{"id":"W4392911444","doi":"10.1117/1.ap.6.2.026005","title":"Deep-learning-empowered synthetic dimension dynamics: morphing of light into topological modes","year":2024,"lang":"en","type":"article","venue":"Advanced Photonics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"European Regional Development Fund; Higher Education Discipline Innovation Project; National Natural Science Foundation of China; Natural Science Foundation of Tianjin City; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Morphing; Dimension (graph theory); Topology (electrical circuits); Dynamics (music); Computer science; Artificial intelligence; Mathematics; Physics; Pure mathematics; Acoustics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001975213,0.0002330698,0.0001397642,0.0001287239,0.000152938,0.0004296293,0.0003282727,0.0003818221,0.0009852762],"category_scores_gemma":[0.0005659334,0.0001613154,0.0001638847,0.0001030424,0.0007138746,0.0005996427,0.00049341,0.0006859121,0.0001465896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003905095,"about_ca_system_score_gemma":0.0002041081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003885232,"about_ca_topic_score_gemma":0.0007864247,"domain_scores_codex":[0.9999472,0.00001498031,0.00000192841,0.00001164976,0.00001551651,0.00000881743],"domain_scores_gemma":[0.9998312,0.00008075763,0.00003095637,0.00002594897,0.00001345025,0.00001761886],"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":[0.0001379401,0.0001144449,0.001377116,0.0001687658,0.00004035573,0.000149887,0.0001940416,0.6882756,0.1633013,0.1000278,0.00185893,0.04435382],"study_design_scores_gemma":[0.000006781306,0.0000231291,0.00009795978,0.000004806516,0.000001641624,0.00001473873,0.00001039675,0.9787641,0.01030012,0.009701853,0.001067344,0.000007213164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4904883,0.0004595719,0.4952441,0.000709923,0.0001321246,0.00003735639,0.0001353994,0.0008154738,0.01197785],"genre_scores_gemma":[0.9319147,0.0001386966,0.06624085,0.00008730315,0.000008863397,0.00003716868,0.00005407774,0.00006863163,0.001449609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009852762,"threshold_uncertainty_score":0.003296018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007422188971753689,"score_gpt":0.2770884840607912,"score_spread":0.2696662950890375,"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."}}