{"id":"W4393372749","doi":"10.1109/ieeeconf59524.2023.10476893","title":"Residual Neural Networks for Learning the Full-Duplex Self-Interference","year":2023,"lang":"en","type":"article","venue":"","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); Memorial University of Newfoundland","funders":"","keywords":"Residual; Computer science; Artificial neural network; Interference (communication); Artificial intelligence; Deep learning; Telecommunications; Algorithm","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":[0.0002292262,0.000139064,0.0001189611,0.00005879108,0.0002777672,0.00008452166,0.0005548469,0.00006864264,0.00006296618],"category_scores_gemma":[0.00007938508,0.0001074571,0.00006299746,0.0003754547,0.00003432833,0.00009340705,0.0001479455,0.000370589,0.00009638253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003357347,"about_ca_system_score_gemma":0.000008556785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001467746,"about_ca_topic_score_gemma":0.0001194445,"domain_scores_codex":[0.9991585,0.00004958159,0.000203474,0.0001400846,0.00009145655,0.000356894],"domain_scores_gemma":[0.998691,0.000703168,0.00002352544,0.0004873381,0.00004790952,0.00004711123],"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":[0.000004575837,0.000004272548,0.00009790962,0.00001322227,0.00002868799,4.901751e-7,0.0003513081,0.9848796,0.0005829563,0.001002321,0.01032323,0.002711478],"study_design_scores_gemma":[0.0001446282,0.00003238827,0.0009120188,0.000009224734,0.00001166227,0.00000370957,0.0003701336,0.9914149,0.0001280804,0.000005418356,0.006830816,0.0001370446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482135,0.0002910115,0.04224209,0.002007078,0.0004827988,0.0005096307,0.000006240681,0.004904611,0.001343107],"genre_scores_gemma":[0.9966245,0.000143866,0.001539743,0.00004980038,0.0001590782,0.0001877085,0.00004812775,0.00005111843,0.001196107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04841102,"threshold_uncertainty_score":0.4381975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377758973945705,"score_gpt":0.2482696657416842,"score_spread":0.2244920760022271,"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."}}