{"id":"W2999263473","doi":"10.1109/tmc.2020.2967038","title":"The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Probabilistic logic; Popularity; Dynamic web page; Markov chain; Exploit; Markov decision process; Markov process; Mathematical optimization; Artificial intelligence; Machine learning; Computer security; Mathematics; Statistics","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":[0.001555067,0.0007951447,0.001021951,0.0004665729,0.0005288976,0.001035992,0.002289046,0.001081151,0.0009294027],"category_scores_gemma":[0.007512134,0.0006947208,0.0005021715,0.0007178817,0.0008171028,0.001744765,0.001057878,0.0009612293,0.0002691833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247185,"about_ca_system_score_gemma":0.002132813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003541837,"about_ca_topic_score_gemma":0.003146277,"domain_scores_codex":[0.9982817,0.0004843308,0.0001002971,0.0004498177,0.000474302,0.00020953],"domain_scores_gemma":[0.9969387,0.001213713,0.0004692064,0.0003697822,0.0008533409,0.0001553643],"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":[0.0001130182,0.00007507188,0.001289092,0.0001269446,0.00004486339,0.0001533484,0.000107468,0.9165619,0.01165309,0.02801106,0.001151765,0.04071236],"study_design_scores_gemma":[0.000008817599,0.00003235717,0.00007548695,0.000004264274,0.00001126895,0.00005300979,0.000007969874,0.9957098,0.001253602,0.002371268,0.0004651055,0.000007043041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00805473,0.0001032494,0.9906507,0.000097616,0.00001744305,0.00004904627,0.00001977654,0.0002149342,0.0007926477],"genre_scores_gemma":[0.8606416,0.0002932613,0.137294,0.000123414,0.00004037725,0.0002378221,0.00006349435,0.0000603145,0.001245765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003541837,"threshold_uncertainty_score":0.009048998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03543056412763705,"score_gpt":0.2323286326801966,"score_spread":0.1968980685525596,"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."}}