{"id":"W4318774098","doi":"10.1016/j.sigpro.2023.108939","title":"One-bit compressed sensing via total variation minimization method","year":2023,"lang":"en","type":"article","venue":"Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","keywords":"Compressed sensing; Piecewise; Algorithm; Thresholding; Minification; Computer science; Series (stratigraphy); Binary number; Bit (key); Total variation denoising; Mathematics; Artificial intelligence; Noise reduction; Image (mathematics); Arithmetic","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":[],"consensus_categories":[],"category_scores_codex":[0.0001757773,0.0001613401,0.0001911013,0.0001991028,0.0001627248,0.0001284636,0.00008215544,0.0001088228,0.00001983843],"category_scores_gemma":[0.0000146788,0.0001906799,0.00004473831,0.0005794154,0.00001886606,0.0002694006,0.0000372226,0.000161113,0.0000350706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004681496,"about_ca_system_score_gemma":0.00002124414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000167957,"about_ca_topic_score_gemma":0.000001331656,"domain_scores_codex":[0.9989728,0.00005252083,0.000243939,0.0002219433,0.0002315091,0.0002772752],"domain_scores_gemma":[0.9995667,0.00007655826,0.00006339455,0.0001313324,0.0001099262,0.00005209512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001020972,0.00001043144,0.000009133942,0.0000793997,0.00002757293,0.00001015611,0.0006150536,0.2467641,0.4795908,0.00003146505,0.0004851529,0.2723665],"study_design_scores_gemma":[0.0001296579,0.00001283342,0.0005066734,0.000157083,0.00002648359,0.0000112616,0.00003180387,0.8701795,0.1260687,0.002542497,0.0001288265,0.0002046108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01562178,0.0001097535,0.9792138,0.00005623412,0.0001233105,0.0001330955,0.000001927576,0.002861833,0.001878218],"genre_scores_gemma":[0.9081444,0.000006093282,0.09146854,0.00004478389,0.0002005405,0.000003860592,0.00003261971,0.00005603937,0.00004310696],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8925226,"threshold_uncertainty_score":0.7775701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02488369356063026,"score_gpt":0.2618927505724298,"score_spread":0.2370090570117995,"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."}}