[{"data":1,"prerenderedAt":15640},["ShallowReactive",2],{"lesson:\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples":3,"course-wordcounts":9912,"ref-card-index":10824,"nav:artificial-intelligence":15505,"tikz:2a91108b07d792afb608b61c34bbd5baa94dcffa940b248bf74a8e38b285206f":15632,"tikz:adbe7c6f779445b949e93d334f927db44c417eba8542a957b167c6c31c676a1f":15633,"tikz:d82954b3a3bf5a15eef1d13730304601fc4ab9f79696e5cc6fab4ca9d8d264c3":15634,"tikz:57e298e23cccb2a4437e16761bc30721d48a212269db34063adb385fd143b988":15635,"tikz:2be899f4103131464a5db4914237b0addb904074d8a3b16bcfebaf8937abb0ba":15636,"tikz:cb12c97e4644559e2e005f0928296f8ddbf41d2616eb15ea16a1ed5916b1ef82":15637,"tikz:ef1adddd02fe350956986dbb1c7622764d1818bef3dca016649b63e4acd5455c":15638,"tikz:f92011cbfc209df8af4dfce18b0f81c3eddb69f25d88f39bd8c017fe04b21283":15639},{"id":4,"title":5,"blurb":6,"body":7,"brief":9884,"category":9885,"description":9886,"draft":9887,"extension":9888,"meta":9889,"module":9891,"navigation":9892,"path":9893,"practice":9894,"rawbody":9895,"readingTime":9896,"seo":9901,"sources":9902,"status":9907,"stem":9908,"summary":9909,"topics":9910,"__hash__":9911},"course\u002F08.artificial-intelligence\u002F05.learning\u002F01.learning-from-examples.md","Learning from Examples","",{"type":8,"value":9,"toc":9870},"minimark",[10,46,62,67,100,648,726,730,735,836,839,850,866,870,1003,1223,1226,1384,1388,1403,1406,1577,2131,2840,3350,3373,3557,3565,3714,4298,4325,5043,5082,5135,5863,6318,6678,6790,7139,7212,7320,7360,7938,8213,8216,8260,8360,8468,8472,8487,8665,8672,8676,8701,8813,9004,9007,9018,9022,9033,9036,9040,9066,9069,9089,9192,9223,9235,9866],[11,12,13,14,19,20,24,25,29,30,34,35,45],"p",{},"Every agent we have built so far came with its competence fully assembled. The\n",[15,16,18],"a",{"href":17},"\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search","search agent"," was handed a\nsuccessor function and a heuristic; the\n",[15,21,23],{"href":22},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic","logical agent","\nwas handed a knowledge base; the\n",[15,26,28],{"href":27},"\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions","decision-theoretic agent","\nwas handed a transition model and a utility. In each case a human sat down in\nadvance and wrote the machinery down. An agent ",[31,32,33],"strong",{},"learns"," when it improves its\nperformance on future tasks after making observations about the world, and there\nare three reasons a designer would want that.",[36,37,38],"sup",{},[15,39,44],{"href":40,"ariaDescribedBy":41,"dataFootnoteRef":6,"id":43},"#user-content-fn-aima-forms",[42],"footnote-label","user-content-fnref-aima-forms","1"," The designer cannot\nanticipate every situation the agent will face; the world changes, so a fixed\nprogram goes stale; and for some tasks — recognizing a face, driving a car — no\nhuman knows how to write the rules in the first place, so the only route is to\nlet the agent find them from data.",[11,47,48,49,52,53,57,58,61],{},"This chapter narrows learning to one class of problem, restrictive-looking but\nvery general: ",[31,50,51],{},"from a collection of input-output pairs, learn\na function that predicts the output for new inputs",". That is ",[54,55,56],"em",{},"inductive"," learning\n— inferring a general rule from specific examples — and its central difficulty is\nnot fitting the examples you have but predicting the ones you do not. Everything\nbelow is machinery for that one problem, and the recurring word is\n",[31,59,60],{},"generalization",".",[63,64,66],"h2",{"id":65},"supervised-learning-as-function-approximation","Supervised learning as function approximation",[11,68,69,70,78,79,82,83,86,87,90,91,95,96,99],{},"Learning divides by the kind of feedback the data carries.",[36,71,72],{},[15,73,77],{"href":74,"ariaDescribedBy":75,"dataFootnoteRef":6,"id":76},"#user-content-fn-aima-feedback",[42],"user-content-fnref-aima-feedback","2"," In\n",[31,80,81],{},"unsupervised learning"," the agent sees inputs with no labels and must find\nstructure on its own (the canonical task is ",[54,84,85],{},"clustering","). In ",[31,88,89],{},"reinforcement\nlearning"," the agent acts and receives a scalar reward, and must decide which of\nits past actions earned it — the subject of\n",[15,92,94],{"href":93},"\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning","reinforcement learning",".\nThe setting of this lesson is ",[31,97,98],{},"supervised learning",", where the data supplies the\ncorrect output for each input.",[101,102,104],"callout",{"type":103},"definition",[11,105,106,109,110,113,114,140,141,485,486,543,544,592,593,610,611,627,628,643,644,647],{},[31,107,108],{},"Definition (Supervised learning)."," Given a ",[31,111,112],{},"training set"," of ",[115,116,119],"span",{"className":117},[118],"katex",[115,120,124],{"className":121,"ariaHidden":123},[122],"katex-html","true",[115,125,128,133],{"className":126},[127],"base",[115,129],{"className":130,"style":132},[131],"strut","height:0.6833em;",[115,134,139],{"className":135,"style":138},[136,137],"mord","mathnormal","margin-right:0.109em;","N","\ninput-output pairs ",[115,142,144],{"className":143},[118],[115,145,147],{"className":146,"ariaHidden":123},[122],[115,148,150,154,159,216,221,226,269,274,277,280,283,323,326,329,369,372,375,378,383,386,389,392,395,436,439,442,482],{"className":149},[127],[115,151],{"className":152,"style":153},[131],"height:1em;vertical-align:-0.25em;",[115,155,158],{"className":156},[157],"mopen","(",[115,160,162,166],{"className":161},[136],[115,163,165],{"className":164},[136,137],"x",[115,167,170],{"className":168},[169],"msupsub",[115,171,175,207],{"className":172},[173,174],"vlist-t","vlist-t2",[115,176,179,202],{"className":177},[178],"vlist-r",[115,180,184],{"className":181,"style":183},[182],"vlist","height:0.3011em;",[115,185,187,192],{"style":186},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[115,188],{"className":189,"style":191},[190],"pstrut","height:2.7em;",[115,193,199],{"className":194},[195,196,197,198],"sizing","reset-size6","size3","mtight",[115,200,44],{"className":201},[136,198],[115,203,206],{"className":204},[205],"vlist-s","​",[115,208,210],{"className":209},[178],[115,211,214],{"className":212,"style":213},[182],"height:0.15em;",[115,215],{},[115,217,220],{"className":218},[219],"mpunct",",",[115,222],{"className":223,"style":225},[224],"mspace","margin-right:0.1667em;",[115,227,229,234],{"className":228},[136],[115,230,233],{"className":231,"style":232},[136,137],"margin-right:0.0359em;","y",[115,235,237],{"className":236},[169],[115,238,240,261],{"className":239},[173,174],[115,241,243,258],{"className":242},[178],[115,244,246],{"className":245,"style":183},[182],[115,247,249,252],{"style":248},"top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;",[115,250],{"className":251,"style":191},[190],[115,253,255],{"className":254},[195,196,197,198],[115,256,44],{"className":257},[136,198],[115,259,206],{"className":260},[205],[115,262,264],{"className":263},[178],[115,265,267],{"className":266,"style":213},[182],[115,268],{},[115,270,273],{"className":271},[272],"mclose",")",[115,275,220],{"className":276},[219],[115,278],{"className":279,"style":225},[224],[115,281,158],{"className":282},[157],[115,284,286,289],{"className":285},[136],[115,287,165],{"className":288},[136,137],[115,290,292],{"className":291},[169],[115,293,295,315],{"className":294},[173,174],[115,296,298,312],{"className":297},[178],[115,299,301],{"className":300,"style":183},[182],[115,302,303,306],{"style":186},[115,304],{"className":305,"style":191},[190],[115,307,309],{"className":308},[195,196,197,198],[115,310,77],{"className":311},[136,198],[115,313,206],{"className":314},[205],[115,316,318],{"className":317},[178],[115,319,321],{"className":320,"style":213},[182],[115,322],{},[115,324,220],{"className":325},[219],[115,327],{"className":328,"style":225},[224],[115,330,332,335],{"className":331},[136],[115,333,233],{"className":334,"style":232},[136,137],[115,336,338],{"className":337},[169],[115,339,341,361],{"className":340},[173,174],[115,342,344,358],{"className":343},[178],[115,345,347],{"className":346,"style":183},[182],[115,348,349,352],{"style":248},[115,350],{"className":351,"style":191},[190],[115,353,355],{"className":354},[195,196,197,198],[115,356,77],{"className":357},[136,198],[115,359,206],{"className":360},[205],[115,362,364],{"className":363},[178],[115,365,367],{"className":366,"style":213},[182],[115,368],{},[115,370,273],{"className":371},[272],[115,373,220],{"className":374},[219],[115,376],{"className":377,"style":225},[224],[115,379,382],{"className":380},[381],"minner","…",[115,384],{"className":385,"style":225},[224],[115,387,220],{"className":388},[219],[115,390],{"className":391,"style":225},[224],[115,393,158],{"className":394},[157],[115,396,398,401],{"className":397},[136],[115,399,165],{"className":400},[136,137],[115,402,404],{"className":403},[169],[115,405,407,428],{"className":406},[173,174],[115,408,410,425],{"className":409},[178],[115,411,414],{"className":412,"style":413},[182],"height:0.3283em;",[115,415,416,419],{"style":186},[115,417],{"className":418,"style":191},[190],[115,420,422],{"className":421},[195,196,197,198],[115,423,139],{"className":424,"style":138},[136,137,198],[115,426,206],{"className":427},[205],[115,429,431],{"className":430},[178],[115,432,434],{"className":433,"style":213},[182],[115,435],{},[115,437,220],{"className":438},[219],[115,440],{"className":441,"style":225},[224],[115,443,445,448],{"className":444},[136],[115,446,233],{"className":447,"style":232},[136,137],[115,449,451],{"className":450},[169],[115,452,454,474],{"className":453},[173,174],[115,455,457,471],{"className":456},[178],[115,458,460],{"className":459,"style":413},[182],[115,461,462,465],{"style":248},[115,463],{"className":464,"style":191},[190],[115,466,468],{"className":467},[195,196,197,198],[115,469,139],{"className":470,"style":138},[136,137,198],[115,472,206],{"className":473},[205],[115,475,477],{"className":476},[178],[115,478,480],{"className":479,"style":213},[182],[115,481],{},[115,483,273],{"className":484},[272],", where each\n",[115,487,489],{"className":488},[118],[115,490,492],{"className":491,"ariaHidden":123},[122],[115,493,495,499],{"className":494},[127],[115,496],{"className":497,"style":498},[131],"height:0.7167em;vertical-align:-0.2861em;",[115,500,502,505],{"className":501},[136],[115,503,233],{"className":504,"style":232},[136,137],[115,506,508],{"className":507},[169],[115,509,511,534],{"className":510},[173,174],[115,512,514,531],{"className":513},[178],[115,515,518],{"className":516,"style":517},[182],"height:0.3117em;",[115,519,520,523],{"style":248},[115,521],{"className":522,"style":191},[190],[115,524,526],{"className":525},[195,196,197,198],[115,527,530],{"className":528,"style":529},[136,137,198],"margin-right:0.0572em;","j",[115,532,206],{"className":533},[205],[115,535,537],{"className":536},[178],[115,538,541],{"className":539,"style":540},[182],"height:0.2861em;",[115,542],{}," was generated by an unknown function ",[115,545,547],{"className":546},[118],[115,548,550,572],{"className":549,"ariaHidden":123},[122],[115,551,553,557,560,564,569],{"className":552},[127],[115,554],{"className":555,"style":556},[131],"height:0.625em;vertical-align:-0.1944em;",[115,558,233],{"className":559,"style":232},[136,137],[115,561],{"className":562,"style":563},[224],"margin-right:0.2778em;",[115,565,568],{"className":566},[567],"mrel","=",[115,570],{"className":571,"style":563},[224],[115,573,575,578,583,586,589],{"className":574},[127],[115,576],{"className":577,"style":153},[131],[115,579,582],{"className":580,"style":581},[136,137],"margin-right:0.1076em;","f",[115,584,158],{"className":585},[157],[115,587,165],{"className":588},[136,137],[115,590,273],{"className":591},[272],", discover a function ",[115,594,596],{"className":595},[118],[115,597,599],{"className":598,"ariaHidden":123},[122],[115,600,602,606],{"className":601},[127],[115,603],{"className":604,"style":605},[131],"height:0.6944em;",[115,607,609],{"className":608},[136,137],"h","\nthat approximates the true ",[115,612,614],{"className":613},[118],[115,615,617],{"className":616,"ariaHidden":123},[122],[115,618,620,624],{"className":619},[127],[115,621],{"className":622,"style":623},[131],"height:0.8889em;vertical-align:-0.1944em;",[115,625,582],{"className":626,"style":581},[136,137],". The function ",[115,629,631],{"className":630},[118],[115,632,634],{"className":633,"ariaHidden":123},[122],[115,635,637,640],{"className":636},[127],[115,638],{"className":639,"style":605},[131],[115,641,609],{"className":642},[136,137]," is a ",[31,645,646],{},"hypothesis","; learning\nis a search through the space of possible hypotheses for one that predicts well\non inputs beyond the training set.",[11,649,650,651,666,667,670,671,670,674,677,678,681,682,697,698,701,702,705,706,709,710,725],{},"The output type names the problem. When ",[115,652,654],{"className":653},[118],[115,655,657],{"className":656,"ariaHidden":123},[122],[115,658,660,663],{"className":659},[127],[115,661],{"className":662,"style":556},[131],[115,664,233],{"className":665,"style":232},[136,137]," ranges over a finite set of values\n(",[54,668,669],{},"sunny",", ",[54,672,673],{},"cloudy",[54,675,676],{},"rainy",") the problem is ",[31,679,680],{},"classification"," — Boolean or binary\nif there are only two values. When ",[115,683,685],{"className":684},[118],[115,686,688],{"className":687,"ariaHidden":123},[122],[115,689,691,694],{"className":690},[127],[115,692],{"className":693,"style":556},[131],[115,695,233],{"className":696,"style":232},[136,137]," is a number (tomorrow's temperature) the\nproblem is ",[31,699,700],{},"regression",". We measure a hypothesis not on the data it was trained\non but on a separate ",[31,703,704],{},"test set"," of examples it has never seen; a hypothesis\n",[31,707,708],{},"generalizes"," well if it predicts ",[115,711,713],{"className":712},[118],[115,714,716],{"className":715,"ariaHidden":123},[122],[115,717,719,722],{"className":718},[127],[115,720],{"className":721,"style":556},[131],[115,723,233],{"className":724,"style":232},[136,137]," accurately for those novel inputs.",[727,728],"tikz-figure",{"hash":729},"2a91108b07d792afb608b61c34bbd5baa94dcffa940b248bf74a8e38b285206f",[731,732,734],"h3",{"id":733},"the-hypothesis-space-and-the-generalization-problem","The hypothesis space and the generalization problem",[11,736,737,738,741,742,760,761,776,777,780,781,796,797,835],{},"We never search all functions; we fix a ",[31,739,740],{},"hypothesis space"," ",[115,743,745],{"className":744},[118],[115,746,748],{"className":747,"ariaHidden":123},[122],[115,749,751,754],{"className":750},[127],[115,752],{"className":753,"style":132},[131],[115,755,759],{"className":756,"style":758},[136,757],"mathcal","margin-right:0.0097em;","H"," and\nsearch within it. For fitting a curve to points in the plane, ",[115,762,764],{"className":763},[118],[115,765,767],{"className":766,"ariaHidden":123},[122],[115,768,770,773],{"className":769},[127],[115,771],{"className":772,"style":132},[131],[115,774,759],{"className":775,"style":758},[136,757]," might\nbe the polynomials up to some degree. A hypothesis is ",[31,778,779],{},"consistent"," if it agrees\nwith all the training data. The trouble is that consistency is cheap and\nmisleading: given ",[115,782,784],{"className":783},[118],[115,785,787],{"className":786,"ariaHidden":123},[122],[115,788,790,793],{"className":789},[127],[115,791],{"className":792,"style":132},[131],[115,794,139],{"className":795,"style":138},[136,137]," points, a polynomial of degree ",[115,798,800],{"className":799},[118],[115,801,803,825],{"className":802,"ariaHidden":123},[122],[115,804,806,810,813,817,822],{"className":805},[127],[115,807],{"className":808,"style":809},[131],"height:0.7667em;vertical-align:-0.0833em;",[115,811,139],{"className":812,"style":138},[136,137],[115,814],{"className":815,"style":816},[224],"margin-right:0.2222em;",[115,818,821],{"className":819},[820],"mbin","−",[115,823],{"className":824,"style":816},[224],[115,826,828,832],{"className":827},[127],[115,829],{"className":830,"style":831},[131],"height:0.6444em;",[115,833,44],{"className":834},[136]," passes through every\none of them exactly, yet wiggles wildly between them and predicts nonsense off the\ndata. A straight line may miss every point slightly and still predict far better.",[727,837],{"hash":838},"adbe7c6f779445b949e93d334f927db44c417eba8542a957b167c6c31c676a1f",[11,840,841,842,845,846,849],{},"A hypothesis space too simple to\ncapture the signal ",[31,843,844],{},"underfits","; one flexible enough to chase the noise\n",[31,847,848],{},"overfits",". Between them sits the fit that captures the trend and ignores the\nnoise, and choosing it is the problem of the rest of the lesson. This tradeoff —\ncomplex hypotheses that fit the training data versus simpler ones that generalize\nbetter — recurs below, along with a classical answer, Ockham's razor.",[101,851,852],{"type":103},[11,853,854,857,858],{},[31,855,856],{},"Definition (Overfitting)."," A hypothesis overfits when it fits the training\nexamples, including their noise and accidents, so closely that its test error is\nworse than a simpler hypothesis would achieve. Overfitting grows more likely as\nthe hypothesis space and the number of input attributes grow, and less likely as\nthe training set grows.",[36,859,860],{},[15,861,865],{"href":862,"ariaDescribedBy":863,"dataFootnoteRef":6,"id":864},"#user-content-fn-aima-overfit",[42],"user-content-fnref-aima-overfit","3",[63,867,869],{"id":868},"decision-trees","Decision trees",[11,871,872,873,876,877,932,933,985,986,994,995,999,1000,1002],{},"A ",[31,874,875],{},"decision tree"," is one of the simplest and most successful hypothesis spaces.\nIt represents a function that takes a vector of attribute values and returns a\nsingle output by running a sequence of tests. Each internal node tests one\nattribute ",[115,878,880],{"className":879},[118],[115,881,883],{"className":882,"ariaHidden":123},[122],[115,884,886,890],{"className":885},[127],[115,887],{"className":888,"style":889},[131],"height:0.8333em;vertical-align:-0.15em;",[115,891,893,897],{"className":892},[136],[115,894,896],{"className":895},[136,137],"A",[115,898,900],{"className":899},[169],[115,901,903,924],{"className":902},[173,174],[115,904,906,921],{"className":905},[178],[115,907,909],{"className":908,"style":517},[182],[115,910,911,914],{"style":186},[115,912],{"className":913,"style":191},[190],[115,915,917],{"className":916},[195,196,197,198],[115,918,920],{"className":919},[136,137,198],"i",[115,922,206],{"className":923},[205],[115,925,927],{"className":926},[178],[115,928,930],{"className":929,"style":213},[182],[115,931],{},"; each branch out of that node is labelled with a value of ",[115,934,936],{"className":935},[118],[115,937,939],{"className":938,"ariaHidden":123},[122],[115,940,942,945],{"className":941},[127],[115,943],{"className":944,"style":889},[131],[115,946,948,951],{"className":947},[136],[115,949,896],{"className":950},[136,137],[115,952,954],{"className":953},[169],[115,955,957,977],{"className":956},[173,174],[115,958,960,974],{"className":959},[178],[115,961,963],{"className":962,"style":517},[182],[115,964,965,968],{"style":186},[115,966],{"className":967,"style":191},[190],[115,969,971],{"className":970},[195,196,197,198],[115,972,920],{"className":973},[136,137,198],[115,975,206],{"className":976},[205],[115,978,980],{"className":979},[178],[115,981,983],{"className":982,"style":213},[182],[115,984],{},";\neach leaf specifies the output to return.",[36,987,988],{},[15,989,993],{"href":990,"ariaDescribedBy":991,"dataFootnoteRef":6,"id":992},"#user-content-fn-aima-dt-rep",[42],"user-content-fnref-aima-dt-rep","4"," The representation is\nnatural for humans — many ",[996,997,998],"q",{},"How To"," manuals are one long decision tree — and, for\nBoolean classification, a tree is logically equivalent to the assertion that the\ngoal is true exactly when the input satisfies one of the paths leading to a\n",[54,1001,123],{}," leaf.",[11,1004,1005,1006,1045,1046,1085,1086,670,1089,670,1092,1095,1096,1130,1131,1157,1158,1183,1184,61],{},"The running example is deciding whether to wait for a table at a restaurant. The\ngoal predicate ",[115,1007,1009],{"className":1008},[118],[115,1010,1012],{"className":1011,"ariaHidden":123},[122],[115,1013,1015,1018,1023,1026,1031,1034,1037,1041],{"className":1014},[127],[115,1016],{"className":1017,"style":605},[131],[115,1019,1022],{"className":1020,"style":1021},[136,137],"margin-right:0.1389em;","W",[115,1024,920],{"className":1025},[136,137],[115,1027,1030],{"className":1028,"style":1029},[136,137],"margin-right:0.0197em;","l",[115,1032,1030],{"className":1033,"style":1029},[136,137],[115,1035,1022],{"className":1036,"style":1021},[136,137],[115,1038,1040],{"className":1039},[136,137],"ai",[115,1042,1044],{"className":1043},[136,137],"t"," is a Boolean function of ten attributes, among them\n",[115,1047,1049],{"className":1048},[118],[115,1050,1052],{"className":1051,"ariaHidden":123},[122],[115,1053,1055,1058,1062,1065,1068,1073,1077,1081],{"className":1054},[127],[115,1056],{"className":1057,"style":132},[131],[115,1059,1061],{"className":1060,"style":1021},[136,137],"P",[115,1063,15],{"className":1064},[136,137],[115,1066,1044],{"className":1067},[136,137],[115,1069,1072],{"className":1070,"style":1071},[136,137],"margin-right:0.0278em;","r",[115,1074,1076],{"className":1075},[136,137],"o",[115,1078,1080],{"className":1079},[136,137],"n",[115,1082,1084],{"className":1083},[136,137],"s"," (how full the restaurant is: ",[54,1087,1088],{},"None",[54,1090,1091],{},"Some",[54,1093,1094],{},"Full","), ",[115,1097,1099],{"className":1098},[118],[115,1100,1102],{"className":1101,"ariaHidden":123},[122],[115,1103,1105,1109,1113,1117,1120,1124,1127],{"className":1104},[127],[115,1106],{"className":1107,"style":1108},[131],"height:0.8778em;vertical-align:-0.1944em;",[115,1110,759],{"className":1111,"style":1112},[136,137],"margin-right:0.0813em;",[115,1114,1116],{"className":1115},[136,137],"u",[115,1118,1080],{"className":1119},[136,137],[115,1121,1123],{"className":1122,"style":232},[136,137],"g",[115,1125,1072],{"className":1126,"style":1071},[136,137],[115,1128,233],{"className":1129,"style":232},[136,137],",\n",[115,1132,1134],{"className":1133},[118],[115,1135,1137],{"className":1136,"ariaHidden":123},[122],[115,1138,1140,1143,1147,1150,1153],{"className":1139},[127],[115,1141],{"className":1142,"style":1108},[131],[115,1144,1146],{"className":1145,"style":1021},[136,137],"T",[115,1148,233],{"className":1149,"style":232},[136,137],[115,1151,11],{"className":1152},[136,137],[115,1154,1156],{"className":1155},[136,137],"e"," (French, Italian, Thai, or burger), ",[115,1159,1161],{"className":1160},[118],[115,1162,1164],{"className":1163,"ariaHidden":123},[122],[115,1165,1167,1170,1173,1176,1179],{"className":1166},[127],[115,1168],{"className":1169,"style":132},[131],[115,1171,1061],{"className":1172,"style":1021},[136,137],[115,1174,1072],{"className":1175,"style":1071},[136,137],[115,1177,920],{"className":1178},[136,137],[115,1180,1182],{"className":1181},[136,137],"ce",", and ",[115,1185,1187],{"className":1186},[118],[115,1188,1190],{"className":1189,"ariaHidden":123},[122],[115,1191,1193,1196,1199,1202,1207,1210,1213,1217,1220],{"className":1192},[127],[115,1194],{"className":1195,"style":132},[131],[115,1197,1022],{"className":1198,"style":1021},[136,137],[115,1200,1040],{"className":1201},[136,137],[115,1203,1206],{"className":1204,"style":1205},[136,137],"margin-right:0.0576em;","tE",[115,1208,1084],{"className":1209},[136,137],[115,1211,1044],{"className":1212},[136,137],[115,1214,1216],{"className":1215},[136,137],"ima",[115,1218,1044],{"className":1219},[136,137],[115,1221,1156],{"className":1222},[136,137],[727,1224],{"hash":1225},"d82954b3a3bf5a15eef1d13730304601fc4ab9f79696e5cc6fab4ca9d8d264c3",[11,1227,1228,1229,1306,1307,1323,1324,1374,1375,1383],{},"Decision trees can represent any Boolean function — but not always concisely. The\nmajority function, true when more than half its inputs are, needs an exponentially\nlarge tree. And the space is enormous: there are ",[115,1230,1232],{"className":1231},[118],[115,1233,1235],{"className":1234,"ariaHidden":123},[122],[115,1236,1238,1242],{"className":1237},[127],[115,1239],{"className":1240,"style":1241},[131],"height:0.88em;",[115,1243,1245,1248],{"className":1244},[136],[115,1246,77],{"className":1247},[136],[115,1249,1251],{"className":1250},[169],[115,1252,1254],{"className":1253},[173],[115,1255,1257],{"className":1256},[178],[115,1258,1260],{"className":1259,"style":1241},[182],[115,1261,1263,1266],{"style":1262},"top:-3.063em;margin-right:0.05em;",[115,1264],{"className":1265,"style":191},[190],[115,1267,1269],{"className":1268},[195,196,197,198],[115,1270,1272],{"className":1271},[136,198],[115,1273,1275,1278],{"className":1274},[136,198],[115,1276,77],{"className":1277},[136,198],[115,1279,1281],{"className":1280},[169],[115,1282,1284],{"className":1283},[173],[115,1285,1287],{"className":1286},[178],[115,1288,1291],{"className":1289,"style":1290},[182],"height:0.7385em;",[115,1292,1294,1298],{"style":1293},"top:-2.931em;margin-right:0.0714em;",[115,1295],{"className":1296,"style":1297},[190],"height:2.5em;",[115,1299,1303],{"className":1300},[195,1301,1302,198],"reset-size3","size1",[115,1304,1080],{"className":1305},[136,137,198]," Boolean functions of\n",[115,1308,1310],{"className":1309},[118],[115,1311,1313],{"className":1312,"ariaHidden":123},[122],[115,1314,1316,1320],{"className":1315},[127],[115,1317],{"className":1318,"style":1319},[131],"height:0.4306em;",[115,1321,1080],{"className":1322},[136,137]," attributes, so with the ten attributes of the restaurant problem we are\nchoosing among roughly ",[115,1325,1327],{"className":1326},[118],[115,1328,1330],{"className":1329,"ariaHidden":123},[122],[115,1331,1333,1337,1340],{"className":1332},[127],[115,1334],{"className":1335,"style":1336},[131],"height:0.8141em;",[115,1338,44],{"className":1339},[136],[115,1341,1343,1347],{"className":1342},[136],[115,1344,1346],{"className":1345},[136],"0",[115,1348,1350],{"className":1349},[169],[115,1351,1353],{"className":1352},[173],[115,1354,1356],{"className":1355},[178],[115,1357,1359],{"className":1358,"style":1336},[182],[115,1360,1361,1364],{"style":1262},[115,1362],{"className":1363,"style":191},[190],[115,1365,1367],{"className":1366},[195,196,197,198],[115,1368,1370],{"className":1369},[136,198],[115,1371,1373],{"className":1372},[136,198],"308"," functions.",[36,1376,1377],{},[15,1378,1382],{"href":1379,"ariaDescribedBy":1380,"dataFootnoteRef":6,"id":1381},"#user-content-fn-aima-dt-expr",[42],"user-content-fnref-aima-dt-expr","5"," Finding a good tree in\nthat space requires a heuristic.",[731,1385,1387],{"id":1386},"learning-a-tree-by-information-gain","Learning a tree by information gain",[11,1389,1390,1391,1394,1395,1398,1399,1402],{},"We want a tree that is consistent with the examples and as small as possible —\nsmall trees are shallow, which means few tests and better generalization. Finding\nthe ",[54,1392,1393],{},"smallest"," consistent tree is intractable, but a greedy heuristic finds a\nsmall one: ",[31,1396,1397],{},"always test the most important attribute first",", where ",[996,1400,1401],{},"most important"," means the one that makes the most difference to the classification. A\ngood split leaves subsets that are as close to all-positive or all-negative as\npossible; a useless split leaves subsets with the same mix as the parent.",[727,1404],{"hash":1405},"57e298e23cccb2a4437e16761bc30721d48a212269db34063adb385fd143b988",[11,1407,1408,1409,1411,1412,1415,1416,1424,1425,1440,1441,1457,1458,1515,1516,220],{},"To make ",[996,1410,1401],{}," precise we borrow ",[31,1413,1414],{},"entropy"," from information\ntheory.",[36,1417,1418],{},[15,1419,1423],{"href":1420,"ariaDescribedBy":1421,"dataFootnoteRef":6,"id":1422},"#user-content-fn-aima-entropy",[42],"user-content-fnref-aima-entropy","6"," Entropy measures the uncertainty of a random variable;\nacquiring information reduces it. A variable with one certain value has entropy\nzero; a fair coin has entropy ",[115,1426,1428],{"className":1427},[118],[115,1429,1431],{"className":1430,"ariaHidden":123},[122],[115,1432,1434,1437],{"className":1433},[127],[115,1435],{"className":1436,"style":831},[131],[115,1438,44],{"className":1439},[136]," bit. For a variable ",[115,1442,1444],{"className":1443},[118],[115,1445,1447],{"className":1446,"ariaHidden":123},[122],[115,1448,1450,1453],{"className":1449},[127],[115,1451],{"className":1452,"style":132},[131],[115,1454,1456],{"className":1455,"style":816},[136,137],"V"," with values ",[115,1459,1461],{"className":1460},[118],[115,1462,1464],{"className":1463,"ariaHidden":123},[122],[115,1465,1467,1471],{"className":1466},[127],[115,1468],{"className":1469,"style":1470},[131],"height:0.5806em;vertical-align:-0.15em;",[115,1472,1474,1478],{"className":1473},[136],[115,1475,1477],{"className":1476,"style":232},[136,137],"v",[115,1479,1481],{"className":1480},[169],[115,1482,1484,1507],{"className":1483},[173,174],[115,1485,1487,1504],{"className":1486},[178],[115,1488,1491],{"className":1489,"style":1490},[182],"height:0.3361em;",[115,1492,1493,1496],{"style":248},[115,1494],{"className":1495,"style":191},[190],[115,1497,1499],{"className":1498},[195,196,197,198],[115,1500,1503],{"className":1501,"style":1502},[136,137,198],"margin-right:0.0315em;","k",[115,1505,206],{"className":1506},[205],[115,1508,1510],{"className":1509},[178],[115,1511,1513],{"className":1512,"style":213},[182],[115,1514],{},"\noccurring with probability ",[115,1517,1519],{"className":1518},[118],[115,1520,1522],{"className":1521,"ariaHidden":123},[122],[115,1523,1525,1528,1531,1534,1574],{"className":1524},[127],[115,1526],{"className":1527,"style":153},[131],[115,1529,1061],{"className":1530,"style":1021},[136,137],[115,1532,158],{"className":1533},[157],[115,1535,1537,1540],{"className":1536},[136],[115,1538,1477],{"className":1539,"style":232},[136,137],[115,1541,1543],{"className":1542},[169],[115,1544,1546,1566],{"className":1545},[173,174],[115,1547,1549,1563],{"className":1548},[178],[115,1550,1552],{"className":1551,"style":1490},[182],[115,1553,1554,1557],{"style":248},[115,1555],{"className":1556,"style":191},[190],[115,1558,1560],{"className":1559},[195,196,197,198],[115,1561,1503],{"className":1562,"style":1502},[136,137,198],[115,1564,206],{"className":1565},[205],[115,1567,1569],{"className":1568},[178],[115,1570,1572],{"className":1571,"style":213},[182],[115,1573],{},[115,1575,273],{"className":1576},[272],[115,1578,1581],{"className":1579},[1580],"katex-display",[115,1582,1584],{"className":1583},[118],[115,1585,1587,1620,1921],{"className":1586,"ariaHidden":123},[122],[115,1588,1590,1593,1596,1599,1602,1605,1608,1611,1614,1617],{"className":1589},[127],[115,1591],{"className":1592,"style":153},[131],[115,1594,759],{"className":1595,"style":1112},[136,137],[115,1597,158],{"className":1598},[157],[115,1600,1456],{"className":1601,"style":816},[136,137],[115,1603,273],{"className":1604},[272],[115,1606],{"className":1607,"style":563},[224],[115,1609],{"className":1610,"style":563},[224],[115,1612,568],{"className":1613},[567],[115,1615],{"className":1616,"style":563},[224],[115,1618],{"className":1619,"style":563},[224],[115,1621,1623,1627,1681,1684,1687,1690,1730,1733,1736,1785,1788,1906,1909,1912,1915,1918],{"className":1622},[127],[115,1624],{"className":1625,"style":1626},[131],"height:2.6236em;vertical-align:-1.3021em;",[115,1628,1632],{"className":1629},[1630,1631],"mop","op-limits",[115,1633,1635,1672],{"className":1634},[173,174],[115,1636,1638,1669],{"className":1637},[178],[115,1639,1642,1655],{"className":1640,"style":1641},[182],"height:1.05em;",[115,1643,1645,1649],{"style":1644},"top:-1.8479em;margin-left:0em;",[115,1646],{"className":1647,"style":1648},[190],"height:3.05em;",[115,1650,1652],{"className":1651},[195,196,197,198],[115,1653,1503],{"className":1654,"style":1502},[136,137,198],[115,1656,1658,1661],{"style":1657},"top:-3.05em;",[115,1659],{"className":1660,"style":1648},[190],[115,1662,1663],{},[115,1664,1668],{"className":1665},[1630,1666,1667],"op-symbol","large-op","∑",[115,1670,206],{"className":1671},[205],[115,1673,1675],{"className":1674},[178],[115,1676,1679],{"className":1677,"style":1678},[182],"height:1.3021em;",[115,1680],{},[115,1682],{"className":1683,"style":225},[224],[115,1685,1061],{"className":1686,"style":1021},[136,137],[115,1688,158],{"className":1689},[157],[115,1691,1693,1696],{"className":1692},[136],[115,1694,1477],{"className":1695,"style":232},[136,137],[115,1697,1699],{"className":1698},[169],[115,1700,1702,1722],{"className":1701},[173,174],[115,1703,1705,1719],{"className":1704},[178],[115,1706,1708],{"className":1707,"style":1490},[182],[115,1709,1710,1713],{"style":248},[115,1711],{"className":1712,"style":191},[190],[115,1714,1716],{"className":1715},[195,196,197,198],[115,1717,1503],{"className":1718,"style":1502},[136,137,198],[115,1720,206],{"className":1721},[205],[115,1723,1725],{"className":1724},[178],[115,1726,1728],{"className":1727,"style":213},[182],[115,1729],{},[115,1731,273],{"className":1732},[272],[115,1734],{"className":1735,"style":225},[224],[115,1737,1739,1748],{"className":1738},[1630],[115,1740,1742],{"className":1741},[1630],[115,1743,1747],{"className":1744,"style":1746},[136,1745],"mathrm","margin-right:0.0139em;","log",[115,1749,1751],{"className":1750},[169],[115,1752,1754,1776],{"className":1753},[173,174],[115,1755,1757,1773],{"className":1756},[178],[115,1758,1761],{"className":1759,"style":1760},[182],"height:0.207em;",[115,1762,1764,1767],{"style":1763},"top:-2.4559em;margin-right:0.05em;",[115,1765],{"className":1766,"style":191},[190],[115,1768,1770],{"className":1769},[195,196,197,198],[115,1771,77],{"className":1772},[136,198],[115,1774,206],{"className":1775},[205],[115,1777,1779],{"className":1778},[178],[115,1780,1783],{"className":1781,"style":1782},[182],"height:0.2441em;",[115,1784],{},[115,1786],{"className":1787,"style":225},[224],[115,1789,1791,1795,1903],{"className":1790},[136],[115,1792],{"className":1793},[157,1794],"nulldelimiter",[115,1796,1799],{"className":1797},[1798],"mfrac",[115,1800,1802,1894],{"className":1801},[173,174],[115,1803,1805,1891],{"className":1804},[178],[115,1806,1809,1868,1879],{"className":1807,"style":1808},[182],"height:1.3214em;",[115,1810,1812,1816],{"style":1811},"top:-2.314em;",[115,1813],{"className":1814,"style":1815},[190],"height:3em;",[115,1817,1819,1822,1825,1865],{"className":1818},[136],[115,1820,1061],{"className":1821,"style":1021},[136,137],[115,1823,158],{"className":1824},[157],[115,1826,1828,1831],{"className":1827},[136],[115,1829,1477],{"className":1830,"style":232},[136,137],[115,1832,1834],{"className":1833},[169],[115,1835,1837,1857],{"className":1836},[173,174],[115,1838,1840,1854],{"className":1839},[178],[115,1841,1843],{"className":1842,"style":1490},[182],[115,1844,1845,1848],{"style":248},[115,1846],{"className":1847,"style":191},[190],[115,1849,1851],{"className":1850},[195,196,197,198],[115,1852,1503],{"className":1853,"style":1502},[136,137,198],[115,1855,206],{"className":1856},[205],[115,1858,1860],{"className":1859},[178],[115,1861,1863],{"className":1862,"style":213},[182],[115,1864],{},[115,1866,273],{"className":1867},[272],[115,1869,1871,1874],{"style":1870},"top:-3.23em;",[115,1872],{"className":1873,"style":1815},[190],[115,1875],{"className":1876,"style":1878},[1877],"frac-line","border-bottom-width:0.04em;",[115,1880,1882,1885],{"style":1881},"top:-3.677em;",[115,1883],{"className":1884,"style":1815},[190],[115,1886,1888],{"className":1887},[136],[115,1889,44],{"className":1890},[136],[115,1892,206],{"className":1893},[205],[115,1895,1897],{"className":1896},[178],[115,1898,1901],{"className":1899,"style":1900},[182],"height:0.936em;",[115,1902],{},[115,1904],{"className":1905},[272,1794],[115,1907],{"className":1908,"style":563},[224],[115,1910],{"className":1911,"style":563},[224],[115,1913,568],{"className":1914},[567],[115,1916],{"className":1917,"style":563},[224],[115,1919],{"className":1920,"style":563},[224],[115,1922,1924,1928,1931,1934,1978,1981,1984,1987,2027,2030,2033,2076,2079,2082,2085,2125,2128],{"className":1923},[127],[115,1925],{"className":1926,"style":1927},[131],"height:2.3521em;vertical-align:-1.3021em;",[115,1929,821],{"className":1930},[136],[115,1932],{"className":1933,"style":225},[224],[115,1935,1937],{"className":1936},[1630,1631],[115,1938,1940,1970],{"className":1939},[173,174],[115,1941,1943,1967],{"className":1942},[178],[115,1944,1946,1957],{"className":1945,"style":1641},[182],[115,1947,1948,1951],{"style":1644},[115,1949],{"className":1950,"style":1648},[190],[115,1952,1954],{"className":1953},[195,196,197,198],[115,1955,1503],{"className":1956,"style":1502},[136,137,198],[115,1958,1959,1962],{"style":1657},[115,1960],{"className":1961,"style":1648},[190],[115,1963,1964],{},[115,1965,1668],{"className":1966},[1630,1666,1667],[115,1968,206],{"className":1969},[205],[115,1971,1973],{"className":1972},[178],[115,1974,1976],{"className":1975,"style":1678},[182],[115,1977],{},[115,1979],{"className":1980,"style":225},[224],[115,1982,1061],{"className":1983,"style":1021},[136,137],[115,1985,158],{"className":1986},[157],[115,1988,1990,1993],{"className":1989},[136],[115,1991,1477],{"className":1992,"style":232},[136,137],[115,1994,1996],{"className":1995},[169],[115,1997,1999,2019],{"className":1998},[173,174],[115,2000,2002,2016],{"className":2001},[178],[115,2003,2005],{"className":2004,"style":1490},[182],[115,2006,2007,2010],{"style":248},[115,2008],{"className":2009,"style":191},[190],[115,2011,2013],{"className":2012},[195,196,197,198],[115,2014,1503],{"className":2015,"style":1502},[136,137,198],[115,2017,206],{"className":2018},[205],[115,2020,2022],{"className":2021},[178],[115,2023,2025],{"className":2024,"style":213},[182],[115,2026],{},[115,2028,273],{"className":2029},[272],[115,2031],{"className":2032,"style":225},[224],[115,2034,2036,2042],{"className":2035},[1630],[115,2037,2039],{"className":2038},[1630],[115,2040,1747],{"className":2041,"style":1746},[136,1745],[115,2043,2045],{"className":2044},[169],[115,2046,2048,2068],{"className":2047},[173,174],[115,2049,2051,2065],{"className":2050},[178],[115,2052,2054],{"className":2053,"style":1760},[182],[115,2055,2056,2059],{"style":1763},[115,2057],{"className":2058,"style":191},[190],[115,2060,2062],{"className":2061},[195,196,197,198],[115,2063,77],{"className":2064},[136,198],[115,2066,206],{"className":2067},[205],[115,2069,2071],{"className":2070},[178],[115,2072,2074],{"className":2073,"style":1782},[182],[115,2075],{},[115,2077],{"className":2078,"style":225},[224],[115,2080,1061],{"className":2081,"style":1021},[136,137],[115,2083,158],{"className":2084},[157],[115,2086,2088,2091],{"className":2087},[136],[115,2089,1477],{"className":2090,"style":232},[136,137],[115,2092,2094],{"className":2093},[169],[115,2095,2097,2117],{"className":2096},[173,174],[115,2098,2100,2114],{"className":2099},[178],[115,2101,2103],{"className":2102,"style":1490},[182],[115,2104,2105,2108],{"style":248},[115,2106],{"className":2107,"style":191},[190],[115,2109,2111],{"className":2110},[195,196,197,198],[115,2112,1503],{"className":2113,"style":1502},[136,137,198],[115,2115,206],{"className":2116},[205],[115,2118,2120],{"className":2119},[178],[115,2121,2123],{"className":2122,"style":213},[182],[115,2124],{},[115,2126,273],{"className":2127},[272],[115,2129,61],{"className":2130},[136],[11,2132,2133,2134,2149,2150,2379,2380,2395,2396,2411,2412,2518,2519,2534,2535,2551,2552,2664,2665,2717,2718,2770,2771,2823,2824,2839],{},"For a Boolean variable that is true with probability ",[115,2135,2137],{"className":2136},[118],[115,2138,2140],{"className":2139,"ariaHidden":123},[122],[115,2141,2143,2146],{"className":2142},[127],[115,2144],{"className":2145,"style":556},[131],[115,2147,996],{"className":2148,"style":232},[136,137],", write\n",[115,2151,2153],{"className":2152},[118],[115,2154,2156,2185,2267,2288,2361],{"className":2155,"ariaHidden":123},[122],[115,2157,2159,2162,2167,2170,2173,2176,2179,2182],{"className":2158},[127],[115,2160],{"className":2161,"style":153},[131],[115,2163,2166],{"className":2164,"style":2165},[136,137],"margin-right:0.0502em;","B",[115,2168,158],{"className":2169},[157],[115,2171,996],{"className":2172,"style":232},[136,137],[115,2174,273],{"className":2175},[272],[115,2177],{"className":2178,"style":563},[224],[115,2180,568],{"className":2181},[567],[115,2183],{"className":2184,"style":563},[224],[115,2186,2188,2192,2195,2202,2205,2208,2251,2254,2257,2260,2264],{"className":2187},[127],[115,2189],{"className":2190,"style":2191},[131],"height:1.2em;vertical-align:-0.35em;",[115,2193,821],{"className":2194},[136],[115,2196,2198],{"className":2197},[157],[115,2199,158],{"className":2200},[2201,1302],"delimsizing",[115,2203,996],{"className":2204,"style":232},[136,137],[115,2206],{"className":2207,"style":225},[224],[115,2209,2211,2217],{"className":2210},[1630],[115,2212,2214],{"className":2213},[1630],[115,2215,1747],{"className":2216,"style":1746},[136,1745],[115,2218,2220],{"className":2219},[169],[115,2221,2223,2243],{"className":2222},[173,174],[115,2224,2226,2240],{"className":2225},[178],[115,2227,2229],{"className":2228,"style":1760},[182],[115,2230,2231,2234],{"style":1763},[115,2232],{"className":2233,"style":191},[190],[115,2235,2237],{"className":2236},[195,196,197,198],[115,2238,77],{"className":2239},[136,198],[115,2241,206],{"className":2242},[205],[115,2244,2246],{"className":2245},[178],[115,2247,2249],{"className":2248,"style":1782},[182],[115,2250],{},[115,2252],{"className":2253,"style":225},[224],[115,2255,996],{"className":2256,"style":232},[136,137],[115,2258],{"className":2259,"style":816},[224],[115,2261,2263],{"className":2262},[820],"+",[115,2265],{"className":2266,"style":816},[224],[115,2268,2270,2273,2276,2279,2282,2285],{"className":2269},[127],[115,2271],{"className":2272,"style":153},[131],[115,2274,158],{"className":2275},[157],[115,2277,44],{"className":2278},[136],[115,2280],{"className":2281,"style":816},[224],[115,2283,821],{"className":2284},[820],[115,2286],{"className":2287,"style":816},[224],[115,2289,2291,2294,2297,2300,2303,2346,2349,2352,2355,2358],{"className":2290},[127],[115,2292],{"className":2293,"style":153},[131],[115,2295,996],{"className":2296,"style":232},[136,137],[115,2298,273],{"className":2299},[272],[115,2301],{"className":2302,"style":225},[224],[115,2304,2306,2312],{"className":2305},[1630],[115,2307,2309],{"className":2308},[1630],[115,2310,1747],{"className":2311,"style":1746},[136,1745],[115,2313,2315],{"className":2314},[169],[115,2316,2318,2338],{"className":2317},[173,174],[115,2319,2321,2335],{"className":2320},[178],[115,2322,2324],{"className":2323,"style":1760},[182],[115,2325,2326,2329],{"style":1763},[115,2327],{"className":2328,"style":191},[190],[115,2330,2332],{"className":2331},[195,196,197,198],[115,2333,77],{"className":2334},[136,198],[115,2336,206],{"className":2337},[205],[115,2339,2341],{"className":2340},[178],[115,2342,2344],{"className":2343,"style":1782},[182],[115,2345],{},[115,2347,158],{"className":2348},[157],[115,2350,44],{"className":2351},[136],[115,2353],{"className":2354,"style":816},[224],[115,2356,821],{"className":2357},[820],[115,2359],{"className":2360,"style":816},[224],[115,2362,2364,2367,2370,2373],{"className":2363},[127],[115,2365],{"className":2366,"style":2191},[131],[115,2368,996],{"className":2369,"style":232},[136,137],[115,2371,273],{"className":2372},[272],[115,2374,2376],{"className":2375},[272],[115,2377,273],{"className":2378},[2201,1302],". A training set with ",[115,2381,2383],{"className":2382},[118],[115,2384,2386],{"className":2385,"ariaHidden":123},[122],[115,2387,2389,2392],{"className":2388},[127],[115,2390],{"className":2391,"style":556},[131],[115,2393,11],{"className":2394},[136,137],"\npositive and ",[115,2397,2399],{"className":2398},[118],[115,2400,2402],{"className":2401,"ariaHidden":123},[122],[115,2403,2405,2408],{"className":2404},[127],[115,2406],{"className":2407,"style":1319},[131],[115,2409,1080],{"className":2410},[136,137]," negative examples has goal entropy ",[115,2413,2415],{"className":2414},[118],[115,2416,2418],{"className":2417,"ariaHidden":123},[122],[115,2419,2421,2425,2428,2434,2512],{"className":2420},[127],[115,2422],{"className":2423,"style":2424},[131],"height:1.3311em;vertical-align:-0.4811em;",[115,2426,2166],{"className":2427,"style":2165},[136,137],[115,2429,2431],{"className":2430},[157],[115,2432,158],{"className":2433},[2201,1302],[115,2435,2437,2440,2509],{"className":2436},[136],[115,2438],{"className":2439},[157,1794],[115,2441,2443],{"className":2442},[1798],[115,2444,2446,2500],{"className":2445},[173,174],[115,2447,2449,2497],{"className":2448},[178],[115,2450,2453,2474,2482],{"className":2451,"style":2452},[182],"height:0.7475em;",[115,2454,2456,2459],{"style":2455},"top:-2.655em;",[115,2457],{"className":2458,"style":1815},[190],[115,2460,2462],{"className":2461},[195,196,197,198],[115,2463,2465,2468,2471],{"className":2464},[136,198],[115,2466,11],{"className":2467},[136,137,198],[115,2469,2263],{"className":2470},[820,198],[115,2472,1080],{"className":2473},[136,137,198],[115,2475,2476,2479],{"style":1870},[115,2477],{"className":2478,"style":1815},[190],[115,2480],{"className":2481,"style":1878},[1877],[115,2483,2485,2488],{"style":2484},"top:-3.4461em;",[115,2486],{"className":2487,"style":1815},[190],[115,2489,2491],{"className":2490},[195,196,197,198],[115,2492,2494],{"className":2493},[136,198],[115,2495,11],{"className":2496},[136,137,198],[115,2498,206],{"className":2499},[205],[115,2501,2503],{"className":2502},[178],[115,2504,2507],{"className":2505,"style":2506},[182],"height:0.4811em;",[115,2508],{},[115,2510],{"className":2511},[272,1794],[115,2513,2515],{"className":2514},[272],[115,2516,273],{"className":2517},[2201,1302],".\nAn attribute ",[115,2520,2522],{"className":2521},[118],[115,2523,2525],{"className":2524,"ariaHidden":123},[122],[115,2526,2528,2531],{"className":2527},[127],[115,2529],{"className":2530,"style":132},[131],[115,2532,896],{"className":2533},[136,137]," with ",[115,2536,2538],{"className":2537},[118],[115,2539,2541],{"className":2540,"ariaHidden":123},[122],[115,2542,2544,2547],{"className":2543},[127],[115,2545],{"className":2546,"style":605},[131],[115,2548,2550],{"className":2549},[136,137],"d"," values splits the set into subsets ",[115,2553,2555],{"className":2554},[118],[115,2556,2558],{"className":2557,"ariaHidden":123},[122],[115,2559,2561,2564,2606,2609,2612,2615,2618,2621,2624],{"className":2560},[127],[115,2562],{"className":2563,"style":1108},[131],[115,2565,2567,2571],{"className":2566},[136],[115,2568,2570],{"className":2569,"style":1205},[136,137],"E",[115,2572,2574],{"className":2573},[169],[115,2575,2577,2598],{"className":2576},[173,174],[115,2578,2580,2595],{"className":2579},[178],[115,2581,2583],{"className":2582,"style":183},[182],[115,2584,2586,2589],{"style":2585},"top:-2.55em;margin-left:-0.0576em;margin-right:0.05em;",[115,2587],{"className":2588,"style":191},[190],[115,2590,2592],{"className":2591},[195,196,197,198],[115,2593,44],{"className":2594},[136,198],[115,2596,206],{"className":2597},[205],[115,2599,2601],{"className":2600},[178],[115,2602,2604],{"className":2603,"style":213},[182],[115,2605],{},[115,2607,220],{"className":2608},[219],[115,2610],{"className":2611,"style":225},[224],[115,2613,382],{"className":2614},[381],[115,2616],{"className":2617,"style":225},[224],[115,2619,220],{"className":2620},[219],[115,2622],{"className":2623,"style":225},[224],[115,2625,2627,2630],{"className":2626},[136],[115,2628,2570],{"className":2629,"style":1205},[136,137],[115,2631,2633],{"className":2632},[169],[115,2634,2636,2656],{"className":2635},[173,174],[115,2637,2639,2653],{"className":2638},[178],[115,2640,2642],{"className":2641,"style":1490},[182],[115,2643,2644,2647],{"style":2585},[115,2645],{"className":2646,"style":191},[190],[115,2648,2650],{"className":2649},[195,196,197,198],[115,2651,2550],{"className":2652},[136,137,198],[115,2654,206],{"className":2655},[205],[115,2657,2659],{"className":2658},[178],[115,2660,2662],{"className":2661,"style":213},[182],[115,2663],{},",\nwhere subset ",[115,2666,2668],{"className":2667},[118],[115,2669,2671],{"className":2670,"ariaHidden":123},[122],[115,2672,2674,2677],{"className":2673},[127],[115,2675],{"className":2676,"style":889},[131],[115,2678,2680,2683],{"className":2679},[136],[115,2681,2570],{"className":2682,"style":1205},[136,137],[115,2684,2686],{"className":2685},[169],[115,2687,2689,2709],{"className":2688},[173,174],[115,2690,2692,2706],{"className":2691},[178],[115,2693,2695],{"className":2694,"style":1490},[182],[115,2696,2697,2700],{"style":2585},[115,2698],{"className":2699,"style":191},[190],[115,2701,2703],{"className":2702},[195,196,197,198],[115,2704,1503],{"className":2705,"style":1502},[136,137,198],[115,2707,206],{"className":2708},[205],[115,2710,2712],{"className":2711},[178],[115,2713,2715],{"className":2714,"style":213},[182],[115,2716],{}," has ",[115,2719,2721],{"className":2720},[118],[115,2722,2724],{"className":2723,"ariaHidden":123},[122],[115,2725,2727,2730],{"className":2726},[127],[115,2728],{"className":2729,"style":556},[131],[115,2731,2733,2736],{"className":2732},[136],[115,2734,11],{"className":2735},[136,137],[115,2737,2739],{"className":2738},[169],[115,2740,2742,2762],{"className":2741},[173,174],[115,2743,2745,2759],{"className":2744},[178],[115,2746,2748],{"className":2747,"style":1490},[182],[115,2749,2750,2753],{"style":186},[115,2751],{"className":2752,"style":191},[190],[115,2754,2756],{"className":2755},[195,196,197,198],[115,2757,1503],{"className":2758,"style":1502},[136,137,198],[115,2760,206],{"className":2761},[205],[115,2763,2765],{"className":2764},[178],[115,2766,2768],{"className":2767,"style":213},[182],[115,2769],{}," positive and ",[115,2772,2774],{"className":2773},[118],[115,2775,2777],{"className":2776,"ariaHidden":123},[122],[115,2778,2780,2783],{"className":2779},[127],[115,2781],{"className":2782,"style":1470},[131],[115,2784,2786,2789],{"className":2785},[136],[115,2787,1080],{"className":2788},[136,137],[115,2790,2792],{"className":2791},[169],[115,2793,2795,2815],{"className":2794},[173,174],[115,2796,2798,2812],{"className":2797},[178],[115,2799,2801],{"className":2800,"style":1490},[182],[115,2802,2803,2806],{"style":186},[115,2804],{"className":2805,"style":191},[190],[115,2807,2809],{"className":2808},[195,196,197,198],[115,2810,1503],{"className":2811,"style":1502},[136,137,198],[115,2813,206],{"className":2814},[205],[115,2816,2818],{"className":2817},[178],[115,2819,2821],{"className":2820,"style":213},[182],[115,2822],{}," negative examples. The expected\nentropy remaining after testing ",[115,2825,2827],{"className":2826},[118],[115,2828,2830],{"className":2829,"ariaHidden":123},[122],[115,2831,2833,2836],{"className":2832},[127],[115,2834],{"className":2835,"style":132},[131],[115,2837,896],{"className":2838},[136,137]," is a weighted average over the branches,",[115,2841,2843],{"className":2842},[1580],[115,2844,2846],{"className":2845},[118],[115,2847,2849,2887],{"className":2848,"ariaHidden":123},[122],[115,2850,2852,2855,2863,2866,2869,2872,2875,2878,2881,2884],{"className":2851},[127],[115,2853],{"className":2854,"style":153},[131],[115,2856,2858],{"className":2857},[136],[115,2859,2862],{"className":2860},[136,2861],"mathit","Remainder",[115,2864,158],{"className":2865},[157],[115,2867,896],{"className":2868},[136,137],[115,2870,273],{"className":2871},[272],[115,2873],{"className":2874,"style":563},[224],[115,2876],{"className":2877,"style":563},[224],[115,2879,568],{"className":2880},[567],[115,2882],{"className":2883,"style":563},[224],[115,2885],{"className":2886,"style":563},[224],[115,2888,2890,2894,2963,2966,3128,3131,3134,3138,3141,3344,3347],{"className":2889},[127],[115,2891],{"className":2892,"style":2893},[131],"height:3.1382em;vertical-align:-1.3021em;",[115,2895,2897],{"className":2896},[1630,1631],[115,2898,2900,2955],{"className":2899},[173,174],[115,2901,2903,2952],{"className":2902},[178],[115,2904,2907,2927,2937],{"className":2905,"style":2906},[182],"height:1.8361em;",[115,2908,2909,2912],{"style":1644},[115,2910],{"className":2911,"style":1648},[190],[115,2913,2915],{"className":2914},[195,196,197,198],[115,2916,2918,2921,2924],{"className":2917},[136,198],[115,2919,1503],{"className":2920,"style":1502},[136,137,198],[115,2922,568],{"className":2923},[567,198],[115,2925,44],{"className":2926},[136,198],[115,2928,2929,2932],{"style":1657},[115,2930],{"className":2931,"style":1648},[190],[115,2933,2934],{},[115,2935,1668],{"className":2936},[1630,1666,1667],[115,2938,2940,2943],{"style":2939},"top:-4.3em;margin-left:0em;",[115,2941],{"className":2942,"style":1648},[190],[115,2944,2946],{"className":2945},[195,196,197,198],[115,2947,2949],{"className":2948},[136,198],[115,2950,2550],{"className":2951},[136,137,198],[115,2953,206],{"className":2954},[205],[115,2956,2958],{"className":2957},[178],[115,2959,2961],{"className":2960,"style":1678},[182],[115,2962],{},[115,2964],{"className":2965,"style":225},[224],[115,2967,2969,2972,3125],{"className":2968},[136],[115,2970],{"className":2971},[157,1794],[115,2973,2975],{"className":2974},[1798],[115,2976,2978,3116],{"className":2977},[173,174],[115,2979,2981,3113],{"className":2980},[178],[115,2982,2985,3008,3016],{"className":2983,"style":2984},[182],"height:1.2603em;",[115,2986,2987,2990],{"style":1811},[115,2988],{"className":2989,"style":1815},[190],[115,2991,2993,2996,2999,3002,3005],{"className":2992},[136],[115,2994,11],{"className":2995},[136,137],[115,2997],{"className":2998,"style":816},[224],[115,3000,2263],{"className":3001},[820],[115,3003],{"className":3004,"style":816},[224],[115,3006,1080],{"className":3007},[136,137],[115,3009,3010,3013],{"style":1870},[115,3011],{"className":3012,"style":1815},[190],[115,3014],{"className":3015,"style":1878},[1877],[115,3017,3018,3021],{"style":1881},[115,3019],{"className":3020,"style":1815},[190],[115,3022,3024,3064,3067,3070,3073],{"className":3023},[136],[115,3025,3027,3030],{"className":3026},[136],[115,3028,11],{"className":3029},[136,137],[115,3031,3033],{"className":3032},[169],[115,3034,3036,3056],{"className":3035},[173,174],[115,3037,3039,3053],{"className":3038},[178],[115,3040,3042],{"className":3041,"style":1490},[182],[115,3043,3044,3047],{"style":186},[115,3045],{"className":3046,"style":191},[190],[115,3048,3050],{"className":3049},[195,196,197,198],[115,3051,1503],{"className":3052,"style":1502},[136,137,198],[115,3054,206],{"className":3055},[205],[115,3057,3059],{"className":3058},[178],[115,3060,3062],{"className":3061,"style":213},[182],[115,3063],{},[115,3065],{"className":3066,"style":816},[224],[115,3068,2263],{"className":3069},[820],[115,3071],{"className":3072,"style":816},[224],[115,3074,3076,3079],{"className":3075},[136],[115,3077,1080],{"className":3078},[136,137],[115,3080,3082],{"className":3081},[169],[115,3083,3085,3105],{"className":3084},[173,174],[115,3086,3088,3102],{"className":3087},[178],[115,3089,3091],{"className":3090,"style":1490},[182],[115,3092,3093,3096],{"style":186},[115,3094],{"className":3095,"style":191},[190],[115,3097,3099],{"className":3098},[195,196,197,198],[115,3100,1503],{"className":3101,"style":1502},[136,137,198],[115,3103,206],{"className":3104},[205],[115,3106,3108],{"className":3107},[178],[115,3109,3111],{"className":3110,"style":213},[182],[115,3112],{},[115,3114,206],{"className":3115},[205],[115,3117,3119],{"className":3118},[178],[115,3120,3123],{"className":3121,"style":3122},[182],"height:0.8804em;",[115,3124],{},[115,3126],{"className":3127},[272,1794],[115,3129],{"className":3130,"style":225},[224],[115,3132,2166],{"className":3133,"style":2165},[136,137],[115,3135],{"className":3136,"style":3137},[224],"margin-right:-0.1667em;",[115,3139],{"className":3140,"style":225},[224],[115,3142,3144,3152,3338],{"className":3143},[381],[115,3145,3149],{"className":3146,"style":3148},[157,3147],"delimcenter","top:0em;",[115,3150,158],{"className":3151},[2201,197],[115,3153,3155,3158,3335],{"className":3154},[136],[115,3156],{"className":3157},[157,1794],[115,3159,3161],{"className":3160},[1798],[115,3162,3164,3327],{"className":3163},[173,174],[115,3165,3167,3324],{"className":3166},[178],[115,3168,3171,3268,3276],{"className":3169,"style":3170},[182],"height:1.1076em;",[115,3172,3173,3176],{"style":1811},[115,3174],{"className":3175,"style":1815},[190],[115,3177,3179,3219,3222,3225,3228],{"className":3178},[136],[115,3180,3182,3185],{"className":3181},[136],[115,3183,11],{"className":3184},[136,137],[115,3186,3188],{"className":3187},[169],[115,3189,3191,3211],{"className":3190},[173,174],[115,3192,3194,3208],{"className":3193},[178],[115,3195,3197],{"className":3196,"style":1490},[182],[115,3198,3199,3202],{"style":186},[115,3200],{"className":3201,"style":191},[190],[115,3203,3205],{"className":3204},[195,196,197,198],[115,3206,1503],{"className":3207,"style":1502},[136,137,198],[115,3209,206],{"className":3210},[205],[115,3212,3214],{"className":3213},[178],[115,3215,3217],{"className":3216,"style":213},[182],[115,3218],{},[115,3220],{"className":3221,"style":816},[224],[115,3223,2263],{"className":3224},[820],[115,3226],{"className":3227,"style":816},[224],[115,3229,3231,3234],{"className":3230},[136],[115,3232,1080],{"className":3233},[136,137],[115,3235,3237],{"className":3236},[169],[115,3238,3240,3260],{"className":3239},[173,174],[115,3241,3243,3257],{"className":3242},[178],[115,3244,3246],{"className":3245,"style":1490},[182],[115,3247,3248,3251],{"style":186},[115,3249],{"className":3250,"style":191},[190],[115,3252,3254],{"className":3253},[195,196,197,198],[115,3255,1503],{"className":3256,"style":1502},[136,137,198],[115,3258,206],{"className":3259},[205],[115,3261,3263],{"className":3262},[178],[115,3264,3266],{"className":3265,"style":213},[182],[115,3267],{},[115,3269,3270,3273],{"style":1870},[115,3271],{"className":3272,"style":1815},[190],[115,3274],{"className":3275,"style":1878},[1877],[115,3277,3278,3281],{"style":1881},[115,3279],{"className":3280,"style":1815},[190],[115,3282,3284],{"className":3283},[136],[115,3285,3287,3290],{"className":3286},[136],[115,3288,11],{"className":3289},[136,137],[115,3291,3293],{"className":3292},[169],[115,3294,3296,3316],{"className":3295},[173,174],[115,3297,3299,3313],{"className":3298},[178],[115,3300,3302],{"className":3301,"style":1490},[182],[115,3303,3304,3307],{"style":186},[115,3305],{"className":3306,"style":191},[190],[115,3308,3310],{"className":3309},[195,196,197,198],[115,3311,1503],{"className":3312,"style":1502},[136,137,198],[115,3314,206],{"className":3315},[205],[115,3317,3319],{"className":3318},[178],[115,3320,3322],{"className":3321,"style":213},[182],[115,3323],{},[115,3325,206],{"className":3326},[205],[115,3328,3330],{"className":3329},[178],[115,3331,3333],{"className":3332,"style":3122},[182],[115,3334],{},[115,3336],{"className":3337},[272,1794],[115,3339,3341],{"className":3340,"style":3148},[272,3147],[115,3342,273],{"className":3343},[2201,197],[115,3345],{"className":3346,"style":225},[224],[115,3348,220],{"className":3349},[219],[11,3351,3352,3353,3356,3357,3372],{},"and the ",[31,3354,3355],{},"information gain"," from testing ",[115,3358,3360],{"className":3359},[118],[115,3361,3363],{"className":3362,"ariaHidden":123},[122],[115,3364,3366,3369],{"className":3365},[127],[115,3367],{"className":3368,"style":132},[131],[115,3370,896],{"className":3371},[136,137]," is the expected reduction in entropy:",[115,3374,3376],{"className":3375},[1580],[115,3377,3379],{"className":3378},[118],[115,3380,3382,3419,3533],{"className":3381,"ariaHidden":123},[122],[115,3383,3385,3388,3395,3398,3401,3404,3407,3410,3413,3416],{"className":3384},[127],[115,3386],{"className":3387,"style":153},[131],[115,3389,3391],{"className":3390},[136],[115,3392,3394],{"className":3393},[136,2861],"Gain",[115,3396,158],{"className":3397},[157],[115,3399,896],{"className":3400},[136,137],[115,3402,273],{"className":3403},[272],[115,3405],{"className":3406,"style":563},[224],[115,3408],{"className":3409,"style":563},[224],[115,3411,568],{"className":3412},[567],[115,3414],{"className":3415,"style":563},[224],[115,3417],{"className":3418,"style":563},[224],[115,3420,3422,3426,3429,3432,3435,3524,3527,3530],{"className":3421},[127],[115,3423],{"className":3424,"style":3425},[131],"height:2.4em;vertical-align:-0.95em;",[115,3427,2166],{"className":3428,"style":2165},[136,137],[115,3430],{"className":3431,"style":3137},[224],[115,3433],{"className":3434,"style":225},[224],[115,3436,3438,3444,3518],{"className":3437},[381],[115,3439,3441],{"className":3440,"style":3148},[157,3147],[115,3442,158],{"className":3443},[2201,197],[115,3445,3447,3450,3515],{"className":3446},[136],[115,3448],{"className":3449},[157,1794],[115,3451,3453],{"className":3452},[1798],[115,3454,3456,3507],{"className":3455},[173,174],[115,3457,3459,3504],{"className":3458},[178],[115,3460,3462,3485,3493],{"className":3461,"style":3170},[182],[115,3463,3464,3467],{"style":1811},[115,3465],{"className":3466,"style":1815},[190],[115,3468,3470,3473,3476,3479,3482],{"className":3469},[136],[115,3471,11],{"className":3472},[136,137],[115,3474],{"className":3475,"style":816},[224],[115,3477,2263],{"className":3478},[820],[115,3480],{"className":3481,"style":816},[224],[115,3483,1080],{"className":3484},[136,137],[115,3486,3487,3490],{"style":1870},[115,3488],{"className":3489,"style":1815},[190],[115,3491],{"className":3492,"style":1878},[1877],[115,3494,3495,3498],{"style":1881},[115,3496],{"className":3497,"style":1815},[190],[115,3499,3501],{"className":3500},[136],[115,3502,11],{"className":3503},[136,137],[115,3505,206],{"className":3506},[205],[115,3508,3510],{"className":3509},[178],[115,3511,3513],{"className":3512,"style":3122},[182],[115,3514],{},[115,3516],{"className":3517},[272,1794],[115,3519,3521],{"className":3520,"style":3148},[272,3147],[115,3522,273],{"className":3523},[2201,197],[115,3525],{"className":3526,"style":816},[224],[115,3528,821],{"className":3529},[820],[115,3531],{"className":3532,"style":816},[224],[115,3534,3536,3539,3545,3548,3551,3554],{"className":3535},[127],[115,3537],{"className":3538,"style":153},[131],[115,3540,3542],{"className":3541},[136],[115,3543,2862],{"className":3544},[136,2861],[115,3546,158],{"className":3547},[157],[115,3549,896],{"className":3550},[136,137],[115,3552,273],{"className":3553},[272],[115,3555,61],{"className":3556},[136],[101,3558,3559],{"type":103},[11,3560,3561,3564],{},[31,3562,3563],{},"Definition (Information gain)."," The expected reduction in goal entropy that\nresults from testing an attribute. The greedy tree learner splits, at each node,\non the attribute of maximum information gain — the attribute that most reduces\nthe uncertainty about the classification.",[11,3566,3567,3568,3619,3620,3663,3664,3697,3698,3701,3702,3704,3705,3701,3708,3710,3711,3713],{},"The restaurant set has ",[115,3569,3571],{"className":3570},[118],[115,3572,3574,3592,3610],{"className":3573,"ariaHidden":123},[122],[115,3575,3577,3580,3583,3586,3589],{"className":3576},[127],[115,3578],{"className":3579,"style":556},[131],[115,3581,11],{"className":3582},[136,137],[115,3584],{"className":3585,"style":563},[224],[115,3587,568],{"className":3588},[567],[115,3590],{"className":3591,"style":563},[224],[115,3593,3595,3598,3601,3604,3607],{"className":3594},[127],[115,3596],{"className":3597,"style":1319},[131],[115,3599,1080],{"className":3600},[136,137],[115,3602],{"className":3603,"style":563},[224],[115,3605,568],{"className":3606},[567],[115,3608],{"className":3609,"style":563},[224],[115,3611,3613,3616],{"className":3612},[127],[115,3614],{"className":3615,"style":831},[131],[115,3617,1423],{"className":3618},[136],", so the goal entropy is ",[115,3621,3623],{"className":3622},[118],[115,3624,3626,3654],{"className":3625,"ariaHidden":123},[122],[115,3627,3629,3632,3635,3638,3642,3645,3648,3651],{"className":3628},[127],[115,3630],{"className":3631,"style":153},[131],[115,3633,2166],{"className":3634,"style":2165},[136,137],[115,3636,158],{"className":3637},[157],[115,3639,3641],{"className":3640},[136],"0.5",[115,3643,273],{"className":3644},[272],[115,3646],{"className":3647,"style":563},[224],[115,3649,568],{"className":3650},[567],[115,3652],{"className":3653,"style":563},[224],[115,3655,3657,3660],{"className":3656},[127],[115,3658],{"className":3659,"style":831},[131],[115,3661,44],{"className":3662},[136]," bit.\nWorking out the two splits above confirms the intuition. ",[115,3665,3667],{"className":3666},[118],[115,3668,3670],{"className":3669,"ariaHidden":123},[122],[115,3671,3673,3676,3679,3682,3685,3688,3691,3694],{"className":3672},[127],[115,3674],{"className":3675,"style":132},[131],[115,3677,1061],{"className":3678,"style":1021},[136,137],[115,3680,15],{"className":3681},[136,137],[115,3683,1044],{"className":3684},[136,137],[115,3686,1072],{"className":3687,"style":1071},[136,137],[115,3689,1076],{"className":3690},[136,137],[115,3692,1080],{"className":3693},[136,137],[115,3695,1084],{"className":3696},[136,137]," carves off a\npure-",[54,3699,3700],{},"No"," subset (",[54,3703,1088],{},") and a pure-",[54,3706,3707],{},"Yes",[54,3709,1091],{},"), leaving only ",[54,3712,1094],{},"\nmixed, and gains most of that bit:",[115,3715,3717],{"className":3716},[1580],[115,3718,3720],{"className":3719},[118],[115,3721,3723,3771,3790,4280],{"className":3722,"ariaHidden":123},[122],[115,3724,3726,3729,3735,3738,3741,3744,3747,3750,3753,3756,3759,3762,3765,3768],{"className":3725},[127],[115,3727],{"className":3728,"style":153},[131],[115,3730,3732],{"className":3731},[136],[115,3733,3394],{"className":3734},[136,2861],[115,3736,158],{"className":3737},[157],[115,3739,1061],{"className":3740,"style":1021},[136,137],[115,3742,15],{"className":3743},[136,137],[115,3745,1044],{"className":3746},[136,137],[115,3748,1072],{"className":3749,"style":1071},[136,137],[115,3751,1076],{"className":3752},[136,137],[115,3754,1080],{"className":3755},[136,137],[115,3757,1084],{"className":3758},[136,137],[115,3760,273],{"className":3761},[272],[115,3763],{"className":3764,"style":563},[224],[115,3766,568],{"className":3767},[567],[115,3769],{"className":3770,"style":563},[224],[115,3772,3774,3778,3781,3784,3787],{"className":3773},[127],[115,3775],{"className":3776,"style":3777},[131],"height:0.7278em;vertical-align:-0.0833em;",[115,3779,44],{"className":3780},[136],[115,3782],{"className":3783,"style":816},[224],[115,3785,821],{"className":3786},[820],[115,3788],{"className":3789,"style":816},[224],[115,3791,3793,3796,4270,4273,4277],{"className":3792},[127],[115,3794],{"className":3795,"style":2191},[131],[115,3797,3799,3806,3878,3881,3884,3952,3955,3958,3961,3964,4032,4035,4038,4106,4109,4112,4115,4118,4186,4189,4192,4260,4263],{"className":3798},[381],[115,3800,3802],{"className":3801,"style":3148},[157,3147],[115,3803,3805],{"className":3804},[2201,1302],"[",[115,3807,3809,3812,3875],{"className":3808},[136],[115,3810],{"className":3811},[157,1794],[115,3813,3815],{"className":3814},[1798],[115,3816,3818,3866],{"className":3817},[173,174],[115,3819,3821,3863],{"className":3820},[178],[115,3822,3825,3840,3848],{"className":3823,"style":3824},[182],"height:0.8451em;",[115,3826,3827,3830],{"style":2455},[115,3828],{"className":3829,"style":1815},[190],[115,3831,3833],{"className":3832},[195,196,197,198],[115,3834,3836],{"className":3835},[136,198],[115,3837,3839],{"className":3838},[136,198],"12",[115,3841,3842,3845],{"style":1870},[115,3843],{"className":3844,"style":1815},[190],[115,3846],{"className":3847,"style":1878},[1877],[115,3849,3851,3854],{"style":3850},"top:-3.394em;",[115,3852],{"className":3853,"style":1815},[190],[115,3855,3857],{"className":3856},[195,196,197,198],[115,3858,3860],{"className":3859},[136,198],[115,3861,77],{"className":3862},[136,198],[115,3864,206],{"className":3865},[205],[115,3867,3869],{"className":3868},[178],[115,3870,3873],{"className":3871,"style":3872},[182],"height:0.345em;",[115,3874],{},[115,3876],{"className":3877},[272,1794],[115,3879,2166],{"className":3880,"style":2165},[136,137],[115,3882,158],{"className":3883},[157],[115,3885,3887,3890,3949],{"className":3886},[136],[115,3888],{"className":3889},[157,1794],[115,3891,3893],{"className":3892},[1798],[115,3894,3896,3941],{"className":3895},[173,174],[115,3897,3899,3938],{"className":3898},[178],[115,3900,3902,3916,3924],{"className":3901,"style":3824},[182],[115,3903,3904,3907],{"style":2455},[115,3905],{"className":3906,"style":1815},[190],[115,3908,3910],{"className":3909},[195,196,197,198],[115,3911,3913],{"className":3912},[136,198],[115,3914,77],{"className":3915},[136,198],[115,3917,3918,3921],{"style":1870},[115,3919],{"className":3920,"style":1815},[190],[115,3922],{"className":3923,"style":1878},[1877],[115,3925,3926,3929],{"style":3850},[115,3927],{"className":3928,"style":1815},[190],[115,3930,3932],{"className":3931},[195,196,197,198],[115,3933,3935],{"className":3934},[136,198],[115,3936,1346],{"className":3937},[136,198],[115,3939,206],{"className":3940},[205],[115,3942,3944],{"className":3943},[178],[115,3945,3947],{"className":3946,"style":3872},[182],[115,3948],{},[115,3950],{"className":3951},[272,1794],[115,3953,273],{"className":3954},[272],[115,3956],{"className":3957,"style":816},[224],[115,3959,2263],{"className":3960},[820],[115,3962],{"className":3963,"style":816},[224],[115,3965,3967,3970,4029],{"className":3966},[136],[115,3968],{"className":3969},[157,1794],[115,3971,3973],{"className":3972},[1798],[115,3974,3976,4021],{"className":3975},[173,174],[115,3977,3979,4018],{"className":3978},[178],[115,3980,3982,3996,4004],{"className":3981,"style":3824},[182],[115,3983,3984,3987],{"style":2455},[115,3985],{"className":3986,"style":1815},[190],[115,3988,3990],{"className":3989},[195,196,197,198],[115,3991,3993],{"className":3992},[136,198],[115,3994,3839],{"className":3995},[136,198],[115,3997,3998,4001],{"style":1870},[115,3999],{"className":4000,"style":1815},[190],[115,4002],{"className":4003,"style":1878},[1877],[115,4005,4006,4009],{"style":3850},[115,4007],{"className":4008,"style":1815},[190],[115,4010,4012],{"className":4011},[195,196,197,198],[115,4013,4015],{"className":4014},[136,198],[115,4016,993],{"className":4017},[136,198],[115,4019,206],{"className":4020},[205],[115,4022,4024],{"className":4023},[178],[115,4025,4027],{"className":4026,"style":3872},[182],[115,4028],{},[115,4030],{"className":4031},[272,1794],[115,4033,2166],{"className":4034,"style":2165},[136,137],[115,4036,158],{"className":4037},[157],[115,4039,4041,4044,4103],{"className":4040},[136],[115,4042],{"className":4043},[157,1794],[115,4045,4047],{"className":4046},[1798],[115,4048,4050,4095],{"className":4049},[173,174],[115,4051,4053,4092],{"className":4052},[178],[115,4054,4056,4070,4078],{"className":4055,"style":3824},[182],[115,4057,4058,4061],{"style":2455},[115,4059],{"className":4060,"style":1815},[190],[115,4062,4064],{"className":4063},[195,196,197,198],[115,4065,4067],{"className":4066},[136,198],[115,4068,993],{"className":4069},[136,198],[115,4071,4072,4075],{"style":1870},[115,4073],{"className":4074,"style":1815},[190],[115,4076],{"className":4077,"style":1878},[1877],[115,4079,4080,4083],{"style":3850},[115,4081],{"className":4082,"style":1815},[190],[115,4084,4086],{"className":4085},[195,196,197,198],[115,4087,4089],{"className":4088},[136,198],[115,4090,993],{"className":4091},[136,198],[115,4093,206],{"className":4094},[205],[115,4096,4098],{"className":4097},[178],[115,4099,4101],{"className":4100,"style":3872},[182],[115,4102],{},[115,4104],{"className":4105},[272,1794],[115,4107,273],{"className":4108},[272],[115,4110],{"className":4111,"style":816},[224],[115,4113,2263],{"className":4114},[820],[115,4116],{"className":4117,"style":816},[224],[115,4119,4121,4124,4183],{"className":4120},[136],[115,4122],{"className":4123},[157,1794],[115,4125,4127],{"className":4126},[1798],[115,4128,4130,4175],{"className":4129},[173,174],[115,4131,4133,4172],{"className":4132},[178],[115,4134,4136,4150,4158],{"className":4135,"style":3824},[182],[115,4137,4138,4141],{"style":2455},[115,4139],{"className":4140,"style":1815},[190],[115,4142,4144],{"className":4143},[195,196,197,198],[115,4145,4147],{"className":4146},[136,198],[115,4148,3839],{"className":4149},[136,198],[115,4151,4152,4155],{"style":1870},[115,4153],{"className":4154,"style":1815},[190],[115,4156],{"className":4157,"style":1878},[1877],[115,4159,4160,4163],{"style":3850},[115,4161],{"className":4162,"style":1815},[190],[115,4164,4166],{"className":4165},[195,196,197,198],[115,4167,4169],{"className":4168},[136,198],[115,4170,1423],{"className":4171},[136,198],[115,4173,206],{"className":4174},[205],[115,4176,4178],{"className":4177},[178],[115,4179,4181],{"className":4180,"style":3872},[182],[115,4182],{},[115,4184],{"className":4185},[272,1794],[115,4187,2166],{"className":4188,"style":2165},[136,137],[115,4190,158],{"className":4191},[157],[115,4193,4195,4198,4257],{"className":4194},[136],[115,4196],{"className":4197},[157,1794],[115,4199,4201],{"className":4200},[1798],[115,4202,4204,4249],{"className":4203},[173,174],[115,4205,4207,4246],{"className":4206},[178],[115,4208,4210,4224,4232],{"className":4209,"style":3824},[182],[115,4211,4212,4215],{"style":2455},[115,4213],{"className":4214,"style":1815},[190],[115,4216,4218],{"className":4217},[195,196,197,198],[115,4219,4221],{"className":4220},[136,198],[115,4222,1423],{"className":4223},[136,198],[115,4225,4226,4229],{"style":1870},[115,4227],{"className":4228,"style":1815},[190],[115,4230],{"className":4231,"style":1878},[1877],[115,4233,4234,4237],{"style":3850},[115,4235],{"className":4236,"style":1815},[190],[115,4238,4240],{"className":4239},[195,196,197,198],[115,4241,4243],{"className":4242},[136,198],[115,4244,77],{"className":4245},[136,198],[115,4247,206],{"className":4248},[205],[115,4250,4252],{"className":4251},[178],[115,4253,4255],{"className":4254,"style":3872},[182],[115,4256],{},[115,4258],{"className":4259},[272,1794],[115,4261,273],{"className":4262},[272],[115,4264,4266],{"className":4265,"style":3148},[272,3147],[115,4267,4269],{"className":4268},[2201,1302],"]",[115,4271],{"className":4272,"style":563},[224],[115,4274,4276],{"className":4275},[567],"≈",[115,4278],{"className":4279,"style":563},[224],[115,4281,4283,4286,4290],{"className":4282},[127],[115,4284],{"className":4285,"style":605},[131],[115,4287,4289],{"className":4288},[136],"0.541",[115,4291,4294],{"className":4292},[136,4293],"text",[115,4295,4297],{"className":4296},[136]," bits.",[11,4299,4300,4324],{},[115,4301,4303],{"className":4302},[118],[115,4304,4306],{"className":4305,"ariaHidden":123},[122],[115,4307,4309,4312,4315,4318,4321],{"className":4308},[127],[115,4310],{"className":4311,"style":1108},[131],[115,4313,1146],{"className":4314,"style":1021},[136,137],[115,4316,233],{"className":4317,"style":232},[136,137],[115,4319,11],{"className":4320},[136,137],[115,4322,1156],{"className":4323},[136,137],", by contrast, leaves four subsets each evenly split, and gains nothing:",[115,4326,4328],{"className":4327},[1580],[115,4329,4331],{"className":4330},[118],[115,4332,4334,4373,4391,5028],{"className":4333,"ariaHidden":123},[122],[115,4335,4337,4340,4346,4349,4352,4355,4358,4361,4364,4367,4370],{"className":4336},[127],[115,4338],{"className":4339,"style":153},[131],[115,4341,4343],{"className":4342},[136],[115,4344,3394],{"className":4345},[136,2861],[115,4347,158],{"className":4348},[157],[115,4350,1146],{"className":4351,"style":1021},[136,137],[115,4353,233],{"className":4354,"style":232},[136,137],[115,4356,11],{"className":4357},[136,137],[115,4359,1156],{"className":4360},[136,137],[115,4362,273],{"className":4363},[272],[115,4365],{"className":4366,"style":563},[224],[115,4368,568],{"className":4369},[567],[115,4371],{"className":4372,"style":563},[224],[115,4374,4376,4379,4382,4385,4388],{"className":4375},[127],[115,4377],{"className":4378,"style":3777},[131],[115,4380,44],{"className":4381},[136],[115,4383],{"className":4384,"style":816},[224],[115,4386,821],{"className":4387},[820],[115,4389],{"className":4390,"style":816},[224],[115,4392,4394,4397,5019,5022,5025],{"className":4393},[127],[115,4395],{"className":4396,"style":2191},[131],[115,4398,4400,4406,4474,4477,4480,4548,4551,4554,4557,4560,4628,4631,4634,4702,4705,4708,4711,4714,4782,4785,4788,4856,4859,4862,4865,4868,4936,4939,4942,5010,5013],{"className":4399},[381],[115,4401,4403],{"className":4402,"style":3148},[157,3147],[115,4404,3805],{"className":4405},[2201,1302],[115,4407,4409,4412,4471],{"className":4408},[136],[115,4410],{"className":4411},[157,1794],[115,4413,4415],{"className":4414},[1798],[115,4416,4418,4463],{"className":4417},[173,174],[115,4419,4421,4460],{"className":4420},[178],[115,4422,4424,4438,4446],{"className":4423,"style":3824},[182],[115,4425,4426,4429],{"style":2455},[115,4427],{"className":4428,"style":1815},[190],[115,4430,4432],{"className":4431},[195,196,197,198],[115,4433,4435],{"className":4434},[136,198],[115,4436,3839],{"className":4437},[136,198],[115,4439,4440,4443],{"style":1870},[115,4441],{"className":4442,"style":1815},[190],[115,4444],{"className":4445,"style":1878},[1877],[115,4447,4448,4451],{"style":3850},[115,4449],{"className":4450,"style":1815},[190],[115,4452,4454],{"className":4453},[195,196,197,198],[115,4455,4457],{"className":4456},[136,198],[115,4458,77],{"className":4459},[136,198],[115,4461,206],{"className":4462},[205],[115,4464,4466],{"className":4465},[178],[115,4467,4469],{"className":4468,"style":3872},[182],[115,4470],{},[115,4472],{"className":4473},[272,1794],[115,4475,2166],{"className":4476,"style":2165},[136,137],[115,4478,158],{"className":4479},[157],[115,4481,4483,4486,4545],{"className":4482},[136],[115,4484],{"className":4485},[157,1794],[115,4487,4489],{"className":4488},[1798],[115,4490,4492,4537],{"className":4491},[173,174],[115,4493,4495,4534],{"className":4494},[178],[115,4496,4498,4512,4520],{"className":4497,"style":3824},[182],[115,4499,4500,4503],{"style":2455},[115,4501],{"className":4502,"style":1815},[190],[115,4504,4506],{"className":4505},[195,196,197,198],[115,4507,4509],{"className":4508},[136,198],[115,4510,77],{"className":4511},[136,198],[115,4513,4514,4517],{"style":1870},[115,4515],{"className":4516,"style":1815},[190],[115,4518],{"className":4519,"style":1878},[1877],[115,4521,4522,4525],{"style":3850},[115,4523],{"className":4524,"style":1815},[190],[115,4526,4528],{"className":4527},[195,196,197,198],[115,4529,4531],{"className":4530},[136,198],[115,4532,44],{"className":4533},[136,198],[115,4535,206],{"className":4536},[205],[115,4538,4540],{"className":4539},[178],[115,4541,4543],{"className":4542,"style":3872},[182],[115,4544],{},[115,4546],{"className":4547},[272,1794],[115,4549,273],{"className":4550},[272],[115,4552],{"className":4553,"style":816},[224],[115,4555,2263],{"className":4556},[820],[115,4558],{"className":4559,"style":816},[224],[115,4561,4563,4566,4625],{"className":4562},[136],[115,4564],{"className":4565},[157,1794],[115,4567,4569],{"className":4568},[1798],[115,4570,4572,4617],{"className":4571},[173,174],[115,4573,4575,4614],{"className":4574},[178],[115,4576,4578,4592,4600],{"className":4577,"style":3824},[182],[115,4579,4580,4583],{"style":2455},[115,4581],{"className":4582,"style":1815},[190],[115,4584,4586],{"className":4585},[195,196,197,198],[115,4587,4589],{"className":4588},[136,198],[115,4590,3839],{"className":4591},[136,198],[115,4593,4594,4597],{"style":1870},[115,4595],{"className":4596,"style":1815},[190],[115,4598],{"className":4599,"style":1878},[1877],[115,4601,4602,4605],{"style":3850},[115,4603],{"className":4604,"style":1815},[190],[115,4606,4608],{"className":4607},[195,196,197,198],[115,4609,4611],{"className":4610},[136,198],[115,4612,77],{"className":4613},[136,198],[115,4615,206],{"className":4616},[205],[115,4618,4620],{"className":4619},[178],[115,4621,4623],{"className":4622,"style":3872},[182],[115,4624],{},[115,4626],{"className":4627},[272,1794],[115,4629,2166],{"className":4630,"style":2165},[136,137],[115,4632,158],{"className":4633},[157],[115,4635,4637,4640,4699],{"className":4636},[136],[115,4638],{"className":4639},[157,1794],[115,4641,4643],{"className":4642},[1798],[115,4644,4646,4691],{"className":4645},[173,174],[115,4647,4649,4688],{"className":4648},[178],[115,4650,4652,4666,4674],{"className":4651,"style":3824},[182],[115,4653,4654,4657],{"style":2455},[115,4655],{"className":4656,"style":1815},[190],[115,4658,4660],{"className":4659},[195,196,197,198],[115,4661,4663],{"className":4662},[136,198],[115,4664,77],{"className":4665},[136,198],[115,4667,4668,4671],{"style":1870},[115,4669],{"className":4670,"style":1815},[190],[115,4672],{"className":4673,"style":1878},[1877],[115,4675,4676,4679],{"style":3850},[115,4677],{"className":4678,"style":1815},[190],[115,4680,4682],{"className":4681},[195,196,197,198],[115,4683,4685],{"className":4684},[136,198],[115,4686,44],{"className":4687},[136,198],[115,4689,206],{"className":4690},[205],[115,4692,4694],{"className":4693},[178],[115,4695,4697],{"className":4696,"style":3872},[182],[115,4698],{},[115,4700],{"className":4701},[272,1794],[115,4703,273],{"className":4704},[272],[115,4706],{"className":4707,"style":816},[224],[115,4709,2263],{"className":4710},[820],[115,4712],{"className":4713,"style":816},[224],[115,4715,4717,4720,4779],{"className":4716},[136],[115,4718],{"className":4719},[157,1794],[115,4721,4723],{"className":4722},[1798],[115,4724,4726,4771],{"className":4725},[173,174],[115,4727,4729,4768],{"className":4728},[178],[115,4730,4732,4746,4754],{"className":4731,"style":3824},[182],[115,4733,4734,4737],{"style":2455},[115,4735],{"className":4736,"style":1815},[190],[115,4738,4740],{"className":4739},[195,196,197,198],[115,4741,4743],{"className":4742},[136,198],[115,4744,3839],{"className":4745},[136,198],[115,4747,4748,4751],{"style":1870},[115,4749],{"className":4750,"style":1815},[190],[115,4752],{"className":4753,"style":1878},[1877],[115,4755,4756,4759],{"style":3850},[115,4757],{"className":4758,"style":1815},[190],[115,4760,4762],{"className":4761},[195,196,197,198],[115,4763,4765],{"className":4764},[136,198],[115,4766,993],{"className":4767},[136,198],[115,4769,206],{"className":4770},[205],[115,4772,4774],{"className":4773},[178],[115,4775,4777],{"className":4776,"style":3872},[182],[115,4778],{},[115,4780],{"className":4781},[272,1794],[115,4783,2166],{"className":4784,"style":2165},[136,137],[115,4786,158],{"className":4787},[157],[115,4789,4791,4794,4853],{"className":4790},[136],[115,4792],{"className":4793},[157,1794],[115,4795,4797],{"className":4796},[1798],[115,4798,4800,4845],{"className":4799},[173,174],[115,4801,4803,4842],{"className":4802},[178],[115,4804,4806,4820,4828],{"className":4805,"style":3824},[182],[115,4807,4808,4811],{"style":2455},[115,4809],{"className":4810,"style":1815},[190],[115,4812,4814],{"className":4813},[195,196,197,198],[115,4815,4817],{"className":4816},[136,198],[115,4818,993],{"className":4819},[136,198],[115,4821,4822,4825],{"style":1870},[115,4823],{"className":4824,"style":1815},[190],[115,4826],{"className":4827,"style":1878},[1877],[115,4829,4830,4833],{"style":3850},[115,4831],{"className":4832,"style":1815},[190],[115,4834,4836],{"className":4835},[195,196,197,198],[115,4837,4839],{"className":4838},[136,198],[115,4840,77],{"className":4841},[136,198],[115,4843,206],{"className":4844},[205],[115,4846,4848],{"className":4847},[178],[115,4849,4851],{"className":4850,"style":3872},[182],[115,4852],{},[115,4854],{"className":4855},[272,1794],[115,4857,273],{"className":4858},[272],[115,4860],{"className":4861,"style":816},[224],[115,4863,2263],{"className":4864},[820],[115,4866],{"className":4867,"style":816},[224],[115,4869,4871,4874,4933],{"className":4870},[136],[115,4872],{"className":4873},[157,1794],[115,4875,4877],{"className":4876},[1798],[115,4878,4880,4925],{"className":4879},[173,174],[115,4881,4883,4922],{"className":4882},[178],[115,4884,4886,4900,4908],{"className":4885,"style":3824},[182],[115,4887,4888,4891],{"style":2455},[115,4889],{"className":4890,"style":1815},[190],[115,4892,4894],{"className":4893},[195,196,197,198],[115,4895,4897],{"className":4896},[136,198],[115,4898,3839],{"className":4899},[136,198],[115,4901,4902,4905],{"style":1870},[115,4903],{"className":4904,"style":1815},[190],[115,4906],{"className":4907,"style":1878},[1877],[115,4909,4910,4913],{"style":3850},[115,4911],{"className":4912,"style":1815},[190],[115,4914,4916],{"className":4915},[195,196,197,198],[115,4917,4919],{"className":4918},[136,198],[115,4920,993],{"className":4921},[136,198],[115,4923,206],{"className":4924},[205],[115,4926,4928],{"className":4927},[178],[115,4929,4931],{"className":4930,"style":3872},[182],[115,4932],{},[115,4934],{"className":4935},[272,1794],[115,4937,2166],{"className":4938,"style":2165},[136,137],[115,4940,158],{"className":4941},[157],[115,4943,4945,4948,5007],{"className":4944},[136],[115,4946],{"className":4947},[157,1794],[115,4949,4951],{"className":4950},[1798],[115,4952,4954,4999],{"className":4953},[173,174],[115,4955,4957,4996],{"className":4956},[178],[115,4958,4960,4974,4982],{"className":4959,"style":3824},[182],[115,4961,4962,4965],{"style":2455},[115,4963],{"className":4964,"style":1815},[190],[115,4966,4968],{"className":4967},[195,196,197,198],[115,4969,4971],{"className":4970},[136,198],[115,4972,993],{"className":4973},[136,198],[115,4975,4976,4979],{"style":1870},[115,4977],{"className":4978,"style":1815},[190],[115,4980],{"className":4981,"style":1878},[1877],[115,4983,4984,4987],{"style":3850},[115,4985],{"className":4986,"style":1815},[190],[115,4988,4990],{"className":4989},[195,196,197,198],[115,4991,4993],{"className":4992},[136,198],[115,4994,77],{"className":4995},[136,198],[115,4997,206],{"className":4998},[205],[115,5000,5002],{"className":5001},[178],[115,5003,5005],{"className":5004,"style":3872},[182],[115,5006],{},[115,5008],{"className":5009},[272,1794],[115,5011,273],{"className":5012},[272],[115,5014,5016],{"className":5015,"style":3148},[272,3147],[115,5017,4269],{"className":5018},[2201,1302],[115,5020],{"className":5021,"style":563},[224],[115,5023,568],{"className":5024},[567],[115,5026],{"className":5027,"style":563},[224],[115,5029,5031,5034,5037],{"className":5030},[127],[115,5032],{"className":5033,"style":605},[131],[115,5035,1346],{"className":5036},[136],[115,5038,5040],{"className":5039},[136,4293],[115,5041,4297],{"className":5042},[136],[5044,5045,5047,5048,5081],"h4",{"id":5046},"carrying-the-patrons-computation-all-the-way-through","Carrying the ",[115,5049,5051],{"className":5050},[118],[115,5052,5054],{"className":5053,"ariaHidden":123},[122],[115,5055,5057,5060,5063,5066,5069,5072,5075,5078],{"className":5056},[127],[115,5058],{"className":5059,"style":132},[131],[115,5061,1061],{"className":5062,"style":1021},[136,137],[115,5064,15],{"className":5065},[136,137],[115,5067,1044],{"className":5068},[136,137],[115,5070,1072],{"className":5071,"style":1071},[136,137],[115,5073,1076],{"className":5074},[136,137],[115,5076,1080],{"className":5077},[136,137],[115,5079,1084],{"className":5080},[136,137]," computation all the way through",[11,5083,5084,5085,5100,5101,5134],{},"Deriving the ",[115,5086,5088],{"className":5087},[118],[115,5089,5091],{"className":5090,"ariaHidden":123},[122],[115,5092,5094,5097],{"className":5093},[127],[115,5095],{"className":5096,"style":831},[131],[115,5098,4289],{"className":5099},[136]," in full shows how the entropy formula is used. The twelve\nrestaurant examples split under ",[115,5102,5104],{"className":5103},[118],[115,5105,5107],{"className":5106,"ariaHidden":123},[122],[115,5108,5110,5113,5116,5119,5122,5125,5128,5131],{"className":5109},[127],[115,5111],{"className":5112,"style":132},[131],[115,5114,1061],{"className":5115,"style":1021},[136,137],[115,5117,15],{"className":5118},[136,137],[115,5120,1044],{"className":5121},[136,137],[115,5123,1072],{"className":5124,"style":1071},[136,137],[115,5126,1076],{"className":5127},[136,137],[115,5129,1080],{"className":5130},[136,137],[115,5132,1084],{"className":5133},[136,137]," into three branches:",[5136,5137,5138,5250,5359],"ul",{},[5139,5140,5141,5143,5144,5146,5147,5162,5163,5178,5179,5249],"li",{},[31,5142,1088],{}," (2 examples): both are ",[54,5145,3700],{},", so ",[115,5148,5150],{"className":5149},[118],[115,5151,5153],{"className":5152,"ariaHidden":123},[122],[115,5154,5156,5159],{"className":5155},[127],[115,5157],{"className":5158,"style":831},[131],[115,5160,1346],{"className":5161},[136]," positive, ",[115,5164,5166],{"className":5165},[118],[115,5167,5169],{"className":5168,"ariaHidden":123},[122],[115,5170,5172,5175],{"className":5171},[127],[115,5173],{"className":5174,"style":831},[131],[115,5176,77],{"className":5177},[136]," negative. Its Boolean\nentropy is ",[115,5180,5182],{"className":5181},[118],[115,5183,5185,5213,5240],{"className":5184,"ariaHidden":123},[122],[115,5186,5188,5191,5194,5197,5201,5204,5207,5210],{"className":5187},[127],[115,5189],{"className":5190,"style":153},[131],[115,5192,2166],{"className":5193,"style":2165},[136,137],[115,5195,158],{"className":5196},[157],[115,5198,5200],{"className":5199},[136],"0\u002F2",[115,5202,273],{"className":5203},[272],[115,5205],{"className":5206,"style":563},[224],[115,5208,568],{"className":5209},[567],[115,5211],{"className":5212,"style":563},[224],[115,5214,5216,5219,5222,5225,5228,5231,5234,5237],{"className":5215},[127],[115,5217],{"className":5218,"style":153},[131],[115,5220,2166],{"className":5221,"style":2165},[136,137],[115,5223,158],{"className":5224},[157],[115,5226,1346],{"className":5227},[136],[115,5229,273],{"className":5230},[272],[115,5232],{"className":5233,"style":563},[224],[115,5235,568],{"className":5236},[567],[115,5238],{"className":5239,"style":563},[224],[115,5241,5243,5246],{"className":5242},[127],[115,5244],{"className":5245,"style":831},[131],[115,5247,1346],{"className":5248},[136]," — a pure subset carries no uncertainty.",[5139,5251,5252,5254,5255,5146,5257,5162,5272,5287,5288,5358],{},[31,5253,1091],{}," (4 examples): all four are ",[54,5256,3707],{},[115,5258,5260],{"className":5259},[118],[115,5261,5263],{"className":5262,"ariaHidden":123},[122],[115,5264,5266,5269],{"className":5265},[127],[115,5267],{"className":5268,"style":831},[131],[115,5270,993],{"className":5271},[136],[115,5273,5275],{"className":5274},[118],[115,5276,5278],{"className":5277,"ariaHidden":123},[122],[115,5279,5281,5284],{"className":5280},[127],[115,5282],{"className":5283,"style":831},[131],[115,5285,1346],{"className":5286},[136]," negative.\n",[115,5289,5291],{"className":5290},[118],[115,5292,5294,5322,5349],{"className":5293,"ariaHidden":123},[122],[115,5295,5297,5300,5303,5306,5310,5313,5316,5319],{"className":5296},[127],[115,5298],{"className":5299,"style":153},[131],[115,5301,2166],{"className":5302,"style":2165},[136,137],[115,5304,158],{"className":5305},[157],[115,5307,5309],{"className":5308},[136],"4\u002F4",[115,5311,273],{"className":5312},[272],[115,5314],{"className":5315,"style":563},[224],[115,5317,568],{"className":5318},[567],[115,5320],{"className":5321,"style":563},[224],[115,5323,5325,5328,5331,5334,5337,5340,5343,5346],{"className":5324},[127],[115,5326],{"className":5327,"style":153},[131],[115,5329,2166],{"className":5330,"style":2165},[136,137],[115,5332,158],{"className":5333},[157],[115,5335,44],{"className":5336},[136],[115,5338,273],{"className":5339},[272],[115,5341],{"className":5342,"style":563},[224],[115,5344,568],{"className":5345},[567],[115,5347],{"className":5348,"style":563},[224],[115,5350,5352,5355],{"className":5351},[127],[115,5353],{"className":5354,"style":831},[131],[115,5356,1346],{"className":5357},[136]," — pure again.",[5139,5360,5361,5363,5364,5162,5379,5394,5395,61],{},[31,5362,1094],{}," (6 examples): ",[115,5365,5367],{"className":5366},[118],[115,5368,5370],{"className":5369,"ariaHidden":123},[122],[115,5371,5373,5376],{"className":5372},[127],[115,5374],{"className":5375,"style":831},[131],[115,5377,77],{"className":5378},[136],[115,5380,5382],{"className":5381},[118],[115,5383,5385],{"className":5384,"ariaHidden":123},[122],[115,5386,5388,5391],{"className":5387},[127],[115,5389],{"className":5390,"style":831},[131],[115,5392,993],{"className":5393},[136]," negative. Here\n",[115,5396,5398],{"className":5397},[118],[115,5399,5401,5429,5457,5666],{"className":5400,"ariaHidden":123},[122],[115,5402,5404,5407,5410,5413,5417,5420,5423,5426],{"className":5403},[127],[115,5405],{"className":5406,"style":153},[131],[115,5408,2166],{"className":5409,"style":2165},[136,137],[115,5411,158],{"className":5412},[157],[115,5414,5416],{"className":5415},[136],"2\u002F6",[115,5418,273],{"className":5419},[272],[115,5421],{"className":5422,"style":563},[224],[115,5424,568],{"className":5425},[567],[115,5427],{"className":5428,"style":563},[224],[115,5430,5432,5435,5438,5441,5445,5448,5451,5454],{"className":5431},[127],[115,5433],{"className":5434,"style":153},[131],[115,5436,2166],{"className":5437,"style":2165},[136,137],[115,5439,158],{"className":5440},[157],[115,5442,5444],{"className":5443},[136],"1\u002F3",[115,5446,273],{"className":5447},[272],[115,5449],{"className":5450,"style":563},[224],[115,5452,568],{"className":5453},[567],[115,5455],{"className":5456,"style":563},[224],[115,5458,5460,5463,5466,5472,5540,5543,5586,5589,5657,5660,5663],{"className":5459},[127],[115,5461],{"className":5462,"style":2191},[131],[115,5464,821],{"className":5465},[136],[115,5467,5469],{"className":5468},[136],[115,5470,158],{"className":5471},[2201,1302],[115,5473,5475,5478,5537],{"className":5474},[136],[115,5476],{"className":5477},[157,1794],[115,5479,5481],{"className":5480},[1798],[115,5482,5484,5529],{"className":5483},[173,174],[115,5485,5487,5526],{"className":5486},[178],[115,5488,5490,5504,5512],{"className":5489,"style":3824},[182],[115,5491,5492,5495],{"style":2455},[115,5493],{"className":5494,"style":1815},[190],[115,5496,5498],{"className":5497},[195,196,197,198],[115,5499,5501],{"className":5500},[136,198],[115,5502,865],{"className":5503},[136,198],[115,5505,5506,5509],{"style":1870},[115,5507],{"className":5508,"style":1815},[190],[115,5510],{"className":5511,"style":1878},[1877],[115,5513,5514,5517],{"style":3850},[115,5515],{"className":5516,"style":1815},[190],[115,5518,5520],{"className":5519},[195,196,197,198],[115,5521,5523],{"className":5522},[136,198],[115,5524,44],{"className":5525},[136,198],[115,5527,206],{"className":5528},[205],[115,5530,5532],{"className":5531},[178],[115,5533,5535],{"className":5534,"style":3872},[182],[115,5536],{},[115,5538],{"className":5539},[272,1794],[115,5541],{"className":5542,"style":225},[224],[115,5544,5546,5552],{"className":5545},[1630],[115,5547,5549],{"className":5548},[1630],[115,5550,1747],{"className":5551,"style":1746},[136,1745],[115,5553,5555],{"className":5554},[169],[115,5556,5558,5578],{"className":5557},[173,174],[115,5559,5561,5575],{"className":5560},[178],[115,5562,5564],{"className":5563,"style":1760},[182],[115,5565,5566,5569],{"style":1763},[115,5567],{"className":5568,"style":191},[190],[115,5570,5572],{"className":5571},[195,196,197,198],[115,5573,77],{"className":5574},[136,198],[115,5576,206],{"className":5577},[205],[115,5579,5581],{"className":5580},[178],[115,5582,5584],{"className":5583,"style":1782},[182],[115,5585],{},[115,5587],{"className":5588,"style":225},[224],[115,5590,5592,5595,5654],{"className":5591},[136],[115,5593],{"className":5594},[157,1794],[115,5596,5598],{"className":5597},[1798],[115,5599,5601,5646],{"className":5600},[173,174],[115,5602,5604,5643],{"className":5603},[178],[115,5605,5607,5621,5629],{"className":5606,"style":3824},[182],[115,5608,5609,5612],{"style":2455},[115,5610],{"className":5611,"style":1815},[190],[115,5613,5615],{"className":5614},[195,196,197,198],[115,5616,5618],{"className":5617},[136,198],[115,5619,865],{"className":5620},[136,198],[115,5622,5623,5626],{"style":1870},[115,5624],{"className":5625,"style":1815},[190],[115,5627],{"className":5628,"style":1878},[1877],[115,5630,5631,5634],{"style":3850},[115,5632],{"className":5633,"style":1815},[190],[115,5635,5637],{"className":5636},[195,196,197,198],[115,5638,5640],{"className":5639},[136,198],[115,5641,44],{"className":5642},[136,198],[115,5644,206],{"className":5645},[205],[115,5647,5649],{"className":5648},[178],[115,5650,5652],{"className":5651,"style":3872},[182],[115,5653],{},[115,5655],{"className":5656},[272,1794],[115,5658],{"className":5659,"style":816},[224],[115,5661,2263],{"className":5662},[820],[115,5664],{"className":5665,"style":816},[224],[115,5667,5669,5672,5740,5743,5786,5789,5857],{"className":5668},[127],[115,5670],{"className":5671,"style":2191},[131],[115,5673,5675,5678,5737],{"className":5674},[136],[115,5676],{"className":5677},[157,1794],[115,5679,5681],{"className":5680},[1798],[115,5682,5684,5729],{"className":5683},[173,174],[115,5685,5687,5726],{"className":5686},[178],[115,5688,5690,5704,5712],{"className":5689,"style":3824},[182],[115,5691,5692,5695],{"style":2455},[115,5693],{"className":5694,"style":1815},[190],[115,5696,5698],{"className":5697},[195,196,197,198],[115,5699,5701],{"className":5700},[136,198],[115,5702,865],{"className":5703},[136,198],[115,5705,5706,5709],{"style":1870},[115,5707],{"className":5708,"style":1815},[190],[115,5710],{"className":5711,"style":1878},[1877],[115,5713,5714,5717],{"style":3850},[115,5715],{"className":5716,"style":1815},[190],[115,5718,5720],{"className":5719},[195,196,197,198],[115,5721,5723],{"className":5722},[136,198],[115,5724,77],{"className":5725},[136,198],[115,5727,206],{"className":5728},[205],[115,5730,5732],{"className":5731},[178],[115,5733,5735],{"className":5734,"style":3872},[182],[115,5736],{},[115,5738],{"className":5739},[272,1794],[115,5741],{"className":5742,"style":225},[224],[115,5744,5746,5752],{"className":5745},[1630],[115,5747,5749],{"className":5748},[1630],[115,5750,1747],{"className":5751,"style":1746},[136,1745],[115,5753,5755],{"className":5754},[169],[115,5756,5758,5778],{"className":5757},[173,174],[115,5759,5761,5775],{"className":5760},[178],[115,5762,5764],{"className":5763,"style":1760},[182],[115,5765,5766,5769],{"style":1763},[115,5767],{"className":5768,"style":191},[190],[115,5770,5772],{"className":5771},[195,196,197,198],[115,5773,77],{"className":5774},[136,198],[115,5776,206],{"className":5777},[205],[115,5779,5781],{"className":5780},[178],[115,5782,5784],{"className":5783,"style":1782},[182],[115,5785],{},[115,5787],{"className":5788,"style":225},[224],[115,5790,5792,5795,5854],{"className":5791},[136],[115,5793],{"className":5794},[157,1794],[115,5796,5798],{"className":5797},[1798],[115,5799,5801,5846],{"className":5800},[173,174],[115,5802,5804,5843],{"className":5803},[178],[115,5805,5807,5821,5829],{"className":5806,"style":3824},[182],[115,5808,5809,5812],{"style":2455},[115,5810],{"className":5811,"style":1815},[190],[115,5813,5815],{"className":5814},[195,196,197,198],[115,5816,5818],{"className":5817},[136,198],[115,5819,865],{"className":5820},[136,198],[115,5822,5823,5826],{"style":1870},[115,5824],{"className":5825,"style":1815},[190],[115,5827],{"className":5828,"style":1878},[1877],[115,5830,5831,5834],{"style":3850},[115,5832],{"className":5833,"style":1815},[190],[115,5835,5837],{"className":5836},[195,196,197,198],[115,5838,5840],{"className":5839},[136,198],[115,5841,77],{"className":5842},[136,198],[115,5844,206],{"className":5845},[205],[115,5847,5849],{"className":5848},[178],[115,5850,5852],{"className":5851,"style":3872},[182],[115,5853],{},[115,5855],{"className":5856},[272,1794],[115,5858,5860],{"className":5859},[136],[115,5861,273],{"className":5862},[2201,1302],[11,5864,5865,5866,6086,6087,6317],{},"Evaluate that last term. ",[115,5867,5869],{"className":5868},[118],[115,5870,5872,6002,6073],{"className":5871,"ariaHidden":123},[122],[115,5873,5875,5879,5922,5925,5993,5996,5999],{"className":5874},[127],[115,5876],{"className":5877,"style":5878},[131],"height:1.1901em;vertical-align:-0.345em;",[115,5880,5882,5888],{"className":5881},[1630],[115,5883,5885],{"className":5884},[1630],[115,5886,1747],{"className":5887,"style":1746},[136,1745],[115,5889,5891],{"className":5890},[169],[115,5892,5894,5914],{"className":5893},[173,174],[115,5895,5897,5911],{"className":5896},[178],[115,5898,5900],{"className":5899,"style":1760},[182],[115,5901,5902,5905],{"style":1763},[115,5903],{"className":5904,"style":191},[190],[115,5906,5908],{"className":5907},[195,196,197,198],[115,5909,77],{"className":5910},[136,198],[115,5912,206],{"className":5913},[205],[115,5915,5917],{"className":5916},[178],[115,5918,5920],{"className":5919,"style":1782},[182],[115,5921],{},[115,5923],{"className":5924,"style":225},[224],[115,5926,5928,5931,5990],{"className":5927},[136],[115,5929],{"className":5930},[157,1794],[115,5932,5934],{"className":5933},[1798],[115,5935,5937,5982],{"className":5936},[173,174],[115,5938,5940,5979],{"className":5939},[178],[115,5941,5943,5957,5965],{"className":5942,"style":3824},[182],[115,5944,5945,5948],{"style":2455},[115,5946],{"className":5947,"style":1815},[190],[115,5949,5951],{"className":5950},[195,196,197,198],[115,5952,5954],{"className":5953},[136,198],[115,5955,865],{"className":5956},[136,198],[115,5958,5959,5962],{"style":1870},[115,5960],{"className":5961,"style":1815},[190],[115,5963],{"className":5964,"style":1878},[1877],[115,5966,5967,5970],{"style":3850},[115,5968],{"className":5969,"style":1815},[190],[115,5971,5973],{"className":5972},[195,196,197,198],[115,5974,5976],{"className":5975},[136,198],[115,5977,44],{"className":5978},[136,198],[115,5980,206],{"className":5981},[205],[115,5983,5985],{"className":5984},[178],[115,5986,5988],{"className":5987,"style":3872},[182],[115,5989],{},[115,5991],{"className":5992},[272,1794],[115,5994],{"className":5995,"style":563},[224],[115,5997,568],{"className":5998},[567],[115,6000],{"className":6001,"style":563},[224],[115,6003,6005,6009,6012,6015,6058,6061,6064,6067,6070],{"className":6004},[127],[115,6006],{"className":6007,"style":6008},[131],"height:0.9386em;vertical-align:-0.2441em;",[115,6010,821],{"className":6011},[136],[115,6013],{"className":6014,"style":225},[224],[115,6016,6018,6024],{"className":6017},[1630],[115,6019,6021],{"className":6020},[1630],[115,6022,1747],{"className":6023,"style":1746},[136,1745],[115,6025,6027],{"className":6026},[169],[115,6028,6030,6050],{"className":6029},[173,174],[115,6031,6033,6047],{"className":6032},[178],[115,6034,6036],{"className":6035,"style":1760},[182],[115,6037,6038,6041],{"style":1763},[115,6039],{"className":6040,"style":191},[190],[115,6042,6044],{"className":6043},[195,196,197,198],[115,6045,77],{"className":6046},[136,198],[115,6048,206],{"className":6049},[205],[115,6051,6053],{"className":6052},[178],[115,6054,6056],{"className":6055,"style":1782},[182],[115,6057],{},[115,6059],{"className":6060,"style":225},[224],[115,6062,865],{"className":6063},[136],[115,6065],{"className":6066,"style":563},[224],[115,6068,568],{"className":6069},[567],[115,6071],{"className":6072,"style":563},[224],[115,6074,6076,6079,6082],{"className":6075},[127],[115,6077],{"className":6078,"style":3777},[131],[115,6080,821],{"className":6081},[136],[115,6083,6085],{"className":6084},[136],"1.585"," and\n",[115,6088,6090],{"className":6089},[118],[115,6091,6093,6222,6240,6304],{"className":6092,"ariaHidden":123},[122],[115,6094,6096,6099,6142,6145,6213,6216,6219],{"className":6095},[127],[115,6097],{"className":6098,"style":5878},[131],[115,6100,6102,6108],{"className":6101},[1630],[115,6103,6105],{"className":6104},[1630],[115,6106,1747],{"className":6107,"style":1746},[136,1745],[115,6109,6111],{"className":6110},[169],[115,6112,6114,6134],{"className":6113},[173,174],[115,6115,6117,6131],{"className":6116},[178],[115,6118,6120],{"className":6119,"style":1760},[182],[115,6121,6122,6125],{"style":1763},[115,6123],{"className":6124,"style":191},[190],[115,6126,6128],{"className":6127},[195,196,197,198],[115,6129,77],{"className":6130},[136,198],[115,6132,206],{"className":6133},[205],[115,6135,6137],{"className":6136},[178],[115,6138,6140],{"className":6139,"style":1782},[182],[115,6141],{},[115,6143],{"className":6144,"style":225},[224],[115,6146,6148,6151,6210],{"className":6147},[136],[115,6149],{"className":6150},[157,1794],[115,6152,6154],{"className":6153},[1798],[115,6155,6157,6202],{"className":6156},[173,174],[115,6158,6160,6199],{"className":6159},[178],[115,6161,6163,6177,6185],{"className":6162,"style":3824},[182],[115,6164,6165,6168],{"style":2455},[115,6166],{"className":6167,"style":1815},[190],[115,6169,6171],{"className":6170},[195,196,197,198],[115,6172,6174],{"className":6173},[136,198],[115,6175,865],{"className":6176},[136,198],[115,6178,6179,6182],{"style":1870},[115,6180],{"className":6181,"style":1815},[190],[115,6183],{"className":6184,"style":1878},[1877],[115,6186,6187,6190],{"style":3850},[115,6188],{"className":6189,"style":1815},[190],[115,6191,6193],{"className":6192},[195,196,197,198],[115,6194,6196],{"className":6195},[136,198],[115,6197,77],{"className":6198},[136,198],[115,6200,206],{"className":6201},[205],[115,6203,6205],{"className":6204},[178],[115,6206,6208],{"className":6207,"style":3872},[182],[115,6209],{},[115,6211],{"className":6212},[272,1794],[115,6214],{"className":6215,"style":563},[224],[115,6217,568],{"className":6218},[567],[115,6220],{"className":6221,"style":563},[224],[115,6223,6225,6228,6231,6234,6237],{"className":6224},[127],[115,6226],{"className":6227,"style":3777},[131],[115,6229,44],{"className":6230},[136],[115,6232],{"className":6233,"style":816},[224],[115,6235,821],{"className":6236},[820],[115,6238],{"className":6239,"style":816},[224],[115,6241,6243,6246,6289,6292,6295,6298,6301],{"className":6242},[127],[115,6244],{"className":6245,"style":6008},[131],[115,6247,6249,6255],{"className":6248},[1630],[115,6250,6252],{"className":6251},[1630],[115,6253,1747],{"className":6254,"style":1746},[136,1745],[115,6256,6258],{"className":6257},[169],[115,6259,6261,6281],{"className":6260},[173,174],[115,6262,6264,6278],{"className":6263},[178],[115,6265,6267],{"className":6266,"style":1760},[182],[115,6268,6269,6272],{"style":1763},[115,6270],{"className":6271,"style":191},[190],[115,6273,6275],{"className":6274},[195,196,197,198],[115,6276,77],{"className":6277},[136,198],[115,6279,206],{"className":6280},[205],[115,6282,6284],{"className":6283},[178],[115,6285,6287],{"className":6286,"style":1782},[182],[115,6288],{},[115,6290],{"className":6291,"style":225},[224],[115,6293,865],{"className":6294},[136],[115,6296],{"className":6297,"style":563},[224],[115,6299,568],{"className":6300},[567],[115,6302],{"className":6303,"style":563},[224],[115,6305,6307,6310,6313],{"className":6306},[127],[115,6308],{"className":6309,"style":3777},[131],[115,6311,821],{"className":6312},[136],[115,6314,6316],{"className":6315},[136],"0.585",", so",[115,6319,6321],{"className":6320},[1580],[115,6322,6324],{"className":6323},[118],[115,6325,6327,6419,6523,6624,6643,6662],{"className":6326,"ariaHidden":123},[122],[115,6328,6330,6333,6336,6339,6407,6410,6413,6416],{"className":6329},[127],[115,6331],{"className":6332,"style":5878},[131],[115,6334,2166],{"className":6335,"style":2165},[136,137],[115,6337,158],{"className":6338},[157],[115,6340,6342,6345,6404],{"className":6341},[136],[115,6343],{"className":6344},[157,1794],[115,6346,6348],{"className":6347},[1798],[115,6349,6351,6396],{"className":6350},[173,174],[115,6352,6354,6393],{"className":6353},[178],[115,6355,6357,6371,6379],{"className":6356,"style":3824},[182],[115,6358,6359,6362],{"style":2455},[115,6360],{"className":6361,"style":1815},[190],[115,6363,6365],{"className":6364},[195,196,197,198],[115,6366,6368],{"className":6367},[136,198],[115,6369,865],{"className":6370},[136,198],[115,6372,6373,6376],{"style":1870},[115,6374],{"className":6375,"style":1815},[190],[115,6377],{"className":6378,"style":1878},[1877],[115,6380,6381,6384],{"style":3850},[115,6382],{"className":6383,"style":1815},[190],[115,6385,6387],{"className":6386},[195,196,197,198],[115,6388,6390],{"className":6389},[136,198],[115,6391,44],{"className":6392},[136,198],[115,6394,206],{"className":6395},[205],[115,6397,6399],{"className":6398},[178],[115,6400,6402],{"className":6401,"style":3872},[182],[115,6403],{},[115,6405],{"className":6406},[272,1794],[115,6408,273],{"className":6409},[272],[115,6411],{"className":6412,"style":563},[224],[115,6414,568],{"className":6415},[567],[115,6417],{"className":6418,"style":563},[224],[115,6420,6422,6425,6428,6434,6502,6505,6508,6511,6514,6517,6520],{"className":6421},[127],[115,6423],{"className":6424,"style":2191},[131],[115,6426,821],{"className":6427},[136],[115,6429,6431],{"className":6430},[136],[115,6432,158],{"className":6433},[2201,1302],[115,6435,6437,6440,6499],{"className":6436},[136],[115,6438],{"className":6439},[157,1794],[115,6441,6443],{"className":6442},[1798],[115,6444,6446,6491],{"className":6445},[173,174],[115,6447,6449,6488],{"className":6448},[178],[115,6450,6452,6466,6474],{"className":6451,"style":3824},[182],[115,6453,6454,6457],{"style":2455},[115,6455],{"className":6456,"style":1815},[190],[115,6458,6460],{"className":6459},[195,196,197,198],[115,6461,6463],{"className":6462},[136,198],[115,6464,865],{"className":6465},[136,198],[115,6467,6468,6471],{"style":1870},[115,6469],{"className":6470,"style":1815},[190],[115,6472],{"className":6473,"style":1878},[1877],[115,6475,6476,6479],{"style":3850},[115,6477],{"className":6478,"style":1815},[190],[115,6480,6482],{"className":6481},[195,196,197,198],[115,6483,6485],{"className":6484},[136,198],[115,6486,44],{"className":6487},[136,198],[115,6489,206],{"className":6490},[205],[115,6492,6494],{"className":6493},[178],[115,6495,6497],{"className":6496,"style":3872},[182],[115,6498],{},[115,6500],{"className":6501},[272,1794],[115,6503,158],{"className":6504},[157],[115,6506,821],{"className":6507},[136],[115,6509,6085],{"className":6510},[136],[115,6512,273],{"className":6513},[272],[115,6515],{"className":6516,"style":816},[224],[115,6518,2263],{"className":6519},[820],[115,6521],{"className":6522,"style":816},[224],[115,6524,6526,6529,6597,6600,6603,6606,6609,6615,6618,6621],{"className":6525},[127],[115,6527],{"className":6528,"style":2191},[131],[115,6530,6532,6535,6594],{"className":6531},[136],[115,6533],{"className":6534},[157,1794],[115,6536,6538],{"className":6537},[1798],[115,6539,6541,6586],{"className":6540},[173,174],[115,6542,6544,6583],{"className":6543},[178],[115,6545,6547,6561,6569],{"className":6546,"style":3824},[182],[115,6548,6549,6552],{"style":2455},[115,6550],{"className":6551,"style":1815},[190],[115,6553,6555],{"className":6554},[195,196,197,198],[115,6556,6558],{"className":6557},[136,198],[115,6559,865],{"className":6560},[136,198],[115,6562,6563,6566],{"style":1870},[115,6564],{"className":6565,"style":1815},[190],[115,6567],{"className":6568,"style":1878},[1877],[115,6570,6571,6574],{"style":3850},[115,6572],{"className":6573,"style":1815},[190],[115,6575,6577],{"className":6576},[195,196,197,198],[115,6578,6580],{"className":6579},[136,198],[115,6581,77],{"className":6582},[136,198],[115,6584,206],{"className":6585},[205],[115,6587,6589],{"className":6588},[178],[115,6590,6592],{"className":6591,"style":3872},[182],[115,6593],{},[115,6595],{"className":6596},[272,1794],[115,6598,158],{"className":6599},[157],[115,6601,821],{"className":6602},[136],[115,6604,6316],{"className":6605},[136],[115,6607,273],{"className":6608},[272],[115,6610,6612],{"className":6611},[136],[115,6613,273],{"className":6614},[2201,1302],[115,6616],{"className":6617,"style":563},[224],[115,6619,568],{"className":6620},[567],[115,6622],{"className":6623,"style":563},[224],[115,6625,6627,6630,6634,6637,6640],{"className":6626},[127],[115,6628],{"className":6629,"style":3777},[131],[115,6631,6633],{"className":6632},[136],"0.528",[115,6635],{"className":6636,"style":816},[224],[115,6638,2263],{"className":6639},[820],[115,6641],{"className":6642,"style":816},[224],[115,6644,6646,6649,6653,6656,6659],{"className":6645},[127],[115,6647],{"className":6648,"style":831},[131],[115,6650,6652],{"className":6651},[136],"0.390",[115,6654],{"className":6655,"style":563},[224],[115,6657,568],{"className":6658},[567],[115,6660],{"className":6661,"style":563},[224],[115,6663,6665,6668,6672],{"className":6664},[127],[115,6666],{"className":6667,"style":605},[131],[115,6669,6671],{"className":6670},[136],"0.918",[115,6673,6675],{"className":6674},[136,4293],[115,6676,4297],{"className":6677},[136],[11,6679,6680,6681,6683,6684,6686,6687,6689,6690,6789],{},"Now weight each branch by its share of the twelve examples. ",[54,6682,1088],{}," and ",[54,6685,1091],{},"\ncontribute nothing because their entropies are zero; only ",[54,6688,1094],{}," survives, with\nweight ",[115,6691,6693],{"className":6692},[118],[115,6694,6696,6715],{"className":6695,"ariaHidden":123},[122],[115,6697,6699,6702,6706,6709,6712],{"className":6698},[127],[115,6700],{"className":6701,"style":153},[131],[115,6703,6705],{"className":6704},[136],"6\u002F12",[115,6707],{"className":6708,"style":563},[224],[115,6710,568],{"className":6711},[567],[115,6713],{"className":6714,"style":563},[224],[115,6716,6718,6721],{"className":6717},[127],[115,6719],{"className":6720,"style":5878},[131],[115,6722,6724,6727,6786],{"className":6723},[136],[115,6725],{"className":6726},[157,1794],[115,6728,6730],{"className":6729},[1798],[115,6731,6733,6778],{"className":6732},[173,174],[115,6734,6736,6775],{"className":6735},[178],[115,6737,6739,6753,6761],{"className":6738,"style":3824},[182],[115,6740,6741,6744],{"style":2455},[115,6742],{"className":6743,"style":1815},[190],[115,6745,6747],{"className":6746},[195,196,197,198],[115,6748,6750],{"className":6749},[136,198],[115,6751,77],{"className":6752},[136,198],[115,6754,6755,6758],{"style":1870},[115,6756],{"className":6757,"style":1815},[190],[115,6759],{"className":6760,"style":1878},[1877],[115,6762,6763,6766],{"style":3850},[115,6764],{"className":6765,"style":1815},[190],[115,6767,6769],{"className":6768},[195,196,197,198],[115,6770,6772],{"className":6771},[136,198],[115,6773,44],{"className":6774},[136,198],[115,6776,206],{"className":6777},[205],[115,6779,6781],{"className":6780},[178],[115,6782,6784],{"className":6783,"style":3872},[182],[115,6785],{},[115,6787],{"className":6788},[272,1794],":",[115,6791,6793],{"className":6792},[1580],[115,6794,6796],{"className":6795},[118],[115,6797,6799,6847,6939,7031,7123],{"className":6798,"ariaHidden":123},[122],[115,6800,6802,6805,6811,6814,6817,6820,6823,6826,6829,6832,6835,6838,6841,6844],{"className":6801},[127],[115,6803],{"className":6804,"style":153},[131],[115,6806,6808],{"className":6807},[136],[115,6809,2862],{"className":6810},[136,2861],[115,6812,158],{"className":6813},[157],[115,6815,1061],{"className":6816,"style":1021},[136,137],[115,6818,15],{"className":6819},[136,137],[115,6821,1044],{"className":6822},[136,137],[115,6824,1072],{"className":6825,"style":1071},[136,137],[115,6827,1076],{"className":6828},[136,137],[115,6830,1080],{"className":6831},[136,137],[115,6833,1084],{"className":6834},[136,137],[115,6836,273],{"className":6837},[272],[115,6839],{"className":6840,"style":563},[224],[115,6842,568],{"className":6843},[567],[115,6845],{"className":6846,"style":563},[224],[115,6848,6850,6853,6921,6924,6927,6930,6933,6936],{"className":6849},[127],[115,6851],{"className":6852,"style":5878},[131],[115,6854,6856,6859,6918],{"className":6855},[136],[115,6857],{"className":6858},[157,1794],[115,6860,6862],{"className":6861},[1798],[115,6863,6865,6910],{"className":6864},[173,174],[115,6866,6868,6907],{"className":6867},[178],[115,6869,6871,6885,6893],{"className":6870,"style":3824},[182],[115,6872,6873,6876],{"style":2455},[115,6874],{"className":6875,"style":1815},[190],[115,6877,6879],{"className":6878},[195,196,197,198],[115,6880,6882],{"className":6881},[136,198],[115,6883,3839],{"className":6884},[136,198],[115,6886,6887,6890],{"style":1870},[115,6888],{"className":6889,"style":1815},[190],[115,6891],{"className":6892,"style":1878},[1877],[115,6894,6895,6898],{"style":3850},[115,6896],{"className":6897,"style":1815},[190],[115,6899,6901],{"className":6900},[195,196,197,198],[115,6902,6904],{"className":6903},[136,198],[115,6905,77],{"className":6906},[136,198],[115,6908,206],{"className":6909},[205],[115,6911,6913],{"className":6912},[178],[115,6914,6916],{"className":6915,"style":3872},[182],[115,6917],{},[115,6919],{"className":6920},[272,1794],[115,6922,158],{"className":6923},[157],[115,6925,1346],{"className":6926},[136],[115,6928,273],{"className":6929},[272],[115,6931],{"className":6932,"style":816},[224],[115,6934,2263],{"className":6935},[820],[115,6937],{"className":6938,"style":816},[224],[115,6940,6942,6945,7013,7016,7019,7022,7025,7028],{"className":6941},[127],[115,6943],{"className":6944,"style":5878},[131],[115,6946,6948,6951,7010],{"className":6947},[136],[115,6949],{"className":6950},[157,1794],[115,6952,6954],{"className":6953},[1798],[115,6955,6957,7002],{"className":6956},[173,174],[115,6958,6960,6999],{"className":6959},[178],[115,6961,6963,6977,6985],{"className":6962,"style":3824},[182],[115,6964,6965,6968],{"style":2455},[115,6966],{"className":6967,"style":1815},[190],[115,6969,6971],{"className":6970},[195,196,197,198],[115,6972,6974],{"className":6973},[136,198],[115,6975,3839],{"className":6976},[136,198],[115,6978,6979,6982],{"style":1870},[115,6980],{"className":6981,"style":1815},[190],[115,6983],{"className":6984,"style":1878},[1877],[115,6986,6987,6990],{"style":3850},[115,6988],{"className":6989,"style":1815},[190],[115,6991,6993],{"className":6992},[195,196,197,198],[115,6994,6996],{"className":6995},[136,198],[115,6997,993],{"className":6998},[136,198],[115,7000,206],{"className":7001},[205],[115,7003,7005],{"className":7004},[178],[115,7006,7008],{"className":7007,"style":3872},[182],[115,7009],{},[115,7011],{"className":7012},[272,1794],[115,7014,158],{"className":7015},[157],[115,7017,1346],{"className":7018},[136],[115,7020,273],{"className":7021},[272],[115,7023],{"className":7024,"style":816},[224],[115,7026,2263],{"className":7027},[820],[115,7029],{"className":7030,"style":816},[224],[115,7032,7034,7037,7105,7108,7111,7114,7117,7120],{"className":7033},[127],[115,7035],{"className":7036,"style":5878},[131],[115,7038,7040,7043,7102],{"className":7039},[136],[115,7041],{"className":7042},[157,1794],[115,7044,7046],{"className":7045},[1798],[115,7047,7049,7094],{"className":7048},[173,174],[115,7050,7052,7091],{"className":7051},[178],[115,7053,7055,7069,7077],{"className":7054,"style":3824},[182],[115,7056,7057,7060],{"style":2455},[115,7058],{"className":7059,"style":1815},[190],[115,7061,7063],{"className":7062},[195,196,197,198],[115,7064,7066],{"className":7065},[136,198],[115,7067,3839],{"className":7068},[136,198],[115,7070,7071,7074],{"style":1870},[115,7072],{"className":7073,"style":1815},[190],[115,7075],{"className":7076,"style":1878},[1877],[115,7078,7079,7082],{"style":3850},[115,7080],{"className":7081,"style":1815},[190],[115,7083,7085],{"className":7084},[195,196,197,198],[115,7086,7088],{"className":7087},[136,198],[115,7089,1423],{"className":7090},[136,198],[115,7092,206],{"className":7093},[205],[115,7095,7097],{"className":7096},[178],[115,7098,7100],{"className":7099,"style":3872},[182],[115,7101],{},[115,7103],{"className":7104},[272,1794],[115,7106,158],{"className":7107},[157],[115,7109,6671],{"className":7110},[136],[115,7112,273],{"className":7113},[272],[115,7115],{"className":7116,"style":563},[224],[115,7118,568],{"className":7119},[567],[115,7121],{"className":7122,"style":563},[224],[115,7124,7126,7129,7133],{"className":7125},[127],[115,7127],{"className":7128,"style":605},[131],[115,7130,7132],{"className":7131},[136],"0.459",[115,7134,7136],{"className":7135},[136,4293],[115,7137,4297],{"className":7138},[136],[11,7140,7141,7142,7211],{},"The goal entropy before the split was ",[115,7143,7145],{"className":7144},[118],[115,7146,7148,7175,7202],{"className":7147,"ariaHidden":123},[122],[115,7149,7151,7154,7157,7160,7163,7166,7169,7172],{"className":7150},[127],[115,7152],{"className":7153,"style":153},[131],[115,7155,2166],{"className":7156,"style":2165},[136,137],[115,7158,158],{"className":7159},[157],[115,7161,6705],{"className":7162},[136],[115,7164,273],{"className":7165},[272],[115,7167],{"className":7168,"style":563},[224],[115,7170,568],{"className":7171},[567],[115,7173],{"className":7174,"style":563},[224],[115,7176,7178,7181,7184,7187,7190,7193,7196,7199],{"className":7177},[127],[115,7179],{"className":7180,"style":153},[131],[115,7182,2166],{"className":7183,"style":2165},[136,137],[115,7185,158],{"className":7186},[157],[115,7188,3641],{"className":7189},[136],[115,7191,273],{"className":7192},[272],[115,7194],{"className":7195,"style":563},[224],[115,7197,568],{"className":7198},[567],[115,7200],{"className":7201,"style":563},[224],[115,7203,7205,7208],{"className":7204},[127],[115,7206],{"className":7207,"style":831},[131],[115,7209,44],{"className":7210},[136]," bit, so",[115,7213,7215],{"className":7214},[1580],[115,7216,7218],{"className":7217},[118],[115,7219,7221,7269,7287,7305],{"className":7220,"ariaHidden":123},[122],[115,7222,7224,7227,7233,7236,7239,7242,7245,7248,7251,7254,7257,7260,7263,7266],{"className":7223},[127],[115,7225],{"className":7226,"style":153},[131],[115,7228,7230],{"className":7229},[136],[115,7231,3394],{"className":7232},[136,2861],[115,7234,158],{"className":7235},[157],[115,7237,1061],{"className":7238,"style":1021},[136,137],[115,7240,15],{"className":7241},[136,137],[115,7243,1044],{"className":7244},[136,137],[115,7246,1072],{"className":7247,"style":1071},[136,137],[115,7249,1076],{"className":7250},[136,137],[115,7252,1080],{"className":7253},[136,137],[115,7255,1084],{"className":7256},[136,137],[115,7258,273],{"className":7259},[272],[115,7261],{"className":7262,"style":563},[224],[115,7264,568],{"className":7265},[567],[115,7267],{"className":7268,"style":563},[224],[115,7270,7272,7275,7278,7281,7284],{"className":7271},[127],[115,7273],{"className":7274,"style":3777},[131],[115,7276,44],{"className":7277},[136],[115,7279],{"className":7280,"style":816},[224],[115,7282,821],{"className":7283},[820],[115,7285],{"className":7286,"style":816},[224],[115,7288,7290,7293,7296,7299,7302],{"className":7289},[127],[115,7291],{"className":7292,"style":831},[131],[115,7294,7132],{"className":7295},[136],[115,7297],{"className":7298,"style":563},[224],[115,7300,568],{"className":7301},[567],[115,7303],{"className":7304,"style":563},[224],[115,7306,7308,7311,7314],{"className":7307},[127],[115,7309],{"className":7310,"style":605},[131],[115,7312,4289],{"className":7313},[136],[115,7315,7317],{"className":7316},[136,4293],[115,7318,4297],{"className":7319},[136],[11,7321,7322,7323,7353,7354,7356,7357,7359],{},"Compare this against a middling attribute to see the ranking the learner uses.