{"id":"W2998178972","doi":"10.1109/tit.2019.2962495","title":"Optimization of Heterogeneous Coded Caching","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Defence Research and Development Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cache; Optimization problem; Scheme (mathematics); Function (biology); Mathematical optimization; CPU cache; Perspective (graphical); Encoding (memory); Distributed computing; Algorithm; Parallel computing; Mathematics","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":[0.001566907,0.0007909649,0.001008475,0.0005159889,0.0004558486,0.001648178,0.001420757,0.0009327866,0.002338609],"category_scores_gemma":[0.006670818,0.0003856437,0.0003385373,0.0008187087,0.001321184,0.00187326,0.00131638,0.001030332,0.0002189088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002816719,"about_ca_system_score_gemma":0.001670491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005052641,"about_ca_topic_score_gemma":0.002942536,"domain_scores_codex":[0.9989865,0.0003852054,0.00003207222,0.0001576923,0.0002449645,0.0001935701],"domain_scores_gemma":[0.997524,0.001601564,0.0002080545,0.0002066265,0.0003394264,0.0001204171],"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":[0.00005329914,0.00002404168,0.0001946482,0.00004318767,0.00001129547,0.00005084695,0.00001834567,0.9508281,0.001053642,0.04020005,0.000712448,0.006809988],"study_design_scores_gemma":[0.000006796474,0.00001448774,0.00007788432,0.000005163182,0.000004162985,0.00001299498,0.00001072799,0.9896837,0.0003550218,0.009432321,0.0003920394,0.000004622375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07172047,0.0005392423,0.9114619,0.0004953639,0.00006640379,0.0001086818,0.0002138907,0.0001919375,0.015202],"genre_scores_gemma":[0.9174957,0.0003011429,0.07558236,0.0001176766,0.00003995356,0.0001258486,0.0001502072,0.00009186476,0.006095304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005052641,"threshold_uncertainty_score":0.02043682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006795102457840474,"score_gpt":0.1968553848828865,"score_spread":0.1900602824250461,"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."}}