{"id":"W4384575251","doi":"10.23952/jnva.7.2023.4.02","title":"Sparse broadband beamformer design via proximal optimization Techniques","year":2023,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Adaptive beamformer; Beamforming; Computer science; Term (time); Optimization problem; Algorithm; Recursive least squares filter; Mathematical optimization; Regularization (linguistics); Least-squares function approximation; Adaptive filter; Mathematics; Telecommunications; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001027435,0.00009375071,0.0002686483,0.001034276,0.00007834727,0.0000756581,0.0002422209,0.00006032283,0.00003057262],"category_scores_gemma":[0.0001385973,0.00007821744,0.0001590243,0.002116864,0.00002811747,0.0007086197,0.00005556904,0.00009259247,0.000002047487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002889127,"about_ca_system_score_gemma":0.0001009948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001504715,"about_ca_topic_score_gemma":9.884617e-7,"domain_scores_codex":[0.9987086,0.00008026406,0.0005460932,0.0001359729,0.0004294991,0.00009955568],"domain_scores_gemma":[0.9984803,0.0001678897,0.000545312,0.0001300796,0.0006128522,0.00006361099],"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":[0.0001554215,0.0005237968,0.006619869,0.00006460652,0.002530709,0.00001743196,0.0008390114,0.9025896,0.007189433,0.0102378,0.001243048,0.0679893],"study_design_scores_gemma":[0.0001198937,0.0001514952,0.003893554,0.00001489529,0.000223194,0.00002174033,0.000004953151,0.9857092,0.006212465,0.003360421,0.0002009249,0.00008730077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001043239,0.00002222131,0.9981171,0.000503034,0.00004880939,0.00007939206,0.000004787392,0.00007948891,0.0001019649],"genre_scores_gemma":[0.05196801,0.00009599382,0.9477165,0.00004551694,0.00008979256,0.000004264629,0.00001285909,0.000005710561,0.00006137024],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08311959,"threshold_uncertainty_score":0.3189614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0208525109966032,"score_gpt":0.274930023273085,"score_spread":0.2540775122764818,"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."}}