{"id":"https://openalex.org/W3130456161","doi":"https://doi.org/10.1109/jstars.2021.3059991","title":"Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data","display_name":"Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3130456161","doi":"https://doi.org/10.1109/jstars.2021.3059991","mag":"3130456161"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2021.3059991","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3059991","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09356129.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09356129.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058685996","display_name":"Nathan Inkawhich","orcid":"https://orcid.org/0000-0002-6429-5632"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nathan Inkawhich","raw_affiliation_strings":["Electrical and Computer Engineering Department, Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060006716","display_name":"Matthew Inkawhich","orcid":"https://orcid.org/0000-0002-2422-7483"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew J. Inkawhich","raw_affiliation_strings":["Electrical and Computer Engineering Department, Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087645632","display_name":"Eric K. Davis","orcid":"https://orcid.org/0000-0003-0679-5724"},"institutions":[{"id":"https://openalex.org/I48697060","display_name":"SRC","ror":"https://ror.org/00tt53p64","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I48697060"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric K. Davis","raw_affiliation_strings":["Machine Learning & AI, SRC Inc., North Syracuse, NY, USA"],"affiliations":[{"raw_affiliation_string":"Machine Learning & AI, SRC Inc., North Syracuse, NY, USA","institution_ids":["https://openalex.org/I48697060"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102874846","display_name":"Uttam Majumder","orcid":"https://orcid.org/0000-0002-5244-2500"},"institutions":[{"id":"https://openalex.org/I1280414376","display_name":"United States Air Force Research Laboratory","ror":"https://ror.org/02e2egq70","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4389425425"]},{"id":"https://openalex.org/I4388482673","display_name":"U.S. Air Force Research Laboratory Information Directorate","ror":"https://ror.org/00z65zs77","country_code":null,"type":"government","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4388482673","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Uttam K. Majumder","raw_affiliation_strings":["Computing and Communications, Air Force Research Laboratory Information Directorate, Rome, NY, USA"],"affiliations":[{"raw_affiliation_string":"Computing and Communications, Air Force Research Laboratory Information Directorate, Rome, NY, USA","institution_ids":["https://openalex.org/I1280414376","https://openalex.org/I4388482673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017618189","display_name":"Erin E. Tripp","orcid":"https://orcid.org/0000-0002-5476-8213"},"institutions":[{"id":"https://openalex.org/I4388482673","display_name":"U.S. Air Force Research Laboratory Information Directorate","ror":"https://ror.org/00z65zs77","country_code":null,"type":"government","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4388482673","https://openalex.org/I4389425425"]},{"id":"https://openalex.org/I1280414376","display_name":"United States Air Force Research Laboratory","ror":"https://ror.org/02e2egq70","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erin Tripp","raw_affiliation_strings":["High Performance Systems Branch, Air Force Research Laboratory Information Directorate, Rome, NY, USA"],"affiliations":[{"raw_affiliation_string":"High Performance Systems Branch, Air Force Research Laboratory Information Directorate, Rome, NY, USA","institution_ids":["https://openalex.org/I1280414376","https://openalex.org/I4388482673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052097001","display_name":"Chris Capraro","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145244","display_name":"Radar (United States)","ror":"https://ror.org/05c5a2504","country_code":"US","type":"company","lineage":["https://openalex.org/I4210145244"]},{"id":"https://openalex.org/I48697060","display_name":"SRC","ror":"https://ror.org/00tt53p64","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I48697060"]},{"id":"https://openalex.org/I4210090800","display_name":"Sensors (United States)","ror":"https://ror.org/00b4mz884","country_code":"US","type":"company","lineage":["https://openalex.org/I4210090800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chris Capraro","raw_affiliation_strings":["Radars and Sensors, SRC Inc., North Syracuse, NY, USA"],"affiliations":[{"raw_affiliation_string":"Radars and Sensors, SRC Inc., North Syracuse, NY, USA","institution_ids":["https://openalex.org/I48697060","https://openalex.org/I4210090800","https://openalex.org/I4210145244"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058073627","display_name":"Yiran Chen","orcid":"https://orcid.org/0000-0002-1486-8412"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiran Chen","raw_affiliation_strings":["Electrical and Computer Engineering Department, Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5058685996"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":24.4798,"has_fulltext":true,"cited_by_count":90,"citation_normalized_percentile":{"value":0.99628167,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"14","issue":null,"first_page":"2942","last_page":"2955"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7603391408920288},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.7514572143554688},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.7473748922348022},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6359496116638184},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5540689826011658},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5019681453704834},{"id":"https://openalex.org/keywords/automatic-target-recognition","display_name":"Automatic target recognition","score":0.47486400604248047},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.47257745265960693},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.43760567903518677},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43192368745803833},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.42020082473754883},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4105857312679291},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.406902015209198},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39387762546539307},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09577387571334839}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7603391408920288},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7514572143554688},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.7473748922348022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6359496116638184},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5540689826011658},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5019681453704834},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.47486400604248047},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.47257745265960693},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.43760567903518677},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43192368745803833},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.42020082473754883},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4105857312679291},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.406902015209198},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39387762546539307},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09577387571334839},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2021.3059991","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3059991","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09356129.