2.705-56.870L-2.174-56.870L-2.174-56.421L-4.240-56.421L-4.240-56.870L-3.713-56.870L-3.713-58.835Q-3.713-59.210-3.787-59.435Q-3.861-59.659-4.158-59.659Q-4.670-59.659-5.047-59.323Q-5.424-58.987-5.424-58.483L-5.424-56.870L-4.896-56.870L-4.896-56.421L-6.959-56.421L-6.959-56.870L-6.431-56.870L-6.431-58.835Q-6.431-59.210-6.510-59.435Q-6.588-59.659-6.881-59.659Q-7.392-59.659-7.767-59.325Q-8.142-58.991-8.142-58.483L-8.142-56.870L-7.615-56.870L-7.615-56.421M0.463-54.870L-1.599-54.870L-1.599-55.316L-1.072-55.316L-1.072-59.351Q-1.072-59.519-1.599-59.519L-1.599-59.964L-0.111-60.030L-0.111-59.659Q0.440-60.030 1.194-60.030Q1.748-60.030 2.197-59.812Q2.647-59.593 2.904-59.177Q3.162-58.761 3.162-58.198Q3.162-57.749 2.990-57.403Q2.819-57.058 2.518-56.825Q2.217-56.593 1.826-56.480Q1.436-56.366 1.010-56.366Q0.408-56.366-0.064-56.710L-0.064-55.316L0.463-55.316L0.463-54.870M-0.064-59.148L-0.064-57.284Q0.108-57.034 0.369-56.884Q0.631-56.733 0.928-56.733Q1.490-56.733 1.770-57.167Q2.049-57.601 2.049-58.198Q2.049-58.546 1.949-58.870Q1.850-59.194 1.625-59.407Q1.401-59.620 1.041-59.620Q0.717-59.620 0.422-59.497Q0.127-59.374-0.064-59.148M5.963-56.421L3.947-56.421L3.947-56.870L4.475-56.870L4.475-61.237Q4.475-61.386 4.328-61.423Q4.182-61.460 3.947-61.460L3.947-61.909L5.436-61.972L5.436-56.870L5.963-56.870L5.963-56.421M6.483-57.351Q6.483-57.722 6.778-57.974Q7.072-58.226 7.524-58.357Q7.975-58.487 8.410-58.534Q8.846-58.581 9.233-58.581L9.233-58.835Q9.233-59.233 9.002-59.464Q8.772-59.694 8.377-59.694Q7.951-59.694 7.744-59.644Q7.904-59.499 7.904-59.245Q7.904-59.011 7.746-58.853Q7.588-58.694 7.354-58.694Q7.112-58.694 6.953-58.853Q6.795-59.011 6.795-59.245Q6.795-59.757 7.260-59.909Q7.725-60.062 8.377-60.062Q8.799-60.062 9.229-59.946Q9.658-59.831 9.949-59.560Q10.240-59.288 10.240-58.855L10.240-56.995Q10.272-56.870 10.811-56.870Q10.869-56.870 10.916-56.823Q10.963-56.776 10.963-56.718L10.963-56.581Q10.963-56.515 10.916-56.468Q10.869-56.421 10.811-56.421L10.264-56.421Q9.385-56.421 9.385-56.925Q9.197-56.648 8.852-56.507Q8.506-56.366 8.139-56.366Q7.764-56.366 7.383-56.450Q7.002-56.534 6.742-56.757Q6.483-56.980 6.483-57.351M7.490-57.351Q7.490-57.163 7.602-57.023Q7.713-56.882 7.889-56.808Q8.065-56.733 8.248-56.733Q8.479-56.733 8.707-56.814Q8.936-56.894 9.084-57.054Q9.233-57.214 9.233-57.452L9.233-58.245Q8.897-58.245 8.494-58.161Q8.092-58.077 7.791-57.876Q7.490-57.675 7.490-57.351M11.826-57.343L11.826-59.526L11.154-59.526L11.154-59.894Q11.541-59.894 11.815-60.140Q12.088-60.386 12.221-60.761Q12.354-61.136 12.354-61.499L12.834-61.499L12.834-59.972L14.069-59.972L14.069-59.526L12.834-59.526L12.834-57.358Q12.834-56.773 13.291-56.773Q13.502-56.773 13.625-56.956Q13.748-57.140 13.748-57.358L13.748-57.812L14.229-57.812L14.229-57.343Q14.229-57.069 14.082-56.847Q13.936-56.624 13.696-56.495Q13.455-56.366 13.186-56.366Q12.612-56.366 12.219-56.589Q11.826-56.812 11.826-57.343M15.053-58.222Q15.053-58.800 15.336-59.220Q15.619-59.640 16.104-59.851Q16.588-60.062 17.154-60.062Q17.596-60.062 17.932-59.950Q18.268-59.839 18.502-59.618Q18.737-59.398 18.862-59.067Q18.987-58.737 18.987-58.300Q18.987-58.144 18.834-58.116L16.162-58.116Q16.162-56.773 17.420-56.773Q17.772-56.773 18.078-56.929Q18.385-57.085 18.506-57.382Q18.580-57.511 18.666-57.511L18.834-57.511Q18.987-57.476 18.987-57.343Q18.987-57.308 18.979-57.292Q18.795-56.816 18.330-56.591Q17.865-56.366 17.291-56.366Q16.694-56.366 16.184-56.569Q15.674-56.773 15.363-57.194Q15.053-57.616 15.053-58.222M16.162-58.460L18.170-58.460Q18.170-58.999 17.922-59.347Q17.674-59.694 17.154-59.694Q16.811-59.694 16.586-59.528Q16.362-59.362 16.262-59.085Q16.162-58.808 16.162-58.460\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(9.223 13.825)\">\u003Cpath d=\"M-58.994-56.741Q-59.088-56.894-59.139-57.114Q-59.190-57.335-59.217-57.636Q-59.244-57.937-59.250-58.212Q-59.256-58.487-59.256-58.870Q-59.256-59.382-59.244-59.743Q-59.233-60.105-59.174-60.452Q-59.115-60.800-58.994-60.987Q-58.815-61.249-58.455-61.319Q-58.096-61.390-57.592-61.390Q-57.088-61.390-56.731-61.319Q-56.373-61.249-56.193-60.987Q-56.068-60.804-56.010-60.454Q-55.951-60.105-55.940-59.743Q-55.928-59.382-55.928-58.870Q-55.928-58.351-55.940-57.989Q-55.951-57.628-56.010-57.276Q-56.068-56.925-56.193-56.741Q-56.377-56.483-56.733-56.413Q-57.088-56.343-57.592-56.343Q-58.096-56.343-58.453-56.413Q-58.811-56.483-58.994-56.741M-58.455-57.230Q-58.354-57.023-58.129-56.962Q-57.904-56.901-57.592-56.901Q-57.279-56.901-57.057-56.962Q-56.834-57.023-56.729-57.230Q-56.623-57.444-56.596-57.882Q-56.568-58.319-56.568-58.964Q-56.568-60.237-56.729-60.534Q-56.834-60.722-57.053-60.775Q-57.272-60.827-57.592-60.827Q-57.912-60.827-58.129-60.776Q-58.346-60.726-58.455-60.534Q-58.615-60.237-58.615-58.964Q-58.615-58.655-58.613-58.487Q-58.611-58.319-58.594-58.034Q-58.576-57.749-58.543-57.548Q-58.510-57.347-58.455-57.230M-53.893-56.421L-55.076-56.421Q-55.272-56.444-55.322-56.659L-55.322-56.749Q-55.272-56.956-55.076-56.980L-54.803-56.980L-54.803-60.749L-55.076-60.749Q-55.272-60.773-55.322-60.987L-55.322-61.077Q-55.272-61.284-55.076-61.308L-53.506-61.308Q-53.119-61.308-52.764-61.138Q-52.408-60.968-52.186-60.657Q-51.963-60.347-51.963-59.948Q-51.963-59.722-52.045-59.513Q-52.127-59.304-52.270-59.136Q-52.412-58.968-52.604-58.847Q-52.408-58.694-52.293-58.478Q-52.178-58.261-52.178-58.023L-52.178-57.444Q-52.178-56.901-51.979-56.901Q-51.932-56.901-51.906-57.030Q-51.881-57.159-51.881-57.261Q-51.830-57.468-51.635-57.491L-51.490-57.491Q-51.295-57.472-51.244-57.261L-51.244-57.206Q-51.244-56.874-51.449-56.608Q-51.654-56.343-51.971-56.343Q-52.385-56.343-52.602-56.650Q-52.818-56.956-52.818-57.390L-52.818-57.964Q-52.818-58.253-53.027-58.421Q-53.236-58.589-53.522-58.589L-54.162-58.589L-54.162-56.980L-53.893-56.980Q-53.693-56.956-53.643-56.749L-53.643-56.659Q-53.693-56.444-53.893-56.421M-54.162-60.749L-54.162-59.148L-53.588-59.148Q-53.354-59.148-53.127-59.243Q-52.901-59.339-52.752-59.523Q-52.604-59.706-52.604-59.948Q-52.604-60.191-52.752-60.374Q-52.901-60.558-53.125-60.653Q-53.350-60.749-53.588-60.749L-54.162-60.749M-50.604-56.659L-50.604-56.749Q-50.553-56.956-50.358-56.980L-49.420-56.980L-49.420-60.749L-50.358-60.749Q-50.553-60.773-50.604-60.987L-50.604-61.077Q-50.553-61.284-50.358-61.308L-47.846-61.308Q-47.647-61.284-47.596-61.077L-47.596-60.987Q-47.647-60.773-47.846-60.749L-48.779-60.749L-48.779-56.980L-47.846-56.980Q-47.647-56.956-47.596-56.749L-47.596-56.659Q-47.647-56.444-47.846-56.421L-50.358-56.421Q-50.553-56.444-50.604-56.659M-44.869-56.343Q-45.447-56.343-45.858-56.737Q-46.268-57.132-46.469-57.720Q-46.670-58.308-46.670-58.870Q-46.670-59.421-46.469-60.011Q-46.268-60.601-45.858-60.995Q-45.447-61.390-44.869-61.390Q-44.369-61.390-43.967-61.062Q-43.920-61.148-43.877-61.220Q-43.834-61.292-43.781-61.335Q-43.729-61.378-43.654-61.390L-43.576-61.390Q-43.479-61.378-43.416-61.316Q-43.354-61.253-43.342-61.155L-43.342-59.827Q-43.354-59.733-43.416-59.667Q-43.479-59.601-43.576-59.589L-43.744-59.589Q-43.940-59.616-43.975-59.796Q-44.029-60.214-44.240-60.521Q-44.451-60.827-44.822-60.827Q-45.217-60.827-45.492-60.503Q-45.768-60.179-45.899-59.718Q-46.029-59.257-46.029-58.870Q-46.029-58.468-45.901-58.009Q-45.772-57.550-45.502-57.226Q-45.233-56.901-44.838-56.901Q-44.455-56.901-44.250-57.255Q-44.045-57.608-43.998-58.054L-44.416-58.054Q-44.627-58.077-44.662-58.292L-44.662-58.382Q-44.623-58.589-44.416-58.612L-43.213-58.612Q-43.006-58.589-42.967-58.382L-42.967-58.292Q-43.002-58.077-43.213-58.054L-43.342-58.054L-43.342-56.644Q-43.393-56.441-43.592-56.405L-43.736-56.405Q-43.932-56.437-43.983-56.644L-43.983-56.694Q-44.354-56.343-44.869-56.343M-42.111-56.659L-42.111-56.749Q-42.061-56.956-41.865-56.980L-40.928-56.980L-40.928-60.749L-41.865-60.749Q-42.061-60.773-42.111-60.987L-42.111-61.077Q-42.061-61.284-41.865-61.308L-39.354-61.308Q-39.154-61.284-39.104-61.077L-39.104-60.987Q-39.154-60.773-39.354-60.749L-40.287-60.749L-40.287-56.980L-39.354-56.980Q-39.154-56.956-39.104-56.749L-39.104-56.659Q-39.154-56.444-39.354-56.421L-41.865-56.421Q-42.061-56.444-42.111-56.659M-38.283-56.659L-38.283-56.749Q-38.233-56.956-38.033-56.980L-37.764-56.980L-37.764-60.749L-38.033-60.749Q-38.233-60.773-38.283-60.987L-38.283-61.077Q-38.233-61.284-38.033-61.308L-37.252-61.308Q-37.123-61.308-37.018-61.233Q-36.912-61.159-36.873-61.046Q-36.596-60.257-36.436-59.808Q-36.276-59.358-36.022-58.640Q-35.768-57.921-35.629-57.511Q-35.490-57.101-35.490-57.062L-35.490-60.749L-35.764-60.749Q-35.959-60.773-36.010-60.987L-36.010-61.077Q-35.959-61.284-35.764-61.308L-34.690-61.308Q-34.494-61.284-34.443-61.077L-34.443-60.987Q-34.494-60.773-34.690-60.749L-34.963-60.749L-34.963-56.659Q-35.014-56.444-35.209-56.421L-35.475-56.421Q-35.600-56.421-35.703-56.495Q-35.807-56.569-35.850-56.687Q-36.147-57.534-36.520-58.583Q-36.893-59.632-37.063-60.120Q-37.233-60.608-37.236-60.667L-37.236-56.980L-36.963-56.980Q-36.772-56.956-36.713-56.749L-36.713-56.659Q-36.772-56.444-36.963-56.421L-38.033-56.421Q-38.233-56.444-38.283-56.659M-30.740-58.558L-33.483-58.558Q-33.608-58.569-33.693-58.655Q-33.779-58.741-33.779-58.870Q-33.779-58.999-33.693-59.081Q-33.608-59.163-33.483-59.175L-30.740-59.175Q-30.615-59.163-30.533-59.081Q-30.451-58.999-30.451-58.870Q-30.451-58.741-30.533-58.655Q-30.615-58.569-30.740-58.558M-27.725-56.343Q-28.174-56.343-28.539-56.567Q-28.904-56.792-29.158-57.175Q-29.412-57.558-29.537-58.001Q-29.662-58.444-29.662-58.870Q-29.662-59.296-29.537-59.735Q-29.412-60.175-29.158-60.558Q-28.904-60.941-28.545-61.165Q-28.186-61.390-27.725-61.390Q-27.447-61.390-27.190-61.298Q-26.932-61.206-26.717-61.038L-26.584-61.276Q-26.557-61.327-26.502-61.358Q-26.447-61.390-26.389-61.390L-26.311-61.390Q-26.217-61.378-26.154-61.319Q-26.092-61.261-26.080-61.155L-26.080-59.827Q-26.092-59.726-26.154-59.663Q-26.217-59.601-26.311-59.589L-26.479-59.589Q-26.580-59.601-26.643-59.667Q-26.705-59.733-26.717-59.827Q-26.756-60.093-26.879-60.317Q-27.002-60.542-27.205-60.685Q-27.408-60.827-27.670-60.827Q-28.002-60.827-28.254-60.644Q-28.506-60.460-28.678-60.159Q-28.850-59.858-28.936-59.517Q-29.022-59.175-29.022-58.870Q-29.022-58.566-28.938-58.224Q-28.854-57.882-28.682-57.579Q-28.510-57.276-28.252-57.089Q-27.994-56.901-27.662-56.901Q-27.279-56.901-26.998-57.175Q-26.717-57.448-26.717-57.835Q-26.690-58.046-26.479-58.069L-26.311-58.069Q-26.080-58.030-26.080-57.804Q-26.080-57.487-26.217-57.216Q-26.354-56.944-26.588-56.747Q-26.822-56.550-27.113-56.446Q-27.404-56.343-27.725-56.343M-25.127-56.659L-25.127-56.749Q-25.076-56.956-24.881-56.980L-23.943-56.980L-23.943-60.749L-24.881-60.749Q-25.076-60.773-25.127-60.987L-25.127-61.077Q-25.076-61.284-24.881-61.308L-22.369-61.308Q-22.170-61.284-22.119-61.077L-22.119-60.987Q-22.170-60.773-22.369-60.749L-23.303-60.749L-23.303-56.980L-22.369-56.980Q-22.170-56.956-22.119-56.749L-22.119-56.659Q-22.170-56.444-22.369-56.421L-24.881-56.421Q-25.076-56.444-25.127-56.659M-20.436-56.659L-20.436-56.749Q-20.377-56.956-20.186-56.980L-19.697-56.980L-19.697-60.749L-20.643-60.749L-20.643-60.237Q-20.690-60.023-20.889-59.995L-21.033-59.995Q-21.233-60.023-21.283-60.237L-21.283-61.077Q-21.233-61.284-21.033-61.308L-17.721-61.308Q-17.526-61.288-17.475-61.077L-17.475-60.237Q-17.522-60.023-17.721-59.995L-17.865-59.995Q-18.065-60.023-18.115-60.237L-18.115-60.749L-19.057-60.749L-19.057-56.980L-18.568-56.980Q-18.373-56.956-18.322-56.749L-18.322-56.659Q-18.373-56.444-18.568-56.421L-20.186-56.421Q-20.377-56.444-20.436-56.659M-15.971-56.659L-15.971-56.749Q-15.920-56.956-15.725-56.980L-15.451-56.980L-15.451-58.374L-16.580-60.749L-16.838-60.749Q-17.033-60.773-17.084-60.987L-17.084-61.077Q-17.033-61.284-16.838-61.308L-15.772-61.308Q-15.565-61.284-15.526-61.077L-15.526-60.987Q-15.565-60.773-15.772-60.749L-15.869-60.749Q-15.131-59.202-15.131-59.023Q-15.119-59.230-14.397-60.749L-14.490-60.749Q-14.701-60.773-14.740-60.987L-14.740-61.077Q-14.701-61.284-14.490-61.308L-13.428-61.308Q-13.233-61.284-13.182-61.077L-13.182-60.987Q-13.233-60.773-13.428-60.749L-13.686-60.749L-14.811-58.374L-14.811-56.980L-14.541-56.980Q-14.342-56.956-14.291-56.749L-14.291-56.659Q-14.342-56.444-14.541-56.421L-15.725-56.421Q-15.920-56.444-15.971-56.659\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(117.344 12.936)\">\u003Cpath d=\"M-59.154-58.148Q-59.154-58.628-58.910-59.042Q-58.666-59.456-58.250-59.694Q-57.834-59.933-57.354-59.933Q-56.799-59.933-56.420-59.823Q-56.041-59.714-56.041-59.308Q-56.041-59.140-56.154-59.017Q-56.268-58.894-56.440-58.894Q-56.611-58.894-56.731-59.009Q-56.850-59.124-56.850-59.292L-56.850-59.351Q-57.010-59.374-57.346-59.374Q-57.674-59.374-57.942-59.204Q-58.209-59.034-58.361-58.751Q-58.514-58.468-58.514-58.148Q-58.514-57.827-58.342-57.546Q-58.170-57.265-57.885-57.103Q-57.600-56.941-57.272-56.941Q-56.959-56.941-56.832-57.044Q-56.705-57.148-56.588-57.337Q-56.471-57.526-56.354-57.542L-56.186-57.542Q-56.080-57.530-56.016-57.462Q-55.951-57.394-55.951-57.292Q-55.951-57.245-55.971-57.206Q-56.080-56.913-56.283-56.733Q-56.486-56.554-56.762-56.468Q-57.037-56.382-57.354-56.382Q-57.838-56.382-58.254-56.620Q-58.670-56.858-58.912-57.261Q-59.154-57.663-59.154-58.148M-54.850-56.659L-54.850-56.749Q-54.799-56.956-54.604-56.980L-53.565-56.980L-53.565-59.308L-54.537-59.308Q-54.736-59.331-54.787-59.550L-54.787-59.636Q-54.736-59.847-54.537-59.870L-53.170-59.870Q-52.975-59.851-52.924-59.636L-52.924-56.980L-52.010-56.980Q-51.815-56.956-51.764-56.749L-51.764-56.659Q-51.815-56.444-52.010-56.421L-54.604-56.421Q-54.799-56.444-54.850-56.659M-53.818-60.847L-53.818-60.901Q-53.818-61.073-53.682-61.194Q-53.545-61.316-53.369-61.316Q-53.197-61.316-53.061-61.194Q-52.924-61.073-52.924-60.901L-52.924-60.847Q-52.924-60.671-53.061-60.550Q-53.197-60.429-53.369-60.429Q-53.545-60.429-53.682-60.550Q-53.818-60.671-53.818-60.847M-50.022-57.526L-50.022-59.308L-50.772-59.308Q-50.971-59.331-51.022-59.550L-51.022-59.636Q-50.971-59.847-50.772-59.870L-50.022-59.870L-50.022-60.620Q-49.971-60.827-49.772-60.855L-49.627-60.855Q-49.432-60.827-49.381-60.620L-49.381-59.870L-48.022-59.870Q-47.830-59.851-47.772-59.636L-47.772-59.550Q-47.826-59.331-48.022-59.308L-49.381-59.308L-49.381-57.558Q-49.381-56.941-48.807-56.941Q-48.557-56.941-48.393-57.126Q-48.229-57.312-48.229-57.558Q-48.229-57.651-48.156-57.722Q-48.084-57.792-47.983-57.804L-47.838-57.804Q-47.639-57.780-47.588-57.573L-47.588-57.526Q-47.588-57.202-47.772-56.939Q-47.955-56.675-48.248-56.528Q-48.541-56.382-48.861-56.382Q-49.373-56.382-49.697-56.692Q-50.022-57.003-50.022-57.526M-46.631-55.300Q-46.631-55.409-46.580-55.501Q-46.529-55.593-46.438-55.644Q-46.346-55.694-46.240-55.694Q-46.131-55.694-46.039-55.644Q-45.947-55.593-45.897-55.501Q-45.846-55.409-45.846-55.300L-45.990-55.300Q-45.990-55.163-45.967-55.163Q-45.709-55.163-45.522-55.355Q-45.334-55.546-45.248-55.812L-45.037-56.421L-46.174-59.308L-46.502-59.308Q-46.697-59.331-46.752-59.550L-46.752-59.636Q-46.693-59.847-46.502-59.870L-45.342-59.870Q-45.147-59.847-45.096-59.636L-45.096-59.550Q-45.147-59.331-45.342-59.308L-45.608-59.308Q-45.272-58.460-45.027-57.806Q-44.783-57.151-44.783-57.062L-44.776-57.062Q-44.776-57.120-44.684-57.413Q-44.592-57.706-44.399-58.290Q-44.205-58.874-44.065-59.308L-44.342-59.308Q-44.553-59.331-44.592-59.550L-44.592-59.636Q-44.541-59.851-44.342-59.870L-43.190-59.870Q-42.983-59.847-42.943-59.636L-42.943-59.550Q-42.983-59.331-43.190-59.308L-43.510-59.308L-44.686-55.812Q-44.854-55.312-45.180-54.958Q-45.506-54.605-45.967-54.605Q-46.240-54.605-46.436-54.816Q-46.631-55.026-46.631-55.300\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M-56.791-56.421L-59.377-56.421L-59.377-56.718Q-59.057-56.718-58.813-56.765Q-58.568-56.812-58.568-56.980L-58.568-61.323Q-58.568-61.495-58.813-61.542Q-59.057-61.589-59.377-61.589L-59.377-61.886L-54.760-61.886L-54.529-60.038L-54.811-60.038Q-54.897-60.722-55.059-61.042Q-55.221-61.362-55.561-61.476Q-55.901-61.589-56.592-61.589L-57.401-61.589Q-57.619-61.589-57.711-61.546Q-57.803-61.503-57.803-61.323L-57.803-59.300L-57.193-59.300Q-56.768-59.300-56.570-59.366Q-56.373-59.433-56.291-59.626Q-56.209-59.819-56.209-60.237L-55.928-60.237L-55.928-58.069L-56.209-58.069Q-56.209-58.487-56.291-58.681Q-56.373-58.874-56.570-58.941Q-56.768-59.007-57.193-59.007L-57.803-59.007L-57.803-56.980Q-57.803-56.816-57.484-56.767Q-57.166-56.718-56.791-56.718\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M-52.631-56.421L-54.611-56.421L-54.611-56.718Q-54.342-56.718-54.174-56.763Q-54.006-56.808-54.006-56.980L-54.006-59.116Q-54.006-59.331-54.068-59.427Q-54.131-59.523-54.248-59.544Q-54.365-59.566-54.611-59.566L-54.611-59.862L-53.443-59.948L-53.443-59.163Q-53.365-59.374-53.213-59.560Q-53.061-59.745-52.861-59.847Q-52.662-59.948-52.436-59.948Q-52.190-59.948-51.998-59.804Q-51.807-59.659-51.807-59.429Q-51.807-59.273-51.912-59.163Q-52.018-59.054-52.174-59.054Q-52.330-59.054-52.440-59.163Q-52.549-59.273-52.549-59.429Q-52.549-59.589-52.443-59.694Q-52.768-59.694-52.982-59.466Q-53.197-59.237-53.293-58.898Q-53.389-58.558-53.389-58.253L-53.389-56.980Q-53.389-56.812-53.162-56.765Q-52.936-56.718-52.631-56.718L-52.631-56.421M-51.326-58.116Q-51.326-58.620-51.070-59.052Q-50.815-59.483-50.379-59.735Q-49.943-59.987-49.443-59.987Q-49.057-59.987-48.715-59.843Q-48.373-59.698-48.111-59.437Q-47.850-59.175-47.707-58.839Q-47.565-58.503-47.565-58.116Q-47.565-57.624-47.828-57.214Q-48.092-56.804-48.522-56.573Q-48.951-56.343-49.443-56.343Q-49.936-56.343-50.369-56.575Q-50.803-56.808-51.065-57.216Q-51.326-57.624-51.326-58.116M-49.443-56.620Q-48.986-56.620-48.734-56.843Q-48.482-57.066-48.395-57.417Q-48.307-57.769-48.307-58.214Q-48.307-58.644-48.400-58.982Q-48.494-59.319-48.748-59.526Q-49.002-59.733-49.443-59.733Q-50.092-59.733-50.336-59.317Q-50.580-58.901-50.580-58.214Q-50.580-57.769-50.492-57.417Q-50.404-57.066-50.152-56.843Q-49.900-56.620-49.443-56.620M-45.150-56.421L-47.006-56.421L-47.006-56.718Q-46.732-56.718-46.565-56.765Q-46.397-56.812-46.397-56.980L-46.397-59.116Q-46.397-59.331-46.459-59.427Q-46.522-59.523-46.641-59.544Q-46.760-59.566-47.006-59.566L-47.006-59.862L-45.815-59.948L-45.815-59.214Q-45.701-59.429-45.508-59.597Q-45.315-59.765-45.076-59.857Q-44.838-59.948-44.584-59.948Q-43.623-59.948-43.447-59.237Q-43.264-59.566-42.936-59.757Q-42.607-59.948-42.229-59.948Q-41.053-59.948-41.053-58.870L-41.053-56.980Q-41.053-56.812-40.885-56.765Q-40.717-56.718-40.447-56.718L-40.447-56.421L-42.303-56.421L-42.303-56.718Q-42.029-56.718-41.861-56.763Q-41.693-56.808-41.693-56.980L-41.693-58.855Q-41.693-59.241-41.818-59.468Q-41.943-59.694-42.295-59.694Q-42.600-59.694-42.856-59.532Q-43.111-59.370-43.260-59.101Q-43.408-58.831-43.408-58.534L-43.408-56.980Q-43.408-56.812-43.238-56.765Q-43.068-56.718-42.799-56.718L-42.799-56.421L-44.654-56.421L-44.654-56.718Q-44.381-56.718-44.213-56.765Q-44.045-56.812-44.045-56.980L-44.045-58.855Q-44.045-59.241-44.170-59.468Q-44.295-59.694-44.647-59.694Q-44.951-59.694-45.207-59.532Q-45.463-59.370-45.611-59.101Q-45.760-58.831-45.760-58.534L-45.760-56.980Q-45.760-56.812-45.590-56.765Q-45.420-56.718-45.150-56.718\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M-35.573-56.452L-36.643-59.308Q-36.709-59.487-36.840-59.530Q-36.971-59.573-37.229-59.573L-37.229-59.870L-35.549-59.870L-35.549-59.573Q-35.999-59.573-35.999-59.374Q-35.995-59.358-35.993-59.341Q-35.991-59.323-35.991-59.308L-35.198-57.214L-34.487-59.124Q-34.522-59.218-34.522-59.263Q-34.522-59.308-34.557-59.308Q-34.624-59.487-34.754-59.530Q-34.885-59.573-35.139-59.573L-35.139-59.870L-33.549-59.870L-33.549-59.573Q-33.999-59.573-33.999-59.374Q-33.995-59.355-33.993-59.337Q-33.991-59.319-33.991-59.308L-33.159-57.093L-32.405-59.093Q-32.381-59.151-32.381-59.222Q-32.381-59.382-32.518-59.478Q-32.655-59.573-32.823-59.573L-32.823-59.870L-31.436-59.870L-31.436-59.573Q-31.670-59.573-31.848-59.446Q-32.026-59.319-32.108-59.093L-33.092-56.452Q-33.147-56.343-33.260-56.343L-33.319-56.343Q-33.432-56.343-33.475-56.452L-34.334-58.726L-35.190-56.452Q-35.229-56.343-35.350-56.343L-35.405-56.343Q-35.518-56.343-35.573-56.452M-29.092-56.421L-30.948-56.421L-30.948-56.718Q-30.674-56.718-30.506-56.765Q-30.338-56.812-30.338-56.980L-30.338-61.140Q-30.338-61.355-30.401-61.450Q-30.463-61.546-30.583-61.567Q-30.702-61.589-30.948-61.589L-30.948-61.886L-29.725-61.972L-29.725-59.269Q-29.600-59.480-29.413-59.630Q-29.225-59.780-28.999-59.864Q-28.772-59.948-28.526-59.948Q-27.358-59.948-27.358-58.870L-27.358-56.980Q-27.358-56.812-27.188-56.765Q-27.018-56.718-26.749-56.718L-26.749-56.421L-28.604-56.421L-28.604-56.718Q-28.331-56.718-28.163-56.765Q-27.995-56.812-27.995-56.980L-27.995-58.855Q-27.995-59.237-28.116-59.466Q-28.237-59.694-28.588-59.694Q-28.901-59.694-29.155-59.532Q-29.409-59.370-29.555-59.101Q-29.702-58.831-29.702-58.534L-29.702-56.980Q-29.702-56.812-29.532-56.765Q-29.362-56.718-29.092-56.718L-29.092-56.421M-26.206-57.253Q-26.206-57.737-25.803-58.032Q-25.401-58.327-24.850-58.446Q-24.299-58.566-23.807-58.566L-23.807-58.855Q-23.807-59.081-23.922-59.288Q-24.038-59.495-24.235-59.614Q-24.432-59.733-24.663-59.733Q-25.088-59.733-25.374-59.628Q-25.303-59.601-25.256-59.546Q-25.209-59.491-25.184-59.421Q-25.159-59.351-25.159-59.276Q-25.159-59.171-25.209-59.079Q-25.260-58.987-25.352-58.937Q-25.444-58.886-25.549-58.886Q-25.655-58.886-25.747-58.937Q-25.838-58.987-25.889-59.079Q-25.940-59.171-25.940-59.276Q-25.940-59.694-25.551-59.841Q-25.163-59.987-24.663-59.987Q-24.331-59.987-23.977-59.857Q-23.624-59.726-23.395-59.472Q-23.167-59.218-23.167-58.870L-23.167-57.069Q-23.167-56.937-23.094-56.827Q-23.022-56.718-22.893-56.718Q-22.768-56.718-22.700-56.823Q-22.631-56.929-22.631-57.069L-22.631-57.581L-22.350-57.581L-22.350-57.069Q-22.350-56.866-22.467-56.708Q-22.584-56.550-22.766-56.466Q-22.948-56.382-23.151-56.382Q-23.381-56.382-23.534-56.554Q-23.686-56.726-23.717-56.956Q-23.877-56.675-24.186-56.509Q-24.495-56.343-24.846-56.343Q-25.358-56.343-25.782-56.566Q-26.206-56.788-26.206-57.253M-25.518-57.253Q-25.518-56.968-25.292-56.782Q-25.065-56.597-24.772-56.597Q-24.526-56.597-24.301-56.714Q-24.077-56.831-23.942-57.034Q-23.807-57.237-23.807-57.491L-23.807-58.323Q-24.073-58.323-24.358-58.269Q-24.643-58.214-24.915-58.085Q-25.186-57.956-25.352-57.749Q-25.518-57.542-25.518-57.253M-21.432-57.382L-21.432-59.573L-22.135-59.573L-22.135-59.827Q-21.780-59.827-21.538-60.060Q-21.295-60.292-21.184-60.640Q-21.073-60.987-21.073-61.343L-20.792-61.343L-20.792-59.870L-19.616-59.870L-19.616-59.573L-20.792-59.573L-20.792-57.398Q-20.792-57.077-20.672-56.849Q-20.553-56.620-20.272-56.620Q-20.092-56.620-19.975-56.743Q-19.858-56.866-19.805-57.046Q-19.752-57.226-19.752-57.398L-19.752-57.870L-19.471-57.870L-19.471-57.382Q-19.471-57.128-19.577-56.888Q-19.682-56.648-19.879-56.495Q-20.077-56.343-20.334-56.343Q-20.651-56.343-20.903-56.466Q-21.155-56.589-21.293-56.823Q-21.432-57.058-21.432-57.382\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M-15.866-58.148Q-15.866-58.644-15.616-59.069Q-15.366-59.495-14.946-59.741Q-14.526-59.987-14.026-59.987Q-13.487-59.987-13.096-59.862Q-12.706-59.737-12.706-59.323Q-12.706-59.218-12.756-59.126Q-12.807-59.034-12.899-58.983Q-12.991-58.933-13.100-58.933Q-13.206-58.933-13.297-58.983Q-13.389-59.034-13.440-59.126Q-13.491-59.218-13.491-59.323Q-13.491-59.546-13.323-59.651Q-13.545-59.710-14.018-59.710Q-14.315-59.710-14.530-59.571Q-14.745-59.433-14.876-59.202Q-15.006-58.972-15.065-58.702Q-15.124-58.433-15.124-58.148Q-15.124-57.753-14.991-57.403Q-14.858-57.054-14.586-56.837Q-14.315-56.620-13.917-56.620Q-13.542-56.620-13.266-56.837Q-12.991-57.054-12.889-57.413Q-12.874-57.476-12.811-57.476L-12.706-57.476Q-12.670-57.476-12.645-57.448Q-12.620-57.421-12.620-57.382L-12.620-57.358Q-12.752-56.878-13.137-56.610Q-13.522-56.343-14.026-56.343Q-14.389-56.343-14.723-56.480Q-15.057-56.616-15.317-56.866Q-15.577-57.116-15.721-57.452Q-15.866-57.788-15.866-58.148M-10.272-56.421L-12.049-56.421L-12.049-56.718Q-11.776-56.718-11.608-56.765Q-11.440-56.812-11.440-56.980L-11.440-59.116Q-11.440-59.331-11.497-59.427Q-11.553-59.523-11.667-59.544Q-11.780-59.566-12.026-59.566L-12.026-59.862L-10.827-59.948L-10.827-56.980Q-10.827-56.812-10.680-56.765Q-10.534-56.718-10.272-56.718L-10.272-56.421M-11.713-61.343Q-11.713-61.534-11.579-61.665Q-11.444-61.796-11.249-61.796Q-11.127-61.796-11.024-61.733Q-10.920-61.671-10.858-61.567Q-10.795-61.464-10.795-61.343Q-10.795-61.148-10.926-61.013Q-11.057-60.878-11.249-60.878Q-11.448-60.878-11.581-61.011Q-11.713-61.144-11.713-61.343M-9.147-57.382L-9.147-59.573L-9.850-59.573L-9.850-59.827Q-9.495-59.827-9.252-60.060Q-9.010-60.292-8.899-60.640Q-8.788-60.987-8.788-61.343L-8.506-61.343L-8.506-59.870L-7.331-59.870L-7.331-59.573L-8.506-59.573L-8.506-57.398Q-8.506-57.077-8.387-56.849Q-8.268-56.620-7.987-56.620Q-7.807-56.620-7.690-56.743Q-7.573-56.866-7.520-57.046Q-7.467-57.226-7.467-57.398L-7.467-57.870L-7.186-57.870L-7.186-57.382Q-7.186-57.128-7.292-56.888Q-7.397-56.648-7.594-56.495Q-7.792-56.343-8.049-56.343Q-8.366-56.343-8.618-56.466Q-8.870-56.589-9.008-56.823Q-9.147-57.058-9.147-57.382\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M-6.283-55.124Q-6.169-55.046-5.994-55.046Q-5.705-55.046-5.484-55.259Q-5.263-55.472-5.138-55.773L-4.849-56.421L-6.123-59.308Q-6.205-59.483-6.349-59.528Q-6.494-59.573-6.763-59.573L-6.763-59.870L-5.044-59.870L-5.044-59.573Q-5.466-59.573-5.466-59.390Q-5.466-59.378-5.451-59.308L-4.513-57.183L-3.681-59.093Q-3.642-59.183-3.642-59.261Q-3.642-59.401-3.744-59.487Q-3.845-59.573-3.986-59.573L-3.986-59.870L-2.634-59.870L-2.634-59.573Q-2.888-59.573-3.082-59.448Q-3.275-59.323-3.380-59.093L-4.826-55.773Q-4.939-55.519-5.105-55.296Q-5.271-55.073-5.500-54.931Q-5.728-54.788-5.994-54.788Q-6.291-54.788-6.531-54.980Q-6.771-55.171-6.771-55.460Q-6.771-55.616-6.666-55.718Q-6.560-55.819-6.412-55.819Q-6.306-55.819-6.226-55.773Q-6.146-55.726-6.099-55.648Q-6.052-55.569-6.052-55.460Q-6.052-55.339-6.113-55.251Q-6.173-55.163-6.283-55.124\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M0.717-57.253Q0.717-57.737 1.119-58.032Q1.522-58.327 2.072-58.446Q2.623-58.566 3.115-58.566L3.115-58.855Q3.115-59.081 3-59.288Q2.885-59.495 2.688-59.614Q2.490-59.733 2.260-59.733Q1.834-59.733 1.549-59.628Q1.619-59.601 1.666-59.546Q1.713-59.491 1.738-59.421Q1.764-59.351 1.764-59.276Q1.764-59.171 1.713-59.079Q1.662-58.987 1.570-58.937Q1.479-58.886 1.373-58.886Q1.268-58.886 1.176-58.937Q1.084-58.987 1.033-59.079Q0.983-59.171 0.983-59.276Q0.983-59.694 1.371-59.841Q1.760-59.987 2.260-59.987Q2.592-59.987 2.945-59.857Q3.299-59.726 3.527-59.472Q3.756-59.218 3.756-58.870L3.756-57.069Q3.756-56.937 3.828-56.827Q3.901-56.718 4.029-56.718Q4.154-56.718 4.223-56.823Q4.291-56.929 4.291-57.069L4.291-57.581L4.572-57.581L4.572-57.069Q4.572-56.866 4.455-56.708Q4.338-56.550 4.156-56.466Q3.975-56.382 3.772-56.382Q3.541-56.382 3.389-56.554Q3.236-56.726 3.205-56.956Q3.045-56.675 2.736-56.509Q2.428-56.343 2.076-56.343Q1.565-56.343 1.141-56.566Q0.717-56.788 0.717-57.253M1.404-57.253Q1.404-56.968 1.631-56.782Q1.858-56.597 2.151-56.597Q2.397-56.597 2.621-56.714Q2.846-56.831 2.981-57.034Q3.115-57.237 3.115-57.491L3.115-58.323Q2.850-58.323 2.565-58.269Q2.279-58.214 2.008-58.085Q1.736-57.956 1.570-57.749Q1.404-57.542 1.404-57.253M6.873-56.421L4.893-56.421L4.893-56.718Q5.162-56.718 5.330-56.763Q5.498-56.808 5.498-56.980L5.498-59.116Q5.498-59.331 5.436-59.427Q5.373-59.523 5.256-59.544Q5.139-59.566 4.893-59.566L4.893-59.862L6.061-59.948L6.061-59.163Q6.139-59.374 6.291-59.560Q6.444-59.745 6.643-59.847Q6.842-59.948 7.069-59.948Q7.315-59.948 7.506-59.804Q7.697-59.659 7.697-59.429Q7.697-59.273 7.592-59.163Q7.486-59.054 7.330-59.054Q7.174-59.054 7.065-59.163Q6.955-59.273 6.955-59.429Q6.955-59.589 7.061-59.694Q6.736-59.694 6.522-59.466Q6.307-59.237 6.211-58.898Q6.115-58.558 6.115-58.253L6.115-56.980Q6.115-56.812 6.342-56.765Q6.569-56.718 6.873-56.718L6.873-56.421M8.178-58.175Q8.178-58.655 8.410-59.071Q8.643-59.487 9.053-59.737Q9.463-59.987 9.940-59.987Q10.670-59.987 11.069-59.546Q11.467-59.105 11.467-58.374Q11.467-58.269 11.373-58.245L8.924-58.245L8.924-58.175Q8.924-57.765 9.045-57.409Q9.166-57.054 9.438-56.837Q9.709-56.620 10.139-56.620Q10.502-56.620 10.799-56.849Q11.096-57.077 11.197-57.429Q11.205-57.476 11.291-57.491L11.373-57.491Q11.467-57.464 11.467-57.382Q11.467-57.374 11.459-57.343Q11.397-57.116 11.258-56.933Q11.119-56.749 10.928-56.616Q10.736-56.483 10.518-56.413Q10.299-56.343 10.061-56.343Q9.690-56.343 9.352-56.480Q9.014-56.616 8.746-56.868Q8.479-57.120 8.328-57.460Q8.178-57.800 8.178-58.175M8.932-58.483L10.893-58.483Q10.893-58.788 10.791-59.079Q10.690-59.370 10.473-59.552Q10.256-59.733 9.940-59.733Q9.639-59.733 9.408-59.546Q9.178-59.358 9.055-59.067Q8.932-58.776 8.932-58.483\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M15.211-55.124Q15.325-55.046 15.500-55.046Q15.789-55.046 16.010-55.259Q16.231-55.472 16.356-55.773L16.645-56.421L15.371-59.308Q15.289-59.483 15.145-59.528Q15-59.573 14.731-59.573L14.731-59.870L16.450-59.870L16.450-59.573Q16.028-59.573 16.028-59.390Q16.028-59.378 16.043-59.308L16.981-57.183L17.813-59.093Q17.852-59.183 17.852-59.261Q17.852-59.401 17.750-59.487Q17.649-59.573 17.508-59.573L17.508-59.870L18.860-59.870L18.860-59.573Q18.606-59.573 18.412-59.448Q18.219-59.323 18.114-59.093L16.668-55.773Q16.555-55.519 16.389-55.296Q16.223-55.073 15.994-54.931Q15.766-54.788 15.500-54.788Q15.203-54.788 14.963-54.980Q14.723-55.171 14.723-55.460Q14.723-55.616 14.828-55.718Q14.934-55.819 15.082-55.819Q15.188-55.819 15.268-55.773Q15.348-55.726 15.395-55.648Q15.442-55.569 15.442-55.460Q15.442-55.339 15.381-55.251Q15.321-55.163 15.211-55.124\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M19.043-58.116Q19.043-58.620 19.299-59.052Q19.555-59.483 19.991-59.735Q20.426-59.987 20.926-59.987Q21.313-59.987 21.655-59.843Q21.996-59.698 22.258-59.437Q22.520-59.175 22.662-58.839Q22.805-58.503 22.805-58.116Q22.805-57.624 22.541-57.214Q22.278-56.804 21.848-56.573Q21.418-56.343 20.926-56.343Q20.434-56.343 20-56.575Q19.567-56.808 19.305-57.216Q19.043-57.624 19.043-58.116M20.926-56.620Q21.383-56.620 21.635-56.843Q21.887-57.066 21.975-57.417Q22.063-57.769 22.063-58.214Q22.063-58.644 21.969-58.982Q21.875-59.319 21.621-59.526Q21.367-59.733 20.926-59.733Q20.278-59.733 20.034-59.317Q19.789-58.901 19.789-58.214Q19.789-57.769 19.877-57.417Q19.965-57.066 20.217-56.843Q20.469-56.620 20.926-56.620M23.973-57.374L23.973-59.116Q23.973-59.331 23.910-59.427Q23.848-59.523 23.729-59.544Q23.610-59.566 23.364-59.566L23.364-59.862L24.610-59.948L24.610-57.398L24.610-57.374Q24.610-57.062 24.664-56.900Q24.719-56.737 24.869-56.667Q25.020-56.597 25.340-56.597Q25.770-56.597 26.043-56.935Q26.317-57.273 26.317-57.718L26.317-59.116Q26.317-59.331 26.254-59.427Q26.192-59.523 26.073-59.544Q25.953-59.566 25.707-59.566L25.707-59.862L26.953-59.948L26.953-57.163Q26.953-56.952 27.016-56.857Q27.078-56.761 27.198-56.739Q27.317-56.718 27.563-56.718L27.563-56.421L26.340-56.343L26.340-56.964Q26.172-56.675 25.891-56.509Q25.610-56.343 25.289-56.343Q23.973-56.343 23.973-57.374\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M32.763-56.421L30.931-56.421L30.931-56.718Q31.205-56.718 31.373-56.765Q31.541-56.812 31.541-56.980L31.541-61.140Q31.541-61.355 31.478-61.450Q31.416-61.546 31.297-61.567Q31.177-61.589 30.931-61.589L30.931-61.886L32.154-61.972L32.154-56.980Q32.154-56.812 32.322-56.765Q32.490-56.718 32.763-56.718L32.763-56.421M33.209-58.175Q33.209-58.655 33.441-59.071Q33.674-59.487 34.084-59.737Q34.494-59.987 34.970-59.987Q35.701-59.987 36.099-59.546Q36.498-59.105 36.498-58.374Q36.498-58.269 36.404-58.245L33.955-58.245L33.955-58.175Q33.955-57.765 34.076-57.409Q34.197-57.054 34.468-56.837Q34.740-56.620 35.170-56.620Q35.533-56.620 35.830-56.849Q36.127-57.077 36.228-57.429Q36.236-57.476 36.322-57.491L36.404-57.491Q36.498-57.464 36.498-57.382Q36.498-57.374 36.490-57.343Q36.427-57.116 36.289-56.933Q36.150-56.749 35.959-56.616Q35.767-56.483 35.549-56.413Q35.330-56.343 35.091-56.343Q34.720-56.343 34.382-56.480Q34.045-56.616 33.777-56.868Q33.509-57.120 33.359-57.460Q33.209-57.800 33.209-58.175M33.963-58.483L35.924-58.483Q35.924-58.788 35.822-59.079Q35.720-59.370 35.504-59.552Q35.287-59.733 34.970-59.733Q34.670-59.733 34.439-59.546Q34.209-59.358 34.086-59.067Q33.963-58.776 33.963-58.483M37.084-57.253Q37.084-57.737 37.486-58.032Q37.888-58.327 38.439-58.446Q38.990-58.566 39.482-58.566L39.482-58.855Q39.482-59.081 39.367-59.288Q39.252-59.495 39.054-59.614Q38.857-59.733 38.627-59.733Q38.201-59.733 37.916-59.628Q37.986-59.601 38.033-59.546Q38.080-59.491 38.105-59.421Q38.131-59.351 38.131-59.276Q38.131-59.171 38.080-59.079Q38.029-58.987 37.937-58.937Q37.845-58.886 37.740-58.886Q37.634-58.886 37.543-58.937Q37.451-58.987 37.400-59.079Q37.349-59.171 37.349-59.276Q37.349-59.694 37.738-59.841Q38.127-59.987 38.627-59.987Q38.959-59.987 39.312-59.857Q39.666-59.726 39.894-59.472Q40.123-59.218 40.123-58.870L40.123-57.069Q40.123-56.937 40.195-56.827Q40.267-56.718 40.396-56.718Q40.521-56.718 40.590-56.823Q40.658-56.929 40.658-57.069L40.658-57.581L40.939-57.581L40.939-57.069Q40.939-56.866 40.822-56.708Q40.705-56.550 40.523-56.466Q40.341-56.382 40.138-56.382Q39.908-56.382 39.756-56.554Q39.603-56.726 39.572-56.956Q39.412-56.675 39.103-56.509Q38.795-56.343 38.443-56.343Q37.931-56.343 37.507-56.566Q37.084-56.788 37.084-57.253M37.771-57.253Q37.771-56.968 37.998-56.782Q38.224-56.597 38.517-56.597Q38.763-56.597 38.988-56.714Q39.213-56.831 39.347-57.034Q39.482-57.237 39.482-57.491L39.482-58.323Q39.216-58.323 38.931-58.269Q38.646-58.214 38.375-58.085Q38.103-57.956 37.937-57.749Q37.771-57.542 37.771-57.253\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 13.38)\">\u003Cpath d=\"M42.803-56.452L41.580-59.308Q41.498-59.483 41.354-59.528Q41.209-59.573 40.940-59.573L40.940-59.870L42.651-59.870L42.651-59.573Q42.229-59.573 42.229-59.390Q42.229-59.355 42.244-59.308L43.190-57.116L44.030-59.093Q44.069-59.171 44.069-59.261Q44.069-59.401 43.963-59.487Q43.858-59.573 43.717-59.573L43.717-59.870L45.069-59.870L45.069-59.573Q44.545-59.573 44.330-59.093L43.205-56.452Q43.143-56.343 43.037-56.343L42.971-56.343Q42.858-56.343 42.803-56.452M47.342-56.421L45.565-56.421L45.565-56.718Q45.838-56.718 46.006-56.765Q46.174-56.812 46.174-56.980L46.174-59.116Q46.174-59.331 46.118-59.427Q46.061-59.523 45.948-59.544Q45.834-59.566 45.588-59.566L45.588-59.862L46.787-59.948L46.787-56.980Q46.787-56.812 46.934-56.765Q47.080-56.718 47.342-56.718L47.342-56.421M45.901-61.343Q45.901-61.534 46.035-61.665Q46.170-61.796 46.366-61.796Q46.487-61.796 46.590-61.733Q46.694-61.671 46.756-61.567Q46.819-61.464 46.819-61.343Q46.819-61.148 46.688-61.013Q46.557-60.878 46.366-60.878Q46.166-60.878 46.034-61.011Q45.901-61.144 45.901-61.343M49.772-56.421L47.916-56.421L47.916-56.718Q48.190-56.718 48.358-56.765Q48.526-56.812 48.526-56.980L48.526-59.116Q48.526-59.331 48.463-59.427Q48.401-59.523 48.282-59.544Q48.162-59.566 47.916-59.566L47.916-59.862L49.108-59.948L49.108-59.214Q49.221-59.429 49.414-59.597Q49.608-59.765 49.846-59.857Q50.084-59.948 50.338-59.948Q51.506-59.948 51.506-58.870L51.506-56.980Q51.506-56.812 51.676-56.765Q51.846-56.718 52.116-56.718L52.116-56.421L50.260-56.421L50.260-56.718Q50.534-56.718 50.702-56.765Q50.869-56.812 50.869-56.980L50.869-58.855Q50.869-59.237 50.748-59.466Q50.627-59.694 50.276-59.694Q49.963-59.694 49.709-59.532Q49.455-59.370 49.309-59.101Q49.162-58.831 49.162-58.534L49.162-56.980Q49.162-56.812 49.332-56.765Q49.502-56.718 49.772-56.718L49.772-56.421M52.561-55.812Q52.561-56.093 52.772-56.304Q52.983-56.515 53.268-56.605Q53.112-56.730 53.034-56.919Q52.955-57.108 52.955-57.308Q52.955-57.663 53.186-57.956Q52.819-58.296 52.819-58.765Q52.819-59.116 53.022-59.386Q53.225-59.655 53.545-59.802Q53.866-59.948 54.209-59.948Q54.729-59.948 55.100-59.667Q55.463-60.038 56.010-60.038Q56.190-60.038 56.317-59.911Q56.444-59.784 56.444-59.605Q56.444-59.499 56.366-59.421Q56.287-59.343 56.178-59.343Q56.069-59.343 55.993-59.419Q55.916-59.495 55.916-59.605Q55.916-59.706 55.955-59.757Q55.963-59.765 55.967-59.771Q55.971-59.776 55.971-59.780Q55.596-59.780 55.276-59.526Q55.596-59.187 55.596-58.765Q55.596-58.495 55.479-58.278Q55.362-58.062 55.157-57.903Q54.952-57.745 54.709-57.663Q54.467-57.581 54.209-57.581Q53.991-57.581 53.778-57.640Q53.565-57.698 53.369-57.819Q53.276-57.679 53.276-57.499Q53.276-57.292 53.412-57.140Q53.549-56.987 53.756-56.987L54.452-56.987Q54.940-56.987 55.352-56.903Q55.764-56.819 56.043-56.562Q56.323-56.304 56.323-55.812Q56.323-55.448 56.002-55.216Q55.682-54.983 55.241-54.882Q54.799-54.780 54.444-54.780Q54.088-54.780 53.645-54.882Q53.202-54.983 52.881-55.216Q52.561-55.448 52.561-55.812M53.065-55.812Q53.065-55.616 53.209-55.468Q53.354-55.319 53.567-55.230Q53.780-55.140 54.020-55.093Q54.260-55.046 54.444-55.046Q54.686-55.046 55.016-55.124Q55.346-55.202 55.582-55.376Q55.819-55.550 55.819-55.812Q55.819-56.218 55.409-56.327Q54.998-56.437 54.436-56.437L53.756-56.437Q53.487-56.437 53.276-56.259Q53.065-56.081 53.065-55.812M54.209-57.847Q54.932-57.847 54.932-58.765Q54.932-59.687 54.209-59.687Q53.483-59.687 53.483-58.765Q53.483-57.847 54.209-57.847M57.994-56.886Q57.994-57.077 58.127-57.210Q58.260-57.343 58.455-57.343Q58.647-57.343 58.780-57.210Q58.912-57.077 58.912-56.886Q58.912-56.687 58.780-56.554Q58.647-56.421 58.455-56.421Q58.260-56.421 58.127-56.554Q57.994-56.687 57.994-56.886M58.311-58.062L58.311-58.460Q58.311-58.858 58.440-59.259Q58.569-59.659 58.815-59.995Q58.893-60.101 59.059-60.276Q59.225-60.452 59.293-60.589Q59.362-60.726 59.362-60.964Q59.362-61.452 59.159-61.624Q58.955-61.796 58.455-61.796Q58.147-61.796 57.869-61.685Q57.592-61.573 57.440-61.351Q57.612-61.351 57.719-61.237Q57.827-61.124 57.827-60.956Q57.827-60.855 57.776-60.763Q57.725-60.671 57.635-60.618Q57.545-60.566 57.432-60.566Q57.323-60.566 57.233-60.618Q57.143-60.671 57.092-60.763Q57.041-60.855 57.041-60.956Q57.041-61.304 57.252-61.554Q57.463-61.804 57.787-61.929Q58.112-62.054 58.455-62.054Q59.116-62.054 59.610-61.804Q60.104-61.554 60.104-60.956Q60.104-60.706 59.981-60.491Q59.858-60.276 59.647-60.140Q59.334-59.937 59.102-59.681Q58.869-59.425 58.731-59.108Q58.592-58.792 58.592-58.444L58.592-58.062Q58.592-58.026 58.565-57.999Q58.537-57.972 58.506-57.972L58.401-57.972Q58.369-57.972 58.340-57.997Q58.311-58.023 58.311-58.062\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(9.223 30.897)\">\u003Cpath d=\"M-59.576-56.659L-59.576-56.749Q-59.537-56.956-59.330-56.980L-59.080-56.980L-59.080-60.749L-59.330-60.749Q-59.537-60.773-59.576-60.987L-59.576-61.077Q-59.537-61.284-59.330-61.308L-57.514-61.308Q-56.963-61.308-56.568-60.915Q-56.174-60.523-55.979-59.944Q-55.783-59.366-55.783-58.819Q-55.783-58.292-55.983-57.728Q-56.182-57.163-56.574-56.792Q-56.967-56.421-57.514-56.421L-59.330-56.421Q-59.537-56.444-59.576-56.659M-58.440-60.749L-58.440-56.980L-57.674-56.980Q-57.080-56.980-56.752-57.575Q-56.424-58.171-56.424-58.819Q-56.424-59.226-56.559-59.675Q-56.693-60.124-56.979-60.437Q-57.264-60.749-57.674-60.749L-58.440-60.749M-55.283-56.659L-55.283-56.749Q-55.244-56.956-55.033-56.980L-54.725-56.980L-54.725-60.749L-55.033-60.749Q-55.244-60.773-55.283-60.987L-55.283-61.077Q-55.244-61.284-55.033-61.308L-51.826-61.308Q-51.631-61.284-51.580-61.077L-51.580-60.237Q-51.631-60.023-51.826-59.995L-51.971-59.995Q-52.166-60.023-52.221-60.237L-52.221-60.749L-54.084-60.749L-54.084-59.230L-53.108-59.230L-53.108-59.444Q-53.057-59.651-52.858-59.679L-52.713-59.679Q-52.518-59.651-52.467-59.444L-52.467-58.460Q-52.518-58.245-52.713-58.222L-52.858-58.222Q-53.057-58.245-53.108-58.460L-53.108-58.667L-54.084-58.667L-54.084-56.980L-52.041-56.980L-52.041-57.620Q-51.990-57.827-51.795-57.855L-51.651-57.855Q-51.455-57.827-51.404-57.620L-51.404-56.659Q-51.455-56.441-51.651-56.421L-55.033-56.421Q-55.244-56.444-55.283-56.659M-50.795-56.581L-50.795-57.812Q-50.768-58.023-50.557-58.046L-50.389-58.046Q-50.295-58.034-50.233-57.976Q-50.170-57.917-50.158-57.812Q-50.158-57.319-49.793-57.110Q-49.428-56.901-48.893-56.901Q-48.658-56.901-48.455-57.009Q-48.252-57.116-48.129-57.310Q-48.006-57.503-48.006-57.733Q-48.006-58.030-48.201-58.253Q-48.397-58.476-48.686-58.542L-49.693-58.773Q-49.994-58.843-50.246-59.028Q-50.498-59.214-50.647-59.482Q-50.795-59.749-50.795-60.062Q-50.795-60.441-50.586-60.745Q-50.377-61.050-50.035-61.220Q-49.693-61.390-49.318-61.390Q-48.643-61.390-48.213-61.062L-48.143-61.245Q-48.068-61.366-47.932-61.390L-47.854-61.390Q-47.756-61.378-47.693-61.316Q-47.631-61.253-47.619-61.155L-47.619-59.925Q-47.643-59.714-47.854-59.687L-48.022-59.687Q-48.217-59.710-48.252-59.894Q-48.315-60.347-48.582-60.587Q-48.850-60.827-49.311-60.827Q-49.518-60.827-49.725-60.739Q-49.932-60.651-50.065-60.483Q-50.197-60.316-50.197-60.101Q-50.197-59.847-50.004-59.650Q-49.811-59.452-49.541-59.390L-48.533-59.163Q-48.205-59.085-47.951-58.882Q-47.697-58.679-47.551-58.384Q-47.404-58.089-47.404-57.773Q-47.404-57.374-47.608-57.048Q-47.811-56.722-48.154-56.532Q-48.498-56.343-48.885-56.343Q-49.686-56.343-50.205-56.659L-50.276-56.483Q-50.346-56.366-50.486-56.343L-50.557-56.343Q-50.772-56.366-50.795-56.581M-45.912-56.659L-45.912-56.749Q-45.854-56.956-45.662-56.980L-45.174-56.980L-45.174-60.749L-46.119-60.749L-46.119-60.237Q-46.166-60.023-46.365-59.995L-46.510-59.995Q-46.709-60.023-46.760-60.237L-46.760-61.077Q-46.709-61.284-46.510-61.308L-43.197-61.308Q-43.002-61.288-42.951-61.077L-42.951-60.237Q-42.998-60.023-43.197-59.995L-43.342-59.995Q-43.541-60.023-43.592-60.237L-43.592-60.749L-44.533-60.749L-44.533-56.980L-44.045-56.980Q-43.850-56.956-43.799-56.749L-43.799-56.659Q-43.850-56.444-44.045-56.421L-45.662-56.421Q-45.854-56.444-45.912-56.659M-39.233-58.558L-41.975-58.558Q-42.100-58.569-42.186-58.655Q-42.272-58.741-42.272-58.870Q-42.272-58.999-42.186-59.081Q-42.100-59.163-41.975-59.175L-39.233-59.175Q-39.108-59.163-39.026-59.081Q-38.943-58.999-38.943-58.870Q-38.943-58.741-39.026-58.655Q-39.108-58.569-39.233-58.558M-36.217-56.343Q-36.666-56.343-37.031-56.567Q-37.397-56.792-37.651-57.175Q-37.904-57.558-38.029-58.001Q-38.154-58.444-38.154-58.870Q-38.154-59.296-38.029-59.735Q-37.904-60.175-37.651-60.558Q-37.397-60.941-37.037-61.165Q-36.678-61.390-36.217-61.390Q-35.940-61.390-35.682-61.298Q-35.424-61.206-35.209-61.038L-35.076-61.276Q-35.049-61.327-34.994-61.358Q-34.940-61.390-34.881-61.390L-34.803-61.390Q-34.709-61.378-34.647-61.319Q-34.584-61.261-34.572-61.155L-34.572-59.827Q-34.584-59.726-34.647-59.663Q-34.709-59.601-34.803-59.589L-34.971-59.589Q-35.072-59.601-35.135-59.667Q-35.197-59.733-35.209-59.827Q-35.248-60.093-35.371-60.317Q-35.494-60.542-35.697-60.685Q-35.901-60.827-36.162-60.827Q-36.494-60.827-36.746-60.644Q-36.998-60.460-37.170-60.159Q-37.342-59.858-37.428-59.517Q-37.514-59.175-37.514-58.870Q-37.514-58.566-37.430-58.224Q-37.346-57.882-37.174-57.579Q-37.002-57.276-36.744-57.089Q-36.486-56.901-36.154-56.901Q-35.772-56.901-35.490-57.175Q-35.209-57.448-35.209-57.835Q-35.182-58.046-34.971-58.069L-34.803-58.069Q-34.572-58.030-34.572-57.804Q-34.572-57.487-34.709-57.216Q-34.846-56.944-35.080-56.747Q-35.315-56.550-35.606-56.446Q-35.897-56.343-36.217-56.343M-33.619-56.659L-33.619-56.749Q-33.568-56.956-33.373-56.980L-32.436-56.980L-32.436-60.749L-33.373-60.749Q-33.568-60.773-33.619-60.987L-33.619-61.077Q-33.568-61.284-33.373-61.308L-30.861-61.308Q-30.662-61.284-30.611-61.077L-30.611-60.987Q-30.662-60.773-30.861-60.749L-31.795-60.749L-31.795-56.980L-30.861-56.980Q-30.662-56.956-30.611-56.749L-30.611-56.659Q-30.662-56.444-30.861-56.421L-33.373-56.421Q-33.568-56.444-33.619-56.659M-28.928-56.659L-28.928-56.749Q-28.869-56.956-28.678-56.980L-28.190-56.980L-28.190-60.749L-29.135-60.749L-29.135-60.237Q-29.182-60.023-29.381-59.995L-29.526-59.995Q-29.725-60.023-29.776-60.237L-29.776-61.077Q-29.725-61.284-29.526-61.308L-26.213-61.308Q-26.018-61.288-25.967-61.077L-25.967-60.237Q-26.014-60.023-26.213-59.995L-26.358-59.995Q-26.557-60.023-26.608-60.237L-26.608-60.749L-27.549-60.749L-27.549-56.980L-27.061-56.980Q-26.865-56.956-26.815-56.749L-26.815-56.659Q-26.865-56.444-27.061-56.421L-28.678-56.421Q-28.869-56.444-28.928-56.659M-24.463-56.659L-24.463-56.749Q-24.412-56.956-24.217-56.980L-23.943-56.980L-23.943-58.374L-25.072-60.749L-25.330-60.749Q-25.526-60.773-25.576-60.987L-25.576-61.077Q-25.526-61.284-25.330-61.308L-24.264-61.308Q-24.057-61.284-24.018-61.077L-24.018-60.987Q-24.057-60.773-24.264-60.749L-24.361-60.749Q-23.623-59.202-23.623-59.023Q-23.611-59.230-22.889-60.749L-22.983-60.749Q-23.193-60.773-23.233-60.987L-23.233-61.077Q-23.193-61.284-22.983-61.308L-21.920-61.308Q-21.725-61.284-21.674-61.077L-21.674-60.987Q-21.725-60.773-21.920-60.749L-22.178-60.749L-23.303-58.374L-23.303-56.980L-23.033-56.980Q-22.834-56.956-22.783-56.749L-22.783-56.659Q-22.834-56.444-23.033-56.421L-24.217-56.421Q-24.412-56.444-24.463-56.659\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(117.344 30.008)\">\u003Cpath d=\"M-59.154-58.148Q-59.154-58.628-58.910-59.042Q-58.666-59.456-58.250-59.694Q-57.834-59.933-57.354-59.933Q-56.799-59.933-56.420-59.823Q-56.041-59.714-56.041-59.308Q-56.041-59.140-56.154-59.017Q-56.268-58.894-56.440-58.894Q-56.611-58.894-56.731-59.009Q-56.850-59.124-56.850-59.292L-56.850-59.351Q-57.010-59.374-57.346-59.374Q-57.674-59.374-57.942-59.204Q-58.209-59.034-58.361-58.751Q-58.514-58.468-58.514-58.148Q-58.514-57.827-58.342-57.546Q-58.170-57.265-57.885-57.103Q-57.600-56.941-57.272-56.941Q-56.959-56.941-56.832-57.044Q-56.705-57.148-56.588-57.337Q-56.471-57.526-56.354-57.542L-56.186-57.542Q-56.080-57.530-56.016-57.462Q-55.951-57.394-55.951-57.292Q-55.951-57.245-55.971-57.206Q-56.080-56.913-56.283-56.733Q-56.486-56.554-56.762-56.468Q-57.037-56.382-57.354-56.382Q-57.838-56.382-58.254-56.620Q-58.670-56.858-58.912-57.261Q-59.154-57.663-59.154-58.148M-54.850-56.659L-54.850-56.749Q-54.799-56.956-54.604-56.980L-53.565-56.980L-53.565-59.308L-54.537-59.308Q-54.736-59.331-54.787-59.550L-54.787-59.636Q-54.736-59.847-54.537-59.870L-53.170-59.870Q-52.975-59.851-52.924-59.636L-52.924-56.980L-52.010-56.980Q-51.815-56.956-51.764-56.749L-51.764-56.659Q-51.815-56.444-52.010-56.421L-54.604-56.421Q-54.799-56.444-54.850-56.659M-53.818-60.847L-53.818-60.901Q-53.818-61.073-53.682-61.194Q-53.545-61.316-53.369-61.316Q-53.197-61.316-53.061-61.194Q-52.924-61.073-52.924-60.901L-52.924-60.847Q-52.924-60.671-53.061-60.550Q-53.197-60.429-53.369-60.429Q-53.545-60.429-53.682-60.550Q-53.818-60.671-53.818-60.847M-50.022-57.526L-50.022-59.308L-50.772-59.308Q-50.971-59.331-51.022-59.550L-51.022-59.636Q-50.971-59.847-50.772-59.870L-50.022-59.870L-50.022-60.620Q-49.971-60.827-49.772-60.855L-49.627-60.855Q-49.432-60.827-49.381-60.620L-49.381-59.870L-48.022-59.870Q-47.830-59.851-47.772-59.636L-47.772-59.550Q-47.826-59.331-48.022-59.308L-49.381-59.308L-49.381-57.558Q-49.381-56.941-48.807-56.941Q-48.557-56.941-48.393-57.126Q-48.229-57.312-48.229-57.558Q-48.229-57.651-48.156-57.722Q-48.084-57.792-47.983-57.804L-47.838-57.804Q-47.639-57.780-47.588-57.573L-47.588-57.526Q-47.588-57.202-47.772-56.939Q-47.955-56.675-48.248-56.528Q-48.541-56.382-48.861-56.382Q-49.373-56.382-49.697-56.692Q-50.022-57.003-50.022-57.526M-46.631-55.300Q-46.631-55.409-46.580-55.501Q-46.529-55.593-46.438-55.644Q-46.346-55.694-46.240-55.694Q-46.131-55.694-46.039-55.644Q-45.947-55.593-45.897-55.501Q-45.846-55.409-45.846-55.300L-45.990-55.300Q-45.990-55.163-45.967-55.163Q-45.709-55.163-45.522-55.355Q-45.334-55.546-45.248-55.812L-45.037-56.421L-46.174-59.308L-46.502-59.308Q-46.697-59.331-46.752-59.550L-46.752-59.636Q-46.693-59.847-46.502-59.870L-45.342-59.870Q-45.147-59.847-45.096-59.636L-45.096-59.550Q-45.147-59.331-45.342-59.308L-45.608-59.308Q-45.272-58.460-45.027-57.806Q-44.783-57.151-44.783-57.062L-44.776-57.062Q-44.776-57.120-44.684-57.413Q-44.592-57.706-44.399-58.290Q-44.205-58.874-44.065-59.308L-44.342-59.308Q-44.553-59.331-44.592-59.550L-44.592-59.636Q-44.541-59.851-44.342-59.870L-43.190-59.870Q-42.983-59.847-42.943-59.636L-42.943-59.550Q-42.983-59.331-43.190-59.308L-43.510-59.308L-44.686-55.812Q-44.854-55.312-45.180-54.958Q-45.506-54.605-45.967-54.605Q-46.240-54.605-46.436-54.816Q-46.631-55.026-46.631-55.300\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(162.869 30.453)\">\u003Cpath d=\"M-57.139-56.366L-58.842-61.323Q-58.908-61.499-59.084-61.544Q-59.260-61.589-59.553-61.589L-59.553-61.886L-57.408-61.886L-57.408-61.589Q-58.065-61.589-58.065-61.374Q-58.061-61.362-58.059-61.353Q-58.057-61.343-58.057-61.323L-56.705-57.390L-55.506-60.894L-55.651-61.323Q-55.721-61.499-55.895-61.544Q-56.068-61.589-56.361-61.589L-56.361-61.886L-54.225-61.886L-54.225-61.589Q-54.873-61.589-54.873-61.374Q-54.873-61.331-54.865-61.323L-53.514-57.390L-52.240-61.108Q-52.225-61.155-52.225-61.191Q-52.225-61.331-52.334-61.423Q-52.443-61.515-52.600-61.552Q-52.756-61.589-52.889-61.589L-52.889-61.886L-51.154-61.886L-51.154-61.589Q-51.772-61.589-51.943-61.108L-53.568-56.366Q-53.588-56.316-53.631-56.284Q-53.674-56.253-53.729-56.253L-53.783-56.253Q-53.904-56.253-53.943-56.366L-55.354-60.460L-56.760-56.366Q-56.779-56.316-56.822-56.284Q-56.865-56.253-56.920-56.253L-56.979-56.253Q-57.092-56.253-57.139-56.366M-48.818-56.421L-50.674-56.421L-50.674-56.718Q-50.401-56.718-50.233-56.765Q-50.065-56.812-50.065-56.980L-50.065-61.140Q-50.065-61.355-50.127-61.450Q-50.190-61.546-50.309-61.567Q-50.428-61.589-50.674-61.589L-50.674-61.886L-49.451-61.972L-49.451-59.269Q-49.326-59.480-49.139-59.630Q-48.951-59.780-48.725-59.864Q-48.498-59.948-48.252-59.948Q-47.084-59.948-47.084-58.870L-47.084-56.980Q-47.084-56.812-46.914-56.765Q-46.744-56.718-46.475-56.718L-46.475-56.421L-48.330-56.421L-48.330-56.718Q-48.057-56.718-47.889-56.765Q-47.721-56.812-47.721-56.980L-47.721-58.855Q-47.721-59.237-47.842-59.466Q-47.963-59.694-48.315-59.694Q-48.627-59.694-48.881-59.532Q-49.135-59.370-49.281-59.101Q-49.428-58.831-49.428-58.534L-49.428-56.980Q-49.428-56.812-49.258-56.765Q-49.088-56.718-48.818-56.718L-48.818-56.421M-46.029-58.175Q-46.029-58.655-45.797-59.071Q-45.565-59.487-45.154-59.737Q-44.744-59.987-44.268-59.987Q-43.537-59.987-43.139-59.546Q-42.740-59.105-42.740-58.374Q-42.740-58.269-42.834-58.245L-45.283-58.245L-45.283-58.175Q-45.283-57.765-45.162-57.409Q-45.041-57.054-44.770-56.837Q-44.498-56.620-44.068-56.620Q-43.705-56.620-43.408-56.849Q-43.111-57.077-43.010-57.429Q-43.002-57.476-42.916-57.491L-42.834-57.491Q-42.740-57.464-42.740-57.382Q-42.740-57.374-42.748-57.343Q-42.811-57.116-42.949-56.933Q-43.088-56.749-43.279-56.616Q-43.471-56.483-43.690-56.413Q-43.908-56.343-44.147-56.343Q-44.518-56.343-44.856-56.480Q-45.193-56.616-45.461-56.868Q-45.729-57.120-45.879-57.460Q-46.029-57.800-46.029-58.175M-45.276-58.483L-43.315-58.483Q-43.315-58.788-43.416-59.079Q-43.518-59.370-43.734-59.552Q-43.951-59.733-44.268-59.733Q-44.568-59.733-44.799-59.546Q-45.029-59.358-45.152-59.067Q-45.276-58.776-45.276-58.483M-40.244-56.421L-42.225-56.421L-42.225-56.718Q-41.955-56.718-41.787-56.763Q-41.619-56.808-41.619-56.980L-41.619-59.116Q-41.619-59.331-41.682-59.427Q-41.744-59.523-41.861-59.544Q-41.979-59.566-42.225-59.566L-42.225-59.862L-41.057-59.948L-41.057-59.163Q-40.979-59.374-40.826-59.560Q-40.674-59.745-40.475-59.847Q-40.276-59.948-40.049-59.948Q-39.803-59.948-39.611-59.804Q-39.420-59.659-39.420-59.429Q-39.420-59.273-39.526-59.163Q-39.631-59.054-39.787-59.054Q-39.943-59.054-40.053-59.163Q-40.162-59.273-40.162-59.429Q-40.162-59.589-40.057-59.694Q-40.381-59.694-40.596-59.466Q-40.811-59.237-40.906-58.898Q-41.002-58.558-41.002-58.253L-41.002-56.980Q-41.002-56.812-40.776-56.765Q-40.549-56.718-40.244-56.718L-40.244-56.421M-38.940-58.175Q-38.940-58.655-38.707-59.071Q-38.475-59.487-38.065-59.737Q-37.654-59.987-37.178-59.987Q-36.447-59.987-36.049-59.546Q-35.651-59.105-35.651-58.374Q-35.651-58.269-35.744-58.245L-38.193-58.245L-38.193-58.175Q-38.193-57.765-38.072-57.409Q-37.951-57.054-37.680-56.837Q-37.408-56.620-36.979-56.620Q-36.615-56.620-36.318-56.849Q-36.022-57.077-35.920-57.429Q-35.912-57.476-35.826-57.491L-35.744-57.491Q-35.651-57.464-35.651-57.382Q-35.651-57.374-35.658-57.343Q-35.721-57.116-35.859-56.933Q-35.998-56.749-36.190-56.616Q-36.381-56.483-36.600-56.413Q-36.818-56.343-37.057-56.343Q-37.428-56.343-37.766-56.480Q-38.104-56.616-38.371-56.868Q-38.639-57.120-38.789-57.460Q-38.940-57.800-38.940-58.175M-38.186-58.483L-36.225-58.483Q-36.225-58.788-36.326-59.079Q-36.428-59.370-36.645-59.552Q-36.861-59.733-37.178-59.733Q-37.479-59.733-37.709-59.546Q-37.940-59.358-38.063-59.067Q-38.186-58.776-38.186-58.483\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 30.453)\">\u003Cpath