{"id":"W2963378461","doi":"10.1049/iet-spr.2018.5037","title":"Sharp sufficient condition of block signal recovery via <i>l</i> <sub>2</sub> / <i>l</i> <sub>1</sub> ‐minimisation","year":2019,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Minimisation (clinical trials); Signal recovery; Signal processing; Computer science; Mathematics; Minification; Algorithm; Mathematical optimization; Telecommunications; Statistics; Compressed sensing","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"],"consensus_categories":[],"category_scores_codex":[0.0002906804,0.0004209886,0.0004843322,0.0002655144,0.000141406,0.0001293448,0.0002516017,0.0002713718,0.00002679082],"category_scores_gemma":[0.00001430576,0.0004605241,0.0001790325,0.0004588706,0.00009261374,0.0005730581,0.00006999631,0.000405283,0.00009123843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001392455,"about_ca_system_score_gemma":0.00009900625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005923216,"about_ca_topic_score_gemma":0.000003285553,"domain_scores_codex":[0.9976639,0.00006457923,0.000679381,0.0004988512,0.0005695286,0.0005237592],"domain_scores_gemma":[0.9988446,0.000122968,0.0003122438,0.0003094983,0.0002892994,0.000121398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006110493,0.00008434668,0.00008835942,0.0002440977,0.00004118595,0.0000102694,0.0002352644,0.04280116,0.9131985,0.00001215404,0.001684775,0.04153879],"study_design_scores_gemma":[0.0003880968,0.0001872986,0.0002033516,0.0007016205,0.00006344273,0.00003670964,0.00006584174,0.1122129,0.8848032,0.0006900282,0.0001804316,0.0004670365],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9463788,0.0004916504,0.05021802,0.00003537535,0.0002610833,0.0004179644,0.00002750514,0.0007751372,0.001394478],"genre_scores_gemma":[0.9987002,0.0000650702,0.0005576187,0.0002305008,0.0002248544,0.00002556374,0.00008272125,0.0001069126,0.000006566063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06941175,"threshold_uncertainty_score":0.9997846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008403849006667102,"score_gpt":0.2038306158277703,"score_spread":0.1954267668211032,"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."}}