{"id":"W4386075162","doi":"10.1109/isit54713.2023.10206487","title":"Gaussian Broadcast Channels with Bidirectional Conferencing Decoders and Correlated Noises","year":2023,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaussian; Channel (broadcasting); Upper and lower bounds; Decoding methods; Lambda; Noise (video); Computer science; Gaussian noise; Topology (electrical circuits); Mathematics; Telecommunications; Algorithm; Computer network; Discrete mathematics; Physics; Combinatorics; Mathematical analysis; Image (mathematics); Optics; Artificial intelligence; Quantum mechanics","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":[0.00007129674,0.00009977373,0.0001021946,0.0001579089,0.0000696819,0.00004690299,0.00008800579,0.00008381636,0.00006409681],"category_scores_gemma":[0.000008738217,0.00008981237,0.00001246995,0.0003097775,0.00004318191,0.0001139434,0.00003741733,0.0001786257,0.00003956155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002355226,"about_ca_system_score_gemma":0.00001211443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006672916,"about_ca_topic_score_gemma":0.00008415845,"domain_scores_codex":[0.9995424,0.00001362266,0.000108801,0.0001026838,0.00009043527,0.0001420746],"domain_scores_gemma":[0.9996643,0.00006238469,0.00001410988,0.0001704499,0.00003425309,0.00005451277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002536467,0.0003396654,0.08639974,0.001318724,0.00207633,0.0001907908,0.03283403,0.2299519,0.09105247,0.1149993,0.1692588,0.2713246],"study_design_scores_gemma":[0.0008618089,0.0001067989,0.02054878,0.0002743053,0.00003043682,0.0001014901,0.001539174,0.8990084,0.05291494,0.000955888,0.02280552,0.0008524494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9192727,0.0002724164,0.02638723,0.0005955683,0.0001880487,0.0002856007,0.000009635856,0.008381424,0.04460743],"genre_scores_gemma":[0.9976379,0.0003912752,0.001530932,0.00003267456,0.00001394973,0.00002953146,0.00002170444,0.00002707975,0.000314965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6690565,"threshold_uncertainty_score":0.3662442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01634843439340376,"score_gpt":0.225672171244634,"score_spread":0.2093237368512302,"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."}}