d=\"M-32.225-57.253Q-32.225-57.737-31.823-58.032Q-31.420-58.327-30.870-58.446Q-30.319-58.566-29.827-58.566L-29.827-58.855Q-29.827-59.081-29.942-59.288Q-30.057-59.495-30.254-59.614Q-30.452-59.733-30.682-59.733Q-31.108-59.733-31.393-59.628Q-31.323-59.601-31.276-59.546Q-31.229-59.491-31.204-59.421Q-31.178-59.351-31.178-59.276Q-31.178-59.171-31.229-59.079Q-31.280-58.987-31.372-58.937Q-31.463-58.886-31.569-58.886Q-31.674-58.886-31.766-58.937Q-31.858-58.987-31.909-59.079Q-31.959-59.171-31.959-59.276Q-31.959-59.694-31.571-59.841Q-31.182-59.987-30.682-59.987Q-30.350-59.987-29.997-59.857Q-29.643-59.726-29.415-59.472Q-29.186-59.218-29.186-58.870L-29.186-57.069Q-29.186-56.937-29.114-56.827Q-29.041-56.718-28.913-56.718Q-28.788-56.718-28.719-56.823Q-28.651-56.929-28.651-57.069L-28.651-57.581L-28.370-57.581L-28.370-57.069Q-28.370-56.866-28.487-56.708Q-28.604-56.550-28.786-56.466Q-28.967-56.382-29.170-56.382Q-29.401-56.382-29.553-56.554Q-29.706-56.726-29.737-56.956Q-29.897-56.675-30.206-56.509Q-30.514-56.343-30.866-56.343Q-31.377-56.343-31.801-56.566Q-32.225-56.788-32.225-57.253M-31.538-57.253Q-31.538-56.968-31.311-56.782Q-31.084-56.597-30.791-56.597Q-30.545-56.597-30.321-56.714Q-30.096-56.831-29.961-57.034Q-29.827-57.237-29.827-57.491L-29.827-58.323Q-30.092-58.323-30.377-58.269Q-30.663-58.214-30.934-58.085Q-31.206-57.956-31.372-57.749Q-31.538-57.542-31.538-57.253M-26.069-56.421L-28.049-56.421L-28.049-56.718Q-27.780-56.718-27.612-56.763Q-27.444-56.808-27.444-56.980L-27.444-59.116Q-27.444-59.331-27.506-59.427Q-27.569-59.523-27.686-59.544Q-27.803-59.566-28.049-59.566L-28.049-59.862L-26.881-59.948L-26.881-59.163Q-26.803-59.374-26.651-59.560Q-26.498-59.745-26.299-59.847Q-26.100-59.948-25.873-59.948Q-25.627-59.948-25.436-59.804Q-25.245-59.659-25.245-59.429Q-25.245-59.273-25.350-59.163Q-25.456-59.054-25.612-59.054Q-25.768-59.054-25.877-59.163Q-25.987-59.273-25.987-59.429Q-25.987-59.589-25.881-59.694Q-26.206-59.694-26.420-59.466Q-26.635-59.237-26.731-58.898Q-26.827-58.558-26.827-58.253L-26.827-56.980Q-26.827-56.812-26.600-56.765Q-26.373-56.718-26.069-56.718L-26.069-56.421M-24.764-58.175Q-24.764-58.655-24.532-59.071Q-24.299-59.487-23.889-59.737Q-23.479-59.987-23.002-59.987Q-22.272-59.987-21.873-59.546Q-21.475-59.105-21.475-58.374Q-21.475-58.269-21.569-58.245L-24.018-58.245L-24.018-58.175Q-24.018-57.765-23.897-57.409Q-23.776-57.054-23.504-56.837Q-23.233-56.620-22.803-56.620Q-22.440-56.620-22.143-56.849Q-21.846-57.077-21.745-57.429Q-21.737-57.476-21.651-57.491L-21.569-57.491Q-21.475-57.464-21.475-57.382Q-21.475-57.374-21.483-57.343Q-21.545-57.116-21.684-56.933Q-21.823-56.749-22.014-56.616Q-22.206-56.483-22.424-56.413Q-22.643-56.343-22.881-56.343Q-23.252-56.343-23.590-56.480Q-23.928-56.616-24.196-56.868Q-24.463-57.120-24.614-57.460Q-24.764-57.800-24.764-58.175M-24.010-58.483L-22.049-58.483Q-22.049-58.788-22.151-59.079Q-22.252-59.370-22.469-59.552Q-22.686-59.733-23.002-59.733Q-23.303-59.733-23.534-59.546Q-23.764-59.358-23.887-59.067Q-24.010-58.776-24.010-58.483\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 30.453)\">\u003Cpath d=\"M-17.731-55.124Q-17.617-55.046-17.442-55.046Q-17.153-55.046-16.932-55.259Q-16.711-55.472-16.586-55.773L-16.297-56.421L-17.571-59.308Q-17.653-59.483-17.797-59.528Q-17.942-59.573-18.211-59.573L-18.211-59.870L-16.492-59.870L-16.492-59.573Q-16.914-59.573-16.914-59.390Q-16.914-59.378-16.899-59.308L-15.961-57.183L-15.129-59.093Q-15.090-59.183-15.090-59.261Q-15.090-59.401-15.192-59.487Q-15.293-59.573-15.434-59.573L-15.434-59.870L-14.082-59.870L-14.082-59.573Q-14.336-59.573-14.530-59.448Q-14.723-59.323-14.828-59.093L-16.274-55.773Q-16.387-55.519-16.553-55.296Q-16.719-55.073-16.948-54.931Q-17.176-54.788-17.442-54.788Q-17.739-54.788-17.979-54.980Q-18.219-55.171-18.219-55.460Q-18.219-55.616-18.114-55.718Q-18.008-55.819-17.860-55.819Q-17.754-55.819-17.674-55.773Q-17.594-55.726-17.547-55.648Q-17.500-55.569-17.500-55.460Q-17.500-55.339-17.561-55.251Q-17.621-55.163-17.731-55.124\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 30.453)\">\u003Cpath d=\"M-13.899-58.116Q-13.899-58.620-13.643-59.052Q-13.387-59.483-12.951-59.735Q-12.516-59.987-12.016-59.987Q-11.629-59.987-11.287-59.843Q-10.946-59.698-10.684-59.437Q-10.422-59.175-10.280-58.839Q-10.137-58.503-10.137-58.116Q-10.137-57.624-10.401-57.214Q-10.664-56.804-11.094-56.573Q-11.524-56.343-12.016-56.343Q-12.508-56.343-12.942-56.575Q-13.375-56.808-13.637-57.216Q-13.899-57.624-13.899-58.116M-12.016-56.620Q-11.559-56.620-11.307-56.843Q-11.055-57.066-10.967-57.417Q-10.879-57.769-10.879-58.214Q-10.879-58.644-10.973-58.982Q-11.067-59.319-11.321-59.526Q-11.575-59.733-12.016-59.733Q-12.664-59.733-12.908-59.317Q-13.153-58.901-13.153-58.214Q-13.153-57.769-13.065-57.417Q-12.977-57.066-12.725-56.843Q-12.473-56.620-12.016-56.620M-8.969-57.374L-8.969-59.116Q-8.969-59.331-9.032-59.427Q-9.094-59.523-9.213-59.544Q-9.332-59.566-9.578-59.566L-9.578-59.862L-8.332-59.948L-8.332-57.398L-8.332-57.374Q-8.332-57.062-8.278-56.900Q-8.223-56.737-8.073-56.667Q-7.922-56.597-7.602-56.597Q-7.172-56.597-6.899-56.935Q-6.625-57.273-6.625-57.718L-6.625-59.116Q-6.625-59.331-6.688-59.427Q-6.750-59.523-6.869-59.544Q-6.989-59.566-7.235-59.566L-7.235-59.862L-5.989-59.948L-5.989-57.163Q-5.989-56.952-5.926-56.857Q-5.864-56.761-5.744-56.739Q-5.625-56.718-5.379-56.718L-5.379-56.421L-6.602-56.343L-6.602-56.964Q-6.770-56.675-7.051-56.509Q-7.332-56.343-7.653-56.343Q-8.969-56.343-8.969-57.374\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 30.453)\">\u003Cpath d=\"M-2.094-55.812Q-2.094-56.093-1.883-56.304Q-1.672-56.515-1.387-56.605Q-1.543-56.730-1.621-56.919Q-1.699-57.108-1.699-57.308Q-1.699-57.663-1.469-57.956Q-1.836-58.296-1.836-58.765Q-1.836-59.116-1.633-59.386Q-1.430-59.655-1.109-59.802Q-0.789-59.948-0.445-59.948Q0.074-59.948 0.445-59.667Q0.809-60.038 1.356-60.038Q1.535-60.038 1.662-59.911Q1.789-59.784 1.789-59.605Q1.789-59.499 1.711-59.421Q1.633-59.343 1.523-59.343Q1.414-59.343 1.338-59.419Q1.262-59.495 1.262-59.605Q1.262-59.706 1.301-59.757Q1.309-59.765 1.313-59.771Q1.316-59.776 1.316-59.780Q0.941-59.780 0.621-59.526Q0.941-59.187 0.941-58.765Q0.941-58.495 0.824-58.278Q0.707-58.062 0.502-57.903Q0.297-57.745 0.055-57.663Q-0.187-57.581-0.445-57.581Q-0.664-57.581-0.877-57.640Q-1.090-57.698-1.285-57.819Q-1.379-57.679-1.379-57.499Q-1.379-57.292-1.242-57.140Q-1.105-56.987-0.898-56.987L-0.203-56.987Q0.285-56.987 0.697-56.903Q1.109-56.819 1.389-56.562Q1.668-56.304 1.668-55.812Q1.668-55.448 1.348-55.216Q1.027-54.983 0.586-54.882Q0.145-54.780-0.211-54.780Q-0.566-54.780-1.010-54.882Q-1.453-54.983-1.773-55.216Q-2.094-55.448-2.094-55.812M-1.590-55.812Q-1.590-55.616-1.445-55.468Q-1.301-55.319-1.088-55.230Q-0.875-55.140-0.635-55.093Q-0.394-55.046-0.211-55.046Q0.031-55.046 0.361-55.124Q0.691-55.202 0.928-55.376Q1.164-55.550 1.164-55.812Q1.164-56.218 0.754-56.327Q0.344-56.437-0.219-56.437L-0.898-56.437Q-1.168-56.437-1.379-56.259Q-1.590-56.081-1.590-55.812M-0.445-57.847Q0.277-57.847 0.277-58.765Q0.277-59.687-0.445-59.687Q-1.172-59.687-1.172-58.765Q-1.172-57.847-0.445-57.847M2.152-58.116Q2.152-58.620 2.408-59.052Q2.664-59.483 3.100-59.735Q3.535-59.987 4.035-59.987Q4.422-59.987 4.764-59.843Q5.106-59.698 5.367-59.437Q5.629-59.175 5.772-58.839Q5.914-58.503 5.914-58.116Q5.914-57.624 5.650-57.214Q5.387-56.804 4.957-56.573Q4.527-56.343 4.035-56.343Q3.543-56.343 3.109-56.575Q2.676-56.808 2.414-57.216Q2.152-57.624 2.152-58.116M4.035-56.620Q4.492-56.620 4.744-56.843Q4.996-57.066 5.084-57.417Q5.172-57.769 5.172-58.214Q5.172-58.644 5.078-58.982Q4.984-59.319 4.731-59.526Q4.477-59.733 4.035-59.733Q3.387-59.733 3.143-59.317Q2.898-58.901 2.898-58.214Q2.898-57.769 2.986-57.417Q3.074-57.066 3.326-56.843Q3.578-56.620 4.035-56.620M8.258-56.421L6.481-56.421L6.481-56.718Q6.754-56.718 6.922-56.765Q7.090-56.812 7.090-56.980L7.090-59.116Q7.090-59.331 7.033-59.427Q6.977-59.523 6.863-59.544Q6.750-59.566 6.504-59.566L6.504-59.862L7.703-59.948L7.703-56.980Q7.703-56.812 7.850-56.765Q7.996-56.718 8.258-56.718L8.258-56.421M6.816-61.343Q6.816-61.534 6.951-61.665Q7.086-61.796 7.281-61.796Q7.402-61.796 7.506-61.733Q7.609-61.671 7.672-61.567Q7.734-61.464 7.734-61.343Q7.734-61.148 7.604-61.013Q7.473-60.878 7.281-60.878Q7.082-60.878 6.949-61.011Q6.816-61.144 6.816-61.343M10.688-56.421L8.832-56.421L8.832-56.718Q9.105-56.718 9.273-56.765Q9.441-56.812 9.441-56.980L9.441-59.116Q9.441-59.331 9.379-59.427Q9.316-59.523 9.197-59.544Q9.078-59.566 8.832-59.566L8.832-59.862L10.023-59.948L10.023-59.214Q10.137-59.429 10.330-59.597Q10.523-59.765 10.762-59.857Q11-59.948 11.254-59.948Q12.422-59.948 12.422-58.870L12.422-56.980Q12.422-56.812 12.592-56.765Q12.762-56.718 13.031-56.718L13.031-56.421L11.176-56.421L11.176-56.718Q11.449-56.718 11.617-56.765Q11.785-56.812 11.785-56.980L11.785-58.855Q11.785-59.237 11.664-59.466Q11.543-59.694 11.191-59.694Q10.879-59.694 10.625-59.532Q10.371-59.370 10.225-59.101Q10.078-58.831 10.078-58.534L10.078-56.980Q10.078-56.812 10.248-56.765Q10.418-56.718 10.688-56.718L10.688-56.421M13.477-55.812Q13.477-56.093 13.688-56.304Q13.898-56.515 14.184-56.605Q14.027-56.730 13.949-56.919Q13.871-57.108 13.871-57.308Q13.871-57.663 14.102-57.956Q13.734-58.296 13.734-58.765Q13.734-59.116 13.938-59.386Q14.141-59.655 14.461-59.802Q14.781-59.948 15.125-59.948Q15.645-59.948 16.016-59.667Q16.379-60.038 16.926-60.038Q17.105-60.038 17.232-59.911Q17.359-59.784 17.359-59.605Q17.359-59.499 17.281-59.421Q17.203-59.343 17.094-59.343Q16.984-59.343 16.908-59.419Q16.832-59.495 16.832-59.605Q16.832-59.706 16.871-59.757Q16.879-59.765 16.883-59.771Q16.887-59.776 16.887-59.780Q16.512-59.780 16.191-59.526Q16.512-59.187 16.512-58.765Q16.512-58.495 16.395-58.278Q16.277-58.062 16.072-57.903Q15.867-57.745 15.625-57.663Q15.383-57.581 15.125-57.581Q14.906-57.581 14.693-57.640Q14.480-57.698 14.285-57.819Q14.191-57.679 14.191-57.499Q14.191-57.292 14.328-57.140Q14.465-56.987 14.672-56.987L15.367-56.987Q15.855-56.987 16.268-56.903Q16.680-56.819 16.959-56.562Q17.238-56.304 17.238-55.812Q17.238-55.448 16.918-55.216Q16.598-54.983 16.156-54.882Q15.715-54.780 15.359-54.780Q15.004-54.780 14.561-54.882Q14.117-54.983 13.797-55.216Q13.477-55.448 13.477-55.812M13.980-55.812Q13.980-55.616 14.125-55.468Q14.270-55.319 14.482-55.230Q14.695-55.140 14.936-55.093Q15.176-55.046 15.359-55.046Q15.602-55.046 15.932-55.124Q16.262-55.202 16.498-55.376Q16.734-55.550 16.734-55.812Q16.734-56.218 16.324-56.327Q15.914-56.437 15.352-56.437L14.672-56.437Q14.402-56.437 14.191-56.259Q13.980-56.081 13.980-55.812M15.125-57.847Q15.848-57.847 15.848-58.765Q15.848-59.687 15.125-59.687Q14.398-59.687 14.398-58.765Q14.398-57.847 15.125-57.847M18.910-56.886Q18.910-57.077 19.043-57.210Q19.176-57.343 19.371-57.343Q19.563-57.343 19.695-57.210Q19.828-57.077 19.828-56.886Q19.828-56.687 19.695-56.554Q19.563-56.421 19.371-56.421Q19.176-56.421 19.043-56.554Q18.910-56.687 18.910-56.886M19.227-58.062L19.227-58.460Q19.227-58.858 19.355-59.259Q19.484-59.659 19.730-59.995Q19.809-60.101 19.975-60.276Q20.141-60.452 20.209-60.589Q20.277-60.726 20.277-60.964Q20.277-61.452 20.074-61.624Q19.871-61.796 19.371-61.796Q19.063-61.796 18.785-61.685Q18.508-61.573 18.355-61.351Q18.527-61.351 18.635-61.237Q18.742-61.124 18.742-60.956Q18.742-60.855 18.691-60.763Q18.641-60.671 18.551-60.618Q18.461-60.566 18.348-60.566Q18.238-60.566 18.148-60.618Q18.059-60.671 18.008-60.763Q17.957-60.855 17.957-60.956Q17.957-61.304 18.168-61.554Q18.379-61.804 18.703-61.929Q19.027-62.054 19.371-62.054Q20.031-62.054 20.525-61.804Q21.020-61.554 21.020-60.956Q21.020-60.706 20.897-60.491Q20.773-60.276 20.563-60.140Q20.250-59.937 20.018-59.681Q19.785-59.425 19.647-59.108Q19.508-58.792 19.508-58.444L19.508-58.062Q19.508-58.026 19.480-57.999Q19.453-57.972 19.422-57.972L19.316-57.972Q19.285-57.972 19.256-57.997Q19.227-58.023 19.227-58.062\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(9.223 47.969)\">\u003Cpath d=\"M-59.576-56.659L-59.576-56.749Q-59.537-56.956-59.330-56.980L-59.080-56.980L-59.080-60.749L-59.330-60.749Q-59.537-60.773-59.576-60.987L-59.576-61.077Q-59.537-61.284-59.330-61.308L-57.514-61.308Q-56.963-61.308-56.568-60.915Q-56.174-60.523-55.979-59.944Q-55.783-59.366-55.783-58.819Q-55.783-58.292-55.983-57.728Q-56.182-57.163-56.574-56.792Q-56.967-56.421-57.514-56.421L-59.330-56.421Q-59.537-56.444-59.576-56.659M-58.440-60.749L-58.440-56.980L-57.674-56.980Q-57.080-56.980-56.752-57.575Q-56.424-58.171-56.424-58.819Q-56.424-59.226-56.559-59.675Q-56.693-60.124-56.979-60.437Q-57.264-60.749-57.674-60.749L-58.440-60.749M-55.283-56.659L-55.283-56.749Q-55.244-56.956-55.033-56.980L-54.725-56.980L-54.725-60.749L-55.033-60.749Q-55.244-60.773-55.283-60.987L-55.283-61.077Q-55.244-61.284-55.033-61.308L-51.826-61.308Q-51.631-61.284-51.580-61.077L-51.580-60.237Q-51.631-60.023-51.826-59.995L-51.971-59.995Q-52.166-60.023-52.221-60.237L-52.221-60.749L-54.084-60.749L-54.084-59.230L-53.108-59.230L-53.108-59.444Q-53.057-59.651-52.858-59.679L-52.713-59.679Q-52.518-59.651-52.467-59.444L-52.467-58.460Q-52.518-58.245-52.713-58.222L-52.858-58.222Q-53.057-58.245-53.108-58.460L-53.108-58.667L-54.084-58.667L-54.084-56.980L-52.041-56.980L-52.041-57.620Q-51.990-57.827-51.795-57.855L-51.651-57.855Q-51.455-57.827-51.404-57.620L-51.404-56.659Q-51.455-56.441-51.651-56.421L-55.033-56.421Q-55.244-56.444-55.283-56.659M-51.037-56.659L-51.037-56.749Q-50.998-56.956-50.787-56.980L-50.479-56.980L-50.479-60.749L-50.787-60.749Q-50.998-60.773-51.037-60.987L-51.037-61.077Q-50.998-61.284-50.787-61.308L-48.838-61.308Q-48.440-61.308-48.094-61.107Q-47.748-60.905-47.541-60.560Q-47.334-60.214-47.334-59.812Q-47.334-59.405-47.539-59.062Q-47.744-58.718-48.090-58.513Q-48.436-58.308-48.838-58.308L-49.838-58.308L-49.838-56.980L-49.526-56.980Q-49.315-56.956-49.276-56.749L-49.276-56.659Q-49.315-56.444-49.526-56.421L-50.787-56.421Q-50.998-56.444-51.037-56.659M-49.838-60.749L-49.838-58.870L-48.998-58.870Q-48.736-58.870-48.500-58.993Q-48.264-59.116-48.119-59.331Q-47.975-59.546-47.975-59.812Q-47.975-60.081-48.119-60.292Q-48.264-60.503-48.500-60.626Q-48.736-60.749-48.998-60.749L-49.838-60.749M-46.744-56.659L-46.744-56.749Q-46.686-56.960-46.494-56.980L-46.272-56.980L-45.318-61.163Q-45.295-61.280-45.199-61.355Q-45.104-61.429-44.990-61.429L-44.717-61.429Q-44.604-61.429-44.508-61.353Q-44.412-61.276-44.389-61.163L-43.440-56.980L-43.213-56.980Q-43.006-56.956-42.967-56.749L-42.967-56.659Q-43.018-56.444-43.213-56.421L-44.295-56.421Q-44.490-56.444-44.541-56.659L-44.541-56.749Q-44.490-56.956-44.295-56.980L-44.096-56.980Q-44.225-57.546-44.248-57.628L-45.463-57.628Q-45.506-57.444-45.615-56.980L-45.416-56.980Q-45.217-56.956-45.166-56.749L-45.166-56.659Q-45.217-56.444-45.416-56.421L-46.494-56.421Q-46.701-56.444-46.744-56.659M-45.342-58.191L-44.365-58.191Q-44.846-60.323-44.846-60.667L-44.854-60.667Q-44.854-60.483-44.994-59.800Q-45.135-59.116-45.342-58.191M-41.154-56.421L-42.338-56.421Q-42.533-56.444-42.584-56.659L-42.584-56.749Q-42.533-56.956-42.338-56.980L-42.065-56.980L-42.065-60.749L-42.338-60.749Q-42.533-60.773-42.584-60.987L-42.584-61.077Q-42.533-61.284-42.338-61.308L-40.768-61.308Q-40.381-61.308-40.026-61.138Q-39.670-60.968-39.447-60.657Q-39.225-60.347-39.225-59.948Q-39.225-59.722-39.307-59.513Q-39.389-59.304-39.531-59.136Q-39.674-58.968-39.865-58.847Q-39.670-58.694-39.555-58.478Q-39.440-58.261-39.440-58.023L-39.440-57.444Q-39.440-56.901-39.240-56.901Q-39.193-56.901-39.168-57.030Q-39.143-57.159-39.143-57.261Q-39.092-57.468-38.897-57.491L-38.752-57.491Q-38.557-57.472-38.506-57.261L-38.506-57.206Q-38.506-56.874-38.711-56.608Q-38.916-56.343-39.233-56.343Q-39.647-56.343-39.863-56.650Q-40.080-56.956-40.080-57.390L-40.080-57.964Q-40.080-58.253-40.289-58.421Q-40.498-58.589-40.783-58.589L-41.424-58.589L-41.424-56.980L-41.154-56.980Q-40.955-56.956-40.904-56.749L-40.904-56.659Q-40.955-56.444-41.154-56.421M-41.424-60.749L-41.424-59.148L-40.850-59.148Q-40.615-59.148-40.389-59.243Q-40.162-59.339-40.014-59.523Q-39.865-59.706-39.865-59.948Q-39.865-60.191-40.014-60.374Q-40.162-60.558-40.387-60.653Q-40.611-60.749-40.850-60.749L-41.424-60.749M-37.420-56.659L-37.420-56.749Q-37.361-56.956-37.170-56.980L-36.682-56.980L-36.682-60.749L-37.627-60.749L-37.627-60.237Q-37.674-60.023-37.873-59.995L-38.018-59.995Q-38.217-60.023-38.268-60.237L-38.268-61.077Q-38.217-61.284-38.018-61.308L-34.705-61.308Q-34.510-61.288-34.459-61.077L-34.459-60.237Q-34.506-60.023-34.705-59.995L-34.850-59.995Q-35.049-60.023-35.100-60.237L-35.100-60.749L-36.041-60.749L-36.041-56.980L-35.553-56.980Q-35.358-56.956-35.307-56.749L-35.307-56.659Q-35.358-56.444-35.553-56.421L-37.170-56.421Q-37.361-56.444-37.420-56.659M-30.740-58.558L-33.483-58.558Q-33.608-58.569-33.693-58.655Q-33.779-58.741-33.779-58.870Q-33.779-58.999-33.693-59.081Q-33.608-59.163-33.483-59.175L-30.740-59.175Q-30.615-59.163-30.533-59.081Q-30.451-58.999-30.451-58.870Q-30.451-58.741-30.533-58.655Q-30.615-58.569-30.740-58.558M-28.928-56.659L-28.928-56.749Q-28.869-56.956-28.678-56.980L-28.190-56.980L-28.190-60.749L-29.135-60.749L-29.135-60.237Q-29.182-60.023-29.381-59.995L-29.526-59.995Q-29.725-60.023-29.776-60.237L-29.776-61.077Q-29.725-61.284-29.526-61.308L-26.213-61.308Q-26.018-61.288-25.967-61.077L-25.967-60.237Q-26.014-60.023-26.213-59.995L-26.358-59.995Q-26.557-60.023-26.608-60.237L-26.608-60.749L-27.549-60.749L-27.549-56.980L-27.061-56.980Q-26.865-56.956-26.815-56.749L-26.815-56.659Q-26.865-56.444-27.061-56.421L-28.678-56.421Q-28.869-56.444-28.928-56.659M-25.127-56.659L-25.127-56.749Q-25.076-56.956-24.881-56.980L-23.943-56.980L-23.943-60.749L-24.881-60.749Q-25.076-60.773-25.127-60.987L-25.127-61.077Q-25.076-61.284-24.881-61.308L-22.369-61.308Q-22.170-61.284-22.119-61.077L-22.119-60.987Q-22.170-60.773-22.369-60.749L-23.303-60.749L-23.303-56.980L-22.369-56.980Q-22.170-56.956-22.119-56.749L-22.119-56.659Q-22.170-56.444-22.369-56.421L-24.881-56.421Q-25.076-56.444-25.127-56.659M-21.385-56.659L-21.385-56.749Q-21.334-56.956-21.139-56.980L-20.963-56.980L-20.963-60.749L-21.139-60.749Q-21.334-60.773-21.385-60.987L-21.385-61.077Q-21.334-61.284-21.139-61.308L-20.451-61.308Q-20.322-61.308-20.223-61.232Q-20.123-61.155-20.084-61.046Q-20.053-60.941-19.830-60.251Q-19.608-59.562-19.492-59.165Q-19.377-58.769-19.377-58.694Q-19.369-58.773-19.315-58.970Q-19.260-59.167-19.147-59.538Q-19.033-59.909-18.887-60.372Q-18.740-60.835-18.674-61.046Q-18.635-61.155-18.531-61.232Q-18.428-61.308-18.307-61.308L-17.619-61.308Q-17.420-61.284-17.369-61.077L-17.369-60.987Q-17.420-60.773-17.619-60.749L-17.795-60.749L-17.795-56.980L-17.619-56.980Q-17.420-56.956-17.369-56.749L-17.369-56.659Q-17.420-56.444-17.619-56.421L-18.498-56.421Q-18.693-56.444-18.744-56.659L-18.744-56.749Q-18.693-56.956-18.498-56.980L-18.322-56.980L-18.322-60.667Q-18.322-60.608-18.406-60.310Q-18.490-60.011-18.594-59.679Q-18.697-59.347-18.828-58.939Q-18.959-58.530-19.026-58.316Q-19.065-58.202-19.164-58.128Q-19.264-58.054-19.377-58.054Q-19.490-58.054-19.590-58.128Q-19.690-58.202-19.729-58.316Q-19.795-58.530-19.930-58.948Q-20.065-59.366-20.201-59.810Q-20.338-60.253-20.383-60.417Q-20.428-60.581-20.436-60.667L-20.436-56.980L-20.260-56.980Q-20.061-56.956-20.010-56.749L-20.010-56.659Q-20.061-56.444-20.260-56.421L-21.139-56.421Q-21.334-56.444-21.385-56.659M-17.068-56.659L-17.068-56.749Q-17.029-56.956-16.818-56.980L-16.510-56.980L-16.510-60.749L-16.818-60.749Q-17.029-60.773-17.068-60.987L-17.068-61.077Q-17.029-61.284-16.818-61.308L-13.611-61.308Q-13.416-61.284-13.365-61.077L-13.365-60.237Q-13.416-60.023-13.611-59.995L-13.756-59.995Q-13.951-60.023-14.006-60.237L-14.006-60.749L-15.869-60.749L-15.869-59.230L-14.893-59.230L-14.893-59.444Q-14.842-59.651-14.643-59.679L-14.498-59.679Q-14.303-59.651-14.252-59.444L-14.252-58.460Q-14.303-58.245-14.498-58.222L-14.643-58.222Q-14.842-58.245-14.893-58.460L-14.893-58.667L-15.869-58.667L-15.869-56.980L-13.826-56.980L-13.826-57.620Q-13.776-57.827-13.580-57.855L-13.436-57.855Q-13.240-57.827-13.190-57.620L-13.190-56.659Q-13.240-56.441-13.436-56.421L-16.818-56.421Q-17.029-56.444-17.068-56.659\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(117.344 47.969)\">\u003Cpath d=\"M-58.514-57.526L-58.514-59.308L-59.264-59.308Q-59.463-59.331-59.514-59.550L-59.514-59.636Q-59.463-59.847-59.264-59.870L-58.514-59.870L-58.514-60.620Q-58.463-60.827-58.264-60.855L-58.119-60.855Q-57.924-60.827-57.873-60.620L-57.873-59.870L-56.514-59.870Q-56.322-59.851-56.264-59.636L-56.264-59.550Q-56.318-59.331-56.514-59.308L-57.873-59.308L-57.873-57.558Q-57.873-56.941-57.299-56.941Q-57.049-56.941-56.885-57.126Q-56.721-57.312-56.721-57.558Q-56.721-57.651-56.649-57.722Q-56.576-57.792-56.475-57.804L-56.330-57.804Q-56.131-57.780-56.080-57.573L-56.080-57.526Q-56.080-57.202-56.264-56.939Q-56.447-56.675-56.740-56.528Q-57.033-56.382-57.354-56.382Q-57.865-56.382-58.190-56.692Q-58.514-57.003-58.514-57.526M-54.850-56.659L-54.850-56.749Q-54.799-56.956-54.604-56.980L-53.565-56.980L-53.565-59.308L-54.537-59.308Q-54.736-59.331-54.787-59.550L-54.787-59.636Q-54.736-59.847-54.537-59.870L-53.170-59.870Q-52.975-59.851-52.924-59.636L-52.924-56.980L-52.010-56.980Q-51.815-56.956-51.764-56.749L-51.764-56.659Q-51.815-56.444-52.010-56.421L-54.604-56.421Q-54.799-56.444-54.850-56.659M-53.818-60.847L-53.818-60.901Q-53.818-61.073-53.682-61.194Q-53.545-61.316-53.369-61.316Q-53.197-61.316-53.061-61.194Q-52.924-61.073-52.924-60.901L-52.924-60.847Q-52.924-60.671-53.061-60.550Q-53.197-60.429-53.369-60.429Q-53.545-60.429-53.682-60.550Q-53.818-60.671-53.818-60.847M-51.276-56.659L-51.276-56.749Q-51.225-56.960-51.029-56.980L-50.830-56.980L-50.830-59.308L-51.029-59.308Q-51.236-59.331-51.276-59.550L-51.276-59.636Q-51.225-59.851-51.029-59.870L-50.549-59.870Q-50.358-59.847-50.299-59.636Q-49.979-59.909-49.565-59.909Q-49.373-59.909-49.205-59.800Q-49.037-59.691-48.963-59.519Q-48.787-59.710-48.565-59.810Q-48.342-59.909-48.100-59.909Q-47.682-59.909-47.527-59.567Q-47.373-59.226-47.373-58.757L-47.373-56.980L-47.174-56.980Q-46.963-56.956-46.924-56.749L-46.924-56.659Q-46.975-56.444-47.174-56.421L-47.990-56.421Q-48.186-56.444-48.236-56.659L-48.236-56.749Q-48.186-56.956-47.990-56.980L-47.901-56.980L-47.901-58.726Q-47.901-59.351-48.151-59.351Q-48.483-59.351-48.660-59.040Q-48.838-58.730-48.838-58.366L-48.838-56.980L-48.635-56.980Q-48.428-56.956-48.389-56.749L-48.389-56.659Q-48.440-56.444-48.635-56.421L-49.451-56.421Q-49.651-56.444-49.701-56.659L-49.701-56.749Q-49.651-56.956-49.451-56.980L-49.365-56.980L-49.365-58.726Q-49.365-59.351-49.611-59.351Q-49.943-59.351-50.121-59.038Q-50.299-58.726-50.299-58.366L-50.299-56.980L-50.100-56.980Q-49.893-56.956-49.854-56.749L-49.854-56.659Q-49.904-56.441-50.100-56.421L-51.029-56.421Q-51.236-56.444-51.276-56.659M-43.463-57.909L-45.904-57.909Q-45.850-57.632-45.652-57.409Q-45.455-57.187-45.178-57.064Q-44.901-56.941-44.615-56.941Q-44.143-56.941-43.920-57.230Q-43.912-57.241-43.856-57.347Q-43.799-57.452-43.750-57.495Q-43.701-57.538-43.608-57.550L-43.463-57.550Q-43.272-57.530-43.213-57.316L-43.213-57.261Q-43.279-56.960-43.510-56.763Q-43.740-56.566-44.053-56.474Q-44.365-56.382-44.670-56.382Q-45.154-56.382-45.594-56.610Q-46.033-56.839-46.301-57.239Q-46.568-57.640-46.568-58.132L-46.568-58.191Q-46.568-58.659-46.322-59.062Q-46.076-59.464-45.668-59.698Q-45.260-59.933-44.791-59.933Q-44.287-59.933-43.934-59.710Q-43.580-59.487-43.397-59.099Q-43.213-58.710-43.213-58.206L-43.213-58.148Q-43.272-57.933-43.463-57.909M-45.897-58.460L-43.869-58.460Q-43.916-58.870-44.154-59.122Q-44.393-59.374-44.791-59.374Q-45.186-59.374-45.492-59.112Q-45.799-58.851-45.897-58.460\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M-57.139-56.366L-58.842-61.323Q-58.908-61.499-59.084-61.544Q-59.260-61.589-59.553-61.589L-59.553-61.886L-57.408-61.886L-57.408-61.589Q-58.065-61.589-58.065-61.374Q-58.061-61.362-58.059-61.353Q-58.057-61.343-58.057-61.323L-56.705-57.390L-55.506-60.894L-55.651-61.323Q-55.721-61.499-55.895-61.544Q-56.068-61.589-56.361-61.589L-56.361-61.886L-54.225-61.886L-54.225-61.589Q-54.873-61.589-54.873-61.374Q-54.873-61.331-54.865-61.323L-53.514-57.390L-52.240-61.108Q-52.225-61.155-52.225-61.191Q-52.225-61.331-52.334-61.423Q-52.443-61.515-52.600-61.552Q-52.756-61.589-52.889-61.589L-52.889-61.886L-51.154-61.886L-51.154-61.589Q-51.772-61.589-51.943-61.108L-53.568-56.366Q-53.588-56.316-53.631-56.284Q-53.674-56.253-53.729-56.253L-53.783-56.253Q-53.904-56.253-53.943-56.366L-55.354-60.460L-56.760-56.366Q-56.779-56.316-56.822-56.284Q-56.865-56.253-56.920-56.253L-56.979-56.253Q-57.092-56.253-57.139-56.366M-48.818-56.421L-50.674-56.421L-50.674-56.718Q-50.401-56.718-50.233-56.765Q-50.065-56.812-50.065-56.980L-50.065-61.140Q-50.065-61.355-50.127-61.450Q-50.190-61.546-50.309-61.567Q-50.428-61.589-50.674-61.589L-50.674-61.886L-49.451-61.972L-49.451-59.269Q-49.326-59.480-49.139-59.630Q-48.951-59.780-48.725-59.864Q-48.498-59.948-48.252-59.948Q-47.084-59.948-47.084-58.870L-47.084-56.980Q-47.084-56.812-46.914-56.765Q-46.744-56.718-46.475-56.718L-46.475-56.421L-48.330-56.421L-48.330-56.718Q-48.057-56.718-47.889-56.765Q-47.721-56.812-47.721-56.980L-47.721-58.855Q-47.721-59.237-47.842-59.466Q-47.963-59.694-48.315-59.694Q-48.627-59.694-48.881-59.532Q-49.135-59.370-49.281-59.101Q-49.428-58.831-49.428-58.534L-49.428-56.980Q-49.428-56.812-49.258-56.765Q-49.088-56.718-48.818-56.718L-48.818-56.421M-46.029-58.175Q-46.029-58.655-45.797-59.071Q-45.565-59.487-45.154-59.737Q-44.744-59.987-44.268-59.987Q-43.537-59.987-43.139-59.546Q-42.740-59.105-42.740-58.374Q-42.740-58.269-42.834-58.245L-45.283-58.245L-45.283-58.175Q-45.283-57.765-45.162-57.409Q-45.041-57.054-44.770-56.837Q-44.498-56.620-44.068-56.620Q-43.705-56.620-43.408-56.849Q-43.111-57.077-43.010-57.429Q-43.002-57.476-42.916-57.491L-42.834-57.491Q-42.740-57.464-42.740-57.382Q-42.740-57.374-42.748-57.343Q-42.811-57.116-42.949-56.933Q-43.088-56.749-43.279-56.616Q-43.471-56.483-43.690-56.413Q-43.908-56.343-44.147-56.343Q-44.518-56.343-44.856-56.480Q-45.193-56.616-45.461-56.868Q-45.729-57.120-45.879-57.460Q-46.029-57.800-46.029-58.175M-45.276-58.483L-43.315-58.483Q-43.315-58.788-43.416-59.079Q-43.518-59.370-43.734-59.552Q-43.951-59.733-44.268-59.733Q-44.568-59.733-44.799-59.546Q-45.029-59.358-45.152-59.067Q-45.276-58.776-45.276-58.483M-40.322-56.421L-42.178-56.421L-42.178-56.718Q-41.904-56.718-41.736-56.765Q-41.568-56.812-41.568-56.980L-41.568-59.116Q-41.568-59.331-41.631-59.427Q-41.693-59.523-41.813-59.544Q-41.932-59.566-42.178-59.566L-42.178-59.862L-40.986-59.948L-40.986-59.214Q-40.873-59.429-40.680-59.597Q-40.486-59.765-40.248-59.857Q-40.010-59.948-39.756-59.948Q-38.588-59.948-38.588-58.870L-38.588-56.980Q-38.588-56.812-38.418-56.765Q-38.248-56.718-37.979-56.718L-37.979-56.421L-39.834-56.421L-39.834-56.718Q-39.561-56.718-39.393-56.765Q-39.225-56.812-39.225-56.980L-39.225-58.855Q-39.225-59.237-39.346-59.466Q-39.467-59.694-39.818-59.694Q-40.131-59.694-40.385-59.532Q-40.639-59.370-40.785-59.101Q-40.932-58.831-40.932-58.534L-40.932-56.980Q-40.932-56.812-40.762-56.765Q-40.592-56.718-40.322-56.718\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M-33.105-56.452L-34.175-59.308Q-34.242-59.487-34.372-59.530Q-34.503-59.573-34.761-59.573L-34.761-59.870L-33.081-59.870L-33.081-59.573Q-33.531-59.573-33.531-59.374Q-33.527-59.358-33.525-59.341Q-33.523-59.323-33.523-59.308L-32.730-57.214L-32.019-59.124Q-32.054-59.218-32.054-59.263Q-32.054-59.308-32.089-59.308Q-32.156-59.487-32.286-59.530Q-32.417-59.573-32.671-59.573L-32.671-59.870L-31.081-59.870L-31.081-59.573Q-31.531-59.573-31.531-59.374Q-31.527-59.355-31.525-59.337Q-31.523-59.319-31.523-59.308L-30.691-57.093L-29.937-59.093Q-29.913-59.151-29.913-59.222Q-29.913-59.382-30.050-59.478Q-30.187-59.573-30.355-59.573L-30.355-59.870L-28.968-59.870L-28.968-59.573Q-29.202-59.573-29.380-59.446Q-29.558-59.319-29.640-59.093L-30.624-56.452Q-30.679-56.343-30.792-56.343L-30.851-56.343Q-30.964-56.343-31.007-56.452L-31.867-58.726L-32.722-56.452Q-32.761-56.343-32.882-56.343L-32.937-56.343Q-33.050-56.343-33.105-56.452\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M-28.788-58.116Q-28.788-58.620-28.532-59.052Q-28.276-59.483-27.840-59.735Q-27.405-59.987-26.905-59.987Q-26.518-59.987-26.176-59.843Q-25.835-59.698-25.573-59.437Q-25.311-59.175-25.169-58.839Q-25.026-58.503-25.026-58.116Q-25.026-57.624-25.290-57.214Q-25.553-56.804-25.983-56.573Q-26.413-56.343-26.905-56.343Q-27.397-56.343-27.831-56.575Q-28.264-56.808-28.526-57.216Q-28.788-57.624-28.788-58.116M-26.905-56.620Q-26.448-56.620-26.196-56.843Q-25.944-57.066-25.856-57.417Q-25.768-57.769-25.768-58.214Q-25.768-58.644-25.862-58.982Q-25.956-59.319-26.210-59.526Q-26.463-59.733-26.905-59.733Q-27.553-59.733-27.797-59.317Q-28.042-58.901-28.042-58.214Q-28.042-57.769-27.954-57.417Q-27.866-57.066-27.614-56.843Q-27.362-56.620-26.905-56.620M-23.858-57.374L-23.858-59.116Q-23.858-59.331-23.921-59.427Q-23.983-59.523-24.102-59.544Q-24.221-59.566-24.467-59.566L-24.467-59.862L-23.221-59.948L-23.221-57.398L-23.221-57.374Q-23.221-57.062-23.167-56.900Q-23.112-56.737-22.962-56.667Q-22.811-56.597-22.491-56.597Q-22.061-56.597-21.788-56.935Q-21.514-57.273-21.514-57.718L-21.514-59.116Q-21.514-59.331-21.577-59.427Q-21.639-59.523-21.758-59.544Q-21.878-59.566-22.124-59.566L-22.124-59.862L-20.878-59.948L-20.878-57.163Q-20.878-56.952-20.815-56.857Q-20.753-56.761-20.633-56.739Q-20.514-56.718-20.268-56.718L-20.268-56.421L-21.491-56.343L-21.491-56.964Q-21.659-56.675-21.940-56.509Q-22.221-56.343-22.542-56.343Q-23.858-56.343-23.858-57.374M-17.909-56.421L-19.741-56.421L-19.741-56.718Q-19.467-56.718-19.299-56.765Q-19.131-56.812-19.131-56.980L-19.131-61.140Q-19.131-61.355-19.194-61.450Q-19.256-61.546-19.376-61.567Q-19.495-61.589-19.741-61.589L-19.741-61.886L-18.518-61.972L-18.518-56.980Q-18.518-56.812-18.350-56.765Q-18.182-56.718-17.909-56.718L-17.909-56.421M-15.647-56.343Q-16.128-56.343-16.536-56.587Q-16.944-56.831-17.182-57.245Q-17.421-57.659-17.421-58.148Q-17.421-58.640-17.163-59.056Q-16.905-59.472-16.473-59.710Q-16.042-59.948-15.549-59.948Q-14.928-59.948-14.479-59.511L-14.479-61.140Q-14.479-61.355-14.542-61.450Q-14.604-61.546-14.721-61.567Q-14.838-61.589-15.085-61.589L-15.085-61.886L-13.862-61.972L-13.862-57.163Q-13.862-56.952-13.799-56.857Q-13.737-56.761-13.620-56.739Q-13.503-56.718-13.253-56.718L-13.253-56.421L-14.503-56.343L-14.503-56.827Q-14.967-56.343-15.647-56.343M-15.581-56.597Q-15.241-56.597-14.948-56.788Q-14.655-56.980-14.503-57.276L-14.503-59.108Q-14.651-59.382-14.913-59.538Q-15.174-59.694-15.487-59.694Q-16.112-59.694-16.395-59.247Q-16.678-58.800-16.678-58.140Q-16.678-57.495-16.426-57.046Q-16.174-56.597-15.581-56.597\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M-9.481-55.124Q-9.367-55.046-9.192-55.046Q-8.903-55.046-8.682-55.259Q-8.461-55.472-8.336-55.773L-8.047-56.421L-9.321-59.308Q-9.403-59.483-9.547-59.528Q-9.692-59.573-9.961-59.573L-9.961-59.870L-8.242-59.870L-8.242-59.573Q-8.664-59.573-8.664-59.390Q-8.664-59.378-8.649-59.308L-7.711-57.183L-6.879-59.093Q-6.840-59.183-6.840-59.261Q-6.840-59.401-6.942-59.487Q-7.043-59.573-7.184-59.573L-7.184-59.870L-5.832-59.870L-5.832-59.573Q-6.086-59.573-6.280-59.448Q-6.473-59.323-6.578-59.093L-8.024-55.773Q-8.137-55.519-8.303-55.296Q-8.469-55.073-8.698-54.931Q-8.926-54.788-9.192-54.788Q-9.489-54.788-9.729-54.980Q-9.969-55.171-9.969-55.460Q-9.969-55.616-9.864-55.718Q-9.758-55.819-9.610-55.819Q-9.504-55.819-9.424-55.773Q-9.344-55.726-9.297-55.648Q-9.250-55.569-9.250-55.460Q-9.250-55.339-9.311-55.251Q-9.371-55.163-9.481-55.124\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M-5.649-58.116Q-5.649-58.620-5.393-59.052Q-5.137-59.483-4.701-59.735Q-4.266-59.987-3.766-59.987Q-3.379-59.987-3.037-59.843Q-2.696-59.698-2.434-59.437Q-2.172-59.175-2.030-58.839Q-1.887-58.503-1.887-58.116Q-1.887-57.624-2.151-57.214Q-2.414-56.804-2.844-56.573Q-3.274-56.343-3.766-56.343Q-4.258-56.343-4.692-56.575Q-5.125-56.808-5.387-57.216Q-5.649-57.624-5.649-58.116M-3.766-56.620Q-3.309-56.620-3.057-56.843Q-2.805-57.066-2.717-57.417Q-2.629-57.769-2.629-58.214Q-2.629-58.644-2.723-58.982Q-2.817-59.319-3.071-59.526Q-3.324-59.733-3.766-59.733Q-4.414-59.733-4.658-59.317Q-4.903-58.901-4.903-58.214Q-4.903-57.769-4.815-57.417Q-4.727-57.066-4.475-56.843Q-4.223-56.620-3.766-56.620M-0.719-57.374L-0.719-59.116Q-0.719-59.331-0.782-59.427Q-0.844-59.523-0.963-59.544Q-1.082-59.566-1.328-59.566L-1.328-59.862L-0.082-59.948L-0.082-57.398L-0.082-57.374Q-0.082-57.062-0.028-56.900Q0.027-56.737 0.177-56.667Q0.328-56.597 0.648-56.597Q1.078-56.597 1.351-56.935Q1.625-57.273 1.625-57.718L1.625-59.116Q1.625-59.331 1.562-59.427Q1.500-59.523 1.381-59.544Q1.261-59.566 1.015-59.566L1.015-59.862L2.261-59.948L2.261-57.163Q2.261-56.952 2.324-56.857Q2.386-56.761 2.506-56.739Q2.625-56.718 2.871-56.718L2.871-56.421L1.648-56.343L1.648-56.964Q1.480-56.675 1.199-56.509Q0.918-56.343 0.597-56.343Q-0.719-56.343-0.719-57.374\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M8.071-56.421L6.239-56.421L6.239-56.718Q6.513-56.718 6.681-56.765Q6.849-56.812 6.849-56.980L6.849-61.140Q6.849-61.355 6.786-61.450Q6.724-61.546 6.605-61.567Q6.485-61.589 6.239-61.589L6.239-61.886L7.462-61.972L7.462-56.980Q7.462-56.812 7.630-56.765Q7.798-56.718 8.071-56.718L8.071-56.421M10.376-56.421L8.599-56.421L8.599-56.718Q8.872-56.718 9.040-56.765Q9.208-56.812 9.208-56.980L9.208-59.116Q9.208-59.331 9.151-59.427Q9.095-59.523 8.982-59.544Q8.868-59.566 8.622-59.566L8.622-59.862L9.821-59.948L9.821-56.980Q9.821-56.812 9.968-56.765Q10.114-56.718 10.376-56.718L10.376-56.421M8.935-61.343Q8.935-61.534 9.069-61.665Q9.204-61.796 9.399-61.796Q9.521-61.796 9.624-61.733Q9.728-61.671 9.790-61.567Q9.853-61.464 9.853-61.343Q9.853-61.148 9.722-61.013Q9.591-60.878 9.399-60.878Q9.200-60.878 9.067-61.011Q8.935-61.144 8.935-61.343M12.700-56.421L10.903-56.421L10.903-56.718Q11.173-56.718 11.341-56.763Q11.509-56.808 11.509-56.980L11.509-61.140Q11.509-61.355 11.446-61.450Q11.384-61.546 11.267-61.567Q11.149-61.589 10.903-61.589L10.903-61.886L12.126-61.972L12.126-58.206L13.224-59.093Q13.431-59.273 13.431-59.421Q13.431-59.487 13.378-59.530Q13.325-59.573 13.255-59.573L13.255-59.870L14.790-59.870L14.790-59.573Q14.259-59.573 13.661-59.093L13.052-58.597L14.126-57.198Q14.263-57.023 14.370-56.915Q14.478-56.808 14.612-56.763Q14.747-56.718 14.974-56.718L14.974-56.421L13.349-56.421L13.349-56.718Q13.591-56.718 13.591-56.870Q13.591-56.948 13.548-57.019Q13.505-57.089 13.423-57.198L12.622-58.245L12.095-57.819L12.095-56.980Q12.095-56.812 12.263-56.765Q12.431-56.718 12.700-56.718\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M15.129-58.175Q15.129-58.655 15.362-59.071Q15.594-59.487 16.004-59.737Q16.414-59.987 16.891-59.987Q17.621-59.987 18.020-59.546Q18.418-59.105 18.418-58.374Q18.418-58.269 18.325-58.245L15.875-58.245L15.875-58.175Q15.875-57.765 15.996-57.409Q16.118-57.054 16.389-56.837Q16.661-56.620 17.090-56.620Q17.453-56.620 17.750-56.849Q18.047-57.077 18.149-57.429Q18.157-57.476 18.243-57.491L18.325-57.491Q18.418-57.464 18.418-57.382Q18.418-57.374 18.411-57.343Q18.348-57.116 18.209-56.933Q18.071-56.749 17.879-56.616Q17.688-56.483 17.469-56.413Q17.250-56.343 17.012-56.343Q16.641-56.343 16.303-56.480Q15.965-56.616 15.698-56.868Q15.430-57.120 15.280-57.460Q15.129-57.800 15.129-58.175M15.883-58.483L17.844-58.483Q17.844-58.788 17.743-59.079Q17.641-59.370 17.424-59.552Q17.207-59.733 16.891-59.733Q16.590-59.733 16.360-59.546Q16.129-59.358 16.006-59.067Q15.883-58.776 15.883-58.483\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M22.365-57.382L22.365-59.573L21.662-59.573L21.662-59.827Q22.018-59.827 22.260-60.060Q22.502-60.292 22.613-60.640Q22.725-60.987 22.725-61.343L23.006-61.343L23.006-59.870L24.182-59.870L24.182-59.573L23.006-59.573L23.006-57.398Q23.006-57.077 23.125-56.849Q23.244-56.620 23.525-56.620Q23.705-56.620 23.822-56.743Q23.939-56.866 23.992-57.046Q24.045-57.226 24.045-57.398L24.045-57.870L24.326-57.870L24.326-57.382Q24.326-57.128 24.221-56.888Q24.115-56.648 23.918-56.495Q23.721-56.343 23.463-56.343Q23.147-56.343 22.895-56.466Q22.643-56.589 22.504-56.823Q22.365-57.058 22.365-57.382M25.045-58.116Q25.045-58.620 25.301-59.052Q25.557-59.483 25.992-59.735Q26.428-59.987 26.928-59.987Q27.314-59.987 27.656-59.843Q27.998-59.698 28.260-59.437Q28.522-59.175 28.664-58.839Q28.807-58.503 28.807-58.116Q28.807-57.624 28.543-57.214Q28.279-56.804 27.850-56.573Q27.420-56.343 26.928-56.343Q26.436-56.343 26.002-56.575Q25.568-56.808 25.307-57.216Q25.045-57.624 25.045-58.116M26.928-56.620Q27.385-56.620 27.637-56.843Q27.889-57.066 27.977-57.417Q28.064-57.769 28.064-58.214Q28.064-58.644 27.971-58.982Q27.877-59.319 27.623-59.526Q27.369-59.733 26.928-59.733Q26.279-59.733 26.035-59.317Q25.791-58.901 25.791-58.214Q25.791-57.769 25.879-57.417Q25.967-57.066 26.219-56.843Q26.471-56.620 26.928-56.620\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M34.043-56.421L32.211-56.421L32.211-56.718Q32.485-56.718 32.653-56.765Q32.821-56.812 32.821-56.980L32.821-61.140Q32.821-61.355 32.758-61.450Q32.696-61.546 32.577-61.567Q32.457-61.589 32.211-61.589L32.211-61.886L33.434-61.972L33.434-56.980Q33.434-56.812 33.602-56.765Q33.770-56.718 34.043-56.718L34.043-56.421M34.489-58.175Q34.489-58.655 34.721-59.071Q34.953-59.487 35.364-59.737Q35.774-59.987 36.250-59.987Q36.981-59.987 37.379-59.546Q37.778-59.105 37.778-58.374Q37.778-58.269 37.684-58.245L35.235-58.245L35.235-58.175Q35.235-57.765 35.356-57.409Q35.477-57.054 35.748-56.837Q36.020-56.620 36.450-56.620Q36.813-56.620 37.110-56.849Q37.407-57.077 37.508-57.429Q37.516-57.476 37.602-57.491L37.684-57.491Q37.778-57.464 37.778-57.382Q37.778-57.374 37.770-57.343Q37.707-57.116 37.569-56.933Q37.430-56.749 37.239-56.616Q37.047-56.483 36.828-56.413Q36.610-56.343 36.371-56.343Q36-56.343 35.662-56.480Q35.325-56.616 35.057-56.868Q34.789-57.120 34.639-57.460Q34.489-57.800 34.489-58.175M35.243-58.483L37.203-58.483Q37.203-58.788 37.102-59.079Q37-59.370 36.784-59.552Q36.567-59.733 36.250-59.733Q35.950-59.733 35.719-59.546Q35.489-59.358 35.366-59.067Q35.243-58.776 35.243-58.483M38.364-57.253Q38.364-57.737 38.766-58.032Q39.168-58.327 39.719-58.446Q40.270-58.566 40.762-58.566L40.762-58.855Q40.762-59.081 40.647-59.288Q40.532-59.495 40.334-59.614Q40.137-59.733 39.907-59.733Q39.481-59.733 39.196-59.628Q39.266-59.601 39.313-59.546Q39.360-59.491 39.385-59.421Q39.411-59.351 39.411-59.276Q39.411-59.171 39.360-59.079Q39.309-58.987 39.217-58.937Q39.125-58.886 39.020-58.886Q38.914-58.886 38.823-58.937Q38.731-58.987 38.680-59.079Q38.629-59.171 38.629-59.276Q38.629-59.694 39.018-59.841Q39.407-59.987 39.907-59.987Q40.239-59.987 40.592-59.857Q40.946-59.726 41.174-59.472Q41.403-59.218 41.403-58.870L41.403-57.069Q41.403-56.937 41.475-56.827Q41.547-56.718 41.676-56.718Q41.801-56.718 41.870-56.823Q41.938-56.929 41.938-57.069L41.938-57.581L42.219-57.581L42.219-57.069Q42.219-56.866 42.102-56.708Q41.985-56.550 41.803-56.466Q41.621-56.382 41.418-56.382Q41.188-56.382 41.036-56.554Q40.883-56.726 40.852-56.956Q40.692-56.675 40.383-56.509Q40.075-56.343 39.723-56.343Q39.211-56.343 38.787-56.566Q38.364-56.788 38.364-57.253M39.051-57.253Q39.051-56.968 39.278-56.782Q39.504-56.597 39.797-56.597Q40.043-56.597 40.268-56.714Q40.493-56.831 40.627-57.034Q40.762-57.237 40.762-57.491L40.762-58.323Q40.496-58.323 40.211-58.269Q39.926-58.214 39.655-58.085Q39.383-57.956 39.217-57.749Q39.051-57.542 39.051-57.253\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M44.083-56.452L42.860-59.308Q42.778-59.483 42.634-59.528Q42.489-59.573 42.220-59.573L42.220-59.870L43.931-59.870L43.931-59.573Q43.509-59.573 43.509-59.390Q43.509-59.355 43.524-59.308L44.470-57.116L45.310-59.093Q45.349-59.171 45.349-59.261Q45.349-59.401 45.243-59.487Q45.138-59.573 44.997-59.573L44.997-59.870L46.349-59.870L46.349-59.573Q45.825-59.573 45.610-59.093L44.485-56.452Q44.423-56.343 44.317-56.343L44.251-56.343Q44.138-56.343 44.083-56.452\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 47.525)\">\u003Cpath d=\"M46.532-58.175Q46.532-58.655 46.765-59.071Q46.997-59.487 47.407-59.737Q47.817-59.987 48.294-59.987Q49.024-59.987 49.423-59.546Q49.821-59.105 49.821-58.374Q49.821-58.269 49.728-58.245L47.278-58.245L47.278-58.175Q47.278-57.765 47.399-57.409Q47.521-57.054 47.792-56.837Q48.064-56.620 48.493-56.620Q48.856-56.620 49.153-56.849Q49.450-57.077 49.552-57.429Q49.560-57.476 49.646-57.491L49.728-57.491Q49.821-57.464 49.821-57.382Q49.821-57.374 49.814-57.343Q49.751-57.116 49.612-56.933Q49.474-56.749 49.282-56.616Q49.091-56.483 48.872-56.413Q48.653-56.343 48.415-56.343Q48.044-56.343 47.706-56.480Q47.368-56.616 47.101-56.868Q46.833-57.120 46.683-57.460Q46.532-57.800 46.532-58.175M47.286-58.483L49.247-58.483Q49.247-58.788 49.146-59.079Q49.044-59.370 48.827-59.552Q48.610-59.733 48.294-59.733Q47.993-59.733 47.763-59.546Q47.532-59.358 47.409-59.067Q47.286-58.776 47.286-58.483M51.497-56.886Q51.497-57.077 51.630-57.210Q51.763-57.343 51.958-57.343Q52.149-57.343 52.282-57.210Q52.415-57.077 52.415-56.886Q52.415-56.687 52.282-56.554Q52.149-56.421 51.958-56.421Q51.763-56.421 51.630-56.554Q51.497-56.687 51.497-56.886M51.814-58.062L51.814-58.460Q51.814-58.858 51.942-59.259Q52.071-59.659 52.317-59.995Q52.396-60.101 52.562-60.276Q52.728-60.452 52.796-60.589Q52.864-60.726 52.864-60.964Q52.864-61.452 52.661-61.624Q52.458-61.796 51.958-61.796Q51.649-61.796 51.372-61.685Q51.095-61.573 50.942-61.351Q51.114-61.351 51.222-61.237Q51.329-61.124 51.329-60.956Q51.329-60.855 51.278-60.763Q51.228-60.671 51.138-60.618Q51.048-60.566 50.935-60.566Q50.825-60.566 50.735-60.618Q50.646-60.671 50.595-60.763Q50.544-60.855 50.544-60.956Q50.544-61.304 50.755-61.554Q50.966-61.804 51.290-61.929Q51.614-62.054 51.958-62.054Q52.618-62.054 53.112-61.804Q53.606-61.554 53.606-60.956Q53.606-60.706 53.483-60.491Q53.360-60.276 53.149-60.140Q52.837-59.937 52.605-59.681Q52.372-59.425 52.233-59.108Q52.095-58.792 52.095-58.444L52.095-58.062Q52.095-58.026 52.067-57.999Q52.040-57.972 52.009-57.972L51.903-57.972Q51.872-57.972 51.843-57.997Q51.814-58.023 51.814-58.062\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(9.223 65.04)\">\u003Cpath d=\"M-59.576-56.659L-59.576-56.749Q-59.537-56.956-59.330-56.980L-59.080-56.980L-59.080-60.749L-59.330-60.749Q-59.537-60.773-59.576-60.987L-59.576-61.077Q-59.537-61.284-59.330-61.308L-57.514-61.308Q-56.963-61.308-56.568-60.915Q-56.174-60.523-55.979-59.944Q-55.783-59.366-55.783-58.819Q-55.783-58.292-55.983-57.728Q-56.182-57.163-56.574-56.792Q-56.967-56.421-57.514-56.421L-59.330-56.421Q-59.537-56.444-59.576-56.659M-58.440-60.749L-58.440-56.980L-57.674-56.980Q-57.080-56.980-56.752-57.575Q-56.424-58.171-56.424-58.819Q-56.424-59.226-56.559-59.675Q-56.693-60.124-56.979-60.437Q-57.264-60.749-57.674-60.749L-58.440-60.749M-55.283-56.659L-55.283-56.749Q-55.244-56.956-55.033-56.980L-54.725-56.980L-54.725-60.749L-55.033-60.749Q-55.244-60.773-55.283-60.987L-55.283-61.077Q-55.244-61.284-55.033-61.308L-51.826-61.308Q-51.631-61.284-51.580-61.077L-51.580-60.237Q-51.631-60.023-51.826-59.995L-51.971-59.995Q-52.166-60.023-52.221-60.237L-52.221-60.749L-54.084-60.749L-54.084-59.230L-53.108-59.230L-53.108-59.444Q-53.057-59.651-52.858-59.679L-52.713-59.679Q-52.518-59.651-52.467-59.444L-52.467-58.460Q-52.518-58.245-52.713-58.222L-52.858-58.222Q-53.057-58.245-53.108-58.460L-53.108-58.667L-54.084-58.667L-54.084-56.980L-52.041-56.980L-52.041-57.620Q-51.990-57.827-51.795-57.855L-51.651-57.855Q-51.455-57.827-51.404-57.620L-51.404-56.659Q-51.455-56.441-51.651-56.421L-55.033-56.421Q-55.244-56.444-55.283-56.659M-51.037-56.659L-51.037-56.749Q-50.998-56.956-50.787-56.980L-50.479-56.980L-50.479-60.749L-50.787-60.749Q-50.998-60.773-51.037-60.987L-51.037-61.077Q-50.998-61.284-50.787-61.308L-48.838-61.308Q-48.440-61.308-48.094-61.107Q-47.748-60.905-47.541-60.560Q-47.334-60.214-47.334-59.812Q-47.334-59.405-47.539-59.062Q-47.744-58.718-48.090-58.513Q-48.436-58.308-48.838-58.308L-49.838-58.308L-49.838-56.980L-49.526-56.980Q-49.315-56.956-49.276-56.749L-49.276-56.659Q-49.315-56.444-49.526-56.421L-50.787-56.421Q-50.998-56.444-51.037-56.659M-49.838-60.749L-49.838-58.870L-48.998-58.870Q-48.736-58.870-48.500-58.993Q-48.264-59.116-48.119-59.331Q-47.975-59.546-47.975-59.812Q-47.975-60.081-48.119-60.292Q-48.264-60.503-48.500-60.626Q-48.736-60.749-48.998-60.749L-49.838-60.749M-46.744-56.659L-46.744-56.749Q-46.686-56.960-46.494-56.980L-46.272-56.980L-45.318-61.163Q-45.295-61.280-45.199-61.355Q-45.104-61.429-44.990-61.429L-44.717-61.429Q-44.604-61.429-44.508-61.353Q-44.412-61.276-44.389-61.163L-43.440-56.980L-43.213-56.980Q-43.006-56.956-42.967-56.749L-42.967-56.659Q-43.018-56.444-43.213-56.421L-44.295-56.421Q-44.490-56.444-44.541-56.659L-44.541-56.749Q-44.490-56.956-44.295-56.980L-44.096-56.980Q-44.225-57.546-44.248-57.628L-45.463-57.628Q-45.506-57.444-45.615-56.980L-45.416-56.980Q-45.217-56.956-45.166-56.749L-45.166-56.659Q-45.217-56.444-45.416-56.421L-46.494-56.421Q-46.701-56.444-46.744-56.659M-45.342-58.191L-44.365-58.191Q-44.846-60.323-44.846-60.667L-44.854-60.667Q-44.854-60.483-44.994-59.800Q-45.135-59.116-45.342-58.191M-41.154-56.421L-42.338-56.421Q-42.533-56.444-42.584-56.659L-42.584-56.749Q-42.533-56.956-42.338-56.980L-42.065-56.980L-42.065-60.749L-42.338-60.749Q-42.533-60.773-42.584-60.987L-42.584-61.077Q-42.533-61.284-42.338-61.308L-40.768-61.308Q-40.381-61.308-40.026-61.138Q-39.670-60.968-39.447-60.657Q-39.225-60.347-39.225-59.948Q-39.225-59.722-39.307-59.513Q-39.389-59.304-39.531-59.136Q-39.674-58.968-39.865-58.847Q-39.670-58.694-39.555-58.478Q-39.440-58.261-39.440-58.023L-39.440-57.444Q-39.440-56.901-39.240-56.901Q-39.193-56.901-39.168-57.030Q-39.143-57.159-39.143-57.261Q-39.092-57.468-38.897-57.491L-38.752-57.491Q-38.557-57.472-38.506-57.261L-38.506-57.206Q-38.506-56.874-38.711-56.608Q-38.916-56.343-39.233-56.343Q-39.647-56.343-39.863-56.650Q-40.080-56.956-40.080-57.390L-40.080-57.964Q-40.080-58.253-40.289-58.421Q-40.498-58.589-40.783-58.589L-41.424-58.589L-41.424-56.980L-41.154-56.980Q-40.955-56.956-40.904-56.749L-40.904-56.659Q-40.955-56.444-41.154-56.421M-41.424-60.749L-41.424-59.148L-40.850-59.148Q-40.615-59.148-40.389-59.243Q-40.162-59.339-40.014-59.523Q-39.865-59.706-39.865-59.948Q-39.865-60.191-40.014-60.374Q-40.162-60.558-40.387-60.653Q-40.611-60.749-40.850-60.749L-41.424-60.749M-37.420-56.659L-37.420-56.749Q-37.361-56.956-37.170-56.980L-36.682-56.980L-36.682-60.749L-37.627-60.749L-37.627-60.237Q-37.674-60.023-37.873-59.995L-38.018-59.995Q-38.217-60.023-38.268-60.237L-38.268-61.077Q-38.217-61.284-38.018-61.308L-34.705-61.308Q-34.510-61.288-34.459-61.077L-34.459-60.237Q-34.506-60.023-34.705-59.995L-34.850-59.995Q-35.049-60.023-35.100-60.237L-35.100-60.749L-36.041-60.749L-36.041-56.980L-35.553-56.980Q-35.358-56.956-35.307-56.749L-35.307-56.659Q-35.358-56.444-35.553-56.421L-37.170-56.421Q-37.361-56.444-37.420-56.659M-30.740-58.558L-33.483-58.558Q-33.608-58.569-33.693-58.655Q-33.779-58.741-33.779-58.870Q-33.779-58.999-33.693-59.081Q-33.608-59.163-33.483-59.175L-30.740-59.175Q-30.615-59.163-30.533-59.081Q-30.451-58.999-30.451-58.870Q-30.451-58.741-30.533-58.655Q-30.615-58.569-30.740-58.558M-29.854-56.659L-29.854-56.749Q-29.815-56.956-29.608-56.980L-29.358-56.980L-29.358-60.749L-29.608-60.749Q-29.815-60.773-29.854-60.987L-29.854-61.077Q-29.815-61.284-29.608-61.308L-27.791-61.308Q-27.240-61.308-26.846-60.915Q-26.451-60.523-26.256-59.944Q-26.061-59.366-26.061-58.819Q-26.061-58.292-26.260-57.728Q-26.459-57.163-26.852-56.792Q-27.244-56.421-27.791-56.421L-29.608-56.421Q-29.815-56.444-29.854-56.659M-28.717-60.749L-28.717-56.980L-27.951-56.980Q-27.358-56.980-27.029-57.575Q-26.701-58.171-26.701-58.819Q-26.701-59.226-26.836-59.675Q-26.971-60.124-27.256-60.437Q-27.541-60.749-27.951-60.749L-28.717-60.749M-25.514-56.659L-25.514-56.749Q-25.455-56.960-25.264-56.980L-25.041-56.980L-24.088-61.163Q-24.065-61.280-23.969-61.355Q-23.873-61.429-23.760-61.429L-23.486-61.429Q-23.373-61.429-23.277-61.353Q-23.182-61.276-23.158-61.163L-22.209-56.980L-21.983-56.980Q-21.776-56.956-21.736-56.749L-21.736-56.659Q-21.787-56.444-21.983-56.421L-23.065-56.421Q-23.260-56.444-23.311-56.659L-23.311-56.749Q-23.260-56.956-23.065-56.980L-22.865-56.980Q-22.994-57.546-23.018-57.628L-24.233-57.628Q-24.276-57.444-24.385-56.980L-24.186-56.980Q-23.986-56.956-23.936-56.749L-23.936-56.659Q-23.986-56.444-24.186-56.421L-25.264-56.421Q-25.471-56.444-25.514-56.659M-24.111-58.191L-23.135-58.191Q-23.615-60.323-23.615-60.667L-23.623-60.667Q-23.623-60.483-23.764-59.800Q-23.904-59.116-24.111-58.191M-20.436-56.659L-20.436-56.749Q-20.377-56.956-20.186-56.980L-19.697-56.980L-19.697-60.749L-20.643-60.749L-20.643-60.237Q-20.690-60.023-20.889-59.995L-21.033-59.995Q-21.233-60.023-21.283-60.237L-21.283-61.077Q-21.233-61.284-21.033-61.308L-17.721-61.308Q-17.526-61.288-17.475-61.077L-17.475-60.237Q-17.522-60.023-17.721-59.995L-17.865-59.995Q-18.065-60.023-18.115-60.237L-18.115-60.749L-19.057-60.749L-19.057-56.980L-18.568-56.980Q-18.373-56.956-18.322-56.749L-18.322-56.659Q-18.373-56.444-18.568-56.421L-20.186-56.421Q-20.377-56.444-20.436-56.659M-17.068-56.659L-17.068-56.749Q-17.029-56.956-16.818-56.980L-16.510-56.980L-16.510-60.749L-16.818-60.749Q-17.029-60.773-17.068-60.987L-17.068-61.077Q-17.029-61.284-16.818-61.308L-13.611-61.308Q-13.416-61.284-13.365-61.077L-13.365-60.237Q-13.416-60.023-13.611-59.995L-13.756-59.995Q-13.951-60.023-14.006-60.237L-14.006-60.749L-15.869-60.749L-15.869-59.230L-14.893-59.230L-14.893-59.444Q-14.842-59.651-14.643-59.679L-14.498-59.679Q-14.303-59.651-14.252-59.444L-14.252-58.460Q-14.303-58.245-14.498-58.222L-14.643-58.222Q-14.842-58.245-14.893-58.460L-14.893-58.667L-15.869-58.667L-15.869-56.980L-13.826-56.980L-13.826-57.620Q-13.776-57.827-13.580-57.855L-13.436-57.855Q-13.240-57.827-13.190-57.620L-13.190-56.659Q-13.240-56.441-13.436-56.421L-16.818-56.421Q-17.029-56.444-17.068-56.659\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(117.344 65.04)\">\u003Cpath