{"id":"W2950634431","doi":"10.48550/arxiv.1008.1366","title":"Efficient Dealiased Convolutions without Padding","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Padding; Computer science; Convolution (computer science); Decoupling (probability); Computation; Fast Fourier transform; Fourier transform; Parallel computing; Computational science; Algorithm; Mathematics; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008214724,0.001182924,0.001090566,0.0009566019,0.0007663044,0.001839053,0.001958968,0.0008169195,0.01393294],"category_scores_gemma":[0.004064314,0.0006194241,0.00095005,0.001139918,0.0008729321,0.002170988,0.001940161,0.001457942,0.007569368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006753316,"about_ca_system_score_gemma":0.001025667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008629066,"about_ca_topic_score_gemma":0.0016883,"domain_scores_codex":[0.9991924,0.00009160747,0.00007538854,0.000121917,0.0004233843,0.00009527933],"domain_scores_gemma":[0.998764,0.0004070897,0.0001021219,0.0003752277,0.0002968983,0.0000545357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000283015,0.0001337316,0.0007184831,0.0003143835,0.0001001927,0.0002573121,0.0002715204,0.05507578,0.03642089,0.1544621,0.01052392,0.7414387],"study_design_scores_gemma":[0.00004447289,0.00008951384,0.0002982956,0.00003695493,0.00003578422,0.0004631074,0.00006931707,0.8335728,0.06619832,0.07820398,0.02092788,0.00005966111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002054383,0.00004674792,0.9952275,0.00002008342,0.00002912641,0.00002276227,0.00003560986,0.001280547,0.001283235],"genre_scores_gemma":[0.04643398,0.0001373822,0.9468632,0.00005361887,0.00003891013,0.0001381292,0.0002385941,0.0007176719,0.005378478],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01393294,"threshold_uncertainty_score":0.04661036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026362704294891,"score_gpt":0.2147010992791457,"score_spread":0.1120648288496566,"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."}}