{"id":"https://openalex.org/W7164006862","doi":"https://doi.org/10.48550/arxiv.2606.08191","title":"Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation","display_name":"Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation","publication_year":2026,"publication_date":"2026-06-06","ids":{"openalex":"https://openalex.org/W7164006862","doi":"https://doi.org/10.48550/arxiv.2606.08191"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08191","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.08191","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138287102","display_name":"Kewei Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Kewei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138235635","display_name":"Rongying Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Rongying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138253295","display_name":"Xueli Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xueli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138286657","display_name":"Xiwen Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Xiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138279538","display_name":"Zhongjian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhongjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138263374","display_name":"Lan Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Lan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138272518","display_name":"Ruochi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ruochi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138241260","display_name":"Fengfeng Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Fengfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11103","display_name":"Antimicrobial Peptides and Activities","score":0.2736000120639801,"subfield":{"id":"https://openalex.org/subfields/2404","display_name":"Microbiology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11103","display_name":"Antimicrobial Peptides and Activities","score":0.2736000120639801,"subfield":{"id":"https://openalex.org/subfields/2404","display_name":"Microbiology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.18649999797344208,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.09520000219345093,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.7777000069618225},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.49549999833106995},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.40310001373291016},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.3686000108718872},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.3212999999523163},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.3050999939441681}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7777000069618225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6270999908447266},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.49549999833106995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48190000653266907},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C81444415","wikidata":"https://www.wikidata.org/wiki/Q7243535","display_name":"Priming (agriculture)","level":3,"score":0.2854999899864197},{"id":"https://openalex.org/C2781188995","wikidata":"https://www.wikidata.org/wiki/Q6934982","display_name":"Multiplex","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.258899986743927},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08191","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.08191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08191","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Token":[0],"aggregation":[1,31],"is":[2,148],"a":[3,29,49,155],"common":[4],"bottleneck":[5],"in":[6,21,179],"models":[7],"that":[8,33,136],"map":[9],"token":[10,24,35],"representations":[11,36],"to":[12],"sample-level":[13],"predictions,":[14],"yet":[15],"most":[16,141],"pooling":[17],"methods":[18],"operate":[19],"only":[20],"the":[22,38,94,119,140,144,162],"original":[23],"domain.":[25],"We":[26,60,134],"propose":[27],"FLaG,":[28],"plug-in":[30],"module":[32],"transforms":[34],"with":[37,44,69,73,82,105,122,167],"real":[39],"FFT,":[40],"summarizes":[41],"spectral":[42,158,177],"components":[43],"learnable":[45],"latent":[46],"queries,":[47],"applies":[48],"channel-wise":[50],"gate,":[51],"and":[52,77,79,86,99,111,131,143,161,171,187,193],"reconstructs":[53],"enhanced":[54],"time-domain":[55],"tokens":[56],"for":[57],"final":[58],"pooling.":[59],"evaluate":[61],"FLaG":[62,88],"on":[63,75,84,93,100,109,118],"antimicrobial":[64,96],"peptide":[65,97],"(AMP)":[66],"activity":[67],"prediction":[68],"ESM2,":[70],"image":[71],"classification":[72,81],"ResNet18":[74],"CIFAR-10":[76],"CIFAR-100,":[78,101],"text":[80,107],"RoBERTa":[83],"IMDB":[85,110],"GLUE.":[87,112],"achieves":[89],"its":[90,116],"clearest":[91],"gains":[92],"ESM2-8M":[95],"tasks":[98],"while":[102],"remaining":[103,145],"competitive":[104],"strong":[106],"baselines":[108],"Then":[113],"we":[114],"probe":[115],"behavior":[117],"AMP":[120],"setting":[121],"band":[123],"knockouts,":[124],"gate":[125,152],"summaries,":[126],"residue":[127],"perturbations,":[128],"latent-query":[129],"readouts,":[130],"structure-proxy":[132],"stratification.":[133],"find":[135],"low-frequency":[137],"bands":[138],"contribute":[139],"overall,":[142],"higher-band":[146],"pattern":[147],"more":[149],"sample-specific.":[150],"The":[151,182],"acts":[153],"as":[154],"broadly":[156],"shared":[157],"reweighting":[159],"stage":[160],"cross-attention":[163],"patterns":[164],"are":[165,189],"sample-specific":[166],"mild":[168],"query-wise":[169],"differentiation,":[170],"higher-helix":[172],"peptides":[173],"exhibit":[174],"stronger":[175],"average":[176],"sensitivity":[178],"both":[180],"bacteria.":[181],"supplementary":[183],"materials,":[184],"source":[185],"code":[186],"data":[188],"released":[190],"at":[191],"https://www.healthinformaticslab.org/supp/":[192],"https://github.com/Kewei2023/AMPCliff/tree/FLaG.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}