d=\"M-57.850-56.382Q-58.315-56.382-58.680-56.632Q-59.045-56.882-59.250-57.286Q-59.455-57.691-59.455-58.148Q-59.455-58.491-59.330-58.812Q-59.205-59.132-58.973-59.382Q-58.740-59.632-58.436-59.771Q-58.131-59.909-57.776-59.909Q-57.264-59.909-56.858-59.589L-56.858-60.749L-57.279-60.749Q-57.490-60.773-57.529-60.987L-57.529-61.077Q-57.490-61.284-57.279-61.308L-56.467-61.308Q-56.268-61.284-56.217-61.077L-56.217-56.980L-55.791-56.980Q-55.584-56.956-55.545-56.749L-55.545-56.659Q-55.584-56.444-55.791-56.421L-56.608-56.421Q-56.807-56.444-56.858-56.659L-56.858-56.788Q-57.053-56.597-57.315-56.489Q-57.576-56.382-57.850-56.382M-57.811-56.941Q-57.451-56.941-57.199-57.210Q-56.947-57.480-56.858-57.855L-56.858-58.687Q-56.916-58.874-57.043-59.025Q-57.170-59.175-57.348-59.263Q-57.526-59.351-57.721-59.351Q-58.029-59.351-58.279-59.181Q-58.529-59.011-58.674-58.726Q-58.818-58.441-58.818-58.140Q-58.818-57.691-58.533-57.316Q-58.248-56.941-57.811-56.941M-55.061-57.534Q-55.061-57.980-54.647-58.237Q-54.233-58.495-53.692-58.595Q-53.151-58.694-52.643-58.702Q-52.643-58.917-52.777-59.069Q-52.912-59.222-53.119-59.298Q-53.326-59.374-53.537-59.374Q-53.881-59.374-54.041-59.351L-54.041-59.292Q-54.041-59.124-54.160-59.009Q-54.279-58.894-54.443-58.894Q-54.619-58.894-54.734-59.017Q-54.850-59.140-54.850-59.308Q-54.850-59.714-54.469-59.823Q-54.088-59.933-53.529-59.933Q-53.260-59.933-52.992-59.855Q-52.725-59.776-52.500-59.626Q-52.276-59.476-52.139-59.255Q-52.002-59.034-52.002-58.757L-52.002-57.038Q-52.002-56.980-51.475-56.980Q-51.279-56.960-51.229-56.749L-51.229-56.659Q-51.279-56.444-51.475-56.421L-51.619-56.421Q-51.963-56.421-52.192-56.468Q-52.420-56.515-52.565-56.702Q-53.026-56.382-53.733-56.382Q-54.068-56.382-54.373-56.523Q-54.678-56.663-54.869-56.925Q-55.061-57.187-55.061-57.534M-54.420-57.526Q-54.420-57.253-54.178-57.097Q-53.936-56.941-53.651-56.941Q-53.432-56.941-53.199-56.999Q-52.967-57.058-52.805-57.196Q-52.643-57.335-52.643-57.558L-52.643-58.148Q-52.924-58.148-53.340-58.091Q-53.756-58.034-54.088-57.896Q-54.420-57.757-54.420-57.526M-50.022-57.526L-50.022-59.308L-50.772-59.308Q-50.971-59.331-51.022-59.550L-51.022-59.636Q-50.971-59.847-50.772-59.870L-50.022-59.870L-50.022-60.620Q-49.971-60.827-49.772-60.855L-49.627-60.855Q-49.432-60.827-49.381-60.620L-49.381-59.870L-48.022-59.870Q-47.830-59.851-47.772-59.636L-47.772-59.550Q-47.826-59.331-48.022-59.308L-49.381-59.308L-49.381-57.558Q-49.381-56.941-48.807-56.941Q-48.557-56.941-48.393-57.126Q-48.229-57.312-48.229-57.558Q-48.229-57.651-48.156-57.722Q-48.084-57.792-47.983-57.804L-47.838-57.804Q-47.639-57.780-47.588-57.573L-47.588-57.526Q-47.588-57.202-47.772-56.939Q-47.955-56.675-48.248-56.528Q-48.541-56.382-48.861-56.382Q-49.373-56.382-49.697-56.692Q-50.022-57.003-50.022-57.526M-43.463-57.909L-45.904-57.909Q-45.850-57.632-45.652-57.409Q-45.455-57.187-45.178-57.064Q-44.901-56.941-44.615-56.941Q-44.143-56.941-43.920-57.230Q-43.912-57.241-43.856-57.347Q-43.799-57.452-43.750-57.495Q-43.701-57.538-43.608-57.550L-43.463-57.550Q-43.272-57.530-43.213-57.316L-43.213-57.261Q-43.279-56.960-43.510-56.763Q-43.740-56.566-44.053-56.474Q-44.365-56.382-44.670-56.382Q-45.154-56.382-45.594-56.610Q-46.033-56.839-46.301-57.239Q-46.568-57.640-46.568-58.132L-46.568-58.191Q-46.568-58.659-46.322-59.062Q-46.076-59.464-45.668-59.698Q-45.260-59.933-44.791-59.933Q-44.287-59.933-43.934-59.710Q-43.580-59.487-43.397-59.099Q-43.213-58.710-43.213-58.206L-43.213-58.148Q-43.272-57.933-43.463-57.909M-45.897-58.460L-43.869-58.460Q-43.916-58.870-44.154-59.122Q-44.393-59.374-44.791-59.374Q-45.186-59.374-45.492-59.112Q-45.799-58.851-45.897-58.460\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M-57.139-56.366L-58.842-61.323Q-58.908-61.499-59.084-61.544Q-59.260-61.589-59.553-61.589L-59.553-61.886L-57.408-61.886L-57.408-61.589Q-58.065-61.589-58.065-61.374Q-58.061-61.362-58.059-61.353Q-58.057-61.343-58.057-61.323L-56.705-57.390L-55.506-60.894L-55.651-61.323Q-55.721-61.499-55.895-61.544Q-56.068-61.589-56.361-61.589L-56.361-61.886L-54.225-61.886L-54.225-61.589Q-54.873-61.589-54.873-61.374Q-54.873-61.331-54.865-61.323L-53.514-57.390L-52.240-61.108Q-52.225-61.155-52.225-61.191Q-52.225-61.331-52.334-61.423Q-52.443-61.515-52.600-61.552Q-52.756-61.589-52.889-61.589L-52.889-61.886L-51.154-61.886L-51.154-61.589Q-51.772-61.589-51.943-61.108L-53.568-56.366Q-53.588-56.316-53.631-56.284Q-53.674-56.253-53.729-56.253L-53.783-56.253Q-53.904-56.253-53.943-56.366L-55.354-60.460L-56.760-56.366Q-56.779-56.316-56.822-56.284Q-56.865-56.253-56.920-56.253L-56.979-56.253Q-57.092-56.253-57.139-56.366M-48.818-56.421L-50.674-56.421L-50.674-56.718Q-50.401-56.718-50.233-56.765Q-50.065-56.812-50.065-56.980L-50.065-61.140Q-50.065-61.355-50.127-61.450Q-50.190-61.546-50.309-61.567Q-50.428-61.589-50.674-61.589L-50.674-61.886L-49.451-61.972L-49.451-59.269Q-49.326-59.480-49.139-59.630Q-48.951-59.780-48.725-59.864Q-48.498-59.948-48.252-59.948Q-47.084-59.948-47.084-58.870L-47.084-56.980Q-47.084-56.812-46.914-56.765Q-46.744-56.718-46.475-56.718L-46.475-56.421L-48.330-56.421L-48.330-56.718Q-48.057-56.718-47.889-56.765Q-47.721-56.812-47.721-56.980L-47.721-58.855Q-47.721-59.237-47.842-59.466Q-47.963-59.694-48.315-59.694Q-48.627-59.694-48.881-59.532Q-49.135-59.370-49.281-59.101Q-49.428-58.831-49.428-58.534L-49.428-56.980Q-49.428-56.812-49.258-56.765Q-49.088-56.718-48.818-56.718L-48.818-56.421M-45.932-57.253Q-45.932-57.737-45.529-58.032Q-45.127-58.327-44.576-58.446Q-44.026-58.566-43.533-58.566L-43.533-58.855Q-43.533-59.081-43.649-59.288Q-43.764-59.495-43.961-59.614Q-44.158-59.733-44.389-59.733Q-44.815-59.733-45.100-59.628Q-45.029-59.601-44.983-59.546Q-44.936-59.491-44.910-59.421Q-44.885-59.351-44.885-59.276Q-44.885-59.171-44.936-59.079Q-44.986-58.987-45.078-58.937Q-45.170-58.886-45.276-58.886Q-45.381-58.886-45.473-58.937Q-45.565-58.987-45.615-59.079Q-45.666-59.171-45.666-59.276Q-45.666-59.694-45.277-59.841Q-44.889-59.987-44.389-59.987Q-44.057-59.987-43.703-59.857Q-43.350-59.726-43.121-59.472Q-42.893-59.218-42.893-58.870L-42.893-57.069Q-42.893-56.937-42.820-56.827Q-42.748-56.718-42.619-56.718Q-42.494-56.718-42.426-56.823Q-42.358-56.929-42.358-57.069L-42.358-57.581L-42.076-57.581L-42.076-57.069Q-42.076-56.866-42.193-56.708Q-42.311-56.550-42.492-56.466Q-42.674-56.382-42.877-56.382Q-43.108-56.382-43.260-56.554Q-43.412-56.726-43.443-56.956Q-43.604-56.675-43.912-56.509Q-44.221-56.343-44.572-56.343Q-45.084-56.343-45.508-56.566Q-45.932-56.788-45.932-57.253M-45.244-57.253Q-45.244-56.968-45.018-56.782Q-44.791-56.597-44.498-56.597Q-44.252-56.597-44.027-56.714Q-43.803-56.831-43.668-57.034Q-43.533-57.237-43.533-57.491L-43.533-58.323Q-43.799-58.323-44.084-58.269Q-44.369-58.214-44.641-58.085Q-44.912-57.956-45.078-57.749Q-45.244-57.542-45.244-57.253M-41.158-57.382L-41.158-59.573L-41.861-59.573L-41.861-59.827Q-41.506-59.827-41.264-60.060Q-41.022-60.292-40.910-60.640Q-40.799-60.987-40.799-61.343L-40.518-61.343L-40.518-59.870L-39.342-59.870L-39.342-59.573L-40.518-59.573L-40.518-57.398Q-40.518-57.077-40.399-56.849Q-40.279-56.620-39.998-56.620Q-39.818-56.620-39.701-56.743Q-39.584-56.866-39.531-57.046Q-39.479-57.226-39.479-57.398L-39.479-57.870L-39.197-57.870L-39.197-57.382Q-39.197-57.128-39.303-56.888Q-39.408-56.648-39.606-56.495Q-39.803-56.343-40.061-56.343Q-40.377-56.343-40.629-56.466Q-40.881-56.589-41.020-56.823Q-41.158-57.058-41.158-57.382\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M-33.819-56.343Q-34.300-56.343-34.708-56.587Q-35.116-56.831-35.354-57.245Q-35.593-57.659-35.593-58.148Q-35.593-58.640-35.335-59.056Q-35.077-59.472-34.645-59.710Q-34.214-59.948-33.722-59.948Q-33.101-59.948-32.651-59.511L-32.651-61.140Q-32.651-61.355-32.714-61.450Q-32.776-61.546-32.894-61.567Q-33.011-61.589-33.257-61.589L-33.257-61.886L-32.034-61.972L-32.034-57.163Q-32.034-56.952-31.972-56.857Q-31.909-56.761-31.792-56.739Q-31.675-56.718-31.425-56.718L-31.425-56.421L-32.675-56.343L-32.675-56.827Q-33.140-56.343-33.819-56.343M-33.753-56.597Q-33.413-56.597-33.120-56.788Q-32.827-56.980-32.675-57.276L-32.675-59.108Q-32.823-59.382-33.085-59.538Q-33.347-59.694-33.659-59.694Q-34.284-59.694-34.567-59.247Q-34.851-58.800-34.851-58.140Q-34.851-57.495-34.599-57.046Q-34.347-56.597-33.753-56.597M-30.819-57.253Q-30.819-57.737-30.417-58.032Q-30.015-58.327-29.464-58.446Q-28.913-58.566-28.421-58.566L-28.421-58.855Q-28.421-59.081-28.536-59.288Q-28.651-59.495-28.849-59.614Q-29.046-59.733-29.276-59.733Q-29.702-59.733-29.987-59.628Q-29.917-59.601-29.870-59.546Q-29.823-59.491-29.798-59.421Q-29.772-59.351-29.772-59.276Q-29.772-59.171-29.823-59.079Q-29.874-58.987-29.966-58.937Q-30.058-58.886-30.163-58.886Q-30.269-58.886-30.360-58.937Q-30.452-58.987-30.503-59.079Q-30.554-59.171-30.554-59.276Q-30.554-59.694-30.165-59.841Q-29.776-59.987-29.276-59.987Q-28.944-59.987-28.591-59.857Q-28.237-59.726-28.009-59.472Q-27.780-59.218-27.780-58.870L-27.780-57.069Q-27.780-56.937-27.708-56.827Q-27.636-56.718-27.507-56.718Q-27.382-56.718-27.313-56.823Q-27.245-56.929-27.245-57.069L-27.245-57.581L-26.964-57.581L-26.964-57.069Q-26.964-56.866-27.081-56.708Q-27.198-56.550-27.380-56.466Q-27.562-56.382-27.765-56.382Q-27.995-56.382-28.147-56.554Q-28.300-56.726-28.331-56.956Q-28.491-56.675-28.800-56.509Q-29.108-56.343-29.460-56.343Q-29.972-56.343-30.395-56.566Q-30.819-56.788-30.819-57.253M-30.132-57.253Q-30.132-56.968-29.905-56.782Q-29.679-56.597-29.386-56.597Q-29.140-56.597-28.915-56.714Q-28.690-56.831-28.556-57.034Q-28.421-57.237-28.421-57.491L-28.421-58.323Q-28.687-58.323-28.972-58.269Q-29.257-58.214-29.528-58.085Q-29.800-57.956-29.966-57.749Q-30.132-57.542-30.132-57.253\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M-26.481-55.124Q-26.367-55.046-26.192-55.046Q-25.903-55.046-25.682-55.259Q-25.461-55.472-25.336-55.773L-25.047-56.421L-26.321-59.308Q-26.403-59.483-26.547-59.528Q-26.692-59.573-26.961-59.573L-26.961-59.870L-25.242-59.870L-25.242-59.573Q-25.664-59.573-25.664-59.390Q-25.664-59.378-25.649-59.308L-24.711-57.183L-23.879-59.093Q-23.840-59.183-23.840-59.261Q-23.840-59.401-23.942-59.487Q-24.043-59.573-24.184-59.573L-24.184-59.870L-22.832-59.870L-22.832-59.573Q-23.086-59.573-23.280-59.448Q-23.473-59.323-23.578-59.093L-25.024-55.773Q-25.137-55.519-25.303-55.296Q-25.469-55.073-25.698-54.931Q-25.926-54.788-26.192-54.788Q-26.489-54.788-26.729-54.980Q-26.969-55.171-26.969-55.460Q-26.969-55.616-26.864-55.718Q-26.758-55.819-26.610-55.819Q-26.504-55.819-26.424-55.773Q-26.344-55.726-26.297-55.648Q-26.250-55.569-26.250-55.460Q-26.250-55.339-26.311-55.251Q-26.371-55.163-26.481-55.124\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M-17.994-56.452L-19.064-59.308Q-19.131-59.487-19.261-59.530Q-19.392-59.573-19.650-59.573L-19.650-59.870L-17.970-59.870L-17.970-59.573Q-18.420-59.573-18.420-59.374Q-18.416-59.358-18.414-59.341Q-18.412-59.323-18.412-59.308L-17.619-57.214L-16.908-59.124Q-16.943-59.218-16.943-59.263Q-16.943-59.308-16.978-59.308Q-17.045-59.487-17.175-59.530Q-17.306-59.573-17.560-59.573L-17.560-59.870L-15.970-59.870L-15.970-59.573Q-16.420-59.573-16.420-59.374Q-16.416-59.355-16.414-59.337Q-16.412-59.319-16.412-59.308L-15.580-57.093L-14.826-59.093Q-14.802-59.151-14.802-59.222Q-14.802-59.382-14.939-59.478Q-15.076-59.573-15.244-59.573L-15.244-59.870L-13.857-59.870L-13.857-59.573Q-14.091-59.573-14.269-59.446Q-14.447-59.319-14.529-59.093L-15.513-56.452Q-15.568-56.343-15.681-56.343L-15.740-56.343Q-15.853-56.343-15.896-56.452L-16.756-58.726L-17.611-56.452Q-17.650-56.343-17.771-56.343L-17.826-56.343Q-17.939-56.343-17.994-56.452\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M-13.677-58.116Q-13.677-58.620-13.421-59.052Q-13.165-59.483-12.729-59.735Q-12.294-59.987-11.794-59.987Q-11.407-59.987-11.065-59.843Q-10.724-59.698-10.462-59.437Q-10.200-59.175-10.058-58.839Q-9.915-58.503-9.915-58.116Q-9.915-57.624-10.179-57.214Q-10.442-56.804-10.872-56.573Q-11.302-56.343-11.794-56.343Q-12.286-56.343-12.720-56.575Q-13.153-56.808-13.415-57.216Q-13.677-57.624-13.677-58.116M-11.794-56.620Q-11.337-56.620-11.085-56.843Q-10.833-57.066-10.745-57.417Q-10.657-57.769-10.657-58.214Q-10.657-58.644-10.751-58.982Q-10.845-59.319-11.099-59.526Q-11.352-59.733-11.794-59.733Q-12.442-59.733-12.686-59.317Q-12.931-58.901-12.931-58.214Q-12.931-57.769-12.843-57.417Q-12.755-57.066-12.503-56.843Q-12.251-56.620-11.794-56.620M-8.747-57.374L-8.747-59.116Q-8.747-59.331-8.810-59.427Q-8.872-59.523-8.991-59.544Q-9.110-59.566-9.356-59.566L-9.356-59.862L-8.110-59.948L-8.110-57.398L-8.110-57.374Q-8.110-57.062-8.056-56.900Q-8.001-56.737-7.851-56.667Q-7.700-56.597-7.380-56.597Q-6.950-56.597-6.677-56.935Q-6.403-57.273-6.403-57.718L-6.403-59.116Q-6.403-59.331-6.466-59.427Q-6.528-59.523-6.647-59.544Q-6.767-59.566-7.013-59.566L-7.013-59.862L-5.767-59.948L-5.767-57.163Q-5.767-56.952-5.704-56.857Q-5.642-56.761-5.522-56.739Q-5.403-56.718-5.157-56.718L-5.157-56.421L-6.380-56.343L-6.380-56.964Q-6.548-56.675-6.829-56.509Q-7.110-56.343-7.431-56.343Q-8.747-56.343-8.747-57.374M-2.798-56.421L-4.630-56.421L-4.630-56.718Q-4.356-56.718-4.188-56.765Q-4.020-56.812-4.020-56.980L-4.020-61.140Q-4.020-61.355-4.083-61.450Q-4.145-61.546-4.265-61.567Q-4.384-61.589-4.630-61.589L-4.630-61.886L-3.407-61.972L-3.407-56.980Q-3.407-56.812-3.239-56.765Q-3.071-56.718-2.798-56.718L-2.798-56.421M-0.536-56.343Q-1.017-56.343-1.425-56.587Q-1.833-56.831-2.071-57.245Q-2.310-57.659-2.310-58.148Q-2.310-58.640-2.052-59.056Q-1.794-59.472-1.362-59.710Q-0.931-59.948-0.438-59.948Q0.183-59.948 0.632-59.511L0.632-61.140Q0.632-61.355 0.569-61.450Q0.507-61.546 0.390-61.567Q0.273-61.589 0.026-61.589L0.026-61.886L1.249-61.972L1.249-57.163Q1.249-56.952 1.312-56.857Q1.374-56.761 1.491-56.739Q1.608-56.718 1.858-56.718L1.858-56.421L0.608-56.343L0.608-56.827Q0.144-56.343-0.536-56.343M-0.470-56.597Q-0.130-56.597 0.163-56.788Q0.456-56.980 0.608-57.276L0.608-59.108Q0.460-59.382 0.198-59.538Q-0.063-59.694-0.376-59.694Q-1.001-59.694-1.284-59.247Q-1.567-58.800-1.567-58.140Q-1.567-57.495-1.315-57.046Q-1.063-56.597-0.470-56.597\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M5.630-55.124Q5.744-55.046 5.919-55.046Q6.208-55.046 6.429-55.259Q6.650-55.472 6.775-55.773L7.064-56.421L5.790-59.308Q5.708-59.483 5.564-59.528Q5.419-59.573 5.150-59.573L5.150-59.870L6.869-59.870L6.869-59.573Q6.447-59.573 6.447-59.390Q6.447-59.378 6.462-59.308L7.400-57.183L8.232-59.093Q8.271-59.183 8.271-59.261Q8.271-59.401 8.169-59.487Q8.068-59.573 7.927-59.573L7.927-59.870L9.279-59.870L9.279-59.573Q9.025-59.573 8.831-59.448Q8.638-59.323 8.533-59.093L7.087-55.773Q6.974-55.519 6.808-55.296Q6.642-55.073 6.413-54.931Q6.185-54.788 5.919-54.788Q5.622-54.788 5.382-54.980Q5.142-55.171 5.142-55.460Q5.142-55.616 5.247-55.718Q5.353-55.819 5.501-55.819Q5.607-55.819 5.687-55.773Q5.767-55.726 5.814-55.648Q5.861-55.569 5.861-55.460Q5.861-55.339 5.800-55.251Q5.740-55.163 5.630-55.124\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M9.462-58.116Q9.462-58.620 9.718-59.052Q9.974-59.483 10.410-59.735Q10.845-59.987 11.345-59.987Q11.732-59.987 12.074-59.843Q12.415-59.698 12.677-59.437Q12.939-59.175 13.081-58.839Q13.224-58.503 13.224-58.116Q13.224-57.624 12.960-57.214Q12.697-56.804 12.267-56.573Q11.837-56.343 11.345-56.343Q10.853-56.343 10.419-56.575Q9.986-56.808 9.724-57.216Q9.462-57.624 9.462-58.116M11.345-56.620Q11.802-56.620 12.054-56.843Q12.306-57.066 12.394-57.417Q12.482-57.769 12.482-58.214Q12.482-58.644 12.388-58.982Q12.294-59.319 12.040-59.526Q11.787-59.733 11.345-59.733Q10.697-59.733 10.453-59.317Q10.208-58.901 10.208-58.214Q10.208-57.769 10.296-57.417Q10.384-57.066 10.636-56.843Q10.888-56.620 11.345-56.620M14.392-57.374L14.392-59.116Q14.392-59.331 14.329-59.427Q14.267-59.523 14.148-59.544Q14.029-59.566 13.783-59.566L13.783-59.862L15.029-59.948L15.029-57.398L15.029-57.374Q15.029-57.062 15.083-56.900Q15.138-56.737 15.288-56.667Q15.439-56.597 15.759-56.597Q16.189-56.597 16.462-56.935Q16.736-57.273 16.736-57.718L16.736-59.116Q16.736-59.331 16.673-59.427Q16.611-59.523 16.492-59.544Q16.372-59.566 16.126-59.566L16.126-59.862L17.372-59.948L17.372-57.163Q17.372-56.952 17.435-56.857Q17.497-56.761 17.617-56.739Q17.736-56.718 17.982-56.718L17.982-56.421L16.759-56.343L16.759-56.964Q16.591-56.675 16.310-56.509Q16.029-56.343 15.708-56.343Q14.392-56.343 14.392-57.374\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M23.182-56.421L21.350-56.421L21.350-56.718Q21.624-56.718 21.792-56.765Q21.960-56.812 21.960-56.980L21.960-61.140Q21.960-61.355 21.897-61.450Q21.835-61.546 21.716-61.567Q21.596-61.589 21.350-61.589L21.350-61.886L22.573-61.972L22.573-56.980Q22.573-56.812 22.741-56.765Q22.909-56.718 23.182-56.718L23.182-56.421M25.487-56.421L23.710-56.421L23.710-56.718Q23.983-56.718 24.151-56.765Q24.319-56.812 24.319-56.980L24.319-59.116Q24.319-59.331 24.262-59.427Q24.206-59.523 24.093-59.544Q23.979-59.566 23.733-59.566L23.733-59.862L24.932-59.948L24.932-56.980Q24.932-56.812 25.079-56.765Q25.225-56.718 25.487-56.718L25.487-56.421M24.046-61.343Q24.046-61.534 24.180-61.665Q24.315-61.796 24.510-61.796Q24.632-61.796 24.735-61.733Q24.839-61.671 24.901-61.567Q24.964-61.464 24.964-61.343Q24.964-61.148 24.833-61.013Q24.702-60.878 24.510-60.878Q24.311-60.878 24.178-61.011Q24.046-61.144 24.046-61.343M27.811-56.421L26.014-56.421L26.014-56.718Q26.284-56.718 26.452-56.763Q26.620-56.808 26.620-56.980L26.620-61.140Q26.620-61.355 26.557-61.450Q26.495-61.546 26.378-61.567Q26.260-61.589 26.014-61.589L26.014-61.886L27.237-61.972L27.237-58.206L28.335-59.093Q28.542-59.273 28.542-59.421Q28.542-59.487 28.489-59.530Q28.436-59.573 28.366-59.573L28.366-59.870L29.901-59.870L29.901-59.573Q29.370-59.573 28.772-59.093L28.163-58.597L29.237-57.198Q29.374-57.023 29.481-56.915Q29.589-56.808 29.723-56.763Q29.858-56.718 30.085-56.718L30.085-56.421L28.460-56.421L28.460-56.718Q28.702-56.718 28.702-56.870Q28.702-56.948 28.659-57.019Q28.616-57.089 28.534-57.198L27.733-58.245L27.206-57.819L27.206-56.980Q27.206-56.812 27.374-56.765Q27.542-56.718 27.811-56.718\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M30.240-58.175Q30.240-58.655 30.473-59.071Q30.705-59.487 31.115-59.737Q31.525-59.987 32.002-59.987Q32.732-59.987 33.131-59.546Q33.529-59.105 33.529-58.374Q33.529-58.269 33.436-58.245L30.986-58.245L30.986-58.175Q30.986-57.765 31.107-57.409Q31.229-57.054 31.500-56.837Q31.772-56.620 32.201-56.620Q32.564-56.620 32.861-56.849Q33.158-57.077 33.260-57.429Q33.268-57.476 33.354-57.491L33.436-57.491Q33.529-57.464 33.529-57.382Q33.529-57.374 33.522-57.343Q33.459-57.116 33.320-56.933Q33.182-56.749 32.990-56.616Q32.799-56.483 32.580-56.413Q32.361-56.343 32.123-56.343Q31.752-56.343 31.414-56.480Q31.076-56.616 30.809-56.868Q30.541-57.120 30.391-57.460Q30.240-57.800 30.240-58.175M30.994-58.483L32.955-58.483Q32.955-58.788 32.854-59.079Q32.752-59.370 32.535-59.552Q32.318-59.733 32.002-59.733Q31.701-59.733 31.471-59.546Q31.240-59.358 31.117-59.067Q30.994-58.776 30.994-58.483\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M37.476-57.382L37.476-59.573L36.773-59.573L36.773-59.827Q37.129-59.827 37.371-60.060Q37.613-60.292 37.724-60.640Q37.836-60.987 37.836-61.343L38.117-61.343L38.117-59.870L39.293-59.870L39.293-59.573L38.117-59.573L38.117-57.398Q38.117-57.077 38.236-56.849Q38.355-56.620 38.636-56.620Q38.816-56.620 38.933-56.743Q39.050-56.866 39.103-57.046Q39.156-57.226 39.156-57.398L39.156-57.870L39.437-57.870L39.437-57.382Q39.437-57.128 39.332-56.888Q39.226-56.648 39.029-56.495Q38.832-56.343 38.574-56.343Q38.258-56.343 38.006-56.466Q37.754-56.589 37.615-56.823Q37.476-57.058 37.476-57.382M40.156-58.116Q40.156-58.620 40.412-59.052Q40.668-59.483 41.103-59.735Q41.539-59.987 42.039-59.987Q42.425-59.987 42.767-59.843Q43.109-59.698 43.371-59.437Q43.633-59.175 43.775-58.839Q43.918-58.503 43.918-58.116Q43.918-57.624 43.654-57.214Q43.390-56.804 42.961-56.573Q42.531-56.343 42.039-56.343Q41.547-56.343 41.113-56.575Q40.679-56.808 40.418-57.216Q40.156-57.624 40.156-58.116M42.039-56.620Q42.496-56.620 42.748-56.843Q43-57.066 43.088-57.417Q43.175-57.769 43.175-58.214Q43.175-58.644 43.082-58.982Q42.988-59.319 42.734-59.526Q42.480-59.733 42.039-59.733Q41.390-59.733 41.146-59.317Q40.902-58.901 40.902-58.214Q40.902-57.769 40.990-57.417Q41.078-57.066 41.330-56.843Q41.582-56.620 42.039-56.620\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M49.155-56.421L47.323-56.421L47.323-56.718Q47.597-56.718 47.765-56.765Q47.933-56.812 47.933-56.980L47.933-61.140Q47.933-61.355 47.870-61.450Q47.808-61.546 47.689-61.567Q47.569-61.589 47.323-61.589L47.323-61.886L48.546-61.972L48.546-56.980Q48.546-56.812 48.714-56.765Q48.882-56.718 49.155-56.718L49.155-56.421M49.601-58.175Q49.601-58.655 49.833-59.071Q50.066-59.487 50.476-59.737Q50.886-59.987 51.362-59.987Q52.093-59.987 52.491-59.546Q52.890-59.105 52.890-58.374Q52.890-58.269 52.796-58.245L50.347-58.245L50.347-58.175Q50.347-57.765 50.468-57.409Q50.589-57.054 50.860-56.837Q51.132-56.620 51.562-56.620Q51.925-56.620 52.222-56.849Q52.519-57.077 52.620-57.429Q52.628-57.476 52.714-57.491L52.796-57.491Q52.890-57.464 52.890-57.382Q52.890-57.374 52.882-57.343Q52.819-57.116 52.681-56.933Q52.542-56.749 52.351-56.616Q52.159-56.483 51.941-56.413Q51.722-56.343 51.483-56.343Q51.112-56.343 50.774-56.480Q50.437-56.616 50.169-56.868Q49.901-57.120 49.751-57.460Q49.601-57.800 49.601-58.175M50.355-58.483L52.316-58.483Q52.316-58.788 52.214-59.079Q52.112-59.370 51.896-59.552Q51.679-59.733 51.362-59.733Q51.062-59.733 50.831-59.546Q50.601-59.358 50.478-59.067Q50.355-58.776 50.355-58.483M53.476-57.253Q53.476-57.737 53.878-58.032Q54.280-58.327 54.831-58.446Q55.382-58.566 55.874-58.566L55.874-58.855Q55.874-59.081 55.759-59.288Q55.644-59.495 55.446-59.614Q55.249-59.733 55.019-59.733Q54.593-59.733 54.308-59.628Q54.378-59.601 54.425-59.546Q54.472-59.491 54.497-59.421Q54.523-59.351 54.523-59.276Q54.523-59.171 54.472-59.079Q54.421-58.987 54.329-58.937Q54.237-58.886 54.132-58.886Q54.026-58.886 53.935-58.937Q53.843-58.987 53.792-59.079Q53.741-59.171 53.741-59.276Q53.741-59.694 54.130-59.841Q54.519-59.987 55.019-59.987Q55.351-59.987 55.704-59.857Q56.058-59.726 56.286-59.472Q56.515-59.218 56.515-58.870L56.515-57.069Q56.515-56.937 56.587-56.827Q56.659-56.718 56.788-56.718Q56.913-56.718 56.982-56.823Q57.050-56.929 57.050-57.069L57.050-57.581L57.331-57.581L57.331-57.069Q57.331-56.866 57.214-56.708Q57.097-56.550 56.915-56.466Q56.733-56.382 56.530-56.382Q56.300-56.382 56.148-56.554Q55.995-56.726 55.964-56.956Q55.804-56.675 55.495-56.509Q55.187-56.343 54.835-56.343Q54.323-56.343 53.899-56.566Q53.476-56.788 53.476-57.253M54.163-57.253Q54.163-56.968 54.390-56.782Q54.616-56.597 54.909-56.597Q55.155-56.597 55.380-56.714Q55.605-56.831 55.739-57.034Q55.874-57.237 55.874-57.491L55.874-58.323Q55.608-58.323 55.323-58.269Q55.038-58.214 54.767-58.085Q54.495-57.956 54.329-57.749Q54.163-57.542 54.163-57.253\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M59.194-56.452L57.971-59.308Q57.889-59.483 57.745-59.528Q57.600-59.573 57.331-59.573L57.331-59.870L59.042-59.870L59.042-59.573Q58.620-59.573 58.620-59.390Q58.620-59.355 58.635-59.308L59.581-57.116L60.421-59.093Q60.460-59.171 60.460-59.261Q60.460-59.401 60.354-59.487Q60.249-59.573 60.108-59.573L60.108-59.870L61.460-59.870L61.460-59.573Q60.936-59.573 60.721-59.093L59.596-56.452Q59.534-56.343 59.428-56.343L59.362-56.343Q59.249-56.343 59.194-56.452\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 64.596)\">\u003Cpath d=\"M61.644-58.175Q61.644-58.655 61.877-59.071Q62.109-59.487 62.519-59.737Q62.929-59.987 63.406-59.987Q64.136-59.987 64.535-59.546Q64.933-59.105 64.933-58.374Q64.933-58.269 64.840-58.245L62.390-58.245L62.390-58.175Q62.390-57.765 62.511-57.409Q62.633-57.054 62.904-56.837Q63.176-56.620 63.605-56.620Q63.968-56.620 64.265-56.849Q64.562-57.077 64.664-57.429Q64.672-57.476 64.758-57.491L64.840-57.491Q64.933-57.464 64.933-57.382Q64.933-57.374 64.926-57.343Q64.863-57.116 64.724-56.933Q64.586-56.749 64.394-56.616Q64.203-56.483 63.984-56.413Q63.765-56.343 63.527-56.343Q63.156-56.343 62.818-56.480Q62.480-56.616 62.213-56.868Q61.945-57.120 61.795-57.460Q61.644-57.800 61.644-58.175M62.398-58.483L64.359-58.483Q64.359-58.788 64.258-59.079Q64.156-59.370 63.939-59.552Q63.722-59.733 63.406-59.733Q63.105-59.733 62.875-59.546Q62.644-59.358 62.521-59.067Q62.398-58.776 62.398-58.483M66.609-56.886Q66.609-57.077 66.742-57.210Q66.875-57.343 67.070-57.343Q67.261-57.343 67.394-57.210Q67.527-57.077 67.527-56.886Q67.527-56.687 67.394-56.554Q67.261-56.421 67.070-56.421Q66.875-56.421 66.742-56.554Q66.609-56.687 66.609-56.886M66.926-58.062L66.926-58.460Q66.926-58.858 67.054-59.259Q67.183-59.659 67.429-59.995Q67.508-60.101 67.674-60.276Q67.840-60.452 67.908-60.589Q67.976-60.726 67.976-60.964Q67.976-61.452 67.773-61.624Q67.570-61.796 67.070-61.796Q66.761-61.796 66.484-61.685Q66.207-61.573 66.054-61.351Q66.226-61.351 66.334-61.237Q66.441-61.124 66.441-60.956Q66.441-60.855 66.390-60.763Q66.340-60.671 66.250-60.618Q66.160-60.566 66.047-60.566Q65.937-60.566 65.847-60.618Q65.758-60.671 65.707-60.763Q65.656-60.855 65.656-60.956Q65.656-61.304 65.867-61.554Q66.078-61.804 66.402-61.929Q66.726-62.054 67.070-62.054Q67.730-62.054 68.224-61.804Q68.719-61.554 68.719-60.956Q68.719-60.706 68.595-60.491Q68.472-60.276 68.261-60.140Q67.949-59.937 67.717-59.681Q67.484-59.425 67.345-59.108Q67.207-58.792 67.207-58.444L67.207-58.062Q67.207-58.026 67.179-57.999Q67.152-57.972 67.121-57.972L67.015-57.972Q66.984-57.972 66.955-57.997Q66.926-58.023 66.926-58.062\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(9.223 82.112)\">\u003Cpath d=\"M-59.483-56.659L-59.483-56.749Q-59.424-56.960-59.233-56.980L-59.010-56.980L-58.057-61.163Q-58.033-61.280-57.938-61.355Q-57.842-61.429-57.729-61.429L-57.455-61.429Q-57.342-61.429-57.246-61.353Q-57.151-61.276-57.127-61.163L-56.178-56.980L-55.951-56.980Q-55.744-56.956-55.705-56.749L-55.705-56.659Q-55.756-56.444-55.951-56.421L-57.033-56.421Q-57.229-56.444-57.279-56.659L-57.279-56.749Q-57.229-56.956-57.033-56.980L-56.834-56.980Q-56.963-57.546-56.986-57.628L-58.201-57.628Q-58.244-57.444-58.354-56.980L-58.154-56.980Q-57.955-56.956-57.904-56.749L-57.904-56.659Q-57.955-56.444-58.154-56.421L-59.233-56.421Q-59.440-56.444-59.483-56.659M-58.080-58.191L-57.104-58.191Q-57.584-60.323-57.584-60.667L-57.592-60.667Q-57.592-60.483-57.733-59.800Q-57.873-59.116-58.080-58.191M-53.893-56.421L-55.076-56.421Q-55.272-56.444-55.322-56.659L-55.322-56.749Q-55.272-56.956-55.076-56.980L-54.803-56.980L-54.803-60.749L-55.076-60.749Q-55.272-60.773-55.322-60.987L-55.322-61.077Q-55.272-61.284-55.076-61.308L-53.506-61.308Q-53.119-61.308-52.764-61.138Q-52.408-60.968-52.186-60.657Q-51.963-60.347-51.963-59.948Q-51.963-59.722-52.045-59.513Q-52.127-59.304-52.270-59.136Q-52.412-58.968-52.604-58.847Q-52.408-58.694-52.293-58.478Q-52.178-58.261-52.178-58.023L-52.178-57.444Q-52.178-56.901-51.979-56.901Q-51.932-56.901-51.906-57.030Q-51.881-57.159-51.881-57.261Q-51.830-57.468-51.635-57.491L-51.490-57.491Q-51.295-57.472-51.244-57.261L-51.244-57.206Q-51.244-56.874-51.449-56.608Q-51.654-56.343-51.971-56.343Q-52.385-56.343-52.602-56.650Q-52.818-56.956-52.818-57.390L-52.818-57.964Q-52.818-58.253-53.027-58.421Q-53.236-58.589-53.522-58.589L-54.162-58.589L-54.162-56.980L-53.893-56.980Q-53.693-56.956-53.643-56.749L-53.643-56.659Q-53.693-56.444-53.893-56.421M-54.162-60.749L-54.162-59.148L-53.588-59.148Q-53.354-59.148-53.127-59.243Q-52.901-59.339-52.752-59.523Q-52.604-59.706-52.604-59.948Q-52.604-60.191-52.752-60.374Q-52.901-60.558-53.125-60.653Q-53.350-60.749-53.588-60.749L-54.162-60.749M-49.647-56.421L-50.830-56.421Q-51.026-56.444-51.076-56.659L-51.076-56.749Q-51.026-56.956-50.830-56.980L-50.557-56.980L-50.557-60.749L-50.830-60.749Q-51.026-60.773-51.076-60.987L-51.076-61.077Q-51.026-61.284-50.830-61.308L-49.260-61.308Q-48.873-61.308-48.518-61.138Q-48.162-60.968-47.940-60.657Q-47.717-60.347-47.717-59.948Q-47.717-59.722-47.799-59.513Q-47.881-59.304-48.024-59.136Q-48.166-58.968-48.358-58.847Q-48.162-58.694-48.047-58.478Q-47.932-58.261-47.932-58.023L-47.932-57.444Q-47.932-56.901-47.733-56.901Q-47.686-56.901-47.660-57.030Q-47.635-57.159-47.635-57.261Q-47.584-57.468-47.389-57.491L-47.244-57.491Q-47.049-57.472-46.998-57.261L-46.998-57.206Q-46.998-56.874-47.203-56.608Q-47.408-56.343-47.725-56.343Q-48.139-56.343-48.356-56.650Q-48.572-56.956-48.572-57.390L-48.572-57.964Q-48.572-58.253-48.781-58.421Q-48.990-58.589-49.276-58.589L-49.916-58.589L-49.916-56.980L-49.647-56.980Q-49.447-56.956-49.397-56.749L-49.397-56.659Q-49.447-56.444-49.647-56.421M-49.916-60.749L-49.916-59.148L-49.342-59.148Q-49.108-59.148-48.881-59.243Q-48.654-59.339-48.506-59.523Q-48.358-59.706-48.358-59.948Q-48.358-60.191-48.506-60.374Q-48.654-60.558-48.879-60.653Q-49.104-60.749-49.342-60.749L-49.916-60.749M-46.358-56.659L-46.358-56.749Q-46.307-56.956-46.111-56.980L-45.174-56.980L-45.174-60.749L-46.111-60.749Q-46.307-60.773-46.358-60.987L-46.358-61.077Q-46.307-61.284-46.111-61.308L-43.600-61.308Q-43.401-61.284-43.350-61.077L-43.350-60.987Q-43.401-60.773-43.600-60.749L-44.533-60.749L-44.533-56.980L-43.600-56.980Q-43.401-56.956-43.350-56.749L-43.350-56.659Q-43.401-56.444-43.600-56.421L-46.111-56.421Q-46.307-56.444-46.358-56.659M-41.080-56.605L-42.119-60.749L-42.330-60.749Q-42.526-60.773-42.576-60.987L-42.576-61.077Q-42.526-61.284-42.330-61.308L-41.272-61.308Q-41.065-61.284-41.026-61.077L-41.026-60.987Q-41.065-60.773-41.272-60.749L-41.455-60.749Q-41.350-60.343-41.213-59.788Q-41.076-59.233-40.922-58.589Q-40.768-57.944-40.688-57.558Q-40.608-57.171-40.608-57.062Q-40.600-57.183-40.522-57.552Q-40.443-57.921-40.297-58.538Q-40.151-59.155-40.014-59.716Q-39.877-60.276-39.760-60.749L-39.943-60.749Q-40.154-60.773-40.193-60.987L-40.193-61.077Q-40.154-61.284-39.943-61.308L-38.889-61.308Q-38.697-61.284-38.639-61.077L-38.639-60.987Q-38.697-60.773-38.889-60.749L-39.096-60.749L-40.135-56.605Q-40.162-56.495-40.258-56.419Q-40.354-56.343-40.471-56.343L-40.744-56.343Q-40.861-56.343-40.955-56.417Q-41.049-56.491-41.080-56.605M-38.299-56.659L-38.299-56.749Q-38.260-56.956-38.049-56.980L-37.740-56.980L-37.740-60.749L-38.049-60.749Q-38.260-60.773-38.299-60.987L-38.299-61.077Q-38.260-61.284-38.049-61.308L-34.842-61.308Q-34.647-61.284-34.596-61.077L-34.596-60.237Q-34.647-60.023-34.842-59.995L-34.986-59.995Q-35.182-60.023-35.236-60.237L-35.236-60.749L-37.100-60.749L-37.100-59.230L-36.123-59.230L-36.123-59.444Q-36.072-59.651-35.873-59.679L-35.729-59.679Q-35.533-59.651-35.483-59.444L-35.483-58.460Q-35.533-58.245-35.729-58.222L-35.873-58.222Q-36.072-58.245-36.123-58.460L-36.123-58.667L-37.100-58.667L-37.100-56.980L-35.057-56.980L-35.057-57.620Q-35.006-57.827-34.811-57.855L-34.666-57.855Q-34.471-57.827-34.420-57.620L-34.420-56.659Q-34.471-56.441-34.666-56.421L-38.049-56.421Q-38.260-56.444-38.299-56.659M-30.740-58.558L-33.483-58.558Q-33.608-58.569-33.693-58.655Q-33.779-58.741-33.779-58.870Q-33.779-58.999-33.693-59.081Q-33.608-59.163-33.483-59.175L-30.740-59.175Q-30.615-59.163-30.533-59.081Q-30.451-58.999-30.451-58.870Q-30.451-58.741-30.533-58.655Q-30.615-58.569-30.740-58.558M-28.928-56.659L-28.928-56.749Q-28.869-56.956-28.678-56.980L-28.190-56.980L-28.190-60.749L-29.135-60.749L-29.135-60.237Q-29.182-60.023-29.381-59.995L-29.526-59.995Q-29.725-60.023-29.776-60.237L-29.776-61.077Q-29.725-61.284-29.526-61.308L-26.213-61.308Q-26.018-61.288-25.967-61.077L-25.967-60.237Q-26.014-60.023-26.213-59.995L-26.358-59.995Q-26.557-60.023-26.608-60.237L-26.608-60.749L-27.549-60.749L-27.549-56.980L-27.061-56.980Q-26.865-56.956-26.815-56.749L-26.815-56.659Q-26.865-56.444-27.061-56.421L-28.678-56.421Q-28.869-56.444-28.928-56.659M-25.127-56.659L-25.127-56.749Q-25.076-56.956-24.881-56.980L-23.943-56.980L-23.943-60.749L-24.881-60.749Q-25.076-60.773-25.127-60.987L-25.127-61.077Q-25.076-61.284-24.881-61.308L-22.369-61.308Q-22.170-61.284-22.119-61.077L-22.119-60.987Q-22.170-60.773-22.369-60.749L-23.303-60.749L-23.303-56.980L-22.369-56.980Q-22.170-56.956-22.119-56.749L-22.119-56.659Q-22.170-56.444-22.369-56.421L-24.881-56.421Q-25.076-56.444-25.127-56.659M-21.385-56.659L-21.385-56.749Q-21.334-56.956-21.139-56.980L-20.963-56.980L-20.963-60.749L-21.139-60.749Q-21.334-60.773-21.385-60.987L-21.385-61.077Q-21.334-61.284-21.139-61.308L-20.451-61.308Q-20.322-61.308-20.223-61.232Q-20.123-61.155-20.084-61.046Q-20.053-60.941-19.830-60.251Q-19.608-59.562-19.492-59.165Q-19.377-58.769-19.377-58.694Q-19.369-58.773-19.315-58.970Q-19.260-59.167-19.147-59.538Q-19.033-59.909-18.887-60.372Q-18.740-60.835-18.674-61.046Q-18.635-61.155-18.531-61.232Q-18.428-61.308-18.307-61.308L-17.619-61.308Q-17.420-61.284-17.369-61.077L-17.369-60.987Q-17.420-60.773-17.619-60.749L-17.795-60.749L-17.795-56.980L-17.619-56.980Q-17.420-56.956-17.369-56.749L-17.369-56.659Q-17.420-56.444-17.619-56.421L-18.498-56.421Q-18.693-56.444-18.744-56.659L-18.744-56.749Q-18.693-56.956-18.498-56.980L-18.322-56.980L-18.322-60.667Q-18.322-60.608-18.406-60.310Q-18.490-60.011-18.594-59.679Q-18.697-59.347-18.828-58.939Q-18.959-58.530-19.026-58.316Q-19.065-58.202-19.164-58.128Q-19.264-58.054-19.377-58.054Q-19.490-58.054-19.590-58.128Q-19.690-58.202-19.729-58.316Q-19.795-58.530-19.930-58.948Q-20.065-59.366-20.201-59.810Q-20.338-60.253-20.383-60.417Q-20.428-60.581-20.436-60.667L-20.436-56.980L-20.260-56.980Q-20.061-56.956-20.010-56.749L-20.010-56.659Q-20.061-56.444-20.260-56.421L-21.139-56.421Q-21.334-56.444-21.385-56.659M-17.068-56.659L-17.068-56.749Q-17.029-56.956-16.818-56.980L-16.510-56.980L-16.510-60.749L-16.818-60.749Q-17.029-60.773-17.068-60.987L-17.068-61.077Q-17.029-61.284-16.818-61.308L-13.611-61.308Q-13.416-61.284-13.365-61.077L-13.365-60.237Q-13.416-60.023-13.611-59.995L-13.756-59.995Q-13.951-60.023-14.006-60.237L-14.006-60.749L-15.869-60.749L-15.869-59.230L-14.893-59.230L-14.893-59.444Q-14.842-59.651-14.643-59.679L-14.498-59.679Q-14.303-59.651-14.252-59.444L-14.252-58.460Q-14.303-58.245-14.498-58.222L-14.643-58.222Q-14.842-58.245-14.893-58.460L-14.893-58.667L-15.869-58.667L-15.869-56.980L-13.826-56.980L-13.826-57.620Q-13.776-57.827-13.580-57.855L-13.436-57.855Q-13.240-57.827-13.190-57.620L-13.190-56.659Q-13.240-56.441-13.436-56.421L-16.818-56.421Q-17.029-56.444-17.068-56.659\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(117.344 82.112)\">\u003Cpath d=\"M-58.514-57.526L-58.514-59.308L-59.264-59.308Q-59.463-59.331-59.514-59.550L-59.514-59.636Q-59.463-59.847-59.264-59.870L-58.514-59.870L-58.514-60.620Q-58.463-60.827-58.264-60.855L-58.119-60.855Q-57.924-60.827-57.873-60.620L-57.873-59.870L-56.514-59.870Q-56.322-59.851-56.264-59.636L-56.264-59.550Q-56.318-59.331-56.514-59.308L-57.873-59.308L-57.873-57.558Q-57.873-56.941-57.299-56.941Q-57.049-56.941-56.885-57.126Q-56.721-57.312-56.721-57.558Q-56.721-57.651-56.649-57.722Q-56.576-57.792-56.475-57.804L-56.330-57.804Q-56.131-57.780-56.080-57.573L-56.080-57.526Q-56.080-57.202-56.264-56.939Q-56.447-56.675-56.740-56.528Q-57.033-56.382-57.354-56.382Q-57.865-56.382-58.190-56.692Q-58.514-57.003-58.514-57.526M-54.850-56.659L-54.850-56.749Q-54.799-56.956-54.604-56.980L-53.565-56.980L-53.565-59.308L-54.537-59.308Q-54.736-59.331-54.787-59.550L-54.787-59.636Q-54.736-59.847-54.537-59.870L-53.170-59.870Q-52.975-59.851-52.924-59.636L-52.924-56.980L-52.010-56.980Q-51.815-56.956-51.764-56.749L-51.764-56.659Q-51.815-56.444-52.010-56.421L-54.604-56.421Q-54.799-56.444-54.850-56.659M-53.818-60.847L-53.818-60.901Q-53.818-61.073-53.682-61.194Q-53.545-61.316-53.369-61.316Q-53.197-61.316-53.061-61.194Q-52.924-61.073-52.924-60.901L-52.924-60.847Q-52.924-60.671-53.061-60.550Q-53.197-60.429-53.369-60.429Q-53.545-60.429-53.682-60.550Q-53.818-60.671-53.818-60.847M-51.276-56.659L-51.276-56.749Q-51.225-56.960-51.029-56.980L-50.830-56.980L-50.830-59.308L-51.029-59.308Q-51.236-59.331-51.276-59.550L-51.276-59.636Q-51.225-59.851-51.029-59.870L-50.549-59.870Q-50.358-59.847-50.299-59.636Q-49.979-59.909-49.565-59.909Q-49.373-59.909-49.205-59.800Q-49.037-59.691-48.963-59.519Q-48.787-59.710-48.565-59.810Q-48.342-59.909-48.100-59.909Q-47.682-59.909-47.527-59.567Q-47.373-59.226-47.373-58.757L-47.373-56.980L-47.174-56.980Q-46.963-56.956-46.924-56.749L-46.924-56.659Q-46.975-56.444-47.174-56.421L-47.990-56.421Q-48.186-56.444-48.236-56.659L-48.236-56.749Q-48.186-56.956-47.990-56.980L-47.901-56.980L-47.901-58.726Q-47.901-59.351-48.151-59.351Q-48.483-59.351-48.660-59.040Q-48.838-58.730-48.838-58.366L-48.838-56.980L-48.635-56.980Q-48.428-56.956-48.389-56.749L-48.389-56.659Q-48.440-56.444-48.635-56.421L-49.451-56.421Q-49.651-56.444-49.701-56.659L-49.701-56.749Q-49.651-56.956-49.451-56.980L-49.365-56.980L-49.365-58.726Q-49.365-59.351-49.611-59.351Q-49.943-59.351-50.121-59.038Q-50.299-58.726-50.299-58.366L-50.299-56.980L-50.100-56.980Q-49.893-56.956-49.854-56.749L-49.854-56.659Q-49.904-56.441-50.100-56.421L-51.029-56.421Q-51.236-56.444-51.276-56.659M-43.463-57.909L-45.904-57.909Q-45.850-57.632-45.652-57.409Q-45.455-57.187-45.178-57.064Q-44.901-56.941-44.615-56.941Q-44.143-56.941-43.920-57.230Q-43.912-57.241-43.856-57.347Q-43.799-57.452-43.750-57.495Q-43.701-57.538-43.608-57.550L-43.463-57.550Q-43.272-57.530-43.213-57.316L-43.213-57.261Q-43.279-56.960-43.510-56.763Q-43.740-56.566-44.053-56.474Q-44.365-56.382-44.670-56.382Q-45.154-56.382-45.594-56.610Q-46.033-56.839-46.301-57.239Q-46.568-57.640-46.568-58.132L-46.568-58.191Q-46.568-58.659-46.322-59.062Q-46.076-59.464-45.668-59.698Q-45.260-59.933-44.791-59.933Q-44.287-59.933-43.934-59.710Q-43.580-59.487-43.397-59.099Q-43.213-58.710-43.213-58.206L-43.213-58.148Q-43.272-57.933-43.463-57.909M-45.897-58.460L-43.869-58.460Q-43.916-58.870-44.154-59.122Q-44.393-59.374-44.791-59.374Q-45.186-59.374-45.492-59.112Q-45.799-58.851-45.897-58.460\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M-57.139-56.366L-58.842-61.323Q-58.908-61.499-59.084-61.544Q-59.260-61.589-59.553-61.589L-59.553-61.886L-57.408-61.886L-57.408-61.589Q-58.065-61.589-58.065-61.374Q-58.061-61.362-58.059-61.353Q-58.057-61.343-58.057-61.323L-56.705-57.390L-55.506-60.894L-55.651-61.323Q-55.721-61.499-55.895-61.544Q-56.068-61.589-56.361-61.589L-56.361-61.886L-54.225-61.886L-54.225-61.589Q-54.873-61.589-54.873-61.374Q-54.873-61.331-54.865-61.323L-53.514-57.390L-52.240-61.108Q-52.225-61.155-52.225-61.191Q-52.225-61.331-52.334-61.423Q-52.443-61.515-52.600-61.552Q-52.756-61.589-52.889-61.589L-52.889-61.886L-51.154-61.886L-51.154-61.589Q-51.772-61.589-51.943-61.108L-53.568-56.366Q-53.588-56.316-53.631-56.284Q-53.674-56.253-53.729-56.253L-53.783-56.253Q-53.904-56.253-53.943-56.366L-55.354-60.460L-56.760-56.366Q-56.779-56.316-56.822-56.284Q-56.865-56.253-56.920-56.253L-56.979-56.253Q-57.092-56.253-57.139-56.366M-48.818-56.421L-50.674-56.421L-50.674-56.718Q-50.401-56.718-50.233-56.765Q-50.065-56.812-50.065-56.980L-50.065-61.140Q-50.065-61.355-50.127-61.450Q-50.190-61.546-50.309-61.567Q-50.428-61.589-50.674-61.589L-50.674-61.886L-49.451-61.972L-49.451-59.269Q-49.326-59.480-49.139-59.630Q-48.951-59.780-48.725-59.864Q-48.498-59.948-48.252-59.948Q-47.084-59.948-47.084-58.870L-47.084-56.980Q-47.084-56.812-46.914-56.765Q-46.744-56.718-46.475-56.718L-46.475-56.421L-48.330-56.421L-48.330-56.718Q-48.057-56.718-47.889-56.765Q-47.721-56.812-47.721-56.980L-47.721-58.855Q-47.721-59.237-47.842-59.466Q-47.963-59.694-48.315-59.694Q-48.627-59.694-48.881-59.532Q-49.135-59.370-49.281-59.101Q-49.428-58.831-49.428-58.534L-49.428-56.980Q-49.428-56.812-49.258-56.765Q-49.088-56.718-48.818-56.718L-48.818-56.421M-46.029-58.175Q-46.029-58.655-45.797-59.071Q-45.565-59.487-45.154-59.737Q-44.744-59.987-44.268-59.987Q-43.537-59.987-43.139-59.546Q-42.740-59.105-42.740-58.374Q-42.740-58.269-42.834-58.245L-45.283-58.245L-45.283-58.175Q-45.283-57.765-45.162-57.409Q-45.041-57.054-44.770-56.837Q-44.498-56.620-44.068-56.620Q-43.705-56.620-43.408-56.849Q-43.111-57.077-43.010-57.429Q-43.002-57.476-42.916-57.491L-42.834-57.491Q-42.740-57.464-42.740-57.382Q-42.740-57.374-42.748-57.343Q-42.811-57.116-42.949-56.933Q-43.088-56.749-43.279-56.616Q-43.471-56.483-43.690-56.413Q-43.908-56.343-44.147-56.343Q-44.518-56.343-44.856-56.480Q-45.193-56.616-45.461-56.868Q-45.729-57.120-45.879-57.460Q-46.029-57.800-46.029-58.175M-45.276-58.483L-43.315-58.483Q-43.315-58.788-43.416-59.079Q-43.518-59.370-43.734-59.552Q-43.951-59.733-44.268-59.733Q-44.568-59.733-44.799-59.546Q-45.029-59.358-45.152-59.067Q-45.276-58.776-45.276-58.483M-40.322-56.421L-42.178-56.421L-42.178-56.718Q-41.904-56.718-41.736-56.765Q-41.568-56.812-41.568-56.980L-41.568-59.116Q-41.568-59.331-41.631-59.427Q-41.693-59.523-41.813-59.544Q-41.932-59.566-42.178-59.566L-42.178-59.862L-40.986-59.948L-40.986-59.214Q-40.873-59.429-40.680-59.597Q-40.486-59.765-40.248-59.857Q-40.010-59.948-39.756-59.948Q-38.588-59.948-38.588-58.870L-38.588-56.980Q-38.588-56.812-38.418-56.765Q-38.248-56.718-37.979-56.718L-37.979-56.421L-39.834-56.421L-39.834-56.718Q-39.561-56.718-39.393-56.765Q-39.225-56.812-39.225-56.980L-39.225-58.855Q-39.225-59.237-39.346-59.466Q-39.467-59.694-39.818-59.694Q-40.131-59.694-40.385-59.532Q-40.639-59.370-40.785-59.101Q-40.932-58.831-40.932-58.534L-40.932-56.980Q-40.932-56.812-40.762-56.765Q-40.592-56.718-40.322-56.718\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M-32.874-56.343Q-33.355-56.343-33.763-56.587Q-34.171-56.831-34.409-57.245Q-34.648-57.659-34.648-58.148Q-34.648-58.640-34.390-59.056Q-34.132-59.472-33.700-59.710Q-33.269-59.948-32.777-59.948Q-32.156-59.948-31.706-59.511L-31.706-61.140Q-31.706-61.355-31.769-61.450Q-31.831-61.546-31.949-61.567Q-32.066-61.589-32.312-61.589L-32.312-61.886L-31.089-61.972L-31.089-57.163Q-31.089-56.952-31.027-56.857Q-30.964-56.761-30.847-56.739Q-30.730-56.718-30.480-56.718L-30.480-56.421L-31.730-56.343L-31.730-56.827Q-32.195-56.343-32.874-56.343M-32.808-56.597Q-32.468-56.597-32.175-56.788Q-31.882-56.980-31.730-57.276L-31.730-59.108Q-31.878-59.382-32.140-59.538Q-32.402-59.694-32.714-59.694Q-33.339-59.694-33.622-59.247Q-33.906-58.800-33.906-58.140Q-33.906-57.495-33.654-57.046Q-33.402-56.597-32.808-56.597M-29.972-58.116Q-29.972-58.620-29.716-59.052Q-29.460-59.483-29.025-59.735Q-28.589-59.987-28.089-59.987Q-27.702-59.987-27.361-59.843Q-27.019-59.698-26.757-59.437Q-26.495-59.175-26.353-58.839Q-26.210-58.503-26.210-58.116Q-26.210-57.624-26.474-57.214Q-26.738-56.804-27.167-56.573Q-27.597-56.343-28.089-56.343Q-28.581-56.343-29.015-56.575Q-29.449-56.808-29.710-57.216Q-29.972-57.624-29.972-58.116M-28.089-56.620Q-27.632-56.620-27.380-56.843Q-27.128-57.066-27.040-57.417Q-26.952-57.769-26.952-58.214Q-26.952-58.644-27.046-58.982Q-27.140-59.319-27.394-59.526Q-27.648-59.733-28.089-59.733Q-28.738-59.733-28.982-59.317Q-29.226-58.901-29.226-58.214Q-29.226-57.769-29.138-57.417Q-29.050-57.066-28.798-56.843Q-28.546-56.620-28.089-56.620\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M-22.467-55.124Q-22.353-55.046-22.178-55.046Q-21.889-55.046-21.668-55.259Q-21.447-55.472-21.322-55.773L-21.033-56.421L-22.307-59.308Q-22.389-59.483-22.533-59.528Q-22.678-59.573-22.947-59.573L-22.947-59.870L-21.228-59.870L-21.228-59.573Q-21.650-59.573-21.650-59.390Q-21.650-59.378-21.635-59.308L-20.697-57.183L-19.865-59.093Q-19.826-59.183-19.826-59.261Q-19.826-59.401-19.928-59.487Q-20.029-59.573-20.170-59.573L-20.170-59.870L-18.818-59.870L-18.818-59.573Q-19.072-59.573-19.266-59.448Q-19.459-59.323-19.564-59.093L-21.010-55.773Q-21.123-55.519-21.289-55.296Q-21.455-55.073-21.684-54.931Q-21.912-54.788-22.178-54.788Q-22.475-54.788-22.715-54.980Q-22.955-55.171-22.955-55.460Q-22.955-55.616-22.850-55.718Q-22.744-55.819-22.596-55.819Q-22.490-55.819-22.410-55.773Q-22.330-55.726-22.283-55.648Q-22.236-55.569-22.236-55.460Q-22.236-55.339-22.297-55.251Q-22.357-55.163-22.467-55.124\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M-18.635-58.116Q-18.635-58.620-18.379-59.052Q-18.123-59.483-17.687-59.735Q-17.252-59.987-16.752-59.987Q-16.365-59.987-16.023-59.843Q-15.682-59.698-15.420-59.437Q-15.158-59.175-15.016-58.839Q-14.873-58.503-14.873-58.116Q-14.873-57.624-15.137-57.214Q-15.400-56.804-15.830-56.573Q-16.260-56.343-16.752-56.343Q-17.244-56.343-17.678-56.575Q-18.111-56.808-18.373-57.216Q-18.635-57.624-18.635-58.116M-16.752-56.620Q-16.295-56.620-16.043-56.843Q-15.791-57.066-15.703-57.417Q-15.615-57.769-15.615-58.214Q-15.615-58.644-15.709-58.982Q-15.803-59.319-16.057-59.526Q-16.311-59.733-16.752-59.733Q-17.400-59.733-17.644-59.317Q-17.889-58.901-17.889-58.214Q-17.889-57.769-17.801-57.417Q-17.713-57.066-17.461-56.843Q-17.209-56.620-16.752-56.620M-13.705-57.374L-13.705-59.116Q-13.705-59.331-13.768-59.427Q-13.830-59.523-13.949-59.544Q-14.068-59.566-14.314-59.566L-14.314-59.862L-13.068-59.948L-13.068-57.398L-13.068-57.374Q-13.068-57.062-13.014-56.900Q-12.959-56.737-12.809-56.667Q-12.658-56.597-12.338-56.597Q-11.908-56.597-11.635-56.935Q-11.361-57.273-11.361-57.718L-11.361-59.116Q-11.361-59.331-11.424-59.427Q-11.486-59.523-11.605-59.544Q-11.725-59.566-11.971-59.566L-11.971-59.862L-10.725-59.948L-10.725-57.163Q-10.725-56.952-10.662-56.857Q-10.600-56.761-10.480-56.739Q-10.361-56.718-10.115-56.718L-10.115-56.421L-11.338-56.343L-11.338-56.964Q-11.506-56.675-11.787-56.509Q-12.068-56.343-12.389-56.343Q-13.705-56.343-13.705-57.374\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M-5.244-56.452L-6.314-59.308Q-6.380-59.487-6.511-59.530Q-6.642-59.573-6.900-59.573L-6.900-59.870L-5.220-59.870L-5.220-59.573Q-5.670-59.573-5.670-59.374Q-5.666-59.358-5.664-59.341Q-5.662-59.323-5.662-59.308L-4.869-57.214L-4.158-59.124Q-4.193-59.218-4.193-59.263Q-4.193-59.308-4.228-59.308Q-4.295-59.487-4.425-59.530Q-4.556-59.573-4.810-59.573L-4.810-59.870L-3.220-59.870L-3.220-59.573Q-3.670-59.573-3.670-59.374Q-3.666-59.355-3.664-59.337Q-3.662-59.319-3.662-59.308L-2.830-57.093L-2.076-59.093Q-2.052-59.151-2.052-59.222Q-2.052-59.382-2.189-59.478Q-2.326-59.573-2.494-59.573L-2.494-59.870L-1.107-59.870L-1.107-59.573Q-1.341-59.573-1.519-59.446Q-1.697-59.319-1.779-59.093L-2.763-56.452Q-2.818-56.343-2.931-56.343L-2.990-56.343Q-3.103-56.343-3.146-56.452L-4.005-58.726L-4.861-56.452Q-4.900-56.343-5.021-56.343L-5.076-56.343Q-5.189-56.343-5.244-56.452\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M-0.829-57.253Q-0.829-57.737-0.427-58.032Q-0.024-58.327 0.526-58.446Q1.077-58.566 1.569-58.566L1.569-58.855Q1.569-59.081 1.454-59.288Q1.339-59.495 1.142-59.614Q0.944-59.733 0.714-59.733Q0.288-59.733 0.003-59.628Q0.073-59.601 0.120-59.546Q0.167-59.491 0.192-59.421Q0.218-59.351 0.218-59.276Q0.218-59.171 0.167-59.079Q0.116-58.987 0.024-58.937Q-0.067-58.886-0.173-58.886Q-0.278-58.886-0.370-58.937Q-0.462-58.987-0.513-59.079Q-0.563-59.171-0.563-59.276Q-0.563-59.694-0.175-59.841Q0.214-59.987 0.714-59.987Q1.046-59.987 1.399-59.857Q1.753-59.726 1.981-59.472Q2.210-59.218 2.210-58.870L2.210-57.069Q2.210-56.937 2.282-56.827Q2.355-56.718 2.483-56.718Q2.608-56.718 2.677-56.823Q2.745-56.929 2.745-57.069L2.745-57.581L3.026-57.581L3.026-57.069Q3.026-56.866 2.909-56.708Q2.792-56.550 2.610-56.466Q2.429-56.382 2.226-56.382Q1.995-56.382 1.843-56.554Q1.690-56.726 1.659-56.956Q1.499-56.675 1.190-56.509Q0.882-56.343 0.530-56.343Q0.019-56.343-0.405-56.566Q-0.829-56.788-0.829-57.253M-0.142-57.253Q-0.142-56.968 0.085-56.782Q0.312-56.597 0.605-56.597Q0.851-56.597 1.075-56.714Q1.300-56.831 1.435-57.034Q1.569-57.237 1.569-57.491L1.569-58.323Q1.304-58.323 1.019-58.269Q0.733-58.214 0.462-58.085Q0.190-57.956 0.024-57.749Q-0.142-57.542-0.142-57.253M5.249-56.421L3.394-56.421L3.394-56.718Q3.667-56.718 3.835-56.765Q4.003-56.812 4.003-56.980L4.003-59.116Q4.003-59.331 3.940-59.427Q3.878-59.523 3.759-59.544Q3.640-59.566 3.394-59.566L3.394-59.862L4.585-59.948L4.585-59.214Q4.698-59.429 4.892-59.597Q5.085-59.765 5.323-59.857Q5.562-59.948 5.815-59.948Q6.983-59.948 6.983-58.870L6.983-56.980Q6.983-56.812 7.153-56.765Q7.323-56.718 7.593-56.718L7.593-56.421L5.737-56.421L5.737-56.718Q6.011-56.718 6.179-56.765Q6.347-56.812 6.347-56.980L6.347-58.855Q6.347-59.237 6.226-59.466Q6.105-59.694 5.753-59.694Q5.440-59.694 5.187-59.532Q4.933-59.370 4.786-59.101Q4.640-58.831 4.640-58.534L4.640-56.980Q4.640-56.812 4.810-56.765Q4.980-56.718 5.249-56.718\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M8.434-57.382L8.434-59.573L7.731-59.573L7.731-59.827Q8.087-59.827 8.329-60.060Q8.571-60.292 8.682-60.640Q8.794-60.987 8.794-61.343L9.075-61.343L9.075-59.870L10.251-59.870L10.251-59.573L9.075-59.573L9.075-57.398Q9.075-57.077 9.194-56.849Q9.313-56.620 9.594-56.620Q9.774-56.620 9.891-56.743Q10.008-56.866 10.061-57.046Q10.114-57.226 10.114-57.398L10.114-57.870L10.395-57.870L10.395-57.382Q10.395-57.128 10.290-56.888Q10.184-56.648 9.987-56.495Q9.790-56.343 9.532-56.343Q9.216-56.343 8.964-56.466Q8.712-56.589 8.573-56.823Q8.434-57.058 8.434-57.382\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M14.573-57.382L14.573-59.573L13.870-59.573L13.870-59.827Q14.226-59.827 14.468-60.060Q14.710-60.292 14.821-60.640Q14.933-60.987 14.933-61.343L15.214-61.343L15.214-59.870L16.390-59.870L16.390-59.573L15.214-59.573L15.214-57.398Q15.214-57.077 15.333-56.849Q15.452-56.620 15.733-56.620Q15.913-56.620 16.030-56.743Q16.148-56.866 16.200-57.046Q16.253-57.226 16.253-57.398L16.253-57.870L16.534-57.870L16.534-57.382Q16.534-57.128 16.429-56.888Q16.323-56.648 16.126-56.495Q15.929-56.343 15.671-56.343Q15.355-56.343 15.103-56.466Q14.851-56.589 14.712-56.823Q14.573-57.058 14.573-57.382M17.253-58.116Q17.253-58.620 17.509-59.052Q17.765-59.483 18.200-59.735Q18.636-59.987 19.136-59.987Q19.523-59.987 19.864-59.843Q20.206-59.698 20.468-59.437Q20.730-59.175 20.872-58.839Q21.015-58.503 21.015-58.116Q21.015-57.624 20.751-57.214Q20.487-56.804 20.058-56.573Q19.628-56.343 19.136-56.343Q18.644-56.343 18.210-56.575Q17.776-56.808 17.515-57.216Q17.253-57.624 17.253-58.116M19.136-56.620Q19.593-56.620 19.845-56.843Q20.097-57.066 20.185-57.417Q20.273-57.769 20.273-58.214Q20.273-58.644 20.179-58.982Q20.085-59.319 19.831-59.526Q19.577-59.733 19.136-59.733Q18.487-59.733 18.243-59.317Q17.999-58.901 17.999-58.214Q17.999-57.769 18.087-57.417Q18.175-57.066 18.427-56.843Q18.679-56.620 19.136-56.620\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M24.435-57.253Q24.435-57.737 24.837-58.032Q25.240-58.327 25.790-58.446Q26.341-58.566 26.833-58.566L26.833-58.855Q26.833-59.081 26.718-59.288Q26.603-59.495 26.406-59.614Q26.208-59.733 25.978-59.733Q25.552-59.733 25.267-59.628Q25.337-59.601 25.384-59.546Q25.431-59.491 25.456-59.421Q25.482-59.351 25.482-59.276Q25.482-59.171 25.431-59.079Q25.380-58.987 25.288-58.937Q25.197-58.886 25.091-58.886Q24.986-58.886 24.894-58.937Q24.802-58.987 24.751-59.079Q24.701-59.171 24.701-59.276Q24.701-59.694 25.089-59.841Q25.478-59.987 25.978-59.987Q26.310-59.987 26.663-59.857Q27.017-59.726 27.245-59.472Q27.474-59.218 27.474-58.870L27.474-57.069Q27.474-56.937 27.546-56.827Q27.619-56.718 27.747-56.718Q27.872-56.718 27.941-56.823Q28.009-56.929 28.009-57.069L28.009-57.581L28.290-57.581L28.290-57.069Q28.290-56.866 28.173-56.708Q28.056-56.550 27.874-56.466Q27.693-56.382 27.490-56.382Q27.259-56.382 27.107-56.554Q26.954-56.726 26.923-56.956Q26.763-56.675 26.454-56.509Q26.146-56.343 25.794-56.343Q25.283-56.343 24.859-56.566Q24.435-56.788 24.435-57.253M25.122-57.253Q25.122-56.968 25.349-56.782Q25.576-56.597 25.869-56.597Q26.115-56.597 26.339-56.714Q26.564-56.831 26.699-57.034Q26.833-57.237 26.833-57.491L26.833-58.323Q26.568-58.323 26.283-58.269Q25.997-58.214 25.726-58.085Q25.454-57.956 25.288-57.749Q25.122-57.542 25.122-57.253M30.591-56.421L28.611-56.421L28.611-56.718Q28.880-56.718 29.048-56.763Q29.216-56.808 29.216-56.980L29.216-59.116Q29.216-59.331 29.154-59.427Q29.091-59.523 28.974-59.544Q28.857-59.566 28.611-59.566L28.611-59.862L29.779-59.948L29.779-59.163Q29.857-59.374 30.009-59.560Q30.162-59.745 30.361-59.847Q30.560-59.948 30.787-59.948Q31.033-59.948 31.224-59.804Q31.415-59.659 31.415-59.429Q31.415-59.273 31.310-59.163Q31.204-59.054 31.048-59.054Q30.892-59.054 30.783-59.163Q30.673-59.273 30.673-59.429Q30.673-59.589 30.779-59.694Q30.454-59.694 30.240-59.466Q30.025-59.237 29.929-58.898Q29.833-58.558 29.833-58.253L29.833-56.980Q29.833-56.812 30.060-56.765Q30.287-56.718 30.591-56.718L30.591-56.421M33.904-56.421L31.923-56.421L31.923-56.718Q32.193-56.718 32.361-56.763Q32.529-56.808 32.529-56.980L32.529-59.116Q32.529-59.331 32.466-59.427Q32.404-59.523 32.287-59.544Q32.169-59.566 31.923-59.566L31.923-59.862L33.091-59.948L33.091-59.163Q33.169-59.374 33.322-59.560Q33.474-59.745 33.673-59.847Q33.872-59.948 34.099-59.948Q34.345-59.948 34.537-59.804Q34.728-59.659 34.728-59.429Q34.728-59.273 34.622-59.163Q34.517-59.054 34.361-59.054Q34.204-59.054 34.095-59.163Q33.986-59.273 33.986-59.429Q33.986-59.589 34.091-59.694Q33.767-59.694 33.552-59.466Q33.337-59.237 33.242-58.898Q33.146-58.558 33.146-58.253L33.146-56.980Q33.146-56.812 33.372-56.765Q33.599-56.718 33.904-56.718L33.904-56.421M37.068-56.421L35.290-56.421L35.290-56.718Q35.564-56.718 35.732-56.765Q35.900-56.812 35.900-56.980L35.900-59.116Q35.900-59.331 35.843-59.427Q35.787-59.523 35.673-59.544Q35.560-59.566 35.314-59.566L35.314-59.862L36.513-59.948L36.513-56.980Q36.513-56.812 36.660-56.765Q36.806-56.718 37.068-56.718L37.068-56.421M35.626-61.343Q35.626-61.534 35.761-61.665Q35.896-61.796 36.091-61.796Q36.212-61.796 36.316-61.733Q36.419-61.671 36.482-61.567Q36.544-61.464 36.544-61.343Q36.544-61.148 36.413-61.013Q36.283-60.878 36.091-60.878Q35.892-60.878 35.759-61.011Q35.626-61.144 35.626-61.343M39.369-56.452L38.146-59.308Q38.064-59.483 37.919-59.528Q37.775-59.573 37.505-59.573L37.505-59.870L39.216-59.870L39.216-59.573Q38.794-59.573 38.794-59.390Q38.794-59.355 38.810-59.308L39.755-57.116L40.595-59.093Q40.634-59.171 40.634-59.261Q40.634-59.401 40.529-59.487Q40.423-59.573 40.283-59.573L40.283-59.870L41.634-59.870L41.634-59.573Q41.111-59.573 40.896-59.093L39.771-56.452Q39.708-56.343 39.603-56.343L39.537-56.343Q39.423-56.343 39.369-56.452\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 81.668)\">\u003Cpath d=\"M41.824-58.175Q41.824-58.655 42.057-59.071Q42.289-59.487 42.699-59.737Q43.109-59.987 43.586-59.987Q44.316-59.987 44.715-59.546Q45.113-59.105 45.113-58.374Q45.113-58.269 45.020-58.245L42.570-58.245L42.570-58.175Q42.570-57.765 42.691-57.409Q42.813-57.054 43.084-56.837Q43.356-56.620 43.785-56.620Q44.148-56.620 44.445-56.849Q44.742-57.077 44.844-57.429Q44.852-57.476 44.938-57.491L45.020-57.491Q45.113-57.464 45.113-57.382Q45.113-57.374 45.106-57.343Q45.043-57.116 44.904-56.933Q44.766-56.749 44.574-56.616Q44.383-56.483 44.164-56.413Q43.945-56.343 43.707-56.343Q43.336-56.343 42.998-56.480Q42.660-56.616 42.393-56.868Q42.125-57.120 41.975-57.460Q41.824-57.800 41.824-58.175M42.578-58.483L44.539-58.483Q44.539-58.788 44.438-59.079Q44.336-59.370 44.119-59.552Q43.902-59.733 43.586-59.733Q43.285-59.733 43.055-59.546Q42.824-59.358 42.701-59.067Q42.578-58.776 42.578-58.483M46.789-56.886Q46.789-57.077 46.922-57.210Q47.055-57.343 47.250-57.343Q47.441-57.343 47.574-57.210Q47.707-57.077 47.707-56.886Q47.707-56.687 47.574-56.554Q47.441-56.421 47.250-56.421Q47.055-56.421 46.922-56.554Q46.789-56.687 46.789-56.886M47.106-58.062L47.106-58.460Q47.106-58.858 47.234-59.259Q47.363-59.659 47.609-59.995Q47.688-60.101 47.854-60.276Q48.020-60.452 48.088-60.589Q48.156-60.726 48.156-60.964Q48.156-61.452 47.953-61.624Q47.750-61.796 47.250-61.796Q46.941-61.796 46.664-61.685Q46.387-61.573 46.234-61.351Q46.406-61.351 46.514-61.237Q46.621-61.124 46.621-60.956Q46.621-60.855 46.570-60.763Q46.520-60.671 46.430-60.618Q46.340-60.566 46.227-60.566Q46.117-60.566 46.027-60.618Q45.938-60.671 45.887-60.763Q45.836-60.855 45.836-60.956Q45.836-61.304 46.047-61.554Q46.258-61.804 46.582-61.929Q46.906-62.054 47.250-62.054Q47.910-62.054 48.404-61.804Q48.898-61.554 48.898-60.956Q48.898-60.706 48.775-60.491Q48.652-60.276 48.441-60.140Q48.129-59.937 47.897-59.681Q47.664-59.425 47.525-59.108Q47.387-58.792 47.387-58.444L47.387-58.062Q47.387-58.026 47.359-57.999Q47.332-57.972 47.301-57.972L47.195-57.972Q47.164-57.972 47.135-57.997Q47.106-58.023 47.106-58.062\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(9.223 99.184)\">\u003Cpath