{"id":"https://openalex.org/W1973773065","doi":"https://doi.org/10.1142/s0129065791000200","title":"USE OF NEURAL NETS TO MEASURE THE \u03c4 POLARIZATION AND ITS BAYESIAN INTERPRETATION","display_name":"USE OF NEURAL NETS TO MEASURE THE \u03c4 POLARIZATION AND ITS BAYESIAN INTERPRETATION","publication_year":1991,"publication_date":"1991-01-01","ids":{"openalex":"https://openalex.org/W1973773065","doi":"https://doi.org/10.1142/s0129065791000200","mag":"1973773065"},"language":"en","primary_location":{"id":"doi:10.1142/s0129065791000200","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0129065791000200","pdf_url":null,"source":{"id":"https://openalex.org/S197665576","display_name":"International Journal of Neural Systems","issn_l":"0129-0657","issn":["0129-0657","1793-6462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Neural Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019003392","display_name":"L. Garrido","orcid":"https://orcid.org/0000-0001-8883-6539"},"institutions":[{"id":"https://openalex.org/I123044942","display_name":"Universitat Aut\u00f2noma de Barcelona","ror":"https://ror.org/052g8jq94","country_code":"ES","type":"education","lineage":["https://openalex.org/I123044942"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Llu\u00eds Garrido","raw_affiliation_strings":["Laboratori de F\u00edsica d\u2019Altes Energies, Universitat Aut\u00f2noma de Barcelona E-08193 Bellaterra (Barcelona), Spain"],"affiliations":[{"raw_affiliation_string":"Laboratori de F\u00edsica d\u2019Altes Energies, Universitat Aut\u00f2noma de Barcelona E-08193 Bellaterra (Barcelona), Spain","institution_ids":["https://openalex.org/I123044942"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073375207","display_name":"V. Gaitan","orcid":"https://orcid.org/0000-0003-4009-2061"},"institutions":[{"id":"https://openalex.org/I123044942","display_name":"Universitat Aut\u00f2noma de Barcelona","ror":"https://ror.org/052g8jq94","country_code":"ES","type":"education","lineage":["https://openalex.org/I123044942"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Vicens Gaitan","raw_affiliation_strings":["Laboratori de F\u00edsica d\u2019Altes Energies, Universitat Aut\u00f2noma de Barcelona E-08193 Bellaterra (Barcelona), Spain"],"affiliations":[{"raw_affiliation_string":"Laboratori de F\u00edsica d\u2019Altes Energies, Universitat Aut\u00f2noma de Barcelona E-08193 Bellaterra (Barcelona), Spain","institution_ids":["https://openalex.org/I123044942"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019003392"],"corresponding_institution_ids":["https://openalex.org/I123044942"],"apc_list":null,"apc_paid":null,"fwci":3.6237,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.9261582,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"02","issue":"03","first_page":"221","last_page":"228"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.8014000058174133,"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/T10320","display_name":"Neural Networks and Applications","score":0.8014000058174133,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.7813000082969666,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.7049773931503296},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6917762160301208},{"id":"https://openalex.org/keywords/helicity","display_name":"Helicity","score":0.6357758045196533},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.6322023272514343},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6110255122184753},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5101516842842102},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.47888725996017456},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47749564051628113},{"id":"https://openalex.org/keywords/polarization","display_name":"Polarization (electrochemistry)","score":0.44057339429855347},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.34733083844184875},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.2627420723438263},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.19263619184494019},{"id":"https://openalex.org/keywords/particle-physics","display_name":"Particle physics","score":0.12800860404968262},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.12385281920433044}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.7049773931503296},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6917762160301208},{"id":"https://openalex.org/C2775859700","wikidata":"https://www.wikidata.org/wiki/Q568855","display_name":"Helicity","level":2,"score":0.6357758045196533},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.6322023272514343},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6110255122184753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5101516842842102},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.47888725996017456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47749564051628113},{"id":"https://openalex.org/C205049153","wikidata":"https://www.wikidata.org/wiki/Q2698605","display_name":"Polarization (electrochemistry)","level":2,"score":0.44057339429855347},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.34733083844184875},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2627420723438263},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.19263619184494019},{"id":"https://openalex.org/C109214941","wikidata":"https://www.wikidata.org/wiki/Q18334","display_name":"Particle physics","level":1,"score":0.12800860404968262},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.12385281920433044},{"id":"https://openalex.org/C147789679","wikidata":"https://www.wikidata.org/wiki/Q11372","display_name":"Physical chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0129065791000200","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0129065791000200","pdf_url":null,"source":{"id":"https://openalex.org/S197665576","display_name":"International Journal of Neural Systems","issn_l":"0129-0657","issn":["0129-0657","1793-6462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Neural Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2407375987","https://openalex.org/W2505726097","https://openalex.org/W2950975704","https://openalex.org/W3049691116","https://openalex.org/W2010643158","https://openalex.org/W2106867672","https://openalex.org/W4310268968","https://openalex.org/W3081214562","https://openalex.org/W2753713401","https://openalex.org/W2053745677"],"abstract_inverted_index":{"We":[0,58],"have":[1,47,59],"tested":[2],"a":[3,9,39,64,85],"neural":[4,79],"network":[5],"(NN)":[6],"technique":[7],"as":[8],"method":[10],"to":[11,54,67,84],"determine":[12],"the":[13,16,20,42,55,69,73],"helicity":[14,49],"of":[15,72],"\u03c4":[17,31],"particles":[18],"in":[19,38],"process:":[21],"e":[22,24],"+":[23,30],"\u2212":[25,32],"\u2192(Z":[26],"0":[27],",":[28],"\u03b3*)\u2192\u03c4":[29],"\u2192(\u03c1\u03bd)(\u03c1\u03bd).":[33],"It":[34],"takes":[35],"into":[36],"account":[37],"natural":[40],"way":[41,66],"fact":[43],"that":[44,77],"both":[45],"taus":[46],"different":[48],"and":[50],"gives":[51],"efficiencies":[52],"comparable":[53],"Bayesian":[56,86],"method.":[57],"found":[60],"this":[61],"\u201cacademic\u201d":[62],"example":[63],"nice":[65],"introduce":[68],"analytical":[70],"interpretation":[71],"net":[74],"output,":[75],"showing":[76],"these":[78],"nets":[80],"techniques":[81],"are":[82],"equivalent":[83],"Decision":[87],"Rule.":[88]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
