{"id":"https://openalex.org/W7148471670","doi":"https://doi.org/10.48550/arxiv.2604.00800","title":"MIRANDA: MId-feature RANk-adversarial Domain Adaptation toward climate change-robust ecological forecasting with deep learning","display_name":"MIRANDA: MId-feature RANk-adversarial Domain Adaptation toward climate change-robust ecological forecasting with deep learning","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148471670","doi":"https://doi.org/10.48550/arxiv.2604.00800"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00800","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00800","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.00800","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042426111","display_name":"Yuchang Jiang","orcid":"https://orcid.org/0000-0001-7293-1304"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Yuchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132791493","display_name":"Jan Dirk Wegner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wegner, Jan Dirk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132819866","display_name":"Vivien Sainte Fare Garnot","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garnot, Vivien Sainte Fare","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/T10895","display_name":"Species Distribution and Climate Change","score":0.1817999929189682,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10895","display_name":"Species Distribution and Climate Change","score":0.1817999929189682,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.14730000495910645,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.10760000348091125,"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/adversarial-system","display_name":"Adversarial system","score":0.6553999781608582},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5656999945640564},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5649999976158142},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5281999707221985},{"id":"https://openalex.org/keywords/climate-change","display_name":"Climate change","score":0.5159000158309937},{"id":"https://openalex.org/keywords/phenology","display_name":"Phenology","score":0.41269999742507935},{"id":"https://openalex.org/keywords/climate-model","display_name":"Climate model","score":0.40389999747276306},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.38920000195503235}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6553999781608582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6233999729156494},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6129999756813049},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5656999945640564},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5281999707221985},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.5159000158309937},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4706000089645386},{"id":"https://openalex.org/C51417038","wikidata":"https://www.wikidata.org/wiki/Q272737","display_name":"Phenology","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C168754636","wikidata":"https://www.wikidata.org/wiki/Q620920","display_name":"Climate model","level":3,"score":0.40389999747276306},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3668999969959259},{"id":"https://openalex.org/C96240277","wikidata":"https://www.wikidata.org/wiki/Q5333237","display_name":"Ecological forecasting","level":4,"score":0.3594000041484833},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.32839998602867126},{"id":"https://openalex.org/C110872660","wikidata":"https://www.wikidata.org/wiki/Q37813","display_name":"Ecosystem","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C103215972","wikidata":"https://www.wikidata.org/wiki/Q5381340","display_name":"Environmental niche modelling","level":4,"score":0.271699994802475},{"id":"https://openalex.org/C2778851808","wikidata":"https://www.wikidata.org/wiki/Q260607","display_name":"Climate change adaptation","level":3,"score":0.2646999955177307},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00800","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00800","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.00800","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00800","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.8400886058807373,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Plant":[0],"phenology":[1,32],"modelling":[2,33],"aims":[3],"to":[4,28,59,145,194],"predict":[5],"the":[6,164,200],"timing":[7],"of":[8,92,108,150,180],"seasonal":[9],"phases,":[10],"such":[11],"as":[12,47],"leaf-out":[13],"or":[14],"flowering,":[15],"from":[16],"meteorological":[17,166],"time":[18],"series.":[19],"Reliable":[20],"predictions":[21],"are":[22,67],"crucial":[23],"for":[24],"anticipating":[25],"ecosystem":[26],"responses":[27],"climate":[29,70,86],"change.":[30,71],"While":[31],"has":[34],"traditionally":[35],"relied":[36],"on":[37,128],"mechanistic":[38,56,204],"approaches,":[39,190],"deep":[40,61],"learning":[41],"methods":[42,124],"have":[43],"recently":[44],"been":[45],"proposed":[46],"flexible,":[48],"data-driven":[49],"alternatives":[50],"with":[51,102,203],"often":[52],"superior":[53],"performance.":[54],"However,":[55],"models":[57],"tend":[58],"outperform":[60],"networks":[62],"when":[63],"data":[64],"distribution":[65,196],"shifts":[66,197],"induced":[68],"by":[69],"Domain":[72,118],"Adaptation":[73,119],"(DA)":[74],"techniques":[75],"could":[76],"help":[77],"address":[78,138],"this":[79,112],"limitation.":[80],"Yet,":[81],"unlike":[82,187],"standard":[83],"DA":[84,189],"settings,":[85],"change":[87],"induces":[88],"a":[89,97,151,157,169],"temporal":[90],"continuum":[91],"domains":[93],"and":[94,99,105,175,198],"involves":[95],"both":[96],"covariate":[98],"label":[100,139],"shift,":[101,140],"warmer":[103],"records":[104],"earlier":[106],"start":[107],"spring.":[109],"To":[110],"tackle":[111],"challenge,":[113],"we":[114,141,155,184],"introduce":[115],"Mid-feature":[116],"Rank-adversarial":[117],"(MIRANDA).":[120],"Whereas":[121],"conventional":[122,188],"adversarial":[123,143],"enforce":[125],"domain":[126],"invariance":[127],"final":[129],"latent":[130],"representations,":[131],"an":[132],"approach":[133],"that":[134,160],"does":[135],"not":[136],"explicitly":[137],"apply":[142],"regularization":[144],"intermediate":[146],"features.":[147],"Moreover,":[148],"instead":[149],"binary":[152],"domain-classification":[153],"objective,":[154],"employ":[156],"rank-based":[158],"objective":[159],"enforces":[161],"year-invariance":[162],"in":[163],"learned":[165],"representations.":[167],"On":[168],"country-scale":[170],"dataset":[171],"spanning":[172],"70":[173],"years":[174],"comprising":[176],"67,800":[177],"phenological":[178],"observations":[179],"5":[181],"tree":[182],"species,":[183],"demonstrate":[185],"that,":[186],"MIRANDA":[191],"improves":[192],"robustness":[193],"climatic":[195],"narrows":[199],"performance":[201],"gap":[202],"models.":[205]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-03T00:00:00"}