{"id":"W2086164463","doi":"10.1109/tsp.2014.2304438","title":"Robust Beamforming by Linear Programming","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"University of Hong Kong; Defence Research and Development Canada; McMaster University; City University of Hong Kong","keywords":"Beamforming; Robustness (evolution); Linear programming; Computer science; Second-order cone programming; Ellipsoid; Mathematical optimization; Robust optimization; Algorithm; Norm (philosophy); Set (abstract data type); Gaussian; Mathematics; Convex optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000406346,0.0002644179,0.0002262072,0.00018626,0.0008654215,0.0005169874,0.0006195087,0.0001126839,0.0000203846],"category_scores_gemma":[0.000006193799,0.0002495568,0.0001032986,0.0007207394,0.00007284722,0.001405824,0.000004146556,0.0004201417,0.00005403814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006243735,"about_ca_system_score_gemma":0.0001091698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006816973,"about_ca_topic_score_gemma":0.000003133857,"domain_scores_codex":[0.9980487,0.00004830208,0.0003406339,0.0005773733,0.0004304199,0.0005545804],"domain_scores_gemma":[0.9991831,0.00008385342,0.0001499233,0.0002708317,0.0001233516,0.0001889224],"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":[0.000008703669,0.0001125143,0.000005048406,0.00006026155,0.000008815947,0.000002407894,0.0002606755,0.01321534,0.01351095,0.00001173494,0.0000553157,0.9727482],"study_design_scores_gemma":[0.0004703708,0.0001959923,0.000001784327,0.0002537362,0.00002055318,0.00004348754,0.00006658071,0.3288288,0.6629549,0.0002715315,0.006432057,0.0004601017],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002099651,0.0001439696,0.9957523,0.0003692321,0.0001766876,0.0001302059,0.000001495627,0.0005868368,0.0007396619],"genre_scores_gemma":[0.745938,0.000004073359,0.2531617,0.0004623602,0.0000997735,0.00002791544,0.000001169974,0.00002717839,0.0002777962],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9722881,"threshold_uncertainty_score":0.9999956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02176824992461652,"score_gpt":0.2401050113679314,"score_spread":0.2183367614433149,"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."}}