\n",[115,7324,7326],{"className":7325},[118],[115,7327,7329],{"className":7328,"ariaHidden":123},[122],[115,7330,7332,7335,7338,7341,7344,7347,7350],{"className":7331},[127],[115,7333],{"className":7334,"style":1108},[131],[115,7336,759],{"className":7337,"style":1112},[136,137],[115,7339,1116],{"className":7340},[136,137],[115,7342,1080],{"className":7343},[136,137],[115,7345,1123],{"className":7346,"style":232},[136,137],[115,7348,1072],{"className":7349,"style":1071},[136,137],[115,7351,233],{"className":7352,"style":232},[136,137]," splits the twelve into ",[54,7355,3707],{}," (5 pos, 2 neg) and ",[54,7358,3700],{}," (1 pos, 4 neg):",[115,7361,7363],{"className":7362},[1580],[115,7364,7366],{"className":7365},[118],[115,7367,7369,7414,7575,7735,7828,7921],{"className":7368,"ariaHidden":123},[122],[115,7370,7372,7375,7381,7384,7387,7390,7393,7396,7399,7402,7405,7408,7411],{"className":7371},[127],[115,7373],{"className":7374,"style":153},[131],[115,7376,7378],{"className":7377},[136],[115,7379,2862],{"className":7380},[136,2861],[115,7382,158],{"className":7383},[157],[115,7385,759],{"className":7386,"style":1112},[136,137],[115,7388,1116],{"className":7389},[136,137],[115,7391,1080],{"className":7392},[136,137],[115,7394,1123],{"className":7395,"style":232},[136,137],[115,7397,1072],{"className":7398,"style":1071},[136,137],[115,7400,233],{"className":7401,"style":232},[136,137],[115,7403,273],{"className":7404},[272],[115,7406],{"className":7407,"style":563},[224],[115,7409,568],{"className":7410},[567],[115,7412],{"className":7413,"style":563},[224],[115,7415,7417,7420,7489,7492,7495,7563,7566,7569,7572],{"className":7416},[127],[115,7418],{"className":7419,"style":5878},[131],[115,7421,7423,7426,7486],{"className":7422},[136],[115,7424],{"className":7425},[157,1794],[115,7427,7429],{"className":7428},[1798],[115,7430,7432,7478],{"className":7431},[173,174],[115,7433,7435,7475],{"className":7434},[178],[115,7436,7438,7452,7460],{"className":7437,"style":3824},[182],[115,7439,7440,7443],{"style":2455},[115,7441],{"className":7442,"style":1815},[190],[115,7444,7446],{"className":7445},[195,196,197,198],[115,7447,7449],{"className":7448},[136,198],[115,7450,3839],{"className":7451},[136,198],[115,7453,7454,7457],{"style":1870},[115,7455],{"className":7456,"style":1815},[190],[115,7458],{"className":7459,"style":1878},[1877],[115,7461,7462,7465],{"style":3850},[115,7463],{"className":7464,"style":1815},[190],[115,7466,7468],{"className":7467},[195,196,197,198],[115,7469,7471],{"className":7470},[136,198],[115,7472,7474],{"className":7473},[136,198],"7",[115,7476,206],{"className":7477},[205],[115,7479,7481],{"className":7480},[178],[115,7482,7484],{"className":7483,"style":3872},[182],[115,7485],{},[115,7487],{"className":7488},[272,1794],[115,7490,2166],{"className":7491,"style":2165},[136,137],[115,7493,158],{"className":7494},[157],[115,7496,7498,7501,7560],{"className":7497},[136],[115,7499],{"className":7500},[157,1794],[115,7502,7504],{"className":7503},[1798],[115,7505,7507,7552],{"className":7506},[173,174],[115,7508,7510,7549],{"className":7509},[178],[115,7511,7513,7527,7535],{"className":7512,"style":3824},[182],[115,7514,7515,7518],{"style":2455},[115,7516],{"className":7517,"style":1815},[190],[115,7519,7521],{"className":7520},[195,196,197,198],[115,7522,7524],{"className":7523},[136,198],[115,7525,7474],{"className":7526},[136,198],[115,7528,7529,7532],{"style":1870},[115,7530],{"className":7531,"style":1815},[190],[115,7533],{"className":7534,"style":1878},[1877],[115,7536,7537,7540],{"style":3850},[115,7538],{"className":7539,"style":1815},[190],[115,7541,7543],{"className":7542},[195,196,197,198],[115,7544,7546],{"className":7545},[136,198],[115,7547,1382],{"className":7548},[136,198],[115,7550,206],{"className":7551},[205],[115,7553,7555],{"className":7554},[178],[115,7556,7558],{"className":7557,"style":3872},[182],[115,7559],{},[115,7561],{"className":7562},[272,1794],[115,7564,273],{"className":7565},[272],[115,7567],{"className":7568,"style":816},[224],[115,7570,2263],{"className":7571},[820],[115,7573],{"className":7574,"style":816},[224],[115,7576,7578,7581,7649,7652,7655,7723,7726,7729,7732],{"className":7577},[127],[115,7579],{"className":7580,"style":5878},[131],[115,7582,7584,7587,7646],{"className":7583},[136],[115,7585],{"className":7586},[157,1794],[115,7588,7590],{"className":7589},[1798],[115,7591,7593,7638],{"className":7592},[173,174],[115,7594,7596,7635],{"className":7595},[178],[115,7597,7599,7613,7621],{"className":7598,"style":3824},[182],[115,7600,7601,7604],{"style":2455},[115,7602],{"className":7603,"style":1815},[190],[115,7605,7607],{"className":7606},[195,196,197,198],[115,7608,7610],{"className":7609},[136,198],[115,7611,3839],{"className":7612},[136,198],[115,7614,7615,7618],{"style":1870},[115,7616],{"className":7617,"style":1815},[190],[115,7619],{"className":7620,"style":1878},[1877],[115,7622,7623,7626],{"style":3850},[115,7624],{"className":7625,"style":1815},[190],[115,7627,7629],{"className":7628},[195,196,197,198],[115,7630,7632],{"className":7631},[136,198],[115,7633,1382],{"className":7634},[136,198],[115,7636,206],{"className":7637},[205],[115,7639,7641],{"className":7640},[178],[115,7642,7644],{"className":7643,"style":3872},[182],[115,7645],{},[115,7647],{"className":7648},[272,1794],[115,7650,2166],{"className":7651,"style":2165},[136,137],[115,7653,158],{"className":7654},[157],[115,7656,7658,7661,7720],{"className":7657},[136],[115,7659],{"className":7660},[157,1794],[115,7662,7664],{"className":7663},[1798],[115,7665,7667,7712],{"className":7666},[173,174],[115,7668,7670,7709],{"className":7669},[178],[115,7671,7673,7687,7695],{"className":7672,"style":3824},[182],[115,7674,7675,7678],{"style":2455},[115,7676],{"className":7677,"style":1815},[190],[115,7679,7681],{"className":7680},[195,196,197,198],[115,7682,7684],{"className":7683},[136,198],[115,7685,1382],{"className":7686},[136,198],[115,7688,7689,7692],{"style":1870},[115,7690],{"className":7691,"style":1815},[190],[115,7693],{"className":7694,"style":1878},[1877],[115,7696,7697,7700],{"style":3850},[115,7698],{"className":7699,"style":1815},[190],[115,7701,7703],{"className":7702},[195,196,197,198],[115,7704,7706],{"className":7705},[136,198],[115,7707,44],{"className":7708},[136,198],[115,7710,206],{"className":7711},[205],[115,7713,7715],{"className":7714},[178],[115,7716,7718],{"className":7717,"style":3872},[182],[115,7719],{},[115,7721],{"className":7722},[272,1794],[115,7724,273],{"className":7725},[272],[115,7727],{"className":7728,"style":563},[224],[115,7730,568],{"className":7731},[567],[115,7733],{"className":7734,"style":563},[224],[115,7736,7738,7741,7809,7812,7816,7819,7822,7825],{"className":7737},[127],[115,7739],{"className":7740,"style":5878},[131],[115,7742,7744,7747,7806],{"className":7743},[136],[115,7745],{"className":7746},[157,1794],[115,7748,7750],{"className":7749},[1798],[115,7751,7753,7798],{"className":7752},[173,174],[115,7754,7756,7795],{"className":7755},[178],[115,7757,7759,7773,7781],{"className":7758,"style":3824},[182],[115,7760,7761,7764],{"style":2455},[115,7762],{"className":7763,"style":1815},[190],[115,7765,7767],{"className":7766},[195,196,197,198],[115,7768,7770],{"className":7769},[136,198],[115,7771,3839],{"className":7772},[136,198],[115,7774,7775,7778],{"style":1870},[115,7776],{"className":7777,"style":1815},[190],[115,7779],{"className":7780,"style":1878},[1877],[115,7782,7783,7786],{"style":3850},[115,7784],{"className":7785,"style":1815},[190],[115,7787,7789],{"className":7788},[195,196,197,198],[115,7790,7792],{"className":7791},[136,198],[115,7793,7474],{"className":7794},[136,198],[115,7796,206],{"className":7797},[205],[115,7799,7801],{"className":7800},[178],[115,7802,7804],{"className":7803,"style":3872},[182],[115,7805],{},[115,7807],{"className":7808},[272,1794],[115,7810,158],{"className":7811},[157],[115,7813,7815],{"className":7814},[136],"0.863",[115,7817,273],{"className":7818},[272],[115,7820],{"className":7821,"style":816},[224],[115,7823,2263],{"className":7824},[820],[115,7826],{"className":7827,"style":816},[224],[115,7829,7831,7834,7902,7905,7909,7912,7915,7918],{"className":7830},[127],[115,7832],{"className":7833,"style":5878},[131],[115,7835,7837,7840,7899],{"className":7836},[136],[115,7838],{"className":7839},[157,1794],[115,7841,7843],{"className":7842},[1798],[115,7844,7846,7891],{"className":7845},[173,174],[115,7847,7849,7888],{"className":7848},[178],[115,7850,7852,7866,7874],{"className":7851,"style":3824},[182],[115,7853,7854,7857],{"style":2455},[115,7855],{"className":7856,"style":1815},[190],[115,7858,7860],{"className":7859},[195,196,197,198],[115,7861,7863],{"className":7862},[136,198],[115,7864,3839],{"className":7865},[136,198],[115,7867,7868,7871],{"style":1870},[115,7869],{"className":7870,"style":1815},[190],[115,7872],{"className":7873,"style":1878},[1877],[115,7875,7876,7879],{"style":3850},[115,7877],{"className":7878,"style":1815},[190],[115,7880,7882],{"className":7881},[195,196,197,198],[115,7883,7885],{"className":7884},[136,198],[115,7886,1382],{"className":7887},[136,198],[115,7889,206],{"className":7890},[205],[115,7892,7894],{"className":7893},[178],[115,7895,7897],{"className":7896,"style":3872},[182],[115,7898],{},[115,7900],{"className":7901},[272,1794],[115,7903,158],{"className":7904},[157],[115,7906,7908],{"className":7907},[136],"0.722",[115,7910,273],{"className":7911},[272],[115,7913],{"className":7914,"style":563},[224],[115,7916,568],{"className":7917},[567],[115,7919],{"className":7920,"style":563},[224],[115,7922,7924,7927,7931],{"className":7923},[127],[115,7925],{"className":7926,"style":623},[131],[115,7928,7930],{"className":7929},[136],"0.804",[115,7932,7934],{"className":7933},[136,4293],[115,7935,7937],{"className":7936},[136]," bits,",[11,7939,7940,7941,7993,7994,8027,8028,8178,8179,8212],{},"for a gain of ",[115,7942,7944],{"className":7943},[118],[115,7945,7947,7965,7983],{"className":7946,"ariaHidden":123},[122],[115,7948,7950,7953,7956,7959,7962],{"className":7949},[127],[115,7951],{"className":7952,"style":3777},[131],[115,7954,44],{"className":7955},[136],[115,7957],{"className":7958,"style":816},[224],[115,7960,821],{"className":7961},[820],[115,7963],{"className":7964,"style":816},[224],[115,7966,7968,7971,7974,7977,7980],{"className":7967},[127],[115,7969],{"className":7970,"style":831},[131],[115,7972,7930],{"className":7973},[136],[115,7975],{"className":7976,"style":563},[224],[115,7978,568],{"className":7979},[567],[115,7981],{"className":7982,"style":563},[224],[115,7984,7986,7989],{"className":7985},[127],[115,7987],{"className":7988,"style":831},[131],[115,7990,7992],{"className":7991},[136],"0.196"," bits — real, but less than half of ",[115,7995,7997],{"className":7996},[118],[115,7998,8000],{"className":7999,"ariaHidden":123},[122],[115,8001,8003,8006,8009,8012,8015,8018,8021,8024],{"className":8002},[127],[115,8004],{"className":8005,"style":132},[131],[115,8007,1061],{"className":8008,"style":1021},[136,137],[115,8010,15],{"className":8011},[136,137],[115,8013,1044],{"className":8014},[136,137],[115,8016,1072],{"className":8017,"style":1071},[136,137],[115,8019,1076],{"className":8020},[136,137],[115,8022,1080],{"className":8023},[136,137],[115,8025,1084],{"className":8026},[136,137],"'s.\nThe greedy learner therefore ranks the candidate root attributes\n",[115,8029,8031],{"className":8030},[118],[115,8032,8034,8083,8128,8148],{"className":8033,"ariaHidden":123},[122],[115,8035,8037,8040,8043,8046,8049,8052,8055,8058,8061,8064,8067,8070,8073,8076,8080],{"className":8036},[127],[115,8038],{"className":8039,"style":153},[131],[115,8041,1061],{"className":8042,"style":1021},[136,137],[115,8044,15],{"className":8045},[136,137],[115,8047,1044],{"className":8048},[136,137],[115,8050,1072],{"className":8051,"style":1071},[136,137],[115,8053,1076],{"className":8054},[136,137],[115,8056,1080],{"className":8057},[136,137],[115,8059,1084],{"className":8060},[136,137],[115,8062],{"className":8063,"style":563},[224],[115,8065,158],{"className":8066},[157],[115,8068,4289],{"className":8069},[136],[115,8071,273],{"className":8072},[272],[115,8074],{"className":8075,"style":563},[224],[115,8077,8079],{"className":8078},[567],">",[115,8081],{"className":8082,"style":563},[224],[115,8084,8086,8089,8092,8095,8098,8101,8104,8107,8110,8113,8116,8119,8122,8125],{"className":8085},[127],[115,8087],{"className":8088,"style":153},[131],[115,8090,759],{"className":8091,"style":1112},[136,137],[115,8093,1116],{"className":8094},[136,137],[115,8096,1080],{"className":8097},[136,137],[115,8099,1123],{"className":8100,"style":232},[136,137],[115,8102,1072],{"className":8103,"style":1071},[136,137],[115,8105,233],{"className":8106,"style":232},[136,137],[115,8108],{"className":8109,"style":563},[224],[115,8111,158],{"className":8112},[157],[115,8114,7992],{"className":8115},[136],[115,8117,273],{"className":8118},[272],[115,8120],{"className":8121,"style":563},[224],[115,8123,8079],{"className":8124},[567],[115,8126],{"className":8127,"style":563},[224],[115,8129,8131,8135,8139,8142,8145],{"className":8130},[127],[115,8132],{"className":8133,"style":8134},[131],"height:0.5782em;vertical-align:-0.0391em;",[115,8136,8138],{"className":8137},[381],"⋯",[115,8140],{"className":8141,"style":563},[224],[115,8143,8079],{"className":8144},[567],[115,8146],{"className":8147,"style":563},[224],[115,8149,8151,8154,8157,8160,8163,8166,8169,8172,8175],{"className":8150},[127],[115,8152],{"className":8153,"style":153},[131],[115,8155,1146],{"className":8156,"style":1021},[136,137],[115,8158,233],{"className":8159,"style":232},[136,137],[115,8161,11],{"className":8162},[136,137],[115,8164,1156],{"className":8165},[136,137],[115,8167],{"className":8168,"style":563},[224],[115,8170,158],{"className":8171},[157],[115,8173,1346],{"className":8174},[136],[115,8176,273],{"className":8177},[272]," and tests ",[115,8180,8182],{"className":8181},[118],[115,8183,8185],{"className":8184,"ariaHidden":123},[122],[115,8186,8188,8191,8194,8197,8200,8203,8206,8209],{"className":8187},[127],[115,8189],{"className":8190,"style":132},[131],[115,8192,1061],{"className":8193,"style":1021},[136,137],[115,8195,15],{"className":8196},[136,137],[115,8198,1044],{"className":8199},[136,137],[115,8201,1072],{"className":8202,"style":1071},[136,137],[115,8204,1076],{"className":8205},[136,137],[115,8207,1080],{"className":8208},[136,137],[115,8210,1084],{"className":8211},[136,137]," first.",[727,8214],{"hash":8215},"2be899f4103131464a5db4914237b0addb904074d8a3b16bcfebaf8937abb0ba",[11,8217,8218,8219,8252,8253],{},"So ",[115,8220,8222],{"className":8221},[118],[115,8223,8225],{"className":8224,"ariaHidden":123},[122],[115,8226,8228,8231,8234,8237,8240,8243,8246,8249],{"className":8227},[127],[115,8229],{"className":8230,"style":132},[131],[115,8232,1061],{"className":8233,"style":1021},[136,137],[115,8235,15],{"className":8236},[136,137],[115,8238,1044],{"className":8239},[136,137],[115,8241,1072],{"className":8242,"style":1071},[136,137],[115,8244,1076],{"className":8245},[136,137],[115,8247,1080],{"className":8248},[136,137],[115,8250,1084],{"className":8251},[136,137]," has the maximum gain and is chosen as the root. After splitting, each\nnon-pure branch becomes a smaller decision-tree problem with one fewer attribute,\nsolved recursively. The full algorithm handles four cases: a pure subset returns\nits class; a mixed subset splits on the best remaining attribute; an empty subset\nreturns the plurality vote of the parent's examples; and a subset with no\nattributes left but mixed labels (noise, or an unobservable distinction) returns\nits own plurality vote.",[36,8254,8255],{},[15,8256,7474],{"href":8257,"ariaDescribedBy":8258,"dataFootnoteRef":6,"id":8259},"#user-content-fn-aima-dt-induce",[42],"user-content-fnref-aima-dt-induce",[8261,8262,8266],"pre",{"className":8263,"code":8264,"language":8265,"meta":6,"style":6},"language-algorithm shiki shiki-themes Vesper Light - Orange Boost (Quick Open Adjusted) vesper","caption: $\\textsc{Decision-Tree-Learning}(examples, attributes, parent)$ — greedy induction\nif $examples$ is empty then\n  return $\\textsc{Plurality-Value}(parent)$\nelse if all $examples$ have the same classification then\n  return that classification\nelse if $attributes$ is empty then\n  return $\\textsc{Plurality-Value}(examples)$\nelse\n  $A \\gets \\arg\\max_{a \\in attributes} \\textsc{Importance}(a, examples)$\n  $tree \\gets$ a new decision tree with root test $A$\n  for each value $v_k$ of $A$ do\n    $exs \\gets \\{e \\in examples : e.A = v_k\\}$\n    $subtree \\gets \\textsc{Decision-Tree-Learning}(exs, attributes - A, examples)$\n    add a branch to $tree$ with label $(A = v_k)$ and subtree $subtree$\n  return $tree$\n","algorithm",[8267,8268,8269,8276,8282,8288,8294,8300,8306,8312,8318,8324,8330,8336,8342,8348,8354],"code",{"__ignoreMap":6},[115,8270,8273],{"class":8271,"line":8272},"line",1,[115,8274,8275],{},"caption: $\\textsc{Decision-Tree-Learning}(examples, attributes, parent)$ — greedy induction\n",[115,8277,8279],{"class":8271,"line":8278},2,[115,8280,8281],{},"if $examples$ is empty then\n",[115,8283,8285],{"class":8271,"line":8284},3,[115,8286,8287],{},"  return $\\textsc{Plurality-Value}(parent)$\n",[115,8289,8291],{"class":8271,"line":8290},4,[115,8292,8293],{},"else if all $examples$ have the same classification then\n",[115,8295,8297],{"class":8271,"line":8296},5,[115,8298,8299],{},"  return that classification\n",[115,8301,8303],{"class":8271,"line":8302},6,[115,8304,8305],{},"else if $attributes$ is empty then\n",[115,8307,8309],{"class":8271,"line":8308},7,[115,8310,8311],{},"  return $\\textsc{Plurality-Value}(examples)$\n",[115,8313,8315],{"class":8271,"line":8314},8,[115,8316,8317],{},"else\n",[115,8319,8321],{"class":8271,"line":8320},9,[115,8322,8323],{},"  $A \\gets \\arg\\max_{a \\in attributes} \\textsc{Importance}(a, examples)$\n",[115,8325,8327],{"class":8271,"line":8326},10,[115,8328,8329],{},"  $tree \\gets$ a new decision tree with root test $A$\n",[115,8331,8333],{"class":8271,"line":8332},11,[115,8334,8335],{},"  for each value $v_k$ of $A$ do\n",[115,8337,8339],{"class":8271,"line":8338},12,[115,8340,8341],{},"    $exs \\gets \\{e \\in examples : e.A = v_k\\}$\n",[115,8343,8345],{"class":8271,"line":8344},13,[115,8346,8347],{},"    $subtree \\gets \\textsc{Decision-Tree-Learning}(exs, attributes - A, examples)$\n",[115,8349,8351],{"class":8271,"line":8350},14,[115,8352,8353],{},"    add a branch to $tree$ with label $(A = v_k)$ and subtree $subtree$\n",[115,8355,8357],{"class":8271,"line":8356},15,[115,8358,8359],{},"  return $tree$\n",[11,8361,8362,8363,8387,8388,8410,8411,8414,8415,8439,8440,8464,8465,8467],{},"Here ",[115,8364,8366],{"className":8365},[118],[115,8367,8369],{"className":8368,"ariaHidden":123},[122],[115,8370,8372,8375],{"className":8371},[127],[115,8373],{"className":8374,"style":1108},[131],[115,8376,8380],{"className":8377},[8378,8379],"enclosing","textsc",[115,8381,8383],{"className":8382},[136,4293],[115,8384,8386],{"className":8385},[136],"Importance"," is information gain and ",[115,8389,8391],{"className":8390},[118],[115,8392,8394],{"className":8393,"ariaHidden":123},[122],[115,8395,8397,8400],{"className":8396},[127],[115,8398],{"className":8399,"style":623},[131],[115,8401,8403],{"className":8402},[8378,8379],[115,8404,8406],{"className":8405},[136,4293],[115,8407,8409],{"className":8408},[136],"Plurality-Value","\nreturns the most common output, breaking ties at random. Run on the 12-example\ntraining set, the algorithm produces a tree that is consistent with the data and\n",[54,8412,8413],{},"simpler"," than the tree a human wrote — it never tests ",[115,8416,8418],{"className":8417},[118],[115,8419,8421],{"className":8420,"ariaHidden":123},[122],[115,8422,8424,8427,8430,8433,8436],{"className":8423},[127],[115,8425],{"className":8426,"style":132},[131],[115,8428,1061],{"className":8429,"style":1021},[136,137],[115,8431,1072],{"className":8432,"style":1071},[136,137],[115,8434,920],{"className":8435},[136,137],[115,8437,1182],{"className":8438},[136,137]," or ",[115,8441,8443],{"className":8442},[118],[115,8444,8446],{"className":8445,"ariaHidden":123},[122],[115,8447,8449,8452,8455,8458,8461],{"className":8448},[127],[115,8450],{"className":8451,"style":1108},[131],[115,8453,1146],{"className":8454,"style":1021},[136,137],[115,8456,233],{"className":8457,"style":232},[136,137],[115,8459,11],{"className":8460},[136,137],[115,8462,1156],{"className":8463},[136,137]," at all,\nbecause it can classify every example without them. That is the algorithm working\nas intended: it fits the examples, not the ",[996,8466,123],{}," function, and among consistent\ntrees it prefers the small one.",[731,8469,8471],{"id":8470},"overfitting-and-pruning","Overfitting and pruning",[11,8473,8474,8475,8478,8479],{},"Left unchecked, the greedy learner grows a large tree whenever it finds any\npattern in the input, even when there is no pattern to find. Give it dice rolls\nlabelled by the die's color, weight, and the time of the roll, and if two fair\nrolls happen to come up six, it will build a path predicting six from those\nirrelevant attributes. To address this, ",[31,8476,8477],{},"decision tree pruning"," grows the full\ntree, then eliminates nodes that are not clearly relevant.",[36,8480,8481],{},[15,8482,8486],{"href":8483,"ariaDescribedBy":8484,"dataFootnoteRef":6,"id":8485},"#user-content-fn-aima-prune",[42],"user-content-fnref-aima-prune","8",[11,8488,8489,8490,2770,8505,8520,8521,8567,8568,8571,8572,8575,8576,8619,8620,8664],{},"Pruning works from the bottom. Consider a test node all of whose descendants are\nleaves. If the attribute it tests appears to be irrelevant — detecting only noise\n— replace the whole node with a single leaf. How do we tell? An irrelevant\nattribute splits a set of ",[115,8491,8493],{"className":8492},[118],[115,8494,8496],{"className":8495,"ariaHidden":123},[122],[115,8497,8499,8502],{"className":8498},[127],[115,8500],{"className":8501,"style":556},[131],[115,8503,11],{"className":8504},[136,137],[115,8506,8508],{"className":8507},[118],[115,8509,8511],{"className":8510,"ariaHidden":123},[122],[115,8512,8514,8517],{"className":8513},[127],[115,8515],{"className":8516,"style":1319},[131],[115,8518,1080],{"className":8519},[136,137]," negative examples into subsets\nwhose positive fractions are all close to the parent's ",[115,8522,8524],{"className":8523},[118],[115,8525,8527,8555],{"className":8526,"ariaHidden":123},[122],[115,8528,8530,8533,8536,8540,8543,8546,8549,8552],{"className":8529},[127],[115,8531],{"className":8532,"style":153},[131],[115,8534,11],{"className":8535},[136,137],[115,8537,8539],{"className":8538},[136],"\u002F",[115,8541,158],{"className":8542},[157],[115,8544,11],{"className":8545},[136,137],[115,8547],{"className":8548,"style":816},[224],[115,8550,2263],{"className":8551},[820],[115,8553],{"className":8554,"style":816},[224],[115,8556,8558,8561,8564],{"className":8557},[127],[115,8559],{"className":8560,"style":153},[131],[115,8562,1080],{"className":8563},[136,137],[115,8565,273],{"className":8566},[272],", so its\ninformation gain is close to zero. A ",[31,8569,8570],{},"significance test"," makes ",[996,8573,8574],{},"close to zero","\nprecise: assume the null hypothesis that the attribute is irrelevant, compute how\nfar the observed split deviates from the even split that null predicts, and prune\nunless the deviation is statistically unlikely (the standard ",[115,8577,8579],{"className":8578},[118],[115,8580,8582],{"className":8581,"ariaHidden":123},[122],[115,8583,8585,8589],{"className":8584},[127],[115,8586],{"className":8587,"style":8588},[131],"height:1.0085em;vertical-align:-0.1944em;",[115,8590,8592,8596],{"className":8591},[136],[115,8593,8595],{"className":8594},[136,137],"χ",[115,8597,8599],{"className":8598},[169],[115,8600,8602],{"className":8601},[173],[115,8603,8605],{"className":8604},[178],[115,8606,8608],{"className":8607,"style":1336},[182],[115,8609,8610,8613],{"style":1262},[115,8611],{"className":8612,"style":191},[190],[115,8614,8616],{"className":8615},[195,196,197,198],[115,8617,77],{"className":8618},[136,198]," test at the\n5% level). This is ",[31,8621,8622,8663],{},[115,8623,8625],{"className":8624},[118],[115,8626,8628],{"className":8627,"ariaHidden":123},[122],[115,8629,8631,8634],{"className":8630},[127],[115,8632],{"className":8633,"style":8588},[131],[115,8635,8637,8640],{"className":8636},[136],[115,8638,8595],{"className":8639},[136,137],[115,8641,8643],{"className":8642},[169],[115,8644,8646],{"className":8645},[173],[115,8647,8649],{"className":8648},[178],[115,8650,8652],{"className":8651,"style":1336},[182],[115,8653,8654,8657],{"style":1262},[115,8655],{"className":8656,"style":191},[190],[115,8658,8660],{"className":8659},[195,196,197,198],[115,8661,77],{"className":8662},[136,198]," pruning",", and pruned trees are both more accurate\nin the presence of noise and smaller, hence easier for a person to read.",[11,8666,8667,8668,8671],{},"Growing then pruning beats ",[31,8669,8670],{},"early stopping"," — halting growth when no attribute\nlooks good enough. Early stopping is fooled by attributes that are useless alone\nbut informative in combination, like the two inputs of an XOR: neither has any\ngain at the root, so early stopping quits, but splitting on either one exposes an\ninformative split at the next level. Grow-then-prune sees the whole tree first and\nkeeps such structure.",[63,8673,8675],{"id":8674},"evaluating-a-hypothesis","Evaluating a hypothesis",[11,8677,8678,8679,8682,8683,8571,8686,8688,8689,8692,8693],{},"To choose among hypotheses we need to measure how well each will predict the\n",[54,8680,8681],{},"future",". The ",[31,8684,8685],{},"stationarity assumption",[996,8687,8681],{}," meaningful: examples are\ndrawn independently from a probability distribution that does not change over time\n— the examples are ",[31,8690,8691],{},"independent and identically distributed (i.i.d.)",". Without\nsome such link between past and future, no prediction is justified.",[36,8694,8695],{},[15,8696,8700],{"href":8697,"ariaDescribedBy":8698,"dataFootnoteRef":6,"id":8699},"#user-content-fn-aima-eval",[42],"user-content-fnref-aima-eval","9",[11,8702,8703,8704,8707,8708,8790,8791,8806,8807,8809,8810,8812],{},"The ",[31,8705,8706],{},"error rate"," of a hypothesis is the proportion of examples it misclassifies,\n",[115,8709,8711],{"className":8710},[118],[115,8712,8714,8781],{"className":8713,"ariaHidden":123},[122],[115,8715,8717,8720,8723,8726,8729,8732,8735,8778],{"className":8716},[127],[115,8718],{"className":8719,"style":153},[131],[115,8721,609],{"className":8722},[136,137],[115,8724,158],{"className":8725},[157],[115,8727,165],{"className":8728},[136,137],[115,8730,273],{"className":8731},[272],[115,8733],{"className":8734,"style":563},[224],[115,8736,8738,8771,8775],{"className":8737},[567],[115,8739,8741],{"className":8740},[567],[115,8742,8745],{"className":8743},[136,8744],"vbox",[115,8746,8749],{"className":8747},[8748],"thinbox",[115,8750,8753,8756,8767],{"className":8751},[8752],"rlap",[115,8754],{"className":8755,"style":623},[131],[115,8757,8760],{"className":8758},[8759],"inner",[115,8761,8763],{"className":8762},[136],[115,8764,8766],{"className":8765},[567],"",[115,8768],{"className":8769},[8770],"fix",[115,8772],{"className":8773},[224,8774],"nobreak",[115,8776,568],{"className":8777},[567],[115,8779],{"className":8780,"style":563},[224],[115,8782,8784,8787],{"className":8783},[127],[115,8785],{"className":8786,"style":556},[131],[115,8788,233],{"className":8789,"style":232},[136,137],". A low error rate on the training set proves nothing — a professor\nknows an exam will not measure students who have already seen the questions. To\nestimate how ",[115,8792,8794],{"className":8793},[118],[115,8795,8797],{"className":8796,"ariaHidden":123},[122],[115,8798,8800,8803],{"className":8799},[127],[115,8801],{"className":8802,"style":605},[131],[115,8804,609],{"className":8805},[136,137]," generalizes we test it on held-out examples. The simplest\nprotocol splits the data into a ",[31,8808,112],{},", which the learner sees, and a\n",[31,8811,704],{},", which measures the final hypothesis. But a single split wastes data:\nreserve half for testing and you train on half as much; reserve only a tenth and a\nstatistical accident in that tenth can give a bad estimate.",[11,8814,8815,8833,8834,8849,8850,8865,8866,8900,8901,8916,8917,8439,8950,8966,8967,9000,9001,61],{},[31,8816,8817,8832],{},[115,8818,8820],{"className":8819},[118],[115,8821,8823],{"className":8822,"ariaHidden":123},[122],[115,8824,8826,8829],{"className":8825},[127],[115,8827],{"className":8828,"style":605},[131],[115,8830,1503],{"className":8831,"style":1502},[136,137],"-fold cross-validation"," squeezes more from the same data by letting every\nexample serve as both training and test data. Split the data into ",[115,8835,8837],{"className":8836},[118],[115,8838,8840],{"className":8839,"ariaHidden":123},[122],[115,8841,8843,8846],{"className":8842},[127],[115,8844],{"className":8845,"style":605},[131],[115,8847,1503],{"className":8848,"style":1502},[136,137]," equal folds;\nrun ",[115,8851,8853],{"className":8852},[118],[115,8854,8856],{"className":8855,"ariaHidden":123},[122],[115,8857,8859,8862],{"className":8858},[127],[115,8860],{"className":8861,"style":605},[131],[115,8863,1503],{"className":8864,"style":1502},[136,137]," rounds, each holding out one fold as the test set and training on the other\n",[115,8867,8869],{"className":8868},[118],[115,8870,8872,8891],{"className":8871,"ariaHidden":123},[122],[115,8873,8875,8879,8882,8885,8888],{"className":8874},[127],[115,8876],{"className":8877,"style":8878},[131],"height:0.7778em;vertical-align:-0.0833em;",[115,8880,1503],{"className":8881,"style":1502},[136,137],[115,8883],{"className":8884,"style":816},[224],[115,8886,821],{"className":8887},[820],[115,8889],{"className":8890,"style":816},[224],[115,8892,8894,8897],{"className":8893},[127],[115,8895],{"className":8896,"style":831},[131],[115,8898,44],{"className":8899},[136],"; average the ",[115,8902,8904],{"className":8903},[118],[115,8905,8907],{"className":8906,"ariaHidden":123},[122],[115,8908,8910,8913],{"className":8909},[127],[115,8911],{"className":8912,"style":605},[131],[115,8914,1503],{"className":8915,"style":1502},[136,137]," scores. Popular values are ",[115,8918,8920],{"className":8919},[118],[115,8921,8923,8941],{"className":8922,"ariaHidden":123},[122],[115,8924,8926,8929,8932,8935,8938],{"className":8925},[127],[115,8927],{"className":8928,"style":605},[131],[115,8930,1503],{"className":8931,"style":1502},[136,137],[115,8933],{"className":8934,"style":563},[224],[115,8936,568],{"className":8937},[567],[115,8939],{"className":8940,"style":563},[224],[115,8942,8944,8947],{"className":8943},[127],[115,8945],{"className":8946,"style":831},[131],[115,8948,1382],{"className":8949},[136],[115,8951,8953],{"className":8952},[118],[115,8954,8956],{"className":8955,"ariaHidden":123},[122],[115,8957,8959,8962],{"className":8958},[127],[115,8960],{"className":8961,"style":831},[131],[115,8963,8965],{"className":8964},[136],"10","; the extreme\n",[115,8968,8970],{"className":8969},[118],[115,8971,8973,8991],{"className":8972,"ariaHidden":123},[122],[115,8974,8976,8979,8982,8985,8988],{"className":8975},[127],[115,8977],{"className":8978,"style":605},[131],[115,8980,1503],{"className":8981,"style":1502},[136,137],[115,8983],{"className":8984,"style":563},[224],[115,8986,568],{"className":8987},[567],[115,8989],{"className":8990,"style":563},[224],[115,8992,8994,8997],{"className":8993},[127],[115,8995],{"className":8996,"style":132},[131],[115,8998,139],{"className":8999,"style":138},[136,137]," is ",[31,9002,9003],{},"leave-one-out cross-validation",[727,9005],{"hash":9006},"cb12c97e4644559e2e005f0928296f8ddbf41d2616eb15ea16a1ed5916b1ef82",[11,9008,9009,9010,9013,9014,9017],{},"One discipline matters above all: never let the test set influence the choice of\nhypothesis. ",[31,9011,9012],{},"Peeking"," ruins the estimate — if you try many settings of a\nlearner's knobs, measure each on the test set, and report the best, then the test\nset has leaked into learning and the reported accuracy is a fiction. To avoid this,\nhold the test set out until every choice is made; to compare models along the way,\ncarve a third ",[31,9015,9016],{},"validation set"," out of the training data.",[731,9019,9021],{"id":9020},"the-learning-curve","The learning curve",[11,9023,9024,9025,9028,9029,9032],{},"Plotting a learner's test accuracy against training-set size gives a ",[31,9026,9027],{},"learning\ncurve",". Accuracy rises as the training set grows — more examples make the right\npattern easier to distinguish from noise — which is why learning curves are also\ncalled ",[996,9030,9031],{},"happy graphs."," The shape says something practical: a curve still climbing\nat the largest available size means more data would help; a curve that has\nflattened means the hypothesis space, not the data, is now the limit.",[727,9034],{"hash":9035},"ef1adddd02fe350956986dbb1c7622764d1818bef3dca016649b63e4acd5455c",[731,9037,9039],{"id":9038},"model-selection-and-occams-razor","Model selection and Occam's razor",[11,9041,9042,9043,9046,9047,9049,9050,9053,9054,9061,9062,9065],{},"Two questions remain: which hypothesis space, and how complex a\nhypothesis within it? Choosing the polynomial degree, or the number of nodes in a\ntree, is ",[31,9044,9045],{},"model selection",", and it splits the job of finding the best hypothesis\ninto two parts — ",[54,9048,9045],{}," fixes the hypothesis space, then ",[54,9051,9052],{},"optimization","\nfinds the best hypothesis in it.",[36,9055,9056],{},[15,9057,8965],{"href":9058,"ariaDescribedBy":9059,"dataFootnoteRef":6,"id":9060},"#user-content-fn-aima-modelsel",[42],"user-content-fnref-aima-modelsel"," A cross-validation wrapper solves\nmodel selection directly: enumerate models from simplest to most complex, and for\neach, use cross-validation to estimate its error. As complexity grows the training\nerror falls monotonically, but the validation error falls and then ",[54,9063,9064],{},"rises"," — the\nU-shaped signature of underfitting giving way to overfitting. Pick the model at the\nbottom of the U.",[727,9067],{"hash":9068},"f92011cbfc209df8af4dfce18b0f81c3eddb69f25d88f39bd8c017fe04b21283",[11,9070,9071,9072,9075,9076,9084,9085,9088],{},"Why prefer the simpler model at all? Because of ",[31,9073,9074],{},"Ockham's razor",": among\nhypotheses consistent with the data, prefer the simplest.",[36,9077,9078],{},[15,9079,9083],{"href":9080,"ariaDescribedBy":9081,"dataFootnoteRef":6,"id":9082},"#user-content-fn-aima-occam",[42],"user-content-fnref-aima-occam","11"," A\ndegree-1 polynomial is simpler than a degree-7 one, and simplicity is a bet on\ngeneralization — a simple hypothesis has fewer ways to have latched onto noise. An\nequivalent, weight-based route to the same preference is ",[31,9086,9087],{},"regularization",":\ninstead of enumerating model sizes, minimize a total cost that adds a complexity\npenalty to the empirical loss,",[115,9090,9092],{"className":9091},[1580],[115,9093,9095],{"className":9094},[118],[115,9096,9098,9129,9160],{"className":9097,"ariaHidden":123},[122],[115,9099,9101,9104,9111,9114,9117,9120,9123,9126],{"className":9100},[127],[115,9102],{"className":9103,"style":153},[131],[115,9105,9107],{"className":9106},[136],[115,9108,9110],{"className":9109},[136,2861],"Cost",[115,9112,158],{"className":9113},[157],[115,9115,609],{"className":9116},[136,137],[115,9118,273],{"className":9119},[272],[115,9121],{"className":9122,"style":563},[224],[115,9124,568],{"className":9125},[567],[115,9127],{"className":9128,"style":563},[224],[115,9130,9132,9135,9142,9145,9148,9151,9154,9157],{"className":9131},[127],[115,9133],{"className":9134,"style":153},[131],[115,9136,9138],{"className":9137},[136],[115,9139,9141],{"className":9140},[136,2861],"EmpLoss",[115,9143,158],{"className":9144},[157],[115,9146,609],{"className":9147},[136,137],[115,9149,273],{"className":9150},[272],[115,9152],{"className":9153,"style":816},[224],[115,9155,2263],{"className":9156},[820],[115,9158],{"className":9159,"style":816},[224],[115,9161,9163,9166,9170,9173,9180,9183,9186,9189],{"className":9162},[127],[115,9164],{"className":9165,"style":153},[131],[115,9167,9169],{"className":9168},[136,137],"λ",[115,9171],{"className":9172,"style":225},[224],[115,9174,9176],{"className":9175},[136],[115,9177,9179],{"className":9178},[136,2861],"Complexity",[115,9181,158],{"className":9182},[157],[115,9184,609],{"className":9185},[136,137],[115,9187,273],{"className":9188},[272],[115,9190,220],{"className":9191},[219],[11,9193,9194,9195,9210,9211,9214,9215,9218,9219,9222],{},"where ",[115,9196,9198],{"className":9197},[118],[115,9199,9201],{"className":9200,"ariaHidden":123},[122],[115,9202,9204,9207],{"className":9203},[127],[115,9205],{"className":9206,"style":605},[131],[115,9208,9169],{"className":9209},[136,137]," trades loss against complexity. The name is apt — it searches for a\nhypothesis that is more ",[54,9212,9213],{},"regular",", less wiggly. There is also a tradeoff between\nthe ",[54,9216,9217],{},"expressiveness"," of a hypothesis space and the ",[54,9220,9221],{},"complexity of finding"," a good\nhypothesis within it: a richer language may let a simple hypothesis fit the data,\nbut searching it can be intractable, which is why most learning sticks to simple\nrepresentations.",[11,9224,9225,9226,9229,9230,9234],{},"This is the foundation: a hypothesis space, a search for a hypothesis that\ngeneralizes, and a discipline for measuring whether it does. What we have not yet\ndone is ",[54,9227,9228],{},"prove"," that generalization is possible, or survey the model families\nbeyond the decision tree. That is the work of the second part,\n",[15,9231,9233],{"href":9232},"\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families","The Theory of Learning and Model Families",",\nwhich opens with the question these measurements leave unanswered: how can we be\nsure a hypothesis will predict well on inputs it has never seen?",[9236,9237,9240,9245],"section",{"className":9238,"dataFootnotes":6},[9239],"footnotes",[63,9241,9244],{"className":9242,"id":42},[9243],"sr-only","Footnotes",[9246,9247,9248,9265,9278,9291,9338,9438,9675,9711,9766,9795,9853],"ol",{},[5139,9249,9251,670,9254,9257,9258],{"id":9250},"user-content-fn-aima-forms",[31,9252,9253],{},"Russell & Norvig",[54,9255,9256],{},"AIMA"," (3rd ed.), Ch. 18 opening and §18.1 — Forms of Learning: an agent learns if it improves on future tasks after observing the world; the three reasons a designer wants learning (unanticipated situations, changing environments, tasks no one can program by hand). ",[15,9259,9264],{"href":9260,"ariaLabel":9261,"className":9262,"dataFootnoteBackref":6},"#user-content-fnref-aima-forms","Back to reference 1",[9263],"data-footnote-backref","↩",[5139,9266,9268,670,9270,9272,9273],{"id":9267},"user-content-fn-aima-feedback",[31,9269,9253],{},[54,9271,9256],{}," (3rd ed.), §18.1 — the three types of feedback: unsupervised (patterns with no labels, e.g. clustering), reinforcement (a reward signal), and supervised (correct outputs supplied). ",[15,9274,9264],{"href":9275,"ariaLabel":9276,"className":9277,"dataFootnoteBackref":6},"#user-content-fnref-aima-feedback","Back to reference 2",[9263],[5139,9279,9281,670,9283,9285,9286],{"id":9280},"user-content-fn-aima-overfit",[31,9282,9253],{},[54,9284,9256],{}," (3rd ed.), §18.2 and §18.3.5 — Fig. 18.1's underfit\u002Fconsistent\u002Foverfit polynomial fits; overfitting grows more likely as the hypothesis space and attribute count grow, less likely as training data grows. ",[15,9287,9264],{"href":9288,"ariaLabel":9289,"className":9290,"dataFootnoteBackref":6},"#user-content-fnref-aima-overfit","Back to reference 3",[9263],[5139,9292,9294,670,9296,9298,9299,9332,9333],{"id":9293},"user-content-fn-aima-dt-rep",[31,9295,9253],{},[54,9297,9256],{}," (3rd ed.), §18.3.1–18.3.2 — the decision-tree representation (interior tests, branch values, leaf outputs), the ",[115,9300,9302],{"className":9301},[118],[115,9303,9305],{"className":9304,"ariaHidden":123},[122],[115,9306,9308,9311,9314,9317,9320,9323,9326,9329],{"className":9307},[127],[115,9309],{"className":9310,"style":605},[131],[115,9312,1022],{"className":9313,"style":1021},[136,137],[115,9315,920],{"className":9316},[136,137],[115,9318,1030],{"className":9319,"style":1029},[136,137],[115,9321,1030],{"className":9322,"style":1029},[136,137],[115,9324,1022],{"className":9325,"style":1021},[136,137],[115,9327,1040],{"className":9328},[136,137],[115,9330,1044],{"className":9331},[136,137]," example and its attributes, and equivalence to a DNF assertion over root-to-leaf paths. ",[15,9334,9264],{"href":9335,"ariaLabel":9336,"className":9337,"dataFootnoteBackref":6},"#user-content-fnref-aima-dt-rep","Back to reference 4",[9263],[5139,9339,9341,670,9343,9345,9346,9416,9417,9432,9433],{"id":9340},"user-content-fn-aima-dt-expr",[31,9342,9253],{},[54,9344,9256],{}," (3rd ed.), §18.3.2 — decision trees express any Boolean function but not always concisely (the majority function); there are ",[115,9347,9349],{"className":9348},[118],[115,9350,9352],{"className":9351,"ariaHidden":123},[122],[115,9353,9355,9358],{"className":9354},[127],[115,9356],{"className":9357,"style":1241},[131],[115,9359,9361,9364],{"className":9360},[136],[115,9362,77],{"className":9363},[136],[115,9365,9367],{"className":9366},[169],[115,9368,9370],{"className":9369},[173],[115,9371,9373],{"className":9372},[178],[115,9374,9376],{"className":9375,"style":1241},[182],[115,9377,9378,9381],{"style":1262},[115,9379],{"className":9380,"style":191},[190],[115,9382,9384],{"className":9383},[195,196,197,198],[115,9385,9387],{"className":9386},[136,198],[115,9388,9390,9393],{"className":9389},[136,198],[115,9391,77],{"className":9392},[136,198],[115,9394,9396],{"className":9395},[169],[115,9397,9399],{"className":9398},[173],[115,9400,9402],{"className":9401},[178],[115,9403,9405],{"className":9404,"style":1290},[182],[115,9406,9407,9410],{"style":1293},[115,9408],{"className":9409,"style":1297},[190],[115,9411,9413],{"className":9412},[195,1301,1302,198],[115,9414,1080],{"className":9415},[136,137,198]," Boolean functions of ",[115,9418,9420],{"className":9419},[118],[115,9421,9423],{"className":9422,"ariaHidden":123},[122],[115,9424,9426,9429],{"className":9425},[127],[115,9427],{"className":9428,"style":1319},[131],[115,9430,1080],{"className":9431},[136,137]," attributes. ",[15,9434,9264],{"href":9435,"ariaLabel":9436,"className":9437,"dataFootnoteBackref":6},"#user-content-fnref-aima-dt-expr","Back to reference 5",[9263],[5139,9439,9441,670,9443,9445,9446,9470,9471,670,9495,9522,9523,9550,9551,9614,9615,9669,9670],{"id":9440},"user-content-fn-aima-entropy",[31,9442,9253],{},[54,9444,9256],{}," (3rd ed.), §18.3.4 — entropy ",[115,9447,9449],{"className":9448},[118],[115,9450,9452],{"className":9451,"ariaHidden":123},[122],[115,9453,9455,9458,9461,9464,9467],{"className":9454},[127],[115,9456],{"className":9457,"style":153},[131],[115,9459,759],{"className":9460,"style":1112},[136,137],[115,9462,158],{"className":9463},[157],[115,9465,1456],{"className":9466,"style":816},[136,137],[115,9468,273],{"className":9469},[272],", the Boolean-entropy shorthand ",[115,9472,9474],{"className":9473},[118],[115,9475,9477],{"className":9476,"ariaHidden":123},[122],[115,9478,9480,9483,9486,9489,9492],{"className":9479},[127],[115,9481],{"className":9482,"style":153},[131],[115,9484,2166],{"className":9485,"style":2165},[136,137],[115,9487,158],{"className":9488},[157],[115,9490,996],{"className":9491,"style":232},[136,137],[115,9493,273],{"className":9494},[272],[115,9496,9498],{"className":9497},[118],[115,9499,9501],{"className":9500,"ariaHidden":123},[122],[115,9502,9504,9507,9513,9516,9519],{"className":9503},[127],[115,9505],{"className":9506,"style":153},[131],[115,9508,9510],{"className":9509},[136],[115,9511,2862],{"className":9512},[136,2861],[115,9514,158],{"className":9515},[157],[115,9517,896],{"className":9518},[136,137],[115,9520,273],{"className":9521},[272],", and the information-gain heuristic ",[115,9524,9526],{"className":9525},[118],[115,9527,9529],{"className":9528,"ariaHidden":123},[122],[115,9530,9532,9535,9541,9544,9547],{"className":9531},[127],[115,9533],{"className":9534,"style":153},[131],[115,9536,9538],{"className":9537},[136],[115,9539,3394],{"className":9540},[136,2861],[115,9542,158],{"className":9543},[157],[115,9545,896],{"className":9546},[136,137],[115,9548,273],{"className":9549},[272],"; the worked ",[115,9552,9554],{"className":9553},[118],[115,9555,9557,9605],{"className":9556,"ariaHidden":123},[122],[115,9558,9560,9563,9569,9572,9575,9578,9581,9584,9587,9590,9593,9596,9599,9602],{"className":9559},[127],[115,9561],{"className":9562,"style":153},[131],[115,9564,9566],{"className":9565},[136],[115,9567,3394],{"className":9568},[136,2861],[115,9570,158],{"className":9571},[157],[115,9573,1061],{"className":9574,"style":1021},[136,137],[115,9576,15],{"className":9577},[136,137],[115,9579,1044],{"className":9580},[136,137],[115,9582,1072],{"className":9583,"style":1071},[136,137],[115,9585,1076],{"className":9586},[136,137],[115,9588,1080],{"className":9589},[136,137],[115,9591,1084],{"className":9592},[136,137],[115,9594,273],{"className":9595},[272],[115,9597],{"className":9598,"style":563},[224],[115,9600,4276],{"className":9601},[567],[115,9603],{"className":9604,"style":563},[224],[115,9606,9608,9611],{"className":9607},[127],[115,9609],{"className":9610,"style":831},[131],[115,9612,4289],{"className":9613},[136]," vs. ",[115,9616,9618],{"className":9617},[118],[115,9619,9621,9660],{"className":9620,"ariaHidden":123},[122],[115,9622,9624,9627,9633,9636,9639,9642,9645,9648,9651,9654,9657],{"className":9623},[127],[115,9625],{"className":9626,"style":153},[131],[115,9628,9630],{"className":9629},[136],[115,9631,3394],{"className":9632},[136,2861],[115,9634,158],{"className":9635},[157],[115,9637,1146],{"className":9638,"style":1021},[136,137],[115,9640,233],{"className":9641,"style":232},[136,137],[115,9643,11],{"className":9644},[136,137],[115,9646,1156],{"className":9647},[136,137],[115,9649,273],{"className":9650},[272],[115,9652],{"className":9653,"style":563},[224],[115,9655,568],{"className":9656},[567],[115,9658],{"className":9659,"style":563},[224],[115,9661,9663,9666],{"className":9662},[127],[115,9664],{"className":9665,"style":831},[131],[115,9667,1346],{"className":9668},[136],". ",[15,9671,9264],{"href":9672,"ariaLabel":9673,"className":9674,"dataFootnoteBackref":6},"#user-content-fnref-aima-entropy","Back to reference 6",[9263],[5139,9676,9678,670,9680,9682,9683,9705,9706],{"id":9677},"user-content-fn-aima-dt-induce",[31,9679,9253],{},[54,9681,9256],{}," (3rd ed.), §18.3.3 and Fig. 18.5 — the greedy divide-and-conquer ",[115,9684,9686],{"className":9685},[118],[115,9687,9689],{"className":9688,"ariaHidden":123},[122],[115,9690,9692,9695],{"className":9691},[127],[115,9693],{"className":9694,"style":1108},[131],[115,9696,9698],{"className":9697},[8378,8379],[115,9699,9701],{"className":9700},[136,4293],[115,9702,9704],{"className":9703},[136],"Decision-Tree-Learning"," algorithm and its four recursive cases; the induced tree is consistent with and simpler than the hand-built one. ",[15,9707,9264],{"href":9708,"ariaLabel":9709,"className":9710,"dataFootnoteBackref":6},"#user-content-fnref-aima-dt-induce","Back to reference 7",[9263],[5139,9712,9714,670,9716,9718,9719,9760,9761],{"id":9713},"user-content-fn-aima-prune",[31,9715,9253],{},[54,9717,9256],{}," (3rd ed.), §18.3.5 — generalization and overfitting; decision-tree pruning via a ",[115,9720,9722],{"className":9721},[118],[115,9723,9725],{"className":9724,"ariaHidden":123},[122],[115,9726,9728,9731],{"className":9727},[127],[115,9729],{"className":9730,"style":8588},[131],[115,9732,9734,9737],{"className":9733},[136],[115,9735,8595],{"className":9736},[136,137],[115,9738,9740],{"className":9739},[169],[115,9741,9743],{"className":9742},[173],[115,9744,9746],{"className":9745},[178],[115,9747,9749],{"className":9748,"style":1336},[182],[115,9750,9751,9754],{"style":1262},[115,9752],{"className":9753,"style":191},[190],[115,9755,9757],{"className":9756},[195,196,197,198],[115,9758,77],{"className":9759},[136,198]," significance test on the null hypothesis of an irrelevant attribute, and why grow-then-prune beats early stopping (the XOR case). ",[15,9762,9264],{"href":9763,"ariaLabel":9764,"className":9765,"dataFootnoteBackref":6},"#user-content-fnref-aima-prune","Back to reference 8",[9263],[5139,9767,9769,670,9771,9773,9774,9789,9790],{"id":9768},"user-content-fn-aima-eval",[31,9770,9253],{},[54,9772,9256],{}," (3rd ed.), §18.4 — the stationarity\u002Fi.i.d. assumption, error rate, holdout vs. ",[115,9775,9777],{"className":9776},[118],[115,9778,9780],{"className":9779,"ariaHidden":123},[122],[115,9781,9783,9786],{"className":9782},[127],[115,9784],{"className":9785,"style":605},[131],[115,9787,1503],{"className":9788,"style":1502},[136,137],"-fold and leave-one-out cross-validation, peeking, and the training\u002Fvalidation\u002Ftest discipline; the learning curve of §18.3 (Fig. 18.7). ",[15,9791,9264],{"href":9792,"ariaLabel":9793,"className":9794,"dataFootnoteBackref":6},"#user-content-fnref-aima-eval","Back to reference 9",[9263],[5139,9796,9798,670,9800,9802,9803,9669,9848],{"id":9797},"user-content-fn-aima-modelsel",[31,9799,9253],{},[54,9801,9256],{}," (3rd ed.), §18.4.1 and §18.4.3 — model selection as a wrapper over a size parameter, the U-shaped validation curve (Fig. 18.9), and regularization as minimizing ",[115,9804,9806],{"className":9805},[118],[115,9807,9809,9830],{"className":9808,"ariaHidden":123},[122],[115,9810,9812,9815,9821,9824,9827],{"className":9811},[127],[115,9813],{"className":9814,"style":1108},[131],[115,9816,9818],{"className":9817},[136],[115,9819,9141],{"className":9820},[136,2861],[115,9822],{"className":9823,"style":816},[224],[115,9825,2263],{"className":9826},[820],[115,9828],{"className":9829,"style":816},[224],[115,9831,9833,9836,9839,9842],{"className":9832},[127],[115,9834],{"className":9835,"style":623},[131],[115,9837,9169],{"className":9838},[136,137],[115,9840],{"className":9841,"style":225},[224],[115,9843,9845],{"className":9844},[136],[115,9846,9179],{"className":9847},[136,2861],[15,9849,9264],{"href":9850,"ariaLabel":9851,"className":9852,"dataFootnoteBackref":6},"#user-content-fnref-aima-modelsel","Back to reference 10",[9263],[5139,9854,9856,670,9858,9860,9861],{"id":9855},"user-content-fn-aima-occam",[31,9857,9253],{},[54,9859,9256],{}," (3rd ed.), §18.2 and §18.3 — Ockham's razor (prefer the simplest consistent hypothesis) and the expressiveness-versus-search tradeoff in choosing a hypothesis space. ",[15,9862,9264],{"href":9863,"ariaLabel":9864,"className":9865,"dataFootnoteBackref":6},"#user-content-fnref-aima-occam","Back to reference 11",[9263],[9867,9868,9869],"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":8278,"depth":8278,"links":9871},[9872,9875,9879,9883],{"id":65,"depth":8278,"text":66,"children":9873},[9874],{"id":733,"depth":8284,"text":734},{"id":868,"depth":8278,"text":869,"children":9876},[9877,9878],{"id":1386,"depth":8284,"text":1387},{"id":8470,"depth":8284,"text":8471},{"id":8674,"depth":8278,"text":8675,"children":9880},[9881,9882],{"id":9020,"depth":8284,"text":9021},{"id":9038,"depth":8284,"text":9039},{"id":42,"depth":8278,"text":9244},[],"computer-science","Every agent we have built so far came with its competence fully assembled. The\nsearch agent was handed a\nsuccessor function and a heuristic; the\nlogical agent\nwas handed a knowledge base; the\ndecision-theoretic agent\nwas handed a transition model and a utility. In each case a human sat down in\nadvance and wrote the machinery down. An agent learns when it improves its\nperformance on future tasks after making observations about the world, and there\nare three reasons a designer would want that.1 The designer cannot\nanticipate every situation the agent will face; the world changes, so a fixed\nprogram goes stale; and for some tasks — recognizing a face, driving a car — no\nhuman knows how to write the rules in the first place, so the only route is to\nlet the agent find them from data.",false,"md",{"moduleNumber":8296,"lessonNumber":8272,"order":9890},501,"Learning",true,"\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples",[],"---\ntitle: Learning from Examples\nmodule: Learning\nmoduleNumber: 5\nlessonNumber: 1\norder: 501\nsummary: >\n  An agent that improves with experience does not need its designer to anticipate\n  every situation. Inductive learning takes that ambition and narrows it to one\n  tractable problem: from labelled input-output pairs, recover a function that\n  predicts the output for inputs never seen. This first part builds the foundation\n  around a single organizing question — generalization — through decision trees and\n  information gain, and the training\u002Fvalidation\u002Ftest discipline for evaluating and\n  choosing hypotheses. A second part takes up the theory of learning and the main\n  model families.\ntopics: [Learning]\nsources:\n  - book: AIMA\n    ref: \"Ch. 18 — Learning from Examples; §18.1 Forms of Learning; §18.2 Supervised Learning\"\n  - book: AIMA\n    ref: \"§18.3 Learning Decision Trees; §18.4 Evaluating and Choosing the Best Hypothesis\"\n---\n\n\nEvery agent we have built so far came with its competence fully assembled. The\n[search agent](\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search) was handed a\nsuccessor function and a heuristic; the\n[logical agent](\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic)\nwas handed a knowledge base; the\n[decision-theoretic agent](\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions)\nwas handed a transition model and a utility. In each case a human sat down in\nadvance and wrote the machinery down. An agent **learns** when it improves its\nperformance on future tasks after making observations about the world, and there\nare three reasons a designer would want that.[^aima-forms] The designer cannot\nanticipate every situation the agent will face; the world changes, so a fixed\nprogram goes stale; and for some tasks — recognizing a face, driving a car — no\nhuman knows how to write the rules in the first place, so the only route is to\nlet the agent find them from data.\n\nThis chapter narrows learning to one class of problem, restrictive-looking but\nvery general: **from a collection of input-output pairs, learn\na function that predicts the output for new inputs**. That is _inductive_ learning\n— inferring a general rule from specific examples — and its central difficulty is\nnot fitting the examples you have but predicting the ones you do not. Everything\nbelow is machinery for that one problem, and the recurring word is\n**generalization**.\n\n## Supervised learning as function approximation\n\nLearning divides by the kind of feedback the data carries.[^aima-feedback] In\n**unsupervised learning** the agent sees inputs with no labels and must find\nstructure on its own (the canonical task is _clustering_). In **reinforcement\nlearning** the agent acts and receives a scalar reward, and must decide which of\nits past actions earned it — the subject of\n[reinforcement learning](\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning).\nThe setting of this lesson is **supervised learning**, where the data supplies the\ncorrect output for each input.\n\n> **Definition (Supervised learning).** Given a **training set** of $N$\n> input-output pairs $(x_1, y_1), (x_2, y_2), \\ldots, (x_N, y_N)$, where each\n> $y_j$ was generated by an unknown function $y = f(x)$, discover a function $h$\n> that approximates the true $f$. The function $h$ is a **hypothesis**; learning\n> is a search through the space of possible hypotheses for one that predicts well\n> on inputs beyond the training set.\n\nThe output type names the problem. When $y$ ranges over a finite set of values\n(_sunny_, _cloudy_, _rainy_) the problem is **classification** — Boolean or binary\nif there are only two values. When $y$ is a number (tomorrow's temperature) the\nproblem is **regression**. We measure a hypothesis not on the data it was trained\non but on a separate **test set** of examples it has never seen; a hypothesis\n**generalizes** well if it predicts $y$ accurately for those novel inputs.\n\n$$\n% caption: Supervised learning as function approximation: a training set sampled\n% from an unknown $f$ is fed to a learner, which returns a hypothesis $h$; the\n% test set, drawn from the same distribution, measures how well $h$ generalizes.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize,\n  box\u002F.style={draw, minimum width=22mm, minimum height=11mm, align=center}]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\node[box] (train) at (0,0)   {training set\\\\(x, y) pairs};\n  \\node[box, draw=acc, text=acc, thick] (learn) at (3.9,0) {learner};\n  \\node[box] (hyp)   at (7.8,0)  {hypothesis h};\n  \\node[box] (test)  at (7.8,-2.1) {test set\\\\(new x)};\n  \\node[box] (err)   at (3.9,-2.1) {test error};\n  \\draw[->, acc, thick] (train) -- (learn);\n  \\draw[->, acc, thick] (learn) -- (hyp);\n  \\draw[->, thick] (hyp) -- (test) node[midway, right, font=\\scriptsize] {predict};\n  \\draw[->, thick] (test) -- (err) node[midway, above, font=\\scriptsize, xshift=-5mm] {compare to true y};\n\\end{tikzpicture}\n$$\n\n### The hypothesis space and the generalization problem\n\nWe never search all functions; we fix a **hypothesis space** $\\mathcal{H}$ and\nsearch within it. For fitting a curve to points in the plane, $\\mathcal{H}$ might\nbe the polynomials up to some degree. A hypothesis is **consistent** if it agrees\nwith all the training data. The trouble is that consistency is cheap and\nmisleading: given $N$ points, a polynomial of degree $N-1$ passes through every\none of them exactly, yet wiggles wildly between them and predicts nonsense off the\ndata. A straight line may miss every point slightly and still predict far better.\n\n$$\n% caption: The bias-variance tension. Left, a straight line underfits — too rigid\n% to follow the trend. Center, a moderate curve fits the signal and generalizes.\n% Right, a high-degree polynomial overfits — it threads every point but swings\n% wildly between them.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{red}{HTML}{C0392B}\n  % --- panel 1: underfit ---\n  \\begin{scope}\n    \\draw[->, black] (-0.2,0) -- (3.3,0) node[right, font=\\scriptsize, black] {x};\n    \\draw[->, black] (0,-0.2) -- (0,2.6) node[above, font=\\scriptsize, black] {y};\n    \\foreach \\p in {(0.4,0.5),(0.8,1.3),(1.2,0.9),(1.6,1.8),(2.0,1.4),(2.4,2.2),(2.8,1.9)}\n      \\fill[black] \\p circle (1.6pt);\n    \\draw[acc, very thick] (0.2,0.7) -- (3.0,2.0);\n    \\node[font=\\scriptsize, anchor=north] at (1.5,-0.35) {too simple};\n  \\end{scope}\n  % --- panel 2: good fit ---\n  \\begin{scope}[xshift=4.6cm]\n    \\draw[->, black] (-0.2,0) -- (3.3,0) node[right, font=\\scriptsize, black] {x};\n    \\draw[->, black] (0,-0.2) -- (0,2.6) node[above, font=\\scriptsize, black] {y};\n    \\foreach \\p in {(0.4,0.5),(0.8,1.3),(1.2,0.9),(1.6,1.8),(2.0,1.4),(2.4,2.2),(2.8,1.9)}\n      \\fill[black] \\p circle (1.6pt);\n    \\draw[acc, very thick] (0.2,0.6) .. controls (1.0,1.6) and (1.8,1.2) .. (3.0,2.1);\n    \\node[font=\\scriptsize, anchor=north] at (1.5,-0.35) {just right};\n  \\end{scope}\n  % --- panel 3: overfit ---\n  \\begin{scope}[xshift=9.2cm]\n    \\draw[->, black] (-0.2,0) -- (3.3,0) node[right, font=\\scriptsize, black] {x};\n    \\draw[->, black] (0,-0.2) -- (0,2.6) node[above, font=\\scriptsize, black] {y};\n    \\foreach \\p in {(0.4,0.5),(0.8,1.3),(1.2,0.9),(1.6,1.8),(2.0,1.4),(2.4,2.2),(2.8,1.9)}\n      \\fill[black] \\p circle (1.6pt);\n    \\draw[red, very thick] (0.4,0.5) .. controls (0.6,2.3) and (0.7,-0.2) ..\n      (0.8,1.3) .. controls (1.0,2.4) and (1.1,0.2) ..\n      (1.2,0.9) .. controls (1.4,2.2) and (1.5,0.4) ..\n      (1.6,1.8) .. controls (1.8,0.4) and (1.9,2.5) ..\n      (2.0,1.4) .. controls (2.2,2.6) and (2.3,0.6) ..\n      (2.4,2.2) .. controls (2.6,0.5) and (2.7,2.6) .. (2.8,1.9);\n    \\node[font=\\scriptsize, anchor=north] at (1.5,-0.35) {too complex};\n  \\end{scope}\n\\end{tikzpicture}\n$$\n\nA hypothesis space too simple to\ncapture the signal **underfits**; one flexible enough to chase the noise\n**overfits**. Between them sits the fit that captures the trend and ignores the\nnoise, and choosing it is the problem of the rest of the lesson. This tradeoff —\ncomplex hypotheses that fit the training data versus simpler ones that generalize\nbetter — recurs below, along with a classical answer, Ockham's razor.\n\n> **Definition (Overfitting).** A hypothesis overfits when it fits the training\n> examples, including their noise and accidents, so closely that its test error is\n> worse than a simpler hypothesis would achieve. Overfitting grows more likely as\n> the hypothesis space and the number of input attributes grow, and less likely as\n> the training set grows.[^aima-overfit]\n\n## Decision trees\n\nA **decision tree** is one of the simplest and most successful hypothesis spaces.\nIt represents a function that takes a vector of attribute values and returns a\nsingle output by running a sequence of tests. Each internal node tests one\nattribute $A_i$; each branch out of that node is labelled with a value of $A_i$;\neach leaf specifies the output to return.[^aima-dt-rep] The representation is\nnatural for humans — many \"How To\" manuals are one long decision tree — and, for\nBoolean classification, a tree is logically equivalent to the assertion that the\ngoal is true exactly when the input satisfies one of the paths leading to a\n_true_ leaf.\n\nThe running example is deciding whether to wait for a table at a restaurant. The\ngoal predicate $WillWait$ is a Boolean function of ten attributes, among them\n$Patrons$ (how full the restaurant is: _None_, _Some_, _Full_), $Hungry$,\n$Type$ (French, Italian, Thai, or burger), $Price$, and $WaitEstimate$.\n\n$$\n% caption: A decision tree for the restaurant problem. Interior nodes test one\n% attribute; branches are attribute values; leaves are the WillWait verdict. An\n% example with Patrons = Full and WaitEstimate = 0-10 is classified Yes.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize,\n  test\u002F.style={draw, minimum height=7mm, inner sep=3pt},\n  leaf\u002F.style={draw, minimum width=8mm, minimum height=6mm, inner sep=2pt}]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\node[test, draw=acc, text=acc] (pat) at (5,4.4) {Patrons?};\n  \\node[leaf] (no1)  at (2.2,3.2) {No};\n  \\node[leaf] (yes1) at (4.0,3.2) {Yes};\n  \\node[test] (wait) at (7.4,3.2) {WaitEstimate?};\n  \\node[leaf] (no2)  at (4.2,1.9) {No};\n  \\node[test] (alt)  at (6.1,1.9) {Alternate?};\n  \\node[test] (hun)  at (8.7,1.9) {Hungry?};\n  \\node[leaf] (yes2) at (10.6,1.9) {Yes};\n  \\node[leaf] (no3)  at (5.4,0.6) {No};\n  \\node[leaf] (yes3) at (6.8,0.6) {Yes};\n  \\node[leaf] (yes4) at (8.1,0.6) {Yes};\n  \\node[leaf] (no4)  at (9.4,0.6) {No};\n  \\draw[->] (pat) -- (no1)  node[midway, above left, font=\\scriptsize, inner sep=1pt] {None};\n  \\draw[->] (pat) -- (yes1) node[midway, left, font=\\scriptsize, inner sep=1pt] {Some};\n  \\draw[->] (pat) -- (wait) node[midway, above right, font=\\scriptsize, inner sep=1pt] {Full};\n  \\draw[->] (wait) -- (no2)  node[midway, above left, font=\\scriptsize, inner sep=1pt] {$>$60};\n  \\draw[->] (wait) -- (alt)  node[midway, left, font=\\scriptsize, inner sep=1pt] {30-60};\n  \\draw[->] (wait) -- (hun)  node[midway, right, font=\\scriptsize, inner sep=1pt] {10-30};\n  \\draw[->] (wait) -- (yes2) node[midway, above right, font=\\scriptsize, inner sep=1pt] {0-10};\n  \\draw[->] (alt) -- (no3)  node[midway, left, font=\\scriptsize, inner sep=1pt] {No};\n  \\draw[->] (alt) -- (yes3) node[midway, right, font=\\scriptsize, inner sep=1pt] {Yes};\n  \\draw[->] (hun) -- (yes4) node[midway, left, font=\\scriptsize, inner sep=1pt] {No};\n  \\draw[->] (hun) -- (no4)  node[midway, right, font=\\scriptsize, inner sep=1pt] {Yes};\n\\end{tikzpicture}\n$$\n\nDecision trees can represent any Boolean function — but not always concisely. The\nmajority function, true when more than half its inputs are, needs an exponentially\nlarge tree. And the space is enormous: there are $2^{2^n}$ Boolean functions of\n$n$ attributes, so with the ten attributes of the restaurant problem we are\nchoosing among roughly $10^{308}$ functions.[^aima-dt-expr] Finding a good tree in\nthat space requires a heuristic.\n\n### Learning a tree by information gain\n\nWe want a tree that is consistent with the examples and as small as possible —\nsmall trees are shallow, which means few tests and better generalization. Finding\nthe _smallest_ consistent tree is intractable, but a greedy heuristic finds a\nsmall one: **always test the most important attribute first**, where \"most\nimportant\" means the one that makes the most difference to the classification. A\ngood split leaves subsets that are as close to all-positive or all-negative as\npossible; a useless split leaves subsets with the same mix as the parent.\n\n$$\n% caption: Splitting the 12 restaurant examples. Testing Type (left) leaves four\n% subsets each still evenly split between positive and negative — no progress.\n% Testing Patrons (right) isolates a pure-negative subset (None) and a\n% pure-positive subset (Some), a far better split.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize,\n  pool\u002F.style={draw, minimum width=13mm, minimum height=6mm, align=center, font=\\scriptsize},\n  leaf\u002F.style={draw, minimum width=8mm, minimum height=5mm, inner sep=2pt, font=\\scriptsize}]\n  \\definecolor{acc}{HTML}{2348F2}\n  % --- left: Type ---\n  \\node[pool] (typ) at (0,3.0) {Type?};\n  \\node[pool] (t1) at (-2.4,1.4) {1 pos\\\\1 neg};\n  \\node[pool] (t2) at (-0.8,1.4) {1 pos\\\\1 neg};\n  \\node[pool] (t3) at (0.8,1.4)  {2 pos\\\\2 neg};\n  \\node[pool] (t4) at (2.4,1.4)  {2 pos\\\\2 neg};\n  \\draw[->] (typ) -- (t1) node[midway, above left, font=\\tiny, inner sep=0.5pt] {French};\n  \\draw[->] (typ) -- (t2) node[pos=0.35, left, font=\\tiny, inner sep=0.5pt] {Italian};\n  \\draw[->] (typ) -- (t3) node[pos=0.35, right, font=\\tiny, inner sep=0.5pt] {Thai};\n  \\draw[->] (typ) -- (t4) node[midway, above right, font=\\tiny, inner sep=0.5pt] {Burger};\n  \\node[font=\\scriptsize, text=black] at (0,0.5) {gain = 0 bits};\n  % --- right: Patrons ---\n  \\begin{scope}[xshift=7.2cm]\n    \\node[pool, draw=acc, text=acc] (pat) at (0,3.0) {Patrons?};\n    \\node[leaf] (p1) at (-2.0,1.4) {No};\n    \\node[leaf] (p2) at (0,1.4)    {Yes};\n    \\node[pool] (p3) at (2.1,1.4)  {2 pos\\\\4 neg};\n    \\draw[->] (pat) -- (p1) node[midway, above left, font=\\tiny, inner sep=0.5pt] {None};\n    \\draw[->] (pat) -- (p2) node[pos=0.4, left, font=\\tiny, inner sep=0.5pt] {Some};\n    \\draw[->] (pat) -- (p3) node[midway, above right, font=\\tiny, inner sep=0.5pt] {Full};\n    \\node[font=\\scriptsize, text=acc] at (0,0.5) {gain = 0.541 bits};\n  \\end{scope}\n\\end{tikzpicture}\n$$\n\nTo make \"most important\" precise we borrow **entropy** from information\ntheory.[^aima-entropy] Entropy measures the uncertainty of a random variable;\nacquiring information reduces it. A variable with one certain value has entropy\nzero; a fair coin has entropy $1$ bit. For a variable $V$ with values $v_k$\noccurring with probability $P(v_k)$,\n\n$$\nH(V) \\;=\\; \\sum_k P(v_k) \\log_2 \\frac{1}{P(v_k)} \\;=\\; -\\sum_k P(v_k)\\log_2 P(v_k).\n$$\n\nFor a Boolean variable that is true with probability $q$, write\n$B(q) = -\\bigl(q\\log_2 q + (1-q)\\log_2(1-q)\\bigr)$. A training set with $p$\npositive and $n$ negative examples has goal entropy $B\\bigl(\\tfrac{p}{p+n}\\bigr)$.\nAn attribute $A$ with $d$ values splits the set into subsets $E_1, \\ldots, E_d$,\nwhere subset $E_k$ has $p_k$ positive and $n_k$ negative examples. The expected\nentropy remaining after testing $A$ is a weighted average over the branches,\n\n$$\n\\mathit{Remainder}(A) \\;=\\; \\sum_{k=1}^{d} \\frac{p_k + n_k}{p+n}\\, B\\!\\left(\\frac{p_k}{p_k+n_k}\\right),\n$$\n\nand the **information gain** from testing $A$ is the expected reduction in entropy:\n\n$$\n\\mathit{Gain}(A) \\;=\\; B\\!\\left(\\frac{p}{p+n}\\right) - \\mathit{Remainder}(A).\n$$\n\n> **Definition (Information gain).** The expected reduction in goal entropy that\n> results from testing an attribute. The greedy tree learner splits, at each node,\n> on the attribute of maximum information gain — the attribute that most reduces\n> the uncertainty about the classification.\n\nThe restaurant set has $p = n = 6$, so the goal entropy is $B(0.5) = 1$ bit.\nWorking out the two splits above confirms the intuition. $Patrons$ carves off a\npure-_No_ subset (_None_) and a pure-_Yes_ subset (_Some_), leaving only _Full_\nmixed, and gains most of that bit:\n\n$$\n\\mathit{Gain}(Patrons) = 1 - \\left[\\tfrac{2}{12}B(\\tfrac{0}{2}) + \\tfrac{4}{12}B(\\tfrac{4}{4}) + \\tfrac{6}{12}B(\\tfrac{2}{6})\\right] \\approx 0.541 \\text{ bits.}\n$$\n\n$Type$, by contrast, leaves four subsets each evenly split, and gains nothing:\n\n$$\n\\mathit{Gain}(Type) = 1 - \\left[\\tfrac{2}{12}B(\\tfrac{1}{2}) + \\tfrac{2}{12}B(\\tfrac{1}{2}) + \\tfrac{4}{12}B(\\tfrac{2}{4}) + \\tfrac{4}{12}B(\\tfrac{2}{4})\\right] = 0 \\text{ bits.}\n$$\n\n#### Carrying the $Patrons$ computation all the way through\n\nDeriving the $0.541$ in full shows how the entropy formula is used. The twelve\nrestaurant examples split under $Patrons$ into three branches:\n\n- **None** (2 examples): both are _No_, so $0$ positive, $2$ negative. Its Boolean\n  entropy is $B(0\u002F2) = B(0) = 0$ — a pure subset carries no uncertainty.\n- **Some** (4 examples): all four are _Yes_, so $4$ positive, $0$ negative.\n  $B(4\u002F4) = B(1) = 0$ — pure again.\n- **Full** (6 examples): $2$ positive, $4$ negative. Here\n  $B(2\u002F6) = B(1\u002F3) = -\\big(\\tfrac13\\log_2\\tfrac13 + \\tfrac23\\log_2\\tfrac23\\big)$.\n\nEvaluate that last term. $\\log_2\\tfrac13 = -\\log_2 3 = -1.585$ and\n$\\log_2\\tfrac23 = 1 - \\log_2 3 = -0.585$, so\n\n$$\nB(\\tfrac13) = -\\big(\\tfrac13(-1.585) + \\tfrac23(-0.585)\\big) = 0.528 + 0.390 = 0.918 \\text{ bits.}\n$$\n\nNow weight each branch by its share of the twelve examples. _None_ and _Some_\ncontribute nothing because their entropies are zero; only _Full_ survives, with\nweight $6\u002F12 = \\tfrac12$:\n\n$$\n\\mathit{Remainder}(Patrons) = \\tfrac{2}{12}(0) + \\tfrac{4}{12}(0) + \\tfrac{6}{12}(0.918) = 0.459 \\text{ bits.}\n$$\n\nThe goal entropy before the split was $B(6\u002F12) = B(0.5) = 1$ bit, so\n\n$$\n\\mathit{Gain}(Patrons) = 1 - 0.459 = 0.541 \\text{ bits.}\n$$\n\nCompare this against a middling attribute to see the ranking the learner uses.\n$Hungry$ splits the twelve into _Yes_ (5 pos, 2 neg) and _No_ (1 pos, 4 neg):\n\n$$\n\\mathit{Remainder}(Hungry) = \\tfrac{7}{12}B(\\tfrac57) + \\tfrac{5}{12}B(\\tfrac15)\n= \\tfrac{7}{12}(0.863) + \\tfrac{5}{12}(0.722) = 0.804 \\text{ bits,}\n$$\n\nfor a gain of $1 - 0.804 = 0.196$ bits — real, but less than half of $Patrons$'s.\nThe greedy learner therefore ranks the candidate root attributes\n$Patrons\\;(0.541) > Hungry\\;(0.196) > \\cdots > Type\\;(0)$ and tests $Patrons$ first.\n\n$$\n% caption: Information gain of four candidate root attributes on the 12 restaurant\n% examples. Patrons (0.541 bits) wins because it isolates two pure subsets; Type\n% (0 bits) loses because every branch keeps the parent's even split. The greedy\n% learner tests the highest bar first.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{red}{HTML}{C0392B}\n  \\draw[->, black] (0,0) -- (0,3.4) node[above, black, font=\\scriptsize] {gain (bits)};\n  \\draw[black] (0,0) -- (7.2,0);\n  % gridline at 0.541\n  \\node[anchor=east, black, font=\\scriptsize] at (-0.08,3.0) {0.541};\n  \\draw[black, dashed] (0,3.0) -- (7.2,3.0);\n  % bars: scale 0.541 -> 3.0, so factor 5.545\n  \\fill[acc!20, draw=acc] (0.6,0) rectangle (1.8,3.0);\n  \\node[acc, anchor=south, font=\\scriptsize] at (1.2,3.05) {Patrons};\n  \\node[anchor=north, font=\\scriptsize] at (1.2,-0.05) {0.541};\n  \\fill[black, draw=black] (2.4,0) rectangle (3.6,1.09);\n  \\node[anchor=south, font=\\scriptsize, text=black] at (3.0,1.1) {Hungry};\n  \\node[anchor=north, font=\\scriptsize] at (3.0,-0.05) {0.196};\n  \\fill[black, draw=black] (4.2,0) rectangle (5.4,0.42);\n  \\node[anchor=south, font=\\scriptsize, text=black] at (4.8,0.45) {Rain};\n  \\node[anchor=north, font=\\scriptsize] at (4.8,-0.05) {0.076};\n  \\fill[red!14, draw=red] (6.0,0) rectangle (7.2,0.06);\n  \\node[red, anchor=south, font=\\scriptsize] at (6.6,0.1) {Type};\n  \\node[anchor=north, font=\\scriptsize] at (6.6,-0.05) {0};\n\\end{tikzpicture}\n$$\n\nSo $Patrons$ has the maximum gain and is chosen as the root. After splitting, each\nnon-pure branch becomes a smaller decision-tree problem with one fewer attribute,\nsolved recursively. The full algorithm handles four cases: a pure subset returns\nits class; a mixed subset splits on the best remaining attribute; an empty subset\nreturns the plurality vote of the parent's examples; and a subset with no\nattributes left but mixed labels (noise, or an unobservable distinction) returns\nits own plurality vote.[^aima-dt-induce]\n\n```algorithm\ncaption: $\\textsc{Decision-Tree-Learning}(examples, attributes, parent)$ — greedy induction\nif $examples$ is empty then\n  return $\\textsc{Plurality-Value}(parent)$\nelse if all $examples$ have the same classification then\n  return that classification\nelse if $attributes$ is empty then\n  return $\\textsc{Plurality-Value}(examples)$\nelse\n  $A \\gets \\arg\\max_{a \\in attributes} \\textsc{Importance}(a, examples)$\n  $tree \\gets$ a new decision tree with root test $A$\n  for each value $v_k$ of $A$ do\n    $exs \\gets \\{e \\in examples : e.A = v_k\\}$\n    $subtree \\gets \\textsc{Decision-Tree-Learning}(exs, attributes - A, examples)$\n    add a branch to $tree$ with label $(A = v_k)$ and subtree $subtree$\n  return $tree$\n```\n\nHere $\\textsc{Importance}$ is information gain and $\\textsc{Plurality-Value}$\nreturns the most common output, breaking ties at random. Run on the 12-example\ntraining set, the algorithm produces a tree that is consistent with the data and\n_simpler_ than the tree a human wrote — it never tests $Price$ or $Type$ at all,\nbecause it can classify every example without them. That is the algorithm working\nas intended: it fits the examples, not the \"true\" function, and among consistent\ntrees it prefers the small one.\n\n### Overfitting and pruning\n\nLeft unchecked, the greedy learner grows a large tree whenever it finds any\npattern in the input, even when there is no pattern to find. Give it dice rolls\nlabelled by the die's color, weight, and the time of the roll, and if two fair\nrolls happen to come up six, it will build a path predicting six from those\nirrelevant attributes. To address this, **decision tree pruning** grows the full\ntree, then eliminates nodes that are not clearly relevant.[^aima-prune]\n\nPruning works from the bottom. Consider a test node all of whose descendants are\nleaves. If the attribute it tests appears to be irrelevant — detecting only noise\n— replace the whole node with a single leaf. How do we tell? An irrelevant\nattribute splits a set of $p$ positive and $n$ negative examples into subsets\nwhose positive fractions are all close to the parent's $p\u002F(p+n)$, so its\ninformation gain is close to zero. A **significance test** makes \"close to zero\"\nprecise: assume the null hypothesis that the attribute is irrelevant, compute how\nfar the observed split deviates from the even split that null predicts, and prune\nunless the deviation is statistically unlikely (the standard $\\chi^2$ test at the\n5% level). This is **$\\chi^2$ pruning**, and pruned trees are both more accurate\nin the presence of noise and smaller, hence easier for a person to read.\n\nGrowing then pruning beats **early stopping** — halting growth when no attribute\nlooks good enough. Early stopping is fooled by attributes that are useless alone\nbut informative in combination, like the two inputs of an XOR: neither has any\ngain at the root, so early stopping quits, but splitting on either one exposes an\ninformative split at the next level. Grow-then-prune sees the whole tree first and\nkeeps such structure.\n\n## Evaluating a hypothesis\n\nTo choose among hypotheses we need to measure how well each will predict the\n_future_. The **stationarity assumption** makes \"future\" meaningful: examples are\ndrawn independently from a probability distribution that does not change over time\n— the examples are **independent and identically distributed (i.i.d.)**. Without\nsome such link between past and future, no prediction is justified.[^aima-eval]\n\nThe **error rate** of a hypothesis is the proportion of examples it misclassifies,\n$h(x) \\neq y$. A low error rate on the training set proves nothing — a professor\nknows an exam will not measure students who have already seen the questions. To\nestimate how $h$ generalizes we test it on held-out examples. The simplest\nprotocol splits the data into a **training set**, which the learner sees, and a\n**test set**, which measures the final hypothesis. But a single split wastes data:\nreserve half for testing and you train on half as much; reserve only a tenth and a\nstatistical accident in that tenth can give a bad estimate.\n\n**$k$-fold cross-validation** squeezes more from the same data by letting every\nexample serve as both training and test data. Split the data into $k$ equal folds;\nrun $k$ rounds, each holding out one fold as the test set and training on the other\n$k-1$; average the $k$ scores. Popular values are $k = 5$ or $10$; the extreme\n$k = N$ is **leave-one-out cross-validation**.\n\n$$\n% caption: Five-fold cross-validation. In each round a different fold (dark) is\n% the held-out test set and the rest (light) is training data; the five test\n% scores are averaged so every example is tested exactly once.\n\\begin{tikzpicture}[>=stealth, font=\\scriptsize]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{red}{HTML}{C0392B}\n  \\foreach \\r in {0,1,2,3,4} {\n    \\pgfmathsetmacro\\yy{-\\r*0.85}\n    \\foreach \\c in {0,1,2,3,4} {\n      \\ifnum\\c=\\r\n        \\fill[red!25] (\\c*1.15, \\yy) rectangle ++(1.05,0.6);\n        \\draw[red, thick] (\\c*1.15, \\yy) rectangle ++(1.05,0.6);\n      \\else\n        \\fill[acc!12] (\\c*1.15, \\yy) rectangle ++(1.05,0.6);\n        \\draw[acc] (\\c*1.15, \\yy) rectangle ++(1.05,0.6);\n      \\fi\n    }\n    \\node[anchor=east, font=\\scriptsize] at (-0.25, \\yy+0.3) {round \\the\\numexpr\\r+1\\relax};\n  }\n  \\node[font=\\scriptsize, text=red, anchor=west] at (6.1,-1.4) {test fold};\n  \\node[font=\\scriptsize, text=acc, anchor=west] at (6.1,-1.9) {training folds};\n\\end{tikzpicture}\n$$\n\nOne discipline matters above all: never let the test set influence the choice of\nhypothesis. **Peeking** ruins the estimate — if you try many settings of a\nlearner's knobs, measure each on the test set, and report the best, then the test\nset has leaked into learning and the reported accuracy is a fiction. To avoid this,\nhold the test set out until every choice is made; to compare models along the way,\ncarve a third **validation set** out of the training data.\n\n### The learning curve\n\nPlotting a learner's test accuracy against training-set size gives a **learning\ncurve**. Accuracy rises as the training set grows — more examples make the right\npattern easier to distinguish from noise — which is why learning curves are also\ncalled \"happy graphs.\" The shape says something practical: a curve still climbing\nat the largest available size means more data would help; a curve that has\nflattened means the hypothesis space, not the data, is now the limit.\n\n$$\n% caption: A learning curve. Test accuracy rises with training-set size and then\n% flattens as the hypothesis space, rather than the amount of data, becomes the\n% binding constraint.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\draw[->, black] (0,0) -- (6.4,0) node[right, font=\\scriptsize, black] {training set size};\n  \\draw[->, black] (0,0) -- (0,3.4) node[above, font=\\scriptsize, black] {test accuracy};\n  \\node[font=\\scriptsize, anchor=east] at (-0.05,3.0) {1.0};\n  \\node[font=\\scriptsize, anchor=east] at (-0.05,1.5) {0.5};\n  \\draw[acc, very thick] (0.2,1.2)\n    .. controls (0.9,2.6) and (1.6,2.75) .. (2.6,2.85)\n    .. controls (3.8,2.95) and (5.0,2.98) .. (6.0,3.0);\n  \\node[acc, anchor=south west, font=\\scriptsize] at (4.0,2.55) {plateau};\n\\end{tikzpicture}\n$$\n\n### Model selection and Occam's razor\n\nTwo questions remain: which hypothesis space, and how complex a\nhypothesis within it? Choosing the polynomial degree, or the number of nodes in a\ntree, is **model selection**, and it splits the job of finding the best hypothesis\ninto two parts — _model selection_ fixes the hypothesis space, then _optimization_\nfinds the best hypothesis in it.[^aima-modelsel] A cross-validation wrapper solves\nmodel selection directly: enumerate models from simplest to most complex, and for\neach, use cross-validation to estimate its error. As complexity grows the training\nerror falls monotonically, but the validation error falls and then _rises_ — the\nU-shaped signature of underfitting giving way to overfitting. Pick the model at the\nbottom of the U.\n\n$$\n% caption: Model selection by validation error. Training error falls monotonically\n% with complexity; validation error is U-shaped, and its minimum marks the model\n% that best balances underfitting against overfitting.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{red}{HTML}{C0392B}\n  \\draw[->, black] (0,0) -- (6.4,0) node[right, font=\\scriptsize, black] {model complexity};\n  \\draw[->, black] (0,0) -- (0,3.6) node[above, font=\\scriptsize, black] {error};\n  % training error: monotonically decreasing\n  \\draw[acc, very thick] (0.3,3.1) .. controls (1.6,1.4) and (3.0,0.7) .. (6.0,0.35);\n  \\node[acc, anchor=west, font=\\scriptsize] at (6.05,0.35) {training};\n  % validation error: U-shaped\n  \\draw[red, very thick] (0.3,3.3) .. controls (1.8,1.5) and (2.6,1.1) .. (3.4,1.05)\n    .. controls (4.4,1.0) and (5.2,2.0) .. (6.0,2.9);\n  \\node[red, anchor=west, font=\\scriptsize] at (6.05,2.9) {validation};\n  \\draw[dashed, black] (3.4,0) -- (3.4,1.05);\n  \\fill[red] (3.4,1.05) circle (2pt);\n  \\node[font=\\scriptsize, anchor=north] at (3.4,-0.1) {best};\n\\end{tikzpicture}\n$$\n\nWhy prefer the simpler model at all? Because of **Ockham's razor**: among\nhypotheses consistent with the data, prefer the simplest.[^aima-occam] A\ndegree-1 polynomial is simpler than a degree-7 one, and simplicity is a bet on\ngeneralization — a simple hypothesis has fewer ways to have latched onto noise. An\nequivalent, weight-based route to the same preference is **regularization**:\ninstead of enumerating model sizes, minimize a total cost that adds a complexity\npenalty to the empirical loss,\n\n$$\n\\mathit{Cost}(h) = \\mathit{EmpLoss}(h) + \\lambda\\, \\mathit{Complexity}(h),\n$$\n\nwhere $\\lambda$ trades loss against complexity. The name is apt — it searches for a\nhypothesis that is more _regular_, less wiggly. There is also a tradeoff between\nthe _expressiveness_ of a hypothesis space and the _complexity of finding_ a good\nhypothesis within it: a richer language may let a simple hypothesis fit the data,\nbut searching it can be intractable, which is why most learning sticks to simple\nrepresentations.\n\nThis is the foundation: a hypothesis space, a search for a hypothesis that\ngeneralizes, and a discipline for measuring whether it does. What we have not yet\ndone is _prove_ that generalization is possible, or survey the model families\nbeyond the decision tree. That is the work of the second part,\n[The Theory of Learning and Model Families](\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families),\nwhich opens with the question these measurements leave unanswered: how can we be\nsure a hypothesis will predict well on inputs it has never seen?\n\n[^aima-forms]: **Russell & Norvig**, _AIMA_ (3rd ed.), Ch. 18 opening and §18.1 — Forms of Learning: an agent learns if it improves on future tasks after observing the world; the three reasons a designer wants learning (unanticipated situations, changing environments, tasks no one can program by hand).\n[^aima-feedback]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.1 — the three types of feedback: unsupervised (patterns with no labels, e.g. clustering), reinforcement (a reward signal), and supervised (correct outputs supplied).\n[^aima-overfit]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.2 and §18.3.5 — Fig. 18.1's underfit\u002Fconsistent\u002Foverfit polynomial fits; overfitting grows more likely as the hypothesis space and attribute count grow, less likely as training data grows.\n[^aima-dt-rep]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.3.1–18.3.2 — the decision-tree representation (interior tests, branch values, leaf outputs), the $WillWait$ example and its attributes, and equivalence to a DNF assertion over root-to-leaf paths.\n[^aima-dt-expr]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.3.2 — decision trees express any Boolean function but not always concisely (the majority function); there are $2^{2^n}$ Boolean functions of $n$ attributes.\n[^aima-dt-induce]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.3.3 and Fig. 18.5 — the greedy divide-and-conquer $\\textsc{Decision-Tree-Learning}$ algorithm and its four recursive cases; the induced tree is consistent with and simpler than the hand-built one.\n[^aima-entropy]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.3.4 — entropy $H(V)$, the Boolean-entropy shorthand $B(q)$, $\\mathit{Remainder}(A)$, and the information-gain heuristic $\\mathit{Gain}(A)$; the worked $\\mathit{Gain}(Patrons) \\approx 0.541$ vs. $\\mathit{Gain}(Type) = 0$.\n[^aima-prune]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.3.5 — generalization and overfitting; decision-tree pruning via a $\\chi^2$ significance test on the null hypothesis of an irrelevant attribute, and why grow-then-prune beats early stopping (the XOR case).\n[^aima-eval]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.4 — the stationarity\u002Fi.i.d. assumption, error rate, holdout vs. $k$-fold and leave-one-out cross-validation, peeking, and the training\u002Fvalidation\u002Ftest discipline; the learning curve of §18.3 (Fig. 18.7).\n[^aima-modelsel]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.4.1 and §18.4.3 — model selection as a wrapper over a size parameter, the U-shaped validation curve (Fig. 18.9), and regularization as minimizing $\\mathit{EmpLoss} + \\lambda\\,\\mathit{Complexity}$.\n[^aima-occam]: **Russell & Norvig**, _AIMA_ (3rd ed.), §18.2 and §18.3 — Ockham's razor (prefer the simplest consistent hypothesis) and the expressiveness-versus-search tradeoff in choosing a hypothesis space.\n",{"text":9897,"minutes":9898,"time":9899,"words":9900},"15 min read",14.42,865200,2884,{"title":5,"description":9886},[9903,9905],{"book":9256,"ref":9904},"Ch. 18 — Learning from Examples; §18.1 Forms of Learning; §18.2 Supervised Learning",{"book":9256,"ref":9906},"§18.3 Learning Decision Trees; §18.4 Evaluating and Choosing the Best Hypothesis","available","08.artificial-intelligence\u002F05.learning\u002F01.learning-from-examples","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",[9891],"HIBDUCj_Hj6msf5hhiS_ygTAqiOdO4jTw1tvIA0bUxs",{"\u002Falgorithms\u002Ffoundations\u002Fwhat-is-an-algorithm":9913,"\u002Falgorithms\u002Ffoundations\u002Fproof-techniques":9914,"\u002Falgorithms\u002Ffoundations\u002Fasymptotic-analysis":9915,"\u002Falgorithms\u002Ffoundations\u002Fgrowth-rates-and-loop-analysis":9916,"\u002Falgorithms\u002Ffoundations\u002Frecurrences":9917,"\u002Falgorithms\u002Ffoundations\u002Famortized-analysis":9918,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fmergesort":9919,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fquicksort":9920,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fselection":9921,"\u002Falgorithms\u002Fdivide-and-conquer\u002Ffast-multiplication":9922,"\u002Falgorithms\u002Fsorting\u002Fheaps-and-heapsort":9923,"\u002Falgorithms\u002Fsorting\u002Fsorting-lower-bounds":9924,"\u002Falgorithms\u002Fsorting\u002Flinear-time-sorting":9925,"\u002Falgorithms\u002Fsorting\u002Fexternal-sorting":9926,"\u002Falgorithms\u002Fdata-structures\u002Felementary-structures":9927,"\u002Falgorithms\u002Fdata-structures\u002Fhash-tables":9928,"\u002Falgorithms\u002Fdata-structures\u002Fbinary-search-trees":9929,"\u002Falgorithms\u002Fdata-structures\u002Favl-trees":9930,"\u002Falgorithms\u002Fdata-structures\u002Fbalanced-trees":9931,"\u002Falgorithms\u002Fdata-structures\u002Funion-find":9932,"\u002Falgorithms\u002Fdata-structures\u002Ffenwick-and-segment-trees":9933,"\u002Falgorithms\u002Fdata-structures\u002Fspatial-data-structures":9934,"\u002Falgorithms\u002Fdata-structures\u002Fskip-lists-and-probabilistic-structures":9935,"\u002Falgorithms\u002Fdata-structures\u002Fb-trees":9936,"\u002Falgorithms\u002Fdata-structures\u002Fdata-stream-algorithms":9937,"\u002Falgorithms\u002Fdata-structures\u002Fstreaming-sketches":9938,"\u002Falgorithms\u002Fsequences\u002Ftwo-pointers-and-windows":9939,"\u002Falgorithms\u002Fsequences\u002Fprefix-sums":9940,"\u002Falgorithms\u002Fsequences\u002Fmonotonic-stacks":9941,"\u002Falgorithms\u002Fsequences\u002Fbinary-search-on-the-answer":9942,"\u002Falgorithms\u002Fsequences\u002Fstring-matching":9943,"\u002Falgorithms\u002Fsequences\u002Fkmp-and-z-function":9944,"\u002Falgorithms\u002Fsequences\u002Ftries":9945,"\u002Falgorithms\u002Fsequences\u002Fsuffix-arrays-and-aho-corasick":9946,"\u002Falgorithms\u002Fgraphs\u002Frepresentations-and-traversal":9947,"\u002Falgorithms\u002Fgraphs\u002Fdepth-first-search":9948,"\u002Falgorithms\u002Fgraphs\u002Ftopological-sort-and-scc":9949,"\u002Falgorithms\u002Fgraphs\u002Fminimum-spanning-trees":9950,"\u002Falgorithms\u002Fgraphs\u002Fkruskal-and-prim":9951,"\u002Falgorithms\u002Fgraphs\u002Fshortest-paths":9952,"\u002Falgorithms\u002Fgraphs\u002Fall-pairs-and-negative-weights":9953,"\u002Falgorithms\u002Fgraphs\u002Fnetwork-flow":9954,"\u002Falgorithms\u002Fgraphs\u002Fmax-flow-min-cut":9955,"\u002Falgorithms\u002Fgraphs\u002Fbridges-and-articulation-points":9956,"\u002Falgorithms\u002Fgraphs\u002Flowest-common-ancestor":9957,"\u002Falgorithms\u002Fgraphs\u002Ftwo-sat":9958,"\u002Falgorithms\u002Fgraphs\u002Feulerian-tours":9959,"\u002Falgorithms\u002Fgraphs\u002Fbipartite-matching":9960,"\u002Falgorithms\u002Fgreedy\u002Fthe-greedy-method":9961,"\u002Falgorithms\u002Fgreedy\u002Fscheduling-and-intervals":9962,"\u002Falgorithms\u002Fgreedy\u002Fhuffman-codes":9963,"\u002Falgorithms\u002Fgreedy\u002Fmatroids":9964,"\u002Falgorithms\u002Fgreedy\u002Fstable-matching":9965,"\u002Falgorithms\u002Fdynamic-programming\u002Fprinciples":9966,"\u002Falgorithms\u002Fdynamic-programming\u002Fsequence-dp":9967,"\u002Falgorithms\u002Fdynamic-programming\u002Flongest-increasing-subsequence":9968,"\u002Falgorithms\u002Fdynamic-programming\u002Fknapsack":9969,"\u002Falgorithms\u002Fdynamic-programming\u002Fcoin-change-and-unbounded":9970,"\u002Falgorithms\u002Fdynamic-programming\u002Finterval-dp":9971,"\u002Falgorithms\u002Fdynamic-programming\u002Ftree-dp":9972,"\u002Falgorithms\u002Fdynamic-programming\u002Fbitmask-dp":9973,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-optimizations":9974,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-on-graphs":9975,"\u002Falgorithms\u002Fdynamic-programming\u002Fdigit-and-probability-dp":9976,"\u002Falgorithms\u002Fbacktracking\u002Fbacktracking-fundamentals":9977,"\u002Falgorithms\u002Fbacktracking\u002Fconstraint-search":9978,"\u002Falgorithms\u002Fbacktracking\u002Fbranch-and-bound":9979,"\u002Falgorithms\u002Fbacktracking\u002Fgraph-backtracking":9980,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fnumber-theory-basics":9981,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmodular-exponentiation-and-primality":9982,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fsieve-and-factorization":9983,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fcombinatorics":9984,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmatrix-exponentiation":9985,"\u002Falgorithms\u002Fmathematical-algorithms\u002Ffast-fourier-transform":9986,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fgradient-descent":9987,"\u002Falgorithms\u002Fcomputational-geometry\u002Fgeometric-primitives":9988,"\u002Falgorithms\u002Fcomputational-geometry\u002Fconvex-hull":9989,"\u002Falgorithms\u002Fcomputational-geometry\u002Fsweep-line":9990,"\u002Falgorithms\u002Fcomputational-geometry\u002Fpolygons-and-proximity":9991,"\u002Falgorithms\u002Fintractability\u002Fp-np-reductions":9992,"\u002Falgorithms\u002Fintractability\u002Fnp-completeness":9993,"\u002Falgorithms\u002Fintractability\u002Fcoping-with-hardness":9994,"\u002Falgorithms\u002Fintractability\u002Fapproximation-algorithms":9995,"\u002Falgorithms":9996,"\u002Fcalculus\u002Flimits-and-continuity\u002Ffunctions-and-models":9997,"\u002Fcalculus\u002Flimits-and-continuity\u002Fthe-limit-of-a-function":9998,"\u002Fcalculus\u002Flimits-and-continuity\u002Flimit-laws-and-the-precise-definition":9999,"\u002Fcalculus\u002Flimits-and-continuity\u002Fcontinuity":10000,"\u002Fcalculus\u002Fderivatives\u002Fthe-derivative-and-rates-of-change":10001,"\u002Fcalculus\u002Fderivatives\u002Fdifferentiation-rules-and-the-chain-rule":10002,"\u002Fcalculus\u002Fderivatives\u002Fimplicit-differentiation-and-related-rates":10003,"\u002Fcalculus\u002Fderivatives\u002Flinear-approximations-and-differentials":10004,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fextrema-and-the-mean-value-theorem":10005,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fhow-derivatives-shape-a-graph":10006,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fcurve-sketching-and-optimization":10007,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fnewtons-method-and-antiderivatives":10008,"\u002Fcalculus\u002Fintegrals\u002Farea-and-the-definite-integral":10009,"\u002Fcalculus\u002Fintegrals\u002Fthe-fundamental-theorem-of-calculus":10010,"\u002Fcalculus\u002Fintegrals\u002Fthe-substitution-rule":10011,"\u002Fcalculus\u002Fapplications-of-integration\u002Fareas-and-volumes":10012,"\u002Fcalculus\u002Fapplications-of-integration\u002Fwork-average-value-and-arc-length":10013,"\u002Fcalculus\u002Fapplications-of-integration\u002Fphysics-economics-and-probability":10014,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Finverse-functions-logarithms-and-exponentials":10015,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Fgrowth-decay-inverse-trig-and-hyperbolic-functions":10016,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Flhospitals-rule":10017,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fintegration-by-parts":10018,"\u002Fcalculus\u002Ftechniques-of-integration\u002Ftrigonometric-integrals-and-substitution":10019,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fpartial-fractions-and-integration-strategy":10020,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fapproximate-and-improper-integrals":10021,"\u002Fcalculus\u002Fparametric-and-polar\u002Fparametric-curves-and-their-calculus":10022,"\u002Fcalculus\u002Fparametric-and-polar\u002Fpolar-coordinates":10023,"\u002Fcalculus\u002Fparametric-and-polar\u002Fconic-sections":10024,"\u002Fcalculus\u002Fsequences-and-series\u002Fsequences":10025,"\u002Fcalculus\u002Fsequences-and-series\u002Fseries-and-the-integral-test":10026,"\u002Fcalculus\u002Fsequences-and-series\u002Fthe-convergence-tests":10027,"\u002Fcalculus\u002Fsequences-and-series\u002Fpower-series":10028,"\u002Fcalculus\u002Fsequences-and-series\u002Ftaylor-and-maclaurin-series":10029,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvectors-and-the-dot-product":10030,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fthe-cross-product-lines-and-planes":10011,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fcylinders-and-quadric-surfaces":10031,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvector-functions-and-space-curves":10032,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Farc-length-curvature-and-motion":10033,"\u002Fcalculus\u002Fpartial-derivatives\u002Ffunctions-of-several-variables":10001,"\u002Fcalculus\u002Fpartial-derivatives\u002Fpartial-derivatives":10034,"\u002Fcalculus\u002Fpartial-derivatives\u002Ftangent-planes-and-the-chain-rule":10035,"\u002Fcalculus\u002Fpartial-derivatives\u002Fdirectional-derivatives-and-the-gradient":10036,"\u002Fcalculus\u002Fpartial-derivatives\u002Foptimization-and-lagrange-multipliers":10037,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fdouble-integrals":10038,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Ftriple-integrals-and-coordinate-systems":10039,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fvector-fields-and-line-integrals":10040,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fgreens-theorem-curl-and-divergence":10041,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fsurface-integrals":10042,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fstokes-and-the-divergence-theorem":10043,"\u002Fcalculus":10044,"\u002Fmechanics\u002Ffoundations\u002Fmeasurement-and-dimensions":10045,"\u002Fmechanics\u002Ffoundations\u002Fvector-algebra":10046,"\u002Fmechanics\u002Fkinematics\u002Fone-dimensional-motion":10047,"\u002Fmechanics\u002Fkinematics\u002Fmotion-graphs":10048,"\u002Fmechanics\u002Fkinematics\u002Fprojectile-motion":10049,"\u002Fmechanics\u002Fkinematics\u002Frelative-motion":10050,"\u002Fmechanics\u002Fkinematics\u002Fcircular-motion":10051,"\u002Fmechanics\u002Fdynamics\u002Fnewtons-laws":10052,"\u002Fmechanics\u002Fdynamics\u002Ffree-body-diagrams":10053,"\u002Fmechanics\u002Fdynamics\u002Ffriction-and-curved-motion":10054,"\u002Fmechanics\u002Fdynamics\u002Fnumerical-dynamics":10055,"\u002Fmechanics\u002Fdynamics\u002Fcenter-of-mass-systems":10056,"\u002Fmechanics\u002Fenergy\u002Fwork-and-kinetic-energy":10057,"\u002Fmechanics\u002Fenergy\u002Fpotential-energy":10058,"\u002Fmechanics\u002Fenergy\u002Fmultiparticle-work":10059,"\u002Fmechanics\u002Fenergy\u002Fmass-energy-and-binding":10060,"\u002Fmechanics\u002Fenergy\u002Fphotons-and-quantization":10061,"\u002Fmechanics\u002Fmomentum\u002Fmomentum-and-collisions":10062,"\u002Fmechanics\u002Fmomentum\u002Fcenter-of-mass-collisions":10063,"\u002Fmechanics\u002Fmomentum\u002Frocket-propulsion":10064,"\u002Fmechanics\u002Frotation\u002Frotational-inertia":10065,"\u002Fmechanics\u002Frotation\u002Frotational-dynamics":10066,"\u002Fmechanics\u002Frotation\u002Frolling-motion":10067,"\u002Fmechanics\u002Frotation\u002Fangular-momentum":10068,"\u002Fmechanics\u002Frotation\u002Frolling-resistance":10069,"\u002Fmechanics\u002Frotation\u002Fgyroscopic-precession":10070,"\u002Fmechanics\u002Fgravity-and-matter\u002Fkeplerian-orbits":10071,"\u002Fmechanics\u002Fgravity-and-matter\u002Fgravitational-fields":10072,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstatic-equilibrium":10073,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-statics":10074,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-flow":10075,"\u002Fmechanics\u002Fgravity-and-matter\u002Forbital-motion":10076,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstress-and-elasticity":10077,"\u002Fmechanics\u002Foscillations-waves\u002Fdamped-oscillators":10078,"\u002Fmechanics\u002Foscillations-waves\u002Ftravelling-waves":10079,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-superposition":10080,"\u002Fmechanics\u002Foscillations-waves\u002Fstanding-waves":10081,"\u002Fmechanics\u002Foscillations-waves\u002Fsound-waves":10082,"\u002Fmechanics\u002Foscillations-waves\u002Fdoppler-effect":10083,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-packets":10084,"\u002Fmechanics\u002Foscillations-waves\u002Fbeats-and-coupling":10085,"\u002Fmechanics\u002Foscillations-waves\u002Fsimple-harmonic-motion":10086,"\u002Fmechanics\u002Foscillations-waves\u002Fpendulum-motion":10087,"\u002Fmechanics\u002Foscillations-waves\u002Fdriven-oscillators":10088,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-boundaries":10089,"\u002Fmechanics\u002Fthermodynamics\u002Fkinetic-theory-of-ideal-gases":10090,"\u002Fmechanics\u002Fthermodynamics\u002Ffirst-law-of-thermodynamics":10091,"\u002Fmechanics\u002Fthermodynamics\u002Fentropy-and-the-second-law":10092,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-processes":10093,"\u002Fmechanics\u002Fthermodynamics\u002Fphase-changes":10094,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-machines":10095,"\u002Fmechanics":10096,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcharge-and-conductors":10097,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcoulombs-law":10098,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-and-force":10099,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-maps":10100,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-dipoles":10101,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fcontinuous-charge-fields":10102,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fgauss-law-and-conductors":10103,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpoint-charge-potential":10104,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpotential-gradients-and-equipotentials":10105,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Felectrostatic-energy-and-pressure":10106,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Flaplace-boundary-problems":10107,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fcontinuous-charge-potentials":10108,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitance-fundamentals":10085,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-networks":10109,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-energy-and-force":10110,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fdielectric-polarization-and-breakdown":10111,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fcurrent-and-resistance":10081,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fkirchhoff-network-analysis":9946,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Frc-transients":10112,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-trajectories":10072,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fhall-effect":10113,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-force-on-conductors":10114,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-dipoles":10115,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmass-spectrometry":10116,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmoving-charge-fields":10117,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fbiot-savart-law":10118,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fcircular-current-loops":10119,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Famperes-law":10120,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fgauss-law-for-magnetism":10121,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmagnetic-materials":10046,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-flux":10122,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Ffaradays-law":10123,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Flenzs-law":10124,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmotional-emf":10125,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Feddy-currents":10126,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fself-inductance":10127,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-energy":10128,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Frl-circuits":10129,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-fundamentals":10064,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Freactance":10063,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Frlc-resonance":10130,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-power":10131,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Ftransformers":10132,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdisplacement-current":10133,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-waves":10134,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-momentum":10135,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdipole-radiation":10136,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fpolarization":10137,"\u002Felectricity-and-magnetism\u002Foptics\u002Freflection-and-refraction":10138,"\u002Felectricity-and-magnetism\u002Foptics\u002Fthin-lenses":10090,"\u002Felectricity-and-magnetism\u002Foptics\u002Fspherical-mirrors":10088,"\u002Felectricity-and-magnetism":10139,"\u002Flinear-algebra\u002Flinear-systems\u002Fsystems-and-echelon-forms":10140,"\u002Flinear-algebra\u002Flinear-systems\u002Fvector-and-matrix-equations":10141,"\u002Flinear-algebra\u002Flinear-systems\u002Fsolution-sets-and-applications":10142,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-independence":10143,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-transformations":10144,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-operations":10145,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-inverse-and-invertibility":10146,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fpartitioned-matrices-and-lu":10147,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fsubspaces-dimension-rank":10148,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fapplications-leontief-and-graphics":9998,"\u002Flinear-algebra\u002Fdeterminants\u002Fdeterminants-and-cofactors":10149,"\u002Flinear-algebra\u002Fdeterminants\u002Fproperties-of-determinants":10150,"\u002Flinear-algebra\u002Fdeterminants\u002Fcramer-volume-and-area":10002,"\u002Flinear-algebra\u002Fvector-spaces\u002Fvector-spaces-and-subspaces":10151,"\u002Flinear-algebra\u002Fvector-spaces\u002Fnull-and-column-spaces":10152,"\u002Flinear-algebra\u002Fvector-spaces\u002Fbases-and-independent-sets":10153,"\u002Flinear-algebra\u002Fvector-spaces\u002Fcoordinate-systems":10154,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdimension-and-rank":10155,"\u002Flinear-algebra\u002Fvector-spaces\u002Fchange-of-basis":10156,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdifference-equations-and-markov":10157,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-eigenvalues":10158,"\u002Flinear-algebra\u002Feigenvalues\u002Fthe-characteristic-equation":10159,"\u002Flinear-algebra\u002Feigenvalues\u002Fdiagonalization":10160,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-linear-transformations":10161,"\u002Flinear-algebra\u002Feigenvalues\u002Fcomplex-eigenvalues":10162,"\u002Flinear-algebra\u002Feigenvalues\u002Fdynamical-systems":10163,"\u002Flinear-algebra\u002Feigenvalues\u002Fpower-method":10164,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-length-orthogonality":10165,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Forthogonal-sets-and-projections":10166,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fgram-schmidt-and-qr":10167,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-problems":10168,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-applications":10169,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-spaces":10170,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fdiagonalizing-symmetric-matrices":10037,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fquadratic-forms":10171,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fconstrained-optimization":10172,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsingular-value-decomposition":10173,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsvd-applications-pca-imaging":10174,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-thinking-and-matrix-computation":10175,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Flu-and-cholesky":10176,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fconditioning-and-floating-point":10177,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fstability-and-error-analysis":10178,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fqr-and-numerical-least-squares":10179,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-eigenvalues-and-svd":10180,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-combinations":10181,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-independence-and-barycentric-coordinates":10182,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fconvex-combinations-and-convex-sets":10183,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fhyperplanes-and-polytopes":10184,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fcurves-and-surfaces":10185,"\u002Flinear-algebra":10186,"\u002Ftheory-of-computation":10187,"\u002Fcomputer-architecture\u002Ffoundations\u002Fbits-bytes-and-words":10188,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-representation":10189,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-arithmetic":10190,"\u002Fcomputer-architecture\u002Ffoundations\u002Ffloating-point":10191,"\u002Fcomputer-architecture\u002Ffoundations\u002Fboolean-algebra-and-bit-manipulation":10192,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fthe-machines-view":10193,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fdata-movement":10194,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farithmetic-and-logic":10195,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fcontrol-flow":10196,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fprocedures":10197,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farrays-structs-and-alignment":10198,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fmemory-layout-and-buffer-overflows":10199,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fwhat-an-isa-is":10200,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Finstruction-formats-and-operands":10201,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Faddressing-modes":10202,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fthe-y86-64-instruction-set":10203,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fy86-64-programming":10204,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Ftransistors-gates-and-boolean-functions":10205,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fcombinational-logic-and-hcl":10206,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmultiplexers-decoders-and-the-alu":10207,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmemory-elements-latches-flip-flops-and-clocking":10208,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fregister-files-and-random-access-memory":10209,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-fetch-decode-execute-cycle":10210,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-seq-stages":10211,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fcontrol-logic-and-sequencing":10212,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fassembling-seq":10213,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Ftracing-a-program":10214,"\u002Fcomputer-architecture\u002Fpipelining\u002Fpipelining-principles":10215,"\u002Fcomputer-architecture\u002Fpipelining\u002Ffrom-seq-to-pipe":10216,"\u002Fcomputer-architecture\u002Fpipelining\u002Fdata-hazards-stalling-and-forwarding":10217,"\u002Fcomputer-architecture\u002Fpipelining\u002Fcontrol-hazards-and-branch-prediction":10218,"\u002Fcomputer-architecture\u002Fpipelining\u002Fthe-complete-pipe-processor":10219,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fstorage-technologies-and-the-latency-gap":10220,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Flocality":10221,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-memories-direct-mapped":10222,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fset-associative-and-write-policies":10223,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-performance-and-cache-friendly-code":10224,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Faddress-spaces-and-translation":10225,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fpage-tables-and-page-faults":10226,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fthe-tlb-and-multi-level-page-tables":10227,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Fexceptional-control-flow":10228,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Finterrupts-and-the-kernel":10229,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fprocesses-threads-and-parallelism":10230,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fhardware-multithreading":10231,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fcache-coherence":10232,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmemory-consistency-and-synchronization":10233,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmulticore-organization":10234,"\u002Fcomputer-architecture\u002Fcapstone\u002Fthe-whole-machine":10235,"\u002Fcomputer-architecture\u002Fcapstone\u002Fassembling-a-complete-cpu":10236,"\u002Fcomputer-architecture":10187,"\u002Fdifferential-equations\u002Ffoundations\u002Fmodels-and-direction-fields":10237,"\u002Fdifferential-equations\u002Ffoundations\u002Fclassification-and-terminology":10238,"\u002Fdifferential-equations\u002Ffirst-order\u002Flinear-first-order-integrating-factors":10239,"\u002Fdifferential-equations\u002Ffirst-order\u002Fseparable-and-exact":10002,"\u002Fdifferential-equations\u002Ffirst-order\u002Fmodeling-first-order":10240,"\u002Fdifferential-equations\u002Ffirst-order\u002Fautonomous-and-population-dynamics":10001,"\u002Fdifferential-equations\u002Ffirst-order\u002Fexistence-uniqueness-euler":10008,"\u002Fdifferential-equations\u002Ffirst-order\u002Ffirst-order-difference-equations":10241,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhomogeneous-constant-coefficients":10242,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fcomplex-and-repeated-roots":10042,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fnonhomogeneous-undetermined-coefficients":10243,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fvariation-of-parameters":10244,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fmechanical-electrical-vibrations":10245,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhigher-order-linear":10246,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fpower-series-ordinary-points":10247,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fregular-singular-frobenius":10248,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fbessel-and-special-functions":10249,"\u002Fdifferential-equations\u002Flaplace\u002Flaplace-definition-ivps":10250,"\u002Fdifferential-equations\u002Flaplace\u002Fstep-impulse-convolution":10251,"\u002Fdifferential-equations\u002Fsystems\u002Fmatrices-eigenvalues-review":10252,"\u002Fdifferential-equations\u002Fsystems\u002Fconstant-coefficient-systems-phase-portraits":10253,"\u002Fdifferential-equations\u002Fsystems\u002Frepeated-eigenvalues-fundamental-matrices":10254,"\u002Fdifferential-equations\u002Fnumerical\u002Feuler-and-runge-kutta":10249,"\u002Fdifferential-equations\u002Fnumerical\u002Fmultistep-systems-stability":10255,"\u002Fdifferential-equations\u002Fnonlinear\u002Fphase-plane-autonomous-stability":10256,"\u002Fdifferential-equations\u002Fnonlinear\u002Flocally-linear-and-liapunov":10257,"\u002Fdifferential-equations\u002Fnonlinear\u002Fcompeting-species-predator-prey-limit-cycles":10258,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Ffourier-series":10259,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fheat-wave-laplace-equations":10260,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fsturm-liouville":10261,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fcalculus-of-variations":10262,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fhistorical-notes":10263,"\u002Fdifferential-equations":10264,"\u002Frelativity\u002Ffoundations\u002Fspecial-relativity-postulates":10265,"\u002Frelativity\u002Ffoundations\u002Florentz-transformation-spacetime":10266,"\u002Frelativity\u002Ffoundations\u002Ftime-dilation-length-contraction":10267,"\u002Frelativity\u002Ffoundations\u002Frelativistic-momentum-energy":10268,"\u002Frelativity\u002Ffoundations\u002Fgeneral-relativity":10147,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fminkowski-spacetime-and-the-interval":10269,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Ffour-vectors-and-index-notation":10270,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fthe-lorentz-group-and-rapidity":10271,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fdoppler-aberration-and-appearance":10272,"\u002Frelativity\u002Frelativistic-dynamics\u002Ffour-momentum-force-and-accelerated-motion":10273,"\u002Frelativity\u002Frelativistic-dynamics\u002Fparticle-decays-and-two-body-kinematics":10274,"\u002Frelativity\u002Frelativistic-dynamics\u002Fcollisions-thresholds-and-the-cm-frame":10017,"\u002Frelativity\u002Frelativistic-dynamics\u002Fmandelstam-variables-and-invariants":10275,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ffour-current-and-the-four-potential":10276,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fthe-electromagnetic-field-tensor":10277,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ftransformation-of-electric-and-magnetic-fields":10278,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fcovariant-maxwell-and-the-stress-energy-tensor":10279,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-equivalence-principle-formalized":10280,"\u002Frelativity\u002Fcurved-spacetime\u002Fmanifolds-vectors-and-the-metric":10281,"\u002Frelativity\u002Fcurved-spacetime\u002Fcovariant-derivative-and-christoffel-symbols":10282,"\u002Frelativity\u002Fcurved-spacetime\u002Fgeodesics-and-the-geodesic-equation":10283,"\u002Frelativity\u002Fcurved-spacetime\u002Fcurvature-riemann-and-geodesic-deviation":10284,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-einstein-field-equations":10239,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fthe-schwarzschild-metric":10285,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fgeodesics-and-orbits-in-schwarzschild":10286,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Flight-bending-and-null-geodesics":10287,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fperihelion-precession-of-mercury":10288,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fdeflection-of-light-and-gravitational-lensing":10289,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fgravitational-redshift-and-shapiro-delay":10168,"\u002Frelativity\u002Ftests-of-general-relativity\u002Frelativity-in-technology-gps":10290,"\u002Frelativity\u002Fblack-holes\u002Fhorizons-and-coordinate-singularities":10291,"\u002Frelativity\u002Fblack-holes\u002Frotating-and-charged-black-holes":10178,"\u002Frelativity\u002Fblack-holes\u002Fblack-hole-thermodynamics":10292,"\u002Frelativity\u002Fgravitational-waves\u002Flinearized-gravity-and-wave-solutions":10293,"\u002Frelativity\u002Fgravitational-waves\u002Fgeneration-and-the-quadrupole-formula":10294,"\u002Frelativity\u002Fgravitational-waves\u002Fdetection-ligo-and-the-first-events":10295,"\u002Frelativity\u002Fcosmological-bridge\u002Fthe-cosmological-principle-and-flrw-metric":10296,"\u002Frelativity\u002Fcosmological-bridge\u002Ffriedmann-equations-and-cosmic-dynamics":10297,"\u002Frelativity":10298,"\u002Fphysical-computing":10187,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fblackbody-radiation-and-the-planck-quantum":10299,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-photoelectric-effect-and-the-photon":10278,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fx-rays-and-the-compton-effect":10300,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-old-quantum-theory-bohr-and-sommerfeld":10301,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fde-broglie-waves-and-electron-diffraction":10302,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fwave-packets-and-the-probability-interpretation":10303,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fthe-uncertainty-principle":10304,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-schrodinger-equation-in-one-dimension":10305,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-free-particle-and-wave-packet-dynamics":10306,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fparticle-in-infinite-and-finite-square-wells":10254,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Foperators-expectation-values-and-the-harmonic-oscillator":10178,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-dirac-delta-potential":10307,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fbarrier-penetration-and-quantum-tunneling":10308,"\u002Fquantum-mechanics\u002Fformalism\u002Fhilbert-space-and-dirac-notation":10309,"\u002Fquantum-mechanics\u002Fformalism\u002Fobservables-hermitian-operators-and-eigenvalues":10310,"\u002Fquantum-mechanics\u002Fformalism\u002Fthe-postulates-and-quantum-measurement":10306,"\u002Fquantum-mechanics\u002Fformalism\u002Fposition-momentum-and-continuous-spectra":10311,"\u002Fquantum-mechanics\u002Fformalism\u002Fcommutators-and-the-generalized-uncertainty-principle":10286,"\u002Fquantum-mechanics\u002Fformalism\u002Ftime-evolution-schrodinger-and-heisenberg-pictures":10039,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fladder-operators-and-the-number-states":10312,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fcoherent-and-squeezed-states":10313,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fsymmetries-generators-and-conservation-laws":10019,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fparity-time-reversal-and-discrete-symmetries":10314,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Forbital-angular-momentum-and-spherical-harmonics":10315,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Fthe-angular-momentum-algebra":10316,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Faddition-of-angular-momenta-and-clebsch-gordan":10317,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-schrodinger-equation-in-three-dimensions":10318,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-hydrogen-atom":10319,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-isotropic-oscillator-and-hidden-symmetry":10320,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-half-pauli-matrices-and-stern-gerlach":10321,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-in-a-magnetic-field-precession-and-resonance":10322,"\u002Fquantum-mechanics\u002Fspin\u002Ftwo-level-systems-and-the-bloch-sphere":10278,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fidentical-particles-and-exchange-symmetry":10323,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fthe-pauli-principle-atoms-and-the-periodic-table":10324,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ftime-independent-perturbation-theory":10325,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ffine-structure-and-the-real-hydrogen-atom":10312,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-zeeman-and-stark-effects":10000,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-variational-method":10326,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-wkb-approximation":10327,"\u002Fquantum-mechanics":10328,"\u002Freal-analysis\u002Ffoundations\u002Fsets-logic-functions":10262,"\u002Freal-analysis\u002Ffoundations\u002Fordered-fields-completeness":10329,"\u002Freal-analysis\u002Ffoundations\u002Fabsolute-value-bounds":10330,"\u002Freal-analysis\u002Ffoundations\u002Fintervals-uncountability":10151,"\u002Freal-analysis\u002Fsequences-series\u002Fsequences-limits":10331,"\u002Freal-analysis\u002Fsequences-series\u002Flimit-laws-monotone":10037,"\u002Freal-analysis\u002Fsequences-series\u002Flimsup-bolzano-weierstrass":10332,"\u002Freal-analysis\u002Fsequences-series\u002Fcauchy-completeness":10333,"\u002Freal-analysis\u002Fsequences-series\u002Fseries-convergence":10174,"\u002Freal-analysis\u002Fsequences-series\u002Fabsolute-conditional-rearrangement":10284,"\u002Freal-analysis\u002Fmetric-spaces\u002Fmetric-spaces-norms":10334,"\u002Freal-analysis\u002Fmetric-spaces\u002Fopen-closed-sets":10335,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconvergence-completeness":10336,"\u002Freal-analysis\u002Fmetric-spaces\u002Fcompactness":10337,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconnectedness":10338,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-of-functions":10311,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuous-functions":10339,"\u002Freal-analysis\u002Fcontinuity\u002Fevt-ivt":10144,"\u002Freal-analysis\u002Fcontinuity\u002Funiform-continuity":10340,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuity-metric-spaces":10341,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-infinity-monotone":9998,"\u002Freal-analysis\u002Fdifferentiation\u002Fthe-derivative":10342,"\u002Freal-analysis\u002Fdifferentiation\u002Fmean-value-theorem":10343,"\u002Freal-analysis\u002Fdifferentiation\u002Ftaylors-theorem":10300,"\u002Freal-analysis\u002Fdifferentiation\u002Finverse-function-1d":10027,"\u002Freal-analysis\u002Friemann-integration\u002Fdarboux-integral":10178,"\u002Freal-analysis\u002Friemann-integration\u002Fintegrability-classes":10344,"\u002Freal-analysis\u002Friemann-integration\u002Fproperties-of-the-integral":10345,"\u002Freal-analysis\u002Friemann-integration\u002Ffundamental-theorem":10163,"\u002Freal-analysis\u002Friemann-integration\u002Flog-exp-improper":10288,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpointwise-uniform-convergence":10346,"\u002Freal-analysis\u002Ffunction-sequences\u002Finterchange-of-limits":10347,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpower-series-weierstrass":10348,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpicard-ode":10183,"\u002Freal-analysis\u002Fseveral-variables\u002Fdifferentiability-rn":10349,"\u002Freal-analysis\u002Fseveral-variables\u002Fgradient-chain-rule":10350,"\u002Freal-analysis\u002Fseveral-variables\u002Fhigher-derivatives-taylor-extrema":10351,"\u002Freal-analysis\u002Fseveral-variables\u002Finverse-implicit-theorems":10351,"\u002Freal-analysis\u002Fseveral-variables\u002Fmultiple-integrals":10352,"\u002Freal-analysis":10353,"\u002Fabstract-algebra\u002Ffoundations\u002Fsets-functions-relations":10354,"\u002Fabstract-algebra\u002Ffoundations\u002Fintegers-and-modular-arithmetic":10355,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fgroup-axioms-and-first-examples":10356,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fdihedral-and-symmetric-groups":10357,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fmatrix-and-quaternion-groups":10358,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fhomomorphisms-and-group-actions":10359,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fsubgroups-and-substructures":10360,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcyclic-groups":10361,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fgeneration-and-subgroup-lattices":10362,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcosets-lagrange-and-normal-subgroups":10363,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fisomorphism-theorems":10327,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcomposition-series-and-the-alternating-group":10364,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Factions-and-cayleys-theorem":10356,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fconjugation-and-the-class-equation":10273,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fsylow-theorems":10365,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fautomorphisms-and-simple-groups":10366,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fdirect-products-and-finite-abelian-groups":10367,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fsemidirect-products":10368,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fnilpotent-and-solvable-groups":10369,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fclassifying-small-groups":10370,"\u002Fabstract-algebra\u002Fring-theory\u002Frings-definitions-and-examples":10371,"\u002Fabstract-algebra\u002Fring-theory\u002Fideals-quotients-and-homomorphisms":10372,"\u002Fabstract-algebra\u002Fring-theory\u002Ffractions-and-the-chinese-remainder-theorem":10366,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Feuclidean-domains-pids-ufds":10373,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fpolynomial-rings-over-fields":10342,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fgauss-lemma-and-unique-factorization":10374,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Firreducibility-criteria-and-groebner":10375,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fintroduction-to-modules":10376,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ffree-modules-and-direct-sums":10377,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ftensor-products-and-exact-sequences":10378,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fvector-spaces-and-linear-maps":10379,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fstructure-theorem-over-pids":10380,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Frational-canonical-form":10381,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fjordan-canonical-form":10382,"\u002Fabstract-algebra\u002Ffield-theory\u002Ffield-extensions-and-algebraic-elements":10383,"\u002Fabstract-algebra\u002Ffield-theory\u002Fstraightedge-and-compass-constructions":10016,"\u002Fabstract-algebra\u002Ffield-theory\u002Fsplitting-fields-and-algebraic-closure":10384,"\u002Fabstract-algebra\u002Ffield-theory\u002Fseparable-and-cyclotomic-extensions":10385,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fthe-galois-correspondence":10258,"\u002Fabstract-algebra\u002Fgalois-theory\u002Ffinite-fields":10386,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fcyclotomic-and-abelian-extensions":10387,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fgalois-groups-of-polynomials":10315,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fsolvability-by-radicals-and-the-quintic":10387,"\u002Fabstract-algebra\u002Fcapstone\u002Fcommutative-algebra-and-algebraic-geometry":10388,"\u002Fabstract-algebra\u002Fcapstone\u002Frepresentation-and-character-theory":10389,"\u002Fabstract-algebra":10390,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fatomic-spectra-rutherford":10391,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-model-hydrogen":10392,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fx-ray-spectra-franck-hertz":10393,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-sommerfeld-old-quantum-theory":10394,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fold-quantum-theory-limits-wkb":10395,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fschrodinger-3d-hydrogen":10307,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fhydrogen-wave-functions":10396,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fradial-equation-in-full":10397,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fsymmetry-degeneracy-runge-lenz":10398,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fexpectation-values-virial":10399,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fquantum-defects-alkali-spectra":10400,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Frydberg-atoms":10401,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Frelativistic-kinetic-correction":10402,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fspin-orbit-thomas-precession":10141,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdarwin-term-fine-structure-formula":10270,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdirac-equation-hydrogen":10016,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Flamb-shift-qed":10403,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fhyperfine-structure-21cm":10000,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fnuclear-effects-isotope-shift":10404,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fperiodic-table-atomic-spectra":10405,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fcentral-field-self-consistent":10038,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fidentical-particles-hartree-fock":10332,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhelium-two-electron-atom":10406,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fls-jj-coupling-term-symbols":10407,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhund-rules-ground-terms":10408,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fzeeman-effect":10409,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fpaschen-back-intermediate":10410,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fstark-effect-polarizability":10411,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Ftime-dependent-perturbation-golden-rule":10412,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fdipole-approximation-einstein-coefficients":10413,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fselection-rules-forbidden-transitions":10414,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Flifetimes-and-line-shapes":10415,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Flaser-principles":10416,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fspectroscopy-techniques":10417,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fline-catalog-nist-asd":10418,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Flaser-cooling-doppler":10419,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fsub-doppler-trapping":10363,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fbose-einstein-condensation":10420,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Foptical-clocks-precision":10421,"\u002Fatomic-physics":10422,"\u002Fdatabases":10187,"\u002Fcategory-theory\u002Ffoundations\u002Fwhat-is-a-category":10423,"\u002Fcategory-theory\u002Ffoundations\u002Fexamples-of-categories":10424,"\u002Fcategory-theory\u002Ffoundations\u002Fspecial-morphisms":10425,"\u002Fcategory-theory\u002Ffoundations\u002Ffunctors":10354,"\u002Fcategory-theory\u002Ffoundations\u002Fnatural-transformations":10426,"\u002Fcategory-theory\u002Ffoundations\u002Fsize-and-set-theory":10427,"\u002Fcategory-theory\u002Funiversal-properties\u002Funiversal-properties":10428,"\u002Fcategory-theory\u002Funiversal-properties\u002Fproducts-and-coproducts":10429,"\u002Fcategory-theory\u002Funiversal-properties\u002Fconstructions-on-categories":10430,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Frepresentable-functors":10431,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-lemma":10432,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-consequences":10433,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits":10434,"\u002Fcategory-theory\u002Flimits-colimits\u002Fproducts-equalizers-pullbacks":10435,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcolimits":10436,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcomputing-limits":10437,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits-and-functors":10438,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions":10439,"\u002Fcategory-theory\u002Fadjunctions\u002Funits-and-counits":10440,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions-via-universal-arrows":10441,"\u002Fcategory-theory\u002Fadjunctions\u002Ffree-forgetful-adjunctions":10442,"\u002Fcategory-theory\u002Fadjoints-limits\u002Flimits-via-adjoints":10443,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fpresheaf-limits-colimits":10444,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoints-preserve-limits":10445,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoint-functor-theorem":10436,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fmonads":10446,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-eilenberg-moore":10447,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fkleisli-and-programming":10448,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-for-endofunctors":10449,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Fcartesian-closed-categories":10450,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Flambda-calculus-correspondence":10395,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Ffixed-points-and-recursion":10451,"\u002Fcategory-theory":10452,"\u002Fdeep-learning\u002Fmathematical-background\u002Flinear-algebra-for-deep-learning":10453,"\u002Fdeep-learning\u002Fmathematical-background\u002Fprobability-and-information-theory":10454,"\u002Fdeep-learning\u002Fmathematical-background\u002Fnumerical-computation":10455,"\u002Fdeep-learning\u002Fmathematical-background\u002Fcalculus":10456,"\u002Fdeep-learning\u002Ffoundations\u002Fwhat-is-deep-learning":10457,"\u002Fdeep-learning\u002Ffoundations\u002Fmachine-learning-refresher":10458,"\u002Fdeep-learning\u002Ffoundations\u002Flinear-models-and-the-perceptron":10417,"\u002Fdeep-learning\u002Fneural-networks\u002Fthe-multilayer-perceptron":10459,"\u002Fdeep-learning\u002Fneural-networks\u002Factivation-functions":10460,"\u002Fdeep-learning\u002Fneural-networks\u002Funiversal-approximation":10461,"\u002Fdeep-learning\u002Fneural-networks\u002Fbackpropagation":10462,"\u002Fdeep-learning\u002Fneural-networks\u002Floss-functions-and-output-units":10463,"\u002Fdeep-learning\u002Foptimization\u002Fgradient-descent-and-sgd":10464,"\u002Fdeep-learning\u002Foptimization\u002Fmomentum-and-adaptive-methods":10465,"\u002Fdeep-learning\u002Foptimization\u002Finitialization":10466,"\u002Fdeep-learning\u002Foptimization\u002Fthe-optimization-landscape":10467,"\u002Fdeep-learning\u002Foptimization\u002Fsecond-order-and-approximate-methods":10468,"\u002Fdeep-learning\u002Fregularization\u002Fregularization-overview":10469,"\u002Fdeep-learning\u002Fregularization\u002Fdropout-and-data-augmentation":10470,"\u002Fdeep-learning\u002Fregularization\u002Fearly-stopping-and-parameter-sharing":10471,"\u002Fdeep-learning\u002Fregularization\u002Fnormalization":10472,"\u002Fdeep-learning\u002Farchitectures\u002Fconvolutional-networks":10473,"\u002Fdeep-learning\u002Farchitectures\u002Fcnn-architectures":10474,"\u002Fdeep-learning\u002Farchitectures\u002Frecurrent-networks":10475,"\u002Fdeep-learning\u002Farchitectures\u002Flstm-and-gru":10476,"\u002Fdeep-learning\u002Farchitectures\u002Fattention-and-transformers":10477,"\u002Fdeep-learning\u002Farchitectures\u002Fthe-transformer-architecture":10478,"\u002Fdeep-learning\u002Farchitectures\u002Ftransformers-in-practice":10479,"\u002Fdeep-learning\u002Farchitectures\u002Fgraph-neural-networks":10480,"\u002Fdeep-learning\u002Farchitectures\u002Fstate-space-models":10481,"\u002Fdeep-learning\u002Ftheory\u002Fgeneralization-theory":10482,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-robustness":10483,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-defenses":10484,"\u002Fdeep-learning\u002Ftheory\u002Fbayesian-and-ensemble-methods":10485,"\u002Fdeep-learning\u002Ftheory\u002Fdeep-equilibrium-models":10429,"\u002Fdeep-learning\u002Fgenerative-models\u002Flinear-factor-models":10486,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoencoders":10487,"\u002Fdeep-learning\u002Fgenerative-models\u002Fvariational-autoencoders":10488,"\u002Fdeep-learning\u002Fgenerative-models\u002Fgenerative-adversarial-networks":10489,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoregressive-and-normalizing-flows":10490,"\u002Fdeep-learning\u002Fgenerative-models\u002Fenergy-based-and-boltzmann-machines":10491,"\u002Fdeep-learning\u002Fgenerative-models\u002Fdiffusion-and-score-based-models":10492,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fstructured-probabilistic-models":9981,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fmonte-carlo-and-mcmc":10493,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fapproximate-inference":10494,"\u002Fdeep-learning\u002Fpractical\u002Fpractical-methodology":10217,"\u002Fdeep-learning\u002Fpractical\u002Fhyperparameters-and-debugging":10495,"\u002Fdeep-learning\u002Fpractical\u002Frepresentation-learning":10496,"\u002Fdeep-learning\u002Fpractical\u002Ftransfer-learning":10497,"\u002Fdeep-learning\u002Fpractical\u002Fapplications":10498,"\u002Fdeep-learning\u002Fpractical\u002Fmodel-compression-and-distillation":10499,"\u002Fdeep-learning\u002Fpractical\u002Fmeta-learning-and-few-shot":10500,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Flarge-language-models":10501,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fscaling-inference-and-alignment":10502,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fseq2seq-pretraining-and-bart":10503,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ftext-to-text-transfer-and-conditional-generation":10504,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fspeech-and-audio-models":10505,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fself-supervised-speech-and-synthesis":10506,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fai-agents":10190,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fagent-memory-retrieval-and-orchestration":10507,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmixture-of-experts":10508,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmultimodal-models":10509,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ffusion-and-vision-language-models":10510,"\u002Fdeep-learning\u002Freinforcement-learning\u002Ffoundations-of-reinforcement-learning":10227,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fmodel-free-prediction-and-control":10511,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fdeep-q-networks":10512,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fpolicy-gradients-and-actor-critic":10513,"\u002Fdeep-learning\u002Freinforcement-learning\u002Frl-from-human-feedback":10514,"\u002Fdeep-learning":10187,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fequilibrium-state-variables-zeroth-law":10515,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Ffirst-law-heat-and-work":10312,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fsecond-law-entropy-and-the-carnot-bound":10516,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fthermodynamic-potentials-and-maxwell-relations":10517,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fstability-response-functions-and-the-third-law":10518,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fclassical-statistics-and-equipartition":10519,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fphase-space-and-liouvilles-theorem":10520,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fensembles-and-the-equal-probability-postulate":10521,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fstatistical-entropy-boltzmann-and-gibbs":10522,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fmicrocanonical-ensemble-and-entropy":10523,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fequilibrium-conditions-temperature-pressure-chemical-potential":10359,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fideal-gas-phase-space-and-the-sackur-tetrode-entropy":10524,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Ftwo-state-systems-paramagnets-and-negative-temperature":10525,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fcanonical-ensemble-and-the-boltzmann-distribution":10526,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fpartition-function-and-the-helmholtz-free-energy":10246,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fenergy-fluctuations-and-ensemble-equivalence":10039,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fthe-einstein-solid-and-harmonic-systems":10527,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fparamagnetism-and-the-schottky-anomaly":10528,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fideal-gas-partition-function-and-the-gibbs-paradox":10529,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fequipartition-and-the-virial-theorem":10173,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fmolecular-gases-rotation-and-vibration":10530,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fgrand-canonical-ensemble-and-the-grand-partition-function":10531,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fchemical-potential-fugacity-and-number-fluctuations":10251,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fensemble-summary-and-the-thermodynamic-web":10178,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fquantum-statistics-bose-einstein-and-fermi-dirac":10532,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fderiving-the-quantum-distributions":10147,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fthe-classical-limit-and-quantum-concentration":10017,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fideal-quantum-gases-general-framework":10033,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-and-the-fermion-gas":10533,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthe-photon-gas-and-plancks-radiation-law":10534,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fblackbody-thermodynamics-and-radiation-pressure":10535,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fphonons-and-the-debye-model":10165,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-derived":10536,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthermodynamics-of-the-bose-gas-and-superfluidity":10027,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fthe-ideal-fermi-gas-at-zero-temperature":10537,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fsommerfeld-expansion-and-electrons-in-metals":10538,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":10539,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fneutron-stars-and-nuclear-matter":10540,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-cluster-expansion-and-virial-coefficients":10361,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-van-der-waals-gas-and-liquid-gas-coexistence":10541,"\u002Fstatistical-mechanics\u002Finteractions\u002Fquantum-gases-with-interactions-and-exchange":10542,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fphases-coexistence-and-classification":10543,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-ising-model-and-exact-solutions":10544,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fmean-field-theory-and-the-weiss-model":10307,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fcritical-exponents-and-landau-theory":10545,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-renormalization-group-idea":10285,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fthermodynamic-fluctuations-and-response":10546,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fbrownian-motion-and-the-langevin-equation":10146,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Flinear-response-and-the-fluctuation-dissipation-theorem":10547,"\u002Fstatistical-mechanics":10548,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fbonding-mechanisms":10549,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fmolecular-orbitals-and-h2-plus":10012,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fhydrogen-molecule-and-exchange":10272,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fvan-der-waals-forces":10550,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Frotational-vibrational-spectra":10551,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Fanharmonicity-and-rovibrational-structure":10552,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Framan-and-electronic-bands":10553,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Flasers-and-masers":10554,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fstructure-of-solids":10555,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fbravais-lattices-and-crystal-systems":10145,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Freciprocal-lattice-and-brillouin-zones":10556,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fdiffraction-and-structure-factors":10557,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonon-dispersion":10558,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonons-quantization-and-dos":10559,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fdebye-einstein-heat-capacity":10288,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fanharmonicity-and-thermal-transport":10560,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ffree-electron-gas-and-conduction":10561,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fsommerfeld-model-and-heat-capacity":10562,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ftransport-and-the-hall-effect":10563,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fscreening-and-plasmons":10564,"\u002Fcondensed-matter\u002Fband-theory\u002Fblochs-theorem-and-energy-bands":10343,"\u002Fcondensed-matter\u002Fband-theory\u002Fnearly-free-electron-model":10287,"\u002Fcondensed-matter\u002Fband-theory\u002Ftight-binding-method":10565,"\u002Fcondensed-matter\u002Fband-theory\u002Ffermi-surfaces-and-semiclassical-dynamics":10566,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fsemiconductor-bands-and-junctions":10567,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fintrinsic-and-extrinsic-semiconductors":10568,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fcarrier-transport-and-recombination":10278,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fthe-pn-junction":10569,"\u002Fcondensed-matter\u002Fsemiconductors\u002Ftransistors-and-optoelectronics":10570,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fdielectrics-and-polarization":10515,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fferroelectrics-and-piezoelectrics":10415,"\u002Fcondensed-matter\u002Fmagnetism\u002Fdiamagnetism-and-paramagnetism":10142,"\u002Fcondensed-matter\u002Fmagnetism\u002Fexchange-and-ferromagnetism":10571,"\u002Fcondensed-matter\u002Fmagnetism\u002Fantiferromagnetism-and-domains":10008,"\u002Fcondensed-matter\u002Fmagnetism\u002Fspin-waves-and-magnons":10572,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fsuperconductivity-phenomenology":10573,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Flondon-theory-and-the-meissner-effect":10153,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fginzburg-landau-theory":10574,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fbcs-theory":10408,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fjosephson-and-high-tc":10575,"\u002Fcondensed-matter\u002Fnanostructures\u002Fquantum-wells-wires-and-dots":10000,"\u002Fcondensed-matter\u002Fnanostructures\u002Finteger-quantum-hall-effect":10576,"\u002Fcondensed-matter\u002Fnanostructures\u002Ffractional-quantum-hall-and-topology":10011,"\u002Fcondensed-matter\u002Fnanostructures\u002Fgraphene-and-dirac-materials":10577,"\u002Fcondensed-matter":10328,"\u002Flogic\u002Ffoundations\u002Flogic-as-a-mathematical-model":10578,"\u002Flogic\u002Fsentential-logic\u002Fformal-languages-and-well-formed-formulas":10579,"\u002Flogic\u002Fsentential-logic\u002Ftruth-assignments-and-tautologies":10580,"\u002Flogic\u002Fsentential-logic\u002Funique-readability-and-parsing":10581,"\u002Flogic\u002Fsentential-logic\u002Finduction-and-recursion":10025,"\u002Flogic\u002Fsentential-logic\u002Fexpressive-completeness-and-normal-forms":10582,"\u002Flogic\u002Fsentential-logic\u002Fboolean-circuits":10583,"\u002Flogic\u002Fsentential-logic\u002Fcompactness-and-effectiveness":10025,"\u002Flogic\u002Ffirst-order-languages\u002Ffirst-order-languages":10584,"\u002Flogic\u002Ffirst-order-languages\u002Fstructures-truth-and-satisfaction":10438,"\u002Flogic\u002Ffirst-order-languages\u002Fdefinability-and-elementary-equivalence":10585,"\u002Flogic\u002Ffirst-order-languages\u002Fterms-substitution-and-parsing":10586,"\u002Flogic\u002Fdeductive-calculus\u002Fa-deductive-calculus":10587,"\u002Flogic\u002Fdeductive-calculus\u002Fdeduction-theorem-and-derived-rules":10585,"\u002Flogic\u002Fdeductive-calculus\u002Fsoundness":10588,"\u002Flogic\u002Fdeductive-calculus\u002Fcompleteness-and-consistency":10589,"\u002Flogic\u002Fmodels-and-theories\u002Fcompactness-and-lowenheim-skolem":10590,"\u002Flogic\u002Fmodels-and-theories\u002Ftheories-elementary-classes-and-categoricity":10591,"\u002Flogic\u002Fmodels-and-theories\u002Finterpretations-between-theories":10592,"\u002Flogic\u002Fmodels-and-theories\u002Fnonstandard-analysis":10593,"\u002Flogic\u002Farithmetic-and-definability\u002Fdefinability-in-arithmetic":10594,"\u002Flogic\u002Farithmetic-and-definability\u002Fnatural-numbers-with-successor":10595,"\u002Flogic\u002Farithmetic-and-definability\u002Fpresburger-and-reducts":10516,"\u002Flogic\u002Farithmetic-and-definability\u002Fa-subtheory-and-representability":10596,"\u002Flogic\u002Fincompleteness\u002Farithmetization-of-syntax":10589,"\u002Flogic\u002Fincompleteness\u002Fincompleteness-and-undecidability":10597,"\u002Flogic\u002Fincompleteness\u002Fsecond-incompleteness-theorem":10598,"\u002Flogic\u002Fcomputability-and-representability\u002Frecursive-functions":10231,"\u002Flogic\u002Fcomputability-and-representability\u002Frepresenting-exponentiation":10599,"\u002Flogic\u002Fsecond-order-logic\u002Fsecond-order-languages":10416,"\u002Flogic\u002Fsecond-order-logic\u002Fskolem-functions-and-many-sorted-logic":10600,"\u002Flogic\u002Fsecond-order-logic\u002Fgeneral-structures":10601,"\u002Flogic":10602,"\u002Freinforcement-learning\u002Ffoundations\u002Fwhat-is-reinforcement-learning":10603,"\u002Freinforcement-learning\u002Ffoundations\u002Fa-brief-history-of-rl":10604,"\u002Freinforcement-learning\u002Ffoundations\u002Fmulti-armed-bandits":10217,"\u002Freinforcement-learning\u002Ffoundations\u002Fbandit-exploration-algorithms":10605,"\u002Freinforcement-learning\u002Ffoundations\u002Fmarkov-decision-processes":10606,"\u002Freinforcement-learning\u002Ffoundations\u002Fvalue-functions-and-optimality":10607,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdynamic-programming":10608,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdp-async-and-gpi":10598,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-methods":10609,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-off-policy":10610,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftemporal-difference-learning":10611,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftd-control-sarsa-and-q-learning":10509,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-bootstrapping":10612,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-off-policy-methods":10613,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-and-learning":10614,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-focusing-and-decision-time":10615,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdecision-time-planning":10616,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-tree-search":10234,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-prediction":10617,"\u002Freinforcement-learning\u002Fapproximation\u002Ffeature-construction-and-nonlinear":10618,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-control":10619,"\u002Freinforcement-learning\u002Fapproximation\u002Faverage-reward-control":10620,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-and-the-deadly-triad":10425,"\u002Freinforcement-learning\u002Fapproximation\u002Fbellman-error-and-gradient-td":10621,"\u002Freinforcement-learning\u002Fapproximation\u002Feligibility-traces":10597,"\u002Freinforcement-learning\u002Fapproximation\u002Ftrue-online-and-sarsa-lambda":10622,"\u002Freinforcement-learning\u002Fapproximation\u002Fpolicy-gradient-methods":10623,"\u002Freinforcement-learning\u002Fapproximation\u002Factor-critic-and-continuous-actions":10624,"\u002Freinforcement-learning\u002Fapproximation\u002Fleast-squares-and-memory-based-methods":10245,"\u002Freinforcement-learning\u002Fapproximation\u002Fmemory-and-kernel-methods":10625,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-eligibility-traces":10375,"\u002Freinforcement-learning\u002Fapproximation\u002Fstable-off-policy-traces":10626,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdeep-q-networks":10627,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdqn-improvements":10207,"\u002Freinforcement-learning\u002Fdeep-rl\u002Factor-critic-and-ppo":10628,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fppo-and-continuous-control":10629,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fcase-studies":10630,"\u002Freinforcement-learning\u002Fdeep-rl\u002Frl-beyond-games":10631,"\u002Freinforcement-learning\u002Fdeep-rl\u002Ffrontiers":10632,"\u002Freinforcement-learning\u002Fdeep-rl\u002Freward-design-and-open-problems":10485,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow":10633,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow-part-2":10634,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control":10635,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control-part-2":10520,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl":10636,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl-part-2":10637,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration":10638,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration-part-2":10234,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl":10301,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl-part-2":10639,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl":10640,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl-part-2":10641,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl":10642,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl-part-2":10643,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl":10644,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl-part-2":10645,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Frlhf-and-language-models":10646,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps":10647,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps-part-2":10648,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl":10649,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl-part-2":10650,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmeta-rl-and-generalization":10651,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fpsychology-of-reinforcement":10652,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Finstrumental-conditioning-and-control":10653,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-and-td-error":10654,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-in-the-brain":10655,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fanimal-learning-and-cognition":10656,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fcognitive-maps-and-planning":10657,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fneuroscience-of-reinforcement":10658,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fseveral-learning-systems":10659,"\u002Freinforcement-learning":10187,"\u002Fartificial-intelligence\u002Ffoundations\u002Fwhat-is-ai":10660,"\u002Fartificial-intelligence\u002Ffoundations\u002Ffoundations-of-ai":10661,"\u002Fartificial-intelligence\u002Ffoundations\u002Fintelligent-agents":10662,"\u002Fartificial-intelligence\u002Ffoundations\u002Fagent-architectures":10663,"\u002Fartificial-intelligence\u002Fsearch\u002Funinformed-search":10664,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-strategies-compared":10665,"\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search":10666,"\u002Fartificial-intelligence\u002Fsearch\u002Fheuristic-functions":10667,"\u002Fartificial-intelligence\u002Fsearch\u002Flocal-search":10668,"\u002Fartificial-intelligence\u002Fsearch\u002Fpopulation-and-continuous-search":10669,"\u002Fartificial-intelligence\u002Fsearch\u002Fadversarial-search":10670,"\u002Fartificial-intelligence\u002Fsearch\u002Fgames-of-chance-and-imperfect-information":10671,"\u002Fartificial-intelligence\u002Fsearch\u002Fconstraint-satisfaction":10672,"\u002Fartificial-intelligence\u002Fsearch\u002Fcsp-search-and-structure":10514,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-under-uncertainty":10369,"\u002Fartificial-intelligence\u002Fsearch\u002Fbelief-state-and-online-search":10673,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic":10674,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-inference":10675,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic":10676,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic-in-use":10677,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Finference-and-resolution":10678,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-resolution":10496,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fclassical-planning":10679,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-graphs-and-graphplan":10680,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-in-the-real-world":10681,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-under-uncertainty":10682,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fknowledge-representation":10683,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Freasoning-systems-and-defaults":10684,"\u002Fartificial-intelligence\u002Funcertainty\u002Fprobability-and-bayes":10685,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayes-rule-and-naive-bayes":10686,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayesian-networks":10687,"\u002Fartificial-intelligence\u002Funcertainty\u002Finference-in-bayesian-networks":10688,"\u002Fartificial-intelligence\u002Funcertainty\u002Freasoning-over-time":10689,"\u002Fartificial-intelligence\u002Funcertainty\u002Ftracking-and-data-association":10690,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions":10428,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmarkov-decision-processes":10680,"\u002Fartificial-intelligence\u002Funcertainty\u002Fdecision-networks-and-game-theory":10691,"\u002Fartificial-intelligence\u002Funcertainty\u002Fgame-theory-and-mechanism-design":9968,"\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples":9900,"\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families":10692,"\u002Fartificial-intelligence\u002Flearning\u002Fprobabilistic-learning":10693,"\u002Fartificial-intelligence\u002Flearning\u002Fexpectation-maximization":10694,"\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning":10695,"\u002Fartificial-intelligence\u002Flearning\u002Fgeneralization-and-policy-search":10477,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-in-learning":10696,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-based-learning-methods":10697,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fvision-and-perception":10698,"\u002Fartificial-intelligence\u002Ffrontiers\u002Freconstructing-the-3d-world":10699,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobotics":10700,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobot-planning-and-control":10701,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnatural-language-in-ai":10702,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnlp-grammar-translation-and-speech":10703,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fphilosophy-and-future":10704,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fai-ethics-and-future":10705,"\u002Fartificial-intelligence":10187,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-constituents-nuclide-chart":10444,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-size-charge-distributions":10706,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-masses-binding-energy":10004,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fsemi-empirical-mass-formula":10002,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-moments-multipoles":10538,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnuclear-force-shell-overview":10707,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fthe-deuteron":10030,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnucleon-nucleon-scattering":10340,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fmeson-theory-isospin":10708,"\u002Fnuclear-physics\u002Fnuclear-models\u002Ffermi-gas-model":10709,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fliquid-drop-collective-coordinates":10710,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fshell-model-single-particle":10400,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fcollective-model-rotations-vibrations":10711,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-law-modes":10712,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-kinetics-equilibrium":10713,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-decay-gamow-theory":10599,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-fine-structure-hindrance":10714,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fbeta-decay-energetics-neutrino":10715,"\u002Fnuclear-physics\u002Fbeta-decay\u002Ffermi-theory-beta-decay":10014,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fweak-interaction-parity-violation":10243,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fdouble-beta-decay-neutrino-mass":10716,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fgamma-multipole-radiation":10344,"\u002Fnuclear-physics\u002Fgamma-decay\u002Finternal-conversion-isomers":10717,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fangular-correlations-mossbauer":10718,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Freaction-kinematics-cross-sections":10239,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fcompound-nucleus-resonances":10327,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fdirect-reactions-optical-model":10719,"\u002Fnuclear-physics\u002Ffission\u002Ffission-barrier-dynamics":10720,"\u002Fnuclear-physics\u002Ffission\u002Fchain-reactions-reactor-physics":10721,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Ffusion-reactions-confinement":10009,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fstellar-nucleosynthesis":10384,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fbig-bang-nucleosynthesis":10246,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fcharged-particle-stopping-power":10722,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fphoton-neutron-interactions":10294,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fradiation-detectors":10358,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fdosimetry-radiation-biology":10723,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fnuclear-applications-dating-medicine":10724,"\u002Fnuclear-physics":10725,"\u002Fnatural-language-processing\u002Ffoundations\u002Fwhat-is-nlp":10726,"\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization":10727,"\u002Fnatural-language-processing\u002Ffoundations\u002Fminimum-edit-distance":10365,"\u002Fnatural-language-processing\u002Ffoundations\u002Fn-gram-language-models":10728,"\u002Fnatural-language-processing\u002Ffoundations\u002Fsmoothing-and-backoff":10729,"\u002Fnatural-language-processing\u002Fclassification\u002Fnaive-bayes-and-sentiment":10730,"\u002Fnatural-language-processing\u002Fclassification\u002Fevaluating-classifiers":10213,"\u002Fnatural-language-processing\u002Fclassification\u002Flogistic-regression":10731,"\u002Fnatural-language-processing\u002Fclassification\u002Fsentiment-and-affect-lexicons":10732,"\u002Fnatural-language-processing\u002Fsemantics\u002Fvector-semantics-and-embeddings":10510,"\u002Fnatural-language-processing\u002Fsemantics\u002Fstatic-word-embeddings":10733,"\u002Fnatural-language-processing\u002Fsemantics\u002Fneural-language-models":10679,"\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling":10734,"\u002Fnatural-language-processing\u002Fsequences\u002Fcrfs-and-neural-taggers":10735,"\u002Fnatural-language-processing\u002Fsequences\u002Frnns-and-lstms":10736,"\u002Fnatural-language-processing\u002Ftransformers\u002Ftransformers-and-attention":10737,"\u002Fnatural-language-processing\u002Ftransformers\u002Fthe-transformer-architecture":10738,"\u002Fnatural-language-processing\u002Ftransformers\u002Flarge-language-models":10739,"\u002Fnatural-language-processing\u002Ftransformers\u002Fllm-pretraining-and-scaling":10740,"\u002Fnatural-language-processing\u002Ftransformers\u002Ffine-tuning-and-prompting":10191,"\u002Fnatural-language-processing\u002Ftransformers\u002Fprompting-and-alignment":10741,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-parsing":10742,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcky-scoring-and-evaluation":10685,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdependency-parsing":10743,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fgraph-based-and-neural-dependency-parsing":10744,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fword-senses-and-wsd":10745,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fwsd-in-practice-and-induction":10746,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-roles-and-information-extraction":10747,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Frelations-events-and-templates":10748,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoreference-and-discourse":10749,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoherence-and-discourse-structure":10750,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Flogical-semantics":10609,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcompositional-semantics-and-description-logics":10751,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-parsing":10752,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fneural-semantic-parsing":10753,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Finformation-extraction":10754,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftimes-events-and-templates":10755,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdiscourse-coherence":10756,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fentity-based-and-global-coherence":10757,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-grammars":10758,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftreebanks-and-lexicalized-grammars":10759,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation":10760,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation-decoding-and-evaluation":10761,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering":10762,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering-knowledge-and-llms":10463,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots":10630,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants":10219,"\u002Fnatural-language-processing\u002Fapplications\u002Ftext-summarization":10763,"\u002Fnatural-language-processing\u002Fapplications\u002Fabstractive-summarization-and-evaluation":10764,"\u002Fnatural-language-processing\u002Fspeech\u002Fphonetics":10765,"\u002Fnatural-language-processing\u002Fspeech\u002Facoustic-phonetics":10766,"\u002Fnatural-language-processing\u002Fspeech\u002Fautomatic-speech-recognition":10477,"\u002Fnatural-language-processing\u002Fspeech\u002Fasr-evaluation-and-applications":10767,"\u002Fnatural-language-processing":10187,"\u002Fparticle-physics\u002Ffoundations\u002Fhistorical-overview-particle-zoo":10768,"\u002Fparticle-physics\u002Ffoundations\u002Fparticle-physics-basic-concepts":10385,"\u002Fparticle-physics\u002Ffoundations\u002Ffundamental-interactions-force-carriers":10769,"\u002Fparticle-physics\u002Funits-kinematics\u002Fnatural-units-and-scales":10530,"\u002Fparticle-physics\u002Funits-kinematics\u002Ffour-vectors-invariant-mass":10770,"\u002Fparticle-physics\u002Funits-kinematics\u002Fdecay-scattering-kinematics-mandelstam":10771,"\u002Fparticle-physics\u002Funits-kinematics\u002Fcross-sections-golden-rule":10719,"\u002Fparticle-physics\u002Fsymmetries\u002Fconservation-laws-symmetries":10772,"\u002Fparticle-physics\u002Fsymmetries\u002Fdiscrete-symmetries-cpt":10773,"\u002Fparticle-physics\u002Fsymmetries\u002Fparity-violation-weak":10335,"\u002Fparticle-physics\u002Fsymmetries\u002Fsu2-su3-flavor-symmetry":10037,"\u002Fparticle-physics\u002Fquark-model\u002Feightfold-way-su3":10774,"\u002Fparticle-physics\u002Fquark-model\u002Fmeson-spectroscopy":10289,"\u002Fparticle-physics\u002Fquark-model\u002Fbaryon-spectroscopy":10775,"\u002Fparticle-physics\u002Fquark-model\u002Fcolor-confinement-exotics":10322,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fklein-gordon-equation":10776,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fdirac-equation-spinors":10576,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fantiparticles-hole-theory":10777,"\u002Fparticle-physics\u002Fqed\u002Ffeynman-rules-qed":10778,"\u002Fparticle-physics\u002Fqed\u002Fqed-tree-processes":10545,"\u002Fparticle-physics\u002Fqed\u002Frenormalization-running-coupling":10779,"\u002Fparticle-physics\u002Fqed\u002Felectron-g-2":10008,"\u002Fparticle-physics\u002Fweak-interaction\u002Fva-structure-weak":10780,"\u002Fparticle-physics\u002Fweak-interaction\u002Fw-z-bosons-decays":10781,"\u002Fparticle-physics\u002Fweak-interaction\u002Fckm-matrix":10782,"\u002Fparticle-physics\u002Fweak-interaction\u002Fcp-violation-kaons-b-mesons":10326,"\u002Fparticle-physics\u002Fqcd\u002Fcolor-su3-gluons":10542,"\u002Fparticle-physics\u002Fqcd\u002Fasymptotic-freedom-confinement":10783,"\u002Fparticle-physics\u002Fqcd\u002Fdeep-inelastic-scattering-partons":10784,"\u002Fparticle-physics\u002Fqcd\u002Fjets-hadronization":10401,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Felectroweak-su2-u1":10785,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fspontaneous-symmetry-breaking":10446,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-mechanism":10786,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-boson-discovery":10787,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fstandard-model":10335,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-oscillations":10368,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-mass-pmns":10788,"\u002Fparticle-physics\u002Fneutrinos\u002Fdirac-majorana-experiments":10789,"\u002Fparticle-physics\u002Fexperiment\u002Faccelerators-luminosity":10790,"\u002Fparticle-physics\u002Fexperiment\u002Fdetectors-subsystems":10424,"\u002Fparticle-physics\u002Fexperiment\u002Fhow-discoveries-are-made":10791,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fbeyond-standard-model":10165,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fgrand-unified-theories":10792,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fsupersymmetry":10268,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fhierarchy-problem-naturalness":10793,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fdark-matter-candidates":10794,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fmatter-antimatter-open-questions":10592,"\u002Fparticle-physics":10795,"\u002Fastrophysics-cosmology\u002Forientation\u002Fthe-sun-and-stars":10386,"\u002Fastrophysics-cosmology\u002Forientation\u002Fstellar-death-final-states":10540,"\u002Fastrophysics-cosmology\u002Forientation\u002Fgalaxies-and-cosmology":10796,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fmagnitudes-fluxes-and-the-distance-modulus":10325,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fstellar-spectra-and-spectral-classification":10797,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Ftelescopes-and-detectors-across-the-spectrum":10385,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fthe-cosmic-distance-ladder":10297,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fblackbody-radiation-and-specific-intensity":10798,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fradiative-transfer-and-the-transfer-equation":10799,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fspectral-line-formation-and-broadening":10800,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fopacity-and-the-rosseland-mean":10801,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fhydrostatic-equilibrium-and-the-virial-theorem":10802,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equations-of-stellar-structure":10591,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equation-of-state-and-polytropes":10522,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-standard-solar-model":10803,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fthermonuclear-reaction-rates-and-the-gamow-peak":10804,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhydrogen-burning-pp-chains-and-cno":10805,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhelium-burning-and-the-triple-alpha-process":10806,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fadvanced-burning-and-neutron-capture-nucleosynthesis":10718,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fphases-of-the-interstellar-medium":10807,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fmolecular-clouds-and-gravitational-collapse":10281,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fprotostars-and-the-pre-main-sequence":10344,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-main-sequence-and-its-structure":10326,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fpost-main-sequence-low-mass-evolution":10808,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-evolution-of-massive-stars":10410,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fstellar-pulsation-and-the-instability-strip":10026,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":10809,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fcore-collapse-supernovae":10810,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fthermonuclear-supernovae-type-ia":10541,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fneutron-stars-and-pulsars":10811,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fblack-holes-schwarzschild-and-kerr":10812,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fbinary-systems-and-mass-transfer":10813,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Faccreting-compact-objects":10814,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fgravitational-waves-from-inspiraling-binaries":10007,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fmultimessenger-astronomy-and-gamma-ray-bursts":10815,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fthe-milky-way":10816,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-morphology-and-classification":10268,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-rotation-curves-and-dark-matter":10817,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Factive-galactic-nuclei-and-supermassive-black-holes":10818,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-clusters-and-large-scale-structure":10534,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-expanding-universe-and-hubbles-law":10189,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-frw-metric-and-cosmological-redshift":10819,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-friedmann-equations-and-cosmic-dynamics":10820,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fcosmological-models-and-distances":10446,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fdark-energy-and-the-accelerating-universe":10432,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fthe-thermal-history-of-the-universe":10444,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fbig-bang-nucleosynthesis":10541,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Frecombination-and-the-cosmic-microwave-background":10821,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcmb-anisotropies-and-cosmological-parameters":10006,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcosmic-inflation":10198,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fstructure-formation-and-the-growth-of-perturbations":10822,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fdark-matter-dark-energy-and-open-questions":10285,"\u002Fastrophysics-cosmology":10390,"\u002Fcolophon":10823,"\u002F":10187},4250,4808,3626,2682,4109,4786,3878,3875,3751,3415,4067,3153,3000,4042,5461,5808,3961,3749,4327,5067,4246,4655,4154,5436,2640,4003,3601,2158,4331,4189,2273,3252,4633,4964,4172,3131,5524,3160,4031,2309,4207,3226,2648,4842,5340,3307,5701,4977,4039,2615,3472,4460,3848,4075,4400,3382,3010,3602,3737,3740,3707,3922,5191,4043,3804,4542,4214,5062,2850,4361,3443,3627,4044,3766,4140,3860,4006,5199,4334,5234,3651,5509,5680,153,1375,1073,1093,1125,1146,1014,1132,876,1541,1189,1173,984,1402,1301,950,1268,1063,1107,1408,1161,925,1012,866,964,1090,1142,1085,1020,1207,973,980,728,764,1225,1329,796,929,801,878,774,1044,1488,1175,1130,890,814,870,154,4073,5140,4961,5127,4870,5382,5195,4955,5369,4501,5576,3824,4132,4289,4307,4570,3403,5084,5105,5201,5116,5341,5175,5368,5188,5211,5499,5155,4981,5125,5415,5255,5304,5130,5167,5552,5164,5094,5239,5036,5190,5004,5099,5035,5159,5088,5026,4937,5023,5264,5244,133,5114,5078,5043,5312,5170,5342,5139,5151,5049,5212,5013,5068,5079,5102,5121,5081,5029,5379,5854,5110,2139,3798,5055,5364,4984,4935,4895,4972,5289,5112,5156,4987,5031,5025,5149,5302,5042,5002,4979,4922,4960,5279,126,1877,1180,1129,907,958,1112,1300,1053,1250,1181,1241,1234,966,1050,734,1190,484,1082,926,733,761,571,607,798,804,952,977,731,784,645,771,1017,742,1004,1000,1562,1254,1288,1101,1011,1486,1061,856,992,1169,988,137,0,2037,1782,2384,2254,2123,2332,1643,1714,2089,1751,1367,1660,2511,1998,1892,1854,1791,2438,2487,1917,2375,2525,2266,1845,2275,1810,1631,2310,2166,2233,2113,2505,2347,2672,2112,2473,2592,2380,3013,2513,3256,3218,2194,2173,2205,2326,2081,3342,3152,1799,1670,1027,960,1095,1291,986,897,1209,1055,1817,1801,1593,1465,1196,1464,1201,1230,1435,1684,1461,1926,1500,1409,1284,1774,1869,162,1487,1122,1188,1001,1351,982,1005,979,1325,1046,943,1279,824,1008,989,1798,1277,1025,987,1043,1211,1074,981,939,1002,739,1139,1108,1013,1070,978,1458,1317,157,1357,1077,2355,1116,1037,1178,1637,1314,1109,1056,1702,1474,1071,1158,832,993,1404,1024,1068,1339,1106,1264,1248,913,1848,1328,1633,1224,1143,135,1378,959,1028,998,911,1527,1203,1266,1483,1165,990,938,965,1257,1418,1099,942,1352,956,1035,1398,1003,1094,1292,138,1721,1827,1449,1354,1148,1184,1285,1281,1213,1290,1271,1252,1274,1778,1591,1503,1437,1571,1584,1957,1117,1781,1648,1342,1667,1510,1965,1607,1365,1849,1259,1303,1356,1238,2208,1564,173,1671,1286,1227,1638,1529,668,1078,918,709,865,880,940,1534,1015,874,922,841,794,1194,822,1105,1658,1359,1296,1438,1921,1844,1570,1429,1324,1400,140,1787,1558,1654,1492,1747,2224,2002,2009,1323,1349,1785,1573,1722,1829,1353,1548,1552,1583,1624,1585,1245,1364,1514,1343,1397,1355,2211,1481,1770,160,2388,2293,2256,2552,2569,2478,2039,2496,2578,2814,2519,2461,2587,2492,2714,3278,2654,3050,2447,2849,2238,2369,2061,2214,2602,2563,2186,2985,2749,3364,2038,2282,2409,2126,2573,2206,2176,2268,2182,2402,2705,2633,2414,2213,2801,3313,3410,3195,1952,2017,1509,2537,2645,2027,2415,2838,2356,1906,3184,2950,2807,2954,1683,1316,1034,1138,1763,1822,1705,1246,1701,1097,1104,1187,1032,1083,1228,916,1489,1033,1652,997,692,837,1023,888,864,1089,1231,1214,1675,1156,1075,1520,1309,139,1205,1051,735,1123,1072,915,567,768,825,1253,983,1007,762,1058,861,862,971,1208,1149,1145,1029,1084,927,810,838,857,807,936,949,2321,1622,1069,1113,1057,854,1958,1528,1618,2049,1432,1679,1796,1685,1346,1275,1476,1505,1610,2018,1599,1215,1838,1909,132,3902,2215,2240,3266,3208,3073,2454,2969,2451,1875,2728,1884,2371,2516,2842,1690,1904,2346,3146,1386,2607,1966,2668,1665,2885,1606,2577,3074,2869,2403,2433,2082,1939,1587,2460,2747,2032,2642,1619,3123,1993,2090,2339,3829,1737,2622,2340,2322,3828,4409,2305,3411,2510,4527,3030,3569,3043,2457,1946,2277,2044,2909,1693,1945,2093,2399,2115,2898,2742,2242,3895,3378,3376,2769,2223,3062,3262,2651,2949,2768,3128,2423,1977,2087,2866,3388,2830,2210,2489,3945,2099,2713,3402,1692,2931,4195,3989,3206,4391,3004,3704,3494,2902,999,881,901,919,748,869,1018,1045,1049,1333,954,1092,1019,976,1771,1480,1396,953,1026,161,3533,2495,1818,3007,2595,3427,3537,2216,1895,2304,3396,1739,2073,1962,2203,1767,2666,2264,2276,2852,1807,3735,1560,4144,1669,1676,1972,2418,3291,1525,2040,2766,2337,2220,2800,3001,2078,1759,2836,1896,2026,1758,1543,1047,896,946,1060,1384,1482,815,1414,1322,1440,1240,1468,1098,1133,847,1009,1381,1052,1191,1258,1370,1712,1441,1199,957,1079,150,1262,1417,1368,1219,1136,1064,1463,1636,1059,931,1115,1736,1174,1376,1363,1411,1247,1746,1313,1299,1617,1102,1076,1495,1265,1193,1263,80,{"\u002Falgorithms\u002Ffoundations\u002Fwhat-is-an-algorithm":10825,"\u002Falgorithms\u002Ffoundations\u002Fproof-techniques":10830,"\u002Falgorithms\u002Ffoundations\u002Fasymptotic-analysis":10834,"\u002Falgorithms\u002Ffoundations\u002Fgrowth-rates-and-loop-analysis":10838,"\u002Falgorithms\u002Ffoundations\u002Frecurrences":10842,"\u002Falgorithms\u002Ffoundations\u002Famortized-analysis":10846,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fmergesort":10850,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fquicksort":10855,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fselection":10859,"\u002Falgorithms\u002Fdivide-and-conquer\u002Ffast-multiplication":10863,"\u002Falgorithms\u002Fsorting\u002Fheaps-and-heapsort":10867,"\u002Falgorithms\u002Fsorting\u002Fsorting-lower-bounds":10872,"\u002Falgorithms\u002Fsorting\u002Flinear-time-sorting":10876,"\u002Falgorithms\u002Fsorting\u002Fexternal-sorting":10880,"\u002Falgorithms\u002Fdata-structures\u002Felementary-structures":10884,"\u002Falgorithms\u002Fdata-structures\u002Fhash-tables":10889,"\u002Falgorithms\u002Fdata-structures\u002Fbinary-search-trees":10893,"\u002Falgorithms\u002Fdata-structures\u002Favl-trees":10897,"\u002Falgorithms\u002Fdata-structures\u002Fbalanced-trees":10901,"\u002Falgorithms\u002Fdata-structures\u002Funion-find":10905,"\u002Falgorithms\u002Fdata-structures\u002Ffenwick-and-segment-trees":10909,"\u002Falgorithms\u002Fdata-structures\u002Fspatial-data-structures":10913,"\u002Falgorithms\u002Fdata-structures\u002Fskip-lists-and-probabilistic-structures":10917,"\u002Falgorithms\u002Fdata-structures\u002Fb-trees":10921,"\u002Falgorithms\u002Fdata-structures\u002Fdata-stream-algorithms":10925,"\u002Falgorithms\u002Fdata-structures\u002Fstreaming-sketches":10929,"\u002Falgorithms\u002Fsequences\u002Ftwo-pointers-and-windows":10933,"\u002Falgorithms\u002Fsequences\u002Fprefix-sums":10938,"\u002Falgorithms\u002Fsequences\u002Fmonotonic-stacks":10942,"\u002Falgorithms\u002Fsequences\u002Fbinary-search-on-the-answer":10946,"\u002Falgorithms\u002Fsequences\u002Fstring-matching":10950,"\u002Falgorithms\u002Fsequences\u002Fkmp-and-z-function":10954,"\u002Falgorithms\u002Fsequences\u002Ftries":10958,"\u002Falgorithms\u002Fsequences\u002Fsuffix-arrays-and-aho-corasick":10962,"\u002Falgorithms\u002Fgraphs\u002Frepresentations-and-traversal":10966,"\u002Falgorithms\u002Fgraphs\u002Fdepth-first-search":10971,"\u002Falgorithms\u002Fgraphs\u002Ftopological-sort-and-scc":10975,"\u002Falgorithms\u002Fgraphs\u002Fminimum-spanning-trees":10979,"\u002Falgorithms\u002Fgraphs\u002Fkruskal-and-prim":10983,"\u002Falgorithms\u002Fgraphs\u002Fshortest-paths":10987,"\u002Falgorithms\u002Fgraphs\u002Fall-pairs-and-negative-weights":10991,"\u002Falgorithms\u002Fgraphs\u002Fnetwork-flow":10995,"\u002Falgorithms\u002Fgraphs\u002Fmax-flow-min-cut":10999,"\u002Falgorithms\u002Fgraphs\u002Fbridges-and-articulation-points":11003,"\u002Falgorithms\u002Fgraphs\u002Flowest-common-ancestor":11007,"\u002Falgorithms\u002Fgraphs\u002Ftwo-sat":11011,"\u002Falgorithms\u002Fgraphs\u002Feulerian-tours":11015,"\u002Falgorithms\u002Fgraphs\u002Fbipartite-matching":11019,"\u002Falgorithms\u002Fgreedy\u002Fthe-greedy-method":11023,"\u002Falgorithms\u002Fgreedy\u002Fscheduling-and-intervals":11028,"\u002Falgorithms\u002Fgreedy\u002Fhuffman-codes":11032,"\u002Falgorithms\u002Fgreedy\u002Fmatroids":11036,"\u002Falgorithms\u002Fgreedy\u002Fstable-matching":11040,"\u002Falgorithms\u002Fdynamic-programming\u002Fprinciples":11044,"\u002Falgorithms\u002Fdynamic-programming\u002Fsequence-dp":11049,"\u002Falgorithms\u002Fdynamic-programming\u002Flongest-increasing-subsequence":11053,"\u002Falgorithms\u002Fdynamic-programming\u002Fknapsack":11057,"\u002Falgorithms\u002Fdynamic-programming\u002Fcoin-change-and-unbounded":11061,"\u002Falgorithms\u002Fdynamic-programming\u002Finterval-dp":11065,"\u002Falgorithms\u002Fdynamic-programming\u002Ftree-dp":11069,"\u002Falgorithms\u002Fdynamic-programming\u002Fbitmask-dp":11073,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-optimizations":11077,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-on-graphs":11081,"\u002Falgorithms\u002Fdynamic-programming\u002Fdigit-and-probability-dp":11085,"\u002Falgorithms\u002Fbacktracking\u002Fbacktracking-fundamentals":11089,"\u002Falgorithms\u002Fbacktracking\u002Fconstraint-search":11094,"\u002Falgorithms\u002Fbacktracking\u002Fbranch-and-bound":11098,"\u002Falgorithms\u002Fbacktracking\u002Fgraph-backtracking":11102,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fnumber-theory-basics":11106,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmodular-exponentiation-and-primality":11111,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fsieve-and-factorization":11115,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fcombinatorics":11119,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmatrix-exponentiation":11123,"\u002Falgorithms\u002Fmathematical-algorithms\u002Ffast-fourier-transform":11127,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fgradient-descent":11131,"\u002Falgorithms\u002Fcomputational-geometry\u002Fgeometric-primitives":11135,"\u002Falgorithms\u002Fcomputational-geometry\u002Fconvex-hull":11140,"\u002Falgorithms\u002Fcomputational-geometry\u002Fsweep-line":11144,"\u002Falgorithms\u002Fcomputational-geometry\u002Fpolygons-and-proximity":11148,"\u002Falgorithms\u002Fintractability\u002Fp-np-reductions":11152,"\u002Falgorithms\u002Fintractability\u002Fnp-completeness":11157,"\u002Falgorithms\u002Fintractability\u002Fcoping-with-hardness":11161,"\u002Falgorithms\u002Fintractability\u002Fapproximation-algorithms":11165,"\u002Falgorithms":11169,"\u002Fcalculus\u002Flimits-and-continuity\u002Ffunctions-and-models":11172,"\u002Fcalculus\u002Flimits-and-continuity\u002Fthe-limit-of-a-function":11177,"\u002Fcalculus\u002Flimits-and-continuity\u002Flimit-laws-and-the-precise-definition":11181,"\u002Fcalculus\u002Flimits-and-continuity\u002Fcontinuity":11185,"\u002Fcalculus\u002Fderivatives\u002Fthe-derivative-and-rates-of-change":11189,"\u002Fcalculus\u002Fderivatives\u002Fdifferentiation-rules-and-the-chain-rule":11194,"\u002Fcalculus\u002Fderivatives\u002Fimplicit-differentiation-and-related-rates":11198,"\u002Fcalculus\u002Fderivatives\u002Flinear-approximations-and-differentials":11202,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fextrema-and-the-mean-value-theorem":11206,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fhow-derivatives-shape-a-graph":11211,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fcurve-sketching-and-optimization":11215,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fnewtons-method-and-antiderivatives":11219,"\u002Fcalculus\u002Fintegrals\u002Farea-and-the-definite-integral":11223,"\u002Fcalculus\u002Fintegrals\u002Fthe-fundamental-theorem-of-calculus":11228,"\u002Fcalculus\u002Fintegrals\u002Fthe-substitution-rule":11232,"\u002Fcalculus\u002Fapplications-of-integration\u002Fareas-and-volumes":11236,"\u002Fcalculus\u002Fapplications-of-integration\u002Fwork-average-value-and-arc-length":11241,"\u002Fcalculus\u002Fapplications-of-integration\u002Fphysics-economics-and-probability":11245,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Finverse-functions-logarithms-and-exponentials":11249,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Fgrowth-decay-inverse-trig-and-hyperbolic-functions":11254,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Flhospitals-rule":11258,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fintegration-by-parts":11262,"\u002Fcalculus\u002Ftechniques-of-integration\u002Ftrigonometric-integrals-and-substitution":11267,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fpartial-fractions-and-integration-strategy":11271,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fapproximate-and-improper-integrals":11275,"\u002Fcalculus\u002Fparametric-and-polar\u002Fparametric-curves-and-their-calculus":11279,"\u002Fcalculus\u002Fparametric-and-polar\u002Fpolar-coordinates":11284,"\u002Fcalculus\u002Fparametric-and-polar\u002Fconic-sections":11288,"\u002Fcalculus\u002Fsequences-and-series\u002Fsequences":11292,"\u002Fcalculus\u002Fsequences-and-series\u002Fseries-and-the-integral-test":11297,"\u002Fcalculus\u002Fsequences-and-series\u002Fthe-convergence-tests":11301,"\u002Fcalculus\u002Fsequences-and-series\u002Fpower-series":11305,"\u002Fcalculus\u002Fsequences-and-series\u002Ftaylor-and-maclaurin-series":11309,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvectors-and-the-dot-product":11313,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fthe-cross-product-lines-and-planes":11318,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fcylinders-and-quadric-surfaces":11322,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvector-functions-and-space-curves":11326,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Farc-length-curvature-and-motion":11330,"\u002Fcalculus\u002Fpartial-derivatives\u002Ffunctions-of-several-variables":11334,"\u002Fcalculus\u002Fpartial-derivatives\u002Fpartial-derivatives":11339,"\u002Fcalculus\u002Fpartial-derivatives\u002Ftangent-planes-and-the-chain-rule":11342,"\u002Fcalculus\u002Fpartial-derivatives\u002Fdirectional-derivatives-and-the-gradient":11346,"\u002Fcalculus\u002Fpartial-derivatives\u002Foptimization-and-lagrange-multipliers":11350,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fdouble-integrals":11354,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Ftriple-integrals-and-coordinate-systems":11359,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fvector-fields-and-line-integrals":11363,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fgreens-theorem-curl-and-divergence":11367,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fsurface-integrals":11371,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fstokes-and-the-divergence-theorem":11375,"\u002Fcalculus":11379,"\u002Fmechanics\u002Ffoundations\u002Fmeasurement-and-dimensions":11382,"\u002Fmechanics\u002Ffoundations\u002Fvector-algebra":11386,"\u002Fmechanics\u002Fkinematics\u002Fone-dimensional-motion":11390,"\u002Fmechanics\u002Fkinematics\u002Fmotion-graphs":11395,"\u002Fmechanics\u002Fkinematics\u002Fprojectile-motion":11399,"\u002Fmechanics\u002Fkinematics\u002Frelative-motion":11403,"\u002Fmechanics\u002Fkinematics\u002Fcircular-motion":11407,"\u002Fmechanics\u002Fdynamics\u002Fnewtons-laws":11411,"\u002Fmechanics\u002Fdynamics\u002Ffree-body-diagrams":11416,"\u002Fmechanics\u002Fdynamics\u002Ffriction-and-curved-motion":11420,"\u002Fmechanics\u002Fdynamics\u002Fnumerical-dynamics":11424,"\u002Fmechanics\u002Fdynamics\u002Fcenter-of-mass-systems":11428,"\u002Fmechanics\u002Fenergy\u002Fwork-and-kinetic-energy":11432,"\u002Fmechanics\u002Fenergy\u002Fpotential-energy":11437,"\u002Fmechanics\u002Fenergy\u002Fmultiparticle-work":11441,"\u002Fmechanics\u002Fenergy\u002Fmass-energy-and-binding":11445,"\u002Fmechanics\u002Fenergy\u002Fphotons-and-quantization":11449,"\u002Fmechanics\u002Fmomentum\u002Fmomentum-and-collisions":11453,"\u002Fmechanics\u002Fmomentum\u002Fcenter-of-mass-collisions":11458,"\u002Fmechanics\u002Fmomentum\u002Frocket-propulsion":11462,"\u002Fmechanics\u002Frotation\u002Frotational-inertia":11466,"\u002Fmechanics\u002Frotation\u002Frotational-dynamics":11471,"\u002Fmechanics\u002Frotation\u002Frolling-motion":11475,"\u002Fmechanics\u002Frotation\u002Fangular-momentum":11479,"\u002Fmechanics\u002Frotation\u002Frolling-resistance":11483,"\u002Fmechanics\u002Frotation\u002Fgyroscopic-precession":11487,"\u002Fmechanics\u002Fgravity-and-matter\u002Fkeplerian-orbits":11491,"\u002Fmechanics\u002Fgravity-and-matter\u002Fgravitational-fields":11496,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstatic-equilibrium":11500,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-statics":11504,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-flow":11508,"\u002Fmechanics\u002Fgravity-and-matter\u002Forbital-motion":11512,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstress-and-elasticity":11516,"\u002Fmechanics\u002Foscillations-waves\u002Fdamped-oscillators":11520,"\u002Fmechanics\u002Foscillations-waves\u002Ftravelling-waves":11525,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-superposition":11529,"\u002Fmechanics\u002Foscillations-waves\u002Fstanding-waves":11533,"\u002Fmechanics\u002Foscillations-waves\u002Fsound-waves":11537,"\u002Fmechanics\u002Foscillations-waves\u002Fdoppler-effect":11541,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-packets":11545,"\u002Fmechanics\u002Foscillations-waves\u002Fbeats-and-coupling":11549,"\u002Fmechanics\u002Foscillations-waves\u002Fsimple-harmonic-motion":11553,"\u002Fmechanics\u002Foscillations-waves\u002Fpendulum-motion":11557,"\u002Fmechanics\u002Foscillations-waves\u002Fdriven-oscillators":11561,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-boundaries":11565,"\u002Fmechanics\u002Fthermodynamics\u002Fkinetic-theory-of-ideal-gases":11569,"\u002Fmechanics\u002Fthermodynamics\u002Ffirst-law-of-thermodynamics":11574,"\u002Fmechanics\u002Fthermodynamics\u002Fentropy-and-the-second-law":11578,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-processes":11582,"\u002Fmechanics\u002Fthermodynamics\u002Fphase-changes":11586,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-machines":11590,"\u002Fmechanics":11594,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcharge-and-conductors":11597,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcoulombs-law":11602,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-and-force":11606,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-maps":11610,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-dipoles":11614,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fcontinuous-charge-fields":11618,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fgauss-law-and-conductors":11623,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpoint-charge-potential":11627,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpotential-gradients-and-equipotentials":11632,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Felectrostatic-energy-and-pressure":11636,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Flaplace-boundary-problems":11640,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fcontinuous-charge-potentials":11644,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitance-fundamentals":11648,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-networks":11653,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-energy-and-force":11657,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fdielectric-polarization-and-breakdown":11661,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fcurrent-and-resistance":11665,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fkirchhoff-network-analysis":11670,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Frc-transients":11674,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-trajectories":11678,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fhall-effect":11683,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-force-on-conductors":11687,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-dipoles":11691,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmass-spectrometry":11695,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmoving-charge-fields":11699,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fbiot-savart-law":11704,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fcircular-current-loops":11708,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Famperes-law":11712,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fgauss-law-for-magnetism":11716,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmagnetic-materials":11720,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-flux":11724,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Ffaradays-law":11729,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Flenzs-law":11733,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmotional-emf":11737,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Feddy-currents":11741,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fself-inductance":11745,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-energy":11749,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Frl-circuits":11753,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-fundamentals":11757,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Freactance":11762,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Frlc-resonance":11766,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-power":11770,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Ftransformers":11774,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdisplacement-current":11778,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-waves":11783,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-momentum":11787,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdipole-radiation":11791,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fpolarization":11795,"\u002Felectricity-and-magnetism\u002Foptics\u002Freflection-and-refraction":11799,"\u002Felectricity-and-magnetism\u002Foptics\u002Fthin-lenses":11804,"\u002Felectricity-and-magnetism\u002Foptics\u002Fspherical-mirrors":11808,"\u002Felectricity-and-magnetism":11812,"\u002Flinear-algebra\u002Flinear-systems\u002Fsystems-and-echelon-forms":11815,"\u002Flinear-algebra\u002Flinear-systems\u002Fvector-and-matrix-equations":11820,"\u002Flinear-algebra\u002Flinear-systems\u002Fsolution-sets-and-applications":11824,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-independence":11828,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-transformations":11832,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-operations":11836,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-inverse-and-invertibility":11841,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fpartitioned-matrices-and-lu":11845,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fsubspaces-dimension-rank":11849,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fapplications-leontief-and-graphics":11853,"\u002Flinear-algebra\u002Fdeterminants\u002Fdeterminants-and-cofactors":11857,"\u002Flinear-algebra\u002Fdeterminants\u002Fproperties-of-determinants":11862,"\u002Flinear-algebra\u002Fdeterminants\u002Fcramer-volume-and-area":11866,"\u002Flinear-algebra\u002Fvector-spaces\u002Fvector-spaces-and-subspaces":11870,"\u002Flinear-algebra\u002Fvector-spaces\u002Fnull-and-column-spaces":11875,"\u002Flinear-algebra\u002Fvector-spaces\u002Fbases-and-independent-sets":11879,"\u002Flinear-algebra\u002Fvector-spaces\u002Fcoordinate-systems":11883,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdimension-and-rank":11887,"\u002Flinear-algebra\u002Fvector-spaces\u002Fchange-of-basis":11891,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdifference-equations-and-markov":11895,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-eigenvalues":11899,"\u002Flinear-algebra\u002Feigenvalues\u002Fthe-characteristic-equation":11904,"\u002Flinear-algebra\u002Feigenvalues\u002Fdiagonalization":11908,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-linear-transformations":11912,"\u002Flinear-algebra\u002Feigenvalues\u002Fcomplex-eigenvalues":11916,"\u002Flinear-algebra\u002Feigenvalues\u002Fdynamical-systems":11920,"\u002Flinear-algebra\u002Feigenvalues\u002Fpower-method":11924,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-length-orthogonality":11928,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Forthogonal-sets-and-projections":11933,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fgram-schmidt-and-qr":11937,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-problems":11941,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-applications":11945,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-spaces":11949,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fdiagonalizing-symmetric-matrices":11953,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fquadratic-forms":11958,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fconstrained-optimization":11962,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsingular-value-decomposition":11966,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsvd-applications-pca-imaging":11970,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-thinking-and-matrix-computation":11974,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Flu-and-cholesky":11979,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fconditioning-and-floating-point":11983,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fstability-and-error-analysis":11987,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fqr-and-numerical-least-squares":11991,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-eigenvalues-and-svd":11995,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-combinations":11999,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-independence-and-barycentric-coordinates":12004,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fconvex-combinations-and-convex-sets":12008,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fhyperplanes-and-polytopes":12012,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fcurves-and-surfaces":12016,"\u002Flinear-algebra":12020,"\u002Ftheory-of-computation":12023,"\u002Fcomputer-architecture\u002Ffoundations\u002Fbits-bytes-and-words":12026,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-representation":12030,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-arithmetic":12034,"\u002Fcomputer-architecture\u002Ffoundations\u002Ffloating-point":12038,"\u002Fcomputer-architecture\u002Ffoundations\u002Fboolean-algebra-and-bit-manipulation":12042,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fthe-machines-view":12046,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fdata-movement":12051,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farithmetic-and-logic":12055,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fcontrol-flow":12059,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fprocedures":12063,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farrays-structs-and-alignment":12067,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fmemory-layout-and-buffer-overflows":12071,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fwhat-an-isa-is":12075,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Finstruction-formats-and-operands":12080,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Faddressing-modes":12084,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fthe-y86-64-instruction-set":12088,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fy86-64-programming":12092,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Ftransistors-gates-and-boolean-functions":12096,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fcombinational-logic-and-hcl":12101,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmultiplexers-decoders-and-the-alu":12105,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmemory-elements-latches-flip-flops-and-clocking":12109,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fregister-files-and-random-access-memory":12113,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-fetch-decode-execute-cycle":12117,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-seq-stages":12122,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fcontrol-logic-and-sequencing":12126,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fassembling-seq":12130,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Ftracing-a-program":12134,"\u002Fcomputer-architecture\u002Fpipelining\u002Fpipelining-principles":12138,"\u002Fcomputer-architecture\u002Fpipelining\u002Ffrom-seq-to-pipe":12143,"\u002Fcomputer-architecture\u002Fpipelining\u002Fdata-hazards-stalling-and-forwarding":12147,"\u002Fcomputer-architecture\u002Fpipelining\u002Fcontrol-hazards-and-branch-prediction":12151,"\u002Fcomputer-architecture\u002Fpipelining\u002Fthe-complete-pipe-processor":12155,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fstorage-technologies-and-the-latency-gap":12159,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Flocality":12164,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-memories-direct-mapped":12168,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fset-associative-and-write-policies":12172,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-performance-and-cache-friendly-code":12176,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Faddress-spaces-and-translation":12180,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fpage-tables-and-page-faults":12185,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fthe-tlb-and-multi-level-page-tables":12189,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Fexceptional-control-flow":12193,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Finterrupts-and-the-kernel":12198,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fprocesses-threads-and-parallelism":12202,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fhardware-multithreading":12207,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fcache-coherence":12211,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmemory-consistency-and-synchronization":12215,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmulticore-organization":12219,"\u002Fcomputer-architecture\u002Fcapstone\u002Fthe-whole-machine":12223,"\u002Fcomputer-architecture\u002Fcapstone\u002Fassembling-a-complete-cpu":12228,"\u002Fcomputer-architecture":12232,"\u002Fdifferential-equations\u002Ffoundations\u002Fmodels-and-direction-fields":12235,"\u002Fdifferential-equations\u002Ffoundations\u002Fclassification-and-terminology":12239,"\u002Fdifferential-equations\u002Ffirst-order\u002Flinear-first-order-integrating-factors":12243,"\u002Fdifferential-equations\u002Ffirst-order\u002Fseparable-and-exact":12248,"\u002Fdifferential-equations\u002Ffirst-order\u002Fmodeling-first-order":12252,"\u002Fdifferential-equations\u002Ffirst-order\u002Fautonomous-and-population-dynamics":12256,"\u002Fdifferential-equations\u002Ffirst-order\u002Fexistence-uniqueness-euler":12260,"\u002Fdifferential-equations\u002Ffirst-order\u002Ffirst-order-difference-equations":12264,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhomogeneous-constant-coefficients":12268,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fcomplex-and-repeated-roots":12273,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fnonhomogeneous-undetermined-coefficients":12277,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fvariation-of-parameters":12281,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fmechanical-electrical-vibrations":12285,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhigher-order-linear":12289,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fpower-series-ordinary-points":12293,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fregular-singular-frobenius":12298,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fbessel-and-special-functions":12302,"\u002Fdifferential-equations\u002Flaplace\u002Flaplace-definition-ivps":12306,"\u002Fdifferential-equations\u002Flaplace\u002Fstep-impulse-convolution":12311,"\u002Fdifferential-equations\u002Fsystems\u002Fmatrices-eigenvalues-review":12315,"\u002Fdifferential-equations\u002Fsystems\u002Fconstant-coefficient-systems-phase-portraits":12320,"\u002Fdifferential-equations\u002Fsystems\u002Frepeated-eigenvalues-fundamental-matrices":12324,"\u002Fdifferential-equations\u002Fnumerical\u002Feuler-and-runge-kutta":12328,"\u002Fdifferential-equations\u002Fnumerical\u002Fmultistep-systems-stability":12333,"\u002Fdifferential-equations\u002Fnonlinear\u002Fphase-plane-autonomous-stability":12337,"\u002Fdifferential-equations\u002Fnonlinear\u002Flocally-linear-and-liapunov":12342,"\u002Fdifferential-equations\u002Fnonlinear\u002Fcompeting-species-predator-prey-limit-cycles":12346,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Ffourier-series":12350,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fheat-wave-laplace-equations":12355,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fsturm-liouville":12359,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fcalculus-of-variations":12363,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fhistorical-notes":12368,"\u002Fdifferential-equations":12372,"\u002Frelativity\u002Ffoundations\u002Fspecial-relativity-postulates":12375,"\u002Frelativity\u002Ffoundations\u002Florentz-transformation-spacetime":12380,"\u002Frelativity\u002Ffoundations\u002Ftime-dilation-length-contraction":12384,"\u002Frelativity\u002Ffoundations\u002Frelativistic-momentum-energy":12388,"\u002Frelativity\u002Ffoundations\u002Fgeneral-relativity":12392,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fminkowski-spacetime-and-the-interval":12396,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Ffour-vectors-and-index-notation":12401,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fthe-lorentz-group-and-rapidity":12405,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fdoppler-aberration-and-appearance":12409,"\u002Frelativity\u002Frelativistic-dynamics\u002Ffour-momentum-force-and-accelerated-motion":12413,"\u002Frelativity\u002Frelativistic-dynamics\u002Fparticle-decays-and-two-body-kinematics":12418,"\u002Frelativity\u002Frelativistic-dynamics\u002Fcollisions-thresholds-and-the-cm-frame":12422,"\u002Frelativity\u002Frelativistic-dynamics\u002Fmandelstam-variables-and-invariants":12426,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ffour-current-and-the-four-potential":12430,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fthe-electromagnetic-field-tensor":12435,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ftransformation-of-electric-and-magnetic-fields":12439,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fcovariant-maxwell-and-the-stress-energy-tensor":12443,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-equivalence-principle-formalized":12447,"\u002Frelativity\u002Fcurved-spacetime\u002Fmanifolds-vectors-and-the-metric":12452,"\u002Frelativity\u002Fcurved-spacetime\u002Fcovariant-derivative-and-christoffel-symbols":12456,"\u002Frelativity\u002Fcurved-spacetime\u002Fgeodesics-and-the-geodesic-equation":12460,"\u002Frelativity\u002Fcurved-spacetime\u002Fcurvature-riemann-and-geodesic-deviation":12464,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-einstein-field-equations":12468,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fthe-schwarzschild-metric":12472,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fgeodesics-and-orbits-in-schwarzschild":12477,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Flight-bending-and-null-geodesics":12481,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fperihelion-precession-of-mercury":12485,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fdeflection-of-light-and-gravitational-lensing":12490,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fgravitational-redshift-and-shapiro-delay":12494,"\u002Frelativity\u002Ftests-of-general-relativity\u002Frelativity-in-technology-gps":12498,"\u002Frelativity\u002Fblack-holes\u002Fhorizons-and-coordinate-singularities":12502,"\u002Frelativity\u002Fblack-holes\u002Frotating-and-charged-black-holes":12507,"\u002Frelativity\u002Fblack-holes\u002Fblack-hole-thermodynamics":12511,"\u002Frelativity\u002Fgravitational-waves\u002Flinearized-gravity-and-wave-solutions":12515,"\u002Frelativity\u002Fgravitational-waves\u002Fgeneration-and-the-quadrupole-formula":12520,"\u002Frelativity\u002Fgravitational-waves\u002Fdetection-ligo-and-the-first-events":12524,"\u002Frelativity\u002Fcosmological-bridge\u002Fthe-cosmological-principle-and-flrw-metric":12528,"\u002Frelativity\u002Fcosmological-bridge\u002Ffriedmann-equations-and-cosmic-dynamics":12533,"\u002Frelativity":12537,"\u002Fphysical-computing":12540,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fblackbody-radiation-and-the-planck-quantum":12543,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-photoelectric-effect-and-the-photon":12548,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fx-rays-and-the-compton-effect":12552,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-old-quantum-theory-bohr-and-sommerfeld":12556,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fde-broglie-waves-and-electron-diffraction":12560,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fwave-packets-and-the-probability-interpretation":12565,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fthe-uncertainty-principle":12569,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-schrodinger-equation-in-one-dimension":12573,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-free-particle-and-wave-packet-dynamics":12578,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fparticle-in-infinite-and-finite-square-wells":12582,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Foperators-expectation-values-and-the-harmonic-oscillator":12586,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-dirac-delta-potential":12590,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fbarrier-penetration-and-quantum-tunneling":12594,"\u002Fquantum-mechanics\u002Fformalism\u002Fhilbert-space-and-dirac-notation":12598,"\u002Fquantum-mechanics\u002Fformalism\u002Fobservables-hermitian-operators-and-eigenvalues":12603,"\u002Fquantum-mechanics\u002Fformalism\u002Fthe-postulates-and-quantum-measurement":12607,"\u002Fquantum-mechanics\u002Fformalism\u002Fposition-momentum-and-continuous-spectra":12611,"\u002Fquantum-mechanics\u002Fformalism\u002Fcommutators-and-the-generalized-uncertainty-principle":12615,"\u002Fquantum-mechanics\u002Fformalism\u002Ftime-evolution-schrodinger-and-heisenberg-pictures":12619,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fladder-operators-and-the-number-states":12623,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fcoherent-and-squeezed-states":12628,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fsymmetries-generators-and-conservation-laws":12632,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fparity-time-reversal-and-discrete-symmetries":12636,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Forbital-angular-momentum-and-spherical-harmonics":12640,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Fthe-angular-momentum-algebra":12644,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Faddition-of-angular-momenta-and-clebsch-gordan":12648,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-schrodinger-equation-in-three-dimensions":12652,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-hydrogen-atom":12657,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-isotropic-oscillator-and-hidden-symmetry":12661,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-half-pauli-matrices-and-stern-gerlach":12665,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-in-a-magnetic-field-precession-and-resonance":12670,"\u002Fquantum-mechanics\u002Fspin\u002Ftwo-level-systems-and-the-bloch-sphere":12674,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fidentical-particles-and-exchange-symmetry":12678,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fthe-pauli-principle-atoms-and-the-periodic-table":12683,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ftime-independent-perturbation-theory":12687,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ffine-structure-and-the-real-hydrogen-atom":12692,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-zeeman-and-stark-effects":12696,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-variational-method":12700,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-wkb-approximation":12704,"\u002Fquantum-mechanics":12708,"\u002Freal-analysis\u002Ffoundations\u002Fsets-logic-functions":12711,"\u002Freal-analysis\u002Ffoundations\u002Fordered-fields-completeness":12716,"\u002Freal-analysis\u002Ffoundations\u002Fabsolute-value-bounds":12720,"\u002Freal-analysis\u002Ffoundations\u002Fintervals-uncountability":12724,"\u002Freal-analysis\u002Fsequences-series\u002Fsequences-limits":12728,"\u002Freal-analysis\u002Fsequences-series\u002Flimit-laws-monotone":12733,"\u002Freal-analysis\u002Fsequences-series\u002Flimsup-bolzano-weierstrass":12737,"\u002Freal-analysis\u002Fsequences-series\u002Fcauchy-completeness":12741,"\u002Freal-analysis\u002Fsequences-series\u002Fseries-convergence":12745,"\u002Freal-analysis\u002Fsequences-series\u002Fabsolute-conditional-rearrangement":12749,"\u002Freal-analysis\u002Fmetric-spaces\u002Fmetric-spaces-norms":12753,"\u002Freal-analysis\u002Fmetric-spaces\u002Fopen-closed-sets":12758,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconvergence-completeness":12762,"\u002Freal-analysis\u002Fmetric-spaces\u002Fcompactness":12766,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconnectedness":12770,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-of-functions":12774,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuous-functions":12778,"\u002Freal-analysis\u002Fcontinuity\u002Fevt-ivt":12782,"\u002Freal-analysis\u002Fcontinuity\u002Funiform-continuity":12786,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuity-metric-spaces":12790,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-infinity-monotone":12794,"\u002Freal-analysis\u002Fdifferentiation\u002Fthe-derivative":12798,"\u002Freal-analysis\u002Fdifferentiation\u002Fmean-value-theorem":12803,"\u002Freal-analysis\u002Fdifferentiation\u002Ftaylors-theorem":12807,"\u002Freal-analysis\u002Fdifferentiation\u002Finverse-function-1d":12811,"\u002Freal-analysis\u002Friemann-integration\u002Fdarboux-integral":12815,"\u002Freal-analysis\u002Friemann-integration\u002Fintegrability-classes":12820,"\u002Freal-analysis\u002Friemann-integration\u002Fproperties-of-the-integral":12824,"\u002Freal-analysis\u002Friemann-integration\u002Ffundamental-theorem":12828,"\u002Freal-analysis\u002Friemann-integration\u002Flog-exp-improper":12831,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpointwise-uniform-convergence":12835,"\u002Freal-analysis\u002Ffunction-sequences\u002Finterchange-of-limits":12840,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpower-series-weierstrass":12844,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpicard-ode":12848,"\u002Freal-analysis\u002Fseveral-variables\u002Fdifferentiability-rn":12852,"\u002Freal-analysis\u002Fseveral-variables\u002Fgradient-chain-rule":12857,"\u002Freal-analysis\u002Fseveral-variables\u002Fhigher-derivatives-taylor-extrema":12861,"\u002Freal-analysis\u002Fseveral-variables\u002Finverse-implicit-theorems":12865,"\u002Freal-analysis\u002Fseveral-variables\u002Fmultiple-integrals":12869,"\u002Freal-analysis":12873,"\u002Fabstract-algebra\u002Ffoundations\u002Fsets-functions-relations":12876,"\u002Fabstract-algebra\u002Ffoundations\u002Fintegers-and-modular-arithmetic":12880,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fgroup-axioms-and-first-examples":12884,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fdihedral-and-symmetric-groups":12889,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fmatrix-and-quaternion-groups":12893,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fhomomorphisms-and-group-actions":12897,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fsubgroups-and-substructures":12901,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcyclic-groups":12906,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fgeneration-and-subgroup-lattices":12910,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcosets-lagrange-and-normal-subgroups":12914,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fisomorphism-theorems":12918,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcomposition-series-and-the-alternating-group":12922,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Factions-and-cayleys-theorem":12926,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fconjugation-and-the-class-equation":12931,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fsylow-theorems":12935,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fautomorphisms-and-simple-groups":12939,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fdirect-products-and-finite-abelian-groups":12943,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fsemidirect-products":12948,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fnilpotent-and-solvable-groups":12952,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fclassifying-small-groups":12956,"\u002Fabstract-algebra\u002Fring-theory\u002Frings-definitions-and-examples":12960,"\u002Fabstract-algebra\u002Fring-theory\u002Fideals-quotients-and-homomorphisms":12965,"\u002Fabstract-algebra\u002Fring-theory\u002Ffractions-and-the-chinese-remainder-theorem":12969,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Feuclidean-domains-pids-ufds":12973,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fpolynomial-rings-over-fields":12978,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fgauss-lemma-and-unique-factorization":12982,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Firreducibility-criteria-and-groebner":12986,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fintroduction-to-modules":12990,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ffree-modules-and-direct-sums":12995,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ftensor-products-and-exact-sequences":12999,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fvector-spaces-and-linear-maps":13003,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fstructure-theorem-over-pids":13007,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Frational-canonical-form":13012,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fjordan-canonical-form":13016,"\u002Fabstract-algebra\u002Ffield-theory\u002Ffield-extensions-and-algebraic-elements":13020,"\u002Fabstract-algebra\u002Ffield-theory\u002Fstraightedge-and-compass-constructions":13025,"\u002Fabstract-algebra\u002Ffield-theory\u002Fsplitting-fields-and-algebraic-closure":13029,"\u002Fabstract-algebra\u002Ffield-theory\u002Fseparable-and-cyclotomic-extensions":13033,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fthe-galois-correspondence":13037,"\u002Fabstract-algebra\u002Fgalois-theory\u002Ffinite-fields":13042,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fcyclotomic-and-abelian-extensions":13046,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fgalois-groups-of-polynomials":13050,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fsolvability-by-radicals-and-the-quintic":13054,"\u002Fabstract-algebra\u002Fcapstone\u002Fcommutative-algebra-and-algebraic-geometry":13058,"\u002Fabstract-algebra\u002Fcapstone\u002Frepresentation-and-character-theory":13063,"\u002Fabstract-algebra":13067,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fatomic-spectra-rutherford":13070,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-model-hydrogen":13075,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fx-ray-spectra-franck-hertz":13079,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-sommerfeld-old-quantum-theory":13083,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fold-quantum-theory-limits-wkb":13087,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fschrodinger-3d-hydrogen":13091,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fhydrogen-wave-functions":13096,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fradial-equation-in-full":13100,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fsymmetry-degeneracy-runge-lenz":13104,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fexpectation-values-virial":13108,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fquantum-defects-alkali-spectra":13112,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Frydberg-atoms":13116,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Frelativistic-kinetic-correction":13120,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fspin-orbit-thomas-precession":13125,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdarwin-term-fine-structure-formula":13129,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdirac-equation-hydrogen":13133,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Flamb-shift-qed":13137,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fhyperfine-structure-21cm":13142,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fnuclear-effects-isotope-shift":13146,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fperiodic-table-atomic-spectra":13150,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fcentral-field-self-consistent":13155,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fidentical-particles-hartree-fock":13159,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhelium-two-electron-atom":13163,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fls-jj-coupling-term-symbols":13167,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhund-rules-ground-terms":13171,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fzeeman-effect":13175,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fpaschen-back-intermediate":13180,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fstark-effect-polarizability":13184,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Ftime-dependent-perturbation-golden-rule":13188,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fdipole-approximation-einstein-coefficients":13193,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fselection-rules-forbidden-transitions":13197,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Flifetimes-and-line-shapes":13201,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Flaser-principles":13205,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fspectroscopy-techniques":13210,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fline-catalog-nist-asd":13214,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Flaser-cooling-doppler":13218,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fsub-doppler-trapping":13223,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fbose-einstein-condensation":13227,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Foptical-clocks-precision":13231,"\u002Fatomic-physics":13235,"\u002Fdatabases":13238,"\u002Fcategory-theory\u002Ffoundations\u002Fwhat-is-a-category":13241,"\u002Fcategory-theory\u002Ffoundations\u002Fexamples-of-categories":13245,"\u002Fcategory-theory\u002Ffoundations\u002Fspecial-morphisms":13249,"\u002Fcategory-theory\u002Ffoundations\u002Ffunctors":13253,"\u002Fcategory-theory\u002Ffoundations\u002Fnatural-transformations":13257,"\u002Fcategory-theory\u002Ffoundations\u002Fsize-and-set-theory":13261,"\u002Fcategory-theory\u002Funiversal-properties\u002Funiversal-properties":13265,"\u002Fcategory-theory\u002Funiversal-properties\u002Fproducts-and-coproducts":13270,"\u002Fcategory-theory\u002Funiversal-properties\u002Fconstructions-on-categories":13274,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Frepresentable-functors":13278,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-lemma":13283,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-consequences":13287,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits":13291,"\u002Fcategory-theory\u002Flimits-colimits\u002Fproducts-equalizers-pullbacks":13296,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcolimits":13300,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcomputing-limits":13304,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits-and-functors":13308,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions":13312,"\u002Fcategory-theory\u002Fadjunctions\u002Funits-and-counits":13317,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions-via-universal-arrows":13321,"\u002Fcategory-theory\u002Fadjunctions\u002Ffree-forgetful-adjunctions":13325,"\u002Fcategory-theory\u002Fadjoints-limits\u002Flimits-via-adjoints":13329,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fpresheaf-limits-colimits":13334,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoints-preserve-limits":13338,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoint-functor-theorem":13342,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fmonads":13346,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-eilenberg-moore":13351,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fkleisli-and-programming":13355,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-for-endofunctors":13359,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Fcartesian-closed-categories":13363,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Flambda-calculus-correspondence":13368,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Ffixed-points-and-recursion":13372,"\u002Fcategory-theory":13376,"\u002Fdeep-learning\u002Fmathematical-background\u002Flinear-algebra-for-deep-learning":13379,"\u002Fdeep-learning\u002Fmathematical-background\u002Fprobability-and-information-theory":13383,"\u002Fdeep-learning\u002Fmathematical-background\u002Fnumerical-computation":13387,"\u002Fdeep-learning\u002Fmathematical-background\u002Fcalculus":13391,"\u002Fdeep-learning\u002Ffoundations\u002Fwhat-is-deep-learning":13394,"\u002Fdeep-learning\u002Ffoundations\u002Fmachine-learning-refresher":13398,"\u002Fdeep-learning\u002Ffoundations\u002Flinear-models-and-the-perceptron":13402,"\u002Fdeep-learning\u002Fneural-networks\u002Fthe-multilayer-perceptron":13406,"\u002Fdeep-learning\u002Fneural-networks\u002Factivation-functions":13411,"\u002Fdeep-learning\u002Fneural-networks\u002Funiversal-approximation":13415,"\u002Fdeep-learning\u002Fneural-networks\u002Fbackpropagation":13419,"\u002Fdeep-learning\u002Fneural-networks\u002Floss-functions-and-output-units":13423,"\u002Fdeep-learning\u002Foptimization\u002Fgradient-descent-and-sgd":13427,"\u002Fdeep-learning\u002Foptimization\u002Fmomentum-and-adaptive-methods":13432,"\u002Fdeep-learning\u002Foptimization\u002Finitialization":13436,"\u002Fdeep-learning\u002Foptimization\u002Fthe-optimization-landscape":13440,"\u002Fdeep-learning\u002Foptimization\u002Fsecond-order-and-approximate-methods":13444,"\u002Fdeep-learning\u002Fregularization\u002Fregularization-overview":13448,"\u002Fdeep-learning\u002Fregularization\u002Fdropout-and-data-augmentation":13453,"\u002Fdeep-learning\u002Fregularization\u002Fearly-stopping-and-parameter-sharing":13457,"\u002Fdeep-learning\u002Fregularization\u002Fnormalization":13461,"\u002Fdeep-learning\u002Farchitectures\u002Fconvolutional-networks":13465,"\u002Fdeep-learning\u002Farchitectures\u002Fcnn-architectures":13470,"\u002Fdeep-learning\u002Farchitectures\u002Frecurrent-networks":13474,"\u002Fdeep-learning\u002Farchitectures\u002Flstm-and-gru":13478,"\u002Fdeep-learning\u002Farchitectures\u002Fattention-and-transformers":13482,"\u002Fdeep-learning\u002Farchitectures\u002Fthe-transformer-architecture":13486,"\u002Fdeep-learning\u002Farchitectures\u002Ftransformers-in-practice":13490,"\u002Fdeep-learning\u002Farchitectures\u002Fgraph-neural-networks":13494,"\u002Fdeep-learning\u002Farchitectures\u002Fstate-space-models":13498,"\u002Fdeep-learning\u002Ftheory\u002Fgeneralization-theory":13502,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-robustness":13507,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-defenses":13511,"\u002Fdeep-learning\u002Ftheory\u002Fbayesian-and-ensemble-methods":13515,"\u002Fdeep-learning\u002Ftheory\u002Fdeep-equilibrium-models":13519,"\u002Fdeep-learning\u002Fgenerative-models\u002Flinear-factor-models":13523,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoencoders":13528,"\u002Fdeep-learning\u002Fgenerative-models\u002Fvariational-autoencoders":13532,"\u002Fdeep-learning\u002Fgenerative-models\u002Fgenerative-adversarial-networks":13536,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoregressive-and-normalizing-flows":13540,"\u002Fdeep-learning\u002Fgenerative-models\u002Fenergy-based-and-boltzmann-machines":13544,"\u002Fdeep-learning\u002Fgenerative-models\u002Fdiffusion-and-score-based-models":13548,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fstructured-probabilistic-models":13552,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fmonte-carlo-and-mcmc":13557,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fapproximate-inference":13561,"\u002Fdeep-learning\u002Fpractical\u002Fpractical-methodology":13565,"\u002Fdeep-learning\u002Fpractical\u002Fhyperparameters-and-debugging":13570,"\u002Fdeep-learning\u002Fpractical\u002Frepresentation-learning":13574,"\u002Fdeep-learning\u002Fpractical\u002Ftransfer-learning":13578,"\u002Fdeep-learning\u002Fpractical\u002Fapplications":13582,"\u002Fdeep-learning\u002Fpractical\u002Fmodel-compression-and-distillation":13586,"\u002Fdeep-learning\u002Fpractical\u002Fmeta-learning-and-few-shot":13590,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Flarge-language-models":13594,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fscaling-inference-and-alignment":13599,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fseq2seq-pretraining-and-bart":13603,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ftext-to-text-transfer-and-conditional-generation":13607,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fspeech-and-audio-models":13611,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fself-supervised-speech-and-synthesis":13615,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fai-agents":13619,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fagent-memory-retrieval-and-orchestration":13623,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmixture-of-experts":13627,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmultimodal-models":13631,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ffusion-and-vision-language-models":13635,"\u002Fdeep-learning\u002Freinforcement-learning\u002Ffoundations-of-reinforcement-learning":13639,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fmodel-free-prediction-and-control":13644,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fdeep-q-networks":13648,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fpolicy-gradients-and-actor-critic":13652,"\u002Fdeep-learning\u002Freinforcement-learning\u002Frl-from-human-feedback":13656,"\u002Fdeep-learning":13660,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fequilibrium-state-variables-zeroth-law":13663,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Ffirst-law-heat-and-work":13667,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fsecond-law-entropy-and-the-carnot-bound":13671,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fthermodynamic-potentials-and-maxwell-relations":13675,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fstability-response-functions-and-the-third-law":13679,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fclassical-statistics-and-equipartition":13683,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fphase-space-and-liouvilles-theorem":13688,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fensembles-and-the-equal-probability-postulate":13692,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fstatistical-entropy-boltzmann-and-gibbs":13696,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fmicrocanonical-ensemble-and-entropy":13700,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fequilibrium-conditions-temperature-pressure-chemical-potential":13705,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fideal-gas-phase-space-and-the-sackur-tetrode-entropy":13709,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Ftwo-state-systems-paramagnets-and-negative-temperature":13713,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fcanonical-ensemble-and-the-boltzmann-distribution":13717,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fpartition-function-and-the-helmholtz-free-energy":13722,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fenergy-fluctuations-and-ensemble-equivalence":13726,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fthe-einstein-solid-and-harmonic-systems":13730,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fparamagnetism-and-the-schottky-anomaly":13734,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fideal-gas-partition-function-and-the-gibbs-paradox":13738,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fequipartition-and-the-virial-theorem":13743,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fmolecular-gases-rotation-and-vibration":13747,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fgrand-canonical-ensemble-and-the-grand-partition-function":13751,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fchemical-potential-fugacity-and-number-fluctuations":13756,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fensemble-summary-and-the-thermodynamic-web":13760,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fquantum-statistics-bose-einstein-and-fermi-dirac":13764,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fderiving-the-quantum-distributions":13769,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fthe-classical-limit-and-quantum-concentration":13773,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fideal-quantum-gases-general-framework":13777,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-and-the-fermion-gas":13781,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthe-photon-gas-and-plancks-radiation-law":13786,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fblackbody-thermodynamics-and-radiation-pressure":13790,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fphonons-and-the-debye-model":13794,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-derived":13798,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthermodynamics-of-the-bose-gas-and-superfluidity":13802,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fthe-ideal-fermi-gas-at-zero-temperature":13806,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fsommerfeld-expansion-and-electrons-in-metals":13811,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":13815,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fneutron-stars-and-nuclear-matter":13819,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-cluster-expansion-and-virial-coefficients":13823,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-van-der-waals-gas-and-liquid-gas-coexistence":13828,"\u002Fstatistical-mechanics\u002Finteractions\u002Fquantum-gases-with-interactions-and-exchange":13832,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fphases-coexistence-and-classification":13836,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-ising-model-and-exact-solutions":13841,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fmean-field-theory-and-the-weiss-model":13845,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fcritical-exponents-and-landau-theory":13849,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-renormalization-group-idea":13853,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fthermodynamic-fluctuations-and-response":13857,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fbrownian-motion-and-the-langevin-equation":13862,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Flinear-response-and-the-fluctuation-dissipation-theorem":13866,"\u002Fstatistical-mechanics":13870,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fbonding-mechanisms":13873,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fmolecular-orbitals-and-h2-plus":13878,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fhydrogen-molecule-and-exchange":13882,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