{"id":"W4387870545","doi":"10.1109/icc45041.2023.10279094","title":"Active Sensing for Localization with Reconfigurable Intelligent Surface","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Codebook; Scalability; Frame (networking); Artificial neural network; Sequence (biology); User equipment; Real-time computing; Artificial intelligence; State (computer science); Telecommunications link; Position (finance); Base station; Algorithm; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027903,0.0005323676,0.0004261315,0.0002592964,0.0002226179,0.000480091,0.0008497629,0.0006738706,0.001008541],"category_scores_gemma":[0.0008970893,0.0002538522,0.0003852558,0.000305321,0.0007527665,0.001129126,0.0007037655,0.000706442,0.0002464355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004295318,"about_ca_system_score_gemma":0.0003311776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001430277,"about_ca_topic_score_gemma":0.00174144,"domain_scores_codex":[0.9997715,0.00005272073,0.000008632581,0.0000684913,0.0000704524,0.00002818288],"domain_scores_gemma":[0.9997194,0.0001485548,0.00003567288,0.00003999275,0.0000434524,0.00001284242],"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":[0.0002677919,0.00007956191,0.001168734,0.0002170565,0.00006589979,0.0002680924,0.0002835606,0.6071472,0.1015902,0.02675172,0.001579438,0.2605807],"study_design_scores_gemma":[0.000006219591,0.00005996089,0.0001544874,0.000006720449,0.000007530114,0.00003838247,0.00001926252,0.9872079,0.006666462,0.004624484,0.001199657,0.000008967116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01526516,0.0002874012,0.9824698,0.000113523,0.00004206716,0.00001083728,0.00001583792,0.000272997,0.00152238],"genre_scores_gemma":[0.8284923,0.0003157627,0.1675131,0.0001667793,0.00005309316,0.00005738468,0.00006435686,0.00004863018,0.003288664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001430277,"threshold_uncertainty_score":0.003373921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02561715278305318,"score_gpt":0.2525468721531204,"score_spread":0.2269297193700672,"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."}}