{"id":"https://openalex.org/W7161131593","doi":"https://doi.org/10.48550/arxiv.2605.13248","title":"Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis","display_name":"Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161131593","doi":"https://doi.org/10.48550/arxiv.2605.13248"},"language":"en","primary_location":{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/47cbf6e1-ee90-43f4-95cd-5394a1a52ddd","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/47cbf6e1-ee90-43f4-95cd-5394a1a52ddd","pdf_url":"https://ris.utwente.nl/ws/files/536916549/2605.13248v1.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Cui, B, Song, X, Zhang, Y, Zhang, S, van Beijnum, B J F, Tabak, M & Wang, Y 2026 'Compact Latent Manifold Translation : A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis' ArXiv.org. https://doi.org/10.48550/arXiv.2605.13248","raw_type":"info:eu-repo/semantics/preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ris.utwente.nl/ws/files/536916549/2605.13248v1.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136151490","display_name":"Bo Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136095600","display_name":"Xiaowen Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Xiaowen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136099920","display_name":"Yaowen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yaowen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136185564","display_name":"Shunzhe Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shunzhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136145252","display_name":"B. J. F. van Beijnum","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"van Beijnum, B. J. F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059115485","display_name":"Monique Tabak","orcid":"https://orcid.org/0000-0001-5082-1112"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tabak, Monique","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136146341","display_name":"Ying Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ying","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":true,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.36230000853538513,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.36230000853538513,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11021","display_name":"ECG Monitoring and Analysis","score":0.2590000033378601,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.09070000052452087,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.5620999932289124},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4690000116825104},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.42989999055862427},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.41830000281333923},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.39980000257492065},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.3610999882221222},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.35740000009536743},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.33559998869895935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6503999829292297},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.5620999932289124},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5284000039100647},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4690000116825104},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.41830000281333923},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40639999508857727},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.35740000009536743},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3441999852657318},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33660000562667847},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.33559998869895935},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.2615000009536743}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/47cbf6e1-ee90-43f4-95cd-5394a1a52ddd","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/47cbf6e1-ee90-43f4-95cd-5394a1a52ddd","pdf_url":"https://ris.utwente.nl/ws/files/536916549/2605.13248v1.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Cui, B, Song, X, Zhang, Y, Zhang, S, van Beijnum, B J F, Tabak, M & Wang, Y 2026 'Compact Latent Manifold Translation : A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis' ArXiv.org. https://doi.org/10.48550/arXiv.2605.13248","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2605.13248","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13248","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,"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":"pmh:oai:ris.utwente.nl:openaire_cris_publications/47cbf6e1-ee90-43f4-95cd-5394a1a52ddd","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/47cbf6e1-ee90-43f4-95cd-5394a1a52ddd","pdf_url":"https://ris.utwente.nl/ws/files/536916549/2605.13248v1.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Cui, B, Song, X, Zhang, Y, Zhang, S, van Beijnum, B J F, Tabak, M & Wang, Y 2026 'Compact Latent Manifold Translation : A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis' ArXiv.org. https://doi.org/10.48550/arXiv.2605.13248","raw_type":"info:eu-repo/semantics/preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7161131593.pdf","grobid_xml":"https://content.openalex.org/works/W7161131593.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"analysis":[1],"of":[2,188,202],"physiological":[3,123],"time":[4],"series,":[5],"such":[6],"as":[7,129],"electrocardiograms":[8],"(ECG)":[9],"and":[10,18,47,156],"photoplethysmograms":[11],"(PPG),":[12],"is":[13],"persistently":[14],"hindered":[15],"by":[16,120],"modality":[17,40],"frequency":[19],"gaps":[20,75],"stemming":[21],"from":[22,38,164],"heterogeneous":[23,97],"recording":[24],"devices.":[25],"Existing":[26],"foundation":[27,216],"models":[28],"typically":[29],"rely":[30],"on":[31],"continuous":[32],"latent":[33,103,132],"spaces,":[34],"which":[35],"frequently":[36],"suffer":[37],"severe":[39],"entanglement,":[41],"lack":[42],"high-fidelity":[43],"cross-frequency":[44,172],"generative":[45],"capacity,":[46],"impose":[48],"high":[49],"computational":[50,204],"costs":[51],"that":[52,72,139],"prohibit":[53],"edge-device":[54],"deployment.":[55],"In":[56,147],"this":[57],"paper,":[58],"we":[59,84],"propose":[60],"Compact":[61],"Latent":[62,112],"Manifold":[63],"Translation":[64],"(CLMT),":[65],"a":[66,77,86,110,130,192,200,209],"highly":[67],"parameter-efficient":[68],"(0.09B)":[69],"unified":[70],"framework":[71],"bridges":[73],"these":[74,115],"through":[76],"novel":[78],"two-stage":[79],"discrete":[80,102,116,194],"translation":[81,134],"paradigm.":[82],"First,":[83],"introduce":[85],"Universal":[87],"Tokenizer":[88],"utilizing":[89],"Hierarchical":[90],"Residual":[91],"Vector":[92],"Quantization":[93],"(RVQ)":[94],"to":[95,167,175],"decouple":[96],"signals":[98,198],"into":[99],"isolated,":[100],"well-structured":[101],"manifolds,":[104],"effectively":[105],"preventing":[106],"inter-modality":[107],"interference.":[108],"Second,":[109],"Context-Prompted":[111],"Translator":[113],"maps":[114],"tokens":[117],"across":[118],"modalities":[119],"integrating":[121],"static":[122],"priors,":[124],"reframing":[125],"complex":[126],"signal":[127],"synthesis":[128],"pure":[131],"sequence":[133],"task.":[135],"Extensive":[136],"evaluations":[137],"demonstrate":[138],"our":[140,206],"0.09B":[141],"model":[142],"significantly":[143],"outperforms":[144],"massive":[145],"baselines.":[146],"cross-modal":[148],"PPG-to-ECG":[149],"synthesis,":[150],"it":[151,177],"resolves":[152],"temporal":[153],"phase":[154],"drift":[155],"dramatically":[157],"improves":[158],"the":[159,203],"clinical":[160],"R-peak":[161],"detection":[162],"F1-score":[163],"0.37":[165],"(baseline)":[166],"0.83.":[168],"Furthermore,":[169],"in":[170],"extreme":[171],"super-resolution":[173],"(25Hz":[174],"100Hz),":[176],"successfully":[178],"recovers":[179],"high-frequency":[180],"diagnostic":[181],"landmarks,":[182],"achieving":[183],"an":[184],"unprecedented":[185],"Pearson":[186],"correlation":[187],"0.9956.":[189],"By":[190],"learning":[191],"universal":[193],"language":[195],"for":[196,212],"biological":[197],"with":[199],"fraction":[201],"footprint,":[205],"approach":[207],"sets":[208],"new":[210],"trajectory":[211],"edge-deployable,":[213],"multi-modal":[214],"medical":[215],"models.":[217]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-15T00:00:00"}