d=\"M-59.483-56.659L-59.483-56.749Q-59.424-56.960-59.233-56.980L-59.010-56.980L-58.057-61.163Q-58.033-61.280-57.938-61.355Q-57.842-61.429-57.729-61.429L-57.455-61.429Q-57.342-61.429-57.246-61.353Q-57.151-61.276-57.127-61.163L-56.178-56.980L-55.951-56.980Q-55.744-56.956-55.705-56.749L-55.705-56.659Q-55.756-56.444-55.951-56.421L-57.033-56.421Q-57.229-56.444-57.279-56.659L-57.279-56.749Q-57.229-56.956-57.033-56.980L-56.834-56.980Q-56.963-57.546-56.986-57.628L-58.201-57.628Q-58.244-57.444-58.354-56.980L-58.154-56.980Q-57.955-56.956-57.904-56.749L-57.904-56.659Q-57.955-56.444-58.154-56.421L-59.233-56.421Q-59.440-56.444-59.483-56.659M-58.080-58.191L-57.104-58.191Q-57.584-60.323-57.584-60.667L-57.592-60.667Q-57.592-60.483-57.733-59.800Q-57.873-59.116-58.080-58.191M-53.893-56.421L-55.076-56.421Q-55.272-56.444-55.322-56.659L-55.322-56.749Q-55.272-56.956-55.076-56.980L-54.803-56.980L-54.803-60.749L-55.076-60.749Q-55.272-60.773-55.322-60.987L-55.322-61.077Q-55.272-61.284-55.076-61.308L-53.506-61.308Q-53.119-61.308-52.764-61.138Q-52.408-60.968-52.186-60.657Q-51.963-60.347-51.963-59.948Q-51.963-59.722-52.045-59.513Q-52.127-59.304-52.270-59.136Q-52.412-58.968-52.604-58.847Q-52.408-58.694-52.293-58.478Q-52.178-58.261-52.178-58.023L-52.178-57.444Q-52.178-56.901-51.979-56.901Q-51.932-56.901-51.906-57.030Q-51.881-57.159-51.881-57.261Q-51.830-57.468-51.635-57.491L-51.490-57.491Q-51.295-57.472-51.244-57.261L-51.244-57.206Q-51.244-56.874-51.449-56.608Q-51.654-56.343-51.971-56.343Q-52.385-56.343-52.602-56.650Q-52.818-56.956-52.818-57.390L-52.818-57.964Q-52.818-58.253-53.027-58.421Q-53.236-58.589-53.522-58.589L-54.162-58.589L-54.162-56.980L-53.893-56.980Q-53.693-56.956-53.643-56.749L-53.643-56.659Q-53.693-56.444-53.893-56.421M-54.162-60.749L-54.162-59.148L-53.588-59.148Q-53.354-59.148-53.127-59.243Q-52.901-59.339-52.752-59.523Q-52.604-59.706-52.604-59.948Q-52.604-60.191-52.752-60.374Q-52.901-60.558-53.125-60.653Q-53.350-60.749-53.588-60.749L-54.162-60.749M-49.647-56.421L-50.830-56.421Q-51.026-56.444-51.076-56.659L-51.076-56.749Q-51.026-56.956-50.830-56.980L-50.557-56.980L-50.557-60.749L-50.830-60.749Q-51.026-60.773-51.076-60.987L-51.076-61.077Q-51.026-61.284-50.830-61.308L-49.260-61.308Q-48.873-61.308-48.518-61.138Q-48.162-60.968-47.940-60.657Q-47.717-60.347-47.717-59.948Q-47.717-59.722-47.799-59.513Q-47.881-59.304-48.024-59.136Q-48.166-58.968-48.358-58.847Q-48.162-58.694-48.047-58.478Q-47.932-58.261-47.932-58.023L-47.932-57.444Q-47.932-56.901-47.733-56.901Q-47.686-56.901-47.660-57.030Q-47.635-57.159-47.635-57.261Q-47.584-57.468-47.389-57.491L-47.244-57.491Q-47.049-57.472-46.998-57.261L-46.998-57.206Q-46.998-56.874-47.203-56.608Q-47.408-56.343-47.725-56.343Q-48.139-56.343-48.356-56.650Q-48.572-56.956-48.572-57.390L-48.572-57.964Q-48.572-58.253-48.781-58.421Q-48.990-58.589-49.276-58.589L-49.916-58.589L-49.916-56.980L-49.647-56.980Q-49.447-56.956-49.397-56.749L-49.397-56.659Q-49.447-56.444-49.647-56.421M-49.916-60.749L-49.916-59.148L-49.342-59.148Q-49.108-59.148-48.881-59.243Q-48.654-59.339-48.506-59.523Q-48.358-59.706-48.358-59.948Q-48.358-60.191-48.506-60.374Q-48.654-60.558-48.879-60.653Q-49.104-60.749-49.342-60.749L-49.916-60.749M-46.358-56.659L-46.358-56.749Q-46.307-56.956-46.111-56.980L-45.174-56.980L-45.174-60.749L-46.111-60.749Q-46.307-60.773-46.358-60.987L-46.358-61.077Q-46.307-61.284-46.111-61.308L-43.600-61.308Q-43.401-61.284-43.350-61.077L-43.350-60.987Q-43.401-60.773-43.600-60.749L-44.533-60.749L-44.533-56.980L-43.600-56.980Q-43.401-56.956-43.350-56.749L-43.350-56.659Q-43.401-56.444-43.600-56.421L-46.111-56.421Q-46.307-56.444-46.358-56.659M-41.080-56.605L-42.119-60.749L-42.330-60.749Q-42.526-60.773-42.576-60.987L-42.576-61.077Q-42.526-61.284-42.330-61.308L-41.272-61.308Q-41.065-61.284-41.026-61.077L-41.026-60.987Q-41.065-60.773-41.272-60.749L-41.455-60.749Q-41.350-60.343-41.213-59.788Q-41.076-59.233-40.922-58.589Q-40.768-57.944-40.688-57.558Q-40.608-57.171-40.608-57.062Q-40.600-57.183-40.522-57.552Q-40.443-57.921-40.297-58.538Q-40.151-59.155-40.014-59.716Q-39.877-60.276-39.760-60.749L-39.943-60.749Q-40.154-60.773-40.193-60.987L-40.193-61.077Q-40.154-61.284-39.943-61.308L-38.889-61.308Q-38.697-61.284-38.639-61.077L-38.639-60.987Q-38.697-60.773-38.889-60.749L-39.096-60.749L-40.135-56.605Q-40.162-56.495-40.258-56.419Q-40.354-56.343-40.471-56.343L-40.744-56.343Q-40.861-56.343-40.955-56.417Q-41.049-56.491-41.080-56.605M-38.299-56.659L-38.299-56.749Q-38.260-56.956-38.049-56.980L-37.740-56.980L-37.740-60.749L-38.049-60.749Q-38.260-60.773-38.299-60.987L-38.299-61.077Q-38.260-61.284-38.049-61.308L-34.842-61.308Q-34.647-61.284-34.596-61.077L-34.596-60.237Q-34.647-60.023-34.842-59.995L-34.986-59.995Q-35.182-60.023-35.236-60.237L-35.236-60.749L-37.100-60.749L-37.100-59.230L-36.123-59.230L-36.123-59.444Q-36.072-59.651-35.873-59.679L-35.729-59.679Q-35.533-59.651-35.483-59.444L-35.483-58.460Q-35.533-58.245-35.729-58.222L-35.873-58.222Q-36.072-58.245-36.123-58.460L-36.123-58.667L-37.100-58.667L-37.100-56.980L-35.057-56.980L-35.057-57.620Q-35.006-57.827-34.811-57.855L-34.666-57.855Q-34.471-57.827-34.420-57.620L-34.420-56.659Q-34.471-56.441-34.666-56.421L-38.049-56.421Q-38.260-56.444-38.299-56.659M-30.740-58.558L-33.483-58.558Q-33.608-58.569-33.693-58.655Q-33.779-58.741-33.779-58.870Q-33.779-58.999-33.693-59.081Q-33.608-59.163-33.483-59.175L-30.740-59.175Q-30.615-59.163-30.533-59.081Q-30.451-58.999-30.451-58.870Q-30.451-58.741-30.533-58.655Q-30.615-58.569-30.740-58.558M-29.854-56.659L-29.854-56.749Q-29.815-56.956-29.608-56.980L-29.358-56.980L-29.358-60.749L-29.608-60.749Q-29.815-60.773-29.854-60.987L-29.854-61.077Q-29.815-61.284-29.608-61.308L-27.791-61.308Q-27.240-61.308-26.846-60.915Q-26.451-60.523-26.256-59.944Q-26.061-59.366-26.061-58.819Q-26.061-58.292-26.260-57.728Q-26.459-57.163-26.852-56.792Q-27.244-56.421-27.791-56.421L-29.608-56.421Q-29.815-56.444-29.854-56.659M-28.717-60.749L-28.717-56.980L-27.951-56.980Q-27.358-56.980-27.029-57.575Q-26.701-58.171-26.701-58.819Q-26.701-59.226-26.836-59.675Q-26.971-60.124-27.256-60.437Q-27.541-60.749-27.951-60.749L-28.717-60.749M-25.514-56.659L-25.514-56.749Q-25.455-56.960-25.264-56.980L-25.041-56.980L-24.088-61.163Q-24.065-61.280-23.969-61.355Q-23.873-61.429-23.760-61.429L-23.486-61.429Q-23.373-61.429-23.277-61.353Q-23.182-61.276-23.158-61.163L-22.209-56.980L-21.983-56.980Q-21.776-56.956-21.736-56.749L-21.736-56.659Q-21.787-56.444-21.983-56.421L-23.065-56.421Q-23.260-56.444-23.311-56.659L-23.311-56.749Q-23.260-56.956-23.065-56.980L-22.865-56.980Q-22.994-57.546-23.018-57.628L-24.233-57.628Q-24.276-57.444-24.385-56.980L-24.186-56.980Q-23.986-56.956-23.936-56.749L-23.936-56.659Q-23.986-56.444-24.186-56.421L-25.264-56.421Q-25.471-56.444-25.514-56.659M-24.111-58.191L-23.135-58.191Q-23.615-60.323-23.615-60.667L-23.623-60.667Q-23.623-60.483-23.764-59.800Q-23.904-59.116-24.111-58.191M-20.436-56.659L-20.436-56.749Q-20.377-56.956-20.186-56.980L-19.697-56.980L-19.697-60.749L-20.643-60.749L-20.643-60.237Q-20.690-60.023-20.889-59.995L-21.033-59.995Q-21.233-60.023-21.283-60.237L-21.283-61.077Q-21.233-61.284-21.033-61.308L-17.721-61.308Q-17.526-61.288-17.475-61.077L-17.475-60.237Q-17.522-60.023-17.721-59.995L-17.865-59.995Q-18.065-60.023-18.115-60.237L-18.115-60.749L-19.057-60.749L-19.057-56.980L-18.568-56.980Q-18.373-56.956-18.322-56.749L-18.322-56.659Q-18.373-56.444-18.568-56.421L-20.186-56.421Q-20.377-56.444-20.436-56.659M-17.068-56.659L-17.068-56.749Q-17.029-56.956-16.818-56.980L-16.510-56.980L-16.510-60.749L-16.818-60.749Q-17.029-60.773-17.068-60.987L-17.068-61.077Q-17.029-61.284-16.818-61.308L-13.611-61.308Q-13.416-61.284-13.365-61.077L-13.365-60.237Q-13.416-60.023-13.611-59.995L-13.756-59.995Q-13.951-60.023-14.006-60.237L-14.006-60.749L-15.869-60.749L-15.869-59.230L-14.893-59.230L-14.893-59.444Q-14.842-59.651-14.643-59.679L-14.498-59.679Q-14.303-59.651-14.252-59.444L-14.252-58.460Q-14.303-58.245-14.498-58.222L-14.643-58.222Q-14.842-58.245-14.893-58.460L-14.893-58.667L-15.869-58.667L-15.869-56.980L-13.826-56.980L-13.826-57.620Q-13.776-57.827-13.580-57.855L-13.436-57.855Q-13.240-57.827-13.190-57.620L-13.190-56.659Q-13.240-56.441-13.436-56.421L-16.818-56.421Q-17.029-56.444-17.068-56.659\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(117.344 99.184)\">\u003Cpath d=\"M-57.850-56.382Q-58.315-56.382-58.680-56.632Q-59.045-56.882-59.250-57.286Q-59.455-57.691-59.455-58.148Q-59.455-58.491-59.330-58.812Q-59.205-59.132-58.973-59.382Q-58.740-59.632-58.436-59.771Q-58.131-59.909-57.776-59.909Q-57.264-59.909-56.858-59.589L-56.858-60.749L-57.279-60.749Q-57.490-60.773-57.529-60.987L-57.529-61.077Q-57.490-61.284-57.279-61.308L-56.467-61.308Q-56.268-61.284-56.217-61.077L-56.217-56.980L-55.791-56.980Q-55.584-56.956-55.545-56.749L-55.545-56.659Q-55.584-56.444-55.791-56.421L-56.608-56.421Q-56.807-56.444-56.858-56.659L-56.858-56.788Q-57.053-56.597-57.315-56.489Q-57.576-56.382-57.850-56.382M-57.811-56.941Q-57.451-56.941-57.199-57.210Q-56.947-57.480-56.858-57.855L-56.858-58.687Q-56.916-58.874-57.043-59.025Q-57.170-59.175-57.348-59.263Q-57.526-59.351-57.721-59.351Q-58.029-59.351-58.279-59.181Q-58.529-59.011-58.674-58.726Q-58.818-58.441-58.818-58.140Q-58.818-57.691-58.533-57.316Q-58.248-56.941-57.811-56.941M-55.061-57.534Q-55.061-57.980-54.647-58.237Q-54.233-58.495-53.692-58.595Q-53.151-58.694-52.643-58.702Q-52.643-58.917-52.777-59.069Q-52.912-59.222-53.119-59.298Q-53.326-59.374-53.537-59.374Q-53.881-59.374-54.041-59.351L-54.041-59.292Q-54.041-59.124-54.160-59.009Q-54.279-58.894-54.443-58.894Q-54.619-58.894-54.734-59.017Q-54.850-59.140-54.850-59.308Q-54.850-59.714-54.469-59.823Q-54.088-59.933-53.529-59.933Q-53.260-59.933-52.992-59.855Q-52.725-59.776-52.500-59.626Q-52.276-59.476-52.139-59.255Q-52.002-59.034-52.002-58.757L-52.002-57.038Q-52.002-56.980-51.475-56.980Q-51.279-56.960-51.229-56.749L-51.229-56.659Q-51.279-56.444-51.475-56.421L-51.619-56.421Q-51.963-56.421-52.192-56.468Q-52.420-56.515-52.565-56.702Q-53.026-56.382-53.733-56.382Q-54.068-56.382-54.373-56.523Q-54.678-56.663-54.869-56.925Q-55.061-57.187-55.061-57.534M-54.420-57.526Q-54.420-57.253-54.178-57.097Q-53.936-56.941-53.651-56.941Q-53.432-56.941-53.199-56.999Q-52.967-57.058-52.805-57.196Q-52.643-57.335-52.643-57.558L-52.643-58.148Q-52.924-58.148-53.340-58.091Q-53.756-58.034-54.088-57.896Q-54.420-57.757-54.420-57.526M-50.022-57.526L-50.022-59.308L-50.772-59.308Q-50.971-59.331-51.022-59.550L-51.022-59.636Q-50.971-59.847-50.772-59.870L-50.022-59.870L-50.022-60.620Q-49.971-60.827-49.772-60.855L-49.627-60.855Q-49.432-60.827-49.381-60.620L-49.381-59.870L-48.022-59.870Q-47.830-59.851-47.772-59.636L-47.772-59.550Q-47.826-59.331-48.022-59.308L-49.381-59.308L-49.381-57.558Q-49.381-56.941-48.807-56.941Q-48.557-56.941-48.393-57.126Q-48.229-57.312-48.229-57.558Q-48.229-57.651-48.156-57.722Q-48.084-57.792-47.983-57.804L-47.838-57.804Q-47.639-57.780-47.588-57.573L-47.588-57.526Q-47.588-57.202-47.772-56.939Q-47.955-56.675-48.248-56.528Q-48.541-56.382-48.861-56.382Q-49.373-56.382-49.697-56.692Q-50.022-57.003-50.022-57.526M-43.463-57.909L-45.904-57.909Q-45.850-57.632-45.652-57.409Q-45.455-57.187-45.178-57.064Q-44.901-56.941-44.615-56.941Q-44.143-56.941-43.920-57.230Q-43.912-57.241-43.856-57.347Q-43.799-57.452-43.750-57.495Q-43.701-57.538-43.608-57.550L-43.463-57.550Q-43.272-57.530-43.213-57.316L-43.213-57.261Q-43.279-56.960-43.510-56.763Q-43.740-56.566-44.053-56.474Q-44.365-56.382-44.670-56.382Q-45.154-56.382-45.594-56.610Q-46.033-56.839-46.301-57.239Q-46.568-57.640-46.568-58.132L-46.568-58.191Q-46.568-58.659-46.322-59.062Q-46.076-59.464-45.668-59.698Q-45.260-59.933-44.791-59.933Q-44.287-59.933-43.934-59.710Q-43.580-59.487-43.397-59.099Q-43.213-58.710-43.213-58.206L-43.213-58.148Q-43.272-57.933-43.463-57.909M-45.897-58.460L-43.869-58.460Q-43.916-58.870-44.154-59.122Q-44.393-59.374-44.791-59.374Q-45.186-59.374-45.492-59.112Q-45.799-58.851-45.897-58.460\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M-57.139-56.366L-58.842-61.323Q-58.908-61.499-59.084-61.544Q-59.260-61.589-59.553-61.589L-59.553-61.886L-57.408-61.886L-57.408-61.589Q-58.065-61.589-58.065-61.374Q-58.061-61.362-58.059-61.353Q-58.057-61.343-58.057-61.323L-56.705-57.390L-55.506-60.894L-55.651-61.323Q-55.721-61.499-55.895-61.544Q-56.068-61.589-56.361-61.589L-56.361-61.886L-54.225-61.886L-54.225-61.589Q-54.873-61.589-54.873-61.374Q-54.873-61.331-54.865-61.323L-53.514-57.390L-52.240-61.108Q-52.225-61.155-52.225-61.191Q-52.225-61.331-52.334-61.423Q-52.443-61.515-52.600-61.552Q-52.756-61.589-52.889-61.589L-52.889-61.886L-51.154-61.886L-51.154-61.589Q-51.772-61.589-51.943-61.108L-53.568-56.366Q-53.588-56.316-53.631-56.284Q-53.674-56.253-53.729-56.253L-53.783-56.253Q-53.904-56.253-53.943-56.366L-55.354-60.460L-56.760-56.366Q-56.779-56.316-56.822-56.284Q-56.865-56.253-56.920-56.253L-56.979-56.253Q-57.092-56.253-57.139-56.366M-48.818-56.421L-50.674-56.421L-50.674-56.718Q-50.401-56.718-50.233-56.765Q-50.065-56.812-50.065-56.980L-50.065-61.140Q-50.065-61.355-50.127-61.450Q-50.190-61.546-50.309-61.567Q-50.428-61.589-50.674-61.589L-50.674-61.886L-49.451-61.972L-49.451-59.269Q-49.326-59.480-49.139-59.630Q-48.951-59.780-48.725-59.864Q-48.498-59.948-48.252-59.948Q-47.084-59.948-47.084-58.870L-47.084-56.980Q-47.084-56.812-46.914-56.765Q-46.744-56.718-46.475-56.718L-46.475-56.421L-48.330-56.421L-48.330-56.718Q-48.057-56.718-47.889-56.765Q-47.721-56.812-47.721-56.980L-47.721-58.855Q-47.721-59.237-47.842-59.466Q-47.963-59.694-48.315-59.694Q-48.627-59.694-48.881-59.532Q-49.135-59.370-49.281-59.101Q-49.428-58.831-49.428-58.534L-49.428-56.980Q-49.428-56.812-49.258-56.765Q-49.088-56.718-48.818-56.718L-48.818-56.421M-45.932-57.253Q-45.932-57.737-45.529-58.032Q-45.127-58.327-44.576-58.446Q-44.026-58.566-43.533-58.566L-43.533-58.855Q-43.533-59.081-43.649-59.288Q-43.764-59.495-43.961-59.614Q-44.158-59.733-44.389-59.733Q-44.815-59.733-45.100-59.628Q-45.029-59.601-44.983-59.546Q-44.936-59.491-44.910-59.421Q-44.885-59.351-44.885-59.276Q-44.885-59.171-44.936-59.079Q-44.986-58.987-45.078-58.937Q-45.170-58.886-45.276-58.886Q-45.381-58.886-45.473-58.937Q-45.565-58.987-45.615-59.079Q-45.666-59.171-45.666-59.276Q-45.666-59.694-45.277-59.841Q-44.889-59.987-44.389-59.987Q-44.057-59.987-43.703-59.857Q-43.350-59.726-43.121-59.472Q-42.893-59.218-42.893-58.870L-42.893-57.069Q-42.893-56.937-42.820-56.827Q-42.748-56.718-42.619-56.718Q-42.494-56.718-42.426-56.823Q-42.358-56.929-42.358-57.069L-42.358-57.581L-42.076-57.581L-42.076-57.069Q-42.076-56.866-42.193-56.708Q-42.311-56.550-42.492-56.466Q-42.674-56.382-42.877-56.382Q-43.108-56.382-43.260-56.554Q-43.412-56.726-43.443-56.956Q-43.604-56.675-43.912-56.509Q-44.221-56.343-44.572-56.343Q-45.084-56.343-45.508-56.566Q-45.932-56.788-45.932-57.253M-45.244-57.253Q-45.244-56.968-45.018-56.782Q-44.791-56.597-44.498-56.597Q-44.252-56.597-44.027-56.714Q-43.803-56.831-43.668-57.034Q-43.533-57.237-43.533-57.491L-43.533-58.323Q-43.799-58.323-44.084-58.269Q-44.369-58.214-44.641-58.085Q-44.912-57.956-45.078-57.749Q-45.244-57.542-45.244-57.253M-41.158-57.382L-41.158-59.573L-41.861-59.573L-41.861-59.827Q-41.506-59.827-41.264-60.060Q-41.022-60.292-40.910-60.640Q-40.799-60.987-40.799-61.343L-40.518-61.343L-40.518-59.870L-39.342-59.870L-39.342-59.573L-40.518-59.573L-40.518-57.398Q-40.518-57.077-40.399-56.849Q-40.279-56.620-39.998-56.620Q-39.818-56.620-39.701-56.743Q-39.584-56.866-39.531-57.046Q-39.479-57.226-39.479-57.398L-39.479-57.870L-39.197-57.870L-39.197-57.382Q-39.197-57.128-39.303-56.888Q-39.408-56.648-39.606-56.495Q-39.803-56.343-40.061-56.343Q-40.377-56.343-40.629-56.466Q-40.881-56.589-41.020-56.823Q-41.158-57.058-41.158-57.382\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M-33.819-56.343Q-34.300-56.343-34.708-56.587Q-35.116-56.831-35.354-57.245Q-35.593-57.659-35.593-58.148Q-35.593-58.640-35.335-59.056Q-35.077-59.472-34.645-59.710Q-34.214-59.948-33.722-59.948Q-33.101-59.948-32.651-59.511L-32.651-61.140Q-32.651-61.355-32.714-61.450Q-32.776-61.546-32.894-61.567Q-33.011-61.589-33.257-61.589L-33.257-61.886L-32.034-61.972L-32.034-57.163Q-32.034-56.952-31.972-56.857Q-31.909-56.761-31.792-56.739Q-31.675-56.718-31.425-56.718L-31.425-56.421L-32.675-56.343L-32.675-56.827Q-33.140-56.343-33.819-56.343M-33.753-56.597Q-33.413-56.597-33.120-56.788Q-32.827-56.980-32.675-57.276L-32.675-59.108Q-32.823-59.382-33.085-59.538Q-33.347-59.694-33.659-59.694Q-34.284-59.694-34.567-59.247Q-34.851-58.800-34.851-58.140Q-34.851-57.495-34.599-57.046Q-34.347-56.597-33.753-56.597M-30.819-57.253Q-30.819-57.737-30.417-58.032Q-30.015-58.327-29.464-58.446Q-28.913-58.566-28.421-58.566L-28.421-58.855Q-28.421-59.081-28.536-59.288Q-28.651-59.495-28.849-59.614Q-29.046-59.733-29.276-59.733Q-29.702-59.733-29.987-59.628Q-29.917-59.601-29.870-59.546Q-29.823-59.491-29.798-59.421Q-29.772-59.351-29.772-59.276Q-29.772-59.171-29.823-59.079Q-29.874-58.987-29.966-58.937Q-30.058-58.886-30.163-58.886Q-30.269-58.886-30.360-58.937Q-30.452-58.987-30.503-59.079Q-30.554-59.171-30.554-59.276Q-30.554-59.694-30.165-59.841Q-29.776-59.987-29.276-59.987Q-28.944-59.987-28.591-59.857Q-28.237-59.726-28.009-59.472Q-27.780-59.218-27.780-58.870L-27.780-57.069Q-27.780-56.937-27.708-56.827Q-27.636-56.718-27.507-56.718Q-27.382-56.718-27.313-56.823Q-27.245-56.929-27.245-57.069L-27.245-57.581L-26.964-57.581L-26.964-57.069Q-26.964-56.866-27.081-56.708Q-27.198-56.550-27.380-56.466Q-27.562-56.382-27.765-56.382Q-27.995-56.382-28.147-56.554Q-28.300-56.726-28.331-56.956Q-28.491-56.675-28.800-56.509Q-29.108-56.343-29.460-56.343Q-29.972-56.343-30.395-56.566Q-30.819-56.788-30.819-57.253M-30.132-57.253Q-30.132-56.968-29.905-56.782Q-29.679-56.597-29.386-56.597Q-29.140-56.597-28.915-56.714Q-28.690-56.831-28.556-57.034Q-28.421-57.237-28.421-57.491L-28.421-58.323Q-28.687-58.323-28.972-58.269Q-29.257-58.214-29.528-58.085Q-29.800-57.956-29.966-57.749Q-30.132-57.542-30.132-57.253\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M-26.481-55.124Q-26.367-55.046-26.192-55.046Q-25.903-55.046-25.682-55.259Q-25.461-55.472-25.336-55.773L-25.047-56.421L-26.321-59.308Q-26.403-59.483-26.547-59.528Q-26.692-59.573-26.961-59.573L-26.961-59.870L-25.242-59.870L-25.242-59.573Q-25.664-59.573-25.664-59.390Q-25.664-59.378-25.649-59.308L-24.711-57.183L-23.879-59.093Q-23.840-59.183-23.840-59.261Q-23.840-59.401-23.942-59.487Q-24.043-59.573-24.184-59.573L-24.184-59.870L-22.832-59.870L-22.832-59.573Q-23.086-59.573-23.280-59.448Q-23.473-59.323-23.578-59.093L-25.024-55.773Q-25.137-55.519-25.303-55.296Q-25.469-55.073-25.698-54.931Q-25.926-54.788-26.192-54.788Q-26.489-54.788-26.729-54.980Q-26.969-55.171-26.969-55.460Q-26.969-55.616-26.864-55.718Q-26.758-55.819-26.610-55.819Q-26.504-55.819-26.424-55.773Q-26.344-55.726-26.297-55.648Q-26.250-55.569-26.250-55.460Q-26.250-55.339-26.311-55.251Q-26.371-55.163-26.481-55.124\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M-17.994-56.452L-19.064-59.308Q-19.131-59.487-19.261-59.530Q-19.392-59.573-19.650-59.573L-19.650-59.870L-17.970-59.870L-17.970-59.573Q-18.420-59.573-18.420-59.374Q-18.416-59.358-18.414-59.341Q-18.412-59.323-18.412-59.308L-17.619-57.214L-16.908-59.124Q-16.943-59.218-16.943-59.263Q-16.943-59.308-16.978-59.308Q-17.045-59.487-17.175-59.530Q-17.306-59.573-17.560-59.573L-17.560-59.870L-15.970-59.870L-15.970-59.573Q-16.420-59.573-16.420-59.374Q-16.416-59.355-16.414-59.337Q-16.412-59.319-16.412-59.308L-15.580-57.093L-14.826-59.093Q-14.802-59.151-14.802-59.222Q-14.802-59.382-14.939-59.478Q-15.076-59.573-15.244-59.573L-15.244-59.870L-13.857-59.870L-13.857-59.573Q-14.091-59.573-14.269-59.446Q-14.447-59.319-14.529-59.093L-15.513-56.452Q-15.568-56.343-15.681-56.343L-15.740-56.343Q-15.853-56.343-15.896-56.452L-16.756-58.726L-17.611-56.452Q-17.650-56.343-17.771-56.343L-17.826-56.343Q-17.939-56.343-17.994-56.452\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M-13.677-58.116Q-13.677-58.620-13.421-59.052Q-13.165-59.483-12.729-59.735Q-12.294-59.987-11.794-59.987Q-11.407-59.987-11.065-59.843Q-10.724-59.698-10.462-59.437Q-10.200-59.175-10.058-58.839Q-9.915-58.503-9.915-58.116Q-9.915-57.624-10.179-57.214Q-10.442-56.804-10.872-56.573Q-11.302-56.343-11.794-56.343Q-12.286-56.343-12.720-56.575Q-13.153-56.808-13.415-57.216Q-13.677-57.624-13.677-58.116M-11.794-56.620Q-11.337-56.620-11.085-56.843Q-10.833-57.066-10.745-57.417Q-10.657-57.769-10.657-58.214Q-10.657-58.644-10.751-58.982Q-10.845-59.319-11.099-59.526Q-11.352-59.733-11.794-59.733Q-12.442-59.733-12.686-59.317Q-12.931-58.901-12.931-58.214Q-12.931-57.769-12.843-57.417Q-12.755-57.066-12.503-56.843Q-12.251-56.620-11.794-56.620M-8.747-57.374L-8.747-59.116Q-8.747-59.331-8.810-59.427Q-8.872-59.523-8.991-59.544Q-9.110-59.566-9.356-59.566L-9.356-59.862L-8.110-59.948L-8.110-57.398L-8.110-57.374Q-8.110-57.062-8.056-56.900Q-8.001-56.737-7.851-56.667Q-7.700-56.597-7.380-56.597Q-6.950-56.597-6.677-56.935Q-6.403-57.273-6.403-57.718L-6.403-59.116Q-6.403-59.331-6.466-59.427Q-6.528-59.523-6.647-59.544Q-6.767-59.566-7.013-59.566L-7.013-59.862L-5.767-59.948L-5.767-57.163Q-5.767-56.952-5.704-56.857Q-5.642-56.761-5.522-56.739Q-5.403-56.718-5.157-56.718L-5.157-56.421L-6.380-56.343L-6.380-56.964Q-6.548-56.675-6.829-56.509Q-7.110-56.343-7.431-56.343Q-8.747-56.343-8.747-57.374M-2.798-56.421L-4.630-56.421L-4.630-56.718Q-4.356-56.718-4.188-56.765Q-4.020-56.812-4.020-56.980L-4.020-61.140Q-4.020-61.355-4.083-61.450Q-4.145-61.546-4.265-61.567Q-4.384-61.589-4.630-61.589L-4.630-61.886L-3.407-61.972L-3.407-56.980Q-3.407-56.812-3.239-56.765Q-3.071-56.718-2.798-56.718L-2.798-56.421M-0.536-56.343Q-1.017-56.343-1.425-56.587Q-1.833-56.831-2.071-57.245Q-2.310-57.659-2.310-58.148Q-2.310-58.640-2.052-59.056Q-1.794-59.472-1.362-59.710Q-0.931-59.948-0.438-59.948Q0.183-59.948 0.632-59.511L0.632-61.140Q0.632-61.355 0.569-61.450Q0.507-61.546 0.390-61.567Q0.273-61.589 0.026-61.589L0.026-61.886L1.249-61.972L1.249-57.163Q1.249-56.952 1.312-56.857Q1.374-56.761 1.491-56.739Q1.608-56.718 1.858-56.718L1.858-56.421L0.608-56.343L0.608-56.827Q0.144-56.343-0.536-56.343M-0.470-56.597Q-0.130-56.597 0.163-56.788Q0.456-56.980 0.608-57.276L0.608-59.108Q0.460-59.382 0.198-59.538Q-0.063-59.694-0.376-59.694Q-1.001-59.694-1.284-59.247Q-1.567-58.800-1.567-58.140Q-1.567-57.495-1.315-57.046Q-1.063-56.597-0.470-56.597\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M5.630-55.124Q5.744-55.046 5.919-55.046Q6.208-55.046 6.429-55.259Q6.650-55.472 6.775-55.773L7.064-56.421L5.790-59.308Q5.708-59.483 5.564-59.528Q5.419-59.573 5.150-59.573L5.150-59.870L6.869-59.870L6.869-59.573Q6.447-59.573 6.447-59.390Q6.447-59.378 6.462-59.308L7.400-57.183L8.232-59.093Q8.271-59.183 8.271-59.261Q8.271-59.401 8.169-59.487Q8.068-59.573 7.927-59.573L7.927-59.870L9.279-59.870L9.279-59.573Q9.025-59.573 8.831-59.448Q8.638-59.323 8.533-59.093L7.087-55.773Q6.974-55.519 6.808-55.296Q6.642-55.073 6.413-54.931Q6.185-54.788 5.919-54.788Q5.622-54.788 5.382-54.980Q5.142-55.171 5.142-55.460Q5.142-55.616 5.247-55.718Q5.353-55.819 5.501-55.819Q5.607-55.819 5.687-55.773Q5.767-55.726 5.814-55.648Q5.861-55.569 5.861-55.460Q5.861-55.339 5.800-55.251Q5.740-55.163 5.630-55.124\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M9.462-58.116Q9.462-58.620 9.718-59.052Q9.974-59.483 10.410-59.735Q10.845-59.987 11.345-59.987Q11.732-59.987 12.074-59.843Q12.415-59.698 12.677-59.437Q12.939-59.175 13.081-58.839Q13.224-58.503 13.224-58.116Q13.224-57.624 12.960-57.214Q12.697-56.804 12.267-56.573Q11.837-56.343 11.345-56.343Q10.853-56.343 10.419-56.575Q9.986-56.808 9.724-57.216Q9.462-57.624 9.462-58.116M11.345-56.620Q11.802-56.620 12.054-56.843Q12.306-57.066 12.394-57.417Q12.482-57.769 12.482-58.214Q12.482-58.644 12.388-58.982Q12.294-59.319 12.040-59.526Q11.787-59.733 11.345-59.733Q10.697-59.733 10.453-59.317Q10.208-58.901 10.208-58.214Q10.208-57.769 10.296-57.417Q10.384-57.066 10.636-56.843Q10.888-56.620 11.345-56.620M14.392-57.374L14.392-59.116Q14.392-59.331 14.329-59.427Q14.267-59.523 14.148-59.544Q14.029-59.566 13.783-59.566L13.783-59.862L15.029-59.948L15.029-57.398L15.029-57.374Q15.029-57.062 15.083-56.900Q15.138-56.737 15.288-56.667Q15.439-56.597 15.759-56.597Q16.189-56.597 16.462-56.935Q16.736-57.273 16.736-57.718L16.736-59.116Q16.736-59.331 16.673-59.427Q16.611-59.523 16.492-59.544Q16.372-59.566 16.126-59.566L16.126-59.862L17.372-59.948L17.372-57.163Q17.372-56.952 17.435-56.857Q17.497-56.761 17.617-56.739Q17.736-56.718 17.982-56.718L17.982-56.421L16.759-56.343L16.759-56.964Q16.591-56.675 16.310-56.509Q16.029-56.343 15.708-56.343Q14.392-56.343 14.392-57.374\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M23.182-56.421L21.350-56.421L21.350-56.718Q21.624-56.718 21.792-56.765Q21.960-56.812 21.960-56.980L21.960-61.140Q21.960-61.355 21.897-61.450Q21.835-61.546 21.716-61.567Q21.596-61.589 21.350-61.589L21.350-61.886L22.573-61.972L22.573-56.980Q22.573-56.812 22.741-56.765Q22.909-56.718 23.182-56.718L23.182-56.421M25.487-56.421L23.710-56.421L23.710-56.718Q23.983-56.718 24.151-56.765Q24.319-56.812 24.319-56.980L24.319-59.116Q24.319-59.331 24.262-59.427Q24.206-59.523 24.093-59.544Q23.979-59.566 23.733-59.566L23.733-59.862L24.932-59.948L24.932-56.980Q24.932-56.812 25.079-56.765Q25.225-56.718 25.487-56.718L25.487-56.421M24.046-61.343Q24.046-61.534 24.180-61.665Q24.315-61.796 24.510-61.796Q24.632-61.796 24.735-61.733Q24.839-61.671 24.901-61.567Q24.964-61.464 24.964-61.343Q24.964-61.148 24.833-61.013Q24.702-60.878 24.510-60.878Q24.311-60.878 24.178-61.011Q24.046-61.144 24.046-61.343M27.811-56.421L26.014-56.421L26.014-56.718Q26.284-56.718 26.452-56.763Q26.620-56.808 26.620-56.980L26.620-61.140Q26.620-61.355 26.557-61.450Q26.495-61.546 26.378-61.567Q26.260-61.589 26.014-61.589L26.014-61.886L27.237-61.972L27.237-58.206L28.335-59.093Q28.542-59.273 28.542-59.421Q28.542-59.487 28.489-59.530Q28.436-59.573 28.366-59.573L28.366-59.870L29.901-59.870L29.901-59.573Q29.370-59.573 28.772-59.093L28.163-58.597L29.237-57.198Q29.374-57.023 29.481-56.915Q29.589-56.808 29.723-56.763Q29.858-56.718 30.085-56.718L30.085-56.421L28.460-56.421L28.460-56.718Q28.702-56.718 28.702-56.870Q28.702-56.948 28.659-57.019Q28.616-57.089 28.534-57.198L27.733-58.245L27.206-57.819L27.206-56.980Q27.206-56.812 27.374-56.765Q27.542-56.718 27.811-56.718\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M30.240-58.175Q30.240-58.655 30.473-59.071Q30.705-59.487 31.115-59.737Q31.525-59.987 32.002-59.987Q32.732-59.987 33.131-59.546Q33.529-59.105 33.529-58.374Q33.529-58.269 33.436-58.245L30.986-58.245L30.986-58.175Q30.986-57.765 31.107-57.409Q31.229-57.054 31.500-56.837Q31.772-56.620 32.201-56.620Q32.564-56.620 32.861-56.849Q33.158-57.077 33.260-57.429Q33.268-57.476 33.354-57.491L33.436-57.491Q33.529-57.464 33.529-57.382Q33.529-57.374 33.522-57.343Q33.459-57.116 33.320-56.933Q33.182-56.749 32.990-56.616Q32.799-56.483 32.580-56.413Q32.361-56.343 32.123-56.343Q31.752-56.343 31.414-56.480Q31.076-56.616 30.809-56.868Q30.541-57.120 30.391-57.460Q30.240-57.800 30.240-58.175M30.994-58.483L32.955-58.483Q32.955-58.788 32.854-59.079Q32.752-59.370 32.535-59.552Q32.318-59.733 32.002-59.733Q31.701-59.733 31.471-59.546Q31.240-59.358 31.117-59.067Q30.994-58.776 30.994-58.483\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M37.476-57.382L37.476-59.573L36.773-59.573L36.773-59.827Q37.129-59.827 37.371-60.060Q37.613-60.292 37.724-60.640Q37.836-60.987 37.836-61.343L38.117-61.343L38.117-59.870L39.293-59.870L39.293-59.573L38.117-59.573L38.117-57.398Q38.117-57.077 38.236-56.849Q38.355-56.620 38.636-56.620Q38.816-56.620 38.933-56.743Q39.050-56.866 39.103-57.046Q39.156-57.226 39.156-57.398L39.156-57.870L39.437-57.870L39.437-57.382Q39.437-57.128 39.332-56.888Q39.226-56.648 39.029-56.495Q38.832-56.343 38.574-56.343Q38.258-56.343 38.006-56.466Q37.754-56.589 37.615-56.823Q37.476-57.058 37.476-57.382M40.156-58.116Q40.156-58.620 40.412-59.052Q40.668-59.483 41.103-59.735Q41.539-59.987 42.039-59.987Q42.425-59.987 42.767-59.843Q43.109-59.698 43.371-59.437Q43.633-59.175 43.775-58.839Q43.918-58.503 43.918-58.116Q43.918-57.624 43.654-57.214Q43.390-56.804 42.961-56.573Q42.531-56.343 42.039-56.343Q41.547-56.343 41.113-56.575Q40.679-56.808 40.418-57.216Q40.156-57.624 40.156-58.116M42.039-56.620Q42.496-56.620 42.748-56.843Q43-57.066 43.088-57.417Q43.175-57.769 43.175-58.214Q43.175-58.644 43.082-58.982Q42.988-59.319 42.734-59.526Q42.480-59.733 42.039-59.733Q41.390-59.733 41.146-59.317Q40.902-58.901 40.902-58.214Q40.902-57.769 40.990-57.417Q41.078-57.066 41.330-56.843Q41.582-56.620 42.039-56.620\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M47.339-57.253Q47.339-57.737 47.741-58.032Q48.144-58.327 48.694-58.446Q49.245-58.566 49.737-58.566L49.737-58.855Q49.737-59.081 49.622-59.288Q49.507-59.495 49.310-59.614Q49.112-59.733 48.882-59.733Q48.456-59.733 48.171-59.628Q48.241-59.601 48.288-59.546Q48.335-59.491 48.360-59.421Q48.386-59.351 48.386-59.276Q48.386-59.171 48.335-59.079Q48.284-58.987 48.192-58.937Q48.101-58.886 47.995-58.886Q47.890-58.886 47.798-58.937Q47.706-58.987 47.655-59.079Q47.605-59.171 47.605-59.276Q47.605-59.694 47.993-59.841Q48.382-59.987 48.882-59.987Q49.214-59.987 49.567-59.857Q49.921-59.726 50.149-59.472Q50.378-59.218 50.378-58.870L50.378-57.069Q50.378-56.937 50.450-56.827Q50.523-56.718 50.651-56.718Q50.776-56.718 50.845-56.823Q50.913-56.929 50.913-57.069L50.913-57.581L51.194-57.581L51.194-57.069Q51.194-56.866 51.077-56.708Q50.960-56.550 50.778-56.466Q50.597-56.382 50.394-56.382Q50.163-56.382 50.011-56.554Q49.858-56.726 49.827-56.956Q49.667-56.675 49.358-56.509Q49.050-56.343 48.698-56.343Q48.187-56.343 47.763-56.566Q47.339-56.788 47.339-57.253M48.026-57.253Q48.026-56.968 48.253-56.782Q48.480-56.597 48.773-56.597Q49.019-56.597 49.243-56.714Q49.468-56.831 49.603-57.034Q49.737-57.237 49.737-57.491L49.737-58.323Q49.472-58.323 49.187-58.269Q48.901-58.214 48.630-58.085Q48.358-57.956 48.192-57.749Q48.026-57.542 48.026-57.253M53.495-56.421L51.515-56.421L51.515-56.718Q51.784-56.718 51.952-56.763Q52.120-56.808 52.120-56.980L52.120-59.116Q52.120-59.331 52.058-59.427Q51.995-59.523 51.878-59.544Q51.761-59.566 51.515-59.566L51.515-59.862L52.683-59.948L52.683-59.163Q52.761-59.374 52.913-59.560Q53.066-59.745 53.265-59.847Q53.464-59.948 53.691-59.948Q53.937-59.948 54.128-59.804Q54.319-59.659 54.319-59.429Q54.319-59.273 54.214-59.163Q54.108-59.054 53.952-59.054Q53.796-59.054 53.687-59.163Q53.577-59.273 53.577-59.429Q53.577-59.589 53.683-59.694Q53.358-59.694 53.144-59.466Q52.929-59.237 52.833-58.898Q52.737-58.558 52.737-58.253L52.737-56.980Q52.737-56.812 52.964-56.765Q53.191-56.718 53.495-56.718L53.495-56.421M56.808-56.421L54.827-56.421L54.827-56.718Q55.097-56.718 55.265-56.763Q55.433-56.808 55.433-56.980L55.433-59.116Q55.433-59.331 55.370-59.427Q55.308-59.523 55.191-59.544Q55.073-59.566 54.827-59.566L54.827-59.862L55.995-59.948L55.995-59.163Q56.073-59.374 56.226-59.560Q56.378-59.745 56.577-59.847Q56.776-59.948 57.003-59.948Q57.249-59.948 57.441-59.804Q57.632-59.659 57.632-59.429Q57.632-59.273 57.526-59.163Q57.421-59.054 57.265-59.054Q57.108-59.054 56.999-59.163Q56.890-59.273 56.890-59.429Q56.890-59.589 56.995-59.694Q56.671-59.694 56.456-59.466Q56.241-59.237 56.146-58.898Q56.050-58.558 56.050-58.253L56.050-56.980Q56.050-56.812 56.276-56.765Q56.503-56.718 56.808-56.718L56.808-56.421M59.972-56.421L58.194-56.421L58.194-56.718Q58.468-56.718 58.636-56.765Q58.804-56.812 58.804-56.980L58.804-59.116Q58.804-59.331 58.747-59.427Q58.691-59.523 58.577-59.544Q58.464-59.566 58.218-59.566L58.218-59.862L59.417-59.948L59.417-56.980Q59.417-56.812 59.564-56.765Q59.710-56.718 59.972-56.718L59.972-56.421M58.530-61.343Q58.530-61.534 58.665-61.665Q58.800-61.796 58.995-61.796Q59.116-61.796 59.220-61.733Q59.323-61.671 59.386-61.567Q59.448-61.464 59.448-61.343Q59.448-61.148 59.317-61.013Q59.187-60.878 58.995-60.878Q58.796-60.878 58.663-61.011Q58.530-61.144 58.530-61.343M62.273-56.452L61.050-59.308Q60.968-59.483 60.823-59.528Q60.679-59.573 60.409-59.573L60.409-59.870L62.120-59.870L62.120-59.573Q61.698-59.573 61.698-59.390Q61.698-59.355 61.714-59.308L62.659-57.116L63.499-59.093Q63.538-59.171 63.538-59.261Q63.538-59.401 63.433-59.487Q63.327-59.573 63.187-59.573L63.187-59.870L64.538-59.870L64.538-59.573Q64.015-59.573 63.800-59.093L62.675-56.452Q62.612-56.343 62.507-56.343L62.441-56.343Q62.327-56.343 62.273-56.452\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(162.869 98.74)\">\u003Cpath d=\"M64.727-58.175Q64.727-58.655 64.960-59.071Q65.192-59.487 65.602-59.737Q66.012-59.987 66.489-59.987Q67.219-59.987 67.618-59.546Q68.016-59.105 68.016-58.374Q68.016-58.269 67.923-58.245L65.473-58.245L65.473-58.175Q65.473-57.765 65.594-57.409Q65.716-57.054 65.987-56.837Q66.259-56.620 66.688-56.620Q67.052-56.620 67.348-56.849Q67.645-57.077 67.747-57.429Q67.755-57.476 67.841-57.491L67.923-57.491Q68.016-57.464 68.016-57.382Q68.016-57.374 68.009-57.343Q67.946-57.116 67.807-56.933Q67.669-56.749 67.477-56.616Q67.286-56.483 67.067-56.413Q66.848-56.343 66.610-56.343Q66.239-56.343 65.901-56.480Q65.563-56.616 65.296-56.868Q65.028-57.120 64.878-57.460Q64.727-57.800 64.727-58.175M65.481-58.483L67.442-58.483Q67.442-58.788 67.341-59.079Q67.239-59.370 67.022-59.552Q66.805-59.733 66.489-59.733Q66.188-59.733 65.958-59.546Q65.727-59.358 65.604-59.067Q65.481-58.776 65.481-58.483M69.692-56.886Q69.692-57.077 69.825-57.210Q69.958-57.343 70.153-57.343Q70.344-57.343 70.477-57.210Q70.610-57.077 70.610-56.886Q70.610-56.687 70.477-56.554Q70.344-56.421 70.153-56.421Q69.958-56.421 69.825-56.554Q69.692-56.687 69.692-56.886M70.009-58.062L70.009-58.460Q70.009-58.858 70.137-59.259Q70.266-59.659 70.512-59.995Q70.591-60.101 70.757-60.276Q70.923-60.452 70.991-60.589Q71.059-60.726 71.059-60.964Q71.059-61.452 70.856-61.624Q70.653-61.796 70.153-61.796Q69.844-61.796 69.567-61.685Q69.290-61.573 69.137-61.351Q69.309-61.351 69.417-61.237Q69.524-61.124 69.524-60.956Q69.524-60.855 69.473-60.763Q69.423-60.671 69.333-60.618Q69.243-60.566 69.130-60.566Q69.020-60.566 68.930-60.618Q68.841-60.671 68.790-60.763Q68.739-60.855 68.739-60.956Q68.739-61.304 68.950-61.554Q69.161-61.804 69.485-61.929Q69.809-62.054 70.153-62.054Q70.813-62.054 71.307-61.804Q71.802-61.554 71.802-60.956Q71.802-60.706 71.678-60.491Q71.555-60.276 71.344-60.140Q71.032-59.937 70.800-59.681Q70.567-59.425 70.428-59.108Q70.290-58.792 70.290-58.444L70.290-58.062Q70.290-58.026 70.262-57.999Q70.235-57.972 70.204-57.972L70.098-57.972Q70.067-57.972 70.038-57.997Q70.009-58.023 70.009-58.062\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">A frame for the air-travel domain. Each slot has a semantic type and a question template the system uses to fill it; the goal of the dialogue is to fill every slot, then perform the action (query the flight database).\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:434.078px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 325.559 179.962\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg fill=\"var(--tk-soft-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath d=\"M-68.737 28.432H8.086V-4.701h-76.823Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-25.76 11.233)\">\u003Cpath d=\"M-11.328-7.135L-13.250-7.135L-13.250-7.432Q-12.441-7.432-12.441-7.912L-12.441-12.256Q-12.699-12.303-13.250-12.303L-13.250-12.600L-11.754-12.600Q-11.695-12.580-11.684-12.569L-8.676-8.381L-8.676-11.822Q-8.676-12.303-9.481-12.303L-9.481-12.600L-7.563-12.600L-7.563-12.303Q-8.371-12.303-8.371-11.822L-8.371-7.240Q-8.391-7.155-8.465-7.135L-8.570-7.135Q-8.625-7.147-8.641-7.174L-12.137-12.030L-12.137-7.912Q-12.137-7.432-11.328-7.432L-11.328-7.135M-2.508-7.135L-6.891-7.135L-6.891-7.432Q-6.570-7.432-6.326-7.479Q-6.082-7.526-6.082-7.694L-6.082-12.037Q-6.082-12.209-6.326-12.256Q-6.570-12.303-6.891-12.303L-6.891-12.600L-4.305-12.600L-4.305-12.303Q-5.316-12.303-5.316-12.037L-5.316-7.694Q-5.316-7.522-5.225-7.477Q-5.133-7.432-4.914-7.432L-4.227-7.432Q-3.754-7.432-3.445-7.557Q-3.137-7.682-2.959-7.912Q-2.781-8.143-2.690-8.469Q-2.598-8.795-2.555-9.248L-2.274-9.248L-2.508-7.135M-0.777-8.944L-0.777-12.037Q-0.777-12.209-1.022-12.256Q-1.266-12.303-1.586-12.303L-1.586-12.600L0.797-12.600L0.797-12.303Q0.476-12.303 0.232-12.254Q-0.012-12.205-0.012-12.037L-0.012-8.967Q-0.012-8.244 0.332-7.754Q0.676-7.264 1.375-7.264Q1.840-7.264 2.211-7.494Q2.582-7.725 2.791-8.117Q3-8.510 3-8.967L3-11.822Q3-12.303 2.191-12.303L2.191-12.600L4.101-12.600L4.101-12.303Q3.293-12.303 3.293-11.822L3.293-8.944Q3.293-8.424 3.039-7.967Q2.785-7.510 2.344-7.239Q1.902-6.967 1.375-6.967Q0.965-6.967 0.584-7.110Q0.203-7.252-0.109-7.522Q-0.422-7.791-0.600-8.158Q-0.777-8.526-0.777-8.944\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.76 11.233)\">\u003Cpath d=\"M-27.198 4.357Q-27.811 3.900-28.213 3.265Q-28.616 2.631-28.811 1.885Q-29.006 1.138-29.006 0.365Q-29.006-0.408-28.811-1.155Q-28.616-1.901-28.213-2.535Q-27.811-3.170-27.198-3.627Q-27.186-3.631-27.178-3.633Q-27.170-3.635-27.159-3.635L-27.081-3.635Q-27.042-3.635-27.016-3.608Q-26.991-3.580-26.991-3.537Q-26.991-3.487-27.022-3.467Q-27.530-3.014-27.852-2.391Q-28.174-1.768-28.315-1.072Q-28.456-0.377-28.456 0.365Q-28.456 1.099-28.317 1.799Q-28.178 2.498-27.854 3.123Q-27.530 3.748-27.022 4.197Q-26.991 4.217-26.991 4.267Q-26.991 4.310-27.016 4.338Q-27.042 4.365-27.081 4.365L-27.159 4.365Q-27.167 4.361-27.176 4.359Q-27.186 4.357-27.198 4.357M-26.229 2.357L-26.229 1.135Q-26.229 1.107-26.198 1.076Q-26.167 1.045-26.143 1.045L-26.038 1.045Q-25.967 1.045-25.952 1.107Q-25.889 1.428-25.751 1.668Q-25.612 1.908-25.379 2.049Q-25.147 2.189-24.838 2.189Q-24.600 2.189-24.391 2.129Q-24.182 2.068-24.045 1.920Q-23.909 1.771-23.909 1.525Q-23.909 1.271-24.120 1.105Q-24.331 0.939-24.600 0.885L-25.221 0.771Q-25.628 0.693-25.928 0.437Q-26.229 0.181-26.229-0.194Q-26.229-0.561-26.028-0.783Q-25.827-1.006-25.503-1.104Q-25.178-1.201-24.838-1.201Q-24.374-1.201-24.077-0.994L-23.854-1.178Q-23.831-1.201-23.799-1.201L-23.749-1.201Q-23.717-1.201-23.690-1.174Q-23.663-1.147-23.663-1.115L-23.663-0.131Q-23.663-0.100-23.688-0.071Q-23.713-0.041-23.749-0.041L-23.854-0.041Q-23.889-0.041-23.917-0.069Q-23.944-0.096-23.944-0.131Q-23.944-0.530-24.196-0.750Q-24.448-0.971-24.846-0.971Q-25.202-0.971-25.485-0.848Q-25.768-0.725-25.768-0.420Q-25.768-0.201-25.567-0.069Q-25.366 0.064-25.120 0.107L-24.495 0.220Q-24.065 0.310-23.756 0.607Q-23.448 0.904-23.448 1.318Q-23.448 1.888-23.846 2.166Q-24.245 2.443-24.838 2.443Q-25.389 2.443-25.741 2.107L-26.038 2.420Q-26.061 2.443-26.096 2.443L-26.143 2.443Q-26.167 2.443-26.198 2.412Q-26.229 2.381-26.229 2.357M-21.006 2.365L-22.838 2.365L-22.838 2.068Q-22.565 2.068-22.397 2.021Q-22.229 1.974-22.229 1.806L-22.229-2.354Q-22.229-2.569-22.292-2.664Q-22.354-2.760-22.473-2.781Q-22.592-2.803-22.838-2.803L-22.838-3.100L-21.616-3.186L-21.616 1.806Q-21.616 1.974-21.448 2.021Q-21.280 2.068-21.006 2.068L-21.006 2.365M-20.561 0.670Q-20.561 0.166-20.305-0.266Q-20.049-0.697-19.614-0.949Q-19.178-1.201-18.678-1.201Q-18.292-1.201-17.950-1.057Q-17.608-0.912-17.346-0.651Q-17.085-0.389-16.942-0.053Q-16.799 0.283-16.799 0.670Q-16.799 1.162-17.063 1.572Q-17.327 1.982-17.756 2.213Q-18.186 2.443-18.678 2.443Q-19.170 2.443-19.604 2.211Q-20.038 1.978-20.299 1.570Q-20.561 1.162-20.561 0.670M-18.678 2.166Q-18.221 2.166-17.969 1.943Q-17.717 1.720-17.629 1.369Q-17.542 1.017-17.542 0.572Q-17.542 0.142-17.635-0.196Q-17.729-0.533-17.983-0.740Q-18.237-0.947-18.678-0.947Q-19.327-0.947-19.571-0.531Q-19.815-0.115-19.815 0.572Q-19.815 1.017-19.727 1.369Q-19.639 1.720-19.387 1.943Q-19.135 2.166-18.678 2.166M-15.690 1.404L-15.690-0.787L-16.393-0.787L-16.393-1.041Q-16.038-1.041-15.795-1.274Q-15.553-1.506-15.442-1.854Q-15.331-2.201-15.331-2.557L-15.049-2.557L-15.049-1.084L-13.874-1.084L-13.874-0.787L-15.049-0.787L-15.049 1.388Q-15.049 1.709-14.930 1.937Q-14.811 2.166-14.530 2.166Q-14.350 2.166-14.233 2.043Q-14.116 1.920-14.063 1.740Q-14.010 1.560-14.010 1.388L-14.010 0.916L-13.729 0.916L-13.729 1.404Q-13.729 1.658-13.835 1.898Q-13.940 2.138-14.137 2.291Q-14.335 2.443-14.592 2.443Q-14.909 2.443-15.161 2.320Q-15.413 2.197-15.551 1.963Q-15.690 1.728-15.690 1.404\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.76 11.233)\">\u003Cpath d=\"M-8.101 2.365L-10.086 2.365L-10.086 2.068Q-9.812 2.068-9.644 2.021Q-9.476 1.974-9.476 1.806L-9.476-0.787L-10.117-0.787L-10.117-1.084L-9.476-1.084L-9.476-2.018Q-9.476-2.283-9.359-2.520Q-9.242-2.756-9.049-2.920Q-8.855-3.084-8.607-3.176Q-8.359-3.268-8.094-3.268Q-7.808-3.268-7.584-3.110Q-7.359-2.951-7.359-2.674Q-7.359-2.518-7.465-2.408Q-7.570-2.299-7.734-2.299Q-7.890-2.299-8-2.408Q-8.109-2.518-8.109-2.674Q-8.109-2.881-7.949-2.987Q-8.047-3.010-8.140-3.010Q-8.371-3.010-8.543-2.854Q-8.715-2.697-8.801-2.461Q-8.886-2.225-8.886-2.002L-8.886-1.084L-7.918-1.084L-7.918-0.787L-8.863-0.787L-8.863 1.806Q-8.863 1.974-8.636 2.021Q-8.410 2.068-8.101 2.068\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.76 11.233)\">\u003Cpath d=\"M-5.079 2.365L-6.857 2.365L-6.857 2.068Q-6.583 2.068-6.415 2.021Q-6.247 1.974-6.247 1.806L-6.247-0.330Q-6.247-0.545-6.304-0.641Q-6.361-0.737-6.474-0.758Q-6.587-0.780-6.833-0.780L-6.833-1.076L-5.634-1.162L-5.634 1.806Q-5.634 1.974-5.488 2.021Q-5.341 2.068-5.079 2.068L-5.079 2.365M-6.521-2.557Q-6.521-2.748-6.386-2.879Q-6.251-3.010-6.056-3.010Q-5.935-3.010-5.831-2.947Q-5.728-2.885-5.665-2.781Q-5.603-2.678-5.603-2.557Q-5.603-2.362-5.734-2.227Q-5.864-2.092-6.056-2.092Q-6.255-2.092-6.388-2.225Q-6.521-2.358-6.521-2.557M-2.665 2.365L-4.497 2.365L-4.497 2.068Q-4.224 2.068-4.056 2.021Q-3.888 1.974-3.888 1.806L-3.888-2.354Q-3.888-2.569-3.950-2.664Q-4.013-2.760-4.132-2.781Q-4.251-2.803-4.497-2.803L-4.497-3.100L-3.275-3.186L-3.275 1.806Q-3.275 1.974-3.107 2.021Q-2.939 2.068-2.665 2.068L-2.665 2.365M-0.306 2.365L-2.138 2.365L-2.138 2.068Q-1.864 2.068-1.697 2.021Q-1.529 1.974-1.529 1.806L-1.529-2.354Q-1.529-2.569-1.591-2.664Q-1.654-2.760-1.773-2.781Q-1.892-2.803-2.138-2.803L-2.138-3.100L-0.915-3.186L-0.915 1.806Q-0.915 1.974-0.747 2.021Q-0.579 2.068-0.306 2.068L-0.306 2.365M1.999 2.365L0.221 2.365L0.221 2.068Q0.495 2.068 0.663 2.021Q0.831 1.974 0.831 1.806L0.831-0.330Q0.831-0.545 0.774-0.641Q0.718-0.737 0.604-0.758Q0.491-0.780 0.245-0.780L0.245-1.076L1.444-1.162L1.444 1.806Q1.444 1.974 1.591 2.021Q1.737 2.068 1.999 2.068L1.999 2.365M0.557-2.557Q0.557-2.748 0.692-2.879Q0.827-3.010 1.022-3.010Q1.143-3.010 1.247-2.947Q1.350-2.885 1.413-2.781Q1.475-2.678 1.475-2.557Q1.475-2.362 1.344-2.227Q1.214-2.092 1.022-2.092Q0.823-2.092 0.690-2.225Q0.557-2.358 0.557-2.557M4.428 2.365L2.573 2.365L2.573 2.068Q2.846 2.068 3.014 2.021Q3.182 1.974 3.182 1.806L3.182-0.330Q3.182-0.545 3.120-0.641Q3.057-0.737 2.938-0.758Q2.819-0.780 2.573-0.780L2.573-1.076L3.764-1.162L3.764-0.428Q3.878-0.643 4.071-0.811Q4.264-0.979 4.503-1.071Q4.741-1.162 4.995-1.162Q6.163-1.162 6.163-0.084L6.163 1.806Q6.163 1.974 6.333 2.021Q6.503 2.068 6.772 2.068L6.772 2.365L4.917 2.365L4.917 2.068Q5.190 2.068 5.358 2.021Q5.526 1.974 5.526 1.806L5.526-0.069Q5.526-0.451 5.405-0.680Q5.284-0.908 4.932-0.908Q4.620-0.908 4.366-0.746Q4.112-0.584 3.966-0.315Q3.819-0.045 3.819 0.252L3.819 1.806Q3.819 1.974 3.989 2.021Q4.159 2.068 4.428 2.068L4.428 2.365M7.218 2.974Q7.218 2.693 7.428 2.482Q7.639 2.271 7.925 2.181Q7.768 2.056 7.690 1.867Q7.612 1.678 7.612 1.478Q7.612 1.123 7.843 0.830Q7.475 0.490 7.475 0.021Q7.475-0.330 7.678-0.600Q7.882-0.869 8.202-1.016Q8.522-1.162 8.866-1.162Q9.386-1.162 9.757-0.881Q10.120-1.252 10.667-1.252Q10.846-1.252 10.973-1.125Q11.100-0.998 11.100-0.819Q11.100-0.713 11.022-0.635Q10.944-0.557 10.835-0.557Q10.725-0.557 10.649-0.633Q10.573-0.709 10.573-0.819Q10.573-0.920 10.612-0.971Q10.620-0.979 10.624-0.985Q10.628-0.990 10.628-0.994Q10.253-0.994 9.932-0.740Q10.253-0.401 10.253 0.021Q10.253 0.291 10.136 0.508Q10.018 0.724 9.813 0.883Q9.608 1.041 9.366 1.123Q9.124 1.205 8.866 1.205Q8.647 1.205 8.434 1.146Q8.221 1.088 8.026 0.967Q7.932 1.107 7.932 1.287Q7.932 1.494 8.069 1.646Q8.206 1.799 8.413 1.799L9.108 1.799Q9.596 1.799 10.009 1.883Q10.421 1.967 10.700 2.224Q10.979 2.482 10.979 2.974Q10.979 3.338 10.659 3.570Q10.339 3.803 9.897 3.904Q9.456 4.006 9.100 4.006Q8.745 4.006 8.302 3.904Q7.858 3.803 7.538 3.570Q7.218 3.338 7.218 2.974M7.721 2.974Q7.721 3.170 7.866 3.318Q8.011 3.467 8.223 3.556Q8.436 3.646 8.677 3.693Q8.917 3.740 9.100 3.740Q9.343 3.740 9.673 3.662Q10.003 3.584 10.239 3.410Q10.475 3.236 10.475 2.974Q10.475 2.568 10.065 2.459Q9.655 2.349 9.093 2.349L8.413 2.349Q8.143 2.349 7.932 2.527Q7.721 2.705 7.721 2.974M8.866 0.939Q9.589 0.939 9.589 0.021Q9.589-0.901 8.866-0.901Q8.139-0.901 8.139 0.021Q8.139 0.939 8.866 0.939\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.76 11.233)\">\u003Cpath d=\"M17.194 0.549L14.721 0.549Q14.643 0.537 14.594 0.488Q14.546 0.439 14.546 0.365Q14.546 0.291 14.594 0.242Q14.643 0.193 14.721 0.181L17.194 0.181L17.194-2.299Q17.221-2.467 17.378-2.467Q17.452-2.467 17.501-2.418Q17.550-2.369 17.561-2.299L17.561 0.181L20.034 0.181Q20.202 0.213 20.202 0.365Q20.202 0.517 20.034 0.549L17.561 0.549L17.561 3.029Q17.550 3.099 17.501 3.148Q17.452 3.197 17.378 3.197Q17.221 3.197 17.194 3.029\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.76 11.233)\">\u003Cpath d=\"M-28.270 11.943Q-28.751 11.943-29.159 11.699Q-29.567 11.455-29.805 11.041Q-30.044 10.627-30.044 10.138Q-30.044 9.646-29.786 9.230Q-29.528 8.814-29.096 8.576Q-28.665 8.338-28.173 8.338Q-27.552 8.338-27.102 8.775L-27.102 7.146Q-27.102 6.931-27.165 6.836Q-27.227 6.740-27.345 6.719Q-27.462 6.697-27.708 6.697L-27.708 6.400L-26.485 6.314L-26.485 11.123Q-26.485 11.334-26.423 11.429Q-26.360 11.525-26.243 11.547Q-26.126 11.568-25.876 11.568L-25.876 11.865L-27.126 11.943L-27.126 11.459Q-27.591 11.943-28.270 11.943M-28.204 11.689Q-27.864 11.689-27.571 11.498Q-27.278 11.306-27.126 11.010L-27.126 9.178Q-27.274 8.904-27.536 8.748Q-27.798 8.592-28.110 8.592Q-28.735 8.592-29.018 9.039Q-29.302 9.486-29.302 10.146Q-29.302 10.791-29.050 11.240Q-28.798 11.689-28.204 11.689M-23.509 11.865L-25.286 11.865L-25.286 11.568Q-25.012 11.568-24.845 11.521Q-24.677 11.474-24.677 11.306L-24.677 9.170Q-24.677 8.955-24.733 8.859Q-24.790 8.763-24.903 8.742Q-25.016 8.720-25.262 8.720L-25.262 8.424L-24.063 8.338L-24.063 11.306Q-24.063 11.474-23.917 11.521Q-23.770 11.568-23.509 11.568L-23.509 11.865M-24.950 6.943Q-24.950 6.752-24.815 6.621Q-24.680 6.490-24.485 6.490Q-24.364 6.490-24.261 6.553Q-24.157 6.615-24.095 6.719Q-24.032 6.822-24.032 6.943Q-24.032 7.138-24.163 7.273Q-24.294 7.408-24.485 7.408Q-24.684 7.408-24.817 7.275Q-24.950 7.142-24.950 6.943M-22.911 11.033Q-22.911 10.549-22.509 10.254Q-22.106 9.959-21.555 9.840Q-21.005 9.720-20.512 9.720L-20.512 9.431Q-20.512 9.205-20.628 8.998Q-20.743 8.791-20.940 8.672Q-21.137 8.553-21.368 8.553Q-21.794 8.553-22.079 8.658Q-22.009 8.685-21.962 8.740Q-21.915 8.795-21.889 8.865Q-21.864 8.935-21.864 9.010Q-21.864 9.115-21.915 9.207Q-21.966 9.299-22.057 9.349Q-22.149 9.400-22.255 9.400Q-22.360 9.400-22.452 9.349Q-22.544 9.299-22.595 9.207Q-22.645 9.115-22.645 9.010Q-22.645 8.592-22.257 8.445Q-21.868 8.299-21.368 8.299Q-21.036 8.299-20.682 8.429Q-20.329 8.560-20.100 8.814Q-19.872 9.068-19.872 9.416L-19.872 11.217Q-19.872 11.349-19.800 11.459Q-19.727 11.568-19.598 11.568Q-19.473 11.568-19.405 11.463Q-19.337 11.357-19.337 11.217L-19.337 10.705L-19.055 10.705L-19.055 11.217Q-19.055 11.420-19.173 11.578Q-19.290 11.736-19.471 11.820Q-19.653 11.904-19.856 11.904Q-20.087 11.904-20.239 11.732Q-20.391 11.560-20.423 11.330Q-20.583 11.611-20.891 11.777Q-21.200 11.943-21.552 11.943Q-22.063 11.943-22.487 11.720Q-22.911 11.498-22.911 11.033M-22.223 11.033Q-22.223 11.318-21.997 11.504Q-21.770 11.689-21.477 11.689Q-21.231 11.689-21.007 11.572Q-20.782 11.455-20.647 11.252Q-20.512 11.049-20.512 10.795L-20.512 9.963Q-20.778 9.963-21.063 10.017Q-21.348 10.072-21.620 10.201Q-21.891 10.330-22.057 10.537Q-22.223 10.744-22.223 11.033M-16.848 11.865L-18.680 11.865L-18.680 11.568Q-18.407 11.568-18.239 11.521Q-18.071 11.474-18.071 11.306L-18.071 7.146Q-18.071 6.931-18.134 6.836Q-18.196 6.740-18.315 6.719Q-18.434 6.697-18.680 6.697L-18.680 6.400L-17.458 6.314L-17.458 11.306Q-17.458 11.474-17.290 11.521Q-17.122 11.568-16.848 11.568L-16.848 11.865M-16.403 10.170Q-16.403 9.666-16.147 9.234Q-15.891 8.803-15.456 8.551Q-15.020 8.299-14.520 8.299Q-14.134 8.299-13.792 8.443Q-13.450 8.588-13.188 8.849Q-12.927 9.111-12.784 9.447Q-12.641 9.783-12.641 10.170Q-12.641 10.662-12.905 11.072Q-13.169 11.482-13.598 11.713Q-14.028 11.943-14.520 11.943Q-15.012 11.943-15.446 11.711Q-15.880 11.478-16.141 11.070Q-16.403 10.662-16.403 10.170M-14.520 11.666Q-14.063 11.666-13.811 11.443Q-13.559 11.220-13.471 10.869Q-13.384 10.517-13.384 10.072Q-13.384 9.642-13.477 9.304Q-13.571 8.967-13.825 8.760Q-14.079 8.553-14.520 8.553Q-15.169 8.553-15.413 8.969Q-15.657 9.385-15.657 10.072Q-15.657 10.517-15.569 10.869Q-15.481 11.220-15.229 11.443Q-14.977 11.666-14.520 11.666M-12.157 12.474Q-12.157 12.193-11.946 11.982Q-11.735 11.771-11.450 11.681Q-11.606 11.556-11.684 11.367Q-11.762 11.178-11.762 10.978Q-11.762 10.623-11.532 10.330Q-11.899 9.990-11.899 9.521Q-11.899 9.170-11.696 8.900Q-11.493 8.631-11.173 8.484Q-10.852 8.338-10.509 8.338Q-9.989 8.338-9.618 8.619Q-9.255 8.248-8.708 8.248Q-8.528 8.248-8.401 8.375Q-8.274 8.502-8.274 8.681Q-8.274 8.787-8.352 8.865Q-8.430 8.943-8.540 8.943Q-8.649 8.943-8.725 8.867Q-8.802 8.791-8.802 8.681Q-8.802 8.580-8.762 8.529Q-8.755 8.521-8.751 8.515Q-8.747 8.510-8.747 8.506Q-9.122 8.506-9.442 8.760Q-9.122 9.099-9.122 9.521Q-9.122 9.791-9.239 10.008Q-9.356 10.224-9.561 10.383Q-9.766 10.541-10.009 10.623Q-10.251 10.705-10.509 10.705Q-10.727 10.705-10.940 10.646Q-11.153 10.588-11.348 10.467Q-11.442 10.607-11.442 10.787Q-11.442 10.994-11.305 11.146Q-11.169 11.299-10.962 11.299L-10.266 11.299Q-9.778 11.299-9.366 11.383Q-8.954 11.467-8.675 11.724Q-8.395 11.982-8.395 12.474Q-8.395 12.838-8.716 13.070Q-9.036 13.303-9.477 13.404Q-9.919 13.506-10.274 13.506Q-10.630 13.506-11.073 13.404Q-11.516 13.303-11.837 13.070Q-12.157 12.838-12.157 12.474M-11.653 12.474Q-11.653 12.670-11.509 12.818Q-11.364 12.967-11.151 13.056Q-10.938 13.146-10.698 13.193Q-10.458 13.240-10.274 13.240Q-10.032 13.240-9.702 13.162Q-9.372 13.084-9.136 12.910Q-8.899 12.736-8.899 12.474Q-8.899 12.068-9.309 11.959Q-9.720 11.849-10.282 11.849L-10.962 11.849Q-11.231 11.849-11.442 12.027Q-11.653 12.205-11.653 12.474M-10.509 10.439Q-9.786 10.439-9.786 9.521Q-9.786 8.599-10.509 8.599Q-11.235 8.599-11.235 9.521Q-11.235 10.439-10.509 10.439M-7.227 10.912L-7.227 9.170Q-7.227 8.955-7.290 8.859Q-7.352 8.763-7.471 8.742Q-7.591 8.720-7.837 8.720L-7.837 8.424L-6.591 8.338L-6.591 10.888L-6.591 10.912Q-6.591 11.224-6.536 11.386Q-6.481 11.549-6.331 11.619Q-6.180 11.689-5.860 11.689Q-5.430 11.689-5.157 11.351Q-4.884 11.013-4.884 10.568L-4.884 9.170Q-4.884 8.955-4.946 8.859Q-5.009 8.763-5.128 8.742Q-5.247 8.720-5.493 8.720L-5.493 8.424L-4.247 8.338L-4.247 11.123Q-4.247 11.334-4.184 11.429Q-4.122 11.525-4.003 11.547Q-3.884 11.568-3.637 11.568L-3.637 11.865L-4.860 11.943L-4.860 11.322Q-5.028 11.611-5.309 11.777Q-5.591 11.943-5.911 11.943Q-7.227 11.943-7.227 10.912M-3.192 10.111Q-3.192 9.631-2.960 9.215Q-2.727 8.799-2.317 8.549Q-1.907 8.299-1.430 8.299Q-0.700 8.299-0.302 8.740Q0.097 9.181 0.097 9.912Q0.097 10.017 0.003 10.041L-2.446 10.041L-2.446 10.111Q-2.446 10.521-2.325 10.877Q-2.204 11.232-1.932 11.449Q-1.661 11.666-1.231 11.666Q-0.868 11.666-0.571 11.437Q-0.274 11.209-0.173 10.857Q-0.165 10.810-0.079 10.795L0.003 10.795Q0.097 10.822 0.097 10.904Q0.097 10.912 0.089 10.943Q0.027 11.170-0.112 11.353Q-0.251 11.537-0.442 11.670Q-0.634 11.803-0.852 11.873Q-1.071 11.943-1.309 11.943Q-1.680 11.943-2.018 11.806Q-2.356 11.670-2.624 11.418Q-2.891 11.166-3.042 10.826Q-3.192 10.486-3.192 10.111M-2.438 9.803L-0.477 9.803Q-0.477 9.498-0.579 9.207Q-0.680 8.916-0.897 8.734Q-1.114 8.553-1.430 8.553Q-1.731 8.553-1.962 8.740Q-2.192 8.928-2.315 9.219Q-2.438 9.510-2.438 9.803\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.76 11.233)\">\u003Cpath d=\"M3.539 11.033Q3.539 10.549 3.941 10.254Q4.344 9.959 4.894 9.840Q5.445 9.720 5.937 9.720L5.937 9.431Q5.937 9.205 5.822 8.998Q5.707 8.791 5.510 8.672Q5.312 8.553 5.082 8.553Q4.656 8.553 4.371 8.658Q4.441 8.685 4.488 8.740Q4.535 8.795 4.560 8.865Q4.586 8.935 4.586 9.010Q4.586 9.115 4.535 9.207Q4.484 9.299 4.392 9.349Q4.301 9.400 4.195 9.400Q4.090 9.400 3.998 9.349Q3.906 9.299 3.855 9.207Q3.805 9.115 3.805 9.010Q3.805 8.592 4.193 8.445Q4.582 8.299 5.082 8.299Q5.414 8.299 5.767 8.429Q6.121 8.560 6.349 8.814Q6.578 9.068 6.578 9.416L6.578 11.217Q6.578 11.349 6.650 11.459Q6.723 11.568 6.851 11.568Q6.976 11.568 7.045 11.463Q7.113 11.357 7.113 11.217L7.113 10.705L7.394 10.705L7.394 11.217Q7.394 11.420 7.277 11.578Q7.160 11.736 6.978 11.820Q6.797 11.904 6.594 11.904Q6.363 11.904 6.211 11.732Q6.058 11.560 6.027 11.330Q5.867 11.611 5.558 11.777Q5.250 11.943 4.898 11.943Q4.387 11.943 3.963 11.720Q3.539 11.498 3.539 11.033M4.226 11.033Q4.226 11.318 4.453 11.504Q4.680 11.689 4.973 11.689Q5.219 11.689 5.443 11.572Q5.668 11.455 5.803 11.252Q5.937 11.049 5.937 10.795L5.937 9.963Q5.672 9.963 5.387 10.017Q5.101 10.072 4.830 10.201Q4.558 10.330 4.392 10.537Q4.226 10.744 4.226 11.033M7.730 10.138Q7.730 9.642 7.980 9.217Q8.230 8.791 8.650 8.545Q9.070 8.299 9.570 8.299Q10.109 8.299 10.500 8.424Q10.890 8.549 10.890 8.963Q10.890 9.068 10.840 9.160Q10.789 9.252 10.697 9.303Q10.605 9.353 10.496 9.353Q10.390 9.353 10.299 9.303Q10.207 9.252 10.156 9.160Q10.105 9.068 10.105 8.963Q10.105 8.740 10.273 8.635Q10.051 8.576 9.578 8.576Q9.281 8.576 9.066 8.715Q8.851 8.853 8.721 9.084Q8.590 9.314 8.531 9.584Q8.473 9.853 8.473 10.138Q8.473 10.533 8.605 10.883Q8.738 11.232 9.010 11.449Q9.281 11.666 9.680 11.666Q10.055 11.666 10.330 11.449Q10.605 11.232 10.707 10.873Q10.723 10.810 10.785 10.810L10.890 10.810Q10.926 10.810 10.951 10.838Q10.976 10.865 10.976 10.904L10.976 10.928Q10.844 11.408 10.459 11.676Q10.074 11.943 9.570 11.943Q9.207 11.943 8.873 11.806Q8.539 11.670 8.279 11.420Q8.019 11.170 7.875 10.834Q7.730 10.498 7.730 10.138M12.090 10.904L12.090 8.713L11.387 8.713L11.387 8.459Q11.742 8.459 11.984 8.226Q12.226 7.994 12.338 7.646Q12.449 7.299 12.449 6.943L12.730 6.943L12.730 8.416L13.906 8.416L13.906 8.713L12.730 8.713L12.730 10.888Q12.730 11.209 12.849 11.437Q12.969 11.666 13.250 11.666Q13.430 11.666 13.547 11.543Q13.664 11.420 13.717 11.240Q13.769 11.060 13.769 10.888L13.769 10.416L14.051 10.416L14.051 10.904Q14.051 11.158 13.945 11.398Q13.840 11.638 13.642 11.791Q13.445 11.943 13.187 11.943Q12.871 11.943 12.619 11.820Q12.367 11.697 12.228 11.463Q12.090 11.228 12.090 10.904M14.812 11.857L14.812 10.635Q14.812 10.607 14.844 10.576Q14.875 10.545 14.898 10.545L15.004 10.545Q15.074 10.545 15.090 10.607Q15.152 10.928 15.291 11.168Q15.430 11.408 15.662 11.549Q15.894 11.689 16.203 11.689Q16.441 11.689 16.650 11.629Q16.859 11.568 16.996 11.420Q17.133 