{"id":"W1647263699","doi":"10.1109/isit.2015.7282682","title":"Optimality of Walrand-Varaiya type policies and approximation results for zero delay coding of Markov sources","year":2015,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Markov chain; Quantization (signal processing); Markov process; Mathematics; Coding (social sciences); Finite state; Mathematical optimization; Markov decision process; Zero (linguistics); Optimal control; Applied mathematics; Computer science; Algorithm; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004410716,0.00006726768,0.0001597922,0.00006970071,0.00001619982,0.00001023104,0.0001224407,0.00005791165,0.000001347362],"category_scores_gemma":[0.0001407455,0.00006380628,0.00002094231,0.00009922954,0.00005339282,0.000104251,0.00004750728,0.000042287,2.381625e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001651992,"about_ca_system_score_gemma":0.000009715221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001062746,"about_ca_topic_score_gemma":0.00001347695,"domain_scores_codex":[0.9994679,0.00002273415,0.0002820179,0.00006360603,0.00008648761,0.00007722461],"domain_scores_gemma":[0.9993539,0.0001264007,0.00007066656,0.0002440533,0.0001729546,0.00003202647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002831968,0.00067124,0.009008585,0.007334196,0.000795125,9.635427e-7,0.1069732,0.03097429,0.2596615,0.4563122,0.07695618,0.04848056],"study_design_scores_gemma":[0.002304403,0.0002993508,0.002322924,0.0002150197,0.00004926191,0.000006864773,0.001297659,0.378456,0.5968519,0.01080466,0.006956013,0.0004359307],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8386417,0.0004539153,0.1408533,0.0001341506,0.00003358285,0.0003714068,0.00004921348,0.0003173123,0.01914538],"genre_scores_gemma":[0.9224254,0.00009430684,0.07739974,0.000008938719,0.000006292035,0.00001024146,0.00001727201,0.000009488248,0.0000282723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4455076,"threshold_uncertainty_score":0.2601945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04641831960546004,"score_gpt":0.2883114087872172,"score_spread":0.2418930891817572,"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."}}