{"id":"W2592025126","doi":"10.1109/tc.2017.2676763","title":"Efficient Composited de Bruijn Sequence Generators","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Standards and Technology; Cisco Systems","keywords":"De Bruijn sequence; Sequence (biology); Computer science; Application-specific integrated circuit; Algorithm; Stream cipher; Parallel computing; Discrete mathematics; Mathematics; Cryptography; Computer hardware","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":[0.0003898305,0.0005395115,0.0006011798,0.0006031871,0.0004261163,0.0007976969,0.000934674,0.0005326061,0.006363661],"category_scores_gemma":[0.001530158,0.0002986578,0.0003110019,0.0005855257,0.000417304,0.001273541,0.00106986,0.0006775486,0.002140992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007378039,"about_ca_system_score_gemma":0.001060812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005189565,"about_ca_topic_score_gemma":0.001088192,"domain_scores_codex":[0.9990687,0.000133553,0.00007679257,0.0001816224,0.000395786,0.0001436438],"domain_scores_gemma":[0.9992811,0.0002281077,0.00007181643,0.0001629222,0.000195133,0.00006091808],"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":[0.002242343,0.0003528712,0.001552125,0.0006247563,0.000073666,0.0007942314,0.0003270744,0.06120986,0.2560742,0.1053572,0.01443642,0.5569551],"study_design_scores_gemma":[0.0005469968,0.0009819615,0.001394648,0.00008664193,0.0000614698,0.001581138,0.00009132746,0.5441895,0.3417335,0.040068,0.06915189,0.0001128518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1354914,0.001133907,0.8297938,0.0003962238,0.0003418498,0.0004750133,0.0006070176,0.005535763,0.02622505],"genre_scores_gemma":[0.5951011,0.0002464835,0.3821203,0.0001781779,0.0000885231,0.0002849829,0.001015002,0.0001891306,0.02077637],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006363661,"threshold_uncertainty_score":0.02128857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02651520188252763,"score_gpt":0.2631694293226665,"score_spread":0.2366542274401388,"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."}}