{"id":"W2141937678","doi":"10.1109/milcom.1992.244162","title":"Simulated annealing optimization in blind equalization","year":2003,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Maxima and minima; Blind equalization; Gradient descent; Simulated annealing; Mathematical optimization; Computer science; Stochastic gradient descent; Convergence (economics); Gradient method; Algorithm; Minification; Equalization (audio); Mathematics; Artificial intelligence; Artificial neural network; Decoding methods","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":[0.0004701003,0.00006331132,0.00006765244,0.0001954974,0.00003419657,0.0001147799,0.0001668719,0.00006263309,0.00004417932],"category_scores_gemma":[0.0001060949,0.00006338494,0.00001453644,0.000756619,0.000006754066,0.0005736857,0.00002344348,0.00005634853,0.000009756984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002675716,"about_ca_system_score_gemma":0.00003811441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001806519,"about_ca_topic_score_gemma":0.000009113429,"domain_scores_codex":[0.9992287,0.0001461325,0.0002035699,0.0001849939,0.0001238339,0.0001127502],"domain_scores_gemma":[0.9995973,0.00004535547,0.00005010521,0.0002065607,0.00007288051,0.00002776279],"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":[9.488035e-7,0.00001698242,0.0001734363,9.92505e-7,7.317144e-7,6.469353e-7,0.000289868,0.7205405,0.00008100612,0.2785869,0.00003497518,0.0002729952],"study_design_scores_gemma":[0.00030235,0.00001693443,0.0000371184,0.000005422469,4.720233e-7,0.000001183182,0.00001288811,0.9863283,0.008910946,0.003733906,0.0005626018,0.00008785447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005993996,0.00001226902,0.9810492,0.0001869101,0.00004223519,0.0001421148,8.080787e-8,0.00034052,0.0122327],"genre_scores_gemma":[0.7349362,0.000005183192,0.2644412,0.0004719782,0.000003258215,0.000002835553,0.00000427384,0.000004656505,0.0001304017],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7289423,"threshold_uncertainty_score":0.2584763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0290326412622156,"score_gpt":0.2962071686987487,"score_spread":0.2671745274365331,"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."}}