[{"data":1,"prerenderedAt":19777},["ShallowReactive",2],{"lesson:\u002Fdeep-learning\u002Ftheory\u002Fgeneralization-theory":3,"course-wordcounts":13999,"ref-card-index":14911,"nav:deep-learning":19590,"tikz:ba20426fc01f94d2ed27084bfc83090249c7b5a21869e6025315893031972725":19770,"tikz:da6e8d45d686af82195a5202a8f7fadacd8ff4a810fff8f7df1110a25f1705d2":19771,"tikz:49480abebc3b447e544942b1662625853d989a986d0e6a0b09011ca0be4c98eb":19772,"tikz:37893215db287baad82953b1fa4b3753797c7edcad5f249b0836ab7de7ecd099":19773,"tikz:c834927dafa54ab7d1c1e2b65bf97e6c45a9631e397838f1b21de1583ba28048":19774,"tikz:78f7e7a520af78447d08c56ab796f14e534aa3e658138bd83345d2974b19ea9a":19775,"tikz:fa8cdea36b497c464e5a9f76ca2e967b6fa540d45d49c5e403a80f08ca52e760":19776},{"id":4,"title":5,"blurb":6,"body":7,"brief":13967,"category":13968,"description":13969,"draft":13970,"extension":13971,"meta":13972,"module":13976,"navigation":13977,"path":13978,"practice":13979,"rawbody":13980,"readingTime":13981,"seo":13986,"sources":13987,"status":13994,"stem":13995,"summary":13996,"topics":13997,"__hash__":13998},"course\u002F06.deep-learning\u002F06.theory\u002F01.generalization-theory.md","Generalization Theory","",{"type":8,"value":9,"toc":13951},"minimark",[10,279,867,1018,1301,1828,1833,1868,1908,1919,2252,2644,2648,2651,3254,4215,4218,4679,4835,4838,4843,5245,6328,6675,6682,7431,8278,8730,9017,9108,9112,9157,9201,9673,9676,9692,10093,10114,10132,10135,10139,10150,10162,10165,10172,10225,10241,10245,10333,10782,10801,11504,11613,11616,11620,11638,11646,11649,11848,12441,13316,13328,13331,13334,13351,13355,13362,13365,13395,13408,13412,13415,13673,13677,13689,13812,13815,13819,13834,13860],[11,12,13,14,18,19,253,254,257,258,261,262,133],"p",{},"Everything else in this subject is machinery for driving the ",[15,16,17],"strong",{},"training"," loss\ndown. Generalization theory addresses the question that remains once the training\nloss is small: does a low loss on the sample ",[20,21,24],"span",{"className":22},[23],"katex",[20,25,29,58],{"className":26,"ariaHidden":28},[27],"katex-html","true",[20,30,33,38,45,50,55],{"className":31},[32],"base",[20,34],{"className":35,"style":37},[36],"strut","height:0.6833em;",[20,39,44],{"className":40,"style":43},[41,42],"mord","mathcal","margin-right:0.0278em;","D",[20,46],{"className":47,"style":49},[48],"mspace","margin-right:0.2778em;",[20,51,54],{"className":52},[53],"mrel","=",[20,56],{"className":57,"style":49},[48],[20,59,61,65,70,129,134,138,181,186],{"className":60},[32],[20,62],{"className":63,"style":64},[36],"height:1.0087em;vertical-align:-0.2587em;",[20,66,69],{"className":67},[68],"mopen","{(",[20,71,73,78],{"className":72},[41],[20,74,77],{"className":75},[41,76],"mathnormal","x",[20,79,82],{"className":80},[81],"msupsub",[20,83,87,120],{"className":84},[85,86],"vlist-t","vlist-t2",[20,88,91,115],{"className":89},[90],"vlist-r",[20,92,96],{"className":93,"style":95},[94],"vlist","height:0.3117em;",[20,97,99,104],{"style":98},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[20,100],{"className":101,"style":103},[102],"pstrut","height:2.7em;",[20,105,111],{"className":106},[107,108,109,110],"sizing","reset-size6","size3","mtight",[20,112,114],{"className":113},[41,76,110],"i",[20,116,119],{"className":117},[118],"vlist-s","​",[20,121,123],{"className":122},[90],[20,124,127],{"className":125,"style":126},[94],"height:0.15em;",[20,128],{},[20,130,133],{"className":131},[132],"mpunct",",",[20,135],{"className":136,"style":137},[48],"margin-right:0.1667em;",[20,139,141,146],{"className":140},[41],[20,142,145],{"className":143,"style":144},[41,76],"margin-right:0.0359em;","y",[20,147,149],{"className":148},[81],[20,150,152,173],{"className":151},[85,86],[20,153,155,170],{"className":154},[90],[20,156,158],{"className":157,"style":95},[94],[20,159,161,164],{"style":160},"top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;",[20,162],{"className":163,"style":103},[102],[20,165,167],{"className":166},[107,108,109,110],[20,168,114],{"className":169},[41,76,110],[20,171,119],{"className":172},[118],[20,174,176],{"className":175},[90],[20,177,179],{"className":178,"style":126},[94],[20,180],{},[20,182,185],{"className":183},[184],"mclose",")",[20,187,189,193],{"className":188},[184],[20,190,192],{"className":191},[184],"}",[20,194,196],{"className":195},[81],[20,197,199,244],{"className":198},[85,86],[20,200,202,241],{"className":201},[90],[20,203,206,228],{"className":204,"style":205},[94],"height:0.6644em;",[20,207,209,212],{"style":208},"top:-2.4413em;margin-left:0em;margin-right:0.05em;",[20,210],{"className":211,"style":103},[102],[20,213,215],{"className":214},[107,108,109,110],[20,216,218,221,224],{"className":217},[41,110],[20,219,114],{"className":220},[41,76,110],[20,222,54],{"className":223},[53,110],[20,225,227],{"className":226},[41,110],"1",[20,229,231,234],{"style":230},"top:-3.063em;margin-right:0.05em;",[20,232],{"className":233,"style":103},[102],[20,235,237],{"className":236},[107,108,109,110],[20,238,240],{"className":239},[41,76,110],"n",[20,242,119],{"className":243},[118],[20,245,247],{"className":246},[90],[20,248,251],{"className":249,"style":250},[94],"height:0.2587em;",[20,252],{},"\nsay anything about loss on data we have not seen? Write the two quantities the\ntheory compares, the ",[15,255,256],{},"empirical risk"," on the sample and the ",[15,259,260],{},"population risk","\nunder the true distribution ",[20,263,265],{"className":264},[23],[20,266,268],{"className":267,"ariaHidden":28},[27],[20,269,271,274],{"className":270},[32],[20,272],{"className":273,"style":37},[36],[20,275,278],{"className":276,"style":277},[41,42],"margin-right:0.0822em;","P",[20,280,283],{"className":281},[282],"katex-display",[20,284,286],{"className":285},[23],[20,287,289,357,705],{"className":288,"ariaHidden":28},[27],[20,290,292,296,337,341,345,348,351,354],{"className":291},[32],[20,293],{"className":294,"style":295},[36],"height:1.1968em;vertical-align:-0.25em;",[20,297,300],{"className":298},[41,299],"accent",[20,301,303],{"className":302},[85],[20,304,306],{"className":305},[90],[20,307,310,322],{"className":308,"style":309},[94],"height:0.9468em;",[20,311,313,317],{"style":312},"top:-3em;",[20,314],{"className":315,"style":316},[102],"height:3em;",[20,318,321],{"className":319,"style":320},[41,76],"margin-right:0.0077em;","R",[20,323,325,328],{"style":324},"top:-3.2523em;",[20,326],{"className":327,"style":316},[102],[20,329,333],{"className":330,"style":332},[331],"accent-body","left:-0.1667em;",[20,334,336],{"className":335},[41],"^",[20,338,340],{"className":339},[68],"(",[20,342,344],{"className":343,"style":43},[41,76],"θ",[20,346,185],{"className":347},[184],[20,349],{"className":350,"style":49},[48],[20,352,54],{"className":353},[53],[20,355],{"className":356,"style":49},[48],[20,358,360,364,435,438,513,516,520,523,671,674,677,681,684,687,690,693,696,699,702],{"className":359},[32],[20,361],{"className":362,"style":363},[36],"height:2.9291em;vertical-align:-1.2777em;",[20,365,367,371,432],{"className":366},[41],[20,368],{"className":369},[68,370],"nulldelimiter",[20,372,375],{"className":373},[374],"mfrac",[20,376,378,423],{"className":377},[85,86],[20,379,381,420],{"className":380},[90],[20,382,385,397,408],{"className":383,"style":384},[94],"height:1.3214em;",[20,386,388,391],{"style":387},"top:-2.314em;",[20,389],{"className":390,"style":316},[102],[20,392,394],{"className":393},[41],[20,395,240],{"className":396},[41,76],[20,398,400,403],{"style":399},"top:-3.23em;",[20,401],{"className":402,"style":316},[102],[20,404],{"className":405,"style":407},[406],"frac-line","border-bottom-width:0.04em;",[20,409,411,414],{"style":410},"top:-3.677em;",[20,412],{"className":413,"style":316},[102],[20,415,417],{"className":416},[41],[20,418,227],{"className":419},[41],[20,421,119],{"className":422},[118],[20,424,426],{"className":425},[90],[20,427,430],{"className":428,"style":429},[94],"height:0.686em;",[20,431],{},[20,433],{"className":434},[184,370],[20,436],{"className":437,"style":137},[48],[20,439,443],{"className":440},[441,442],"mop","op-limits",[20,444,446,504],{"className":445},[85,86],[20,447,449,501],{"className":448},[90],[20,450,453,475,489],{"className":451,"style":452},[94],"height:1.6514em;",[20,454,456,460],{"style":455},"top:-1.8723em;margin-left:0em;",[20,457],{"className":458,"style":459},[102],"height:3.05em;",[20,461,463],{"className":462},[107,108,109,110],[20,464,466,469,472],{"className":465},[41,110],[20,467,114],{"className":468},[41,76,110],[20,470,54],{"className":471},[53,110],[20,473,227],{"className":474},[41,110],[20,476,478,481],{"style":477},"top:-3.05em;",[20,479],{"className":480,"style":459},[102],[20,482,483],{},[20,484,488],{"className":485},[441,486,487],"op-symbol","large-op","∑",[20,490,492,495],{"style":491},"top:-4.3em;margin-left:0em;",[20,493],{"className":494,"style":459},[102],[20,496,498],{"className":497},[107,108,109,110],[20,499,240],{"className":500},[41,76,110],[20,502,119],{"className":503},[118],[20,505,507],{"className":506},[90],[20,508,511],{"className":509,"style":510},[94],"height:1.2777em;",[20,512],{},[20,514],{"className":515,"style":137},[48],[20,517,519],{"className":518},[41],"ℓ",[20,521],{"className":522,"style":137},[48],[20,524,527,532,576,579,619,622,625,628,668],{"className":525},[526],"minner",[20,528,340],{"className":529,"style":531},[68,530],"delimcenter","top:0em;",[20,533,535,540],{"className":534},[41],[20,536,539],{"className":537,"style":538},[41,76],"margin-right:0.1076em;","f",[20,541,543],{"className":542},[81],[20,544,546,568],{"className":545},[85,86],[20,547,549,565],{"className":548},[90],[20,550,553],{"className":551,"style":552},[94],"height:0.3361em;",[20,554,556,559],{"style":555},"top:-2.55em;margin-left:-0.1076em;margin-right:0.05em;",[20,557],{"className":558,"style":103},[102],[20,560,562],{"className":561},[107,108,109,110],[20,563,344],{"className":564,"style":43},[41,76,110],[20,566,119],{"className":567},[118],[20,569,571],{"className":570},[90],[20,572,574],{"className":573,"style":126},[94],[20,575],{},[20,577,340],{"className":578},[68],[20,580,582,585],{"className":581},[41],[20,583,77],{"className":584},[41,76],[20,586,588],{"className":587},[81],[20,589,591,611],{"className":590},[85,86],[20,592,594,608],{"className":593},[90],[20,595,597],{"className":596,"style":95},[94],[20,598,599,602],{"style":98},[20,600],{"className":601,"style":103},[102],[20,603,605],{"className":604},[107,108,109,110],[20,606,114],{"className":607},[41,76,110],[20,609,119],{"className":610},[118],[20,612,614],{"className":613},[90],[20,615,617],{"className":616,"style":126},[94],[20,618],{},[20,620,185],{"className":621},[184],[20,623,133],{"className":624},[132],[20,626],{"className":627,"style":137},[48],[20,629,631,634],{"className":630},[41],[20,632,145],{"className":633,"style":144},[41,76],[20,635,637],{"className":636},[81],[20,638,640,660],{"className":639},[85,86],[20,641,643,657],{"className":642},[90],[20,644,646],{"className":645,"style":95},[94],[20,647,648,651],{"style":160},[20,649],{"className":650,"style":103},[102],[20,652,654],{"className":653},[107,108,109,110],[20,655,114],{"className":656},[41,76,110],[20,658,119],{"className":659},[118],[20,661,663],{"className":662},[90],[20,664,666],{"className":665,"style":126},[94],[20,667],{},[20,669,185],{"className":670,"style":531},[184,530],[20,672],{"className":673,"style":137},[48],[20,675,133],{"className":676},[132],[20,678],{"className":679,"style":680},[48],"margin-right:2em;",[20,682],{"className":683,"style":137},[48],[20,685,321],{"className":686,"style":320},[41,76],[20,688,340],{"className":689},[68],[20,691,344],{"className":692,"style":43},[41,76],[20,694,185],{"className":695},[184],[20,697],{"className":698,"style":49},[48],[20,700,54],{"className":701},[53],[20,703],{"className":704,"style":49},[48],[20,706,708,712,779,782,860,863],{"className":707},[32],[20,709],{"className":710,"style":711},[36],"height:1.1052em;vertical-align:-0.3552em;",[20,713,715,720],{"className":714},[41],[20,716,719],{"className":717},[41,718],"mathbb","E",[20,721,723],{"className":722},[81],[20,724,726,770],{"className":725},[85,86],[20,727,729,767],{"className":728},[90],[20,730,733],{"className":731,"style":732},[94],"height:0.3448em;",[20,734,736,739],{"style":735},"top:-2.5198em;margin-left:0em;margin-right:0.05em;",[20,737],{"className":738,"style":103},[102],[20,740,742],{"className":741},[107,108,109,110],[20,743,745,748,751,754,757,760,764],{"className":744},[41,110],[20,746,340],{"className":747},[68,110],[20,749,77],{"className":750},[41,76,110],[20,752,133],{"className":753},[132,110],[20,755,145],{"className":756,"style":144},[41,76,110],[20,758,185],{"className":759},[184,110],[20,761,763],{"className":762},[53,110],"∼",[20,765,278],{"className":766,"style":277},[41,42,110],[20,768,119],{"className":769},[118],[20,771,773],{"className":772},[90],[20,774,777],{"className":775,"style":776},[94],"height:0.3552em;",[20,778],{},[20,780],{"className":781,"style":137},[48],[20,783,785,789,792,795,835,838,841,844,847,850,853,856],{"className":784},[526],[20,786,788],{"className":787,"style":531},[68,530],"[",[20,790,519],{"className":791},[41],[20,793,340],{"className":794},[68],[20,796,798,801],{"className":797},[41],[20,799,539],{"className":800,"style":538},[41,76],[20,802,804],{"className":803},[81],[20,805,807,827],{"className":806},[85,86],[20,808,810,824],{"className":809},[90],[20,811,813],{"className":812,"style":552},[94],[20,814,815,818],{"style":555},[20,816],{"className":817,"style":103},[102],[20,819,821],{"className":820},[107,108,109,110],[20,822,344],{"className":823,"style":43},[41,76,110],[20,825,119],{"className":826},[118],[20,828,830],{"className":829},[90],[20,831,833],{"className":832,"style":126},[94],[20,834],{},[20,836,340],{"className":837},[68],[20,839,77],{"className":840},[41,76],[20,842,185],{"className":843},[184],[20,845,133],{"className":846},[132],[20,848],{"className":849,"style":137},[48],[20,851,145],{"className":852,"style":144},[41,76],[20,854,185],{"className":855},[184],[20,857,859],{"className":858,"style":531},[184,530],"]",[20,861],{"className":862,"style":137},[48],[20,864,866],{"className":865},[41],".",[11,868,869,870,913,914,929,930,933,934,1017],{},"Training minimizes ",[20,871,873],{"className":872},[23],[20,874,876],{"className":875,"ariaHidden":28},[27],[20,877,879,882],{"className":878},[32],[20,880],{"className":881,"style":309},[36],[20,883,885],{"className":884},[41,299],[20,886,888],{"className":887},[85],[20,889,891],{"className":890},[90],[20,892,894,902],{"className":893,"style":309},[94],[20,895,896,899],{"style":312},[20,897],{"className":898,"style":316},[102],[20,900,321],{"className":901,"style":320},[41,76],[20,903,904,907],{"style":324},[20,905],{"className":906,"style":316},[102],[20,908,910],{"className":909,"style":332},[331],[20,911,336],{"className":912},[41],"; we care about ",[20,915,917],{"className":916},[23],[20,918,920],{"className":919,"ariaHidden":28},[27],[20,921,923,926],{"className":922},[32],[20,924],{"className":925,"style":37},[36],[20,927,321],{"className":928,"style":320},[41,76],". The ",[15,931,932],{},"generalization gap"," is the\ndifference ",[20,935,937],{"className":936},[23],[20,938,940,971],{"className":939,"ariaHidden":28},[27],[20,941,943,947,950,953,956,959,963,968],{"className":942},[32],[20,944],{"className":945,"style":946},[36],"height:1em;vertical-align:-0.25em;",[20,948,321],{"className":949,"style":320},[41,76],[20,951,340],{"className":952},[68],[20,954,344],{"className":955,"style":43},[41,76],[20,957,185],{"className":958},[184],[20,960],{"className":961,"style":962},[48],"margin-right:0.2222em;",[20,964,967],{"className":965},[966],"mbin","−",[20,969],{"className":970,"style":962},[48],[20,972,974,977,1008,1011,1014],{"className":973},[32],[20,975],{"className":976,"style":295},[36],[20,978,980],{"className":979},[41,299],[20,981,983],{"className":982},[85],[20,984,986],{"className":985},[90],[20,987,989,997],{"className":988,"style":309},[94],[20,990,991,994],{"style":312},[20,992],{"className":993,"style":316},[102],[20,995,321],{"className":996,"style":320},[41,76],[20,998,999,1002],{"style":324},[20,1000],{"className":1001,"style":316},[102],[20,1003,1005],{"className":1004,"style":332},[331],[20,1006,336],{"className":1007},[41],[20,1009,340],{"className":1010},[68],[20,1012,344],{"className":1013,"style":43},[41,76],[20,1015,185],{"className":1016},[184],", and the entire classical program is one\nmove: bound that gap by something we can compute or control.",[1019,1020,1022],"callout",{"type":1021},"definition",[11,1023,1024,1027,1028,1081,1082,1134,1135,1151,1152,1176,1177,1256,1257,1300],{},[15,1025,1026],{},"Definition (Generalization gap)."," For a model ",[20,1029,1031],{"className":1030},[23],[20,1032,1034],{"className":1033,"ariaHidden":28},[27],[20,1035,1037,1041],{"className":1036},[32],[20,1038],{"className":1039,"style":1040},[36],"height:0.8889em;vertical-align:-0.1944em;",[20,1042,1044,1047],{"className":1043},[41],[20,1045,539],{"className":1046,"style":538},[41,76],[20,1048,1050],{"className":1049},[81],[20,1051,1053,1073],{"className":1052},[85,86],[20,1054,1056,1070],{"className":1055},[90],[20,1057,1059],{"className":1058,"style":552},[94],[20,1060,1061,1064],{"style":555},[20,1062],{"className":1063,"style":103},[102],[20,1065,1067],{"className":1066},[107,108,109,110],[20,1068,344],{"className":1069,"style":43},[41,76,110],[20,1071,119],{"className":1072},[118],[20,1074,1076],{"className":1075},[90],[20,1077,1079],{"className":1078,"style":126},[94],[20,1080],{}," with empirical risk\n",[20,1083,1085],{"className":1084},[23],[20,1086,1088],{"className":1087,"ariaHidden":28},[27],[20,1089,1091,1094,1125,1128,1131],{"className":1090},[32],[20,1092],{"className":1093,"style":295},[36],[20,1095,1097],{"className":1096},[41,299],[20,1098,1100],{"className":1099},[85],[20,1101,1103],{"className":1102},[90],[20,1104,1106,1114],{"className":1105,"style":309},[94],[20,1107,1108,1111],{"style":312},[20,1109],{"className":1110,"style":316},[102],[20,1112,321],{"className":1113,"style":320},[41,76],[20,1115,1116,1119],{"style":324},[20,1117],{"className":1118,"style":316},[102],[20,1120,1122],{"className":1121,"style":332},[331],[20,1123,336],{"className":1124},[41],[20,1126,340],{"className":1127},[68],[20,1129,344],{"className":1130,"style":43},[41,76],[20,1132,185],{"className":1133},[184]," on ",[20,1136,1138],{"className":1137},[23],[20,1139,1141],{"className":1140,"ariaHidden":28},[27],[20,1142,1144,1148],{"className":1143},[32],[20,1145],{"className":1146,"style":1147},[36],"height:0.4306em;",[20,1149,240],{"className":1150},[41,76]," i.i.d. samples and population risk ",[20,1153,1155],{"className":1154},[23],[20,1156,1158],{"className":1157,"ariaHidden":28},[27],[20,1159,1161,1164,1167,1170,1173],{"className":1160},[32],[20,1162],{"className":1163,"style":946},[36],[20,1165,321],{"className":1166,"style":320},[41,76],[20,1168,340],{"className":1169},[68],[20,1171,344],{"className":1172,"style":43},[41,76],[20,1174,185],{"className":1175},[184],", the\ngeneralization gap is ",[20,1178,1180],{"className":1179},[23],[20,1181,1183,1210],{"className":1182,"ariaHidden":28},[27],[20,1184,1186,1189,1192,1195,1198,1201,1204,1207],{"className":1185},[32],[20,1187],{"className":1188,"style":946},[36],[20,1190,321],{"className":1191,"style":320},[41,76],[20,1193,340],{"className":1194},[68],[20,1196,344],{"className":1197,"style":43},[41,76],[20,1199,185],{"className":1200},[184],[20,1202],{"className":1203,"style":962},[48],[20,1205,967],{"className":1206},[966],[20,1208],{"className":1209,"style":962},[48],[20,1211,1213,1216,1247,1250,1253],{"className":1212},[32],[20,1214],{"className":1215,"style":295},[36],[20,1217,1219],{"className":1218},[41,299],[20,1220,1222],{"className":1221},[85],[20,1223,1225],{"className":1224},[90],[20,1226,1228,1236],{"className":1227,"style":309},[94],[20,1229,1230,1233],{"style":312},[20,1231],{"className":1232,"style":316},[102],[20,1234,321],{"className":1235,"style":320},[41,76],[20,1237,1238,1241],{"style":324},[20,1239],{"className":1240,"style":316},[102],[20,1242,1244],{"className":1243,"style":332},[331],[20,1245,336],{"className":1246},[41],[20,1248,340],{"className":1249},[68],[20,1251,344],{"className":1252,"style":43},[41,76],[20,1254,185],{"className":1255},[184],". A learner generalizes when\nthis gap is small; it overfits when ",[20,1258,1260],{"className":1259},[23],[20,1261,1263],{"className":1262,"ariaHidden":28},[27],[20,1264,1266,1269],{"className":1265},[32],[20,1267],{"className":1268,"style":309},[36],[20,1270,1272],{"className":1271},[41,299],[20,1273,1275],{"className":1274},[85],[20,1276,1278],{"className":1277},[90],[20,1279,1281,1289],{"className":1280,"style":309},[94],[20,1282,1283,1286],{"style":312},[20,1284],{"className":1285,"style":316},[102],[20,1287,321],{"className":1288,"style":320},[41,76],[20,1290,1291,1294],{"style":324},[20,1292],{"className":1293,"style":316},[102],[20,1295,1297],{"className":1296,"style":332},[331],[20,1298,336],{"className":1299},[41]," is small but the gap is large.",[11,1302,1303,1304,1308,1324,1325,1452,1453,1606,1607,1631,1632,1635,1636,1681,1682,1725,1726,1741,1742,1823,1824,1827],{},"The empirical risk is an unbiased estimate of the population risk for any ",[1305,1306,1307],"em",{},"fixed",[20,1309,1311],{"className":1310},[23],[20,1312,1314],{"className":1313,"ariaHidden":28},[27],[20,1315,1317,1321],{"className":1316},[32],[20,1318],{"className":1319,"style":1320},[36],"height:0.6944em;",[20,1322,344],{"className":1323,"style":43},[41,76]," chosen before seeing the data: ",[20,1326,1328],{"className":1327},[23],[20,1329,1331,1434],{"className":1330,"ariaHidden":28},[27],[20,1332,1334,1337,1381,1384,1415,1418,1421,1425,1428,1431],{"className":1333},[32],[20,1335],{"className":1336,"style":295},[36],[20,1338,1340,1343],{"className":1339},[41],[20,1341,719],{"className":1342},[41,718],[20,1344,1346],{"className":1345},[81],[20,1347,1349,1373],{"className":1348},[85,86],[20,1350,1352,1370],{"className":1351},[90],[20,1353,1356],{"className":1354,"style":1355},[94],"height:0.3283em;",[20,1357,1358,1361],{"style":98},[20,1359],{"className":1360,"style":103},[102],[20,1362,1364],{"className":1363},[107,108,109,110],[20,1365,1367],{"className":1366},[41,110],[20,1368,44],{"className":1369,"style":43},[41,42,110],[20,1371,119],{"className":1372},[118],[20,1374,1376],{"className":1375},[90],[20,1377,1379],{"className":1378,"style":126},[94],[20,1380],{},[20,1382,788],{"className":1383},[68],[20,1385,1387],{"className":1386},[41,299],[20,1388,1390],{"className":1389},[85],[20,1391,1393],{"className":1392},[90],[20,1394,1396,1404],{"className":1395,"style":309},[94],[20,1397,1398,1401],{"style":312},[20,1399],{"className":1400,"style":316},[102],[20,1402,321],{"className":1403,"style":320},[41,76],[20,1405,1406,1409],{"style":324},[20,1407],{"className":1408,"style":316},[102],[20,1410,1412],{"className":1411,"style":332},[331],[20,1413,336],{"className":1414},[41],[20,1416,340],{"className":1417},[68],[20,1419,344],{"className":1420,"style":43},[41,76],[20,1422,1424],{"className":1423},[184],")]",[20,1426],{"className":1427,"style":49},[48],[20,1429,54],{"className":1430},[53],[20,1432],{"className":1433,"style":49},[48],[20,1435,1437,1440,1443,1446,1449],{"className":1436},[32],[20,1438],{"className":1439,"style":946},[36],[20,1441,321],{"className":1442,"style":320},[41,76],[20,1444,340],{"className":1445},[68],[20,1447,344],{"className":1448,"style":43},[41,76],[20,1450,185],{"className":1451},[184]," by linearity, since each term ",[20,1454,1456],{"className":1455},[23],[20,1457,1459],{"className":1458,"ariaHidden":28},[27],[20,1460,1462,1465,1468,1471,1511,1514,1554,1557,1560,1563,1603],{"className":1461},[32],[20,1463],{"className":1464,"style":946},[36],[20,1466,519],{"className":1467},[41],[20,1469,340],{"className":1470},[68],[20,1472,1474,1477],{"className":1473},[41],[20,1475,539],{"className":1476,"style":538},[41,76],[20,1478,1480],{"className":1479},[81],[20,1481,1483,1503],{"className":1482},[85,86],[20,1484,1486,1500],{"className":1485},[90],[20,1487,1489],{"className":1488,"style":552},[94],[20,1490,1491,1494],{"style":555},[20,1492],{"className":1493,"style":103},[102],[20,1495,1497],{"className":1496},[107,108,109,110],[20,1498,344],{"className":1499,"style":43},[41,76,110],[20,1501,119],{"className":1502},[118],[20,1504,1506],{"className":1505},[90],[20,1507,1509],{"className":1508,"style":126},[94],[20,1510],{},[20,1512,340],{"className":1513},[68],[20,1515,1517,1520],{"className":1516},[41],[20,1518,77],{"className":1519},[41,76],[20,1521,1523],{"className":1522},[81],[20,1524,1526,1546],{"className":1525},[85,86],[20,1527,1529,1543],{"className":1528},[90],[20,1530,1532],{"className":1531,"style":95},[94],[20,1533,1534,1537],{"style":98},[20,1535],{"className":1536,"style":103},[102],[20,1538,1540],{"className":1539},[107,108,109,110],[20,1541,114],{"className":1542},[41,76,110],[20,1544,119],{"className":1545},[118],[20,1547,1549],{"className":1548},[90],[20,1550,1552],{"className":1551,"style":126},[94],[20,1553],{},[20,1555,185],{"className":1556},[184],[20,1558,133],{"className":1559},[132],[20,1561],{"className":1562,"style":137},[48],[20,1564,1566,1569],{"className":1565},[41],[20,1567,145],{"className":1568,"style":144},[41,76],[20,1570,1572],{"className":1571},[81],[20,1573,1575,1595],{"className":1574},[85,86],[20,1576,1578,1592],{"className":1577},[90],[20,1579,1581],{"className":1580,"style":95},[94],[20,1582,1583,1586],{"style":160},[20,1584],{"className":1585,"style":103},[102],[20,1587,1589],{"className":1588},[107,108,109,110],[20,1590,114],{"className":1591},[41,76,110],[20,1593,119],{"className":1594},[118],[20,1596,1598],{"className":1597},[90],[20,1599,1601],{"className":1600,"style":126},[94],[20,1602],{},[20,1604,185],{"className":1605},[184]," is an i.i.d.\ndraw with mean ",[20,1608,1610],{"className":1609},[23],[20,1611,1613],{"className":1612,"ariaHidden":28},[27],[20,1614,1616,1619,1622,1625,1628],{"className":1615},[32],[20,1617],{"className":1618,"style":946},[36],[20,1620,321],{"className":1621,"style":320},[41,76],[20,1623,340],{"className":1624},[68],[20,1626,344],{"className":1627,"style":43},[41,76],[20,1629,185],{"className":1630},[184],". The difficulty is that training ",[1305,1633,1634],{},"chooses"," ",[20,1637,1639],{"className":1638},[23],[20,1640,1642],{"className":1641,"ariaHidden":28},[27],[20,1643,1645,1649],{"className":1644},[32],[20,1646],{"className":1647,"style":1648},[36],"height:0.9579em;",[20,1650,1652],{"className":1651},[41,299],[20,1653,1655],{"className":1654},[85],[20,1656,1658],{"className":1657},[90],[20,1659,1661,1669],{"className":1660,"style":1648},[94],[20,1662,1663,1666],{"style":312},[20,1664],{"className":1665,"style":316},[102],[20,1667,344],{"className":1668,"style":43},[41,76],[20,1670,1672,1675],{"style":1671},"top:-3.2634em;",[20,1673],{"className":1674,"style":316},[102],[20,1676,1678],{"className":1677,"style":332},[331],[20,1679,336],{"className":1680},[41],"\nby looking at the sample, so ",[20,1683,1685],{"className":1684},[23],[20,1686,1688],{"className":1687,"ariaHidden":28},[27],[20,1689,1691,1694],{"className":1690},[32],[20,1692],{"className":1693,"style":1648},[36],[20,1695,1697],{"className":1696},[41,299],[20,1698,1700],{"className":1699},[85],[20,1701,1703],{"className":1702},[90],[20,1704,1706,1714],{"className":1705,"style":1648},[94],[20,1707,1708,1711],{"style":312},[20,1709],{"className":1710,"style":316},[102],[20,1712,344],{"className":1713,"style":43},[41,76],[20,1715,1716,1719],{"style":1671},[20,1717],{"className":1718,"style":316},[102],[20,1720,1722],{"className":1721,"style":332},[331],[20,1723,336],{"className":1724},[41]," and ",[20,1727,1729],{"className":1728},[23],[20,1730,1732],{"className":1731,"ariaHidden":28},[27],[20,1733,1735,1738],{"className":1734},[32],[20,1736],{"className":1737,"style":37},[36],[20,1739,44],{"className":1740,"style":43},[41,42]," are dependent and the\nunbiasedness breaks. The learner picks whichever function happened to fit the\nparticular noise in this sample, and ",[20,1743,1745],{"className":1744},[23],[20,1746,1748],{"className":1747,"ariaHidden":28},[27],[20,1749,1751,1755,1786,1789,1820],{"className":1750},[32],[20,1752],{"className":1753,"style":1754},[36],"height:1.2079em;vertical-align:-0.25em;",[20,1756,1758],{"className":1757},[41,299],[20,1759,1761],{"className":1760},[85],[20,1762,1764],{"className":1763},[90],[20,1765,1767,1775],{"className":1766,"style":309},[94],[20,1768,1769,1772],{"style":312},[20,1770],{"className":1771,"style":316},[102],[20,1773,321],{"className":1774,"style":320},[41,76],[20,1776,1777,1780],{"style":324},[20,1778],{"className":1779,"style":316},[102],[20,1781,1783],{"className":1782,"style":332},[331],[20,1784,336],{"className":1785},[41],[20,1787,340],{"className":1788},[68],[20,1790,1792],{"className":1791},[41,299],[20,1793,1795],{"className":1794},[85],[20,1796,1798],{"className":1797},[90],[20,1799,1801,1809],{"className":1800,"style":1648},[94],[20,1802,1803,1806],{"style":312},[20,1804],{"className":1805,"style":316},[102],[20,1807,344],{"className":1808,"style":43},[41,76],[20,1810,1811,1814],{"style":1671},[20,1812],{"className":1813,"style":316},[102],[20,1815,1817],{"className":1816,"style":332},[331],[20,1818,336],{"className":1819},[41],[20,1821,185],{"className":1822},[184]," is optimistically low.\nEvery bound below accounts for that dependence by controlling the gap\n",[1305,1825,1826],{},"uniformly"," over the whole class the optimizer could have selected from, not just\nat one fixed point.",[1829,1830,1832],"h2",{"id":1831},"classical-theory-capacity-controls-the-gap","Classical theory: capacity controls the gap",[11,1834,1835,1836,1635,1839,1856,1857,1867],{},"The classical answer is that the gap is governed not by the single function the\noptimizer returns but by the ",[15,1837,1838],{},"richness of the hypothesis class",[20,1840,1842],{"className":1841},[23],[20,1843,1845],{"className":1844,"ariaHidden":28},[27],[20,1846,1848,1851],{"className":1847},[32],[20,1849],{"className":1850,"style":37},[36],[20,1852,1855],{"className":1853,"style":1854},[41,42],"margin-right:0.0097em;","H"," it\nwas chosen from.",[1858,1859,1860],"sup",{},[1861,1862,227],"a",{"href":1863,"ariaDescribedBy":1864,"dataFootnoteRef":6,"id":1866},"#user-content-fn-gf-capacity",[1865],"footnote-label","user-content-fnref-gf-capacity"," A learner that searches a tiny class cannot overfit\nmuch, because there are not enough functions to fit the noise; a learner with a\nvast class can fit anything, including the noise, and the noise does not transfer\nto test data.",[1019,1869,1870],{"type":1021},[11,1871,1872,1875,1876,1891,1892,1907],{},[15,1873,1874],{},"Definition (Capacity)."," The capacity of a hypothesis class ",[20,1877,1879],{"className":1878},[23],[20,1880,1882],{"className":1881,"ariaHidden":28},[27],[20,1883,1885,1888],{"className":1884},[32],[20,1886],{"className":1887,"style":37},[36],[20,1889,1855],{"className":1890,"style":1854},[41,42]," is a\nmeasure of how many distinct labelings of a dataset its members can realize.\nHigher capacity means ",[20,1893,1895],{"className":1894},[23],[20,1896,1898],{"className":1897,"ariaHidden":28},[27],[20,1899,1901,1904],{"className":1900},[32],[20,1902],{"className":1903,"style":37},[36],[20,1905,1855],{"className":1906,"style":1854},[41,42]," can fit more intricate patterns, and more\nnoise. Goodfellow §5.2 frames the whole bias–variance story around it.",[11,1909,1910,1911,1914,1915,1918],{},"The sharpest combinatorial measure of capacity for binary classifiers is the\n",[15,1912,1913],{},"Vapnik–Chervonenkis dimension",": the size of the largest set of points the class\ncan label in ",[1305,1916,1917],{},"every"," possible way.",[1019,1920,1921],{"type":1021},[11,1922,1923,1926,1927,2068,2069,2084,2085,2127,2128,2143,2144,2180,2181,2251],{},[15,1924,1925],{},"Definition (Shattering, VC dimension)."," A set ",[20,1928,1930],{"className":1929},[23],[20,1931,1933,1953],{"className":1932,"ariaHidden":28},[27],[20,1934,1936,1939,1944,1947,1950],{"className":1935},[32],[20,1937],{"className":1938,"style":37},[36],[20,1940,1943],{"className":1941,"style":1942},[41,76],"margin-right:0.0576em;","S",[20,1945],{"className":1946,"style":49},[48],[20,1948,54],{"className":1949},[53],[20,1951],{"className":1952,"style":49},[48],[20,1954,1956,1959,1963,2004,2007,2010,2014,2017,2020,2023,2065],{"className":1955},[32],[20,1957],{"className":1958,"style":946},[36],[20,1960,1962],{"className":1961},[68],"{",[20,1964,1966,1969],{"className":1965},[41],[20,1967,77],{"className":1968},[41,76],[20,1970,1972],{"className":1971},[81],[20,1973,1975,1996],{"className":1974},[85,86],[20,1976,1978,1993],{"className":1977},[90],[20,1979,1982],{"className":1980,"style":1981},[94],"height:0.3011em;",[20,1983,1984,1987],{"style":98},[20,1985],{"className":1986,"style":103},[102],[20,1988,1990],{"className":1989},[107,108,109,110],[20,1991,227],{"className":1992},[41,110],[20,1994,119],{"className":1995},[118],[20,1997,1999],{"className":1998},[90],[20,2000,2002],{"className":2001,"style":126},[94],[20,2003],{},[20,2005,133],{"className":2006},[132],[20,2008],{"className":2009,"style":137},[48],[20,2011,2013],{"className":2012},[526],"…",[20,2015],{"className":2016,"style":137},[48],[20,2018,133],{"className":2019},[132],[20,2021],{"className":2022,"style":137},[48],[20,2024,2026,2029],{"className":2025},[41],[20,2027,77],{"className":2028},[41,76],[20,2030,2032],{"className":2031},[81],[20,2033,2035,2057],{"className":2034},[85,86],[20,2036,2038,2054],{"className":2037},[90],[20,2039,2042],{"className":2040,"style":2041},[94],"height:0.1514em;",[20,2043,2044,2047],{"style":98},[20,2045],{"className":2046,"style":103},[102],[20,2048,2050],{"className":2049},[107,108,109,110],[20,2051,2053],{"className":2052},[41,76,110],"m",[20,2055,119],{"className":2056},[118],[20,2058,2060],{"className":2059},[90],[20,2061,2063],{"className":2062,"style":126},[94],[20,2064],{},[20,2066,192],{"className":2067},[184]," is\nshattered by ",[20,2070,2072],{"className":2071},[23],[20,2073,2075],{"className":2074,"ariaHidden":28},[27],[20,2076,2078,2081],{"className":2077},[32],[20,2079],{"className":2080,"style":37},[36],[20,2082,1855],{"className":2083,"style":1854},[41,42]," if for every one of the ",[20,2086,2088],{"className":2087},[23],[20,2089,2091],{"className":2090,"ariaHidden":28},[27],[20,2092,2094,2097],{"className":2093},[32],[20,2095],{"className":2096,"style":205},[36],[20,2098,2100,2104],{"className":2099},[41],[20,2101,2103],{"className":2102},[41],"2",[20,2105,2107],{"className":2106},[81],[20,2108,2110],{"className":2109},[85],[20,2111,2113],{"className":2112},[90],[20,2114,2116],{"className":2115,"style":205},[94],[20,2117,2118,2121],{"style":230},[20,2119],{"className":2120,"style":103},[102],[20,2122,2124],{"className":2123},[107,108,109,110],[20,2125,2053],{"className":2126},[41,76,110]," labelings of ",[20,2129,2131],{"className":2130},[23],[20,2132,2134],{"className":2133,"ariaHidden":28},[27],[20,2135,2137,2140],{"className":2136},[32],[20,2138],{"className":2139,"style":37},[36],[20,2141,1943],{"className":2142,"style":1942},[41,76]," there\nis some ",[20,2145,2147],{"className":2146},[23],[20,2148,2150,2171],{"className":2149,"ariaHidden":28},[27],[20,2151,2153,2157,2161,2164,2168],{"className":2152},[32],[20,2154],{"className":2155,"style":2156},[36],"height:0.7335em;vertical-align:-0.0391em;",[20,2158,2160],{"className":2159},[41,76],"h",[20,2162],{"className":2163,"style":49},[48],[20,2165,2167],{"className":2166},[53],"∈",[20,2169],{"className":2170,"style":49},[48],[20,2172,2174,2177],{"className":2173},[32],[20,2175],{"className":2176,"style":37},[36],[20,2178,1855],{"className":2179,"style":1854},[41,42]," realizing it. The VC dimension ",[20,2182,2184],{"className":2183},[23],[20,2185,2187],{"className":2186,"ariaHidden":28},[27],[20,2188,2190,2193,2242,2245,2248],{"className":2189},[32],[20,2191],{"className":2192,"style":946},[36],[20,2194,2196,2200],{"className":2195},[41],[20,2197,2199],{"className":2198},[41,76],"d",[20,2201,2203],{"className":2202},[81],[20,2204,2206,2234],{"className":2205},[85,86],[20,2207,2209,2231],{"className":2208},[90],[20,2210,2212],{"className":2211,"style":1355},[94],[20,2213,2214,2217],{"style":98},[20,2215],{"className":2216,"style":103},[102],[20,2218,2220],{"className":2219},[107,108,109,110],[20,2221,2223],{"className":2222},[41,110],[20,2224,2226],{"className":2225},[41,110],[20,2227,2230],{"className":2228},[41,2229,110],"mathrm","VC",[20,2232,119],{"className":2233},[118],[20,2235,2237],{"className":2236},[90],[20,2238,2240],{"className":2239,"style":126},[94],[20,2241],{},[20,2243,340],{"className":2244},[68],[20,2246,1855],{"className":2247,"style":1854},[41,42],[20,2249,185],{"className":2250},[184]," is the cardinality of the largest shattered set (possibly\ninfinite).",[11,2253,2254,2255,2318,2319,2336,2337,2352,2353,2368,2369,2384,2385,2462,2463,2504,2505,2600,2601,2643],{},"Take the concrete case of linear classifiers in the plane. Three points in general\nposition can be shattered: for each of the ",[20,2256,2258],{"className":2257},[23],[20,2259,2261,2307],{"className":2260,"ariaHidden":28},[27],[20,2262,2264,2268,2298,2301,2304],{"className":2263},[32],[20,2265],{"className":2266,"style":2267},[36],"height:0.8141em;",[20,2269,2271,2274],{"className":2270},[41],[20,2272,2103],{"className":2273},[41],[20,2275,2277],{"className":2276},[81],[20,2278,2280],{"className":2279},[85],[20,2281,2283],{"className":2282},[90],[20,2284,2286],{"className":2285,"style":2267},[94],[20,2287,2288,2291],{"style":230},[20,2289],{"className":2290,"style":103},[102],[20,2292,2294],{"className":2293},[107,108,109,110],[20,2295,2297],{"className":2296},[41,110],"3",[20,2299],{"className":2300,"style":49},[48],[20,2302,54],{"className":2303},[53],[20,2305],{"className":2306,"style":49},[48],[20,2308,2310,2314],{"className":2309},[32],[20,2311],{"className":2312,"style":2313},[36],"height:0.6444em;",[20,2315,2317],{"className":2316},[41],"8"," sign patterns there is a line\nputting the ",[20,2320,2322],{"className":2321},[23],[20,2323,2325],{"className":2324,"ariaHidden":28},[27],[20,2326,2328,2332],{"className":2327},[32],[20,2329],{"className":2330,"style":2331},[36],"height:0.6667em;vertical-align:-0.0833em;",[20,2333,2335],{"className":2334},[41],"+"," points on one side and the ",[20,2338,2340],{"className":2339},[23],[20,2341,2343],{"className":2342,"ariaHidden":28},[27],[20,2344,2346,2349],{"className":2345},[32],[20,2347],{"className":2348,"style":2331},[36],[20,2350,967],{"className":2351},[41]," points on the other. Four points\ncannot: label two diagonal points ",[20,2354,2356],{"className":2355},[23],[20,2357,2359],{"className":2358,"ariaHidden":28},[27],[20,2360,2362,2365],{"className":2361},[32],[20,2363],{"className":2364,"style":2331},[36],[20,2366,2335],{"className":2367},[41]," and the other two ",[20,2370,2372],{"className":2371},[23],[20,2373,2375],{"className":2374,"ariaHidden":28},[27],[20,2376,2378,2381],{"className":2377},[32],[20,2379],{"className":2380,"style":2331},[36],[20,2382,967],{"className":2383},[41]," (the XOR pattern) and\nno line separates them. So ",[20,2386,2388],{"className":2387},[23],[20,2389,2391,2453],{"className":2390,"ariaHidden":28},[27],[20,2392,2394,2398,2444,2447,2450],{"className":2393},[32],[20,2395],{"className":2396,"style":2397},[36],"height:0.8444em;vertical-align:-0.15em;",[20,2399,2401,2404],{"className":2400},[41],[20,2402,2199],{"className":2403},[41,76],[20,2405,2407],{"className":2406},[81],[20,2408,2410,2436],{"className":2409},[85,86],[20,2411,2413,2433],{"className":2412},[90],[20,2414,2416],{"className":2415,"style":1355},[94],[20,2417,2418,2421],{"style":98},[20,2419],{"className":2420,"style":103},[102],[20,2422,2424],{"className":2423},[107,108,109,110],[20,2425,2427],{"className":2426},[41,110],[20,2428,2430],{"className":2429},[41,110],[20,2431,2230],{"className":2432},[41,2229,110],[20,2434,119],{"className":2435},[118],[20,2437,2439],{"className":2438},[90],[20,2440,2442],{"className":2441,"style":126},[94],[20,2443],{},[20,2445],{"className":2446,"style":49},[48],[20,2448,54],{"className":2449},[53],[20,2451],{"className":2452,"style":49},[48],[20,2454,2456,2459],{"className":2455},[32],[20,2457],{"className":2458,"style":2313},[36],[20,2460,2297],{"className":2461},[41]," for lines in ",[20,2464,2466],{"className":2465},[23],[20,2467,2469],{"className":2468,"ariaHidden":28},[27],[20,2470,2472,2475],{"className":2471},[32],[20,2473],{"className":2474,"style":2267},[36],[20,2476,2478,2481],{"className":2477},[41],[20,2479,321],{"className":2480},[41,718],[20,2482,2484],{"className":2483},[81],[20,2485,2487],{"className":2486},[85],[20,2488,2490],{"className":2489},[90],[20,2491,2493],{"className":2492,"style":2267},[94],[20,2494,2495,2498],{"style":230},[20,2496],{"className":2497,"style":103},[102],[20,2499,2501],{"className":2500},[107,108,109,110],[20,2502,2103],{"className":2503},[41,110],", and\nin general ",[20,2506,2508],{"className":2507},[23],[20,2509,2511,2572,2591],{"className":2510,"ariaHidden":28},[27],[20,2512,2514,2517,2563,2566,2569],{"className":2513},[32],[20,2515],{"className":2516,"style":2397},[36],[20,2518,2520,2523],{"className":2519},[41],[20,2521,2199],{"className":2522},[41,76],[20,2524,2526],{"className":2525},[81],[20,2527,2529,2555],{"className":2528},[85,86],[20,2530,2532,2552],{"className":2531},[90],[20,2533,2535],{"className":2534,"style":1355},[94],[20,2536,2537,2540],{"style":98},[20,2538],{"className":2539,"style":103},[102],[20,2541,2543],{"className":2542},[107,108,109,110],[20,2544,2546],{"className":2545},[41,110],[20,2547,2549],{"className":2548},[41,110],[20,2550,2230],{"className":2551},[41,2229,110],[20,2553,119],{"className":2554},[118],[20,2556,2558],{"className":2557},[90],[20,2559,2561],{"className":2560,"style":126},[94],[20,2562],{},[20,2564],{"className":2565,"style":49},[48],[20,2567,54],{"className":2568},[53],[20,2570],{"className":2571,"style":49},[48],[20,2573,2575,2579,2582,2585,2588],{"className":2574},[32],[20,2576],{"className":2577,"style":2578},[36],"height:0.7778em;vertical-align:-0.0833em;",[20,2580,2199],{"className":2581},[41,76],[20,2583],{"className":2584,"style":962},[48],[20,2586,2335],{"className":2587},[966],[20,2589],{"className":2590,"style":962},[48],[20,2592,2594,2597],{"className":2593},[32],[20,2595],{"className":2596,"style":2313},[36],[20,2598,227],{"className":2599},[41]," for a linear classifier in ",[20,2602,2604],{"className":2603},[23],[20,2605,2607],{"className":2606,"ariaHidden":28},[27],[20,2608,2610,2614],{"className":2609},[32],[20,2611],{"className":2612,"style":2613},[36],"height:0.8491em;",[20,2615,2617,2620],{"className":2616},[41],[20,2618,321],{"className":2619},[41,718],[20,2621,2623],{"className":2622},[81],[20,2624,2626],{"className":2625},[85],[20,2627,2629],{"className":2628},[90],[20,2630,2632],{"className":2631,"style":2613},[94],[20,2633,2634,2637],{"style":230},[20,2635],{"className":2636,"style":103},[102],[20,2638,2640],{"className":2639},[107,108,109,110],[20,2641,2199],{"className":2642},[41,76,110]," with\na bias term.",[2645,2646],"tikz-figure",{"hash":2647},"ba20426fc01f94d2ed27084bfc83090249c7b5a21869e6025315893031972725",[11,2649,2650],{},"Capacity for this family equals the parameter count, and that coincidence is\nthe intuition classical theory leans on. The central result turns VC dimension into\na uniform bound on the gap.",[1019,2652,2654,2926],{"type":2653},"theorem",[11,2655,2656,2659,2660,2675,2676,2754,2755,2808,2809,2836,2837,2852,2853,2889,2890,1635,2893,133],{},[15,2657,2658],{},"Theorem (VC generalization bound)."," Let ",[20,2661,2663],{"className":2662},[23],[20,2664,2666],{"className":2665,"ariaHidden":28},[27],[20,2667,2669,2672],{"className":2668},[32],[20,2670],{"className":2671,"style":37},[36],[20,2673,1855],{"className":2674,"style":1854},[41,42]," have VC dimension\n",[20,2677,2679],{"className":2678},[23],[20,2680,2682,2743],{"className":2681,"ariaHidden":28},[27],[20,2683,2685,2688,2734,2737,2740],{"className":2684},[32],[20,2686],{"className":2687,"style":2397},[36],[20,2689,2691,2694],{"className":2690},[41],[20,2692,2199],{"className":2693},[41,76],[20,2695,2697],{"className":2696},[81],[20,2698,2700,2726],{"className":2699},[85,86],[20,2701,2703,2723],{"className":2702},[90],[20,2704,2706],{"className":2705,"style":1355},[94],[20,2707,2708,2711],{"style":98},[20,2709],{"className":2710,"style":103},[102],[20,2712,2714],{"className":2713},[107,108,109,110],[20,2715,2717],{"className":2716},[41,110],[20,2718,2720],{"className":2719},[41,110],[20,2721,2230],{"className":2722},[41,2229,110],[20,2724,119],{"className":2725},[118],[20,2727,2729],{"className":2728},[90],[20,2730,2732],{"className":2731,"style":126},[94],[20,2733],{},[20,2735],{"className":2736,"style":49},[48],[20,2738,54],{"className":2739},[53],[20,2741],{"className":2742,"style":49},[48],[20,2744,2746,2749],{"className":2745},[32],[20,2747],{"className":2748,"style":37},[36],[20,2750,2753],{"className":2751,"style":2752},[41,76],"margin-right:0.0715em;","C"," and let ",[20,2756,2758],{"className":2757},[23],[20,2759,2761],{"className":2760,"ariaHidden":28},[27],[20,2762,2764,2768,2799,2802,2805],{"className":2763},[32],[20,2765],{"className":2766,"style":2767},[36],"height:1.1412em;vertical-align:-0.1944em;",[20,2769,2771],{"className":2770},[41,299],[20,2772,2774],{"className":2773},[85],[20,2775,2777],{"className":2776},[90],[20,2778,2780,2788],{"className":2779,"style":309},[94],[20,2781,2782,2785],{"style":312},[20,2783],{"className":2784,"style":316},[102],[20,2786,321],{"className":2787,"style":320},[41,76],[20,2789,2790,2793],{"style":324},[20,2791],{"className":2792,"style":316},[102],[20,2794,2796],{"className":2795,"style":332},[331],[20,2797,336],{"className":2798},[41],[20,2800,133],{"className":2801},[132],[20,2803],{"className":2804,"style":137},[48],[20,2806,321],{"className":2807,"style":320},[41,76]," be the empirical and population\n",[20,2810,2812],{"className":2811},[23],[20,2813,2815],{"className":2814,"ariaHidden":28},[27],[20,2816,2818,2821,2825,2833],{"className":2817},[32],[20,2819],{"className":2820,"style":2313},[36],[20,2822,2824],{"className":2823},[41],"0",[20,2826,2829],{"className":2827},[41,2828],"text",[20,2830,2832],{"className":2831},[41],"–",[20,2834,227],{"className":2835},[41]," risk on ",[20,2838,2840],{"className":2839},[23],[20,2841,2843],{"className":2842,"ariaHidden":28},[27],[20,2844,2846,2849],{"className":2845},[32],[20,2847],{"className":2848,"style":1147},[36],[20,2850,240],{"className":2851},[41,76]," i.i.d. samples. Then with probability at least\n",[20,2854,2856],{"className":2855},[23],[20,2857,2859,2878],{"className":2858,"ariaHidden":28},[27],[20,2860,2862,2866,2869,2872,2875],{"className":2861},[32],[20,2863],{"className":2864,"style":2865},[36],"height:0.7278em;vertical-align:-0.0833em;",[20,2867,227],{"className":2868},[41],[20,2870],{"className":2871,"style":962},[48],[20,2873,967],{"className":2874},[966],[20,2876],{"className":2877,"style":962},[48],[20,2879,2881,2884],{"className":2880},[32],[20,2882],{"className":2883,"style":1320},[36],[20,2885,2888],{"className":2886,"style":2887},[41,76],"margin-right:0.0379em;","δ",", ",[1305,2891,2892],{},"simultaneously for every",[20,2894,2896],{"className":2895},[23],[20,2897,2899,2917],{"className":2898,"ariaHidden":28},[27],[20,2900,2902,2905,2908,2911,2914],{"className":2901},[32],[20,2903],{"className":2904,"style":2156},[36],[20,2906,2160],{"className":2907},[41,76],[20,2909],{"className":2910,"style":49},[48],[20,2912,2167],{"className":2913},[53],[20,2915],{"className":2916,"style":49},[48],[20,2918,2920,2923],{"className":2919},[32],[20,2921],{"className":2922,"style":37},[36],[20,2924,1855],{"className":2925,"style":1854},[41,42],[20,2927,2929],{"className":2928},[282],[20,2930,2932],{"className":2931},[23],[20,2933,2935,2969,3030],{"className":2934,"ariaHidden":28},[27],[20,2936,2938,2941,2944,2947,2950,2953,2956,2959,2963,2966],{"className":2937},[32],[20,2939],{"className":2940,"style":946},[36],[20,2942,321],{"className":2943,"style":320},[41,76],[20,2945,340],{"className":2946},[68],[20,2948,2160],{"className":2949},[41,76],[20,2951,185],{"className":2952},[184],[20,2954],{"className":2955,"style":49},[48],[20,2957],{"className":2958,"style":49},[48],[20,2960,2962],{"className":2961},[53],"≤",[20,2964],{"className":2965,"style":49},[48],[20,2967],{"className":2968,"style":49},[48],[20,2970,2972,2975,3006,3009,3012,3015,3018,3021,3024,3027],{"className":2971},[32],[20,2973],{"className":2974,"style":295},[36],[20,2976,2978],{"className":2977},[41,299],[20,2979,2981],{"className":2980},[85],[20,2982,2984],{"className":2983},[90],[20,2985,2987,2995],{"className":2986,"style":309},[94],[20,2988,2989,2992],{"style":312},[20,2990],{"className":2991,"style":316},[102],[20,2993,321],{"className":2994,"style":320},[41,76],[20,2996,2997,3000],{"style":324},[20,2998],{"className":2999,"style":316},[102],[20,3001,3003],{"className":3002,"style":332},[331],[20,3004,336],{"className":3005},[41],[20,3007,340],{"className":3008},[68],[20,3010,2160],{"className":3011},[41,76],[20,3013,185],{"className":3014},[184],[20,3016],{"className":3017,"style":49},[48],[20,3019],{"className":3020,"style":962},[48],[20,3022,2335],{"className":3023},[966],[20,3025],{"className":3026,"style":49},[48],[20,3028],{"className":3029,"style":962},[48],[20,3031,3033,3037,3041,3045,3048,3248,3251],{"className":3032},[32],[20,3034],{"className":3035,"style":3036},[36],"height:3em;vertical-align:-1.25em;",[20,3038,3040],{"className":3039,"style":43},[41,76],"O",[20,3042],{"className":3043,"style":3044},[48],"margin-right:-0.1667em;",[20,3046],{"className":3047,"style":137},[48],[20,3049,3051,3059,3242],{"className":3050},[526],[20,3052,3054],{"className":3053,"style":531},[68,530],[20,3055,340],{"className":3056},[3057,3058],"delimsizing","size4",[20,3060,3063],{"className":3061},[41,3062],"sqrt",[20,3064,3066,3233],{"className":3065},[85,86],[20,3067,3069,3230],{"className":3068},[90],[20,3070,3073,3207],{"className":3071,"style":3072},[94],"height:1.7044em;",[20,3074,3078,3082],{"className":3075,"style":3077},[3076],"svg-align","top:-4.4em;",[20,3079],{"className":3080,"style":3081},[102],"height:4.4em;",[20,3083,3086],{"className":3084,"style":3085},[41],"padding-left:1em;",[20,3087,3089,3092,3204],{"className":3088},[41],[20,3090],{"className":3091},[68,370],[20,3093,3095],{"className":3094},[374],[20,3096,3098,3196],{"className":3097},[85,86],[20,3099,3101,3193],{"className":3100},[90],[20,3102,3105,3116,3124],{"className":3103,"style":3104},[94],"height:1.427em;",[20,3106,3107,3110],{"style":387},[20,3108],{"className":3109,"style":316},[102],[20,3111,3113],{"className":3112},[41],[20,3114,240],{"className":3115},[41,76],[20,3117,3118,3121],{"style":399},[20,3119],{"className":3120,"style":316},[102],[20,3122],{"className":3123,"style":407},[406],[20,3125,3126,3129],{"style":410},[20,3127],{"className":3128,"style":316},[102],[20,3130,3132,3135,3138,3141,3149,3152,3155,3159,3162,3165,3168,3171,3174,3180,3183,3187,3190],{"className":3131},[41],[20,3133,2753],{"className":3134,"style":2752},[41,76],[20,3136],{"className":3137,"style":137},[48],[20,3139],{"className":3140,"style":137},[48],[20,3142,3144],{"className":3143},[441],[20,3145,3148],{"className":3146,"style":3147},[41,2229],"margin-right:0.0139em;","log",[20,3150,340],{"className":3151},[68],[20,3153,240],{"className":3154},[41,76],[20,3156,3158],{"className":3157},[41],"\u002F",[20,3160,2753],{"className":3161,"style":2752},[41,76],[20,3163,185],{"className":3164},[184],[20,3166],{"className":3167,"style":962},[48],[20,3169,2335],{"className":3170},[966],[20,3172],{"className":3173,"style":962},[48],[20,3175,3177],{"className":3176},[441],[20,3178,3148],{"className":3179,"style":3147},[41,2229],[20,3181,340],{"className":3182},[68],[20,3184,3186],{"className":3185},[41],"1\u002F",[20,3188,2888],{"className":3189,"style":2887},[41,76],[20,3191,185],{"className":3192},[184],[20,3194,119],{"className":3195},[118],[20,3197,3199],{"className":3198},[90],[20,3200,3202],{"className":3201,"style":429},[94],[20,3203],{},[20,3205],{"className":3206},[184,370],[20,3208,3210,3213],{"style":3209},"top:-3.6644em;",[20,3211],{"className":3212,"style":3081},[102],[20,3214,3218],{"className":3215,"style":3217},[3216],"hide-tail","min-width:1.02em;height:2.48em;",[3219,3220,3226],"svg",{"xmlns":3221,"width":3222,"height":3223,"viewBox":3224,"preserveAspectRatio":3225},"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg","400em","2.48em","0 0 400000 2592","xMinYMin slice",[3227,3228],"path",{"d":3229},"M424,2478\nc-1.3,-0.7,-38.5,-172,-111.5,-514c-73,-342,-109.8,-513.3,-110.5,-514\nc0,-2,-10.7,14.3,-32,49c-4.7,7.3,-9.8,15.7,-15.5,25c-5.7,9.3,-9.8,16,-12.5,20\ns-5,7,-5,7c-4,-3.3,-8.3,-7.7,-13,-13s-13,-13,-13,-13s76,-122,76,-122s77,-121,77,-121\ns209,968,209,968c0,-2,84.7,-361.7,254,-1079c169.3,-717.3,254.7,-1077.7,256,-1081\nl0 -0c4,-6.7,10,-10,18,-10 H400000\nv40H1014.6\ns-87.3,378.7,-272.6,1166c-185.3,787.3,-279.3,1182.3,-282,1185\nc-2,6,-10,9,-24,9\nc-8,0,-12,-0.7,-12,-2z M1001 80\nh400000v40h-400000z",[20,3231,119],{"className":3232},[118],[20,3234,3236],{"className":3235},[90],[20,3237,3240],{"className":3238,"style":3239},[94],"height:0.7356em;",[20,3241],{},[20,3243,3245],{"className":3244,"style":531},[184,530],[20,3246,185],{"className":3247},[3057,3058],[20,3249],{"className":3250,"style":137},[48],[20,3252,866],{"className":3253},[41],[11,3255,3256,3257,3276,3277,3292,3293,3296,3297,3314,3315,3330,3331,3532,3533,3548,3549,3564,3565,3782,3783,3798,3799,3814,3815,3930,3931,3955,3956,3971,3972,4024,4025,4040,4041,4100,4101,4125,4126,866],{},"Deep networks break the mechanism behind the ",[3258,3259,3260,3261],"q",{},"simultaneously for every ",[20,3262,3264],{"className":3263},[23],[20,3265,3267],{"className":3266,"ariaHidden":28},[27],[20,3268,3270,3273],{"className":3269},[32],[20,3271],{"className":3272,"style":1320},[36],[20,3274,2160],{"className":3275},[41,76],"\nguarantee. If ",[20,3278,3280],{"className":3279},[23],[20,3281,3283],{"className":3282,"ariaHidden":28},[27],[20,3284,3286,3289],{"className":3285},[32],[20,3287],{"className":3288,"style":37},[36],[20,3290,1855],{"className":3291,"style":1854},[41,42]," were a ",[1305,3294,3295],{},"finite"," set of ",[20,3298,3300],{"className":3299},[23],[20,3301,3303],{"className":3302,"ariaHidden":28},[27],[20,3304,3306,3309],{"className":3305},[32],[20,3307],{"className":3308,"style":37},[36],[20,3310,3313],{"className":3311,"style":3312},[41,76],"margin-right:0.109em;","M","\nfunctions, each fixed ",[20,3316,3318],{"className":3317},[23],[20,3319,3321],{"className":3320,"ariaHidden":28},[27],[20,3322,3324,3327],{"className":3323},[32],[20,3325],{"className":3326,"style":1320},[36],[20,3328,2160],{"className":3329},[41,76]," has ",[20,3332,3334],{"className":3333},[23],[20,3335,3337,3374,3430,3452],{"className":3336,"ariaHidden":28},[27],[20,3338,3340,3343,3350,3353,3356,3359,3362,3365,3368,3371],{"className":3339},[32],[20,3341],{"className":3342,"style":946},[36],[20,3344,3346],{"className":3345},[441],[20,3347,3349],{"className":3348},[41,2229],"Pr",[20,3351,788],{"className":3352},[68],[20,3354,321],{"className":3355,"style":320},[41,76],[20,3357,340],{"className":3358},[68],[20,3360,2160],{"className":3361},[41,76],[20,3363,185],{"className":3364},[184],[20,3366],{"className":3367,"style":962},[48],[20,3369,967],{"className":3370},[966],[20,3372],{"className":3373,"style":962},[48],[20,3375,3377,3380,3411,3414,3417,3420,3423,3427],{"className":3376},[32],[20,3378],{"className":3379,"style":295},[36],[20,3381,3383],{"className":3382},[41,299],[20,3384,3386],{"className":3385},[85],[20,3387,3389],{"className":3388},[90],[20,3390,3392,3400],{"className":3391,"style":309},[94],[20,3393,3394,3397],{"style":312},[20,3395],{"className":3396,"style":316},[102],[20,3398,321],{"className":3399,"style":320},[41,76],[20,3401,3402,3405],{"style":324},[20,3403],{"className":3404,"style":316},[102],[20,3406,3408],{"className":3407,"style":332},[331],[20,3409,336],{"className":3410},[41],[20,3412,340],{"className":3413},[68],[20,3415,2160],{"className":3416},[41,76],[20,3418,185],{"className":3419},[184],[20,3421],{"className":3422,"style":49},[48],[20,3424,3426],{"className":3425},[53],">",[20,3428],{"className":3429,"style":49},[48],[20,3431,3433,3436,3440,3443,3446,3449],{"className":3432},[32],[20,3434],{"className":3435,"style":946},[36],[20,3437,3439],{"className":3438},[41,76],"ε",[20,3441,859],{"className":3442},[184],[20,3444],{"className":3445,"style":49},[48],[20,3447,2962],{"className":3448},[53],[20,3450],{"className":3451,"style":49},[48],[20,3453,3455,3459],{"className":3454},[32],[20,3456],{"className":3457,"style":3458},[36],"height:0.9869em;",[20,3460,3462,3466],{"className":3461},[41],[20,3463,3465],{"className":3464},[41,76],"e",[20,3467,3469],{"className":3468},[81],[20,3470,3472],{"className":3471},[85],[20,3473,3475],{"className":3474},[90],[20,3476,3478],{"className":3477,"style":3458},[94],[20,3479,3480,3483],{"style":230},[20,3481],{"className":3482,"style":103},[102],[20,3484,3486],{"className":3485},[107,108,109,110],[20,3487,3489,3492,3495,3498],{"className":3488},[41,110],[20,3490,967],{"className":3491},[41,110],[20,3493,2103],{"className":3494},[41,110],[20,3496,240],{"className":3497},[41,76,110],[20,3499,3501,3504],{"className":3500},[41,110],[20,3502,3439],{"className":3503},[41,76,110],[20,3505,3507],{"className":3506},[81],[20,3508,3510],{"className":3509},[85],[20,3511,3513],{"className":3512},[90],[20,3514,3517],{"className":3515,"style":3516},[94],"height:0.8913em;",[20,3518,3520,3524],{"style":3519},"top:-2.931em;margin-right:0.0714em;",[20,3521],{"className":3522,"style":3523},[102],"height:2.5em;",[20,3525,3529],{"className":3526},[107,3527,3528,110],"reset-size3","size1",[20,3530,2103],{"className":3531},[41,110]," by Hoeffding's inequality (the empirical risk is an average of ",[20,3534,3536],{"className":3535},[23],[20,3537,3539],{"className":3538,"ariaHidden":28},[27],[20,3540,3542,3545],{"className":3541},[32],[20,3543],{"className":3544,"style":1147},[36],[20,3546,240],{"className":3547},[41,76],"\nbounded i.i.d. terms). A union bound over all ",[20,3550,3552],{"className":3551},[23],[20,3553,3555],{"className":3554,"ariaHidden":28},[27],[20,3556,3558,3561],{"className":3557},[32],[20,3559],{"className":3560,"style":37},[36],[20,3562,3313],{"className":3563,"style":3312},[41,76]," functions gives ",[20,3566,3568],{"className":3567},[23],[20,3569,3571,3603,3630,3685,3706],{"className":3570,"ariaHidden":28},[27],[20,3572,3574,3577,3583,3586,3590,3593,3596,3600],{"className":3573},[32],[20,3575],{"className":3576,"style":946},[36],[20,3578,3580],{"className":3579},[441],[20,3581,3349],{"className":3582},[41,2229],[20,3584,788],{"className":3585},[68],[20,3587,3589],{"className":3588},[41],"∃",[20,3591,2160],{"className":3592},[41,76],[20,3594],{"className":3595,"style":49},[48],[20,3597,3599],{"className":3598},[53],":",[20,3601],{"className":3602,"style":49},[48],[20,3604,3606,3609,3612,3615,3618,3621,3624,3627],{"className":3605},[32],[20,3607],{"className":3608,"style":946},[36],[20,3610,321],{"className":3611,"style":320},[41,76],[20,3613,340],{"className":3614},[68],[20,3616,2160],{"className":3617},[41,76],[20,3619,185],{"className":3620},[184],[20,3622],{"className":3623,"style":962},[48],[20,3625,967],{"className":3626},[966],[20,3628],{"className":3629,"style":962},[48],[20,3631,3633,3636,3667,3670,3673,3676,3679,3682],{"className":3632},[32],[20,3634],{"className":3635,"style":295},[36],[20,3637,3639],{"className":3638},[41,299],[20,3640,3642],{"className":3641},[85],[20,3643,3645],{"className":3644},[90],[20,3646,3648,3656],{"className":3647,"style":309},[94],[20,3649,3650,3653],{"style":312},[20,3651],{"className":3652,"style":316},[102],[20,3654,321],{"className":3655,"style":320},[41,76],[20,3657,3658,3661],{"style":324},[20,3659],{"className":3660,"style":316},[102],[20,3662,3664],{"className":3663,"style":332},[331],[20,3665,336],{"className":3666},[41],[20,3668,340],{"className":3669},[68],[20,3671,2160],{"className":3672},[41,76],[20,3674,185],{"className":3675},[184],[20,3677],{"className":3678,"style":49},[48],[20,3680,3426],{"className":3681},[53],[20,3683],{"className":3684,"style":49},[48],[20,3686,3688,3691,3694,3697,3700,3703],{"className":3687},[32],[20,3689],{"className":3690,"style":946},[36],[20,3692,3439],{"className":3693},[41,76],[20,3695,859],{"className":3696},[184],[20,3698],{"className":3699,"style":49},[48],[20,3701,2962],{"className":3702},[53],[20,3704],{"className":3705,"style":49},[48],[20,3707,3709,3712,3715],{"className":3708},[32],[20,3710],{"className":3711,"style":3458},[36],[20,3713,3313],{"className":3714,"style":3312},[41,76],[20,3716,3718,3721],{"className":3717},[41],[20,3719,3465],{"className":3720},[41,76],[20,3722,3724],{"className":3723},[81],[20,3725,3727],{"className":3726},[85],[20,3728,3730],{"className":3729},[90],[20,3731,3733],{"className":3732,"style":3458},[94],[20,3734,3735,3738],{"style":230},[20,3736],{"className":3737,"style":103},[102],[20,3739,3741],{"className":3740},[107,108,109,110],[20,3742,3744,3747,3750,3753],{"className":3743},[41,110],[20,3745,967],{"className":3746},[41,110],[20,3748,2103],{"className":3749},[41,110],[20,3751,240],{"className":3752},[41,76,110],[20,3754,3756,3759],{"className":3755},[41,110],[20,3757,3439],{"className":3758},[41,76,110],[20,3760,3762],{"className":3761},[81],[20,3763,3765],{"className":3764},[85],[20,3766,3768],{"className":3767},[90],[20,3769,3771],{"className":3770,"style":3516},[94],[20,3772,3773,3776],{"style":3519},[20,3774],{"className":3775,"style":3523},[102],[20,3777,3779],{"className":3778},[107,3527,3528,110],[20,3780,2103],{"className":3781},[41,110],"; setting the right side\nto ",[20,3784,3786],{"className":3785},[23],[20,3787,3789],{"className":3788,"ariaHidden":28},[27],[20,3790,3792,3795],{"className":3791},[32],[20,3793],{"className":3794,"style":1320},[36],[20,3796,2888],{"className":3797,"style":2887},[41,76]," and solving for ",[20,3800,3802],{"className":3801},[23],[20,3803,3805],{"className":3804,"ariaHidden":28},[27],[20,3806,3808,3811],{"className":3807},[32],[20,3809],{"className":3810,"style":1147},[36],[20,3812,3439],{"className":3813},[41,76]," yields a uniform gap of order\n",[20,3816,3818],{"className":3817},[23],[20,3819,3821],{"className":3820,"ariaHidden":28},[27],[20,3822,3824,3828],{"className":3823},[32],[20,3825],{"className":3826,"style":3827},[36],"height:1.24em;vertical-align:-0.305em;",[20,3829,3831],{"className":3830},[41,3062],[20,3832,3834,3921],{"className":3833},[85,86],[20,3835,3837,3918],{"className":3836},[90],[20,3838,3841,3901],{"className":3839,"style":3840},[94],"height:0.935em;",[20,3842,3845,3849],{"className":3843,"style":3844},[3076],"top:-3.2em;",[20,3846],{"className":3847,"style":3848},[102],"height:3.2em;",[20,3850,3852,3855,3861,3864,3867,3870,3873,3876,3882,3885,3888,3891,3895,3898],{"className":3851,"style":3085},[41],[20,3853,340],{"className":3854},[68],[20,3856,3858],{"className":3857},[441],[20,3859,3148],{"className":3860,"style":3147},[41,2229],[20,3862],{"className":3863,"style":137},[48],[20,3865,3313],{"className":3866,"style":3312},[41,76],[20,3868],{"className":3869,"style":962},[48],[20,3871,2335],{"className":3872},[966],[20,3874],{"className":3875,"style":962},[48],[20,3877,3879],{"className":3878},[441],[20,3880,3148],{"className":3881,"style":3147},[41,2229],[20,3883,340],{"className":3884},[68],[20,3886,3186],{"className":3887},[41],[20,3889,2888],{"className":3890,"style":2887},[41,76],[20,3892,3894],{"className":3893},[184],"))",[20,3896,3158],{"className":3897},[41],[20,3899,240],{"className":3900},[41,76],[20,3902,3904,3907],{"style":3903},"top:-2.895em;",[20,3905],{"className":3906,"style":3848},[102],[20,3908,3911],{"className":3909,"style":3910},[3216],"min-width:1.02em;height:1.28em;",[3219,3912,3915],{"xmlns":3221,"width":3222,"height":3913,"viewBox":3914,"preserveAspectRatio":3225},"1.28em","0 0 400000 1296",[3227,3916],{"d":3917},"M263,681c0.7,0,18,39.7,52,119\nc34,79.3,68.167,158.7,102.5,238c34.3,79.3,51.8,119.3,52.5,120\nc340,-704.7,510.7,-1060.3,512,-1067\nl0 -0\nc4.7,-7.3,11,-11,19,-11\nH40000v40H1012.3\ns-271.3,567,-271.3,567c-38.7,80.7,-84,175,-136,283c-52,108,-89.167,185.3,-111.5,232\nc-22.3,46.7,-33.8,70.3,-34.5,71c-4.7,4.7,-12.3,7,-23,7s-12,-1,-12,-1\ns-109,-253,-109,-253c-72.7,-168,-109.3,-252,-110,-252c-10.7,8,-22,16.7,-34,26\nc-22,17.3,-33.3,26,-34,26s-26,-26,-26,-26s76,-59,76,-59s76,-60,76,-60z\nM1001 80h400000v40h-400000z",[20,3919,119],{"className":3920},[118],[20,3922,3924],{"className":3923},[90],[20,3925,3928],{"className":3926,"style":3927},[94],"height:0.305em;",[20,3929],{},". The log-cardinality ",[20,3932,3934],{"className":3933},[23],[20,3935,3937],{"className":3936,"ariaHidden":28},[27],[20,3938,3940,3943,3949,3952],{"className":3939},[32],[20,3941],{"className":3942,"style":1040},[36],[20,3944,3946],{"className":3945},[441],[20,3947,3148],{"className":3948,"style":3147},[41,2229],[20,3950],{"className":3951,"style":137},[48],[20,3953,3313],{"className":3954,"style":3312},[41,76]," is the capacity.\nThe Sauer–Shelah lemma extends this to infinite classes: a class of VC dimension\n",[20,3957,3959],{"className":3958},[23],[20,3960,3962],{"className":3961,"ariaHidden":28},[27],[20,3963,3965,3968],{"className":3964},[32],[20,3966],{"className":3967,"style":37},[36],[20,3969,2753],{"className":3970,"style":2752},[41,76]," realizes at most ",[20,3973,3975],{"className":3974},[23],[20,3976,3978],{"className":3977,"ariaHidden":28},[27],[20,3979,3981,3985,3988,3991,4021],{"className":3980},[32],[20,3982],{"className":3983,"style":3984},[36],"height:1.0913em;vertical-align:-0.25em;",[20,3986,3040],{"className":3987,"style":43},[41,76],[20,3989,340],{"className":3990},[68],[20,3992,3994,3997],{"className":3993},[41],[20,3995,240],{"className":3996},[41,76],[20,3998,4000],{"className":3999},[81],[20,4001,4003],{"className":4002},[85],[20,4004,4006],{"className":4005},[90],[20,4007,4010],{"className":4008,"style":4009},[94],"height:0.8413em;",[20,4011,4012,4015],{"style":230},[20,4013],{"className":4014,"style":103},[102],[20,4016,4018],{"className":4017},[107,108,109,110],[20,4019,2753],{"className":4020,"style":2752},[41,76,110],[20,4022,185],{"className":4023},[184]," distinct labelings on any ",[20,4026,4028],{"className":4027},[23],[20,4029,4031],{"className":4030,"ariaHidden":28},[27],[20,4032,4034,4037],{"className":4033},[32],[20,4035],{"className":4036,"style":1147},[36],[20,4038,240],{"className":4039},[41,76]," points, so\n",[20,4042,4044],{"className":4043},[23],[20,4045,4047,4076],{"className":4046,"ariaHidden":28},[27],[20,4048,4050,4053,4059,4062,4065,4068,4073],{"className":4049},[32],[20,4051],{"className":4052,"style":1040},[36],[20,4054,4056],{"className":4055},[441],[20,4057,3148],{"className":4058,"style":3147},[41,2229],[20,4060],{"className":4061,"style":137},[48],[20,4063,3313],{"className":4064,"style":3312},[41,76],[20,4066],{"className":4067,"style":49},[48],[20,4069,4072],{"className":4070},[53,4071],"amsrm","⇝",[20,4074],{"className":4075,"style":49},[48],[20,4077,4079,4082,4085,4088,4094,4097],{"className":4078},[32],[20,4080],{"className":4081,"style":1040},[36],[20,4083,2753],{"className":4084,"style":2752},[41,76],[20,4086],{"className":4087,"style":137},[48],[20,4089,4091],{"className":4090},[441],[20,4092,3148],{"className":4093,"style":3147},[41,2229],[20,4095],{"className":4096,"style":137},[48],[20,4098,240],{"className":4099},[41,76]," takes the place of ",[20,4102,4104],{"className":4103},[23],[20,4105,4107],{"className":4106,"ariaHidden":28},[27],[20,4108,4110,4113,4119,4122],{"className":4109},[32],[20,4111],{"className":4112,"style":1040},[36],[20,4114,4116],{"className":4115},[441],[20,4117,3148],{"className":4118,"style":3147},[41,2229],[20,4120],{"className":4121,"style":137},[48],[20,4123,3313],{"className":4124,"style":3312},[41,76],", giving the theorem's\n",[20,4127,4129],{"className":4128},[23],[20,4130,4132],{"className":4131,"ariaHidden":28},[27],[20,4133,4135,4138],{"className":4134},[32],[20,4136],{"className":4137,"style":3827},[36],[20,4139,4141],{"className":4140},[41,3062],[20,4142,4144,4207],{"className":4143},[85,86],[20,4145,4147,4204],{"className":4146},[90],[20,4148,4150,4192],{"className":4149,"style":3840},[94],[20,4151,4153,4156],{"className":4152,"style":3844},[3076],[20,4154],{"className":4155,"style":3848},[102],[20,4157,4159,4162,4165,4171,4174,4177,4180,4183,4186,4189],{"className":4158,"style":3085},[41],[20,4160,2753],{"className":4161,"style":2752},[41,76],[20,4163],{"className":4164,"style":137},[48],[20,4166,4168],{"className":4167},[441],[20,4169,3148],{"className":4170,"style":3147},[41,2229],[20,4172,340],{"className":4173},[68],[20,4175,240],{"className":4176},[41,76],[20,4178,3158],{"className":4179},[41],[20,4181,2753],{"className":4182,"style":2752},[41,76],[20,4184,185],{"className":4185},[184],[20,4187,3158],{"className":4188},[41],[20,4190,240],{"className":4191},[41,76],[20,4193,4194,4197],{"style":3903},[20,4195],{"className":4196,"style":3848},[102],[20,4198,4200],{"className":4199,"style":3910},[3216],[3219,4201,4202],{"xmlns":3221,"width":3222,"height":3913,"viewBox":3914,"preserveAspectRatio":3225},[3227,4203],{"d":3917},[20,4205,119],{"className":4206},[118],[20,4208,4210],{"className":4209},[90],[20,4211,4213],{"className":4212,"style":3927},[94],[20,4214],{},[11,4216,4217],{},"Stripped of the logarithmic and confidence terms, the bound has the shape the\nwhole field quotes:",[20,4219,4221],{"className":4220},[282],[20,4222,4224],{"className":4223},[23],[20,4225,4227,4263,4324],{"className":4226,"ariaHidden":28},[27],[20,4228,4230,4233,4236,4239,4242,4245,4248,4251,4254,4257,4260],{"className":4229},[32],[20,4231],{"className":4232,"style":946},[36],[20,4234],{"className":4235,"style":49},[48],[20,4237,321],{"className":4238,"style":320},[41,76],[20,4240,340],{"className":4241},[68],[20,4243,344],{"className":4244,"style":43},[41,76],[20,4246,185],{"className":4247},[184],[20,4249],{"className":4250,"style":49},[48],[20,4252],{"className":4253,"style":49},[48],[20,4255,2962],{"className":4256},[53],[20,4258],{"className":4259,"style":49},[48],[20,4261],{"className":4262,"style":49},[48],[20,4264,4266,4269,4300,4303,4306,4309,4312,4315,4318,4321],{"className":4265},[32],[20,4267],{"className":4268,"style":295},[36],[20,4270,4272],{"className":4271},[41,299],[20,4273,4275],{"className":4274},[85],[20,4276,4278],{"className":4277},[90],[20,4279,4281,4289],{"className":4280,"style":309},[94],[20,4282,4283,4286],{"style":312},[20,4284],{"className":4285,"style":316},[102],[20,4287,321],{"className":4288,"style":320},[41,76],[20,4290,4291,4294],{"style":324},[20,4292],{"className":4293,"style":316},[102],[20,4295,4297],{"className":4296,"style":332},[331],[20,4298,336],{"className":4299},[41],[20,4301,340],{"className":4302},[68],[20,4304,344],{"className":4305,"style":43},[41,76],[20,4307,185],{"className":4308},[184],[20,4310],{"className":4311,"style":49},[48],[20,4313],{"className":4314,"style":962},[48],[20,4316,2335],{"className":4317},[966],[20,4319],{"className":4320,"style":49},[48],[20,4322],{"className":4323,"style":962},[48],[20,4325,4327,4331,4334,4337,4340,4480,4483,4486,4489],{"className":4326},[32],[20,4328],{"className":4329,"style":4330},[36],"height:2.7em;vertical-align:-1.1em;",[20,4332,3040],{"className":4333,"style":43},[41,76],[20,4335],{"className":4336,"style":3044},[48],[20,4338],{"className":4339,"style":137},[48],[20,4341,4343,4349,4474],{"className":4342},[526],[20,4344,4346],{"className":4345,"style":531},[68,530],[20,4347,340],{"className":4348},[3057,109],[20,4350,4352],{"className":4351},[41,3062],[20,4353,4355,4465],{"className":4354},[85,86],[20,4356,4358,4462],{"className":4357},[90],[20,4359,4362,4445],{"className":4360,"style":4361},[94],"height:1.2975em;",[20,4363,4366,4370],{"className":4364,"style":4365},[3076],"top:-3.8em;",[20,4367],{"className":4368,"style":4369},[102],"height:3.8em;",[20,4371,4373],{"className":4372,"style":3085},[41],[20,4374,4376,4379,4442],{"className":4375},[41],[20,4377],{"className":4378},[68,370],[20,4380,4382],{"className":4381},[374],[20,4383,4385,4433],{"className":4384},[85,86],[20,4386,4388,4430],{"className":4387},[90],[20,4389,4392,4407,4415],{"className":4390,"style":4391},[94],"height:0.8723em;",[20,4393,4395,4398],{"style":4394},"top:-2.655em;",[20,4396],{"className":4397,"style":316},[102],[20,4399,4401],{"className":4400},[107,108,109,110],[20,4402,4404],{"className":4403},[41,110],[20,4405,240],{"className":4406},[41,76,110],[20,4408,4409,4412],{"style":399},[20,4410],{"className":4411,"style":316},[102],[20,4413],{"className":4414,"style":407},[406],[20,4416,4418,4421],{"style":4417},"top:-3.394em;",[20,4419],{"className":4420,"style":316},[102],[20,4422,4424],{"className":4423},[107,108,109,110],[20,4425,4427],{"className":4426},[41,110],[20,4428,2753],{"className":4429,"style":2752},[41,76,110],[20,4431,119],{"className":4432},[118],[20,4434,4436],{"className":4435},[90],[20,4437,4440],{"className":4438,"style":4439},[94],"height:0.345em;",[20,4441],{},[20,4443],{"className":4444},[184,370],[20,4446,4448,4451],{"style":4447},"top:-3.2575em;",[20,4449],{"className":4450,"style":4369},[102],[20,4452,4455],{"className":4453,"style":4454},[3216],"min-width:1.02em;height:1.88em;",[3219,4456,4459],{"xmlns":3221,"width":3222,"height":4457,"viewBox":4458,"preserveAspectRatio":3225},"1.88em","0 0 400000 1944",[3227,4460],{"d":4461},"M983 90\nl0 -0\nc4,-6.7,10,-10,18,-10 H400000v40\nH1013.1s-83.4,268,-264.1,840c-180.7,572,-277,876.3,-289,913c-4.7,4.7,-12.7,7,-24,7\ns-12,0,-12,0c-1.3,-3.3,-3.7,-11.7,-7,-25c-35.3,-125.3,-106.7,-373.3,-214,-744\nc-10,12,-21,25,-33,39s-32,39,-32,39c-6,-5.3,-15,-14,-27,-26s25,-30,25,-30\nc26.7,-32.7,52,-63,76,-91s52,-60,52,-60s208,722,208,722\nc56,-175.3,126.3,-397.3,211,-666c84.7,-268.7,153.8,-488.2,207.5,-658.5\nc53.7,-170.3,84.5,-266.8,92.5,-289.5z\nM1001 80h400000v40h-400000z",[20,4463,119],{"className":4464},[118],[20,4466,4468],{"className":4467},[90],[20,4469,4472],{"className":4470,"style":4471},[94],"height:0.5425em;",[20,4473],{},[20,4475,4477],{"className":4476,"style":531},[184,530],[20,4478,185],{"className":4479},[3057,109],[20,4481],{"className":4482,"style":49},[48],[20,4484],{"className":4485,"style":680},[48],[20,4487],{"className":4488,"style":137},[48],[20,4490,4492],{"className":4491},[41],[20,4493,4496,4541],{"className":4494},[4495],"mtable",[20,4497,4500],{"className":4498},[4499],"col-align-r",[20,4501,4503,4532],{"className":4502},[85,86],[20,4504,4506,4529],{"className":4505},[90],[20,4507,4510,4520],{"className":4508,"style":4509},[94],"height:1.6em;",[20,4511,4513,4517],{"style":4512},"top:-3.6em;",[20,4514],{"className":4515,"style":4516},[102],"height:2.84em;",[20,4518],{"className":4519},[41],[20,4521,4523,4526],{"style":4522},"top:-2.1em;",[20,4524],{"className":4525,"style":4516},[102],[20,4527],{"className":4528},[41],[20,4530,119],{"className":4531},[118],[20,4533,4535],{"className":4534},[90],[20,4536,4539],{"className":4537,"style":4538},[94],"height:1.1em;",[20,4540],{},[20,4542,4545],{"className":4543},[4544],"col-align-l",[20,4546,4548,4671],{"className":4547},[85,86],[20,4549,4551,4668],{"className":4550},[90],[20,4552,4554,4637],{"className":4553,"style":4509},[94],[20,4555,4557,4560],{"style":4556},"top:-3.76em;",[20,4558],{"className":4559,"style":316},[102],[20,4561,4563,4566,4569,4572,4575,4578,4585,4631,4634],{"className":4562},[41],[20,4564],{"className":4565},[41],[20,4567,2753],{"className":4568,"style":2752},[41,76],[20,4570],{"className":4571,"style":49},[48],[20,4573,54],{"className":4574},[53],[20,4576],{"className":4577,"style":49},[48],[20,4579,4581],{"className":4580},[41,2828],[20,4582,4584],{"className":4583},[41],"capacity (e.g. ",[20,4586,4588,4591],{"className":4587},[41],[20,4589,2199],{"className":4590},[41,76],[20,4592,4594],{"className":4593},[81],[20,4595,4597,4623],{"className":4596},[85,86],[20,4598,4600,4620],{"className":4599},[90],[20,4601,4603],{"className":4602,"style":1355},[94],[20,4604,4605,4608],{"style":98},[20,4606],{"className":4607,"style":103},[102],[20,4609,4611],{"className":4610},[107,108,109,110],[20,4612,4614],{"className":4613},[41,110],[20,4615,4617],{"className":4616},[41,110],[20,4618,2230],{"className":4619},[41,2229,110],[20,4621,119],{"className":4622},[118],[20,4624,4626],{"className":4625},[90],[20,4627,4629],{"className":4628,"style":126},[94],[20,4630],{},[20,4632,185],{"className":4633},[184],[20,4635,133],{"className":4636},[132],[20,4638,4640,4643],{"style":4639},"top:-2.26em;",[20,4641],{"className":4642,"style":316},[102],[20,4644,4646,4649,4652,4655,4658,4661],{"className":4645},[41],[20,4647],{"className":4648},[41],[20,4650,240],{"className":4651},[41,76],[20,4653],{"className":4654,"style":49},[48],[20,4656,54],{"className":4657},[53],[20,4659],{"className":4660,"style":49},[48],[20,4662,4664],{"className":4663},[41,2828],[20,4665,4667],{"className":4666},[41],"number of samples.",[20,4669,119],{"className":4670},[118],[20,4672,4674],{"className":4673},[90],[20,4675,4677],{"className":4676,"style":4538},[94],[20,4678],{},[11,4680,4681,4682,4725,4726,4741,4742,4807,4808,4823,4824,4827,4828],{},"Two terms trade off: ",[20,4683,4685],{"className":4684},[23],[20,4686,4688],{"className":4687,"ariaHidden":28},[27],[20,4689,4691,4694],{"className":4690},[32],[20,4692],{"className":4693,"style":309},[36],[20,4695,4697],{"className":4696},[41,299],[20,4698,4700],{"className":4699},[85],[20,4701,4703],{"className":4702},[90],[20,4704,4706,4714],{"className":4705,"style":309},[94],[20,4707,4708,4711],{"style":312},[20,4709],{"className":4710,"style":316},[102],[20,4712,321],{"className":4713,"style":320},[41,76],[20,4715,4716,4719],{"style":324},[20,4717],{"className":4718,"style":316},[102],[20,4720,4722],{"className":4721,"style":332},[331],[20,4723,336],{"className":4724},[41]," falls as capacity ",[20,4727,4729],{"className":4728},[23],[20,4730,4732],{"className":4731,"ariaHidden":28},[27],[20,4733,4735,4738],{"className":4734},[32],[20,4736],{"className":4737,"style":37},[36],[20,4739,2753],{"className":4740,"style":2752},[41,76]," rises\n(a richer class fits the sample better), while the gap term ",[20,4743,4745],{"className":4744},[23],[20,4746,4748],{"className":4747,"ariaHidden":28},[27],[20,4749,4751,4754],{"className":4750},[32],[20,4752],{"className":4753,"style":3827},[36],[20,4755,4757],{"className":4756},[41,3062],[20,4758,4760,4799],{"className":4759},[85,86],[20,4761,4763,4796],{"className":4762},[90],[20,4764,4766,4784],{"className":4765,"style":3840},[94],[20,4767,4769,4772],{"className":4768,"style":3844},[3076],[20,4770],{"className":4771,"style":3848},[102],[20,4773,4775,4778,4781],{"className":4774,"style":3085},[41],[20,4776,2753],{"className":4777,"style":2752},[41,76],[20,4779,3158],{"className":4780},[41],[20,4782,240],{"className":4783},[41,76],[20,4785,4786,4789],{"style":3903},[20,4787],{"className":4788,"style":3848},[102],[20,4790,4792],{"className":4791,"style":3910},[3216],[3219,4793,4794],{"xmlns":3221,"width":3222,"height":3913,"viewBox":3914,"preserveAspectRatio":3225},[3227,4795],{"d":3917},[20,4797,119],{"className":4798},[118],[20,4800,4802],{"className":4801},[90],[20,4803,4805],{"className":4804,"style":3927},[94],[20,4806],{}," rises with\n",[20,4809,4811],{"className":4810},[23],[20,4812,4814],{"className":4813,"ariaHidden":28},[27],[20,4815,4817,4820],{"className":4816},[32],[20,4818],{"className":4819,"style":37},[36],[20,4821,2753],{"className":4822,"style":2752},[41,76],". Their sum is U-shaped in capacity, minimized at an intermediate ",[3258,4825,4826],{},"sweet spot",",\nthe classical bias–variance tradeoff.",[1858,4829,4830],{},[1861,4831,2103],{"href":4832,"ariaDescribedBy":4833,"dataFootnoteRef":6,"id":4834},"#user-content-fn-gf-biasvar",[1865],"user-content-fnref-gf-biasvar",[2645,4836],{"hash":4837},"da6e8d45d686af82195a5202a8f7fadacd8ff4a810fff8f7df1110a25f1705d2",[4839,4840,4842],"h3",{"id":4841},"the-biasvariance-decomposition","The bias–variance decomposition",[11,4844,4845,4846,4861,4862,4925,4926,2889,4968,5041,5042,5196,5197,5212,5213,5228,5229,5244],{},"The U-shape has an exact algebraic source. Fix a query point ",[20,4847,4849],{"className":4848},[23],[20,4850,4852],{"className":4851,"ariaHidden":28},[27],[20,4853,4855,4858],{"className":4854},[32],[20,4856],{"className":4857,"style":1147},[36],[20,4859,77],{"className":4860},[41,76],", let ",[20,4863,4865],{"className":4864},[23],[20,4866,4868,4887,4915],{"className":4867,"ariaHidden":28},[27],[20,4869,4871,4875,4878,4881,4884],{"className":4870},[32],[20,4872],{"className":4873,"style":4874},[36],"height:0.625em;vertical-align:-0.1944em;",[20,4876,145],{"className":4877,"style":144},[41,76],[20,4879],{"className":4880,"style":49},[48],[20,4882,54],{"className":4883},[53],[20,4885],{"className":4886,"style":49},[48],[20,4888,4890,4893,4897,4900,4903,4906,4909,4912],{"className":4889},[32],[20,4891],{"className":4892,"style":946},[36],[20,4894,4896],{"className":4895,"style":144},[41,76],"g",[20,4898,340],{"className":4899},[68],[20,4901,77],{"className":4902},[41,76],[20,4904,185],{"className":4905},[184],[20,4907],{"className":4908,"style":962},[48],[20,4910,2335],{"className":4911},[966],[20,4913],{"className":4914,"style":962},[48],[20,4916,4918,4921],{"className":4917},[32],[20,4919],{"className":4920,"style":1147},[36],[20,4922,4924],{"className":4923},[41,76],"ϵ"," with noise ",[20,4927,4929],{"className":4928},[23],[20,4930,4932,4959],{"className":4931,"ariaHidden":28},[27],[20,4933,4935,4938,4941,4944,4947,4950,4953,4956],{"className":4934},[32],[20,4936],{"className":4937,"style":946},[36],[20,4939,719],{"className":4940},[41,718],[20,4942,788],{"className":4943},[68],[20,4945,4924],{"className":4946},[41,76],[20,4948,859],{"className":4949},[184],[20,4951],{"className":4952,"style":49},[48],[20,4954,54],{"className":4955},[53],[20,4957],{"className":4958,"style":49},[48],[20,4960,4962,4965],{"className":4961},[32],[20,4963],{"className":4964,"style":2313},[36],[20,4966,2824],{"className":4967},[41],[20,4969,4971],{"className":4970},[23],[20,4972,4974,5005],{"className":4973,"ariaHidden":28},[27],[20,4975,4977,4980,4987,4990,4993,4996,4999,5002],{"className":4976},[32],[20,4978],{"className":4979,"style":946},[36],[20,4981,4983],{"className":4982},[41],[20,4984,4986],{"className":4985},[41,2229],"Var",[20,4988,340],{"className":4989},[68],[20,4991,4924],{"className":4992},[41,76],[20,4994,185],{"className":4995},[184],[20,4997],{"className":4998,"style":49},[48],[20,5000,54],{"className":5001},[53],[20,5003],{"className":5004,"style":49},[48],[20,5006,5008,5011],{"className":5007},[32],[20,5009],{"className":5010,"style":2267},[36],[20,5012,5014,5018],{"className":5013},[41],[20,5015,5017],{"className":5016,"style":144},[41,76],"σ",[20,5019,5021],{"className":5020},[81],[20,5022,5024],{"className":5023},[85],[20,5025,5027],{"className":5026},[90],[20,5028,5030],{"className":5029,"style":2267},[94],[20,5031,5032,5035],{"style":230},[20,5033],{"className":5034,"style":103},[102],[20,5036,5038],{"className":5037},[107,108,109,110],[20,5039,2103],{"className":5040},[41,110],",\nand let ",[20,5043,5045],{"className":5044},[23],[20,5046,5048,5108],{"className":5047,"ariaHidden":28},[27],[20,5049,5051,5055,5099,5102,5105],{"className":5050},[32],[20,5052],{"className":5053,"style":5054},[36],"height:1.1523em;vertical-align:-0.1944em;",[20,5056,5058],{"className":5057},[41,299],[20,5059,5061,5090],{"className":5060},[85,86],[20,5062,5064,5087],{"className":5063},[90],[20,5065,5067,5075],{"className":5066,"style":1648},[94],[20,5068,5069,5072],{"style":312},[20,5070],{"className":5071,"style":316},[102],[20,5073,539],{"className":5074,"style":538},[41,76],[20,5076,5077,5080],{"style":1671},[20,5078],{"className":5079,"style":316},[102],[20,5081,5084],{"className":5082,"style":5083},[331],"left:-0.0833em;",[20,5085,336],{"className":5086},[41],[20,5088,119],{"className":5089},[118],[20,5091,5093],{"className":5092},[90],[20,5094,5097],{"className":5095,"style":5096},[94],"height:0.1944em;",[20,5098],{},[20,5100],{"className":5101,"style":49},[48],[20,5103,54],{"className":5104},[53],[20,5106],{"className":5107,"style":49},[48],[20,5109,5111,5114],{"className":5110},[32],[20,5112],{"className":5113,"style":5054},[36],[20,5115,5117,5159],{"className":5116},[41],[20,5118,5120],{"className":5119},[41,299],[20,5121,5123,5151],{"className":5122},[85,86],[20,5124,5126,5148],{"className":5125},[90],[20,5127,5129,5137],{"className":5128,"style":1648},[94],[20,5130,5131,5134],{"style":312},[20,5132],{"className":5133,"style":316},[102],[20,5135,539],{"className":5136,"style":538},[41,76],[20,5138,5139,5142],{"style":1671},[20,5140],{"className":5141,"style":316},[102],[20,5143,5145],{"className":5144,"style":5083},[331],[20,5146,336],{"className":5147},[41],[20,5149,119],{"className":5150},[118],[20,5152,5154],{"className":5153},[90],[20,5155,5157],{"className":5156,"style":5096},[94],[20,5158],{},[20,5160,5162],{"className":5161},[81],[20,5163,5165,5188],{"className":5164},[85,86],[20,5166,5168,5185],{"className":5167},[90],[20,5169,5171],{"className":5170,"style":1355},[94],[20,5172,5173,5176],{"style":555},[20,5174],{"className":5175,"style":103},[102],[20,5177,5179],{"className":5178},[107,108,109,110],[20,5180,5182],{"className":5181},[41,110],[20,5183,44],{"className":5184,"style":43},[41,42,110],[20,5186,119],{"className":5187},[118],[20,5189,5191],{"className":5190},[90],[20,5192,5194],{"className":5193,"style":126},[94],[20,5195],{}," be the model fit on a random training set\n",[20,5198,5200],{"className":5199},[23],[20,5201,5203],{"className":5202,"ariaHidden":28},[27],[20,5204,5206,5209],{"className":5205},[32],[20,5207],{"className":5208,"style":37},[36],[20,5210,44],{"className":5211,"style":43},[41,42],". The expected squared error at ",[20,5214,5216],{"className":5215},[23],[20,5217,5219],{"className":5218,"ariaHidden":28},[27],[20,5220,5222,5225],{"className":5221},[32],[20,5223],{"className":5224,"style":1147},[36],[20,5226,77],{"className":5227},[41,76],", averaged over both the label noise\nand the draw of ",[20,5230,5232],{"className":5231},[23],[20,5233,5235],{"className":5234,"ariaHidden":28},[27],[20,5236,5238,5241],{"className":5237},[32],[20,5239],{"className":5240,"style":37},[36],[20,5242,44],{"className":5243,"style":43},[41,42],", decomposes into three non-negative pieces:",[20,5246,5248],{"className":5247},[282],[20,5249,5251],{"className":5250},[23],[20,5252,5254,5439,5777,6175],{"className":5253,"ariaHidden":28},[27],[20,5255,5257,5261,5311,5314,5317,5430,5433,5436],{"className":5256},[32],[20,5258],{"className":5259,"style":5260},[36],"height:1.8em;vertical-align:-0.65em;",[20,5262,5264,5267],{"className":5263},[41],[20,5265,719],{"className":5266},[41,718],[20,5268,5270],{"className":5269},[81],[20,5271,5273,5302],{"className":5272},[85,86],[20,5274,5276,5299],{"className":5275},[90],[20,5277,5279],{"className":5278,"style":1355},[94],[20,5280,5281,5284],{"style":98},[20,5282],{"className":5283,"style":103},[102],[20,5285,5287],{"className":5286},[107,108,109,110],[20,5288,5290,5293,5296],{"className":5289},[41,110],[20,5291,44],{"className":5292,"style":43},[41,42,110],[20,5294,133],{"className":5295},[132,110],[20,5297,4924],{"className":5298},[41,76,110],[20,5300,119],{"className":5301},[118],[20,5303,5305],{"className":5304},[90],[20,5306,5309],{"className":5307,"style":5308},[94],"height:0.2861em;",[20,5310],{},[20,5312],{"className":5313,"style":3044},[48],[20,5315],{"className":5316,"style":137},[48],[20,5318,5320,5327,5330,5333,5336,5339,5342,5384,5387,5390,5393,5424],{"className":5319},[526],[20,5321,5323],{"className":5322,"style":531},[68,530],[20,5324,788],{"className":5325},[3057,5326],"size2",[20,5328,340],{"className":5329},[68],[20,5331,145],{"className":5332,"style":144},[41,76],[20,5334],{"className":5335,"style":962},[48],[20,5337,967],{"className":5338},[966],[20,5340],{"className":5341,"style":962},[48],[20,5343,5345],{"className":5344},[41,299],[20,5346,5348,5376],{"className":5347},[85,86],[20,5349,5351,5373],{"className":5350},[90],[20,5352,5354,5362],{"className":5353,"style":1648},[94],[20,5355,5356,5359],{"style":312},[20,5357],{"className":5358,"style":316},[102],[20,5360,539],{"className":5361,"style":538},[41,76],[20,5363,5364,5367],{"style":1671},[20,5365],{"className":5366,"style":316},[102],[20,5368,5370],{"className":5369,"style":5083},[331],[20,5371,336],{"className":5372},[41],[20,5374,119],{"className":5375},[118],[20,5377,5379],{"className":5378},[90],[20,5380,5382],{"className":5381,"style":5096},[94],[20,5383],{},[20,5385,340],{"className":5386},[68],[20,5388,77],{"className":5389},[41,76],[20,5391,185],{"className":5392},[184],[20,5394,5396,5399],{"className":5395},[184],[20,5397,185],{"className":5398},[184],[20,5400,5402],{"className":5401},[81],[20,5403,5405],{"className":5404},[85],[20,5406,5408],{"className":5407},[90],[20,5409,5412],{"className":5410,"style":5411},[94],"height:0.8641em;",[20,5413,5415,5418],{"style":5414},"top:-3.113em;margin-right:0.05em;",[20,5416],{"className":5417,"style":103},[102],[20,5419,5421],{"className":5420},[107,108,109,110],[20,5422,2103],{"className":5423},[41,110],[20,5425,5427],{"className":5426,"style":531},[184,530],[20,5428,859],{"className":5429},[3057,5326],[20,5431],{"className":5432,"style":49},[48],[20,5434,54],{"className":5435},[53],[20,5437],{"className":5438,"style":49},[48],[20,5440,5442,5446,5762,5765,5768,5771,5774],{"className":5441},[32],[20,5443],{"className":5444,"style":5445},[36],"height:3.4134em;vertical-align:-2.0593em;",[20,5447,5450],{"className":5448},[526,5449],"munder",[20,5451,5453,5753],{"className":5452},[85,86],[20,5454,5456,5750],{"className":5455},[90],[20,5457,5460,5508],{"className":5458,"style":5459},[94],"height:1.354em;",[20,5461,5463,5467],{"style":5462},"top:-1.2947em;",[20,5464],{"className":5465,"style":5466},[102],"height:3.354em;",[20,5468,5470],{"className":5469},[107,108,109,110],[20,5471,5473],{"className":5472},[41,110],[20,5474,5476,5483],{"className":5475},[41,110],[20,5477,5479],{"className":5478},[41,2828,110],[20,5480,5482],{"className":5481},[41,110],"bias",[20,5484,5486],{"className":5485},[81],[20,5487,5489],{"className":5488},[85],[20,5490,5492],{"className":5491},[90],[20,5493,5496],{"className":5494,"style":5495},[94],"height:0.8019em;",[20,5497,5499,5502],{"style":5498},"top:-2.8416em;margin-right:0.0714em;",[20,5500],{"className":5501,"style":3523},[102],[20,5503,5505],{"className":5504},[107,3527,3528,110],[20,5506,2103],{"className":5507},[41,110],[20,5509,5511,5514],{"style":5510},"top:-3.354em;",[20,5512],{"className":5513,"style":5466},[102],[20,5515,5517],{"className":5516},[526,5449],[20,5518,5520,5741],{"className":5519},[85,86],[20,5521,5523,5738],{"className":5522},[90],[20,5524,5526,5570],{"className":5525,"style":5459},[94],[20,5527,5530,5533],{"className":5528,"style":5529},[3076],"top:-2.056em;",[20,5531],{"className":5532,"style":5466},[102],[20,5534,5538,5550,5560],{"className":5535,"style":5537},[5536],"stretchy","height:0.548em;min-width:1.6em;",[20,5539,5543],{"className":5540,"style":5542},[5541],"brace-left","height:0.548em;",[3219,5544,5547],{"xmlns":3221,"width":3222,"height":5545,"viewBox":5546,"preserveAspectRatio":3225},"0.548em","0 0 400000 548",[3227,5548],{"d":5549},"M0 6l6-6h17c12.688 0 19.313.3 20 1 4 4 7.313 8.3 10 13\n 35.313 51.3 80.813 93.8 136.5 127.5 55.688 33.7 117.188 55.8 184.5 66.5.688\n 0 2 .3 4 1 18.688 2.7 76 4.3 172 5h399450v120H429l-6-1c-124.688-8-235-61.7\n-331-161C60.687 138.7 32.312 99.3 7 54L0 41V6z",[20,5551,5554],{"className":5552,"style":5542},[5553],"brace-center",[3219,5555,5557],{"xmlns":3221,"width":3222,"height":5545,"viewBox":5546,"preserveAspectRatio":5556},"xMidYMin slice",[3227,5558],{"d":5559},"M199572 214\nc100.7 8.3 195.3 44 280 108 55.3 42 101.7 93 139 153l9 14c2.7-4 5.7-8.7 9-14\n 53.3-86.7 123.7-153 211-199 66.7-36 137.3-56.3 212-62h199568v120H200432c-178.3\n 11.7-311.7 78.3-403 201-6 8-9.7 12-11 12-.7.7-6.7 1-18 1s-17.3-.3-18-1c-1.3 0\n-5-4-11-12-44.7-59.3-101.3-106.3-170-141s-145.3-54.3-229-60H0V214z",[20,5561,5564],{"className":5562,"style":5542},[5563],"brace-right",[3219,5565,5567],{"xmlns":3221,"width":3222,"height":5545,"viewBox":5546,"preserveAspectRatio":5566},"xMaxYMin slice",[3227,5568],{"d":5569},"M399994 0l6 6v35l-6 11c-56 104-135.3 181.3-238 232-57.3\n 28.7-117 45-179 50H-300V214h399897c43.3-7 81-15 113-26 100.7-33 179.7-91 237\n-174 2.7-5 6-9 10-13 .7-1 7.3-1 20-1h17z",[20,5571,5572,5575],{"style":5510},[20,5573],{"className":5574,"style":5466},[102],[20,5576,5578],{"className":5577},[41],[20,5579,5581,5714],{"className":5580},[526],[20,5582,5584,5590,5593,5596,5599,5602,5605,5608,5611,5654,5657,5699,5702,5705,5708],{"className":5583},[526],[20,5585,5587],{"className":5586,"style":531},[68,530],[20,5588,340],{"className":5589},[3057,5326],[20,5591,4896],{"className":5592,"style":144},[41,76],[20,5594,340],{"className":5595},[68],[20,5597,77],{"className":5598},[41,76],[20,5600,185],{"className":5601},[184],[20,5603],{"className":5604,"style":962},[48],[20,5606,967],{"className":5607},[966],[20,5609],{"className":5610,"style":962},[48],[20,5612,5614,5617],{"className":5613},[41],[20,5615,719],{"className":5616},[41,718],[20,5618,5620],{"className":5619},[81],[20,5621,5623,5646],{"className":5622},[85,86],[20,5624,5626,5643],{"className":5625},[90],[20,5627,5629],{"className":5628,"style":1355},[94],[20,5630,5631,5634],{"style":98},[20,5632],{"className":5633,"style":103},[102],[20,5635,5637],{"className":5636},[107,108,109,110],[20,5638,5640],{"className":5639},[41,110],[20,5641,44],{"className":5642,"style":43},[41,42,110],[20,5644,119],{"className":5645},[118],[20,5647,5649],{"className":5648},[90],[20,5650,5652],{"className":5651,"style":126},[94],[20,5653],{},[20,5655,788],{"className":5656},[68],[20,5658,5660],{"className":5659},[41,299],[20,5661,5663,5691],{"className":5662},[85,86],[20,5664,5666,5688],{"className":5665},[90],[20,5667,5669,5677],{"className":5668,"style":1648},[94],[20,5670,5671,5674],{"style":312},[20,5672],{"className":5673,"style":316},[102],[20,5675,539],{"className":5676,"style":538},[41,76],[20,5678,5679,5682],{"style":1671},[20,5680],{"className":5681,"style":316},[102],[20,5683,5685],{"className":5684,"style":5083},[331],[20,5686,336],{"className":5687},[41],[20,5689,119],{"className":5690},[118],[20,5692,5694],{"className":5693},[90],[20,5695,5697],{"className":5696,"style":5096},[94],[20,5698],{},[20,5700,340],{"className":5701},[68],[20,5703,77],{"className":5704},[41,76],[20,5706,1424],{"className":5707},[184],[20,5709,5711],{"className":5710,"style":531},[184,530],[20,5712,185],{"className":5713},[3057,5326],[20,5715,5717],{"className":5716},[81],[20,5718,5720],{"className":5719},[85],[20,5721,5723],{"className":5722},[90],[20,5724,5726],{"className":5725,"style":5459},[94],[20,5727,5729,5732],{"style":5728},"top:-3.6029em;margin-right:0.05em;",[20,5730],{"className":5731,"style":103},[102],[20,5733,5735],{"className":5734},[107,108,109,110],[20,5736,2103],{"className":5737},[41,110],[20,5739,119],{"className":5740},[118],[20,5742,5744],{"className":5743},[90],[20,5745,5748],{"className":5746,"style":5747},[94],"height:1.298em;",[20,5749],{},[20,5751,119],{"className":5752},[118],[20,5754,5756],{"className":5755},[90],[20,5757,5760],{"className":5758,"style":5759},[94],"height:2.0593em;",[20,5761],{},[20,5763],{"className":5764,"style":49},[48],[20,5766],{"className":5767,"style":962},[48],[20,5769,2335],{"className":5770},[966],[20,5772],{"className":5773,"style":49},[48],[20,5775],{"className":5776,"style":962},[48],[20,5778,5780,5784,6160,6163,6166,6169,6172],{"className":5779},[32],[20,5781],{"className":5782,"style":5783},[36],"height:3.7155em;vertical-align:-2.2655em;",[20,5785,5787],{"className":5786},[526,5449],[20,5788,5790,6151],{"className":5789},[85,86],[20,5791,5793,6148],{"className":5792},[90],[20,5794,5797,5817],{"className":5795,"style":5796},[94],"height:1.45em;",[20,5798,5800,5804],{"style":5799},"top:-1.1845em;",[20,5801],{"className":5802,"style":5803},[102],"height:3.45em;",[20,5805,5807],{"className":5806},[107,108,109,110],[20,5808,5810],{"className":5809},[41,110],[20,5811,5813],{"className":5812},[41,2828,110],[20,5814,5816],{"className":5815},[41,110],"variance",[20,5818,5820,5823],{"style":5819},"top:-3.45em;",[20,5821],{"className":5822,"style":5803},[102],[20,5824,5826],{"className":5825},[526,5449],[20,5827,5829,6139],{"className":5828},[85,86],[20,5830,5832,6136],{"className":5831},[90],[20,5833,5835,5866],{"className":5834,"style":5796},[94],[20,5836,5839,5842],{"className":5837,"style":5838},[3076],"top:-1.852em;",[20,5840],{"className":5841,"style":5803},[102],[20,5843,5845,5852,5859],{"className":5844,"style":5537},[5536],[20,5846,5848],{"className":5847,"style":5542},[5541],[3219,5849,5850],{"xmlns":3221,"width":3222,"height":5545,"viewBox":5546,"preserveAspectRatio":3225},[3227,5851],{"d":5549},[20,5853,5855],{"className":5854,"style":5542},[5553],[3219,5856,5857],{"xmlns":3221,"width":3222,"height":5545,"viewBox":5546,"preserveAspectRatio":5556},[3227,5858],{"d":5559},[20,5860,5862],{"className":5861,"style":5542},[5563],[3219,5863,5864],{"xmlns":3221,"width":3222,"height":5545,"viewBox":5546,"preserveAspectRatio":5566},[3227,5865],{"d":5569},[20,5867,5868,5871],{"style":5819},[20,5869],{"className":5870,"style":5803},[102],[20,5872,5874,5917,5920,5923],{"className":5873},[41],[20,5875,5877,5880],{"className":5876},[41],[20,5878,719],{"className":5879},[41,718],[20,5881,5883],{"className":5882},[81],[20,5884,5886,5909],{"className":5885},[85,86],[20,5887,5889,5906],{"className":5888},[90],[20,5890,5892],{"className":5891,"style":1355},[94],[20,5893,5894,5897],{"style":98},[20,5895],{"className":5896,"style":103},[102],[20,5898,5900],{"className":5899},[107,108,109,110],[20,5901,5903],{"className":5902},[41,110],[20,5904,44],{"className":5905,"style":43},[41,42,110],[20,5907,119],{"className":5908},[118],[20,5910,5912],{"className":5911},[90],[20,5913,5915],{"className":5914,"style":126},[94],[20,5916],{},[20,5918],{"className":5919,"style":3044},[48],[20,5921],{"className":5922,"style":137},[48],[20,5924,5926,5932,6130],{"className":5925},[526],[20,5927,5929],{"className":5928,"style":531},[68,530],[20,5930,788],{"className":5931},[3057,109],[20,5933,5935,6107],{"className":5934},[526],[20,5936,5938,5944,5986,5989,5992,5995,5998,6001,6004,6047,6050,6092,6095,6098,6101],{"className":5937},[526],[20,5939,5941],{"className":5940,"style":531},[68,530],[20,5942,340],{"className":5943},[3057,5326],[20,5945,5947],{"className":5946},[41,299],[20,5948,5950,5978],{"className":5949},[85,86],[20,5951,5953,5975],{"className":5952},[90],[20,5954,5956,5964],{"className":5955,"style":1648},[94],[20,5957,5958,5961],{"style":312},[20,5959],{"className":5960,"style":316},[102],[20,5962,539],{"className":5963,"style":538},[41,76],[20,5965,5966,5969],{"style":1671},[20,5967],{"className":5968,"style":316},[102],[20,5970,5972],{"className":5971,"style":5083},[331],[20,5973,336],{"className":5974},[41],[20,5976,119],{"className":5977},[118],[20,5979,5981],{"className":5980},[90],[20,5982,5984],{"className":5983,"style":5096},[94],[20,5985],{},[20,5987,340],{"className":5988},[68],[20,5990,77],{"className":5991},[41,76],[20,5993,185],{"className":5994},[184],[20,5996],{"className":5997,"style":962},[48],[20,5999,967],{"className":6000},[966],[20,6002],{"className":6003,"style":962},[48],[20,6005,6007,6010],{"className":6006},[41],[20,6008,719],{"className":6009},[41,718],[20,6011,6013],{"className":6012},[81],[20,6014,6016,6039],{"className":6015},[85,86],[20,6017,6019,6036],{"className":6018},[90],[20,6020,6022],{"className":6021,"style":1355},[94],[20,6023,6024,6027],{"style":98},[20,6025],{"className":6026,"style":103},[102],[20,6028,6030],{"className":6029},[107,108,109,110],[20,6031,6033],{"className":6032},[41,110],[20,6034,44],{"className":6035,"style":43},[41,42,110],[20,6037,119],{"className":6038},[118],[20,6040,6042],{"className":6041},[90],[20,6043,6045],{"className":6044,"style":126},[94],[20,6046],{},[20,6048,788],{"className":6049},[68],[20,6051,6053],{"className":6052},[41,299],[20,6054,6056,6084],{"className":6055},[85,86],[20,6057,6059,6081],{"className":6058},[90],[20,6060,6062,6070],{"className":6061,"style":1648},[94],[20,6063,6064,6067],{"style":312},[20,6065],{"className":6066,"style":316},[102],[20,6068,539],{"className":6069,"style":538},[41,76],[20,6071,6072,6075],{"style":1671},[20,6073],{"className":6074,"style":316},[102],[20,6076,6078],{"className":6077,"style":5083},[331],[20,6079,336],{"className":6080},[41],[20,6082,119],{"className":6083},[118],[20,6085,6087],{"className":6086},[90],[20,6088,6090],{"className":6089,"style":5096},[94],[20,6091],{},[20,6093,340],{"className":6094},[68],[20,6096,77],{"className":6097},[41,76],[20,6099,1424],{"className":6100},[184],[20,6102,6104],{"className":6103,"style":531},[184,530],[20,6105,185],{"className":6106},[3057,5326],[20,6108,6110],{"className":6109},[81],[20,6111,6113],{"className":6112},[85],[20,6114,6116],{"className":6115},[90],[20,6117,6119],{"className":6118,"style":5459},[94],[20,6120,6121,6124],{"style":5728},[20,6122],{"className":6123,"style":103},[102],[20,6125,6127],{"className":6126},[107,108,109,110],[20,6128,2103],{"className":6129},[41,110],[20,6131,6133],{"className":6132,"style":531},[184,530],[20,6134,859],{"className":6135},[3057,109],[20,6137,119],{"className":6138},[118],[20,6140,6142],{"className":6141},[90],[20,6143,6146],{"className":6144,"style":6145},[94],"height:1.598em;",[20,6147],{},[20,6149,119],{"className":6150},[118],[20,6152,6154],{"className":6153},[90],[20,6155,6158],{"className":6156,"style":6157},[94],"height:2.2655em;",[20,6159],{},[20,6161],{"className":6162,"style":49},[48],[20,6164],{"className":6165,"style":962},[48],[20,6167,2335],{"className":6168},[966],[20,6170],{"className":6171,"style":49},[48],[20,6173],{"className":6174,"style":962},[48],[20,6176,6178,6182,6322,6325],{"className":6177},[32],[20,6179],{"className":6180,"style":6181},[36],"height:2.1796em;vertical-align:-1.3155em;",[20,6183,6185],{"className":6184},[526,5449],[20,6186,6188,6313],{"className":6187},[85,86],[20,6189,6191,6310],{"className":6190},[90],[20,6192,6194,6213],{"className":6193,"style":5411},[94],[20,6195,6197,6200],{"style":6196},"top:-1.6845em;",[20,6198],{"className":6199,"style":316},[102],[20,6201,6203],{"className":6202},[107,108,109,110],[20,6204,6206],{"className":6205},[41,110],[20,6207,6209],{"className":6208},[41,2828,110],[20,6210,6212],{"className":6211},[41,110],"noise",[20,6214,6215,6218],{"style":312},[20,6216],{"className":6217,"style":316},[102],[20,6219,6221],{"className":6220},[526,5449],[20,6222,6224,6301],{"className":6223},[85,86],[20,6225,6227,6298],{"className":6226},[90],[20,6228,6230,6261],{"className":6229,"style":5411},[94],[20,6231,6234,6237],{"className":6232,"style":6233},[3076],"top:-2.352em;",[20,6235],{"className":6236,"style":316},[102],[20,6238,6240,6247,6254],{"className":6239,"style":5537},[5536],[20,6241,6243],{"className":6242,"style":5542},[5541],[3219,6244,6245],{"xmlns":3221,"width":3222,"height":5545,"viewBox":5546,"preserveAspectRatio":3225},[3227,6246],{"d":5549},[20,6248,6250],{"className":6249,"style":5542},[5553],[3219,6251,6252],{"xmlns":3221,"width":3222,"height":5545,"viewBox":5546,"preserveAspectRatio":5556},[3227,6253],{"d":5559},[20,6255,6257],{"className":6256,"style":5542},[5563],[3219,6258,6259],{"xmlns":3221,"width":3222,"height":5545,"viewBox":5546,"preserveAspectRatio":5566},[3227,6260],{"d":5569},[20,6262,6263,6266],{"style":312},[20,6264],{"className":6265,"style":316},[102],[20,6267,6269],{"className":6268},[41],[20,6270,6272,6275],{"className":6271},[41],[20,6273,5017],{"className":6274,"style":144},[41,76],[20,6276,6278],{"className":6277},[81],[20,6279,6281],{"className":6280},[85],[20,6282,6284],{"className":6283},[90],[20,6285,6287],{"className":6286,"style":5411},[94],[20,6288,6289,6292],{"style":5414},[20,6290],{"className":6291,"style":103},[102],[20,6293,6295],{"className":6294},[107,108,109,110],[20,6296,2103],{"className":6297},[41,110],[20,6299,119],{"className":6300},[118],[20,6302,6304],{"className":6303},[90],[20,6305,6308],{"className":6306,"style":6307},[94],"height:0.648em;",[20,6309],{},[20,6311,119],{"className":6312},[118],[20,6314,6316],{"className":6315},[90],[20,6317,6320],{"className":6318,"style":6319},[94],"height:1.3155em;",[20,6321],{},[20,6323],{"className":6324,"style":137},[48],[20,6326,866],{"className":6327},[41],[11,6329,6330,6331,6346,6347,6401,6402,6480,6481,6544,6545,6548,6549,6564,6565,6568,6569,6623,6624,1635,6627,6668,6669],{},"The cross terms vanish: ",[20,6332,6334],{"className":6333},[23],[20,6335,6337],{"className":6336,"ariaHidden":28},[27],[20,6338,6340,6343],{"className":6339},[32],[20,6341],{"className":6342,"style":1147},[36],[20,6344,4924],{"className":6345},[41,76]," is independent of ",[20,6348,6350],{"className":6349},[23],[20,6351,6353],{"className":6352,"ariaHidden":28},[27],[20,6354,6356,6359],{"className":6355},[32],[20,6357],{"className":6358,"style":5054},[36],[20,6360,6362],{"className":6361},[41,299],[20,6363,6365,6393],{"className":6364},[85,86],[20,6366,6368,6390],{"className":6367},[90],[20,6369,6371,6379],{"className":6370,"style":1648},[94],[20,6372,6373,6376],{"style":312},[20,6374],{"className":6375,"style":316},[102],[20,6377,539],{"className":6378,"style":538},[41,76],[20,6380,6381,6384],{"style":1671},[20,6382],{"className":6383,"style":316},[102],[20,6385,6387],{"className":6386,"style":5083},[331],[20,6388,336],{"className":6389},[41],[20,6391,119],{"className":6392},[118],[20,6394,6396],{"className":6395},[90],[20,6397,6399],{"className":6398,"style":5096},[94],[20,6400],{}," and mean-zero, and\nthe split of ",[20,6403,6405],{"className":6404},[23],[20,6406,6408,6429],{"className":6407,"ariaHidden":28},[27],[20,6409,6411,6414,6417,6420,6423,6426],{"className":6410},[32],[20,6412],{"className":6413,"style":946},[36],[20,6415,340],{"className":6416},[68],[20,6418,4896],{"className":6419,"style":144},[41,76],[20,6421],{"className":6422,"style":962},[48],[20,6424,967],{"className":6425},[966],[20,6427],{"className":6428,"style":962},[48],[20,6430,6432,6435,6477],{"className":6431},[32],[20,6433],{"className":6434,"style":1754},[36],[20,6436,6438],{"className":6437},[41,299],[20,6439,6441,6469],{"className":6440},[85,86],[20,6442,6444,6466],{"className":6443},[90],[20,6445,6447,6455],{"className":6446,"style":1648},[94],[20,6448,6449,6452],{"style":312},[20,6450],{"className":6451,"style":316},[102],[20,6453,539],{"className":6454,"style":538},[41,76],[20,6456,6457,6460],{"style":1671},[20,6458],{"className":6459,"style":316},[102],[20,6461,6463],{"className":6462,"style":5083},[331],[20,6464,336],{"className":6465},[41],[20,6467,119],{"className":6468},[118],[20,6470,6472],{"className":6471},[90],[20,6473,6475],{"className":6474,"style":5096},[94],[20,6476],{},[20,6478,185],{"className":6479},[184]," around ",[20,6482,6484],{"className":6483},[23],[20,6485,6487],{"className":6486,"ariaHidden":28},[27],[20,6488,6490,6493,6496,6499,6541],{"className":6489},[32],[20,6491],{"className":6492,"style":1754},[36],[20,6494,719],{"className":6495},[41,718],[20,6497,788],{"className":6498},[68],[20,6500,6502],{"className":6501},[41,299],[20,6503,6505,6533],{"className":6504},[85,86],[20,6506,6508,6530],{"className":6507},[90],[20,6509,6511,6519],{"className":6510,"style":1648},[94],[20,6512,6513,6516],{"style":312},[20,6514],{"className":6515,"style":316},[102],[20,6517,539],{"className":6518,"style":538},[41,76],[20,6520,6521,6524],{"style":1671},[20,6522],{"className":6523,"style":316},[102],[20,6525,6527],{"className":6526,"style":5083},[331],[20,6528,336],{"className":6529},[41],[20,6531,119],{"className":6532},[118],[20,6534,6536],{"className":6535},[90],[20,6537,6539],{"className":6538,"style":5096},[94],[20,6540],{},[20,6542,859],{"className":6543},[184]," leaves a zero-mean residual\nthat is uncorrelated with the constant bias. ",[15,6546,6547],{},"Bias"," is the error a low-capacity\nclass cannot avoid even with infinite data — its best member is still far from ",[20,6550,6552],{"className":6551},[23],[20,6553,6555],{"className":6554,"ariaHidden":28},[27],[20,6556,6558,6561],{"className":6557},[32],[20,6559],{"className":6560,"style":4874},[36],[20,6562,4896],{"className":6563,"style":144},[41,76],".