11.271 17.133 11.025Q17.133 10.771 16.922 10.605Q16.711 10.439 16.441 10.385L15.820 10.271Q15.414 10.193 15.113 9.937Q14.812 9.681 14.812 9.306Q14.812 8.939 15.014 8.717Q15.215 8.494 15.539 8.396Q15.863 8.299 16.203 8.299Q16.668 8.299 16.965 8.506L17.187 8.322Q17.211 8.299 17.242 8.299L17.293 8.299Q17.324 8.299 17.351 8.326Q17.379 8.353 17.379 8.385L17.379 9.369Q17.379 9.400 17.353 9.429Q17.328 9.459 17.293 9.459L17.187 9.459Q17.152 9.459 17.125 9.431Q17.098 9.404 17.098 9.369Q17.098 8.970 16.846 8.750Q16.594 8.529 16.195 8.529Q15.840 8.529 15.557 8.652Q15.273 8.775 15.273 9.080Q15.273 9.299 15.474 9.431Q15.676 9.564 15.922 9.607L16.547 9.720Q16.976 9.810 17.285 10.107Q17.594 10.404 17.594 10.818Q17.594 11.388 17.195 11.666Q16.797 11.943 16.203 11.943Q15.652 11.943 15.301 11.607L15.004 11.920Q14.980 11.943 14.945 11.943L14.898 11.943Q14.875 11.943 14.844 11.912Q14.812 11.881 14.812 11.857M18.523 13.865L18.441 13.865Q18.406 13.865 18.381 13.836Q18.355 13.806 18.355 13.767Q18.355 13.717 18.387 13.697Q18.773 13.361 19.057 12.912Q19.340 12.463 19.506 11.963Q19.672 11.463 19.746 10.945Q19.820 10.428 19.820 9.865Q19.820 9.295 19.746 8.779Q19.672 8.263 19.506 7.767Q19.340 7.271 19.060 6.824Q18.781 6.377 18.387 6.033Q18.355 6.013 18.355 5.963Q18.355 5.924 18.381 5.894Q18.406 5.865 18.441 5.865L18.523 5.865Q18.535 5.865 18.545 5.867Q18.555 5.869 18.562 5.873Q19.176 6.330 19.578 6.965Q19.980 7.599 20.176 8.345Q20.371 9.092 20.371 9.865Q20.371 10.638 20.176 11.385Q19.980 12.131 19.578 12.765Q19.176 13.400 18.562 13.857Q18.551 13.857 18.543 13.859Q18.535 13.861 18.523 13.865\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-soft-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath d=\"M50.765 27.514h76.822V-3.784H50.765Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(93.741 7.528)\">\u003Cpath d=\"M-28.270 2.443Q-28.751 2.443-29.159 2.199Q-29.567 1.955-29.805 1.541Q-30.044 1.127-30.044 0.638Q-30.044 0.146-29.786-0.270Q-29.528-0.686-29.096-0.924Q-28.665-1.162-28.173-1.162Q-27.552-1.162-27.102-0.725L-27.102-2.354Q-27.102-2.569-27.165-2.664Q-27.227-2.760-27.345-2.781Q-27.462-2.803-27.708-2.803L-27.708-3.100L-26.485-3.186L-26.485 1.623Q-26.485 1.834-26.423 1.929Q-26.360 2.025-26.243 2.047Q-26.126 2.068-25.876 2.068L-25.876 2.365L-27.126 2.443L-27.126 1.959Q-27.591 2.443-28.270 2.443M-28.204 2.189Q-27.864 2.189-27.571 1.998Q-27.278 1.806-27.126 1.510L-27.126-0.322Q-27.274-0.596-27.536-0.752Q-27.798-0.908-28.110-0.908Q-28.735-0.908-29.018-0.461Q-29.302-0.014-29.302 0.646Q-29.302 1.291-29.050 1.740Q-28.798 2.189-28.204 2.189M-23.509 2.365L-25.286 2.365L-25.286 2.068Q-25.012 2.068-24.845 2.021Q-24.677 1.974-24.677 1.806L-24.677-0.330Q-24.677-0.545-24.733-0.641Q-24.790-0.737-24.903-0.758Q-25.016-0.780-25.262-0.780L-25.262-1.076L-24.063-1.162L-24.063 1.806Q-24.063 1.974-23.917 2.021Q-23.770 2.068-23.509 2.068L-23.509 2.365M-24.950-2.557Q-24.950-2.748-24.815-2.879Q-24.680-3.010-24.485-3.010Q-24.364-3.010-24.261-2.947Q-24.157-2.885-24.095-2.781Q-24.032-2.678-24.032-2.557Q-24.032-2.362-24.163-2.227Q-24.294-2.092-24.485-2.092Q-24.684-2.092-24.817-2.225Q-24.950-2.358-24.950-2.557M-22.911 1.533Q-22.911 1.049-22.509 0.754Q-22.106 0.459-21.555 0.340Q-21.005 0.220-20.512 0.220L-20.512-0.069Q-20.512-0.295-20.628-0.502Q-20.743-0.709-20.940-0.828Q-21.137-0.947-21.368-0.947Q-21.794-0.947-22.079-0.842Q-22.009-0.815-21.962-0.760Q-21.915-0.705-21.889-0.635Q-21.864-0.565-21.864-0.490Q-21.864-0.385-21.915-0.293Q-21.966-0.201-22.057-0.151Q-22.149-0.100-22.255-0.100Q-22.360-0.100-22.452-0.151Q-22.544-0.201-22.595-0.293Q-22.645-0.385-22.645-0.490Q-22.645-0.908-22.257-1.055Q-21.868-1.201-21.368-1.201Q-21.036-1.201-20.682-1.071Q-20.329-0.940-20.100-0.686Q-19.872-0.432-19.872-0.084L-19.872 1.717Q-19.872 1.849-19.800 1.959Q-19.727 2.068-19.598 2.068Q-19.473 2.068-19.405 1.963Q-19.337 1.857-19.337 1.717L-19.337 1.205L-19.055 1.205L-19.055 1.717Q-19.055 1.920-19.173 2.078Q-19.290 2.236-19.471 2.320Q-19.653 2.404-19.856 2.404Q-20.087 2.404-20.239 2.232Q-20.391 2.060-20.423 1.830Q-20.583 2.111-20.891 2.277Q-21.200 2.443-21.552 2.443Q-22.063 2.443-22.487 2.220Q-22.911 1.998-22.911 1.533M-22.223 1.533Q-22.223 1.818-21.997 2.004Q-21.770 2.189-21.477 2.189Q-21.231 2.189-21.007 2.072Q-20.782 1.955-20.647 1.752Q-20.512 1.549-20.512 1.295L-20.512 0.463Q-20.778 0.463-21.063 0.517Q-21.348 0.572-21.620 0.701Q-21.891 0.830-22.057 1.037Q-22.223 1.244-22.223 1.533M-16.848 2.365L-18.680 2.365L-18.680 2.068Q-18.407 2.068-18.239 2.021Q-18.071 1.974-18.071 1.806L-18.071-2.354Q-18.071-2.569-18.134-2.664Q-18.196-2.760-18.315-2.781Q-18.434-2.803-18.680-2.803L-18.680-3.100L-17.458-3.186L-17.458 1.806Q-17.458 1.974-17.290 2.021Q-17.122 2.068-16.848 2.068L-16.848 2.365M-16.403 0.670Q-16.403 0.166-16.147-0.266Q-15.891-0.697-15.456-0.949Q-15.020-1.201-14.520-1.201Q-14.134-1.201-13.792-1.057Q-13.450-0.912-13.188-0.651Q-12.927-0.389-12.784-0.053Q-12.641 0.283-12.641 0.670Q-12.641 1.162-12.905 1.572Q-13.169 1.982-13.598 2.213Q-14.028 2.443-14.520 2.443Q-15.012 2.443-15.446 2.211Q-15.880 1.978-16.141 1.570Q-16.403 1.162-16.403 0.670M-14.520 2.166Q-14.063 2.166-13.811 1.943Q-13.559 1.720-13.471 1.369Q-13.384 1.017-13.384 0.572Q-13.384 0.142-13.477-0.196Q-13.571-0.533-13.825-0.740Q-14.079-0.947-14.520-0.947Q-15.169-0.947-15.413-0.531Q-15.657-0.115-15.657 0.572Q-15.657 1.017-15.569 1.369Q-15.481 1.720-15.229 1.943Q-14.977 2.166-14.520 2.166M-12.157 2.974Q-12.157 2.693-11.946 2.482Q-11.735 2.271-11.450 2.181Q-11.606 2.056-11.684 1.867Q-11.762 1.678-11.762 1.478Q-11.762 1.123-11.532 0.830Q-11.899 0.490-11.899 0.021Q-11.899-0.330-11.696-0.600Q-11.493-0.869-11.173-1.016Q-10.852-1.162-10.509-1.162Q-9.989-1.162-9.618-0.881Q-9.255-1.252-8.708-1.252Q-8.528-1.252-8.401-1.125Q-8.274-0.998-8.274-0.819Q-8.274-0.713-8.352-0.635Q-8.430-0.557-8.540-0.557Q-8.649-0.557-8.725-0.633Q-8.802-0.709-8.802-0.819Q-8.802-0.920-8.762-0.971Q-8.755-0.979-8.751-0.985Q-8.747-0.990-8.747-0.994Q-9.122-0.994-9.442-0.740Q-9.122-0.401-9.122 0.021Q-9.122 0.291-9.239 0.508Q-9.356 0.724-9.561 0.883Q-9.766 1.041-10.009 1.123Q-10.251 1.205-10.509 1.205Q-10.727 1.205-10.940 1.146Q-11.153 1.088-11.348 0.967Q-11.442 1.107-11.442 1.287Q-11.442 1.494-11.305 1.646Q-11.169 1.799-10.962 1.799L-10.266 1.799Q-9.778 1.799-9.366 1.883Q-8.954 1.967-8.675 2.224Q-8.395 2.482-8.395 2.974Q-8.395 3.338-8.716 3.570Q-9.036 3.803-9.477 3.904Q-9.919 4.006-10.274 4.006Q-10.630 4.006-11.073 3.904Q-11.516 3.803-11.837 3.570Q-12.157 3.338-12.157 2.974M-11.653 2.974Q-11.653 3.170-11.509 3.318Q-11.364 3.467-11.151 3.556Q-10.938 3.646-10.698 3.693Q-10.458 3.740-10.274 3.740Q-10.032 3.740-9.702 3.662Q-9.372 3.584-9.136 3.410Q-8.899 3.236-8.899 2.974Q-8.899 2.568-9.309 2.459Q-9.720 2.349-10.282 2.349L-10.962 2.349Q-11.231 2.349-11.442 2.527Q-11.653 2.705-11.653 2.974M-10.509 0.939Q-9.786 0.939-9.786 0.021Q-9.786-0.901-10.509-0.901Q-11.235-0.901-11.235 0.021Q-11.235 0.939-10.509 0.939M-7.227 1.412L-7.227-0.330Q-7.227-0.545-7.290-0.641Q-7.352-0.737-7.471-0.758Q-7.591-0.780-7.837-0.780L-7.837-1.076L-6.591-1.162L-6.591 1.388L-6.591 1.412Q-6.591 1.724-6.536 1.886Q-6.481 2.049-6.331 2.119Q-6.180 2.189-5.860 2.189Q-5.430 2.189-5.157 1.851Q-4.884 1.513-4.884 1.068L-4.884-0.330Q-4.884-0.545-4.946-0.641Q-5.009-0.737-5.128-0.758Q-5.247-0.780-5.493-0.780L-5.493-1.076L-4.247-1.162L-4.247 1.623Q-4.247 1.834-4.184 1.929Q-4.122 2.025-4.003 2.047Q-3.884 2.068-3.637 2.068L-3.637 2.365L-4.860 2.443L-4.860 1.822Q-5.028 2.111-5.309 2.277Q-5.591 2.443-5.911 2.443Q-7.227 2.443-7.227 1.412M-3.192 0.611Q-3.192 0.131-2.960-0.285Q-2.727-0.701-2.317-0.951Q-1.907-1.201-1.430-1.201Q-0.700-1.201-0.302-0.760Q0.097-0.319 0.097 0.412Q0.097 0.517 0.003 0.541L-2.446 0.541L-2.446 0.611Q-2.446 1.021-2.325 1.377Q-2.204 1.732-1.932 1.949Q-1.661 2.166-1.231 2.166Q-0.868 2.166-0.571 1.937Q-0.274 1.709-0.173 1.357Q-0.165 1.310-0.079 1.295L0.003 1.295Q0.097 1.322 0.097 1.404Q0.097 1.412 0.089 1.443Q0.027 1.670-0.112 1.853Q-0.251 2.037-0.442 2.170Q-0.634 2.303-0.852 2.373Q-1.071 2.443-1.309 2.443Q-1.680 2.443-2.018 2.306Q-2.356 2.170-2.624 1.918Q-2.891 1.666-3.042 1.326Q-3.192 0.986-3.192 0.611M-2.438 0.303L-0.477 0.303Q-0.477-0.002-0.579-0.293Q-0.680-0.584-0.897-0.766Q-1.114-0.947-1.430-0.947Q-1.731-0.947-1.962-0.760Q-2.192-0.572-2.315-0.281Q-2.438 0.010-2.438 0.303M2.698 0.916L0.445 0.916L0.445 0.365L2.698 0.365L2.698 0.916M3.460 2.357L3.460 1.135Q3.460 1.107 3.491 1.076Q3.523 1.045 3.546 1.045L3.652 1.045Q3.722 1.045 3.738 1.107Q3.800 1.428 3.939 1.668Q4.077 1.908 4.310 2.049Q4.542 2.189 4.851 2.189Q5.089 2.189 5.298 2.129Q5.507 2.068 5.644 1.920Q5.780 1.771 5.780 1.525Q5.780 1.271 5.570 1.105Q5.359 0.939 5.089 0.885L4.468 0.771Q4.062 0.693 3.761 0.437Q3.460 0.181 3.460-0.194Q3.460-0.561 3.661-0.783Q3.863-1.006 4.187-1.104Q4.511-1.201 4.851-1.201Q5.316-1.201 5.613-0.994L5.835-1.178Q5.859-1.201 5.890-1.201L5.941-1.201Q5.972-1.201 5.999-1.174Q6.027-1.147 6.027-1.115L6.027-0.131Q6.027-0.100 6.001-0.071Q5.976-0.041 5.941-0.041L5.835-0.041Q5.800-0.041 5.773-0.069Q5.745-0.096 5.745-0.131Q5.745-0.530 5.493-0.750Q5.241-0.971 4.843-0.971Q4.488-0.971 4.204-0.848Q3.921-0.725 3.921-0.420Q3.921-0.201 4.122-0.069Q4.323 0.064 4.570 0.107L5.195 0.220Q5.624 0.310 5.933 0.607Q6.241 0.904 6.241 1.318Q6.241 1.888 5.843 2.166Q5.445 2.443 4.851 2.443Q4.300 2.443 3.948 2.107L3.652 2.420Q3.628 2.443 3.593 2.443L3.546 2.443Q3.523 2.443 3.491 2.412Q3.460 2.381 3.460 2.357M7.394 1.404L7.394-0.787L6.691-0.787L6.691-1.041Q7.046-1.041 7.288-1.274Q7.530-1.506 7.642-1.854Q7.753-2.201 7.753-2.557L8.034-2.557L8.034-1.084L9.210-1.084L9.210-0.787L8.034-0.787L8.034 1.388Q8.034 1.709 8.154 1.937Q8.273 2.166 8.554 2.166Q8.734 2.166 8.851 2.043Q8.968 1.920 9.021 1.740Q9.073 1.560 9.073 1.388L9.073 0.916L9.355 0.916L9.355 1.404Q9.355 1.658 9.249 1.898Q9.144 2.138 8.946 2.291Q8.749 2.443 8.491 2.443Q8.175 2.443 7.923 2.320Q7.671 2.197 7.532 1.963Q7.394 1.728 7.394 1.404M10.171 1.533Q10.171 1.049 10.573 0.754Q10.976 0.459 11.527 0.340Q12.077 0.220 12.570 0.220L12.570-0.069Q12.570-0.295 12.454-0.502Q12.339-0.709 12.142-0.828Q11.945-0.947 11.714-0.947Q11.288-0.947 11.003-0.842Q11.073-0.815 11.120-0.760Q11.167-0.705 11.193-0.635Q11.218-0.565 11.218-0.490Q11.218-0.385 11.167-0.293Q11.116-0.201 11.025-0.151Q10.933-0.100 10.827-0.100Q10.722-0.100 10.630-0.151Q10.538-0.201 10.488-0.293Q10.437-0.385 10.437-0.490Q10.437-0.908 10.825-1.055Q11.214-1.201 11.714-1.201Q12.046-1.201 12.400-1.071Q12.753-0.940 12.982-0.686Q13.210-0.432 13.210-0.084L13.210 1.717Q13.210 1.849 13.282 1.959Q13.355 2.068 13.484 2.068Q13.609 2.068 13.677 1.963Q13.745 1.857 13.745 1.717L13.745 1.205L14.027 1.205L14.027 1.717Q14.027 1.920 13.909 2.078Q13.792 2.236 13.611 2.320Q13.429 2.404 13.226 2.404Q12.995 2.404 12.843 2.232Q12.691 2.060 12.659 1.830Q12.499 2.111 12.191 2.277Q11.882 2.443 11.530 2.443Q11.019 2.443 10.595 2.220Q10.171 1.998 10.171 1.533M10.859 1.533Q10.859 1.818 11.085 2.004Q11.312 2.189 11.605 2.189Q11.851 2.189 12.075 2.072Q12.300 1.955 12.435 1.752Q12.570 1.549 12.570 1.295L12.570 0.463Q12.304 0.463 12.019 0.517Q11.734 0.572 11.462 0.701Q11.191 0.830 11.025 1.037Q10.859 1.244 10.859 1.533M14.945 1.404L14.945-0.787L14.241-0.787L14.241-1.041Q14.597-1.041 14.839-1.274Q15.081-1.506 15.193-1.854Q15.304-2.201 15.304-2.557L15.585-2.557L15.585-1.084L16.761-1.084L16.761-0.787L15.585-0.787L15.585 1.388Q15.585 1.709 15.704 1.937Q15.823 2.166 16.105 2.166Q16.284 2.166 16.402 2.043Q16.519 1.920 16.571 1.740Q16.624 1.560 16.624 1.388L16.624 0.916L16.905 0.916L16.905 1.404Q16.905 1.658 16.800 1.898Q16.695 2.138 16.497 2.291Q16.300 2.443 16.042 2.443Q15.726 2.443 15.474 2.320Q15.222 2.197 15.083 1.963Q14.945 1.728 14.945 1.404M17.624 0.611Q17.624 0.131 17.857-0.285Q18.089-0.701 18.499-0.951Q18.909-1.201 19.386-1.201Q20.116-1.201 20.515-0.760Q20.913-0.319 20.913 0.412Q20.913 0.517 20.820 0.541L18.370 0.541L18.370 0.611Q18.370 1.021 18.491 1.377Q18.613 1.732 18.884 1.949Q19.155 2.166 19.585 2.166Q19.948 2.166 20.245 1.937Q20.542 1.709 20.644 1.357Q20.652 1.310 20.738 1.295L20.820 1.295Q20.913 1.322 20.913 1.404Q20.913 1.412 20.905 1.443Q20.843 1.670 20.704 1.853Q20.566 2.037 20.374 2.170Q20.183 2.303 19.964 2.373Q19.745 2.443 19.507 2.443Q19.136 2.443 18.798 2.306Q18.460 2.170 18.193 1.918Q17.925 1.666 17.775 1.326Q17.624 0.986 17.624 0.611M18.378 0.303L20.339 0.303Q20.339-0.002 20.238-0.293Q20.136-0.584 19.919-0.766Q19.702-0.947 19.386-0.947Q19.085-0.947 18.855-0.760Q18.624-0.572 18.501-0.281Q18.378 0.010 18.378 0.303\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(93.741 7.528)\">\u003Cpath d=\"M-16.578 10.904L-16.578 8.713L-17.281 8.713L-17.281 8.459Q-16.925 8.459-16.683 8.226Q-16.441 7.994-16.330 7.646Q-16.218 7.299-16.218 6.943L-15.937 6.943L-15.937 8.416L-14.761 8.416L-14.761 8.713L-15.937 8.713L-15.937 10.888Q-15.937 11.209-15.818 11.437Q-15.699 11.666-15.418 11.666Q-15.238 11.666-15.121 11.543Q-15.003 11.420-14.951 11.240Q-14.898 11.060-14.898 10.888L-14.898 10.416L-14.617 10.416L-14.617 10.904Q-14.617 11.158-14.722 11.398Q-14.828 11.638-15.025 11.791Q-15.222 11.943-15.480 11.943Q-15.796 11.943-16.048 11.820Q-16.300 11.697-16.439 11.463Q-16.578 11.228-16.578 10.904M-11.890 11.865L-13.871 11.865L-13.871 11.568Q-13.601 11.568-13.433 11.523Q-13.265 11.478-13.265 11.306L-13.265 9.170Q-13.265 8.955-13.328 8.859Q-13.390 8.763-13.507 8.742Q-13.625 8.720-13.871 8.720L-13.871 8.424L-12.703 8.338L-12.703 9.123Q-12.625 8.912-12.472 8.726Q-12.320 8.541-12.121 8.439Q-11.921 8.338-11.695 8.338Q-11.449 8.338-11.257 8.482Q-11.066 8.627-11.066 8.857Q-11.066 9.013-11.171 9.123Q-11.277 9.232-11.433 9.232Q-11.589 9.232-11.699 9.123Q-11.808 9.013-11.808 8.857Q-11.808 8.697-11.703 8.592Q-12.027 8.592-12.242 8.820Q-12.457 9.049-12.552 9.388Q-12.648 9.728-12.648 10.033L-12.648 11.306Q-12.648 11.474-12.421 11.521Q-12.195 11.568-11.890 11.568L-11.890 11.865M-10.488 11.033Q-10.488 10.549-10.086 10.254Q-9.683 9.959-9.132 9.840Q-8.582 9.720-8.089 9.720L-8.089 9.431Q-8.089 9.205-8.205 8.998Q-8.320 8.791-8.517 8.672Q-8.714 8.553-8.945 8.553Q-9.371 8.553-9.656 8.658Q-9.586 8.685-9.539 8.740Q-9.492 8.795-9.466 8.865Q-9.441 8.935-9.441 9.010Q-9.441 9.115-9.492 9.207Q-9.543 9.299-9.634 9.349Q-9.726 9.400-9.832 9.400Q-9.937 9.400-10.029 9.349Q-10.121 9.299-10.171 9.207Q-10.222 9.115-10.222 9.010Q-10.222 8.592-9.834 8.445Q-9.445 8.299-8.945 8.299Q-8.613 8.299-8.259 8.429Q-7.906 8.560-7.677 8.814Q-7.449 9.068-7.449 9.416L-7.449 11.217Q-7.449 11.349-7.377 11.459Q-7.304 11.568-7.175 11.568Q-7.050 11.568-6.982 11.463Q-6.914 11.357-6.914 11.217L-6.914 10.705L-6.632 10.705L-6.632 11.217Q-6.632 11.420-6.750 11.578Q-6.867 11.736-7.048 11.820Q-7.230 11.904-7.433 11.904Q-7.664 11.904-7.816 11.732Q-7.968 11.560-8 11.330Q-8.160 11.611-8.468 11.777Q-8.777 11.943-9.128 11.943Q-9.640 11.943-10.064 11.720Q-10.488 11.498-10.488 11.033M-9.800 11.033Q-9.800 11.318-9.574 11.504Q-9.347 11.689-9.054 11.689Q-8.808 11.689-8.584 11.572Q-8.359 11.455-8.224 11.252Q-8.089 11.049-8.089 10.795L-8.089 9.963Q-8.355 9.963-8.640 10.017Q-8.925 10.072-9.197 10.201Q-9.468 10.330-9.634 10.537Q-9.800 10.744-9.800 11.033M-6.296 10.138Q-6.296 9.642-6.046 9.217Q-5.796 8.791-5.377 8.545Q-4.957 8.299-4.457 8.299Q-3.918 8.299-3.527 8.424Q-3.136 8.549-3.136 8.963Q-3.136 9.068-3.187 9.160Q-3.238 9.252-3.330 9.303Q-3.421 9.353-3.531 9.353Q-3.636 9.353-3.728 9.303Q-3.820 9.252-3.871 9.160Q-3.921 9.068-3.921 8.963Q-3.921 8.740-3.753 8.635Q-3.976 8.576-4.449 8.576Q-4.746 8.576-4.961 8.715Q-5.175 8.853-5.306 9.084Q-5.437 9.314-5.496 9.584Q-5.554 9.853-5.554 10.138Q-5.554 10.533-5.421 10.883Q-5.289 11.232-5.017 11.449Q-4.746 11.666-4.347 11.666Q-3.972 11.666-3.697 11.449Q-3.421 11.232-3.320 10.873Q-3.304 10.810-3.242 10.810L-3.136 10.810Q-3.101 10.810-3.076 10.838Q-3.050 10.865-3.050 10.904L-3.050 10.928Q-3.183 11.408-3.568 11.676Q-3.953 11.943-4.457 11.943Q-4.820 11.943-5.154 11.806Q-5.488 11.670-5.748 11.420Q-6.007 11.170-6.152 10.834Q-6.296 10.498-6.296 10.138\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(93.741 7.528)\">\u003Cpath d=\"M-0.969 11.865L-2.765 11.865L-2.765 11.568Q-2.496 11.568-2.328 11.523Q-2.160 11.478-2.160 11.306L-2.160 7.146Q-2.160 6.931-2.222 6.836Q-2.285 6.740-2.402 6.719Q-2.519 6.697-2.765 6.697L-2.765 6.400L-1.543 6.314L-1.543 10.080L-0.445 9.193Q-0.238 9.013-0.238 8.865Q-0.238 8.799-0.291 8.756Q-0.344 8.713-0.414 8.713L-0.414 8.416L1.121 8.416L1.121 8.713Q0.590 8.713-0.008 9.193L-0.617 9.689L0.457 11.088Q0.594 11.263 0.701 11.371Q0.809 11.478 0.944 11.523Q1.078 11.568 1.305 11.568L1.305 11.865L-0.320 11.865L-0.320 11.568Q-0.078 11.568-0.078 11.416Q-0.078 11.338-0.121 11.267Q-0.164 11.197-0.246 11.088L-1.047 10.041L-1.574 10.467L-1.574 11.306Q-1.574 11.474-1.406 11.521Q-1.238 11.568-0.969 11.568\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(93.741 7.528)\">\u003Cpath d=\"M1.457 10.111Q1.457 9.631 1.690 9.215Q1.922 8.799 2.332 8.549Q2.742 8.299 3.219 8.299Q3.949 8.299 4.348 8.740Q4.746 9.181 4.746 9.912Q4.746 10.017 4.653 10.041L2.203 10.041L2.203 10.111Q2.203 10.521 2.324 10.877Q2.446 11.232 2.717 11.449Q2.989 11.666 3.418 11.666Q3.782 11.666 4.078 11.437Q4.375 11.209 4.477 10.857Q4.485 10.810 4.571 10.795L4.653 10.795Q4.746 10.822 4.746 10.904Q4.746 10.912 4.739 10.943Q4.676 11.170 4.537 11.353Q4.399 11.537 4.207 11.670Q4.016 11.803 3.797 11.873Q3.578 11.943 3.340 11.943Q2.969 11.943 2.631 11.806Q2.293 11.670 2.026 11.418Q1.758 11.166 1.608 10.826Q1.457 10.486 1.457 10.111M2.211 9.803L4.172 9.803Q4.172 9.498 4.071 9.207Q3.969 8.916 3.752 8.734Q3.535 8.553 3.219 8.553Q2.918 8.553 2.688 8.740Q2.457 8.928 2.334 9.219Q2.211 9.510 2.211 9.803M7.242 11.865L5.262 11.865L5.262 11.568Q5.532 11.568 5.699 11.523Q5.867 11.478 5.867 11.306L5.867 9.170Q5.867 8.955 5.805 8.859Q5.742 8.763 5.625 8.742Q5.508 8.720 5.262 8.720L5.262 8.424L6.430 8.338L6.430 9.123Q6.508 8.912 6.660 8.726Q6.813 8.541 7.012 8.439Q7.211 8.338 7.438 8.338Q7.684 8.338 7.875 8.482Q8.067 8.627 8.067 8.857Q8.067 9.013 7.961 9.123Q7.856 9.232 7.699 9.232Q7.543 9.232 7.434 9.123Q7.324 9.013 7.324 8.857Q7.324 8.697 7.430 8.592Q7.106 8.592 6.891 8.820Q6.676 9.049 6.580 9.388Q6.485 9.728 6.485 10.033L6.485 11.306Q6.485 11.474 6.711 11.521Q6.938 11.568 7.242 11.568\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-soft-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath d=\"M170.266 27.514h76.823V-3.784h-76.823Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(223.655 6.75)\">\u003Cpath d=\"M-28.270 2.443Q-28.751 2.443-29.159 2.199Q-29.567 1.955-29.805 1.541Q-30.044 1.127-30.044 0.638Q-30.044 0.146-29.786-0.270Q-29.528-0.686-29.096-0.924Q-28.665-1.162-28.173-1.162Q-27.552-1.162-27.102-0.725L-27.102-2.354Q-27.102-2.569-27.165-2.664Q-27.227-2.760-27.345-2.781Q-27.462-2.803-27.708-2.803L-27.708-3.100L-26.485-3.186L-26.485 1.623Q-26.485 1.834-26.423 1.929Q-26.360 2.025-26.243 2.047Q-26.126 2.068-25.876 2.068L-25.876 2.365L-27.126 2.443L-27.126 1.959Q-27.591 2.443-28.270 2.443M-28.204 2.189Q-27.864 2.189-27.571 1.998Q-27.278 1.806-27.126 1.510L-27.126-0.322Q-27.274-0.596-27.536-0.752Q-27.798-0.908-28.110-0.908Q-28.735-0.908-29.018-0.461Q-29.302-0.014-29.302 0.646Q-29.302 1.291-29.050 1.740Q-28.798 2.189-28.204 2.189M-23.509 2.365L-25.286 2.365L-25.286 2.068Q-25.012 2.068-24.845 2.021Q-24.677 1.974-24.677 1.806L-24.677-0.330Q-24.677-0.545-24.733-0.641Q-24.790-0.737-24.903-0.758Q-25.016-0.780-25.262-0.780L-25.262-1.076L-24.063-1.162L-24.063 1.806Q-24.063 1.974-23.917 2.021Q-23.770 2.068-23.509 2.068L-23.509 2.365M-24.950-2.557Q-24.950-2.748-24.815-2.879Q-24.680-3.010-24.485-3.010Q-24.364-3.010-24.261-2.947Q-24.157-2.885-24.095-2.781Q-24.032-2.678-24.032-2.557Q-24.032-2.362-24.163-2.227Q-24.294-2.092-24.485-2.092Q-24.684-2.092-24.817-2.225Q-24.950-2.358-24.950-2.557M-22.911 1.533Q-22.911 1.049-22.509 0.754Q-22.106 0.459-21.555 0.340Q-21.005 0.220-20.512 0.220L-20.512-0.069Q-20.512-0.295-20.628-0.502Q-20.743-0.709-20.940-0.828Q-21.137-0.947-21.368-0.947Q-21.794-0.947-22.079-0.842Q-22.009-0.815-21.962-0.760Q-21.915-0.705-21.889-0.635Q-21.864-0.565-21.864-0.490Q-21.864-0.385-21.915-0.293Q-21.966-0.201-22.057-0.151Q-22.149-0.100-22.255-0.100Q-22.360-0.100-22.452-0.151Q-22.544-0.201-22.595-0.293Q-22.645-0.385-22.645-0.490Q-22.645-0.908-22.257-1.055Q-21.868-1.201-21.368-1.201Q-21.036-1.201-20.682-1.071Q-20.329-0.940-20.100-0.686Q-19.872-0.432-19.872-0.084L-19.872 1.717Q-19.872 1.849-19.800 1.959Q-19.727 2.068-19.598 2.068Q-19.473 2.068-19.405 1.963Q-19.337 1.857-19.337 1.717L-19.337 1.205L-19.055 1.205L-19.055 1.717Q-19.055 1.920-19.173 2.078Q-19.290 2.236-19.471 2.320Q-19.653 2.404-19.856 2.404Q-20.087 2.404-20.239 2.232Q-20.391 2.060-20.423 1.830Q-20.583 2.111-20.891 2.277Q-21.200 2.443-21.552 2.443Q-22.063 2.443-22.487 2.220Q-22.911 1.998-22.911 1.533M-22.223 1.533Q-22.223 1.818-21.997 2.004Q-21.770 2.189-21.477 2.189Q-21.231 2.189-21.007 2.072Q-20.782 1.955-20.647 1.752Q-20.512 1.549-20.512 1.295L-20.512 0.463Q-20.778 0.463-21.063 0.517Q-21.348 0.572-21.620 0.701Q-21.891 0.830-22.057 1.037Q-22.223 1.244-22.223 1.533M-16.848 2.365L-18.680 2.365L-18.680 2.068Q-18.407 2.068-18.239 2.021Q-18.071 1.974-18.071 1.806L-18.071-2.354Q-18.071-2.569-18.134-2.664Q-18.196-2.760-18.315-2.781Q-18.434-2.803-18.680-2.803L-18.680-3.100L-17.458-3.186L-17.458 1.806Q-17.458 1.974-17.290 2.021Q-17.122 2.068-16.848 2.068L-16.848 2.365M-16.403 0.670Q-16.403 0.166-16.147-0.266Q-15.891-0.697-15.456-0.949Q-15.020-1.201-14.520-1.201Q-14.134-1.201-13.792-1.057Q-13.450-0.912-13.188-0.651Q-12.927-0.389-12.784-0.053Q-12.641 0.283-12.641 0.670Q-12.641 1.162-12.905 1.572Q-13.169 1.982-13.598 2.213Q-14.028 2.443-14.520 2.443Q-15.012 2.443-15.446 2.211Q-15.880 1.978-16.141 1.570Q-16.403 1.162-16.403 0.670M-14.520 2.166Q-14.063 2.166-13.811 1.943Q-13.559 1.720-13.471 1.369Q-13.384 1.017-13.384 0.572Q-13.384 0.142-13.477-0.196Q-13.571-0.533-13.825-0.740Q-14.079-0.947-14.520-0.947Q-15.169-0.947-15.413-0.531Q-15.657-0.115-15.657 0.572Q-15.657 1.017-15.569 1.369Q-15.481 1.720-15.229 1.943Q-14.977 2.166-14.520 2.166M-12.157 2.974Q-12.157 2.693-11.946 2.482Q-11.735 2.271-11.450 2.181Q-11.606 2.056-11.684 1.867Q-11.762 1.678-11.762 1.478Q-11.762 1.123-11.532 0.830Q-11.899 0.490-11.899 0.021Q-11.899-0.330-11.696-0.600Q-11.493-0.869-11.173-1.016Q-10.852-1.162-10.509-1.162Q-9.989-1.162-9.618-0.881Q-9.255-1.252-8.708-1.252Q-8.528-1.252-8.401-1.125Q-8.274-0.998-8.274-0.819Q-8.274-0.713-8.352-0.635Q-8.430-0.557-8.540-0.557Q-8.649-0.557-8.725-0.633Q-8.802-0.709-8.802-0.819Q-8.802-0.920-8.762-0.971Q-8.755-0.979-8.751-0.985Q-8.747-0.990-8.747-0.994Q-9.122-0.994-9.442-0.740Q-9.122-0.401-9.122 0.021Q-9.122 0.291-9.239 0.508Q-9.356 0.724-9.561 0.883Q-9.766 1.041-10.009 1.123Q-10.251 1.205-10.509 1.205Q-10.727 1.205-10.940 1.146Q-11.153 1.088-11.348 0.967Q-11.442 1.107-11.442 1.287Q-11.442 1.494-11.305 1.646Q-11.169 1.799-10.962 1.799L-10.266 1.799Q-9.778 1.799-9.366 1.883Q-8.954 1.967-8.675 2.224Q-8.395 2.482-8.395 2.974Q-8.395 3.338-8.716 3.570Q-9.036 3.803-9.477 3.904Q-9.919 4.006-10.274 4.006Q-10.630 4.006-11.073 3.904Q-11.516 3.803-11.837 3.570Q-12.157 3.338-12.157 2.974M-11.653 2.974Q-11.653 3.170-11.509 3.318Q-11.364 3.467-11.151 3.556Q-10.938 3.646-10.698 3.693Q-10.458 3.740-10.274 3.740Q-10.032 3.740-9.702 3.662Q-9.372 3.584-9.136 3.410Q-8.899 3.236-8.899 2.974Q-8.899 2.568-9.309 2.459Q-9.720 2.349-10.282 2.349L-10.962 2.349Q-11.231 2.349-11.442 2.527Q-11.653 2.705-11.653 2.974M-10.509 0.939Q-9.786 0.939-9.786 0.021Q-9.786-0.901-10.509-0.901Q-11.235-0.901-11.235 0.021Q-11.235 0.939-10.509 0.939M-7.227 1.412L-7.227-0.330Q-7.227-0.545-7.290-0.641Q-7.352-0.737-7.471-0.758Q-7.591-0.780-7.837-0.780L-7.837-1.076L-6.591-1.162L-6.591 1.388L-6.591 1.412Q-6.591 1.724-6.536 1.886Q-6.481 2.049-6.331 2.119Q-6.180 2.189-5.860 2.189Q-5.430 2.189-5.157 1.851Q-4.884 1.513-4.884 1.068L-4.884-0.330Q-4.884-0.545-4.946-0.641Q-5.009-0.737-5.128-0.758Q-5.247-0.780-5.493-0.780L-5.493-1.076L-4.247-1.162L-4.247 1.623Q-4.247 1.834-4.184 1.929Q-4.122 2.025-4.003 2.047Q-3.884 2.068-3.637 2.068L-3.637 2.365L-4.860 2.443L-4.860 1.822Q-5.028 2.111-5.309 2.277Q-5.591 2.443-5.911 2.443Q-7.227 2.443-7.227 1.412M-3.192 0.611Q-3.192 0.131-2.960-0.285Q-2.727-0.701-2.317-0.951Q-1.907-1.201-1.430-1.201Q-0.700-1.201-0.302-0.760Q0.097-0.319 0.097 0.412Q0.097 0.517 0.003 0.541L-2.446 0.541L-2.446 0.611Q-2.446 1.021-2.325 1.377Q-2.204 1.732-1.932 1.949Q-1.661 2.166-1.231 2.166Q-0.868 2.166-0.571 1.937Q-0.274 1.709-0.173 1.357Q-0.165 1.310-0.079 1.295L0.003 1.295Q0.097 1.322 0.097 1.404Q0.097 1.412 0.089 1.443Q0.027 1.670-0.112 1.853Q-0.251 2.037-0.442 2.170Q-0.634 2.303-0.852 2.373Q-1.071 2.443-1.309 2.443Q-1.680 2.443-2.018 2.306Q-2.356 2.170-2.624 1.918Q-2.891 1.666-3.042 1.326Q-3.192 0.986-3.192 0.611M-2.438 0.303L-0.477 0.303Q-0.477-0.002-0.579-0.293Q-0.680-0.584-0.897-0.766Q-1.114-0.947-1.430-0.947Q-1.731-0.947-1.962-0.760Q-2.192-0.572-2.315-0.281Q-2.438 0.010-2.438 0.303\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(223.655 6.75)\">\u003Cpath d=\"M-23.954 13.416L-25.809 13.416L-25.809 13.123Q-25.540 13.123-25.372 13.078Q-25.204 13.033-25.204 12.857L-25.204 9.033Q-25.204 8.826-25.360 8.773Q-25.516 8.720-25.809 8.720L-25.809 8.424L-24.587 8.338L-24.587 8.803Q-24.356 8.580-24.042 8.459Q-23.727 8.338-23.387 8.338Q-22.915 8.338-22.511 8.584Q-22.106 8.830-21.874 9.246Q-21.641 9.662-21.641 10.138Q-21.641 10.513-21.790 10.842Q-21.938 11.170-22.208 11.422Q-22.477 11.674-22.821 11.808Q-23.165 11.943-23.524 11.943Q-23.813 11.943-24.085 11.822Q-24.356 11.701-24.563 11.490L-24.563 12.857Q-24.563 13.033-24.395 13.078Q-24.227 13.123-23.954 13.123L-23.954 13.416M-24.563 9.201L-24.563 11.041Q-24.411 11.330-24.149 11.510Q-23.887 11.689-23.579 11.689Q-23.294 11.689-23.071 11.551Q-22.848 11.412-22.696 11.181Q-22.544 10.951-22.466 10.679Q-22.387 10.408-22.387 10.138Q-22.387 9.806-22.512 9.449Q-22.637 9.092-22.886 8.855Q-23.134 8.619-23.481 8.619Q-23.805 8.619-24.100 8.775Q-24.395 8.931-24.563 9.201\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(223.655 6.75)\">\u003Cpath d=\"M-20.879 10.170Q-20.879 9.666-20.623 9.234Q-20.367 8.803-19.931 8.551Q-19.496 8.299-18.996 8.299Q-18.609 8.299-18.267 8.443Q-17.926 8.588-17.664 8.849Q-17.402 9.111-17.260 9.447Q-17.117 9.783-17.117 10.170Q-17.117 10.662-17.381 11.072Q-17.644 11.482-18.074 11.713Q-18.504 11.943-18.996 11.943Q-19.488 11.943-19.922 11.711Q-20.355 11.478-20.617 11.070Q-20.879 10.662-20.879 10.170M-18.996 11.666Q-18.539 11.666-18.287 11.443Q-18.035 11.220-17.947 10.869Q-17.859 10.517-17.859 10.072Q-17.859 9.642-17.953 9.304Q-18.047 8.967-18.301 8.760Q-18.555 8.553-18.996 8.553Q-19.644 8.553-19.888 8.969Q-20.133 9.385-20.133 10.072Q-20.133 10.517-20.045 10.869Q-19.957 11.220-19.705 11.443Q-19.453 11.666-18.996 11.666M-14.719 11.865L-16.551 11.865L-16.551 11.568Q-16.277 11.568-16.109 11.521Q-15.941 11.474-15.941 11.306L-15.941 7.146Q-15.941 6.931-16.004 6.836Q-16.066 6.740-16.185 6.719Q-16.305 6.697-16.551 6.697L-16.551 6.400L-15.328 6.314L-15.328 11.306Q-15.328 11.474-15.160 11.521Q-14.992 11.568-14.719 11.568L-14.719 11.865M-12.414 11.865L-14.191 11.865L-14.191 11.568Q-13.918 11.568-13.750 11.521Q-13.582 11.474-13.582 11.306L-13.582 9.170Q-13.582 8.955-13.638 8.859Q-13.695 8.763-13.808 8.742Q-13.922 8.720-14.168 8.720L-14.168 8.424L-12.969 8.338L-12.969 11.306Q-12.969 11.474-12.822 11.521Q-12.676 11.568-12.414 11.568L-12.414 11.865M-13.855 6.943Q-13.855 6.752-13.721 6.621Q-13.586 6.490-13.390 6.490Q-13.269 6.490-13.166 6.553Q-13.062 6.615-13 6.719Q-12.937 6.822-12.937 6.943Q-12.937 7.138-13.068 7.273Q-13.199 7.408-13.390 7.408Q-13.590 7.408-13.722 7.275Q-13.855 7.142-13.855 6.943M-11.871 10.138Q-11.871 9.642-11.621 9.217Q-11.371 8.791-10.951 8.545Q-10.531 8.299-10.031 8.299Q-9.492 8.299-9.101 8.424Q-8.711 8.549-8.711 8.963Q-8.711 9.068-8.762 9.160Q-8.812 9.252-8.904 9.303Q-8.996 9.353-9.105 9.353Q-9.211 9.353-9.303 9.303Q-9.394 9.252-9.445 9.160Q-9.496 9.068-9.496 8.963Q-9.496 8.740-9.328 8.635Q-9.551 8.576-10.023 8.576Q-10.320 8.576-10.535 8.715Q-10.750 8.853-10.881 9.084Q-11.012 9.314-11.070 9.584Q-11.129 9.853-11.129 10.138Q-11.129 10.533-10.996 10.883Q-10.863 11.232-10.592 11.449Q-10.320 11.666-9.922 11.666Q-9.547 11.666-9.271 11.449Q-8.996 11.232-8.894 10.873Q-8.879 10.810-8.816 10.810L-8.711 10.810Q-8.676 10.810-8.650 10.838Q-8.625 10.865-8.625 10.904L-8.625 10.928Q-8.758 11.408-9.142 11.676Q-9.527 11.943-10.031 11.943Q-10.394 11.943-10.728 11.806Q-11.062 11.670-11.322 11.420Q-11.582 11.170-11.726 10.834Q-11.871 10.498-11.871 10.138M-7.719 13.162Q-7.605 13.240-7.430 13.240Q-7.140 13.240-6.920 13.027Q-6.699 12.814-6.574 12.513L-6.285 11.865L-7.558 8.978Q-7.640 8.803-7.785 8.758Q-7.930 8.713-8.199 8.713L-8.199 8.416L-6.480 8.416L-6.480 8.713Q-6.902 8.713-6.902 8.896Q-6.902 8.908-6.887 8.978L-5.949 11.103L-5.117 9.193Q-5.078 9.103-5.078 9.025Q-5.078 8.885-5.180 8.799Q-5.281 8.713-5.422 8.713L-5.422 8.416L-4.070 8.416L-4.070 8.713Q-4.324 8.713-4.517 8.838Q-4.711 8.963-4.816 9.193L-6.262 12.513Q-6.375 12.767-6.541 12.990Q-6.707 13.213-6.935 13.355Q-7.164 13.498-7.430 13.498Q-7.726 13.498-7.967 13.306Q-8.207 13.115-8.207 12.826Q-8.207 12.670-8.101 12.568Q-7.996 12.467-7.847 12.467Q-7.742 12.467-7.662 12.513Q-7.582 12.560-7.535 12.638Q-7.488 12.717-7.488 12.826Q-7.488 12.947-7.549 13.035Q-7.609 13.123-7.719 13.162\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-soft-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath d=\"M170.266 101.492h76.823V70.194h-76.823Z\"\u002F>\u003Cg fill=\"currentColor\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(211.09 80.46)\">\u003Cpath d=\"M-9.327 2.365L-11.249 2.365L-11.249 2.068Q-10.440 2.068-10.440 1.588L-10.440-2.756Q-10.698-2.803-11.249-2.803L-11.249-3.100L-9.753-3.100Q-9.694-3.080-9.683-3.069L-6.675 1.119L-6.675-2.322Q-6.675-2.803-7.480-2.803L-7.480-3.100L-5.562-3.100L-5.562-2.803Q-6.370-2.803-6.370-2.322L-6.370 2.260Q-6.390 2.345-6.464 2.365L-6.569 2.365Q-6.624 2.353-6.640 2.326L-10.136-2.530L-10.136 1.588Q-10.136 2.068-9.327 2.068L-9.327 2.365M-0.507 2.365L-4.890 2.365L-4.890 2.068Q-4.569 2.068-4.325 2.021Q-4.081 1.974-4.081 1.806L-4.081-2.537Q-4.081-2.709-4.325-2.756Q-4.569-2.803-4.890-2.803L-4.890-3.100L-2.304-3.100L-2.304-2.803Q-3.315-2.803-3.315-2.537L-3.315 1.806Q-3.315 1.978-3.224 2.023Q-3.132 2.068-2.913 2.068L-2.226 2.068Q-1.753 2.068-1.444 1.943Q-1.136 1.818-0.958 1.588Q-0.780 1.357-0.689 1.031Q-0.597 0.705-0.554 0.252L-0.273 0.252L-0.507 2.365M0.552-0.369Q0.552-0.963 0.790-1.498Q1.028-2.033 1.440-2.430Q1.852-2.826 2.388-3.047Q2.923-3.268 3.520-3.268Q3.743-3.268 3.956-3.221Q4.169-3.174 4.364-3.084Q4.560-2.994 4.739-2.867Q4.919-2.740 5.071-2.572L5.544-3.237Q5.575-3.268 5.606-3.268L5.653-3.268Q5.681-3.268 5.712-3.237Q5.743-3.205 5.743-3.178L5.743-1.041Q5.743-1.018 5.712-0.987Q5.681-0.955 5.653-0.955L5.536-0.955Q5.462-0.955 5.446-1.041Q5.399-1.530 5.159-1.977Q4.919-2.424 4.519-2.697Q4.118-2.971 3.614-2.971Q3.075-2.971 2.657-2.766Q2.239-2.561 1.964-2.199Q1.688-1.838 1.552-1.371Q1.415-0.905 1.415-0.369Q1.415 0.162 1.556 0.635Q1.696 1.107 1.983 1.470Q2.270 1.834 2.694 2.035Q3.118 2.236 3.673 2.236Q3.966 2.236 4.265 2.152Q4.563 2.068 4.769 1.877Q4.974 1.685 4.974 1.388L4.974 0.717Q4.974 0.455 3.911 0.455L3.911 0.158L6.337 0.158L6.337 0.455Q6.067 0.455 5.905 0.502Q5.743 0.549 5.743 0.717L5.743 2.275Q5.743 2.314 5.714 2.340Q5.685 2.365 5.645 2.365Q5.571 2.365 5.394 2.185Q5.216 2.006 5.134 1.885Q4.903 2.232 4.462 2.383Q4.020 2.533 3.520 2.533Q2.938 2.533 2.395 2.312Q1.852 2.092 1.438 1.691Q1.024 1.291 0.788 0.758Q0.552 0.224 0.552-0.369\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(211.09 80.46)\">\u003Cpath d=\"M-27.708 13.857Q-28.321 13.400-28.723 12.765Q-29.126 12.131-29.321 11.385Q-29.516 10.638-29.516 9.865Q-29.516 9.092-29.321 8.345Q-29.126 7.599-28.723 6.965Q-28.321 6.330-27.708 5.873Q-27.696 5.869-27.688 5.867Q-27.680 5.865-27.669 5.865L-27.591 5.865Q-27.552 5.865-27.526 5.892Q-27.501 5.920-27.501 5.963Q-27.501 6.013-27.532 6.033Q-28.040 6.486-28.362 7.109Q-28.684 7.732-28.825 8.428Q-28.966 9.123-28.966 9.865Q-28.966 10.599-28.827 11.299Q-28.688 11.998-28.364 12.623Q-28.040 13.248-27.532 13.697Q-27.501 13.717-27.501 13.767Q-27.501 13.810-27.526 13.838Q-27.552 13.865-27.591 13.865L-27.669 13.865Q-27.677 13.861-27.686 13.859Q-27.696 13.857-27.708 13.857M-24.774 11.865L-26.755 11.865L-26.755 11.568Q-26.485 11.568-26.317 11.523Q-26.149 11.478-26.149 11.306L-26.149 9.170Q-26.149 8.955-26.212 8.859Q-26.274 8.763-26.391 8.742Q-26.509 8.720-26.755 8.720L-26.755 8.424L-25.587 8.338L-25.587 9.123Q-25.509 8.912-25.356 8.726Q-25.204 8.541-25.005 8.439Q-24.805 8.338-24.579 8.338Q-24.333 8.338-24.141 8.482Q-23.950 8.627-23.950 8.857Q-23.950 9.013-24.055 9.123Q-24.161 9.232-24.317 9.232Q-24.473 9.232-24.583 9.123Q-24.692 9.013-24.692 8.857Q-24.692 8.697-24.587 8.592Q-24.911 8.592-25.126 8.820Q-25.341 9.049-25.436 9.388Q-25.532 9.728-25.532 10.033L-25.532 11.306Q-25.532 11.474-25.305 11.521Q-25.079 11.568-24.774 11.568L-24.774 11.865M-23.470 10.111Q-23.470 9.631-23.237 9.215Q-23.005 8.799-22.595 8.549Q-22.184 8.299-21.708 8.299Q-20.977 8.299-20.579 8.740Q-20.180 9.181-20.180 9.912Q-20.180 10.017-20.274 10.041L-22.723 10.041L-22.723 10.111Q-22.723 10.521-22.602 10.877Q-22.481 11.232-22.210 11.449Q-21.938 11.666-21.509 11.666Q-21.145 11.666-20.848 11.437Q-20.552 11.209-20.450 10.857Q-20.442 10.810-20.356 10.795L-20.274 10.795Q-20.180 10.822-20.180 10.904Q-20.180 10.912-20.188 10.943Q-20.251 11.170-20.389 11.353Q-20.528 11.537-20.720 11.670Q-20.911 11.803-21.130 11.873Q-21.348 11.943-21.587 11.943Q-21.958 11.943-22.296 11.806Q-22.634 11.670-22.901 11.418Q-23.169 11.166-23.319 10.826Q-23.470 10.486-23.470 10.111M-22.716 9.803L-20.755 9.803Q-20.755 9.498-20.856 9.207Q-20.958 8.916-21.175 8.734Q-21.391 8.553-21.708 8.553Q-22.009 8.553-22.239 8.740Q-22.470 8.928-22.593 9.219Q-22.716 9.510-22.716 9.803M-19.649 11.857L-19.649 10.635Q-19.649 10.607-19.618 10.576Q-19.587 10.545-19.563 10.545L-19.458 10.545Q-19.387 10.545-19.372 10.607Q-19.309 10.928-19.171 11.168Q-19.032 11.408-18.800 11.549Q-18.567 11.689-18.259 11.689Q-18.020 11.689-17.811 11.629Q-17.602 11.568-17.466 11.420Q-17.329 11.271-17.329 11.025Q-17.329 10.771-17.540 10.605Q-17.751 10.439-18.020 10.385L-18.641 10.271Q-19.048 10.193-19.348 9.937Q-19.649 9.681-19.649 9.306Q-19.649 8.939-19.448 8.717Q-19.247 8.494-18.923 8.396Q-18.598 8.299-18.259 8.299Q-17.794 8.299-17.497 8.506L-17.274 8.322Q-17.251 8.299-17.220 8.299L-17.169 8.299Q-17.137 8.299-17.110 8.326Q-17.083 8.353-17.083 8.385L-17.083 9.369Q-17.083 9.400-17.108 9.429Q-17.134 9.459-17.169 9.459L-17.274 9.459Q-17.309 9.459-17.337 9.431Q-17.364 9.404-17.364 9.369Q-17.364 8.970-17.616 8.750Q-17.868 8.529-18.266 8.529Q-18.622 8.529-18.905 8.652Q-19.188 8.775-19.188 9.080Q-19.188 9.299-18.987 9.431Q-18.786 9.564-18.540 9.607L-17.915 9.720Q-17.485 9.810-17.177 10.107Q-16.868 10.404-16.868 10.818Q-16.868 11.388-17.266 11.666Q-17.665 11.943-18.259 11.943Q-18.809 11.943-19.161 11.607L-19.458 11.920Q-19.481 11.943-19.516 11.943L-19.563 11.943Q-19.587 11.943-19.618 11.912Q-19.649 11.881-19.649 11.857M-14.458 13.416L-16.313 13.416L-16.313 13.123Q-16.044 13.123-15.876 13.078Q-15.708 13.033-15.708 12.857L-15.708 9.033Q-15.708 8.826-15.864 8.773Q-16.020 8.720-16.313 8.720L-16.313 8.424L-15.091 8.338L-15.091 8.803Q-14.860 8.580-14.546 8.459Q-14.231 8.338-13.891 8.338Q-13.419 8.338-13.014 8.584Q-12.610 8.830-12.378 9.246Q-12.145 9.662-12.145 10.138Q-12.145 10.513-12.294 10.842Q-12.442 11.170-12.712 11.422Q-12.981 11.674-13.325 11.808Q-13.669 11.943-14.028 11.943Q-14.317 11.943-14.589 11.822Q-14.860 11.701-15.067 11.490L-15.067 12.857Q-15.067 13.033-14.899 13.078Q-14.731 13.123-14.458 13.123L-14.458 13.416M-15.067 9.201L-15.067 11.041Q-14.915 11.330-14.653 11.510Q-14.391 11.689-14.083 11.689Q-13.798 11.689-13.575 11.551Q-13.352 11.412-13.200 11.181Q-13.048 10.951-12.970 10.679Q-12.891 10.408-12.891 10.138Q-12.891 9.806-13.016 9.449Q-13.141 9.092-13.389 8.855Q-13.637 8.619-13.985 8.619Q-14.309 8.619-14.604 8.775Q-14.899 8.931-15.067 9.201\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(211.09 80.46)\">\u003Cpath d=\"M-11.380 10.170Q-11.380 9.666-11.124 9.234Q-10.868 8.803-10.432 8.551Q-9.997 8.299-9.497 8.299Q-9.110 8.299-8.768 8.443Q-8.427 8.588-8.165 8.849Q-7.903 9.111-7.761 9.447Q-7.618 9.783-7.618 10.170Q-7.618 10.662-7.882 11.072Q-8.145 11.482-8.575 11.713Q-9.005 11.943-9.497 11.943Q-9.989 11.943-10.423 11.711Q-10.856 11.478-11.118 11.070Q-11.380 10.662-11.380 10.170M-9.497 11.666Q-9.040 11.666-8.788 11.443Q-8.536 11.220-8.448 10.869Q-8.360 10.517-8.360 10.072Q-8.360 9.642-8.454 9.304Q-8.548 8.967-8.802 8.760Q-9.056 8.553-9.497 8.553Q-10.145 8.553-10.389 8.969Q-10.634 9.385-10.634 10.072Q-10.634 10.517-10.546 10.869Q-10.458 11.220-10.206 11.443Q-9.954 11.666-9.497 11.666M-5.204 11.865L-7.059 11.865L-7.059 11.568Q-6.786 11.568-6.618 11.521Q-6.450 11.474-6.450 11.306L-6.450 9.170Q-6.450 8.955-6.513 8.859Q-6.575 8.763-6.694 8.742Q-6.813 8.720-7.059 8.720L-7.059 8.424L-5.868 8.338L-5.868 9.072Q-5.755 8.857-5.561 8.689Q-5.368 8.521-5.130 8.429Q-4.891 8.338-4.638 8.338Q-3.470 8.338-3.470 9.416L-3.470 11.306Q-3.470 11.474-3.300 11.521Q-3.130 11.568-2.860 11.568L-2.860 11.865L-4.716 11.865L-4.716 11.568Q-4.442 11.568-4.274 11.521Q-4.106 11.474-4.106 11.306L-4.106 9.431Q-4.106 9.049-4.227 8.820Q-4.348 8.592-4.700 8.592Q-5.013 8.592-5.266 8.754Q-5.520 8.916-5.667 9.185Q-5.813 9.455-5.813 9.752L-5.813 11.306Q-5.813 11.474-5.643 11.521Q-5.473 11.568-5.204 11.568L-5.204 11.865M-2.372 11.857L-2.372 10.635Q-2.372 10.607-2.341 10.576Q-2.309 10.545-2.286 10.545L-2.181 10.545Q-2.110 10.545-2.095 10.607Q-2.032 10.928-1.893 11.168Q-1.755 11.408-1.522 11.549Q-1.290 11.689-0.981 11.689Q-0.743 11.689-0.534 11.629Q-0.325 11.568-0.188 11.420Q-0.052 11.271-0.052 11.025Q-0.052 10.771-0.263 10.605Q-0.473 10.439-0.743 10.385L-1.364 10.271Q-1.770 10.193-2.071 9.937Q-2.372 9.681-2.372 9.306Q-2.372 8.939-2.171 8.717Q-1.970 8.494-1.645 8.396Q-1.321 8.299-0.981 8.299Q-0.516 8.299-0.220 8.506L0.003 8.322Q0.027 8.299 0.058 8.299L0.109 8.299Q0.140 8.299 0.167 8.326Q0.194 8.353 0.194 8.385L0.194 9.369Q0.194 9.400 0.169 9.429Q0.144 9.459 0.109 9.459L0.003 9.459Q-0.032 9.459-0.059 9.431Q-0.087 9.404-0.087 9.369Q-0.087 8.970-0.339 8.750Q-0.591 8.529-0.989 8.529Q-1.345 8.529-1.628 8.652Q-1.911 8.775-1.911 9.080Q-1.911 9.299-1.710 9.431Q-1.509 9.564-1.263 9.607L-0.638 9.720Q-0.208 9.810 0.101 10.107Q0.409 10.404 0.409 10.818Q0.409 11.388 0.011 11.666Q-0.388 11.943-0.981 11.943Q-1.532 11.943-1.884 11.607L-2.181 11.920Q-2.204 11.943-2.239 11.943L-2.286 11.943Q-2.309 11.943-2.341 11.912Q-2.372 11.881-2.372 11.857M0.937 10.111Q0.937 9.631 1.169 9.215Q1.402 8.799 1.812 8.549Q2.222 8.299 2.698 8.299Q3.429 8.299 3.827 8.740Q4.226 9.181 4.226 9.912Q4.226 10.017 4.132 10.041L1.683 10.041L1.683 10.111Q1.683 10.521 1.804 10.877Q1.925 11.232 2.196 11.449Q2.468 11.666 2.898 11.666Q3.261 11.666 3.558 11.437Q3.855 11.209 3.956 10.857Q3.964 10.810 4.050 10.795L4.132 10.795Q4.226 10.822 4.226 10.904Q4.226 10.912 4.218 10.943Q4.155 11.170 4.017 11.353Q3.878 11.537 3.687 11.670Q3.495 11.803 3.277 11.873Q3.058 11.943 2.819 11.943Q2.448 11.943 2.111 11.806Q1.773 11.670 1.505 11.418Q1.237 11.166 1.087 10.826Q0.937 10.486 0.937 10.111M1.691 9.803L3.652 9.803Q3.652 9.498 3.550 9.207Q3.448 8.916 3.232 8.734Q3.015 8.553 2.698 8.553Q2.398 8.553 2.167 8.740Q1.937 8.928 1.814 9.219Q1.691 9.510 1.691 9.803\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(211.09 80.46)\">\u003Cpath d=\"M8.181 10.904L8.181 8.713L7.478 8.713L7.478 8.459Q7.834 8.459 8.076 8.226Q8.318 7.994 8.429 7.646Q8.541 7.299 8.541 6.943L8.822 6.943L8.822 8.416L9.998 8.416L9.998 8.713L8.822 8.713L8.822 10.888Q8.822 11.209 8.941 11.437Q9.060 11.666 9.341 11.666Q9.521 11.666 9.638 11.543Q9.755 11.420 9.808 11.240Q9.861 11.060 9.861 10.888L9.861 10.416L10.142 10.416L10.142 10.904Q10.142 11.158 10.037 11.398Q9.931 11.638 9.734 11.791Q9.537 11.943 9.279 11.943Q8.963 11.943 8.711 11.820Q8.459 11.697 8.320 11.463Q8.181 11.228 8.181 10.904M10.861 10.111Q10.861 9.631 11.093 9.215Q11.326 8.799 11.736 8.549Q12.146 8.299 12.623 8.299Q13.353 8.299 13.752 8.740Q14.150 9.181 14.150 9.912Q14.150 10.017 14.056 10.041L11.607 10.041L11.607 10.111Q11.607 10.521 11.728 10.877Q11.849 11.232 12.121 11.449Q12.392 11.666 12.822 11.666Q13.185 11.666 13.482 11.437Q13.779 11.209 13.880 10.857Q13.888 10.810 13.974 10.795L14.056 10.795Q14.150 10.822 14.150 10.904Q14.150 10.912 14.142 10.943Q14.080 11.170 13.941 11.353Q13.802 11.537 13.611 11.670Q13.420 11.803 13.201 11.873Q12.982 11.943 12.744 11.943Q12.373 11.943 12.035 11.806Q11.697 11.670 11.429 11.418Q11.162 11.166 11.011 10.826Q10.861 10.486 10.861 10.111M11.615 9.803L13.576 9.803Q13.576 9.498 13.474 9.207Q13.373 8.916 13.156 8.734Q12.939 8.553 12.623 8.553Q12.322 8.553 12.091 8.740Q11.861 8.928 11.738 9.219Q11.615 9.510 11.615 9.803M16.025 11.865L14.529 11.865L14.529 11.568Q15.162 11.568 15.584 11.088L16.353 10.178L15.361 8.978Q15.205 8.799 15.043 8.756Q14.880 8.713 14.576 8.713L14.576 8.416L16.263 8.416L16.263 8.713Q16.170 8.713 16.093 8.756Q16.017 8.799 16.017 8.888Q16.017 8.931 16.048 8.978L16.705 9.767L17.185 9.193Q17.302 9.056 17.302 8.920Q17.302 8.830 17.252 8.771Q17.201 8.713 17.119 8.713L17.119 8.416L18.607 8.416L18.607 8.713Q17.970 8.713 17.560 9.193L16.880 9.994L17.966 11.306Q18.127 11.482 18.287 11.525Q18.447 11.568 18.752 11.568L18.752 11.865L17.064 11.865L17.064 11.568Q17.154 11.568 17.232 11.525Q17.310 11.482 17.310 11.392Q17.310 11.369 17.279 11.306L16.537 10.400L15.951 11.088Q15.834 11.224 15.834 11.361Q15.834 11.447 15.884 11.508Q15.935 11.568 16.025 11.568L16.025 11.865M19.744 10.904L19.744 8.713L19.041 8.713L19.041 8.459Q19.396 8.459 19.638 8.226Q19.880 7.994 19.992 7.646Q20.103 7.299 20.103 6.943L20.384 6.943L20.384 8.416L21.560 8.416L21.560 8.713L20.384 8.713L20.384 10.888Q20.384 11.209 20.504 11.437Q20.623 11.666 20.904 11.666Q21.084 11.666 21.201 11.543Q21.318 11.420 21.371 11.240Q21.423 11.060 21.423 10.888L21.423 10.416L21.705 10.416L21.705 10.904Q21.705 11.158 21.599 11.398Q21.494 11.638 21.297 11.791Q21.099 11.943 20.841 11.943Q20.525 11.943 20.273 11.820Q20.021 11.697 19.882 11.463Q19.744 11.228 19.744 10.904M22.826 13.865L22.744 13.865Q22.709 13.865 22.683 13.836Q22.658 13.806 22.658 13.767Q22.658 13.717 22.689 13.697Q23.076 13.361 23.359 12.912Q23.642 12.463 23.808 11.963Q23.974 11.463 24.048 10.945Q24.123 10.428 24.123 9.865Q24.123 9.295 24.048 8.779Q23.974 8.263 23.808 7.767Q23.642 7.271 23.363 6.824Q23.084 6.377 22.689 6.033Q22.658 6.013 22.658 5.963Q22.658 5.924 22.683 5.894Q22.709 5.865 22.744 5.865L22.826 5.865Q22.838 5.865 22.847 5.867Q22.857 5.869 22.865 5.873Q23.478 6.330 23.880 6.965Q24.283 7.599 24.478 8.345Q24.673 9.092 24.673 9.865Q24.673 10.638 24.478 11.385Q24.283 12.131 23.880 12.765Q23.478 13.400 22.865 13.857Q22.853 13.857 22.845 13.859Q22.838 13.861 22.826 13.865\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-68.737-40.772H8.086V-72.07h-76.823Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-27.295 -61.803)\">\u003Cpath d=\"M-9.663 2.365L-11.413 2.365L-11.413 2.068Q-10.714 2.068-10.526 1.588L-8.725-3.237Q-8.671-3.346-8.557-3.346L-8.487-3.346Q-8.374-3.346-8.319-3.237L-6.429 1.806Q-6.350 1.974-6.147 2.021Q-5.944 2.068-5.632 2.068L-5.632 2.365L-7.854 2.365L-7.854 2.068Q-7.214 2.068-7.214 1.853Q-7.214 1.834-7.216 1.824Q-7.218 1.814-7.222 1.806L-7.686 0.572L-9.831 0.572L-10.214 1.588Q-10.218 1.603-10.223 1.633Q-10.229 1.662-10.229 1.685Q-10.229 1.826-10.139 1.910Q-10.050 1.994-9.917 2.031Q-9.784 2.068-9.663 2.068L-9.663 2.365M-8.757-2.291L-9.725 0.275L-7.800 0.275L-8.757-2.291M-4.870 2.443L-4.870 0.638Q-4.870 0.611-4.839 0.580Q-4.807 0.549-4.784 0.549L-4.679 0.549Q-4.647 0.549-4.618 0.578Q-4.589 0.607-4.589 0.638Q-4.589 1.420-4.073 1.828Q-3.557 2.236-2.749 2.236Q-2.452 2.236-2.196 2.086Q-1.940 1.935-1.790 1.679Q-1.639 1.424-1.639 1.127Q-1.639 0.728-1.886 0.424Q-2.132 0.119-2.503 0.037L-3.624-0.221Q-3.964-0.295-4.251-0.516Q-4.538-0.737-4.704-1.055Q-4.870-1.373-4.870-1.725Q-4.870-2.155-4.639-2.510Q-4.409-2.865-4.028-3.067Q-3.647-3.268-3.222-3.268Q-2.972-3.268-2.725-3.209Q-2.479-3.151-2.261-3.028Q-2.042-2.905-1.878-2.725L-1.550-3.221Q-1.518-3.268-1.479-3.268L-1.432-3.268Q-1.405-3.268-1.374-3.237Q-1.343-3.205-1.343-3.178L-1.343-1.369Q-1.343-1.346-1.374-1.315Q-1.405-1.283-1.432-1.283L-1.534-1.283Q-1.565-1.283-1.595-1.313Q-1.624-1.342-1.624-1.369Q-1.624-1.502-1.667-1.688Q-1.710-1.873-1.774-2.028Q-1.839-2.182-1.938-2.340Q-2.038-2.498-2.128-2.588Q-2.557-2.994-3.222-2.994Q-3.499-2.994-3.759-2.862Q-4.018-2.729-4.177-2.494Q-4.335-2.260-4.335-1.979Q-4.335-1.623-4.095-1.352Q-3.854-1.080-3.487-0.994L-2.374-0.740Q-2.097-0.674-1.864-0.520Q-1.632-0.365-1.462-0.147Q-1.292 0.072-1.198 0.330Q-1.104 0.588-1.104 0.877Q-1.104 1.205-1.229 1.508Q-1.354 1.810-1.589 2.047Q-1.823 2.283-2.116 2.408Q-2.409 2.533-2.749 2.533Q-3.764 2.533-4.335 1.990L-4.663 2.486Q-4.694 2.533-4.733 2.533L-4.784 2.533Q-4.807 2.533-4.839 2.502Q-4.870 2.470-4.870 2.443M2.087 2.365L-0.272 2.365L-0.272 2.068Q0.052 2.068 0.294 2.021Q0.536 1.974 0.536 1.806L0.536-2.537Q0.536-2.709 0.294-2.756Q0.052-2.803-0.272-2.803L-0.272-3.100L2.321-3.100Q2.653-3.100 3.040-3.014Q3.427-2.928 3.775-2.754Q4.122-2.580 4.341-2.299Q4.560-2.018 4.560-1.651Q4.560-1.326 4.359-1.061Q4.157-0.795 3.851-0.619Q3.544-0.444 3.216-0.354Q3.583-0.233 3.843 0.037Q4.103 0.306 4.153 0.670L4.247 1.365Q4.318 1.814 4.415 2.045Q4.513 2.275 4.810 2.275Q5.056 2.275 5.189 2.058Q5.321 1.842 5.321 1.580Q5.341 1.506 5.423 1.486L5.505 1.486Q5.599 1.510 5.599 1.603Q5.599 1.834 5.501 2.049Q5.403 2.263 5.226 2.398Q5.048 2.533 4.818 2.533Q4.220 2.533 3.802 2.246Q3.384 1.959 3.384 1.388L3.384 0.693Q3.384 0.412 3.232 0.197Q3.079-0.018 2.829-0.131Q2.579-0.244 2.306-0.244L1.278-0.244L1.278 1.806Q1.278 1.970 1.523 2.019Q1.767 2.068 2.087 2.068L2.087 2.365M1.278-2.537L1.278-0.498L2.208-0.498Q2.528-0.498 2.796-0.551Q3.064-0.604 3.263-0.731Q3.462-0.858 3.579-1.090Q3.696-1.322 3.696-1.651Q3.696-2.303 3.300-2.553Q2.903-2.803 2.208-2.803L1.681-2.803Q1.462-2.803 1.370-2.760Q1.278-2.717 1.278-2.537\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-27.295 -61.803)\">\u003Cpath d=\"M-27.708 13.857Q-28.321 13.400-28.723 12.765Q-29.126 12.131-29.321 11.385Q-29.516 10.638-29.516 9.865Q-29.516 9.092-29.321 8.345Q-29.126 7.599-28.723 6.965Q-28.321 6.330-27.708 5.873Q-27.696 5.869-27.688 5.867Q-27.680 5.865-27.669 5.865L-27.591 5.865Q-27.552 5.865-27.526 5.892Q-27.501 5.920-27.501 5.963Q-27.501 6.013-27.532 6.033Q-28.040 6.486-28.362 7.109Q-28.684 7.732-28.825 8.428Q-28.966 9.123-28.966 9.865Q-28.966 10.599-28.827 11.299Q-28.688 11.998-28.364 12.623Q-28.040 13.248-27.532 13.697Q-27.501 13.717-27.501 13.767Q-27.501 13.810-27.526 13.838Q-27.552 13.865-27.591 13.865L-27.669 13.865Q-27.677 13.861-27.686 13.859Q-27.696 13.857-27.708 13.857M-26.739 11.857L-26.739 10.635Q-26.739 10.607-26.708 10.576Q-26.677 10.545-26.653 10.545L-26.548 10.545Q-26.477 10.545-26.462 10.607Q-26.399 10.928-26.261 11.168Q-26.122 11.408-25.889 11.549Q-25.657 11.689-25.348 11.689Q-25.110 11.689-24.901 11.629Q-24.692 11.568-24.555 11.420Q-24.419 11.271-24.419 11.025Q-24.419 10.771-24.630 10.605Q-24.841 10.439-25.110 10.385L-25.731 10.271Q-26.137 10.193-26.438 9.937Q-26.739 9.681-26.739 9.306Q-26.739 8.939-26.538 8.717Q-26.337 8.494-26.012 8.396Q-25.688 8.299-25.348 8.299Q-24.884 8.299-24.587 8.506L-24.364 8.322Q-24.341 8.299-24.309 8.299L-24.259 8.299Q-24.227 8.299-24.200 8.326Q-24.173 8.353-24.173 8.385L-24.173 9.369Q-24.173 9.400-24.198 9.429Q-24.223 9.459-24.259 9.459L-24.364 9.459Q-24.399 9.459-24.427 9.431Q-24.454 9.404-24.454 9.369Q-24.454 8.970-24.706 8.750Q-24.958 8.529-25.356 8.529Q-25.712 8.529-25.995 8.652Q-26.278 8.775-26.278 9.080Q-26.278 9.299-26.077 9.431Q-25.876 9.564-25.630 9.607L-25.005 9.720Q-24.575 9.810-24.266 10.107Q-23.958 10.404-23.958 10.818Q-23.958 11.388-24.356 11.666Q-24.755 11.943-25.348 11.943Q-25.899 11.943-26.251 11.607L-26.548 11.920Q-26.571 11.943-26.606 11.943L-26.653 11.943Q-26.677 11.943-26.708 11.912Q-26.739 11.881-26.739 11.857M-21.548 13.416L-23.403 13.416L-23.403 13.123Q-23.134 13.123-22.966 13.078Q-22.798 13.033-22.798 12.857L-22.798 9.033Q-22.798 8.826-22.954 8.773Q-23.110 8.720-23.403 8.720L-23.403 8.424L-22.180 8.338L-22.180 8.803Q-21.950 8.580-21.636 8.459Q-21.321 8.338-20.981 8.338Q-20.509 8.338-20.104 8.584Q-19.700 8.830-19.468 9.246Q-19.235 9.662-19.235 10.138Q-19.235 10.513-19.384 10.842Q-19.532 11.170-19.802 11.422Q-20.071 11.674-20.415 11.808Q-20.759 11.943-21.118 11.943Q-21.407 11.943-21.679 11.822Q-21.950 11.701-22.157 11.490L-22.157 12.857Q-22.157 13.033-21.989 13.078Q-21.821 13.123-21.548 13.123L-21.548 13.416M-22.157 9.201L-22.157 11.041Q-22.005 11.330-21.743 11.510Q-21.481 11.689-21.173 11.689Q-20.887 11.689-20.665 11.551Q-20.442 11.412-20.290 11.181Q-20.137 10.951-20.059 10.679Q-19.981 10.408-19.981 10.138Q-19.981 9.806-20.106 9.449Q-20.231 9.092-20.479 8.855Q-20.727 8.619-21.075 8.619Q-21.399 8.619-21.694 8.775Q-21.989 8.931-22.157 9.201\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-27.295 -61.803)\">\u003Cpath d=\"M-18.471 10.170Q-18.471 9.666-18.215 9.234Q-17.959 8.803-17.523 8.551Q-17.088 8.299-16.588 8.299Q-16.201 8.299-15.859 8.443Q-15.518 8.588-15.256 8.849Q-14.994 9.111-14.852 9.447Q-14.709 9.783-14.709 10.170Q-14.709 10.662-14.973 11.072Q-15.236 11.482-15.666 11.713Q-16.096 11.943-16.588 11.943Q-17.080 11.943-17.514 11.711Q-17.947 11.478-18.209 11.070Q-18.471 10.662-18.471 10.170M-16.588 11.666Q-16.131 11.666-15.879 11.443Q-15.627 11.220-15.539 10.869Q-15.451 10.517-15.451 10.072Q-15.451 9.642-15.545 9.304Q-15.639 8.967-15.893 8.760Q-16.146 8.553-16.588 8.553Q-17.236 8.553-17.480 8.969Q-17.725 9.385-17.725 10.072Q-17.725 10.517-17.637 10.869Q-17.549 11.220-17.297 11.443Q-17.045 11.666-16.588 11.666M-12.400 11.865L-14.197 11.865L-14.197 11.568Q-13.928 11.568-13.760 11.523Q-13.592 11.478-13.592 11.306L-13.592 7.146Q-13.592 6.931-13.654 6.836Q-13.717 6.740-13.834 6.719Q-13.951 6.697-14.197 6.697L-14.197 6.400L-12.975 6.314L-12.975 10.080L-11.877 9.193Q-11.670 9.013-11.670 8.865Q-11.670 8.799-11.723 8.756Q-11.775 8.713-11.846 8.713L-11.846 8.416L-10.311 8.416L-10.311 8.713Q-10.842 8.713-11.439 9.193L-12.049 9.689L-10.975 11.088Q-10.838 11.263-10.730 11.371Q-10.623 11.478-10.488 11.523Q-10.354 11.568-10.127 11.568L-10.127 11.865L-11.752 11.865L-11.752 11.568Q-11.510 11.568-11.510 11.416Q-11.510 11.338-11.553 11.267Q-11.596 11.197-11.678 11.088L-12.479 10.041L-13.006 10.467L-13.006 11.306Q-13.006 11.474-12.838 11.521Q-12.670 11.568-12.400 11.568\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-27.295 -61.803)\">\u003Cpath d=\"M-9.971 10.111Q-9.971 9.631-9.738 9.215Q-9.506 8.799-9.096 8.549Q-8.686 8.299-8.209 8.299Q-7.479 8.299-7.080 8.740Q-6.682 9.181-6.682 9.912Q-6.682 10.017-6.775 10.041L-9.225 10.041L-9.225 10.111Q-9.225 10.521-9.104 10.877Q-8.982 11.232-8.711 11.449Q-8.439 11.666-8.010 11.666Q-7.646 11.666-7.350 11.437Q-7.053 11.209-6.951 10.857Q-6.943 10.810-6.857 10.795L-6.775 10.795Q-6.682 10.822-6.682 10.904Q-6.682 10.912-6.689 10.943Q-6.752 11.170-6.891 11.353Q-7.029 11.537-7.221 11.670Q-7.412 11.803-7.631 11.873Q-7.850 11.943-8.088 11.943Q-8.459 11.943-8.797 11.806Q-9.135 11.670-9.402 11.418Q-9.670 11.166-9.820 10.826Q-9.971 10.486-9.971 10.111M-9.217 9.803L-7.256 9.803Q-7.256 9.498-7.357 9.207Q-7.459 8.916-7.676 8.734Q-7.893 8.553-8.209 8.553Q-8.510 8.553-8.740 8.740Q-8.971 8.928-9.094 9.219Q-9.217 9.510-9.217 9.803M-4.264 11.865L-6.119 11.865L-6.119 11.568Q-5.846 11.568-5.678 11.521Q-5.510 11.474-5.510 11.306L-5.510 9.170Q-5.510 8.955-5.572 8.859Q-5.635 8.763-5.754 8.742Q-5.873 8.720-6.119 8.720L-6.119 8.424L-4.928 8.338L-4.928 9.072Q-4.814 8.857-4.621 8.689Q-4.428 8.521-4.189 8.429Q-3.951 8.338-3.697 8.338Q-2.529 8.338-2.529 9.416L-2.529 11.306Q-2.529 11.474-2.359 11.521Q-2.189 11.568-1.920 11.568L-1.920 11.865L-3.775 11.865L-3.775 11.568Q-3.502 11.568-3.334 11.521Q-3.166 11.474-3.166 11.306L-3.166 9.431Q-3.166 9.049-3.287 8.820Q-3.408 8.592-3.760 8.592Q-4.072 8.592-4.326 8.754Q-4.580 8.916-4.727 9.185Q-4.873 9.455-4.873 9.752L-4.873 11.306Q-4.873 11.474-4.703 11.521Q-4.533 11.568-4.264 11.568\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-27.295 -61.803)\">\u003Cpath d=\"M3.223 11.865L1.445 11.865L1.445 11.568Q1.719 11.568 1.887 11.521Q2.055 11.474 2.055 11.306L2.055 9.170Q2.055 8.955 1.998 8.859Q1.941 8.763 1.828 8.742Q1.715 8.720 1.469 8.720L1.469 8.424L2.668 8.338L2.668 11.306Q2.668 11.474 2.814 11.521Q2.961 11.568 3.223 11.568L3.223 11.865M1.781 6.943Q1.781 6.752 1.916 6.621Q2.051 6.490 2.246 6.490Q2.367 6.490 2.471 6.553Q2.574 6.615 2.637 6.719Q2.699 6.822 2.699 6.943Q2.699 7.138 2.568 7.273Q2.438 7.408 2.246 7.408Q2.047 7.408 1.914 7.275Q1.781 7.142 1.781 6.943M5.652 11.865L3.797 11.865L3.797 11.568Q4.070 11.568 4.238 11.521Q4.406 11.474 4.406 11.306L4.406 9.170Q4.406 8.955 4.344 8.859Q4.281 8.763 4.162 8.742Q4.043 8.720 3.797 8.720L3.797 8.424L4.988 