fvan-der-waals-forces":13886,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Frotational-vibrational-spectra":13890,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Fanharmonicity-and-rovibrational-structure":13895,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Framan-and-electronic-bands":13899,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Flasers-and-masers":13903,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fstructure-of-solids":13907,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fbravais-lattices-and-crystal-systems":13912,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Freciprocal-lattice-and-brillouin-zones":13916,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fdiffraction-and-structure-factors":13920,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonon-dispersion":13924,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonons-quantization-and-dos":13929,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fdebye-einstein-heat-capacity":13933,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fanharmonicity-and-thermal-transport":13937,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ffree-electron-gas-and-conduction":13941,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fsommerfeld-model-and-heat-capacity":13946,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ftransport-and-the-hall-effect":13950,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fscreening-and-plasmons":13954,"\u002Fcondensed-matter\u002Fband-theory\u002Fblochs-theorem-and-energy-bands":13958,"\u002Fcondensed-matter\u002Fband-theory\u002Fnearly-free-electron-model":13963,"\u002Fcondensed-matter\u002Fband-theory\u002Ftight-binding-method":13967,"\u002Fcondensed-matter\u002Fband-theory\u002Ffermi-surfaces-and-semiclassical-dynamics":13971,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fsemiconductor-bands-and-junctions":13975,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fintrinsic-and-extrinsic-semiconductors":13980,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fcarrier-transport-and-recombination":13984,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fthe-pn-junction":13988,"\u002Fcondensed-matter\u002Fsemiconductors\u002Ftransistors-and-optoelectronics":13992,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fdielectrics-and-polarization":13996,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fferroelectrics-and-piezoelectrics":14001,"\u002Fcondensed-matter\u002Fmagnetism\u002Fdiamagnetism-and-paramagnetism":14005,"\u002Fcondensed-matter\u002Fmagnetism\u002Fexchange-and-ferromagnetism":14010,"\u002Fcondensed-matter\u002Fmagnetism\u002Fantiferromagnetism-and-domains":14014,"\u002Fcondensed-matter\u002Fmagnetism\u002Fspin-waves-and-magnons":14018,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fsuperconductivity-phenomenology":14022,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Flondon-theory-and-the-meissner-effect":14027,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fginzburg-landau-theory":14031,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fbcs-theory":14035,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fjosephson-and-high-tc":14039,"\u002Fcondensed-matter\u002Fnanostructures\u002Fquantum-wells-wires-and-dots":14043,"\u002Fcondensed-matter\u002Fnanostructures\u002Finteger-quantum-hall-effect":14048,"\u002Fcondensed-matter\u002Fnanostructures\u002Ffractional-quantum-hall-and-topology":14052,"\u002Fcondensed-matter\u002Fnanostructures\u002Fgraphene-and-dirac-materials":14056,"\u002Fcondensed-matter":14060,"\u002Flogic\u002Ffoundations\u002Flogic-as-a-mathematical-model":14063,"\u002Flogic\u002Fsentential-logic\u002Fformal-languages-and-well-formed-formulas":14067,"\u002Flogic\u002Fsentential-logic\u002Ftruth-assignments-and-tautologies":14072,"\u002Flogic\u002Fsentential-logic\u002Funique-readability-and-parsing":14076,"\u002Flogic\u002Fsentential-logic\u002Finduction-and-recursion":14080,"\u002Flogic\u002Fsentential-logic\u002Fexpressive-completeness-and-normal-forms":14084,"\u002Flogic\u002Fsentential-logic\u002Fboolean-circuits":14088,"\u002Flogic\u002Fsentential-logic\u002Fcompactness-and-effectiveness":14092,"\u002Flogic\u002Ffirst-order-languages\u002Ffirst-order-languages":14096,"\u002Flogic\u002Ffirst-order-languages\u002Fstructures-truth-and-satisfaction":14101,"\u002Flogic\u002Ffirst-order-languages\u002Fdefinability-and-elementary-equivalence":14105,"\u002Flogic\u002Ffirst-order-languages\u002Fterms-substitution-and-parsing":14109,"\u002Flogic\u002Fdeductive-calculus\u002Fa-deductive-calculus":14113,"\u002Flogic\u002Fdeductive-calculus\u002Fdeduction-theorem-and-derived-rules":14118,"\u002Flogic\u002Fdeductive-calculus\u002Fsoundness":14122,"\u002Flogic\u002Fdeductive-calculus\u002Fcompleteness-and-consistency":14126,"\u002Flogic\u002Fmodels-and-theories\u002Fcompactness-and-lowenheim-skolem":14130,"\u002Flogic\u002Fmodels-and-theories\u002Ftheories-elementary-classes-and-categoricity":14135,"\u002Flogic\u002Fmodels-and-theories\u002Finterpretations-between-theories":14139,"\u002Flogic\u002Fmodels-and-theories\u002Fnonstandard-analysis":14143,"\u002Flogic\u002Farithmetic-and-definability\u002Fdefinability-in-arithmetic":14147,"\u002Flogic\u002Farithmetic-and-definability\u002Fnatural-numbers-with-successor":14152,"\u002Flogic\u002Farithmetic-and-definability\u002Fpresburger-and-reducts":14156,"\u002Flogic\u002Farithmetic-and-definability\u002Fa-subtheory-and-representability":14160,"\u002Flogic\u002Fincompleteness\u002Farithmetization-of-syntax":14164,"\u002Flogic\u002Fincompleteness\u002Fincompleteness-and-undecidability":14169,"\u002Flogic\u002Fincompleteness\u002Fsecond-incompleteness-theorem":14173,"\u002Flogic\u002Fcomputability-and-representability\u002Frecursive-functions":14177,"\u002Flogic\u002Fcomputability-and-representability\u002Frepresenting-exponentiation":14182,"\u002Flogic\u002Fsecond-order-logic\u002Fsecond-order-languages":14186,"\u002Flogic\u002Fsecond-order-logic\u002Fskolem-functions-and-many-sorted-logic":14191,"\u002Flogic\u002Fsecond-order-logic\u002Fgeneral-structures":14195,"\u002Flogic":14199,"\u002Freinforcement-learning\u002Ffoundations\u002Fwhat-is-reinforcement-learning":14202,"\u002Freinforcement-learning\u002Ffoundations\u002Fa-brief-history-of-rl":14206,"\u002Freinforcement-learning\u002Ffoundations\u002Fmulti-armed-bandits":14210,"\u002Freinforcement-learning\u002Ffoundations\u002Fbandit-exploration-algorithms":14214,"\u002Freinforcement-learning\u002Ffoundations\u002Fmarkov-decision-processes":14218,"\u002Freinforcement-learning\u002Ffoundations\u002Fvalue-functions-and-optimality":14222,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdynamic-programming":14226,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdp-async-and-gpi":14230,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-methods":14234,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-off-policy":14238,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftemporal-difference-learning":14242,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftd-control-sarsa-and-q-learning":14246,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-bootstrapping":14250,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-off-policy-methods":14254,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-and-learning":14258,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-focusing-and-decision-time":14262,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdecision-time-planning":14266,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-tree-search":14270,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-prediction":14274,"\u002Freinforcement-learning\u002Fapproximation\u002Ffeature-construction-and-nonlinear":14279,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-control":14283,"\u002Freinforcement-learning\u002Fapproximation\u002Faverage-reward-control":14287,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-and-the-deadly-triad":14291,"\u002Freinforcement-learning\u002Fapproximation\u002Fbellman-error-and-gradient-td":14295,"\u002Freinforcement-learning\u002Fapproximation\u002Feligibility-traces":14299,"\u002Freinforcement-learning\u002Fapproximation\u002Ftrue-online-and-sarsa-lambda":14303,"\u002Freinforcement-learning\u002Fapproximation\u002Fpolicy-gradient-methods":14307,"\u002Freinforcement-learning\u002Fapproximation\u002Factor-critic-and-continuous-actions":14311,"\u002Freinforcement-learning\u002Fapproximation\u002Fleast-squares-and-memory-based-methods":14315,"\u002Freinforcement-learning\u002Fapproximation\u002Fmemory-and-kernel-methods":14319,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-eligibility-traces":14323,"\u002Freinforcement-learning\u002Fapproximation\u002Fstable-off-policy-traces":14327,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdeep-q-networks":14331,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdqn-improvements":14335,"\u002Freinforcement-learning\u002Fdeep-rl\u002Factor-critic-and-ppo":14339,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fppo-and-continuous-control":14343,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fcase-studies":14347,"\u002Freinforcement-learning\u002Fdeep-rl\u002Frl-beyond-games":14351,"\u002Freinforcement-learning\u002Fdeep-rl\u002Ffrontiers":14355,"\u002Freinforcement-learning\u002Fdeep-rl\u002Freward-design-and-open-problems":14359,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow":14363,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow-part-2":14368,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control":14372,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control-part-2":14376,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl":14380,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl-part-2":14384,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration":14388,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration-part-2":14392,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl":14396,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl-part-2":14400,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl":14404,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl-part-2":14408,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl":14412,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl-part-2":14416,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl":14420,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl-part-2":14424,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Frlhf-and-language-models":14428,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps":14432,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps-part-2":14436,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl":14440,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl-part-2":14444,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmeta-rl-and-generalization":14448,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fpsychology-of-reinforcement":14452,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Finstrumental-conditioning-and-control":14457,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-and-td-error":14461,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-in-the-brain":14465,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fanimal-learning-and-cognition":14469,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fcognitive-maps-and-planning":14473,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fneuroscience-of-reinforcement":14477,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fseveral-learning-systems":14481,"\u002Freinforcement-learning":14485,"\u002Fartificial-intelligence\u002Ffoundations\u002Fwhat-is-ai":14487,"\u002Fartificial-intelligence\u002Ffoundations\u002Ffoundations-of-ai":14491,"\u002Fartificial-intelligence\u002Ffoundations\u002Fintelligent-agents":14495,"\u002Fartificial-intelligence\u002Ffoundations\u002Fagent-architectures":14499,"\u002Fartificial-intelligence\u002Fsearch\u002Funinformed-search":14503,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-strategies-compared":14508,"\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search":14512,"\u002Fartificial-intelligence\u002Fsearch\u002Fheuristic-functions":14515,"\u002Fartificial-intelligence\u002Fsearch\u002Flocal-search":14519,"\u002Fartificial-intelligence\u002Fsearch\u002Fpopulation-and-continuous-search":14523,"\u002Fartificial-intelligence\u002Fsearch\u002Fadversarial-search":14527,"\u002Fartificial-intelligence\u002Fsearch\u002Fgames-of-chance-and-imperfect-information":14531,"\u002Fartificial-intelligence\u002Fsearch\u002Fconstraint-satisfaction":14535,"\u002Fartificial-intelligence\u002Fsearch\u002Fcsp-search-and-structure":14539,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-under-uncertainty":14543,"\u002Fartificial-intelligence\u002Fsearch\u002Fbelief-state-and-online-search":14547,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic":14551,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-inference":14555,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic":14559,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic-in-use":14563,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Finference-and-resolution":14567,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-resolution":14571,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fclassical-planning":14575,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-graphs-and-graphplan":14579,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-in-the-real-world":14583,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-under-uncertainty":14587,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fknowledge-representation":14591,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Freasoning-systems-and-defaults":14595,"\u002Fartificial-intelligence\u002Funcertainty\u002Fprobability-and-bayes":14599,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayes-rule-and-naive-bayes":14604,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayesian-networks":14608,"\u002Fartificial-intelligence\u002Funcertainty\u002Finference-in-bayesian-networks":14612,"\u002Fartificial-intelligence\u002Funcertainty\u002Freasoning-over-time":14616,"\u002Fartificial-intelligence\u002Funcertainty\u002Ftracking-and-data-association":14620,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions":14624,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmarkov-decision-processes":14627,"\u002Fartificial-intelligence\u002Funcertainty\u002Fdecision-networks-and-game-theory":14630,"\u002Fartificial-intelligence\u002Funcertainty\u002Fgame-theory-and-mechanism-design":14634,"\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples":14638,"\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families":14639,"\u002Fartificial-intelligence\u002Flearning\u002Fprobabilistic-learning":14641,"\u002Fartificial-intelligence\u002Flearning\u002Fexpectation-maximization":14645,"\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning":14649,"\u002Fartificial-intelligence\u002Flearning\u002Fgeneralization-and-policy-search":14651,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-in-learning":14655,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-based-learning-methods":14659,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fvision-and-perception":14663,"\u002Fartificial-intelligence\u002Ffrontiers\u002Freconstructing-the-3d-world":14668,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobotics":14672,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobot-planning-and-control":14676,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnatural-language-in-ai":14680,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnlp-grammar-translation-and-speech":14684,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fphilosophy-and-future":14688,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fai-ethics-and-future":14692,"\u002Fartificial-intelligence":14696,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-constituents-nuclide-chart":14699,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-size-charge-distributions":14704,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-masses-binding-energy":14708,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fsemi-empirical-mass-formula":14712,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-moments-multipoles":14716,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnuclear-force-shell-overview":14720,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fthe-deuteron":14725,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnucleon-nucleon-scattering":14729,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fmeson-theory-isospin":14733,"\u002Fnuclear-physics\u002Fnuclear-models\u002Ffermi-gas-model":14737,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fliquid-drop-collective-coordinates":14742,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fshell-model-single-particle":14746,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fcollective-model-rotations-vibrations":14750,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-law-modes":14754,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-kinetics-equilibrium":14759,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-decay-gamow-theory":14763,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-fine-structure-hindrance":14768,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fbeta-decay-energetics-neutrino":14772,"\u002Fnuclear-physics\u002Fbeta-decay\u002Ffermi-theory-beta-decay":14777,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fweak-interaction-parity-violation":14781,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fdouble-beta-decay-neutrino-mass":14785,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fgamma-multipole-radiation":14789,"\u002Fnuclear-physics\u002Fgamma-decay\u002Finternal-conversion-isomers":14794,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fangular-correlations-mossbauer":14798,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Freaction-kinematics-cross-sections":14802,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fcompound-nucleus-resonances":14807,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fdirect-reactions-optical-model":14811,"\u002Fnuclear-physics\u002Ffission\u002Ffission-barrier-dynamics":14815,"\u002Fnuclear-physics\u002Ffission\u002Fchain-reactions-reactor-physics":14820,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Ffusion-reactions-confinement":14824,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fstellar-nucleosynthesis":14829,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fbig-bang-nucleosynthesis":14833,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fcharged-particle-stopping-power":14837,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fphoton-neutron-interactions":14842,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fradiation-detectors":14846,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fdosimetry-radiation-biology":14850,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fnuclear-applications-dating-medicine":14854,"\u002Fnuclear-physics":14858,"\u002Fnatural-language-processing\u002Ffoundations\u002Fwhat-is-nlp":14861,"\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization":14865,"\u002Fnatural-language-processing\u002Ffoundations\u002Fminimum-edit-distance":14869,"\u002Fnatural-language-processing\u002Ffoundations\u002Fn-gram-language-models":14873,"\u002Fnatural-language-processing\u002Ffoundations\u002Fsmoothing-and-backoff":14877,"\u002Fnatural-language-processing\u002Fclassification\u002Fnaive-bayes-and-sentiment":14881,"\u002Fnatural-language-processing\u002Fclassification\u002Fevaluating-classifiers":14886,"\u002Fnatural-language-processing\u002Fclassification\u002Flogistic-regression":14890,"\u002Fnatural-language-processing\u002Fclassification\u002Fsentiment-and-affect-lexicons":14894,"\u002Fnatural-language-processing\u002Fsemantics\u002Fvector-semantics-and-embeddings":14898,"\u002Fnatural-language-processing\u002Fsemantics\u002Fstatic-word-embeddings":14903,"\u002Fnatural-language-processing\u002Fsemantics\u002Fneural-language-models":14907,"\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling":14911,"\u002Fnatural-language-processing\u002Fsequences\u002Fcrfs-and-neural-taggers":14915,"\u002Fnatural-language-processing\u002Fsequences\u002Frnns-and-lstms":14919,"\u002Fnatural-language-processing\u002Ftransformers\u002Ftransformers-and-attention":14923,"\u002Fnatural-language-processing\u002Ftransformers\u002Fthe-transformer-architecture":14927,"\u002Fnatural-language-processing\u002Ftransformers\u002Flarge-language-models":14930,"\u002Fnatural-language-processing\u002Ftransformers\u002Fllm-pretraining-and-scaling":14933,"\u002Fnatural-language-processing\u002Ftransformers\u002Ffine-tuning-and-prompting":14937,"\u002Fnatural-language-processing\u002Ftransformers\u002Fprompting-and-alignment":14941,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-parsing":14945,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcky-scoring-and-evaluation":14950,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdependency-parsing":14954,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fgraph-based-and-neural-dependency-parsing":14958,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fword-senses-and-wsd":14962,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fwsd-in-practice-and-induction":14966,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-roles-and-information-extraction":14970,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Frelations-events-and-templates":14974,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoreference-and-discourse":14978,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoherence-and-discourse-structure":14982,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Flogical-semantics":14986,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcompositional-semantics-and-description-logics":14990,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-parsing":14994,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fneural-semantic-parsing":14998,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Finformation-extraction":15002,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftimes-events-and-templates":15006,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdiscourse-coherence":15010,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fentity-based-and-global-coherence":15014,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-grammars":15018,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftreebanks-and-lexicalized-grammars":15022,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation":15026,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation-decoding-and-evaluation":15030,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering":15034,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering-knowledge-and-llms":15038,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots":15042,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants":15046,"\u002Fnatural-language-processing\u002Fapplications\u002Ftext-summarization":15050,"\u002Fnatural-language-processing\u002Fapplications\u002Fabstractive-summarization-and-evaluation":15054,"\u002Fnatural-language-processing\u002Fspeech\u002Fphonetics":15058,"\u002Fnatural-language-processing\u002Fspeech\u002Facoustic-phonetics":15063,"\u002Fnatural-language-processing\u002Fspeech\u002Fautomatic-speech-recognition":15067,"\u002Fnatural-language-processing\u002Fspeech\u002Fasr-evaluation-and-applications":15071,"\u002Fnatural-language-processing":15075,"\u002Fparticle-physics\u002Ffoundations\u002Fhistorical-overview-particle-zoo":15078,"\u002Fparticle-physics\u002Ffoundations\u002Fparticle-physics-basic-concepts":15082,"\u002Fparticle-physics\u002Ffoundations\u002Ffundamental-interactions-force-carriers":15086,"\u002Fparticle-physics\u002Funits-kinematics\u002Fnatural-units-and-scales":15090,"\u002Fparticle-physics\u002Funits-kinematics\u002Ffour-vectors-invariant-mass":15095,"\u002Fparticle-physics\u002Funits-kinematics\u002Fdecay-scattering-kinematics-mandelstam":15099,"\u002Fparticle-physics\u002Funits-kinematics\u002Fcross-sections-golden-rule":15103,"\u002Fparticle-physics\u002Fsymmetries\u002Fconservation-laws-symmetries":15107,"\u002Fparticle-physics\u002Fsymmetries\u002Fdiscrete-symmetries-cpt":15112,"\u002Fparticle-physics\u002Fsymmetries\u002Fparity-violation-weak":15116,"\u002Fparticle-physics\u002Fsymmetries\u002Fsu2-su3-flavor-symmetry":15120,"\u002Fparticle-physics\u002Fquark-model\u002Feightfold-way-su3":15124,"\u002Fparticle-physics\u002Fquark-model\u002Fmeson-spectroscopy":15129,"\u002Fparticle-physics\u002Fquark-model\u002Fbaryon-spectroscopy":15133,"\u002Fparticle-physics\u002Fquark-model\u002Fcolor-confinement-exotics":15137,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fklein-gordon-equation":15141,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fdirac-equation-spinors":15146,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fantiparticles-hole-theory":15150,"\u002Fparticle-physics\u002Fqed\u002Ffeynman-rules-qed":15154,"\u002Fparticle-physics\u002Fqed\u002Fqed-tree-processes":15159,"\u002Fparticle-physics\u002Fqed\u002Frenormalization-running-coupling":15163,"\u002Fparticle-physics\u002Fqed\u002Felectron-g-2":15167,"\u002Fparticle-physics\u002Fweak-interaction\u002Fva-structure-weak":15171,"\u002Fparticle-physics\u002Fweak-interaction\u002Fw-z-bosons-decays":15176,"\u002Fparticle-physics\u002Fweak-interaction\u002Fckm-matrix":15180,"\u002Fparticle-physics\u002Fweak-interaction\u002Fcp-violation-kaons-b-mesons":15184,"\u002Fparticle-physics\u002Fqcd\u002Fcolor-su3-gluons":15188,"\u002Fparticle-physics\u002Fqcd\u002Fasymptotic-freedom-confinement":15193,"\u002Fparticle-physics\u002Fqcd\u002Fdeep-inelastic-scattering-partons":15197,"\u002Fparticle-physics\u002Fqcd\u002Fjets-hadronization":15201,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Felectroweak-su2-u1":15205,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fspontaneous-symmetry-breaking":15210,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-mechanism":15214,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-boson-discovery":15218,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fstandard-model":15222,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-oscillations":15226,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-mass-pmns":15231,"\u002Fparticle-physics\u002Fneutrinos\u002Fdirac-majorana-experiments":15235,"\u002Fparticle-physics\u002Fexperiment\u002Faccelerators-luminosity":15239,"\u002Fparticle-physics\u002Fexperiment\u002Fdetectors-subsystems":15244,"\u002Fparticle-physics\u002Fexperiment\u002Fhow-discoveries-are-made":15248,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fbeyond-standard-model":15252,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fgrand-unified-theories":15256,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fsupersymmetry":15260,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fhierarchy-problem-naturalness":15264,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fdark-matter-candidates":15268,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fmatter-antimatter-open-questions":15272,"\u002Fparticle-physics":15276,"\u002Fastrophysics-cosmology\u002Forientation\u002Fthe-sun-and-stars":15279,"\u002Fastrophysics-cosmology\u002Forientation\u002Fstellar-death-final-states":15284,"\u002Fastrophysics-cosmology\u002Forientation\u002Fgalaxies-and-cosmology":15288,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fmagnitudes-fluxes-and-the-distance-modulus":15292,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fstellar-spectra-and-spectral-classification":15297,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Ftelescopes-and-detectors-across-the-spectrum":15301,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fthe-cosmic-distance-ladder":15305,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fblackbody-radiation-and-specific-intensity":15309,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fradiative-transfer-and-the-transfer-equation":15314,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fspectral-line-formation-and-broadening":15318,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fopacity-and-the-rosseland-mean":15322,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fhydrostatic-equilibrium-and-the-virial-theorem":15326,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equations-of-stellar-structure":15331,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equation-of-state-and-polytropes":15335,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-standard-solar-model":15339,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fthermonuclear-reaction-rates-and-the-gamow-peak":15343,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhydrogen-burning-pp-chains-and-cno":15348,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhelium-burning-and-the-triple-alpha-process":15352,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fadvanced-burning-and-neutron-capture-nucleosynthesis":15356,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fphases-of-the-interstellar-medium":15360,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fmolecular-clouds-and-gravitational-collapse":15365,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fprotostars-and-the-pre-main-sequence":15369,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-main-sequence-and-its-structure":15373,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fpost-main-sequence-low-mass-evolution":15378,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-evolution-of-massive-stars":15382,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fstellar-pulsation-and-the-instability-strip":15386,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":15390,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fcore-collapse-supernovae":15394,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fthermonuclear-supernovae-type-ia":15398,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fneutron-stars-and-pulsars":15402,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fblack-holes-schwarzschild-and-kerr":15406,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fbinary-systems-and-mass-transfer":15410,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Faccreting-compact-objects":15415,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fgravitational-waves-from-inspiraling-binaries":15419,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fmultimessenger-astronomy-and-gamma-ray-bursts":15423,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fthe-milky-way":15427,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-morphology-and-classification":15432,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-rotation-curves-and-dark-matter":15436,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Factive-galactic-nuclei-and-supermassive-black-holes":15440,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-clusters-and-large-scale-structure":15444,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-expanding-universe-and-hubbles-law":15448,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-frw-metric-and-cosmological-redshift":15453,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-friedmann-equations-and-cosmic-dynamics":15457,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fcosmological-models-and-distances":15460,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fdark-energy-and-the-accelerating-universe":15464,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fthe-thermal-history-of-the-universe":15468,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fbig-bang-nucleosynthesis":15473,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Frecombination-and-the-cosmic-microwave-background":15477,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcmb-anisotropies-and-cosmological-parameters":15481,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcosmic-inflation":15485,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fstructure-formation-and-the-growth-of-perturbations":15489,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fdark-matter-dark-energy-and-open-questions":15493,"\u002Fastrophysics-cosmology":15497,"\u002Fcolophon":15500,"\u002F":15503},{"path":10826,"title":10827,"module":10828,"summary":10829},"\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":10831,"title":10832,"module":10828,"summary":10833},"\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":10835,"title":10836,"module":10828,"summary":10837},"\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":10839,"title":10840,"module":10828,"summary":10841},"\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":10843,"title":10844,"module":10828,"summary":10845},"\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":10847,"title":10848,"module":10828,"summary":10849},"\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":10851,"title":10852,"module":10853,"summary":10854},"\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":10856,"title":10857,"module":10853,"summary":10858},"\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":10860,"title":10861,"module":10853,"summary":10862},"\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":10864,"title":10865,"module":10853,"summary":10866},"\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":10868,"title":10869,"module":10870,"summary":10871},"\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":10873,"title":10874,"module":10870,"summary":10875},"\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":10877,"title":10878,"module":10870,"summary":10879},"\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":10881,"title":10882,"module":10870,"summary":10883},"\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":10885,"title":10886,"module":10887,"summary":10888},"\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":10890,"title":10891,"module":10887,"summary":10892},"\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":10894,"title":10895,"module":10887,"summary":10896},"\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":10898,"title":10899,"module":10887,"summary":10900},"\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":10902,"title":10903,"module":10887,"summary":10904},"\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":10906,"title":10907,"module":10887,"summary":10908},"\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":10910,"title":10911,"module":10887,"summary":10912},"\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":10914,"title":10915,"module":10887,"summary":10916},"\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":10918,"title":10919,"module":10887,"summary":10920},"\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":10922,"title":10923,"module":10887,"summary":10924},"\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":10926,"title":10927,"module":10887,"summary":10928},"\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":10930,"title":10931,"module":10887,"summary":10932},"\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":10934,"title":10935,"module":10936,"summary":10937},"\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":10939,"title":10940,"module":10936,"summary":10941},"\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":10943,"title":10944,"module":10936,"summary":10945},"\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":10947,"title":10948,"module":10936,"summary":10949},"\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":10951,"title":10952,"module":10936,"summary":10953},"\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":10955,"title":10956,"module":10936,"summary":10957},"\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":10959,"title":10960,"module":10936,"summary":10961},"\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":10963,"title":10964,"module":10936,"summary":10965},"\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":10967,"title":10968,"module":10969,"summary":10970},"\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":10972,"title":10973,"module":10969,"summary":10974},"\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":10976,"title":10977,"module":10969,"summary":10978},"\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":10980,"title":10981,"module":10969,"summary":10982},"\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":10984,"title":10985,"module":10969,"summary":10986},"\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":10988,"title":10989,"module":10969,"summary":10990},"\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":10992,"title":10993,"module":10969,"summary":10994},"\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":10996,"title":10997,"module":10969,"summary":10998},"\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":11000,"title":11001,"module":10969,"summary":11002},"\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":11004,"title":11005,"module":10969,"summary":11006},"\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":11008,"title":11009,"module":10969,"summary":11010},"\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":11012,"title":11013,"module":10969,"summary":11014},"\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":11016,"title":11017,"module":10969,"summary":11018},"\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":11020,"title":11021,"module":10969,"summary":11022},"\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":11024,"title":11025,"module":11026,"summary":11027},"\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":11029,"title":11030,"module":11026,"summary":11031},"\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":11033,"title":11034,"module":11026,"summary":11035},"\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":11037,"title":11038,"module":11026,"summary":11039},"\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":11041,"title":11042,"module":11026,"summary":11043},"\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":11045,"title":11046,"module":11047,"summary":11048},"\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":11050,"title":11051,"module":11047,"summary":11052},"\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":11054,"title":11055,"module":11047,"summary":11056},"\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":11058,"title":11059,"module":11047,"summary":11060},"\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":11062,"title":11063,"module":11047,"summary":11064},"\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":11066,"title":11067,"module":11047,"summary":11068},"\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":11070,"title":11071,"module":11047,"summary":11072},"\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":11074,"title":11075,"module":11047,"summary":11076},"\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":11078,"title":11079,"module":11047,"summary":11080},"\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":11082,"title":11083,"module":11047,"summary":11084},"\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":11086,"title":11087,"module":11047,"summary":11088},"\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":11090,"title":11091,"module":11092,"summary":11093},"\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":11095,"title":11096,"module":11092,"summary":11097},"\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":11099,"title":11100,"module":11092,"summary":11101},"\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":11103,"title":11104,"module":11092,"summary":11105},"\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":11107,"title":11108,"module":11109,"summary":11110},"\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":11112,"title":11113,"module":11109,"summary":11114},"\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":11116,"title":11117,"module":11109,"summary":11118},"\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":11120,"title":11121,"module":11109,"summary":11122},"\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":11124,"title":11125,"module":11109,"summary":11126},"\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":11128,"title":11129,"module":11109,"summary":11130},"\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":11132,"title":11133,"module":11109,"summary":11134},"\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":11136,"title":11137,"module":11138,"summary":11139},"\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":11141,"title":11142,"module":11138,"summary":11143},"\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":11145,"title":11146,"module":11138,"summary":11147},"\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":11149,"title":11150,"module":11138,"summary":11151},"\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":11153,"title":11154,"module":11155,"summary":11156},"\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":11158,"title":11159,"module":11155,"summary":11160},"\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":11162,"title":11163,"module":11155,"summary":11164},"\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":11166,"title":11167,"module":11155,"summary":11168},"\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":11170,"title":11171,"module":6,"summary":6},"\u002Falgorithms","Algorithms",{"path":11173,"title":11174,"module":11175,"summary":11176},"\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":11178,"title":11179,"module":11175,"summary":11180},"\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":11182,"title":11183,"module":11175,"summary":11184},"\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":11186,"title":11187,"module":11175,"summary":11188},"\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":11190,"title":11191,"module":11192,"summary":11193},"\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":11195,"title":11196,"module":11192,"summary":11197},"\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":11199,"title":11200,"module":11192,"summary":11201},"\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":11203,"title":11204,"module":11192,"summary":11205},"\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":11207,"title":11208,"module":11209,"summary":11210},"\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":11212,"title":11213,"module":11209,"summary":11214},"\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":11216,"title":11217,"module":11209,"summary":11218},"\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":11220,"title":11221,"module":11209,"summary":11222},"\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":11224,"title":11225,"module":11226,"summary":11227},"\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":11229,"title":11230,"module":11226,"summary":11231},"\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":11233,"title":11234,"module":11226,"summary":11235},"\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":11237,"title":11238,"module":11239,"summary":11240},"\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":11242,"title":11243,"module":11239,"summary":11244},"\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":11246,"title":11247,"module":11239,"summary":11248},"\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":11250,"title":11251,"module":11252,"summary":11253},"\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":11255,"title":11256,"module":11252,"summary":11257},"\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":11259,"title":11260,"module":11252,"summary":11261},"\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":11263,"title":11264,"module":11265,"summary":11266},"\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":11268,"title":11269,"module":11265,"summary":11270},"\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":11272,"title":11273,"module":11265,"summary":11274},"\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":11276,"title":11277,"module":11265,"summary":11278},"\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":11280,"title":11281,"module":11282,"summary":11283},"\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":11285,"title":11286,"module":11282,"summary":11287},"\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":11289,"title":11290,"module":11282,"summary":11291},"\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":11293,"title":11294,"module":11295,"summary":11296},"\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":11298,"title":11299,"module":11295,"summary":11300},"\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":11302,"title":11303,"module":11295,"summary":11304},"\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":11306,"title":11307,"module":11295,"summary":11308},"\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":11310,"title":11311,"module":11295,"summary":11312},"\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":11314,"title":11315,"module":11316,"summary":11317},"\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":11319,"title":11320,"module":11316,"summary":11321},"\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":11323,"title":11324,"module":11316,"summary":11325},"\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":11327,"title":11328,"module":11316,"summary":11329},"\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":11331,"title":11332,"module":11316,"summary":11333},"\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":11335,"title":11336,"module":11337,"summary":11338},"\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":11340,"title":11337,"module":11337,"summary":11341},"\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":11343,"title":11344,"module":11337,"summary":11345},"\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":11347,"title":11348,"module":11337,"summary":11349},"\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":11351,"title":11352,"module":11337,"summary":11353},"\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":11355,"title":11356,"module":11357,"summary":11358},"\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":11360,"title":11361,"module":11357,"summary":11362},"\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":11364,"title":11365,"module":11357,"summary":11366},"\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":11368,"title":11369,"module":11357,"summary":11370},"\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":11372,"title":11373,"module":11357,"summary":11374},"\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":11376,"title":11377,"module":11357,"summary":11378},"\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":11380,"title":11381,"module":6,"summary":6},"\u002Fcalculus","Calculus",{"path":11383,"title":11384,"module":10828,"summary":11385},"\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":11387,"title":11388,"module":10828,"summary":11389},"\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":11391,"title":11392,"module":11393,"summary":11394},"\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":11396,"title":11397,"module":11393,"summary":11398},"\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":11400,"title":11401,"module":11393,"summary":11402},"\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":11404,"title":11405,"module":11393,"summary":11406},"\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":11408,"title":11409,"module":11393,"summary":11410},"\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":11412,"title":11413,"module":11414,"summary":11415},"\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":11417,"title":11418,"module":11414,"summary":11419},"\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":11421,"title":11422,"module":11414,"summary":11423},"\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":11425,"title":11426,"module":11414,"summary":11427},"\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":11429,"title":11430,"module":11414,"summary":11431},"\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":11433,"title":11434,"module":11435,"summary":11436},"\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":11438,"title":11439,"module":11435,"summary":11440},"\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":11442,"title":11443,"module":11435,"summary":11444},"\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":11446,"title":11447,"module":11435,"summary":11448},"\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":11450,"title":11451,"module":11435,"summary":11452},"\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":11454,"title":11455,"module":11456,"summary":11457},"\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":11459,"title":11460,"module":11456,"summary":11461},"\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":11463,"title":11464,"module":11456,"summary":11465},"\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":11467,"title":11468,"module":11469,"summary":11470},"\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":11472,"title":11473,"module":11469,"summary":11474},"\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":11476,"title":11477,"module":11469,"summary":11478},"\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":11480,"title":11481,"module":11469,"summary":11482},"\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":11484,"title":11485,"module":11469,"summary":11486},"\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":11488,"title":11489,"module":11469,"summary":11490},"\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":11492,"title":11493,"module":11494,"summary":11495},"\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":11497,"title":11498,"module":11494,"summary":11499},"\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":11501,"title":11502,"module":11494,"summary":11503},"\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":11505,"title":11506,"module":11494,"summary":11507},"\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":11509,"title":11510,"module":11494,"summary":11511},"\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":11513,"title":11514,"module":11494,"summary":11515},"\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":11517,"title":11518,"module":11494,"summary":11519},"\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":11521,"title":11522,"module":11523,"summary":11524},"\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":11526,"title":11527,"module":11523,"summary":11528},"\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":11530,"title":11531,"module":11523,"summary":11532},"\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":11534,"title":11535,"module":11523,"summary":11536},"\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":11538,"title":11539,"module":11523,"summary":11540},"\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":11542,"title":11543,"module":11523,"summary":11544},"\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":11546,"title":11547,"module":11523,"summary":11548},"\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":11550,"title":11551,"module":11523,"summary":11552},"\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":11554,"title":11555,"module":11523,"summary":11556},"\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":11558,"title":11559,"module":11523,"summary":11560},"\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":11562,"title":11563,"module":11523,"summary":11564},"\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":11566,"title":11567,"module":11523,"summary":11568},"\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":11570,"title":11571,"module":11572,"summary":11573},"\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":11575,"title":11576,"module":11572,"summary":11577},"\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":11579,"title":11580,"module":11572,"summary":11581},"\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":11583,"title":11584,"module":11572,"summary":11585},"\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":11587,"title":11588,"module":11572,"summary":11589},"\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":11591,"title":11592,"module":11572,"summary":11593},"\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":11595,"title":11596,"module":6,"summary":6},"\u002Fmechanics","Mechanics & Dynamics",{"path":11598,"title":11599,"module":11600,"summary":11601},"\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":11603,"title":11604,"module":11600,"summary":11605},"\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":11607,"title":11608,"module":11600,"summary":11609},"\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":11611,"title":11612,"module":11600,"summary":11613},"\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":11615,"title":11616,"module":11600,"summary":11617},"\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":11619,"title":11620,"module":11621,"summary":11622},"\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":11624,"title":11625,"module":11621,"summary":11626},"\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":11628,"title":11629,"module":11630,"summary":11631},"\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":11633,"title":11634,"module":11630,"summary":11635},"\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":11637,"title":11638,"module":11630,"summary":11639},"\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":11641,"title":11642,"module":11630,"summary":11643},"\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":11645,"title":11646,"module":11630,"summary":11647},"\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":11649,"title":11650,"module":11651,"summary":11652},"\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":11654,"title":11655,"module":11651,"summary":11656},"\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":11658,"title":11659,"module":11651,"summary":11660},"\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":11662,"title":11663,"module":11651,"summary":11664},"\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":11666,"title":11667,"module":11668,"summary":11669},"\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":11671,"title":11672,"module":11668,"summary":11673},"\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":11675,"title":11676,"module":11668,"summary":11677},"\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":11679,"title":11680,"module":11681,"summary":11682},"\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":11684,"title":11685,"module":11681,"summary":11686},"\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":11688,"title":11689,"module":11681,"summary":11690},"\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":11692,"title":11693,"module":11681,"summary":11694},"\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":11696,"title":11697,"module":11681,"summary":11698},"\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":11700,"title":11701,"module":11702,"summary":11703},"\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":11705,"title":11706,"module":11702,"summary":11707},"\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":11709,"title":11710,"module":11702,"summary":11711},"\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":11713,"title":11714,"module":11702,"summary":11715},"\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":11717,"title":11718,"module":11702,"summary":11719},"\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":11721,"title":11722,"module":11702,"summary":11723},"\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":11725,"title":11726,"module":11727,"summary":11728},"\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":11730,"title":11731,"module":11727,"summary":11732},"\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":11734,"title":11735,"module":11727,"summary":11736},"\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":11738,"title":11739,"module":11727,"summary":11740},"\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":11742,"title":11743,"module":11727,"summary":11744},"\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":11746,"title":11747,"module":11727,"summary":11748},"\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":11750,"title":11751,"module":11727,"summary":11752},"\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":11754,"title":11755,"module":11727,"summary":11756},"\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":11758,"title":11759,"module":11760,"summary":11761},"\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":11763,"title":11764,"module":11760,"summary":11765},"\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":11767,"title":11768,"module":11760,"summary":11769},"\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":11771,"title":11772,"module":11760,"summary":11773},"\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":11775,"title":11776,"module":11760,"summary":11777},"\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":11779,"title":11780,"module":11781,"summary":11782},"\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":11784,"title":11785,"module":11781,"summary":11786},"\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":11788,"title":11789,"module":11781,"summary":11790},"\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":11792,"title":11793,"module":11781,"summary":11794},"\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":11796,"title":11797,"module":11781,"summary":11798},"\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":11800,"title":11801,"module":11802,"summary":11803},"\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":11805,"title":11806,"module":11802,"summary":11807},"\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":11809,"title":11810,"module":11802,"summary":11811},"\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":11813,"title":11814,"module":6,"summary":6},"\u002Felectricity-and-magnetism","Electricity & Magnetism",{"path":11816,"title":11817,"module":11818,"summary":11819},"\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":11821,"title":11822,"module":11818,"summary":11823},"\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":11825,"title":11826,"module":11818,"summary":11827},"\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":11829,"title":11830,"module":11818,"summary":11831},"\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":11833,"title":11834,"module":11818,"summary":11835},"\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":11837,"title":11838,"module":11839,"summary":11840},"\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":11842,"title":11843,"module":11839,"summary":11844},"\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":11846,"title":11847,"module":11839,"summary":11848},"\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":11850,"title":11851,"module":11839,"summary":11852},"\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":11854,"title":11855,"module":11839,"summary":11856},"\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":11858,"title":11859,"module":11860,"summary":11861},"\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":11863,"title":11864,"module":11860,"summary":11865},"\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":11867,"title":11868,"module":11860,"summary":11869},"\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":11871,"title":11872,"module":11873,"summary":11874},"\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":11876,"title":11877,"module":11873,"summary":11878},"\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":11880,"title":11881,"module":11873,"summary":11882},"\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":11884,"title":11885,"module":11873,"summary":11886},"\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":11888,"title":11889,"module":11873,"summary":11890},"\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":11892,"title":11893,"module":11873,"summary":11894},"\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":11896,"title":11897,"module":11873,"summary":11898},"\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":11900,"title":11901,"module":11902,"summary":11903},"\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":11905,"title":11906,"module":11902,"summary":11907},"\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":11909,"title":11910,"module":11902,"summary":11911},"\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":11913,"title":11914,"module":11902,"summary":11915},"\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":11917,"title":11918,"module":11902,"summary":11919},"\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":11921,"title":11922,"module":11902,"summary":11923},"\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":11925,"title":11926,"module":11902,"summary":11927},"\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":11929,"title":11930,"module":11931,"summary":11932},"\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":11934,"title":11935,"module":11931,"summary":11936},"\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":11938,"title":11939,"module":11931,"summary":11940},"\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":11942,"title":11943,"module":11931,"summary":11944},"\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":11946,"title":11947,"module":11931,"summary":11948},"\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":11950,"title":11951,"module":11931,"summary":11952},"\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":11954,"title":11955,"module":11956,"summary":11957},"\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":11959,"title":11960,"module":11956,"summary":11961},"\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":11963,"title":11964,"module":11956,"summary":11965},"\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":11967,"title":11968,"module":11956,"summary":11969},"\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":11971,"title":11972,"module":11956,"summary":11973},"\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":11975,"title":11976,"module":11977,"summary":11978},"\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":11980,"title":11981,"module":11977,"summary":11982},"\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":11984,"title":11985,"module":11977,"summary":11986},"\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":11988,"title":11989,"module":11977,"summary":11990},"\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":11992,"title":11993,"module":11977,"summary":11994},"\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":11996,"title":11997,"module":11977,"summary":11998},"\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":12000,"title":12001,"module":12002,"summary":12003},"\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":12005,"title":12006,"module":12002,"summary":12007},"\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":12009,"title":12010,"module":12002,"summary":12011},"\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":12013,"title":12014,"module":12002,"summary":12015},"\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":12017,"title":12018,"module":12002,"summary":12019},"\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":12021,"title":12022,"module":6,"summary":6},"\u002Flinear-algebra","Linear Algebra",{"path":12024,"title":12025,"module":6,"summary":6},"\u002Ftheory-of-computation","Theory of Computation",{"path":12027,"title":12028,"module":10828,"summary":12029},"\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":12031,"title":12032,"module":10828,"summary":12033},"\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":12035,"title":12036,"module":10828,"summary":12037},"\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":12039,"title":12040,"module":10828,"summary":12041},"\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":12043,"title":12044,"module":10828,"summary":12045},"\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":12047,"title":12048,"module":12049,"summary":12050},"\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":12052,"title":12053,"module":12049,"summary":12054},"\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":12056,"title":12057,"module":12049,"summary":12058},"\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":12060,"title":12061,"module":12049,"summary":12062},"\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":12064,"title":12065,"module":12049,"summary":12066},"\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":12068,"title":12069,"module":12049,"summary":12070},"\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":12072,"title":12073,"module":12049,"summary":12074},"\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":12076,"title":12077,"module":12078,"summary":12079},"\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":12081,"title":12082,"module":12078,"summary":12083},"\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":12085,"title":12086,"module":12078,"summary":12087},"\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":12089,"title":12090,"module":12078,"summary":12091},"\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":12093,"title":12094,"module":12078,"summary":12095},"\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":12097,"title":12098,"module":12099,"summary":12100},"\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":12102,"title":12103,"module":12099,"summary":12104},"\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":12106,"title":12107,"module":12099,"summary":12108},"\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":12110,"title":12111,"module":12099,"summary":12112},"\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":12114,"title":12115,"module":12099,"summary":12116},"\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":12118,"title":12119,"module":12120,"summary":12121},"\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":12123,"title":12124,"module":12120,"summary":12125},"\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":12127,"title":12128,"module":12120,"summary":12129},"\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":12131,"title":12132,"module":12120,"summary":12133},"\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":12135,"title":12136,"module":12120,"summary":12137},"\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":12139,"title":12140,"module":12141,"summary":12142},"\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":12144,"title":12145,"module":12141,"summary":12146},"\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":12148,"title":12149,"module":12141,"summary":12150},"\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":12152,"title":12153,"module":12141,"summary":12154},"\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":12156,"title":12157,"module":12141,"summary":12158},"\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":12160,"title":12161,"module":12162,"summary":12163},"\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":12165,"title":12166,"module":12162,"summary":12167},"\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":12169,"title":12170,"module":12162,"summary":12171},"\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":12173,"title":12174,"module":12162,"summary":12175},"\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":12177,"title":12178,"module":12162,"summary":12179},"\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":12181,"title":12182,"module":12183,"summary":12184},"\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":12186,"title":12187,"module":12183,"summary":12188},"\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":12190,"title":12191,"module":12183,"summary":12192},"\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":12194,"title":12195,"module":12196,"summary":12197},"\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":12199,"title":12200,"module":12196,"summary":12201},"\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":12203,"title":12204,"module":12205,"summary":12206},"\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":12208,"title":12209,"module":12205,"summary":12210},"\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":12212,"title":12213,"module":12205,"summary":12214},"\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":12216,"title":12217,"module":12205,"summary":12218},"\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":12220,"title":12221,"module":12205,"summary":12222},"\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":12224,"title":12225,"module":12226,"summary":12227},"\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":12229,"title":12230,"module":12226,"summary":12231},"\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":12233,"title":12234,"module":6,"summary":6},"\u002Fcomputer-architecture","Computer Architecture",{"path":12236,"title":12237,"module":10828,"summary":12238},"\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":12240,"title":12241,"module":10828,"summary":12242},"\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":12244,"title":12245,"module":12246,"summary":12247},"\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":12249,"title":12250,"module":12246,"summary":12251},"\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":12253,"title":12254,"module":12246,"summary":12255},"\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":12257,"title":12258,"module":12246,"summary":12259},"\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":12261,"title":12262,"module":12246,"summary":12263},"\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":12265,"title":12266,"module":12246,"summary":12267},"\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":12269,"title":12270,"module":12271,"summary":12272},"\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":12274,"title":12275,"module":12271,"summary":12276},"\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":12278,"title":12279,"module":12271,"summary":12280},"\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":12282,"title":12283,"module":12271,"summary":12284},"\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":12286,"title":12287,"module":12271,"summary":12288},"\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":12290,"title":12291,"module":12271,"summary":12292},"\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":12294,"title":12295,"module":12296,"summary":12297},"\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":12299,"title":12300,"module":12296,"summary":12301},"\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":12303,"title":12304,"module":12296,"summary":12305},"\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":12307,"title":12308,"module":12309,"summary":12310},"\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":12312,"title":12313,"module":12309,"summary":12314},"\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":12316,"title":12317,"module":12318,"summary":12319},"\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":12321,"title":12322,"module":12318,"summary":12323},"\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":12325,"title":12326,"module":12318,"summary":12327},"\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":12329,"title":12330,"module":12331,"summary":12332},"\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":12334,"title":12335,"module":12331,"summary":12336},"\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":12338,"title":12339,"module":12340,"summary":12341},"\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":12343,"title":12344,"module":12340,"summary":12345},"\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":12347,"title":12348,"module":12340,"summary":12349},"\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":12351,"title":12352,"module":12353,"summary":12354},"\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":12356,"title":12357,"module":12353,"summary":12358},"\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":12360,"title":12361,"module":12353,"summary":12362},"\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":12364,"title":12365,"module":12366,"summary":12367},"\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":12369,"title":12370,"module":12366,"summary":12371},"\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":12373,"title":12374,"module":6,"summary":6},"\u002Fdifferential-equations","Differential Equations",{"path":12376,"title":12377,"module":12378,"summary":12379},"\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":12381,"title":12382,"module":12378,"summary":12383},"\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":12385,"title":12386,"module":12378,"summary":12387},"\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":12389,"title":12390,"module":12378,"summary":12391},"\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":12393,"title":12394,"module":12378,"summary":12395},"\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":12397,"title":12398,"module":12399,"summary":12400},"\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":12402,"title":12403,"module":12399,"summary":12404},"\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":12406,"title":12407,"module":12399,"summary":12408},"\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":12410,"title":12411,"module":12399,"summary":12412},"\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":12414,"title":12415,"module":12416,"summary":12417},"\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":12419,"title":12420,"module":12416,"summary":12421},"\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":12423,"title":12424,"module":12416,"summary":12425},"\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":12427,"title":12428,"module":12416,"summary":12429},"\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":12431,"title":12432,"module":12433,"summary":12434},"\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":12436,"title":12437,"module":12433,"summary":12438},"\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":12440,"title":12441,"module":12433,"summary":12442},"\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":12444,"title":12445,"module":12433,"summary":12446},"\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":12448,"title":12449,"module":12450,"summary":12451},"\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":12453,"title":12454,"module":12450,"summary":12455},"\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":12457,"title":12458,"module":12450,"summary":12459},"\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":12461,"title":12462,"module":12450,"summary":12463},"\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":12465,"title":12466,"module":12450,"summary":12467},"\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":12469,"title":12470,"module":12450,"summary":12471},"\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":12473,"title":12474,"module":12475,"summary":12476},"\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":12478,"title":12479,"module":12475,"summary":12480},"\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":12482,"title":12483,"module":12475,"summary":12484},"\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":12486,"title":12487,"module":12488,"summary":12489},"\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":12491,"title":12492,"module":12488,"summary":12493},"\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":12495,"title":12496,"module":12488,"summary":12497},"\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":12499,"title":12500,"module":12488,"summary":12501},"\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":12503,"title":12504,"module":12505,"summary":12506},"\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":12508,"title":12509,"module":12505,"summary":12510},"\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":12512,"title":12513,"module":12505,"summary":12514},"\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":12516,"title":12517,"module":12518,"summary":12519},"\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":12521,"title":12522,"module":12518,"summary":12523},"\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":12525,"title":12526,"module":12518,"summary":12527},"\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":12529,"title":12530,"module":12531,"summary":12532},"\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":12534,"title":12535,"module":12531,"summary":12536},"\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":12538,"title":12539,"module":6,"summary":6},"\u002Frelativity","Relativity",{"path":12541,"title":12542,"module":6,"summary":6},"\u002Fphysical-computing","Physical Computing",{"path":12544,"title":12545,"module":12546,"summary":12547},"\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":12549,"title":12550,"module":12546,"summary":12551},"\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":12553,"title":12554,"module":12546,"summary":12555},"\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":12557,"title":12558,"module":12546,"summary":12559},"\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":12561,"title":12562,"module":12563,"summary":12564},"\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":12566,"title":12567,"module":12563,"summary":12568},"\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":12570,"title":12571,"module":12563,"summary":12572},"\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":12574,"title":12575,"module":12576,"summary":12577},"\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":12579,"title":12580,"module":12576,"summary":12581},"\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":12583,"title":12584,"module":12576,"summary":12585},"\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":12587,"title":12588,"module":12576,"summary":12589},"\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":12591,"title":12592,"module":12576,"summary":12593},"\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":12595,"title":12596,"module":12576,"summary":12597},"\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":12599,"title":12600,"module":12601,"summary":12602},"\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":12604,"title":12605,"module":12601,"summary":12606},"\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":12608,"title":12609,"module":12601,"summary":12610},"\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":12612,"title":12613,"module":12601,"summary":12614},"\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":12616,"title":12617,"module":12601,"summary":12618},"\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":12620,"title":12621,"module":12601,"summary":12622},"\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":12624,"title":12625,"module":12626,"summary":12627},"\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":12629,"title":12630,"module":12626,"summary":12631},"\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":12633,"title":12634,"module":12626,"summary":12635},"\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":12637,"title":12638,"module":12626,"summary":12639},"\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":12641,"title":12642,"module":11481,"summary":12643},"\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":12645,"title":12646,"module":11481,"summary":12647},"\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":12649,"title":12650,"module":11481,"summary":12651},"\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":12653,"title":12654,"module":12655,"summary":12656},"\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":12658,"title":12659,"module":12655,"summary":12660},"\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":12662,"title":12663,"module":12655,"summary":12664},"\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":12666,"title":12667,"module":12668,"summary":12669},"\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":12671,"title":12672,"module":12668,"summary":12673},"\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":12675,"title":12676,"module":12668,"summary":12677},"\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":12679,"title":12680,"module":12681,"summary":12682},"\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":12684,"title":12685,"module":12681,"summary":12686},"\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":12688,"title":12689,"module":12690,"summary":12691},"\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":12693,"title":12694,"module":12690,"summary":12695},"\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":12697,"title":12698,"module":12690,"summary":12699},"\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":12701,"title":12702,"module":12690,"summary":12703},"\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":12705,"title":12706,"module":12690,"summary":12707},"\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":12709,"title":12710,"module":6,"summary":6},"\u002Fquantum-mechanics","Quantum Mechanics",{"path":12712,"title":12713,"module":12714,"summary":12715},"\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":12717,"title":12718,"module":12714,"summary":12719},"\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":12721,"title":12722,"module":12714,"summary":12723},"\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":12725,"title":12726,"module":12714,"summary":12727},"\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":12729,"title":12730,"module":12731,"summary":12732},"\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":12734,"title":12735,"module":12731,"summary":12736},"\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":12738,"title":12739,"module":12731,"summary":12740},"\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":12742,"title":12743,"module":12731,"summary":12744},"\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":12746,"title":12747,"module":12731,"summary":12748},"\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":12750,"title":12751,"module":12731,"summary":12752},"\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":12754,"title":12755,"module":12756,"summary":12757},"\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":12759,"title":12760,"module":12756,"summary":12761},"\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":12763,"title":12764,"module":12756,"summary":12765},"\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":12767,"title":12768,"module":12756,"summary":12769},"\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":12771,"title":12772,"module":12756,"summary":12773},"\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":12775,"title":12776,"module":11175,"summary":12777},"\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":12779,"title":12780,"module":11175,"summary":12781},"\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":12783,"title":12784,"module":11175,"summary":12785},"\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":12787,"title":12788,"module":11175,"summary":12789},"\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":12791,"title":12792,"module":11175,"summary":12793},"\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":12795,"title":12796,"module":11175,"summary":12797},"\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":12799,"title":12800,"module":12801,"summary":12802},"\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":12804,"title":12805,"module":12801,"summary":12806},"\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":12808,"title":12809,"module":12801,"summary":12810},"\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":12812,"title":12813,"module":12801,"summary":12814},"\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":12816,"title":12817,"module":12818,"summary":12819},"\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":12821,"title":12822,"module":12818,"summary":12823},"\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":12825,"title":12826,"module":12818,"summary":12827},"\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":12829,"title":11230,"module":12818,"summary":12830},"\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":12832,"title":12833,"module":12818,"summary":12834},"\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":12836,"title":12837,"module":12838,"summary":12839},"\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":12841,"title":12842,"module":12838,"summary":12843},"\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":12845,"title":12846,"module":12838,"summary":12847},"\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":12849,"title":12850,"module":12838,"summary":12851},"\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":12853,"title":12854,"module":12855,"summary":12856},"\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":12858,"title":12859,"module":12855,"summary":12860},"\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":12862,"title":12863,"module":12855,"summary":12864},"\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":12866,"title":12867,"module":12855,"summary":12868},"\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":12870,"title":12871,"module":12855,"summary":12872},"\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":12874,"title":12875,"module":6,"summary":6},"\u002Freal-analysis","Real Analysis",{"path":12877,"title":12878,"module":10828,"summary":12879},"\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":12881,"title":12882,"module":10828,"summary":12883},"\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":12885,"title":12886,"module":12887,"summary":12888},"\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":12890,"title":12891,"module":12887,"summary":12892},"\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":12894,"title":12895,"module":12887,"summary":12896},"\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":12898,"title":12899,"module":12887,"summary":12900},"\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":12902,"title":12903,"module":12904,"summary":12905},"\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":12907,"title":12908,"module":12904,"summary":12909},"\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":12911,"title":12912,"module":12904,"summary":12913},"\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":12915,"title":12916,"module":12904,"summary":12917},"\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":12919,"title":12920,"module":12904,"summary":12921},"\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":12923,"title":12924,"module":12904,"summary":12925},"\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":12927,"title":12928,"module":12929,"summary":12930},"\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":12932,"title":12933,"module":12929,"summary":12934},"\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":12936,"title":12937,"module":12929,"summary":12938},"\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":12940,"title":12941,"module":12929,"summary":12942},"\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":12944,"title":12945,"module":12946,"summary":12947},"\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":12949,"title":12950,"module":12946,"summary":12951},"\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":12953,"title":12954,"module":12946,"summary":12955},"\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":12957,"title":12958,"module":12946,"summary":12959},"\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":12961,"title":12962,"module":12963,"summary":12964},"\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":12966,"title":12967,"module":12963,"summary":12968},"\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":12970,"title":12971,"module":12963,"summary":12972},"\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":12974,"title":12975,"module":12976,"summary":12977},"\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":12979,"title":12980,"module":12976,"summary":12981},"\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":12983,"title":12984,"module":12976,"summary":12985},"\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":12987,"title":12988,"module":12976,"summary":12989},"\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":12991,"title":12992,"module":12993,"summary":12994},"\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":12996,"title":12997,"module":12993,"summary":12998},"\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":13000,"title":13001,"module":12993,"summary":13002},"\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":13004,"title":13005,"module":12993,"summary":13006},"\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":13008,"title":13009,"module":13010,"summary":13011},"\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":13013,"title":13014,"module":13010,"summary":13015},"\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":13017,"title":13018,"module":13010,"summary":13019},"\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":13021,"title":13022,"module":13023,"summary":13024},"\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":13026,"title":13027,"module":13023,"summary":13028},"\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":13030,"title":13031,"module":13023,"summary":13032},"\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":13034,"title":13035,"module":13023,"summary":13036},"\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":13038,"title":13039,"module":13040,"summary":13041},"\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":13043,"title":13044,"module":13040,"summary":13045},"\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":13047,"title":13048,"module":13040,"summary":13049},"\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":13051,"title":13052,"module":13040,"summary":13053},"\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":13055,"title":13056,"module":13040,"summary":13057},"\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":13059,"title":13060,"module":13061,"summary":13062},"\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":13064,"title":13065,"module":13061,"summary":13066},"\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":13068,"title":13069,"module":6,"summary":6},"\u002Fabstract-algebra","Abstract Algebra",{"path":13071,"title":13072,"module":13073,"summary":13074},"\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":13076,"title":13077,"module":13073,"summary":13078},"\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":13080,"title":13081,"module":13073,"summary":13082},"\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":13084,"title":13085,"module":13073,"summary":13086},"\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":13088,"title":13089,"module":13073,"summary":13090},"\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":13092,"title":13093,"module":13094,"summary":13095},"\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":13097,"title":13098,"module":13094,"summary":13099},"\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":13101,"title":13102,"module":13094,"summary":13103},"\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":13105,"title":13106,"module":13094,"summary":13107},"\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":13109,"title":13110,"module":13094,"summary":13111},"\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":13113,"title":13114,"module":13094,"summary":13115},"\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":13117,"title":13118,"module":13094,"summary":13119},"\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":13121,"title":13122,"module":13123,"summary":13124},"\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":13126,"title":13127,"module":13123,"summary":13128},"\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":13130,"title":13131,"module":13123,"summary":13132},"\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":13134,"title":13135,"module":13123,"summary":13136},"\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":13138,"title":13139,"module":13140,"summary":13141},"\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":13143,"title":13144,"module":13140,"summary":13145},"\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":13147,"title":13148,"module":13140,"summary":13149},"\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":13151,"title":13152,"module":13153,"summary":13154},"\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":13156,"title":13157,"module":13153,"summary":13158},"\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":13160,"title":13161,"module":13153,"summary":13162},"\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":13164,"title":13165,"module":13153,"summary":13166},"\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":13168,"title":13169,"module":13153,"summary":13170},"\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":13172,"title":13173,"module":13153,"summary":13174},"\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":13176,"title":13177,"module":13178,"summary":13179},"\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":13181,"title":13182,"module":13178,"summary":13183},"\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":13185,"title":13186,"module":13178,"summary":13187},"\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":13189,"title":13190,"module":13191,"summary":13192},"\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":13194,"title":13195,"module":13191,"summary":13196},"\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":13198,"title":13199,"module":13191,"summary":13200},"\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":13202,"title":13203,"module":13191,"summary":13204},"\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":13206,"title":13207,"module":13208,"summary":13209},"\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":13211,"title":13212,"module":13208,"summary":13213},"\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":13215,"title":13216,"module":13208,"summary":13217},"\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":13219,"title":13220,"module":13221,"summary":13222},"\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":13224,"title":13225,"module":13221,"summary":13226},"\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":13228,"title":13229,"module":13221,"summary":13230},"\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":13232,"title":13233,"module":13221,"summary":13234},"\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":13236,"title":13237,"module":6,"summary":6},"\u002Fatomic-physics","Atomic Physics",{"path":13239,"title":13240,"module":6,"summary":6},"\u002Fdatabases","Databases",{"path":13242,"title":13243,"module":10828,"summary":13244},"\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":13246,"title":13247,"module":10828,"summary":13248},"\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":13250,"title":13251,"module":10828,"summary":13252},"\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":13254,"title":13255,"module":10828,"summary":13256},"\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":13258,"title":13259,"module":10828,"summary":13260},"\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":13262,"title":13263,"module":10828,"summary":13264},"\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":13266,"title":13267,"module":13268,"summary":13269},"\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":13271,"title":13272,"module":13268,"summary":13273},"\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":13275,"title":13276,"module":13268,"summary":13277},"\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":13279,"title":13280,"module":13281,"summary":13282},"\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":13284,"title":13285,"module":13281,"summary":13286},"\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":13288,"title":13289,"module":13281,"summary":13290},"\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":13292,"title":13293,"module":13294,"summary":13295},"\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":13297,"title":13298,"module":13294,"summary":13299},"\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":13301,"title":13302,"module":13294,"summary":13303},"\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":13305,"title":13306,"module":13294,"summary":13307},"\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":13309,"title":13310,"module":13294,"summary":13311},"\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":13313,"title":13314,"module":13315,"summary":13316},"\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":13318,"title":13319,"module":13315,"summary":13320},"\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":13322,"title":13323,"module":13315,"summary":13324},"\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":13326,"title":13327,"module":13315,"summary":13328},"\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":13330,"title":13331,"module":13332,"summary":13333},"\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":13335,"title":13336,"module":13332,"summary":13337},"\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":13339,"title":13340,"module":13332,"summary":13341},"\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":13343,"title":13344,"module":13332,"summary":13345},"\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":13347,"title":13348,"module":13349,"summary":13350},"\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":13352,"title":13353,"module":13349,"summary":13354},"\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":13356,"title":13357,"module":13349,"summary":13358},"\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":13360,"title":13361,"module":13349,"summary":13362},"\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":13364,"title":13365,"module":13366,"summary":13367},"\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":13369,"title":13370,"module":13366,"summary":13371},"\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":13373,"title":13374,"module":13366,"summary":13375},"\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":13377,"title":13378,"module":6,"summary":6},"\u002Fcategory-theory","Category Theory",{"path":13380,"title":12022,"module":13381,"summary":13382},"\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":13384,"title":13385,"module":13381,"summary":13386},"\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":13388,"title":13389,"module":13381,"summary":13390},"\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":13392,"title":11381,"module":13381,"summary":13393},"\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":13395,"title":13396,"module":10828,"summary":13397},"\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":13399,"title":13400,"module":10828,"summary":13401},"\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":13403,"title":13404,"module":10828,"summary":13405},"\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":13407,"title":13408,"module":13409,"summary":13410},"\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":13412,"title":13413,"module":13409,"summary":13414},"\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":13416,"title":13417,"module":13409,"summary":13418},"\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":13420,"title":13421,"module":13409,"summary":13422},"\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":13424,"title":13425,"module":13409,"summary":13426},"\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":13428,"title":13429,"module":13430,"summary":13431},"\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":13433,"title":13434,"module":13430,"summary":13435},"\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":13437,"title":13438,"module":13430,"summary":13439},"\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":13441,"title":13442,"module":13430,"summary":13443},"\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":13445,"title":13446,"module":13430,"summary":13447},"\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":13449,"title":13450,"module":13451,"summary":13452},"\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":13454,"title":13455,"module":13451,"summary":13456},"\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":13458,"title":13459,"module":13451,"summary":13460},"\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":13462,"title":13463,"module":13451,"summary":13464},"\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":13466,"title":13467,"module":13468,"summary":13469},"\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":13471,"title":13472,"module":13468,"summary":13473},"\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":13475,"title":13476,"module":13468,"summary":13477},"\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":13479,"title":13480,"module":13468,"summary":13481},"\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":13483,"title":13484,"module":13468,"summary":13485},"\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":13487,"title":13488,"module":13468,"summary":13489},"\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":13491,"title":13492,"module":13468,"summary":13493},"\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":13495,"title":13496,"module":13468,"summary":13497},"\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":13499,"title":13500,"module":13468,"summary":13501},"\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":13503,"title":13504,"module":13505,"summary":13506},"\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":13508,"title":13509,"module":13505,"summary":13510},"\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":13512,"title":13513,"module":13505,"summary":13514},"\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":13516,"title":13517,"module":13505,"summary":13518},"\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":13520,"title":13521,"module":13505,"summary":13522},"\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":13524,"title":13525,"module":13526,"summary":13527},"\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":13529,"title":13530,"module":13526,"summary":13531},"\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":13533,"title":13534,"module":13526,"summary":13535},"\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":13537,"title":13538,"module":13526,"summary":13539},"\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":13541,"title":13542,"module":13526,"summary":13543},"\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":13545,"title":13546,"module":13526,"summary":13547},"\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":13549,"title":13550,"module":13526,"summary":13551},"\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":13553,"title":13554,"module":13555,"summary":13556},"\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":13558,"title":13559,"module":13555,"summary":13560},"\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":13562,"title":13563,"module":13555,"summary":13564},"\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":13566,"title":13567,"module":13568,"summary":13569},"\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":13571,"title":13572,"module":13568,"summary":13573},"\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":13575,"title":13576,"module":13568,"summary":13577},"\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":13579,"title":13580,"module":13568,"summary":13581},"\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":13583,"title":13584,"module":13568,"summary":13585},"\u002Fdeep-learning\u002Fpractical\u002Fapplications","Applications","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":13587,"title":13588,"module":13568,"summary":13589},"\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":13591,"title":13592,"module":13568,"summary":13593},"\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":13595,"title":13596,"module":13597,"summary":13598},"\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":13600,"title":13601,"module":13597,"summary":13602},"\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":13604,"title":13605,"module":13597,"summary":13606},"\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":13608,"title":13609,"module":13597,"summary":13610},"\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":13612,"title":13613,"module":13597,"summary":13614},"\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":13616,"title":13617,"module":13597,"summary":13618},"\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":13620,"title":13621,"module":13597,"summary":13622},"\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":13624,"title":13625,"module":13597,"summary":13626},"\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":13628,"title":13629,"module":13597,"summary":13630},"\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":13632,"title":13633,"module":13597,"summary":13634},"\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":13636,"title":13637,"module":13597,"summary":13638},"\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":13640,"title":13641,"module":13642,"summary":13643},"\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":13645,"title":13646,"module":13642,"summary":13647},"\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":13649,"title":13650,"module":13642,"summary":13651},"\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":13653,"title":13654,"module":13642,"summary":13655},"\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":13657,"title":13658,"module":13642,"summary":13659},"\u002Fdeep-learning\u002Freinforcement-learning\u002Frl-from-human-feedback","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":13661,"title":13662,"module":6,"summary":6},"\u002Fdeep-learning","Deep Learning",{"path":13664,"title":13665,"module":11572,"summary":13666},"\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":13668,"title":13669,"module":11572,"summary":13670},"\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":13672,"title":13673,"module":11572,"summary":13674},"\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":13676,"title":13677,"module":11572,"summary":13678},"\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":13680,"title":13681,"module":11572,"summary":13682},"\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":13684,"title":13685,"module":13686,"summary":13687},"\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":13689,"title":13690,"module":13686,"summary":13691},"\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":13693,"title":13694,"module":13686,"summary":13695},"\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":13697,"title":13698,"module":13686,"summary":13699},"\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":13701,"title":13702,"module":13703,"summary":13704},"\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":13706,"title":13707,"module":13703,"summary":13708},"\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":13710,"title":13711,"module":13703,"summary":13712},"\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":13714,"title":13715,"module":13703,"summary":13716},"\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":13718,"title":13719,"module":13720,"summary":13721},"\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":13723,"title":13724,"module":13720,"summary":13725},"\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":13727,"title":13728,"module":13720,"summary":13729},"\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":13731,"title":13732,"module":13720,"summary":13733},"\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":13735,"title":13736,"module":13720,"summary":13737},"\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":13739,"title":13740,"module":13741,"summary":13742},"\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":13744,"title":13745,"module":13741,"summary":13746},"\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":13748,"title":13749,"module":13741,"summary":13750},"\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":13752,"title":13753,"module":13754,"summary":13755},"\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":13757,"title":13758,"module":13754,"summary":13759},"\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":13761,"title":13762,"module":13754,"summary":13763},"\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":13765,"title":13766,"module":13767,"summary":13768},"\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":13770,"title":13771,"module":13767,"summary":13772},"\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":13774,"title":13775,"module":13767,"summary":13776},"\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":13778,"title":13779,"module":13767,"summary":13780},"\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":13782,"title":13783,"module":13784,"summary":13785},"\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":13787,"title":13788,"module":13784,"summary":13789},"\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":13791,"title":13792,"module":13784,"summary":13793},"\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":13795,"title":13796,"module":13784,"summary":13797},"\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":13799,"title":13800,"module":13784,"summary":13801},"\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":13803,"title":13804,"module":13784,"summary":13805},"\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":13807,"title":13808,"module":13809,"summary":13810},"\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":13812,"title":13813,"module":13809,"summary":13814},"\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":13816,"title":13817,"module":13809,"summary":13818},"\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":13820,"title":13821,"module":13809,"summary":13822},"\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":13824,"title":13825,"module":13826,"summary":13827},"\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":13829,"title":13830,"module":13826,"summary":13831},"\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":13833,"title":13834,"module":13826,"summary":13835},"\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":13837,"title":13838,"module":13839,"summary":13840},"\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":13842,"title":13843,"module":13839,"summary":13844},"\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":13846,"title":13847,"module":13839,"summary":13848},"\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":13850,"title":13851,"module":13839,"summary":13852},"\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":13854,"title":13855,"module":13839,"summary":13856},"\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":13858,"title":13859,"module":13860,"summary":13861},"\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":13863,"title":13864,"module":13860,"summary":13865},"\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":13867,"title":13868,"module":13860,"summary":13869},"\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":13871,"title":13872,"module":6,"summary":6},"\u002Fstatistical-mechanics","Statistical Mechanics",{"path":13874,"title":13875,"module":13876,"summary":13877},"\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":13879,"title":13880,"module":13876,"summary":13881},"\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":13883,"title":13884,"module":13876,"summary":13885},"\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":13887,"title":13888,"module":13876,"summary":13889},"\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":13891,"title":13892,"module":13893,"summary":13894},"\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":13896,"title":13897,"module":13893,"summary":13898},"\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":13900,"title":13901,"module":13893,"summary":13902},"\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":13904,"title":13905,"module":13893,"summary":13906},"\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":13908,"title":13909,"module":13910,"summary":13911},"\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":13913,"title":13914,"module":13910,"summary":13915},"\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":13917,"title":13918,"module":13910,"summary":13919},"\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":13921,"title":13922,"module":13910,"summary":13923},"\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":13925,"title":13926,"module":13927,"summary":13928},"\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":13930,"title":13931,"module":13927,"summary":13932},"\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":13934,"title":13935,"module":13927,"summary":13936},"\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":13938,"title":13939,"module":13927,"summary":13940},"\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":13942,"title":13943,"module":13944,"summary":13945},"\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":13947,"title":13948,"module":13944,"summary":13949},"\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":13951,"title":13952,"module":13944,"summary":13953},"\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":13955,"title":13956,"module":13944,"summary":13957},"\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":13959,"title":13960,"module":13961,"summary":13962},"\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":13964,"title":13965,"module":13961,"summary":13966},"\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":13968,"title":13969,"module":13961,"summary":13970},"\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":13972,"title":13973,"module":13961,"summary":13974},"\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":13976,"title":13977,"module":13978,"summary":13979},"\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":13981,"title":13982,"module":13978,"summary":13983},"\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":13985,"title":13986,"module":13978,"summary":13987},"\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":13989,"title":13990,"module":13978,"summary":13991},"\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":13993,"title":13994,"module":13978,"summary":13995},"\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":13997,"title":13998,"module":13999,"summary":14000},"\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":14002,"title":14003,"module":13999,"summary":14004},"\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":14006,"title":14007,"module":14008,"summary":14009},"\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":14011,"title":14012,"module":14008,"summary":14013},"\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":14015,"title":14016,"module":14008,"summary":14017},"\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":14019,"title":14020,"module":14008,"summary":14021},"\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":14023,"title":14024,"module":14025,"summary":14026},"\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":14028,"title":14029,"module":14025,"summary":14030},"\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":14032,"title":14033,"module":14025,"summary":14034},"\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":14036,"title":14037,"module":14025,"summary":14038},"\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":14040,"title":14041,"module":14025,"summary":14042},"\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":14044,"title":14045,"module":14046,"summary":14047},"\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":14049,"title":14050,"module":14046,"summary":14051},"\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":14053,"title":14054,"module":14046,"summary":14055},"\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":14057,"title":14058,"module":14046,"summary":14059},"\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":14061,"title":14062,"module":6,"summary":6},"\u002Fcondensed-matter","Condensed Matter