\n",[15,6566,6567],{},"Variance"," is how much ",[20,6570,6572],{"className":6571},[23],[20,6573,6575],{"className":6574,"ariaHidden":28},[27],[20,6576,6578,6581],{"className":6577},[32],[20,6579],{"className":6580,"style":5054},[36],[20,6582,6584],{"className":6583},[41,299],[20,6585,6587,6615],{"className":6586},[85,86],[20,6588,6590,6612],{"className":6589},[90],[20,6591,6593,6601],{"className":6592,"style":1648},[94],[20,6594,6595,6598],{"style":312},[20,6596],{"className":6597,"style":316},[102],[20,6599,539],{"className":6600,"style":538},[41,76],[20,6602,6603,6606],{"style":1671},[20,6604],{"className":6605,"style":316},[102],[20,6607,6609],{"className":6608,"style":5083},[331],[20,6610,336],{"className":6611},[41],[20,6613,119],{"className":6614},[118],[20,6616,6618],{"className":6617},[90],[20,6619,6621],{"className":6620,"style":5096},[94],[20,6622],{}," swings as the sample is redrawn — a high-capacity\nclass chases the sample noise, so its predictions are unstable. ",[15,6625,6626],{},"Noise",[20,6628,6630],{"className":6629},[23],[20,6631,6633],{"className":6632,"ariaHidden":28},[27],[20,6634,6636,6639],{"className":6635},[32],[20,6637],{"className":6638,"style":2267},[36],[20,6640,6642,6645],{"className":6641},[41],[20,6643,5017],{"className":6644,"style":144},[41,76],[20,6646,6648],{"className":6647},[81],[20,6649,6651],{"className":6650},[85],[20,6652,6654],{"className":6653},[90],[20,6655,6657],{"className":6656,"style":2267},[94],[20,6658,6659,6662],{"style":230},[20,6660],{"className":6661,"style":103},[102],[20,6663,6665],{"className":6664},[107,108,109,110],[20,6666,2103],{"className":6667},[41,110],"\nis irreducible. Raising capacity lowers bias and raises variance; the sum is\nU-shaped, and the sweet spot is where the two derivatives cancel. This is the same\nU as the VC budget, seen through squared error instead of a uniform bound.",[1858,6670,6671],{},[1861,6672,2103],{"href":4832,"ariaDescribedBy":6673,"dataFootnoteRef":6,"id":6674},[1865],"user-content-fnref-gf-biasvar-2",[11,6676,6677,6678,6681],{},"A second, distribution-aware measure replaces the worst-case combinatorics of VC\nwith an average over the actual data: ",[15,6679,6680],{},"Rademacher complexity"," measures how well\nthe class can fit a labeling of pure random signs.",[1019,6683,6684,6937,7412],{"type":1021},[11,6685,6686,6689,6690,6824,6825,6920,6921,6936],{},[15,6687,6688],{},"Definition (Rademacher complexity)."," For a sample ",[20,6691,6693],{"className":6692},[23],[20,6694,6696,6714],{"className":6695,"ariaHidden":28},[27],[20,6697,6699,6702,6705,6708,6711],{"className":6698},[32],[20,6700],{"className":6701,"style":37},[36],[20,6703,1943],{"className":6704,"style":1942},[41,76],[20,6706],{"className":6707,"style":49},[48],[20,6709,54],{"className":6710},[53],[20,6712],{"className":6713,"style":49},[48],[20,6715,6717,6720,6723,6763,6766,6769,6772,6775,6778,6781,6821],{"className":6716},[32],[20,6718],{"className":6719,"style":946},[36],[20,6721,1962],{"className":6722},[68],[20,6724,6726,6729],{"className":6725},[41],[20,6727,77],{"className":6728},[41,76],[20,6730,6732],{"className":6731},[81],[20,6733,6735,6755],{"className":6734},[85,86],[20,6736,6738,6752],{"className":6737},[90],[20,6739,6741],{"className":6740,"style":1981},[94],[20,6742,6743,6746],{"style":98},[20,6744],{"className":6745,"style":103},[102],[20,6747,6749],{"className":6748},[107,108,109,110],[20,6750,227],{"className":6751},[41,110],[20,6753,119],{"className":6754},[118],[20,6756,6758],{"className":6757},[90],[20,6759,6761],{"className":6760,"style":126},[94],[20,6762],{},[20,6764,133],{"className":6765},[132],[20,6767],{"className":6768,"style":137},[48],[20,6770,2013],{"className":6771},[526],[20,6773],{"className":6774,"style":137},[48],[20,6776,133],{"className":6777},[132],[20,6779],{"className":6780,"style":137},[48],[20,6782,6784,6787],{"className":6783},[41],[20,6785,77],{"className":6786},[41,76],[20,6788,6790],{"className":6789},[81],[20,6791,6793,6813],{"className":6792},[85,86],[20,6794,6796,6810],{"className":6795},[90],[20,6797,6799],{"className":6798,"style":2041},[94],[20,6800,6801,6804],{"style":98},[20,6802],{"className":6803,"style":103},[102],[20,6805,6807],{"className":6806},[107,108,109,110],[20,6808,240],{"className":6809},[41,76,110],[20,6811,119],{"className":6812},[118],[20,6814,6816],{"className":6815},[90],[20,6817,6819],{"className":6818,"style":126},[94],[20,6820],{},[20,6822,192],{"className":6823},[184]," and\nindependent random signs ",[20,6826,6828],{"className":6827},[23],[20,6829,6831,6890],{"className":6830,"ariaHidden":28},[27],[20,6832,6834,6838,6881,6884,6887],{"className":6833},[32],[20,6835],{"className":6836,"style":6837},[36],"height:0.6891em;vertical-align:-0.15em;",[20,6839,6841,6846],{"className":6840},[41],[20,6842,6845],{"className":6843,"style":6844},[41,76],"margin-right:0.0799em;","ς",[20,6847,6849],{"className":6848},[81],[20,6850,6852,6873],{"className":6851},[85,86],[20,6853,6855,6870],{"className":6854},[90],[20,6856,6858],{"className":6857,"style":95},[94],[20,6859,6861,6864],{"style":6860},"top:-2.55em;margin-left:-0.0799em;margin-right:0.05em;",[20,6862],{"className":6863,"style":103},[102],[20,6865,6867],{"className":6866},[107,108,109,110],[20,6868,114],{"className":6869},[41,76,110],[20,6871,119],{"className":6872},[118],[20,6874,6876],{"className":6875},[90],[20,6877,6879],{"className":6878,"style":126},[94],[20,6880],{},[20,6882],{"className":6883,"style":49},[48],[20,6885,2167],{"className":6886},[53],[20,6888],{"className":6889,"style":49},[48],[20,6891,6893,6896,6899,6902,6905,6908,6911,6914,6917],{"className":6892},[32],[20,6894],{"className":6895,"style":946},[36],[20,6897,1962],{"className":6898},[68],[20,6900,967],{"className":6901},[41],[20,6903,227],{"className":6904},[41],[20,6906,133],{"className":6907},[132],[20,6909],{"className":6910,"style":137},[48],[20,6912,2335],{"className":6913},[41],[20,6915,227],{"className":6916},[41],[20,6918,192],{"className":6919},[184]," drawn uniformly, the\nempirical Rademacher complexity of ",[20,6922,6924],{"className":6923},[23],[20,6925,6927],{"className":6926,"ariaHidden":28},[27],[20,6928,6930,6933],{"className":6929},[32],[20,6931],{"className":6932,"style":37},[36],[20,6934,1855],{"className":6935,"style":1854},[41,42]," is",[20,6938,6940],{"className":6939},[282],[20,6941,6943],{"className":6942},[23],[20,6944,6946,7049],{"className":6945,"ariaHidden":28},[27],[20,6947,6949,6953,7031,7034,7037,7040,7043,7046],{"className":6948},[32],[20,6950],{"className":6951,"style":6952},[36],"height:1.1814em;vertical-align:-0.25em;",[20,6954,6956,6997],{"className":6955},[41],[20,6957,6959],{"className":6958},[41,299],[20,6960,6962],{"className":6961},[85],[20,6963,6965],{"className":6964},[90],[20,6966,6969,6978],{"className":6967,"style":6968},[94],"height:0.9314em;",[20,6970,6971,6974],{"style":312},[20,6972],{"className":6973,"style":316},[102],[20,6975,321],{"className":6976},[41,6977],"mathfrak",[20,6979,6982,6985],{"className":6980,"style":6981},[3076],"top:-3.6914em;",[20,6983],{"className":6984,"style":316},[102],[20,6986,6988],{"style":6987},"height:0.24em;",[3219,6989,6994],{"xmlns":3221,"width":6990,"height":6991,"viewBox":6992,"preserveAspectRatio":6993},"100%","0.24em","0 0 1062 239","none",[3227,6995],{"d":6996},"M529 0h5l519 115c5 1 9 5 9 10 0 1-1 2-1 3l-4 22\nc-1 5-5 9-11 9h-2L532 67 19 159h-2c-5 0-9-4-11-9l-5-22c-1-6 2-12 8-13z",[20,6998,7000],{"className":6999},[81],[20,7001,7003,7023],{"className":7002},[85,86],[20,7004,7006,7020],{"className":7005},[90],[20,7007,7009],{"className":7008,"style":1355},[94],[20,7010,7011,7014],{"style":98},[20,7012],{"className":7013,"style":103},[102],[20,7015,7017],{"className":7016},[107,108,109,110],[20,7018,1943],{"className":7019,"style":1942},[41,76,110],[20,7021,119],{"className":7022},[118],[20,7024,7026],{"className":7025},[90],[20,7027,7029],{"className":7028,"style":126},[94],[20,7030],{},[20,7032,340],{"className":7033},[68],[20,7035,1855],{"className":7036,"style":1854},[41,42],[20,7038,185],{"className":7039},[184],[20,7041],{"className":7042,"style":49},[48],[20,7044,54],{"className":7045},[53],[20,7047],{"className":7048,"style":49},[48],[20,7050,7052,7056,7100,7103,7106,7406,7409],{"className":7051},[32],[20,7053],{"className":7054,"style":7055},[36],"height:3.0277em;vertical-align:-1.2777em;",[20,7057,7059,7062],{"className":7058},[41],[20,7060,719],{"className":7061},[41,718],[20,7063,7065],{"className":7064},[81],[20,7066,7068,7091],{"className":7067},[85,86],[20,7069,7071,7088],{"className":7070},[90],[20,7072,7074],{"className":7073,"style":2041},[94],[20,7075,7076,7079],{"style":98},[20,7077],{"className":7078,"style":103},[102],[20,7080,7082],{"className":7081},[107,108,109,110],[20,7083,7085],{"className":7084},[41,110],[20,7086,6845],{"className":7087,"style":6844},[41,76,110],[20,7089,119],{"className":7090},[118],[20,7092,7094],{"className":7093},[90],[20,7095,7098],{"className":7096,"style":7097},[94],"height:0.2181em;",[20,7099],{},[20,7101],{"className":7102,"style":3044},[48],[20,7104],{"className":7105,"style":137},[48],[20,7107,7109,7115,7173,7176,7238,7241,7305,7308,7348,7351,7354,7357,7397,7400],{"className":7108},[526],[20,7110,7112],{"className":7111,"style":531},[68,530],[20,7113,788],{"className":7114},[3057,3058],[20,7116,7118],{"className":7117},[441,442],[20,7119,7121,7164],{"className":7120},[85,86],[20,7122,7124,7161],{"className":7123},[90],[20,7125,7127,7148],{"className":7126,"style":1147},[94],[20,7128,7130,7133],{"style":7129},"top:-2.1535em;margin-left:0em;",[20,7131],{"className":7132,"style":316},[102],[20,7134,7136],{"className":7135},[107,108,109,110],[20,7137,7139,7142,7145],{"className":7138},[41,110],[20,7140,2160],{"className":7141},[41,76,110],[20,7143,2167],{"className":7144},[53,110],[20,7146,1855],{"className":7147,"style":1854},[41,42,110],[20,7149,7150,7153],{"style":312},[20,7151],{"className":7152,"style":316},[102],[20,7154,7155],{},[20,7156,7158],{"className":7157},[441],[20,7159,1858],{"className":7160},[41,2229],[20,7162,119],{"className":7163},[118],[20,7165,7167],{"className":7166},[90],[20,7168,7171],{"className":7169,"style":7170},[94],"height:0.9739em;",[20,7172],{},[20,7174],{"className":7175,"style":137},[48],[20,7177,7179,7182,7235],{"className":7178},[41],[20,7180],{"className":7181},[68,370],[20,7183,7185],{"className":7184},[374],[20,7186,7188,7227],{"className":7187},[85,86],[20,7189,7191,7224],{"className":7190},[90],[20,7192,7194,7205,7213],{"className":7193,"style":384},[94],[20,7195,7196,7199],{"style":387},[20,7197],{"className":7198,"style":316},[102],[20,7200,7202],{"className":7201},[41],[20,7203,240],{"className":7204},[41,76],[20,7206,7207,7210],{"style":399},[20,7208],{"className":7209,"style":316},[102],[20,7211],{"className":7212,"style":407},[406],[20,7214,7215,7218],{"style":410},[20,7216],{"className":7217,"style":316},[102],[20,7219,7221],{"className":7220},[41],[20,7222,227],{"className":7223},[41],[20,7225,119],{"className":7226},[118],[20,7228,7230],{"className":7229},[90],[20,7231,7233],{"className":7232,"style":429},[94],[20,7234],{},[20,7236],{"className":7237},[184,370],[20,7239],{"className":7240,"style":137},[48],[20,7242,7244],{"className":7243},[441,442],[20,7245,7247,7297],{"className":7246},[85,86],[20,7248,7250,7294],{"className":7249},[90],[20,7251,7253,7273,7283],{"className":7252,"style":452},[94],[20,7254,7255,7258],{"style":455},[20,7256],{"className":7257,"style":459},[102],[20,7259,7261],{"className":7260},[107,108,109,110],[20,7262,7264,7267,7270],{"className":7263},[41,110],[20,7265,114],{"className":7266},[41,76,110],[20,7268,54],{"className":7269},[53,110],[20,7271,227],{"className":7272},[41,110],[20,7274,7275,7278],{"style":477},[20,7276],{"className":7277,"style":459},[102],[20,7279,7280],{},[20,7281,488],{"className":7282},[441,486,487],[20,7284,7285,7288],{"style":491},[20,7286],{"className":7287,"style":459},[102],[20,7289,7291],{"className":7290},[107,108,109,110],[20,7292,240],{"className":7293},[41,76,110],[20,7295,119],{"className":7296},[118],[20,7298,7300],{"className":7299},[90],[20,7301,7303],{"className":7302,"style":510},[94],[20,7304],{},[20,7306],{"className":7307,"style":137},[48],[20,7309,7311,7314],{"className":7310},[41],[20,7312,6845],{"className":7313,"style":6844},[41,76],[20,7315,7317],{"className":7316},[81],[20,7318,7320,7340],{"className":7319},[85,86],[20,7321,7323,7337],{"className":7322},[90],[20,7324,7326],{"className":7325,"style":95},[94],[20,7327,7328,7331],{"style":6860},[20,7329],{"className":7330,"style":103},[102],[20,7332,7334],{"className":7333},[107,108,109,110],[20,7335,114],{"className":7336},[41,76,110],[20,7338,119],{"className":7339},[118],[20,7341,7343],{"className":7342},[90],[20,7344,7346],{"className":7345,"style":126},[94],[20,7347],{},[20,7349],{"className":7350,"style":137},[48],[20,7352,2160],{"className":7353},[41,76],[20,7355,340],{"className":7356},[68],[20,7358,7360,7363],{"className":7359},[41],[20,7361,77],{"className":7362},[41,76],[20,7364,7366],{"className":7365},[81],[20,7367,7369,7389],{"className":7368},[85,86],[20,7370,7372,7386],{"className":7371},[90],[20,7373,7375],{"className":7374,"style":95},[94],[20,7376,7377,7380],{"style":98},[20,7378],{"className":7379,"style":103},[102],[20,7381,7383],{"className":7382},[107,108,109,110],[20,7384,114],{"className":7385},[41,76,110],[20,7387,119],{"className":7388},[118],[20,7390,7392],{"className":7391},[90],[20,7393,7395],{"className":7394,"style":126},[94],[20,7396],{},[20,7398,185],{"className":7399},[184],[20,7401,7403],{"className":7402,"style":531},[184,530],[20,7404,859],{"className":7405},[3057,3058],[20,7407],{"className":7408,"style":137},[48],[20,7410,866],{"className":7411},[41],[11,7413,7414,7415,7430],{},"It is the expected best correlation between a class member and a random\nlabeling, large when ",[20,7416,7418],{"className":7417},[23],[20,7419,7421],{"className":7420,"ariaHidden":28},[27],[20,7422,7424,7427],{"className":7423},[32],[20,7425],{"className":7426,"style":37},[36],[20,7428,1855],{"className":7429,"style":1854},[41,42]," can mimic noise.",[11,7432,7433,7434,7534,7535,7868,7869,7968,7969,8086,8087,8246,8247,1725,8262,8277],{},"Consider two extreme cases. A single constant function has\n",[20,7435,7437],{"className":7436},[23],[20,7438,7440,7525],{"className":7439,"ariaHidden":28},[27],[20,7441,7443,7447,7516,7519,7522],{"className":7442},[32],[20,7444],{"className":7445,"style":7446},[36],"height:1.0814em;vertical-align:-0.15em;",[20,7448,7450,7482],{"className":7449},[41],[20,7451,7453],{"className":7452},[41,299],[20,7454,7456],{"className":7455},[85],[20,7457,7459],{"className":7458},[90],[20,7460,7462,7470],{"className":7461,"style":6968},[94],[20,7463,7464,7467],{"style":312},[20,7465],{"className":7466,"style":316},[102],[20,7468,321],{"className":7469},[41,6977],[20,7471,7473,7476],{"className":7472,"style":6981},[3076],[20,7474],{"className":7475,"style":316},[102],[20,7477,7478],{"style":6987},[3219,7479,7480],{"xmlns":3221,"width":6990,"height":6991,"viewBox":6992,"preserveAspectRatio":6993},[3227,7481],{"d":6996},[20,7483,7485],{"className":7484},[81],[20,7486,7488,7508],{"className":7487},[85,86],[20,7489,7491,7505],{"className":7490},[90],[20,7492,7494],{"className":7493,"style":1355},[94],[20,7495,7496,7499],{"style":98},[20,7497],{"className":7498,"style":103},[102],[20,7500,7502],{"className":7501},[107,108,109,110],[20,7503,1943],{"className":7504,"style":1942},[41,76,110],[20,7506,119],{"className":7507},[118],[20,7509,7511],{"className":7510},[90],[20,7512,7514],{"className":7513,"style":126},[94],[20,7515],{},[20,7517],{"className":7518,"style":49},[48],[20,7520,54],{"className":7521},[53],[20,7523],{"className":7524,"style":49},[48],[20,7526,7528,7531],{"className":7527},[32],[20,7529],{"className":7530,"style":2313},[36],[20,7532,2824],{"className":7533},[41],": the sum ",[20,7536,7538],{"className":7537},[23],[20,7539,7541,7726,7745],{"className":7540,"ariaHidden":28},[27],[20,7542,7544,7548,7617,7620,7625,7628,7668,7671,7674,7714,7717,7720,7723],{"className":7543},[32],[20,7545],{"className":7546,"style":7547},[36],"height:1.1901em;vertical-align:-0.345em;",[20,7549,7551,7554,7614],{"className":7550},[41],[20,7552],{"className":7553},[68,370],[20,7555,7557],{"className":7556},[374],[20,7558,7560,7606],{"className":7559},[85,86],[20,7561,7563,7603],{"className":7562},[90],[20,7564,7567,7581,7589],{"className":7565,"style":7566},[94],"height:0.8451em;",[20,7568,7569,7572],{"style":4394},[20,7570],{"className":7571,"style":316},[102],[20,7573,7575],{"className":7574},[107,108,109,110],[20,7576,7578],{"className":7577},[41,110],[20,7579,240],{"className":7580},[41,76,110],[20,7582,7583,7586],{"style":399},[20,7584],{"className":7585,"style":316},[102],[20,7587],{"className":7588,"style":407},[406],[20,7590,7591,7594],{"style":4417},[20,7592],{"className":7593,"style":316},[102],[20,7595,7597],{"className":7596},[107,108,109,110],[20,7598,7600],{"className":7599},[41,110],[20,7601,227],{"className":7602},[41,110],[20,7604,119],{"className":7605},[118],[20,7607,7609],{"className":7608},[90],[20,7610,7612],{"className":7611,"style":4439},[94],[20,7613],{},[20,7615],{"className":7616},[184,370],[20,7618],{"className":7619,"style":137},[48],[20,7621,488],{"className":7622,"style":7624},[441,486,7623],"small-op","position:relative;top:0em;",[20,7626],{"className":7627,"style":137},[48],[20,7629,7631,7634],{"className":7630},[41],[20,7632,6845],{"className":7633,"style":6844},[41,76],[20,7635,7637],{"className":7636},[81],[20,7638,7640,7660],{"className":7639},[85,86],[20,7641,7643,7657],{"className":7642},[90],[20,7644,7646],{"className":7645,"style":95},[94],[20,7647,7648,7651],{"style":6860},[20,7649],{"className":7650,"style":103},[102],[20,7652,7654],{"className":7653},[107,108,109,110],[20,7655,114],{"className":7656},[41,76,110],[20,7658,119],{"className":7659},[118],[20,7661,7663],{"className":7662},[90],[20,7664,7666],{"className":7665,"style":126},[94],[20,7667],{},[20,7669,2160],{"className":7670},[41,76],[20,7672,340],{"className":7673},[68],[20,7675,7677,7680],{"className":7676},[41],[20,7678,77],{"className":7679},[41,76],[20,7681,7683],{"className":7682},[81],[20,7684,7686,7706],{"className":7685},[85,86],[20,7687,7689,7703],{"className":7688},[90],[20,7690,7692],{"className":7691,"style":95},[94],[20,7693,7694,7697],{"style":98},[20,7695],{"className":7696,"style":103},[102],[20,7698,7700],{"className":7699},[107,108,109,110],[20,7701,114],{"className":7702},[41,76,110],[20,7704,119],{"className":7705},[118],[20,7707,7709],{"className":7708},[90],[20,7710,7712],{"className":7711,"style":126},[94],[20,7713],{},[20,7715,185],{"className":7716},[184],[20,7718],{"className":7719,"style":49},[48],[20,7721,54],{"className":7722},[53],[20,7724],{"className":7725,"style":49},[48],[20,7727,7729,7732,7735,7738,7742],{"className":7728},[32],[20,7730],{"className":7731,"style":1320},[36],[20,7733,2160],{"className":7734},[41,76],[20,7736],{"className":7737,"style":962},[48],[20,7739,7741],{"className":7740},[966],"⋅",[20,7743],{"className":7744,"style":962},[48],[20,7746,7748,7751,7819,7822,7825,7828],{"className":7747},[32],[20,7749],{"className":7750,"style":7547},[36],[20,7752,7754,7757,7816],{"className":7753},[41],[20,7755],{"className":7756},[68,370],[20,7758,7760],{"className":7759},[374],[20,7761,7763,7808],{"className":7762},[85,86],[20,7764,7766,7805],{"className":7765},[90],[20,7767,7769,7783,7791],{"className":7768,"style":7566},[94],[20,7770,7771,7774],{"style":4394},[20,7772],{"className":7773,"style":316},[102],[20,7775,7777],{"className":7776},[107,108,109,110],[20,7778,7780],{"className":7779},[41,110],[20,7781,240],{"className":7782},[41,76,110],[20,7784,7785,7788],{"style":399},[20,7786],{"className":7787,"style":316},[102],[20,7789],{"className":7790,"style":407},[406],[20,7792,7793,7796],{"style":4417},[20,7794],{"className":7795,"style":316},[102],[20,7797,7799],{"className":7798},[107,108,109,110],[20,7800,7802],{"className":7801},[41,110],[20,7803,227],{"className":7804},[41,110],[20,7806,119],{"className":7807},[118],[20,7809,7811],{"className":7810},[90],[20,7812,7814],{"className":7813,"style":4439},[94],[20,7815],{},[20,7817],{"className":7818},[184,370],[20,7820],{"className":7821,"style":137},[48],[20,7823,488],{"className":7824,"style":7624},[441,486,7623],[20,7826],{"className":7827,"style":137},[48],[20,7829,7831,7834],{"className":7830},[41],[20,7832,6845],{"className":7833,"style":6844},[41,76],[20,7835,7837],{"className":7836},[81],[20,7838,7840,7860],{"className":7839},[85,86],[20,7841,7843,7857],{"className":7842},[90],[20,7844,7846],{"className":7845,"style":95},[94],[20,7847,7848,7851],{"style":6860},[20,7849],{"className":7850,"style":103},[102],[20,7852,7854],{"className":7853},[107,108,109,110],[20,7855,114],{"className":7856},[41,76,110],[20,7858,119],{"className":7859},[118],[20,7861,7863],{"className":7862},[90],[20,7864,7866],{"className":7865,"style":126},[94],[20,7867],{}," has expectation zero because the signs average out. A class\nthat can hit any target on the sample has ",[20,7870,7872],{"className":7871},[23],[20,7873,7875,7959],{"className":7874,"ariaHidden":28},[27],[20,7876,7878,7881,7950,7953,7956],{"className":7877},[32],[20,7879],{"className":7880,"style":7446},[36],[20,7882,7884,7916],{"className":7883},[41],[20,7885,7887],{"className":7886},[41,299],[20,7888,7890],{"className":7889},[85],[20,7891,7893],{"className":7892},[90],[20,7894,7896,7904],{"className":7895,"style":6968},[94],[20,7897,7898,7901],{"style":312},[20,7899],{"className":7900,"style":316},[102],[20,7902,321],{"className":7903},[41,6977],[20,7905,7907,7910],{"className":7906,"style":6981},[3076],[20,7908],{"className":7909,"style":316},[102],[20,7911,7912],{"style":6987},[3219,7913,7914],{"xmlns":3221,"width":6990,"height":6991,"viewBox":6992,"preserveAspectRatio":6993},[3227,7915],{"d":6996},[20,7917,7919],{"className":7918},[81],[20,7920,7922,7942],{"className":7921},[85,86],[20,7923,7925,7939],{"className":7924},[90],[20,7926,7928],{"className":7927,"style":1355},[94],[20,7929,7930,7933],{"style":98},[20,7931],{"className":7932,"style":103},[102],[20,7934,7936],{"className":7935},[107,108,109,110],[20,7937,1943],{"className":7938,"style":1942},[41,76,110],[20,7940,119],{"className":7941},[118],[20,7943,7945],{"className":7944},[90],[20,7946,7948],{"className":7947,"style":126},[94],[20,7949],{},[20,7951],{"className":7952,"style":49},[48],[20,7954,54],{"className":7955},[53],[20,7957],{"className":7958,"style":49},[48],[20,7960,7962,7965],{"className":7961},[32],[20,7963],{"className":7964,"style":2313},[36],[20,7966,227],{"className":7967},[41],": it sets\n",[20,7970,7972],{"className":7971},[23],[20,7973,7975,8039],{"className":7974,"ariaHidden":28},[27],[20,7976,7978,7981,7984,7987,8027,8030,8033,8036],{"className":7977},[32],[20,7979],{"className":7980,"style":946},[36],[20,7982,2160],{"className":7983},[41,76],[20,7985,340],{"className":7986},[68],[20,7988,7990,7993],{"className":7989},[41],[20,7991,77],{"className":7992},[41,76],[20,7994,7996],{"className":7995},[81],[20,7997,7999,8019],{"className":7998},[85,86],[20,8000,8002,8016],{"className":8001},[90],[20,8003,8005],{"className":8004,"style":95},[94],[20,8006,8007,8010],{"style":98},[20,8008],{"className":8009,"style":103},[102],[20,8011,8013],{"className":8012},[107,108,109,110],[20,8014,114],{"className":8015},[41,76,110],[20,8017,119],{"className":8018},[118],[20,8020,8022],{"className":8021},[90],[20,8023,8025],{"className":8024,"style":126},[94],[20,8026],{},[20,8028,185],{"className":8029},[184],[20,8031],{"className":8032,"style":49},[48],[20,8034,54],{"className":8035},[53],[20,8037],{"className":8038,"style":49},[48],[20,8040,8042,8046],{"className":8041},[32],[20,8043],{"className":8044,"style":8045},[36],"height:0.5806em;vertical-align:-0.15em;",[20,8047,8049,8052],{"className":8048},[41],[20,8050,6845],{"className":8051,"style":6844},[41,76],[20,8053,8055],{"className":8054},[81],[20,8056,8058,8078],{"className":8057},[85,86],[20,8059,8061,8075],{"className":8060},[90],[20,8062,8064],{"className":8063,"style":95},[94],[20,8065,8066,8069],{"style":6860},[20,8067],{"className":8068,"style":103},[102],[20,8070,8072],{"className":8071},[107,108,109,110],[20,8073,114],{"className":8074},[41,76,110],[20,8076,119],{"className":8077},[118],[20,8079,8081],{"className":8080},[90],[20,8082,8084],{"className":8083,"style":126},[94],[20,8085],{}," for every draw, so the correlation is ",[20,8088,8090],{"className":8089},[23],[20,8091,8093,8237],{"className":8092,"ariaHidden":28},[27],[20,8094,8096,8099,8167,8170,8173,8176,8228,8231,8234],{"className":8095},[32],[20,8097],{"className":8098,"style":7547},[36],[20,8100,8102,8105,8164],{"className":8101},[41],[20,8103],{"className":8104},[68,370],[20,8106,8108],{"className":8107},[374],[20,8109,8111,8156],{"className":8110},[85,86],[20,8112,8114,8153],{"className":8113},[90],[20,8115,8117,8131,8139],{"className":8116,"style":7566},[94],[20,8118,8119,8122],{"style":4394},[20,8120],{"className":8121,"style":316},[102],[20,8123,8125],{"className":8124},[107,108,109,110],[20,8126,8128],{"className":8127},[41,110],[20,8129,240],{"className":8130},[41,76,110],[20,8132,8133,8136],{"style":399},[20,8134],{"className":8135,"style":316},[102],[20,8137],{"className":8138,"style":407},[406],[20,8140,8141,8144],{"style":4417},[20,8142],{"className":8143,"style":316},[102],[20,8145,8147],{"className":8146},[107,108,109,110],[20,8148,8150],{"className":8149},[41,110],[20,8151,227],{"className":8152},[41,110],[20,8154,119],{"className":8155},[118],[20,8157,8159],{"className":8158},[90],[20,8160,8162],{"className":8161,"style":4439},[94],[20,8163],{},[20,8165],{"className":8166},[184,370],[20,8168],{"className":8169,"style":137},[48],[20,8171,488],{"className":8172,"style":7624},[441,486,7623],[20,8174],{"className":8175,"style":137},[48],[20,8177,8179,8182],{"className":8178},[41],[20,8180,6845],{"className":8181,"style":6844},[41,76],[20,8183,8185],{"className":8184},[81],[20,8186,8188,8220],{"className":8187},[85,86],[20,8189,8191,8217],{"className":8190},[90],[20,8192,8194,8206],{"className":8193,"style":2267},[94],[20,8195,8197,8200],{"style":8196},"top:-2.4413em;margin-left:-0.0799em;margin-right:0.05em;",[20,8198],{"className":8199,"style":103},[102],[20,8201,8203],{"className":8202},[107,108,109,110],[20,8204,114],{"className":8205},[41,76,110],[20,8207,8208,8211],{"style":230},[20,8209],{"className":8210,"style":103},[102],[20,8212,8214],{"className":8213},[107,108,109,110],[20,8215,2103],{"className":8216},[41,110],[20,8218,119],{"className":8219},[118],[20,8221,8223],{"className":8222},[90],[20,8224,8226],{"className":8225,"style":250},[94],[20,8227],{},[20,8229],{"className":8230,"style":49},[48],[20,8232,54],{"className":8233},[53],[20,8235],{"className":8236,"style":49},[48],[20,8238,8240,8243],{"className":8239},[32],[20,8241],{"className":8242,"style":2313},[36],[20,8244,227],{"className":8245},[41],". Rademacher complexity thus interpolates between ",[20,8248,8250],{"className":8249},[23],[20,8251,8253],{"className":8252,"ariaHidden":28},[27],[20,8254,8256,8259],{"className":8255},[32],[20,8257],{"className":8258,"style":2313},[36],[20,8260,2824],{"className":8261},[41],[20,8263,8265],{"className":8264},[23],[20,8266,8268],{"className":8267,"ariaHidden":28},[27],[20,8269,8271,8274],{"className":8270},[32],[20,8272],{"className":8273,"style":2313},[36],[20,8275,227],{"className":8276},[41]," as\nthe class goes from trivial to able-to-shatter, and it plugs straight into a bound.",[1019,8279,8280,8384],{"type":2653},[11,8281,8282,8285,8286,8319,8320,8353,8354,133],{},[15,8283,8284],{},"Theorem (Rademacher generalization bound)."," With probability at least\n",[20,8287,8289],{"className":8288},[23],[20,8290,8292,8310],{"className":8291,"ariaHidden":28},[27],[20,8293,8295,8298,8301,8304,8307],{"className":8294},[32],[20,8296],{"className":8297,"style":2865},[36],[20,8299,227],{"className":8300},[41],[20,8302],{"className":8303,"style":962},[48],[20,8305,967],{"className":8306},[966],[20,8308],{"className":8309,"style":962},[48],[20,8311,8313,8316],{"className":8312},[32],[20,8314],{"className":8315,"style":1320},[36],[20,8317,2888],{"className":8318,"style":2887},[41,76],", simultaneously for all ",[20,8321,8323],{"className":8322},[23],[20,8324,8326,8344],{"className":8325,"ariaHidden":28},[27],[20,8327,8329,8332,8335,8338,8341],{"className":8328},[32],[20,8330],{"className":8331,"style":2156},[36],[20,8333,2160],{"className":8334},[41,76],[20,8336],{"className":8337,"style":49},[48],[20,8339,2167],{"className":8340},[53],[20,8342],{"className":8343,"style":49},[48],[20,8345,8347,8350],{"className":8346},[32],[20,8348],{"className":8349,"style":37},[36],[20,8351,1855],{"className":8352,"style":1854},[41,42]," with loss in ",[20,8355,8357],{"className":8356},[23],[20,8358,8360],{"className":8359,"ariaHidden":28},[27],[20,8361,8363,8366,8369,8372,8375,8378,8381],{"className":8362},[32],[20,8364],{"className":8365,"style":946},[36],[20,8367,788],{"className":8368},[68],[20,8370,2824],{"className":8371},[41],[20,8373,133],{"className":8374},[132],[20,8376],{"className":8377,"style":137},[48],[20,8379,227],{"className":8380},[41],[20,8382,859],{"className":8383},[184],[20,8385,8387],{"className":8386},[282],[20,8388,8390],{"className":8389},[23],[20,8391,8393,8426,8487,8592],{"className":8392,"ariaHidden":28},[27],[20,8394,8396,8399,8402,8405,8408,8411,8414,8417,8420,8423],{"className":8395},[32],[20,8397],{"className":8398,"style":946},[36],[20,8400,321],{"className":8401,"style":320},[41,76],[20,8403,340],{"className":8404},[68],[20,8406,2160],{"className":8407},[41,76],[20,8409,185],{"className":8410},[184],[20,8412],{"className":8413,"style":49},[48],[20,8415],{"className":8416,"style":49},[48],[20,8418,2962],{"className":8419},[53],[20,8421],{"className":8422,"style":49},[48],[20,8424],{"className":8425,"style":49},[48],[20,8427,8429,8432,8463,8466,8469,8472,8475,8478,8481,8484],{"className":8428},[32],[20,8430],{"className":8431,"style":295},[36],[20,8433,8435],{"className":8434},[41,299],[20,8436,8438],{"className":8437},[85],[20,8439,8441],{"className":8440},[90],[20,8442,8444,8452],{"className":8443,"style":309},[94],[20,8445,8446,8449],{"style":312},[20,8447],{"className":8448,"style":316},[102],[20,8450,321],{"className":8451,"style":320},[41,76],[20,8453,8454,8457],{"style":324},[20,8455],{"className":8456,"style":316},[102],[20,8458,8460],{"className":8459,"style":332},[331],[20,8461,336],{"className":8462},[41],[20,8464,340],{"className":8465},[68],[20,8467,2160],{"className":8468},[41,76],[20,8470,185],{"className":8471},[184],[20,8473],{"className":8474,"style":49},[48],[20,8476],{"className":8477,"style":962},[48],[20,8479,2335],{"className":8480},[966],[20,8482],{"className":8483,"style":49},[48],[20,8485],{"className":8486,"style":962},[48],[20,8488,8490,8493,8496,8499,8568,8571,8574,8577,8580,8583,8586,8589],{"className":8489},[32],[20,8491],{"className":8492,"style":6952},[36],[20,8494,2103],{"className":8495},[41],[20,8497],{"className":8498,"style":137},[48],[20,8500,8502,8534],{"className":8501},[41],[20,8503,8505],{"className":8504},[41,299],[20,8506,8508],{"className":8507},[85],[20,8509,8511],{"className":8510},[90],[20,8512,8514,8522],{"className":8513,"style":6968},[94],[20,8515,8516,8519],{"style":312},[20,8517],{"className":8518,"style":316},[102],[20,8520,321],{"className":8521},[41,6977],[20,8523,8525,8528],{"className":8524,"style":6981},[3076],[20,8526],{"className":8527,"style":316},[102],[20,8529,8530],{"style":6987},[3219,8531,8532],{"xmlns":3221,"width":6990,"height":6991,"viewBox":6992,"preserveAspectRatio":6993},[3227,8533],{"d":6996},[20,8535,8537],{"className":8536},[81],[20,8538,8540,8560],{"className":8539},[85,86],[20,8541,8543,8557],{"className":8542},[90],[20,8544,8546],{"className":8545,"style":1355},[94],[20,8547,8548,8551],{"style":98},[20,8549],{"className":8550,"style":103},[102],[20,8552,8554],{"className":8553},[107,108,109,110],[20,8555,1943],{"className":8556,"style":1942},[41,76,110],[20,8558,119],{"className":8559},[118],[20,8561,8563],{"className":8562},[90],[20,8564,8566],{"className":8565,"style":126},[94],[20,8567],{},[20,8569,340],{"className":8570},[68],[20,8572,1855],{"className":8573,"style":1854},[41,42],[20,8575,185],{"className":8576},[184],[20,8578],{"className":8579,"style":49},[48],[20,8581],{"className":8582,"style":962},[48],[20,8584,2335],{"className":8585},[966],[20,8587],{"className":8588,"style":49},[48],[20,8590],{"className":8591,"style":962},[48],[20,8593,8595,8599,8602,8727],{"className":8594},[32],[20,8596],{"className":8597,"style":8598},[36],"height:2.44em;vertical-align:-0.7356em;",[20,8600,2297],{"className":8601},[41],[20,8603,8605],{"className":8604},[41,3062],[20,8606,8608,8719],{"className":8607},[85,86],[20,8609,8611,8716],{"className":8610},[90],[20,8612,8614,8704],{"className":8613,"style":3072},[94],[20,8615,8617,8620],{"className":8616,"style":3077},[3076],[20,8618],{"className":8619,"style":3081},[102],[20,8621,8623],{"className":8622,"style":3085},[41],[20,8624,8626,8629,8701],{"className":8625},[41],[20,8627],{"className":8628},[68,370],[20,8630,8632],{"className":8631},[374],[20,8633,8635,8693],{"className":8634},[85,86],[20,8636,8638,8690],{"className":8637},[90],[20,8639,8641,8655,8663],{"className":8640,"style":3104},[94],[20,8642,8643,8646],{"style":387},[20,8644],{"className":8645,"style":316},[102],[20,8647,8649,8652],{"className":8648},[41],[20,8650,2103],{"className":8651},[41],[20,8653,240],{"className":8654},[41,76],[20,8656,8657,8660],{"style":399},[20,8658],{"className":8659,"style":316},[102],[20,8661],{"className":8662,"style":407},[406],[20,8664,8665,8668],{"style":410},[20,8666],{"className":8667,"style":316},[102],[20,8669,8671,8677,8680,8684,8687],{"className":8670},[41],[20,8672,8674],{"className":8673},[441],[20,8675,3148],{"className":8676,"style":3147},[41,2229],[20,8678,340],{"className":8679},[68],[20,8681,8683],{"className":8682},[41],"2\u002F",[20,8685,2888],{"className":8686,"style":2887},[41,76],[20,8688,185],{"className":8689},[184],[20,8691,119],{"className":8692},[118],[20,8694,8696],{"className":8695},[90],[20,8697,8699],{"className":8698,"style":429},[94],[20,8700],{},[20,8702],{"className":8703},[184,370],[20,8705,8706,8709],{"style":3209},[20,8707],{"className":8708,"style":3081},[102],[20,8710,8712],{"className":8711,"style":3217},[3216],[3219,8713,8714],{"xmlns":3221,"width":3222,"height":3223,"viewBox":3224,"preserveAspectRatio":3225},[3227,8715],{"d":3229},[20,8717,119],{"className":8718},[118],[20,8720,8722],{"className":8721},[90],[20,8723,8725],{"className":8724,"style":3239},[94],[20,8726],{},[20,8728,866],{"className":8729},[41],[11,8731,8732,8733,8777,8778,8819,8820,8981,8982,1635,8985,9000,9001,9016],{},"For linear predictors with ",[20,8734,8736],{"className":8735},[23],[20,8737,8739,8766],{"className":8738,"ariaHidden":28},[27],[20,8740,8742,8745,8749,8754,8757,8760,8763],{"className":8741},[32],[20,8743],{"className":8744,"style":946},[36],[20,8746,8748],{"className":8747},[68],"∥",[20,8750,8753],{"className":8751,"style":8752},[41,76],"margin-right:0.0269em;","w",[20,8755,8748],{"className":8756},[184],[20,8758],{"className":8759,"style":49},[48],[20,8761,2962],{"className":8762},[53],[20,8764],{"className":8765,"style":49},[48],[20,8767,8769,8772],{"className":8768},[32],[20,8770],{"className":8771,"style":37},[36],[20,8773,8776],{"className":8774,"style":8775},[41,76],"margin-right:0.0502em;","B"," over inputs with ",[20,8779,8781],{"className":8780},[23],[20,8782,8784,8808],{"className":8783,"ariaHidden":28},[27],[20,8785,8787,8790,8793,8796,8799,8802,8805],{"className":8786},[32],[20,8788],{"className":8789,"style":946},[36],[20,8791,8748],{"className":8792},[68],[20,8794,77],{"className":8795},[41,76],[20,8797,8748],{"className":8798},[184],[20,8800],{"className":8801,"style":49},[48],[20,8803,2962],{"className":8804},[53],[20,8806],{"className":8807,"style":49},[48],[20,8809,8811,8814],{"className":8810},[32],[20,8812],{"className":8813,"style":37},[36],[20,8815,8818],{"className":8816,"style":8817},[41,76],"margin-right:0.0785em;","X",", one\ncan show ",[20,8821,8823],{"className":8822},[23],[20,8824,8826,8910],{"className":8825,"ariaHidden":28},[27],[20,8827,8829,8832,8901,8904,8907],{"className":8828},[32],[20,8830],{"className":8831,"style":7446},[36],[20,8833,8835,8867],{"className":8834},[41],[20,8836,8838],{"className":8837},[41,299],[20,8839,8841],{"className":8840},[85],[20,8842,8844],{"className":8843},[90],[20,8845,8847,8855],{"className":8846,"style":6968},[94],[20,8848,8849,8852],{"style":312},[20,8850],{"className":8851,"style":316},[102],[20,8853,321],{"className":8854},[41,6977],[20,8856,8858,8861],{"className":8857,"style":6981},[3076],[20,8859],{"className":8860,"style":316},[102],[20,8862,8863],{"style":6987},[3219,8864,8865],{"xmlns":3221,"width":6990,"height":6991,"viewBox":6992,"preserveAspectRatio":6993},[3227,8866],{"d":6996},[20,8868,8870],{"className":8869},[81],[20,8871,8873,8893],{"className":8872},[85,86],[20,8874,8876,8890],{"className":8875},[90],[20,8877,8879],{"className":8878,"style":1355},[94],[20,8880,8881,8884],{"style":98},[20,8882],{"className":8883,"style":103},[102],[20,8885,8887],{"className":8886},[107,108,109,110],[20,8888,1943],{"className":8889,"style":1942},[41,76,110],[20,8891,119],{"className":8892},[118],[20,8894,8896],{"className":8895},[90],[20,8897,8899],{"className":8898,"style":126},[94],[20,8900],{},[20,8902],{"className":8903,"style":49},[48],[20,8905,2962],{"className":8906},[53],[20,8908],{"className":8909,"style":49},[48],[20,8911,8913,8917,8920,8923,8926],{"className":8912},[32],[20,8914],{"className":8915,"style":8916},[36],"height:1.0503em;vertical-align:-0.25em;",[20,8918,8776],{"className":8919,"style":8775},[41,76],[20,8921,8818],{"className":8922,"style":8817},[41,76],[20,8924,3158],{"className":8925},[41],[20,8927,8929],{"className":8928},[41,3062],[20,8930,8932,8972],{"className":8931},[85,86],[20,8933,8935,8969],{"className":8934},[90],[20,8936,8939,8952],{"className":8937,"style":8938},[94],"height:0.8003em;",[20,8940,8942,8945],{"className":8941,"style":312},[3076],[20,8943],{"className":8944,"style":316},[102],[20,8946,8949],{"className":8947,"style":8948},[41],"padding-left:0.833em;",[20,8950,240],{"className":8951},[41,76],[20,8953,8955,8958],{"style":8954},"top:-2.7603em;",[20,8956],{"className":8957,"style":316},[102],[20,8959,8962],{"className":8960,"style":8961},[3216],"min-width:0.853em;height:1.08em;",[3219,8963,8966],{"xmlns":3221,"width":3222,"height":8964,"viewBox":8965,"preserveAspectRatio":3225},"1.08em","0 0 400000 1080",[3227,8967],{"d":8968},"M95,702\nc-2.7,0,-7.17,-2.7,-13.5,-8c-5.8,-5.3,-9.5,-10,-9.5,-14\nc0,-2,0.3,-3.3,1,-4c1.3,-2.7,23.83,-20.7,67.5,-54\nc44.2,-33.3,65.8,-50.3,66.5,-51c1.3,-1.3,3,-2,5,-2c4.7,0,8.7,3.3,12,10\ns173,378,173,378c0.7,0,35.3,-71,104,-213c68.7,-142,137.5,-285,206.5,-429\nc69,-144,104.5,-217.7,106.5,-221\nl0 -0\nc5.3,-9.3,12,-14,20,-14\nH400000v40H845.2724\ns-225.272,467,-225.272,467s-235,486,-235,486c-2.7,4.7,-9,7,-19,7\nc-6,0,-10,-1,-12,-3s-194,-422,-194,-422s-65,47,-65,47z\nM834 80h400000v40h-400000z",[20,8970,119],{"className":8971},[118],[20,8973,8975],{"className":8974},[90],[20,8976,8979],{"className":8977,"style":8978},[94],"height:0.2397em;",[20,8980],{},": the complexity is controlled\nby the ",[1305,8983,8984],{},"norm",[20,8986,8988],{"className":8987},[23],[20,8989,8991],{"className":8990,"ariaHidden":28},[27],[20,8992,8994,8997],{"className":8993},[32],[20,8995],{"className":8996,"style":37},[36],[20,8998,8776],{"className":8999,"style":8775},[41,76],", not the ambient dimension ",[20,9002,9004],{"className":9003},[23],[20,9005,9007],{"className":9006,"ariaHidden":28},[27],[20,9008,9010,9013],{"className":9009},[32],[20,9011],{"className":9012,"style":1320},[36],[20,9014,2199],{"className":9015},[41,76],". That norm dependence is what\ncarries over to the modern setting: it remains meaningful in high dimension, where\nthe dimension-based VC bound does not.",[11,9018,9019,9020,9107],{},"Both theorems are valid and both are tight for the classes they were built for:\nlinear models, kernel machines, shallow trees. Their common shape, gap ",[20,9021,9023],{"className":9022},[23],[20,9024,9026,9039],{"className":9025,"ariaHidden":28},[27],[20,9027,9029,9033,9036],{"className":9028},[32],[20,9030],{"className":9031,"style":9032},[36],"height:0.7719em;vertical-align:-0.136em;",[20,9034,2962],{"className":9035},[53],[20,9037],{"className":9038,"style":49},[48],[20,9040,9042,9045,9048,9051,9104],{"className":9041},[32],[20,9043],{"className":9044,"style":3827},[36],[20,9046,3040],{"className":9047,"style":43},[41,76],[20,9049,340],{"className":9050},[68],[20,9052,9054],{"className":9053},[41,3062],[20,9055,9057,9096],{"className":9056},[85,86],[20,9058,9060,9093],{"className":9059},[90],[20,9061,9063,9081],{"className":9062,"style":3840},[94],[20,9064,9066,9069],{"className":9065,"style":3844},[3076],[20,9067],{"className":9068,"style":3848},[102],[20,9070,9072,9075,9078],{"className":9071,"style":3085},[41],[20,9073,2753],{"className":9074,"style":2752},[41,76],[20,9076,3158],{"className":9077},[41],[20,9079,240],{"className":9080},[41,76],[20,9082,9083,9086],{"style":3903},[20,9084],{"className":9085,"style":3848},[102],[20,9087,9089],{"className":9088,"style":3910},[3216],[3219,9090,9091],{"xmlns":3221,"width":3222,"height":3913,"viewBox":3914,"preserveAspectRatio":3225},[3227,9092],{"d":3917},[20,9094,119],{"className":9095},[118],[20,9097,9099],{"className":9098},[90],[20,9100,9102],{"className":9101,"style":3927},[94],[20,9103],{},[20,9105,185],{"className":9106},[184],", predicts that capacity exceeding the sample size is fatal. Deep\nnetworks break that prediction so completely that the bounds become numbers larger\nthan one and tell us nothing.",[1829,9109,9111],{"id":9110},"the-deep-learning-puzzle","The deep-learning puzzle",[11,9113,9114,9115,9130,9131,9146,9147,866,9150],{},"Modern networks live in a regime classical theory was never meant to describe.\nTheir parameter count ",[20,9116,9118],{"className":9117},[23],[20,9119,9121],{"className":9120,"ariaHidden":28},[27],[20,9122,9124,9127],{"className":9123},[32],[20,9125],{"className":9126,"style":4874},[36],[20,9128,11],{"className":9129},[41,76]," (and any reasonable capacity surrogate) vastly exceeds\nthe sample size ",[20,9132,9134],{"className":9133},[23],[20,9135,9137],{"className":9136,"ariaHidden":28},[27],[20,9138,9140,9143],{"className":9139},[32],[20,9141],{"className":9142,"style":1147},[36],[20,9144,240],{"className":9145},[41,76],". They are ",[15,9148,9149],{},"overparameterized",[1858,9151,9152],{},[1861,9153,2297],{"href":9154,"ariaDescribedBy":9155,"dataFootnoteRef":6,"id":9156},"#user-content-fn-postdate",[1865],"user-content-fnref-postdate",[1019,9158,9159],{"type":1021},[11,9160,9161,9164,9165,9200],{},[15,9162,9163],{},"Definition (Overparameterization)."," A model is overparameterized when it has\nmore free parameters than training examples, ",[20,9166,9168],{"className":9167},[23],[20,9169,9171,9191],{"className":9170,"ariaHidden":28},[27],[20,9172,9174,9178,9181,9184,9188],{"className":9173},[32],[20,9175],{"className":9176,"style":9177},[36],"height:0.7335em;vertical-align:-0.1944em;",[20,9179,11],{"className":9180},[41,76],[20,9182],{"className":9183,"style":49},[48],[20,9185,9187],{"className":9186},[53],"≫",[20,9189],{"className":9190,"style":49},[48],[20,9192,9194,9197],{"className":9193},[32],[20,9195],{"className":9196,"style":1147},[36],[20,9198,240],{"className":9199},[41,76],", so that infinitely many\nparameter settings achieve zero training loss. The set of such global minimizers\nis a high-dimensional manifold, not a point.",[11,9202,9203,9204,9289,9290,9373,9374,9414,9415,9430,9431,9625,9626,9641,9642,9672],{},"For example, a residual image network can carry\n",[20,9205,9207],{"className":9206},[23],[20,9208,9210,9230,9250],{"className":9209,"ariaHidden":28},[27],[20,9211,9213,9217,9220,9223,9227],{"className":9212},[32],[20,9214],{"className":9215,"style":9216},[36],"height:0.6776em;vertical-align:-0.1944em;",[20,9218,11],{"className":9219},[41,76],[20,9221],{"className":9222,"style":49},[48],[20,9224,9226],{"className":9225},[53],"≈",[20,9228],{"className":9229,"style":49},[48],[20,9231,9233,9236,9240,9243,9247],{"className":9232},[32],[20,9234],{"className":9235,"style":2865},[36],[20,9237,9239],{"className":9238},[41],"2.5",[20,9241],{"className":9242,"style":962},[48],[20,9244,9246],{"className":9245},[966],"×",[20,9248],{"className":9249,"style":962},[48],[20,9251,9253,9256,9259],{"className":9252},[32],[20,9254],{"className":9255,"style":2267},[36],[20,9257,227],{"className":9258},[41],[20,9260,9262,9265],{"className":9261},[41],[20,9263,2824],{"className":9264},[41],[20,9266,9268],{"className":9267},[81],[20,9269,9271],{"className":9270},[85],[20,9272,9274],{"className":9273},[90],[20,9275,9277],{"className":9276,"style":2267},[94],[20,9278,9279,9282],{"style":230},[20,9280],{"className":9281,"style":103},[102],[20,9283,9285],{"className":9284},[107,108,109,110],[20,9286,9288],{"className":9287},[41,110],"7"," parameters and train on ",[20,9291,9293],{"className":9292},[23],[20,9294,9296,9315,9334],{"className":9295,"ariaHidden":28},[27],[20,9297,9299,9303,9306,9309,9312],{"className":9298},[32],[20,9300],{"className":9301,"style":9302},[36],"height:0.4831em;",[20,9304,240],{"className":9305},[41,76],[20,9307],{"className":9308,"style":49},[48],[20,9310,9226],{"className":9311},[53],[20,9313],{"className":9314,"style":49},[48],[20,9316,9318,9321,9325,9328,9331],{"className":9317},[32],[20,9319],{"className":9320,"style":2865},[36],[20,9322,9324],{"className":9323},[41],"5",[20,9326],{"className":9327,"style":962},[48],[20,9329,9246],{"className":9330},[966],[20,9332],{"className":9333,"style":962},[48],[20,9335,9337,9340,9343],{"className":9336},[32],[20,9338],{"className":9339,"style":2267},[36],[20,9341,227],{"className":9342},[41],[20,9344,9346,9349],{"className":9345},[41],[20,9347,2824],{"className":9348},[41],[20,9350,9352],{"className":9351},[81],[20,9353,9355],{"className":9354},[85],[20,9356,9358],{"className":9357},[90],[20,9359,9361],{"className":9360,"style":2267},[94],[20,9362,9363,9366],{"style":230},[20,9364],{"className":9365,"style":103},[102],[20,9367,9369],{"className":9368},[107,108,109,110],[20,9370,9372],{"className":9371},[41,110],"4","\nlabeled images, so ",[20,9375,9377],{"className":9376},[23],[20,9378,9380,9404],{"className":9379,"ariaHidden":28},[27],[20,9381,9383,9386,9389,9392,9395,9398,9401],{"className":9382},[32],[20,9384],{"className":9385,"style":946},[36],[20,9387,11],{"className":9388},[41,76],[20,9390,3158],{"className":9391},[41],[20,9393,240],{"className":9394},[41,76],[20,9396],{"className":9397,"style":49},[48],[20,9399,9226],{"className":9400},[53],[20,9402],{"className":9403,"style":49},[48],[20,9405,9407,9410],{"className":9406},[32],[20,9408],{"className":9409,"style":2313},[36],[20,9411,9413],{"className":9412},[41],"500",". The VC dimension of a piecewise-linear\nnetwork grows at least linearly in ",[20,9416,9418],{"className":9417},[23],[20,9419,9421],{"className":9420,"ariaHidden":28},[27],[20,9422,9424,9427],{"className":9423},[32],[20,9425],{"className":9426,"style":4874},[36],[20,9428,11],{"className":9429},[41,76],", so ",[20,9432,9434],{"className":9433},[23],[20,9435,9437,9549,9615],{"className":9436,"ariaHidden":28},[27],[20,9438,9440,9443,9539,9542,9546],{"className":9439},[32],[20,9441],{"className":9442,"style":3827},[36],[20,9444,9446],{"className":9445},[41,3062],[20,9447,9449,9531],{"className":9448},[85,86],[20,9450,9452,9528],{"className":9451},[90],[20,9453,9455,9516],{"className":9454,"style":3840},[94],[20,9456,9458,9461],{"className":9457,"style":3844},[3076],[20,9459],{"className":9460,"style":3848},[102],[20,9462,9464,9510,9513],{"className":9463,"style":3085},[41],[20,9465,9467,9470],{"className":9466},[41],[20,9468,2199],{"className":9469},[41,76],[20,9471,9473],{"className":9472},[81],[20,9474,9476,9502],{"className":9475},[85,86],[20,9477,9479,9499],{"className":9478},[90],[20,9480,9482],{"className":9481,"style":1355},[94],[20,9483,9484,9487],{"style":98},[20,9485],{"className":9486,"style":103},[102],[20,9488,9490],{"className":9489},[107,108,109,110],[20,9491,9493],{"className":9492},[41,110],[20,9494,9496],{"className":9495},[41,110],[20,9497,2230],{"className":9498},[41,2229,110],[20,9500,119],{"className":9501},[118],[20,9503,9505],{"className":9504},[90],[20,9506,9508],{"className":9507,"style":126},[94],[20,9509],{},[20,9511,3158],{"className":9512},[41],[20,9514,240],{"className":9515},[41,76],[20,9517,9518,9521],{"style":3903},[20,9519],{"className":9520,"style":3848},[102],[20,9522,9524],{"className":9523,"style":3910},[3216],[3219,9525,9526],{"xmlns":3221,"width":3222,"height":3913,"viewBox":3914,"preserveAspectRatio":3225},[3227,9527],{"d":3917},[20,9529,119],{"className":9530},[118],[20,9532,9534],{"className":9533},[90],[20,9535,9537],{"className":9536,"style":3927},[94],[20,9538],{},[20,9540],{"className":9541,"style":49},[48],[20,9543,9545],{"className":9544},[53,4071],"≳",[20,9547],{"className":9548,"style":49},[48],[20,9550,9552,9556,9606,9609,9612],{"className":9551},[32],[20,9553],{"className":9554,"style":9555},[36],"height:1.04em;vertical-align:-0.1328em;",[20,9557,9559],{"className":9558},[41,3062],[20,9560,9562,9597],{"className":9561},[85,86],[20,9563,9565,9594],{"className":9564},[90],[20,9566,9569,9581],{"className":9567,"style":9568},[94],"height:0.9072em;",[20,9570,9572,9575],{"className":9571,"style":312},[3076],[20,9573],{"className":9574,"style":316},[102],[20,9576,9578],{"className":9577,"style":8948},[41],[20,9579,9413],{"className":9580},[41],[20,9582,9584,9587],{"style":9583},"top:-2.8672em;",[20,9585],{"className":9586,"style":316},[102],[20,9588,9590],{"className":9589,"style":8961},[3216],[3219,9591,9592],{"xmlns":3221,"width":3222,"height":8964,"viewBox":8965,"preserveAspectRatio":3225},[3227,9593],{"d":8968},[20,9595,119],{"className":9596},[118],[20,9598,9600],{"className":9599},[90],[20,9601,9604],{"className":9602,"style":9603},[94],"height:0.1328em;",[20,9605],{},[20,9607],{"className":9608,"style":49},[48],[20,9610,9226],{"className":9611},[53],[20,9613],{"className":9614,"style":49},[48],[20,9616,9618,9621],{"className":9617},[32],[20,9619],{"className":9620,"style":2313},[36],[20,9622,9624],{"className":9623},[41],"22",": the bound guarantees a gap of at most ",[20,9627,9629],{"className":9628},[23],[20,9630,9632],{"className":9631,"ariaHidden":28},[27],[20,9633,9635,9638],{"className":9634},[32],[20,9636],{"className":9637,"style":2313},[36],[20,9639,9624],{"className":9640},[41],", on a risk that\nlives in ",[20,9643,9645],{"className":9644},[23],[20,9646,9648],{"className":9647,"ariaHidden":28},[27],[20,9649,9651,9654,9657,9660,9663,9666,9669],{"className":9650},[32],[20,9652],{"className":9653,"style":946},[36],[20,9655,788],{"className":9656},[68],[20,9658,2824],{"className":9659},[41],[20,9661,133],{"className":9662},[132],[20,9664],{"className":9665,"style":137},[48],[20,9667,227],{"className":9668},[41],[20,9670,859],{"className":9671},[184],". The bound is vacuous.",[11,9674,9675],{},"Two facts about these models cannot both be accommodated by the classical bound,\nand together they are the central tension of the field. The post-2016 literature\nthat established them is more recent than Goodfellow's textbook, which still\npresents capacity control as the operative theory.",[1019,9677,9679,9689],{"type":9678},"lemma",[11,9680,9681,9684,9685,9688],{},[15,9682,9683],{},"Fact (Networks fit random labels)."," A standard image network trained on a\ndataset whose labels have been replaced by uniform random noise reaches ",[1305,9686,9687],{},"zero","\ntraining error. Its effective capacity is therefore large enough to memorize an\narbitrary labeling of the training set — exactly the condition VC theory calls",[3258,9690,9691],{},"able to shatter the sample.",[11,9693,9694,9695,9794,9795,9872,9873,9938,9939,9964,9965,10062,10063,866],{},"If the class can shatter the training set, then ",[20,9696,9698],{"className":9697},[23],[20,9699,9701,9785],{"className":9700,"ariaHidden":28},[27],[20,9702,9704,9707,9776,9779,9782],{"className":9703},[32],[20,9705],{"className":9706,"style":7446},[36],[20,9708,9710,9742],{"className":9709},[41],[20,9711,9713],{"className":9712},[41,299],[20,9714,9716],{"className":9715},[85],[20,9717,9719],{"className":9718},[90],[20,9720,9722,9730],{"className":9721,"style":6968},[94],[20,9723,9724,9727],{"style":312},[20,9725],{"className":9726,"style":316},[102],[20,9728,321],{"className":9729},[41,6977],[20,9731,9733,9736],{"className":9732,"style":6981},[3076],[20,9734],{"className":9735,"style":316},[102],[20,9737,9738],{"style":6987},[3219,9739,9740],{"xmlns":3221,"width":6990,"height":6991,"viewBox":6992,"preserveAspectRatio":6993},[3227,9741],{"d":6996},[20,9743,9745],{"className":9744},[81],[20,9746,9748,9768],{"className":9747},[85,86],[20,9749,9751,9765],{"className":9750},[90],[20,9752,9754],{"className":9753,"style":1355},[94],[20,9755,9756,9759],{"style":98},[20,9757],{"className":9758,"style":103},[102],[20,9760,9762],{"className":9761},[107,108,109,110],[20,9763,1943],{"className":9764,"style":1942},[41,76,110],[20,9766,119],{"className":9767},[118],[20,9769,9771],{"className":9770},[90],[20,9772,9774],{"className":9773,"style":126},[94],[20,9775],{},[20,9777],{"className":9778,"style":49},[48],[20,9780,9226],{"className":9781},[53],[20,9783],{"className":9784,"style":49},[48],[20,9786,9788,9791],{"className":9787},[32],[20,9789],{"className":9790,"style":2313},[36],[20,9792,227],{"className":9793},[41],"\nand ",[20,9796,9798],{"className":9797},[23],[20,9799,9801,9863],{"className":9800,"ariaHidden":28},[27],[20,9802,9804,9808,9854,9857,9860],{"className":9803},[32],[20,9805],{"className":9806,"style":9807},[36],"height:0.9592em;vertical-align:-0.2296em;",[20,9809,9811,9814],{"className":9810},[41],[20,9812,2199],{"className":9813},[41,76],[20,9815,9817],{"className":9816},[81],[20,9818,9820,9846],{"className":9819},[85,86],[20,9821,9823,9843],{"className":9822},[90],[20,9824,9826],{"className":9825,"style":1355},[94],[20,9827,9828,9831],{"style":98},[20,9829],{"className":9830,"style":103},[102],[20,9832,9834],{"className":9833},[107,108,109,110],[20,9835,9837],{"className":9836},[41,110],[20,9838,9840],{"className":9839},[41,110],[20,9841,2230],{"className":9842},[41,2229,110],[20,9844,119],{"className":9845},[118],[20,9847,9849],{"className":9848},[90],[20,9850,9852],{"className":9851,"style":126},[94],[20,9853],{},[20,9855],{"className":9856,"style":49},[48],[20,9858,9545],{"className":9859},[53,4071],[20,9861],{"className":9862,"style":49},[48],[20,9864,9866,9869],{"className":9865},[32],[20,9867],{"className":9868,"style":1147},[36],[20,9870,240],{"className":9871},[41,76],", so the bound term ",[20,9874,9876],{"className":9875},[23],[20,9877,9879],{"className":9878,"ariaHidden":28},[27],[20,9880,9882,9885],{"className":9881},[32],[20,9883],{"className":9884,"style":3827},[36],[20,9886,9888],{"className":9887},[41,3062],[20,9889,9891,9930],{"className":9890},[85,86],[20,9892,9894,9927],{"className":9893},[90],[20,9895,9897,9915],{"className":9896,"style":3840},[94],[20,9898,9900,9903],{"className":9899,"style":3844},[3076],[20,9901],{"className":9902,"style":3848},[102],[20,9904,9906,9909,9912],{"className":9905,"style":3085},[41],[20,9907,2753],{"className":9908,"style":2752},[41,76],[20,9910,3158],{"className":9911},[41],[20,9913,240],{"className":9914},[41,76],[20,9916,9917,9920],{"style":3903},[20,9918],{"className":9919,"style":3848},[102],[20,9921,9923],{"className":9922,"style":3910},[3216],[3219,9924,9925],{"xmlns":3221,"width":3222,"height":3913,"viewBox":3914,"preserveAspectRatio":3225},[3227,9926],{"d":3917},[20,9928,119],{"className":9929},[118],[20,9931,9933],{"className":9932},[90],[20,9934,9936],{"className":9935,"style":3927},[94],[20,9937],{}," is ",[20,9940,9942],{"className":9941},[23],[20,9943,9945],{"className":9944,"ariaHidden":28},[27],[20,9946,9948,9951,9955,9958,9961],{"className":9947},[32],[20,9949],{"className":9950,"style":946},[36],[20,9952,9954],{"className":9953},[41],"Ω",[20,9956,340],{"className":9957},[68],[20,9959,227],{"className":9960},[41],[20,9962,185],{"className":9963},[184],": the\nguaranteed gap is at least a constant, and ",[20,9966,9968],{"className":9967},[23],[20,9969,9971,9998,10053],{"className":9970,"ariaHidden":28},[27],[20,9972,9974,9977,9980,9983,9986,9989,9992,9995],{"className":9973},[32],[20,9975],{"className":9976,"style":946},[36],[20,9978,321],{"className":9979,"style":320},[41,76],[20,9981,340],{"className":9982},[68],[20,9984,344],{"className":9985,"style":43},[41,76],[20,9987,185],{"className":9988},[184],[20,9990],{"className":9991,"style":49},[48],[20,9993,2962],{"className":9994},[53],[20,9996],{"className":9997,"style":49},[48],[20,9999,10001,10004,10035,10038,10041,10044,10047,10050],{"className":10000},[32],[20,10002],{"className":10003,"style":295},[36],[20,10005,10007],{"className":10006},[41,299],[20,10008,10010],{"className":10009},[85],[20,10011,10013],{"className":10012},[90],[20,10014,10016,10024],{"className":10015,"style":309},[94],[20,10017,10018,10021],{"style":312},[20,10019],{"className":10020,"style":316},[102],[20,10022,321],{"className":10023,"style":320},[41,76],[20,10025,10026,10029],{"style":324},[20,10027],{"className":10028,"style":316},[102],[20,10030,10032],{"className":10031,"style":332},[331],[20,10033,336],{"className":10034},[41],[20,10036,340],{"className":10037},[68],[20,10039,344],{"className":10040,"style":43},[41,76],[20,10042,185],{"className":10043},[184],[20,10045],{"className":10046,"style":962},[48],[20,10048,2335],{"className":10049},[966],[20,10051],{"className":10052,"style":962},[48],[20,10054,10056,10059],{"className":10055},[32],[20,10057],{"className":10058,"style":2313},[36],[20,10060,227],{"className":10061},[41]," is\nvacuous because risk already lives in ",[20,10064,10066],{"className":10065},[23],[20,10067,10069],{"className":10068,"ariaHidden":28},[27],[20,10070,10072,10075,10078,10081,10084,10087,10090],{"className":10071},[32],[20,10073],{"className":10074,"style":946},[36],[20,10076,788],{"className":10077},[68],[20,10079,2824],{"className":10080},[41],[20,10082,133],{"className":10083},[132],[20,10085],{"className":10086,"style":137},[48],[20,10088,227],{"className":10089},[41],[20,10091,859],{"className":10092},[184],[1019,10094,10095],{"type":9678},[11,10096,10097,10100,10101,10104,10105,10107,10108,10110,10111,10113],{},[15,10098,10099],{},"Fact (...yet they generalize on real data)."," The ",[1305,10102,10103],{},"same"," architecture, ",[1305,10106,10103],{},"\noptimizer, and ",[1305,10109,10103],{}," epoch budget that memorizes random labels instead learns a\nfunction with a small generalization gap when trained on the ",[1305,10112,28],{}," labels. The\nmodel has the capacity to memorize but, on structured data, does not have to.",[11,10115,10116,10117,866],{},"The two facts isolate the puzzle precisely: capacity is necessary to explain the\nrandom-label result and useless to explain the real-data result. Whatever controls\nthe gap on real data, it is not the size of ",[20,10118,10120],{"className":10119},[23],[20,10121,10123],{"className":10122,"ariaHidden":28},[27],[20,10124,10126,10129],{"className":10125},[32],[20,10127],{"className":10128,"style":37},[36],[20,10130,1855],{"className":10131,"style":1854},[41,42],[2645,10133],{"hash":10134},"49480abebc3b447e544942b1662625853d989a986d0e6a0b09011ca0be4c98eb",[1829,10136,10138],{"id":10137},"double-descent","Double descent",[11,10140,10141,10142,10145,10146,10149],{},"The cleanest empirical refutation of the classical U-curve is ",[15,10143,10144],{},"double descent",".\nSweep model size from tiny to huge and plot test error. At first the classical\nstory holds: test error falls, then rises as the model begins to overfit, peaking\nright where the model becomes just barely able to fit the training set. Past that\npoint, the regime classical theory forbids, test error ",[1305,10147,10148],{},"falls again",", often\nbelow the classical sweet spot.",[1019,10151,10152],{"type":1021},[11,10153,10154,10157,10158,10161],{},[15,10155,10156],{},"Definition (Interpolation threshold)."," The model size at which the network\nfirst achieves zero training error — it exactly ",[1305,10159,10160],{},"interpolates"," the training set.\nBelow it the model is underparameterized and cannot fit the data; above it the\nmodel is overparameterized and many zero-error solutions exist. The double-descent\npeak sits at this threshold.",[2645,10163],{"hash":10164},"37893215db287baad82953b1fa4b3753797c7edcad5f249b0836ab7de7ecd099",[11,10166,10167,10168,10171],{},"Why the second descent happens is the active question. The working explanation is\nthat beyond the threshold there is a ",[1305,10169,10170],{},"choice"," of zero-error solution, and the\noptimizer picks a simple one rather than an arbitrary one. At the threshold\nitself there is essentially a unique interpolating solution and the optimizer is\nforced into it, however contorted; that forced, brittle fit is the spike. With\nmore parameters, the optimizer finds a smoother interpolant, and test error drops.",[11,10173,10174,10175,10178,10179,10182,10183,10204,10205,10220,10221,10224],{},"The same peak appears when the axis is ",[1305,10176,10177],{},"training time"," or ",[1305,10180,10181],{},"data size"," rather than\nmodel width, which is why the phenomenon is read as being about the ratio of\neffective capacity to sample size, ",[20,10184,10186],{"className":10185},[23],[20,10187,10189],{"className":10188,"ariaHidden":28},[27],[20,10190,10192,10195,10198,10201],{"className":10191},[32],[20,10193],{"className":10194,"style":946},[36],[20,10196,2753],{"className":10197,"style":2752},[41,76],[20,10199,3158],{"className":10200},[41],[20,10202,240],{"className":10203},[41,76],", crossing ",[20,10206,10208],{"className":10207},[23],[20,10209,10211],{"className":10210,"ariaHidden":28},[27],[20,10212,10214,10217],{"className":10213},[32],[20,10215],{"className":10216,"style":2313},[36],[20,10218,227],{"className":10219},[41]," — not about parameter count\nalone. The peak sits wherever the model is ",[1305,10222,10223],{},"just"," able to interpolate, whichever\nknob pushed it there.",[1019,10226,10228],{"type":10227},"remark",[11,10229,10230,10233,10234,10236,10237,10240],{},[15,10231,10232],{},"Remark (A capacity surrogate that does not double-descend)."," The phenomenon\nmotivates measuring capacity by quantities the optimizer actually controls — the\n",[1305,10235,8984],{}," of the learned weights, or the ",[1305,10238,10239],{},"margin",", rather than the parameter count.\nNorm-based complexity grows then shrinks across the threshold, tracking the\ndouble-descent curve where parameter-counting fails.",[1829,10242,10244],{"id":10243},"margin-and-norm-based-bounds","Margin and norm-based bounds",[11,10246,10247,10248,10313,10314,10329,10330,10332],{},"The Rademacher bound already hinted at the fix: for linear predictors the\ncomplexity was ",[20,10249,10251],{"className":10250},[23],[20,10252,10254],{"className":10253,"ariaHidden":28},[27],[20,10255,10257,10260,10263,10266,10269],{"className":10256},[32],[20,10258],{"className":10259,"style":8916},[36],[20,10261,8776],{"className":10262,"style":8775},[41,76],[20,10264,8818],{"className":10265,"style":8817},[41,76],[20,10267,3158],{"className":10268},[41],[20,10270,10272],{"className":10271},[41,3062],[20,10273,10275,10305],{"className":10274},[85,86],[20,10276,10278,10302],{"className":10277},[90],[20,10279,10281,10290],{"className":10280,"style":8938},[94],[20,10282,10284,10287],{"className":10283,"style":312},[3076],[20,10285],{"className":10286,"style":316},[102],[20,10288,240],{"className":10289,"style":8948},[41,76],[20,10291,10292,10295],{"style":8954},[20,10293],{"className":10294,"style":316},[102],[20,10296,10298],{"className":10297,"style":8961},[3216],[3219,10299,10300],{"xmlns":3221,"width":3222,"height":8964,"viewBox":8965,"preserveAspectRatio":3225},[3227,10301],{"d":8968},[20,10303,119],{"className":10304},[118],[20,10306,10308],{"className":10307},[90],[20,10309,10311],{"className":10310,"style":8978},[94],[20,10312],{},", controlled by the weight norm ",[20,10315,10317],{"className":10316},[23],[20,10318,10320],{"className":10319,"ariaHidden":28},[27],[20,10321,10323,10326],{"className":10322},[32],[20,10324],{"className":10325,"style":37},[36],[20,10327,8776],{"className":10328,"style":8775},[41,76]," and not the\ndimension. The margin turns this into a bound that stays finite for interpolating\nnetworks. Define the ",[15,10331,10239],{}," of a correctly classified example as the gap\nbetween the score of the true class and the best competing class,",[20,10334,10336],{"className":10335},[282],[20,10337,10339],{"className":10338},[23],[20,10340,10342,10400,10545],{"className":10341,"ariaHidden":28},[27],[20,10343,10345,10348,10391,10394,10397],{"className":10344},[32],[20,10346],{"className":10347,"style":4874},[36],[20,10349,10351,10356],{"className":10350},[41],[20,10352,10355],{"className":10353,"style":10354},[41,76],"margin-right:0.0556em;","γ",[20,10357,10359],{"className":10358},[81],[20,10360,10362,10383],{"className":10361},[85,86],[20,10363,10365,10380],{"className":10364},[90],[20,10366,10368],{"className":10367,"style":95},[94],[20,10369,10371,10374],{"style":10370},"top:-2.55em;margin-left:-0.0556em;margin-right:0.05em;",[20,10372],{"className":10373,"style":103},[102],[20,10375,10377],{"className":10376},[107,108,109,110],[20,10378,114],{"className":10379},[41,76,110],[20,10381,119],{"className":10382},[118],[20,10384,10386],{"className":10385},[90],[20,10387,10389],{"className":10388,"style":126},[94],[20,10390],{},[20,10392],{"className":10393,"style":49},[48],[20,10395,54],{"className":10396},[53],[20,10398],{"className":10399,"style":49},[48],[20,10401,10403,10407,10490,10493,10533,10536,10539,10542],{"className":10402},[32],[20,10404],{"className":10405,"style":10406},[36],"height:1.0361em;vertical-align:-0.2861em;",[20,10408,10410,10413],{"className":10409},[41],[20,10411,539],{"className":10412,"style":538},[41,76],[20,10414,10416],{"className":10415},[81],[20,10417,10419,10482],{"className":10418},[85,86],[20,10420,10422,10479],{"className":10421},[90],[20,10423,10425],{"className":10424,"style":2041},[94],[20,10426,10427,10430],{"style":555},[20,10428],{"className":10429,"style":103},[102],[20,10431,10433],{"className":10432},[107,108,109,110],[20,10434,10436],{"className":10435},[41,110],[20,10437,10439,10442],{"className":10438},[41,110],[20,10440,145],{"className":10441,"style":144},[41,76,110],[20,10443,10445],{"className":10444},[81],[20,10446,10448,10470],{"className":10447},[85,86],[20,10449,10451,10467],{"className":10450},[90],[20,10452,10455],{"className":10453,"style":10454},[94],"height:0.3281em;",[20,10456,10458,10461],{"style":10457},"top:-2.357em;margin-left:-0.0359em;margin-right:0.0714em;",[20,10459],{"className":10460,"style":3523},[102],[20,10462,10464],{"className":10463},[107,3527,3528,110],[20,10465,114],{"className":10466},[41,76,110],[20,10468,119],{"className":10469},[118],[20,10471,10473],{"className":10472},[90],[20,10474,10477],{"className":10475,"style":10476},[94],"height:0.143em;",[20,10478],{},[20,10480,119],{"className":10481},[118],[20,10483,10485],{"className":10484},[90],[20,10486,10488],{"className":10487,"style":5308},[94],[20,10489],{},[20,10491,340],{"className":10492},[68],[20,10494,10496,10499],{"className":10495},[41],[20,10497,77],{"className":10498},[41,76],[20,10500,10502],{"className":10501},[81],[20,10503,10505,10525],{"className":10504},[85,86],[20,10506,10508,10522],{"className":10507},[90],[20,10509,10511],{"className":10510,"style":95},[94],[20,10512,10513,10516],{"style":98},[20,10514],{"className":10515,"style":103},[102],[20,10517,10519],{"className":10518},[107,108,109,110],[20,10520,114],{"className":10521},[41,76,110],[20,10523,119],{"className":10524},[118],[20,10526,10528],{"className":10527},[90],[20,10529,10531],{"className":10530,"style":126},[94],[20,10532],{},[20,10534,185],{"className":10535},[184],[20,10537],{"className":10538,"style":962},[48],[20,10540,967],{"className":10541},[966],[20,10543],{"className":10544,"style":962},[48],[20,10546,10548,10552,10690,10693,10733,10736,10776,10779],{"className":10547},[32],[20,10549],{"className":10550,"style":10551},[36],"height:1.6382em;vertical-align:-0.8882em;",[20,10553,10555],{"className":10554},[441,442],[20,10556,10558,10681],{"className":10557},[85,86],[20,10559,10561,10678],{"className":10560},[90],[20,10562,10564,10664],{"className":10563,"style":1147},[94],[20,10565,10567,10570],{"style":10566},"top:-2.3479em;margin-left:0em;",[20,10568],{"className":10569,"style":316},[102],[20,10571,10573],{"className":10572},[107,108,109,110],[20,10574,10576,10581,10624],{"className":10575},[41,110],[20,10577,10580],{"className":10578,"style":10579},[41,76,110],"margin-right:0.0315em;","k",[20,10582,10584,10617,10621],{"className":10583},[53,110],[20,10585,10587],{"className":10586},[53,110],[20,10588,10591],{"className":10589},[41,10590,110],"vbox",[20,10592,10595],{"className":10593},[10594,110],"thinbox",[20,10596,10599,10602,10613],{"className":10597},[10598,110],"rlap",[20,10600],{"className":10601,"style":1040},[36],[20,10603,10606],{"className":10604},[10605],"inner",[20,10607,10609],{"className":10608},[41,110],[20,10610,10612],{"className":10611},[53,110],"",[20,10614],{"className":10615},[10616],"fix",[20,10618],{"className":10619},[48,10620,110],"nobreak",[20,10622,54],{"className":10623},[53,110],[20,10625,10627,10630],{"className":10626},[41,110],[20,10628,145],{"className":10629,"style":144},[41,76,110],[20,10631,10633],{"className":10632},[81],[20,10634,10636,10656],{"className":10635},[85,86],[20,10637,10639,10653],{"className":10638},[90],[20,10640,10642],{"className":10641,"style":10454},[94],[20,10643,10644,10647],{"style":10457},[20,10645],{"className":10646,"style":3523},[102],[20,10648,10650],{"className":10649},[107,3527,3528,110],[20,10651,114],{"className":10652},[41,76,110],[20,10654,119],{"className":10655},[118],[20,10657,10659],{"className":10658},[90],[20,10660,10662],{"className":10661,"style":10476},[94],[20,10663],{},[20,10665,10666,10669],{"style":312},[20,10667],{"className":10668,"style":316},[102],[20,10670,10671],{},[20,10672,10674],{"className":10673},[441],[20,10675,10677],{"className":10676},[41,2229],"max",[20,10679,119],{"className":10680},[118],[20,10682,10684],{"className":10683},[90],[20,10685,10688],{"className":10686,"style":10687},[94],"height:0.8882em;",[20,10689],{},[20,10691],{"className":10692,"style":137},[48],[20,10694,10696,10699],{"className":10695},[41],[20,10697,539],{"className":10698,"style":538},[41,76],[20,10700,10702],{"className":10701},[81],[20,10703,10705,10725],{"className":10704},[85,86],[20,10706,10708,10722],{"className":10707},[90],[20,10709,10711],{"className":10710,"style":552},[94],[20,10712,10713,10716],{"style":555},[20,10714],{"className":10715,"style":103},[102],[20,10717,10719],{"className":10718},[107,108,109,110],[20,10720,10580],{"className":10721,"style":10579},[41,76,110],[20,10723,119],{"className":10724},[118],[20,10726,10728],{"className":10727},[90],[20,10729,10731],{"className":10730,"style":126},[94],[20,10732],{},[20,10734,340],{"className":10735},[68],[20,10737,10739,10742],{"className":10738},[41],[20,10740,77],{"className":10741},[41,76],[20,10743,10745],{"className":10744},[81],[20,10746,10748,10768],{"className":10747},[85,86],[20,10749,10751,10765],{"className":10750},[90],[20,10752,10754],{"className":10753,"style":95},[94],[20,10755,10756,10759],{"style":98},[20,10757],{"className":10758,"style":103},[102],[20,10760,10762],{"className":10761},[107,108,109,110],[20,10763,114],{"className":10764},[41,76,110],[20,10766,119],{"className":10767},[118],[20,10769,10771],{"className":10770},[90],[20,10772,10774],{"className":10773,"style":126},[94],[20,10775],{},[20,10777,185],{"className":10778},[184],[20,10780,133],{"className":10781},[132],[11,10783,10784,10785,10800],{},"and let ",[20,10786,10788],{"className":10787},[23],[20,10789,10791],{"className":10790,"ariaHidden":28},[27],[20,10792,10794,10797],{"className":10793},[32],[20,10795],{"className":10796,"style":4874},[36],[20,10798,10355],{"className":10799,"style":10354},[41,76]," be the margin achieved on the whole sample. A predictor that\nseparates the data with a large margin sits far from every decision boundary, so\nsmall perturbations of the input do not flip its label — a smoothness property that\ntransfers to unseen points.",[1019,10802,10803,10918,11389],{"type":2653},[11,10804,10805,10808,10809,10870,10871,10917],{},[15,10806,10807],{},"Theorem (Margin bound, schematic)."," For a network with weight matrices of\nspectral norms ",[20,10810,10812],{"className":10811},[23],[20,10813,10815],{"className":10814,"ariaHidden":28},[27],[20,10816,10818,10821,10824,10867],{"className":10817},[32],[20,10819],{"className":10820,"style":946},[36],[20,10822,8748],{"className":10823},[68],[20,10825,10827,10832],{"className":10826},[41],[20,10828,10831],{"className":10829,"style":10830},[41,76],"margin-right:0.1389em;","W",[20,10833,10835],{"className":10834},[81],[20,10836,10838,10859],{"className":10837},[85,86],[20,10839,10841,10856],{"className":10840},[90],[20,10842,10844],{"className":10843,"style":552},[94],[20,10845,10847,10850],{"style":10846},"top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;",[20,10848],{"className":10849,"style":103},[102],[20,10851,10853],{"className":10852},[107,108,109,110],[20,10854,519],{"className":10855},[41,110],[20,10857,119],{"className":10858},[118],[20,10860,10862],{"className":10861},[90],[20,10863,10865],{"className":10864,"style":126},[94],[20,10866],{},[20,10868,8748],{"className":10869},[184],", with probability ",[20,10872,10874],{"className":10873},[23],[20,10875,10877,10890,10908],{"className":10876,"ariaHidden":28},[27],[20,10878,10880,10883,10887],{"className":10879},[32],[20,10881],{"className":10882,"style":9032},[36],[20,10884,10886],{"className":10885},[53],"≥",[20,10888],{"className":10889,"style":49},[48],[20,10891,10893,10896,10899,10902,10905],{"className":10892},[32],[20,10894],{"className":10895,"style":2865},[36],[20,10897,227],{"className":10898},[41],[20,10900],{"className":10901,"style":962},[48],[20,10903,967],{"className":10904},[966],[20,10906],{"className":10907,"style":962},[48],[20,10909,10911,10914],{"className":10910},[32],[20,10912],{"className":10913,"style":1320},[36],[20,10915,2888],{"className":10916,"style":2887},[41,76]," the\nmisclassification risk obeys",[20,10919,10921],{"className":10920},[282],[20,10922,10924],{"className":10923},[23],[20,10925,10927,11002,11102],{"className":10926,"ariaHidden":28},[27],[20,10928,10930,10933,10978,10981,10984,10987,10990,10993,10996,10999],{"className":10929},[32],[20,10931],{"className":10932,"style":711},[36],[20,10934,10936,10939],{"className":10935},[41],[20,10937,321],{"className":10938,"style":320},[41,76],[20,10940,10942],{"className":10941},[81],[20,10943,10945,10970],{"className":10944},[85,86],[20,10946,10948,10967],{"className":10947},[90],[20,10949,10951],{"className":10950,"style":732},[94],[20,10952,10954,10957],{"style":10953},"top:-2.5198em;margin-left:-0.0077em;margin-right:0.05em;",[20,10955],{"className":10956,"style":103},[102],[20,10958,10960],{"className":10959},[107,108,109,110],[20,10961,10963],{"className":10962},[41,110],[20,10964,10966],{"className":10965},[41,110],"0\u002F1",[20,10968,119],{"className":10969},[118],[20,10971,10973],{"className":10972},[90],[20,10974,10976],{"className":10975,"style":776},[94],[20,10977],{},[20,10979,340],{"className":10980},[68],[20,10982,539],{"className":10983,"style":538},[41,76],[20,10985,185],{"className":10986},[184],[20,10988],{"className":10989,"style":49},[48],[20,10991],{"className":10992,"style":49},[48],[20,10994,2962],{"className":10995},[53],[20,10997],{"className":10998,"style":49},[48],[20,11000],{"className":11001,"style":49},[48],[20,11003,11005,11009,11078,11081,11084,11087,11090,11093,11096,11099],{"className":11004},[32],[20,11006],{"className":11007,"style":11008},[36],"height:1.2329em;vertical-align:-0.2861em;",[20,11010,11012,11043],{"className":11011},[41],[20,11013,11015],{"className":11014},[41,299],[20,11016,11018],{"className":11017},[85],[20,11019,11021],{"className":11020},[90],[20,11022,11024,11032],{"className":11023,"style":309},[94],[20,11025,11026,11029],{"style":312},[20,11027],{"className":11028,"style":316},[102],[20,11030,321],{"className":11031,"style":320},[41,76],[20,11033,11034,11037],{"style":324},[20,11035],{"className":11036,"style":316},[102],[20,11038,11040],{"className":11039,"style":332},[331],[20,11041,336],{"className":11042},[41],[20,11044,11046],{"className":11045},[81],[20,11047,11049,11070],{"className":11048},[85,86],[20,11050,11052,11067],{"className":11051},[90],[20,11053,11055],{"className":11054,"style":2041},[94],[20,11056,11058,11061],{"style":11057},"top:-2.55em;margin-left:-0.0077em;margin-right:0.05em;",[20,11059],{"className":11060,"style":103},[102],[20,11062,11064],{"className":11063},[107,108,109,110],[20,11065,10355],{"className":11066,"style":10354},[41,76,110],[20,11068,119],{"className":11069},[118],[20,11071,11073],{"className":11072},[90],[20,11074,11076],{"className":11075,"style":5308},[94],[20,11077],{},[20,11079,340],{"className":11080},[68],[20,11082,539],{"className":11083,"style":538},[41,76],[20,11085,185],{"className":11086},[184],[20,11088],{"className":11089,"style":49},[48],[20,11091],{"className":11092,"style":962},[48],[20,11094,2335],{"className":11095},[966],[20,11097],{"className":11098,"style":49},[48],[20,11100],{"className":11101,"style":962},[48],[20,11103,11105,11109,11143,11146,11149,11383,11386],{"className":11104},[32],[20,11106],{"className":11107,"style":11108},[36],"height:2.4em;vertical-align:-0.95em;",[20,11110,11112],{"className":11111},[41,299],[20,11113,11115],{"className":11114},[85],[20,11116,11118],{"className":11117},[90],[20,11119,11122,11130],{"className":11120,"style":11121},[94],"height:0.9202em;",[20,11123,11124,11127],{"style":312},[20,11125],{"className":11126,"style":316},[102],[20,11128,3040],{"className":11129,"style":43},[41,76],[20,11131,11133,11136],{"style":11132},"top:-3.6023em;",[20,11134],{"className":11135,"style":316},[102],[20,11137,11139],{"className":11138,"style":332},[331],[20,11140,11142],{"className":11141},[41],"~",[20,11144],{"className":11145,"style":3044},[48],[20,11147],{"className":11148,"style":137},[48],[20,11150,11152,11158,11377],{"className":11151},[526],[20,11153,11155],{"className":11154,"style":531},[68,530],[20,11156,340],{"className":11157},[3057,109],[20,11159,11161,11164,11374],{"className":11160},[41],[20,11162],{"className":11163},[68,370],[20,11165,11167],{"className":11166},[374],[20,11168,11170,11365],{"className":11169},[85,86],[20,11171,11173,11362],{"className":11172},[90],[20,11174,11177,11236,11244],{"className":11175,"style":11176},[94],"height:1.4397em;",[20,11178,11180,11183],{"style":11179},"top:-2.3097em;",[20,11181],{"className":11182,"style":316},[102],[20,11184,11186,11189,11192],{"className":11185},[41],[20,11187,10355],{"className":11188,"style":10354},[41,76],[20,11190],{"className":11191,"style":137},[48],[20,11193,11195],{"className":11194},[41,3062],[20,11196,11198,11228],{"className":11197},[85,86],[20,11199,11201,11225],{"className":11200},[90],[20,11202,11204,11213],{"className":11203,"style":8938},[94],[20,11205,11207,11210],{"className":11206,"style":312},[3076],[20,11208],{"className":11209,"style":316},[102],[20,11211,240],{"className":11212,"style":8948},[41,76],[20,11214,11215,11218],{"style":8954},[20,11216],{"className":11217,"style":316},[102],[20,11219,11221],{"className":11220,"style":8961},[3216],[3219,11222,11223],{"xmlns":3221,"width":3222,"height":8964,"viewBox":8965,"preserveAspectRatio":3225},[3227,11224],{"d":8968},[20,11226,119],{"className":11227},[118],[20,11229,11231],{"className":11230},[90],[20,11232,11234],{"className":11233,"style":8978},[94],[20,11235],{},[20,11237,11238,11241],{"style":399},[20,11239],{"className":11240,"style":316},[102],[20,11242],{"className":11243,"style":407},[406],[20,11245,11247,11250],{"style":11246},"top:-3.6897em;",[20,11248],{"className":11249,"style":316},[102],[20,11251,11253,11297,11300,11340,11343,11346,11349,11352,11355,11358],{"className":11252},[41],[20,11254,11256,11260],{"className":11255},[441],[20,11257,11259],{"className":11258,"style":7624},[441,486,7623],"∏",[20,11261,11263],{"className":11262},[81],[20,11264,11266,11288],{"className":11265},[85,86],[20,11267,11269,11285],{"className":11268},[90],[20,11270,11273],{"className":11271,"style":11272},[94],"height:0.1864em;",[20,11274,11276,11279],{"style":11275},"top:-2.4003em;margin-left:0em;margin-right:0.05em;",[20,11277],{"className":11278,"style":103},[102],[20,11280,11282],{"className":11281},[107,108,109,110],[20,11283,519],{"className":11284},[41,110],[20,11286,119],{"className":11287},[118],[20,11289,11291],{"className":11290},[90],[20,11292,11295],{"className":11293,"style":11294},[94],"height:0.2997em;",[20,11296],{},[20,11298,8748],{"className":11299},[68],[20,11301,11303,11306],{"className":11302},[41],[20,11304,10831],{"className":11305,"style":10830},[41,76],[20,11307,11309],{"className":11308},[81],[20,11310,11312,11332],{"className":11311},[85,86],[20,11313,11315,11329],{"className":11314},[90],[20,11316,11318],{"className":11317,"style":552},[94],[20,11319,11320,11323],{"style":10846},[20,11321],{"className":11322,"style":103},[102],[20,11324,11326],{"className":11325},[107,108,109,110],[20,11327,519],{"className":11328},[41,110],[20,11330,119],{"className":11331},[118],[20,11333,11335],{"className":11334},[90],[20,11336,11338],{"className":11337,"style":126},[94],[20,11339],{},[20,11341,8748],{"className":11342},[184],[20,11344],{"className":11345,"style":137},[48],[20,11347],{"className":11348,"style":962},[48],[20,11350,7741],{"className":11351},[966],[20,11353],{"className":11354,"style":137},[48],[20,11356],{"className":11357,"style":962},[48],[20,11359,11361],{"className":11360,"style":43},[41,76],"r",[20,11363,119],{"className":11364},[118],[20,11366,11368],{"className":11367},[90],[20,11369,11372],{"className":11370,"style":11371},[94],"height:0.93em;",[20,11373],{},[20,11375],{"className":11376},[184,370],[20,11378,11380],{"className":11379,"style":531},[184,530],[20,11381,185],{"className":11382},[3057,109],[20,11384],{"className":11385,"style":137},[48],[20,11387,133],{"className":11388},[132],[11,11390,11391,11392,11472,11473,1725,11488,11503],{},"where ",[20,11393,11395],{"className":11394},[23],[20,11396,11398],{"className":11397,"ariaHidden":28},[27],[20,11399,11401,11404],{"className":11400},[32],[20,11402],{"className":11403,"style":11008},[36],[20,11405,11407,11438],{"className":11406},[41],[20,11408,11410],{"className":11409},[41,299],[20,11411,11413],{"className":11412},[85],[20,11414,11416],{"className":11415},[90],[20,11417,11419,11427],{"className":11418,"style":309},[94],[20,11420,11421,11424],{"style":312},[20,11422],{"className":11423,"style":316},[102],[20,11425,321],{"className":11426,"style":320},[41,76],[20,11428,11429,11432],{"style":324},[20,11430],{"className":11431,"style":316},[102],[20,11433,11435],{"className":11434,"style":332},[331],[20,11436,336],{"className":11437},[41],[20,11439,11441],{"className":11440},[81],[20,11442,11444,11464],{"className":11443},[85,86],[20,11445,11447,11461],{"className":11446},[90],[20,11448,11450],{"className":11449,"style":2041},[94],[20,11451,11452,11455],{"style":11057},[20,11453],{"className":11454,"style":103},[102],[20,11456,11458],{"className":11457},[107,108,109,110],[20,11459,10355],{"className":11460,"style":10354},[41,76,110],[20,11462,119],{"className":11463},[118],[20,11465,11467],{"className":11466},[90],[20,11468,11470],{"className":11469,"style":5308},[94],[20,11471],{}," is the fraction of training points with margin below\n",[20,11474,11476],{"className":11475},[23],[20,11477,11479],{"className":11478,"ariaHidden":28},[27],[20,11480,11482,11485],{"className":11481},[32],[20,11483],{"className":11484,"style":4874},[36],[20,11486,10355],{"className":11487,"style":10354},[41,76],[20,11489,11491],{"className":11490},[23],[20,11492,11494],{"className":11493,"ariaHidden":28},[27],[20,11495,11497,11500],{"className":11496},[32],[20,11498],{"className":11499,"style":1147},[36],[20,11501,11361],{"className":11502,"style":43},[41,76]," collects the (weaker) dependence on architecture.",[11,11505,11506,11507,11612],{},"The numerator measures the size of the function; the denominator, its distance\nfrom the boundary. Two networks with identical parameter counts can differ\nby orders of magnitude in ",[20,11508,11510],{"className":11509},[23],[20,11511,11513],{"className":11512,"ariaHidden":28},[27],[20,11514,11516,11520,11560,11563,11603,11606,11609],{"className":11515},[32],[20,11517],{"className":11518,"style":11519},[36],"height:1.0497em;vertical-align:-0.2997em;",[20,11521,11523,11526],{"className":11522},[441],[20,11524,11259],{"className":11525,"style":7624},[441,486,7623],[20,11527,11529],{"className":11528},[81],[20,11530,11532,11552],{"className":11531},[85,86],[20,11533,11535,11549],{"className":11534},[90],[20,11536,11538],{"className":11537,"style":11272},[94],[20,11539,11540,11543],{"style":11275},[20,11541],{"className":11542,"style":103},[102],[20,11544,11546],{"className":11545},[107,108,109,110],[20,11547,519],{"className":11548},[41,110],[20,11550,119],{"className":11551},[118],[20,11553,11555],{"className":11554},[90],[20,11556,11558],{"className":11557,"style":11294},[94],[20,11559],{},[20,11561,8748],{"className":11562},[68],[20,11564,11566,11569],{"className":11565},[41],[20,11567,10831],{"className":11568,"style":10830},[41,76],[20,11570,11572],{"className":11571},[81],[20,11573,11575,11595],{"className":11574},[85,86],[20,11576,11578,11592],{"className":11577},[90],[20,11579,11581],{"className":11580,"style":552},[94],[20,11582,11583,11586],{"style":10846},[20,11584],{"className":11585,"style":103},[102],[20,11587,11589],{"className":11588},[107,108,109,110],[20,11590,519],{"className":11591},[41,110],[20,11593,119],{"className":11594},[118],[20,11596,11598],{"className":11597},[90],[20,11599,11601],{"className":11600,"style":126},[94],[20,11602],{},[20,11604,8748],{"className":11605},[184],[20,11607,3158],{"className":11608},[41],[20,11610,10355],{"className":11611,"style":10354},[41,76],": the one the optimizer\nhappens to find on real data has small weight norm and large margin, so its bound\nis small; the one that memorizes random labels needs enormous weights to force the\ncontorted fit, so its bound is large. Parameter counting cannot tell them apart;\nthe norm-over-margin ratio can. This is the same quantity that shrinks past the\ninterpolation threshold and tracks the second descent.",[2645,11614],{"hash":11615},"c834927dafa54ab7d1c1e2b65bf97e6c45a9631e397838f1b21de1583ba28048",[1829,11617,11619],{"id":11618},"implicit-regularization","Implicit regularization",[11,11621,11622,11623,11626,11627,11630,11631],{},"The missing ingredient is that the ",[15,11624,11625],{},"optimizer is not neutral",". Among the\ninfinitely many parameter vectors that interpolate the training data, gradient\ndescent does not return a random one; its trajectory is biased toward a particular,\nwell-behaved corner of the solution manifold. This is ",[15,11628,11629],{},"implicit regularization",":\na preference for simple solutions that nobody wrote into the loss.",[1858,11632,11633],{},[1861,11634,9372],{"href":11635,"ariaDescribedBy":11636,"dataFootnoteRef":6,"id":11637},"#user-content-fn-gf-reg",[1865],"user-content-fnref-gf-reg",[1019,11639,11640],{"type":1021},[11,11641,11642,11645],{},[15,11643,11644],{},"Definition (Implicit regularization)."," The tendency of an optimization\nprocedure (gradient descent, SGD) to converge to solutions with special\nstructure (low norm, large margin, flat curvature) among all those that minimize\nthe training loss, without any explicit penalty term enforcing that structure.",[11,11647,11648],{},"The canonical exact result is for the simplest interpolating problem, where the bias\ncan be named precisely.",[1019,11650,11651],{"type":2653},[11,11652,11653,11656,11657,11738,11739,11742,11843,11844,11847],{},[15,11654,11655],{},"Theorem (Implicit bias of gradient descent, separable case)."," For logistic\nregression on linearly separable data, gradient descent on the (unregularized)\nempirical loss drives ",[20,11658,11660],{"className":11659},[23],[20,11661,11663,11728],{"className":11662,"ariaHidden":28},[27],[20,11664,11666,11669,11672,11715,11718,11721,11725],{"className":11665},[32],[20,11667],{"className":11668,"style":946},[36],[20,11670,8748],{"className":11671},[68],[20,11673,11675,11678],{"className":11674},[41],[20,11676,8753],{"className":11677,"style":8752},[41,76],[20,11679,11681],{"className":11680},[81],[20,11682,11684,11707],{"className":11683},[85,86],[20,11685,11687,11704],{"className":11686},[90],[20,11688,11691],{"className":11689,"style":11690},[94],"height:0.2806em;",[20,11692,11694,11697],{"style":11693},"top:-2.55em;margin-left:-0.0269em;margin-right:0.05em;",[20,11695],{"className":11696,"style":103},[102],[20,11698,11700],{"className":11699},[107,108,109,110],[20,11701,11703],{"className":11702},[41,76,110],"t",[20,11705,119],{"className":11706},[118],[20,11708,11710],{"className":11709},[90],[20,11711,11713],{"className":11712,"style":126},[94],[20,11714],{},[20,11716,8748],{"className":11717},[184],[20,11719],{"className":11720,"style":49},[48],[20,11722,11724],{"className":11723},[53],"→",[20,11726],{"className":11727,"style":49},[48],[20,11729,11731,11734],{"className":11730},[32],[20,11732],{"className":11733,"style":1147},[36],[20,11735,11737],{"className":11736},[41],"∞",", but the ",[1305,11740,11741],{},"direction",[20,11743,11745],{"className":11744},[23],[20,11746,11748],{"className":11747,"ariaHidden":28},[27],[20,11749,11751,11754,11794,11797,11800,11840],{"className":11750},[32],[20,11752],{"className":11753,"style":946},[36],[20,11755,11757,11760],{"className":11756},[41],[20,11758,8753],{"className":11759,"style":8752},[41,76],[20,11761,11763],{"className":11762},[81],[20,11764,11766,11786],{"className":11765},[85,86],[20,11767,11769,11783],{"className":11768},[90],[20,11770,11772],{"className":11771,"style":11690},[94],[20,11773,11774,11777],{"style":11693},[20,11775],{"className":11776,"style":103},[102],[20,11778,11780],{"className":11779},[107,108,109,110],[20,11781,11703],{"className":11782},[41,76,110],[20,11784,119],{"className":11785},[118],[20,11787,11789],{"className":11788},[90],[20,11790,11792],{"className":11791,"style":126},[94],[20,11793],{},[20,11795,3158],{"className":11796},[41],[20,11798,8748],{"className":11799},[68],[20,11801,11803,11806],{"className":11802},[41],[20,11804,8753],{"className":11805,"style":8752},[41,76],[20,11807,11809],{"className":11808},[81],[20,11810,11812,11832],{"className":11811},[85,86],[20,11813,11815,11829],{"className":11814},[90],[20,11816,11818],{"className":11817,"style":11690},[94],[20,11819,11820,11823],{"style":11693},[20,11821],{"className":11822,"style":103},[102],[20,11824,11826],{"className":11825},[107,108,109,110],[20,11827,11703],{"className":11828},[41,76,110],[20,11830,119],{"className":11831},[118],[20,11833,11835],{"className":11834},[90],[20,11836,11838],{"className":11837,"style":126},[94],[20,11839],{},[20,11841,8748],{"className":11842},[184]," converges to the ",[15,11845,11846],{},"maximum-margin"," separator — the same\nsolution a hard-margin SVM returns. The optimizer regularizes toward maximum\nmargin with no margin term in the objective.",[1019,11849,11851],{"type":11850},"proof",[11,11852,11853,11856,11857,11872,11873,11931,11932,11947,11948,11951,11952,12078,12079,12210,12211,12226,12227,12418,12419],{},[15,11854,11855],{},"Proof (sketch)."," On separable data no finite ",[20,11858,11860],{"className":11859},[23],[20,11861,11863],{"className":11862,"ariaHidden":28},[27],[20,11864,11866,11869],{"className":11865},[32],[20,11867],{"className":11868,"style":1147},[36],[20,11870,8753],{"className":11871,"style":8752},[41,76]," achieves zero logistic loss,\nso ",[20,11874,11876],{"className":11875},[23],[20,11877,11879],{"className":11878,"ariaHidden":28},[27],[20,11880,11882,11885,11888,11928],{"className":11881},[32],[20,11883],{"className":11884,"style":946},[36],[20,11886,8748],{"className":11887},[68],[20,11889,11891,11894],{"className":11890},[41],[20,11892,8753],{"className":11893,"style":8752},[41,76],[20,11895,11897],{"className":11896},[81],[20,11898,11900,11920],{"className":11899},[85,86],[20,11901,11903,11917],{"className":11902},[90],[20,11904,11906],{"className":11905,"style":11690},[94],[20,11907,11908,11911],{"style":11693},[20,11909],{"className":11910,"style":103},[102],[20,11912,11914],{"className":11913},[107,108,109,110],[20,11915,11703],{"className":11916},[41,76,110],[20,11918,119],{"className":11919},[118],[20,11921,11923],{"className":11922},[90],[20,11924,11926],{"className":11925,"style":126},[94],[20,11927],{},[20,11929,8748],{"className":11930},[184]," diverges as the loss is pushed toward ",[20,11933,11935],{"className":11934},[23],[20,11936,11938],{"className":11937,"ariaHidden":28},[27],[20,11939,11941,11944],{"className":11940},[32],[20,11942],{"className":11943,"style":2313},[36],[20,11945,2824],{"className":11946},[41],". The loss is\ndominated by the example with the ",[1305,11949,11950],{},"smallest"," margin ",[20,11953,11955],{"className":11954},[23],[20,11956,11958],{"className":11957,"ariaHidden":28},[27],[20,11959,11961,11965,12005,12008,12038],{"className":11960},[32],[20,11962],{"className":11963,"style":11964},[36],"height:1.0435em;vertical-align:-0.1944em;",[20,11966,11968,11971],{"className":11967},[41],[20,11969,145],{"className":11970,"style":144},[41,76],[20,11972,11974],{"className":11973},[81],[20,11975,11977,11997],{"className":11976},[85,86],[20,11978,11980,11994],{"className":11979},[90],[20,11981,11983],{"className":11982,"style":95},[94],[20,11984,11985,11988],{"style":160},[20,11986],{"className":11987,"style":103},[102],[20,11989,11991],{"className":11990},[107,108,109,110],[20,11992,114],{"className":11993},[41,76,110],[20,11995,119],{"className":11996},[118],[20,11998,12000],{"className":11999},[90],[20,12001,12003],{"className":12002,"style":126},[94],[20,12004],{},[20,12006],{"className":12007,"style":137},[48],[20,12009,12011,12014],{"className":12010},[41],[20,12012,8753],{"className":12013,"style":8752},[41,76],[20,12015,12017],{"className":12016},[81],[20,12018,12020],{"className":12019},[85],[20,12021,12023],{"className":12022},[90],[20,12024,12026],{"className":12025,"style":2613},[94],[20,12027,12028,12031],{"style":230},[20,12029],{"className":12030,"style":103},[102],[20,12032,12034],{"className":12033},[107,108,109,110],[20,12035,12037],{"className":12036},[41,110],"⊤",[20,12039,12041,12044],{"className":12040},[41],[20,12042,77],{"className":12043},[41,76],[20,12045,12047],{"className":12046},[81],[20,12048,12050,12070],{"className":12049},[85,86],[20,12051,12053,12067],{"className":12052},[90],[20,12054,12056],{"className":12055,"style":95},[94],[20,12057,12058,12061],{"style":98},[20,12059],{"className":12060,"style":103},[102],[20,12062,12064],{"className":12063},[107,108,109,110],[20,12065,114],{"className":12066},[41,76,110],[20,12068,119],{"className":12069},[118],[20,12071,12073],{"className":12072},[90],[20,12074,12076],{"className":12075,"style":126},[94],[20,12077],{},", since the\nlogistic tail ",[20,12080,12082],{"className":12081},[23],[20,12083,12085,12112,12169],{"className":12084,"ariaHidden":28},[27],[20,12086,12088,12091,12097,12100,12103,12106,12109],{"className":12087},[32],[20,12089],{"className":12090,"style":946},[36],[20,12092,12094],{"className":12093},[441],[20,12095,3148],{"className":12096,"style":3147},[41,2229],[20,12098,340],{"className":12099},[68],[20,12101,227],{"className":12102},[41],[20,12104],{"className":12105,"style":962},[48],[20,12107,2335],{"className":12108},[966],[20,12110],{"className":12111,"style":962},[48],[20,12113,12115,12119,12157,12160,12163,12166],{"className":12114},[32],[20,12116],{"className":12117,"style":12118},[36],"height:1.0213em;vertical-align:-0.25em;",[20,12120,12122,12125],{"className":12121},[41],[20,12123,3465],{"className":12124},[41,76],[20,12126,12128],{"className":12127},[81],[20,12129,12131],{"className":12130},[85],[20,12132,12134],{"className":12133},[90],[20,12135,12138],{"className":12136,"style":12137},[94],"height:0.7713em;",[20,12139,12140,12143],{"style":230},[20,12141],{"className":12142,"style":103},[102],[20,12144,12146],{"className":12145},[107,108,109,110],[20,12147,12149,12152],{"className":12148},[41,110],[20,12150,967],{"className":12151},[41,110],[20,12153,12156],{"className":12154,"style":12155},[41,76,110],"margin-right:0.044em;","z",[20,12158,185],{"className":12159},[184],[20,12161],{"className":12162,"style":49},[48],[20,12164,9226],{"className":12165},[53],[20,12167],{"className":12168,"style":49},[48],[20,12170,12172,12175],{"className":12171},[32],[20,12173],{"className":12174,"style":12137},[36],[20,12176,12178,12181],{"className":12177},[41],[20,12179,3465],{"className":12180},[41,76],[20,12182,12184],{"className":12183},[81],[20,12185,12187],{"className":12186},[85],[20,12188,12190],{"className":12189},[90],[20,12191,12193],{"className":12192,"style":12137},[94],[20,12194,12195,12198],{"style":230},[20,12196],{"className":12197,"style":103},[102],[20,12199,12201],{"className":12200},[107,108,109,110],[20,12202,12204,12207],{"className":12203},[41,110],[20,12205,967],{"className":12206},[41,110],[20,12208,12156],{"className":12209,"style":12155},[41,76,110]," decays fastest for the largest ",[20,12212,12214],{"className":12213},[23],[20,12215,12217],{"className":12216,"ariaHidden":28},[27],[20,12218,12220,12223],{"className":12219},[32],[20,12221],{"className":12222,"style":1147},[36],[20,12224,12156],{"className":12225,"style":12155},[41,76],".\nMinimizing therefore pours all reduction into raising the worst-case margin, i.e.\nmaximizing ",[20,12228,12230],{"className":12229},[23],[20,12231,12233],{"className":12232,"ariaHidden":28},[27],[20,12234,12236,12240,12285,12288,12328,12331,12334,12337,12340,12343,12346,12349,12378],{"className":12235},[32],[20,12237],{"className":12238,"style":12239},[36],"height:1.0991em;vertical-align:-0.25em;",[20,12241,12243,12250],{"className":12242},[441],[20,12244,12246],{"className":12245},[441],[20,12247,12249],{"className":12248},[41,2229],"min",[20,12251,12253],{"className":12252},[81],[20,12254,12256,12277],{"className":12255},[85,86],[20,12257,12259,12274],{"className":12258},[90],[20,12260,12262],{"className":12261,"style":95},[94],[20,12263,12265,12268],{"style":12264},"top:-2.55em;margin-right:0.05em;",[20,12266],{"className":12267,"style":103},[102],[20,12269,12271],{"className":12270},[107,108,109,110],[20,12272,114],{"className":12273},[41,76,110],[20,12275,119],{"className":12276},[118],[20,12278,12280],{"className":12279},[90],[20,12281,12283],{"className":12282,"style":126},[94],[20,12284],{},[20,12286],{"className":12287,"style":137},[48],[20,12289,12291,12294],{"className":12290},[41],[20,12292,145],{"className":12293,"style":144},[41,76],[20,12295,12297],{"className":12296},[81],[20,12298,12300,12320],{"className":12299},[85,86],[20,12301,12303,12317],{"className":12302},[90],[20,12304,12306],{"className":12305,"style":95},[94],[20,12307,12308,12311],{"style":160},[20,12309],{"className":12310,"style":103},[102],[20,12312,12314],{"className":12313},[107,108,109,110],[20,12315,114],{"className":12316},[41,76,110],[20,12318,119],{"className":12319},[118],[20,12321,12323],{"className":12322},[90],[20,12324,12326],{"className":12325,"style":126},[94],[20,12327],{},[20,12329],{"className":12330,"style":137},[48],[20,12332,340],{"className":12333},[68],[20,12335,8753],{"className":12336,"style":8752},[41,76],[20,12338,3158],{"className":12339},[41],[20,12341,8748],{"className":12342},[68],[20,12344,8753],{"className":12345,"style":8752},[41,76],[20,12347,8748],{"className":12348},[184],[20,12350,12352,12355],{"className":12351},[184],[20,12353,185],{"className":12354},[184],[20,12356,12358],{"className":12357},[81],[20,12359,12361],{"className":12360},[85],[20,12362,12364],{"className":12363},[90],[20,12365,12367],{"className":12366,"style":2613},[94],[20,12368,12369,12372],{"style":230},[20,12370],{"className":12371,"style":103},[102],[20,12373,12375],{"className":12374},[107,108,109,110],[20,12376,12037],{"className":12377},[41,110],[20,12379,12381,12384],{"className":12380},[41],[20,12382,77],{"className":12383},[41,76],[20,12385,12387],{"className":12386},[81],[20,12388,12390,12410],{"className":12389},[85,86],[20,12391,12393,12407],{"className":12392},[90],[20,12394,12396],{"className":12395,"style":95},[94],[20,12397,12398,12401],{"style":98},[20,12399],{"className":12400,"style":103},[102],[20,12402,12404],{"className":12403},[107,108,109,110],[20,12405,114],{"className":12406},[41,76,110],[20,12408,119],{"className":12409},[118],[20,12411,12413],{"className":12412},[90],[20,12414,12416],{"className":12415,"style":126},[94],[20,12417],{},". The normalized iterate\nconverges to the maximizer of that minimum margin. ",[20,12420,12422],{"className":12421},[23],[20,12423,12425],{"className":12424,"ariaHidden":28},[27],[20,12426,12428,12432],{"className":12427},[32],[20,12429],{"className":12430,"style":12431},[36],"height:0.675em;",[20,12433,12437],{"className":12434},[12435,12436],"enclosing","qed",[20,12438,12440],{"className":12439},[41,4071],"□",[11,12442,12443,12444,12838,12839,12854,12855,12994,12995,13072,13073,13088,13089,13245,13246,13262,13263,13315],{},"To see the tail argument in coordinates, write the gradient of the logistic loss,\n",[20,12445,12447],{"className":12446},[23],[20,12448,12450,12509],{"className":12449,"ariaHidden":28},[27],[20,12451,12453,12456,12460,12491,12494,12497,12500,12503,12506],{"className":12452},[32],[20,12454],{"className":12455,"style":295},[36],[20,12457,12459],{"className":12458},[41],"∇",[20,12461,12463],{"className":12462},[41,299],[20,12464,12466],{"className":12465},[85],[20,12467,12469],{"className":12468},[90],[20,12470,12472,12480],{"className":12471,"style":309},[94],[20,12473,12474,12477],{"style":312},[20,12475],{"className":12476,"style":316},[102],[20,12478,321],{"className":12479,"style":320},[41,76],[20,12481,12482,12485],{"style":324},[20,12483],{"className":12484,"style":316},[102],[20,12486,12488],{"className":12487,"style":332},[331],[20,12489,336],{"className":12490},[41],[20,12492,340],{"className":12493},[68],[20,12495,8753],{"className":12496,"style":8752},[41,76],[20,12498,185],{"className":12499},[184],[20,12501],{"className":12502,"style":49},[48],[20,12504,54],{"className":12505},[53],[20,12507],{"className":12508,"style":49},[48],[20,12510,12512,12516,12519,12587,12590,12631,12634,12674,12714,12717,12720,12723,12726,12766,12795,12835],{"className":12511},[32],[20,12513],{"className":12514,"style":12515},[36],"height:1.1941em;vertical-align:-0.345em;",[20,12517,967],{"className":12518},[41],[20,12520,12522,12525,12584],{"className":12521},[41],[20,12523],{"className":12524},[68,370],[20,12526,12528],{"className":12527},[374],[20,12529,12531,12576],{"className":12530},[85,86],[20,12532,12534,12573],{"className":12533},[90],[20,12535,12537,12551,12559],{"className":12536,"style":7566},[94],[20,12538,12539,12542],{"style":4394},[20,12540],{"className":12541,"style":316},[102],[20,12543,12545],{"className":12544},[107,108,109,110],[20,12546,12548],{"className":12547},[41,110],[20,12549,240],{"className":12550},[41,76,110],[20,12552,12553,12556],{"style":399},[20,12554],{"className":12555,"style":316},[102],[20,12557],{"className":12558,"style":407},[406],[20,12560,12561,12564],{"style":4417},[20,12562],{"className":12563,"style":316},[102],[20,12565,12567],{"className":12566},[107,108,109,110],[20,12568,12570],{"className":12569},[41,110],[20,12571,227],{"className":12572},[41,110],[20,12574,119],{"className":12575},[118],[20,12577,12579],{"className":12578},[90],[20,12580,12582],{"className":12581,"style":4439},[94],[20,12583],{},[20,12585],{"className":12586},[184,370],[20,12588],{"className":12589,"style":137},[48],[20,12591,12593,12596],{"className":12592},[441],[20,12594,488],{"className":12595,"style":7624},[441,486,7623],[20,12597,12599],{"className":12598},[81],[20,12600,12602,12623],{"className":12601},[85,86],[20,12603,12605,12620],{"className":12604},[90],[20,12606,12609],{"className":12607,"style":12608},[94],"height:0.162em;",[20,12610,12611,12614],{"style":11275},[20,12612],{"className":12613,"style":103},[102],[20,12615,12617],{"className":12616},[107,108,109,110],[20,12618,114],{"className":12619},[41,76,110],[20,12621,119],{"className":12622},[118],[20,12624,12626],{"className":12625},[90],[20,12627,12629],{"className":12628,"style":11294},[94],[20,12630],{},[20,12632],{"className":12633,"style":137},[48],[20,12635,12637,12640],{"className":12636},[41],[20,12638,145],{"className":12639,"style":144},[41,76],[20,12641,12643],{"className":12642},[81],[20,12644,12646,12666],{"className":12645},[85,86],[20,12647,12649,12663],{"className":12648},[90],[20,12650,12652],{"className":12651,"style":95},[94],[20,12653,12654,12657],{"style":160},[20,12655],{"className":12656,"style":103},[102],[20,12658,12660],{"className":12659},[107,108,109,110],[20,12661,114],{"className":12662},[41,76,110],[20,12664,119],{"className":12665},[118],[20,12667,12669],{"className":12668},[90],[20,12670,12672],{"className":12671,"style":126},[94],[20,12673],{},[20,12675,12677,12680],{"className":12676},[41],[20,12678,77],{"className":12679},[41,76],[20,12681,12683],{"className":12682},[81],[20,12684,12686,12706],{"className":12685},[85,86],[20,12687,12689,12703],{"className":12688},[90],[20,12690,12692],{"className":12691,"style":95},[94],[20,12693,12694,12697],{"style":98},[20,12695],{"className":12696,"style":103},[102],[20,12698,12700],{"className":12699},[107,108,109,110],[20,12701,114],{"className":12702},[41,76,110],[20,12704,119],{"className":12705},[118],[20,12707,12709],{"className":12708},[90],[20,12710,12712],{"className":12711,"style":126},[94],[20,12713],{},[20,12715],{"className":12716,"style":137},[48],[20,12718,5017],{"className":12719,"style":144},[41,76],[20,12721,340],{"className":12722},[68],[20,12724,967],{"className":12725},[41],[20,12727,12729,12732],{"className":12728},[41],[20,12730,145],{"className":12731,"style":144},[41,76],[20,12733,12735],{"className":12734},[81],[20,12736,12738,12758],{"className":12737},[85,86],[20,12739,12741,12755],{"className":12740},[90],[20,12742,12744],{"className":12743,"style":95},[94],[20,12745,12746,12749],{"style":160},[20,12747],{"className":12748,"style":103},[102],[20,12750,12752],{"className":12751},[107,108,109,110],[20,12753,114],{"className":12754},[41,76,110],[20,12756,119],{"className":12757},[118],[20,12759,12761],{"className":12760},[90],[20,12762,12764],{"className":12763,"style":126},[94],[20,12765],{},[20,12767,12769,12772],{"className":12768},[41],[20,12770,8753],{"className":12771,"style":8752},[41,76],[20,12773,12775],{"className":12774},[81],[20,12776,12778],{"className":12777},[85],[20,12779,12781],{"className":12780},[90],[20,12782,12784],{"className":12783,"style":2613},[94],[20,12785,12786,12789],{"style":230},[20,12787],{"className":12788,"style":103},[102],[20,12790,12792],{"className":12791},[107,108,109,110],[20,12793,12037],{"className":12794},[41,110],[20,12796,12798,12801],{"className":12797},[41],[20,12799,77],{"className":12800},[41,76],[20,12802,12804],{"className":12803},[81],[20,12805,12807,12827],{"className":12806},[85,86],[20,12808,12810,12824],{"className":12809},[90],[20,12811,12813],{"className":12812,"style":95},[94],[20,12814,12815,12818],{"style":98},[20,12816],{"className":12817,"style":103},[102],[20,12819,12821],{"className":12820},[107,108,109,110],[20,12822,114],{"className":12823},[41,76,110],[20,12825,119],{"className":12826},[118],[20,12828,12830],{"className":12829},[90],[20,12831,12833],{"className":12832,"style":126},[94],[20,12834],{},[20,12836,185],{"className":12837},[184]," with ",[20,12840,12842],{"className":12841},[23],[20,12843,12845],{"className":12844,"ariaHidden":28},[27],[20,12846,12848,12851],{"className":12847},[32],[20,12849],{"className":12850,"style":1147},[36],[20,12852,5017],{"className":12853,"style":144},[41,76],"\nthe logistic sigmoid. Once the data is separated, every ",[20,12856,12858],{"className":12857},[23],[20,12859,12861,12985],{"className":12860,"ariaHidden":28},[27],[20,12862,12864,12867,12907,12936,12976,12979,12982],{"className":12863},[32],[20,12865],{"className":12866,"style":11964},[36],[20,12868,12870,12873],{"className":12869},[41],[20,12871,145],{"className":12872,"style":144},[41,76],[20,12874,12876],{"className":12875},[81],[20,12877,12879,12899],{"className":12878},[85,86],[20,12880,12882,12896],{"className":12881},[90],[20,12883,12885],{"className":12884,"style":95},[94],[20,12886,12887,12890],{"style":160},[20,12888],{"className":12889,"style":103},[102],[20,12891,12893],{"className":12892},[107,108,109,110],[20,12894,114],{"className":12895},[41,76,110],[20,12897,119],{"className":12898},[118],[20,12900,12902],{"className":12901},[90],[20,12903,12905],{"className":12904,"style":126},[94],[20,12906],{},[20,12908,12910,12913],{"className":12909},[41],[20,12911,8753],{"className":12912,"style":8752},[41,76],[20,12914,12916],{"className":12915},[81],[20,12917,12919],{"className":12918},[85],[20,12920,12922],{"className":12921},[90],[20,12923,12925],{"className":12924,"style":2613},[94],[20,12926,12927,12930],{"style":230},[20,12928],{"className":12929,"style":103},[102],[20,12931,12933],{"className":12932},[107,108,109,110],[20,12934,12037],{"className":12935},[41,110],[20,12937,12939,12942],{"className":12938},[41],[20,12940,77],{"className":12941},[41,76],[20,12943,12945],{"className":12944},[81],[20,12946,12948,12968],{"className":12947},[85,86],[20,12949,12951,12965],{"className":12950},[90],[20,12952,12954],{"className":12953,"style":95},[94],[20,12955,12956,12959],{"style":98},[20,12957],{"className":12958,"style":103},[102],[20,12960,12962],{"className":12961},[107,108,109,110],[20,12963,114],{"className":12964},[41,76,110],[20,12966,119],{"className":12967},[118],[20,12969,12971],{"className":12970},[90],[20,12972,12974],{"className":12973,"style":126},[94],[20,12975],{},[20,12977],{"className":12978,"style":49},[48],[20,12980,3426],{"className":12981},[53],[20,12983],{"className":12984,"style":49},[48],[20,12986,12988,12991],{"className":12987},[32],[20,12989],{"className":12990,"style":2313},[36],[20,12992,2824],{"className":12993},[41],", and\n",[20,12996,12998],{"className":12997},[23],[20,12999,13001,13031],{"className":13000,"ariaHidden":28},[27],[20,13002,13004,13007,13010,13013,13016,13019,13022,13025,13028],{"className":13003},[32],[20,13005],{"className":13006,"style":946},[36],[20,13008,5017],{"className":13009,"style":144},[41,76],[20,13011,340],{"className":13012},[68],[20,13014,967],{"className":13015},[41],[20,13017,12156],{"className":13018,"style":12155},[41,76],[20,13020,185],{"className":13021},[184],[20,13023],{"className":13024,"style":49},[48],[20,13026,9226],{"className":13027},[53],[20,13029],{"className":13030,"style":49},[48],[20,13032,13034,13037],{"className":13033},[32],[20,13035],{"className":13036,"style":12137},[36],[20,13038,13040,13043],{"className":13039},[41],[20,13041,3465],{"className":13042},[41,76],[20,13044,13046],{"className":13045},[81],[20,13047,13049],{"className":13048},[85],[20,13050,13052],{"className":13051},[90],[20,13053,13055],{"className":13054,"style":12137},[94],[20,13056,13057,13060],{"style":230},[20,13058],{"className":13059,"style":103},[102],[20,13061,13063],{"className":13062},[107,108,109,110],[20,13064,13066,13069],{"className":13065},[41,110],[20,13067,967],{"className":13068},[41,110],[20,13070,12156],{"className":13071,"style":12155},[41,76,110]," for large ",[20,13074,13076],{"className":13075},[23],[20,13077,13079],{"className":13078,"ariaHidden":28},[27],[20,13080,13082,13085],{"className":13081},[32],[20,13083],{"className":13084,"style":1147},[36],[20,13086,12156],{"className":13087,"style":12155},[41,76],", so the weight ",[20,13090,13092],{"className":13091},[23],[20,13093,13095],{"className":13094,"ariaHidden":28},[27],[20,13096,13098,13102],{"className":13097},[32],[20,13099],{"className":13100,"style":13101},[36],"height:1.0119em;",[20,13103,13105,13108],{"className":13104},[41],[20,13106,3465],{"className":13107},[41,76],[20,13109,13111],{"className":13110},[81],[20,13112,13114],{"className":13113},[85],[20,13115,13117],{"className":13116},[90],[20,13118,13120],{"className":13119,"style":13101},[94],[20,13121,13122,13125],{"style":230},[20,13123],{"className":13124,"style":103},[102],[20,13126,13128],{"className":13127},[107,108,109,110],[20,13129,13131,13134,13174,13204],{"className":13130},[41,110],[20,13132,967],{"className":13133},[41,110],[20,13135,13137,13140],{"className":13136},[41,110],[20,13138,145],{"className":13139,"style":144},[41,76,110],[20,13141,13143],{"className":13142},[81],[20,13144,13146,13166],{"className":13145},[85,86],[20,13147,13149,13163],{"className":13148},[90],[20,13150,13152],{"className":13151,"style":10454},[94],[20,13153,13154,13157],{"style":10457},[20,13155],{"className":13156,"style":3523},[102],[20,13158,13160],{"className":13159},[107,3527,3528,110],[20,13161,114],{"className":13162},[41,76,110],[20,13164,119],{"className":13165},[118],[20,13167,13169],{"className":13168},[90],[20,13170,13172],{"className":13171,"style":10476},[94],[20,13173],{},[20,13175,13177,13180],{"className":13176},[41,110],[20,13178,8753],{"className":13179,"style":8752},[41,76,110],[20,13181,13183],{"className":13182},[81],[20,13184,13186],{"className":13185},[85],[20,13187,13189],{"className":13188},[90],[20,13190,13193],{"className":13191,"style":13192},[94],"height:0.927em;",[20,13194,13195,13198],{"style":3519},[20,13196],{"className":13197,"style":3523},[102],[20,13199,13201],{"className":13200},[107,3527,3528,110],[20,13202,12037],{"className":13203},[41,110],[20,13205,13207,13210],{"className":13206},[41,110],[20,13208,77],{"className":13209},[41,76,110],[20,13211,13213],{"className":13212},[81],[20,13214,13216,13237],{"className":13215},[85,86],[20,13217,13219,13234],{"className":13218},[90],[20,13220,13222],{"className":13221,"style":10454},[94],[20,13223,13225,13228],{"style":13224},"top:-2.357em;margin-left:0em;margin-right:0.0714em;",[20,13226],{"className":13227,"style":3523},[102],[20,13229,13231],{"className":13230},[107,3527,3528,110],[20,13232,114],{"className":13233},[41,76,110],[20,13235,119],{"className":13236},[118],[20,13238,13240],{"className":13239},[90],[20,13241,13243],{"className":13242,"style":10476},[94],[20,13244],{}," on\nexample ",[20,13247,13249],{"className":13248},[23],[20,13250,13252],{"className":13251,"ariaHidden":28},[27],[20,13253,13255,13259],{"className":13254},[32],[20,13256],{"className":13257,"style":13258},[36],"height:0.6595em;",[20,13260,114],{"className":13261},[41,76]," is exponentially smaller for well-separated points than for the\nworst-margin point. The gradient is therefore, up to a vanishing remainder, a\ncombination of the few smallest-margin examples — the support vectors — and ",[20,13264,13266],{"className":13265},[23],[20,13267,13269],{"className":13268,"ariaHidden":28},[27],[20,13270,13272,13275],{"className":13271},[32],[20,13273],{"className":13274,"style":8045},[36],[20,13276,13278,13281],{"className":13277},[41],[20,13279,8753],{"className":13280,"style":8752},[41,76],[20,13282,13284],{"className":13283},[81],[20,13285,13287,13307],{"className":13286},[85,86],[20,13288,13290,13304],{"className":13289},[90],[20,13291,13293],{"className":13292,"style":11690},[94],[20,13294,13295,13298],{"style":11693},[20,13296],{"className":13297,"style":103},[102],[20,13299,13301],{"className":13300},[107,108,109,110],[20,13302,11703],{"className":13303},[41,76,110],[20,13305,119],{"className":13306},[118],[20,13308,13310],{"className":13309},[90],[20,13311,13313],{"className":13312,"style":126},[94],[20,13314],{},"\ngrows in exactly the direction that pushes those margins up. The endpoint is the\nmax-margin direction, reached with no penalty term.",[11,13317,13318,13319,13323,13324,13327],{},"For deep networks no such clean theorem exists, but the same flavor of bias is\nobserved empirically and connects directly to the geometry of the loss surface from\n",[1861,13320,13322],{"href":13321},"\u002Fdeep-learning\u002Foptimization\u002Fthe-optimization-landscape","the optimization landscape",":\nSGD prefers ",[15,13325,13326],{},"flat minima",", regions where the loss is insensitive to small\nparameter perturbations, over sharp ones. Flatness is a stability property, and\nstability bounds generalization: a function that barely changes when the parameters\nwobble also barely changes when the training set is resampled.",[11,13329,13330],{},"The minibatch noise supplies the mechanism. An SGD update is the full gradient plus\na zero-mean stochastic term whose covariance scales with the local loss curvature.\nIn a sharp basin that noise kicks the iterate up the steep walls and out; in a wide\nflat basin the same noise is absorbed. The stationary distribution of the SGD\niterate therefore concentrates in flat regions — an entropic pull toward flatness\nthat is stronger the larger the learning-rate-to-batch-size ratio.",[2645,13332],{"hash":13333},"78f7e7a520af78447d08c56ab796f14e534aa3e658138bd83345d2974b19ea9a",[11,13335,13336,13337,13340,13341,13344,13345,1725,13348],{},"Optimization and generalization are therefore ",[15,13338,13339],{},"entangled"," in deep learning in a\nway they are not in convex learning. For a convex objective every minimizer is\nequally good and the optimizer is just a means to reach the unique answer; for an\noverparameterized network the optimizer ",[1305,13342,13343],{},"selects"," which of many zero-loss answers\nyou get, so the choice of algorithm is itself a regularizer. There is no clean\nseparation between ",[3258,13346,13347],{},"how we optimize",[3258,13349,13350],{},"how well we generalize.",[1829,13352,13354],{"id":13353},"the-interpolation-regime","The interpolation regime",[11,13356,13357,13358,13361],{},"The practical upshot inverts the classical rule: zero\ntraining error is not, by itself, a sign of overfitting. In the\noverparameterized regime, pushing the model past the interpolation threshold and\n",[1305,13359,13360],{},"continuing"," to grow it keeps improving test error even though training error has\nalready bottomed out at zero.",[2645,13363],{"hash":13364},"fa8cdea36b497c464e5a9f76ca2e967b6fa540d45d49c5e403a80f08ca52e760",[11,13366,13367,13368,13372,13373,13377,13378,13382,13383,13390,13391,13394],{},"This is why the dominant practical recipe is to make models as large as compute\nallows and lean on ",[1861,13369,13371],{"href":13370},"\u002Fdeep-learning\u002Fregularization\u002Fregularization-overview","regularization",",\nexplicit (weight decay, ",[1861,13374,13376],{"href":13375},"\u002Fdeep-learning\u002Fregularization\u002Fdropout-and-data-augmentation","dropout",")\nand implicit (SGD, ",[1861,13379,13381],{"href":13380},"\u002Fdeep-learning\u002Fregularization\u002Fearly-stopping-and-parameter-sharing","early stopping","),\nto steer the interpolating solution toward the well-behaved corner, rather than\nto shrink the model to fit a capacity budget.",[1858,13384,13385],{},[1861,13386,9324],{"href":13387,"ariaDescribedBy":13388,"dataFootnoteRef":6,"id":13389},"#user-content-fn-chollet-overfit",[1865],"user-content-fnref-chollet-overfit"," The regularizers do\nnot reduce capacity; they bias the search ",[1305,13392,13393],{},"within"," the huge class toward the\nlow-norm, large-margin, flat solutions the bounds above favor.",[1019,13396,13398],{"type":13397},"note",[11,13399,13400,13403,13404,13407],{},[15,13401,13402],{},"Takeaway."," In the overparameterized regime, zero training error is not\noverfitting — it is interpolation, and the test error is set by ",[1305,13405,13406],{},"which","\ninterpolating solution the optimizer lands on. Grow the model, then bias the\nsearch (SGD, weight decay, early stopping) toward low norm and large margin;\ndo not shrink the model to fit a VC budget.",[1829,13409,13411],{"id":13410},"classical-versus-modern","Classical versus modern",[11,13413,13414],{},"The two views disagree on every major question. The classical column\nis Goodfellow §5.2; the modern column post-dates Goodfellow (2016).",[13416,13417,13418,13434],"table",{},[13419,13420,13421],"thead",{},[13422,13423,13424,13428,13431],"tr",{},[13425,13426,13427],"th",{},"Question",[13425,13429,13430],{},"Classical view",[13425,13432,13433],{},"Modern (overparameterized) view",[13435,13436,13437,13508,13523,13534,13545,13559,13662],"tbody",{},[13422,13438,13439,13443,13505],{},[13440,13441,13442],"td",{},"What controls the gap?",[13440,13444,13445,13446,13504],{},"hypothesis-class capacity (",[20,13447,13449],{"className":13448},[23],[20,13450,13452],{"className":13451,"ariaHidden":28},[27],[20,13453,13455,13458],{"className":13454},[32],[20,13456],{"className":13457,"style":2397},[36],[20,13459,13461,13464],{"className":13460},[41],[20,13462,2199],{"className":13463},[41,76],[20,13465,13467],{"className":13466},[81],[20,13468,13470,13496],{"className":13469},[85,86],[20,13471,13473,13493],{"className":13472},[90],[20,13474,13476],{"className":13475,"style":1355},[94],[20,13477,13478,13481],{"style":98},[20,13479],{"className":13480,"style":103},[102],[20,13482,13484],{"className":13483},[107,108,109,110],[20,13485,13487],{"className":13486},[41,110],[20,13488,13490],{"className":13489},[41,110],[20,13491,2230],{"className":13492},[41,2229,110],[20,13494,119],{"className":13495},[118],[20,13497,13499],{"className":13498},[90],[20,13500,13502],{"className":13501,"style":126},[94],[20,13503],{},", Rademacher)",[13440,13506,13507],{},"implicit bias of the optimizer (norm, margin, flatness)",[13422,13509,13510,13513,13516],{},[13440,13511,13512],{},"Effect of more parameters than data",[13440,13514,13515],{},"catastrophic overfitting",[13440,13517,13518,13519,13522],{},"often ",[1305,13520,13521],{},"improves"," test error (second descent)",[13422,13524,13525,13528,13531],{},[13440,13526,13527],{},"Test error vs. model size",[13440,13529,13530],{},"U-shaped; stop at the sweet spot",[13440,13532,13533],{},"double descent; grow past the threshold",[13422,13535,13536,13539,13542],{},[13440,13537,13538],{},"Zero training error means",[13440,13540,13541],{},"overfitting (a red flag)",[13440,13543,13544],{},"interpolation (routine, often optimal)",[13422,13546,13547,13550,13553],{},[13440,13548,13549],{},"Role of the optimizer",[13440,13551,13552],{},"a neutral means to a unique minimum",[13440,13554,13555,13556,13558],{},"a regularizer that ",[1305,13557,13343],{}," among many minima",[13422,13560,13561,13564,13567],{},[13440,13562,13563],{},"Are the bounds tight?",[13440,13565,13566],{},"yes, for linear \u002F kernel \u002F tree classes",[13440,13568,13569,13570,185],{},"vacuous for deep nets (",[20,13571,13573],{"className":13572},[23],[20,13574,13576,13644],{"className":13575,"ariaHidden":28},[27],[20,13577,13579,13582,13635,13638,13641],{"className":13578},[32],[20,13580],{"className":13581,"style":3827},[36],[20,13583,13585],{"className":13584},[41,3062],[20,13586,13588,13627],{"className":13587},[85,86],[20,13589,13591,13624],{"className":13590},[90],[20,13592,13594,13612],{"className":13593,"style":3840},[94],[20,13595,13597,13600],{"className":13596,"style":3844},[3076],[20,13598],{"className":13599,"style":3848},[102],[20,13601,13603,13606,13609],{"className":13602,"style":3085},[41],[20,13604,2753],{"className":13605,"style":2752},[41,76],[20,13607,3158],{"className":13608},[41],[20,13610,240],{"className":13611},[41,76],[20,13613,13614,13617],{"style":3903},[20,13615],{"className":13616,"style":3848},[102],[20,13618,13620],{"className":13619,"style":3910},[3216],[3219,13621,13622],{"xmlns":3221,"width":3222,"height":3913,"viewBox":3914,"preserveAspectRatio":3225},[3227,13623],{"d":3917},[20,13625,119],{"className":13626},[118],[20,13628,13630],{"className":13629},[90],[20,13631,13633],{"className":13632,"style":3927},[94],[20,13634],{},[20,13636],{"className":13637,"style":49},[48],[20,13639,54],{"className":13640},[53],[20,13642],{"className":13643,"style":49},[48],[20,13645,13647,13650,13653,13656,13659],{"className":13646},[32],[20,13648],{"className":13649,"style":946},[36],[20,13651,9954],{"className":13652},[41],[20,13654,340],{"className":13655},[68],[20,13657,227],{"className":13658},[41],[20,13660,185],{"className":13661},[184],[13422,13663,13664,13667,13670],{},[13440,13665,13666],{},"How to control overfitting",[13440,13668,13669],{},"restrict the class (fewer parameters)",[13440,13671,13672],{},"keep the class huge; bias the search (SGD, weight decay)",[1829,13674,13676],{"id":13675},"the-papers-behind-the-puzzle","The papers behind the puzzle",[11,13678,13679,13680,13685,13686,13688],{},"Goodfellow §5.2 presents capacity control as the operative theory; every result in\nthis lesson that contradicts it comes from the public literature that appeared after\nthe 2016 text, and the citations are worth naming. The empirical trigger was\n",[15,13681,13682],{},[3258,13683,13684],{},"Understanding deep learning requires rethinking generalization"," (Zhang et al.,\n2017), the random-label experiment above: the demonstration that standard networks\nmemorize pure noise to zero training error, which is what rendered the VC and\nRademacher bounds vacuous in the regime that matters. ",[15,13687,10138],{}," was named\nand generalized by Belkin et al. (2019) and mapped across model size, data size, and\ntraining time by Nakkiran et al. (2020), establishing that the classical U-curve is\nonly the left half of the picture.",[11,13690,13691,13692,13695,13696,13800,13801,13804,13805,13807,13808,13811],{},"The constructive replies came in two families. ",[15,13693,13694],{},"Margin- and norm-based bounds","\n(Bartlett, Foster & Telgarsky, 2017) replaced the parameter count with the product\nof spectral norms over the margin — the ",[20,13697,13699],{"className":13698},[23],[20,13700,13702],{"className":13701,"ariaHidden":28},[27],[20,13703,13705,13708,13748,13751,13791,13794,13797],{"className":13704},[32],[20,13706],{"className":13707,"style":11519},[36],[20,13709,13711,13714],{"className":13710},[441],[20,13712,11259],{"className":13713,"style":7624},[441,486,7623],[20,13715,13717],{"className":13716},[81],[20,13718,13720,13740],{"className":13719},[85,86],[20,13721,13723,13737],{"className":13722},[90],[20,13724,13726],{"className":13725,"style":11272},[94],[20,13727,13728,13731],{"style":11275},[20,13729],{"className":13730,"style":103},[102],[20,13732,13734],{"className":13733},[107,108,109,110],[20,13735,519],{"className":13736},[41,110],[20,13738,119],{"className":13739},[118],[20,13741,13743],{"className":13742},[90],[20,13744,13746],{"className":13745,"style":11294},[94],[20,13747],{},[20,13749,8748],{"className":13750},[68],[20,13752,13754,13757],{"className":13753},[41],[20,13755,10831],{"className":13756,"style":10830},[41,76],[20,13758,13760],{"className":13759},[81],[20,13761,13763,13783],{"className":13762},[85,86],[20,13764,13766,13780],{"className":13765},[90],[20,13767,13769],{"className":13768,"style":552},[94],[20,13770,13771,13774],{"style":10846},[20,13772],{"className":13773,"style":103},[102],[20,13775,13777],{"className":13776},[107,108,109,110],[20,13778,519],{"className":13779},[41,110],[20,13781,119],{"className":13782},[118],[20,13784,13786],{"className":13785},[90],[20,13787,13789],{"className":13788,"style":126},[94],[20,13790],{},[20,13792,8748],{"className":13793},[184],[20,13795,3158],{"className":13796},[41],[20,13798,10355],{"className":13799,"style":10354},[41,76]," quantity\nabove — giving a complexity measure that stays finite for interpolating networks and\ntracks the second descent. ",[15,13802,13803],{},"Implicit bias"," was characterized exactly for the\nseparable-logistic case by Soudry et al. (2018), whose result that gradient descent\nconverges in direction to the max-margin separator is the theorem this lesson\nsketched, and connected to ",[15,13806,13326],{}," and generalization through the stability\nlens (Keskar et al., 2017, on the sharp-minima cost of large-batch training). No\nsingle paper yet closes the loop into a predictive, algorithm-dependent theory, but\ntogether these mark the shift from ",[3258,13809,13810],{},"count the functions the class contains"," to",[3258,13813,13814],{},"characterize the function the optimizer returns.",[1829,13816,13818],{"id":13817},"what-the-theory-has-and-has-not-settled","What the theory has and has not settled",[11,13820,13821,13822,13825,13826,13829,13830,13833],{},"The classical bounds are valid, and tight for the families\nthey were derived for. What fails is their ",[1305,13823,13824],{},"relevance",": capacity is the wrong knob\nfor overparameterized networks because the optimizer, not the class, picks the\nfunction. A predictive theory of deep generalization is still open, but its shape is\nclear: it must be ",[15,13827,13828],{},"algorithm-dependent",", folding the trajectory of SGD and the\ngeometry of the minimum it finds into the bound, not just counting the functions the\narchitecture ",[1305,13831,13832],{},"could"," in principle represent. The norm- and margin-based bounds are\nthe first honest step in that direction, because they measure the function the\noptimizer actually returned rather than the class it was drawn from.",[11,13835,13836,13837,13841,13842,13846,13847,13851,13852,13855,13856,13859],{},"The threads this opens are picked up across the rest of the module: the brittleness\nthat survives small test error in ",[1861,13838,13840],{"href":13839},"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-robustness","adversarial robustness",",\nthe use of the solution manifold's width to quantify uncertainty in\n",[1861,13843,13845],{"href":13844},"\u002Fdeep-learning\u002Ftheory\u002Fbayesian-and-ensemble-methods","Bayesian and ensemble methods",",\nand the limit of infinite implicit depth in\n",[1861,13848,13850],{"href":13849},"\u002Fdeep-learning\u002Ftheory\u002Fdeep-equilibrium-models","deep equilibrium models",". The\nsingle idea underneath all of them is the one this lesson isolated: in the\noverparameterized world, ",[1305,13853,13854],{},"how"," you search the hypothesis class matters more than\n",[1305,13857,13858],{},"how big"," it is.",[13861,13862,13865,13870],"section",{"className":13863,"dataFootnotes":6},[13864],"footnotes",[1829,13866,13869],{"className":13867,"id":1865},[13868],"sr-only","Footnotes",[13871,13872,13873,13891,13911,13923,13936],"ol",{},[13874,13875,13877,2889,13880,13883,13884],"li",{"id":13876},"user-content-fn-gf-capacity",[15,13878,13879],{},"Goodfellow",[1305,13881,13882],{},"Deep Learning",", §5.2 — Capacity, Overfitting and Underfitting: the representational capacity of a hypothesis class as the knob governing the train\u002Ftest gap. ",[1861,13885,13890],{"href":13886,"ariaLabel":13887,"className":13888,"dataFootnoteBackref":6},"#user-content-fnref-gf-capacity","Back to reference 1",[13889],"data-footnote-backref","↩",[13874,13892,13894,2889,13896,13898,13899,1635,13904],{"id":13893},"user-content-fn-gf-biasvar",[15,13895,13879],{},[1305,13897,13882],{},", §5.4 — Estimators, Bias and Variance: the U-shaped decomposition of error into bias and variance and the resulting capacity sweet spot. ",[1861,13900,13890],{"href":13901,"ariaLabel":13902,"className":13903,"dataFootnoteBackref":6},"#user-content-fnref-gf-biasvar","Back to reference 2",[13889],[1861,13905,13890,13909],{"href":13906,"ariaLabel":13907,"className":13908,"dataFootnoteBackref":6},"#user-content-fnref-gf-biasvar-2","Back to reference 2-2",[13889],[1858,13910,2103],{},[13874,13912,13914,13915,13917,13918],{"id":13913},"user-content-fn-postdate","The overparameterization phenomena here — fitting random labels, double descent, implicit bias toward max-margin and flat minima — postdate ",[15,13916,13879],{}," (2016), which still presents capacity control (§5.2) as the operative theory of generalization. ",[1861,13919,13890],{"href":13920,"ariaLabel":13921,"className":13922,"dataFootnoteBackref":6},"#user-content-fnref-postdate","Back to reference 3",[13889],[13874,13924,13926,2889,13928,13930,13931],{"id":13925},"user-content-fn-gf-reg",[15,13927,13879],{},[1305,13929,13882],{},", Ch. 7 — Regularization for Deep Learning: penalties and procedures that bias the learned function toward simpler solutions. ",[1861,13932,13890],{"href":13933,"ariaLabel":13934,"className":13935,"dataFootnoteBackref":6},"#user-content-fnref-gf-reg","Back to reference 4",[13889],[13874,13937,13939,2889,13942,13945,13946],{"id":13938},"user-content-fn-chollet-overfit",[15,13940,13941],{},"Chollet",[1305,13943,13944],{},"Deep Learning with Python",", Ch. 5 — Fundamentals of ML: the practitioner's recipe of large models plus regularization rather than capacity-matching. ",[1861,13947,13890],{"href":13948,"ariaLabel":13949,"className":13950,"dataFootnoteBackref":6},"#user-content-fnref-chollet-overfit","Back to reference 5",[13889],{"title":6,"searchDepth":13952,"depth":13952,"links":13953},2,[13954,13958,13959,13960,13961,13962,13963,13964,13965,13966],{"id":1831,"depth":13952,"text":1832,"children":13955},[13956],{"id":4841,"depth":13957,"text":4842},3,{"id":9110,"depth":13952,"text":9111},{"id":10137,"depth":13952,"text":10138},{"id":10243,"depth":13952,"text":10244},{"id":11618,"depth":13952,"text":11619},{"id":13353,"depth":13952,"text":13354},{"id":13410,"depth":13952,"text":13411},{"id":13675,"depth":13952,"text":13676},{"id":13817,"depth":13952,"text":13818},{"id":1865,"depth":13952,"text":13869},[],"computer-science","Everything else in this subject is machinery for driving the training loss\ndown. Generalization theory addresses the question that remains once the training\nloss is small: does a low loss on the sample D={(xi​,yi​)}i=1n​\nsay anything about loss on data we have not seen? Write the two quantities the\ntheory compares, the empirical risk on the sample and the population risk\nunder the true distribution P,",false,"md",{"moduleNumber":13973,"lessonNumber":13974,"order":13975},6,1,601,"Theory & Frontiers",true,"\u002Fdeep-learning\u002Ftheory\u002Fgeneralization-theory",[],"---\ntitle: Generalization Theory\nmodule: Theory & Frontiers\nmoduleNumber: 6\nlessonNumber: 1\norder: 601\nsummary: >\n  Classical learning theory bounds the gap between training and test error by a\n  model's capacity (VC dimension, Rademacher complexity), and predicts that a\n  model with more parameters than data should overfit catastrophically. Modern\n  networks do the opposite: they interpolate, even fit pure noise, and still\n  generalize. We derive the classical bounds, work the bias-variance\n  decomposition, show why the bounds go vacuous, and survey what replaced them:\n  double descent, the interpolation threshold, margin and norm-based bounds, and\n  the implicit bias of the optimizer itself.\ntopics: [Theory & Frontiers]\nsources:\n  - book: Goodfellow\n    ref: \"§5.2 — Capacity, Overfitting and Underfitting; §5.4.2 VC Dimension\"\n  - book: Goodfellow\n    ref: \"Ch. 7 — Regularization for Deep Learning\"\n  - book: Goodfellow\n    ref: \"§5.5 — Maximum Likelihood Estimation; §5.4 Estimators, Bias, Variance\"\n---\n\nEverything else in this subject is machinery for driving the **training** loss\ndown. Generalization theory addresses the question that remains once the training\nloss is small: does a low loss on the sample $\\mathcal{D} = \\{(x_i, y_i)\\}_{i=1}^n$\nsay anything about loss on data we have not seen? Write the two quantities the\ntheory compares, the **empirical risk** on the sample and the **population risk**\nunder the true distribution $\\mathcal{P}$,\n\n$$\n\\hat R(\\theta) = \\frac{1}{n}\\sum_{i=1}^n \\ell\\parens{f_\\theta(x_i), y_i},\n\\qquad\nR(\\theta) = \\mathbb{E}_{(x,y)\\sim\\mathcal{P}}\\brackets{\\ell(f_\\theta(x), y)}.\n$$\n\nTraining minimizes $\\hat R$; we care about $R$. The **generalization gap** is the\ndifference $R(\\theta) - \\hat R(\\theta)$, and the entire classical program is one\nmove: bound that gap by something we can compute or control.\n\n> **Definition (Generalization gap).** For a model $f_\\theta$ with empirical risk\n> $\\hat R(\\theta)$ on $n$ i.i.d. samples and population risk $R(\\theta)$, the\n> generalization gap is $R(\\theta) - \\hat R(\\theta)$. A learner generalizes when\n> this gap is small; it overfits when $\\hat R$ is small but the gap is large.\n\nThe empirical risk is an unbiased estimate of the population risk for any _fixed_\n$\\theta$ chosen before seeing the data: $\\mathbb{E}_{\\mathcal D}[\\hat R(\\theta)] =\nR(\\theta)$ by linearity, since each term $\\ell(f_\\theta(x_i),y_i)$ is an i.i.d.\ndraw with mean $R(\\theta)$. The difficulty is that training _chooses_ $\\hat\\theta$\nby looking at the sample, so $\\hat\\theta$ and $\\mathcal{D}$ are dependent and the\nunbiasedness breaks. The learner picks whichever function happened to fit the\nparticular noise in this sample, and $\\hat R(\\hat\\theta)$ is optimistically low.\nEvery bound below accounts for that dependence by controlling the gap\n_uniformly_ over the whole class the optimizer could have selected from, not just\nat one fixed point.\n\n## Classical theory: capacity controls the gap\n\nThe classical answer is that the gap is governed not by the single function the\noptimizer returns but by the **richness of the hypothesis class** $\\mathcal{H}$ it\nwas chosen from.[^gf-capacity] A learner that searches a tiny class cannot overfit\nmuch, because there are not enough functions to fit the noise; a learner with a\nvast class can fit anything, including the noise, and the noise does not transfer\nto test data.\n\n> **Definition (Capacity).** The capacity of a hypothesis class $\\mathcal{H}$ is a\n> measure of how many distinct labelings of a dataset its members can realize.\n> Higher capacity means $\\mathcal{H}$ can fit more intricate patterns, and more\n> noise. Goodfellow §5.2 frames the whole bias–variance story around it.\n\nThe sharpest combinatorial measure of capacity for binary classifiers is the\n**Vapnik–Chervonenkis dimension**: the size of the largest set of points the class\ncan label in _every_ possible way.\n\n> **Definition (Shattering, VC dimension).** A set $S = \\{x_1,\\dots,x_m\\}$ is\n> shattered by $\\mathcal{H}$ if for every one of the $2^m$ labelings of $S$ there\n> is some $h \\in \\mathcal{H}$ realizing it. The VC dimension $d_{\\mathrm{VC}}\n> (\\mathcal{H})$ is the cardinality of the largest shattered set (possibly\n> infinite).\n\nTake the concrete case of linear classifiers in the plane. Three points in general\nposition can be shattered: for each of the $2^3 = 8$ sign patterns there is a line\nputting the $+$ points on one side and the $-$ points on the other. Four points\ncannot: label two diagonal points $+$ and the other two $-$ (the XOR pattern) and\nno line separates them. So $d_{\\mathrm{VC}} = 3$ for lines in $\\mathbb{R}^2$, and\nin general $d_{\\mathrm{VC}} = d + 1$ for a linear classifier in $\\mathbb{R}^d$ with\na bias term.\n\n$$\n% caption: VC dimension of lines in the plane. Any of the $2^3=8$ labelings of three points is realized by some line, so three points are shattered; the XOR labeling of four points admits no separating line, so $d_{\\mathrm{VC}}=3$.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{grn}{HTML}{1F9D4D}\n  \\definecolor{rd}{HTML}{C0392B}\n  % ---- left panel: 3 points, one labeling, separated by a line ----\n  \\begin{scope}[shift={(0,0)}]\n    \\node[anchor=south, font=\\footnotesize] at (1.5,2.7) {\\texttt{3 points: shattered}};\n    \\draw[black] (0,0) rectangle (3,2.4);\n    % separating line\n    \\draw[acc, very thick] (0.3,2.1) -- (2.7,0.15);\n    % + points (green, white plus), - point (red, white minus)\n    \\fill[grn] (0.7,0.6) circle (5pt);\n    \\node[white, font=\\scriptsize] at (0.7,0.6) {+};\n    \\fill[grn] (1.35,0.5) circle (5pt);\n    \\node[white, font=\\scriptsize] at (1.35,0.5) {+};\n    \\fill[rd] (2.1,1.7) circle (5pt);\n    \\draw[white, line width=1pt] (1.94,1.7) -- (2.26,1.7);\n  \\end{scope}\n  % ---- right panel: 4 points, XOR, no separating line ----\n  \\begin{scope}[shift={(4.4,0)}]\n    \\node[anchor=south, font=\\footnotesize] at (1.5,2.7) {\\texttt{4 points: XOR}};\n    \\draw[black] (0,0) rectangle (3,2.4);\n    % corners\n    \\fill[grn] (0.5,0.5) circle (5pt);\n    \\node[white, font=\\scriptsize] at (0.5,0.5) {+};\n    \\fill[rd] (2.5,0.5) circle (5pt);\n    \\draw[white, line width=1pt] (2.34,0.5) -- (2.66,0.5);\n    \\fill[rd] (0.5,1.9) circle (5pt);\n    \\draw[white, line width=1pt] (0.34,1.9) -- (0.66,1.9);\n    \\fill[grn] (2.5,1.9) circle (5pt);\n    \\node[white, font=\\scriptsize] at (2.5,1.9) {+};\n    % a candidate line that fails\n    \\draw[acc, very thick, dashed] (0.15,1.3) -- (2.85,1.1);\n    \\node[acc, anchor=north, font=\\footnotesize] at (1.5,1.0) {\\texttt{no line works}};\n  \\end{scope}\n\\end{tikzpicture}\n$$\n\nCapacity for this family equals the parameter count, and that coincidence is\nthe intuition classical theory leans on. The central result turns VC dimension into\na uniform bound on the gap.