{"id":"W4312050893","doi":"10.1109/jsac.2022.3221991","title":"Deep Learning-Enabled Semantic Communication Systems With Task-Unaware Transmitter and Dynamic Data","year":2022,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":232,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Transmitter; Task (project management); Artificial intelligence; Data transmission; Machine learning; Computer network; Channel (broadcasting)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001203693,0.0007254771,0.0007323491,0.0004312252,0.0004772477,0.0008564873,0.001730596,0.001033778,0.00133387],"category_scores_gemma":[0.003381184,0.000321159,0.0004141671,0.0006511736,0.0009379074,0.003125264,0.002177074,0.001858522,0.000450874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009120526,"about_ca_system_score_gemma":0.001275027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003828184,"about_ca_topic_score_gemma":0.003385166,"domain_scores_codex":[0.9994592,0.0001205201,0.00003842066,0.0001650012,0.0001397689,0.00007715505],"domain_scores_gemma":[0.9989667,0.0004127768,0.000115996,0.0002246976,0.0002253134,0.00005458735],"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":[0.0006720258,0.000364022,0.001718644,0.0002183067,0.0001299645,0.0003306742,0.0004500421,0.5389986,0.02405241,0.04188507,0.00529633,0.3858838],"study_design_scores_gemma":[0.00001007891,0.00003785853,0.0001172007,0.000006372362,0.000009856247,0.00002903625,0.00001986605,0.9834133,0.0047931,0.01091301,0.0006406152,0.000009636985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02814564,0.000238019,0.9687682,0.0002107095,0.00005693866,0.00003184322,0.0000755997,0.001080549,0.001392517],"genre_scores_gemma":[0.8160024,0.0002653038,0.1786405,0.000321919,0.00008120035,0.0001242213,0.0003534719,0.000106817,0.004104146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003828184,"threshold_uncertainty_score":0.007611811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02747285909964657,"score_gpt":0.2735198466886392,"score_spread":0.2460469875889926,"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."}}