{"id":"W4366563381","doi":"10.48550/arxiv.2304.09691","title":"DarSwin: Distortion Aware Radial Swin Transformer","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Computer science; Encoder; Artificial intelligence; Distortion (music); Computer vision; Pixel; Transformer; Algorithm; Physics; Bandwidth (computing); Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002645657,0.0002928892,0.000295557,0.0002629878,0.0001201596,0.0001412422,0.001692693,0.0003108677,0.00005080161],"category_scores_gemma":[0.00002481105,0.0003194665,0.0002636535,0.000439687,0.00009601749,0.0005074955,0.0004844945,0.0005759869,0.0002454816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002837709,"about_ca_system_score_gemma":0.0001451499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001273314,"about_ca_topic_score_gemma":0.00006970669,"domain_scores_codex":[0.9982391,0.00008203068,0.0002227902,0.0009384056,0.000150945,0.0003667261],"domain_scores_gemma":[0.998693,0.00004275952,0.0001141367,0.0008537393,0.0001340999,0.0001622314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002543725,0.0007374098,0.01604486,0.0006347034,0.0005484066,0.0009998566,0.001634484,0.01264447,0.002333445,0.9268003,0.0172203,0.02014745],"study_design_scores_gemma":[0.001803477,0.00104219,0.01556716,0.001231905,0.0003779047,0.000008409642,0.0002336097,0.491925,0.01809562,0.4602105,0.005749977,0.00375427],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04414229,0.00001636573,0.9458421,0.000363351,0.001177084,0.0003557491,0.00001549957,0.001256883,0.00683066],"genre_scores_gemma":[0.9974061,0.0001260918,0.0006005618,0.00005671729,0.0001073314,0.000002723452,0.00002246725,0.00002015422,0.001657893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9532638,"threshold_uncertainty_score":0.9999257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1786210083205381,"score_gpt":0.2111178859179526,"score_spread":0.03249687759741446,"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."}}