Physics",{"path":14064,"title":14065,"module":10828,"summary":14066},"\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":14068,"title":14069,"module":14070,"summary":14071},"\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":14073,"title":14074,"module":14070,"summary":14075},"\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":14077,"title":14078,"module":14070,"summary":14079},"\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":14081,"title":14082,"module":14070,"summary":14083},"\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":14085,"title":14086,"module":14070,"summary":14087},"\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":14089,"title":14090,"module":14070,"summary":14091},"\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":14093,"title":14094,"module":14070,"summary":14095},"\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":14097,"title":14098,"module":14099,"summary":14100},"\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":14102,"title":14103,"module":14099,"summary":14104},"\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":14106,"title":14107,"module":14099,"summary":14108},"\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":14110,"title":14111,"module":14099,"summary":14112},"\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":14114,"title":14115,"module":14116,"summary":14117},"\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":14119,"title":14120,"module":14116,"summary":14121},"\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":14123,"title":14124,"module":14116,"summary":14125},"\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":14127,"title":14128,"module":14116,"summary":14129},"\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":14131,"title":14132,"module":14133,"summary":14134},"\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":14136,"title":14137,"module":14133,"summary":14138},"\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":14140,"title":14141,"module":14133,"summary":14142},"\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":14144,"title":14145,"module":14133,"summary":14146},"\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":14148,"title":14149,"module":14150,"summary":14151},"\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":14153,"title":14154,"module":14150,"summary":14155},"\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":14157,"title":14158,"module":14150,"summary":14159},"\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":14161,"title":14162,"module":14150,"summary":14163},"\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":14165,"title":14166,"module":14167,"summary":14168},"\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":14170,"title":14171,"module":14167,"summary":14172},"\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":14174,"title":14175,"module":14167,"summary":14176},"\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":14178,"title":14179,"module":14180,"summary":14181},"\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":14183,"title":14184,"module":14180,"summary":14185},"\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":14187,"title":14188,"module":14189,"summary":14190},"\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":14192,"title":14193,"module":14189,"summary":14194},"\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":14196,"title":14197,"module":14189,"summary":14198},"\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":14200,"title":14201,"module":6,"summary":6},"\u002Flogic","Logic",{"path":14203,"title":14204,"module":10828,"summary":14205},"\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":14207,"title":14208,"module":10828,"summary":14209},"\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":14211,"title":14212,"module":10828,"summary":14213},"\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":14215,"title":14216,"module":10828,"summary":14217},"\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":14219,"title":14220,"module":10828,"summary":14221},"\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":14223,"title":14224,"module":10828,"summary":14225},"\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":14227,"title":11047,"module":14228,"summary":14229},"\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":14231,"title":14232,"module":14228,"summary":14233},"\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":14235,"title":14236,"module":14228,"summary":14237},"\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":14239,"title":14240,"module":14228,"summary":14241},"\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":14243,"title":14244,"module":14228,"summary":14245},"\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":14247,"title":14248,"module":14228,"summary":14249},"\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":14251,"title":14252,"module":14228,"summary":14253},"\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":14255,"title":14256,"module":14228,"summary":14257},"\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":14259,"title":14260,"module":14228,"summary":14261},"\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":14263,"title":14264,"module":14228,"summary":14265},"\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":14267,"title":14268,"module":14228,"summary":14269},"\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":14271,"title":14272,"module":14228,"summary":14273},"\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":14275,"title":14276,"module":14277,"summary":14278},"\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":14280,"title":14281,"module":14277,"summary":14282},"\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":14284,"title":14285,"module":14277,"summary":14286},"\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":14288,"title":14289,"module":14277,"summary":14290},"\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":14292,"title":14293,"module":14277,"summary":14294},"\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":14296,"title":14297,"module":14277,"summary":14298},"\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":14300,"title":14301,"module":14277,"summary":14302},"\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":14304,"title":14305,"module":14277,"summary":14306},"\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":14308,"title":14309,"module":14277,"summary":14310},"\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":14312,"title":14313,"module":14277,"summary":14314},"\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":14316,"title":14317,"module":14277,"summary":14318},"\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":14320,"title":14321,"module":14277,"summary":14322},"\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":14324,"title":14325,"module":14277,"summary":14326},"\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":14328,"title":14329,"module":14277,"summary":14330},"\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":14332,"title":13650,"module":14333,"summary":14334},"\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":14336,"title":14337,"module":14333,"summary":14338},"\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":14340,"title":14341,"module":14333,"summary":14342},"\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":14344,"title":14345,"module":14333,"summary":14346},"\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":14348,"title":14349,"module":14333,"summary":14350},"\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":14352,"title":14353,"module":14333,"summary":14354},"\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":14356,"title":14357,"module":14333,"summary":14358},"\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":14360,"title":14361,"module":14333,"summary":14362},"\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":14364,"title":14365,"module":14366,"summary":14367},"\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":14369,"title":14370,"module":14366,"summary":14371},"\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":14373,"title":14374,"module":14366,"summary":14375},"\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":14377,"title":14378,"module":14366,"summary":14379},"\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":14381,"title":14382,"module":14366,"summary":14383},"\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":14385,"title":14386,"module":14366,"summary":14387},"\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":14389,"title":14390,"module":14366,"summary":14391},"\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":14393,"title":14394,"module":14366,"summary":14395},"\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":14397,"title":14398,"module":14366,"summary":14399},"\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":14401,"title":14402,"module":14366,"summary":14403},"\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":14405,"title":14406,"module":14366,"summary":14407},"\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":14409,"title":14410,"module":14366,"summary":14411},"\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":14413,"title":14414,"module":14366,"summary":14415},"\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":14417,"title":14418,"module":14366,"summary":14419},"\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":14421,"title":14422,"module":14366,"summary":14423},"\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":14425,"title":14426,"module":14366,"summary":14427},"\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":14429,"title":14430,"module":14366,"summary":14431},"\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":14433,"title":14434,"module":14366,"summary":14435},"\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":14437,"title":14438,"module":14366,"summary":14439},"\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":14441,"title":14442,"module":14366,"summary":14443},"\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":14445,"title":14446,"module":14366,"summary":14447},"\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":14449,"title":14450,"module":14366,"summary":14451},"\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":14453,"title":14454,"module":14455,"summary":14456},"\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":14458,"title":14459,"module":14455,"summary":14460},"\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":14462,"title":14463,"module":14455,"summary":14464},"\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":14466,"title":14467,"module":14455,"summary":14468},"\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":14470,"title":14471,"module":14455,"summary":14472},"\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":14474,"title":14475,"module":14455,"summary":14476},"\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":14478,"title":14479,"module":14455,"summary":14480},"\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":14482,"title":14483,"module":14455,"summary":14484},"\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":14486,"title":13642,"module":6,"summary":6},"\u002Freinforcement-learning",{"path":14488,"title":14489,"module":10828,"summary":14490},"\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":14492,"title":14493,"module":10828,"summary":14494},"\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":14496,"title":14497,"module":10828,"summary":14498},"\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":14500,"title":14501,"module":10828,"summary":14502},"\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":14504,"title":14505,"module":14506,"summary":14507},"\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":14509,"title":14510,"module":14506,"summary":14511},"\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":17,"title":14513,"module":14506,"summary":14514},"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":14516,"title":14517,"module":14506,"summary":14518},"\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":14520,"title":14521,"module":14506,"summary":14522},"\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":14524,"title":14525,"module":14506,"summary":14526},"\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":14528,"title":14529,"module":14506,"summary":14530},"\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":14532,"title":14533,"module":14506,"summary":14534},"\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":14536,"title":14537,"module":14506,"summary":14538},"\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":14540,"title":14541,"module":14506,"summary":14542},"\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":14544,"title":14545,"module":14506,"summary":14546},"\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":14548,"title":14549,"module":14506,"summary":14550},"\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":22,"title":14552,"module":14553,"summary":14554},"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":14556,"title":14557,"module":14553,"summary":14558},"\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":14560,"title":14561,"module":14553,"summary":14562},"\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":14564,"title":14565,"module":14553,"summary":14566},"\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":14568,"title":14569,"module":14553,"summary":14570},"\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":14572,"title":14573,"module":14553,"summary":14574},"\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":14576,"title":14577,"module":14553,"summary":14578},"\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":14580,"title":14581,"module":14553,"summary":14582},"\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":14584,"title":14585,"module":14553,"summary":14586},"\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":14588,"title":14589,"module":14553,"summary":14590},"\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":14592,"title":14593,"module":14553,"summary":14594},"\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":14596,"title":14597,"module":14553,"summary":14598},"\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":14600,"title":14601,"module":14602,"summary":14603},"\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":14605,"title":14606,"module":14602,"summary":14607},"\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":14609,"title":14610,"module":14602,"summary":14611},"\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":14613,"title":14614,"module":14602,"summary":14615},"\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":14617,"title":14618,"module":14602,"summary":14619},"\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":14621,"title":14622,"module":14602,"summary":14623},"\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":27,"title":14625,"module":14602,"summary":14626},"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":14628,"title":14220,"module":14602,"summary":14629},"\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":14631,"title":14632,"module":14602,"summary":14633},"\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":14635,"title":14636,"module":14602,"summary":14637},"\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":9893,"title":5,"module":9891,"summary":9909},{"path":9232,"title":9233,"module":9891,"summary":14640},"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":14642,"title":14643,"module":9891,"summary":14644},"\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":14646,"title":14647,"module":9891,"summary":14648},"\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":93,"title":13642,"module":9891,"summary":14650},"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":14652,"title":14653,"module":9891,"summary":14654},"\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":14656,"title":14657,"module":9891,"summary":14658},"\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":14660,"title":14661,"module":9891,"summary":14662},"\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":14664,"title":14665,"module":14666,"summary":14667},"\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":14669,"title":14670,"module":14666,"summary":14671},"\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":14673,"title":14674,"module":14666,"summary":14675},"\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":14677,"title":14678,"module":14666,"summary":14679},"\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":14681,"title":14682,"module":14666,"summary":14683},"\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":14685,"title":14686,"module":14666,"summary":14687},"\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":14689,"title":14690,"module":14666,"summary":14691},"\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":14693,"title":14694,"module":14666,"summary":14695},"\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":14697,"title":14698,"module":6,"summary":6},"\u002Fartificial-intelligence","Artificial Intelligence",{"path":14700,"title":14701,"module":14702,"summary":14703},"\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":14705,"title":14706,"module":14702,"summary":14707},"\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":14709,"title":14710,"module":14702,"summary":14711},"\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":14713,"title":14714,"module":14702,"summary":14715},"\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":14717,"title":14718,"module":14702,"summary":14719},"\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":14721,"title":14722,"module":14723,"summary":14724},"\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":14726,"title":14727,"module":14723,"summary":14728},"\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":14730,"title":14731,"module":14723,"summary":14732},"\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":14734,"title":14735,"module":14723,"summary":14736},"\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":14738,"title":14739,"module":14740,"summary":14741},"\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":14743,"title":14744,"module":14740,"summary":14745},"\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":14747,"title":14748,"module":14740,"summary":14749},"\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":14751,"title":14752,"module":14740,"summary":14753},"\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":14755,"title":14756,"module":14757,"summary":14758},"\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":14760,"title":14761,"module":14757,"summary":14762},"\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":14764,"title":14765,"module":14766,"summary":14767},"\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":14769,"title":14770,"module":14766,"summary":14771},"\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":14773,"title":14774,"module":14775,"summary":14776},"\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":14778,"title":14779,"module":14775,"summary":14780},"\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":14782,"title":14783,"module":14775,"summary":14784},"\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":14786,"title":14787,"module":14775,"summary":14788},"\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":14790,"title":14791,"module":14792,"summary":14793},"\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":14795,"title":14796,"module":14792,"summary":14797},"\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":14799,"title":14800,"module":14792,"summary":14801},"\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":14803,"title":14804,"module":14805,"summary":14806},"\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":14808,"title":14809,"module":14805,"summary":14810},"\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":14812,"title":14813,"module":14805,"summary":14814},"\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":14816,"title":14817,"module":14818,"summary":14819},"\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":14821,"title":14822,"module":14818,"summary":14823},"\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":14825,"title":14826,"module":14827,"summary":14828},"\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":14830,"title":14831,"module":14827,"summary":14832},"\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":14834,"title":14835,"module":14827,"summary":14836},"\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":14838,"title":14839,"module":14840,"summary":14841},"\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":14843,"title":14844,"module":14840,"summary":14845},"\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":14847,"title":14848,"module":14840,"summary":14849},"\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":14851,"title":14852,"module":14840,"summary":14853},"\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":14855,"title":14856,"module":14840,"summary":14857},"\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":14859,"title":14860,"module":6,"summary":6},"\u002Fnuclear-physics","Nuclear Physics",{"path":14862,"title":14863,"module":10828,"summary":14864},"\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":14866,"title":14867,"module":10828,"summary":14868},"\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization","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":14870,"title":14871,"module":10828,"summary":14872},"\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":14874,"title":14875,"module":10828,"summary":14876},"\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":14878,"title":14879,"module":10828,"summary":14880},"\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":14882,"title":14883,"module":14884,"summary":14885},"\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":14887,"title":14888,"module":14884,"summary":14889},"\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":14891,"title":14892,"module":14884,"summary":14893},"\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":14895,"title":14896,"module":14884,"summary":14897},"\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":14899,"title":14900,"module":14901,"summary":14902},"\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":14904,"title":14905,"module":14901,"summary":14906},"\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":14908,"title":14909,"module":14901,"summary":14910},"\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":14912,"title":14913,"module":11294,"summary":14914},"\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling","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":14916,"title":14917,"module":11294,"summary":14918},"\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":14920,"title":14921,"module":11294,"summary":14922},"\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":14924,"title":14925,"module":11776,"summary":14926},"\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":14928,"title":13488,"module":11776,"summary":14929},"\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":14931,"title":13596,"module":11776,"summary":14932},"\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":14934,"title":14935,"module":11776,"summary":14936},"\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":14938,"title":14939,"module":11776,"summary":14940},"\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":14942,"title":14943,"module":11776,"summary":14944},"\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":14946,"title":14947,"module":14948,"summary":14949},"\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":14951,"title":14952,"module":14948,"summary":14953},"\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":14955,"title":14956,"module":14948,"summary":14957},"\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":14959,"title":14960,"module":14948,"summary":14961},"\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":14963,"title":14964,"module":14948,"summary":14965},"\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":14967,"title":14968,"module":14948,"summary":14969},"\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":14971,"title":14972,"module":14948,"summary":14973},"\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":14975,"title":14976,"module":14948,"summary":14977},"\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":14979,"title":14980,"module":14948,"summary":14981},"\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":14983,"title":14984,"module":14948,"summary":14985},"\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":14987,"title":14988,"module":14948,"summary":14989},"\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":14991,"title":14992,"module":14948,"summary":14993},"\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":14995,"title":14996,"module":14948,"summary":14997},"\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":14999,"title":15000,"module":14948,"summary":15001},"\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":15003,"title":15004,"module":14948,"summary":15005},"\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":15007,"title":15008,"module":14948,"summary":15009},"\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":15011,"title":15012,"module":14948,"summary":15013},"\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":15015,"title":15016,"module":14948,"summary":15017},"\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":15019,"title":15020,"module":14948,"summary":15021},"\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":15023,"title":15024,"module":14948,"summary":15025},"\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":15027,"title":15028,"module":13584,"summary":15029},"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation","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":15031,"title":15032,"module":13584,"summary":15033},"\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":15035,"title":15036,"module":13584,"summary":15037},"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering","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":15039,"title":15040,"module":13584,"summary":15041},"\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":15043,"title":15044,"module":13584,"summary":15045},"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots","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",{"path":15047,"title":15048,"module":13584,"summary":15049},"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants","Dialogue Systems: LLM Assistants, Evaluation, and Design","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":15051,"title":15052,"module":13584,"summary":15053},"\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":15055,"title":15056,"module":13584,"summary":15057},"\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":15059,"title":15060,"module":15061,"summary":15062},"\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":15064,"title":15065,"module":15061,"summary":15066},"\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":15068,"title":15069,"module":15061,"summary":15070},"\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":15072,"title":15073,"module":15061,"summary":15074},"\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":15076,"title":15077,"module":6,"summary":6},"\u002Fnatural-language-processing","Natural Language Processing",{"path":15079,"title":15080,"module":10828,"summary":15081},"\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":15083,"title":15084,"module":10828,"summary":15085},"\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":15087,"title":15088,"module":10828,"summary":15089},"\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":15091,"title":15092,"module":15093,"summary":15094},"\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":15096,"title":15097,"module":15093,"summary":15098},"\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":15100,"title":15101,"module":15093,"summary":15102},"\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":15104,"title":15105,"module":15093,"summary":15106},"\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":15108,"title":15109,"module":15110,"summary":15111},"\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":15113,"title":15114,"module":15110,"summary":15115},"\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":15117,"title":15118,"module":15110,"summary":15119},"\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":15121,"title":15122,"module":15110,"summary":15123},"\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":15125,"title":15126,"module":15127,"summary":15128},"\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":15130,"title":15131,"module":15127,"summary":15132},"\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":15134,"title":15135,"module":15127,"summary":15136},"\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":15138,"title":15139,"module":15127,"summary":15140},"\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":15142,"title":15143,"module":15144,"summary":15145},"\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":15147,"title":15148,"module":15144,"summary":15149},"\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":15151,"title":15152,"module":15144,"summary":15153},"\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":15155,"title":15156,"module":15157,"summary":15158},"\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":15160,"title":15161,"module":15157,"summary":15162},"\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":15164,"title":15165,"module":15157,"summary":15166},"\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":15168,"title":15169,"module":15157,"summary":15170},"\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":15172,"title":15173,"module":15174,"summary":15175},"\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":15177,"title":15178,"module":15174,"summary":15179},"\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":15181,"title":15182,"module":15174,"summary":15183},"\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":15185,"title":15186,"module":15174,"summary":15187},"\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":15189,"title":15190,"module":15191,"summary":15192},"\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":15194,"title":15195,"module":15191,"summary":15196},"\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":15198,"title":15199,"module":15191,"summary":15200},"\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":15202,"title":15203,"module":15191,"summary":15204},"\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":15206,"title":15207,"module":15208,"summary":15209},"\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":15211,"title":15212,"module":15208,"summary":15213},"\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":15215,"title":15216,"module":15208,"summary":15217},"\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":15219,"title":15220,"module":15208,"summary":15221},"\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":15223,"title":15224,"module":15208,"summary":15225},"\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":15227,"title":15228,"module":15229,"summary":15230},"\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":15232,"title":15233,"module":15229,"summary":15234},"\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":15236,"title":15237,"module":15229,"summary":15238},"\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":15240,"title":15241,"module":15242,"summary":15243},"\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":15245,"title":15246,"module":15242,"summary":15247},"\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":15249,"title":15250,"module":15242,"summary":15251},"\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":15253,"title":15254,"module":15254,"summary":15255},"\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":15257,"title":15258,"module":15254,"summary":15259},"\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":15261,"title":15262,"module":15254,"summary":15263},"\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":15265,"title":15266,"module":15254,"summary":15267},"\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":15269,"title":15270,"module":15254,"summary":15271},"\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":15273,"title":15274,"module":15254,"summary":15275},"\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":15277,"title":15278,"module":6,"summary":6},"\u002Fparticle-physics","Particle Physics",{"path":15280,"title":15281,"module":15282,"summary":15283},"\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":15285,"title":15286,"module":15282,"summary":15287},"\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":15289,"title":15290,"module":15282,"summary":15291},"\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":15293,"title":15294,"module":15295,"summary":15296},"\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":15298,"title":15299,"module":15295,"summary":15300},"\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":15302,"title":15303,"module":15295,"summary":15304},"\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":15306,"title":15307,"module":15295,"summary":15308},"\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":15310,"title":15311,"module":15312,"summary":15313},"\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":15315,"title":15316,"module":15312,"summary":15317},"\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":15319,"title":15320,"module":15312,"summary":15321},"\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":15323,"title":15324,"module":15312,"summary":15325},"\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":15327,"title":15328,"module":15329,"summary":15330},"\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":15332,"title":15333,"module":15329,"summary":15334},"\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":15336,"title":15337,"module":15329,"summary":15338},"\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":15340,"title":15341,"module":15329,"summary":15342},"\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":15344,"title":15345,"module":15346,"summary":15347},"\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":15349,"title":15350,"module":15346,"summary":15351},"\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":15353,"title":15354,"module":15346,"summary":15355},"\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":15357,"title":15358,"module":15346,"summary":15359},"\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":15361,"title":15362,"module":15363,"summary":15364},"\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":15366,"title":15367,"module":15363,"summary":15368},"\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":15370,"title":15371,"module":15363,"summary":15372},"\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":15374,"title":15375,"module":15376,"summary":15377},"\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":15379,"title":15380,"module":15376,"summary":15381},"\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":15383,"title":15384,"module":15376,"summary":15385},"\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":15387,"title":15388,"module":15376,"summary":15389},"\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":15391,"title":13817,"module":15392,"summary":15393},"\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":15395,"title":15396,"module":15392,"summary":15397},"\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":15399,"title":15400,"module":15392,"summary":15401},"\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":15403,"title":15404,"module":15392,"summary":15405},"\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":15407,"title":15408,"module":15392,"summary":15409},"\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":15411,"title":15412,"module":15413,"summary":15414},"\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":15416,"title":15417,"module":15413,"summary":15418},"\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":15420,"title":15421,"module":15413,"summary":15422},"\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":15424,"title":15425,"module":15413,"summary":15426},"\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":15428,"title":15429,"module":15430,"summary":15431},"\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":15433,"title":15434,"module":15430,"summary":15435},"\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":15437,"title":15438,"module":15430,"summary":15439},"\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":15441,"title":15442,"module":15430,"summary":15443},"\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":15445,"title":15446,"module":15430,"summary":15447},"\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":15449,"title":15450,"module":15451,"summary":15452},"\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":15454,"title":15455,"module":15451,"summary":15456},"\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":15458,"title":12535,"module":15451,"summary":15459},"\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":15461,"title":15462,"module":15451,"summary":15463},"\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":15465,"title":15466,"module":15451,"summary":15467},"\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":15469,"title":15470,"module":15471,"summary":15472},"\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":15474,"title":15475,"module":15471,"summary":15476},"\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":15478,"title":15479,"module":15471,"summary":15480},"\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":15482,"title":15483,"module":15471,"summary":15484},"\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":15486,"title":15487,"module":15471,"summary":15488},"\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":15490,"title":15491,"module":15471,"summary":15492},"\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":15494,"title":15495,"module":15471,"summary":15496},"\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":15498,"title":15499,"module":6,"summary":6},"\u002Fastrophysics-cosmology","Astrophysics & Cosmology",{"path":15501,"title":15502,"module":6,"summary":6},"\u002Fcolophon","Colophon",{"path":8539,"title":15504,"module":6,"summary":6},"Study Notes",[15506,15517,15544,15571,15594,15613],{"module":10828,"moduleNumber":8272,"slug":15507,"lessons":15508},"foundations",[15509,15511,15513,15515],{"title":14489,"path":14488,"lessonNumber":8272,"topics":15510,"summary":14490},[10828],{"title":14493,"path":14492,"lessonNumber":8278,"topics":15512,"summary":14494},[10828],{"title":14497,"path":14496,"lessonNumber":8284,"topics":15514,"summary":14498},[10828],{"title":14501,"path":14500,"lessonNumber":8290,"topics":15516,"summary":14502},[10828],{"module":14506,"moduleNumber":8278,"slug":15518,"lessons":15519},"search",[15520,15522,15524,15526,15528,15530,15532,15534,15536,15538,15540,15542],{"title":14505,"path":14504,"lessonNumber":8272,"topics":15521,"summary":14507},[14506],{"title":14510,"path":14509,"lessonNumber":8278,"topics":15523,"summary":14511},[14506],{"title":14513,"path":17,"lessonNumber":8284,"topics":15525,"summary":14514},[14506],{"title":14517,"path":14516,"lessonNumber":8290,"topics":15527,"summary":14518},[14506],{"title":14521,"path":14520,"lessonNumber":8296,"topics":15529,"summary":14522},[14506],{"title":14525,"path":14524,"lessonNumber":8302,"topics":15531,"summary":14526},[14506],{"title":14529,"path":14528,"lessonNumber":8308,"topics":15533,"summary":14530},[14506],{"title":14533,"path":14532,"lessonNumber":8314,"topics":15535,"summary":14534},[14506],{"title":14537,"path":14536,"lessonNumber":8320,"topics":15537,"summary":14538},[14506],{"title":14541,"path":14540,"lessonNumber":8326,"topics":15539,"summary":14542},[14506],{"title":14545,"path":14544,"lessonNumber":8332,"topics":15541,"summary":14546},[14506],{"title":14549,"path":14548,"lessonNumber":8338,"topics":15543,"summary":14550},[14506],{"module":14553,"moduleNumber":8284,"slug":15545,"lessons":15546},"logic-and-planning",[15547,15549,15551,15553,15555,15557,15559,15561,15563,15565,15567,15569],{"title":14552,"path":22,"lessonNumber":8272,"topics":15548,"summary":14554},[14201],{"title":14557,"path":14556,"lessonNumber":8278,"topics":15550,"summary":14558},[14201],{"title":14561,"path":14560,"lessonNumber":8284,"topics":15552,"summary":14562},[14201],{"title":14565,"path":14564,"lessonNumber":8290,"topics":15554,"summary":14566},[14201],{"title":14569,"path":14568,"lessonNumber":8296,"topics":15556,"summary":14570},[14201],{"title":14573,"path":14572,"lessonNumber":8302,"topics":15558,"summary":14574},[14201],{"title":14577,"path":14576,"lessonNumber":8308,"topics":15560,"summary":14578},[14201],{"title":14581,"path":14580,"lessonNumber":8314,"topics":15562,"summary":14582},[14201],{"title":14585,"path":14584,"lessonNumber":8320,"topics":15564,"summary":14586},[14201],{"title":14589,"path":14588,"lessonNumber":8326,"topics":15566,"summary":14590},[14201],{"title":14593,"path":14592,"lessonNumber":8332,"topics":15568,"summary":14594},[14201],{"title":14597,"path":14596,"lessonNumber":8338,"topics":15570,"summary":14598},[14201],{"module":14602,"moduleNumber":8290,"slug":15572,"lessons":15573},"uncertainty",[15574,15576,15578,15580,15582,15584,15586,15588,15590,15592],{"title":14601,"path":14600,"lessonNumber":8272,"topics":15575,"summary":14603},[14602],{"title":14606,"path":14605,"lessonNumber":8278,"topics":15577,"summary":14607},[14602],{"title":14610,"path":14609,"lessonNumber":8284,"topics":15579,"summary":14611},[14602],{"title":14614,"path":14613,"lessonNumber":8290,"topics":15581,"summary":14615},[14602],{"title":14618,"path":14617,"lessonNumber":8296,"topics":15583,"summary":14619},[14602],{"title":14622,"path":14621,"lessonNumber":8302,"topics":15585,"summary":14623},[14602],{"title":14625,"path":27,"lessonNumber":8308,"topics":15587,"summary":14626},[14602],{"title":14220,"path":14628,"lessonNumber":8314,"topics":15589,"summary":14629},[14602],{"title":14632,"path":14631,"lessonNumber":8320,"topics":15591,"summary":14633},[14602],{"title":14636,"path":14635,"lessonNumber":8326,"topics":15593,"summary":14637},[14602],{"module":9891,"moduleNumber":8296,"slug":15595,"lessons":15596},"learning",[15597,15599,15601,15603,15605,15607,15609,15611],{"title":5,"path":9893,"lessonNumber":8272,"topics":15598,"summary":9909},[9891],{"title":9233,"path":9232,"lessonNumber":8278,"topics":15600,"summary":14640},[9891],{"title":14643,"path":14642,"lessonNumber":8284,"topics":15602,"summary":14644},[9891],{"title":14647,"path":14646,"lessonNumber":8290,"topics":15604,"summary":14648},[9891],{"title":13642,"path":93,"lessonNumber":8296,"topics":15606,"summary":14650},[9891],{"title":14653,"path":14652,"lessonNumber":8302,"topics":15608,"summary":14654},[9891],{"title":14657,"path":14656,"lessonNumber":8308,"topics":15610,"summary":14658},[9891],{"title":14661,"path":14660,"lessonNumber":8314,"topics":15612,"summary":14662},[9891],{"module":14666,"moduleNumber":8302,"slug":15614,"lessons":15615},"frontiers",[15616,15618,15620,15622,15624,15626,15628,15630],{"title":14665,"path":14664,"lessonNumber":8272,"topics":15617,"summary":14667},[14666],{"title":14670,"path":14669,"lessonNumber":8278,"topics":15619,"summary":14671},[14666],{"title":14674,"path":14673,"lessonNumber":8284,"topics":15621,"summary":14675},[14666],{"title":14678,"path":14677,"lessonNumber":8290,"topics":15623,"summary":14679},[14666],{"title":14682,"path":14681,"lessonNumber":8296,"topics":15625,"summary":14683},[14666],{"title":14686,"path":14685,"lessonNumber":8302,"topics":15627,"summary":14687},[14666],{"title":14690,"path":14689,"lessonNumber":8308,"topics":15629,"summary":14691},[14666],{"title":14694,"path":14693,"lessonNumber":8314,"topics":15631,"summary":14695},[14666],"\u003Csvg style=\"width:100%;max-width:396.827px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 297.621 97.649\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cpath fill=\"none\" d=\"M-65.403-40.572h62.596V-71.87h-62.596Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-21.277 6.433)\">\u003Cpath d=\"M-33.242-66.682L-33.242-68.873L-33.945-68.873L-33.945-69.127Q-33.589-69.127-33.347-69.360Q-33.105-69.592-32.994-69.940Q-32.882-70.287-32.882-70.643L-32.601-70.643L-32.601-69.170L-31.425-69.170L-31.425-68.873L-32.601-68.873L-32.601-66.698Q-32.601-66.377-32.482-66.149Q-32.363-65.920-32.082-65.920Q-31.902-65.920-31.785-66.043Q-31.667-66.166-31.615-66.346Q-31.562-66.526-31.562-66.698L-31.562-67.170L-31.281-67.170L-31.281-66.682Q-31.281-66.428-31.386-66.188Q-31.492-65.948-31.689-65.795Q-31.886-65.643-32.144-65.643Q-32.460-65.643-32.712-65.766Q-32.964-65.889-33.103-66.123Q-33.242-66.358-33.242-66.682M-28.554-65.721L-30.535-65.721L-30.535-66.018Q-30.265-66.018-30.097-66.063Q-29.929-66.108-29.929-66.280L-29.929-68.416Q-29.929-68.631-29.992-68.727Q-30.054-68.823-30.171-68.844Q-30.289-68.866-30.535-68.866L-30.535-69.162L-29.367-69.248L-29.367-68.463Q-29.289-68.674-29.136-68.860Q-28.984-69.045-28.785-69.147Q-28.585-69.248-28.359-69.248Q-28.113-69.248-27.921-69.104Q-27.730-68.959-27.730-68.729Q-27.730-68.573-27.835-68.463Q-27.941-68.354-28.097-68.354Q-28.253-68.354-28.363-68.463Q-28.472-68.573-28.472-68.729Q-28.472-68.889-28.367-68.994Q-28.691-68.994-28.906-68.766Q-29.121-68.537-29.216-68.198Q-29.312-67.858-29.312-67.553L-29.312-66.280Q-29.312-66.112-29.085-66.065Q-28.859-66.018-28.554-66.018L-28.554-65.721M-27.152-66.553Q-27.152-67.037-26.750-67.332Q-26.347-67.627-25.796-67.746Q-25.246-67.866-24.753-67.866L-24.753-68.155Q-24.753-68.381-24.869-68.588Q-24.984-68.795-25.181-68.914Q-25.378-69.034-25.609-69.034Q-26.035-69.034-26.320-68.928Q-26.250-68.901-26.203-68.846Q-26.156-68.791-26.130-68.721Q-26.105-68.651-26.105-68.576Q-26.105-68.471-26.156-68.379Q-26.207-68.287-26.298-68.237Q-26.390-68.186-26.496-68.186Q-26.601-68.186-26.693-68.237Q-26.785-68.287-26.835-68.379Q-26.886-68.471-26.886-68.576Q-26.886-68.994-26.498-69.141Q-26.109-69.287-25.609-69.287Q-25.277-69.287-24.923-69.157Q-24.570-69.026-24.341-68.772Q-24.113-68.518-24.113-68.170L-24.113-66.369Q-24.113-66.237-24.041-66.127Q-23.968-66.018-23.839-66.018Q-23.714-66.018-23.646-66.123Q-23.578-66.229-23.578-66.369L-23.578-66.881L-23.296-66.881L-23.296-66.369Q-23.296-66.166-23.414-66.008Q-23.531-65.850-23.712-65.766Q-23.894-65.682-24.097-65.682Q-24.328-65.682-24.480-65.854Q-24.632-66.026-24.664-66.256Q-24.824-65.975-25.132-65.809Q-25.441-65.643-25.792-65.643Q-26.304-65.643-26.728-65.866Q-27.152-66.088-27.152-66.553M-26.464-66.553Q-26.464-66.268-26.238-66.082Q-26.011-65.897-25.718-65.897Q-25.472-65.897-25.248-66.014Q-25.023-66.131-24.888-66.334Q-24.753-66.537-24.753-66.791L-24.753-67.623Q-25.019-67.623-25.304-67.569Q-25.589-67.514-25.861-67.385Q-26.132-67.256-26.298-67.049Q-26.464-66.842-26.464-66.553M-21.144-65.721L-22.921-65.721L-22.921-66.018Q-22.648-66.018-22.480-66.065Q-22.312-66.112-22.312-66.280L-22.312-68.416Q-22.312-68.631-22.369-68.727Q-22.425-68.823-22.539-68.844Q-22.652-68.866-22.898-68.866L-22.898-69.162L-21.699-69.248L-21.699-66.280Q-21.699-66.112-21.552-66.065Q-21.406-66.018-21.144-66.018L-21.144-65.721M-22.585-70.643Q-22.585-70.834-22.451-70.965Q-22.316-71.096-22.121-71.096Q-22-71.096-21.896-71.034Q-21.792-70.971-21.730-70.867Q-21.667-70.764-21.667-70.643Q-21.667-70.448-21.798-70.313Q-21.929-70.178-22.121-70.178Q-22.320-70.178-22.453-70.311Q-22.585-70.444-22.585-70.643M-18.714-65.721L-20.570-65.721L-20.570-66.018Q-20.296-66.018-20.128-66.065Q-19.960-66.112-19.960-66.280L-19.960-68.416Q-19.960-68.631-20.023-68.727Q-20.085-68.823-20.205-68.844Q-20.324-68.866-20.570-68.866L-20.570-69.162L-19.378-69.248L-19.378-68.514Q-19.265-68.729-19.072-68.897Q-18.878-69.065-18.640-69.157Q-18.402-69.248-18.148-69.248Q-16.980-69.248-16.980-68.170L-16.980-66.280Q-16.980-66.112-16.810-66.065Q-16.640-66.018-16.371-66.018L-16.371-65.721L-18.226-65.721L-18.226-66.018Q-17.953-66.018-17.785-66.065Q-17.617-66.112-17.617-66.280L-17.617-68.155Q-17.617-68.537-17.738-68.766Q-17.859-68.994-18.210-68.994Q-18.523-68.994-18.777-68.832Q-19.031-68.670-19.177-68.401Q-19.324-68.131-19.324-67.834L-19.324-66.280Q-19.324-66.112-19.154-66.065Q-18.984-66.018-18.714-66.018L-18.714-65.721M-14.066-65.721L-15.843-65.721L-15.843-66.018Q-15.570-66.018-15.402-66.065Q-15.234-66.112-15.234-66.280L-15.234-68.416Q-15.234-68.631-15.291-68.727Q-15.347-68.823-15.460-68.844Q-15.574-68.866-15.820-68.866L-15.820-69.162L-14.621-69.248L-14.621-66.280Q-14.621-66.112-14.474-66.065Q-14.328-66.018-14.066-66.018L-14.066-65.721M-15.507-70.643Q-15.507-70.834-15.373-70.965Q-15.238-71.096-15.042-71.096Q-14.921-71.096-14.818-71.034Q-14.714-70.971-14.652-70.867Q-14.589-70.764-14.589-70.643Q-14.589-70.448-14.720-70.313Q-14.851-70.178-15.042-70.178Q-15.242-70.178-15.375-70.311Q-15.507-70.444-15.507-70.643M-11.636-65.721L-13.492-65.721L-13.492-66.018Q-13.218-66.018-13.050-66.065Q-12.882-66.112-12.882-66.280L-12.882-68.416Q-12.882-68.631-12.945-68.727Q-13.007-68.823-13.126-68.844Q-13.246-68.866-13.492-68.866L-13.492-69.162L-12.300-69.248L-12.300-68.514Q-12.187-68.729-11.994-68.897Q-11.800-69.065-11.562-69.157Q-11.324-69.248-11.070-69.248Q-9.902-69.248-9.902-68.170L-9.902-66.280Q-9.902-66.112-9.732-66.065Q-9.562-66.018-9.292-66.018L-9.292-65.721L-11.148-65.721L-11.148-66.018Q-10.875-66.018-10.707-66.065Q-10.539-66.112-10.539-66.280L-10.539-68.155Q-10.539-68.537-10.660-68.766Q-10.781-68.994-11.132-68.994Q-11.445-68.994-11.699-68.832Q-11.953-68.670-12.099-68.401Q-12.246-68.131-12.246-67.834L-12.246-66.280Q-12.246-66.112-12.076-66.065Q-11.906-66.018-11.636-66.018L-11.636-65.721M-8.847-65.112Q-8.847-65.393-8.636-65.604Q-8.425-65.815-8.140-65.905Q-8.296-66.030-8.375-66.219Q-8.453-66.409-8.453-66.608Q-8.453-66.963-8.222-67.256Q-8.589-67.596-8.589-68.065Q-8.589-68.416-8.386-68.686Q-8.183-68.955-7.863-69.102Q-7.542-69.248-7.199-69.248Q-6.679-69.248-6.308-68.967Q-5.945-69.338-5.398-69.338Q-5.218-69.338-5.091-69.211Q-4.964-69.084-4.964-68.905Q-4.964-68.799-5.042-68.721Q-5.121-68.643-5.230-68.643Q-5.339-68.643-5.416-68.719Q-5.492-68.795-5.492-68.905Q-5.492-69.006-5.453-69.057Q-5.445-69.065-5.441-69.071Q-5.437-69.076-5.437-69.080Q-5.812-69.080-6.132-68.826Q-5.812-68.487-5.812-68.065Q-5.812-67.795-5.929-67.578Q-6.046-67.362-6.251-67.203Q-6.457-67.045-6.699-66.963Q-6.941-66.881-7.199-66.881Q-7.417-66.881-7.630-66.940Q-7.843-66.998-8.039-67.119Q-8.132-66.979-8.132-66.799Q-8.132-66.592-7.996-66.440Q-7.859-66.287-7.652-66.287L-6.957-66.287Q-6.468-66.287-6.056-66.203Q-5.644-66.119-5.365-65.862Q-5.085-65.604-5.085-65.112Q-5.085-64.748-5.406-64.516Q-5.726-64.284-6.167-64.182Q-6.609-64.080-6.964-64.080Q-7.320-64.080-7.763-64.182Q-8.207-64.284-8.527-64.516Q-8.847-64.748-8.847-65.112M-8.343-65.112Q-8.343-64.916-8.199-64.768Q-8.054-64.619-7.841-64.530Q-7.628-64.440-7.388-64.393Q-7.148-64.346-6.964-64.346Q-6.722-64.346-6.392-64.424Q-6.062-64.502-5.826-64.676Q-5.589-64.850-5.589-65.112Q-5.589-65.518-6-65.627Q-6.410-65.737-6.972-65.737L-7.652-65.737Q-7.921-65.737-8.132-65.559Q-8.343-65.381-8.343-65.112M-7.199-67.147Q-6.476-67.147-6.476-68.065Q-6.476-68.987-7.199-68.987Q-7.925-68.987-7.925-68.065Q-7.925-67.147-7.199-67.147\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-21.277 6.433)\">\u003Cpath d=\"M-1.706-65.729L-1.706-66.951Q-1.706-66.979-1.675-67.010Q-1.643-67.041-1.620-67.041L-1.514-67.041Q-1.444-67.041-1.428-66.979Q-1.366-66.659-1.227-66.418Q-1.089-66.178-0.856-66.037Q-0.624-65.897-0.315-65.897Q-0.077-65.897 0.132-65.957Q0.341-66.018 0.478-66.166Q0.615-66.315 0.615-66.561Q0.615-66.815 0.404-66.981Q0.193-67.147-0.077-67.201L-0.698-67.315Q-1.104-67.393-1.405-67.649Q-1.706-67.905-1.706-68.280Q-1.706-68.647-1.505-68.869Q-1.303-69.092-0.979-69.190Q-0.655-69.287-0.315-69.287Q0.150-69.287 0.447-69.080L0.669-69.264Q0.693-69.287 0.724-69.287L0.775-69.287Q0.806-69.287 0.833-69.260Q0.861-69.233 0.861-69.201L0.861-68.217Q0.861-68.186 0.835-68.157Q0.810-68.127 0.775-68.127L0.669-68.127Q0.634-68.127 0.607-68.155Q0.579-68.182 0.579-68.217Q0.579-68.616 0.327-68.836Q0.075-69.057-0.323-69.057Q-0.678-69.057-0.962-68.934Q-1.245-68.811-1.245-68.506Q-1.245-68.287-1.044-68.155Q-0.842-68.022-0.596-67.979L0.029-67.866Q0.458-67.776 0.767-67.479Q1.075-67.182 1.075-66.768Q1.075-66.198 0.677-65.920Q0.279-65.643-0.315-65.643Q-0.866-65.643-1.217-65.979L-1.514-65.666Q-1.538-65.643-1.573-65.643L-1.620-65.643Q-1.643-65.643-1.675-65.674Q-1.706-65.705-1.706-65.729M1.603-67.475Q1.603-67.955 1.835-68.371Q2.068-68.787 2.478-69.037Q2.888-69.287 3.365-69.287Q4.095-69.287 4.493-68.846Q4.892-68.405 4.892-67.674Q4.892-67.569 4.798-67.545L2.349-67.545L2.349-67.475Q2.349-67.065 2.470-66.709Q2.591-66.354 2.863-66.137Q3.134-65.920 3.564-65.920Q3.927-65.920 4.224-66.149Q4.521-66.377 4.622-66.729Q4.630-66.776 4.716-66.791L4.798-66.791Q4.892-66.764 4.892-66.682Q4.892-66.674 4.884-66.643Q4.822-66.416 4.683-66.233Q4.544-66.049 4.353-65.916Q4.161-65.784 3.943-65.713Q3.724-65.643 3.486-65.643Q3.115-65.643 2.777-65.780Q2.439-65.916 2.171-66.168Q1.904-66.420 1.753-66.760Q1.603-67.100 1.603-67.475M2.357-67.784L4.318-67.784Q4.318-68.088 4.216-68.379Q4.115-68.670 3.898-68.852Q3.681-69.034 3.365-69.034Q3.064-69.034 2.833-68.846Q2.603-68.659 2.480-68.367Q2.357-68.076 2.357-67.784M6.005-66.682L6.005-68.873L5.302-68.873L5.302-69.127Q5.658-69.127 5.900-69.360Q6.142-69.592 6.253-69.940Q6.365-70.287 6.365-70.643L6.646-70.643L6.646-69.170L7.822-69.170L7.822-68.873L6.646-68.873L6.646-66.698Q6.646-66.377 6.765-66.149Q6.884-65.920 7.165-65.920Q7.345-65.920 7.462-66.043Q7.579-66.166 7.632-66.346Q7.685-66.526 7.685-66.698L7.685-67.170L7.966-67.170L7.966-66.682Q7.966-66.428 7.861-66.188Q7.755-65.948 7.558-65.795Q7.361-65.643 7.103-65.643Q6.786-65.643 6.534-65.766Q6.283-65.889 6.144-66.123Q6.005-66.358 6.005-66.682\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-21.277 6.433)\">\u003Cpath d=\"M-31.016-54.229Q-31.629-54.686-32.031-55.321Q-32.434-55.955-32.629-56.701Q-32.824-57.448-32.824-58.221Q-32.824-58.994-32.629-59.741Q-32.434-60.487-32.031-61.121Q-31.629-61.756-31.016-62.213Q-31.004-62.217-30.996-62.219Q-30.988-62.221-30.977-62.221L-30.899-62.221Q-30.860-62.221-30.834-62.194Q-30.809-62.166-30.809-62.123Q-30.809-62.073-30.840-62.053Q-31.348-61.600-31.670-60.977Q-31.992-60.354-32.133-59.658Q-32.274-58.963-32.274-58.221Q-32.274-57.487-32.135-56.787Q-31.996-56.088-31.672-55.463Q-31.348-54.838-30.840-54.389Q-30.809-54.369-30.809-54.319Q-30.809-54.276-30.834-54.248Q-30.860-54.221-30.899-54.221L-30.977-54.221Q-30.985-54.225-30.994-54.227Q-31.004-54.229-31.016-54.229M-28.703-56.221L-30.199-56.221L-30.199-56.518Q-29.567-56.518-29.145-56.998L-28.375-57.908L-29.367-59.108Q-29.524-59.287-29.686-59.330Q-29.848-59.373-30.153-59.373L-30.153-59.670L-28.465-59.670L-28.465-59.373Q-28.559-59.373-28.635-59.330Q-28.711-59.287-28.711-59.198Q-28.711-59.155-28.680-59.108L-28.024-58.319L-27.543-58.893Q-27.426-59.030-27.426-59.166Q-27.426-59.256-27.477-59.315Q-27.528-59.373-27.610-59.373L-27.610-59.670L-26.121-59.670L-26.121-59.373Q-26.758-59.373-27.168-58.893L-27.848-58.092L-26.762-56.780Q-26.602-56.604-26.442-56.561Q-26.281-56.518-25.977-56.518L-25.977-56.221L-27.664-56.221L-27.664-56.518Q-27.574-56.518-27.496-56.561Q-27.418-56.604-27.418-56.694Q-27.418-56.717-27.449-56.780L-28.192-57.686L-28.778-56.998Q-28.895-56.862-28.895-56.725Q-28.895-56.639-28.844-56.578Q-28.793-56.518-28.703-56.518L-28.703-56.221M-25.024-54.815Q-25.024-54.838-24.992-54.885Q-24.699-55.147-24.533-55.514Q-24.367-55.881-24.367-56.268L-24.367-56.326Q-24.496-56.221-24.664-56.221Q-24.856-56.221-24.992-56.354Q-25.129-56.487-25.129-56.686Q-25.129-56.877-24.992-57.010Q-24.856-57.143-24.664-57.143Q-24.363-57.143-24.238-56.873Q-24.113-56.604-24.113-56.268Q-24.113-55.819-24.295-55.405Q-24.477-54.991-24.817-54.694Q-24.840-54.670-24.879-54.670Q-24.926-54.670-24.975-54.715Q-25.024-54.760-25.024-54.815\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-21.277 6.433)\">\u003Cpath d=\"M-19.991-54.924Q-19.877-54.846-19.702-54.846Q-19.413-54.846-19.192-55.059Q-18.971-55.272-18.846-55.573L-18.557-56.221L-19.831-59.108Q-19.913-59.283-20.057-59.328Q-20.202-59.373-20.471-59.373L-20.471-59.670L-18.752-59.670L-18.752-59.373Q-19.174-59.373-19.174-59.190Q-19.174-59.178-19.159-59.108L-18.221-56.983L-17.389-58.893Q-17.350-58.983-17.350-59.061Q-17.350-59.201-17.452-59.287Q-17.553-59.373-17.694-59.373L-17.694-59.670L-16.342-59.670L-16.342-59.373Q-16.596-59.373-16.790-59.248Q-16.983-59.123-17.088-58.893L-18.534-55.573Q-18.647-55.319-18.813-55.096Q-18.979-54.873-19.208-54.731Q-19.436-54.588-19.702-54.588Q-19.999-54.588-20.239-54.780Q-20.479-54.971-20.479-55.260Q-20.479-55.416-20.374-55.518Q-20.268-55.619-20.120-55.619Q-20.014-55.619-19.934-55.573Q-19.854-55.526-19.807-55.448Q-19.760-55.369-19.760-55.260Q-19.760-55.139-19.821-55.051Q-19.881-54.963-19.991-54.924M-15.526-54.221L-15.608-54.221Q-15.643-54.221-15.668-54.250Q-15.694-54.280-15.694-54.319Q-15.694-54.369-15.663-54.389Q-15.276-54.725-14.993-55.174Q-14.709-55.623-14.543-56.123Q-14.377-56.623-14.303-57.141Q-14.229-57.658-14.229-58.221Q-14.229-58.791-14.303-59.307Q-14.377-59.823-14.543-60.319Q-14.709-60.815-14.989-61.262Q-15.268-61.709-15.663-62.053Q-15.694-62.073-15.694-62.123Q-15.694-62.162-15.668-62.192Q-15.643-62.221-15.608-62.221L-15.526-62.221Q-15.514-62.221-15.504-62.219Q-15.495-62.217-15.487-62.213Q-14.874-61.756-14.471-61.121Q-14.069-60.487-13.874-59.741Q-13.678-58.994-13.678-58.221Q-13.678-57.448-13.874-56.701Q-14.069-55.955-14.471-55.321Q-14.874-54.686-15.487-54.229Q-15.499-54.229-15.506-54.227Q-15.514-54.225-15.526-54.221\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-21.277 6.433)\">\u003Cpath d=\"M-7.901-54.670L-9.756-54.670L-9.756-54.963Q-9.487-54.963-9.319-55.008Q-9.151-55.053-9.151-55.229L-9.151-59.053Q-9.151-59.260-9.307-59.313Q-9.463-59.366-9.756-59.366L-9.756-59.662L-8.534-59.748L-8.534-59.283Q-8.303-59.506-7.989-59.627Q-7.674-59.748-7.335-59.748Q-6.862-59.748-6.458-59.502Q-6.053-59.256-5.821-58.840Q-5.588-58.424-5.588-57.948Q-5.588-57.573-5.737-57.244Q-5.885-56.916-6.155-56.664Q-6.424-56.412-6.768-56.278Q-7.112-56.143-7.471-56.143Q-7.760-56.143-8.032-56.264Q-8.303-56.385-8.510-56.596L-8.510-55.229Q-8.510-55.053-8.342-55.008Q-8.174-54.963-7.901-54.963L-7.901-54.670M-8.510-58.885L-8.510-57.045Q-8.358-56.756-8.096-56.576Q-7.835-56.397-7.526-56.397Q-7.241-56.397-7.018-56.535Q-6.795-56.674-6.643-56.905Q-6.491-57.135-6.413-57.407Q-6.335-57.678-6.335-57.948Q-6.335-58.280-6.460-58.637Q-6.585-58.994-6.833-59.231Q-7.081-59.467-7.428-59.467Q-7.752-59.467-8.047-59.311Q-8.342-59.155-8.510-58.885M-4.967-57.053Q-4.967-57.537-4.565-57.832Q-4.163-58.127-3.612-58.246Q-3.061-58.366-2.569-58.366L-2.569-58.655Q-2.569-58.881-2.684-59.088Q-2.799-59.295-2.997-59.414Q-3.194-59.533-3.424-59.533Q-3.850-59.533-4.135-59.428Q-4.065-59.401-4.018-59.346Q-3.971-59.291-3.946-59.221Q-3.920-59.151-3.920-59.076Q-3.920-58.971-3.971-58.879Q-4.022-58.787-4.114-58.737Q-4.206-58.686-4.311-58.686Q-4.417-58.686-4.508-58.737Q-4.600-58.787-4.651-58.879Q-4.702-58.971-4.702-59.076Q-4.702-59.494-4.313-59.641Q-3.924-59.787-3.424-59.787Q-3.092-59.787-2.739-59.657Q-2.385-59.526-2.157-59.272Q-1.928-59.018-1.928-58.670L-1.928-56.869Q-1.928-56.737-1.856-56.627Q-1.784-56.518-1.655-56.518Q-1.530-56.518-1.461-56.623Q-1.393-56.729-1.393-56.869L-1.393-57.381L-1.112-57.381L-1.112-56.869Q-1.112-56.666-1.229-56.508Q-1.346-56.350-1.528-56.266Q-1.710-56.182-1.913-56.182Q-2.143-56.182-2.295-56.354Q-2.448-56.526-2.479-56.756Q-2.639-56.475-2.948-56.309Q-3.256-56.143-3.608-56.143Q-4.120-56.143-4.543-56.366Q-4.967-56.588-4.967-57.053M-4.280-57.053Q-4.280-56.768-4.053-56.582Q-3.827-56.397-3.534-56.397Q-3.288-56.397-3.063-56.514Q-2.838-56.631-2.704-56.834Q-2.569-57.037-2.569-57.291L-2.569-58.123Q-2.835-58.123-3.120-58.069Q-3.405-58.014-3.676-57.885Q-3.948-57.756-4.114-57.549Q-4.280-57.342-4.280-57.053M1.040-56.221L-0.737-56.221L-0.737-56.518Q-0.463-56.518-0.295-56.565Q-0.127-56.612-0.127-56.780L-0.127-58.916Q-0.127-59.131-0.184-59.227Q-0.241-59.323-0.354-59.344Q-0.467-59.366-0.713-59.366L-0.713-59.662L0.486-59.748L0.486-56.780Q0.486-56.612 0.632-56.565Q0.779-56.518 1.040-56.518L1.040-56.221M-0.401-61.143Q-0.401-61.334-0.266-61.465Q-0.131-61.596 0.064-61.596Q0.185-61.596 0.289-61.533Q0.392-61.471 0.455-61.367Q0.517-61.264 0.517-61.143Q0.517-60.948 0.386-60.813Q0.255-60.678 0.064-60.678Q-0.135-60.678-0.268-60.811Q-0.401-60.944-0.401-61.143M3.548-56.221L1.568-56.221L1.568-56.518Q1.837-56.518 2.005-56.563Q2.173-56.608 2.173-56.780L2.173-58.916Q2.173-59.131 2.111-59.227Q2.048-59.323 1.931-59.344Q1.814-59.366 1.568-59.366L1.568-59.662L2.736-59.748L2.736-58.963Q2.814-59.174 2.966-59.360Q3.119-59.545 3.318-59.647Q3.517-59.748 3.744-59.748Q3.990-59.748 4.181-59.604Q4.373-59.459 4.373-59.229Q4.373-59.073 4.267-58.963Q4.162-58.854 4.005-58.854Q3.849-58.854 3.740-58.963Q3.630-59.073 3.630-59.229Q3.630-59.389 3.736-59.494Q3.412-59.494 3.197-59.266Q2.982-59.037 2.886-58.698Q2.790-58.358 2.790-58.053L2.790-56.780Q2.790-56.612 3.017-56.565Q3.244-56.518 3.548-56.518L3.548-56.221M4.896-56.229L4.896-57.451Q4.896-57.479 4.927-57.510Q4.958-57.541 4.982-57.541L5.087-57.541Q5.158-57.541 5.173-57.479Q5.236-57.158 5.374-56.918Q5.513-56.678 5.746-56.537Q5.978-56.397 6.287-56.397Q6.525-56.397 6.734-56.457Q6.943-56.518 7.080-56.666Q7.216-56.815 7.216-57.061Q7.216-57.315 7.005-57.481Q6.794-57.647 6.525-57.701L5.904-57.815Q5.498-57.893 5.197-58.149Q4.896-58.405 4.896-58.780Q4.896-59.147 5.097-59.369Q5.298-59.592 5.623-59.690Q5.947-59.787 6.287-59.787Q6.751-59.787 7.048-59.580L7.271-59.764Q7.294-59.787 7.326-59.787L7.376-59.787Q7.408-59.787 7.435-59.760Q7.462-59.733 7.462-59.701L7.462-58.717Q7.462-58.686 7.437-58.657Q7.412-58.627 7.376-58.627L7.271-58.627Q7.236-58.627 7.208-58.655Q7.181-58.682 7.181-58.717Q7.181-59.116 6.929-59.336Q6.677-59.557 6.279-59.557Q5.923-59.557 5.640-59.434Q5.357-59.311 5.357-59.006Q5.357-58.787 5.558-58.655Q5.759-58.522 6.005-58.479L6.630-58.366Q7.060-58.276 7.369-57.979Q7.677-57.682 7.677-57.268Q7.677-56.698 7.279-56.420Q6.880-56.143 6.287-56.143Q5.736-56.143 5.384-56.479L5.087-56.166Q5.064-56.143 5.029-56.143L4.982-56.143Q4.958-56.143 4.927-56.174Q4.896-56.205 4.896-56.229\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"var(--tk-accent)\" style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M45.562-40.572h62.596V-71.87H45.562Z\"\u002F>\u003Cg transform=\"translate(98.208 2.778)\">\u003Cpath d=\"M-31.953-56.221L-33.785-56.221L-33.785-56.518Q-33.511-56.518-33.343-56.565Q-33.175-56.612-33.175-56.780L-33.175-60.940Q-33.175-61.155-33.238-61.250Q-33.300-61.346-33.419-61.367Q-33.539-61.389-33.785-61.389L-33.785-61.686L-32.562-61.772L-32.562-56.780Q-32.562-56.612-32.394-56.565Q-32.226-56.518-31.953-56.518L-31.953-56.221M-31.507-57.975Q-31.507-58.455-31.275-58.871Q-31.042-59.287-30.632-59.537Q-30.222-59.787-29.746-59.787Q-29.015-59.787-28.617-59.346Q-28.218-58.905-28.218-58.174Q-28.218-58.069-28.312-58.045L-30.761-58.045L-30.761-57.975Q-30.761-57.565-30.640-57.209Q-30.519-56.854-30.248-56.637Q-29.976-56.420-29.546-56.420Q-29.183-56.420-28.886-56.649Q-28.589-56.877-28.488-57.229Q-28.480-57.276-28.394-57.291L-28.312-57.291Q-28.218-57.264-28.218-57.182Q-28.218-57.174-28.226-57.143Q-28.289-56.916-28.427-56.733Q-28.566-56.549-28.757-56.416Q-28.949-56.283-29.167-56.213Q-29.386-56.143-29.625-56.143Q-29.996-56.143-30.334-56.280Q-30.671-56.416-30.939-56.668Q-31.207-56.920-31.357-57.260Q-31.507-57.600-31.507-57.975M-30.753-58.283L-28.792-58.283Q-28.792-58.588-28.894-58.879Q-28.996-59.170-29.212-59.352Q-29.429-59.533-29.746-59.533Q-30.046-59.533-30.277-59.346Q-30.507-59.158-30.630-58.867Q-30.753-58.576-30.753-58.283M-27.632-57.053Q-27.632-57.537-27.230-57.832Q-26.828-58.127-26.277-58.246Q-25.726-58.366-25.234-58.366L-25.234-58.655Q-25.234-58.881-25.349-59.088Q-25.464-59.295-25.662-59.414Q-25.859-59.533-26.089-59.533Q-26.515-59.533-26.800-59.428Q-26.730-59.401-26.683-59.346Q-26.636-59.291-26.611-59.221Q-26.585-59.151-26.585-59.076Q-26.585-58.971-26.636-58.879Q-26.687-58.787-26.779-58.737Q-26.871-58.686-26.976-58.686Q-27.082-58.686-27.173-58.737Q-27.265-58.787-27.316-58.879Q-27.367-58.971-27.367-59.076Q-27.367-59.494-26.978-59.641Q-26.589-59.787-26.089-59.787Q-25.757-59.787-25.404-59.657Q-25.050-59.526-24.822-59.272Q-24.593-59.018-24.593-58.670L-24.593-56.869Q-24.593-56.737-24.521-56.627Q-24.449-56.518-24.320-56.518Q-24.195-56.518-24.126-56.623Q-24.058-56.729-24.058-56.869L-24.058-57.381L-23.777-57.381L-23.777-56.869Q-23.777-56.666-23.894-56.508Q-24.011-56.350-24.193-56.266Q-24.375-56.182-24.578-56.182Q-24.808-56.182-24.960-56.354Q-25.113-56.526-25.144-56.756Q-25.304-56.475-25.613-56.309Q-25.921-56.143-26.273-56.143Q-26.785-56.143-27.209-56.366Q-27.632-56.588-27.632-57.053M-26.945-57.053Q-26.945-56.768-26.718-56.582Q-26.492-56.397-26.199-56.397Q-25.953-56.397-25.728-56.514Q-25.503-56.631-25.369-56.834Q-25.234-57.037-25.234-57.291L-25.234-58.123Q-25.500-58.123-25.785-58.069Q-26.070-58.014-26.341-57.885Q-26.613-57.756-26.779-57.549Q-26.945-57.342-26.945-57.053M-21.476-56.221L-23.457-56.221L-23.457-56.518Q-23.187-56.518-23.019-56.563Q-22.851-56.608-22.851-56.780L-22.851-58.916Q-22.851-59.131-22.914-59.227Q-22.976-59.323-23.093-59.344Q-23.210-59.366-23.457-59.366L-23.457-59.662L-22.289-59.748L-22.289-58.963Q-22.210-59.174-22.058-59.360Q-21.906-59.545-21.707-59.647Q-21.507-59.748-21.281-59.748Q-21.035-59.748-20.843-59.604Q-20.652-59.459-20.652-59.229Q-20.652-59.073-20.757-58.963Q-20.863-58.854-21.019-58.854Q-21.175-58.854-21.285-58.963Q-21.394-59.073-21.394-59.229Q-21.394-59.389-21.289-59.494Q-21.613-59.494-21.828-59.266Q-22.042-59.037-22.138-58.698Q-22.234-58.358-22.234-58.053L-22.234-56.780Q-22.234-56.612-22.007-56.565Q-21.781-56.518-21.476-56.518L-21.476-56.221M-18.242-56.221L-20.097-56.221L-20.097-56.518Q-19.824-56.518-19.656-56.565Q-19.488-56.612-19.488-56.780L-19.488-58.916Q-19.488-59.131-19.550-59.227Q-19.613-59.323-19.732-59.344Q-19.851-59.366-20.097-59.366L-20.097-59.662L-18.906-59.748L-18.906-59.014Q-18.792-59.229-18.599-59.397Q-18.406-59.565-18.167-59.657Q-17.929-59.748-17.675-59.748Q-16.507-59.748-16.507-58.670L-16.507-56.780Q-16.507-56.612-16.337-56.565Q-16.167-56.518-15.898-56.518L-15.898-56.221L-17.753-56.221L-17.753-56.518Q-17.480-56.518-17.312-56.565Q-17.144-56.612-17.144-56.780L-17.144-58.655Q-17.144-59.037-17.265-59.266Q-17.386-59.494-17.738-59.494Q-18.050-59.494-18.304-59.332Q-18.558-59.170-18.705-58.901Q-18.851-58.631-18.851-58.334L-18.851-56.780Q-18.851-56.612-18.681-56.565Q-18.511-56.518-18.242-56.518L-18.242-56.221M-15.453-57.975Q-15.453-58.455-15.220-58.871Q-14.988-59.287-14.578-59.537Q-14.167-59.787-13.691-59.787Q-12.960-59.787-12.562-59.346Q-12.164-58.905-12.164-58.174Q-12.164-58.069-12.257-58.045L-14.707-58.045L-14.707-57.975Q-14.707-57.565-14.585-57.209Q-14.464-56.854-14.193-56.637Q-13.921-56.420-13.492-56.420Q-13.128-56.420-12.832-56.649Q-12.535-56.877-12.433-57.229Q-12.425-57.276-12.339-57.291L-12.257-57.291Q-12.164-57.264-12.164-57.182Q-12.164-57.174-12.171-57.143Q-12.234-56.916-12.373-56.733Q-12.511-56.549-12.703-56.416Q-12.894-56.283-13.113-56.213Q-13.332-56.143-13.570-56.143Q-13.941-56.143-14.279-56.280Q-14.617-56.416-14.884-56.668Q-15.152-56.920-15.302-57.260Q-15.453-57.600-15.453-57.975M-14.699-58.283L-12.738-58.283Q-12.738-58.588-12.839-58.879Q-12.941-59.170-13.158-59.352Q-13.375-59.533-13.691-59.533Q-13.992-59.533-14.222-59.346Q-14.453-59.158-14.576-58.867Q-14.699-58.576-14.699-58.283M-9.667-56.221L-11.648-56.221L-11.648-56.518Q-11.378-56.518-11.210-56.563Q-11.042-56.608-11.042-56.780L-11.042-58.916Q-11.042-59.131-11.105-59.227Q-11.167-59.323-11.285-59.344Q-11.402-59.366-11.648-59.366L-11.648-59.662L-10.480-59.748L-10.480-58.963Q-10.402-59.174-10.250-59.360Q-10.097-59.545-9.898-59.647Q-9.699-59.748-9.472-59.748Q-9.226-59.748-9.035-59.604Q-8.843-59.459-8.843-59.229Q-8.843-59.073-8.949-58.963Q-9.054-58.854-9.210-58.854Q-9.367-58.854-9.476-58.963Q-9.585-59.073-9.585-59.229Q-9.585-59.389-9.480-59.494Q-9.804-59.494-10.019-59.266Q-10.234-59.037-10.330-58.698Q-10.425-58.358-10.425-58.053L-10.425-56.780Q-10.425-56.612-10.199-56.565Q-9.972-56.518-9.667-56.518\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M156.528-40.572h62.596V-71.87h-62.596Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(198.627 2)\">\u003Cpath d=\"M-31.937-56.221L-33.792-56.221L-33.792-56.518Q-33.519-56.518-33.351-56.565Q-33.183-56.612-33.183-56.780L-33.183-60.940Q-33.183-61.155-33.246-61.250Q-33.308-61.346-33.427-61.367Q-33.546-61.389-33.792-61.389L-33.792-61.686L-32.570-61.772L-32.570-59.069Q-32.445-59.280-32.257-59.430Q-32.070-59.580-31.843-59.664Q-31.617-59.748-31.371-59.748Q-30.203-59.748-30.203-58.670L-30.203-56.780Q-30.203-56.612-30.033-56.565Q-29.863-56.518-29.593-56.518L-29.593-56.221L-31.449-56.221L-31.449-56.518Q-31.175-56.518-31.007-56.565Q-30.839-56.612-30.839-56.780L-30.839-58.655Q-30.839-59.037-30.960-59.266Q-31.082-59.494-31.433-59.494Q-31.746-59.494-32-59.332Q-32.253-59.170-32.400-58.901Q-32.546-58.631-32.546-58.334L-32.546-56.780Q-32.546-56.612-32.376-56.565Q-32.207-56.518-31.937-56.518\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(198.627 2)\">\u003Cpath d=\"M-28.963-54.924Q-28.849-54.846-28.674-54.846Q-28.385-54.846-28.164-55.059Q-27.943-55.272-27.818-55.573L-27.529-56.221L-28.803-59.108Q-28.885-59.283-29.029-59.328Q-29.174-59.373-29.443-59.373L-29.443-59.670L-27.724-59.670L-27.724-59.373Q-28.146-59.373-28.146-59.190Q-28.146-59.178-28.131-59.108L-27.193-56.983L-26.361-58.893Q-26.322-58.983-26.322-59.061Q-26.322-59.201-26.424-59.287Q-26.525-59.373-26.666-59.373L-26.666-59.670L-25.314-59.670L-25.314-59.373Q-25.568-59.373-25.762-59.248Q-25.955-59.123-26.060-58.893L-27.506-55.573Q-27.619-55.319-27.785-55.096Q-27.951-54.873-28.180-54.731Q-28.408-54.588-28.674-54.588Q-28.971-54.588-29.211-54.780Q-29.451-54.971-29.451-55.260Q-29.451-55.416-29.346-55.518Q-29.240-55.619-29.092-55.619Q-28.986-55.619-28.906-55.573Q-28.826-55.526-28.779-55.448Q-28.732-55.369-28.732-55.260Q-28.732-55.139-28.793-55.051Q-28.853-54.963-28.963-54.924M-23.017-54.670L-24.873-54.670L-24.873-54.963Q-24.603-54.963-24.435-55.008Q-24.267-55.053-24.267-55.229L-24.267-59.053Q-24.267-59.260-24.424-59.313Q-24.580-59.366-24.873-59.366L-24.873-59.662L-23.650-59.748L-23.650-59.283Q-23.420-59.506-23.105-59.627Q-22.791-59.748-22.451-59.748Q-21.978-59.748-21.574-59.502Q-21.170-59.256-20.937-58.840Q-20.705-58.424-20.705-57.948Q-20.705-57.573-20.853-57.244Q-21.002-56.916-21.271-56.664Q-21.541-56.412-21.885-56.278Q-22.228-56.143-22.588-56.143Q-22.877-56.143-23.148-56.264Q-23.420-56.385-23.627-56.596L-23.627-55.229Q-23.627-55.053-23.459-55.008Q-23.291-54.963-23.017-54.963L-23.017-54.670M-23.627-58.885L-23.627-57.045Q-23.474-56.756-23.213-56.576Q-22.951-56.397-22.642-56.397Q-22.357-56.397-22.135-56.535Q-21.912-56.674-21.760-56.905Q-21.607-57.135-21.529-57.407Q-21.451-57.678-21.451-57.948Q-21.451-58.280-21.576-58.637Q-21.701-58.994-21.949-59.231Q-22.197-59.467-22.545-59.467Q-22.869-59.467-23.164-59.311Q-23.459-59.155-23.627-58.885\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(198.627 2)\">\u003Cpath d=\"M-19.937-57.916Q-19.937-58.420-19.681-58.852Q-19.425-59.283-18.989-59.535Q-18.554-59.787-18.054-59.787Q-17.667-59.787-17.325-59.643Q-16.984-59.498-16.722-59.237Q-16.460-58.975-16.318-58.639Q-16.175-58.303-16.175-57.916Q-16.175-57.424-16.439-57.014Q-16.702-56.604-17.132-56.373Q-17.562-56.143-18.054-56.143Q-18.546-56.143-18.980-56.375Q-19.413-56.608-19.675-57.016Q-19.937-57.424-19.937-57.916M-18.054-56.420Q-17.597-56.420-17.345-56.643Q-17.093-56.866-17.005-57.217Q-16.917-57.569-16.917-58.014Q-16.917-58.444-17.011-58.782Q-17.105-59.119-17.359-59.326Q-17.613-59.533-18.054-59.533Q-18.702-59.533-18.946-59.117Q-19.191-58.701-19.191-58.014Q-19.191-57.569-19.103-57.217Q-19.015-56.866-18.763-56.643Q-18.511-56.420-18.054-56.420M-15.066-57.182L-15.066-59.373L-15.769-59.373L-15.769-59.627Q-15.413-59.627-15.171-59.860Q-14.929-60.092-14.818-60.440Q-14.706-60.787-14.706-61.143L-14.425-61.143L-14.425-59.670L-13.249-59.670L-13.249-59.373L-14.425-59.373L-14.425-57.198Q-14.425-56.877-14.306-56.649Q-14.187-56.420-13.905-56.420Q-13.726-56.420-13.609-56.543Q-13.491-56.666-13.439-56.846Q-13.386-57.026-13.386-57.198L-13.386-57.670L-13.105-57.670L-13.105-57.182Q-13.105-56.928-13.210-56.688Q-13.316-56.448-13.513-56.295Q-13.710-56.143-13.968-56.143Q-14.284-56.143-14.536-56.266Q-14.788-56.389-14.927-56.623Q-15.066-56.858-15.066-57.182M-10.456-56.221L-12.312-56.221L-12.312-56.518Q-12.038-56.518-11.870-56.565Q-11.702-56.612-11.702-56.780L-11.702-60.940Q-11.702-61.155-11.765-61.250Q-11.827-61.346-11.946-61.367Q-12.066-61.389-12.312-61.389L-12.312-61.686L-11.089-61.772L-11.089-59.069Q-10.964-59.280-10.777-59.430Q-10.589-59.580-10.363-59.664Q-10.136-59.748-9.890-59.748Q-8.722-59.748-8.722-58.670L-8.722-56.780Q-8.722-56.612-8.552-56.565Q-8.382-56.518-8.113-56.518L-8.113-56.221L-9.968-56.221L-9.968-56.518Q-9.695-56.518-9.527-56.565Q-9.359-56.612-9.359-56.780L-9.359-58.655Q-9.359-59.037-9.480-59.266Q-9.601-59.494-9.952-59.494Q-10.265-59.494-10.519-59.332Q-10.773-59.170-10.919-58.901Q-11.066-58.631-11.066-58.334L-11.066-56.780Q-11.066-56.612-10.896-56.565Q-10.726-56.518-10.456-56.518L-10.456-56.221M-7.667-57.975Q-7.667-58.455-7.435-58.871Q-7.202-59.287-6.792-59.537Q-6.382-59.787-5.905-59.787Q-5.175-59.787-4.777-59.346Q-4.378-58.905-4.378-58.174Q-4.378-58.069-4.472-58.045L-6.921-58.045L-6.921-57.975Q-6.921-57.565-6.800-57.209Q-6.679-56.854-6.407-56.637Q-6.136-56.420-5.706-56.420Q-5.343-56.420-5.046-56.649Q-4.749-56.877-4.648-57.229Q-4.640-57.276-4.554-57.291L-4.472-57.291Q-4.378-57.264-4.378-57.182Q-4.378-57.174-4.386-57.143Q-4.448-56.916-4.587-56.733Q-4.726-56.549-4.917-56.416Q-5.109-56.283-5.327-56.213Q-5.546-56.143-5.784-56.143Q-6.155-56.143-6.493-56.280Q-6.831-56.416-7.099-56.668Q-7.366-56.920-7.517-57.260Q-7.667-57.600-7.667-57.975M-6.913-58.283L-4.952-58.283Q-4.952-58.588-5.054-58.879Q-5.155-59.170-5.372-59.352Q-5.589-59.533-5.905-59.533Q-6.206-59.533-6.437-59.346Q-6.667-59.158-6.790-58.867Q-6.913-58.576-6.913-58.283M-3.847-56.229L-3.847-57.451Q-3.847-57.479-3.816-57.510Q-3.784-57.541-3.761-57.541L-3.655-57.541Q-3.585-57.541-3.570-57.479Q-3.507-57.158-3.368-56.918Q-3.230-56.678-2.997-56.537Q-2.765-56.397-2.456-56.397Q-2.218-56.397-2.009-56.457Q-1.800-56.518-1.663-56.666Q-1.527-56.815-1.527-57.061Q-1.527-57.315-1.738-57.481Q-1.948-57.647-2.218-57.701L-2.839-57.815Q-3.245-57.893-3.546-58.149Q-3.847-58.405-3.847-58.780Q-3.847-59.147-3.646-59.369Q-3.445-59.592-3.120-59.690Q-2.796-59.787-2.456-59.787Q-1.991-59.787-1.695-59.580L-1.472-59.764Q-1.448-59.787-1.417-59.787L-1.366-59.787Q-1.335-59.787-1.308-59.760Q-1.280-59.733-1.280-59.701L-1.280-58.717Q-1.280-58.686-1.306-58.657Q-1.331-58.627-1.366-58.627L-1.472-58.627Q-1.507-58.627-1.534-58.655Q-1.562-58.682-1.562-58.717Q-1.562-59.116-1.814-59.336Q-2.066-59.557-2.464-59.557Q-2.820-59.557-3.103-59.434Q-3.386-59.311-3.386-59.006Q-3.386-58.787-3.185-58.655Q-2.984-58.522-2.738-58.479L-2.113-58.366Q-1.683-58.276-1.374-57.979Q-1.066-57.682-1.066-57.268Q-1.066-56.698-1.464-56.420Q-1.863-56.143-2.456-56.143Q-3.007-56.143-3.359-56.479L-3.655-56.166Q-3.679-56.143-3.714-56.143L-3.761-56.143Q-3.784-56.143-3.816-56.174Q-3.847-56.205-3.847-56.229M1.321-56.221L-0.456-56.221L-0.456-56.518Q-0.183-56.518-0.015-56.565Q0.153-56.612 0.153-56.780L0.153-58.916Q0.153-59.131 0.096-59.227Q0.040-59.323-0.073-59.344Q-0.187-59.366-0.433-59.366L-0.433-59.662L0.766-59.748L0.766-56.780Q0.766-56.612 0.913-56.565Q1.059-56.518 1.321-56.518L1.321-56.221M-0.120-61.143Q-0.120-61.334 0.014-61.465Q0.149-61.596 0.345-61.596Q0.466-61.596 0.569-61.533Q0.673-61.471 0.735-61.367Q0.798-61.264 0.798-61.143Q0.798-60.948 0.667-60.813Q0.536-60.678 0.345-60.678Q0.145-60.678 0.012-60.811Q-0.120-60.944-0.120-61.143M1.864-56.229L1.864-57.451Q1.864-57.479 1.895-57.510Q1.927-57.541 1.950-57.541L2.055-57.541Q2.126-57.541 2.141-57.479Q2.204-57.158 2.343-56.918Q2.481-56.678 2.714-56.537Q2.946-56.397 3.255-56.397Q3.493-56.397 3.702-56.457Q3.911-56.518 4.048-56.666Q4.184-56.815 4.184-57.061Q4.184-57.315 3.973-57.481Q3.762-57.647 3.493-57.701L2.872-57.815Q2.466-57.893 2.165-58.149Q1.864-58.405 1.864-58.780Q1.864-59.147 2.065-59.369Q2.266-59.592 2.591-59.690Q2.915-59.787 3.255-59.787Q3.720-59.787 4.016-59.580L4.239-59.764Q4.262-59.787 4.294-59.787L4.345-59.787Q4.376-59.787 4.403-59.760Q4.430-59.733 4.430-59.701L4.430-58.717Q4.430-58.686 4.405-58.657Q4.380-58.627 4.345-58.627L4.239-58.627Q4.204-58.627 4.177-58.655Q4.149-58.682 4.149-58.717Q4.149-59.116 3.897-59.336Q3.645-59.557 3.247-59.557Q2.891-59.557 2.608-59.434Q2.325-59.311 2.325-59.006Q2.325-58.787 2.526-58.655Q2.727-58.522 2.973-58.479L3.598-58.366Q4.028-58.276 4.337-57.979Q4.645-57.682 4.645-57.268Q4.645-56.698 4.247-56.420Q3.848-56.143 3.255-56.143Q2.704-56.143 2.352-56.479L2.055-56.166Q2.032-56.143 1.997-56.143L1.950-56.143Q1.927-56.143 1.895-56.174Q1.864-56.205 1.864-56.229\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(198.627 2)\">\u003Cpath d=\"M9.949-56.221L8.093-56.221L8.093-56.518Q8.367-56.518 8.535-56.565Q8.703-56.612 8.703-56.780L8.703-60.940Q8.703-61.155 8.640-61.250Q8.578-61.346 8.459-61.367Q8.340-61.389 8.093-61.389L8.093-61.686L9.316-61.772L9.316-59.069Q9.441-59.280 9.629-59.430Q9.816-59.580 10.043-59.664Q10.269-59.748 10.515-59.748Q11.683-59.748 11.683-58.670L11.683-56.780Q11.683-56.612 11.853-56.565Q12.023-56.518 12.293-56.518L12.293-56.221L10.437-56.221L10.437-56.518Q10.711-56.518 10.879-56.565Q11.047-56.612 11.047-56.780L11.047-58.655Q11.047-59.037 10.926-59.266Q10.804-59.494 10.453-59.494Q10.140-59.494 9.886-59.332Q9.633-59.170 9.486-58.901Q9.340-58.631 9.340-58.334L9.340-56.780Q9.340-56.612 9.510-56.565Q9.679-56.518 9.949-56.518\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M156.528 19.179h62.596V-12.12h-62.596Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(207.647 65.961)\">\u003Cpath d=\"M-32.463-66.682L-32.463-68.873L-33.166-68.873L-33.166-69.127Q-32.810-69.127-32.568-69.360Q-32.326-69.592-32.215-69.940Q-32.103-70.287-32.103-70.643L-31.822-70.643L-31.822-69.170L-30.646-69.170L-30.646-68.873L-31.822-68.873L-31.822-66.698Q-31.822-66.377-31.703-66.149Q-31.584-65.920-31.303-65.920Q-31.123-65.920-31.006-66.043Q-30.889-66.166-30.836-66.346Q-30.783-66.526-30.783-66.698L-30.783-67.170L-30.502-67.170L-30.502-66.682Q-30.502-66.428-30.607-66.188Q-30.713-65.948-30.910-65.795Q-31.107-65.643-31.365-65.643Q-31.681-65.643-31.933-65.766Q-32.185-65.889-32.324-66.123Q-32.463-66.358-32.463-66.682M-29.783-67.475Q-29.783-67.955-29.551-68.371Q-29.318-68.787-28.908-69.037Q-28.498-69.287-28.021-69.287Q-27.291-69.287-26.892-68.846Q-26.494-68.405-26.494-67.674Q-26.494-67.569-26.588-67.545L-29.037-67.545L-29.037-67.475Q-29.037-67.065-28.916-66.709Q-28.795-66.354-28.523-66.137Q-28.252-65.920-27.822-65.920Q-27.459-65.920-27.162-66.149Q-26.865-66.377-26.764-66.729Q-26.756-66.776-26.670-66.791L-26.588-66.791Q-26.494-66.764-26.494-66.682Q-26.494-66.674-26.502-66.643Q-26.564-66.416-26.703-66.233Q-26.842-66.049-27.033-65.916Q-27.224-65.784-27.443-65.713Q-27.662-65.643-27.900-65.643Q-28.271-65.643-28.609-65.780Q-28.947-65.916-29.215-66.168Q-29.482-66.420-29.633-66.760Q-29.783-67.100-29.783-67.475M-29.029-67.784L-27.068-67.784Q-27.068-68.088-27.170-68.379Q-27.271-68.670-27.488-68.852Q-27.705-69.034-28.021-69.034Q-28.322-69.034-28.553-68.846Q-28.783-68.659-28.906-68.367Q-29.029-68.076-29.029-67.784M-25.963-65.729L-25.963-66.951Q-25.963-66.979-25.931-67.010Q-25.900-67.041-25.877-67.041L-25.771-67.041Q-25.701-67.041-25.685-66.979Q-25.623-66.659-25.484-66.418Q-25.346-66.178-25.113-66.037Q-24.881-65.897-24.572-65.897Q-24.334-65.897-24.125-65.957Q-23.916-66.018-23.779-66.166Q-23.642-66.315-23.642-66.561Q-23.642-66.815-23.853-66.981Q-24.064-67.147-24.334-67.201L-24.955-67.315Q-25.361-67.393-25.662-67.649Q-25.963-67.905-25.963-68.280Q-25.963-68.647-25.762-68.869Q-25.560-69.092-25.236-69.190Q-24.912-69.287-24.572-69.287Q-24.107-69.287-23.810-69.080L-23.588-69.264Q-23.564-69.287-23.533-69.287L-23.482-69.287Q-23.451-69.287-23.424-69.260Q-23.396-69.233-23.396-69.201L-23.396-68.217Q-23.396-68.186-23.422-68.157Q-23.447-68.127-23.482-68.127L-23.588-68.127Q-23.623-68.127-23.650-68.155Q-23.678-68.182-23.678-68.217Q-23.678-68.616-23.930-68.836Q-24.181-69.057-24.580-69.057Q-24.935-69.057-25.219-68.934Q-25.502-68.811-25.502-68.506Q-25.502-68.287-25.301-68.155Q-25.099-68.022-24.853-67.979L-24.228-67.866Q-23.799-67.776-23.490-67.479Q-23.181-67.182-23.181-66.768Q-23.181-66.198-23.580-65.920Q-23.978-65.643-24.572-65.643Q-25.123-65.643-25.474-65.979L-25.771-65.666Q-25.795-65.643-25.830-65.643L-25.877-65.643Q-25.900-65.643-25.931-65.674Q-25.963-65.705-25.963-65.729M-22.029-66.682L-22.029-68.873L-22.732-68.873L-22.732-69.127Q-22.377-69.127-22.135-69.360Q-21.892-69.592-21.781-69.940Q-21.670-70.287-21.670-70.643L-21.389-70.643L-21.389-69.170L-20.213-69.170L-20.213-68.873L-21.389-68.873L-21.389-66.698Q-21.389-66.377-21.269-66.149Q-21.150-65.920-20.869-65.920Q-20.689-65.920-20.572-66.043Q-20.455-66.166-20.402-66.346Q-20.349-66.526-20.349-66.698L-20.349-67.170L-20.068-67.170L-20.068-66.682Q-20.068-66.428-20.174-66.188Q-20.279-65.948-20.476-65.795Q-20.674-65.643-20.931-65.643Q-21.248-65.643-21.500-65.766Q-21.752-65.889-21.890-66.123Q-22.029-66.358-22.029-66.682\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(207.647 65.961)\">\u003Cpath d=\"M-16.470-65.729L-16.470-66.951Q-16.470-66.979-16.439-67.010Q-16.407-67.041-16.384-67.041L-16.278-67.041Q-16.208-67.041-16.192-66.979Q-16.130-66.659-15.991-66.418Q-15.853-66.178-15.620-66.037Q-15.388-65.897-15.079-65.897Q-14.841-65.897-14.632-65.957Q-14.423-66.018-14.286-66.166Q-14.149-66.315-14.149-66.561Q-14.149-66.815-14.360-66.981Q-14.571-67.147-14.841-67.201L-15.462-67.315Q-15.868-67.393-16.169-67.649Q-16.470-67.905-16.470-68.280Q-16.470-68.647-16.269-68.869Q-16.067-69.092-15.743-69.190Q-15.419-69.287-15.079-69.287Q-14.614-69.287-14.317-69.080L-14.095-69.264Q-14.071-69.287-14.040-69.287L-13.989-69.287Q-13.958-69.287-13.931-69.260Q-13.903-69.233-13.903-69.201L-13.903-68.217Q-13.903-68.186-13.929-68.157Q-13.954-68.127-13.989-68.127L-14.095-68.127Q-14.130-68.127-14.157-68.155Q-14.185-68.182-14.185-68.217Q-14.185-68.616-14.437-68.836Q-14.689-69.057-15.087-69.057Q-15.442-69.057-15.726-68.934Q-16.009-68.811-16.009-68.506Q-16.009-68.287-15.808-68.155Q-15.606-68.022-15.360-67.979L-14.735-67.866Q-14.306-67.776-13.997-67.479Q-13.689-67.182-13.689-66.768Q-13.689-66.198-14.087-65.920Q-14.485-65.643-15.079-65.643Q-15.630-65.643-15.981-65.979L-16.278-65.666Q-16.302-65.643-16.337-65.643L-16.384-65.643Q-16.407-65.643-16.439-65.674Q-16.470-65.705-16.470-65.729M-13.161-67.475Q-13.161-67.955-12.929-68.371Q-12.696-68.787-12.286-69.037Q-11.876-69.287-11.399-69.287Q-10.669-69.287-10.271-68.846Q-9.872-68.405-9.872-67.674Q-9.872-67.569-9.966-67.545L-12.415-67.545L-12.415-67.475Q-12.415-67.065-12.294-66.709Q-12.173-66.354-11.901-66.137Q-11.630-65.920-11.200-65.920Q-10.837-65.920-10.540-66.149Q-10.243-66.377-10.142-66.729Q-10.134-66.776-10.048-66.791L-9.966-66.791Q-9.872-66.764-9.872-66.682Q-9.872-66.674-9.880-66.643Q-9.942-66.416-10.081-66.233Q-10.220-66.049-10.411-65.916Q-10.603-65.784-10.821-65.713Q-11.040-65.643-11.278-65.643Q-11.649-65.643-11.987-65.780Q-12.325-65.916-12.593-66.168Q-12.860-66.420-13.011-66.760Q-13.161-67.100-13.161-67.475M-12.407-67.784L-10.446-67.784Q-10.446-68.088-10.548-68.379Q-10.649-68.670-10.866-68.852Q-11.083-69.034-11.399-69.034Q-11.700-69.034-11.931-68.846Q-12.161-68.659-12.284-68.367Q-12.407-68.076-12.407-67.784M-8.759-66.682L-8.759-68.873L-9.462-68.873L-9.462-69.127Q-9.106-69.127-8.864-69.360Q-8.622-69.592-8.511-69.940Q-8.399-70.287-8.399-70.643L-8.118-70.643L-8.118-69.170L-6.942-69.170L-6.942-68.873L-8.118-68.873L-8.118-66.698Q-8.118-66.377-7.999-66.149Q-7.880-65.920-7.599-65.920Q-7.419-65.920-7.302-66.043Q-7.185-66.166-7.132-66.346Q-7.079-66.526-7.079-66.698L-7.079-67.170L-6.798-67.170L-6.798-66.682Q-6.798-66.428-6.903-66.188Q-7.009-65.948-7.206-65.795Q-7.403-65.643-7.661-65.643Q-7.978-65.643-8.230-65.766Q-8.481-65.889-8.620-66.123Q-8.759-66.358-8.759-66.682\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(207.647 65.961)\">\u003Cpath d=\"M-31.488-54.229Q-32.101-54.686-32.503-55.321Q-32.906-55.955-33.101-56.701Q-33.296-57.448-33.296-58.221Q-33.296-58.994-33.101-59.741Q-32.906-60.487-32.503-61.121Q-32.101-61.756-31.488-62.213Q-31.476-62.217-31.468-62.219Q-31.460-62.221-31.449-62.221L-31.371-62.221Q-31.332-62.221-31.306-62.194Q-31.281-62.166-31.281-62.123Q-31.281-62.073-31.312-62.053Q-31.820-61.600-32.142-60.977Q-32.464-60.354-32.605-59.658Q-32.746-58.963-32.746-58.221Q-32.746-57.487-32.607-56.787Q-32.468-56.088-32.144-55.463Q-31.820-54.838-31.312-54.389Q-31.281-54.369-31.281-54.319Q-31.281-54.276-31.306-54.248Q-31.332-54.221-31.371-54.221L-31.449-54.221Q-31.457-54.225-31.466-54.227Q-31.476-54.229-31.488-54.229M-28.632-56.221L-30.488-56.221L-30.488-56.518Q-30.214-56.518-30.046-56.565Q-29.878-56.612-29.878-56.780L-29.878-58.916Q-29.878-59.131-29.941-59.227Q-30.003-59.323-30.123-59.344Q-30.242-59.366-30.488-59.366L-30.488-59.662L-29.296-59.748L-29.296-59.014Q-29.183-59.229-28.990-59.397Q-28.796-59.565-28.558-59.657Q-28.320-59.748-28.066-59.748Q-26.898-59.748-26.898-58.670L-26.898-56.780Q-26.898-56.612-26.728-56.565Q-26.558-56.518-26.289-56.518L-26.289-56.221L-28.144-56.221L-28.144-56.518Q-27.871-56.518-27.703-56.565Q-27.535-56.612-27.535-56.780L-27.535-58.655Q-27.535-59.037-27.656-59.266Q-27.777-59.494-28.128-59.494Q-28.441-59.494-28.695-59.332Q-28.949-59.170-29.095-58.901Q-29.242-58.631-29.242-58.334L-29.242-56.780Q-29.242-56.612-29.072-56.565Q-28.902-56.518-28.632-56.518L-28.632-56.221M-25.843-57.975Q-25.843-58.455-25.611-58.871Q-25.378-59.287-24.968-59.537Q-24.558-59.787-24.082-59.787Q-23.351-59.787-22.953-59.346Q-22.554-58.905-22.554-58.174Q-22.554-58.069-22.648-58.045L-25.097-58.045L-25.097-57.975Q-25.097-57.565-24.976-57.209Q-24.855-56.854-24.584-56.637Q-24.312-56.420-23.882-56.420Q-23.519-56.420-23.222-56.649Q-22.925-56.877-22.824-57.229Q-22.816-57.276-22.730-57.291L-22.648-57.291Q-22.554-57.264-22.554-57.182Q-22.554-57.174-22.562-57.143Q-22.625-56.916-22.763-56.733Q-22.902-56.549-23.093-56.416Q-23.285-56.283-23.503-56.213Q-23.722-56.143-23.960-56.143Q-24.332-56.143-24.669-56.280Q-25.007-56.416-25.275-56.668Q-25.542-56.920-25.693-57.260Q-25.843-57.600-25.843-57.975M-25.089-58.283L-23.128-58.283Q-23.128-58.588-23.230-58.879Q-23.332-59.170-23.548-59.352Q-23.765-59.533-24.082-59.533Q-24.382-59.533-24.613-59.346Q-24.843-59.158-24.966-58.867Q-25.089-58.576-25.089-58.283M-20.480-56.252L-21.550-59.108Q-21.617-59.287-21.748-59.330Q-21.878-59.373-22.136-59.373L-22.136-59.670L-20.457-59.670L-20.457-59.373Q-20.906-59.373-20.906-59.174Q-20.902-59.158-20.900-59.141Q-20.898-59.123-20.898-59.108L-20.105-57.014L-19.394-58.924Q-19.429-59.018-19.429-59.063Q-19.429-59.108-19.464-59.108Q-19.531-59.287-19.662-59.330Q-19.792-59.373-20.046-59.373L-20.046-59.670L-18.457-59.670L-18.457-59.373Q-18.906-59.373-18.906-59.174Q-18.902-59.155-18.900-59.137Q-18.898-59.119-18.898-59.108L-18.066-56.893L-17.312-58.893Q-17.289-58.951-17.289-59.022Q-17.289-59.182-17.425-59.278Q-17.562-59.373-17.730-59.373L-17.730-59.670L-16.343-59.670L-16.343-59.373Q-16.578-59.373-16.755-59.246Q-16.933-59.119-17.015-58.893L-18-56.252Q-18.054-56.143-18.167-56.143L-18.226-56.143Q-18.339-56.143-18.382-56.252L-19.242-58.526L-20.097-56.252Q-20.136-56.143-20.257-56.143L-20.312-56.143Q-20.425-56.143-20.480-56.252\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(207.647 65.961)\">\u003Cpath d=\"M-11.702-56.221L-13.198-56.221L-13.198-56.518Q-12.565-56.518-12.143-56.998L-11.374-57.908L-12.366-59.108Q-12.522-59.287-12.684-59.330Q-12.847-59.373-13.151-59.373L-13.151-59.670L-11.464-59.670L-11.464-59.373Q-11.557-59.373-11.634-59.330Q-11.710-59.287-11.710-59.198Q-11.710-59.155-11.679-59.108L-11.022-58.319L-10.542-58.893Q-10.425-59.030-10.425-59.166Q-10.425-59.256-10.475-59.315Q-10.526-59.373-10.608-59.373L-10.608-59.670L-9.120-59.670L-9.120-59.373Q-9.757-59.373-10.167-58.893L-10.847-58.092L-9.761-56.780Q-9.600-56.604-9.440-56.561Q-9.280-56.518-8.975-56.518L-8.975-56.221L-10.663-56.221L-10.663-56.518Q-10.573-56.518-10.495-56.561Q-10.417-56.604-10.417-56.694Q-10.417-56.717-10.448-56.780L-11.190-57.686L-11.776-56.998Q-11.893-56.862-11.893-56.725Q-11.893-56.639-11.843-56.578Q-11.792-56.518-11.702-56.518L-11.702-56.221M-8.206-54.221L-8.288-54.221Q-8.323-54.221-8.348-54.250Q-8.374-54.280-8.374-54.319Q-8.374-54.369-8.343-54.389Q-7.956-54.725-7.673-55.174Q-7.389-55.623-7.223-56.123Q-7.057-56.623-6.983-57.141Q-6.909-57.658-6.909-58.221Q-6.909-58.791-6.983-59.307Q-7.057-59.823-7.223-60.319Q-7.389-60.815-7.669-61.262Q-7.948-61.709-8.343-62.053Q-8.374-62.073-8.374-62.123Q-8.374-62.162-8.348-62.192Q-8.323-62.221-8.288-62.221L-8.206-62.221Q-8.194-62.221-8.184-62.219Q-8.175-62.217-8.167-62.213Q-7.554-61.756-7.151-61.121Q-6.749-60.487-6.554-59.741Q-6.358-58.994-6.358-58.221Q-6.358-57.448-6.554-56.701Q-6.749-55.955-7.151-55.321Q-7.554-54.686-8.167-54.229Q-8.179-54.229-8.186-54.227Q-8.194-54.225-8.206-54.221\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M45.562 19.179h62.596V-12.12H45.562Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(93.695 62.211)\">\u003Cpath d=\"M-33.242-57.182L-33.242-59.373L-33.945-59.373L-33.945-59.627Q-33.589-59.627-33.347-59.860Q-33.105-60.092-32.994-60.440Q-32.882-60.787-32.882-61.143L-32.601-61.143L-32.601-59.670L-31.425-59.670L-31.425-59.373L-32.601-59.373L-32.601-57.198Q-32.601-56.877-32.482-56.649Q-32.363-56.420-32.082-56.420Q-31.902-56.420-31.785-56.543Q-31.667-56.666-31.615-56.846Q-31.562-57.026-31.562-57.198L-31.562-57.670L-31.281-57.670L-31.281-57.182Q-31.281-56.928-31.386-56.688Q-31.492-56.448-31.689-56.295Q-31.886-56.143-32.144-56.143Q-32.460-56.143-32.712-56.266Q-32.964-56.389-33.103-56.623Q-33.242-56.858-33.242-57.182M-30.562-57.975Q-30.562-58.455-30.330-58.871Q-30.097-59.287-29.687-59.537Q-29.277-59.787-28.800-59.787Q-28.070-59.787-27.671-59.346Q-27.273-58.905-27.273-58.174Q-27.273-58.069-27.367-58.045L-29.816-58.045L-29.816-57.975Q-29.816-57.565-29.695-57.209Q-29.574-56.854-29.302-56.637Q-29.031-56.420-28.601-56.420Q-28.238-56.420-27.941-56.649Q-27.644-56.877-27.542-57.229Q-27.535-57.276-27.449-57.291L-27.367-57.291Q-27.273-57.264-27.273-57.182Q-27.273-57.174-27.281-57.143Q-27.343-56.916-27.482-56.733Q-27.621-56.549-27.812-56.416Q-28.003-56.283-28.222-56.213Q-28.441-56.143-28.679-56.143Q-29.050-56.143-29.388-56.280Q-29.726-56.416-29.994-56.668Q-30.261-56.920-30.412-57.260Q-30.562-57.600-30.562-57.975M-29.808-58.283L-27.847-58.283Q-27.847-58.588-27.949-58.879Q-28.050-59.170-28.267-59.352Q-28.484-59.533-28.800-59.533Q-29.101-59.533-29.332-59.346Q-29.562-59.158-29.685-58.867Q-29.808-58.576-29.808-58.283M-26.742-56.229L-26.742-57.451Q-26.742-57.479-26.710-57.510Q-26.679-57.541-26.656-57.541L-26.550-57.541Q-26.480-57.541-26.464-57.479Q-26.402-57.158-26.263-56.918Q-26.125-56.678-25.892-56.537Q-25.660-56.397-25.351-56.397Q-25.113-56.397-24.904-56.457Q-24.695-56.518-24.558-56.666Q-24.421-56.815-24.421-57.061Q-24.421-57.315-24.632-57.481Q-24.843-57.647-25.113-57.701L-25.734-57.815Q-26.140-57.893-26.441-58.149Q-26.742-58.405-26.742-58.780Q-26.742-59.147-26.541-59.369Q-26.339-59.592-26.015-59.690Q-25.691-59.787-25.351-59.787Q-24.886-59.787-24.589-59.580L-24.367-59.764Q-24.343-59.787-24.312-59.787L-24.261-59.787Q-24.230-59.787-24.203-59.760Q-24.175-59.733-24.175-59.701L-24.175-58.717Q-24.175-58.686-24.201-58.657Q-24.226-58.627-24.261-58.627L-24.367-58.627Q-24.402-58.627-24.429-58.655Q-24.457-58.682-24.457-58.717Q-24.457-59.116-24.709-59.336Q-24.960-59.557-25.359-59.557Q-25.714-59.557-25.998-59.434Q-26.281-59.311-26.281-59.006Q-26.281-58.787-26.080-58.655Q-25.878-58.522-25.632-58.479L-25.007-58.366Q-24.578-58.276-24.269-57.979Q-23.960-57.682-23.960-57.268Q-23.960-56.698-24.359-56.420Q-24.757-56.143-25.351-56.143Q-25.902-56.143-26.253-56.479L-26.550-56.166Q-26.574-56.143-26.609-56.143L-26.656-56.143Q-26.679-56.143-26.710-56.174Q-26.742-56.205-26.742-56.229M-22.808-57.182L-22.808-59.373L-23.511-59.373L-23.511-59.627Q-23.156-59.627-22.914-59.860Q-22.671-60.092-22.560-60.440Q-22.449-60.787-22.449-61.143L-22.167-61.143L-22.167-59.670L-20.992-59.670L-20.992-59.373L-22.167-59.373L-22.167-57.198Q-22.167-56.877-22.048-56.649Q-21.929-56.420-21.648-56.420Q-21.468-56.420-21.351-56.543Q-21.234-56.666-21.181-56.846Q-21.128-57.026-21.128-57.198L-21.128-57.670L-20.847-57.670L-20.847-57.182Q-20.847-56.928-20.953-56.688Q-21.058-56.448-21.255-56.295Q-21.453-56.143-21.710-56.143Q-22.027-56.143-22.279-56.266Q-22.531-56.389-22.669-56.623Q-22.808-56.858-22.808-57.182\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(93.695 62.211)\">\u003Cpath d=\"M-17.292-57.975Q-17.292-58.455-17.059-58.871Q-16.827-59.287-16.417-59.537Q-16.007-59.787-15.530-59.787Q-14.800-59.787-14.401-59.346Q-14.003-58.905-14.003-58.174Q-14.003-58.069-14.096-58.045L-16.546-58.045L-16.546-57.975Q-16.546-57.565-16.425-57.209Q-16.303-56.854-16.032-56.637Q-15.760-56.420-15.331-56.420Q-14.968-56.420-14.671-56.649Q-14.374-56.877-14.272-57.229Q-14.264-57.276-14.178-57.291L-14.096-57.291Q-14.003-57.264-14.003-57.182Q-14.003-57.174-14.010-57.143Q-14.073-56.916-14.212-56.733Q-14.350-56.549-14.542-56.416Q-14.733-56.283-14.952-56.213Q-15.171-56.143-15.409-56.143Q-15.780-56.143-16.118-56.280Q-16.456-56.416-16.723-56.668Q-16.991-56.920-17.141-57.260Q-17.292-57.600-17.292-57.975M-16.538-58.283L-14.577-58.283Q-14.577-58.588-14.678-58.879Q-14.780-59.170-14.997-59.352Q-15.214-59.533-15.530-59.533Q-15.831-59.533-16.061-59.346Q-16.292-59.158-16.415-58.867Q-16.538-58.576-16.538-58.283M-11.507-56.221L-13.487-56.221L-13.487-56.518Q-13.218-56.518-13.050-56.563Q-12.882-56.608-12.882-56.780L-12.882-58.916Q-12.882-59.131-12.944-59.227Q-13.007-59.323-13.124-59.344Q-13.241-59.366-13.487-59.366L-13.487-59.662L-12.319-59.748L-12.319-58.963Q-12.241-59.174-12.089-59.360Q-11.936-59.545-11.737-59.647Q-11.538-59.748-11.311-59.748Q-11.065-59.748-10.874-59.604Q-10.682-59.459-10.682-59.229Q-10.682-59.073-10.788-58.963Q-10.893-58.854-11.050-58.854Q-11.206-58.854-11.315-58.963Q-11.425-59.073-11.425-59.229Q-11.425-59.389-11.319-59.494Q-11.643-59.494-11.858-59.266Q-12.073-59.037-12.169-58.698Q-12.264-58.358-12.264-58.053L-12.264-56.780Q-12.264-56.612-12.038-56.565Q-11.811-56.518-11.507-56.518L-11.507-56.221M-8.194-56.221L-10.175-56.221L-10.175-56.518Q-9.905-56.518-9.737-56.563Q-9.569-56.608-9.569-56.780L-9.569-58.916Q-9.569-59.131-9.632-59.227Q-9.694-59.323-9.811-59.344Q-9.928-59.366-10.175-59.366L-10.175-59.662L-9.007-59.748L-9.007-58.963Q-8.928-59.174-8.776-59.360Q-8.624-59.545-8.425-59.647Q-8.225-59.748-7.999-59.748Q-7.753-59.748-7.561-59.604Q-7.370-59.459-7.370-59.229Q-7.370-59.073-7.475-58.963Q-7.581-58.854-7.737-58.854Q-7.893-58.854-8.003-58.963Q-8.112-59.073-8.112-59.229Q-8.112-59.389-8.007-59.494Q-8.331-59.494-8.546-59.266Q-8.760-59.037-8.856-58.698Q-8.952-58.358-8.952-58.053L-8.952-56.780Q-8.952-56.612-8.725-56.565Q-8.499-56.518-8.194-56.518L-8.194-56.221M-6.889-57.916Q-6.889-58.420-6.634-58.852Q-6.378-59.283-5.942-59.535Q-5.507-59.787-5.007-59.787Q-4.620-59.787-4.278-59.643Q-3.936-59.498-3.675-59.237Q-3.413-58.975-3.270-58.639Q-3.128-58.303-3.128-57.916Q-3.128-57.424-3.391-57.014Q-3.655-56.604-4.085-56.373Q-4.514-56.143-5.007-56.143Q-5.499-56.143-5.932-56.375Q-6.366-56.608-6.628-57.016Q-6.889-57.424-6.889-57.916M-5.007-56.420Q-4.550-56.420-4.298-56.643Q-4.046-56.866-3.958-57.217Q-3.870-57.569-3.870-58.014Q-3.870-58.444-3.964-58.782Q-4.057-59.119-4.311-59.326Q-4.565-59.533-5.007-59.533Q-5.655-59.533-5.899-59.117Q-6.143-58.701-6.143-58.014Q-6.143-57.569-6.055-57.217Q-5.968-56.866-5.716-56.643Q-5.464-56.420-5.007-56.420M-0.635-56.221L-2.616-56.221L-2.616-56.518Q-2.346-56.518-2.178-56.563Q-2.010-56.608-2.010-56.780L-2.010-58.916Q-2.010-59.131-2.073-59.227Q-2.135-59.323-2.253-59.344Q-2.370-59.366-2.616-59.366L-2.616-59.662L-1.448-59.748L-1.448-58.963Q-1.370-59.174-1.218-59.360Q-1.065-59.545-0.866-59.647Q-0.667-59.748-0.440-59.748Q-0.194-59.748-0.003-59.604Q0.189-59.459 0.189-59.229Q0.189-59.073 0.083-58.963Q-0.022-58.854-0.178-58.854Q-0.335-58.854-0.444-58.963Q-0.553-59.073-0.553-59.229Q-0.553-59.389-0.448-59.494Q-0.772-59.494-0.987-59.266Q-1.202-59.037-1.298-58.698Q-1.393-58.358-1.393-58.053L-1.393-56.780Q-1.393-56.612-1.167-56.565Q-0.940-56.518-0.635-56.518\" 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=\"M-2.607-56.221h45.17\"\u002F>\u003Cpath stroke=\"none\" d=\"m45.162-56.221-4.16-2.08 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=\"M108.558-56.221h45.17\"\u002F>\u003Cpath stroke=\"none\" d=\"m156.328-56.221-4.16-2.08 1.56 2.08-1.56 2.08\"\u002F>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M187.826-40.372v25.453\"\u002F>\u003Cpath stroke=\"none\" d=\"m187.826-12.32 2.08-4.159-2.08 1.56-2.08-1.56\"\u002F>\u003Cg transform=\"translate(225.664 31.625)\">\u003Cpath d=\"M-32.147-54.864L-33.777-54.864L-33.777-55.144Q-33.548-55.144-33.399-55.179Q-33.251-55.213-33.251-55.353L-33.251-58.699Q-33.251-58.870-33.387-58.911Q-33.524-58.952-33.777-58.952L-33.777-59.232L-32.697-59.307L-32.697-58.901Q-32.475-59.102-32.188-59.205Q-31.900-59.307-31.593-59.307Q-31.166-59.307-30.802-59.094Q-30.438-58.880-30.224-58.516Q-30.010-58.152-30.010-57.732Q-30.010-57.287-30.250-56.923Q-30.489-56.559-30.882-56.356Q-31.275-56.153-31.719-56.153Q-31.986-56.153-32.234-56.253Q-32.481-56.354-32.669-56.535L-32.669-55.353Q-32.669-55.216-32.521-55.180Q-32.372-55.144-32.147-55.144L-32.147-54.864M-32.669-58.552L-32.669-56.942Q-32.536-56.689-32.293-56.532Q-32.051-56.375-31.774-56.375Q-31.446-56.375-31.193-56.576Q-30.940-56.778-30.807-57.096Q-30.673-57.414-30.673-57.732Q-30.673-57.961-30.738-58.190Q-30.803-58.419-30.931-58.617Q-31.060-58.815-31.254-58.935Q-31.449-59.054-31.682-59.054Q-31.976-59.054-32.244-58.925Q-32.512-58.795-32.669-58.552M-27.625-56.221L-29.361-56.221L-29.361-56.501Q-29.132-56.501-28.983-56.535Q-28.834-56.570-28.834-56.710L-28.834-58.559Q-28.834-58.829-28.942-58.890Q-29.050-58.952-29.361-58.952L-29.361-59.232L-28.332-59.307L-28.332-58.600Q-28.202-58.908-27.959-59.107Q-27.717-59.307-27.399-59.307Q-27.180-59.307-27.009-59.183Q-26.838-59.058-26.838-58.846Q-26.838-58.709-26.938-58.610Q-27.037-58.511-27.170-58.511Q-27.307-58.511-27.406-58.610Q-27.505-58.709-27.505-58.846Q-27.505-58.986-27.406-59.085Q-27.696-59.085-27.896-58.889Q-28.096-58.692-28.188-58.398Q-28.281-58.104-28.281-57.824L-28.281-56.710Q-28.281-56.501-27.625-56.501L-27.625-56.221M-26.295-57.756Q-26.295-58.077-26.170-58.366Q-26.045-58.655-25.820-58.878Q-25.594-59.102-25.299-59.222Q-25.003-59.342-24.685-59.342Q-24.357-59.342-24.095-59.242Q-23.834-59.143-23.658-58.961Q-23.482-58.778-23.388-58.520Q-23.294-58.262-23.294-57.930Q-23.294-57.838-23.376-57.817L-25.632-57.817L-25.632-57.756Q-25.632-57.168-25.348-56.785Q-25.064-56.402-24.497-56.402Q-24.176-56.402-23.907-56.595Q-23.639-56.788-23.550-57.103Q-23.543-57.144-23.468-57.158L-23.376-57.158Q-23.294-57.134-23.294-57.062Q-23.294-57.055-23.301-57.028Q-23.414-56.631-23.784-56.392Q-24.155-56.153-24.579-56.153Q-25.017-56.153-25.417-56.361Q-25.816-56.570-26.056-56.937Q-26.295-57.304-26.295-57.756M-25.625-58.026L-23.810-58.026Q-23.810-58.303-23.907-58.555Q-24.005-58.808-24.203-58.964Q-24.401-59.119-24.685-59.119Q-24.962-59.119-25.176-58.961Q-25.389-58.802-25.507-58.547Q-25.625-58.292-25.625-58.026M-22.706-57.732Q-22.706-58.070-22.566-58.361Q-22.426-58.651-22.181-58.865Q-21.937-59.078-21.633-59.193Q-21.329-59.307-21.004-59.307Q-20.734-59.307-20.471-59.208Q-20.208-59.109-20.016-58.931L-20.016-60.329Q-20.016-60.599-20.124-60.661Q-20.231-60.722-20.542-60.722L-20.542-61.003L-19.466-61.078L-19.466-56.894Q-19.466-56.706-19.411-56.623Q-19.356-56.539-19.256-56.520Q-19.155-56.501-18.939-56.501L-18.939-56.221L-20.047-56.153L-20.047-56.570Q-20.464-56.153-21.089-56.153Q-21.520-56.153-21.893-56.365Q-22.265-56.576-22.486-56.937Q-22.706-57.298-22.706-57.732M-21.031-56.375Q-20.823-56.375-20.636-56.447Q-20.450-56.518-20.296-56.655Q-20.143-56.792-20.047-56.970L-20.047-58.579Q-20.132-58.726-20.278-58.846Q-20.423-58.966-20.592-59.025Q-20.761-59.085-20.942-59.085Q-21.503-59.085-21.771-58.696Q-22.040-58.306-22.040-57.725Q-22.040-57.154-21.805-56.764Q-21.571-56.375-21.031-56.375M-16.673-56.221L-18.225-56.221L-18.225-56.501Q-18-56.501-17.851-56.535Q-17.702-56.570-17.702-56.710L-17.702-58.559Q-17.702-58.747-17.750-58.831Q-17.798-58.914-17.895-58.933Q-17.993-58.952-18.205-58.952L-18.205-59.232L-17.148-59.307L-17.148-56.710Q-17.148-56.570-17.017-56.535Q-16.885-56.501-16.673-56.501L-16.673-56.221M-17.945-60.528Q-17.945-60.699-17.822-60.818Q-17.699-60.938-17.528-60.938Q-17.360-60.938-17.237-60.818Q-17.114-60.699-17.114-60.528Q-17.114-60.353-17.237-60.230Q-17.360-60.107-17.528-60.107Q-17.699-60.107-17.822-60.230Q-17.945-60.353-17.945-60.528M-16.027-57.732Q-16.027-58.060-15.892-58.361Q-15.757-58.661-15.522-58.882Q-15.286-59.102-14.981-59.222Q-14.677-59.342-14.353-59.342Q-13.847-59.342-13.498-59.239Q-13.149-59.137-13.149-58.761Q-13.149-58.614-13.247-58.513Q-13.344-58.412-13.491-58.412Q-13.645-58.412-13.744-58.511Q-13.843-58.610-13.843-58.761Q-13.843-58.949-13.703-59.041Q-13.905-59.092-14.346-59.092Q-14.701-59.092-14.930-58.896Q-15.159-58.699-15.260-58.390Q-15.361-58.080-15.361-57.732Q-15.361-57.383-15.234-57.077Q-15.108-56.771-14.853-56.587Q-14.599-56.402-14.243-56.402Q-14.021-56.402-13.836-56.486Q-13.652-56.570-13.517-56.725Q-13.382-56.881-13.324-57.089Q-13.310-57.144-13.255-57.144L-13.143-57.144Q-13.112-57.144-13.090-57.120Q-13.067-57.096-13.067-57.062L-13.067-57.041Q-13.153-56.754-13.341-56.556Q-13.529-56.358-13.794-56.255Q-14.059-56.153-14.353-56.153Q-14.783-56.153-15.171-56.359Q-15.559-56.566-15.793-56.929Q-16.027-57.291-16.027-57.732M-11.953-57.062L-11.953-58.959L-12.592-58.959L-12.592-59.181Q-12.274-59.181-12.057-59.391Q-11.840-59.601-11.740-59.911Q-11.639-60.220-11.639-60.528L-11.372-60.528L-11.372-59.239L-10.295-59.239L-10.295-58.959L-11.372-58.959L-11.372-57.075Q-11.372-56.799-11.268-56.600Q-11.164-56.402-10.904-56.402Q-10.747-56.402-10.641-56.506Q-10.535-56.611-10.485-56.764Q-10.436-56.918-10.436-57.075L-10.436-57.489L-10.169-57.489L-10.169-57.062Q-10.169-56.836-10.268-56.626Q-10.367-56.416-10.552-56.284Q-10.736-56.153-10.965-56.153Q-11.403-56.153-11.678-56.390Q-11.953-56.628-11.953-57.062\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M156.328 3.53h-45.37\"\u002F>\u003Cpath stroke=\"none\" d=\"m108.358 3.53 4.16 2.08-1.56-2.08 1.56-2.08\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(120.816 54.657)\">\u003Cpath d=\"M-33.791-57.732Q-33.791-58.060-33.656-58.361Q-33.521-58.661-33.285-58.882Q-33.049-59.102-32.745-59.222Q-32.440-59.342-32.116-59.342Q-31.610-59.342-31.261-59.239Q-30.913-59.137-30.913-58.761Q-30.913-58.614-31.010-58.513Q-31.107-58.412-31.254-58.412Q-31.408-58.412-31.507-58.511Q-31.606-58.610-31.606-58.761Q-31.606-58.949-31.466-59.041Q-31.668-59.092-32.109-59.092Q-32.464-59.092-32.693-58.896Q-32.922-58.699-33.023-58.390Q-33.124-58.080-33.124-57.732Q-33.124-57.383-32.998-57.077Q-32.871-56.771-32.616-56.587Q-32.362-56.402-32.006-56.402Q-31.784-56.402-31.600-56.486Q-31.415-56.570-31.280-56.725Q-31.145-56.881-31.087-57.089Q-31.073-57.144-31.019-57.144L-30.906-57.144Q-30.875-57.144-30.853-57.120Q-30.831-57.096-30.831-57.062L-30.831-57.041Q-30.916-56.754-31.104-56.556Q-31.292-56.358-31.557-56.255Q-31.822-56.153-32.116-56.153Q-32.546-56.153-32.934-56.359Q-33.322-56.566-33.556-56.929Q-33.791-57.291-33.791-57.732M-30.284-57.704Q-30.284-58.046-30.149-58.345Q-30.014-58.644-29.774-58.868Q-29.535-59.092-29.217-59.217Q-28.899-59.342-28.568-59.342Q-28.124-59.342-27.724-59.126Q-27.324-58.911-27.090-58.533Q-26.855-58.156-26.855-57.704Q-26.855-57.363-26.997-57.079Q-27.139-56.795-27.384-56.588Q-27.628-56.382-27.937-56.267Q-28.247-56.153-28.568-56.153Q-28.999-56.153-29.400-56.354Q-29.802-56.556-30.043-56.908Q-30.284-57.260-30.284-57.704M-28.568-56.402Q-27.966-56.402-27.742-56.780Q-27.519-57.158-27.519-57.790Q-27.519-58.402-27.753-58.761Q-27.987-59.119-28.568-59.119Q-29.621-59.119-29.621-57.790Q-29.621-57.158-29.395-56.780Q-29.169-56.402-28.568-56.402M-24.579-56.221L-26.213-56.221L-26.213-56.501Q-25.984-56.501-25.835-56.535Q-25.687-56.570-25.687-56.710L-25.687-58.559Q-25.687-58.829-25.794-58.890Q-25.902-58.952-26.213-58.952L-26.213-59.232L-25.153-59.307L-25.153-58.658Q-24.982-58.966-24.678-59.137Q-24.374-59.307-24.029-59.307Q-23.629-59.307-23.352-59.167Q-23.075-59.027-22.990-58.679Q-22.822-58.972-22.523-59.140Q-22.224-59.307-21.879-59.307Q-21.373-59.307-21.089-59.084Q-20.806-58.860-20.806-58.364L-20.806-56.710Q-20.806-56.573-20.657-56.537Q-20.508-56.501-20.283-56.501L-20.283-56.221L-21.913-56.221L-21.913-56.501Q-21.688-56.501-21.537-56.537Q-21.387-56.573-21.387-56.710L-21.387-58.350Q-21.387-58.685-21.506-58.885Q-21.626-59.085-21.940-59.085Q-22.210-59.085-22.445-58.949Q-22.679-58.812-22.817-58.578Q-22.956-58.344-22.956-58.070L-22.956-56.710Q-22.956-56.573-22.807-56.537Q-22.658-56.501-22.433-56.501L-22.433-56.221L-24.063-56.221L-24.063-56.501Q-23.834-56.501-23.685-56.535Q-23.537-56.570-23.537-56.710L-23.537-58.350Q-23.537-58.685-23.656-58.885Q-23.776-59.085-24.090-59.085Q-24.360-59.085-24.595-58.949Q-24.829-58.812-24.967-58.578Q-25.105-58.344-25.105-58.070L-25.105-56.710Q-25.105-56.573-24.955-56.537Q-24.805-56.501-24.579-56.501L-24.579-56.221M-18.051-54.864L-19.681-54.864L-19.681-55.144Q-19.452-55.144-19.303-55.179Q-19.155-55.213-19.155-55.353L-19.155-58.699Q-19.155-58.870-19.292-58.911Q-19.428-58.952-19.681-58.952L-19.681-59.232L-18.601-59.307L-18.601-58.901Q-18.379-59.102-18.092-59.205Q-17.805-59.307-17.497-59.307Q-17.070-59.307-16.706-59.094Q-16.342-58.880-16.128-58.516Q-15.915-58.152-15.915-57.732Q-15.915-57.287-16.154-56.923Q-16.393-56.559-16.786-56.356Q-17.179-56.153-17.624-56.153Q-17.890-56.153-18.138-56.253Q-18.386-56.354-18.574-56.535L-18.574-55.353Q-18.574-55.216-18.425-55.180Q-18.276-55.144-18.051-55.144L-18.051-54.864M-18.574-58.552L-18.574-56.942Q-18.440-56.689-18.198-56.532Q-17.955-56.375-17.678-56.375Q-17.350-56.375-17.097-56.576Q-16.844-56.778-16.711-57.096Q-16.578-57.414-16.578-57.732Q-16.578-57.961-16.643-58.190Q-16.708-58.419-16.836-58.617Q-16.964-58.815-17.159-58.935Q-17.354-59.054-17.586-59.054Q-17.880-59.054-18.148-58.925Q-18.417-58.795-18.574-58.552M-15.221-56.949Q-15.221-57.281-14.997-57.508Q-14.773-57.735-14.429-57.863Q-14.086-57.992-13.713-58.044Q-13.341-58.097-13.037-58.097L-13.037-58.350Q-13.037-58.555-13.144-58.735Q-13.252-58.914-13.433-59.017Q-13.614-59.119-13.823-59.119Q-14.230-59.119-14.465-59.027Q-14.376-58.990-14.330-58.906Q-14.284-58.822-14.284-58.720Q-14.284-58.624-14.330-58.545Q-14.376-58.467-14.457-58.422Q-14.537-58.378-14.626-58.378Q-14.776-58.378-14.877-58.475Q-14.978-58.573-14.978-58.720Q-14.978-59.342-13.823-59.342Q-13.611-59.342-13.361-59.278Q-13.112-59.215-12.910-59.096Q-12.709-58.976-12.582-58.791Q-12.456-58.607-12.456-58.364L-12.456-56.788Q-12.456-56.672-12.394-56.576Q-12.333-56.481-12.220-56.481Q-12.110-56.481-12.045-56.575Q-11.980-56.669-11.980-56.788L-11.980-57.236L-11.714-57.236L-11.714-56.788Q-11.714-56.518-11.941-56.353Q-12.168-56.187-12.449-56.187Q-12.657-56.187-12.794-56.341Q-12.931-56.494-12.955-56.710Q-13.102-56.443-13.384-56.298Q-13.666-56.153-13.990-56.153Q-14.267-56.153-14.551-56.228Q-14.834-56.303-15.028-56.482Q-15.221-56.662-15.221-56.949M-14.605-56.949Q-14.605-56.775-14.505-56.645Q-14.404-56.515-14.248-56.445Q-14.093-56.375-13.929-56.375Q-13.710-56.375-13.501-56.472Q-13.293-56.570-13.165-56.751Q-13.037-56.932-13.037-57.158L-13.037-57.886Q-13.361-57.886-13.727-57.795Q-14.093-57.704-14.349-57.492Q-14.605-57.281-14.605-56.949M-9.547-56.221L-11.283-56.221L-11.283-56.501Q-11.054-56.501-10.906-56.535Q-10.757-56.570-10.757-56.710L-10.757-58.559Q-10.757-58.829-10.865-58.890Q-10.972-58.952-11.283-58.952L-11.283-59.232L-10.254-59.307L-10.254-58.600Q-10.125-58.908-9.882-59.107Q-9.639-59.307-9.321-59.307Q-9.103-59.307-8.932-59.183Q-8.761-59.058-8.761-58.846Q-8.761-58.709-8.860-58.610Q-8.959-58.511-9.092-58.511Q-9.229-58.511-9.328-58.610Q-9.427-58.709-9.427-58.846Q-9.427-58.986-9.328-59.085Q-9.619-59.085-9.819-58.889Q-10.019-58.692-10.111-58.398Q-10.203-58.104-10.203-57.824L-10.203-56.710Q-10.203-56.501-9.547-56.501L-9.547-56.221M-8.217-57.756Q-8.217-58.077-8.093-58.366Q-7.968-58.655-7.742-58.878Q-7.517-59.102-7.221-59.222Q-6.925-59.342-6.607-59.342Q-6.279-59.342-6.018-59.242Q-5.756-59.143-5.580-58.961Q-5.404-58.778-5.310-58.520Q-5.216-58.262-5.216-57.930Q-5.216-57.838-5.298-57.817L-7.554-57.817L-7.554-57.756Q-7.554-57.168-7.271-56.785Q-6.987-56.402-6.419-56.402Q-6.098-56.402-5.830-56.595Q-5.562-56.788-5.473-57.103Q-5.466-57.144-5.391-57.158L-5.298-57.158Q-5.216-57.134-5.216-57.062Q-5.216-57.055-5.223-57.028Q-5.336-56.631-5.707-56.392Q-6.078-56.153-6.501-56.153Q-6.939-56.153-7.339-56.361Q-7.739-56.570-7.978-56.937Q-8.217-57.304-8.217-57.756M-7.547-58.026L-5.732-58.026Q-5.732-58.303-5.830-58.555Q-5.927-58.808-6.126-58.964Q-6.324-59.119-6.607-59.119Q-6.884-59.119-7.098-58.961Q-7.312-58.802-7.429-58.547Q-7.547-58.292-7.547-58.026\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(120.816 54.657)\">\u003Cpath d=\"M-1.375-57.062L-1.375-58.959L-2.014-58.959L-2.014-59.181Q-1.696-59.181-1.479-59.391Q-1.262-59.601-1.162-59.911Q-1.061-60.220-1.061-60.528L-0.794-60.528L-0.794-59.239L0.283-59.239L0.283-58.959L-0.794-58.959L-0.794-57.075Q-0.794-56.799-0.690-56.600Q-0.586-56.402-0.326-56.402Q-0.169-56.402-0.063-56.506Q0.043-56.611 0.093-56.764Q0.142-56.918 0.142-57.075L0.142-57.489L0.409-57.489L0.409-57.062Q0.409-56.836 0.310-56.626Q0.211-56.416 0.026-56.284Q-0.158-56.153-0.387-56.153Q-0.825-56.153-1.100-56.390Q-1.375-56.628-1.375-57.062M1.178-57.704Q1.178-58.046 1.313-58.345Q1.448-58.644 1.687-58.868Q1.927-59.092 2.244-59.217Q2.562-59.342 2.894-59.342Q3.338-59.342 3.738-59.126Q4.138-58.911 4.372-58.533Q4.606-58.156 4.606-57.704Q4.606-57.363 4.464-57.079Q4.323-56.795 4.078-56.588Q3.834-56.382 3.524-56.267Q3.215-56.153 2.894-56.153Q2.463-56.153 2.062-56.354Q1.660-56.556 1.419-56.908Q1.178-57.260 1.178-57.704M2.894-56.402Q3.495-56.402 3.719-56.780Q3.943-57.158 3.943-57.790Q3.943-58.402 3.709-58.761Q3.475-59.119 2.894-59.119Q1.841-59.119 1.841-57.790Q1.841-57.158 2.067-56.780Q2.292-56.402 2.894-56.402\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(120.816 54.657)\">\u003Cpath d=\"M8.430-57.062L8.430-58.959L7.791-58.959L7.791-59.181Q8.109-59.181 8.326-59.391Q8.543-59.601 8.643-59.911Q8.744-60.220 8.744-60.528L9.011-60.528L9.011-59.239L10.088-59.239L10.088-58.959L9.011-58.959L9.011-57.075Q9.011-56.799 9.115-56.600Q9.219-56.402 9.479-56.402Q9.636-56.402 9.742-56.506Q9.848-56.611 9.898-56.764Q9.947-56.918 9.947-57.075L9.947-57.489L10.214-57.489L10.214-57.062Q10.214-56.836 10.115-56.626Q10.016-56.416 9.831-56.284Q9.647-56.153 9.418-56.153Q8.980-56.153 8.705-56.390Q8.430-56.628 8.430-57.062M12.774-56.221L11.038-56.221L11.038-56.501Q11.267-56.501 11.415-56.535Q11.564-56.570 11.564-56.710L11.564-58.559Q11.564-58.829 11.456-58.890Q11.349-58.952 11.038-58.952L11.038-59.232L12.067-59.307L12.067-58.600Q12.196-58.908 12.439-59.107Q12.682-59.307 13-59.307Q13.218-59.307 13.389-59.183Q13.560-59.058 13.560-58.846Q13.560-58.709 13.461-58.610Q13.362-58.511 13.229-58.511Q13.092-58.511 12.993-58.610Q12.894-58.709 12.894-58.846Q12.894-58.986 12.993-59.085Q12.702-59.085 12.502-58.889Q12.302-58.692 12.210-58.398Q12.118-58.104 12.118-57.824L12.118-56.710Q12.118-56.501 12.774-56.501L12.774-56.221M14.719-57.055L14.719-58.559Q14.719-58.829 14.611-58.890Q14.504-58.952 14.193-58.952L14.193-59.232L15.300-59.307L15.300-57.075L15.300-57.055Q15.300-56.775 15.351-56.631Q15.402-56.488 15.544-56.431Q15.686-56.375 15.973-56.375Q16.226-56.375 16.431-56.515Q16.636-56.655 16.753-56.881Q16.869-57.106 16.869-57.356L16.869-58.559Q16.869-58.829 16.761-58.890Q16.653-58.952 16.342-58.952L16.342-59.232L17.450-59.307L17.450-56.894Q17.450-56.703 17.503-56.621Q17.556-56.539 17.657-56.520Q17.757-56.501 17.973-56.501L17.973-56.221L16.896-56.153L16.896-56.717Q16.787-56.535 16.641-56.412Q16.496-56.289 16.310-56.221Q16.124-56.153 15.922-56.153Q14.719-56.153 14.719-57.055M18.520-57.756Q18.520-58.077 18.644-58.366Q18.769-58.655 18.995-58.878Q19.220-59.102 19.516-59.222Q19.812-59.342 20.130-59.342Q20.458-59.342 20.719-59.242Q20.981-59.143 21.157-58.961Q21.333-58.778 21.427-58.520Q21.521-58.262 21.521-57.930Q21.521-57.838 21.439-57.817L19.183-57.817L19.183-57.756Q19.183-57.168 19.466-56.785Q19.750-56.402 20.318-56.402Q20.639-56.402 20.907-56.595Q21.175-56.788 21.264-57.103Q21.271-57.144 21.346-57.158L21.439-57.158Q21.521-57.134 21.521-57.062Q21.521-57.055 21.514-57.028Q21.401-56.631 21.030-56.392Q20.659-56.153 20.235-56.153Q19.798-56.153 19.398-56.361Q18.998-56.570 18.759-56.937Q18.520-57.304 18.520-57.756M19.190-58.026L21.005-58.026Q21.005-58.303 20.907-58.555Q20.810-58.808 20.611-58.964Q20.413-59.119 20.130-59.119Q19.853-59.119 19.639-58.961Q19.425-58.802 19.308-58.547Q19.190-58.292 19.190-58.026\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(120.816 54.657)\">\u003Cpath d=\"M25.155-55.086Q25.285-55.018 25.422-55.018Q25.593-55.018 25.743-55.107Q25.894-55.196 26.005-55.341Q26.116-55.486 26.194-55.654L26.458-56.221L25.289-58.747Q25.214-58.894 25.084-58.926Q24.954-58.959 24.721-58.959L24.721-59.239L26.242-59.239L26.242-58.959Q25.894-58.959 25.894-58.812Q25.897-58.791 25.899-58.774Q25.901-58.757 25.901-58.747L26.758-56.888L27.531-58.559Q27.565-58.627 27.565-58.706Q27.565-58.819 27.481-58.889Q27.398-58.959 27.285-58.959L27.285-59.239L28.481-59.239L28.481-58.959Q28.262-58.959 28.090-58.855Q27.917-58.750 27.825-58.559L26.488-55.654Q26.318-55.284 26.048-55.038Q25.777-54.792 25.422-54.792Q25.152-54.792 24.933-54.958Q24.714-55.124 24.714-55.387Q24.714-55.524 24.807-55.613Q24.899-55.701 25.039-55.701Q25.176-55.701 25.265-55.613Q25.354-55.524 25.354-55.387Q25.354-55.284 25.301-55.206Q25.248-55.127 25.155-55.086\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">Supervised learning as function approximation: a training set sampled from an unknown \u003Cspan class=\"katex\">\u003Cspan class=\"katex-html\" aria-hidden=\"true\">\u003Cspan class=\"base\">\u003Cspan class=\"strut\" style=\"height:0.8889em;vertical-align:-0.1944em;\">\u003C\u002Fspan>\u003Cspan class=\"mord mathnormal\" style=\"margin-right:0.1076em;\">f\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan> is fed to a learner, which returns a hypothesis \u003Cspan class=\"katex\">\u003Cspan class=\"katex-html\" aria-hidden=\"true\">\u003Cspan class=\"base\">\u003Cspan class=\"strut\" style=\"height:0.6944em;\">\u003C\u002Fspan>\u003Cspan class=\"mord mathnormal\">h\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>; the test set, drawn from the same distribution, measures how well \u003Cspan class=\"katex\">\u003Cspan class=\"katex-html\" aria-hidden=\"true\">\u003Cspan class=\"base\">\u003Cspan class=\"strut\" style=\"height:0.6944em;\">\u003C\u002Fspan>\u003Cspan class=\"mord mathnormal\">h\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan> generalizes.