{"id":"https://openalex.org/W7154380308","doi":"https://doi.org/10.48550/arxiv.2604.10821","title":"Slithering Through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging","display_name":"Slithering Through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7154380308","doi":"https://doi.org/10.48550/arxiv.2604.10821"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.10821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10821","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.2604.10821","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mohanty, Pinaki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohanty, Pinaki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133607601","display_name":"Ruqi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ruqi","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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.6053000092506409,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.6053000092506409,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.14069999754428864,"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/T12261","display_name":"Statistical Mechanics and Entropy","score":0.03590000048279762,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.6197999715805054},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.5809999704360962},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5001000165939331},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.47609999775886536},{"id":"https://openalex.org/keywords/discrete-time-and-continuous-time","display_name":"Discrete time and continuous time","score":0.4731000065803528},{"id":"https://openalex.org/keywords/ising-model","display_name":"Ising model","score":0.44920000433921814},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.44909998774528503},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.42640000581741333},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.37860000133514404}],"concepts":[{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.6197999715805054},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.5809999704360962},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5001000165939331},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48410001397132874},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.47609999775886536},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C51329190","wikidata":"https://www.wikidata.org/wiki/Q1076349","display_name":"Ising model","level":2,"score":0.44920000433921814},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.42640000581741333},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42309999465942383},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.412200003862381},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.37860000133514404},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.353300005197525},{"id":"https://openalex.org/C2987477279","wikidata":"https://www.wikidata.org/wiki/Q19885330","display_name":"Discrete variable","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3158999979496002},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3077999949455261},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2987000048160553},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2912999987602234},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C190669063","wikidata":"https://www.wikidata.org/wiki/Q5282043","display_name":"Discrete choice","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.10821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10821","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.2604.10821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10821","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":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.5767387747764587}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-dimensional":[0],"and":[1,45,116,125,146],"complex":[2],"discrete":[3,19,50,86],"distributions":[4],"often":[5],"exhibit":[6],"multimodal":[7,49],"behavior":[8],"due":[9],"to":[10,41,73,83,104],"inherent":[11],"discontinuities,":[12],"posing":[13],"significant":[14],"challenges":[15],"for":[16],"sampling.":[17],"Gradient-based":[18],"samplers,":[20],"while":[21,110],"effective,":[22],"frequently":[23],"become":[24],"trapped":[25],"in":[26,47,94],"local":[27],"modes":[28],"when":[29],"confronted":[30],"with":[31,89],"rugged":[32],"or":[33],"disconnected":[34,117],"energy":[35],"landscapes.":[36],"This":[37,98],"limits":[38],"their":[39],"ability":[40],"achieve":[42],"adequate":[43],"mixing":[44,75],"convergence":[46,124],"high-dimensional":[48],"spaces.":[51],"To":[52],"address":[53],"these":[54],"challenges,":[55],"we":[56],"propose":[57],"\\emph{Hyperbolic":[58],"Secant-squared":[59],"Gibbs-Sampling":[60],"(HiSS)},":[61],"a":[62,70,79,95,135],"novel":[63],"family":[64],"of":[65,123,138],"sampling":[66,87],"algorithms":[67],"that":[68,128],"integrates":[69],"\\emph{Metropolis-within-Gibbs}":[71],"framework":[72],"enhance":[74],"efficiency.":[76],"HiSS":[77,129],"leverages":[78],"logistic":[80],"convolution":[81],"kernel":[82],"couple":[84],"the":[85,90,101,106],"variable":[88,93,103],"continuous":[91],"auxiliary":[92,102],"joint":[96],"distribution.":[97],"design":[99],"allows":[100],"encapsulate":[105],"true":[107],"target":[108],"distribution":[109],"facilitating":[111],"easy":[112],"transitions":[113],"between":[114],"distant":[115],"modes.":[118],"We":[119],"provide":[120],"theoretical":[121],"guarantees":[122],"demonstrate":[126],"empirically":[127],"outperforms":[130],"many":[131],"popular":[132],"alternatives":[133],"on":[134],"wide":[136],"variety":[137],"tasks,":[139],"including":[140],"Ising":[141],"models,":[142],"binary":[143],"neural":[144],"networks,":[145],"combinatorial":[147],"optimization.":[148]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-15T00:00:00"}