{"id":"W3110737355","doi":"10.1109/lwc.2020.3043365","title":"Robust Cooperative Communication Optimization for Multi-UAV-Aided Vehicular Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Beihang University; Royal Society","keywords":"Computer science; Robustness (evolution); Mathematical optimization; Quality of service; Robust optimization; Parametric statistics; Minimax; Minification; Optimization problem; Distributed computing; Computer network; Algorithm; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001669916,0.00025585,0.0002662341,0.00008403218,0.0005442144,0.000138016,0.001116549,0.0001419109,0.00002090958],"category_scores_gemma":[0.00003093555,0.0002972247,0.000101127,0.0006018631,0.0001663166,0.0003617741,0.0001023775,0.0003365887,0.00001910402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001170265,"about_ca_system_score_gemma":0.00002061209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001776672,"about_ca_topic_score_gemma":0.00003442381,"domain_scores_codex":[0.9987249,0.0001333338,0.0004895346,0.0002630215,0.0001193432,0.0002698218],"domain_scores_gemma":[0.9978323,0.000193734,0.0001231905,0.001486189,0.0002358174,0.0001287582],"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":[0.00000847005,0.0000492414,0.00005174718,0.00002630293,0.00006280924,1.30691e-7,0.0004987186,0.9901969,0.002777231,0.0008700265,0.004130628,0.001327761],"study_design_scores_gemma":[0.0007209622,0.00001595075,0.00004867019,0.00003342494,0.00005235215,0.000001122342,0.0001269146,0.9951316,0.0007072393,0.000002768436,0.002839035,0.0003199235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006163886,0.000640109,0.9845451,0.006647131,0.00008741964,0.001106091,0.00004426828,0.0006217471,0.0001442261],"genre_scores_gemma":[0.6522828,0.001596613,0.3419726,0.001916576,0.00007684932,0.0009452124,0.001104677,0.00009465504,0.000009947798],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6461189,"threshold_uncertainty_score":0.999948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05399239494067296,"score_gpt":0.244701013867771,"score_spread":0.190708618927098,"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."}}