{"id":"https://openalex.org/W7131615703","doi":"https://doi.org/10.1109/ickg66886.2025.00010","title":"BETWEEN: Boundary Estimation through Time Warping, Energy, &amp; Entropy Neutralization","display_name":"BETWEEN: Boundary Estimation through Time Warping, Energy, &amp; Entropy Neutralization","publication_year":2025,"publication_date":"2025-11-13","ids":{"openalex":"https://openalex.org/W7131615703","doi":"https://doi.org/10.1109/ickg66886.2025.00010"},"language":null,"primary_location":{"id":"doi:10.1109/ickg66886.2025.00010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickg66886.2025.00010","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Knowledge Graph (ICKG)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126928067","display_name":"Calvin Breseman","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107489","display_name":"American Standard (United States)","ror":"https://ror.org/013b0rk29","country_code":"US","type":"company","lineage":["https://openalex.org/I4210107489"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Calvin Breseman","raw_affiliation_strings":["Standard AI,San Francisco,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Standard AI,San Francisco,United States","institution_ids":["https://openalex.org/I4210107489"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126896511","display_name":"Igor Moiseev","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107489","display_name":"American Standard (United States)","ror":"https://ror.org/013b0rk29","country_code":"US","type":"company","lineage":["https://openalex.org/I4210107489"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Igor Moiseev","raw_affiliation_strings":["Standard AI,San Francisco,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Standard AI,San Francisco,United States","institution_ids":["https://openalex.org/I4210107489"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126592788","display_name":"Bruno Abbate","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107489","display_name":"American Standard (United States)","ror":"https://ror.org/013b0rk29","country_code":"US","type":"company","lineage":["https://openalex.org/I4210107489"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bruno Abbate","raw_affiliation_strings":["Standard AI,San Francisco,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Standard AI,San Francisco,United States","institution_ids":["https://openalex.org/I4210107489"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122150090","display_name":"David M. Woollard","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107489","display_name":"American Standard (United States)","ror":"https://ror.org/013b0rk29","country_code":"US","type":"company","lineage":["https://openalex.org/I4210107489"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David M. Woollard","raw_affiliation_strings":["Standard AI,San Francisco,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Standard AI,San Francisco,United States","institution_ids":["https://openalex.org/I4210107489"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210107489"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"17","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.08860000222921371,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.08860000222921371,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.08179999887943268,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.06689999997615814,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5113999843597412},{"id":"https://openalex.org/keywords/entropy-estimation","display_name":"Entropy estimation","score":0.4909000098705292},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4528999924659729},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.42480000853538513},{"id":"https://openalex.org/keywords/bernoullis-principle","display_name":"Bernoulli's principle","score":0.413100004196167},{"id":"https://openalex.org/keywords/differential-entropy","display_name":"Differential entropy","score":0.3921000063419342},{"id":"https://openalex.org/keywords/kullback\u2013leibler-divergence","display_name":"Kullback\u2013Leibler divergence","score":0.3919999897480011},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.38580000400543213},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.3822000026702881},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.3783000111579895}],"concepts":[{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5113999843597412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5044999718666077},{"id":"https://openalex.org/C95546049","wikidata":"https://www.wikidata.org/wiki/Q1345207","display_name":"Entropy estimation","level":3,"score":0.4909000098705292},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47589999437332153},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4528999924659729},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42750000953674316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42730000615119934},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.42480000853538513},{"id":"https://openalex.org/C152361515","wikidata":"https://www.wikidata.org/wiki/Q181328","display_name":"Bernoulli's