{"id":"https://openalex.org/W4415536619","doi":"https://doi.org/10.1145/3746027.3758146","title":"Financial Models meets Generative Art: Black-Scholes-Inspired Concept Blending in Text-to-Image Diffusion","display_name":"Financial Models meets Generative Art: Black-Scholes-Inspired Concept Blending in Text-to-Image Diffusion","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415536619","doi":"https://doi.org/10.1145/3746027.3758146"},"language":"en","primary_location":{"id":"doi:10.1145/3746027.3758146","doi":"https://doi.org/10.1145/3746027.3758146","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3758146","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"ACM International Conference on Multimedia (ACM MM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2405.13685","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101761284","display_name":"Divya Kothandaraman","orcid":"https://orcid.org/0000-0002-6276-4968","observed_orcids":["https://orcid.org/0000-0002-6276-4968"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Divya Kothandaraman","raw_affiliation_strings":["University of Maryland College Park, College Park, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-6276-4968","affiliations":[{"raw_affiliation_string":"University of Maryland College Park, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102878981","display_name":"Ming C. Lin","orcid":"https://orcid.org/0000-0003-3736-6949","observed_orcids":["https://orcid.org/0000-0003-3736-6949"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming Lin","raw_affiliation_strings":["University of Maryland College Park, College Park, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-3736-6949","affiliations":[{"raw_affiliation_string":"University of Maryland College Park, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004194238","display_name":"Dinesh Manocha","orcid":"https://orcid.org/0000-0001-7047-9801","observed_orcids":["https://orcid.org/0000-0001-7047-9801"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dinesh Manocha","raw_affiliation_strings":["University of Maryland College Park, College Park, MD, USA"],"raw_orcid":"https://orcid.org/0000-0001-7047-9801","affiliations":[{"raw_affiliation_string":"University of Maryland College Park, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":1.034,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.81892053,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"12209","last_page":"12217"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":1.0,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":1.0,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0,"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":0.0,"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/text-to-image-diffusion-models","display_name":"text-to-image diffusion models","score":0.9620000123977661},{"id":"https://openalex.org/keywords/black-scholes-model","display_name":"Black-Scholes model","score":0.9399999976158142},{"id":"https://openalex.org/keywords/text-conditioning","display_name":"text conditioning","score":0.609000027179718},{"id":"https://openalex.org/keywords/diffusion-denoising","display_name":"diffusion denoising","score":0.8709999918937683},{"id":"https://openalex.org/keywords/non-equilibrium-thermodynamics","display_name":"non-equilibrium thermodynamics","score":0.8130000233650208},{"id":"https://openalex.org/keywords/markovian-dynamics","display_name":"Markovian dynamics","score":0.8180000185966492}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5112000107765198},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4147000014781952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41040000319480896},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.37860000133514404},{"id":"https://openalex.org/C23925645","wikidata":"https://www.wikidata.org/wiki/Q5449731","display_name":"Financial modeling","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.29499998688697815},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2791999876499176},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27630001306533813},{"id":"https://openalex.org/C76969082","wikidata":"https://www.wikidata.org/wiki/Q486902","display_name":"Mathematical model","level":2,"score":0.27219998836517334}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3746027.3758146","doi":"https://doi.org/10.1145/3746027.3758146","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3758146","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"ACM International Conference on Multimedia (ACM MM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2405.13685","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.13685","pdf_url":"https://arxiv.org/pdf/2405.13685","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2405.13685","doi":"https://doi.org/10.48550/arxiv.2405.13685","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.13685","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":"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2405.13685","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.13685","pdf_url":"https://arxiv.org/pdf/2405.13685","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4344294614","display_name":null,"funder_award_id":"W911NF2110026","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415536619.pdf","grobid_xml":"https://content.openalex.org/works/W4415536619.