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:513.712px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 385.284 114.265\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cpath fill=\"none\" d=\"M-65.403 12.948H32.18\"\u002F>\u003Cpath stroke=\"none\" d=\"m34.181 12.948-3.2-1.6 1.2 1.6-1.2 1.6\"\u002F>\u003Cg transform=\"translate(97.427 1.507)\">\u003Cpath d=\"M-58.216 12.948L-59.539 12.948L-59.539 12.668Q-58.978 12.668-58.599 12.268L-57.884 11.471L-58.797 10.422Q-58.934 10.275-59.082 10.243Q-59.231 10.210-59.498 10.210L-59.498 9.930L-57.997 9.930L-57.997 10.210Q-58.189 10.210-58.189 10.344Q-58.189 10.374-58.158 10.422L-57.563 11.106L-57.122 10.610Q-57.009 10.480-57.009 10.364Q-57.009 10.302-57.047 10.256Q-57.085 10.210-57.143 10.210L-57.143 9.930L-55.827 9.930L-55.827 10.210Q-56.387 10.210-56.767 10.610L-57.389 11.311L-56.394 12.459Q-56.295 12.558-56.194 12.603Q-56.093 12.647-55.982 12.657Q-55.871 12.668-55.693 12.668L-55.693 12.948L-57.187 12.948L-57.187 12.668Q-57.122 12.668-57.062 12.634Q-57.003 12.599-57.003 12.534Q-57.003 12.487-57.033 12.459L-57.710 11.673L-58.243 12.268Q-58.356 12.398-58.356 12.514Q-58.356 12.579-58.315 12.623Q-58.274 12.668-58.216 12.668\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-59.713 18.639V-59.03\"\u002F>\u003Cpath stroke=\"none\" d=\"m-59.713-61.03-1.6 3.2 1.6-1.2 1.6 1.2\"\u002F>\u003Cg transform=\"translate(-2.1 -78.871)\">\u003Cpath d=\"M-59.064 14.083Q-58.934 14.151-58.797 14.151Q-58.626 14.151-58.476 14.062Q-58.325 13.973-58.214 13.828Q-58.103 13.683-58.025 13.515L-57.761 12.948L-58.930 10.422Q-59.005 10.275-59.135 10.243Q-59.265 10.210-59.498 10.210L-59.498 9.930L-57.977 9.930L-57.977 10.210Q-58.325 10.210-58.325 10.357Q-58.322 10.378-58.320 10.395Q-58.318 10.412-58.318 10.422L-57.461 12.281L-56.688 10.610Q-56.654 10.542-56.654 10.463Q-56.654 10.350-56.738 10.280Q-56.821 10.210-56.934 10.210L-56.934 9.930L-55.738 9.930L-55.738 10.210Q-55.957 10.210-56.129 10.314Q-56.302 10.419-56.394 10.610L-57.731 13.515Q-57.901 13.885-58.171 14.131Q-58.442 14.377-58.797 14.377Q-59.067 14.377-59.286 14.211Q-59.505 14.045-59.505 13.782Q-59.505 13.645-59.412 13.556Q-59.320 13.468-59.180 13.468Q-59.043 13.468-58.954 13.556Q-58.865 13.645-58.865 13.782Q-58.865 13.885-58.918 13.963Q-58.971 14.042-59.064 14.083\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath stroke=\"none\" d=\"M-46.732-1.278a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M-35.35-24.04a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M-23.97-12.66a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M-12.588000000000001-38.267a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M-1.2069999999999999-26.885a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M10.174-49.648a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M21.555-41.112a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0m-1.6 0\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"m-54.022-6.969 79.667-36.988\" style=\"stroke-width:1.2\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(24.557 18.353)\">\u003Cpath d=\"M-58.872 12.107L-58.872 10.210L-59.511 10.210L-59.511 9.988Q-59.193 9.988-58.976 9.778Q-58.759 9.568-58.659 9.258Q-58.558 8.949-58.558 8.641L-58.291 8.641L-58.291 9.930L-57.214 9.930L-57.214 10.210L-58.291 10.210L-58.291 12.094Q-58.291 12.370-58.187 12.569Q-58.083 12.767-57.823 12.767Q-57.666 12.767-57.560 12.663Q-57.454 12.558-57.404 12.405Q-57.355 12.251-57.355 12.094L-57.355 11.680L-57.088 11.680L-57.088 12.107Q-57.088 12.333-57.187 12.543Q-57.286 12.753-57.471 12.885Q-57.655 13.016-57.884 13.016Q-58.322 13.016-58.597 12.779Q-58.872 12.541-58.872 12.107M-56.319 11.465Q-56.319 11.123-56.184 10.824Q-56.049 10.525-55.810 10.301Q-55.570 10.077-55.253 9.952Q-54.935 9.827-54.603 9.827Q-54.159 9.827-53.759 10.043Q-53.359 10.258-53.125 10.636Q-52.891 11.013-52.891 11.465Q-52.891 11.806-53.033 12.090Q-53.174 12.374-53.419 12.581Q-53.663 12.787-53.973 12.902Q-54.282 13.016-54.603 13.016Q-55.034 13.016-55.435 12.815Q-55.837 12.613-56.078 12.261Q-56.319 11.909-56.319 11.465M-54.603 12.767Q-54.002 12.767-53.778 12.389Q-53.554 12.011-53.554 11.379Q-53.554 10.767-53.788 10.408Q-54.022 10.050-54.603 10.050Q-55.656 10.050-55.656 11.379Q-55.656 12.011-55.430 12.389Q-55.205 12.767-54.603 12.767\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(24.557 18.353)\">\u003Cpath d=\"M-52.113 11.465Q-52.113 11.123-51.978 10.824Q-51.843 10.525-51.603 10.301Q-51.364 10.077-51.046 9.952Q-50.728 9.827-50.397 9.827Q-49.952 9.827-49.553 10.043Q-49.153 10.258-48.918 10.636Q-48.684 11.013-48.684 11.465Q-48.684 11.806-48.826 12.090Q-48.968 12.374-49.212 12.581Q-49.457 12.787-49.766 12.902Q-50.075 13.016-50.397 13.016Q-50.827 13.016-51.229 12.815Q-51.631 12.613-51.872 12.261Q-52.113 11.909-52.113 11.465M-50.397 12.767Q-49.795 12.767-49.571 12.389Q-49.347 12.011-49.347 11.379Q-49.347 10.767-49.582 10.408Q-49.816 10.050-50.397 10.050Q-51.449 10.050-51.449 11.379Q-51.449 12.011-51.224 12.389Q-50.998 12.767-50.397 12.767\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(24.557 18.353)\">\u003Cpath d=\"M-45.392 12.941L-45.392 11.878Q-45.392 11.854-45.364 11.827Q-45.337 11.800-45.313 11.800L-45.204 11.800Q-45.139 11.800-45.125 11.858Q-45.029 12.292-44.783 12.543Q-44.537 12.794-44.123 12.794Q-43.782 12.794-43.529 12.661Q-43.276 12.528-43.276 12.220Q-43.276 12.063-43.370 11.948Q-43.464 11.834-43.602 11.765Q-43.741 11.697-43.908 11.659L-44.489 11.560Q-44.845 11.492-45.118 11.271Q-45.392 11.051-45.392 10.709Q-45.392 10.460-45.280 10.285Q-45.169 10.111-44.983 10.012Q-44.797 9.913-44.581 9.870Q-44.366 9.827-44.123 9.827Q-43.710 9.827-43.430 10.009L-43.214 9.834Q-43.204 9.831-43.197 9.829Q-43.190 9.827-43.180 9.827L-43.129 9.827Q-43.102 9.827-43.078 9.851Q-43.054 9.875-43.054 9.903L-43.054 10.750Q-43.054 10.771-43.078 10.798Q-43.102 10.825-43.129 10.825L-43.242 10.825Q-43.269 10.825-43.295 10.800Q-43.320 10.774-43.320 10.750Q-43.320 10.514-43.426 10.350Q-43.532 10.186-43.715 10.104Q-43.898 10.022-44.130 10.022Q-44.458 10.022-44.715 10.125Q-44.971 10.227-44.971 10.504Q-44.971 10.699-44.788 10.808Q-44.605 10.918-44.376 10.959L-43.802 11.065Q-43.556 11.113-43.342 11.241Q-43.129 11.369-42.992 11.572Q-42.855 11.776-42.855 12.025Q-42.855 12.538-43.221 12.777Q-43.587 13.016-44.123 13.016Q-44.619 13.016-44.951 12.722L-45.217 12.996Q-45.238 13.016-45.265 13.016L-45.313 13.016Q-45.337 13.016-45.364 12.989Q-45.392 12.962-45.392 12.941M-40.610 12.948L-42.162 12.948L-42.162 12.668Q-41.936 12.668-41.787 12.634Q-41.639 12.599-41.639 12.459L-41.639 10.610Q-41.639 10.422-41.686 10.338Q-41.734 10.255-41.832 10.236Q-41.929 10.217-42.141 10.217L-42.141 9.937L-41.085 9.862L-41.085 12.459Q-41.085 12.599-40.953 12.634Q-40.822 12.668-40.610 12.668L-40.610 12.948M-41.881 8.641Q-41.881 8.470-41.758 8.351Q-41.635 8.231-41.464 8.231Q-41.297 8.231-41.174 8.351Q-41.051 8.470-41.051 8.641Q-41.051 8.816-41.174 8.939Q-41.297 9.062-41.464 9.062Q-41.635 9.062-41.758 8.939Q-41.881 8.816-41.881 8.641M-38.282 12.948L-39.916 12.948L-39.916 12.668Q-39.687 12.668-39.538 12.634Q-39.390 12.599-39.390 12.459L-39.390 10.610Q-39.390 10.340-39.497 10.279Q-39.605 10.217-39.916 10.217L-39.916 9.937L-38.856 9.862L-38.856 10.511Q-38.685 10.203-38.381 10.032Q-38.077 9.862-37.732 9.862Q-37.332 9.862-37.055 10.002Q-36.778 10.142-36.693 10.490Q-36.525 10.197-36.226 10.029Q-35.927 9.862-35.582 9.862Q-35.076 9.862-34.792 10.085Q-34.509 10.309-34.509 10.805L-34.509 12.459Q-34.509 12.596-34.360 12.632Q-34.211 12.668-33.986 12.668L-33.986 12.948L-35.616 12.948L-35.616 12.668Q-35.391 12.668-35.240 12.632Q-35.090 12.596-35.090 12.459L-35.090 10.819Q-35.090 10.484-35.209 10.284Q-35.329 10.084-35.644 10.084Q-35.914 10.084-36.148 10.220Q-36.382 10.357-36.520 10.591Q-36.659 10.825-36.659 11.099L-36.659 12.459Q-36.659 12.596-36.510 12.632Q-36.361 12.668-36.136 12.668L-36.136 12.948L-37.766 12.948L-37.766 12.668Q-37.537 12.668-37.388 12.634Q-37.240 12.599-37.240 12.459L-37.240 10.819Q-37.240 10.484-37.359 10.284Q-37.479 10.084-37.793 10.084Q-38.063 10.084-38.298 10.220Q-38.532 10.357-38.670 10.591Q-38.809 10.825-38.809 11.099L-38.809 12.459Q-38.809 12.596-38.658 12.632Q-38.508 12.668-38.282 12.668L-38.282 12.948M-31.754 14.305L-33.384 14.305L-33.384 14.025Q-33.155 14.025-33.007 13.990Q-32.858 13.956-32.858 13.816L-32.858 10.470Q-32.858 10.299-32.995 10.258Q-33.131 10.217-33.384 10.217L-33.384 9.937L-32.304 9.862L-32.304 10.268Q-32.082 10.067-31.795 9.964Q-31.508 9.862-31.200 9.862Q-30.773 9.862-30.409 10.075Q-30.045 10.289-29.831 10.653Q-29.618 11.017-29.618 11.437Q-29.618 11.882-29.857 12.246Q-30.096 12.610-30.489 12.813Q-30.882 13.016-31.327 13.016Q-31.593 13.016-31.841 12.916Q-32.089 12.815-32.277 12.634L-32.277 13.816Q-32.277 13.953-32.128 13.989Q-31.979 14.025-31.754 14.025L-31.754 14.305M-32.277 10.617L-32.277 12.227Q-32.144 12.480-31.901 12.637Q-31.658 12.794-31.381 12.794Q-31.053 12.794-30.800 12.593Q-30.547 12.391-30.414 12.073Q-30.281 11.755-30.281 11.437Q-30.281 11.208-30.346 10.979Q-30.411 10.750-30.539 10.552Q-30.667 10.354-30.862 10.234Q-31.057 10.115-31.289 10.115Q-31.583 10.115-31.851 10.244Q-32.120 10.374-32.277 10.617M-27.314 12.948L-28.917 12.948L-28.917 12.668Q-28.691 12.668-28.543 12.634Q-28.394 12.599-28.394 12.459L-28.394 8.840Q-28.394 8.570-28.502 8.508Q-28.609 8.447-28.917 8.447L-28.917 8.166L-27.840 8.091L-27.840 12.459Q-27.840 12.596-27.690 12.632Q-27.539 12.668-27.314 12.668L-27.314 12.948M-26.760 11.413Q-26.760 11.092-26.635 10.803Q-26.511 10.514-26.285 10.291Q-26.060 10.067-25.764 9.947Q-25.468 9.827-25.150 9.827Q-24.822 9.827-24.561 9.927Q-24.299 10.026-24.123 10.208Q-23.947 10.391-23.853 10.649Q-23.759 10.907-23.759 11.239Q-23.759 11.331-23.841 11.352L-26.097 11.352L-26.097 11.413Q-26.097 12.001-25.813 12.384Q-25.530 12.767-24.962 12.767Q-24.641 12.767-24.373 12.574Q-24.104 12.381-24.016 12.066Q-24.009 12.025-23.934 12.011L-23.841 12.011Q-23.759 12.035-23.759 12.107Q-23.759 12.114-23.766 12.141Q-23.879 12.538-24.250 12.777Q-24.621 13.016-25.044 13.016Q-25.482 13.016-25.882 12.808Q-26.282 12.599-26.521 12.232Q-26.760 11.865-26.760 11.413M-26.090 11.143L-24.275 11.143Q-24.275 10.866-24.373 10.614Q-24.470 10.361-24.668 10.205Q-24.867 10.050-25.150 10.050Q-25.427 10.050-25.641 10.208Q-25.854 10.367-25.972 10.622Q-26.090 10.877-26.090 11.143\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M65.48 12.948h97.584\"\u002F>\u003Cpath stroke=\"none\" d=\"m165.064 12.948-3.2-1.6 1.2 1.6-1.2 1.6\"\u002F>\u003Cg transform=\"translate(228.31 1.507)\">\u003Cpath d=\"M-58.216 12.948L-59.539 12.948L-59.539 12.668Q-58.978 12.668-58.599 12.268L-57.884 11.471L-58.797 10.422Q-58.934 10.275-59.082 10.243Q-59.231 10.210-59.498 10.210L-59.498 9.930L-57.997 9.930L-57.997 10.210Q-58.189 10.210-58.189 10.344Q-58.189 10.374-58.158 10.422L-57.563 11.106L-57.122 10.610Q-57.009 10.480-57.009 10.364Q-57.009 10.302-57.047 10.256Q-57.085 10.210-57.143 10.210L-57.143 9.930L-55.827 9.930L-55.827 10.210Q-56.387 10.210-56.767 10.610L-57.389 11.311L-56.394 12.459Q-56.295 12.558-56.194 12.603Q-56.093 12.647-55.982 12.657Q-55.871 12.668-55.693 12.668L-55.693 12.948L-57.187 12.948L-57.187 12.668Q-57.122 12.668-57.062 12.634Q-57.003 12.599-57.003 12.534Q-57.003 12.487-57.033 12.459L-57.710 11.673L-58.243 12.268Q-58.356 12.398-58.356 12.514Q-58.356 12.579-58.315 12.623Q-58.274 12.668-58.216 12.668\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M71.17 18.639V-59.03\"\u002F>\u003Cpath stroke=\"none\" d=\"m71.17-61.03-1.6 3.2 1.6-1.2 1.6 1.2\"\u002F>\u003Cg transform=\"translate(128.782 -78.871)\">\u003Cpath d=\"M-59.064 14.083Q-58.934 14.151-58.797 14.151Q-58.626 14.151-58.476 14.062Q-58.325 13.973-58.214 13.828Q-58.103 13.683-58.025 13.515L-57.761 12.948L-58.930 10.422Q-59.005 10.275-59.135 10.243Q-59.265 10.210-59.498 10.210L-59.498 9.930L-57.977 9.930L-57.977 10.210Q-58.325 10.210-58.325 10.357Q-58.322 10.378-58.320 10.395Q-58.318 10.412-58.318 10.422L-57.461 12.281L-56.688 10.610Q-56.654 10.542-56.654 10.463Q-56.654 10.350-56.738 10.280Q-56.821 10.210-56.934 10.210L-56.934 9.930L-55.738 9.930L-55.738 10.210Q-55.957 10.210-56.129 10.314Q-56.302 10.419-56.394 10.610L-57.731 13.515Q-57.901 13.885-58.171 14.131Q-58.442 14.377-58.797 14.377Q-59.067 14.377-59.286 14.211Q-59.505 14.045-59.505 13.782Q-59.505 13.645-59.412 13.556Q-59.320 13.468-59.180 13.468Q-59.043 13.468-58.954 13.556Q-58.865 13.645-58.865 13.782Q-58.865 13.885-58.918 13.963Q-58.971 14.042-59.064 14.083\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath stroke=\"none\" d=\"M84.14999999999999-1.278a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M95.532-24.04a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M106.913-12.66a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M118.29499999999999-38.267a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M129.67499999999998-26.885a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M141.05599999999998-49.648a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M152.438-41.112a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0m-1.6 0\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M76.86-4.124c22.763-28.452 45.525-17.071 79.668-42.679\" style=\"stroke-width:1.2\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(157.27 18.353)\">\u003Cpath d=\"M-59.928 13.796Q-59.928 13.645-59.828 13.548Q-59.727 13.450-59.580 13.450Q-59.491 13.450-59.411 13.495Q-59.330 13.539-59.284 13.618Q-59.238 13.697-59.238 13.796Q-59.238 13.997-59.399 14.096Q-59.258 14.151-59.053 14.151Q-58.783 14.151-58.667 13.878Q-58.551 13.604-58.551 13.290L-58.551 10.610Q-58.551 10.344-58.677 10.280Q-58.804 10.217-59.132 10.217L-59.132 9.937L-57.997 9.862L-57.997 13.310Q-57.997 13.597-58.144 13.842Q-58.291 14.086-58.542 14.231Q-58.794 14.377-59.070 14.377Q-59.399 14.377-59.663 14.235Q-59.928 14.093-59.928 13.796M-58.831 8.641Q-58.831 8.470-58.708 8.351Q-58.585 8.231-58.411 8.231Q-58.243 8.231-58.120 8.351Q-57.997 8.470-57.997 8.641Q-57.997 8.816-58.120 8.939Q-58.243 9.062-58.411 9.062Q-58.585 9.062-58.708 8.939Q-58.831 8.816-58.831 8.641M-56.346 12.114L-56.346 10.610Q-56.346 10.340-56.454 10.279Q-56.562 10.217-56.873 10.217L-56.873 9.937L-55.765 9.862L-55.765 12.094L-55.765 12.114Q-55.765 12.394-55.714 12.538Q-55.663 12.681-55.521 12.738Q-55.379 12.794-55.092 12.794Q-54.839 12.794-54.634 12.654Q-54.429 12.514-54.313 12.288Q-54.196 12.063-54.196 11.813L-54.196 10.610Q-54.196 10.340-54.304 10.279Q-54.412 10.217-54.723 10.217L-54.723 9.937L-53.615 9.862L-53.615 12.275Q-53.615 12.466-53.562 12.548Q-53.509 12.630-53.409 12.649Q-53.308 12.668-53.092 12.668L-53.092 12.948L-54.169 13.016L-54.169 12.452Q-54.278 12.634-54.424 12.757Q-54.569 12.880-54.755 12.948Q-54.942 13.016-55.143 13.016Q-56.346 13.016-56.346 12.114M-52.505 12.941L-52.505 11.878Q-52.505 11.854-52.477 11.827Q-52.450 11.800-52.426 11.800L-52.317 11.800Q-52.252 11.800-52.238 11.858Q-52.142 12.292-51.896 12.543Q-51.650 12.794-51.236 12.794Q-50.895 12.794-50.642 12.661Q-50.389 12.528-50.389 12.220Q-50.389 12.063-50.483 11.948Q-50.577 11.834-50.715 11.765Q-50.854 11.697-51.021 11.659L-51.602 11.560Q-51.958 11.492-52.231 11.271Q-52.505 11.051-52.505 10.709Q-52.505 10.460-52.393 10.285Q-52.282 10.111-52.096 10.012Q-51.910 9.913-51.694 9.870Q-51.479 9.827-51.236 9.827Q-50.823 9.827-50.543 10.009L-50.327 9.834Q-50.317 9.831-50.310 9.829Q-50.303 9.827-50.293 9.827L-50.242 9.827Q-50.214 9.827-50.191 9.851Q-50.167 9.875-50.167 9.903L-50.167 10.750Q-50.167 10.771-50.191 10.798Q-50.214 10.825-50.242 10.825L-50.355 10.825Q-50.382 10.825-50.408 10.800Q-50.433 10.774-50.433 10.750Q-50.433 10.514-50.539 10.350Q-50.645 10.186-50.828 10.104Q-51.011 10.022-51.243 10.022Q-51.571 10.022-51.828 10.125Q-52.084 10.227-52.084 10.504Q-52.084 10.699-51.901 10.808Q-51.718 10.918-51.489 10.959L-50.915 11.065Q-50.669 11.113-50.455 11.241Q-50.242 11.369-50.105 11.572Q-49.968 11.776-49.968 12.025Q-49.968 12.538-50.334 12.777Q-50.700 13.016-51.236 13.016Q-51.732 13.016-52.064 12.722L-52.330 12.996Q-52.351 13.016-52.378 13.016L-52.426 13.016Q-52.450 13.016-52.477 12.989Q-52.505 12.962-52.505 12.941M-48.813 12.107L-48.813 10.210L-49.452 10.210L-49.452 9.988Q-49.134 9.988-48.917 9.778Q-48.700 9.568-48.599 9.258Q-48.499 8.949-48.499 8.641L-48.232 8.641L-48.232 9.930L-47.155 9.930L-47.155 10.210L-48.232 10.210L-48.232 12.094Q-48.232 12.370-48.128 12.569Q-48.024 12.767-47.764 12.767Q-47.607 12.767-47.501 12.663Q-47.395 12.558-47.345 12.405Q-47.296 12.251-47.296 12.094L-47.296 11.680L-47.029 11.680L-47.029 12.107Q-47.029 12.333-47.128 12.543Q-47.227 12.753-47.412 12.885Q-47.596 13.016-47.825 13.016Q-48.263 13.016-48.538 12.779Q-48.813 12.541-48.813 12.107\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(157.27 18.353)\">\u003Cpath d=\"M-41.765 12.948L-43.501 12.948L-43.501 12.668Q-43.272 12.668-43.123 12.634Q-42.975 12.599-42.975 12.459L-42.975 10.610Q-42.975 10.340-43.082 10.279Q-43.190 10.217-43.501 10.217L-43.501 9.937L-42.472 9.862L-42.472 10.569Q-42.342 10.261-42.100 10.062Q-41.857 9.862-41.539 9.862Q-41.320 9.862-41.149 9.986Q-40.978 10.111-40.978 10.323Q-40.978 10.460-41.078 10.559Q-41.177 10.658-41.310 10.658Q-41.447 10.658-41.546 10.559Q-41.645 10.460-41.645 10.323Q-41.645 10.183-41.546 10.084Q-41.836 10.084-42.036 10.280Q-42.236 10.477-42.329 10.771Q-42.421 11.065-42.421 11.345L-42.421 12.459Q-42.421 12.668-41.765 12.668L-41.765 12.948M-38.777 12.948L-40.329 12.948L-40.329 12.668Q-40.103 12.668-39.955 12.634Q-39.806 12.599-39.806 12.459L-39.806 10.610Q-39.806 10.422-39.854 10.338Q-39.902 10.255-39.999 10.236Q-40.097 10.217-40.308 10.217L-40.308 9.937L-39.252 9.862L-39.252 12.459Q-39.252 12.599-39.121 12.634Q-38.989 12.668-38.777 12.668L-38.777 12.948M-40.049 8.641Q-40.049 8.470-39.926 8.351Q-39.803 8.231-39.632 8.231Q-39.464 8.231-39.341 8.351Q-39.218 8.470-39.218 8.641Q-39.218 8.816-39.341 8.939Q-39.464 9.062-39.632 9.062Q-39.803 9.062-39.926 8.939Q-40.049 8.816-40.049 8.641M-38.172 13.481Q-38.172 13.235-37.976 13.051Q-37.779 12.866-37.523 12.787Q-37.660 12.675-37.731 12.514Q-37.803 12.353-37.803 12.172Q-37.803 11.851-37.591 11.605Q-37.926 11.307-37.926 10.897Q-37.926 10.436-37.537 10.149Q-37.147 9.862-36.668 9.862Q-36.197 9.862-35.862 10.108Q-35.687 9.954-35.477 9.872Q-35.267 9.790-35.038 9.790Q-34.874 9.790-34.753 9.897Q-34.631 10.005-34.631 10.169Q-34.631 10.265-34.703 10.337Q-34.775 10.408-34.867 10.408Q-34.966 10.408-35.036 10.335Q-35.106 10.261-35.106 10.162Q-35.106 10.108-35.093 10.077L-35.086 10.063Q-35.079 10.043-35.070 10.032Q-35.062 10.022-35.058 10.015Q-35.414 10.015-35.701 10.238Q-35.414 10.531-35.414 10.897Q-35.414 11.212-35.599 11.444Q-35.783 11.677-36.072 11.805Q-36.361 11.933-36.668 11.933Q-36.870 11.933-37.061 11.883Q-37.253 11.834-37.431 11.724Q-37.523 11.851-37.523 11.994Q-37.523 12.176-37.395 12.311Q-37.267 12.446-37.082 12.446L-36.450 12.446Q-36.002 12.446-35.633 12.517Q-35.264 12.589-35.004 12.818Q-34.744 13.047-34.744 13.481Q-34.744 13.802-35.040 14.004Q-35.335 14.206-35.739 14.295Q-36.142 14.384-36.456 14.384Q-36.774 14.384-37.178 14.295Q-37.581 14.206-37.877 14.004Q-38.172 13.802-38.172 13.481M-37.718 13.481Q-37.718 13.710-37.499 13.859Q-37.280 14.008-36.988 14.076Q-36.696 14.144-36.456 14.144Q-36.292 14.144-36.084 14.108Q-35.875 14.073-35.669 13.992Q-35.462 13.912-35.330 13.784Q-35.199 13.656-35.199 13.481Q-35.199 13.129-35.580 13.035Q-35.961 12.941-36.463 12.941L-37.082 12.941Q-37.321 12.941-37.519 13.092Q-37.718 13.242-37.718 13.481M-36.668 11.694Q-36.002 11.694-36.002 10.897Q-36.002 10.097-36.668 10.097Q-37.338 10.097-37.338 10.897Q-37.338 11.694-36.668 11.694M-32.468 12.948L-34.101 12.948L-34.101 12.668Q-33.872 12.668-33.724 12.634Q-33.575 12.599-33.575 12.459L-33.575 8.840Q-33.575 8.570-33.683 8.508Q-33.790 8.447-34.101 8.447L-34.101 8.166L-33.021 8.091L-33.021 10.477Q-32.915 10.292-32.738 10.150Q-32.560 10.009-32.351 9.935Q-32.143 9.862-31.917 9.862Q-31.412 9.862-31.128 10.085Q-30.844 10.309-30.844 10.805L-30.844 12.459Q-30.844 12.596-30.695 12.632Q-30.547 12.668-30.321 12.668L-30.321 12.948L-31.952 12.948L-31.952 12.668Q-31.723 12.668-31.574 12.634Q-31.425 12.599-31.425 12.459L-31.425 10.819Q-31.425 10.484-31.545 10.284Q-31.664 10.084-31.979 10.084Q-32.249 10.084-32.483 10.220Q-32.717 10.357-32.856 10.591Q-32.994 10.825-32.994 11.099L-32.994 12.459Q-32.994 12.596-32.844 12.632Q-32.693 12.668-32.468 12.668\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(157.27 18.353)\">\u003Cpath d=\"M-29.412 12.107L-29.412 10.210L-30.051 10.210L-30.051 9.988Q-29.733 9.988-29.516 9.778Q-29.299 9.568-29.199 9.258Q-29.098 8.949-29.098 8.641L-28.831 8.641L-28.831 9.930L-27.754 9.930L-27.754 10.210L-28.831 10.210L-28.831 12.094Q-28.831 12.370-28.727 12.569Q-28.623 12.767-28.363 12.767Q-28.206 12.767-28.100 12.663Q-27.994 12.558-27.944 12.405Q-27.895 12.251-27.895 12.094L-27.895 11.680L-27.628 11.680L-27.628 12.107Q-27.628 12.333-27.727 12.543Q-27.826 12.753-28.011 12.885Q-28.195 13.016-28.424 13.016Q-28.862 13.016-29.137 12.779Q-29.412 12.541-29.412 12.107\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M196.362 12.948h97.585\"\u002F>\u003Cpath stroke=\"none\" d=\"m295.946 12.948-3.2-1.6 1.2 1.6-1.2 1.6\"\u002F>\u003Cg transform=\"translate(359.192 1.507)\">\u003Cpath d=\"M-58.216 12.948L-59.539 12.948L-59.539 12.668Q-58.978 12.668-58.599 12.268L-57.884 11.471L-58.797 10.422Q-58.934 10.275-59.082 10.243Q-59.231 10.210-59.498 10.210L-59.498 9.930L-57.997 9.930L-57.997 10.210Q-58.189 10.210-58.189 10.344Q-58.189 10.374-58.158 10.422L-57.563 11.106L-57.122 10.610Q-57.009 10.480-57.009 10.364Q-57.009 10.302-57.047 10.256Q-57.085 10.210-57.143 10.210L-57.143 9.930L-55.827 9.930L-55.827 10.210Q-56.387 10.210-56.767 10.610L-57.389 11.311L-56.394 12.459Q-56.295 12.558-56.194 12.603Q-56.093 12.647-55.982 12.657Q-55.871 12.668-55.693 12.668L-55.693 12.948L-57.187 12.948L-57.187 12.668Q-57.122 12.668-57.062 12.634Q-57.003 12.599-57.003 12.534Q-57.003 12.487-57.033 12.459L-57.710 11.673L-58.243 12.268Q-58.356 12.398-58.356 12.514Q-58.356 12.579-58.315 12.623Q-58.274 12.668-58.216 12.668\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M202.052 18.639V-59.03\"\u002F>\u003Cpath stroke=\"none\" d=\"m202.052-61.03-1.6 3.2 1.6-1.2 1.6 1.2\"\u002F>\u003Cg transform=\"translate(259.665 -78.871)\">\u003Cpath d=\"M-59.064 14.083Q-58.934 14.151-58.797 14.151Q-58.626 14.151-58.476 14.062Q-58.325 13.973-58.214 13.828Q-58.103 13.683-58.025 13.515L-57.761 12.948L-58.930 10.422Q-59.005 10.275-59.135 10.243Q-59.265 10.210-59.498 10.210L-59.498 9.930L-57.977 9.930L-57.977 10.210Q-58.325 10.210-58.325 10.357Q-58.322 10.378-58.320 10.395Q-58.318 10.412-58.318 10.422L-57.461 12.281L-56.688 10.610Q-56.654 10.542-56.654 10.463Q-56.654 10.350-56.738 10.280Q-56.821 10.210-56.934 10.210L-56.934 9.930L-55.738 9.930L-55.738 10.210Q-55.957 10.210-56.129 10.314Q-56.302 10.419-56.394 10.610L-57.731 13.515Q-57.901 13.885-58.171 14.131Q-58.442 14.377-58.797 14.377Q-59.067 14.377-59.286 14.211Q-59.505 14.045-59.505 13.782Q-59.505 13.645-59.412 13.556Q-59.320 13.468-59.180 13.468Q-59.043 13.468-58.954 13.556Q-58.865 13.645-58.865 13.782Q-58.865 13.885-58.918 13.963Q-58.971 14.042-59.064 14.083\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath stroke=\"none\" d=\"M215.033-1.278a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M226.415-24.04a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M237.796-12.66a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M249.177-38.267a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M260.55800000000005-26.885a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M271.939-49.648a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0M283.32000000000005-41.112a1.6 1.6 0 1 0-3.2 0 1.6 1.6 0 0 0 3.2 0m-1.6 0\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-warn)\" d=\"M213.433-1.278c5.691-51.215 8.536 19.917 11.382-22.762 5.69-31.298 8.536 31.298 11.38 11.38 5.691-36.988 8.536 14.227 11.382-25.607 5.69 39.834 8.535-19.917 11.38 11.382 5.691-34.144 8.537 22.761 11.382-22.763 5.69 48.37 8.536-11.381 11.381 8.536\" style=\"stroke-width:1.2\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(283.166 18.353)\">\u003Cpath d=\"M-58.872 12.107L-58.872 10.210L-59.511 10.210L-59.511 9.988Q-59.193 9.988-58.976 9.778Q-58.759 9.568-58.659 9.258Q-58.558 8.949-58.558 8.641L-58.291 8.641L-58.291 9.930L-57.214 9.930L-57.214 10.210L-58.291 10.210L-58.291 12.094Q-58.291 12.370-58.187 12.569Q-58.083 12.767-57.823 12.767Q-57.666 12.767-57.560 12.663Q-57.454 12.558-57.404 12.405Q-57.355 12.251-57.355 12.094L-57.355 11.680L-57.088 11.680L-57.088 12.107Q-57.088 12.333-57.187 12.543Q-57.286 12.753-57.471 12.885Q-57.655 13.016-57.884 13.016Q-58.322 13.016-58.597 12.779Q-58.872 12.541-58.872 12.107M-56.319 11.465Q-56.319 11.123-56.184 10.824Q-56.049 10.525-55.810 10.301Q-55.570 10.077-55.253 9.952Q-54.935 9.827-54.603 9.827Q-54.159 9.827-53.759 10.043Q-53.359 10.258-53.125 10.636Q-52.891 11.013-52.891 11.465Q-52.891 11.806-53.033 12.090Q-53.174 12.374-53.419 12.581Q-53.663 12.787-53.973 12.902Q-54.282 13.016-54.603 13.016Q-55.034 13.016-55.435 12.815Q-55.837 12.613-56.078 12.261Q-56.319 11.909-56.319 11.465M-54.603 12.767Q-54.002 12.767-53.778 12.389Q-53.554 12.011-53.554 11.379Q-53.554 10.767-53.788 10.408Q-54.022 10.050-54.603 10.050Q-55.656 10.050-55.656 11.379Q-55.656 12.011-55.430 12.389Q-55.205 12.767-54.603 12.767\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(283.166 18.353)\">\u003Cpath d=\"M-52.113 11.465Q-52.113 11.123-51.978 10.824Q-51.843 10.525-51.603 10.301Q-51.364 10.077-51.046 9.952Q-50.728 9.827-50.397 9.827Q-49.952 9.827-49.553 10.043Q-49.153 10.258-48.918 10.636Q-48.684 11.013-48.684 11.465Q-48.684 11.806-48.826 12.090Q-48.968 12.374-49.212 12.581Q-49.457 12.787-49.766 12.902Q-50.075 13.016-50.397 13.016Q-50.827 13.016-51.229 12.815Q-51.631 12.613-51.872 12.261Q-52.113 11.909-52.113 11.465M-50.397 12.767Q-49.795 12.767-49.571 12.389Q-49.347 12.011-49.347 11.379Q-49.347 10.767-49.582 10.408Q-49.816 10.050-50.397 10.050Q-51.449 10.050-51.449 11.379Q-51.449 12.011-51.224 12.389Q-50.998 12.767-50.397 12.767\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(283.166 18.353)\">\u003Cpath d=\"M-45.392 11.437Q-45.392 11.109-45.257 10.808Q-45.122 10.508-44.886 10.287Q-44.650 10.067-44.346 9.947Q-44.041 9.827-43.717 9.827Q-43.211 9.827-42.862 9.930Q-42.514 10.032-42.514 10.408Q-42.514 10.555-42.611 10.656Q-42.708 10.757-42.855 10.757Q-43.009 10.757-43.108 10.658Q-43.207 10.559-43.207 10.408Q-43.207 10.220-43.067 10.128Q-43.269 10.077-43.710 10.077Q-44.065 10.077-44.294 10.273Q-44.523 10.470-44.624 10.779Q-44.725 11.089-44.725 11.437Q-44.725 11.786-44.599 12.092Q-44.472 12.398-44.217 12.582Q-43.963 12.767-43.607 12.767Q-43.385 12.767-43.201 12.683Q-43.016 12.599-42.881 12.444Q-42.746 12.288-42.688 12.080Q-42.674 12.025-42.620 12.025L-42.507 12.025Q-42.476 12.025-42.454 12.049Q-42.432 12.073-42.432 12.107L-42.432 12.128Q-42.517 12.415-42.705 12.613Q-42.893 12.811-43.158 12.914Q-43.423 13.016-43.717 13.016Q-44.147 13.016-44.535 12.810Q-44.923 12.603-45.157 12.240Q-45.392 11.878-45.392 11.437M-41.885 11.465Q-41.885 11.123-41.750 10.824Q-41.615 10.525-41.375 10.301Q-41.136 10.077-40.818 9.952Q-40.500 9.827-40.169 9.827Q-39.725 9.827-39.325 10.043Q-38.925 10.258-38.691 10.636Q-38.456 11.013-38.456 11.465Q-38.456 11.806-38.598 12.090Q-38.740 12.374-38.985 12.581Q-39.229 12.787-39.538 12.902Q-39.848 13.016-40.169 13.016Q-40.600 13.016-41.001 12.815Q-41.403 12.613-41.644 12.261Q-41.885 11.909-41.885 11.465M-40.169 12.767Q-39.567 12.767-39.343 12.389Q-39.120 12.011-39.120 11.379Q-39.120 10.767-39.354 10.408Q-39.588 10.050-40.169 10.050Q-41.222 10.050-41.222 11.379Q-41.222 12.011-40.996 12.389Q-40.770 12.767-40.169 12.767M-36.180 12.948L-37.814 12.948L-37.814 12.668Q-37.585 12.668-37.436 12.634Q-37.288 12.599-37.288 12.459L-37.288 10.610Q-37.288 10.340-37.395 10.279Q-37.503 10.217-37.814 10.217L-37.814 9.937L-36.754 9.862L-36.754 10.511Q-36.583 10.203-36.279 10.032Q-35.975 9.862-35.630 9.862Q-35.230 9.862-34.953 10.002Q-34.676 10.142-34.591 10.490Q-34.423 10.197-34.124 10.029Q-33.825 9.862-33.480 9.862Q-32.974 9.862-32.690 10.085Q-32.407 10.309-32.407 10.805L-32.407 12.459Q-32.407 12.596-32.258 12.632Q-32.109 12.668-31.884 12.668L-31.884 12.948L-33.514 12.948L-33.514 12.668Q-33.289 12.668-33.138 12.632Q-32.988 12.596-32.988 12.459L-32.988 10.819Q-32.988 10.484-33.107 10.284Q-33.227 10.084-33.541 10.084Q-33.811 10.084-34.046 10.220Q-34.280 10.357-34.418 10.591Q-34.557 10.825-34.557 11.099L-34.557 12.459Q-34.557 12.596-34.408 12.632Q-34.259 12.668-34.034 12.668L-34.034 12.948L-35.664 12.948L-35.664 12.668Q-35.435 12.668-35.286 12.634Q-35.138 12.599-35.138 12.459L-35.138 10.819Q-35.138 10.484-35.257 10.284Q-35.377 10.084-35.691 10.084Q-35.961 10.084-36.196 10.220Q-36.430 10.357-36.568 10.591Q-36.706 10.825-36.706 11.099L-36.706 12.459Q-36.706 12.596-36.556 12.632Q-36.406 12.668-36.180 12.668L-36.180 12.948M-29.652 14.305L-31.282 14.305L-31.282 14.025Q-31.053 14.025-30.904 13.990Q-30.756 13.956-30.756 13.816L-30.756 10.470Q-30.756 10.299-30.893 10.258Q-31.029 10.217-31.282 10.217L-31.282 9.937L-30.202 9.862L-30.202 10.268Q-29.980 10.067-29.693 9.964Q-29.406 9.862-29.098 9.862Q-28.671 9.862-28.307 10.075Q-27.943 10.289-27.729 10.653Q-27.516 11.017-27.516 11.437Q-27.516 11.882-27.755 12.246Q-27.994 12.610-28.387 12.813Q-28.780 13.016-29.225 13.016Q-29.491 13.016-29.739 12.916Q-29.987 12.815-30.175 12.634L-30.175 13.816Q-30.175 13.953-30.026 13.989Q-29.877 14.025-29.652 14.025L-29.652 14.305M-30.175 10.617L-30.175 12.227Q-30.041 12.480-29.799 12.637Q-29.556 12.794-29.279 12.794Q-28.951 12.794-28.698 12.593Q-28.445 12.391-28.312 12.073Q-28.179 11.755-28.179 11.437Q-28.179 11.208-28.244 10.979Q-28.309 10.750-28.437 10.552Q-28.565 10.354-28.760 10.234Q-28.955 10.115-29.187 10.115Q-29.481 10.115-29.749 10.244Q-30.018 10.374-30.175 10.617M-25.212 12.948L-26.815 12.948L-26.815 12.668Q-26.589 12.668-26.441 12.634Q-26.292 12.599-26.292 12.459L-26.292 8.840Q-26.292 8.570-26.400 8.508Q-26.507 8.447-26.815 8.447L-26.815 8.166L-25.738 8.091L-25.738 12.459Q-25.738 12.596-25.588 12.632Q-25.437 12.668-25.212 12.668L-25.212 12.948M-24.658 11.413Q-24.658 11.092-24.533 10.803Q-24.409 10.514-24.183 10.291Q-23.957 10.067-23.662 9.947Q-23.366 9.827-23.048 9.827Q-22.720 9.827-22.459 9.927Q-22.197 10.026-22.021 10.208Q-21.845 10.391-21.751 10.649Q-21.657 10.907-21.657 11.239Q-21.657 11.331-21.739 11.352L-23.995 11.352L-23.995 11.413Q-23.995 12.001-23.711 12.384Q-23.428 12.767-22.860 12.767Q-22.539 12.767-22.271 12.574Q-22.002 12.381-21.914 12.066Q-21.907 12.025-21.831 12.011L-21.739 12.011Q-21.657 12.035-21.657 12.107Q-21.657 12.114-21.664 12.141Q-21.777 12.538-22.148 12.777Q-22.519 13.016-22.942 13.016Q-23.380 13.016-23.780 12.808Q-24.180 12.599-24.419 12.232Q-24.658 11.865-24.658 11.413M-23.988 11.143L-22.173 11.143Q-22.173 10.866-22.271 10.614Q-22.368 10.361-22.566 10.205Q-22.765 10.050-23.048 10.050Q-23.325 10.050-23.539 10.208Q-23.752 10.367-23.870 10.622Q-23.988 10.877-23.988 11.143M-19.887 12.948L-21.209 12.948L-21.209 12.668Q-20.649 12.668-20.269 12.268L-19.555 11.471L-20.468 10.422Q-20.604 10.275-20.753 10.243Q-20.902 10.210-21.168 10.210L-21.168 9.930L-19.668 9.930L-19.668 10.210Q-19.859 10.210-19.859 10.344Q-19.859 10.374-19.829 10.422L-19.234 11.106L-18.793 10.610Q-18.680 10.480-18.680 10.364Q-18.680 10.302-18.718 10.256Q-18.755 10.210-18.813 10.210L-18.813 9.930L-17.498 9.930L-17.498 10.210Q-18.058 10.210-18.437 10.610L-19.060 11.311L-18.065 12.459Q-17.966 12.558-17.865 12.603Q-17.764 12.647-17.653 12.657Q-17.542 12.668-17.364 12.668L-17.364 12.948L-18.858 12.948L-18.858 12.668Q-18.793 12.668-18.733 12.634Q-18.673 12.599-18.673 12.534Q-18.673 12.487-18.704 12.459L-19.381 11.673L-19.914 12.268Q-20.027 12.398-20.027 12.514Q-20.027 12.579-19.986 12.623Q-19.945 12.668-19.887 12.668\" 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 bias-variance tension. Left, a straight line underfits — too rigid to follow the trend. Center, a moderate curve fits the signal and generalizes. Right, a high-degree polynomial overfits — it threads every point but swings wildly between them.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:361.998px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 271.499 133.014\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg stroke=\"var(--tk-accent)\">\u003Cpath fill=\"none\" d=\"M2.936-52.153h38.752V-72.07H2.936Z\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(125.888 -122.414)\">\u003Cpath d=\"M-117.218 63.080L-119.600 63.080L-119.600 62.783Q-119.276 62.783-119.034 62.736Q-118.792 62.689-118.792 62.521L-118.792 58.178Q-118.792 58.006-119.034 57.959Q-119.276 57.912-119.600 57.912L-119.600 57.615L-116.655 57.615Q-116.311 57.615-115.956 57.715Q-115.600 57.814-115.306 58.006Q-115.011 58.197-114.829 58.482Q-114.647 58.767-114.647 59.127Q-114.647 59.600-114.958 59.935Q-115.268 60.271-115.733 60.443Q-116.198 60.615-116.655 60.615L-118.022 60.615L-118.022 62.521Q-118.022 62.689-117.780 62.736Q-117.538 62.783-117.218 62.783L-117.218 63.080M-118.050 58.178L-118.050 60.346L-116.874 60.346Q-116.186 60.346-115.848 60.070Q-115.511 59.795-115.511 59.127Q-115.511 58.463-115.848 58.187Q-116.186 57.912-116.874 57.912L-117.647 57.912Q-117.866 57.912-117.958 57.955Q-118.050 57.998-118.050 58.178\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(125.888 -122.414)\">\u003Cpath d=\"M-114.071 62.248Q-114.071 61.764-113.669 61.469Q-113.266 61.174-112.716 61.055Q-112.165 60.935-111.673 60.935L-111.673 60.646Q-111.673 60.420-111.788 60.213Q-111.903 60.006-112.100 59.887Q-112.298 59.767-112.528 59.767Q-112.954 59.767-113.239 59.873Q-113.169 59.900-113.122 59.955Q-113.075 60.010-113.050 60.080Q-113.024 60.150-113.024 60.225Q-113.024 60.330-113.075 60.422Q-113.126 60.514-113.218 60.564Q-113.309 60.615-113.415 60.615Q-113.520 60.615-113.612 60.564Q-113.704 60.514-113.755 60.422Q-113.805 60.330-113.805 60.225Q-113.805 59.807-113.417 59.660Q-113.028 59.514-112.528 59.514Q-112.196 59.514-111.843 59.644Q-111.489 59.775-111.261 60.029Q-111.032 60.283-111.032 60.631L-111.032 62.432Q-111.032 62.564-110.960 62.674Q-110.887 62.783-110.759 62.783Q-110.634 62.783-110.565 62.678Q-110.497 62.572-110.497 62.432L-110.497 61.920L-110.216 61.920L-110.216 62.432Q-110.216 62.635-110.333 62.793Q-110.450 62.951-110.632 63.035Q-110.813 63.119-111.016 63.119Q-111.247 63.119-111.399 62.947Q-111.552 62.775-111.583 62.545Q-111.743 62.826-112.052 62.992Q-112.360 63.158-112.712 63.158Q-113.223 63.158-113.647 62.935Q-114.071 62.713-114.071 62.248M-113.384 62.248Q-113.384 62.533-113.157 62.719Q-112.930 62.904-112.637 62.904Q-112.391 62.904-112.167 62.787Q-111.942 62.670-111.807 62.467Q-111.673 62.264-111.673 62.010L-111.673 61.178Q-111.938 61.178-112.223 61.232Q-112.509 61.287-112.780 61.416Q-113.052 61.545-113.218 61.752Q-113.384 61.959-113.384 62.248M-109.298 62.119L-109.298 59.928L-110.001 59.928L-110.001 59.674Q-109.645 59.674-109.403 59.441Q-109.161 59.209-109.050 58.861Q-108.938 58.514-108.938 58.158L-108.657 58.158L-108.657 59.631L-107.481 59.631L-107.481 59.928L-108.657 59.928L-108.657 62.103Q-108.657 62.424-108.538 62.652Q-108.419 62.881-108.137 62.881Q-107.958 62.881-107.841 62.758Q-107.723 62.635-107.671 62.455Q-107.618 62.275-107.618 62.103L-107.618 61.631L-107.337 61.631L-107.337 62.119Q-107.337 62.373-107.442 62.613Q-107.548 62.853-107.745 63.006Q-107.942 63.158-108.200 63.158Q-108.516 63.158-108.768 63.035Q-109.020 62.912-109.159 62.678Q-109.298 62.443-109.298 62.119M-104.610 63.080L-106.591 63.080L-106.591 62.783Q-106.321 62.783-106.153 62.738Q-105.985 62.693-105.985 62.521L-105.985 60.385Q-105.985 60.170-106.048 60.074Q-106.110 59.978-106.227 59.957Q-106.344 59.935-106.591 59.935L-106.591 59.639L-105.423 59.553L-105.423 60.338Q-105.344 60.127-105.192 59.941Q-105.040 59.756-104.841 59.654Q-104.641 59.553-104.415 59.553Q-104.169 59.553-103.977 59.697Q-103.786 59.842-103.786 60.072Q-103.786 60.228-103.891 60.338Q-103.997 60.447-104.153 60.447Q-104.309 60.447-104.419 60.338Q-104.528 60.228-104.528 60.072Q-104.528 59.912-104.423 59.807Q-104.747 59.807-104.962 60.035Q-105.177 60.264-105.272 60.603Q-105.368 60.943-105.368 61.248L-105.368 62.521Q-105.368 62.689-105.141 62.736Q-104.915 62.783-104.610 62.783L-104.610 63.080M-103.305 61.385Q-103.305 60.881-103.050 60.449Q-102.794 60.017-102.358 59.766Q-101.923 59.514-101.423 59.514Q-101.036 59.514-100.694 59.658Q-100.352 59.803-100.091 60.064Q-99.829 60.326-99.686 60.662Q-99.544 60.998-99.544 61.385Q-99.544 61.877-99.807 62.287Q-100.071 62.697-100.501 62.928Q-100.930 63.158-101.423 63.158Q-101.915 63.158-102.348 62.926Q-102.782 62.693-103.044 62.285Q-103.305 61.877-103.305 61.385M-101.423 62.881Q-100.966 62.881-100.714 62.658Q-100.462 62.435-100.374 62.084Q-100.286 61.732-100.286 61.287Q-100.286 60.857-100.380 60.519Q-100.473 60.182-100.727 59.975Q-100.981 59.767-101.423 59.767Q-102.071 59.767-102.315 60.184Q-102.559 60.600-102.559 61.287Q-102.559 61.732-102.471 62.084Q-102.384 62.435-102.132 62.658Q-101.880 62.881-101.423 62.881M-97.130 63.080L-98.985 63.080L-98.985 62.783Q-98.712 62.783-98.544 62.736Q-98.376 62.689-98.376 62.521L-98.376 60.385Q-98.376 60.170-98.438 60.074Q-98.501 59.978-98.620 59.957Q-98.739 59.935-98.985 59.935L-98.985 59.639L-97.794 59.553L-97.794 60.287Q-97.680 60.072-97.487 59.904Q-97.294 59.736-97.055 59.644Q-96.817 59.553-96.563 59.553Q-95.395 59.553-95.395 60.631L-95.395 62.521Q-95.395 62.689-95.225 62.736Q-95.055 62.783-94.786 62.783L-94.786 63.080L-96.641 63.080L-96.641 62.783Q-96.368 62.783-96.200 62.736Q-96.032 62.689-96.032 62.521L-96.032 60.646Q-96.032 60.264-96.153 60.035Q-96.274 59.807-96.626 59.807Q-96.938 59.807-97.192 59.969Q-97.446 60.131-97.593 60.400Q-97.739 60.670-97.739 60.967L-97.739 62.521Q-97.739 62.689-97.569 62.736Q-97.399 62.783-97.130 62.783L-97.130 63.080M-94.298 63.072L-94.298 61.850Q-94.298 61.822-94.266 61.791Q-94.235 61.760-94.212 61.760L-94.106 61.760Q-94.036 61.760-94.020 61.822Q-93.958 62.142-93.819 62.383Q-93.680 62.623-93.448 62.764Q-93.216 62.904-92.907 62.904Q-92.669 62.904-92.460 62.844Q-92.251 62.783-92.114 62.635Q-91.977 62.486-91.977 62.240Q-91.977 61.986-92.188 61.820Q-92.399 61.654-92.669 61.600L-93.290 61.486Q-93.696 61.408-93.997 61.152Q-94.298 60.896-94.298 60.521Q-94.298 60.154-94.096 59.932Q-93.895 59.709-93.571 59.611Q-93.247 59.514-92.907 59.514Q-92.442 59.514-92.145 59.721L-91.923 59.537Q-91.899 59.514-91.868 59.514L-91.817 59.514Q-91.786 59.514-91.759 59.541Q-91.731 59.568-91.731 59.600L-91.731 60.584Q-91.731 60.615-91.757 60.644Q-91.782 60.674-91.817 60.674L-91.923 60.674Q-91.958 60.674-91.985 60.646Q-92.012 60.619-92.012 60.584Q-92.012 60.185-92.264 59.965Q-92.516 59.744-92.915 59.744Q-93.270 59.744-93.553 59.867Q-93.837 59.990-93.837 60.295Q-93.837 60.514-93.636 60.646Q-93.434 60.779-93.188 60.822L-92.563 60.935Q-92.134 61.025-91.825 61.322Q-91.516 61.619-91.516 62.033Q-91.516 62.603-91.915 62.881Q-92.313 63.158-92.907 63.158Q-93.458 63.158-93.809 62.822L-94.106 63.135Q-94.130 63.158-94.165 63.158L-94.212 63.158Q-94.235 63.158-94.266 63.127Q-94.298 63.096-94.298 63.072M-89.802 62.615Q-89.802 62.424-89.669 62.291Q-89.536 62.158-89.341 62.158Q-89.149 62.158-89.016 62.291Q-88.884 62.424-88.884 62.615Q-88.884 62.814-89.016 62.947Q-89.149 63.080-89.341 63.080Q-89.536 63.080-89.669 62.947Q-89.802 62.814-89.802 62.615M-89.485 61.439L-89.485 61.041Q-89.485 60.642-89.356 60.242Q-89.227 59.842-88.981 59.506Q-88.903 59.400-88.737 59.225Q-88.571 59.049-88.503 58.912Q-88.434 58.775-88.434 58.537Q-88.434 58.049-88.637 57.877Q-88.841 57.705-89.341 57.705Q-89.649 57.705-89.927 57.816Q-90.204 57.928-90.356 58.150Q-90.184 58.150-90.077 58.264Q-89.969 58.377-89.969 58.545Q-89.969 58.646-90.020 58.738Q-90.071 58.830-90.161 58.883Q-90.251 58.935-90.364 58.935Q-90.473 58.935-90.563 58.883Q-90.653 58.830-90.704 58.738Q-90.755 58.646-90.755 58.545Q-90.755 58.197-90.544 57.947Q-90.333 57.697-90.009 57.572Q-89.684 57.447-89.341 57.447Q-88.680 57.447-88.186 57.697Q-87.692 57.947-87.692 58.545Q-87.692 58.795-87.815 59.010Q-87.938 59.225-88.149 59.361Q-88.462 59.564-88.694 59.820Q-88.927 60.076-89.065 60.392Q-89.204 60.709-89.204 61.057L-89.204 61.439Q-89.204 61.475-89.231 61.502Q-89.259 61.529-89.290 61.529L-89.395 61.529Q-89.427 61.529-89.456 61.504Q-89.485 61.478-89.485 61.439\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-68.737-19.433h22.763v-17.071h-22.763Z\"\u002F>\u003Cg transform=\"translate(57.288 -88.315)\">\u003Cpath d=\"M-117.694 63.080L-119.616 63.080L-119.616 62.783Q-118.807 62.783-118.807 62.303L-118.807 57.959Q-119.065 57.912-119.616 57.912L-119.616 57.615L-118.120 57.615Q-118.061 57.635-118.050 57.646L-115.042 61.834L-115.042 58.392Q-115.042 57.912-115.847 57.912L-115.847 57.615L-113.929 57.615L-113.929 57.912Q-114.737 57.912-114.737 58.392L-114.737 62.975Q-114.757 63.060-114.831 63.080L-114.936 63.080Q-114.991 63.068-115.007 63.041L-118.503 58.185L-118.503 62.303Q-118.503 62.783-117.694 62.783L-117.694 63.080M-113.354 61.385Q-113.354 60.881-113.098 60.449Q-112.843 60.017-112.407 59.766Q-111.972 59.514-111.472 59.514Q-111.085 59.514-110.743 59.658Q-110.401 59.803-110.139 60.064Q-109.878 60.326-109.735 60.662Q-109.593 60.998-109.593 61.385Q-109.593 61.877-109.856 62.287Q-110.120 62.697-110.550 62.928Q-110.979 63.158-111.472 63.158Q-111.964 63.158-112.397 62.926Q-112.831 62.693-113.093 62.285Q-113.354 61.877-113.354 61.385M-111.472 62.881Q-111.014 62.881-110.763 62.658Q-110.511 62.435-110.423 62.084Q-110.335 61.732-110.335 61.287Q-110.335 60.857-110.429 60.519Q-110.522 60.182-110.776 59.975Q-111.030 59.767-111.472 59.767Q-112.120 59.767-112.364 60.184Q-112.608 60.600-112.608 61.287Q-112.608 61.732-112.520 62.084Q-112.432 62.435-112.181 62.658Q-111.929 62.881-111.472 62.881\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-17.522-19.433H5.241v-17.071h-22.763Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(107.416 -88.315)\">\u003Cpath d=\"M-115.600 63.080L-117.952 63.080L-117.952 62.783Q-117.632 62.783-117.388 62.736Q-117.143 62.689-117.143 62.521L-117.143 60.896L-118.952 58.178Q-119.085 58.002-119.292 57.957Q-119.499 57.912-119.831 57.912L-119.831 57.615L-117.577 57.615L-117.577 57.912Q-118.089 57.912-118.089 58.088Q-118.089 58.123-118.065 58.178L-116.503 60.514L-115.097 58.392Q-115.022 58.287-115.022 58.193Q-115.022 58.045-115.145 57.978Q-115.268 57.912-115.432 57.912L-115.432 57.615L-113.714 57.615L-113.714 57.912Q-114.409 57.912-114.737 58.392L-116.401 60.896L-116.401 62.521Q-116.401 62.783-115.600 62.783\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(107.416 -88.315)\">\u003Cpath d=\"M-114.055 61.326Q-114.055 60.846-113.822 60.430Q-113.590 60.014-113.180 59.764Q-112.770 59.514-112.293 59.514Q-111.563 59.514-111.164 59.955Q-110.766 60.396-110.766 61.127Q-110.766 61.232-110.859 61.256L-113.309 61.256L-113.309 61.326Q-113.309 61.736-113.188 62.092Q-113.066 62.447-112.795 62.664Q-112.523 62.881-112.094 62.881Q-111.731 62.881-111.434 62.652Q-111.137 62.424-111.035 62.072Q-111.027 62.025-110.941 62.010L-110.859 62.010Q-110.766 62.037-110.766 62.119Q-110.766 62.127-110.773 62.158Q-110.836 62.385-110.975 62.568Q-111.113 62.752-111.305 62.885Q-111.496 63.017-111.715 63.088Q-111.934 63.158-112.172 63.158Q-112.543 63.158-112.881 63.021Q-113.219 62.885-113.486 62.633Q-113.754 62.381-113.904 62.041Q-114.055 61.701-114.055 61.326M-113.301 61.017L-111.340 61.017Q-111.340 60.713-111.441 60.422Q-111.543 60.131-111.760 59.949Q-111.977 59.767-112.293 59.767Q-112.594 59.767-112.824 59.955Q-113.055 60.142-113.178 60.434Q-113.301 60.725-113.301 61.017M-110.234 63.072L-110.234 61.850Q-110.234 61.822-110.203 61.791Q-110.172 61.760-110.148 61.760L-110.043 61.760Q-109.973 61.760-109.957 61.822Q-109.895 62.142-109.756 62.383Q-109.617 62.623-109.385 62.764Q-109.152 62.904-108.844 62.904Q-108.606 62.904-108.397 62.844Q-108.188 62.783-108.051 62.635Q-107.914 62.486-107.914 62.240Q-107.914 61.986-108.125 61.820Q-108.336 61.654-108.606 61.600L-109.227 61.486Q-109.633 61.408-109.934 61.152Q-110.234 60.896-110.234 60.521Q-110.234 60.154-110.033 59.932Q-109.832 59.709-109.508 59.611Q-109.184 59.514-108.844 59.514Q-108.379 59.514-108.082 59.721L-107.859 59.537Q-107.836 59.514-107.805 59.514L-107.754 59.514Q-107.723 59.514-107.695 59.541Q-107.668 59.568-107.668 59.600L-107.668 60.584Q-107.668 60.615-107.693 60.644Q-107.719 60.674-107.754 60.674L-107.859 60.674Q-107.895 60.674-107.922 60.646Q-107.949 60.619-107.949 60.584Q-107.949 60.185-108.201 59.965Q-108.453 59.744-108.852 59.744Q-109.207 59.744-109.490 59.867Q-109.773 59.990-109.773 60.295Q-109.773 60.514-109.572 60.646Q-109.371 60.779-109.125 60.822L-108.500 60.935Q-108.070 61.025-107.762 61.322Q-107.453 61.619-107.453 62.033Q-107.453 62.603-107.852 62.881Q-108.250 63.158-108.844 63.158Q-109.395 63.158-109.746 62.822L-110.043 63.135Q-110.066 63.158-110.102 63.158L-110.148 63.158Q-110.172 63.158-110.203 63.127Q-110.234 63.096-110.234 63.072\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M60.015-18.01h61.167v-19.917H60.015Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(182.966 -88.27)\">\u003Cpath d=\"M-117.378 63.135L-119.081 58.178Q-119.147 58.002-119.323 57.957Q-119.499 57.912-119.792 57.912L-119.792 57.615L-117.647 57.615L-117.647 57.912Q-118.304 57.912-118.304 58.127Q-118.300 58.139-118.298 58.148Q-118.296 58.158-118.296 58.178L-116.944 62.111L-115.745 58.607L-115.889 58.178Q-115.960 58.002-116.134 57.957Q-116.307 57.912-116.600 57.912L-116.600 57.615L-114.464 57.615L-114.464 57.912Q-115.112 57.912-115.112 58.127Q-115.112 58.170-115.104 58.178L-113.753 62.111L-112.479 58.392Q-112.464 58.346-112.464 58.310Q-112.464 58.170-112.573 58.078Q-112.682 57.986-112.839 57.949Q-112.995 57.912-113.128 57.912L-113.128 57.615L-111.393 57.615L-111.393 57.912Q-112.011 57.912-112.182 58.392L-113.807 63.135Q-113.827 63.185-113.870 63.217Q-113.913 63.248-113.968 63.248L-114.022 63.248Q-114.143 63.248-114.182 63.135L-115.593 59.041L-116.999 63.135Q-117.018 63.185-117.061 63.217Q-117.104 63.248-117.159 63.248L-117.218 63.248Q-117.331 63.248-117.378 63.135\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(182.966 -88.27)\">\u003Cpath d=\"M-111.596 62.248Q-111.596 61.764-111.194 61.469Q-110.791 61.174-110.241 61.055Q-109.690 60.935-109.198 60.935L-109.198 60.646Q-109.198 60.420-109.313 60.213Q-109.428 60.006-109.625 59.887Q-109.823 59.767-110.053 59.767Q-110.479 59.767-110.764 59.873Q-110.694 59.900-110.647 59.955Q-110.600 60.010-110.575 60.080Q-110.549 60.150-110.549 60.225Q-110.549 60.330-110.600 60.422Q-110.651 60.514-110.743 60.564Q-110.834 60.615-110.940 60.615Q-111.045 60.615-111.137 60.564Q-111.229 60.514-111.280 60.422Q-111.330 60.330-111.330 60.225Q-111.330 59.807-110.942 59.660Q-110.553 59.514-110.053 59.514Q-109.721 59.514-109.368 59.644Q-109.014 59.775-108.786 60.029Q-108.557 60.283-108.557 60.631L-108.557 62.432Q-108.557 62.564-108.485 62.674Q-108.412 62.783-108.284 62.783Q-108.159 62.783-108.090 62.678Q-108.022 62.572-108.022 62.432L-108.022 61.920L-107.741 61.920L-107.741 62.432Q-107.741 62.635-107.858 62.793Q-107.975 62.951-108.157 63.035Q-108.338 63.119-108.541 63.119Q-108.772 63.119-108.924 62.947Q-109.077 62.775-109.108 62.545Q-109.268 62.826-109.577 62.992Q-109.885 63.158-110.237 63.158Q-110.748 63.158-111.172 62.935Q-111.596 62.713-111.596 62.248M-110.909 62.248Q-110.909 62.533-110.682 62.719Q-110.455 62.904-110.162 62.904Q-109.916 62.904-109.692 62.787Q-109.467 62.670-109.332 62.467Q-109.198 62.264-109.198 62.010L-109.198 61.178Q-109.463 61.178-109.748 61.232Q-110.034 61.287-110.305 61.416Q-110.577 61.545-110.743 61.752Q-110.909 61.959-110.909 62.248M-105.588 63.080L-107.366 63.080L-107.366 62.783Q-107.092 62.783-106.924 62.736Q-106.756 62.689-106.756 62.521L-106.756 60.385Q-106.756 60.170-106.813 60.074Q-106.870 59.978-106.983 59.957Q-107.096 59.935-107.342 59.935L-107.342 59.639L-106.143 59.553L-106.143 62.521Q-106.143 62.689-105.996 62.736Q-105.850 62.783-105.588 62.783L-105.588 63.080M-107.030 58.158Q-107.030 57.967-106.895 57.836Q-106.760 57.705-106.565 57.705Q-106.444 57.705-106.340 57.767Q-106.237 57.830-106.174 57.934Q-106.112 58.037-106.112 58.158Q-106.112 58.353-106.243 58.488Q-106.373 58.623-106.565 58.623Q-106.764 58.623-106.897 58.490Q-107.030 58.357-107.030 58.158M-104.463 62.119L-104.463 59.928L-105.166 59.928L-105.166 59.674Q-104.811 59.674-104.569 59.441Q-104.327 59.209-104.215 58.861Q-104.104 58.514-104.104 58.158L-103.823 58.158L-103.823 59.631L-102.647 59.631L-102.647 59.928L-103.823 59.928L-103.823 62.103Q-103.823 62.424-103.703 62.652Q-103.584 62.881-103.303 62.881Q-103.123 62.881-103.006 62.758Q-102.889 62.635-102.836 62.455Q-102.784 62.275-102.784 62.103L-102.784 61.631L-102.502 61.631L-102.502 62.119Q-102.502 62.373-102.608 62.613Q-102.713 62.853-102.911 63.006Q-103.108 63.158-103.366 63.158Q-103.682 63.158-103.934 63.035Q-104.186 62.912-104.325 62.678Q-104.463 62.443-104.463 62.119M-96.838 63.080L-101.686 63.080L-101.686 62.783Q-101.366 62.783-101.121 62.736Q-100.877 62.689-100.877 62.521L-100.877 58.178Q-100.877 58.006-101.121 57.959Q-101.366 57.912-101.686 57.912L-101.686 57.615L-96.952 57.615L-96.717 59.463L-96.998 59.463Q-97.080 58.767-97.256 58.447Q-97.432 58.127-97.780 58.019Q-98.127 57.912-98.846 57.912L-99.709 57.912Q-99.928 57.912-100.020 57.955Q-100.112 57.998-100.112 58.178L-100.112 60.096L-99.479 60.096Q-99.053 60.096-98.846 60.033Q-98.639 59.971-98.551 59.775Q-98.463 59.580-98.463 59.158L-98.182 59.158L-98.182 61.326L-98.463 61.326Q-98.463 60.904-98.551 60.711Q-98.639 60.517-98.846 60.455Q-99.053 60.392-99.479 60.392L-100.112 60.392L-100.112 62.521Q-100.112 62.693-100.020 62.738Q-99.928 62.783-99.709 62.783L-98.784 62.783Q-98.202 62.783-97.848 62.701Q-97.495 62.619-97.289 62.422Q-97.084 62.225-96.971 61.887Q-96.858 61.549-96.764 60.967L-96.487 60.967L-96.838 63.080M-95.963 63.072L-95.963 61.850Q-95.963 61.822-95.932 61.791Q-95.901 61.760-95.877 61.760L-95.772 61.760Q-95.702 61.760-95.686 61.822Q-95.623 62.142-95.485 62.383Q-95.346 62.623-95.114 62.764Q-94.881 62.904-94.573 62.904Q-94.334 62.904-94.125 62.844Q-93.916 62.783-93.780 62.635Q-93.643 62.486-93.643 62.240Q-93.643 61.986-93.854 61.820Q-94.065 61.654-94.334 61.600L-94.955 61.486Q-95.362 61.408-95.662 61.152Q-95.963 60.896-95.963 60.521Q-95.963 60.154-95.762 59.932Q-95.561 59.709-95.237 59.611Q-94.912 59.514-94.573 59.514Q-94.108 59.514-93.811 59.721L-93.588 59.537Q-93.565 59.514-93.534 59.514L-93.483 59.514Q-93.452 59.514-93.424 59.541Q-93.397 59.568-93.397 59.600L-93.397 60.584Q-93.397 60.615-93.422 60.644Q-93.448 60.674-93.483 60.674L-93.588 60.674Q-93.623 60.674-93.651 60.646Q-93.678 60.619-93.678 60.584Q-93.678 60.185-93.930 59.965Q-94.182 59.744-94.580 59.744Q-94.936 59.744-95.219 59.867Q-95.502 59.990-95.502 60.295Q-95.502 60.514-95.301 60.646Q-95.100 60.779-94.854 60.822L-94.229 60.935Q-93.799 61.025-93.491 61.322Q-93.182 61.619-93.182 62.033Q-93.182 62.603-93.580 62.881Q-93.979 63.158-94.573 63.158Q-95.123 63.158-95.475 62.822L-95.772 63.135Q-95.795 63.158-95.830 63.158L-95.877 63.158Q-95.901 63.158-95.932 63.127Q-95.963 63.096-95.963 63.072M-92.030 62.119L-92.030 59.928L-92.733 59.928L-92.733 59.674Q-92.377 59.674-92.135 59.441Q-91.893 59.209-91.782 58.861Q-91.670 58.514-91.670 58.158L-91.389 58.158L-91.389 59.631L-90.213 59.631L-90.213 59.928L-91.389 59.928L-91.389 62.103Q-91.389 62.424-91.270 62.652Q-91.151 62.881-90.870 62.881Q-90.690 62.881-90.573 62.758Q-90.455 62.635-90.403 62.455Q-90.350 62.275-90.350 62.103L-90.350 61.631L-90.069 61.631L-90.069 62.119Q-90.069 62.373-90.174 62.613Q-90.280 62.853-90.477 63.006Q-90.674 63.158-90.932 63.158Q-91.248 63.158-91.500 63.035Q-91.752 62.912-91.891 62.678Q-92.030 62.443-92.030 62.119M-87.491 63.080L-89.268 63.080L-89.268 62.783Q-88.995 62.783-88.827 62.736Q-88.659 62.689-88.659 62.521L-88.659 60.385Q-88.659 60.170-88.715 60.074Q-88.772 59.978-88.885 59.957Q-88.998 59.935-89.245 59.935L-89.245 59.639L-88.045 59.553L-88.045 62.521Q-88.045 62.689-87.899 62.736Q-87.752 62.783-87.491 62.783L-87.491 63.080M-88.932 58.158Q-88.932 57.967-88.797 57.836Q-88.662 57.705-88.467 57.705Q-88.346 57.705-88.243 57.767Q-88.139 57.830-88.077 57.934Q-88.014 58.037-88.014 58.158Q-88.014 58.353-88.145 58.488Q-88.276 58.623-88.467 58.623Q-88.666 58.623-88.799 58.490Q-88.932 58.357-88.932 58.158M-85.061 63.080L-86.916 63.080L-86.916 62.783Q-86.643 62.783-86.475 62.736Q-86.307 62.689-86.307 62.521L-86.307 60.385Q-86.307 60.170-86.370 60.074Q-86.432 59.978-86.551 59.957Q-86.670 59.935-86.916 59.935L-86.916 59.639L-85.725 59.553L-85.725 60.287Q-85.612 60.072-85.418 59.904Q-85.225 59.736-84.987 59.644Q-84.748 59.553-84.495 59.553Q-83.534 59.553-83.358 60.264Q-83.174 59.935-82.846 59.744Q-82.518 59.553-82.139 59.553Q-80.963 59.553-80.963 60.631L-80.963 62.521Q-80.963 62.689-80.795 62.736Q-80.627 62.783-80.358 62.783L-80.358 63.080L-82.213 63.080L-82.213 62.783Q-81.940 62.783-81.772 62.738Q-81.604 62.693-81.604 62.521L-81.604 60.646Q-81.604 60.260-81.729 60.033Q-81.854 59.807-82.205 59.807Q-82.510 59.807-82.766 59.969Q-83.022 60.131-83.170 60.400Q-83.319 60.670-83.319 60.967L-83.319 62.521Q-83.319 62.689-83.149 62.736Q-82.979 62.783-82.709 62.783L-82.709 63.080L-84.565 63.080L-84.565 62.783Q-84.291 62.783-84.123 62.736Q-83.955 62.689-83.955 62.521L-83.955 60.646Q-83.955 60.260-84.080 60.033Q-84.205 59.807-84.557 59.807Q-84.862 59.807-85.118 59.969Q-85.373 60.131-85.522 60.400Q-85.670 60.670-85.670 60.967L-85.670 62.521Q-85.670 62.689-85.500 62.736Q-85.330 62.783-85.061 62.783L-85.061 63.080M-79.815 62.248Q-79.815 61.764-79.412 61.469Q-79.010 61.174-78.459 61.055Q-77.909 60.935-77.416 60.935L-77.416 60.646Q-77.416 60.420-77.532 60.213Q-77.647 60.006-77.844 59.887Q-78.041 59.767-78.272 59.767Q-78.698 59.767-78.983 59.873Q-78.912 59.900-78.866 59.955Q-78.819 60.010-78.793 60.080Q-78.768 60.150-78.768 60.225Q-78.768 60.330-78.819 60.422Q-78.870 60.514-78.961 60.564Q-79.053 60.615-79.159 60.615Q-79.264 60.615-79.356 60.564Q-79.448 60.514-79.498 60.422Q-79.549 60.330-79.549 60.225Q-79.549 59.807-79.161 59.660Q-78.772 59.514-78.272 59.514Q-77.940 59.514-77.586 59.644Q-77.233 59.775-77.004 60.029Q-76.776 60.283-76.776 60.631L-76.776 62.432Q-76.776 62.564-76.703 62.674Q-76.631 62.783-76.502 62.783Q-76.377 62.783-76.309 62.678Q-76.241 62.572-76.241 62.432L-76.241 61.920L-75.959 61.920L-75.959 62.432Q-75.959 62.635-76.077 62.793Q-76.194 62.951-76.375 63.035Q-76.557 63.119-76.760 63.119Q-76.991 63.119-77.143 62.947Q-77.295 62.775-77.327 62.545Q-77.487 62.826-77.795 62.992Q-78.104 63.158-78.455 63.158Q-78.967 63.158-79.391 62.935Q-79.815 62.713-79.815 62.248M-79.127 62.248Q-79.127 62.533-78.901 62.719Q-78.674 62.904-78.381 62.904Q-78.135 62.904-77.911 62.787Q-77.686 62.670-77.551 62.467Q-77.416 62.264-77.416 62.010L-77.416 61.178Q-77.682 61.178-77.967 61.232Q-78.252 61.287-78.524 61.416Q-78.795 61.545-78.961 61.752Q-79.127 61.959-79.127 62.248M-75.041 62.119L-75.041 59.928L-75.745 59.928L-75.745 59.674Q-75.389 59.674-75.147 59.441Q-74.905 59.209-74.793 58.861Q-74.682 58.514-74.682 58.158L-74.401 58.158L-74.401 59.631L-73.225 59.631L-73.225 59.928L-74.401 59.928L-74.401 62.103Q-74.401 62.424-74.282 62.652Q-74.162 62.881-73.881 62.881Q-73.702 62.881-73.584 62.758Q-73.467 62.635-73.414 62.455Q-73.362 62.275-73.362 62.103L-73.362 61.631L-73.080 61.631L-73.080 62.119Q-73.080 62.373-73.186 62.613Q-73.291 62.853-73.489 63.006Q-73.686 63.158-73.944 63.158Q-74.260 63.158-74.512 63.035Q-74.764 62.912-74.903 62.678Q-75.041 62.443-75.041 62.119M-72.362 61.326Q-72.362 60.846-72.129 60.430Q-71.897 60.014-71.487 59.764Q-71.077 59.514-70.600 59.514Q-69.870 59.514-69.471 59.955Q-69.073 60.396-69.073 61.127Q-69.073 61.232-69.166 61.256L-71.616 61.256L-71.616 61.326Q-71.616 61.736-71.495 62.092Q-71.373 62.447-71.102 62.664Q-70.830 62.881-70.401 62.881Q-70.037 62.881-69.741 62.652Q-69.444 62.424-69.342 62.072Q-69.334 62.025-69.248 62.010L-69.166 62.010Q-69.073 62.037-69.073 62.119Q-69.073 62.127-69.080 62.158Q-69.143 62.385-69.282 62.568Q-69.420 62.752-69.612 62.885Q-69.803 63.017-70.022 63.088Q-70.241 63.158-70.479 63.158Q-70.850 63.158-71.188 63.021Q-71.526 62.885-71.793 62.633Q-72.061 62.381-72.211 62.041Q-72.362 61.701-72.362 61.326M-71.608 61.017L-69.647 61.017Q-69.647 60.713-69.748 60.422Q-69.850 60.131-70.067 59.949Q-70.284 59.767-70.600 59.767Q-70.901 59.767-71.131 59.955Q-71.362 60.142-71.485 60.434Q-71.608 60.725-71.608 61.017M-67.397 62.615Q-67.397 62.424-67.264 62.291Q-67.131 62.158-66.936 62.158Q-66.745 62.158-66.612 62.291Q-66.479 62.424-66.479 62.615Q-66.479 62.814-66.612 62.947Q-66.745 63.080-66.936 63.080Q-67.131 63.080-67.264 62.947Q-67.397 62.814-67.397 62.615M-67.080 61.439L-67.080 61.041Q-67.080 60.642-66.952 60.242Q-66.823 59.842-66.577 59.506Q-66.498 59.400-66.332 59.225Q-66.166 59.049-66.098 58.912Q-66.030 58.775-66.030 58.537Q-66.030 58.049-66.233 57.877Q-66.436 57.705-66.936 57.705Q-67.245 57.705-67.522 57.816Q-67.799 57.928-67.952 58.150Q-67.780 58.150-67.672 58.264Q-67.565 58.377-67.565 58.545Q-67.565 58.646-67.616 58.738Q-67.666 58.830-67.756 58.883Q-67.846 58.935-67.959 58.935Q-68.069 58.935-68.159 58.883Q-68.248 58.830-68.299 58.738Q-68.350 58.646-68.350 58.545Q-68.350 58.197-68.139 57.947Q-67.928 57.697-67.604 57.572Q-67.280 57.447-66.936 57.447Q-66.276 57.447-65.782 57.697Q-65.287 57.947-65.287 58.545Q-65.287 58.795-65.411 59.010Q-65.534 59.225-65.745 59.361Q-66.057 59.564-66.289 59.820Q-66.522 60.076-66.661 60.392Q-66.799 60.709-66.799 61.057L-66.799 61.439Q-66.799 61.475-66.827 61.502Q-66.854 61.529-66.885 61.529L-66.991 61.529Q-67.022 61.529-67.051 61.504Q-67.080 61.478-67.080 61.439\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-11.831 17.556H10.93V.484h-22.762Z\"\u002F>\u003Cg transform=\"translate(114.193 -51.327)\">\u003Cpath d=\"M-117.694 63.080L-119.616 63.080L-119.616 62.783Q-118.807 62.783-118.807 62.303L-118.807 57.959Q-119.065 57.912-119.616 57.912L-119.616 57.615L-118.120 57.615Q-118.061 57.635-118.050 57.646L-115.042 61.834L-115.042 58.392Q-115.042 57.912-115.847 57.912L-115.847 57.615L-113.929 57.615L-113.929 57.912Q-114.737 57.912-114.737 58.392L-114.737 62.975Q-114.757 63.060-114.831 63.080L-114.936 63.080Q-114.991 63.068-115.007 63.041L-118.503 58.185L-118.503 62.303Q-118.503 62.783-117.694 62.783L-117.694 63.080M-113.354 61.385Q-113.354 60.881-113.098 60.449Q-112.843 60.017-112.407 59.766Q-111.972 59.514-111.472 59.514Q-111.085 59.514-110.743 59.658Q-110.401 59.803-110.139 60.064Q-109.878 60.326-109.735 60.662Q-109.593 60.998-109.593 61.385Q-109.593 61.877-109.856 62.287Q-110.120 62.697-110.550 62.928Q-110.979 63.158-111.472 63.158Q-111.964 63.158-112.397 62.926Q-112.831 62.693-113.093 62.285Q-113.354 61.877-113.354 61.385M-111.472 62.881Q-111.014 62.881-110.763 62.658Q-110.511 62.435-110.423 62.084Q-110.335 61.732-110.335 61.287Q-110.335 60.857-110.429 60.519Q-110.522 60.182-110.776 59.975Q-111.030 59.767-111.472 59.767Q-112.120 59.767-112.364 60.184Q-112.608 60.600-112.608 61.287Q-112.608 61.732-112.520 62.084Q-112.432 62.435-112.181 62.658Q-111.929 62.881-111.472 62.881\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M31.014 18.979h45.193V-.938H31.014Z\"\u002F>\u003Cg transform=\"translate(153.965 -51.282)\">\u003Cpath d=\"M-117.913 63.080L-119.663 63.080L-119.663 62.783Q-118.964 62.783-118.776 62.303L-116.975 57.478Q-116.921 57.369-116.807 57.369L-116.737 57.369Q-116.624 57.369-116.569 57.478L-114.679 62.521Q-114.600 62.689-114.397 62.736Q-114.194 62.783-113.882 62.783L-113.882 63.080L-116.104 63.080L-116.104 62.783Q-115.464 62.783-115.464 62.568Q-115.464 62.549-115.466 62.539Q-115.468 62.529-115.472 62.521L-115.936 61.287L-118.081 61.287L-118.464 62.303Q-118.468 62.318-118.473 62.348Q-118.479 62.377-118.479 62.400Q-118.479 62.541-118.389 62.625Q-118.300 62.709-118.167 62.746Q-118.034 62.783-117.913 62.783L-117.913 63.080M-117.007 58.424L-117.975 60.990L-116.050 60.990L-117.007 58.424M-111.440 63.080L-113.272 63.080L-113.272 62.783Q-112.999 62.783-112.831 62.736Q-112.663 62.689-112.663 62.521L-112.663 58.361Q-112.663 58.146-112.725 58.051Q-112.788 57.955-112.907 57.934Q-113.026 57.912-113.272 57.912L-113.272 57.615L-112.050 57.529L-112.050 62.521Q-112.050 62.689-111.882 62.736Q-111.714 62.783-111.440 62.783L-111.440 63.080M-110.370 62.119L-110.370 59.928L-111.073 59.928L-111.073 59.674Q-110.718 59.674-110.475 59.441Q-110.233 59.209-110.122 58.861Q-110.011 58.514-110.011 58.158L-109.729 58.158L-109.729 59.631L-108.554 59.631L-108.554 59.928L-109.729 59.928L-109.729 62.103Q-109.729 62.424-109.610 62.652Q-109.491 62.881-109.210 62.881Q-109.030 62.881-108.913 62.758Q-108.796 62.635-108.743 62.455Q-108.690 62.275-108.690 62.103L-108.690 61.631L-108.409 61.631L-108.409 62.119Q-108.409 62.373-108.514 62.613Q-108.620 62.853-108.817 63.006Q-109.014 63.158-109.272 63.158Q-109.589 63.158-109.841 63.035Q-110.093 62.912-110.231 62.678Q-110.370 62.443-110.370 62.119M-107.690 61.326Q-107.690 60.846-107.458 60.430Q-107.225 60.014-106.815 59.764Q-106.405 59.514-105.929 59.514Q-105.198 59.514-104.800 59.955Q-104.401 60.396-104.401 61.127Q-104.401 61.232-104.495 61.256L-106.944 61.256L-106.944 61.326Q-106.944 61.736-106.823 62.092Q-106.702 62.447-106.431 62.664Q-106.159 62.881-105.729 62.881Q-105.366 62.881-105.069 62.652Q-104.772 62.424-104.671 62.072Q-104.663 62.025-104.577 62.010L-104.495 62.010Q-104.401 62.037-104.401 62.119Q-104.401 62.127-104.409 62.158Q-104.472 62.385-104.610 62.568Q-104.749 62.752-104.940 62.885Q-105.132 63.017-105.350 63.088Q-105.569 63.158-105.807 63.158Q-106.179 63.158-106.516 63.021Q-106.854 62.885-107.122 62.633Q-107.389 62.381-107.540 62.041Q-107.690 61.701-107.690 61.326M-106.936 61.017L-104.975 61.017Q-104.975 60.713-105.077 60.422Q-105.179 60.131-105.395 59.949Q-105.612 59.767-105.929 59.767Q-106.229 59.767-106.460 59.955Q-106.690 60.142-106.813 60.434Q-106.936 60.725-106.936 61.017M-101.905 63.080L-103.886 63.080L-103.886 62.783Q-103.616 62.783-103.448 62.738Q-103.280 62.693-103.280 62.521L-103.280 60.385Q-103.280 60.170-103.343 60.074Q-103.405 59.978-103.522 59.957Q-103.639 59.935-103.886 59.935L-103.886 59.639L-102.718 59.553L-102.718 60.338Q-102.639 60.127-102.487 59.941Q-102.335 59.756-102.136 59.654Q-101.936 59.553-101.710 59.553Q-101.464 59.553-101.272 59.697Q-101.081 59.842-101.081 60.072Q-101.081 60.228-101.186 60.338Q-101.292 60.447-101.448 60.447Q-101.604 60.447-101.714 60.338Q-101.823 60.228-101.823 60.072Q-101.823 59.912-101.718 59.807Q-102.042 59.807-102.257 60.035Q-102.472 60.264-102.567 60.603Q-102.663 60.943-102.663 61.248L-102.663 62.521Q-102.663 62.689-102.436 62.736Q-102.210 62.783-101.905 62.783L-101.905 63.080M-98.671 63.080L-100.526 63.080L-100.526 62.783Q-100.253 62.783-100.085 62.736Q-99.917 62.689-99.917 62.521L-99.917 60.385Q-99.917 60.170-99.979 60.074Q-100.042 59.978-100.161 59.957Q-100.280 59.935-100.526 59.935L-100.526 59.639L-99.335 59.553L-99.335 60.287Q-99.222 60.072-99.028 59.904Q-98.835 59.736-98.597 59.644Q-98.358 59.553-98.104 59.553Q-96.936 59.553-96.936 60.631L-96.936 62.521Q-96.936 62.689-96.766 62.736Q-96.597 62.783-96.327 62.783L-96.327 63.080L-98.182 63.080L-98.182 62.783Q-97.909 62.783-97.741 62.736Q-97.573 62.689-97.573 62.521L-97.573 60.646Q-97.573 60.264-97.694 60.035Q-97.815 59.807-98.167 59.807Q-98.479 59.807-98.733 59.969Q-98.987 60.131-99.134 60.400Q-99.280 60.670-99.280 60.967L-99.280 62.521Q-99.280 62.689-99.110 62.736Q-98.940 62.783-98.671 62.783L-98.671 63.080M-95.784 62.248Q-95.784 61.764-95.382 61.469Q-94.979 61.174-94.429 61.055Q-93.878 60.935-93.386 60.935L-93.386 60.646Q-93.386 60.420-93.501 60.213Q-93.616 60.006-93.813 59.887Q-94.011 59.767-94.241 59.767Q-94.667 59.767-94.952 59.873Q-94.882 59.900-94.835 59.955Q-94.788 60.010-94.763 60.080Q-94.737 60.150-94.737 60.225Q-94.737 60.330-94.788 60.422Q-94.839 60.514-94.931 60.564Q-95.022 60.615-95.128 60.615Q-95.233 60.615-95.325 60.564Q-95.417 60.514-95.468 60.422Q-95.518 60.330-95.518 60.225Q-95.518 59.807-95.130 59.660Q-94.741 59.514-94.241 59.514Q-93.909 59.514-93.556 59.644Q-93.202 59.775-92.973 60.029Q-92.745 60.283-92.745 60.631L-92.745 62.432Q-92.745 62.564-92.673 62.674Q-92.600 62.783-92.472 62.783Q-92.347 62.783-92.278 62.678Q-92.210 62.572-92.210 62.432L-92.210 61.920L-91.929 61.920L-91.929 62.432Q-91.929 62.635-92.046 62.793Q-92.163 62.951-92.345 63.035Q-92.526 63.119-92.729 63.119Q-92.960 63.119-93.112 62.947Q-93.264 62.775-93.296 62.545Q-93.456 62.826-93.764 62.992Q-94.073 63.158-94.425 63.158Q-94.936 63.158-95.360 62.935Q-95.784 62.713-95.784 62.248M-95.097 62.248Q-95.097 62.533-94.870 62.719Q-94.643 62.904-94.350 62.904Q-94.104 62.904-93.880 62.787Q-93.655 62.670-93.520 62.467Q-93.386 62.264-93.386 62.010L-93.386 61.178Q-93.651 61.178-93.936 61.232Q-94.222 61.287-94.493 61.416Q-94.764 61.545-94.931 61.752Q-95.097 61.959-95.097 62.248M-91.011 62.119L-91.011 59.928L-91.714 59.928L-91.714 59.674Q-91.358 59.674-91.116 59.441Q-90.874 59.209-90.763 58.861Q-90.651 58.514-90.651 58.158L-90.370 58.158L-90.370 59.631L-89.194 59.631L-89.194 59.928L-90.370 59.928L-90.370 62.103Q-90.370 62.424-90.251 62.652Q-90.132 62.881-89.850 62.881Q-89.671 62.881-89.554 62.758Q-89.436 62.635-89.384 62.455Q-89.331 62.275-89.331 62.103L-89.331 61.631L-89.050 61.631L-89.050 62.119Q-89.050 62.373-89.155 62.613Q-89.261 62.853-89.458 63.006Q-89.655 63.158-89.913 63.158Q-90.229 63.158-90.481 63.035Q-90.733 62.912-90.872 62.678Q-91.011 62.443-91.011 62.119M-88.331 61.326Q-88.331 60.846-88.098 60.430Q-87.866 60.014-87.456 59.764Q-87.046 59.514-86.569 59.514Q-85.839 59.514-85.440 59.955Q-85.042 60.396-85.042 61.127Q-85.042 61.232-85.136 61.256L-87.585 61.256L-87.585 61.326Q-87.585 61.736-87.464 62.092Q-87.343 62.447-87.071 62.664Q-86.800 62.881-86.370 62.881Q-86.007 62.881-85.710 62.652Q-85.413 62.424-85.311 62.072Q-85.304 62.025-85.218 62.010L-85.136 62.010Q-85.042 62.037-85.042 62.119Q-85.042 62.127-85.050 62.158Q-85.112 62.385-85.251 62.568Q-85.389 62.752-85.581 62.885Q-85.772 63.017-85.991 63.088Q-86.210 63.158-86.448 63.158Q-86.819 63.158-87.157 63.021Q-87.495 62.885-87.763 62.633Q-88.030 62.381-88.181 62.041Q-88.331 61.701-88.331 61.326M-87.577 61.017L-85.616 61.017Q-85.616 60.713-85.718 60.422Q-85.819 60.131-86.036 59.949Q-86.253 59.767-86.569 59.767Q-86.870 59.767-87.100 59.955Q-87.331 60.142-87.454 60.434Q-87.577 60.725-87.577 61.017M-83.366 62.615Q-83.366 62.424-83.233 62.291Q-83.100 62.158-82.905 62.158Q-82.714 62.158-82.581 62.291Q-82.448 62.424-82.448 62.615Q-82.448 62.814-82.581 62.947Q-82.714 63.080-82.905 63.080Q-83.100 63.080-83.233 62.947Q-83.366 62.814-83.366 62.615M-83.050 61.439L-83.050 61.041Q-83.050 60.642-82.921 60.242Q-82.792 59.842-82.546 59.506Q-82.468 59.400-82.302 59.225Q-82.136 59.049-82.067 58.912Q-81.999 58.775-81.999 58.537Q-81.999 58.049-82.202 57.877Q-82.405 57.705-82.905 57.705Q-83.214 57.705-83.491 57.816Q-83.768 57.928-83.921 58.150Q-83.749 58.150-83.641 58.264Q-83.534 58.377-83.534 58.545Q-83.534 58.646-83.585 58.738Q-83.636 58.830-83.725 58.883Q-83.815 58.935-83.929 58.935Q-84.038 58.935-84.128 58.883Q-84.218 58.830-84.268 58.738Q-84.319 58.646-84.319 58.545Q-84.319 58.197-84.108 57.947Q-83.897 57.697-83.573 57.572Q-83.249 57.447-82.905 57.447Q-82.245 57.447-81.751 57.697Q-81.257 57.947-81.257 58.545Q-81.257 58.795-81.380 59.010Q-81.503 59.225-81.714 59.361Q-82.026 59.564-82.259 59.820Q-82.491 60.076-82.630 60.392Q-82.768 60.709-82.768 61.057L-82.768 61.439Q-82.768 61.475-82.796 61.502Q-82.823 61.529-82.854 61.529L-82.960 61.529Q-82.991 61.529-83.020 61.504Q-83.050 61.478-83.050 61.439\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M108.65 18.979h37.874V-.938H108.65Z\"\u002F>\u003Cg transform=\"translate(231.602 -52.06)\">\u003Cpath d=\"M-117.233 63.080L-119.616 63.080L-119.616 62.783Q-119.296 62.783-119.052 62.736Q-118.807 62.689-118.807 62.521L-118.807 58.178Q-118.807 58.006-119.052 57.959Q-119.296 57.912-119.616 57.912L-119.616 57.615L-117.233 57.615L-117.233 57.912Q-117.554 57.912-117.798 57.961Q-118.042 58.010-118.042 58.178L-118.042 60.088L-115.503 60.088L-115.503 58.178Q-115.503 58.006-115.747 57.959Q-115.991 57.912-116.311 57.912L-116.311 57.615L-113.929 57.615L-113.929 57.912Q-114.249 57.912-114.493 57.961Q-114.737 58.010-114.737 58.178L-114.737 62.521Q-114.737 62.685-114.493 62.734Q-114.249 62.783-113.929 62.783L-113.929 63.080L-116.311 63.080L-116.311 62.783Q-115.991 62.783-115.747 62.736Q-115.503 62.689-115.503 62.521L-115.503 60.385L-118.042 60.385L-118.042 62.521Q-118.042 62.685-117.798 62.734Q-117.554 62.783-117.233 62.783L-117.233 63.080M-112.671 62.127L-112.671 60.385Q-112.671 60.170-112.733 60.074Q-112.796 59.978-112.915 59.957Q-113.034 59.935-113.280 59.935L-113.280 59.639L-112.034 59.553L-112.034 62.103L-112.034 62.127Q-112.034 62.439-111.979 62.601Q-111.925 62.764-111.774 62.834Q-111.624 62.904-111.304 62.904Q-110.874 62.904-110.600 62.566Q-110.327 62.228-110.327 61.783L-110.327 60.385Q-110.327 60.170-110.389 60.074Q-110.452 59.978-110.571 59.957Q-110.690 59.935-110.936 59.935L-110.936 59.639L-109.690 59.553L-109.690 62.338Q-109.690 62.549-109.628 62.644Q-109.565 62.740-109.446 62.762Q-109.327 62.783-109.081 62.783L-109.081 63.080L-110.304 63.158L-110.304 62.537Q-110.472 62.826-110.753 62.992Q-111.034 63.158-111.354 63.158Q-112.671 63.158-112.671 62.127M-106.706 63.080L-108.561 63.080L-108.561 62.783Q-108.288 62.783-108.120 62.736Q-107.952 62.689-107.952 62.521L-107.952 60.385Q-107.952 60.170-108.014 60.074Q-108.077 59.978-108.196 59.957Q-108.315 59.935-108.561 59.935L-108.561 59.639L-107.370 59.553L-107.370 60.287Q-107.257 60.072-107.063 59.904Q-106.870 59.736-106.632 59.644Q-106.393 59.553-106.139 59.553Q-104.972 59.553-104.972 60.631L-104.972 62.521Q-104.972 62.689-104.802 62.736Q-104.632 62.783-104.362 62.783L-104.362 63.080L-106.218 63.080L-106.218 62.783Q-105.944 62.783-105.776 62.736Q-105.608 62.689-105.608 62.521L-105.608 60.646Q-105.608 60.264-105.729 60.035Q-105.850 59.807-106.202 59.807Q-106.514 59.807-106.768 59.969Q-107.022 60.131-107.169 60.400Q-107.315 60.670-107.315 60.967L-107.315 62.521Q-107.315 62.689-107.145 62.736Q-106.975 62.783-106.706 62.783L-106.706 63.080M-103.917 63.689Q-103.917 63.408-103.706 63.197Q-103.495 62.986-103.210 62.896Q-103.366 62.771-103.444 62.582Q-103.522 62.392-103.522 62.193Q-103.522 61.838-103.292 61.545Q-103.659 61.205-103.659 60.736Q-103.659 60.385-103.456 60.115Q-103.253 59.846-102.932 59.699Q-102.612 59.553-102.268 59.553Q-101.749 59.553-101.378 59.834Q-101.014 59.463-100.468 59.463Q-100.288 59.463-100.161 59.590Q-100.034 59.717-100.034 59.896Q-100.034 60.002-100.112 60.080Q-100.190 60.158-100.300 60.158Q-100.409 60.158-100.485 60.082Q-100.561 60.006-100.561 59.896Q-100.561 59.795-100.522 59.744Q-100.514 59.736-100.511 59.730Q-100.507 59.725-100.507 59.721Q-100.882 59.721-101.202 59.975Q-100.882 60.314-100.882 60.736Q-100.882 61.006-100.999 61.223Q-101.116 61.439-101.321 61.598Q-101.526 61.756-101.768 61.838Q-102.011 61.920-102.268 61.920Q-102.487 61.920-102.700 61.861Q-102.913 61.803-103.108 61.682Q-103.202 61.822-103.202 62.002Q-103.202 62.209-103.065 62.361Q-102.929 62.514-102.722 62.514L-102.026 62.514Q-101.538 62.514-101.126 62.598Q-100.714 62.682-100.434 62.939Q-100.155 63.197-100.155 63.689Q-100.155 64.053-100.475 64.285Q-100.796 64.517-101.237 64.619Q-101.679 64.721-102.034 64.721Q-102.389 64.721-102.833 64.619Q-103.276 64.517-103.597 64.285Q-103.917 64.053-103.917 63.689M-103.413 63.689Q-103.413 63.885-103.268 64.033Q-103.124 64.182-102.911 64.271Q-102.698 64.361-102.458 64.408Q-102.218 64.455-102.034 64.455Q-101.792 64.455-101.462 64.377Q-101.132 64.299-100.895 64.125Q-100.659 63.951-100.659 63.689Q-100.659 63.283-101.069 63.174Q-101.479 63.064-102.042 63.064L-102.722 63.064Q-102.991 63.064-103.202 63.242Q-103.413 63.420-103.413 63.689M-102.268 61.654Q-101.546 61.654-101.546 60.736Q-101.546 59.814-102.268 59.814Q-102.995 59.814-102.995 60.736Q-102.995 61.654-102.268 61.654M-97.663 63.080L-99.643 63.080L-99.643 62.783Q-99.374 62.783-99.206 62.738Q-99.038 62.693-99.038 62.521L-99.038 60.385Q-99.038 60.170-99.100 60.074Q-99.163 59.978-99.280 59.957Q-99.397 59.935-99.643 59.935L-99.643 59.639L-98.475 59.553L-98.475 60.338Q-98.397 60.127-98.245 59.941Q-98.093 59.756-97.893 59.654Q-97.694 59.553-97.468 59.553Q-97.222 59.553-97.030 59.697Q-96.839 59.842-96.839 60.072Q-96.839 60.228-96.944 60.338Q-97.050 60.447-97.206 60.447Q-97.362 60.447-97.472 60.338Q-97.581 60.228-97.581 60.072Q-97.581 59.912-97.475 59.807Q-97.800 59.807-98.014 60.035Q-98.229 60.264-98.325 60.603Q-98.421 60.943-98.421 61.248L-98.421 62.521Q-98.421 62.689-98.194 62.736Q-97.968 62.783-97.663 62.783L-97.663 63.080M-95.940 64.377Q-95.827 64.455-95.651 64.455Q-95.362 64.455-95.141 64.242Q-94.921 64.029-94.796 63.728L-94.507 63.080L-95.780 60.193Q-95.862 60.017-96.007 59.973Q-96.151 59.928-96.421 59.928L-96.421 59.631L-94.702 59.631L-94.702 59.928Q-95.124 59.928-95.124 60.111Q-95.124 60.123-95.108 60.193L-94.171 62.318L-93.339 60.408Q-93.300 60.318-93.300 60.240Q-93.300 60.100-93.401 60.014Q-93.503 59.928-93.643 59.928L-93.643 59.631L-92.292 59.631L-92.292 59.928Q-92.546 59.928-92.739 60.053Q-92.932 60.178-93.038 60.408L-94.483 63.728Q-94.597 63.982-94.763 64.205Q-94.929 64.428-95.157 64.570Q-95.386 64.713-95.651 64.713Q-95.948 64.713-96.188 64.521Q-96.429 64.330-96.429 64.041Q-96.429 63.885-96.323 63.783Q-96.218 63.682-96.069 63.682Q-95.964 63.682-95.884 63.728Q-95.804 63.775-95.757 63.853Q-95.710 63.932-95.710 64.041Q-95.710 64.162-95.770 64.250Q-95.831 64.338-95.940 64.377M-90.690 62.615Q-90.690 62.424-90.557 62.291Q-90.425 62.158-90.229 62.158Q-90.038 62.158-89.905 62.291Q-89.772 62.424-89.772 62.615Q-89.772 62.814-89.905 62.947Q-90.038 63.080-90.229 63.080Q-90.425 63.080-90.557 62.947Q-90.690 62.814-90.690 62.615M-90.374 61.439L-90.374 61.041Q-90.374 60.642-90.245 60.242Q-90.116 59.842-89.870 59.506Q-89.792 59.400-89.626 59.225Q-89.460 59.049-89.391 58.912Q-89.323 58.775-89.323 58.537Q-89.323 58.049-89.526 57.877Q-89.729 57.705-90.229 57.705Q-90.538 57.705-90.815 57.816Q-91.093 57.928-91.245 58.150Q-91.073 58.150-90.966 58.264Q-90.858 58.377-90.858 58.545Q-90.858 58.646-90.909 58.738Q-90.960 58.830-91.050 58.883Q-91.139 58.935-91.253 58.935Q-91.362 58.935-91.452 58.883Q-91.542 58.830-91.593 58.738Q-91.643 58.646-91.643 58.545Q-91.643 58.197-91.432 57.947Q-91.222 57.697-90.897 57.572Q-90.573 57.447-90.229 57.447Q-89.569 57.447-89.075 57.697Q-88.581 57.947-88.581 58.545Q-88.581 58.795-88.704 59.010Q-88.827 59.225-89.038 59.361Q-89.350 59.564-89.583 59.820Q-89.815 60.076-89.954 60.392Q-90.093 60.709-90.093 61.057L-90.093 61.439Q-90.093 61.475-90.120 61.502Q-90.147 61.529-90.179 61.529L-90.284 61.529Q-90.315 61.529-90.345 61.504Q-90.374 61.478-90.374 61.439\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M170.267 17.556h22.762V.484h-22.762Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(295.205 -51.327)\">\u003Cpath d=\"M-115.600 63.080L-117.952 63.080L-117.952 62.783Q-117.632 62.783-117.388 62.736Q-117.143 62.689-117.143 62.521L-117.143 60.896L-118.952 58.178Q-119.085 58.002-119.292 57.957Q-119.499 57.912-119.831 57.912L-119.831 57.615L-117.577 57.615L-117.577 57.912Q-118.089 57.912-118.089 58.088Q-118.089 58.123-118.065 58.178L-116.503 60.514L-115.097 58.392Q-115.022 58.287-115.022 58.193Q-115.022 58.045-115.145 57.978Q-115.268 57.912-115.432 57.912L-115.432 57.615L-113.714 57.615L-113.714 57.912Q-114.409 57.912-114.737 58.392L-116.401 60.896L-116.401 62.521Q-116.401 62.783-115.600 62.783\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(295.205 -51.327)\">\u003Cpath d=\"M-114.055 61.326Q-114.055 60.846-113.822 60.430Q-113.590 60.014-113.180 59.764Q-112.770 59.514-112.293 59.514Q-111.563 59.514-111.164 59.955Q-110.766 60.396-110.766 61.127Q-110.766 61.232-110.859 61.256L-113.309 61.256L-113.309 61.326Q-113.309 61.736-113.188 62.092Q-113.066 62.447-112.795 62.664Q-112.523 62.881-112.094 62.881Q-111.731 62.881-111.434 62.652Q-111.137 62.424-111.035 62.072Q-111.027 62.025-110.941 62.010L-110.859 62.010Q-110.766 62.037-110.766 62.119Q-110.766 62.127-110.773 62.158Q-110.836 62.385-110.975 62.568Q-111.113 62.752-111.305 62.885Q-111.496 63.017-111.715 63.088Q-111.934 63.158-112.172 63.158Q-112.543 63.158-112.881 63.021Q-113.219 62.885-113.486 62.633Q-113.754 62.381-113.904 62.041Q-114.055 61.701-114.055 61.326M-113.301 61.017L-111.340 61.017Q-111.340 60.713-111.441 60.422Q-111.543 60.131-111.760 59.949Q-111.977 59.767-112.293 59.767Q-112.594 59.767-112.824 59.955Q-113.055 60.142-113.178 60.434Q-113.301 60.725-113.301 61.017M-110.234 63.072L-110.234 61.850Q-110.234 61.822-110.203 61.791Q-110.172 61.760-110.148 61.760L-110.043 61.760Q-109.973 61.760-109.957 61.822Q-109.895 62.142-109.756 62.383Q-109.617 62.623-109.385 62.764Q-109.152 62.904-108.844 62.904Q-108.606 62.904-108.397 62.844Q-108.188 62.783-108.051 62.635Q-107.914 62.486-107.914 62.240Q-107.914 61.986-108.125 61.820Q-108.336 61.654-108.606 61.600L-109.227 61.486Q-109.633 61.408-109.934 61.152Q-110.234 60.896-110.234 60.521Q-110.234 60.154-110.033 59.932Q-109.832 59.709-109.508 59.611Q-109.184 59.514-108.844 59.514Q-108.379 59.514-108.082 59.721L-107.859 59.537Q-107.836 59.514-107.805 59.514L-107.754 59.514Q-107.723 59.514-107.695 59.541Q-107.668 59.568-107.668 59.600L-107.668 60.584Q-107.668 60.615-107.693 60.644Q-107.719 60.674-107.754 60.674L-107.859 60.674Q-107.895 60.674-107.922 60.646Q-107.949 60.619-107.949 60.584Q-107.949 60.185-108.201 59.965Q-108.453 59.744-108.852 59.744Q-109.207 59.744-109.490 59.867Q-109.773 59.990-109.773 60.295Q-109.773 60.514-109.572 60.646Q-109.371 60.779-109.125 60.822L-108.500 60.935Q-108.070 61.025-107.762 61.322Q-107.453 61.619-107.453 62.033Q-107.453 62.603-107.852 62.881Q-108.250 63.158-108.844 63.158Q-109.395 63.158-109.746 62.822L-110.043 63.135Q-110.066 63.158-110.102 63.158L-110.148 63.158Q-110.172 63.158-110.203 63.127Q-110.234 63.096-110.234 63.072\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M22.312 54.544h22.762V37.473H22.312Z\"\u002F>\u003Cg transform=\"translate(148.336 -14.338)\">\u003Cpath d=\"M-117.694 63.080L-119.616 63.080L-119.616 62.783Q-118.807 62.783-118.807 62.303L-118.807 57.959Q-119.065 57.912-119.616 57.912L-119.616 57.615L-118.120 57.615Q-118.061 57.635-118.050 57.646L-115.042 61.834L-115.042 58.392Q-115.042 57.912-115.847 57.912L-115.847 57.615L-113.929 57.615L-113.929 57.912Q-114.737 57.912-114.737 58.392L-114.737 62.975Q-114.757 63.060-114.831 63.080L-114.936 63.080Q-114.991 63.068-115.007 63.041L-118.503 58.185L-118.503 62.303Q-118.503 62.783-117.694 62.783L-117.694 63.080M-113.354 61.385Q-113.354 60.881-113.098 60.449Q-112.843 60.017-112.407 59.766Q-111.972 59.514-111.472 59.514Q-111.085 59.514-110.743 59.658Q-110.401 59.803-110.139 60.064Q-109.878 60.326-109.735 60.662Q-109.593 60.998-109.593 61.385Q-109.593 61.877-109.856 62.287Q-110.120 62.697-110.550 62.928Q-110.979 63.158-111.472 63.158Q-111.964 63.158-112.397 62.926Q-112.831 62.693-113.093 62.285Q-113.354 61.877-113.354 61.385M-111.472 62.881Q-111.014 62.881-110.763 62.658Q-110.511 62.435-110.423 62.084Q-110.335 61.732-110.335 61.287Q-110.335 60.857-110.429 60.519Q-110.522 60.182-110.776 59.975Q-111.030 59.767-111.472 59.767Q-112.120 59.767-112.364 60.184Q-112.608 60.600-112.608 61.287Q-112.608 61.732-112.520 62.084Q-112.432 62.435-112.181 62.658Q-111.929 62.881-111.472 62.881\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M62.146 54.544h22.762V37.473H62.146Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(187.084 -14.338)\">\u003Cpath d=\"M-115.600 63.080L-117.952 63.080L-117.952 62.783Q-117.632 62.783-117.388 62.736Q-117.143 62.689-117.143 62.521L-117.143 60.896L-118.952 58.178Q-119.085 58.002-119.292 57.957Q-119.499 57.912-119.831 57.912L-119.831 57.615L-117.577 57.615L-117.577 57.912Q-118.089 57.912-118.089 58.088Q-118.089 58.123-118.065 58.178L-116.503 60.514L-115.097 58.392Q-115.022 58.287-115.022 58.193Q-115.022 58.045-115.145 57.978Q-115.268 57.912-115.432 57.912L-115.432 57.615L-113.714 57.615L-113.714 57.912Q-114.409 57.912-114.737 58.392L-116.401 60.896L-116.401 62.521Q-116.401 62.783-115.600 62.783\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(187.084 -14.338)\">\u003Cpath d=\"M-114.055 61.326Q-114.055 60.846-113.822 60.430Q-113.590 60.014-113.180 59.764Q-112.770 59.514-112.293 59.514Q-111.563 59.514-111.164 59.955Q-110.766 60.396-110.766 61.127Q-110.766 61.232-110.859 61.256L-113.309 61.256L-113.309 61.326Q-113.309 61.736-113.188 62.092Q-113.066 62.447-112.795 62.664Q-112.523 62.881-112.094 62.881Q-111.731 62.881-111.434 62.652Q-111.137 62.424-111.035 62.072Q-111.027 62.025-110.941 62.010L-110.859 62.010Q-110.766 62.037-110.766 62.119Q-110.766 62.127-110.773 62.158Q-110.836 62.385-110.975 62.568Q-111.113 62.752-111.305 62.885Q-111.496 63.017-111.715 63.088Q-111.934 63.158-112.172 63.158Q-112.543 63.158-112.881 63.021Q-113.219 62.885-113.486 62.633Q-113.754 62.381-113.904 62.041Q-114.055 61.701-114.055 61.326M-113.301 61.017L-111.340 61.017Q-111.340 60.713-111.441 60.422Q-111.543 60.131-111.760 59.949Q-111.977 59.767-112.293 59.767Q-112.594 59.767-112.824 59.955Q-113.055 60.142-113.178 60.434Q-113.301 60.725-113.301 61.017M-110.234 63.072L-110.234 61.850Q-110.234 61.822-110.203 61.791Q-110.172 61.760-110.148 61.760L-110.043 61.760Q-109.973 61.760-109.957 61.822Q-109.895 62.142-109.756 62.383Q-109.617 62.623-109.385 62.764Q-109.152 62.904-108.844 62.904Q-108.606 62.904-108.397 62.844Q-108.188 62.783-108.051 62.635Q-107.914 62.486-107.914 62.240Q-107.914 61.986-108.125 61.820Q-108.336 61.654-108.606 61.600L-109.227 61.486Q-109.633 61.408-109.934 61.152Q-110.234 60.896-110.234 60.521Q-110.234 60.154-110.033 59.932Q-109.832 59.709-109.508 59.611Q-109.184 59.514-108.844 59.514Q-108.379 59.514-108.082 59.721L-107.859 59.537Q-107.836 59.514-107.805 59.514L-107.754 59.514Q-107.723 59.514-107.695 59.541Q-107.668 59.568-107.668 59.600L-107.668 60.584Q-107.668 60.615-107.693 60.644Q-107.719 60.674-107.754 60.674L-107.859 60.674Q-107.895 60.674-107.922 60.646Q-107.949 60.619-107.949 60.584Q-107.949 60.185-108.201 59.965Q-108.453 59.744-108.852 59.744Q-109.207 59.744-109.490 59.867Q-109.773 59.990-109.773 60.295Q-109.773 60.514-109.572 60.646Q-109.371 60.779-109.125 60.822L-108.500 60.935Q-108.070 61.025-107.762 61.322Q-107.453 61.619-107.453 62.033Q-107.453 62.603-107.852 62.881Q-108.250 63.158-108.844 63.158Q-109.395 63.158-109.746 62.822L-110.043 63.135Q-110.066 63.158-110.102 63.158L-110.148 63.158Q-110.172 63.158-110.203 63.127Q-110.234 63.096-110.234 63.072\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M99.135 54.544h22.762V37.473H99.135Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(224.073 -14.338)\">\u003Cpath d=\"M-115.600 63.080L-117.952 63.080L-117.952 62.783Q-117.632 62.783-117.388 62.736Q-117.143 62.689-117.143 62.521L-117.143 60.896L-118.952 58.178Q-119.085 58.002-119.292 57.957Q-119.499 57.912-119.831 57.912L-119.831 57.615L-117.577 57.615L-117.577 57.912Q-118.089 57.912-118.089 58.088Q-118.089 58.123-118.065 58.178L-116.503 60.514L-115.097 58.392Q-115.022 58.287-115.022 58.193Q-115.022 58.045-115.145 57.978Q-115.268 57.912-115.432 57.912L-115.432 57.615L-113.714 57.615L-113.714 57.912Q-114.409 57.912-114.737 58.392L-116.401 60.896L-116.401 62.521Q-116.401 62.783-115.600 62.783\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(224.073 -14.338)\">\u003Cpath d=\"M-114.055 61.326Q-114.055 60.846-113.822 60.430Q-113.590 60.014-113.180 59.764Q-112.770 59.514-112.293 59.514Q-111.563 59.514-111.164 59.955Q-110.766 60.396-110.766 61.127Q-110.766 61.232-110.859 61.256L-113.309 61.256L-113.309 61.326Q-113.309 61.736-113.188 62.092Q-113.066 62.447-112.795 62.664Q-112.523 62.881-112.094 62.881Q-111.731 62.881-111.434 62.652Q-111.137 62.424-111.035 62.072Q-111.027 62.025-110.941 62.010L-110.859 62.010Q-110.766 62.037-110.766 62.119Q-110.766 62.127-110.773 62.158Q-110.836 62.385-110.975 62.568Q-111.113 62.752-111.305 62.885Q-111.496 63.017-111.715 63.088Q-111.934 63.158-112.172 63.158Q-112.543 63.158-112.881 63.021Q-113.219 62.885-113.486 62.633Q-113.754 62.381-113.904 62.041Q-114.055 61.701-114.055 61.326M-113.301 61.017L-111.340 61.017Q-111.340 60.713-111.441 60.422Q-111.543 60.131-111.760 59.949Q-111.977 59.767-112.293 59.767Q-112.594 59.767-112.824 59.955Q-113.055 60.142-113.178 60.434Q-113.301 60.725-113.301 61.017M-110.234 63.072L-110.234 61.850Q-110.234 61.822-110.203 61.791Q-110.172 61.760-110.148 61.760L-110.043 61.760Q-109.973 61.760-109.957 61.822Q-109.895 62.142-109.756 62.383Q-109.617 62.623-109.385 62.764Q-109.152 62.904-108.844 62.904Q-108.606 62.904-108.397 62.844Q-108.188 62.783-108.051 62.635Q-107.914 62.486-107.914 62.240Q-107.914 61.986-108.125 61.820Q-108.336 61.654-108.606 61.600L-109.227 61.486Q-109.633 61.408-109.934 61.152Q-110.234 60.896-110.234 60.521Q-110.234 60.154-110.033 59.932Q-109.832 59.709-109.508 59.611Q-109.184 59.514-108.844 59.514Q-108.379 59.514-108.082 59.721L-107.859 59.537Q-107.836 59.514-107.805 59.514L-107.754 59.514Q-107.723 59.514-107.695 59.541Q-107.668 59.568-107.668 59.600L-107.668 60.584Q-107.668 60.615-107.693 60.644Q-107.719 60.674-107.754 60.674L-107.859 60.674Q-107.895 60.674-107.922 60.646Q-107.949 60.619-107.949 60.584Q-107.949 60.185-108.201 59.965Q-108.453 59.744-108.852 59.744Q-109.207 59.744-109.490 59.867Q-109.773 59.990-109.773 60.295Q-109.773 60.514-109.572 60.646Q-109.371 60.779-109.125 60.822L-108.500 60.935Q-108.070 61.025-107.762 61.322Q-107.453 61.619-107.453 62.033Q-107.453 62.603-107.852 62.881Q-108.250 63.158-108.844 63.158Q-109.395 63.158-109.746 62.822L-110.043 63.135Q-110.066 63.158-110.102 63.158L-110.148 63.158Q-110.172 63.158-110.203 63.127Q-110.234 63.096-110.234 63.072\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M136.123 54.544h22.762V37.473h-22.762Z\"\u002F>\u003Cg transform=\"translate(262.147 -14.338)\">\u003Cpath d=\"M-117.694 63.080L-119.616 63.080L-119.616 62.783Q-118.807 62.783-118.807 62.303L-118.807 57.959Q-119.065 57.912-119.616 57.912L-119.616 57.615L-118.120 57.615Q-118.061 57.635-118.050 57.646L-115.042 61.834L-115.042 58.392Q-115.042 57.912-115.847 57.912L-115.847 57.615L-113.929 57.615L-113.929 57.912Q-114.737 57.912-114.737 58.392L-114.737 62.975Q-114.757 63.060-114.831 63.080L-114.936 63.080Q-114.991 63.068-115.007 63.041L-118.503 58.185L-118.503 62.303Q-118.503 62.783-117.694 62.783L-117.694 63.080M-113.354 61.385Q-113.354 60.881-113.098 60.449Q-112.843 60.017-112.407 59.766Q-111.972 59.514-111.472 59.514Q-111.085 59.514-110.743 59.658Q-110.401 59.803-110.139 60.064Q-109.878 60.326-109.735 60.662Q-109.593 60.998-109.593 61.385Q-109.593 61.877-109.856 62.287Q-110.120 62.697-110.550 62.928Q-110.979 63.158-111.472 63.158Q-111.964 63.158-112.397 62.926Q-112.831 62.693-113.093 62.285Q-113.354 61.877-113.354 61.385M-111.472 62.881Q-111.014 62.881-110.763 62.658Q-110.511 62.435-110.423 62.084Q-110.335 61.732-110.335 61.287Q-110.335 60.857-110.429 60.519Q-110.522 60.182-110.776 59.975Q-111.030 59.767-111.472 59.767Q-112.120 59.767-112.364 60.184Q-112.608 60.600-112.608 61.287Q-112.608 61.732-112.520 62.084Q-112.432 62.435-112.181 62.658Q-111.929 62.881-111.472 62.881\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m2.736-53.726-46.672 20.01\"\u002F>\u003Cpath stroke=\"none\" d=\"m-45.775-32.928 3.572.21-1.733-.998.472-1.943\"\u002F>\u003Cg transform=\"translate(79.371 -107.607)\">\u003Cpath d=\"M-117.840 63.080L-119.573 63.080L-119.573 62.800Q-118.851 62.800-118.851 62.400L-118.851 58.613Q-119.084 58.579-119.573 58.579L-119.573 58.298L-118.216 58.298Q-118.175 58.312-118.154 58.326L-115.451 61.973L-115.451 58.978Q-115.451 58.579-116.172 58.579L-116.172 58.298L-114.435 58.298L-114.435 58.579Q-115.157 58.579-115.157 58.978L-115.157 62.988Q-115.174 63.063-115.242 63.080L-115.362 63.080Q-115.410 63.070-115.423 63.046L-118.557 58.825L-118.557 62.400Q-118.557 62.800-117.840 62.800L-117.840 63.080M-113.779 61.597Q-113.779 61.255-113.644 60.956Q-113.509 60.657-113.270 60.433Q-113.031 60.209-112.713 60.084Q-112.395 59.959-112.063 59.959Q-111.619 59.959-111.219 60.175Q-110.819 60.390-110.585 60.768Q-110.351 61.145-110.351 61.597Q-110.351 61.938-110.493 62.222Q-110.635 62.506-110.879 62.713Q-111.123 62.919-111.433 63.034Q-111.742 63.148-112.063 63.148Q-112.494 63.148-112.896 62.947Q-113.297 62.745-113.538 62.393Q-113.779 62.041-113.779 61.597M-112.063 62.899Q-111.462 62.899-111.238 62.521Q-111.014 62.143-111.014 61.511Q-111.014 60.899-111.248 60.540Q-111.482 60.182-112.063 60.182Q-113.116 60.182-113.116 61.511Q-113.116 62.143-112.890 62.521Q-112.665 62.899-112.063 62.899M-108.075 63.080L-109.708 63.080L-109.708 62.800Q-109.479 62.800-109.331 62.766Q-109.182 62.731-109.182 62.591L-109.182 60.742Q-109.182 60.472-109.290 60.411Q-109.397 60.349-109.708 60.349L-109.708 60.069L-108.649 59.994L-108.649 60.643Q-108.478 60.335-108.174 60.164Q-107.869 59.994-107.524 59.994Q-107.018 59.994-106.735 60.217Q-106.451 60.441-106.451 60.937L-106.451 62.591Q-106.451 62.728-106.302 62.764Q-106.154 62.800-105.928 62.800L-105.928 63.080L-107.558 63.080L-107.558 62.800Q-107.329 62.800-107.181 62.766Q-107.032 62.731-107.032 62.591L-107.032 60.951Q-107.032 60.616-107.152 60.416Q-107.271 60.216-107.586 60.216Q-107.856 60.216-108.090 60.352Q-108.324 60.489-108.462 60.723Q-108.601 60.957-108.601 61.231L-108.601 62.591Q-108.601 62.728-108.451 62.764Q-108.300 62.800-108.075 62.800L-108.075 63.080M-105.381 61.545Q-105.381 61.224-105.256 60.935Q-105.132 60.646-104.906 60.423Q-104.681 60.199-104.385 60.079Q-104.089 59.959-103.771 59.959Q-103.443 59.959-103.182 60.059Q-102.920 60.158-102.744 60.340Q-102.568 60.523-102.474 60.781Q-102.380 61.039-102.380 61.371Q-102.380 61.463-102.462 61.484L-104.718 61.484L-104.718 61.545Q-104.718 62.133-104.434 62.516Q-104.151 62.899-103.583 62.899Q-103.262 62.899-102.994 62.706Q-102.725 62.513-102.637 62.198Q-102.630 62.157-102.555 62.143L-102.462 62.143Q-102.380 62.167-102.380 62.239Q-102.380 62.246-102.387 62.273Q-102.500 62.670-102.871 62.909Q-103.242 63.148-103.665 63.148Q-104.103 63.148-104.503 62.940Q-104.903 62.731-105.142 62.364Q-105.381 61.997-105.381 61.545M-104.711 61.275L-102.896 61.275Q-102.896 60.998-102.994 60.746Q-103.091 60.493-103.289 60.337Q-103.488 60.182-103.771 60.182Q-104.048 60.182-104.262 60.340Q-104.475 60.499-104.593 60.754Q-104.711 61.009-104.711 61.275\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M13.847-51.953 2.418-38.24\"\u002F>\u003Cpath stroke=\"none\" d=\"m1.137-36.704 3.278-1.434-1.997-.102-.46-1.946\"\u002F>\u003Cg transform=\"translate(107.716 -105.017)\">\u003Cpath d=\"M-119.463 63.142L-119.463 61.569Q-119.463 61.542-119.438 61.516Q-119.412 61.491-119.385 61.491L-119.272 61.491Q-119.244 61.491-119.221 61.518Q-119.197 61.545-119.197 61.569Q-119.197 61.914-119.065 62.178Q-118.933 62.441-118.704 62.610Q-118.475 62.779-118.173 62.860Q-117.870 62.940-117.529 62.940Q-117.262 62.940-117.026 62.812Q-116.790 62.684-116.645 62.461Q-116.500 62.239-116.500 61.973Q-116.500 61.750-116.606 61.554Q-116.712 61.357-116.893 61.222Q-117.074 61.087-117.300 61.036L-118.328 60.804Q-118.639 60.732-118.899 60.546Q-119.159 60.359-119.311 60.088Q-119.463 59.816-119.463 59.501Q-119.463 59.115-119.250 58.808Q-119.036 58.500-118.689 58.329Q-118.342 58.158-117.963 58.158Q-117.734 58.158-117.505 58.211Q-117.276 58.264-117.077 58.372Q-116.879 58.479-116.725 58.643L-116.431 58.203Q-116.408 58.158-116.367 58.158L-116.319 58.158Q-116.288 58.158-116.266 58.184Q-116.244 58.209-116.244 58.237L-116.244 59.812Q-116.244 59.833-116.267 59.860Q-116.291 59.888-116.319 59.888L-116.431 59.888Q-116.493 59.888-116.507 59.812Q-116.548 59.399-116.729 59.079Q-116.910 58.760-117.221 58.585Q-117.532 58.411-117.963 58.411Q-118.212 58.411-118.452 58.522Q-118.691 58.633-118.841 58.831Q-118.992 59.030-118.992 59.293Q-118.992 59.505-118.884 59.686Q-118.776 59.867-118.600 59.987Q-118.424 60.106-118.216 60.147L-117.187 60.376Q-116.869 60.448-116.602 60.653Q-116.336 60.858-116.184 61.152Q-116.032 61.446-116.032 61.778Q-116.032 62.171-116.237 62.506Q-116.442 62.841-116.787 63.030Q-117.132 63.220-117.529 63.220Q-117.949 63.220-118.328 63.107Q-118.708 62.995-118.978 62.745L-119.272 63.179Q-119.299 63.220-119.337 63.220L-119.385 63.220Q-119.412 63.220-119.438 63.195Q-119.463 63.169-119.463 63.142M-115.263 61.597Q-115.263 61.255-115.128 60.956Q-114.993 60.657-114.753 60.433Q-114.514 60.209-114.196 60.084Q-113.878 59.959-113.547 59.959Q-113.102 59.959-112.702 60.175Q-112.303 60.390-112.068 60.768Q-111.834 61.145-111.834 61.597Q-111.834 61.938-111.976 62.222Q-112.118 62.506-112.362 62.713Q-112.607 62.919-112.916 63.034Q-113.225 63.148-113.547 63.148Q-113.977 63.148-114.379 62.947Q-114.781 62.745-115.022 62.393Q-115.263 62.041-115.263 61.597M-113.547 62.899Q-112.945 62.899-112.721 62.521Q-112.497 62.143-112.497 61.511Q-112.497 60.899-112.732 60.540Q-112.966 60.182-113.547 60.182Q-114.599 60.182-114.599 61.511Q-114.599 62.143-114.374 62.521Q-114.148 62.899-113.547 62.899M-109.558 63.080L-111.192 63.080L-111.192 62.800Q-110.963 62.800-110.814 62.766Q-110.665 62.731-110.665 62.591L-110.665 60.742Q-110.665 60.472-110.773 60.411Q-110.881 60.349-111.192 60.349L-111.192 60.069L-110.132 59.994L-110.132 60.643Q-109.961 60.335-109.657 60.164Q-109.353 59.994-109.008 59.994Q-108.608 59.994-108.331 60.134Q-108.054 60.274-107.969 60.622Q-107.801 60.329-107.502 60.161Q-107.203 59.994-106.858 59.994Q-106.352 59.994-106.068 60.217Q-105.785 60.441-105.785 60.937L-105.785 62.591Q-105.785 62.728-105.636 62.764Q-105.487 62.800-105.262 62.800L-105.262 63.080L-106.892 63.080L-106.892 62.800Q-106.666 62.800-106.516 62.764Q-106.366 62.728-106.366 62.591L-106.366 60.951Q-106.366 60.616-106.485 60.416Q-106.605 60.216-106.919 60.216Q-107.189 60.216-107.423 60.352Q-107.658 60.489-107.796 60.723Q-107.934 60.957-107.934 61.231L-107.934 62.591Q-107.934 62.728-107.786 62.764Q-107.637 62.800-107.411 62.800L-107.411 63.080L-109.042 63.080L-109.042 62.800Q-108.813 62.800-108.664 62.766Q-108.515 62.731-108.515 62.591L-108.515 60.951Q-108.515 60.616-108.635 60.416Q-108.755 60.216-109.069 60.216Q-109.339 60.216-109.573 60.352Q-109.807 60.489-109.946 60.723Q-110.084 60.957-110.084 61.231L-110.084 62.591Q-110.084 62.728-109.934 62.764Q-109.784 62.800-109.558 62.800L-109.558 63.080M-104.715 61.545Q-104.715 61.224-104.590 60.935Q-104.465 60.646-104.240 60.423Q-104.014 60.199-103.718 60.079Q-103.423 59.959-103.105 59.959Q-102.777 59.959-102.515 60.059Q-102.254 60.158-102.078 60.340Q-101.902 60.523-101.808 60.781Q-101.714 61.039-101.714 61.371Q-101.714 61.463-101.796 61.484L-104.052 61.484L-104.052 61.545Q-104.052 62.133-103.768 62.516Q-103.484 62.899-102.917 62.899Q-102.596 62.899-102.327 62.706Q-102.059 62.513-101.970 62.198Q-101.963 62.157-101.888 62.143L-101.796 62.143Q-101.714 62.167-101.714 62.239Q-101.714 62.246-101.721 62.273Q-101.833 62.670-102.204 62.909Q-102.575 63.148-102.999 63.148Q-103.436 63.148-103.836 62.940Q-104.236 62.731-104.475 62.364Q-104.715 61.997-104.715 61.545M-104.045 61.275L-102.230 61.275Q-102.230 60.998-102.327 60.746Q-102.425 60.493-102.623 60.337Q-102.821 60.182-103.105 60.182Q-103.382 60.182-103.595 60.340Q-103.809 60.499-103.927 60.754Q-104.045 61.009-104.045 61.275\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m41.888-52.325 26.61 13.304\"\u002F>\u003Cpath stroke=\"none\" d=\"m70.287-38.127-2.147-2.862.358 1.968-1.789.894\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(177.239 -109.506)\">\u003Cpath d=\"M-117.259 63.080L-119.573 63.080L-119.573 62.800Q-118.851 62.800-118.851 62.591L-118.851 58.790Q-118.851 58.579-119.573 58.579L-119.573 58.298L-115.389 58.298L-115.177 59.935L-115.444 59.935Q-115.522 59.324-115.674 59.045Q-115.827 58.767-116.131 58.673Q-116.435 58.579-117.060 58.579L-117.795 58.579Q-117.983 58.579-118.072 58.613Q-118.161 58.647-118.161 58.790L-118.161 60.547L-117.607 60.547Q-117.238 60.547-117.054 60.493Q-116.869 60.438-116.790 60.265Q-116.712 60.093-116.712 59.727L-116.445 59.727L-116.445 61.644L-116.712 61.644Q-116.712 61.279-116.790 61.106Q-116.869 60.934-117.054 60.881Q-117.238 60.828-117.607 60.828L-118.161 60.828L-118.161 62.591Q-118.161 62.800-117.259 62.800\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(177.239 -109.506)\">\u003Cpath d=\"M-114.549 62.246L-114.549 60.742Q-114.549 60.472-114.657 60.411Q-114.765 60.349-115.076 60.349L-115.076 60.069L-113.968 59.994L-113.968 62.226L-113.968 62.246Q-113.968 62.526-113.917 62.670Q-113.866 62.813-113.724 62.870Q-113.582 62.926-113.295 62.926Q-113.042 62.926-112.837 62.786Q-112.632 62.646-112.516 62.420Q-112.399 62.195-112.399 61.945L-112.399 60.742Q-112.399 60.472-112.507 60.411Q-112.615 60.349-112.926 60.349L-112.926 60.069L-111.818 59.994L-111.818 62.407Q-111.818 62.598-111.765 62.680Q-111.712 62.762-111.612 62.781Q-111.511 62.800-111.295 62.800L-111.295 63.080L-112.372 63.148L-112.372 62.584Q-112.481 62.766-112.627 62.889Q-112.772 63.012-112.958 63.080Q-113.145 63.148-113.346 63.148Q-114.549 63.148-114.549 62.246M-109.040 63.080L-110.643 63.080L-110.643 62.800Q-110.417 62.800-110.268 62.766Q-110.120 62.731-110.120 62.591L-110.120 58.972Q-110.120 58.702-110.227 58.640Q-110.335 58.579-110.643 58.579L-110.643 58.298L-109.566 58.223L-109.566 62.591Q-109.566 62.728-109.416 62.764Q-109.265 62.800-109.040 62.800L-109.040 63.080M-106.777 63.080L-108.380 63.080L-108.380 62.800Q-108.154 62.800-108.006 62.766Q-107.857 62.731-107.857 62.591L-107.857 58.972Q-107.857 58.702-107.965 58.640Q-108.072 58.579-108.380 58.579L-108.380 58.298L-107.303 58.223L-107.303 62.591Q-107.303 62.728-107.153 62.764Q-107.002 62.800-106.777 62.800\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M65.606-17.81 12.984 3.568\"\u002F>\u003Cpath stroke=\"none\" d=\"m11.131 4.32 3.567.279-1.714-1.03.51-1.935\"\u002F>\u003Cg stroke=\"none\" font-size=\"7\">\u003Cg transform=\"translate(142.898 -71.555)\">\u003Cpath d=\"M-119.197 63.442Q-119.197 63.333-119.104 63.295L-115.030 61.330L-119.104 59.372Q-119.197 59.334-119.197 59.225Q-119.197 59.153-119.147 59.100Q-119.098 59.047-119.022 59.047Q-119.019 59.047-118.937 59.067L-114.548 61.183Q-114.463 61.231-114.463 61.330Q-114.463 61.436-114.548 61.484L-118.937 63.600Q-118.964 63.606-118.985 63.613Q-119.005 63.620-119.022 63.620Q-119.098 63.620-119.147 63.564Q-119.197 63.507-119.197 63.442\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(142.898 -71.555)\">\u003Cpath d=\"M-111.713 63.220Q-112.171 63.220-112.489 63.005Q-112.806 62.789-112.988 62.437Q-113.169 62.085-113.246 61.665Q-113.323 61.245-113.323 60.817Q-113.323 60.233-113.070 59.677Q-112.817 59.122-112.347 58.777Q-111.877 58.432-111.279 58.432Q-110.869 58.432-110.585 58.630Q-110.301 58.828-110.301 59.231Q-110.301 59.327-110.347 59.406Q-110.393 59.484-110.474 59.529Q-110.554 59.573-110.643 59.573Q-110.790 59.573-110.891 59.476Q-110.992 59.378-110.992 59.231Q-110.992 59.101-110.901 58.994Q-110.810 58.886-110.677 58.886Q-110.865 58.664-111.279 58.664Q-111.593 58.664-111.867 58.828Q-112.140 58.992-112.307 59.266Q-112.495 59.556-112.560 59.922Q-112.625 60.288-112.625 60.742Q-112.475 60.448-112.210 60.270Q-111.945 60.093-111.631 60.093Q-111.200 60.093-110.851 60.299Q-110.503 60.506-110.303 60.862Q-110.103 61.217-110.103 61.644Q-110.103 62.089-110.320 62.449Q-110.537 62.810-110.910 63.015Q-111.282 63.220-111.713 63.220M-111.713 62.967Q-111.337 62.967-111.133 62.784Q-110.930 62.601-110.867 62.318Q-110.804 62.034-110.804 61.644Q-110.804 61.258-110.858 60.978Q-110.913 60.698-111.108 60.506Q-111.303 60.315-111.672 60.315Q-111.962 60.315-112.174 60.491Q-112.386 60.667-112.494 60.940Q-112.601 61.214-112.601 61.497L-112.601 61.638L-112.601 61.679Q-112.601 62.184-112.389 62.576Q-112.178 62.967-111.713 62.967M-107.731 63.220Q-108.367 63.220-108.731 62.875Q-109.095 62.530-109.230 62.005Q-109.365 61.480-109.365 60.855Q-109.365 59.830-109.009 59.131Q-108.654 58.432-107.731 58.432Q-106.805 58.432-106.452 59.131Q-106.100 59.830-106.100 60.855Q-106.100 61.480-106.235 62.005Q-106.370 62.530-106.733 62.875Q-107.095 63.220-107.731 63.220M-107.731 62.995Q-107.293 62.995-107.080 62.620Q-106.866 62.246-106.817 61.779Q-106.767 61.313-106.767 60.735Q-106.767 60.182-106.817 59.754Q-106.866 59.327-107.078 58.992Q-107.290 58.657-107.731 58.657Q-108.073 58.657-108.276 58.864Q-108.479 59.071-108.567 59.383Q-108.654 59.696-108.676 60.012Q-108.698 60.329-108.698 60.735Q-108.698 61.152-108.676 61.494Q-108.654 61.836-108.565 62.184Q-108.476 62.533-108.271 62.764Q-108.066 62.995-107.731 62.995\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M80.441-17.81 65.18-2.552\"\u002F>\u003Cpath stroke=\"none\" d=\"m63.764-1.138 3.394-1.13-1.98-.284-.282-1.98\"\u002F>\u003Cg transform=\"translate(172.215 -70.299)\">\u003Cpath d=\"M-119.156 62.533Q-119.036 62.690-118.845 62.789Q-118.653 62.889-118.438 62.928Q-118.223 62.967-118 62.967Q-117.703 62.967-117.508 62.812Q-117.313 62.656-117.223 62.402Q-117.132 62.147-117.132 61.863Q-117.132 61.569-117.224 61.318Q-117.317 61.067-117.515 60.911Q-117.713 60.756-118.007 60.756L-118.523 60.756Q-118.551 60.756-118.576 60.730Q-118.602 60.705-118.602 60.681L-118.602 60.609Q-118.602 60.578-118.576 60.556Q-118.551 60.534-118.523 60.534L-118.082 60.503Q-117.720 60.503-117.500 60.146Q-117.279 59.788-117.279 59.399Q-117.279 59.071-117.474 58.867Q-117.669 58.664-118 58.664Q-118.287 58.664-118.540 58.748Q-118.793 58.831-118.957 59.019Q-118.810 59.019-118.710 59.134Q-118.609 59.248-118.609 59.399Q-118.609 59.549-118.715 59.659Q-118.821 59.768-118.978 59.768Q-119.139 59.768-119.248 59.659Q-119.357 59.549-119.357 59.399Q-119.357 59.074-119.149 58.855Q-118.940 58.637-118.624 58.534Q-118.308 58.432-118 58.432Q-117.682 58.432-117.354 58.536Q-117.026 58.640-116.799 58.862Q-116.572 59.084-116.572 59.399Q-116.572 59.833-116.859 60.158Q-117.146 60.482-117.580 60.629Q-117.269 60.694-116.989 60.860Q-116.708 61.026-116.531 61.284Q-116.353 61.542-116.353 61.863Q-116.353 62.273-116.597 62.583Q-116.842 62.892-117.223 63.056Q-117.604 63.220-118 63.220Q-118.369 63.220-118.727 63.107Q-119.084 62.995-119.328 62.745Q-119.573 62.496-119.573 62.126Q-119.573 61.955-119.456 61.843Q-119.340 61.730-119.169 61.730Q-119.060 61.730-118.969 61.781Q-118.879 61.832-118.824 61.925Q-118.769 62.017-118.769 62.126Q-118.769 62.294-118.882 62.413Q-118.995 62.533-119.156 62.533M-113.981 63.220Q-114.617 63.220-114.981 62.875Q-115.345 62.530-115.480 62.005Q-115.615 61.480-115.615 60.855Q-115.615 59.830-115.259 59.131Q-114.904 58.432-113.981 58.432Q-113.055 58.432-112.702 59.131Q-112.350 59.830-112.350 60.855Q-112.350 61.480-112.485 62.005Q-112.620 62.530-112.983 62.875Q-113.345 63.220-113.981 63.220M-113.981 62.995Q-113.543 62.995-113.330 62.620Q-113.116 62.246-113.067 61.779Q-113.017 61.313-113.017 60.735Q-113.017 60.182-113.067 59.754Q-113.116 59.327-113.328 58.992Q-113.540 58.657-113.981 58.657Q-114.323 58.657-114.526 58.864Q-114.729 59.071-114.817 59.383Q-114.904 59.696-114.926 60.012Q-114.948 60.329-114.948 60.735Q-114.948 61.152-114.926 61.494Q-114.904 61.836-114.815 62.184Q-114.726 62.533-114.521 62.764Q-114.316 62.995-113.981 62.995M-109.790 61.826L-111.848 61.826L-111.848 61.323L-109.790 61.323L-109.790 61.826M-107.312 63.220Q-107.770 63.220-108.088 63.005Q-108.406 62.789-108.587 62.437Q-108.768 62.085-108.845 61.665Q-108.922 61.245-108.922 60.817Q-108.922 60.233-108.669 59.677Q-108.416 59.122-107.946 58.777Q-107.476 58.432-106.878 58.432Q-106.468 58.432-106.184 58.630Q-105.901 58.828-105.901 59.231Q-105.901 59.327-105.947 59.406Q-105.993 59.484-106.073 59.529Q-106.154 59.573-106.243 59.573Q-106.389 59.573-106.490 59.476Q-106.591 59.378-106.591 59.231Q-106.591 59.101-106.501 58.994Q-106.410 58.886-106.277 58.886Q-106.465 58.664-106.878 58.664Q-107.193 58.664-107.466 58.828Q-107.740 58.992-107.907 59.266Q-108.095 59.556-108.160 59.922Q-108.225 60.288-108.225 60.742Q-108.075 60.448-107.810 60.270Q-107.545 60.093-107.230 60.093Q-106.800 60.093-106.451 60.299Q-106.102 60.506-105.902 60.862Q-105.702 61.217-105.702 61.644Q-105.702 62.089-105.920 62.449Q-106.137 62.810-106.509 63.015Q-106.882 63.220-107.312 63.220M-107.312 62.967Q-106.936 62.967-106.733 62.784Q-106.530 62.601-106.466 62.318Q-106.403 62.034-106.403 61.644Q-106.403 61.258-106.458 60.978Q-106.513 60.698-106.707 60.506Q-106.902 60.315-107.271 60.315Q-107.562 60.315-107.774 60.491Q-107.986 60.667-108.093 60.940Q-108.201 61.214-108.201 61.497L-108.201 61.638L-108.201 61.679Q-108.201 62.184-107.989 62.576Q-107.777 62.967-107.312 62.967M-103.330 63.220Q-103.966 63.220-104.330 62.875Q-104.694 62.530-104.829 62.005Q-104.964 61.480-104.964 60.855Q-104.964 59.830-104.609 59.131Q-104.253 58.432-103.330 58.432Q-102.404 58.432-102.052 59.131Q-101.700 59.830-101.700 60.855Q-101.700 61.480-101.835 62.005Q-101.970 62.530-102.332 62.875Q-102.695 63.220-103.330 63.220M-103.330 62.995Q-102.893 62.995-102.679 62.620Q-102.466 62.246-102.416 61.779Q-102.367 61.313-102.367 60.735Q-102.367 60.182-102.416 59.754Q-102.466 59.327-102.678 58.992Q-102.889 58.657-103.330 58.657Q-103.672 58.657-103.876 58.864Q-104.079 59.071-104.166 59.383Q-104.253 59.696-104.275 60.012Q-104.298 60.329-104.298 60.735Q-104.298 61.152-104.275 61.494Q-104.253 61.836-104.164 62.184Q-104.076 62.533-103.870 62.764Q-103.665 62.995-103.330 62.995\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M100.756-17.81 116.02-2.552\"\u002F>\u003Cpath stroke=\"none\" d=\"m117.433-1.138-1.131-3.394-.283 1.98-1.98.283\"\u002F>\u003Cg transform=\"translate(230.246 -70.299)\">\u003Cpath d=\"M-116.626 63.080L-119.156 63.080L-119.156 62.800Q-118.188 62.800-118.188 62.591L-118.188 58.972Q-118.581 59.160-119.203 59.160L-119.203 58.879Q-118.786 58.879-118.422 58.778Q-118.058 58.678-117.802 58.432L-117.676 58.432Q-117.611 58.449-117.594 58.517L-117.594 62.591Q-117.594 62.800-116.626 62.800L-116.626 63.080M-113.981 63.220Q-114.617 63.220-114.981 62.875Q-115.345 62.530-115.480 62.005Q-115.615 61.480-115.615 60.855Q-115.615 59.830-115.259 59.131Q-114.904 58.432-113.981 58.432Q-113.055 58.432-112.702 59.131Q-112.350 59.830-112.350 60.855Q-112.350 61.480-112.485 62.005Q-112.620 62.530-112.983 62.875Q-113.345 63.220-113.981 63.220M-113.981 62.995Q-113.543 62.995-113.330 62.620Q-113.116 62.246-113.067 61.779Q-113.017 61.313-113.017 60.735Q-113.017 60.182-113.067 59.754Q-113.116 59.327-113.328 58.992Q-113.540 58.657-113.981 58.657Q-114.323 58.657-114.526 58.864Q-114.729 59.071-114.817 59.383Q-114.904 59.696-114.926 60.012Q-114.948 60.329-114.948 60.735Q-114.948 61.152-114.926 61.494Q-114.904 61.836-114.815 62.184Q-114.726 62.533-114.521 62.764Q-114.316 62.995-113.981 62.995M-109.790 61.826L-111.848 61.826L-111.848 61.323L-109.790 61.323L-109.790 61.826M-108.505 62.533Q-108.386 62.690-108.194 62.789Q-108.003 62.889-107.787 62.928Q-107.572 62.967-107.350 62.967Q-107.053 62.967-106.858 62.812Q-106.663 62.656-106.572 62.402Q-106.482 62.147-106.482 61.863Q-106.482 61.569-106.574 61.318Q-106.666 61.067-106.865 60.911Q-107.063 60.756-107.357 60.756L-107.873 60.756Q-107.900 60.756-107.926 60.730Q-107.952 60.705-107.952 60.681L-107.952 60.609Q-107.952 60.578-107.926 60.556Q-107.900 60.534-107.873 60.534L-107.432 60.503Q-107.070 60.503-106.849 60.146Q-106.629 59.788-106.629 59.399Q-106.629 59.071-106.824 58.867Q-107.018 58.664-107.350 58.664Q-107.637 58.664-107.890 58.748Q-108.143 58.831-108.307 59.019Q-108.160 59.019-108.059 59.134Q-107.958 59.248-107.958 59.399Q-107.958 59.549-108.064 59.659Q-108.170 59.768-108.327 59.768Q-108.488 59.768-108.598 59.659Q-108.707 59.549-108.707 59.399Q-108.707 59.074-108.498 58.855Q-108.290 58.637-107.974 58.534Q-107.658 58.432-107.350 58.432Q-107.032 58.432-106.704 58.536Q-106.376 58.640-106.149 58.862Q-105.921 59.084-105.921 59.399Q-105.921 59.833-106.208 60.158Q-106.495 60.482-106.930 60.629Q-106.619 60.694-106.338 60.860Q-106.058 61.026-105.880 61.284Q-105.702 61.542-105.702 61.863Q-105.702 62.273-105.947 62.583Q-106.191 62.892-106.572 63.056Q-106.953 63.220-107.350 63.220Q-107.719 63.220-108.076 63.107Q-108.433 62.995-108.678 62.745Q-108.922 62.496-108.922 62.126Q-108.922 61.955-108.806 61.843Q-108.690 61.730-108.519 61.730Q-108.410 61.730-108.319 61.781Q-108.228 61.832-108.174 61.925Q-108.119 62.017-108.119 62.126Q-108.119 62.294-108.232 62.413Q-108.345 62.533-108.505 62.533M-103.330 63.220Q-103.966 63.220-104.330 62.875Q-104.694 62.530-104.829 62.005Q-104.964 61.480-104.964 60.855Q-104.964 59.830-104.609 59.131Q-104.253 58.432-103.330 58.432Q-102.404 58.432-102.052 59.131Q-101.700 59.830-101.700 60.855Q-101.700 61.480-101.835 62.005Q-101.970 62.530-102.332 62.875Q-102.695 63.220-103.330 63.220M-103.330 62.995Q-102.893 62.995-102.679 62.620Q-102.466 62.246-102.416 61.779Q-102.367 61.313-102.367 60.735Q-102.367 60.182-102.416 59.754Q-102.466 59.327-102.678 58.992Q-102.889 58.657-103.330 58.657Q-103.672 58.657-103.876 58.864Q-104.079 59.071-104.166 59.383Q-104.253 59.696-104.275 60.012Q-104.298 60.329-104.298 60.735Q-104.298 61.152-104.275 61.494Q-104.253 61.836-104.164 62.184Q-104.076 62.533-103.870 62.764Q-103.665 62.995-103.330 62.995\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m115.592-17.81 52.622 21.378\"\u002F>\u003Cpath stroke=\"none\" d=\"m170.067 4.32-2.363-2.686.51 1.934-1.714 1.03\"\u002F>\u003Cg transform=\"translate(263.98 -71.025)\">\u003Cpath d=\"M-117.963 63.220Q-118.598 63.220-118.962 62.875Q-119.327 62.530-119.462 62.005Q-119.597 61.480-119.597 60.855Q-119.597 59.830-119.241 59.131Q-118.886 58.432-117.963 58.432Q-117.036 58.432-116.684 59.131Q-116.332 59.830-116.332 60.855Q-116.332 61.480-116.467 62.005Q-116.602 62.530-116.965 62.875Q-117.327 63.220-117.963 63.220M-117.963 62.995Q-117.525 62.995-117.312 62.620Q-117.098 62.246-117.048 61.779Q-116.999 61.313-116.999 60.735Q-116.999 60.182-117.048 59.754Q-117.098 59.327-117.310 58.992Q-117.522 58.657-117.963 58.657Q-118.305 58.657-118.508 58.864Q-118.711 59.071-118.798 59.383Q-118.886 59.696-118.908 60.012Q-118.930 60.329-118.930 60.735Q-118.930 61.152-118.908 61.494Q-118.886 61.836-118.797 62.184Q-118.708 62.533-118.503 62.764Q-118.298 62.995-117.963 62.995M-113.772 61.826L-115.830 61.826L-115.830 61.323L-113.772 61.323L-113.772 61.826M-109.958 63.080L-112.487 63.080L-112.487 62.800Q-111.520 62.800-111.520 62.591L-111.520 58.972Q-111.913 59.160-112.535 59.160L-112.535 58.879Q-112.118 58.879-111.754 58.778Q-111.390 58.678-111.134 58.432L-111.007 58.432Q-110.942 58.449-110.925 58.517L-110.925 62.591Q-110.925 62.800-109.958 62.800L-109.958 63.080M-107.312 63.220Q-107.948 63.220-108.312 62.875Q-108.676 62.530-108.811 62.005Q-108.946 61.480-108.946 60.855Q-108.946 59.830-108.591 59.131Q-108.235 58.432-107.312 58.432Q-106.386 58.432-106.034 59.131Q-105.682 59.830-105.682 60.855Q-105.682 61.480-105.817 62.005Q-105.952 62.530-106.314 62.875Q-106.677 63.220-107.312 63.220M-107.312 62.995Q-106.875 62.995-106.661 62.620Q-106.448 62.246-106.398 61.779Q-106.348 61.313-106.348 60.735Q-106.348 60.182-106.398 59.754Q-106.448 59.327-106.660 58.992Q-106.871 58.657-107.312 58.657Q-107.654 58.657-107.858 58.864Q-108.061 59.071-108.148 59.383Q-108.235 59.696-108.257 60.012Q-108.280 60.329-108.280 60.735Q-108.280 61.152-108.257 61.494Q-108.235 61.836-108.146 62.184Q-108.057 62.533-107.852 62.764Q-107.647 62.995-107.312 62.995\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m48.141 19.179-8.798 16.333\"\u002F>\u003Cpath stroke=\"none\" d=\"m38.395 37.273 2.926-2.059-1.978.298-.84-1.815\"\u002F>\u003Cg transform=\"translate(152.13 -32.463)\">\u003Cpath d=\"M-117.840 63.080L-119.573 63.080L-119.573 62.800Q-118.851 62.800-118.851 62.400L-118.851 58.613Q-119.084 58.579-119.573 58.579L-119.573 58.298L-118.216 58.298Q-118.175 58.312-118.154 58.326L-115.451 61.973L-115.451 58.978Q-115.451 58.579-116.172 58.579L-116.172 58.298L-114.435 58.298L-114.435 58.579Q-115.157 58.579-115.157 58.978L-115.157 62.988Q-115.174 63.063-115.242 63.080L-115.362 63.080Q-115.410 63.070-115.423 63.046L-118.557 58.825L-118.557 62.400Q-118.557 62.800-117.840 62.800L-117.840 63.080M-113.779 61.597Q-113.779 61.255-113.644 60.956Q-113.509 60.657-113.270 60.433Q-113.031 60.209-112.713 60.084Q-112.395 59.959-112.063 59.959Q-111.619 59.959-111.219 60.175Q-110.819 60.390-110.585 60.768Q-110.351 61.145-110.351 61.597Q-110.351 61.938-110.493 62.222Q-110.635 62.506-110.879 62.713Q-111.123 62.919-111.433 63.034Q-111.742 63.148-112.063 63.148Q-112.494 63.148-112.896 62.947Q-113.297 62.745-113.538 62.393Q-113.779 62.041-113.779 61.597M-112.063 62.899Q-111.462 62.899-111.238 62.521Q-111.014 62.143-111.014 61.511Q-111.014 60.899-111.248 60.540Q-111.482 60.182-112.063 60.182Q-113.116 60.182-113.116 61.511Q-113.116 62.143-112.890 62.521Q-112.665 62.899-112.063 62.899\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m59.08 19.179 8.797 16.333\"\u002F>\u003Cpath stroke=\"none\" d=\"m68.826 37.273-.11-3.576-.839 1.815-1.977-.298\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(185.104 -32.463)\">\u003Cpath d=\"M-115.950 63.080L-118.062 63.080L-118.062 62.800Q-117.341 62.800-117.341 62.591L-117.341 61.169L-118.992 58.790Q-119.111 58.647-119.304 58.613Q-119.497 58.579-119.785 58.579L-119.785 58.298L-117.754 58.298L-117.754 58.579Q-118.202 58.579-118.202 58.719Q-118.202 58.753-118.181 58.790L-116.773 60.817L-115.492 58.978Q-115.437 58.903-115.437 58.811Q-115.437 58.688-115.539 58.633Q-115.642 58.579-115.785 58.579L-115.785 58.298L-114.227 58.298L-114.227 58.579Q-114.521 58.579-114.762 58.673Q-115.003 58.767-115.150 58.978L-116.678 61.169L-116.678 62.591Q-116.678 62.800-115.950 62.800\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(185.104 -32.463)\">\u003Cpath d=\"M-114.422 61.545Q-114.422 61.224-114.297 60.935Q-114.172 60.646-113.946 60.423Q-113.721 60.199-113.425 60.079Q-113.130 59.959-112.812 59.959Q-112.484 59.959-112.222 60.059Q-111.961 60.158-111.785 60.340Q-111.609 60.523-111.515 60.781Q-111.421 61.039-111.421 61.371Q-111.421 61.463-111.503 61.484L-113.758 61.484L-113.758 61.545Q-113.758 62.133-113.475 62.516Q-113.191 62.899-112.624 62.899Q-112.302 62.899-112.034 62.706Q-111.766 62.513-111.677 62.198Q-111.670 62.157-111.595 62.143L-111.503 62.143Q-111.421 62.167-111.421 62.239Q-111.421 62.246-111.427 62.273Q-111.540 62.670-111.911 62.909Q-112.282 63.148-112.706 63.148Q-113.143 63.148-113.543 62.940Q-113.943 62.731-114.182 62.364Q-114.422 61.997-114.422 61.545M-113.752 61.275L-111.937 61.275Q-111.937 60.998-112.034 60.746Q-112.132 60.493-112.330 60.337Q-112.528 60.182-112.812 60.182Q-113.089 60.182-113.302 60.340Q-113.516 60.499-113.634 60.754Q-113.752 61.009-113.752 61.275M-110.833 63.073L-110.833 62.010Q-110.833 61.986-110.805 61.959Q-110.778 61.932-110.754 61.932L-110.645 61.932Q-110.580 61.932-110.566 61.990Q-110.470 62.424-110.224 62.675Q-109.978 62.926-109.565 62.926Q-109.223 62.926-108.970 62.793Q-108.717 62.660-108.717 62.352Q-108.717 62.195-108.811 62.080Q-108.905 61.966-109.043 61.897Q-109.182 61.829-109.349 61.791L-109.930 61.692Q-110.286 61.624-110.559 61.403Q-110.833 61.183-110.833 60.841Q-110.833 60.592-110.722 60.417Q-110.611 60.243-110.424 60.144Q-110.238 60.045-110.023 60.002Q-109.807 59.959-109.565 59.959Q-109.151 59.959-108.871 60.141L-108.655 59.966Q-108.645 59.963-108.638 59.961Q-108.632 59.959-108.621 59.959L-108.570 59.959Q-108.543 59.959-108.519 59.983Q-108.495 60.007-108.495 60.035L-108.495 60.882Q-108.495 60.903-108.519 60.930Q-108.543 60.957-108.570 60.957L-108.683 60.957Q-108.710 60.957-108.736 60.932Q-108.761 60.906-108.761 60.882Q-108.761 60.646-108.867 60.482Q-108.973 60.318-109.156 60.236Q-109.339 60.154-109.571 60.154Q-109.900 60.154-110.156 60.257Q-110.412 60.359-110.412 60.636Q-110.412 60.831-110.229 60.940Q-110.047 61.050-109.818 61.091L-109.243 61.197Q-108.997 61.245-108.784 61.373Q-108.570 61.501-108.433 61.704Q-108.297 61.908-108.297 62.157Q-108.297 62.670-108.662 62.909Q-109.028 63.148-109.565 63.148Q-110.060 63.148-110.392 62.854L-110.658 63.128Q-110.679 63.148-110.706 63.148L-110.754 63.148Q-110.778 63.148-110.805 63.121Q-110.833 63.094-110.833 63.073\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m122.9 19.179-7.516 16.278\"\u002F>\u003Cpath stroke=\"none\" d=\"m114.546 37.273 2.794-2.235-1.956.419-.95-1.76\"\u002F>\u003Cg transform=\"translate(227.585 -32.463)\">\u003Cpath d=\"M-117.840 63.080L-119.573 63.080L-119.573 62.800Q-118.851 62.800-118.851 62.400L-118.851 58.613Q-119.084 58.579-119.573 58.579L-119.573 58.298L-118.216 58.298Q-118.175 58.312-118.154 58.326L-115.451 61.973L-115.451 58.978Q-115.451 58.579-116.172 58.579L-116.172 58.298L-114.435 58.298L-114.435 58.579Q-115.157 58.579-115.157 58.978L-115.157 62.988Q-115.174 63.063-115.242 63.080L-115.362 63.080Q-115.410 63.070-115.423 63.046L-118.557 58.825L-118.557 62.400Q-118.557 62.800-117.840 62.800L-117.840 63.080M-113.779 61.597Q-113.779 61.255-113.644 60.956Q-113.509 60.657-113.270 60.433Q-113.031 60.209-112.713 60.084Q-112.395 59.959-112.063 59.959Q-111.619 59.959-111.219 60.175Q-110.819 60.390-110.585 60.768Q-110.351 61.145-110.351 61.597Q-110.351 61.938-110.493 62.222Q-110.635 62.506-110.879 62.713Q-111.123 62.919-111.433 63.034Q-111.742 63.148-112.063 63.148Q-112.494 63.148-112.896 62.947Q-113.297 62.745-113.538 62.393Q-113.779 62.041-113.779 61.597M-112.063 62.899Q-111.462 62.899-111.238 62.521Q-111.014 62.143-111.014 61.511Q-111.014 60.899-111.248 60.540Q-111.482 60.182-112.063 60.182Q-113.116 60.182-113.116 61.511Q-113.116 62.143-112.890 62.521Q-112.665 62.899-112.063 62.899\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m133.056 19.179 8.798 16.333\"\u002F>\u003Cpath stroke=\"none\" d=\"m142.802 37.273-.108-3.576-.84 1.815-1.978-.298\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(259.08 -32.463)\">\u003Cpath d=\"M-115.950 63.080L-118.062 63.080L-118.062 62.800Q-117.341 62.800-117.341 62.591L-117.341 61.169L-118.992 58.790Q-119.111 58.647-119.304 58.613Q-119.497 58.579-119.785 58.579L-119.785 58.298L-117.754 58.298L-117.754 58.579Q-118.202 58.579-118.202 58.719Q-118.202 58.753-118.181 58.790L-116.773 60.817L-115.492 58.978Q-115.437 58.903-115.437 58.811Q-115.437 58.688-115.539 58.633Q-115.642 58.579-115.785 58.579L-115.785 58.298L-114.227 58.298L-114.227 58.579Q-114.521 58.579-114.762 58.673Q-115.003 58.767-115.150 58.978L-116.678 61.169L-116.678 62.591Q-116.678 62.800-115.950 62.800\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(259.08 -32.463)\">\u003Cpath d=\"M-114.422 61.545Q-114.422 61.224-114.297 60.935Q-114.172 60.646-113.946 60.423Q-113.721 60.199-113.425 60.079Q-113.130 59.959-112.812 59.959Q-112.484 59.959-112.222 60.059Q-111.961 60.158-111.785 60.340Q-111.609 60.523-111.515 60.781Q-111.421 61.039-111.421 61.371Q-111.421 61.463-111.503 61.484L-113.758 61.484L-113.758 61.545Q-113.758 62.133-113.475 62.516Q-113.191 62.899-112.624 62.899Q-112.302 62.899-112.034 62.706Q-111.766 62.513-111.677 62.198Q-111.670 62.157-111.595 62.143L-111.503 62.143Q-111.421 62.167-111.421 62.239Q-111.421 62.246-111.427 62.273Q-111.540 62.670-111.911 62.909Q-112.282 63.148-112.706 63.148Q-113.143 63.148-113.543 62.940Q-113.943 62.731-114.182 62.364Q-114.422 61.997-114.422 61.545M-113.752 61.275L-111.937 61.275Q-111.937 60.998-112.034 60.746Q-112.132 60.493-112.330 60.337Q-112.528 60.182-112.812 60.182Q-113.089 60.182-113.302 60.340Q-113.516 60.499-113.634 60.754Q-113.752 61.009-113.752 61.275M-110.833 63.073L-110.833 62.010Q-110.833 61.986-110.805 61.959Q-110.778 61.932-110.754 61.932L-110.645 61.932Q-110.580 61.932-110.566 61.990Q-110.470 62.424-110.224 62.675Q-109.978 62.926-109.565 62.926Q-109.223 62.926-108.970 62.793Q-108.717 62.660-108.717 62.352Q-108.717 62.195-108.811 62.080Q-108.905 61.966-109.043 61.897Q-109.182 61.829-109.349 61.791L-109.930 61.692Q-110.286 61.624-110.559 61.403Q-110.833 61.183-110.833 60.841Q-110.833 60.592-110.722 60.417Q-110.611 60.243-110.424 60.144Q-110.238 60.045-110.023 60.002Q-109.807 59.959-109.565 59.959Q-109.151 59.959-108.871 60.141L-108.655 59.966Q-108.645 59.963-108.638 59.961Q-108.632 59.959-108.621 59.959L-108.570 59.959Q-108.543 59.959-108.519 59.983Q-108.495 60.007-108.495 60.035L-108.495 60.882Q-108.495 60.903-108.519 60.930Q-108.543 60.957-108.570 60.957L-108.683 60.957Q-108.710 60.957-108.736 60.932Q-108.761 60.906-108.761 60.882Q-108.761 60.646-108.867 60.482Q-108.973 60.318-109.156 60.236Q-109.339 60.154-109.571 60.154Q-109.900 60.154-110.156 60.257Q-110.412 60.359-110.412 60.636Q-110.412 60.831-110.229 60.940Q-110.047 61.050-109.818 61.091L-109.243 61.197Q-108.997 61.245-108.784 61.373Q-108.570 61.501-108.433 61.704Q-108.297 61.908-108.297 62.157Q-108.297 62.670-108.662 62.909Q-109.028 63.148-109.565 63.148Q-110.060 63.148-110.392 62.854L-110.658 63.128Q-110.679 63.148-110.706 63.148L-110.754 63.148Q-110.778 63.148-110.805 63.121Q-110.833 63.094-110.833 63.073\" 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\">A decision tree for the restaurant problem. Interior nodes test one attribute; branches are attribute values; leaves are the WillWait verdict. An example with Patrons = Full and WaitEstimate = 0-10 is classified Yes.