\n\n> **Theorem (VC generalization bound).** Let $\\mathcal{H}$ have VC dimension\n> $d_{\\mathrm{VC}} = C$ and let $\\hat R, R$ be the empirical and population\n> $0\\text{–}1$ risk on $n$ i.i.d. samples. Then with probability at least\n> $1-\\delta$, _simultaneously for every_ $h \\in \\mathcal{H}$,\n> $$\n> R(h) \\;\\le\\; \\hat R(h) \\;+\\; O\\!\\parens{\\sqrt{\\frac{C\\,\\log(n\u002FC) + \\log(1\u002F\\delta)}{n}}}.\n> $$\n\nDeep networks break the mechanism behind the \"simultaneously for every $h$\"\nguarantee. If $\\mathcal{H}$ were a _finite_ set of $M$\nfunctions, each fixed $h$ has $\\Pr[R(h) - \\hat R(h) > \\varepsilon] \\le e^{-2n\n\\varepsilon^2}$ by Hoeffding's inequality (the empirical risk is an average of $n$\nbounded i.i.d. terms). A union bound over all $M$ functions gives $\\Pr[\\exists h :\nR(h) - \\hat R(h) > \\varepsilon] \\le M e^{-2n\\varepsilon^2}$; setting the right side\nto $\\delta$ and solving for $\\varepsilon$ yields a uniform gap of order\n$\\sqrt{(\\log M + \\log(1\u002F\\delta))\u002Fn}$. The log-cardinality $\\log M$ is the capacity.\nThe Sauer–Shelah lemma extends this to infinite classes: a class of VC dimension\n$C$ realizes at most $O(n^C)$ distinct labelings on any $n$ points, so\n$\\log M \\rightsquigarrow C\\log n$ takes the place of $\\log M$, giving the theorem's\n$\\sqrt{C\\log(n\u002FC)\u002Fn}$.\n\nStripped of the logarithmic and confidence terms, the bound has the shape the\nwhole field quotes:\n\n$$\n\\;R(\\theta) \\;\\le\\; \\hat R(\\theta) \\;+\\; O\\!\\parens{\\sqrt{\\tfrac{C}{n}}}\\;\n\\qquad\n\\begin{aligned}\n&C = \\text{capacity (e.g. } d_{\\mathrm{VC}}),\\\\\n&n = \\text{number of samples.}\n\\end{aligned}\n$$\n\nTwo terms trade off: $\\hat R$ falls as capacity $C$ rises\n(a richer class fits the sample better), while the gap term $\\sqrt{C\u002Fn}$ rises with\n$C$. Their sum is U-shaped in capacity, minimized at an intermediate \"sweet spot\",\nthe classical bias–variance tradeoff.[^gf-biasvar]\n\n$$\n% caption: Classical bias--variance: training risk falls and the gap term grows with capacity, so test risk is U-shaped with an intermediate sweet spot.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize, scale=1.0]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{green}{HTML}{1F9D4D}\n  \\definecolor{red}{HTML}{C0392B}\n  % axes\n  \\draw[->, thick] (0,0) -- (7.4,0) node[right, font=\\footnotesize] {capacity};\n  \\draw[->, thick] (0,0) -- (0,4.6) node[above, font=\\footnotesize] {risk};\n  % training risk: decreasing\n  \\draw[green, very thick] plot[domain=0.35:7, samples=60] (\\x, {3.6\u002F(\\x*0.7+0.5)});\n  \\node[green, anchor=west] at (5.0,0.3) {\\texttt{train risk}};\n  % gap term sqrt(C\u002Fn): increasing\n  \\draw[acc, very thick] plot[domain=0.2:7, samples=60] (\\x, {1.05*sqrt(\\x)});\n  \\node[acc, anchor=north, align=center, font=\\footnotesize] at (5.3,1.85) {\\texttt{gap term}\\\\\\texttt{(grows with C)}};\n  % test risk = sum: U-shaped\n  \\draw[red, very thick] plot[domain=0.35:7, samples=70] (\\x, {3.6\u002F(\\x*0.7+0.5) + 1.05*sqrt(\\x)});\n  \\node[red, anchor=west] at (4.2,3.95) {\\texttt{test risk}};\n  % sweet spot marker at the U minimum (x ~ 3.5, where red curve bottoms)\n  \\draw[black, dashed] (3.5,0) -- (3.5,3.18);\n  \\node[black, anchor=north, font=\\footnotesize] at (3.5,-0.05) {\\texttt{sweet spot}};\n  \\fill[red] (3.5,3.18) circle (2.4pt);\n\\end{tikzpicture}\n$$\n\n### The bias–variance decomposition\n\nThe U-shape has an exact algebraic source. Fix a query point $x$, let $y = g(x) +\n\\epsilon$ with noise $\\mathbb{E}[\\epsilon]=0$, $\\mathrm{Var}(\\epsilon)=\\sigma^2$,\nand let $\\hat f = \\hat f_{\\mathcal{D}}$ be the model fit on a random training set\n$\\mathcal{D}$. The expected squared error at $x$, averaged over both the label noise\nand the draw of $\\mathcal{D}$, decomposes into three non-negative pieces:\n\n$$\n\\mathbb{E}_{\\mathcal{D},\\epsilon}\\!\\brackets{(y - \\hat f(x))^2}\n=\n\\underbrace{\\parens{g(x) - \\mathbb{E}_{\\mathcal D}[\\hat f(x)]}^2}_{\\text{bias}^2}\n\\;+\\;\n\\underbrace{\\mathbb{E}_{\\mathcal D}\\!\\brackets{\\parens{\\hat f(x) - \\mathbb{E}_{\\mathcal D}[\\hat f(x)]}^2}}_{\\text{variance}}\n\\;+\\;\n\\underbrace{\\sigma^2}_{\\text{noise}}.\n$$\n\nThe cross terms vanish: $\\epsilon$ is independent of $\\hat f$ and mean-zero, and\nthe split of $(g - \\hat f)$ around $\\mathbb{E}[\\hat f]$ leaves a zero-mean residual\nthat is uncorrelated with the constant bias. **Bias** is the error a low-capacity\nclass cannot avoid even with infinite data — its best member is still far from $g$.\n**Variance** is how much $\\hat f$ swings as the sample is redrawn — a high-capacity\nclass chases the sample noise, so its predictions are unstable. **Noise** $\\sigma^2$\nis irreducible. Raising capacity lowers bias and raises variance; the sum is\nU-shaped, and the sweet spot is where the two derivatives cancel. This is the same\nU as the VC budget, seen through squared error instead of a uniform bound.[^gf-biasvar]\n\nA second, distribution-aware measure replaces the worst-case combinatorics of VC\nwith an average over the actual data: **Rademacher complexity** measures how well\nthe class can fit a labeling of pure random signs.\n\n> **Definition (Rademacher complexity).** For a sample $S = \\{x_1,\\dots,x_n\\}$ and\n> independent random signs $\\varsigma_i \\in \\{-1,+1\\}$ drawn uniformly, the\n> empirical Rademacher complexity of $\\mathcal{H}$ is\n> $$\n> \\widehat{\\mathfrak{R}}_S(\\mathcal{H})\n> = \\mathbb{E}_{\\varsigma}\\!\\brackets{\\sup_{h\\in\\mathcal{H}} \\frac{1}{n}\\sum_{i=1}^n \\varsigma_i\\, h(x_i)}.\n> $$\n> It is the expected best correlation between a class member and a random\n> labeling, large when $\\mathcal{H}$ can mimic noise.\n\nConsider two extreme cases. A single constant function has\n$\\widehat{\\mathfrak{R}}_S = 0$: the sum $\\frac1n\\sum \\varsigma_i h(x_i) = h\\cdot\n\\frac1n\\sum\\varsigma_i$ has expectation zero because the signs average out. A class\nthat can hit any target on the sample has $\\widehat{\\mathfrak{R}}_S = 1$: it sets\n$h(x_i) = \\varsigma_i$ for every draw, so the correlation is $\\frac1n\\sum\n\\varsigma_i^2 = 1$. Rademacher complexity thus interpolates between $0$ and $1$ as\nthe class goes from trivial to able-to-shatter, and it plugs straight into a bound.\n\n> **Theorem (Rademacher generalization bound).** With probability at least\n> $1-\\delta$, simultaneously for all $h \\in \\mathcal{H}$ with loss in $[0,1]$,\n> $$\n> R(h) \\;\\le\\; \\hat R(h) \\;+\\; 2\\,\\widehat{\\mathfrak{R}}_S(\\mathcal{H})\n> \\;+\\; 3\\sqrt{\\frac{\\log(2\u002F\\delta)}{2n}}.\n> $$\n\nFor linear predictors with $\\norm{w}\\le B$ over inputs with $\\norm{x}\\le X$, one\ncan show $\\widehat{\\mathfrak{R}}_S \\le BX\u002F\\sqrt{n}$: the complexity is controlled\nby the _norm_ $B$, not the ambient dimension $d$. That norm dependence is what\ncarries over to the modern setting: it remains meaningful in high dimension, where\nthe dimension-based VC bound does not.\n\nBoth theorems are valid and both are tight for the classes they were built for:\nlinear models, kernel machines, shallow trees. Their common shape, gap $\\le\nO(\\sqrt{C\u002Fn})$, predicts that capacity exceeding the sample size is fatal. Deep\nnetworks break that prediction so completely that the bounds become numbers larger\nthan one and tell us nothing.\n\n## The deep-learning puzzle\n\nModern networks live in a regime classical theory was never meant to describe.\nTheir parameter count $p$ (and any reasonable capacity surrogate) vastly exceeds\nthe sample size $n$. They are **overparameterized**.[^postdate]\n\n> **Definition (Overparameterization).** A model is overparameterized when it has\n> more free parameters than training examples, $p \\gg n$, so that infinitely many\n> parameter settings achieve zero training loss. The set of such global minimizers\n> is a high-dimensional manifold, not a point.\n\nFor example, a residual image network can carry\n$p \\approx 2.5\\times 10^7$ parameters and train on $n \\approx 5\\times 10^4$\nlabeled images, so $p\u002Fn \\approx 500$. The VC dimension of a piecewise-linear\nnetwork grows at least linearly in $p$, so $\\sqrt{d_{\\mathrm{VC}}\u002Fn} \\gtrsim\n\\sqrt{500} \\approx 22$: the bound guarantees a gap of at most $22$, on a risk that\nlives in $[0,1]$. The bound is vacuous.\n\nTwo facts about these models cannot both be accommodated by the classical bound,\nand together they are the central tension of the field. The post-2016 literature\nthat established them is more recent than Goodfellow's textbook, which still\npresents capacity control as the operative theory.\n\n> **Fact (Networks fit random labels).** A standard image network trained on a\n> dataset whose labels have been replaced by uniform random noise reaches _zero_\n> training error. Its effective capacity is therefore large enough to memorize an\n> arbitrary labeling of the training set — exactly the condition VC theory calls\n> \"able to shatter the sample.\"\n\nIf the class can shatter the training set, then $\\widehat{\\mathfrak{R}}_S \\approx 1$\nand $d_{\\mathrm{VC}} \\gtrsim n$, so the bound term $\\sqrt{C\u002Fn}$ is $\\Omega(1)$: the\nguaranteed gap is at least a constant, and $R(\\theta) \\le \\hat R(\\theta) + 1$ is\nvacuous because risk already lives in $[0,1]$.\n\n> **Fact (...yet they generalize on real data).** The _same_ architecture, _same_\n> optimizer, and _same_ epoch budget that memorizes random labels instead learns a\n> function with a small generalization gap when trained on the _true_ labels. The\n> model has the capacity to memorize but, on structured data, does not have to.\n\nThe two facts isolate the puzzle precisely: capacity is necessary to explain the\nrandom-label result and useless to explain the real-data result. Whatever controls\nthe gap on real data, it is not the size of $\\mathcal{H}$.\n\n$$\n% caption: The model drives training risk to zero on both real and random labels, yet the test gap is small on real labels and near-chance on random ones.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize, scale=1.0]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{green}{HTML}{1F9D4D}\n  \\definecolor{red}{HTML}{C0392B}\n  % axes\n  \\draw[->, thick] (0,0) -- (7.4,0) node[right, font=\\footnotesize] {training epoch};\n  \\draw[->, thick] (0,0) -- (0,4.4) node[above, font=\\footnotesize] {error};\n  \\node[anchor=east, font=\\footnotesize] at (-0.05,4.0) {\\texttt{high}};\n  \\node[anchor=east, font=\\scriptsize] at (-0.05,0.0) {0};\n  % train error real: -> 0\n  \\draw[green, very thick] plot[domain=0.2:7, samples=50] (\\x, {4.0*exp(-1.1*\\x)});\n  \\node[green, anchor=west, font=\\footnotesize] at (1.7,0.35) {\\texttt{train (real)}};\n  % train error random: -> 0 slower\n  \\draw[green, very thick, dashed] plot[domain=0.2:7, samples=50] (\\x, {4.0*exp(-0.55*\\x)});\n  \\node[green, anchor=west, font=\\footnotesize] at (2.55,1.85) {\\texttt{train (random)}};\n  % test error real: settles low\n  \\draw[acc, very thick] plot[domain=0.2:7, samples=50] (\\x, {0.9 + 1.6*exp(-1.0*\\x)});\n  \\node[acc, anchor=west, font=\\footnotesize] at (5.1,0.74) {\\texttt{test (real)}};\n  % test error random: stays high (chance)\n  \\draw[red, very thick] plot[domain=0.2:7, samples=50] (\\x, {3.5 - 0.12*\\x});\n  \\node[red, anchor=south, font=\\footnotesize] at (3.0,3.1) {\\texttt{test (random)}};\n\\end{tikzpicture}\n$$\n\n## Double descent\n\nThe cleanest empirical refutation of the classical U-curve is **double descent**.\nSweep model size from tiny to huge and plot test error. At first the classical\nstory holds: test error falls, then rises as the model begins to overfit, peaking\nright where the model becomes just barely able to fit the training set. Past that\npoint, the regime classical theory forbids, test error _falls again_, often\nbelow the classical sweet spot.\n\n> **Definition (Interpolation threshold).** The model size at which the network\n> first achieves zero training error — it exactly _interpolates_ the training set.\n> Below it the model is underparameterized and cannot fit the data; above it the\n> model is overparameterized and many zero-error solutions exist. The double-descent\n> peak sits at this threshold.\n\n$$\n% caption: Double descent: test risk follows the classical U, spikes at the interpolation threshold, then descends a second time in the overparameterized regime $p > n$.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize, scale=1.0]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{red}{HTML}{C0392B}\n  \\definecolor{green}{HTML}{1F9D4D}\n  % axes\n  \\draw[->, thick] (0,0) -- (9.0,0) node[right, font=\\footnotesize] {model size (params)};\n  \\draw[->, thick] (0,0) -- (0,4.6) node[above, font=\\footnotesize] {test risk};\n  % interpolation threshold marker\n  \\draw[black, dashed] (4.3,0) -- (4.3,4.3);\n  \\node[black, anchor=south, align=center, font=\\footnotesize] at (4.3,4.25) {\\texttt{interpolation}\\\\\\texttt{threshold}};\n  % regime labels\n  \\node[black, anchor=north, font=\\footnotesize] at (2.1,-0.05) {\\texttt{under-param.}};\n  \\node[black, anchor=north, font=\\footnotesize] at (6.7,-0.05) {\\texttt{over-param.} ($p > n$)};\n  % classical U (left of threshold): down then up to spike\n  \\draw[red, very thick] plot[domain=0.4:4.3, samples=60]\n    (\\x, {1.1 + 1.7\u002F(\\x+0.4) + 0.42*(\\x)*(\\x)\u002F4.0});\n  % spike continues into descent (right of threshold): steep drop then flat-ish\n  \\draw[red, very thick] plot[domain=4.3:8.6, samples=70]\n    (\\x, {1.05 + 2.3*exp(-1.3*(\\x-4.3)) });\n  % marker dots: classical min (left U bottom ~ x=1.6) and modern min\n  \\fill[red] (1.6,2.07) circle (2.2pt);\n  \\node[red, anchor=west, font=\\footnotesize] at (0.45,1.45) {\\texttt{classical min}};\n  \\fill[green] (8.2,1.06) circle (2.6pt);\n  \\node[green, anchor=south, font=\\footnotesize] at (7.7,1.7) {\\texttt{modern min}};\n\\end{tikzpicture}\n$$\n\nWhy the second descent happens is the active question. The working explanation is\nthat beyond the threshold there is a _choice_ of zero-error solution, and the\noptimizer picks a simple one rather than an arbitrary one. At the threshold\nitself there is essentially a unique interpolating solution and the optimizer is\nforced into it, however contorted; that forced, brittle fit is the spike. With\nmore parameters, the optimizer finds a smoother interpolant, and test error drops.\n\nThe same peak appears when the axis is _training time_ or _data size_ rather than\nmodel width, which is why the phenomenon is read as being about the ratio of\neffective capacity to sample size, $C\u002Fn$, crossing $1$ — not about parameter count\nalone. The peak sits wherever the model is _just_ able to interpolate, whichever\nknob pushed it there.\n\n> **Remark (A capacity surrogate that does not double-descend).** The phenomenon\n> motivates measuring capacity by quantities the optimizer actually controls — the\n> _norm_ of the learned weights, or the _margin_, rather than the parameter count.\n> Norm-based complexity grows then shrinks across the threshold, tracking the\n> double-descent curve where parameter-counting fails.\n\n## Margin and norm-based bounds\n\nThe Rademacher bound already hinted at the fix: for linear predictors the\ncomplexity was $BX\u002F\\sqrt n$, controlled by the weight norm $B$ and not the\ndimension. The margin turns this into a bound that stays finite for interpolating\nnetworks. Define the **margin** of a correctly classified example as the gap\nbetween the score of the true class and the best competing class,\n\n$$\n\\gamma_i = f_{y_i}(x_i) - \\max_{k \\ne y_i} f_k(x_i),\n$$\n\nand let $\\gamma$ be the margin achieved on the whole sample. A predictor that\nseparates the data with a large margin sits far from every decision boundary, so\nsmall perturbations of the input do not flip its label — a smoothness property that\ntransfers to unseen points.\n\n> **Theorem (Margin bound, schematic).** For a network with weight matrices of\n> spectral norms $\\norm{W_\\ell}$, with probability $\\ge 1-\\delta$ the\n> misclassification risk obeys\n> $$\n> R_{0\u002F1}(f) \\;\\le\\; \\hat R_\\gamma(f) \\;+\\; \\tilde O\\!\\parens{\\frac{\\prod_\\ell \\norm{W_\\ell}\\,\\cdot\\, r}{\\gamma\\,\\sqrt n}},\n> $$\n> where $\\hat R_\\gamma$ is the fraction of training points with margin below\n> $\\gamma$ and $r$ collects the (weaker) dependence on architecture.\n\nThe numerator measures the size of the function; the denominator, its distance\nfrom the boundary. Two networks with identical parameter counts can differ\nby orders of magnitude in $\\prod_\\ell\\norm{W_\\ell}\u002F\\gamma$: the one the optimizer\nhappens to find on real data has small weight norm and large margin, so its bound\nis small; the one that memorizes random labels needs enormous weights to force the\ncontorted fit, so its bound is large. Parameter counting cannot tell them apart;\nthe norm-over-margin ratio can. This is the same quantity that shrinks past the\ninterpolation threshold and tracks the second descent.\n\n$$\n% caption: A large-margin separator (right) leaves a wide corridor around the boundary, so nearby test points keep their labels; a small-margin fit (left) threads between points and flips labels under tiny shifts.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{grn}{HTML}{1F9D4D}\n  \\definecolor{rd}{HTML}{C0392B}\n  % ---- left: small margin ----\n  \\begin{scope}[shift={(0,0)}]\n    \\node[anchor=south, font=\\footnotesize] at (1.6,3.2) {\\texttt{small margin}};\n    \\draw[black] (0,0) rectangle (3.2,3.0);\n    % wiggly tight boundary\n    \\draw[acc, very thick] (0.1,2.6) .. controls (1.4,1.2) and (1.8,2.2) .. (3.1,0.6);\n    % points close to boundary\n    \\fill[grn] (0.6,0.7) circle (2.6pt);\n    \\fill[grn] (1.5,0.9) circle (2.6pt);\n    \\fill[grn] (1.1,1.5) circle (2.6pt);\n    \\fill[rd] (1.9,2.4) circle (2.6pt);\n    \\fill[rd] (2.5,1.6) circle (2.6pt);\n    \\fill[rd] (0.9,2.3) circle (2.6pt);\n    \\node[black, anchor=north, font=\\footnotesize] at (1.6,-0.05) {\\texttt{flips easily}};\n  \\end{scope}\n  % ---- right: large margin ----\n  \\begin{scope}[shift={(4.6,0)}]\n    \\node[anchor=south, font=\\footnotesize] at (1.6,3.2) {\\texttt{large margin}};\n    \\draw[black] (0,0) rectangle (3.2,3.0);\n    % straight boundary\n    \\draw[acc, very thick] (0.3,2.7) -- (2.9,0.3);\n    % margin corridor\n    \\draw[acc!45, dashed] (0.75,2.85) -- (3.05,0.75);\n    \\draw[acc!45, dashed] (-0.05,2.25) -- (2.55,-0.05);\n    % points far from boundary\n    \\fill[grn] (0.55,0.55) circle (2.6pt);\n    \\fill[grn] (1.15,0.5) circle (2.6pt);\n    \\fill[grn] (0.5,1.1) circle (2.6pt);\n    \\fill[rd] (2.6,2.4) circle (2.6pt);\n    \\fill[rd] (2.0,2.6) circle (2.6pt);\n    \\fill[rd] (2.65,1.8) circle (2.6pt);\n    \\node[black, anchor=north, font=\\footnotesize] at (1.6,-0.05) {\\texttt{stable}};\n  \\end{scope}\n\\end{tikzpicture}\n$$\n\n## Implicit regularization\n\nThe missing ingredient is that the **optimizer is not neutral**. Among the\ninfinitely many parameter vectors that interpolate the training data, gradient\ndescent does not return a random one; its trajectory is biased toward a particular,\nwell-behaved corner of the solution manifold. This is **implicit regularization**:\na preference for simple solutions that nobody wrote into the loss.[^gf-reg]\n\n> **Definition (Implicit regularization).** The tendency of an optimization\n> procedure (gradient descent, SGD) to converge to solutions with special\n> structure (low norm, large margin, flat curvature) among all those that minimize\n> the training loss, without any explicit penalty term enforcing that structure.\n\nThe canonical exact result is for the simplest interpolating problem, where the bias\ncan be named precisely.\n\n> **Theorem (Implicit bias of gradient descent, separable case).** For logistic\n> regression on linearly separable data, gradient descent on the (unregularized)\n> empirical loss drives $\\norm{w_t} \\to \\infty$, but the _direction_\n> $w_t \u002F \\norm{w_t}$ converges to the **maximum-margin** separator — the same\n> solution a hard-margin SVM returns. The optimizer regularizes toward maximum\n> margin with no margin term in the objective.\n\n> **Proof (sketch).** On separable data no finite $w$ achieves zero logistic loss,\n> so $\\norm{w_t}$ diverges as the loss is pushed toward $0$. The loss is\n> dominated by the example with the _smallest_ margin $y_i\\, w^\\top x_i$, since the\n> logistic tail $\\log(1+e^{-z})\\approx e^{-z}$ decays fastest for the largest $z$.\n> Minimizing therefore pours all reduction into raising the worst-case margin, i.e.\n> maximizing $\\min_i y_i\\,(w\u002F\\norm{w})^\\top x_i$. The normalized iterate\n> converges to the maximizer of that minimum margin. $\\qed$\n\nTo see the tail argument in coordinates, write the gradient of the logistic loss,\n$\\nabla \\hat R(w) = -\\frac1n\\sum_i y_i x_i\\, \\sigma(-y_i w^\\top x_i)$ with $\\sigma$\nthe logistic sigmoid. Once the data is separated, every $y_i w^\\top x_i > 0$, and\n$\\sigma(-z) \\approx e^{-z}$ for large $z$, so the weight $e^{-y_i w^\\top x_i}$ on\nexample $i$ is exponentially smaller for well-separated points than for the\nworst-margin point. The gradient is therefore, up to a vanishing remainder, a\ncombination of the few smallest-margin examples — the support vectors — and $w_t$\ngrows in exactly the direction that pushes those margins up. The endpoint is the\nmax-margin direction, reached with no penalty term.\n\nFor deep networks no such clean theorem exists, but the same flavor of bias is\nobserved empirically and connects directly to the geometry of the loss surface from\n[the optimization landscape](\u002Fdeep-learning\u002Foptimization\u002Fthe-optimization-landscape):\nSGD prefers **flat minima**, regions where the loss is insensitive to small\nparameter perturbations, over sharp ones. Flatness is a stability property, and\nstability bounds generalization: a function that barely changes when the parameters\nwobble also barely changes when the training set is resampled.\n\nThe minibatch noise supplies the mechanism. An SGD update is the full gradient plus\na zero-mean stochastic term whose covariance scales with the local loss curvature.\nIn a sharp basin that noise kicks the iterate up the steep walls and out; in a wide\nflat basin the same noise is absorbed. The stationary distribution of the SGD\niterate therefore concentrates in flat regions — an entropic pull toward flatness\nthat is stronger the larger the learning-rate-to-batch-size ratio.\n\n$$\n% caption: Implicit bias: among the many interpolating minima, minibatch noise drives SGD into the wide flat basin rather than the narrow sharp one.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize, scale=1.0]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{green}{HTML}{1F9D4D}\n  \\definecolor{red}{HTML}{C0392B}\n  % axes\n  \\draw[->, thick] (0,0) -- (9.0,0) node[right, font=\\footnotesize] {parameters};\n  \\draw[->, thick] (0,0) -- (0,3.9) node[above, font=\\footnotesize] {train loss};\n  % a double-well landscape: sharp minimum (left) and flat minimum (right)\n  % sharp narrow well around x=2\n  \\draw[black, very thick] plot[domain=0.3:3.3, samples=80]\n    (\\x, {0.35 + 2.6*(\\x-2.0)*(\\x-2.0)});\n  % flat wide well around x=6.4\n  \\draw[black, very thick] plot[domain=4.2:8.6, samples=90]\n    (\\x, {0.35 + 0.28*(\\x-6.4)*(\\x-6.4)});\n  % connecting ridge\n  \\draw[black, very thick] plot[domain=3.3:4.2, samples=30]\n    (\\x, {0.35 + 2.6*(3.3-2.0)*(3.3-2.0) - (\\x-3.3)\u002F0.9*((2.6*(3.3-2.0)*(3.3-2.0)) - (0.28*(4.2-6.4)*(4.2-6.4)))});\n  % sharp minimum point + label\n  \\fill[red] (2.0,0.35) circle (2.6pt);\n  \\node[red, anchor=south, align=center, font=\\footnotesize] at (2.55,0.62) {\\texttt{sharp min}\\\\\\texttt{(poor test)}};\n  % flat minimum point selected by SGD + label\n  \\fill[green] (6.4,0.35) circle (2.8pt);\n  \\node[green, anchor=south, align=center, font=\\footnotesize] at (6.4,0.62) {\\texttt{flat min}\\\\\\texttt{(SGD picks this)}};\n  % zero-train-loss line\n  \\draw[acc, dashed] (0,0.35) -- (8.7,0.35);\n  \\node[acc, anchor=south west, font=\\footnotesize] at (0.15,0.42) {\\texttt{train loss} $= 0$};\n\\end{tikzpicture}\n$$\n\nOptimization and generalization are therefore **entangled** in deep learning in a\nway they are not in convex learning. For a convex objective every minimizer is\nequally good and the optimizer is just a means to reach the unique answer; for an\noverparameterized network the optimizer _selects_ which of many zero-loss answers\nyou get, so the choice of algorithm is itself a regularizer. There is no clean\nseparation between \"how we optimize\" and \"how well we generalize.\"\n\n## The interpolation regime\n\nThe practical upshot inverts the classical rule: zero\ntraining error is not, by itself, a sign of overfitting. In the\noverparameterized regime, pushing the model past the interpolation threshold and\n_continuing_ to grow it keeps improving test error even though training error has\nalready bottomed out at zero.\n\n$$\n% caption: Past the interpolation threshold train error stays at zero while test error keeps falling as the model grows: bigger is better.\n\\begin{tikzpicture}[>=stealth, font=\\footnotesize, scale=1.0]\n  \\definecolor{acc}{HTML}{2348F2}\n  \\definecolor{green}{HTML}{1F9D4D}\n  \\definecolor{red}{HTML}{C0392B}\n  % axes\n  \\draw[->, thick] (0,0) -- (8.6,0) node[right, font=\\footnotesize] {model size};\n  \\draw[->, thick] (0,0) -- (0,4.4) node[above, font=\\footnotesize] {error};\n  % interpolation threshold\n  \\draw[black, dashed] (3.2,0) -- (3.2,4.1);\n  \\node[black, anchor=south, align=center, font=\\footnotesize] at (3.2,4.05) {\\texttt{threshold}};\n  % train error: falls to 0 at threshold then stays 0\n  \\draw[green, very thick] plot[domain=0.3:3.2, samples=40] (\\x, {3.6*exp(-1.3*\\x)});\n  \\draw[green, very thick] (3.2,{3.6*exp(-1.3*3.2)}) -- (8.3,0.06);\n  \\node[green, anchor=south, font=\\footnotesize] at (5.8,0.12) {\\texttt{train error} $= 0$};\n  % test error: keeps falling past threshold\n  \\draw[acc, very thick] plot[domain=0.3:8.3, samples=70] (\\x, {0.85 + 2.6*exp(-0.45*\\x)});\n  \\node[acc, anchor=west, font=\\footnotesize] at (5.9,0.62) {\\texttt{test error}};\n  % arrow emphasizing continued improvement\n  \\draw[red, ->, thick] (4.0,1.95) -- (7.2,1.02);\n  \\node[red, anchor=south west, font=\\footnotesize] at (4.7,2.0) {\\texttt{bigger still helps}};\n\\end{tikzpicture}\n$$\n\nThis is why the dominant practical recipe is to make models as large as compute\nallows and lean on [regularization](\u002Fdeep-learning\u002Fregularization\u002Fregularization-overview),\nexplicit (weight decay, [dropout](\u002Fdeep-learning\u002Fregularization\u002Fdropout-and-data-augmentation))\nand implicit (SGD, [early stopping](\u002Fdeep-learning\u002Fregularization\u002Fearly-stopping-and-parameter-sharing)),\nto steer the interpolating solution toward the well-behaved corner, rather than\nto shrink the model to fit a capacity budget.[^chollet-overfit] The regularizers do\nnot reduce capacity; they bias the search _within_ the huge class toward the\nlow-norm, large-margin, flat solutions the bounds above favor.\n\n> **Takeaway.** In the overparameterized regime, zero training error is not\n> overfitting — it is interpolation, and the test error is set by _which_\n> interpolating solution the optimizer lands on. Grow the model, then bias the\n> search (SGD, weight decay, early stopping) toward low norm and large margin;\n> do not shrink the model to fit a VC budget.\n\n## Classical versus modern\n\nThe two views disagree on every major question. The classical column\nis Goodfellow §5.2; the modern column post-dates Goodfellow (2016).\n\n| Question | Classical view | Modern (overparameterized) view |\n| --- | --- | --- |\n| What controls the gap? | hypothesis-class capacity ($d_{\\mathrm{VC}}$, Rademacher) | implicit bias of the optimizer (norm, margin, flatness) |\n| Effect of more parameters than data | catastrophic overfitting | often _improves_ test error (second descent) |\n| Test error vs. model size | U-shaped; stop at the sweet spot | double descent; grow past the threshold |\n| Zero training error means | overfitting (a red flag) | interpolation (routine, often optimal) |\n| Role of the optimizer | a neutral means to a unique minimum | a regularizer that _selects_ among many minima |\n| Are the bounds tight? | yes, for linear \u002F kernel \u002F tree classes | vacuous for deep nets ($\\sqrt{C\u002Fn} = \\Omega(1)$) |\n| How to control overfitting | restrict the class (fewer parameters) | keep the class huge; bias the search (SGD, weight decay) |\n\n## The papers behind the puzzle\n\nGoodfellow §5.2 presents capacity control as the operative theory; every result in\nthis lesson that contradicts it comes from the public literature that appeared after\nthe 2016 text, and the citations are worth naming. The empirical trigger was\n**\"Understanding deep learning requires rethinking generalization\"** (Zhang et al.,\n2017), the random-label experiment above: the demonstration that standard networks\nmemorize pure noise to zero training error, which is what rendered the VC and\nRademacher bounds vacuous in the regime that matters. **Double descent** was named\nand generalized by Belkin et al. (2019) and mapped across model size, data size, and\ntraining time by Nakkiran et al. (2020), establishing that the classical U-curve is\nonly the left half of the picture.\n\nThe constructive replies came in two families. **Margin- and norm-based bounds**\n(Bartlett, Foster & Telgarsky, 2017) replaced the parameter count with the product\nof spectral norms over the margin — the $\\prod_\\ell\\norm{W_\\ell}\u002F\\gamma$ quantity\nabove — giving a complexity measure that stays finite for interpolating networks and\ntracks the second descent. **Implicit bias** was characterized exactly for the\nseparable-logistic case by Soudry et al. (2018), whose result that gradient descent\nconverges in direction to the max-margin separator is the theorem this lesson\nsketched, and connected to **flat minima** and generalization through the stability\nlens (Keskar et al., 2017, on the sharp-minima cost of large-batch training). No\nsingle paper yet closes the loop into a predictive, algorithm-dependent theory, but\ntogether these mark the shift from \"count the functions the class contains\" to\n\"characterize the function the optimizer returns.\"\n\n## What the theory has and has not settled\n\nThe classical bounds are valid, and tight for the families\nthey were derived for. What fails is their _relevance_: capacity is the wrong knob\nfor overparameterized networks because the optimizer, not the class, picks the\nfunction. A predictive theory of deep generalization is still open, but its shape is\nclear: it must be **algorithm-dependent**, folding the trajectory of SGD and the\ngeometry of the minimum it finds into the bound, not just counting the functions the\narchitecture _could_ in principle represent. The norm- and margin-based bounds are\nthe first honest step in that direction, because they measure the function the\noptimizer actually returned rather than the class it was drawn from.\n\nThe threads this opens are picked up across the rest of the module: the brittleness\nthat survives small test error in [adversarial robustness](\u002Fdeep-learning\u002Ftheory\u002Fadversarial-robustness),\nthe use of the solution manifold's width to quantify uncertainty in\n[Bayesian and ensemble methods](\u002Fdeep-learning\u002Ftheory\u002Fbayesian-and-ensemble-methods),\nand the limit of infinite implicit depth in\n[deep equilibrium models](\u002Fdeep-learning\u002Ftheory\u002Fdeep-equilibrium-models). The\nsingle idea underneath all of them is the one this lesson isolated: in the\noverparameterized world, _how_ you search the hypothesis class matters more than\n_how big_ it is.\n\n[^gf-capacity]: **Goodfellow**, _Deep Learning_, §5.2 — Capacity, Overfitting and Underfitting: the representational capacity of a hypothesis class as the knob governing the train\u002Ftest gap.\n[^gf-biasvar]: **Goodfellow**, _Deep Learning_, §5.4 — Estimators, Bias and Variance: the U-shaped decomposition of error into bias and variance and the resulting capacity sweet spot.\n[^gf-reg]: **Goodfellow**, _Deep Learning_, Ch. 7 — Regularization for Deep Learning: penalties and procedures that bias the learned function toward simpler solutions.\n[^chollet-overfit]: **Chollet**, _Deep Learning with Python_, Ch. 5 — Fundamentals of ML: the practitioner's recipe of large models plus regularization rather than capacity-matching.\n[^postdate]: The overparameterization phenomena here — fitting random labels, double descent, implicit bias toward max-margin and flat minima — postdate **Goodfellow** (2016), which still presents capacity control (§5.2) as the operative theory of generalization.\n",{"text":13982,"minutes":13983,"time":13984,"words":13985},"17 min read",16.82,1009200,3364,{"title":5,"description":13969},[13988,13990,13992],{"book":13879,"ref":13989},"§5.2 — Capacity, Overfitting and Underfitting; §5.4.2 VC Dimension",{"book":13879,"ref":13991},"Ch. 7 — Regularization for Deep Learning",{"book":13879,"ref":13993},"§5.5 — Maximum Likelihood Estimation; §5.4 Estimators, Bias, Variance","available","06.deep-learning\u002F06.theory\u002F01.generalization-theory","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",[13976],"1-ZBZsT64NVwHR77-zGTJEQsg11ea0mrjY6Qu4D_s0s",{"\u002Falgorithms\u002Ffoundations\u002Fwhat-is-an-algorithm":14000,"\u002Falgorithms\u002Ffoundations\u002Fproof-techniques":14001,"\u002Falgorithms\u002Ffoundations\u002Fasymptotic-analysis":14002,"\u002Falgorithms\u002Ffoundations\u002Fgrowth-rates-and-loop-analysis":14003,"\u002Falgorithms\u002Ffoundations\u002Frecurrences":14004,"\u002Falgorithms\u002Ffoundations\u002Famortized-analysis":14005,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fmergesort":14006,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fquicksort":14007,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fselection":14008,"\u002Falgorithms\u002Fdivide-and-conquer\u002Ffast-multiplication":14009,"\u002Falgorithms\u002Fsorting\u002Fheaps-and-heapsort":14010,"\u002Falgorithms\u002Fsorting\u002Fsorting-lower-bounds":14011,"\u002Falgorithms\u002Fsorting\u002Flinear-time-sorting":14012,"\u002Falgorithms\u002Fsorting\u002Fexternal-sorting":14013,"\u002Falgorithms\u002Fdata-structures\u002Felementary-structures":14014,"\u002Falgorithms\u002Fdata-structures\u002Fhash-tables":14015,"\u002Falgorithms\u002Fdata-structures\u002Fbinary-search-trees":14016,"\u002Falgorithms\u002Fdata-structures\u002Favl-trees":14017,"\u002Falgorithms\u002Fdata-structures\u002Fbalanced-trees":14018,"\u002Falgorithms\u002Fdata-structures\u002Funion-find":14019,"\u002Falgorithms\u002Fdata-structures\u002Ffenwick-and-segment-trees":14020,"\u002Falgorithms\u002Fdata-structures\u002Fspatial-data-structures":14021,"\u002Falgorithms\u002Fdata-structures\u002Fskip-lists-and-probabilistic-structures":14022,"\u002Falgorithms\u002Fdata-structures\u002Fb-trees":14023,"\u002Falgorithms\u002Fdata-structures\u002Fdata-stream-algorithms":14024,"\u002Falgorithms\u002Fdata-structures\u002Fstreaming-sketches":14025,"\u002Falgorithms\u002Fsequences\u002Ftwo-pointers-and-windows":14026,"\u002Falgorithms\u002Fsequences\u002Fprefix-sums":14027,"\u002Falgorithms\u002Fsequences\u002Fmonotonic-stacks":14028,"\u002Falgorithms\u002Fsequences\u002Fbinary-search-on-the-answer":14029,"\u002Falgorithms\u002Fsequences\u002Fstring-matching":14030,"\u002Falgorithms\u002Fsequences\u002Fkmp-and-z-function":14031,"\u002Falgorithms\u002Fsequences\u002Ftries":14032,"\u002Falgorithms\u002Fsequences\u002Fsuffix-arrays-and-aho-corasick":14033,"\u002Falgorithms\u002Fgraphs\u002Frepresentations-and-traversal":14034,"\u002Falgorithms\u002Fgraphs\u002Fdepth-first-search":14035,"\u002Falgorithms\u002Fgraphs\u002Ftopological-sort-and-scc":14036,"\u002Falgorithms\u002Fgraphs\u002Fminimum-spanning-trees":14037,"\u002Falgorithms\u002Fgraphs\u002Fkruskal-and-prim":14038,"\u002Falgorithms\u002Fgraphs\u002Fshortest-paths":14039,"\u002Falgorithms\u002Fgraphs\u002Fall-pairs-and-negative-weights":14040,"\u002Falgorithms\u002Fgraphs\u002Fnetwork-flow":14041,"\u002Falgorithms\u002Fgraphs\u002Fmax-flow-min-cut":14042,"\u002Falgorithms\u002Fgraphs\u002Fbridges-and-articulation-points":14043,"\u002Falgorithms\u002Fgraphs\u002Flowest-common-ancestor":14044,"\u002Falgorithms\u002Fgraphs\u002Ftwo-sat":14045,"\u002Falgorithms\u002Fgraphs\u002Feulerian-tours":14046,"\u002Falgorithms\u002Fgraphs\u002Fbipartite-matching":14047,"\u002Falgorithms\u002Fgreedy\u002Fthe-greedy-method":14048,"\u002Falgorithms\u002Fgreedy\u002Fscheduling-and-intervals":14049,"\u002Falgorithms\u002Fgreedy\u002Fhuffman-codes":14050,"\u002Falgorithms\u002Fgreedy\u002Fmatroids":14051,"\u002Falgorithms\u002Fgreedy\u002Fstable-matching":14052,"\u002Falgorithms\u002Fdynamic-programming\u002Fprinciples":14053,"\u002Falgorithms\u002Fdynamic-programming\u002Fsequence-dp":14054,"\u002Falgorithms\u002Fdynamic-programming\u002Flongest-increasing-subsequence":14055,"\u002Falgorithms\u002Fdynamic-programming\u002Fknapsack":14056,"\u002Falgorithms\u002Fdynamic-programming\u002Fcoin-change-and-unbounded":14057,"\u002Falgorithms\u002Fdynamic-programming\u002Finterval-dp":14058,"\u002Falgorithms\u002Fdynamic-programming\u002Ftree-dp":14059,"\u002Falgorithms\u002Fdynamic-programming\u002Fbitmask-dp":14060,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-optimizations":14061,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-on-graphs":14062,"\u002Falgorithms\u002Fdynamic-programming\u002Fdigit-and-probability-dp":14063,"\u002Falgorithms\u002Fbacktracking\u002Fbacktracking-fundamentals":14064,"\u002Falgorithms\u002Fbacktracking\u002Fconstraint-search":14065,"\u002Falgorithms\u002Fbacktracking\u002Fbranch-and-bound":14066,"\u002Falgorithms\u002Fbacktracking\u002Fgraph-backtracking":14067,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fnumber-theory-basics":14068,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmodular-exponentiation-and-primality":14069,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fsieve-and-factorization":14070,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fcombinatorics":14071,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmatrix-exponentiation":14072,"\u002Falgorithms\u002Fmathematical-algorithms\u002Ffast-fourier-transform":14073,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fgradient-descent":14074,"\u002Falgorithms\u002Fcomputational-geometry\u002Fgeometric-primitives":14075,"\u002Falgorithms\u002Fcomputational-geometry\u002Fconvex-hull":14076,"\u002Falgorithms\u002Fcomputational-geometry\u002Fsweep-line":14077,"\u002Falgorithms\u002Fcomputational-geometry\u002Fpolygons-and-proximity":14078,"\u002Falgorithms\u002Fintractability\u002Fp-np-reductions":14079,"\u002Falgorithms\u002Fintractability\u002Fnp-completeness":14080,"\u002Falgorithms\u002Fintractability\u002Fcoping-with-hardness":14081,"\u002Falgorithms\u002Fintractability\u002Fapproximation-algorithms":14082,"\u002Falgorithms":14083,"\u002Fcalculus\u002Flimits-and-continuity\u002Ffunctions-and-models":14084,"\u002Fcalculus\u002Flimits-and-continuity\u002Fthe-limit-of-a-function":14085,"\u002Fcalculus\u002Flimits-and-continuity\u002Flimit-laws-and-the-precise-definition":14086,"\u002Fcalculus\u002Flimits-and-continuity\u002Fcontinuity":14087,"\u002Fcalculus\u002Fderivatives\u002Fthe-derivative-and-rates-of-change":14088,"\u002Fcalculus\u002Fderivatives\u002Fdifferentiation-rules-and-the-chain-rule":14089,"\u002Fcalculus\u002Fderivatives\u002Fimplicit-differentiation-and-related-rates":14090,"\u002Fcalculus\u002Fderivatives\u002Flinear-approximations-and-differentials":14091,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fextrema-and-the-mean-value-theorem":14092,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fhow-derivatives-shape-a-graph":14093,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fcurve-sketching-and-optimization":14094,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fnewtons-method-and-antiderivatives":14095,"\u002Fcalculus\u002Fintegrals\u002Farea-and-the-definite-integral":14096,"\u002Fcalculus\u002Fintegrals\u002Fthe-fundamental-theorem-of-calculus":14097,"\u002Fcalculus\u002Fintegrals\u002Fthe-substitution-rule":14098,"\u002Fcalculus\u002Fapplications-of-integration\u002Fareas-and-volumes":14099,"\u002Fcalculus\u002Fapplications-of-integration\u002Fwork-average-value-and-arc-length":14100,"\u002Fcalculus\u002Fapplications-of-integration\u002Fphysics-economics-and-probability":14101,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Finverse-functions-logarithms-and-exponentials":14102,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Fgrowth-decay-inverse-trig-and-hyperbolic-functions":14103,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Flhospitals-rule":14104,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fintegration-by-parts":14105,"\u002Fcalculus\u002Ftechniques-of-integration\u002Ftrigonometric-integrals-and-substitution":14106,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fpartial-fractions-and-integration-strategy":14107,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fapproximate-and-improper-integrals":14108,"\u002Fcalculus\u002Fparametric-and-polar\u002Fparametric-curves-and-their-calculus":14109,"\u002Fcalculus\u002Fparametric-and-polar\u002Fpolar-coordinates":14110,"\u002Fcalculus\u002Fparametric-and-polar\u002Fconic-sections":14111,"\u002Fcalculus\u002Fsequences-and-series\u002Fsequences":14112,"\u002Fcalculus\u002Fsequences-and-series\u002Fseries-and-the-integral-test":14113,"\u002Fcalculus\u002Fsequences-and-series\u002Fthe-convergence-tests":14114,"\u002Fcalculus\u002Fsequences-and-series\u002Fpower-series":14115,"\u002Fcalculus\u002Fsequences-and-series\u002Ftaylor-and-maclaurin-series":14116,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvectors-and-the-dot-product":14117,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fthe-cross-product-lines-and-planes":14098,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fcylinders-and-quadric-surfaces":14118,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvector-functions-and-space-curves":14119,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Farc-length-curvature-and-motion":14120,"\u002Fcalculus\u002Fpartial-derivatives\u002Ffunctions-of-several-variables":14088,"\u002Fcalculus\u002Fpartial-derivatives\u002Fpartial-derivatives":14121,"\u002Fcalculus\u002Fpartial-derivatives\u002Ftangent-planes-and-the-chain-rule":14122,"\u002Fcalculus\u002Fpartial-derivatives\u002Fdirectional-derivatives-and-the-gradient":14123,"\u002Fcalculus\u002Fpartial-derivatives\u002Foptimization-and-lagrange-multipliers":14124,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fdouble-integrals":14125,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Ftriple-integrals-and-coordinate-systems":14126,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fvector-fields-and-line-integrals":14127,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fgreens-theorem-curl-and-divergence":14128,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fsurface-integrals":14129,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fstokes-and-the-divergence-theorem":14130,"\u002Fcalculus":14131,"\u002Fmechanics\u002Ffoundations\u002Fmeasurement-and-dimensions":14132,"\u002Fmechanics\u002Ffoundations\u002Fvector-algebra":14133,"\u002Fmechanics\u002Fkinematics\u002Fone-dimensional-motion":14134,"\u002Fmechanics\u002Fkinematics\u002Fmotion-graphs":14135,"\u002Fmechanics\u002Fkinematics\u002Fprojectile-motion":14136,"\u002Fmechanics\u002Fkinematics\u002Frelative-motion":14137,"\u002Fmechanics\u002Fkinematics\u002Fcircular-motion":14138,"\u002Fmechanics\u002Fdynamics\u002Fnewtons-laws":14139,"\u002Fmechanics\u002Fdynamics\u002Ffree-body-diagrams":14140,"\u002Fmechanics\u002Fdynamics\u002Ffriction-and-curved-motion":14141,"\u002Fmechanics\u002Fdynamics\u002Fnumerical-dynamics":14142,"\u002Fmechanics\u002Fdynamics\u002Fcenter-of-mass-systems":14143,"\u002Fmechanics\u002Fenergy\u002Fwork-and-kinetic-energy":14144,"\u002Fmechanics\u002Fenergy\u002Fpotential-energy":14145,"\u002Fmechanics\u002Fenergy\u002Fmultiparticle-work":14146,"\u002Fmechanics\u002Fenergy\u002Fmass-energy-and-binding":14147,"\u002Fmechanics\u002Fenergy\u002Fphotons-and-quantization":14148,"\u002Fmechanics\u002Fmomentum\u002Fmomentum-and-collisions":14149,"\u002Fmechanics\u002Fmomentum\u002Fcenter-of-mass-collisions":14150,"\u002Fmechanics\u002Fmomentum\u002Frocket-propulsion":14151,"\u002Fmechanics\u002Frotation\u002Frotational-inertia":14152,"\u002Fmechanics\u002Frotation\u002Frotational-dynamics":14153,"\u002Fmechanics\u002Frotation\u002Frolling-motion":14154,"\u002Fmechanics\u002Frotation\u002Fangular-momentum":14155,"\u002Fmechanics\u002Frotation\u002Frolling-resistance":14156,"\u002Fmechanics\u002Frotation\u002Fgyroscopic-precession":14157,"\u002Fmechanics\u002Fgravity-and-matter\u002Fkeplerian-orbits":14158,"\u002Fmechanics\u002Fgravity-and-matter\u002Fgravitational-fields":14159,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstatic-equilibrium":14160,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-statics":14161,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-flow":14162,"\u002Fmechanics\u002Fgravity-and-matter\u002Forbital-motion":14163,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstress-and-elasticity":14164,"\u002Fmechanics\u002Foscillations-waves\u002Fdamped-oscillators":14165,"\u002Fmechanics\u002Foscillations-waves\u002Ftravelling-waves":14166,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-superposition":14167,"\u002Fmechanics\u002Foscillations-waves\u002Fstanding-waves":14168,"\u002Fmechanics\u002Foscillations-waves\u002Fsound-waves":14169,"\u002Fmechanics\u002Foscillations-waves\u002Fdoppler-effect":14170,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-packets":14171,"\u002Fmechanics\u002Foscillations-waves\u002Fbeats-and-coupling":14172,"\u002Fmechanics\u002Foscillations-waves\u002Fsimple-harmonic-motion":14173,"\u002Fmechanics\u002Foscillations-waves\u002Fpendulum-motion":14174,"\u002Fmechanics\u002Foscillations-waves\u002Fdriven-oscillators":14175,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-boundaries":14176,"\u002Fmechanics\u002Fthermodynamics\u002Fkinetic-theory-of-ideal-gases":14177,"\u002Fmechanics\u002Fthermodynamics\u002Ffirst-law-of-thermodynamics":14178,"\u002Fmechanics\u002Fthermodynamics\u002Fentropy-and-the-second-law":14179,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-processes":14180,"\u002Fmechanics\u002Fthermodynamics\u002Fphase-changes":14181,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-machines":14182,"\u002Fmechanics":14183,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcharge-and-conductors":14184,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcoulombs-law":14185,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-and-force":14186,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-maps":14187,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-dipoles":14188,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fcontinuous-charge-fields":14189,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fgauss-law-and-conductors":14190,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpoint-charge-potential":14191,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpotential-gradients-and-equipotentials":14192,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Felectrostatic-energy-and-pressure":14193,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Flaplace-boundary-problems":14194,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fcontinuous-charge-potentials":14195,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitance-fundamentals":14172,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-networks":14196,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-energy-and-force":14197,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fdielectric-polarization-and-breakdown":14198,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fcurrent-and-resistance":14168,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fkirchhoff-network-analysis":14033,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Frc-transients":14199,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-trajectories":14159,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fhall-effect":14200,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-force-on-conductors":14201,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-dipoles":14202,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmass-spectrometry":14203,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmoving-charge-fields":14204,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fbiot-savart-law":14205,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fcircular-current-loops":14206,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Famperes-law":14207,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fgauss-law-for-magnetism":14208,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmagnetic-materials":14133,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-flux":14209,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Ffaradays-law":14210,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Flenzs-law":14211,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmotional-emf":14212,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Feddy-currents":14213,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fself-inductance":14214,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-energy":14215,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Frl-circuits":14216,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-fundamentals":14151,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Freactance":14150,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Frlc-resonance":14217,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-power":14218,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Ftransformers":14219,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdisplacement-current":14220,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-waves":14221,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-momentum":14222,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdipole-radiation":14223,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fpolarization":14224,"\u002Felectricity-and-magnetism\u002Foptics\u002Freflection-and-refraction":14225,"\u002Felectricity-and-magnetism\u002Foptics\u002Fthin-lenses":14177,"\u002Felectricity-and-magnetism\u002Foptics\u002Fspherical-mirrors":14175,"\u002Felectricity-and-magnetism":14226,"\u002Flinear-algebra\u002Flinear-systems\u002Fsystems-and-echelon-forms":14227,"\u002Flinear-algebra\u002Flinear-systems\u002Fvector-and-matrix-equations":14228,"\u002Flinear-algebra\u002Flinear-systems\u002Fsolution-sets-and-applications":14229,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-independence":14230,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-transformations":14231,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-operations":14232,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-inverse-and-invertibility":14233,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fpartitioned-matrices-and-lu":14234,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fsubspaces-dimension-rank":14235,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fapplications-leontief-and-graphics":14085,"\u002Flinear-algebra\u002Fdeterminants\u002Fdeterminants-and-cofactors":14236,"\u002Flinear-algebra\u002Fdeterminants\u002Fproperties-of-determinants":14237,"\u002Flinear-algebra\u002Fdeterminants\u002Fcramer-volume-and-area":14089,"\u002Flinear-algebra\u002Fvector-spaces\u002Fvector-spaces-and-subspaces":14238,"\u002Flinear-algebra\u002Fvector-spaces\u002Fnull-and-column-spaces":14239,"\u002Flinear-algebra\u002Fvector-spaces\u002Fbases-and-independent-sets":14240,"\u002Flinear-algebra\u002Fvector-spaces\u002Fcoordinate-systems":14241,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdimension-and-rank":14242,"\u002Flinear-algebra\u002Fvector-spaces\u002Fchange-of-basis":14243,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdifference-equations-and-markov":14244,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-eigenvalues":14245,"\u002Flinear-algebra\u002Feigenvalues\u002Fthe-characteristic-equation":14246,"\u002Flinear-algebra\u002Feigenvalues\u002Fdiagonalization":14247,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-linear-transformations":14248,"\u002Flinear-algebra\u002Feigenvalues\u002Fcomplex-eigenvalues":14249,"\u002Flinear-algebra\u002Feigenvalues\u002Fdynamical-systems":14250,"\u002Flinear-algebra\u002Feigenvalues\u002Fpower-method":14251,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-length-orthogonality":14252,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Forthogonal-sets-and-projections":14253,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fgram-schmidt-and-qr":14254,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-problems":14255,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-applications":14256,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-spaces":14257,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fdiagonalizing-symmetric-matrices":14124,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fquadratic-forms":14258,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fconstrained-optimization":14259,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsingular-value-decomposition":14260,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsvd-applications-pca-imaging":14261,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-thinking-and-matrix-computation":14262,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Flu-and-cholesky":14263,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fconditioning-and-floating-point":14264,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fstability-and-error-analysis":14265,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fqr-and-numerical-least-squares":14266,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-eigenvalues-and-svd":14267,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-combinations":14268,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-independence-and-barycentric-coordinates":14269,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fconvex-combinations-and-convex-sets":14270,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fhyperplanes-and-polytopes":14271,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fcurves-and-surfaces":14272,"\u002Flinear-algebra":14273,"\u002Ftheory-of-computation":14274,"\u002Fcomputer-architecture\u002Ffoundations\u002Fbits-bytes-and-words":14275,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-representation":14276,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-arithmetic":14277,"\u002Fcomputer-architecture\u002Ffoundations\u002Ffloating-point":14278,"\u002Fcomputer-architecture\u002Ffoundations\u002Fboolean-algebra-and-bit-manipulation":14279,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fthe-machines-view":14280,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fdata-movement":14281,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farithmetic-and-logic":14282,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fcontrol-flow":14283,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fprocedures":14284,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farrays-structs-and-alignment":14285,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fmemory-layout-and-buffer-overflows":14286,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fwhat-an-isa-is":14287,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Finstruction-formats-and-operands":14288,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Faddressing-modes":14289,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fthe-y86-64-instruction-set":14290,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fy86-64-programming":14291,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Ftransistors-gates-and-boolean-functions":14292,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fcombinational-logic-and-hcl":14293,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmultiplexers-decoders-and-the-alu":14294,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmemory-elements-latches-flip-flops-and-clocking":14295,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fregister-files-and-random-access-memory":14296,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-fetch-decode-execute-cycle":14297,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-seq-stages":14298,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fcontrol-logic-and-sequencing":14299,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fassembling-seq":14300,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Ftracing-a-program":14301,"\u002Fcomputer-architecture\u002Fpipelining\u002Fpipelining-principles":14302,"\u002Fcomputer-architecture\u002Fpipelining\u002Ffrom-seq-to-pipe":14303,"\u002Fcomputer-architecture\u002Fpipelining\u002Fdata-hazards-stalling-and-forwarding":14304,"\u002Fcomputer-architecture\u002Fpipelining\u002Fcontrol-hazards-and-branch-prediction":14305,"\u002Fcomputer-architecture\u002Fpipelining\u002Fthe-complete-pipe-processor":14306,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fstorage-technologies-and-the-latency-gap":14307,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Flocality":14308,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-memories-direct-mapped":14309,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fset-associative-and-write-policies":14310,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-performance-and-cache-friendly-code":14311,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Faddress-spaces-and-translation":14312,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fpage-tables-and-page-faults":14313,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fthe-tlb-and-multi-level-page-tables":14314,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Fexceptional-control-flow":14315,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Finterrupts-and-the-kernel":14316,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fprocesses-threads-and-parallelism":14317,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fhardware-multithreading":14318,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fcache-coherence":14319,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmemory-consistency-and-synchronization":14320,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmulticore-organization":14321,"\u002Fcomputer-architecture\u002Fcapstone\u002Fthe-whole-machine":14322,"\u002Fcomputer-architecture\u002Fcapstone\u002Fassembling-a-complete-cpu":14323,"\u002Fcomputer-architecture":14274,"\u002Fdifferential-equations\u002Ffoundations\u002Fmodels-and-direction-fields":14324,"\u002Fdifferential-equations\u002Ffoundations\u002Fclassification-and-terminology":14325,"\u002Fdifferential-equations\u002Ffirst-order\u002Flinear-first-order-integrating-factors":14326,"\u002Fdifferential-equations\u002Ffirst-order\u002Fseparable-and-exact":14089,"\u002Fdifferential-equations\u002Ffirst-order\u002Fmodeling-first-order":14327,"\u002Fdifferential-equations\u002Ffirst-order\u002Fautonomous-and-population-dynamics":14088,"\u002Fdifferential-equations\u002Ffirst-order\u002Fexistence-uniqueness-euler":14095,"\u002Fdifferential-equations\u002Ffirst-order\u002Ffirst-order-difference-equations":14328,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhomogeneous-constant-coefficients":14329,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fcomplex-and-repeated-roots":14129,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fnonhomogeneous-undetermined-coefficients":14330,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fvariation-of-parameters":14331,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fmechanical-electrical-vibrations":14332,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhigher-order-linear":14333,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fpower-series-ordinary-points":14334,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fregular-singular-frobenius":14335,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fbessel-and-special-functions":14336,"\u002Fdifferential-equations\u002Flaplace\u002Flaplace-definition-ivps":14337,"\u002Fdifferential-equations\u002Flaplace\u002Fstep-impulse-convolution":14338,"\u002Fdifferential-equations\u002Fsystems\u002Fmatrices-eigenvalues-review":14339,"\u002Fdifferential-equations\u002Fsystems\u002Fconstant-coefficient-systems-phase-portraits":14340,"\u002Fdifferential-equations\u002Fsystems\u002Frepeated-eigenvalues-fundamental-matrices":14341,"\u002Fdifferential-equations\u002Fnumerical\u002Feuler-and-runge-kutta":14336,"\u002Fdifferential-equations\u002Fnumerical\u002Fmultistep-systems-stability":14342,"\u002Fdifferential-equations\u002Fnonlinear\u002Fphase-plane-autonomous-stability":14343,"\u002Fdifferential-equations\u002Fnonlinear\u002Flocally-linear-and-liapunov":14344,"\u002Fdifferential-equations\u002Fnonlinear\u002Fcompeting-species-predator-prey-limit-cycles":14345,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Ffourier-series":14346,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fheat-wave-laplace-equations":14347,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fsturm-liouville":14348,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fcalculus-of-variations":14349,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fhistorical-notes":14350,"\u002Fdifferential-equations":14351,"\u002Frelativity\u002Ffoundations\u002Fspecial-relativity-postulates":14352,"\u002Frelativity\u002Ffoundations\u002Florentz-transformation-spacetime":14353,"\u002Frelativity\u002Ffoundations\u002Ftime-dilation-length-contraction":14354,"\u002Frelativity\u002Ffoundations\u002Frelativistic-momentum-energy":14355,"\u002Frelativity\u002Ffoundations\u002Fgeneral-relativity":14234,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fminkowski-spacetime-and-the-interval":14356,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Ffour-vectors-and-index-notation":14357,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fthe-lorentz-group-and-rapidity":14358,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fdoppler-aberration-and-appearance":14359,"\u002Frelativity\u002Frelativistic-dynamics\u002Ffour-momentum-force-and-accelerated-motion":14360,"\u002Frelativity\u002Frelativistic-dynamics\u002Fparticle-decays-and-two-body-kinematics":14361,"\u002Frelativity\u002Frelativistic-dynamics\u002Fcollisions-thresholds-and-the-cm-frame":14104,"\u002Frelativity\u002Frelativistic-dynamics\u002Fmandelstam-variables-and-invariants":14362,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ffour-current-and-the-four-potential":14363,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fthe-electromagnetic-field-tensor":14364,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ftransformation-of-electric-and-magnetic-fields":14365,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fcovariant-maxwell-and-the-stress-energy-tensor":14366,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-equivalence-principle-formalized":14367,"\u002Frelativity\u002Fcurved-spacetime\u002Fmanifolds-vectors-and-the-metric":14368,"\u002Frelativity\u002Fcurved-spacetime\u002Fcovariant-derivative-and-christoffel-symbols":14369,"\u002Frelativity\u002Fcurved-spacetime\u002Fgeodesics-and-the-geodesic-equation":14370,"\u002Frelativity\u002Fcurved-spacetime\u002Fcurvature-riemann-and-geodesic-deviation":14371,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-einstein-field-equations":14326,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fthe-schwarzschild-metric":14372,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fgeodesics-and-orbits-in-schwarzschild":14373,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Flight-bending-and-null-geodesics":14374,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fperihelion-precession-of-mercury":14375,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fdeflection-of-light-and-gravitational-lensing":14376,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fgravitational-redshift-and-shapiro-delay":14255,"\u002Frelativity\u002Ftests-of-general-relativity\u002Frelativity-in-technology-gps":14377,"\u002Frelativity\u002Fblack-holes\u002Fhorizons-and-coordinate-singularities":14378,"\u002Frelativity\u002Fblack-holes\u002Frotating-and-charged-black-holes":14265,"\u002Frelativity\u002Fblack-holes\u002Fblack-hole-thermodynamics":14379,"\u002Frelativity\u002Fgravitational-waves\u002Flinearized-gravity-and-wave-solutions":14380,"\u002Frelativity\u002Fgravitational-waves\u002Fgeneration-and-the-quadrupole-formula":14381,"\u002Frelativity\u002Fgravitational-waves\u002Fdetection-ligo-and-the-first-events":14382,"\u002Frelativity\u002Fcosmological-bridge\u002Fthe-cosmological-principle-and-flrw-metric":14383,"\u002Frelativity\u002Fcosmological-bridge\u002Ffriedmann-equations-and-cosmic-dynamics":14384,"\u002Frelativity":14385,"\u002Fphysical-computing":14274,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fblackbody-radiation-and-the-planck-quantum":14386,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-photoelectric-effect-and-the-photon":14365,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fx-rays-and-the-compton-effect":14387,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-old-quantum-theory-bohr-and-sommerfeld":14388,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fde-broglie-waves-and-electron-diffraction":14389,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fwave-packets-and-the-probability-interpretation":14390,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fthe-uncertainty-principle":14391,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-schrodinger-equation-in-one-dimension":14392,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-free-particle-and-wave-packet-dynamics":14393,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fparticle-in-infinite-and-finite-square-wells":14341,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Foperators-expectation-values-and-the-harmonic-oscillator":14265,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-dirac-delta-potential":14394,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fbarrier-penetration-and-quantum-tunneling":14395,"\u002Fquantum-mechanics\u002Fformalism\u002Fhilbert-space-and-dirac-notation":14396,"\u002Fquantum-mechanics\u002Fformalism\u002Fobservables-hermitian-operators-and-eigenvalues":14397,"\u002Fquantum-mechanics\u002Fformalism\u002Fthe-postulates-and-quantum-measurement":14393,"\u002Fquantum-mechanics\u002Fformalism\u002Fposition-momentum-and-continuous-spectra":14398,"\u002Fquantum-mechanics\u002Fformalism\u002Fcommutators-and-the-generalized-uncertainty-principle":14373,"\u002Fquantum-mechanics\u002Fformalism\u002Ftime-evolution-schrodinger-and-heisenberg-pictures":14126,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fladder-operators-and-the-number-states":14399,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fcoherent-and-squeezed-states":14400,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fsymmetries-generators-and-conservation-laws":14106,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fparity-time-reversal-and-discrete-symmetries":14401,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Forbital-angular-momentum-and-spherical-harmonics":14402,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Fthe-angular-momentum-algebra":14403,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Faddition-of-angular-momenta-and-clebsch-gordan":14404,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-schrodinger-equation-in-three-dimensions":14405,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-hydrogen-atom":14406,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-isotropic-oscillator-and-hidden-symmetry":14407,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-half-pauli-matrices-and-stern-gerlach":14408,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-in-a-magnetic-field-precession-and-resonance":14409,"\u002Fquantum-mechanics\u002Fspin\u002Ftwo-level-systems-and-the-bloch-sphere":14365,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fidentical-particles-and-exchange-symmetry":14410,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fthe-pauli-principle-atoms-and-the-periodic-table":14411,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ftime-independent-perturbation-theory":14412,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ffine-structure-and-the-real-hydrogen-atom":14399,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-zeeman-and-stark-effects":14087,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-variational-method":14413,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-wkb-approximation":14414,"\u002Fquantum-mechanics":14415,"\u002Freal-analysis\u002Ffoundations\u002Fsets-logic-functions":14349,"\u002Freal-analysis\u002Ffoundations\u002Fordered-fields-completeness":14416,"\u002Freal-analysis\u002Ffoundations\u002Fabsolute-value-bounds":14417,"\u002Freal-analysis\u002Ffoundations\u002Fintervals-uncountability":14238,"\u002Freal-analysis\u002Fsequences-series\u002Fsequences-limits":14418,"\u002Freal-analysis\u002Fsequences-series\u002Flimit-laws-monotone":14124,"\u002Freal-analysis\u002Fsequences-series\u002Flimsup-bolzano-weierstrass":14419,"\u002Freal-analysis\u002Fsequences-series\u002Fcauchy-completeness":14420,"\u002Freal-analysis\u002Fsequences-series\u002Fseries-convergence":14261,"\u002Freal-analysis\u002Fsequences-series\u002Fabsolute-conditional-rearrangement":14371,"\u002Freal-analysis\u002Fmetric-spaces\u002Fmetric-spaces-norms":14421,"\u002Freal-analysis\u002Fmetric-spaces\u002Fopen-closed-sets":14422,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconvergence-completeness":14423,"\u002Freal-analysis\u002Fmetric-spaces\u002Fcompactness":14424,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconnectedness":14425,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-of-functions":14398,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuous-functions":14426,"\u002Freal-analysis\u002Fcontinuity\u002Fevt-ivt":14231,"\u002Freal-analysis\u002Fcontinuity\u002Funiform-continuity":14427,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuity-metric-spaces":14428,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-infinity-monotone":14085,"\u002Freal-analysis\u002Fdifferentiation\u002Fthe-derivative":14429,"\u002Freal-analysis\u002Fdifferentiation\u002Fmean-value-theorem":14430,"\u002Freal-analysis\u002Fdifferentiation\u002Ftaylors-theorem":14387,"\u002Freal-analysis\u002Fdifferentiation\u002Finverse-function-1d":14114,"\u002Freal-analysis\u002Friemann-integration\u002Fdarboux-integral":14265,"\u002Freal-analysis\u002Friemann-integration\u002Fintegrability-classes":14431,"\u002Freal-analysis\u002Friemann-integration\u002Fproperties-of-the-integral":14432,"\u002Freal-analysis\u002Friemann-integration\u002Ffundamental-theorem":14250,"\u002Freal-analysis\u002Friemann-integration\u002Flog-exp-improper":14375,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpointwise-uniform-convergence":14433,"\u002Freal-analysis\u002Ffunction-sequences\u002Finterchange-of-limits":14434,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpower-series-weierstrass":14435,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpicard-ode":14270,"\u002Freal-analysis\u002Fseveral-variables\u002Fdifferentiability-rn":14436,"\u002Freal-analysis\u002Fseveral-variables\u002Fgradient-chain-rule":14437,"\u002Freal-analysis\u002Fseveral-variables\u002Fhigher-derivatives-taylor-extrema":14438,"\u002Freal-analysis\u002Fseveral-variables\u002Finverse-implicit-theorems":14438,"\u002Freal-analysis\u002Fseveral-variables\u002Fmultiple-integrals":14439,"\u002Freal-analysis":14440,"\u002Fabstract-algebra\u002Ffoundations\u002Fsets-functions-relations":14441,"\u002Fabstract-algebra\u002Ffoundations\u002Fintegers-and-modular-arithmetic":14442,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fgroup-axioms-and-first-examples":14443,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fdihedral-and-symmetric-groups":14444,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fmatrix-and-quaternion-groups":14445,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fhomomorphisms-and-group-actions":14446,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fsubgroups-and-substructures":14447,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcyclic-groups":14448,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fgeneration-and-subgroup-lattices":14449,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcosets-lagrange-and-normal-subgroups":14450,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fisomorphism-theorems":14414,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcomposition-series-and-the-alternating-group":14451,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Factions-and-cayleys-theorem":14443,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fconjugation-and-the-class-equation":14360,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fsylow-theorems":14452,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fautomorphisms-and-simple-groups":14453,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fdirect-products-and-finite-abelian-groups":14454,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fsemidirect-products":14455,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fnilpotent-and-solvable-groups":14456,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fclassifying-small-groups":14457,"\u002Fabstract-algebra\u002Fring-theory\u002Frings-definitions-and-examples":14458,"\u002Fabstract-algebra\u002Fring-theory\u002Fideals-quotients-and-homomorphisms":14459,"\u002Fabstract-algebra\u002Fring-theory\u002Ffractions-and-the-chinese-remainder-theorem":14453,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Feuclidean-domains-pids-ufds":14460,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fpolynomial-rings-over-fields":14429,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fgauss-lemma-and-unique-factorization":14461,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Firreducibility-criteria-and-groebner":14462,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fintroduction-to-modules":14463,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ffree-modules-and-direct-sums":14464,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ftensor-products-and-exact-sequences":14465,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fvector-spaces-and-linear-maps":14466,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fstructure-theorem-over-pids":14467,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Frational-canonical-form":14468,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fjordan-canonical-form":14469,"\u002Fabstract-algebra\u002Ffield-theory\u002Ffield-extensions-and-algebraic-elements":14470,"\u002Fabstract-algebra\u002Ffield-theory\u002Fstraightedge-and-compass-constructions":14103,"\u002Fabstract-algebra\u002Ffield-theory\u002Fsplitting-fields-and-algebraic-closure":14471,"\u002Fabstract-algebra\u002Ffield-theory\u002Fseparable-and-cyclotomic-extensions":14472,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fthe-galois-correspondence":14345,"\u002Fabstract-algebra\u002Fgalois-theory\u002Ffinite-fields":14473,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fcyclotomic-and-abelian-extensions":14474,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fgalois-groups-of-polynomials":14402,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fsolvability-by-radicals-and-the-quintic":14474,"\u002Fabstract-algebra\u002Fcapstone\u002Fcommutative-algebra-and-algebraic-geometry":14475,"\u002Fabstract-algebra\u002Fcapstone\u002Frepresentation-and-character-theory":14476,"\u002Fabstract-algebra":14477,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fatomic-spectra-rutherford":14478,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-model-hydrogen":14479,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fx-ray-spectra-franck-hertz":14480,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-sommerfeld-old-quantum-theory":14481,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fold-quantum-theory-limits-wkb":14482,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fschrodinger-3d-hydrogen":14394,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fhydrogen-wave-functions":14483,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fradial-equation-in-full":14484,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fsymmetry-degeneracy-runge-lenz":14485,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fexpectation-values-virial":14486,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fquantum-defects-alkali-spectra":14487,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Frydberg-atoms":14488,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Frelativistic-kinetic-correction":14489,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fspin-orbit-thomas-precession":14228,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdarwin-term-fine-structure-formula":14357,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdirac-equation-hydrogen":14103,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Flamb-shift-qed":14490,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fhyperfine-structure-21cm":14087,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fnuclear-effects-isotope-shift":14491,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fperiodic-table-atomic-spectra":14492,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fcentral-field-self-consistent":14125,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fidentical-particles-hartree-fock":14419,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhelium-two-electron-atom":14493,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fls-jj-coupling-term-symbols":14494,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhund-rules-ground-terms":14495,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fzeeman-effect":14496,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fpaschen-back-intermediate":14497,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fstark-effect-polarizability":14498,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Ftime-dependent-perturbation-golden-rule":14499,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fdipole-approximation-einstein-coefficients":14500,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fselection-rules-forbidden-transitions":14501,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Flifetimes-and-line-shapes":14502,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Flaser-principles":14503,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fspectroscopy-techniques":14504,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fline-catalog-nist-asd":14505,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Flaser-cooling-doppler":14506,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fsub-doppler-trapping":14450,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fbose-einstein-condensation":14507,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Foptical-clocks-precision":14508,"\u002Fatomic-physics":14509,"\u002Fdatabases":14274,"\u002Fcategory-theory\u002Ffoundations\u002Fwhat-is-a-category":14510,"\u002Fcategory-theory\u002Ffoundations\u002Fexamples-of-categories":14511,"\u002Fcategory-theory\u002Ffoundations\u002Fspecial-morphisms":14512,"\u002Fcategory-theory\u002Ffoundations\u002Ffunctors":14441,"\u002Fcategory-theory\u002Ffoundations\u002Fnatural-transformations":14513,"\u002Fcategory-theory\u002Ffoundations\u002Fsize-and-set-theory":14514,"\u002Fcategory-theory\u002Funiversal-properties\u002Funiversal-properties":14515,"\u002Fcategory-theory\u002Funiversal-properties\u002Fproducts-and-coproducts":14516,"\u002Fcategory-theory\u002Funiversal-properties\u002Fconstructions-on-categories":14517,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Frepresentable-functors":14518,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-lemma":14519,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-consequences":14520,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits":14521,"\u002Fcategory-theory\u002Flimits-colimits\u002Fproducts-equalizers-pullbacks":14522,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcolimits":14523,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcomputing-limits":14524,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits-and-functors":14525,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions":14526,"\u002Fcategory-theory\u002Fadjunctions\u002Funits-and-counits":14527,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions-via-universal-arrows":14528,"\u002Fcategory-theory\u002Fadjunctions\u002Ffree-forgetful-adjunctions":14529,"\u002Fcategory-theory\u002Fadjoints-limits\u002Flimits-via-adjoints":14530,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fpresheaf-limits-colimits":14531,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoints-preserve-limits":14532,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoint-functor-theorem":14523,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fmonads":14533,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-eilenberg-moore":14534,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fkleisli-and-programming":14535,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-for-endofunctors":14536,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Fcartesian-closed-categories":14537,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Flambda-calculus-correspondence":14482,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Ffixed-points-and-recursion":14538,"\u002Fcategory-theory":14539,"\u002Fdeep-learning\u002Fmathematical-background\u002Flinear-algebra-for-deep-learning":14540,"\u002Fdeep-learning\u002Fmathematical-background\u002Fprobability-and-information-theory":14541,"\u002Fdeep-learning\u002Fmathematical-background\u002Fnumerical-computation":14542,"\u002Fdeep-learning\u002Fmathematical-background\u002Fcalculus":14543,"\u002Fdeep-learning\u002Ffoundations\u002Fwhat-is-deep-learning":14544,"\u002Fdeep-learning\u002Ffoundations\u002Fmachine-learning-refresher":14545,"\u002Fdeep-learning\u002Ffoundations\u002Flinear-models-and-the-perceptron":14504,"\u002Fdeep-learning\u002Fneural-networks\u002Fthe-multilayer-perceptron":14546,"\u002Fdeep-learning\u002Fneural-networks\u002Factivation-functions":14547,"\u002Fdeep-learning\u002Fneural-networks\u002Funiversal-approximation":14548,"\u002Fdeep-learning\u002Fneural-networks\u002Fbackpropagation":14549,"\u002Fdeep-learning\u002Fneural-networks\u002Floss-functions-and-output-units":14550,"\u002Fdeep-learning\u002Foptimization\u002Fgradient-descent-and-sgd":14551,"\u002Fdeep-learning\u002Foptimization\u002Fmomentum-and-adaptive-methods":14552,"\u002Fdeep-learning\u002Foptimization\u002Finitialization":14553,"\u002Fdeep-learning\u002Foptimization\u002Fthe-optimization-landscape":14554,"\u002Fdeep-learning\u002Foptimization\u002Fsecond-order-and-approximate-methods":14555,"\u002Fdeep-learning\u002Fregularization\u002Fregularization-overview":14556,"\u002Fdeep-learning\u002Fregularization\u002Fdropout-and-data-augmentation":14557,"\u002Fdeep-learning\u002Fregularization\u002Fearly-stopping-and-parameter-sharing":14558,"\u002Fdeep-learning\u002Fregularization\u002Fnormalization":14559,"\u002Fdeep-learning\u002Farchitectures\u002Fconvolutional-networks":14560,"\u002Fdeep-learning\u002Farchitectures\u002Fcnn-architectures":14561,"\u002Fdeep-learning\u002Farchitectures\u002Frecurrent-networks":14562,"\u002Fdeep-learning\u002Farchitectures\u002Flstm-and-gru":14563,"\u002Fdeep-learning\u002Farchitectures\u002Fattention-and-transformers":14564,"\u002Fdeep-learning\u002Farchitectures\u002Fthe-transformer-architecture":14565,"\u002Fdeep-learning\u002Farchitectures\u002Ftransformers-in-practice":14566,"\u002Fdeep-learning\u002Farchitectures\u002Fgraph-neural-networks":14567,"\u002Fdeep-learning\u002Farchitectures\u002Fstate-space-models":14568,"\u002Fdeep-learning\u002Ftheory\u002Fgeneralization-theory":13985,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-robustness":14569,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-defenses":14570,"\u002Fdeep-learning\u002Ftheory\u002Fbayesian-and-ensemble-methods":14571,"\u002Fdeep-learning\u002Ftheory\u002Fdeep-equilibrium-models":14516,"\u002Fdeep-learning\u002Fgenerative-models\u002Flinear-factor-models":14572,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoencoders":14573,"\u002Fdeep-learning\u002Fgenerative-models\u002Fvariational-autoencoders":14574,"\u002Fdeep-learning\u002Fgenerative-models\u002Fgenerative-adversarial-networks":14575,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoregressive-and-normalizing-flows":14576,"\u002Fdeep-learning\u002Fgenerative-models\u002Fenergy-based-and-boltzmann-machines":14577,"\u002Fdeep-learning\u002Fgenerative-models\u002Fdiffusion-and-score-based-models":14578,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fstructured-probabilistic-models":14068,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fmonte-carlo-and-mcmc":14579,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fapproximate-inference":14580,"\u002Fdeep-learning\u002Fpractical\u002Fpractical-methodology":14304,"\u002Fdeep-learning\u002Fpractical\u002Fhyperparameters-and-debugging":14581,"\u002Fdeep-learning\u002Fpractical\u002Frepresentation-learning":14582,"\u002Fdeep-learning\u002Fpractical\u002Ftransfer-learning":14583,"\u002Fdeep-learning\u002Fpractical\u002Fapplications":14584,"\u002Fdeep-learning\u002Fpractical\u002Fmodel-compression-and-distillation":14585,"\u002Fdeep-learning\u002Fpractical\u002Fmeta-learning-and-few-shot":14586,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Flarge-language-models":14587,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fscaling-inference-and-alignment":14588,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fseq2seq-pretraining-and-bart":14589,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ftext-to-text-transfer-and-conditional-generation":14590,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fspeech-and-audio-models":14591,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fself-supervised-speech-and-synthesis":14592,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fai-agents":14277,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fagent-memory-retrieval-and-orchestration":14593,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmixture-of-experts":14594,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmultimodal-models":14595,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ffusion-and-vision-language-models":14596,"\u002Fdeep-learning\u002Freinforcement-learning\u002Ffoundations-of-reinforcement-learning":14314,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fmodel-free-prediction-and-control":14597,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fdeep-q-networks":14598,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fpolicy-gradients-and-actor-critic":14599,"\u002Fdeep-learning\u002Freinforcement-learning\u002Frl-from-human-feedback":14600,"\u002Fdeep-learning":14274,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fequilibrium-state-variables-zeroth-law":14601,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Ffirst-law-heat-and-work":14399,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fsecond-law-entropy-and-the-carnot-bound":14602,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fthermodynamic-potentials-and-maxwell-relations":14603,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fstability-response-functions-and-the-third-law":14604,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fclassical-statistics-and-equipartition":14605,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fphase-space-and-liouvilles-theorem":14606,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fensembles-and-the-equal-probability-postulate":14607,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fstatistical-entropy-boltzmann-and-gibbs":14608,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fmicrocanonical-ensemble-and-entropy":14609,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fequilibrium-conditions-temperature-pressure-chemical-potential":14446,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fideal-gas-phase-space-and-the-sackur-tetrode-entropy":14610,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Ftwo-state-systems-paramagnets-and-negative-temperature":14611,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fcanonical-ensemble-and-the-boltzmann-distribution":14612,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fpartition-function-and-the-helmholtz-free-energy":14333,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fenergy-fluctuations-and-ensemble-equivalence":14126,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fthe-einstein-solid-and-harmonic-systems":14613,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fparamagnetism-and-the-schottky-anomaly":14614,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fideal-gas-partition-function-and-the-gibbs-paradox":14615,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fequipartition-and-the-virial-theorem":14260,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fmolecular-gases-rotation-and-vibration":14616,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fgrand-canonical-ensemble-and-the-grand-partition-function":14617,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fchemical-potential-fugacity-and-number-fluctuations":14338,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fensemble-summary-and-the-thermodynamic-web":14265,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fquantum-statistics-bose-einstein-and-fermi-dirac":14618,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fderiving-the-quantum-distributions":14234,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fthe-classical-limit-and-quantum-concentration":14104,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fideal-quantum-gases-general-framework":14120,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-and-the-fermion-gas":14619,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthe-photon-gas-and-plancks-radiation-law":14620,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fblackbody-thermodynamics-and-radiation-pressure":14621,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fphonons-and-the-debye-model":14252,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-derived":14622,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthermodynamics-of-the-bose-gas-and-superfluidity":14114,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fthe-ideal-fermi-gas-at-zero-temperature":14623,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fsommerfeld-expansion-and-electrons-in-metals":14624,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":14625,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fneutron-stars-and-nuclear-matter":14626,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-cluster-expansion-and-virial-coefficients":14448,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-van-der-waals-gas-and-liquid-gas-coexistence":14627,"\u002Fstatistical-mechanics\u002Finteractions\u002Fquantum-gases-with-interactions-and-exchange":14628,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fphases-coexistence-and-classification":14629,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-ising-model-and-exact-solutions":14630,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fmean-field-theory-and-the-weiss-model":14394,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fcritical-exponents-and-landau-theory":14631,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-renormalization-group-idea":14372,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fthermodynamic-fluctuations-and-response":14632,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fbrownian-motion-and-the-langevin-equation":14233,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Flinear-response-and-the-fluctuation-dissipation-theorem":14633,"\u002Fstatistical-mechanics":14634,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fbonding-mechanisms":14635,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fmolecular-orbitals-and-h2-plus":14099,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fhydrogen-molecule-and-exchange":14359,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fvan-der-waals-forces":14636,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Frotational-vibrational-spectra":14637,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Fanharmonicity-and-rovibrational-structure":14638,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Framan-and-electronic-bands":14639,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Flasers-and-masers":14640,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fstructure-of-solids":14641,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fbravais-lattices-and-crystal-systems":14232,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Freciprocal-lattice-and-brillouin-zones":14642,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fdiffraction-and-structure-factors":14643,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonon-dispersion":14644,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonons-quantization-and-dos":14645,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fdebye-einstein-heat-capacity":14375,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fanharmonicity-and-thermal-transport":14646,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ffree-electron-gas-and-conduction":14647,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fsommerfeld-model-and-heat-capacity":14648,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ftransport-and-the-hall-effect":14649,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fscreening-and-plasmons":14650,"\u002Fcondensed-matter\u002Fband-theory\u002Fblochs-theorem-and-energy-bands":14430,"\u002Fcondensed-matter\u002Fband-theory\u002Fnearly-free-electron-model":14374,"\u002Fcondensed-matter\u002Fband-theory\u002Ftight-binding-method":14651,"\u002Fcondensed-matter\u002Fband-theory\u002Ffermi-surfaces-and-semiclassical-dynamics":14652,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fsemiconductor-bands-and-junctions":14653,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fintrinsic-and-extrinsic-semiconductors":14654,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fcarrier-transport-and-recombination":14365,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fthe-pn-junction":14655,"\u002Fcondensed-matter\u002Fsemiconductors\u002Ftransistors-and-optoelectronics":14656,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fdielectrics-and-polarization":14601,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fferroelectrics-and-piezoelectrics":14502,"\u002Fcondensed-matter\u002Fmagnetism\u002Fdiamagnetism-and-paramagnetism":14229,"\u002Fcondensed-matter\u002Fmagnetism\u002Fexchange-and-ferromagnetism":14657,"\u002Fcondensed-matter\u002Fmagnetism\u002Fantiferromagnetism-and-domains":14095,"\u002Fcondensed-matter\u002Fmagnetism\u002Fspin-waves-and-magnons":14658,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fsuperconductivity-phenomenology":14659,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Flondon-theory-and-the-meissner-effect":14240,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fginzburg-landau-theory":14660,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fbcs-theory":14495,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fjosephson-and-high-tc":14661,"\u002Fcondensed-matter\u002Fnanostructures\u002Fquantum-wells-wires-and-dots":14087,"\u002Fcondensed-matter\u002Fnanostructures\u002Finteger-quantum-hall-effect":14662,"\u002Fcondensed-matter\u002Fnanostructures\u002Ffractional-quantum-hall-and-topology":14098,"\u002Fcondensed-matter\u002Fnanostructures\u002Fgraphene-and-dirac-materials":14663,"\u002Fcondensed-matter":14415,"\u002Flogic\u002Ffoundations\u002Flogic-as-a-mathematical-model":14664,"\u002Flogic\u002Fsentential-logic\u002Fformal-languages-and-well-formed-formulas":14665,"\u002Flogic\u002Fsentential-logic\u002Ftruth-assignments-and-tautologies":14666,"\u002Flogic\u002Fsentential-logic\u002Funique-readability-and-parsing":14667,"\u002Flogic\u002Fsentential-logic\u002Finduction-and-recursion":14112,"\u002Flogic\u002Fsentential-logic\u002Fexpressive-completeness-and-normal-forms":14668,"\u002Flogic\u002Fsentential-logic\u002Fboolean-circuits":14669,"\u002Flogic\u002Fsentential-logic\u002Fcompactness-and-effectiveness":14112,"\u002Flogic\u002Ffirst-order-languages\u002Ffirst-order-languages":14670,"\u002Flogic\u002Ffirst-order-languages\u002Fstructures-truth-and-satisfaction":14525,"\u002Flogic\u002Ffirst-order-languages\u002Fdefinability-and-elementary-equivalence":14671,"\u002Flogic\u002Ffirst-order-languages\u002Fterms-substitution-and-parsing":14672,"\u002Flogic\u002Fdeductive-calculus\u002Fa-deductive-calculus":14673,"\u002Flogic\u002Fdeductive-calculus\u002Fdeduction-theorem-and-derived-rules":14671,"\u002Flogic\u002Fdeductive-calculus\u002Fsoundness":14674,"\u002Flogic\u002Fdeductive-calculus\u002Fcompleteness-and-consistency":14675,"\u002Flogic\u002Fmodels-and-theories\u002Fcompactness-and-lowenheim-skolem":14676,"\u002Flogic\u002Fmodels-and-theories\u002Ftheories-elementary-classes-and-categoricity":14677,"\u002Flogic\u002Fmodels-and-theories\u002Finterpretations-between-theories":14678,"\u002Flogic\u002Fmodels-and-theories\u002Fnonstandard-analysis":14679,"\u002Flogic\u002Farithmetic-and-definability\u002Fdefinability-in-arithmetic":14680,"\u002Flogic\u002Farithmetic-and-definability\u002Fnatural-numbers-with-successor":14681,"\u002Flogic\u002Farithmetic-and-definability\u002Fpresburger-and-reducts":14602,"\u002Flogic\u002Farithmetic-and-definability\u002Fa-subtheory-and-representability":14682,"\u002Flogic\u002Fincompleteness\u002Farithmetization-of-syntax":14675,"\u002Flogic\u002Fincompleteness\u002Fincompleteness-and-undecidability":14683,"\u002Flogic\u002Fincompleteness\u002Fsecond-incompleteness-theorem":14684,"\u002Flogic\u002Fcomputability-and-representability\u002Frecursive-functions":14318,"\u002Flogic\u002Fcomputability-and-representability\u002Frepresenting-exponentiation":14685,"\u002Flogic\u002Fsecond-order-logic\u002Fsecond-order-languages":14503,"\u002Flogic\u002Fsecond-order-logic\u002Fskolem-functions-and-many-sorted-logic":14686,"\u002Flogic\u002Fsecond-order-logic\u002Fgeneral-structures":14687,"\u002Flogic":14688,"\u002Freinforcement-learning\u002Ffoundations\u002Fwhat-is-reinforcement-learning":14689,"\u002Freinforcement-learning\u002Ffoundations\u002Fa-brief-history-of-rl":14690,"\u002Freinforcement-learning\u002Ffoundations\u002Fmulti-armed-bandits":14304,"\u002Freinforcement-learning\u002Ffoundations\u002Fbandit-exploration-algorithms":14691,"\u002Freinforcement-learning\u002Ffoundations\u002Fmarkov-decision-processes":14692,"\u002Freinforcement-learning\u002Ffoundations\u002Fvalue-functions-and-optimality":14693,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdynamic-programming":14694,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdp-async-and-gpi":14684,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-methods":14695,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-off-policy":14696,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftemporal-difference-learning":14697,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftd-control-sarsa-and-q-learning":14595,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-bootstrapping":14698,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-off-policy-methods":14699,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-and-learning":14700,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-focusing-and-decision-time":14701,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdecision-time-planning":14702,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-tree-search":14321,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-prediction":14703,"\u002Freinforcement-learning\u002Fapproximation\u002Ffeature-construction-and-nonlinear":14704,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-control":14705,"\u002Freinforcement-learning\u002Fapproximation\u002Faverage-reward-control":14706,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-and-the-deadly-triad":14512,"\u002Freinforcement-learning\u002Fapproximation\u002Fbellman-error-and-gradient-td":14707,"\u002Freinforcement-learning\u002Fapproximation\u002Feligibility-traces":14683,"\u002Freinforcement-learning\u002Fapproximation\u002Ftrue-online-and-sarsa-lambda":14708,"\u002Freinforcement-learning\u002Fapproximation\u002Fpolicy-gradient-methods":14709,"\u002Freinforcement-learning\u002Fapproximation\u002Factor-critic-and-continuous-actions":14710,"\u002Freinforcement-learning\u002Fapproximation\u002Fleast-squares-and-memory-based-methods":14332,"\u002Freinforcement-learning\u002Fapproximation\u002Fmemory-and-kernel-methods":14711,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-eligibility-traces":14462,"\u002Freinforcement-learning\u002Fapproximation\u002Fstable-off-policy-traces":14712,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdeep-q-networks":14713,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdqn-improvements":14294,"\u002Freinforcement-learning\u002Fdeep-rl\u002Factor-critic-and-ppo":14714,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fppo-and-continuous-control":14715,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fcase-studies":14716,"\u002Freinforcement-learning\u002Fdeep-rl\u002Frl-beyond-games":14717,"\u002Freinforcement-learning\u002Fdeep-rl\u002Ffrontiers":14718,"\u002Freinforcement-learning\u002Fdeep-rl\u002Freward-design-and-open-problems":14571,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow":14719,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow-part-2":14720,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control":14721,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control-part-2":14606,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl":14722,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl-part-2":14723,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration":14724,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration-part-2":14321,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl":14388,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl-part-2":14725,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl":14726,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl-part-2":14727,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl":14728,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl-part-2":14729,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl":14730,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl-part-2":14731,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Frlhf-and-language-models":14732,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps":14733,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps-part-2":14734,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl":14735,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl-part-2":14736,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmeta-rl-and-generalization":14737,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fpsychology-of-reinforcement":14738,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Finstrumental-conditioning-and-control":14739,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-and-td-error":14740,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-in-the-brain":14741,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fanimal-learning-and-cognition":14742,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fcognitive-maps-and-planning":14743,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fneuroscience-of-reinforcement":14744,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fseveral-learning-systems":14745,"\u002Freinforcement-learning":14274,"\u002Fartificial-intelligence\u002Ffoundations\u002Fwhat-is-ai":14746,"\u002Fartificial-intelligence\u002Ffoundations\u002Ffoundations-of-ai":14747,"\u002Fartificial-intelligence\u002Ffoundations\u002Fintelligent-agents":14748,"\u002Fartificial-intelligence\u002Ffoundations\u002Fagent-architectures":14749,"\u002Fartificial-intelligence\u002Fsearch\u002Funinformed-search":14750,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-strategies-compared":14751,"\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search":14752,"\u002Fartificial-intelligence\u002Fsearch\u002Fheuristic-functions":14753,"\u002Fartificial-intelligence\u002Fsearch\u002Flocal-search":14754,"\u002Fartificial-intelligence\u002Fsearch\u002Fpopulation-and-continuous-search":14755,"\u002Fartificial-intelligence\u002Fsearch\u002Fadversarial-search":14756,"\u002Fartificial-intelligence\u002Fsearch\u002Fgames-of-chance-and-imperfect-information":14757,"\u002Fartificial-intelligence\u002Fsearch\u002Fconstraint-satisfaction":14758,"\u002Fartificial-intelligence\u002Fsearch\u002Fcsp-search-and-structure":14600,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-under-uncertainty":14456,"\u002Fartificial-intelligence\u002Fsearch\u002Fbelief-state-and-online-search":14759,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic":14760,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-inference":14761,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic":14762,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic-in-use":14763,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Finference-and-resolution":14764,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-resolution":14582,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fclassical-planning":14765,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-graphs-and-graphplan":14766,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-in-the-real-world":14767,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-under-uncertainty":14768,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fknowledge-representation":14769,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Freasoning-systems-and-defaults":14770,"\u002Fartificial-intelligence\u002Funcertainty\u002Fprobability-and-bayes":14771,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayes-rule-and-naive-bayes":14772,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayesian-networks":14773,"\u002Fartificial-intelligence\u002Funcertainty\u002Finference-in-bayesian-networks":14774,"\u002Fartificial-intelligence\u002Funcertainty\u002Freasoning-over-time":14775,"\u002Fartificial-intelligence\u002Funcertainty\u002Ftracking-and-data-association":14776,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions":14515,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmarkov-decision-processes":14766,"\u002Fartificial-intelligence\u002Funcertainty\u002Fdecision-networks-and-game-theory":14777,"\u002Fartificial-intelligence\u002Funcertainty\u002Fgame-theory-and-mechanism-design":14055,"\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples":14778,"\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families":14779,"\u002Fartificial-intelligence\u002Flearning\u002Fprobabilistic-learning":14780,"\u002Fartificial-intelligence\u002Flearning\u002Fexpectation-maximization":14781,"\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning":14782,"\u002Fartificial-intelligence\u002Flearning\u002Fgeneralization-and-policy-search":14564,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-in-learning":14783,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-based-learning-methods":14784,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fvision-and-perception":14785,"\u002Fartificial-intelligence\u002Ffrontiers\u002Freconstructing-the-3d-world":14786,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobotics":14787,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobot-planning-and-control":14788,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnatural-language-in-ai":14789,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnlp-grammar-translation-and-speech":14790,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fphilosophy-and-future":14791,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fai-ethics-and-future":14792,"\u002Fartificial-intelligence":14274,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-constituents-nuclide-chart":14531,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-size-charge-distributions":14793,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-masses-binding-energy":14091,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fsemi-empirical-mass-formula":14089,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-moments-multipoles":14624,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnuclear-force-shell-overview":14794,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fthe-deuteron":14117,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnucleon-nucleon-scattering":14427,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fmeson-theory-isospin":14795,"\u002Fnuclear-physics\u002Fnuclear-models\u002Ffermi-gas-model":14796,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fliquid-drop-collective-coordinates":14797,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fshell-model-single-particle":14487,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fcollective-model-rotations-vibrations":14798,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-law-modes":14799,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-kinetics-equilibrium":14800,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-decay-gamow-theory":14685,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-fine-structure-hindrance":14801,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fbeta-decay-energetics-neutrino":14802,"\u002Fnuclear-physics\u002Fbeta-decay\u002Ffermi-theory-beta-decay":14101,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fweak-interaction-parity-violation":14330,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fdouble-beta-decay-neutrino-mass":14803,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fgamma-multipole-radiation":14431,"\u002Fnuclear-physics\u002Fgamma-decay\u002Finternal-conversion-isomers":14804,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fangular-correlations-mossbauer":14805,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Freaction-kinematics-cross-sections":14326,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fcompound-nucleus-resonances":14414,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fdirect-reactions-optical-model":14806,"\u002Fnuclear-physics\u002Ffission\u002Ffission-barrier-dynamics":14807,"\u002Fnuclear-physics\u002Ffission\u002Fchain-reactions-reactor-physics":14808,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Ffusion-reactions-confinement":14096,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fstellar-nucleosynthesis":14471,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fbig-bang-nucleosynthesis":14333,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fcharged-particle-stopping-power":14809,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fphoton-neutron-interactions":14381,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fradiation-detectors":14445,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fdosimetry-radiation-biology":14810,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fnuclear-applications-dating-medicine":14811,"\u002Fnuclear-physics":14812,"\u002Fnatural-language-processing\u002Ffoundations\u002Fwhat-is-nlp":14813,"\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization":14814,"\u002Fnatural-language-processing\u002Ffoundations\u002Fminimum-edit-distance":14452,"\u002Fnatural-language-processing\u002Ffoundations\u002Fn-gram-language-models":14815,"\u002Fnatural-language-processing\u002Ffoundations\u002Fsmoothing-and-backoff":14816,"\u002Fnatural-language-processing\u002Fclassification\u002Fnaive-bayes-and-sentiment":14817,"\u002Fnatural-language-processing\u002Fclassification\u002Fevaluating-classifiers":14300,"\u002Fnatural-language-processing\u002Fclassification\u002Flogistic-regression":14818,"\u002Fnatural-language-processing\u002Fclassification\u002Fsentiment-and-affect-lexicons":14819,"\u002Fnatural-language-processing\u002Fsemantics\u002Fvector-semantics-and-embeddings":14596,"\u002Fnatural-language-processing\u002Fsemantics\u002Fstatic-word-embeddings":14820,"\u002Fnatural-language-processing\u002Fsemantics\u002Fneural-language-models":14765,"\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling":14821,"\u002Fnatural-language-processing\u002Fsequences\u002Fcrfs-and-neural-taggers":14822,"\u002Fnatural-language-processing\u002Fsequences\u002Frnns-and-lstms":14823,"\u002Fnatural-language-processing\u002Ftransformers\u002Ftransformers-and-attention":14824,"\u002Fnatural-language-processing\u002Ftransformers\u002Fthe-transformer-architecture":14825,"\u002Fnatural-language-processing\u002Ftransformers\u002Flarge-language-models":14826,"\u002Fnatural-language-processing\u002Ftransformers\u002Fllm-pretraining-and-scaling":14827,"\u002Fnatural-language-processing\u002Ftransformers\u002Ffine-tuning-and-prompting":14278,"\u002Fnatural-language-processing\u002Ftransformers\u002Fprompting-and-alignment":14828,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-parsing":14829,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcky-scoring-and-evaluation":14771,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdependency-parsing":14830,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fgraph-based-and-neural-dependency-parsing":14831,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fword-senses-and-wsd":14832,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fwsd-in-practice-and-induction":14833,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-roles-and-information-extraction":14834,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Frelations-events-and-templates":14835,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoreference-and-discourse":14836,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoherence-and-discourse-structure":14837,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Flogical-semantics":14695,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcompositional-semantics-and-description-logics":14838,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-parsing":14839,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fneural-semantic-parsing":14840,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Finformation-extraction":14841,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftimes-events-and-templates":14842,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdiscourse-coherence":14843,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fentity-based-and-global-coherence":14844,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-grammars":14845,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftreebanks-and-lexicalized-grammars":14846,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation":14847,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation-decoding-and-evaluation":14848,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering":14849,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering-knowledge-and-llms":14550,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots":14716,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants":14306,"\u002Fnatural-language-processing\u002Fapplications\u002Ftext-summarization":14850,"\u002Fnatural-language-processing\u002Fapplications\u002Fabstractive-summarization-and-evaluation":14851,"\u002Fnatural-language-processing\u002Fspeech\u002Fphonetics":14852,"\u002Fnatural-language-processing\u002Fspeech\u002Facoustic-phonetics":14853,"\u002Fnatural-language-processing\u002Fspeech\u002Fautomatic-speech-recognition":14564,"\u002Fnatural-language-processing\u002Fspeech\u002Fasr-evaluation-and-applications":14854,"\u002Fnatural-language-processing":14274,"\u002Fparticle-physics\u002Ffoundations\u002Fhistorical-overview-particle-zoo":14855,"\u002Fparticle-physics\u002Ffoundations\u002Fparticle-physics-basic-concepts":14472,"\u002Fparticle-physics\u002Ffoundations\u002Ffundamental-interactions-force-carriers":14856,"\u002Fparticle-physics\u002Funits-kinematics\u002Fnatural-units-and-scales":14616,"\u002Fparticle-physics\u002Funits-kinematics\u002Ffour-vectors-invariant-mass":14857,"\u002Fparticle-physics\u002Funits-kinematics\u002Fdecay-scattering-kinematics-mandelstam":14858,"\u002Fparticle-physics\u002Funits-kinematics\u002Fcross-sections-golden-rule":14806,"\u002Fparticle-physics\u002Fsymmetries\u002Fconservation-laws-symmetries":14859,"\u002Fparticle-physics\u002Fsymmetries\u002Fdiscrete-symmetries-cpt":14860,"\u002Fparticle-physics\u002Fsymmetries\u002Fparity-violation-weak":14422,"\u002Fparticle-physics\u002Fsymmetries\u002Fsu2-su3-flavor-symmetry":14124,"\u002Fparticle-physics\u002Fquark-model\u002Feightfold-way-su3":14861,"\u002Fparticle-physics\u002Fquark-model\u002Fmeson-spectroscopy":14376,"\u002Fparticle-physics\u002Fquark-model\u002Fbaryon-spectroscopy":14862,"\u002Fparticle-physics\u002Fquark-model\u002Fcolor-confinement-exotics":14409,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fklein-gordon-equation":14863,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fdirac-equation-spinors":14662,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fantiparticles-hole-theory":14864,"\u002Fparticle-physics\u002Fqed\u002Ffeynman-rules-qed":14865,"\u002Fparticle-physics\u002Fqed\u002Fqed-tree-processes":14631,"\u002Fparticle-physics\u002Fqed\u002Frenormalization-running-coupling":14866,"\u002Fparticle-physics\u002Fqed\u002Felectron-g-2":14095,"\u002Fparticle-physics\u002Fweak-interaction\u002Fva-structure-weak":14867,"\u002Fparticle-physics\u002Fweak-interaction\u002Fw-z-bosons-decays":14868,"\u002Fparticle-physics\u002Fweak-interaction\u002Fckm-matrix":14869,"\u002Fparticle-physics\u002Fweak-interaction\u002Fcp-violation-kaons-b-mesons":14413,"\u002Fparticle-physics\u002Fqcd\u002Fcolor-su3-gluons":14628,"\u002Fparticle-physics\u002Fqcd\u002Fasymptotic-freedom-confinement":14870,"\u002Fparticle-physics\u002Fqcd\u002Fdeep-inelastic-scattering-partons":14871,"\u002Fparticle-physics\u002Fqcd\u002Fjets-hadronization":14488,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Felectroweak-su2-u1":14872,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fspontaneous-symmetry-breaking":14533,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-mechanism":14873,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-boson-discovery":14874,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fstandard-model":14422,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-oscillations":14455,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-mass-pmns":14875,"\u002Fparticle-physics\u002Fneutrinos\u002Fdirac-majorana-experiments":14876,"\u002Fparticle-physics\u002Fexperiment\u002Faccelerators-luminosity":14877,"\u002Fparticle-physics\u002Fexperiment\u002Fdetectors-subsystems":14511,"\u002Fparticle-physics\u002Fexperiment\u002Fhow-discoveries-are-made":14878,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fbeyond-standard-model":14252,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fgrand-unified-theories":14879,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fsupersymmetry":14355,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fhierarchy-problem-naturalness":14880,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fdark-matter-candidates":14881,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fmatter-antimatter-open-questions":14678,"\u002Fparticle-physics":14882,"\u002Fastrophysics-cosmology\u002Forientation\u002Fthe-sun-and-stars":14473,"\u002Fastrophysics-cosmology\u002Forientation\u002Fstellar-death-final-states":14626,"\u002Fastrophysics-cosmology\u002Forientation\u002Fgalaxies-and-cosmology":14883,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fmagnitudes-fluxes-and-the-distance-modulus":14412,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fstellar-spectra-and-spectral-classification":14884,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Ftelescopes-and-detectors-across-the-spectrum":14472,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fthe-cosmic-distance-ladder":14384,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fblackbody-radiation-and-specific-intensity":14885,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fradiative-transfer-and-the-transfer-equation":14886,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fspectral-line-formation-and-broadening":14887,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fopacity-and-the-rosseland-mean":14888,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fhydrostatic-equilibrium-and-the-virial-theorem":14889,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equations-of-stellar-structure":14677,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equation-of-state-and-polytropes":14608,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-standard-solar-model":14890,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fthermonuclear-reaction-rates-and-the-gamow-peak":14891,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhydrogen-burning-pp-chains-and-cno":14892,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhelium-burning-and-the-triple-alpha-process":14893,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fadvanced-burning-and-neutron-capture-nucleosynthesis":14805,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fphases-of-the-interstellar-medium":14894,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fmolecular-clouds-and-gravitational-collapse":14368,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fprotostars-and-the-pre-main-sequence":14431,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-main-sequence-and-its-structure":14413,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fpost-main-sequence-low-mass-evolution":14895,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-evolution-of-massive-stars":14497,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fstellar-pulsation-and-the-instability-strip":14113,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":14896,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fcore-collapse-supernovae":14897,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fthermonuclear-supernovae-type-ia":14627,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fneutron-stars-and-pulsars":14898,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fblack-holes-schwarzschild-and-kerr":14899,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fbinary-systems-and-mass-transfer":14900,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Faccreting-compact-objects":14901,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fgravitational-waves-from-inspiraling-binaries":14094,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fmultimessenger-astronomy-and-gamma-ray-bursts":14902,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fthe-milky-way":14903,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-morphology-and-classification":14355,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-rotation-curves-and-dark-matter":14904,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Factive-galactic-nuclei-and-supermassive-black-holes":14905,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-clusters-and-large-scale-structure":14620,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-expanding-universe-and-hubbles-law":14276,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-frw-metric-and-cosmological-redshift":14906,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-friedmann-equations-and-cosmic-dynamics":14907,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fcosmological-models-and-distances":14533,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fdark-energy-and-the-accelerating-universe":14519,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fthe-thermal-history-of-the-universe":14531,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fbig-bang-nucleosynthesis":14627,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Frecombination-and-the-cosmic-microwave-background":14908,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcmb-anisotropies-and-cosmological-parameters":14093,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcosmic-inflation":14285,