{"id":"W2896082286","doi":"10.3997/2214-4609.201800882","title":"Blind Deconvolution with Toeplitz-structured Sparse Total Least Squares Algorithm","year":2018,"lang":"en","type":"article","venue":"Proceedings","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Deconvolution; Blind deconvolution; Toeplitz matrix; Algorithm; Estimator; Wavelet; Compressed sensing; Computer science; Regularization (linguistics); Iterative method; Least-squares function approximation; Synthetic data; Mathematics; Mathematical optimization; Artificial intelligence; Statistics","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":[0.0002378974,0.0001755927,0.0001473915,0.0001571253,0.0001806665,0.0003773918,0.0004832953,0.00009605913,0.00002374183],"category_scores_gemma":[0.00002883143,0.0001478623,0.00003604793,0.0005010895,0.0001439497,0.001178476,0.0001499063,0.0001574619,0.00006747138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005106671,"about_ca_system_score_gemma":0.00006883754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002141584,"about_ca_topic_score_gemma":0.00000704573,"domain_scores_codex":[0.9987123,0.000008697516,0.0001919694,0.0004645644,0.000329007,0.0002934723],"domain_scores_gemma":[0.9990854,0.00001136854,0.0001324695,0.0001966011,0.0004664318,0.0001077759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003323685,0.0003543217,0.008906503,0.0001052647,0.0001443253,0.00001496813,0.028691,0.00001300633,0.01857984,0.4087544,0.02803168,0.5060723],"study_design_scores_gemma":[0.005280286,0.005230073,0.05718223,0.0003157012,0.00007963134,0.00130236,0.001202349,0.3606315,0.4443865,0.06726505,0.05425314,0.002871127],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2326974,0.00003818393,0.7561057,0.001015108,0.0001758169,0.0003882828,0.000002947236,0.001044797,0.008531736],"genre_scores_gemma":[0.749891,0.000002023887,0.2492167,0.0003173907,0.000224365,0.00002897124,0.000002073577,0.00001367575,0.0003037081],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5171936,"threshold_uncertainty_score":0.6029651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137802238907955,"score_gpt":0.2488251782530233,"score_spread":0.2350449543622278,"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."}}