{"id":"W3208277967","doi":"10.1364/sppcom.2021.spf2e.2","title":"Deep Neural Network Assisted Second-Order Perturbation-Based Nonlinearity Compensation","year":2021,"lang":"en","type":"article","venue":"OSA Advanced Photonics Congress 2021","topic":"Optical Network Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of British Columbia","funders":"","keywords":"Nonlinear system; Compensation (psychology); Quadrature amplitude modulation; Artificial neural network; Perturbation (astronomy); Computer science; QAM; Control theory (sociology); Optics; Physics; Telecommunications; Artificial intelligence; Bit error rate; Decoding methods; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001126403,0.0003163309,0.0004090056,0.00005803525,0.000151519,0.0001205814,0.0002463712,0.0002629519,0.001288939],"category_scores_gemma":[0.0002801882,0.0003555863,0.0001211446,0.0008226237,0.0001027799,0.0002160457,0.00009262425,0.0005865584,0.00004151856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001452505,"about_ca_system_score_gemma":0.00007812426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001358625,"about_ca_topic_score_gemma":0.0002972474,"domain_scores_codex":[0.9982446,0.00006377366,0.0004448124,0.0004341099,0.0002592235,0.000553412],"domain_scores_gemma":[0.998339,0.0004292411,0.00008633821,0.000655692,0.0003803642,0.0001093604],"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":[0.00001970374,0.00005061162,0.0005026736,0.00006912731,0.00005716768,0.00007782578,0.00001472873,0.9725713,0.002320779,0.002432393,0.0002430365,0.02164062],"study_design_scores_gemma":[0.0007447309,0.00002478856,0.0009668655,0.0000569201,0.00002761044,0.00001147467,0.00004940265,0.9732586,0.007893711,0.0008869244,0.0156745,0.0004044621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8562241,0.01559553,0.08314995,0.001809859,0.01136826,0.00156268,0.0002266182,0.00405669,0.02600637],"genre_scores_gemma":[0.7100165,0.0002223946,0.2877961,0.0004016366,0.0001272317,0.0001083387,0.0007133963,0.0001087552,0.0005056263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2046462,"threshold_uncertainty_score":0.9998896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00966972241928233,"score_gpt":0.2283442200067377,"score_spread":0.2186744975874554,"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."}}