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fstructure-formation-and-the-growth-of-perturbations":14909,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fdark-matter-dark-energy-and-open-questions":14372,"\u002Fastrophysics-cosmology":14477,"\u002Fcolophon":14910,"\u002F":14274},4250,4808,3626,2682,4109,4786,3878,3875,3751,3415,4067,3153,3000,4042,5461,5808,3961,3749,4327,5067,4246,4655,4154,5436,2640,4003,3601,2158,4331,4189,2273,3252,4633,4964,4172,3131,5524,3160,4031,2309,4207,3226,2648,4842,5340,3307,5701,4977,4039,2615,3472,4460,3848,4075,4400,3382,3010,3602,3737,3740,3707,3922,5191,4043,3804,4542,4214,5062,2850,4361,3443,3627,4044,3766,4140,3860,4006,5199,4334,5234,3651,5509,5680,153,1375,1073,1093,1125,1146,1014,1132,876,1541,1189,1173,984,1402,1301,950,1268,1063,1107,1408,1161,925,1012,866,964,1090,1142,1085,1020,1207,973,980,728,764,1225,1329,796,929,801,878,774,1044,1488,1175,1130,890,814,870,154,4073,5140,4961,5127,4870,5382,5195,4955,5369,4501,5576,3824,4132,4289,4307,4570,3403,5084,5105,5201,5116,5341,5175,5368,5188,5211,5499,5155,4981,5125,5415,5255,5304,5130,5167,5552,5164,5094,5239,5036,5190,5004,5099,5035,5159,5088,5026,4937,5023,5264,5244,133,5114,5078,5043,5312,5170,5342,5139,5151,5049,5212,5013,5068,5079,5102,5121,5081,5029,5379,5854,5110,2139,3798,5055,5364,4984,4935,4895,4972,5289,5112,5156,4987,5031,5025,5149,5302,5042,5002,4979,4922,4960,5279,126,1877,1180,1129,907,958,1112,1300,1053,1250,1181,1241,1234,966,1050,734,1190,484,1082,926,733,761,571,607,798,804,952,977,731,784,645,771,1017,742,1004,1000,1562,1254,1288,1101,1011,1486,1061,856,992,1169,988,137,0,2037,1782,2384,2254,2123,2332,1643,1714,2089,1751,1367,1660,2511,1998,1892,1854,1791,2438,2487,1917,2375,2525,2266,1845,2275,1810,1631,2310,2166,2233,2113,2505,2347,2672,2112,2473,2592,2380,3013,2513,3256,3218,2194,2173,2205,2326,2081,3342,3152,1799,1670,1027,960,1095,1291,986,897,1209,1055,1817,1801,1593,1465,1196,1464,1201,1230,1435,1684,1461,1926,1500,1409,1284,1774,1869,162,1487,1122,1188,1001,1351,982,1005,979,1325,1046,943,1279,824,1008,989,1798,1277,1025,987,1043,1211,1074,981,939,1002,739,1139,1108,1013,1070,978,1458,1317,157,1357,1077,2355,1116,1037,1178,1637,1314,1109,1056,1702,1474,1071,1158,832,993,1404,1024,1068,1339,1106,1264,1248,913,1848,1328,1633,1224,1143,135,1378,959,1028,998,911,1527,1203,1266,1483,1165,990,938,965,1257,1418,1099,942,1352,956,1035,1398,1003,1094,1292,138,1721,1827,1449,1354,1148,1184,1285,1281,1213,1290,1271,1252,1274,1778,1591,1503,1437,1571,1584,1957,1117,1781,1648,1342,1667,1510,1965,1607,1365,1849,1259,1303,1356,1238,2208,1564,173,1671,1286,1227,1638,1529,668,1078,918,709,865,880,940,1534,1015,874,922,841,794,1194,822,1105,1658,1359,1296,1438,1921,1844,1570,1429,1324,1400,140,1787,1558,1654,1492,1747,2224,2002,2009,1323,1349,1785,1573,1722,1829,1353,1548,1552,1583,1624,1585,1245,1364,1514,1343,1397,1355,2211,1481,1770,160,2388,2293,2256,2552,2569,2478,2039,2496,2578,2814,2519,2461,2587,2492,2714,3278,2654,3050,2447,2849,2238,2369,2061,2214,2602,2563,2186,2985,2749,2038,2282,2409,2126,2573,2206,2176,2268,2182,2402,2705,2633,2414,2213,2801,3313,3410,3195,1952,2017,1509,2537,2645,2027,2415,2838,2356,1906,3184,2950,2807,2954,1683,1316,1034,1138,1763,1822,1705,1246,1701,1097,1104,1187,1032,1083,1228,916,1489,1033,1652,997,692,837,1023,888,864,1089,1231,1214,1675,1156,1075,1520,1309,139,1205,1051,735,1123,1072,915,567,768,825,1253,983,1007,762,1058,861,862,971,1208,1149,1145,1029,1084,927,810,838,857,807,936,949,2321,1622,1069,1113,1057,854,1958,1528,1618,2049,1432,1679,1796,1685,1346,1275,1476,1505,1610,2018,1599,1215,1838,1909,132,3902,2215,2240,3266,3208,3073,2454,2969,2451,1875,2728,1884,2371,2516,2842,1690,1904,2346,3146,1386,2607,1966,2668,1665,2885,1606,2577,3074,2869,2403,2433,2082,1939,1587,2460,2747,2032,2642,1619,3123,1993,2090,2339,3829,1737,2622,2340,2322,3828,4409,2305,3411,2510,4527,3030,3569,3043,2457,1946,2277,2044,2909,1693,1945,2093,2399,2115,2898,2742,2242,3895,3378,3376,2769,2223,3062,3262,2651,2949,2768,3128,2423,1977,2087,2866,3388,2830,2210,2489,2884,3945,2099,2713,3402,1692,2931,4195,3989,3206,4391,3004,3704,3494,2902,999,881,901,919,748,869,1018,1045,1049,1333,954,1092,1019,976,1771,1480,1396,953,1026,161,3533,2495,1818,3007,2595,3427,3537,2216,1895,2304,3396,1739,2073,1962,2203,1767,2666,2264,2276,2852,1807,3735,1560,4144,1669,1676,1972,2418,3291,1525,2040,2766,2337,2220,2800,3001,2078,1759,2836,1896,2026,1758,1543,1047,896,946,1060,1384,1482,815,1414,1322,1440,1240,1468,1098,1133,847,1009,1381,1052,1191,1258,1370,1712,1441,1199,957,1079,150,1262,1417,1368,1219,1136,1064,1463,1636,1059,931,1115,1736,1174,1376,1363,1411,1247,1746,1313,1299,1617,1102,1076,1495,1265,1193,1263,80,{"\u002Falgorithms\u002Ffoundations\u002Fwhat-is-an-algorithm":14912,"\u002Falgorithms\u002Ffoundations\u002Fproof-techniques":14917,"\u002Falgorithms\u002Ffoundations\u002Fasymptotic-analysis":14921,"\u002Falgorithms\u002Ffoundations\u002Fgrowth-rates-and-loop-analysis":14925,"\u002Falgorithms\u002Ffoundations\u002Frecurrences":14929,"\u002Falgorithms\u002Ffoundations\u002Famortized-analysis":14933,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fmergesort":14937,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fquicksort":14942,"\u002Falgorithms\u002Fdivide-and-conquer\u002Fselection":14946,"\u002Falgorithms\u002Fdivide-and-conquer\u002Ffast-multiplication":14950,"\u002Falgorithms\u002Fsorting\u002Fheaps-and-heapsort":14954,"\u002Falgorithms\u002Fsorting\u002Fsorting-lower-bounds":14959,"\u002Falgorithms\u002Fsorting\u002Flinear-time-sorting":14963,"\u002Falgorithms\u002Fsorting\u002Fexternal-sorting":14967,"\u002Falgorithms\u002Fdata-structures\u002Felementary-structures":14971,"\u002Falgorithms\u002Fdata-structures\u002Fhash-tables":14976,"\u002Falgorithms\u002Fdata-structures\u002Fbinary-search-trees":14980,"\u002Falgorithms\u002Fdata-structures\u002Favl-trees":14984,"\u002Falgorithms\u002Fdata-structures\u002Fbalanced-trees":14988,"\u002Falgorithms\u002Fdata-structures\u002Funion-find":14992,"\u002Falgorithms\u002Fdata-structures\u002Ffenwick-and-segment-trees":14996,"\u002Falgorithms\u002Fdata-structures\u002Fspatial-data-structures":15000,"\u002Falgorithms\u002Fdata-structures\u002Fskip-lists-and-probabilistic-structures":15004,"\u002Falgorithms\u002Fdata-structures\u002Fb-trees":15008,"\u002Falgorithms\u002Fdata-structures\u002Fdata-stream-algorithms":15012,"\u002Falgorithms\u002Fdata-structures\u002Fstreaming-sketches":15016,"\u002Falgorithms\u002Fsequences\u002Ftwo-pointers-and-windows":15020,"\u002Falgorithms\u002Fsequences\u002Fprefix-sums":15025,"\u002Falgorithms\u002Fsequences\u002Fmonotonic-stacks":15029,"\u002Falgorithms\u002Fsequences\u002Fbinary-search-on-the-answer":15033,"\u002Falgorithms\u002Fsequences\u002Fstring-matching":15037,"\u002Falgorithms\u002Fsequences\u002Fkmp-and-z-function":15041,"\u002Falgorithms\u002Fsequences\u002Ftries":15045,"\u002Falgorithms\u002Fsequences\u002Fsuffix-arrays-and-aho-corasick":15049,"\u002Falgorithms\u002Fgraphs\u002Frepresentations-and-traversal":15053,"\u002Falgorithms\u002Fgraphs\u002Fdepth-first-search":15058,"\u002Falgorithms\u002Fgraphs\u002Ftopological-sort-and-scc":15062,"\u002Falgorithms\u002Fgraphs\u002Fminimum-spanning-trees":15066,"\u002Falgorithms\u002Fgraphs\u002Fkruskal-and-prim":15070,"\u002Falgorithms\u002Fgraphs\u002Fshortest-paths":15074,"\u002Falgorithms\u002Fgraphs\u002Fall-pairs-and-negative-weights":15078,"\u002Falgorithms\u002Fgraphs\u002Fnetwork-flow":15082,"\u002Falgorithms\u002Fgraphs\u002Fmax-flow-min-cut":15086,"\u002Falgorithms\u002Fgraphs\u002Fbridges-and-articulation-points":15090,"\u002Falgorithms\u002Fgraphs\u002Flowest-common-ancestor":15094,"\u002Falgorithms\u002Fgraphs\u002Ftwo-sat":15098,"\u002Falgorithms\u002Fgraphs\u002Feulerian-tours":15102,"\u002Falgorithms\u002Fgraphs\u002Fbipartite-matching":15106,"\u002Falgorithms\u002Fgreedy\u002Fthe-greedy-method":15110,"\u002Falgorithms\u002Fgreedy\u002Fscheduling-and-intervals":15115,"\u002Falgorithms\u002Fgreedy\u002Fhuffman-codes":15119,"\u002Falgorithms\u002Fgreedy\u002Fmatroids":15123,"\u002Falgorithms\u002Fgreedy\u002Fstable-matching":15127,"\u002Falgorithms\u002Fdynamic-programming\u002Fprinciples":15131,"\u002Falgorithms\u002Fdynamic-programming\u002Fsequence-dp":15136,"\u002Falgorithms\u002Fdynamic-programming\u002Flongest-increasing-subsequence":15140,"\u002Falgorithms\u002Fdynamic-programming\u002Fknapsack":15144,"\u002Falgorithms\u002Fdynamic-programming\u002Fcoin-change-and-unbounded":15148,"\u002Falgorithms\u002Fdynamic-programming\u002Finterval-dp":15152,"\u002Falgorithms\u002Fdynamic-programming\u002Ftree-dp":15156,"\u002Falgorithms\u002Fdynamic-programming\u002Fbitmask-dp":15160,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-optimizations":15164,"\u002Falgorithms\u002Fdynamic-programming\u002Fdp-on-graphs":15168,"\u002Falgorithms\u002Fdynamic-programming\u002Fdigit-and-probability-dp":15172,"\u002Falgorithms\u002Fbacktracking\u002Fbacktracking-fundamentals":15176,"\u002Falgorithms\u002Fbacktracking\u002Fconstraint-search":15181,"\u002Falgorithms\u002Fbacktracking\u002Fbranch-and-bound":15185,"\u002Falgorithms\u002Fbacktracking\u002Fgraph-backtracking":15189,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fnumber-theory-basics":15193,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmodular-exponentiation-and-primality":15198,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fsieve-and-factorization":15202,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fcombinatorics":15206,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fmatrix-exponentiation":15210,"\u002Falgorithms\u002Fmathematical-algorithms\u002Ffast-fourier-transform":15214,"\u002Falgorithms\u002Fmathematical-algorithms\u002Fgradient-descent":15218,"\u002Falgorithms\u002Fcomputational-geometry\u002Fgeometric-primitives":15222,"\u002Falgorithms\u002Fcomputational-geometry\u002Fconvex-hull":15227,"\u002Falgorithms\u002Fcomputational-geometry\u002Fsweep-line":15231,"\u002Falgorithms\u002Fcomputational-geometry\u002Fpolygons-and-proximity":15235,"\u002Falgorithms\u002Fintractability\u002Fp-np-reductions":15239,"\u002Falgorithms\u002Fintractability\u002Fnp-completeness":15244,"\u002Falgorithms\u002Fintractability\u002Fcoping-with-hardness":15248,"\u002Falgorithms\u002Fintractability\u002Fapproximation-algorithms":15252,"\u002Falgorithms":15256,"\u002Fcalculus\u002Flimits-and-continuity\u002Ffunctions-and-models":15259,"\u002Fcalculus\u002Flimits-and-continuity\u002Fthe-limit-of-a-function":15264,"\u002Fcalculus\u002Flimits-and-continuity\u002Flimit-laws-and-the-precise-definition":15268,"\u002Fcalculus\u002Flimits-and-continuity\u002Fcontinuity":15272,"\u002Fcalculus\u002Fderivatives\u002Fthe-derivative-and-rates-of-change":15276,"\u002Fcalculus\u002Fderivatives\u002Fdifferentiation-rules-and-the-chain-rule":15281,"\u002Fcalculus\u002Fderivatives\u002Fimplicit-differentiation-and-related-rates":15285,"\u002Fcalculus\u002Fderivatives\u002Flinear-approximations-and-differentials":15289,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fextrema-and-the-mean-value-theorem":15293,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fhow-derivatives-shape-a-graph":15298,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fcurve-sketching-and-optimization":15302,"\u002Fcalculus\u002Fapplications-of-derivatives\u002Fnewtons-method-and-antiderivatives":15306,"\u002Fcalculus\u002Fintegrals\u002Farea-and-the-definite-integral":15310,"\u002Fcalculus\u002Fintegrals\u002Fthe-fundamental-theorem-of-calculus":15315,"\u002Fcalculus\u002Fintegrals\u002Fthe-substitution-rule":15319,"\u002Fcalculus\u002Fapplications-of-integration\u002Fareas-and-volumes":15323,"\u002Fcalculus\u002Fapplications-of-integration\u002Fwork-average-value-and-arc-length":15328,"\u002Fcalculus\u002Fapplications-of-integration\u002Fphysics-economics-and-probability":15332,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Finverse-functions-logarithms-and-exponentials":15336,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Fgrowth-decay-inverse-trig-and-hyperbolic-functions":15341,"\u002Fcalculus\u002Fexponential-logarithmic-and-inverse-functions\u002Flhospitals-rule":15345,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fintegration-by-parts":15349,"\u002Fcalculus\u002Ftechniques-of-integration\u002Ftrigonometric-integrals-and-substitution":15354,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fpartial-fractions-and-integration-strategy":15358,"\u002Fcalculus\u002Ftechniques-of-integration\u002Fapproximate-and-improper-integrals":15362,"\u002Fcalculus\u002Fparametric-and-polar\u002Fparametric-curves-and-their-calculus":15366,"\u002Fcalculus\u002Fparametric-and-polar\u002Fpolar-coordinates":15371,"\u002Fcalculus\u002Fparametric-and-polar\u002Fconic-sections":15375,"\u002Fcalculus\u002Fsequences-and-series\u002Fsequences":15379,"\u002Fcalculus\u002Fsequences-and-series\u002Fseries-and-the-integral-test":15384,"\u002Fcalculus\u002Fsequences-and-series\u002Fthe-convergence-tests":15388,"\u002Fcalculus\u002Fsequences-and-series\u002Fpower-series":15392,"\u002Fcalculus\u002Fsequences-and-series\u002Ftaylor-and-maclaurin-series":15396,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvectors-and-the-dot-product":15400,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fthe-cross-product-lines-and-planes":15405,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fcylinders-and-quadric-surfaces":15409,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Fvector-functions-and-space-curves":15413,"\u002Fcalculus\u002Fvectors-and-space-curves\u002Farc-length-curvature-and-motion":15417,"\u002Fcalculus\u002Fpartial-derivatives\u002Ffunctions-of-several-variables":15421,"\u002Fcalculus\u002Fpartial-derivatives\u002Fpartial-derivatives":15426,"\u002Fcalculus\u002Fpartial-derivatives\u002Ftangent-planes-and-the-chain-rule":15429,"\u002Fcalculus\u002Fpartial-derivatives\u002Fdirectional-derivatives-and-the-gradient":15433,"\u002Fcalculus\u002Fpartial-derivatives\u002Foptimization-and-lagrange-multipliers":15437,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fdouble-integrals":15441,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Ftriple-integrals-and-coordinate-systems":15446,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fvector-fields-and-line-integrals":15450,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fgreens-theorem-curl-and-divergence":15454,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fsurface-integrals":15458,"\u002Fcalculus\u002Fmultiple-integrals-and-vector-calculus\u002Fstokes-and-the-divergence-theorem":15462,"\u002Fcalculus":15466,"\u002Fmechanics\u002Ffoundations\u002Fmeasurement-and-dimensions":15469,"\u002Fmechanics\u002Ffoundations\u002Fvector-algebra":15473,"\u002Fmechanics\u002Fkinematics\u002Fone-dimensional-motion":15477,"\u002Fmechanics\u002Fkinematics\u002Fmotion-graphs":15482,"\u002Fmechanics\u002Fkinematics\u002Fprojectile-motion":15486,"\u002Fmechanics\u002Fkinematics\u002Frelative-motion":15490,"\u002Fmechanics\u002Fkinematics\u002Fcircular-motion":15494,"\u002Fmechanics\u002Fdynamics\u002Fnewtons-laws":15498,"\u002Fmechanics\u002Fdynamics\u002Ffree-body-diagrams":15503,"\u002Fmechanics\u002Fdynamics\u002Ffriction-and-curved-motion":15507,"\u002Fmechanics\u002Fdynamics\u002Fnumerical-dynamics":15511,"\u002Fmechanics\u002Fdynamics\u002Fcenter-of-mass-systems":15515,"\u002Fmechanics\u002Fenergy\u002Fwork-and-kinetic-energy":15519,"\u002Fmechanics\u002Fenergy\u002Fpotential-energy":15524,"\u002Fmechanics\u002Fenergy\u002Fmultiparticle-work":15528,"\u002Fmechanics\u002Fenergy\u002Fmass-energy-and-binding":15532,"\u002Fmechanics\u002Fenergy\u002Fphotons-and-quantization":15536,"\u002Fmechanics\u002Fmomentum\u002Fmomentum-and-collisions":15540,"\u002Fmechanics\u002Fmomentum\u002Fcenter-of-mass-collisions":15545,"\u002Fmechanics\u002Fmomentum\u002Frocket-propulsion":15549,"\u002Fmechanics\u002Frotation\u002Frotational-inertia":15553,"\u002Fmechanics\u002Frotation\u002Frotational-dynamics":15558,"\u002Fmechanics\u002Frotation\u002Frolling-motion":15562,"\u002Fmechanics\u002Frotation\u002Fangular-momentum":15566,"\u002Fmechanics\u002Frotation\u002Frolling-resistance":15570,"\u002Fmechanics\u002Frotation\u002Fgyroscopic-precession":15574,"\u002Fmechanics\u002Fgravity-and-matter\u002Fkeplerian-orbits":15578,"\u002Fmechanics\u002Fgravity-and-matter\u002Fgravitational-fields":15583,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstatic-equilibrium":15587,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-statics":15591,"\u002Fmechanics\u002Fgravity-and-matter\u002Ffluid-flow":15595,"\u002Fmechanics\u002Fgravity-and-matter\u002Forbital-motion":15599,"\u002Fmechanics\u002Fgravity-and-matter\u002Fstress-and-elasticity":15603,"\u002Fmechanics\u002Foscillations-waves\u002Fdamped-oscillators":15607,"\u002Fmechanics\u002Foscillations-waves\u002Ftravelling-waves":15612,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-superposition":15616,"\u002Fmechanics\u002Foscillations-waves\u002Fstanding-waves":15620,"\u002Fmechanics\u002Foscillations-waves\u002Fsound-waves":15624,"\u002Fmechanics\u002Foscillations-waves\u002Fdoppler-effect":15628,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-packets":15632,"\u002Fmechanics\u002Foscillations-waves\u002Fbeats-and-coupling":15636,"\u002Fmechanics\u002Foscillations-waves\u002Fsimple-harmonic-motion":15640,"\u002Fmechanics\u002Foscillations-waves\u002Fpendulum-motion":15644,"\u002Fmechanics\u002Foscillations-waves\u002Fdriven-oscillators":15648,"\u002Fmechanics\u002Foscillations-waves\u002Fwave-boundaries":15652,"\u002Fmechanics\u002Fthermodynamics\u002Fkinetic-theory-of-ideal-gases":15656,"\u002Fmechanics\u002Fthermodynamics\u002Ffirst-law-of-thermodynamics":15661,"\u002Fmechanics\u002Fthermodynamics\u002Fentropy-and-the-second-law":15665,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-processes":15669,"\u002Fmechanics\u002Fthermodynamics\u002Fphase-changes":15673,"\u002Fmechanics\u002Fthermodynamics\u002Fthermal-machines":15677,"\u002Fmechanics":15681,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcharge-and-conductors":15684,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Fcoulombs-law":15689,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-and-force":15693,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-field-maps":15697,"\u002Felectricity-and-magnetism\u002Felectric-fields\u002Felectric-dipoles":15701,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fcontinuous-charge-fields":15705,"\u002Felectricity-and-magnetism\u002Fcontinuous-charge-distributions\u002Fgauss-law-and-conductors":15710,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpoint-charge-potential":15714,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fpotential-gradients-and-equipotentials":15719,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Felectrostatic-energy-and-pressure":15723,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Flaplace-boundary-problems":15727,"\u002Felectricity-and-magnetism\u002Felectric-potential\u002Fcontinuous-charge-potentials":15731,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitance-fundamentals":15735,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-networks":15740,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fcapacitor-energy-and-force":15744,"\u002Felectricity-and-magnetism\u002Fcapacitance\u002Fdielectric-polarization-and-breakdown":15748,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fcurrent-and-resistance":15752,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Fkirchhoff-network-analysis":15757,"\u002Felectricity-and-magnetism\u002Fdirect-current-circuits\u002Frc-transients":15761,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-trajectories":15765,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fhall-effect":15770,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-force-on-conductors":15774,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmagnetic-dipoles":15778,"\u002Felectricity-and-magnetism\u002Fmagnetic-field\u002Fmass-spectrometry":15782,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmoving-charge-fields":15786,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fbiot-savart-law":15791,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fcircular-current-loops":15795,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Famperes-law":15799,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fgauss-law-for-magnetism":15803,"\u002Felectricity-and-magnetism\u002Fmagnetic-sources\u002Fmagnetic-materials":15807,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-flux":15811,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Ffaradays-law":15816,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Flenzs-law":15820,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmotional-emf":15824,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Feddy-currents":15828,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fself-inductance":15832,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Fmagnetic-energy":15836,"\u002Felectricity-and-magnetism\u002Felectromagnetic-induction\u002Frl-circuits":15840,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-fundamentals":15844,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Freactance":15849,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Frlc-resonance":15853,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Fac-power":15857,"\u002Felectricity-and-magnetism\u002Falternating-current\u002Ftransformers":15861,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdisplacement-current":15865,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-waves":15870,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Felectromagnetic-momentum":15874,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fdipole-radiation":15878,"\u002Felectricity-and-magnetism\u002Fmaxwell-electromagnetic-waves\u002Fpolarization":15882,"\u002Felectricity-and-magnetism\u002Foptics\u002Freflection-and-refraction":15886,"\u002Felectricity-and-magnetism\u002Foptics\u002Fthin-lenses":15891,"\u002Felectricity-and-magnetism\u002Foptics\u002Fspherical-mirrors":15895,"\u002Felectricity-and-magnetism":15899,"\u002Flinear-algebra\u002Flinear-systems\u002Fsystems-and-echelon-forms":15902,"\u002Flinear-algebra\u002Flinear-systems\u002Fvector-and-matrix-equations":15907,"\u002Flinear-algebra\u002Flinear-systems\u002Fsolution-sets-and-applications":15911,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-independence":15915,"\u002Flinear-algebra\u002Flinear-systems\u002Flinear-transformations":15919,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-operations":15923,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fmatrix-inverse-and-invertibility":15928,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fpartitioned-matrices-and-lu":15932,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fsubspaces-dimension-rank":15936,"\u002Flinear-algebra\u002Fmatrix-algebra\u002Fapplications-leontief-and-graphics":15940,"\u002Flinear-algebra\u002Fdeterminants\u002Fdeterminants-and-cofactors":15944,"\u002Flinear-algebra\u002Fdeterminants\u002Fproperties-of-determinants":15949,"\u002Flinear-algebra\u002Fdeterminants\u002Fcramer-volume-and-area":15953,"\u002Flinear-algebra\u002Fvector-spaces\u002Fvector-spaces-and-subspaces":15957,"\u002Flinear-algebra\u002Fvector-spaces\u002Fnull-and-column-spaces":15962,"\u002Flinear-algebra\u002Fvector-spaces\u002Fbases-and-independent-sets":15966,"\u002Flinear-algebra\u002Fvector-spaces\u002Fcoordinate-systems":15970,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdimension-and-rank":15974,"\u002Flinear-algebra\u002Fvector-spaces\u002Fchange-of-basis":15978,"\u002Flinear-algebra\u002Fvector-spaces\u002Fdifference-equations-and-markov":15982,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-eigenvalues":15986,"\u002Flinear-algebra\u002Feigenvalues\u002Fthe-characteristic-equation":15991,"\u002Flinear-algebra\u002Feigenvalues\u002Fdiagonalization":15995,"\u002Flinear-algebra\u002Feigenvalues\u002Feigenvectors-and-linear-transformations":15999,"\u002Flinear-algebra\u002Feigenvalues\u002Fcomplex-eigenvalues":16003,"\u002Flinear-algebra\u002Feigenvalues\u002Fdynamical-systems":16007,"\u002Flinear-algebra\u002Feigenvalues\u002Fpower-method":16011,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-length-orthogonality":16015,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Forthogonal-sets-and-projections":16020,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fgram-schmidt-and-qr":16024,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-problems":16028,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Fleast-squares-applications":16032,"\u002Flinear-algebra\u002Forthogonality-least-squares\u002Finner-product-spaces":16036,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fdiagonalizing-symmetric-matrices":16040,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fquadratic-forms":16045,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fconstrained-optimization":16049,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsingular-value-decomposition":16053,"\u002Flinear-algebra\u002Fsymmetric-quadratic-svd\u002Fsvd-applications-pca-imaging":16057,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-thinking-and-matrix-computation":16061,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Flu-and-cholesky":16066,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fconditioning-and-floating-point":16070,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fstability-and-error-analysis":16074,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fqr-and-numerical-least-squares":16078,"\u002Flinear-algebra\u002Fnumerical-linear-algebra\u002Fnumerical-eigenvalues-and-svd":16082,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-combinations":16086,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Faffine-independence-and-barycentric-coordinates":16091,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fconvex-combinations-and-convex-sets":16095,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fhyperplanes-and-polytopes":16099,"\u002Flinear-algebra\u002Fgeometry-of-vector-spaces\u002Fcurves-and-surfaces":16103,"\u002Flinear-algebra":16107,"\u002Ftheory-of-computation":16110,"\u002Fcomputer-architecture\u002Ffoundations\u002Fbits-bytes-and-words":16113,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-representation":16117,"\u002Fcomputer-architecture\u002Ffoundations\u002Finteger-arithmetic":16121,"\u002Fcomputer-architecture\u002Ffoundations\u002Ffloating-point":16125,"\u002Fcomputer-architecture\u002Ffoundations\u002Fboolean-algebra-and-bit-manipulation":16129,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fthe-machines-view":16133,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fdata-movement":16138,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farithmetic-and-logic":16142,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fcontrol-flow":16146,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fprocedures":16150,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Farrays-structs-and-alignment":16154,"\u002Fcomputer-architecture\u002Fmachine-level-x86-64\u002Fmemory-layout-and-buffer-overflows":16158,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fwhat-an-isa-is":16162,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Finstruction-formats-and-operands":16167,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Faddressing-modes":16171,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fthe-y86-64-instruction-set":16175,"\u002Fcomputer-architecture\u002Finstruction-set-architecture\u002Fy86-64-programming":16179,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Ftransistors-gates-and-boolean-functions":16183,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fcombinational-logic-and-hcl":16188,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmultiplexers-decoders-and-the-alu":16192,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fmemory-elements-latches-flip-flops-and-clocking":16196,"\u002Fcomputer-architecture\u002Fdigital-logic\u002Fregister-files-and-random-access-memory":16200,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-fetch-decode-execute-cycle":16204,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fthe-seq-stages":16209,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fcontrol-logic-and-sequencing":16213,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Fassembling-seq":16217,"\u002Fcomputer-architecture\u002Fprocessor-design\u002Ftracing-a-program":16221,"\u002Fcomputer-architecture\u002Fpipelining\u002Fpipelining-principles":16225,"\u002Fcomputer-architecture\u002Fpipelining\u002Ffrom-seq-to-pipe":16230,"\u002Fcomputer-architecture\u002Fpipelining\u002Fdata-hazards-stalling-and-forwarding":16234,"\u002Fcomputer-architecture\u002Fpipelining\u002Fcontrol-hazards-and-branch-prediction":16238,"\u002Fcomputer-architecture\u002Fpipelining\u002Fthe-complete-pipe-processor":16242,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fstorage-technologies-and-the-latency-gap":16246,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Flocality":16251,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-memories-direct-mapped":16255,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fset-associative-and-write-policies":16259,"\u002Fcomputer-architecture\u002Fmemory-hierarchy\u002Fcache-performance-and-cache-friendly-code":16263,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Faddress-spaces-and-translation":16267,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fpage-tables-and-page-faults":16272,"\u002Fcomputer-architecture\u002Fvirtual-memory\u002Fthe-tlb-and-multi-level-page-tables":16276,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Fexceptional-control-flow":16280,"\u002Fcomputer-architecture\u002Fexceptions-and-io\u002Finterrupts-and-the-kernel":16285,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fprocesses-threads-and-parallelism":16289,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fhardware-multithreading":16294,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fcache-coherence":16298,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmemory-consistency-and-synchronization":16302,"\u002Fcomputer-architecture\u002Fmultithreading-and-multicore\u002Fmulticore-organization":16306,"\u002Fcomputer-architecture\u002Fcapstone\u002Fthe-whole-machine":16310,"\u002Fcomputer-architecture\u002Fcapstone\u002Fassembling-a-complete-cpu":16315,"\u002Fcomputer-architecture":16319,"\u002Fdifferential-equations\u002Ffoundations\u002Fmodels-and-direction-fields":16322,"\u002Fdifferential-equations\u002Ffoundations\u002Fclassification-and-terminology":16326,"\u002Fdifferential-equations\u002Ffirst-order\u002Flinear-first-order-integrating-factors":16330,"\u002Fdifferential-equations\u002Ffirst-order\u002Fseparable-and-exact":16335,"\u002Fdifferential-equations\u002Ffirst-order\u002Fmodeling-first-order":16339,"\u002Fdifferential-equations\u002Ffirst-order\u002Fautonomous-and-population-dynamics":16343,"\u002Fdifferential-equations\u002Ffirst-order\u002Fexistence-uniqueness-euler":16347,"\u002Fdifferential-equations\u002Ffirst-order\u002Ffirst-order-difference-equations":16351,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhomogeneous-constant-coefficients":16355,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fcomplex-and-repeated-roots":16360,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fnonhomogeneous-undetermined-coefficients":16364,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fvariation-of-parameters":16368,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fmechanical-electrical-vibrations":16372,"\u002Fdifferential-equations\u002Fsecond-order-linear\u002Fhigher-order-linear":16376,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fpower-series-ordinary-points":16380,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fregular-singular-frobenius":16385,"\u002Fdifferential-equations\u002Fseries-solutions\u002Fbessel-and-special-functions":16389,"\u002Fdifferential-equations\u002Flaplace\u002Flaplace-definition-ivps":16393,"\u002Fdifferential-equations\u002Flaplace\u002Fstep-impulse-convolution":16398,"\u002Fdifferential-equations\u002Fsystems\u002Fmatrices-eigenvalues-review":16402,"\u002Fdifferential-equations\u002Fsystems\u002Fconstant-coefficient-systems-phase-portraits":16407,"\u002Fdifferential-equations\u002Fsystems\u002Frepeated-eigenvalues-fundamental-matrices":16411,"\u002Fdifferential-equations\u002Fnumerical\u002Feuler-and-runge-kutta":16415,"\u002Fdifferential-equations\u002Fnumerical\u002Fmultistep-systems-stability":16420,"\u002Fdifferential-equations\u002Fnonlinear\u002Fphase-plane-autonomous-stability":16424,"\u002Fdifferential-equations\u002Fnonlinear\u002Flocally-linear-and-liapunov":16429,"\u002Fdifferential-equations\u002Fnonlinear\u002Fcompeting-species-predator-prey-limit-cycles":16433,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Ffourier-series":16437,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fheat-wave-laplace-equations":16442,"\u002Fdifferential-equations\u002Fpdes-fourier-bvp\u002Fsturm-liouville":16446,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fcalculus-of-variations":16450,"\u002Fdifferential-equations\u002Fhistory-variations\u002Fhistorical-notes":16455,"\u002Fdifferential-equations":16459,"\u002Frelativity\u002Ffoundations\u002Fspecial-relativity-postulates":16462,"\u002Frelativity\u002Ffoundations\u002Florentz-transformation-spacetime":16467,"\u002Frelativity\u002Ffoundations\u002Ftime-dilation-length-contraction":16471,"\u002Frelativity\u002Ffoundations\u002Frelativistic-momentum-energy":16475,"\u002Frelativity\u002Ffoundations\u002Fgeneral-relativity":16479,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fminkowski-spacetime-and-the-interval":16483,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Ffour-vectors-and-index-notation":16488,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fthe-lorentz-group-and-rapidity":16492,"\u002Frelativity\u002Fspacetime-and-the-lorentz-group\u002Fdoppler-aberration-and-appearance":16496,"\u002Frelativity\u002Frelativistic-dynamics\u002Ffour-momentum-force-and-accelerated-motion":16500,"\u002Frelativity\u002Frelativistic-dynamics\u002Fparticle-decays-and-two-body-kinematics":16505,"\u002Frelativity\u002Frelativistic-dynamics\u002Fcollisions-thresholds-and-the-cm-frame":16509,"\u002Frelativity\u002Frelativistic-dynamics\u002Fmandelstam-variables-and-invariants":16513,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ffour-current-and-the-four-potential":16517,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fthe-electromagnetic-field-tensor":16522,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Ftransformation-of-electric-and-magnetic-fields":16526,"\u002Frelativity\u002Fcovariant-electrodynamics\u002Fcovariant-maxwell-and-the-stress-energy-tensor":16530,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-equivalence-principle-formalized":16534,"\u002Frelativity\u002Fcurved-spacetime\u002Fmanifolds-vectors-and-the-metric":16539,"\u002Frelativity\u002Fcurved-spacetime\u002Fcovariant-derivative-and-christoffel-symbols":16543,"\u002Frelativity\u002Fcurved-spacetime\u002Fgeodesics-and-the-geodesic-equation":16547,"\u002Frelativity\u002Fcurved-spacetime\u002Fcurvature-riemann-and-geodesic-deviation":16551,"\u002Frelativity\u002Fcurved-spacetime\u002Fthe-einstein-field-equations":16555,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fthe-schwarzschild-metric":16559,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Fgeodesics-and-orbits-in-schwarzschild":16564,"\u002Frelativity\u002Fthe-schwarzschild-solution\u002Flight-bending-and-null-geodesics":16568,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fperihelion-precession-of-mercury":16572,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fdeflection-of-light-and-gravitational-lensing":16577,"\u002Frelativity\u002Ftests-of-general-relativity\u002Fgravitational-redshift-and-shapiro-delay":16581,"\u002Frelativity\u002Ftests-of-general-relativity\u002Frelativity-in-technology-gps":16585,"\u002Frelativity\u002Fblack-holes\u002Fhorizons-and-coordinate-singularities":16589,"\u002Frelativity\u002Fblack-holes\u002Frotating-and-charged-black-holes":16594,"\u002Frelativity\u002Fblack-holes\u002Fblack-hole-thermodynamics":16598,"\u002Frelativity\u002Fgravitational-waves\u002Flinearized-gravity-and-wave-solutions":16602,"\u002Frelativity\u002Fgravitational-waves\u002Fgeneration-and-the-quadrupole-formula":16607,"\u002Frelativity\u002Fgravitational-waves\u002Fdetection-ligo-and-the-first-events":16611,"\u002Frelativity\u002Fcosmological-bridge\u002Fthe-cosmological-principle-and-flrw-metric":16615,"\u002Frelativity\u002Fcosmological-bridge\u002Ffriedmann-equations-and-cosmic-dynamics":16620,"\u002Frelativity":16624,"\u002Fphysical-computing":16627,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fblackbody-radiation-and-the-planck-quantum":16630,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-photoelectric-effect-and-the-photon":16635,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fx-rays-and-the-compton-effect":16639,"\u002Fquantum-mechanics\u002Fold-quantum-theory\u002Fthe-old-quantum-theory-bohr-and-sommerfeld":16643,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fde-broglie-waves-and-electron-diffraction":16647,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fwave-packets-and-the-probability-interpretation":16652,"\u002Fquantum-mechanics\u002Fmatter-waves\u002Fthe-uncertainty-principle":16656,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-schrodinger-equation-in-one-dimension":16660,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-free-particle-and-wave-packet-dynamics":16665,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fparticle-in-infinite-and-finite-square-wells":16669,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Foperators-expectation-values-and-the-harmonic-oscillator":16673,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fthe-dirac-delta-potential":16677,"\u002Fquantum-mechanics\u002Fwave-mechanics-1d\u002Fbarrier-penetration-and-quantum-tunneling":16681,"\u002Fquantum-mechanics\u002Fformalism\u002Fhilbert-space-and-dirac-notation":16685,"\u002Fquantum-mechanics\u002Fformalism\u002Fobservables-hermitian-operators-and-eigenvalues":16690,"\u002Fquantum-mechanics\u002Fformalism\u002Fthe-postulates-and-quantum-measurement":16694,"\u002Fquantum-mechanics\u002Fformalism\u002Fposition-momentum-and-continuous-spectra":16698,"\u002Fquantum-mechanics\u002Fformalism\u002Fcommutators-and-the-generalized-uncertainty-principle":16702,"\u002Fquantum-mechanics\u002Fformalism\u002Ftime-evolution-schrodinger-and-heisenberg-pictures":16706,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fladder-operators-and-the-number-states":16710,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fcoherent-and-squeezed-states":16715,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fsymmetries-generators-and-conservation-laws":16719,"\u002Fquantum-mechanics\u002Foscillator-and-symmetry\u002Fparity-time-reversal-and-discrete-symmetries":16723,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Forbital-angular-momentum-and-spherical-harmonics":16727,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Fthe-angular-momentum-algebra":16731,"\u002Fquantum-mechanics\u002Fangular-momentum\u002Faddition-of-angular-momenta-and-clebsch-gordan":16735,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-schrodinger-equation-in-three-dimensions":16739,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-hydrogen-atom":16744,"\u002Fquantum-mechanics\u002Fcentral-potentials\u002Fthe-isotropic-oscillator-and-hidden-symmetry":16748,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-half-pauli-matrices-and-stern-gerlach":16752,"\u002Fquantum-mechanics\u002Fspin\u002Fspin-in-a-magnetic-field-precession-and-resonance":16757,"\u002Fquantum-mechanics\u002Fspin\u002Ftwo-level-systems-and-the-bloch-sphere":16761,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fidentical-particles-and-exchange-symmetry":16765,"\u002Fquantum-mechanics\u002Fidentical-particles\u002Fthe-pauli-principle-atoms-and-the-periodic-table":16770,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ftime-independent-perturbation-theory":16774,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Ffine-structure-and-the-real-hydrogen-atom":16779,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-zeeman-and-stark-effects":16783,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-variational-method":16787,"\u002Fquantum-mechanics\u002Fapproximation-methods\u002Fthe-wkb-approximation":16791,"\u002Fquantum-mechanics":16795,"\u002Freal-analysis\u002Ffoundations\u002Fsets-logic-functions":16798,"\u002Freal-analysis\u002Ffoundations\u002Fordered-fields-completeness":16803,"\u002Freal-analysis\u002Ffoundations\u002Fabsolute-value-bounds":16807,"\u002Freal-analysis\u002Ffoundations\u002Fintervals-uncountability":16811,"\u002Freal-analysis\u002Fsequences-series\u002Fsequences-limits":16815,"\u002Freal-analysis\u002Fsequences-series\u002Flimit-laws-monotone":16820,"\u002Freal-analysis\u002Fsequences-series\u002Flimsup-bolzano-weierstrass":16824,"\u002Freal-analysis\u002Fsequences-series\u002Fcauchy-completeness":16828,"\u002Freal-analysis\u002Fsequences-series\u002Fseries-convergence":16832,"\u002Freal-analysis\u002Fsequences-series\u002Fabsolute-conditional-rearrangement":16836,"\u002Freal-analysis\u002Fmetric-spaces\u002Fmetric-spaces-norms":16840,"\u002Freal-analysis\u002Fmetric-spaces\u002Fopen-closed-sets":16845,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconvergence-completeness":16849,"\u002Freal-analysis\u002Fmetric-spaces\u002Fcompactness":16853,"\u002Freal-analysis\u002Fmetric-spaces\u002Fconnectedness":16857,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-of-functions":16861,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuous-functions":16865,"\u002Freal-analysis\u002Fcontinuity\u002Fevt-ivt":16869,"\u002Freal-analysis\u002Fcontinuity\u002Funiform-continuity":16873,"\u002Freal-analysis\u002Fcontinuity\u002Fcontinuity-metric-spaces":16877,"\u002Freal-analysis\u002Fcontinuity\u002Flimits-infinity-monotone":16881,"\u002Freal-analysis\u002Fdifferentiation\u002Fthe-derivative":16885,"\u002Freal-analysis\u002Fdifferentiation\u002Fmean-value-theorem":16890,"\u002Freal-analysis\u002Fdifferentiation\u002Ftaylors-theorem":16894,"\u002Freal-analysis\u002Fdifferentiation\u002Finverse-function-1d":16898,"\u002Freal-analysis\u002Friemann-integration\u002Fdarboux-integral":16902,"\u002Freal-analysis\u002Friemann-integration\u002Fintegrability-classes":16907,"\u002Freal-analysis\u002Friemann-integration\u002Fproperties-of-the-integral":16911,"\u002Freal-analysis\u002Friemann-integration\u002Ffundamental-theorem":16915,"\u002Freal-analysis\u002Friemann-integration\u002Flog-exp-improper":16918,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpointwise-uniform-convergence":16922,"\u002Freal-analysis\u002Ffunction-sequences\u002Finterchange-of-limits":16927,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpower-series-weierstrass":16931,"\u002Freal-analysis\u002Ffunction-sequences\u002Fpicard-ode":16935,"\u002Freal-analysis\u002Fseveral-variables\u002Fdifferentiability-rn":16939,"\u002Freal-analysis\u002Fseveral-variables\u002Fgradient-chain-rule":16944,"\u002Freal-analysis\u002Fseveral-variables\u002Fhigher-derivatives-taylor-extrema":16948,"\u002Freal-analysis\u002Fseveral-variables\u002Finverse-implicit-theorems":16952,"\u002Freal-analysis\u002Fseveral-variables\u002Fmultiple-integrals":16956,"\u002Freal-analysis":16960,"\u002Fabstract-algebra\u002Ffoundations\u002Fsets-functions-relations":16963,"\u002Fabstract-algebra\u002Ffoundations\u002Fintegers-and-modular-arithmetic":16967,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fgroup-axioms-and-first-examples":16971,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fdihedral-and-symmetric-groups":16976,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fmatrix-and-quaternion-groups":16980,"\u002Fabstract-algebra\u002Fgroups-and-symmetry\u002Fhomomorphisms-and-group-actions":16984,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fsubgroups-and-substructures":16988,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcyclic-groups":16993,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fgeneration-and-subgroup-lattices":16997,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcosets-lagrange-and-normal-subgroups":17001,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fisomorphism-theorems":17005,"\u002Fabstract-algebra\u002Fsubgroups-and-quotients\u002Fcomposition-series-and-the-alternating-group":17009,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Factions-and-cayleys-theorem":17013,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fconjugation-and-the-class-equation":17018,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fsylow-theorems":17022,"\u002Fabstract-algebra\u002Fgroup-actions-and-sylow\u002Fautomorphisms-and-simple-groups":17026,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fdirect-products-and-finite-abelian-groups":17030,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fsemidirect-products":17035,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fnilpotent-and-solvable-groups":17039,"\u002Fabstract-algebra\u002Fproducts-and-group-structure\u002Fclassifying-small-groups":17043,"\u002Fabstract-algebra\u002Fring-theory\u002Frings-definitions-and-examples":17047,"\u002Fabstract-algebra\u002Fring-theory\u002Fideals-quotients-and-homomorphisms":17052,"\u002Fabstract-algebra\u002Fring-theory\u002Ffractions-and-the-chinese-remainder-theorem":17056,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Feuclidean-domains-pids-ufds":17060,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fpolynomial-rings-over-fields":17065,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Fgauss-lemma-and-unique-factorization":17069,"\u002Fabstract-algebra\u002Ffactorization-and-polynomials\u002Firreducibility-criteria-and-groebner":17073,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fintroduction-to-modules":17077,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ffree-modules-and-direct-sums":17082,"\u002Fabstract-algebra\u002Fmodule-theory\u002Ftensor-products-and-exact-sequences":17086,"\u002Fabstract-algebra\u002Fmodule-theory\u002Fvector-spaces-and-linear-maps":17090,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fstructure-theorem-over-pids":17094,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Frational-canonical-form":17099,"\u002Fabstract-algebra\u002Fmodules-over-pids\u002Fjordan-canonical-form":17103,"\u002Fabstract-algebra\u002Ffield-theory\u002Ffield-extensions-and-algebraic-elements":17107,"\u002Fabstract-algebra\u002Ffield-theory\u002Fstraightedge-and-compass-constructions":17112,"\u002Fabstract-algebra\u002Ffield-theory\u002Fsplitting-fields-and-algebraic-closure":17116,"\u002Fabstract-algebra\u002Ffield-theory\u002Fseparable-and-cyclotomic-extensions":17120,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fthe-galois-correspondence":17124,"\u002Fabstract-algebra\u002Fgalois-theory\u002Ffinite-fields":17129,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fcyclotomic-and-abelian-extensions":17133,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fgalois-groups-of-polynomials":17137,"\u002Fabstract-algebra\u002Fgalois-theory\u002Fsolvability-by-radicals-and-the-quintic":17141,"\u002Fabstract-algebra\u002Fcapstone\u002Fcommutative-algebra-and-algebraic-geometry":17145,"\u002Fabstract-algebra\u002Fcapstone\u002Frepresentation-and-character-theory":17150,"\u002Fabstract-algebra":17154,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fatomic-spectra-rutherford":17157,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-model-hydrogen":17162,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fx-ray-spectra-franck-hertz":17166,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fbohr-sommerfeld-old-quantum-theory":17170,"\u002Fatomic-physics\u002Fearly-models-and-old-quantum-theory\u002Fold-quantum-theory-limits-wkb":17174,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fschrodinger-3d-hydrogen":17178,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fhydrogen-wave-functions":17183,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fradial-equation-in-full":17187,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fsymmetry-degeneracy-runge-lenz":17191,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fexpectation-values-virial":17195,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Fquantum-defects-alkali-spectra":17199,"\u002Fatomic-physics\u002Fquantum-hydrogen-atom\u002Frydberg-atoms":17203,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Frelativistic-kinetic-correction":17207,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fspin-orbit-thomas-precession":17212,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdarwin-term-fine-structure-formula":17216,"\u002Fatomic-physics\u002Ffine-structure-and-the-dirac-atom\u002Fdirac-equation-hydrogen":17220,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Flamb-shift-qed":17224,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fhyperfine-structure-21cm":17229,"\u002Fatomic-physics\u002Fqed-corrections-and-hyperfine-structure\u002Fnuclear-effects-isotope-shift":17233,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fperiodic-table-atomic-spectra":17237,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fcentral-field-self-consistent":17242,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fidentical-particles-hartree-fock":17246,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhelium-two-electron-atom":17250,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fls-jj-coupling-term-symbols":17254,"\u002Fatomic-physics\u002Fmany-electron-atoms\u002Fhund-rules-ground-terms":17258,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fzeeman-effect":17262,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fpaschen-back-intermediate":17267,"\u002Fatomic-physics\u002Fatoms-in-external-fields\u002Fstark-effect-polarizability":17271,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Ftime-dependent-perturbation-golden-rule":17275,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fdipole-approximation-einstein-coefficients":17280,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Fselection-rules-forbidden-transitions":17284,"\u002Fatomic-physics\u002Fradiative-transitions-and-line-shapes\u002Flifetimes-and-line-shapes":17288,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Flaser-principles":17292,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fspectroscopy-techniques":17297,"\u002Fatomic-physics\u002Flasers-and-spectroscopy\u002Fline-catalog-nist-asd":17301,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Flaser-cooling-doppler":17305,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fsub-doppler-trapping":17310,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Fbose-einstein-condensation":17314,"\u002Fatomic-physics\u002Fmodern-atomic-physics\u002Foptical-clocks-precision":17318,"\u002Fatomic-physics":17322,"\u002Fdatabases":17325,"\u002Fcategory-theory\u002Ffoundations\u002Fwhat-is-a-category":17328,"\u002Fcategory-theory\u002Ffoundations\u002Fexamples-of-categories":17332,"\u002Fcategory-theory\u002Ffoundations\u002Fspecial-morphisms":17336,"\u002Fcategory-theory\u002Ffoundations\u002Ffunctors":17340,"\u002Fcategory-theory\u002Ffoundations\u002Fnatural-transformations":17344,"\u002Fcategory-theory\u002Ffoundations\u002Fsize-and-set-theory":17348,"\u002Fcategory-theory\u002Funiversal-properties\u002Funiversal-properties":17352,"\u002Fcategory-theory\u002Funiversal-properties\u002Fproducts-and-coproducts":17357,"\u002Fcategory-theory\u002Funiversal-properties\u002Fconstructions-on-categories":17361,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Frepresentable-functors":17365,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-lemma":17370,"\u002Fcategory-theory\u002Frepresentables-yoneda\u002Fyoneda-consequences":17374,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits":17378,"\u002Fcategory-theory\u002Flimits-colimits\u002Fproducts-equalizers-pullbacks":17383,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcolimits":17387,"\u002Fcategory-theory\u002Flimits-colimits\u002Fcomputing-limits":17391,"\u002Fcategory-theory\u002Flimits-colimits\u002Flimits-and-functors":17395,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions":17399,"\u002Fcategory-theory\u002Fadjunctions\u002Funits-and-counits":17404,"\u002Fcategory-theory\u002Fadjunctions\u002Fadjunctions-via-universal-arrows":17408,"\u002Fcategory-theory\u002Fadjunctions\u002Ffree-forgetful-adjunctions":17412,"\u002Fcategory-theory\u002Fadjoints-limits\u002Flimits-via-adjoints":17416,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fpresheaf-limits-colimits":17421,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoints-preserve-limits":17425,"\u002Fcategory-theory\u002Fadjoints-limits\u002Fadjoint-functor-theorem":17429,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fmonads":17433,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-eilenberg-moore":17438,"\u002Fcategory-theory\u002Fmonads-algebras\u002Fkleisli-and-programming":17442,"\u002Fcategory-theory\u002Fmonads-algebras\u002Falgebras-for-endofunctors":17446,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Fcartesian-closed-categories":17450,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Flambda-calculus-correspondence":17455,"\u002Fcategory-theory\u002Fcartesian-closed-lambda\u002Ffixed-points-and-recursion":17459,"\u002Fcategory-theory":17463,"\u002Fdeep-learning\u002Fmathematical-background\u002Flinear-algebra-for-deep-learning":17466,"\u002Fdeep-learning\u002Fmathematical-background\u002Fprobability-and-information-theory":17470,"\u002Fdeep-learning\u002Fmathematical-background\u002Fnumerical-computation":17474,"\u002Fdeep-learning\u002Fmathematical-background\u002Fcalculus":17478,"\u002Fdeep-learning\u002Ffoundations\u002Fwhat-is-deep-learning":17481,"\u002Fdeep-learning\u002Ffoundations\u002Fmachine-learning-refresher":17485,"\u002Fdeep-learning\u002Ffoundations\u002Flinear-models-and-the-perceptron":17489,"\u002Fdeep-learning\u002Fneural-networks\u002Fthe-multilayer-perceptron":17493,"\u002Fdeep-learning\u002Fneural-networks\u002Factivation-functions":17498,"\u002Fdeep-learning\u002Fneural-networks\u002Funiversal-approximation":17502,"\u002Fdeep-learning\u002Fneural-networks\u002Fbackpropagation":17506,"\u002Fdeep-learning\u002Fneural-networks\u002Floss-functions-and-output-units":17510,"\u002Fdeep-learning\u002Foptimization\u002Fgradient-descent-and-sgd":17514,"\u002Fdeep-learning\u002Foptimization\u002Fmomentum-and-adaptive-methods":17519,"\u002Fdeep-learning\u002Foptimization\u002Finitialization":17523,"\u002Fdeep-learning\u002Foptimization\u002Fthe-optimization-landscape":17527,"\u002Fdeep-learning\u002Foptimization\u002Fsecond-order-and-approximate-methods":17530,"\u002Fdeep-learning\u002Fregularization\u002Fregularization-overview":17534,"\u002Fdeep-learning\u002Fregularization\u002Fdropout-and-data-augmentation":17538,"\u002Fdeep-learning\u002Fregularization\u002Fearly-stopping-and-parameter-sharing":17541,"\u002Fdeep-learning\u002Fregularization\u002Fnormalization":17544,"\u002Fdeep-learning\u002Farchitectures\u002Fconvolutional-networks":17548,"\u002Fdeep-learning\u002Farchitectures\u002Fcnn-architectures":17553,"\u002Fdeep-learning\u002Farchitectures\u002Frecurrent-networks":17557,"\u002Fdeep-learning\u002Farchitectures\u002Flstm-and-gru":17561,"\u002Fdeep-learning\u002Farchitectures\u002Fattention-and-transformers":17565,"\u002Fdeep-learning\u002Farchitectures\u002Fthe-transformer-architecture":17569,"\u002Fdeep-learning\u002Farchitectures\u002Ftransformers-in-practice":17573,"\u002Fdeep-learning\u002Farchitectures\u002Fgraph-neural-networks":17577,"\u002Fdeep-learning\u002Farchitectures\u002Fstate-space-models":17581,"\u002Fdeep-learning\u002Ftheory\u002Fgeneralization-theory":17585,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-robustness":17586,"\u002Fdeep-learning\u002Ftheory\u002Fadversarial-defenses":17589,"\u002Fdeep-learning\u002Ftheory\u002Fbayesian-and-ensemble-methods":17593,"\u002Fdeep-learning\u002Ftheory\u002Fdeep-equilibrium-models":17596,"\u002Fdeep-learning\u002Fgenerative-models\u002Flinear-factor-models":17599,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoencoders":17604,"\u002Fdeep-learning\u002Fgenerative-models\u002Fvariational-autoencoders":17608,"\u002Fdeep-learning\u002Fgenerative-models\u002Fgenerative-adversarial-networks":17612,"\u002Fdeep-learning\u002Fgenerative-models\u002Fautoregressive-and-normalizing-flows":17616,"\u002Fdeep-learning\u002Fgenerative-models\u002Fenergy-based-and-boltzmann-machines":17620,"\u002Fdeep-learning\u002Fgenerative-models\u002Fdiffusion-and-score-based-models":17624,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fstructured-probabilistic-models":17628,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fmonte-carlo-and-mcmc":17633,"\u002Fdeep-learning\u002Fprobabilistic-methods\u002Fapproximate-inference":17637,"\u002Fdeep-learning\u002Fpractical\u002Fpractical-methodology":17641,"\u002Fdeep-learning\u002Fpractical\u002Fhyperparameters-and-debugging":17646,"\u002Fdeep-learning\u002Fpractical\u002Frepresentation-learning":17650,"\u002Fdeep-learning\u002Fpractical\u002Ftransfer-learning":17654,"\u002Fdeep-learning\u002Fpractical\u002Fapplications":17658,"\u002Fdeep-learning\u002Fpractical\u002Fmodel-compression-and-distillation":17662,"\u002Fdeep-learning\u002Fpractical\u002Fmeta-learning-and-few-shot":17666,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Flarge-language-models":17670,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fscaling-inference-and-alignment":17675,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fseq2seq-pretraining-and-bart":17679,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ftext-to-text-transfer-and-conditional-generation":17683,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fspeech-and-audio-models":17687,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fself-supervised-speech-and-synthesis":17691,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fai-agents":17695,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fagent-memory-retrieval-and-orchestration":17699,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmixture-of-experts":17703,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Fmultimodal-models":17707,"\u002Fdeep-learning\u002Flarge-models-and-agents\u002Ffusion-and-vision-language-models":17711,"\u002Fdeep-learning\u002Freinforcement-learning\u002Ffoundations-of-reinforcement-learning":17715,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fmodel-free-prediction-and-control":17720,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fdeep-q-networks":17724,"\u002Fdeep-learning\u002Freinforcement-learning\u002Fpolicy-gradients-and-actor-critic":17728,"\u002Fdeep-learning\u002Freinforcement-learning\u002Frl-from-human-feedback":17732,"\u002Fdeep-learning":17736,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fequilibrium-state-variables-zeroth-law":17738,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Ffirst-law-heat-and-work":17742,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fsecond-law-entropy-and-the-carnot-bound":17746,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fthermodynamic-potentials-and-maxwell-relations":17750,"\u002Fstatistical-mechanics\u002Fthermodynamics\u002Fstability-response-functions-and-the-third-law":17754,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fclassical-statistics-and-equipartition":17758,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fphase-space-and-liouvilles-theorem":17763,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fensembles-and-the-equal-probability-postulate":17767,"\u002Fstatistical-mechanics\u002Ffoundations\u002Fstatistical-entropy-boltzmann-and-gibbs":17771,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fmicrocanonical-ensemble-and-entropy":17775,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fequilibrium-conditions-temperature-pressure-chemical-potential":17780,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Fideal-gas-phase-space-and-the-sackur-tetrode-entropy":17784,"\u002Fstatistical-mechanics\u002Fmicrocanonical\u002Ftwo-state-systems-paramagnets-and-negative-temperature":17788,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fcanonical-ensemble-and-the-boltzmann-distribution":17792,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fpartition-function-and-the-helmholtz-free-energy":17797,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fenergy-fluctuations-and-ensemble-equivalence":17801,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fthe-einstein-solid-and-harmonic-systems":17805,"\u002Fstatistical-mechanics\u002Fcanonical\u002Fparamagnetism-and-the-schottky-anomaly":17809,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fideal-gas-partition-function-and-the-gibbs-paradox":17813,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fequipartition-and-the-virial-theorem":17818,"\u002Fstatistical-mechanics\u002Fclassical-gas\u002Fmolecular-gases-rotation-and-vibration":17822,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fgrand-canonical-ensemble-and-the-grand-partition-function":17826,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fchemical-potential-fugacity-and-number-fluctuations":17831,"\u002Fstatistical-mechanics\u002Fgrand-canonical\u002Fensemble-summary-and-the-thermodynamic-web":17835,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fquantum-statistics-bose-einstein-and-fermi-dirac":17839,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fderiving-the-quantum-distributions":17844,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fthe-classical-limit-and-quantum-concentration":17848,"\u002Fstatistical-mechanics\u002Fquantum-statistics\u002Fideal-quantum-gases-general-framework":17852,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-and-the-fermion-gas":17856,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthe-photon-gas-and-plancks-radiation-law":17861,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fblackbody-thermodynamics-and-radiation-pressure":17865,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fphonons-and-the-debye-model":17869,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fbose-einstein-condensation-derived":17873,"\u002Fstatistical-mechanics\u002Fbose-systems\u002Fthermodynamics-of-the-bose-gas-and-superfluidity":17877,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fthe-ideal-fermi-gas-at-zero-temperature":17881,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fsommerfeld-expansion-and-electrons-in-metals":17886,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":17890,"\u002Fstatistical-mechanics\u002Ffermi-gas\u002Fneutron-stars-and-nuclear-matter":17894,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-cluster-expansion-and-virial-coefficients":17898,"\u002Fstatistical-mechanics\u002Finteractions\u002Fthe-van-der-waals-gas-and-liquid-gas-coexistence":17903,"\u002Fstatistical-mechanics\u002Finteractions\u002Fquantum-gases-with-interactions-and-exchange":17907,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fphases-coexistence-and-classification":17911,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-ising-model-and-exact-solutions":17916,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fmean-field-theory-and-the-weiss-model":17920,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fcritical-exponents-and-landau-theory":17924,"\u002Fstatistical-mechanics\u002Fphase-transitions\u002Fthe-renormalization-group-idea":17928,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fthermodynamic-fluctuations-and-response":17932,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Fbrownian-motion-and-the-langevin-equation":17937,"\u002Fstatistical-mechanics\u002Ffluctuations\u002Flinear-response-and-the-fluctuation-dissipation-theorem":17941,"\u002Fstatistical-mechanics":17945,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fbonding-mechanisms":17948,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fmolecular-orbitals-and-h2-plus":17953,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fhydrogen-molecule-and-exchange":17957,"\u002Fcondensed-matter\u002Fmolecules-and-bonding\u002Fvan-der-waals-forces":17961,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Frotational-vibrational-spectra":17965,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Fanharmonicity-and-rovibrational-structure":17970,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Framan-and-electronic-bands":17974,"\u002Fcondensed-matter\u002Fmolecular-spectra\u002Flasers-and-masers":17978,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fstructure-of-solids":17982,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fbravais-lattices-and-crystal-systems":17987,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Freciprocal-lattice-and-brillouin-zones":17991,"\u002Fcondensed-matter\u002Fcrystal-structure\u002Fdiffraction-and-structure-factors":17995,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonon-dispersion":17999,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fphonons-quantization-and-dos":18004,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fdebye-einstein-heat-capacity":18008,"\u002Fcondensed-matter\u002Flattice-dynamics\u002Fanharmonicity-and-thermal-transport":18012,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ffree-electron-gas-and-conduction":18016,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fsommerfeld-model-and-heat-capacity":18021,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Ftransport-and-the-hall-effect":18025,"\u002Fcondensed-matter\u002Ffree-electron-fermi-gas\u002Fscreening-and-plasmons":18029,"\u002Fcondensed-matter\u002Fband-theory\u002Fblochs-theorem-and-energy-bands":18033,"\u002Fcondensed-matter\u002Fband-theory\u002Fnearly-free-electron-model":18038,"\u002Fcondensed-matter\u002Fband-theory\u002Ftight-binding-method":18042,"\u002Fcondensed-matter\u002Fband-theory\u002Ffermi-surfaces-and-semiclassical-dynamics":18046,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fsemiconductor-bands-and-junctions":18050,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fintrinsic-and-extrinsic-semiconductors":18055,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fcarrier-transport-and-recombination":18059,"\u002Fcondensed-matter\u002Fsemiconductors\u002Fthe-pn-junction":18063,"\u002Fcondensed-matter\u002Fsemiconductors\u002Ftransistors-and-optoelectronics":18067,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fdielectrics-and-polarization":18071,"\u002Fcondensed-matter\u002Fdielectrics-and-ferroelectrics\u002Fferroelectrics-and-piezoelectrics":18076,"\u002Fcondensed-matter\u002Fmagnetism\u002Fdiamagnetism-and-paramagnetism":18080,"\u002Fcondensed-matter\u002Fmagnetism\u002Fexchange-and-ferromagnetism":18085,"\u002Fcondensed-matter\u002Fmagnetism\u002Fantiferromagnetism-and-domains":18089,"\u002Fcondensed-matter\u002Fmagnetism\u002Fspin-waves-and-magnons":18093,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fsuperconductivity-phenomenology":18097,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Flondon-theory-and-the-meissner-effect":18102,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fginzburg-landau-theory":18106,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fbcs-theory":18110,"\u002Fcondensed-matter\u002Fsuperconductivity\u002Fjosephson-and-high-tc":18114,"\u002Fcondensed-matter\u002Fnanostructures\u002Fquantum-wells-wires-and-dots":18118,"\u002Fcondensed-matter\u002Fnanostructures\u002Finteger-quantum-hall-effect":18123,"\u002Fcondensed-matter\u002Fnanostructures\u002Ffractional-quantum-hall-and-topology":18127,"\u002Fcondensed-matter\u002Fnanostructures\u002Fgraphene-and-dirac-materials":18131,"\u002Fcondensed-matter":18135,"\u002Flogic\u002Ffoundations\u002Flogic-as-a-mathematical-model":18138,"\u002Flogic\u002Fsentential-logic\u002Fformal-languages-and-well-formed-formulas":18142,"\u002Flogic\u002Fsentential-logic\u002Ftruth-assignments-and-tautologies":18147,"\u002Flogic\u002Fsentential-logic\u002Funique-readability-and-parsing":18151,"\u002Flogic\u002Fsentential-logic\u002Finduction-and-recursion":18155,"\u002Flogic\u002Fsentential-logic\u002Fexpressive-completeness-and-normal-forms":18159,"\u002Flogic\u002Fsentential-logic\u002Fboolean-circuits":18163,"\u002Flogic\u002Fsentential-logic\u002Fcompactness-and-effectiveness":18167,"\u002Flogic\u002Ffirst-order-languages\u002Ffirst-order-languages":18171,"\u002Flogic\u002Ffirst-order-languages\u002Fstructures-truth-and-satisfaction":18176,"\u002Flogic\u002Ffirst-order-languages\u002Fdefinability-and-elementary-equivalence":18180,"\u002Flogic\u002Ffirst-order-languages\u002Fterms-substitution-and-parsing":18184,"\u002Flogic\u002Fdeductive-calculus\u002Fa-deductive-calculus":18188,"\u002Flogic\u002Fdeductive-calculus\u002Fdeduction-theorem-and-derived-rules":18193,"\u002Flogic\u002Fdeductive-calculus\u002Fsoundness":18197,"\u002Flogic\u002Fdeductive-calculus\u002Fcompleteness-and-consistency":18201,"\u002Flogic\u002Fmodels-and-theories\u002Fcompactness-and-lowenheim-skolem":18205,"\u002Flogic\u002Fmodels-and-theories\u002Ftheories-elementary-classes-and-categoricity":18210,"\u002Flogic\u002Fmodels-and-theories\u002Finterpretations-between-theories":18214,"\u002Flogic\u002Fmodels-and-theories\u002Fnonstandard-analysis":18218,"\u002Flogic\u002Farithmetic-and-definability\u002Fdefinability-in-arithmetic":18222,"\u002Flogic\u002Farithmetic-and-definability\u002Fnatural-numbers-with-successor":18227,"\u002Flogic\u002Farithmetic-and-definability\u002Fpresburger-and-reducts":18231,"\u002Flogic\u002Farithmetic-and-definability\u002Fa-subtheory-and-representability":18235,"\u002Flogic\u002Fincompleteness\u002Farithmetization-of-syntax":18239,"\u002Flogic\u002Fincompleteness\u002Fincompleteness-and-undecidability":18244,"\u002Flogic\u002Fincompleteness\u002Fsecond-incompleteness-theorem":18248,"\u002Flogic\u002Fcomputability-and-representability\u002Frecursive-functions":18252,"\u002Flogic\u002Fcomputability-and-representability\u002Frepresenting-exponentiation":18257,"\u002Flogic\u002Fsecond-order-logic\u002Fsecond-order-languages":18261,"\u002Flogic\u002Fsecond-order-logic\u002Fskolem-functions-and-many-sorted-logic":18266,"\u002Flogic\u002Fsecond-order-logic\u002Fgeneral-structures":18270,"\u002Flogic":18274,"\u002Freinforcement-learning\u002Ffoundations\u002Fwhat-is-reinforcement-learning":18277,"\u002Freinforcement-learning\u002Ffoundations\u002Fa-brief-history-of-rl":18281,"\u002Freinforcement-learning\u002Ffoundations\u002Fmulti-armed-bandits":18285,"\u002Freinforcement-learning\u002Ffoundations\u002Fbandit-exploration-algorithms":18289,"\u002Freinforcement-learning\u002Ffoundations\u002Fmarkov-decision-processes":18293,"\u002Freinforcement-learning\u002Ffoundations\u002Fvalue-functions-and-optimality":18297,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdynamic-programming":18301,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdp-async-and-gpi":18305,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-methods":18309,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-off-policy":18313,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftemporal-difference-learning":18317,"\u002Freinforcement-learning\u002Ftabular-methods\u002Ftd-control-sarsa-and-q-learning":18321,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-bootstrapping":18325,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fn-step-off-policy-methods":18329,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-and-learning":18333,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fplanning-focusing-and-decision-time":18337,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fdecision-time-planning":18341,"\u002Freinforcement-learning\u002Ftabular-methods\u002Fmonte-carlo-tree-search":18345,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-prediction":18349,"\u002Freinforcement-learning\u002Fapproximation\u002Ffeature-construction-and-nonlinear":18354,"\u002Freinforcement-learning\u002Fapproximation\u002Fon-policy-control":18358,"\u002Freinforcement-learning\u002Fapproximation\u002Faverage-reward-control":18362,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-and-the-deadly-triad":18366,"\u002Freinforcement-learning\u002Fapproximation\u002Fbellman-error-and-gradient-td":18370,"\u002Freinforcement-learning\u002Fapproximation\u002Feligibility-traces":18374,"\u002Freinforcement-learning\u002Fapproximation\u002Ftrue-online-and-sarsa-lambda":18378,"\u002Freinforcement-learning\u002Fapproximation\u002Fpolicy-gradient-methods":18382,"\u002Freinforcement-learning\u002Fapproximation\u002Factor-critic-and-continuous-actions":18386,"\u002Freinforcement-learning\u002Fapproximation\u002Fleast-squares-and-memory-based-methods":18390,"\u002Freinforcement-learning\u002Fapproximation\u002Fmemory-and-kernel-methods":18394,"\u002Freinforcement-learning\u002Fapproximation\u002Foff-policy-eligibility-traces":18398,"\u002Freinforcement-learning\u002Fapproximation\u002Fstable-off-policy-traces":18402,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdeep-q-networks":18406,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fdqn-improvements":18410,"\u002Freinforcement-learning\u002Fdeep-rl\u002Factor-critic-and-ppo":18414,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fppo-and-continuous-control":18418,"\u002Freinforcement-learning\u002Fdeep-rl\u002Fcase-studies":18422,"\u002Freinforcement-learning\u002Fdeep-rl\u002Frl-beyond-games":18426,"\u002Freinforcement-learning\u002Fdeep-rl\u002Ffrontiers":18430,"\u002Freinforcement-learning\u002Fdeep-rl\u002Freward-design-and-open-problems":18434,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow":18438,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fdistributional-and-rainbow-part-2":18443,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control":18447,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fcontinuous-control-part-2":18451,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl":18455,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmodel-based-rl-part-2":18459,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration":18463,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fexploration-part-2":18467,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl":18471,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Foffline-rl-part-2":18475,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl":18479,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fimitation-and-inverse-rl-part-2":18483,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl":18487,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmulti-agent-rl-part-2":18491,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl":18495,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fhierarchical-rl-part-2":18499,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Frlhf-and-language-models":18503,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps":18507,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fpartial-observability-pomdps-part-2":18511,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl":18515,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fsafe-and-constrained-rl-part-2":18519,"\u002Freinforcement-learning\u002Fmodern-deep-rl\u002Fmeta-rl-and-generalization":18523,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fpsychology-of-reinforcement":18527,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Finstrumental-conditioning-and-control":18532,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-and-td-error":18536,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fdopamine-in-the-brain":18540,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fanimal-learning-and-cognition":18544,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fcognitive-maps-and-planning":18548,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fneuroscience-of-reinforcement":18552,"\u002Freinforcement-learning\u002Fminds-and-brains\u002Fseveral-learning-systems":18556,"\u002Freinforcement-learning":18560,"\u002Fartificial-intelligence\u002Ffoundations\u002Fwhat-is-ai":18562,"\u002Fartificial-intelligence\u002Ffoundations\u002Ffoundations-of-ai":18566,"\u002Fartificial-intelligence\u002Ffoundations\u002Fintelligent-agents":18570,"\u002Fartificial-intelligence\u002Ffoundations\u002Fagent-architectures":18574,"\u002Fartificial-intelligence\u002Fsearch\u002Funinformed-search":18578,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-strategies-compared":18583,"\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search":18587,"\u002Fartificial-intelligence\u002Fsearch\u002Fheuristic-functions":18591,"\u002Fartificial-intelligence\u002Fsearch\u002Flocal-search":18595,"\u002Fartificial-intelligence\u002Fsearch\u002Fpopulation-and-continuous-search":18599,"\u002Fartificial-intelligence\u002Fsearch\u002Fadversarial-search":18603,"\u002Fartificial-intelligence\u002Fsearch\u002Fgames-of-chance-and-imperfect-information":18607,"\u002Fartificial-intelligence\u002Fsearch\u002Fconstraint-satisfaction":18611,"\u002Fartificial-intelligence\u002Fsearch\u002Fcsp-search-and-structure":18615,"\u002Fartificial-intelligence\u002Fsearch\u002Fsearch-under-uncertainty":18619,"\u002Fartificial-intelligence\u002Fsearch\u002Fbelief-state-and-online-search":18623,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic":18627,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-inference":18632,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic":18636,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-logic-in-use":18640,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Finference-and-resolution":18644,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Ffirst-order-resolution":18648,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fclassical-planning":18652,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-graphs-and-graphplan":18656,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-in-the-real-world":18660,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fplanning-under-uncertainty":18664,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fknowledge-representation":18668,"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Freasoning-systems-and-defaults":18672,"\u002Fartificial-intelligence\u002Funcertainty\u002Fprobability-and-bayes":18676,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayes-rule-and-naive-bayes":18681,"\u002Fartificial-intelligence\u002Funcertainty\u002Fbayesian-networks":18685,"\u002Fartificial-intelligence\u002Funcertainty\u002Finference-in-bayesian-networks":18689,"\u002Fartificial-intelligence\u002Funcertainty\u002Freasoning-over-time":18693,"\u002Fartificial-intelligence\u002Funcertainty\u002Ftracking-and-data-association":18697,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions":18701,"\u002Fartificial-intelligence\u002Funcertainty\u002Fmarkov-decision-processes":18705,"\u002Fartificial-intelligence\u002Funcertainty\u002Fdecision-networks-and-game-theory":18708,"\u002Fartificial-intelligence\u002Funcertainty\u002Fgame-theory-and-mechanism-design":18712,"\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples":18716,"\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families":18721,"\u002Fartificial-intelligence\u002Flearning\u002Fprobabilistic-learning":18725,"\u002Fartificial-intelligence\u002Flearning\u002Fexpectation-maximization":18729,"\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning":18733,"\u002Fartificial-intelligence\u002Flearning\u002Fgeneralization-and-policy-search":18736,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-in-learning":18740,"\u002Fartificial-intelligence\u002Flearning\u002Fknowledge-based-learning-methods":18744,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fvision-and-perception":18748,"\u002Fartificial-intelligence\u002Ffrontiers\u002Freconstructing-the-3d-world":18753,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobotics":18757,"\u002Fartificial-intelligence\u002Ffrontiers\u002Frobot-planning-and-control":18761,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnatural-language-in-ai":18765,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fnlp-grammar-translation-and-speech":18769,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fphilosophy-and-future":18773,"\u002Fartificial-intelligence\u002Ffrontiers\u002Fai-ethics-and-future":18777,"\u002Fartificial-intelligence":18781,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-constituents-nuclide-chart":18784,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-size-charge-distributions":18789,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-masses-binding-energy":18793,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fsemi-empirical-mass-formula":18797,"\u002Fnuclear-physics\u002Fnuclear-properties\u002Fnuclear-moments-multipoles":18801,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnuclear-force-shell-overview":18805,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fthe-deuteron":18810,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fnucleon-nucleon-scattering":18814,"\u002Fnuclear-physics\u002Fnuclear-force-deuteron\u002Fmeson-theory-isospin":18818,"\u002Fnuclear-physics\u002Fnuclear-models\u002Ffermi-gas-model":18822,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fliquid-drop-collective-coordinates":18827,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fshell-model-single-particle":18831,"\u002Fnuclear-physics\u002Fnuclear-models\u002Fcollective-model-rotations-vibrations":18835,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-law-modes":18839,"\u002Fnuclear-physics\u002Fradioactive-decay\u002Fdecay-kinetics-equilibrium":18844,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-decay-gamow-theory":18848,"\u002Fnuclear-physics\u002Falpha-decay\u002Falpha-fine-structure-hindrance":18853,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fbeta-decay-energetics-neutrino":18857,"\u002Fnuclear-physics\u002Fbeta-decay\u002Ffermi-theory-beta-decay":18862,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fweak-interaction-parity-violation":18866,"\u002Fnuclear-physics\u002Fbeta-decay\u002Fdouble-beta-decay-neutrino-mass":18870,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fgamma-multipole-radiation":18874,"\u002Fnuclear-physics\u002Fgamma-decay\u002Finternal-conversion-isomers":18879,"\u002Fnuclear-physics\u002Fgamma-decay\u002Fangular-correlations-mossbauer":18883,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Freaction-kinematics-cross-sections":18887,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fcompound-nucleus-resonances":18892,"\u002Fnuclear-physics\u002Fnuclear-reactions\u002Fdirect-reactions-optical-model":18896,"\u002Fnuclear-physics\u002Ffission\u002Ffission-barrier-dynamics":18900,"\u002Fnuclear-physics\u002Ffission\u002Fchain-reactions-reactor-physics":18905,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Ffusion-reactions-confinement":18909,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fstellar-nucleosynthesis":18914,"\u002Fnuclear-physics\u002Ffusion-nucleosynthesis\u002Fbig-bang-nucleosynthesis":18918,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fcharged-particle-stopping-power":18922,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fphoton-neutron-interactions":18927,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fradiation-detectors":18931,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fdosimetry-radiation-biology":18935,"\u002Fnuclear-physics\u002Fradiation-matter-applications\u002Fnuclear-applications-dating-medicine":18939,"\u002Fnuclear-physics":18943,"\u002Fnatural-language-processing\u002Ffoundations\u002Fwhat-is-nlp":18946,"\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization":18950,"\u002Fnatural-language-processing\u002Ffoundations\u002Fminimum-edit-distance":18954,"\u002Fnatural-language-processing\u002Ffoundations\u002Fn-gram-language-models":18958,"\u002Fnatural-language-processing\u002Ffoundations\u002Fsmoothing-and-backoff":18962,"\u002Fnatural-language-processing\u002Fclassification\u002Fnaive-bayes-and-sentiment":18966,"\u002Fnatural-language-processing\u002Fclassification\u002Fevaluating-classifiers":18971,"\u002Fnatural-language-processing\u002Fclassification\u002Flogistic-regression":18975,"\u002Fnatural-language-processing\u002Fclassification\u002Fsentiment-and-affect-lexicons":18979,"\u002Fnatural-language-processing\u002Fsemantics\u002Fvector-semantics-and-embeddings":18983,"\u002Fnatural-language-processing\u002Fsemantics\u002Fstatic-word-embeddings":18988,"\u002Fnatural-language-processing\u002Fsemantics\u002Fneural-language-models":18992,"\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling":18996,"\u002Fnatural-language-processing\u002Fsequences\u002Fcrfs-and-neural-taggers":19000,"\u002Fnatural-language-processing\u002Fsequences\u002Frnns-and-lstms":19004,"\u002Fnatural-language-processing\u002Ftransformers\u002Ftransformers-and-attention":19008,"\u002Fnatural-language-processing\u002Ftransformers\u002Fthe-transformer-architecture":19012,"\u002Fnatural-language-processing\u002Ftransformers\u002Flarge-language-models":19015,"\u002Fnatural-language-processing\u002Ftransformers\u002Fllm-pretraining-and-scaling":19018,"\u002Fnatural-language-processing\u002Ftransformers\u002Ffine-tuning-and-prompting":19022,"\u002Fnatural-language-processing\u002Ftransformers\u002Fprompting-and-alignment":19026,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-parsing":19030,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcky-scoring-and-evaluation":19035,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdependency-parsing":19039,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fgraph-based-and-neural-dependency-parsing":19043,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fword-senses-and-wsd":19047,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fwsd-in-practice-and-induction":19051,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-roles-and-information-extraction":19055,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Frelations-events-and-templates":19059,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoreference-and-discourse":19063,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoherence-and-discourse-structure":19067,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Flogical-semantics":19071,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcompositional-semantics-and-description-logics":19075,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-parsing":19079,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fneural-semantic-parsing":19083,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Finformation-extraction":19087,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftimes-events-and-templates":19091,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdiscourse-coherence":19095,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fentity-based-and-global-coherence":19099,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-grammars":19103,"\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftreebanks-and-lexicalized-grammars":19107,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation":19111,"\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation-decoding-and-evaluation":19115,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering":19119,"\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering-knowledge-and-llms":19123,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots":19127,"\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants":19131,"\u002Fnatural-language-processing\u002Fapplications\u002Ftext-summarization":19135,"\u002Fnatural-language-processing\u002Fapplications\u002Fabstractive-summarization-and-evaluation":19139,"\u002Fnatural-language-processing\u002Fspeech\u002Fphonetics":19143,"\u002Fnatural-language-processing\u002Fspeech\u002Facoustic-phonetics":19148,"\u002Fnatural-language-processing\u002Fspeech\u002Fautomatic-speech-recognition":19152,"\u002Fnatural-language-processing\u002Fspeech\u002Fasr-evaluation-and-applications":19156,"\u002Fnatural-language-processing":19160,"\u002Fparticle-physics\u002Ffoundations\u002Fhistorical-overview-particle-zoo":19163,"\u002Fparticle-physics\u002Ffoundations\u002Fparticle-physics-basic-concepts":19167,"\u002Fparticle-physics\u002Ffoundations\u002Ffundamental-interactions-force-carriers":19171,"\u002Fparticle-physics\u002Funits-kinematics\u002Fnatural-units-and-scales":19175,"\u002Fparticle-physics\u002Funits-kinematics\u002Ffour-vectors-invariant-mass":19180,"\u002Fparticle-physics\u002Funits-kinematics\u002Fdecay-scattering-kinematics-mandelstam":19184,"\u002Fparticle-physics\u002Funits-kinematics\u002Fcross-sections-golden-rule":19188,"\u002Fparticle-physics\u002Fsymmetries\u002Fconservation-laws-symmetries":19192,"\u002Fparticle-physics\u002Fsymmetries\u002Fdiscrete-symmetries-cpt":19197,"\u002Fparticle-physics\u002Fsymmetries\u002Fparity-violation-weak":19201,"\u002Fparticle-physics\u002Fsymmetries\u002Fsu2-su3-flavor-symmetry":19205,"\u002Fparticle-physics\u002Fquark-model\u002Feightfold-way-su3":19209,"\u002Fparticle-physics\u002Fquark-model\u002Fmeson-spectroscopy":19214,"\u002Fparticle-physics\u002Fquark-model\u002Fbaryon-spectroscopy":19218,"\u002Fparticle-physics\u002Fquark-model\u002Fcolor-confinement-exotics":19222,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fklein-gordon-equation":19226,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fdirac-equation-spinors":19231,"\u002Fparticle-physics\u002Frelativistic-wave-equations\u002Fantiparticles-hole-theory":19235,"\u002Fparticle-physics\u002Fqed\u002Ffeynman-rules-qed":19239,"\u002Fparticle-physics\u002Fqed\u002Fqed-tree-processes":19244,"\u002Fparticle-physics\u002Fqed\u002Frenormalization-running-coupling":19248,"\u002Fparticle-physics\u002Fqed\u002Felectron-g-2":19252,"\u002Fparticle-physics\u002Fweak-interaction\u002Fva-structure-weak":19256,"\u002Fparticle-physics\u002Fweak-interaction\u002Fw-z-bosons-decays":19261,"\u002Fparticle-physics\u002Fweak-interaction\u002Fckm-matrix":19265,"\u002Fparticle-physics\u002Fweak-interaction\u002Fcp-violation-kaons-b-mesons":19269,"\u002Fparticle-physics\u002Fqcd\u002Fcolor-su3-gluons":19273,"\u002Fparticle-physics\u002Fqcd\u002Fasymptotic-freedom-confinement":19278,"\u002Fparticle-physics\u002Fqcd\u002Fdeep-inelastic-scattering-partons":19282,"\u002Fparticle-physics\u002Fqcd\u002Fjets-hadronization":19286,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Felectroweak-su2-u1":19290,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fspontaneous-symmetry-breaking":19295,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-mechanism":19299,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fhiggs-boson-discovery":19303,"\u002Fparticle-physics\u002Felectroweak-higgs\u002Fstandard-model":19307,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-oscillations":19311,"\u002Fparticle-physics\u002Fneutrinos\u002Fneutrino-mass-pmns":19316,"\u002Fparticle-physics\u002Fneutrinos\u002Fdirac-majorana-experiments":19320,"\u002Fparticle-physics\u002Fexperiment\u002Faccelerators-luminosity":19324,"\u002Fparticle-physics\u002Fexperiment\u002Fdetectors-subsystems":19329,"\u002Fparticle-physics\u002Fexperiment\u002Fhow-discoveries-are-made":19333,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fbeyond-standard-model":19337,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fgrand-unified-theories":19341,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fsupersymmetry":19345,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fhierarchy-problem-naturalness":19349,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fdark-matter-candidates":19353,"\u002Fparticle-physics\u002Fbeyond-standard-model\u002Fmatter-antimatter-open-questions":19357,"\u002Fparticle-physics":19361,"\u002Fastrophysics-cosmology\u002Forientation\u002Fthe-sun-and-stars":19364,"\u002Fastrophysics-cosmology\u002Forientation\u002Fstellar-death-final-states":19369,"\u002Fastrophysics-cosmology\u002Forientation\u002Fgalaxies-and-cosmology":19373,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fmagnitudes-fluxes-and-the-distance-modulus":19377,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fstellar-spectra-and-spectral-classification":19382,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Ftelescopes-and-detectors-across-the-spectrum":19386,"\u002Fastrophysics-cosmology\u002Fobservational-foundations\u002Fthe-cosmic-distance-ladder":19390,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fblackbody-radiation-and-specific-intensity":19394,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fradiative-transfer-and-the-transfer-equation":19399,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fspectral-line-formation-and-broadening":19403,"\u002Fastrophysics-cosmology\u002Fradiation-and-matter\u002Fopacity-and-the-rosseland-mean":19407,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fhydrostatic-equilibrium-and-the-virial-theorem":19411,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equations-of-stellar-structure":19416,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-equation-of-state-and-polytropes":19420,"\u002Fastrophysics-cosmology\u002Fstellar-structure\u002Fthe-standard-solar-model":19424,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fthermonuclear-reaction-rates-and-the-gamow-peak":19428,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhydrogen-burning-pp-chains-and-cno":19433,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fhelium-burning-and-the-triple-alpha-process":19437,"\u002Fastrophysics-cosmology\u002Fnuclear-astrophysics\u002Fadvanced-burning-and-neutron-capture-nucleosynthesis":19441,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fphases-of-the-interstellar-medium":19445,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fmolecular-clouds-and-gravitational-collapse":19450,"\u002Fastrophysics-cosmology\u002Fism-and-star-formation\u002Fprotostars-and-the-pre-main-sequence":19454,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-main-sequence-and-its-structure":19458,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fpost-main-sequence-low-mass-evolution":19463,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fthe-evolution-of-massive-stars":19467,"\u002Fastrophysics-cosmology\u002Fstellar-evolution\u002Fstellar-pulsation-and-the-instability-strip":19471,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fwhite-dwarfs-and-the-chandrasekhar-limit":19475,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fcore-collapse-supernovae":19479,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fthermonuclear-supernovae-type-ia":19483,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fneutron-stars-and-pulsars":19487,"\u002Fastrophysics-cosmology\u002Fstellar-death-and-compact-remnants\u002Fblack-holes-schwarzschild-and-kerr":19491,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fbinary-systems-and-mass-transfer":19495,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Faccreting-compact-objects":19500,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fgravitational-waves-from-inspiraling-binaries":19504,"\u002Fastrophysics-cosmology\u002Fbinaries-and-gravitational-waves\u002Fmultimessenger-astronomy-and-gamma-ray-bursts":19508,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fthe-milky-way":19512,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-morphology-and-classification":19517,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-rotation-curves-and-dark-matter":19521,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Factive-galactic-nuclei-and-supermassive-black-holes":19525,"\u002Fastrophysics-cosmology\u002Fgalaxies\u002Fgalaxy-clusters-and-large-scale-structure":19529,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-expanding-universe-and-hubbles-law":19533,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-frw-metric-and-cosmological-redshift":19538,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fthe-friedmann-equations-and-cosmic-dynamics":19542,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fcosmological-models-and-distances":19545,"\u002Fastrophysics-cosmology\u002Fcosmology-expansion-and-dynamics\u002Fdark-energy-and-the-accelerating-universe":19549,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fthe-thermal-history-of-the-universe":19553,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fbig-bang-nucleosynthesis":19558,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Frecombination-and-the-cosmic-microwave-background":19562,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcmb-anisotropies-and-cosmological-parameters":19566,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fcosmic-inflation":19570,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fstructure-formation-and-the-growth-of-perturbations":19574,"\u002Fastrophysics-cosmology\u002Fthe-hot-big-bang\u002Fdark-matter-dark-energy-and-open-questions":19578,"\u002Fastrophysics-cosmology":19582,"\u002Fcolophon":19585,"\u002F":19588},{"path":14913,"title":14914,"module":14915,"summary":14916},"\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":14918,"title":14919,"module":14915,"summary":14920},"\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":14922,"title":14923,"module":14915,"summary":14924},"\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":14926,"title":14927,"module":14915,"summary":14928},"\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":14930,"title":14931,"module":14915,"summary":14932},"\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":14934,"title":14935,"module":14915,"summary":14936},"\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":14938,"title":14939,"module":14940,"summary":14941},"\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":14943,"title":14944,"module":14940,"summary":14945},"\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":14947,"title":14948,"module":14940,"summary":14949},"\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":14951,"title":14952,"module":14940,"summary":14953},"\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":14955,"title":14956,"module":14957,"summary":14958},"\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":14960,"title":14961,"module":14957,"summary":14962},"\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":14964,"title":14965,"module":14957,"summary":14966},"\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":14968,"title":14969,"module":14957,"summary":14970},"\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":14972,"title":14973,"module":14974,"summary":14975},"\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":14977,"title":14978,"module":14974,"summary":14979},"\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":14981,"title":14982,"module":14974,"summary":14983},"\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":14985,"title":14986,"module":14974,"summary":14987},"\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":14989,"title":14990,"module":14974,"summary":14991},"\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":14993,"title":14994,"module":14974,"summary":14995},"\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":14997,"title":14998,"module":14974,"summary":14999},"\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":15001,"title":15002,"module":14974,"summary":15003},"\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":15005,"title":15006,"module":14974,"summary":15007},"\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":15009,"title":15010,"module":14974,"summary":15011},"\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":15013,"title":15014,"module":14974,"summary":15015},"\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":15017,"title":15018,"module":14974,"summary":15019},"\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":15021,"title":15022,"module":15023,"summary":15024},"\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":15026,"title":15027,"module":15023,"summary":15028},"\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":15030,"title":15031,"module":15023,"summary":15032},"\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":15034,"title":15035,"module":15023,"summary":15036},"\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":15038,"title":15039,"module":15023,"summary":15040},"\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":15042,"title":15043,"module":15023,"summary":15044},"\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":15046,"title":15047,"module":15023,"summary":15048},"\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":15050,"title":15051,"module":15023,"summary":15052},"\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":15054,"title":15055,"module":15056,"summary":15057},"\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":15059,"title":15060,"module":15056,"summary":15061},"\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":15063,"title":15064,"module":15056,"summary":15065},"\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":15067,"title":15068,"module":15056,"summary":15069},"\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":15071,"title":15072,"module":15056,"summary":15073},"\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":15075,"title":15076,"module":15056,"summary":15077},"\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":15079,"title":15080,"module":15056,"summary":15081},"\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":15083,"title":15084,"module":15056,"summary":15085},"\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":15087,"title":15088,"module":15056,"summary":15089},"\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":15091,"title":15092,"module":15056,"summary":15093},"\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":15095,"title":15096,"module":15056,"summary":15097},"\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":15099,"title":15100,"module":15056,"summary":15101},"\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":15103,"title":15104,"module":15056,"summary":15105},"\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":15107,"title":15108,"module":15056,"summary":15109},"\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":15111,"title":15112,"module":15113,"summary":15114},"\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":15116,"title":15117,"module":15113,"summary":15118},"\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":15120,"title":15121,"module":15113,"summary":15122},"\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":15124,"title":15125,"module":15113,"summary":15126},"\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":15128,"title":15129,"module":15113,"summary":15130},"\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":15132,"title":15133,"module":15134,"summary":15135},"\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":15137,"title":15138,"module":15134,"summary":15139},"\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":15141,"title":15142,"module":15134,"summary":15143},"\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":15145,"title":15146,"module":15134,"summary":15147},"\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":15149,"title":15150,"module":15134,"summary":15151},"\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":15153,"title":15154,"module":15134,"summary":15155},"\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":15157,"title":15158,"module":15134,"summary":15159},"\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":15161,"title":15162,"module":15134,"summary":15163},"\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":15165,"title":15166,"module":15134,"summary":15167},"\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":15169,"title":15170,"module":15134,"summary":15171},"\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":15173,"title":15174,"module":15134,"summary":15175},"\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":15177,"title":15178,"module":15179,"summary":15180},"\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":15182,"title":15183,"module":15179,"summary":15184},"\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":15186,"title":15187,"module":15179,"summary":15188},"\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":15190,"title":15191,"module":15179,"summary":15192},"\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":15194,"title":15195,"module":15196,"summary":15197},"\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":15199,"title":15200,"module":15196,"summary":15201},"\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":15203,"title":15204,"module":15196,"summary":15205},"\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":15207,"title":15208,"module":15196,"summary":15209},"\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":15211,"title":15212,"module":15196,"summary":15213},"\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":15215,"title":15216,"module":15196,"summary":15217},"\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":15219,"title":15220,"module":15196,"summary":15221},"\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":15223,"title":15224,"module":15225,"summary":15226},"\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":15228,"title":15229,"module":15225,"summary":15230},"\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":15232,"title":15233,"module":15225,"summary":15234},"\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":15236,"title":15237,"module":15225,"summary":15238},"\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":15240,"title":15241,"module":15242,"summary":15243},"\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":15245,"title":15246,"module":15242,"summary":15247},"\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":15249,"title":15250,"module":15242,"summary":15251},"\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":15253,"title":15254,"module":15242,"summary":15255},"\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":15257,"title":15258,"module":6,"summary":6},"\u002Falgorithms","Algorithms",{"path":15260,"title":15261,"module":15262,"summary":15263},"\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":15265,"title":15266,"module":15262,"summary":15267},"\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":15269,"title":15270,"module":15262,"summary":15271},"\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":15273,"title":15274,"module":15262,"summary":15275},"\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":15277,"title":15278,"module":15279,"summary":15280},"\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":15282,"title":15283,"module":15279,"summary":15284},"\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":15286,"title":15287,"module":15279,"summary":15288},"\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":15290,"title":15291,"module":15279,"summary":15292},"\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":15294,"title":15295,"module":15296,"summary":15297},"\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":15299,"title":15300,"module":15296,"summary":15301},"\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":15303,"title":15304,"module":15296,"summary":15305},"\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":15307,"title":15308,"module":15296,"summary":15309},"\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":15311,"title":15312,"module":15313,"summary":15314},"\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":15316,"title":15317,"module":15313,"summary":15318},"\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":15320,"title":15321,"module":15313,"summary":15322},"\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":15324,"title":15325,"module":15326,"summary":15327},"\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":15329,"title":15330,"module":15326,"summary":15331},"\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":15333,"title":15334,"module":15326,"summary":15335},"\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":15337,"title":15338,"module":15339,"summary":15340},"\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":15342,"title":15343,"module":15339,"summary":15344},"\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":15346,"title":15347,"module":15339,"summary":15348},"\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":15350,"title":15351,"module":15352,"summary":15353},"\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":15355,"title":15356,"module":15352,"summary":15357},"\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":15359,"title":15360,"module":15352,"summary":15361},"\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":15363,"title":15364,"module":15352,"summary":15365},"\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":15367,"title":15368,"module":15369,"summary":15370},"\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":15372,"title":15373,"module":15369,"summary":15374},"\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":15376,"title":15377,"module":15369,"summary":15378},"\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":15380,"title":15381,"module":15382,"summary":15383},"\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":15385,"title":15386,"module":15382,"summary":15387},"\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":15389,"title":15390,"module":15382,"summary":15391},"\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":15393,"title":15394,"module":15382,"summary":15395},"\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":15397,"title":15398,"module":15382,"summary":15399},"\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":15401,"title":15402,"module":15403,"summary":15404},"\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":15406,"title":15407,"module":15403,"summary":15408},"\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":15410,"title":15411,"module":15403,"summary":15412},"\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":15414,"title":15415,"module":15403,"summary":15416},"\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":15418,"title":15419,"module":15403,"summary":15420},"\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":15422,"title":15423,"module":15424,"summary":15425},"\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":15427,"title":15424,"module":15424,"summary":15428},"\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":15430,"title":15431,"module":15424,"summary":15432},"\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":15434,"title":15435,"module":15424,"summary":15436},"\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":15438,"title":15439,"module":15424,"summary":15440},"\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":15442,"title":15443,"module":15444,"summary":15445},"\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":15447,"title":15448,"module":15444,"summary":15449},"\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":15451,"title":15452,"module":15444,"summary":15453},"\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":15455,"title":15456,"module":15444,"summary":15457},"\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":15459,"title":15460,"module":15444,"summary":15461},"\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":15463,"title":15464,"module":15444,"summary":15465},"\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":15467,"title":15468,"module":6,"summary":6},"\u002Fcalculus","Calculus",{"path":15470,"title":15471,"module":14915,"summary":15472},"\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":15474,"title":15475,"module":14915,"summary":15476},"\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":15478,"title":15479,"module":15480,"summary":15481},"\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":15483,"title":15484,"module":15480,"summary":15485},"\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":15487,"title":15488,"module":15480,"summary":15489},"\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":15491,"title":15492,"module":15480,"summary":15493},"\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":15495,"title":15496,"module":15480,"summary":15497},"\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":15499,"title":15500,"module":15501,"summary":15502},"\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":15504,"title":15505,"module":15501,"summary":15506},"\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":15508,"title":15509,"module":15501,"summary":15510},"\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":15512,"title":15513,"module":15501,"summary":15514},"\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":15516,"title":15517,"module":15501,"summary":15518},"\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":15520,"title":15521,"module":15522,"summary":15523},"\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":15525,"title":15526,"module":15522,"summary":15527},"\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":15529,"title":15530,"module":15522,"summary":15531},"\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":15533,"title":15534,"module":15522,"summary":15535},"\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":15537,"title":15538,"module":15522,"summary":15539},"\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":15541,"title":15542,"module":15543,"summary":15544},"\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":15546,"title":15547,"module":15543,"summary":15548},"\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":15550,"title":15551,"module":15543,"summary":15552},"\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":15554,"title":15555,"module":15556,"summary":15557},"\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":15559,"title":15560,"module":15556,"summary":15561},"\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":15563,"title":15564,"module":15556,"summary":15565},"\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":15567,"title":15568,"module":15556,"summary":15569},"\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":15571,"title":15572,"module":15556,"summary":15573},"\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":15575,"title":15576,"module":15556,"summary":15577},"\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":15579,"title":15580,"module":15581,"summary":15582},"\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":15584,"title":15585,"module":15581,"summary":15586},"\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":15588,"title":15589,"module":15581,"summary":15590},"\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":15592,"title":15593,"module":15581,"summary":15594},"\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":15596,"title":15597,"module":15581,"summary":15598},"\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":15600,"title":15601,"module":15581,"summary":15602},"\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":15604,"title":15605,"module":15581,"summary":15606},"\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":15608,"title":15609,"module":15610,"summary":15611},"\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":15613,"title":15614,"module":15610,"summary":15615},"\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":15617,"title":15618,"module":15610,"summary":15619},"\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":15621,"title":15622,"module":15610,"summary":15623},"\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":15625,"title":15626,"module":15610,"summary":15627},"\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":15629,"title":15630,"module":15610,"summary":15631},"\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":15633,"title":15634,"module":15610,"summary":15635},"\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":15637,"title":15638,"module":15610,"summary":15639},"\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":15641,"title":15642,"module":15610,"summary":15643},"\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":15645,"title":15646,"module":15610,"summary":15647},"\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":15649,"title":15650,"module":15610,"summary":15651},"\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":15653,"title":15654,"module":15610,"summary":15655},"\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":15657,"title":15658,"module":15659,"summary":15660},"\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":15662,"title":15663,"module":15659,"summary":15664},"\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":15666,"title":15667,"module":15659,"summary":15668},"\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":15670,"title":15671,"module":15659,"summary":15672},"\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":15674,"title":15675,"module":15659,"summary":15676},"\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":15678,"title":15679,"module":15659,"summary":15680},"\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":15682,"title":15683,"module":6,"summary":6},"\u002Fmechanics","Mechanics & Dynamics",{"path":15685,"title":15686,"module":15687,"summary":15688},"\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":15690,"title":15691,"module":15687,"summary":15692},"\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":15694,"title":15695,"module":15687,"summary":15696},"\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":15698,"title":15699,"module":15687,"summary":15700},"\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":15702,"title":15703,"module":15687,"summary":15704},"\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":15706,"title":15707,"module":15708,"summary":15709},"\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":15711,"title":15712,"module":15708,"summary":15713},"\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":15715,"title":15716,"module":15717,"summary":15718},"\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":15720,"title":15721,"module":15717,"summary":15722},"\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":15724,"title":15725,"module":15717,"summary":15726},"\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":15728,"title":15729,"module":15717,"summary":15730},"\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":15732,"title":15733,"module":15717,"summary":15734},"\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":15736,"title":15737,"module":15738,"summary":15739},"\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":15741,"title":15742,"module":15738,"summary":15743},"\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":15745,"title":15746,"module":15738,"summary":15747},"\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":15749,"title":15750,"module":15738,"summary":15751},"\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":15753,"title":15754,"module":15755,"summary":15756},"\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":15758,"title":15759,"module":15755,"summary":15760},"\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":15762,"title":15763,"module":15755,"summary":15764},"\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":15766,"title":15767,"module":15768,"summary":15769},"\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":15771,"title":15772,"module":15768,"summary":15773},"\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":15775,"title":15776,"module":15768,"summary":15777},"\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":15779,"title":15780,"module":15768,"summary":15781},"\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":15783,"title":15784,"module":15768,"summary":15785},"\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":15787,"title":15788,"module":15789,"summary":15790},"\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":15792,"title":15793,"module":15789,"summary":15794},"\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":15796,"title":15797,"module":15789,"summary":15798},"\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":15800,"title":15801,"module":15789,"summary":15802},"\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":15804,"title":15805,"module":15789,"summary":15806},"\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":15808,"title":15809,"module":15789,"summary":15810},"\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":15812,"title":15813,"module":15814,"summary":15815},"\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":15817,"title":15818,"module":15814,"summary":15819},"\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":15821,"title":15822,"module":15814,"summary":15823},"\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":15825,"title":15826,"module":15814,"summary":15827},"\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":15829,"title":15830,"module":15814,"summary":15831},"\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":15833,"title":15834,"module":15814,"summary":15835},"\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":15837,"title":15838,"module":15814,"summary":15839},"\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":15841,"title":15842,"module":15814,"summary":15843},"\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":15845,"title":15846,"module":15847,"summary":15848},"\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":15850,"title":15851,"module":15847,"summary":15852},"\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":15854,"title":15855,"module":15847,"summary":15856},"\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":15858,"title":15859,"module":15847,"summary":15860},"\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":15862,"title":15863,"module":15847,"summary":15864},"\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":15866,"title":15867,"module":15868,"summary":15869},"\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":15871,"title":15872,"module":15868,"summary":15873},"\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":15875,"title":15876,"module":15868,"summary":15877},"\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":15879,"title":15880,"module":15868,"summary":15881},"\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":15883,"title":15884,"module":15868,"summary":15885},"\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":15887,"title":15888,"module":15889,"summary":15890},"\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":15892,"title":15893,"module":15889,"summary":15894},"\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":15896,"title":15897,"module":15889,"summary":15898},"\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":15900,"title":15901,"module":6,"summary":6},"\u002Felectricity-and-magnetism","Electricity & Magnetism",{"path":15903,"title":15904,"module":15905,"summary":15906},"\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":15908,"title":15909,"module":15905,"summary":15910},"\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":15912,"title":15913,"module":15905,"summary":15914},"\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":15916,"title":15917,"module":15905,"summary":15918},"\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":15920,"title":15921,"module":15905,"summary":15922},"\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":15924,"title":15925,"module":15926,"summary":15927},"\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":15929,"title":15930,"module":15926,"summary":15931},"\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":15933,"title":15934,"module":15926,"summary":15935},"\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":15937,"title":15938,"module":15926,"summary":15939},"\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":15941,"title":15942,"module":15926,"summary":15943},"\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":15945,"title":15946,"module":15947,"summary":15948},"\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":15950,"title":15951,"module":15947,"summary":15952},"\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":15954,"title":15955,"module":15947,"summary":15956},"\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":15958,"title":15959,"module":15960,"summary":15961},"\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":15963,"title":15964,"module":15960,"summary":15965},"\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":15967,"title":15968,"module":15960,"summary":15969},"\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":15971,"title":15972,"module":15960,"summary":15973},"\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":15975,"title":15976,"module":15960,"summary":15977},"\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":15979,"title":15980,"module":15960,"summary":15981},"\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":15983,"title":15984,"module":15960,"summary":15985},"\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":15987,"title":15988,"module":15989,"summary":15990},"\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":15992,"title":15993,"module":15989,"summary":15994},"\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":15996,"title":15997,"module":15989,"summary":15998},"\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":16000,"title":16001,"module":15989,"summary":16002},"\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":16004,"title":16005,"module":15989,"summary":16006},"\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":16008,"title":16009,"module":15989,"summary":16010},"\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":16012,"title":16013,"module":15989,"summary":16014},"\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":16016,"title":16017,"module":16018,"summary":16019},"\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":16021,"title":16022,"module":16018,"summary":16023},"\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":16025,"title":16026,"module":16018,"summary":16027},"\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":16029,"title":16030,"module":16018,"summary":16031},"\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":16033,"title":16034,"module":16018,"summary":16035},"\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":16037,"title":16038,"module":16018,"summary":16039},"\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":16041,"title":16042,"module":16043,"summary":16044},"\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":16046,"title":16047,"module":16043,"summary":16048},"\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":16050,"title":16051,"module":16043,"summary":16052},"\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":16054,"title":16055,"module":16043,"summary":16056},"\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":16058,"title":16059,"module":16043,"summary":16060},"\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":16062,"title":16063,"module":16064,"summary":16065},"\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":16067,"title":16068,"module":16064,"summary":16069},"\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":16071,"title":16072,"module":16064,"summary":16073},"\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":16075,"title":16076,"module":16064,"summary":16077},"\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":16079,"title":16080,"module":16064,"summary":16081},"\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":16083,"title":16084,"module":16064,"summary":16085},"\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":16087,"title":16088,"module":16089,"summary":16090},"\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":16092,"title":16093,"module":16089,"summary":16094},"\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":16096,"title":16097,"module":16089,"summary":16098},"\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":16100,"title":16101,"module":16089,"summary":16102},"\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":16104,"title":16105,"module":16089,"summary":16106},"\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":16108,"title":16109,"module":6,"summary":6},"\u002Flinear-algebra","Linear Algebra",{"path":16111,"title":16112,"module":6,"summary":6},"\u002Ftheory-of-computation","Theory of Computation",{"path":16114,"title":16115,"module":14915,"summary":16116},"\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":16118,"title":16119,"module":14915,"summary":16120},"\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":16122,"title":16123,"module":14915,"summary":16124},"\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":16126,"title":16127,"module":14915,"summary":16128},"\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":16130,"title":16131,"module":14915,"summary":16132},"\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":16134,"title":16135,"module":16136,"summary":16137},"\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":16139,"title":16140,"module":16136,"summary":16141},"\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":16143,"title":16144,"module":16136,"summary":16145},"\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":16147,"title":16148,"module":16136,"summary":16149},"\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":16151,"title":16152,"module":16136,"summary":16153},"\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":16155,"title":16156,"module":16136,"summary":16157},"\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":16159,"title":16160,"module":16136,"summary":16161},"\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":16163,"title":16164,"module":16165,"summary":16166},"\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":16168,"title":16169,"module":16165,"summary":16170},"\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":16172,"title":16173,"module":16165,"summary":16174},"\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":16176,"title":16177,"module":16165,"summary":16178},"\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":16180,"title":16181,"module":16165,"summary":16182},"\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":16184,"title":16185,"module":16186,"summary":16187},"\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":16189,"title":16190,"module":16186,"summary":16191},"\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":16193,"title":16194,"module":16186,"summary":16195},"\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":16197,"title":16198,"module":16186,"summary":16199},"\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":16201,"title":16202,"module":16186,"summary":16203},"\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":16205,"title":16206,"module":16207,"summary":16208},"\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":16210,"title":16211,"module":16207,"summary":16212},"\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":16214,"title":16215,"module":16207,"summary":16216},"\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":16218,"title":16219,"module":16207,"summary":16220},"\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":16222,"title":16223,"module":16207,"summary":16224},"\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":16226,"title":16227,"module":16228,"summary":16229},"\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":16231,"title":16232,"module":16228,"summary":16233},"\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":16235,"title":16236,"module":16228,"summary":16237},"\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":16239,"title":16240,"module":16228,"summary":16241},"\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":16243,"title":16244,"module":16228,"summary":16245},"\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":16247,"title":16248,"module":16249,"summary":16250},"\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":16252,"title":16253,"module":16249,"summary":16254},"\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":16256,"title":16257,"module":16249,"summary":16258},"\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":16260,"title":16261,"module":16249,"summary":16262},"\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":16264,"title":16265,"module":16249,"summary":16266},"\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":16268,"title":16269,"module":16270,"summary":16271},"\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":16273,"title":16274,"module":16270,"summary":16275},"\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":16277,"title":16278,"module":16270,"summary":16279},"\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":16281,"title":16282,"module":16283,"summary":16284},"\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":16286,"title":16287,"module":16283,"summary":16288},"\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":16290,"title":16291,"module":16292,"summary":16293},"\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":16295,"title":16296,"module":16292,"summary":16297},"\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":16299,"title":16300,"module":16292,"summary":16301},"\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":16303,"title":16304,"module":16292,"summary":16305},"\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":16307,"title":16308,"module":16292,"summary":16309},"\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":16311,"title":16312,"module":16313,"summary":16314},"\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":16316,"title":16317,"module":16313,"summary":16318},"\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":16320,"title":16321,"module":6,"summary":6},"\u002Fcomputer-architecture","Computer Architecture",{"path":16323,"title":16324,"module":14915,"summary":16325},"\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":16327,"title":16328,"module":14915,"summary":16329},"\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":16331,"title":16332,"module":16333,"summary":16334},"\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":16336,"title":16337,"module":16333,"summary":16338},"\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":16340,"title":16341,"module":16333,"summary":16342},"\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":16344,"title":16345,"module":16333,"summary":16346},"\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":16348,"title":16349,"module":16333,"summary":16350},"\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":16352,"title":16353,"module":16333,"summary":16354},"\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":16356,"title":16357,"module":16358,"summary":16359},"\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":16361,"title":16362,"module":16358,"summary":16363},"\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":16365,"title":16366,"module":16358,"summary":16367},"\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":16369,"title":16370,"module":16358,"summary":16371},"\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":16373,"title":16374,"module":16358,"summary":16375},"\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":16377,"title":16378,"module":16358,"summary":16379},"\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":16381,"title":16382,"module":16383,"summary":16384},"\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":16386,"title":16387,"module":16383,"summary":16388},"\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":16390,"title":16391,"module":16383,"summary":16392},"\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":16394,"title":16395,"module":16396,"summary":16397},"\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":16399,"title":16400,"module":16396,"summary":16401},"\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":16403,"title":16404,"module":16405,"summary":16406},"\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":16408,"title":16409,"module":16405,"summary":16410},"\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":16412,"title":16413,"module":16405,"summary":16414},"\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":16416,"title":16417,"module":16418,"summary":16419},"\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":16421,"title":16422,"module":16418,"summary":16423},"\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":16425,"title":16426,"module":16427,"summary":16428},"\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":16430,"title":16431,"module":16427,"summary":16432},"\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":16434,"title":16435,"module":16427,"summary":16436},"\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":16438,"title":16439,"module":16440,"summary":16441},"\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":16443,"title":16444,"module":16440,"summary":16445},"\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":16447,"title":16448,"module":16440,"summary":16449},"\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":16451,"title":16452,"module":16453,"summary":16454},"\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":16456,"title":16457,"module":16453,"summary":16458},"\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":16460,"title":16461,"module":6,"summary":6},"\u002Fdifferential-equations","Differential Equations",{"path":16463,"title":16464,"module":16465,"summary":16466},"\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":16468,"title":16469,"module":16465,"summary":16470},"\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":16472,"title":16473,"module":16465,"summary":16474},"\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":16476,"title":16477,"module":16465,"summary":16478},"\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":16480,"title":16481,"module":16465,"summary":16482},"\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":16484,"title":16485,"module":16486,"summary":16487},"\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":16489,"title":16490,"module":16486,"summary":16491},"\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":16493,"title":16494,"module":16486,"summary":16495},"\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":16497,"title":16498,"module":16486,"summary":16499},"\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":16501,"title":16502,"module":16503,"summary":16504},"\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":16506,"title":16507,"module":16503,"summary":16508},"\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":16510,"title":16511,"module":16503,"summary":16512},"\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":16514,"title":16515,"module":16503,"summary":16516},"\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":16518,"title":16519,"module":16520,"summary":16521},"\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":16523,"title":16524,"module":16520,"summary":16525},"\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":16527,"title":16528,"module":16520,"summary":16529},"\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":16531,"title":16532,"module":16520,"summary":16533},"\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":16535,"title":16536,"module":16537,"summary":16538},"\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":16540,"title":16541,"module":16537,"summary":16542},"\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":16544,"title":16545,"module":16537,"summary":16546},"\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":16548,"title":16549,"module":16537,"summary":16550},"\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":16552,"title":16553,"module":16537,"summary":16554},"\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":16556,"title":16557,"module":16537,"summary":16558},"\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":16560,"title":16561,"module":16562,"summary":16563},"\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":16565,"title":16566,"module":16562,"summary":16567},"\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":16569,"title":16570,"module":16562,"summary":16571},"\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":16573,"title":16574,"module":16575,"summary":16576},"\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":16578,"title":16579,"module":16575,"summary":16580},"\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":16582,"title":16583,"module":16575,"summary":16584},"\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":16586,"title":16587,"module":16575,"summary":16588},"\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":16590,"title":16591,"module":16592,"summary":16593},"\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":16595,"title":16596,"module":16592,"summary":16597},"\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":16599,"title":16600,"module":16592,"summary":16601},"\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":16603,"title":16604,"module":16605,"summary":16606},"\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":16608,"title":16609,"module":16605,"summary":16610},"\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":16612,"title":16613,"module":16605,"summary":16614},"\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":16616,"title":16617,"module":16618,"summary":16619},"\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":16621,"title":16622,"module":16618,"summary":16623},"\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":16625,"title":16626,"module":6,"summary":6},"\u002Frelativity","Relativity",{"path":16628,"title":16629,"module":6,"summary":6},"\u002Fphysical-computing","Physical Computing",{"path":16631,"title":16632,"module":16633,"summary":16634},"\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":16636,"title":16637,"module":16633,"summary":16638},"\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":16640,"title":16641,"module":16633,"summary":16642},"\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":16644,"title":16645,"module":16633,"summary":16646},"\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":16648,"title":16649,"module":16650,"summary":16651},"\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":16653,"title":16654,"module":16650,"summary":16655},"\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":16657,"title":16658,"module":16650,"summary":16659},"\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":16661,"title":16662,"module":16663,"summary":16664},"\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":16666,"title":16667,"module":16663,"summary":16668},"\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":16670,"title":16671,"module":16663,"summary":16672},"\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":16674,"title":16675,"module":16663,"summary":16676},"\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":16678,"title":16679,"module":16663,"summary":16680},"\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":16682,"title":16683,"module":16663,"summary":16684},"\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":16686,"title":16687,"module":16688,"summary":16689},"\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":16691,"title":16692,"module":16688,"summary":16693},"\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":16695,"title":16696,"module":16688,"summary":16697},"\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":16699,"title":16700,"module":16688,"summary":16701},"\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":16703,"title":16704,"module":16688,"summary":16705},"\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":16707,"title":16708,"module":16688,"summary":16709},"\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":16711,"title":16712,"module":16713,"summary":16714},"\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":16716,"title":16717,"module":16713,"summary":16718},"\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":16720,"title":16721,"module":16713,"summary":16722},"\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":16724,"title":16725,"module":16713,"summary":16726},"\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":16728,"title":16729,"module":15568,"summary":16730},"\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":16732,"title":16733,"module":15568,"summary":16734},"\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":16736,"title":16737,"module":15568,"summary":16738},"\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":16740,"title":16741,"module":16742,"summary":16743},"\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":16745,"title":16746,"module":16742,"summary":16747},"\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":16749,"title":16750,"module":16742,"summary":16751},"\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":16753,"title":16754,"module":16755,"summary":16756},"\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":16758,"title":16759,"module":16755,"summary":16760},"\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":16762,"title":16763,"module":16755,"summary":16764},"\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":16766,"title":16767,"module":16768,"summary":16769},"\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":16771,"title":16772,"module":16768,"summary":16773},"\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":16775,"title":16776,"module":16777,"summary":16778},"\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":16780,"title":16781,"module":16777,"summary":16782},"\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":16784,"title":16785,"module":16777,"summary":16786},"\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":16788,"title":16789,"module":16777,"summary":16790},"\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":16792,"title":16793,"module":16777,"summary":16794},"\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":16796,"title":16797,"module":6,"summary":6},"\u002Fquantum-mechanics","Quantum Mechanics",{"path":16799,"title":16800,"module":16801,"summary":16802},"\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":16804,"title":16805,"module":16801,"summary":16806},"\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":16808,"title":16809,"module":16801,"summary":16810},"\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":16812,"title":16813,"module":16801,"summary":16814},"\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":16816,"title":16817,"module":16818,"summary":16819},"\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":16821,"title":16822,"module":16818,"summary":16823},"\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":16825,"title":16826,"module":16818,"summary":16827},"\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":16829,"title":16830,"module":16818,"summary":16831},"\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":16833,"title":16834,"module":16818,"summary":16835},"\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":16837,"title":16838,"module":16818,"summary":16839},"\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":16841,"title":16842,"module":16843,"summary":16844},"\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":16846,"title":16847,"module":16843,"summary":16848},"\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":16850,"title":16851,"module":16843,"summary":16852},"\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":16854,"title":16855,"module":16843,"summary":16856},"\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":16858,"title":16859,"module":16843,"summary":16860},"\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":16862,"title":16863,"module":15262,"summary":16864},"\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":16866,"title":16867,"module":15262,"summary":16868},"\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":16870,"title":16871,"module":15262,"summary":16872},"\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":16874,"title":16875,"module":15262,"summary":16876},"\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":16878,"title":16879,"module":15262,"summary":16880},"\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":16882,"title":16883,"module":15262,"summary":16884},"\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":16886,"title":16887,"module":16888,"summary":16889},"\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":16891,"title":16892,"module":16888,"summary":16893},"\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":16895,"title":16896,"module":16888,"summary":16897},"\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":16899,"title":16900,"module":16888,"summary":16901},"\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":16903,"title":16904,"module":16905,"summary":16906},"\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":16908,"title":16909,"module":16905,"summary":16910},"\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":16912,"title":16913,"module":16905,"summary":16914},"\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":16916,"title":15317,"module":16905,"summary":16917},"\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":16919,"title":16920,"module":16905,"summary":16921},"\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":16923,"title":16924,"module":16925,"summary":16926},"\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":16928,"title":16929,"module":16925,"summary":16930},"\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":16932,"title":16933,"module":16925,"summary":16934},"\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":16936,"title":16937,"module":16925,"summary":16938},"\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":16940,"title":16941,"module":16942,"summary":16943},"\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":16945,"title":16946,"module":16942,"summary":16947},"\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":16949,"title":16950,"module":16942,"summary":16951},"\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":16953,"title":16954,"module":16942,"summary":16955},"\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":16957,"title":16958,"module":16942,"summary":16959},"\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":16961,"title":16962,"module":6,"summary":6},"\u002Freal-analysis","Real Analysis",{"path":16964,"title":16965,"module":14915,"summary":16966},"\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":16968,"title":16969,"module":14915,"summary":16970},"\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":16972,"title":16973,"module":16974,"summary":16975},"\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":16977,"title":16978,"module":16974,"summary":16979},"\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":16981,"title":16982,"module":16974,"summary":16983},"\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":16985,"title":16986,"module":16974,"summary":16987},"\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":16989,"title":16990,"module":16991,"summary":16992},"\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":16994,"title":16995,"module":16991,"summary":16996},"\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":16998,"title":16999,"module":16991,"summary":17000},"\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":17002,"title":17003,"module":16991,"summary":17004},"\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":17006,"title":17007,"module":16991,"summary":17008},"\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":17010,"title":17011,"module":16991,"summary":17012},"\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":17014,"title":17015,"module":17016,"summary":17017},"\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":17019,"title":17020,"module":17016,"summary":17021},"\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":17023,"title":17024,"module":17016,"summary":17025},"\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":17027,"title":17028,"module":17016,"summary":17029},"\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":17031,"title":17032,"module":17033,"summary":17034},"\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":17036,"title":17037,"module":17033,"summary":17038},"\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":17040,"title":17041,"module":17033,"summary":17042},"\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":17044,"title":17045,"module":17033,"summary":17046},"\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":17048,"title":17049,"module":17050,"summary":17051},"\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":17053,"title":17054,"module":17050,"summary":17055},"\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":17057,"title":17058,"module":17050,"summary":17059},"\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":17061,"title":17062,"module":17063,"summary":17064},"\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":17066,"title":17067,"module":17063,"summary":17068},"\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":17070,"title":17071,"module":17063,"summary":17072},"\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":17074,"title":17075,"module":17063,"summary":17076},"\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":17078,"title":17079,"module":17080,"summary":17081},"\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":17083,"title":17084,"module":17080,"summary":17085},"\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":17087,"title":17088,"module":17080,"summary":17089},"\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":17091,"title":17092,"module":17080,"summary":17093},"\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":17095,"title":17096,"module":17097,"summary":17098},"\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":17100,"title":17101,"module":17097,"summary":17102},"\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":17104,"title":17105,"module":17097,"summary":17106},"\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":17108,"title":17109,"module":17110,"summary":17111},"\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":17113,"title":17114,"module":17110,"summary":17115},"\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":17117,"title":17118,"module":17110,"summary":17119},"\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":17121,"title":17122,"module":17110,"summary":17123},"\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":17125,"title":17126,"module":17127,"summary":17128},"\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":17130,"title":17131,"module":17127,"summary":17132},"\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":17134,"title":17135,"module":17127,"summary":17136},"\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":17138,"title":17139,"module":17127,"summary":17140},"\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":17142,"title":17143,"module":17127,"summary":17144},"\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":17146,"title":17147,"module":17148,"summary":17149},"\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":17151,"title":17152,"module":17148,"summary":17153},"\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":17155,"title":17156,"module":6,"summary":6},"\u002Fabstract-algebra","Abstract Algebra",{"path":17158,"title":17159,"module":17160,"summary":17161},"\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":17163,"title":17164,"module":17160,"summary":17165},"\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":17167,"title":17168,"module":17160,"summary":17169},"\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":17171,"title":17172,"module":17160,"summary":17173},"\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":17175,"title":17176,"module":17160,"summary":17177},"\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":17179,"title":17180,"module":17181,"summary":17182},"\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":17184,"title":17185,"module":17181,"summary":17186},"\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":17188,"title":17189,"module":17181,"summary":17190},"\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":17192,"title":17193,"module":17181,"summary":17194},"\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":17196,"title":17197,"module":17181,"summary":17198},"\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":17200,"title":17201,"module":17181,"summary":17202},"\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":17204,"title":17205,"module":17181,"summary":17206},"\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":17208,"title":17209,"module":17210,"summary":17211},"\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":17213,"title":17214,"module":17210,"summary":17215},"\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":17217,"title":17218,"module":17210,"summary":17219},"\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":17221,"title":17222,"module":17210,"summary":17223},"\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":17225,"title":17226,"module":17227,"summary":17228},"\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":17230,"title":17231,"module":17227,"summary":17232},"\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":17234,"title":17235,"module":17227,"summary":17236},"\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":17238,"title":17239,"module":17240,"summary":17241},"\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":17243,"title":17244,"module":17240,"summary":17245},"\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":17247,"title":17248,"module":17240,"summary":17249},"\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":17251,"title":17252,"module":17240,"summary":17253},"\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":17255,"title":17256,"module":17240,"summary":17257},"\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":17259,"title":17260,"module":17240,"summary":17261},"\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":17263,"title":17264,"module":17265,"summary":17266},"\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":17268,"title":17269,"module":17265,"summary":17270},"\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":17272,"title":17273,"module":17265,"summary":17274},"\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":17276,"title":17277,"module":17278,"summary":17279},"\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":17281,"title":17282,"module":17278,"summary":17283},"\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":17285,"title":17286,"module":17278,"summary":17287},"\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":17289,"title":17290,"module":17278,"summary":17291},"\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":17293,"title":17294,"module":17295,"summary":17296},"\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":17298,"title":17299,"module":17295,"summary":17300},"\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":17302,"title":17303,"module":17295,"summary":17304},"\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":17306,"title":17307,"module":17308,"summary":17309},"\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":17311,"title":17312,"module":17308,"summary":17313},"\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":17315,"title":17316,"module":17308,"summary":17317},"\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":17319,"title":17320,"module":17308,"summary":17321},"\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":17323,"title":17324,"module":6,"summary":6},"\u002Fatomic-physics","Atomic Physics",{"path":17326,"title":17327,"module":6,"summary":6},"\u002Fdatabases","Databases",{"path":17329,"title":17330,"module":14915,"summary":17331},"\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":17333,"title":17334,"module":14915,"summary":17335},"\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":17337,"title":17338,"module":14915,"summary":17339},"\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":17341,"title":17342,"module":14915,"summary":17343},"\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":17345,"title":17346,"module":14915,"summary":17347},"\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":17349,"title":17350,"module":14915,"summary":17351},"\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":17353,"title":17354,"module":17355,"summary":17356},"\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":17358,"title":17359,"module":17355,"summary":17360},"\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":17362,"title":17363,"module":17355,"summary":17364},"\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":17366,"title":17367,"module":17368,"summary":17369},"\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":17371,"title":17372,"module":17368,"summary":17373},"\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":17375,"title":17376,"module":17368,"summary":17377},"\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":17379,"title":17380,"module":17381,"summary":17382},"\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":17384,"title":17385,"module":17381,"summary":17386},"\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":17388,"title":17389,"module":17381,"summary":17390},"\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":17392,"title":17393,"module":17381,"summary":17394},"\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":17396,"title":17397,"module":17381,"summary":17398},"\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":17400,"title":17401,"module":17402,"summary":17403},"\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":17405,"title":17406,"module":17402,"summary":17407},"\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":17409,"title":17410,"module":17402,"summary":17411},"\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":17413,"title":17414,"module":17402,"summary":17415},"\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":17417,"title":17418,"module":17419,"summary":17420},"\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":17422,"title":17423,"module":17419,"summary":17424},"\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":17426,"title":17427,"module":17419,"summary":17428},"\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":17430,"title":17431,"module":17419,"summary":17432},"\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":17434,"title":17435,"module":17436,"summary":17437},"\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":17439,"title":17440,"module":17436,"summary":17441},"\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":17443,"title":17444,"module":17436,"summary":17445},"\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":17447,"title":17448,"module":17436,"summary":17449},"\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":17451,"title":17452,"module":17453,"summary":17454},"\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":17456,"title":17457,"module":17453,"summary":17458},"\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":17460,"title":17461,"module":17453,"summary":17462},"\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":17464,"title":17465,"module":6,"summary":6},"\u002Fcategory-theory","Category Theory",{"path":17467,"title":16109,"module":17468,"summary":17469},"\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":17471,"title":17472,"module":17468,"summary":17473},"\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":17475,"title":17476,"module":17468,"summary":17477},"\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":17479,"title":15468,"module":17468,"summary":17480},"\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":17482,"title":17483,"module":14915,"summary":17484},"\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":17486,"title":17487,"module":14915,"summary":17488},"\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":17490,"title":17491,"module":14915,"summary":17492},"\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":17494,"title":17495,"module":17496,"summary":17497},"\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":17499,"title":17500,"module":17496,"summary":17501},"\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":17503,"title":17504,"module":17496,"summary":17505},"\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":17507,"title":17508,"module":17496,"summary":17509},"\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":17511,"title":17512,"module":17496,"summary":17513},"\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":17515,"title":17516,"module":17517,"summary":17518},"\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":17520,"title":17521,"module":17517,"summary":17522},"\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":17524,"title":17525,"module":17517,"summary":17526},"\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":13321,"title":17528,"module":17517,"summary":17529},"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":17531,"title":17532,"module":17517,"summary":17533},"\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":13370,"title":17535,"module":17536,"summary":17537},"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":13375,"title":17539,"module":17536,"summary":17540},"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":13380,"title":17542,"module":17536,"summary":17543},"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":17545,"title":17546,"module":17536,"summary":17547},"\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":17549,"title":17550,"module":17551,"summary":17552},"\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":17554,"title":17555,"module":17551,"summary":17556},"\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":17558,"title":17559,"module":17551,"summary":17560},"\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":17562,"title":17563,"module":17551,"summary":17564},"\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":17566,"title":17567,"module":17551,"summary":17568},"\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":17570,"title":17571,"module":17551,"summary":17572},"\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":17574,"title":17575,"module":17551,"summary":17576},"\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":17578,"title":17579,"module":17551,"summary":17580},"\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":17582,"title":17583,"module":17551,"summary":17584},"\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":13978,"title":5,"module":13976,"summary":13996},{"path":13839,"title":17587,"module":13976,"summary":17588},"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":17590,"title":17591,"module":13976,"summary":17592},"\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":13844,"title":17594,"module":13976,"summary":17595},"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":13849,"title":17597,"module":13976,"summary":17598},"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":17600,"title":17601,"module":17602,"summary":17603},"\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":17605,"title":17606,"module":17602,"summary":17607},"\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":17609,"title":17610,"module":17602,"summary":17611},"\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":17613,"title":17614,"module":17602,"summary":17615},"\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":17617,"title":17618,"module":17602,"summary":17619},"\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":17621,"title":17622,"module":17602,"summary":17623},"\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":17625,"title":17626,"module":17602,"summary":17627},"\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":17629,"title":17630,"module":17631,"summary":17632},"\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":17634,"title":17635,"module":17631,"summary":17636},"\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":17638,"title":17639,"module":17631,"summary":17640},"\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":17642,"title":17643,"module":17644,"summary":17645},"\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":17647,"title":17648,"module":17644,"summary":17649},"\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":17651,"title":17652,"module":17644,"summary":17653},"\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":17655,"title":17656,"module":17644,"summary":17657},"\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":17659,"title":17660,"module":17644,"summary":17661},"\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":17663,"title":17664,"module":17644,"summary":17665},"\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":17667,"title":17668,"module":17644,"summary":17669},"\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":17671,"title":17672,"module":17673,"summary":17674},"\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":17676,"title":17677,"module":17673,"summary":17678},"\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":17680,"title":17681,"module":17673,"summary":17682},"\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":17684,"title":17685,"module":17673,"summary":17686},"\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":17688,"title":17689,"module":17673,"summary":17690},"\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":17692,"title":17693,"module":17673,"summary":17694},"\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":17696,"title":17697,"module":17673,"summary":17698},"\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":17700,"title":17701,"module":17673,"summary":17702},"\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":17704,"title":17705,"module":17673,"summary":17706},"\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":17708,"title":17709,"module":17673,"summary":17710},"\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":17712,"title":17713,"module":17673,"summary":17714},"\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":17716,"title":17717,"module":17718,"summary":17719},"\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":17721,"title":17722,"module":17718,"summary":17723},"\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":17725,"title":17726,"module":17718,"summary":17727},"\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":17729,"title":17730,"module":17718,"summary":17731},"\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":17733,"title":17734,"module":17718,"summary":17735},"\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":17737,"title":13882,"module":6,"summary":6},"\u002Fdeep-learning",{"path":17739,"title":17740,"module":15659,"summary":17741},"\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":17743,"title":17744,"module":15659,"summary":17745},"\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":17747,"title":17748,"module":15659,"summary":17749},"\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":17751,"title":17752,"module":15659,"summary":17753},"\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":17755,"title":17756,"module":15659,"summary":17757},"\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":17759,"title":17760,"module":17761,"summary":17762},"\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":17764,"title":17765,"module":17761,"summary":17766},"\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":17768,"title":17769,"module":17761,"summary":17770},"\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":17772,"title":17773,"module":17761,"summary":17774},"\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":17776,"title":17777,"module":17778,"summary":17779},"\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":17781,"title":17782,"module":17778,"summary":17783},"\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":17785,"title":17786,"module":17778,"summary":17787},"\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":17789,"title":17790,"module":17778,"summary":17791},"\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":17793,"title":17794,"module":17795,"summary":17796},"\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":17798,"title":17799,"module":17795,"summary":17800},"\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":17802,"title":17803,"module":17795,"summary":17804},"\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":17806,"title":17807,"module":17795,"summary":17808},"\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":17810,"title":17811,"module":17795,"summary":17812},"\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":17814,"title":17815,"module":17816,"summary":17817},"\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":17819,"title":17820,"module":17816,"summary":17821},"\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":17823,"title":17824,"module":17816,"summary":17825},"\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":17827,"title":17828,"module":17829,"summary":17830},"\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":17832,"title":17833,"module":17829,"summary":17834},"\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":17836,"title":17837,"module":17829,"summary":17838},"\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":17840,"title":17841,"module":17842,"summary":17843},"\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":17845,"title":17846,"module":17842,"summary":17847},"\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":17849,"title":17850,"module":17842,"summary":17851},"\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":17853,"title":17854,"module":17842,"summary":17855},"\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":17857,"title":17858,"module":17859,"summary":17860},"\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":17862,"title":17863,"module":17859,"summary":17864},"\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":17866,"title":17867,"module":17859,"summary":17868},"\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":17870,"title":17871,"module":17859,"summary":17872},"\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":17874,"title":17875,"module":17859,"summary":17876},"\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":17878,"title":17879,"module":17859,"summary":17880},"\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":17882,"title":17883,"module":17884,"summary":17885},"\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":17887,"title":17888,"module":17884,"summary":17889},"\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":17891,"title":17892,"module":17884,"summary":17893},"\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":17895,"title":17896,"module":17884,"summary":17897},"\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":17899,"title":17900,"module":17901,"summary":17902},"\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":17904,"title":17905,"module":17901,"summary":17906},"\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":17908,"title":17909,"module":17901,"summary":17910},"\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":17912,"title":17913,"module":17914,"summary":17915},"\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":17917,"title":17918,"module":17914,"summary":17919},"\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":17921,"title":17922,"module":17914,"summary":17923},"\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":17925,"title":17926,"module":17914,"summary":17927},"\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":17929,"title":17930,"module":17914,"summary":17931},"\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":17933,"title":17934,"module":17935,"summary":17936},"\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":17938,"title":17939,"module":17935,"summary":17940},"\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":17942,"title":17943,"module":17935,"summary":17944},"\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":17946,"title":17947,"module":6,"summary":6},"\u002Fstatistical-mechanics","Statistical Mechanics",{"path":17949,"title":17950,"module":17951,"summary":17952},"\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":17954,"title":17955,"module":17951,"summary":17956},"\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":17958,"title":17959,"module":17951,"summary":17960},"\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":17962,"title":17963,"module":17951,"summary":17964},"\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":17966,"title":17967,"module":17968,"summary":17969},"\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":17971,"title":17972,"module":17968,"summary":17973},"\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":17975,"title":17976,"module":17968,"summary":17977},"\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":17979,"title":17980,"module":17968,"summary":17981},"\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":17983,"title":17984,"module":17985,"summary":17986},"\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":17988,"title":17989,"module":17985,"summary":17990},"\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":17992,"title":17993,"module":17985,"summary":17994},"\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":17996,"title":17997,"module":17985,"summary":17998},"\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":18000,"title":18001,"module":18002,"summary":18003},"\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":18005,"title":18006,"module":18002,"summary":18007},"\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":18009,"title":18010,"module":18002,"summary":18011},"\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":18013,"title":18014,"module":18002,"summary":18015},"\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":18017,"title":18018,"module":18019,"summary":18020},"\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":18022,"title":18023,"module":18019,"summary":18024},"\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":18026,"title":18027,"module":18019,"summary":18028},"\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":18030,"title":18031,"module":18019,"summary":18032},"\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":18034,"title":18035,"module":18036,"summary":18037},"\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":18039,"title":18040,"module":18036,"summary":18041},"\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":18043,"title":18044,"module":18036,"summary":18045},"\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":18047,"title":18048,"module":18036,"summary":18049},"\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":18051,"title":18052,"module":18053,"summary":18054},"\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":18056,"title":18057,"module":18053,"summary":18058},"\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":18060,"title":18061,"module":18053,"summary":18062},"\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":18064,"title":18065,"module":18053,"summary":18066},"\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":18068,"title":18069,"module":18053,"summary":18070},"\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":18072,"title":18073,"module":18074,"summary":18075},"\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":18077,"title":18078,"module":18074,"summary":18079},"\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":18081,"title":18082,"module":18083,"summary":18084},"\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":18086,"title":18087,"module":18083,"summary":18088},"\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":18090,"title":18091,"module":18083,"summary":18092},"\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":18094,"title":18095,"module":18083,"summary":18096},"\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":18098,"title":18099,"module":18100,"summary":18101},"\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":18103,"title":18104,"module":18100,"summary":18105},"\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":18107,"title":18108,"module":18100,"summary":18109},"\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":18111,"title":18112,"module":18100,"summary":18113},"\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":18115,"title":18116,"module":18100,"summary":18117},"\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":18119,"title":18120,"module":18121,"summary":18122},"\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":18124,"title":18125,"module":18121,"summary":18126},"\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":18128,"title":18129,"module":18121,"summary":18130},"\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":18132,"title":18133,"module":18121,"summary":18134},"\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":18136,"title":18137,"module":6,"summary":6},"\u002Fcondensed-matter","Condensed Matter Physics",{"path":18139,"title":18140,"module":14915,"summary":18141},"\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":18143,"title":18144,"module":18145,"summary":18146},"\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":18148,"title":18149,"module":18145,"summary":18150},"\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":18152,"title":18153,"module":18145,"summary":18154},"\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":18156,"title":18157,"module":18145,"summary":18158},"\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":18160,"title":18161,"module":18145,"summary":18162},"\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":18164,"title":18165,"module":18145,"summary":18166},"\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":18168,"title":18169,"module":18145,"summary":18170},"\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":18172,"title":18173,"module":18174,"summary":18175},"\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":18177,"title":18178,"module":18174,"summary":18179},"\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":18181,"title":18182,"module":18174,"summary":18183},"\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":18185,"title":18186,"module":18174,"summary":18187},"\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":18189,"title":18190,"module":18191,"summary":18192},"\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":18194,"title":18195,"module":18191,"summary":18196},"\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":18198,"title":18199,"module":18191,"summary":18200},"\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":18202,"title":18203,"module":18191,"summary":18204},"\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":18206,"title":18207,"module":18208,"summary":18209},"\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":18211,"title":18212,"module":18208,"summary":18213},"\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":18215,"title":18216,"module":18208,"summary":18217},"\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":18219,"title":18220,"module":18208,"summary":18221},"\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":18223,"title":18224,"module":18225,"summary":18226},"\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":18228,"title":18229,"module":18225,"summary":18230},"\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":18232,"title":18233,"module":18225,"summary":18234},"\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":18236,"title":18237,"module":18225,"summary":18238},"\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":18240,"title":18241,"module":18242,"summary":18243},"\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":18245,"title":18246,"module":18242,"summary":18247},"\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":18249,"title":18250,"module":18242,"summary":18251},"\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":18253,"title":18254,"module":18255,"summary":18256},"\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":18258,"title":18259,"module":18255,"summary":18260},"\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":18262,"title":18263,"module":18264,"summary":18265},"\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":18267,"title":18268,"module":18264,"summary":18269},"\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":18271,"title":18272,"module":18264,"summary":18273},"\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":18275,"title":18276,"module":6,"summary":6},"\u002Flogic","Logic",{"path":18278,"title":18279,"module":14915,"summary":18280},"\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":18282,"title":18283,"module":14915,"summary":18284},"\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":18286,"title":18287,"module":14915,"summary":18288},"\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":18290,"title":18291,"module":14915,"summary":18292},"\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":18294,"title":18295,"module":14915,"summary":18296},"\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":18298,"title":18299,"module":14915,"summary":18300},"\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":18302,"title":15134,"module":18303,"summary":18304},"\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":18306,"title":18307,"module":18303,"summary":18308},"\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":18310,"title":18311,"module":18303,"summary":18312},"\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":18314,"title":18315,"module":18303,"summary":18316},"\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":18318,"title":18319,"module":18303,"summary":18320},"\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":18322,"title":18323,"module":18303,"summary":18324},"\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":18326,"title":18327,"module":18303,"summary":18328},"\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":18330,"title":18331,"module":18303,"summary":18332},"\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":18334,"title":18335,"module":18303,"summary":18336},"\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":18338,"title":18339,"module":18303,"summary":18340},"\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":18342,"title":18343,"module":18303,"summary":18344},"\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":18346,"title":18347,"module":18303,"summary":18348},"\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":18350,"title":18351,"module":18352,"summary":18353},"\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":18355,"title":18356,"module":18352,"summary":18357},"\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":18359,"title":18360,"module":18352,"summary":18361},"\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":18363,"title":18364,"module":18352,"summary":18365},"\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":18367,"title":18368,"module":18352,"summary":18369},"\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":18371,"title":18372,"module":18352,"summary":18373},"\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":18375,"title":18376,"module":18352,"summary":18377},"\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":18379,"title":18380,"module":18352,"summary":18381},"\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":18383,"title":18384,"module":18352,"summary":18385},"\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":18387,"title":18388,"module":18352,"summary":18389},"\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":18391,"title":18392,"module":18352,"summary":18393},"\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":18395,"title":18396,"module":18352,"summary":18397},"\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":18399,"title":18400,"module":18352,"summary":18401},"\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":18403,"title":18404,"module":18352,"summary":18405},"\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":18407,"title":17726,"module":18408,"summary":18409},"\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":18411,"title":18412,"module":18408,"summary":18413},"\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":18415,"title":18416,"module":18408,"summary":18417},"\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":18419,"title":18420,"module":18408,"summary":18421},"\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":18423,"title":18424,"module":18408,"summary":18425},"\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":18427,"title":18428,"module":18408,"summary":18429},"\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":18431,"title":18432,"module":18408,"summary":18433},"\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":18435,"title":18436,"module":18408,"summary":18437},"\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":18439,"title":18440,"module":18441,"summary":18442},"\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":18444,"title":18445,"module":18441,"summary":18446},"\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":18448,"title":18449,"module":18441,"summary":18450},"\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":18452,"title":18453,"module":18441,"summary":18454},"\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":18456,"title":18457,"module":18441,"summary":18458},"\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":18460,"title":18461,"module":18441,"summary":18462},"\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":18464,"title":18465,"module":18441,"summary":18466},"\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":18468,"title":18469,"module":18441,"summary":18470},"\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":18472,"title":18473,"module":18441,"summary":18474},"\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":18476,"title":18477,"module":18441,"summary":18478},"\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":18480,"title":18481,"module":18441,"summary":18482},"\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":18484,"title":18485,"module":18441,"summary":18486},"\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":18488,"title":18489,"module":18441,"summary":18490},"\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":18492,"title":18493,"module":18441,"summary":18494},"\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":18496,"title":18497,"module":18441,"summary":18498},"\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":18500,"title":18501,"module":18441,"summary":18502},"\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":18504,"title":18505,"module":18441,"summary":18506},"\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":18508,"title":18509,"module":18441,"summary":18510},"\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":18512,"title":18513,"module":18441,"summary":18514},"\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":18516,"title":18517,"module":18441,"summary":18518},"\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":18520,"title":18521,"module":18441,"summary":18522},"\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":18524,"title":18525,"module":18441,"summary":18526},"\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":18528,"title":18529,"module":18530,"summary":18531},"\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":18533,"title":18534,"module":18530,"summary":18535},"\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":18537,"title":18538,"module":18530,"summary":18539},"\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":18541,"title":18542,"module":18530,"summary":18543},"\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":18545,"title":18546,"module":18530,"summary":18547},"\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":18549,"title":18550,"module":18530,"summary":18551},"\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":18553,"title":18554,"module":18530,"summary":18555},"\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":18557,"title":18558,"module":18530,"summary":18559},"\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":18561,"title":17718,"module":6,"summary":6},"\u002Freinforcement-learning",{"path":18563,"title":18564,"module":14915,"summary":18565},"\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":18567,"title":18568,"module":14915,"summary":18569},"\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":18571,"title":18572,"module":14915,"summary":18573},"\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":18575,"title":18576,"module":14915,"summary":18577},"\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":18579,"title":18580,"module":18581,"summary":18582},"\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":18584,"title":18585,"module":18581,"summary":18586},"\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":18588,"title":18589,"module":18581,"summary":18590},"\u002Fartificial-intelligence\u002Fsearch\u002Finformed-search","Informed Search and A*","An informed search uses a heuristic $h(n)$, an estimate of the cost from a node to the goal, to decide what to expand next. Greedy best-first search follows the heuristic blindly and gives up optimality; A* corrects it by ranking nodes on $f(n) = g(n) + h(n)$, and is optimal when the heuristic is admissible (tree search) or consistent (graph search). This lesson defines the heuristic, builds best-first search, and proves why A* is optimal, with the contour picture that explains its pruning. Where good heuristics come from is the next lesson.\n",{"path":18592,"title":18593,"module":18581,"summary":18594},"\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":18596,"title":18597,"module":18581,"summary":18598},"\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":18600,"title":18601,"module":18581,"summary":18602},"\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":18604,"title":18605,"module":18581,"summary":18606},"\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":18608,"title":18609,"module":18581,"summary":18610},"\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":18612,"title":18613,"module":18581,"summary":18614},"\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":18616,"title":18617,"module":18581,"summary":18618},"\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":18620,"title":18621,"module":18581,"summary":18622},"\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":18624,"title":18625,"module":18581,"summary":18626},"\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":18628,"title":18629,"module":18630,"summary":18631},"\u002Fartificial-intelligence\u002Flogic-and-planning\u002Fpropositional-logic","Logical Agents and Propositional Logic","Logic and Planning","A knowledge-based agent keeps a store of sentences and acts by asking it what to do. To make \"asking\" mean something we need entailment — the relation $KB \\models \\alpha$ that holds when every model of the knowledge base is a model of the query. Propositional logic gives a syntax and a truth-table semantics for which entailment is decidable. This first part builds the foundations: the agent loop, the Wumpus World, models and entailment, the connectives and truth tables, theorem proving by refutation, and the resolution rule with its CNF conversion — a single complete inference procedure for all of propositional logic.\n",{"path":18633,"title":18634,"module":18630,"summary":18635},"\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":18637,"title":18638,"module":18630,"summary":18639},"\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":18641,"title":18642,"module":18630,"summary":18643},"\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":18645,"title":18646,"module":18630,"summary":18647},"\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":18649,"title":18650,"module":18630,"summary":18651},"\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":18653,"title":18654,"module":18630,"summary":18655},"\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":18657,"title":18658,"module":18630,"summary":18659},"\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":18661,"title":18662,"module":18630,"summary":18663},"\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":18665,"title":18666,"module":18630,"summary":18667},"\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":18669,"title":18670,"module":18630,"summary":18671},"\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":18673,"title":18674,"module":18630,"summary":18675},"\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":18677,"title":18678,"module":18679,"summary":18680},"\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":18682,"title":18683,"module":18679,"summary":18684},"\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":18686,"title":18687,"module":18679,"summary":18688},"\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":18690,"title":18691,"module":18679,"summary":18692},"\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":18694,"title":18695,"module":18679,"summary":18696},"\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":18698,"title":18699,"module":18679,"summary":18700},"\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":18702,"title":18703,"module":18679,"summary":18704},"\u002Fartificial-intelligence\u002Funcertainty\u002Fmaking-decisions","Making Decisions: Utility Theory","A rational agent chooses the action that maximizes expected utility — the probability of each outcome weighted by how much the agent wants it. We derive the utility function from six axioms on preferences, so maximizing expected utility is forced by consistency rather than assumed; look at risk aversion in the utility-of-money curve; package one-shot choices into decision networks; and quantify what an observation is worth with the value of information.\n",{"path":18706,"title":18295,"module":18679,"summary":18707},"\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":18709,"title":18710,"module":18679,"summary":18711},"\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":18713,"title":18714,"module":18679,"summary":18715},"\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":18717,"title":18718,"module":18719,"summary":18720},"\u002Fartificial-intelligence\u002Flearning\u002Flearning-from-examples","Learning from Examples","Learning","An agent that improves with experience does not need its designer to anticipate every situation. Inductive learning takes that ambition and narrows it to one tractable problem: from labelled input-output pairs, recover a function that predicts the output for inputs never seen. This first part builds the foundation around a single organizing question — generalization — through decision trees and information gain, and the training\u002Fvalidation\u002Ftest discipline for evaluating and choosing hypotheses. A second part takes up the theory of learning and the main model families.\n",{"path":18722,"title":18723,"module":18719,"summary":18724},"\u002Fartificial-intelligence\u002Flearning\u002Ftheory-and-model-families","The Theory of Learning and Model Families","Cross-validation measures generalization but does not explain it. This part supplies the theory — PAC learning, sample complexity, and the VC dimension — that says when a hypothesis consistent with enough data is probably approximately correct, and why an unrestricted hypothesis space can never generalize. It then surveys the model families a practitioner reaches for: linear regression and gradient descent, the perceptron and logistic regression, support vector machines and the kernel trick, and ensembles by bagging and boosting — closing with what deep learning changed about the classical picture.\n",{"path":18726,"title":18727,"module":18719,"summary":18728},"\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":18730,"title":18731,"module":18719,"summary":18732},"\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":18734,"title":17718,"module":18719,"summary":18735},"\u002Fartificial-intelligence\u002Flearning\u002Freinforcement-learning","Reinforcement learning is an MDP with the model unknown: the agent knows neither how its actions move the world nor which states are rewarded, and must recover good behaviour from experienced transitions and rewards alone. This first part builds the classical tabular theory — passive learning (fix a policy, learn its value, by direct estimation, adaptive dynamic programming, and temporal differences) and active learning (choose actions, trade exploration against exploitation, and learn control with Q-learning and SARSA). A second part lifts it off the lookup table with function approximation and policy search.\n",{"path":18737,"title":18738,"module":18719,"summary":18739},"\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":18741,"title":18742,"module":18719,"summary":18743},"\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":18745,"title":18746,"module":18719,"summary":18747},"\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":18749,"title":18750,"module":18751,"summary":18752},"\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":18754,"title":18755,"module":18751,"summary":18756},"\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":18758,"title":18759,"module":18751,"summary":18760},"\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":18762,"title":18763,"module":18751,"summary":18764},"\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":18766,"title":18767,"module":18751,"summary":18768},"\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":18770,"title":18771,"module":18751,"summary":18772},"\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":18774,"title":18775,"module":18751,"summary":18776},"\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":18778,"title":18779,"module":18751,"summary":18780},"\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":18782,"title":18783,"module":6,"summary":6},"\u002Fartificial-intelligence","Artificial Intelligence",{"path":18785,"title":18786,"module":18787,"summary":18788},"\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":18790,"title":18791,"module":18787,"summary":18792},"\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":18794,"title":18795,"module":18787,"summary":18796},"\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":18798,"title":18799,"module":18787,"summary":18800},"\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":18802,"title":18803,"module":18787,"summary":18804},"\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":18806,"title":18807,"module":18808,"summary":18809},"\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":18811,"title":18812,"module":18808,"summary":18813},"\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":18815,"title":18816,"module":18808,"summary":18817},"\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":18819,"title":18820,"module":18808,"summary":18821},"\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":18823,"title":18824,"module":18825,"summary":18826},"\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":18828,"title":18829,"module":18825,"summary":18830},"\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":18832,"title":18833,"module":18825,"summary":18834},"\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":18836,"title":18837,"module":18825,"summary":18838},"\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":18840,"title":18841,"module":18842,"summary":18843},"\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":18845,"title":18846,"module":18842,"summary":18847},"\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":18849,"title":18850,"module":18851,"summary":18852},"\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":18854,"title":18855,"module":18851,"summary":18856},"\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":18858,"title":18859,"module":18860,"summary":18861},"\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":18863,"title":18864,"module":18860,"summary":18865},"\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":18867,"title":18868,"module":18860,"summary":18869},"\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":18871,"title":18872,"module":18860,"summary":18873},"\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":18875,"title":18876,"module":18877,"summary":18878},"\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":18880,"title":18881,"module":18877,"summary":18882},"\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":18884,"title":18885,"module":18877,"summary":18886},"\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":18888,"title":18889,"module":18890,"summary":18891},"\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":18893,"title":18894,"module":18890,"summary":18895},"\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":18897,"title":18898,"module":18890,"summary":18899},"\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":18901,"title":18902,"module":18903,"summary":18904},"\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":18906,"title":18907,"module":18903,"summary":18908},"\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":18910,"title":18911,"module":18912,"summary":18913},"\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":18915,"title":18916,"module":18912,"summary":18917},"\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":18919,"title":18920,"module":18912,"summary":18921},"\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":18923,"title":18924,"module":18925,"summary":18926},"\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":18928,"title":18929,"module":18925,"summary":18930},"\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":18932,"title":18933,"module":18925,"summary":18934},"\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":18936,"title":18937,"module":18925,"summary":18938},"\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":18940,"title":18941,"module":18925,"summary":18942},"\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":18944,"title":18945,"module":6,"summary":6},"\u002Fnuclear-physics","Nuclear Physics",{"path":18947,"title":18948,"module":14915,"summary":18949},"\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":18951,"title":18952,"module":14915,"summary":18953},"\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":18955,"title":18956,"module":14915,"summary":18957},"\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":18959,"title":18960,"module":14915,"summary":18961},"\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":18963,"title":18964,"module":14915,"summary":18965},"\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":18967,"title":18968,"module":18969,"summary":18970},"\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":18972,"title":18973,"module":18969,"summary":18974},"\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":18976,"title":18977,"module":18969,"summary":18978},"\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":18980,"title":18981,"module":18969,"summary":18982},"\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":18984,"title":18985,"module":18986,"summary":18987},"\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":18989,"title":18990,"module":18986,"summary":18991},"\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":18993,"title":18994,"module":18986,"summary":18995},"\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":18997,"title":18998,"module":15381,"summary":18999},"\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":19001,"title":19002,"module":15381,"summary":19003},"\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":19005,"title":19006,"module":15381,"summary":19007},"\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":19009,"title":19010,"module":15863,"summary":19011},"\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":19013,"title":17571,"module":15863,"summary":19014},"\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":19016,"title":17672,"module":15863,"summary":19017},"\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":19019,"title":19020,"module":15863,"summary":19021},"\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":19023,"title":19024,"module":15863,"summary":19025},"\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":19027,"title":19028,"module":15863,"summary":19029},"\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":19031,"title":19032,"module":19033,"summary":19034},"\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":19036,"title":19037,"module":19033,"summary":19038},"\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":19040,"title":19041,"module":19033,"summary":19042},"\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":19044,"title":19045,"module":19033,"summary":19046},"\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":19048,"title":19049,"module":19033,"summary":19050},"\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":19052,"title":19053,"module":19033,"summary":19054},"\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":19056,"title":19057,"module":19033,"summary":19058},"\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":19060,"title":19061,"module":19033,"summary":19062},"\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":19064,"title":19065,"module":19033,"summary":19066},"\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":19068,"title":19069,"module":19033,"summary":19070},"\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":19072,"title":19073,"module":19033,"summary":19074},"\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":19076,"title":19077,"module":19033,"summary":19078},"\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":19080,"title":19081,"module":19033,"summary":19082},"\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":19084,"title":19085,"module":19033,"summary":19086},"\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":19088,"title":19089,"module":19033,"summary":19090},"\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":19092,"title":19093,"module":19033,"summary":19094},"\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":19096,"title":19097,"module":19033,"summary":19098},"\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":19100,"title":19101,"module":19033,"summary":19102},"\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":19104,"title":19105,"module":19033,"summary":19106},"\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":19108,"title":19109,"module":19033,"summary":19110},"\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":19112,"title":19113,"module":17660,"summary":19114},"\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":19116,"title":19117,"module":17660,"summary":19118},"\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":19120,"title":19121,"module":17660,"summary":19122},"\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":19124,"title":19125,"module":17660,"summary":19126},"\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":19128,"title":19129,"module":17660,"summary":19130},"\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":19132,"title":19133,"module":17660,"summary":19134},"\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":19136,"title":19137,"module":17660,"summary":19138},"\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":19140,"title":19141,"module":17660,"summary":19142},"\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":19144,"title":19145,"module":19146,"summary":19147},"\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":19149,"title":19150,"module":19146,"summary":19151},"\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":19153,"title":19154,"module":19146,"summary":19155},"\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":19157,"title":19158,"module":19146,"summary":19159},"\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":19161,"title":19162,"module":6,"summary":6},"\u002Fnatural-language-processing","Natural Language Processing",{"path":19164,"title":19165,"module":14915,"summary":19166},"\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":19168,"title":19169,"module":14915,"summary":19170},"\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":19172,"title":19173,"module":14915,"summary":19174},"\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":19176,"title":19177,"module":19178,"summary":19179},"\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":19181,"title":19182,"module":19178,"summary":19183},"\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":19185,"title":19186,"module":19178,"summary":19187},"\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":19189,"title":19190,"module":19178,"summary":19191},"\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":19193,"title":19194,"module":19195,"summary":19196},"\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":19198,"title":19199,"module":19195,"summary":19200},"\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":19202,"title":19203,"module":19195,"summary":19204},"\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":19206,"title":19207,"module":19195,"summary":19208},"\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":19210,"title":19211,"module":19212,"summary":19213},"\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":19215,"title":19216,"module":19212,"summary":19217},"\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":19219,"title":19220,"module":19212,"summary":19221},"\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":19223,"title":19224,"module":19212,"summary":19225},"\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":19227,"title":19228,"module":19229,"summary":19230},"\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":19232,"title":19233,"module":19229,"summary":19234},"\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":19236,"title":19237,"module":19229,"summary":19238},"\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":19240,"title":19241,"module":19242,"summary":19243},"\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":19245,"title":19246,"module":19242,"summary":19247},"\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":19249,"title":19250,"module":19242,"summary":19251},"\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":19253,"title":19254,"module":19242,"summary":19255},"\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":19257,"title":19258,"module":19259,"summary":19260},"\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":19262,"title":19263,"module":19259,"summary":19264},"\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":19266,"title":19267,"module":19259,"summary":19268},"\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":19270,"title":19271,"module":19259,"summary":19272},"\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":19274,"title":19275,"module":19276,"summary":19277},"\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":19279,"title":19280,"module":19276,"summary":19281},"\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":19283,"title":19284,"module":19276,"summary":19285},"\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":19287,"title":19288,"module":19276,"summary":19289},"\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":19291,"title":19292,"module":19293,"summary":19294},"\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":19296,"title":19297,"module":19293,"summary":19298},"\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":19300,"title":19301,"module":19293,"summary":19302},"\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":19304,"title":19305,"module":19293,"summary":19306},"\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":19308,"title":19309,"module":19293,"summary":19310},"\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":19312,"title":19313,"module":19314,"summary":19315},"\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":19317,"title":19318,"module":19314,"summary":19319},"\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":19321,"title":19322,"module":19314,"summary":19323},"\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":19325,"title":19326,"module":19327,"summary":19328},"\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":19330,"title":19331,"module":19327,"summary":19332},"\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":19334,"title":19335,"module":19327,"summary":19336},"\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":19338,"title":19339,"module":19339,"summary":19340},"\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":19342,"title":19343,"module":19339,"summary":19344},"\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":19346,"title":19347,"module":19339,"summary":19348},"\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":19350,"title":19351,"module":19339,"summary":19352},"\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":19354,"title":19355,"module":19339,"summary":19356},"\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":19358,"title":19359,"module":19339,"summary":19360},"\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":19362,"title":19363,"module":6,"summary":6},"\u002Fparticle-physics","Particle Physics",{"path":19365,"title":19366,"module":19367,"summary":19368},"\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":19370,"title":19371,"module":19367,"summary":19372},"\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":19374,"title":19375,"module":19367,"summary":19376},"\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":19378,"title":19379,"module":19380,"summary":19381},"\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":19383,"title":19384,"module":19380,"summary":19385},"\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":19387,"title":19388,"module":19380,"summary":19389},"\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":19391,"title":19392,"module":19380,"summary":19393},"\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":19395,"title":19396,"module":19397,"summary":19398},"\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":19400,"title":19401,"module":19397,"summary":19402},"\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":19404,"title":19405,"module":19397,"summary":19406},"\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":19408,"title":19409,"module":19397,"summary":19410},"\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":19412,"title":19413,"module":19414,"summary":19415},"\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":19417,"title":19418,"module":19414,"summary":19419},"\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":19421,"title":19422,"module":19414,"summary":19423},"\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":19425,"title":19426,"module":19414,"summary":19427},"\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":19429,"title":19430,"module":19431,"summary":19432},"\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":19434,"title":19435,"module":19431,"summary":19436},"\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":19438,"title":19439,"module":19431,"summary":19440},"\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":19442,"title":19443,"module":19431,"summary":19444},"\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":19446,"title":19447,"module":19448,"summary":19449},"\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":19451,"title":19452,"module":19448,"summary":19453},"\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":19455,"title":19456,"module":19448,"summary":19457},"\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":19459,"title":19460,"module":19461,"summary":19462},"\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":19464,"title":19465,"module":19461,"summary":19466},"\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":19468,"title":19469,"module":19461,"summary":19470},"\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":19472,"title":19473,"module":19461,"summary":19474},"\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":19476,"title":17892,"module":19477,"summary":19478},"\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":19480,"title":19481,"module":19477,"summary":19482},"\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":19484,"title":19485,"module":19477,"summary":19486},"\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":19488,"title":19489,"module":19477,"summary":19490},"\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":19492,"title":19493,"module":19477,"summary":19494},"\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":19496,"title":19497,"module":19498,"summary":19499},"\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":19501,"title":19502,"module":19498,"summary":19503},"\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":19505,"title":19506,"module":19498,"summary":19507},"\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":19509,"title":19510,"module":19498,"summary":19511},"\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":19513,"title":19514,"module":19515,"summary":19516},"\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":19518,"title":19519,"module":19515,"summary":19520},"\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":19522,"title":19523,"module":19515,"summary":19524},"\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":19526,"title":19527,"module":19515,"summary":19528},"\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":19530,"title":19531,"module":19515,"summary":19532},"\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":19534,"title":19535,"module":19536,"summary":19537},"\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":19539,"title":19540,"module":19536,"summary":19541},"\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":19543,"title":16622,"module":19536,"summary":19544},"\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":19546,"title":19547,"module":19536,"summary":19548},"\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":19550,"title":19551,"module":19536,"summary":19552},"\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":19554,"title":19555,"module":19556,"summary":19557},"\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":19559,"title":19560,"module":19556,"summary":19561},"\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":19563,"title":19564,"module":19556,"summary":19565},"\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":19567,"title":19568,"module":19556,"summary":19569},"\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":19571,"title":19572,"module":19556,"summary":19573},"\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":19575,"title":19576,"module":19556,"summary":19577},"\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":19579,"title":19580,"module":19556,"summary":19581},"\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":19583,"title":19584,"module":6,"summary":6},"\u002Fastrophysics-cosmology","Astrophysics & Cosmology",{"path":19586,"title":19587,"module":6,"summary":6},"\u002Fcolophon","Colophon",{"path":3158,"title":19589,"module":6,"summary":6},"Study Notes",[19591,19603,19612,19626,19639,19649,19673,19686,19703,19712,19730,19756],{"module":17468,"moduleNumber":13974,"slug":19592,"lessons":19593},"mathematical-background",[19594,19596,19598,19600],{"title":16109,"path":17467,"lessonNumber":13974,"topics":19595,"summary":17469},[17468],{"title":17472,"path":17471,"lessonNumber":13952,"topics":19597,"summary":17473},[17468],{"title":17476,"path":17475,"lessonNumber":13957,"topics":19599,"summary":17477},[17468],{"title":15468,"path":17479,"lessonNumber":19601,"topics":19602,"summary":17480},4,[17468],{"module":14915,"moduleNumber":13952,"slug":19604,"lessons":19605},"foundations",[19606,19608,19610],{"title":17483,"path":17482,"lessonNumber":13974,"topics":19607,"summary":17484},[14915],{"title":17487,"path":17486,"lessonNumber":13952,"topics":19609,"summary":17488},[14915],{"title":17491,"path":17490,"lessonNumber":13957,"topics":19611,"summary":17492},[14915],{"module":17496,"moduleNumber":13957,"slug":19613,"lessons":19614},"neural-networks",[19615,19617,19619,19621,19623],{"title":17495,"path":17494,"lessonNumber":13974,"topics":19616,"summary":17497},[17496],{"title":17500,"path":17499,"lessonNumber":13952,"topics":19618,"summary":17501},[17496],{"title":17504,"path":17503,"lessonNumber":13957,"topics":19620,"summary":17505},[17496],{"title":17508,"path":17507,"lessonNumber":19601,"topics":19622,"summary":17509},[17496],{"title":17512,"path":17511,"lessonNumber":19624,"topics":19625,"summary":17513},5,[17496],{"module":17517,"moduleNumber":19601,"slug":19627,"lessons":19628},"optimization",[19629,19631,19633,19635,19637],{"title":17516,"path":17515,"lessonNumber":13974,"topics":19630,"summary":17518},[17517],{"title":17521,"path":17520,"lessonNumber":13952,"topics":19632,"summary":17522},[17517],{"title":17525,"path":17524,"lessonNumber":13957,"topics":19634,"summary":17526},[17517],{"title":17528,"path":13321,"lessonNumber":19601,"topics":19636,"summary":17529},[17517],{"title":17532,"path":17531,"lessonNumber":19624,"topics":19638,"summary":17533},[17517],{"module":17536,"moduleNumber":19624,"slug":13371,"lessons":19640},[19641,19643,19645,19647],{"title":17535,"path":13370,"lessonNumber":13974,"topics":19642,"summary":17537},[17536],{"title":17539,"path":13375,"lessonNumber":13952,"topics":19644,"summary":17540},[17536],{"title":17542,"path":13380,"lessonNumber":13957,"topics":19646,"summary":17543},[17536],{"title":17546,"path":17545,"lessonNumber":19601,"topics":19648,"summary":17547},[17536],{"module":17551,"moduleNumber":13973,"slug":19650,"lessons":19651},"architectures",[19652,19654,19656,19658,19660,19662,19664,19667,19670],{"title":17550,"path":17549,"lessonNumber":13974,"topics":19653,"summary":17552},[17551],{"title":17555,"path":17554,"lessonNumber":13952,"topics":19655,"summary":17556},[17551],{"title":17559,"path":17558,"lessonNumber":13957,"topics":19657,"summary":17560},[17551],{"title":17563,"path":17562,"lessonNumber":19601,"topics":19659,"summary":17564},[17551],{"title":17567,"path":17566,"lessonNumber":19624,"topics":19661,"summary":17568},[17551],{"title":17571,"path":17570,"lessonNumber":13973,"topics":19663,"summary":17572},[17551],{"title":17575,"path":17574,"lessonNumber":19665,"topics":19666,"summary":17576},7,[17551],{"title":17579,"path":17578,"lessonNumber":19668,"topics":19669,"summary":17580},8,[17551],{"title":17583,"path":17582,"lessonNumber":19671,"topics":19672,"summary":17584},9,[17551],{"module":13976,"moduleNumber":19665,"slug":19674,"lessons":19675},"theory",[19676,19678,19680,19682,19684],{"title":5,"path":13978,"lessonNumber":13974,"topics":19677,"summary":13996},[13976],{"title":17587,"path":13839,"lessonNumber":13952,"topics":19679,"summary":17588},[13976],{"title":17591,"path":17590,"lessonNumber":13957,"topics":19681,"summary":17592},[13976],{"title":17594,"path":13844,"lessonNumber":19601,"topics":19683,"summary":17595},[13976],{"title":17597,"path":13849,"lessonNumber":19624,"topics":19685,"summary":17598},[13976],{"module":17602,"moduleNumber":19668,"slug":19687,"lessons":19688},"generative-models",[19689,19691,19693,19695,19697,19699,19701],{"title":17601,"path":17600,"lessonNumber":13974,"topics":19690,"summary":17603},[17602],{"title":17606,"path":17605,"lessonNumber":13952,"topics":19692,"summary":17607},[17602],{"title":17610,"path":17609,"lessonNumber":13957,"topics":19694,"summary":17611},[17602],{"title":17614,"path":17613,"lessonNumber":19601,"topics":19696,"summary":17615},[17602],{"title":17618,"path":17617,"lessonNumber":19624,"topics":19698,"summary":17619},[17602],{"title":17622,"path":17621,"lessonNumber":13973,"topics":19700,"summary":17623},[17602],{"title":17626,"path":17625,"lessonNumber":19665,"topics":19702,"summary":17627},[17602],{"module":17631,"moduleNumber":19671,"slug":19704,"lessons":19705},"probabilistic-methods",[19706,19708,19710],{"title":17630,"path":17629,"lessonNumber":13974,"topics":19707,"summary":17632},[17631],{"title":17635,"path":17634,"lessonNumber":13952,"topics":19709,"summary":17636},[17631],{"title":17639,"path":17638,"lessonNumber":13957,"topics":19711,"summary":17640},[17631],{"module":17644,"moduleNumber":19713,"slug":19714,"lessons":19715},10,"practical",[19716,19718,19720,19722,19724,19726,19728],{"title":17643,"path":17642,"lessonNumber":13974,"topics":19717,"summary":17645},[17644],{"title":17648,"path":17647,"lessonNumber":13952,"topics":19719,"summary":17649},[17644],{"title":17652,"path":17651,"lessonNumber":13957,"topics":19721,"summary":17653},[17644],{"title":17656,"path":17655,"lessonNumber":19601,"topics":19723,"summary":17657},[17644],{"title":17660,"path":17659,"lessonNumber":19624,"topics":19725,"summary":17661},[17644],{"title":17664,"path":17663,"lessonNumber":13973,"topics":19727,"summary":17665},[17644],{"title":17668,"path":17667,"lessonNumber":19665,"topics":19729,"summary":17669},[17644],{"module":17673,"moduleNumber":19731,"slug":19732,"lessons":19733},11,"large-models-and-agents",[19734,19736,19738,19740,19742,19744,19746,19748,19750,19752,19754],{"title":17672,"path":17671,"lessonNumber":13974,"topics":19735,"summary":17674},[17673],{"title":17677,"path":17676,"lessonNumber":13952,"topics":19737,"summary":17678},[17673],{"title":17681,"path":17680,"lessonNumber":13957,"topics":19739,"summary":17682},[17673],{"title":17685,"path":17684,"lessonNumber":19601,"topics":19741,"summary":17686},[17673],{"title":17689,"path":17688,"lessonNumber":19624,"topics":19743,"summary":17690},[17673],{"title":17693,"path":17692,"lessonNumber":13973,"topics":19745,"summary":17694},[17673],{"title":17697,"path":17696,"lessonNumber":19665,"topics":19747,"summary":17698},[17673],{"title":17701,"path":17700,"lessonNumber":19668,"topics":19749,"summary":17702},[17673],{"title":17705,"path":17704,"lessonNumber":19671,"topics":19751,"summary":17706},[17673],{"title":17709,"path":17708,"lessonNumber":19713,"topics":19753,"summary":17710},[17673],{"title":17713,"path":17712,"lessonNumber":19731,"topics":19755,"summary":17714},[17673],{"module":17718,"moduleNumber":19757,"slug":19758,"lessons":19759},12,"reinforcement-learning",[19760,19762,19764,19766,19768],{"title":17717,"path":17716,"lessonNumber":13974,"topics":19761,"summary":17719},[17718],{"title":17722,"path":17721,"lessonNumber":13952,"topics":19763,"summary":17723},[17718],{"title":17726,"path":17725,"lessonNumber":13957,"topics":19765,"summary":17727},[17718],{"title":17730,"path":17729,"lessonNumber":19601,"topics":19767,"summary":17731},[17718],{"title":17734,"path":17733,"lessonNumber":19624,"topics":19769,"summary":17735},[17718],"\u003Csvg style=\"width:100%;max-width:306.539px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 229.905 96.555\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(.178 -82.133)\">\u003Cpath d=\"M-58.764 17.038Q-58.764 16.862-58.647 16.741Q-58.530 16.620-58.354 16.620Q-58.272 16.620-58.196 16.651Q-58.120 16.683-58.069 16.733Q-58.018 16.784-57.987 16.864Q-57.956 16.944-57.956 17.023Q-57.956 17.155-58.038 17.269Q-57.768 17.605-56.979 17.605Q-56.553 17.605-56.212 17.339Q-55.870 17.073-55.870 16.659Q-55.870 16.386-56.036 16.167Q-56.202 15.948-56.462 15.837Q-56.721 15.726-56.995 15.726L-57.460 15.726Q-57.671 15.702-57.702 15.483L-57.702 15.398Q-57.671 15.194-57.460 15.163L-56.932 15.124Q-56.718 15.124-56.526 15.001Q-56.335 14.878-56.221 14.675Q-56.108 14.472-56.108 14.261Q-56.108 13.987-56.391 13.833Q-56.675 13.679-56.979 13.679Q-57.542 13.679-57.780 13.870Q-57.718 13.983-57.718 14.093Q-57.718 14.265-57.831 14.378Q-57.944 14.491-58.116 14.491Q-58.292 14.491-58.407 14.368Q-58.522 14.245-58.522 14.077Q-58.522 13.550-58.050 13.333Q-57.577 13.116-56.979 13.116Q-56.639 13.116-56.284 13.247Q-55.928 13.378-55.698 13.636Q-55.468 13.894-55.468 14.261Q-55.468 14.605-55.636 14.909Q-55.803 15.214-56.100 15.413Q-55.729 15.593-55.479 15.929Q-55.229 16.265-55.229 16.659Q-55.229 17.105-55.483 17.446Q-55.737 17.788-56.139 17.976Q-56.542 18.163-56.979 18.163Q-57.264 18.163-57.569 18.108Q-57.874 18.054-58.149 17.927Q-58.425 17.800-58.595 17.577Q-58.764 17.355-58.764 17.038\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(.178 -82.133)\">\u003Cpath d=\"M-50.545 19.628L-50.545 19.542Q-50.502 19.323-50.295 19.300L-49.873 19.300L-49.873 15.198L-50.295 15.198Q-50.502 15.175-50.545 14.956L-50.545 14.870Q-50.498 14.659-50.295 14.636L-49.478 14.636Q-49.283 14.655-49.232 14.870L-49.232 14.940Q-49.021 14.773-48.758 14.685Q-48.494 14.597-48.224 14.597Q-47.885 14.597-47.588 14.741Q-47.291 14.886-47.076 15.138Q-46.861 15.390-46.746 15.702Q-46.631 16.015-46.631 16.358Q-46.631 16.823-46.857 17.233Q-47.084 17.644-47.470 17.884Q-47.857 18.124-48.326 18.124Q-48.845 18.124-49.232 17.757L-49.232 19.300L-48.806 19.300Q-48.599 19.323-48.560 19.542L-48.560 19.628Q-48.599 19.839-48.806 19.862L-50.295 19.862Q-50.498 19.839-50.545 19.628M-48.369 17.565Q-48.060 17.565-47.810 17.396Q-47.560 17.226-47.416 16.944Q-47.271 16.663-47.271 16.358Q-47.271 16.069-47.396 15.790Q-47.521 15.511-47.754 15.333Q-47.986 15.155-48.287 15.155Q-48.607 15.155-48.869 15.341Q-49.131 15.526-49.232 15.827L-49.232 16.679Q-49.142 17.046-48.922 17.306Q-48.701 17.565-48.369 17.565M-44.248 18.124Q-44.720 18.124-45.105 17.880Q-45.490 17.636-45.713 17.226Q-45.935 16.815-45.935 16.358Q-45.935 16.015-45.810 15.692Q-45.685 15.370-45.455 15.116Q-45.224 14.862-44.918 14.718Q-44.611 14.573-44.248 14.573Q-43.885 14.573-43.572 14.720Q-43.260 14.866-43.037 15.112Q-42.814 15.358-42.687 15.679Q-42.560 15.999-42.560 16.358Q-42.560 16.815-42.785 17.228Q-43.010 17.640-43.394 17.882Q-43.779 18.124-44.248 18.124M-44.248 17.565Q-43.783 17.565-43.492 17.171Q-43.201 16.776-43.201 16.292Q-43.201 15.999-43.336 15.731Q-43.470 15.464-43.711 15.298Q-43.951 15.132-44.248 15.132Q-44.553 15.132-44.791 15.298Q-45.029 15.464-45.164 15.731Q-45.299 15.999-45.299 16.292Q-45.299 16.773-45.006 17.169Q-44.713 17.565-44.248 17.565M-41.506 17.847L-41.506 17.757Q-41.455 17.550-41.260 17.526L-40.220 17.526L-40.220 15.198L-41.193 15.198Q-41.392 15.175-41.443 14.956L-41.443 14.870Q-41.392 14.659-41.193 14.636L-39.826 14.636Q-39.631 14.655-39.580 14.870L-39.580 17.526L-38.666 17.526Q-38.470 17.550-38.420 17.757L-38.420 17.847Q-38.470 18.062-38.666 18.085L-41.260 18.085Q-41.455 18.062-41.506 17.847M-40.474 13.659L-40.474 13.605Q-40.474 13.433-40.338 13.312Q-40.201 13.190-40.025 13.190Q-39.853 13.190-39.717 13.312Q-39.580 13.433-39.580 13.605L-39.580 13.659Q-39.580 13.835-39.717 13.956Q-39.853 14.077-40.025 14.077Q-40.201 14.077-40.338 13.956Q-40.474 13.835-40.474 13.659M-37.806 17.847L-37.806 17.757Q-37.763 17.550-37.556 17.526L-37.135 17.526L-37.135 15.198L-37.556 15.198Q-37.763 15.175-37.806 14.956L-37.806 14.870Q-37.760 14.659-37.556 14.636L-36.740 14.636Q-36.545 14.659-36.494 14.870L-36.494 14.956L-36.502 14.980Q-36.275 14.800-36.002 14.698Q-35.728 14.597-35.435 14.597Q-35.088 14.597-34.849 14.737Q-34.611 14.878-34.496 15.136Q-34.381 15.394-34.381 15.749L-34.381 17.526L-33.955 17.526Q-33.748 17.550-33.709 17.757L-33.709 17.847Q-33.748 18.062-33.955 18.085L-35.349 18.085Q-35.545 18.062-35.595 17.847L-35.595 17.757Q-35.545 17.546-35.349 17.526L-35.021 17.526L-35.021 15.780Q-35.021 15.472-35.111 15.314Q-35.201 15.155-35.494 15.155Q-35.763 15.155-35.992 15.286Q-36.220 15.417-36.357 15.646Q-36.494 15.874-36.494 16.140L-36.494 17.526L-36.068 17.526Q-35.861 17.550-35.822 17.757L-35.822 17.847Q-35.861 18.062-36.068 18.085L-37.556 18.085Q-37.763 18.062-37.806 17.847M-32.431 16.980L-32.431 15.198L-33.181 15.198Q-33.381 15.175-33.431 14.956L-33.431 14.870Q-33.381 14.659-33.181 14.636L-32.431 14.636L-32.431 13.886Q-32.381 13.679-32.181 13.651L-32.037 13.651Q-31.842 13.679-31.791 13.886L-31.791 14.636L-30.431 14.636Q-30.240 14.655-30.181 14.870L-30.181 14.956Q-30.236 15.175-30.431 15.198L-31.791 15.198L-31.791 16.948Q-31.791 17.565-31.217 17.565Q-30.967 17.565-30.803 17.380Q-30.638 17.194-30.638 16.948Q-30.638 16.855-30.566 16.784Q-30.494 16.714-30.392 16.702L-30.248 16.702Q-30.049 16.726-29.998 16.933L-29.998 16.980Q-29.998 17.304-30.181 17.567Q-30.365 17.831-30.658 17.978Q-30.951 18.124-31.271 18.124Q-31.783 18.124-32.107 17.814Q-32.431 17.503-32.431 16.980M-28.799 17.886L-28.799 16.972Q-28.771 16.765-28.560 16.741L-28.392 16.741Q-28.228 16.765-28.170 16.925Q-27.967 17.565-27.240 17.565Q-27.033 17.565-26.804 17.530Q-26.576 17.495-26.408 17.380Q-26.240 17.265-26.240 17.062Q-26.240 16.851-26.463 16.737Q-26.685 16.624-26.959 16.581L-27.658 16.468Q-28.799 16.257-28.799 15.534Q-28.799 15.245-28.654 15.056Q-28.510 14.866-28.269 14.759Q-28.029 14.651-27.773 14.612Q-27.517 14.573-27.240 14.573Q-26.990 14.573-26.797 14.603Q-26.603 14.632-26.439 14.710Q-26.361 14.593-26.232 14.573L-26.154 14.573Q-26.056 14.585-25.994 14.648Q-25.931 14.710-25.920 14.804L-25.920 15.511Q-25.931 15.605-25.994 15.671Q-26.056 15.737-26.154 15.749L-26.322 15.749Q-26.416 15.737-26.482 15.671Q-26.549 15.605-26.560 15.511Q-26.560 15.132-27.256 15.132Q-27.603 15.132-27.922 15.214Q-28.240 15.296-28.240 15.542Q-28.240 15.808-27.568 15.917L-26.865 16.038Q-26.381 16.120-26.031 16.368Q-25.681 16.616-25.681 17.062Q-25.681 17.452-25.918 17.694Q-26.154 17.937-26.504 18.030Q-26.853 18.124-27.240 18.124Q-27.818 18.124-28.217 17.870Q-28.287 17.995-28.336 18.052Q-28.385 18.108-28.490 18.124L-28.560 18.124Q-28.775 18.101-28.799 17.886M-23.572 17.526Q-23.572 17.304-23.406 17.138Q-23.240 16.972-23.010 16.972Q-22.861 16.972-22.734 17.050Q-22.607 17.128-22.533 17.253Q-22.459 17.378-22.459 17.526Q-22.459 17.753-22.625 17.919Q-22.791 18.085-23.010 18.085Q-23.236 18.085-23.404 17.917Q-23.572 17.749-23.572 17.526M-23.572 15.190Q-23.572 14.968-23.406 14.802Q-23.240 14.636-23.010 14.636Q-22.861 14.636-22.734 14.714Q-22.607 14.792-22.533 14.917Q-22.459 15.042-22.459 15.190Q-22.459 15.417-22.625 15.583Q-22.791 15.749-23.010 15.749Q-23.236 15.749-23.404 15.581Q-23.572 15.413-23.572 15.190\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(.178 -82.133)\">\u003Cpath d=\"M-11.779 17.886L-11.779 16.972Q-11.752 16.765-11.541 16.741L-11.373 16.741Q-11.209 16.765-11.150 16.925Q-10.947 17.565-10.220 17.565Q-10.013 17.565-9.785 17.530Q-9.556 17.495-9.388 17.380Q-9.220 17.265-9.220 17.062Q-9.220 16.851-9.443 16.737Q-9.666 16.624-9.939 16.581L-10.638 16.468Q-11.779 16.257-11.779 15.534Q-11.779 15.245-11.635 15.056Q-11.490 14.866-11.250 14.759Q-11.010 14.651-10.754 14.612Q-10.498 14.573-10.220 14.573Q-9.970 14.573-9.777 14.603Q-9.584 14.632-9.420 14.710Q-9.342 14.593-9.213 14.573L-9.135 14.573Q-9.037 14.585-8.974 14.648Q-8.912 14.710-8.900 14.804L-8.900 15.511Q-8.912 15.605-8.974 15.671Q-9.037 15.737-9.135 15.749L-9.303 15.749Q-9.396 15.737-9.463 15.671Q-9.529 15.605-9.541 15.511Q-9.541 15.132-10.236 15.132Q-10.584 15.132-10.902 15.214Q-11.220 15.296-11.220 15.542Q-11.220 15.808-10.549 15.917L-9.845 16.038Q-9.361 16.120-9.011 16.368Q-8.662 16.616-8.662 17.062Q-8.662 17.452-8.898 17.694Q-9.135 17.937-9.484 18.030Q-9.834 18.124-10.220 18.124Q-10.799 18.124-11.197 17.870Q-11.267 17.995-11.316 18.052Q-11.365 18.108-11.470 18.124L-11.541 18.124Q-11.756 18.101-11.779 17.886M-8.049 17.847L-8.049 17.757Q-8.006 17.550-7.799 17.526L-7.377 17.526L-7.377 13.757L-7.799 13.757Q-8.006 13.733-8.049 13.519L-8.049 13.429Q-8.006 13.222-7.799 13.198L-6.982 13.198Q-6.787 13.222-6.736 13.429L-6.736 14.980Q-6.275 14.597-5.678 14.597Q-5.330 14.597-5.092 14.737Q-4.853 14.878-4.738 15.136Q-4.623 15.394-4.623 15.749L-4.623 17.526L-4.197 17.526Q-3.990 17.550-3.951 17.757L-3.951 17.847Q-3.990 18.062-4.197 18.085L-5.592 18.085Q-5.787 18.062-5.838 17.847L-5.838 17.757Q-5.787 17.546-5.592 17.526L-5.263 17.526L-5.263 15.780Q-5.263 15.472-5.353 15.314Q-5.443 15.155-5.736 15.155Q-6.006 15.155-6.234 15.286Q-6.463 15.417-6.599 15.646Q-6.736 15.874-6.736 16.140L-6.736 17.526L-6.310 17.526Q-6.103 17.550-6.064 17.757L-6.064 17.847Q-6.103 18.062-6.310 18.085L-7.799 18.085Q-8.006 18.062-8.049 17.847M-3.467 16.972Q-3.467 16.526-3.053 16.269Q-2.638 16.011-2.097 15.911Q-1.556 15.812-1.049 15.804Q-1.049 15.589-1.183 15.437Q-1.318 15.284-1.525 15.208Q-1.732 15.132-1.943 15.132Q-2.287 15.132-2.447 15.155L-2.447 15.214Q-2.447 15.382-2.566 15.497Q-2.685 15.612-2.849 15.612Q-3.025 15.612-3.140 15.489Q-3.256 15.366-3.256 15.198Q-3.256 14.792-2.875 14.683Q-2.494 14.573-1.935 14.573Q-1.666 14.573-1.398 14.651Q-1.131 14.730-0.906 14.880Q-0.681 15.030-0.545 15.251Q-0.408 15.472-0.408 15.749L-0.408 17.468Q-0.408 17.526 0.119 17.526Q0.315 17.546 0.365 17.757L0.365 17.847Q0.315 18.062 0.119 18.085L-0.025 18.085Q-0.369 18.085-0.597 18.038Q-0.826 17.991-0.970 17.804Q-1.431 18.124-2.138 18.124Q-2.474 18.124-2.779 17.983Q-3.084 17.843-3.275 17.581Q-3.467 17.319-3.467 16.972M-2.826 16.980Q-2.826 17.253-2.584 17.409Q-2.342 17.565-2.056 17.565Q-1.838 17.565-1.605 17.507Q-1.373 17.448-1.211 17.310Q-1.049 17.171-1.049 16.948L-1.049 16.358Q-1.330 16.358-1.746 16.415Q-2.162 16.472-2.494 16.610Q-2.826 16.749-2.826 16.980M1.572 16.980L1.572 15.198L0.822 15.198Q0.623 15.175 0.572 14.956L0.572 14.870Q0.623 14.659 0.822 14.636L1.572 14.636L1.572 13.886Q1.623 13.679 1.822 13.651L1.967 13.651Q2.162 13.679 2.213 13.886L2.213 14.636L3.572 14.636Q3.764 14.655 3.822 14.870L3.822 14.956Q3.768 15.175 3.572 15.198L2.213 15.198L2.213 16.948Q2.213 17.565 2.787 17.565Q3.037 17.565 3.201 17.380Q3.365 17.194 3.365 16.948Q3.365 16.855 3.438 16.784Q3.510 16.714 3.612 16.702L3.756 16.702Q3.955 16.726 4.006 16.933L4.006 16.980Q4.006 17.304 3.822 17.567Q3.639 17.831 3.346 17.978Q3.053 18.124 2.733 18.124Q2.221 18.124 1.897 17.814Q1.572 17.503 1.572 16.980M5.819 16.980L5.819 15.198L5.069 15.198Q4.869 15.175 4.819 14.956L4.819 14.870Q4.869 14.659 5.069 14.636L5.819 14.636L5.819 13.886Q5.869 13.679 6.069 13.651L6.213 13.651Q6.408 13.679 6.459 13.886L6.459 14.636L7.819 14.636Q8.010 14.655 8.069 14.870L8.069 14.956Q8.014 15.175 7.819 15.198L6.459 15.198L6.459 16.948Q6.459 17.565 7.033 17.565Q7.283 17.565 7.447 17.380Q7.612 17.194 7.612 16.948Q7.612 16.855 7.684 16.784Q7.756 16.714 7.858 16.702L8.002 16.702Q8.201 16.726 8.252 16.933L8.252 16.980Q8.252 17.304 8.069 17.567Q7.885 17.831 7.592 17.978Q7.299 18.124 6.979 18.124Q6.467 18.124 6.143 17.814Q5.819 17.503 5.819 16.980M12.377 16.597L9.936 16.597Q9.990 16.874 10.188 17.097Q10.385 17.319 10.662 17.442Q10.940 17.565 11.225 17.565Q11.697 17.565 11.920 17.276Q11.928 17.265 11.985 17.159Q12.041 17.054 12.090 17.011Q12.139 16.968 12.233 16.956L12.377 16.956Q12.569 16.976 12.627 17.190L12.627 17.245Q12.561 17.546 12.330 17.743Q12.100 17.940 11.787 18.032Q11.475 18.124 11.170 18.124Q10.686 18.124 10.246 17.896Q9.807 17.667 9.539 17.267Q9.272 16.866 9.272 16.374L9.272 16.315Q9.272 15.847 9.518 15.444Q9.764 15.042 10.172 14.808Q10.580 14.573 11.049 14.573Q11.553 14.573 11.906 14.796Q12.260 15.019 12.444 15.407Q12.627 15.796 12.627 16.300L12.627 16.358Q12.569 16.573 12.377 16.597M9.944 16.046L11.971 16.046Q11.924 15.636 11.686 15.384Q11.447 15.132 11.049 15.132Q10.655 15.132 10.348 15.394Q10.041 15.655 9.944 16.046M13.334 17.847L13.334 17.757Q13.393 17.550 13.584 17.526L14.295 17.526L14.295 15.198L13.584 15.198Q13.389 15.175 13.334 14.956L13.334 14.870Q13.393 14.659 13.584 14.636L14.686 14.636Q14.885 14.655 14.936 14.870L14.936 15.198Q15.197 14.913 15.553 14.755Q15.908 14.597 16.295 14.597Q16.588 14.597 16.822 14.731Q17.057 14.866 17.057 15.132Q17.057 15.300 16.947 15.417Q16.838 15.534 16.670 15.534Q16.518 15.534 16.403 15.423Q16.287 15.312 16.287 15.155Q15.912 15.155 15.598 15.356Q15.283 15.558 15.110 15.892Q14.936 16.226 14.936 16.605L14.936 17.526L15.881 17.526Q16.088 17.550 16.127 17.757L16.127 17.847Q16.088 18.062 15.881 18.085L13.584 18.085Q13.393 18.062 13.334 17.847M20.869 16.597L18.428 16.597Q18.483 16.874 18.680 17.097Q18.877 17.319 19.155 17.442Q19.432 17.565 19.717 17.565Q20.190 17.565 20.412 17.276Q20.420 17.265 20.477 17.159Q20.533 17.054 20.582 17.011Q20.631 16.968 20.725 16.956L20.869 16.956Q21.061 16.976 21.119 17.190L21.119 17.245Q21.053 17.546 20.822 17.743Q20.592 17.940 20.280 18.032Q19.967 18.124 19.662 18.124Q19.178 18.124 18.739 17.896Q18.299 17.667 18.031 17.267Q17.764 16.866 17.764 16.374L17.764 16.315Q17.764 15.847 18.010 15.444Q18.256 15.042 18.664 14.808Q19.072 14.573 19.541 14.573Q20.045 14.573 20.399 14.796Q20.752 15.019 20.936 15.407Q21.119 15.796 21.119 16.300L21.119 16.358Q21.061 16.573 20.869 16.597M18.436 16.046L20.463 16.046Q20.416 15.636 20.178 15.384Q19.940 15.132 19.541 15.132Q19.147 15.132 18.840 15.394Q18.533 15.655 18.436 16.046M23.467 18.124Q23.002 18.124 22.637 17.874Q22.272 17.624 22.067 17.220Q21.862 16.815 21.862 16.358Q21.862 16.015 21.987 15.694Q22.112 15.374 22.344 15.124Q22.576 14.874 22.881 14.735Q23.186 14.597 23.541 14.597Q24.053 14.597 24.459 14.917L24.459 13.757L24.037 13.757Q23.826 13.733 23.787 13.519L23.787 13.429Q23.826 13.222 24.037 13.198L24.850 13.198Q25.049 13.222 25.100 13.429L25.100 17.526L25.526 17.526Q25.733 17.550 25.772 17.757L25.772 17.847Q25.733 18.062 25.526 18.085L24.709 18.085Q24.510 18.062 24.459 17.847L24.459 17.718Q24.264 17.909 24.002 18.017Q23.740 18.124 23.467 18.124M23.506 17.565Q23.865 17.565 24.117 17.296Q24.369 17.026 24.459 16.651L24.459 15.819Q24.401 15.632 24.274 15.481Q24.147 15.331 23.969 15.243Q23.791 15.155 23.596 15.155Q23.287 15.155 23.037 15.325Q22.787 15.495 22.643 15.780Q22.498 16.065 22.498 16.366Q22.498 16.815 22.783 17.190Q23.069 17.565 23.506 17.565\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-59.116 18.085v-68.286h85.359v68.286Zm85.359-68.286\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"m-50.58-41.666 68.287 55.483\" style=\"stroke-width:1.2\"\u002F>\u003Cpath fill=\"var(--tk-good)\" stroke=\"none\" d=\"M-34.199 1.013a5 5 0 1 0-10 0 5 5 0 0 0 10 0m-5 0\"\u002F>\u003Cg fill=\"var(--tk-bg)\" stroke=\"var(--tk-bg)\">\u003Cg transform=\"translate(16.847 -15.322)\">\u003Cpath d=\"M-56.218 18.765L-56.218 16.509L-58.467 16.509Q-58.535 16.499-58.581 16.453Q-58.627 16.407-58.627 16.335Q-58.627 16.191-58.467 16.168L-56.218 16.168L-56.218 13.912Q-56.207 13.843-56.161 13.797Q-56.115 13.751-56.043 13.751Q-55.900 13.751-55.876 13.912L-55.876 16.168L-53.634 16.168Q-53.473 16.191-53.473 16.335Q-53.473 16.407-53.519 16.453Q-53.565 16.499-53.634 16.509L-55.876 16.509L-55.876 18.765Q-55.900 18.926-56.043 18.926Q-56.115 18.926-56.161 18.880Q-56.207 18.834-56.218 18.765\" fill=\"var(--tk-bg)\" stroke=\"var(--tk-bg)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-good)\" stroke=\"none\" d=\"M-15.704 3.859a5 5 0 1 0-10 0 5 5 0 0 0 10 0m-5 0\"\u002F>\u003Cg fill=\"var(--tk-bg)\" stroke=\"var(--tk-bg)\">\u003Cg transform=\"translate(35.342 -12.476)\">\u003Cpath d=\"M-56.218 18.765L-56.218 16.509L-58.467 16.509Q-58.535 16.499-58.581 16.453Q-58.627 16.407-58.627 16.335Q-58.627 16.191-58.467 16.168L-56.218 16.168L-56.218 13.912Q-56.207 13.843-56.161 13.797Q-56.115 13.751-56.043 13.751Q-55.900 13.751-55.876 13.912L-55.876 16.168L-53.634 16.168Q-53.473 16.191-53.473 16.335Q-53.473 16.407-53.519 16.453Q-53.565 16.499-53.634 16.509L-55.876 16.509L-55.876 18.765Q-55.900 18.926-56.043 18.926Q-56.115 18.926-56.161 18.880Q-56.207 18.834-56.218 18.765\" fill=\"var(--tk-bg)\" stroke=\"var(--tk-bg)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-warn)\" stroke=\"none\" d=\"M5.635-30.285a5 5 0 1 0-10 0 5 5 0 0 0 10 0m-5 0\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-bg)\" d=\"M-3.917-30.285h9.105\" style=\"stroke-width:1\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(138.12 -82.133)\">\u003Cpath d=\"M-56.557 16.741L-58.628 16.741Q-58.823 16.718-58.878 16.499L-58.878 16.261Q-58.878 16.187-58.835 16.116L-56.987 13.245Q-56.901 13.116-56.749 13.116L-56.292 13.116Q-56.182 13.116-56.104 13.194Q-56.026 13.273-56.026 13.382L-56.026 16.183L-55.362 16.183Q-55.167 16.206-55.116 16.413L-55.116 16.499Q-55.167 16.718-55.362 16.741L-56.026 16.741L-56.026 17.526L-55.452 17.526Q-55.245 17.550-55.206 17.757L-55.206 17.847Q-55.245 18.062-55.452 18.085L-57.132 18.085Q-57.339 18.062-57.382 17.847L-57.382 17.757Q-57.339 17.550-57.132 17.526L-56.557 17.526L-56.557 16.741M-56.557 13.589L-58.229 16.183L-56.557 16.183\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(138.12 -82.133)\">\u003Cpath d=\"M-50.545 19.628L-50.545 19.542Q-50.502 19.323-50.295 19.300L-49.873 19.300L-49.873 15.198L-50.295 15.198Q-50.502 15.175-50.545 14.956L-50.545 14.870Q-50.498 14.659-50.295 14.636L-49.478 14.636Q-49.283 14.655-49.232 14.870L-49.232 14.940Q-49.021 14.773-48.758 14.685Q-48.494 14.597-48.224 14.597Q-47.885 14.597-47.588 14.741Q-47.291 14.886-47.076 15.138Q-46.861 15.390-46.746 15.702Q-46.631 16.015-46.631 16.358Q-46.631 16.823-46.857 17.233Q-47.084 17.644-47.470 17.884Q-47.857 18.124-48.326 18.124Q-48.845 18.124-49.232 17.757L-49.232 19.300L-48.806 19.300Q-48.599 19.323-48.560 19.542L-48.560 19.628Q-48.599 19.839-48.806 19.862L-50.295 19.862Q-50.498 19.839-50.545 19.628M-48.369 17.565Q-48.060 17.565-47.810 17.396Q-47.560 17.226-47.416 16.944Q-47.271 16.663-47.271 16.358Q-47.271 16.069-47.396 15.790Q-47.521 15.511-47.754 15.333Q-47.986 15.155-48.287 15.155Q-48.607 15.155-48.869 15.341Q-49.131 15.526-49.232 15.827L-49.232 16.679Q-49.142 17.046-48.922 17.306Q-48.701 17.565-48.369 17.565M-44.248 18.124Q-44.720 18.124-45.105 17.880Q-45.490 17.636-45.713 17.226Q-45.935 16.815-45.935 16.358Q-45.935 16.015-45.810 15.692Q-45.685 15.370-45.455 15.116Q-45.224 14.862-44.918 14.718Q-44.611 14.573-44.248 14.573Q-43.885 14.573-43.572 14.720Q-43.260 14.866-43.037 15.112Q-42.814 15.358-42.687 15.679Q-42.560 15.999-42.560 16.358Q-42.560 16.815-42.785 17.228Q-43.010 17.640-43.394 17.882Q-43.779 18.124-44.248 18.124M-44.248 17.565Q-43.783 17.565-43.492 17.171Q-43.201 16.776-43.201 16.292Q-43.201 15.999-43.336 15.731Q-43.470 15.464-43.711 15.298Q-43.951 15.132-44.248 15.132Q-44.553 15.132-44.791 15.298Q-45.029 15.464-45.164 15.731Q-45.299 15.999-45.299 16.292Q-45.299 16.773-45.006 17.169Q-44.713 17.565-44.248 17.565M-41.506 17.847L-41.506 17.757Q-41.455 17.550-41.260 17.526L-40.220 17.526L-40.220 15.198L-41.193 15.198Q-41.392 15.175-41.443 14.956L-41.443 14.870Q-41.392 14.659-41.193 14.636L-39.826 14.636Q-39.631 14.655-39.580 14.870L-39.580 17.526L-38.666 17.526Q-38.470 17.550-38.420 17.757L-38.420 17.847Q-38.470 18.062-38.666 18.085L-41.260 18.085Q-41.455 18.062-41.506 17.847M-40.474 13.659L-40.474 13.605Q-40.474 13.433-40.338 13.312Q-40.201 13.190-40.025 13.190Q-39.853 13.190-39.717 13.312Q-39.580 13.433-39.580 13.605L-39.580 13.659Q-39.580 13.835-39.717 13.956Q-39.853 14.077-40.025 14.077Q-40.201 14.077-40.338 13.956Q-40.474 13.835-40.474 13.659M-37.806 17.847L-37.806 17.757Q-37.763 17.550-37.556 17.526L-37.135 17.526L-37.135 15.198L-37.556 15.198Q-37.763 15.175-37.806 14.956L-37.806 14.870Q-37.760 14.659-37.556 14.636L-36.740 14.636Q-36.545 14.659-36.494 