{"id":"W4390204032","doi":"10.1109/tce.2023.3347229","title":"Accelerating Huffman Encoding Using 512-Bit SIMD Instructions","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Huffman coding; SIMD; Computer science; Canonical Huffman code; Parallel computing; Table (database); Initialization; Lookup table; Encoding (memory); Arithmetic; Arithmetic coding; Data compression; Algorithm; Decoding methods; Context-adaptive binary arithmetic coding; Operating system; Programming language; Mathematics; Code rate; Database; Systematic code; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002329805,0.00024133,0.0002098645,0.0003932139,0.001125526,0.0002887159,0.0006387547,0.0001187581,0.00004278851],"category_scores_gemma":[0.00000749174,0.0002486878,0.0001228024,0.001429269,0.00005371073,0.0009209508,0.00001440879,0.0005913177,0.0001536131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001836289,"about_ca_system_score_gemma":0.0002800169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006999107,"about_ca_topic_score_gemma":0.0000806184,"domain_scores_codex":[0.9979925,0.00007819258,0.0003392989,0.0005634012,0.0003593614,0.000667208],"domain_scores_gemma":[0.9988229,0.000166231,0.0001002356,0.0006867821,0.00009038568,0.0001334246],"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":[0.00003180215,0.0003321034,0.00007227785,0.00005248272,0.0002746614,0.00004489735,0.00105116,0.1257738,0.07806188,0.01189975,0.001879083,0.7805261],"study_design_scores_gemma":[0.000482952,0.0001180116,0.00003120787,0.00005575388,0.00003841826,0.0000872401,0.00006296204,0.9472429,0.04032603,0.001053037,0.01007061,0.0004308496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04720441,0.0001497971,0.9498361,0.0001773786,0.001278509,0.0001939013,0.00002181998,0.000765493,0.0003725843],"genre_scores_gemma":[0.9681711,0.0003889578,0.03079243,0.0001615513,0.000080294,0.00004069045,0.000009633658,0.00003886174,0.0003165219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9209666,"threshold_uncertainty_score":0.9999965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04897455348123104,"score_gpt":0.2875480179570735,"score_spread":0.2385734644758424,"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."}}