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:506.159px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 379.619 92.312\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cpath fill=\"none\" d=\"M-.45-54.998h36.988V-72.07H-.45Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-10.826 -83.608)\">\u003Cpath d=\"M22.265 21.824L19.527 21.824L19.527 21.544Q19.876 21.544 20.213 21.508Q20.549 21.472 20.549 21.335L20.549 17.534Q20.549 17.391 20.461 17.357Q20.372 17.323 20.187 17.323L19.828 17.323Q19.527 17.323 19.312 17.370Q19.097 17.418 18.940 17.575Q18.803 17.709 18.743 17.987Q18.683 18.266 18.646 18.679L18.379 18.679L18.526 17.042L23.260 17.042L23.407 18.679L23.140 18.679Q23.103 18.266 23.046 17.989Q22.990 17.712 22.846 17.575Q22.686 17.415 22.474 17.369Q22.262 17.323 21.958 17.323L21.606 17.323Q21.421 17.323 21.332 17.357Q21.243 17.391 21.243 17.534L21.243 21.335Q21.243 21.472 21.580 21.508Q21.917 21.544 22.265 21.544\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-10.826 -83.608)\">\u003Cpath d=\"M24.186 22.959Q24.316 23.027 24.453 23.027Q24.624 23.027 24.774 22.938Q24.925 22.849 25.036 22.704Q25.147 22.559 25.225 22.391L25.489 21.824L24.320 19.298Q24.245 19.151 24.115 19.119Q23.985 19.086 23.752 19.086L23.752 18.806L25.273 18.806L25.273 19.086Q24.925 19.086 24.925 19.233Q24.928 19.254 24.930 19.271Q24.932 19.288 24.932 19.298L25.789 21.157L26.562 19.486Q26.596 19.418 26.596 19.339Q26.596 19.226 26.512 19.156Q26.429 19.086 26.316 19.086L26.316 18.806L27.512 18.806L27.512 19.086Q27.293 19.086 27.121 19.190Q26.948 19.295 26.856 19.486L25.519 22.391Q25.349 22.761 25.079 23.007Q24.808 23.253 24.453 23.253Q24.183 23.253 23.964 23.087Q23.745 22.921 23.745 22.658Q23.745 22.521 23.838 22.432Q23.930 22.344 24.070 22.344Q24.207 22.344 24.296 22.432Q24.385 22.521 24.385 22.658Q24.385 22.761 24.332 22.839Q24.279 22.918 24.186 22.959M29.696 23.181L28.066 23.181L28.066 22.901Q28.295 22.901 28.443 22.866Q28.592 22.832 28.592 22.692L28.592 19.346Q28.592 19.175 28.455 19.134Q28.319 19.093 28.066 19.093L28.066 18.813L29.146 18.738L29.146 19.144Q29.368 18.943 29.655 18.840Q29.942 18.738 30.250 18.738Q30.677 18.738 31.041 18.951Q31.405 19.165 31.619 19.529Q31.832 19.893 31.832 20.313Q31.832 20.758 31.593 21.122Q31.354 21.486 30.961 21.689Q30.568 21.892 30.123 21.892Q29.857 21.892 29.609 21.792Q29.361 21.691 29.173 21.510L29.173 22.692Q29.173 22.829 29.322 22.865Q29.471 22.901 29.696 22.901L29.696 23.181M29.173 19.493L29.173 21.103Q29.307 21.356 29.549 21.513Q29.792 21.670 30.069 21.670Q30.397 21.670 30.650 21.469Q30.903 21.267 31.036 20.949Q31.169 20.631 31.169 20.313Q31.169 20.084 31.104 19.855Q31.039 19.626 30.911 19.428Q30.783 19.230 30.588 19.110Q30.393 18.991 30.161 18.991Q29.867 18.991 29.599 19.120Q29.330 19.250 29.173 19.493\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-10.826 -83.608)\">\u003Cpath d=\"M32.643 20.289Q32.643 19.968 32.768 19.679Q32.893 19.390 33.119 19.167Q33.344 18.943 33.640 18.823Q33.935 18.703 34.253 18.703Q34.581 18.703 34.843 18.803Q35.104 18.902 35.280 19.084Q35.456 19.267 35.550 19.525Q35.644 19.783 35.644 20.115Q35.644 20.207 35.562 20.228L33.307 20.228L33.307 20.289Q33.307 20.877 33.590 21.260Q33.874 21.643 34.441 21.643Q34.763 21.643 35.031 21.450Q35.299 21.257 35.388 20.942Q35.395 20.901 35.470 20.887L35.562 20.887Q35.644 20.911 35.644 20.983Q35.644 20.990 35.638 21.017Q35.525 21.414 35.154 21.653Q34.783 21.892 34.359 21.892Q33.922 21.892 33.522 21.684Q33.122 21.475 32.883 21.108Q32.643 20.741 32.643 20.289M33.313 20.019L35.128 20.019Q35.128 19.742 35.031 19.490Q34.933 19.237 34.735 19.081Q34.537 18.926 34.253 18.926Q33.976 18.926 33.763 19.084Q33.549 19.243 33.431 19.498Q33.313 19.753 33.313 20.019M37.275 21.404Q37.275 21.236 37.401 21.113Q37.528 20.990 37.695 20.990Q37.863 20.990 37.986 21.113Q38.109 21.236 38.109 21.404Q38.109 21.578 37.986 21.701Q37.863 21.824 37.695 21.824Q37.528 21.824 37.401 21.701Q37.275 21.578 37.275 21.404M37.562 20.368L37.562 20.026Q37.562 19.678 37.675 19.339Q37.787 19.001 37.989 18.731Q38.051 18.649 38.169 18.532Q38.287 18.416 38.363 18.326Q38.440 18.235 38.485 18.122Q38.529 18.010 38.529 17.849Q38.529 17.422 38.336 17.271Q38.143 17.121 37.695 17.121Q37.418 17.121 37.169 17.215Q36.919 17.309 36.786 17.504Q36.923 17.504 37.012 17.610Q37.100 17.716 37.100 17.849Q37.100 17.945 37.054 18.023Q37.008 18.102 36.928 18.146Q36.848 18.191 36.759 18.191Q36.608 18.191 36.507 18.093Q36.407 17.996 36.407 17.849Q36.407 17.538 36.600 17.321Q36.793 17.104 37.090 17Q37.388 16.895 37.695 16.895Q37.952 16.895 38.213 16.938Q38.474 16.981 38.693 17.083Q38.912 17.186 39.052 17.377Q39.192 17.569 39.192 17.849Q39.192 18.064 39.083 18.252Q38.974 18.440 38.782 18.563Q38.505 18.734 38.292 18.956Q38.078 19.178 37.953 19.457Q37.828 19.736 37.828 20.040L37.828 20.368Q37.828 20.399 37.805 20.423Q37.781 20.447 37.753 20.447L37.641 20.447Q37.610 20.447 37.586 20.423Q37.562 20.399 37.562 20.368\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-68.737-7.74h36.989V-28.28h-36.989Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-77.606 -34.259)\">\u003Cpath d=\"M21.456 13.824L18.926 13.824L18.926 13.544Q19.894 13.544 19.894 13.335L19.894 9.716Q19.501 9.904 18.879 9.904L18.879 9.623Q19.296 9.623 19.660 9.522Q20.024 9.422 20.280 9.176L20.406 9.176Q20.471 9.193 20.488 9.261L20.488 13.335Q20.488 13.544 21.456 13.544\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-77.606 -34.259)\">\u003Cpath d=\"M26.769 15.181L25.139 15.181L25.139 14.901Q25.368 14.901 25.517 14.866Q25.665 14.832 25.665 14.692L25.665 11.346Q25.665 11.175 25.529 11.134Q25.392 11.093 25.139 11.093L25.139 10.813L26.219 10.738L26.219 11.144Q26.441 10.943 26.728 10.840Q27.016 10.738 27.323 10.738Q27.750 10.738 28.114 10.951Q28.478 11.165 28.692 11.529Q28.906 11.893 28.906 12.313Q28.906 12.758 28.666 13.122Q28.427 13.486 28.034 13.689Q27.641 13.892 27.197 13.892Q26.930 13.892 26.682 13.792Q26.435 13.691 26.247 13.510L26.247 14.692Q26.247 14.829 26.395 14.865Q26.544 14.901 26.769 14.901L26.769 15.181M26.247 11.493L26.247 13.103Q26.380 13.356 26.623 13.513Q26.865 13.670 27.142 13.670Q27.470 13.670 27.723 13.469Q27.976 13.267 28.109 12.949Q28.243 12.631 28.243 12.313Q28.243 12.084 28.178 11.855Q28.113 11.626 27.985 11.428Q27.856 11.230 27.662 11.110Q27.467 10.991 27.234 10.991Q26.940 10.991 26.672 11.120Q26.404 11.250 26.247 11.493\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-77.606 -34.259)\">\u003Cpath d=\"M29.716 12.341Q29.716 11.999 29.851 11.700Q29.986 11.401 30.226 11.177Q30.465 10.953 30.783 10.828Q31.101 10.703 31.432 10.703Q31.877 10.703 32.276 10.919Q32.676 11.134 32.911 11.512Q33.145 11.889 33.145 12.341Q33.145 12.682 33.003 12.966Q32.861 13.250 32.617 13.457Q32.372 13.663 32.063 13.778Q31.754 13.892 31.432 13.892Q31.002 13.892 30.600 13.691Q30.198 13.489 29.957 13.137Q29.716 12.785 29.716 12.341M31.432 13.643Q32.034 13.643 32.258 13.265Q32.482 12.887 32.482 12.255Q32.482 11.643 32.247 11.284Q32.013 10.926 31.432 10.926Q30.380 10.926 30.380 12.255Q30.380 12.887 30.605 13.265Q30.831 13.643 31.432 13.643M33.739 13.817L33.739 12.754Q33.739 12.730 33.767 12.703Q33.794 12.676 33.818 12.676L33.927 12.676Q33.992 12.676 34.006 12.734Q34.102 13.168 34.348 13.419Q34.594 13.670 35.007 13.670Q35.349 13.670 35.602 13.537Q35.855 13.404 35.855 13.096Q35.855 12.939 35.761 12.824Q35.667 12.710 35.529 12.641Q35.390 12.573 35.223 12.535L34.642 12.436Q34.286 12.368 34.013 12.147Q33.739 11.927 33.739 11.585Q33.739 11.336 33.850 11.161Q33.962 10.987 34.148 10.888Q34.334 10.789 34.549 10.746Q34.765 10.703 35.007 10.703Q35.421 10.703 35.701 10.885L35.917 10.710Q35.927 10.707 35.934 10.705Q35.941 10.703 35.951 10.703L36.002 10.703Q36.029 10.703 36.053 10.727Q36.077 10.751 36.077 10.779L36.077 11.626Q36.077 11.647 36.053 11.674Q36.029 11.701 36.002 11.701L35.889 11.701Q35.862 11.701 35.836 11.676Q35.811 11.650 35.811 11.626Q35.811 11.390 35.705 11.226Q35.599 11.062 35.416 10.980Q35.233 10.898 35.001 10.898Q34.672 10.898 34.416 11.001Q34.160 11.103 34.160 11.380Q34.160 11.575 34.343 11.684Q34.526 11.794 34.755 11.835L35.329 11.941Q35.575 11.989 35.788 12.117Q36.002 12.245 36.139 12.448Q36.276 12.652 36.276 12.901Q36.276 13.414 35.910 13.653Q35.544 13.892 35.007 13.892Q34.512 13.892 34.180 13.598L33.914 13.872Q33.893 13.892 33.866 13.892L33.818 13.892Q33.794 13.892 33.767 13.865Q33.739 13.838 33.739 13.817\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-77.606 -34.259)\">\u003Cpath d=\"M21.370 21.824L18.840 21.824L18.840 21.544Q19.808 21.544 19.808 21.335L19.808 17.716Q19.415 17.904 18.793 17.904L18.793 17.623Q19.210 17.623 19.574 17.522Q19.938 17.422 20.194 17.176L20.320 17.176Q20.385 17.193 20.402 17.261L20.402 21.335Q20.402 21.544 21.370 21.544\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-77.606 -34.259)\">\u003Cpath d=\"M26.721 21.824L25.087 21.824L25.087 21.544Q25.316 21.544 25.465 21.510Q25.614 21.475 25.614 21.335L25.614 19.486Q25.614 19.216 25.506 19.155Q25.398 19.093 25.087 19.093L25.087 18.813L26.147 18.738L26.147 19.387Q26.318 19.079 26.622 18.908Q26.926 18.738 27.271 18.738Q27.777 18.738 28.061 18.961Q28.345 19.185 28.345 19.681L28.345 21.335Q28.345 21.472 28.493 21.508Q28.642 21.544 28.868 21.544L28.868 21.824L27.237 21.824L27.237 21.544Q27.466 21.544 27.615 21.510Q27.764 21.475 27.764 21.335L27.764 19.695Q27.764 19.360 27.644 19.160Q27.524 18.960 27.210 18.960Q26.940 18.960 26.706 19.096Q26.472 19.233 26.333 19.467Q26.195 19.701 26.195 19.975L26.195 21.335Q26.195 21.472 26.345 21.508Q26.496 21.544 26.721 21.544L26.721 21.824M29.414 20.289Q29.414 19.968 29.539 19.679Q29.664 19.390 29.890 19.167Q30.115 18.943 30.411 18.823Q30.706 18.703 31.024 18.703Q31.352 18.703 31.614 18.803Q31.875 18.902 32.051 19.084Q32.227 19.267 32.321 19.525Q32.415 19.783 32.415 20.115Q32.415 20.207 32.333 20.228L30.078 20.228L30.078 20.289Q30.078 20.877 30.361 21.260Q30.645 21.643 31.212 21.643Q31.534 21.643 31.802 21.450Q32.070 21.257 32.159 20.942Q32.166 20.901 32.241 20.887L32.333 20.887Q32.415 20.911 32.415 20.983Q32.415 20.990 32.409 21.017Q32.296 21.414 31.925 21.653Q31.554 21.892 31.130 21.892Q30.693 21.892 30.293 21.684Q29.893 21.475 29.654 21.108Q29.414 20.741 29.414 20.289M30.084 20.019L31.899 20.019Q31.899 19.742 31.802 19.490Q31.704 19.237 31.506 19.081Q31.308 18.926 31.024 18.926Q30.747 18.926 30.534 19.084Q30.320 19.243 30.202 19.498Q30.084 19.753 30.084 20.019M32.962 22.357Q32.962 22.111 33.159 21.927Q33.355 21.742 33.612 21.663Q33.475 21.551 33.403 21.390Q33.331 21.229 33.331 21.048Q33.331 20.727 33.543 20.481Q33.208 20.183 33.208 19.773Q33.208 19.312 33.598 19.025Q33.988 18.738 34.466 18.738Q34.938 18.738 35.273 18.984Q35.447 18.830 35.657 18.748Q35.868 18.666 36.097 18.666Q36.261 18.666 36.382 18.773Q36.503 18.881 36.503 19.045Q36.503 19.141 36.432 19.213Q36.360 19.284 36.267 19.284Q36.168 19.284 36.098 19.211Q36.028 19.137 36.028 19.038Q36.028 18.984 36.042 18.953L36.049 18.939Q36.056 18.919 36.064 18.908Q36.073 18.898 36.076 18.891Q35.721 18.891 35.433 19.114Q35.721 19.407 35.721 19.773Q35.721 20.088 35.536 20.320Q35.351 20.553 35.063 20.681Q34.774 20.809 34.466 20.809Q34.265 20.809 34.073 20.759Q33.882 20.710 33.704 20.600Q33.612 20.727 33.612 20.870Q33.612 21.052 33.740 21.187Q33.868 21.322 34.053 21.322L34.685 21.322Q35.133 21.322 35.502 21.393Q35.871 21.465 36.131 21.694Q36.391 21.923 36.391 22.357Q36.391 22.678 36.095 22.880Q35.799 23.082 35.396 23.171Q34.993 23.260 34.678 23.260Q34.360 23.260 33.957 23.171Q33.554 23.082 33.258 22.880Q32.962 22.678 32.962 22.357M33.417 22.357Q33.417 22.586 33.636 22.735Q33.854 22.884 34.147 22.952Q34.439 23.020 34.678 23.020Q34.842 23.020 35.051 22.984Q35.259 22.949 35.466 22.868Q35.673 22.788 35.804 22.660Q35.936 22.532 35.936 22.357Q35.936 22.005 35.555 21.911Q35.174 21.817 34.671 21.817L34.053 21.817Q33.813 21.817 33.615 21.968Q33.417 22.118 33.417 22.357M34.466 20.570Q35.133 20.570 35.133 19.773Q35.133 18.973 34.466 18.973Q33.796 18.973 33.796 19.773Q33.796 20.570 34.466 20.570\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-23.213-7.74h36.989V-28.28h-36.989Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-32.082 -34.259)\">\u003Cpath d=\"M21.456 13.824L18.926 13.824L18.926 13.544Q19.894 13.544 19.894 13.335L19.894 9.716Q19.501 9.904 18.879 9.904L18.879 9.623Q19.296 9.623 19.660 9.522Q20.024 9.422 20.280 9.176L20.406 9.176Q20.471 9.193 20.488 9.261L20.488 13.335Q20.488 13.544 21.456 13.544\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-32.082 -34.259)\">\u003Cpath d=\"M26.769 15.181L25.139 15.181L25.139 14.901Q25.368 14.901 25.517 14.866Q25.665 14.832 25.665 14.692L25.665 11.346Q25.665 11.175 25.529 11.134Q25.392 11.093 25.139 11.093L25.139 10.813L26.219 10.738L26.219 11.144Q26.441 10.943 26.728 10.840Q27.016 10.738 27.323 10.738Q27.750 10.738 28.114 10.951Q28.478 11.165 28.692 11.529Q28.906 11.893 28.906 12.313Q28.906 12.758 28.666 13.122Q28.427 13.486 28.034 13.689Q27.641 13.892 27.197 13.892Q26.930 13.892 26.682 13.792Q26.435 13.691 26.247 13.510L26.247 14.692Q26.247 14.829 26.395 14.865Q26.544 14.901 26.769 14.901L26.769 15.181M26.247 11.493L26.247 13.103Q26.380 13.356 26.623 13.513Q26.865 13.670 27.142 13.670Q27.470 13.670 27.723 13.469Q27.976 13.267 28.109 12.949Q28.243 12.631 28.243 12.313Q28.243 12.084 28.178 11.855Q28.113 11.626 27.985 11.428Q27.856 11.230 27.662 11.110Q27.467 10.991 27.234 10.991Q26.940 10.991 26.672 11.120Q26.404 11.250 26.247 11.493\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-32.082 -34.259)\">\u003Cpath d=\"M29.716 12.341Q29.716 11.999 29.851 11.700Q29.986 11.401 30.226 11.177Q30.465 10.953 30.783 10.828Q31.101 10.703 31.432 10.703Q31.877 10.703 32.276 10.919Q32.676 11.134 32.911 11.512Q33.145 11.889 33.145 12.341Q33.145 12.682 33.003 12.966Q32.861 13.250 32.617 13.457Q32.372 13.663 32.063 13.778Q31.754 13.892 31.432 13.892Q31.002 13.892 30.600 13.691Q30.198 13.489 29.957 13.137Q29.716 12.785 29.716 12.341M31.432 13.643Q32.034 13.643 32.258 13.265Q32.482 12.887 32.482 12.255Q32.482 11.643 32.247 11.284Q32.013 10.926 31.432 10.926Q30.380 10.926 30.380 12.255Q30.380 12.887 30.605 13.265Q30.831 13.643 31.432 13.643M33.739 13.817L33.739 12.754Q33.739 12.730 33.767 12.703Q33.794 12.676 33.818 12.676L33.927 12.676Q33.992 12.676 34.006 12.734Q34.102 13.168 34.348 13.419Q34.594 13.670 35.007 13.670Q35.349 13.670 35.602 13.537Q35.855 13.404 35.855 13.096Q35.855 12.939 35.761 12.824Q35.667 12.710 35.529 12.641Q35.390 12.573 35.223 12.535L34.642 12.436Q34.286 12.368 34.013 12.147Q33.739 11.927 33.739 11.585Q33.739 11.336 33.850 11.161Q33.962 10.987 34.148 10.888Q34.334 10.789 34.549 10.746Q34.765 10.703 35.007 10.703Q35.421 10.703 35.701 10.885L35.917 10.710Q35.927 10.707 35.934 10.705Q35.941 10.703 35.951 10.703L36.002 10.703Q36.029 10.703 36.053 10.727Q36.077 10.751 36.077 10.779L36.077 11.626Q36.077 11.647 36.053 11.674Q36.029 11.701 36.002 11.701L35.889 11.701Q35.862 11.701 35.836 11.676Q35.811 11.650 35.811 11.626Q35.811 11.390 35.705 11.226Q35.599 11.062 35.416 10.980Q35.233 10.898 35.001 10.898Q34.672 10.898 34.416 11.001Q34.160 11.103 34.160 11.380Q34.160 11.575 34.343 11.684Q34.526 11.794 34.755 11.835L35.329 11.941Q35.575 11.989 35.788 12.117Q36.002 12.245 36.139 12.448Q36.276 12.652 36.276 12.901Q36.276 13.414 35.910 13.653Q35.544 13.892 35.007 13.892Q34.512 13.892 34.180 13.598L33.914 13.872Q33.893 13.892 33.866 13.892L33.818 13.892Q33.794 13.892 33.767 13.865Q33.739 13.838 33.739 13.817\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-32.082 -34.259)\">\u003Cpath d=\"M21.370 21.824L18.840 21.824L18.840 21.544Q19.808 21.544 19.808 21.335L19.808 17.716Q19.415 17.904 18.793 17.904L18.793 17.623Q19.210 17.623 19.574 17.522Q19.938 17.422 20.194 17.176L20.320 17.176Q20.385 17.193 20.402 17.261L20.402 21.335Q20.402 21.544 21.370 21.544\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-32.082 -34.259)\">\u003Cpath d=\"M26.721 21.824L25.087 21.824L25.087 21.544Q25.316 21.544 25.465 21.510Q25.614 21.475 25.614 21.335L25.614 19.486Q25.614 19.216 25.506 19.155Q25.398 19.093 25.087 19.093L25.087 18.813L26.147 18.738L26.147 19.387Q26.318 19.079 26.622 18.908Q26.926 18.738 27.271 18.738Q27.777 18.738 28.061 18.961Q28.345 19.185 28.345 19.681L28.345 21.335Q28.345 21.472 28.493 21.508Q28.642 21.544 28.868 21.544L28.868 21.824L27.237 21.824L27.237 21.544Q27.466 21.544 27.615 21.510Q27.764 21.475 27.764 21.335L27.764 19.695Q27.764 19.360 27.644 19.160Q27.524 18.960 27.210 18.960Q26.940 18.960 26.706 19.096Q26.472 19.233 26.333 19.467Q26.195 19.701 26.195 19.975L26.195 21.335Q26.195 21.472 26.345 21.508Q26.496 21.544 26.721 21.544L26.721 21.824M29.414 20.289Q29.414 19.968 29.539 19.679Q29.664 19.390 29.890 19.167Q30.115 18.943 30.411 18.823Q30.706 18.703 31.024 18.703Q31.352 18.703 31.614 18.803Q31.875 18.902 32.051 19.084Q32.227 19.267 32.321 19.525Q32.415 19.783 32.415 20.115Q32.415 20.207 32.333 20.228L30.078 20.228L30.078 20.289Q30.078 20.877 30.361 21.260Q30.645 21.643 31.212 21.643Q31.534 21.643 31.802 21.450Q32.070 21.257 32.159 20.942Q32.166 20.901 32.241 20.887L32.333 20.887Q32.415 20.911 32.415 20.983Q32.415 20.990 32.409 21.017Q32.296 21.414 31.925 21.653Q31.554 21.892 31.130 21.892Q30.693 21.892 30.293 21.684Q29.893 21.475 29.654 21.108Q29.414 20.741 29.414 20.289M30.084 20.019L31.899 20.019Q31.899 19.742 31.802 19.490Q31.704 19.237 31.506 19.081Q31.308 18.926 31.024 18.926Q30.747 18.926 30.534 19.084Q30.320 19.243 30.202 19.498Q30.084 19.753 30.084 20.019M32.962 22.357Q32.962 22.111 33.159 21.927Q33.355 21.742 33.612 21.663Q33.475 21.551 33.403 21.390Q33.331 21.229 33.331 21.048Q33.331 20.727 33.543 20.481Q33.208 20.183 33.208 19.773Q33.208 19.312 33.598 19.025Q33.988 18.738 34.466 18.738Q34.938 18.738 35.273 18.984Q35.447 18.830 35.657 18.748Q35.868 18.666 36.097 18.666Q36.261 18.666 36.382 18.773Q36.503 18.881 36.503 19.045Q36.503 19.141 36.432 19.213Q36.360 19.284 36.267 19.284Q36.168 19.284 36.098 19.211Q36.028 19.137 36.028 19.038Q36.028 18.984 36.042 18.953L36.049 18.939Q36.056 18.919 36.064 18.908Q36.073 18.898 36.076 18.891Q35.721 18.891 35.433 19.114Q35.721 19.407 35.721 19.773Q35.721 20.088 35.536 20.320Q35.351 20.553 35.063 20.681Q34.774 20.809 34.466 20.809Q34.265 20.809 34.073 20.759Q33.882 20.710 33.704 20.600Q33.612 20.727 33.612 20.870Q33.612 21.052 33.740 21.187Q33.868 21.322 34.053 21.322L34.685 21.322Q35.133 21.322 35.502 21.393Q35.871 21.465 36.131 21.694Q36.391 21.923 36.391 22.357Q36.391 22.678 36.095 22.880Q35.799 23.082 35.396 23.171Q34.993 23.260 34.678 23.260Q34.360 23.260 33.957 23.171Q33.554 23.082 33.258 22.880Q32.962 22.678 32.962 22.357M33.417 22.357Q33.417 22.586 33.636 22.735Q33.854 22.884 34.147 22.952Q34.439 23.020 34.678 23.020Q34.842 23.020 35.051 22.984Q35.259 22.949 35.466 22.868Q35.673 22.788 35.804 22.660Q35.936 22.532 35.936 22.357Q35.936 22.005 35.555 21.911Q35.174 21.817 34.671 21.817L34.053 21.817Q33.813 21.817 33.615 21.968Q33.417 22.118 33.417 22.357M34.466 20.570Q35.133 20.570 35.133 19.773Q35.133 18.973 34.466 18.973Q33.796 18.973 33.796 19.773Q33.796 20.570 34.466 20.570\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M22.312-7.74h36.989V-28.28H22.312Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(13.443 -34.259)\">\u003Cpath d=\"M21.456 13.824L18.571 13.824L18.571 13.622Q18.571 13.592 18.598 13.564L19.846 12.347Q19.918 12.272 19.960 12.230Q20.003 12.187 20.082 12.108Q20.495 11.695 20.726 11.337Q20.957 10.980 20.957 10.556Q20.957 10.324 20.878 10.121Q20.799 9.917 20.658 9.767Q20.516 9.616 20.321 9.536Q20.126 9.456 19.894 9.456Q19.583 9.456 19.325 9.615Q19.067 9.774 18.937 10.051L18.957 10.051Q19.125 10.051 19.232 10.162Q19.340 10.273 19.340 10.437Q19.340 10.594 19.231 10.707Q19.121 10.820 18.957 10.820Q18.797 10.820 18.684 10.707Q18.571 10.594 18.571 10.437Q18.571 10.061 18.779 9.774Q18.988 9.487 19.323 9.331Q19.658 9.176 20.013 9.176Q20.437 9.176 20.817 9.334Q21.196 9.493 21.430 9.810Q21.664 10.126 21.664 10.556Q21.664 10.867 21.524 11.136Q21.384 11.404 21.179 11.609Q20.974 11.814 20.611 12.096Q20.249 12.378 20.140 12.474L19.285 13.202L19.928 13.202Q20.191 13.202 20.480 13.200Q20.769 13.199 20.987 13.190Q21.206 13.181 21.223 13.164Q21.285 13.099 21.322 12.932Q21.360 12.764 21.398 12.522L21.664 12.522\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(13.443 -34.259)\">\u003Cpath d=\"M26.769 15.181L25.139 15.181L25.139 14.901Q25.368 14.901 25.517 14.866Q25.665 14.832 25.665 14.692L25.665 11.346Q25.665 11.175 25.529 11.134Q25.392 11.093 25.139 11.093L25.139 10.813L26.219 10.738L26.219 11.144Q26.441 10.943 26.728 10.840Q27.016 10.738 27.323 10.738Q27.750 10.738 28.114 10.951Q28.478 11.165 28.692 11.529Q28.906 11.893 28.906 12.313Q28.906 12.758 28.666 13.122Q28.427 13.486 28.034 13.689Q27.641 13.892 27.197 13.892Q26.930 13.892 26.682 13.792Q26.435 13.691 26.247 13.510L26.247 14.692Q26.247 14.829 26.395 14.865Q26.544 14.901 26.769 14.901L26.769 15.181M26.247 11.493L26.247 13.103Q26.380 13.356 26.623 13.513Q26.865 13.670 27.142 13.670Q27.470 13.670 27.723 13.469Q27.976 13.267 28.109 12.949Q28.243 12.631 28.243 12.313Q28.243 12.084 28.178 11.855Q28.113 11.626 27.985 11.428Q27.856 11.230 27.662 11.110Q27.467 10.991 27.234 10.991Q26.940 10.991 26.672 11.120Q26.404 11.250 26.247 11.493\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(13.443 -34.259)\">\u003Cpath d=\"M29.716 12.341Q29.716 11.999 29.851 11.700Q29.986 11.401 30.226 11.177Q30.465 10.953 30.783 10.828Q31.101 10.703 31.432 10.703Q31.877 10.703 32.276 10.919Q32.676 11.134 32.911 11.512Q33.145 11.889 33.145 12.341Q33.145 12.682 33.003 12.966Q32.861 13.250 32.617 13.457Q32.372 13.663 32.063 13.778Q31.754 13.892 31.432 13.892Q31.002 13.892 30.600 13.691Q30.198 13.489 29.957 13.137Q29.716 12.785 29.716 12.341M31.432 13.643Q32.034 13.643 32.258 13.265Q32.482 12.887 32.482 12.255Q32.482 11.643 32.247 11.284Q32.013 10.926 31.432 10.926Q30.380 10.926 30.380 12.255Q30.380 12.887 30.605 13.265Q30.831 13.643 31.432 13.643M33.739 13.817L33.739 12.754Q33.739 12.730 33.767 12.703Q33.794 12.676 33.818 12.676L33.927 12.676Q33.992 12.676 34.006 12.734Q34.102 13.168 34.348 13.419Q34.594 13.670 35.007 13.670Q35.349 13.670 35.602 13.537Q35.855 13.404 35.855 13.096Q35.855 12.939 35.761 12.824Q35.667 12.710 35.529 12.641Q35.390 12.573 35.223 12.535L34.642 12.436Q34.286 12.368 34.013 12.147Q33.739 11.927 33.739 11.585Q33.739 11.336 33.850 11.161Q33.962 10.987 34.148 10.888Q34.334 10.789 34.549 10.746Q34.765 10.703 35.007 10.703Q35.421 10.703 35.701 10.885L35.917 10.710Q35.927 10.707 35.934 10.705Q35.941 10.703 35.951 10.703L36.002 10.703Q36.029 10.703 36.053 10.727Q36.077 10.751 36.077 10.779L36.077 11.626Q36.077 11.647 36.053 11.674Q36.029 11.701 36.002 11.701L35.889 11.701Q35.862 11.701 35.836 11.676Q35.811 11.650 35.811 11.626Q35.811 11.390 35.705 11.226Q35.599 11.062 35.416 10.980Q35.233 10.898 35.001 10.898Q34.672 10.898 34.416 11.001Q34.160 11.103 34.160 11.380Q34.160 11.575 34.343 11.684Q34.526 11.794 34.755 11.835L35.329 11.941Q35.575 11.989 35.788 12.117Q36.002 12.245 36.139 12.448Q36.276 12.652 36.276 12.901Q36.276 13.414 35.910 13.653Q35.544 13.892 35.007 13.892Q34.512 13.892 34.180 13.598L33.914 13.872Q33.893 13.892 33.866 13.892L33.818 13.892Q33.794 13.892 33.767 13.865Q33.739 13.838 33.739 13.817\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(13.443 -34.259)\">\u003Cpath d=\"M21.370 21.824L18.485 21.824L18.485 21.622Q18.485 21.592 18.512 21.564L19.760 20.347Q19.832 20.272 19.874 20.230Q19.917 20.187 19.996 20.108Q20.409 19.695 20.640 19.337Q20.871 18.980 20.871 18.556Q20.871 18.324 20.792 18.121Q20.713 17.917 20.572 17.767Q20.430 17.616 20.235 17.536Q20.040 17.456 19.808 17.456Q19.497 17.456 19.239 17.615Q18.981 17.774 18.851 18.051L18.871 18.051Q19.039 18.051 19.146 18.162Q19.254 18.273 19.254 18.437Q19.254 18.594 19.145 18.707Q19.035 18.820 18.871 18.820Q18.711 18.820 18.598 18.707Q18.485 18.594 18.485 18.437Q18.485 18.061 18.693 17.774Q18.902 17.487 19.237 17.331Q19.572 17.176 19.927 17.176Q20.351 17.176 20.731 17.334Q21.110 17.493 21.344 17.810Q21.578 18.126 21.578 18.556Q21.578 18.867 21.438 19.136Q21.298 19.404 21.093 19.609Q20.888 19.814 20.525 20.096Q20.163 20.378 20.054 20.474L19.199 21.202L19.842 21.202Q20.105 21.202 20.394 21.200Q20.683 21.199 20.901 21.190Q21.120 21.181 21.137 21.164Q21.199 21.099 21.236 20.932Q21.274 20.764 21.312 20.522L21.578 20.522\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(13.443 -34.259)\">\u003Cpath d=\"M26.721 21.824L25.087 21.824L25.087 21.544Q25.316 21.544 25.465 21.510Q25.614 21.475 25.614 21.335L25.614 19.486Q25.614 19.216 25.506 19.155Q25.398 19.093 25.087 19.093L25.087 18.813L26.147 18.738L26.147 19.387Q26.318 19.079 26.622 18.908Q26.926 18.738 27.271 18.738Q27.777 18.738 28.061 18.961Q28.345 19.185 28.345 19.681L28.345 21.335Q28.345 21.472 28.493 21.508Q28.642 21.544 28.868 21.544L28.868 21.824L27.237 21.824L27.237 21.544Q27.466 21.544 27.615 21.510Q27.764 21.475 27.764 21.335L27.764 19.695Q27.764 19.360 27.644 19.160Q27.524 18.960 27.210 18.960Q26.940 18.960 26.706 19.096Q26.472 19.233 26.333 19.467Q26.195 19.701 26.195 19.975L26.195 21.335Q26.195 21.472 26.345 21.508Q26.496 21.544 26.721 21.544L26.721 21.824M29.414 20.289Q29.414 19.968 29.539 19.679Q29.664 19.390 29.890 19.167Q30.115 18.943 30.411 18.823Q30.706 18.703 31.024 18.703Q31.352 18.703 31.614 18.803Q31.875 18.902 32.051 19.084Q32.227 19.267 32.321 19.525Q32.415 19.783 32.415 20.115Q32.415 20.207 32.333 20.228L30.078 20.228L30.078 20.289Q30.078 20.877 30.361 21.260Q30.645 21.643 31.212 21.643Q31.534 21.643 31.802 21.450Q32.070 21.257 32.159 20.942Q32.166 20.901 32.241 20.887L32.333 20.887Q32.415 20.911 32.415 20.983Q32.415 20.990 32.409 21.017Q32.296 21.414 31.925 21.653Q31.554 21.892 31.130 21.892Q30.693 21.892 30.293 21.684Q29.893 21.475 29.654 21.108Q29.414 20.741 29.414 20.289M30.084 20.019L31.899 20.019Q31.899 19.742 31.802 19.490Q31.704 19.237 31.506 19.081Q31.308 18.926 31.024 18.926Q30.747 18.926 30.534 19.084Q30.320 19.243 30.202 19.498Q30.084 19.753 30.084 20.019M32.962 22.357Q32.962 22.111 33.159 21.927Q33.355 21.742 33.612 21.663Q33.475 21.551 33.403 21.390Q33.331 21.229 33.331 21.048Q33.331 20.727 33.543 20.481Q33.208 20.183 33.208 19.773Q33.208 19.312 33.598 19.025Q33.988 18.738 34.466 18.738Q34.938 18.738 35.273 18.984Q35.447 18.830 35.657 18.748Q35.868 18.666 36.097 18.666Q36.261 18.666 36.382 18.773Q36.503 18.881 36.503 19.045Q36.503 19.141 36.432 19.213Q36.360 19.284 36.267 19.284Q36.168 19.284 36.098 19.211Q36.028 19.137 36.028 19.038Q36.028 18.984 36.042 18.953L36.049 18.939Q36.056 18.919 36.064 18.908Q36.073 18.898 36.076 18.891Q35.721 18.891 35.433 19.114Q35.721 19.407 35.721 19.773Q35.721 20.088 35.536 20.320Q35.351 20.553 35.063 20.681Q34.774 20.809 34.466 20.809Q34.265 20.809 34.073 20.759Q33.882 20.710 33.704 20.600Q33.612 20.727 33.612 20.870Q33.612 21.052 33.740 21.187Q33.868 21.322 34.053 21.322L34.685 21.322Q35.133 21.322 35.502 21.393Q35.871 21.465 36.131 21.694Q36.391 21.923 36.391 22.357Q36.391 22.678 36.095 22.880Q35.799 23.082 35.396 23.171Q34.993 23.260 34.678 23.260Q34.360 23.260 33.957 23.171Q33.554 23.082 33.258 22.880Q32.962 22.678 32.962 22.357M33.417 22.357Q33.417 22.586 33.636 22.735Q33.854 22.884 34.147 22.952Q34.439 23.020 34.678 23.020Q34.842 23.020 35.051 22.984Q35.259 22.949 35.466 22.868Q35.673 22.788 35.804 22.660Q35.936 22.532 35.936 22.357Q35.936 22.005 35.555 21.911Q35.174 21.817 34.671 21.817L34.053 21.817Q33.813 21.817 33.615 21.968Q33.417 22.118 33.417 22.357M34.466 20.570Q35.133 20.570 35.133 19.773Q35.133 18.973 34.466 18.973Q33.796 18.973 33.796 19.773Q33.796 20.570 34.466 20.570\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M67.836-7.74h36.989V-28.28H67.836Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(58.967 -34.259)\">\u003Cpath d=\"M21.456 13.824L18.571 13.824L18.571 13.622Q18.571 13.592 18.598 13.564L19.846 12.347Q19.918 12.272 19.960 12.230Q20.003 12.187 20.082 12.108Q20.495 11.695 20.726 11.337Q20.957 10.980 20.957 10.556Q20.957 10.324 20.878 10.121Q20.799 9.917 20.658 9.767Q20.516 9.616 20.321 9.536Q20.126 9.456 19.894 9.456Q19.583 9.456 19.325 9.615Q19.067 9.774 18.937 10.051L18.957 10.051Q19.125 10.051 19.232 10.162Q19.340 10.273 19.340 10.437Q19.340 10.594 19.231 10.707Q19.121 10.820 18.957 10.820Q18.797 10.820 18.684 10.707Q18.571 10.594 18.571 10.437Q18.571 10.061 18.779 9.774Q18.988 9.487 19.323 9.331Q19.658 9.176 20.013 9.176Q20.437 9.176 20.817 9.334Q21.196 9.493 21.430 9.810Q21.664 10.126 21.664 10.556Q21.664 10.867 21.524 11.136Q21.384 11.404 21.179 11.609Q20.974 11.814 20.611 12.096Q20.249 12.378 20.140 12.474L19.285 13.202L19.928 13.202Q20.191 13.202 20.480 13.200Q20.769 13.199 20.987 13.190Q21.206 13.181 21.223 13.164Q21.285 13.099 21.322 12.932Q21.360 12.764 21.398 12.522L21.664 12.522\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(58.967 -34.259)\">\u003Cpath d=\"M26.769 15.181L25.139 15.181L25.139 14.901Q25.368 14.901 25.517 14.866Q25.665 14.832 25.665 14.692L25.665 11.346Q25.665 11.175 25.529 11.134Q25.392 11.093 25.139 11.093L25.139 10.813L26.219 10.738L26.219 11.144Q26.441 10.943 26.728 10.840Q27.016 10.738 27.323 10.738Q27.750 10.738 28.114 10.951Q28.478 11.165 28.692 11.529Q28.906 11.893 28.906 12.313Q28.906 12.758 28.666 13.122Q28.427 13.486 28.034 13.689Q27.641 13.892 27.197 13.892Q26.930 13.892 26.682 13.792Q26.435 13.691 26.247 13.510L26.247 14.692Q26.247 14.829 26.395 14.865Q26.544 14.901 26.769 14.901L26.769 15.181M26.247 11.493L26.247 13.103Q26.380 13.356 26.623 13.513Q26.865 13.670 27.142 13.670Q27.470 13.670 27.723 13.469Q27.976 13.267 28.109 12.949Q28.243 12.631 28.243 12.313Q28.243 12.084 28.178 11.855Q28.113 11.626 27.985 11.428Q27.856 11.230 27.662 11.110Q27.467 10.991 27.234 10.991Q26.940 10.991 26.672 11.120Q26.404 11.250 26.247 11.493\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(58.967 -34.259)\">\u003Cpath d=\"M29.716 12.341Q29.716 11.999 29.851 11.700Q29.986 11.401 30.226 11.177Q30.465 10.953 30.783 10.828Q31.101 10.703 31.432 10.703Q31.877 10.703 32.276 10.919Q32.676 11.134 32.911 11.512Q33.145 11.889 33.145 12.341Q33.145 12.682 33.003 12.966Q32.861 13.250 32.617 13.457Q32.372 13.663 32.063 13.778Q31.754 13.892 31.432 13.892Q31.002 13.892 30.600 13.691Q30.198 13.489 29.957 13.137Q29.716 12.785 29.716 12.341M31.432 13.643Q32.034 13.643 32.258 13.265Q32.482 12.887 32.482 12.255Q32.482 11.643 32.247 11.284Q32.013 10.926 31.432 10.926Q30.380 10.926 30.380 12.255Q30.380 12.887 30.605 13.265Q30.831 13.643 31.432 13.643M33.739 13.817L33.739 12.754Q33.739 12.730 33.767 12.703Q33.794 12.676 33.818 12.676L33.927 12.676Q33.992 12.676 34.006 12.734Q34.102 13.168 34.348 13.419Q34.594 13.670 35.007 13.670Q35.349 13.670 35.602 13.537Q35.855 13.404 35.855 13.096Q35.855 12.939 35.761 12.824Q35.667 12.710 35.529 12.641Q35.390 12.573 35.223 12.535L34.642 12.436Q34.286 12.368 34.013 12.147Q33.739 11.927 33.739 11.585Q33.739 11.336 33.850 11.161Q33.962 10.987 34.148 10.888Q34.334 10.789 34.549 10.746Q34.765 10.703 35.007 10.703Q35.421 10.703 35.701 10.885L35.917 10.710Q35.927 10.707 35.934 10.705Q35.941 10.703 35.951 10.703L36.002 10.703Q36.029 10.703 36.053 10.727Q36.077 10.751 36.077 10.779L36.077 11.626Q36.077 11.647 36.053 11.674Q36.029 11.701 36.002 11.701L35.889 11.701Q35.862 11.701 35.836 11.676Q35.811 11.650 35.811 11.626Q35.811 11.390 35.705 11.226Q35.599 11.062 35.416 10.980Q35.233 10.898 35.001 10.898Q34.672 10.898 34.416 11.001Q34.160 11.103 34.160 11.380Q34.160 11.575 34.343 11.684Q34.526 11.794 34.755 11.835L35.329 11.941Q35.575 11.989 35.788 12.117Q36.002 12.245 36.139 12.448Q36.276 12.652 36.276 12.901Q36.276 13.414 35.910 13.653Q35.544 13.892 35.007 13.892Q34.512 13.892 34.180 13.598L33.914 13.872Q33.893 13.892 33.866 13.892L33.818 13.892Q33.794 13.892 33.767 13.865Q33.739 13.838 33.739 13.817\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(58.967 -34.259)\">\u003Cpath d=\"M21.370 21.824L18.485 21.824L18.485 21.622Q18.485 21.592 18.512 21.564L19.760 20.347Q19.832 20.272 19.874 20.230Q19.917 20.187 19.996 20.108Q20.409 19.695 20.640 19.337Q20.871 18.980 20.871 18.556Q20.871 18.324 20.792 18.121Q20.713 17.917 20.572 17.767Q20.430 17.616 20.235 17.536Q20.040 17.456 19.808 17.456Q19.497 17.456 19.239 17.615Q18.981 17.774 18.851 18.051L18.871 18.051Q19.039 18.051 19.146 18.162Q19.254 18.273 19.254 18.437Q19.254 18.594 19.145 18.707Q19.035 18.820 18.871 18.820Q18.711 18.820 18.598 18.707Q18.485 18.594 18.485 18.437Q18.485 18.061 18.693 17.774Q18.902 17.487 19.237 17.331Q19.572 17.176 19.927 17.176Q20.351 17.176 20.731 17.334Q21.110 17.493 21.344 17.810Q21.578 18.126 21.578 18.556Q21.578 18.867 21.438 19.136Q21.298 19.404 21.093 19.609Q20.888 19.814 20.525 20.096Q20.163 20.378 20.054 20.474L19.199 21.202L19.842 21.202Q20.105 21.202 20.394 21.200Q20.683 21.199 20.901 21.190Q21.120 21.181 21.137 21.164Q21.199 21.099 21.236 20.932Q21.274 20.764 21.312 20.522L21.578 20.522\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(58.967 -34.259)\">\u003Cpath d=\"M26.721 21.824L25.087 21.824L25.087 21.544Q25.316 21.544 25.465 21.510Q25.614 21.475 25.614 21.335L25.614 19.486Q25.614 19.216 25.506 19.155Q25.398 19.093 25.087 19.093L25.087 18.813L26.147 18.738L26.147 19.387Q26.318 19.079 26.622 18.908Q26.926 18.738 27.271 18.738Q27.777 18.738 28.061 18.961Q28.345 19.185 28.345 19.681L28.345 21.335Q28.345 21.472 28.493 21.508Q28.642 21.544 28.868 21.544L28.868 21.824L27.237 21.824L27.237 21.544Q27.466 21.544 27.615 21.510Q27.764 21.475 27.764 21.335L27.764 19.695Q27.764 19.360 27.644 19.160Q27.524 18.960 27.210 18.960Q26.940 18.960 26.706 19.096Q26.472 19.233 26.333 19.467Q26.195 19.701 26.195 19.975L26.195 21.335Q26.195 21.472 26.345 21.508Q26.496 21.544 26.721 21.544L26.721 21.824M29.414 20.289Q29.414 19.968 29.539 19.679Q29.664 19.390 29.890 19.167Q30.115 18.943 30.411 18.823Q30.706 18.703 31.024 18.703Q31.352 18.703 31.614 18.803Q31.875 18.902 32.051 19.084Q32.227 19.267 32.321 19.525Q32.415 19.783 32.415 20.115Q32.415 20.207 32.333 20.228L30.078 20.228L30.078 20.289Q30.078 20.877 30.361 21.260Q30.645 21.643 31.212 21.643Q31.534 21.643 31.802 21.450Q32.070 21.257 32.159 20.942Q32.166 20.901 32.241 20.887L32.333 20.887Q32.415 20.911 32.415 20.983Q32.415 20.990 32.409 21.017Q32.296 21.414 31.925 21.653Q31.554 21.892 31.130 21.892Q30.693 21.892 30.293 21.684Q29.893 21.475 29.654 21.108Q29.414 20.741 29.414 20.289M30.084 20.019L31.899 20.019Q31.899 19.742 31.802 19.490Q31.704 19.237 31.506 19.081Q31.308 18.926 31.024 18.926Q30.747 18.926 30.534 19.084Q30.320 19.243 30.202 19.498Q30.084 19.753 30.084 20.019M32.962 22.357Q32.962 22.111 33.159 21.927Q33.355 21.742 33.612 21.663Q33.475 21.551 33.403 21.390Q33.331 21.229 33.331 21.048Q33.331 20.727 33.543 20.481Q33.208 20.183 33.208 19.773Q33.208 19.312 33.598 19.025Q33.988 18.738 34.466 18.738Q34.938 18.738 35.273 18.984Q35.447 18.830 35.657 18.748Q35.868 18.666 36.097 18.666Q36.261 18.666 36.382 18.773Q36.503 18.881 36.503 19.045Q36.503 19.141 36.432 19.213Q36.360 19.284 36.267 19.284Q36.168 19.284 36.098 19.211Q36.028 19.137 36.028 19.038Q36.028 18.984 36.042 18.953L36.049 18.939Q36.056 18.919 36.064 18.908Q36.073 18.898 36.076 18.891Q35.721 18.891 35.433 19.114Q35.721 19.407 35.721 19.773Q35.721 20.088 35.536 20.320Q35.351 20.553 35.063 20.681Q34.774 20.809 34.466 20.809Q34.265 20.809 34.073 20.759Q33.882 20.710 33.704 20.600Q33.612 20.727 33.612 20.870Q33.612 21.052 33.740 21.187Q33.868 21.322 34.053 21.322L34.685 21.322Q35.133 21.322 35.502 21.393Q35.871 21.465 36.131 21.694Q36.391 21.923 36.391 22.357Q36.391 22.678 36.095 22.880Q35.799 23.082 35.396 23.171Q34.993 23.260 34.678 23.260Q34.360 23.260 33.957 23.171Q33.554 23.082 33.258 22.880Q32.962 22.678 32.962 22.357M33.417 22.357Q33.417 22.586 33.636 22.735Q33.854 22.884 34.147 22.952Q34.439 23.020 34.678 23.020Q34.842 23.020 35.051 22.984Q35.259 22.949 35.466 22.868Q35.673 22.788 35.804 22.660Q35.936 22.532 35.936 22.357Q35.936 22.005 35.555 21.911Q35.174 21.817 34.671 21.817L34.053 21.817Q33.813 21.817 33.615 21.968Q33.417 22.118 33.417 22.357M34.466 20.570Q35.133 20.570 35.133 19.773Q35.133 18.973 34.466 18.973Q33.796 18.973 33.796 19.773Q33.796 20.570 34.466 20.570\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m4.942-54.798-37.823 25.21\"\u002F>\u003Cpath stroke=\"none\" d=\"m-34.545-28.479 3.55-.443-1.886-.666.111-1.997\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr5\" font-size=\"5\">\u003Cg transform=\"translate(-53.497 -64.163)\">\u003Cpath d=\"M20.249 21.824L18.449 21.824L18.449 21.585Q19.003 21.585 19.003 21.465L19.003 18.770Q19.003 18.650 18.449 18.650L18.449 18.408L21.799 18.408L21.965 19.619L21.733 19.619Q21.689 19.278 21.599 19.082Q21.508 18.887 21.366 18.795Q21.223 18.704 21.018 18.677Q20.813 18.650 20.439 18.650L19.834 18.650Q19.707 18.650 19.630 18.670Q19.553 18.689 19.553 18.770L19.553 19.993L20 19.993Q20.212 19.993 20.336 19.980Q20.461 19.966 20.549 19.910Q20.637 19.854 20.678 19.738Q20.720 19.622 20.720 19.409L20.949 19.409L20.949 20.818L20.720 20.818Q20.720 20.603 20.677 20.486Q20.634 20.369 20.550 20.315Q20.466 20.262 20.350 20.248Q20.234 20.235 20 20.235L19.553 20.235L19.553 21.465Q19.553 21.541 19.785 21.563Q20.017 21.585 20.249 21.585\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-53.497 -64.163)\">\u003Cpath d=\"M23.600 21.824L22.221 21.824L22.221 21.585Q22.636 21.585 22.636 21.465L22.636 20.188Q22.636 19.998 22.546 19.954Q22.455 19.910 22.221 19.910L22.221 19.668L22.980 19.619Q22.978 19.617 22.974 19.616Q22.971 19.615 22.966 19.615L23.039 19.615L23.039 20.139Q23.151 19.907 23.355 19.761Q23.559 19.615 23.806 19.615Q23.908 19.615 24.013 19.657Q24.118 19.700 24.182 19.781Q24.245 19.861 24.245 19.969Q24.245 20.078 24.169 20.157Q24.094 20.235 23.984 20.235Q23.876 20.235 23.798 20.157Q23.720 20.078 23.720 19.969Q23.720 19.871 23.781 19.800Q23.618 19.800 23.487 19.878Q23.356 19.956 23.267 20.086Q23.178 20.215 23.129 20.380Q23.080 20.545 23.080 20.694L23.080 21.465Q23.080 21.585 23.600 21.585L23.600 21.824M24.870 20.728Q24.870 20.479 24.979 20.266Q25.087 20.054 25.274 19.901Q25.461 19.749 25.694 19.666Q25.927 19.583 26.164 19.583Q26.494 19.583 26.747 19.698Q27.001 19.812 27.145 20.045Q27.289 20.279 27.289 20.608Q27.289 20.645 27.270 20.672Q27.250 20.699 27.216 20.699L25.390 20.699L25.390 20.728Q25.390 21.165 25.634 21.422Q25.878 21.680 26.310 21.680Q26.472 21.680 26.628 21.623Q26.784 21.565 26.900 21.453Q27.016 21.341 27.070 21.189Q27.075 21.172 27.097 21.157Q27.119 21.143 27.136 21.143L27.216 21.143Q27.246 21.143 27.267 21.168Q27.289 21.194 27.289 21.223Q27.289 21.236 27.288 21.243Q27.287 21.250 27.285 21.260Q27.214 21.465 27.054 21.604Q26.894 21.743 26.680 21.812Q26.467 21.880 26.254 21.880Q25.927 21.880 25.601 21.739Q25.275 21.597 25.073 21.333Q24.870 21.070 24.870 20.728M25.400 20.525L26.884 20.525Q26.884 20.303 26.799 20.132Q26.713 19.961 26.551 19.865Q26.389 19.768 26.164 19.768Q25.842 19.768 25.640 19.980Q25.439 20.191 25.400 20.525M29.304 21.824L28.010 21.824L28.010 21.585Q28.425 21.585 28.425 21.465L28.425 20.188Q28.425 19.998 28.334 19.954Q28.244 19.910 28.010 19.910L28.010 19.668L28.850 19.615L28.850 20.078Q28.945 19.929 29.088 19.826Q29.230 19.722 29.402 19.668Q29.575 19.615 29.755 19.615Q30.620 19.615 30.620 20.293L30.620 21.465Q30.620 21.585 31.035 21.585L31.035 21.824L29.738 21.824L29.738 21.585Q30.153 21.585 30.153 21.465L30.153 20.308Q30.153 20.052 30.053 19.926Q29.953 19.800 29.704 19.800Q29.499 19.800 29.308 19.888Q29.118 19.976 29.003 20.137Q28.889 20.298 28.889 20.503L28.889 21.465Q28.889 21.585 29.304 21.585L29.304 21.824M31.708 20.745Q31.708 20.489 31.823 20.272Q31.938 20.056 32.134 19.901Q32.331 19.746 32.569 19.665Q32.807 19.583 33.058 19.583Q33.464 19.583 33.745 19.663Q34.025 19.744 34.025 20.039Q34.025 20.110 33.989 20.176Q33.952 20.242 33.886 20.279Q33.820 20.315 33.749 20.315Q33.637 20.315 33.555 20.236Q33.474 20.157 33.474 20.039Q33.474 19.910 33.579 19.820Q33.430 19.783 33.063 19.783Q32.856 19.783 32.702 19.856Q32.548 19.929 32.440 20.066Q32.331 20.203 32.280 20.375Q32.228 20.547 32.228 20.745Q32.228 21.011 32.343 21.226Q32.458 21.441 32.672 21.560Q32.885 21.680 33.149 21.680Q33.405 21.680 33.599 21.555Q33.793 21.431 33.869 21.194Q33.889 21.162 33.930 21.155L34.035 21.155Q34.094 21.172 34.094 21.223Q34.094 21.228 34.089 21.248Q34.018 21.463 33.860 21.605Q33.703 21.748 33.497 21.814Q33.290 21.880 33.058 21.880Q32.726 21.880 32.414 21.740Q32.101 21.599 31.905 21.341Q31.708 21.082 31.708 20.745\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-53.497 -64.163)\">\u003Cpath d=\"M35.940 21.824L34.646 21.824L34.646 21.585Q35.061 21.585 35.061 21.465L35.061 18.928Q35.061 18.738 34.971 18.694Q34.881 18.650 34.646 18.650L34.646 18.408L35.506 18.355L35.506 20.044Q35.645 19.834 35.888 19.724Q36.131 19.615 36.392 19.615Q37.256 19.615 37.256 20.293L37.256 21.465Q37.256 21.585 37.671 21.585L37.671 21.824L36.375 21.824L36.375 21.585Q36.790 21.585 36.790 21.465L36.790 20.308Q36.790 20.052 36.690 19.926Q36.590 19.800 36.341 19.800Q36.136 19.800 35.945 19.888Q35.755 19.976 35.640 20.137Q35.525 20.298 35.525 20.503L35.525 21.465Q35.525 21.585 35.940 21.585\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M13.677-54.798 1.409-30.268\"\u002F>\u003Cpath stroke=\"none\" d=\"m.514-28.479 2.862-2.146-1.967.358-.895-1.79\"\u002F>\u003Cg transform=\"translate(-29.438 -65.674)\">\u003Cpath d=\"M20.139 21.824L18.420 21.824L18.420 21.585Q19.003 21.585 19.003 21.465L19.003 18.770Q19.003 18.650 18.420 18.650L18.420 18.408L20.139 18.408L20.139 18.650Q19.553 18.650 19.553 18.770L19.553 21.465Q19.553 21.585 20.139 21.585L20.139 21.824M21.289 21.228L21.289 19.910L20.769 19.910L20.769 19.724Q20.947 19.724 21.091 19.638Q21.235 19.551 21.331 19.409Q21.428 19.268 21.477 19.095Q21.525 18.921 21.525 18.748L21.755 18.748L21.755 19.668L22.629 19.668L22.629 19.910L21.755 19.910L21.755 21.214Q21.755 21.414 21.844 21.547Q21.933 21.680 22.131 21.680Q22.297 21.680 22.401 21.535Q22.504 21.389 22.504 21.214L22.504 20.918L22.734 20.918L22.734 21.228Q22.734 21.399 22.645 21.551Q22.556 21.702 22.407 21.791Q22.258 21.880 22.080 21.880Q21.743 21.880 21.516 21.718Q21.289 21.555 21.289 21.228M23.620 21.284Q23.620 20.970 23.928 20.785Q24.235 20.601 24.643 20.532Q25.051 20.464 25.380 20.464L25.380 20.308Q25.380 20.152 25.289 20.027Q25.197 19.903 25.051 19.835Q24.904 19.768 24.746 19.768Q24.409 19.768 24.255 19.815Q24.365 19.893 24.365 20.039Q24.365 20.115 24.329 20.177Q24.294 20.240 24.228 20.277Q24.162 20.315 24.089 20.315Q23.979 20.315 23.897 20.236Q23.815 20.157 23.815 20.039Q23.815 19.746 24.088 19.665Q24.360 19.583 24.746 19.583Q24.914 19.583 25.105 19.624Q25.295 19.666 25.462 19.752Q25.629 19.839 25.737 19.976Q25.844 20.113 25.844 20.293L25.844 21.419Q25.844 21.497 25.898 21.555Q25.952 21.614 26.030 21.614Q26.110 21.614 26.163 21.555Q26.215 21.497 26.215 21.419L26.215 21.099L26.445 21.099L26.445 21.419Q26.445 21.638 26.242 21.746Q26.040 21.853 25.800 21.853Q25.659 21.853 25.564 21.745Q25.468 21.636 25.444 21.490Q25.361 21.619 25.224 21.708Q25.087 21.797 24.925 21.839Q24.763 21.880 24.599 21.880Q24.377 21.880 24.155 21.828Q23.933 21.775 23.776 21.642Q23.620 21.509 23.620 21.284M24.099 21.284Q24.099 21.475 24.276 21.585Q24.453 21.695 24.660 21.695Q24.829 21.695 24.996 21.635Q25.163 21.575 25.272 21.452Q25.380 21.328 25.380 21.155L25.380 20.635Q24.956 20.635 24.527 20.789Q24.099 20.943 24.099 21.284M28.300 21.824L27.026 21.824L27.026 21.585Q27.441 21.585 27.441 21.465L27.441 18.928Q27.441 18.738 27.351 18.694Q27.260 18.650 27.026 18.650L27.026 18.408L27.885 18.355L27.885 21.465Q27.885 21.585 28.300 21.585L28.300 21.824M30.271 21.824L29.035 21.824L29.035 21.585Q29.450 21.585 29.450 21.465L29.450 20.188Q29.450 19.995 29.367 19.953Q29.284 19.910 29.055 19.910L29.055 19.668L29.895 19.615L29.895 21.465Q29.895 21.585 30.271 21.585L30.271 21.824M29.274 18.755Q29.274 18.628 29.368 18.534Q29.462 18.440 29.589 18.440Q29.646 18.440 29.708 18.466Q29.770 18.491 29.812 18.533Q29.853 18.574 29.879 18.637Q29.904 18.699 29.904 18.755Q29.904 18.882 29.810 18.976Q29.716 19.070 29.589 19.070Q29.509 19.070 29.436 19.026Q29.362 18.982 29.318 18.909Q29.274 18.836 29.274 18.755M31.040 21.284Q31.040 20.970 31.347 20.785Q31.655 20.601 32.063 20.532Q32.470 20.464 32.800 20.464L32.800 20.308Q32.800 20.152 32.708 20.027Q32.617 19.903 32.470 19.835Q32.324 19.768 32.165 19.768Q31.828 19.768 31.674 19.815Q31.784 19.893 31.784 20.039Q31.784 20.115 31.749 20.177Q31.713 20.240 31.648 20.277Q31.582 20.315 31.508 20.315Q31.398 20.315 31.317 20.236Q31.235 20.157 31.235 20.039Q31.235 19.746 31.507 19.665Q31.779 19.583 32.165 19.583Q32.334 19.583 32.524 19.624Q32.714 19.666 32.882 19.752Q33.049 19.839 33.156 19.976Q33.264 20.113 33.264 20.293L33.264 21.419Q33.264 21.497 33.317 21.555Q33.371 21.614 33.449 21.614Q33.530 21.614 33.582 21.555Q33.635 21.497 33.635 21.419L33.635 21.099L33.864 21.099L33.864 21.419Q33.864 21.638 33.662 21.746Q33.459 21.853 33.220 21.853Q33.078 21.853 32.983 21.745Q32.888 21.636 32.863 21.490Q32.780 21.619 32.644 21.708Q32.507 21.797 32.345 21.839Q32.182 21.880 32.019 21.880Q31.796 21.880 31.574 21.828Q31.352 21.775 31.196 21.642Q31.040 21.509 31.040 21.284M31.518 21.284Q31.518 21.475 31.695 21.585Q31.872 21.695 32.080 21.695Q32.248 21.695 32.415 21.635Q32.583 21.575 32.691 21.452Q32.800 21.328 32.800 21.155L32.800 20.635Q32.375 20.635 31.947 20.789Q31.518 20.943 31.518 21.284M35.734 21.824L34.440 21.824L34.440 21.585Q34.856 21.585 34.856 21.465L34.856 20.188Q34.856 19.998 34.765 19.954Q34.675 19.910 34.440 19.910L34.440 19.668L35.280 19.615L35.280 20.078Q35.376 19.929 35.518 19.826Q35.661 19.722 35.833 19.668Q36.005 19.615 36.186 19.615Q37.050 19.615 37.050 20.293L37.050 21.465Q37.050 21.585 37.465 21.585L37.465 21.824L36.169 21.824L36.169 21.585Q36.584 21.585 36.584 21.465L36.584 20.308Q36.584 20.052 36.484 19.926Q36.384 19.800 36.135 19.800Q35.930 19.800 35.739 19.888Q35.549 19.976 35.434 20.137Q35.319 20.298 35.319 20.503L35.319 21.465Q35.319 21.585 35.734 21.585\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m22.411-54.798 12.268 24.53\"\u002F>\u003Cpath stroke=\"none\" d=\"M35.574-28.479v-3.577l-.895 1.789-1.967-.358\"\u002F>\u003Cg transform=\"translate(9.674 -65.674)\">\u003Cpath d=\"M21.499 21.824L19.379 21.824L19.379 21.585Q20.163 21.585 20.163 21.465L20.163 18.770Q20.163 18.692 20.087 18.671Q20.012 18.650 19.885 18.650L19.585 18.650Q19.326 18.650 19.156 18.682Q18.986 18.714 18.864 18.833Q18.757 18.941 18.711 19.137Q18.664 19.334 18.640 19.619L18.410 19.619L18.525 18.408L22.348 18.408L22.463 19.619L22.233 19.619Q22.209 19.334 22.165 19.140Q22.121 18.946 22.009 18.833Q21.887 18.714 21.717 18.682Q21.547 18.650 21.289 18.650L20.993 18.650Q20.866 18.650 20.791 18.671Q20.715 18.692 20.715 18.770L20.715 21.465Q20.715 21.585 21.499 21.585L21.499 21.824M24.528 21.824L23.234 21.824L23.234 21.585Q23.649 21.585 23.649 21.465L23.649 18.928Q23.649 18.738 23.559 18.694Q23.469 18.650 23.234 18.650L23.234 18.408L24.094 18.355L24.094 20.044Q24.233 19.834 24.476 19.724Q24.719 19.615 24.980 19.615Q25.844 19.615 25.844 20.293L25.844 21.465Q25.844 21.585 26.259 21.585L26.259 21.824L24.963 21.824L24.963 21.585Q25.378 21.585 25.378 21.465L25.378 20.308Q25.378 20.052 25.278 19.926Q25.178 19.800 24.929 19.800Q24.724 19.800 24.533 19.888Q24.343 19.976 24.228 20.137Q24.113 20.298 24.113 20.503L24.113 21.465Q24.113 21.585 24.528 21.585L24.528 21.824M26.984 21.284Q26.984 20.970 27.292 20.785Q27.600 20.601 28.007 20.532Q28.415 20.464 28.745 20.464L28.745 20.308Q28.745 20.152 28.653 20.027Q28.562 19.903 28.415 19.835Q28.269 19.768 28.110 19.768Q27.773 19.768 27.619 19.815Q27.729 19.893 27.729 20.039Q27.729 20.115 27.694 20.177Q27.658 20.240 27.592 20.277Q27.526 20.315 27.453 20.315Q27.343 20.315 27.262 20.236Q27.180 20.157 27.180 20.039Q27.180 19.746 27.452 19.665Q27.724 19.583 28.110 19.583Q28.278 19.583 28.469 19.624Q28.659 19.666 28.826 19.752Q28.994 19.839 29.101 19.976Q29.209 20.113 29.209 20.293L29.209 21.419Q29.209 21.497 29.262 21.555Q29.316 21.614 29.394 21.614Q29.475 21.614 29.527 21.555Q29.580 21.497 29.580 21.419L29.580 21.099L29.809 21.099L29.809 21.419Q29.809 21.638 29.607 21.746Q29.404 21.853 29.165 21.853Q29.023 21.853 28.928 21.745Q28.833 21.636 28.808 21.490Q28.725 21.619 28.588 21.708Q28.452 21.797 28.289 21.839Q28.127 21.880 27.963 21.880Q27.741 21.880 27.519 21.828Q27.297 21.775 27.141 21.642Q26.984 21.509 26.984 21.284M27.463 21.284Q27.463 21.475 27.640 21.585Q27.817 21.695 28.024 21.695Q28.193 21.695 28.360 21.635Q28.527 21.575 28.636 21.452Q28.745 21.328 28.745 21.155L28.745 20.635Q28.320 20.635 27.891 20.789Q27.463 20.943 27.463 21.284M31.626 21.824L30.390 21.824L30.390 21.585Q30.805 21.585 30.805 21.465L30.805 20.188Q30.805 19.995 30.722 19.953Q30.639 19.910 30.410 19.910L30.410 19.668L31.250 19.615L31.250 21.465Q31.250 21.585 31.626 21.585L31.626 21.824M30.629 18.755Q30.629 18.628 30.723 18.534Q30.817 18.440 30.944 18.440Q31.001 18.440 31.063 18.466Q31.125 18.491 31.167 18.533Q31.208 18.574 31.234 18.637Q31.259 18.699 31.259 18.755Q31.259 18.882 31.165 18.976Q31.071 19.070 30.944 19.070Q30.864 19.070 30.791 19.026Q30.717 18.982 30.673 18.909Q30.629 18.836 30.629 18.755\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m31.146-54.798 37.823 25.21\"\u002F>\u003Cpath stroke=\"none\" d=\"m70.633-28.479-1.775-3.106.111 1.997-1.886.666\"\u002F>\u003Cg transform=\"translate(33.545 -65.135)\">\u003Cpath d=\"M20.839 21.824L18.469 21.824L18.469 21.585Q19.023 21.585 19.023 21.465L19.023 18.770Q19.023 18.650 18.469 18.650L18.469 18.408L20.664 18.408Q20.944 18.408 21.272 18.501Q21.599 18.594 21.826 18.788Q22.053 18.982 22.053 19.268Q22.053 19.583 21.742 19.778Q21.430 19.973 21.064 20.039Q21.289 20.039 21.566 20.150Q21.843 20.262 22.036 20.459Q22.229 20.657 22.229 20.894Q22.229 21.121 22.096 21.300Q21.962 21.480 21.748 21.596Q21.533 21.712 21.296 21.768Q21.059 21.824 20.839 21.824M19.533 20.139L19.533 21.465Q19.533 21.546 19.610 21.565Q19.687 21.585 19.814 21.585L20.664 21.585Q20.874 21.585 21.104 21.508Q21.335 21.431 21.490 21.275Q21.645 21.118 21.645 20.894Q21.645 20.674 21.514 20.501Q21.384 20.327 21.178 20.233Q20.971 20.139 20.754 20.139L19.533 20.139M19.533 18.770L19.533 19.954L20.495 19.954Q20.644 19.954 20.817 19.912Q20.991 19.871 21.147 19.784Q21.303 19.698 21.396 19.567Q21.489 19.436 21.489 19.268Q21.489 19.075 21.363 18.936Q21.237 18.797 21.046 18.723Q20.854 18.650 20.664 18.650L19.814 18.650Q19.687 18.650 19.610 18.670Q19.533 18.689 19.533 18.770M23.530 21.228L23.530 20.188Q23.530 19.998 23.440 19.954Q23.349 19.910 23.115 19.910L23.115 19.668L23.994 19.615L23.994 21.228Q23.994 21.428 24.045 21.531Q24.096 21.634 24.206 21.664Q24.316 21.695 24.533 21.695Q24.680 21.695 24.810 21.645Q24.941 21.595 25.041 21.502Q25.141 21.409 25.200 21.281Q25.258 21.153 25.258 21.009L25.258 20.188Q25.258 19.998 25.168 19.954Q25.078 19.910 24.843 19.910L24.843 19.668L25.725 19.615L25.725 21.304Q25.725 21.438 25.771 21.498Q25.817 21.558 25.898 21.571Q25.979 21.585 26.140 21.585L26.140 21.824L25.280 21.880L25.280 21.475Q25.156 21.663 24.942 21.772Q24.729 21.880 24.494 21.880Q24.299 21.880 24.137 21.855Q23.974 21.829 23.835 21.761Q23.696 21.692 23.613 21.562Q23.530 21.431 23.530 21.228M28.215 21.824L26.836 21.824L26.836 21.585Q27.251 21.585 27.251 21.465L27.251 20.188Q27.251 19.998 27.160 19.954Q27.070 19.910 26.836 19.910L26.836 19.668L27.595 19.619Q27.592 19.617 27.589 19.616Q27.585 19.615 27.580 19.615L27.653 19.615L27.653 20.139Q27.766 19.907 27.970 19.761Q28.173 19.615 28.420 19.615Q28.523 19.615 28.627 19.657Q28.732 19.700 28.796 19.781Q28.859 19.861 28.859 19.969Q28.859 20.078 28.784 20.157Q28.708 20.235 28.598 20.235Q28.491 20.235 28.413 20.157Q28.335 20.078 28.335 19.969Q28.335 19.871 28.396 19.800Q28.232 19.800 28.101 19.878Q27.971 19.956 27.882 20.086Q27.793 20.215 27.744 20.380Q27.695 20.545 27.695 20.694L27.695 21.465Q27.695 21.585 28.215 21.585L28.215 21.824M29.484 22.205Q29.484 22.075 29.563 21.977Q29.641 21.878 29.764 21.811Q29.887 21.743 30.004 21.714Q29.909 21.631 29.854 21.510Q29.799 21.389 29.799 21.255Q29.799 21.033 29.948 20.850Q29.831 20.752 29.760 20.624Q29.690 20.496 29.690 20.354Q29.690 20.120 29.846 19.951Q30.002 19.783 30.236 19.699Q30.471 19.615 30.700 19.615Q31.066 19.615 31.350 19.788Q31.482 19.680 31.656 19.619Q31.831 19.558 32.014 19.558Q32.099 19.558 32.174 19.595Q32.248 19.632 32.296 19.700Q32.343 19.768 32.343 19.854Q32.343 19.937 32.287 19.993Q32.231 20.049 32.150 20.049Q32.072 20.049 32.014 19.991Q31.955 19.932 31.955 19.854Q31.955 19.834 31.965 19.795Q31.965 19.778 31.975 19.759L31.989 19.744Q31.728 19.744 31.484 19.893Q31.582 19.981 31.643 20.102Q31.704 20.222 31.704 20.354Q31.704 20.586 31.550 20.755Q31.396 20.923 31.160 21.009Q30.925 21.094 30.700 21.094Q30.354 21.094 30.090 20.945Q30.029 21.016 30.029 21.118Q30.029 21.253 30.130 21.348Q30.232 21.443 30.371 21.443L30.878 21.443Q31.230 21.443 31.525 21.493Q31.821 21.543 32.038 21.713Q32.255 21.883 32.255 22.205Q32.255 22.390 32.105 22.519Q31.955 22.647 31.733 22.718Q31.511 22.788 31.280 22.821Q31.049 22.854 30.869 22.854Q30.627 22.854 30.300 22.794Q29.973 22.735 29.729 22.589Q29.484 22.444 29.484 22.205M29.851 22.205Q29.851 22.368 30.034 22.472Q30.217 22.576 30.461 22.622Q30.705 22.669 30.869 22.669Q31.032 22.669 31.278 22.622Q31.523 22.576 31.706 22.472Q31.889 22.368 31.889 22.205Q31.889 21.973 31.572 21.908Q31.254 21.844 30.878 21.844L30.371 21.844Q30.188 21.844 30.019 21.938Q29.851 22.032 29.851 22.205M30.695 20.908L30.700 20.908Q30.964 20.908 31.104 20.764Q31.245 20.620 31.245 20.354Q31.245 20.086 31.107 19.943Q30.969 19.800 30.700 19.800Q30.427 19.800 30.288 19.942Q30.148 20.083 30.148 20.354Q30.148 20.620 30.288 20.764Q30.427 20.908 30.695 20.908M32.885 20.728Q32.885 20.479 32.994 20.266Q33.103 20.054 33.289 19.901Q33.476 19.749 33.709 19.666Q33.942 19.583 34.179 19.583Q34.509 19.583 34.763 19.698Q35.017 19.812 35.161 20.045Q35.305 20.279 35.305 20.608Q35.305 20.645 35.285 20.672Q35.266 20.699 35.231 20.699L33.405 20.699L33.405 20.728Q33.405 21.165 33.649 21.422Q33.894 21.680 34.326 21.680Q34.487 21.680 34.643 21.623Q34.799 21.565 34.915 21.453Q35.031 21.341 35.085 21.189Q35.090 21.172 35.112 21.157Q35.134 21.143 35.151 21.143L35.231 21.143Q35.261 21.143 35.283 21.168Q35.305 21.194 35.305 21.223Q35.305 21.236 35.304 21.243Q35.302 21.250 35.300 21.260Q35.229 21.465 35.069 21.604Q34.909 21.743 34.696 21.812Q34.482 21.880 34.270 21.880Q33.942 21.880 33.617 21.739Q33.291 21.597 33.088 21.333Q32.885 21.070 32.885 20.728M33.415 20.525L34.899 20.525Q34.899 20.303 34.814 20.132Q34.729 19.961 34.566 19.865Q34.404 19.768 34.179 19.768Q33.857 19.768 33.656 19.980Q33.454 20.191 33.415 20.525M37.375 21.824L35.996 21.824L35.996 21.585Q36.411 21.585 36.411 21.465L36.411 20.188Q36.411 19.998 36.320 19.954Q36.230 19.910 35.996 19.910L35.996 19.668L36.755 19.619Q36.752 19.617 36.749 19.616Q36.745 19.615 36.740 19.615L36.814 19.615L36.814 20.139Q36.926 19.907 37.130 19.761Q37.334 19.615 37.580 19.615Q37.683 19.615 37.788 19.657Q37.893 19.700 37.956 19.781Q38.020 19.861 38.020 19.969Q38.020 20.078 37.944 20.157Q37.868 20.235 37.758 20.235Q37.651 20.235 37.573 20.157Q37.495 20.078 37.495 19.969Q37.495 19.871 37.556 19.800Q37.392 19.800 37.262 19.878Q37.131 19.956 37.042 20.086Q36.953 20.215 36.904 20.380Q36.855 20.545 36.855 20.694L36.855 21.465Q36.855 21.585 37.375 21.585\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-22.917 -12.476)\">\u003Cpath d=\"M18.317 22.357Q18.317 22.111 18.514 21.927Q18.711 21.742 18.967 21.663Q18.830 21.551 18.758 21.390Q18.687 21.229 18.687 21.048Q18.687 20.727 18.898 20.481Q18.564 20.183 18.564 19.773Q18.564 19.312 18.953 19.025Q19.343 18.738 19.821 18.738Q20.293 18.738 20.628 18.984Q20.802 18.830 21.013 18.748Q21.223 18.666 21.452 18.666Q21.616 18.666 21.737 18.773Q21.858 18.881 21.858 19.045Q21.858 19.141 21.787 19.213Q21.715 19.284 21.623 19.284Q21.523 19.284 21.453 19.211Q21.383 19.137 21.383 19.038Q21.383 18.984 21.397 18.953L21.404 18.939Q21.411 18.919 21.419 18.908Q21.428 18.898 21.431 18.891Q21.076 18.891 20.789 19.114Q21.076 19.407 21.076 19.773Q21.076 20.088 20.891 20.320Q20.707 20.553 20.418 20.681Q20.129 20.809 19.821 20.809Q19.620 20.809 19.428 20.759Q19.237 20.710 19.059 20.600Q18.967 20.727 18.967 20.870Q18.967 21.052 19.095 21.187Q19.223 21.322 19.408 21.322L20.040 21.322Q20.488 21.322 20.857 21.393Q21.226 21.465 21.486 21.694Q21.746 21.923 21.746 22.357Q21.746 22.678 21.450 22.880Q21.154 23.082 20.751 23.171Q20.348 23.260 20.033 23.260Q19.715 23.260 19.312 23.171Q18.909 23.082 18.613 22.880Q18.317 22.678 18.317 22.357M18.772 22.357Q18.772 22.586 18.991 22.735Q19.210 22.884 19.502 22.952Q19.794 23.020 20.033 23.020Q20.197 23.020 20.406 22.984Q20.614 22.949 20.821 22.868Q21.028 22.788 21.159 22.660Q21.291 22.532 21.291 22.357Q21.291 22.005 20.910 21.911Q20.529 21.817 20.026 21.817L19.408 21.817Q19.169 21.817 18.970 21.968Q18.772 22.118 18.772 22.357M19.821 20.570Q20.488 20.570 20.488 19.773Q20.488 18.973 19.821 18.973Q19.151 18.973 19.151 19.773Q19.151 20.570 19.821 20.570M22.398 21.096Q22.398 20.764 22.622 20.537Q22.846 20.310 23.190 20.182Q23.533 20.053 23.906 20.001Q24.278 19.948 24.583 19.948L24.583 19.695Q24.583 19.490 24.475 19.310Q24.367 19.131 24.186 19.028Q24.005 18.926 23.796 18.926Q23.390 18.926 23.154 19.018Q23.243 19.055 23.289 19.139Q23.335 19.223 23.335 19.325Q23.335 19.421 23.289 19.500Q23.243 19.578 23.162 19.623Q23.082 19.667 22.993 19.667Q22.843 19.667 22.742 19.570Q22.641 19.472 22.641 19.325Q22.641 18.703 23.796 18.703Q24.008 18.703 24.258 18.767Q24.507 18.830 24.709 18.949Q24.911 19.069 25.037 19.254Q25.164 19.438 25.164 19.681L25.164 21.257Q25.164 21.373 25.225 21.469Q25.287 21.564 25.399 21.564Q25.509 21.564 25.574 21.470Q25.639 21.376 25.639 21.257L25.639 20.809L25.905 20.809L25.905 21.257Q25.905 21.527 25.678 21.692Q25.451 21.858 25.170 21.858Q24.962 21.858 24.825 21.704Q24.689 21.551 24.665 21.335Q24.518 21.602 24.236 21.747Q23.954 21.892 23.629 21.892Q23.352 21.892 23.068 21.817Q22.785 21.742 22.592 21.563Q22.398 21.383 22.398 21.096M23.014 21.096Q23.014 21.270 23.115 21.400Q23.215 21.530 23.371 21.600Q23.526 21.670 23.690 21.670Q23.909 21.670 24.118 21.573Q24.326 21.475 24.454 21.294Q24.583 21.113 24.583 20.887L24.583 20.159Q24.258 20.159 23.892 20.250Q23.526 20.341 23.270 20.553Q23.014 20.764 23.014 21.096M27.939 21.824L26.387 21.824L26.387 21.544Q26.613 21.544 26.762 21.510Q26.910 21.475 26.910 21.335L26.910 19.486Q26.910 19.298 26.862 19.214Q26.815 19.131 26.717 19.112Q26.620 19.093 26.408 19.093L26.408 18.813L27.464 18.738L27.464 21.335Q27.464 21.475 27.596 21.510Q27.727 21.544 27.939 21.544L27.939 21.824M26.668 17.517Q26.668 17.346 26.791 17.227Q26.914 17.107 27.085 17.107Q27.252 17.107 27.375 17.227Q27.498 17.346 27.498 17.517Q27.498 17.692 27.375 17.815Q27.252 17.938 27.085 17.938Q26.914 17.938 26.791 17.815Q26.668 17.692 26.668 17.517M30.267 21.824L28.633 21.824L28.633 21.544Q28.862 21.544 29.011 21.510Q29.159 21.475 29.159 21.335L29.159 19.486Q29.159 19.216 29.052 19.155Q28.944 19.093 28.633 19.093L28.633 18.813L29.692 18.738L29.692 19.387Q29.863 19.079 30.168 18.908Q30.472 18.738 30.817 18.738Q31.323 18.738 31.607 18.961Q31.890 19.185 31.890 19.681L31.890 21.335Q31.890 21.472 32.039 21.508Q32.188 21.544 32.413 21.544L32.413 21.824L30.783 21.824L30.783 21.544Q31.012 21.544 31.160 21.510Q31.309 21.475 31.309 21.335L31.309 19.695Q31.309 19.360 31.190 19.160Q31.070 18.960 30.755 18.960Q30.485 18.960 30.251 19.096Q30.017 19.233 29.879 19.467Q29.740 19.701 29.740 19.975L29.740 21.335Q29.740 21.472 29.891 21.508Q30.041 21.544 30.267 21.544\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-22.917 -12.476)\">\u003Cpath d=\"M40.873 21.017L36.040 21.017Q35.972 21.007 35.926 20.961Q35.880 20.915 35.880 20.843Q35.880 20.778 35.926 20.732Q35.972 20.686 36.040 20.676L40.873 20.676Q40.942 20.686 40.988 20.732Q41.034 20.778 41.034 20.843Q41.034 20.915 40.988 20.961Q40.942 21.007 40.873 21.017M40.873 19.479L36.040 19.479Q35.972 19.469 35.926 19.423Q35.880 19.377 35.880 19.305Q35.880 19.161 36.040 19.137L40.873 19.137Q41.034 19.161 41.034 19.305Q41.034 19.377 40.988 19.423Q40.942 19.469 40.873 19.479\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-22.917 -12.476)\">\u003Cpath d=\"M46.214 21.964Q45.579 21.964 45.215 21.619Q44.850 21.274 44.715 20.749Q44.580 20.224 44.580 19.599Q44.580 18.574 44.936 17.875Q45.291 17.176 46.214 17.176Q47.141 17.176 47.493 17.875Q47.845 18.574 47.845 19.599Q47.845 20.224 47.710 20.749Q47.575 21.274 47.212 21.619Q46.850 21.964 46.214 21.964M46.214 21.739Q46.652 21.739 46.865 21.364Q47.079 20.990 47.129 20.523Q47.178 20.057 47.178 19.479Q47.178 18.926 47.129 18.498Q47.079 18.071 46.867 17.736Q46.655 17.401 46.214 17.401Q45.872 17.401 45.669 17.608Q45.466 17.815 45.379 18.127Q45.291 18.440 45.269 18.756Q45.247 19.073 45.247 19.479Q45.247 19.896 45.269 20.238Q45.291 20.580 45.380 20.928Q45.469 21.277 45.674 21.508Q45.879 21.739 46.214 21.739\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-22.917 -12.476)\">\u003Cpath d=\"M52.026 21.824L51.759 21.824L51.759 17.716Q51.759 17.446 51.652 17.384Q51.544 17.323 51.233 17.323L51.233 17.042L52.313 16.967L52.313 19.137Q52.522 18.946 52.807 18.842Q53.093 18.738 53.390 18.738Q53.708 18.738 54.005 18.859Q54.302 18.980 54.525 19.196Q54.747 19.411 54.873 19.696Q55 19.982 55 20.313Q55 20.758 54.760 21.122Q54.521 21.486 54.128 21.689Q53.735 21.892 53.291 21.892Q53.096 21.892 52.906 21.836Q52.717 21.780 52.556 21.675Q52.395 21.571 52.255 21.410L52.026 21.824M52.341 19.479L52.341 21.096Q52.477 21.356 52.718 21.513Q52.959 21.670 53.236 21.670Q53.530 21.670 53.742 21.563Q53.954 21.455 54.087 21.263Q54.220 21.072 54.279 20.833Q54.337 20.594 54.337 20.313Q54.337 19.954 54.243 19.650Q54.149 19.346 53.921 19.153Q53.694 18.960 53.328 18.960Q53.028 18.960 52.761 19.096Q52.494 19.233 52.341 19.479M57.252 21.824L55.700 21.824L55.700 21.544Q55.926 21.544 56.075 21.510Q56.223 21.475 56.223 21.335L56.223 19.486Q56.223 19.298 56.176 19.214Q56.128 19.131 56.030 19.112Q55.933 19.093 55.721 19.093L55.721 18.813L56.777 18.738L56.777 21.335Q56.777 21.475 56.909 21.510Q57.040 21.544 57.252 21.544L57.252 21.824M55.981 17.517Q55.981 17.346 56.104 17.227Q56.227 17.107 56.398 17.107Q56.565 17.107 56.688 17.227Q56.811 17.346 56.811 17.517Q56.811 17.692 56.688 17.815Q56.565 17.938 56.398 17.938Q56.227 17.938 56.104 17.815Q55.981 17.692 55.981 17.517M58.425 20.983L58.425 19.086L57.785 19.086L57.785 18.864Q58.103 18.864 58.320 18.654Q58.537 18.444 58.638 18.134Q58.739 17.825 58.739 17.517L59.006 17.517L59.006 18.806L60.082 18.806L60.082 19.086L59.006 19.086L59.006 20.970Q59.006 21.246 59.110 21.445Q59.214 21.643 59.474 21.643Q59.631 21.643 59.737 21.539Q59.843 21.434 59.893 21.281Q59.942 21.127 59.942 20.970L59.942 20.556L60.209 20.556L60.209 20.983Q60.209 21.209 60.110 21.419Q60.010 21.629 59.826 21.761Q59.641 21.892 59.412 21.892Q58.975 21.892 58.700 21.655Q58.425 21.417 58.425 20.983M61.019 21.817L61.019 20.754Q61.019 20.730 61.046 20.703Q61.073 20.676 61.097 20.676L61.207 20.676Q61.272 20.676 61.285 20.734Q61.381 21.168 61.627 21.419Q61.873 21.670 62.287 21.670Q62.629 21.670 62.882 21.537Q63.134 21.404 63.134 21.096Q63.134 20.939 63.040 20.824Q62.947 20.710 62.808 20.641Q62.670 20.573 62.502 20.535L61.921 20.436Q61.566 20.368 61.292 20.147Q61.019 19.927 61.019 19.585Q61.019 19.336 61.130 19.161Q61.241 18.987 61.427 18.888Q61.613 18.789 61.829 18.746Q62.044 18.703 62.287 18.703Q62.700 18.703 62.981 18.885L63.196 18.710Q63.206 18.707 63.213 18.705Q63.220 18.703 63.230 18.703L63.281 18.703Q63.309 18.703 63.333 18.727Q63.357 18.751 63.357 18.779L63.357 19.626Q63.357 19.647 63.333 19.674Q63.309 19.701 63.281 19.701L63.169 19.701Q63.141 19.701 63.116 19.676Q63.090 19.650 63.090 19.626Q63.090 19.390 62.984 19.226Q62.878 19.062 62.695 18.980Q62.512 18.898 62.280 18.898Q61.952 18.898 61.696 19.001Q61.439 19.103 61.439 19.380Q61.439 19.575 61.622 19.684Q61.805 19.794 62.034 19.835L62.608 19.941Q62.854 19.989 63.068 20.117Q63.281 20.245 63.418 20.448Q63.555 20.652 63.555 20.901Q63.555 21.414 63.189 21.653Q62.823 21.892 62.287 21.892Q61.791 21.892 61.460 21.598L61.193 21.872Q61.173 21.892 61.145 21.892L61.097 21.892Q61.073 21.892 61.046 21.865Q61.019 21.838 61.019 21.817\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"var(--tk-accent)\">\u003Cpath fill=\"none\" d=\"M204.202-54.998h37.404V-72.07h-37.404Z\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(189.49 -82.928)\">\u003Cpath d=\"M20.570 21.824L18.437 21.824L18.437 21.544Q19.158 21.544 19.158 21.335L19.158 17.534Q19.158 17.323 18.437 17.323L18.437 17.042L21.103 17.042Q21.513 17.042 21.934 17.196Q22.354 17.350 22.638 17.654Q22.921 17.958 22.921 18.372Q22.921 18.690 22.754 18.936Q22.586 19.182 22.310 19.348Q22.033 19.513 21.711 19.597Q21.390 19.681 21.103 19.681L19.849 19.681L19.849 21.335Q19.849 21.544 20.570 21.544L20.570 21.824M19.821 17.534L19.821 19.431L20.908 19.431Q21.517 19.431 21.831 19.194Q22.146 18.956 22.146 18.372Q22.146 17.979 22 17.745Q21.855 17.511 21.583 17.417Q21.312 17.323 20.908 17.323L20.187 17.323Q19.999 17.323 19.910 17.357Q19.821 17.391 19.821 17.534\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(189.49 -82.928)\">\u003Cpath d=\"M23.577 21.096Q23.577 20.764 23.800 20.537Q24.024 20.310 24.368 20.182Q24.711 20.053 25.084 20.001Q25.456 19.948 25.761 19.948L25.761 19.695Q25.761 19.490 25.653 19.310Q25.545 19.131 25.364 19.028Q25.183 18.926 24.975 18.926Q24.568 18.926 24.332 19.018Q24.421 19.055 24.467 19.139Q24.513 19.223 24.513 19.325Q24.513 19.421 24.467 19.500Q24.421 19.578 24.340 19.623Q24.260 19.667 24.171 19.667Q24.021 19.667 23.920 19.570Q23.819 19.472 23.819 19.325Q23.819 18.703 24.975 18.703Q25.186 18.703 25.436 18.767Q25.685 18.830 25.887 18.949Q26.089 19.069 26.215 19.254Q26.342 19.438 26.342 19.681L26.342 21.257Q26.342 21.373 26.403 21.469Q26.465 21.564 26.578 21.564Q26.687 21.564 26.752 21.470Q26.817 21.376 26.817 21.257L26.817 20.809L27.083 20.809L27.083 21.257Q27.083 21.527 26.856 21.692Q26.629 21.858 26.349 21.858Q26.140 21.858 26.003 21.704Q25.867 21.551 25.843 21.335Q25.696 21.602 25.414 21.747Q25.132 21.892 24.807 21.892Q24.530 21.892 24.246 21.817Q23.963 21.742 23.770 21.563Q23.577 21.383 23.577 21.096M24.192 21.096Q24.192 21.270 24.293 21.400Q24.393 21.530 24.549 21.600Q24.704 21.670 24.869 21.670Q25.087 21.670 25.296 21.573Q25.504 21.475 25.632 21.294Q25.761 21.113 25.761 20.887L25.761 20.159Q25.436 20.159 25.070 20.250Q24.704 20.341 24.448 20.553Q24.192 20.764 24.192 21.096M28.027 20.983L28.027 19.086L27.388 19.086L27.388 18.864Q27.705 18.864 27.923 18.654Q28.140 18.444 28.240 18.134Q28.341 17.825 28.341 17.517L28.608 17.517L28.608 18.806L29.684 18.806L29.684 19.086L28.608 19.086L28.608 20.970Q28.608 21.246 28.712 21.445Q28.816 21.643 29.076 21.643Q29.233 21.643 29.339 21.539Q29.445 21.434 29.495 21.281Q29.544 21.127 29.544 20.970L29.544 20.556L29.811 20.556L29.811 20.983Q29.811 21.209 29.712 21.419Q29.613 21.629 29.428 21.761Q29.244 21.892 29.015 21.892Q28.577 21.892 28.302 21.655Q28.027 21.417 28.027 20.983M32.371 21.824L30.635 21.824L30.635 21.544Q30.864 21.544 31.012 21.510Q31.161 21.475 31.161 21.335L31.161 19.486Q31.161 19.216 31.053 19.155Q30.946 19.093 30.635 19.093L30.635 18.813L31.663 18.738L31.663 19.445Q31.793 19.137 32.036 18.938Q32.279 18.738 32.597 18.738Q32.815 18.738 32.986 18.862Q33.157 18.987 33.157 19.199Q33.157 19.336 33.058 19.435Q32.959 19.534 32.826 19.534Q32.689 19.534 32.590 19.435Q32.491 19.336 32.491 19.199Q32.491 19.059 32.590 18.960Q32.299 18.960 32.099 19.156Q31.899 19.353 31.807 19.647Q31.715 19.941 31.715 20.221L31.715 21.335Q31.715 21.544 32.371 21.544L32.371 21.824M33.701 20.341Q33.701 19.999 33.836 19.700Q33.971 19.401 34.210 19.177Q34.449 18.953 34.767 18.828Q35.085 18.703 35.416 18.703Q35.861 18.703 36.261 18.919Q36.661 19.134 36.895 19.512Q37.129 19.889 37.129 20.341Q37.129 20.682 36.987 20.966Q36.845 21.250 36.601 21.457Q36.356 21.663 36.047 21.778Q35.738 21.892 35.416 21.892Q34.986 21.892 34.584 21.691Q34.183 21.489 33.942 21.137Q33.701 20.785 33.701 20.341M35.416 21.643Q36.018 21.643 36.242 21.265Q36.466 20.887 36.466 20.255Q36.466 19.643 36.232 19.284Q35.997 18.926 35.416 18.926Q34.364 18.926 34.364 20.255Q34.364 20.887 34.589 21.265Q34.815 21.643 35.416 21.643M39.405 21.824L37.771 21.824L37.771 21.544Q38 21.544 38.149 21.510Q38.298 21.475 38.298 21.335L38.298 19.486Q38.298 19.216 38.190 19.155Q38.082 19.093 37.771 19.093L37.771 18.813L38.831 18.738L38.831 19.387Q39.002 19.079 39.306 18.908Q39.610 18.738 39.955 18.738Q40.461 18.738 40.745 18.961Q41.029 19.185 41.029 19.681L41.029 21.335Q41.029 21.472 41.177 21.508Q41.326 21.544 41.552 21.544L41.552 21.824L39.921 21.824L39.921 21.544Q40.150 21.544 40.299 21.510Q40.448 21.475 40.448 21.335L40.448 19.695Q40.448 19.360 40.328 19.160Q40.208 18.960 39.894 18.960Q39.624 18.960 39.390 19.096Q39.156 19.233 39.017 19.467Q38.879 19.701 38.879 19.975L38.879 21.335Q38.879 21.472 39.029 21.508Q39.180 21.544 39.405 21.544L39.405 21.824M42.140 21.817L42.140 20.754Q42.140 20.730 42.167 20.703Q42.194 20.676 42.218 20.676L42.328 20.676Q42.392 20.676 42.406 20.734Q42.502 21.168 42.748 21.419Q42.994 21.670 43.408 21.670Q43.749 21.670 44.002 21.537Q44.255 21.404 44.255 21.096Q44.255 20.939 44.161 20.824Q44.067 20.710 43.929 20.641Q43.790 20.573 43.623 20.535L43.042 20.436Q42.686 20.368 42.413 20.147Q42.140 19.927 42.140 19.585Q42.140 19.336 42.251 19.161Q42.362 18.987 42.548 18.888Q42.734 18.789 42.950 18.746Q43.165 18.703 43.408 18.703Q43.821 18.703 44.101 18.885L44.317 18.710Q44.327 18.707 44.334 18.705Q44.341 18.703 44.351 18.703L44.402 18.703Q44.430 18.703 44.454 18.727Q44.477 18.751 44.477 18.779L44.477 19.626Q44.477 19.647 44.454 19.674Q44.430 19.701 44.402 19.701L44.289 19.701Q44.262 19.701 44.236 19.676Q44.211 19.650 44.211 19.626Q44.211 19.390 44.105 19.226Q43.999 19.062 43.816 18.980Q43.633 18.898 43.401 18.898Q43.073 18.898 42.816 19.001Q42.560 19.103 42.560 19.380Q42.560 19.575 42.743 19.684Q42.926 19.794 43.155 19.835L43.729 19.941Q43.975 19.989 44.189 20.117Q44.402 20.245 44.539 20.448Q44.676 20.652 44.676 20.901Q44.676 21.414 44.310 21.653Q43.944 21.892 43.408 21.892Q42.912 21.892 42.580 21.598L42.314 21.872Q42.293 21.892 42.266 21.892L42.218 21.892Q42.194 21.892 42.167 21.865Q42.140 21.838 42.140 21.817M46.347 21.404Q46.347 21.236 46.474 21.113Q46.600 20.990 46.767 20.990Q46.935 20.990 47.058 21.113Q47.181 21.236 47.181 21.404Q47.181 21.578 47.058 21.701Q46.935 21.824 46.767 21.824Q46.600 21.824 46.474 21.701Q46.347 21.578 46.347 21.404M46.634 20.368L46.634 20.026Q46.634 19.678 46.747 19.339Q46.860 19.001 47.061 18.731Q47.123 18.649 47.241 18.532Q47.359 18.416 47.436 18.326Q47.513 18.235 47.557 18.122Q47.601 18.010 47.601 17.849Q47.601 17.422 47.408 17.271Q47.215 17.121 46.767 17.121Q46.491 17.121 46.241 17.215Q45.992 17.309 45.858 17.504Q45.995 17.504 46.084 17.610Q46.173 17.716 46.173 17.849Q46.173 17.945 46.127 18.023Q46.080 18.102 46 18.146Q45.920 18.191 45.831 18.191Q45.681 18.191 45.580 18.093Q45.479 17.996 45.479 17.849Q45.479 17.538 45.672 17.321Q45.865 17.104 46.162 17Q46.460 16.895 46.767 16.895Q47.024 16.895 47.285 16.938Q47.547 16.981 47.766 17.083Q47.984 17.186 48.124 17.377Q48.265 17.569 48.265 17.849Q48.265 18.064 48.155 18.252Q48.046 18.440 47.854 18.563Q47.578 18.734 47.364 18.956Q47.150 19.178 47.026 19.457Q46.901 19.736 46.901 20.040L46.901 20.368Q46.901 20.399 46.877 20.423Q46.853 20.447 46.826 20.447L46.713 20.447Q46.682 20.447 46.658 20.423Q46.634 20.399 46.634 20.368\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M154.617-10.896h22.762v-14.227h-22.762Z\"\u002F>\u003Cg transform=\"translate(143.01 -37.442)\">\u003Cpath d=\"M20.156 21.824L18.423 21.824L18.423 21.544Q19.145 21.544 19.145 21.144L19.145 17.357Q18.912 17.323 18.423 17.323L18.423 17.042L19.780 17.042Q19.821 17.056 19.842 17.070L22.545 20.717L22.545 17.722Q22.545 17.323 21.824 17.323L21.824 17.042L23.561 17.042L23.561 17.323Q22.839 17.323 22.839 17.722L22.839 21.732Q22.822 21.807 22.754 21.824L22.634 21.824Q22.586 21.814 22.573 21.790L19.439 17.569L19.439 21.144Q19.439 21.544 20.156 21.544L20.156 21.824M24.217 20.341Q24.217 19.999 24.352 19.700Q24.487 19.401 24.726 19.177Q24.965 18.953 25.283 18.828Q25.601 18.703 25.933 18.703Q26.377 18.703 26.777 18.919Q27.177 19.134 27.411 19.512Q27.645 19.889 27.645 20.341Q27.645 20.682 27.503 20.966Q27.361 21.250 27.117 21.457Q26.873 21.663 26.563 21.778Q26.254 21.892 25.933 21.892Q25.502 21.892 25.100 21.691Q24.699 21.489 24.458 21.137Q24.217 20.785 24.217 20.341M25.933 21.643Q26.534 21.643 26.758 21.265Q26.982 20.887 26.982 20.255Q26.982 19.643 26.748 19.284Q26.514 18.926 25.933 18.926Q24.880 18.926 24.880 20.255Q24.880 20.887 25.106 21.265Q25.331 21.643 25.933 21.643\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M211.523-10.896h22.762v-14.227h-22.762Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(198.87 -37.442)\">\u003Cpath d=\"M22.046 21.824L19.934 21.824L19.934 21.544Q20.655 21.544 20.655 21.335L20.655 19.913L19.004 17.534Q18.885 17.391 18.692 17.357Q18.499 17.323 18.211 17.323L18.211 17.042L20.242 17.042L20.242 17.323Q19.794 17.323 19.794 17.463Q19.794 17.497 19.815 17.534L21.223 19.561L22.504 17.722Q22.559 17.647 22.559 17.555Q22.559 17.432 22.457 17.377Q22.354 17.323 22.211 17.323L22.211 17.042L23.769 17.042L23.769 17.323Q23.475 17.323 23.234 17.417Q22.993 17.511 22.846 17.722L21.318 19.913L21.318 21.335Q21.318 21.544 22.046 21.544\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(198.87 -37.442)\">\u003Cpath d=\"M23.574 20.289Q23.574 19.968 23.699 19.679Q23.824 19.390 24.050 19.167Q24.275 18.943 24.571 18.823Q24.866 18.703 25.184 18.703Q25.512 18.703 25.774 18.803Q26.035 18.902 26.211 19.084Q26.387 19.267 26.481 19.525Q26.575 19.783 26.575 20.115Q26.575 20.207 26.493 20.228L24.238 20.228L24.238 20.289Q24.238 20.877 24.521 21.260Q24.805 21.643 25.372 21.643Q25.694 21.643 25.962 21.450Q26.230 21.257 26.319 20.942Q26.326 20.901 26.401 20.887L26.493 20.887Q26.575 20.911 26.575 20.983Q26.575 20.990 26.569 21.017Q26.456 21.414 26.085 21.653Q25.714 21.892 25.290 21.892Q24.853 21.892 24.453 21.684Q24.053 21.475 23.814 21.108Q23.574 20.741 23.574 20.289M24.244 20.019L26.059 20.019Q26.059 19.742 25.962 19.490Q25.864 19.237 25.666 19.081Q25.468 18.926 25.184 18.926Q24.907 18.926 24.694 19.084Q24.480 19.243 24.362 19.498Q24.244 19.753 24.244 20.019M27.163 21.817L27.163 20.754Q27.163 20.730 27.191 20.703Q27.218 20.676 27.242 20.676L27.351 20.676Q27.416 20.676 27.430 20.734Q27.526 21.168 27.772 21.419Q28.018 21.670 28.431 21.670Q28.773 21.670 29.026 21.537Q29.279 21.404 29.279 21.096Q29.279 20.939 29.185 20.824Q29.091 20.710 28.953 20.641Q28.814 20.573 28.647 20.535L28.066 20.436Q27.710 20.368 27.437 20.147Q27.163 19.927 27.163 19.585Q27.163 19.336 27.274 19.161Q27.385 18.987 27.572 18.888Q27.758 18.789 27.973 18.746Q28.189 18.703 28.431 18.703Q28.845 18.703 29.125 18.885L29.341 18.710Q29.351 18.707 29.358 18.705Q29.364 18.703 29.375 18.703L29.426 18.703Q29.453 18.703 29.477 18.727Q29.501 18.751 29.501 18.779L29.501 19.626Q29.501 19.647 29.477 19.674Q29.453 19.701 29.426 19.701L29.313 19.701Q29.286 19.701 29.260 19.676Q29.235 19.650 29.235 19.626Q29.235 19.390 29.129 19.226Q29.023 19.062 28.840 18.980Q28.657 18.898 28.425 18.898Q28.096 18.898 27.840 19.001Q27.584 19.103 27.584 19.380Q27.584 19.575 27.767 19.684Q27.949 19.794 28.178 19.835L28.753 19.941Q28.999 19.989 29.212 20.117Q29.426 20.245 29.563 20.448Q29.699 20.652 29.699 20.901Q29.699 21.414 29.334 21.653Q28.968 21.892 28.431 21.892Q27.936 21.892 27.604 21.598L27.338 21.872Q27.317 21.892 27.290 21.892L27.242 21.892Q27.218 21.892 27.191 21.865Q27.163 21.838 27.163 21.817\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M264.16-7.74h36.989V-28.28H264.16Z\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(255.291 -34.259)\">\u003Cpath d=\"M21.456 13.824L18.571 13.824L18.571 13.622Q18.571 13.592 18.598 13.564L19.846 12.347Q19.918 12.272 19.960 12.230Q20.003 12.187 20.082 12.108Q20.495 11.695 20.726 11.337Q20.957 10.980 20.957 10.556Q20.957 10.324 20.878 10.121Q20.799 9.917 20.658 9.767Q20.516 9.616 20.321 9.536Q20.126 9.456 19.894 9.456Q19.583 9.456 19.325 9.615Q19.067 9.774 18.937 10.051L18.957 10.051Q19.125 10.051 19.232 10.162Q19.340 10.273 19.340 10.437Q19.340 10.594 19.231 10.707Q19.121 10.820 18.957 10.820Q18.797 10.820 18.684 10.707Q18.571 10.594 18.571 10.437Q18.571 10.061 18.779 9.774Q18.988 9.487 19.323 9.331Q19.658 9.176 20.013 9.176Q20.437 9.176 20.817 9.334Q21.196 9.493 21.430 9.810Q21.664 10.126 21.664 10.556Q21.664 10.867 21.524 11.136Q21.384 11.404 21.179 11.609Q20.974 11.814 20.611 12.096Q20.249 12.378 20.140 12.474L19.285 13.202L19.928 13.202Q20.191 13.202 20.480 13.200Q20.769 13.199 20.987 13.190Q21.206 13.181 21.223 13.164Q21.285 13.099 21.322 12.932Q21.360 12.764 21.398 12.522L21.664 12.522\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(255.291 -34.259)\">\u003Cpath d=\"M26.769 15.181L25.139 15.181L25.139 14.901Q25.368 14.901 25.517 14.866Q25.665 14.832 25.665 14.692L25.665 11.346Q25.665 11.175 25.529 11.134Q25.392 11.093 25.139 11.093L25.139 10.813L26.219 10.738L26.219 11.144Q26.441 10.943 26.728 10.840Q27.016 10.738 27.323 10.738Q27.750 10.738 28.114 10.951Q28.478 11.165 28.692 11.529Q28.906 11.893 28.906 12.313Q28.906 12.758 28.666 13.122Q28.427 13.486 28.034 13.689Q27.641 13.892 27.197 13.892Q26.930 13.892 26.682 13.792Q26.435 13.691 26.247 13.510L26.247 14.692Q26.247 14.829 26.395 14.865Q26.544 14.901 26.769 14.901L26.769 15.181M26.247 11.493L26.247 13.103Q26.380 13.356 26.623 13.513Q26.865 13.670 27.142 13.670Q27.470 13.670 27.723 13.469Q27.976 13.267 28.109 12.949Q28.243 12.631 28.243 12.313Q28.243 12.084 28.178 11.855Q28.113 11.626 27.985 11.428Q27.856 11.230 27.662 11.110Q27.467 10.991 27.234 10.991Q26.940 10.991 26.672 11.120Q26.404 11.250 26.247 11.493\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(255.291 -34.259)\">\u003Cpath d=\"M29.716 12.341Q29.716 11.999 29.851 11.700Q29.986 11.401 30.226 11.177Q30.465 10.953 30.783 10.828Q31.101 10.703 31.432 10.703Q31.877 10.703 32.276 10.919Q32.676 11.134 32.911 11.512Q33.145 11.889 33.145 12.341Q33.145 12.682 33.003 12.966Q32.861 13.250 32.617 13.457Q32.372 13.663 32.063 13.778Q31.754 13.892 31.432 13.892Q31.002 13.892 30.600 13.691Q30.198 13.489 29.957 13.137Q29.716 12.785 29.716 12.341M31.432 13.643Q32.034 13.643 32.258 13.265Q32.482 12.887 32.482 12.255Q32.482 11.643 32.247 11.284Q32.013 10.926 31.432 10.926Q30.380 10.926 30.380 12.255Q30.380 12.887 30.605 13.265Q30.831 13.643 31.432 13.643M33.739 13.817L33.739 12.754Q33.739 12.730 33.767 12.703Q33.794 12.676 33.818 12.676L33.927 12.676Q33.992 12.676 34.006 12.734Q34.102 13.168 34.348 13.419Q34.594 13.670 35.007 13.670Q35.349 13.670 35.602 13.537Q35.855 13.404 35.855 13.096Q35.855 12.939 35.761 12.824Q35.667 12.710 35.529 12.641Q35.390 12.573 35.223 12.535L34.642 12.436Q34.286 12.368 34.013 12.147Q33.739 11.927 33.739 11.585Q33.739 11.336 33.850 11.161Q33.962 10.987 34.148 10.888Q34.334 10.789 34.549 10.746Q34.765 10.703 35.007 10.703Q35.421 10.703 35.701 10.885L35.917 10.710Q35.927 10.707 35.934 10.705Q35.941 10.703 35.951 10.703L36.002 10.703Q36.029 10.703 36.053 10.727Q36.077 10.751 36.077 10.779L36.077 11.626Q36.077 11.647 36.053 11.674Q36.029 11.701 36.002 11.701L35.889 11.701Q35.862 11.701 35.836 11.676Q35.811 11.650 35.811 11.626Q35.811 11.390 35.705 11.226Q35.599 11.062 35.416 10.980Q35.233 10.898 35.001 10.898Q34.672 10.898 34.416 11.001Q34.160 11.103 34.160 11.380Q34.160 11.575 34.343 11.684Q34.526 11.794 34.755 11.835L35.329 11.941Q35.575 11.989 35.788 12.117Q36.002 12.245 36.139 12.448Q36.276 12.652 36.276 12.901Q36.276 13.414 35.910 13.653Q35.544 13.892 35.007 13.892Q34.512 13.892 34.180 13.598L33.914 13.872Q33.893 13.892 33.866 13.892L33.818 13.892Q33.794 13.892 33.767 13.865Q33.739 13.838 33.739 13.817\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(255.291 -34.259)\">\u003Cpath d=\"M20.361 20.676L18.317 20.676L18.317 20.395L20.648 17.223Q20.683 17.176 20.748 17.176L20.884 17.176Q20.929 17.176 20.956 17.203Q20.983 17.230 20.983 17.275L20.983 20.395L21.746 20.395L21.746 20.676L20.983 20.676L20.983 21.335Q20.983 21.544 21.739 21.544L21.739 21.824L19.606 21.824L19.606 21.544Q20.361 21.544 20.361 21.335L20.361 20.676M20.409 17.951L18.618 20.395L20.409 20.395\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(255.291 -34.259)\">\u003Cpath d=\"M26.721 21.824L25.087 21.824L25.087 21.544Q25.316 21.544 25.465 21.510Q25.614 21.475 25.614 21.335L25.614 19.486Q25.614 19.216 25.506 19.155Q25.398 19.093 25.087 19.093L25.087 18.813L26.147 18.738L26.147 19.387Q26.318 19.079 26.622 18.908Q26.926 18.738 27.271 18.738Q27.777 18.738 28.061 18.961Q28.345 19.185 28.345 19.681L28.345 21.335Q28.345 21.472 28.493 21.508Q28.642 21.544 28.868 21.544L28.868 21.824L27.237 21.824L27.237 21.544Q27.466 21.544 27.615 21.510Q27.764 21.475 27.764 21.335L27.764 19.695Q27.764 19.360 27.644 19.160Q27.524 18.960 27.210 18.960Q26.940 18.960 26.706 19.096Q26.472 19.233 26.333 19.467Q26.195 19.701 26.195 19.975L26.195 21.335Q26.195 21.472 26.345 21.508Q26.496 21.544 26.721 21.544L26.721 21.824M29.414 20.289Q29.414 19.968 29.539 19.679Q29.664 19.390 29.890 19.167Q30.115 18.943 30.411 18.823Q30.706 18.703 31.024 18.703Q31.352 18.703 31.614 18.803Q31.875 18.902 32.051 19.084Q32.227 19.267 32.321 19.525Q32.415 19.783 32.415 20.115Q32.415 20.207 32.333 20.228L30.078 20.228L30.078 20.289Q30.078 20.877 30.361 21.260Q30.645 21.643 31.212 21.643Q31.534 21.643 31.802 21.450Q32.070 21.257 32.159 20.942Q32.166 20.901 32.241 20.887L32.333 20.887Q32.415 20.911 32.415 20.983Q32.415 20.990 32.409 21.017Q32.296 21.414 31.925 21.653Q31.554 21.892 31.130 21.892Q30.693 21.892 30.293 21.684Q29.893 21.475 29.654 21.108Q29.414 20.741 29.414 20.289M30.084 20.019L31.899 20.019Q31.899 19.742 31.802 19.490Q31.704 19.237 31.506 19.081Q31.308 18.926 31.024 18.926Q30.747 18.926 30.534 19.084Q30.320 19.243 30.202 19.498Q30.084 19.753 30.084 20.019M32.962 22.357Q32.962 22.111 33.159 21.927Q33.355 21.742 33.612 21.663Q33.475 21.551 33.403 21.390Q33.331 21.229 33.331 21.048Q33.331 20.727 33.543 20.481Q33.208 20.183 33.208 19.773Q33.208 19.312 33.598 19.025Q33.988 18.738 34.466 18.738Q34.938 18.738 35.273 18.984Q35.447 18.830 35.657 18.748Q35.868 18.666 36.097 18.666Q36.261 18.666 36.382 18.773Q36.503 18.881 36.503 19.045Q36.503 19.141 36.432 19.213Q36.360 19.284 36.267 19.284Q36.168 19.284 36.098 19.211Q36.028 19.137 36.028 19.038Q36.028 18.984 36.042 18.953L36.049 18.939Q36.056 18.919 36.064 18.908Q36.073 18.898 36.076 18.891Q35.721 18.891 35.433 19.114Q35.721 19.407 35.721 19.773Q35.721 20.088 35.536 20.320Q35.351 20.553 35.063 20.681Q34.774 20.809 34.466 20.809Q34.265 20.809 34.073 20.759Q33.882 20.710 33.704 20.600Q33.612 20.727 33.612 20.870Q33.612 21.052 33.740 21.187Q33.868 21.322 34.053 21.322L34.685 21.322Q35.133 21.322 35.502 21.393Q35.871 21.465 36.131 21.694Q36.391 21.923 36.391 22.357Q36.391 22.678 36.095 22.880Q35.799 23.082 35.396 23.171Q34.993 23.260 34.678 23.260Q34.360 23.260 33.957 23.171Q33.554 23.082 33.258 22.880Q32.962 22.678 32.962 22.357M33.417 22.357Q33.417 22.586 33.636 22.735Q33.854 22.884 34.147 22.952Q34.439 23.020 34.678 23.020Q34.842 23.020 35.051 22.984Q35.259 22.949 35.466 22.868Q35.673 22.788 35.804 22.660Q35.936 22.532 35.936 22.357Q35.936 22.005 35.555 21.911Q35.174 21.817 34.671 21.817L34.053 21.817Q33.813 21.817 33.615 21.968Q33.417 22.118 33.417 22.357M34.466 20.570Q35.133 20.570 35.133 19.773Q35.133 18.973 34.466 18.973Q33.796 18.973 33.796 19.773Q33.796 20.570 34.466 20.570\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m211.984-54.798-35.286 28.226\"\u002F>\u003Cpath stroke=\"none\" d=\"m175.136-25.323 3.498-.75-1.936-.5-.063-1.998\"\u002F>\u003Cg transform=\"translate(159.705 -62.585)\">\u003Cpath d=\"M19.809 21.824L18.449 21.824L18.449 21.585Q19.003 21.585 19.003 21.324L19.003 18.670Q18.874 18.650 18.449 18.650L18.449 18.408L19.519 18.408Q19.558 18.408 19.575 18.433L21.733 21.004L21.733 18.909Q21.733 18.650 21.179 18.650L21.179 18.408L22.539 18.408L22.539 18.650Q21.984 18.650 21.984 18.909L21.984 21.748Q21.984 21.775 21.960 21.800Q21.936 21.824 21.909 21.824L21.809 21.824Q21.777 21.824 21.760 21.800L19.255 18.819L19.255 21.324Q19.255 21.585 19.809 21.585L19.809 21.824M23.259 20.760Q23.259 20.501 23.376 20.280Q23.493 20.059 23.696 19.903Q23.898 19.746 24.144 19.665Q24.389 19.583 24.643 19.583Q24.899 19.583 25.146 19.665Q25.393 19.746 25.593 19.903Q25.793 20.059 25.911 20.276Q26.030 20.493 26.030 20.760Q26.030 21.016 25.908 21.227Q25.786 21.438 25.582 21.584Q25.378 21.729 25.129 21.804Q24.880 21.880 24.643 21.880Q24.406 21.880 24.161 21.806Q23.916 21.731 23.708 21.582Q23.501 21.433 23.380 21.225Q23.259 21.016 23.259 20.760M24.643 21.680Q25.109 21.680 25.310 21.428Q25.510 21.177 25.510 20.703Q25.510 20.249 25.304 20.009Q25.097 19.768 24.643 19.768Q24.191 19.768 23.985 20.009Q23.779 20.249 23.779 20.703Q23.779 21.177 23.979 21.428Q24.179 21.680 24.643 21.680M28.039 21.824L26.745 21.824L26.745 21.585Q27.160 21.585 27.160 21.465L27.160 20.188Q27.160 19.998 27.070 19.954Q26.980 19.910 26.745 19.910L26.745 19.668L27.585 19.615L27.585 20.078Q27.680 19.929 27.823 19.826Q27.966 19.722 28.138 19.668Q28.310 19.615 28.491 19.615Q29.355 19.615 29.355 20.293L29.355 21.465Q29.355 21.585 29.770 21.585L29.770 21.824L28.474 21.824L28.474 21.585Q28.889 21.585 28.889 21.465L28.889 20.308Q28.889 20.052 28.789 19.926Q28.689 19.800 28.440 19.800Q28.234 19.800 28.044 19.888Q27.854 19.976 27.739 20.137Q27.624 20.298 27.624 20.503L27.624 21.465Q27.624 21.585 28.039 21.585L28.039 21.824M30.410 20.728Q30.410 20.479 30.518 20.266Q30.627 20.054 30.814 19.901Q31.001 19.749 31.234 19.666Q31.467 19.583 31.704 19.583Q32.033 19.583 32.287 19.698Q32.541 19.812 32.685 20.045Q32.829 20.279 32.829 20.608Q32.829 20.645 32.810 20.672Q32.790 20.699 32.756 20.699L30.930 20.699L30.930 20.728Q30.930 21.165 31.174 21.422Q31.418 21.680 31.850 21.680Q32.011 21.680 32.168 21.623Q32.324 21.565 32.440 21.453Q32.556 21.341 32.609 21.189Q32.614 21.172 32.636 21.157Q32.658 21.143 32.675 21.143L32.756 21.143Q32.785 21.143 32.807 21.168Q32.829 21.194 32.829 21.223Q32.829 21.236 32.828 21.243Q32.827 21.250 32.824 21.260Q32.753 21.465 32.594 21.604Q32.434 21.743 32.220 21.812Q32.006 21.880 31.794 21.880Q31.467 21.880 31.141 21.739Q30.815 21.597 30.612 21.333Q30.410 21.070 30.410 20.728M30.940 20.525L32.424 20.525Q32.424 20.303 32.338 20.132Q32.253 19.961 32.091 19.865Q31.928 19.768 31.704 19.768Q31.381 19.768 31.180 19.980Q30.979 20.191 30.940 20.525\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M222.904-54.798v27.475\"\u002F>\u003Cpath stroke=\"none\" d=\"m222.904-25.323 1.6-3.2-1.6 1.2-1.6-1.2\"\u002F>\u003Cg transform=\"translate(188.465 -63.133)\">\u003Cpath d=\"M18.530 21.873L18.530 20.750Q18.542 20.701 18.593 20.689L18.698 20.689Q18.747 20.701 18.759 20.750Q18.759 21.104 18.952 21.315Q19.145 21.526 19.447 21.610Q19.748 21.695 20.090 21.695Q20.293 21.695 20.500 21.613Q20.708 21.531 20.843 21.372Q20.979 21.214 20.979 21.004Q20.979 20.825 20.883 20.688Q20.788 20.550 20.636 20.459Q20.483 20.369 20.315 20.335L19.448 20.164Q19.072 20.088 18.801 19.843Q18.530 19.597 18.530 19.243Q18.530 18.955 18.712 18.739Q18.894 18.523 19.177 18.411Q19.460 18.299 19.743 18.299Q20.029 18.299 20.293 18.383Q20.556 18.467 20.744 18.643L20.979 18.318Q20.991 18.306 21.025 18.299L21.069 18.299Q21.118 18.313 21.130 18.360L21.130 19.478Q21.118 19.529 21.069 19.544L20.964 19.544Q20.913 19.532 20.898 19.478Q20.861 19.165 20.706 18.950Q20.551 18.736 20.306 18.629Q20.061 18.523 19.743 18.523Q19.550 18.523 19.342 18.595Q19.133 18.667 18.994 18.810Q18.855 18.953 18.855 19.153Q18.855 19.392 19.051 19.550Q19.248 19.707 19.499 19.759L20.363 19.929Q20.544 19.961 20.717 20.049Q20.891 20.137 21.023 20.265Q21.154 20.393 21.229 20.557Q21.303 20.720 21.303 20.918Q21.303 21.219 21.125 21.452Q20.947 21.685 20.666 21.809Q20.385 21.934 20.090 21.934Q19.326 21.934 18.908 21.599L18.684 21.914Q18.669 21.924 18.640 21.934L18.593 21.934Q18.542 21.922 18.530 21.873M22.109 20.760Q22.109 20.501 22.226 20.280Q22.343 20.059 22.546 19.903Q22.749 19.746 22.994 19.665Q23.239 19.583 23.493 19.583Q23.750 19.583 23.996 19.665Q24.243 19.746 24.443 19.903Q24.643 20.059 24.762 20.276Q24.880 20.493 24.880 20.760Q24.880 21.016 24.758 21.227Q24.636 21.438 24.432 21.584Q24.228 21.729 23.979 21.804Q23.730 21.880 23.493 21.880Q23.256 21.880 23.011 21.806Q22.766 21.731 22.558 21.582Q22.351 21.433 22.230 21.225Q22.109 21.016 22.109 20.760M23.493 21.680Q23.960 21.680 24.160 21.428Q24.360 21.177 24.360 20.703Q24.360 20.249 24.154 20.009Q23.947 19.768 23.493 19.768Q23.042 19.768 22.835 20.009Q22.629 20.249 22.629 20.703Q22.629 21.177 22.829 21.428Q23.029 21.680 23.493 21.680M26.889 21.824L25.595 21.824L25.595 21.585Q26.010 21.585 26.010 21.465L26.010 20.188Q26.010 19.998 25.920 19.954Q25.830 19.910 25.595 19.910L25.595 19.668L26.435 19.615L26.435 20.078Q26.530 19.929 26.674 19.824Q26.818 19.720 26.991 19.667Q27.163 19.615 27.346 19.615Q27.485 19.615 27.616 19.633Q27.746 19.651 27.856 19.698Q27.966 19.744 28.054 19.835Q28.142 19.927 28.181 20.064Q28.320 19.846 28.565 19.730Q28.811 19.615 29.079 19.615Q29.946 19.615 29.946 20.293L29.946 21.465Q29.946 21.585 30.361 21.585L30.361 21.824L29.065 21.824L29.065 21.585Q29.480 21.585 29.480 21.465L29.480 20.308Q29.480 20.052 29.377 19.926Q29.274 19.800 29.025 19.800Q28.877 19.800 28.731 19.849Q28.586 19.898 28.466 19.993Q28.347 20.088 28.278 20.218Q28.210 20.347 28.210 20.503L28.210 21.465Q28.210 21.585 28.625 21.585L28.625 21.824L27.329 21.824L27.329 21.585Q27.744 21.585 27.744 21.465L27.744 20.308Q27.744 20.052 27.641 19.926Q27.539 19.800 27.290 19.800Q27.141 19.800 26.995 19.849Q26.850 19.898 26.731 19.993Q26.611 20.088 26.543 20.218Q26.474 20.347 26.474 20.503L26.474 21.465Q26.474 21.585 26.889 21.585L26.889 21.824M30.996 20.728Q30.996 20.479 31.104 20.266Q31.213 20.054 31.400 19.901Q31.586 19.749 31.820 19.666Q32.053 19.583 32.290 19.583Q32.619 19.583 32.873 19.698Q33.127 19.812 33.271 20.045Q33.415 20.279 33.415 20.608Q33.415 20.645 33.396 20.672Q33.376 20.699 33.342 20.699L31.516 20.699L31.516 20.728Q31.516 21.165 31.760 21.422Q32.004 21.680 32.436 21.680Q32.597 21.680 32.753 21.623Q32.910 21.565 33.026 21.453Q33.142 21.341 33.195 21.189Q33.200 21.172 33.222 21.157Q33.244 21.143 33.261 21.143L33.342 21.143Q33.371 21.143 33.393 21.168Q33.415 21.194 33.415 21.223Q33.415 21.236 33.414 21.243Q33.413 21.250 33.410 21.260Q33.339 21.465 33.179 21.604Q33.020 21.743 32.806 21.812Q32.592 21.880 32.380 21.880Q32.053 21.880 31.727 21.739Q31.401 21.597 31.198 21.333Q30.996 21.070 30.996 20.728M31.525 20.525L33.010 20.525Q33.010 20.303 32.924 20.132Q32.839 19.961 32.677 19.865Q32.514 19.768 32.290 19.768Q31.967 19.768 31.766 19.980Q31.565 20.191 31.525 20.525\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m234.368-54.798 32.96 25.107\"\u002F>\u003Cpath stroke=\"none\" d=\"m268.92-28.479-1.576-3.211-.015 2-1.924.545\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr5\" font-size=\"5\">\u003Cg transform=\"translate(234.3 -64.163)\">\u003Cpath d=\"M20.249 21.824L18.449 21.824L18.449 21.585Q19.003 21.585 19.003 21.465L19.003 18.770Q19.003 18.650 18.449 18.650L18.449 18.408L21.799 18.408L21.965 19.619L21.733 19.619Q21.689 19.278 21.599 19.082Q21.508 18.887 21.366 18.795Q21.223 18.704 21.018 18.677Q20.813 18.650 20.439 18.650L19.834 18.650Q19.707 18.650 19.630 18.670Q19.553 18.689 19.553 18.770L19.553 19.993L20 19.993Q20.212 19.993 20.336 19.980Q20.461 19.966 20.549 19.910Q20.637 19.854 20.678 19.738Q20.720 19.622 20.720 19.409L20.949 19.409L20.949 20.818L20.720 20.818Q20.720 20.603 20.677 20.486Q20.634 20.369 20.550 20.315Q20.466 20.262 20.350 20.248Q20.234 20.235 20 20.235L19.553 20.235L19.553 21.465Q19.553 21.541 19.785 21.563Q20.017 21.585 20.249 21.585\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(234.3 -64.163)\">\u003Cpath d=\"M22.665 21.228L22.665 20.188Q22.665 19.998 22.575 19.954Q22.485 19.910 22.250 19.910L22.250 19.668L23.129 19.615L23.129 21.228Q23.129 21.428 23.181 21.531Q23.232 21.634 23.342 21.664Q23.452 21.695 23.669 21.695Q23.815 21.695 23.946 21.645Q24.077 21.595 24.177 21.502Q24.277 21.409 24.335 21.281Q24.394 21.153 24.394 21.009L24.394 20.188Q24.394 19.998 24.304 19.954Q24.213 19.910 23.979 19.910L23.979 19.668L24.860 19.615L24.860 21.304Q24.860 21.438 24.907 21.498Q24.953 21.558 25.034 21.571Q25.114 21.585 25.275 21.585L25.275 21.824L24.416 21.880L24.416 21.475Q24.291 21.663 24.078 21.772Q23.864 21.880 23.630 21.880Q23.434 21.880 23.272 21.855Q23.110 21.829 22.971 21.761Q22.831 21.692 22.748 21.562Q22.665 21.431 22.665 21.228M27.280 21.824L26.005 21.824L26.005 21.585Q26.420 21.585 26.420 21.465L26.420 18.928Q26.420 18.738 26.330 18.694Q26.240 18.650 26.005 18.650L26.005 18.408L26.865 18.355L26.865 21.465Q26.865 21.585 27.280 21.585L27.280 21.824M29.289 21.824L28.015 21.824L28.015 21.585Q28.430 21.585 28.430 21.465L28.430 18.928Q28.430 18.738 28.339 18.694Q28.249 18.650 28.015 18.650L28.015 18.408L28.874 18.355L28.874 21.465Q28.874 21.585 29.289 21.585\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.150\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(174.831 -12.476)\">\u003Cpath d=\"M18.317 22.357Q18.317 22.111 18.514 21.927Q18.711 21.742 18.967 21.663Q18.830 21.551 18.758 21.390Q18.687 21.229 18.687 21.048Q18.687 20.727 18.898 20.481Q18.564 20.183 18.564 19.773Q18.564 19.312 18.953 19.025Q19.343 18.738 19.821 18.738Q20.293 18.738 20.628 18.984Q20.802 18.830 21.013 18.748Q21.223 18.666 21.452 18.666Q21.616 18.666 21.737 18.773Q21.858 18.881 21.858 19.045Q21.858 19.141 21.787 19.213Q21.715 19.284 21.623 19.284Q21.523 19.284 21.453 19.211Q21.383 19.137 21.383 19.038Q21.383 18.984 21.397 18.953L21.404 18.939Q21.411 18.919 21.419 18.908Q21.428 18.898 21.431 18.891Q21.076 18.891 20.789 19.114Q21.076 19.407 21.076 19.773Q21.076 20.088 20.891 20.320Q20.707 20.553 20.418 20.681Q20.129 20.809 19.821 20.809Q19.620 20.809 19.428 20.759Q19.237 20.710 19.059 20.600Q18.967 20.727 18.967 20.870Q18.967 21.052 19.095 21.187Q19.223 21.322 19.408 21.322L20.040 21.322Q20.488 21.322 20.857 21.393Q21.226 21.465 21.486 21.694Q21.746 21.923 21.746 22.357Q21.746 22.678 21.450 22.880Q21.154 23.082 20.751 23.171Q20.348 23.260 20.033 23.260Q19.715 23.260 19.312 23.171Q18.909 23.082 18.613 22.880Q18.317 22.678 18.317 22.357M18.772 22.357Q18.772 22.586 18.991 22.735Q19.210 22.884 19.502 22.952Q19.794 23.020 20.033 23.020Q20.197 23.020 20.406 22.984Q20.614 22.949 20.821 22.868Q21.028 22.788 21.159 22.660Q21.291 22.532 21.291 22.357Q21.291 22.005 20.910 21.911Q20.529 21.817 20.026 21.817L19.408 21.817Q19.169 21.817 18.970 21.968Q18.772 22.118 18.772 22.357M19.821 20.570Q20.488 20.570 20.488 19.773Q20.488 18.973 19.821 18.973Q19.151 18.973 19.151 19.773Q19.151 20.570 19.821 20.570M22.398 21.096Q22.398 20.764 22.622 20.537Q22.846 20.310 23.190 20.182Q23.533 20.053 23.906 20.001Q24.278 19.948 24.583 19.948L24.583 19.695Q24.583 19.490 24.475 19.310Q24.367 19.131 24.186 19.028Q24.005 18.926 23.796 18.926Q23.390 18.926 23.154 19.018Q23.243 19.055 23.289 19.139Q23.335 19.223 23.335 19.325Q23.335 19.421 23.289 19.500Q23.243 19.578 23.162 19.623Q23.082 19.667 22.993 19.667Q22.843 19.667 22.742 19.570Q22.641 19.472 22.641 19.325Q22.641 18.703 23.796 18.703Q24.008 18.703 24.258 18.767Q24.507 18.830 24.709 18.949Q24.911 19.069 25.037 19.254Q25.164 19.438 25.164 19.681L25.164 21.257Q25.164 21.373 25.225 21.469Q25.287 21.564 25.399 21.564Q25.509 21.564 25.574 21.470Q25.639 21.376 25.639 21.257L25.639 20.809L25.905 20.809L25.905 21.257Q25.905 21.527 25.678 21.692Q25.451 21.858 25.170 21.858Q24.962 21.858 24.825 21.704Q24.689 21.551 24.665 21.335Q24.518 21.602 24.236 21.747Q23.954 21.892 23.629 21.892Q23.352 21.892 23.068 21.817Q22.785 21.742 22.592 21.563Q22.398 21.383 22.398 21.096M23.014 21.096Q23.014 21.270 23.115 21.400Q23.215 21.530 23.371 21.600Q23.526 21.670 23.690 21.670Q23.909 21.670 24.118 21.573Q24.326 21.475 24.454 21.294Q24.583 21.113 24.583 20.887L24.583 20.159Q24.258 20.159 23.892 20.250Q23.526 20.341 23.270 20.553Q23.014 20.764 23.014 21.096M27.939 21.824L26.387 21.824L26.387 21.544Q26.613 21.544 26.762 21.510Q26.910 21.475 26.910 21.335L26.910 19.486Q26.910 19.298 26.862 19.214Q26.815 19.131 26.717 19.112Q26.620 19.093 26.408 19.093L26.408 18.813L27.464 18.738L27.464 21.335Q27.464 21.475 27.596 21.510Q27.727 21.544 27.939 21.544L27.939 21.824M26.668 17.517Q26.668 17.346 26.791 17.227Q26.914 17.107 27.085 17.107Q27.252 17.107 27.375 17.227Q27.498 17.346 27.498 17.517Q27.498 17.692 27.375 17.815Q27.252 17.938 27.085 17.938Q26.914 17.938 26.791 17.815Q26.668 17.692 26.668 17.517M30.267 21.824L28.633 21.824L28.633 21.544Q28.862 21.544 29.011 21.510Q29.159 21.475 29.159 21.335L29.159 19.486Q29.159 19.216 29.052 19.155Q28.944 19.093 28.633 19.093L28.633 18.813L29.692 18.738L29.692 19.387Q29.863 19.079 30.168 18.908Q30.472 18.738 30.817 18.738Q31.323 18.738 31.607 18.961Q31.890 19.185 31.890 19.681L31.890 21.335Q31.890 21.472 32.039 21.508Q32.188 21.544 32.413 21.544L32.413 21.824L30.783 21.824L30.783 21.544Q31.012 21.544 31.160 21.510Q31.309 21.475 31.309 21.335L31.309 19.695Q31.309 19.360 31.190 19.160Q31.070 18.960 30.755 18.960Q30.485 18.960 30.251 19.096Q30.017 19.233 29.879 19.467Q29.740 19.701 29.740 19.975L29.740 21.335Q29.740 21.472 29.891 21.508Q30.041 21.544 30.267 21.544\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(174.831 -12.476)\">\u003Cpath d=\"M40.873 21.017L36.040 21.017Q35.972 21.007 35.926 20.961Q35.880 20.915 35.880 20.843Q35.880 20.778 35.926 20.732Q35.972 20.686 36.040 20.676L40.873 20.676Q40.942 20.686 40.988 20.732Q41.034 20.778 41.034 20.843Q41.034 20.915 40.988 20.961Q40.942 21.007 40.873 21.017M40.873 19.479L36.040 19.479Q35.972 19.469 35.926 19.423Q35.880 19.377 35.880 19.305Q35.880 19.161 36.040 19.137L40.873 19.137Q41.034 19.161 41.034 19.305Q41.034 19.377 40.988 19.423Q40.942 19.469 40.873 19.479\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(174.831 -12.476)\">\u003Cpath d=\"M46.214 21.964Q45.579 21.964 45.215 21.619Q44.850 21.274 44.715 20.749Q44.580 20.224 44.580 19.599Q44.580 18.574 44.936 17.875Q45.291 17.176 46.214 17.176Q47.141 17.176 47.493 17.875Q47.845 18.574 47.845 19.599Q47.845 20.224 47.710 20.749Q47.575 21.274 47.212 21.619Q46.850 21.964 46.214 21.964M46.214 21.739Q46.652 21.739 46.865 21.364Q47.079 20.990 47.129 20.523Q47.178 20.057 47.178 19.479Q47.178 18.926 47.129 18.498Q47.079 18.071 46.867 17.736Q46.655 17.401 46.214 17.401Q45.872 17.401 45.669 17.608Q45.466 17.815 45.379 18.127Q45.291 18.440 45.269 18.756Q45.247 19.073 45.247 19.479Q45.247 19.896 45.269 20.238Q45.291 20.580 45.380 20.928Q45.469 21.277 45.674 21.508Q45.879 21.739 46.214 21.739M48.921 21.404Q48.921 21.236 49.044 21.113Q49.167 20.990 49.342 20.990Q49.509 20.990 49.632 21.113Q49.755 21.236 49.755 21.404Q49.755 21.578 49.632 21.701Q49.509 21.824 49.342 21.824Q49.167 21.824 49.044 21.701Q48.921 21.578 48.921 21.404M51.276 21.062L51.246 21.062Q51.382 21.359 51.680 21.535Q51.977 21.711 52.305 21.711Q52.667 21.711 52.895 21.533Q53.122 21.356 53.216 21.067Q53.310 20.778 53.310 20.416Q53.310 20.101 53.255 19.816Q53.201 19.531 53.028 19.325Q52.855 19.120 52.541 19.120Q52.267 19.120 52.085 19.187Q51.902 19.254 51.798 19.343Q51.693 19.431 51.598 19.541Q51.502 19.650 51.457 19.660L51.379 19.660Q51.307 19.643 51.290 19.572L51.290 17.254Q51.290 17.220 51.314 17.198Q51.338 17.176 51.372 17.176L51.399 17.176Q51.686 17.292 51.955 17.346Q52.223 17.401 52.500 17.401Q52.777 17.401 53.047 17.346Q53.317 17.292 53.597 17.176L53.621 17.176Q53.655 17.176 53.679 17.199Q53.703 17.223 53.703 17.254L53.703 17.323Q53.703 17.350 53.683 17.370Q53.409 17.685 53.025 17.861Q52.640 18.037 52.226 18.037Q51.888 18.037 51.570 17.951L51.570 19.233Q51.967 18.898 52.541 18.898Q52.944 18.898 53.281 19.108Q53.618 19.319 53.811 19.671Q54.004 20.023 54.004 20.423Q54.004 20.754 53.864 21.040Q53.724 21.325 53.479 21.535Q53.235 21.745 52.932 21.855Q52.630 21.964 52.312 21.964Q51.953 21.964 51.627 21.800Q51.300 21.636 51.105 21.344Q50.911 21.052 50.911 20.689Q50.911 20.539 51.017 20.433Q51.122 20.327 51.276 20.327Q51.430 20.327 51.534 20.431Q51.639 20.535 51.639 20.689Q51.639 20.846 51.534 20.954Q51.430 21.062 51.276 21.062M56.769 20.676L54.725 20.676L54.725 20.395L57.056 17.223Q57.090 17.176 57.155 17.176L57.292 17.176Q57.336 17.176 57.364 17.203Q57.391 17.230 57.391 17.275L57.391 20.395L58.153 20.395L58.153 20.676L57.391 20.676L57.391 21.335Q57.391 21.544 58.146 21.544L58.146 21.824L56.014 21.824L56.014 21.544Q56.769 21.544 56.769 21.335L56.769 20.676M56.817 17.951L55.026 20.395L56.817 20.395L56.817 17.951M61.759 21.824L59.230 21.824L59.230 21.544Q60.197 21.544 60.197 21.335L60.197 17.716Q59.804 17.904 59.182 17.904L59.182 17.623Q59.599 17.623 59.963 17.522Q60.327 17.422 60.583 17.176L60.710 17.176Q60.775 17.193 60.792 17.261L60.792 21.335Q60.792 21.544 61.759 21.544\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(174.831 -12.476)\">\u003Cpath d=\"M66.249 21.824L65.982 21.824L65.982 17.716Q65.982 17.446 65.875 17.384Q65.767 17.323 65.456 17.323L65.456 17.042L66.536 16.967L66.536 19.137Q66.745 18.946 67.030 18.842Q67.316 18.738 67.613 18.738Q67.931 18.738 68.228 18.859Q68.525 18.980 68.748 19.196Q68.970 19.411 69.096 19.696Q69.223 19.982 69.223 20.313Q69.223 20.758 68.983 21.122Q68.744 21.486 68.351 21.689Q67.958 21.892 67.514 21.892Q67.319 21.892 67.129 21.836Q66.940 21.780 66.779 21.675Q66.618 21.571 66.478 21.410L66.249 21.824M66.564 19.479L66.564 21.096Q66.700 21.356 66.941 21.513Q67.182 21.670 67.459 21.670Q67.753 21.670 67.965 21.563Q68.177 21.455 68.310 21.263Q68.443 21.072 68.502 20.833Q68.560 20.594 68.560 20.313Q68.560 19.954 68.466 19.650Q68.372 19.346 68.144 19.153Q67.917 18.960 67.551 18.960Q67.251 18.960 66.984 19.096Q66.717 19.233 66.564 19.479M71.475 21.824L69.923 21.824L69.923 21.544Q70.149 21.544 70.298 21.510Q70.446 21.475 70.446 21.335L70.446 19.486Q70.446 19.298 70.399 19.214Q70.351 19.131 70.253 19.112Q70.156 19.093 69.944 19.093L69.944 18.813L71 18.738L71 21.335Q71 21.475 71.132 21.510Q71.263 21.544 71.475 21.544L71.475 21.824M70.204 17.517Q70.204 17.346 70.327 17.227Q70.450 17.107 70.621 17.107Q70.788 17.107 70.911 17.227Q71.034 17.346 71.034 17.517Q71.034 17.692 70.911 17.815Q70.788 17.938 70.621 17.938Q70.450 17.938 70.327 17.815Q70.204 17.692 70.204 17.517M72.648 20.983L72.648 19.086L72.008 19.086L72.008 18.864Q72.326 18.864 72.543 18.654Q72.760 18.444 72.861 18.134Q72.962 17.825 72.962 17.517L73.229 17.517L73.229 18.806L74.305 18.806L74.305 19.086L73.229 19.086L73.229 20.970Q73.229 21.246 73.333 21.445Q73.437 21.643 73.697 21.643Q73.854 21.643 73.960 21.539Q74.066 21.434 74.116 21.281Q74.165 21.127 74.165 20.970L74.165 20.556L74.432 20.556L74.432 20.983Q74.432 21.209 74.333 21.419Q74.233 21.629 74.049 21.761Q73.864 21.892 73.635 21.892Q73.198 21.892 72.923 21.655Q72.648 21.417 72.648 20.983M75.242 21.817L75.242 20.754Q75.242 20.730 75.269 20.703Q75.296 20.676 75.320 20.676L75.430 20.676Q75.495 20.676 75.508 20.734Q75.604 21.168 75.850 21.419Q76.096 21.670 76.510 21.670Q76.852 21.670 77.105 21.537Q77.357 21.404 77.357 21.096Q77.357 20.939 77.263 20.824Q77.170 20.710 77.031 20.641Q76.893 20.573 76.725 20.535L76.144 20.436Q75.789 20.368 75.515 20.147Q75.242 19.927 75.242 19.585Q75.242 19.336 75.353 19.161Q75.464 18.987 75.650 18.888Q75.836 18.789 76.052 18.746Q76.267 18.703 76.510 18.703Q76.923 18.703 77.204 18.885L77.419 18.710Q77.429 18.707 77.436 18.705Q77.443 18.703 77.453 18.703L77.504 18.703Q77.532 18.703 77.556 18.727Q77.580 18.751 77.580 18.779L77.580 19.626Q77.580 19.647 77.556 19.674Q77.532 19.701 77.504 19.701L77.392 19.701Q77.364 19.701 77.339 19.676Q77.313 19.650 77.313 19.626Q77.313 19.390 77.207 19.226Q77.101 19.062 76.918 18.980Q76.735 18.898 76.503 18.898Q76.175 18.898 75.919 19.001Q75.662 19.103 75.662 19.380Q75.662 19.575 75.845 19.684Q76.028 19.794 76.257 19.835L76.831 19.941Q77.077 19.989 77.291 20.117Q77.504 20.245 77.641 20.448Q77.778 20.652 77.778 20.901Q77.778 21.414 77.412 21.653Q77.046 21.892 76.510 21.892Q76.014 21.892 75.683 21.598L75.416 21.872Q75.396 21.892 75.368 21.892L75.320 21.892Q75.296 21.892 75.269 21.865Q75.242 21.838 75.242 21.817\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">Splitting the 12 restaurant examples. Testing Type (left) leaves four subsets each still evenly split between positive and negative — no progress. Testing Patrons (right) isolates a pure-negative subset (None) and a pure-positive subset (Some), a far better split.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:326.769px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 245.077 129.405\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cpath fill=\"none\" d=\"M-38.253 38.335v-94.74\"\u002F>\u003Cpath stroke=\"none\" d=\"m-38.253-58.404-1.6 3.2 1.6-1.2 1.6 1.2\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-18.285 -102.022)\">\u003Cpath d=\"M-37.980 38.868Q-37.980 38.622-37.783 38.438Q-37.586 38.253-37.330 38.174Q-37.467 38.062-37.539 37.901Q-37.610 37.740-37.610 37.559Q-37.610 37.238-37.399 36.992Q-37.733 36.694-37.733 36.284Q-37.733 35.823-37.344 35.536Q-36.954 35.249-36.476 35.249Q-36.004 35.249-35.669 35.495Q-35.495 35.341-35.284 35.259Q-35.074 35.177-34.845 35.177Q-34.681 35.177-34.560 35.284Q-34.439 35.392-34.439 35.556Q-34.439 35.652-34.510 35.724Q-34.582 35.795-34.674 35.795Q-34.774 35.795-34.844 35.722Q-34.914 35.648-34.914 35.549Q-34.914 35.495-34.900 35.464L-34.893 35.450Q-34.886 35.430-34.878 35.419Q-34.869 35.409-34.866 35.402Q-35.221 35.402-35.508 35.625Q-35.221 35.918-35.221 36.284Q-35.221 36.599-35.406 36.831Q-35.590 37.064-35.879 37.192Q-36.168 37.320-36.476 37.320Q-36.677 37.320-36.869 37.270Q-37.060 37.221-37.238 37.111Q-37.330 37.238-37.330 37.381Q-37.330 37.563-37.202 37.698Q-37.074 37.833-36.889 37.833L-36.257 37.833Q-35.809 37.833-35.440 37.904Q-35.071 37.976-34.811 38.205Q-34.551 38.434-34.551 38.868Q-34.551 39.189-34.847 39.391Q-35.143 39.593-35.546 39.682Q-35.949 39.771-36.264 39.771Q-36.582 39.771-36.985 39.682Q-37.388 39.593-37.684 39.391Q-37.980 39.189-37.980 38.868M-37.525 38.868Q-37.525 39.097-37.306 39.246Q-37.087 39.395-36.795 39.463Q-36.503 39.531-36.264 39.531Q-36.100 39.531-35.891 39.495Q-35.683 39.460-35.476 39.379Q-35.269 39.299-35.138 39.171Q-35.006 39.043-35.006 38.868Q-35.006 38.516-35.387 38.422Q-35.768 38.328-36.271 38.328L-36.889 38.328Q-37.128 38.328-37.327 38.479Q-37.525 38.629-37.525 38.868M-36.476 37.081Q-35.809 37.081-35.809 36.284Q-35.809 35.484-36.476 35.484Q-37.146 35.484-37.146 36.284Q-37.146 37.081-36.476 37.081M-33.899 37.607Q-33.899 37.275-33.675 37.048Q-33.451 36.821-33.107 36.693Q-32.764 36.564-32.391 36.512Q-32.019 36.459-31.714 36.459L-31.714 36.206Q-31.714 36.001-31.822 35.821Q-31.930 35.642-32.111 35.539Q-32.292 35.437-32.501 35.437Q-32.907 35.437-33.143 35.529Q-33.054 35.566-33.008 35.650Q-32.962 35.734-32.962 35.836Q-32.962 35.932-33.008 36.011Q-33.054 36.089-33.135 36.134Q-33.215 36.178-33.304 36.178Q-33.454 36.178-33.555 36.081Q-33.656 35.983-33.656 35.836Q-33.656 35.214-32.501 35.214Q-32.289 35.214-32.039 35.278Q-31.790 35.341-31.588 35.460Q-31.386 35.580-31.260 35.765Q-31.133 35.949-31.133 36.192L-31.133 37.768Q-31.133 37.884-31.072 37.980Q-31.010 38.075-30.898 38.075Q-30.788 38.075-30.723 37.981Q-30.658 37.887-30.658 37.768L-30.658 37.320L-30.392 37.320L-30.392 37.768Q-30.392 38.038-30.619 38.203Q-30.846 38.369-31.127 38.369Q-31.335 38.369-31.472 38.215Q-31.608 38.062-31.632 37.846Q-31.779 38.113-32.061 38.258Q-32.343 38.403-32.668 38.403Q-32.945 38.403-33.229 38.328Q-33.512 38.253-33.705 38.074Q-33.899 37.894-33.899 37.607M-33.283 37.607Q-33.283 37.781-33.182 37.911Q-33.082 38.041-32.926 38.111Q-32.771 38.181-32.607 38.181Q-32.388 38.181-32.179 38.084Q-31.971 37.986-31.843 37.805Q-31.714 37.624-31.714 37.398L-31.714 36.670Q-32.039 36.670-32.405 36.761Q-32.771 36.852-33.027 37.064Q-33.283 37.275-33.283 37.607M-28.358 38.335L-29.910 38.335L-29.910 38.055Q-29.684 38.055-29.535 38.021Q-29.387 37.986-29.387 37.846L-29.387 35.997Q-29.387 35.809-29.435 35.725Q-29.482 35.642-29.580 35.623Q-29.677 35.604-29.889 35.604L-29.889 35.324L-28.833 35.249L-28.833 37.846Q-28.833 37.986-28.701 38.021Q-28.570 38.055-28.358 38.055L-28.358 38.335M-29.629 34.028Q-29.629 33.857-29.506 33.738Q-29.383 33.618-29.212 33.618Q-29.045 33.618-28.922 33.738Q-28.799 33.857-28.799 34.028Q-28.799 34.203-28.922 34.326Q-29.045 34.449-29.212 34.449Q-29.383 34.449-29.506 34.326Q-29.629 34.203-29.629 34.028M-26.030 38.335L-27.664 38.335L-27.664 38.055Q-27.435 38.055-27.286 38.021Q-27.138 37.986-27.138 37.846L-27.138 35.997Q-27.138 35.727-27.245 35.666Q-27.353 35.604-27.664 35.604L-27.664 35.324L-26.605 35.249L-26.605 35.898Q-26.434 35.590-26.129 35.419Q-25.825 35.249-25.480 35.249Q-24.974 35.249-24.691 35.472Q-24.407 35.696-24.407 36.192L-24.407 37.846Q-24.407 37.983-24.258 38.019Q-24.109 38.055-23.884 38.055L-23.884 38.335L-25.514 38.335L-25.514 38.055Q-25.285 38.055-25.137 38.021Q-24.988 37.986-24.988 37.846L-24.988 36.206Q-24.988 35.871-25.107 35.671Q-25.227 35.471-25.542 35.471Q-25.812 35.471-26.046 35.607Q-26.280 35.744-26.418 35.978Q-26.557 36.212-26.557 36.486L-26.557 37.846Q-26.557 37.983-26.406 38.019Q-26.256 38.055-26.030 38.055\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-18.285 -102.022)\">\u003Cpath d=\"M-18.461 40.085Q-19.011 39.685-19.382 39.130Q-19.753 38.574-19.934 37.928Q-20.115 37.282-20.115 36.585Q-20.115 36.072-20.015 35.577Q-19.914 35.081-19.709 34.630Q-19.504 34.179-19.191 33.787Q-18.878 33.396-18.461 33.092Q-18.451 33.088-18.444 33.087Q-18.437 33.085-18.427 33.085L-18.359 33.085Q-18.324 33.085-18.302 33.109Q-18.280 33.133-18.280 33.170Q-18.280 33.215-18.307 33.232Q-18.656 33.533-18.909 33.917Q-19.162 34.302-19.314 34.743Q-19.466 35.184-19.538 35.640Q-19.610 36.096-19.610 36.585Q-19.610 37.586-19.300 38.473Q-18.991 39.360-18.307 39.945Q-18.280 39.962-18.280 40.006Q-18.280 40.044-18.302 40.068Q-18.324 40.092-18.359 40.092L-18.427 40.092Q-18.434 40.088-18.442 40.087Q-18.451 40.085-18.461 40.085M-16.663 38.335L-16.930 38.335L-16.930 34.227Q-16.930 33.957-17.038 33.895Q-17.145 33.834-17.456 33.834L-17.456 33.553L-16.376 33.478L-16.376 35.648Q-16.168 35.457-15.882 35.353Q-15.597 35.249-15.300 35.249Q-14.982 35.249-14.684 35.370Q-14.387 35.491-14.165 35.707Q-13.943 35.922-13.816 36.207Q-13.690 36.493-13.690 36.824Q-13.690 37.269-13.929 37.633Q-14.168 37.997-14.561 38.200Q-14.954 38.403-15.399 38.403Q-15.593 38.403-15.783 38.347Q-15.973 38.291-16.134 38.186Q-16.294 38.082-16.434 37.921L-16.663 38.335M-16.349 35.990L-16.349 37.607Q-16.212 37.867-15.971 38.024Q-15.730 38.181-15.453 38.181Q-15.159 38.181-14.947 38.074Q-14.736 37.966-14.602 37.774Q-14.469 37.583-14.411 37.344Q-14.353 37.105-14.353 36.824Q-14.353 36.465-14.447 36.161Q-14.541 35.857-14.768 35.664Q-14.995 35.471-15.361 35.471Q-15.662 35.471-15.928 35.607Q-16.195 35.744-16.349 35.990M-11.437 38.335L-12.989 38.335L-12.989 38.055Q-12.763 38.055-12.615 38.021Q-12.466 37.986-12.466 37.846L-12.466 35.997Q-12.466 35.809-12.514 35.725Q-12.562 35.642-12.659 35.623Q-12.757 35.604-12.968 35.604L-12.968 35.324L-11.912 35.249L-11.912 37.846Q-11.912 37.986-11.781 38.021Q-11.649 38.055-11.437 38.055L-11.437 38.335M-12.709 34.028Q-12.709 33.857-12.586 33.738Q-12.463 33.618-12.292 33.618Q-12.124 33.618-12.001 33.738Q-11.878 33.857-11.878 34.028Q-11.878 34.203-12.001 34.326Q-12.124 34.449-12.292 34.449Q-12.463 34.449-12.586 34.326Q-12.709 34.203-12.709 34.028M-10.265 37.494L-10.265 35.597L-10.904 35.597L-10.904 35.375Q-10.586 35.375-10.369 35.165Q-10.152 34.955-10.051 34.645Q-9.950 34.336-9.950 34.028L-9.684 34.028L-9.684 35.317L-8.607 35.317L-8.607 35.597L-9.684 35.597L-9.684 37.481Q-9.684 37.757-9.580 37.956Q-9.475 38.154-9.216 38.154Q-9.058 38.154-8.952 38.050Q-8.846 37.945-8.797 37.792Q-8.747 37.638-8.747 37.481L-8.747 37.067L-8.481 37.067L-8.481 37.494Q-8.481 37.720-8.580 37.930Q-8.679 38.140-8.863 38.272Q-9.048 38.403-9.277 38.403Q-9.715 38.403-9.990 38.166Q-10.265 37.928-10.265 37.494M-7.671 38.328L-7.671 37.265Q-7.671 37.241-7.643 37.214Q-7.616 37.187-7.592 37.187L-7.483 37.187Q-7.418 37.187-7.404 37.245Q-7.308 37.679-7.062 37.930Q-6.816 38.181-6.403 38.181Q-6.061 38.181-5.808 38.048Q-5.555 37.915-5.555 37.607Q-5.555 37.450-5.649 37.335Q-5.743 37.221-5.881 37.152Q-6.020 37.084-6.187 37.046L-6.768 36.947Q-7.124 36.879-7.397 36.658Q-7.671 36.438-7.671 36.096Q-7.671 35.847-7.560 35.672Q-7.448 35.498-7.262 35.399Q-7.076 35.300-6.861 35.257Q-6.645 35.214-6.403 35.214Q-5.989 35.214-5.709 35.396L-5.493 35.221Q-5.483 35.218-5.476 35.216Q-5.469 35.214-5.459 35.214L-5.408 35.214Q-5.381 35.214-5.357 35.238Q-5.333 35.262-5.333 35.290L-5.333 36.137Q-5.333 36.158-5.357 36.185Q-5.381 36.212-5.408 36.212L-5.521 36.212Q-5.548 36.212-5.574 36.187Q-5.599 36.161-5.599 36.137Q-5.599 35.901-5.705 35.737Q-5.811 35.573-5.994 35.491Q-6.177 35.409-6.409 35.409Q-6.738 35.409-6.994 35.512Q-7.250 35.614-7.250 35.891Q-7.250 36.086-7.067 36.195Q-6.884 36.305-6.655 36.346L-6.081 36.452Q-5.835 36.500-5.622 36.628Q-5.408 36.756-5.271 36.959Q-5.134 37.163-5.134 37.412Q-5.134 37.925-5.500 38.164Q-5.866 38.403-6.403 38.403Q-6.898 38.403-7.230 38.109L-7.496 38.383Q-7.517 38.403-7.544 38.403L-7.592 38.403Q-7.616 38.403-7.643 38.376Q-7.671 38.349-7.671 38.328M-4.184 40.092L-4.253 40.092Q-4.287 40.092-4.309 40.066Q-4.331 40.041-4.331 40.006Q-4.331 39.962-4.301 39.945Q-3.945 39.641-3.696 39.251Q-3.446 38.861-3.294 38.429Q-3.142 37.997-3.072 37.528Q-3.002 37.060-3.002 36.585Q-3.002 36.106-3.072 35.640Q-3.142 35.173-3.296 34.738Q-3.449 34.302-3.701 33.914Q-3.952 33.526-4.301 33.232Q-4.331 33.215-4.331 33.170Q-4.331 33.136-4.309 33.111Q-4.287 33.085-4.253 33.085L-4.184 33.085Q-4.174 33.085-4.165 33.087Q-4.157 33.088-4.147 33.092Q-3.603 33.492-3.231 34.045Q-2.858 34.599-2.677 35.245Q-2.496 35.891-2.496 36.585Q-2.496 37.286-2.677 37.933Q-2.858 38.581-3.232 39.135Q-3.607 39.689-4.147 40.085Q-4.157 40.085-4.165 40.087Q-4.174 40.088-4.184 40.092\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-38.253 38.335h204.86\"\u002F>\u003Cg transform=\"translate(-24.018 -83.103)\">\u003Cpath d=\"M-36.264 38.475Q-36.899 38.475-37.263 38.130Q-37.628 37.785-37.763 37.260Q-37.898 36.735-37.898 36.110Q-37.898 35.085-37.542 34.386Q-37.187 33.687-36.264 33.687Q-35.337 33.687-34.985 34.386Q-34.633 35.085-34.633 36.110Q-34.633 36.735-34.768 37.260Q-34.903 37.785-35.266 38.130Q-35.628 38.475-36.264 38.475M-36.264 38.250Q-35.826 38.250-35.613 37.875Q-35.399 37.501-35.349 37.034Q-35.300 36.568-35.300 35.990Q-35.300 35.437-35.349 35.009Q-35.399 34.582-35.611 34.247Q-35.823 33.912-36.264 33.912Q-36.606 33.912-36.809 34.119Q-37.012 34.326-37.099 34.638Q-37.187 34.951-37.209 35.267Q-37.231 35.584-37.231 35.990Q-37.231 36.407-37.209 36.749Q-37.187 37.091-37.098 37.439Q-37.009 37.788-36.804 38.019Q-36.599 38.250-36.264 38.250M-33.557 37.915Q-33.557 37.747-33.434 37.624Q-33.311 37.501-33.136 37.501Q-32.969 37.501-32.846 37.624Q-32.723 37.747-32.723 37.915Q-32.723 38.089-32.846 38.212Q-32.969 38.335-33.136 38.335Q-33.311 38.335-33.434 38.212Q-33.557 38.089-33.557 37.915M-31.202 37.573L-31.232 37.573Q-31.096 37.870-30.798 38.046Q-30.501 38.222-30.173 38.222Q-29.811 38.222-29.583 38.044Q-29.356 37.867-29.262 37.578Q-29.168 37.289-29.168 36.927Q-29.168 36.612-29.223 36.327Q-29.277 36.042-29.450 35.836Q-29.623 35.631-29.937 35.631Q-30.211 35.631-30.393 35.698Q-30.576 35.765-30.680 35.854Q-30.785 35.942-30.880 36.052Q-30.976 36.161-31.021 36.171L-31.099 36.171Q-31.171 36.154-31.188 36.083L-31.188 33.765Q-31.188 33.731-31.164 33.709Q-31.140 33.687-31.106 33.687L-31.079 33.687Q-30.792 33.803-30.523 33.857Q-30.255 33.912-29.978 33.912Q-29.701 33.912-29.431 33.857Q-29.161 33.803-28.881 33.687L-28.857 33.687Q-28.823 33.687-28.799 33.710Q-28.775 33.734-28.775 33.765L-28.775 33.834Q-28.775 33.861-28.795 33.881Q-29.069 34.196-29.453 34.372Q-29.838 34.548-30.252 34.548Q-30.590 34.548-30.908 34.462L-30.908 35.744Q-30.511 35.409-29.937 35.409Q-29.534 35.409-29.197 35.619Q-28.860 35.830-28.667 36.182Q-28.474 36.534-28.474 36.934Q-28.474 37.265-28.614 37.551Q-28.754 37.836-28.999 38.046Q-29.243 38.256-29.546 38.366Q-29.848 38.475-30.166 38.475Q-30.525 38.475-30.851 38.311Q-31.178 38.147-31.373 37.855Q-31.567 37.563-31.567 37.200Q-31.567 37.050-31.461 36.944Q-31.356 36.838-31.202 36.838Q-31.048 36.838-30.944 36.942Q-30.839 37.046-30.839 37.200Q-30.839 37.357-30.944 37.465Q-31.048 37.573-31.202 37.573M-25.709 37.187L-27.753 37.187L-27.753 36.906L-25.422 33.734Q-25.388 33.687-25.323 33.687L-25.186 33.687Q-25.142 33.687-25.114 33.714Q-25.087 33.741-25.087 33.786L-25.087 36.906L-24.325 36.906L-24.325 37.187L-25.087 37.187L-25.087 37.846Q-25.087 38.055-24.332 38.055L-24.332 38.335L-26.464 38.335L-26.464 38.055Q-25.709 38.055-25.709 37.846L-25.709 37.187M-25.661 34.462L-27.452 36.906L-25.661 36.906L-25.661 34.462M-20.719 38.335L-23.248 38.335L-23.248 38.055Q-22.281 38.055-22.281 37.846L-22.281 34.227Q-22.674 34.415-23.296 34.415L-23.296 34.134Q-22.879 34.134-22.515 34.033Q-22.151 33.933-21.895 33.687L-21.768 33.687Q-21.703 33.704-21.686 33.772L-21.686 37.846Q-21.686 38.055-20.719 38.055\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-38.253-47.023h204.86\" style=\"stroke-dasharray:3.0,3.0\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"var(--tk-accent)\" d=\"M-21.18 38.335v-85.358h34.142v85.358Zm34.142-85.358\"\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=\"translate(20.66 -90.314)\">\u003Cpath d=\"M-35.727 38.335L-37.860 38.335L-37.860 38.055Q-37.139 38.055-37.139 37.846L-37.139 34.045Q-37.139 33.834-37.860 33.834L-37.860 33.553L-35.194 33.553Q-34.784 33.553-34.363 33.707Q-33.943 33.861-33.659 34.165Q-33.376 34.469-33.376 34.883Q-33.376 35.201-33.543 35.447Q-33.711 35.693-33.987 35.859Q-34.264 36.024-34.586 36.108Q-34.907 36.192-35.194 36.192L-36.448 36.192L-36.448 37.846Q-36.448 38.055-35.727 38.055L-35.727 38.335M-36.476 34.045L-36.476 35.942L-35.389 35.942Q-34.780 35.942-34.466 35.705Q-34.151 35.467-34.151 34.883Q-34.151 34.490-34.297 34.256Q-34.442 34.022-34.714 33.928Q-34.985 33.834-35.389 33.834L-36.110 33.834Q-36.298 33.834-36.387 33.868Q-36.476 33.902-36.476 34.045\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(20.66 -90.314)\">\u003Cpath d=\"M-32.720 37.607Q-32.720 37.275-32.497 37.048Q-32.273 36.821-31.929 36.693Q-31.586 36.564-31.213 36.512Q-30.841 36.459-30.536 36.459L-30.536 36.206Q-30.536 36.001-30.644 35.821Q-30.752 35.642-30.933 35.539Q-31.114 35.437-31.322 35.437Q-31.729 35.437-31.965 35.529Q-31.876 35.566-31.830 35.650Q-31.784 35.734-31.784 35.836Q-31.784 35.932-31.830 36.011Q-31.876 36.089-31.957 36.134Q-32.037 36.178-32.126 36.178Q-32.276 36.178-32.377 36.081Q-32.478 35.983-32.478 35.836Q-32.478 35.214-31.322 35.214Q-31.111 35.214-30.861 35.278Q-30.612 35.341-30.410 35.460Q-30.208 35.580-30.082 35.765Q-29.955 35.949-29.955 36.192L-29.955 37.768Q-29.955 37.884-29.894 37.980Q-29.832 38.075-29.719 38.075Q-29.610 38.075-29.545 37.981Q-29.480 37.887-29.480 37.768L-29.480 37.320L-29.214 37.320L-29.214 37.768Q-29.214 38.038-29.441 38.203Q-29.668 38.369-29.948 38.369Q-30.157 38.369-30.294 38.215Q-30.430 38.062-30.454 37.846Q-30.601 38.113-30.883 38.258Q-31.165 38.403-31.490 38.403Q-31.767 38.403-32.051 38.328Q-32.334 38.253-32.527 38.074Q-32.720 37.894-32.720 37.607M-32.105 37.607Q-32.105 37.781-32.004 37.911Q-31.904 38.041-31.748 38.111Q-31.593 38.181-31.428 38.181Q-31.210 38.181-31.001 38.084Q-30.793 37.986-30.665 37.805Q-30.536 37.624-30.536 37.398L-30.536 36.670Q-30.861 36.670-31.227 36.761Q-31.593 36.852-31.849 37.064Q-32.105 37.275-32.105 37.607M-28.270 37.494L-28.270 35.597L-28.909 35.597L-28.909 35.375Q-28.592 35.375-28.374 35.165Q-28.157 34.955-28.057 34.645Q-27.956 34.336-27.956 34.028L-27.689 34.028L-27.689 35.317L-26.613 35.317L-26.613 35.597L-27.689 35.597L-27.689 37.481Q-27.689 37.757-27.585 37.956Q-27.481 38.154-27.221 38.154Q-27.064 38.154-26.958 38.050Q-26.852 37.945-26.802 37.792Q-26.753 37.638-26.753 37.481L-26.753 37.067L-26.486 37.067L-26.486 37.494Q-26.486 37.720-26.585 37.930Q-26.684 38.140-26.869 38.272Q-27.053 38.403-27.282 38.403Q-27.720 38.403-27.995 38.166Q-28.270 37.928-28.270 37.494M-23.926 38.335L-25.662 38.335L-25.662 38.055Q-25.433 38.055-25.285 38.021Q-25.136 37.986-25.136 37.846L-25.136 35.997Q-25.136 35.727-25.244 35.666Q-25.351 35.604-25.662 35.604L-25.662 35.324L-24.634 35.249L-24.634 35.956Q-24.504 35.648-24.261 35.449Q-24.018 35.249-23.700 35.249Q-23.482 35.249-23.311 35.373Q-23.140 35.498-23.140 35.710Q-23.140 35.847-23.239 35.946Q-23.338 36.045-23.471 36.045Q-23.608 36.045-23.707 35.946Q-23.806 35.847-23.806 35.710Q-23.806 35.570-23.707 35.471Q-23.998 35.471-24.198 35.667Q-24.398 35.864-24.490 36.158Q-24.582 36.452-24.582 36.732L-24.582 37.846Q-24.582 38.055-23.926 38.055L-23.926 38.335M-22.596 36.852Q-22.596 36.510-22.461 36.211Q-22.326 35.912-22.087 35.688Q-21.848 35.464-21.530 35.339Q-21.212 35.214-20.881 35.214Q-20.436 35.214-20.036 35.430Q-19.636 35.645-19.402 36.023Q-19.168 36.400-19.168 36.852Q-19.168 37.193-19.310 37.477Q-19.452 37.761-19.696 37.968Q-19.941 38.174-20.250 38.289Q-20.559 38.403-20.881 38.403Q-21.311 38.403-21.713 38.202Q-22.114 38-22.355 37.648Q-22.596 37.296-22.596 36.852M-20.881 38.154Q-20.279 38.154-20.055 37.776Q-19.831 37.398-19.831 36.766Q-19.831 36.154-20.065 35.795Q-20.300 35.437-20.881 35.437Q-21.933 35.437-21.933 36.766Q-21.933 37.398-21.708 37.776Q-21.482 38.154-20.881 38.154M-16.892 38.335L-18.526 38.335L-18.526 38.055Q-18.297 38.055-18.148 38.021Q-17.999 37.986-17.999 37.846L-17.999 35.997Q-17.999 35.727-18.107 35.666Q-18.215 35.604-18.526 35.604L-18.526 35.324L-17.466 35.249L-17.466 35.898Q-17.295 35.590-16.991 35.419Q-16.687 35.249-16.342 35.249Q-15.836 35.249-15.552 35.472Q-15.268 35.696-15.268 36.192L-15.268 37.846Q-15.268 37.983-15.120 38.019Q-14.971 38.055-14.745 38.055L-14.745 38.335L-16.376 38.335L-16.376 38.055Q-16.147 38.055-15.998 38.021Q-15.849 37.986-15.849 37.846L-15.849 36.206Q-15.849 35.871-15.969 35.671Q-16.089 35.471-16.403 35.471Q-16.673 35.471-16.907 35.607Q-17.141 35.744-17.280 35.978Q-17.418 36.212-17.418 36.486L-17.418 37.846Q-17.418 37.983-17.268 38.019Q-17.117 38.055-16.892 38.055L-16.892 38.335M-14.157 38.328L-14.157 37.265Q-14.157 37.241-14.130 37.214Q-14.103 37.187-14.079 37.187L-13.969 37.187Q-13.905 37.187-13.891 37.245Q-13.795 37.679-13.549 37.930Q-13.303 38.181-12.889 38.181Q-12.548 38.181-12.295 38.048Q-12.042 37.915-12.042 37.607Q-12.042 37.450-12.136 37.335Q-12.230 37.221-12.368 37.152Q-12.507 37.084-12.674 37.046L-13.255 36.947Q-13.611 36.879-13.884 36.658Q-14.157 36.438-14.157 36.096Q-14.157 35.847-14.046 35.672Q-13.935 35.498-13.749 35.399Q-13.563 35.300-13.347 35.257Q-13.132 35.214-12.889 35.214Q-12.476 35.214-12.196 35.396L-11.980 35.221Q-11.970 35.218-11.963 35.216Q-11.956 35.214-11.946 35.214L-11.895 35.214Q-11.867 35.214-11.843 35.238Q-11.820 35.262-11.820 35.290L-11.820 36.137Q-11.820 36.158-11.843 36.185Q-11.867 36.212-11.895 36.212L-12.008 36.212Q-12.035 36.212-12.061 36.187Q-12.086 36.161-12.086 36.137Q-12.086 35.901-12.192 35.737Q-12.298 35.573-12.481 35.491Q-12.664 35.409-12.896 35.409Q-13.224 35.409-13.481 35.512Q-13.737 35.614-13.737 35.891Q-13.737 36.086-13.554 36.195Q-13.371 36.305-13.142 36.346L-12.568 36.452Q-12.322 36.500-12.108 36.628Q-11.895 36.756-11.758 36.959Q-11.621 37.163-11.621 37.412Q-11.621 37.925-11.987 38.164Q-12.353 38.403-12.889 38.403Q-13.385 38.403-13.717 38.109L-13.983 38.383Q-14.004 38.403-14.031 38.403L-14.079 38.403Q-14.103 38.403-14.130 38.376Q-14.157 38.349-14.157 38.328\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(25.039 9.467)\">\u003Cpath d=\"M-36.264 38.475Q-36.899 38.475-37.263 38.130Q-37.628 37.785-37.763 37.260Q-37.898 36.735-37.898 36.110Q-37.898 35.085-37.542 34.386Q-37.187 33.687-36.264 33.687Q-35.337 33.687-34.985 34.386Q-34.633 35.085-34.633 36.110Q-34.633 36.735-34.768 37.260Q-34.903 37.785-35.266 38.130Q-35.628 38.475-36.264 38.475M-36.264 38.250Q-35.826 38.250-35.613 37.875Q-35.399 37.501-35.349 37.034Q-35.300 36.568-35.300 35.990Q-35.300 35.437-35.349 35.009Q-35.399 34.582-35.611 34.247Q-35.823 33.912-36.264 33.912Q-36.606 33.912-36.809 34.119Q-37.012 34.326-37.099 34.638Q-37.187 34.951-37.209 35.267Q-37.231 35.584-37.231 35.990Q-37.231 36.407-37.209 36.749Q-37.187 37.091-37.098 37.439Q-37.009 37.788-36.804 38.019Q-36.599 38.250-36.264 38.250M-33.557 37.915Q-33.557 37.747-33.434 37.624Q-33.311 37.501-33.136 37.501Q-32.969 37.501-32.846 37.624Q-32.723 37.747-32.723 37.915Q-32.723 38.089-32.846 38.212Q-32.969 38.335-33.136 38.335Q-33.311 38.335-33.434 38.212Q-33.557 38.089-33.557 37.915M-31.202 37.573L-31.232 37.573Q-31.096 37.870-30.798 38.046Q-30.501 38.222-30.173 38.222Q-29.811 38.222-29.583 38.044Q-29.356 37.867-29.262 37.578Q-29.168 37.289-29.168 36.927Q-29.168 36.612-29.223 36.327Q-29.277 36.042-29.450 35.836Q-29.623 35.631-29.937 35.631Q-30.211 35.631-30.393 35.698Q-30.576 35.765-30.680 35.854Q-30.785 35.942-30.880 36.052Q-30.976 36.161-31.021 36.171L-31.099 36.171Q-31.171 36.154-31.188 36.083L-31.188 33.765Q-31.188 33.731-31.164 33.709Q-31.140 33.687-31.106 33.687L-31.079 33.687Q-30.792 33.803-30.523 33.857Q-30.255 33.912-29.978 33.912Q-29.701 33.912-29.431 33.857Q-29.161 33.803-28.881 33.687L-28.857 33.687Q-28.823 33.687-28.799 33.710Q-28.775 33.734-28.775 33.765L-28.775 33.834Q-28.775 33.861-28.795 33.881Q-29.069 34.196-29.453 34.372Q-29.838 34.548-30.252 34.548Q-30.590 34.548-30.908 34.462L-30.908 35.744Q-30.511 35.409-29.937 35.409Q-29.534 35.409-29.197 35.619Q-28.860 35.830-28.667 36.182Q-28.474 36.534-28.474 36.934Q-28.474 37.265-28.614 37.551Q-28.754 37.836-28.999 38.046Q-29.243 38.256-29.546 38.366Q-29.848 38.475-30.166 38.475Q-30.525 38.475-30.851 38.311Q-31.178 38.147-31.373 37.855Q-31.567 37.563-31.567 37.200Q-31.567 37.050-31.461 36.944Q-31.356 36.838-31.202 36.838Q-31.048 36.838-30.944 36.942Q-30.839 37.046-30.839 37.200Q-30.839 37.357-30.944 37.465Q-31.048 37.573-31.202 37.573M-25.709 37.187L-27.753 37.187L-27.753 36.906L-25.422 33.734Q-25.388 33.687-25.323 33.687L-25.186 33.687Q-25.142 33.687-25.114 33.714Q-25.087 33.741-25.087 33.786L-25.087 36.906L-24.325 36.906L-24.325 37.187L-25.087 37.187L-25.087 37.846Q-25.087 38.055-24.332 38.055L-24.332 38.335L-26.464 38.335L-26.464 38.055Q-25.709 38.055-25.709 37.846L-25.709 37.187M-25.661 34.462L-27.452 36.906L-25.661 36.906L-25.661 34.462M-20.719 38.335L-23.248 38.335L-23.248 38.055Q-22.281 38.055-22.281 37.846L-22.281 34.227Q-22.674 34.415-23.296 34.415L-23.296 34.134Q-22.879 34.134-22.515 34.033Q-22.151 33.933-21.895 33.687L-21.768 33.687Q-21.703 33.704-21.686 33.772L-21.686 37.846Q-21.686 38.055-20.719 38.055\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath d=\"M30.034 38.335V7.322h34.143v31.013ZM64.177 7.322\"\u002F>\u003Cg transform=\"translate(72.334 -36.192)\">\u003Cpath d=\"M-35.741 38.335L-37.874 38.335L-37.874 38.055Q-37.152 38.055-37.152 37.846L-37.152 34.045Q-37.152 33.834-37.874 33.834L-37.874 33.553L-35.741 33.553L-35.741 33.834Q-36.462 33.834-36.462 34.045L-36.462 35.710L-34.151 35.710L-34.151 34.045Q-34.151 33.834-34.873 33.834L-34.873 33.553L-32.736 33.553L-32.736 33.834Q-33.458 33.834-33.458 34.045L-33.458 37.846Q-33.458 38.055-32.736 38.055L-32.736 38.335L-34.873 38.335L-34.873 38.055Q-34.151 38.055-34.151 37.846L-34.151 35.990L-36.462 35.990L-36.462 37.846Q-36.462 38.055-35.741 38.055L-35.741 38.335M-31.465 37.501L-31.465 35.997Q-31.465 35.727-31.573 35.666Q-31.680 35.604-31.991 35.604L-31.991 35.324L-30.884 35.249L-30.884 37.481L-30.884 37.501Q-30.884 37.781-30.833 37.925Q-30.781 38.068-30.639 38.125Q-30.498 38.181-30.211 38.181Q-29.958 38.181-29.753 38.041Q-29.547 37.901-29.431 37.675Q-29.315 37.450-29.315 37.200L-29.315 35.997Q-29.315 35.727-29.423 35.666Q-29.530 35.604-29.841 35.604L-29.841 35.324L-28.734 35.249L-28.734 37.662Q-28.734 37.853-28.681 37.935Q-28.628 38.017-28.527 38.036Q-28.426 38.055-28.211 38.055L-28.211 38.335L-29.288 38.403L-29.288 37.839Q-29.397 38.021-29.542 38.144Q-29.688 38.267-29.874 38.335Q-30.060 38.403-30.262 38.403Q-31.465 38.403-31.465 37.501M-25.941 38.335L-27.575 38.335L-27.575 38.055Q-27.346 38.055-27.198 38.021Q-27.049 37.986-27.049 37.846L-27.049 35.997Q-27.049 35.727-27.157 35.666Q-27.264 35.604-27.575 35.604L-27.575 35.324L-26.516 35.249L-26.516 35.898Q-26.345 35.590-26.041 35.419Q-25.736 35.249-25.391 35.249Q-24.885 35.249-24.602 35.472Q-24.318 35.696-24.318 36.192L-24.318 37.846Q-24.318 37.983-24.169 38.019Q-24.021 38.055-23.795 38.055L-23.795 38.335L-25.425 38.335L-25.425 38.055Q-25.196 38.055-25.048 38.021Q-24.899 37.986-24.899 37.846L-24.899 36.206Q-24.899 35.871-25.019 35.671Q-25.138 35.471-25.453 35.471Q-25.723 35.471-25.957 35.607Q-26.191 35.744-26.329 35.978Q-26.468 36.212-26.468 36.486L-26.468 37.846Q-26.468 37.983-26.317 38.019Q-26.167 38.055-25.941 38.055L-25.941 38.335M-23.248 38.868Q-23.248 38.622-23.052 38.438Q-22.855 38.253-22.599 38.174Q-22.735 38.062-22.807 37.901Q-22.879 37.740-22.879 37.559Q-22.879 37.238-22.667 36.992Q-23.002 36.694-23.002 36.284Q-23.002 35.823-22.612 35.536Q-22.223 35.249-21.744 35.249Q-21.273 35.249-20.938 35.495Q-20.763 35.341-20.553 35.259Q-20.343 35.177-20.114 35.177Q-19.950 35.177-19.828 35.284Q-19.707 35.392-19.707 35.556Q-19.707 35.652-19.779 35.724Q-19.851 35.795-19.943 35.795Q-20.042 35.795-20.112 35.722Q-20.182 35.648-20.182 35.549Q-20.182 35.495-20.169 35.464L-20.162 35.450Q-20.155 35.430-20.146 35.419Q-20.138 35.409-20.134 35.402Q-20.490 35.402-20.777 35.625Q-20.490 35.918-20.490 36.284Q-20.490 36.599-20.674 36.831Q-20.859 37.064-21.148 37.192Q-21.437 37.320-21.744 37.320Q-21.946 37.320-22.137 37.270Q-22.329 37.221-22.506 37.111Q-22.599 37.238-22.599 37.381Q-22.599 37.563-22.471 37.698Q-22.342 37.833-22.158 37.833L-21.525 37.833Q-21.078 37.833-20.709 37.904Q-20.339 37.976-20.080 38.205Q-19.820 38.434-19.820 38.868Q-19.820 39.189-20.116 39.391Q-20.411 39.593-20.815 39.682Q-21.218 39.771-21.532 39.771Q-21.850 39.771-22.253 39.682Q-22.657 39.593-22.952 39.391Q-23.248 39.189-23.248 38.868M-22.794 38.868Q-22.794 39.097-22.575 39.246Q-22.356 39.395-22.064 39.463Q-21.772 39.531-21.532 39.531Q-21.368 39.531-21.160 39.495Q-20.951 39.460-20.744 39.379Q-20.538 39.299-20.406 39.171Q-20.274 39.043-20.274 38.868Q-20.274 38.516-20.656 38.422Q-21.037 38.328-21.539 38.328L-22.158 38.328Q-22.397 38.328-22.595 38.479Q-22.794 38.629-22.794 38.868M-21.744 37.081Q-21.078 37.081-21.078 36.284Q-21.078 35.484-21.744 35.484Q-22.414 35.484-22.414 36.284Q-22.414 37.081-21.744 37.081M-17.475 38.335L-19.211 38.335L-19.211 38.055Q-18.982 38.055-18.834 38.021Q-18.685 37.986-18.685 37.846L-18.685 35.997Q-18.685 35.727-18.793 35.666Q-18.900 35.604-19.211 35.604L-19.211 35.324L-18.183 35.249L-18.183 35.956Q-18.053 35.648-17.810 35.449Q-17.567 35.249-17.250 35.249Q-17.031 35.249-16.860 35.373Q-16.689 35.498-16.689 35.710Q-16.689 35.847-16.788 35.946Q-16.887 36.045-17.021 36.045Q-17.157 36.045-17.256 35.946Q-17.356 35.847-17.356 35.710Q-17.356 35.570-17.256 35.471Q-17.547 35.471-17.747 35.667Q-17.947 35.864-18.039 36.158Q-18.131 36.452-18.131 36.732L-18.131 37.846Q-18.131 38.055-17.475 38.055L-17.475 38.335M-15.770 39.470Q-15.640 39.538-15.503 39.538Q-15.332 39.538-15.182 39.449Q-15.031 39.360-14.920 39.215Q-14.809 39.070-14.731 38.902L-14.467 38.335L-15.636 35.809Q-15.711 35.662-15.841 35.630Q-15.971 35.597-16.204 35.597L-16.204 35.317L-14.683 35.317L-14.683 35.597Q-15.031 35.597-15.031 35.744Q-15.028 35.765-15.026 35.782Q-15.024 35.799-15.024 35.809L-14.167 37.668L-13.394 35.997Q-13.360 35.929-13.360 35.850Q-13.360 35.737-13.444 35.667Q-13.527 35.597-13.640 35.597L-13.640 35.317L-12.444 35.317L-12.444 35.597Q-12.663 35.597-12.835 35.701Q-13.008 35.806-13.100 35.997L-14.437 38.902Q-14.607 39.272-14.878 39.518Q-15.148 39.764-15.503 39.764Q-15.773 39.764-15.992 39.598Q-16.211 39.432-16.211 39.169Q-16.211 39.032-16.118 38.943Q-16.026 38.855-15.886 38.855Q-15.749 38.855-15.660 38.943Q-15.571 39.032-15.571 39.169Q-15.571 39.272-15.624 39.350Q-15.677 39.429-15.770 39.470\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(76.254 9.467)\">\u003Cpath d=\"M-36.264 38.475Q-36.899 38.475-37.263 38.130Q-37.628 37.785-37.763 37.260Q-37.898 36.735-37.898 36.110Q-37.898 35.085-37.542 34.386Q-37.187 33.687-36.264 33.687Q-35.337 33.687-34.985 34.386Q-34.633 35.085-34.633 36.110Q-34.633 36.735-34.768 37.260Q-34.903 37.785-35.266 38.130Q-35.628 38.475-36.264 38.475M-36.264 38.250Q-35.826 38.250-35.613 37.875Q-35.399 37.501-35.349 37.034Q-35.300 36.568-35.300 35.990Q-35.300 35.437-35.349 35.009Q-35.399 34.582-35.611 34.247Q-35.823 33.912-36.264 33.912Q-36.606 33.912-36.809 34.119Q-37.012 34.326-37.099 34.638Q-37.187 34.951-37.209 35.267Q-37.231 35.584-37.231 35.990Q-37.231 36.407-37.209 36.749Q-37.187 37.091-37.098 37.439Q-37.009 37.788-36.804 38.019Q-36.599 38.250-36.264 38.250M-33.557 37.915Q-33.557 37.747-33.434 37.624Q-33.311 37.501-33.136 37.501Q-32.969 37.501-32.846 37.624Q-32.723 37.747-32.723 37.915Q-32.723 38.089-32.846 38.212Q-32.969 38.335-33.136 38.335Q-33.311 38.335-33.434 38.212Q-33.557 38.089-33.557 37.915M-28.683 38.335L-31.212 38.335L-31.212 38.055Q-30.245 38.055-30.245 37.846L-30.245 34.227Q-30.638 34.415-31.260 34.415L-31.260 34.134Q-30.843 34.134-30.479 34.033Q-30.115 33.933-29.858 33.687L-29.732 33.687Q-29.667 33.704-29.650 33.772L-29.650 37.846Q-29.650 38.055-28.683 38.055L-28.683 38.335M-27.052 38.021Q-26.933 38.137-26.755 38.179Q-26.577 38.222-26.362 38.222Q-26.123 38.222-25.912 38.113Q-25.702 38.003-25.548 37.821Q-25.395 37.638-25.295 37.405Q-25.128 36.978-25.128 36.158Q-25.278 36.452-25.542 36.631Q-25.805 36.811-26.123 36.811Q-26.557 36.811-26.904 36.602Q-27.251 36.394-27.449 36.033Q-27.647 35.672-27.647 35.249Q-27.647 34.914-27.517 34.625Q-27.387 34.336-27.157 34.122Q-26.926 33.909-26.627 33.798Q-26.328 33.687-25.996 33.687Q-25.138 33.687-24.783 34.401Q-24.427 35.115-24.427 36.072Q-24.427 36.489-24.555 36.917Q-24.684 37.344-24.940 37.699Q-25.196 38.055-25.559 38.265Q-25.921 38.475-26.362 38.475Q-26.816 38.475-27.134 38.287Q-27.452 38.099-27.452 37.675Q-27.452 37.525-27.353 37.426Q-27.254 37.327-27.104 37.327Q-27.035 37.327-26.969 37.354Q-26.902 37.381-26.857 37.426Q-26.813 37.470-26.786 37.537Q-26.758 37.604-26.758 37.675Q-26.758 37.805-26.839 37.903Q-26.919 38-27.052 38.021M-26.082 36.585Q-25.788 36.585-25.572 36.407Q-25.357 36.230-25.249 35.954Q-25.142 35.679-25.142 35.389Q-25.142 35.344-25.143 35.317Q-25.145 35.290-25.149 35.255Q-25.145 35.245-25.143 35.238Q-25.142 35.231-25.142 35.221Q-25.142 34.719-25.340 34.319Q-25.538 33.919-25.996 33.919Q-26.564 33.919-26.757 34.278Q-26.950 34.637-26.950 35.249Q-26.950 35.635-26.895 35.918Q-26.840 36.202-26.646 36.394Q-26.451 36.585-26.082 36.585M-22.055 38.475Q-22.513 38.475-22.831 38.260Q-23.149 38.044-23.330 37.692Q-23.511 37.340-23.588 36.920Q-23.665 36.500-23.665 36.072Q-23.665 35.488-23.412 34.932Q-23.159 34.377-22.689 34.032Q-22.219 33.687-21.621 33.687Q-21.211 33.687-20.927 33.885Q-20.644 34.083-20.644 34.486Q-20.644 34.582-20.690 34.661Q-20.736 34.739-20.816 34.784Q-20.897 34.828-20.985 34.828Q-21.132 34.828-21.233 34.731Q-21.334 34.633-21.334 34.486Q-21.334 34.356-21.243 34.249Q-21.153 34.141-21.020 34.141Q-21.208 33.919-21.621 33.919Q-21.936 33.919-22.209 34.083Q-22.482 34.247-22.650 34.521Q-22.838 34.811-22.903 35.177Q-22.968 35.543-22.968 35.997Q-22.817 35.703-22.553 35.525Q-22.288 35.348-21.973 35.348Q-21.543 35.348-21.194 35.554Q-20.845 35.761-20.645 36.117Q-20.445 36.472-20.445 36.899Q-20.445 37.344-20.662 37.704Q-20.879 38.065-21.252 38.270Q-21.625 38.475-22.055 38.475M-22.055 38.222Q-21.679 38.222-21.476 38.039Q-21.273 37.856-21.209 37.573Q-21.146 37.289-21.146 36.899Q-21.146 36.513-21.201 36.233Q-21.255 35.953-21.450 35.761Q-21.645 35.570-22.014 35.570Q-22.305 35.570-22.517 35.746Q-22.729 35.922-22.836 36.195Q-22.944 36.469-22.944 36.752L-22.944 36.893L-22.944 36.934Q-22.944 37.439-22.732 37.831Q-22.520 38.222-22.055 38.222\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath d=\"M81.249 38.335v-11.95h34.143v11.95Zm34.143-11.95\"\u002F>\u003Cg transform=\"translate(128.337 -16.337)\">\u003Cpath d=\"M-35.754 38.335L-37.860 38.335L-37.860 38.055Q-37.139 38.055-37.139 37.846L-37.139 34.045Q-37.139 33.834-37.860 33.834L-37.860 33.553L-35.522 33.553Q-35.211 33.553-34.868 33.627Q-34.524 33.700-34.203 33.856Q-33.881 34.011-33.680 34.257Q-33.478 34.503-33.478 34.828Q-33.478 35.115-33.666 35.346Q-33.854 35.577-34.131 35.725Q-34.408 35.874-34.698 35.956Q-34.363 36.059-34.129 36.291Q-33.895 36.523-33.851 36.852L-33.765 37.460Q-33.738 37.672-33.692 37.841Q-33.646 38.010-33.541 38.130Q-33.437 38.250-33.249 38.250Q-33.034 38.250-32.918 38.065Q-32.801 37.880-32.801 37.648Q-32.784 37.583-32.709 37.566L-32.617 37.566Q-32.535 37.586-32.535 37.668Q-32.535 37.874-32.625 38.060Q-32.716 38.246-32.882 38.361Q-33.047 38.475-33.249 38.475Q-33.587 38.475-33.881 38.374Q-34.175 38.273-34.360 38.051Q-34.545 37.829-34.545 37.481L-34.545 36.872Q-34.545 36.623-34.688 36.436Q-34.832 36.250-35.055 36.151Q-35.279 36.052-35.529 36.052L-36.476 36.052L-36.476 37.846Q-36.476 38.055-35.754 38.055L-35.754 38.335M-36.476 34.045L-36.476 35.830L-35.621 35.830Q-35.334 35.830-35.095 35.787Q-34.856 35.744-34.668 35.635Q-34.480 35.525-34.368 35.327Q-34.257 35.129-34.257 34.828Q-34.257 34.251-34.625 34.042Q-34.992 33.834-35.621 33.834L-36.110 33.834Q-36.298 33.834-36.387 33.868Q-36.476 33.902-36.476 34.045M-32.077 37.607Q-32.077 37.275-31.853 37.048Q-31.629 36.821-31.285 36.693Q-30.942 36.564-30.569 36.512Q-30.197 36.459-29.893 36.459L-29.893 36.206Q-29.893 36.001-30 35.821Q-30.108 35.642-30.289 35.539Q-30.470 35.437-30.679 35.437Q-31.086 35.437-31.321 35.529Q-31.232 35.566-31.186 35.650Q-31.140 35.734-31.140 35.836Q-31.140 35.932-31.186 36.011Q-31.232 36.089-31.313 36.134Q-31.393 36.178-31.482 36.178Q-31.632 36.178-31.733 36.081Q-31.834 35.983-31.834 35.836Q-31.834 35.214-30.679 35.214Q-30.467 35.214-30.217 35.278Q-29.968 35.341-29.766 35.460Q-29.565 35.580-29.438 35.765Q-29.312 35.949-29.312 36.192L-29.312 37.768Q-29.312 37.884-29.250 37.980Q-29.189 38.075-29.076 38.075Q-28.966 38.075-28.901 37.981Q-28.836 37.887-28.836 37.768L-28.836 37.320L-28.570 37.320L-28.570 37.768Q-28.570 38.038-28.797 38.203Q-29.024 38.369-29.305 38.369Q-29.513 38.369-29.650 38.215Q-29.787 38.062-29.811 37.846Q-29.958 38.113-30.240 38.258Q-30.522 38.403-30.846 38.403Q-31.123 38.403-31.407 38.328Q-31.691 38.253-31.884 38.074Q-32.077 37.894-32.077 37.607M-31.461 37.607Q-31.461 37.781-31.361 37.911Q-31.260 38.041-31.104 38.111Q-30.949 38.181-30.785 38.181Q-30.566 38.181-30.357 38.084Q-30.149 37.986-30.021 37.805Q-29.893 37.624-29.893 37.398L-29.893 36.670Q-30.217 36.670-30.583 36.761Q-30.949 36.852-31.205 37.064Q-31.461 37.275-31.461 37.607M-26.536 38.335L-28.088 38.335L-28.088 38.055Q-27.862 38.055-27.714 38.021Q-27.565 37.986-27.565 37.846L-27.565 35.997Q-27.565 35.809-27.613 35.725Q-27.661 35.642-27.758 35.623Q-27.856 35.604-28.067 35.604L-28.067 35.324L-27.011 35.249L-27.011 37.846Q-27.011 37.986-26.880 38.021Q-26.748 38.055-26.536 38.055L-26.536 38.335M-27.808 34.028Q-27.808 33.857-27.685 33.738Q-27.562 33.618-27.391 33.618Q-27.223 33.618-27.100 33.738Q-26.977 33.857-26.977 34.028Q-26.977 34.203-27.100 34.326Q-27.223 34.449-27.391 34.449Q-27.562 34.449-27.685 34.326Q-27.808 34.203-27.808 34.028M-24.209 38.335L-25.842 38.335L-25.842 38.055Q-25.613 38.055-25.465 38.021Q-25.316 37.986-25.316 37.846L-25.316 35.997Q-25.316 35.727-25.424 35.666Q-25.531 35.604-25.842 35.604L-25.842 35.324L-24.783 35.249L-24.783 35.898Q-24.612 35.590-24.308 35.419Q-24.003 35.249-23.658 35.249Q-23.152 35.249-22.869 35.472Q-22.585 35.696-22.585 36.192L-22.585 37.846Q-22.585 37.983-22.436 38.019Q-22.288 38.055-22.062 38.055L-22.062 38.335L-23.692 38.335L-23.692 38.055Q-23.463 38.055-23.315 38.021Q-23.166 37.986-23.166 37.846L-23.166 36.206Q-23.166 35.871-23.286 35.671Q-23.405 35.471-23.720 35.471Q-23.990 35.471-24.224 35.607Q-24.458 35.744-24.597 35.978Q-24.735 36.212-24.735 36.486L-24.735 37.846Q-24.735 37.983-24.585 38.019Q-24.434 38.055-24.209 38.055\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(127.469 9.467)\">\u003Cpath d=\"M-36.264 38.475Q-36.899 38.475-37.263 38.130Q-37.628 37.785-37.763 37.260Q-37.898 36.735-37.898 36.110Q-37.898 35.085-37.542 34.386Q-37.187 33.687-36.264 33.687Q-35.337 33.687-34.985 34.386Q-34.633 35.085-34.633 36.110Q-34.633 36.735-34.768 37.260Q-34.903 37.785-35.266 38.130Q-35.628 38.475-36.264 38.475M-36.264 38.250Q-35.826 38.250-35.613 37.875Q-35.399 37.501-35.349 37.034Q-35.300 36.568-35.300 35.990Q-35.300 35.437-35.349 35.009Q-35.399 34.582-35.611 34.247Q-35.823 33.912-36.264 33.912Q-36.606 33.912-36.809 34.119Q-37.012 34.326-37.099 34.638Q-37.187 34.951-37.209 35.267Q-37.231 35.584-37.231 35.990Q-37.231 36.407-37.209 36.749Q-37.187 37.091-37.098 37.439Q-37.009 37.788-36.804 38.019Q-36.599 38.250-36.264 38.250M-33.557 37.915Q-33.557 37.747-33.434 37.624Q-33.311 37.501-33.136 37.501Q-32.969 37.501-32.846 37.624Q-32.723 37.747-32.723 37.915Q-32.723 38.089-32.846 38.212Q-32.969 38.335-33.136 38.335Q-33.311 38.335-33.434 38.212Q-33.557 38.089-33.557 37.915M-30.019 38.475Q-30.655 38.475-31.019 38.130Q-31.383 37.785-31.518 37.260Q-31.653 36.735-31.653 36.110Q-31.653 35.085-31.297 34.386Q-30.942 33.687-30.019 33.687Q-29.093 33.687-28.741 34.386Q-28.389 35.085-28.389 36.110Q-28.389 36.735-28.524 37.260Q-28.659 37.785-29.021 38.130Q-29.383 38.475-30.019 38.475M-30.019 38.250Q-29.582 38.250-29.368 37.875Q-29.154 37.501-29.105 37.034Q-29.055 36.568-29.055 35.990Q-29.055 35.437-29.105 35.009Q-29.154 34.582-29.366 34.247Q-29.578 33.912-30.019 33.912Q-30.361 33.912-30.564 34.119Q-30.768 34.326-30.855 34.638Q-30.942 34.951-30.964 35.267Q-30.986 35.584-30.986 35.990Q-30.986 36.407-30.964 36.749Q-30.942 37.091-30.853 37.439Q-30.764 37.788-30.559 38.019Q-30.354 38.250-30.019 38.250M-26.591 38.127Q-26.591 37.621-26.461 37.113Q-26.331 36.606-26.094 36.144Q-25.856 35.683-25.521 35.262L-24.875 34.449L-25.689 34.449Q-26.273 34.449-26.670 34.457Q-27.066 34.466-27.090 34.486Q-27.192 34.603-27.271 35.129L-27.538 35.129L-27.292 33.605L-27.025 33.605L-27.025 33.625Q-27.025 33.693-26.950 33.736Q-26.875 33.779-26.796 33.786Q-26.605 33.810-26.410 33.816Q-26.215 33.823-26.024 33.825Q-25.832 33.827-25.634 33.827L-24.212 33.827L-24.212 34.015Q-24.222 34.063-24.232 34.073L-25.289 35.396Q-25.507 35.669-25.630 35.982Q-25.753 36.294-25.812 36.643Q-25.870 36.992-25.883 37.323Q-25.897 37.655-25.897 38.127Q-25.897 38.277-25.996 38.376Q-26.095 38.475-26.242 38.475Q-26.393 38.475-26.492 38.376Q-26.591 38.277-26.591 38.127M-22.055 38.475Q-22.513 38.475-22.831 38.260Q-23.149 38.044-23.330 37.692Q-23.511 37.340-23.588 36.920Q-23.665 36.500-23.665 36.072Q-23.665 35.488-23.412 34.932Q-23.159 34.377-22.689 34.032Q-22.219 33.687-21.621 33.687Q-21.211 33.687-20.927 33.885Q-20.644 34.083-20.644 34.486Q-20.644 34.582-20.690 34.661Q-20.736 34.739-20.816 34.784Q-20.897 34.828-20.985 34.828Q-21.132 34.828-21.233 34.731Q-21.334 34.633-21.334 34.486Q-21.334 34.356-21.243 34.249Q-21.153 34.141-21.020 34.141Q-21.208 33.919-21.621 33.919Q-21.936 33.919-22.209 34.083Q-22.482 34.247-22.650 34.521Q-22.838 34.811-22.903 35.177Q-22.968 35.543-22.968 35.997Q-22.817 35.703-22.553 35.525Q-22.288 35.348-21.973 35.348Q-21.543 35.348-21.194 35.554Q-20.845 35.761-20.645 36.117Q-20.445 36.472-20.445 36.899Q-20.445 37.344-20.662 37.704Q-20.879 38.065-21.252 38.270Q-21.625 38.475-22.055 38.475M-22.055 38.222Q-21.679 38.222-21.476 38.039Q-21.273 37.856-21.209 37.573Q-21.146 37.289-21.146 36.899Q-21.146 36.513-21.201 36.233Q-21.255 35.953-21.450 35.761Q-21.645 35.570-22.014 35.570Q-22.305 35.570-22.517 35.746Q-22.729 35.922-22.836 36.195Q-22.944 36.469-22.944 36.752L-22.944 36.893L-22.944 36.934Q-22.944 37.439-22.732 37.831Q-22.520 38.222-22.055 38.222\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-soft-warn)\" stroke=\"var(--tk-warn)\" d=\"M132.464 38.335v-1.707h34.143v1.707Zm34.143-1.707\"\u002F>\u003Cg fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\">\u003Cg fill=\"var(--tk-warn)\" stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(178.847 -7.74)\">\u003Cpath d=\"M-34.032 38.335L-36.770 38.335L-36.770 38.055Q-36.421 38.055-36.084 38.019Q-35.748 37.983-35.748 37.846L-35.748 34.045Q-35.748 33.902-35.836 33.868Q-35.925 33.834-36.110 33.834L-36.469 33.834Q-36.770 33.834-36.985 33.881Q-37.200 33.929-37.357 34.086Q-37.494 34.220-37.554 34.498Q-37.614 34.777-37.651 35.190L-37.918 35.190L-37.771 33.553L-33.037 33.553L-32.890 35.190L-33.157 35.190Q-33.194 34.777-33.251 34.500Q-33.307 34.223-33.451 34.086Q-33.611 33.926-33.823 33.880Q-34.035 33.834-34.339 33.834L-34.691 33.834Q-34.876 33.834-34.965 33.868Q-35.054 33.902-35.054 34.045L-35.054 37.846Q-35.054 37.983-34.717 38.019Q-34.380 38.055-34.032 38.055\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(178.847 -7.74)\">\u003Cpath d=\"M-32.111 39.470Q-31.981 39.538-31.844 39.538Q-31.673 39.538-31.523 39.449Q-31.372 39.360-31.261 39.215Q-31.150 39.070-31.072 38.902L-30.808 38.335L-31.977 35.809Q-32.052 35.662-32.182 35.630Q-32.312 35.597-32.545 35.597L-32.545 35.317L-31.024 35.317L-31.024 35.597Q-31.372 35.597-31.372 35.744Q-31.369 35.765-31.367 35.782Q-31.365 35.799-31.365 35.809L-30.508 37.668L-29.735 35.997Q-29.701 35.929-29.701 35.850Q-29.701 35.737-29.785 35.667Q-29.868 35.597-29.981 35.597L-29.981 35.317L-28.785 35.317L-28.785 35.597Q-29.004 35.597-29.176 35.701Q-29.349 35.806-29.441 35.997L-30.778 38.902Q-30.948 39.272-31.218 39.518Q-31.489 39.764-31.844 39.764Q-32.114 39.764-32.333 39.598Q-32.552 39.432-32.552 39.169Q-32.552 39.032-32.459 38.943Q-32.367 38.855-32.227 38.855Q-32.090 38.855-32.001 38.943Q-31.912 39.032-31.912 39.169Q-31.912 39.272-31.965 39.350Q-32.018 39.429-32.111 39.470M-26.601 39.692L-28.231 39.692L-28.231 39.412Q-28.002 39.412-27.854 39.377Q-27.705 39.343-27.705 39.203L-27.705 35.857Q-27.705 35.686-27.842 35.645Q-27.978 35.604-28.231 35.604L-28.231 35.324L-27.151 35.249L-27.151 35.655Q-26.929 35.454-26.642 35.351Q-26.355 35.249-26.047 35.249Q-25.620 35.249-25.256 35.462Q-24.892 35.676-24.678 36.040Q-24.465 36.404-24.465 36.824Q-24.465 37.269-24.704 37.633Q-24.943 37.997-25.336 38.200Q-25.729 38.403-26.174 38.403Q-26.440 38.403-26.688 38.303Q-26.936 38.202-27.124 38.021L-27.124 39.203Q-27.124 39.340-26.975 39.376Q-26.826 39.412-26.601 39.412L-26.601 39.692M-27.124 36.004L-27.124 37.614Q-26.990 37.867-26.748 38.024Q-26.505 38.181-26.228 38.181Q-25.900 38.181-25.647 37.980Q-25.394 37.778-25.261 37.460Q-25.128 37.142-25.128 36.824Q-25.128 36.595-25.193 36.366Q-25.258 36.137-25.386 35.939Q-25.514 35.741-25.709 35.621Q-25.904 35.502-26.136 35.502Q-26.430 35.502-26.698 35.631Q-26.967 35.761-27.124 36.004\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(178.847 -7.74)\">\u003Cpath d=\"M-23.653 36.800Q-23.653 36.479-23.528 36.190Q-23.403 35.901-23.177 35.678Q-22.952 35.454-22.656 35.334Q-22.361 35.214-22.043 35.214Q-21.715 35.214-21.453 35.314Q-21.192 35.413-21.016 35.595Q-20.840 35.778-20.746 36.036Q-20.652 36.294-20.652 36.626Q-20.652 36.718-20.734 36.739L-22.989 36.739L-22.989 36.800Q-22.989 37.388-22.706 37.771Q-22.422 38.154-21.855 38.154Q-21.533 38.154-21.265 37.961Q-20.997 37.768-20.908 37.453Q-20.901 37.412-20.826 37.398L-20.734 37.398Q-20.652 37.422-20.652 37.494Q-20.652 37.501-20.658 37.528Q-20.771 37.925-21.142 38.164Q-21.513 38.403-21.937 38.403Q-22.374 38.403-22.774 38.195Q-23.174 37.986-23.413 37.619Q-23.653 37.252-23.653 36.800M-22.983 36.530L-21.168 36.530Q-21.168 36.253-21.265 36.001Q-21.363 35.748-21.561 35.592Q-21.759 35.437-22.043 35.437Q-22.320 35.437-22.533 35.595Q-22.747 35.754-22.865 36.009Q-22.983 36.264-22.983 36.530\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(185.795 9.467)\">\u003Cpath d=\"M-36.264 38.475Q-36.899 38.475-37.263 38.130Q-37.628 37.785-37.763 37.260Q-37.898 36.735-37.898 36.110Q-37.898 35.085-37.542 34.386Q-37.187 33.687-36.264 33.687Q-35.337 33.687-34.985 34.386Q-34.633 35.085-34.633 36.110Q-34.633 36.735-34.768 37.260Q-34.903 37.785-35.266 38.130Q-35.628 38.475-36.264 38.475M-36.264 38.250Q-35.826 38.250-35.613 37.875Q-35.399 37.501-35.349 37.034Q-35.300 36.568-35.300 35.990Q-35.300 35.437-35.349 35.009Q-35.399 34.582-35.611 34.247Q-35.823 33.912-36.264 33.912Q-36.606 33.912-36.809 34.119Q-37.012 34.326-37.099 34.638Q-37.187 34.951-37.209 35.267Q-37.231 35.584-37.231 35.990Q-37.231 36.407-37.209 36.749Q-37.187 37.091-37.098 37.439Q-37.009 37.788-36.804 38.019Q-36.599 38.250-36.264 38.250\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">Information gain of four candidate root attributes on the 12 restaurant examples. Patrons (0.541 bits) wins because it isolates two pure subsets; Type (0 bits) loses because every branch keeps the parent's even split. The greedy learner tests the highest bar first.