{"id":"W1182960591","doi":"10.1016/j.comcom.2015.08.003","title":"A compression approach to reducing power consumption of TCAMs in regular expression matching","year":2015,"lang":"en","type":"article","venue":"Computer Communications","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Computer science; Regular expression; Throughput; Parallel computing; Content-addressable memory; Matching (statistics); Set (abstract data type); Artificial intelligence; 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":[0.0003619037,0.0006413315,0.0005045848,0.0009372664,0.0005383051,0.0006852389,0.001408324,0.0004793223,0.00529049],"category_scores_gemma":[0.002225813,0.0002863087,0.0004247546,0.001640177,0.0004283046,0.00128942,0.000595673,0.0006858017,0.0008487619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006034154,"about_ca_system_score_gemma":0.001051258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878302,"about_ca_topic_score_gemma":0.003851679,"domain_scores_codex":[0.9993681,0.0001032309,0.00004050887,0.00007095411,0.0003362455,0.00008095706],"domain_scores_gemma":[0.9990163,0.0003027567,0.00005878673,0.0003594567,0.0002387117,0.00002396614],"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":[0.0005273563,0.0002316508,0.0009565961,0.0002790264,0.00007713036,0.0003129929,0.0002457984,0.09884406,0.09178212,0.05356894,0.01076278,0.7424116],"study_design_scores_gemma":[0.00008176501,0.0004624204,0.0007277493,0.00006491937,0.00009390913,0.0006302378,0.0001035816,0.8059693,0.1386223,0.03228544,0.0209208,0.00003754902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06263138,0.001125586,0.9193288,0.00049385,0.0002103369,0.0002294821,0.0001976375,0.003514151,0.01226881],"genre_scores_gemma":[0.5765468,0.000758519,0.4082057,0.0005758239,0.0001878208,0.0002230089,0.000490124,0.0004506489,0.01256147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00529049,"threshold_uncertainty_score":0.01769841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06838275011301369,"score_gpt":0.2977764274054741,"score_spread":0.2293936772924604,"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."}}