{"id":"W2022401652","doi":"10.1109/lsp.2012.2205142","title":"Tractable Bound for Spherical Section Property in the Presence of Side-Information","year":2012,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Semidefinite programming; Relaxation (psychology); Upper and lower bounds; Compressed sensing; Semidefinite embedding; Linear programming; Constraint (computer-aided design); Dual (grammatical number); Property (philosophy); Mathematics; Computer science; Algorithm; Mathematical optimization; Computational complexity theory; Combinatorics; Quadratically constrained quadratic program; Quadratic programming; Mathematical analysis; Geometry","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":[0.0002239965,0.00008610331,0.00009594182,0.00004322041,0.00005430538,0.00005562252,0.0001076345,0.00004468827,0.000002151391],"category_scores_gemma":[0.00001259734,0.00005537595,0.0000301392,0.0001434427,0.00003499453,0.000857133,0.00000421482,0.0001245444,0.000001220745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003186749,"about_ca_system_score_gemma":0.00001118448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003772703,"about_ca_topic_score_gemma":0.000002079301,"domain_scores_codex":[0.9993944,0.00002147547,0.0001865802,0.00005383986,0.0001382922,0.0002054396],"domain_scores_gemma":[0.9997624,0.0000558666,0.00005182725,0.00007784076,0.00003375157,0.00001832934],"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":[0.00005124131,0.00005599044,0.0004457575,0.0002994424,0.00001341795,9.218485e-7,0.003937678,0.04405677,0.8675424,0.00000949207,0.01768049,0.06590637],"study_design_scores_gemma":[0.000459168,0.00009151951,0.002086182,0.000378211,0.00003812831,0.00003783337,0.000530418,0.3300993,0.6476911,0.0002465277,0.01793131,0.0004102471],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5589653,0.0001427613,0.4388092,0.0002803765,0.0002339028,0.0003713033,0.000001794295,0.0001801557,0.001015242],"genre_scores_gemma":[0.9967272,0.000002105402,0.002677101,0.0003535285,0.0001826643,0.00003956648,0.000002421975,0.00001040467,0.000005057067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4377618,"threshold_uncertainty_score":0.2258165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02191508015645217,"score_gpt":0.237234963508795,"score_spread":0.2153198833523428,"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."}}