principle","level":2,"score":0.413100004196167},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40689998865127563},{"id":"https://openalex.org/C124551494","wikidata":"https://www.wikidata.org/wiki/Q3055345","display_name":"Differential entropy","level":4,"score":0.3921000063419342},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3822000026702881},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3783000111579895},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.33239999413490295},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3231000006198883},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C9136319","wikidata":"https://www.wikidata.org/wiki/Q362640","display_name":"Covariant transformation","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.2630000114440918},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.2547999918460846},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ickg66886.2025.00010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickg66886.2025.00010","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Knowledge Graph (ICKG)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1975684011","https://openalex.org/W2118673550","https://openalex.org/W2128922623","https://openalex.org/W2148477345","https://openalex.org/W2167311767","https://openalex.org/W2330210193","https://openalex.org/W4404468887"],"related_works":[],"abstract_inverted_index":{"We":[0],"introduce":[1],"BETWEEN\u2013Boundary":[2],"Estimation":[3],"through":[4],"Time-Warping,":[5],"Energy,":[6],"&":[7],"Entropy":[8],"Neutralization\u2013an":[9],"unsupervised":[10],"framework":[11],"for":[12,197],"change":[13],"point":[14],"detection":[15],"(CPD)":[16],"with":[17,26],"multidimensional":[18],"spatiotemporal":[19,156],"data.":[20],"BETWEEN":[21,130,175],"augments":[22],"greedy":[23],"binary":[24],"segmentation":[25],"iterative":[27],"revision":[28,111],"and":[29,96,112,126,137,149,162,179,195],"consolidation":[30,113],"steps":[31],"that":[32,76,187],"steer":[33],"partitions":[34],"toward":[35],"global":[36],"optimality":[37],"without":[38],"the":[39,78,87,215],"restrictive":[40],"monotonic-additive":[41],"assumptions":[42],"of":[43,94,168,201],"PELT<sup":[44],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[45,47,54,56],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup><sup":[46],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>PELT\u2013Pruned":[48],"Exact":[49],"Linear":[50],"Time":[51],"or":[52],"FPOP<sup":[53],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup><sup":[55],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>FPOP\u2013Functional":[57],"Pruning":[58],"Optimal":[59],"Partitioning.":[60],"Guided":[61],"by":[62,90],"principled":[63],"limits":[64],"on":[65,153],"what":[66],"CPD":[67,172,226],"can":[68,212],"resolve,":[69],"we":[70,224],"apply":[71],"an":[72,228,236],"adaptive":[73],"Butterworth":[74],"filter":[75],"trims":[77],"search":[79],"space":[80],"to":[81,105,131,144],"genuine":[82],"functional":[83],"inflection":[84],"points,":[85],"slashing":[86],"effective":[88],"workload":[89],"roughly":[91],"a":[92,98,198],"factor":[93],"twenty-five":[95],"yielding":[97],"comparable":[99],"reduction":[100],"in":[101],"computational":[102],"overhead":[103],"relative":[104],"Binary":[106],"Segmentation\u2013even":[107],"after":[108],"BETWEEN's":[109],"additional":[110],"passes.":[114],"A":[115],"novel":[116],"gain":[117],"function":[118],"couples":[119],"dynamic":[120],"time":[121],"warping,":[122],"energy":[123],"density":[124],"ratios,":[125],"Kullback-Leibler":[127],"divergence,":[128],"allowing":[129],"recognize":[132],"both":[133],"subtle":[134],"shape":[135],"shifts":[136],"large":[138],"distributional":[139],"jumps":[140],"while":[141],"remaining":[142],"agnostic":[143],"class":[145],"labels,":[146],"sampling":[147],"rates,":[148],"segment":[150],"length.":[151],"Experiments":[152],"two":[154],"multivariate":[155],"benchmarks,":[157],"Human":[158],"Activity":[159],"Recognition":[160],"(HAR)":[161],"Bee":[163],"Waggle":[164],"Dance,":[165],"show":[166],"improvements":[167],"10-20%":[169],"over":[170],"state-of-the-art":[171],"baselines.":[173],"Crucially,":[174],"segments":[176],"unstructured":[177],"human":[178,207],"animal":[180],"behavior":[181],"streams":[182],"into":[183,190],"coherent,":[184],"self-similar":[185],"\u201ctokens\u201d":[186],"feed":[188],"directly":[189],"downstream":[191],"deep":[192],"learning":[193],"pipelines":[194],"allow":[196],"graphical":[199],"representation":[200],"complex":[202],"real-world":[203],"processes":[204],"such":[205],"as":[206,227],"behavior,":[208],"enabling":[209],"methodologies":[210],"which":[211,231],"help":[213],"examine":[214],"transitions":[216],"between":[217],"discrete":[218],"behavioral":[219],"states.":[220],"By":[221],"doing":[222],"so,":[223],"re-frame":[225],"pre-processing":[229],"step":[230],"reduces":[232],"dimensionality,":[233],"rather":[234],"than":[235],"end":[237],"goal":[238],"itself.":[239]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-02-27T00:00:00"}