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:356c7363d8704a60a64d03178bb4743b","is_oa":true,"landing_page_url":"https://doaj.org/article/356c7363d8704a60a64d03178bb4743b","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 2942-2955 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2021.3059991","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3059991","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09356129.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G6405843661","display_name":null,"funder_award_id":"FA8750-18-C-0148","funder_id":"https://openalex.org/F4320338294","funder_display_name":"Air Force Research Laboratory"}],"funders":[{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"},{"id":"https://openalex.org/F4320332467","display_name":"U.S. Air Force","ror":"https://ror.org/006gmme17"},{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3130456161.pdf","grobid_xml":"https://content.openalex.org/works/W3130456161.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1995443109","https://openalex.org/W2067532361","https://openalex.org/W2095705004","https://openalex.org/W2111847198","https://openalex.org/W2112796928","https://openalex.org/W2151239833","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2531327146","https://openalex.org/W2730249686","https://openalex.org/W2765407302","https://openalex.org/W2785325870","https://openalex.org/W2800255161","https://openalex.org/W2801973217","https://openalex.org/W2867167548","https://openalex.org/W2890897299","https://openalex.org/W2945014618","https://openalex.org/W2945137370","https://openalex.org/W2945637781","https://openalex.org/W2946348251","https://openalex.org/W2948210185","https://openalex.org/W2954996726","https://openalex.org/W2962851944","https://openalex.org/W2963399829","https://openalex.org/W2964116600","https://openalex.org/W2964137095","https://openalex.org/W2964253222","https://openalex.org/W2970206392","https://openalex.org/W3010290582","https://openalex.org/W3021910653","https://openalex.org/W3021955884","https://openalex.org/W3021960028","https://openalex.org/W3034309483","https://openalex.org/W3035050044","https://openalex.org/W3142349132","https://openalex.org/W4293846201","https://openalex.org/W4293861706","https://openalex.org/W6649065943","https://openalex.org/W6674330103","https://openalex.org/W6682208247","https://openalex.org/W6685133223","https://openalex.org/W6728622933","https://openalex.org/W6739575509","https://openalex.org/W6739868092","https://openalex.org/W6745136726","https://openalex.org/W6747899497","https://openalex.org/W6752760542","https://openalex.org/W6755310938"],"related_works":["https://openalex.org/W4283015542","https://openalex.org/W4380446748","https://openalex.org/W4312659495","https://openalex.org/W3175965105","https://openalex.org/W2945014618","https://openalex.org/W4385366257","https://openalex.org/W3101007570","https://openalex.org/W4387910575","https://openalex.org/W2002992601","https://openalex.org/W3186268266"],"abstract_inverted_index":{"Obtaining":[0],"measured":[1,57,71,91,189,218],"synthetic":[2,40,84,112,152,191,247],"aperture":[3],"radar":[4],"(SAR)":[5],"data":[6,42,113,135],"for":[7,38,43],"training":[8,58,85,125,227],"automatic":[9],"target":[10],"recognition":[11],"(ATR)":[12],"models":[13,172,203,243],"can":[14],"be":[15],"too":[16],"expensive":[17],"(in":[18],"terms":[19],"of":[20,26,67,70,81,169,187,200,240],"time":[21],"and":[22,24,102,122,142,154,175,178,190,204,233],"money)":[23],"complex":[25],"a":[27,183],"process":[28],"in":[29],"many":[30],"situations.":[31],"In":[32,73],"response,":[33],"researchers":[34],"have":[35],"developed":[36],"methods":[37,215],"creating":[39],"SAR":[41],"targets":[44],"using":[45,173,241],"electro-magnetic":[46],"prediction":[47],"software,":[48],"which":[49],"is":[50],"then":[51,165],"used":[52],"to":[53,106,115,145,181,225],"enrich":[54],"an":[55],"existing":[56],"dataset.":[59,163],"However,":[60],"this":[61,74],"approach":[62],"relies":[63],"on":[64,78,89,160,246],"the":[65,79,95,111,119,147,151,161,167,188,196,226,238],"availability":[66],"some":[68],"amount":[69],"data.":[72,92,192,248],"work,":[75],"we":[76,133,194],"focus":[77],"case":[80],"having":[82],"100%":[83],"data,":[86,153],"while":[87],"testing":[88],"only":[90],"We":[93,164],"use":[94],"SAMPLE":[96,162],"dataset":[97],"public":[98],"released":[99],"by":[100],"AFRL,":[101],"find":[103,179,205],"significant":[104],"challenges":[105],"learning":[107],"generalizable":[108],"representations":[109],"from":[110,150],"due":[114],"distributional":[116],"differences":[117],"between":[118],"two":[120],"modalities":[121],"extremely":[123],"limited":[124],"sample":[126],"quantities.":[127],"Using":[128],"deep":[129],"learning-based":[130],"ATR":[131,171,242],"models,":[132],"propose":[134],"augmentation,":[136],"model":[137],"construction,":[138],"loss":[139],"function":[140],"choices,":[141],"ensembling":[143],"techniques":[144,232],"enhance":[146,237],"representation":[148,186],"learned":[149],"ultimately":[155],"achieved":[156],"over":[157],"95%":[158],"accuracy":[159],"analyze":[166],"functionality":[168],"our":[170,201,231],"saliency":[174],"feature-space":[176],"investigations":[177],"them":[180],"learn":[182],"more":[184,211],"cohesive":[185],"Finally,":[193],"evaluate":[195],"out-of-library":[197],"detection":[198],"performance":[199],"synthetic-only":[202],"that":[206,221],"they":[207],"are":[208],"nearly":[209],"10%":[210],"effective":[212],"than":[213],"baseline":[214],"at":[216],"identifying":[217],"test":[219],"samples":[220],"do":[222],"not":[223],"belong":[224],"class":[228],"set.":[229],"Overall,":[230],"their":[234],"compositions":[235],"significantly":[236],"feasibility":[239],"trained":[244],"exclusively":[245]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":30},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}