{"id":"W3032977126","doi":"10.1049/iet-spr.2019.0245","title":"Learning‐based design of random measurement matrix for compressed sensing with inter‐column correlation using copula function","year":2020,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Copula (linguistics); Computer science; Correlation; Pattern recognition (psychology); Compressed sensing; Random matrix; Artificial intelligence; Algorithm; Mathematics; Econometrics; Eigenvalues and eigenvectors; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001530202,0.001200387,0.0009655796,0.0006018534,0.0003485749,0.0006971742,0.001044312,0.0009448896,0.001535746],"category_scores_gemma":[0.005869271,0.0005276501,0.0006313791,0.0007643244,0.0008445583,0.001350625,0.001093737,0.001442797,0.000722048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005347701,"about_ca_system_score_gemma":0.001314862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001428335,"about_ca_topic_score_gemma":0.001851897,"domain_scores_codex":[0.9986916,0.0004964899,0.00006082376,0.0002934677,0.0003724199,0.00008522072],"domain_scores_gemma":[0.9980003,0.001099476,0.0002403902,0.0001826578,0.0004108039,0.00006630436],"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":[0.0002757718,0.0001497083,0.001064466,0.0004639183,0.0001502334,0.0002003464,0.000178562,0.6277053,0.03518537,0.05582152,0.003965016,0.2748398],"study_design_scores_gemma":[0.00001021215,0.00005167451,0.0001184764,0.00001149059,0.00001086072,0.00005906239,0.0000076332,0.9921645,0.003098538,0.003557743,0.0008960201,0.00001385314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00130853,0.0000764917,0.998251,0.00004191295,0.00001006579,0.00001910457,0.0000146541,0.00007594726,0.0002022381],"genre_scores_gemma":[0.2520922,0.0005799704,0.7445176,0.0003065664,0.0001107218,0.0003406459,0.0003401476,0.0001274437,0.001584728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001535746,"threshold_uncertainty_score":0.008092582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05881248745552867,"score_gpt":0.2476509266920826,"score_spread":0.1888384392365539,"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."}}