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W2151065060","https://openalex.org/W2519536754","https://openalex.org/W2766527293","https://openalex.org/W2934375473","https://openalex.org/W3034431451","https://openalex.org/W3102061158","https://openalex.org/W3216352822","https://openalex.org/W3217030260","https://openalex.org/W4286611278","https://openalex.org/W4307076412","https://openalex.org/W4308163867","https://openalex.org/W4310428424","https://openalex.org/W4312824283","https://openalex.org/W4312933868","https://openalex.org/W4321392876","https://openalex.org/W4353113390","https://openalex.org/W4378446358","https://openalex.org/W4380993193","https://openalex.org/W4385270985","https://openalex.org/W4386057725","https://openalex.org/W4386072096","https://openalex.org/W4386076403","https://openalex.org/W4386076425","https://openalex.org/W4386083141","https://openalex.org/W4387688000","https://openalex.org/W4389116046","https://openalex.org/W4390872477","https://openalex.org/W4390873084","https://openalex.org/W4390873875","https://openalex.org/W4392538976","https://openalex.org/W4402726956","https://openalex.org/W4402753507"],"related_works":["https://openalex.org/W7169790141","https://openalex.org/W4398795924","https://openalex.org/W7133185348","https://openalex.org/W7196944467","https://openalex.org/W7160168470","https://openalex.org/W7196932732","https://openalex.org/W7133195264","https://openalex.org/W7164164983","https://openalex.org/W4415538077","https://openalex.org/W7196960967"],"abstract_inverted_index":{"We":[0,120],"introduce":[1],"a":[2,81],"novel":[3],"approach":[4,126],"for":[5,66,84,189],"concept":[6,85,99,137,181],"blending":[7,86,100,138,182],"in":[8,59,76,183],"pretrained":[9],"text-to-image":[10,180],"diffusion":[11,29,54],"models,":[12,55],"aiming":[13],"to":[14,46,133],"generate":[15],"images":[16],"at":[17,195],"the":[18,36,50,63,90,94,122,187],"intersection":[19],"of":[20,93,124],"multiple":[21,162],"text":[22,42,135,160,165],"prompts.":[23],"At":[24],"each":[25],"time":[26],"step":[27],"during":[28],"denoising,":[30],"our":[31,47,125],"algorithm":[32,83,101],"forecasts":[33],"predictions":[34],"w.r.t.":[35],"generated":[37],"image":[38],"and":[39,62,131,148,167],"makes":[40],"informed":[41],"conditioning":[43],"decisions.":[44],"Central":[45],"method":[48],"is":[49,102,193],"unique":[51],"analogy":[52],"between":[53,73],"which":[56],"are":[57],"rooted":[58],"non-equilibrium":[60],"thermodynamics,":[61],"Black-Scholes":[64,95],"model":[65],"financial":[67],"option":[68],"pricing.":[69],"By":[70],"drawing":[71],"parallels":[72],"key":[74],"variables":[75],"both":[77],"domains,":[78],"we":[79],"derive":[80],"robust":[82],"that":[87,174],"capitalizes":[88],"on":[89],"Markovian":[91],"dynamics":[92],"framework.":[96],"Our":[97,171],"text-based":[98],"data-efficient,":[103],"meaning":[104],"it":[105,112,129],"does":[106],"not":[107],"need":[108],"additional":[109],"training.":[110],"Furthermore,":[111],"operates":[113],"without":[114],"human":[115],"intervention":[116],"or":[117],"hyperparameter":[118],"tuning.":[119],"highlight":[121],"benefits":[123],"by":[127],"comparing":[128],"qualitatively":[130],"quantitatively":[132],"other":[134],"based":[136],"techniques,":[139],"including":[140],"linear":[141],"interpolation,":[142],"alternating":[143],"prompts,":[144],"step-wise":[145],"prompt":[146,150,166],"switching,":[147],"CLIP-guided":[149],"selection":[151],"across":[152],"various":[153],"scenarios":[154],"such":[155],"as":[156],"single":[157],"object":[158],"per":[159,164],"prompt,":[161],"objects":[163,168],"against":[169],"backgrounds.":[170],"work":[172],"shows":[173],"financially":[175],"inspired":[176],"techniques":[177],"can":[178],"enhance":[179],"generative":[184],"AI,":[185],"paving":[186],"way":[188],"broader":[190],"innovation.":[191],"Code":[192],"available":[194],"https://github.com/divyakraman/BlackScholesDiffusion2024.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2025-10-25T00:00:00"}
