{"id":"W1695582736","doi":"10.1007/3-540-45537-x_18","title":"Fast Normal Basis Multiplication Using General Purpose Processors","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cryptography and Residue Arithmetic","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Normal basis; Multiplication (music); Basis (linear algebra); Finite field; Multiplication algorithm; Flexibility (engineering); Software; Cryptography; Parallel computing; Field (mathematics); Binary number; Arithmetic; Algorithm; Galois theory; Mathematics; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003644103,0.0009960918,0.0006408726,0.000734993,0.0004435088,0.001089481,0.0006560683,0.0004189185,0.01281767],"category_scores_gemma":[0.0009252791,0.0003636964,0.0003741223,0.001217591,0.0004802739,0.002143246,0.0008464298,0.0009047127,0.00581125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003918862,"about_ca_system_score_gemma":0.0004357118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002245581,"about_ca_topic_score_gemma":0.0004617753,"domain_scores_codex":[0.9997242,0.00005962922,0.00001484453,0.00003753437,0.0001244022,0.00003929863],"domain_scores_gemma":[0.9996674,0.00009349522,0.00001439567,0.0001146538,0.00009179491,0.00001823596],"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":[0.0004691608,0.00008572914,0.0003330078,0.0003927259,0.00003923559,0.0002209654,0.0002307332,0.008578398,0.04151353,0.2194602,0.02269208,0.7059842],"study_design_scores_gemma":[0.0002261581,0.000654897,0.001060692,0.0001946868,0.0001296354,0.002308646,0.0001180425,0.1485379,0.1196494,0.516995,0.2100169,0.0001080757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04953779,0.003022421,0.8449912,0.0003048335,0.0006761927,0.0001075351,0.0001433583,0.005746626,0.09547006],"genre_scores_gemma":[0.3988314,0.003145918,0.4818449,0.0002170926,0.0003710483,0.0001367851,0.0006631236,0.0008365338,0.1139533],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01281767,"threshold_uncertainty_score":0.04287934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713141384492867,"score_gpt":0.2457914859349635,"score_spread":0.2286600720900349,"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."}}