{"id":"https://openalex.org/W7224092546","doi":"https://doi.org/10.48550/arxiv.2610.36743","title":"Readout and Operating-Point Sensitivity in Event-Stream Candidate Selection","display_name":"Readout and Operating-Point Sensitivity in Event-Stream Candidate Selection","publication_year":2026,"publication_date":"2026-10-27","ids":{"openalex":"https://openalex.org/W7224092546","doi":"https://doi.org/10.48550/arxiv.2610.36743"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.36743","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.36743","pdf_url":"https://arxiv.org/pdf/2610.36743","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.36743","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052729137","display_name":"Yifan Shuai","orcid":"https://orcid.org/0009-0006-1052-984X","observed_orcids":["https://orcid.org/0009-0006-1052-984X"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shuai, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5052729137"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.3472000062465668,"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"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.3472000062465668,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.2134000062942505,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.21060000360012054,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/event-cameras","display_name":"event cameras","score":0.5519999861717224},{"id":"https://openalex.org/keywords/false-triggers","display_name":"false triggers","score":0.46299999952316284},{"id":"https://openalex.org/keywords/threshold-calibration","display_name":"threshold calibration","score":0.8339999914169312},{"id":"https://openalex.org/keywords/operating-point","display_name":"operating point","score":0.6299999952316284},{"id":"https://openalex.org/keywords/mobilenetv3","display_name":"MobileNetV3","score":0.7429999709129333},{"id":"https://openalex.org/keywords/jepa","display_name":"JEPA","score":0.3880000114440918},{"id":"https://openalex.org/keywords/fall-detection","display_name":"fall detection","score":0.42500001192092896},{"id":"https://openalex.org/keywords/partial-auc","display_name":"partial AUC","score":0.4749999940395355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5895000100135803},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5864999890327454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5756000280380249},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5475000143051147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C2780527195","wikidata":"https://www.wikidata.org/wiki/Q5422874","display_name":"Eyewear","level":2,"score":0.45739999413490295},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.39469999074935913},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.38749998807907104},{"id":"https://openalex.org/C190729725","wikidata":"https://www.wikidata.org/wiki/Q5012817","display_name":"CLs upper limits","level":2,"score":0.3831999897956848},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.33899998664855957},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32919999957084656},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3070000112056732},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2825999855995178},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C206041023","wikidata":"https://www.wikidata.org/wiki/Q1751970","display_name":"Wilcoxon signed-rank test","level":3,"score":0.27129998803138733},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C193244246","wikidata":"https://www.wikidata.org/wiki/Q5432696","display_name":"False discovery rate","level":3,"score":0.25369998812675476},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.36743","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.36743","pdf_url":"https://arxiv.org/pdf/2610.36743","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.36743","doi":"https://doi.org/10.48550/arxiv.2610.36743","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.36743","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.36743","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.36743","pdf_url":"https://arxiv.org/pdf/2610.36743","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224092546.pdf","grobid_xml":"https://content.openalex.org/works/W7224092546.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7203607926","https://openalex.org/W7131351627","https://openalex.org/W7165202636","https://openalex.org/W7131815288","https://openalex.org/W4360584268","https://openalex.org/W7223220926","https://openalex.org/W4327644546","https://openalex.org/W4213412898","https://openalex.org/W4402592998","https://openalex.org/W7214082408"],"abstract_inverted_index":{"Event-triggered":[0],"recording":[1,193],"needs":[2],"a":[3,53,61],"useful":[4],"combination":[5],"of":[6],"target":[7],"capture":[8,105,152],"and":[9,19,31,38,49,72,95,126,144,164],"false":[10,91,120,196],"triggers,":[11,92,121],"yet":[12],"representation":[13],"comparisons":[14],"can":[15],"change":[16],"with":[17,65,89,118],"readout":[18],"threshold":[20],"calibration.":[21],"We":[22,170],"evaluate":[23],"real":[24],"event":[25],"accumulations":[26],"from":[27],"520":[28],"DailyDVS-200":[29],"clips":[30],"47":[32],"participants":[33],"using":[34],"participant-disjoint":[35],"training,":[36],"validation,":[37],"testing.":[39],"The":[40,148,176],"original":[41,149],"study":[42],"varies":[43],"frozen":[44],"encoder":[45],"scale,":[46],"accumulation":[47],"duration,":[48],"temporal":[50],"view":[51],"count;":[52],"post-review":[54],"extension":[55],"adds":[56],"two":[57],"supervised-pretrained":[58],"convolutional":[59],"encoders,":[60],"61,586-parameter":[62],"trained":[63],"CNN":[64],"three":[66],"seeds,":[67],"ten":[68,132],"repeated":[69],"participant":[70],"partitions,":[71],"calibration":[73],"sensitivity.":[74],"At":[75],"50":[76],"ms/eight":[77],"views":[78],"under":[79],"the":[80],"primary":[81],"handshake-or-fall":[82],"policy,":[83],"DINO-S":[84],"CLS":[85,101,143],"captures":[86,116],"101/132":[87],"targets":[88],"40/388":[90],"versus":[93,113],"78/132":[94],"35/388":[96],"for":[97,141,146],"patch":[98],"mean.":[99],"DINO-B":[100,142],"has":[102],"lower":[103],"selected-threshold":[104],"but":[106,157],"higher":[107],"fold-wise":[108],"low-FPR":[109],"partial":[110,137],"AUC":[111,138],"(0.746":[112],"0.732).":[114],"MobileNetV3-Small":[115],"97/132":[117],"53/388":[119],"demonstrating":[122],"why":[123],"lightweight":[124],"baselines":[125],"actual":[127],"operating":[128],"points":[129],"matter.":[130],"Across":[131],"dependent":[133],"repartitions,":[134],"mean":[135],"normalized":[136],"is":[139,154],"0.738":[140],"0.674":[145],"MobileNet.":[147],"LeJEPA":[150],"S-to-B":[151],"difference":[153],"8.3":[155],"points,":[156],"its":[158],"multiplicity-adjusted":[159],"interval":[160],"includes":[161],"zero.":[162],"Duration":[163],"view-count":[165],"effects":[166],"remain":[167],"policy":[168],"dependent.":[169],"find":[171],"no":[172],"established":[173],"JEPA-specific":[174],"advantage.":[175],"evidence":[177],"concerns":[178],"offline":[179],"action-defined":[180],"candidate":[181],"selection":[182],"on":[183],"previously":[184],"used":[185],"development":[186],"data;":[187],"it":[188],"does":[189],"not":[190],"establish":[191],"personal":[192],"value,":[194],"continuous":[195],"alarms":[197],"per":[198],"hour,":[199],"or":[200],"eyewear":[201],"energy.":[202]},"counts_by_year":[],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2026-10-29T00:00:00"}