8.338L4.988 9.072Q5.102 8.857 5.295 8.689Q5.488 8.521 5.727 8.429Q5.965 8.338 6.219 8.338Q7.387 8.338 7.387 9.416L7.387 11.306Q7.387 11.474 7.557 11.521Q7.727 11.568 7.996 11.568L7.996 11.865L6.141 11.865L6.141 11.568Q6.414 11.568 6.582 11.521Q6.750 11.474 6.750 11.306L6.750 9.431Q6.750 9.049 6.629 8.820Q6.508 8.592 6.156 8.592Q5.844 8.592 5.590 8.754Q5.336 8.916 5.189 9.185Q5.043 9.455 5.043 9.752L5.043 11.306Q5.043 11.474 5.213 11.521Q5.383 11.568 5.652 11.568L5.652 11.865M10.324 13.416L8.469 13.416L8.469 13.123Q8.738 13.123 8.906 13.078Q9.074 13.033 9.074 12.857L9.074 9.033Q9.074 8.826 8.918 8.773Q8.762 8.720 8.469 8.720L8.469 8.424L9.691 8.338L9.691 8.803Q9.922 8.580 10.236 8.459Q10.551 8.338 10.891 8.338Q11.363 8.338 11.768 8.584Q12.172 8.830 12.404 9.246Q12.637 9.662 12.637 10.138Q12.637 10.513 12.488 10.842Q12.340 11.170 12.070 11.422Q11.801 11.674 11.457 11.808Q11.113 11.943 10.754 11.943Q10.465 11.943 10.193 11.822Q9.922 11.701 9.715 11.490L9.715 12.857Q9.715 13.033 9.883 13.078Q10.051 13.123 10.324 13.123L10.324 13.416M9.715 9.201L9.715 11.041Q9.867 11.330 10.129 11.510Q10.391 11.689 10.699 11.689Q10.984 11.689 11.207 11.551Q11.430 11.412 11.582 11.181Q11.734 10.951 11.813 10.679Q11.891 10.408 11.891 10.138Q11.891 9.806 11.766 9.449Q11.641 9.092 11.393 8.855Q11.145 8.619 10.797 8.619Q10.473 8.619 10.178 8.775Q9.883 8.931 9.715 9.201M13.844 10.912L13.844 9.170Q13.844 8.955 13.781 8.859Q13.719 8.763 13.600 8.742Q13.480 8.720 13.234 8.720L13.234 8.424L14.480 8.338L14.480 10.888L14.480 10.912Q14.480 11.224 14.535 11.386Q14.590 11.549 14.740 11.619Q14.891 11.689 15.211 11.689Q15.641 11.689 15.914 11.351Q16.188 11.013 16.188 10.568L16.188 9.170Q16.188 8.955 16.125 8.859Q16.063 8.763 15.943 8.742Q15.824 8.720 15.578 8.720L15.578 8.424L16.824 8.338L16.824 11.123Q16.824 11.334 16.887 11.429Q16.949 11.525 17.068 11.547Q17.188 11.568 17.434 11.568L17.434 11.865L16.211 11.943L16.211 11.322Q16.043 11.611 15.762 11.777Q15.480 11.943 15.160 11.943Q13.844 11.943 13.844 10.912M18.504 10.904L18.504 8.713L17.801 8.713L17.801 8.459Q18.156 8.459 18.398 8.226Q18.641 7.994 18.752 7.646Q18.863 7.299 18.863 6.943L19.145 6.943L19.145 8.416L20.320 8.416L20.320 8.713L19.145 8.713L19.145 10.888Q19.145 11.209 19.264 11.437Q19.383 11.666 19.664 11.666Q19.844 11.666 19.961 11.543Q20.078 11.420 20.131 11.240Q20.184 11.060 20.184 10.888L20.184 10.416L20.465 10.416L20.465 10.904Q20.465 11.158 20.359 11.398Q20.254 11.638 20.057 11.791Q19.859 11.943 19.602 11.943Q19.285 11.943 19.033 11.820Q18.781 11.697 18.643 11.463Q18.504 11.228 18.504 10.904M21.586 13.865L21.504 13.865Q21.469 13.865 21.443 13.836Q21.418 13.806 21.418 13.767Q21.418 13.717 21.449 13.697Q21.836 13.361 22.119 12.912Q22.402 12.463 22.568 11.963Q22.734 11.463 22.809 10.945Q22.883 10.428 22.883 9.865Q22.883 9.295 22.809 8.779Q22.734 8.263 22.568 7.767Q22.402 7.271 22.123 6.824Q21.844 6.377 21.449 6.033Q21.418 6.013 21.418 5.963Q21.418 5.924 21.443 5.894Q21.469 5.865 21.504 5.865L21.586 5.865Q21.598 5.865 21.607 5.867Q21.617 5.869 21.625 5.873Q22.238 6.330 22.641 6.965Q23.043 7.599 23.238 8.345Q23.434 9.092 23.434 9.865Q23.434 10.638 23.238 11.385Q23.043 12.131 22.641 12.765Q22.238 13.400 21.625 13.857Q21.613 13.857 21.605 13.859Q21.598 13.861 21.586 13.865\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-68.737 101.492H8.086V70.194h-76.823Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-29.892 80.46)\">\u003Cpath d=\"M-4.348 2.365L-7.391 2.365L-7.391 2.068Q-6.629 2.068-6.446 2.029Q-6.403 2.017-6.354 1.984Q-6.305 1.951-6.280 1.908Q-6.254 1.865-6.254 1.806L-6.254-2.537Q-6.254-2.713-6.346-2.758Q-6.438-2.803-6.653-2.803L-7.047-2.803Q-7.743-2.803-8.032-2.514Q-8.180-2.365-8.243-2.045Q-8.305-1.725-8.348-1.252L-8.629-1.252L-8.469-3.100L-3.270-3.100L-3.110-1.252L-3.391-1.252Q-3.434-1.760-3.496-2.063Q-3.559-2.365-3.711-2.514Q-3.996-2.803-4.696-2.803L-5.086-2.803Q-5.301-2.803-5.393-2.760Q-5.485-2.717-5.485-2.537L-5.485 1.806Q-5.485 1.881-5.430 1.945Q-5.375 2.010-5.293 2.029Q-5.110 2.068-4.348 2.068L-4.348 2.365M1.789 2.365L-1.254 2.365L-1.254 2.068Q-0.493 2.068-0.309 2.029Q-0.266 2.017-0.217 1.984Q-0.168 1.951-0.143 1.908Q-0.118 1.865-0.118 1.806L-0.118-2.537Q-0.118-2.713-0.209-2.758Q-0.301-2.803-0.516-2.803L-0.911-2.803Q-1.606-2.803-1.895-2.514Q-2.043-2.365-2.106-2.045Q-2.168-1.725-2.211-1.252L-2.493-1.252L-2.332-3.100L2.867-3.100L3.027-1.252L2.746-1.252Q2.703-1.760 2.640-2.063Q2.578-2.365 2.425-2.514Q2.140-2.803 1.441-2.803L1.050-2.803Q0.836-2.803 0.744-2.760Q0.652-2.717 0.652-2.537L0.652 1.806Q0.652 1.881 0.707 1.945Q0.761 2.010 0.843 2.029Q1.027 2.068 1.789 2.068L1.789 2.365M3.812 2.443L3.812 0.638Q3.812 0.611 3.843 0.580Q3.875 0.549 3.898 0.549L4.004 0.549Q4.035 0.549 4.064 0.578Q4.093 0.607 4.093 0.638Q4.093 1.420 4.609 1.828Q5.125 2.236 5.933 2.236Q6.230 2.236 6.486 2.086Q6.742 1.935 6.892 1.679Q7.043 1.424 7.043 1.127Q7.043 0.728 6.796 0.424Q6.550 0.119 6.179 0.037L5.058-0.221Q4.718-0.295 4.431-0.516Q4.144-0.737 3.978-1.055Q3.812-1.373 3.812-1.725Q3.812-2.155 4.043-2.510Q4.273-2.865 4.654-3.067Q5.035-3.268 5.461-3.268Q5.711-3.268 5.957-3.209Q6.203-3.151 6.421-3.028Q6.640-2.905 6.804-2.725L7.132-3.221Q7.164-3.268 7.203-3.268L7.250-3.268Q7.277-3.268 7.308-3.237Q7.339-3.205 7.339-3.178L7.339-1.369Q7.339-1.346 7.308-1.315Q7.277-1.283 7.250-1.283L7.148-1.283Q7.117-1.283 7.087-1.313Q7.058-1.342 7.058-1.369Q7.058-1.502 7.015-1.688Q6.972-1.873 6.908-2.028Q6.843-2.182 6.744-2.340Q6.644-2.498 6.554-2.588Q6.125-2.994 5.461-2.994Q5.183-2.994 4.923-2.862Q4.664-2.729 4.505-2.494Q4.347-2.260 4.347-1.979Q4.347-1.623 4.587-1.352Q4.828-1.080 5.195-0.994L6.308-0.740Q6.586-0.674 6.818-0.520Q7.050-0.365 7.220-0.147Q7.390 0.072 7.484 0.330Q7.578 0.588 7.578 0.877Q7.578 1.205 7.453 1.508Q7.328 1.810 7.093 2.047Q6.859 2.283 6.566 2.408Q6.273 2.533 5.933 2.533Q4.918 2.533 4.347 1.990L4.019 2.486Q3.988 2.533 3.949 2.533L3.898 2.533Q3.875 2.533 3.843 2.502Q3.812 2.470 3.812 2.443\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-29.892 80.46)\">\u003Cpath d=\"M-27.708 13.857Q-28.321 13.400-28.723 12.765Q-29.126 12.131-29.321 11.385Q-29.516 10.638-29.516 9.865Q-29.516 9.092-29.321 8.345Q-29.126 7.599-28.723 6.965Q-28.321 6.330-27.708 5.873Q-27.696 5.869-27.688 5.867Q-27.680 5.865-27.669 5.865L-27.591 5.865Q-27.552 5.865-27.526 5.892Q-27.501 5.920-27.501 5.963Q-27.501 6.013-27.532 6.033Q-28.040 6.486-28.362 7.109Q-28.684 7.732-28.825 8.428Q-28.966 9.123-28.966 9.865Q-28.966 10.599-28.827 11.299Q-28.688 11.998-28.364 12.623Q-28.040 13.248-27.532 13.697Q-27.501 13.717-27.501 13.767Q-27.501 13.810-27.526 13.838Q-27.552 13.865-27.591 13.865L-27.669 13.865Q-27.677 13.861-27.686 13.859Q-27.696 13.857-27.708 13.857M-26.739 11.857L-26.739 10.635Q-26.739 10.607-26.708 10.576Q-26.677 10.545-26.653 10.545L-26.548 10.545Q-26.477 10.545-26.462 10.607Q-26.399 10.928-26.261 11.168Q-26.122 11.408-25.889 11.549Q-25.657 11.689-25.348 11.689Q-25.110 11.689-24.901 11.629Q-24.692 11.568-24.555 11.420Q-24.419 11.271-24.419 11.025Q-24.419 10.771-24.630 10.605Q-24.841 10.439-25.110 10.385L-25.731 10.271Q-26.137 10.193-26.438 9.937Q-26.739 9.681-26.739 9.306Q-26.739 8.939-26.538 8.717Q-26.337 8.494-26.012 8.396Q-25.688 8.299-25.348 8.299Q-24.884 8.299-24.587 8.506L-24.364 8.322Q-24.341 8.299-24.309 8.299L-24.259 8.299Q-24.227 8.299-24.200 8.326Q-24.173 8.353-24.173 8.385L-24.173 9.369Q-24.173 9.400-24.198 9.429Q-24.223 9.459-24.259 9.459L-24.364 9.459Q-24.399 9.459-24.427 9.431Q-24.454 9.404-24.454 9.369Q-24.454 8.970-24.706 8.750Q-24.958 8.529-25.356 8.529Q-25.712 8.529-25.995 8.652Q-26.278 8.775-26.278 9.080Q-26.278 9.299-26.077 9.431Q-25.876 9.564-25.630 9.607L-25.005 9.720Q-24.575 9.810-24.266 10.107Q-23.958 10.404-23.958 10.818Q-23.958 11.388-24.356 11.666Q-24.755 11.943-25.348 11.943Q-25.899 11.943-26.251 11.607L-26.548 11.920Q-26.571 11.943-26.606 11.943L-26.653 11.943Q-26.677 11.943-26.708 11.912Q-26.739 11.881-26.739 11.857M-21.548 13.416L-23.403 13.416L-23.403 13.123Q-23.134 13.123-22.966 13.078Q-22.798 13.033-22.798 12.857L-22.798 9.033Q-22.798 8.826-22.954 8.773Q-23.110 8.720-23.403 8.720L-23.403 8.424L-22.180 8.338L-22.180 8.803Q-21.950 8.580-21.636 8.459Q-21.321 8.338-20.981 8.338Q-20.509 8.338-20.104 8.584Q-19.700 8.830-19.468 9.246Q-19.235 9.662-19.235 10.138Q-19.235 10.513-19.384 10.842Q-19.532 11.170-19.802 11.422Q-20.071 11.674-20.415 11.808Q-20.759 11.943-21.118 11.943Q-21.407 11.943-21.679 11.822Q-21.950 11.701-22.157 11.490L-22.157 12.857Q-22.157 13.033-21.989 13.078Q-21.821 13.123-21.548 13.123L-21.548 13.416M-22.157 9.201L-22.157 11.041Q-22.005 11.330-21.743 11.510Q-21.481 11.689-21.173 11.689Q-20.887 11.689-20.665 11.551Q-20.442 11.412-20.290 11.181Q-20.137 10.951-20.059 10.679Q-19.981 10.408-19.981 10.138Q-19.981 9.806-20.106 9.449Q-20.231 9.092-20.479 8.855Q-20.727 8.619-21.075 8.619Q-21.399 8.619-21.694 8.775Q-21.989 8.931-22.157 9.201\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-29.892 80.46)\">\u003Cpath d=\"M-18.471 10.170Q-18.471 9.666-18.215 9.234Q-17.959 8.803-17.523 8.551Q-17.088 8.299-16.588 8.299Q-16.201 8.299-15.859 8.443Q-15.518 8.588-15.256 8.849Q-14.994 9.111-14.852 9.447Q-14.709 9.783-14.709 10.170Q-14.709 10.662-14.973 11.072Q-15.236 11.482-15.666 11.713Q-16.096 11.943-16.588 11.943Q-17.080 11.943-17.514 11.711Q-17.947 11.478-18.209 11.070Q-18.471 10.662-18.471 10.170M-16.588 11.666Q-16.131 11.666-15.879 11.443Q-15.627 11.220-15.539 10.869Q-15.451 10.517-15.451 10.072Q-15.451 9.642-15.545 9.304Q-15.639 8.967-15.893 8.760Q-16.146 8.553-16.588 8.553Q-17.236 8.553-17.480 8.969Q-17.725 9.385-17.725 10.072Q-17.725 10.517-17.637 10.869Q-17.549 11.220-17.297 11.443Q-17.045 11.666-16.588 11.666M-12.400 11.865L-14.197 11.865L-14.197 11.568Q-13.928 11.568-13.760 11.523Q-13.592 11.478-13.592 11.306L-13.592 7.146Q-13.592 6.931-13.654 6.836Q-13.717 6.740-13.834 6.719Q-13.951 6.697-14.197 6.697L-14.197 6.400L-12.975 6.314L-12.975 10.080L-11.877 9.193Q-11.670 9.013-11.670 8.865Q-11.670 8.799-11.723 8.756Q-11.775 8.713-11.846 8.713L-11.846 8.416L-10.311 8.416L-10.311 8.713Q-10.842 8.713-11.439 9.193L-12.049 9.689L-10.975 11.088Q-10.838 11.263-10.730 11.371Q-10.623 11.478-10.488 11.523Q-10.354 11.568-10.127 11.568L-10.127 11.865L-11.752 11.865L-11.752 11.568Q-11.510 11.568-11.510 11.416Q-11.510 11.338-11.553 11.267Q-11.596 11.197-11.678 11.088L-12.479 10.041L-13.006 10.467L-13.006 11.306Q-13.006 11.474-12.838 11.521Q-12.670 11.568-12.400 11.568\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-29.892 80.46)\">\u003Cpath d=\"M-9.971 10.111Q-9.971 9.631-9.738 9.215Q-9.506 8.799-9.096 8.549Q-8.686 8.299-8.209 8.299Q-7.479 8.299-7.080 8.740Q-6.682 9.181-6.682 9.912Q-6.682 10.017-6.775 10.041L-9.225 10.041L-9.225 10.111Q-9.225 10.521-9.104 10.877Q-8.982 11.232-8.711 11.449Q-8.439 11.666-8.010 11.666Q-7.646 11.666-7.350 11.437Q-7.053 11.209-6.951 10.857Q-6.943 10.810-6.857 10.795L-6.775 10.795Q-6.682 10.822-6.682 10.904Q-6.682 10.912-6.689 10.943Q-6.752 11.170-6.891 11.353Q-7.029 11.537-7.221 11.670Q-7.412 11.803-7.631 11.873Q-7.850 11.943-8.088 11.943Q-8.459 11.943-8.797 11.806Q-9.135 11.670-9.402 11.418Q-9.670 11.166-9.820 10.826Q-9.971 10.486-9.971 10.111M-9.217 9.803L-7.256 9.803Q-7.256 9.498-7.357 9.207Q-7.459 8.916-7.676 8.734Q-7.893 8.553-8.209 8.553Q-8.510 8.553-8.740 8.740Q-8.971 8.928-9.094 9.219Q-9.217 9.510-9.217 9.803M-4.264 11.865L-6.119 11.865L-6.119 11.568Q-5.846 11.568-5.678 11.521Q-5.510 11.474-5.510 11.306L-5.510 9.170Q-5.510 8.955-5.572 8.859Q-5.635 8.763-5.754 8.742Q-5.873 8.720-6.119 8.720L-6.119 8.424L-4.928 8.338L-4.928 9.072Q-4.814 8.857-4.621 8.689Q-4.428 8.521-4.189 8.429Q-3.951 8.338-3.697 8.338Q-2.529 8.338-2.529 9.416L-2.529 11.306Q-2.529 11.474-2.359 11.521Q-2.189 11.568-1.920 11.568L-1.920 11.865L-3.775 11.865L-3.775 11.568Q-3.502 11.568-3.334 11.521Q-3.166 11.474-3.166 11.306L-3.166 9.431Q-3.166 9.049-3.287 8.820Q-3.408 8.592-3.760 8.592Q-4.072 8.592-4.326 8.754Q-4.580 8.916-4.727 9.185Q-4.873 9.455-4.873 9.752L-4.873 11.306Q-4.873 11.474-4.703 11.521Q-4.533 11.568-4.264 11.568\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-29.892 80.46)\">\u003Cpath d=\"M1.363 10.170Q1.363 9.666 1.619 9.234Q1.875 8.803 2.311 8.551Q2.746 8.299 3.246 8.299Q3.633 8.299 3.975 8.443Q4.316 8.588 4.578 8.849Q4.840 9.111 4.982 9.447Q5.125 9.783 5.125 10.170Q5.125 10.662 4.861 11.072Q4.598 11.482 4.168 11.713Q3.738 11.943 3.246 11.943Q2.754 11.943 2.320 11.711Q1.887 11.478 1.625 11.070Q1.363 10.662 1.363 10.170M3.246 11.666Q3.703 11.666 3.955 11.443Q4.207 11.220 4.295 10.869Q4.383 10.517 4.383 10.072Q4.383 9.642 4.289 9.304Q4.195 8.967 3.941 8.760Q3.688 8.553 3.246 8.553Q2.598 8.553 2.354 8.969Q2.109 9.385 2.109 10.072Q2.109 10.517 2.197 10.869Q2.285 11.220 2.537 11.443Q2.789 11.666 3.246 11.666M6.293 10.912L6.293 9.170Q6.293 8.955 6.230 8.859Q6.168 8.763 6.049 8.742Q5.930 8.720 5.684 8.720L5.684 8.424L6.930 8.338L6.930 10.888L6.930 10.912Q6.930 11.224 6.984 11.386Q7.039 11.549 7.189 11.619Q7.340 11.689 7.660 11.689Q8.090 11.689 8.363 11.351Q8.637 11.013 8.637 10.568L8.637 9.170Q8.637 8.955 8.574 8.859Q8.512 8.763 8.393 8.742Q8.273 8.720 8.027 8.720L8.027 8.424L9.273 8.338L9.273 11.123Q9.273 11.334 9.336 11.429Q9.398 11.525 9.518 11.547Q9.637 11.568 9.883 11.568L9.883 11.865L8.660 11.943L8.660 11.322Q8.492 11.611 8.211 11.777Q7.930 11.943 7.609 11.943Q6.293 11.943 6.293 10.912M10.953 10.904L10.953 8.713L10.250 8.713L10.250 8.459Q10.605 8.459 10.848 8.226Q11.090 7.994 11.201 7.646Q11.313 7.299 11.313 6.943L11.594 6.943L11.594 8.416L12.770 8.416L12.770 8.713L11.594 8.713L11.594 10.888Q11.594 11.209 11.713 11.437Q11.832 11.666 12.113 11.666Q12.293 11.666 12.410 11.543Q12.527 11.420 12.580 11.240Q12.633 11.060 12.633 10.888L12.633 10.416L12.914 10.416L12.914 10.904Q12.914 11.158 12.809 11.398Q12.703 11.638 12.506 11.791Q12.309 11.943 12.051 11.943Q11.734 11.943 11.482 11.820Q11.230 11.697 11.092 11.463Q10.953 11.228 10.953 10.904M15.516 13.416L13.660 13.416L13.660 13.123Q13.930 13.123 14.098 13.078Q14.266 13.033 14.266 12.857L14.266 9.033Q14.266 8.826 14.109 8.773Q13.953 8.720 13.660 8.720L13.660 8.424L14.883 8.338L14.883 8.803Q15.113 8.580 15.428 8.459Q15.742 8.338 16.082 8.338Q16.555 8.338 16.959 8.584Q17.363 8.830 17.596 9.246Q17.828 9.662 17.828 10.138Q17.828 10.513 17.680 10.842Q17.531 11.170 17.262 11.422Q16.992 11.674 16.648 11.808Q16.305 11.943 15.945 11.943Q15.656 11.943 15.385 11.822Q15.113 11.701 14.906 11.490L14.906 12.857Q14.906 13.033 15.074 13.078Q15.242 13.123 15.516 13.123L15.516 13.416M14.906 9.201L14.906 11.041Q15.059 11.330 15.320 11.510Q15.582 11.689 15.891 11.689Q16.176 11.689 16.398 11.551Q16.621 11.412 16.773 11.181Q16.926 10.951 17.004 10.679Q17.082 10.408 17.082 10.138Q17.082 9.806 16.957 9.449Q16.832 9.092 16.584 8.855Q16.336 8.619 15.988 8.619Q15.664 8.619 15.369 8.775Q15.074 8.931 14.906 9.201M19.035 10.912L19.035 9.170Q19.035 8.955 18.973 8.859Q18.910 8.763 18.791 8.742Q18.672 8.720 18.426 8.720L18.426 8.424L19.672 8.338L19.672 10.888L19.672 10.912Q19.672 11.224 19.727 11.386Q19.781 11.549 19.932 11.619Q20.082 11.689 20.402 11.689Q20.832 11.689 21.105 11.351Q21.379 11.013 21.379 10.568L21.379 9.170Q21.379 8.955 21.316 8.859Q21.254 8.763 21.135 8.742Q21.016 8.720 20.770 8.720L20.770 8.424L22.016 8.338L22.016 11.123Q22.016 11.334 22.078 11.429Q22.141 11.525 22.260 11.547Q22.379 11.568 22.625 11.568L22.625 11.865L21.402 11.943L21.402 11.322Q21.234 11.611 20.953 11.777Q20.672 11.943 20.352 11.943Q19.035 11.943 19.035 10.912M23.695 10.904L23.695 8.713L22.992 8.713L22.992 8.459Q23.348 8.459 23.590 8.226Q23.832 7.994 23.943 7.646Q24.055 7.299 24.055 6.943L24.336 6.943L24.336 8.416L25.512 8.416L25.512 8.713L24.336 8.713L24.336 10.888Q24.336 11.209 24.455 11.437Q24.574 11.666 24.855 11.666Q25.035 11.666 25.152 11.543Q25.270 11.420 25.322 11.240Q25.375 11.060 25.375 10.888L25.375 10.416L25.656 10.416L25.656 10.904Q25.656 11.158 25.551 11.398Q25.445 11.638 25.248 11.791Q25.051 11.943 24.793 11.943Q24.477 11.943 24.225 11.820Q23.973 11.697 23.834 11.463Q23.695 11.228 23.695 10.904M26.777 13.865L26.695 13.865Q26.660 13.865 26.635 13.836Q26.609 13.806 26.609 13.767Q26.609 13.717 26.641 13.697Q27.027 13.361 27.311 12.912Q27.594 12.463 27.760 11.963Q27.926 11.463 28 10.945Q28.074 10.428 28.074 9.865Q28.074 9.295 28 8.779Q27.926 8.263 27.760 7.767Q27.594 7.271 27.314 6.824Q27.035 6.377 26.641 6.033Q26.609 6.013 26.609 5.963Q26.609 5.924 26.635 5.894Q26.660 5.865 26.695 5.865L26.777 5.865Q26.789 5.865 26.799 5.867Q26.809 5.869 26.816 5.873Q27.430 6.330 27.832 6.965Q28.234 7.599 28.430 8.345Q28.625 9.092 28.625 9.865Q28.625 10.638 28.430 11.385Q28.234 12.131 27.832 12.765Q27.430 13.400 26.816 13.857Q26.805 13.857 26.797 13.859Q26.789 13.861 26.777 13.865\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M8.286 11.865h39.679\"\u002F>\u003Cpath stroke=\"none\" d=\"m50.565 11.865-4.16-2.08 1.56 2.08-1.56 2.08\"\u002F>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M127.787 11.865h39.68\"\u002F>\u003Cpath stroke=\"none\" d=\"m170.066 11.865-4.16-2.08 1.56 2.08-1.56 2.08\"\u002F>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M208.677 27.714v39.68\"\u002F>\u003Cpath stroke=\"none\" d=\"m208.677 69.994 2.08-4.16-2.08 1.56-2.08-1.56\"\u002F>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-30.325-40.572v33.071\"\u002F>\u003Cpath stroke=\"none\" d=\"m-30.325-4.9 2.08-4.16-2.08 1.56-2.08-1.56\"\u002F>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M170.066 85.843H10.886\"\u002F>\u003Cpath stroke=\"none\" d=\"m8.286 85.843 4.16 2.08-1.56-2.08 1.56-2.08\"\u002F>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M170.066 85.843C55.033 85.843 32.27 57.39-27.963 29.717\"\u002F>\u003Cpath stroke=\"none\" d=\"m-30.325 28.632 2.911 3.626-.549-2.541 2.286-1.239\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(111.5 61.908)\">\u003Cpath d=\"M-28.329 11.865L-29.963 11.865L-29.963 11.585Q-29.734 11.585-29.585 11.551Q-29.436 11.516-29.436 11.376L-29.436 9.527Q-29.436 9.257-29.544 9.196Q-29.652 9.134-29.963 9.134L-29.963 8.854L-28.903 8.779L-28.903 9.428Q-28.732 9.120-28.428 8.949Q-28.124 8.779-27.779 8.779Q-27.273 8.779-26.989 9.002Q-26.705 9.226-26.705 9.722L-26.705 11.376Q-26.705 11.513-26.557 11.549Q-26.408 11.585-26.182 11.585L-26.182 11.865L-27.813 11.865L-27.813 11.585Q-27.584 11.585-27.435 11.551Q-27.286 11.516-27.286 11.376L-27.286 9.736Q-27.286 9.401-27.406 9.201Q-27.526 9.001-27.840 9.001Q-28.110 9.001-28.344 9.137Q-28.578 9.274-28.717 9.508Q-28.855 9.742-28.855 10.016L-28.855 11.376Q-28.855 11.513-28.705 11.549Q-28.554 11.585-28.329 11.585L-28.329 11.865M-25.636 10.330Q-25.636 10.009-25.511 9.720Q-25.386 9.431-25.160 9.208Q-24.935 8.984-24.639 8.864Q-24.344 8.744-24.026 8.744Q-23.698 8.744-23.436 8.844Q-23.175 8.943-22.999 9.125Q-22.823 9.308-22.729 9.566Q-22.635 9.824-22.635 10.156Q-22.635 10.248-22.717 10.269L-24.972 10.269L-24.972 10.330Q-24.972 10.918-24.689 11.301Q-24.405 11.684-23.838 11.684Q-23.516 11.684-23.248 11.491Q-22.980 11.298-22.891 10.983Q-22.884 10.942-22.809 10.928L-22.717 10.928Q-22.635 10.952-22.635 11.024Q-22.635 11.031-22.641 11.058Q-22.754 11.455-23.125 11.694Q-23.496 11.933-23.920 11.933Q-24.357 11.933-24.757 11.725Q-25.157 11.516-25.396 11.149Q-25.636 10.782-25.636 10.330M-24.966 10.060L-23.151 10.060Q-23.151 9.783-23.248 9.531Q-23.346 9.278-23.544 9.122Q-23.742 8.967-24.026 8.967Q-24.303 8.967-24.516 9.125Q-24.730 9.284-24.848 9.539Q-24.966 9.794-24.966 10.060M-20.864 11.865L-22.187 11.865L-22.187 11.585Q-21.626 11.585-21.247 11.185L-20.533 10.388L-21.445 9.339Q-21.582 9.192-21.731 9.160Q-21.879 9.127-22.146 9.127L-22.146 8.847L-20.645 8.847L-20.645 9.127Q-20.837 9.127-20.837 9.261Q-20.837 9.291-20.806 9.339L-20.211 10.023L-19.770 9.527Q-19.658 9.397-19.658 9.281Q-19.658 9.219-19.695 9.173Q-19.733 9.127-19.791 9.127L-19.791 8.847L-18.475 8.847L-18.475 9.127Q-19.035 9.127-19.415 9.527L-20.037 10.228L-19.042 11.376Q-18.943 11.475-18.842 11.520Q-18.742 11.564-18.630 11.574Q-18.519 11.585-18.342 11.585L-18.342 11.865L-19.835 11.865L-19.835 11.585Q-19.770 11.585-19.710 11.551Q-19.651 11.516-19.651 11.451Q-19.651 11.404-19.681 11.376L-20.358 10.590L-20.891 11.185Q-21.004 11.315-21.004 11.431Q-21.004 11.496-20.963 11.540Q-20.922 11.585-20.864 11.585L-20.864 11.865M-17.320 11.024L-17.320 9.127L-17.959 9.127L-17.959 8.905Q-17.641 8.905-17.424 8.695Q-17.207 8.485-17.106 8.175Q-17.005 7.866-17.005 7.558L-16.739 7.558L-16.739 8.847L-15.662 8.847L-15.662 9.127L-16.739 9.127L-16.739 11.011Q-16.739 11.287-16.634 11.486Q-16.530 11.684-16.270 11.684Q-16.113 11.684-16.007 11.580Q-15.901 11.475-15.852 11.322Q-15.802 11.168-15.802 11.011L-15.802 10.597L-15.535 10.597L-15.535 11.024Q-15.535 11.250-15.635 11.460Q-15.734 11.670-15.918 11.802Q-16.103 11.933-16.332 11.933Q-16.769 11.933-17.044 11.696Q-17.320 11.458-17.320 11.024\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(111.5 61.908)\">\u003Cpath d=\"M-11.491 11.024L-11.491 9.127L-12.130 9.127L-12.130 8.905Q-11.812 8.905-11.595 8.695Q-11.378 8.485-11.278 8.175Q-11.177 7.866-11.177 7.558L-10.910 7.558L-10.910 8.847L-9.833 8.847L-9.833 9.127L-10.910 9.127L-10.910 11.011Q-10.910 11.287-10.806 11.486Q-10.702 11.684-10.442 11.684Q-10.285 11.684-10.179 11.580Q-10.073 11.475-10.023 11.322Q-9.974 11.168-9.974 11.011L-9.974 10.597L-9.707 10.597L-9.707 11.024Q-9.707 11.250-9.806 11.460Q-9.905 11.670-10.090 11.802Q-10.274 11.933-10.503 11.933Q-10.941 11.933-11.216 11.696Q-11.491 11.458-11.491 11.024M-8.323 11.031L-8.323 9.527Q-8.323 9.257-8.430 9.196Q-8.538 9.134-8.849 9.134L-8.849 8.854L-7.742 8.779L-7.742 11.011L-7.742 11.031Q-7.742 11.311-7.690 11.455Q-7.639 11.598-7.497 11.655Q-7.355 11.711-7.068 11.711Q-6.815 11.711-6.610 11.571Q-6.405 11.431-6.289 11.205Q-6.173 10.980-6.173 10.730L-6.173 9.527Q-6.173 9.257-6.280 9.196Q-6.388 9.134-6.699 9.134L-6.699 8.854L-5.592 8.779L-5.592 11.192Q-5.592 11.383-5.539 11.465Q-5.486 11.547-5.385 11.566Q-5.284 11.585-5.069 11.585L-5.069 11.865L-6.145 11.933L-6.145 11.369Q-6.255 11.551-6.400 11.674Q-6.545 11.797-6.732 11.865Q-6.918 11.933-7.120 11.933Q-8.323 11.933-8.323 11.031M-2.731 11.865L-4.467 11.865L-4.467 11.585Q-4.238 11.585-4.090 11.551Q-3.941 11.516-3.941 11.376L-3.941 9.527Q-3.941 9.257-4.049 9.196Q-4.156 9.134-4.467 9.134L-4.467 8.854L-3.438 8.779L-3.438 9.486Q-3.309 9.178-3.066 8.979Q-2.823 8.779-2.505 8.779Q-2.287 8.779-2.116 8.903Q-1.945 9.028-1.945 9.240Q-1.945 9.377-2.044 9.476Q-2.143 9.575-2.276 9.575Q-2.413 9.575-2.512 9.476Q-2.611 9.377-2.611 9.240Q-2.611 9.100-2.512 9.001Q-2.803 9.001-3.003 9.197Q-3.203 9.394-3.295 9.688Q-3.387 9.982-3.387 10.262L-3.387 11.376Q-3.387 11.585-2.731 11.585L-2.731 11.865M0.321 11.865L-1.312 11.865L-1.312 11.585Q-1.083 11.585-0.935 11.551Q-0.786 11.516-0.786 11.376L-0.786 9.527Q-0.786 9.257-0.894 9.196Q-1.001 9.134-1.312 9.134L-1.312 8.854L-0.253 8.779L-0.253 9.428Q-0.082 9.120 0.222 8.949Q0.526 8.779 0.872 8.779Q1.377 8.779 1.661 9.002Q1.945 9.226 1.945 9.722L1.945 11.376Q1.945 11.513 2.094 11.549Q2.242 11.585 2.468 11.585L2.468 11.865L0.837 11.865L0.837 11.585Q1.066 11.585 1.215 11.551Q1.364 11.516 1.364 11.376L1.364 9.736Q1.364 9.401 1.244 9.201Q1.125 9.001 0.810 9.001Q0.540 9.001 0.306 9.137Q0.072 9.274-0.067 9.508Q-0.205 9.742-0.205 10.016L-0.205 11.376Q-0.205 11.513-0.055 11.549Q0.096 11.585 0.321 11.585\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">The dialogue-state pipeline. NLU extracts dialogue acts and slot fillers from the user's turn; the state tracker accumulates them into the full frame; the policy chooses the next system act; NLG realizes it as text (and, for spoken systems, ASR and TTS wrap the whole loop).\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:363.761px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 272.821 205.569\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cpath fill=\"none\" d=\"M-65.403-35.081h113.81V-72.07h-113.81Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-24.58 1.711)\">\u003Cpath d=\"M-8.225-55.059Q-8.225-55.401-8.090-55.700Q-7.955-55.999-7.715-56.223Q-7.476-56.447-7.158-56.572Q-6.840-56.697-6.509-56.697Q-6.064-56.697-5.665-56.481Q-5.265-56.266-5.030-55.888Q-4.796-55.511-4.796-55.059Q-4.796-54.718-4.938-54.434Q-5.080-54.150-5.324-53.943Q-5.569-53.737-5.878-53.622Q-6.187-53.508-6.509-53.508Q-6.939-53.508-7.341-53.709Q-7.743-53.911-7.984-54.263Q-8.225-54.615-8.225-55.059M-6.509-53.757Q-5.907-53.757-5.683-54.135Q-5.459-54.513-5.459-55.145Q-5.459-55.757-5.694-56.116Q-5.928-56.474-6.509-56.474Q-7.561-56.474-7.561-55.145Q-7.561-54.513-7.336-54.135Q-7.110-53.757-6.509-53.757M-2.452-53.576L-4.188-53.576L-4.188-53.856Q-3.959-53.856-3.810-53.890Q-3.662-53.925-3.662-54.065L-3.662-55.914Q-3.662-56.184-3.769-56.245Q-3.877-56.307-4.188-56.307L-4.188-56.587L-3.159-56.662L-3.159-55.955Q-3.029-56.263-2.787-56.462Q-2.544-56.662-2.226-56.662Q-2.007-56.662-1.836-56.538Q-1.665-56.413-1.665-56.201Q-1.665-56.064-1.765-55.965Q-1.864-55.866-1.997-55.866Q-2.134-55.866-2.233-55.965Q-2.332-56.064-2.332-56.201Q-2.332-56.341-2.233-56.440Q-2.523-56.440-2.723-56.244Q-2.923-56.047-3.016-55.753Q-3.108-55.459-3.108-55.179L-3.108-54.065Q-3.108-53.856-2.452-53.856L-2.452-53.576M0.536-53.576L-1.016-53.576L-1.016-53.856Q-0.790-53.856-0.642-53.890Q-0.493-53.925-0.493-54.065L-0.493-55.914Q-0.493-56.102-0.541-56.186Q-0.589-56.269-0.686-56.288Q-0.784-56.307-0.996-56.307L-0.996-56.587L0.061-56.662L0.061-54.065Q0.061-53.925 0.192-53.890Q0.324-53.856 0.536-53.856L0.536-53.576M-0.736-57.883Q-0.736-58.054-0.613-58.173Q-0.490-58.293-0.319-58.293Q-0.151-58.293-0.028-58.173Q0.095-58.054 0.095-57.883Q0.095-57.708-0.028-57.585Q-0.151-57.462-0.319-57.462Q-0.490-57.462-0.613-57.585Q-0.736-57.708-0.736-57.883M1.141-53.043Q1.141-53.289 1.337-53.473Q1.534-53.658 1.790-53.737Q1.653-53.849 1.582-54.010Q1.510-54.171 1.510-54.352Q1.510-54.673 1.722-54.919Q1.387-55.217 1.387-55.627Q1.387-56.088 1.776-56.375Q2.166-56.662 2.645-56.662Q3.116-56.662 3.451-56.416Q3.626-56.570 3.836-56.652Q4.046-56.734 4.275-56.734Q4.439-56.734 4.560-56.627Q4.682-56.519 4.682-56.355Q4.682-56.259 4.610-56.187Q4.538-56.116 4.446-56.116Q4.347-56.116 4.277-56.189Q4.207-56.263 4.207-56.362Q4.207-56.416 4.220-56.447L4.227-56.461Q4.234-56.481 4.242-56.492Q4.251-56.502 4.254-56.509Q3.899-56.509 3.612-56.286Q3.899-55.993 3.899-55.627Q3.899-55.312 3.714-55.080Q3.530-54.847 3.241-54.719Q2.952-54.591 2.645-54.591Q2.443-54.591 2.252-54.641Q2.060-54.690 1.882-54.800Q1.790-54.673 1.790-54.530Q1.790-54.348 1.918-54.213Q2.046-54.078 2.231-54.078L2.863-54.078Q3.311-54.078 3.680-54.007Q4.049-53.935 4.309-53.706Q4.569-53.477 4.569-53.043Q4.569-52.722 4.273-52.520Q3.978-52.318 3.574-52.229Q3.171-52.140 2.856-52.140Q2.539-52.140 2.135-52.229Q1.732-52.318 1.436-52.520Q1.141-52.722 1.141-53.043M1.595-53.043Q1.595-52.814 1.814-52.665Q2.033-52.516 2.325-52.448Q2.617-52.380 2.856-52.380Q3.021-52.380 3.229-52.416Q3.438-52.451 3.644-52.532Q3.851-52.612 3.983-52.740Q4.114-52.868 4.114-53.043Q4.114-53.395 3.733-53.489Q3.352-53.583 2.850-53.583L2.231-53.583Q1.992-53.583 1.794-53.432Q1.595-53.282 1.595-53.043M2.645-54.830Q3.311-54.830 3.311-55.627Q3.311-56.427 2.645-56.427Q1.975-56.427 1.975-55.627Q1.975-54.830 2.645-54.830\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-24.58 1.711)\">\u003Cpath d=\"M13.040-54.383L8.207-54.383Q8.139-54.393 8.093-54.439Q8.047-54.485 8.047-54.557Q8.047-54.622 8.093-54.668Q8.139-54.714 8.207-54.724L13.040-54.724Q13.109-54.714 13.155-54.668Q13.201-54.622 13.201-54.557Q13.201-54.485 13.155-54.439Q13.109-54.393 13.040-54.383M13.040-55.921L8.207-55.921Q8.139-55.931 8.093-55.977Q8.047-56.023 8.047-56.095Q8.047-56.239 8.207-56.263L13.040-56.263Q13.201-56.239 13.201-56.095Q13.201-56.023 13.155-55.977Q13.109-55.931 13.040-55.921\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-24.58 1.711)\">\u003Cpath d=\"M19.771-53.576L16.798-53.576L16.798-53.856Q17.519-53.856 17.519-54.065L17.519-57.866Q17.519-58.077 16.798-58.077L16.798-58.358L19.556-58.358Q19.823-58.358 20.129-58.283Q20.434-58.207 20.694-58.060Q20.954-57.913 21.116-57.684Q21.279-57.455 21.279-57.161Q21.279-56.731 20.892-56.449Q20.506-56.167 20.024-56.075Q20.332-56.075 20.681-55.910Q21.029-55.746 21.258-55.468Q21.487-55.189 21.487-54.871Q21.487-54.465 21.224-54.171Q20.961-53.877 20.563-53.726Q20.164-53.576 19.771-53.576M18.162-55.948L18.162-54.065Q18.162-53.925 18.250-53.890Q18.339-53.856 18.527-53.856L19.556-53.856Q19.843-53.856 20.120-53.983Q20.397-54.109 20.568-54.343Q20.739-54.577 20.739-54.871Q20.739-55.087 20.657-55.283Q20.575-55.480 20.424-55.630Q20.274-55.781 20.077-55.864Q19.881-55.948 19.665-55.948L18.162-55.948M18.162-57.866L18.162-56.174L19.344-56.174Q19.631-56.174 19.912-56.293Q20.192-56.413 20.371-56.640Q20.551-56.868 20.551-57.161Q20.551-57.411 20.412-57.625Q20.274-57.838 20.045-57.958Q19.816-58.077 19.556-58.077L18.527-58.077Q18.339-58.077 18.250-58.043Q18.162-58.009 18.162-57.866M22.249-55.059Q22.249-55.401 22.384-55.700Q22.519-55.999 22.759-56.223Q22.998-56.447 23.316-56.572Q23.634-56.697 23.965-56.697Q24.410-56.697 24.809-56.481Q25.209-56.266 25.443-55.888Q25.678-55.511 25.678-55.059Q25.678-54.718 25.536-54.434Q25.394-54.150 25.150-53.943Q24.905-53.737 24.596-53.622Q24.287-53.508 23.965-53.508Q23.535-53.508 23.133-53.709Q22.731-53.911 22.490-54.263Q22.249-54.615 22.249-55.059M23.965-53.757Q24.567-53.757 24.791-54.135Q25.015-54.513 25.015-55.145Q25.015-55.757 24.780-56.116Q24.546-56.474 23.965-56.474Q22.912-56.474 22.912-55.145Q22.912-54.513 23.138-54.135Q23.364-53.757 23.965-53.757M26.272-53.583L26.272-54.646Q26.272-54.670 26.300-54.697Q26.327-54.724 26.351-54.724L26.460-54.724Q26.525-54.724 26.539-54.666Q26.635-54.232 26.881-53.981Q27.127-53.730 27.540-53.730Q27.882-53.730 28.135-53.863Q28.388-53.996 28.388-54.304Q28.388-54.461 28.294-54.576Q28.200-54.690 28.062-54.759Q27.923-54.827 27.756-54.865L27.175-54.964Q26.819-55.032 26.546-55.253Q26.272-55.473 26.272-55.815Q26.272-56.064 26.383-56.239Q26.495-56.413 26.681-56.512Q26.867-56.611 27.082-56.654Q27.298-56.697 27.540-56.697Q27.954-56.697 28.234-56.515L28.450-56.690Q28.460-56.693 28.467-56.695Q28.474-56.697 28.484-56.697L28.535-56.697Q28.562-56.697 28.586-56.673Q28.610-56.649 28.610-56.621L28.610-55.774Q28.610-55.753 28.586-55.726Q28.562-55.699 28.535-55.699L28.422-55.699Q28.395-55.699 28.369-55.724Q28.344-55.750 28.344-55.774Q28.344-56.010 28.238-56.174Q28.132-56.338 27.949-56.420Q27.766-56.502 27.534-56.502Q27.205-56.502 26.949-56.399Q26.693-56.297 26.693-56.020Q26.693-55.825 26.876-55.716Q27.058-55.606 27.287-55.565L27.862-55.459Q28.108-55.411 28.321-55.283Q28.535-55.155 28.672-54.952Q28.808-54.748 28.808-54.499Q28.808-53.986 28.443-53.747Q28.077-53.508 27.540-53.508Q27.045-53.508 26.713-53.802L26.447-53.528Q26.426-53.508 26.399-53.508L26.351-53.508Q26.327-53.508 26.300-53.535Q26.272-53.562 26.272-53.583M29.964-54.417L29.964-56.314L29.325-56.314L29.325-56.536Q29.642-56.536 29.860-56.746Q30.077-56.956 30.177-57.266Q30.278-57.575 30.278-57.883L30.545-57.883L30.545-56.594L31.621-56.594L31.621-56.314L30.545-56.314L30.545-54.430Q30.545-54.154 30.649-53.955Q30.753-53.757 31.013-53.757Q31.170-53.757 31.276-53.861Q31.382-53.966 31.432-54.119Q31.481-54.273 31.481-54.430L31.481-54.844L31.748-54.844L31.748-54.417Q31.748-54.191 31.649-53.981Q31.550-53.771 31.365-53.639Q31.181-53.508 30.952-53.508Q30.514-53.508 30.239-53.745Q29.964-53.983 29.964-54.417M32.517-55.059Q32.517-55.401 32.652-55.700Q32.787-55.999 33.026-56.223Q33.266-56.447 33.583-56.572Q33.901-56.697 34.233-56.697Q34.677-56.697 35.077-56.481Q35.477-56.266 35.711-55.888Q35.945-55.511 35.945-55.059Q35.945-54.718 35.803-54.434Q35.662-54.150 35.417-53.943Q35.173-53.737 34.863-53.622Q34.554-53.508 34.233-53.508Q33.802-53.508 33.401-53.709Q32.999-53.911 32.758-54.263Q32.517-54.615 32.517-55.059M34.233-53.757Q34.834-53.757 35.058-54.135Q35.282-54.513 35.282-55.145Q35.282-55.757 35.048-56.116Q34.814-56.474 34.233-56.474Q33.180-56.474 33.180-55.145Q33.180-54.513 33.406-54.135Q33.631-53.757 34.233-53.757M38.222-53.576L36.588-53.576L36.588-53.856Q36.817-53.856 36.965-53.890Q37.114-53.925 37.114-54.065L37.114-55.914Q37.114-56.184 37.006-56.245Q36.899-56.307 36.588-56.307L36.588-56.587L37.647-56.662L37.647-56.013Q37.818-56.321 38.122-56.492Q38.427-56.662 38.772-56.662Q39.278-56.662 39.561-56.439Q39.845-56.215 39.845-55.719L39.845-54.065Q39.845-53.928 39.994-53.892Q40.142-53.856 40.368-53.856L40.368-53.576L38.738-53.576L38.738-53.856Q38.967-53.856 39.115-53.890Q39.264-53.925 39.264-54.065L39.264-55.705Q39.264-56.040 39.144-56.240Q39.025-56.440 38.710-56.440Q38.440-56.440 38.206-56.304Q37.972-56.167 37.834-55.933Q37.695-55.699 37.695-55.425L37.695-54.065Q37.695-53.928 37.846-53.892Q37.996-53.856 38.222-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-65.403 10.443h113.81v-36.988h-113.81Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-25.119 51.916)\">\u003Cpath d=\"M-8.225-63.059Q-8.225-63.401-8.090-63.700Q-7.955-63.999-7.715-64.223Q-7.476-64.447-7.158-64.572Q-6.840-64.697-6.509-64.697Q-6.064-64.697-5.665-64.481Q-5.265-64.266-5.030-63.888Q-4.796-63.511-4.796-63.059Q-4.796-62.718-4.938-62.434Q-5.080-62.150-5.324-61.943Q-5.569-61.737-5.878-61.622Q-6.187-61.508-6.509-61.508Q-6.939-61.508-7.341-61.709Q-7.743-61.911-7.984-62.263Q-8.225-62.615-8.225-63.059M-6.509-61.757Q-5.907-61.757-5.683-62.135Q-5.459-62.513-5.459-63.145Q-5.459-63.757-5.694-64.116Q-5.928-64.474-6.509-64.474Q-7.561-64.474-7.561-63.145Q-7.561-62.513-7.336-62.135Q-7.110-61.757-6.509-61.757M-2.452-61.576L-4.188-61.576L-4.188-61.856Q-3.959-61.856-3.810-61.890Q-3.662-61.925-3.662-62.065L-3.662-63.914Q-3.662-64.184-3.769-64.245Q-3.877-64.307-4.188-64.307L-4.188-64.587L-3.159-64.662L-3.159-63.955Q-3.029-64.263-2.787-64.462Q-2.544-64.662-2.226-64.662Q-2.007-64.662-1.836-64.538Q-1.665-64.413-1.665-64.201Q-1.665-64.064-1.765-63.965Q-1.864-63.866-1.997-63.866Q-2.134-63.866-2.233-63.965Q-2.332-64.064-2.332-64.201Q-2.332-64.341-2.233-64.440Q-2.523-64.440-2.723-64.244Q-2.923-64.047-3.016-63.753Q-3.108-63.459-3.108-63.179L-3.108-62.065Q-3.108-61.856-2.452-61.856L-2.452-61.576M0.536-61.576L-1.016-61.576L-1.016-61.856Q-0.790-61.856-0.642-61.890Q-0.493-61.925-0.493-62.065L-0.493-63.914Q-0.493-64.102-0.541-64.186Q-0.589-64.269-0.686-64.288Q-0.784-64.307-0.996-64.307L-0.996-64.587L0.061-64.662L0.061-62.065Q0.061-61.925 0.192-61.890Q0.324-61.856 0.536-61.856L0.536-61.576M-0.736-65.883Q-0.736-66.054-0.613-66.173Q-0.490-66.293-0.319-66.293Q-0.151-66.293-0.028-66.173Q0.095-66.054 0.095-65.883Q0.095-65.708-0.028-65.585Q-0.151-65.462-0.319-65.462Q-0.490-65.462-0.613-65.585Q-0.736-65.708-0.736-65.883M1.141-61.043Q1.141-61.289 1.337-61.473Q1.534-61.658 1.790-61.737Q1.653-61.849 1.582-62.010Q1.510-62.171 1.510-62.352Q1.510-62.673 1.722-62.919Q1.387-63.217 1.387-63.627Q1.387-64.088 1.776-64.375Q2.166-64.662 2.645-64.662Q3.116-64.662 3.451-64.416Q3.626-64.570 3.836-64.652Q4.046-64.734 4.275-64.734Q4.439-64.734 4.560-64.627Q4.682-64.519 4.682-64.355Q4.682-64.259 4.610-64.187Q4.538-64.116 4.446-64.116Q4.347-64.116 4.277-64.189Q4.207-64.263 4.207-64.362Q4.207-64.416 4.220-64.447L4.227-64.461Q4.234-64.481 4.242-64.492Q4.251-64.502 4.254-64.509Q3.899-64.509 3.612-64.286Q3.899-63.993 3.899-63.627Q3.899-63.312 3.714-63.080Q3.530-62.847 3.241-62.719Q2.952-62.591 2.645-62.591Q2.443-62.591 2.252-62.641Q2.060-62.690 1.882-62.800Q1.790-62.673 1.790-62.530Q1.790-62.348 1.918-62.213Q2.046-62.078 2.231-62.078L2.863-62.078Q3.311-62.078 3.680-62.007Q4.049-61.935 4.309-61.706Q4.569-61.477 4.569-61.043Q4.569-60.722 4.273-60.520Q3.978-60.318 3.574-60.229Q3.171-60.140 2.856-60.140Q2.539-60.140 2.135-60.229Q1.732-60.318 1.436-60.520Q1.141-60.722 1.141-61.043M1.595-61.043Q1.595-60.814 1.814-60.665Q2.033-60.516 2.325-60.448Q2.617-60.380 2.856-60.380Q3.021-60.380 3.229-60.416Q3.438-60.451 3.644-60.532Q3.851-60.612 3.983-60.740Q4.114-60.868 4.114-61.043Q4.114-61.395 3.733-61.489Q3.352-61.583 2.850-61.583L2.231-61.583Q1.992-61.583 1.794-61.432Q1.595-61.282 1.595-61.043M2.645-62.830Q3.311-62.830 3.311-63.627Q3.311-64.427 2.645-64.427Q1.975-64.427 1.975-63.627Q1.975-62.830 2.645-62.830\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 51.916)\">\u003Cpath d=\"M13.040-62.383L8.207-62.383Q8.139-62.393 8.093-62.439Q8.047-62.485 8.047-62.557Q8.047-62.622 8.093-62.668Q8.139-62.714 8.207-62.724L13.040-62.724Q13.109-62.714 13.155-62.668Q13.201-62.622 13.201-62.557Q13.201-62.485 13.155-62.439Q13.109-62.393 13.040-62.383M13.040-63.921L8.207-63.921Q8.139-63.931 8.093-63.977Q8.047-64.023 8.047-64.095Q8.047-64.239 8.207-64.263L13.040-64.263Q13.201-64.239 13.201-64.095Q13.201-64.023 13.155-63.977Q13.109-63.931 13.040-63.921\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 51.916)\">\u003Cpath d=\"M19.771-61.576L16.798-61.576L16.798-61.856Q17.519-61.856 17.519-62.065L17.519-65.866Q17.519-66.077 16.798-66.077L16.798-66.358L19.556-66.358Q19.823-66.358 20.129-66.283Q20.434-66.207 20.694-66.060Q20.954-65.913 21.116-65.684Q21.279-65.455 21.279-65.161Q21.279-64.731 20.892-64.449Q20.506-64.167 20.024-64.075Q20.332-64.075 20.681-63.910Q21.029-63.746 21.258-63.468Q21.487-63.189 21.487-62.871Q21.487-62.465 21.224-62.171Q20.961-61.877 20.563-61.726Q20.164-61.576 19.771-61.576M18.162-63.948L18.162-62.065Q18.162-61.925 18.250-61.890Q18.339-61.856 18.527-61.856L19.556-61.856Q19.843-61.856 20.120-61.983Q20.397-62.109 20.568-62.343Q20.739-62.577 20.739-62.871Q20.739-63.087 20.657-63.283Q20.575-63.480 20.424-63.630Q20.274-63.781 20.077-63.864Q19.881-63.948 19.665-63.948L18.162-63.948M18.162-65.866L18.162-64.174L19.344-64.174Q19.631-64.174 19.912-64.293Q20.192-64.413 20.371-64.640Q20.551-64.868 20.551-65.161Q20.551-65.411 20.412-65.625Q20.274-65.838 20.045-65.958Q19.816-66.077 19.556-66.077L18.527-66.077Q18.339-66.077 18.250-66.043Q18.162-66.009 18.162-65.866M22.249-63.059Q22.249-63.401 22.384-63.700Q22.519-63.999 22.759-64.223Q22.998-64.447 23.316-64.572Q23.634-64.697 23.965-64.697Q24.410-64.697 24.809-64.481Q25.209-64.266 25.443-63.888Q25.678-63.511 25.678-63.059Q25.678-62.718 25.536-62.434Q25.394-62.150 25.150-61.943Q24.905-61.737 24.596-61.622Q24.287-61.508 23.965-61.508Q23.535-61.508 23.133-61.709Q22.731-61.911 22.490-62.263Q22.249-62.615 22.249-63.059M23.965-61.757Q24.567-61.757 24.791-62.135Q25.015-62.513 25.015-63.145Q25.015-63.757 24.780-64.116Q24.546-64.474 23.965-64.474Q22.912-64.474 22.912-63.145Q22.912-62.513 23.138-62.135Q23.364-61.757 23.965-61.757M26.272-61.583L26.272-62.646Q26.272-62.670 26.300-62.697Q26.327-62.724 26.351-62.724L26.460-62.724Q26.525-62.724 26.539-62.666Q26.635-62.232 26.881-61.981Q27.127-61.730 27.540-61.730Q27.882-61.730 28.135-61.863Q28.388-61.996 28.388-62.304Q28.388-62.461 28.294-62.576Q28.200-62.690 28.062-62.759Q27.923-62.827 27.756-62.865L27.175-62.964Q26.819-63.032 26.546-63.253Q26.272-63.473 26.272-63.815Q26.272-64.064 26.383-64.239Q26.495-64.413 26.681-64.512Q26.867-64.611 27.082-64.654Q27.298-64.697 27.540-64.697Q27.954-64.697 28.234-64.515L28.450-64.690Q28.460-64.693 28.467-64.695Q28.474-64.697 28.484-64.697L28.535-64.697Q28.562-64.697 28.586-64.673Q28.610-64.649 28.610-64.621L28.610-63.774Q28.610-63.753 28.586-63.726Q28.562-63.699 28.535-63.699L28.422-63.699Q28.395-63.699 28.369-63.724Q28.344-63.750 28.344-63.774Q28.344-64.010 28.238-64.174Q28.132-64.338 27.949-64.420Q27.766-64.502 27.534-64.502Q27.205-64.502 26.949-64.399Q26.693-64.297 26.693-64.020Q26.693-63.825 26.876-63.716Q27.058-63.606 27.287-63.565L27.862-63.459Q28.108-63.411 28.321-63.283Q28.535-63.155 28.672-62.952Q28.808-62.748 28.808-62.499Q28.808-61.986 28.443-61.747Q28.077-61.508 27.540-61.508Q27.045-61.508 26.713-61.802L26.447-61.528Q26.426-61.508 26.399-61.508L26.351-61.508Q26.327-61.508 26.300-61.535Q26.272-61.562 26.272-61.583M29.964-62.417L29.964-64.314L29.325-64.314L29.325-64.536Q29.642-64.536 29.860-64.746Q30.077-64.956 30.177-65.266Q30.278-65.575 30.278-65.883L30.545-65.883L30.545-64.594L31.621-64.594L31.621-64.314L30.545-64.314L30.545-62.430Q30.545-62.154 30.649-61.955Q30.753-61.757 31.013-61.757Q31.170-61.757 31.276-61.861Q31.382-61.966 31.432-62.119Q31.481-62.273 31.481-62.430L31.481-62.844L31.748-62.844L31.748-62.417Q31.748-62.191 31.649-61.981Q31.550-61.771 31.365-61.639Q31.181-61.508 30.952-61.508Q30.514-61.508 30.239-61.745Q29.964-61.983 29.964-62.417M32.517-63.059Q32.517-63.401 32.652-63.700Q32.787-63.999 33.026-64.223Q33.266-64.447 33.583-64.572Q33.901-64.697 34.233-64.697Q34.677-64.697 35.077-64.481Q35.477-64.266 35.711-63.888Q35.945-63.511 35.945-63.059Q35.945-62.718 35.803-62.434Q35.662-62.150 35.417-61.943Q35.173-61.737 34.863-61.622Q34.554-61.508 34.233-61.508Q33.802-61.508 33.401-61.709Q32.999-61.911 32.758-62.263Q32.517-62.615 32.517-63.059M34.233-61.757Q34.834-61.757 35.058-62.135Q35.282-62.513 35.282-63.145Q35.282-63.757 35.048-64.116Q34.814-64.474 34.233-64.474Q33.180-64.474 33.180-63.145Q33.180-62.513 33.406-62.135Q33.631-61.757 34.233-61.757M38.222-61.576L36.588-61.576L36.588-61.856Q36.817-61.856 36.965-61.890Q37.114-61.925 37.114-62.065L37.114-63.914Q37.114-64.184 37.006-64.245Q36.899-64.307 36.588-64.307L36.588-64.587L37.647-64.662L37.647-64.013Q37.818-64.321 38.122-64.492Q38.427-64.662 38.772-64.662Q39.278-64.662 39.561-64.439Q39.845-64.215 39.845-63.719L39.845-62.065Q39.845-61.928 39.994-61.892Q40.142-61.856 40.368-61.856L40.368-61.576L38.738-61.576L38.738-61.856Q38.967-61.856 39.115-61.890Q39.264-61.925 39.264-62.065L39.264-63.705Q39.264-64.040 39.144-64.240Q39.025-64.440 38.710-64.440Q38.440-64.440 38.206-64.304Q37.972-64.167 37.834-63.933Q37.695-63.699 37.695-63.425L37.695-62.065Q37.695-61.928 37.846-61.892Q37.996-61.856 38.222-61.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 51.916)\">\u003Cpath d=\"M-8.184-55.087Q-8.184-55.425-8.043-55.716Q-7.903-56.006-7.659-56.220Q-7.415-56.433-7.110-56.548Q-6.806-56.662-6.481-56.662Q-6.211-56.662-5.948-56.563Q-5.685-56.464-5.494-56.286L-5.494-57.684Q-5.494-57.954-5.601-58.016Q-5.709-58.077-6.020-58.077L-6.020-58.358L-4.943-58.433L-4.943-54.249Q-4.943-54.061-4.889-53.978Q-4.834-53.894-4.733-53.875Q-4.632-53.856-4.417-53.856L-4.417-53.576L-5.524-53.508L-5.524-53.925Q-5.941-53.508-6.567-53.508Q-6.998-53.508-7.370-53.720Q-7.743-53.931-7.963-54.292Q-8.184-54.653-8.184-55.087M-6.509-53.730Q-6.300-53.730-6.114-53.802Q-5.928-53.873-5.774-54.010Q-5.620-54.147-5.524-54.325L-5.524-55.934Q-5.610-56.081-5.755-56.201Q-5.900-56.321-6.070-56.380Q-6.239-56.440-6.420-56.440Q-6.980-56.440-7.249-56.051Q-7.517-55.661-7.517-55.080Q-7.517-54.509-7.283-54.119Q-7.049-53.730-6.509-53.730M-3.809-55.111Q-3.809-55.432-3.684-55.721Q-3.559-56.010-3.333-56.233Q-3.108-56.457-2.812-56.577Q-2.517-56.697-2.199-56.697Q-1.871-56.697-1.609-56.597Q-1.348-56.498-1.172-56.316Q-0.996-56.133-0.902-55.875Q-0.808-55.617-0.808-55.285Q-0.808-55.193-0.890-55.172L-3.145-55.172L-3.145-55.111Q-3.145-54.523-2.862-54.140Q-2.578-53.757-2.011-53.757Q-1.689-53.757-1.421-53.950Q-1.153-54.143-1.064-54.458Q-1.057-54.499-0.982-54.513L-0.890-54.513Q-0.808-54.489-0.808-54.417Q-0.808-54.410-0.814-54.383Q-0.927-53.986-1.298-53.747Q-1.669-53.508-2.093-53.508Q-2.530-53.508-2.930-53.716Q-3.330-53.925-3.569-54.292Q-3.809-54.659-3.809-55.111M-3.139-55.381L-1.324-55.381Q-1.324-55.658-1.421-55.910Q-1.519-56.163-1.717-56.319Q-1.915-56.474-2.199-56.474Q-2.476-56.474-2.689-56.316Q-2.903-56.157-3.021-55.902Q-3.139-55.647-3.139-55.381M-0.220-53.583L-0.220-54.646Q-0.220-54.670-0.192-54.697Q-0.165-54.724-0.141-54.724L-0.032-54.724Q0.033-54.724 0.047-54.666Q0.143-54.232 0.389-53.981Q0.635-53.730 1.048-53.730Q1.390-53.730 1.643-53.863Q1.896-53.996 1.896-54.304Q1.896-54.461 1.802-54.576Q1.708-54.690 1.570-54.759Q1.431-54.827 1.264-54.865L0.683-54.964Q0.327-55.032 0.054-55.253Q-0.220-55.473-0.220-55.815Q-0.220-56.064-0.109-56.239Q0.002-56.413 0.189-56.512Q0.375-56.611 0.590-56.654Q0.806-56.697 1.048-56.697Q1.462-56.697 1.742-56.515L1.958-56.690Q1.968-56.693 1.975-56.695Q1.981-56.697 1.992-56.697L2.043-56.697Q2.070-56.697 2.094-56.673Q2.118-56.649 2.118-56.621L2.118-55.774Q2.118-55.753 2.094-55.726Q2.070-55.699 2.043-55.699L1.930-55.699Q1.903-55.699 1.877-55.724Q1.852-55.750 1.852-55.774Q1.852-56.010 1.746-56.174Q1.640-56.338 1.457-56.420Q1.274-56.502 1.042-56.502Q0.713-56.502 0.457-56.399Q0.201-56.297 0.201-56.020Q0.201-55.825 0.384-55.716Q0.566-55.606 0.795-55.565L1.370-55.459Q1.616-55.411 1.829-55.283Q2.043-55.155 2.180-54.952Q2.316-54.748 2.316-54.499Q2.316-53.986 1.951-53.747Q1.585-53.508 1.048-53.508Q0.553-53.508 0.221-53.802L-0.045-53.528Q-0.066-53.508-0.093-53.508L-0.141-53.508Q-0.165-53.508-0.192-53.535Q-0.220-53.562-0.220-53.583M3.472-54.417L3.472-56.314L2.833-56.314L2.833-56.536Q3.150-56.536 3.367-56.746Q3.585-56.956 3.685-57.266Q3.786-57.575 3.786-57.883L4.053-57.883L4.053-56.594L5.129-56.594L5.129-56.314L4.053-56.314L4.053-54.430Q4.053-54.154 4.157-53.955Q4.261-53.757 4.521-53.757Q4.678-53.757 4.784-53.861Q4.890-53.966 4.940-54.119Q4.989-54.273 4.989-54.430L4.989-54.844L5.256-54.844L5.256-54.417Q5.256-54.191 5.157-53.981Q5.058-53.771 4.873-53.639Q4.689-53.508 4.460-53.508Q4.022-53.508 3.747-53.745Q3.472-53.983 3.472-54.417\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 51.916)\">\u003Cpath d=\"M13.944-54.383L9.111-54.383Q9.043-54.393 8.997-54.439Q8.951-54.485 8.951-54.557Q8.951-54.622 8.997-54.668Q9.043-54.714 9.111-54.724L13.944-54.724Q14.013-54.714 14.059-54.668Q14.105-54.622 14.105-54.557Q14.105-54.485 14.059-54.439Q14.013-54.393 13.944-54.383M13.944-55.921L9.111-55.921Q9.043-55.931 8.997-55.977Q8.951-56.023 8.951-56.095Q8.951-56.239 9.111-56.263L13.944-56.263Q14.105-56.239 14.105-56.095Q14.105-56.023 14.059-55.977Q14.013-55.931 13.944-55.921\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 51.916)\">\u003Cpath d=\"M20.460-53.576L17.688-53.576L17.688-53.856Q18.409-53.856 18.409-54.065L18.409-57.866Q18.409-58.077 17.688-58.077L17.688-58.358L20.460-58.358Q20.945-58.358 21.381-58.163Q21.817-57.968 22.140-57.626Q22.463-57.284 22.641-56.844Q22.818-56.403 22.818-55.921Q22.818-55.435 22.634-55.012Q22.449-54.588 22.126-54.266Q21.803-53.945 21.371-53.761Q20.939-53.576 20.460-53.576M19.072-57.866L19.072-54.065Q19.072-53.925 19.161-53.890Q19.250-53.856 19.438-53.856L20.262-53.856Q20.887-53.856 21.291-54.118Q21.694-54.379 21.882-54.846Q22.070-55.312 22.070-55.921Q22.070-56.399 21.978-56.792Q21.885-57.185 21.636-57.483Q21.397-57.770 21.033-57.924Q20.669-58.077 20.262-58.077L19.438-58.077Q19.250-58.077 19.161-58.043Q19.072-58.009 19.072-57.866M23.587-55.111Q23.587-55.432 23.712-55.721Q23.837-56.010 24.063-56.233Q24.288-56.457 24.584-56.577Q24.879-56.697 25.197-56.697Q25.525-56.697 25.787-56.597Q26.048-56.498 26.224-56.316Q26.400-56.133 26.494-55.875Q26.588-55.617 26.588-55.285Q26.588-55.193 26.506-55.172L24.251-55.172L24.251-55.111Q24.251-54.523 24.534-54.140Q24.818-53.757 25.385-53.757Q25.707-53.757 25.975-53.950Q26.243-54.143 26.332-54.458Q26.339-54.499 26.414-54.513L26.506-54.513Q26.588-54.489 26.588-54.417Q26.588-54.410 26.582-54.383Q26.469-53.986 26.098-53.747Q25.727-53.508 25.303-53.508Q24.866-53.508 24.466-53.716Q24.066-53.925 23.827-54.292Q23.587-54.659 23.587-55.111M24.257-55.381L26.072-55.381Q26.072-55.658 25.975-55.910Q25.878-56.163 25.679-56.319Q25.481-56.474 25.197-56.474Q24.920-56.474 24.707-56.316Q24.493-56.157 24.375-55.902Q24.257-55.647 24.257-55.381M28.858-53.576L27.224-53.576L27.224-53.856Q27.453-53.856 27.602-53.890Q27.751-53.925 27.751-54.065L27.751-55.914Q27.751-56.184 27.643-56.245Q27.535-56.307 27.224-56.307L27.224-56.587L28.284-56.662L28.284-56.013Q28.455-56.321 28.759-56.492Q29.063-56.662 29.408-56.662Q29.914-56.662 30.198-56.439Q30.482-56.215 30.482-55.719L30.482-54.065Q30.482-53.928 30.630-53.892Q30.779-53.856 31.004-53.856L31.004-53.576L29.374-53.576L29.374-53.856Q29.603-53.856 29.752-53.890Q29.900-53.925 29.900-54.065L29.900-55.705Q29.900-56.040 29.781-56.240Q29.661-56.440 29.347-56.440Q29.077-56.440 28.843-56.304Q28.608-56.167 28.470-55.933Q28.332-55.699 28.332-55.425L28.332-54.065Q28.332-53.928 28.482-53.892Q28.632-53.856 28.858-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 51.916)\">\u003Cpath d=\"M32.977-53.603L31.849-56.102Q31.777-56.249 31.647-56.281Q31.517-56.314 31.288-56.314L31.288-56.594L32.802-56.594L32.802-56.314Q32.450-56.314 32.450-56.167Q32.450-56.122 32.461-56.102L33.325-54.184L34.105-55.914Q34.139-55.982 34.139-56.061Q34.139-56.174 34.055-56.244Q33.971-56.314 33.852-56.314L33.852-56.594L35.048-56.594L35.048-56.314Q34.829-56.314 34.658-56.211Q34.488-56.109 34.399-55.914L33.363-53.603Q33.315-53.508 33.209-53.508L33.131-53.508Q33.025-53.508 32.977-53.603\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 51.916)\">\u003Cpath d=\"M35.332-55.111Q35.332-55.432 35.457-55.721Q35.582-56.010 35.808-56.233Q36.033-56.457 36.329-56.577Q36.624-56.697 36.942-56.697Q37.270-56.697 37.532-56.597Q37.793-56.498 37.969-56.316Q38.145-56.133 38.239-55.875Q38.333-55.617 38.333-55.285Q38.333-55.193 38.251-55.172L35.996-55.172L35.996-55.111Q35.996-54.523 36.279-54.140Q36.563-53.757 37.130-53.757Q37.452-53.757 37.720-53.950Q37.988-54.143 38.077-54.458Q38.084-54.499 38.159-54.513L38.251-54.513Q38.333-54.489 38.333-54.417Q38.333-54.410 38.327-54.383Q38.214-53.986 37.843-53.747Q37.472-53.508 37.048-53.508Q36.611-53.508 36.211-53.716Q35.811-53.925 35.572-54.292Q35.332-54.659 35.332-55.111M36.002-55.381L37.817-55.381Q37.817-55.658 37.720-55.910Q37.622-56.163 37.424-56.319Q37.226-56.474 36.942-56.474Q36.665-56.474 36.452-56.316Q36.238-56.157 36.120-55.902Q36.002-55.647 36.002-55.381M40.671-53.576L38.935-53.576L38.935-53.856Q39.164-53.856 39.313-53.890Q39.461-53.925 39.461-54.065L39.461-55.914Q39.461-56.184 39.354-56.245Q39.246-56.307 38.935-56.307L38.935-56.587L39.964-56.662L39.964-55.955Q40.094-56.263 40.336-56.462Q40.579-56.662 40.897-56.662Q41.116-56.662 41.287-56.538Q41.457-56.413 41.457-56.201Q41.457-56.064 41.358-55.965Q41.259-55.866 41.126-55.866Q40.989-55.866 40.890-55.965Q40.791-56.064 40.791-56.201Q40.791-56.341 40.890-56.440Q40.600-56.440 40.400-56.244Q40.200-56.047 40.107-55.753Q40.015-55.459 40.015-55.179L40.015-54.065Q40.015-53.856 40.671-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-65.403 64.503h113.81V27.515h-113.81Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-25.119 105.976)\">\u003Cpath d=\"M-8.225-63.059Q-8.225-63.401-8.090-63.700Q-7.955-63.999-7.715-64.223Q-7.476-64.447-7.158-64.572Q-6.840-64.697-6.509-64.697Q-6.064-64.697-5.665-64.481Q-5.265-64.266-5.030-63.888Q-4.796-63.511-4.796-63.059Q-4.796-62.718-4.938-62.434Q-5.080-62.150-5.324-61.943Q-5.569-61.737-5.878-61.622Q-6.187-61.508-6.509-61.508Q-6.939-61.508-7.341-61.709Q-7.743-61.911-7.984-62.263Q-8.225-62.615-8.225-63.059M-6.509-61.757Q-5.907-61.757-5.683-62.135Q-5.459-62.513-5.459-63.145Q-5.459-63.757-5.694-64.116Q-5.928-64.474-6.509-64.474Q-7.561-64.474-7.561-63.145Q-7.561-62.513-7.336-62.135Q-7.110-61.757-6.509-61.757M-2.452-61.576L-4.188-61.576L-4.188-61.856Q-3.959-61.856-3.810-61.890Q-3.662-61.925-3.662-62.065L-3.662-63.914Q-3.662-64.184-3.769-64.245Q-3.877-64.307-4.188-64.307L-4.188-64.587L-3.159-64.662L-3.159-63.955Q-3.029-64.263-2.787-64.462Q-2.544-64.662-2.226-64.662Q-2.007-64.662-1.836-64.538Q-1.665-64.413-1.665-64.201Q-1.665-64.064-1.765-63.965Q-1.864-63.866-1.997-63.866Q-2.134-63.866-2.233-63.965Q-2.332-64.064-2.332-64.201Q-2.332-64.341-2.233-64.440Q-2.523-64.440-2.723-64.244Q-2.923-64.047-3.016-63.753Q-3.108-63.459-3.108-63.179L-3.108-62.065Q-3.108-61.856-2.452-61.856L-2.452-61.576M0.536-61.576L-1.016-61.576L-1.016-61.856Q-0.790-61.856-0.642-61.890Q-0.493-61.925-0.493-62.065L-0.493-63.914Q-0.493-64.102-0.541-64.186Q-0.589-64.269-0.686-64.288Q-0.784-64.307-0.996-64.307L-0.996-64.587L0.061-64.662L0.061-62.065Q0.061-61.925 0.192-61.890Q0.324-61.856 0.536-61.856L0.536-61.576M-0.736-65.883Q-0.736-66.054-0.613-66.173Q-0.490-66.293-0.319-66.293Q-0.151-66.293-0.028-66.173Q0.095-66.054 0.095-65.883Q0.095-65.708-0.028-65.585Q-0.151-65.462-0.319-65.462Q-0.490-65.462-0.613-65.585Q-0.736-65.708-0.736-65.883M1.141-61.043Q1.141-61.289 1.337-61.473Q1.534-61.658 1.790-61.737Q1.653-61.849 1.582-62.010Q1.510-62.171 1.510-62.352Q1.510-62.673 1.722-62.919Q1.387-63.217 1.387-63.627Q1.387-64.088 1.776-64.375Q2.166-64.662 2.645-64.662Q3.116-64.662 3.451-64.416Q3.626-64.570 3.836-64.652Q4.046-64.734 4.275-64.734Q4.439-64.734 4.560-64.627Q4.682-64.519 4.682-64.355Q4.682-64.259 4.610-64.187Q4.538-64.116 4.446-64.116Q4.347-64.116 4.277-64.189Q4.207-64.263 4.207-64.362Q4.207-64.416 4.220-64.447L4.227-64.461Q4.234-64.481 4.242-64.492Q4.251-64.502 4.254-64.509Q3.899-64.509 3.612-64.286Q3.899-63.993 3.899-63.627Q3.899-63.312 3.714-63.080Q3.530-62.847 3.241-62.719Q2.952-62.591 2.645-62.591Q2.443-62.591 2.252-62.641Q2.060-62.690 1.882-62.800Q1.790-62.673 1.790-62.530Q1.790-62.348 1.918-62.213Q2.046-62.078 2.231-62.078L2.863-62.078Q3.311-62.078 3.680-62.007Q4.049-61.935 4.309-61.706Q4.569-61.477 4.569-61.043Q4.569-60.722 4.273-60.520Q3.978-60.318 3.574-60.229Q3.171-60.140 2.856-60.140Q2.539-60.140 2.135-60.229Q1.732-60.318 1.436-60.520Q1.141-60.722 1.141-61.043M1.595-61.043Q1.595-60.814 1.814-60.665Q2.033-60.516 2.325-60.448Q2.617-60.380 2.856-60.380Q3.021-60.380 3.229-60.416Q3.438-60.451 3.644-60.532Q3.851-60.612 3.983-60.740Q4.114-60.868 4.114-61.043Q4.114-61.395 3.733-61.489Q3.352-61.583 2.850-61.583L2.231-61.583Q1.992-61.583 1.794-61.432Q1.595-61.282 1.595-61.043M2.645-62.830Q3.311-62.830 3.311-63.627Q3.311-64.427 2.645-64.427Q1.975-64.427 1.975-63.627Q1.975-62.830 2.645-62.830\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 105.976)\">\u003Cpath d=\"M13.040-62.383L8.207-62.383Q8.139-62.393 8.093-62.439Q8.047-62.485 8.047-62.557Q8.047-62.622 8.093-62.668Q8.139-62.714 8.207-62.724L13.040-62.724Q13.109-62.714 13.155-62.668Q13.201-62.622 13.201-62.557Q13.201-62.485 13.155-62.439Q13.109-62.393 13.040-62.383M13.040-63.921L8.207-63.921Q8.139-63.931 8.093-63.977Q8.047-64.023 8.047-64.095Q8.047-64.239 8.207-64.263L13.040-64.263Q13.201-64.239 13.201-64.095Q13.201-64.023 13.155-63.977Q13.109-63.931 13.040-63.921\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 105.976)\">\u003Cpath d=\"M18.295-61.576L16.705-61.576L16.705-61.856Q17.348-61.856 17.505-62.256L19.149-66.471Q19.183-66.566 19.296-66.566L19.378-66.566Q19.488-66.566 19.529-66.471L21.248-62.065Q21.316-61.925 21.506-61.890Q21.696-61.856 21.969-61.856L21.969-61.576L19.970-61.576L19.970-61.856Q20.534-61.856 20.534-62.031Q20.534-62.048 20.532-62.055Q20.530-62.061 20.527-62.065L20.106-63.131L18.148-63.131L17.806-62.256Q17.792-62.256 17.792-62.178Q17.792-62.017 17.955-61.937Q18.117-61.856 18.295-61.856L18.295-61.576M19.129-65.650L18.261-63.411L20.004-63.411L19.129-65.650M23.179-62.410L23.179-63.914Q23.179-64.184 23.071-64.245Q22.964-64.307 22.653-64.307L22.653-64.587L23.760-64.662L23.760-62.430L23.760-62.410Q23.760-62.130 23.811-61.986Q23.863-61.843 24.005-61.786Q24.146-61.730 24.433-61.730Q24.686-61.730 24.891-61.870Q25.097-62.010 25.213-62.236Q25.329-62.461 25.329-62.711L25.329-63.914Q25.329-64.184 25.221-64.245Q25.114-64.307 24.803-64.307L24.803-64.587L25.910-64.662L25.910-62.249Q25.910-62.058 25.963-61.976Q26.016-61.894 26.117-61.875Q26.218-61.856 26.433-61.856L26.433-61.576L25.356-61.508L25.356-62.072Q25.247-61.890 25.102-61.767Q24.956-61.644 24.770-61.576Q24.584-61.508 24.382-61.508Q23.179-61.508 23.179-62.410M27.021-61.583L27.021-62.646Q27.021-62.670 27.048-62.697Q27.076-62.724 27.099-62.724L27.209-62.724Q27.274-62.724 27.287-62.666Q27.383-62.232 27.629-61.981Q27.875-61.730 28.289-61.730Q28.631-61.730 28.884-61.863Q29.137-61.996 29.137-62.304Q29.137-62.461 29.043-62.576Q28.949-62.690 28.810-62.759Q28.672-62.827 28.504-62.865L27.923-62.964Q27.568-63.032 27.294-63.253Q27.021-63.473 27.021-63.815Q27.021-64.064 27.132-64.239Q27.243-64.413 27.429-64.512Q27.616-64.611 27.831-64.654Q28.046-64.697 28.289-64.697Q28.703-64.697 28.983-64.515L29.198-64.690Q29.208-64.693 29.215-64.695Q29.222-64.697 29.232-64.697L29.284-64.697Q29.311-64.697 29.335-64.673Q29.359-64.649 29.359-64.621L29.359-63.774Q29.359-63.753 29.335-63.726Q29.311-63.699 29.284-63.699L29.171-63.699Q29.143-63.699 29.118-63.724Q29.092-63.750 29.092-63.774Q29.092-64.010 28.986-64.174Q28.880-64.338 28.697-64.420Q28.515-64.502 28.282-64.502Q27.954-64.502 27.698-64.399Q27.441-64.297 27.441-64.020Q27.441-63.825 27.624-63.716Q27.807-63.606 28.036-63.565L28.610-63.459Q28.856-63.411 29.070-63.283Q29.284-63.155 29.420-62.952Q29.557-62.748 29.557-62.499Q29.557-61.986 29.191-61.747Q28.826-61.508 28.289-61.508Q27.793-61.508 27.462-61.802L27.195-61.528Q27.175-61.508 27.147-61.508L27.099-61.508Q27.076-61.508 27.048-61.535Q27.021-61.562 27.021-61.583M30.712-62.417L30.712-64.314L30.073-64.314L30.073-64.536Q30.391-64.536 30.608-64.746Q30.825-64.956 30.926-65.266Q31.027-65.575 31.027-65.883L31.293-65.883L31.293-64.594L32.370-64.594L32.370-64.314L31.293-64.314L31.293-62.430Q31.293-62.154 31.398-61.955Q31.502-61.757 31.762-61.757Q31.919-61.757 32.025-61.861Q32.131-61.966 32.180-62.119Q32.230-62.273 32.230-62.430L32.230-62.844L32.496-62.844L32.496-62.417Q32.496-62.191 32.397-61.981Q32.298-61.771 32.114-61.639Q31.929-61.508 31.700-61.508Q31.263-61.508 30.987-61.745Q30.712-61.983 30.712-62.417M34.923-61.576L33.371-61.576L33.371-61.856Q33.597-61.856 33.746-61.890Q33.894-61.925 33.894-62.065L33.894-63.914Q33.894-64.102 33.847-64.186Q33.799-64.269 33.701-64.288Q33.604-64.307 33.392-64.307L33.392-64.587L34.448-64.662L34.448-62.065Q34.448-61.925 34.580-61.890Q34.711-61.856 34.923-61.856L34.923-61.576M33.652-65.883Q33.652-66.054 33.775-66.173Q33.898-66.293 34.069-66.293Q34.236-66.293 34.359-66.173Q34.482-66.054 34.482-65.883Q34.482-65.708 34.359-65.585Q34.236-65.462 34.069-65.462Q33.898-65.462 33.775-65.585Q33.652-65.708 33.652-65.883M37.251-61.576L35.617-61.576L35.617-61.856Q35.846-61.856 35.995-61.890Q36.143-61.925 36.143-62.065L36.143-63.914Q36.143-64.184 36.036-64.245Q35.928-64.307 35.617-64.307L35.617-64.587L36.677-64.662L36.677-64.013Q36.848-64.321 37.152-64.492Q37.456-64.662 37.801-64.662Q38.307-64.662 38.591-64.439Q38.874-64.215 38.874-63.719L38.874-62.065Q38.874-61.928 39.023-61.892Q39.172-61.856 39.397-61.856L39.397-61.576L37.767-61.576L37.767-61.856Q37.996-61.856 38.145-61.890Q38.293-61.925 38.293-62.065L38.293-63.705Q38.293-64.040 38.174-64.240Q38.054-64.440 37.740-64.440Q37.470-64.440 37.235-64.304Q37.001-64.167 36.863-63.933Q36.724-63.699 36.724-63.425L36.724-62.065Q36.724-61.928 36.875-61.892Q37.025-61.856 37.251-61.