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:378.721px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 284.041 120.612\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cpath fill=\"var(--tk-soft-warn)\" stroke=\"none\" d=\"M-21.25-54.798V-71.87H8.627v17.072ZM8.627-71.87\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-warn)\" d=\"M-21.25-54.798V-71.87H8.627v17.072ZM8.627-71.87\" style=\"stroke-width:.8\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M11.471-54.798V-71.87h29.876v17.072ZM41.347-71.87\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M11.471-54.798V-71.87h29.876v17.072ZM41.347-71.87\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M44.192-54.798V-71.87h29.875v17.072ZM74.067-71.87\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M44.192-54.798V-71.87h29.875v17.072ZM74.067-71.87\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M76.912-54.798V-71.87h29.876v17.072Zm29.876-17.072\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M76.912-54.798V-71.87h29.876v17.072Zm29.876-17.072\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M109.633-54.798V-71.87h29.875v17.072Zm29.875-17.072\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M109.633-54.798V-71.87h29.875v17.072Zm29.875-17.072\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-37.688 -6.105)\">\u003Cpath d=\"M-19.185-54.798L-20.921-54.798L-20.921-55.078Q-20.692-55.078-20.543-55.112Q-20.395-55.147-20.395-55.287L-20.395-57.136Q-20.395-57.406-20.502-57.467Q-20.610-57.529-20.921-57.529L-20.921-57.809L-19.892-57.884L-19.892-57.177Q-19.762-57.485-19.520-57.684Q-19.277-57.884-18.959-57.884Q-18.740-57.884-18.569-57.760Q-18.398-57.635-18.398-57.423Q-18.398-57.286-18.498-57.187Q-18.597-57.088-18.730-57.088Q-18.867-57.088-18.966-57.187Q-19.065-57.286-19.065-57.423Q-19.065-57.563-18.966-57.662Q-19.256-57.662-19.456-57.466Q-19.656-57.269-19.749-56.975Q-19.841-56.681-19.841-56.401L-19.841-55.287Q-19.841-55.078-19.185-55.078L-19.185-54.798M-17.855-56.281Q-17.855-56.623-17.720-56.922Q-17.585-57.221-17.346-57.445Q-17.106-57.669-16.789-57.794Q-16.471-57.919-16.139-57.919Q-15.695-57.919-15.295-57.703Q-14.895-57.488-14.661-57.110Q-14.427-56.733-14.427-56.281Q-14.427-55.940-14.569-55.656Q-14.710-55.372-14.955-55.165Q-15.199-54.959-15.509-54.844Q-15.818-54.730-16.139-54.730Q-16.570-54.730-16.971-54.931Q-17.373-55.133-17.614-55.485Q-17.855-55.837-17.855-56.281M-16.139-54.979Q-15.538-54.979-15.314-55.357Q-15.090-55.735-15.090-56.367Q-15.090-56.979-15.324-57.338Q-15.558-57.696-16.139-57.696Q-17.192-57.696-17.192-56.367Q-17.192-55.735-16.966-55.357Q-16.741-54.979-16.139-54.979M-13.258-55.632L-13.258-57.136Q-13.258-57.406-13.365-57.467Q-13.473-57.529-13.784-57.529L-13.784-57.809L-12.677-57.884L-12.677-55.652L-12.677-55.632Q-12.677-55.352-12.625-55.208Q-12.574-55.065-12.432-55.008Q-12.291-54.952-12.003-54.952Q-11.750-54.952-11.545-55.092Q-11.340-55.232-11.224-55.458Q-11.108-55.683-11.108-55.933L-11.108-57.136Q-11.108-57.406-11.216-57.467Q-11.323-57.529-11.634-57.529L-11.634-57.809L-10.527-57.884L-10.527-55.471Q-10.527-55.280-10.474-55.198Q-10.421-55.116-10.320-55.097Q-10.219-55.078-10.004-55.078L-10.004-54.798L-11.081-54.730L-11.081-55.294Q-11.190-55.112-11.335-54.989Q-11.480-54.866-11.667-54.798Q-11.853-54.730-12.055-54.730Q-13.258-54.730-13.258-55.632M-7.734-54.798L-9.368-54.798L-9.368-55.078Q-9.139-55.078-8.990-55.112Q-8.842-55.147-8.842-55.287L-8.842-57.136Q-8.842-57.406-8.949-57.467Q-9.057-57.529-9.368-57.529L-9.368-57.809L-8.309-57.884L-8.309-57.235Q-8.138-57.543-7.833-57.714Q-7.529-57.884-7.184-57.884Q-6.678-57.884-6.395-57.661Q-6.111-57.437-6.111-56.941L-6.111-55.287Q-6.111-55.150-5.962-55.114Q-5.813-55.078-5.588-55.078L-5.588-54.798L-7.218-54.798L-7.218-55.078Q-6.989-55.078-6.841-55.112Q-6.692-55.147-6.692-55.287L-6.692-56.927Q-6.692-57.262-6.811-57.462Q-6.931-57.662-7.246-57.662Q-7.516-57.662-7.750-57.526Q-7.984-57.389-8.122-57.155Q-8.261-56.921-8.261-56.647L-8.261-55.287Q-8.261-55.150-8.110-55.114Q-7.960-55.078-7.734-55.078L-7.734-54.798M-5-56.309Q-5-56.647-4.860-56.938Q-4.720-57.228-4.475-57.442Q-4.231-57.655-3.927-57.770Q-3.623-57.884-3.298-57.884Q-3.028-57.884-2.765-57.785Q-2.501-57.686-2.310-57.508L-2.310-58.906Q-2.310-59.176-2.418-59.238Q-2.525-59.299-2.836-59.299L-2.836-59.580L-1.760-59.655L-1.760-55.471Q-1.760-55.283-1.705-55.200Q-1.650-55.116-1.550-55.097Q-1.449-55.078-1.233-55.078L-1.233-54.798L-2.341-54.730L-2.341-55.147Q-2.758-54.730-3.383-54.730Q-3.814-54.730-4.186-54.942Q-4.559-55.153-4.780-55.514Q-5-55.875-5-56.309M-3.325-54.952Q-3.117-54.952-2.930-55.024Q-2.744-55.095-2.590-55.232Q-2.436-55.369-2.341-55.547L-2.341-57.156Q-2.426-57.303-2.572-57.423Q-2.717-57.543-2.886-57.602Q-3.055-57.662-3.236-57.662Q-3.797-57.662-4.065-57.273Q-4.333-56.883-4.333-56.302Q-4.333-55.731-4.099-55.341Q-3.865-54.952-3.325-54.952\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-37.688 -6.105)\">\u003Cpath d=\"M5.133-54.798L2.603-54.798L2.603-55.078Q3.571-55.078 3.571-55.287L3.571-58.906Q3.178-58.718 2.556-58.718L2.556-58.999Q2.973-58.999 3.337-59.100Q3.701-59.200 3.957-59.446L4.083-59.446Q4.148-59.429 4.165-59.361L4.165-55.287Q4.165-55.078 5.133-55.078\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M-21.25-30.613v-17.072H8.627v17.072ZM8.627-47.685\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-21.25-30.613v-17.072H8.627v17.072ZM8.627-47.685\"\u002F>\u003Cpath fill=\"var(--tk-soft-warn)\" stroke=\"none\" d=\"M11.471-30.613v-17.072h29.876v17.072Zm29.876-17.072\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-warn)\" d=\"M11.471-30.613v-17.072h29.876v17.072Zm29.876-17.072\" style=\"stroke-width:.8\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M44.192-30.613v-17.072h29.875v17.072Zm29.875-17.072\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M44.192-30.613v-17.072h29.875v17.072Zm29.875-17.072\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M76.912-30.613v-17.072h29.876v17.072Zm29.876-17.072\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M76.912-30.613v-17.072h29.876v17.072Zm29.876-17.072\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M109.633-30.613v-17.072h29.875v17.072Zm29.875-17.072\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M109.633-30.613v-17.072h29.875v17.072Zm29.875-17.072\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-37.688 18.08)\">\u003Cpath d=\"M-19.185-54.798L-20.921-54.798L-20.921-55.078Q-20.692-55.078-20.543-55.112Q-20.395-55.147-20.395-55.287L-20.395-57.136Q-20.395-57.406-20.502-57.467Q-20.610-57.529-20.921-57.529L-20.921-57.809L-19.892-57.884L-19.892-57.177Q-19.762-57.485-19.520-57.684Q-19.277-57.884-18.959-57.884Q-18.740-57.884-18.569-57.760Q-18.398-57.635-18.398-57.423Q-18.398-57.286-18.498-57.187Q-18.597-57.088-18.730-57.088Q-18.867-57.088-18.966-57.187Q-19.065-57.286-19.065-57.423Q-19.065-57.563-18.966-57.662Q-19.256-57.662-19.456-57.466Q-19.656-57.269-19.749-56.975Q-19.841-56.681-19.841-56.401L-19.841-55.287Q-19.841-55.078-19.185-55.078L-19.185-54.798M-17.855-56.281Q-17.855-56.623-17.720-56.922Q-17.585-57.221-17.346-57.445Q-17.106-57.669-16.789-57.794Q-16.471-57.919-16.139-57.919Q-15.695-57.919-15.295-57.703Q-14.895-57.488-14.661-57.110Q-14.427-56.733-14.427-56.281Q-14.427-55.940-14.569-55.656Q-14.710-55.372-14.955-55.165Q-15.199-54.959-15.509-54.844Q-15.818-54.730-16.139-54.730Q-16.570-54.730-16.971-54.931Q-17.373-55.133-17.614-55.485Q-17.855-55.837-17.855-56.281M-16.139-54.979Q-15.538-54.979-15.314-55.357Q-15.090-55.735-15.090-56.367Q-15.090-56.979-15.324-57.338Q-15.558-57.696-16.139-57.696Q-17.192-57.696-17.192-56.367Q-17.192-55.735-16.966-55.357Q-16.741-54.979-16.139-54.979M-13.258-55.632L-13.258-57.136Q-13.258-57.406-13.365-57.467Q-13.473-57.529-13.784-57.529L-13.784-57.809L-12.677-57.884L-12.677-55.652L-12.677-55.632Q-12.677-55.352-12.625-55.208Q-12.574-55.065-12.432-55.008Q-12.291-54.952-12.003-54.952Q-11.750-54.952-11.545-55.092Q-11.340-55.232-11.224-55.458Q-11.108-55.683-11.108-55.933L-11.108-57.136Q-11.108-57.406-11.216-57.467Q-11.323-57.529-11.634-57.529L-11.634-57.809L-10.527-57.884L-10.527-55.471Q-10.527-55.280-10.474-55.198Q-10.421-55.116-10.320-55.097Q-10.219-55.078-10.004-55.078L-10.004-54.798L-11.081-54.730L-11.081-55.294Q-11.190-55.112-11.335-54.989Q-11.480-54.866-11.667-54.798Q-11.853-54.730-12.055-54.730Q-13.258-54.730-13.258-55.632M-7.734-54.798L-9.368-54.798L-9.368-55.078Q-9.139-55.078-8.990-55.112Q-8.842-55.147-8.842-55.287L-8.842-57.136Q-8.842-57.406-8.949-57.467Q-9.057-57.529-9.368-57.529L-9.368-57.809L-8.309-57.884L-8.309-57.235Q-8.138-57.543-7.833-57.714Q-7.529-57.884-7.184-57.884Q-6.678-57.884-6.395-57.661Q-6.111-57.437-6.111-56.941L-6.111-55.287Q-6.111-55.150-5.962-55.114Q-5.813-55.078-5.588-55.078L-5.588-54.798L-7.218-54.798L-7.218-55.078Q-6.989-55.078-6.841-55.112Q-6.692-55.147-6.692-55.287L-6.692-56.927Q-6.692-57.262-6.811-57.462Q-6.931-57.662-7.246-57.662Q-7.516-57.662-7.750-57.526Q-7.984-57.389-8.122-57.155Q-8.261-56.921-8.261-56.647L-8.261-55.287Q-8.261-55.150-8.110-55.114Q-7.960-55.078-7.734-55.078L-7.734-54.798M-5-56.309Q-5-56.647-4.860-56.938Q-4.720-57.228-4.475-57.442Q-4.231-57.655-3.927-57.770Q-3.623-57.884-3.298-57.884Q-3.028-57.884-2.765-57.785Q-2.501-57.686-2.310-57.508L-2.310-58.906Q-2.310-59.176-2.418-59.238Q-2.525-59.299-2.836-59.299L-2.836-59.580L-1.760-59.655L-1.760-55.471Q-1.760-55.283-1.705-55.200Q-1.650-55.116-1.550-55.097Q-1.449-55.078-1.233-55.078L-1.233-54.798L-2.341-54.730L-2.341-55.147Q-2.758-54.730-3.383-54.730Q-3.814-54.730-4.186-54.942Q-4.559-55.153-4.780-55.514Q-5-55.875-5-56.309M-3.325-54.952Q-3.117-54.952-2.930-55.024Q-2.744-55.095-2.590-55.232Q-2.436-55.369-2.341-55.547L-2.341-57.156Q-2.426-57.303-2.572-57.423Q-2.717-57.543-2.886-57.602Q-3.055-57.662-3.236-57.662Q-3.797-57.662-4.065-57.273Q-4.333-56.883-4.333-56.302Q-4.333-55.731-4.099-55.341Q-3.865-54.952-3.325-54.952\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-37.688 18.08)\">\u003Cpath d=\"M5.133-54.798L2.248-54.798L2.248-55Q2.248-55.030 2.275-55.058L3.523-56.275Q3.595-56.350 3.637-56.392Q3.680-56.435 3.759-56.514Q4.172-56.927 4.403-57.285Q4.634-57.642 4.634-58.066Q4.634-58.298 4.555-58.501Q4.476-58.705 4.335-58.855Q4.193-59.006 3.998-59.086Q3.803-59.166 3.571-59.166Q3.260-59.166 3.002-59.007Q2.744-58.848 2.614-58.571L2.634-58.571Q2.802-58.571 2.909-58.460Q3.017-58.349 3.017-58.185Q3.017-58.028 2.908-57.915Q2.798-57.802 2.634-57.802Q2.474-57.802 2.361-57.915Q2.248-58.028 2.248-58.185Q2.248-58.561 2.456-58.848Q2.665-59.135 3-59.291Q3.335-59.446 3.690-59.446Q4.114-59.446 4.494-59.288Q4.873-59.129 5.107-58.812Q5.341-58.496 5.341-58.066Q5.341-57.755 5.201-57.486Q5.061-57.218 4.856-57.013Q4.651-56.808 4.288-56.526Q3.926-56.244 3.817-56.148L2.962-55.420L3.605-55.420Q3.868-55.420 4.157-55.422Q4.446-55.423 4.664-55.432Q4.883-55.441 4.900-55.458Q4.962-55.523 4.999-55.690Q5.037-55.858 5.075-56.100L5.341-56.100\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M-21.25-6.428V-23.5H8.627v17.072ZM8.627-23.5\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-21.25-6.428V-23.5H8.627v17.072ZM8.627-23.5\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M11.471-6.428V-23.5h29.876v17.072ZM41.347-23.5\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M11.471-6.428V-23.5h29.876v17.072ZM41.347-23.5\"\u002F>\u003Cpath fill=\"var(--tk-soft-warn)\" stroke=\"none\" d=\"M44.192-6.428V-23.5h29.875v17.072ZM74.067-23.5\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-warn)\" d=\"M44.192-6.428V-23.5h29.875v17.072ZM74.067-23.5\" style=\"stroke-width:.8\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M76.912-6.428V-23.5h29.876v17.072ZM106.788-23.5\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M76.912-6.428V-23.5h29.876v17.072ZM106.788-23.5\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M109.633-6.428V-23.5h29.875v17.072ZM139.508-23.5\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M109.633-6.428V-23.5h29.875v17.072ZM139.508-23.5\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-37.688 42.265)\">\u003Cpath d=\"M-19.185-54.798L-20.921-54.798L-20.921-55.078Q-20.692-55.078-20.543-55.112Q-20.395-55.147-20.395-55.287L-20.395-57.136Q-20.395-57.406-20.502-57.467Q-20.610-57.529-20.921-57.529L-20.921-57.809L-19.892-57.884L-19.892-57.177Q-19.762-57.485-19.520-57.684Q-19.277-57.884-18.959-57.884Q-18.740-57.884-18.569-57.760Q-18.398-57.635-18.398-57.423Q-18.398-57.286-18.498-57.187Q-18.597-57.088-18.730-57.088Q-18.867-57.088-18.966-57.187Q-19.065-57.286-19.065-57.423Q-19.065-57.563-18.966-57.662Q-19.256-57.662-19.456-57.466Q-19.656-57.269-19.749-56.975Q-19.841-56.681-19.841-56.401L-19.841-55.287Q-19.841-55.078-19.185-55.078L-19.185-54.798M-17.855-56.281Q-17.855-56.623-17.720-56.922Q-17.585-57.221-17.346-57.445Q-17.106-57.669-16.789-57.794Q-16.471-57.919-16.139-57.919Q-15.695-57.919-15.295-57.703Q-14.895-57.488-14.661-57.110Q-14.427-56.733-14.427-56.281Q-14.427-55.940-14.569-55.656Q-14.710-55.372-14.955-55.165Q-15.199-54.959-15.509-54.844Q-15.818-54.730-16.139-54.730Q-16.570-54.730-16.971-54.931Q-17.373-55.133-17.614-55.485Q-17.855-55.837-17.855-56.281M-16.139-54.979Q-15.538-54.979-15.314-55.357Q-15.090-55.735-15.090-56.367Q-15.090-56.979-15.324-57.338Q-15.558-57.696-16.139-57.696Q-17.192-57.696-17.192-56.367Q-17.192-55.735-16.966-55.357Q-16.741-54.979-16.139-54.979M-13.258-55.632L-13.258-57.136Q-13.258-57.406-13.365-57.467Q-13.473-57.529-13.784-57.529L-13.784-57.809L-12.677-57.884L-12.677-55.652L-12.677-55.632Q-12.677-55.352-12.625-55.208Q-12.574-55.065-12.432-55.008Q-12.291-54.952-12.003-54.952Q-11.750-54.952-11.545-55.092Q-11.340-55.232-11.224-55.458Q-11.108-55.683-11.108-55.933L-11.108-57.136Q-11.108-57.406-11.216-57.467Q-11.323-57.529-11.634-57.529L-11.634-57.809L-10.527-57.884L-10.527-55.471Q-10.527-55.280-10.474-55.198Q-10.421-55.116-10.320-55.097Q-10.219-55.078-10.004-55.078L-10.004-54.798L-11.081-54.730L-11.081-55.294Q-11.190-55.112-11.335-54.989Q-11.480-54.866-11.667-54.798Q-11.853-54.730-12.055-54.730Q-13.258-54.730-13.258-55.632M-7.734-54.798L-9.368-54.798L-9.368-55.078Q-9.139-55.078-8.990-55.112Q-8.842-55.147-8.842-55.287L-8.842-57.136Q-8.842-57.406-8.949-57.467Q-9.057-57.529-9.368-57.529L-9.368-57.809L-8.309-57.884L-8.309-57.235Q-8.138-57.543-7.833-57.714Q-7.529-57.884-7.184-57.884Q-6.678-57.884-6.395-57.661Q-6.111-57.437-6.111-56.941L-6.111-55.287Q-6.111-55.150-5.962-55.114Q-5.813-55.078-5.588-55.078L-5.588-54.798L-7.218-54.798L-7.218-55.078Q-6.989-55.078-6.841-55.112Q-6.692-55.147-6.692-55.287L-6.692-56.927Q-6.692-57.262-6.811-57.462Q-6.931-57.662-7.246-57.662Q-7.516-57.662-7.750-57.526Q-7.984-57.389-8.122-57.155Q-8.261-56.921-8.261-56.647L-8.261-55.287Q-8.261-55.150-8.110-55.114Q-7.960-55.078-7.734-55.078L-7.734-54.798M-5-56.309Q-5-56.647-4.860-56.938Q-4.720-57.228-4.475-57.442Q-4.231-57.655-3.927-57.770Q-3.623-57.884-3.298-57.884Q-3.028-57.884-2.765-57.785Q-2.501-57.686-2.310-57.508L-2.310-58.906Q-2.310-59.176-2.418-59.238Q-2.525-59.299-2.836-59.299L-2.836-59.580L-1.760-59.655L-1.760-55.471Q-1.760-55.283-1.705-55.200Q-1.650-55.116-1.550-55.097Q-1.449-55.078-1.233-55.078L-1.233-54.798L-2.341-54.730L-2.341-55.147Q-2.758-54.730-3.383-54.730Q-3.814-54.730-4.186-54.942Q-4.559-55.153-4.780-55.514Q-5-55.875-5-56.309M-3.325-54.952Q-3.117-54.952-2.930-55.024Q-2.744-55.095-2.590-55.232Q-2.436-55.369-2.341-55.547L-2.341-57.156Q-2.426-57.303-2.572-57.423Q-2.717-57.543-2.886-57.602Q-3.055-57.662-3.236-57.662Q-3.797-57.662-4.065-57.273Q-4.333-56.883-4.333-56.302Q-4.333-55.731-4.099-55.341Q-3.865-54.952-3.325-54.952\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-37.688 42.265)\">\u003Cpath d=\"M2.603-55.345Q2.723-55.188 2.914-55.089Q3.106-54.989 3.321-54.950Q3.536-54.911 3.759-54.911Q4.056-54.911 4.251-55.066Q4.446-55.222 4.536-55.476Q4.627-55.731 4.627-56.015Q4.627-56.309 4.535-56.560Q4.442-56.811 4.244-56.967Q4.046-57.122 3.752-57.122L3.236-57.122Q3.208-57.122 3.183-57.148Q3.157-57.173 3.157-57.197L3.157-57.269Q3.157-57.300 3.183-57.322Q3.208-57.344 3.236-57.344L3.677-57.375Q4.039-57.375 4.259-57.732Q4.480-58.090 4.480-58.479Q4.480-58.807 4.285-59.011Q4.090-59.214 3.759-59.214Q3.472-59.214 3.219-59.130Q2.966-59.047 2.802-58.859Q2.949-58.859 3.049-58.744Q3.150-58.630 3.150-58.479Q3.150-58.329 3.044-58.219Q2.938-58.110 2.781-58.110Q2.620-58.110 2.511-58.219Q2.402-58.329 2.402-58.479Q2.402-58.804 2.610-59.023Q2.819-59.241 3.135-59.344Q3.451-59.446 3.759-59.446Q4.077-59.446 4.405-59.342Q4.733-59.238 4.960-59.016Q5.187-58.794 5.187-58.479Q5.187-58.045 4.900-57.720Q4.613-57.396 4.179-57.249Q4.490-57.184 4.770-57.018Q5.051-56.852 5.228-56.594Q5.406-56.336 5.406-56.015Q5.406-55.605 5.162-55.295Q4.917-54.986 4.536-54.822Q4.155-54.658 3.759-54.658Q3.390-54.658 3.032-54.771Q2.675-54.883 2.431-55.133Q2.186-55.382 2.186-55.752Q2.186-55.923 2.303-56.035Q2.419-56.148 2.590-56.148Q2.699-56.148 2.790-56.097Q2.880-56.046 2.935-55.953Q2.990-55.861 2.990-55.752Q2.990-55.584 2.877-55.465Q2.764-55.345 2.603-55.345\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M-21.25 17.757V.685H8.627v17.072ZM8.627.685\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-21.25 17.757V.685H8.627v17.072ZM8.627.685\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M11.471 17.757V.685h29.876v17.072ZM41.347.685\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M11.471 17.757V.685h29.876v17.072ZM41.347.685\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M44.192 17.757V.685h29.875v17.072ZM74.067.685\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M44.192 17.757V.685h29.875v17.072ZM74.067.685\"\u002F>\u003Cpath fill=\"var(--tk-soft-warn)\" stroke=\"none\" d=\"M76.912 17.757V.685h29.876v17.072ZM106.788.685\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-warn)\" d=\"M76.912 17.757V.685h29.876v17.072ZM106.788.685\" style=\"stroke-width:.8\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M109.633 17.757V.685h29.875v17.072ZM139.508.685\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M109.633 17.757V.685h29.875v17.072ZM139.508.685\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-37.688 66.45)\">\u003Cpath d=\"M-19.185-54.798L-20.921-54.798L-20.921-55.078Q-20.692-55.078-20.543-55.112Q-20.395-55.147-20.395-55.287L-20.395-57.136Q-20.395-57.406-20.502-57.467Q-20.610-57.529-20.921-57.529L-20.921-57.809L-19.892-57.884L-19.892-57.177Q-19.762-57.485-19.520-57.684Q-19.277-57.884-18.959-57.884Q-18.740-57.884-18.569-57.760Q-18.398-57.635-18.398-57.423Q-18.398-57.286-18.498-57.187Q-18.597-57.088-18.730-57.088Q-18.867-57.088-18.966-57.187Q-19.065-57.286-19.065-57.423Q-19.065-57.563-18.966-57.662Q-19.256-57.662-19.456-57.466Q-19.656-57.269-19.749-56.975Q-19.841-56.681-19.841-56.401L-19.841-55.287Q-19.841-55.078-19.185-55.078L-19.185-54.798M-17.855-56.281Q-17.855-56.623-17.720-56.922Q-17.585-57.221-17.346-57.445Q-17.106-57.669-16.789-57.794Q-16.471-57.919-16.139-57.919Q-15.695-57.919-15.295-57.703Q-14.895-57.488-14.661-57.110Q-14.427-56.733-14.427-56.281Q-14.427-55.940-14.569-55.656Q-14.710-55.372-14.955-55.165Q-15.199-54.959-15.509-54.844Q-15.818-54.730-16.139-54.730Q-16.570-54.730-16.971-54.931Q-17.373-55.133-17.614-55.485Q-17.855-55.837-17.855-56.281M-16.139-54.979Q-15.538-54.979-15.314-55.357Q-15.090-55.735-15.090-56.367Q-15.090-56.979-15.324-57.338Q-15.558-57.696-16.139-57.696Q-17.192-57.696-17.192-56.367Q-17.192-55.735-16.966-55.357Q-16.741-54.979-16.139-54.979M-13.258-55.632L-13.258-57.136Q-13.258-57.406-13.365-57.467Q-13.473-57.529-13.784-57.529L-13.784-57.809L-12.677-57.884L-12.677-55.652L-12.677-55.632Q-12.677-55.352-12.625-55.208Q-12.574-55.065-12.432-55.008Q-12.291-54.952-12.003-54.952Q-11.750-54.952-11.545-55.092Q-11.340-55.232-11.224-55.458Q-11.108-55.683-11.108-55.933L-11.108-57.136Q-11.108-57.406-11.216-57.467Q-11.323-57.529-11.634-57.529L-11.634-57.809L-10.527-57.884L-10.527-55.471Q-10.527-55.280-10.474-55.198Q-10.421-55.116-10.320-55.097Q-10.219-55.078-10.004-55.078L-10.004-54.798L-11.081-54.730L-11.081-55.294Q-11.190-55.112-11.335-54.989Q-11.480-54.866-11.667-54.798Q-11.853-54.730-12.055-54.730Q-13.258-54.730-13.258-55.632M-7.734-54.798L-9.368-54.798L-9.368-55.078Q-9.139-55.078-8.990-55.112Q-8.842-55.147-8.842-55.287L-8.842-57.136Q-8.842-57.406-8.949-57.467Q-9.057-57.529-9.368-57.529L-9.368-57.809L-8.309-57.884L-8.309-57.235Q-8.138-57.543-7.833-57.714Q-7.529-57.884-7.184-57.884Q-6.678-57.884-6.395-57.661Q-6.111-57.437-6.111-56.941L-6.111-55.287Q-6.111-55.150-5.962-55.114Q-5.813-55.078-5.588-55.078L-5.588-54.798L-7.218-54.798L-7.218-55.078Q-6.989-55.078-6.841-55.112Q-6.692-55.147-6.692-55.287L-6.692-56.927Q-6.692-57.262-6.811-57.462Q-6.931-57.662-7.246-57.662Q-7.516-57.662-7.750-57.526Q-7.984-57.389-8.122-57.155Q-8.261-56.921-8.261-56.647L-8.261-55.287Q-8.261-55.150-8.110-55.114Q-7.960-55.078-7.734-55.078L-7.734-54.798M-5-56.309Q-5-56.647-4.860-56.938Q-4.720-57.228-4.475-57.442Q-4.231-57.655-3.927-57.770Q-3.623-57.884-3.298-57.884Q-3.028-57.884-2.765-57.785Q-2.501-57.686-2.310-57.508L-2.310-58.906Q-2.310-59.176-2.418-59.238Q-2.525-59.299-2.836-59.299L-2.836-59.580L-1.760-59.655L-1.760-55.471Q-1.760-55.283-1.705-55.200Q-1.650-55.116-1.550-55.097Q-1.449-55.078-1.233-55.078L-1.233-54.798L-2.341-54.730L-2.341-55.147Q-2.758-54.730-3.383-54.730Q-3.814-54.730-4.186-54.942Q-4.559-55.153-4.780-55.514Q-5-55.875-5-56.309M-3.325-54.952Q-3.117-54.952-2.930-55.024Q-2.744-55.095-2.590-55.232Q-2.436-55.369-2.341-55.547L-2.341-57.156Q-2.426-57.303-2.572-57.423Q-2.717-57.543-2.886-57.602Q-3.055-57.662-3.236-57.662Q-3.797-57.662-4.065-57.273Q-4.333-56.883-4.333-56.302Q-4.333-55.731-4.099-55.341Q-3.865-54.952-3.325-54.952\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-37.688 66.45)\">\u003Cpath d=\"M4.124-55.946L2.080-55.946L2.080-56.227L4.411-59.399Q4.446-59.446 4.511-59.446L4.647-59.446Q4.692-59.446 4.719-59.419Q4.746-59.392 4.746-59.347L4.746-56.227L5.509-56.227L5.509-55.946L4.746-55.946L4.746-55.287Q4.746-55.078 5.502-55.078L5.502-54.798L3.369-54.798L3.369-55.078Q4.124-55.078 4.124-55.287L4.124-55.946M4.172-58.671L2.381-56.227L4.172-56.227\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M-21.25 41.942V24.87H8.627v17.072ZM8.627 24.87\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-21.25 41.942V24.87H8.627v17.072ZM8.627 24.87\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M11.471 41.942V24.87h29.876v17.072ZM41.347 24.87\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M11.471 41.942V24.87h29.876v17.072ZM41.347 24.87\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M44.192 41.942V24.87h29.875v17.072ZM74.067 24.87\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M44.192 41.942V24.87h29.875v17.072ZM74.067 24.87\"\u002F>\u003Cpath fill=\"var(--tk-soft-accent)\" stroke=\"none\" d=\"M76.912 41.942V24.87h29.876v17.072Zm29.876-17.072\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M76.912 41.942V24.87h29.876v17.072Zm29.876-17.072\"\u002F>\u003Cpath fill=\"var(--tk-soft-warn)\" stroke=\"none\" d=\"M109.633 41.942V24.87h29.875v17.072Zm29.875-17.072\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-warn)\" d=\"M109.633 41.942V24.87h29.875v17.072Zm29.875-17.072\" style=\"stroke-width:.8\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-37.688 90.635)\">\u003Cpath d=\"M-19.185-54.798L-20.921-54.798L-20.921-55.078Q-20.692-55.078-20.543-55.112Q-20.395-55.147-20.395-55.287L-20.395-57.136Q-20.395-57.406-20.502-57.467Q-20.610-57.529-20.921-57.529L-20.921-57.809L-19.892-57.884L-19.892-57.177Q-19.762-57.485-19.520-57.684Q-19.277-57.884-18.959-57.884Q-18.740-57.884-18.569-57.760Q-18.398-57.635-18.398-57.423Q-18.398-57.286-18.498-57.187Q-18.597-57.088-18.730-57.088Q-18.867-57.088-18.966-57.187Q-19.065-57.286-19.065-57.423Q-19.065-57.563-18.966-57.662Q-19.256-57.662-19.456-57.466Q-19.656-57.269-19.749-56.975Q-19.841-56.681-19.841-56.401L-19.841-55.287Q-19.841-55.078-19.185-55.078L-19.185-54.798M-17.855-56.281Q-17.855-56.623-17.720-56.922Q-17.585-57.221-17.346-57.445Q-17.106-57.669-16.789-57.794Q-16.471-57.919-16.139-57.919Q-15.695-57.919-15.295-57.703Q-14.895-57.488-14.661-57.110Q-14.427-56.733-14.427-56.281Q-14.427-55.940-14.569-55.656Q-14.710-55.372-14.955-55.165Q-15.199-54.959-15.509-54.844Q-15.818-54.730-16.139-54.730Q-16.570-54.730-16.971-54.931Q-17.373-55.133-17.614-55.485Q-17.855-55.837-17.855-56.281M-16.139-54.979Q-15.538-54.979-15.314-55.357Q-15.090-55.735-15.090-56.367Q-15.090-56.979-15.324-57.338Q-15.558-57.696-16.139-57.696Q-17.192-57.696-17.192-56.367Q-17.192-55.735-16.966-55.357Q-16.741-54.979-16.139-54.979M-13.258-55.632L-13.258-57.136Q-13.258-57.406-13.365-57.467Q-13.473-57.529-13.784-57.529L-13.784-57.809L-12.677-57.884L-12.677-55.652L-12.677-55.632Q-12.677-55.352-12.625-55.208Q-12.574-55.065-12.432-55.008Q-12.291-54.952-12.003-54.952Q-11.750-54.952-11.545-55.092Q-11.340-55.232-11.224-55.458Q-11.108-55.683-11.108-55.933L-11.108-57.136Q-11.108-57.406-11.216-57.467Q-11.323-57.529-11.634-57.529L-11.634-57.809L-10.527-57.884L-10.527-55.471Q-10.527-55.280-10.474-55.198Q-10.421-55.116-10.320-55.097Q-10.219-55.078-10.004-55.078L-10.004-54.798L-11.081-54.730L-11.081-55.294Q-11.190-55.112-11.335-54.989Q-11.480-54.866-11.667-54.798Q-11.853-54.730-12.055-54.730Q-13.258-54.730-13.258-55.632M-7.734-54.798L-9.368-54.798L-9.368-55.078Q-9.139-55.078-8.990-55.112Q-8.842-55.147-8.842-55.287L-8.842-57.136Q-8.842-57.406-8.949-57.467Q-9.057-57.529-9.368-57.529L-9.368-57.809L-8.309-57.884L-8.309-57.235Q-8.138-57.543-7.833-57.714Q-7.529-57.884-7.184-57.884Q-6.678-57.884-6.395-57.661Q-6.111-57.437-6.111-56.941L-6.111-55.287Q-6.111-55.150-5.962-55.114Q-5.813-55.078-5.588-55.078L-5.588-54.798L-7.218-54.798L-7.218-55.078Q-6.989-55.078-6.841-55.112Q-6.692-55.147-6.692-55.287L-6.692-56.927Q-6.692-57.262-6.811-57.462Q-6.931-57.662-7.246-57.662Q-7.516-57.662-7.750-57.526Q-7.984-57.389-8.122-57.155Q-8.261-56.921-8.261-56.647L-8.261-55.287Q-8.261-55.150-8.110-55.114Q-7.960-55.078-7.734-55.078L-7.734-54.798M-5-56.309Q-5-56.647-4.860-56.938Q-4.720-57.228-4.475-57.442Q-4.231-57.655-3.927-57.770Q-3.623-57.884-3.298-57.884Q-3.028-57.884-2.765-57.785Q-2.501-57.686-2.310-57.508L-2.310-58.906Q-2.310-59.176-2.418-59.238Q-2.525-59.299-2.836-59.299L-2.836-59.580L-1.760-59.655L-1.760-55.471Q-1.760-55.283-1.705-55.200Q-1.650-55.116-1.550-55.097Q-1.449-55.078-1.233-55.078L-1.233-54.798L-2.341-54.730L-2.341-55.147Q-2.758-54.730-3.383-54.730Q-3.814-54.730-4.186-54.942Q-4.559-55.153-4.780-55.514Q-5-55.875-5-56.309M-3.325-54.952Q-3.117-54.952-2.930-55.024Q-2.744-55.095-2.590-55.232Q-2.436-55.369-2.341-55.547L-2.341-57.156Q-2.426-57.303-2.572-57.423Q-2.717-57.543-2.886-57.602Q-3.055-57.662-3.236-57.662Q-3.797-57.662-4.065-57.273Q-4.333-56.883-4.333-56.302Q-4.333-55.731-4.099-55.341Q-3.865-54.952-3.325-54.952\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-37.688 90.635)\">\u003Cpath d=\"M2.614-55.560L2.583-55.560Q2.720-55.263 3.017-55.087Q3.314-54.911 3.642-54.911Q4.005-54.911 4.232-55.089Q4.459-55.266 4.553-55.555Q4.647-55.844 4.647-56.206Q4.647-56.521 4.593-56.806Q4.538-57.091 4.365-57.297Q4.193-57.502 3.878-57.502Q3.605-57.502 3.422-57.435Q3.239-57.368 3.135-57.279Q3.031-57.191 2.935-57.081Q2.839-56.972 2.795-56.962L2.716-56.962Q2.644-56.979 2.627-57.050L2.627-59.368Q2.627-59.402 2.651-59.424Q2.675-59.446 2.709-59.446L2.737-59.446Q3.024-59.330 3.292-59.276Q3.560-59.221 3.837-59.221Q4.114-59.221 4.384-59.276Q4.654-59.330 4.934-59.446L4.958-59.446Q4.993-59.446 5.016-59.423Q5.040-59.399 5.040-59.368L5.040-59.299Q5.040-59.272 5.020-59.252Q4.746-58.937 4.362-58.761Q3.977-58.585 3.564-58.585Q3.225-58.585 2.908-58.671L2.908-57.389Q3.304-57.724 3.878-57.724Q4.282-57.724 4.618-57.514Q4.955-57.303 5.148-56.951Q5.341-56.599 5.341-56.199Q5.341-55.868 5.201-55.582Q5.061-55.297 4.817-55.087Q4.572-54.877 4.270-54.767Q3.967-54.658 3.649-54.658Q3.290-54.658 2.964-54.822Q2.638-54.986 2.443-55.278Q2.248-55.570 2.248-55.933Q2.248-56.083 2.354-56.189Q2.460-56.295 2.614-56.295Q2.767-56.295 2.872-56.191Q2.976-56.087 2.976-55.933Q2.976-55.776 2.872-55.668Q2.767-55.560 2.614-55.560\" 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(177.095 42.264)\">\u003Cpath d=\"M-20.408-55.639L-20.408-57.536L-21.047-57.536L-21.047-57.758Q-20.729-57.758-20.512-57.968Q-20.295-58.178-20.195-58.488Q-20.094-58.797-20.094-59.105L-19.827-59.105L-19.827-57.816L-18.750-57.816L-18.750-57.536L-19.827-57.536L-19.827-55.652Q-19.827-55.376-19.723-55.177Q-19.619-54.979-19.359-54.979Q-19.202-54.979-19.096-55.083Q-18.990-55.188-18.940-55.341Q-18.891-55.495-18.891-55.652L-18.891-56.066L-18.624-56.066L-18.624-55.639Q-18.624-55.413-18.723-55.203Q-18.822-54.993-19.007-54.861Q-19.191-54.730-19.420-54.730Q-19.858-54.730-20.133-54.967Q-20.408-55.205-20.408-55.639M-17.855-56.333Q-17.855-56.654-17.730-56.943Q-17.605-57.232-17.380-57.455Q-17.154-57.679-16.859-57.799Q-16.563-57.919-16.245-57.919Q-15.917-57.919-15.655-57.819Q-15.394-57.720-15.218-57.538Q-15.042-57.355-14.948-57.097Q-14.854-56.839-14.854-56.507Q-14.854-56.415-14.936-56.394L-17.192-56.394L-17.192-56.333Q-17.192-55.745-16.908-55.362Q-16.624-54.979-16.057-54.979Q-15.736-54.979-15.468-55.172Q-15.199-55.365-15.110-55.680Q-15.103-55.721-15.028-55.735L-14.936-55.735Q-14.854-55.711-14.854-55.639Q-14.854-55.632-14.861-55.605Q-14.974-55.208-15.344-54.969Q-15.715-54.730-16.139-54.730Q-16.577-54.730-16.977-54.938Q-17.376-55.147-17.616-55.514Q-17.855-55.881-17.855-56.333M-17.185-56.603L-15.370-56.603Q-15.370-56.880-15.468-57.132Q-15.565-57.385-15.763-57.541Q-15.961-57.696-16.245-57.696Q-16.522-57.696-16.736-57.538Q-16.949-57.379-17.067-57.124Q-17.185-56.869-17.185-56.603M-14.266-54.805L-14.266-55.868Q-14.266-55.892-14.239-55.919Q-14.211-55.946-14.187-55.946L-14.078-55.946Q-14.013-55.946-13.999-55.888Q-13.904-55.454-13.658-55.203Q-13.412-54.952-12.998-54.952Q-12.656-54.952-12.403-55.085Q-12.150-55.218-12.150-55.526Q-12.150-55.683-12.244-55.798Q-12.338-55.912-12.477-55.981Q-12.615-56.049-12.783-56.087L-13.364-56.186Q-13.719-56.254-13.993-56.475Q-14.266-56.695-14.266-57.037Q-14.266-57.286-14.155-57.461Q-14.044-57.635-13.858-57.734Q-13.671-57.833-13.456-57.876Q-13.241-57.919-12.998-57.919Q-12.584-57.919-12.304-57.737L-12.089-57.912Q-12.079-57.915-12.072-57.917Q-12.065-57.919-12.055-57.919L-12.003-57.919Q-11.976-57.919-11.952-57.895Q-11.928-57.871-11.928-57.843L-11.928-56.996Q-11.928-56.975-11.952-56.948Q-11.976-56.921-12.003-56.921L-12.116-56.921Q-12.144-56.921-12.169-56.946Q-12.195-56.972-12.195-56.996Q-12.195-57.232-12.301-57.396Q-12.407-57.560-12.590-57.642Q-12.772-57.724-13.005-57.724Q-13.333-57.724-13.589-57.621Q-13.846-57.519-13.846-57.242Q-13.846-57.047-13.663-56.938Q-13.480-56.828-13.251-56.787L-12.677-56.681Q-12.431-56.633-12.217-56.505Q-12.003-56.377-11.867-56.174Q-11.730-55.970-11.730-55.721Q-11.730-55.208-12.096-54.969Q-12.461-54.730-12.998-54.730Q-13.494-54.730-13.825-55.024L-14.092-54.750Q-14.112-54.730-14.140-54.730L-14.187-54.730Q-14.211-54.730-14.239-54.757Q-14.266-54.784-14.266-54.805M-10.575-55.639L-10.575-57.536L-11.214-57.536L-11.214-57.758Q-10.896-57.758-10.679-57.968Q-10.462-58.178-10.361-58.488Q-10.260-58.797-10.260-59.105L-9.994-59.105L-9.994-57.816L-8.917-57.816L-8.917-57.536L-9.994-57.536L-9.994-55.652Q-9.994-55.376-9.889-55.177Q-9.785-54.979-9.525-54.979Q-9.368-54.979-9.262-55.083Q-9.156-55.188-9.107-55.341Q-9.057-55.495-9.057-55.652L-9.057-56.066L-8.791-56.066L-8.791-55.639Q-8.791-55.413-8.890-55.203Q-8.989-54.993-9.173-54.861Q-9.358-54.730-9.587-54.730Q-10.024-54.730-10.300-54.967Q-10.575-55.205-10.575-55.639\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(177.095 42.264)\">\u003Cpath d=\"M-3.469-54.798L-5.202-54.798L-5.202-55.078Q-4.976-55.078-4.827-55.112Q-4.679-55.147-4.679-55.287L-4.679-57.536L-5.267-57.536L-5.267-57.816L-4.679-57.816L-4.679-58.633Q-4.679-58.951-4.501-59.199Q-4.323-59.446-4.033-59.587Q-3.742-59.727-3.431-59.727Q-3.175-59.727-2.971-59.585Q-2.768-59.443-2.768-59.200Q-2.768-59.064-2.867-58.965Q-2.966-58.865-3.103-58.865Q-3.240-58.865-3.339-58.965Q-3.438-59.064-3.438-59.200Q-3.438-59.381-3.298-59.474Q-3.376-59.501-3.476-59.501Q-3.684-59.501-3.838-59.368Q-3.992-59.235-4.072-59.031Q-4.152-58.828-4.152-58.619L-4.152-57.816L-3.264-57.816L-3.264-57.536L-4.125-57.536L-4.125-55.287Q-4.125-55.078-3.469-55.078L-3.469-54.798M-2.830-56.281Q-2.830-56.623-2.695-56.922Q-2.560-57.221-2.320-57.445Q-2.081-57.669-1.763-57.794Q-1.445-57.919-1.114-57.919Q-0.669-57.919-0.269-57.703Q0.130-57.488 0.365-57.110Q0.599-56.733 0.599-56.281Q0.599-55.940 0.457-55.656Q0.315-55.372 0.071-55.165Q-0.174-54.959-0.483-54.844Q-0.792-54.730-1.114-54.730Q-1.544-54.730-1.946-54.931Q-2.348-55.133-2.589-55.485Q-2.830-55.837-2.830-56.281M-1.114-54.979Q-0.512-54.979-0.288-55.357Q-0.064-55.735-0.064-56.367Q-0.064-56.979-0.299-57.338Q-0.533-57.696-1.114-57.696Q-2.166-57.696-2.166-56.367Q-2.166-55.735-1.941-55.357Q-1.715-54.979-1.114-54.979M2.861-54.798L1.258-54.798L1.258-55.078Q1.484-55.078 1.633-55.112Q1.781-55.147 1.781-55.287L1.781-58.906Q1.781-59.176 1.674-59.238Q1.566-59.299 1.258-59.299L1.258-59.580L2.335-59.655L2.335-55.287Q2.335-55.150 2.485-55.114Q2.636-55.078 2.861-55.078L2.861-54.798M3.456-56.309Q3.456-56.647 3.596-56.938Q3.736-57.228 3.981-57.442Q4.225-57.655 4.529-57.770Q4.834-57.884 5.158-57.884Q5.428-57.884 5.691-57.785Q5.955-57.686 6.146-57.508L6.146-58.906Q6.146-59.176 6.038-59.238Q5.931-59.299 5.620-59.299L5.620-59.580L6.696-59.655L6.696-55.471Q6.696-55.283 6.751-55.200Q6.806-55.116 6.907-55.097Q7.007-55.078 7.223-55.078L7.223-54.798L6.115-54.730L6.115-55.147Q5.698-54.730 5.073-54.730Q4.642-54.730 4.270-54.942Q3.897-55.153 3.677-55.514Q3.456-55.875 3.456-56.309M5.131-54.952Q5.339-54.952 5.526-55.024Q5.712-55.095 5.866-55.232Q6.020-55.369 6.115-55.547L6.115-57.156Q6.030-57.303 5.885-57.423Q5.739-57.543 5.570-57.602Q5.401-57.662 5.220-57.662Q4.659-57.662 4.391-57.273Q4.123-56.883 4.123-56.302Q4.123-55.731 4.357-55.341Q4.591-54.952 5.131-54.952\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(177.095 55.81)\">\u003Cpath d=\"M-20.408-55.639L-20.408-57.536L-21.047-57.536L-21.047-57.758Q-20.729-57.758-20.512-57.968Q-20.295-58.178-20.195-58.488Q-20.094-58.797-20.094-59.105L-19.827-59.105L-19.827-57.816L-18.750-57.816L-18.750-57.536L-19.827-57.536L-19.827-55.652Q-19.827-55.376-19.723-55.177Q-19.619-54.979-19.359-54.979Q-19.202-54.979-19.096-55.083Q-18.990-55.188-18.940-55.341Q-18.891-55.495-18.891-55.652L-18.891-56.066L-18.624-56.066L-18.624-55.639Q-18.624-55.413-18.723-55.203Q-18.822-54.993-19.007-54.861Q-19.191-54.730-19.420-54.730Q-19.858-54.730-20.133-54.967Q-20.408-55.205-20.408-55.639M-16.064-54.798L-17.800-54.798L-17.800-55.078Q-17.571-55.078-17.423-55.112Q-17.274-55.147-17.274-55.287L-17.274-57.136Q-17.274-57.406-17.382-57.467Q-17.489-57.529-17.800-57.529L-17.800-57.809L-16.771-57.884L-16.771-57.177Q-16.642-57.485-16.399-57.684Q-16.156-57.884-15.838-57.884Q-15.620-57.884-15.449-57.760Q-15.278-57.635-15.278-57.423Q-15.278-57.286-15.377-57.187Q-15.476-57.088-15.609-57.088Q-15.746-57.088-15.845-57.187Q-15.944-57.286-15.944-57.423Q-15.944-57.563-15.845-57.662Q-16.136-57.662-16.336-57.466Q-16.536-57.269-16.628-56.975Q-16.720-56.681-16.720-56.401L-16.720-55.287Q-16.720-55.078-16.064-55.078L-16.064-54.798M-14.635-55.526Q-14.635-55.858-14.411-56.085Q-14.187-56.312-13.844-56.440Q-13.500-56.569-13.128-56.621Q-12.755-56.674-12.451-56.674L-12.451-56.927Q-12.451-57.132-12.559-57.312Q-12.666-57.491-12.848-57.594Q-13.029-57.696-13.237-57.696Q-13.644-57.696-13.880-57.604Q-13.791-57.567-13.745-57.483Q-13.699-57.399-13.699-57.297Q-13.699-57.201-13.745-57.122Q-13.791-57.044-13.871-56.999Q-13.952-56.955-14.041-56.955Q-14.191-56.955-14.292-57.052Q-14.393-57.150-14.393-57.297Q-14.393-57.919-13.237-57.919Q-13.025-57.919-12.776-57.855Q-12.526-57.792-12.325-57.673Q-12.123-57.553-11.997-57.368Q-11.870-57.184-11.870-56.941L-11.870-55.365Q-11.870-55.249-11.809-55.153Q-11.747-55.058-11.634-55.058Q-11.525-55.058-11.460-55.152Q-11.395-55.246-11.395-55.365L-11.395-55.813L-11.128-55.813L-11.128-55.365Q-11.128-55.095-11.356-54.930Q-11.583-54.764-11.863-54.764Q-12.072-54.764-12.208-54.918Q-12.345-55.071-12.369-55.287Q-12.516-55.020-12.798-54.875Q-13.080-54.730-13.405-54.730Q-13.682-54.730-13.965-54.805Q-14.249-54.880-14.442-55.059Q-14.635-55.239-14.635-55.526M-14.020-55.526Q-14.020-55.352-13.919-55.222Q-13.818-55.092-13.663-55.022Q-13.507-54.952-13.343-54.952Q-13.124-54.952-12.916-55.049Q-12.707-55.147-12.579-55.328Q-12.451-55.509-12.451-55.735L-12.451-56.463Q-12.776-56.463-13.142-56.372Q-13.507-56.281-13.764-56.069Q-14.020-55.858-14.020-55.526M-9.095-54.798L-10.646-54.798L-10.646-55.078Q-10.421-55.078-10.272-55.112Q-10.124-55.147-10.124-55.287L-10.124-57.136Q-10.124-57.324-10.171-57.408Q-10.219-57.491-10.317-57.510Q-10.414-57.529-10.626-57.529L-10.626-57.809L-9.570-57.884L-9.570-55.287Q-9.570-55.147-9.438-55.112Q-9.307-55.078-9.095-55.078L-9.095-54.798M-10.366-59.105Q-10.366-59.276-10.243-59.395Q-10.120-59.515-9.949-59.515Q-9.782-59.515-9.659-59.395Q-9.536-59.276-9.536-59.105Q-9.536-58.930-9.659-58.807Q-9.782-58.684-9.949-58.684Q-10.120-58.684-10.243-58.807Q-10.366-58.930-10.366-59.105M-6.767-54.798L-8.401-54.798L-8.401-55.078Q-8.172-55.078-8.023-55.112Q-7.874-55.147-7.874-55.287L-7.874-57.136Q-7.874-57.406-7.982-57.467Q-8.090-57.529-8.401-57.529L-8.401-57.809L-7.341-57.884L-7.341-57.235Q-7.170-57.543-6.866-57.714Q-6.562-57.884-6.217-57.884Q-5.711-57.884-5.427-57.661Q-5.144-57.437-5.144-56.941L-5.144-55.287Q-5.144-55.150-4.995-55.114Q-4.846-55.078-4.621-55.078L-4.621-54.798L-6.251-54.798L-6.251-55.078Q-6.022-55.078-5.873-55.112Q-5.725-55.147-5.725-55.287L-5.725-56.927Q-5.725-57.262-5.844-57.462Q-5.964-57.662-6.278-57.662Q-6.548-57.662-6.782-57.526Q-7.017-57.389-7.155-57.155Q-7.293-56.921-7.293-56.647L-7.293-55.287Q-7.293-55.150-7.143-55.114Q-6.993-55.078-6.767-55.078L-6.767-54.798M-2.416-54.798L-3.968-54.798L-3.968-55.078Q-3.742-55.078-3.593-55.112Q-3.445-55.147-3.445-55.287L-3.445-57.136Q-3.445-57.324-3.493-57.408Q-3.541-57.491-3.638-57.510Q-3.735-57.529-3.947-57.529L-3.947-57.809L-2.891-57.884L-2.891-55.287Q-2.891-55.147-2.759-55.112Q-2.628-55.078-2.416-55.078L-2.416-54.798M-3.687-59.105Q-3.687-59.276-3.564-59.395Q-3.441-59.515-3.270-59.515Q-3.103-59.515-2.980-59.395Q-2.857-59.276-2.857-59.105Q-2.857-58.930-2.980-58.807Q-3.103-58.684-3.270-58.684Q-3.441-58.684-3.564-58.807Q-3.687-58.930-3.687-59.105M-0.088-54.798L-1.722-54.798L-1.722-55.078Q-1.493-55.078-1.344-55.112Q-1.196-55.147-1.196-55.287L-1.196-57.136Q-1.196-57.406-1.303-57.467Q-1.411-57.529-1.722-57.529L-1.722-57.809L-0.663-57.884L-0.663-57.235Q-0.492-57.543-0.187-57.714Q0.117-57.884 0.462-57.884Q0.968-57.884 1.251-57.661Q1.535-57.437 1.535-56.941L1.535-55.287Q1.535-55.150 1.684-55.114Q1.833-55.078 2.058-55.078L2.058-54.798L0.428-54.798L0.428-55.078Q0.657-55.078 0.805-55.112Q0.954-55.147 0.954-55.287L0.954-56.927Q0.954-57.262 0.834-57.462Q0.715-57.662 0.400-57.662Q0.130-57.662-0.104-57.526Q-0.338-57.389-0.476-57.155Q-0.615-56.921-0.615-56.647L-0.615-55.287Q-0.615-55.150-0.464-55.114Q-0.314-55.078-0.088-55.078L-0.088-54.798M2.605-54.265Q2.605-54.511 2.802-54.695Q2.998-54.880 3.254-54.959Q3.118-55.071 3.046-55.232Q2.974-55.393 2.974-55.574Q2.974-55.895 3.186-56.141Q2.851-56.439 2.851-56.849Q2.851-57.310 3.241-57.597Q3.630-57.884 4.109-57.884Q4.581-57.884 4.916-57.638Q5.090-57.792 5.300-57.874Q5.510-57.956 5.739-57.956Q5.903-57.956 6.025-57.849Q6.146-57.741 6.146-57.577Q6.146-57.481 6.074-57.409Q6.002-57.338 5.910-57.338Q5.811-57.338 5.741-57.411Q5.671-57.485 5.671-57.584Q5.671-57.638 5.685-57.669L5.691-57.683Q5.698-57.703 5.707-57.714Q5.715-57.724 5.719-57.731Q5.363-57.731 5.076-57.508Q5.363-57.215 5.363-56.849Q5.363-56.534 5.179-56.302Q4.994-56.069 4.705-55.941Q4.417-55.813 4.109-55.813Q3.907-55.813 3.716-55.863Q3.524-55.912 3.347-56.022Q3.254-55.895 3.254-55.752Q3.254-55.570 3.383-55.435Q3.511-55.300 3.695-55.300L4.328-55.300Q4.775-55.300 5.145-55.229Q5.514-55.157 5.773-54.928Q6.033-54.699 6.033-54.265Q6.033-53.944 5.738-53.742Q5.442-53.540 5.039-53.451Q4.635-53.362 4.321-53.362Q4.003-53.362 3.600-53.451Q3.196-53.540 2.901-53.742Q2.605-53.944 2.605-54.265M3.060-54.265Q3.060-54.036 3.278-53.887Q3.497-53.738 3.789-53.670Q4.082-53.602 4.321-53.602Q4.485-53.602 4.693-53.638Q4.902-53.673 5.109-53.754Q5.315-53.834 5.447-53.962Q5.579-54.090 5.579-54.265Q5.579-54.617 5.198-54.711Q4.816-54.805 4.314-54.805L3.695-54.805Q3.456-54.805 3.258-54.654Q3.060-54.504 3.060-54.265M4.109-56.052Q4.775-56.052 4.775-56.849Q4.775-57.649 4.109-57.649Q3.439-57.649 3.439-56.849Q3.439-56.052 4.109-56.052\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(177.095 55.81)\">\u003Cpath d=\"M11.141-54.798L9.408-54.798L9.408-55.078Q9.634-55.078 9.783-55.112Q9.931-55.147 9.931-55.287L9.931-57.536L9.343-57.536L9.343-57.816L9.931-57.816L9.931-58.633Q9.931-58.951 10.109-59.199Q10.287-59.446 10.577-59.587Q10.868-59.727 11.179-59.727Q11.435-59.727 11.639-59.585Q11.842-59.443 11.842-59.200Q11.842-59.064 11.743-58.965Q11.644-58.865 11.507-58.865Q11.370-58.865 11.271-58.965Q11.172-59.064 11.172-59.200Q11.172-59.381 11.312-59.474Q11.234-59.501 11.134-59.501Q10.926-59.501 10.772-59.368Q10.618-59.235 10.538-59.031Q10.458-58.828 10.458-58.619L10.458-57.816L11.346-57.816L11.346-57.536L10.485-57.536L10.485-55.287Q10.485-55.078 11.141-55.078L11.141-54.798M11.780-56.281Q11.780-56.623 11.915-56.922Q12.050-57.221 12.290-57.445Q12.529-57.669 12.847-57.794Q13.165-57.919 13.496-57.919Q13.941-57.919 14.341-57.703Q14.740-57.488 14.975-57.110Q15.209-56.733 15.209-56.281Q15.209-55.940 15.067-55.656Q14.925-55.372 14.681-55.165Q14.436-54.959 14.127-54.844Q13.818-54.730 13.496-54.730Q13.066-54.730 12.664-54.931Q12.262-55.133 12.021-55.485Q11.780-55.837 11.780-56.281M13.496-54.979Q14.098-54.979 14.322-55.357Q14.546-55.735 14.546-56.367Q14.546-56.979 14.311-57.338Q14.077-57.696 13.496-57.696Q12.444-57.696 12.444-56.367Q12.444-55.735 12.669-55.357Q12.895-54.979 13.496-54.979M17.471-54.798L15.868-54.798L15.868-55.078Q16.094-55.078 16.243-55.112Q16.391-55.147 16.391-55.287L16.391-58.906Q16.391-59.176 16.284-59.238Q16.176-59.299 15.868-59.299L15.868-59.580L16.945-59.655L16.945-55.287Q16.945-55.150 17.095-55.114Q17.246-55.078 17.471-55.078L17.471-54.798M18.066-56.309Q18.066-56.647 18.206-56.938Q18.346-57.228 18.591-57.442Q18.835-57.655 19.139-57.770Q19.444-57.884 19.768-57.884Q20.038-57.884 20.301-57.785Q20.565-57.686 20.756-57.508L20.756-58.906Q20.756-59.176 20.648-59.238Q20.541-59.299 20.230-59.299L20.230-59.580L21.306-59.655L21.306-55.471Q21.306-55.283 21.361-55.200Q21.416-55.116 21.517-55.097Q21.617-55.078 21.833-55.078L21.833-54.798L20.725-54.730L20.725-55.147Q20.308-54.730 19.683-54.730Q19.252-54.730 18.880-54.942Q18.507-55.153 18.287-55.514Q18.066-55.875 18.066-56.309M19.741-54.952Q19.949-54.952 20.136-55.024Q20.322-55.095 20.476-55.232Q20.630-55.369 20.725-55.547L20.725-57.156Q20.640-57.303 20.495-57.423Q20.349-57.543 20.180-57.602Q20.011-57.662 19.830-57.662Q19.269-57.662 19.001-57.273Q18.733-56.883 18.733-56.302Q18.733-55.731 18.967-55.341Q19.201-54.952 19.741-54.952M22.482-54.805L22.482-55.868Q22.482-55.892 22.509-55.919Q22.537-55.946 22.561-55.946L22.670-55.946Q22.735-55.946 22.749-55.888Q22.844-55.454 23.091-55.203Q23.337-54.952 23.750-54.952Q24.092-54.952 24.345-55.085Q24.598-55.218 24.598-55.526Q24.598-55.683 24.504-55.798Q24.410-55.912 24.271-55.981Q24.133-56.049 23.966-56.087L23.384-56.186Q23.029-56.254 22.756-56.475Q22.482-56.695 22.482-57.037Q22.482-57.286 22.593-57.461Q22.704-57.635 22.891-57.734Q23.077-57.833 23.292-57.876Q23.508-57.919 23.750-57.919Q24.164-57.919 24.444-57.737L24.659-57.912Q24.670-57.915 24.676-57.917Q24.683-57.919 24.694-57.919L24.745-57.919Q24.772-57.919 24.796-57.895Q24.820-57.871 24.820-57.843L24.820-56.996Q24.820-56.975 24.796-56.948Q24.772-56.921 24.745-56.921L24.632-56.921Q24.605-56.921 24.579-56.946Q24.553-56.972 24.553-56.996Q24.553-57.232 24.447-57.396Q24.341-57.560 24.159-57.642Q23.976-57.724 23.743-57.724Q23.415-57.724 23.159-57.621Q22.903-57.519 22.903-57.242Q22.903-57.047 23.085-56.938Q23.268-56.828 23.497-56.787L24.071-56.681Q24.318-56.633 24.531-56.505Q24.745-56.377 24.882-56.174Q25.018-55.970 25.018-55.721Q25.018-55.208 24.653-54.969Q24.287-54.730 23.750-54.730Q23.255-54.730 22.923-55.024L22.656-54.750Q22.636-54.730 22.609-54.730L22.561-54.730Q22.537-54.730 22.509-54.757Q22.482-54.784 22.482-54.805\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">Five-fold cross-validation. In each round a different fold (dark) is the held-out test set and the rest (light) is training data; the five test scores are averaged so every example is tested exactly once.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:373.359px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 280.019 121.639\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cpath fill=\"none\" d=\"M-42.579 37.002H137.52\"\u002F>\u003Cpath stroke=\"none\" d=\"m139.519 37.002-3.2-1.6 1.2 1.6-1.2 1.6\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(185.63 1.674)\">\u003Cpath d=\"M-41.738 36.161L-41.738 34.264L-42.377 34.264L-42.377 34.042Q-42.059 34.042-41.842 33.832Q-41.625 33.622-41.525 33.312Q-41.424 33.003-41.424 32.695L-41.157 32.695L-41.157 33.984L-40.080 33.984L-40.080 34.264L-41.157 34.264L-41.157 36.148Q-41.157 36.424-41.053 36.623Q-40.949 36.821-40.689 36.821Q-40.532 36.821-40.426 36.717Q-40.320 36.612-40.270 36.459Q-40.221 36.305-40.221 36.148L-40.221 35.734L-39.954 35.734L-39.954 36.161Q-39.954 36.387-40.053 36.597Q-40.152 36.807-40.337 36.939Q-40.521 37.070-40.750 37.070Q-41.188 37.070-41.463 36.833Q-41.738 36.595-41.738 36.161M-37.394 37.002L-39.130 37.002L-39.130 36.722Q-38.901 36.722-38.753 36.688Q-38.604 36.653-38.604 36.513L-38.604 34.664Q-38.604 34.394-38.712 34.333Q-38.819 34.271-39.130 34.271L-39.130 33.991L-38.101 33.916L-38.101 34.623Q-37.972 34.315-37.729 34.116Q-37.486 33.916-37.168 33.916Q-36.950 33.916-36.779 34.040Q-36.608 34.165-36.608 34.377Q-36.608 34.514-36.707 34.613Q-36.806 34.712-36.939 34.712Q-37.076 34.712-37.175 34.613Q-37.274 34.514-37.274 34.377Q-37.274 34.237-37.175 34.138Q-37.466 34.138-37.666 34.334Q-37.866 34.531-37.958 34.825Q-38.050 35.119-38.050 35.399L-38.050 36.513Q-38.050 36.722-37.394 36.722L-37.394 37.002M-35.965 36.274Q-35.965 35.942-35.741 35.715Q-35.517 35.488-35.174 35.360Q-34.830 35.231-34.458 35.179Q-34.085 35.126-33.781 35.126L-33.781 34.873Q-33.781 34.668-33.889 34.488Q-33.996 34.309-34.178 34.206Q-34.359 34.104-34.567 34.104Q-34.974 34.104-35.210 34.196Q-35.121 34.233-35.075 34.317Q-35.029 34.401-35.029 34.503Q-35.029 34.599-35.075 34.678Q-35.121 34.756-35.201 34.801Q-35.282 34.845-35.371 34.845Q-35.521 34.845-35.622 34.748Q-35.723 34.650-35.723 34.503Q-35.723 33.881-34.567 33.881Q-34.355 33.881-34.106 33.945Q-33.856 34.008-33.655 34.127Q-33.453 34.247-33.327 34.432Q-33.200 34.616-33.200 34.859L-33.200 36.435Q-33.200 36.551-33.139 36.647Q-33.077 36.742-32.964 36.742Q-32.855 36.742-32.790 36.648Q-32.725 36.554-32.725 36.435L-32.725 35.987L-32.458 35.987L-32.458 36.435Q-32.458 36.705-32.686 36.870Q-32.913 37.036-33.193 37.036Q-33.402 37.036-33.538 36.882Q-33.675 36.729-33.699 36.513Q-33.846 36.780-34.128 36.925Q-34.410 37.070-34.735 37.070Q-35.012 37.070-35.295 36.995Q-35.579 36.920-35.772 36.741Q-35.965 36.561-35.965 36.274M-35.350 36.274Q-35.350 36.448-35.249 36.578Q-35.148 36.708-34.993 36.778Q-34.837 36.848-34.673 36.848Q-34.454 36.848-34.246 36.751Q-34.037 36.653-33.909 36.472Q-33.781 36.291-33.781 36.065L-33.781 35.337Q-34.106 35.337-34.472 35.428Q-34.837 35.519-35.094 35.731Q-35.350 35.942-35.350 36.274M-30.425 37.002L-31.976 37.002L-31.976 36.722Q-31.751 36.722-31.602 36.688Q-31.454 36.653-31.454 36.513L-31.454 34.664Q-31.454 34.476-31.501 34.392Q-31.549 34.309-31.647 34.290Q-31.744 34.271-31.956 34.271L-31.956 33.991L-30.900 33.916L-30.900 36.513Q-30.900 36.653-30.768 36.688Q-30.637 36.722-30.425 36.722L-30.425 37.002M-31.696 32.695Q-31.696 32.524-31.573 32.405Q-31.450 32.285-31.279 32.285Q-31.112 32.285-30.989 32.405Q-30.866 32.524-30.866 32.695Q-30.866 32.870-30.989 32.993Q-31.112 33.116-31.279 33.116Q-31.450 33.116-31.573 32.993Q-31.696 32.870-31.696 32.695M-28.097 37.002L-29.731 37.002L-29.731 36.722Q-29.502 36.722-29.353 36.688Q-29.204 36.653-29.204 36.513L-29.204 34.664Q-29.204 34.394-29.312 34.333Q-29.420 34.271-29.731 34.271L-29.731 33.991L-28.671 33.916L-28.671 34.565Q-28.500 34.257-28.196 34.086Q-27.892 33.916-27.547 33.916Q-27.041 33.916-26.757 34.139Q-26.474 34.363-26.474 34.859L-26.474 36.513Q-26.474 36.650-26.325 36.686Q-26.176 36.722-25.951 36.722L-25.951 37.002L-27.581 37.002L-27.581 36.722Q-27.352 36.722-27.203 36.688Q-27.055 36.653-27.055 36.513L-27.055 34.873Q-27.055 34.538-27.174 34.338Q-27.294 34.138-27.608 34.138Q-27.878 34.138-28.112 34.274Q-28.347 34.411-28.485 34.645Q-28.623 34.879-28.623 35.153L-28.623 36.513Q-28.623 36.650-28.473 36.686Q-28.323 36.722-28.097 36.722L-28.097 37.002M-23.746 37.002L-25.298 37.002L-25.298 36.722Q-25.072 36.722-24.923 36.688Q-24.775 36.653-24.775 36.513L-24.775 34.664Q-24.775 34.476-24.823 34.392Q-24.871 34.309-24.968 34.290Q-25.065 34.271-25.277 34.271L-25.277 33.991L-24.221 33.916L-24.221 36.513Q-24.221 36.653-24.089 36.688Q-23.958 36.722-23.746 36.722L-23.746 37.002M-25.017 32.695Q-25.017 32.524-24.894 32.405Q-24.771 32.285-24.600 32.285Q-24.433 32.285-24.310 32.405Q-24.187 32.524-24.187 32.695Q-24.187 32.870-24.310 32.993Q-24.433 33.116-24.600 33.116Q-24.771 33.116-24.894 32.993Q-25.017 32.870-25.017 32.695M-21.418 37.002L-23.052 37.002L-23.052 36.722Q-22.823 36.722-22.674 36.688Q-22.526 36.653-22.526 36.513L-22.526 34.664Q-22.526 34.394-22.633 34.333Q-22.741 34.271-23.052 34.271L-23.052 33.991L-21.993 33.916L-21.993 34.565Q-21.822 34.257-21.517 34.086Q-21.213 33.916-20.868 33.916Q-20.362 33.916-20.079 34.139Q-19.795 34.363-19.795 34.859L-19.795 36.513Q-19.795 36.650-19.646 36.686Q-19.497 36.722-19.272 36.722L-19.272 37.002L-20.902 37.002L-20.902 36.722Q-20.673 36.722-20.525 36.688Q-20.376 36.653-20.376 36.513L-20.376 34.873Q-20.376 34.538-20.496 34.338Q-20.615 34.138-20.930 34.138Q-21.200 34.138-21.434 34.274Q-21.668 34.411-21.806 34.645Q-21.945 34.879-21.945 35.153L-21.945 36.513Q-21.945 36.650-21.794 36.686Q-21.644 36.722-21.418 36.722L-21.418 37.002M-18.725 37.535Q-18.725 37.289-18.528 37.105Q-18.332 36.920-18.076 36.841Q-18.212 36.729-18.284 36.568Q-18.356 36.407-18.356 36.226Q-18.356 35.905-18.144 35.659Q-18.479 35.361-18.479 34.951Q-18.479 34.490-18.089 34.203Q-17.700 33.916-17.221 33.916Q-16.749 33.916-16.414 34.162Q-16.240 34.008-16.030 33.926Q-15.820 33.844-15.591 33.844Q-15.427 33.844-15.305 33.951Q-15.184 34.059-15.184 34.223Q-15.184 34.319-15.256 34.391Q-15.328 34.462-15.420 34.462Q-15.519 34.462-15.589 34.389Q-15.659 34.315-15.659 34.216Q-15.659 34.162-15.645 34.131L-15.639 34.117Q-15.632 34.097-15.623 34.086Q-15.615 34.076-15.611 34.069Q-15.967 34.069-16.254 34.292Q-15.967 34.585-15.967 34.951Q-15.967 35.266-16.151 35.498Q-16.336 35.731-16.625 35.859Q-16.913 35.987-17.221 35.987Q-17.423 35.987-17.614 35.937Q-17.806 35.888-17.983 35.778Q-18.076 35.905-18.076 36.048Q-18.076 36.230-17.947 36.365Q-17.819 36.500-17.635 36.500L-17.002 36.500Q-16.555 36.500-16.185 36.571Q-15.816 36.643-15.557 36.872Q-15.297 37.101-15.297 37.535Q-15.297 37.856-15.592 38.058Q-15.888 38.260-16.291 38.349Q-16.695 38.438-17.009 38.438Q-17.327 38.438-17.730 38.349Q-18.134 38.260-18.429 38.058Q-18.725 37.856-18.725 37.535M-18.270 37.535Q-18.270 37.764-18.052 37.913Q-17.833 38.062-17.541 38.130Q-17.248 38.198-17.009 38.198Q-16.845 38.198-16.637 38.162Q-16.428 38.127-16.221 38.046Q-16.015 37.966-15.883 37.838Q-15.751 37.710-15.751 37.535Q-15.751 37.183-16.132 37.089Q-16.514 36.995-17.016 36.995L-17.635 36.995Q-17.874 36.995-18.072 37.146Q-18.270 37.296-18.270 37.535M-17.221 35.748Q-16.555 35.748-16.555 34.951Q-16.555 34.151-17.221 34.151Q-17.891 34.151-17.891 34.951Q-17.891 35.748-17.221 35.748\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(185.63 1.674)\">\u003Cpath d=\"M-11.987 36.995L-11.987 35.932Q-11.987 35.908-11.959 35.881Q-11.932 35.854-11.908 35.854L-11.799 35.854Q-11.734 35.854-11.720 35.912Q-11.624 36.346-11.378 36.597Q-11.132 36.848-10.718 36.848Q-10.377 36.848-10.124 36.715Q-9.871 36.582-9.871 36.274Q-9.871 36.117-9.965 36.002Q-10.059 35.888-10.197 35.819Q-10.336 35.751-10.503 35.713L-11.084 35.614Q-11.440 35.546-11.713 35.325Q-11.987 35.105-11.987 34.763Q-11.987 34.514-11.875 34.339Q-11.764 34.165-11.578 34.066Q-11.392 33.967-11.176 33.924Q-10.961 33.881-10.718 33.881Q-10.305 33.881-10.025 34.063L-9.809 33.888Q-9.799 33.885-9.792 33.883Q-9.785 33.881-9.775 33.881L-9.724 33.881Q-9.697 33.881-9.673 33.905Q-9.649 33.929-9.649 33.957L-9.649 34.804Q-9.649 34.825-9.673 34.852Q-9.697 34.879-9.724 34.879L-9.837 34.879Q-9.864 34.879-9.890 34.854Q-9.915 34.828-9.915 34.804Q-9.915 34.568-10.021 34.404Q-10.127 34.240-10.310 34.158Q-10.493 34.076-10.725 34.076Q-11.053 34.076-11.310 34.179Q-11.566 34.281-11.566 34.558Q-11.566 34.753-11.383 34.862Q-11.200 34.972-10.971 35.013L-10.397 35.119Q-10.151 35.167-9.937 35.295Q-9.724 35.423-9.587 35.626Q-9.450 35.830-9.450 36.079Q-9.450 36.592-9.816 36.831Q-10.182 37.070-10.718 37.070Q-11.214 37.070-11.546 36.776L-11.812 37.050Q-11.833 37.070-11.860 37.070L-11.908 37.070Q-11.932 37.070-11.959 37.043Q-11.987 37.016-11.987 36.995M-8.863 35.467Q-8.863 35.146-8.738 34.857Q-8.613 34.568-8.387 34.345Q-8.162 34.121-7.866 34.001Q-7.571 33.881-7.253 33.881Q-6.925 33.881-6.663 33.981Q-6.402 34.080-6.226 34.262Q-6.050 34.445-5.956 34.703Q-5.862 34.961-5.862 35.293Q-5.862 35.385-5.944 35.406L-8.199 35.406L-8.199 35.467Q-8.199 36.055-7.916 36.438Q-7.632 36.821-7.065 36.821Q-6.743 36.821-6.475 36.628Q-6.207 36.435-6.118 36.120Q-6.111 36.079-6.036 36.065L-5.944 36.065Q-5.862 36.089-5.862 36.161Q-5.862 36.168-5.868 36.195Q-5.981 36.592-6.352 36.831Q-6.723 37.070-7.147 37.070Q-7.584 37.070-7.984 36.862Q-8.384 36.653-8.623 36.286Q-8.863 35.919-8.863 35.467M-8.193 35.197L-6.378 35.197Q-6.378 34.920-6.475 34.668Q-6.572 34.415-6.771 34.259Q-6.969 34.104-7.253 34.104Q-7.530 34.104-7.743 34.262Q-7.957 34.421-8.075 34.676Q-8.193 34.931-8.193 35.197M-4.747 36.161L-4.747 34.264L-5.386 34.264L-5.386 34.042Q-5.069 34.042-4.852 33.832Q-4.634 33.622-4.534 33.312Q-4.433 33.003-4.433 32.695L-4.166 32.695L-4.166 33.984L-3.090 33.984L-3.090 34.264L-4.166 34.264L-4.166 36.148Q-4.166 36.424-4.062 36.623Q-3.958 36.821-3.698 36.821Q-3.541 36.821-3.435 36.717Q-3.329 36.612-3.279 36.459Q-3.230 36.305-3.230 36.148L-3.230 35.734L-2.963 35.734L-2.963 36.161Q-2.963 36.387-3.062 36.597Q-3.161 36.807-3.346 36.939Q-3.530 37.070-3.759 37.070Q-4.197 37.070-4.472 36.833Q-4.747 36.595-4.747 36.161\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(185.63 1.674)\">\u003Cpath d=\"M0.556 36.995L0.556 35.932Q0.556 35.908 0.584 35.881Q0.611 35.854 0.635 35.854L0.744 35.854Q0.809 35.854 0.823 35.912Q0.919 36.346 1.165 36.597Q1.411 36.848 1.825 36.848Q2.166 36.848 2.419 36.715Q2.672 36.582 2.672 36.274Q2.672 36.117 2.578 36.002Q2.484 35.888 2.346 35.819Q2.207 35.751 2.040 35.713L1.459 35.614Q1.103 35.546 0.830 35.325Q0.556 35.105 0.556 34.763Q0.556 34.514 0.668 34.339Q0.779 34.165 0.965 34.066Q1.151 33.967 1.367 33.924Q1.582 33.881 1.825 33.881Q2.238 33.881 2.518 34.063L2.734 33.888Q2.744 33.885 2.751 33.883Q2.758 33.881 2.768 33.881L2.819 33.881Q2.846 33.881 2.870 33.905Q2.894 33.929 2.894 33.957L2.894 34.804Q2.894 34.825 2.870 34.852Q2.846 34.879 2.819 34.879L2.706 34.879Q2.679 34.879 2.653 34.854Q2.628 34.828 2.628 34.804Q2.628 34.568 2.522 34.404Q2.416 34.240 2.233 34.158Q2.050 34.076 1.818 34.076Q1.490 34.076 1.233 34.179Q0.977 34.281 0.977 34.558Q0.977 34.753 1.160 34.862Q1.343 34.972 1.572 35.013L2.146 35.119Q2.392 35.167 2.606 35.295Q2.819 35.423 2.956 35.626Q3.093 35.830 3.093 36.079Q3.093 36.592 2.727 36.831Q2.361 37.070 1.825 37.070Q1.329 37.070 0.997 36.776L0.731 37.050Q0.710 37.070 0.683 37.070L0.635 37.070Q0.611 37.070 0.584 37.043Q0.556 37.016 0.556 36.995M5.338 37.002L3.786 37.002L3.786 36.722Q4.012 36.722 4.161 36.688Q4.309 36.653 4.309 36.513L4.309 34.664Q4.309 34.476 4.262 34.392Q4.214 34.309 4.116 34.290Q4.019 34.271 3.807 34.271L3.807 33.991L4.863 33.916L4.863 36.513Q4.863 36.653 4.995 36.688Q5.126 36.722 5.338 36.722L5.338 37.002M4.067 32.695Q4.067 32.524 4.190 32.405Q4.313 32.285 4.484 32.285Q4.651 32.285 4.774 32.405Q4.897 32.524 4.897 32.695Q4.897 32.870 4.774 32.993Q4.651 33.116 4.484 33.116Q4.313 33.116 4.190 32.993Q4.067 32.870 4.067 32.695M8.715 37.002L6.042 37.002Q5.998 37.002 5.971 36.975Q5.943 36.947 5.943 36.903L5.943 36.835Q5.943 36.794 5.971 36.763L8.079 34.210L7.440 34.210Q7.016 34.210 6.784 34.276Q6.552 34.343 6.432 34.553Q6.312 34.763 6.312 35.184L6.049 35.184L6.131 33.984L8.722 33.984Q8.763 33.984 8.792 34.011Q8.821 34.039 8.821 34.083L8.821 34.131Q8.821 34.175 8.797 34.203L6.692 36.749L7.372 36.749Q7.700 36.749 7.917 36.713Q8.134 36.677 8.302 36.527Q8.442 36.390 8.495 36.166Q8.548 35.942 8.575 35.614L8.842 35.614L8.715 37.002M9.491 35.467Q9.491 35.146 9.616 34.857Q9.741 34.568 9.966 34.345Q10.192 34.121 10.487 34.001Q10.783 33.881 11.101 33.881Q11.429 33.881 11.690 33.981Q11.952 34.080 12.128 34.262Q12.304 34.445 12.398 34.703Q12.492 34.961 12.492 35.293Q12.492 35.385 12.410 35.406L10.154 35.406L10.154 35.467Q10.154 36.055 10.438 36.438Q10.721 36.821 11.289 36.821Q11.610 36.821 11.878 36.628Q12.147 36.435 12.236 36.120Q12.242 36.079 12.318 36.065L12.410 36.065Q12.492 36.089 12.492 36.161Q12.492 36.168 12.485 36.195Q12.372 36.592 12.002 36.831Q11.631 37.070 11.207 37.070Q10.769 37.070 10.369 36.862Q9.970 36.653 9.730 36.286Q9.491 35.919 9.491 35.467M10.161 35.197L11.976 35.197Q11.976 34.920 11.878 34.668Q11.781 34.415 11.583 34.259Q11.385 34.104 11.101 34.104Q10.824 34.104 10.610 34.262Q10.397 34.421 10.279 34.676Q10.161 34.931 10.161 35.197\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-42.579 37.002v-94.74\"\u002F>\u003Cpath stroke=\"none\" d=\"m-42.579-59.737-1.6 3.2 1.6-1.2 1.6 1.2\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(-23.025 -101.633)\">\u003Cpath d=\"M-41.738 36.161L-41.738 34.264L-42.377 34.264L-42.377 34.042Q-42.059 34.042-41.842 33.832Q-41.625 33.622-41.525 33.312Q-41.424 33.003-41.424 32.695L-41.157 32.695L-41.157 33.984L-40.080 33.984L-40.080 34.264L-41.157 34.264L-41.157 36.148Q-41.157 36.424-41.053 36.623Q-40.949 36.821-40.689 36.821Q-40.532 36.821-40.426 36.717Q-40.320 36.612-40.270 36.459Q-40.221 36.305-40.221 36.148L-40.221 35.734L-39.954 35.734L-39.954 36.161Q-39.954 36.387-40.053 36.597Q-40.152 36.807-40.337 36.939Q-40.521 37.070-40.750 37.070Q-41.188 37.070-41.463 36.833Q-41.738 36.595-41.738 36.161M-39.185 35.467Q-39.185 35.146-39.060 34.857Q-38.935 34.568-38.710 34.345Q-38.484 34.121-38.189 34.001Q-37.893 33.881-37.575 33.881Q-37.247 33.881-36.985 33.981Q-36.724 34.080-36.548 34.262Q-36.372 34.445-36.278 34.703Q-36.184 34.961-36.184 35.293Q-36.184 35.385-36.266 35.406L-38.522 35.406L-38.522 35.467Q-38.522 36.055-38.238 36.438Q-37.954 36.821-37.387 36.821Q-37.066 36.821-36.798 36.628Q-36.529 36.435-36.440 36.120Q-36.433 36.079-36.358 36.065L-36.266 36.065Q-36.184 36.089-36.184 36.161Q-36.184 36.168-36.191 36.195Q-36.304 36.592-36.674 36.831Q-37.045 37.070-37.469 37.070Q-37.907 37.070-38.307 36.862Q-38.706 36.653-38.946 36.286Q-39.185 35.919-39.185 35.467M-38.515 35.197L-36.700 35.197Q-36.700 34.920-36.798 34.668Q-36.895 34.415-37.093 34.259Q-37.291 34.104-37.575 34.104Q-37.852 34.104-38.066 34.262Q-38.279 34.421-38.397 34.676Q-38.515 34.931-38.515 35.197M-35.596 36.995L-35.596 35.932Q-35.596 35.908-35.569 35.881Q-35.541 35.854-35.517 35.854L-35.408 35.854Q-35.343 35.854-35.329 35.912Q-35.234 36.346-34.988 36.597Q-34.742 36.848-34.328 36.848Q-33.986 36.848-33.733 36.715Q-33.480 36.582-33.480 36.274Q-33.480 36.117-33.574 36.002Q-33.668 35.888-33.807 35.819Q-33.945 35.751-34.113 35.713L-34.694 35.614Q-35.049 35.546-35.323 35.325Q-35.596 35.105-35.596 34.763Q-35.596 34.514-35.485 34.339Q-35.374 34.165-35.188 34.066Q-35.001 33.967-34.786 33.924Q-34.571 33.881-34.328 33.881Q-33.914 33.881-33.634 34.063L-33.419 33.888Q-33.409 33.885-33.402 33.883Q-33.395 33.881-33.385 33.881L-33.333 33.881Q-33.306 33.881-33.282 33.905Q-33.258 33.929-33.258 33.957L-33.258 34.804Q-33.258 34.825-33.282 34.852Q-33.306 34.879-33.333 34.879L-33.446 34.879Q-33.474 34.879-33.499 34.854Q-33.525 34.828-33.525 34.804Q-33.525 34.568-33.631 34.404Q-33.737 34.240-33.920 34.158Q-34.102 34.076-34.335 34.076Q-34.663 34.076-34.919 34.179Q-35.176 34.281-35.176 34.558Q-35.176 34.753-34.993 34.862Q-34.810 34.972-34.581 35.013L-34.007 35.119Q-33.761 35.167-33.547 35.295Q-33.333 35.423-33.197 35.626Q-33.060 35.830-33.060 36.079Q-33.060 36.592-33.426 36.831Q-33.791 37.070-34.328 37.070Q-34.824 37.070-35.155 36.776L-35.422 37.050Q-35.442 37.070-35.470 37.070L-35.517 37.070Q-35.541 37.070-35.569 37.043Q-35.596 37.016-35.596 36.995M-31.905 36.161L-31.905 34.264L-32.544 34.264L-32.544 34.042Q-32.226 34.042-32.009 33.832Q-31.792 33.622-31.691 33.312Q-31.590 33.003-31.590 32.695L-31.324 32.695L-31.324 33.984L-30.247 33.984L-30.247 34.264L-31.324 34.264L-31.324 36.148Q-31.324 36.424-31.219 36.623Q-31.115 36.821-30.855 36.821Q-30.698 36.821-30.592 36.717Q-30.486 36.612-30.437 36.459Q-30.387 36.305-30.387 36.148L-30.387 35.734L-30.121 35.734L-30.121 36.161Q-30.121 36.387-30.220 36.597Q-30.319 36.807-30.503 36.939Q-30.688 37.070-30.917 37.070Q-31.354 37.070-31.630 36.833Q-31.905 36.595-31.905 36.161\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-23.025 -101.633)\">\u003Cpath d=\"M-26.537 36.274Q-26.537 35.942-26.314 35.715Q-26.090 35.488-25.746 35.360Q-25.403 35.231-25.030 35.179Q-24.658 35.126-24.353 35.126L-24.353 34.873Q-24.353 34.668-24.461 34.488Q-24.569 34.309-24.750 34.206Q-24.931 34.104-25.139 34.104Q-25.546 34.104-25.782 34.196Q-25.693 34.233-25.647 34.317Q-25.601 34.401-25.601 34.503Q-25.601 34.599-25.647 34.678Q-25.693 34.756-25.774 34.801Q-25.854 34.845-25.943 34.845Q-26.093 34.845-26.194 34.748Q-26.295 34.650-26.295 34.503Q-26.295 33.881-25.139 33.881Q-24.928 33.881-24.678 33.945Q-24.429 34.008-24.227 34.127Q-24.025 34.247-23.899 34.432Q-23.772 34.616-23.772 34.859L-23.772 36.435Q-23.772 36.551-23.711 36.647Q-23.649 36.742-23.536 36.742Q-23.427 36.742-23.362 36.648Q-23.297 36.554-23.297 36.435L-23.297 35.987L-23.031 35.987L-23.031 36.435Q-23.031 36.705-23.258 36.870Q-23.485 37.036-23.765 37.036Q-23.974 37.036-24.111 36.882Q-24.247 36.729-24.271 36.513Q-24.418 36.780-24.700 36.925Q-24.982 37.070-25.307 37.070Q-25.584 37.070-25.868 36.995Q-26.151 36.920-26.344 36.741Q-26.537 36.561-26.537 36.274M-25.922 36.274Q-25.922 36.448-25.821 36.578Q-25.721 36.708-25.565 36.778Q-25.410 36.848-25.245 36.848Q-25.027 36.848-24.818 36.751Q-24.610 36.653-24.482 36.472Q-24.353 36.291-24.353 36.065L-24.353 35.337Q-24.678 35.337-25.044 35.428Q-25.410 35.519-25.666 35.731Q-25.922 35.942-25.922 36.274M-22.614 35.491Q-22.614 35.163-22.479 34.862Q-22.344 34.562-22.108 34.341Q-21.872 34.121-21.568 34.001Q-21.264 33.881-20.939 33.881Q-20.433 33.881-20.084 33.984Q-19.736 34.086-19.736 34.462Q-19.736 34.609-19.833 34.710Q-19.931 34.811-20.077 34.811Q-20.231 34.811-20.330 34.712Q-20.430 34.613-20.430 34.462Q-20.430 34.274-20.289 34.182Q-20.491 34.131-20.932 34.131Q-21.287 34.131-21.516 34.327Q-21.745 34.524-21.846 34.833Q-21.947 35.143-21.947 35.491Q-21.947 35.840-21.821 36.146Q-21.694 36.452-21.440 36.636Q-21.185 36.821-20.829 36.821Q-20.607 36.821-20.423 36.737Q-20.238 36.653-20.103 36.498Q-19.968 36.342-19.910 36.134Q-19.896 36.079-19.842 36.079L-19.729 36.079Q-19.698 36.079-19.676 36.103Q-19.654 36.127-19.654 36.161L-19.654 36.182Q-19.739 36.469-19.927 36.667Q-20.115 36.865-20.380 36.968Q-20.645 37.070-20.939 37.070Q-21.369 37.070-21.757 36.864Q-22.145 36.657-22.379 36.294Q-22.614 35.932-22.614 35.491M-19.066 35.491Q-19.066 35.163-18.931 34.862Q-18.796 34.562-18.560 34.341Q-18.324 34.121-18.020 34.001Q-17.716 33.881-17.391 33.881Q-16.885 33.881-16.536 33.984Q-16.188 34.086-16.188 34.462Q-16.188 34.609-16.285 34.710Q-16.383 34.811-16.530 34.811Q-16.683 34.811-16.783 34.712Q-16.882 34.613-16.882 34.462Q-16.882 34.274-16.742 34.182Q-16.943 34.131-17.384 34.131Q-17.740 34.131-17.969 34.327Q-18.198 34.524-18.298 34.833Q-18.399 35.143-18.399 35.491Q-18.399 35.840-18.273 36.146Q-18.146 36.452-17.892 36.636Q-17.637 36.821-17.282 36.821Q-17.059 36.821-16.875 36.737Q-16.690 36.653-16.555 36.498Q-16.420 36.342-16.362 36.134Q-16.348 36.079-16.294 36.079L-16.181 36.079Q-16.150 36.079-16.128 36.103Q-16.106 36.127-16.106 36.161L-16.106 36.182Q-16.191 36.469-16.379 36.667Q-16.567 36.865-16.832 36.968Q-17.097 37.070-17.391 37.070Q-17.822 37.070-18.210 36.864Q-18.598 36.657-18.832 36.294Q-19.066 35.932-19.066 35.491M-14.944 36.168L-14.944 34.664Q-14.944 34.394-15.051 34.333Q-15.159 34.271-15.470 34.271L-15.470 33.991L-14.363 33.916L-14.363 36.148L-14.363 36.168Q-14.363 36.448-14.311 36.592Q-14.260 36.735-14.118 36.792Q-13.976 36.848-13.689 36.848Q-13.436 36.848-13.231 36.708Q-13.026 36.568-12.910 36.342Q-12.794 36.117-12.794 35.867L-12.794 34.664Q-12.794 34.394-12.901 34.333Q-13.009 34.271-13.320 34.271L-13.320 33.991L-12.213 33.916L-12.213 36.329Q-12.213 36.520-12.160 36.602Q-12.107 36.684-12.006 36.703Q-11.905 36.722-11.690 36.722L-11.690 37.002L-12.766 37.070L-12.766 36.506Q-12.876 36.688-13.021 36.811Q-13.166 36.934-13.353 37.002Q-13.539 37.070-13.741 37.070Q-14.944 37.070-14.944 36.168M-9.352 37.002L-11.088 37.002L-11.088 36.722Q-10.859 36.722-10.711 36.688Q-10.562 36.653-10.562 36.513L-10.562 34.664Q-10.562 34.394-10.670 34.333Q-10.777 34.271-11.088 34.271L-11.088 33.991L-10.059 33.916L-10.059 34.623Q-9.930 34.315-9.687 34.116Q-9.444 33.916-9.126 33.916Q-8.908 33.916-8.737 34.040Q-8.566 34.165-8.566 34.377Q-8.566 34.514-8.665 34.613Q-8.764 34.712-8.897 34.712Q-9.034 34.712-9.133 34.613Q-9.232 34.514-9.232 34.377Q-9.232 34.237-9.133 34.138Q-9.424 34.138-9.624 34.334Q-9.824 34.531-9.916 34.825Q-10.008 35.119-10.008 35.399L-10.008 36.513Q-10.008 36.722-9.352 36.722L-9.352 37.002M-7.923 36.274Q-7.923 35.942-7.699 35.715Q-7.475 35.488-7.132 35.360Q-6.788 35.231-6.416 35.179Q-6.043 35.126-5.739 35.126L-5.739 34.873Q-5.739 34.668-5.847 34.488Q-5.954 34.309-6.136 34.206Q-6.317 34.104-6.525 34.104Q-6.932 34.104-7.168 34.196Q-7.079 34.233-7.033 34.317Q-6.987 34.401-6.987 34.503Q-6.987 34.599-7.033 34.678Q-7.079 34.756-7.159 34.801Q-7.240 34.845-7.328 34.845Q-7.479 34.845-7.580 34.748Q-7.681 34.650-7.681 34.503Q-7.681 33.881-6.525 33.881Q-6.313 33.881-6.064 33.945Q-5.814 34.008-5.613 34.127Q-5.411 34.247-5.285 34.432Q-5.158 34.616-5.158 34.859L-5.158 36.435Q-5.158 36.551-5.097 36.647Q-5.035 36.742-4.922 36.742Q-4.813 36.742-4.748 36.648Q-4.683 36.554-4.683 36.435L-4.683 35.987L-4.416 35.987L-4.416 36.435Q-4.416 36.705-4.644 36.870Q-4.871 37.036-5.151 37.036Q-5.360 37.036-5.496 36.882Q-5.633 36.729-5.657 36.513Q-5.804 36.780-6.086 36.925Q-6.368 37.070-6.693 37.070Q-6.970 37.070-7.253 36.995Q-7.537 36.920-7.730 36.741Q-7.923 36.561-7.923 36.274M-7.308 36.274Q-7.308 36.448-7.207 36.578Q-7.106 36.708-6.951 36.778Q-6.795 36.848-6.631 36.848Q-6.412 36.848-6.204 36.751Q-5.995 36.653-5.867 36.472Q-5.739 36.291-5.739 36.065L-5.739 35.337Q-6.064 35.337-6.430 35.428Q-6.795 35.519-7.052 35.731Q-7.308 35.942-7.308 36.274M-3.999 35.491Q-3.999 35.163-3.864 34.862Q-3.729 34.562-3.493 34.341Q-3.258 34.121-2.953 34.001Q-2.649 33.881-2.325 33.881Q-1.819 33.881-1.470 33.984Q-1.121 34.086-1.121 34.462Q-1.121 34.609-1.219 34.710Q-1.316 34.811-1.463 34.811Q-1.617 34.811-1.716 34.712Q-1.815 34.613-1.815 34.462Q-1.815 34.274-1.675 34.182Q-1.877 34.131-2.318 34.131Q-2.673 34.131-2.902 34.327Q-3.131 34.524-3.232 34.833Q-3.333 35.143-3.333 35.491Q-3.333 35.840-3.206 36.146Q-3.080 36.452-2.825 36.636Q-2.571 36.821-2.215 36.821Q-1.993 36.821-1.808 36.737Q-1.624 36.653-1.489 36.498Q-1.354 36.342-1.296 36.134Q-1.282 36.079-1.227 36.079L-1.115 36.079Q-1.084 36.079-1.062 36.103Q-1.039 36.127-1.039 36.161L-1.039 36.182Q-1.125 36.469-1.313 36.667Q-1.501 36.865-1.766 36.968Q-2.031 37.070-2.325 37.070Q-2.755 37.070-3.143 36.864Q-3.531 36.657-3.765 36.294Q-3.999 35.932-3.999 35.491M-0.117 38.137Q0.013 38.205 0.150 38.205Q0.321 38.205 0.471 38.116Q0.622 38.027 0.733 37.882Q0.844 37.737 0.923 37.569L1.186 37.002L0.017 34.476Q-0.058 34.329-0.188 34.297Q-0.318 34.264-0.551 34.264L-0.551 33.984L0.970 33.984L0.970 34.264Q0.622 34.264 0.622 34.411Q0.625 34.432 0.627 34.449Q0.629 34.466 0.629 34.476L1.486 36.335L2.259 34.664Q2.293 34.596 2.293 34.517Q2.293 34.404 2.209 34.334Q2.126 34.264 2.013 34.264L2.013 33.984L3.209 33.984L3.209 34.264Q2.990 34.264 2.818 34.368Q2.645 34.473 2.553 34.664L1.216 37.569Q1.046 37.939 0.776 38.185Q0.506 38.431 0.150 38.431Q-0.120 38.431-0.339 38.265Q-0.557 38.099-0.557 37.836Q-0.557 37.699-0.465 37.610Q-0.373 37.522-0.233 37.522Q-0.096 37.522-0.007 37.610Q0.082 37.699 0.082 37.836Q0.082 37.939 0.029 38.017Q-0.024 38.096-0.117 38.137\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(-15.192 -83.103)\">\u003Cpath d=\"M-39.253 37.002L-41.783 37.002L-41.783 36.722Q-40.815 36.722-40.815 36.513L-40.815 32.894Q-41.208 33.082-41.830 33.082L-41.830 32.801Q-41.413 32.801-41.049 32.700Q-40.685 32.600-40.429 32.354L-40.303 32.354Q-40.238 32.371-40.221 32.439L-40.221 36.513Q-40.221 36.722-39.253 36.722L-39.253 37.002M-37.883 36.582Q-37.883 36.414-37.760 36.291Q-37.637 36.168-37.462 36.168Q-37.295 36.168-37.172 36.291Q-37.049 36.414-37.049 36.582Q-37.049 36.756-37.172 36.879Q-37.295 37.002-37.462 37.002Q-37.637 37.002-37.760 36.879Q-37.883 36.756-37.883 36.582M-34.345 37.142Q-34.981 37.142-35.345 36.797Q-35.709 36.452-35.844 35.927Q-35.979 35.402-35.979 34.777Q-35.979 33.752-35.623 33.053Q-35.268 32.354-34.345 32.354Q-33.419 32.354-33.067 33.053Q-32.715 33.752-32.715 34.777Q-32.715 35.402-32.850 35.927Q-32.985 36.452-33.347 36.797Q-33.709 37.142-34.345 37.142M-34.345 36.917Q-33.908 36.917-33.694 36.542Q-33.480 36.168-33.431 35.701Q-33.381 35.235-33.381 34.657Q-33.381 34.104-33.431 33.676Q-33.480 33.249-33.692 32.914Q-33.904 32.579-34.345 32.579Q-34.687 32.579-34.890 32.786Q-35.094 32.993-35.181 33.305Q-35.268 33.618-35.290 33.934Q-35.312 34.251-35.312 34.657Q-35.312 35.074-35.290 35.416Q-35.268 35.758-35.179 36.106Q-35.090 36.455-34.885 36.686Q-34.680 36.917-34.345 36.917\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-15.192 -40.424)\">\u003Cpath d=\"M-40.590 37.142Q-41.225 37.142-41.589 36.797Q-41.954 36.452-42.089 35.927Q-42.224 35.402-42.224 34.777Q-42.224 33.752-41.868 33.053Q-41.513 32.354-40.590 32.354Q-39.663 32.354-39.311 33.053Q-38.959 33.752-38.959 34.777Q-38.959 35.402-39.094 35.927Q-39.229 36.452-39.592 36.797Q-39.954 37.142-40.590 37.142M-40.590 36.917Q-40.152 36.917-39.939 36.542Q-39.725 36.168-39.675 35.701Q-39.626 35.235-39.626 34.657Q-39.626 34.104-39.675 33.676Q-39.725 33.249-39.937 32.914Q-40.149 32.579-40.590 32.579Q-40.932 32.579-41.135 32.786Q-41.338 32.993-41.425 33.305Q-41.513 33.618-41.535 33.934Q-41.557 34.251-41.557 34.657Q-41.557 35.074-41.535 35.416Q-41.513 35.758-41.424 36.106Q-41.335 36.455-41.130 36.686Q-40.925 36.917-40.590 36.917M-37.883 36.582Q-37.883 36.414-37.760 36.291Q-37.637 36.168-37.462 36.168Q-37.295 36.168-37.172 36.291Q-37.049 36.414-37.049 36.582Q-37.049 36.756-37.172 36.879Q-37.295 37.002-37.462 37.002Q-37.637 37.002-37.760 36.879Q-37.883 36.756-37.883 36.582M-35.528 36.240L-35.558 36.240Q-35.422 36.537-35.124 36.713Q-34.827 36.889-34.499 36.889Q-34.137 36.889-33.909 36.711Q-33.682 36.534-33.588 36.245Q-33.494 35.956-33.494 35.594Q-33.494 35.279-33.549 34.994Q-33.603 34.709-33.776 34.503Q-33.949 34.298-34.263 34.298Q-34.537 34.298-34.719 34.365Q-34.902 34.432-35.006 34.521Q-35.111 34.609-35.206 34.719Q-35.302 34.828-35.347 34.838L-35.425 34.838Q-35.497 34.821-35.514 34.750L-35.514 32.432Q-35.514 32.398-35.490 32.376Q-35.466 32.354-35.432 32.354L-35.405 32.354Q-35.118 32.470-34.849 32.524Q-34.581 32.579-34.304 32.579Q-34.027 32.579-33.757 32.524Q-33.487 32.470-33.207 32.354L-33.183 32.354Q-33.149 32.354-33.125 32.377Q-33.101 32.401-33.101 32.432L-33.101 32.501Q-33.101 32.528-33.121 32.548Q-33.395 32.863-33.779 33.039Q-34.164 33.215-34.578 33.215Q-34.916 33.215-35.234 33.129L-35.234 34.411Q-34.837 34.076-34.263 34.076Q-33.860 34.076-33.523 34.286Q-33.186 34.497-32.993 34.849Q-32.800 35.201-32.800 35.601Q-32.800 35.932-32.940 36.218Q-33.080 36.503-33.325 36.713Q-33.569 36.923-33.872 37.033Q-34.174 37.142-34.492 37.142Q-34.851 37.142-35.177 36.978Q-35.504 36.814-35.699 36.522Q-35.893 36.230-35.893 35.867Q-35.893 35.717-35.787 35.611Q-35.682 35.505-35.528 35.505Q-35.374 35.505-35.270 35.609Q-35.165 35.713-35.165 35.867Q-35.165 36.024-35.270 36.132Q-35.374 36.240-35.528 36.240\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-36.888 2.859C-16.971-36.976 2.946-41.243 31.399-44.09c34.143-2.845 68.286-3.698 96.739-4.267\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cg transform=\"translate(117.344 -77.449)\">\u003Cpath d=\"M-40.621 38.359L-42.251 38.359L-42.251 38.079Q-42.022 38.079-41.873 38.044Q-41.725 38.010-41.725 37.870L-41.725 34.524Q-41.725 34.353-41.861 34.312Q-41.998 34.271-42.251 34.271L-42.251 33.991L-41.171 33.916L-41.171 34.322Q-40.949 34.121-40.662 34.018Q-40.374 33.916-40.067 33.916Q-39.640 33.916-39.276 34.129Q-38.912 34.343-38.698 34.707Q-38.484 35.071-38.484 35.491Q-38.484 35.936-38.724 36.300Q-38.963 36.664-39.356 36.867Q-39.749 37.070-40.193 37.070Q-40.460 37.070-40.708 36.970Q-40.955 36.869-41.143 36.688L-41.143 37.870Q-41.143 38.007-40.995 38.043Q-40.846 38.079-40.621 38.079L-40.621 38.359M-41.143 34.671L-41.143 36.281Q-41.010 36.534-40.767 36.691Q-40.525 36.848-40.248 36.848Q-39.920 36.848-39.667 36.647Q-39.414 36.445-39.281 36.127Q-39.147 35.809-39.147 35.491Q-39.147 35.262-39.212 35.033Q-39.277 34.804-39.405 34.606Q-39.534 34.408-39.728 34.288Q-39.923 34.169-40.156 34.169Q-40.450 34.169-40.718 34.298Q-40.986 34.428-41.143 34.671M-36.181 37.002L-37.784 37.002L-37.784 36.722Q-37.558 36.722-37.409 36.688Q-37.261 36.653-37.261 36.513L-37.261 32.894Q-37.261 32.624-37.368 32.562Q-37.476 32.501-37.784 32.501L-37.784 32.220L-36.707 32.145L-36.707 36.513Q-36.707 36.650-36.557 36.686Q-36.406 36.722-36.181 36.722L-36.181 37.002M-35.528 36.274Q-35.528 35.942-35.304 35.715Q-35.080 35.488-34.736 35.360Q-34.393 35.231-34.020 35.179Q-33.648 35.126-33.344 35.126L-33.344 34.873Q-33.344 34.668-33.451 34.488Q-33.559 34.309-33.740 34.206Q-33.921 34.104-34.130 34.104Q-34.537 34.104-34.772 34.196Q-34.683 34.233-34.637 34.317Q-34.591 34.401-34.591 34.503Q-34.591 34.599-34.637 34.678Q-34.683 34.756-34.764 34.801Q-34.844 34.845-34.933 34.845Q-35.083 34.845-35.184 34.748Q-35.285 34.650-35.285 34.503Q-35.285 33.881-34.130 33.881Q-33.918 33.881-33.668 33.945Q-33.419 34.008-33.217 34.127Q-33.016 34.247-32.889 34.432Q-32.763 34.616-32.763 34.859L-32.763 36.435Q-32.763 36.551-32.701 36.647Q-32.640 36.742-32.527 36.742Q-32.417 36.742-32.352 36.648Q-32.287 36.554-32.287 36.435L-32.287 35.987L-32.021 35.987L-32.021 36.435Q-32.021 36.705-32.248 36.870Q-32.475 37.036-32.756 37.036Q-32.964 37.036-33.101 36.882Q-33.238 36.729-33.262 36.513Q-33.409 36.780-33.691 36.925Q-33.973 37.070-34.297 37.070Q-34.574 37.070-34.858 36.995Q-35.142 36.920-35.335 36.741Q-35.528 36.561-35.528 36.274M-34.912 36.274Q-34.912 36.448-34.812 36.578Q-34.711 36.708-34.555 36.778Q-34.400 36.848-34.236 36.848Q-34.017 36.848-33.808 36.751Q-33.600 36.653-33.472 36.472Q-33.344 36.291-33.344 36.065L-33.344 35.337Q-33.668 35.337-34.034 35.428Q-34.400 35.519-34.656 35.731Q-34.912 35.942-34.912 36.274M-31.078 36.161L-31.078 34.264L-31.717 34.264L-31.717 34.042Q-31.399 34.042-31.182 33.832Q-30.965 33.622-30.864 33.312Q-30.763 33.003-30.763 32.695L-30.496 32.695L-30.496 33.984L-29.420 33.984L-29.420 34.264L-30.496 34.264L-30.496 36.148Q-30.496 36.424-30.392 36.623Q-30.288 36.821-30.028 36.821Q-29.871 36.821-29.765 36.717Q-29.659 36.612-29.610 36.459Q-29.560 36.305-29.560 36.148L-29.560 35.734L-29.293 35.734L-29.293 36.161Q-29.293 36.387-29.392 36.597Q-29.492 36.807-29.676 36.939Q-29.861 37.070-30.090 37.070Q-30.527 37.070-30.802 36.833Q-31.078 36.595-31.078 36.161M-28.524 35.467Q-28.524 35.146-28.400 34.857Q-28.275 34.568-28.049 34.345Q-27.824 34.121-27.528 34.001Q-27.232 33.881-26.914 33.881Q-26.586 33.881-26.325 33.981Q-26.063 34.080-25.887 34.262Q-25.711 34.445-25.617 34.703Q-25.523 34.961-25.523 35.293Q-25.523 35.385-25.605 35.406L-27.861 35.406L-27.861 35.467Q-27.861 36.055-27.578 36.438Q-27.294 36.821-26.726 36.821Q-26.405 36.821-26.137 36.628Q-25.869 36.435-25.780 36.120Q-25.773 36.079-25.698 36.065L-25.605 36.065Q-25.523 36.089-25.523 36.161Q-25.523 36.168-25.530 36.195Q-25.643 36.592-26.014 36.831Q-26.385 37.070-26.808 37.070Q-27.246 37.070-27.646 36.862Q-28.046 36.653-28.285 36.286Q-28.524 35.919-28.524 35.467M-27.854 35.197L-26.039 35.197Q-26.039 34.920-26.137 34.668Q-26.234 34.415-26.433 34.259Q-26.631 34.104-26.914 34.104Q-27.191 34.104-27.405 34.262Q-27.619 34.421-27.736 34.676Q-27.854 34.931-27.854 35.197M-24.877 36.274Q-24.877 35.942-24.653 35.715Q-24.430 35.488-24.086 35.360Q-23.743 35.231-23.370 35.179Q-22.997 35.126-22.693 35.126L-22.693 34.873Q-22.693 34.668-22.801 34.488Q-22.909 34.309-23.090 34.206Q-23.271 34.104-23.479 34.104Q-23.886 34.104-24.122 34.196Q-24.033 34.233-23.987 34.317Q-23.941 34.401-23.941 34.503Q-23.941 34.599-23.987 34.678Q-24.033 34.756-24.113 34.801Q-24.194 34.845-24.283 34.845Q-24.433 34.845-24.534 34.748Q-24.635 34.650-24.635 34.503Q-24.635 33.881-23.479 33.881Q-23.267 33.881-23.018 33.945Q-22.768 34.008-22.567 34.127Q-22.365 34.247-22.239 34.432Q-22.112 34.616-22.112 34.859L-22.112 36.435Q-22.112 36.551-22.051 36.647Q-21.989 36.742-21.876 36.742Q-21.767 36.742-21.702 36.648Q-21.637 36.554-21.637 36.435L-21.637 35.987L-21.371 35.987L-21.371 36.435Q-21.371 36.705-21.598 36.870Q-21.825 37.036-22.105 37.036Q-22.314 37.036-22.451 36.882Q-22.587 36.729-22.611 36.513Q-22.758 36.780-23.040 36.925Q-23.322 37.070-23.647 37.070Q-23.924 37.070-24.207 36.995Q-24.491 36.920-24.684 36.741Q-24.877 36.561-24.877 36.274M-24.262 36.274Q-24.262 36.448-24.161 36.578Q-24.060 36.708-23.905 36.778Q-23.749 36.848-23.585 36.848Q-23.367 36.848-23.158 36.751Q-22.950 36.653-22.821 36.472Q-22.693 36.291-22.693 36.065L-22.693 35.337Q-23.018 35.337-23.384 35.428Q-23.749 35.519-24.006 35.731Q-24.262 35.942-24.262 36.274M-20.379 36.168L-20.379 34.664Q-20.379 34.394-20.487 34.333Q-20.595 34.271-20.906 34.271L-20.906 33.991L-19.798 33.916L-19.798 36.148L-19.798 36.168Q-19.798 36.448-19.747 36.592Q-19.696 36.735-19.554 36.792Q-19.412 36.848-19.125 36.848Q-18.872 36.848-18.667 36.708Q-18.462 36.568-18.346 36.342Q-18.229 36.117-18.229 35.867L-18.229 34.664Q-18.229 34.394-18.337 34.333Q-18.445 34.271-18.756 34.271L-18.756 33.991L-17.648 33.916L-17.648 36.329Q-17.648 36.520-17.595 36.602Q-17.542 36.684-17.442 36.703Q-17.341 36.722-17.125 36.722L-17.125 37.002L-18.202 37.070L-18.202 36.506Q-18.311 36.688-18.457 36.811Q-18.602 36.934-18.788 37.002Q-18.975 37.070-19.176 37.070Q-20.379 37.070-20.379 36.168\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">A learning curve. Test accuracy rises with training-set size and then flattens as the hypothesis space, rather than the amount of data, becomes the binding constraint.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:366.729px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 275.047 132.882\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cpath fill=\"none\" d=\"M-53.812 40.04h180.097\"\u002F>\u003Cpath stroke=\"none\" d=\"m128.285 40.04-3.2-1.6 1.2 1.6-1.2 1.6\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(185.63 1.75)\">\u003Cpath d=\"M-51.816 40.040L-53.450 40.040L-53.450 39.760Q-53.221 39.760-53.072 39.726Q-52.923 39.691-52.923 39.551L-52.923 37.702Q-52.923 37.432-53.031 37.371Q-53.139 37.309-53.450 37.309L-53.450 37.029L-52.390 36.954L-52.390 37.603Q-52.219 37.295-51.915 37.124Q-51.611 36.954-51.266 36.954Q-50.866 36.954-50.589 37.094Q-50.312 37.234-50.227 37.582Q-50.059 37.289-49.760 37.121Q-49.461 36.954-49.116 36.954Q-48.610 36.954-48.326 37.177Q-48.042 37.401-48.042 37.897L-48.042 39.551Q-48.042 39.688-47.894 39.724Q-47.745 39.760-47.520 39.760L-47.520 40.040L-49.150 40.040L-49.150 39.760Q-48.924 39.760-48.774 39.724Q-48.624 39.688-48.624 39.551L-48.624 37.911Q-48.624 37.576-48.743 37.376Q-48.863 37.176-49.177 37.176Q-49.447 37.176-49.681 37.312Q-49.916 37.449-50.054 37.683Q-50.192 37.917-50.192 38.191L-50.192 39.551Q-50.192 39.688-50.044 39.724Q-49.895 39.760-49.669 39.760L-49.669 40.040L-51.300 40.040L-51.300 39.760Q-51.071 39.760-50.922 39.726Q-50.773 39.691-50.773 39.551L-50.773 37.911Q-50.773 37.576-50.893 37.376Q-51.013 37.176-51.327 37.176Q-51.597 37.176-51.831 37.312Q-52.065 37.449-52.204 37.683Q-52.342 37.917-52.342 38.191L-52.342 39.551Q-52.342 39.688-52.192 39.724Q-52.041 39.760-51.816 39.760L-51.816 40.040M-46.973 38.557Q-46.973 38.215-46.838 37.916Q-46.703 37.617-46.463 37.393Q-46.224 37.169-45.906 37.044Q-45.588 36.919-45.257 36.919Q-44.812 36.919-44.413 37.135Q-44.013 37.350-43.779 37.728Q-43.544 38.105-43.544 38.557Q-43.544 38.898-43.686 39.182Q-43.828 39.466-44.072 39.673Q-44.317 39.879-44.626 39.994Q-44.936 40.108-45.257 40.108Q-45.687 40.108-46.089 39.907Q-46.491 39.705-46.732 39.353Q-46.973 39.001-46.973 38.557M-45.257 39.859Q-44.655 39.859-44.431 39.481Q-44.208 39.103-44.208 38.471Q-44.208 37.859-44.442 37.500Q-44.676 37.142-45.257 37.142Q-46.310 37.142-46.310 38.471Q-46.310 39.103-46.084 39.481Q-45.858 39.859-45.257 39.859\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(185.63 1.75)\">\u003Cpath d=\"M-42.727 38.529Q-42.727 38.191-42.586 37.900Q-42.446 37.610-42.202 37.396Q-41.958 37.183-41.653 37.068Q-41.349 36.954-41.024 36.954Q-40.754 36.954-40.491 37.053Q-40.228 37.152-40.037 37.330L-40.037 35.932Q-40.037 35.662-40.144 35.600Q-40.252 35.539-40.563 35.539L-40.563 35.258L-39.486 35.183L-39.486 39.367Q-39.486 39.555-39.432 39.638Q-39.377 39.722-39.276 39.741Q-39.175 39.760-38.960 39.760L-38.960 40.040L-40.067 40.108L-40.067 39.691Q-40.484 40.108-41.110 40.108Q-41.541 40.108-41.913 39.896Q-42.286 39.685-42.506 39.324Q-42.727 38.963-42.727 38.529M-41.052 39.886Q-40.843 39.886-40.657 39.814Q-40.471 39.743-40.317 39.606Q-40.163 39.469-40.067 39.291L-40.067 37.682Q-40.153 37.535-40.298 37.415Q-40.443 37.295-40.613 37.236Q-40.782 37.176-40.963 37.176Q-41.523 37.176-41.792 37.565Q-42.060 37.955-42.060 38.536Q-42.060 39.107-41.826 39.497Q-41.592 39.886-41.052 39.886M-38.352 38.505Q-38.352 38.184-38.227 37.895Q-38.102 37.606-37.876 37.383Q-37.651 37.159-37.355 37.039Q-37.060 36.919-36.742 36.919Q-36.414 36.919-36.152 37.019Q-35.891 37.118-35.715 37.300Q-35.539 37.483-35.445 37.741Q-35.351 37.999-35.351 38.331Q-35.351 38.423-35.433 38.444L-37.688 38.444L-37.688 38.505Q-37.688 39.093-37.405 39.476Q-37.121 39.859-36.554 39.859Q-36.232 39.859-35.964 39.666Q-35.696 39.473-35.607 39.158Q-35.600 39.117-35.525 39.103L-35.433 39.103Q-35.351 39.127-35.351 39.199Q-35.351 39.206-35.357 39.233Q-35.470 39.630-35.841 39.869Q-36.212 40.108-36.636 40.108Q-37.073 40.108-37.473 39.900Q-37.873 39.691-38.112 39.324Q-38.352 38.957-38.352 38.505M-37.682 38.235L-35.867 38.235Q-35.867 37.958-35.964 37.706Q-36.062 37.453-36.260 37.297Q-36.458 37.142-36.742 37.142Q-37.019 37.142-37.232 37.300Q-37.446 37.459-37.564 37.714Q-37.682 37.969-37.682 38.235M-33.095 40.040L-34.698 40.040L-34.698 39.760Q-34.472 39.760-34.323 39.726Q-34.175 39.691-34.175 39.551L-34.175 35.932Q-34.175 35.662-34.282 35.600Q-34.390 35.539-34.698 35.539L-34.698 35.258L-33.621 35.183L-33.621 39.551Q-33.621 39.688-33.471 39.724Q-33.320 39.760-33.095 39.760\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(185.63 1.75)\">\u003Cpath d=\"M-29.796 38.529Q-29.796 38.201-29.661 37.900Q-29.526 37.600-29.290 37.379Q-29.054 37.159-28.750 37.039Q-28.445 36.919-28.121 36.919Q-27.615 36.919-27.266 37.022Q-26.918 37.124-26.918 37.500Q-26.918 37.647-27.015 37.748Q-27.112 37.849-27.259 37.849Q-27.413 37.849-27.512 37.750Q-27.611 37.651-27.611 37.500Q-27.611 37.312-27.471 37.220Q-27.673 37.169-28.114 37.169Q-28.469 37.169-28.698 37.365Q-28.927 37.562-29.028 37.871Q-29.129 38.181-29.129 38.529Q-29.129 38.878-29.003 39.184Q-28.876 39.490-28.621 39.674Q-28.367 39.859-28.011 39.859Q-27.789 39.859-27.605 39.775Q-27.420 39.691-27.285 39.536Q-27.150 39.380-27.092 39.172Q-27.078 39.117-27.024 39.117L-26.911 39.117Q-26.880 39.117-26.858 39.141Q-26.836 39.165-26.836 39.199L-26.836 39.220Q-26.921 39.507-27.109 39.705Q-27.297 39.903-27.562 40.006Q-27.827 40.108-28.121 40.108Q-28.551 40.108-28.939 39.902Q-29.327 39.695-29.561 39.332Q-29.796 38.970-29.796 38.529M-26.289 38.557Q-26.289 38.215-26.154 37.916Q-26.019 37.617-25.779 37.393Q-25.540 37.169-25.222 37.044Q-24.904 36.919-24.573 36.919Q-24.129 36.919-23.729 37.135Q-23.329 37.350-23.095 37.728Q-22.860 38.105-22.860 38.557Q-22.860 38.898-23.002 39.182Q-23.144 39.466-23.389 39.673Q-23.633 39.879-23.942 39.994Q-24.252 40.108-24.573 40.108Q-25.004 40.108-25.405 39.907Q-25.807 39.705-26.048 39.353Q-26.289 39.001-26.289 38.557M-24.573 39.859Q-23.971 39.859-23.747 39.481Q-23.524 39.103-23.524 38.471Q-23.524 37.859-23.758 37.500Q-23.992 37.142-24.573 37.142Q-25.626 37.142-25.626 38.471Q-25.626 39.103-25.400 39.481Q-25.174 39.859-24.573 39.859M-20.584 40.040L-22.218 40.040L-22.218 39.760Q-21.989 39.760-21.840 39.726Q-21.692 39.691-21.692 39.551L-21.692 37.702Q-21.692 37.432-21.799 37.371Q-21.907 37.309-22.218 37.309L-22.218 37.029L-21.158 36.954L-21.158 37.603Q-20.987 37.295-20.683 37.124Q-20.379 36.954-20.034 36.954Q-19.634 36.954-19.357 37.094Q-19.080 37.234-18.995 37.582Q-18.827 37.289-18.528 37.121Q-18.229 36.954-17.884 36.954Q-17.378 36.954-17.094 37.177Q-16.811 37.401-16.811 37.897L-16.811 39.551Q-16.811 39.688-16.662 39.724Q-16.513 39.760-16.288 39.760L-16.288 40.040L-17.918 40.040L-17.918 39.760Q-17.693 39.760-17.542 39.724Q-17.392 39.688-17.392 39.551L-17.392 37.911Q-17.392 37.576-17.511 37.376Q-17.631 37.176-17.945 37.176Q-18.215 37.176-18.450 37.312Q-18.684 37.449-18.822 37.683Q-18.961 37.917-18.961 38.191L-18.961 39.551Q-18.961 39.688-18.812 39.724Q-18.663 39.760-18.438 39.760L-18.438 40.040L-20.068 40.040L-20.068 39.760Q-19.839 39.760-19.690 39.726Q-19.542 39.691-19.542 39.551L-19.542 37.911Q-19.542 37.576-19.661 37.376Q-19.781 37.176-20.095 37.176Q-20.365 37.176-20.600 37.312Q-20.834 37.449-20.972 37.683Q-21.110 37.917-21.110 38.191L-21.110 39.551Q-21.110 39.688-20.960 39.724Q-20.810 39.760-20.584 39.760L-20.584 40.040M-14.056 41.397L-15.686 41.397L-15.686 41.117Q-15.457 41.117-15.308 41.082Q-15.160 41.048-15.160 40.908L-15.160 37.562Q-15.160 37.391-15.297 37.350Q-15.433 37.309-15.686 37.309L-15.686 37.029L-14.606 36.954L-14.606 37.360Q-14.384 37.159-14.097 37.056Q-13.810 36.954-13.502 36.954Q-13.075 36.954-12.711 37.167Q-12.347 37.381-12.133 37.745Q-11.920 38.109-11.920 38.529Q-11.920 38.974-12.159 39.338Q-12.398 39.702-12.791 39.905Q-13.184 40.108-13.629 40.108Q-13.895 40.108-14.143 40.008Q-14.391 39.907-14.579 39.726L-14.579 40.908Q-14.579 41.045-14.430 41.081Q-14.281 41.117-14.056 41.117L-14.056 41.397M-14.579 37.709L-14.579 39.319Q-14.445 39.572-14.203 39.729Q-13.960 39.886-13.683 39.886Q-13.355 39.886-13.102 39.685Q-12.849 39.483-12.716 39.165Q-12.583 38.847-12.583 38.529Q-12.583 38.300-12.648 38.071Q-12.713 37.842-12.841 37.644Q-12.969 37.446-13.164 37.326Q-13.359 37.207-13.591 37.207Q-13.885 37.207-14.153 37.336Q-14.422 37.466-14.579 37.709M-9.616 40.040L-11.219 40.040L-11.219 39.760Q-10.993 39.760-10.845 39.726Q-10.696 39.691-10.696 39.551L-10.696 35.932Q-10.696 35.662-10.804 35.600Q-10.911 35.539-11.219 35.539L-11.219 35.258L-10.142 35.183L-10.142 39.551Q-10.142 39.688-9.992 39.724Q-9.841 39.760-9.616 39.760L-9.616 40.040M-9.062 38.505Q-9.062 38.184-8.937 37.895Q-8.813 37.606-8.587 37.383Q-8.361 37.159-8.066 37.039Q-7.770 36.919-7.452 36.919Q-7.124 36.919-6.863 37.019Q-6.601 37.118-6.425 37.300Q-6.249 37.483-6.155 37.741Q-6.061 37.999-6.061 38.331Q-6.061 38.423-6.143 38.444L-8.399 38.444L-8.399 38.505Q-8.399 39.093-8.115 39.476Q-7.832 39.859-7.264 39.859Q-6.943 39.859-6.675 39.666Q-6.406 39.473-6.318 39.158Q-6.311 39.117-6.235 39.103L-6.143 39.103Q-6.061 39.127-6.061 39.199Q-6.061 39.206-6.068 39.233Q-6.181 39.630-6.552 39.869Q-6.922 40.108-7.346 40.108Q-7.784 40.108-8.184 39.900Q-8.584 39.691-8.823 39.324Q-9.062 38.957-9.062 38.505M-8.392 38.235L-6.577 38.235Q-6.577 37.958-6.675 37.706Q-6.772 37.453-6.970 37.297Q-7.169 37.142-7.452 37.142Q-7.729 37.142-7.943 37.300Q-8.156 37.459-8.274 37.714Q-8.392 37.969-8.392 38.235M-4.291 40.040L-5.613 40.040L-5.613 39.760Q-5.053 39.760-4.673 39.360L-3.959 38.563L-4.872 37.514Q-5.008 37.367-5.157 37.335Q-5.306 37.302-5.572 37.302L-5.572 37.022L-4.072 37.022L-4.072 37.302Q-4.263 37.302-4.263 37.436Q-4.263 37.466-4.233 37.514L-3.638 38.198L-3.197 37.702Q-3.084 37.572-3.084 37.456Q-3.084 37.394-3.122 37.348Q-3.159 37.302-3.217 37.302L-3.217 37.022L-1.902 37.022L-1.902 37.302Q-2.462 37.302-2.841 37.702L-3.464 38.403L-2.469 39.551Q-2.370 39.650-2.269 39.695Q-2.168 39.739-2.057 39.749Q-1.946 39.760-1.768 39.760L-1.768 40.040L-3.262 40.040L-3.262 39.760Q-3.197 39.760-3.137 39.726Q-3.077 39.691-3.077 39.626Q-3.077 39.579-3.108 39.551L-3.785 38.765L-4.318 39.360Q-4.431 39.490-4.431 39.606Q-4.431 39.671-4.390 39.715Q-4.349 39.760-4.291 39.760L-4.291 40.040M0.344 40.040L-1.208 40.040L-1.208 39.760Q-0.982 39.760-0.833 39.726Q-0.685 39.691-0.685 39.551L-0.685 37.702Q-0.685 37.514-0.733 37.430Q-0.780 37.347-0.878 37.328Q-0.975 37.309-1.187 37.309L-1.187 37.029L-0.131 36.954L-0.131 39.551Q-0.131 39.691 0.001 39.726Q0.132 39.760 0.344 39.760L0.344 40.040M-0.927 35.733Q-0.927 35.562-0.804 35.443Q-0.681 35.323-0.510 35.323Q-0.343 35.323-0.220 35.443Q-0.097 35.562-0.097 35.733Q-0.097 35.908-0.220 36.031Q-0.343 36.154-0.510 36.154Q-0.681 36.154-0.804 36.031Q-0.927 35.908-0.927 35.733M1.516 39.199L1.516 37.302L0.877 37.302L0.877 37.080Q1.195 37.080 1.412 36.870Q1.629 36.660 1.730 36.350Q1.831 36.041 1.831 35.733L2.098 35.733L2.098 37.022L3.174 37.022L3.174 37.302L2.098 37.302L2.098 39.186Q2.098 39.462 2.202 39.661Q2.306 39.859 2.566 39.859Q2.723 39.859 2.829 39.755Q2.935 39.650 2.984 39.497Q3.034 39.343 3.034 39.186L3.034 38.772L3.301 38.772L3.301 39.199Q3.301 39.425 3.202 39.635Q3.102 39.845 2.918 39.977Q2.733 40.108 2.504 40.108Q2.067 40.108 1.792 39.871Q1.516 39.633 1.516 39.199\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(185.63 1.75)\">\u003Cpath d=\"M4.261 41.175Q4.391 41.243 4.528 41.243Q4.699 41.243 4.849 41.154Q5 41.065 5.111 40.920Q5.222 40.775 5.300 40.607L5.564 40.040L4.395 37.514Q4.320 37.367 4.190 37.335Q4.060 37.302 3.827 37.302L3.827 37.022L5.348 37.022L5.348 37.302Q5 37.302 5 37.449Q5.003 37.470 5.005 37.487Q5.007 37.504 5.007 37.514L5.864 39.373L6.637 37.702Q6.671 37.634 6.671 37.555Q6.671 37.442 6.587 37.372Q6.504 37.302 6.391 37.302L6.391 37.022L7.587 37.022L7.587 37.302Q7.368 37.302 7.196 37.406Q7.023 37.511 6.931 37.702L5.594 40.607Q5.424 40.977 5.154 41.223Q4.883 41.469 4.528 41.469Q4.258 41.469 4.039 41.303Q3.820 41.137 3.820 40.874Q3.820 40.737 3.913 40.648Q4.005 40.560 4.145 40.560Q4.282 40.560 4.371 40.648Q4.460 40.737 4.460 40.874Q4.460 40.977 4.407 41.055Q4.354 41.134 4.261 41.175\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-53.812 40.04V-60.39\"\u002F>\u003Cpath stroke=\"none\" d=\"m-53.812-62.39-1.6 3.2 1.6-1.2 1.6 1.2\"\u002F>\u003Cg transform=\"translate(-8.458 -105.963)\">\u003Cpath d=\"M-53.539 38.505Q-53.539 38.184-53.414 37.895Q-53.289 37.606-53.063 37.383Q-52.838 37.159-52.542 37.039Q-52.247 36.919-51.929 36.919Q-51.601 36.919-51.339 37.019Q-51.078 37.118-50.902 37.300Q-50.726 37.483-50.632 37.741Q-50.538 37.999-50.538 38.331Q-50.538 38.423-50.620 38.444L-52.875 38.444L-52.875 38.505Q-52.875 39.093-52.592 39.476Q-52.308 39.859-51.741 39.859Q-51.419 39.859-51.151 39.666Q-50.883 39.473-50.794 39.158Q-50.787 39.117-50.712 39.103L-50.620 39.103Q-50.538 39.127-50.538 39.199Q-50.538 39.206-50.544 39.233Q-50.657 39.630-51.028 39.869Q-51.399 40.108-51.823 40.108Q-52.260 40.108-52.660 39.900Q-53.060 39.691-53.299 39.324Q-53.539 38.957-53.539 38.505M-52.869 38.235L-51.054 38.235Q-51.054 37.958-51.151 37.706Q-51.249 37.453-51.447 37.297Q-51.645 37.142-51.929 37.142Q-52.206 37.142-52.419 37.300Q-52.633 37.459-52.751 37.714Q-52.869 37.969-52.869 38.235M-48.200 40.040L-49.936 40.040L-49.936 39.760Q-49.707 39.760-49.558 39.726Q-49.410 39.691-49.410 39.551L-49.410 37.702Q-49.410 37.432-49.517 37.371Q-49.625 37.309-49.936 37.309L-49.936 37.029L-48.907 36.954L-48.907 37.661Q-48.777 37.353-48.535 37.154Q-48.292 36.954-47.974 36.954Q-47.755 36.954-47.584 37.078Q-47.414 37.203-47.414 37.415Q-47.414 37.552-47.513 37.651Q-47.612 37.750-47.745 37.750Q-47.882 37.750-47.981 37.651Q-48.080 37.552-48.080 37.415Q-48.080 37.275-47.981 37.176Q-48.271 37.176-48.471 37.372Q-48.671 37.569-48.764 37.863Q-48.856 38.157-48.856 38.437L-48.856 39.551Q-48.856 39.760-48.200 39.760L-48.200 40.040M-45.079 40.040L-46.815 40.040L-46.815 39.760Q-46.586 39.760-46.438 39.726Q-46.289 39.691-46.289 39.551L-46.289 37.702Q-46.289 37.432-46.397 37.371Q-46.504 37.309-46.815 37.309L-46.815 37.029L-45.787 36.954L-45.787 37.661Q-45.657 37.353-45.414 37.154Q-45.171 36.954-44.854 36.954Q-44.635 36.954-44.464 37.078Q-44.293 37.203-44.293 37.415Q-44.293 37.552-44.392 37.651Q-44.491 37.750-44.624 37.750Q-44.761 37.750-44.860 37.651Q-44.959 37.552-44.959 37.415Q-44.959 37.275-44.860 37.176Q-45.151 37.176-45.351 37.372Q-45.551 37.569-45.643 37.863Q-45.735 38.157-45.735 38.437L-45.735 39.551Q-45.735 39.760-45.079 39.760L-45.079 40.040M-43.749 38.557Q-43.749 38.215-43.614 37.916Q-43.479 37.617-43.240 37.393Q-43.001 37.169-42.683 37.044Q-42.365 36.919-42.034 36.919Q-41.589 36.919-41.189 37.135Q-40.790 37.350-40.555 37.728Q-40.321 38.105-40.321 38.557Q-40.321 38.898-40.463 39.182Q-40.605 39.466-40.849 39.673Q-41.094 39.879-41.403 39.994Q-41.712 40.108-42.034 40.108Q-42.464 40.108-42.866 39.907Q-43.268 39.705-43.509 39.353Q-43.749 39.001-43.749 38.557M-42.034 39.859Q-41.432 39.859-41.208 39.481Q-40.984 39.103-40.984 38.471Q-40.984 37.859-41.218 37.500Q-41.453 37.142-42.034 37.142Q-43.086 37.142-43.086 38.471Q-43.086 39.103-42.861 39.481Q-42.635 39.859-42.034 39.859M-37.977 40.040L-39.713 40.040L-39.713 39.760Q-39.484 39.760-39.335 39.726Q-39.187 39.691-39.187 39.551L-39.187 37.702Q-39.187 37.432-39.294 37.371Q-39.402 37.309-39.713 37.309L-39.713 37.029L-38.684 36.954L-38.684 37.661Q-38.554 37.353-38.312 37.154Q-38.069 36.954-37.751 36.954Q-37.532 36.954-37.361 37.078Q-37.190 37.203-37.190 37.415Q-37.190 37.552-37.290 37.651Q-37.389 37.750-37.522 37.750Q-37.659 37.750-37.758 37.651Q-37.857 37.552-37.857 37.415Q-37.857 37.275-37.758 37.176Q-38.048 37.176-38.248 37.372Q-38.448 37.569-38.541 37.863Q-38.633 38.157-38.633 38.437L-38.633 39.551Q-38.633 39.760-37.977 39.760\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-45.276-48.164C-8.287.206 31.546 20.123 116.904 30.081\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cg transform=\"translate(175.672 -8.285)\">\u003Cpath d=\"M-52.971 39.199L-52.971 37.302L-53.610 37.302L-53.610 37.080Q-53.292 37.080-53.075 36.870Q-52.858 36.660-52.758 36.350Q-52.657 36.041-52.657 35.733L-52.390 35.733L-52.390 37.022L-51.313 37.022L-51.313 37.302L-52.390 37.302L-52.390 39.186Q-52.390 39.462-52.286 39.661Q-52.182 39.859-51.922 39.859Q-51.765 39.859-51.659 39.755Q-51.553 39.650-51.503 39.497Q-51.454 39.343-51.454 39.186L-51.454 38.772L-51.187 38.772L-51.187 39.199Q-51.187 39.425-51.286 39.635Q-51.385 39.845-51.570 39.977Q-51.754 40.108-51.983 40.108Q-52.421 40.108-52.696 39.871Q-52.971 39.633-52.971 39.199M-48.627 40.040L-50.363 40.040L-50.363 39.760Q-50.134 39.760-49.986 39.726Q-49.837 39.691-49.837 39.551L-49.837 37.702Q-49.837 37.432-49.945 37.371Q-50.052 37.309-50.363 37.309L-50.363 37.029L-49.334 36.954L-49.334 37.661Q-49.205 37.353-48.962 37.154Q-48.719 36.954-48.401 36.954Q-48.183 36.954-48.012 37.078Q-47.841 37.203-47.841 37.415Q-47.841 37.552-47.940 37.651Q-48.039 37.750-48.172 37.750Q-48.309 37.750-48.408 37.651Q-48.507 37.552-48.507 37.415Q-48.507 37.275-48.408 37.176Q-48.699 37.176-48.899 37.372Q-49.099 37.569-49.191 37.863Q-49.283 38.157-49.283 38.437L-49.283 39.551Q-49.283 39.760-48.627 39.760L-48.627 40.040M-47.198 39.312Q-47.198 38.980-46.974 38.753Q-46.750 38.526-46.407 38.398Q-46.063 38.269-45.691 38.217Q-45.318 38.164-45.014 38.164L-45.014 37.911Q-45.014 37.706-45.122 37.526Q-45.229 37.347-45.411 37.244Q-45.592 37.142-45.800 37.142Q-46.207 37.142-46.443 37.234Q-46.354 37.271-46.308 37.355Q-46.262 37.439-46.262 37.541Q-46.262 37.637-46.308 37.716Q-46.354 37.794-46.434 37.839Q-46.515 37.883-46.604 37.883Q-46.754 37.883-46.855 37.786Q-46.956 37.688-46.956 37.541Q-46.956 36.919-45.800 36.919Q-45.588 36.919-45.339 36.983Q-45.089 37.046-44.888 37.165Q-44.686 37.285-44.560 37.470Q-44.433 37.654-44.433 37.897L-44.433 39.473Q-44.433 39.589-44.372 39.685Q-44.310 39.780-44.197 39.780Q-44.088 39.780-44.023 39.686Q-43.958 39.592-43.958 39.473L-43.958 39.025L-43.691 39.025L-43.691 39.473Q-43.691 39.743-43.919 39.908Q-44.146 40.074-44.426 40.074Q-44.635 40.074-44.771 39.920Q-44.908 39.767-44.932 39.551Q-45.079 39.818-45.361 39.963Q-45.643 40.108-45.968 40.108Q-46.245 40.108-46.528 40.033Q-46.812 39.958-47.005 39.779Q-47.198 39.599-47.198 39.312M-46.583 39.312Q-46.583 39.486-46.482 39.616Q-46.381 39.746-46.226 39.816Q-46.070 39.886-45.906 39.886Q-45.687 39.886-45.479 39.789Q-45.270 39.691-45.142 39.510Q-45.014 39.329-45.014 39.103L-45.014 38.375Q-45.339 38.375-45.705 38.466Q-46.070 38.557-46.327 38.769Q-46.583 38.980-46.583 39.312M-41.658 40.040L-43.209 40.040L-43.209 39.760Q-42.984 39.760-42.835 39.726Q-42.687 39.691-42.687 39.551L-42.687 37.702Q-42.687 37.514-42.734 37.430Q-42.782 37.347-42.880 37.328Q-42.977 37.309-43.189 37.309L-43.189 37.029L-42.133 36.954L-42.133 39.551Q-42.133 39.691-42.001 39.726Q-41.870 39.760-41.658 39.760L-41.658 40.040M-42.929 35.733Q-42.929 35.562-42.806 35.443Q-42.683 35.323-42.512 35.323Q-42.345 35.323-42.222 35.443Q-42.099 35.562-42.099 35.733Q-42.099 35.908-42.222 36.031Q-42.345 36.154-42.512 36.154Q-42.683 36.154-42.806 36.031Q-42.929 35.908-42.929 35.733M-39.330 40.040L-40.964 40.040L-40.964 39.760Q-40.735 39.760-40.586 39.726Q-40.437 39.691-40.437 39.551L-40.437 37.702Q-40.437 37.432-40.545 37.371Q-40.653 37.309-40.964 37.309L-40.964 37.029L-39.904 36.954L-39.904 37.603Q-39.733 37.295-39.429 37.124Q-39.125 36.954-38.780 36.954Q-38.274 36.954-37.990 37.177Q-37.707 37.401-37.707 37.897L-37.707 39.551Q-37.707 39.688-37.558 39.724Q-37.409 39.760-37.184 39.760L-37.184 40.040L-38.814 40.040L-38.814 39.760Q-38.585 39.760-38.436 39.726Q-38.288 39.691-38.288 39.551L-38.288 37.911Q-38.288 37.576-38.407 37.376Q-38.527 37.176-38.841 37.176Q-39.111 37.176-39.345 37.312Q-39.580 37.449-39.718 37.683Q-39.856 37.917-39.856 38.191L-39.856 39.551Q-39.856 39.688-39.706 39.724Q-39.556 39.760-39.330 39.760L-39.330 40.040M-34.979 40.040L-36.531 40.040L-36.531 39.760Q-36.305 39.760-36.156 39.726Q-36.008 39.691-36.008 39.551L-36.008 37.702Q-36.008 37.514-36.056 37.430Q-36.104 37.347-36.201 37.328Q-36.298 37.309-36.510 37.309L-36.510 37.029L-35.454 36.954L-35.454 39.551Q-35.454 39.691-35.322 39.726Q-35.191 39.760-34.979 39.760L-34.979 40.040M-36.250 35.733Q-36.250 35.562-36.127 35.443Q-36.004 35.323-35.833 35.323Q-35.666 35.323-35.543 35.443Q-35.420 35.562-35.420 35.733Q-35.420 35.908-35.543 36.031Q-35.666 36.154-35.833 36.154Q-36.004 36.154-36.127 36.031Q-36.250 35.908-36.250 35.733M-32.651 40.040L-34.285 40.040L-34.285 39.760Q-34.056 39.760-33.907 39.726Q-33.759 39.691-33.759 39.551L-33.759 37.702Q-33.759 37.432-33.866 37.371Q-33.974 37.309-34.285 37.309L-34.285 37.029L-33.226 36.954L-33.226 37.603Q-33.055 37.295-32.750 37.124Q-32.446 36.954-32.101 36.954Q-31.595 36.954-31.312 37.177Q-31.028 37.401-31.028 37.897L-31.028 39.551Q-31.028 39.688-30.879 39.724Q-30.730 39.760-30.505 39.760L-30.505 40.040L-32.135 40.040L-32.135 39.760Q-31.906 39.760-31.758 39.726Q-31.609 39.691-31.609 39.551L-31.609 37.911Q-31.609 37.576-31.729 37.376Q-31.848 37.176-32.163 37.176Q-32.433 37.176-32.667 37.312Q-32.901 37.449-33.039 37.683Q-33.178 37.917-33.178 38.191L-33.178 39.551Q-33.178 39.688-33.027 39.724Q-32.877 39.760-32.651 39.760L-32.651 40.040M-29.958 40.573Q-29.958 40.327-29.761 40.143Q-29.565 39.958-29.309 39.879Q-29.445 39.767-29.517 39.606Q-29.589 39.445-29.589 39.264Q-29.589 38.943-29.377 38.697Q-29.712 38.399-29.712 37.989Q-29.712 37.528-29.322 37.241Q-28.933 36.954-28.454 36.954Q-27.982 36.954-27.647 37.200Q-27.473 37.046-27.263 36.964Q-27.053 36.882-26.824 36.882Q-26.660 36.882-26.538 36.989Q-26.417 37.097-26.417 37.261Q-26.417 37.357-26.489 37.429Q-26.561 37.500-26.653 37.500Q-26.752 37.500-26.822 37.427Q-26.892 37.353-26.892 37.254Q-26.892 37.200-26.878 37.169L-26.872 37.155Q-26.865 37.135-26.856 37.124Q-26.848 37.114-26.844 37.107Q-27.200 37.107-27.487 37.330Q-27.200 37.623-27.200 37.989Q-27.200 38.304-27.384 38.536Q-27.569 38.769-27.858 38.897Q-28.146 39.025-28.454 39.025Q-28.656 39.025-28.847 38.975Q-29.039 38.926-29.216 38.816Q-29.309 38.943-29.309 39.086Q-29.309 39.268-29.180 39.403Q-29.052 39.538-28.868 39.538L-28.235 39.538Q-27.788 39.538-27.418 39.609Q-27.049 39.681-26.790 39.910Q-26.530 40.139-26.530 40.573Q-26.530 40.894-26.825 41.096Q-27.121 41.298-27.524 41.387Q-27.928 41.476-28.242 41.476Q-28.560 41.476-28.963 41.387Q-29.367 41.298-29.662 41.096Q-29.958 40.894-29.958 40.573M-29.503 40.573Q-29.503 40.802-29.285 40.951Q-29.066 41.100-28.774 41.168Q-28.481 41.236-28.242 41.236Q-28.078 41.236-27.870 41.200Q-27.661 41.165-27.454 41.084Q-27.248 41.004-27.116 40.876Q-26.984 40.748-26.984 40.573Q-26.984 40.221-27.365 40.127Q-27.747 40.033-28.249 40.033L-28.868 40.033Q-29.107 40.033-29.305 40.184Q-29.503 40.334-29.503 40.573M-28.454 38.786Q-27.788 38.786-27.788 37.989Q-27.788 37.189-28.454 37.189Q-29.124 37.189-29.124 37.989Q-29.124 38.786-28.454 38.786\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-warn)\" d=\"M-45.276-53.854C-2.597-2.64 20.165 8.742 42.927 10.164c28.453 1.423 51.215-27.03 73.977-52.637\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\">\u003Cg fill=\"var(--tk-warn)\" stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(175.672 -80.082)\">\u003Cpath d=\"M-51.908 40.013L-53.036 37.514Q-53.108 37.367-53.238 37.335Q-53.368 37.302-53.597 37.302L-53.597 37.022L-52.083 37.022L-52.083 37.302Q-52.435 37.302-52.435 37.449Q-52.435 37.494-52.424 37.514L-51.560 39.432L-50.780 37.702Q-50.746 37.634-50.746 37.555Q-50.746 37.442-50.830 37.372Q-50.914 37.302-51.033 37.302L-51.033 37.022L-49.837 37.022L-49.837 37.302Q-50.056 37.302-50.227 37.405Q-50.397 37.507-50.486 37.702L-51.522 40.013Q-51.570 40.108-51.676 40.108L-51.754 40.108Q-51.860 40.108-51.908 40.013\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(175.672 -80.082)\">\u003Cpath d=\"M-49.668 39.312Q-49.668 38.980-49.445 38.753Q-49.221 38.526-48.877 38.398Q-48.534 38.269-48.161 38.217Q-47.789 38.164-47.484 38.164L-47.484 37.911Q-47.484 37.706-47.592 37.526Q-47.700 37.347-47.881 37.244Q-48.062 37.142-48.270 37.142Q-48.677 37.142-48.913 37.234Q-48.824 37.271-48.778 37.355Q-48.732 37.439-48.732 37.541Q-48.732 37.637-48.778 37.716Q-48.824 37.794-48.905 37.839Q-48.985 37.883-49.074 37.883Q-49.224 37.883-49.325 37.786Q-49.426 37.688-49.426 37.541Q-49.426 36.919-48.270 36.919Q-48.059 36.919-47.809 36.983Q-47.560 37.046-47.358 37.165Q-47.156 37.285-47.030 37.470Q-46.903 37.654-46.903 37.897L-46.903 39.473Q-46.903 39.589-46.842 39.685Q-46.780 39.780-46.667 39.780Q-46.558 39.780-46.493 39.686Q-46.428 39.592-46.428 39.473L-46.428 39.025L-46.162 39.025L-46.162 39.473Q-46.162 39.743-46.389 39.908Q-46.616 40.074-46.896 40.074Q-47.105 40.074-47.242 39.920Q-47.378 39.767-47.402 39.551Q-47.549 39.818-47.831 39.963Q-48.113 40.108-48.438 40.108Q-48.715 40.108-48.999 40.033Q-49.282 39.958-49.475 39.779Q-49.668 39.599-49.668 39.312M-49.053 39.312Q-49.053 39.486-48.952 39.616Q-48.852 39.746-48.696 39.816Q-48.541 39.886-48.376 39.886Q-48.158 39.886-47.949 39.789Q-47.741 39.691-47.613 39.510Q-47.484 39.329-47.484 39.103L-47.484 38.375Q-47.809 38.375-48.175 38.466Q-48.541 38.557-48.797 38.769Q-49.053 38.980-49.053 39.312M-44.077 40.040L-45.680 40.040L-45.680 39.760Q-45.454 39.760-45.305 39.726Q-45.157 39.691-45.157 39.551L-45.157 35.932Q-45.157 35.662-45.264 35.600Q-45.372 35.539-45.680 35.539L-45.680 35.258L-44.603 35.183L-44.603 39.551Q-44.603 39.688-44.453 39.724Q-44.302 39.760-44.077 39.760L-44.077 40.040M-41.865 40.040L-43.417 40.040L-43.417 39.760Q-43.191 39.760-43.043 39.726Q-42.894 39.691-42.894 39.551L-42.894 37.702Q-42.894 37.514-42.942 37.430Q-42.990 37.347-43.087 37.328Q-43.185 37.309-43.396 37.309L-43.396 37.029L-42.340 36.954L-42.340 39.551Q-42.340 39.691-42.209 39.726Q-42.077 39.760-41.865 39.760L-41.865 40.040M-43.137 35.733Q-43.137 35.562-43.014 35.443Q-42.891 35.323-42.720 35.323Q-42.552 35.323-42.429 35.443Q-42.306 35.562-42.306 35.733Q-42.306 35.908-42.429 36.031Q-42.552 36.154-42.720 36.154Q-42.891 36.154-43.014 36.031Q-43.137 35.908-43.137 35.733M-41.219 38.529Q-41.219 38.191-41.079 37.900Q-40.939 37.610-40.695 37.396Q-40.450 37.183-40.146 37.068Q-39.842 36.954-39.517 36.954Q-39.247 36.954-38.984 37.053Q-38.721 37.152-38.529 37.330L-38.529 35.932Q-38.529 35.662-38.637 35.600Q-38.745 35.539-39.056 35.539L-39.056 35.258L-37.979 35.183L-37.979 39.367Q-37.979 39.555-37.924 39.638Q-37.870 39.722-37.769 39.741Q-37.668 39.760-37.453 39.760L-37.453 40.040L-38.560 40.108L-38.560 39.691Q-38.977 40.108-39.603 40.108Q-40.033 40.108-40.406 39.896Q-40.778 39.685-40.999 39.324Q-41.219 38.963-41.219 38.529M-39.544 39.886Q-39.336 39.886-39.150 39.814Q-38.963 39.743-38.810 39.606Q-38.656 39.469-38.560 39.291L-38.560 37.682Q-38.645 37.535-38.791 37.415Q-38.936 37.295-39.105 37.236Q-39.274 37.176-39.456 37.176Q-40.016 37.176-40.284 37.565Q-40.553 37.955-40.553 38.536Q-40.553 39.107-40.319 39.497Q-40.084 39.886-39.544 39.886M-36.745 39.312Q-36.745 38.980-36.521 38.753Q-36.297 38.526-35.954 38.398Q-35.610 38.269-35.238 38.217Q-34.865 38.164-34.561 38.164L-34.561 37.911Q-34.561 37.706-34.669 37.526Q-34.776 37.347-34.958 37.244Q-35.139 37.142-35.347 37.142Q-35.754 37.142-35.990 37.234Q-35.901 37.271-35.855 37.355Q-35.809 37.439-35.809 37.541Q-35.809 37.637-35.855 37.716Q-35.901 37.794-35.981 37.839Q-36.062 37.883-36.150 37.883Q-36.301 37.883-36.402 37.786Q-36.502 37.688-36.502 37.541Q-36.502 36.919-35.347 36.919Q-35.135 36.919-34.886 36.983Q-34.636 37.046-34.435 37.165Q-34.233 37.285-34.106 37.470Q-33.980 37.654-33.980 37.897L-33.980 39.473Q-33.980 39.589-33.918 39.685Q-33.857 39.780-33.744 39.780Q-33.635 39.780-33.570 39.686Q-33.505 39.592-33.505 39.473L-33.505 39.025L-33.238 39.025L-33.238 39.473Q-33.238 39.743-33.466 39.908Q-33.693 40.074-33.973 40.074Q-34.182 40.074-34.318 39.920Q-34.455 39.767-34.479 39.551Q-34.626 39.818-34.908 39.963Q-35.190 40.108-35.515 40.108Q-35.791 40.108-36.075 40.033Q-36.359 39.958-36.552 39.779Q-36.745 39.599-36.745 39.312M-36.130 39.312Q-36.130 39.486-36.029 39.616Q-35.928 39.746-35.773 39.816Q-35.617 39.886-35.453 39.886Q-35.234 39.886-35.026 39.789Q-34.817 39.691-34.689 39.510Q-34.561 39.329-34.561 39.103L-34.561 38.375Q-34.886 38.375-35.251 38.466Q-35.617 38.557-35.874 38.769Q-36.130 38.980-36.130 39.312M-32.295 39.199L-32.295 37.302L-32.934 37.302L-32.934 37.080Q-32.616 37.080-32.399 36.870Q-32.182 36.660-32.081 36.350Q-31.980 36.041-31.980 35.733L-31.714 35.733L-31.714 37.022L-30.637 37.022L-30.637 37.302L-31.714 37.302L-31.714 39.186Q-31.714 39.462-31.610 39.661Q-31.505 39.859-31.246 39.859Q-31.088 39.859-30.982 39.755Q-30.876 39.650-30.827 39.497Q-30.777 39.343-30.777 39.186L-30.777 38.772L-30.511 38.772L-30.511 39.199Q-30.511 39.425-30.610 39.635Q-30.709 39.845-30.894 39.977Q-31.078 40.108-31.307 40.108Q-31.745 40.108-32.020 39.871Q-32.295 39.633-32.295 39.199M-28.084 40.040L-29.636 40.040L-29.636 39.760Q-29.410 39.760-29.261 39.726Q-29.113 39.691-29.113 39.551L-29.113 37.702Q-29.113 37.514-29.161 37.430Q-29.208 37.347-29.306 37.328Q-29.403 37.309-29.615 37.309L-29.615 37.029L-28.559 36.954L-28.559 39.551Q-28.559 39.691-28.427 39.726Q-28.296 39.760-28.084 39.760L-28.084 40.040M-29.355 35.733Q-29.355 35.562-29.232 35.443Q-29.109 35.323-28.938 35.323Q-28.771 35.323-28.648 35.443Q-28.525 35.562-28.525 35.733Q-28.525 35.908-28.648 36.031Q-28.771 36.154-28.938 36.154Q-29.109 36.154-29.232 36.031Q-29.355 35.908-29.355 35.733M-27.479 38.557Q-27.479 38.215-27.344 37.916Q-27.209 37.617-26.970 37.393Q-26.730 37.169-26.413 37.044Q-26.095 36.919-25.763 36.919Q-25.319 36.919-24.919 37.135Q-24.519 37.350-24.285 37.728Q-24.051 38.105-24.051 38.557Q-24.051 38.898-24.193 39.182Q-24.334 39.466-24.579 39.673Q-24.823 39.879-25.133 39.994Q-25.442 40.108-25.763 40.108Q-26.194 40.108-26.595 39.907Q-26.997 39.705-27.238 39.353Q-27.479 39.001-27.479 38.557M-25.763 39.859Q-25.162 39.859-24.938 39.481Q-24.714 39.103-24.714 38.471Q-24.714 37.859-24.948 37.500Q-25.182 37.142-25.763 37.142Q-26.816 37.142-26.816 38.471Q-26.816 39.103-26.590 39.481Q-26.365 39.859-25.763 39.859M-21.774 40.040L-23.408 40.040L-23.408 39.760Q-23.179 39.760-23.031 39.726Q-22.882 39.691-22.882 39.551L-22.882 37.702Q-22.882 37.432-22.989 37.371Q-23.097 37.309-23.408 37.309L-23.408 37.029L-22.349 36.954L-22.349 37.603Q-22.178 37.295-21.874 37.124Q-21.569 36.954-21.224 36.954Q-20.718 36.954-20.435 37.177Q-20.151 37.401-20.151 37.897L-20.151 39.551Q-20.151 39.688-20.002 39.724Q-19.853 39.760-19.628 39.760L-19.628 40.040L-21.258 40.040L-21.258 39.760Q-21.029 39.760-20.881 39.726Q-20.732 39.691-20.732 39.551L-20.732 37.911Q-20.732 37.576-20.852 37.376Q-20.971 37.176-21.286 37.176Q-21.556 37.176-21.790 37.312Q-22.024 37.449-22.162 37.683Q-22.301 37.917-22.301 38.191L-22.301 39.551Q-22.301 39.688-22.150 39.724Q-22 39.760-21.774 39.760\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M42.927 40.04V10.164\" style=\"stroke-dasharray:3.0,3.0\"\u002F>\u003Cpath fill=\"var(--tk-warn)\" stroke=\"none\" d=\"M44.927 10.164a2 2 0 1 0-4 0 2 2 0 0 0 4 0m-2 0\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr7\" font-size=\"7\">\u003Cg transform=\"translate(89.499 11.24)\">\u003Cpath d=\"M-52.691 40.040L-52.958 40.040L-52.958 35.932Q-52.958 35.662-53.065 35.600Q-53.173 35.539-53.484 35.539L-53.484 35.258L-52.404 35.183L-52.404 37.353Q-52.195 37.162-51.910 37.058Q-51.624 36.954-51.327 36.954Q-51.009 36.954-50.712 37.075Q-50.415 37.196-50.192 37.412Q-49.970 37.627-49.844 37.912Q-49.717 38.198-49.717 38.529Q-49.717 38.974-49.957 39.338Q-50.196 39.702-50.589 39.905Q-50.982 40.108-51.426 40.108Q-51.621 40.108-51.811 40.052Q-52 39.996-52.161 39.891Q-52.322 39.787-52.462 39.626L-52.691 40.040M-52.376 37.695L-52.376 39.312Q-52.240 39.572-51.999 39.729Q-51.758 39.886-51.481 39.886Q-51.187 39.886-50.975 39.779Q-50.763 39.671-50.630 39.479Q-50.497 39.288-50.438 39.049Q-50.380 38.810-50.380 38.529Q-50.380 38.170-50.474 37.866Q-50.568 37.562-50.796 37.369Q-51.023 37.176-51.389 37.176Q-51.689 37.176-51.956 37.312Q-52.223 37.449-52.376 37.695\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(89.499 11.24)\">\u003Cpath d=\"M-48.907 38.505Q-48.907 38.184-48.782 37.895Q-48.657 37.606-48.431 37.383Q-48.206 37.159-47.910 37.039Q-47.615 36.919-47.297 36.919Q-46.969 36.919-46.707 37.019Q-46.446 37.118-46.270 37.300Q-46.094 37.483-46 37.741Q-45.906 37.999-45.906 38.331Q-45.906 38.423-45.988 38.444L-48.243 38.444L-48.243 38.505Q-48.243 39.093-47.960 39.476Q-47.676 39.859-47.109 39.859Q-46.787 39.859-46.519 39.666Q-46.251 39.473-46.162 39.158Q-46.155 39.117-46.080 39.103L-45.988 39.103Q-45.906 39.127-45.906 39.199Q-45.906 39.206-45.912 39.233Q-46.025 39.630-46.396 39.869Q-46.767 40.108-47.191 40.108Q-47.628 40.108-48.028 39.900Q-48.428 39.691-48.667 39.324Q-48.907 38.957-48.907 38.505M-48.237 38.235L-46.422 38.235Q-46.422 37.958-46.519 37.706Q-46.617 37.453-46.815 37.297Q-47.013 37.142-47.297 37.142Q-47.574 37.142-47.787 37.300Q-48.001 37.459-48.119 37.714Q-48.237 37.969-48.237 38.235M-45.318 40.033L-45.318 38.970Q-45.318 38.946-45.290 38.919Q-45.263 38.892-45.239 38.892L-45.130 38.892Q-45.065 38.892-45.051 38.950Q-44.955 39.384-44.709 39.635Q-44.463 39.886-44.050 39.886Q-43.708 39.886-43.455 39.753Q-43.202 39.620-43.202 39.312Q-43.202 39.155-43.296 39.040Q-43.390 38.926-43.528 38.857Q-43.667 38.789-43.834 38.751L-44.415 38.652Q-44.771 38.584-45.044 38.363Q-45.318 38.143-45.318 37.801Q-45.318 37.552-45.207 37.377Q-45.096 37.203-44.909 37.104Q-44.723 37.005-44.508 36.962Q-44.292 36.919-44.050 36.919Q-43.636 36.919-43.356 37.101L-43.140 36.926Q-43.130 36.923-43.123 36.921Q-43.117 36.919-43.106 36.919L-43.055 36.919Q-43.028 36.919-43.004 36.943Q-42.980 36.967-42.980 36.995L-42.980 37.842Q-42.980 37.863-43.004 37.890Q-43.028 37.917-43.055 37.917L-43.168 37.917Q-43.195 37.917-43.221 37.892Q-43.246 37.866-43.246 37.842Q-43.246 37.606-43.352 37.442Q-43.458 37.278-43.641 37.196Q-43.824 37.114-44.056 37.114Q-44.385 37.114-44.641 37.217Q-44.897 37.319-44.897 37.596Q-44.897 37.791-44.714 37.900Q-44.532 38.010-44.303 38.051L-43.728 38.157Q-43.482 38.205-43.269 38.333Q-43.055 38.461-42.918 38.664Q-42.782 38.868-42.782 39.117Q-42.782 39.630-43.147 39.869Q-43.513 40.108-44.050 40.108Q-44.545 40.108-44.877 39.814L-45.143 40.088Q-45.164 40.108-45.191 40.108L-45.239 40.108Q-45.263 40.108-45.290 40.081Q-45.318 40.054-45.318 40.033M-41.626 39.199L-41.626 37.302L-42.265 37.302L-42.265 37.080Q-41.948 37.080-41.731 36.870Q-41.513 36.660-41.413 36.350Q-41.312 36.041-41.312 35.733L-41.045 35.733L-41.045 37.022L-39.969 37.022L-39.969 37.302L-41.045 37.302L-41.045 39.186Q-41.045 39.462-40.941 39.661Q-40.837 39.859-40.577 39.859Q-40.420 39.859-40.314 39.755Q-40.208 39.650-40.158 39.497Q-40.109 39.343-40.109 39.186L-40.109 38.772L-39.842 38.772L-39.842 39.199Q-39.842 39.425-39.941 39.635Q-40.040 39.845-40.225 39.977Q-40.409 40.108-40.638 40.108Q-41.076 40.108-41.351 39.871Q-41.626 39.633-41.626 39.199\" 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\">Model selection by validation error. Training error falls monotonically with complexity; validation error is U-shaped, and its minimum marks the model that best balances underfitting against overfitting.\u003C\u002Ffigcaption>",1785117813676]