{"id":"W4403758818","doi":"10.1109/lnet.2024.3486260","title":"Multi-Modal Transformer and Reinforcement Learning-Based Beam Management","year":2024,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); University of Ottawa","funders":"Mitacs","keywords":"Transformer; Modal; Reinforcement; Reinforcement learning; Computer science; Structural engineering; Engineering; Artificial intelligence; Materials science; Electrical engineering; Voltage; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00119137,0.0007455358,0.000713004,0.0005456632,0.0002691528,0.0006255577,0.00124768,0.0006197149,0.001623174],"category_scores_gemma":[0.00275093,0.0002849045,0.0005989852,0.0005601464,0.0006582155,0.001143421,0.0008290333,0.001119779,0.0004130473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005858224,"about_ca_system_score_gemma":0.0007456846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003400463,"about_ca_topic_score_gemma":0.00391292,"domain_scores_codex":[0.9994441,0.0001530159,0.0000296251,0.0001465615,0.0001562953,0.00007032704],"domain_scores_gemma":[0.9991083,0.0004081217,0.0001252549,0.0001056555,0.0001961131,0.00005643389],"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":[0.000283888,0.0001890672,0.003093053,0.0001121057,0.00009714533,0.0001246512,0.0001217186,0.5967818,0.01797578,0.00828432,0.003607142,0.3693293],"study_design_scores_gemma":[0.000006949123,0.00002038859,0.0001310547,0.000002832618,0.000005120787,0.00001703166,0.000005276314,0.9961603,0.00181376,0.001514241,0.0003180776,0.000004953737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00933926,0.0002335124,0.9887692,0.0001199803,0.00003362308,0.00003543247,0.0000450178,0.0005518977,0.0008720898],"genre_scores_gemma":[0.7558857,0.0002995779,0.2402642,0.0003040266,0.00009075835,0.0001210047,0.0002241818,0.0001094538,0.002701134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003400463,"threshold_uncertainty_score":0.006761372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009480679318488065,"score_gpt":0.2109846926295449,"score_spread":0.2015040133110568,"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."}}