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 105.976)\">\u003Cpath d=\"M-8.184-55.087Q-8.184-55.425-8.043-55.716Q-7.903-56.006-7.659-56.220Q-7.415-56.433-7.110-56.548Q-6.806-56.662-6.481-56.662Q-6.211-56.662-5.948-56.563Q-5.685-56.464-5.494-56.286L-5.494-57.684Q-5.494-57.954-5.601-58.016Q-5.709-58.077-6.020-58.077L-6.020-58.358L-4.943-58.433L-4.943-54.249Q-4.943-54.061-4.889-53.978Q-4.834-53.894-4.733-53.875Q-4.632-53.856-4.417-53.856L-4.417-53.576L-5.524-53.508L-5.524-53.925Q-5.941-53.508-6.567-53.508Q-6.998-53.508-7.370-53.720Q-7.743-53.931-7.963-54.292Q-8.184-54.653-8.184-55.087M-6.509-53.730Q-6.300-53.730-6.114-53.802Q-5.928-53.873-5.774-54.010Q-5.620-54.147-5.524-54.325L-5.524-55.934Q-5.610-56.081-5.755-56.201Q-5.900-56.321-6.070-56.380Q-6.239-56.440-6.420-56.440Q-6.980-56.440-7.249-56.051Q-7.517-55.661-7.517-55.080Q-7.517-54.509-7.283-54.119Q-7.049-53.730-6.509-53.730M-3.809-55.111Q-3.809-55.432-3.684-55.721Q-3.559-56.010-3.333-56.233Q-3.108-56.457-2.812-56.577Q-2.517-56.697-2.199-56.697Q-1.871-56.697-1.609-56.597Q-1.348-56.498-1.172-56.316Q-0.996-56.133-0.902-55.875Q-0.808-55.617-0.808-55.285Q-0.808-55.193-0.890-55.172L-3.145-55.172L-3.145-55.111Q-3.145-54.523-2.862-54.140Q-2.578-53.757-2.011-53.757Q-1.689-53.757-1.421-53.950Q-1.153-54.143-1.064-54.458Q-1.057-54.499-0.982-54.513L-0.890-54.513Q-0.808-54.489-0.808-54.417Q-0.808-54.410-0.814-54.383Q-0.927-53.986-1.298-53.747Q-1.669-53.508-2.093-53.508Q-2.530-53.508-2.930-53.716Q-3.330-53.925-3.569-54.292Q-3.809-54.659-3.809-55.111M-3.139-55.381L-1.324-55.381Q-1.324-55.658-1.421-55.910Q-1.519-56.163-1.717-56.319Q-1.915-56.474-2.199-56.474Q-2.476-56.474-2.689-56.316Q-2.903-56.157-3.021-55.902Q-3.139-55.647-3.139-55.381M-0.220-53.583L-0.220-54.646Q-0.220-54.670-0.192-54.697Q-0.165-54.724-0.141-54.724L-0.032-54.724Q0.033-54.724 0.047-54.666Q0.143-54.232 0.389-53.981Q0.635-53.730 1.048-53.730Q1.390-53.730 1.643-53.863Q1.896-53.996 1.896-54.304Q1.896-54.461 1.802-54.576Q1.708-54.690 1.570-54.759Q1.431-54.827 1.264-54.865L0.683-54.964Q0.327-55.032 0.054-55.253Q-0.220-55.473-0.220-55.815Q-0.220-56.064-0.109-56.239Q0.002-56.413 0.189-56.512Q0.375-56.611 0.590-56.654Q0.806-56.697 1.048-56.697Q1.462-56.697 1.742-56.515L1.958-56.690Q1.968-56.693 1.975-56.695Q1.981-56.697 1.992-56.697L2.043-56.697Q2.070-56.697 2.094-56.673Q2.118-56.649 2.118-56.621L2.118-55.774Q2.118-55.753 2.094-55.726Q2.070-55.699 2.043-55.699L1.930-55.699Q1.903-55.699 1.877-55.724Q1.852-55.750 1.852-55.774Q1.852-56.010 1.746-56.174Q1.640-56.338 1.457-56.420Q1.274-56.502 1.042-56.502Q0.713-56.502 0.457-56.399Q0.201-56.297 0.201-56.020Q0.201-55.825 0.384-55.716Q0.566-55.606 0.795-55.565L1.370-55.459Q1.616-55.411 1.829-55.283Q2.043-55.155 2.180-54.952Q2.316-54.748 2.316-54.499Q2.316-53.986 1.951-53.747Q1.585-53.508 1.048-53.508Q0.553-53.508 0.221-53.802L-0.045-53.528Q-0.066-53.508-0.093-53.508L-0.141-53.508Q-0.165-53.508-0.192-53.535Q-0.220-53.562-0.220-53.583M3.472-54.417L3.472-56.314L2.833-56.314L2.833-56.536Q3.150-56.536 3.367-56.746Q3.585-56.956 3.685-57.266Q3.786-57.575 3.786-57.883L4.053-57.883L4.053-56.594L5.129-56.594L5.129-56.314L4.053-56.314L4.053-54.430Q4.053-54.154 4.157-53.955Q4.261-53.757 4.521-53.757Q4.678-53.757 4.784-53.861Q4.890-53.966 4.940-54.119Q4.989-54.273 4.989-54.430L4.989-54.844L5.256-54.844L5.256-54.417Q5.256-54.191 5.157-53.981Q5.058-53.771 4.873-53.639Q4.689-53.508 4.460-53.508Q4.022-53.508 3.747-53.745Q3.472-53.983 3.472-54.417\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 105.976)\">\u003Cpath d=\"M13.944-54.383L9.111-54.383Q9.043-54.393 8.997-54.439Q8.951-54.485 8.951-54.557Q8.951-54.622 8.997-54.668Q9.043-54.714 9.111-54.724L13.944-54.724Q14.013-54.714 14.059-54.668Q14.105-54.622 14.105-54.557Q14.105-54.485 14.059-54.439Q14.013-54.393 13.944-54.383M13.944-55.921L9.111-55.921Q9.043-55.931 8.997-55.977Q8.951-56.023 8.951-56.095Q8.951-56.239 9.111-56.263L13.944-56.263Q14.105-56.239 14.105-56.095Q14.105-56.023 14.059-55.977Q14.013-55.931 13.944-55.921\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 105.976)\">\u003Cpath d=\"M20.460-53.576L17.688-53.576L17.688-53.856Q18.409-53.856 18.409-54.065L18.409-57.866Q18.409-58.077 17.688-58.077L17.688-58.358L20.460-58.358Q20.945-58.358 21.381-58.163Q21.817-57.968 22.140-57.626Q22.463-57.284 22.641-56.844Q22.818-56.403 22.818-55.921Q22.818-55.435 22.634-55.012Q22.449-54.588 22.126-54.266Q21.803-53.945 21.371-53.761Q20.939-53.576 20.460-53.576M19.072-57.866L19.072-54.065Q19.072-53.925 19.161-53.890Q19.250-53.856 19.438-53.856L20.262-53.856Q20.887-53.856 21.291-54.118Q21.694-54.379 21.882-54.846Q22.070-55.312 22.070-55.921Q22.070-56.399 21.978-56.792Q21.885-57.185 21.636-57.483Q21.397-57.770 21.033-57.924Q20.669-58.077 20.262-58.077L19.438-58.077Q19.250-58.077 19.161-58.043Q19.072-58.009 19.072-57.866M23.587-55.111Q23.587-55.432 23.712-55.721Q23.837-56.010 24.063-56.233Q24.288-56.457 24.584-56.577Q24.879-56.697 25.197-56.697Q25.525-56.697 25.787-56.597Q26.048-56.498 26.224-56.316Q26.400-56.133 26.494-55.875Q26.588-55.617 26.588-55.285Q26.588-55.193 26.506-55.172L24.251-55.172L24.251-55.111Q24.251-54.523 24.534-54.140Q24.818-53.757 25.385-53.757Q25.707-53.757 25.975-53.950Q26.243-54.143 26.332-54.458Q26.339-54.499 26.414-54.513L26.506-54.513Q26.588-54.489 26.588-54.417Q26.588-54.410 26.582-54.383Q26.469-53.986 26.098-53.747Q25.727-53.508 25.303-53.508Q24.866-53.508 24.466-53.716Q24.066-53.925 23.827-54.292Q23.587-54.659 23.587-55.111M24.257-55.381L26.072-55.381Q26.072-55.658 25.975-55.910Q25.878-56.163 25.679-56.319Q25.481-56.474 25.197-56.474Q24.920-56.474 24.707-56.316Q24.493-56.157 24.375-55.902Q24.257-55.647 24.257-55.381M28.858-53.576L27.224-53.576L27.224-53.856Q27.453-53.856 27.602-53.890Q27.751-53.925 27.751-54.065L27.751-55.914Q27.751-56.184 27.643-56.245Q27.535-56.307 27.224-56.307L27.224-56.587L28.284-56.662L28.284-56.013Q28.455-56.321 28.759-56.492Q29.063-56.662 29.408-56.662Q29.914-56.662 30.198-56.439Q30.482-56.215 30.482-55.719L30.482-54.065Q30.482-53.928 30.630-53.892Q30.779-53.856 31.004-53.856L31.004-53.576L29.374-53.576L29.374-53.856Q29.603-53.856 29.752-53.890Q29.900-53.925 29.900-54.065L29.900-55.705Q29.900-56.040 29.781-56.240Q29.661-56.440 29.347-56.440Q29.077-56.440 28.843-56.304Q28.608-56.167 28.470-55.933Q28.332-55.699 28.332-55.425L28.332-54.065Q28.332-53.928 28.482-53.892Q28.632-53.856 28.858-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 105.976)\">\u003Cpath d=\"M32.977-53.603L31.849-56.102Q31.777-56.249 31.647-56.281Q31.517-56.314 31.288-56.314L31.288-56.594L32.802-56.594L32.802-56.314Q32.450-56.314 32.450-56.167Q32.450-56.122 32.461-56.102L33.325-54.184L34.105-55.914Q34.139-55.982 34.139-56.061Q34.139-56.174 34.055-56.244Q33.971-56.314 33.852-56.314L33.852-56.594L35.048-56.594L35.048-56.314Q34.829-56.314 34.658-56.211Q34.488-56.109 34.399-55.914L33.363-53.603Q33.315-53.508 33.209-53.508L33.131-53.508Q33.025-53.508 32.977-53.603\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-25.119 105.976)\">\u003Cpath d=\"M35.332-55.111Q35.332-55.432 35.457-55.721Q35.582-56.010 35.808-56.233Q36.033-56.457 36.329-56.577Q36.624-56.697 36.942-56.697Q37.270-56.697 37.532-56.597Q37.793-56.498 37.969-56.316Q38.145-56.133 38.239-55.875Q38.333-55.617 38.333-55.285Q38.333-55.193 38.251-55.172L35.996-55.172L35.996-55.111Q35.996-54.523 36.279-54.140Q36.563-53.757 37.130-53.757Q37.452-53.757 37.720-53.950Q37.988-54.143 38.077-54.458Q38.084-54.499 38.159-54.513L38.251-54.513Q38.333-54.489 38.333-54.417Q38.333-54.410 38.327-54.383Q38.214-53.986 37.843-53.747Q37.472-53.508 37.048-53.508Q36.611-53.508 36.211-53.716Q35.811-53.925 35.572-54.292Q35.332-54.659 35.332-55.111M36.002-55.381L37.817-55.381Q37.817-55.658 37.720-55.910Q37.622-56.163 37.424-56.319Q37.226-56.474 36.942-56.474Q36.665-56.474 36.452-56.316Q36.238-56.157 36.120-55.902Q36.002-55.647 36.002-55.381M40.671-53.576L38.935-53.576L38.935-53.856Q39.164-53.856 39.313-53.890Q39.461-53.925 39.461-54.065L39.461-55.914Q39.461-56.184 39.354-56.245Q39.246-56.307 38.935-56.307L38.935-56.587L39.964-56.662L39.964-55.955Q40.094-56.263 40.336-56.462Q40.579-56.662 40.897-56.662Q41.116-56.662 41.287-56.538Q41.457-56.413 41.457-56.201Q41.457-56.064 41.358-55.965Q41.259-55.866 41.126-55.866Q40.989-55.866 40.890-55.965Q40.791-56.064 40.791-56.201Q40.791-56.341 40.890-56.440Q40.600-56.440 40.400-56.244Q40.200-56.047 40.107-55.753Q40.015-55.459 40.015-55.179L40.015-54.065Q40.015-53.856 40.671-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-65.403 127.1h113.81V90.11h-113.81Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M-8.225-71.059Q-8.225-71.401-8.090-71.700Q-7.955-71.999-7.715-72.223Q-7.476-72.447-7.158-72.572Q-6.840-72.697-6.509-72.697Q-6.064-72.697-5.665-72.481Q-5.265-72.266-5.030-71.888Q-4.796-71.511-4.796-71.059Q-4.796-70.718-4.938-70.434Q-5.080-70.150-5.324-69.943Q-5.569-69.737-5.878-69.622Q-6.187-69.508-6.509-69.508Q-6.939-69.508-7.341-69.709Q-7.743-69.911-7.984-70.263Q-8.225-70.615-8.225-71.059M-6.509-69.757Q-5.907-69.757-5.683-70.135Q-5.459-70.513-5.459-71.145Q-5.459-71.757-5.694-72.116Q-5.928-72.474-6.509-72.474Q-7.561-72.474-7.561-71.145Q-7.561-70.513-7.336-70.135Q-7.110-69.757-6.509-69.757M-2.452-69.576L-4.188-69.576L-4.188-69.856Q-3.959-69.856-3.810-69.890Q-3.662-69.925-3.662-70.065L-3.662-71.914Q-3.662-72.184-3.769-72.245Q-3.877-72.307-4.188-72.307L-4.188-72.587L-3.159-72.662L-3.159-71.955Q-3.029-72.263-2.787-72.462Q-2.544-72.662-2.226-72.662Q-2.007-72.662-1.836-72.538Q-1.665-72.413-1.665-72.201Q-1.665-72.064-1.765-71.965Q-1.864-71.866-1.997-71.866Q-2.134-71.866-2.233-71.965Q-2.332-72.064-2.332-72.201Q-2.332-72.341-2.233-72.440Q-2.523-72.440-2.723-72.244Q-2.923-72.047-3.016-71.753Q-3.108-71.459-3.108-71.179L-3.108-70.065Q-3.108-69.856-2.452-69.856L-2.452-69.576M0.536-69.576L-1.016-69.576L-1.016-69.856Q-0.790-69.856-0.642-69.890Q-0.493-69.925-0.493-70.065L-0.493-71.914Q-0.493-72.102-0.541-72.186Q-0.589-72.269-0.686-72.288Q-0.784-72.307-0.996-72.307L-0.996-72.587L0.061-72.662L0.061-70.065Q0.061-69.925 0.192-69.890Q0.324-69.856 0.536-69.856L0.536-69.576M-0.736-73.883Q-0.736-74.054-0.613-74.173Q-0.490-74.293-0.319-74.293Q-0.151-74.293-0.028-74.173Q0.095-74.054 0.095-73.883Q0.095-73.708-0.028-73.585Q-0.151-73.462-0.319-73.462Q-0.490-73.462-0.613-73.585Q-0.736-73.708-0.736-73.883M1.141-69.043Q1.141-69.289 1.337-69.473Q1.534-69.658 1.790-69.737Q1.653-69.849 1.582-70.010Q1.510-70.171 1.510-70.352Q1.510-70.673 1.722-70.919Q1.387-71.217 1.387-71.627Q1.387-72.088 1.776-72.375Q2.166-72.662 2.645-72.662Q3.116-72.662 3.451-72.416Q3.626-72.570 3.836-72.652Q4.046-72.734 4.275-72.734Q4.439-72.734 4.560-72.627Q4.682-72.519 4.682-72.355Q4.682-72.259 4.610-72.187Q4.538-72.116 4.446-72.116Q4.347-72.116 4.277-72.189Q4.207-72.263 4.207-72.362Q4.207-72.416 4.220-72.447L4.227-72.461Q4.234-72.481 4.242-72.492Q4.251-72.502 4.254-72.509Q3.899-72.509 3.612-72.286Q3.899-71.993 3.899-71.627Q3.899-71.312 3.714-71.080Q3.530-70.847 3.241-70.719Q2.952-70.591 2.645-70.591Q2.443-70.591 2.252-70.641Q2.060-70.690 1.882-70.800Q1.790-70.673 1.790-70.530Q1.790-70.348 1.918-70.213Q2.046-70.078 2.231-70.078L2.863-70.078Q3.311-70.078 3.680-70.007Q4.049-69.935 4.309-69.706Q4.569-69.477 4.569-69.043Q4.569-68.722 4.273-68.520Q3.978-68.318 3.574-68.229Q3.171-68.140 2.856-68.140Q2.539-68.140 2.135-68.229Q1.732-68.318 1.436-68.520Q1.141-68.722 1.141-69.043M1.595-69.043Q1.595-68.814 1.814-68.665Q2.033-68.516 2.325-68.448Q2.617-68.380 2.856-68.380Q3.021-68.380 3.229-68.416Q3.438-68.451 3.644-68.532Q3.851-68.612 3.983-68.740Q4.114-68.868 4.114-69.043Q4.114-69.395 3.733-69.489Q3.352-69.583 2.850-69.583L2.231-69.583Q1.992-69.583 1.794-69.432Q1.595-69.282 1.595-69.043M2.645-70.830Q3.311-70.830 3.311-71.627Q3.311-72.427 2.645-72.427Q1.975-72.427 1.975-71.627Q1.975-70.830 2.645-70.830\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M13.040-70.383L8.207-70.383Q8.139-70.393 8.093-70.439Q8.047-70.485 8.047-70.557Q8.047-70.622 8.093-70.668Q8.139-70.714 8.207-70.724L13.040-70.724Q13.109-70.714 13.155-70.668Q13.201-70.622 13.201-70.557Q13.201-70.485 13.155-70.439Q13.109-70.393 13.040-70.383M13.040-71.921L8.207-71.921Q8.139-71.931 8.093-71.977Q8.047-72.023 8.047-72.095Q8.047-72.239 8.207-72.263L13.040-72.263Q13.201-72.239 13.201-72.095Q13.201-72.023 13.155-71.977Q13.109-71.931 13.040-71.921\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M18.295-69.576L16.705-69.576L16.705-69.856Q17.348-69.856 17.505-70.256L19.149-74.471Q19.183-74.566 19.296-74.566L19.378-74.566Q19.488-74.566 19.529-74.471L21.248-70.065Q21.316-69.925 21.506-69.890Q21.696-69.856 21.969-69.856L21.969-69.576L19.970-69.576L19.970-69.856Q20.534-69.856 20.534-70.031Q20.534-70.048 20.532-70.055Q20.530-70.061 20.527-70.065L20.106-71.131L18.148-71.131L17.806-70.256Q17.792-70.256 17.792-70.178Q17.792-70.017 17.955-69.937Q18.117-69.856 18.295-69.856L18.295-69.576M19.129-73.650L18.261-71.411L20.004-71.411L19.129-73.650M23.179-70.410L23.179-71.914Q23.179-72.184 23.071-72.245Q22.964-72.307 22.653-72.307L22.653-72.587L23.760-72.662L23.760-70.430L23.760-70.410Q23.760-70.130 23.811-69.986Q23.863-69.843 24.005-69.786Q24.146-69.730 24.433-69.730Q24.686-69.730 24.891-69.870Q25.097-70.010 25.213-70.236Q25.329-70.461 25.329-70.711L25.329-71.914Q25.329-72.184 25.221-72.245Q25.114-72.307 24.803-72.307L24.803-72.587L25.910-72.662L25.910-70.249Q25.910-70.058 25.963-69.976Q26.016-69.894 26.117-69.875Q26.218-69.856 26.433-69.856L26.433-69.576L25.356-69.508L25.356-70.072Q25.247-69.890 25.102-69.767Q24.956-69.644 24.770-69.576Q24.584-69.508 24.382-69.508Q23.179-69.508 23.179-70.410M27.021-69.583L27.021-70.646Q27.021-70.670 27.048-70.697Q27.076-70.724 27.099-70.724L27.209-70.724Q27.274-70.724 27.287-70.666Q27.383-70.232 27.629-69.981Q27.875-69.730 28.289-69.730Q28.631-69.730 28.884-69.863Q29.137-69.996 29.137-70.304Q29.137-70.461 29.043-70.576Q28.949-70.690 28.810-70.759Q28.672-70.827 28.504-70.865L27.923-70.964Q27.568-71.032 27.294-71.253Q27.021-71.473 27.021-71.815Q27.021-72.064 27.132-72.239Q27.243-72.413 27.429-72.512Q27.616-72.611 27.831-72.654Q28.046-72.697 28.289-72.697Q28.703-72.697 28.983-72.515L29.198-72.690Q29.208-72.693 29.215-72.695Q29.222-72.697 29.232-72.697L29.284-72.697Q29.311-72.697 29.335-72.673Q29.359-72.649 29.359-72.621L29.359-71.774Q29.359-71.753 29.335-71.726Q29.311-71.699 29.284-71.699L29.171-71.699Q29.143-71.699 29.118-71.724Q29.092-71.750 29.092-71.774Q29.092-72.010 28.986-72.174Q28.880-72.338 28.697-72.420Q28.515-72.502 28.282-72.502Q27.954-72.502 27.698-72.399Q27.441-72.297 27.441-72.020Q27.441-71.825 27.624-71.716Q27.807-71.606 28.036-71.565L28.610-71.459Q28.856-71.411 29.070-71.283Q29.284-71.155 29.420-70.952Q29.557-70.748 29.557-70.499Q29.557-69.986 29.191-69.747Q28.826-69.508 28.289-69.508Q27.793-69.508 27.462-69.802L27.195-69.528Q27.175-69.508 27.147-69.508L27.099-69.508Q27.076-69.508 27.048-69.535Q27.021-69.562 27.021-69.583M30.712-70.417L30.712-72.314L30.073-72.314L30.073-72.536Q30.391-72.536 30.608-72.746Q30.825-72.956 30.926-73.266Q31.027-73.575 31.027-73.883L31.293-73.883L31.293-72.594L32.370-72.594L32.370-72.314L31.293-72.314L31.293-70.430Q31.293-70.154 31.398-69.955Q31.502-69.757 31.762-69.757Q31.919-69.757 32.025-69.861Q32.131-69.966 32.180-70.119Q32.230-70.273 32.230-70.430L32.230-70.844L32.496-70.844L32.496-70.417Q32.496-70.191 32.397-69.981Q32.298-69.771 32.114-69.639Q31.929-69.508 31.700-69.508Q31.263-69.508 30.987-69.745Q30.712-69.983 30.712-70.417M34.923-69.576L33.371-69.576L33.371-69.856Q33.597-69.856 33.746-69.890Q33.894-69.925 33.894-70.065L33.894-71.914Q33.894-72.102 33.847-72.186Q33.799-72.269 33.701-72.288Q33.604-72.307 33.392-72.307L33.392-72.587L34.448-72.662L34.448-70.065Q34.448-69.925 34.580-69.890Q34.711-69.856 34.923-69.856L34.923-69.576M33.652-73.883Q33.652-74.054 33.775-74.173Q33.898-74.293 34.069-74.293Q34.236-74.293 34.359-74.173Q34.482-74.054 34.482-73.883Q34.482-73.708 34.359-73.585Q34.236-73.462 34.069-73.462Q33.898-73.462 33.775-73.585Q33.652-73.708 33.652-73.883M37.251-69.576L35.617-69.576L35.617-69.856Q35.846-69.856 35.995-69.890Q36.143-69.925 36.143-70.065L36.143-71.914Q36.143-72.184 36.036-72.245Q35.928-72.307 35.617-72.307L35.617-72.587L36.677-72.662L36.677-72.013Q36.848-72.321 37.152-72.492Q37.456-72.662 37.801-72.662Q38.307-72.662 38.591-72.439Q38.874-72.215 38.874-71.719L38.874-70.065Q38.874-69.928 39.023-69.892Q39.172-69.856 39.397-69.856L39.397-69.576L37.767-69.576L37.767-69.856Q37.996-69.856 38.145-69.890Q38.293-69.925 38.293-70.065L38.293-71.705Q38.293-72.040 38.174-72.240Q38.054-72.440 37.740-72.440Q37.470-72.440 37.235-72.304Q37.001-72.167 36.863-71.933Q36.724-71.699 36.724-71.425L36.724-70.065Q36.724-69.928 36.875-69.892Q37.025-69.856 37.251-69.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M-8.184-63.087Q-8.184-63.425-8.043-63.716Q-7.903-64.006-7.659-64.220Q-7.415-64.433-7.110-64.548Q-6.806-64.662-6.481-64.662Q-6.211-64.662-5.948-64.563Q-5.685-64.464-5.494-64.286L-5.494-65.684Q-5.494-65.954-5.601-66.016Q-5.709-66.077-6.020-66.077L-6.020-66.358L-4.943-66.433L-4.943-62.249Q-4.943-62.061-4.889-61.978Q-4.834-61.894-4.733-61.875Q-4.632-61.856-4.417-61.856L-4.417-61.576L-5.524-61.508L-5.524-61.925Q-5.941-61.508-6.567-61.508Q-6.998-61.508-7.370-61.720Q-7.743-61.931-7.963-62.292Q-8.184-62.653-8.184-63.087M-6.509-61.730Q-6.300-61.730-6.114-61.802Q-5.928-61.873-5.774-62.010Q-5.620-62.147-5.524-62.325L-5.524-63.934Q-5.610-64.081-5.755-64.201Q-5.900-64.321-6.070-64.380Q-6.239-64.440-6.420-64.440Q-6.980-64.440-7.249-64.051Q-7.517-63.661-7.517-63.080Q-7.517-62.509-7.283-62.119Q-7.049-61.730-6.509-61.730M-3.809-63.111Q-3.809-63.432-3.684-63.721Q-3.559-64.010-3.333-64.233Q-3.108-64.457-2.812-64.577Q-2.517-64.697-2.199-64.697Q-1.871-64.697-1.609-64.597Q-1.348-64.498-1.172-64.316Q-0.996-64.133-0.902-63.875Q-0.808-63.617-0.808-63.285Q-0.808-63.193-0.890-63.172L-3.145-63.172L-3.145-63.111Q-3.145-62.523-2.862-62.140Q-2.578-61.757-2.011-61.757Q-1.689-61.757-1.421-61.950Q-1.153-62.143-1.064-62.458Q-1.057-62.499-0.982-62.513L-0.890-62.513Q-0.808-62.489-0.808-62.417Q-0.808-62.410-0.814-62.383Q-0.927-61.986-1.298-61.747Q-1.669-61.508-2.093-61.508Q-2.530-61.508-2.930-61.716Q-3.330-61.925-3.569-62.292Q-3.809-62.659-3.809-63.111M-3.139-63.381L-1.324-63.381Q-1.324-63.658-1.421-63.910Q-1.519-64.163-1.717-64.319Q-1.915-64.474-2.199-64.474Q-2.476-64.474-2.689-64.316Q-2.903-64.157-3.021-63.902Q-3.139-63.647-3.139-63.381M-0.220-61.583L-0.220-62.646Q-0.220-62.670-0.192-62.697Q-0.165-62.724-0.141-62.724L-0.032-62.724Q0.033-62.724 0.047-62.666Q0.143-62.232 0.389-61.981Q0.635-61.730 1.048-61.730Q1.390-61.730 1.643-61.863Q1.896-61.996 1.896-62.304Q1.896-62.461 1.802-62.576Q1.708-62.690 1.570-62.759Q1.431-62.827 1.264-62.865L0.683-62.964Q0.327-63.032 0.054-63.253Q-0.220-63.473-0.220-63.815Q-0.220-64.064-0.109-64.239Q0.002-64.413 0.189-64.512Q0.375-64.611 0.590-64.654Q0.806-64.697 1.048-64.697Q1.462-64.697 1.742-64.515L1.958-64.690Q1.968-64.693 1.975-64.695Q1.981-64.697 1.992-64.697L2.043-64.697Q2.070-64.697 2.094-64.673Q2.118-64.649 2.118-64.621L2.118-63.774Q2.118-63.753 2.094-63.726Q2.070-63.699 2.043-63.699L1.930-63.699Q1.903-63.699 1.877-63.724Q1.852-63.750 1.852-63.774Q1.852-64.010 1.746-64.174Q1.640-64.338 1.457-64.420Q1.274-64.502 1.042-64.502Q0.713-64.502 0.457-64.399Q0.201-64.297 0.201-64.020Q0.201-63.825 0.384-63.716Q0.566-63.606 0.795-63.565L1.370-63.459Q1.616-63.411 1.829-63.283Q2.043-63.155 2.180-62.952Q2.316-62.748 2.316-62.499Q2.316-61.986 1.951-61.747Q1.585-61.508 1.048-61.508Q0.553-61.508 0.221-61.802L-0.045-61.528Q-0.066-61.508-0.093-61.508L-0.141-61.508Q-0.165-61.508-0.192-61.535Q-0.220-61.562-0.220-61.583M3.472-62.417L3.472-64.314L2.833-64.314L2.833-64.536Q3.150-64.536 3.367-64.746Q3.585-64.956 3.685-65.266Q3.786-65.575 3.786-65.883L4.053-65.883L4.053-64.594L5.129-64.594L5.129-64.314L4.053-64.314L4.053-62.430Q4.053-62.154 4.157-61.955Q4.261-61.757 4.521-61.757Q4.678-61.757 4.784-61.861Q4.890-61.966 4.940-62.119Q4.989-62.273 4.989-62.430L4.989-62.844L5.256-62.844L5.256-62.417Q5.256-62.191 5.157-61.981Q5.058-61.771 4.873-61.639Q4.689-61.508 4.460-61.508Q4.022-61.508 3.747-61.745Q3.472-61.983 3.472-62.417\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M13.944-62.383L9.111-62.383Q9.043-62.393 8.997-62.439Q8.951-62.485 8.951-62.557Q8.951-62.622 8.997-62.668Q9.043-62.714 9.111-62.724L13.944-62.724Q14.013-62.714 14.059-62.668Q14.105-62.622 14.105-62.557Q14.105-62.485 14.059-62.439Q14.013-62.393 13.944-62.383M13.944-63.921L9.111-63.921Q9.043-63.931 8.997-63.977Q8.951-64.023 8.951-64.095Q8.951-64.239 9.111-64.263L13.944-64.263Q14.105-64.239 14.105-64.095Q14.105-64.023 14.059-63.977Q14.013-63.931 13.944-63.921\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M20.460-61.576L17.688-61.576L17.688-61.856Q18.409-61.856 18.409-62.065L18.409-65.866Q18.409-66.077 17.688-66.077L17.688-66.358L20.460-66.358Q20.945-66.358 21.381-66.163Q21.817-65.968 22.140-65.626Q22.463-65.284 22.641-64.844Q22.818-64.403 22.818-63.921Q22.818-63.435 22.634-63.012Q22.449-62.588 22.126-62.266Q21.803-61.945 21.371-61.761Q20.939-61.576 20.460-61.576M19.072-65.866L19.072-62.065Q19.072-61.925 19.161-61.890Q19.250-61.856 19.438-61.856L20.262-61.856Q20.887-61.856 21.291-62.118Q21.694-62.379 21.882-62.846Q22.070-63.312 22.070-63.921Q22.070-64.399 21.978-64.792Q21.885-65.185 21.636-65.483Q21.397-65.770 21.033-65.924Q20.669-66.077 20.262-66.077L19.438-66.077Q19.250-66.077 19.161-66.043Q19.072-66.009 19.072-65.866M23.587-63.111Q23.587-63.432 23.712-63.721Q23.837-64.010 24.063-64.233Q24.288-64.457 24.584-64.577Q24.879-64.697 25.197-64.697Q25.525-64.697 25.787-64.597Q26.048-64.498 26.224-64.316Q26.400-64.133 26.494-63.875Q26.588-63.617 26.588-63.285Q26.588-63.193 26.506-63.172L24.251-63.172L24.251-63.111Q24.251-62.523 24.534-62.140Q24.818-61.757 25.385-61.757Q25.707-61.757 25.975-61.950Q26.243-62.143 26.332-62.458Q26.339-62.499 26.414-62.513L26.506-62.513Q26.588-62.489 26.588-62.417Q26.588-62.410 26.582-62.383Q26.469-61.986 26.098-61.747Q25.727-61.508 25.303-61.508Q24.866-61.508 24.466-61.716Q24.066-61.925 23.827-62.292Q23.587-62.659 23.587-63.111M24.257-63.381L26.072-63.381Q26.072-63.658 25.975-63.910Q25.878-64.163 25.679-64.319Q25.481-64.474 25.197-64.474Q24.920-64.474 24.707-64.316Q24.493-64.157 24.375-63.902Q24.257-63.647 24.257-63.381M28.858-61.576L27.224-61.576L27.224-61.856Q27.453-61.856 27.602-61.890Q27.751-61.925 27.751-62.065L27.751-63.914Q27.751-64.184 27.643-64.245Q27.535-64.307 27.224-64.307L27.224-64.587L28.284-64.662L28.284-64.013Q28.455-64.321 28.759-64.492Q29.063-64.662 29.408-64.662Q29.914-64.662 30.198-64.439Q30.482-64.215 30.482-63.719L30.482-62.065Q30.482-61.928 30.630-61.892Q30.779-61.856 31.004-61.856L31.004-61.576L29.374-61.576L29.374-61.856Q29.603-61.856 29.752-61.890Q29.900-61.925 29.900-62.065L29.900-63.705Q29.900-64.040 29.781-64.240Q29.661-64.440 29.347-64.440Q29.077-64.440 28.843-64.304Q28.608-64.167 28.470-63.933Q28.332-63.699 28.332-63.425L28.332-62.065Q28.332-61.928 28.482-61.892Q28.632-61.856 28.858-61.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M32.977-61.603L31.849-64.102Q31.777-64.249 31.647-64.281Q31.517-64.314 31.288-64.314L31.288-64.594L32.802-64.594L32.802-64.314Q32.450-64.314 32.450-64.167Q32.450-64.122 32.461-64.102L33.325-62.184L34.105-63.914Q34.139-63.982 34.139-64.061Q34.139-64.174 34.055-64.244Q33.971-64.314 33.852-64.314L33.852-64.594L35.048-64.594L35.048-64.314Q34.829-64.314 34.658-64.211Q34.488-64.109 34.399-63.914L33.363-61.603Q33.315-61.508 33.209-61.508L33.131-61.508Q33.025-61.508 32.977-61.603\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M35.332-63.111Q35.332-63.432 35.457-63.721Q35.582-64.010 35.808-64.233Q36.033-64.457 36.329-64.577Q36.624-64.697 36.942-64.697Q37.270-64.697 37.532-64.597Q37.793-64.498 37.969-64.316Q38.145-64.133 38.239-63.875Q38.333-63.617 38.333-63.285Q38.333-63.193 38.251-63.172L35.996-63.172L35.996-63.111Q35.996-62.523 36.279-62.140Q36.563-61.757 37.130-61.757Q37.452-61.757 37.720-61.950Q37.988-62.143 38.077-62.458Q38.084-62.499 38.159-62.513L38.251-62.513Q38.333-62.489 38.333-62.417Q38.333-62.410 38.327-62.383Q38.214-61.986 37.843-61.747Q37.472-61.508 37.048-61.508Q36.611-61.508 36.211-61.716Q35.811-61.925 35.572-62.292Q35.332-62.659 35.332-63.111M36.002-63.381L37.817-63.381Q37.817-63.658 37.720-63.910Q37.622-64.163 37.424-64.319Q37.226-64.474 36.942-64.474Q36.665-64.474 36.452-64.316Q36.238-64.157 36.120-63.902Q36.002-63.647 36.002-63.381M40.671-61.576L38.935-61.576L38.935-61.856Q39.164-61.856 39.313-61.890Q39.461-61.925 39.461-62.065L39.461-63.914Q39.461-64.184 39.354-64.245Q39.246-64.307 38.935-64.307L38.935-64.587L39.964-64.662L39.964-63.955Q40.094-64.263 40.336-64.462Q40.579-64.662 40.897-64.662Q41.116-64.662 41.287-64.538Q41.457-64.413 41.457-64.201Q41.457-64.064 41.358-63.965Q41.259-63.866 41.126-63.866Q40.989-63.866 40.890-63.965Q40.791-64.064 40.791-64.201Q40.791-64.341 40.890-64.440Q40.600-64.440 40.400-64.244Q40.200-64.047 40.107-63.753Q40.015-63.459 40.015-63.179L40.015-62.065Q40.015-61.856 40.671-61.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M-8.184-55.087Q-8.184-55.425-8.043-55.716Q-7.903-56.006-7.659-56.220Q-7.415-56.433-7.110-56.548Q-6.806-56.662-6.481-56.662Q-6.211-56.662-5.948-56.563Q-5.685-56.464-5.494-56.286L-5.494-57.684Q-5.494-57.954-5.601-58.016Q-5.709-58.077-6.020-58.077L-6.020-58.358L-4.943-58.433L-4.943-54.249Q-4.943-54.061-4.889-53.978Q-4.834-53.894-4.733-53.875Q-4.632-53.856-4.417-53.856L-4.417-53.576L-5.524-53.508L-5.524-53.925Q-5.941-53.508-6.567-53.508Q-6.998-53.508-7.370-53.720Q-7.743-53.931-7.963-54.292Q-8.184-54.653-8.184-55.087M-6.509-53.730Q-6.300-53.730-6.114-53.802Q-5.928-53.873-5.774-54.010Q-5.620-54.147-5.524-54.325L-5.524-55.934Q-5.610-56.081-5.755-56.201Q-5.900-56.321-6.070-56.380Q-6.239-56.440-6.420-56.440Q-6.980-56.440-7.249-56.051Q-7.517-55.661-7.517-55.080Q-7.517-54.509-7.283-54.119Q-7.049-53.730-6.509-53.730M-3.709-54.304Q-3.709-54.636-3.486-54.863Q-3.262-55.090-2.918-55.218Q-2.575-55.347-2.202-55.399Q-1.830-55.452-1.525-55.452L-1.525-55.705Q-1.525-55.910-1.633-56.090Q-1.741-56.269-1.922-56.372Q-2.103-56.474-2.311-56.474Q-2.718-56.474-2.954-56.382Q-2.865-56.345-2.819-56.261Q-2.773-56.177-2.773-56.075Q-2.773-55.979-2.819-55.900Q-2.865-55.822-2.946-55.777Q-3.026-55.733-3.115-55.733Q-3.265-55.733-3.366-55.830Q-3.467-55.928-3.467-56.075Q-3.467-56.697-2.311-56.697Q-2.100-56.697-1.850-56.633Q-1.601-56.570-1.399-56.451Q-1.197-56.331-1.071-56.146Q-0.944-55.962-0.944-55.719L-0.944-54.143Q-0.944-54.027-0.883-53.931Q-0.821-53.836-0.708-53.836Q-0.599-53.836-0.534-53.930Q-0.469-54.024-0.469-54.143L-0.469-54.591L-0.203-54.591L-0.203-54.143Q-0.203-53.873-0.430-53.708Q-0.657-53.542-0.937-53.542Q-1.146-53.542-1.283-53.696Q-1.419-53.849-1.443-54.065Q-1.590-53.798-1.872-53.653Q-2.154-53.508-2.479-53.508Q-2.756-53.508-3.040-53.583Q-3.323-53.658-3.516-53.837Q-3.709-54.017-3.709-54.304M-3.094-54.304Q-3.094-54.130-2.993-54Q-2.893-53.870-2.737-53.800Q-2.581-53.730-2.417-53.730Q-2.199-53.730-1.990-53.827Q-1.782-53.925-1.654-54.106Q-1.525-54.287-1.525-54.513L-1.525-55.241Q-1.850-55.241-2.216-55.150Q-2.581-55.059-2.838-54.847Q-3.094-54.636-3.094-54.304M0.741-54.417L0.741-56.314L0.102-56.314L0.102-56.536Q0.419-56.536 0.637-56.746Q0.854-56.956 0.954-57.266Q1.055-57.575 1.055-57.883L1.322-57.883L1.322-56.594L2.398-56.594L2.398-56.314L1.322-56.314L1.322-54.430Q1.322-54.154 1.426-53.955Q1.530-53.757 1.790-53.757Q1.947-53.757 2.053-53.861Q2.159-53.966 2.209-54.119Q2.258-54.273 2.258-54.430L2.258-54.844L2.525-54.844L2.525-54.417Q2.525-54.191 2.426-53.981Q2.327-53.771 2.142-53.639Q1.958-53.508 1.729-53.508Q1.291-53.508 1.016-53.745Q0.741-53.983 0.741-54.417M3.294-55.111Q3.294-55.432 3.419-55.721Q3.544-56.010 3.769-56.233Q3.995-56.457 4.290-56.577Q4.586-56.697 4.904-56.697Q5.232-56.697 5.493-56.597Q5.755-56.498 5.931-56.316Q6.107-56.133 6.201-55.875Q6.295-55.617 6.295-55.285Q6.295-55.193 6.213-55.172L3.957-55.172L3.957-55.111Q3.957-54.523 4.241-54.140Q4.524-53.757 5.092-53.757Q5.413-53.757 5.681-53.950Q5.950-54.143 6.039-54.458Q6.045-54.499 6.121-54.513L6.213-54.513Q6.295-54.489 6.295-54.417Q6.295-54.410 6.288-54.383Q6.175-53.986 5.804-53.747Q5.434-53.508 5.010-53.508Q4.572-53.508 4.172-53.716Q3.773-53.925 3.533-54.292Q3.294-54.659 3.294-55.111M3.964-55.381L5.779-55.381Q5.779-55.658 5.681-55.910Q5.584-56.163 5.386-56.319Q5.188-56.474 4.904-56.474Q4.627-56.474 4.413-56.316Q4.200-56.157 4.082-55.902Q3.964-55.647 3.964-55.381\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M14.762-54.383L9.929-54.383Q9.861-54.393 9.815-54.439Q9.769-54.485 9.769-54.557Q9.769-54.622 9.815-54.668Q9.861-54.714 9.929-54.724L14.762-54.724Q14.831-54.714 14.877-54.668Q14.923-54.622 14.923-54.557Q14.923-54.485 14.877-54.439Q14.831-54.393 14.762-54.383M14.762-55.921L9.929-55.921Q9.861-55.931 9.815-55.977Q9.769-56.023 9.769-56.095Q9.769-56.239 9.929-56.263L14.762-56.263Q14.923-56.239 14.923-56.095Q14.923-56.023 14.877-55.977Q14.831-55.931 14.762-55.921\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M22.334-53.576L19.596-53.576L19.596-53.856Q19.945-53.856 20.282-53.892Q20.618-53.928 20.618-54.065L20.618-57.866Q20.618-58.009 20.530-58.043Q20.441-58.077 20.256-58.077L19.897-58.077Q19.596-58.077 19.381-58.030Q19.166-57.982 19.009-57.825Q18.872-57.691 18.812-57.413Q18.752-57.134 18.715-56.721L18.448-56.721L18.595-58.358L23.329-58.358L23.476-56.721L23.209-56.721Q23.172-57.134 23.115-57.411Q23.059-57.688 22.915-57.825Q22.755-57.985 22.543-58.031Q22.331-58.077 22.027-58.077L21.675-58.077Q21.490-58.077 21.401-58.043Q21.312-58.009 21.312-57.866L21.312-54.065Q21.312-53.928 21.649-53.892Q21.986-53.856 22.334-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M24.065-54.410L24.065-55.914Q24.065-56.184 23.957-56.245Q23.849-56.307 23.538-56.307L23.538-56.587L24.646-56.662L24.646-54.430L24.646-54.410Q24.646-54.130 24.697-53.986Q24.748-53.843 24.890-53.786Q25.032-53.730 25.319-53.730Q25.572-53.730 25.777-53.870Q25.982-54.010 26.098-54.236Q26.215-54.461 26.215-54.711L26.215-55.914Q26.215-56.184 26.107-56.245Q25.999-56.307 25.688-56.307L25.688-56.587L26.796-56.662L26.796-54.249Q26.796-54.058 26.849-53.976Q26.902-53.894 27.002-53.875Q27.103-53.856 27.319-53.856L27.319-53.576L26.242-53.508L26.242-54.072Q26.133-53.890 25.987-53.767Q25.842-53.644 25.656-53.576Q25.469-53.508 25.268-53.508Q24.065-53.508 24.065-54.410M27.865-55.111Q27.865-55.432 27.990-55.721Q28.115-56.010 28.341-56.233Q28.566-56.457 28.862-56.577Q29.157-56.697 29.475-56.697Q29.803-56.697 30.065-56.597Q30.326-56.498 30.502-56.316Q30.678-56.133 30.772-55.875Q30.866-55.617 30.866-55.285Q30.866-55.193 30.784-55.172L28.529-55.172L28.529-55.111Q28.529-54.523 28.812-54.140Q29.096-53.757 29.663-53.757Q29.985-53.757 30.253-53.950Q30.521-54.143 30.610-54.458Q30.617-54.499 30.692-54.513L30.784-54.513Q30.866-54.489 30.866-54.417Q30.866-54.410 30.860-54.383Q30.747-53.986 30.376-53.747Q30.005-53.508 29.581-53.508Q29.144-53.508 28.744-53.716Q28.344-53.925 28.105-54.292Q27.865-54.659 27.865-55.111M28.535-55.381L30.350-55.381Q30.350-55.658 30.253-55.910Q30.155-56.163 29.957-56.319Q29.759-56.474 29.475-56.474Q29.198-56.474 28.985-56.316Q28.771-56.157 28.653-55.902Q28.535-55.647 28.535-55.381M31.953-52.346Q31.953-52.380 31.981-52.407Q32.251-52.636 32.399-52.959Q32.548-53.282 32.548-53.638L32.548-53.675Q32.439-53.576 32.275-53.576Q32.093-53.576 31.974-53.696Q31.854-53.815 31.854-53.996Q31.854-54.171 31.974-54.290Q32.093-54.410 32.275-54.410Q32.531-54.410 32.651-54.171Q32.770-53.931 32.770-53.638Q32.770-53.238 32.601-52.867Q32.432-52.496 32.134-52.240Q32.104-52.219 32.076-52.219Q32.035-52.219 31.994-52.260Q31.953-52.301 31.953-52.346\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M36.948-54.417L36.948-56.314L36.309-56.314L36.309-56.536Q36.627-56.536 36.844-56.746Q37.061-56.956 37.161-57.266Q37.262-57.575 37.262-57.883L37.529-57.883L37.529-56.594L38.606-56.594L38.606-56.314L37.529-56.314L37.529-54.430Q37.529-54.154 37.633-53.955Q37.737-53.757 37.997-53.757Q38.154-53.757 38.260-53.861Q38.366-53.966 38.416-54.119Q38.465-54.273 38.465-54.430L38.465-54.844L38.732-54.844L38.732-54.417Q38.732-54.191 38.633-53.981Q38.534-53.771 38.349-53.639Q38.165-53.508 37.936-53.508Q37.498-53.508 37.223-53.745Q36.948-53.983 36.948-54.417M41.159-53.576L39.607-53.576L39.607-53.856Q39.833-53.856 39.981-53.890Q40.130-53.925 40.130-54.065L40.130-55.914Q40.130-56.102 40.082-56.186Q40.034-56.269 39.937-56.288Q39.839-56.307 39.628-56.307L39.628-56.587L40.684-56.662L40.684-54.065Q40.684-53.925 40.815-53.890Q40.947-53.856 41.159-53.856L41.159-53.576M39.887-57.883Q39.887-58.054 40.010-58.173Q40.133-58.293 40.304-58.293Q40.472-58.293 40.595-58.173Q40.718-58.054 40.718-57.883Q40.718-57.708 40.595-57.585Q40.472-57.462 40.304-57.462Q40.133-57.462 40.010-57.585Q39.887-57.708 39.887-57.883M43.486-53.576L41.853-53.576L41.853-53.856Q42.082-53.856 42.230-53.890Q42.379-53.925 42.379-54.065L42.379-55.914Q42.379-56.184 42.271-56.245Q42.164-56.307 41.853-56.307L41.853-56.587L42.912-56.662L42.912-56.013Q43.083-56.321 43.387-56.492Q43.691-56.662 44.037-56.662Q44.437-56.662 44.713-56.522Q44.990-56.382 45.076-56.034Q45.243-56.327 45.542-56.495Q45.841-56.662 46.187-56.662Q46.692-56.662 46.976-56.439Q47.260-56.215 47.260-55.719L47.260-54.065Q47.260-53.928 47.409-53.892Q47.557-53.856 47.783-53.856L47.783-53.576L46.152-53.576L46.152-53.856Q46.378-53.856 46.528-53.892Q46.679-53.928 46.679-54.065L46.679-55.705Q46.679-56.040 46.559-56.240Q46.440-56.440 46.125-56.440Q45.855-56.440 45.621-56.304Q45.387-56.167 45.248-55.933Q45.110-55.699 45.110-55.425L45.110-54.065Q45.110-53.928 45.259-53.892Q45.407-53.856 45.633-53.856L45.633-53.576L44.003-53.576L44.003-53.856Q44.232-53.856 44.380-53.890Q44.529-53.925 44.529-54.065L44.529-55.705Q44.529-56.040 44.409-56.240Q44.290-56.440 43.975-56.440Q43.705-56.440 43.471-56.304Q43.237-56.167 43.098-55.933Q42.960-55.699 42.960-55.425L42.960-54.065Q42.960-53.928 43.110-53.892Q43.261-53.856 43.486-53.856L43.486-53.576M48.330-55.111Q48.330-55.432 48.454-55.721Q48.579-56.010 48.805-56.233Q49.030-56.457 49.326-56.577Q49.622-56.697 49.940-56.697Q50.268-56.697 50.529-56.597Q50.791-56.498 50.967-56.316Q51.143-56.133 51.237-55.875Q51.331-55.617 51.331-55.285Q51.331-55.193 51.249-55.172L48.993-55.172L48.993-55.111Q48.993-54.523 49.276-54.140Q49.560-53.757 50.128-53.757Q50.449-53.757 50.717-53.950Q50.985-54.143 51.074-54.458Q51.081-54.499 51.156-54.513L51.249-54.513Q51.331-54.489 51.331-54.417Q51.331-54.410 51.324-54.383Q51.211-53.986 50.840-53.747Q50.469-53.508 50.045-53.508Q49.608-53.508 49.208-53.716Q48.808-53.925 48.569-54.292Q48.330-54.659 48.330-55.111M49-55.381L50.815-55.381Q50.815-55.658 50.717-55.910Q50.620-56.163 50.421-56.319Q50.223-56.474 49.940-56.474Q49.663-56.474 49.449-56.316Q49.235-56.157 49.117-55.902Q49-55.647 49-55.381\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M59.797-54.383L54.964-54.383Q54.896-54.393 54.850-54.439Q54.804-54.485 54.804-54.557Q54.804-54.622 54.850-54.668Q54.896-54.714 54.964-54.724L59.797-54.724Q59.866-54.714 59.912-54.668Q59.958-54.622 59.958-54.557Q59.958-54.485 59.912-54.439Q59.866-54.393 59.797-54.383M59.797-55.921L54.964-55.921Q54.896-55.931 54.850-55.977Q54.804-56.023 54.804-56.095Q54.804-56.239 54.964-56.263L59.797-56.263Q59.958-56.239 59.958-56.095Q59.958-56.023 59.912-55.977Q59.866-55.931 59.797-55.921\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-50.205 171.892)\">\u003Cpath d=\"M65.144-53.576L63.510-53.576L63.510-53.856Q63.739-53.856 63.888-53.890Q64.037-53.925 64.037-54.065L64.037-55.914Q64.037-56.184 63.929-56.245Q63.821-56.307 63.510-56.307L63.510-56.587L64.570-56.662L64.570-56.013Q64.741-56.321 65.045-56.492Q65.349-56.662 65.694-56.662Q66.094-56.662 66.371-56.522Q66.648-56.382 66.733-56.034Q66.901-56.327 67.200-56.495Q67.499-56.662 67.844-56.662Q68.350-56.662 68.634-56.439Q68.918-56.215 68.918-55.719L68.918-54.065Q68.918-53.928 69.066-53.892Q69.215-53.856 69.440-53.856L69.440-53.576L67.810-53.576L67.810-53.856Q68.036-53.856 68.186-53.892Q68.336-53.928 68.336-54.065L68.336-55.705Q68.336-56.040 68.217-56.240Q68.097-56.440 67.783-56.440Q67.513-56.440 67.279-56.304Q67.044-56.167 66.906-55.933Q66.768-55.699 66.768-55.425L66.768-54.065Q66.768-53.928 66.916-53.892Q67.065-53.856 67.291-53.856L67.291-53.576L65.660-53.576L65.660-53.856Q65.889-53.856 66.038-53.890Q66.187-53.925 66.187-54.065L66.187-55.705Q66.187-56.040 66.067-56.240Q65.947-56.440 65.633-56.440Q65.363-56.440 65.129-56.304Q64.895-56.167 64.756-55.933Q64.618-55.699 64.618-55.425L64.618-54.065Q64.618-53.928 64.768-53.892Q64.919-53.856 65.144-53.856L65.144-53.576M69.987-55.059Q69.987-55.401 70.122-55.700Q70.257-55.999 70.497-56.223Q70.736-56.447 71.054-56.572Q71.372-56.697 71.703-56.697Q72.148-56.697 72.547-56.481Q72.947-56.266 73.181-55.888Q73.416-55.511 73.416-55.059Q73.416-54.718 73.274-54.434Q73.132-54.150 72.888-53.943Q72.643-53.737 72.334-53.622Q72.024-53.508 71.703-53.508Q71.273-53.508 70.871-53.709Q70.469-53.911 70.228-54.263Q69.987-54.615 69.987-55.059M71.703-53.757Q72.305-53.757 72.529-54.135Q72.752-54.513 72.752-55.145Q72.752-55.757 72.518-56.116Q72.284-56.474 71.703-56.474Q70.650-56.474 70.650-55.145Q70.650-54.513 70.876-54.135Q71.102-53.757 71.703-53.757M75.760-53.576L74.024-53.576L74.024-53.856Q74.253-53.856 74.402-53.890Q74.550-53.925 74.550-54.065L74.550-55.914Q74.550-56.184 74.443-56.245Q74.335-56.307 74.024-56.307L74.024-56.587L75.053-56.662L75.053-55.955Q75.183-56.263 75.425-56.462Q75.668-56.662 75.986-56.662Q76.205-56.662 76.376-56.538Q76.546-56.413 76.546-56.201Q76.546-56.064 76.447-55.965Q76.348-55.866 76.215-55.866Q76.078-55.866 75.979-55.965Q75.880-56.064 75.880-56.201Q75.880-56.341 75.979-56.440Q75.689-56.440 75.489-56.244Q75.289-56.047 75.196-55.753Q75.104-55.459 75.104-55.179L75.104-54.065Q75.104-53.856 75.760-53.856L75.760-53.576M78.813-53.576L77.179-53.576L77.179-53.856Q77.408-53.856 77.556-53.890Q77.705-53.925 77.705-54.065L77.705-55.914Q77.705-56.184 77.597-56.245Q77.490-56.307 77.179-56.307L77.179-56.587L78.238-56.662L78.238-56.013Q78.409-56.321 78.713-56.492Q79.018-56.662 79.363-56.662Q79.869-56.662 80.152-56.439Q80.436-56.215 80.436-55.719L80.436-54.065Q80.436-53.928 80.585-53.892Q80.733-53.856 80.959-53.856L80.959-53.576L79.329-53.576L79.329-53.856Q79.558-53.856 79.706-53.890Q79.855-53.925 79.855-54.065L79.855-55.705Q79.855-56.040 79.735-56.240Q79.616-56.440 79.301-56.440Q79.031-56.440 78.797-56.304Q78.563-56.167 78.425-55.933Q78.286-55.699 78.286-55.425L78.286-54.065Q78.286-53.928 78.437-53.892Q78.587-53.856 78.813-53.856L78.813-53.576M83.164-53.576L81.612-53.576L81.612-53.856Q81.837-53.856 81.986-53.890Q82.135-53.925 82.135-54.065L82.135-55.914Q82.135-56.102 82.087-56.186Q82.039-56.269 81.942-56.288Q81.844-56.307 81.632-56.307L81.632-56.587L82.689-56.662L82.689-54.065Q82.689-53.925 82.820-53.890Q82.952-53.856 83.164-53.856L83.164-53.576M81.892-57.883Q81.892-58.054 82.015-58.173Q82.138-58.293 82.309-58.293Q82.477-58.293 82.600-58.173Q82.723-58.054 82.723-57.883Q82.723-57.708 82.600-57.585Q82.477-57.462 82.309-57.462Q82.138-57.462 82.015-57.585Q81.892-57.708 81.892-57.883M85.491-53.576L83.857-53.576L83.857-53.856Q84.086-53.856 84.235-53.890Q84.384-53.925 84.384-54.065L84.384-55.914Q84.384-56.184 84.276-56.245Q84.169-56.307 83.857-56.307L83.857-56.587L84.917-56.662L84.917-56.013Q85.088-56.321 85.392-56.492Q85.696-56.662 86.042-56.662Q86.547-56.662 86.831-56.439Q87.115-56.215 87.115-55.719L87.115-54.065Q87.115-53.928 87.263-53.892Q87.412-53.856 87.638-53.856L87.638-53.576L86.007-53.576L86.007-53.856Q86.236-53.856 86.385-53.890Q86.534-53.925 86.534-54.065L86.534-55.705Q86.534-56.040 86.414-56.240Q86.294-56.440 85.980-56.440Q85.710-56.440 85.476-56.304Q85.242-56.167 85.103-55.933Q84.965-55.699 84.965-55.425L84.965-54.065Q84.965-53.928 85.115-53.892Q85.266-53.856 85.491-53.856L85.491-53.576M88.185-53.043Q88.185-53.289 88.381-53.473Q88.578-53.658 88.834-53.737Q88.697-53.849 88.626-54.010Q88.554-54.171 88.554-54.352Q88.554-54.673 88.766-54.919Q88.431-55.217 88.431-55.627Q88.431-56.088 88.820-56.375Q89.210-56.662 89.689-56.662Q90.160-56.662 90.495-56.416Q90.669-56.570 90.880-56.652Q91.090-56.734 91.319-56.734Q91.483-56.734 91.604-56.627Q91.726-56.519 91.726-56.355Q91.726-56.259 91.654-56.187Q91.582-56.116 91.490-56.116Q91.391-56.116 91.321-56.189Q91.251-56.263 91.251-56.362Q91.251-56.416 91.264-56.447L91.271-56.461Q91.278-56.481 91.286-56.492Q91.295-56.502 91.298-56.509Q90.943-56.509 90.656-56.286Q90.943-55.993 90.943-55.627Q90.943-55.312 90.758-55.080Q90.574-54.847 90.285-54.719Q89.996-54.591 89.689-54.591Q89.487-54.591 89.295-54.641Q89.104-54.690 88.926-54.800Q88.834-54.673 88.834-54.530Q88.834-54.348 88.962-54.213Q89.090-54.078 89.275-54.078L89.907-54.078Q90.355-54.078 90.724-54.007Q91.093-53.935 91.353-53.706Q91.613-53.477 91.613-53.043Q91.613-52.722 91.317-52.520Q91.022-52.318 90.618-52.229Q90.215-52.140 89.900-52.140Q89.583-52.140 89.179-52.229Q88.776-52.318 88.480-52.520Q88.185-52.722 88.185-53.043M88.639-53.043Q88.639-52.814 88.858-52.665Q89.077-52.516 89.369-52.448Q89.661-52.380 89.900-52.380Q90.065-52.380 90.273-52.416Q90.481-52.451 90.688-52.532Q90.895-52.612 91.027-52.740Q91.158-52.868 91.158-53.043Q91.158-53.395 90.777-53.489Q90.396-53.583 89.894-53.583L89.275-53.583Q89.036-53.583 88.837-53.432Q88.639-53.282 88.639-53.043M89.689-54.830Q90.355-54.830 90.355-55.627Q90.355-56.427 89.689-56.427Q89.019-56.427 89.019-55.627Q89.019-54.830 89.689-54.830\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(77.51 1.75)\">\u003Cpath d=\"M-7.657-54.417L-7.657-56.314L-8.296-56.314L-8.296-56.536Q-7.978-56.536-7.761-56.746Q-7.544-56.956-7.444-57.266Q-7.343-57.575-7.343-57.883L-7.076-57.883L-7.076-56.594L-5.999-56.594L-5.999-56.314L-7.076-56.314L-7.076-54.430Q-7.076-54.154-6.972-53.955Q-6.868-53.757-6.608-53.757Q-6.451-53.757-6.345-53.861Q-6.239-53.966-6.189-54.119Q-6.140-54.273-6.140-54.430L-6.140-54.844L-5.873-54.844L-5.873-54.417Q-5.873-54.191-5.972-53.981Q-6.071-53.771-6.256-53.639Q-6.440-53.508-6.669-53.508Q-7.107-53.508-7.382-53.745Q-7.657-53.983-7.657-54.417M-4.489-54.410L-4.489-55.914Q-4.489-56.184-4.596-56.245Q-4.704-56.307-5.015-56.307L-5.015-56.587L-3.908-56.662L-3.908-54.430L-3.908-54.410Q-3.908-54.130-3.856-53.986Q-3.805-53.843-3.663-53.786Q-3.521-53.730-3.234-53.730Q-2.981-53.730-2.776-53.870Q-2.571-54.010-2.455-54.236Q-2.339-54.461-2.339-54.711L-2.339-55.914Q-2.339-56.184-2.446-56.245Q-2.554-56.307-2.865-56.307L-2.865-56.587L-1.758-56.662L-1.758-54.249Q-1.758-54.058-1.705-53.976Q-1.652-53.894-1.551-53.875Q-1.450-53.856-1.235-53.856L-1.235-53.576L-2.311-53.508L-2.311-54.072Q-2.421-53.890-2.566-53.767Q-2.711-53.644-2.898-53.576Q-3.084-53.508-3.286-53.508Q-4.489-53.508-4.489-54.410M1.103-53.576L-0.633-53.576L-0.633-53.856Q-0.404-53.856-0.256-53.890Q-0.107-53.925-0.107-54.065L-0.107-55.914Q-0.107-56.184-0.215-56.245Q-0.322-56.307-0.633-56.307L-0.633-56.587L0.396-56.662L0.396-55.955Q0.525-56.263 0.768-56.462Q1.011-56.662 1.329-56.662Q1.547-56.662 1.718-56.538Q1.889-56.413 1.889-56.201Q1.889-56.064 1.790-55.965Q1.691-55.866 1.558-55.866Q1.421-55.866 1.322-55.965Q1.223-56.064 1.223-56.201Q1.223-56.341 1.322-56.440Q1.031-56.440 0.831-56.244Q0.631-56.047 0.539-55.753Q0.447-55.459 0.447-55.179L0.447-54.065Q0.447-53.856 1.103-53.856L1.103-53.576M4.155-53.576L2.522-53.576L2.522-53.856Q2.751-53.856 2.899-53.890Q3.048-53.925 3.048-54.065L3.048-55.914Q3.048-56.184 2.940-56.245Q2.833-56.307 2.522-56.307L2.522-56.587L3.581-56.662L3.581-56.013Q3.752-56.321 4.056-56.492Q4.360-56.662 4.706-56.662Q5.211-56.662 5.495-56.439Q5.779-56.215 5.779-55.719L5.779-54.065Q5.779-53.928 5.928-53.892Q6.076-53.856 6.302-53.856L6.302-53.576L4.671-53.576L4.671-53.856Q4.900-53.856 5.049-53.890Q5.198-53.925 5.198-54.065L5.198-55.705Q5.198-56.040 5.078-56.240Q4.959-56.440 4.644-56.440Q4.374-56.440 4.140-56.304Q3.906-56.167 3.767-55.933Q3.629-55.699 3.629-55.425L3.629-54.065Q3.629-53.928 3.779-53.892Q3.930-53.856 4.155-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 1.75)\">\u003Cpath d=\"M12.606-53.576L10.076-53.576L10.076-53.856Q11.044-53.856 11.044-54.065L11.044-57.684Q10.651-57.496 10.029-57.496L10.029-57.777Q10.446-57.777 10.810-57.878Q11.174-57.978 11.430-58.224L11.556-58.224Q11.621-58.207 11.638-58.139L11.638-54.065Q11.638-53.856 12.606-53.856L12.606-53.576M13.976-53.996Q13.976-54.164 14.099-54.287Q14.222-54.410 14.397-54.410Q14.564-54.410 14.687-54.287Q14.810-54.164 14.810-53.996Q14.810-53.822 14.687-53.699Q14.564-53.576 14.397-53.576Q14.222-53.576 14.099-53.699Q13.976-53.822 13.976-53.996M13.976-56.180Q13.976-56.348 14.099-56.471Q14.222-56.594 14.397-56.594Q14.564-56.594 14.687-56.471Q14.810-56.348 14.810-56.180Q14.810-56.006 14.687-55.883Q14.564-55.760 14.397-55.760Q14.222-55.760 14.099-55.883Q13.976-56.006 13.976-56.180\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 1.75)\">\u003Cpath d=\"M21.017-53.576L19.465-53.576L19.465-53.856Q19.691-53.856 19.840-53.890Q19.988-53.925 19.988-54.065L19.988-55.914Q19.988-56.102 19.940-56.186Q19.893-56.269 19.795-56.288Q19.698-56.307 19.486-56.307L19.486-56.587L20.542-56.662L20.542-54.065Q20.542-53.925 20.674-53.890Q20.805-53.856 21.017-53.856L21.017-53.576M19.746-57.883Q19.746-58.054 19.869-58.173Q19.992-58.293 20.163-58.293Q20.330-58.293 20.453-58.173Q20.576-58.054 20.576-57.883Q20.576-57.708 20.453-57.585Q20.330-57.462 20.163-57.462Q19.992-57.462 19.869-57.585Q19.746-57.708 19.746-57.883M23.345-53.576L21.711-53.576L21.711-53.856Q21.940-53.856 22.089-53.890Q22.237-53.925 22.237-54.065L22.237-55.914Q22.237-56.184 22.130-56.245Q22.022-56.307 21.711-56.307L21.711-56.587L22.771-56.662L22.771-56.013Q22.941-56.321 23.246-56.492Q23.550-56.662 23.895-56.662Q24.401-56.662 24.685-56.439Q24.968-56.215 24.968-55.719L24.968-54.065Q24.968-53.928 25.117-53.892Q25.266-53.856 25.491-53.856L25.491-53.576L23.861-53.576L23.861-53.856Q24.090-53.856 24.239-53.890Q24.387-53.925 24.387-54.065L24.387-55.705Q24.387-56.040 24.268-56.240Q24.148-56.440 23.834-56.440Q23.564-56.440 23.329-56.304Q23.095-56.167 22.957-55.933Q22.818-55.699 22.818-55.425L22.818-54.065Q22.818-53.928 22.969-53.892Q23.119-53.856 23.345-53.856L23.345-53.576M27.877-53.576L26.144-53.576L26.144-53.856Q26.370-53.856 26.518-53.890Q26.667-53.925 26.667-54.065L26.667-56.314L26.079-56.314L26.079-56.594L26.667-56.594L26.667-57.411Q26.667-57.729 26.845-57.977Q27.023-58.224 27.313-58.365Q27.604-58.505 27.915-58.505Q28.171-58.505 28.374-58.363Q28.578-58.221 28.578-57.978Q28.578-57.842 28.479-57.743Q28.379-57.643 28.243-57.643Q28.106-57.643 28.007-57.743Q27.908-57.842 27.908-57.978Q27.908-58.159 28.048-58.252Q27.969-58.279 27.870-58.279Q27.662-58.279 27.508-58.146Q27.354-58.013 27.274-57.809Q27.193-57.606 27.193-57.397L27.193-56.594L28.082-56.594L28.082-56.314L27.221-56.314L27.221-54.065Q27.221-53.856 27.877-53.856L27.877-53.576M28.516-55.059Q28.516-55.401 28.651-55.700Q28.786-55.999 29.025-56.223Q29.265-56.447 29.583-56.572Q29.900-56.697 30.232-56.697Q30.676-56.697 31.076-56.481Q31.476-56.266 31.710-55.888Q31.944-55.511 31.944-55.059Q31.944-54.718 31.803-54.434Q31.661-54.150 31.416-53.943Q31.172-53.737 30.863-53.622Q30.553-53.508 30.232-53.508Q29.801-53.508 29.400-53.709Q28.998-53.911 28.757-54.263Q28.516-54.615 28.516-55.059M30.232-53.757Q30.834-53.757 31.057-54.135Q31.281-54.513 31.281-55.145Q31.281-55.757 31.047-56.116Q30.813-56.474 30.232-56.474Q29.179-56.474 29.179-55.145Q29.179-54.513 29.405-54.135Q29.630-53.757 30.232-53.757M34.289-53.576L32.553-53.576L32.553-53.856Q32.782-53.856 32.930-53.890Q33.079-53.925 33.079-54.065L33.079-55.914Q33.079-56.184 32.971-56.245Q32.864-56.307 32.553-56.307L32.553-56.587L33.582-56.662L33.582-55.955Q33.711-56.263 