{"id":"https://openalex.org/W3160306203","doi":"https://doi.org/10.1109/icpr48806.2021.9412069","title":"CC-Loss: Channel Correlation Loss for Image Classification","display_name":"CC-Loss: Channel Correlation Loss for Image Classification","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3160306203","doi":"https://doi.org/10.1109/icpr48806.2021.9412069","mag":"3160306203"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2010.05469.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101941616","display_name":"Zeyu Song","orcid":"https://orcid.org/0009-0003-3964-7928"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyu Song","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007417490","display_name":"Dongliang Chang","orcid":"https://orcid.org/0000-0002-4081-3001"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongliang Chang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039812471","display_name":"Zhanyu Ma","orcid":"https://orcid.org/0000-0003-2950-2488"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanyu Ma","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100758186","display_name":"Xiaoxu Li","orcid":"https://orcid.org/0000-0001-8833-9401"},"institutions":[{"id":"https://openalex.org/I22716506","display_name":"Lanzhou University of Technology","ror":"https://ror.org/03panb555","country_code":"CN","type":"education","lineage":["https://openalex.org/I22716506"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxu Li","raw_affiliation_strings":["Lanzhou University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lanzhou University of Technology","institution_ids":["https://openalex.org/I22716506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090108098","display_name":"Zheng\u2010Hua Tan","orcid":"https://orcid.org/0000-0001-6856-8928"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Zheng-Hua Tan","raw_affiliation_strings":["Aalborg University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University","institution_ids":["https://openalex.org/I891191580"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7601","last_page":"7608"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998000264167786,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9984999895095825,"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/discriminative-model","display_name":"Discriminative model","score":0.7608948945999146},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5817185044288635},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.5813276767730713},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5770164132118225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5622372627258301},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5518548488616943},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5254232883453369},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5202195048332214},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5110782384872437},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4965255856513977},{"id":"https://openalex.org/keywords/information-loss","display_name":"Information loss","score":0.47942107915878296},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.459919810295105},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.44242429733276367},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.438750684261322},{"id":"https://openalex.org/keywords/hinge-loss","display_name":"Hinge loss","score":0.4243202209472656},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.377393901348114},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35169151425361633},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32843226194381714},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.19032087922096252}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7608948945999146},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5817185044288635},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.5813276767730713},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5770164132118225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5622372627258301},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5518548488616943},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5254232883453369},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5202195048332214},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5110782384872437},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4965255856513977},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.47942107915878296},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.459919810295105},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.44242429733276367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.438750684261322},{"id":"https://openalex.org/C39891107","wikidata":"https://www.wikidata.org/wiki/Q5767098","display_name":"Hinge loss","level":3,"score":0.4243202209472656},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.377393901348114},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35169151425361633},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32843226194381714},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19032087922096252},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/6ca957b0-28ce-4770-8dac-2874326fba60","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/6ca957b0-28ce-4770-8dac-2874326fba60","pdf_url":"https://arxiv.org/pdf/2010.05469.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Song, Z, Chang, D, Ma, Z, Li, X & Tan, Z-H 2021, CC-LOSS: CHANNEL CORRELATION LOSS FOR IMAGE CLASSIFICATION. in 2020 25th International Conference on Pattern Recognition (ICPR)., 9412069, IEEE (Institute of Electrical and Electronics Engineers), Proceeding IEEE International Conference on Pattern Recognition (ICPR), pp. 7601-7608, 2020 25th International Conference on Pattern Recognition (ICPR), Milano, Italy, 10/01/2021. https://doi.org/10.1109/ICPR48806.2021.9412069","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:ir.lzu.edu.cn/:262010/451465","is_oa":false,"landing_page_url":"http://ir.lzu.edu.cn/handle/262010/451465","pdf_url":null,"source":{"id":"https://openalex.org/S4406923049","display_name":"Lanzhou University Institutional Repository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"\u4f1a\u8bae\u8bba\u6587"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/6ca957b0-28ce-4770-8dac-2874326fba60","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/6ca957b0-28ce-4770-8dac-2874326fba60","pdf_url":"https://arxiv.org/pdf/2010.05469.