14.870L-36.494 14.956L-36.502 14.980Q-36.275 14.800-36.002 14.698Q-35.728 14.597-35.435 14.597Q-35.088 14.597-34.849 14.737Q-34.611 14.878-34.496 15.136Q-34.381 15.394-34.381 15.749L-34.381 17.526L-33.955 17.526Q-33.748 17.550-33.709 17.757L-33.709 17.847Q-33.748 18.062-33.955 18.085L-35.349 18.085Q-35.545 18.062-35.595 17.847L-35.595 17.757Q-35.545 17.546-35.349 17.526L-35.021 17.526L-35.021 15.780Q-35.021 15.472-35.111 15.314Q-35.201 15.155-35.494 15.155Q-35.763 15.155-35.992 15.286Q-36.220 15.417-36.357 15.646Q-36.494 15.874-36.494 16.140L-36.494 17.526L-36.068 17.526Q-35.861 17.550-35.822 17.757L-35.822 17.847Q-35.861 18.062-36.068 18.085L-37.556 18.085Q-37.763 18.062-37.806 17.847M-32.431 16.980L-32.431 15.198L-33.181 15.198Q-33.381 15.175-33.431 14.956L-33.431 14.870Q-33.381 14.659-33.181 14.636L-32.431 14.636L-32.431 13.886Q-32.381 13.679-32.181 13.651L-32.037 13.651Q-31.842 13.679-31.791 13.886L-31.791 14.636L-30.431 14.636Q-30.240 14.655-30.181 14.870L-30.181 14.956Q-30.236 15.175-30.431 15.198L-31.791 15.198L-31.791 16.948Q-31.791 17.565-31.217 17.565Q-30.967 17.565-30.803 17.380Q-30.638 17.194-30.638 16.948Q-30.638 16.855-30.566 16.784Q-30.494 16.714-30.392 16.702L-30.248 16.702Q-30.049 16.726-29.998 16.933L-29.998 16.980Q-29.998 17.304-30.181 17.567Q-30.365 17.831-30.658 17.978Q-30.951 18.124-31.271 18.124Q-31.783 18.124-32.107 17.814Q-32.431 17.503-32.431 16.980M-28.799 17.886L-28.799 16.972Q-28.771 16.765-28.560 16.741L-28.392 16.741Q-28.228 16.765-28.170 16.925Q-27.967 17.565-27.240 17.565Q-27.033 17.565-26.804 17.530Q-26.576 17.495-26.408 17.380Q-26.240 17.265-26.240 17.062Q-26.240 16.851-26.463 16.737Q-26.685 16.624-26.959 16.581L-27.658 16.468Q-28.799 16.257-28.799 15.534Q-28.799 15.245-28.654 15.056Q-28.510 14.866-28.269 14.759Q-28.029 14.651-27.773 14.612Q-27.517 14.573-27.240 14.573Q-26.990 14.573-26.797 14.603Q-26.603 14.632-26.439 14.710Q-26.361 14.593-26.232 14.573L-26.154 14.573Q-26.056 14.585-25.994 14.648Q-25.931 14.710-25.920 14.804L-25.920 15.511Q-25.931 15.605-25.994 15.671Q-26.056 15.737-26.154 15.749L-26.322 15.749Q-26.416 15.737-26.482 15.671Q-26.549 15.605-26.560 15.511Q-26.560 15.132-27.256 15.132Q-27.603 15.132-27.922 15.214Q-28.240 15.296-28.240 15.542Q-28.240 15.808-27.568 15.917L-26.865 16.038Q-26.381 16.120-26.031 16.368Q-25.681 16.616-25.681 17.062Q-25.681 17.452-25.918 17.694Q-26.154 17.937-26.504 18.030Q-26.853 18.124-27.240 18.124Q-27.818 18.124-28.217 17.870Q-28.287 17.995-28.336 18.052Q-28.385 18.108-28.490 18.124L-28.560 18.124Q-28.775 18.101-28.799 17.886M-23.572 17.526Q-23.572 17.304-23.406 17.138Q-23.240 16.972-23.010 16.972Q-22.861 16.972-22.734 17.050Q-22.607 17.128-22.533 17.253Q-22.459 17.378-22.459 17.526Q-22.459 17.753-22.625 17.919Q-22.791 18.085-23.010 18.085Q-23.236 18.085-23.404 17.917Q-23.572 17.749-23.572 17.526M-23.572 15.190Q-23.572 14.968-23.406 14.802Q-23.240 14.636-23.010 14.636Q-22.861 14.636-22.734 14.714Q-22.607 14.792-22.533 14.917Q-22.459 15.042-22.459 15.190Q-22.459 15.417-22.625 15.583Q-22.791 15.749-23.010 15.749Q-23.236 15.749-23.404 15.581Q-23.572 15.413-23.572 15.190\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(138.12 -82.133)\">\u003Cpath d=\"M-12.127 17.847L-12.127 17.757Q-12.084 17.550-11.877 17.526L-11.623 17.526L-10.603 15.476L-11.517 13.757L-11.791 13.757Q-11.986 13.733-12.037 13.519L-12.037 13.429Q-11.986 13.222-11.791 13.198L-10.717 13.198Q-10.521 13.222-10.470 13.429L-10.470 13.519Q-10.521 13.733-10.717 13.757L-10.799 13.757L-10.287 14.733L-9.822 13.757L-9.916 13.757Q-10.127 13.733-10.166 13.519L-10.166 13.429Q-10.127 13.222-9.916 13.198L-8.853 13.198Q-8.662 13.222-8.603 13.429L-8.603 13.519Q-8.662 13.733-8.853 13.757L-9.119 13.757L-9.974 15.476L-8.892 17.526L-8.623 17.526Q-8.424 17.550-8.373 17.757L-8.373 17.847Q-8.424 18.062-8.623 18.085L-9.685 18.085Q-9.892 18.062-9.931 17.847L-9.931 17.757Q-9.892 17.550-9.685 17.526L-9.595 17.526L-10.287 16.140L-10.924 17.526L-10.814 17.526Q-10.615 17.550-10.564 17.757L-10.564 17.847Q-10.615 18.062-10.814 18.085L-11.877 18.085Q-12.084 18.062-12.127 17.847M-7.400 17.765Q-7.494 17.612-7.545 17.392Q-7.595 17.171-7.623 16.870Q-7.650 16.569-7.656 16.294Q-7.662 16.019-7.662 15.636Q-7.662 15.124-7.650 14.763Q-7.638 14.401-7.580 14.054Q-7.521 13.706-7.400 13.519Q-7.220 13.257-6.861 13.187Q-6.502 13.116-5.998 13.116Q-5.494 13.116-5.136 13.187Q-4.779 13.257-4.599 13.519Q-4.474 13.702-4.416 14.052Q-4.357 14.401-4.345 14.763Q-4.334 15.124-4.334 15.636Q-4.334 16.155-4.345 16.517Q-4.357 16.878-4.416 17.230Q-4.474 17.581-4.599 17.765Q-4.783 18.023-5.138 18.093Q-5.494 18.163-5.998 18.163Q-6.502 18.163-6.859 18.093Q-7.217 18.023-7.400 17.765M-6.861 17.276Q-6.760 17.483-6.535 17.544Q-6.310 17.605-5.998 17.605Q-5.685 17.605-5.463 17.544Q-5.240 17.483-5.135 17.276Q-5.029 17.062-5.002 16.624Q-4.974 16.187-4.974 15.542Q-4.974 14.269-5.135 13.972Q-5.240 13.784-5.459 13.731Q-5.678 13.679-5.998 13.679Q-6.318 13.679-6.535 13.730Q-6.752 13.780-6.861 13.972Q-7.021 14.269-7.021 15.542Q-7.021 15.851-7.019 16.019Q-7.017 16.187-7 16.472Q-6.982 16.757-6.949 16.958Q-6.916 17.159-6.861 17.276M-2.299 18.085L-3.482 18.085Q-3.678 18.062-3.728 17.847L-3.728 17.757Q-3.678 17.550-3.482 17.526L-3.209 17.526L-3.209 13.757L-3.482 13.757Q-3.678 13.733-3.728 13.519L-3.728 13.429Q-3.678 13.222-3.482 13.198L-1.912 13.198Q-1.525 13.198-1.170 13.368Q-0.814 13.538-0.592 13.849Q-0.369 14.159-0.369 14.558Q-0.369 14.784-0.451 14.993Q-0.533 15.202-0.676 15.370Q-0.818 15.538-1.010 15.659Q-0.814 15.812-0.699 16.028Q-0.584 16.245-0.584 16.483L-0.584 17.062Q-0.584 17.605-0.385 17.605Q-0.338 17.605-0.312 17.476Q-0.287 17.347-0.287 17.245Q-0.236 17.038-0.041 17.015L0.104 17.015Q0.299 17.034 0.350 17.245L0.350 17.300Q0.350 17.632 0.145 17.898Q-0.060 18.163-0.377 18.163Q-0.791 18.163-1.008 17.856Q-1.224 17.550-1.224 17.116L-1.224 16.542Q-1.224 16.253-1.433 16.085Q-1.642 15.917-1.928 15.917L-2.568 15.917L-2.568 17.526L-2.299 17.526Q-2.099 17.550-2.049 17.757L-2.049 17.847Q-2.099 18.062-2.299 18.085M-2.568 13.757L-2.568 15.358L-1.994 15.358Q-1.760 15.358-1.533 15.263Q-1.306 15.167-1.158 14.983Q-1.010 14.800-1.010 14.558Q-1.010 14.315-1.158 14.132Q-1.306 13.948-1.531 13.853Q-1.756 13.757-1.994 13.757\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M66.076 18.085v-68.286h85.359v68.286Zm85.359-68.286\"\u002F>\u003Cpath fill=\"var(--tk-good)\" stroke=\"none\" d=\"M85.303 3.859a5 5 0 1 0-10 0 5 5 0 0 0 10 0m-5 0\"\u002F>\u003Cg fill=\"var(--tk-bg)\" stroke=\"var(--tk-bg)\">\u003Cg transform=\"translate(136.349 -12.476)\">\u003Cpath d=\"M-56.218 18.765L-56.218 16.509L-58.467 16.509Q-58.535 16.499-58.581 16.453Q-58.627 16.407-58.627 16.335Q-58.627 16.191-58.467 16.168L-56.218 16.168L-56.218 13.912Q-56.207 13.843-56.161 13.797Q-56.115 13.751-56.043 13.751Q-55.900 13.751-55.876 13.912L-55.876 16.168L-53.634 16.168Q-53.473 16.191-53.473 16.335Q-53.473 16.407-53.519 16.453Q-53.565 16.499-53.634 16.509L-55.876 16.509L-55.876 18.765Q-55.900 18.926-56.043 18.926Q-56.115 18.926-56.161 18.880Q-56.207 18.834-56.218 18.765\" fill=\"var(--tk-bg)\" stroke=\"var(--tk-bg)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-warn)\" stroke=\"none\" d=\"M142.208 3.859a5 5 0 1 0-10 0 5 5 0 0 0 10 0m-5 0\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-bg)\" d=\"M132.656 3.859h9.105\" style=\"stroke-width:1\"\u002F>\u003Cpath fill=\"var(--tk-warn)\" stroke=\"none\" d=\"M85.303-35.975a5 5 0 1 0-10 0 5 5 0 0 0 10 0m-5 0\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-bg)\" d=\"M75.75-35.975h9.105\" style=\"stroke-width:1\"\u002F>\u003Cpath fill=\"var(--tk-good)\" stroke=\"none\" d=\"M142.208-35.975a5 5 0 1 0-10 0 5 5 0 0 0 10 0m-5 0\"\u002F>\u003Cg fill=\"var(--tk-bg)\" stroke=\"var(--tk-bg)\">\u003Cg transform=\"translate(193.254 -52.31)\">\u003Cpath d=\"M-56.218 18.765L-56.218 16.509L-58.467 16.509Q-58.535 16.499-58.581 16.453Q-58.627 16.407-58.627 16.335Q-58.627 16.191-58.467 16.168L-56.218 16.168L-56.218 13.912Q-56.207 13.843-56.161 13.797Q-56.115 13.751-56.043 13.751Q-55.900 13.751-55.876 13.912L-55.876 16.168L-53.634 16.168Q-53.473 16.191-53.473 16.335Q-53.473 16.407-53.519 16.453Q-53.565 16.499-53.634 16.509L-55.876 16.509L-55.876 18.765Q-55.900 18.926-56.043 18.926Q-56.115 18.926-56.161 18.880Q-56.207 18.834-56.218 18.765\" fill=\"var(--tk-bg)\" stroke=\"var(--tk-bg)\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"m70.344-18.904 76.823 5.69\" style=\"stroke-dasharray:3.0,3.0;stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(140.246 -20.03)\">\u003Cpath d=\"M-59.046 17.847L-59.046 17.757Q-59.003 17.550-58.796 17.526L-58.374 17.526L-58.374 15.198L-58.796 15.198Q-59.003 15.175-59.046 14.956L-59.046 14.870Q-58.999 14.659-58.796 14.636L-57.979 14.636Q-57.784 14.659-57.733 14.870L-57.733 14.956L-57.741 14.980Q-57.514 14.800-57.241 14.698Q-56.968 14.597-56.675 14.597Q-56.327 14.597-56.089 14.737Q-55.850 14.878-55.735 15.136Q-55.620 15.394-55.620 15.749L-55.620 17.526L-55.194 17.526Q-54.987 17.550-54.948 17.757L-54.948 17.847Q-54.987 18.062-55.194 18.085L-56.589 18.085Q-56.784 18.062-56.835 17.847L-56.835 17.757Q-56.784 17.546-56.589 17.526L-56.261 17.526L-56.261 15.780Q-56.261 15.472-56.350 15.314Q-56.440 15.155-56.733 15.155Q-57.003 15.155-57.231 15.286Q-57.460 15.417-57.596 15.646Q-57.733 15.874-57.733 16.140L-57.733 17.526L-57.307 17.526Q-57.100 17.550-57.061 17.757L-57.061 17.847Q-57.100 18.062-57.307 18.085L-58.796 18.085Q-59.003 18.062-59.046 17.847M-52.749 18.124Q-53.221 18.124-53.606 17.880Q-53.991 17.636-54.214 17.226Q-54.436 16.815-54.436 16.358Q-54.436 16.015-54.311 15.692Q-54.186 15.370-53.956 15.116Q-53.725 14.862-53.419 14.718Q-53.112 14.573-52.749 14.573Q-52.386 14.573-52.073 14.720Q-51.761 14.866-51.538 15.112Q-51.315 15.358-51.188 15.679Q-51.061 15.999-51.061 16.358Q-51.061 16.815-51.286 17.228Q-51.511 17.640-51.895 17.882Q-52.280 18.124-52.749 18.124M-52.749 17.565Q-52.284 17.565-51.993 17.171Q-51.702 16.776-51.702 16.292Q-51.702 15.999-51.837 15.731Q-51.971 15.464-52.212 15.298Q-52.452 15.132-52.749 15.132Q-53.053 15.132-53.292 15.298Q-53.530 15.464-53.665 15.731Q-53.800 15.999-53.800 16.292Q-53.800 16.773-53.507 17.169Q-53.214 17.565-52.749 17.565\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(140.246 -20.03)\">\u003Cpath d=\"M-45.916 17.847L-45.916 17.757Q-45.865 17.550-45.670 17.526L-44.564 17.526L-44.564 13.757L-45.670 13.757Q-45.865 13.733-45.916 13.519L-45.916 13.429Q-45.865 13.222-45.670 13.198L-44.174 13.198Q-43.982 13.222-43.924 13.429L-43.924 17.526L-42.822 17.526Q-42.623 17.550-42.572 17.757L-42.572 17.847Q-42.623 18.062-42.822 18.085L-45.670 18.085Q-45.865 18.062-45.916 17.847M-41.502 17.847L-41.502 17.757Q-41.451 17.550-41.256 17.526L-40.217 17.526L-40.217 15.198L-41.189 15.198Q-41.388 15.175-41.439 14.956L-41.439 14.870Q-41.388 14.659-41.189 14.636L-39.822 14.636Q-39.627 14.655-39.576 14.870L-39.576 17.526L-38.662 17.526Q-38.467 17.550-38.416 17.757L-38.416 17.847Q-38.467 18.062-38.662 18.085L-41.256 18.085Q-41.451 18.062-41.502 17.847M-40.470 13.659L-40.470 13.605Q-40.470 13.433-40.334 13.312Q-40.197 13.190-40.021 13.190Q-39.849 13.190-39.713 13.312Q-39.576 13.433-39.576 13.605L-39.576 13.659Q-39.576 13.835-39.713 13.956Q-39.849 14.077-40.021 14.077Q-40.197 14.077-40.334 13.956Q-40.470 13.835-40.470 13.659M-37.803 17.847L-37.803 17.757Q-37.760 17.550-37.553 17.526L-37.131 17.526L-37.131 15.198L-37.553 15.198Q-37.760 15.175-37.803 14.956L-37.803 14.870Q-37.756 14.659-37.553 14.636L-36.736 14.636Q-36.541 14.659-36.490 14.870L-36.490 14.956L-36.498 14.980Q-36.271 14.800-35.998 14.698Q-35.724 14.597-35.431 14.597Q-35.084 14.597-34.845 14.737Q-34.607 14.878-34.492 15.136Q-34.377 15.394-34.377 15.749L-34.377 17.526L-33.951 17.526Q-33.744 17.550-33.705 17.757L-33.705 17.847Q-33.744 18.062-33.951 18.085L-35.345 18.085Q-35.541 18.062-35.592 17.847L-35.592 17.757Q-35.541 17.546-35.345 17.526L-35.017 17.526L-35.017 15.780Q-35.017 15.472-35.107 15.314Q-35.197 15.155-35.490 15.155Q-35.760 15.155-35.988 15.286Q-36.217 15.417-36.353 15.646Q-36.490 15.874-36.490 16.140L-36.490 17.526L-36.064 17.526Q-35.857 17.550-35.818 17.757L-35.818 17.847Q-35.857 18.062-36.064 18.085L-37.553 18.085Q-37.760 18.062-37.803 17.847M-30.115 16.597L-32.556 16.597Q-32.502 16.874-32.304 17.097Q-32.107 17.319-31.830 17.442Q-31.553 17.565-31.267 17.565Q-30.795 17.565-30.572 17.276Q-30.564 17.265-30.508 17.159Q-30.451 17.054-30.402 17.011Q-30.353 16.968-30.260 16.956L-30.115 16.956Q-29.924 16.976-29.865 17.190L-29.865 17.245Q-29.931 17.546-30.162 17.743Q-30.392 17.940-30.705 18.032Q-31.017 18.124-31.322 18.124Q-31.806 18.124-32.246 17.896Q-32.685 17.667-32.953 17.267Q-33.220 16.866-33.220 16.374L-33.220 16.315Q-33.220 15.847-32.974 15.444Q-32.728 15.042-32.320 14.808Q-31.912 14.573-31.443 14.573Q-30.939 14.573-30.586 14.796Q-30.232 15.019-30.049 15.407Q-29.865 15.796-29.865 16.300L-29.865 16.358Q-29.924 16.573-30.115 16.597M-32.549 16.046L-30.521 16.046Q-30.568 15.636-30.806 15.384Q-31.045 15.132-31.443 15.132Q-31.838 15.132-32.144 15.394Q-32.451 15.655-32.549 16.046\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(140.246 -20.03)\">\u003Cpath d=\"M-24.107 17.862L-24.572 15.198L-24.732 15.198Q-24.939 15.175-24.978 14.956L-24.978 14.870Q-24.939 14.659-24.732 14.636L-23.564 14.636Q-23.353 14.659-23.314 14.870L-23.314 14.956Q-23.353 15.175-23.564 15.198L-24.037 15.198Q-23.924 15.843-23.845 16.288Q-23.767 16.733-23.717 17.052Q-23.666 17.370-23.666 17.444Q-23.658 17.273-23.373 16.276Q-23.334 16.163-23.236 16.089Q-23.138 16.015-23.017 16.015L-22.939 16.015Q-22.818 16.015-22.720 16.089Q-22.623 16.163-22.588 16.276Q-22.478 16.659-22.396 16.983Q-22.314 17.308-22.314 17.444Q-22.302 17.155-21.955 15.198L-22.427 15.198Q-22.635 15.175-22.674 14.956L-22.674 14.870Q-22.635 14.659-22.427 14.636L-21.252 14.636Q-21.060 14.659-21.002 14.870L-21.002 14.956Q-21.056 15.175-21.252 15.198L-21.420 15.198L-21.885 17.862Q-21.908 17.980-21.996 18.052Q-22.084 18.124-22.205 18.124L-22.345 18.124Q-22.467 18.124-22.562 18.052Q-22.658 17.980-22.701 17.862Q-22.748 17.683-22.820 17.427Q-22.892 17.171-22.935 16.962Q-22.978 16.753-22.978 16.651Q-22.986 16.933-23.267 17.862Q-23.295 17.972-23.392 18.048Q-23.490 18.124-23.611 18.124L-23.787 18.124Q-23.908 18.124-23.996 18.052Q-24.084 17.980-24.107 17.862M-18.748 18.124Q-19.220 18.124-19.605 17.880Q-19.990 17.636-20.213 17.226Q-20.435 16.815-20.435 16.358Q-20.435 16.015-20.310 15.692Q-20.185 15.370-19.955 15.116Q-19.724 14.862-19.418 14.718Q-19.111 14.573-18.748 14.573Q-18.385 14.573-18.072 14.720Q-17.760 14.866-17.537 15.112Q-17.314 15.358-17.187 15.679Q-17.060 15.999-17.060 16.358Q-17.060 16.815-17.285 17.228Q-17.510 17.640-17.894 17.882Q-18.279 18.124-18.748 18.124M-18.748 17.565Q-18.283 17.565-17.992 17.171Q-17.701 16.776-17.701 16.292Q-17.701 15.999-17.836 15.731Q-17.970 15.464-18.211 15.298Q-18.451 15.132-18.748 15.132Q-19.052 15.132-19.291 15.298Q-19.529 15.464-19.664 15.731Q-19.799 15.999-19.799 16.292Q-19.799 16.773-19.506 17.169Q-19.213 17.565-18.748 17.565M-16.400 17.847L-16.400 17.757Q-16.342 17.550-16.150 17.526L-15.439 17.526L-15.439 15.198L-16.150 15.198Q-16.345 15.175-16.400 14.956L-16.400 14.870Q-16.342 14.659-16.150 14.636L-15.049 14.636Q-14.849 14.655-14.799 14.870L-14.799 15.198Q-14.537 14.913-14.181 14.755Q-13.826 14.597-13.439 14.597Q-13.146 14.597-12.912 14.731Q-12.677 14.866-12.677 15.132Q-12.677 15.300-12.787 15.417Q-12.896 15.534-13.064 15.534Q-13.217 15.534-13.332 15.423Q-13.447 15.312-13.447 15.155Q-13.822 15.155-14.136 15.356Q-14.451 15.558-14.625 15.892Q-14.799 16.226-14.799 16.605L-14.799 17.526L-13.853 17.526Q-13.646 17.550-13.607 17.757L-13.607 17.847Q-13.646 18.062-13.853 18.085L-16.150 18.085Q-16.342 18.062-16.400 17.847M-12.232 17.847L-12.232 17.757Q-12.181 17.550-11.986 17.526L-11.521 17.526L-11.521 13.757L-11.986 13.757Q-12.181 13.733-12.232 13.519L-12.232 13.429Q-12.181 13.222-11.986 13.198L-11.240 13.198Q-11.045 13.218-10.994 13.429L-10.994 16.253L-9.849 15.198L-10.146 15.198Q-10.353 15.175-10.392 14.956L-10.392 14.870Q-10.353 14.659-10.146 14.636L-8.697 14.636Q-8.494 14.659-8.447 14.870L-8.447 14.956Q-8.490 15.175-8.697 15.198L-9.064 15.198L-10.010 16.062L-8.857 17.526L-8.521 17.526Q-8.314 17.550-8.271 17.757L-8.271 17.847Q-8.314 18.062-8.521 18.085L-9.689 18.085Q-9.885 18.062-9.935 17.847L-9.935 17.757Q-9.885 17.550-9.689 17.526L-9.529 17.526L-10.392 16.421L-10.994 16.972L-10.994 17.526L-10.529 17.526Q-10.338 17.550-10.279 17.757L-10.279 17.847Q-10.338 18.062-10.529 18.085L-11.986 18.085Q-12.181 18.062-12.232 17.847M-7.545 17.886L-7.545 16.972Q-7.517 16.765-7.306 16.741L-7.138 16.741Q-6.974 16.765-6.916 16.925Q-6.713 17.565-5.986 17.565Q-5.779 17.565-5.551 17.530Q-5.322 17.495-5.154 17.380Q-4.986 17.265-4.986 17.062Q-4.986 16.851-5.209 16.737Q-5.431 16.624-5.705 16.581L-6.404 16.468Q-7.545 16.257-7.545 15.534Q-7.545 15.245-7.400 15.056Q-7.256 14.866-7.015 14.759Q-6.775 14.651-6.519 14.612Q-6.263 14.573-5.986 14.573Q-5.736 14.573-5.543 14.603Q-5.349 14.632-5.185 14.710Q-5.107 14.593-4.978 14.573L-4.900 14.573Q-4.802 14.585-4.740 14.648Q-4.677 14.710-4.666 14.804L-4.666 15.511Q-4.677 15.605-4.740 15.671Q-4.802 15.737-4.900 15.749L-5.068 15.749Q-5.162 15.737-5.228 15.671Q-5.295 15.605-5.306 15.511Q-5.306 15.132-6.002 15.132Q-6.349 15.132-6.668 15.214Q-6.986 15.296-6.986 15.542Q-6.986 15.808-6.314 15.917L-5.611 16.038Q-5.127 16.120-4.777 16.368Q-4.427 16.616-4.427 17.062Q-4.427 17.452-4.664 17.694Q-4.900 17.937-5.250 18.030Q-5.599 18.124-5.986 18.124Q-6.564 18.124-6.963 17.870Q-7.033 17.995-7.082 18.052Q-7.131 18.108-7.236 18.124L-7.306 18.124Q-7.521 18.101-7.545 17.886\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">VC dimension of lines in the plane. Any of the \u003Cspan class=\"katex\">\u003Cspan class=\"katex-html\" aria-hidden=\"true\">\u003Cspan class=\"base\">\u003Cspan class=\"strut\" style=\"height:0.8141em;\">\u003C\u002Fspan>\u003Cspan class=\"mord\">\u003Cspan class=\"mord\">2\u003C\u002Fspan>\u003Cspan class=\"msupsub\">\u003Cspan class=\"vlist-t\">\u003Cspan class=\"vlist-r\">\u003Cspan class=\"vlist\" style=\"height:0.8141em;\">\u003Cspan style=\"top:-3.063em;margin-right:0.05em;\">\u003Cspan class=\"pstrut\" style=\"height:2.7em;\">\u003C\u002Fspan>\u003Cspan class=\"sizing reset-size6 size3 mtight\">\u003Cspan class=\"mord mtight\">3\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003Cspan class=\"mspace\" style=\"margin-right:0.2778em;\">\u003C\u002Fspan>\u003Cspan class=\"mrel\">=\u003C\u002Fspan>\u003Cspan class=\"mspace\" style=\"margin-right:0.2778em;\">\u003C\u002Fspan>\u003C\u002Fspan>\u003Cspan class=\"base\">\u003Cspan class=\"strut\" style=\"height:0.6444em;\">\u003C\u002Fspan>\u003Cspan class=\"mord\">8\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan> labelings of three points is realized by some line, so three points are shattered; the XOR labeling of four points admits no separating line, so \u003Cspan class=\"katex\">\u003Cspan class=\"katex-html\" aria-hidden=\"true\">\u003Cspan class=\"base\">\u003Cspan class=\"strut\" style=\"height:0.8444em;vertical-align:-0.15em;\">\u003C\u002Fspan>\u003Cspan class=\"mord\">\u003Cspan class=\"mord mathnormal\">d\u003C\u002Fspan>\u003Cspan class=\"msupsub\">\u003Cspan class=\"vlist-t vlist-t2\">\u003Cspan class=\"vlist-r\">\u003Cspan class=\"vlist\" style=\"height:0.3283em;\">\u003Cspan style=\"top:-2.55em;margin-left:0em;margin-right:0.05em;\">\u003Cspan class=\"pstrut\" style=\"height:2.7em;\">\u003C\u002Fspan>\u003Cspan class=\"sizing reset-size6 size3 mtight\">\u003Cspan class=\"mord mtight\">\u003Cspan class=\"mord mtight\">\u003Cspan class=\"mord mathrm mtight\">VC\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003Cspan class=\"vlist-s\">​\u003C\u002Fspan>\u003C\u002Fspan>\u003Cspan class=\"vlist-r\">\u003Cspan class=\"vlist\" style=\"height:0.15em;\">\u003Cspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003Cspan class=\"mspace\" style=\"margin-right:0.2778em;\">\u003C\u002Fspan>\u003Cspan class=\"mrel\">=\u003C\u002Fspan>\u003Cspan class=\"mspace\" style=\"margin-right:0.2778em;\">\u003C\u002Fspan>\u003C\u002Fspan>\u003Cspan class=\"base\">\u003Cspan class=\"strut\" style=\"height:0.6444em;\">\u003C\u002Fspan>\u003Cspan class=\"mord\">3\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:370.311px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 277.734 176.257\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-48.847 83.492h207.95\"\u002F>\u003Cpath stroke=\"none\" d=\"m161.703 83.492-4.16-2.08 1.56 2.08-1.56 2.08\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(214.283 1.905)\">\u003Cpath d=\"M-48.566 81.765Q-48.566 81.269-48.316 80.844Q-48.066 80.418-47.646 80.172Q-47.226 79.926-46.726 79.926Q-46.187 79.926-45.796 80.051Q-45.406 80.176-45.406 80.590Q-45.406 80.695-45.456 80.787Q-45.507 80.879-45.599 80.930Q-45.691 80.980-45.800 80.980Q-45.906 80.980-45.997 80.930Q-46.089 80.879-46.140 80.787Q-46.191 80.695-46.191 80.590Q-46.191 80.367-46.023 80.262Q-46.245 80.203-46.718 80.203Q-47.015 80.203-47.230 80.342Q-47.445 80.480-47.576 80.711Q-47.706 80.941-47.765 81.211Q-47.824 81.480-47.824 81.765Q-47.824 82.160-47.691 82.510Q-47.558 82.859-47.286 83.076Q-47.015 83.293-46.617 83.293Q-46.242 83.293-45.966 83.076Q-45.691 82.859-45.589 82.500Q-45.574 82.437-45.511 82.437L-45.406 82.437Q-45.370 82.437-45.345 82.465Q-45.320 82.492-45.320 82.531L-45.320 82.555Q-45.452 83.035-45.837 83.303Q-46.222 83.570-46.726 83.570Q-47.089 83.570-47.423 83.433Q-47.757 83.297-48.017 83.047Q-48.277 82.797-48.421 82.461Q-48.566 82.125-48.566 81.765M-44.734 82.660Q-44.734 82.176-44.331 81.881Q-43.929 81.586-43.378 81.467Q-42.827 81.347-42.335 81.347L-42.335 81.058Q-42.335 80.832-42.451 80.625Q-42.566 80.418-42.763 80.299Q-42.960 80.180-43.191 80.180Q-43.617 80.180-43.902 80.285Q-43.831 80.312-43.785 80.367Q-43.738 80.422-43.712 80.492Q-43.687 80.562-43.687 80.637Q-43.687 80.742-43.738 80.834Q-43.788 80.926-43.880 80.976Q-43.972 81.027-44.077 81.027Q-44.183 81.027-44.275 80.976Q-44.367 80.926-44.417 80.834Q-44.468 80.742-44.468 80.637Q-44.468 80.219-44.079 80.072Q-43.691 79.926-43.191 79.926Q-42.859 79.926-42.505 80.056Q-42.152 80.187-41.923 80.441Q-41.695 80.695-41.695 81.043L-41.695 82.844Q-41.695 82.976-41.622 83.086Q-41.550 83.195-41.421 83.195Q-41.296 83.195-41.228 83.090Q-41.160 82.984-41.160 82.844L-41.160 82.332L-40.878 82.332L-40.878 82.844Q-40.878 83.047-40.995 83.205Q-41.113 83.363-41.294 83.447Q-41.476 83.531-41.679 83.531Q-41.910 83.531-42.062 83.359Q-42.214 83.187-42.245 82.957Q-42.406 83.238-42.714 83.404Q-43.023 83.570-43.374 83.570Q-43.886 83.570-44.310 83.347Q-44.734 83.125-44.734 82.660M-44.046 82.660Q-44.046 82.945-43.820 83.131Q-43.593 83.316-43.300 83.316Q-43.054 83.316-42.829 83.199Q-42.605 83.082-42.470 82.879Q-42.335 82.676-42.335 82.422L-42.335 81.590Q-42.601 81.590-42.886 81.644Q-43.171 81.699-43.443 81.828Q-43.714 81.957-43.880 82.164Q-44.046 82.371-44.046 82.660M-38.702 85.043L-40.558 85.043L-40.558 84.750Q-40.288 84.750-40.120 84.705Q-39.952 84.660-39.952 84.484L-39.952 80.660Q-39.952 80.453-40.109 80.400Q-40.265 80.347-40.558 80.347L-40.558 80.051L-39.335 79.965L-39.335 80.430Q-39.105 80.207-38.790 80.086Q-38.476 79.965-38.136 79.965Q-37.663 79.965-37.259 80.211Q-36.855 80.457-36.622 80.873Q-36.390 81.289-36.390 81.765Q-36.390 82.140-36.538 82.469Q-36.687 82.797-36.956 83.049Q-37.226 83.301-37.570 83.435Q-37.913 83.570-38.273 83.570Q-38.562 83.570-38.833 83.449Q-39.105 83.328-39.312 83.117L-39.312 84.484Q-39.312 84.660-39.144 84.705Q-38.976 84.750-38.702 84.750L-38.702 85.043M-39.312 80.828L-39.312 82.668Q-39.160 82.957-38.898 83.137Q-38.636 83.316-38.327 83.316Q-38.042 83.316-37.820 83.178Q-37.597 83.039-37.445 82.808Q-37.292 82.578-37.214 82.306Q-37.136 82.035-37.136 81.765Q-37.136 81.433-37.261 81.076Q-37.386 80.719-37.634 80.482Q-37.882 80.246-38.230 80.246Q-38.554 80.246-38.849 80.402Q-39.144 80.558-39.312 80.828M-35.769 82.660Q-35.769 82.176-35.367 81.881Q-34.964 81.586-34.413 81.467Q-33.863 81.347-33.370 81.347L-33.370 81.058Q-33.370 80.832-33.486 80.625Q-33.601 80.418-33.798 80.299Q-33.995 80.180-34.226 80.180Q-34.652 80.180-34.937 80.285Q-34.867 80.312-34.820 80.367Q-34.773 80.422-34.747 80.492Q-34.722 80.562-34.722 80.637Q-34.722 80.742-34.773 80.834Q-34.824 80.926-34.915 80.976Q-35.007 81.027-35.113 81.027Q-35.218 81.027-35.310 80.976Q-35.402 80.926-35.452 80.834Q-35.503 80.742-35.503 80.637Q-35.503 80.219-35.115 80.072Q-34.726 79.926-34.226 79.926Q-33.894 79.926-33.540 80.056Q-33.187 80.187-32.958 80.441Q-32.730 80.695-32.730 81.043L-32.730 82.844Q-32.730 82.976-32.658 83.086Q-32.585 83.195-32.456 83.195Q-32.331 83.195-32.263 83.090Q-32.195 82.984-32.195 82.844L-32.195 82.332L-31.913 82.332L-31.913 82.844Q-31.913 83.047-32.031 83.205Q-32.148 83.363-32.329 83.447Q-32.511 83.531-32.714 83.531Q-32.945 83.531-33.097 83.359Q-33.249 83.187-33.281 82.957Q-33.441 83.238-33.749 83.404Q-34.058 83.570-34.410 83.570Q-34.921 83.570-35.345 83.347Q-35.769 83.125-35.769 82.660M-35.081 82.660Q-35.081 82.945-34.855 83.131Q-34.628 83.316-34.335 83.316Q-34.089 83.316-33.865 83.199Q-33.640 83.082-33.505 82.879Q-33.370 82.676-33.370 82.422L-33.370 81.590Q-33.636 81.590-33.921 81.644Q-34.206 81.699-34.478 81.828Q-34.749 81.957-34.915 82.164Q-35.081 82.371-35.081 82.660M-31.577 81.765Q-31.577 81.269-31.327 80.844Q-31.077 80.418-30.658 80.172Q-30.238 79.926-29.738 79.926Q-29.199 79.926-28.808 80.051Q-28.417 80.176-28.417 80.590Q-28.417 80.695-28.468 80.787Q-28.519 80.879-28.611 80.930Q-28.702 80.980-28.812 80.980Q-28.917 80.980-29.009 80.930Q-29.101 80.879-29.152 80.787Q-29.202 80.695-29.202 80.590Q-29.202 80.367-29.035 80.262Q-29.257 80.203-29.730 80.203Q-30.027 80.203-30.242 80.342Q-30.456 80.480-30.587 80.711Q-30.718 80.941-30.777 81.211Q-30.835 81.480-30.835 81.765Q-30.835 82.160-30.702 82.510Q-30.570 82.859-30.298 83.076Q-30.027 83.293-29.628 83.293Q-29.253 83.293-28.978 83.076Q-28.702 82.859-28.601 82.500Q-28.585 82.437-28.523 82.437L-28.417 82.437Q-28.382 82.437-28.357 82.465Q-28.331 82.492-28.331 82.531L-28.331 82.555Q-28.464 83.035-28.849 83.303Q-29.234 83.570-29.738 83.570Q-30.101 83.570-30.435 83.433Q-30.769 83.297-31.029 83.047Q-31.288 82.797-31.433 82.461Q-31.577 82.125-31.577 81.765M-25.984 83.492L-27.761 83.492L-27.761 83.195Q-27.488 83.195-27.320 83.148Q-27.152 83.101-27.152 82.933L-27.152 80.797Q-27.152 80.582-27.208 80.486Q-27.265 80.390-27.378 80.369Q-27.492 80.347-27.738 80.347L-27.738 80.051L-26.538 79.965L-26.538 82.933Q-26.538 83.101-26.392 83.148Q-26.245 83.195-25.984 83.195L-25.984 83.492M-27.425 78.570Q-27.425 78.379-27.290 78.248Q-27.156 78.117-26.960 78.117Q-26.839 78.117-26.736 78.180Q-26.632 78.242-26.570 78.346Q-26.507 78.449-26.507 78.570Q-26.507 78.765-26.638 78.900Q-26.769 79.035-26.960 79.035Q-27.160 79.035-27.292 78.902Q-27.425 78.769-27.425 78.570M-24.859 82.531L-24.859 80.340L-25.562 80.340L-25.562 80.086Q-25.206 80.086-24.964 79.853Q-24.722 79.621-24.611 79.273Q-24.499 78.926-24.499 78.570L-24.218 78.570L-24.218 80.043L-23.042 80.043L-23.042 80.340L-24.218 80.340L-24.218 82.515Q-24.218 82.836-24.099 83.064Q-23.980 83.293-23.699 83.293Q-23.519 83.293-23.402 83.170Q-23.285 83.047-23.232 82.867Q-23.179 82.687-23.179 82.515L-23.179 82.043L-22.898 82.043L-22.898 82.531Q-22.898 82.785-23.003 83.025Q-23.109 83.265-23.306 83.418Q-23.503 83.570-23.761 83.570Q-24.077 83.570-24.329 83.447Q-24.581 83.324-24.720 83.090Q-24.859 82.855-24.859 82.531\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(214.283 1.905)\">\u003Cpath d=\"M-21.983 84.789Q-21.869 84.867-21.694 84.867Q-21.405 84.867-21.184 84.654Q-20.963 84.441-20.838 84.140L-20.549 83.492L-21.823 80.605Q-21.905 80.430-22.049 80.385Q-22.194 80.340-22.463 80.340L-22.463 80.043L-20.744 80.043L-20.744 80.340Q-21.166 80.340-21.166 80.523Q-21.166 80.535-21.151 80.605L-20.213 82.730L-19.381 80.820Q-19.342 80.730-19.342 80.652Q-19.342 80.512-19.444 80.426Q-19.545 80.340-19.686 80.340L-19.686 80.043L-18.334 80.043L-18.334 80.340Q-18.588 80.340-18.782 80.465Q-18.975 80.590-19.080 80.820L-20.526 84.140Q-20.639 84.394-20.805 84.617Q-20.971 84.840-21.200 84.982Q-21.428 85.125-21.694 85.125Q-21.991 85.125-22.231 84.933Q-22.471 84.742-22.471 84.453Q-22.471 84.297-22.366 84.195Q-22.260 84.094-22.112 84.094Q-22.006 84.094-21.926 84.140Q-21.846 84.187-21.799 84.265Q-21.752 84.344-21.752 84.453Q-21.752 84.574-21.813 84.662Q-21.873 84.750-21.983 84.789\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-48.847 83.492V-44.79\"\u002F>\u003Cpath stroke=\"none\" d=\"m-48.847-47.39-2.08 4.16 2.08-1.56 2.08 1.56\"\u002F>\u003Cg transform=\"translate(-6.756 -134.616)\">\u003Cpath d=\"M-46.601 83.492L-48.581 83.492L-48.581 83.195Q-48.312 83.195-48.144 83.150Q-47.976 83.105-47.976 82.933L-47.976 80.797Q-47.976 80.582-48.038 80.486Q-48.101 80.390-48.218 80.369Q-48.335 80.347-48.581 80.347L-48.581 80.051L-47.413 79.965L-47.413 80.750Q-47.335 80.539-47.183 80.353Q-47.031 80.168-46.831 80.066Q-46.632 79.965-46.406 79.965Q-46.160 79.965-45.968 80.109Q-45.777 80.254-45.777 80.484Q-45.777 80.640-45.882 80.750Q-45.988 80.859-46.144 80.859Q-46.300 80.859-46.410 80.750Q-46.519 80.640-46.519 80.484Q-46.519 80.324-46.413 80.219Q-46.738 80.219-46.952 80.447Q-47.167 80.676-47.263 81.015Q-47.359 81.355-47.359 81.660L-47.359 82.933Q-47.359 83.101-47.132 83.148Q-46.906 83.195-46.601 83.195L-46.601 83.492M-43.437 83.492L-45.214 83.492L-45.214 83.195Q-44.941 83.195-44.773 83.148Q-44.605 83.101-44.605 82.933L-44.605 80.797Q-44.605 80.582-44.661 80.486Q-44.718 80.390-44.831 80.369Q-44.945 80.347-45.191 80.347L-45.191 80.051L-43.992 79.965L-43.992 82.933Q-43.992 83.101-43.845 83.148Q-43.699 83.195-43.437 83.195L-43.437 83.492M-44.878 78.570Q-44.878 78.379-44.743 78.248Q-44.609 78.117-44.413 78.117Q-44.292 78.117-44.189 78.180Q-44.085 78.242-44.023 78.346Q-43.960 78.449-43.960 78.570Q-43.960 78.765-44.091 78.900Q-44.222 79.035-44.413 79.035Q-44.613 79.035-44.745 78.902Q-44.878 78.769-44.878 78.570M-42.894 83.484L-42.894 82.262Q-42.894 82.234-42.863 82.203Q-42.831 82.172-42.808 82.172L-42.702 82.172Q-42.632 82.172-42.617 82.234Q-42.554 82.555-42.415 82.795Q-42.277 83.035-42.044 83.176Q-41.812 83.316-41.503 83.316Q-41.265 83.316-41.056 83.256Q-40.847 83.195-40.710 83.047Q-40.574 82.898-40.574 82.652Q-40.574 82.398-40.785 82.232Q-40.995 82.066-41.265 82.012L-41.886 81.898Q-42.292 81.820-42.593 81.564Q-42.894 81.308-42.894 80.933Q-42.894 80.566-42.693 80.344Q-42.492 80.121-42.167 80.023Q-41.843 79.926-41.503 79.926Q-41.038 79.926-40.742 80.133L-40.519 79.949Q-40.495 79.926-40.464 79.926L-40.413 79.926Q-40.382 79.926-40.355 79.953Q-40.327 79.980-40.327 80.012L-40.327 80.996Q-40.327 81.027-40.353 81.056Q-40.378 81.086-40.413 81.086L-40.519 81.086Q-40.554 81.086-40.581 81.058Q-40.609 81.031-40.609 80.996Q-40.609 80.597-40.861 80.377Q-41.113 80.156-41.511 80.156Q-41.867 80.156-42.150 80.279Q-42.433 80.402-42.433 80.707Q-42.433 80.926-42.232 81.058Q-42.031 81.191-41.785 81.234L-41.160 81.347Q-40.730 81.437-40.421 81.734Q-40.113 82.031-40.113 82.445Q-40.113 83.015-40.511 83.293Q-40.910 83.570-41.503 83.570Q-42.054 83.570-42.406 83.234L-42.702 83.547Q-42.726 83.570-42.761 83.570L-42.808 83.570Q-42.831 83.570-42.863 83.539Q-42.894 83.508-42.894 83.484M-37.761 83.492L-39.558 83.492L-39.558 83.195Q-39.288 83.195-39.120 83.150Q-38.952 83.105-38.952 82.933L-38.952 78.773Q-38.952 78.558-39.015 78.463Q-39.077 78.367-39.195 78.346Q-39.312 78.324-39.558 78.324L-39.558 78.027L-38.335 77.941L-38.335 81.707L-37.238 80.820Q-37.031 80.640-37.031 80.492Q-37.031 80.426-37.083 80.383Q-37.136 80.340-37.206 80.340L-37.206 80.043L-35.671 80.043L-35.671 80.340Q-36.202 80.340-36.800 80.820L-37.410 81.316L-36.335 82.715Q-36.199 82.890-36.091 82.998Q-35.984 83.105-35.849 83.150Q-35.714 83.195-35.488 83.195L-35.488 83.492L-37.113 83.492L-37.113 83.195Q-36.870 83.195-36.870 83.043Q-36.870 82.965-36.913 82.894Q-36.956 82.824-37.038 82.715L-37.839 81.668L-38.367 82.094L-38.367 82.933Q-38.367 83.101-38.199 83.148Q-38.031 83.195-37.761 83.195\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-good)\" d=\"m-38.889-53.996 3.207 13.165 3.207 10.86 3.206 9.128 3.207 7.756 3.207 6.686L-19.65-.58l3.207 5.113 3.207 4.524 3.206 4.04 3.207 3.62 3.207 3.266 3.206 2.962 3.207 2.697 3.207 2.467 3.206 2.268 3.207 2.088 3.207 1.93 3.206 1.789 3.207 1.661 3.207 1.55 3.206 1.449 3.207 1.357 3.207 1.272 3.206 1.196 3.207 1.128 3.207 1.063 3.206 1.005 3.207.952 3.207.903 3.206.854 3.207.815 3.207.774 3.206.738 3.207.704 3.207.673 3.206.643 3.207.615 3.207.59 3.206.564 3.207.543 3.207.52 3.206.5 3.207.482 3.206.463 3.207.446 3.207.43 3.206.415 3.207.4 3.207.387 3.206.374 3.207.36 3.207.351 3.206.338 3.207.328 3.207.318 3.206.307 3.207.299 3.207.29 3.206.281\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\">\u003Cg fill=\"var(--tk-good)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(145.797 -6.091)\">\u003Cpath d=\"M-47.648 82.387L-47.648 80.605L-48.398 80.605Q-48.597 80.582-48.648 80.363L-48.648 80.277Q-48.597 80.066-48.398 80.043L-47.648 80.043L-47.648 79.293Q-47.597 79.086-47.398 79.058L-47.253 79.058Q-47.058 79.086-47.007 79.293L-47.007 80.043L-45.648 80.043Q-45.456 80.062-45.398 80.277L-45.398 80.363Q-45.452 80.582-45.648 80.605L-47.007 80.605L-47.007 82.355Q-47.007 82.972-46.433 82.972Q-46.183 82.972-46.019 82.787Q-45.855 82.601-45.855 82.355Q-45.855 82.262-45.783 82.191Q-45.710 82.121-45.609 82.109L-45.464 82.109Q-45.265 82.133-45.214 82.340L-45.214 82.387Q-45.214 82.711-45.398 82.974Q-45.581 83.238-45.874 83.385Q-46.167 83.531-46.488 83.531Q-46.999 83.531-47.324 83.221Q-47.648 82.910-47.648 82.387M-44.378 83.254L-44.378 83.164Q-44.320 82.957-44.128 82.933L-43.417 82.933L-43.417 80.605L-44.128 80.605Q-44.324 80.582-44.378 80.363L-44.378 80.277Q-44.320 80.066-44.128 80.043L-43.027 80.043Q-42.827 80.062-42.777 80.277L-42.777 80.605Q-42.515 80.320-42.160 80.162Q-41.804 80.004-41.417 80.004Q-41.124 80.004-40.890 80.138Q-40.656 80.273-40.656 80.539Q-40.656 80.707-40.765 80.824Q-40.874 80.941-41.042 80.941Q-41.195 80.941-41.310 80.830Q-41.425 80.719-41.425 80.562Q-41.800 80.562-42.115 80.763Q-42.429 80.965-42.603 81.299Q-42.777 81.633-42.777 82.012L-42.777 82.933L-41.831 82.933Q-41.624 82.957-41.585 83.164L-41.585 83.254Q-41.624 83.469-41.831 83.492L-44.128 83.492Q-44.320 83.469-44.378 83.254M-39.949 82.379Q-39.949 81.933-39.535 81.676Q-39.120 81.418-38.579 81.318Q-38.038 81.219-37.531 81.211Q-37.531 80.996-37.665 80.844Q-37.800 80.691-38.007 80.615Q-38.214 80.539-38.425 80.539Q-38.769 80.539-38.929 80.562L-38.929 80.621Q-38.929 80.789-39.048 80.904Q-39.167 81.019-39.331 81.019Q-39.507 81.019-39.622 80.896Q-39.738 80.773-39.738 80.605Q-39.738 80.199-39.357 80.090Q-38.976 79.980-38.417 79.980Q-38.148 79.980-37.880 80.058Q-37.613 80.137-37.388 80.287Q-37.163 80.437-37.027 80.658Q-36.890 80.879-36.890 81.156L-36.890 82.875Q-36.890 82.933-36.363 82.933Q-36.167 82.953-36.117 83.164L-36.117 83.254Q-36.167 83.469-36.363 83.492L-36.507 83.492Q-36.851 83.492-37.079 83.445Q-37.308 83.398-37.452 83.211Q-37.913 83.531-38.620 83.531Q-38.956 83.531-39.261 83.390Q-39.566 83.250-39.757 82.988Q-39.949 82.726-39.949 82.379M-39.308 82.387Q-39.308 82.660-39.066 82.816Q-38.824 82.972-38.538 82.972Q-38.320 82.972-38.087 82.914Q-37.855 82.855-37.693 82.717Q-37.531 82.578-37.531 82.355L-37.531 81.765Q-37.812 81.765-38.228 81.822Q-38.644 81.879-38.976 82.017Q-39.308 82.156-39.308 82.387M-35.492 83.254L-35.492 83.164Q-35.441 82.957-35.245 82.933L-34.206 82.933L-34.206 80.605L-35.179 80.605Q-35.378 80.582-35.429 80.363L-35.429 80.277Q-35.378 80.066-35.179 80.043L-33.812 80.043Q-33.617 80.062-33.566 80.277L-33.566 82.933L-32.652 82.933Q-32.456 82.957-32.406 83.164L-32.406 83.254Q-32.456 83.469-32.652 83.492L-35.245 83.492Q-35.441 83.469-35.492 83.254M-34.460 79.066L-34.460 79.012Q-34.460 78.840-34.324 78.719Q-34.187 78.597-34.011 78.597Q-33.839 78.597-33.702 78.719Q-33.566 78.840-33.566 79.012L-33.566 79.066Q-33.566 79.242-33.702 79.363Q-33.839 79.484-34.011 79.484Q-34.187 79.484-34.324 79.363Q-34.460 79.242-34.460 79.066M-31.792 83.254L-31.792 83.164Q-31.749 82.957-31.542 82.933L-31.120 82.933L-31.120 80.605L-31.542 80.605Q-31.749 80.582-31.792 80.363L-31.792 80.277Q-31.745 80.066-31.542 80.043L-30.726 80.043Q-30.531 80.066-30.480 80.277L-30.480 80.363L-30.488 80.387Q-30.261 80.207-29.988 80.105Q-29.714 80.004-29.421 80.004Q-29.074 80.004-28.835 80.144Q-28.597 80.285-28.482 80.543Q-28.367 80.801-28.367 81.156L-28.367 82.933L-27.941 82.933Q-27.734 82.957-27.695 83.164L-27.695 83.254Q-27.734 83.469-27.941 83.492L-29.335 83.492Q-29.531 83.469-29.581 83.254L-29.581 83.164Q-29.531 82.953-29.335 82.933L-29.007 82.933L-29.007 81.187Q-29.007 80.879-29.097 80.721Q-29.187 80.562-29.480 80.562Q-29.749 80.562-29.978 80.693Q-30.206 80.824-30.343 81.053Q-30.480 81.281-30.480 81.547L-30.480 82.933L-30.054 82.933Q-29.847 82.957-29.808 83.164L-29.808 83.254Q-29.847 83.469-30.054 83.492L-31.542 83.492Q-31.749 83.469-31.792 83.254\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(145.797 -6.091)\">\u003Cpath d=\"M-23.124 83.254L-23.124 83.164Q-23.066 82.957-22.874 82.933L-22.163 82.933L-22.163 80.605L-22.874 80.605Q-23.070 80.582-23.124 80.363L-23.124 80.277Q-23.066 80.066-22.874 80.043L-21.773 80.043Q-21.574 80.062-21.523 80.277L-21.523 80.605Q-21.261 80.320-20.906 80.162Q-20.550 80.004-20.163 80.004Q-19.870 80.004-19.636 80.138Q-19.402 80.273-19.402 80.539Q-19.402 80.707-19.511 80.824Q-19.620 80.941-19.788 80.941Q-19.941 80.941-20.056 80.830Q-20.171 80.719-20.171 80.562Q-20.546 80.562-20.861 80.763Q-21.175 80.965-21.349 81.299Q-21.523 81.633-21.523 82.012L-21.523 82.933L-20.577 82.933Q-20.370 82.957-20.331 83.164L-20.331 83.254Q-20.370 83.469-20.577 83.492L-22.874 83.492Q-23.066 83.469-23.124 83.254M-18.484 83.254L-18.484 83.164Q-18.433 82.957-18.238 82.933L-17.199 82.933L-17.199 80.605L-18.171 80.605Q-18.370 80.582-18.421 80.363L-18.421 80.277Q-18.370 80.066-18.171 80.043L-16.804 80.043Q-16.609 80.062-16.558 80.277L-16.558 82.933L-15.644 82.933Q-15.449 82.957-15.398 83.164L-15.398 83.254Q-15.449 83.469-15.644 83.492L-18.238 83.492Q-18.433 83.469-18.484 83.254M-17.452 79.066L-17.452 79.012Q-17.452 78.840-17.316 78.719Q-17.179 78.597-17.003 78.597Q-16.831 78.597-16.695 78.719Q-16.558 78.840-16.558 79.012L-16.558 79.066Q-16.558 79.242-16.695 79.363Q-16.831 79.484-17.003 79.484Q-17.179 79.484-17.316 79.363Q-17.452 79.242-17.452 79.066M-14.269 83.293L-14.269 82.379Q-14.242 82.172-14.031 82.148L-13.863 82.148Q-13.699 82.172-13.640 82.332Q-13.437 82.972-12.710 82.972Q-12.503 82.972-12.275 82.937Q-12.046 82.902-11.878 82.787Q-11.710 82.672-11.710 82.469Q-11.710 82.258-11.933 82.144Q-12.156 82.031-12.429 81.988L-13.128 81.875Q-14.269 81.664-14.269 80.941Q-14.269 80.652-14.124 80.463Q-13.980 80.273-13.740 80.166Q-13.499 80.058-13.243 80.019Q-12.988 79.980-12.710 79.980Q-12.460 79.980-12.267 80.010Q-12.074 80.039-11.910 80.117Q-11.831 80-11.702 79.980L-11.624 79.980Q-11.527 79.992-11.464 80.055Q-11.402 80.117-11.390 80.211L-11.390 80.918Q-11.402 81.012-11.464 81.078Q-11.527 81.144-11.624 81.156L-11.792 81.156Q-11.886 81.144-11.952 81.078Q-12.019 81.012-12.031 80.918Q-12.031 80.539-12.726 80.539Q-13.074 80.539-13.392 80.621Q-13.710 80.703-13.710 80.949Q-13.710 81.215-13.038 81.324L-12.335 81.445Q-11.851 81.527-11.501 81.775Q-11.152 82.023-11.152 82.469Q-11.152 82.859-11.388 83.101Q-11.624 83.344-11.974 83.437Q-12.324 83.531-12.710 83.531Q-13.288 83.531-13.687 83.277Q-13.757 83.402-13.806 83.459Q-13.855 83.515-13.960 83.531L-14.031 83.531Q-14.245 83.508-14.269 83.293M-10.464 83.254L-10.464 83.164Q-10.413 82.957-10.218 82.933L-9.753 82.933L-9.753 79.164L-10.218 79.164Q-10.413 79.140-10.464 78.926L-10.464 78.836Q-10.413 78.629-10.218 78.605L-9.472 78.605Q-9.277 78.625-9.226 78.836L-9.226 81.660L-8.081 80.605L-8.378 80.605Q-8.585 80.582-8.624 80.363L-8.624 80.277Q-8.585 80.066-8.378 80.043L-6.929 80.043Q-6.726 80.066-6.679 80.277L-6.679 80.363Q-6.722 80.582-6.929 80.605L-7.296 80.605L-8.242 81.469L-7.089 82.933L-6.753 82.933Q-6.546 82.957-6.503 83.164L-6.503 83.254Q-6.546 83.469-6.753 83.492L-7.921 83.492Q-8.117 83.469-8.167 83.254L-8.167 83.164Q-8.117 82.957-7.921 82.933L-7.761 82.933L-8.624 81.828L-9.226 82.379L-9.226 82.933L-8.761 82.933Q-8.570 82.957-8.511 83.164L-8.511 83.254Q-8.570 83.469-8.761 83.492L-10.218 83.492Q-10.413 83.469-10.464 83.254\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"m-43.157 70.132 3.28-3.413 3.278-2.828 3.28-2.468 3.279-2.22 3.279-2.032 3.28-1.886 3.278-1.768 3.28-1.67 3.279-1.585 3.279-1.514 3.279-1.45 3.28-1.394 3.278-1.344 3.28-1.299 3.279-1.26L9.31 40.78l3.279-1.188 3.28-1.156 3.278-1.127 3.28-1.1 3.279-1.076 3.279-1.052 3.279-1.03 3.28-1.01 3.278-.99 3.28-.972 3.279-.954 3.279-.938 3.279-.922 3.28-.908 3.278-.893 3.28-.88 3.279-.866 3.279-.855 3.279-.842 3.28-.831 3.278-.82 3.28-.81 3.279-.8 3.279-.789 3.279-.78 3.28-.772 3.278-.762 3.28-.754 3.279-.746 3.279-.738 3.279-.73 3.28-.723 3.278-.716 3.28-.707 3.279-.702 3.279-.695 3.279-.689 3.28-.682 3.278-.676 3.28-.671 3.279-.665 3.279-.658 3.279-.654\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(121.05 -35.176)\">\u003Cpath d=\"M-35.859 74.640Q-35.859 74.340-35.710 74.078Q-35.562 73.816-35.312 73.656Q-35.495 73.402-35.495 73.082Q-35.495 72.797-35.343 72.535Q-35.593 72.211-35.593 71.793Q-35.593 71.433-35.402 71.137Q-35.210 70.840-34.892 70.672Q-34.574 70.504-34.210 70.504Q-34.003 70.504-33.800 70.564Q-33.597 70.625-33.433 70.726Q-33.050 70.465-32.577 70.465Q-32.339 70.465-32.158 70.596Q-31.976 70.726-31.976 70.953Q-31.976 71.101-32.077 71.207Q-32.179 71.312-32.335 71.312Q-32.468 71.312-32.564 71.234Q-32.660 71.156-32.691 71.031Q-32.835 71.039-33.035 71.129Q-32.831 71.441-32.831 71.793Q-32.831 72.074-32.943 72.306Q-33.054 72.539-33.249 72.717Q-33.445 72.894-33.697 72.992Q-33.949 73.090-34.210 73.090Q-34.597 73.090-34.929 72.902Q-34.941 72.902-34.951 72.974Q-34.960 73.047-34.960 73.082Q-34.960 73.203-34.894 73.310Q-34.827 73.418-34.706 73.457Q-34.691 73.453-34.677 73.451Q-34.663 73.449-34.640 73.449Q-34.624 73.449-34.593 73.457Q-34.562 73.465-34.554 73.465L-33.960 73.465Q-33.179 73.465-32.638 73.707Q-32.097 73.949-32.097 74.640Q-32.097 74.941-32.277 75.168Q-32.456 75.394-32.753 75.539Q-33.050 75.683-33.370 75.750Q-33.691 75.816-33.976 75.816Q-34.367 75.816-34.808 75.693Q-35.249 75.570-35.554 75.303Q-35.859 75.035-35.859 74.640M-35.320 74.633Q-35.320 74.851-35.079 74.992Q-34.839 75.133-34.515 75.199Q-34.191 75.265-33.976 75.265Q-33.761 75.265-33.437 75.199Q-33.113 75.133-32.872 74.992Q-32.632 74.851-32.632 74.633Q-32.632 74.344-32.857 74.205Q-33.081 74.066-33.363 74.033Q-33.644 74-33.992 74L-34.609 74Q-34.792 74-34.954 74.080Q-35.117 74.160-35.218 74.306Q-35.320 74.453-35.320 74.633M-34.210 72.535Q-33.910 72.535-33.691 72.316Q-33.472 72.097-33.472 71.793Q-33.472 71.637-33.527 71.506Q-33.581 71.375-33.687 71.269Q-33.792 71.164-33.923 71.109Q-34.054 71.055-34.210 71.055Q-34.515 71.055-34.734 71.273Q-34.952 71.492-34.952 71.793Q-34.952 72.090-34.730 72.312Q-34.507 72.535-34.210 72.535M-31.445 72.879Q-31.445 72.433-31.031 72.176Q-30.617 71.918-30.076 71.818Q-29.535 71.719-29.027 71.711Q-29.027 71.496-29.161 71.344Q-29.296 71.191-29.503 71.115Q-29.710 71.039-29.921 71.039Q-30.265 71.039-30.425 71.062L-30.425 71.121Q-30.425 71.289-30.544 71.404Q-30.663 71.519-30.827 71.519Q-31.003 71.519-31.118 71.396Q-31.234 71.273-31.234 71.105Q-31.234 70.699-30.853 70.590Q-30.472 70.480-29.913 70.480Q-29.644 70.480-29.376 70.558Q-29.109 70.637-28.884 70.787Q-28.660 70.937-28.523 71.158Q-28.386 71.379-28.386 71.656L-28.386 73.375Q-28.386 73.433-27.859 73.433Q-27.663 73.453-27.613 73.664L-27.613 73.754Q-27.663 73.969-27.859 73.992L-28.003 73.992Q-28.347 73.992-28.576 73.945Q-28.804 73.898-28.949 73.711Q-29.410 74.031-30.117 74.031Q-30.452 74.031-30.757 73.890Q-31.062 73.750-31.253 73.488Q-31.445 73.226-31.445 72.879M-30.804 72.887Q-30.804 73.160-30.562 73.316Q-30.320 73.472-30.035 73.472Q-29.816 73.472-29.583 73.414Q-29.351 73.355-29.189 73.217Q-29.027 73.078-29.027 72.855L-29.027 72.265Q-29.308 72.265-29.724 72.322Q-30.140 72.379-30.472 72.517Q-30.804 72.656-30.804 72.887M-27.535 75.535L-27.535 75.449Q-27.492 75.230-27.285 75.207L-26.863 75.207L-26.863 71.105L-27.285 71.105Q-27.492 71.082-27.535 70.863L-27.535 70.777Q-27.488 70.566-27.285 70.543L-26.468 70.543Q-26.273 70.562-26.222 70.777L-26.222 70.847Q-26.011 70.680-25.747 70.592Q-25.484 70.504-25.214 70.504Q-24.874 70.504-24.577 70.648Q-24.281 70.793-24.066 71.045Q-23.851 71.297-23.736 71.609Q-23.620 71.922-23.620 72.265Q-23.620 72.730-23.847 73.140Q-24.074 73.551-24.460 73.791Q-24.847 74.031-25.316 74.031Q-25.835 74.031-26.222 73.664L-26.222 75.207L-25.796 75.207Q-25.589 75.230-25.550 75.449L-25.550 75.535Q-25.589 75.746-25.796 75.769L-27.285 75.769Q-27.488 75.746-27.535 75.535M-25.359 73.472Q-25.050 73.472-24.800 73.303Q-24.550 73.133-24.406 72.851Q-24.261 72.570-24.261 72.265Q-24.261 71.976-24.386 71.697Q-24.511 71.418-24.743 71.240Q-24.976 71.062-25.277 71.062Q-25.597 71.062-25.859 71.248Q-26.120 71.433-26.222 71.734L-26.222 72.586Q-26.132 72.953-25.911 73.213Q-25.691 73.472-25.359 73.472\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(121.05 -35.176)\">\u003Cpath d=\"M-17.898 72.887L-17.898 71.105L-18.648 71.105Q-18.847 71.082-18.898 70.863L-18.898 70.777Q-18.847 70.566-18.648 70.543L-17.898 70.543L-17.898 69.793Q-17.847 69.586-17.648 69.558L-17.503 69.558Q-17.308 69.586-17.257 69.793L-17.257 70.543L-15.898 70.543Q-15.706 70.562-15.648 70.777L-15.648 70.863Q-15.702 71.082-15.898 71.105L-17.257 71.105L-17.257 72.855Q-17.257 73.472-16.683 73.472Q-16.433 73.472-16.269 73.287Q-16.105 73.101-16.105 72.855Q-16.105 72.762-16.033 72.691Q-15.960 72.621-15.859 72.609L-15.714 72.609Q-15.515 72.633-15.464 72.840L-15.464 72.887Q-15.464 73.211-15.648 73.474Q-15.831 73.738-16.124 73.885Q-16.417 74.031-16.738 74.031Q-17.249 74.031-17.574 73.721Q-17.898 73.410-17.898 72.887M-11.339 72.504L-13.781 72.504Q-13.726 72.781-13.529 73.004Q-13.331 73.226-13.054 73.349Q-12.777 73.472-12.492 73.472Q-12.019 73.472-11.796 73.183Q-11.788 73.172-11.732 73.066Q-11.675 72.961-11.626 72.918Q-11.577 72.875-11.484 72.863L-11.339 72.863Q-11.148 72.883-11.089 73.097L-11.089 73.152Q-11.156 73.453-11.386 73.650Q-11.617 73.847-11.929 73.939Q-12.242 74.031-12.546 74.031Q-13.031 74.031-13.470 73.803Q-13.910 73.574-14.177 73.174Q-14.445 72.773-14.445 72.281L-14.445 72.222Q-14.445 71.754-14.199 71.351Q-13.952 70.949-13.544 70.715Q-13.136 70.480-12.667 70.480Q-12.163 70.480-11.810 70.703Q-11.456 70.926-11.273 71.314Q-11.089 71.703-11.089 72.207L-11.089 72.265Q-11.148 72.480-11.339 72.504M-13.773 71.953L-11.745 71.953Q-11.792 71.543-12.031 71.291Q-12.269 71.039-12.667 71.039Q-13.062 71.039-13.368 71.301Q-13.675 71.562-13.773 71.953M-10.382 73.754L-10.382 73.664Q-10.324 73.457-10.132 73.433L-9.421 73.433L-9.421 71.105L-10.132 71.105Q-10.327 71.082-10.382 70.863L-10.382 70.777Q-10.324 70.566-10.132 70.543L-9.031 70.543Q-8.831 70.562-8.781 70.777L-8.781 71.105Q-8.519 70.820-8.163 70.662Q-7.808 70.504-7.421 70.504Q-7.128 70.504-6.894 70.638Q-6.660 70.773-6.660 71.039Q-6.660 71.207-6.769 71.324Q-6.878 71.441-7.046 71.441Q-7.199 71.441-7.314 71.330Q-7.429 71.219-7.429 71.062Q-7.804 71.062-8.118 71.263Q-8.433 71.465-8.607 71.799Q-8.781 72.133-8.781 72.512L-8.781 73.433L-7.835 73.433Q-7.628 73.457-7.589 73.664L-7.589 73.754Q-7.628 73.969-7.835 73.992L-10.132 73.992Q-10.324 73.969-10.382 73.754M-6.413 73.754L-6.413 73.664Q-6.363 73.453-6.167 73.433L-5.968 73.433L-5.968 71.105L-6.167 71.105Q-6.374 71.082-6.413 70.863L-6.413 70.777Q-6.363 70.562-6.167 70.543L-5.687 70.543Q-5.495 70.566-5.437 70.777Q-5.117 70.504-4.702 70.504Q-4.511 70.504-4.343 70.613Q-4.175 70.722-4.101 70.894Q-3.925 70.703-3.702 70.603Q-3.480 70.504-3.238 70.504Q-2.820 70.504-2.665 70.846Q-2.511 71.187-2.511 71.656L-2.511 73.433L-2.312 73.433Q-2.101 73.457-2.062 73.664L-2.062 73.754Q-2.113 73.969-2.312 73.992L-3.128 73.992Q-3.324 73.969-3.374 73.754L-3.374 73.664Q-3.324 73.457-3.128 73.433L-3.038 73.433L-3.038 71.687Q-3.038 71.062-3.288 71.062Q-3.620 71.062-3.798 71.373Q-3.976 71.683-3.976 72.047L-3.976 73.433L-3.773 73.433Q-3.566 73.457-3.527 73.664L-3.527 73.754Q-3.577 73.969-3.773 73.992L-4.589 73.992Q-4.788 73.969-4.839 73.754L-4.839 73.664Q-4.788 73.457-4.589 73.433L-4.503 73.433L-4.503 71.687Q-4.503 71.062-4.749 71.062Q-5.081 71.062-5.259 71.375Q-5.437 71.687-5.437 72.047L-5.437 73.433L-5.238 73.433Q-5.031 73.457-4.992 73.664L-4.992 73.754Q-5.042 73.972-5.238 73.992L-6.167 73.992Q-6.374 73.969-6.413 73.754\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(121.05 -35.176)\">\u003Cpath d=\"M-45.734 84.133Q-46.265 83.824-46.665 83.336Q-47.066 82.847-47.277 82.254Q-47.488 81.660-47.488 81.043Q-47.488 80.426-47.279 79.838Q-47.070 79.250-46.673 78.769Q-46.277 78.289-45.742 77.965Q-45.671 77.941-45.640 77.941L-45.550 77.941Q-45.452 77.953-45.386 78.019Q-45.320 78.086-45.320 78.187Q-45.320 78.332-45.421 78.394Q-45.870 78.683-46.197 79.099Q-46.523 79.515-46.685 80.008Q-46.847 80.500-46.847 81.043Q-46.847 81.449-46.751 81.836Q-46.656 82.222-46.478 82.558Q-46.300 82.894-46.040 83.178Q-45.781 83.461-45.433 83.691Q-45.320 83.769-45.320 83.906Q-45.320 84.004-45.386 84.074Q-45.452 84.144-45.550 84.156L-45.640 84.156Q-45.699 84.156-45.734 84.133M-44.363 84.140Q-44.363 83.840-44.214 83.578Q-44.066 83.316-43.816 83.156Q-43.999 82.902-43.999 82.582Q-43.999 82.297-43.847 82.035Q-44.097 81.711-44.097 81.293Q-44.097 80.933-43.906 80.637Q-43.714 80.340-43.396 80.172Q-43.077 80.004-42.714 80.004Q-42.507 80.004-42.304 80.064Q-42.101 80.125-41.937 80.226Q-41.554 79.965-41.081 79.965Q-40.843 79.965-40.661 80.096Q-40.480 80.226-40.480 80.453Q-40.480 80.601-40.581 80.707Q-40.683 80.812-40.839 80.812Q-40.972 80.812-41.068 80.734Q-41.163 80.656-41.195 80.531Q-41.339 80.539-41.538 80.629Q-41.335 80.941-41.335 81.293Q-41.335 81.574-41.447 81.806Q-41.558 82.039-41.753 82.217Q-41.949 82.394-42.201 82.492Q-42.452 82.590-42.714 82.590Q-43.101 82.590-43.433 82.402Q-43.445 82.402-43.454 82.474Q-43.464 82.547-43.464 82.582Q-43.464 82.703-43.398 82.810Q-43.331 82.918-43.210 82.957Q-43.195 82.953-43.181 82.951Q-43.167 82.949-43.144 82.949Q-43.128 82.949-43.097 82.957Q-43.066 82.965-43.058 82.965L-42.464 82.965Q-41.683 82.965-41.142 83.207Q-40.601 83.449-40.601 84.140Q-40.601 84.441-40.781 84.668Q-40.960 84.894-41.257 85.039Q-41.554 85.183-41.874 85.250Q-42.195 85.316-42.480 85.316Q-42.870 85.316-43.312 85.193Q-43.753 85.070-44.058 84.803Q-44.363 84.535-44.363 84.140M-43.824 84.133Q-43.824 84.351-43.583 84.492Q-43.343 84.633-43.019 84.699Q-42.695 84.765-42.480 84.765Q-42.265 84.765-41.941 84.699Q-41.617 84.633-41.376 84.492Q-41.136 84.351-41.136 84.133Q-41.136 83.844-41.361 83.705Q-41.585 83.566-41.867 83.533Q-42.148 83.500-42.495 83.500L-43.113 83.500Q-43.296 83.500-43.458 83.580Q-43.620 83.660-43.722 83.806Q-43.824 83.953-43.824 84.133M-42.714 82.035Q-42.413 82.035-42.195 81.816Q-41.976 81.597-41.976 81.293Q-41.976 81.137-42.031 81.006Q-42.085 80.875-42.191 80.769Q-42.296 80.664-42.427 80.609Q-42.558 80.555-42.714 80.555Q-43.019 80.555-43.238 80.773Q-43.456 80.992-43.456 81.293Q-43.456 81.590-43.234 81.812Q-43.011 82.035-42.714 82.035M-40.132 83.254L-40.132 83.164Q-40.074 82.957-39.882 82.933L-39.171 82.933L-39.171 80.605L-39.882 80.605Q-40.077 80.582-40.132 80.363L-40.132 80.277Q-40.074 80.066-39.882 80.043L-38.781 80.043Q-38.581 80.062-38.531 80.277L-38.531 80.605Q-38.269 80.320-37.913 80.162Q-37.558 80.004-37.171 80.004Q-36.878 80.004-36.644 80.138Q-36.410 80.273-36.410 80.539Q-36.410 80.707-36.519 80.824Q-36.628 80.941-36.796 80.941Q-36.949 80.941-37.064 80.830Q-37.179 80.719-37.179 80.562Q-37.554 80.562-37.868 80.763Q-38.183 80.965-38.357 81.299Q-38.531 81.633-38.531 82.012L-38.531 82.933L-37.585 82.933Q-37.378 82.957-37.339 83.164L-37.339 83.254Q-37.378 83.469-37.585 83.492L-39.882 83.492Q-40.074 83.469-40.132 83.254M-33.988 83.531Q-34.460 83.531-34.845 83.287Q-35.230 83.043-35.452 82.633Q-35.675 82.222-35.675 81.765Q-35.675 81.422-35.550 81.099Q-35.425 80.777-35.195 80.523Q-34.964 80.269-34.658 80.125Q-34.351 79.980-33.988 79.980Q-33.624 79.980-33.312 80.127Q-32.999 80.273-32.777 80.519Q-32.554 80.765-32.427 81.086Q-32.300 81.406-32.300 81.765Q-32.300 82.222-32.525 82.635Q-32.749 83.047-33.134 83.289Q-33.519 83.531-33.988 83.531M-33.988 82.972Q-33.523 82.972-33.232 82.578Q-32.941 82.183-32.941 81.699Q-32.941 81.406-33.076 81.138Q-33.210 80.871-33.451 80.705Q-33.691 80.539-33.988 80.539Q-34.292 80.539-34.531 80.705Q-34.769 80.871-34.904 81.138Q-35.038 81.406-35.038 81.699Q-35.038 82.180-34.745 82.576Q-34.452 82.972-33.988 82.972M-30.855 83.269L-31.320 80.605L-31.480 80.605Q-31.687 80.582-31.726 80.363L-31.726 80.277Q-31.687 80.066-31.480 80.043L-30.312 80.043Q-30.101 80.066-30.062 80.277L-30.062 80.363Q-30.101 80.582-30.312 80.605L-30.785 80.605Q-30.671 81.250-30.593 81.695Q-30.515 82.140-30.464 82.459Q-30.413 82.777-30.413 82.851Q-30.406 82.680-30.120 81.683Q-30.081 81.570-29.984 81.496Q-29.886 81.422-29.765 81.422L-29.687 81.422Q-29.566 81.422-29.468 81.496Q-29.370 81.570-29.335 81.683Q-29.226 82.066-29.144 82.390Q-29.062 82.715-29.062 82.851Q-29.050 82.562-28.702 80.605L-29.175 80.605Q-29.382 80.582-29.421 80.363L-29.421 80.277Q-29.382 80.066-29.175 80.043L-27.999 80.043Q-27.808 80.066-27.749 80.277L-27.749 80.363Q-27.804 80.582-27.999 80.605L-28.167 80.605L-28.632 83.269Q-28.656 83.387-28.743 83.459Q-28.831 83.531-28.952 83.531L-29.093 83.531Q-29.214 83.531-29.310 83.459Q-29.406 83.387-29.449 83.269Q-29.495 83.090-29.568 82.834Q-29.640 82.578-29.683 82.369Q-29.726 82.160-29.726 82.058Q-29.734 82.340-30.015 83.269Q-30.042 83.379-30.140 83.455Q-30.238 83.531-30.359 83.531L-30.535 83.531Q-30.656 83.531-30.743 83.459Q-30.831 83.387-30.855 83.269M-27.031 83.293L-27.031 82.379Q-27.003 82.172-26.792 82.148L-26.624 82.148Q-26.460 82.172-26.402 82.332Q-26.199 82.972-25.472 82.972Q-25.265 82.972-25.036 82.937Q-24.808 82.902-24.640 82.787Q-24.472 82.672-24.472 82.469Q-24.472 82.258-24.695 82.144Q-24.917 82.031-25.191 81.988L-25.890 81.875Q-27.031 81.664-27.031 80.941Q-27.031 80.652-26.886 80.463Q-26.742 80.273-26.501 80.166Q-26.261 80.058-26.005 80.019Q-25.749 79.980-25.472 79.980Q-25.222 79.980-25.029 80.010Q-24.835 80.039-24.671 80.117Q-24.593 80-24.464 79.980L-24.386 79.980Q-24.288 79.992-24.226 80.055Q-24.163 80.117-24.152 80.211L-24.152 80.918Q-24.163 81.012-24.226 81.078Q-24.288 81.144-24.386 81.156L-24.554 81.156Q-24.648 81.144-24.714 81.078Q-24.781 81.012-24.792 80.918Q-24.792 80.539-25.488 80.539Q-25.835 80.539-26.154 80.621Q-26.472 80.703-26.472 80.949Q-26.472 81.215-25.800 81.324L-25.097 81.445Q-24.613 81.527-24.263 81.775Q-23.913 82.023-23.913 82.469Q-23.913 82.859-24.150 83.101Q-24.386 83.344-24.736 83.437Q-25.085 83.531-25.472 83.531Q-26.050 83.531-26.449 83.277Q-26.519 83.402-26.568 83.459Q-26.617 83.515-26.722 83.531L-26.792 83.531Q-27.007 83.508-27.031 83.293\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(121.05 -35.176)\">\u003Cpath d=\"M-18.089 83.269L-18.554 80.605L-18.714 80.605Q-18.921 80.582-18.960 80.363L-18.960 80.277Q-18.921 80.066-18.714 80.043L-17.546 80.043Q-17.335 80.066-17.296 80.277L-17.296 80.363Q-17.335 80.582-17.546 80.605L-18.019 80.605Q-17.906 81.250-17.827 81.695Q-17.749 82.140-17.699 82.459Q-17.648 82.777-17.648 82.851Q-17.640 82.680-17.355 81.683Q-17.316 81.570-17.218 81.496Q-17.120 81.422-16.999 81.422L-16.921 81.422Q-16.800 81.422-16.702 81.496Q-16.605 81.570-16.570 81.683Q-16.460 82.066-16.378 82.390Q-16.296 82.715-16.296 82.851Q-16.285 82.562-15.937 80.605L-16.410 80.605Q-16.617 80.582-16.656 80.363L-16.656 80.277Q-16.617 80.066-16.410 80.043L-15.234 80.043Q-15.042 80.066-14.984 80.277L-14.984 80.363Q-15.038 80.582-15.234 80.605L-15.402 80.605L-15.867 83.269Q-15.890 83.387-15.978 83.459Q-16.066 83.531-16.187 83.531L-16.327 83.531Q-16.449 83.531-16.544 83.459Q-16.640 83.387-16.683 83.269Q-16.730 83.090-16.802 82.834Q-16.874 82.578-16.917 82.369Q-16.960 82.160-16.960 82.058Q-16.968 82.340-17.249 83.269Q-17.277 83.379-17.374 83.455Q-17.472 83.531-17.593 83.531L-17.769 83.531Q-17.890 83.531-17.978 83.459Q-18.066 83.387-18.089 83.269M-14.234 83.254L-14.234 83.164Q-14.183 82.957-13.988 82.933L-12.949 82.933L-12.949 80.605L-13.921 80.605Q-14.120 80.582-14.171 80.363L-14.171 80.277Q-14.120 80.066-13.921 80.043L-12.554 80.043Q-12.359 80.062-12.308 80.277L-12.308 82.933L-11.394 82.933Q-11.199 82.957-11.148 83.164L-11.148 83.254Q-11.199 83.469-11.394 83.492L-13.988 83.492Q-14.183 83.469-14.234 83.254M-13.202 79.066L-13.202 79.012Q-13.202 78.840-13.066 78.719Q-12.929 78.597-12.753 78.597Q-12.581 78.597-12.445 78.719Q-12.308 78.840-12.308 79.012L-12.308 79.066Q-12.308 79.242-12.445 79.363Q-12.581 79.484-12.753 79.484Q-12.929 79.484-13.066 79.363Q-13.202 79.242-13.202 79.066M-9.406 82.387L-9.406 80.605L-10.156 80.605Q-10.355 80.582-10.406 80.363L-10.406 80.277Q-10.355 80.066-10.156 80.043L-9.406 80.043L-9.406 79.293Q-9.355 79.086-9.156 79.058L-9.011 79.058Q-8.816 79.086-8.765 79.293L-8.765 80.043L-7.406 80.043Q-7.214 80.062-7.156 80.277L-7.156 80.363Q-7.210 80.582-7.406 80.605L-8.765 80.605L-8.765 82.355Q-8.765 82.972-8.191 82.972Q-7.941 82.972-7.777 82.787Q-7.613 82.601-7.613 82.355Q-7.613 82.262-7.540 82.191Q-7.468 82.121-7.367 82.109L-7.222 82.109Q-7.023 82.133-6.972 82.340L-6.972 82.387Q-6.972 82.711-7.156 82.974Q-7.339 83.238-7.632 83.385Q-7.925 83.531-8.245 83.531Q-8.757 83.531-9.081 83.221Q-9.406 82.910-9.406 82.387M-6.288 83.254L-6.288 83.164Q-6.245 82.957-6.038 82.933L-5.617 82.933L-5.617 79.164L-6.038 79.164Q-6.245 79.140-6.288 78.926L-6.288 78.836Q-6.245 78.629-6.038 78.605L-5.222 78.605Q-5.027 78.629-4.976 78.836L-4.976 80.387Q-4.515 80.004-3.917 80.004Q-3.570 80.004-3.331 80.144Q-3.093 80.285-2.978 80.543Q-2.863 80.801-2.863 81.156L-2.863 82.933L-2.437 82.933Q-2.230 82.957-2.191 83.164L-2.191 83.254Q-2.230 83.469-2.437 83.492L-3.831 83.492Q-4.027 83.469-4.077 83.254L-4.077 83.164Q-4.027 82.953-3.831 82.933L-3.503 82.933L-3.503 81.187Q-3.503 80.879-3.593 80.721Q-3.683 80.562-3.976 80.562Q-4.245 80.562-4.474 80.693Q-4.702 80.824-4.839 81.053Q-4.976 81.281-4.976 81.547L-4.976 82.933L-4.550 82.933Q-4.343 82.957-4.304 83.164L-4.304 83.254Q-4.343 83.469-4.550 83.492L-6.038 83.492Q-6.245 83.469-6.288 83.254\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(121.05 -35.176)\">\u003Cpath d=\"M4.419 83.570Q3.969 83.570 3.604 83.346Q3.239 83.121 2.985 82.738Q2.731 82.355 2.606 81.912Q2.481 81.469 2.481 81.043Q2.481 80.617 2.606 80.178Q2.731 79.738 2.985 79.355Q3.239 78.972 3.598 78.748Q3.958 78.523 4.419 78.523Q4.696 78.523 4.954 78.615Q5.212 78.707 5.426 78.875L5.559 78.637Q5.587 78.586 5.641 78.555Q5.696 78.523 5.755 78.523L5.833 78.523Q5.926 78.535 5.989 78.594Q6.051 78.652 6.063 78.758L6.063 80.086Q6.051 80.187 5.989 80.250Q5.926 80.312 5.833 80.324L5.665 80.324Q5.563 80.312 5.501 80.246Q5.438 80.180 5.426 80.086Q5.387 79.820 5.264 79.596Q5.141 79.371 4.938 79.228Q4.735 79.086 4.473 79.086Q4.141 79.086 3.889 79.269Q3.637 79.453 3.466 79.754Q3.294 80.055 3.208 80.396Q3.122 80.738 3.122 81.043Q3.122 81.347 3.206 81.689Q3.290 82.031 3.462 82.334Q3.633 82.637 3.891 82.824Q4.149 83.012 4.481 83.012Q4.864 83.012 5.145 82.738Q5.426 82.465 5.426 82.078Q5.454 81.867 5.665 81.844L5.833 81.844Q6.063 81.883 6.063 82.109Q6.063 82.426 5.926 82.697Q5.790 82.969 5.555 83.166Q5.321 83.363 5.030 83.467Q4.739 83.570 4.419 83.570M7.438 84.156L7.352 84.156Q7.247 84.144 7.178 84.072Q7.110 84 7.110 83.906Q7.110 83.765 7.216 83.699Q7.551 83.484 7.821 83.195Q8.091 82.906 8.274 82.558Q8.458 82.211 8.548 81.832Q8.637 81.453 8.637 81.043Q8.637 80.504 8.473 80.012Q8.309 79.519 7.991 79.105Q7.673 78.691 7.231 78.402Q7.110 78.320 7.110 78.187Q7.110 78.090 7.178 78.021Q7.247 77.953 7.352 77.941L7.438 77.941Q7.493 77.941 7.528 77.965Q7.915 78.187 8.255 78.535Q8.594 78.883 8.815 79.277Q9.036 79.672 9.157 80.117Q9.278 80.562 9.278 81.043Q9.278 81.527 9.157 81.978Q9.036 82.430 8.811 82.826Q8.587 83.222 8.262 83.556Q7.938 83.890 7.536 84.133Q7.466 84.156 7.438 84.156\" 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\" stroke=\"var(--tk-warn)\" d=\"m-38.889-71.67 2.742 9.127 2.743 7.621 2.742 6.415 2.742 5.45 2.742 4.662 2.742 4.015 2.742 3.482 2.742 3.026 2.742 2.653 2.742 2.315 2.743 2.041 2.742 1.796 2.742 1.586L-.5-16.078l2.742 1.238 2.742 1.096 2.742.967 2.742.854 2.743.756 2.742.664 2.742.584 2.742.507 2.742.442 2.742.383 2.742.328 2.742.278 2.742.232 2.743.191 2.742.153 2.742.118 2.742.087 2.742.055 2.742.03 2.742.004 2.742-.018 2.743-.04 2.742-.061 2.742-.08 2.742-.096 2.742-.11 2.742-.127 2.742-.14 2.742-.153 2.743-.166 2.742-.175 2.742-.188 2.742-.195 2.742-.206 2.742-.215 2.742-.22 2.742-.23 2.742-.236 2.743-.243 2.742-.25 2.742-.254 2.742-.26 2.742-.266 2.742-.27 2.742-.274 2.742-.278 2.743-.282 2.742-.287 2.742-.29 2.742-.292 2.742-.295 2.742-.3 2.742-.302 2.742-.302 2.742-.307\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\">\u003Cg fill=\"var(--tk-warn)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(123.034 -109.944)\">\u003Cpath d=\"M-47.648 82.387L-47.648 80.605L-48.398 80.605Q-48.597 80.582-48.648 80.363L-48.648 80.277Q-48.597 80.066-48.398 80.043L-47.648 80.043L-47.648 79.293Q-47.597 79.086-47.398 79.058L-47.253 79.058Q-47.058 79.086-47.007 79.293L-47.007 80.043L-45.648 80.043Q-45.456 80.062-45.398 80.277L-45.398 80.363Q-45.452 80.582-45.648 80.605L-47.007 80.605L-47.007 82.355Q-47.007 82.972-46.433 82.972Q-46.183 82.972-46.019 82.787Q-45.855 82.601-45.855 82.355Q-45.855 82.262-45.783 82.191Q-45.710 82.121-45.609 82.109L-45.464 82.109Q-45.265 82.133-45.214 82.340L-45.214 82.387Q-45.214 82.711-45.398 82.974Q-45.581 83.238-45.874 83.385Q-46.167 83.531-46.488 83.531Q-46.999 83.531-47.324 83.221Q-47.648 82.910-47.648 82.387M-41.089 82.004L-43.531 82.004Q-43.476 82.281-43.279 82.504Q-43.081 82.726-42.804 82.849Q-42.527 82.972-42.242 82.972Q-41.769 82.972-41.546 82.683Q-41.538 82.672-41.482 82.566Q-41.425 82.461-41.376 82.418Q-41.327 82.375-41.234 82.363L-41.089 82.363Q-40.898 82.383-40.839 82.597L-40.839 82.652Q-40.906 82.953-41.136 83.150Q-41.367 83.347-41.679 83.439Q-41.992 83.531-42.296 83.531Q-42.781 83.531-43.220 83.303Q-43.660 83.074-43.927 82.674Q-44.195 82.273-44.195 81.781L-44.195 81.722Q-44.195 81.254-43.949 80.851Q-43.702 80.449-43.294 80.215Q-42.886 79.980-42.417 79.980Q-41.913 79.980-41.560 80.203Q-41.206 80.426-41.023 80.814Q-40.839 81.203-40.839 81.707L-40.839 81.765Q-40.898 81.980-41.089 82.004M-43.523 81.453L-41.495 81.453Q-41.542 81.043-41.781 80.791Q-42.019 80.539-42.417 80.539Q-42.812 80.539-43.118 80.801Q-43.425 81.062-43.523 81.453M-39.769 83.293L-39.769 82.379Q-39.742 82.172-39.531 82.148L-39.363 82.148Q-39.199 82.172-39.140 82.332Q-38.937 82.972-38.210 82.972Q-38.003 82.972-37.775 82.937Q-37.546 82.902-37.378 82.787Q-37.210 82.672-37.210 82.469Q-37.210 82.258-37.433 82.144Q-37.656 82.031-37.929 81.988L-38.628 81.875Q-39.769 81.664-39.769 80.941Q-39.769 80.652-39.624 80.463Q-39.480 80.273-39.240 80.166Q-38.999 80.058-38.743 80.019Q-38.488 79.980-38.210 79.980Q-37.960 79.980-37.767 80.010Q-37.574 80.039-37.410 80.117Q-37.331 80-37.202 79.980L-37.124 79.980Q-37.027 79.992-36.964 80.055Q-36.902 80.117-36.890 80.211L-36.890 80.918Q-36.902 81.012-36.964 81.078Q-37.027 81.144-37.124 81.156L-37.292 81.156Q-37.386 81.144-37.452 81.078Q-37.519 81.012-37.531 80.918Q-37.531 80.539-38.226 80.539Q-38.574 80.539-38.892 80.621Q-39.210 80.703-39.210 80.949Q-39.210 81.215-38.538 81.324L-37.835 81.445Q-37.351 81.527-37.001 81.775Q-36.652 82.023-36.652 82.469Q-36.652 82.859-36.888 83.101Q-37.124 83.344-37.474 83.437Q-37.824 83.531-38.210 83.531Q-38.788 83.531-39.187 83.277Q-39.257 83.402-39.306 83.459Q-39.355 83.515-39.460 83.531L-39.531 83.531Q-39.745 83.508-39.769 83.293M-34.910 82.387L-34.910 80.605L-35.660 80.605Q-35.859 80.582-35.910 80.363L-35.910 80.277Q-35.859 80.066-35.660 80.043L-34.910 80.043L-34.910 79.293Q-34.859 79.086-34.660 79.058L-34.515 79.058Q-34.320 79.086-34.269 79.293L-34.269 80.043L-32.910 80.043Q-32.718 80.062-32.660 80.277L-32.660 80.363Q-32.714 80.582-32.910 80.605L-34.269 80.605L-34.269 82.355Q-34.269 82.972-33.695 82.972Q-33.445 82.972-33.281 82.787Q-33.117 82.601-33.117 82.355Q-33.117 82.262-33.044 82.191Q-32.972 82.121-32.870 82.109L-32.726 82.109Q-32.527 82.133-32.476 82.340L-32.476 82.387Q-32.476 82.711-32.660 82.974Q-32.843 83.238-33.136 83.385Q-33.429 83.531-33.749 83.531Q-34.261 83.531-34.585 83.221Q-34.910 82.910-34.910 82.387\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(123.034 -109.944)\">\u003Cpath d=\"M-27.374 83.254L-27.374 83.164Q-27.316 82.957-27.124 82.933L-26.413 82.933L-26.413 80.605L-27.124 80.605Q-27.320 80.582-27.374 80.363L-27.374 80.277Q-27.316 80.066-27.124 80.043L-26.023 80.043Q-25.824 80.062-25.773 80.277L-25.773 80.605Q-25.511 80.320-25.156 80.162Q-24.800 80.004-24.413 80.004Q-24.120 80.004-23.886 80.138Q-23.652 80.273-23.652 80.539Q-23.652 80.707-23.761 80.824Q-23.870 80.941-24.038 80.941Q-24.191 80.941-24.306 80.830Q-24.421 80.719-24.421 80.562Q-24.796 80.562-25.111 80.763Q-25.425 80.965-25.599 81.299Q-25.773 81.633-25.773 82.012L-25.773 82.933L-24.827 82.933Q-24.620 82.957-24.581 83.164L-24.581 83.254Q-24.620 83.469-24.827 83.492L-27.124 83.492Q-27.316 83.469-27.374 83.254M-22.734 83.254L-22.734 83.164Q-22.683 82.957-22.488 82.933L-21.449 82.933L-21.449 80.605L-22.421 80.605Q-22.620 80.582-22.671 80.363L-22.671 80.277Q-22.620 80.066-22.421 80.043L-21.054 80.043Q-20.859 80.062-20.808 80.277L-20.808 82.933L-19.894 82.933Q-19.699 82.957-19.648 83.164L-19.648 83.254Q-19.699 83.469-19.894 83.492L-22.488 83.492Q-22.683 83.469-22.734 83.254M-21.702 79.066L-21.702 79.012Q-21.702 78.840-21.566 78.719Q-21.429 78.597-21.253 78.597Q-21.081 78.597-20.945 78.719Q-20.808 78.840-20.808 79.012L-20.808 79.066Q-20.808 79.242-20.945 79.363Q-21.081 79.484-21.253 79.484Q-21.429 79.484-21.566 79.363Q-21.702 79.242-21.702 79.066M-18.519 83.293L-18.519 82.379Q-18.492 82.172-18.281 82.148L-18.113 82.148Q-17.949 82.172-17.890 82.332Q-17.687 82.972-16.960 82.972Q-16.753 82.972-16.525 82.937Q-16.296 82.902-16.128 82.787Q-15.960 82.672-15.960 82.469Q-15.960 82.258-16.183 82.144Q-16.406 82.031-16.679 81.988L-17.378 81.875Q-18.519 81.664-18.519 80.941Q-18.519 80.652-18.374 80.463Q-18.230 80.273-17.990 80.166Q-17.749 80.058-17.493 80.019Q-17.238 79.980-16.960 79.980Q-16.710 79.980-16.517 80.010Q-16.324 80.039-16.160 80.117Q-16.081 80-15.952 79.980L-15.874 79.980Q-15.777 79.992-15.714 80.055Q-15.652 80.117-15.640 80.211L-15.640 80.918Q-15.652 81.012-15.714 81.078Q-15.777 81.144-15.874 81.156L-16.042 81.156Q-16.136 81.144-16.202 81.078Q-16.269 81.012-16.281 80.918Q-16.281 80.539-16.976 80.539Q-17.324 80.539-17.642 80.621Q-17.960 80.703-17.960 80.949Q-17.960 81.215-17.288 81.324L-16.585 81.445Q-16.101 81.527-15.751 81.775Q-15.402 82.023-15.402 82.469Q-15.402 82.859-15.638 83.101Q-15.874 83.344-16.224 83.437Q-16.574 83.531-16.960 83.531Q-17.538 83.531-17.937 83.277Q-18.007 83.402-18.056 83.459Q-18.105 83.515-18.210 83.531L-18.281 83.531Q-18.495 83.508-18.519 83.293M-14.714 83.254L-14.714 83.164Q-14.663 82.957-14.468 82.933L-14.003 82.933L-14.003 79.164L-14.468 79.164Q-14.663 79.140-14.714 78.926L-14.714 78.836Q-14.663 78.629-14.468 78.605L-13.722 78.605Q-13.527 78.625-13.476 78.836L-13.476 81.660L-12.331 80.605L-12.628 80.605Q-12.835 80.582-12.874 80.363L-12.874 80.277Q-12.835 80.066-12.628 80.043L-11.179 80.043Q-10.976 80.066-10.929 80.277L-10.929 80.363Q-10.972 80.582-11.179 80.605L-11.546 80.605L-12.492 81.469L-11.339 82.933L-11.003 82.933Q-10.796 82.957-10.753 83.164L-10.753 83.254Q-10.796 83.469-11.003 83.492L-12.171 83.492Q-12.367 83.469-12.417 83.254L-12.417 83.164Q-12.367 82.957-12.171 82.933L-12.011 82.933L-12.874 81.828L-13.476 82.379L-13.476 82.933L-13.011 82.933Q-12.820 82.957-12.761 83.164L-12.761 83.254Q-12.820 83.469-13.011 83.492L-14.468 83.492Q-14.663 83.469-14.714 83.254\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M50.737 83.492v-90.48\" style=\"stroke-dasharray:3.0,3.0\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(78.334 9.384)\">\u003Cpath d=\"M-48.261 83.293L-48.261 82.379Q-48.234 82.172-48.023 82.148L-47.855 82.148Q-47.691 82.172-47.632 82.332Q-47.429 82.972-46.702 82.972Q-46.495 82.972-46.267 82.937Q-46.038 82.902-45.870 82.787Q-45.702 82.672-45.702 82.469Q-45.702 82.258-45.925 82.144Q-46.148 82.031-46.421 81.988L-47.120 81.875Q-48.261 81.664-48.261 80.941Q-48.261 80.652-48.117 80.463Q-47.972 80.273-47.732 80.166Q-47.492 80.058-47.236 80.019Q-46.980 79.980-46.702 79.980Q-46.452 79.980-46.259 80.010Q-46.066 80.039-45.902 80.117Q-45.824 80-45.695 79.980L-45.617 79.980Q-45.519 79.992-45.456 80.055Q-45.394 80.117-45.382 80.211L-45.382 80.918Q-45.394 81.012-45.456 81.078Q-45.519 81.144-45.617 81.156L-45.785 81.156Q-45.878 81.144-45.945 81.078Q-46.011 81.012-46.023 80.918Q-46.023 80.539-46.718 80.539Q-47.066 80.539-47.384 80.621Q-47.702 80.703-47.702 80.949Q-47.702 81.215-47.031 81.324L-46.327 81.445Q-45.843 81.527-45.493 81.775Q-45.144 82.023-45.144 82.469Q-45.144 82.859-45.380 83.101Q-45.617 83.344-45.966 83.437Q-46.316 83.531-46.702 83.531Q-47.281 83.531-47.679 83.277Q-47.749 83.402-47.798 83.459Q-47.847 83.515-47.952 83.531L-48.023 83.531Q-48.238 83.508-48.261 83.293M-43.593 83.269L-44.058 80.605L-44.218 80.605Q-44.425 80.582-44.464 80.363L-44.464 80.277Q-44.425 80.066-44.218 80.043L-43.050 80.043Q-42.839 80.066-42.800 80.277L-42.800 80.363Q-42.839 80.582-43.050 80.605L-43.523 80.605Q-43.410 81.250-43.331 81.695Q-43.253 82.140-43.202 82.459Q-43.152 82.777-43.152 82.851Q-43.144 82.680-42.859 81.683Q-42.820 81.570-42.722 81.496Q-42.624 81.422-42.503 81.422L-42.425 81.422Q-42.304 81.422-42.206 81.496Q-42.109 81.570-42.074 81.683Q-41.964 82.066-41.882 82.390Q-41.800 82.715-41.800 82.851Q-41.788 82.562-41.441 80.605L-41.913 80.605Q-42.120 80.582-42.160 80.363L-42.160 80.277Q-42.120 80.066-41.913 80.043L-40.738 80.043Q-40.546 80.066-40.488 80.277L-40.488 80.363Q-40.542 80.582-40.738 80.605L-40.906 80.605L-41.370 83.269Q-41.394 83.387-41.482 83.459Q-41.570 83.531-41.691 83.531L-41.831 83.531Q-41.952 83.531-42.048 83.459Q-42.144 83.387-42.187 83.269Q-42.234 83.090-42.306 82.834Q-42.378 82.578-42.421 82.369Q-42.464 82.160-42.464 82.058Q-42.472 82.340-42.753 83.269Q-42.781 83.379-42.878 83.455Q-42.976 83.531-43.097 83.531L-43.273 83.531Q-43.394 83.531-43.482 83.459Q-43.570 83.387-43.593 83.269M-36.843 82.004L-39.285 82.004Q-39.230 82.281-39.033 82.504Q-38.835 82.726-38.558 82.849Q-38.281 82.972-37.995 82.972Q-37.523 82.972-37.300 82.683Q-37.292 82.672-37.236 82.566Q-37.179 82.461-37.130 82.418Q-37.081 82.375-36.988 82.363L-36.843 82.363Q-36.652 82.383-36.593 82.597L-36.593 82.652Q-36.660 82.953-36.890 83.150Q-37.120 83.347-37.433 83.439Q-37.745 83.531-38.050 83.531Q-38.535 83.531-38.974 83.303Q-39.413 83.074-39.681 82.674Q-39.949 82.273-39.949 81.781L-39.949 81.722Q-39.949 81.254-39.702 80.851Q-39.456 80.449-39.048 80.215Q-38.640 79.980-38.171 79.980Q-37.667 79.980-37.314 80.203Q-36.960 80.426-36.777 80.814Q-36.593 81.203-36.593 81.707L-36.593 81.765Q-36.652 81.980-36.843 82.004M-39.277 81.453L-37.249 81.453Q-37.296 81.043-37.535 80.791Q-37.773 80.539-38.171 80.539Q-38.566 80.539-38.872 80.801Q-39.179 81.062-39.277 81.453M-32.597 82.004L-35.038 82.004Q-34.984 82.281-34.786 82.504Q-34.589 82.726-34.312 82.849Q-34.035 82.972-33.749 82.972Q-33.277 82.972-33.054 82.683Q-33.046 82.672-32.990 82.566Q-32.933 82.461-32.884 82.418Q-32.835 82.375-32.742 82.363L-32.597 82.363Q-32.406 82.383-32.347 82.597L-32.347 82.652Q-32.413 82.953-32.644 83.150Q-32.874 83.347-33.187 83.439Q-33.499 83.531-33.804 83.531Q-34.288 83.531-34.728 83.303Q-35.167 83.074-35.435 82.674Q-35.702 82.273-35.702 81.781L-35.702 81.722Q-35.702 81.254-35.456 80.851Q-35.210 80.449-34.802 80.215Q-34.394 79.980-33.925 79.980Q-33.421 79.980-33.068 80.203Q-32.714 80.426-32.531 80.814Q-32.347 81.203-32.347 81.707L-32.347 81.765Q-32.406 81.980-32.597 82.004M-35.031 81.453L-33.003 81.453Q-33.050 81.043-33.288 80.791Q-33.527 80.539-33.925 80.539Q-34.320 80.539-34.626 80.801Q-34.933 81.062-35.031 81.453M-30.663 82.387L-30.663 80.605L-31.413 80.605Q-31.613 80.582-31.663 80.363L-31.663 80.277Q-31.613 80.066-31.413 80.043L-30.663 80.043L-30.663 79.293Q-30.613 79.086-30.413 79.058L-30.269 79.058Q-30.074 79.086-30.023 79.293L-30.023 80.043L-28.663 80.043Q-28.472 80.062-28.413 80.277L-28.413 80.363Q-28.468 80.582-28.663 80.605L-30.023 80.605L-30.023 82.355Q-30.023 82.972-29.449 82.972Q-29.199 82.972-29.035 82.787Q-28.870 82.601-28.870 82.355Q-28.870 82.262-28.798 82.191Q-28.726 82.121-28.624 82.109L-28.480 82.109Q-28.281 82.133-28.230 82.340L-28.230 82.387Q-28.230 82.711-28.413 82.974Q-28.597 83.238-28.890 83.385Q-29.183 83.531-29.503 83.531Q-30.015 83.531-30.339 83.221Q-30.663 82.910-30.663 82.387\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(78.334 9.384)\">\u003Cpath d=\"M-22.761 83.293L-22.761 82.379Q-22.734 82.172-22.523 82.148L-22.355 82.148Q-22.191 82.172-22.132 82.332Q-21.929 82.972-21.202 82.972Q-20.995 82.972-20.767 82.937Q-20.538 82.902-20.370 82.787Q-20.202 82.672-20.202 82.469Q-20.202 82.258-20.425 82.144Q-20.648 82.031-20.921 81.988L-21.620 81.875Q-22.761 81.664-22.761 80.941Q-22.761 80.652-22.617 80.463Q-22.472 80.273-22.232 80.166Q-21.992 80.058-21.736 80.019Q-21.480 79.980-21.202 79.980Q-20.952 79.980-20.759 80.010Q-20.566 80.039-20.402 80.117Q-20.324 80-20.195 79.980L-20.117 79.980Q-20.019 79.992-19.956 80.055Q-19.894 80.117-19.882 80.211L-19.882 80.918Q-19.894 81.012-19.956 81.078Q-20.019 81.144-20.117 81.156L-20.285 81.156Q-20.378 81.144-20.445 81.078Q-20.511 81.012-20.523 80.918Q-20.523 80.539-21.218 80.539Q-21.566 80.539-21.884 80.621Q-22.202 80.703-22.202 80.949Q-22.202 81.215-21.531 81.324L-20.827 81.445Q-20.343 81.527-19.993 81.775Q-19.644 82.023-19.644 82.469Q-19.644 82.859-19.880 83.101Q-20.117 83.344-20.466 83.437Q-20.816 83.531-21.202 83.531Q-21.781 83.531-22.179 83.277Q-22.249 83.402-22.298 83.459Q-22.347 83.515-22.452 83.531L-22.523 83.531Q-22.738 83.508-22.761 83.293M-19.031 85.035L-19.031 84.949Q-18.988 84.730-18.781 84.707L-18.359 84.707L-18.359 80.605L-18.781 80.605Q-18.988 80.582-19.031 80.363L-19.031 80.277Q-18.984 80.066-18.781 80.043L-17.964 80.043Q-17.769 80.062-17.718 80.277L-17.718 80.347Q-17.507 80.180-17.243 80.092Q-16.980 80.004-16.710 80.004Q-16.370 80.004-16.074 80.148Q-15.777 80.293-15.562 80.545Q-15.347 80.797-15.232 81.109Q-15.117 81.422-15.117 81.765Q-15.117 82.230-15.343 82.640Q-15.570 83.051-15.956 83.291Q-16.343 83.531-16.812 83.531Q-17.331 83.531-17.718 83.164L-17.718 84.707L-17.292 84.707Q-17.085 84.730-17.046 84.949L-17.046 85.035Q-17.085 85.246-17.292 85.269L-18.781 85.269Q-18.984 85.246-19.031 85.035M-16.855 82.972Q-16.546 82.972-16.296 82.803Q-16.046 82.633-15.902 82.351Q-15.757 82.070-15.757 81.765Q-15.757 81.476-15.882 81.197Q-16.007 80.918-16.240 80.740Q-16.472 80.562-16.773 80.562Q-17.093 80.562-17.355 80.748Q-17.617 80.933-17.718 81.234L-17.718 82.086Q-17.628 82.453-17.408 82.713Q-17.187 82.972-16.855 82.972M-12.734 83.531Q-13.206 83.531-13.591 83.287Q-13.976 83.043-14.199 82.633Q-14.421 82.222-14.421 81.765Q-14.421 81.422-14.296 81.099Q-14.171 80.777-13.941 80.523Q-13.710 80.269-13.404 80.125Q-13.097 79.980-12.734 79.980Q-12.370 79.980-12.058 80.127Q-11.745 80.273-11.523 80.519Q-11.300 80.765-11.173 81.086Q-11.046 81.406-11.046 81.765Q-11.046 82.222-11.271 82.635Q-11.495 83.047-11.880 83.289Q-12.265 83.531-12.734 83.531M-12.734 82.972Q-12.269 82.972-11.978 82.578Q-11.687 82.183-11.687 81.699Q-11.687 81.406-11.822 81.138Q-11.956 80.871-12.197 80.705Q-12.437 80.539-12.734 80.539Q-13.038 80.539-13.277 80.705Q-13.515 80.871-13.650 81.138Q-13.785 81.406-13.785 81.699Q-13.785 82.180-13.492 82.576Q-13.199 82.972-12.734 82.972M-9.410 82.387L-9.410 80.605L-10.160 80.605Q-10.359 80.582-10.410 80.363L-10.410 80.277Q-10.359 80.066-10.160 80.043L-9.410 80.043L-9.410 79.293Q-9.359 79.086-9.160 79.058L-9.015 79.058Q-8.820 79.086-8.769 79.293L-8.769 80.043L-7.410 80.043Q-7.218 80.062-7.160 80.277L-7.160 80.363Q-7.214 80.582-7.410 80.605L-8.769 80.605L-8.769 82.355Q-8.769 82.972-8.195 82.972Q-7.945 82.972-7.781 82.787Q-7.617 82.601-7.617 82.355Q-7.617 82.262-7.544 82.191Q-7.472 82.121-7.370 82.109L-7.226 82.109Q-7.027 82.133-6.976 82.340L-6.976 82.387Q-6.976 82.711-7.160 82.974Q-7.343 83.238-7.636 83.385Q-7.929 83.531-8.249 83.531Q-8.761 83.531-9.085 83.221Q-9.410 82.910-9.410 82.387\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-warn)\" stroke=\"none\" d=\"M53.137-6.987a2.4 2.4 0 1 0-4.8 0 2.4 2.4 0 0 0 4.8 0m-2.4 0\"\u002F>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">Classical bias--variance: training risk falls and the gap term grows with capacity, so test risk is U-shaped with an intermediate sweet spot.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:421.124px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 315.843 148.591\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-33.648 63.432h207.95\"\u002F>\u003Cpath stroke=\"none\" d=\"m176.902 63.432-4.16-2.08 1.56 2.08-1.56 2.08\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(214.283 2)\">\u003Cpath d=\"M-32.785 62.471L-32.785 60.280L-33.488 60.280L-33.488 60.026Q-33.132 60.026-32.890 59.793Q-32.648 59.561-32.537 59.213Q-32.425 58.866-32.425 58.510L-32.144 58.510L-32.144 59.983L-30.968 59.983L-30.968 60.280L-32.144 60.280L-32.144 62.455Q-32.144 62.776-32.025 63.004Q-31.906 63.233-31.625 63.233Q-31.445 63.233-31.328 63.110Q-31.211 62.987-31.158 62.807Q-31.105 62.627-31.105 62.455L-31.105 61.983L-30.824 61.983L-30.824 62.471Q-30.824 62.725-30.929 62.965Q-31.035 63.205-31.232 63.358Q-31.429 63.510-31.687 63.510Q-32.003 63.510-32.255 63.387Q-32.507 63.264-32.646 63.030Q-32.785 62.795-32.785 62.471M-28.097 63.432L-30.078 63.432L-30.078 63.135Q-29.808 63.135-29.640 63.090Q-29.472 63.045-29.472 62.873L-29.472 60.737Q-29.472 60.522-29.535 60.426Q-29.597 60.330-29.714 60.309Q-29.832 60.287-30.078 60.287L-30.078 59.991L-28.910 59.905L-28.910 60.690Q-28.832 60.479-28.679 60.293Q-28.527 60.108-28.328 60.006Q-28.128 59.905-27.902 59.905Q-27.656 59.905-27.464 60.049Q-27.273 60.194-27.273 60.424Q-27.273 60.580-27.378 60.690Q-27.484 60.799-27.640 60.799Q-27.796 60.799-27.906 60.690Q-28.015 60.580-28.015 60.424Q-28.015 60.264-27.910 60.159Q-28.234 60.159-28.449 60.387Q-28.664 60.616-28.759 60.955Q-28.855 61.295-28.855 61.600L-28.855 62.873Q-28.855 63.041-28.628 63.088Q-28.402 63.135-28.097 63.135L-28.097 63.432M-26.695 62.600Q-26.695 62.116-26.293 61.821Q-25.890 61.526-25.339 61.407Q-24.789 61.287-24.296 61.287L-24.296 60.998Q-24.296 60.772-24.412 60.565Q-24.527 60.358-24.724 60.239Q-24.921 60.120-25.152 60.120Q-25.578 60.120-25.863 60.225Q-25.793 60.252-25.746 60.307Q-25.699 60.362-25.673 60.432Q-25.648 60.502-25.648 60.577Q-25.648 60.682-25.699 60.774Q-25.750 60.866-25.841 60.916Q-25.933 60.967-26.039 60.967Q-26.144 60.967-26.236 60.916Q-26.328 60.866-26.378 60.774Q-26.429 60.682-26.429 60.577Q-26.429 60.159-26.041 60.012Q-25.652 59.866-25.152 59.866Q-24.820 59.866-24.466 59.996Q-24.113 60.127-23.884 60.381Q-23.656 60.635-23.656 60.983L-23.656 62.784Q-23.656 62.916-23.584 63.026Q-23.511 63.135-23.382 63.135Q-23.257 63.135-23.189 63.030Q-23.121 62.924-23.121 62.784L-23.121 62.272L-22.839 62.272L-22.839 62.784Q-22.839 62.987-22.957 63.145Q-23.074 63.303-23.255 63.387Q-23.437 63.471-23.640 63.471Q-23.871 63.471-24.023 63.299Q-24.175 63.127-24.207 62.897Q-24.367 63.178-24.675 63.344Q-24.984 63.510-25.336 63.510Q-25.847 63.510-26.271 63.287Q-26.695 63.065-26.695 62.600M-26.007 62.600Q-26.007 62.885-25.781 63.071Q-25.554 63.256-25.261 63.256Q-25.015 63.256-24.791 63.139Q-24.566 63.022-24.431 62.819Q-24.296 62.616-24.296 62.362L-24.296 61.530Q-24.562 61.530-24.847 61.584Q-25.132 61.639-25.404 61.768Q-25.675 61.897-25.841 62.104Q-26.007 62.311-26.007 62.600M-20.687 63.432L-22.464 63.432L-22.464 63.135Q-22.191 63.135-22.023 63.088Q-21.855 63.041-21.855 62.873L-21.855 60.737Q-21.855 60.522-21.912 60.426Q-21.968 60.330-22.082 60.309Q-22.195 60.287-22.441 60.287L-22.441 59.991L-21.242 59.905L-21.242 62.873Q-21.242 63.041-21.095 63.088Q-20.949 63.135-20.687 63.135L-20.687 63.432M-22.128 58.510Q-22.128 58.319-21.994 58.188Q-21.859 58.057-21.664 58.057Q-21.543 58.057-21.439 58.120Q-21.336 58.182-21.273 58.286Q-21.211 58.389-21.211 58.510Q-21.211 58.705-21.341 58.840Q-21.472 58.975-21.664 58.975Q-21.863 58.975-21.996 58.842Q-22.128 58.709-22.128 58.510M-18.257 63.432L-20.113 63.432L-20.113 63.135Q-19.839 63.135-19.671 63.088Q-19.503 63.041-19.503 62.873L-19.503 60.737Q-19.503 60.522-19.566 60.426Q-19.628 60.330-19.748 60.309Q-19.867 60.287-20.113 60.287L-20.113 59.991L-18.921 59.905L-18.921 60.639Q-18.808 60.424-18.615 60.256Q-18.421 60.088-18.183 59.996Q-17.945 59.905-17.691 59.905Q-16.523 59.905-16.523 60.983L-16.523 62.873Q-16.523 63.041-16.353 63.088Q-16.183 63.135-15.914 63.135L-15.914 63.432L-17.769 63.432L-17.769 63.135Q-17.496 63.135-17.328 63.088Q-17.160 63.041-17.160 62.873L-17.160 60.998Q-17.160 60.616-17.281 60.387Q-17.402 60.159-17.753 60.159Q-18.066 60.159-18.320 60.321Q-18.574 60.483-18.720 60.752Q-18.867 61.022-18.867 61.319L-18.867 62.873Q-18.867 63.041-18.697 63.088Q-18.527 63.135-18.257 63.135L-18.257 63.432M-13.609 63.432L-15.386 63.432L-15.386 63.135Q-15.113 63.135-14.945 63.088Q-14.777 63.041-14.777 62.873L-14.777 60.737Q-14.777 60.522-14.834 60.426Q-14.890 60.330-15.003 60.309Q-15.117 60.287-15.363 60.287L-15.363 59.991L-14.164 59.905L-14.164 62.873Q-14.164 63.041-14.017 63.088Q-13.871 63.135-13.609 63.135L-13.609 63.432M-15.050 58.510Q-15.050 58.319-14.916 58.188Q-14.781 58.057-14.586 58.057Q-14.464 58.057-14.361 58.120Q-14.257 58.182-14.195 58.286Q-14.132 58.389-14.132 58.510Q-14.132 58.705-14.263 58.840Q-14.394 58.975-14.586 58.975Q-14.785 58.975-14.918 58.842Q-15.050 58.709-15.050 58.510M-11.179 63.432L-13.035 63.432L-13.035 63.135Q-12.761 63.135-12.593 63.088Q-12.425 63.041-12.425 62.873L-12.425 60.737Q-12.425 60.522-12.488 60.426Q-12.550 60.330-12.669 60.309Q-12.789 60.287-13.035 60.287L-13.035 59.991L-11.843 59.905L-11.843 60.639Q-11.730 60.424-11.537 60.256Q-11.343 60.088-11.105 59.996Q-10.867 59.905-10.613 59.905Q-9.445 59.905-9.445 60.983L-9.445 62.873Q-9.445 63.041-9.275 63.088Q-9.105 63.135-8.836 63.135L-8.836 63.432L-10.691 63.432L-10.691 63.135Q-10.418 63.135-10.250 63.088Q-10.082 63.041-10.082 62.873L-10.082 60.998Q-10.082 60.616-10.203 60.387Q-10.324 60.159-10.675 60.159Q-10.988 60.159-11.242 60.321Q-11.496 60.483-11.642 60.752Q-11.789 61.022-11.789 61.319L-11.789 62.873Q-11.789 63.041-11.619 63.088Q-11.449 63.135-11.179 63.135L-11.179 63.432M-8.390 64.041Q-8.390 63.760-8.179 63.549Q-7.968 63.338-7.683 63.248Q-7.839 63.123-7.918 62.934Q-7.996 62.745-7.996 62.545Q-7.996 62.190-7.765 61.897Q-8.132 61.557-8.132 61.088Q-8.132 60.737-7.929 60.467Q-7.726 60.198-7.406 60.051Q-7.086 59.905-6.742 59.905Q-6.222 59.905-5.851 60.186Q-5.488 59.815-4.941 59.815Q-4.761 59.815-4.634 59.942Q-4.507 60.069-4.507 60.248Q-4.507 60.354-4.586 60.432Q-4.664 60.510-4.773 60.510Q-4.882 60.510-4.959 60.434Q-5.035 60.358-5.035 60.248Q-5.035 60.147-4.996 60.096Q-4.988 60.088-4.984 60.082Q-4.980 60.077-4.980 60.073Q-5.355 60.073-5.675 60.327Q-5.355 60.666-5.355 61.088Q-5.355 61.358-5.472 61.575Q-5.589 61.791-5.794 61.950Q-6 62.108-6.242 62.190Q-6.484 62.272-6.742 62.272Q-6.961 62.272-7.173 62.213Q-7.386 62.155-7.582 62.034Q-7.675 62.174-7.675 62.354Q-7.675 62.561-7.539 62.713Q-7.402 62.866-7.195 62.866L-6.500 62.866Q-6.011 62.866-5.599 62.950Q-5.187 63.034-4.908 63.291Q-4.628 63.549-4.628 64.041Q-4.628 64.405-4.949 64.637Q-5.269 64.870-5.711 64.971Q-6.152 65.073-6.507 65.073Q-6.863 65.073-7.306 64.971Q-7.750 64.870-8.070 64.637Q-8.390 64.405-8.390 64.041M-7.886 64.041Q-7.886 64.237-7.742 64.385Q-7.597 64.534-7.384 64.623Q-7.171 64.713-6.931 64.760Q-6.691 64.807-6.507 64.807Q-6.265 64.807-5.935 64.729Q-5.605 64.651-5.369 64.477Q-5.132 64.303-5.132 64.041Q-5.132 63.635-5.543 63.526Q-5.953 63.416-6.515 63.416L-7.195 63.416Q-7.464 63.416-7.675 63.594Q-7.886 63.772-7.886 64.041M-6.742 62.006Q-6.019 62.006-6.019 61.088Q-6.019 60.166-6.742 60.166Q-7.468 60.166-7.468 61.088Q-7.468 62.006-6.742 62.006\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(214.283 2)\">\u003Cpath d=\"M-1.291 61.678Q-1.291 61.198-1.058 60.782Q-0.826 60.366-0.416 60.116Q-0.006 59.866 0.471 59.866Q1.201 59.866 1.600 60.307Q1.998 60.748 1.998 61.479Q1.998 61.584 1.905 61.608L-0.545 61.608L-0.545 61.678Q-0.545 62.088-0.424 62.444Q-0.302 62.799-0.031 63.016Q0.241 63.233 0.670 63.233Q1.034 63.233 1.330 63.004Q1.627 62.776 1.729 62.424Q1.737 62.377 1.823 62.362L1.905 62.362Q1.998 62.389 1.998 62.471Q1.998 62.479 1.991 62.510Q1.928 62.737 1.789 62.920Q1.651 63.104 1.459 63.237Q1.268 63.370 1.049 63.440Q0.830 63.510 0.592 63.510Q0.221 63.510-0.117 63.373Q-0.455 63.237-0.722 62.985Q-0.990 62.733-1.140 62.393Q-1.291 62.053-1.291 61.678M-0.537 61.370L1.424 61.370Q1.424 61.065 1.323 60.774Q1.221 60.483 1.004 60.301Q0.787 60.120 0.471 60.120Q0.170 60.120-0.060 60.307Q-0.291 60.495-0.414 60.786Q-0.537 61.077-0.537 61.370M4.369 64.983L2.514 64.983L2.514 64.690Q2.784 64.690 2.951 64.645Q3.119 64.600 3.119 64.424L3.119 60.600Q3.119 60.393 2.963 60.340Q2.807 60.287 2.514 60.287L2.514 59.991L3.737 59.905L3.737 60.370Q3.967 60.147 4.282 60.026Q4.596 59.905 4.936 59.905Q5.409 59.905 5.813 60.151Q6.217 60.397 6.450 60.813Q6.682 61.229 6.682 61.705Q6.682 62.080 6.534 62.409Q6.385 62.737 6.116 62.989Q5.846 63.241 5.502 63.375Q5.159 63.510 4.799 63.510Q4.510 63.510 4.239 63.389Q3.967 63.268 3.760 63.057L3.760 64.424Q3.760 64.600 3.928 64.645Q4.096 64.690 4.369 64.690L4.369 64.983M3.760 60.768L3.760 62.608Q3.912 62.897 4.174 63.077Q4.436 63.256 4.744 63.256Q5.030 63.256 5.252 63.118Q5.475 62.979 5.627 62.748Q5.780 62.518 5.858 62.246Q5.936 61.975 5.936 61.705Q5.936 61.373 5.811 61.016Q5.686 60.659 5.438 60.422Q5.190 60.186 4.842 60.186Q4.518 60.186 4.223 60.342Q3.928 60.498 3.760 60.768\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(214.283 2)\">\u003Cpath d=\"M7.445 61.737Q7.445 61.233 7.701 60.801Q7.957 60.370 8.393 60.118Q8.828 59.866 9.328 59.866Q9.715 59.866 10.057 60.010Q10.398 60.155 10.660 60.416Q10.922 60.678 11.064 61.014Q11.207 61.350 11.207 61.737Q11.207 62.229 10.943 62.639Q10.680 63.049 10.250 63.280Q9.820 63.510 9.328 63.510Q8.836 63.510 8.402 63.278Q7.969 63.045 7.707 62.637Q7.445 62.229 7.445 61.737M9.328 63.233Q9.785 63.233 10.037 63.010Q10.289 62.787 10.377 62.436Q10.465 62.084 10.465 61.639Q10.465 61.209 10.371 60.871Q10.277 60.534 10.023 60.327Q9.770 60.120 9.328 60.120Q8.680 60.120 8.436 60.536Q8.191 60.952 8.191 61.639Q8.191 62.084 8.279 62.436Q8.367 62.787 8.619 63.010Q8.871 63.233 9.328 63.233\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(214.283 2)\">\u003Cpath d=\"M11.974 61.705Q11.974 61.209 12.224 60.784Q12.474 60.358 12.894 60.112Q13.314 59.866 13.814 59.866Q14.353 59.866 14.744 59.991Q15.134 60.116 15.134 60.530Q15.134 60.635 15.084 60.727Q15.033 60.819 14.941 60.870Q14.849 60.920 14.740 60.920Q14.634 60.920 14.543 60.870Q14.451 60.819 14.400 60.727Q14.349 60.635 14.349 60.530Q14.349 60.307 14.517 60.202Q14.295 60.143 13.822 60.143Q13.525 60.143 13.310 60.282Q13.095 60.420 12.964 60.651Q12.834 60.881 12.775 61.151Q12.716 61.420 12.716 61.705Q12.716 62.100 12.849 62.450Q12.982 62.799 13.254 63.016Q13.525 63.233 13.923 63.233Q14.298 63.233 14.574 63.016Q14.849 62.799 14.951 62.440Q14.966 62.377 15.029 62.377L15.134 62.377Q15.170 62.377 15.195 62.405Q15.220 62.432 15.220 62.471L15.220 62.495Q15.088 62.975 14.703 63.243Q14.318 63.510 13.814 63.510Q13.451 63.510 13.117 63.373Q12.783 63.237 12.523 62.987Q12.263 62.737 12.119 62.401Q11.974 62.065 11.974 61.705\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(214.283 2)\">\u003Cpath d=\"M17.403 63.432L15.547 63.432L15.547 63.135Q15.821 63.135 15.989 63.088Q16.157 63.041 16.157 62.873L16.157 58.713Q16.157 58.498 16.094 58.403Q16.032 58.307 15.913 58.286Q15.794 58.264 15.547 58.264L15.547 57.967L16.770 57.881L16.770 60.584Q16.895 60.373 17.083 60.223Q17.270 60.073 17.497 59.989Q17.723 59.905 17.969 59.905Q19.137 59.905 19.137 60.983L19.137 62.873Q19.137 63.041 19.307 63.088Q19.477 63.135 19.747 63.135L19.747 63.432L17.891 63.432L17.891 63.135Q18.165 63.135 18.333 63.088Q18.501 63.041 18.501 62.873L18.501 60.998Q18.501 60.616 18.380 60.387Q18.258 60.159 17.907 60.159Q17.594 60.159 17.340 60.321Q17.087 60.483 16.940 60.752Q16.794 61.022 16.794 61.319L16.794 62.873Q16.794 63.041 16.964 63.088Q17.133 63.135 17.403 63.135\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-33.648 63.432V-59.16\"\u002F>\u003Cpath stroke=\"none\" d=\"m-33.648-61.76-2.08 4.16 2.08-1.56 2.08 1.56\"\u002F>\u003Cg transform=\"translate(-8.983 -128.925)\">\u003Cpath d=\"M-33.410 61.678Q-33.410 61.198-33.177 60.782Q-32.945 60.366-32.535 60.116Q-32.125 59.866-31.648 59.866Q-30.918 59.866-30.519 60.307Q-30.121 60.748-30.121 61.479Q-30.121 61.584-30.214 61.608L-32.664 61.608L-32.664 61.678Q-32.664 62.088-32.543 62.444Q-32.421 62.799-32.150 63.016Q-31.878 63.233-31.449 63.233Q-31.086 63.233-30.789 63.004Q-30.492 62.776-30.390 62.424Q-30.382 62.377-30.296 62.362L-30.214 62.362Q-30.121 62.389-30.121 62.471Q-30.121 62.479-30.128 62.510Q-30.191 62.737-30.330 62.920Q-30.468 63.104-30.660 63.237Q-30.851 63.370-31.070 63.440Q-31.289 63.510-31.527 63.510Q-31.898 63.510-32.236 63.373Q-32.574 63.237-32.841 62.985Q-33.109 62.733-33.259 62.393Q-33.410 62.053-33.410 61.678M-32.656 61.370L-30.695 61.370Q-30.695 61.065-30.796 60.774Q-30.898 60.483-31.115 60.301Q-31.332 60.120-31.648 60.120Q-31.949 60.120-32.179 60.307Q-32.410 60.495-32.533 60.786Q-32.656 61.077-32.656 61.370M-27.625 63.432L-29.605 63.432L-29.605 63.135Q-29.336 63.135-29.168 63.090Q-29 63.045-29 62.873L-29 60.737Q-29 60.522-29.062 60.426Q-29.125 60.330-29.242 60.309Q-29.359 60.287-29.605 60.287L-29.605 59.991L-28.437 59.905L-28.437 60.690Q-28.359 60.479-28.207 60.293Q-28.054 60.108-27.855 60.006Q-27.656 59.905-27.429 59.905Q-27.183 59.905-26.992 60.049Q-26.800 60.194-26.800 60.424Q-26.800 60.580-26.906 60.690Q-27.011 60.799-27.168 60.799Q-27.324 60.799-27.433 60.690Q-27.543 60.580-27.543 60.424Q-27.543 60.264-27.437 60.159Q-27.761 60.159-27.976 60.387Q-28.191 60.616-28.287 60.955Q-28.382 61.295-28.382 61.600L-28.382 62.873Q-28.382 63.041-28.156 63.088Q-27.929 63.135-27.625 63.135L-27.625 63.432M-24.312 63.432L-26.293 63.432L-26.293 63.135Q-26.023 63.135-25.855 63.090Q-25.687 63.045-25.687 62.873L-25.687 60.737Q-25.687 60.522-25.750 60.426Q-25.812 60.330-25.929 60.309Q-26.046 60.287-26.293 60.287L-26.293 59.991L-25.125 59.905L-25.125 60.690Q-25.046 60.479-24.894 60.293Q-24.742 60.108-24.543 60.006Q-24.343 59.905-24.117 59.905Q-23.871 59.905-23.679 60.049Q-23.488 60.194-23.488 60.424Q-23.488 60.580-23.593 60.690Q-23.699 60.799-23.855 60.799Q-24.011 60.799-24.121 60.690Q-24.230 60.580-24.230 60.424Q-24.230 60.264-24.125 60.159Q-24.449 60.159-24.664 60.387Q-24.878 60.616-24.974 60.955Q-25.070 61.295-25.070 61.600L-25.070 62.873Q-25.070 63.041-24.843 63.088Q-24.617 63.135-24.312 63.135L-24.312 63.432M-23.007 61.737Q-23.007 61.233-22.752 60.801Q-22.496 60.370-22.060 60.118Q-21.625 59.866-21.125 59.866Q-20.738 59.866-20.396 60.010Q-20.054 60.155-19.793 60.416Q-19.531 60.678-19.388 61.014Q-19.246 61.350-19.246 61.737Q-19.246 62.229-19.509 62.639Q-19.773 63.049-20.203 63.280Q-20.632 63.510-21.125 63.510Q-21.617 63.510-22.050 63.278Q-22.484 63.045-22.746 62.637Q-23.007 62.229-23.007 61.737M-21.125 63.233Q-20.668 63.233-20.416 63.010Q-20.164 62.787-20.076 62.436Q-19.988 62.084-19.988 61.639Q-19.988 61.209-20.082 60.871Q-20.175 60.534-20.429 60.327Q-20.683 60.120-21.125 60.120Q-21.773 60.120-22.017 60.536Q-22.261 60.952-22.261 61.639Q-22.261 62.084-22.173 62.436Q-22.086 62.787-21.834 63.010Q-21.582 63.233-21.125 63.233M-16.753 63.432L-18.734 63.432L-18.734 63.135Q-18.464 63.135-18.296 63.090Q-18.128 63.045-18.128 62.873L-18.128 60.737Q-18.128 60.522-18.191 60.426Q-18.253 60.330-18.371 60.309Q-18.488 60.287-18.734 60.287L-18.734 59.991L-17.566 59.905L-17.566 60.690Q-17.488 60.479-17.336 60.293Q-17.183 60.108-16.984 60.006Q-16.785 59.905-16.558 59.905Q-16.312 59.905-16.121 60.049Q-15.929 60.194-15.929 60.424Q-15.929 60.580-16.035 60.690Q-16.140 60.799-16.296 60.799Q-16.453 60.799-16.562 60.690Q-16.671 60.580-16.671 60.424Q-16.671 60.264-16.566 60.159Q-16.890 60.159-17.105 60.387Q-17.320 60.616-17.416 60.955Q-17.511 61.295-17.511 61.600L-17.511 62.873Q-17.511 63.041-17.285 63.088Q-17.058 63.135-16.753 63.135\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(-21.956 -112.255)\">\u003Cpath d=\"M-33.578 63.194L-33.578 63.104Q-33.535 62.897-33.328 62.873L-32.906 62.873L-32.906 59.104L-33.328 59.104Q-33.535 59.080-33.578 58.866L-33.578 58.776Q-33.535 58.569-33.328 58.545L-32.511 58.545Q-32.316 58.569-32.265 58.776L-32.265 60.327Q-31.804 59.944-31.207 59.944Q-30.859 59.944-30.621 60.084Q-30.382 60.225-30.267 60.483Q-30.152 60.741-30.152 61.096L-30.152 62.873L-29.726 62.873Q-29.519 62.897-29.480 63.104L-29.480 63.194Q-29.519 63.409-29.726 63.432L-31.121 63.432Q-31.316 63.409-31.367 63.194L-31.367 63.104Q-31.316 62.893-31.121 62.873L-30.793 62.873L-30.793 61.127Q-30.793 60.819-30.882 60.661Q-30.972 60.502-31.265 60.502Q-31.535 60.502-31.763 60.633Q-31.992 60.764-32.128 60.993Q-32.265 61.221-32.265 61.487L-32.265 62.873L-31.839 62.873Q-31.632 62.897-31.593 63.104L-31.593 63.194Q-31.632 63.409-31.839 63.432L-33.328 63.432Q-33.535 63.409-33.578 63.194M-28.785 63.194L-28.785 63.104Q-28.734 62.897-28.539 62.873L-27.500 62.873L-27.500 60.545L-28.472 60.545Q-28.671 60.522-28.722 60.303L-28.722 60.217Q-28.671 60.006-28.472 59.983L-27.105 59.983Q-26.910 60.002-26.859 60.217L-26.859 62.873L-25.945 62.873Q-25.750 62.897-25.699 63.104L-25.699 63.194Q-25.750 63.409-25.945 63.432L-28.539 63.432Q-28.734 63.409-28.785 63.194M-27.753 59.006L-27.753 58.952Q-27.753 58.780-27.617 58.659Q-27.480 58.537-27.304 58.537Q-27.132 58.537-26.996 58.659Q-26.859 58.780-26.859 58.952L-26.859 59.006Q-26.859 59.182-26.996 59.303Q-27.132 59.424-27.304 59.424Q-27.480 59.424-27.617 59.303Q-27.753 59.182-27.753 59.006M-24.918 64.080Q-24.918 63.780-24.769 63.518Q-24.621 63.256-24.371 63.096Q-24.554 62.842-24.554 62.522Q-24.554 62.237-24.402 61.975Q-24.652 61.651-24.652 61.233Q-24.652 60.873-24.461 60.577Q-24.269 60.280-23.951 60.112Q-23.632 59.944-23.269 59.944Q-23.062 59.944-22.859 60.004Q-22.656 60.065-22.492 60.166Q-22.109 59.905-21.636 59.905Q-21.398 59.905-21.216 60.036Q-21.035 60.166-21.035 60.393Q-21.035 60.541-21.136 60.647Q-21.238 60.752-21.394 60.752Q-21.527 60.752-21.623 60.674Q-21.718 60.596-21.750 60.471Q-21.894 60.479-22.093 60.569Q-21.890 60.881-21.890 61.233Q-21.890 61.514-22.002 61.746Q-22.113 61.979-22.308 62.157Q-22.503 62.334-22.755 62.432Q-23.007 62.530-23.269 62.530Q-23.656 62.530-23.988 62.342Q-24 62.342-24.009 62.414Q-24.019 62.487-24.019 62.522Q-24.019 62.643-23.953 62.750Q-23.886 62.858-23.765 62.897Q-23.750 62.893-23.736 62.891Q-23.722 62.889-23.699 62.889Q-23.683 62.889-23.652 62.897Q-23.621 62.905-23.613 62.905L-23.019 62.905Q-22.238 62.905-21.697 63.147Q-21.156 63.389-21.156 64.080Q-21.156 64.381-21.336 64.608Q-21.515 64.834-21.812 64.979Q-22.109 65.123-22.429 65.190Q-22.750 65.256-23.035 65.256Q-23.425 65.256-23.867 65.133Q-24.308 65.010-24.613 64.743Q-24.918 64.475-24.918 64.080M-24.378 64.073Q-24.378 64.291-24.138 64.432Q-23.898 64.573-23.574 64.639Q-23.250 64.705-23.035 64.705Q-22.820 64.705-22.496 64.639Q-22.171 64.573-21.931 64.432Q-21.691 64.291-21.691 64.073Q-21.691 63.784-21.916 63.645Q-22.140 63.506-22.421 63.473Q-22.703 63.440-23.050 63.440L-23.668 63.440Q-23.851 63.440-24.013 63.520Q-24.175 63.600-24.277 63.746Q-24.378 63.893-24.378 64.073M-23.269 61.975Q-22.968 61.975-22.750 61.756Q-22.531 61.537-22.531 61.233Q-22.531 61.077-22.586 60.946Q-22.640 60.815-22.746 60.709Q-22.851 60.604-22.982 60.549Q-23.113 60.495-23.269 60.495Q-23.574 60.495-23.793 60.713Q-24.011 60.932-24.011 61.233Q-24.011 61.530-23.789 61.752Q-23.566 61.975-23.269 61.975M-20.839 63.194L-20.839 63.104Q-20.796 62.897-20.589 62.873L-20.168 62.873L-20.168 59.104L-20.589 59.104Q-20.796 59.080-20.839 58.866L-20.839 58.776Q-20.796 58.569-20.589 58.545L-19.773 58.545Q-19.578 58.569-19.527 58.776L-19.527 60.327Q-19.066 59.944-18.468 59.944Q-18.121 59.944-17.882 60.084Q-17.644 60.225-17.529 60.483Q-17.414 60.741-17.414 61.096L-17.414 62.873L-16.988 62.873Q-16.781 62.897-16.742 63.104L-16.742 63.194Q-16.781 63.409-16.988 63.432L-18.382 63.432Q-18.578 63.409-18.628 63.194L-18.628 63.104Q-18.578 62.893-18.382 62.873L-18.054 62.873L-18.054 61.127Q-18.054 60.819-18.144 60.661Q-18.234 60.502-18.527 60.502Q-18.796 60.502-19.025 60.633Q-19.253 60.764-19.390 60.993Q-19.527 61.221-19.527 61.487L-19.527 62.873L-19.101 62.873Q-18.894 62.897-18.855 63.104L-18.855 63.194Q-18.894 63.409-19.101 63.432L-20.589 63.432Q-20.796 63.409-20.839 63.194\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-8.942 2.256)\">\u003Cpath d=\"M-31.659 63.572Q-32.294 63.572-32.658 63.227Q-33.023 62.882-33.158 62.357Q-33.293 61.832-33.293 61.207Q-33.293 60.182-32.937 59.483Q-32.582 58.784-31.659 58.784Q-30.732 58.784-30.380 59.483Q-30.028 60.182-30.028 61.207Q-30.028 61.832-30.163 62.357Q-30.298 62.882-30.661 63.227Q-31.023 63.572-31.659 63.572M-31.659 63.347Q-31.221 63.347-31.008 62.972Q-30.794 62.598-30.744 62.131Q-30.695 61.665-30.695 61.087Q-30.695 60.534-30.744 60.106Q-30.794 59.679-31.006 59.344Q-31.218 59.009-31.659 59.009Q-32.001 59.009-32.204 59.216Q-32.407 59.423-32.494 59.735Q-32.582 60.048-32.604 60.364Q-32.626 60.681-32.626 61.087Q-32.626 61.504-32.604 61.846Q-32.582 62.188-32.493 62.536Q-32.404 62.885-32.199 63.116Q-31.994 63.347-31.659 63.347\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.210\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-good)\" d=\"m-27.957-27.923 3.948 12.933 3.948 11.099 3.948 9.54 3.948 8.173 3.949 7.025 3.948 6.033 3.948 5.168L3.628 36.5l3.948 3.814 3.949 3.266 3.948 2.812 3.948 2.414 3.948 2.072 3.948 1.776 3.949 1.525 3.948 1.321 3.948 1.115 3.948.968 3.948.831 3.949.706 3.948.614 3.948.525 3.948.455 3.948.387 3.949.33 3.948.285 3.948.238 3.948.217 3.948.172 3.949.154 3.948.132 3.948.115 3.948.097 3.948.083 3.949.073 3.948.063 3.948.052 3.948.045 3.948.04 3.949.033 3.948.03 3.948.024 3.948.02 3.948.02 3.949.015 3.948.014 3.948.01 3.948.011 3.948.009\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\">\u003Cg fill=\"var(--tk-good)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(51.903 -7.514)\">\u003Cpath d=\"M-32.449 62.327L-32.449 60.545L-33.199 60.545Q-33.398 60.522-33.449 60.303L-33.449 60.217Q-33.398 60.006-33.199 59.983L-32.449 59.983L-32.449 59.233Q-32.398 59.026-32.199 58.998L-32.054 58.998Q-31.859 59.026-31.808 59.233L-31.808 59.983L-30.449 59.983Q-30.257 60.002-30.199 60.217L-30.199 60.303Q-30.253 60.522-30.449 60.545L-31.808 60.545L-31.808 62.295Q-31.808 62.912-31.234 62.912Q-30.984 62.912-30.820 62.727Q-30.656 62.541-30.656 62.295Q-30.656 62.202-30.584 62.131Q-30.511 62.061-30.410 62.049L-30.265 62.049Q-30.066 62.073-30.015 62.280L-30.015 62.327Q-30.015 62.651-30.199 62.914Q-30.382 63.178-30.675 63.325Q-30.968 63.471-31.289 63.471Q-31.800 63.471-32.125 63.161Q-32.449 62.850-32.449 62.327M-29.179 63.194L-29.179 63.104Q-29.121 62.897-28.929 62.873L-28.218 62.873L-28.218 60.545L-28.929 60.545Q-29.125 60.522-29.179 60.303L-29.179 60.217Q-29.121 60.006-28.929 59.983L-27.828 59.983Q-27.628 60.002-27.578 60.217L-27.578 60.545Q-27.316 60.260-26.961 60.102Q-26.605 59.944-26.218 59.944Q-25.925 59.944-25.691 60.078Q-25.457 60.213-25.457 60.479Q-25.457 60.647-25.566 60.764Q-25.675 60.881-25.843 60.881Q-25.996 60.881-26.111 60.770Q-26.226 60.659-26.226 60.502Q-26.601 60.502-26.916 60.703Q-27.230 60.905-27.404 61.239Q-27.578 61.573-27.578 61.952L-27.578 62.873L-26.632 62.873Q-26.425 62.897-26.386 63.104L-26.386 63.194Q-26.425 63.409-26.632 63.432L-28.929 63.432Q-29.121 63.409-29.179 63.194M-24.750 62.319Q-24.750 61.873-24.336 61.616Q-23.921 61.358-23.380 61.258Q-22.839 61.159-22.332 61.151Q-22.332 60.936-22.466 60.784Q-22.601 60.631-22.808 60.555Q-23.015 60.479-23.226 60.479Q-23.570 60.479-23.730 60.502L-23.730 60.561Q-23.730 60.729-23.849 60.844Q-23.968 60.959-24.132 60.959Q-24.308 60.959-24.423 60.836Q-24.539 60.713-24.539 60.545Q-24.539 60.139-24.158 60.030Q-23.777 59.920-23.218 59.920Q-22.949 59.920-22.681 59.998Q-22.414 60.077-22.189 60.227Q-21.964 60.377-21.828 60.598Q-21.691 60.819-21.691 61.096L-21.691 62.815Q-21.691 62.873-21.164 62.873Q-20.968 62.893-20.918 63.104L-20.918 63.194Q-20.968 63.409-21.164 63.432L-21.308 63.432Q-21.652 63.432-21.880 63.385Q-22.109 63.338-22.253 63.151Q-22.714 63.471-23.421 63.471Q-23.757 63.471-24.062 63.330Q-24.367 63.190-24.558 62.928Q-24.750 62.666-24.750 62.319M-24.109 62.327Q-24.109 62.600-23.867 62.756Q-23.625 62.912-23.339 62.912Q-23.121 62.912-22.888 62.854Q-22.656 62.795-22.494 62.657Q-22.332 62.518-22.332 62.295L-22.332 61.705Q-22.613 61.705-23.029 61.762Q-23.445 61.819-23.777 61.957Q-24.109 62.096-24.109 62.327M-20.293 63.194L-20.293 63.104Q-20.242 62.897-20.046 62.873L-19.007 62.873L-19.007 60.545L-19.980 60.545Q-20.179 60.522-20.230 60.303L-20.230 60.217Q-20.179 60.006-19.980 59.983L-18.613 59.983Q-18.418 60.002-18.367 60.217L-18.367 62.873L-17.453 62.873Q-17.257 62.897-17.207 63.104L-17.207 63.194Q-17.257 63.409-17.453 63.432L-20.046 63.432Q-20.242 63.409-20.293 63.194M-19.261 59.006L-19.261 58.952Q-19.261 58.780-19.125 58.659Q-18.988 58.537-18.812 58.537Q-18.640 58.537-18.503 58.659Q-18.367 58.780-18.367 58.952L-18.367 59.006Q-18.367 59.182-18.503 59.303Q-18.640 59.424-18.812 59.424Q-18.988 59.424-19.125 59.303Q-19.261 59.182-19.261 59.006M-16.593 63.194L-16.593 63.104Q-16.550 62.897-16.343 62.873L-15.921 62.873L-15.921 60.545L-16.343 60.545Q-16.550 60.522-16.593 60.303L-16.593 60.217Q-16.546 60.006-16.343 59.983L-15.527 59.983Q-15.332 60.006-15.281 60.217L-15.281 60.303L-15.289 60.327Q-15.062 60.147-14.789 60.045Q-14.515 59.944-14.222 59.944Q-13.875 59.944-13.636 60.084Q-13.398 60.225-13.283 60.483Q-13.168 60.741-13.168 61.096L-13.168 62.873L-12.742 62.873Q-12.535 62.897-12.496 63.104L-12.496 63.194Q-12.535 63.409-12.742 63.432L-14.136 63.432Q-14.332 63.409-14.382 63.194L-14.382 63.104Q-14.332 62.893-14.136 62.873L-13.808 62.873L-13.808 61.127Q-13.808 60.819-13.898 60.661Q-13.988 60.502-14.281 60.502Q-14.550 60.502-14.779 60.633Q-15.007 60.764-15.144 60.993Q-15.281 61.221-15.281 61.487L-15.281 62.873L-14.855 62.873Q-14.648 62.897-14.609 63.104L-14.609 63.194Q-14.648 63.409-14.855 63.432L-16.343 63.432Q-16.550 63.409-16.593 63.194\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(51.903 -7.514)\">\u003Cpath d=\"M-5.034 64.073Q-5.565 63.764-5.965 63.276Q-6.366 62.787-6.577 62.194Q-6.788 61.600-6.788 60.983Q-6.788 60.366-6.579 59.778Q-6.370 59.190-5.973 58.709Q-5.577 58.229-5.042 57.905Q-4.971 57.881-4.940 57.881L-4.850 57.881Q-4.752 57.893-4.686 57.959Q-4.620 58.026-4.620 58.127Q-4.620 58.272-4.721 58.334Q-5.170 58.623-5.497 59.039Q-5.823 59.455-5.985 59.948Q-6.147 60.440-6.147 60.983Q-6.147 61.389-6.051 61.776Q-5.956 62.162-5.778 62.498Q-5.600 62.834-5.340 63.118Q-5.081 63.401-4.733 63.631Q-4.620 63.709-4.620 63.846Q-4.620 63.944-4.686 64.014Q-4.752 64.084-4.850 64.096L-4.940 64.096Q-4.999 64.096-5.034 64.073M-3.678 63.194L-3.678 63.104Q-3.620 62.897-3.428 62.873L-2.717 62.873L-2.717 60.545L-3.428 60.545Q-3.624 60.522-3.678 60.303L-3.678 60.217Q-3.620 60.006-3.428 59.983L-2.327 59.983Q-2.127 60.002-2.077 60.217L-2.077 60.545Q-1.815 60.260-1.460 60.102Q-1.104 59.944-0.717 59.944Q-0.424 59.944-0.190 60.078Q0.044 60.213 0.044 60.479Q0.044 60.647-0.065 60.764Q-0.174 60.881-0.342 60.881Q-0.495 60.881-0.610 60.770Q-0.725 60.659-0.725 60.502Q-1.100 60.502-1.415 60.703Q-1.729 60.905-1.903 61.239Q-2.077 61.573-2.077 61.952L-2.077 62.873L-1.131 62.873Q-0.924 62.897-0.885 63.104L-0.885 63.194Q-0.924 63.409-1.131 63.432L-3.428 63.432Q-3.620 63.409-3.678 63.194M3.857 61.944L1.415 61.944Q1.470 62.221 1.667 62.444Q1.865 62.666 2.142 62.789Q2.419 62.912 2.705 62.912Q3.177 62.912 3.400 62.623Q3.408 62.612 3.464 62.506Q3.521 62.401 3.570 62.358Q3.619 62.315 3.712 62.303L3.857 62.303Q4.048 62.323 4.107 62.537L4.107 62.592Q4.040 62.893 3.810 63.090Q3.580 63.287 3.267 63.379Q2.955 63.471 2.650 63.471Q2.165 63.471 1.726 63.243Q1.287 63.014 1.019 62.614Q0.751 62.213 0.751 61.721L0.751 61.662Q0.751 61.194 0.998 60.791Q1.244 60.389 1.652 60.155Q2.060 59.920 2.529 59.920Q3.033 59.920 3.386 60.143Q3.740 60.366 3.923 60.754Q4.107 61.143 4.107 61.647L4.107 61.705Q4.048 61.920 3.857 61.944M1.423 61.393L3.451 61.393Q3.404 60.983 3.165 60.731Q2.927 60.479 2.529 60.479Q2.134 60.479 1.828 60.741Q1.521 61.002 1.423 61.393M4.998 62.319Q4.998 61.873 5.412 61.616Q5.826 61.358 6.367 61.258Q6.908 61.159 7.415 61.151Q7.415 60.936 7.281 60.784Q7.146 60.631 6.939 60.555Q6.732 60.479 6.521 60.479Q6.177 60.479 6.017 60.502L6.017 60.561Q6.017 60.729 5.898 60.844Q5.779 60.959 5.615 60.959Q5.439 60.959 5.324 60.836Q5.208 60.713 5.208 60.545Q5.208 60.139 5.589 60.030Q5.970 59.920 6.529 59.920Q6.798 59.920 7.066 59.998Q7.333 60.077 7.558 60.227Q7.783 60.377 7.919 60.598Q8.056 60.819 8.056 61.096L8.056 62.815Q8.056 62.873 8.583 62.873Q8.779 62.893 8.830 63.104L8.830 63.194Q8.779 63.409 8.583 63.432L8.439 63.432Q8.095 63.432 7.867 63.385Q7.638 63.338 7.494 63.151Q7.033 63.471 6.326 63.471Q5.990 63.471 5.685 63.330Q5.380 63.190 5.189 62.928Q4.998 62.666 4.998 62.319M5.638 62.327Q5.638 62.600 5.880 62.756Q6.123 62.912 6.408 62.912Q6.626 62.912 6.859 62.854Q7.091 62.795 7.253 62.657Q7.415 62.518 7.415 62.295L7.415 61.705Q7.134 61.705 6.718 61.762Q6.302 61.819 5.970 61.957Q5.638 62.096 5.638 62.327M9.287 63.194L9.287 63.104Q9.337 62.897 9.533 62.873L10.638 62.873L10.638 59.104L9.533 59.104Q9.337 59.080 9.287 58.866L9.287 58.776Q9.337 58.569 9.533 58.545L11.029 58.545Q11.220 58.569 11.279 58.776L11.279 62.873L12.380 62.873Q12.580 62.897 12.630 63.104L12.630 63.194Q12.580 63.409 12.380 63.432L9.533 63.432Q9.337 63.409 9.287 63.194M14.123 64.096L14.037 64.096Q13.931 64.084 13.863 64.012Q13.794 63.940 13.794 63.846Q13.794 63.705 13.900 63.639Q14.236 63.424 14.505 63.135Q14.775 62.846 14.958 62.498Q15.142 62.151 15.232 61.772Q15.322 61.393 15.322 60.983Q15.322 60.444 15.158 59.952Q14.994 59.459 14.675 59.045Q14.357 58.631 13.915 58.342Q13.794 58.260 13.794 58.127Q13.794 58.030 13.863 57.961Q13.931 57.893 14.037 57.881L14.123 57.881Q14.177 57.881 14.212 57.905Q14.599 58.127 14.939 58.475Q15.279 58.823 15.499 59.217Q15.720 59.612 15.841 60.057Q15.962 60.502 15.962 60.983Q15.962 61.467 15.841 61.918Q15.720 62.370 15.496 62.766Q15.271 63.162 14.947 63.496Q14.623 63.830 14.220 64.073Q14.150 64.096 14.123 64.096\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-good)\" d=\"m-27.957-38.544 3.948 7.49 3.948 6.945 3.948 6.433 3.948 5.965 3.949 5.52 3.948 5.111L-.32 3.668l3.948 4.384 3.948 4.074 3.949 3.768 3.948 3.494 3.948 3.234 3.948 3.006 3.948 2.777 3.949 2.573 3.948 2.39 3.948 2.209 3.948 2.05 3.948 1.889 3.949 1.764 3.948 1.627 3.948 1.504 3.948 1.4 3.948 1.297 3.949 1.197 3.948 1.115 3.948 1.024 3.948.957 3.948.887 3.949.82 3.948.762 3.948.695 3.948.66 3.948.604 3.949.558 3.948.512 3.948.49 3.948.443 3.948.41 3.949.376 3.948.363 3.948.32 3.948.307 3.948.285 3.949.262 3.948.238 3.948.228 3.948.205 3.948.194\" style=\"stroke-dasharray:3.0,3.0;stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\">\u003Cg fill=\"var(--tk-good)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(76.088 -50.193)\">\u003Cpath d=\"M-32.449 62.327L-32.449 60.545L-33.199 60.545Q-33.398 60.522-33.449 60.303L-33.449 60.217Q-33.398 60.006-33.199 59.983L-32.449 59.983L-32.449 59.233Q-32.398 59.026-32.199 58.998L-32.054 58.998Q-31.859 59.026-31.808 59.233L-31.808 59.983L-30.449 59.983Q-30.257 60.002-30.199 60.217L-30.199 60.303Q-30.253 60.522-30.449 60.545L-31.808 60.545L-31.808 62.295Q-31.808 62.912-31.234 62.912Q-30.984 62.912-30.820 62.727Q-30.656 62.541-30.656 62.295Q-30.656 62.202-30.584 62.131Q-30.511 62.061-30.410 62.049L-30.265 62.049Q-30.066 62.073-30.015 62.280L-30.015 62.327Q-30.015 62.651-30.199 62.914Q-30.382 63.178-30.675 63.325Q-30.968 63.471-31.289 63.471Q-31.800 63.471-32.125 63.161Q-32.449 62.850-32.449 62.327M-29.179 63.194L-29.179 63.104Q-29.121 62.897-28.929 62.873L-28.218 62.873L-28.218 60.545L-28.929 60.545Q-29.125 60.522-29.179 60.303L-29.179 60.217Q-29.121 60.006-28.929 59.983L-27.828 59.983Q-27.628 60.002-27.578 60.217L-27.578 60.545Q-27.316 60.260-26.961 60.102Q-26.605 59.944-26.218 59.944Q-25.925 59.944-25.691 60.078Q-25.457 60.213-25.457 60.479Q-25.457 60.647-25.566 60.764Q-25.675 60.881-25.843 60.881Q-25.996 60.881-26.111 60.770Q-26.226 60.659-26.226 60.502Q-26.601 60.502-26.916 60.703Q-27.230 60.905-27.404 61.239Q-27.578 61.573-27.578 61.952L-27.578 62.873L-26.632 62.873Q-26.425 62.897-26.386 63.104L-26.386 63.194Q-26.425 63.409-26.632 63.432L-28.929 63.432Q-29.121 63.409-29.179 63.194M-24.750 62.319Q-24.750 61.873-24.336 61.616Q-23.921 61.358-23.380 61.258Q-22.839 61.159-22.332 61.151Q-22.332 60.936-22.466 60.784Q-22.601 60.631-22.808 60.555Q-23.015 60.479-23.226 60.479Q-23.570 60.479-23.730 60.502L-23.730 60.561Q-23.730 60.729-23.849 60.844Q-23.968 60.959-24.132 60.959Q-24.308 60.959-24.423 60.836Q-24.539 60.713-24.539 60.545Q-24.539 60.139-24.158 60.030Q-23.777 59.920-23.218 59.920Q-22.949 59.920-22.681 59.998Q-22.414 60.077-22.189 60.227Q-21.964 60.377-21.828 60.598Q-21.691 60.819-21.691 61.096L-21.691 62.815Q-21.691 62.873-21.164 62.873Q-20.968 62.893-20.918 63.104L-20.918 63.194Q-20.968 63.409-21.164 63.432L-21.308 63.432Q-21.652 63.432-21.880 63.385Q-22.109 63.338-22.253 63.151Q-22.714 63.471-23.421 63.471Q-23.757 63.471-24.062 63.330Q-24.367 63.190-24.558 62.928Q-24.750 62.666-24.750 62.319M-24.109 62.327Q-24.109 62.600-23.867 62.756Q-23.625 62.912-23.339 62.912Q-23.121 62.912-22.888 62.854Q-22.656 62.795-22.494 62.657Q-22.332 62.518-22.332 62.295L-22.332 61.705Q-22.613 61.705-23.029 61.762Q-23.445 61.819-23.777 61.957Q-24.109 62.096-24.109 62.327M-20.293 63.194L-20.293 63.104Q-20.242 62.897-20.046 62.873L-19.007 62.873L-19.007 60.545L-19.980 60.545Q-20.179 60.522-20.230 60.303L-20.230 60.217Q-20.179 60.006-19.980 59.983L-18.613 59.983Q-18.418 60.002-18.367 60.217L-18.367 62.873L-17.453 62.873Q-17.257 62.897-17.207 63.104L-17.207 63.194Q-17.257 63.409-17.453 63.432L-20.046 63.432Q-20.242 63.409-20.293 63.194M-19.261 59.006L-19.261 58.952Q-19.261 58.780-19.125 58.659Q-18.988 58.537-18.812 58.537Q-18.640 58.537-18.503 58.659Q-18.367 58.780-18.367 58.952L-18.367 59.006Q-18.367 59.182-18.503 59.303Q-18.640 59.424-18.812 59.424Q-18.988 59.424-19.125 59.303Q-19.261 59.182-19.261 59.006M-16.593 63.194L-16.593 63.104Q-16.550 62.897-16.343 62.873L-15.921 62.873L-15.921 60.545L-16.343 60.545Q-16.550 60.522-16.593 60.303L-16.593 60.217Q-16.546 60.006-16.343 59.983L-15.527 59.983Q-15.332 60.006-15.281 60.217L-15.281 60.303L-15.289 60.327Q-15.062 60.147-14.789 60.045Q-14.515 59.944-14.222 59.944Q-13.875 59.944-13.636 60.084Q-13.398 60.225-13.283 60.483Q-13.168 60.741-13.168 61.096L-13.168 62.873L-12.742 62.873Q-12.535 62.897-12.496 63.104L-12.496 63.194Q-12.535 63.409-12.742 63.432L-14.136 63.432Q-14.332 63.409-14.382 63.194L-14.382 63.104Q-14.332 62.893-14.136 62.873L-13.808 62.873L-13.808 61.127Q-13.808 60.819-13.898 60.661Q-13.988 60.502-14.281 60.502Q-14.550 60.502-14.779 60.633Q-15.007 60.764-15.144 60.993Q-15.281 61.221-15.281 61.487L-15.281 62.873L-14.855 62.873Q-14.648 62.897-14.609 63.104L-14.609 63.194Q-14.648 63.409-14.855 63.432L-16.343 63.432Q-16.550 63.409-16.593 63.194\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(76.088 -50.193)\">\u003Cpath d=\"M-5.034 64.073Q-5.565 63.764-5.965 63.276Q-6.366 62.787-6.577 62.194Q-6.788 61.600-6.788 60.983Q-6.788 60.366-6.579 59.778Q-6.370 59.190-5.973 58.709Q-5.577 58.229-5.042 57.905Q-4.971 57.881-4.940 57.881L-4.850 57.881Q-4.752 57.893-4.686 57.959Q-4.620 58.026-4.620 58.127Q-4.620 58.272-4.721 58.334Q-5.170 58.623-5.497 59.039Q-5.823 59.455-5.985 59.948Q-6.147 60.440-6.147 60.983Q-6.147 61.389-6.051 61.776Q-5.956 62.162-5.778 62.498Q-5.600 62.834-5.340 63.118Q-5.081 63.401-4.733 63.631Q-4.620 63.709-4.620 63.846Q-4.620 63.944-4.686 64.014Q-4.752 64.084-4.850 64.096L-4.940 64.096Q-4.999 64.096-5.034 64.073M-3.678 63.194L-3.678 63.104Q-3.620 62.897-3.428 62.873L-2.717 62.873L-2.717 60.545L-3.428 60.545Q-3.624 60.522-3.678 60.303L-3.678 60.217Q-3.620 60.006-3.428 59.983L-2.327 59.983Q-2.127 60.002-2.077 60.217L-2.077 60.545Q-1.815 60.260-1.460 60.102Q-1.104 59.944-0.717 59.944Q-0.424 59.944-0.190 60.078Q0.044 60.213 0.044 60.479Q0.044 60.647-0.065 60.764Q-0.174 60.881-0.342 60.881Q-0.495 60.881-0.610 60.770Q-0.725 60.659-0.725 60.502Q-1.100 60.502-1.415 60.703Q-1.729 60.905-1.903 61.239Q-2.077 61.573-2.077 61.952L-2.077 62.873L-1.131 62.873Q-0.924 62.897-0.885 63.104L-0.885 63.194Q-0.924 63.409-1.131 63.432L-3.428 63.432Q-3.620 63.409-3.678 63.194M0.751 62.319Q0.751 61.873 1.165 61.616Q1.580 61.358 2.121 61.258Q2.662 61.159 3.169 61.151Q3.169 60.936 3.035 60.784Q2.900 60.631 2.693 60.555Q2.486 60.479 2.275 60.479Q1.931 60.479 1.771 60.502L1.771 60.561Q1.771 60.729 1.652 60.844Q1.533 60.959 1.369 60.959Q1.193 60.959 1.078 60.836Q0.962 60.713 0.962 60.545Q0.962 60.139 1.343 60.030Q1.724 59.920 2.283 59.920Q2.552 59.920 2.820 59.998Q3.087 60.077 3.312 60.227Q3.537 60.377 3.673 60.598Q3.810 60.819 3.810 61.096L3.810 62.815Q3.810 62.873 4.337 62.873Q4.533 62.893 4.583 63.104L4.583 63.194Q4.533 63.409 4.337 63.432L4.193 63.432Q3.849 63.432 3.621 63.385Q3.392 63.338 3.248 63.151Q2.787 63.471 2.080 63.471Q1.744 63.471 1.439 63.330Q1.134 63.190 0.943 62.928Q0.751 62.666 0.751 62.319M1.392 62.327Q1.392 62.600 1.634 62.756Q1.876 62.912 2.162 62.912Q2.380 62.912 2.613 62.854Q2.845 62.795 3.007 62.657Q3.169 62.518 3.169 62.295L3.169 61.705Q2.888 61.705 2.472 61.762Q2.056 61.819 1.724 61.957Q1.392 62.096 1.392 62.327M4.662 63.194L4.662 63.104Q4.705 62.897 4.912 62.873L5.333 62.873L5.333 60.545L4.912 60.545Q4.705 60.522 4.662 60.303L4.662 60.217Q4.708 60.006 4.912 59.983L5.728 59.983Q5.923 60.006 5.974 60.217L5.974 60.303L5.966 60.327Q6.193 60.147 6.466 60.045Q6.740 59.944 7.033 59.944Q7.380 59.944 7.619 60.084Q7.857 60.225 7.972 60.483Q8.087 60.741 8.087 61.096L8.087 62.873L8.513 62.873Q8.720 62.897 8.759 63.104L8.759 63.194Q8.720 63.409 8.513 63.432L7.119 63.432Q6.923 63.409 6.873 63.194L6.873 63.104Q6.923 62.893 7.119 62.873L7.447 62.873L7.447 61.127Q7.447 60.819 7.357 60.661Q7.267 60.502 6.974 60.502Q6.705 60.502 6.476 60.633Q6.248 60.764 6.111 60.993Q5.974 61.221 5.974 61.487L5.974 62.873L6.400 62.873Q6.607 62.897 6.646 63.104L6.646 63.194Q6.607 63.409 6.400 63.432L4.912 63.432Q4.705 63.409 4.662 63.194M10.701 63.471Q10.236 63.471 9.871 63.221Q9.505 62.971 9.300 62.567Q9.095 62.162 9.095 61.705Q9.095 61.362 9.220 61.041Q9.345 60.721 9.578 60.471Q9.810 60.221 10.115 60.082Q10.419 59.944 10.775 59.944Q11.287 59.944 11.693 60.264L11.693 59.104L11.271 59.104Q11.060 59.080 11.021 58.866L11.021 58.776Q11.060 58.569 11.271 58.545L12.083 58.545Q12.283 58.569 12.333 58.776L12.333 62.873L12.759 62.873Q12.966 62.897 13.005 63.104L13.005 63.194Q12.966 63.409 12.759 63.432L11.943 63.432Q11.744 63.409 11.693 63.194L11.693 63.065Q11.498 63.256 11.236 63.364Q10.974 63.471 10.701 63.471M10.740 62.912Q11.099 62.912 11.351 62.643Q11.603 62.373 11.693 61.998L11.693 61.166Q11.634 60.979 11.507 60.828Q11.380 60.678 11.203 60.590Q11.025 60.502 10.830 60.502Q10.521 60.502 10.271 60.672Q10.021 60.842 9.876 61.127Q9.732 61.412 9.732 61.713Q9.732 62.162 10.017 62.537Q10.302 62.912 10.740 62.912M15.205 63.471Q14.732 63.471 14.347 63.227Q13.962 62.983 13.740 62.573Q13.517 62.162 13.517 61.705Q13.517 61.362 13.642 61.039Q13.767 60.717 13.998 60.463Q14.228 60.209 14.535 60.065Q14.841 59.920 15.205 59.920Q15.568 59.920 15.880 60.067Q16.193 60.213 16.416 60.459Q16.638 60.705 16.765 61.026Q16.892 61.346 16.892 61.705Q16.892 62.162 16.667 62.575Q16.443 62.987 16.058 63.229Q15.673 63.471 15.205 63.471M15.205 62.912Q15.669 62.912 15.960 62.518Q16.251 62.123 16.251 61.639Q16.251 61.346 16.117 61.078Q15.982 60.811 15.742 60.645Q15.501 60.479 15.205 60.479Q14.900 60.479 14.662 60.645Q14.423 60.811 14.289 61.078Q14.154 61.346 14.154 61.639Q14.154 62.120 14.447 62.516Q14.740 62.912 15.205 62.912M17.275 63.194L17.275 63.104Q17.326 62.893 17.521 62.873L17.720 62.873L17.720 60.545L17.521 60.545Q17.314 60.522 17.275 60.303L17.275 60.217Q17.326 60.002 17.521 59.983L18.001 59.983Q18.193 60.006 18.251 60.217Q18.572 59.944 18.986 59.944Q19.177 59.944 19.345 60.053Q19.513 60.162 19.587 60.334Q19.763 60.143 19.986 60.043Q20.208 59.944 20.451 59.944Q20.869 59.944 21.023 60.286Q21.177 60.627 21.177 61.096L21.177 62.873L21.376 62.873Q21.587 62.897 21.626 63.104L21.626 63.194Q21.576 63.409 21.376 63.432L20.560 63.432Q20.365 63.409 20.314 63.194L20.314 63.104Q20.365 62.897 20.560 62.873L20.650 62.873L20.650 61.127Q20.650 60.502 20.400 60.502Q20.068 60.502 19.890 60.813Q19.712 61.123 19.712 61.487L19.712 62.873L19.916 62.873Q20.123 62.897 20.162 63.104L20.162 63.194Q20.111 63.409 19.916 63.432L19.099 63.432Q18.900 63.409 18.849 63.194L18.849 63.104Q18.900 62.897 19.099 62.873L19.185 62.873L19.185 61.127Q19.185 60.502 18.939 60.502Q18.607 60.502 18.429 60.815Q18.251 61.127 18.251 61.487L18.251 62.873L18.451 62.873Q18.658 62.897 18.697 63.104L18.697 63.194Q18.646 63.412 18.451 63.432L17.521 63.432Q17.314 63.409 17.275 63.194M22.615 64.096L22.529 64.096Q22.423 64.084 22.355 64.012Q22.287 63.940 22.287 63.846Q22.287 63.705 22.392 63.639Q22.728 63.424 22.998 63.135Q23.267 62.846 23.451 62.498Q23.634 62.151 23.724 61.772Q23.814 61.393 23.814 60.983Q23.814 60.444 23.650 59.952Q23.486 59.459 23.167 59.045Q22.849 58.631 22.408 58.342Q22.287 58.260 22.287 58.127Q22.287 58.030 22.355 57.961Q22.423 57.893 22.529 57.881L22.615 57.881Q22.669 57.881 22.705 57.905Q23.091 58.127 23.431 58.475Q23.771 58.823 23.992 59.217Q24.212 59.612 24.333 60.057Q24.455 60.502 24.455 60.983Q24.455 61.467 24.333 61.918Q24.212 62.370 23.988 62.766Q23.763 63.162 23.439 63.496Q23.115 63.830 22.712 64.073Q22.642 64.096 22.615 64.096\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"m-27.957.545 3.948 4.832 3.948 4.203 3.948 3.661 3.948 3.183 3.949 2.773 3.948 2.413 3.948 2.104 3.948 1.826 3.948 1.594 3.949 1.384 3.948 1.207 3.948 1.051 3.948.911 3.948.797 3.949.692 3.948.601 3.948.528 3.948.456 3.948.396 3.949.346 3.948.3 3.948.265 3.948.227 3.948.2 3.949.17 3.948.154 3.948.127 3.948.114 3.948.101 3.949.086 3.948.077 3.948.062 3.948.057 3.948.05 3.949.042 3.948.038 3.948.033 3.948.028 3.948.025 3.949.022 3.948.019 3.948.016 3.948.014 3.948.012 3.949.01 3.948.01 3.948.01 3.948.006 3.948.006\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(148.642 -18.61)\">\u003Cpath d=\"M-32.449 62.327L-32.449 60.545L-33.199 60.545Q-33.398 60.522-33.449 60.303L-33.449 60.217Q-33.398 60.006-33.199 59.983L-32.449 59.983L-32.449 59.233Q-32.398 59.026-32.199 58.998L-32.054 58.998Q-31.859 59.026-31.808 59.233L-31.808 59.983L-30.449 59.983Q-30.257 60.002-30.199 60.217L-30.199 60.303Q-30.253 60.522-30.449 60.545L-31.808 60.545L-31.808 62.295Q-31.808 62.912-31.234 62.912Q-30.984 62.912-30.820 62.727Q-30.656 62.541-30.656 62.295Q-30.656 62.202-30.584 62.131Q-30.511 62.061-30.410 62.049L-30.265 62.049Q-30.066 62.073-30.015 62.280L-30.015 62.327Q-30.015 62.651-30.199 62.914Q-30.382 63.178-30.675 63.325Q-30.968 63.471-31.289 63.471Q-31.800 63.471-32.125 63.161Q-32.449 62.850-32.449 62.327M-25.890 61.944L-28.332 61.944Q-28.277 62.221-28.080 62.444Q-27.882 62.666-27.605 62.789Q-27.328 62.912-27.043 62.912Q-26.570 62.912-26.347 62.623Q-26.339 62.612-26.283 62.506Q-26.226 62.401-26.177 62.358Q-26.128 62.315-26.035 62.303L-25.890 62.303Q-25.699 62.323-25.640 62.537L-25.640 62.592Q-25.707 62.893-25.937 63.090Q-26.168 63.287-26.480 63.379Q-26.793 63.471-27.097 63.471Q-27.582 63.471-28.021 63.243Q-28.461 63.014-28.728 62.614Q-28.996 62.213-28.996 61.721L-28.996 61.662Q-28.996 61.194-28.750 60.791Q-28.503 60.389-28.095 60.155Q-27.687 59.920-27.218 59.920Q-26.714 59.920-26.361 60.143Q-26.007 60.366-25.824 60.754Q-25.640 61.143-25.640 61.647L-25.640 61.705Q-25.699 61.920-25.890 61.944M-28.324 61.393L-26.296 61.393Q-26.343 60.983-26.582 60.731Q-26.820 60.479-27.218 60.479Q-27.613 60.479-27.919 60.741Q-28.226 61.002-28.324 61.393M-24.570 63.233L-24.570 62.319Q-24.543 62.112-24.332 62.088L-24.164 62.088Q-24 62.112-23.941 62.272Q-23.738 62.912-23.011 62.912Q-22.804 62.912-22.576 62.877Q-22.347 62.842-22.179 62.727Q-22.011 62.612-22.011 62.409Q-22.011 62.198-22.234 62.084Q-22.457 61.971-22.730 61.928L-23.429 61.815Q-24.570 61.604-24.570 60.881Q-24.570 60.592-24.425 60.403Q-24.281 60.213-24.041 60.106Q-23.800 59.998-23.544 59.959Q-23.289 59.920-23.011 59.920Q-22.761 59.920-22.568 59.950Q-22.375 59.979-22.211 60.057Q-22.132 59.940-22.003 59.920L-21.925 59.920Q-21.828 59.932-21.765 59.995Q-21.703 60.057-21.691 60.151L-21.691 60.858Q-21.703 60.952-21.765 61.018Q-21.828 61.084-21.925 61.096L-22.093 61.096Q-22.187 61.084-22.253 61.018Q-22.320 60.952-22.332 60.858Q-22.332 60.479-23.027 60.479Q-23.375 60.479-23.693 60.561Q-24.011 60.643-24.011 60.889Q-24.011 61.155-23.339 61.264L-22.636 61.385Q-22.152 61.467-21.802 61.715Q-21.453 61.963-21.453 62.409Q-21.453 62.799-21.689 63.041Q-21.925 63.284-22.275 63.377Q-22.625 63.471-23.011 63.471Q-23.589 63.471-23.988 63.217Q-24.058 63.342-24.107 63.399Q-24.156 63.455-24.261 63.471L-24.332 63.471Q-24.546 63.448-24.570 63.233M-19.711 62.327L-19.711 60.545L-20.461 60.545Q-20.660 60.522-20.711 60.303L-20.711 60.217Q-20.660 60.006-20.461 59.983L-19.711 59.983L-19.711 59.233Q-19.660 59.026-19.461 58.998L-19.316 58.998Q-19.121 59.026-19.070 59.233L-19.070 59.983L-17.711 59.983Q-17.519 60.002-17.461 60.217L-17.461 60.303Q-17.515 60.522-17.711 60.545L-19.070 60.545L-19.070 62.295Q-19.070 62.912-18.496 62.912Q-18.246 62.912-18.082 62.727Q-17.918 62.541-17.918 62.295Q-17.918 62.202-17.845 62.131Q-17.773 62.061-17.671 62.049L-17.527 62.049Q-17.328 62.073-17.277 62.280L-17.277 62.327Q-17.277 62.651-17.461 62.914Q-17.644 63.178-17.937 63.325Q-18.230 63.471-18.550 63.471Q-19.062 63.471-19.386 63.161Q-19.711 62.850-19.711 62.327\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(148.642 -18.61)\">\u003Cpath d=\"M-9.284 64.073Q-9.815 63.764-10.215 63.276Q-10.616 62.787-10.827 62.194Q-11.038 61.600-11.038 60.983Q-11.038 60.366-10.829 59.778Q-10.620 59.190-10.223 58.709Q-9.827 58.229-9.292 57.905Q-9.221 57.881-9.190 57.881L-9.100 57.881Q-9.002 57.893-8.936 57.959Q-8.870 58.026-8.870 58.127Q-8.870 58.272-8.971 58.334Q-9.420 58.623-9.747 59.039Q-10.073 59.455-10.235 59.948Q-10.397 60.440-10.397 60.983Q-10.397 61.389-10.301 61.776Q-10.206 62.162-10.028 62.498Q-9.850 62.834-9.590 63.118Q-9.331 63.401-8.983 63.631Q-8.870 63.709-8.870 63.846Q-8.870 63.944-8.936 64.014Q-9.002 64.084-9.100 64.096L-9.190 64.096Q-9.249 64.096-9.284 64.073M-7.928 63.194L-7.928 63.104Q-7.870 62.897-7.678 62.873L-6.967 62.873L-6.967 60.545L-7.678 60.545Q-7.874 60.522-7.928 60.303L-7.928 60.217Q-7.870 60.006-7.678 59.983L-6.577 59.983Q-6.377 60.002-6.327 60.217L-6.327 60.545Q-6.065 60.260-5.710 60.102Q-5.354 59.944-4.967 59.944Q-4.674 59.944-4.440 60.078Q-4.206 60.213-4.206 60.479Q-4.206 60.647-4.315 60.764Q-4.424 60.881-4.592 60.881Q-4.745 60.881-4.860 60.770Q-4.975 60.659-4.975 60.502Q-5.350 60.502-5.665 60.703Q-5.979 60.905-6.153 61.239Q-6.327 61.573-6.327 61.952L-6.327 62.873L-5.381 62.873Q-5.174 62.897-5.135 63.104L-5.135 63.194Q-5.174 63.409-5.381 63.432L-7.678 63.432Q-7.870 63.409-7.928 63.194M-0.393 61.944L-2.835 61.944Q-2.780 62.221-2.583 62.444Q-2.385 62.666-2.108 62.789Q-1.831 62.912-1.545 62.912Q-1.073 62.912-0.850 62.623Q-0.842 62.612-0.786 62.506Q-0.729 62.401-0.680 62.358Q-0.631 62.315-0.538 62.303L-0.393 62.303Q-0.202 62.323-0.143 62.537L-0.143 62.592Q-0.210 62.893-0.440 63.090Q-0.670 63.287-0.983 63.379Q-1.295 63.471-1.600 63.471Q-2.085 63.471-2.524 63.243Q-2.963 63.014-3.231 62.614Q-3.499 62.213-3.499 61.721L-3.499 61.662Q-3.499 61.194-3.252 60.791Q-3.006 60.389-2.598 60.155Q-2.190 59.920-1.721 59.920Q-1.217 59.920-0.864 60.143Q-0.510 60.366-0.327 60.754Q-0.143 61.143-0.143 61.647L-0.143 61.705Q-0.202 61.920-0.393 61.944M-2.827 61.393L-0.799 61.393Q-0.846 60.983-1.085 60.731Q-1.323 60.479-1.721 60.479Q-2.116 60.479-2.422 60.741Q-2.729 61.002-2.827 61.393M0.748 62.319Q0.748 61.873 1.162 61.616Q1.576 61.358 2.117 61.258Q2.658 61.159 3.165 61.151Q3.165 60.936 3.031 60.784Q2.896 60.631 2.689 60.555Q2.482 60.479 2.271 60.479Q1.927 60.479 1.767 60.502L1.767 60.561Q1.767 60.729 1.648 60.844Q1.529 60.959 1.365 60.959Q1.189 60.959 1.074 60.836Q0.958 60.713 0.958 60.545Q0.958 60.139 1.339 60.030Q1.720 59.920 2.279 59.920Q2.548 59.920 2.816 59.998Q3.083 60.077 3.308 60.227Q3.533 60.377 3.669 60.598Q3.806 60.819 3.806 61.096L3.806 62.815Q3.806 62.873 4.333 62.873Q4.529 62.893 4.580 63.104L4.580 63.194Q4.529 63.409 4.333 63.432L4.189 63.432Q3.845 63.432 3.617 63.385Q3.388 63.338 3.244 63.151Q2.783 63.471 2.076 63.471Q1.740 63.471 1.435 63.330Q1.130 63.190 0.939 62.928Q0.748 62.666 0.748 62.319M1.388 62.327Q1.388 62.600 1.630 62.756Q1.873 62.912 2.158 62.912Q2.376 62.912 2.609 62.854Q2.841 62.795 3.003 62.657Q3.165 62.518 3.165 62.295L3.165 61.705Q2.884 61.705 2.468 61.762Q2.052 61.819 1.720 61.957Q1.388 62.096 1.388 62.327M5.037 63.194L5.037 63.104Q5.087 62.897 5.283 62.873L6.388 62.873L6.388 59.104L5.283 59.104Q5.087 59.080 5.037 58.866L5.037 58.776Q5.087 58.569 5.283 58.545L6.779 58.545Q6.970 58.569 7.029 58.776L7.029 62.873L8.130 62.873Q8.330 62.897 8.380 63.104L8.380 63.194Q8.330 63.409 8.130 63.432L5.283 63.432Q5.087 63.409 5.037 63.194M9.873 64.096L9.787 64.096Q9.681 64.084 9.613 64.012Q9.544 63.940 9.544 63.846Q9.544 63.705 9.650 63.639Q9.986 63.424 10.255 63.135Q10.525 62.846 10.708 62.498Q10.892 62.151 10.982 61.772Q11.072 61.393 11.072 60.983Q11.072 60.444 10.908 59.952Q10.744 59.459 10.425 59.045Q10.107 58.631 9.665 58.342Q9.544 58.260 9.544 58.127Q9.544 58.030 9.613 57.961Q9.681 57.893 9.787 57.881L9.873 57.881Q9.927 57.881 9.962 57.905Q10.349 58.127 10.689 58.475Q11.029 58.823 11.249 59.217Q11.470 59.612 11.591 60.057Q11.712 60.502 11.712 60.983Q11.712 61.467 11.591 61.918Q11.470 62.370 11.246 62.766Q11.021 63.162 10.697 63.496Q10.373 63.830 9.970 64.073Q9.900 64.096 9.873 64.096\" 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\" stroke=\"var(--tk-warn)\" d=\"m-27.957-35.47 3.948.474 3.948.474 3.948.474 3.948.473 3.949.474 3.948.474 3.948.474 3.948.473 3.948.474 3.949.474 3.948.474 3.948.473 3.948.474 3.948.474 3.949.474 3.948.474 3.948.473 3.948.474 3.948.474 3.949.474 3.948.473 3.948.474 3.948.474 3.948.474 3.949.473 3.948.474 3.948.474 3.948.474 3.948.473 3.949.474 3.948.474 3.948.474 3.948.473 3.948.474 3.949.474 3.948.474 3.948.474 3.948.473 3.948.474 3.949.474 3.948.474 3.948.473 3.948.474 3.948.474 3.949.474 3.948.473 3.948.474 3.948.474 3.948.474\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\">\u003Cg fill=\"var(--tk-warn)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(57.733 -92.403)\">\u003Cpath d=\"M-32.449 62.327L-32.449 60.545L-33.199 60.545Q-33.398 60.522-33.449 60.303L-33.449 60.217Q-33.398 60.006-33.199 59.983L-32.449 59.983L-32.449 59.233Q-32.398 59.026-32.199 58.998L-32.054 58.998Q-31.859 59.026-31.808 59.233L-31.808 59.983L-30.449 59.983Q-30.257 60.002-30.199 60.217L-30.199 60.303Q-30.253 60.522-30.449 60.545L-31.808 60.545L-31.808 62.295Q-31.808 62.912-31.234 62.912Q-30.984 62.912-30.820 62.727Q-30.656 62.541-30.656 62.295Q-30.656 62.202-30.584 62.131Q-30.511 62.061-30.410 62.049L-30.265 62.049Q-30.066 62.073-30.015 62.280L-30.015 62.327Q-30.015 62.651-30.199 62.914Q-30.382 63.178-30.675 63.325Q-30.968 63.471-31.289 63.471Q-31.800 63.471-32.125 63.161Q-32.449 62.850-32.449 62.327M-25.890 61.944L-28.332 61.944Q-28.277 62.221-28.080 62.444Q-27.882 62.666-27.605 62.789Q-27.328 62.912-27.043 62.912Q-26.570 62.912-26.347 62.623Q-26.339 62.612-26.283 62.506Q-26.226 62.401-26.177 62.358Q-26.128 62.315-26.035 62.303L-25.890 62.303Q-25.699 62.323-25.640 62.537L-25.640 62.592Q-25.707 62.893-25.937 63.090Q-26.168 63.287-26.480 63.379Q-26.793 63.471-27.097 63.471Q-27.582 63.471-28.021 63.243Q-28.461 63.014-28.728 62.614Q-28.996 62.213-28.996 61.721L-28.996 61.662Q-28.996 61.194-28.750 60.791Q-28.503 60.389-28.095 60.155Q-27.687 59.920-27.218 59.920Q-26.714 59.920-26.361 60.143Q-26.007 60.366-25.824 60.754Q-25.640 61.143-25.640 61.647L-25.640 61.705Q-25.699 61.920-25.890 61.944M-28.324 61.393L-26.296 61.393Q-26.343 60.983-26.582 60.731Q-26.820 60.479-27.218 60.479Q-27.613 60.479-27.919 60.741Q-28.226 61.002-28.324 61.393M-24.570 63.233L-24.570 62.319Q-24.543 62.112-24.332 62.088L-24.164 62.088Q-24 62.112-23.941 62.272Q-23.738 62.912-23.011 62.912Q-22.804 62.912-22.576 62.877Q-22.347 62.842-22.179 62.727Q-22.011 62.612-22.011 62.409Q-22.011 62.198-22.234 62.084Q-22.457 61.971-22.730 61.928L-23.429 61.815Q-24.570 61.604-24.570 60.881Q-24.570 60.592-24.425 60.403Q-24.281 60.213-24.041 60.106Q-23.800 59.998-23.544 59.959Q-23.289 59.920-23.011 59.920Q-22.761 59.920-22.568 59.950Q-22.375 59.979-22.211 60.057Q-22.132 59.940-22.003 59.920L-21.925 59.920Q-21.828 59.932-21.765 59.995Q-21.703 60.057-21.691 60.151L-21.691 60.858Q-21.703 60.952-21.765 61.018Q-21.828 61.084-21.925 61.096L-22.093 61.096Q-22.187 61.084-22.253 61.018Q-22.320 60.952-22.332 60.858Q-22.332 60.479-23.027 60.479Q-23.375 60.479-23.693 60.561Q-24.011 60.643-24.011 60.889Q-24.011 61.155-23.339 61.264L-22.636 61.385Q-22.152 61.467-21.802 61.715Q-21.453 61.963-21.453 62.409Q-21.453 62.799-21.689 63.041Q-21.925 63.284-22.275 63.377Q-22.625 63.471-23.011 63.471Q-23.589 63.471-23.988 63.217Q-24.058 63.342-24.107 63.399Q-24.156 63.455-24.261 63.471L-24.332 63.471Q-24.546 63.448-24.570 63.233M-19.711 62.327L-19.711 60.545L-20.461 60.545Q-20.660 60.522-20.711 60.303L-20.711 60.217Q-20.660 60.006-20.461 59.983L-19.711 59.983L-19.711 59.233Q-19.660 59.026-19.461 58.998L-19.316 58.998Q-19.121 59.026-19.070 59.233L-19.070 59.983L-17.711 59.983Q-17.519 60.002-17.461 60.217L-17.461 60.303Q-17.515 60.522-17.711 60.545L-19.070 60.545L-19.070 62.295Q-19.070 62.912-18.496 62.912Q-18.246 62.912-18.082 62.727Q-17.918 62.541-17.918 62.295Q-17.918 62.202-17.845 62.131Q-17.773 62.061-17.671 62.049L-17.527 62.049Q-17.328 62.073-17.277 62.280L-17.277 62.327Q-17.277 62.651-17.461 62.914Q-17.644 63.178-17.937 63.325Q-18.230 63.471-18.550 63.471Q-19.062 63.471-19.386 63.161Q-19.711 62.850-19.711 62.327\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(57.733 -92.403)\">\u003Cpath d=\"M-9.284 64.073Q-9.815 63.764-10.215 63.276Q-10.616 62.787-10.827 62.194Q-11.038 61.600-11.038 60.983Q-11.038 60.366-10.829 59.778Q-10.620 59.190-10.223 58.709Q-9.827 58.229-9.292 57.905Q-9.221 57.881-9.190 57.881L-9.100 57.881Q-9.002 57.893-8.936 57.959Q-8.870 58.026-8.870 58.127Q-8.870 58.272-8.971 58.334Q-9.420 58.623-9.747 59.039Q-10.073 59.455-10.235 59.948Q-10.397 60.440-10.397 60.983Q-10.397 61.389-10.301 61.776Q-10.206 62.162-10.028 62.498Q-9.850 62.834-9.590 63.118Q-9.331 63.401-8.983 63.631Q-8.870 63.709-8.870 63.846Q-8.870 63.944-8.936 64.014Q-9.002 64.084-9.100 64.096L-9.190 64.096Q-9.249 64.096-9.284 64.073M-7.928 63.194L-7.928 63.104Q-7.870 62.897-7.678 62.873L-6.967 62.873L-6.967 60.545L-7.678 60.545Q-7.874 60.522-7.928 60.303L-7.928 60.217Q-7.870 60.006-7.678 59.983L-6.577 59.983Q-6.377 60.002-6.327 60.217L-6.327 60.545Q-6.065 60.260-5.710 60.102Q-5.354 59.944-4.967 59.944Q-4.674 59.944-4.440 60.078Q-4.206 60.213-4.206 60.479Q-4.206 60.647-4.315 60.764Q-4.424 60.881-4.592 60.881Q-4.745 60.881-4.860 60.770Q-4.975 60.659-4.975 60.502Q-5.350 60.502-5.665 60.703Q-5.979 60.905-6.153 61.239Q-6.327 61.573-6.327 61.952L-6.327 62.873L-5.381 62.873Q-5.174 62.897-5.135 63.104L-5.135 63.194Q-5.174 63.409-5.381 63.432L-7.678 63.432Q-7.870 63.409-7.928 63.194M-3.499 62.319Q-3.499 61.873-3.085 61.616Q-2.670 61.358-2.129 61.258Q-1.588 61.159-1.081 61.151Q-1.081 60.936-1.215 60.784Q-1.350 60.631-1.557 60.555Q-1.764 60.479-1.975 60.479Q-2.319 60.479-2.479 60.502L-2.479 60.561Q-2.479 60.729-2.598 60.844Q-2.717 60.959-2.881 60.959Q-3.057 60.959-3.172 60.836Q-3.288 60.713-3.288 60.545Q-3.288 60.139-2.907 60.030Q-2.526 59.920-1.967 59.920Q-1.698 59.920-1.430 59.998Q-1.163 60.077-0.938 60.227Q-0.713 60.377-0.577 60.598Q-0.440 60.819-0.440 61.096L-0.440 62.815Q-0.440 62.873 0.087 62.873Q0.283 62.893 0.333 63.104L0.333 63.194Q0.283 63.409 0.087 63.432L-0.057 63.432Q-0.401 63.432-0.629 63.385Q-0.858 63.338-1.002 63.151Q-1.463 63.471-2.170 63.471Q-2.506 63.471-2.811 63.330Q-3.116 63.190-3.307 62.928Q-3.499 62.666-3.499 62.319M-2.858 62.327Q-2.858 62.600-2.616 62.756Q-2.374 62.912-2.088 62.912Q-1.870 62.912-1.637 62.854Q-1.405 62.795-1.243 62.657Q-1.081 62.518-1.081 62.295L-1.081 61.705Q-1.362 61.705-1.778 61.762Q-2.194 61.819-2.526 61.957Q-2.858 62.096-2.858 62.327M0.412 63.194L0.412 63.104Q0.455 62.897 0.662 62.873L1.083 62.873L1.083 60.545L0.662 60.545Q0.455 60.522 0.412 60.303L0.412 60.217Q0.458 60.006 0.662 59.983L1.478 59.983Q1.673 60.006 1.724 60.217L1.724 60.303L1.716 60.327Q1.943 60.147 2.216 60.045Q2.490 59.944 2.783 59.944Q3.130 59.944 3.369 60.084Q3.607 60.225 3.722 60.483Q3.837 60.741 3.837 61.096L3.837 62.873L4.263 62.873Q4.470 62.897 4.509 63.104L4.509 63.194Q4.470 63.409 4.263 63.432L2.869 63.432Q2.673 63.409 2.623 63.194L2.623 63.104Q2.673 62.893 2.869 62.873L3.197 62.873L3.197 61.127Q3.197 60.819 3.107 60.661Q3.017 60.502 2.724 60.502Q2.455 60.502 2.226 60.633Q1.998 60.764 1.861 60.993Q1.724 61.221 1.724 61.487L1.724 62.873L2.150 62.873Q2.357 62.897 2.396 63.104L2.396 63.194Q2.357 63.409 2.150 63.432L0.662 63.432Q0.455 63.409 0.412 63.194M6.451 63.471Q5.986 63.471 5.621 63.221Q5.255 62.971 5.050 62.567Q4.845 62.162 4.845 61.705Q4.845 61.362 4.970 61.041Q5.095 60.721 5.328 60.471Q5.560 60.221 5.865 60.082Q6.169 59.944 6.525 59.944Q7.037 59.944 7.443 60.264L7.443 59.104L7.021 59.104Q6.810 59.080 6.771 58.866L6.771 58.776Q6.810 58.569 7.021 58.545L7.833 58.545Q8.033 58.569 8.083 58.776L8.083 62.873L8.509 62.873Q8.716 62.897 8.755 63.104L8.755 63.194Q8.716 63.409 8.509 63.432L7.693 63.432Q7.494 63.409 7.443 63.194L7.443 63.065Q7.248 63.256 6.986 63.364Q6.724 63.471 6.451 63.471M6.490 62.912Q6.849 62.912 7.101 62.643Q7.353 62.373 7.443 61.998L7.443 61.166Q7.384 60.979 7.257 60.828Q7.130 60.678 6.953 60.590Q6.775 60.502 6.580 60.502Q6.271 60.502 6.021 60.672Q5.771 60.842 5.626 61.127Q5.482 61.412 5.482 61.713Q5.482 62.162 5.767 62.537Q6.052 62.912 6.490 62.912M10.955 63.471Q10.482 63.471 10.097 63.227Q9.712 62.983 9.490 62.573Q9.267 62.162 9.267 61.705Q9.267 61.362 9.392 61.039Q9.517 60.717 9.748 60.463Q9.978 60.209 10.285 60.065Q10.591 59.920 10.955 59.920Q11.318 59.920 11.630 60.067Q11.943 60.213 12.165 60.459Q12.388 60.705 12.515 61.026Q12.642 61.346 12.642 61.705Q12.642 62.162 12.417 62.575Q12.193 62.987 11.808 63.229Q11.423 63.471 10.955 63.471M10.955 62.912Q11.419 62.912 11.710 62.518Q12.001 62.123 12.001 61.639Q12.001 61.346 11.867 61.078Q11.732 60.811 11.492 60.645Q11.251 60.479 10.955 60.479Q10.650 60.479 10.412 60.645Q10.173 60.811 10.039 61.078Q9.904 61.346 9.904 61.639Q9.904 62.120 10.197 62.516Q10.490 62.912 10.955 62.912M13.025 63.194L13.025 63.104Q13.076 62.893 13.271 62.873L13.470 62.873L13.470 60.545L13.271 60.545Q13.064 60.522 13.025 60.303L13.025 60.217Q13.076 60.002 13.271 59.983L13.751 59.983Q13.943 60.006 14.001 60.217Q14.322 59.944 14.736 59.944Q14.927 59.944 15.095 60.053Q15.263 60.162 15.337 60.334Q15.513 60.143 15.736 60.043Q15.958 59.944 16.201 59.944Q16.619 59.944 16.773 60.286Q16.927 60.627 16.927 61.096L16.927 62.873L17.126 62.873Q17.337 62.897 17.376 63.104L17.376 63.194Q17.326 63.409 17.126 63.432L16.310 63.432Q16.115 63.409 16.064 63.194L16.064 63.104Q16.115 62.897 16.310 62.873L16.400 62.873L16.400 61.127Q16.400 60.502 16.150 60.502Q15.818 60.502 15.640 60.813Q15.462 61.123 15.462 61.487L15.462 62.873L15.665 62.873Q15.873 62.897 15.912 63.104L15.912 63.194Q15.861 63.409 15.665 63.432L14.849 63.432Q14.650 63.409 14.599 63.194L14.599 63.104Q14.650 62.897 14.849 62.873L14.935 62.873L14.935 61.127Q14.935 60.502 14.689 60.502Q14.357 60.502 14.179 60.815Q14.001 61.127 14.001 61.487L14.001 62.873L14.201 62.873Q14.408 62.897 14.447 63.104L14.447 63.194Q14.396 63.412 14.201 63.432L13.271 63.432Q13.064 63.409 13.025 63.194M18.365 64.096L18.279 64.096Q18.173 64.084 18.105 64.012Q18.037 63.940 18.037 63.846Q18.037 63.705 18.142 63.639Q18.478 63.424 18.748 63.135Q19.017 62.846 19.201 62.498Q19.384 62.151 19.474 61.772Q19.564 61.393 19.564 60.983Q19.564 60.444 19.400 59.952Q19.236 59.459 18.917 59.045Q18.599 58.631 18.158 58.342Q18.037 58.260 18.037 58.127Q18.037 58.030 18.105 57.961Q18.173 57.893 18.279 57.881L18.365 57.881Q18.419 57.881 18.455 57.905Q18.841 58.127 19.181 58.475Q19.521 58.823 19.742 59.217Q19.962 59.612 20.083 60.057Q20.205 60.502 20.205 60.983Q20.205 61.467 20.083 61.918Q19.962 62.370 19.738 62.766Q19.513 63.162 19.189 63.496Q18.865 63.830 18.462 64.073Q18.392 64.096 18.365 64.096\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">The model drives training risk to zero on both real and random labels, yet the test gap is small on real labels and near-chance on random ones.