{"id":"W2102564119","doi":"10.1109/92.974900","title":"Efficient exponentiation using weakly dual basis","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Exponentiation; Finite field; Polynomial basis; Basis (linear algebra); Mathematics; Modular exponentiation; Polynomial; Normal basis; Dual (grammatical number); Square (algebra); Topology (electrical circuits); Computer science; Discrete mathematics; Public-key cryptography; Combinatorics; Encryption; Galois theory; Mathematical analysis; Geometry","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"],"consensus_categories":[],"category_scores_codex":[0.0008058936,0.0003027132,0.0002875458,0.000605332,0.0007182563,0.0004213435,0.0004041838,0.000177288,0.00008456518],"category_scores_gemma":[0.000007858955,0.0002879924,0.0003049356,0.001252162,0.00005033825,0.0005531369,0.000004795162,0.0003174744,0.0001432194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001970962,"about_ca_system_score_gemma":0.00006629122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009866993,"about_ca_topic_score_gemma":0.0000992443,"domain_scores_codex":[0.9973698,0.0003877048,0.0005813757,0.0006104482,0.0006056855,0.0004450445],"domain_scores_gemma":[0.9985929,0.0001378625,0.0001933671,0.0007010308,0.0002204352,0.0001544543],"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":[0.0004314949,0.003324255,0.0002931192,0.0001257396,0.0003562361,0.00007849681,0.009318128,0.8029519,0.09781876,0.03854349,0.0007294332,0.04602901],"study_design_scores_gemma":[0.0006075065,0.0001699268,0.0001362567,0.0002150666,0.0000526364,0.0001359635,0.001010639,0.9759878,0.01960317,0.0001786858,0.001474627,0.000427729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2476346,0.00006100981,0.747655,0.00009829905,0.003347797,0.0003215121,0.00002763129,0.0003373449,0.0005167059],"genre_scores_gemma":[0.9970657,0.00001623938,0.002179104,0.00009312898,0.0001500446,0.00007841345,0.000006871585,0.00002389026,0.0003866525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.749431,"threshold_uncertainty_score":0.9999572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01941881574694752,"score_gpt":0.240251609791976,"score_spread":0.2208327940450285,"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."}}