{"id":"W3113239520","doi":"10.1109/twc.2020.3022895","title":"Age of Information Driven Cache Content Update Scheduling for Dynamic Contents in Heterogeneous Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Age of Information Optimization","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cache; Scheduling (production processes); Markov decision process; Reinforcement learning; Distributed computing; Performance metric; Queue; Dynamic web page; Computer network; Markov process; Artificial intelligence; Mathematical optimization; Web service","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":[],"consensus_categories":[],"category_scores_codex":[0.0001538712,0.0001575727,0.0002405887,0.0002256982,0.0002313801,0.0001011373,0.001418228,0.000106248,0.000004014641],"category_scores_gemma":[0.00001994359,0.0001748598,0.0001202597,0.0005602931,0.00008927394,0.001452463,0.00002224408,0.00027452,0.00001628282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001130438,"about_ca_system_score_gemma":0.00005326291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002868499,"about_ca_topic_score_gemma":0.0001056532,"domain_scores_codex":[0.9986008,0.00009844868,0.0007568366,0.0001525417,0.000188112,0.000203242],"domain_scores_gemma":[0.9981756,0.0001866285,0.000324456,0.0009407047,0.0002758038,0.00009684135],"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":[0.00002991315,0.00009254686,0.00001314234,0.00002492853,0.00003094965,3.275951e-7,0.001581582,0.9770046,0.0003181835,0.00217794,0.000005836534,0.01872001],"study_design_scores_gemma":[0.0009174257,0.00008072179,0.00005768125,0.00005693799,0.0000126692,0.000002481957,0.0001862182,0.996514,0.001827763,0.00003429016,0.0001563647,0.0001534454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008113962,0.00003936788,0.9880511,0.002683444,0.000130258,0.0007349313,0.00004498648,0.0001374235,0.00006448908],"genre_scores_gemma":[0.889948,0.0002962995,0.1086524,0.0007794135,0.000003738578,0.0001861975,0.0001176308,0.00001087207,0.000005452159],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8818341,"threshold_uncertainty_score":0.7130576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04134386294706945,"score_gpt":0.2586858165439218,"score_spread":0.2173419535968523,"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."}}