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:501.067px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 375.801 165.793\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-40.56 71.234h253.475\"\u002F>\u003Cpath stroke=\"none\" d=\"m215.515 71.234-4.16-2.08 1.56 2.08-1.56 2.08\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(259.808 2)\">\u003Cpath d=\"M-38.392 71.234L-40.248 71.234L-40.248 70.937Q-39.974 70.937-39.806 70.890Q-39.638 70.843-39.638 70.675L-39.638 68.539Q-39.638 68.324-39.701 68.228Q-39.763 68.132-39.882 68.111Q-40.001 68.089-40.248 68.089L-40.248 67.793L-39.056 67.707L-39.056 68.441Q-38.943 68.226-38.749 68.058Q-38.556 67.890-38.318 67.798Q-38.080 67.707-37.826 67.707Q-36.865 67.707-36.689 68.418Q-36.505 68.089-36.177 67.898Q-35.849 67.707-35.470 67.707Q-34.294 67.707-34.294 68.785L-34.294 70.675Q-34.294 70.843-34.126 70.890Q-33.958 70.937-33.689 70.937L-33.689 71.234L-35.544 71.234L-35.544 70.937Q-35.271 70.937-35.103 70.892Q-34.935 70.847-34.935 70.675L-34.935 68.800Q-34.935 68.414-35.060 68.187Q-35.185 67.961-35.537 67.961Q-35.841 67.961-36.097 68.123Q-36.353 68.285-36.501 68.554Q-36.650 68.824-36.650 69.121L-36.650 70.675Q-36.650 70.843-36.480 70.890Q-36.310 70.937-36.040 70.937L-36.040 71.234L-37.896 71.234L-37.896 70.937Q-37.623 70.937-37.455 70.890Q-37.287 70.843-37.287 70.675L-37.287 68.800Q-37.287 68.414-37.412 68.187Q-37.537 67.961-37.888 67.961Q-38.193 67.961-38.449 68.123Q-38.705 68.285-38.853 68.554Q-39.001 68.824-39.001 69.121L-39.001 70.675Q-39.001 70.843-38.831 70.890Q-38.662 70.937-38.392 70.937L-38.392 71.234M-33.244 69.539Q-33.244 69.035-32.988 68.603Q-32.732 68.171-32.296 67.920Q-31.861 67.668-31.361 67.668Q-30.974 67.668-30.632 67.812Q-30.290 67.957-30.029 68.218Q-29.767 68.480-29.624 68.816Q-29.482 69.152-29.482 69.539Q-29.482 70.031-29.746 70.441Q-30.009 70.851-30.439 71.082Q-30.869 71.312-31.361 71.312Q-31.853 71.312-32.287 71.080Q-32.720 70.847-32.982 70.439Q-33.244 70.031-33.244 69.539M-31.361 71.035Q-30.904 71.035-30.652 70.812Q-30.400 70.589-30.312 70.238Q-30.224 69.886-30.224 69.441Q-30.224 69.011-30.318 68.673Q-30.412 68.336-30.665 68.129Q-30.919 67.921-31.361 67.921Q-32.009 67.921-32.253 68.338Q-32.498 68.754-32.498 69.441Q-32.498 69.886-32.410 70.238Q-32.322 70.589-32.070 70.812Q-31.818 71.035-31.361 71.035\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(259.808 2)\">\u003Cpath d=\"M-26.935 71.312Q-27.416 71.312-27.824 71.068Q-28.232 70.824-28.470 70.410Q-28.709 69.996-28.709 69.507Q-28.709 69.015-28.451 68.599Q-28.193 68.183-27.761 67.945Q-27.330 67.707-26.838 67.707Q-26.217 67.707-25.767 68.144L-25.767 66.515Q-25.767 66.300-25.830 66.205Q-25.892 66.109-26.010 66.088Q-26.127 66.066-26.373 66.066L-26.373 65.769L-25.150 65.683L-25.150 70.492Q-25.150 70.703-25.088 70.798Q-25.025 70.894-24.908 70.916Q-24.791 70.937-24.541 70.937L-24.541 71.234L-25.791 71.312L-25.791 70.828Q-26.256 71.312-26.935 71.312M-26.869 71.058Q-26.529 71.058-26.236 70.867Q-25.943 70.675-25.791 70.379L-25.791 68.546Q-25.939 68.273-26.201 68.117Q-26.463 67.961-26.775 67.961Q-27.400 67.961-27.683 68.408Q-27.967 68.855-27.967 69.515Q-27.967 70.160-27.715 70.609Q-27.463 71.058-26.869 71.058M-24.033 69.480Q-24.033 69-23.801 68.584Q-23.568 68.168-23.158 67.918Q-22.748 67.668-22.271 67.668Q-21.541 67.668-21.142 68.109Q-20.744 68.550-20.744 69.281Q-20.744 69.386-20.838 69.410L-23.287 69.410L-23.287 69.480Q-23.287 69.890-23.166 70.246Q-23.045 70.601-22.773 70.818Q-22.502 71.035-22.072 71.035Q-21.709 71.035-21.412 70.806Q-21.115 70.578-21.013 70.226Q-21.006 70.179-20.920 70.164L-20.838 70.164Q-20.744 70.191-20.744 70.273Q-20.744 70.281-20.752 70.312Q-20.814 70.539-20.953 70.722Q-21.092 70.906-21.283 71.039Q-21.474 71.171-21.693 71.242Q-21.912 71.312-22.150 71.312Q-22.521 71.312-22.859 71.175Q-23.197 71.039-23.465 70.787Q-23.732 70.535-23.883 70.195Q-24.033 69.855-24.033 69.480M-23.279 69.171L-21.318 69.171Q-21.318 68.867-21.420 68.576Q-21.521 68.285-21.738 68.103Q-21.955 67.921-22.271 67.921Q-22.572 67.921-22.802 68.109Q-23.033 68.296-23.156 68.588Q-23.279 68.879-23.279 69.171M-18.342 71.234L-20.174 71.234L-20.174 70.937Q-19.900 70.937-19.732 70.890Q-19.564 70.843-19.564 70.675L-19.564 66.515Q-19.564 66.300-19.627 66.205Q-19.689 66.109-19.808 66.088Q-19.927 66.066-20.174 66.066L-20.174 65.769L-18.951 65.683L-18.951 70.675Q-18.951 70.843-18.783 70.890Q-18.615 70.937-18.342 70.937\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(259.808 2)\">\u003Cpath d=\"M-15.015 71.226L-15.015 70.004Q-15.015 69.976-14.983 69.945Q-14.952 69.914-14.929 69.914L-14.823 69.914Q-14.753 69.914-14.737 69.976Q-14.675 70.296-14.536 70.537Q-14.398 70.777-14.165 70.918Q-13.933 71.058-13.624 71.058Q-13.386 71.058-13.177 70.998Q-12.968 70.937-12.831 70.789Q-12.694 70.640-12.694 70.394Q-12.694 70.140-12.905 69.974Q-13.116 69.808-13.386 69.754L-14.007 69.640Q-14.413 69.562-14.714 69.306Q-15.015 69.050-15.015 68.675Q-15.015 68.308-14.814 68.086Q-14.612 67.863-14.288 67.765Q-13.964 67.668-13.624 67.668Q-13.159 67.668-12.862 67.875L-12.640 67.691Q-12.616 67.668-12.585 67.668L-12.534 67.668Q-12.503 67.668-12.476 67.695Q-12.448 67.722-12.448 67.754L-12.448 68.738Q-12.448 68.769-12.474 68.798Q-12.499 68.828-12.534 68.828L-12.640 68.828Q-12.675 68.828-12.702 68.800Q-12.730 68.773-12.730 68.738Q-12.730 68.339-12.982 68.119Q-13.233 67.898-13.632 67.898Q-13.987 67.898-14.271 68.021Q-14.554 68.144-14.554 68.449Q-14.554 68.668-14.353 68.800Q-14.151 68.933-13.905 68.976L-13.280 69.089Q-12.851 69.179-12.542 69.476Q-12.233 69.773-12.233 70.187Q-12.233 70.757-12.632 71.035Q-13.030 71.312-13.624 71.312Q-14.175 71.312-14.526 70.976L-14.823 71.289Q-14.847 71.312-14.882 71.312L-14.929 71.312Q-14.952 71.312-14.983 71.281Q-15.015 71.250-15.015 71.226M-9.847 71.234L-11.624 71.234L-11.624 70.937Q-11.351 70.937-11.183 70.890Q-11.015 70.843-11.015 70.675L-11.015 68.539Q-11.015 68.324-11.071 68.228Q-11.128 68.132-11.241 68.111Q-11.355 68.089-11.601 68.089L-11.601 67.793L-10.401 67.707L-10.401 70.675Q-10.401 70.843-10.255 70.890Q-10.108 70.937-9.847 70.937L-9.847 71.234M-11.288 66.312Q-11.288 66.121-11.153 65.990Q-11.019 65.859-10.823 65.859Q-10.702 65.859-10.599 65.921Q-10.495 65.984-10.433 66.088Q-10.370 66.191-10.370 66.312Q-10.370 66.507-10.501 66.642Q-10.632 66.777-10.823 66.777Q-11.023 66.777-11.155 66.644Q-11.288 66.511-11.288 66.312M-6.312 71.234L-9.233 71.234Q-9.276 71.234-9.312 71.203Q-9.347 71.171-9.347 71.121L-9.347 71.050Q-9.347 71.004-9.312 70.968L-6.999 68.043L-7.714 68.043Q-8.073 68.043-8.294 68.084Q-8.515 68.125-8.661 68.240Q-8.808 68.355-8.880 68.574Q-8.952 68.793-8.952 69.156L-9.233 69.156L-9.136 67.785L-6.304 67.785Q-6.257 67.785-6.226 67.818Q-6.194 67.851-6.194 67.898L-6.194 67.953Q-6.194 68-6.218 68.035L-8.538 70.953L-7.776 70.953Q-7.417 70.953-7.177 70.912Q-6.937 70.871-6.753 70.707Q-6.601 70.554-6.540 70.295Q-6.480 70.035-6.448 69.660L-6.171 69.660L-6.312 71.234M-5.569 69.480Q-5.569 69-5.337 68.584Q-5.105 68.168-4.694 67.918Q-4.284 67.668-3.808 67.668Q-3.077 67.668-2.679 68.109Q-2.280 68.550-2.280 69.281Q-2.280 69.386-2.374 69.410L-4.823 69.410L-4.823 69.480Q-4.823 69.890-4.702 70.246Q-4.581 70.601-4.310 70.818Q-4.038 71.035-3.608 71.035Q-3.245 71.035-2.948 70.806Q-2.651 70.578-2.550 70.226Q-2.542 70.179-2.456 70.164L-2.374 70.164Q-2.280 70.191-2.280 70.273Q-2.280 70.281-2.288 70.312Q-2.351 70.539-2.489 70.722Q-2.628 70.906-2.819 71.039Q-3.011 71.171-3.230 71.242Q-3.448 71.312-3.687 71.312Q-4.058 71.312-4.396 71.175Q-4.733 71.039-5.001 70.787Q-5.269 70.535-5.419 70.195Q-5.569 69.855-5.569 69.480M-4.816 69.171L-2.855 69.171Q-2.855 68.867-2.956 68.576Q-3.058 68.285-3.275 68.103Q-3.491 67.921-3.808 67.921Q-4.108 67.921-4.339 68.109Q-4.569 68.296-4.692 68.588Q-4.816 68.879-4.816 69.171\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(259.808 2)\">\u003Cpath d=\"M3.425 73.226Q2.812 72.769 2.410 72.134Q2.007 71.500 1.812 70.754Q1.617 70.007 1.617 69.234Q1.617 68.461 1.812 67.714Q2.007 66.968 2.410 66.334Q2.812 65.699 3.425 65.242Q3.437 65.238 3.445 65.236Q3.453 65.234 3.464 65.234L3.542 65.234Q3.581 65.234 3.607 65.261Q3.632 65.289 3.632 65.332Q3.632 65.382 3.601 65.402Q3.093 65.855 2.771 66.478Q2.449 67.101 2.308 67.796Q2.167 68.492 2.167 69.234Q2.167 69.968 2.306 70.668Q2.445 71.367 2.769 71.992Q3.093 72.617 3.601 73.066Q3.632 73.086 3.632 73.136Q3.632 73.179 3.607 73.207Q3.581 73.234 3.542 73.234L3.464 73.234Q3.456 73.230 3.447 73.228Q3.437 73.226 3.425 73.226M6.234 72.785L4.378 72.785L4.378 72.492Q4.648 72.492 4.816 72.447Q4.984 72.402 4.984 72.226L4.984 68.402Q4.984 68.195 4.828 68.142Q4.671 68.089 4.378 68.089L4.378 67.793L5.601 67.707L5.601 68.171Q5.831 67.949 6.146 67.828Q6.460 67.707 6.800 67.707Q7.273 67.707 7.677 67.953Q8.081 68.199 8.314 68.615Q8.546 69.031 8.546 69.507Q8.546 69.882 8.398 70.211Q8.249 70.539 7.980 70.791Q7.710 71.043 7.367 71.177Q7.023 71.312 6.663 71.312Q6.374 71.312 6.103 71.191Q5.831 71.070 5.624 70.859L5.624 72.226Q5.624 72.402 5.792 72.447Q5.960 72.492 6.234 72.492L6.234 72.785M5.624 68.570L5.624 70.410Q5.777 70.699 6.038 70.879Q6.300 71.058 6.609 71.058Q6.894 71.058 7.117 70.920Q7.339 70.781 7.492 70.550Q7.644 70.320 7.722 70.048Q7.800 69.777 7.800 69.507Q7.800 69.175 7.675 68.818Q7.550 68.461 7.302 68.224Q7.054 67.988 6.706 67.988Q6.382 67.988 6.087 68.144Q5.792 68.300 5.624 68.570M9.167 70.402Q9.167 69.918 9.570 69.623Q9.972 69.328 10.523 69.209Q11.074 69.089 11.566 69.089L11.566 68.800Q11.566 68.574 11.451 68.367Q11.335 68.160 11.138 68.041Q10.941 67.921 10.710 67.921Q10.285 67.921 9.999 68.027Q10.070 68.054 10.117 68.109Q10.163 68.164 10.189 68.234Q10.214 68.304 10.214 68.379Q10.214 68.484 10.163 68.576Q10.113 68.668 10.021 68.718Q9.929 68.769 9.824 68.769Q9.718 68.769 9.626 68.718Q9.535 68.668 9.484 68.576Q9.433 68.484 9.433 68.379Q9.433 67.961 9.822 67.814Q10.210 67.668 10.710 67.668Q11.042 67.668 11.396 67.798Q11.749 67.929 11.978 68.183Q12.206 68.437 12.206 68.785L12.206 70.586Q12.206 70.718 12.279 70.828Q12.351 70.937 12.480 70.937Q12.605 70.937 12.673 70.832Q12.742 70.726 12.742 70.586L12.742 70.074L13.023 70.074L13.023 70.586Q13.023 70.789 12.906 70.947Q12.788 71.105 12.607 71.189Q12.425 71.273 12.222 71.273Q11.992 71.273 11.839 71.101Q11.687 70.929 11.656 70.699Q11.495 70.980 11.187 71.146Q10.878 71.312 10.527 71.312Q10.015 71.312 9.591 71.089Q9.167 70.867 9.167 70.402M9.855 70.402Q9.855 70.687 10.081 70.873Q10.308 71.058 10.601 71.058Q10.847 71.058 11.072 70.941Q11.296 70.824 11.431 70.621Q11.566 70.418 11.566 70.164L11.566 69.332Q11.300 69.332 11.015 69.386Q10.730 69.441 10.458 69.570Q10.187 69.699 10.021 69.906Q9.855 70.113 9.855 70.402M15.324 71.234L13.343 71.234L13.343 70.937Q13.613 70.937 13.781 70.892Q13.949 70.847 13.949 70.675L13.949 68.539Q13.949 68.324 13.886 68.228Q13.824 68.132 13.706 68.111Q13.589 68.089 13.343 68.089L13.343 67.793L14.511 67.707L14.511 68.492Q14.589 68.281 14.742 68.095Q14.894 67.910 15.093 67.808Q15.292 67.707 15.519 67.707Q15.765 67.707 15.956 67.851Q16.148 67.996 16.148 68.226Q16.148 68.382 16.042 68.492Q15.937 68.601 15.781 68.601Q15.624 68.601 15.515 68.492Q15.406 68.382 15.406 68.226Q15.406 68.066 15.511 67.961Q15.187 67.961 14.972 68.189Q14.757 68.418 14.662 68.757Q14.566 69.097 14.566 69.402L14.566 70.675Q14.566 70.843 14.792 70.890Q15.019 70.937 15.324 70.937L15.324 71.234M16.726 70.402Q16.726 69.918 17.128 69.623Q17.531 69.328 18.081 69.209Q18.632 69.089 19.124 69.089L19.124 68.800Q19.124 68.574 19.009 68.367Q18.894 68.160 18.697 68.041Q18.499 67.921 18.269 67.921Q17.843 67.921 17.558 68.027Q17.628 68.054 17.675 68.109Q17.722 68.164 17.747 68.234Q17.773 68.304 17.773 68.379Q17.773 68.484 17.722 68.576Q17.671 68.668 17.579 68.718Q17.488 68.769 17.382 68.769Q17.277 68.769 17.185 68.718Q17.093 68.668 17.042 68.576Q16.992 68.484 16.992 68.379Q16.992 67.961 17.380 67.814Q17.769 67.668 18.269 67.668Q18.601 67.668 18.954 67.798Q19.308 67.929 19.537 68.183Q19.765 68.437 19.765 68.785L19.765 70.586Q19.765 70.718 19.837 70.828Q19.910 70.937 20.038 70.937Q20.163 70.937 20.232 70.832Q20.300 70.726 20.300 70.586L20.300 70.074L20.581 70.074L20.581 70.586Q20.581 70.789 20.464 70.947Q20.347 71.105 20.165 71.189Q19.984 71.273 19.781 71.273Q19.550 71.273 19.398 71.101Q19.245 70.929 19.214 70.699Q19.054 70.980 18.745 71.146Q18.437 71.312 18.085 71.312Q17.574 71.312 17.150 71.089Q16.726 70.867 16.726 70.402M17.413 70.402Q17.413 70.687 17.640 70.873Q17.867 71.058 18.160 71.058Q18.406 71.058 18.630 70.941Q18.855 70.824 18.990 70.621Q19.124 70.418 19.124 70.164L19.124 69.332Q18.859 69.332 18.574 69.386Q18.288 69.441 18.017 69.570Q17.745 69.699 17.579 69.906Q17.413 70.113 17.413 70.402M22.804 71.234L20.949 71.234L20.949 70.937Q21.222 70.937 21.390 70.890Q21.558 70.843 21.558 70.675L21.558 68.539Q21.558 68.324 21.495 68.228Q21.433 68.132 21.314 68.111Q21.195 68.089 20.949 68.089L20.949 67.793L22.140 67.707L22.140 68.441Q22.253 68.226 22.447 68.058Q22.640 67.890 22.878 67.798Q23.117 67.707 23.370 67.707Q24.331 67.707 24.507 68.418Q24.691 68.089 25.019 67.898Q25.347 67.707 25.726 67.707Q26.902 67.707 26.902 68.785L26.902 70.675Q26.902 70.843 27.070 70.890Q27.238 70.937 27.507 70.937L27.507 71.234L25.652 71.234L25.652 70.937Q25.925 70.937 26.093 70.892Q26.261 70.847 26.261 70.675L26.261 68.800Q26.261 68.414 26.136 68.187Q26.011 67.961 25.660 67.961Q25.355 67.961 25.099 68.123Q24.843 68.285 24.695 68.554Q24.546 68.824 24.546 69.121L24.546 70.675Q24.546 70.843 24.716 70.890Q24.886 70.937 25.156 70.937L25.156 71.234L23.300 71.234L23.300 70.937Q23.574 70.937 23.742 70.890Q23.910 70.843 23.910 70.675L23.910 68.800Q23.910 68.414 23.785 68.187Q23.660 67.961 23.308 67.961Q23.003 67.961 22.747 68.123Q22.492 68.285 22.343 68.554Q22.195 68.824 22.195 69.121L22.195 70.675Q22.195 70.843 22.365 70.890Q22.535 70.937 22.804 70.937L22.804 71.234M27.995 71.226L27.995 70.004Q27.995 69.976 28.027 69.945Q28.058 69.914 28.081 69.914L28.187 69.914Q28.257 69.914 28.273 69.976Q28.335 70.296 28.474 70.537Q28.613 70.777 28.845 70.918Q29.078 71.058 29.386 71.058Q29.624 71.058 29.833 70.998Q30.042 70.937 30.179 70.789Q30.316 70.640 30.316 70.394Q30.316 70.140 30.105 69.974Q29.894 69.808 29.624 69.754L29.003 69.640Q28.597 69.562 28.296 69.306Q27.995 69.050 27.995 68.675Q27.995 68.308 28.197 68.086Q28.398 67.863 28.722 67.765Q29.046 67.668 29.386 67.668Q29.851 67.668 30.148 67.875L30.370 67.691Q30.394 67.668 30.425 67.668L30.476 67.668Q30.507 67.668 30.535 67.695Q30.562 67.722 30.562 67.754L30.562 68.738Q30.562 68.769 30.537 68.798Q30.511 68.828 30.476 68.828L30.370 68.828Q30.335 68.828 30.308 68.800Q30.281 68.773 30.281 68.738Q30.281 68.339 30.029 68.119Q29.777 67.898 29.378 67.898Q29.023 67.898 28.740 68.021Q28.456 68.144 28.456 68.449Q28.456 68.668 28.658 68.800Q28.859 68.933 29.105 68.976L29.730 69.089Q30.160 69.179 30.468 69.476Q30.777 69.773 30.777 70.187Q30.777 70.757 30.378 71.035Q29.980 71.312 29.386 71.312Q28.835 71.312 28.484 70.976L28.187 71.289Q28.163 71.312 28.128 71.312L28.081 71.312Q28.058 71.312 28.027 71.281Q27.995 71.250 27.995 71.226M31.706 73.234L31.624 73.234Q31.589 73.234 31.564 73.205Q31.538 73.175 31.538 73.136Q31.538 73.086 31.570 73.066Q31.956 72.730 32.240 72.281Q32.523 71.832 32.689 71.332Q32.855 70.832 32.929 70.314Q33.003 69.796 33.003 69.234Q33.003 68.664 32.929 68.148Q32.855 67.632 32.689 67.136Q32.523 66.640 32.244 66.193Q31.964 65.746 31.570 65.402Q31.538 65.382 31.538 65.332Q31.538 65.293 31.564 65.263Q31.589 65.234 31.624 65.234L31.706 65.234Q31.718 65.234 31.728 65.236Q31.738 65.238 31.745 65.242Q32.359 65.699 32.761 66.334Q33.163 66.968 33.359 67.714Q33.554 68.461 33.554 69.234Q33.554 70.007 33.359 70.754Q33.163 71.500 32.761 72.134Q32.359 72.769 31.745 73.226Q31.734 73.226 31.726 73.228Q31.718 73.230 31.706 73.234\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-40.56 71.234V-57.049\"\u002F>\u003Cpath stroke=\"none\" d=\"m-40.56-59.649-2.08 4.16 2.08-1.56 2.08 1.56\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-15.044 -134.616)\">\u003Cpath d=\"M-39.697 70.273L-39.697 68.082L-40.400 68.082L-40.400 67.828Q-40.044 67.828-39.802 67.595Q-39.560 67.363-39.449 67.015Q-39.337 66.668-39.337 66.312L-39.056 66.312L-39.056 67.785L-37.880 67.785L-37.880 68.082L-39.056 68.082L-39.056 70.257Q-39.056 70.578-38.937 70.806Q-38.818 71.035-38.537 71.035Q-38.357 71.035-38.240 70.912Q-38.123 70.789-38.070 70.609Q-38.017 70.429-38.017 70.257L-38.017 69.785L-37.736 69.785L-37.736 70.273Q-37.736 70.527-37.841 70.767Q-37.947 71.007-38.144 71.160Q-38.341 71.312-38.599 71.312Q-38.915 71.312-39.167 71.189Q-39.419 71.066-39.558 70.832Q-39.697 70.597-39.697 70.273M-37.017 69.480Q-37.017 69-36.785 68.584Q-36.552 68.168-36.142 67.918Q-35.732 67.668-35.255 67.668Q-34.525 67.668-34.126 68.109Q-33.728 68.550-33.728 69.281Q-33.728 69.386-33.822 69.410L-36.271 69.410L-36.271 69.480Q-36.271 69.890-36.150 70.246Q-36.029 70.601-35.757 70.818Q-35.486 71.035-35.056 71.035Q-34.693 71.035-34.396 70.806Q-34.099 70.578-33.998 70.226Q-33.990 70.179-33.904 70.164L-33.822 70.164Q-33.728 70.191-33.728 70.273Q-33.728 70.281-33.736 70.312Q-33.798 70.539-33.937 70.722Q-34.076 70.906-34.267 71.039Q-34.458 71.171-34.677 71.242Q-34.896 71.312-35.134 71.312Q-35.505 71.312-35.843 71.175Q-36.181 71.039-36.449 70.787Q-36.716 70.535-36.867 70.195Q-37.017 69.855-37.017 69.480M-36.263 69.171L-34.302 69.171Q-34.302 68.867-34.404 68.576Q-34.505 68.285-34.722 68.103Q-34.939 67.921-35.255 67.921Q-35.556 67.921-35.787 68.109Q-36.017 68.296-36.140 68.588Q-36.263 68.879-36.263 69.171M-33.197 71.226L-33.197 70.004Q-33.197 69.976-33.165 69.945Q-33.134 69.914-33.111 69.914L-33.005 69.914Q-32.935 69.914-32.919 69.976Q-32.857 70.296-32.718 70.537Q-32.580 70.777-32.347 70.918Q-32.115 71.058-31.806 71.058Q-31.568 71.058-31.359 70.998Q-31.150 70.937-31.013 70.789Q-30.876 70.640-30.876 70.394Q-30.876 70.140-31.087 69.974Q-31.298 69.808-31.568 69.754L-32.189 69.640Q-32.595 69.562-32.896 69.306Q-33.197 69.050-33.197 68.675Q-33.197 68.308-32.996 68.086Q-32.794 67.863-32.470 67.765Q-32.146 67.668-31.806 67.668Q-31.341 67.668-31.044 67.875L-30.822 67.691Q-30.798 67.668-30.767 67.668L-30.716 67.668Q-30.685 67.668-30.658 67.695Q-30.630 67.722-30.630 67.754L-30.630 68.738Q-30.630 68.769-30.656 68.798Q-30.681 68.828-30.716 68.828L-30.822 68.828Q-30.857 68.828-30.884 68.800Q-30.912 68.773-30.912 68.738Q-30.912 68.339-31.164 68.119Q-31.415 67.898-31.814 67.898Q-32.169 67.898-32.453 68.021Q-32.736 68.144-32.736 68.449Q-32.736 68.668-32.535 68.800Q-32.333 68.933-32.087 68.976L-31.462 69.089Q-31.033 69.179-30.724 69.476Q-30.415 69.773-30.415 70.187Q-30.415 70.757-30.814 71.035Q-31.212 71.312-31.806 71.312Q-32.357 71.312-32.708 70.976L-33.005 71.289Q-33.029 71.312-33.064 71.312L-33.111 71.312Q-33.134 71.312-33.165 71.281Q-33.197 71.250-33.197 71.226M-29.263 70.273L-29.263 68.082L-29.966 68.082L-29.966 67.828Q-29.611 67.828-29.369 67.595Q-29.126 67.363-29.015 67.015Q-28.904 66.668-28.904 66.312L-28.623 66.312L-28.623 67.785L-27.447 67.785L-27.447 68.082L-28.623 68.082L-28.623 70.257Q-28.623 70.578-28.503 70.806Q-28.384 71.035-28.103 71.035Q-27.923 71.035-27.806 70.912Q-27.689 70.789-27.636 70.609Q-27.583 70.429-27.583 70.257L-27.583 69.785L-27.302 69.785L-27.302 70.273Q-27.302 70.527-27.408 70.767Q-27.513 71.007-27.710 71.160Q-27.908 71.312-28.165 71.312Q-28.482 71.312-28.734 71.189Q-28.986 71.066-29.124 70.832Q-29.263 70.597-29.263 70.273\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-15.044 -134.616)\">\u003Cpath d=\"M-21.739 71.234L-23.719 71.234L-23.719 70.937Q-23.450 70.937-23.282 70.892Q-23.114 70.847-23.114 70.675L-23.114 68.539Q-23.114 68.324-23.176 68.228Q-23.239 68.132-23.356 68.111Q-23.473 68.089-23.719 68.089L-23.719 67.793L-22.551 67.707L-22.551 68.492Q-22.473 68.281-22.321 68.095Q-22.169 67.910-21.969 67.808Q-21.770 67.707-21.544 67.707Q-21.297 67.707-21.106 67.851Q-20.915 67.996-20.915 68.226Q-20.915 68.382-21.020 68.492Q-21.126 68.601-21.282 68.601Q-21.438 68.601-21.547 68.492Q-21.657 68.382-21.657 68.226Q-21.657 68.066-21.551 67.961Q-21.876 67.961-22.090 68.189Q-22.305 68.418-22.401 68.757Q-22.497 69.097-22.497 69.402L-22.497 70.675Q-22.497 70.843-22.270 70.890Q-22.044 70.937-21.739 70.937L-21.739 71.234M-18.575 71.234L-20.352 71.234L-20.352 70.937Q-20.079 70.937-19.911 70.890Q-19.743 70.843-19.743 70.675L-19.743 68.539Q-19.743 68.324-19.799 68.228Q-19.856 68.132-19.969 68.111Q-20.083 68.089-20.329 68.089L-20.329 67.793L-19.130 67.707L-19.130 70.675Q-19.130 70.843-18.983 70.890Q-18.837 70.937-18.575 70.937L-18.575 71.234M-20.016 66.312Q-20.016 66.121-19.881 65.990Q-19.747 65.859-19.551 65.859Q-19.430 65.859-19.327 65.921Q-19.223 65.984-19.161 66.088Q-19.098 66.191-19.098 66.312Q-19.098 66.507-19.229 66.642Q-19.360 66.777-19.551 66.777Q-19.751 66.777-19.883 66.644Q-20.016 66.511-20.016 66.312M-18.032 71.226L-18.032 70.004Q-18.032 69.976-18.001 69.945Q-17.969 69.914-17.946 69.914L-17.840 69.914Q-17.770 69.914-17.755 69.976Q-17.692 70.296-17.553 70.537Q-17.415 70.777-17.182 70.918Q-16.950 71.058-16.641 71.058Q-16.403 71.058-16.194 70.998Q-15.985 70.937-15.848 70.789Q-15.712 70.640-15.712 70.394Q-15.712 70.140-15.922 69.974Q-16.133 69.808-16.403 69.754L-17.024 69.640Q-17.430 69.562-17.731 69.306Q-18.032 69.050-18.032 68.675Q-18.032 68.308-17.831 68.086Q-17.630 67.863-17.305 67.765Q-16.981 67.668-16.641 67.668Q-16.176 67.668-15.880 67.875L-15.657 67.691Q-15.633 67.668-15.602 67.668L-15.551 67.668Q-15.520 67.668-15.493 67.695Q-15.465 67.722-15.465 67.754L-15.465 68.738Q-15.465 68.769-15.491 68.798Q-15.516 68.828-15.551 68.828L-15.657 68.828Q-15.692 68.828-15.719 68.800Q-15.747 68.773-15.747 68.738Q-15.747 68.339-15.999 68.119Q-16.251 67.898-16.649 67.898Q-17.005 67.898-17.288 68.021Q-17.571 68.144-17.571 68.449Q-17.571 68.668-17.370 68.800Q-17.169 68.933-16.922 68.976L-16.297 69.089Q-15.868 69.179-15.559 69.476Q-15.251 69.773-15.251 70.187Q-15.251 70.757-15.649 71.035Q-16.047 71.312-16.641 71.312Q-17.192 71.312-17.544 70.976L-17.840 71.289Q-17.864 71.312-17.899 71.312L-17.946 71.312Q-17.969 71.312-18.001 71.281Q-18.032 71.250-18.032 71.226M-12.899 71.234L-14.696 71.234L-14.696 70.937Q-14.426 70.937-14.258 70.892Q-14.090 70.847-14.090 70.675L-14.090 66.515Q-14.090 66.300-14.153 66.205Q-14.215 66.109-14.333 66.088Q-14.450 66.066-14.696 66.066L-14.696 65.769L-13.473 65.683L-13.473 69.449L-12.376 68.562Q-12.169 68.382-12.169 68.234Q-12.169 68.168-12.221 68.125Q-12.274 68.082-12.344 68.082L-12.344 67.785L-10.809 67.785L-10.809 68.082Q-11.340 68.082-11.938 68.562L-12.547 69.058L-11.473 70.457Q-11.337 70.632-11.229 70.740Q-11.122 70.847-10.987 70.892Q-10.852 70.937-10.626 70.937L-10.626 71.234L-12.251 71.234L-12.251 70.937Q-12.008 70.937-12.008 70.785Q-12.008 70.707-12.051 70.636Q-12.094 70.566-12.176 70.457L-12.977 69.410L-13.505 69.836L-13.505 70.675Q-13.505 70.843-13.337 70.890Q-13.169 70.937-12.899 70.937\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M81.787 71.234V-51.113\" style=\"stroke-dasharray:3.0,3.0\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(94.721 -124.457)\">\u003Cpath d=\"M-39.943 61.496L-39.943 61.406Q-39.892 61.199-39.697 61.175L-38.658 61.175L-38.658 58.847L-39.630 58.847Q-39.830 58.824-39.880 58.605L-39.880 58.519Q-39.830 58.308-39.630 58.285L-38.263 58.285Q-38.068 58.304-38.017 58.519L-38.017 61.175L-37.103 61.175Q-36.908 61.199-36.857 61.406L-36.857 61.496Q-36.908 61.711-37.103 61.734L-39.697 61.734Q-39.892 61.711-39.943 61.496M-38.912 57.308L-38.912 57.254Q-38.912 57.082-38.775 56.961Q-38.638 56.839-38.462 56.839Q-38.290 56.839-38.154 56.961Q-38.017 57.082-38.017 57.254L-38.017 57.308Q-38.017 57.484-38.154 57.605Q-38.290 57.726-38.462 57.726Q-38.638 57.726-38.775 57.605Q-38.912 57.484-38.912 57.308M-36.244 61.496L-36.244 61.406Q-36.201 61.199-35.994 61.175L-35.572 61.175L-35.572 58.847L-35.994 58.847Q-36.201 58.824-36.244 58.605L-36.244 58.519Q-36.197 58.308-35.994 58.285L-35.177 58.285Q-34.982 58.308-34.931 58.519L-34.931 58.605L-34.939 58.629Q-34.712 58.449-34.439 58.347Q-34.165 58.246-33.873 58.246Q-33.525 58.246-33.287 58.386Q-33.048 58.527-32.933 58.785Q-32.818 59.043-32.818 59.398L-32.818 61.175L-32.392 61.175Q-32.185 61.199-32.146 61.406L-32.146 61.496Q-32.185 61.711-32.392 61.734L-33.787 61.734Q-33.982 61.711-34.033 61.496L-34.033 61.406Q-33.982 61.195-33.787 61.175L-33.458 61.175L-33.458 59.429Q-33.458 59.121-33.548 58.963Q-33.638 58.804-33.931 58.804Q-34.201 58.804-34.429 58.935Q-34.658 59.066-34.794 59.295Q-34.931 59.523-34.931 59.789L-34.931 61.175L-34.505 61.175Q-34.298 61.199-34.259 61.406L-34.259 61.496Q-34.298 61.711-34.505 61.734L-35.994 61.734Q-36.201 61.711-36.244 61.496M-30.869 60.629L-30.869 58.847L-31.619 58.847Q-31.818 58.824-31.869 58.605L-31.869 58.519Q-31.818 58.308-31.619 58.285L-30.869 58.285L-30.869 57.535Q-30.818 57.328-30.619 57.300L-30.474 57.300Q-30.279 57.328-30.228 57.535L-30.228 58.285L-28.869 58.285Q-28.677 58.304-28.619 58.519L-28.619 58.605Q-28.673 58.824-28.869 58.847L-30.228 58.847L-30.228 60.597Q-30.228 61.214-29.654 61.214Q-29.404 61.214-29.240 61.029Q-29.076 60.843-29.076 60.597Q-29.076 60.504-29.003 60.433Q-28.931 60.363-28.830 60.351L-28.685 60.351Q-28.486 60.375-28.435 60.582L-28.435 60.629Q-28.435 60.953-28.619 61.216Q-28.802 61.480-29.095 61.627Q-29.388 61.773-29.708 61.773Q-30.220 61.773-30.544 61.463Q-30.869 61.152-30.869 60.629M-24.310 60.246L-26.751 60.246Q-26.697 60.523-26.499 60.746Q-26.302 60.968-26.025 61.091Q-25.748 61.214-25.462 61.214Q-24.990 61.214-24.767 60.925Q-24.759 60.914-24.703 60.808Q-24.646 60.703-24.597 60.660Q-24.548 60.617-24.455 60.605L-24.310 60.605Q-24.119 60.625-24.060 60.839L-24.060 60.894Q-24.126 61.195-24.357 61.392Q-24.587 61.589-24.900 61.681Q-25.212 61.773-25.517 61.773Q-26.001 61.773-26.441 61.545Q-26.880 61.316-27.148 60.916Q-27.415 60.515-27.415 60.023L-27.415 59.964Q-27.415 59.496-27.169 59.093Q-26.923 58.691-26.515 58.457Q-26.107 58.222-25.638 58.222Q-25.134 58.222-24.781 58.445Q-24.427 58.668-24.244 59.056Q-24.060 59.445-24.060 59.949L-24.060 60.007Q-24.119 60.222-24.310 60.246M-26.744 59.695L-24.716 59.695Q-24.763 59.285-25.001 59.033Q-25.240 58.781-25.638 58.781Q-26.033 58.781-26.339 59.043Q-26.646 59.304-26.744 59.695M-23.353 61.496L-23.353 61.406Q-23.294 61.199-23.103 61.175L-22.392 61.175L-22.392 58.847L-23.103 58.847Q-23.298 58.824-23.353 58.605L-23.353 58.519Q-23.294 58.308-23.103 58.285L-22.001 58.285Q-21.802 58.304-21.751 58.519L-21.751 58.847Q-21.490 58.562-21.134 58.404Q-20.779 58.246-20.392 58.246Q-20.099 58.246-19.865 58.380Q-19.630 58.515-19.630 58.781Q-19.630 58.949-19.740 59.066Q-19.849 59.183-20.017 59.183Q-20.169 59.183-20.285 59.072Q-20.400 58.961-20.400 58.804Q-20.775 58.804-21.089 59.005Q-21.404 59.207-21.578 59.541Q-21.751 59.875-21.751 60.254L-21.751 61.175L-20.806 61.175Q-20.599 61.199-20.560 61.406L-20.560 61.496Q-20.599 61.711-20.806 61.734L-23.103 61.734Q-23.294 61.711-23.353 61.496M-19.259 63.277L-19.259 63.191Q-19.216 62.972-19.009 62.949L-18.587 62.949L-18.587 58.847L-19.009 58.847Q-19.216 58.824-19.259 58.605L-19.259 58.519Q-19.212 58.308-19.009 58.285L-18.193 58.285Q-17.998 58.304-17.947 58.519L-17.947 58.589Q-17.736 58.422-17.472 58.334Q-17.208 58.246-16.939 58.246Q-16.599 58.246-16.302 58.390Q-16.005 58.535-15.790 58.787Q-15.576 59.039-15.460 59.351Q-15.345 59.664-15.345 60.007Q-15.345 60.472-15.572 60.882Q-15.798 61.293-16.185 61.533Q-16.572 61.773-17.040 61.773Q-17.560 61.773-17.947 61.406L-17.947 62.949L-17.521 62.949Q-17.314 62.972-17.275 63.191L-17.275 63.277Q-17.314 63.488-17.521 63.511L-19.009 63.511Q-19.212 63.488-19.259 63.277M-17.083 61.214Q-16.775 61.214-16.525 61.045Q-16.275 60.875-16.130 60.593Q-15.986 60.312-15.986 60.007Q-15.986 59.718-16.111 59.439Q-16.236 59.160-16.468 58.982Q-16.701 58.804-17.001 58.804Q-17.322 58.804-17.583 58.990Q-17.845 59.175-17.947 59.476L-17.947 60.328Q-17.857 60.695-17.636 60.955Q-17.415 61.214-17.083 61.214M-12.962 61.773Q-13.435 61.773-13.820 61.529Q-14.205 61.285-14.427 60.875Q-14.650 60.464-14.650 60.007Q-14.650 59.664-14.525 59.341Q-14.400 59.019-14.169 58.765Q-13.939 58.511-13.632 58.367Q-13.326 58.222-12.962 58.222Q-12.599 58.222-12.287 58.369Q-11.974 58.515-11.751 58.761Q-11.529 59.007-11.402 59.328Q-11.275 59.648-11.275 60.007Q-11.275 60.464-11.499 60.877Q-11.724 61.289-12.109 61.531Q-12.494 61.773-12.962 61.773M-12.962 61.214Q-12.498 61.214-12.206 60.820Q-11.915 60.425-11.915 59.941Q-11.915 59.648-12.050 59.380Q-12.185 59.113-12.425 58.947Q-12.665 58.781-12.962 58.781Q-13.267 58.781-13.505 58.947Q-13.744 59.113-13.878 59.380Q-14.013 59.648-14.013 59.941Q-14.013 60.422-13.720 60.818Q-13.427 61.214-12.962 61.214M-10.388 61.496L-10.388 61.406Q-10.337 61.199-10.142 61.175L-9.037 61.175L-9.037 57.406L-10.142 57.406Q-10.337 57.382-10.388 57.168L-10.388 57.078Q-10.337 56.871-10.142 56.847L-8.646 56.847Q-8.455 56.871-8.396 57.078L-8.396 61.175L-7.294 61.175Q-7.095 61.199-7.044 61.406L-7.044 61.496Q-7.095 61.711-7.294 61.734L-10.142 61.734Q-10.337 61.711-10.388 61.496M-6.185 60.621Q-6.185 60.175-5.771 59.918Q-5.357 59.660-4.816 59.560Q-4.275 59.461-3.767 59.453Q-3.767 59.238-3.902 59.086Q-4.037 58.933-4.244 58.857Q-4.451 58.781-4.662 58.781Q-5.005 58.781-5.165 58.804L-5.165 58.863Q-5.165 59.031-5.285 59.146Q-5.404 59.261-5.568 59.261Q-5.744 59.261-5.859 59.138Q-5.974 59.015-5.974 58.847Q-5.974 58.441-5.593 58.332Q-5.212 58.222-4.654 58.222Q-4.384 58.222-4.117 58.300Q-3.849 58.379-3.624 58.529Q-3.400 58.679-3.263 58.900Q-3.126 59.121-3.126 59.398L-3.126 61.117Q-3.126 61.175-2.599 61.175Q-2.404 61.195-2.353 61.406L-2.353 61.496Q-2.404 61.711-2.599 61.734L-2.744 61.734Q-3.087 61.734-3.316 61.687Q-3.544 61.640-3.689 61.453Q-4.150 61.773-4.857 61.773Q-5.193 61.773-5.498 61.632Q-5.802 61.492-5.994 61.230Q-6.185 60.968-6.185 60.621M-5.544 60.629Q-5.544 60.902-5.302 61.058Q-5.060 61.214-4.775 61.214Q-4.556 61.214-4.324 61.156Q-4.091 61.097-3.929 60.959Q-3.767 60.820-3.767 60.597L-3.767 60.007Q-4.048 60.007-4.464 60.064Q-4.880 60.121-5.212 60.259Q-5.544 60.398-5.544 60.629M-1.146 60.629L-1.146 58.847L-1.896 58.847Q-2.095 58.824-2.146 58.605L-2.146 58.519Q-2.095 58.308-1.896 58.285L-1.146 58.285L-1.146 57.535Q-1.095 57.328-0.896 57.300L-0.751 57.300Q-0.556 57.328-0.505 57.535L-0.505 58.285L0.854 58.285Q1.045 58.304 1.104 58.519L1.104 58.605Q1.049 58.824 0.854 58.847L-0.505 58.847L-0.505 60.597Q-0.505 61.214 0.069 61.214Q0.319 61.214 0.483 61.029Q0.647 60.843 0.647 60.597Q0.647 60.504 0.719 60.433Q0.792 60.363 0.893 60.351L1.038 60.351Q1.237 60.375 1.288 60.582L1.288 60.629Q1.288 60.953 1.104 61.216Q0.920 61.480 0.627 61.627Q0.335 61.773 0.014 61.773Q-0.498 61.773-0.822 61.463Q-1.146 61.152-1.146 60.629M2.518 61.496L2.518 61.406Q2.569 61.199 2.764 61.175L3.803 61.175L3.803 58.847L2.831 58.847Q2.631 58.824 2.581 58.605L2.581 58.519Q2.631 58.308 2.831 58.285L4.198 58.285Q4.393 58.304 4.444 58.519L4.444 61.175L5.358 61.175Q5.553 61.199 5.604 61.406L5.604 61.496Q5.553 61.711 5.358 61.734L2.764 61.734Q2.569 61.711 2.518 61.496M3.549 57.308L3.549 57.254Q3.549 57.082 3.686 56.961Q3.823 56.839 3.999 56.839Q4.170 56.839 4.307 56.961Q4.444 57.082 4.444 57.254L4.444 57.308Q4.444 57.484 4.307 57.605Q4.170 57.726 3.999 57.726Q3.823 57.726 3.686 57.605Q3.549 57.484 3.549 57.308M8.268 61.773Q7.795 61.773 7.411 61.529Q7.026 61.285 6.803 60.875Q6.581 60.464 6.581 60.007Q6.581 59.664 6.706 59.341Q6.831 59.019 7.061 58.765Q7.292 58.511 7.598 58.367Q7.905 58.222 8.268 58.222Q8.631 58.222 8.944 58.369Q9.256 58.515 9.479 58.761Q9.702 59.007 9.829 59.328Q9.956 59.648 9.956 60.007Q9.956 60.464 9.731 60.877Q9.506 61.289 9.122 61.531Q8.737 61.773 8.268 61.773M8.268 61.214Q8.733 61.214 9.024 60.820Q9.315 60.425 9.315 59.941Q9.315 59.648 9.180 59.380Q9.045 59.113 8.805 58.947Q8.565 58.781 8.268 58.781Q7.963 58.781 7.725 58.947Q7.487 59.113 7.352 59.380Q7.217 59.648 7.217 59.941Q7.217 60.422 7.510 60.818Q7.803 61.214 8.268 61.214M10.463 61.496L10.463 61.406Q10.506 61.199 10.713 61.175L11.135 61.175L11.135 58.847L10.713 58.847Q10.506 58.824 10.463 58.605L10.463 58.519Q10.510 58.308 10.713 58.285L11.530 58.285Q11.725 58.308 11.776 58.519L11.776 58.605L11.768 58.629Q11.995 58.449 12.268 58.347Q12.542 58.246 12.835 58.246Q13.182 58.246 13.420 58.386Q13.659 58.527 13.774 58.785Q13.889 59.043 13.889 59.398L13.889 61.175L14.315 61.175Q14.522 61.199 14.561 61.406L14.561 61.496Q14.522 61.711 14.315 61.734L12.920 61.734Q12.725 61.711 12.674 61.496L12.674 61.406Q12.725 61.195 12.920 61.175L13.249 61.175L13.249 59.429Q13.249 59.121 13.159 58.963Q13.069 58.804 12.776 58.804Q12.506 58.804 12.278 58.935Q12.049 59.066 11.913 59.295Q11.776 59.523 11.776 59.789L11.776 61.175L12.202 61.175Q12.409 61.199 12.448 61.406L12.448 61.496Q12.409 61.711 12.202 61.734L10.713 61.734Q10.506 61.711 10.463 61.496\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(94.721 -124.457)\">\u003Cpath d=\"M-30.861 70.129L-30.861 68.347L-31.611 68.347Q-31.810 68.324-31.861 68.105L-31.861 68.019Q-31.810 67.808-31.611 67.785L-30.861 67.785L-30.861 67.035Q-30.810 66.828-30.611 66.800L-30.466 66.800Q-30.271 66.828-30.220 67.035L-30.220 67.785L-28.861 67.785Q-28.669 67.804-28.611 68.019L-28.611 68.105Q-28.665 68.324-28.861 68.347L-30.220 68.347L-30.220 70.097Q-30.220 70.714-29.646 70.714Q-29.396 70.714-29.232 70.529Q-29.068 70.343-29.068 70.097Q-29.068 70.004-28.996 69.933Q-28.923 69.863-28.822 69.851L-28.677 69.851Q-28.478 69.875-28.427 70.082L-28.427 70.129Q-28.427 70.453-28.611 70.716Q-28.794 70.980-29.087 71.127Q-29.380 71.273-29.701 71.273Q-30.212 71.273-30.537 70.963Q-30.861 70.652-30.861 70.129M-27.744 70.996L-27.744 70.906Q-27.701 70.699-27.494 70.675L-27.072 70.675L-27.072 66.906L-27.494 66.906Q-27.701 66.882-27.744 66.668L-27.744 66.578Q-27.701 66.371-27.494 66.347L-26.677 66.347Q-26.482 66.371-26.431 66.578L-26.431 68.129Q-25.970 67.746-25.373 67.746Q-25.025 67.746-24.787 67.886Q-24.548 68.027-24.433 68.285Q-24.318 68.543-24.318 68.898L-24.318 70.675L-23.892 70.675Q-23.685 70.699-23.646 70.906L-23.646 70.996Q-23.685 71.211-23.892 71.234L-25.287 71.234Q-25.482 71.211-25.533 70.996L-25.533 70.906Q-25.482 70.695-25.287 70.675L-24.958 70.675L-24.958 68.929Q-24.958 68.621-25.048 68.463Q-25.138 68.304-25.431 68.304Q-25.701 68.304-25.929 68.435Q-26.158 68.566-26.294 68.795Q-26.431 69.023-26.431 69.289L-26.431 70.675L-26.005 70.675Q-25.798 70.699-25.759 70.906L-25.759 70.996Q-25.798 71.211-26.005 71.234L-27.494 71.234Q-27.701 71.211-27.744 70.996M-23.345 70.996L-23.345 70.906Q-23.287 70.699-23.095 70.675L-22.384 70.675L-22.384 68.347L-23.095 68.347Q-23.290 68.324-23.345 68.105L-23.345 68.019Q-23.287 67.808-23.095 67.785L-21.994 67.785Q-21.794 67.804-21.744 68.019L-21.744 68.347Q-21.482 68.062-21.126 67.904Q-20.771 67.746-20.384 67.746Q-20.091 67.746-19.857 67.880Q-19.623 68.015-19.623 68.281Q-19.623 68.449-19.732 68.566Q-19.841 68.683-20.009 68.683Q-20.162 68.683-20.277 68.572Q-20.392 68.461-20.392 68.304Q-20.767 68.304-21.081 68.505Q-21.396 68.707-21.570 69.041Q-21.744 69.375-21.744 69.754L-21.744 70.675L-20.798 70.675Q-20.591 70.699-20.552 70.906L-20.552 70.996Q-20.591 71.211-20.798 71.234L-23.095 71.234Q-23.287 71.211-23.345 70.996M-15.810 69.746L-18.251 69.746Q-18.197 70.023-17.999 70.246Q-17.802 70.468-17.525 70.591Q-17.248 70.714-16.962 70.714Q-16.490 70.714-16.267 70.425Q-16.259 70.414-16.203 70.308Q-16.146 70.203-16.097 70.160Q-16.048 70.117-15.955 70.105L-15.810 70.105Q-15.619 70.125-15.560 70.339L-15.560 70.394Q-15.626 70.695-15.857 70.892Q-16.087 71.089-16.400 71.181Q-16.712 71.273-17.017 71.273Q-17.501 71.273-17.941 71.045Q-18.380 70.816-18.648 70.416Q-18.915 70.015-18.915 69.523L-18.915 69.464Q-18.915 68.996-18.669 68.593Q-18.423 68.191-18.015 67.957Q-17.607 67.722-17.138 67.722Q-16.634 67.722-16.281 67.945Q-15.927 68.168-15.744 68.556Q-15.560 68.945-15.560 69.449L-15.560 69.507Q-15.619 69.722-15.810 69.746M-18.244 69.195L-16.216 69.195Q-16.263 68.785-16.501 68.533Q-16.740 68.281-17.138 68.281Q-17.533 68.281-17.839 68.543Q-18.146 68.804-18.244 69.195M-14.490 71.035L-14.490 70.121Q-14.462 69.914-14.251 69.890L-14.083 69.890Q-13.919 69.914-13.861 70.074Q-13.658 70.714-12.931 70.714Q-12.724 70.714-12.496 70.679Q-12.267 70.644-12.099 70.529Q-11.931 70.414-11.931 70.211Q-11.931 70-12.154 69.886Q-12.376 69.773-12.650 69.730L-13.349 69.617Q-14.490 69.406-14.490 68.683Q-14.490 68.394-14.345 68.205Q-14.201 68.015-13.960 67.908Q-13.720 67.800-13.464 67.761Q-13.208 67.722-12.931 67.722Q-12.681 67.722-12.488 67.752Q-12.294 67.781-12.130 67.859Q-12.052 67.742-11.923 67.722L-11.845 67.722Q-11.748 67.734-11.685 67.796Q-11.623 67.859-11.611 67.953L-11.611 68.660Q-11.623 68.754-11.685 68.820Q-11.748 68.886-11.845 68.898L-12.013 68.898Q-12.107 68.886-12.173 68.820Q-12.240 68.754-12.251 68.660Q-12.251 68.281-12.947 68.281Q-13.294 68.281-13.613 68.363Q-13.931 68.445-13.931 68.691Q-13.931 68.957-13.259 69.066L-12.556 69.187Q-12.072 69.269-11.722 69.517Q-11.373 69.765-11.373 70.211Q-11.373 70.601-11.609 70.843Q-11.845 71.086-12.195 71.179Q-12.544 71.273-12.931 71.273Q-13.509 71.273-13.908 71.019Q-13.978 71.144-14.027 71.201Q-14.076 71.257-14.181 71.273L-14.251 71.273Q-14.466 71.250-14.490 71.035M-10.759 70.996L-10.759 70.906Q-10.716 70.699-10.509 70.675L-10.087 70.675L-10.087 66.906L-10.509 66.906Q-10.716 66.882-10.759 66.668L-10.759 66.578Q-10.716 66.371-10.509 66.347L-9.693 66.347Q-9.498 66.371-9.447 66.578L-9.447 68.129Q-8.986 67.746-8.388 67.746Q-8.040 67.746-7.802 67.886Q-7.564 68.027-7.449 68.285Q-7.333 68.543-7.333 68.898L-7.333 70.675L-6.908 70.675Q-6.701 70.699-6.662 70.906L-6.662 70.996Q-6.701 71.211-6.908 71.234L-8.302 71.234Q-8.498 71.211-8.548 70.996L-8.548 70.906Q-8.498 70.695-8.302 70.675L-7.974 70.675L-7.974 68.929Q-7.974 68.621-8.064 68.463Q-8.154 68.304-8.447 68.304Q-8.716 68.304-8.945 68.435Q-9.173 68.566-9.310 68.795Q-9.447 69.023-9.447 69.289L-9.447 70.675L-9.021 70.675Q-8.814 70.699-8.775 70.906L-8.775 70.996Q-8.814 71.211-9.021 71.234L-10.509 71.234Q-10.716 71.211-10.759 70.996M-4.462 71.273Q-4.935 71.273-5.320 71.029Q-5.705 70.785-5.927 70.375Q-6.150 69.964-6.150 69.507Q-6.150 69.164-6.025 68.841Q-5.900 68.519-5.669 68.265Q-5.439 68.011-5.132 67.867Q-4.826 67.722-4.462 67.722Q-4.099 67.722-3.787 67.869Q-3.474 68.015-3.251 68.261Q-3.029 68.507-2.902 68.828Q-2.775 69.148-2.775 69.507Q-2.775 69.964-2.999 70.377Q-3.224 70.789-3.609 71.031Q-3.994 71.273-4.462 71.273M-4.462 70.714Q-3.998 70.714-3.706 70.320Q-3.415 69.925-3.415 69.441Q-3.415 69.148-3.550 68.880Q-3.685 68.613-3.925 68.447Q-4.165 68.281-4.462 68.281Q-4.767 68.281-5.005 68.447Q-5.244 68.613-5.378 68.880Q-5.513 69.148-5.513 69.441Q-5.513 69.921-5.220 70.318Q-4.927 70.714-4.462 70.714M-1.888 70.996L-1.888 70.906Q-1.837 70.699-1.642 70.675L-0.537 70.675L-0.537 66.906L-1.642 66.906Q-1.837 66.882-1.888 66.668L-1.888 66.578Q-1.837 66.371-1.642 66.347L-0.146 66.347Q0.045 66.371 0.104 66.578L0.104 70.675L1.206 70.675Q1.405 70.699 1.456 70.906L1.456 70.996Q1.405 71.211 1.206 71.234L-1.642 71.234Q-1.837 71.211-1.888 70.996M3.772 71.273Q3.307 71.273 2.942 71.023Q2.577 70.773 2.372 70.369Q2.167 69.964 2.167 69.507Q2.167 69.164 2.292 68.843Q2.417 68.523 2.649 68.273Q2.881 68.023 3.186 67.884Q3.491 67.746 3.846 67.746Q4.358 67.746 4.764 68.066L4.764 66.906L4.342 66.906Q4.131 66.882 4.092 66.668L4.092 66.578Q4.131 66.371 4.342 66.347L5.155 66.347Q5.354 66.371 5.405 66.578L5.405 70.675L5.831 70.675Q6.038 70.699 6.077 70.906L6.077 70.996Q6.038 71.211 5.831 71.234L5.014 71.234Q4.815 71.211 4.764 70.996L4.764 70.867Q4.569 71.058 4.307 71.166Q4.045 71.273 3.772 71.273M3.811 70.714Q4.170 70.714 4.422 70.445Q4.674 70.175 4.764 69.800L4.764 68.968Q4.706 68.781 4.579 68.630Q4.452 68.480 4.274 68.392Q4.096 68.304 3.901 68.304Q3.592 68.304 3.342 68.474Q3.092 68.644 2.948 68.929Q2.803 69.214 2.803 69.515Q2.803 69.964 3.088 70.339Q3.374 70.714 3.811 70.714\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg transform=\"translate(34.25 9.845)\">\u003Cpath d=\"M-39.818 70.379L-39.818 68.347L-40.240 68.347Q-40.447 68.324-40.490 68.105L-40.490 68.019Q-40.443 67.808-40.240 67.785L-39.423 67.785Q-39.228 67.808-39.177 68.019L-39.177 70.347Q-39.177 70.582-39.007 70.648Q-38.837 70.714-38.552 70.714Q-38.345 70.714-38.150 70.638Q-37.955 70.562-37.830 70.412Q-37.705 70.261-37.705 70.050L-37.705 68.347L-38.126 68.347Q-38.337 68.324-38.376 68.105L-38.376 68.019Q-38.337 67.808-38.126 67.785L-37.314 67.785Q-37.115 67.808-37.064 68.019L-37.064 70.675L-36.638 70.675Q-36.431 70.699-36.392 70.906L-36.392 70.996Q-36.431 71.211-36.638 71.234L-37.455 71.234Q-37.654 71.211-37.705 71.011Q-38.107 71.273-38.615 71.273Q-38.849 71.273-39.064 71.232Q-39.279 71.191-39.445 71.089Q-39.611 70.988-39.714 70.810Q-39.818 70.632-39.818 70.379M-36.244 70.996L-36.244 70.906Q-36.201 70.699-35.994 70.675L-35.572 70.675L-35.572 68.347L-35.994 68.347Q-36.201 68.324-36.244 68.105L-36.244 68.019Q-36.197 67.808-35.994 67.785L-35.177 67.785Q-34.982 67.808-34.931 68.019L-34.931 68.105L-34.939 68.129Q-34.712 67.949-34.439 67.847Q-34.165 67.746-33.873 67.746Q-33.525 67.746-33.287 67.886Q-33.048 68.027-32.933 68.285Q-32.818 68.543-32.818 68.898L-32.818 70.675L-32.392 70.675Q-32.185 70.699-32.146 70.906L-32.146 70.996Q-32.185 71.211-32.392 71.234L-33.787 71.234Q-33.982 71.211-34.033 70.996L-34.033 70.906Q-33.982 70.695-33.787 70.675L-33.458 70.675L-33.458 68.929Q-33.458 68.621-33.548 68.463Q-33.638 68.304-33.931 68.304Q-34.201 68.304-34.429 68.435Q-34.658 68.566-34.794 68.795Q-34.931 69.023-34.931 69.289L-34.931 70.675L-34.505 70.675Q-34.298 70.699-34.259 70.906L-34.259 70.996Q-34.298 71.211-34.505 71.234L-35.994 71.234Q-36.201 71.211-36.244 70.996M-30.205 71.273Q-30.669 71.273-31.035 71.023Q-31.400 70.773-31.605 70.369Q-31.810 69.964-31.810 69.507Q-31.810 69.164-31.685 68.843Q-31.560 68.523-31.328 68.273Q-31.095 68.023-30.790 67.884Q-30.486 67.746-30.130 67.746Q-29.619 67.746-29.212 68.066L-29.212 66.906L-29.634 66.906Q-29.845 66.882-29.884 66.668L-29.884 66.578Q-29.845 66.371-29.634 66.347L-28.822 66.347Q-28.623 66.371-28.572 66.578L-28.572 70.675L-28.146 70.675Q-27.939 70.699-27.900 70.906L-27.900 70.996Q-27.939 71.211-28.146 71.234L-28.962 71.234Q-29.162 71.211-29.212 70.996L-29.212 70.867Q-29.408 71.058-29.669 71.166Q-29.931 71.273-30.205 71.273M-30.165 70.714Q-29.806 70.714-29.554 70.445Q-29.302 70.175-29.212 69.800L-29.212 68.968Q-29.271 68.781-29.398 68.630Q-29.525 68.480-29.703 68.392Q-29.880 68.304-30.076 68.304Q-30.384 68.304-30.634 68.474Q-30.884 68.644-31.029 68.929Q-31.173 69.214-31.173 69.515Q-31.173 69.964-30.888 70.339Q-30.603 70.714-30.165 70.714M-24.310 69.746L-26.751 69.746Q-26.697 70.023-26.499 70.246Q-26.302 70.468-26.025 70.591Q-25.748 70.714-25.462 70.714Q-24.990 70.714-24.767 70.425Q-24.759 70.414-24.703 70.308Q-24.646 70.203-24.597 70.160Q-24.548 70.117-24.455 70.105L-24.310 70.105Q-24.119 70.125-24.060 70.339L-24.060 70.394Q-24.126 70.695-24.357 70.892Q-24.587 71.089-24.900 71.181Q-25.212 71.273-25.517 71.273Q-26.001 71.273-26.441 71.045Q-26.880 70.816-27.148 70.416Q-27.415 70.015-27.415 69.523L-27.415 69.464Q-27.415 68.996-27.169 68.593Q-26.923 68.191-26.515 67.957Q-26.107 67.722-25.638 67.722Q-25.134 67.722-24.781 67.945Q-24.427 68.168-24.244 68.556Q-24.060 68.945-24.060 69.449L-24.060 69.507Q-24.119 69.722-24.310 69.746M-26.744 69.195L-24.716 69.195Q-24.763 68.785-25.001 68.533Q-25.240 68.281-25.638 68.281Q-26.033 68.281-26.339 68.543Q-26.646 68.804-26.744 69.195M-23.353 70.996L-23.353 70.906Q-23.294 70.699-23.103 70.675L-22.392 70.675L-22.392 68.347L-23.103 68.347Q-23.298 68.324-23.353 68.105L-23.353 68.019Q-23.294 67.808-23.103 67.785L-22.001 67.785Q-21.802 67.804-21.751 68.019L-21.751 68.347Q-21.490 68.062-21.134 67.904Q-20.779 67.746-20.392 67.746Q-20.099 67.746-19.865 67.880Q-19.630 68.015-19.630 68.281Q-19.630 68.449-19.740 68.566Q-19.849 68.683-20.017 68.683Q-20.169 68.683-20.285 68.572Q-20.400 68.461-20.400 68.304Q-20.775 68.304-21.089 68.505Q-21.404 68.707-21.578 69.041Q-21.751 69.375-21.751 69.754L-21.751 70.675L-20.806 70.675Q-20.599 70.699-20.560 70.906L-20.560 70.996Q-20.599 71.211-20.806 71.234L-23.103 71.234Q-23.294 71.211-23.353 70.996M-15.833 69.097L-18.576 69.097Q-18.701 69.086-18.787 69Q-18.873 68.914-18.873 68.785Q-18.873 68.656-18.787 68.574Q-18.701 68.492-18.576 68.480L-15.833 68.480Q-15.708 68.492-15.626 68.574Q-15.544 68.656-15.544 68.785Q-15.544 68.914-15.626 69Q-15.708 69.086-15.833 69.097M-15.013 72.777L-15.013 72.691Q-14.970 72.472-14.763 72.449L-14.341 72.449L-14.341 68.347L-14.763 68.347Q-14.970 68.324-15.013 68.105L-15.013 68.019Q-14.966 67.808-14.763 67.785L-13.947 67.785Q-13.751 67.804-13.701 68.019L-13.701 68.089Q-13.490 67.921-13.226 67.834Q-12.962 67.746-12.693 67.746Q-12.353 67.746-12.056 67.890Q-11.759 68.035-11.544 68.287Q-11.330 68.539-11.214 68.851Q-11.099 69.164-11.099 69.507Q-11.099 69.972-11.326 70.382Q-11.552 70.793-11.939 71.033Q-12.326 71.273-12.794 71.273Q-13.314 71.273-13.701 70.906L-13.701 72.449L-13.275 72.449Q-13.068 72.472-13.029 72.691L-13.029 72.777Q-13.068 72.988-13.275 73.011L-14.763 73.011Q-14.966 72.988-15.013 72.777M-12.837 70.714Q-12.529 70.714-12.279 70.545Q-12.029 70.375-11.884 70.093Q-11.740 69.812-11.740 69.507Q-11.740 69.218-11.865 68.939Q-11.990 68.660-12.222 68.482Q-12.455 68.304-12.755 68.304Q-13.076 68.304-13.337 68.490Q-13.599 68.675-13.701 68.976L-13.701 69.828Q-13.611 70.195-13.390 70.455Q-13.169 70.714-12.837 70.714M-10.431 70.121Q-10.431 69.675-10.017 69.418Q-9.603 69.160-9.062 69.060Q-8.521 68.961-8.013 68.953Q-8.013 68.738-8.148 68.586Q-8.283 68.433-8.490 68.357Q-8.697 68.281-8.908 68.281Q-9.251 68.281-9.412 68.304L-9.412 68.363Q-9.412 68.531-9.531 68.646Q-9.650 68.761-9.814 68.761Q-9.990 68.761-10.105 68.638Q-10.220 68.515-10.220 68.347Q-10.220 67.941-9.839 67.832Q-9.458 67.722-8.900 67.722Q-8.630 67.722-8.363 67.800Q-8.095 67.879-7.871 68.029Q-7.646 68.179-7.509 68.400Q-7.373 68.621-7.373 68.898L-7.373 70.617Q-7.373 70.675-6.845 70.675Q-6.650 70.695-6.599 70.906L-6.599 70.996Q-6.650 71.211-6.845 71.234L-6.990 71.234Q-7.333 71.234-7.562 71.187Q-7.790 71.140-7.935 70.953Q-8.396 71.273-9.103 71.273Q-9.439 71.273-9.744 71.132Q-10.048 70.992-10.240 70.730Q-10.431 70.468-10.431 70.121M-9.790 70.129Q-9.790 70.402-9.548 70.558Q-9.306 70.714-9.021 70.714Q-8.802 70.714-8.570 70.656Q-8.337 70.597-8.175 70.459Q-8.013 70.320-8.013 70.097L-8.013 69.507Q-8.294 69.507-8.710 69.564Q-9.126 69.621-9.458 69.759Q-9.790 69.898-9.790 70.129M-6.369 70.996L-6.369 70.906Q-6.310 70.699-6.119 70.675L-5.408 70.675L-5.408 68.347L-6.119 68.347Q-6.314 68.324-6.369 68.105L-6.369 68.019Q-6.310 67.808-6.119 67.785L-5.017 67.785Q-4.818 67.804-4.767 68.019L-4.767 68.347Q-4.505 68.062-4.150 67.904Q-3.794 67.746-3.408 67.746Q-3.115 67.746-2.880 67.880Q-2.646 68.015-2.646 68.281Q-2.646 68.449-2.755 68.566Q-2.865 68.683-3.033 68.683Q-3.185 68.683-3.300 68.572Q-3.415 68.461-3.415 68.304Q-3.790 68.304-4.105 68.505Q-4.419 68.707-4.593 69.041Q-4.767 69.375-4.767 69.754L-4.767 70.675L-3.822 70.675Q-3.615 70.699-3.576 70.906L-3.576 70.996Q-3.615 71.211-3.822 71.234L-6.119 71.234Q-6.310 71.211-6.369 70.996M-1.939 70.121Q-1.939 69.675-1.525 69.418Q-1.111 69.160-0.570 69.060Q-0.029 68.961 0.479 68.953Q0.479 68.738 0.344 68.586Q0.210 68.433 0.002 68.357Q-0.205 68.281-0.415 68.281Q-0.759 68.281-0.919 68.304L-0.919 68.363Q-0.919 68.531-1.039 68.646Q-1.158 68.761-1.322 68.761Q-1.498 68.761-1.613 68.638Q-1.728 68.515-1.728 68.347Q-1.728 67.941-1.347 67.832Q-0.966 67.722-0.408 67.722Q-0.138 67.722 0.129 67.800Q0.397 67.879 0.622 68.029Q0.846 68.179 0.983 68.400Q1.120 68.621 1.120 68.898L1.120 70.617Q1.120 70.675 1.647 70.675Q1.842 70.695 1.893 70.906L1.893 70.996Q1.842 71.211 1.647 71.234L1.502 71.234Q1.159 71.234 0.930 71.187Q0.702 71.140 0.557 70.953Q0.096 71.273-0.611 71.273Q-0.947 71.273-1.251 71.132Q-1.556 70.992-1.748 70.730Q-1.939 70.468-1.939 70.121M-1.298 70.129Q-1.298 70.402-1.056 70.558Q-0.814 70.714-0.529 70.714Q-0.310 70.714-0.078 70.656Q0.155 70.597 0.317 70.459Q0.479 70.320 0.479 70.097L0.479 69.507Q0.198 69.507-0.218 69.564Q-0.634 69.621-0.966 69.759Q-1.298 69.898-1.298 70.129M1.846 70.996L1.846 70.906Q1.897 70.695 2.092 70.675L2.292 70.675L2.292 68.347L2.092 68.347Q1.885 68.324 1.846 68.105L1.846 68.019Q1.897 67.804 2.092 67.785L2.573 67.785Q2.764 67.808 2.823 68.019Q3.143 67.746 3.557 67.746Q3.749 67.746 3.917 67.855Q4.085 67.964 4.159 68.136Q4.335 67.945 4.557 67.845Q4.780 67.746 5.022 67.746Q5.440 67.746 5.594 68.088Q5.749 68.429 5.749 68.898L5.749 70.675L5.948 70.675Q6.159 70.699 6.198 70.906L6.198 70.996Q6.147 71.211 5.948 71.234L5.131 71.234Q4.936 71.211 4.885 70.996L4.885 70.906Q4.936 70.699 5.131 70.675L5.221 70.675L5.221 68.929Q5.221 68.304 4.971 68.304Q4.639 68.304 4.461 68.615Q4.284 68.925 4.284 69.289L4.284 70.675L4.487 70.675Q4.694 70.699 4.733 70.906L4.733 70.996Q4.682 71.211 4.487 71.234L3.670 71.234Q3.471 71.211 3.420 70.996L3.420 70.906Q3.471 70.699 3.670 70.675L3.756 70.675L3.756 68.929Q3.756 68.304 3.510 68.304Q3.178 68.304 3.001 68.617Q2.823 68.929 2.823 69.289L2.823 70.675L3.022 70.675Q3.229 70.699 3.268 70.906L3.268 70.996Q3.217 71.214 3.022 71.234L2.092 71.234Q1.885 71.211 1.846 70.996M7.713 70.675Q7.713 70.453 7.879 70.287Q8.045 70.121 8.276 70.121Q8.424 70.121 8.551 70.199Q8.678 70.277 8.752 70.402Q8.827 70.527 8.827 70.675Q8.827 70.902 8.661 71.068Q8.495 71.234 8.276 71.234Q8.049 71.234 7.881 71.066Q7.713 70.898 7.713 70.675\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg stroke=\"none\" font-size=\"8\">\u003Cg transform=\"translate(151.679 10.956)\">\u003Cpath d=\"M-38.439 71.273Q-38.912 71.273-39.296 71.029Q-39.681 70.785-39.904 70.375Q-40.126 69.964-40.126 69.507Q-40.126 69.164-40.001 68.841Q-39.876 68.519-39.646 68.265Q-39.415 68.011-39.109 67.867Q-38.802 67.722-38.439 67.722Q-38.076 67.722-37.763 67.869Q-37.451 68.015-37.228 68.261Q-37.005 68.507-36.878 68.828Q-36.751 69.148-36.751 69.507Q-36.751 69.964-36.976 70.377Q-37.201 70.789-37.585 71.031Q-37.970 71.273-38.439 71.273M-38.439 70.714Q-37.974 70.714-37.683 70.320Q-37.392 69.925-37.392 69.441Q-37.392 69.148-37.527 68.880Q-37.662 68.613-37.902 68.447Q-38.142 68.281-38.439 68.281Q-38.744 68.281-38.982 68.447Q-39.220 68.613-39.355 68.880Q-39.490 69.148-39.490 69.441Q-39.490 69.921-39.197 70.318Q-38.904 70.714-38.439 70.714M-34.658 71.011L-35.544 68.347L-35.865 68.347Q-36.064 68.324-36.115 68.105L-36.115 68.019Q-36.064 67.808-35.865 67.785L-34.705 67.785Q-34.509 67.804-34.458 68.019L-34.458 68.105Q-34.509 68.324-34.705 68.347L-34.986 68.347L-34.193 70.722L-33.404 68.347L-33.681 68.347Q-33.880 68.324-33.931 68.105L-33.931 68.019Q-33.880 67.808-33.681 67.785L-32.521 67.785Q-32.326 67.808-32.275 68.019L-32.275 68.105Q-32.326 68.324-32.521 68.347L-32.841 68.347L-33.728 71.011Q-33.771 71.125-33.873 71.199Q-33.974 71.273-34.099 71.273L-34.290 71.273Q-34.408 71.273-34.511 71.201Q-34.615 71.129-34.658 71.011M-28.556 69.746L-30.998 69.746Q-30.943 70.023-30.746 70.246Q-30.548 70.468-30.271 70.591Q-29.994 70.714-29.708 70.714Q-29.236 70.714-29.013 70.425Q-29.005 70.414-28.949 70.308Q-28.892 70.203-28.843 70.160Q-28.794 70.117-28.701 70.105L-28.556 70.105Q-28.365 70.125-28.306 70.339L-28.306 70.394Q-28.373 70.695-28.603 70.892Q-28.833 71.089-29.146 71.181Q-29.458 71.273-29.763 71.273Q-30.248 71.273-30.687 71.045Q-31.126 70.816-31.394 70.416Q-31.662 70.015-31.662 69.523L-31.662 69.464Q-31.662 68.996-31.415 68.593Q-31.169 68.191-30.761 67.957Q-30.353 67.722-29.884 67.722Q-29.380 67.722-29.027 67.945Q-28.673 68.168-28.490 68.556Q-28.306 68.945-28.306 69.449L-28.306 69.507Q-28.365 69.722-28.556 69.746M-30.990 69.195L-28.962 69.195Q-29.009 68.785-29.248 68.533Q-29.486 68.281-29.884 68.281Q-30.279 68.281-30.585 68.543Q-30.892 68.804-30.990 69.195M-27.599 70.996L-27.599 70.906Q-27.540 70.699-27.349 70.675L-26.638 70.675L-26.638 68.347L-27.349 68.347Q-27.544 68.324-27.599 68.105L-27.599 68.019Q-27.540 67.808-27.349 67.785L-26.248 67.785Q-26.048 67.804-25.998 68.019L-25.998 68.347Q-25.736 68.062-25.380 67.904Q-25.025 67.746-24.638 67.746Q-24.345 67.746-24.111 67.880Q-23.876 68.015-23.876 68.281Q-23.876 68.449-23.986 68.566Q-24.095 68.683-24.263 68.683Q-24.415 68.683-24.531 68.572Q-24.646 68.461-24.646 68.304Q-25.021 68.304-25.335 68.505Q-25.650 68.707-25.824 69.041Q-25.998 69.375-25.998 69.754L-25.998 70.675L-25.052 70.675Q-24.845 70.699-24.806 70.906L-24.806 70.996Q-24.845 71.211-25.052 71.234L-27.349 71.234Q-27.540 71.211-27.599 70.996M-20.080 69.097L-22.822 69.097Q-22.947 69.086-23.033 69Q-23.119 68.914-23.119 68.785Q-23.119 68.656-23.033 68.574Q-22.947 68.492-22.822 68.480L-20.080 68.480Q-19.955 68.492-19.873 68.574Q-19.790 68.656-19.790 68.785Q-19.790 68.914-19.873 69Q-19.955 69.086-20.080 69.097M-19.259 72.777L-19.259 72.691Q-19.216 72.472-19.009 72.449L-18.587 72.449L-18.587 68.347L-19.009 68.347Q-19.216 68.324-19.259 68.105L-19.259 68.019Q-19.212 67.808-19.009 67.785L-18.193 67.785Q-17.998 67.804-17.947 68.019L-17.947 68.089Q-17.736 67.921-17.472 67.834Q-17.208 67.746-16.939 67.746Q-16.599 67.746-16.302 67.890Q-16.005 68.035-15.790 68.287Q-15.576 68.539-15.460 68.851Q-15.345 69.164-15.345 69.507Q-15.345 69.972-15.572 70.382Q-15.798 70.793-16.185 71.033Q-16.572 71.273-17.040 71.273Q-17.560 71.273-17.947 70.906L-17.947 72.449L-17.521 72.449Q-17.314 72.472-17.275 72.691L-17.275 72.777Q-17.314 72.988-17.521 73.011L-19.009 73.011Q-19.212 72.988-19.259 72.777M-17.083 70.714Q-16.775 70.714-16.525 70.545Q-16.275 70.375-16.130 70.093Q-15.986 69.812-15.986 69.507Q-15.986 69.218-16.111 68.939Q-16.236 68.660-16.468 68.482Q-16.701 68.304-17.001 68.304Q-17.322 68.304-17.583 68.490Q-17.845 68.675-17.947 68.976L-17.947 69.828Q-17.857 70.195-17.636 70.455Q-17.415 70.714-17.083 70.714M-14.677 70.121Q-14.677 69.675-14.263 69.418Q-13.849 69.160-13.308 69.060Q-12.767 68.961-12.259 68.953Q-12.259 68.738-12.394 68.586Q-12.529 68.433-12.736 68.357Q-12.943 68.281-13.154 68.281Q-13.498 68.281-13.658 68.304L-13.658 68.363Q-13.658 68.531-13.777 68.646Q-13.896 68.761-14.060 68.761Q-14.236 68.761-14.351 68.638Q-14.466 68.515-14.466 68.347Q-14.466 67.941-14.085 67.832Q-13.705 67.722-13.146 67.722Q-12.876 67.722-12.609 67.800Q-12.341 67.879-12.117 68.029Q-11.892 68.179-11.755 68.400Q-11.619 68.621-11.619 68.898L-11.619 70.617Q-11.619 70.675-11.091 70.675Q-10.896 70.695-10.845 70.906L-10.845 70.996Q-10.896 71.211-11.091 71.234L-11.236 71.234Q-11.580 71.234-11.808 71.187Q-12.037 71.140-12.181 70.953Q-12.642 71.273-13.349 71.273Q-13.685 71.273-13.990 71.132Q-14.294 70.992-14.486 70.730Q-14.677 70.468-14.677 70.121M-14.037 70.129Q-14.037 70.402-13.794 70.558Q-13.552 70.714-13.267 70.714Q-13.048 70.714-12.816 70.656Q-12.583 70.597-12.421 70.459Q-12.259 70.320-12.259 70.097L-12.259 69.507Q-12.540 69.507-12.956 69.564Q-13.373 69.621-13.705 69.759Q-14.037 69.898-14.037 70.129M-10.615 70.996L-10.615 70.906Q-10.556 70.699-10.365 70.675L-9.654 70.675L-9.654 68.347L-10.365 68.347Q-10.560 68.324-10.615 68.105L-10.615 68.019Q-10.556 67.808-10.365 67.785L-9.263 67.785Q-9.064 67.804-9.013 68.019L-9.013 68.347Q-8.751 68.062-8.396 67.904Q-8.040 67.746-7.654 67.746Q-7.361 67.746-7.126 67.880Q-6.892 68.015-6.892 68.281Q-6.892 68.449-7.001 68.566Q-7.111 68.683-7.279 68.683Q-7.431 68.683-7.546 68.572Q-7.662 68.461-7.662 68.304Q-8.037 68.304-8.351 68.505Q-8.665 68.707-8.839 69.041Q-9.013 69.375-9.013 69.754L-9.013 70.675L-8.068 70.675Q-7.861 70.699-7.822 70.906L-7.822 70.996Q-7.861 71.211-8.068 71.234L-10.365 71.234Q-10.556 71.211-10.615 70.996M-6.185 70.121Q-6.185 69.675-5.771 69.418Q-5.357 69.160-4.816 69.060Q-4.275 68.961-3.767 68.953Q-3.767 68.738-3.902 68.586Q-4.037 68.433-4.244 68.357Q-4.451 68.281-4.662 68.281Q-5.005 68.281-5.165 68.304L-5.165 68.363Q-5.165 68.531-5.285 68.646Q-5.404 68.761-5.568 68.761Q-5.744 68.761-5.859 68.638Q-5.974 68.515-5.974 68.347Q-5.974 67.941-5.593 67.832Q-5.212 67.722-4.654 67.722Q-4.384 67.722-4.117 67.800Q-3.849 67.879-3.624 68.029Q-3.400 68.179-3.263 68.400Q-3.126 68.621-3.126 68.898L-3.126 70.617Q-3.126 70.675-2.599 70.675Q-2.404 70.695-2.353 70.906L-2.353 70.996Q-2.404 71.211-2.599 71.234L-2.744 71.234Q-3.087 71.234-3.316 71.187Q-3.544 71.140-3.689 70.953Q-4.150 71.273-4.857 71.273Q-5.193 71.273-5.498 71.132Q-5.802 70.992-5.994 70.730Q-6.185 70.468-6.185 70.121M-5.544 70.129Q-5.544 70.402-5.302 70.558Q-5.060 70.714-4.775 70.714Q-4.556 70.714-4.324 70.656Q-4.091 70.597-3.929 70.459Q-3.767 70.320-3.767 70.097L-3.767 69.507Q-4.048 69.507-4.464 69.564Q-4.880 69.621-5.212 69.759Q-5.544 69.898-5.544 70.129M-2.400 70.996L-2.400 70.906Q-2.349 70.695-2.154 70.675L-1.955 70.675L-1.955 68.347L-2.154 68.347Q-2.361 68.324-2.400 68.105L-2.400 68.019Q-2.349 67.804-2.154 67.785L-1.673 67.785Q-1.482 67.808-1.423 68.019Q-1.103 67.746-0.689 67.746Q-0.498 67.746-0.330 67.855Q-0.162 67.964-0.087 68.136Q0.088 67.945 0.311 67.845Q0.534 67.746 0.776 67.746Q1.194 67.746 1.348 68.088Q1.502 68.429 1.502 68.898L1.502 70.675L1.702 70.675Q1.913 70.699 1.952 70.906L1.952 70.996Q1.901 71.211 1.702 71.234L0.885 71.234Q0.690 71.211 0.639 70.996L0.639 70.906Q0.690 70.699 0.885 70.675L0.975 70.675L0.975 68.929Q0.975 68.304 0.725 68.304Q0.393 68.304 0.215 68.615Q0.038 68.925 0.038 69.289L0.038 70.675L0.241 70.675Q0.448 70.699 0.487 70.906L0.487 70.996Q0.436 71.211 0.241 71.234L-0.576 71.234Q-0.775 71.211-0.826 70.996L-0.826 70.906Q-0.775 70.699-0.576 70.675L-0.490 70.675L-0.490 68.929Q-0.490 68.304-0.736 68.304Q-1.068 68.304-1.246 68.617Q-1.423 68.929-1.423 69.289L-1.423 70.675L-1.224 70.675Q-1.017 70.699-0.978 70.906L-0.978 70.996Q-1.029 71.214-1.224 71.234L-2.154 71.234Q-2.361 71.211-2.400 70.996M3.467 70.675Q3.467 70.453 3.633 70.287Q3.799 70.121 4.030 70.121Q4.178 70.121 4.305 70.199Q4.432 70.277 4.506 70.402Q4.581 70.527 4.581 70.675Q4.581 70.902 4.415 71.068Q4.249 71.234 4.030 71.234Q3.803 71.234 3.635 71.066Q3.467 70.898 3.467 70.675\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(151.679 10.956)\">\u003Cpath d=\"M12.586 73.226Q11.973 72.769 11.571 72.134Q11.168 71.500 10.973 70.754Q10.778 70.007 10.778 69.234Q10.778 68.461 10.973 67.714Q11.168 66.968 11.571 66.334Q11.973 65.699 12.586 65.242Q12.598 65.238 12.606 65.236Q12.614 65.234 12.625 65.234L12.703 65.234Q12.742 65.234 12.768 65.261Q12.793 65.289 12.793 65.332Q12.793 65.382 12.762 65.402Q12.254 65.855 11.932 66.478Q11.610 67.101 11.469 67.796Q11.328 68.492 11.328 69.234Q11.328 69.968 11.467 70.668Q11.606 71.367 11.930 71.992Q12.254 72.617 12.762 73.066Q12.793 73.086 12.793 73.136Q12.793 73.179 12.768 73.207Q12.742 73.234 12.703 73.234L12.625 73.234Q12.617 73.230 12.608 73.228Q12.598 73.226 12.586 73.226\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(151.679 10.956)\">\u003Cpath d=\"M14.762 72.785L13.161 72.785Q13.126 72.785 13.092 72.744Q13.059 72.703 13.059 72.668L13.090 72.562Q13.122 72.500 13.176 72.492Q13.368 72.492 13.452 72.476Q13.536 72.461 13.588 72.394Q13.641 72.328 13.680 72.187L14.571 68.617Q14.610 68.461 14.610 68.324Q14.610 68.175 14.557 68.068Q14.504 67.961 14.372 67.961Q14.192 67.961 14.073 68.130Q13.954 68.300 13.897 68.486Q13.840 68.671 13.770 68.961Q13.758 69.035 13.688 69.035L13.586 69.035Q13.551 69.035 13.524 69Q13.497 68.964 13.497 68.937L13.497 68.906Q13.583 68.574 13.676 68.332Q13.770 68.089 13.946 67.898Q14.122 67.707 14.387 67.707Q14.669 67.707 14.899 67.851Q15.129 67.996 15.196 68.250Q15.715 67.707 16.274 67.707Q16.809 67.707 17.129 68.091Q17.450 68.476 17.450 69.019Q17.450 69.543 17.169 70.082Q16.887 70.621 16.420 70.966Q15.954 71.312 15.419 71.312Q15.180 71.312 14.979 71.195Q14.778 71.078 14.649 70.867L14.305 72.242Q14.294 72.281 14.274 72.402Q14.274 72.492 14.778 72.492Q14.883 72.523 14.883 72.617L14.848 72.722Q14.817 72.777 14.762 72.785M15.204 68.668L14.770 70.394Q14.829 70.668 14.999 70.863Q15.169 71.058 15.434 71.058Q15.770 71.058 16.049 70.767Q16.329 70.476 16.465 70.121Q16.590 69.828 16.692 69.394Q16.794 68.961 16.794 68.683Q16.794 68.398 16.661 68.179Q16.528 67.961 16.258 67.961Q16.047 67.961 15.852 68.062Q15.657 68.164 15.497 68.320Q15.336 68.476 15.204 68.668\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(151.679 10.956)\">\u003Cpath d=\"M20.625 71.562Q20.625 71.457 20.738 71.394L25.195 69.234L20.730 67.066Q20.625 67.027 20.625 66.906Q20.625 66.828 20.678 66.775Q20.730 66.722 20.809 66.722Q20.828 66.722 20.891 66.738L25.707 69.066Q25.801 69.121 25.801 69.234Q25.801 69.339 25.699 69.402L20.891 71.730Q20.828 71.746 20.809 71.746Q20.730 71.746 20.678 71.693Q20.625 71.640 20.625 71.562\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(151.679 10.956)\">\u003Cpath d=\"M29.582 71.058Q29.586 71.039 29.588 71.025Q29.590 71.011 29.590 70.988L30.184 68.617Q30.223 68.461 30.223 68.324Q30.223 68.175 30.170 68.068Q30.117 67.961 29.985 67.961Q29.805 67.961 29.686 68.130Q29.567 68.300 29.510 68.486Q29.453 68.671 29.383 68.961Q29.371 69.035 29.301 69.035L29.200 69.035Q29.164 69.035 29.137 69Q29.110 68.964 29.110 68.937L29.110 68.906Q29.196 68.574 29.289 68.332Q29.383 68.089 29.559 67.898Q29.735 67.707 30 67.707Q30.200 67.707 30.393 67.789Q30.586 67.871 30.713 68.025Q30.840 68.179 30.840 68.386Q31.090 68.070 31.416 67.888Q31.742 67.707 32.117 67.707Q32.567 67.707 32.850 67.933Q33.133 68.160 33.133 68.593Q33.133 68.933 33 69.334Q32.867 69.734 32.614 70.402Q32.520 70.625 32.520 70.808Q32.520 71.058 32.696 71.058Q33.004 71.058 33.225 70.736Q33.446 70.414 33.528 70.058Q33.555 69.988 33.614 69.988L33.719 69.988Q33.758 69.988 33.783 70.021Q33.809 70.054 33.809 70.082Q33.809 70.097 33.797 70.113Q33.684 70.566 33.389 70.939Q33.094 71.312 32.680 71.312Q32.371 71.312 32.153 71.125Q31.934 70.937 31.934 70.632Q31.934 70.464 31.992 70.347Q32.235 69.703 32.377 69.261Q32.520 68.820 32.520 68.492Q32.520 68.261 32.422 68.111Q32.325 67.961 32.102 67.961Q31.278 67.961 30.719 69.035L30.223 71.027Q30.192 71.152 30.086 71.232Q29.981 71.312 29.856 71.312Q29.746 71.312 29.664 71.242Q29.582 71.171 29.582 71.058\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(151.679 10.956)\">\u003Cpath d=\"M34.685 73.234L34.603 73.234Q34.567 73.234 34.542 73.205Q34.517 73.175 34.517 73.136Q34.517 73.086 34.548 73.066Q34.935 72.730 35.218 72.281Q35.501 71.832 35.667 71.332Q35.833 70.832 35.907 70.314Q35.981 69.796 35.981 69.234Q35.981 68.664 35.907 68.148Q35.833 67.632 35.667 67.136Q35.501 66.640 35.222 66.193Q34.942 65.746 34.548 65.402Q34.517 65.382 34.517 65.332Q34.517 65.293 34.542 65.263Q34.567 65.234 34.603 65.234L34.685 65.234Q34.696 65.234 34.706 65.236Q34.716 65.238 34.724 65.242Q35.337 65.699 35.739 66.334Q36.142 66.968 36.337 67.714Q36.532 68.461 36.532 69.234Q36.532 70.007 36.337 70.754Q36.142 71.500 35.739 72.134Q35.337 72.769 34.724 73.226Q34.712 73.226 34.704 73.228Q34.696 73.230 34.685 73.234\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-warn)\" d=\"m-29.179-21.006 1.88 4.445 1.882 3.765 1.88 3.212 1.881 2.763 1.88 2.376 1.882 2.066 1.88 1.786 1.881 1.552 1.881 1.345 1.88 1.164 1.881 1 1.881.856 1.881.725 1.88.607 1.882.497 1.88.4 1.881.306 1.88.22 1.882.14 1.88.065 1.881-.005 1.88-.071 1.882-.135 1.88-.193 1.881-.252 1.88-.305 1.882-.36 1.88-.41 1.881-.456 1.88-.506 1.882-.551 1.88-.595 1.881-.64 1.88-.68 1.882-.724 1.88-.76L40.41.84 42.29 0l1.882-.877 1.88-.914 1.881-.951 1.881-.987 1.88-1.022 1.881-1.056L55.455-6.9l1.881-1.125 1.88-1.159 1.881-1.192 1.881-1.226 1.881-1.257 1.88-1.29 1.882-1.32 1.88-1.354 1.881-1.384 1.88-1.416 1.882-1.448 1.88-1.477 1.881-1.508 1.88-1.538M81.787-24.083l1.773 5.078 1.773 4.7 1.773 4.333L88.88-5.98l1.774 3.686 1.773 3.397 1.773 3.129 1.773 2.893 1.773 2.665 1.773 2.454 1.773 2.265 1.773 2.095 1.773 1.924 1.773 1.774 1.773 1.643 1.774 1.512 1.773 1.394 1.773 1.283 1.773 1.185 1.773 1.093 1.773 1.008 1.773.93 1.773.857 1.773.793 1.773.726 1.773.673 1.773.617 1.774.575 1.773.524 1.773.484 1.773.452 1.773.412 1.773.38 1.773.353 1.773.327 1.773.295 1.773.276 1.773.254 1.773.236 1.774.216 1.773.197 1.773.19 1.773.17 1.773.15 1.773.144 1.773.137 1.773.118 1.773.118 1.773.105 1.773.091 1.774.092 1.773.078 1.773.08 1.773.071 1.773.059 1.773.059 1.773.054 1.773.051 1.773.046 1.773.043 1.773.039 1.773.037 1.774.033 1.773.03 1.773.03 1.773.026 1.773.024 1.773.022 1.773.021\" style=\"stroke-width:1.2\"\u002F>\u003Cpath fill=\"var(--tk-warn)\" stroke=\"none\" d=\"M7.165 12.337a2.2 2.2 0 1 0-4.4 0 2.2 2.2 0 0 0 4.4 0m-2.2 0\"\u002F>\u003Cg fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\">\u003Cg fill=\"var(--tk-warn)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(16.337 -38.812)\">\u003Cpath d=\"M-40.001 69.507Q-40.001 69.027-39.757 68.613Q-39.513 68.199-39.097 67.961Q-38.681 67.722-38.201 67.722Q-37.646 67.722-37.267 67.832Q-36.888 67.941-36.888 68.347Q-36.888 68.515-37.001 68.638Q-37.115 68.761-37.287 68.761Q-37.458 68.761-37.578 68.646Q-37.697 68.531-37.697 68.363L-37.697 68.304Q-37.857 68.281-38.193 68.281Q-38.521 68.281-38.789 68.451Q-39.056 68.621-39.208 68.904Q-39.361 69.187-39.361 69.507Q-39.361 69.828-39.189 70.109Q-39.017 70.390-38.732 70.552Q-38.447 70.714-38.119 70.714Q-37.806 70.714-37.679 70.611Q-37.552 70.507-37.435 70.318Q-37.318 70.129-37.201 70.113L-37.033 70.113Q-36.927 70.125-36.863 70.193Q-36.798 70.261-36.798 70.363Q-36.798 70.410-36.818 70.449Q-36.927 70.742-37.130 70.921Q-37.333 71.101-37.609 71.187Q-37.884 71.273-38.201 71.273Q-38.685 71.273-39.101 71.035Q-39.517 70.796-39.759 70.394Q-40.001 69.992-40.001 69.507M-35.865 70.996L-35.865 70.906Q-35.814 70.699-35.619 70.675L-34.513 70.675L-34.513 66.906L-35.619 66.906Q-35.814 66.882-35.865 66.668L-35.865 66.578Q-35.814 66.371-35.619 66.347L-34.123 66.347Q-33.931 66.371-33.873 66.578L-33.873 70.675L-32.771 70.675Q-32.572 70.699-32.521 70.906L-32.521 70.996Q-32.572 71.211-32.771 71.234L-35.619 71.234Q-35.814 71.211-35.865 70.996M-31.662 70.121Q-31.662 69.675-31.248 69.418Q-30.833 69.160-30.292 69.060Q-29.751 68.961-29.244 68.953Q-29.244 68.738-29.378 68.586Q-29.513 68.433-29.720 68.357Q-29.927 68.281-30.138 68.281Q-30.482 68.281-30.642 68.304L-30.642 68.363Q-30.642 68.531-30.761 68.646Q-30.880 68.761-31.044 68.761Q-31.220 68.761-31.335 68.638Q-31.451 68.515-31.451 68.347Q-31.451 67.941-31.070 67.832Q-30.689 67.722-30.130 67.722Q-29.861 67.722-29.593 67.800Q-29.326 67.879-29.101 68.029Q-28.876 68.179-28.740 68.400Q-28.603 68.621-28.603 68.898L-28.603 70.617Q-28.603 70.675-28.076 70.675Q-27.880 70.695-27.830 70.906L-27.830 70.996Q-27.880 71.211-28.076 71.234L-28.220 71.234Q-28.564 71.234-28.792 71.187Q-29.021 71.140-29.165 70.953Q-29.626 71.273-30.333 71.273Q-30.669 71.273-30.974 71.132Q-31.279 70.992-31.470 70.730Q-31.662 70.468-31.662 70.121M-31.021 70.129Q-31.021 70.402-30.779 70.558Q-30.537 70.714-30.251 70.714Q-30.033 70.714-29.800 70.656Q-29.568 70.597-29.406 70.459Q-29.244 70.320-29.244 70.097L-29.244 69.507Q-29.525 69.507-29.941 69.564Q-30.357 69.621-30.689 69.759Q-31.021 69.898-31.021 70.129M-27.236 71.035L-27.236 70.121Q-27.208 69.914-26.998 69.890L-26.830 69.890Q-26.665 69.914-26.607 70.074Q-26.404 70.714-25.677 70.714Q-25.470 70.714-25.242 70.679Q-25.013 70.644-24.845 70.529Q-24.677 70.414-24.677 70.211Q-24.677 70-24.900 69.886Q-25.123 69.773-25.396 69.730L-26.095 69.617Q-27.236 69.406-27.236 68.683Q-27.236 68.394-27.091 68.205Q-26.947 68.015-26.706 67.908Q-26.466 67.800-26.210 67.761Q-25.955 67.722-25.677 67.722Q-25.427 67.722-25.234 67.752Q-25.040 67.781-24.876 67.859Q-24.798 67.742-24.669 67.722L-24.591 67.722Q-24.494 67.734-24.431 67.796Q-24.369 67.859-24.357 67.953L-24.357 68.660Q-24.369 68.754-24.431 68.820Q-24.494 68.886-24.591 68.898L-24.759 68.898Q-24.853 68.886-24.919 68.820Q-24.986 68.754-24.998 68.660Q-24.998 68.281-25.693 68.281Q-26.040 68.281-26.359 68.363Q-26.677 68.445-26.677 68.691Q-26.677 68.957-26.005 69.066L-25.302 69.187Q-24.818 69.269-24.468 69.517Q-24.119 69.765-24.119 70.211Q-24.119 70.601-24.355 70.843Q-24.591 71.086-24.941 71.179Q-25.290 71.273-25.677 71.273Q-26.255 71.273-26.654 71.019Q-26.724 71.144-26.773 71.201Q-26.822 71.257-26.927 71.273L-26.998 71.273Q-27.212 71.250-27.236 71.035M-22.990 71.035L-22.990 70.121Q-22.962 69.914-22.751 69.890L-22.583 69.890Q-22.419 69.914-22.361 70.074Q-22.158 70.714-21.431 70.714Q-21.224 70.714-20.996 70.679Q-20.767 70.644-20.599 70.529Q-20.431 70.414-20.431 70.211Q-20.431 70-20.654 69.886Q-20.876 69.773-21.150 69.730L-21.849 69.617Q-22.990 69.406-22.990 68.683Q-22.990 68.394-22.845 68.205Q-22.701 68.015-22.460 67.908Q-22.220 67.800-21.964 67.761Q-21.708 67.722-21.431 67.722Q-21.181 67.722-20.988 67.752Q-20.794 67.781-20.630 67.859Q-20.552 67.742-20.423 67.722L-20.345 67.722Q-20.248 67.734-20.185 67.796Q-20.123 67.859-20.111 67.953L-20.111 68.660Q-20.123 68.754-20.185 68.820Q-20.248 68.886-20.345 68.898L-20.513 68.898Q-20.607 68.886-20.673 68.820Q-20.740 68.754-20.751 68.660Q-20.751 68.281-21.447 68.281Q-21.794 68.281-22.113 68.363Q-22.431 68.445-22.431 68.691Q-22.431 68.957-21.759 69.066L-21.056 69.187Q-20.572 69.269-20.222 69.517Q-19.873 69.765-19.873 70.211Q-19.873 70.601-20.109 70.843Q-20.345 71.086-20.695 71.179Q-21.044 71.273-21.431 71.273Q-22.009 71.273-22.408 71.019Q-22.478 71.144-22.527 71.201Q-22.576 71.257-22.681 71.273L-22.751 71.273Q-22.966 71.250-22.990 71.035M-18.712 70.996L-18.712 70.906Q-18.662 70.699-18.466 70.675L-17.427 70.675L-17.427 68.347L-18.400 68.347Q-18.599 68.324-18.650 68.105L-18.650 68.019Q-18.599 67.808-18.400 67.785L-17.033 67.785Q-16.837 67.804-16.787 68.019L-16.787 70.675L-15.873 70.675Q-15.677 70.699-15.626 70.906L-15.626 70.996Q-15.677 71.211-15.873 71.234L-18.466 71.234Q-18.662 71.211-18.712 70.996M-17.681 66.808L-17.681 66.754Q-17.681 66.582-17.544 66.461Q-17.408 66.339-17.232 66.339Q-17.060 66.339-16.923 66.461Q-16.787 66.582-16.787 66.754L-16.787 66.808Q-16.787 66.984-16.923 67.105Q-17.060 67.226-17.232 67.226Q-17.408 67.226-17.544 67.105Q-17.681 66.984-17.681 66.808M-14.525 69.507Q-14.525 69.027-14.281 68.613Q-14.037 68.199-13.621 67.961Q-13.205 67.722-12.724 67.722Q-12.169 67.722-11.790 67.832Q-11.412 67.941-11.412 68.347Q-11.412 68.515-11.525 68.638Q-11.638 68.761-11.810 68.761Q-11.982 68.761-12.101 68.646Q-12.220 68.531-12.220 68.363L-12.220 68.304Q-12.380 68.281-12.716 68.281Q-13.044 68.281-13.312 68.451Q-13.580 68.621-13.732 68.904Q-13.884 69.187-13.884 69.507Q-13.884 69.828-13.712 70.109Q-13.540 70.390-13.255 70.552Q-12.970 70.714-12.642 70.714Q-12.330 70.714-12.203 70.611Q-12.076 70.507-11.958 70.318Q-11.841 70.129-11.724 70.113L-11.556 70.113Q-11.451 70.125-11.386 70.193Q-11.322 70.261-11.322 70.363Q-11.322 70.410-11.341 70.449Q-11.451 70.742-11.654 70.921Q-11.857 71.101-12.132 71.187Q-12.408 71.273-12.724 71.273Q-13.208 71.273-13.624 71.035Q-14.040 70.796-14.283 70.394Q-14.525 69.992-14.525 69.507M-10.431 70.121Q-10.431 69.675-10.017 69.418Q-9.603 69.160-9.062 69.060Q-8.521 68.961-8.013 68.953Q-8.013 68.738-8.148 68.586Q-8.283 68.433-8.490 68.357Q-8.697 68.281-8.908 68.281Q-9.251 68.281-9.412 68.304L-9.412 68.363Q-9.412 68.531-9.531 68.646Q-9.650 68.761-9.814 68.761Q-9.990 68.761-10.105 68.638Q-10.220 68.515-10.220 68.347Q-10.220 67.941-9.839 67.832Q-9.458 67.722-8.900 67.722Q-8.630 67.722-8.363 67.800Q-8.095 67.879-7.871 68.029Q-7.646 68.179-7.509 68.400Q-7.373 68.621-7.373 68.898L-7.373 70.617Q-7.373 70.675-6.845 70.675Q-6.650 70.695-6.599 70.906L-6.599 70.996Q-6.650 71.211-6.845 71.234L-6.990 71.234Q-7.333 71.234-7.562 71.187Q-7.790 71.140-7.935 70.953Q-8.396 71.273-9.103 71.273Q-9.439 71.273-9.744 71.132Q-10.048 70.992-10.240 70.730Q-10.431 70.468-10.431 70.121M-9.790 70.129Q-9.790 70.402-9.548 70.558Q-9.306 70.714-9.021 70.714Q-8.802 70.714-8.570 70.656Q-8.337 70.597-8.175 70.459Q-8.013 70.320-8.013 70.097L-8.013 69.507Q-8.294 69.507-8.710 69.564Q-9.126 69.621-9.458 69.759Q-9.790 69.898-9.790 70.129M-6.142 70.996L-6.142 70.906Q-6.091 70.699-5.896 70.675L-4.790 70.675L-4.790 66.906L-5.896 66.906Q-6.091 66.882-6.142 66.668L-6.142 66.578Q-6.091 66.371-5.896 66.347L-4.400 66.347Q-4.208 66.371-4.150 66.578L-4.150 70.675L-3.048 70.675Q-2.849 70.699-2.798 70.906L-2.798 70.996Q-2.849 71.211-3.048 71.234L-5.896 71.234Q-6.091 71.211-6.142 70.996\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(16.337 -38.812)\">\u003Cpath d=\"M1.886 70.996L1.886 70.906Q1.937 70.695 2.132 70.675L2.332 70.675L2.332 68.347L2.132 68.347Q1.925 68.324 1.886 68.105L1.886 68.019Q1.937 67.804 2.132 67.785L2.613 67.785Q2.804 67.808 2.863 68.019Q3.183 67.746 3.597 67.746Q3.789 67.746 3.957 67.855Q4.125 67.964 4.199 68.136Q4.375 67.945 4.597 67.845Q4.820 67.746 5.062 67.746Q5.480 67.746 5.634 68.088Q5.789 68.429 5.789 68.898L5.789 70.675L5.988 70.675Q6.199 70.699 6.238 70.906L6.238 70.996Q6.187 71.211 5.988 71.234L5.171 71.234Q4.976 71.211 4.925 70.996L4.925 70.906Q4.976 70.699 5.171 70.675L5.261 70.675L5.261 68.929Q5.261 68.304 5.011 68.304Q4.679 68.304 4.502 68.615Q4.324 68.925 4.324 69.289L4.324 70.675L4.527 70.675Q4.734 70.699 4.773 70.906L4.773 70.996Q4.722 71.211 4.527 71.234L3.711 71.234Q3.511 71.211 3.461 70.996L3.461 70.906Q3.511 70.699 3.711 70.675L3.796 70.675L3.796 68.929Q3.796 68.304 3.550 68.304Q3.218 68.304 3.041 68.617Q2.863 68.929 2.863 69.289L2.863 70.675L3.062 70.675Q3.269 70.699 3.308 70.906L3.308 70.996Q3.257 71.214 3.062 71.234L2.132 71.234Q1.925 71.211 1.886 70.996M6.804 70.996L6.804 70.906Q6.855 70.699 7.050 70.675L8.089 70.675L8.089 68.347L7.117 68.347Q6.918 68.324 6.867 68.105L6.867 68.019Q6.918 67.808 7.117 67.785L8.484 67.785Q8.679 67.804 8.730 68.019L8.730 70.675L9.644 70.675Q9.839 70.699 9.890 70.906L9.890 70.996Q9.839 71.211 9.644 71.234L7.050 71.234Q6.855 71.211 6.804 70.996M7.836 66.808L7.836 66.754Q7.836 66.582 7.972 66.461Q8.109 66.339 8.285 66.339Q8.457 66.339 8.593 66.461Q8.730 66.582 8.730 66.754L8.730 66.808Q8.730 66.984 8.593 67.105Q8.457 67.226 8.285 67.226Q8.109 67.226 7.972 67.105Q7.836 66.984 7.836 66.808M10.503 70.996L10.503 70.906Q10.546 70.699 10.753 70.675L11.175 70.675L11.175 68.347L10.753 68.347Q10.546 68.324 10.503 68.105L10.503 68.019Q10.550 67.808 10.753 67.785L11.570 67.785Q11.765 67.808 11.816 68.019L11.816 68.105L11.808 68.129Q12.035 67.949 12.308 67.847Q12.582 67.746 12.875 67.746Q13.222 67.746 13.461 67.886Q13.699 68.027 13.814 68.285Q13.929 68.543 13.929 68.898L13.929 70.675L14.355 70.675Q14.562 70.699 14.601 70.906L14.601 70.996Q14.562 71.211 14.355 71.234L12.961 71.234Q12.765 71.211 12.714 70.996L12.714 70.906Q12.765 70.695 12.961 70.675L13.289 70.675L13.289 68.929Q13.289 68.621 13.199 68.463Q13.109 68.304 12.816 68.304Q12.546 68.304 12.318 68.435Q12.089 68.566 11.953 68.795Q11.816 69.023 11.816 69.289L11.816 70.675L12.242 70.675Q12.449 70.699 12.488 70.906L12.488 70.996Q12.449 71.211 12.242 71.234L10.753 71.234Q10.546 71.211 10.503 70.996\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-good)\" stroke=\"none\" d=\"M195.353 41.074a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0m-2.6 0\"\u002F>\u003Cg fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\">\u003Cg fill=\"var(--tk-good)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(197.836 -51.903)\">\u003Cpath d=\"M-40.615 70.996L-40.615 70.906Q-40.564 70.695-40.369 70.675L-40.169 70.675L-40.169 68.347L-40.369 68.347Q-40.576 68.324-40.615 68.105L-40.615 68.019Q-40.564 67.804-40.369 67.785L-39.888 67.785Q-39.697 67.808-39.638 68.019Q-39.318 67.746-38.904 67.746Q-38.712 67.746-38.544 67.855Q-38.376 67.964-38.302 68.136Q-38.126 67.945-37.904 67.845Q-37.681 67.746-37.439 67.746Q-37.021 67.746-36.867 68.088Q-36.712 68.429-36.712 68.898L-36.712 70.675L-36.513 70.675Q-36.302 70.699-36.263 70.906L-36.263 70.996Q-36.314 71.211-36.513 71.234L-37.330 71.234Q-37.525 71.211-37.576 70.996L-37.576 70.906Q-37.525 70.699-37.330 70.675L-37.240 70.675L-37.240 68.929Q-37.240 68.304-37.490 68.304Q-37.822 68.304-37.999 68.615Q-38.177 68.925-38.177 69.289L-38.177 70.675L-37.974 70.675Q-37.767 70.699-37.728 70.906L-37.728 70.996Q-37.779 71.211-37.974 71.234L-38.790 71.234Q-38.990 71.211-39.040 70.996L-39.040 70.906Q-38.990 70.699-38.790 70.675L-38.705 70.675L-38.705 68.929Q-38.705 68.304-38.951 68.304Q-39.283 68.304-39.460 68.617Q-39.638 68.929-39.638 69.289L-39.638 70.675L-39.439 70.675Q-39.232 70.699-39.193 70.906L-39.193 70.996Q-39.244 71.214-39.439 71.234L-40.369 71.234Q-40.576 71.211-40.615 70.996M-34.193 71.273Q-34.665 71.273-35.050 71.029Q-35.435 70.785-35.658 70.375Q-35.880 69.964-35.880 69.507Q-35.880 69.164-35.755 68.841Q-35.630 68.519-35.400 68.265Q-35.169 68.011-34.863 67.867Q-34.556 67.722-34.193 67.722Q-33.830 67.722-33.517 67.869Q-33.205 68.015-32.982 68.261Q-32.759 68.507-32.632 68.828Q-32.505 69.148-32.505 69.507Q-32.505 69.964-32.730 70.377Q-32.955 70.789-33.339 71.031Q-33.724 71.273-34.193 71.273M-34.193 70.714Q-33.728 70.714-33.437 70.320Q-33.146 69.925-33.146 69.441Q-33.146 69.148-33.281 68.880Q-33.415 68.613-33.656 68.447Q-33.896 68.281-34.193 68.281Q-34.498 68.281-34.736 68.447Q-34.974 68.613-35.109 68.880Q-35.244 69.148-35.244 69.441Q-35.244 69.921-34.951 70.318Q-34.658 70.714-34.193 70.714M-30.205 71.273Q-30.669 71.273-31.035 71.023Q-31.400 70.773-31.605 70.369Q-31.810 69.964-31.810 69.507Q-31.810 69.164-31.685 68.843Q-31.560 68.523-31.328 68.273Q-31.095 68.023-30.790 67.884Q-30.486 67.746-30.130 67.746Q-29.619 67.746-29.212 68.066L-29.212 66.906L-29.634 66.906Q-29.845 66.882-29.884 66.668L-29.884 66.578Q-29.845 66.371-29.634 66.347L-28.822 66.347Q-28.623 66.371-28.572 66.578L-28.572 70.675L-28.146 70.675Q-27.939 70.699-27.900 70.906L-27.900 70.996Q-27.939 71.211-28.146 71.234L-28.962 71.234Q-29.162 71.211-29.212 70.996L-29.212 70.867Q-29.408 71.058-29.669 71.166Q-29.931 71.273-30.205 71.273M-30.165 70.714Q-29.806 70.714-29.554 70.445Q-29.302 70.175-29.212 69.800L-29.212 68.968Q-29.271 68.781-29.398 68.630Q-29.525 68.480-29.703 68.392Q-29.880 68.304-30.076 68.304Q-30.384 68.304-30.634 68.474Q-30.884 68.644-31.029 68.929Q-31.173 69.214-31.173 69.515Q-31.173 69.964-30.888 70.339Q-30.603 70.714-30.165 70.714M-24.310 69.746L-26.751 69.746Q-26.697 70.023-26.499 70.246Q-26.302 70.468-26.025 70.591Q-25.748 70.714-25.462 70.714Q-24.990 70.714-24.767 70.425Q-24.759 70.414-24.703 70.308Q-24.646 70.203-24.597 70.160Q-24.548 70.117-24.455 70.105L-24.310 70.105Q-24.119 70.125-24.060 70.339L-24.060 70.394Q-24.126 70.695-24.357 70.892Q-24.587 71.089-24.900 71.181Q-25.212 71.273-25.517 71.273Q-26.001 71.273-26.441 71.045Q-26.880 70.816-27.148 70.416Q-27.415 70.015-27.415 69.523L-27.415 69.464Q-27.415 68.996-27.169 68.593Q-26.923 68.191-26.515 67.957Q-26.107 67.722-25.638 67.722Q-25.134 67.722-24.781 67.945Q-24.427 68.168-24.244 68.556Q-24.060 68.945-24.060 69.449L-24.060 69.507Q-24.119 69.722-24.310 69.746M-26.744 69.195L-24.716 69.195Q-24.763 68.785-25.001 68.533Q-25.240 68.281-25.638 68.281Q-26.033 68.281-26.339 68.543Q-26.646 68.804-26.744 69.195M-23.353 70.996L-23.353 70.906Q-23.294 70.699-23.103 70.675L-22.392 70.675L-22.392 68.347L-23.103 68.347Q-23.298 68.324-23.353 68.105L-23.353 68.019Q-23.294 67.808-23.103 67.785L-22.001 67.785Q-21.802 67.804-21.751 68.019L-21.751 68.347Q-21.490 68.062-21.134 67.904Q-20.779 67.746-20.392 67.746Q-20.099 67.746-19.865 67.880Q-19.630 68.015-19.630 68.281Q-19.630 68.449-19.740 68.566Q-19.849 68.683-20.017 68.683Q-20.169 68.683-20.285 68.572Q-20.400 68.461-20.400 68.304Q-20.775 68.304-21.089 68.505Q-21.404 68.707-21.578 69.041Q-21.751 69.375-21.751 69.754L-21.751 70.675L-20.806 70.675Q-20.599 70.699-20.560 70.906L-20.560 70.996Q-20.599 71.211-20.806 71.234L-23.103 71.234Q-23.294 71.211-23.353 70.996M-19.259 70.996L-19.259 70.906Q-19.216 70.699-19.009 70.675L-18.587 70.675L-18.587 68.347L-19.009 68.347Q-19.216 68.324-19.259 68.105L-19.259 68.019Q-19.212 67.808-19.009 67.785L-18.193 67.785Q-17.998 67.808-17.947 68.019L-17.947 68.105L-17.955 68.129Q-17.728 67.949-17.455 67.847Q-17.181 67.746-16.888 67.746Q-16.540 67.746-16.302 67.886Q-16.064 68.027-15.949 68.285Q-15.833 68.543-15.833 68.898L-15.833 70.675L-15.408 70.675Q-15.201 70.699-15.162 70.906L-15.162 70.996Q-15.201 71.211-15.408 71.234L-16.802 71.234Q-16.998 71.211-17.048 70.996L-17.048 70.906Q-16.998 70.695-16.802 70.675L-16.474 70.675L-16.474 68.929Q-16.474 68.621-16.564 68.463Q-16.654 68.304-16.947 68.304Q-17.216 68.304-17.445 68.435Q-17.673 68.566-17.810 68.795Q-17.947 69.023-17.947 69.289L-17.947 70.675L-17.521 70.675Q-17.314 70.699-17.275 70.906L-17.275 70.996Q-17.314 71.211-17.521 71.234L-19.009 71.234Q-19.216 71.211-19.259 70.996\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(197.836 -51.903)\">\u003Cpath d=\"M-10.864 70.996L-10.864 70.906Q-10.813 70.695-10.618 70.675L-10.418 70.675L-10.418 68.347L-10.618 68.347Q-10.825 68.324-10.864 68.105L-10.864 68.019Q-10.813 67.804-10.618 67.785L-10.137 67.785Q-9.946 67.808-9.887 68.019Q-9.567 67.746-9.153 67.746Q-8.961 67.746-8.793 67.855Q-8.625 67.964-8.551 68.136Q-8.375 67.945-8.153 67.845Q-7.930 67.746-7.688 67.746Q-7.270 67.746-7.116 68.088Q-6.961 68.429-6.961 68.898L-6.961 70.675L-6.762 70.675Q-6.551 70.699-6.512 70.906L-6.512 70.996Q-6.563 71.211-6.762 71.234L-7.579 71.234Q-7.774 71.211-7.825 70.996L-7.825 70.906Q-7.774 70.699-7.579 70.675L-7.489 70.675L-7.489 68.929Q-7.489 68.304-7.739 68.304Q-8.071 68.304-8.248 68.615Q-8.426 68.925-8.426 69.289L-8.426 70.675L-8.223 70.675Q-8.016 70.699-7.977 70.906L-7.977 70.996Q-8.028 71.211-8.223 71.234L-9.039 71.234Q-9.239 71.211-9.289 70.996L-9.289 70.906Q-9.239 70.699-9.039 70.675L-8.954 70.675L-8.954 68.929Q-8.954 68.304-9.200 68.304Q-9.532 68.304-9.709 68.617Q-9.887 68.929-9.887 69.289L-9.887 70.675L-9.688 70.675Q-9.481 70.699-9.442 70.906L-9.442 70.996Q-9.493 71.214-9.688 71.234L-10.618 71.234Q-10.825 71.211-10.864 70.996M-5.946 70.996L-5.946 70.906Q-5.895 70.699-5.700 70.675L-4.661 70.675L-4.661 68.347L-5.633 68.347Q-5.832 68.324-5.883 68.105L-5.883 68.019Q-5.832 67.808-5.633 67.785L-4.266 67.785Q-4.071 67.804-4.020 68.019L-4.020 70.675L-3.106 70.675Q-2.911 70.699-2.860 70.906L-2.860 70.996Q-2.911 71.211-3.106 71.234L-5.700 71.234Q-5.895 71.211-5.946 70.996M-4.914 66.808L-4.914 66.754Q-4.914 66.582-4.778 66.461Q-4.641 66.339-4.465 66.339Q-4.293 66.339-4.157 66.461Q-4.020 66.582-4.020 66.754L-4.020 66.808Q-4.020 66.984-4.157 67.105Q-4.293 67.226-4.465 67.226Q-4.641 67.226-4.778 67.105Q-4.914 66.984-4.914 66.808M-2.246 70.996L-2.246 70.906Q-2.204 70.699-1.996 70.675L-1.575 70.675L-1.575 68.347L-1.996 68.347Q-2.204 68.324-2.246 68.105L-2.246 68.019Q-2.200 67.808-1.996 67.785L-1.180 67.785Q-0.985 67.808-0.934 68.019L-0.934 68.105L-0.942 68.129Q-0.715 67.949-0.442 67.847Q-0.168 67.746 0.125 67.746Q0.472 67.746 0.711 67.886Q0.949 68.027 1.064 68.285Q1.179 68.543 1.179 68.898L1.179 70.675L1.605 70.675Q1.812 70.699 1.851 70.906L1.851 70.996Q1.812 71.211 1.605 71.234L0.211 71.234Q0.015 71.211-0.036 70.996L-0.036 70.906Q0.015 70.695 0.211 70.675L0.539 70.675L0.539 68.929Q0.539 68.621 0.449 68.463Q0.359 68.304 0.066 68.304Q-0.204 68.304-0.432 68.435Q-0.661 68.566-0.797 68.795Q-0.934 69.023-0.934 69.289L-0.934 70.675L-0.508 70.675Q-0.301 70.699-0.262 70.906L-0.262 70.996Q-0.301 71.211-0.508 71.234L-1.996 71.234Q-2.204 71.211-2.246 70.996\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">Double descent: test risk follows the classical U, spikes at the interpolation threshold, then descends a second time in the overparameterized regime \u003Cspan class=\"katex\">\u003Cspan class=\"katex-html\" aria-hidden=\"true\">\u003Cspan class=\"base\">\u003Cspan class=\"strut\" style=\"height:0.7335em;vertical-align:-0.1944em;\">\u003C\u002Fspan>\u003Cspan class=\"mord mathnormal\">p\u003C\u002Fspan>\u003Cspan class=\"mspace\" style=\"margin-right:0.2778em;\">\u003C\u002Fspan>\u003Cspan class=\"mrel\">>\u003C\u002Fspan>\u003Cspan class=\"mspace\" style=\"margin-right:0.2778em;\">\u003C\u002Fspan>\u003C\u002Fspan>\u003Cspan class=\"base\">\u003Cspan class=\"strut\" style=\"height:0.4306em;\">\u003C\u002Fspan>\u003Cspan class=\"mord mathnormal\">n\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fspan>.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:317.775px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 238.331 125.537\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(20.024 -96.36)\">\u003Cpath d=\"M-61.484 32.112L-61.484 31.198Q-61.457 30.991-61.246 30.967L-61.078 30.967Q-60.914 30.991-60.855 31.151Q-60.652 31.791-59.925 31.791Q-59.718 31.791-59.490 31.756Q-59.261 31.721-59.093 31.606Q-58.925 31.491-58.925 31.288Q-58.925 31.077-59.148 30.963Q-59.371 30.850-59.644 30.807L-60.343 30.694Q-61.484 30.483-61.484 29.760Q-61.484 29.471-61.340 29.282Q-61.195 29.092-60.955 28.985Q-60.715 28.877-60.459 28.838Q-60.203 28.799-59.925 28.799Q-59.675 28.799-59.482 28.829Q-59.289 28.858-59.125 28.936Q-59.047 28.819-58.918 28.799L-58.840 28.799Q-58.742 28.811-58.679 28.873Q-58.617 28.936-58.605 29.030L-58.605 29.737Q-58.617 29.831-58.679 29.897Q-58.742 29.963-58.840 29.975L-59.008 29.975Q-59.101 29.963-59.168 29.897Q-59.234 29.831-59.246 29.737Q-59.246 29.358-59.941 29.358Q-60.289 29.358-60.607 29.440Q-60.925 29.522-60.925 29.768Q-60.925 30.034-60.254 30.143L-59.550 30.264Q-59.066 30.346-58.716 30.594Q-58.367 30.842-58.367 31.288Q-58.367 31.678-58.603 31.920Q-58.840 32.163-59.189 32.256Q-59.539 32.350-59.925 32.350Q-60.504 32.350-60.902 32.096Q-60.972 32.221-61.021 32.278Q-61.070 32.334-61.175 32.350L-61.246 32.350Q-61.461 32.327-61.484 32.112M-57.879 32.073L-57.879 31.983Q-57.828 31.772-57.633 31.752L-57.433 31.752L-57.433 29.424L-57.633 29.424Q-57.840 29.401-57.879 29.182L-57.879 29.096Q-57.828 28.881-57.633 28.862L-57.152 28.862Q-56.961 28.885-56.902 29.096Q-56.582 28.823-56.168 28.823Q-55.976 28.823-55.808 28.932Q-55.640 29.041-55.566 29.213Q-55.390 29.022-55.168 28.922Q-54.945 28.823-54.703 28.823Q-54.285 28.823-54.131 29.165Q-53.976 29.506-53.976 29.975L-53.976 31.752L-53.777 31.752Q-53.566 31.776-53.527 31.983L-53.527 32.073Q-53.578 32.288-53.777 32.311L-54.593 32.311Q-54.789 32.288-54.840 32.073L-54.840 31.983Q-54.789 31.776-54.593 31.752L-54.504 31.752L-54.504 30.006Q-54.504 29.381-54.754 29.381Q-55.086 29.381-55.263 29.692Q-55.441 30.002-55.441 30.366L-55.441 31.752L-55.238 31.752Q-55.031 31.776-54.992 31.983L-54.992 32.073Q-55.043 32.288-55.238 32.311L-56.054 32.311Q-56.254 32.288-56.304 32.073L-56.304 31.983Q-56.254 31.776-56.054 31.752L-55.968 31.752L-55.968 30.006Q-55.968 29.381-56.215 29.381Q-56.547 29.381-56.724 29.694Q-56.902 30.006-56.902 30.366L-56.902 31.752L-56.703 31.752Q-56.496 31.776-56.457 31.983L-56.457 32.073Q-56.508 32.291-56.703 32.311L-57.633 32.311Q-57.840 32.288-57.879 32.073M-53.172 31.198Q-53.172 30.752-52.758 30.495Q-52.343 30.237-51.802 30.137Q-51.261 30.038-50.754 30.030Q-50.754 29.815-50.888 29.663Q-51.023 29.510-51.230 29.434Q-51.437 29.358-51.648 29.358Q-51.992 29.358-52.152 29.381L-52.152 29.440Q-52.152 29.608-52.271 29.723Q-52.390 29.838-52.554 29.838Q-52.730 29.838-52.845 29.715Q-52.961 29.592-52.961 29.424Q-52.961 29.018-52.580 28.909Q-52.199 28.799-51.640 28.799Q-51.371 28.799-51.103 28.877Q-50.836 28.956-50.611 29.106Q-50.386 29.256-50.250 29.477Q-50.113 29.698-50.113 29.975L-50.113 31.694Q-50.113 31.752-49.586 31.752Q-49.390 31.772-49.340 31.983L-49.340 32.073Q-49.390 32.288-49.586 32.311L-49.730 32.311Q-50.074 32.311-50.302 32.264Q-50.531 32.217-50.675 32.030Q-51.136 32.350-51.843 32.350Q-52.179 32.350-52.484 32.209Q-52.789 32.069-52.980 31.807Q-53.172 31.545-53.172 31.198M-52.531 31.206Q-52.531 31.479-52.289 31.635Q-52.047 31.791-51.761 31.791Q-51.543 31.791-51.310 31.733Q-51.078 31.674-50.916 31.536Q-50.754 31.397-50.754 31.174L-50.754 30.584Q-51.035 30.584-51.451 30.641Q-51.867 30.698-52.199 30.836Q-52.531 30.975-52.531 31.206M-48.883 32.073L-48.883 31.983Q-48.832 31.776-48.636 31.752L-47.531 31.752L-47.531 27.983L-48.636 27.983Q-48.832 27.959-48.883 27.745L-48.883 27.655Q-48.832 27.448-48.636 27.424L-47.140 27.424Q-46.949 27.448-46.890 27.655L-46.890 31.752L-45.789 31.752Q-45.590 31.776-45.539 31.983L-45.539 32.073Q-45.590 32.288-45.789 32.311L-48.636 32.311Q-48.832 32.288-48.883 32.073M-44.636 32.073L-44.636 31.983Q-44.586 31.776-44.390 31.752L-43.285 31.752L-43.285 27.983L-44.390 27.983Q-44.586 27.959-44.636 27.745L-44.636 27.655Q-44.586 27.448-44.390 27.424L-42.894 27.424Q-42.703 27.448-42.644 27.655L-42.644 31.752L-41.543 31.752Q-41.343 31.776-41.293 31.983L-41.293 32.073Q-41.343 32.288-41.543 32.311L-44.390 32.311Q-44.586 32.288-44.636 32.073\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(20.024 -96.36)\">\u003Cpath d=\"M-36.625 32.073L-36.625 31.983Q-36.574 31.772-36.379 31.752L-36.179 31.752L-36.179 29.424L-36.379 29.424Q-36.586 29.401-36.625 29.182L-36.625 29.096Q-36.574 28.881-36.379 28.862L-35.898 28.862Q-35.707 28.885-35.648 29.096Q-35.328 28.823-34.914 28.823Q-34.722 28.823-34.554 28.932Q-34.386 29.041-34.312 29.213Q-34.136 29.022-33.914 28.922Q-33.691 28.823-33.449 28.823Q-33.031 28.823-32.877 29.165Q-32.722 29.506-32.722 29.975L-32.722 31.752L-32.523 31.752Q-32.312 31.776-32.273 31.983L-32.273 32.073Q-32.324 32.288-32.523 32.311L-33.340 32.311Q-33.535 32.288-33.586 32.073L-33.586 31.983Q-33.535 31.776-33.340 31.752L-33.250 31.752L-33.250 30.006Q-33.250 29.381-33.500 29.381Q-33.832 29.381-34.009 29.692Q-34.187 30.002-34.187 30.366L-34.187 31.752L-33.984 31.752Q-33.777 31.776-33.738 31.983L-33.738 32.073Q-33.789 32.288-33.984 32.311L-34.800 32.311Q-35 32.288-35.050 32.073L-35.050 31.983Q-35 31.776-34.800 31.752L-34.715 31.752L-34.715 30.006Q-34.715 29.381-34.961 29.381Q-35.293 29.381-35.470 29.694Q-35.648 30.006-35.648 30.366L-35.648 31.752L-35.449 31.752Q-35.242 31.776-35.203 31.983L-35.203 32.073Q-35.254 32.291-35.449 32.311L-36.379 32.311Q-36.586 32.288-36.625 32.073M-31.918 31.198Q-31.918 30.752-31.504 30.495Q-31.090 30.237-30.549 30.137Q-30.008 30.038-29.500 30.030Q-29.500 29.815-29.634 29.663Q-29.769 29.510-29.976 29.434Q-30.183 29.358-30.394 29.358Q-30.738 29.358-30.898 29.381L-30.898 29.440Q-30.898 29.608-31.017 29.723Q-31.136 29.838-31.300 29.838Q-31.476 29.838-31.591 29.715Q-31.707 29.592-31.707 29.424Q-31.707 29.018-31.326 28.909Q-30.945 28.799-30.386 28.799Q-30.117 28.799-29.849 28.877Q-29.582 28.956-29.357 29.106Q-29.133 29.256-28.996 29.477Q-28.859 29.698-28.859 29.975L-28.859 31.694Q-28.859 31.752-28.332 31.752Q-28.136 31.772-28.086 31.983L-28.086 32.073Q-28.136 32.288-28.332 32.311L-28.476 32.311Q-28.820 32.311-29.049 32.264Q-29.277 32.217-29.422 32.030Q-29.883 32.350-30.590 32.350Q-30.925 32.350-31.230 32.209Q-31.535 32.069-31.726 31.807Q-31.918 31.545-31.918 31.198M-31.277 31.206Q-31.277 31.479-31.035 31.635Q-30.793 31.791-30.508 31.791Q-30.289 31.791-30.056 31.733Q-29.824 31.674-29.662 31.536Q-29.500 31.397-29.500 31.174L-29.500 30.584Q-29.781 30.584-30.197 30.641Q-30.613 30.698-30.945 30.836Q-31.277 30.975-31.277 31.206M-27.855 32.073L-27.855 31.983Q-27.797 31.776-27.605 31.752L-26.894 31.752L-26.894 29.424L-27.605 29.424Q-27.800 29.401-27.855 29.182L-27.855 29.096Q-27.797 28.885-27.605 28.862L-26.504 28.862Q-26.304 28.881-26.254 29.096L-26.254 29.424Q-25.992 29.139-25.636 28.981Q-25.281 28.823-24.894 28.823Q-24.601 28.823-24.367 28.957Q-24.133 29.092-24.133 29.358Q-24.133 29.526-24.242 29.643Q-24.351 29.760-24.519 29.760Q-24.672 29.760-24.787 29.649Q-24.902 29.538-24.902 29.381Q-25.277 29.381-25.591 29.582Q-25.906 29.784-26.080 30.118Q-26.254 30.452-26.254 30.831L-26.254 31.752L-25.308 31.752Q-25.101 31.776-25.062 31.983L-25.062 32.073Q-25.101 32.288-25.308 32.311L-27.605 32.311Q-27.797 32.288-27.855 32.073M-23.593 32.959Q-23.593 32.659-23.445 32.397Q-23.297 32.135-23.047 31.975Q-23.230 31.721-23.230 31.401Q-23.230 31.116-23.078 30.854Q-23.328 30.530-23.328 30.112Q-23.328 29.752-23.136 29.456Q-22.945 29.159-22.627 28.991Q-22.308 28.823-21.945 28.823Q-21.738 28.823-21.535 28.883Q-21.332 28.944-21.168 29.045Q-20.785 28.784-20.312 28.784Q-20.074 28.784-19.892 28.915Q-19.711 29.045-19.711 29.272Q-19.711 29.420-19.812 29.526Q-19.914 29.631-20.070 29.631Q-20.203 29.631-20.299 29.553Q-20.394 29.475-20.425 29.350Q-20.570 29.358-20.769 29.448Q-20.566 29.760-20.566 30.112Q-20.566 30.393-20.677 30.625Q-20.789 30.858-20.984 31.036Q-21.179 31.213-21.431 31.311Q-21.683 31.409-21.945 31.409Q-22.332 31.409-22.664 31.221Q-22.675 31.221-22.685 31.293Q-22.695 31.366-22.695 31.401Q-22.695 31.522-22.629 31.629Q-22.562 31.737-22.441 31.776Q-22.425 31.772-22.412 31.770Q-22.398 31.768-22.375 31.768Q-22.359 31.768-22.328 31.776Q-22.297 31.784-22.289 31.784L-21.695 31.784Q-20.914 31.784-20.373 32.026Q-19.832 32.268-19.832 32.959Q-19.832 33.260-20.011 33.487Q-20.191 33.713-20.488 33.858Q-20.785 34.002-21.105 34.069Q-21.425 34.135-21.711 34.135Q-22.101 34.135-22.543 34.012Q-22.984 33.889-23.289 33.622Q-23.593 33.354-23.593 32.959M-23.054 32.952Q-23.054 33.170-22.814 33.311Q-22.574 33.452-22.250 33.518Q-21.925 33.584-21.711 33.584Q-21.496 33.584-21.172 33.518Q-20.847 33.452-20.607 33.311Q-20.367 33.170-20.367 32.952Q-20.367 32.663-20.591 32.524Q-20.816 32.385-21.097 32.352Q-21.379 32.319-21.726 32.319L-22.343 32.319Q-22.527 32.319-22.689 32.399Q-22.851 32.479-22.953 32.625Q-23.054 32.772-23.054 32.952M-21.945 30.854Q-21.644 30.854-21.425 30.635Q-21.207 30.416-21.207 30.112Q-21.207 29.956-21.261 29.825Q-21.316 29.694-21.422 29.588Q-21.527 29.483-21.658 29.428Q-21.789 29.373-21.945 29.373Q-22.250 29.373-22.468 29.592Q-22.687 29.811-22.687 30.112Q-22.687 30.409-22.465 30.631Q-22.242 30.854-21.945 30.854M-18.968 32.073L-18.968 31.983Q-18.918 31.776-18.722 31.752L-17.683 31.752L-17.683 29.424L-18.656 29.424Q-18.855 29.401-18.906 29.182L-18.906 29.096Q-18.855 28.885-18.656 28.862L-17.289 28.862Q-17.093 28.881-17.043 29.096L-17.043 31.752L-16.129 31.752Q-15.933 31.776-15.883 31.983L-15.883 32.073Q-15.933 32.288-16.129 32.311L-18.722 32.311Q-18.918 32.288-18.968 32.073M-17.937 27.885L-17.937 27.831Q-17.937 27.659-17.800 27.538Q-17.664 27.416-17.488 27.416Q-17.316 27.416-17.179 27.538Q-17.043 27.659-17.043 27.831L-17.043 27.885Q-17.043 28.061-17.179 28.182Q-17.316 28.303-17.488 28.303Q-17.664 28.303-17.800 28.182Q-17.937 28.061-17.937 27.885M-15.269 32.073L-15.269 31.983Q-15.226 31.776-15.019 31.752L-14.597 31.752L-14.597 29.424L-15.019 29.424Q-15.226 29.401-15.269 29.182L-15.269 29.096Q-15.222 28.885-15.019 28.862L-14.203 28.862Q-14.008 28.885-13.957 29.096L-13.957 29.182L-13.965 29.206Q-13.738 29.026-13.465 28.924Q-13.191 28.823-12.898 28.823Q-12.550 28.823-12.312 28.963Q-12.074 29.104-11.959 29.362Q-11.843 29.620-11.843 29.975L-11.843 31.752L-11.418 31.752Q-11.211 31.776-11.172 31.983L-11.172 32.073Q-11.211 32.288-11.418 32.311L-12.812 32.311Q-13.008 32.288-13.058 32.073L-13.058 31.983Q-13.008 31.772-12.812 31.752L-12.484 31.752L-12.484 30.006Q-12.484 29.698-12.574 29.540Q-12.664 29.381-12.957 29.381Q-13.226 29.381-13.455 29.512Q-13.683 29.643-13.820 29.872Q-13.957 30.100-13.957 30.366L-13.957 31.752L-13.531 31.752Q-13.324 31.776-13.285 31.983L-13.285 32.073Q-13.324 32.288-13.531 32.311L-15.019 32.311Q-15.226 32.288-15.269 32.073\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M-62.07 32.311v-85.358h91.049v85.358Zm91.049-85.358\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-59.225-41.666c36.989 39.834 48.37 11.381 85.359 56.905\" style=\"stroke-width:1.2\"\u002F>\u003Cpath fill=\"var(--tk-good)\" stroke=\"none\" d=\"M-42.397999999999996 12.394a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0M-16.79 6.704a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0M-28.171999999999997-10.368a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0m-2.6 0\"\u002F>\u003Cpath fill=\"var(--tk-warn)\" stroke=\"none\" d=\"M-5.41-35.975a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0M11.661999999999999-13.213a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0M-33.863-33.13a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0m-2.6 0\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(20.024 9.845)\">\u003Cpath d=\"M-61.750 32.073L-61.750 31.983Q-61.699 31.776-61.504 31.752L-60.621 31.752L-60.621 29.424L-61.476 29.424Q-61.675 29.401-61.726 29.182L-61.726 29.096Q-61.675 28.885-61.476 28.862L-60.621 28.862L-60.621 28.409Q-60.621 27.944-60.215 27.663Q-59.808 27.381-59.328 27.381Q-59.015 27.381-58.771 27.502Q-58.527 27.623-58.527 27.905Q-58.527 28.069-58.636 28.186Q-58.746 28.303-58.910 28.303Q-59.058 28.303-59.179 28.198Q-59.300 28.092-59.300 27.944L-59.383 27.944Q-59.535 27.944-59.672 28.004Q-59.808 28.065-59.894 28.180Q-59.980 28.295-59.980 28.440L-59.980 28.862L-58.949 28.862Q-58.754 28.881-58.703 29.096L-58.703 29.182Q-58.754 29.401-58.949 29.424L-59.980 29.424L-59.980 31.752L-59.101 31.752Q-58.906 31.776-58.855 31.983L-58.855 32.073Q-58.906 32.288-59.101 32.311L-61.504 32.311Q-61.699 32.288-61.750 32.073M-57.375 32.073L-57.375 31.983Q-57.324 31.776-57.129 31.752L-56.023 31.752L-56.023 27.983L-57.129 27.983Q-57.324 27.959-57.375 27.745L-57.375 27.655Q-57.324 27.448-57.129 27.424L-55.633 27.424Q-55.441 27.448-55.383 27.655L-55.383 31.752L-54.281 31.752Q-54.082 31.776-54.031 31.983L-54.031 32.073Q-54.082 32.288-54.281 32.311L-57.129 32.311Q-57.324 32.288-57.375 32.073M-52.961 32.073L-52.961 31.983Q-52.910 31.776-52.715 31.752L-51.675 31.752L-51.675 29.424L-52.648 29.424Q-52.847 29.401-52.898 29.182L-52.898 29.096Q-52.847 28.885-52.648 28.862L-51.281 28.862Q-51.086 28.881-51.035 29.096L-51.035 31.752L-50.121 31.752Q-49.925 31.776-49.875 31.983L-49.875 32.073Q-49.925 32.288-50.121 32.311L-52.715 32.311Q-52.910 32.288-52.961 32.073M-51.929 27.885L-51.929 27.831Q-51.929 27.659-51.793 27.538Q-51.656 27.416-51.480 27.416Q-51.308 27.416-51.172 27.538Q-51.035 27.659-51.035 27.831L-51.035 27.885Q-51.035 28.061-51.172 28.182Q-51.308 28.303-51.480 28.303Q-51.656 28.303-51.793 28.182Q-51.929 28.061-51.929 27.885M-49.261 33.854L-49.261 33.768Q-49.218 33.549-49.011 33.526L-48.590 33.526L-48.590 29.424L-49.011 29.424Q-49.218 29.401-49.261 29.182L-49.261 29.096Q-49.215 28.885-49.011 28.862L-48.195 28.862Q-48 28.881-47.949 29.096L-47.949 29.166Q-47.738 28.998-47.474 28.911Q-47.211 28.823-46.941 28.823Q-46.601 28.823-46.304 28.967Q-46.008 29.112-45.793 29.364Q-45.578 29.616-45.463 29.928Q-45.347 30.241-45.347 30.584Q-45.347 31.049-45.574 31.459Q-45.800 31.870-46.187 32.110Q-46.574 32.350-47.043 32.350Q-47.562 32.350-47.949 31.983L-47.949 33.526L-47.523 33.526Q-47.316 33.549-47.277 33.768L-47.277 33.854Q-47.316 34.065-47.523 34.088L-49.011 34.088Q-49.215 34.065-49.261 33.854M-47.086 31.791Q-46.777 31.791-46.527 31.622Q-46.277 31.452-46.133 31.170Q-45.988 30.889-45.988 30.584Q-45.988 30.295-46.113 30.016Q-46.238 29.737-46.470 29.559Q-46.703 29.381-47.004 29.381Q-47.324 29.381-47.586 29.567Q-47.847 29.752-47.949 30.053L-47.949 30.905Q-47.859 31.272-47.638 31.532Q-47.418 31.791-47.086 31.791M-44.500 32.112L-44.500 31.198Q-44.472 30.991-44.261 30.967L-44.093 30.967Q-43.929 30.991-43.871 31.151Q-43.668 31.791-42.941 31.791Q-42.734 31.791-42.506 31.756Q-42.277 31.721-42.109 31.606Q-41.941 31.491-41.941 31.288Q-41.941 31.077-42.164 30.963Q-42.386 30.850-42.660 30.807L-43.359 30.694Q-44.500 30.483-44.500 29.760Q-44.500 29.471-44.355 29.282Q-44.211 29.092-43.970 28.985Q-43.730 28.877-43.474 28.838Q-43.218 28.799-42.941 28.799Q-42.691 28.799-42.498 28.829Q-42.304 28.858-42.140 28.936Q-42.062 28.819-41.933 28.799L-41.855 28.799Q-41.758 28.811-41.695 28.873Q-41.633 28.936-41.621 29.030L-41.621 29.737Q-41.633 29.831-41.695 29.897Q-41.758 29.963-41.855 29.975L-42.023 29.975Q-42.117 29.963-42.183 29.897Q-42.250 29.831-42.261 29.737Q-42.261 29.358-42.957 29.358Q-43.304 29.358-43.623 29.440Q-43.941 29.522-43.941 29.768Q-43.941 30.034-43.269 30.143L-42.566 30.264Q-42.082 30.346-41.732 30.594Q-41.383 30.842-41.383 31.288Q-41.383 31.678-41.619 31.920Q-41.855 32.163-42.205 32.256Q-42.554 32.350-42.941 32.350Q-43.519 32.350-43.918 32.096Q-43.988 32.221-44.037 32.278Q-44.086 32.334-44.191 32.350L-44.261 32.350Q-44.476 32.327-44.500 32.112\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(20.024 9.845)\">\u003Cpath d=\"M-33.058 30.823L-35.500 30.823Q-35.445 31.100-35.248 31.323Q-35.050 31.545-34.773 31.668Q-34.496 31.791-34.211 31.791Q-33.738 31.791-33.515 31.502Q-33.508 31.491-33.451 31.385Q-33.394 31.280-33.345 31.237Q-33.297 31.194-33.203 31.182L-33.058 31.182Q-32.867 31.202-32.808 31.416L-32.808 31.471Q-32.875 31.772-33.105 31.969Q-33.336 32.166-33.648 32.258Q-33.961 32.350-34.265 32.350Q-34.750 32.350-35.189 32.122Q-35.629 31.893-35.896 31.493Q-36.164 31.092-36.164 30.600L-36.164 30.541Q-36.164 30.073-35.918 29.670Q-35.672 29.268-35.263 29.034Q-34.855 28.799-34.386 28.799Q-33.883 28.799-33.529 29.022Q-33.175 29.245-32.992 29.633Q-32.808 30.022-32.808 30.526L-32.808 30.584Q-32.867 30.799-33.058 30.823M-35.492 30.272L-33.465 30.272Q-33.511 29.862-33.750 29.610Q-33.988 29.358-34.386 29.358Q-34.781 29.358-35.088 29.620Q-35.394 29.881-35.492 30.272M-31.918 31.198Q-31.918 30.752-31.504 30.495Q-31.090 30.237-30.549 30.137Q-30.008 30.038-29.500 30.030Q-29.500 29.815-29.634 29.663Q-29.769 29.510-29.976 29.434Q-30.183 29.358-30.394 29.358Q-30.738 29.358-30.898 29.381L-30.898 29.440Q-30.898 29.608-31.017 29.723Q-31.136 29.838-31.300 29.838Q-31.476 29.838-31.591 29.715Q-31.707 29.592-31.707 29.424Q-31.707 29.018-31.326 28.909Q-30.945 28.799-30.386 28.799Q-30.117 28.799-29.849 28.877Q-29.582 28.956-29.357 29.106Q-29.133 29.256-28.996 29.477Q-28.859 29.698-28.859 29.975L-28.859 31.694Q-28.859 31.752-28.332 31.752Q-28.136 31.772-28.086 31.983L-28.086 32.073Q-28.136 32.288-28.332 32.311L-28.476 32.311Q-28.820 32.311-29.049 32.264Q-29.277 32.217-29.422 32.030Q-29.883 32.350-30.590 32.350Q-30.925 32.350-31.230 32.209Q-31.535 32.069-31.726 31.807Q-31.918 31.545-31.918 31.198M-31.277 31.206Q-31.277 31.479-31.035 31.635Q-30.793 31.791-30.508 31.791Q-30.289 31.791-30.056 31.733Q-29.824 31.674-29.662 31.536Q-29.500 31.397-29.500 31.174L-29.500 30.584Q-29.781 30.584-30.197 30.641Q-30.613 30.698-30.945 30.836Q-31.277 30.975-31.277 31.206M-27.492 32.112L-27.492 31.198Q-27.465 30.991-27.254 30.967L-27.086 30.967Q-26.922 30.991-26.863 31.151Q-26.660 31.791-25.933 31.791Q-25.726 31.791-25.498 31.756Q-25.269 31.721-25.101 31.606Q-24.933 31.491-24.933 31.288Q-24.933 31.077-25.156 30.963Q-25.379 30.850-25.652 30.807L-26.351 30.694Q-27.492 30.483-27.492 29.760Q-27.492 29.471-27.347 29.282Q-27.203 29.092-26.963 28.985Q-26.722 28.877-26.466 28.838Q-26.211 28.799-25.933 28.799Q-25.683 28.799-25.490 28.829Q-25.297 28.858-25.133 28.936Q-25.054 28.819-24.925 28.799L-24.847 28.799Q-24.750 28.811-24.687 28.873Q-24.625 28.936-24.613 29.030L-24.613 29.737Q-24.625 29.831-24.687 29.897Q-24.750 29.963-24.847 29.975L-25.015 29.975Q-25.109 29.963-25.175 29.897Q-25.242 29.831-25.254 29.737Q-25.254 29.358-25.949 29.358Q-26.297 29.358-26.615 29.440Q-26.933 29.522-26.933 29.768Q-26.933 30.034-26.261 30.143L-25.558 30.264Q-25.074 30.346-24.724 30.594Q-24.375 30.842-24.375 31.288Q-24.375 31.678-24.611 31.920Q-24.847 32.163-25.197 32.256Q-25.547 32.350-25.933 32.350Q-26.511 32.350-26.910 32.096Q-26.980 32.221-27.029 32.278Q-27.078 32.334-27.183 32.350L-27.254 32.350Q-27.468 32.327-27.492 32.112M-23.215 32.073L-23.215 31.983Q-23.164 31.776-22.968 31.752L-21.929 31.752L-21.929 29.424L-22.902 29.424Q-23.101 29.401-23.152 29.182L-23.152 29.096Q-23.101 28.885-22.902 28.862L-21.535 28.862Q-21.340 28.881-21.289 29.096L-21.289 31.752L-20.375 31.752Q-20.179 31.776-20.129 31.983L-20.129 32.073Q-20.179 32.288-20.375 32.311L-22.968 32.311Q-23.164 32.288-23.215 32.073M-22.183 27.885L-22.183 27.831Q-22.183 27.659-22.047 27.538Q-21.910 27.416-21.734 27.416Q-21.562 27.416-21.425 27.538Q-21.289 27.659-21.289 27.831L-21.289 27.885Q-21.289 28.061-21.425 28.182Q-21.562 28.303-21.734 28.303Q-21.910 28.303-22.047 28.182Q-22.183 28.061-22.183 27.885M-19.136 32.073L-19.136 31.983Q-19.086 31.776-18.890 31.752L-17.785 31.752L-17.785 27.983L-18.890 27.983Q-19.086 27.959-19.136 27.745L-19.136 27.655Q-19.086 27.448-18.890 27.424L-17.394 27.424Q-17.203 27.448-17.144 27.655L-17.144 31.752L-16.043 31.752Q-15.843 31.776-15.793 31.983L-15.793 32.073Q-15.843 32.288-16.043 32.311L-18.890 32.311Q-19.086 32.288-19.136 32.073M-14.996 33.432Q-14.996 33.323-14.945 33.231Q-14.894 33.139-14.802 33.088Q-14.711 33.038-14.605 33.038Q-14.496 33.038-14.404 33.088Q-14.312 33.139-14.261 33.231Q-14.211 33.323-14.211 33.432L-14.355 33.432Q-14.355 33.569-14.332 33.569Q-14.074 33.569-13.886 33.377Q-13.699 33.186-13.613 32.920L-13.402 32.311L-14.539 29.424L-14.867 29.424Q-15.062 29.401-15.117 29.182L-15.117 29.096Q-15.058 28.885-14.867 28.862L-13.707 28.862Q-13.511 28.885-13.461 29.096L-13.461 29.182Q-13.511 29.401-13.707 29.424L-13.972 29.424Q-13.636 30.272-13.392 30.926Q-13.148 31.581-13.148 31.670L-13.140 31.670Q-13.140 31.612-13.049 31.319Q-12.957 31.026-12.763 30.442Q-12.570 29.858-12.429 29.424L-12.707 29.424Q-12.918 29.401-12.957 29.182L-12.957 29.096Q-12.906 28.881-12.707 28.862L-11.554 28.862Q-11.347 28.885-11.308 29.096L-11.308 29.182Q-11.347 29.401-11.554 29.424L-11.875 29.424L-13.050 32.920Q-13.218 33.420-13.545 33.774Q-13.871 34.127-14.332 34.127Q-14.605 34.127-14.800 33.916Q-14.996 33.706-14.996 33.432\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(150.907 -96.36)\">\u003Cpath d=\"M-61.621 32.073L-61.621 31.983Q-61.570 31.776-61.375 31.752L-60.269 31.752L-60.269 27.983L-61.375 27.983Q-61.570 27.959-61.621 27.745L-61.621 27.655Q-61.570 27.448-61.375 27.424L-59.879 27.424Q-59.687 27.448-59.629 27.655L-59.629 31.752L-58.527 31.752Q-58.328 31.776-58.277 31.983L-58.277 32.073Q-58.328 32.288-58.527 32.311L-61.375 32.311Q-61.570 32.288-61.621 32.073M-57.418 31.198Q-57.418 30.752-57.004 30.495Q-56.590 30.237-56.049 30.137Q-55.508 30.038-55 30.030Q-55 29.815-55.134 29.663Q-55.269 29.510-55.476 29.434Q-55.683 29.358-55.894 29.358Q-56.238 29.358-56.398 29.381L-56.398 29.440Q-56.398 29.608-56.517 29.723Q-56.636 29.838-56.800 29.838Q-56.976 29.838-57.091 29.715Q-57.207 29.592-57.207 29.424Q-57.207 29.018-56.826 28.909Q-56.445 28.799-55.886 28.799Q-55.617 28.799-55.349 28.877Q-55.082 28.956-54.857 29.106Q-54.633 29.256-54.496 29.477Q-54.359 29.698-54.359 29.975L-54.359 31.694Q-54.359 31.752-53.832 31.752Q-53.636 31.772-53.586 31.983L-53.586 32.073Q-53.636 32.288-53.832 32.311L-53.976 32.311Q-54.320 32.311-54.549 32.264Q-54.777 32.217-54.922 32.030Q-55.383 32.350-56.090 32.350Q-56.425 32.350-56.730 32.209Q-57.035 32.069-57.226 31.807Q-57.418 31.545-57.418 31.198M-56.777 31.206Q-56.777 31.479-56.535 31.635Q-56.293 31.791-56.008 31.791Q-55.789 31.791-55.556 31.733Q-55.324 31.674-55.162 31.536Q-55 31.397-55 31.174L-55 30.584Q-55.281 30.584-55.697 30.641Q-56.113 30.698-56.445 30.836Q-56.777 30.975-56.777 31.206M-53.355 32.073L-53.355 31.983Q-53.297 31.776-53.105 31.752L-52.394 31.752L-52.394 29.424L-53.105 29.424Q-53.300 29.401-53.355 29.182L-53.355 29.096Q-53.297 28.885-53.105 28.862L-52.004 28.862Q-51.804 28.881-51.754 29.096L-51.754 29.424Q-51.492 29.139-51.136 28.981Q-50.781 28.823-50.394 28.823Q-50.101 28.823-49.867 28.957Q-49.633 29.092-49.633 29.358Q-49.633 29.526-49.742 29.643Q-49.851 29.760-50.019 29.760Q-50.172 29.760-50.287 29.649Q-50.402 29.538-50.402 29.381Q-50.777 29.381-51.091 29.582Q-51.406 29.784-51.580 30.118Q-51.754 30.452-51.754 30.831L-51.754 31.752L-50.808 31.752Q-50.601 31.776-50.562 31.983L-50.562 32.073Q-50.601 32.288-50.808 32.311L-53.105 32.311Q-53.297 32.288-53.355 32.073M-49.093 32.959Q-49.093 32.659-48.945 32.397Q-48.797 32.135-48.547 31.975Q-48.730 31.721-48.730 31.401Q-48.730 31.116-48.578 30.854Q-48.828 30.530-48.828 30.112Q-48.828 29.752-48.636 29.456Q-48.445 29.159-48.127 28.991Q-47.808 28.823-47.445 28.823Q-47.238 28.823-47.035 28.883Q-46.832 28.944-46.668 29.045Q-46.285 28.784-45.812 28.784Q-45.574 28.784-45.392 28.915Q-45.211 29.045-45.211 29.272Q-45.211 29.420-45.312 29.526Q-45.414 29.631-45.570 29.631Q-45.703 29.631-45.799 29.553Q-45.894 29.475-45.925 29.350Q-46.070 29.358-46.269 29.448Q-46.066 29.760-46.066 30.112Q-46.066 30.393-46.177 30.625Q-46.289 30.858-46.484 31.036Q-46.679 31.213-46.931 31.311Q-47.183 31.409-47.445 31.409Q-47.832 31.409-48.164 31.221Q-48.175 31.221-48.185 31.293Q-48.195 31.366-48.195 31.401Q-48.195 31.522-48.129 31.629Q-48.062 31.737-47.941 31.776Q-47.925 31.772-47.912 31.770Q-47.898 31.768-47.875 31.768Q-47.859 31.768-47.828 31.776Q-47.797 31.784-47.789 31.784L-47.195 31.784Q-46.414 31.784-45.873 32.026Q-45.332 32.268-45.332 32.959Q-45.332 33.260-45.511 33.487Q-45.691 33.713-45.988 33.858Q-46.285 34.002-46.605 34.069Q-46.925 34.135-47.211 34.135Q-47.601 34.135-48.043 34.012Q-48.484 33.889-48.789 33.622Q-49.093 33.354-49.093 32.959M-48.554 32.952Q-48.554 33.170-48.314 33.311Q-48.074 33.452-47.750 33.518Q-47.425 33.584-47.211 33.584Q-46.996 33.584-46.672 33.518Q-46.347 33.452-46.107 33.311Q-45.867 33.170-45.867 32.952Q-45.867 32.663-46.091 32.524Q-46.316 32.385-46.597 32.352Q-46.879 32.319-47.226 32.319L-47.843 32.319Q-48.027 32.319-48.189 32.399Q-48.351 32.479-48.453 32.625Q-48.554 32.772-48.554 32.952M-47.445 30.854Q-47.144 30.854-46.925 30.635Q-46.707 30.416-46.707 30.112Q-46.707 29.956-46.761 29.825Q-46.816 29.694-46.922 29.588Q-47.027 29.483-47.158 29.428Q-47.289 29.373-47.445 29.373Q-47.750 29.373-47.968 29.592Q-48.187 29.811-48.187 30.112Q-48.187 30.409-47.965 30.631Q-47.742 30.854-47.445 30.854M-41.574 30.823L-44.015 30.823Q-43.961 31.100-43.763 31.323Q-43.566 31.545-43.289 31.668Q-43.011 31.791-42.726 31.791Q-42.254 31.791-42.031 31.502Q-42.023 31.491-41.966 31.385Q-41.910 31.280-41.861 31.237Q-41.812 31.194-41.718 31.182L-41.574 31.182Q-41.383 31.202-41.324 31.416L-41.324 31.471Q-41.390 31.772-41.621 31.969Q-41.851 32.166-42.164 32.258Q-42.476 32.350-42.781 32.350Q-43.265 32.350-43.705 32.122Q-44.144 31.893-44.412 31.493Q-44.679 31.092-44.679 30.600L-44.679 30.541Q-44.679 30.073-44.433 29.670Q-44.187 29.268-43.779 29.034Q-43.371 28.799-42.902 28.799Q-42.398 28.799-42.045 29.022Q-41.691 29.245-41.508 29.633Q-41.324 30.022-41.324 30.526L-41.324 30.584Q-41.383 30.799-41.574 30.823M-44.008 30.272L-41.980 30.272Q-42.027 29.862-42.265 29.610Q-42.504 29.358-42.902 29.358Q-43.297 29.358-43.603 29.620Q-43.910 29.881-44.008 30.272\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(150.907 -96.36)\">\u003Cpath d=\"M-36.625 32.073L-36.625 31.983Q-36.574 31.772-36.379 31.752L-36.179 31.752L-36.179 29.424L-36.379 29.424Q-36.586 29.401-36.625 29.182L-36.625 29.096Q-36.574 28.881-36.379 28.862L-35.898 28.862Q-35.707 28.885-35.648 29.096Q-35.328 28.823-34.914 28.823Q-34.722 28.823-34.554 28.932Q-34.386 29.041-34.312 29.213Q-34.136 29.022-33.914 28.922Q-33.691 28.823-33.449 28.823Q-33.031 28.823-32.877 29.165Q-32.722 29.506-32.722 29.975L-32.722 31.752L-32.523 31.752Q-32.312 31.776-32.273 31.983L-32.273 32.073Q-32.324 32.288-32.523 32.311L-33.340 32.311Q-33.535 32.288-33.586 32.073L-33.586 31.983Q-33.535 31.776-33.340 31.752L-33.250 31.752L-33.250 30.006Q-33.250 29.381-33.500 29.381Q-33.832 29.381-34.009 29.692Q-34.187 30.002-34.187 30.366L-34.187 31.752L-33.984 31.752Q-33.777 31.776-33.738 31.983L-33.738 32.073Q-33.789 32.288-33.984 32.311L-34.800 32.311Q-35 32.288-35.050 32.073L-35.050 31.983Q-35 31.776-34.800 31.752L-34.715 31.752L-34.715 30.006Q-34.715 29.381-34.961 29.381Q-35.293 29.381-35.470 29.694Q-35.648 30.006-35.648 30.366L-35.648 31.752L-35.449 31.752Q-35.242 31.776-35.203 31.983L-35.203 32.073Q-35.254 32.291-35.449 32.311L-36.379 32.311Q-36.586 32.288-36.625 32.073M-31.918 31.198Q-31.918 30.752-31.504 30.495Q-31.090 30.237-30.549 30.137Q-30.008 30.038-29.500 30.030Q-29.500 29.815-29.634 29.663Q-29.769 29.510-29.976 29.434Q-30.183 29.358-30.394 29.358Q-30.738 29.358-30.898 29.381L-30.898 29.440Q-30.898 29.608-31.017 29.723Q-31.136 29.838-31.300 29.838Q-31.476 29.838-31.591 29.715Q-31.707 29.592-31.707 29.424Q-31.707 29.018-31.326 28.909Q-30.945 28.799-30.386 28.799Q-30.117 28.799-29.849 28.877Q-29.582 28.956-29.357 29.106Q-29.133 29.256-28.996 29.477Q-28.859 29.698-28.859 29.975L-28.859 31.694Q-28.859 31.752-28.332 31.752Q-28.136 31.772-28.086 31.983L-28.086 32.073Q-28.136 32.288-28.332 32.311L-28.476 32.311Q-28.820 32.311-29.049 32.264Q-29.277 32.217-29.422 32.030Q-29.883 32.350-30.590 32.350Q-30.925 32.350-31.230 32.209Q-31.535 32.069-31.726 31.807Q-31.918 31.545-31.918 31.198M-31.277 31.206Q-31.277 31.479-31.035 31.635Q-30.793 31.791-30.508 31.791Q-30.289 31.791-30.056 31.733Q-29.824 31.674-29.662 31.536Q-29.500 31.397-29.500 31.174L-29.500 30.584Q-29.781 30.584-30.197 30.641Q-30.613 30.698-30.945 30.836Q-31.277 30.975-31.277 31.206M-27.855 32.073L-27.855 31.983Q-27.797 31.776-27.605 31.752L-26.894 31.752L-26.894 29.424L-27.605 29.424Q-27.800 29.401-27.855 29.182L-27.855 29.096Q-27.797 28.885-27.605 28.862L-26.504 28.862Q-26.304 28.881-26.254 29.096L-26.254 29.424Q-25.992 29.139-25.636 28.981Q-25.281 28.823-24.894 28.823Q-24.601 28.823-24.367 28.957Q-24.133 29.092-24.133 29.358Q-24.133 29.526-24.242 29.643Q-24.351 29.760-24.519 29.760Q-24.672 29.760-24.787 29.649Q-24.902 29.538-24.902 29.381Q-25.277 29.381-25.591 29.582Q-25.906 29.784-26.080 30.118Q-26.254 30.452-26.254 30.831L-26.254 31.752L-25.308 31.752Q-25.101 31.776-25.062 31.983L-25.062 32.073Q-25.101 32.288-25.308 32.311L-27.605 32.311Q-27.797 32.288-27.855 32.073M-23.593 32.959Q-23.593 32.659-23.445 32.397Q-23.297 32.135-23.047 31.975Q-23.230 31.721-23.230 31.401Q-23.230 31.116-23.078 30.854Q-23.328 30.530-23.328 30.112Q-23.328 29.752-23.136 29.456Q-22.945 29.159-22.627 28.991Q-22.308 28.823-21.945 28.823Q-21.738 28.823-21.535 28.883Q-21.332 28.944-21.168 29.045Q-20.785 28.784-20.312 28.784Q-20.074 28.784-19.892 28.915Q-19.711 29.045-19.711 29.272Q-19.711 29.420-19.812 29.526Q-19.914 29.631-20.070 29.631Q-20.203 29.631-20.299 29.553Q-20.394 29.475-20.425 29.350Q-20.570 29.358-20.769 29.448Q-20.566 29.760-20.566 30.112Q-20.566 30.393-20.677 30.625Q-20.789 30.858-20.984 31.036Q-21.179 31.213-21.431 31.311Q-21.683 31.409-21.945 31.409Q-22.332 31.409-22.664 31.221Q-22.675 31.221-22.685 31.293Q-22.695 31.366-22.695 31.401Q-22.695 31.522-22.629 31.629Q-22.562 31.737-22.441 31.776Q-22.425 31.772-22.412 31.770Q-22.398 31.768-22.375 31.768Q-22.359 31.768-22.328 31.776Q-22.297 31.784-22.289 31.784L-21.695 31.784Q-20.914 31.784-20.373 32.026Q-19.832 32.268-19.832 32.959Q-19.832 33.260-20.011 33.487Q-20.191 33.713-20.488 33.858Q-20.785 34.002-21.105 34.069Q-21.425 34.135-21.711 34.135Q-22.101 34.135-22.543 34.012Q-22.984 33.889-23.289 33.622Q-23.593 33.354-23.593 32.959M-23.054 32.952Q-23.054 33.170-22.814 33.311Q-22.574 33.452-22.250 33.518Q-21.925 33.584-21.711 33.584Q-21.496 33.584-21.172 33.518Q-20.847 33.452-20.607 33.311Q-20.367 33.170-20.367 32.952Q-20.367 32.663-20.591 32.524Q-20.816 32.385-21.097 32.352Q-21.379 32.319-21.726 32.319L-22.343 32.319Q-22.527 32.319-22.689 32.399Q-22.851 32.479-22.953 32.625Q-23.054 32.772-23.054 32.952M-21.945 30.854Q-21.644 30.854-21.425 30.635Q-21.207 30.416-21.207 30.112Q-21.207 29.956-21.261 29.825Q-21.316 29.694-21.422 29.588Q-21.527 29.483-21.658 29.428Q-21.789 29.373-21.945 29.373Q-22.250 29.373-22.468 29.592Q-22.687 29.811-22.687 30.112Q-22.687 30.409-22.465 30.631Q-22.242 30.854-21.945 30.854M-18.968 32.073L-18.968 31.983Q-18.918 31.776-18.722 31.752L-17.683 31.752L-17.683 29.424L-18.656 29.424Q-18.855 29.401-18.906 29.182L-18.906 29.096Q-18.855 28.885-18.656 28.862L-17.289 28.862Q-17.093 28.881-17.043 29.096L-17.043 31.752L-16.129 31.752Q-15.933 31.776-15.883 31.983L-15.883 32.073Q-15.933 32.288-16.129 32.311L-18.722 32.311Q-18.918 32.288-18.968 32.073M-17.937 27.885L-17.937 27.831Q-17.937 27.659-17.800 27.538Q-17.664 27.416-17.488 27.416Q-17.316 27.416-17.179 27.538Q-17.043 27.659-17.043 27.831L-17.043 27.885Q-17.043 28.061-17.179 28.182Q-17.316 28.303-17.488 28.303Q-17.664 28.303-17.800 28.182Q-17.937 28.061-17.937 27.885M-15.269 32.073L-15.269 31.983Q-15.226 31.776-15.019 31.752L-14.597 31.752L-14.597 29.424L-15.019 29.424Q-15.226 29.401-15.269 29.182L-15.269 29.096Q-15.222 28.885-15.019 28.862L-14.203 28.862Q-14.008 28.885-13.957 29.096L-13.957 29.182L-13.965 29.206Q-13.738 29.026-13.465 28.924Q-13.191 28.823-12.898 28.823Q-12.550 28.823-12.312 28.963Q-12.074 29.104-11.959 29.362Q-11.843 29.620-11.843 29.975L-11.843 31.752L-11.418 31.752Q-11.211 31.776-11.172 31.983L-11.172 32.073Q-11.211 32.288-11.418 32.311L-12.812 32.311Q-13.008 32.288-13.058 32.073L-13.058 31.983Q-13.008 31.772-12.812 31.752L-12.484 31.752L-12.484 30.006Q-12.484 29.698-12.574 29.540Q-12.664 29.381-12.957 29.381Q-13.226 29.381-13.455 29.512Q-13.683 29.643-13.820 29.872Q-13.957 30.100-13.957 30.366L-13.957 31.752L-13.531 31.752Q-13.324 31.776-13.285 31.983L-13.285 32.073Q-13.324 32.288-13.531 32.311L-15.019 32.311Q-15.226 32.288-15.269 32.073\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M68.813 32.311v-85.358h91.048v85.358Zm91.048-85.358\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"m77.349-44.511 73.977 68.286\" style=\"stroke-width:1.2\"\u002F>\u003Cpath fill=\"none\" stroke=\"var(--tk-soft-accent)\" d=\"m90.152-48.78 65.442 59.752M67.39-31.707l73.977 65.441\" style=\"stroke-dasharray:3.0,3.0\"\u002F>\u003Cpath fill=\"var(--tk-good)\" stroke=\"none\" d=\"M87.062 16.662a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0M104.133 18.085a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0M85.64 1.013a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0m-2.6 0\"\u002F>\u003Cpath fill=\"var(--tk-warn)\" stroke=\"none\" d=\"M145.39-35.975a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0M128.318-41.666a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0M146.81199999999998-18.904a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0m-2.6 0\"\u002F>\u003Cg transform=\"translate(163.657 9.845)\">\u003Cpath d=\"M-61.484 32.112L-61.484 31.198Q-61.457 30.991-61.246 30.967L-61.078 30.967Q-60.914 30.991-60.855 31.151Q-60.652 31.791-59.925 31.791Q-59.718 31.791-59.490 31.756Q-59.261 31.721-59.093 31.606Q-58.925 31.491-58.925 31.288Q-58.925 31.077-59.148 30.963Q-59.371 30.850-59.644 30.807L-60.343 30.694Q-61.484 30.483-61.484 29.760Q-61.484 29.471-61.340 29.282Q-61.195 29.092-60.955 28.985Q-60.715 28.877-60.459 28.838Q-60.203 28.799-59.925 28.799Q-59.675 28.799-59.482 28.829Q-59.289 28.858-59.125 28.936Q-59.047 28.819-58.918 28.799L-58.840 28.799Q-58.742 28.811-58.679 28.873Q-58.617 28.936-58.605 29.030L-58.605 29.737Q-58.617 29.831-58.679 29.897Q-58.742 29.963-58.840 29.975L-59.008 29.975Q-59.101 29.963-59.168 29.897Q-59.234 29.831-59.246 29.737Q-59.246 29.358-59.941 29.358Q-60.289 29.358-60.607 29.440Q-60.925 29.522-60.925 29.768Q-60.925 30.034-60.254 30.143L-59.550 30.264Q-59.066 30.346-58.716 30.594Q-58.367 30.842-58.367 31.288Q-58.367 31.678-58.603 31.920Q-58.840 32.163-59.189 32.256Q-59.539 32.350-59.925 32.350Q-60.504 32.350-60.902 32.096Q-60.972 32.221-61.021 32.278Q-61.070 32.334-61.175 32.350L-61.246 32.350Q-61.461 32.327-61.484 32.112M-56.625 31.206L-56.625 29.424L-57.375 29.424Q-57.574 29.401-57.625 29.182L-57.625 29.096Q-57.574 28.885-57.375 28.862L-56.625 28.862L-56.625 28.112Q-56.574 27.905-56.375 27.877L-56.230 27.877Q-56.035 27.905-55.984 28.112L-55.984 28.862L-54.625 28.862Q-54.433 28.881-54.375 29.096L-54.375 29.182Q-54.429 29.401-54.625 29.424L-55.984 29.424L-55.984 31.174Q-55.984 31.791-55.410 31.791Q-55.160 31.791-54.996 31.606Q-54.832 31.420-54.832 31.174Q-54.832 31.081-54.759 31.010Q-54.687 30.940-54.586 30.928L-54.441 30.928Q-54.242 30.952-54.191 31.159L-54.191 31.206Q-54.191 31.530-54.375 31.793Q-54.558 32.057-54.851 32.204Q-55.144 32.350-55.465 32.350Q-55.976 32.350-56.300 32.040Q-56.625 31.729-56.625 31.206M-53.172 31.198Q-53.172 30.752-52.758 30.495Q-52.343 30.237-51.802 30.137Q-51.261 30.038-50.754 30.030Q-50.754 29.815-50.888 29.663Q-51.023 29.510-51.230 29.434Q-51.437 29.358-51.648 29.358Q-51.992 29.358-52.152 29.381L-52.152 29.440Q-52.152 29.608-52.271 29.723Q-52.390 29.838-52.554 29.838Q-52.730 29.838-52.845 29.715Q-52.961 29.592-52.961 29.424Q-52.961 29.018-52.580 28.909Q-52.199 28.799-51.640 28.799Q-51.371 28.799-51.103 28.877Q-50.836 28.956-50.611 29.106Q-50.386 29.256-50.250 29.477Q-50.113 29.698-50.113 29.975L-50.113 31.694Q-50.113 31.752-49.586 31.752Q-49.390 31.772-49.340 31.983L-49.340 32.073Q-49.390 32.288-49.586 32.311L-49.730 32.311Q-50.074 32.311-50.302 32.264Q-50.531 32.217-50.675 32.030Q-51.136 32.350-51.843 32.350Q-52.179 32.350-52.484 32.209Q-52.789 32.069-52.980 31.807Q-53.172 31.545-53.172 31.198M-52.531 31.206Q-52.531 31.479-52.289 31.635Q-52.047 31.791-51.761 31.791Q-51.543 31.791-51.310 31.733Q-51.078 31.674-50.916 31.536Q-50.754 31.397-50.754 31.174L-50.754 30.584Q-51.035 30.584-51.451 30.641Q-51.867 30.698-52.199 30.836Q-52.531 30.975-52.531 31.206M-48.590 32.073L-48.590 27.983L-49.011 27.983Q-49.218 27.959-49.261 27.745L-49.261 27.655Q-49.218 27.448-49.011 27.424L-48.195 27.424Q-48 27.448-47.949 27.655L-47.949 29.166Q-47.738 28.998-47.474 28.911Q-47.211 28.823-46.941 28.823Q-46.601 28.823-46.304 28.967Q-46.008 29.112-45.793 29.364Q-45.578 29.616-45.463 29.928Q-45.347 30.241-45.347 30.584Q-45.347 31.049-45.574 31.459Q-45.800 31.870-46.187 32.110Q-46.574 32.350-47.043 32.350Q-47.562 32.350-47.949 31.983L-47.949 32.073Q-48 32.291-48.195 32.311L-48.340 32.311Q-48.531 32.288-48.590 32.073M-47.086 31.791Q-46.777 31.791-46.527 31.622Q-46.277 31.452-46.133 31.170Q-45.988 30.889-45.988 30.584Q-45.988 30.295-46.113 30.016Q-46.238 29.737-46.470 29.559Q-46.703 29.381-47.004 29.381Q-47.324 29.381-47.586 29.567Q-47.847 29.752-47.949 30.053L-47.949 30.905Q-47.859 31.272-47.638 31.532Q-47.418 31.791-47.086 31.791M-44.636 32.073L-44.636 31.983Q-44.586 31.776-44.390 31.752L-43.285 31.752L-43.285 27.983L-44.390 27.983Q-44.586 27.959-44.636 27.745L-44.636 27.655Q-44.586 27.448-44.390 27.424L-42.894 27.424Q-42.703 27.448-42.644 27.655L-42.644 31.752L-41.543 31.752Q-41.343 31.776-41.293 31.983L-41.293 32.073Q-41.343 32.288-41.543 32.311L-44.390 32.311Q-44.586 32.288-44.636 32.073M-37.328 30.823L-39.769 30.823Q-39.715 31.100-39.517 31.323Q-39.320 31.545-39.043 31.668Q-38.765 31.791-38.480 31.791Q-38.008 31.791-37.785 31.502Q-37.777 31.491-37.720 31.385Q-37.664 31.280-37.615 31.237Q-37.566 31.194-37.472 31.182L-37.328 31.182Q-37.136 31.202-37.078 31.416L-37.078 31.471Q-37.144 31.772-37.375 31.969Q-37.605 32.166-37.918 32.258Q-38.230 32.350-38.535 32.350Q-39.019 32.350-39.459 32.122Q-39.898 31.893-40.166 31.493Q-40.433 31.092-40.433 30.600L-40.433 30.541Q-40.433 30.073-40.187 29.670Q-39.941 29.268-39.533 29.034Q-39.125 28.799-38.656 28.799Q-38.152 28.799-37.799 29.022Q-37.445 29.245-37.261 29.633Q-37.078 30.022-37.078 30.526L-37.078 30.584Q-37.136 30.799-37.328 30.823M-39.761 30.272L-37.734 30.272Q-37.781 29.862-38.019 29.610Q-38.258 29.358-38.656 29.358Q-39.050 29.358-39.357 29.620Q-39.664 29.881-39.761 30.272\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">A large-margin separator (right) leaves a wide corridor around the boundary, so nearby test points keep their labels; a small-margin fit (left) threads between points and flips labels under tiny shifts.