{"id":"https://openalex.org/W7163864662","doi":"https://doi.org/10.48550/arxiv.2606.06554","title":"Multi-Scale Feature Attention Network for Polymer Classification Using Terahertz Spectroscopy","display_name":"Multi-Scale Feature Attention Network for Polymer Classification Using Terahertz Spectroscopy","publication_year":2026,"publication_date":"2026-06-04","ids":{"openalex":"https://openalex.org/W7163864662","doi":"https://doi.org/10.48550/arxiv.2606.06554"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.06554","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06554","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":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.2606.06554","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070291119","display_name":"R. Mahtani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahtani, Roshni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020570581","display_name":"Il\u00e1n Carretero","orcid":"https://orcid.org/0009-0003-9474-6515"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carretero, Il\u00e1n","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138108832","display_name":"Laura Monroy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Monroy, Laura","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090813134","display_name":"Aldo Moreno\u2010Oyervides","orcid":"https://orcid.org/0000-0002-8865-2518"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moreno-Oyervides, Aldo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138118638","display_name":"Oscar El\u00edas Bonilla-Manrique","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bonilla-Manrique, Oscar El\u00edas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5067714499","display_name":"Roc\u00edo del Amor","orcid":"https://orcid.org/0000-0002-5342-2093"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"del Amor, Roc\u00edo","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/T10752","display_name":"Terahertz technology and applications","score":0.9154999852180481,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10752","display_name":"Terahertz technology and applications","score":0.9154999852180481,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11111","display_name":"Spectroscopy and Laser Applications","score":0.034699998795986176,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.006599999964237213,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6046000123023987},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5907999873161316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5570999979972839},{"id":"https://openalex.org/keywords/terahertz-radiation","display_name":"Terahertz radiation","score":0.5152000188827515},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4413999915122986},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.4408000111579895},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42669999599456787}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.667900025844574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6067000031471252},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6046000123023987},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5907999873161316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5570999979972839},{"id":"https://openalex.org/C107816215","wikidata":"https://www.wikidata.org/wiki/Q647887","display_name":"Terahertz radiation","level":2,"score":0.5152000188827515},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.4408000111579895},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3977000117301941},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C32891209","wikidata":"https://www.wikidata.org/wiki/Q483666","display_name":"Spectroscopy","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.2976999878883362},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2922999858856201},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2612999975681305}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.06554","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06554","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":"doi:10.48550/arxiv.2606.06554","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06554","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5766409635543823,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reliable":[0],"polymer":[1,153],"identification":[2],"is":[3],"essential":[4],"for":[5,82,90,148],"ensuring":[6],"the":[7,62,70,114,119,138],"quality":[8],"and":[9,17,35,58,93,110,151],"safety":[10],"of":[11,49,64,133,140],"recycled":[12],"plastics,":[13],"yet":[14],"conventional":[15],"sorting":[16],"spectroscopic":[18],"techniques":[19,147],"often":[20],"struggle":[21],"to":[22,45,97,116],"deliver":[23],"robust":[24],"discrimination.":[25],"Terahertz":[26],"(THz)":[27],"spectroscopy":[28,143],"offers":[29],"a":[30,76,130],"promising":[31],"alternative,":[32],"providing":[33],"high-resolution":[34],"non-destructive":[36],"measurements.":[37],"In":[38],"this":[39],"work,":[40],"we":[41,68],"leverage":[42],"THz":[43,83,122,142],"signals":[44],"classify":[46],"12":[47],"types":[48],"polymers,":[50,53],"including":[51],"pure":[52],"multilayer":[54],"films,":[55],"commercial":[56],"blends,":[57],"biopolymers.":[59],"To":[60],"handle":[61],"complexity":[63],"these":[65],"spectral":[66],"signals,":[67],"propose":[69],"Multi-Scale":[71],"Feature":[72],"Attention":[73],"Network":[74],"(MSFAN),":[75],"novel":[77],"deep":[78,145],"learning":[79,146],"architecture":[80],"tailored":[81],"data.":[84],"The":[85],"framework":[86],"integrates":[87],"feature":[88],"gating":[89],"signal":[91],"recalibration":[92],"multi-scale":[94],"parallel":[95],"convolutions":[96],"capture":[98],"diverse":[99],"frequency":[100],"patterns.":[101],"These":[102],"features":[103],"are":[104],"further":[105],"refined":[106],"through":[107],"cross-feature":[108],"attention":[109,111],"pooling,":[112],"enabling":[113],"model":[115],"intrinsically":[117],"highlight":[118],"most":[120],"informative":[121],"regions.":[123],"MSFAN":[124],"consistently":[125],"outperforms":[126],"state-of-the-art":[127],"models,":[128],"reaching":[129],"classification":[131],"accuracy":[132],"85.2%.":[134],"This":[135],"study":[136],"demonstrates":[137],"potential":[139],"combining":[141],"with":[144],"effective,":[149],"scalable,":[150],"interpretable":[152],"classification.":[154]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-09T00:00:00"}