33.954-56.462Q34.197-56.662 34.515-56.662Q34.733-56.662 34.904-56.538Q35.075-56.413 35.075-56.201Q35.075-56.064 34.976-55.965Q34.877-55.866 34.744-55.866Q34.607-55.866 34.508-55.965Q34.409-56.064 34.409-56.201Q34.409-56.341 34.508-56.440Q34.217-56.440 34.017-56.244Q33.817-56.047 33.725-55.753Q33.633-55.459 33.633-55.179L33.633-54.065Q33.633-53.856 34.289-53.856L34.289-53.576M37.341-53.576L35.708-53.576L35.708-53.856Q35.937-53.856 36.085-53.890Q36.234-53.925 36.234-54.065L36.234-55.914Q36.234-56.184 36.126-56.245Q36.019-56.307 35.708-56.307L35.708-56.587L36.767-56.662L36.767-56.013Q36.938-56.321 37.242-56.492Q37.546-56.662 37.892-56.662Q38.292-56.662 38.568-56.522Q38.845-56.382 38.931-56.034Q39.098-56.327 39.397-56.495Q39.696-56.662 40.042-56.662Q40.547-56.662 40.831-56.439Q41.115-56.215 41.115-55.719L41.115-54.065Q41.115-53.928 41.263-53.892Q41.412-53.856 41.638-53.856L41.638-53.576L40.007-53.576L40.007-53.856Q40.233-53.856 40.383-53.892Q40.534-53.928 40.534-54.065L40.534-55.705Q40.534-56.040 40.414-56.240Q40.294-56.440 39.980-56.440Q39.710-56.440 39.476-56.304Q39.242-56.167 39.103-55.933Q38.965-55.699 38.965-55.425L38.965-54.065Q38.965-53.928 39.114-53.892Q39.262-53.856 39.488-53.856L39.488-53.576L37.857-53.576L37.857-53.856Q38.086-53.856 38.235-53.890Q38.384-53.925 38.384-54.065L38.384-55.705Q38.384-56.040 38.264-56.240Q38.145-56.440 37.830-56.440Q37.560-56.440 37.326-56.304Q37.092-56.167 36.953-55.933Q36.815-55.699 36.815-55.425L36.815-54.065Q36.815-53.928 36.965-53.892Q37.116-53.856 37.341-53.856L37.341-53.576M44.355-51.826Q43.805-52.226 43.434-52.781Q43.063-53.337 42.882-53.983Q42.701-54.629 42.701-55.326Q42.701-55.839 42.802-56.334Q42.902-56.830 43.107-57.281Q43.313-57.732 43.625-58.124Q43.938-58.515 44.355-58.819Q44.365-58.823 44.372-58.824Q44.379-58.826 44.389-58.826L44.458-58.826Q44.492-58.826 44.514-58.802Q44.536-58.778 44.536-58.741Q44.536-58.696 44.509-58.679Q44.160-58.378 43.907-57.994Q43.654-57.609 43.502-57.168Q43.350-56.727 43.278-56.271Q43.207-55.815 43.207-55.326Q43.207-54.325 43.516-53.438Q43.825-52.551 44.509-51.966Q44.536-51.949 44.536-51.905Q44.536-51.867 44.514-51.843Q44.492-51.819 44.458-51.819L44.389-51.819Q44.382-51.823 44.374-51.824Q44.365-51.826 44.355-51.826M45.305-55.059Q45.305-55.401 45.440-55.700Q45.575-55.999 45.815-56.223Q46.054-56.447 46.372-56.572Q46.690-56.697 47.021-56.697Q47.465-56.697 47.865-56.481Q48.265-56.266 48.499-55.888Q48.733-55.511 48.733-55.059Q48.733-54.718 48.592-54.434Q48.450-54.150 48.205-53.943Q47.961-53.737 47.652-53.622Q47.342-53.508 47.021-53.508Q46.590-53.508 46.189-53.709Q45.787-53.911 45.546-54.263Q45.305-54.615 45.305-55.059M47.021-53.757Q47.623-53.757 47.846-54.135Q48.070-54.513 48.070-55.145Q48.070-55.757 47.836-56.116Q47.602-56.474 47.021-56.474Q45.968-56.474 45.968-55.145Q45.968-54.513 46.194-54.135Q46.419-53.757 47.021-53.757M51.078-53.576L49.342-53.576L49.342-53.856Q49.571-53.856 49.720-53.890Q49.868-53.925 49.868-54.065L49.868-55.914Q49.868-56.184 49.761-56.245Q49.653-56.307 49.342-56.307L49.342-56.587L50.371-56.662L50.371-55.955Q50.501-56.263 50.743-56.462Q50.986-56.662 51.304-56.662Q51.523-56.662 51.693-56.538Q51.864-56.413 51.864-56.201Q51.864-56.064 51.765-55.965Q51.666-55.866 51.533-55.866Q51.396-55.866 51.297-55.965Q51.198-56.064 51.198-56.201Q51.198-56.341 51.297-56.440Q51.006-56.440 50.806-56.244Q50.607-56.047 50.514-55.753Q50.422-55.459 50.422-55.179L50.422-54.065Q50.422-53.856 51.078-53.856L51.078-53.576M54.065-53.576L52.514-53.576L52.514-53.856Q52.739-53.856 52.888-53.890Q53.037-53.925 53.037-54.065L53.037-55.914Q53.037-56.102 52.989-56.186Q52.941-56.269 52.844-56.288Q52.746-56.307 52.534-56.307L52.534-56.587L53.590-56.662L53.590-54.065Q53.590-53.925 53.722-53.890Q53.854-53.856 54.065-53.856L54.065-53.576M52.794-57.883Q52.794-58.054 52.917-58.173Q53.040-58.293 53.211-58.293Q53.378-58.293 53.502-58.173Q53.625-58.054 53.625-57.883Q53.625-57.708 53.502-57.585Q53.378-57.462 53.211-57.462Q53.040-57.462 52.917-57.585Q52.794-57.708 52.794-57.883M54.670-53.043Q54.670-53.289 54.867-53.473Q55.064-53.658 55.320-53.737Q55.183-53.849 55.111-54.010Q55.040-54.171 55.040-54.352Q55.040-54.673 55.252-54.919Q54.917-55.217 54.917-55.627Q54.917-56.088 55.306-56.375Q55.696-56.662 56.174-56.662Q56.646-56.662 56.981-56.416Q57.155-56.570 57.366-56.652Q57.576-56.734 57.805-56.734Q57.969-56.734 58.090-56.627Q58.211-56.519 58.211-56.355Q58.211-56.259 58.140-56.187Q58.068-56.116 57.976-56.116Q57.877-56.116 57.806-56.189Q57.736-56.263 57.736-56.362Q57.736-56.416 57.750-56.447L57.757-56.461Q57.764-56.481 57.772-56.492Q57.781-56.502 57.784-56.509Q57.429-56.509 57.142-56.286Q57.429-55.993 57.429-55.627Q57.429-55.312 57.244-55.080Q57.060-54.847 56.771-54.719Q56.482-54.591 56.174-54.591Q55.973-54.591 55.781-54.641Q55.590-54.690 55.412-54.800Q55.320-54.673 55.320-54.530Q55.320-54.348 55.448-54.213Q55.576-54.078 55.761-54.078L56.393-54.078Q56.841-54.078 57.210-54.007Q57.579-53.935 57.839-53.706Q58.099-53.477 58.099-53.043Q58.099-52.722 57.803-52.520Q57.507-52.318 57.104-52.229Q56.701-52.140 56.386-52.140Q56.068-52.140 55.665-52.229Q55.262-52.318 54.966-52.520Q54.670-52.722 54.670-53.043M55.125-53.043Q55.125-52.814 55.344-52.665Q55.563-52.516 55.855-52.448Q56.147-52.380 56.386-52.380Q56.550-52.380 56.759-52.416Q56.967-52.451 57.174-52.532Q57.381-52.612 57.513-52.740Q57.644-52.868 57.644-53.043Q57.644-53.395 57.263-53.489Q56.882-53.583 56.379-53.583L55.761-53.583Q55.522-53.583 55.323-53.432Q55.125-53.282 55.125-53.043M56.174-54.830Q56.841-54.830 56.841-55.627Q56.841-56.427 56.174-56.427Q55.504-56.427 55.504-55.627Q55.504-54.830 56.174-54.830M59.015-51.819L58.946-51.819Q58.912-51.819 58.890-51.845Q58.868-51.870 58.868-51.905Q58.868-51.949 58.898-51.966Q59.254-52.270 59.503-52.660Q59.753-53.050 59.905-53.482Q60.057-53.914 60.127-54.383Q60.197-54.851 60.197-55.326Q60.197-55.805 60.127-56.271Q60.057-56.738 59.903-57.173Q59.750-57.609 59.498-57.997Q59.247-58.385 58.898-58.679Q58.868-58.696 58.868-58.741Q58.868-58.775 58.890-58.800Q58.912-58.826 58.946-58.826L59.015-58.826Q59.025-58.826 59.034-58.824Q59.042-58.823 59.052-58.819Q59.596-58.419 59.968-57.866Q60.341-57.312 60.522-56.666Q60.703-56.020 60.703-55.326Q60.703-54.625 60.522-53.978Q60.341-53.330 59.967-52.776Q59.592-52.222 59.052-51.826Q59.042-51.826 59.034-51.824Q59.025-51.823 59.015-51.819\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(77.51 47.955)\">\u003Cpath d=\"M-7.657-54.417L-7.657-56.314L-8.296-56.314L-8.296-56.536Q-7.978-56.536-7.761-56.746Q-7.544-56.956-7.444-57.266Q-7.343-57.575-7.343-57.883L-7.076-57.883L-7.076-56.594L-5.999-56.594L-5.999-56.314L-7.076-56.314L-7.076-54.430Q-7.076-54.154-6.972-53.955Q-6.868-53.757-6.608-53.757Q-6.451-53.757-6.345-53.861Q-6.239-53.966-6.189-54.119Q-6.140-54.273-6.140-54.430L-6.140-54.844L-5.873-54.844L-5.873-54.417Q-5.873-54.191-5.972-53.981Q-6.071-53.771-6.256-53.639Q-6.440-53.508-6.669-53.508Q-7.107-53.508-7.382-53.745Q-7.657-53.983-7.657-54.417M-4.489-54.410L-4.489-55.914Q-4.489-56.184-4.596-56.245Q-4.704-56.307-5.015-56.307L-5.015-56.587L-3.908-56.662L-3.908-54.430L-3.908-54.410Q-3.908-54.130-3.856-53.986Q-3.805-53.843-3.663-53.786Q-3.521-53.730-3.234-53.730Q-2.981-53.730-2.776-53.870Q-2.571-54.010-2.455-54.236Q-2.339-54.461-2.339-54.711L-2.339-55.914Q-2.339-56.184-2.446-56.245Q-2.554-56.307-2.865-56.307L-2.865-56.587L-1.758-56.662L-1.758-54.249Q-1.758-54.058-1.705-53.976Q-1.652-53.894-1.551-53.875Q-1.450-53.856-1.235-53.856L-1.235-53.576L-2.311-53.508L-2.311-54.072Q-2.421-53.890-2.566-53.767Q-2.711-53.644-2.898-53.576Q-3.084-53.508-3.286-53.508Q-4.489-53.508-4.489-54.410M1.103-53.576L-0.633-53.576L-0.633-53.856Q-0.404-53.856-0.256-53.890Q-0.107-53.925-0.107-54.065L-0.107-55.914Q-0.107-56.184-0.215-56.245Q-0.322-56.307-0.633-56.307L-0.633-56.587L0.396-56.662L0.396-55.955Q0.525-56.263 0.768-56.462Q1.011-56.662 1.329-56.662Q1.547-56.662 1.718-56.538Q1.889-56.413 1.889-56.201Q1.889-56.064 1.790-55.965Q1.691-55.866 1.558-55.866Q1.421-55.866 1.322-55.965Q1.223-56.064 1.223-56.201Q1.223-56.341 1.322-56.440Q1.031-56.440 0.831-56.244Q0.631-56.047 0.539-55.753Q0.447-55.459 0.447-55.179L0.447-54.065Q0.447-53.856 1.103-53.856L1.103-53.576M4.155-53.576L2.522-53.576L2.522-53.856Q2.751-53.856 2.899-53.890Q3.048-53.925 3.048-54.065L3.048-55.914Q3.048-56.184 2.940-56.245Q2.833-56.307 2.522-56.307L2.522-56.587L3.581-56.662L3.581-56.013Q3.752-56.321 4.056-56.492Q4.360-56.662 4.706-56.662Q5.211-56.662 5.495-56.439Q5.779-56.215 5.779-55.719L5.779-54.065Q5.779-53.928 5.928-53.892Q6.076-53.856 6.302-53.856L6.302-53.576L4.671-53.576L4.671-53.856Q4.900-53.856 5.049-53.890Q5.198-53.925 5.198-54.065L5.198-55.705Q5.198-56.040 5.078-56.240Q4.959-56.440 4.644-56.440Q4.374-56.440 4.140-56.304Q3.906-56.167 3.767-55.933Q3.629-55.699 3.629-55.425L3.629-54.065Q3.629-53.928 3.779-53.892Q3.930-53.856 4.155-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 47.955)\">\u003Cpath d=\"M12.606-53.576L9.721-53.576L9.721-53.778Q9.721-53.808 9.748-53.836L10.996-55.053Q11.068-55.128 11.110-55.170Q11.153-55.213 11.232-55.292Q11.645-55.705 11.876-56.063Q12.107-56.420 12.107-56.844Q12.107-57.076 12.028-57.279Q11.949-57.483 11.808-57.633Q11.666-57.784 11.471-57.864Q11.276-57.944 11.044-57.944Q10.733-57.944 10.475-57.785Q10.217-57.626 10.087-57.349L10.107-57.349Q10.275-57.349 10.382-57.238Q10.490-57.127 10.490-56.963Q10.490-56.806 10.381-56.693Q10.271-56.580 10.107-56.580Q9.947-56.580 9.834-56.693Q9.721-56.806 9.721-56.963Q9.721-57.339 9.929-57.626Q10.138-57.913 10.473-58.069Q10.808-58.224 11.163-58.224Q11.587-58.224 11.967-58.066Q12.346-57.907 12.580-57.590Q12.814-57.274 12.814-56.844Q12.814-56.533 12.674-56.264Q12.534-55.996 12.329-55.791Q12.124-55.586 11.761-55.304Q11.399-55.022 11.290-54.926L10.435-54.198L11.078-54.198Q11.341-54.198 11.630-54.200Q11.919-54.201 12.137-54.210Q12.356-54.219 12.373-54.236Q12.435-54.301 12.472-54.468Q12.510-54.636 12.548-54.878L12.814-54.878L12.606-53.576M13.976-53.996Q13.976-54.164 14.099-54.287Q14.222-54.410 14.397-54.410Q14.564-54.410 14.687-54.287Q14.810-54.164 14.810-53.996Q14.810-53.822 14.687-53.699Q14.564-53.576 14.397-53.576Q14.222-53.576 14.099-53.699Q13.976-53.822 13.976-53.996M13.976-56.180Q13.976-56.348 14.099-56.471Q14.222-56.594 14.397-56.594Q14.564-56.594 14.687-56.471Q14.810-56.348 14.810-56.180Q14.810-56.006 14.687-55.883Q14.564-55.760 14.397-55.760Q14.222-55.760 14.099-55.883Q13.976-56.006 13.976-56.180\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 47.955)\">\u003Cpath d=\"M19.459-54.304Q19.459-54.636 19.682-54.863Q19.906-55.090 20.250-55.218Q20.593-55.347 20.966-55.399Q21.338-55.452 21.643-55.452L21.643-55.705Q21.643-55.910 21.535-56.090Q21.427-56.269 21.246-56.372Q21.065-56.474 20.857-56.474Q20.450-56.474 20.214-56.382Q20.303-56.345 20.349-56.261Q20.395-56.177 20.395-56.075Q20.395-55.979 20.349-55.900Q20.303-55.822 20.222-55.777Q20.142-55.733 20.053-55.733Q19.903-55.733 19.802-55.830Q19.701-55.928 19.701-56.075Q19.701-56.697 20.857-56.697Q21.068-56.697 21.318-56.633Q21.567-56.570 21.769-56.451Q21.971-56.331 22.097-56.146Q22.224-55.962 22.224-55.719L22.224-54.143Q22.224-54.027 22.285-53.931Q22.347-53.836 22.460-53.836Q22.569-53.836 22.634-53.930Q22.699-54.024 22.699-54.143L22.699-54.591L22.965-54.591L22.965-54.143Q22.965-53.873 22.738-53.708Q22.511-53.542 22.231-53.542Q22.022-53.542 21.885-53.696Q21.749-53.849 21.725-54.065Q21.578-53.798 21.296-53.653Q21.014-53.508 20.689-53.508Q20.412-53.508 20.128-53.583Q19.845-53.658 19.652-53.837Q19.459-54.017 19.459-54.304M20.074-54.304Q20.074-54.130 20.175-54Q20.275-53.870 20.431-53.800Q20.586-53.730 20.751-53.730Q20.969-53.730 21.178-53.827Q21.386-53.925 21.514-54.106Q21.643-54.287 21.643-54.513L21.643-55.241Q21.318-55.241 20.952-55.150Q20.586-55.059 20.330-54.847Q20.074-54.636 20.074-54.304M23.382-55.087Q23.382-55.425 23.523-55.716Q23.663-56.006 23.907-56.220Q24.151-56.433 24.456-56.548Q24.760-56.662 25.085-56.662Q25.355-56.662 25.618-56.563Q25.881-56.464 26.072-56.286L26.072-57.684Q26.072-57.954 25.965-58.016Q25.857-58.077 25.546-58.077L25.546-58.358L26.623-58.433L26.623-54.249Q26.623-54.061 26.677-53.978Q26.732-53.894 26.833-53.875Q26.934-53.856 27.149-53.856L27.149-53.576L26.042-53.508L26.042-53.925Q25.625-53.508 24.999-53.508Q24.568-53.508 24.196-53.720Q23.823-53.931 23.603-54.292Q23.382-54.653 23.382-55.087M25.057-53.730Q25.266-53.730 25.452-53.802Q25.638-53.873 25.792-54.010Q25.946-54.147 26.042-54.325L26.042-55.934Q25.956-56.081 25.811-56.201Q25.666-56.321 25.496-56.380Q25.327-56.440 25.146-56.440Q24.586-56.440 24.317-56.051Q24.049-55.661 24.049-55.080Q24.049-54.509 24.283-54.119Q24.517-53.730 25.057-53.730M27.798-55.087Q27.798-55.425 27.939-55.716Q28.079-56.006 28.323-56.220Q28.567-56.433 28.872-56.548Q29.176-56.662 29.501-56.662Q29.771-56.662 30.034-56.563Q30.297-56.464 30.488-56.286L30.488-57.684Q30.488-57.954 30.381-58.016Q30.273-58.077 29.962-58.077L29.962-58.358L31.039-58.433L31.039-54.249Q31.039-54.061 31.093-53.978Q31.148-53.894 31.249-53.875Q31.350-53.856 31.565-53.856L31.565-53.576L30.458-53.508L30.458-53.925Q30.041-53.508 29.415-53.508Q28.984-53.508 28.612-53.720Q28.239-53.931 28.019-54.292Q27.798-54.653 27.798-55.087M29.473-53.730Q29.682-53.730 29.868-53.802Q30.054-53.873 30.208-54.010Q30.362-54.147 30.458-54.325L30.458-55.934Q30.372-56.081 30.227-56.201Q30.082-56.321 29.912-56.380Q29.743-56.440 29.562-56.440Q29.002-56.440 28.733-56.051Q28.465-55.661 28.465-55.080Q28.465-54.509 28.699-54.119Q28.933-53.730 29.473-53.730\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 47.955)\">\u003Cpath d=\"M34.914-55.087Q34.914-55.425 35.055-55.716Q35.195-56.006 35.439-56.220Q35.683-56.433 35.988-56.548Q36.292-56.662 36.617-56.662Q36.887-56.662 37.150-56.563Q37.413-56.464 37.604-56.286L37.604-57.684Q37.604-57.954 37.497-58.016Q37.389-58.077 37.078-58.077L37.078-58.358L38.155-58.433L38.155-54.249Q38.155-54.061 38.209-53.978Q38.264-53.894 38.365-53.875Q38.466-53.856 38.681-53.856L38.681-53.576L37.574-53.508L37.574-53.925Q37.157-53.508 36.531-53.508Q36.100-53.508 35.728-53.720Q35.355-53.931 35.135-54.292Q34.914-54.653 34.914-55.087M36.589-53.730Q36.798-53.730 36.984-53.802Q37.170-53.873 37.324-54.010Q37.478-54.147 37.574-54.325L37.574-55.934Q37.488-56.081 37.343-56.201Q37.198-56.321 37.028-56.380Q36.859-56.440 36.678-56.440Q36.118-56.440 35.849-56.051Q35.581-55.661 35.581-55.080Q35.581-54.509 35.815-54.119Q36.049-53.730 36.589-53.730M39.289-55.111Q39.289-55.432 39.414-55.721Q39.539-56.010 39.765-56.233Q39.990-56.457 40.286-56.577Q40.581-56.697 40.899-56.697Q41.227-56.697 41.489-56.597Q41.750-56.498 41.926-56.316Q42.102-56.133 42.196-55.875Q42.290-55.617 42.290-55.285Q42.290-55.193 42.208-55.172L39.953-55.172L39.953-55.111Q39.953-54.523 40.236-54.140Q40.520-53.757 41.087-53.757Q41.409-53.757 41.677-53.950Q41.945-54.143 42.034-54.458Q42.041-54.499 42.116-54.513L42.208-54.513Q42.290-54.489 42.290-54.417Q42.290-54.410 42.284-54.383Q42.171-53.986 41.800-53.747Q41.429-53.508 41.005-53.508Q40.568-53.508 40.168-53.716Q39.768-53.925 39.529-54.292Q39.289-54.659 39.289-55.111M39.959-55.381L41.774-55.381Q41.774-55.658 41.677-55.910Q41.579-56.163 41.381-56.319Q41.183-56.474 40.899-56.474Q40.622-56.474 40.409-56.316Q40.195-56.157 40.077-55.902Q39.959-55.647 39.959-55.381M42.878-53.583L42.878-54.646Q42.878-54.670 42.906-54.697Q42.933-54.724 42.957-54.724L43.066-54.724Q43.131-54.724 43.145-54.666Q43.241-54.232 43.487-53.981Q43.733-53.730 44.146-53.730Q44.488-53.730 44.741-53.863Q44.994-53.996 44.994-54.304Q44.994-54.461 44.900-54.576Q44.806-54.690 44.668-54.759Q44.529-54.827 44.362-54.865L43.781-54.964Q43.425-55.032 43.152-55.253Q42.878-55.473 42.878-55.815Q42.878-56.064 42.989-56.239Q43.100-56.413 43.287-56.512Q43.473-56.611 43.688-56.654Q43.904-56.697 44.146-56.697Q44.560-56.697 44.840-56.515L45.056-56.690Q45.066-56.693 45.073-56.695Q45.079-56.697 45.090-56.697L45.141-56.697Q45.168-56.697 45.192-56.673Q45.216-56.649 45.216-56.621L45.216-55.774Q45.216-55.753 45.192-55.726Q45.168-55.699 45.141-55.699L45.028-55.699Q45.001-55.699 44.975-55.724Q44.950-55.750 44.950-55.774Q44.950-56.010 44.844-56.174Q44.738-56.338 44.555-56.420Q44.372-56.502 44.140-56.502Q43.811-56.502 43.555-56.399Q43.299-56.297 43.299-56.020Q43.299-55.825 43.482-55.716Q43.664-55.606 43.893-55.565L44.468-55.459Q44.714-55.411 44.927-55.283Q45.141-55.155 45.278-54.952Q45.414-54.748 45.414-54.499Q45.414-53.986 45.049-53.747Q44.683-53.508 44.146-53.508Q43.651-53.508 43.319-53.802L43.053-53.528Q43.032-53.508 43.005-53.508L42.957-53.508Q42.933-53.508 42.906-53.535Q42.878-53.562 42.878-53.583M46.570-54.417L46.570-56.314L45.931-56.314L45.931-56.536Q46.248-56.536 46.465-56.746Q46.683-56.956 46.783-57.266Q46.884-57.575 46.884-57.883L47.151-57.883L47.151-56.594L48.227-56.594L48.227-56.314L47.151-56.314L47.151-54.430Q47.151-54.154 47.255-53.955Q47.359-53.757 47.619-53.757Q47.776-53.757 47.882-53.861Q47.988-53.966 48.038-54.119Q48.087-54.273 48.087-54.430L48.087-54.844L48.354-54.844L48.354-54.417Q48.354-54.191 48.255-53.981Q48.156-53.771 47.971-53.639Q47.787-53.508 47.558-53.508Q47.120-53.508 46.845-53.745Q46.570-53.983 46.570-54.417\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-warn)\" stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(77.51 101.335)\">\u003Cpath d=\"M-7.657-54.417L-7.657-56.314L-8.296-56.314L-8.296-56.536Q-7.978-56.536-7.761-56.746Q-7.544-56.956-7.444-57.266Q-7.343-57.575-7.343-57.883L-7.076-57.883L-7.076-56.594L-5.999-56.594L-5.999-56.314L-7.076-56.314L-7.076-54.430Q-7.076-54.154-6.972-53.955Q-6.868-53.757-6.608-53.757Q-6.451-53.757-6.345-53.861Q-6.239-53.966-6.189-54.119Q-6.140-54.273-6.140-54.430L-6.140-54.844L-5.873-54.844L-5.873-54.417Q-5.873-54.191-5.972-53.981Q-6.071-53.771-6.256-53.639Q-6.440-53.508-6.669-53.508Q-7.107-53.508-7.382-53.745Q-7.657-53.983-7.657-54.417M-4.489-54.410L-4.489-55.914Q-4.489-56.184-4.596-56.245Q-4.704-56.307-5.015-56.307L-5.015-56.587L-3.908-56.662L-3.908-54.430L-3.908-54.410Q-3.908-54.130-3.856-53.986Q-3.805-53.843-3.663-53.786Q-3.521-53.730-3.234-53.730Q-2.981-53.730-2.776-53.870Q-2.571-54.010-2.455-54.236Q-2.339-54.461-2.339-54.711L-2.339-55.914Q-2.339-56.184-2.446-56.245Q-2.554-56.307-2.865-56.307L-2.865-56.587L-1.758-56.662L-1.758-54.249Q-1.758-54.058-1.705-53.976Q-1.652-53.894-1.551-53.875Q-1.450-53.856-1.235-53.856L-1.235-53.576L-2.311-53.508L-2.311-54.072Q-2.421-53.890-2.566-53.767Q-2.711-53.644-2.898-53.576Q-3.084-53.508-3.286-53.508Q-4.489-53.508-4.489-54.410M1.103-53.576L-0.633-53.576L-0.633-53.856Q-0.404-53.856-0.256-53.890Q-0.107-53.925-0.107-54.065L-0.107-55.914Q-0.107-56.184-0.215-56.245Q-0.322-56.307-0.633-56.307L-0.633-56.587L0.396-56.662L0.396-55.955Q0.525-56.263 0.768-56.462Q1.011-56.662 1.329-56.662Q1.547-56.662 1.718-56.538Q1.889-56.413 1.889-56.201Q1.889-56.064 1.790-55.965Q1.691-55.866 1.558-55.866Q1.421-55.866 1.322-55.965Q1.223-56.064 1.223-56.201Q1.223-56.341 1.322-56.440Q1.031-56.440 0.831-56.244Q0.631-56.047 0.539-55.753Q0.447-55.459 0.447-55.179L0.447-54.065Q0.447-53.856 1.103-53.856L1.103-53.576M4.155-53.576L2.522-53.576L2.522-53.856Q2.751-53.856 2.899-53.890Q3.048-53.925 3.048-54.065L3.048-55.914Q3.048-56.184 2.940-56.245Q2.833-56.307 2.522-56.307L2.522-56.587L3.581-56.662L3.581-56.013Q3.752-56.321 4.056-56.492Q4.360-56.662 4.706-56.662Q5.211-56.662 5.495-56.439Q5.779-56.215 5.779-55.719L5.779-54.065Q5.779-53.928 5.928-53.892Q6.076-53.856 6.302-53.856L6.302-53.576L4.671-53.576L4.671-53.856Q4.900-53.856 5.049-53.890Q5.198-53.925 5.198-54.065L5.198-55.705Q5.198-56.040 5.078-56.240Q4.959-56.440 4.644-56.440Q4.374-56.440 4.140-56.304Q3.906-56.167 3.767-55.933Q3.629-55.699 3.629-55.425L3.629-54.065Q3.629-53.928 3.779-53.892Q3.930-53.856 4.155-53.856\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 101.335)\">\u003Cpath d=\"M10.076-54.123Q10.196-53.966 10.387-53.867Q10.579-53.767 10.794-53.728Q11.009-53.689 11.232-53.689Q11.529-53.689 11.724-53.844Q11.919-54 12.009-54.254Q12.100-54.509 12.100-54.793Q12.100-55.087 12.008-55.338Q11.915-55.589 11.717-55.745Q11.519-55.900 11.225-55.900L10.709-55.900Q10.681-55.900 10.656-55.926Q10.630-55.951 10.630-55.975L10.630-56.047Q10.630-56.078 10.656-56.100Q10.681-56.122 10.709-56.122L11.150-56.153Q11.512-56.153 11.732-56.510Q11.953-56.868 11.953-57.257Q11.953-57.585 11.758-57.789Q11.563-57.992 11.232-57.992Q10.945-57.992 10.692-57.908Q10.439-57.825 10.275-57.637Q10.422-57.637 10.522-57.522Q10.623-57.408 10.623-57.257Q10.623-57.107 10.517-56.997Q10.411-56.888 10.254-56.888Q10.093-56.888 9.984-56.997Q9.875-57.107 9.875-57.257Q9.875-57.582 10.083-57.801Q10.292-58.019 10.608-58.122Q10.924-58.224 11.232-58.224Q11.550-58.224 11.878-58.120Q12.206-58.016 12.433-57.794Q12.660-57.572 12.660-57.257Q12.660-56.823 12.373-56.498Q12.086-56.174 11.652-56.027Q11.963-55.962 12.243-55.796Q12.524-55.630 12.701-55.372Q12.879-55.114 12.879-54.793Q12.879-54.383 12.635-54.073Q12.390-53.764 12.009-53.600Q11.628-53.436 11.232-53.436Q10.863-53.436 10.505-53.549Q10.148-53.661 9.904-53.911Q9.659-54.160 9.659-54.530Q9.659-54.701 9.776-54.813Q9.892-54.926 10.063-54.926Q10.172-54.926 10.263-54.875Q10.353-54.824 10.408-54.731Q10.463-54.639 10.463-54.530Q10.463-54.362 10.350-54.243Q10.237-54.123 10.076-54.123M13.976-53.996Q13.976-54.164 14.099-54.287Q14.222-54.410 14.397-54.410Q14.564-54.410 14.687-54.287Q14.810-54.164 14.810-53.996Q14.810-53.822 14.687-53.699Q14.564-53.576 14.397-53.576Q14.222-53.576 14.099-53.699Q13.976-53.822 13.976-53.996M13.976-56.180Q13.976-56.348 14.099-56.471Q14.222-56.594 14.397-56.594Q14.564-56.594 14.687-56.471Q14.810-56.348 14.810-56.180Q14.810-56.006 14.687-55.883Q14.564-55.760 14.397-55.760Q14.222-55.760 14.099-55.883Q13.976-56.006 13.976-56.180\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 101.335)\">\u003Cpath d=\"M19.400-55.087Q19.400-55.415 19.535-55.716Q19.670-56.016 19.906-56.237Q20.142-56.457 20.446-56.577Q20.751-56.697 21.075-56.697Q21.581-56.697 21.930-56.594Q22.278-56.492 22.278-56.116Q22.278-55.969 22.181-55.868Q22.084-55.767 21.937-55.767Q21.783-55.767 21.684-55.866Q21.585-55.965 21.585-56.116Q21.585-56.304 21.725-56.396Q21.523-56.447 21.082-56.447Q20.727-56.447 20.498-56.251Q20.269-56.054 20.168-55.745Q20.067-55.435 20.067-55.087Q20.067-54.738 20.193-54.432Q20.320-54.126 20.575-53.942Q20.829-53.757 21.185-53.757Q21.407-53.757 21.591-53.841Q21.776-53.925 21.911-54.080Q22.046-54.236 22.104-54.444Q22.118-54.499 22.172-54.499L22.285-54.499Q22.316-54.499 22.338-54.475Q22.360-54.451 22.360-54.417L22.360-54.396Q22.275-54.109 22.087-53.911Q21.899-53.713 21.634-53.610Q21.369-53.508 21.075-53.508Q20.645-53.508 20.257-53.714Q19.869-53.921 19.635-54.284Q19.400-54.646 19.400-55.087M22.907-55.059Q22.907-55.401 23.042-55.700Q23.177-55.999 23.417-56.223Q23.656-56.447 23.974-56.572Q24.292-56.697 24.623-56.697Q25.067-56.697 25.467-56.481Q25.867-56.266 26.101-55.888Q26.336-55.511 26.336-55.059Q26.336-54.718 26.194-54.434Q26.052-54.150 25.807-53.943Q25.563-53.737 25.254-53.622Q24.944-53.508 24.623-53.508Q24.192-53.508 23.791-53.709Q23.389-53.911 23.148-54.263Q22.907-54.615 22.907-55.059M24.623-53.757Q25.225-53.757 25.449-54.135Q25.672-54.513 25.672-55.145Q25.672-55.757 25.438-56.116Q25.204-56.474 24.623-56.474Q23.570-56.474 23.570-55.145Q23.570-54.513 23.796-54.135Q24.022-53.757 24.623-53.757M28.680-53.576L26.944-53.576L26.944-53.856Q27.173-53.856 27.322-53.890Q27.470-53.925 27.470-54.065L27.470-55.914Q27.470-56.184 27.363-56.245Q27.255-56.307 26.944-56.307L26.944-56.587L27.973-56.662L27.973-55.955Q28.103-56.263 28.345-56.462Q28.588-56.662 28.906-56.662Q29.125-56.662 29.295-56.538Q29.466-56.413 29.466-56.201Q29.466-56.064 29.367-55.965Q29.268-55.866 29.135-55.866Q28.998-55.866 28.899-55.965Q28.800-56.064 28.800-56.201Q28.800-56.341 28.899-56.440Q28.608-56.440 28.409-56.244Q28.209-56.047 28.116-55.753Q28.024-55.459 28.024-55.179L28.024-54.065Q28.024-53.856 28.680-53.856L28.680-53.576M31.801-53.576L30.065-53.576L30.065-53.856Q30.294-53.856 30.442-53.890Q30.591-53.925 30.591-54.065L30.591-55.914Q30.591-56.184 30.483-56.245Q30.376-56.307 30.065-56.307L30.065-56.587L31.093-56.662L31.093-55.955Q31.223-56.263 31.466-56.462Q31.709-56.662 32.026-56.662Q32.245-56.662 32.416-56.538Q32.587-56.413 32.587-56.201Q32.587-56.064 32.488-55.965Q32.389-55.866 32.255-55.866Q32.119-55.866 32.020-55.965Q31.920-56.064 31.920-56.201Q31.920-56.341 32.020-56.440Q31.729-56.440 31.529-56.244Q31.329-56.047 31.237-55.753Q31.145-55.459 31.145-55.179L31.145-54.065Q31.145-53.856 31.801-53.856L31.801-53.576M33.130-55.111Q33.130-55.432 33.255-55.721Q33.380-56.010 33.606-56.233Q33.831-56.457 34.127-56.577Q34.422-56.697 34.740-56.697Q35.068-56.697 35.330-56.597Q35.591-56.498 35.767-56.316Q35.943-56.133 36.037-55.875Q36.131-55.617 36.131-55.285Q36.131-55.193 36.049-55.172L33.794-55.172L33.794-55.111Q33.794-54.523 34.077-54.140Q34.361-53.757 34.928-53.757Q35.250-53.757 35.518-53.950Q35.786-54.143 35.875-54.458Q35.882-54.499 35.957-54.513L36.049-54.513Q36.131-54.489 36.131-54.417Q36.131-54.410 36.125-54.383Q36.012-53.986 35.641-53.747Q35.270-53.508 34.846-53.508Q34.409-53.508 34.009-53.716Q33.609-53.925 33.370-54.292Q33.130-54.659 33.130-55.111M33.800-55.381L35.615-55.381Q35.615-55.658 35.518-55.910Q35.420-56.163 35.222-56.319Q35.024-56.474 34.740-56.474Q34.463-56.474 34.250-56.316Q34.036-56.157 33.918-55.902Q33.800-55.647 33.800-55.381M36.719-55.087Q36.719-55.415 36.854-55.716Q36.989-56.016 37.225-56.237Q37.461-56.457 37.765-56.577Q38.069-56.697 38.394-56.697Q38.900-56.697 39.249-56.594Q39.597-56.492 39.597-56.116Q39.597-55.969 39.500-55.868Q39.402-55.767 39.255-55.767Q39.102-55.767 39.003-55.866Q38.903-55.965 38.903-56.116Q38.903-56.304 39.044-56.396Q38.842-56.447 38.401-56.447Q38.045-56.447 37.816-56.251Q37.587-56.054 37.487-55.745Q37.386-55.435 37.386-55.087Q37.386-54.738 37.512-54.432Q37.639-54.126 37.893-53.942Q38.148-53.757 38.503-53.757Q38.726-53.757 38.910-53.841Q39.095-53.925 39.230-54.080Q39.365-54.236 39.423-54.444Q39.437-54.499 39.491-54.499L39.604-54.499Q39.635-54.499 39.657-54.475Q39.679-54.451 39.679-54.417L39.679-54.396Q39.594-54.109 39.406-53.911Q39.218-53.713 38.953-53.610Q38.688-53.508 38.394-53.508Q37.963-53.508 37.576-53.714Q37.188-53.921 36.953-54.284Q36.719-54.646 36.719-55.087M40.794-54.417L40.794-56.314L40.154-56.314L40.154-56.536Q40.472-56.536 40.689-56.746Q40.906-56.956 41.007-57.266Q41.108-57.575 41.108-57.883L41.375-57.883L41.375-56.594L42.451-56.594L42.451-56.314L41.375-56.314L41.375-54.430Q41.375-54.154 41.479-53.955Q41.583-53.757 41.843-53.757Q42-53.757 42.106-53.861Q42.212-53.966 42.262-54.119Q42.311-54.273 42.311-54.430L42.311-54.844L42.578-54.844L42.578-54.417Q42.578-54.191 42.479-53.981Q42.379-53.771 42.195-53.639Q42.010-53.508 41.781-53.508Q41.344-53.508 41.069-53.745Q40.794-53.983 40.794-54.417M45.517-51.826Q44.967-52.226 44.596-52.781Q44.225-53.337 44.044-53.983Q43.863-54.629 43.863-55.326Q43.863-55.839 43.964-56.334Q44.065-56.830 44.270-57.281Q44.475-57.732 44.787-58.124Q45.100-58.515 45.517-58.819Q45.527-58.823 45.534-58.824Q45.541-58.826 45.551-58.826L45.620-58.826Q45.654-58.826 45.676-58.802Q45.698-58.778 45.698-58.741Q45.698-58.696 45.671-58.679Q45.322-58.378 45.069-57.994Q44.816-57.609 44.664-57.168Q44.512-56.727 44.440-56.271Q44.369-55.815 44.369-55.326Q44.369-54.325 44.678-53.438Q44.987-52.551 45.671-51.966Q45.698-51.949 45.698-51.905Q45.698-51.867 45.676-51.843Q45.654-51.819 45.620-51.819L45.551-51.819Q45.544-51.823 45.536-51.824Q45.527-51.826 45.517-51.826M46.467-55.059Q46.467-55.401 46.602-55.700Q46.737-55.999 46.977-56.223Q47.216-56.447 47.534-56.572Q47.852-56.697 48.183-56.697Q48.628-56.697 49.027-56.481Q49.427-56.266 49.661-55.888Q49.896-55.511 49.896-55.059Q49.896-54.718 49.754-54.434Q49.612-54.150 49.367-53.943Q49.123-53.737 48.814-53.622Q48.504-53.508 48.183-53.508Q47.753-53.508 47.351-53.709Q46.949-53.911 46.708-54.263Q46.467-54.615 46.467-55.059M48.183-53.757Q48.785-53.757 49.009-54.135Q49.232-54.513 49.232-55.145Q49.232-55.757 48.998-56.116Q48.764-56.474 48.183-56.474Q47.130-56.474 47.130-55.145Q47.130-54.513 47.356-54.135Q47.582-53.757 48.183-53.757M52.240-53.576L50.504-53.576L50.504-53.856Q50.733-53.856 50.882-53.890Q51.030-53.925 51.030-54.065L51.030-55.914Q51.030-56.184 50.923-56.245Q50.815-56.307 50.504-56.307L50.504-56.587L51.533-56.662L51.533-55.955Q51.663-56.263 51.905-56.462Q52.148-56.662 52.466-56.662Q52.685-56.662 52.856-56.538Q53.026-56.413 53.026-56.201Q53.026-56.064 52.927-55.965Q52.828-55.866 52.695-55.866Q52.558-55.866 52.459-55.965Q52.360-56.064 52.360-56.201Q52.360-56.341 52.459-56.440Q52.169-56.440 51.969-56.244Q51.769-56.047 51.676-55.753Q51.584-55.459 51.584-55.179L51.584-54.065Q51.584-53.856 52.240-53.856L52.240-53.576M55.228-53.576L53.676-53.576L53.676-53.856Q53.901-53.856 54.050-53.890Q54.199-53.925 54.199-54.065L54.199-55.914Q54.199-56.102 54.151-56.186Q54.103-56.269 54.006-56.288Q53.908-56.307 53.696-56.307L53.696-56.587L54.753-56.662L54.753-54.065Q54.753-53.925 54.884-53.890Q55.016-53.856 55.228-53.856L55.228-53.576M53.956-57.883Q53.956-58.054 54.079-58.173Q54.202-58.293 54.373-58.293Q54.541-58.293 54.664-58.173Q54.787-58.054 54.787-57.883Q54.787-57.708 54.664-57.585Q54.541-57.462 54.373-57.462Q54.202-57.462 54.079-57.585Q53.956-57.708 53.956-57.883M55.833-53.043Q55.833-53.289 56.029-53.473Q56.226-53.658 56.482-53.737Q56.345-53.849 56.273-54.010Q56.202-54.171 56.202-54.352Q56.202-54.673 56.414-54.919Q56.079-55.217 56.079-55.627Q56.079-56.088 56.468-56.375Q56.858-56.662 57.336-56.662Q57.808-56.662 58.143-56.416Q58.317-56.570 58.528-56.652Q58.738-56.734 58.967-56.734Q59.131-56.734 59.252-56.627Q59.374-56.519 59.374-56.355Q59.374-56.259 59.302-56.187Q59.230-56.116 59.138-56.116Q59.039-56.116 58.969-56.189Q58.898-56.263 58.898-56.362Q58.898-56.416 58.912-56.447L58.919-56.461Q58.926-56.481 58.934-56.492Q58.943-56.502 58.946-56.509Q58.591-56.509 58.304-56.286Q58.591-55.993 58.591-55.627Q58.591-55.312 58.406-55.080Q58.222-54.847 57.933-54.719Q57.644-54.591 57.336-54.591Q57.135-54.591 56.943-54.641Q56.752-54.690 56.574-54.800Q56.482-54.673 56.482-54.530Q56.482-54.348 56.610-54.213Q56.738-54.078 56.923-54.078L57.555-54.078Q58.003-54.078 58.372-54.007Q58.741-53.935 59.001-53.706Q59.261-53.477 59.261-53.043Q59.261-52.722 58.965-52.520Q58.669-52.318 58.266-52.229Q57.863-52.140 57.548-52.140Q57.231-52.140 56.827-52.229Q56.424-52.318 56.128-52.520Q55.833-52.722 55.833-53.043M56.287-53.043Q56.287-52.814 56.506-52.665Q56.725-52.516 57.017-52.448Q57.309-52.380 57.548-52.380Q57.712-52.380 57.921-52.416Q58.129-52.451 58.336-52.532Q58.543-52.612 58.675-52.740Q58.806-52.868 58.806-53.043Q58.806-53.395 58.425-53.489Q58.044-53.583 57.542-53.583L56.923-53.583Q56.684-53.583 56.485-53.432Q56.287-53.282 56.287-53.043M57.336-54.830Q58.003-54.830 58.003-55.627Q58.003-56.427 57.336-56.427Q56.667-56.427 56.667-55.627Q56.667-54.830 57.336-54.830M60.177-51.819L60.108-51.819Q60.074-51.819 60.052-51.845Q60.030-51.870 60.030-51.905Q60.030-51.949 60.061-51.966Q60.416-52.270 60.666-52.660Q60.915-53.050 61.067-53.482Q61.219-53.914 61.289-54.383Q61.359-54.851 61.359-55.326Q61.359-55.805 61.289-56.271Q61.219-56.738 61.065-57.173Q60.912-57.609 60.660-57.997Q60.409-58.385 60.061-58.679Q60.030-58.696 60.030-58.741Q60.030-58.775 60.052-58.800Q60.074-58.826 60.108-58.826L60.177-58.826Q60.187-58.826 60.196-58.824Q60.204-58.823 60.214-58.819Q60.758-58.419 61.130-57.866Q61.503-57.312 61.684-56.666Q61.865-56.020 61.865-55.326Q61.865-54.625 61.684-53.978Q61.503-53.330 61.129-52.776Q60.754-52.222 60.214-51.826Q60.204-51.826 60.196-51.824Q60.187-51.823 60.177-51.819\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(77.51 163.93)\">\u003Cpath d=\"M-7.657-54.417L-7.657-56.314L-8.296-56.314L-8.296-56.536Q-7.978-56.536-7.761-56.746Q-7.544-56.956-7.444-57.266Q-7.343-57.575-7.343-57.883L-7.076-57.883L-7.076-56.594L-5.999-56.594L-5.999-56.314L-7.076-56.314L-7.076-54.430Q-7.076-54.154-6.972-53.955Q-6.868-53.757-6.608-53.757Q-6.451-53.757-6.345-53.861Q-6.239-53.966-6.189-54.119Q-6.140-54.273-6.140-54.430L-6.140-54.844L-5.873-54.844L-5.873-54.417Q-5.873-54.191-5.972-53.981Q-6.071-53.771-6.256-53.639Q-6.440-53.508-6.669-53.508Q-7.107-53.508-7.382-53.745Q-7.657-53.983-7.657-54.417M-4.489-54.410L-4.489-55.914Q-4.489-56.184-4.596-56.245Q-4.704-56.307-5.015-56.307L-5.015-56.587L-3.908-56.662L-3.908-54.430L-3.908-54.410Q-3.908-54.130-3.856-53.986Q-3.805-53.843-3.663-53.786Q-3.521-53.730-3.234-53.730Q-2.981-53.730-2.776-53.870Q-2.571-54.010-2.455-54.236Q-2.339-54.461-2.339-54.711L-2.339-55.914Q-2.339-56.184-2.446-56.245Q-2.554-56.307-2.865-56.307L-2.865-56.587L-1.758-56.662L-1.758-54.249Q-1.758-54.058-1.705-53.976Q-1.652-53.894-1.551-53.875Q-1.450-53.856-1.235-53.856L-1.235-53.576L-2.311-53.508L-2.311-54.072Q-2.421-53.890-2.566-53.767Q-2.711-53.644-2.898-53.576Q-3.084-53.508-3.286-53.508Q-4.489-53.508-4.489-54.410M1.103-53.576L-0.633-53.576L-0.633-53.856Q-0.404-53.856-0.256-53.890Q-0.107-53.925-0.107-54.065L-0.107-55.914Q-0.107-56.184-0.215-56.245Q-0.322-56.307-0.633-56.307L-0.633-56.587L0.396-56.662L0.396-55.955Q0.525-56.263 0.768-56.462Q1.011-56.662 1.329-56.662Q1.547-56.662 1.718-56.538Q1.889-56.413 1.889-56.201Q1.889-56.064 1.790-55.965Q1.691-55.866 1.558-55.866Q1.421-55.866 1.322-55.965Q1.223-56.064 1.223-56.201Q1.223-56.341 1.322-56.440Q1.031-56.440 0.831-56.244Q0.631-56.047 0.539-55.753Q0.447-55.459 0.447-55.179L0.447-54.065Q0.447-53.856 1.103-53.856L1.103-53.576M4.155-53.576L2.522-53.576L2.522-53.856Q2.751-53.856 2.899-53.890Q3.048-53.925 3.048-54.065L3.048-55.914Q3.048-56.184 2.940-56.245Q2.833-56.307 2.522-56.307L2.522-56.587L3.581-56.662L3.581-56.013Q3.752-56.321 4.056-56.492Q4.360-56.662 4.706-56.662Q5.211-56.662 5.495-56.439Q5.779-56.215 5.779-55.719L5.779-54.065Q5.779-53.928 5.928-53.892Q6.076-53.856 6.302-53.856L6.302-53.576L4.671-53.576L4.671-53.856Q4.900-53.856 5.049-53.890Q5.198-53.925 5.198-54.065L5.198-55.705Q5.198-56.040 5.078-56.240Q4.959-56.440 4.644-56.440Q4.374-56.440 4.140-56.304Q3.906-56.167 3.767-55.933Q3.629-55.699 3.629-55.425L3.629-54.065Q3.629-53.928 3.779-53.892Q3.930-53.856 4.155-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 163.93)\">\u003Cpath d=\"M11.597-54.724L9.553-54.724L9.553-55.005L11.884-58.177Q11.919-58.224 11.984-58.224L12.120-58.224Q12.165-58.224 12.192-58.197Q12.219-58.170 12.219-58.125L12.219-55.005L12.982-55.005L12.982-54.724L12.219-54.724L12.219-54.065Q12.219-53.856 12.975-53.856L12.975-53.576L10.842-53.576L10.842-53.856Q11.597-53.856 11.597-54.065L11.597-54.724M11.645-57.449L9.854-55.005L11.645-55.005L11.645-57.449M13.976-53.996Q13.976-54.164 14.099-54.287Q14.222-54.410 14.397-54.410Q14.564-54.410 14.687-54.287Q14.810-54.164 14.810-53.996Q14.810-53.822 14.687-53.699Q14.564-53.576 14.397-53.576Q14.222-53.576 14.099-53.699Q13.976-53.822 13.976-53.996M13.976-56.180Q13.976-56.348 14.099-56.471Q14.222-56.594 14.397-56.594Q14.564-56.594 14.687-56.471Q14.810-56.348 14.810-56.180Q14.810-56.006 14.687-55.883Q14.564-55.760 14.397-55.760Q14.222-55.760 14.099-55.883Q13.976-56.006 13.976-56.180\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 163.93)\">\u003Cpath d=\"M21.198-53.576L19.465-53.576L19.465-53.856Q19.691-53.856 19.840-53.890Q19.988-53.925 19.988-54.065L19.988-56.314L19.400-56.314L19.400-56.594L19.988-56.594L19.988-57.411Q19.988-57.729 20.166-57.977Q20.344-58.224 20.634-58.365Q20.925-58.505 21.236-58.505Q21.492-58.505 21.696-58.363Q21.899-58.221 21.899-57.978Q21.899-57.842 21.800-57.743Q21.701-57.643 21.564-57.643Q21.427-57.643 21.328-57.743Q21.229-57.842 21.229-57.978Q21.229-58.159 21.369-58.252Q21.291-58.279 21.191-58.279Q20.983-58.279 20.829-58.146Q20.675-58.013 20.595-57.809Q20.515-57.606 20.515-57.397L20.515-56.594L21.403-56.594L21.403-56.314L20.542-56.314L20.542-54.065Q20.542-53.856 21.198-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 163.93)\">\u003Cpath d=\"M24.055-53.576L22.503-53.576L22.503-53.856Q22.729-53.856 22.878-53.890Q23.026-53.925 23.026-54.065L23.026-55.914Q23.026-56.102 22.978-56.186Q22.931-56.269 22.833-56.288Q22.736-56.307 22.524-56.307L22.524-56.587L23.580-56.662L23.580-54.065Q23.580-53.925 23.712-53.890Q23.843-53.856 24.055-53.856L24.055-53.576M22.784-57.883Q22.784-58.054 22.907-58.173Q23.030-58.293 23.201-58.293Q23.368-58.293 23.491-58.173Q23.614-58.054 23.614-57.883Q23.614-57.708 23.491-57.585Q23.368-57.462 23.201-57.462Q23.030-57.462 22.907-57.585Q22.784-57.708 22.784-57.883M26.369-53.576L24.766-53.576L24.766-53.856Q24.992-53.856 25.140-53.890Q25.289-53.925 25.289-54.065L25.289-57.684Q25.289-57.954 25.181-58.016Q25.074-58.077 24.766-58.077L24.766-58.358L25.843-58.433L25.843-54.065Q25.843-53.928 25.993-53.892Q26.144-53.856 26.369-53.856L26.369-53.576M28.632-53.576L27.029-53.576L27.029-53.856Q27.254-53.856 27.403-53.890Q27.552-53.925 27.552-54.065L27.552-57.684Q27.552-57.954 27.444-58.016Q27.336-58.077 27.029-58.077L27.029-58.358L28.105-58.433L28.105-54.065Q28.105-53.928 28.256-53.892Q28.406-53.856 28.632-53.856\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 163.93)\">\u003Cpath d=\"M31.924-55.087Q31.924-55.425 32.065-55.716Q32.205-56.006 32.449-56.220Q32.693-56.433 32.998-56.548Q33.302-56.662 33.627-56.662Q33.897-56.662 34.160-56.563Q34.423-56.464 34.614-56.286L34.614-57.684Q34.614-57.954 34.507-58.016Q34.399-58.077 34.088-58.077L34.088-58.358L35.165-58.433L35.165-54.249Q35.165-54.061 35.219-53.978Q35.274-53.894 35.375-53.875Q35.476-53.856 35.691-53.856L35.691-53.576L34.584-53.508L34.584-53.925Q34.167-53.508 33.541-53.508Q33.110-53.508 32.738-53.720Q32.365-53.931 32.145-54.292Q31.924-54.653 31.924-55.087M33.599-53.730Q33.808-53.730 33.994-53.802Q34.180-53.873 34.334-54.010Q34.488-54.147 34.584-54.325L34.584-55.934Q34.498-56.081 34.353-56.201Q34.208-56.321 34.038-56.380Q33.869-56.440 33.688-56.440Q33.128-56.440 32.859-56.051Q32.591-55.661 32.591-55.080Q32.591-54.509 32.825-54.119Q33.059-53.730 33.599-53.730M36.399-54.304Q36.399-54.636 36.622-54.863Q36.846-55.090 37.190-55.218Q37.533-55.347 37.906-55.399Q38.278-55.452 38.583-55.452L38.583-55.705Q38.583-55.910 38.475-56.090Q38.367-56.269 38.186-56.372Q38.005-56.474 37.797-56.474Q37.390-56.474 37.154-56.382Q37.243-56.345 37.289-56.261Q37.335-56.177 37.335-56.075Q37.335-55.979 37.289-55.900Q37.243-55.822 37.162-55.777Q37.082-55.733 36.993-55.733Q36.843-55.733 36.742-55.830Q36.641-55.928 36.641-56.075Q36.641-56.697 37.797-56.697Q38.008-56.697 38.258-56.633Q38.507-56.570 38.709-56.451Q38.911-56.331 39.037-56.146Q39.164-55.962 39.164-55.719L39.164-54.143Q39.164-54.027 39.225-53.931Q39.287-53.836 39.400-53.836Q39.509-53.836 39.574-53.930Q39.639-54.024 39.639-54.143L39.639-54.591L39.905-54.591L39.905-54.143Q39.905-53.873 39.678-53.708Q39.451-53.542 39.171-53.542Q38.962-53.542 38.825-53.696Q38.689-53.849 38.665-54.065Q38.518-53.798 38.236-53.653Q37.954-53.508 37.629-53.508Q37.352-53.508 37.068-53.583Q36.785-53.658 36.592-53.837Q36.399-54.017 36.399-54.304M37.014-54.304Q37.014-54.130 37.115-54Q37.215-53.870 37.371-53.800Q37.527-53.730 37.691-53.730Q37.909-53.730 38.118-53.827Q38.326-53.925 38.454-54.106Q38.583-54.287 38.583-54.513L38.583-55.241Q38.258-55.241 37.892-55.150Q37.527-55.059 37.270-54.847Q37.014-54.636 37.014-54.304M40.849-54.417L40.849-56.314L40.210-56.314L40.210-56.536Q40.527-56.536 40.745-56.746Q40.962-56.956 41.062-57.266Q41.163-57.575 41.163-57.883L41.430-57.883L41.430-56.594L42.506-56.594L42.506-56.314L41.430-56.314L41.430-54.430Q41.430-54.154 41.534-53.955Q41.638-53.757 41.898-53.757Q42.055-53.757 42.161-53.861Q42.267-53.966 42.317-54.119Q42.366-54.273 42.366-54.430L42.366-54.844L42.633-54.844L42.633-54.417Q42.633-54.191 42.534-53.981Q42.435-53.771 42.250-53.639Q42.066-53.508 41.837-53.508Q41.399-53.508 41.124-53.745Q40.849-53.983 40.849-54.417M43.402-55.111Q43.402-55.432 43.527-55.721Q43.652-56.010 43.877-56.233Q44.103-56.457 44.398-56.577Q44.694-56.697 45.012-56.697Q45.340-56.697 45.601-56.597Q45.863-56.498 46.039-56.316Q46.215-56.133 46.309-55.875Q46.403-55.617 46.403-55.285Q46.403-55.193 46.321-55.172L44.065-55.172L44.065-55.111Q44.065-54.523 44.349-54.140Q44.632-53.757 45.200-53.757Q45.521-53.757 45.789-53.950Q46.058-54.143 46.147-54.458Q46.153-54.499 46.229-54.513L46.321-54.513Q46.403-54.489 46.403-54.417Q46.403-54.410 46.396-54.383Q46.283-53.986 45.912-53.747Q45.542-53.508 45.118-53.508Q44.680-53.508 44.280-53.716Q43.881-53.925 43.641-54.292Q43.402-54.659 43.402-55.111M44.072-55.381L45.887-55.381Q45.887-55.658 45.789-55.910Q45.692-56.163 45.494-56.319Q45.296-56.474 45.012-56.474Q44.735-56.474 44.521-56.316Q44.308-56.157 44.190-55.902Q44.072-55.647 44.072-55.381\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 163.93)\">\u003Cpath d=\"M52.286-52.896L52.286-55.152L50.037-55.152Q49.969-55.162 49.923-55.208Q49.877-55.254 49.877-55.326Q49.877-55.470 50.037-55.493L52.286-55.493L52.286-57.749Q52.297-57.818 52.343-57.864Q52.389-57.910 52.461-57.910Q52.604-57.910 52.628-57.749L52.628-55.493L54.870-55.493Q55.031-55.470 55.031-55.326Q55.031-55.254 54.985-55.208Q54.939-55.162 54.870-55.152L52.628-55.152L52.628-52.896Q52.604-52.735 52.461-52.735Q52.389-52.735 52.343-52.781Q52.297-52.827 52.286-52.896\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 163.93)\">\u003Cpath d=\"M59.062-54.417L59.062-56.314L58.423-56.314L58.423-56.536Q58.741-56.536 58.958-56.746Q59.175-56.956 59.275-57.266Q59.376-57.575 59.376-57.883L59.643-57.883L59.643-56.594L60.720-56.594L60.720-56.314L59.643-56.314L59.643-54.430Q59.643-54.154 59.747-53.955Q59.851-53.757 60.111-53.757Q60.268-53.757 60.374-53.861Q60.480-53.966 60.530-54.119Q60.579-54.273 60.579-54.430L60.579-54.844L60.846-54.844L60.846-54.417Q60.846-54.191 60.747-53.981Q60.648-53.771 60.463-53.639Q60.279-53.508 60.050-53.508Q59.612-53.508 59.337-53.745Q59.062-53.983 59.062-54.417M63.273-53.576L61.721-53.576L61.721-53.856Q61.947-53.856 62.095-53.890Q62.244-53.925 62.244-54.065L62.244-55.914Q62.244-56.102 62.196-56.186Q62.148-56.269 62.051-56.288Q61.953-56.307 61.742-56.307L61.742-56.587L62.798-56.662L62.798-54.065Q62.798-53.925 62.929-53.890Q63.061-53.856 63.273-53.856L63.273-53.576M62.001-57.883Q62.001-58.054 62.124-58.173Q62.247-58.293 62.418-58.293Q62.586-58.293 62.709-58.173Q62.832-58.054 62.832-57.883Q62.832-57.708 62.709-57.585Q62.586-57.462 62.418-57.462Q62.247-57.462 62.124-57.585Q62.001-57.708 62.001-57.883M65.600-53.576L63.967-53.576L63.967-53.856Q64.196-53.856 64.344-53.890Q64.493-53.925 64.493-54.065L64.493-55.914Q64.493-56.184 64.385-56.245Q64.278-56.307 63.967-56.307L63.967-56.587L65.026-56.662L65.026-56.013Q65.197-56.321 65.501-56.492Q65.805-56.662 66.151-56.662Q66.551-56.662 66.827-56.522Q67.104-56.382 67.190-56.034Q67.357-56.327 67.656-56.495Q67.955-56.662 68.301-56.662Q68.806-56.662 69.090-56.439Q69.374-56.215 69.374-55.719L69.374-54.065Q69.374-53.928 69.523-53.892Q69.671-53.856 69.897-53.856L69.897-53.576L68.266-53.576L68.266-53.856Q68.492-53.856 68.642-53.892Q68.793-53.928 68.793-54.065L68.793-55.705Q68.793-56.040 68.673-56.240Q68.554-56.440 68.239-56.440Q67.969-56.440 67.735-56.304Q67.501-56.167 67.362-55.933Q67.224-55.699 67.224-55.425L67.224-54.065Q67.224-53.928 67.373-53.892Q67.521-53.856 67.747-53.856L67.747-53.576L66.117-53.576L66.117-53.856Q66.346-53.856 66.494-53.890Q66.643-53.925 66.643-54.065L66.643-55.705Q66.643-56.040 66.523-56.240Q66.404-56.440 66.089-56.440Q65.819-56.440 65.585-56.304Q65.351-56.167 65.212-55.933Q65.074-55.699 65.074-55.425L65.074-54.065Q65.074-53.928 65.224-53.892Q65.375-53.856 65.600-53.856L65.600-53.576M70.444-55.111Q70.444-55.432 70.568-55.721Q70.693-56.010 70.919-56.233Q71.144-56.457 71.440-56.577Q71.736-56.697 72.054-56.697Q72.382-56.697 72.643-56.597Q72.905-56.498 73.081-56.316Q73.257-56.133 73.351-55.875Q73.445-55.617 73.445-55.285Q73.445-55.193 73.363-55.172L71.107-55.172L71.107-55.111Q71.107-54.523 71.390-54.140Q71.674-53.757 72.242-53.757Q72.563-53.757 72.831-53.950Q73.099-54.143 73.188-54.458Q73.195-54.499 73.270-54.513L73.363-54.513Q73.445-54.489 73.445-54.417Q73.445-54.410 73.438-54.383Q73.325-53.986 72.954-53.747Q72.583-53.508 72.159-53.508Q71.722-53.508 71.322-53.716Q70.922-53.925 70.683-54.292Q70.444-54.659 70.444-55.111M71.114-55.381L72.929-55.381Q72.929-55.658 72.831-55.910Q72.734-56.163 72.535-56.319Q72.337-56.474 72.054-56.474Q71.777-56.474 71.563-56.316Q71.349-56.157 71.231-55.902Q71.114-55.647 71.114-55.381\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(77.51 163.93)\">\u003Cpath d=\"M78.874-51.826Q78.324-52.226 77.953-52.781Q77.582-53.337 77.401-53.983Q77.220-54.629 77.220-55.326Q77.220-55.839 77.320-56.334Q77.421-56.830 77.626-57.281Q77.831-57.732 78.144-58.124Q78.457-58.515 78.874-58.819Q78.884-58.823 78.891-58.824Q78.898-58.826 78.908-58.826L78.976-58.826Q79.011-58.826 79.033-58.802Q79.055-58.778 79.055-58.741Q79.055-58.696 79.028-58.679Q78.679-58.378 78.426-57.994Q78.173-57.609 78.021-57.168Q77.869-56.727 77.797-56.271Q77.725-55.815 77.725-55.326Q77.725-54.325 78.035-53.438Q78.344-52.551 79.028-51.966Q79.055-51.949 79.055-51.905Q79.055-51.867 79.033-51.843Q79.011-51.819 78.976-51.819L78.908-51.819Q78.901-51.823 78.893-51.824Q78.884-51.826 78.874-51.826M79.865-55.087Q79.865-55.415 80-55.716Q80.135-56.016 80.371-56.237Q80.607-56.457 80.911-56.577Q81.215-56.697 81.540-56.697Q82.046-56.697 82.394-56.594Q82.743-56.492 82.743-56.116Q82.743-55.969 82.646-55.868Q82.548-55.767 82.401-55.767Q82.247-55.767 82.148-55.866Q82.049-55.965 82.049-56.116Q82.049-56.304 82.189-56.396Q81.988-56.447 81.547-56.447Q81.191-56.447 80.962-56.251Q80.733-56.054 80.632-55.745Q80.532-55.435 80.532-55.087Q80.532-54.738 80.658-54.432Q80.784-54.126 81.039-53.942Q81.294-53.757 81.649-53.757Q81.871-53.757 82.056-53.841Q82.241-53.925 82.376-54.080Q82.511-54.236 82.569-54.444Q82.582-54.499 82.637-54.499L82.750-54.499Q82.781-54.499 82.803-54.475Q82.825-54.451 82.825-54.417L82.825-54.396Q82.740-54.109 82.552-53.911Q82.364-53.713 82.099-53.610Q81.834-53.508 81.540-53.508Q81.109-53.508 80.721-53.714Q80.333-53.921 80.099-54.284Q79.865-54.646 79.865-55.087M83.372-55.059Q83.372-55.401 83.507-55.700Q83.642-55.999 83.881-56.223Q84.120-56.447 84.438-56.572Q84.756-56.697 85.088-56.697Q85.532-56.697 85.932-56.481Q86.332-56.266 86.566-55.888Q86.800-55.511 86.800-55.059Q86.800-54.718 86.658-54.434Q86.516-54.150 86.272-53.943Q86.028-53.737 85.718-53.622Q85.409-53.508 85.088-53.508Q84.657-53.508 84.255-53.709Q83.854-53.911 83.613-54.263Q83.372-54.615 83.372-55.059M85.088-53.757Q85.689-53.757 85.913-54.135Q86.137-54.513 86.137-55.145Q86.137-55.757 85.903-56.116Q85.669-56.474 85.088-56.474Q84.035-56.474 84.035-55.145Q84.035-54.513 84.261-54.135Q84.486-53.757 85.088-53.757M89.076-53.576L87.443-53.576L87.443-53.856Q87.672-53.856 87.820-53.890Q87.969-53.925 87.969-54.065L87.969-55.914Q87.969-56.184 87.861-56.245Q87.754-56.307 87.443-56.307L87.443-56.587L88.502-56.662L88.502-56.013Q88.673-56.321 88.977-56.492Q89.282-56.662 89.627-56.662Q90.027-56.662 90.304-56.522Q90.580-56.382 90.666-56.034Q90.833-56.327 91.132-56.495Q91.431-56.662 91.777-56.662Q92.283-56.662 92.566-56.439Q92.850-56.215 92.850-55.719L92.850-54.065Q92.850-53.928 92.999-53.892Q93.147-53.856 93.373-53.856L93.373-53.576L91.743-53.576L91.743-53.856Q91.968-53.856 92.118-53.892Q92.269-53.928 92.269-54.065L92.269-55.705Q92.269-56.040 92.149-56.240Q92.030-56.440 91.715-56.440Q91.445-56.440 91.211-56.304Q90.977-56.167 90.838-55.933Q90.700-55.699 90.700-55.425L90.700-54.065Q90.700-53.928 90.849-53.892Q90.997-53.856 91.223-53.856L91.223-53.576L89.593-53.576L89.593-53.856Q89.822-53.856 89.970-53.890Q90.119-53.925 90.119-54.065L90.119-55.705Q90.119-56.040 89.999-56.240Q89.880-56.440 89.565-56.440Q89.295-56.440 89.061-56.304Q88.827-56.167 88.689-55.933Q88.550-55.699 88.550-55.425L88.550-54.065Q88.550-53.928 88.701-53.892Q88.851-53.856 89.076-53.856L89.076-53.576M95.605-52.219L93.974-52.219L93.974-52.499Q94.203-52.499 94.352-52.534Q94.501-52.568 94.501-52.708L94.501-56.054Q94.501-56.225 94.364-56.266Q94.227-56.307 93.974-56.307L93.974-56.587L95.055-56.662L95.055-56.256Q95.277-56.457 95.564-56.560Q95.851-56.662 96.159-56.662Q96.586-56.662 96.950-56.449Q97.314-56.235 97.527-55.871Q97.741-55.507 97.741-55.087Q97.741-54.642 97.502-54.278Q97.263-53.914 96.869-53.711Q96.476-53.508 96.032-53.508Q95.765-53.508 95.518-53.608Q95.270-53.709 95.082-53.890L95.082-52.708Q95.082-52.571 95.231-52.535Q95.379-52.499 95.605-52.499L95.605-52.219M95.082-55.907L95.082-54.297Q95.215-54.044 95.458-53.887Q95.701-53.730 95.977-53.730Q96.305-53.730 96.558-53.931Q96.811-54.133 96.945-54.451Q97.078-54.769 97.078-55.087Q97.078-55.316 97.013-55.545Q96.948-55.774 96.820-55.972Q96.692-56.170 96.497-56.290Q96.302-56.409 96.070-56.409Q95.776-56.409 95.507-56.280Q95.239-56.150 95.082-55.907M100.045-53.576L98.442-53.576L98.442-53.856Q98.667-53.856 98.816-53.890Q98.965-53.925 98.965-54.065L98.965-57.684Q98.965-57.954 98.857-58.016Q98.749-58.077 98.442-58.077L98.442-58.358L99.518-58.433L99.518-54.065Q99.518-53.928 99.669-53.892Q99.819-53.856 100.045-53.856L100.045-53.576M100.598-55.111Q100.598-55.432 100.723-55.721Q100.848-56.010 101.074-56.233Q101.299-56.457 101.595-56.577Q101.890-56.697 102.208-56.697Q102.536-56.697 102.798-56.597Q103.059-56.498 103.235-56.316Q103.411-56.133 103.505-55.875Q103.599-55.617 103.599-55.285Q103.599-55.193 103.517-55.172L101.262-55.172L101.262-55.111Q101.262-54.523 101.545-54.140Q101.829-53.757 102.396-53.757Q102.718-53.757 102.986-53.950Q103.254-54.143 103.343-54.458Q103.350-54.499 103.425-54.513L103.517-54.513Q103.599-54.489 103.599-54.417Q103.599-54.410 103.593-54.383Q103.480-53.986 103.109-53.747Q102.738-53.508 102.314-53.508Q101.877-53.508 101.477-53.716Q101.077-53.925 100.838-54.292Q100.598-54.659 100.598-55.111M101.268-55.381L103.083-55.381Q103.083-55.658 102.986-55.910Q102.888-56.163 102.690-56.319Q102.492-56.474 102.208-56.474Q101.931-56.474 101.718-56.316Q101.504-56.157 101.386-55.902Q101.268-55.647 101.268-55.381M104.714-54.417L104.714-56.314L104.075-56.314L104.075-56.536Q104.392-56.536 104.609-56.746Q104.826-56.956 104.927-57.266Q105.028-57.575 105.028-57.883L105.295-57.883L105.295-56.594L106.371-56.594L106.371-56.314L105.295-56.314L105.295-54.430Q105.295-54.154 105.399-53.955Q105.503-53.757 105.763-53.757Q105.920-53.757 106.026-53.861Q106.132-53.966 106.182-54.119Q106.231-54.273 106.231-54.430L106.231-54.844L106.498-54.844L106.498-54.417Q106.498-54.191 106.399-53.981Q106.300-53.771 106.115-53.639Q105.930-53.508 105.701-53.508Q105.264-53.508 104.989-53.745Q104.714-53.983 104.714-54.417M107.267-55.111Q107.267-55.432 107.392-55.721Q107.516-56.010 107.742-56.233Q107.968-56.457 108.263-56.577Q108.559-56.697 108.877-56.697Q109.205-56.697 109.466-56.597Q109.728-56.498 109.904-56.316Q110.080-56.133 110.174-55.875Q110.268-55.617 110.268-55.285Q110.268-55.193 110.186-55.172L107.930-55.172L107.930-55.111Q107.930-54.523 108.214-54.140Q108.497-53.757 109.065-53.757Q109.386-53.757 109.654-53.950Q109.923-54.143 110.012-54.458Q110.018-54.499 110.094-54.513L110.186-54.513Q110.268-54.489 110.268-54.417Q110.268-54.410 110.261-54.383Q110.148-53.986 109.777-53.747Q109.407-53.508 108.983-53.508Q108.545-53.508 108.145-53.716Q107.745-53.925 107.506-54.292Q107.267-54.659 107.267-55.111M107.937-55.381L109.752-55.381Q109.752-55.658 109.654-55.910Q109.557-56.163 109.359-56.319Q109.160-56.474 108.877-56.474Q108.600-56.474 108.386-56.316Q108.173-56.157 108.055-55.902Q107.937-55.647 107.937-55.381M111.177-51.819L111.109-51.819Q111.075-51.819 111.052-51.845Q111.030-51.870 111.030-51.905Q111.030-51.949 111.061-51.966Q111.416-52.270 111.666-52.660Q111.915-53.050 112.067-53.482Q112.220-53.914 112.290-54.383Q112.360-54.851 112.360-55.326Q112.360-55.805 112.290-56.271Q112.220-56.738 112.066-57.173Q111.912-57.609 111.661-57.997Q111.409-58.385 111.061-58.679Q111.030-58.696 111.030-58.741Q111.030-58.775 111.052-58.800Q111.075-58.826 111.109-58.826L111.177-58.826Q111.187-58.826 111.196-58.824Q111.204-58.823 111.215-58.819Q111.758-58.419 112.131-57.866Q112.503-57.312 112.684-56.666Q112.866-56.020 112.866-55.326Q112.866-54.625 112.684-53.978Q112.503-53.330 112.129-52.776Q111.755-52.222 111.215-51.826Q111.204-51.826 111.196-51.824Q111.187-51.823 111.177-51.819\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath fill=\"none\" d=\"M-8.498-34.881v6.136\"\u002F>\u003Cpath stroke=\"none\" d=\"m-8.498-26.745 1.6-3.2-1.6 1.2-1.6-1.2\"\u002F>\u003C\u002Fg>\u003Cg fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-8.498 10.643v14.072\"\u002F>\u003Cpath stroke=\"none\" d=\"m-8.498 27.315 2.08-4.16-2.08 1.56-2.08-1.56\"\u002F>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cpath fill=\"none\" d=\"M-8.498 64.703v23.208\"\u002F>\u003Cpath stroke=\"none\" d=\"m-8.498 89.91 1.6-3.2-1.6 1.2-1.6-1.2\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">A dialogue-state tracker accumulating a frame over four turns. Each turn's NLU act updates the running frame: new slots are added (dest at turn 2), a correction act overwrites an existing slot (orig Boston to Austin at turn 3, in red), and unmentioned slots carry forward. The frame is complete after turn 4.\u003C\u002Ffigcaption>",1785117832438]