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:458.671px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 344.003 236.923\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-38.553 152.082h253.475\"\u002F>\u003Cpath stroke=\"none\" d=\"m217.522 152.082-4.16-2.08 1.56 2.08-1.56 2.08\"\u002F>\u003Cg transform=\"translate(259.808 1.683)\">\u003Cpath d=\"M-36.432 153.633L-38.287 153.633L-38.287 153.340Q-38.018 153.340-37.850 153.295Q-37.682 153.250-37.682 153.074L-37.682 149.250Q-37.682 149.043-37.838 148.990Q-37.994 148.937-38.287 148.937L-38.287 148.641L-37.065 148.555L-37.065 149.019Q-36.834 148.797-36.520 148.676Q-36.205 148.555-35.865 148.555Q-35.393 148.555-34.989 148.801Q-34.584 149.047-34.352 149.463Q-34.119 149.879-34.119 150.355Q-34.119 150.730-34.268 151.059Q-34.416 151.387-34.686 151.639Q-34.955 151.891-35.299 152.025Q-35.643 152.160-36.002 152.160Q-36.291 152.160-36.563 152.039Q-36.834 151.918-37.041 151.707L-37.041 153.074Q-37.041 153.250-36.873 153.295Q-36.705 153.340-36.432 153.340L-36.432 153.633M-37.041 149.418L-37.041 151.258Q-36.889 151.547-36.627 151.727Q-36.365 151.906-36.057 151.906Q-35.772 151.906-35.549 151.768Q-35.326 151.629-35.174 151.398Q-35.022 151.168-34.944 150.896Q-34.865 150.625-34.865 150.355Q-34.865 150.023-34.990 149.666Q-35.115 149.309-35.364 149.072Q-35.612 148.836-35.959 148.836Q-36.283 148.836-36.578 148.992Q-36.873 149.148-37.041 149.418M-33.498 151.250Q-33.498 150.766-33.096 150.471Q-32.694 150.176-32.143 150.057Q-31.592 149.937-31.100 149.937L-31.100 149.648Q-31.100 149.422-31.215 149.215Q-31.330 149.008-31.528 148.889Q-31.725 148.769-31.955 148.769Q-32.381 148.769-32.666 148.875Q-32.596 148.902-32.549 148.957Q-32.502 149.012-32.477 149.082Q-32.451 149.152-32.451 149.227Q-32.451 149.332-32.502 149.424Q-32.553 149.516-32.645 149.566Q-32.737 149.617-32.842 149.617Q-32.948 149.617-33.039 149.566Q-33.131 149.516-33.182 149.424Q-33.233 149.332-33.233 149.227Q-33.233 148.809-32.844 148.662Q-32.455 148.516-31.955 148.516Q-31.623 148.516-31.270 148.646Q-30.916 148.777-30.688 149.031Q-30.459 149.285-30.459 149.633L-30.459 151.434Q-30.459 151.566-30.387 151.676Q-30.315 151.785-30.186 151.785Q-30.061 151.785-29.992 151.680Q-29.924 151.574-29.924 151.434L-29.924 150.922L-29.643 150.922L-29.643 151.434Q-29.643 151.637-29.760 151.795Q-29.877 151.953-30.059 152.037Q-30.240 152.121-30.444 152.121Q-30.674 152.121-30.826 151.949Q-30.979 151.777-31.010 151.547Q-31.170 151.828-31.479 151.994Q-31.787 152.160-32.139 152.160Q-32.651 152.160-33.074 151.937Q-33.498 151.715-33.498 151.250M-32.811 151.250Q-32.811 151.535-32.584 151.721Q-32.358 151.906-32.065 151.906Q-31.819 151.906-31.594 151.789Q-31.369 151.672-31.235 151.469Q-31.100 151.266-31.100 151.012L-31.100 150.180Q-31.365 150.180-31.651 150.234Q-31.936 150.289-32.207 150.418Q-32.479 150.547-32.645 150.754Q-32.811 150.961-32.811 151.250M-27.342 152.082L-29.323 152.082L-29.323 151.785Q-29.053 151.785-28.885 151.740Q-28.717 151.695-28.717 151.523L-28.717 149.387Q-28.717 149.172-28.780 149.076Q-28.842 148.980-28.959 148.959Q-29.076 148.937-29.323 148.937L-29.323 148.641L-28.155 148.555L-28.155 149.340Q-28.076 149.129-27.924 148.943Q-27.772 148.758-27.573 148.656Q-27.373 148.555-27.147 148.555Q-26.901 148.555-26.709 148.699Q-26.518 148.844-26.518 149.074Q-26.518 149.230-26.623 149.340Q-26.729 149.449-26.885 149.449Q-27.041 149.449-27.151 149.340Q-27.260 149.230-27.260 149.074Q-27.260 148.914-27.155 148.809Q-27.479 148.809-27.694 149.037Q-27.908 149.266-28.004 149.605Q-28.100 149.945-28.100 150.250L-28.100 151.523Q-28.100 151.691-27.873 151.738Q-27.647 151.785-27.342 151.785L-27.342 152.082M-25.940 151.250Q-25.940 150.766-25.537 150.471Q-25.135 150.176-24.584 150.057Q-24.033 149.937-23.541 149.937L-23.541 149.648Q-23.541 149.422-23.657 149.215Q-23.772 149.008-23.969 148.889Q-24.166 148.769-24.397 148.769Q-24.823 148.769-25.108 148.875Q-25.037 148.902-24.990 148.957Q-24.944 149.012-24.918 149.082Q-24.893 149.152-24.893 149.227Q-24.893 149.332-24.944 149.424Q-24.994 149.516-25.086 149.566Q-25.178 149.617-25.283 149.617Q-25.389 149.617-25.481 149.566Q-25.573 149.516-25.623 149.424Q-25.674 149.332-25.674 149.227Q-25.674 148.809-25.285 148.662Q-24.897 148.516-24.397 148.516Q-24.065 148.516-23.711 148.646Q-23.358 148.777-23.129 149.031Q-22.901 149.285-22.901 149.633L-22.901 151.434Q-22.901 151.566-22.828 151.676Q-22.756 151.785-22.627 151.785Q-22.502 151.785-22.434 151.680Q-22.365 151.574-22.365 151.434L-22.365 150.922L-22.084 150.922L-22.084 151.434Q-22.084 151.637-22.201 151.795Q-22.319 151.953-22.500 152.037Q-22.682 152.121-22.885 152.121Q-23.115 152.121-23.268 151.949Q-23.420 151.777-23.451 151.547Q-23.612 151.828-23.920 151.994Q-24.229 152.160-24.580 152.160Q-25.092 152.160-25.516 151.937Q-25.940 151.715-25.940 151.250M-25.252 151.250Q-25.252 151.535-25.026 151.721Q-24.799 151.906-24.506 151.906Q-24.260 151.906-24.035 151.789Q-23.811 151.672-23.676 151.469Q-23.541 151.266-23.541 151.012L-23.541 150.180Q-23.807 150.180-24.092 150.234Q-24.377 150.289-24.649 150.418Q-24.920 150.547-25.086 150.754Q-25.252 150.961-25.252 151.250M-19.862 152.082L-21.717 152.082L-21.717 151.785Q-21.444 151.785-21.276 151.738Q-21.108 151.691-21.108 151.523L-21.108 149.387Q-21.108 149.172-21.170 149.076Q-21.233 148.980-21.352 148.959Q-21.471 148.937-21.717 148.937L-21.717 148.641L-20.526 148.555L-20.526 149.289Q-20.412 149.074-20.219 148.906Q-20.026 148.738-19.787 148.646Q-19.549 148.555-19.295 148.555Q-18.334 148.555-18.158 149.266Q-17.975 148.937-17.647 148.746Q-17.319 148.555-16.940 148.555Q-15.764 148.555-15.764 149.633L-15.764 151.523Q-15.764 151.691-15.596 151.738Q-15.428 151.785-15.158 151.785L-15.158 152.082L-17.014 152.082L-17.014 151.785Q-16.740 151.785-16.573 151.740Q-16.405 151.695-16.405 151.523L-16.405 149.648Q-16.405 149.262-16.530 149.035Q-16.655 148.809-17.006 148.809Q-17.311 148.809-17.567 148.971Q-17.823 149.133-17.971 149.402Q-18.119 149.672-18.119 149.969L-18.119 151.523Q-18.119 151.691-17.949 151.738Q-17.780 151.785-17.510 151.785L-17.510 152.082L-19.365 152.082L-19.365 151.785Q-19.092 151.785-18.924 151.738Q-18.756 151.691-18.756 151.523L-18.756 149.648Q-18.756 149.262-18.881 149.035Q-19.006 148.809-19.358 148.809Q-19.662 148.809-19.918 148.971Q-20.174 149.133-20.323 149.402Q-20.471 149.672-20.471 149.969L-20.471 151.523Q-20.471 151.691-20.301 151.738Q-20.131 151.785-19.862 151.785L-19.862 152.082M-14.713 150.328Q-14.713 149.848-14.481 149.432Q-14.248 149.016-13.838 148.766Q-13.428 148.516-12.951 148.516Q-12.221 148.516-11.823 148.957Q-11.424 149.398-11.424 150.129Q-11.424 150.234-11.518 150.258L-13.967 150.258L-13.967 150.328Q-13.967 150.738-13.846 151.094Q-13.725 151.449-13.453 151.666Q-13.182 151.883-12.752 151.883Q-12.389 151.883-12.092 151.654Q-11.795 151.426-11.694 151.074Q-11.686 151.027-11.600 151.012L-11.518 151.012Q-11.424 151.039-11.424 151.121Q-11.424 151.129-11.432 151.160Q-11.494 151.387-11.633 151.570Q-11.772 151.754-11.963 151.887Q-12.155 152.019-12.373 152.090Q-12.592 152.160-12.830 152.160Q-13.201 152.160-13.539 152.023Q-13.877 151.887-14.145 151.635Q-14.412 151.383-14.563 151.043Q-14.713 150.703-14.713 150.328M-13.959 150.019L-11.998 150.019Q-11.998 149.715-12.100 149.424Q-12.201 149.133-12.418 148.951Q-12.635 148.769-12.951 148.769Q-13.252 148.769-13.483 148.957Q-13.713 149.144-13.836 149.436Q-13.959 149.727-13.959 150.019M-10.311 151.121L-10.311 148.930L-11.014 148.930L-11.014 148.676Q-10.658 148.676-10.416 148.443Q-10.174 148.211-10.063 147.863Q-9.951 147.516-9.951 147.160L-9.670 147.160L-9.670 148.633L-8.494 148.633L-8.494 148.930L-9.670 148.930L-9.670 151.105Q-9.670 151.426-9.551 151.654Q-9.432 151.883-9.151 151.883Q-8.971 151.883-8.854 151.760Q-8.737 151.637-8.684 151.457Q-8.631 151.277-8.631 151.105L-8.631 150.633L-8.350 150.633L-8.350 151.121Q-8.350 151.375-8.455 151.615Q-8.561 151.855-8.758 152.008Q-8.955 152.160-9.213 152.160Q-9.530 152.160-9.782 152.037Q-10.033 151.914-10.172 151.680Q-10.311 151.445-10.311 151.121M-7.631 150.328Q-7.631 149.848-7.399 149.432Q-7.166 149.016-6.756 148.766Q-6.346 148.516-5.869 148.516Q-5.139 148.516-4.740 148.957Q-4.342 149.398-4.342 150.129Q-4.342 150.234-4.436 150.258L-6.885 150.258L-6.885 150.328Q-6.885 150.738-6.764 151.094Q-6.643 151.449-6.371 151.666Q-6.100 151.883-5.670 151.883Q-5.307 151.883-5.010 151.654Q-4.713 151.426-4.612 151.074Q-4.604 151.027-4.518 151.012L-4.436 151.012Q-4.342 151.039-4.342 151.121Q-4.342 151.129-4.350 151.160Q-4.412 151.387-4.551 151.570Q-4.690 151.754-4.881 151.887Q-5.073 152.019-5.291 152.090Q-5.510 152.160-5.748 152.160Q-6.119 152.160-6.457 152.023Q-6.795 151.887-7.063 151.635Q-7.330 151.383-7.481 151.043Q-7.631 150.703-7.631 150.328M-6.877 150.019L-4.916 150.019Q-4.916 149.715-5.018 149.424Q-5.119 149.133-5.336 148.951Q-5.553 148.769-5.869 148.769Q-6.170 148.769-6.401 148.957Q-6.631 149.144-6.754 149.436Q-6.877 149.727-6.877 150.019M-1.846 152.082L-3.826 152.082L-3.826 151.785Q-3.557 151.785-3.389 151.740Q-3.221 151.695-3.221 151.523L-3.221 149.387Q-3.221 149.172-3.283 149.076Q-3.346 148.980-3.463 148.959Q-3.580 148.937-3.826 148.937L-3.826 148.641L-2.658 148.555L-2.658 149.340Q-2.580 149.129-2.428 148.943Q-2.276 148.758-2.076 148.656Q-1.877 148.555-1.651 148.555Q-1.405 148.555-1.213 148.699Q-1.022 148.844-1.022 149.074Q-1.022 149.230-1.127 149.340Q-1.233 149.449-1.389 149.449Q-1.545 149.449-1.655 149.340Q-1.764 149.230-1.764 149.074Q-1.764 148.914-1.658 148.809Q-1.983 148.809-2.198 149.037Q-2.412 149.266-2.508 149.605Q-2.604 149.945-2.604 150.250L-2.604 151.523Q-2.604 151.691-2.377 151.738Q-2.151 151.785-1.846 151.785L-1.846 152.082M-0.498 152.074L-0.498 150.852Q-0.498 150.824-0.467 150.793Q-0.436 150.762-0.412 150.762L-0.307 150.762Q-0.237 150.762-0.221 150.824Q-0.158 151.144-0.020 151.385Q0.119 151.625 0.351 151.766Q0.584 151.906 0.892 151.906Q1.131 151.906 1.340 151.846Q1.549 151.785 1.685 151.637Q1.822 151.488 1.822 151.242Q1.822 150.988 1.611 150.822Q1.400 150.656 1.131 150.602L0.510 150.488Q0.103 150.410-0.198 150.154Q-0.498 149.898-0.498 149.523Q-0.498 149.156-0.297 148.934Q-0.096 148.711 0.228 148.613Q0.552 148.516 0.892 148.516Q1.357 148.516 1.654 148.723L1.877 148.539Q1.900 148.516 1.931 148.516L1.982 148.516Q2.013 148.516 2.041 148.543Q2.068 148.570 2.068 148.602L2.068 149.586Q2.068 149.617 2.043 149.646Q2.017 149.676 1.982 149.676L1.877 149.676Q1.842 149.676 1.814 149.648Q1.787 149.621 1.787 149.586Q1.787 149.187 1.535 148.967Q1.283 148.746 0.885 148.746Q0.529 148.746 0.246 148.869Q-0.037 148.992-0.037 149.297Q-0.037 149.516 0.164 149.648Q0.365 149.781 0.611 149.824L1.236 149.937Q1.666 150.027 1.974 150.324Q2.283 150.621 2.283 151.035Q2.283 151.605 1.885 151.883Q1.486 152.160 0.892 152.160Q0.342 152.160-0.010 151.824L-0.307 152.137Q-0.330 152.160-0.365 152.160L-0.412 152.160Q-0.436 152.160-0.467 152.129Q-0.498 152.098-0.498 152.074\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-38.553 152.082V43.716\"\u002F>\u003Cpath stroke=\"none\" d=\"m-38.553 41.116-2.08 4.16 2.08-1.56 2.08 1.56\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(-17.05 -114.698)\">\u003Cpath d=\"M-37.690 151.121L-37.690 148.930L-38.393 148.930L-38.393 148.676Q-38.037 148.676-37.795 148.443Q-37.553 148.211-37.442 147.863Q-37.330 147.516-37.330 147.160L-37.049 147.160L-37.049 148.633L-35.873 148.633L-35.873 148.930L-37.049 148.930L-37.049 151.105Q-37.049 151.426-36.930 151.654Q-36.811 151.883-36.530 151.883Q-36.350 151.883-36.233 151.760Q-36.115 151.637-36.063 151.457Q-36.010 151.277-36.010 151.105L-36.010 150.633L-35.729 150.633L-35.729 151.121Q-35.729 151.375-35.834 151.615Q-35.940 151.855-36.137 152.008Q-36.334 152.160-36.592 152.160Q-36.908 152.160-37.160 152.037Q-37.412 151.914-37.551 151.680Q-37.690 151.445-37.690 151.121M-33.002 152.082L-34.983 152.082L-34.983 151.785Q-34.713 151.785-34.545 151.740Q-34.377 151.695-34.377 151.523L-34.377 149.387Q-34.377 149.172-34.440 149.076Q-34.502 148.980-34.619 148.959Q-34.737 148.937-34.983 148.937L-34.983 148.641L-33.815 148.555L-33.815 149.340Q-33.737 149.129-33.584 148.943Q-33.432 148.758-33.233 148.656Q-33.033 148.555-32.807 148.555Q-32.561 148.555-32.369 148.699Q-32.178 148.844-32.178 149.074Q-32.178 149.230-32.283 149.340Q-32.389 149.449-32.545 149.449Q-32.701 149.449-32.811 149.340Q-32.920 149.230-32.920 149.074Q-32.920 148.914-32.815 148.809Q-33.139 148.809-33.354 149.037Q-33.569 149.266-33.664 149.605Q-33.760 149.945-33.760 150.250L-33.760 151.523Q-33.760 151.691-33.533 151.738Q-33.307 151.785-33.002 151.785L-33.002 152.082M-31.600 151.250Q-31.600 150.766-31.198 150.471Q-30.795 150.176-30.244 150.057Q-29.694 149.937-29.201 149.937L-29.201 149.648Q-29.201 149.422-29.317 149.215Q-29.432 149.008-29.629 148.889Q-29.826 148.769-30.057 148.769Q-30.483 148.769-30.768 148.875Q-30.698 148.902-30.651 148.957Q-30.604 149.012-30.578 149.082Q-30.553 149.152-30.553 149.227Q-30.553 149.332-30.604 149.424Q-30.655 149.516-30.746 149.566Q-30.838 149.617-30.944 149.617Q-31.049 149.617-31.141 149.566Q-31.233 149.516-31.283 149.424Q-31.334 149.332-31.334 149.227Q-31.334 148.809-30.946 148.662Q-30.557 148.516-30.057 148.516Q-29.725 148.516-29.371 148.646Q-29.018 148.777-28.789 149.031Q-28.561 149.285-28.561 149.633L-28.561 151.434Q-28.561 151.566-28.489 151.676Q-28.416 151.785-28.287 151.785Q-28.162 151.785-28.094 151.680Q-28.026 151.574-28.026 151.434L-28.026 150.922L-27.744 150.922L-27.744 151.434Q-27.744 151.637-27.862 151.795Q-27.979 151.953-28.160 152.037Q-28.342 152.121-28.545 152.121Q-28.776 152.121-28.928 151.949Q-29.080 151.777-29.112 151.547Q-29.272 151.828-29.580 151.994Q-29.889 152.160-30.240 152.160Q-30.752 152.160-31.176 151.937Q-31.600 151.715-31.600 151.250M-30.912 151.250Q-30.912 151.535-30.686 151.721Q-30.459 151.906-30.166 151.906Q-29.920 151.906-29.696 151.789Q-29.471 151.672-29.336 151.469Q-29.201 151.266-29.201 151.012L-29.201 150.180Q-29.467 150.180-29.752 150.234Q-30.037 150.289-30.309 150.418Q-30.580 150.547-30.746 150.754Q-30.912 150.961-30.912 151.250M-25.592 152.082L-27.369 152.082L-27.369 151.785Q-27.096 151.785-26.928 151.738Q-26.760 151.691-26.760 151.523L-26.760 149.387Q-26.760 149.172-26.817 149.076Q-26.873 148.980-26.987 148.959Q-27.100 148.937-27.346 148.937L-27.346 148.641L-26.147 148.555L-26.147 151.523Q-26.147 151.691-26 151.738Q-25.854 151.785-25.592 151.785L-25.592 152.082M-27.033 147.160Q-27.033 146.969-26.899 146.838Q-26.764 146.707-26.569 146.707Q-26.448 146.707-26.344 146.769Q-26.240 146.832-26.178 146.936Q-26.115 147.039-26.115 147.160Q-26.115 147.355-26.246 147.490Q-26.377 147.625-26.569 147.625Q-26.768 147.625-26.901 147.492Q-27.033 147.359-27.033 147.160M-23.162 152.082L-25.018 152.082L-25.018 151.785Q-24.744 151.785-24.576 151.738Q-24.408 151.691-24.408 151.523L-24.408 149.387Q-24.408 149.172-24.471 149.076Q-24.533 148.980-24.653 148.959Q-24.772 148.937-25.018 148.937L-25.018 148.641L-23.826 148.555L-23.826 149.289Q-23.713 149.074-23.520 148.906Q-23.326 148.738-23.088 148.646Q-22.850 148.555-22.596 148.555Q-21.428 148.555-21.428 149.633L-21.428 151.523Q-21.428 151.691-21.258 151.738Q-21.088 151.785-20.819 151.785L-20.819 152.082L-22.674 152.082L-22.674 151.785Q-22.401 151.785-22.233 151.738Q-22.065 151.691-22.065 151.523L-22.065 149.648Q-22.065 149.266-22.186 149.037Q-22.307 148.809-22.658 148.809Q-22.971 148.809-23.225 148.971Q-23.479 149.133-23.625 149.402Q-23.772 149.672-23.772 149.969L-23.772 151.523Q-23.772 151.691-23.602 151.738Q-23.432 151.785-23.162 151.785\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(-17.05 -114.698)\">\u003Cpath d=\"M-15.616 152.082L-17.448 152.082L-17.448 151.785Q-17.174 151.785-17.006 151.738Q-16.838 151.691-16.838 151.523L-16.838 147.363Q-16.838 147.148-16.901 147.053Q-16.963 146.957-17.082 146.936Q-17.202 146.914-17.448 146.914L-17.448 146.617L-16.225 146.531L-16.225 151.523Q-16.225 151.691-16.057 151.738Q-15.889 151.785-15.616 151.785L-15.616 152.082M-15.170 150.387Q-15.170 149.883-14.914 149.451Q-14.659 149.019-14.223 148.768Q-13.788 148.516-13.288 148.516Q-12.901 148.516-12.559 148.660Q-12.217 148.805-11.956 149.066Q-11.694 149.328-11.551 149.664Q-11.409 150-11.409 150.387Q-11.409 150.879-11.672 151.289Q-11.936 151.699-12.366 151.930Q-12.795 152.160-13.288 152.160Q-13.780 152.160-14.213 151.928Q-14.647 151.695-14.909 151.287Q-15.170 150.879-15.170 150.387M-13.288 151.883Q-12.831 151.883-12.579 151.660Q-12.327 151.437-12.239 151.086Q-12.151 150.734-12.151 150.289Q-12.151 149.859-12.245 149.521Q-12.338 149.184-12.592 148.977Q-12.846 148.769-13.288 148.769Q-13.936 148.769-14.180 149.186Q-14.424 149.602-14.424 150.289Q-14.424 150.734-14.336 151.086Q-14.248 151.437-13.997 151.660Q-13.745 151.883-13.288 151.883M-10.881 152.074L-10.881 150.852Q-10.881 150.824-10.850 150.793Q-10.819 150.762-10.795 150.762L-10.690 150.762Q-10.620 150.762-10.604 150.824Q-10.541 151.144-10.403 151.385Q-10.264 151.625-10.032 151.766Q-9.799 151.906-9.491 151.906Q-9.252 151.906-9.043 151.846Q-8.834 151.785-8.698 151.637Q-8.561 151.488-8.561 151.242Q-8.561 150.988-8.772 150.822Q-8.983 150.656-9.252 150.602L-9.873 150.488Q-10.280 150.410-10.581 150.154Q-10.881 149.898-10.881 149.523Q-10.881 149.156-10.680 148.934Q-10.479 148.711-10.155 148.613Q-9.831 148.516-9.491 148.516Q-9.026 148.516-8.729 148.723L-8.506 148.539Q-8.483 148.516-8.452 148.516L-8.401 148.516Q-8.370 148.516-8.342 148.543Q-8.315 148.570-8.315 148.602L-8.315 149.586Q-8.315 149.617-8.340 149.646Q-8.366 149.676-8.401 149.676L-8.506 149.676Q-8.541 149.676-8.569 149.648Q-8.596 149.621-8.596 149.586Q-8.596 149.187-8.848 148.967Q-9.100 148.746-9.498 148.746Q-9.854 148.746-10.137 148.869Q-10.420 148.992-10.420 149.297Q-10.420 149.516-10.219 149.648Q-10.018 149.781-9.772 149.824L-9.147 149.937Q-8.717 150.027-8.409 150.324Q-8.100 150.621-8.100 151.035Q-8.100 151.605-8.498 151.883Q-8.897 152.160-9.491 152.160Q-10.041 152.160-10.393 151.824L-10.690 152.137Q-10.713 152.160-10.748 152.160L-10.795 152.160Q-10.819 152.160-10.850 152.129Q-10.881 152.098-10.881 152.074M-7.530 152.074L-7.530 150.852Q-7.530 150.824-7.498 150.793Q-7.467 150.762-7.444 150.762L-7.338 150.762Q-7.268 150.762-7.252 150.824Q-7.190 151.144-7.051 151.385Q-6.913 151.625-6.680 151.766Q-6.448 151.906-6.139 151.906Q-5.901 151.906-5.692 151.846Q-5.483 151.785-5.346 151.637Q-5.209 151.488-5.209 151.242Q-5.209 150.988-5.420 150.822Q-5.631 150.656-5.901 150.602L-6.522 150.488Q-6.928 150.410-7.229 150.154Q-7.530 149.898-7.530 149.523Q-7.530 149.156-7.329 148.934Q-7.127 148.711-6.803 148.613Q-6.479 148.516-6.139 148.516Q-5.674 148.516-5.377 148.723L-5.155 148.539Q-5.131 148.516-5.100 148.516L-5.049 148.516Q-5.018 148.516-4.991 148.543Q-4.963 148.570-4.963 148.602L-4.963 149.586Q-4.963 149.617-4.989 149.646Q-5.014 149.676-5.049 149.676L-5.155 149.676Q-5.190 149.676-5.217 149.648Q-5.245 149.621-5.245 149.586Q-5.245 149.187-5.497 148.967Q-5.748 148.746-6.147 148.746Q-6.502 148.746-6.786 148.869Q-7.069 148.992-7.069 149.297Q-7.069 149.516-6.868 149.648Q-6.666 149.781-6.420 149.824L-5.795 149.937Q-5.366 150.027-5.057 150.324Q-4.748 150.621-4.748 151.035Q-4.748 151.605-5.147 151.883Q-5.545 152.160-6.139 152.160Q-6.690 152.160-7.041 151.824L-7.338 152.137Q-7.362 152.160-7.397 152.160L-7.444 152.160Q-7.467 152.160-7.498 152.129Q-7.530 152.098-7.530 152.074\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"m-30.017-71.67 1.08 9.442 1.08 9.23 1.08 9.015 1.08 8.802 1.081 8.59 1.08 8.375 1.08 8.163 1.08 7.95 1.08 7.736 1.08 7.523 1.081 7.31 1.08 7.096 1.08 6.883 1.08 6.67 1.08 6.457 1.08 6.243 1.081 6.03 1.08 5.818 1.08 5.604 1.08 5.39 1.08 5.177 1.081 4.965 1.08 4.75 1.08 4.538 1.08 4.324 1.08 4.111 1.08 3.898 1.081 3.685 1.08 3.471 1.08 3.259 1.08 3.044 1.08 2.832 1.08 2.619 1.081 2.405 1.08 2.192 1.08 1.979 1.08 1.765 1.08 1.552 1.081 1.34 1.08 1.125 1.08.913 1.08.699 1.08.486 1.08.273 1.081.06 1.08-.154 1.08-.368 1.08-.58 1.08-.793 1.08-1.007 1.081-1.22 1.08-1.433 1.08-1.646 1.08-1.86 1.08-2.072 1.081-2.286 1.08-2.5 1.08-2.712 1.08-2.926 1.08-3.14 1.08-3.351 1.081-3.566 1.08-3.779 1.08-3.991 1.08-4.206 1.08-4.418 1.08-4.632 1.081-4.845 1.08-5.058 1.08-5.271 1.08-5.485 1.08-5.698 1.081-5.912 1.08-6.124 1.08-6.337 1.08-6.551 1.08-6.764 1.08-6.978 1.081-7.19M80.949 103.566l1.406 1.711 1.406 1.675 1.406 1.635 1.407 1.596 1.406 1.558 1.406 1.518 1.406 1.48 1.406 1.44 1.407 1.402 1.406 1.363 1.406 1.323 1.406 1.286 1.407 1.245 1.406 1.207 1.406 1.168 1.406 1.13 1.406 1.09 1.407 1.05 1.406 1.014 1.406.973 1.406.935 1.407.896 1.406.856 1.406.818 1.406.779 1.407.74 1.406.7 1.406.663 1.406.623 1.406.584 1.407.546 1.406.506 1.406.468 1.406.429 1.407.39 1.406.35 1.406.312 1.406.273 1.406.234 1.407.195 1.406.156 1.406.117 1.406.079 1.407.04h1.406l1.406-.039 1.406-.077 1.407-.116 1.406-.155 1.406-.194 1.406-.233 1.406-.272 1.407-.31 1.406-.35 1.406-.39 1.406-.427 1.407-.467 1.406-.505 1.406-.544 1.406-.583 1.406-.623 1.407-.66 1.406-.7 1.406-.74 1.406-.778 1.407-.816 1.406-.856 1.406-.895 1.406-.934 1.406-.972 1.407-1.011 1.406-1.051 1.406-1.088 1.406-1.129 1.407-1.167 1.406-1.206 1.406-1.245 1.406-1.284 1.407-1.322 1.406-1.362 1.406-1.4 1.406-1.44 1.406-1.479 1.407-1.517 1.406-1.556 1.406-1.595 1.406-1.634 1.407-1.673 1.406-1.712M55.341 17.102l.883 2.978.883 2.98.882 2.981.883 2.98.883 2.98.882 2.981.883 2.98.882 2.981.883 2.98.883 2.98.882 2.98.883 2.98.883 2.98.882 2.981.883 2.98.883 2.98.882 2.981.883 2.98.882 2.981.883 2.979.883 2.98.882 2.98.883 2.981.883 2.98.882 2.98.883 2.981.883 2.98.882 2.981.883 2.979\" style=\"stroke-width:1.2\"\u002F>\u003Cpath fill=\"var(--tk-warn)\" stroke=\"none\" d=\"M20.953 142.123a2.6 2.6 0 1 0-5.2 0 2.6 2.6 0 0 0 5.2 0m-2.6 0\"\u002F>\u003Cg fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\">\u003Cg fill=\"var(--tk-warn)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(49.18 -22.951)\">\u003Cpath d=\"M-33.717 142.383L-33.717 141.469Q-33.690 141.262-33.479 141.238L-33.311 141.238Q-33.147 141.262-33.088 141.422Q-32.885 142.062-32.158 142.062Q-31.951 142.062-31.723 142.027Q-31.494 141.992-31.326 141.877Q-31.158 141.762-31.158 141.559Q-31.158 141.348-31.381 141.234Q-31.604 141.121-31.877 141.078L-32.576 140.965Q-33.717 140.754-33.717 140.031Q-33.717 139.742-33.573 139.553Q-33.428 139.363-33.188 139.256Q-32.948 139.148-32.692 139.109Q-32.436 139.070-32.158 139.070Q-31.908 139.070-31.715 139.100Q-31.522 139.129-31.358 139.207Q-31.280 139.090-31.151 139.070L-31.073 139.070Q-30.975 139.082-30.912 139.144Q-30.850 139.207-30.838 139.301L-30.838 140.008Q-30.850 140.102-30.912 140.168Q-30.975 140.234-31.073 140.246L-31.240 140.246Q-31.334 140.234-31.401 140.168Q-31.467 140.102-31.479 140.008Q-31.479 139.629-32.174 139.629Q-32.522 139.629-32.840 139.711Q-33.158 139.793-33.158 140.039Q-33.158 140.305-32.487 140.414L-31.783 140.535Q-31.299 140.617-30.949 140.865Q-30.600 141.113-30.600 141.559Q-30.600 141.949-30.836 142.191Q-31.073 142.434-31.422 142.527Q-31.772 142.621-32.158 142.621Q-32.737 142.621-33.135 142.367Q-33.205 142.492-33.254 142.549Q-33.303 142.605-33.408 142.621L-33.479 142.621Q-33.694 142.598-33.717 142.383M-29.987 142.344L-29.987 142.254Q-29.944 142.047-29.737 142.023L-29.315 142.023L-29.315 138.254L-29.737 138.254Q-29.944 138.230-29.987 138.016L-29.987 137.926Q-29.944 137.719-29.737 137.695L-28.920 137.695Q-28.725 137.719-28.674 137.926L-28.674 139.477Q-28.213 139.094-27.615 139.094Q-27.268 139.094-27.030 139.234Q-26.791 139.375-26.676 139.633Q-26.561 139.891-26.561 140.246L-26.561 142.023L-26.135 142.023Q-25.928 142.047-25.889 142.254L-25.889 142.344Q-25.928 142.559-26.135 142.582L-27.530 142.582Q-27.725 142.559-27.776 142.344L-27.776 142.254Q-27.725 142.043-27.530 142.023L-27.201 142.023L-27.201 140.277Q-27.201 139.969-27.291 139.811Q-27.381 139.652-27.674 139.652Q-27.944 139.652-28.172 139.783Q-28.401 139.914-28.537 140.143Q-28.674 140.371-28.674 140.637L-28.674 142.023L-28.248 142.023Q-28.041 142.047-28.002 142.254L-28.002 142.344Q-28.041 142.559-28.248 142.582L-29.737 142.582Q-29.944 142.559-29.987 142.344M-25.405 141.469Q-25.405 141.023-24.990 140.766Q-24.576 140.508-24.035 140.408Q-23.494 140.309-22.987 140.301Q-22.987 140.086-23.121 139.934Q-23.256 139.781-23.463 139.705Q-23.670 139.629-23.881 139.629Q-24.225 139.629-24.385 139.652L-24.385 139.711Q-24.385 139.879-24.504 139.994Q-24.623 140.109-24.787 140.109Q-24.963 140.109-25.078 139.986Q-25.194 139.863-25.194 139.695Q-25.194 139.289-24.813 139.180Q-24.432 139.070-23.873 139.070Q-23.604 139.070-23.336 139.148Q-23.069 139.227-22.844 139.377Q-22.619 139.527-22.483 139.748Q-22.346 139.969-22.346 140.246L-22.346 141.965Q-22.346 142.023-21.819 142.023Q-21.623 142.043-21.573 142.254L-21.573 142.344Q-21.623 142.559-21.819 142.582L-21.963 142.582Q-22.307 142.582-22.535 142.535Q-22.764 142.488-22.908 142.301Q-23.369 142.621-24.076 142.621Q-24.412 142.621-24.717 142.480Q-25.022 142.340-25.213 142.078Q-25.405 141.816-25.405 141.469M-24.764 141.477Q-24.764 141.750-24.522 141.906Q-24.280 142.062-23.994 142.062Q-23.776 142.062-23.543 142.004Q-23.311 141.945-23.149 141.807Q-22.987 141.668-22.987 141.445L-22.987 140.855Q-23.268 140.855-23.684 140.912Q-24.100 140.969-24.432 141.107Q-24.764 141.246-24.764 141.477M-21.342 142.344L-21.342 142.254Q-21.283 142.047-21.092 142.023L-20.381 142.023L-20.381 139.695L-21.092 139.695Q-21.287 139.672-21.342 139.453L-21.342 139.367Q-21.283 139.156-21.092 139.133L-19.990 139.133Q-19.791 139.152-19.740 139.367L-19.740 139.695Q-19.479 139.410-19.123 139.252Q-18.768 139.094-18.381 139.094Q-18.088 139.094-17.854 139.228Q-17.619 139.363-17.619 139.629Q-17.619 139.797-17.729 139.914Q-17.838 140.031-18.006 140.031Q-18.158 140.031-18.274 139.920Q-18.389 139.809-18.389 139.652Q-18.764 139.652-19.078 139.853Q-19.393 140.055-19.567 140.389Q-19.740 140.723-19.740 141.102L-19.740 142.023L-18.795 142.023Q-18.588 142.047-18.549 142.254L-18.549 142.344Q-18.588 142.559-18.795 142.582L-21.092 142.582Q-21.283 142.559-21.342 142.344M-17.248 144.125L-17.248 144.039Q-17.205 143.820-16.998 143.797L-16.576 143.797L-16.576 139.695L-16.998 139.695Q-17.205 139.672-17.248 139.453L-17.248 139.367Q-17.201 139.156-16.998 139.133L-16.182 139.133Q-15.987 139.152-15.936 139.367L-15.936 139.437Q-15.725 139.269-15.461 139.182Q-15.198 139.094-14.928 139.094Q-14.588 139.094-14.291 139.238Q-13.994 139.383-13.780 139.635Q-13.565 139.887-13.449 140.199Q-13.334 140.512-13.334 140.855Q-13.334 141.320-13.561 141.730Q-13.787 142.141-14.174 142.381Q-14.561 142.621-15.030 142.621Q-15.549 142.621-15.936 142.254L-15.936 143.797L-15.510 143.797Q-15.303 143.820-15.264 144.039L-15.264 144.125Q-15.303 144.336-15.510 144.359L-16.998 144.359Q-17.201 144.336-17.248 144.125M-15.073 142.062Q-14.764 142.062-14.514 141.893Q-14.264 141.723-14.119 141.441Q-13.975 141.160-13.975 140.855Q-13.975 140.566-14.100 140.287Q-14.225 140.008-14.457 139.830Q-14.690 139.652-14.990 139.652Q-15.311 139.652-15.573 139.838Q-15.834 140.023-15.936 140.324L-15.936 141.176Q-15.846 141.543-15.625 141.803Q-15.405 142.062-15.073 142.062\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(49.18 -22.951)\">\u003Cpath d=\"M-8.857 142.344L-8.857 142.254Q-8.806 142.043-8.611 142.023L-8.411 142.023L-8.411 139.695L-8.611 139.695Q-8.818 139.672-8.857 139.453L-8.857 139.367Q-8.806 139.152-8.611 139.133L-8.130 139.133Q-7.939 139.156-7.880 139.367Q-7.560 139.094-7.146 139.094Q-6.954 139.094-6.786 139.203Q-6.618 139.312-6.544 139.484Q-6.368 139.293-6.146 139.193Q-5.923 139.094-5.681 139.094Q-5.263 139.094-5.109 139.436Q-4.954 139.777-4.954 140.246L-4.954 142.023L-4.755 142.023Q-4.544 142.047-4.505 142.254L-4.505 142.344Q-4.556 142.559-4.755 142.582L-5.572 142.582Q-5.767 142.559-5.818 142.344L-5.818 142.254Q-5.767 142.047-5.572 142.023L-5.482 142.023L-5.482 140.277Q-5.482 139.652-5.732 139.652Q-6.064 139.652-6.241 139.963Q-6.419 140.273-6.419 140.637L-6.419 142.023L-6.216 142.023Q-6.009 142.047-5.970 142.254L-5.970 142.344Q-6.021 142.559-6.216 142.582L-7.032 142.582Q-7.232 142.559-7.282 142.344L-7.282 142.254Q-7.232 142.047-7.032 142.023L-6.947 142.023L-6.947 140.277Q-6.947 139.652-7.193 139.652Q-7.525 139.652-7.702 139.965Q-7.880 140.277-7.880 140.637L-7.880 142.023L-7.681 142.023Q-7.474 142.047-7.435 142.254L-7.435 142.344Q-7.486 142.562-7.681 142.582L-8.611 142.582Q-8.818 142.559-8.857 142.344M-3.939 142.344L-3.939 142.254Q-3.888 142.047-3.693 142.023L-2.654 142.023L-2.654 139.695L-3.626 139.695Q-3.825 139.672-3.876 139.453L-3.876 139.367Q-3.825 139.156-3.626 139.133L-2.259 139.133Q-2.064 139.152-2.013 139.367L-2.013 142.023L-1.099 142.023Q-0.904 142.047-0.853 142.254L-0.853 142.344Q-0.904 142.559-1.099 142.582L-3.693 142.582Q-3.888 142.559-3.939 142.344M-2.907 138.156L-2.907 138.102Q-2.907 137.930-2.771 137.809Q-2.634 137.687-2.458 137.687Q-2.286 137.687-2.150 137.809Q-2.013 137.930-2.013 138.102L-2.013 138.156Q-2.013 138.332-2.150 138.453Q-2.286 138.574-2.458 138.574Q-2.634 138.574-2.771 138.453Q-2.907 138.332-2.907 138.156M-0.239 142.344L-0.239 142.254Q-0.197 142.047 0.011 142.023L0.432 142.023L0.432 139.695L0.011 139.695Q-0.197 139.672-0.239 139.453L-0.239 139.367Q-0.193 139.156 0.011 139.133L0.827 139.133Q1.022 139.156 1.073 139.367L1.073 139.453L1.065 139.477Q1.292 139.297 1.565 139.195Q1.839 139.094 2.132 139.094Q2.479 139.094 2.718 139.234Q2.956 139.375 3.071 139.633Q3.186 139.891 3.186 140.246L3.186 142.023L3.612 142.023Q3.819 142.047 3.858 142.254L3.858 142.344Q3.819 142.559 3.612 142.582L2.218 142.582Q2.022 142.559 1.971 142.344L1.971 142.254Q2.022 142.043 2.218 142.023L2.546 142.023L2.546 140.277Q2.546 139.969 2.456 139.811Q2.366 139.652 2.073 139.652Q1.803 139.652 1.575 139.783Q1.346 139.914 1.210 140.143Q1.073 140.371 1.073 140.637L1.073 142.023L1.499 142.023Q1.706 142.047 1.745 142.254L1.745 142.344Q1.706 142.559 1.499 142.582L0.011 142.582Q-0.197 142.559-0.239 142.344\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(49.18 -22.951)\">\u003Cpath d=\"M-35.440 152.723Q-35.971 152.414-36.371 151.926Q-36.772 151.437-36.983 150.844Q-37.194 150.250-37.194 149.633Q-37.194 149.016-36.985 148.428Q-36.776 147.840-36.379 147.359Q-35.983 146.879-35.448 146.555Q-35.377 146.531-35.346 146.531L-35.256 146.531Q-35.158 146.543-35.092 146.609Q-35.026 146.676-35.026 146.777Q-35.026 146.922-35.127 146.984Q-35.576 147.273-35.903 147.689Q-36.229 148.105-36.391 148.598Q-36.553 149.090-36.553 149.633Q-36.553 150.039-36.457 150.426Q-36.362 150.812-36.184 151.148Q-36.006 151.484-35.746 151.768Q-35.487 152.051-35.139 152.281Q-35.026 152.359-35.026 152.496Q-35.026 152.594-35.092 152.664Q-35.158 152.734-35.256 152.746L-35.346 152.746Q-35.405 152.746-35.440 152.723M-34.237 153.625L-34.237 153.539Q-34.194 153.320-33.987 153.297L-33.565 153.297L-33.565 149.195L-33.987 149.195Q-34.194 149.172-34.237 148.953L-34.237 148.867Q-34.190 148.656-33.987 148.633L-33.170 148.633Q-32.975 148.652-32.924 148.867L-32.924 148.937Q-32.713 148.769-32.449 148.682Q-32.186 148.594-31.916 148.594Q-31.576 148.594-31.280 148.738Q-30.983 148.883-30.768 149.135Q-30.553 149.387-30.438 149.699Q-30.323 150.012-30.323 150.355Q-30.323 150.820-30.549 151.230Q-30.776 151.641-31.162 151.881Q-31.549 152.121-32.018 152.121Q-32.537 152.121-32.924 151.754L-32.924 153.297L-32.498 153.297Q-32.291 153.320-32.252 153.539L-32.252 153.625Q-32.291 153.836-32.498 153.859L-33.987 153.859Q-34.190 153.836-34.237 153.625M-32.061 151.562Q-31.752 151.562-31.502 151.393Q-31.252 151.223-31.108 150.941Q-30.963 150.660-30.963 150.355Q-30.963 150.066-31.088 149.787Q-31.213 149.508-31.446 149.330Q-31.678 149.152-31.979 149.152Q-32.299 149.152-32.561 149.338Q-32.823 149.523-32.924 149.824L-32.924 150.676Q-32.834 151.043-32.614 151.303Q-32.393 151.562-32.061 151.562M-27.940 152.121Q-28.412 152.121-28.797 151.877Q-29.182 151.633-29.405 151.223Q-29.627 150.812-29.627 150.355Q-29.627 150.012-29.502 149.689Q-29.377 149.367-29.147 149.113Q-28.916 148.859-28.610 148.715Q-28.303 148.570-27.940 148.570Q-27.576 148.570-27.264 148.717Q-26.951 148.863-26.729 149.109Q-26.506 149.355-26.379 149.676Q-26.252 149.996-26.252 150.355Q-26.252 150.812-26.477 151.225Q-26.701 151.637-27.086 151.879Q-27.471 152.121-27.940 152.121M-27.940 151.562Q-27.475 151.562-27.184 151.168Q-26.893 150.773-26.893 150.289Q-26.893 149.996-27.028 149.728Q-27.162 149.461-27.403 149.295Q-27.643 149.129-27.940 149.129Q-28.244 149.129-28.483 149.295Q-28.721 149.461-28.856 149.728Q-28.990 149.996-28.990 150.289Q-28.990 150.769-28.698 151.166Q-28.405 151.562-27.940 151.562M-23.694 152.121Q-24.166 152.121-24.551 151.877Q-24.936 151.633-25.158 151.223Q-25.381 150.812-25.381 150.355Q-25.381 150.012-25.256 149.689Q-25.131 149.367-24.901 149.113Q-24.670 148.859-24.364 148.715Q-24.057 148.570-23.694 148.570Q-23.330 148.570-23.018 148.717Q-22.705 148.863-22.483 149.109Q-22.260 149.355-22.133 149.676Q-22.006 149.996-22.006 150.355Q-22.006 150.812-22.231 151.225Q-22.455 151.637-22.840 151.879Q-23.225 152.121-23.694 152.121M-23.694 151.562Q-23.229 151.562-22.938 151.168Q-22.647 150.773-22.647 150.289Q-22.647 149.996-22.782 149.728Q-22.916 149.461-23.157 149.295Q-23.397 149.129-23.694 149.129Q-23.998 149.129-24.237 149.295Q-24.475 149.461-24.610 149.728Q-24.744 149.996-24.744 150.289Q-24.744 150.769-24.451 151.166Q-24.158 151.562-23.694 151.562M-21.346 151.844L-21.346 151.754Q-21.287 151.547-21.096 151.523L-20.385 151.523L-20.385 149.195L-21.096 149.195Q-21.291 149.172-21.346 148.953L-21.346 148.867Q-21.287 148.656-21.096 148.633L-19.994 148.633Q-19.795 148.652-19.744 148.867L-19.744 149.195Q-19.483 148.910-19.127 148.752Q-18.772 148.594-18.385 148.594Q-18.092 148.594-17.858 148.728Q-17.623 148.863-17.623 149.129Q-17.623 149.297-17.733 149.414Q-17.842 149.531-18.010 149.531Q-18.162 149.531-18.278 149.420Q-18.393 149.309-18.393 149.152Q-18.768 149.152-19.082 149.353Q-19.397 149.555-19.571 149.889Q-19.744 150.223-19.744 150.602L-19.744 151.523L-18.799 151.523Q-18.592 151.547-18.553 151.754L-18.553 151.844Q-18.592 152.059-18.799 152.082L-21.096 152.082Q-21.287 152.059-21.346 151.844\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(49.18 -22.951)\">\u003Cpath d=\"M-11.853 150.977L-11.853 149.195L-12.603 149.195Q-12.802 149.172-12.853 148.953L-12.853 148.867Q-12.802 148.656-12.603 148.633L-11.853 148.633L-11.853 147.883Q-11.802 147.676-11.603 147.648L-11.458 147.648Q-11.263 147.676-11.212 147.883L-11.212 148.633L-9.853 148.633Q-9.661 148.652-9.603 148.867L-9.603 148.953Q-9.657 149.172-9.853 149.195L-11.212 149.195L-11.212 150.945Q-11.212 151.562-10.638 151.562Q-10.388 151.562-10.224 151.377Q-10.060 151.191-10.060 150.945Q-10.060 150.852-9.988 150.781Q-9.915 150.711-9.814 150.699L-9.669 150.699Q-9.470 150.723-9.419 150.930L-9.419 150.977Q-9.419 151.301-9.603 151.564Q-9.786 151.828-10.079 151.975Q-10.372 152.121-10.693 152.121Q-11.204 152.121-11.529 151.811Q-11.853 151.500-11.853 150.977M-5.294 150.594L-7.736 150.594Q-7.681 150.871-7.484 151.094Q-7.286 151.316-7.009 151.439Q-6.732 151.562-6.447 151.562Q-5.974 151.562-5.751 151.273Q-5.743 151.262-5.687 151.156Q-5.630 151.051-5.581 151.008Q-5.532 150.965-5.439 150.953L-5.294 150.953Q-5.103 150.973-5.044 151.187L-5.044 151.242Q-5.111 151.543-5.341 151.740Q-5.572 151.937-5.884 152.029Q-6.197 152.121-6.501 152.121Q-6.986 152.121-7.425 151.893Q-7.864 151.664-8.132 151.264Q-8.400 150.863-8.400 150.371L-8.400 150.312Q-8.400 149.844-8.154 149.441Q-7.907 149.039-7.499 148.805Q-7.091 148.570-6.622 148.570Q-6.118 148.570-5.765 148.793Q-5.411 149.016-5.228 149.404Q-5.044 149.793-5.044 150.297L-5.044 150.355Q-5.103 150.570-5.294 150.594M-7.728 150.043L-5.700 150.043Q-5.747 149.633-5.986 149.381Q-6.224 149.129-6.622 149.129Q-7.017 149.129-7.323 149.391Q-7.630 149.652-7.728 150.043M-3.974 151.883L-3.974 150.969Q-3.947 150.762-3.736 150.738L-3.568 150.738Q-3.404 150.762-3.345 150.922Q-3.142 151.562-2.415 151.562Q-2.208 151.562-1.980 151.527Q-1.751 151.492-1.583 151.377Q-1.415 151.262-1.415 151.059Q-1.415 150.848-1.638 150.734Q-1.861 150.621-2.134 150.578L-2.833 150.465Q-3.974 150.254-3.974 149.531Q-3.974 149.242-3.829 149.053Q-3.685 148.863-3.445 148.756Q-3.204 148.648-2.948 148.609Q-2.693 148.570-2.415 148.570Q-2.165 148.570-1.972 148.600Q-1.779 148.629-1.614 148.707Q-1.536 148.590-1.407 148.570L-1.329 148.570Q-1.232 148.582-1.169 148.644Q-1.107 148.707-1.095 148.801L-1.095 149.508Q-1.107 149.602-1.169 149.668Q-1.232 149.734-1.329 149.746L-1.497 149.746Q-1.591 149.734-1.657 149.668Q-1.724 149.602-1.736 149.508Q-1.736 149.129-2.431 149.129Q-2.779 149.129-3.097 149.211Q-3.415 149.293-3.415 149.539Q-3.415 149.805-2.743 149.914L-2.040 150.035Q-1.556 150.117-1.206 150.365Q-0.857 150.613-0.857 151.059Q-0.857 151.449-1.093 151.691Q-1.329 151.934-1.679 152.027Q-2.029 152.121-2.415 152.121Q-2.993 152.121-3.392 151.867Q-3.462 151.992-3.511 152.049Q-3.560 152.105-3.665 152.121L-3.736 152.121Q-3.950 152.098-3.974 151.883M0.886 150.977L0.886 149.195L0.136 149.195Q-0.064 149.172-0.114 148.953L-0.114 148.867Q-0.064 148.656 0.136 148.633L0.886 148.633L0.886 147.883Q0.936 147.676 1.136 147.648L1.280 147.648Q1.475 147.676 1.526 147.883L1.526 148.633L2.886 148.633Q3.077 148.652 3.136 148.867L3.136 148.953Q3.081 149.172 2.886 149.195L1.526 149.195L1.526 150.945Q1.526 151.562 2.100 151.562Q2.350 151.562 2.514 151.377Q2.678 151.191 2.678 150.945Q2.678 150.852 2.751 150.781Q2.823 150.711 2.925 150.699L3.069 150.699Q3.268 150.723 3.319 150.930L3.319 150.977Q3.319 151.301 3.136 151.564Q2.952 151.828 2.659 151.975Q2.366 152.121 2.046 152.121Q1.534 152.121 1.210 151.811Q0.886 151.500 0.886 150.977M4.971 152.746L4.886 152.746Q4.780 152.734 4.712 152.662Q4.643 152.590 4.643 152.496Q4.643 152.355 4.749 152.289Q5.085 152.074 5.354 151.785Q5.624 151.496 5.807 151.148Q5.991 150.801 6.081 150.422Q6.171 150.043 6.171 149.633Q6.171 149.094 6.007 148.602Q5.843 148.109 5.524 147.695Q5.206 147.281 4.764 146.992Q4.643 146.910 4.643 146.777Q4.643 146.680 4.712 146.611Q4.780 146.543 4.886 146.531L4.971 146.531Q5.026 146.531 5.061 146.555Q5.448 146.777 5.788 147.125Q6.128 147.473 6.348 147.867Q6.569 148.262 6.690 148.707Q6.811 149.152 6.811 149.633Q6.811 150.117 6.690 150.568Q6.569 151.019 6.344 151.416Q6.120 151.812 5.796 152.146Q5.471 152.480 5.069 152.723Q4.999 152.746 4.971 152.746\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"var(--tk-good)\" stroke=\"none\" d=\"M146.345 142.123a2.8 2.8 0 1 0-5.6 0 2.8 2.8 0 0 0 5.6 0m-2.8 0\"\u002F>\u003Cg fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\">\u003Cg fill=\"var(--tk-good)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(148.097 -22.951)\">\u003Cpath d=\"M-21.233 142.344L-21.233 142.254Q-21.182 142.047-20.987 142.023L-20.104 142.023L-20.104 139.695L-20.959 139.695Q-21.158 139.672-21.209 139.453L-21.209 139.367Q-21.158 139.156-20.959 139.133L-20.104 139.133L-20.104 138.680Q-20.104 138.215-19.698 137.934Q-19.291 137.652-18.811 137.652Q-18.498 137.652-18.254 137.773Q-18.010 137.894-18.010 138.176Q-18.010 138.340-18.119 138.457Q-18.229 138.574-18.393 138.574Q-18.541 138.574-18.662 138.469Q-18.783 138.363-18.783 138.215L-18.866 138.215Q-19.018 138.215-19.155 138.275Q-19.291 138.336-19.377 138.451Q-19.463 138.566-19.463 138.711L-19.463 139.133L-18.432 139.133Q-18.237 139.152-18.186 139.367L-18.186 139.453Q-18.237 139.672-18.432 139.695L-19.463 139.695L-19.463 142.023L-18.584 142.023Q-18.389 142.047-18.338 142.254L-18.338 142.344Q-18.389 142.559-18.584 142.582L-20.987 142.582Q-21.182 142.559-21.233 142.344M-16.858 142.344L-16.858 142.254Q-16.807 142.047-16.612 142.023L-15.506 142.023L-15.506 138.254L-16.612 138.254Q-16.807 138.230-16.858 138.016L-16.858 137.926Q-16.807 137.719-16.612 137.695L-15.116 137.695Q-14.924 137.719-14.866 137.926L-14.866 142.023L-13.764 142.023Q-13.565 142.047-13.514 142.254L-13.514 142.344Q-13.565 142.559-13.764 142.582L-16.612 142.582Q-16.807 142.559-16.858 142.344M-12.655 141.469Q-12.655 141.023-12.241 140.766Q-11.826 140.508-11.285 140.408Q-10.744 140.309-10.237 140.301Q-10.237 140.086-10.371 139.934Q-10.506 139.781-10.713 139.705Q-10.920 139.629-11.131 139.629Q-11.475 139.629-11.635 139.652L-11.635 139.711Q-11.635 139.879-11.754 139.994Q-11.873 140.109-12.037 140.109Q-12.213 140.109-12.328 139.986Q-12.444 139.863-12.444 139.695Q-12.444 139.289-12.063 139.180Q-11.682 139.070-11.123 139.070Q-10.854 139.070-10.586 139.148Q-10.319 139.227-10.094 139.377Q-9.869 139.527-9.733 139.748Q-9.596 139.969-9.596 140.246L-9.596 141.965Q-9.596 142.023-9.069 142.023Q-8.873 142.043-8.823 142.254L-8.823 142.344Q-8.873 142.559-9.069 142.582L-9.213 142.582Q-9.557 142.582-9.785 142.535Q-10.014 142.488-10.158 142.301Q-10.619 142.621-11.326 142.621Q-11.662 142.621-11.967 142.480Q-12.272 142.340-12.463 142.078Q-12.655 141.816-12.655 141.469M-12.014 141.477Q-12.014 141.750-11.772 141.906Q-11.530 142.062-11.244 142.062Q-11.026 142.062-10.793 142.004Q-10.561 141.945-10.399 141.807Q-10.237 141.668-10.237 141.445L-10.237 140.855Q-10.518 140.855-10.934 140.912Q-11.350 140.969-11.682 141.107Q-12.014 141.246-12.014 141.477M-7.616 141.477L-7.616 139.695L-8.366 139.695Q-8.565 139.672-8.616 139.453L-8.616 139.367Q-8.565 139.156-8.366 139.133L-7.616 139.133L-7.616 138.383Q-7.565 138.176-7.366 138.148L-7.221 138.148Q-7.026 138.176-6.975 138.383L-6.975 139.133L-5.616 139.133Q-5.424 139.152-5.366 139.367L-5.366 139.453Q-5.420 139.672-5.616 139.695L-6.975 139.695L-6.975 141.445Q-6.975 142.062-6.401 142.062Q-6.151 142.062-5.987 141.877Q-5.823 141.691-5.823 141.445Q-5.823 141.352-5.750 141.281Q-5.678 141.211-5.576 141.199L-5.432 141.199Q-5.233 141.223-5.182 141.430L-5.182 141.477Q-5.182 141.801-5.366 142.064Q-5.549 142.328-5.842 142.475Q-6.135 142.621-6.455 142.621Q-6.967 142.621-7.291 142.311Q-7.616 142-7.616 141.477\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(148.097 -22.951)\">\u003Cpath d=\"M-0.357 142.344L-0.357 142.254Q-0.306 142.043-0.111 142.023L0.089 142.023L0.089 139.695L-0.111 139.695Q-0.318 139.672-0.357 139.453L-0.357 139.367Q-0.306 139.152-0.111 139.133L0.370 139.133Q0.561 139.156 0.620 139.367Q0.940 139.094 1.354 139.094Q1.546 139.094 1.714 139.203Q1.882 139.312 1.956 139.484Q2.132 139.293 2.354 139.193Q2.577 139.094 2.819 139.094Q3.237 139.094 3.391 139.436Q3.546 139.777 3.546 140.246L3.546 142.023L3.745 142.023Q3.956 142.047 3.995 142.254L3.995 142.344Q3.944 142.559 3.745 142.582L2.928 142.582Q2.733 142.559 2.682 142.344L2.682 142.254Q2.733 142.047 2.928 142.023L3.018 142.023L3.018 140.277Q3.018 139.652 2.768 139.652Q2.436 139.652 2.259 139.963Q2.081 140.273 2.081 140.637L2.081 142.023L2.284 142.023Q2.491 142.047 2.530 142.254L2.530 142.344Q2.479 142.559 2.284 142.582L1.468 142.582Q1.268 142.559 1.218 142.344L1.218 142.254Q1.268 142.047 1.468 142.023L1.553 142.023L1.553 140.277Q1.553 139.652 1.307 139.652Q0.975 139.652 0.798 139.965Q0.620 140.277 0.620 140.637L0.620 142.023L0.819 142.023Q1.026 142.047 1.065 142.254L1.065 142.344Q1.014 142.562 0.819 142.582L-0.111 142.582Q-0.318 142.559-0.357 142.344M4.561 142.344L4.561 142.254Q4.612 142.047 4.807 142.023L5.846 142.023L5.846 139.695L4.874 139.695Q4.675 139.672 4.624 139.453L4.624 139.367Q4.675 139.156 4.874 139.133L6.241 139.133Q6.436 139.152 6.487 139.367L6.487 142.023L7.401 142.023Q7.596 142.047 7.647 142.254L7.647 142.344Q7.596 142.559 7.401 142.582L4.807 142.582Q4.612 142.559 4.561 142.344M5.593 138.156L5.593 138.102Q5.593 137.930 5.729 137.809Q5.866 137.687 6.042 137.687Q6.214 137.687 6.350 137.809Q6.487 137.930 6.487 138.102L6.487 138.156Q6.487 138.332 6.350 138.453Q6.214 138.574 6.042 138.574Q5.866 138.574 5.729 138.453Q5.593 138.332 5.593 138.156M8.261 142.344L8.261 142.254Q8.303 142.047 8.511 142.023L8.932 142.023L8.932 139.695L8.511 139.695Q8.303 139.672 8.261 139.453L8.261 139.367Q8.307 139.156 8.511 139.133L9.327 139.133Q9.522 139.156 9.573 139.367L9.573 139.453L9.565 139.477Q9.792 139.297 10.065 139.195Q10.339 139.094 10.632 139.094Q10.979 139.094 11.218 139.234Q11.456 139.375 11.571 139.633Q11.686 139.891 11.686 140.246L11.686 142.023L12.112 142.023Q12.319 142.047 12.358 142.254L12.358 142.344Q12.319 142.559 12.112 142.582L10.718 142.582Q10.522 142.559 10.471 142.344L10.471 142.254Q10.522 142.043 10.718 142.023L11.046 142.023L11.046 140.277Q11.046 139.969 10.956 139.811Q10.866 139.652 10.573 139.652Q10.303 139.652 10.075 139.783Q9.846 139.914 9.710 140.143Q9.573 140.371 9.573 140.637L9.573 142.023L9.999 142.023Q10.206 142.047 10.245 142.254L10.245 142.344Q10.206 142.559 9.999 142.582L8.511 142.582Q8.303 142.559 8.261 142.344\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(148.097 -22.951)\">\u003Cpath d=\"M-35.440 152.723Q-35.971 152.414-36.371 151.926Q-36.772 151.437-36.983 150.844Q-37.194 150.250-37.194 149.633Q-37.194 149.016-36.985 148.428Q-36.776 147.840-36.379 147.359Q-35.983 146.879-35.448 146.555Q-35.377 146.531-35.346 146.531L-35.256 146.531Q-35.158 146.543-35.092 146.609Q-35.026 146.676-35.026 146.777Q-35.026 146.922-35.127 146.984Q-35.576 147.273-35.903 147.689Q-36.229 148.105-36.391 148.598Q-36.553 149.090-36.553 149.633Q-36.553 150.039-36.457 150.426Q-36.362 150.812-36.184 151.148Q-36.006 151.484-35.746 151.768Q-35.487 152.051-35.139 152.281Q-35.026 152.359-35.026 152.496Q-35.026 152.594-35.092 152.664Q-35.158 152.734-35.256 152.746L-35.346 152.746Q-35.405 152.746-35.440 152.723M-33.881 151.922L-33.881 150.691Q-33.854 150.480-33.643 150.457L-33.475 150.457Q-33.381 150.469-33.319 150.527Q-33.256 150.586-33.244 150.691Q-33.244 151.184-32.879 151.393Q-32.514 151.602-31.979 151.602Q-31.744 151.602-31.541 151.494Q-31.338 151.387-31.215 151.193Q-31.092 151-31.092 150.769Q-31.092 150.473-31.287 150.250Q-31.483 150.027-31.772 149.961L-32.780 149.730Q-33.080 149.660-33.332 149.475Q-33.584 149.289-33.733 149.021Q-33.881 148.754-33.881 148.441Q-33.881 148.062-33.672 147.758Q-33.463 147.453-33.121 147.283Q-32.780 147.113-32.405 147.113Q-31.729 147.113-31.299 147.441L-31.229 147.258Q-31.155 147.137-31.018 147.113L-30.940 147.113Q-30.842 147.125-30.780 147.187Q-30.717 147.250-30.705 147.348L-30.705 148.578Q-30.729 148.789-30.940 148.816L-31.108 148.816Q-31.303 148.793-31.338 148.609Q-31.401 148.156-31.668 147.916Q-31.936 147.676-32.397 147.676Q-32.604 147.676-32.811 147.764Q-33.018 147.852-33.151 148.019Q-33.283 148.187-33.283 148.402Q-33.283 148.656-33.090 148.853Q-32.897 149.051-32.627 149.113L-31.619 149.340Q-31.291 149.418-31.037 149.621Q-30.783 149.824-30.637 150.119Q-30.490 150.414-30.490 150.730Q-30.490 151.129-30.694 151.455Q-30.897 151.781-31.240 151.971Q-31.584 152.160-31.971 152.160Q-32.772 152.160-33.291 151.844L-33.362 152.019Q-33.432 152.137-33.573 152.160L-33.643 152.160Q-33.858 152.137-33.881 151.922M-27.955 152.160Q-28.533 152.160-28.944 151.766Q-29.354 151.371-29.555 150.783Q-29.756 150.195-29.756 149.633Q-29.756 149.082-29.555 148.492Q-29.354 147.902-28.944 147.508Q-28.533 147.113-27.955 147.113Q-27.455 147.113-27.053 147.441Q-27.006 147.355-26.963 147.283Q-26.920 147.211-26.867 147.168Q-26.815 147.125-26.740 147.113L-26.662 147.113Q-26.565 147.125-26.502 147.187Q-26.440 147.250-26.428 147.348L-26.428 148.676Q-26.440 148.769-26.502 148.836Q-26.565 148.902-26.662 148.914L-26.830 148.914Q-27.026 148.887-27.061 148.707Q-27.115 148.289-27.326 147.982Q-27.537 147.676-27.908 147.676Q-28.303 147.676-28.578 148Q-28.854 148.324-28.985 148.785Q-29.115 149.246-29.115 149.633Q-29.115 150.035-28.987 150.494Q-28.858 150.953-28.588 151.277Q-28.319 151.602-27.924 151.602Q-27.541 151.602-27.336 151.248Q-27.131 150.894-27.084 150.449L-27.502 150.449Q-27.713 150.426-27.748 150.211L-27.748 150.121Q-27.709 149.914-27.502 149.891L-26.299 149.891Q-26.092 149.914-26.053 150.121L-26.053 150.211Q-26.088 150.426-26.299 150.449L-26.428 150.449L-26.428 151.859Q-26.479 152.062-26.678 152.098L-26.823 152.098Q-27.018 152.066-27.069 151.859L-27.069 151.809Q-27.440 152.160-27.955 152.160M-25.678 151.844L-25.678 151.754Q-25.639 151.547-25.432 151.523L-25.182 151.523L-25.182 147.754L-25.432 147.754Q-25.639 147.730-25.678 147.516L-25.678 147.426Q-25.639 147.219-25.432 147.195L-23.615 147.195Q-23.065 147.195-22.670 147.588Q-22.276 147.980-22.080 148.559Q-21.885 149.137-21.885 149.684Q-21.885 150.211-22.084 150.775Q-22.283 151.340-22.676 151.711Q-23.069 152.082-23.615 152.082L-25.432 152.082Q-25.639 152.059-25.678 151.844M-24.541 147.754L-24.541 151.523L-23.776 151.523Q-23.182 151.523-22.854 150.928Q-22.526 150.332-22.526 149.684Q-22.526 149.277-22.660 148.828Q-22.795 148.379-23.080 148.066Q-23.365 147.754-23.776 147.754\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(148.097 -22.951)\">\u003Cpath d=\"M-17.232 153.625L-17.232 153.539Q-17.189 153.320-16.982 153.297L-16.560 153.297L-16.560 149.195L-16.982 149.195Q-17.189 149.172-17.232 148.953L-17.232 148.867Q-17.185 148.656-16.982 148.633L-16.165 148.633Q-15.970 148.652-15.919 148.867L-15.919 148.937Q-15.708 148.769-15.445 148.682Q-15.181 148.594-14.911 148.594Q-14.572 148.594-14.275 148.738Q-13.978 148.883-13.763 149.135Q-13.548 149.387-13.433 149.699Q-13.318 150.012-13.318 150.355Q-13.318 150.820-13.544 151.230Q-13.771 151.641-14.157 151.881Q-14.544 152.121-15.013 152.121Q-15.532 152.121-15.919 151.754L-15.919 153.297L-15.493 153.297Q-15.286 153.320-15.247 153.539L-15.247 153.625Q-15.286 153.836-15.493 153.859L-16.982 153.859Q-17.185 153.836-17.232 153.625M-15.056 151.562Q-14.747 151.562-14.497 151.393Q-14.247 151.223-14.103 150.941Q-13.958 150.660-13.958 150.355Q-13.958 150.066-14.083 149.787Q-14.208 149.508-14.441 149.330Q-14.673 149.152-14.974 149.152Q-15.294 149.152-15.556 149.338Q-15.818 149.523-15.919 149.824L-15.919 150.676Q-15.829 151.043-15.609 151.303Q-15.388 151.562-15.056 151.562M-12.439 151.844L-12.439 151.754Q-12.388 151.547-12.193 151.523L-11.154 151.523L-11.154 149.195L-12.126 149.195Q-12.325 149.172-12.376 148.953L-12.376 148.867Q-12.325 148.656-12.126 148.633L-10.759 148.633Q-10.564 148.652-10.513 148.867L-10.513 151.523L-9.599 151.523Q-9.404 151.547-9.353 151.754L-9.353 151.844Q-9.404 152.059-9.599 152.082L-12.193 152.082Q-12.388 152.059-12.439 151.844M-11.407 147.656L-11.407 147.602Q-11.407 147.430-11.271 147.309Q-11.134 147.187-10.958 147.187Q-10.786 147.187-10.650 147.309Q-10.513 147.430-10.513 147.602L-10.513 147.656Q-10.513 147.832-10.650 147.953Q-10.786 148.074-10.958 148.074Q-11.134 148.074-11.271 147.953Q-11.407 147.832-11.407 147.656M-8.251 150.355Q-8.251 149.875-8.007 149.461Q-7.763 149.047-7.347 148.809Q-6.931 148.570-6.450 148.570Q-5.896 148.570-5.517 148.680Q-5.138 148.789-5.138 149.195Q-5.138 149.363-5.251 149.486Q-5.364 149.609-5.536 149.609Q-5.708 149.609-5.827 149.494Q-5.947 149.379-5.947 149.211L-5.947 149.152Q-6.107 149.129-6.443 149.129Q-6.771 149.129-7.038 149.299Q-7.306 149.469-7.458 149.752Q-7.611 150.035-7.611 150.355Q-7.611 150.676-7.439 150.957Q-7.267 151.238-6.982 151.400Q-6.697 151.562-6.368 151.562Q-6.056 151.562-5.929 151.459Q-5.802 151.355-5.685 151.166Q-5.568 150.977-5.450 150.961L-5.282 150.961Q-5.177 150.973-5.113 151.041Q-5.048 151.109-5.048 151.211Q-5.048 151.258-5.068 151.297Q-5.177 151.590-5.380 151.769Q-5.583 151.949-5.859 152.035Q-6.134 152.121-6.450 152.121Q-6.935 152.121-7.351 151.883Q-7.767 151.644-8.009 151.242Q-8.251 150.840-8.251 150.355M-4.419 151.844L-4.419 151.754Q-4.368 151.547-4.173 151.523L-3.708 151.523L-3.708 147.754L-4.173 147.754Q-4.368 147.730-4.419 147.516L-4.419 147.426Q-4.368 147.219-4.173 147.195L-3.427 147.195Q-3.232 147.215-3.181 147.426L-3.181 150.250L-2.036 149.195L-2.333 149.195Q-2.540 149.172-2.579 148.953L-2.579 148.867Q-2.540 148.656-2.333 148.633L-0.884 148.633Q-0.681 148.656-0.634 148.867L-0.634 148.953Q-0.677 149.172-0.884 149.195L-1.251 149.195L-2.197 150.059L-1.044 151.523L-0.708 151.523Q-0.501 151.547-0.458 151.754L-0.458 151.844Q-0.501 152.059-0.708 152.082L-1.876 152.082Q-2.072 152.059-2.122 151.844L-2.122 151.754Q-2.072 151.547-1.876 151.523L-1.716 151.523L-2.579 150.418L-3.181 150.969L-3.181 151.523L-2.716 151.523Q-2.525 151.547-2.466 151.754L-2.466 151.844Q-2.525 152.059-2.716 152.082L-4.173 152.082Q-4.368 152.059-4.419 151.844M0.268 151.883L0.268 150.969Q0.296 150.762 0.507 150.738L0.675 150.738Q0.839 150.762 0.897 150.922Q1.100 151.562 1.827 151.562Q2.034 151.562 2.262 151.527Q2.491 151.492 2.659 151.377Q2.827 151.262 2.827 151.059Q2.827 150.848 2.604 150.734Q2.382 150.621 2.108 150.578L1.409 150.465Q0.268 150.254 0.268 149.531Q0.268 149.242 0.413 149.053Q0.557 148.863 0.798 148.756Q1.038 148.648 1.294 148.609Q1.550 148.570 1.827 148.570Q2.077 148.570 2.270 148.600Q2.464 148.629 2.628 148.707Q2.706 148.590 2.835 148.570L2.913 148.570Q3.011 148.582 3.073 148.644Q3.136 148.707 3.147 148.801L3.147 149.508Q3.136 149.602 3.073 149.668Q3.011 149.734 2.913 149.746L2.745 149.746Q2.651 149.734 2.585 149.668Q2.518 149.602 2.507 149.508Q2.507 149.129 1.811 149.129Q1.464 149.129 1.145 149.211Q0.827 149.293 0.827 149.539Q0.827 149.805 1.499 149.914L2.202 150.035Q2.686 150.117 3.036 150.365Q3.386 150.613 3.386 151.059Q3.386 151.449 3.149 151.691Q2.913 151.934 2.563 152.027Q2.214 152.121 1.827 152.121Q1.249 152.121 0.850 151.867Q0.780 151.992 0.731 152.049Q0.682 152.105 0.577 152.121L0.507 152.121Q0.292 152.098 0.268 151.883\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(148.097 -22.951)\">\u003Cpath d=\"M9.397 150.977L9.397 149.195L8.647 149.195Q8.448 149.172 8.397 148.953L8.397 148.867Q8.448 148.656 8.647 148.633L9.397 148.633L9.397 147.883Q9.448 147.676 9.647 147.648L9.792 147.648Q9.987 147.676 10.038 147.883L10.038 148.633L11.397 148.633Q11.589 148.652 11.647 148.867L11.647 148.953Q11.593 149.172 11.397 149.195L10.038 149.195L10.038 150.945Q10.038 151.562 10.612 151.562Q10.862 151.562 11.026 151.377Q11.190 151.191 11.190 150.945Q11.190 150.852 11.262 150.781Q11.335 150.711 11.436 150.699L11.581 150.699Q11.780 150.723 11.831 150.930L11.831 150.977Q11.831 151.301 11.647 151.564Q11.464 151.828 11.171 151.975Q10.878 152.121 10.557 152.121Q10.046 152.121 9.721 151.811Q9.397 151.500 9.397 150.977M12.514 151.844L12.514 151.754Q12.557 151.547 12.764 151.523L13.186 151.523L13.186 147.754L12.764 147.754Q12.557 147.730 12.514 147.516L12.514 147.426Q12.557 147.219 12.764 147.195L13.581 147.195Q13.776 147.219 13.827 147.426L13.827 148.977Q14.288 148.594 14.886 148.594Q15.233 148.594 15.471 148.734Q15.710 148.875 15.825 149.133Q15.940 149.391 15.940 149.746L15.940 151.523L16.366 151.523Q16.573 151.547 16.612 151.754L16.612 151.844Q16.573 152.059 16.366 152.082L14.971 152.082Q14.776 152.059 14.725 151.844L14.725 151.754Q14.776 151.543 14.971 151.523L15.300 151.523L15.300 149.777Q15.300 149.469 15.210 149.311Q15.120 149.152 14.827 149.152Q14.557 149.152 14.329 149.283Q14.100 149.414 13.964 149.643Q13.827 149.871 13.827 150.137L13.827 151.523L14.253 151.523Q14.460 151.547 14.499 151.754L14.499 151.844Q14.460 152.059 14.253 152.082L12.764 152.082Q12.557 152.059 12.514 151.844M17.307 151.844L17.307 151.754Q17.358 151.547 17.553 151.523L18.593 151.523L18.593 149.195L17.620 149.195Q17.421 149.172 17.370 148.953L17.370 148.867Q17.421 148.656 17.620 148.633L18.987 148.633Q19.182 148.652 19.233 148.867L19.233 151.523L20.147 151.523Q20.343 151.547 20.393 151.754L20.393 151.844Q20.343 152.059 20.147 152.082L17.553 152.082Q17.358 152.059 17.307 151.844M18.339 147.656L18.339 147.602Q18.339 147.430 18.475 147.309Q18.612 147.187 18.788 147.187Q18.960 147.187 19.096 147.309Q19.233 147.430 19.233 147.602L19.233 147.656Q19.233 147.832 19.096 147.953Q18.960 148.074 18.788 148.074Q18.612 148.074 18.475 147.953Q18.339 147.832 18.339 147.656M21.522 151.883L21.522 150.969Q21.550 150.762 21.761 150.738L21.928 150.738Q22.093 150.762 22.151 150.922Q22.354 151.562 23.081 151.562Q23.288 151.562 23.516 151.527Q23.745 151.492 23.913 151.377Q24.081 151.262 24.081 151.059Q24.081 150.848 23.858 150.734Q23.636 150.621 23.362 150.578L22.663 150.465Q21.522 150.254 21.522 149.531Q21.522 149.242 21.667 149.053Q21.811 148.863 22.052 148.756Q22.292 148.648 22.548 148.609Q22.803 148.570 23.081 148.570Q23.331 148.570 23.524 148.600Q23.718 148.629 23.882 148.707Q23.960 148.590 24.089 148.570L24.167 148.570Q24.264 148.582 24.327 148.644Q24.389 148.707 24.401 148.801L24.401 149.508Q24.389 149.602 24.327 149.668Q24.264 149.734 24.167 149.746L23.999 149.746Q23.905 149.734 23.839 149.668Q23.772 149.602 23.761 149.508Q23.761 149.129 23.065 149.129Q22.718 149.129 22.399 149.211Q22.081 149.293 22.081 149.539Q22.081 149.805 22.753 149.914L23.456 150.035Q23.940 150.117 24.290 150.365Q24.639 150.613 24.639 151.059Q24.639 151.449 24.403 151.691Q24.167 151.934 23.817 152.027Q23.468 152.121 23.081 152.121Q22.503 152.121 22.104 151.867Q22.034 151.992 21.985 152.049Q21.936 152.105 21.831 152.121L21.761 152.121Q21.546 152.098 21.522 151.883M26.221 152.746L26.136 152.746Q26.030 152.734 25.962 152.662Q25.893 152.590 25.893 152.496Q25.893 152.355 25.999 152.289Q26.335 152.074 26.604 151.785Q26.874 151.496 27.057 151.148Q27.241 150.801 27.331 150.422Q27.421 150.043 27.421 149.633Q27.421 149.094 27.257 148.602Q27.093 148.109 26.774 147.695Q26.456 147.281 26.014 146.992Q25.893 146.910 25.893 146.777Q25.893 146.680 25.962 146.611Q26.030 146.543 26.136 146.531L26.221 146.531Q26.276 146.531 26.311 146.555Q26.698 146.777 27.038 147.125Q27.378 147.473 27.598 147.867Q27.819 148.262 27.940 148.707Q28.061 149.152 28.061 149.633Q28.061 150.117 27.940 150.568Q27.819 151.019 27.594 151.416Q27.370 151.812 27.046 152.146Q26.721 152.480 26.319 152.723Q26.249 152.746 26.221 152.746\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"M-38.553 142.123h247.539\" style=\"stroke-dasharray:3.0,3.0\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-size=\"8\">\u003Cg transform=\"translate(7.8 -15.483)\">\u003Cpath d=\"M-37.354 150.977L-37.354 149.195L-38.104 149.195Q-38.303 149.172-38.354 148.953L-38.354 148.867Q-38.303 148.656-38.104 148.633L-37.354 148.633L-37.354 147.883Q-37.303 147.676-37.104 147.648L-36.959 147.648Q-36.764 147.676-36.713 147.883L-36.713 148.633L-35.354 148.633Q-35.162 148.652-35.104 148.867L-35.104 148.953Q-35.158 149.172-35.354 149.195L-36.713 149.195L-36.713 150.945Q-36.713 151.562-36.139 151.562Q-35.889 151.562-35.725 151.377Q-35.561 151.191-35.561 150.945Q-35.561 150.852-35.489 150.781Q-35.416 150.711-35.315 150.699L-35.170 150.699Q-34.971 150.723-34.920 150.930L-34.920 150.977Q-34.920 151.301-35.104 151.564Q-35.287 151.828-35.580 151.975Q-35.873 152.121-36.194 152.121Q-36.705 152.121-37.030 151.811Q-37.354 151.500-37.354 150.977M-34.084 151.844L-34.084 151.754Q-34.026 151.547-33.834 151.523L-33.123 151.523L-33.123 149.195L-33.834 149.195Q-34.030 149.172-34.084 148.953L-34.084 148.867Q-34.026 148.656-33.834 148.633L-32.733 148.633Q-32.533 148.652-32.483 148.867L-32.483 149.195Q-32.221 148.910-31.865 148.752Q-31.510 148.594-31.123 148.594Q-30.830 148.594-30.596 148.728Q-30.362 148.863-30.362 149.129Q-30.362 149.297-30.471 149.414Q-30.580 149.531-30.748 149.531Q-30.901 149.531-31.016 149.420Q-31.131 149.309-31.131 149.152Q-31.506 149.152-31.821 149.353Q-32.135 149.555-32.309 149.889Q-32.483 150.223-32.483 150.602L-32.483 151.523L-31.537 151.523Q-31.330 151.547-31.291 151.754L-31.291 151.844Q-31.330 152.059-31.537 152.082L-33.834 152.082Q-34.026 152.059-34.084 151.844M-29.655 150.969Q-29.655 150.523-29.240 150.266Q-28.826 150.008-28.285 149.908Q-27.744 149.809-27.237 149.801Q-27.237 149.586-27.371 149.434Q-27.506 149.281-27.713 149.205Q-27.920 149.129-28.131 149.129Q-28.475 149.129-28.635 149.152L-28.635 149.211Q-28.635 149.379-28.754 149.494Q-28.873 149.609-29.037 149.609Q-29.213 149.609-29.328 149.486Q-29.444 149.363-29.444 149.195Q-29.444 148.789-29.063 148.680Q-28.682 148.570-28.123 148.570Q-27.854 148.570-27.586 148.648Q-27.319 148.727-27.094 148.877Q-26.869 149.027-26.733 149.248Q-26.596 149.469-26.596 149.746L-26.596 151.465Q-26.596 151.523-26.069 151.523Q-25.873 151.543-25.823 151.754L-25.823 151.844Q-25.873 152.059-26.069 152.082L-26.213 152.082Q-26.557 152.082-26.785 152.035Q-27.014 151.988-27.158 151.801Q-27.619 152.121-28.326 152.121Q-28.662 152.121-28.967 151.980Q-29.272 151.840-29.463 151.578Q-29.655 151.316-29.655 150.969M-29.014 150.977Q-29.014 151.250-28.772 151.406Q-28.530 151.562-28.244 151.562Q-28.026 151.562-27.793 151.504Q-27.561 151.445-27.399 151.307Q-27.237 151.168-27.237 150.945L-27.237 150.355Q-27.518 150.355-27.934 150.412Q-28.350 150.469-28.682 150.607Q-29.014 150.746-29.014 150.977M-25.198 151.844L-25.198 151.754Q-25.147 151.547-24.951 151.523L-23.912 151.523L-23.912 149.195L-24.885 149.195Q-25.084 149.172-25.135 148.953L-25.135 148.867Q-25.084 148.656-24.885 148.633L-23.518 148.633Q-23.323 148.652-23.272 148.867L-23.272 151.523L-22.358 151.523Q-22.162 151.547-22.112 151.754L-22.112 151.844Q-22.162 152.059-22.358 152.082L-24.951 152.082Q-25.147 152.059-25.198 151.844M-24.166 147.656L-24.166 147.602Q-24.166 147.430-24.030 147.309Q-23.893 147.187-23.717 147.187Q-23.545 147.187-23.408 147.309Q-23.272 147.430-23.272 147.602L-23.272 147.656Q-23.272 147.832-23.408 147.953Q-23.545 148.074-23.717 148.074Q-23.893 148.074-24.030 147.953Q-24.166 147.832-24.166 147.656M-21.498 151.844L-21.498 151.754Q-21.455 151.547-21.248 151.523L-20.826 151.523L-20.826 149.195L-21.248 149.195Q-21.455 149.172-21.498 148.953L-21.498 148.867Q-21.451 148.656-21.248 148.633L-20.432 148.633Q-20.237 148.656-20.186 148.867L-20.186 148.953L-20.194 148.977Q-19.967 148.797-19.694 148.695Q-19.420 148.594-19.127 148.594Q-18.780 148.594-18.541 148.734Q-18.303 148.875-18.188 149.133Q-18.073 149.391-18.073 149.746L-18.073 151.523L-17.647 151.523Q-17.440 151.547-17.401 151.754L-17.401 151.844Q-17.440 152.059-17.647 152.082L-19.041 152.082Q-19.237 152.059-19.287 151.844L-19.287 151.754Q-19.237 151.543-19.041 151.523L-18.713 151.523L-18.713 149.777Q-18.713 149.469-18.803 149.311Q-18.893 149.152-19.186 149.152Q-19.455 149.152-19.684 149.283Q-19.912 149.414-20.049 149.643Q-20.186 149.871-20.186 150.137L-20.186 151.523L-19.760 151.523Q-19.553 151.547-19.514 151.754L-19.514 151.844Q-19.553 152.059-19.760 152.082L-21.248 152.082Q-21.455 152.059-21.498 151.844\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(7.8 -15.483)\">\u003Cpath d=\"M-12.603 151.844L-12.603 151.754Q-12.552 151.547-12.357 151.523L-11.251 151.523L-11.251 147.754L-12.357 147.754Q-12.552 147.730-12.603 147.516L-12.603 147.426Q-12.552 147.219-12.357 147.195L-10.861 147.195Q-10.669 147.219-10.611 147.426L-10.611 151.523L-9.509 151.523Q-9.310 151.547-9.259 151.754L-9.259 151.844Q-9.310 152.059-9.509 152.082L-12.357 152.082Q-12.552 152.059-12.603 151.844M-6.685 152.121Q-7.157 152.121-7.542 151.877Q-7.927 151.633-8.150 151.223Q-8.372 150.812-8.372 150.355Q-8.372 150.012-8.247 149.689Q-8.122 149.367-7.892 149.113Q-7.661 148.859-7.355 148.715Q-7.048 148.570-6.685 148.570Q-6.322 148.570-6.009 148.717Q-5.697 148.863-5.474 149.109Q-5.251 149.355-5.124 149.676Q-4.997 149.996-4.997 150.355Q-4.997 150.812-5.222 151.225Q-5.447 151.637-5.831 151.879Q-6.216 152.121-6.685 152.121M-6.685 151.562Q-6.220 151.562-5.929 151.168Q-5.638 150.773-5.638 150.289Q-5.638 149.996-5.773 149.728Q-5.907 149.461-6.148 149.295Q-6.388 149.129-6.685 149.129Q-6.989 149.129-7.228 149.295Q-7.466 149.461-7.601 149.728Q-7.736 149.996-7.736 150.289Q-7.736 150.769-7.443 151.166Q-7.150 151.562-6.685 151.562M-3.974 151.883L-3.974 150.969Q-3.947 150.762-3.736 150.738L-3.568 150.738Q-3.404 150.762-3.345 150.922Q-3.142 151.562-2.415 151.562Q-2.208 151.562-1.980 151.527Q-1.751 151.492-1.583 151.377Q-1.415 151.262-1.415 151.059Q-1.415 150.848-1.638 150.734Q-1.861 150.621-2.134 150.578L-2.833 150.465Q-3.974 150.254-3.974 149.531Q-3.974 149.242-3.829 149.053Q-3.685 148.863-3.445 148.756Q-3.204 148.648-2.948 148.609Q-2.693 148.570-2.415 148.570Q-2.165 148.570-1.972 148.600Q-1.779 148.629-1.614 148.707Q-1.536 148.590-1.407 148.570L-1.329 148.570Q-1.232 148.582-1.169 148.644Q-1.107 148.707-1.095 148.801L-1.095 149.508Q-1.107 149.602-1.169 149.668Q-1.232 149.734-1.329 149.746L-1.497 149.746Q-1.591 149.734-1.657 149.668Q-1.724 149.602-1.736 149.508Q-1.736 149.129-2.431 149.129Q-2.779 149.129-3.097 149.211Q-3.415 149.293-3.415 149.539Q-3.415 149.805-2.743 149.914L-2.040 150.035Q-1.556 150.117-1.206 150.365Q-0.857 150.613-0.857 151.059Q-0.857 151.449-1.093 151.691Q-1.329 151.934-1.679 152.027Q-2.029 152.121-2.415 152.121Q-2.993 152.121-3.392 151.867Q-3.462 151.992-3.511 152.049Q-3.560 152.105-3.665 152.121L-3.736 152.121Q-3.950 152.098-3.974 151.883M0.272 151.883L0.272 150.969Q0.300 150.762 0.511 150.738L0.678 150.738Q0.843 150.762 0.901 150.922Q1.104 151.562 1.831 151.562Q2.038 151.562 2.266 151.527Q2.495 151.492 2.663 151.377Q2.831 151.262 2.831 151.059Q2.831 150.848 2.608 150.734Q2.386 150.621 2.112 150.578L1.413 150.465Q0.272 150.254 0.272 149.531Q0.272 149.242 0.417 149.053Q0.561 148.863 0.802 148.756Q1.042 148.648 1.298 148.609Q1.553 148.570 1.831 148.570Q2.081 148.570 2.274 148.600Q2.468 148.629 2.632 148.707Q2.710 148.590 2.839 148.570L2.917 148.570Q3.014 148.582 3.077 148.644Q3.139 148.707 3.151 148.801L3.151 149.508Q3.139 149.602 3.077 149.668Q3.014 149.734 2.917 149.746L2.749 149.746Q2.655 149.734 2.589 149.668Q2.522 149.602 2.511 149.508Q2.511 149.129 1.815 149.129Q1.468 149.129 1.149 149.211Q0.831 149.293 0.831 149.539Q0.831 149.805 1.503 149.914L2.206 150.035Q2.690 150.117 3.040 150.365Q3.389 150.613 3.389 151.059Q3.389 151.449 3.153 151.691Q2.917 151.934 2.567 152.027Q2.218 152.121 1.831 152.121Q1.253 152.121 0.854 151.867Q0.784 151.992 0.735 152.049Q0.686 152.105 0.581 152.121L0.511 152.121Q0.296 152.098 0.272 151.883\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(7.8 -15.483)\">\u003Cpath d=\"M12.742 151.105L7.429 151.105Q7.351 151.098 7.302 151.049Q7.254 151 7.254 150.922Q7.254 150.852 7.301 150.801Q7.347 150.750 7.429 150.738L12.742 150.738Q12.816 150.750 12.863 150.801Q12.910 150.852 12.910 150.922Q12.910 151 12.861 151.049Q12.812 151.098 12.742 151.105M12.742 149.418L7.429 149.418Q7.351 149.410 7.302 149.361Q7.254 149.312 7.254 149.234Q7.254 149.164 7.301 149.113Q7.347 149.062 7.429 149.051L12.742 149.051Q12.816 149.062 12.863 149.113Q12.910 149.164 12.910 149.234Q12.910 149.312 12.861 149.361Q12.812 149.410 12.742 149.418\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(7.8 -15.483)\">\u003Cpath d=\"M17.875 152.250Q17.172 152.250 16.772 151.850Q16.371 151.449 16.227 150.840Q16.082 150.230 16.082 149.531Q16.082 149.008 16.152 148.545Q16.223 148.082 16.416 147.670Q16.609 147.258 16.967 147.010Q17.324 146.762 17.875 146.762Q18.426 146.762 18.783 147.010Q19.141 147.258 19.332 147.668Q19.524 148.078 19.594 148.547Q19.664 149.016 19.664 149.531Q19.664 150.230 19.522 150.838Q19.379 151.445 18.979 151.848Q18.578 152.250 17.875 152.250M17.875 151.992Q18.348 151.992 18.580 151.557Q18.813 151.121 18.867 150.582Q18.922 150.043 18.922 149.402Q18.922 148.406 18.738 147.713Q18.555 147.019 17.875 147.019Q17.508 147.019 17.287 147.258Q17.066 147.496 16.971 147.853Q16.875 148.211 16.850 148.582Q16.824 148.953 16.824 149.402Q16.824 150.043 16.879 150.582Q16.934 151.121 17.166 151.557Q17.399 151.992 17.875 151.992\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">Implicit bias: among the many interpolating minima, minibatch noise drives SGD into the wide flat basin rather than the narrow sharp one.\u003C\u002Ffigcaption>","\u003Csvg style=\"width:100%;max-width:429.257px;height:auto\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"-75 -75 321.943 147.813\">\u003Cg stroke=\"currentColor\" style=\"stroke-miterlimit:10;stroke-width:.4\">\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-46.621 63.432h242.094\"\u002F>\u003Cpath stroke=\"none\" d=\"m198.073 63.432-4.16-2.08 1.56 2.08-1.56 2.08\"\u002F>\u003Cg stroke=\"none\" font-family=\"cmr8\" font-size=\"8\">\u003Cg transform=\"translate(248.427 2.778)\">\u003Cpath d=\"M-44.453 63.432L-46.309 63.432L-46.309 63.135Q-46.035 63.135-45.867 63.088Q-45.699 63.041-45.699 62.873L-45.699 60.737Q-45.699 60.522-45.762 60.426Q-45.824 60.330-45.943 60.309Q-46.062 60.287-46.309 60.287L-46.309 59.991L-45.117 59.905L-45.117 60.639Q-45.004 60.424-44.810 60.256Q-44.617 60.088-44.379 59.996Q-44.141 59.905-43.887 59.905Q-42.926 59.905-42.750 60.616Q-42.566 60.287-42.238 60.096Q-41.910 59.905-41.531 59.905Q-40.355 59.905-40.355 60.983L-40.355 62.873Q-40.355 63.041-40.187 63.088Q-40.019 63.135-39.750 63.135L-39.750 63.432L-41.605 63.432L-41.605 63.135Q-41.332 63.135-41.164 63.090Q-40.996 63.045-40.996 62.873L-40.996 60.998Q-40.996 60.612-41.121 60.385Q-41.246 60.159-41.598 60.159Q-41.902 60.159-42.158 60.321Q-42.414 60.483-42.562 60.752Q-42.711 61.022-42.711 61.319L-42.711 62.873Q-42.711 63.041-42.541 63.088Q-42.371 63.135-42.101 63.135L-42.101 63.432L-43.957 63.432L-43.957 63.135Q-43.684 63.135-43.516 63.088Q-43.348 63.041-43.348 62.873L-43.348 60.998Q-43.348 60.612-43.473 60.385Q-43.598 60.159-43.949 60.159Q-44.254 60.159-44.510 60.321Q-44.766 60.483-44.914 60.752Q-45.062 61.022-45.062 61.319L-45.062 62.873Q-45.062 63.041-44.892 63.088Q-44.723 63.135-44.453 63.135L-44.453 63.432M-39.305 61.737Q-39.305 61.233-39.049 60.801Q-38.793 60.370-38.357 60.118Q-37.922 59.866-37.422 59.866Q-37.035 59.866-36.693 60.010Q-36.351 60.155-36.090 60.416Q-35.828 60.678-35.685 61.014Q-35.543 61.350-35.543 61.737Q-35.543 62.229-35.807 62.639Q-36.070 63.049-36.500 63.280Q-36.930 63.510-37.422 63.510Q-37.914 63.510-38.348 63.278Q-38.781 63.045-39.043 62.637Q-39.305 62.229-39.305 61.737M-37.422 63.233Q-36.965 63.233-36.713 63.010Q-36.461 62.787-36.373 62.436Q-36.285 62.084-36.285 61.639Q-36.285 61.209-36.379 60.871Q-36.473 60.534-36.726 60.327Q-36.980 60.120-37.422 60.120Q-38.070 60.120-38.314 60.536Q-38.559 60.952-38.559 61.639Q-38.559 62.084-38.471 62.436Q-38.383 62.787-38.131 63.010Q-37.879 63.233-37.422 63.233\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(248.427 2.778)\">\u003Cpath d=\"M-32.996 63.510Q-33.477 63.510-33.885 63.266Q-34.293 63.022-34.531 62.608Q-34.770 62.194-34.770 61.705Q-34.770 61.213-34.512 60.797Q-34.254 60.381-33.822 60.143Q-33.391 59.905-32.899 59.905Q-32.278 59.905-31.828 60.342L-31.828 58.713Q-31.828 58.498-31.891 58.403Q-31.953 58.307-32.071 58.286Q-32.188 58.264-32.434 58.264L-32.434 57.967L-31.211 57.881L-31.211 62.690Q-31.211 62.901-31.149 62.996Q-31.086 63.092-30.969 63.114Q-30.852 63.135-30.602 63.135L-30.602 63.432L-31.852 63.510L-31.852 63.026Q-32.317 63.510-32.996 63.510M-32.930 63.256Q-32.590 63.256-32.297 63.065Q-32.004 62.873-31.852 62.577L-31.852 60.745Q-32 60.471-32.262 60.315Q-32.524 60.159-32.836 60.159Q-33.461 60.159-33.744 60.606Q-34.028 61.053-34.028 61.713Q-34.028 62.358-33.776 62.807Q-33.524 63.256-32.930 63.256M-30.094 61.678Q-30.094 61.198-29.862 60.782Q-29.629 60.366-29.219 60.116Q-28.809 59.866-28.332 59.866Q-27.602 59.866-27.203 60.307Q-26.805 60.748-26.805 61.479Q-26.805 61.584-26.899 61.608L-29.348 61.608L-29.348 61.678Q-29.348 62.088-29.227 62.444Q-29.106 62.799-28.834 63.016Q-28.563 63.233-28.133 63.233Q-27.770 63.233-27.473 63.004Q-27.176 62.776-27.074 62.424Q-27.067 62.377-26.981 62.362L-26.899 62.362Q-26.805 62.389-26.805 62.471Q-26.805 62.479-26.813 62.510Q-26.875 62.737-27.014 62.920Q-27.153 63.104-27.344 63.237Q-27.535 63.370-27.754 63.440Q-27.973 63.510-28.211 63.510Q-28.582 63.510-28.920 63.373Q-29.258 63.237-29.526 62.985Q-29.793 62.733-29.944 62.393Q-30.094 62.053-30.094 61.678M-29.340 61.370L-27.379 61.370Q-27.379 61.065-27.481 60.774Q-27.582 60.483-27.799 60.301Q-28.016 60.120-28.332 60.120Q-28.633 60.120-28.864 60.307Q-29.094 60.495-29.217 60.786Q-29.340 61.077-29.340 61.370M-24.403 63.432L-26.235 63.432L-26.235 63.135Q-25.961 63.135-25.793 63.088Q-25.625 63.041-25.625 62.873L-25.625 58.713Q-25.625 58.498-25.688 58.403Q-25.750 58.307-25.869 58.286Q-25.989 58.264-26.235 58.264L-26.235 57.967L-25.012 57.881L-25.012 62.873Q-25.012 63.041-24.844 63.088Q-24.676 63.135-24.403 63.135\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(248.427 2.778)\">\u003Cpath d=\"M-21.076 63.424L-21.076 62.202Q-21.076 62.174-21.044 62.143Q-21.013 62.112-20.990 62.112L-20.884 62.112Q-20.814 62.112-20.798 62.174Q-20.736 62.495-20.597 62.735Q-20.459 62.975-20.226 63.116Q-19.994 63.256-19.685 63.256Q-19.447 63.256-19.238 63.196Q-19.029 63.135-18.892 62.987Q-18.755 62.838-18.755 62.592Q-18.755 62.338-18.966 62.172Q-19.177 62.006-19.447 61.952L-20.068 61.838Q-20.474 61.760-20.775 61.504Q-21.076 61.248-21.076 60.873Q-21.076 60.506-20.875 60.284Q-20.673 60.061-20.349 59.963Q-20.025 59.866-19.685 59.866Q-19.220 59.866-18.923 60.073L-18.701 59.889Q-18.677 59.866-18.646 59.866L-18.595 59.866Q-18.564 59.866-18.537 59.893Q-18.509 59.920-18.509 59.952L-18.509 60.936Q-18.509 60.967-18.535 60.996Q-18.560 61.026-18.595 61.026L-18.701 61.026Q-18.736 61.026-18.763 60.998Q-18.791 60.971-18.791 60.936Q-18.791 60.537-19.043 60.317Q-19.294 60.096-19.693 60.096Q-20.048 60.096-20.332 60.219Q-20.615 60.342-20.615 60.647Q-20.615 60.866-20.414 60.998Q-20.212 61.131-19.966 61.174L-19.341 61.287Q-18.912 61.377-18.603 61.674Q-18.294 61.971-18.294 62.385Q-18.294 62.955-18.693 63.233Q-19.091 63.510-19.685 63.510Q-20.236 63.510-20.587 63.174L-20.884 63.487Q-20.908 63.510-20.943 63.510L-20.990 63.510Q-21.013 63.510-21.044 63.479Q-21.076 63.448-21.076 63.424M-15.908 63.432L-17.685 63.432L-17.685 63.135Q-17.412 63.135-17.244 63.088Q-17.076 63.041-17.076 62.873L-17.076 60.737Q-17.076 60.522-17.132 60.426Q-17.189 60.330-17.302 60.309Q-17.416 60.287-17.662 60.287L-17.662 59.991L-16.462 59.905L-16.462 62.873Q-16.462 63.041-16.316 63.088Q-16.169 63.135-15.908 63.135L-15.908 63.432M-17.349 58.510Q-17.349 58.319-17.214 58.188Q-17.080 58.057-16.884 58.057Q-16.763 58.057-16.660 58.120Q-16.556 58.182-16.494 58.286Q-16.431 58.389-16.431 58.510Q-16.431 58.705-16.562 58.840Q-16.693 58.975-16.884 58.975Q-17.084 58.975-17.216 58.842Q-17.349 58.709-17.349 58.510M-12.373 63.432L-15.294 63.432Q-15.337 63.432-15.373 63.401Q-15.408 63.370-15.408 63.319L-15.408 63.248Q-15.408 63.202-15.373 63.166L-13.060 60.241L-13.775 60.241Q-14.134 60.241-14.355 60.282Q-14.576 60.323-14.722 60.438Q-14.869 60.553-14.941 60.772Q-15.013 60.991-15.013 61.354L-15.294 61.354L-15.197 59.983L-12.365 59.983Q-12.318 59.983-12.287 60.016Q-12.255 60.049-12.255 60.096L-12.255 60.151Q-12.255 60.198-12.279 60.233L-14.599 63.151L-13.837 63.151Q-13.478 63.151-13.238 63.110Q-12.998 63.069-12.814 62.905Q-12.662 62.752-12.601 62.493Q-12.541 62.233-12.509 61.858L-12.232 61.858L-12.373 63.432M-11.630 61.678Q-11.630 61.198-11.398 60.782Q-11.166 60.366-10.755 60.116Q-10.345 59.866-9.869 59.866Q-9.138 59.866-8.740 60.307Q-8.341 60.748-8.341 61.479Q-8.341 61.584-8.435 61.608L-10.884 61.608L-10.884 61.678Q-10.884 62.088-10.763 62.444Q-10.642 62.799-10.371 63.016Q-10.099 63.233-9.669 63.233Q-9.306 63.233-9.009 63.004Q-8.712 62.776-8.611 62.424Q-8.603 62.377-8.517 62.362L-8.435 62.362Q-8.341 62.389-8.341 62.471Q-8.341 62.479-8.349 62.510Q-8.412 62.737-8.550 62.920Q-8.689 63.104-8.880 63.237Q-9.072 63.370-9.291 63.440Q-9.509 63.510-9.748 63.510Q-10.119 63.510-10.457 63.373Q-10.794 63.237-11.062 62.985Q-11.330 62.733-11.480 62.393Q-11.630 62.053-11.630 61.678M-10.877 61.370L-8.916 61.370Q-8.916 61.065-9.017 60.774Q-9.119 60.483-9.336 60.301Q-9.552 60.120-9.869 60.120Q-10.169 60.120-10.400 60.307Q-10.630 60.495-10.753 60.786Q-10.877 61.077-10.877 61.370\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"M-46.621 63.432V-59.16\"\u002F>\u003Cpath stroke=\"none\" d=\"m-46.621-61.76-2.08 4.16 2.08-1.56 2.08 1.56\"\u002F>\u003Cg transform=\"translate(-8.983 -128.925)\">\u003Cpath d=\"M-46.383 61.678Q-46.383 61.198-46.150 60.782Q-45.918 60.366-45.508 60.116Q-45.098 59.866-44.621 59.866Q-43.891 59.866-43.492 60.307Q-43.094 60.748-43.094 61.479Q-43.094 61.584-43.187 61.608L-45.637 61.608L-45.637 61.678Q-45.637 62.088-45.516 62.444Q-45.394 62.799-45.123 63.016Q-44.851 63.233-44.422 63.233Q-44.059 63.233-43.762 63.004Q-43.465 62.776-43.363 62.424Q-43.355 62.377-43.269 62.362L-43.187 62.362Q-43.094 62.389-43.094 62.471Q-43.094 62.479-43.101 62.510Q-43.164 62.737-43.303 62.920Q-43.441 63.104-43.633 63.237Q-43.824 63.370-44.043 63.440Q-44.262 63.510-44.500 63.510Q-44.871 63.510-45.209 63.373Q-45.547 63.237-45.814 62.985Q-46.082 62.733-46.232 62.393Q-46.383 62.053-46.383 61.678M-45.629 61.370L-43.668 61.370Q-43.668 61.065-43.769 60.774Q-43.871 60.483-44.088 60.301Q-44.305 60.120-44.621 60.120Q-44.922 60.120-45.152 60.307Q-45.383 60.495-45.506 60.786Q-45.629 61.077-45.629 61.370M-40.598 63.432L-42.578 63.432L-42.578 63.135Q-42.309 63.135-42.141 63.090Q-41.973 63.045-41.973 62.873L-41.973 60.737Q-41.973 60.522-42.035 60.426Q-42.098 60.330-42.215 60.309Q-42.332 60.287-42.578 60.287L-42.578 59.991L-41.410 59.905L-41.410 60.690Q-41.332 60.479-41.180 60.293Q-41.027 60.108-40.828 60.006Q-40.629 59.905-40.402 59.905Q-40.156 59.905-39.965 60.049Q-39.773 60.194-39.773 60.424Q-39.773 60.580-39.879 60.690Q-39.984 60.799-40.141 60.799Q-40.297 60.799-40.406 60.690Q-40.516 60.580-40.516 60.424Q-40.516 60.264-40.410 60.159Q-40.734 60.159-40.949 60.387Q-41.164 60.616-41.260 60.955Q-41.355 61.295-41.355 61.600L-41.355 62.873Q-41.355 63.041-41.129 63.088Q-40.902 63.135-40.598 63.135L-40.598 63.432M-37.285 63.432L-39.266 63.432L-39.266 63.135Q-38.996 63.135-38.828 63.090Q-38.660 63.045-38.660 62.873L-38.660 60.737Q-38.660 60.522-38.723 60.426Q-38.785 60.330-38.902 60.309Q-39.019 60.287-39.266 60.287L-39.266 59.991L-38.098 59.905L-38.098 60.690Q-38.019 60.479-37.867 60.293Q-37.715 60.108-37.516 60.006Q-37.316 59.905-37.090 59.905Q-36.844 59.905-36.652 60.049Q-36.461 60.194-36.461 60.424Q-36.461 60.580-36.566 60.690Q-36.672 60.799-36.828 60.799Q-36.984 60.799-37.094 60.690Q-37.203 60.580-37.203 60.424Q-37.203 60.264-37.098 60.159Q-37.422 60.159-37.637 60.387Q-37.851 60.616-37.947 60.955Q-38.043 61.295-38.043 61.600L-38.043 62.873Q-38.043 63.041-37.816 63.088Q-37.590 63.135-37.285 63.135L-37.285 63.432M-35.980 61.737Q-35.980 61.233-35.725 60.801Q-35.469 60.370-35.033 60.118Q-34.598 59.866-34.098 59.866Q-33.711 59.866-33.369 60.010Q-33.027 60.155-32.766 60.416Q-32.504 60.678-32.361 61.014Q-32.219 61.350-32.219 61.737Q-32.219 62.229-32.482 62.639Q-32.746 63.049-33.176 63.280Q-33.605 63.510-34.098 63.510Q-34.590 63.510-35.023 63.278Q-35.457 63.045-35.719 62.637Q-35.980 62.229-35.980 61.737M-34.098 63.233Q-33.641 63.233-33.389 63.010Q-33.137 62.787-33.049 62.436Q-32.961 62.084-32.961 61.639Q-32.961 61.209-33.055 60.871Q-33.148 60.534-33.402 60.327Q-33.656 60.120-34.098 60.120Q-34.746 60.120-34.990 60.536Q-35.234 60.952-35.234 61.639Q-35.234 62.084-35.146 62.436Q-35.059 62.787-34.807 63.010Q-34.555 63.233-34.098 63.233M-29.726 63.432L-31.707 63.432L-31.707 63.135Q-31.437 63.135-31.269 63.090Q-31.101 63.045-31.101 62.873L-31.101 60.737Q-31.101 60.522-31.164 60.426Q-31.226 60.330-31.344 60.309Q-31.461 60.287-31.707 60.287L-31.707 59.991L-30.539 59.905L-30.539 60.690Q-30.461 60.479-30.309 60.293Q-30.156 60.108-29.957 60.006Q-29.758 59.905-29.531 59.905Q-29.285 59.905-29.094 60.049Q-28.902 60.194-28.902 60.424Q-28.902 60.580-29.008 60.690Q-29.113 60.799-29.269 60.799Q-29.426 60.799-29.535 60.690Q-29.644 60.580-29.644 60.424Q-29.644 60.264-29.539 60.159Q-29.863 60.159-30.078 60.387Q-30.293 60.616-30.389 60.955Q-30.484 61.295-30.484 61.600L-30.484 62.873Q-30.484 63.041-30.258 63.088Q-30.031 63.135-29.726 63.135\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" d=\"M44.428 63.432V-53.224\" style=\"stroke-dasharray:3.0,3.0\"\u002F>\u003Cg transform=\"translate(71.923 -118.767)\">\u003Cpath d=\"M-45.422 62.327L-45.422 60.545L-46.172 60.545Q-46.371 60.522-46.422 60.303L-46.422 60.217Q-46.371 60.006-46.172 59.983L-45.422 59.983L-45.422 59.233Q-45.371 59.026-45.172 58.998L-45.027 58.998Q-44.832 59.026-44.781 59.233L-44.781 59.983L-43.422 59.983Q-43.230 60.002-43.172 60.217L-43.172 60.303Q-43.226 60.522-43.422 60.545L-44.781 60.545L-44.781 62.295Q-44.781 62.912-44.207 62.912Q-43.957 62.912-43.793 62.727Q-43.629 62.541-43.629 62.295Q-43.629 62.202-43.557 62.131Q-43.484 62.061-43.383 62.049L-43.238 62.049Q-43.039 62.073-42.988 62.280L-42.988 62.327Q-42.988 62.651-43.172 62.914Q-43.355 63.178-43.648 63.325Q-43.941 63.471-44.262 63.471Q-44.773 63.471-45.098 63.161Q-45.422 62.850-45.422 62.327M-42.305 63.194L-42.305 63.104Q-42.262 62.897-42.055 62.873L-41.633 62.873L-41.633 59.104L-42.055 59.104Q-42.262 59.080-42.305 58.866L-42.305 58.776Q-42.262 58.569-42.055 58.545L-41.238 58.545Q-41.043 58.569-40.992 58.776L-40.992 60.327Q-40.531 59.944-39.934 59.944Q-39.586 59.944-39.348 60.084Q-39.109 60.225-38.994 60.483Q-38.879 60.741-38.879 61.096L-38.879 62.873L-38.453 62.873Q-38.246 62.897-38.207 63.104L-38.207 63.194Q-38.246 63.409-38.453 63.432L-39.848 63.432Q-40.043 63.409-40.094 63.194L-40.094 63.104Q-40.043 62.893-39.848 62.873L-39.519 62.873L-39.519 61.127Q-39.519 60.819-39.609 60.661Q-39.699 60.502-39.992 60.502Q-40.262 60.502-40.490 60.633Q-40.719 60.764-40.855 60.993Q-40.992 61.221-40.992 61.487L-40.992 62.873L-40.566 62.873Q-40.359 62.897-40.320 63.104L-40.320 63.194Q-40.359 63.409-40.566 63.432L-42.055 63.432Q-42.262 63.409-42.305 63.194M-37.906 63.194L-37.906 63.104Q-37.848 62.897-37.656 62.873L-36.945 62.873L-36.945 60.545L-37.656 60.545Q-37.851 60.522-37.906 60.303L-37.906 60.217Q-37.848 60.006-37.656 59.983L-36.555 59.983Q-36.355 60.002-36.305 60.217L-36.305 60.545Q-36.043 60.260-35.687 60.102Q-35.332 59.944-34.945 59.944Q-34.652 59.944-34.418 60.078Q-34.184 60.213-34.184 60.479Q-34.184 60.647-34.293 60.764Q-34.402 60.881-34.570 60.881Q-34.723 60.881-34.838 60.770Q-34.953 60.659-34.953 60.502Q-35.328 60.502-35.642 60.703Q-35.957 60.905-36.131 61.239Q-36.305 61.573-36.305 61.952L-36.305 62.873L-35.359 62.873Q-35.152 62.897-35.113 63.104L-35.113 63.194Q-35.152 63.409-35.359 63.432L-37.656 63.432Q-37.848 63.409-37.906 63.194M-30.371 61.944L-32.812 61.944Q-32.758 62.221-32.560 62.444Q-32.363 62.666-32.086 62.789Q-31.809 62.912-31.523 62.912Q-31.051 62.912-30.828 62.623Q-30.820 62.612-30.764 62.506Q-30.707 62.401-30.658 62.358Q-30.609 62.315-30.516 62.303L-30.371 62.303Q-30.180 62.323-30.121 62.537L-30.121 62.592Q-30.187 62.893-30.418 63.090Q-30.648 63.287-30.961 63.379Q-31.273 63.471-31.578 63.471Q-32.062 63.471-32.502 63.243Q-32.941 63.014-33.209 62.614Q-33.476 62.213-33.476 61.721L-33.476 61.662Q-33.476 61.194-33.230 60.791Q-32.984 60.389-32.576 60.155Q-32.168 59.920-31.699 59.920Q-31.195 59.920-30.842 60.143Q-30.488 60.366-30.305 60.754Q-30.121 61.143-30.121 61.647L-30.121 61.705Q-30.180 61.920-30.371 61.944M-32.805 61.393L-30.777 61.393Q-30.824 60.983-31.062 60.731Q-31.301 60.479-31.699 60.479Q-32.094 60.479-32.400 60.741Q-32.707 61.002-32.805 61.393M-29.051 63.233L-29.051 62.319Q-29.023 62.112-28.812 62.088L-28.644 62.088Q-28.480 62.112-28.422 62.272Q-28.219 62.912-27.492 62.912Q-27.285 62.912-27.057 62.877Q-26.828 62.842-26.660 62.727Q-26.492 62.612-26.492 62.409Q-26.492 62.198-26.715 62.084Q-26.937 61.971-27.211 61.928L-27.910 61.815Q-29.051 61.604-29.051 60.881Q-29.051 60.592-28.906 60.403Q-28.762 60.213-28.521 60.106Q-28.281 59.998-28.025 59.959Q-27.769 59.920-27.492 59.920Q-27.242 59.920-27.049 59.950Q-26.855 59.979-26.691 60.057Q-26.613 59.940-26.484 59.920L-26.406 59.920Q-26.309 59.932-26.246 59.995Q-26.184 60.057-26.172 60.151L-26.172 60.858Q-26.184 60.952-26.246 61.018Q-26.309 61.084-26.406 61.096L-26.574 61.096Q-26.668 61.084-26.734 61.018Q-26.801 60.952-26.812 60.858Q-26.812 60.479-27.508 60.479Q-27.855 60.479-28.174 60.561Q-28.492 60.643-28.492 60.889Q-28.492 61.155-27.820 61.264L-27.117 61.385Q-26.633 61.467-26.283 61.715Q-25.934 61.963-25.934 62.409Q-25.934 62.799-26.170 63.041Q-26.406 63.284-26.756 63.377Q-27.105 63.471-27.492 63.471Q-28.070 63.471-28.469 63.217Q-28.539 63.342-28.588 63.399Q-28.637 63.455-28.742 63.471L-28.812 63.471Q-29.027 63.448-29.051 63.233M-25.320 63.194L-25.320 63.104Q-25.277 62.897-25.070 62.873L-24.648 62.873L-24.648 59.104L-25.070 59.104Q-25.277 59.080-25.320 58.866L-25.320 58.776Q-25.277 58.569-25.070 58.545L-24.254 58.545Q-24.059 58.569-24.008 58.776L-24.008 60.327Q-23.547 59.944-22.949 59.944Q-22.601 59.944-22.363 60.084Q-22.125 60.225-22.010 60.483Q-21.894 60.741-21.894 61.096L-21.894 62.873L-21.469 62.873Q-21.262 62.897-21.223 63.104L-21.223 63.194Q-21.262 63.409-21.469 63.432L-22.863 63.432Q-23.059 63.409-23.109 63.194L-23.109 63.104Q-23.059 62.893-22.863 62.873L-22.535 62.873L-22.535 61.127Q-22.535 60.819-22.625 60.661Q-22.715 60.502-23.008 60.502Q-23.277 60.502-23.506 60.633Q-23.734 60.764-23.871 60.993Q-24.008 61.221-24.008 61.487L-24.008 62.873L-23.582 62.873Q-23.375 62.897-23.336 63.104L-23.336 63.194Q-23.375 63.409-23.582 63.432L-25.070 63.432Q-25.277 63.409-25.320 63.194M-19.023 63.471Q-19.496 63.471-19.881 63.227Q-20.266 62.983-20.488 62.573Q-20.711 62.162-20.711 61.705Q-20.711 61.362-20.586 61.039Q-20.461 60.717-20.230 60.463Q-20 60.209-19.693 60.065Q-19.387 59.920-19.023 59.920Q-18.660 59.920-18.348 60.067Q-18.035 60.213-17.812 60.459Q-17.590 60.705-17.463 61.026Q-17.336 61.346-17.336 61.705Q-17.336 62.162-17.560 62.575Q-17.785 62.987-18.170 63.229Q-18.555 63.471-19.023 63.471M-19.023 62.912Q-18.559 62.912-18.267 62.518Q-17.976 62.123-17.976 61.639Q-17.976 61.346-18.111 61.078Q-18.246 60.811-18.486 60.645Q-18.726 60.479-19.023 60.479Q-19.328 60.479-19.566 60.645Q-19.805 60.811-19.939 61.078Q-20.074 61.346-20.074 61.639Q-20.074 62.120-19.781 62.516Q-19.488 62.912-19.023 62.912M-16.449 63.194L-16.449 63.104Q-16.398 62.897-16.203 62.873L-15.098 62.873L-15.098 59.104L-16.203 59.104Q-16.398 59.080-16.449 58.866L-16.449 58.776Q-16.398 58.569-16.203 58.545L-14.707 58.545Q-14.516 58.569-14.457 58.776L-14.457 62.873L-13.355 62.873Q-13.156 62.897-13.105 63.104L-13.105 63.194Q-13.156 63.409-13.355 63.432L-16.203 63.432Q-16.398 63.409-16.449 63.194M-10.789 63.471Q-11.254 63.471-11.619 63.221Q-11.984 62.971-12.189 62.567Q-12.394 62.162-12.394 61.705Q-12.394 61.362-12.269 61.041Q-12.144 60.721-11.912 60.471Q-11.680 60.221-11.375 60.082Q-11.070 59.944-10.715 59.944Q-10.203 59.944-9.797 60.264L-9.797 59.104L-10.219 59.104Q-10.430 59.080-10.469 58.866L-10.469 58.776Q-10.430 58.569-10.219 58.545L-9.406 58.545Q-9.207 58.569-9.156 58.776L-9.156 62.873L-8.730 62.873Q-8.523 62.897-8.484 63.104L-8.484 63.194Q-8.523 63.409-8.730 63.432L-9.547 63.432Q-9.746 63.409-9.797 63.194L-9.797 63.065Q-9.992 63.256-10.254 63.364Q-10.516 63.471-10.789 63.471M-10.750 62.912Q-10.391 62.912-10.139 62.643Q-9.887 62.373-9.797 61.998L-9.797 61.166Q-9.855 60.979-9.982 60.828Q-10.109 60.678-10.287 60.590Q-10.465 60.502-10.660 60.502Q-10.969 60.502-11.219 60.672Q-11.469 60.842-11.613 61.127Q-11.758 61.412-11.758 61.713Q-11.758 62.162-11.473 62.537Q-11.187 62.912-10.750 62.912\" fill=\"currentColor\" stroke=\"currentColor\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-good)\" d=\"M-38.085-5.929-35.97.455l2.115 5.81 2.116 5.265 2.115 4.784 2.116 4.345 2.116 3.934 2.115 3.585 2.116 3.248 2.116 2.95 2.115 2.675 2.116 2.428 2.116 2.213 2.115 1.999 2.116 1.824 2.115 1.649 2.116 1.495 2.116 1.363 2.115 1.24 2.116 1.127 2.116 1.014 2.115.922 2.116.84 2.116.769 2.115.686 2.116.625 2.115.573 2.116.524 2.116.46 2.115.43 2.116.39 2.116.348 2.115.318 2.116.297 2.116.256 2.115.246 2.116.215 2.115.196 2.116.184 2.116.153M44.428 61.835l145.109-.11\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\">\u003Cg fill=\"var(--tk-good)\" stroke=\"none\" font-size=\"8\">\u003Cg transform=\"translate(133.623 -6.947)\">\u003Cpath d=\"M-45.422 62.327L-45.422 60.545L-46.172 60.545Q-46.371 60.522-46.422 60.303L-46.422 60.217Q-46.371 60.006-46.172 59.983L-45.422 59.983L-45.422 59.233Q-45.371 59.026-45.172 58.998L-45.027 58.998Q-44.832 59.026-44.781 59.233L-44.781 59.983L-43.422 59.983Q-43.230 60.002-43.172 60.217L-43.172 60.303Q-43.226 60.522-43.422 60.545L-44.781 60.545L-44.781 62.295Q-44.781 62.912-44.207 62.912Q-43.957 62.912-43.793 62.727Q-43.629 62.541-43.629 62.295Q-43.629 62.202-43.557 62.131Q-43.484 62.061-43.383 62.049L-43.238 62.049Q-43.039 62.073-42.988 62.280L-42.988 62.327Q-42.988 62.651-43.172 62.914Q-43.355 63.178-43.648 63.325Q-43.941 63.471-44.262 63.471Q-44.773 63.471-45.098 63.161Q-45.422 62.850-45.422 62.327M-42.152 63.194L-42.152 63.104Q-42.094 62.897-41.902 62.873L-41.191 62.873L-41.191 60.545L-41.902 60.545Q-42.098 60.522-42.152 60.303L-42.152 60.217Q-42.094 60.006-41.902 59.983L-40.801 59.983Q-40.601 60.002-40.551 60.217L-40.551 60.545Q-40.289 60.260-39.934 60.102Q-39.578 59.944-39.191 59.944Q-38.898 59.944-38.664 60.078Q-38.430 60.213-38.430 60.479Q-38.430 60.647-38.539 60.764Q-38.648 60.881-38.816 60.881Q-38.969 60.881-39.084 60.770Q-39.199 60.659-39.199 60.502Q-39.574 60.502-39.889 60.703Q-40.203 60.905-40.377 61.239Q-40.551 61.573-40.551 61.952L-40.551 62.873L-39.605 62.873Q-39.398 62.897-39.359 63.104L-39.359 63.194Q-39.398 63.409-39.605 63.432L-41.902 63.432Q-42.094 63.409-42.152 63.194M-37.723 62.319Q-37.723 61.873-37.309 61.616Q-36.894 61.358-36.353 61.258Q-35.812 61.159-35.305 61.151Q-35.305 60.936-35.439 60.784Q-35.574 60.631-35.781 60.555Q-35.988 60.479-36.199 60.479Q-36.543 60.479-36.703 60.502L-36.703 60.561Q-36.703 60.729-36.822 60.844Q-36.941 60.959-37.105 60.959Q-37.281 60.959-37.396 60.836Q-37.512 60.713-37.512 60.545Q-37.512 60.139-37.131 60.030Q-36.750 59.920-36.191 59.920Q-35.922 59.920-35.654 59.998Q-35.387 60.077-35.162 60.227Q-34.937 60.377-34.801 60.598Q-34.664 60.819-34.664 61.096L-34.664 62.815Q-34.664 62.873-34.137 62.873Q-33.941 62.893-33.891 63.104L-33.891 63.194Q-33.941 63.409-34.137 63.432L-34.281 63.432Q-34.625 63.432-34.853 63.385Q-35.082 63.338-35.226 63.151Q-35.687 63.471-36.394 63.471Q-36.730 63.471-37.035 63.330Q-37.340 63.190-37.531 62.928Q-37.723 62.666-37.723 62.319M-37.082 62.327Q-37.082 62.600-36.840 62.756Q-36.598 62.912-36.312 62.912Q-36.094 62.912-35.861 62.854Q-35.629 62.795-35.467 62.657Q-35.305 62.518-35.305 62.295L-35.305 61.705Q-35.586 61.705-36.002 61.762Q-36.418 61.819-36.750 61.957Q-37.082 62.096-37.082 62.327M-33.266 63.194L-33.266 63.104Q-33.215 62.897-33.019 62.873L-31.980 62.873L-31.980 60.545L-32.953 60.545Q-33.152 60.522-33.203 60.303L-33.203 60.217Q-33.152 60.006-32.953 59.983L-31.586 59.983Q-31.391 60.002-31.340 60.217L-31.340 62.873L-30.426 62.873Q-30.230 62.897-30.180 63.104L-30.180 63.194Q-30.230 63.409-30.426 63.432L-33.019 63.432Q-33.215 63.409-33.266 63.194M-32.234 59.006L-32.234 58.952Q-32.234 58.780-32.098 58.659Q-31.961 58.537-31.785 58.537Q-31.613 58.537-31.476 58.659Q-31.340 58.780-31.340 58.952L-31.340 59.006Q-31.340 59.182-31.476 59.303Q-31.613 59.424-31.785 59.424Q-31.961 59.424-32.098 59.303Q-32.234 59.182-32.234 59.006M-29.566 63.194L-29.566 63.104Q-29.523 62.897-29.316 62.873L-28.894 62.873L-28.894 60.545L-29.316 60.545Q-29.523 60.522-29.566 60.303L-29.566 60.217Q-29.519 60.006-29.316 59.983L-28.500 59.983Q-28.305 60.006-28.254 60.217L-28.254 60.303L-28.262 60.327Q-28.035 60.147-27.762 60.045Q-27.488 59.944-27.195 59.944Q-26.848 59.944-26.609 60.084Q-26.371 60.225-26.256 60.483Q-26.141 60.741-26.141 61.096L-26.141 62.873L-25.715 62.873Q-25.508 62.897-25.469 63.104L-25.469 63.194Q-25.508 63.409-25.715 63.432L-27.109 63.432Q-27.305 63.409-27.355 63.194L-27.355 63.104Q-27.305 62.893-27.109 62.873L-26.781 62.873L-26.781 61.127Q-26.781 60.819-26.871 60.661Q-26.961 60.502-27.254 60.502Q-27.523 60.502-27.752 60.633Q-27.980 60.764-28.117 60.993Q-28.254 61.221-28.254 61.487L-28.254 62.873L-27.828 62.873Q-27.621 62.897-27.582 63.104L-27.582 63.194Q-27.621 63.409-27.828 63.432L-29.316 63.432Q-29.523 63.409-29.566 63.194\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(133.623 -6.947)\">\u003Cpath d=\"M-17.609 61.944L-20.051 61.944Q-19.996 62.221-19.799 62.444Q-19.601 62.666-19.324 62.789Q-19.047 62.912-18.762 62.912Q-18.289 62.912-18.066 62.623Q-18.058 62.612-18.002 62.506Q-17.945 62.401-17.896 62.358Q-17.848 62.315-17.754 62.303L-17.609 62.303Q-17.418 62.323-17.359 62.537L-17.359 62.592Q-17.426 62.893-17.656 63.090Q-17.887 63.287-18.199 63.379Q-18.512 63.471-18.816 63.471Q-19.301 63.471-19.740 63.243Q-20.180 63.014-20.447 62.614Q-20.715 62.213-20.715 61.721L-20.715 61.662Q-20.715 61.194-20.469 60.791Q-20.223 60.389-19.814 60.155Q-19.406 59.920-18.937 59.920Q-18.433 59.920-18.080 60.143Q-17.726 60.366-17.543 60.754Q-17.359 61.143-17.359 61.647L-17.359 61.705Q-17.418 61.920-17.609 61.944M-20.043 61.393L-18.016 61.393Q-18.062 60.983-18.301 60.731Q-18.539 60.479-18.937 60.479Q-19.332 60.479-19.639 60.741Q-19.945 61.002-20.043 61.393M-16.652 63.194L-16.652 63.104Q-16.594 62.897-16.402 62.873L-15.691 62.873L-15.691 60.545L-16.402 60.545Q-16.598 60.522-16.652 60.303L-16.652 60.217Q-16.594 60.006-16.402 59.983L-15.301 59.983Q-15.101 60.002-15.051 60.217L-15.051 60.545Q-14.789 60.260-14.433 60.102Q-14.078 59.944-13.691 59.944Q-13.398 59.944-13.164 60.078Q-12.930 60.213-12.930 60.479Q-12.930 60.647-13.039 60.764Q-13.148 60.881-13.316 60.881Q-13.469 60.881-13.584 60.770Q-13.699 60.659-13.699 60.502Q-14.074 60.502-14.389 60.703Q-14.703 60.905-14.877 61.239Q-15.051 61.573-15.051 61.952L-15.051 62.873L-14.105 62.873Q-13.898 62.897-13.859 63.104L-13.859 63.194Q-13.898 63.409-14.105 63.432L-16.402 63.432Q-16.594 63.409-16.652 63.194M-12.406 63.194L-12.406 63.104Q-12.348 62.897-12.156 62.873L-11.445 62.873L-11.445 60.545L-12.156 60.545Q-12.351 60.522-12.406 60.303L-12.406 60.217Q-12.348 60.006-12.156 59.983L-11.055 59.983Q-10.855 60.002-10.805 60.217L-10.805 60.545Q-10.543 60.260-10.187 60.102Q-9.832 59.944-9.445 59.944Q-9.152 59.944-8.918 60.078Q-8.683 60.213-8.683 60.479Q-8.683 60.647-8.793 60.764Q-8.902 60.881-9.070 60.881Q-9.223 60.881-9.338 60.770Q-9.453 60.659-9.453 60.502Q-9.828 60.502-10.142 60.703Q-10.457 60.905-10.631 61.239Q-10.805 61.573-10.805 61.952L-10.805 62.873L-9.859 62.873Q-9.652 62.897-9.613 63.104L-9.613 63.194Q-9.652 63.409-9.859 63.432L-12.156 63.432Q-12.348 63.409-12.406 63.194M-6.262 63.471Q-6.734 63.471-7.119 63.227Q-7.504 62.983-7.726 62.573Q-7.949 62.162-7.949 61.705Q-7.949 61.362-7.824 61.039Q-7.699 60.717-7.469 60.463Q-7.238 60.209-6.932 60.065Q-6.625 59.920-6.262 59.920Q-5.898 59.920-5.586 60.067Q-5.273 60.213-5.051 60.459Q-4.828 60.705-4.701 61.026Q-4.574 61.346-4.574 61.705Q-4.574 62.162-4.799 62.575Q-5.023 62.987-5.408 63.229Q-5.793 63.471-6.262 63.471M-6.262 62.912Q-5.797 62.912-5.506 62.518Q-5.215 62.123-5.215 61.639Q-5.215 61.346-5.350 61.078Q-5.484 60.811-5.725 60.645Q-5.965 60.479-6.262 60.479Q-6.566 60.479-6.805 60.645Q-7.043 60.811-7.178 61.078Q-7.312 61.346-7.312 61.639Q-7.312 62.120-7.019 62.516Q-6.726 62.912-6.262 62.912M-3.914 63.194L-3.914 63.104Q-3.855 62.897-3.664 62.873L-2.953 62.873L-2.953 60.545L-3.664 60.545Q-3.859 60.522-3.914 60.303L-3.914 60.217Q-3.855 60.006-3.664 59.983L-2.562 59.983Q-2.363 60.002-2.312 60.217L-2.312 60.545Q-2.051 60.260-1.695 60.102Q-1.340 59.944-0.953 59.944Q-0.660 59.944-0.426 60.078Q-0.191 60.213-0.191 60.479Q-0.191 60.647-0.301 60.764Q-0.410 60.881-0.578 60.881Q-0.730 60.881-0.846 60.770Q-0.961 60.659-0.961 60.502Q-1.336 60.502-1.650 60.703Q-1.965 60.905-2.139 61.239Q-2.312 61.573-2.312 61.952L-2.312 62.873L-1.367 62.873Q-1.160 62.897-1.121 63.104L-1.121 63.194Q-1.160 63.409-1.367 63.432L-3.664 63.432Q-3.855 63.409-3.914 63.194\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(133.623 -6.947)\">\u003Cpath d=\"M8.924 62.455L3.611 62.455Q3.533 62.448 3.484 62.399Q3.436 62.350 3.436 62.272Q3.436 62.202 3.483 62.151Q3.529 62.100 3.611 62.088L8.924 62.088Q8.998 62.100 9.045 62.151Q9.092 62.202 9.092 62.272Q9.092 62.350 9.043 62.399Q8.994 62.448 8.924 62.455M8.924 60.768L3.611 60.768Q3.533 60.760 3.484 60.711Q3.436 60.662 3.436 60.584Q3.436 60.514 3.483 60.463Q3.529 60.412 3.611 60.401L8.924 60.401Q8.998 60.412 9.045 60.463Q9.092 60.514 9.092 60.584Q9.092 60.662 9.043 60.711Q8.994 60.760 8.924 60.768\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(133.623 -6.947)\">\u003Cpath d=\"M14.056 63.600Q13.353 63.600 12.953 63.200Q12.552 62.799 12.408 62.190Q12.263 61.580 12.263 60.881Q12.263 60.358 12.333 59.895Q12.404 59.432 12.597 59.020Q12.790 58.608 13.148 58.360Q13.505 58.112 14.056 58.112Q14.607 58.112 14.964 58.360Q15.322 58.608 15.513 59.018Q15.705 59.428 15.775 59.897Q15.845 60.366 15.845 60.881Q15.845 61.580 15.703 62.188Q15.560 62.795 15.160 63.198Q14.759 63.600 14.056 63.600M14.056 63.342Q14.529 63.342 14.761 62.907Q14.994 62.471 15.048 61.932Q15.103 61.393 15.103 60.752Q15.103 59.756 14.919 59.063Q14.736 58.370 14.056 58.370Q13.689 58.370 13.468 58.608Q13.248 58.846 13.152 59.203Q13.056 59.561 13.031 59.932Q13.005 60.303 13.005 60.752Q13.005 61.393 13.060 61.932Q13.115 62.471 13.347 62.907Q13.580 63.342 14.056 63.342\" fill=\"var(--tk-good)\" stroke=\"var(--tk-good)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cpath fill=\"none\" stroke=\"var(--tk-accent)\" d=\"m-38.085-25.401 3.299 3.284 3.298 3.124 3.3 2.96 3.298 2.811 3.298 2.664 3.3 2.53 3.298 2.405 3.299 2.28 3.298 2.168L-5.098.88l3.299 1.947 3.298 1.857 3.3 1.754 3.298 1.665 3.299 1.583 3.298 1.502 3.299 1.428 3.299 1.354 3.298 1.288 3.3 1.22 3.298 1.155 3.298 1.103 3.3 1.043 3.298.984 3.299.94 3.298.895 3.299.844 3.299.806 3.298.762 3.3.726 3.298.687 3.299.652 3.298.614 3.299.592 3.299.555 3.298.532 3.299.504 3.299.474 3.298.451 3.3.43 3.298.405 3.299.393 3.298.363 3.299.356 3.299.325 3.298.318 3.3.296 3.298.28 3.298.275 3.3.252 3.298.244 3.299.228 3.298.215 3.299.208 3.299.2 3.298.185 3.3.177 3.298.17 3.299.155 3.298.156 3.299.14 3.299.134 3.298.133 3.3.125 3.298.111 3.298.112 3.3.103 3.298.103 3.299.096\" style=\"stroke-width:1.2\"\u002F>\u003Cg fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\">\u003Cg fill=\"var(--tk-accent)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(171.404 -15.426)\">\u003Cpath d=\"M-45.422 62.327L-45.422 60.545L-46.172 60.545Q-46.371 60.522-46.422 60.303L-46.422 60.217Q-46.371 60.006-46.172 59.983L-45.422 59.983L-45.422 59.233Q-45.371 59.026-45.172 58.998L-45.027 58.998Q-44.832 59.026-44.781 59.233L-44.781 59.983L-43.422 59.983Q-43.230 60.002-43.172 60.217L-43.172 60.303Q-43.226 60.522-43.422 60.545L-44.781 60.545L-44.781 62.295Q-44.781 62.912-44.207 62.912Q-43.957 62.912-43.793 62.727Q-43.629 62.541-43.629 62.295Q-43.629 62.202-43.557 62.131Q-43.484 62.061-43.383 62.049L-43.238 62.049Q-43.039 62.073-42.988 62.280L-42.988 62.327Q-42.988 62.651-43.172 62.914Q-43.355 63.178-43.648 63.325Q-43.941 63.471-44.262 63.471Q-44.773 63.471-45.098 63.161Q-45.422 62.850-45.422 62.327M-38.863 61.944L-41.305 61.944Q-41.250 62.221-41.053 62.444Q-40.855 62.666-40.578 62.789Q-40.301 62.912-40.016 62.912Q-39.543 62.912-39.320 62.623Q-39.312 62.612-39.256 62.506Q-39.199 62.401-39.150 62.358Q-39.101 62.315-39.008 62.303L-38.863 62.303Q-38.672 62.323-38.613 62.537L-38.613 62.592Q-38.680 62.893-38.910 63.090Q-39.141 63.287-39.453 63.379Q-39.766 63.471-40.070 63.471Q-40.555 63.471-40.994 63.243Q-41.434 63.014-41.701 62.614Q-41.969 62.213-41.969 61.721L-41.969 61.662Q-41.969 61.194-41.723 60.791Q-41.476 60.389-41.068 60.155Q-40.660 59.920-40.191 59.920Q-39.687 59.920-39.334 60.143Q-38.980 60.366-38.797 60.754Q-38.613 61.143-38.613 61.647L-38.613 61.705Q-38.672 61.920-38.863 61.944M-41.297 61.393L-39.269 61.393Q-39.316 60.983-39.555 60.731Q-39.793 60.479-40.191 60.479Q-40.586 60.479-40.892 60.741Q-41.199 61.002-41.297 61.393M-37.543 63.233L-37.543 62.319Q-37.516 62.112-37.305 62.088L-37.137 62.088Q-36.973 62.112-36.914 62.272Q-36.711 62.912-35.984 62.912Q-35.777 62.912-35.549 62.877Q-35.320 62.842-35.152 62.727Q-34.984 62.612-34.984 62.409Q-34.984 62.198-35.207 62.084Q-35.430 61.971-35.703 61.928L-36.402 61.815Q-37.543 61.604-37.543 60.881Q-37.543 60.592-37.398 60.403Q-37.254 60.213-37.014 60.106Q-36.773 59.998-36.517 59.959Q-36.262 59.920-35.984 59.920Q-35.734 59.920-35.541 59.950Q-35.348 59.979-35.184 60.057Q-35.105 59.940-34.976 59.920L-34.898 59.920Q-34.801 59.932-34.738 59.995Q-34.676 60.057-34.664 60.151L-34.664 60.858Q-34.676 60.952-34.738 61.018Q-34.801 61.084-34.898 61.096L-35.066 61.096Q-35.160 61.084-35.226 61.018Q-35.293 60.952-35.305 60.858Q-35.305 60.479-36 60.479Q-36.348 60.479-36.666 60.561Q-36.984 60.643-36.984 60.889Q-36.984 61.155-36.312 61.264L-35.609 61.385Q-35.125 61.467-34.775 61.715Q-34.426 61.963-34.426 62.409Q-34.426 62.799-34.662 63.041Q-34.898 63.284-35.248 63.377Q-35.598 63.471-35.984 63.471Q-36.562 63.471-36.961 63.217Q-37.031 63.342-37.080 63.399Q-37.129 63.455-37.234 63.471L-37.305 63.471Q-37.519 63.448-37.543 63.233M-32.684 62.327L-32.684 60.545L-33.434 60.545Q-33.633 60.522-33.684 60.303L-33.684 60.217Q-33.633 60.006-33.434 59.983L-32.684 59.983L-32.684 59.233Q-32.633 59.026-32.434 58.998L-32.289 58.998Q-32.094 59.026-32.043 59.233L-32.043 59.983L-30.684 59.983Q-30.492 60.002-30.434 60.217L-30.434 60.303Q-30.488 60.522-30.684 60.545L-32.043 60.545L-32.043 62.295Q-32.043 62.912-31.469 62.912Q-31.219 62.912-31.055 62.727Q-30.891 62.541-30.891 62.295Q-30.891 62.202-30.818 62.131Q-30.746 62.061-30.644 62.049L-30.500 62.049Q-30.301 62.073-30.250 62.280L-30.250 62.327Q-30.250 62.651-30.434 62.914Q-30.617 63.178-30.910 63.325Q-31.203 63.471-31.523 63.471Q-32.035 63.471-32.359 63.161Q-32.684 62.850-32.684 62.327\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(171.404 -15.426)\">\u003Cpath d=\"M-21.859 61.944L-24.301 61.944Q-24.246 62.221-24.049 62.444Q-23.851 62.666-23.574 62.789Q-23.297 62.912-23.012 62.912Q-22.539 62.912-22.316 62.623Q-22.308 62.612-22.252 62.506Q-22.195 62.401-22.146 62.358Q-22.098 62.315-22.004 62.303L-21.859 62.303Q-21.668 62.323-21.609 62.537L-21.609 62.592Q-21.676 62.893-21.906 63.090Q-22.137 63.287-22.449 63.379Q-22.762 63.471-23.066 63.471Q-23.551 63.471-23.990 63.243Q-24.430 63.014-24.697 62.614Q-24.965 62.213-24.965 61.721L-24.965 61.662Q-24.965 61.194-24.719 60.791Q-24.473 60.389-24.064 60.155Q-23.656 59.920-23.187 59.920Q-22.683 59.920-22.330 60.143Q-21.976 60.366-21.793 60.754Q-21.609 61.143-21.609 61.647L-21.609 61.705Q-21.668 61.920-21.859 61.944M-24.293 61.393L-22.266 61.393Q-22.312 60.983-22.551 60.731Q-22.789 60.479-23.187 60.479Q-23.582 60.479-23.889 60.741Q-24.195 61.002-24.293 61.393M-20.902 63.194L-20.902 63.104Q-20.844 62.897-20.652 62.873L-19.941 62.873L-19.941 60.545L-20.652 60.545Q-20.848 60.522-20.902 60.303L-20.902 60.217Q-20.844 60.006-20.652 59.983L-19.551 59.983Q-19.351 60.002-19.301 60.217L-19.301 60.545Q-19.039 60.260-18.683 60.102Q-18.328 59.944-17.941 59.944Q-17.648 59.944-17.414 60.078Q-17.180 60.213-17.180 60.479Q-17.180 60.647-17.289 60.764Q-17.398 60.881-17.566 60.881Q-17.719 60.881-17.834 60.770Q-17.949 60.659-17.949 60.502Q-18.324 60.502-18.639 60.703Q-18.953 60.905-19.127 61.239Q-19.301 61.573-19.301 61.952L-19.301 62.873L-18.355 62.873Q-18.148 62.897-18.109 63.104L-18.109 63.194Q-18.148 63.409-18.355 63.432L-20.652 63.432Q-20.844 63.409-20.902 63.194M-16.656 63.194L-16.656 63.104Q-16.598 62.897-16.406 62.873L-15.695 62.873L-15.695 60.545L-16.406 60.545Q-16.601 60.522-16.656 60.303L-16.656 60.217Q-16.598 60.006-16.406 59.983L-15.305 59.983Q-15.105 60.002-15.055 60.217L-15.055 60.545Q-14.793 60.260-14.437 60.102Q-14.082 59.944-13.695 59.944Q-13.402 59.944-13.168 60.078Q-12.933 60.213-12.933 60.479Q-12.933 60.647-13.043 60.764Q-13.152 60.881-13.320 60.881Q-13.473 60.881-13.588 60.770Q-13.703 60.659-13.703 60.502Q-14.078 60.502-14.392 60.703Q-14.707 60.905-14.881 61.239Q-15.055 61.573-15.055 61.952L-15.055 62.873L-14.109 62.873Q-13.902 62.897-13.863 63.104L-13.863 63.194Q-13.902 63.409-14.109 63.432L-16.406 63.432Q-16.598 63.409-16.656 63.194M-10.512 63.471Q-10.984 63.471-11.369 63.227Q-11.754 62.983-11.976 62.573Q-12.199 62.162-12.199 61.705Q-12.199 61.362-12.074 61.039Q-11.949 60.717-11.719 60.463Q-11.488 60.209-11.182 60.065Q-10.875 59.920-10.512 59.920Q-10.148 59.920-9.836 60.067Q-9.523 60.213-9.301 60.459Q-9.078 60.705-8.951 61.026Q-8.824 61.346-8.824 61.705Q-8.824 62.162-9.049 62.575Q-9.273 62.987-9.658 63.229Q-10.043 63.471-10.512 63.471M-10.512 62.912Q-10.047 62.912-9.756 62.518Q-9.465 62.123-9.465 61.639Q-9.465 61.346-9.600 61.078Q-9.734 60.811-9.975 60.645Q-10.215 60.479-10.512 60.479Q-10.816 60.479-11.055 60.645Q-11.293 60.811-11.428 61.078Q-11.562 61.346-11.562 61.639Q-11.562 62.120-11.269 62.516Q-10.976 62.912-10.512 62.912M-8.164 63.194L-8.164 63.104Q-8.105 62.897-7.914 62.873L-7.203 62.873L-7.203 60.545L-7.914 60.545Q-8.109 60.522-8.164 60.303L-8.164 60.217Q-8.105 60.006-7.914 59.983L-6.812 59.983Q-6.613 60.002-6.562 60.217L-6.562 60.545Q-6.301 60.260-5.945 60.102Q-5.590 59.944-5.203 59.944Q-4.910 59.944-4.676 60.078Q-4.441 60.213-4.441 60.479Q-4.441 60.647-4.551 60.764Q-4.660 60.881-4.828 60.881Q-4.980 60.881-5.096 60.770Q-5.211 60.659-5.211 60.502Q-5.586 60.502-5.900 60.703Q-6.215 60.905-6.389 61.239Q-6.562 61.573-6.562 61.952L-6.562 62.873L-5.617 62.873Q-5.410 62.897-5.371 63.104L-5.371 63.194Q-5.410 63.409-5.617 63.432L-7.914 63.432Q-8.105 63.409-8.164 63.194\" fill=\"var(--tk-accent)\" stroke=\"var(--tk-accent)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" style=\"stroke-width:.8\">\u003Cpath fill=\"none\" d=\"m67.19 7.95 88.552 25.735\"\u002F>\u003Cpath stroke=\"none\" d=\"m158.239 34.41-3.414-3.158.917 2.433-2.078 1.562\"\u002F>\u003C\u002Fg>\u003Cg fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\">\u003Cg fill=\"var(--tk-warn)\" stroke=\"none\" font-family=\"cmtt8\" font-size=\"8\">\u003Cg transform=\"translate(137.26 -62.216)\">\u003Cpath d=\"M-45.879 63.194L-45.879 59.104L-46.301 59.104Q-46.508 59.080-46.551 58.866L-46.551 58.776Q-46.508 58.569-46.301 58.545L-45.484 58.545Q-45.289 58.569-45.238 58.776L-45.238 60.287Q-45.027 60.120-44.764 60.032Q-44.500 59.944-44.230 59.944Q-43.891 59.944-43.594 60.088Q-43.297 60.233-43.082 60.485Q-42.867 60.737-42.752 61.049Q-42.637 61.362-42.637 61.705Q-42.637 62.170-42.863 62.580Q-43.090 62.991-43.476 63.231Q-43.863 63.471-44.332 63.471Q-44.851 63.471-45.238 63.104L-45.238 63.194Q-45.289 63.412-45.484 63.432L-45.629 63.432Q-45.820 63.409-45.879 63.194M-44.375 62.912Q-44.066 62.912-43.816 62.743Q-43.566 62.573-43.422 62.291Q-43.277 62.010-43.277 61.705Q-43.277 61.416-43.402 61.137Q-43.527 60.858-43.760 60.680Q-43.992 60.502-44.293 60.502Q-44.613 60.502-44.875 60.688Q-45.137 60.873-45.238 61.174L-45.238 62.026Q-45.148 62.393-44.928 62.653Q-44.707 62.912-44.375 62.912M-41.758 63.194L-41.758 63.104Q-41.707 62.897-41.512 62.873L-40.473 62.873L-40.473 60.545L-41.445 60.545Q-41.644 60.522-41.695 60.303L-41.695 60.217Q-41.644 60.006-41.445 59.983L-40.078 59.983Q-39.883 60.002-39.832 60.217L-39.832 62.873L-38.918 62.873Q-38.723 62.897-38.672 63.104L-38.672 63.194Q-38.723 63.409-38.918 63.432L-41.512 63.432Q-41.707 63.409-41.758 63.194M-40.726 59.006L-40.726 58.952Q-40.726 58.780-40.590 58.659Q-40.453 58.537-40.277 58.537Q-40.105 58.537-39.969 58.659Q-39.832 58.780-39.832 58.952L-39.832 59.006Q-39.832 59.182-39.969 59.303Q-40.105 59.424-40.277 59.424Q-40.453 59.424-40.590 59.303Q-40.726 59.182-40.726 59.006M-37.891 64.080Q-37.891 63.780-37.742 63.518Q-37.594 63.256-37.344 63.096Q-37.527 62.842-37.527 62.522Q-37.527 62.237-37.375 61.975Q-37.625 61.651-37.625 61.233Q-37.625 60.873-37.434 60.577Q-37.242 60.280-36.924 60.112Q-36.605 59.944-36.242 59.944Q-36.035 59.944-35.832 60.004Q-35.629 60.065-35.465 60.166Q-35.082 59.905-34.609 59.905Q-34.371 59.905-34.189 60.036Q-34.008 60.166-34.008 60.393Q-34.008 60.541-34.109 60.647Q-34.211 60.752-34.367 60.752Q-34.500 60.752-34.596 60.674Q-34.691 60.596-34.723 60.471Q-34.867 60.479-35.066 60.569Q-34.863 60.881-34.863 61.233Q-34.863 61.514-34.975 61.746Q-35.086 61.979-35.281 62.157Q-35.476 62.334-35.728 62.432Q-35.980 62.530-36.242 62.530Q-36.629 62.530-36.961 62.342Q-36.973 62.342-36.982 62.414Q-36.992 62.487-36.992 62.522Q-36.992 62.643-36.926 62.750Q-36.859 62.858-36.738 62.897Q-36.723 62.893-36.709 62.891Q-36.695 62.889-36.672 62.889Q-36.656 62.889-36.625 62.897Q-36.594 62.905-36.586 62.905L-35.992 62.905Q-35.211 62.905-34.670 63.147Q-34.129 63.389-34.129 64.080Q-34.129 64.381-34.309 64.608Q-34.488 64.834-34.785 64.979Q-35.082 65.123-35.402 65.190Q-35.723 65.256-36.008 65.256Q-36.398 65.256-36.840 65.133Q-37.281 65.010-37.586 64.743Q-37.891 64.475-37.891 64.080M-37.351 64.073Q-37.351 64.291-37.111 64.432Q-36.871 64.573-36.547 64.639Q-36.223 64.705-36.008 64.705Q-35.793 64.705-35.469 64.639Q-35.144 64.573-34.904 64.432Q-34.664 64.291-34.664 64.073Q-34.664 63.784-34.889 63.645Q-35.113 63.506-35.394 63.473Q-35.676 63.440-36.023 63.440L-36.641 63.440Q-36.824 63.440-36.986 63.520Q-37.148 63.600-37.250 63.746Q-37.351 63.893-37.351 64.073M-36.242 61.975Q-35.941 61.975-35.723 61.756Q-35.504 61.537-35.504 61.233Q-35.504 61.077-35.559 60.946Q-35.613 60.815-35.719 60.709Q-35.824 60.604-35.955 60.549Q-36.086 60.495-36.242 60.495Q-36.547 60.495-36.766 60.713Q-36.984 60.932-36.984 61.233Q-36.984 61.530-36.762 61.752Q-36.539 61.975-36.242 61.975M-33.644 64.080Q-33.644 63.780-33.496 63.518Q-33.348 63.256-33.098 63.096Q-33.281 62.842-33.281 62.522Q-33.281 62.237-33.129 61.975Q-33.379 61.651-33.379 61.233Q-33.379 60.873-33.187 60.577Q-32.996 60.280-32.678 60.112Q-32.359 59.944-31.996 59.944Q-31.789 59.944-31.586 60.004Q-31.383 60.065-31.219 60.166Q-30.836 59.905-30.363 59.905Q-30.125 59.905-29.943 60.036Q-29.762 60.166-29.762 60.393Q-29.762 60.541-29.863 60.647Q-29.965 60.752-30.121 60.752Q-30.254 60.752-30.350 60.674Q-30.445 60.596-30.476 60.471Q-30.621 60.479-30.820 60.569Q-30.617 60.881-30.617 61.233Q-30.617 61.514-30.728 61.746Q-30.840 61.979-31.035 62.157Q-31.230 62.334-31.482 62.432Q-31.734 62.530-31.996 62.530Q-32.383 62.530-32.715 62.342Q-32.726 62.342-32.736 62.414Q-32.746 62.487-32.746 62.522Q-32.746 62.643-32.680 62.750Q-32.613 62.858-32.492 62.897Q-32.476 62.893-32.463 62.891Q-32.449 62.889-32.426 62.889Q-32.410 62.889-32.379 62.897Q-32.348 62.905-32.340 62.905L-31.746 62.905Q-30.965 62.905-30.424 63.147Q-29.883 63.389-29.883 64.080Q-29.883 64.381-30.062 64.608Q-30.242 64.834-30.539 64.979Q-30.836 65.123-31.156 65.190Q-31.476 65.256-31.762 65.256Q-32.152 65.256-32.594 65.133Q-33.035 65.010-33.340 64.743Q-33.644 64.475-33.644 64.080M-33.105 64.073Q-33.105 64.291-32.865 64.432Q-32.625 64.573-32.301 64.639Q-31.976 64.705-31.762 64.705Q-31.547 64.705-31.223 64.639Q-30.898 64.573-30.658 64.432Q-30.418 64.291-30.418 64.073Q-30.418 63.784-30.642 63.645Q-30.867 63.506-31.148 63.473Q-31.430 63.440-31.777 63.440L-32.394 63.440Q-32.578 63.440-32.740 63.520Q-32.902 63.600-33.004 63.746Q-33.105 63.893-33.105 64.073M-31.996 61.975Q-31.695 61.975-31.476 61.756Q-31.258 61.537-31.258 61.233Q-31.258 61.077-31.312 60.946Q-31.367 60.815-31.473 60.709Q-31.578 60.604-31.709 60.549Q-31.840 60.495-31.996 60.495Q-32.301 60.495-32.519 60.713Q-32.738 60.932-32.738 61.233Q-32.738 61.530-32.516 61.752Q-32.293 61.975-31.996 61.975M-26.125 61.944L-28.566 61.944Q-28.512 62.221-28.314 62.444Q-28.117 62.666-27.840 62.789Q-27.562 62.912-27.277 62.912Q-26.805 62.912-26.582 62.623Q-26.574 62.612-26.517 62.506Q-26.461 62.401-26.412 62.358Q-26.363 62.315-26.269 62.303L-26.125 62.303Q-25.934 62.323-25.875 62.537L-25.875 62.592Q-25.941 62.893-26.172 63.090Q-26.402 63.287-26.715 63.379Q-27.027 63.471-27.332 63.471Q-27.816 63.471-28.256 63.243Q-28.695 63.014-28.963 62.614Q-29.230 62.213-29.230 61.721L-29.230 61.662Q-29.230 61.194-28.984 60.791Q-28.738 60.389-28.330 60.155Q-27.922 59.920-27.453 59.920Q-26.949 59.920-26.596 60.143Q-26.242 60.366-26.059 60.754Q-25.875 61.143-25.875 61.647L-25.875 61.705Q-25.934 61.920-26.125 61.944M-28.559 61.393L-26.531 61.393Q-26.578 60.983-26.816 60.731Q-27.055 60.479-27.453 60.479Q-27.848 60.479-28.154 60.741Q-28.461 61.002-28.559 61.393M-25.168 63.194L-25.168 63.104Q-25.109 62.897-24.918 62.873L-24.207 62.873L-24.207 60.545L-24.918 60.545Q-25.113 60.522-25.168 60.303L-25.168 60.217Q-25.109 60.006-24.918 59.983L-23.816 59.983Q-23.617 60.002-23.566 60.217L-23.566 60.545Q-23.305 60.260-22.949 60.102Q-22.594 59.944-22.207 59.944Q-21.914 59.944-21.680 60.078Q-21.445 60.213-21.445 60.479Q-21.445 60.647-21.555 60.764Q-21.664 60.881-21.832 60.881Q-21.984 60.881-22.100 60.770Q-22.215 60.659-22.215 60.502Q-22.590 60.502-22.904 60.703Q-23.219 60.905-23.392 61.239Q-23.566 61.573-23.566 61.952L-23.566 62.873L-22.621 62.873Q-22.414 62.897-22.375 63.104L-22.375 63.194Q-22.414 63.409-22.621 63.432L-24.918 63.432Q-25.109 63.409-25.168 63.194\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(137.26 -62.216)\">\u003Cpath d=\"M-16.285 63.233L-16.285 62.319Q-16.258 62.112-16.047 62.088L-15.879 62.088Q-15.715 62.112-15.656 62.272Q-15.453 62.912-14.726 62.912Q-14.519 62.912-14.291 62.877Q-14.062 62.842-13.894 62.727Q-13.726 62.612-13.726 62.409Q-13.726 62.198-13.949 62.084Q-14.172 61.971-14.445 61.928L-15.144 61.815Q-16.285 61.604-16.285 60.881Q-16.285 60.592-16.141 60.403Q-15.996 60.213-15.756 60.106Q-15.516 59.998-15.260 59.959Q-15.004 59.920-14.726 59.920Q-14.476 59.920-14.283 59.950Q-14.090 59.979-13.926 60.057Q-13.848 59.940-13.719 59.920L-13.641 59.920Q-13.543 59.932-13.480 59.995Q-13.418 60.057-13.406 60.151L-13.406 60.858Q-13.418 60.952-13.480 61.018Q-13.543 61.084-13.641 61.096L-13.808 61.096Q-13.902 61.084-13.969 61.018Q-14.035 60.952-14.047 60.858Q-14.047 60.479-14.742 60.479Q-15.090 60.479-15.408 60.561Q-15.726 60.643-15.726 60.889Q-15.726 61.155-15.055 61.264L-14.351 61.385Q-13.867 61.467-13.517 61.715Q-13.168 61.963-13.168 62.409Q-13.168 62.799-13.404 63.041Q-13.641 63.284-13.990 63.377Q-14.340 63.471-14.726 63.471Q-15.305 63.471-15.703 63.217Q-15.773 63.342-15.822 63.399Q-15.871 63.455-15.976 63.471L-16.047 63.471Q-16.262 63.448-16.285 63.233M-11.426 62.327L-11.426 60.545L-12.176 60.545Q-12.375 60.522-12.426 60.303L-12.426 60.217Q-12.375 60.006-12.176 59.983L-11.426 59.983L-11.426 59.233Q-11.375 59.026-11.176 58.998L-11.031 58.998Q-10.836 59.026-10.785 59.233L-10.785 59.983L-9.426 59.983Q-9.234 60.002-9.176 60.217L-9.176 60.303Q-9.230 60.522-9.426 60.545L-10.785 60.545L-10.785 62.295Q-10.785 62.912-10.211 62.912Q-9.961 62.912-9.797 62.727Q-9.633 62.541-9.633 62.295Q-9.633 62.202-9.560 62.131Q-9.488 62.061-9.387 62.049L-9.242 62.049Q-9.043 62.073-8.992 62.280L-8.992 62.327Q-8.992 62.651-9.176 62.914Q-9.359 63.178-9.652 63.325Q-9.945 63.471-10.266 63.471Q-10.777 63.471-11.101 63.161Q-11.426 62.850-11.426 62.327M-7.762 63.194L-7.762 63.104Q-7.711 62.897-7.516 62.873L-6.476 62.873L-6.476 60.545L-7.449 60.545Q-7.648 60.522-7.699 60.303L-7.699 60.217Q-7.648 60.006-7.449 59.983L-6.082 59.983Q-5.887 60.002-5.836 60.217L-5.836 62.873L-4.922 62.873Q-4.726 62.897-4.676 63.104L-4.676 63.194Q-4.726 63.409-4.922 63.432L-7.516 63.432Q-7.711 63.409-7.762 63.194M-6.730 59.006L-6.730 58.952Q-6.730 58.780-6.594 58.659Q-6.457 58.537-6.281 58.537Q-6.109 58.537-5.973 58.659Q-5.836 58.780-5.836 58.952L-5.836 59.006Q-5.836 59.182-5.973 59.303Q-6.109 59.424-6.281 59.424Q-6.457 59.424-6.594 59.303Q-6.730 59.182-6.730 59.006M-3.683 63.194L-3.683 63.104Q-3.633 62.897-3.437 62.873L-2.332 62.873L-2.332 59.104L-3.437 59.104Q-3.633 59.080-3.683 58.866L-3.683 58.776Q-3.633 58.569-3.437 58.545L-1.941 58.545Q-1.750 58.569-1.691 58.776L-1.691 62.873L-0.590 62.873Q-0.391 62.897-0.340 63.104L-0.340 63.194Q-0.391 63.409-0.590 63.432L-3.437 63.432Q-3.633 63.409-3.683 63.194M0.563 63.194L0.563 63.104Q0.613 62.897 0.809 62.873L1.914 62.873L1.914 59.104L0.809 59.104Q0.613 59.080 0.563 58.866L0.563 58.776Q0.613 58.569 0.809 58.545L2.305 58.545Q2.496 58.569 2.555 58.776L2.555 62.873L3.656 62.873Q3.856 62.897 3.906 63.104L3.906 63.194Q3.856 63.409 3.656 63.432L0.809 63.432Q0.613 63.409 0.563 63.194\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003Cg transform=\"translate(137.26 -62.216)\">\u003Cpath d=\"M8.700 63.194L8.700 63.104Q8.743 62.897 8.950 62.873L9.372 62.873L9.372 59.104L8.950 59.104Q8.743 59.080 8.700 58.866L8.700 58.776Q8.743 58.569 8.950 58.545L9.767 58.545Q9.962 58.569 10.013 58.776L10.013 60.327Q10.474 59.944 11.071 59.944Q11.419 59.944 11.657 60.084Q11.896 60.225 12.011 60.483Q12.126 60.741 12.126 61.096L12.126 62.873L12.552 62.873Q12.759 62.897 12.798 63.104L12.798 63.194Q12.759 63.409 12.552 63.432L11.157 63.432Q10.962 63.409 10.911 63.194L10.911 63.104Q10.962 62.893 11.157 62.873L11.485 62.873L11.485 61.127Q11.485 60.819 11.396 60.661Q11.306 60.502 11.013 60.502Q10.743 60.502 10.515 60.633Q10.286 60.764 10.150 60.993Q10.013 61.221 10.013 61.487L10.013 62.873L10.439 62.873Q10.646 62.897 10.685 63.104L10.685 63.194Q10.646 63.409 10.439 63.432L8.950 63.432Q8.743 63.409 8.700 63.194M16.388 61.944L13.946 61.944Q14.001 62.221 14.198 62.444Q14.396 62.666 14.673 62.789Q14.950 62.912 15.235 62.912Q15.708 62.912 15.931 62.623Q15.939 62.612 15.995 62.506Q16.052 62.401 16.101 62.358Q16.150 62.315 16.243 62.303L16.388 62.303Q16.579 62.323 16.638 62.537L16.638 62.592Q16.571 62.893 16.341 63.090Q16.110 63.287 15.798 63.379Q15.485 63.471 15.181 63.471Q14.696 63.471 14.257 63.243Q13.818 63.014 13.550 62.614Q13.282 62.213 13.282 61.721L13.282 61.662Q13.282 61.194 13.528 60.791Q13.775 60.389 14.183 60.155Q14.591 59.920 15.060 59.920Q15.564 59.920 15.917 60.143Q16.271 60.366 16.454 60.754Q16.638 61.143 16.638 61.647L16.638 61.705Q16.579 61.920 16.388 61.944M13.954 61.393L15.982 61.393Q15.935 60.983 15.696 60.731Q15.458 60.479 15.060 60.479Q14.665 60.479 14.359 60.741Q14.052 61.002 13.954 61.393M17.571 63.194L17.571 63.104Q17.622 62.897 17.818 62.873L18.923 62.873L18.923 59.104L17.818 59.104Q17.622 59.080 17.571 58.866L17.571 58.776Q17.622 58.569 17.818 58.545L19.314 58.545Q19.505 58.569 19.564 58.776L19.564 62.873L20.665 62.873Q20.864 62.897 20.915 63.104L20.915 63.194Q20.864 63.409 20.665 63.432L17.818 63.432Q17.622 63.409 17.571 63.194M21.439 64.975L21.439 64.889Q21.482 64.670 21.689 64.647L22.110 64.647L22.110 60.545L21.689 60.545Q21.482 60.522 21.439 60.303L21.439 60.217Q21.485 60.006 21.689 59.983L22.505 59.983Q22.700 60.002 22.751 60.217L22.751 60.287Q22.962 60.120 23.226 60.032Q23.489 59.944 23.759 59.944Q24.099 59.944 24.396 60.088Q24.693 60.233 24.907 60.485Q25.122 60.737 25.237 61.049Q25.353 61.362 25.353 61.705Q25.353 62.170 25.126 62.580Q24.900 62.991 24.513 63.231Q24.126 63.471 23.657 63.471Q23.138 63.471 22.751 63.104L22.751 64.647L23.177 64.647Q23.384 64.670 23.423 64.889L23.423 64.975Q23.384 65.186 23.177 65.209L21.689 65.209Q21.485 65.186 21.439 64.975M23.614 62.912Q23.923 62.912 24.173 62.743Q24.423 62.573 24.568 62.291Q24.712 62.010 24.712 61.705Q24.712 61.416 24.587 61.137Q24.462 60.858 24.230 60.680Q23.997 60.502 23.696 60.502Q23.376 60.502 23.114 60.688Q22.853 60.873 22.751 61.174L22.751 62.026Q22.841 62.393 23.062 62.653Q23.282 62.912 23.614 62.912M26.200 63.233L26.200 62.319Q26.228 62.112 26.439 62.088L26.607 62.088Q26.771 62.112 26.829 62.272Q27.032 62.912 27.759 62.912Q27.966 62.912 28.194 62.877Q28.423 62.842 28.591 62.727Q28.759 62.612 28.759 62.409Q28.759 62.198 28.536 62.084Q28.314 61.971 28.040 61.928L27.341 61.815Q26.200 61.604 26.200 60.881Q26.200 60.592 26.345 60.403Q26.489 60.213 26.730 60.106Q26.970 59.998 27.226 59.959Q27.482 59.920 27.759 59.920Q28.009 59.920 28.202 59.950Q28.396 59.979 28.560 60.057Q28.638 59.940 28.767 59.920L28.845 59.920Q28.943 59.932 29.005 59.995Q29.068 60.057 29.079 60.151L29.079 60.858Q29.068 60.952 29.005 61.018Q28.943 61.084 28.845 61.096L28.677 61.096Q28.583 61.084 28.517 61.018Q28.450 60.952 28.439 60.858Q28.439 60.479 27.743 60.479Q27.396 60.479 27.077 60.561Q26.759 60.643 26.759 60.889Q26.759 61.155 27.431 61.264L28.134 61.385Q28.618 61.467 28.968 61.715Q29.318 61.963 29.318 62.409Q29.318 62.799 29.081 63.041Q28.845 63.284 28.495 63.377Q28.146 63.471 27.759 63.471Q27.181 63.471 26.782 63.217Q26.712 63.342 26.663 63.399Q26.614 63.455 26.509 63.471L26.439 63.471Q26.224 63.448 26.200 63.233\" fill=\"var(--tk-warn)\" stroke=\"var(--tk-warn)\" class=\"tikz-text\" style=\"stroke-width:0.240\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cfigcaption class=\"tikz-cap\">Past the interpolation threshold train error stays at zero while test error keeps falling as the model grows: bigger is better.\u003C\u002Ffigcaption>",1785117770676]