{"id":"W4224928115","doi":"10.1109/icassp43922.2022.9747338","title":"Massive Unsourced Random Access Based on Bilinear Vector Approximate Message Passing","year":2022,"lang":"en","type":"article","venue":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Bilinear interpolation; Decoding methods; Message passing; Random access; Channel (broadcasting); Scheme (mathematics); Theoretical computer science; Coding (social sciences); Algorithm; Distributed computing; Computer network; Mathematics; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005262873,0.0005672466,0.0005570045,0.0005668491,0.0006621684,0.0008221531,0.000951406,0.0001569611,0.001567391],"category_scores_gemma":[0.0001145477,0.0005763333,0.0001611599,0.0004268184,0.0001816466,0.0002956995,0.0002593926,0.001305004,0.00001444991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003857519,"about_ca_system_score_gemma":0.0002028548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002276026,"about_ca_topic_score_gemma":0.0000022438,"domain_scores_codex":[0.9963735,0.000196428,0.0006328173,0.0008158757,0.001398579,0.0005827857],"domain_scores_gemma":[0.9984464,0.0003094005,0.0003021326,0.0003946948,0.0003239512,0.0002234167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002090993,0.0008101811,0.000395613,0.0002635495,0.0004301106,0.001060562,0.0005046859,0.4770001,0.4136584,0.001193173,0.01600053,0.08659215],"study_design_scores_gemma":[0.001531,0.0002933925,0.00009957811,0.0002192404,0.00006755563,0.00005238009,0.0002856179,0.9720069,0.0216133,0.001845824,0.001314441,0.0006708106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3053402,0.000664229,0.6446289,0.004053317,0.004217709,0.00222478,0.001448152,0.003621757,0.033801],"genre_scores_gemma":[0.9952733,0.00008200041,0.002667424,0.0007663155,0.0003988697,0.0001624802,0.0001532321,0.0001181655,0.0003782485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6899331,"threshold_uncertainty_score":0.9996688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04135109982443126,"score_gpt":0.2879525679200048,"score_spread":0.2466014680955735,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}