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Song, Z, Chang, D, Ma, Z, Li, X & Tan, Z-H 2021, CC-LOSS: CHANNEL CORRELATION LOSS FOR IMAGE CLASSIFICATION. in 2020 25th International Conference on Pattern Recognition (ICPR)., 9412069, IEEE (Institute of Electrical and Electronics Engineers), Proceeding IEEE International Conference on Pattern Recognition (ICPR), pp. 7601-7608, 2020 25th International Conference on Pattern Recognition (ICPR), Milano, Italy, 10/01/2021. https://doi.org/10.1109/ICPR48806.2021.9412069","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1216186352","display_name":null,"funder_award_id":"202006470036","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G1223278680","display_name":null,"funder_award_id":"61773071,61922015,61671030,U19B2036","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5093855108","display_name":null,"funder_award_id":"CX2020105","funder_id":"https://openalex.org/F4320321470","funder_display_name":"Beijing University of Posts and Telecommunications"},{"id":"https://openalex.org/G6510677135","display_name":null,"funder_award_id":"2019YFF0303300,2019YFF0303302","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7697810044","display_name":null,"funder_award_id":"Z200002","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G8074575329","display_name":null,"funder_award_id":"Z191100001119140","funder_id":"https://openalex.org/F4320334978","funder_display_name":"Beijing Nova Program"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321470","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null},{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3160306203.pdf","grobid_xml":"https://content.openalex.org/works/W3160306203.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W1998808035","https://openalex.org/W2007339694","https://openalex.org/W2108598243","https://openalex.org/W2138011018","https://openalex.org/W2145287260","https://openalex.org/W2163605009","https://openalex.org/W2169490875","https://openalex.org/W2194775991","https://openalex.org/W2520774990","https://openalex.org/W2737258237","https://openalex.org/W2752782242","https://openalex.org/W2890834819","https://openalex.org/W2899505139","https://openalex.org/W2904713653","https://openalex.org/W2913125146","https://openalex.org/W2917452308","https://openalex.org/W2951696358","https://openalex.org/W2962898354","https://openalex.org/W2963351448","https://openalex.org/W2963420686","https://openalex.org/W2963446712","https://openalex.org/W2963466847","https://openalex.org/W2969985801","https://openalex.org/W3002653563","https://openalex.org/W3008809756","https://openalex.org/W3034231606","https://openalex.org/W3108870912","https://openalex.org/W3121480429","https://openalex.org/W4246060645","https://openalex.org/W4289238228","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6684191040","https://openalex.org/W6726946684","https://openalex.org/W6737496325","https://openalex.org/W6741437728","https://openalex.org/W6748257384","https://openalex.org/W6754597090","https://openalex.org/W6773968463","https://openalex.org/W6779430660","https://openalex.org/W6788995615"],"related_works":["https://openalex.org/W4281627728","https://openalex.org/W2761785940","https://openalex.org/W2129933262","https://openalex.org/W3037097571","https://openalex.org/W2951959408","https://openalex.org/W2895831313","https://openalex.org/W4308016144","https://openalex.org/W4289406078","https://openalex.org/W4404200101","https://openalex.org/W2572480023"],"abstract_inverted_index":{"The":[0],"loss":[1,14,42,65,90,190],"function":[2,15],"is":[3,18,24,93,138],"a":[4,25,87,116,164],"key":[5],"component":[6],"in":[7,129],"deep":[8],"learning":[9],"models.":[10],"A":[11],"commonly":[12],"used":[13],"for":[16,33,126],"classification":[17,34,195],"the":[19,56,60,70,73,77,97,108,111,130,142,148,156,184,188],"cross":[20],"entropy":[21],"loss,":[22,39],"which":[23],"simple":[26],"yet":[27],"effective":[28],"application":[29],"of":[30,59,124],"information":[31],"theory":[32],"problems.":[35],"Based":[36],"on":[37,192],"this":[38,83],"many":[40],"other":[41],"functions":[43,66,191],"have":[44],"been":[45],"proposed,":[46],"e.g.,":[47],"by":[48],"adding":[49],"intra-class":[50,109,169],"and":[51,76,102,110,153,171],"inter-class":[52,112,172],"constraints":[53],"to":[54,68,95,120,140,154],"enhance":[55],"discriminative":[57],"ability":[58],"learned":[61],"features.":[62],"However,":[63],"these":[64],"fail":[67],"consider":[69],"connections":[71],"between":[72,100,158],"feature":[74,165],"distribution":[75],"model":[78],"structure.":[79],"Aiming":[80],"at":[81],"addressing":[82],"problem,":[84],"we":[85,162],"propose":[86],"channel":[88,117,122,143],"correlation":[89],"(CC-Loss)":[91],"that":[92,177],"able":[94],"constrain":[96],"specific":[98],"relations":[99],"classes":[101],"channels":[103],"as":[104,106],"well":[105],"maintain":[107],"separability.":[113,173],"CC-Loss":[114,186],"uses":[115],"attention":[118,123,144],"module":[119],"generate":[121],"features":[125],"each":[127],"sample":[128],"training":[131],"stage.":[132],"Next,":[133],"an":[134],"Euclidean":[135],"distance":[136],"matrix":[137],"calculated":[139],"make":[141],"vectors":[145],"associated":[146],"with":[147,167,183],"same":[149],"class":[150],"become":[151],"identical":[152],"increase":[155],"difference":[157],"different":[159,179],"classes.":[160],"Finally,":[161],"obtain":[163],"embedding":[166],"good":[168],"compactness":[170],"Experimental":[174],"results":[175],"show":[176],"two":[178],"backbone":[180],"models":[181],"trained":[182],"proposed":[185],"outperform":[187],"state-of-the-art":[189],"three":[193],"image":[194],"datasets.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
