{"id":"W1588766067","doi":"10.1109/iscas.1994.409111","title":"Associative memory architecture for video compression","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Content-addressable memory; Architecture; Computer architecture; Data compression; Very-large-scale integration; Associative property; Modularity (biology); Vector quantization; Quantization (signal processing); Content-addressable storage; Codec; Artificial intelligence; Frame (networking); Computer engineering; Theoretical computer science; Computer hardware; Embedded system; Algorithm; Artificial neural network; Mathematics","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":[0.0001113238,0.0002228339,0.0001823732,0.0003903154,0.0003231924,0.0004859327,0.0008577123,0.0004221347,0.005423161],"category_scores_gemma":[0.0002909179,0.00008128506,0.0001321451,0.0006244194,0.0002358341,0.0009824857,0.0002885721,0.0004790777,0.00111101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003009404,"about_ca_system_score_gemma":0.0003599613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008557792,"about_ca_topic_score_gemma":0.001624321,"domain_scores_codex":[0.9999185,0.000009468422,0.000006199808,0.000017152,0.00003711292,0.00001162629],"domain_scores_gemma":[0.9999083,0.00001790932,0.000007330229,0.00001719133,0.00004346981,0.000005833322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002771088,0.000150958,0.0005627652,0.0004503989,0.0000671778,0.0003531792,0.00009719645,0.01996035,0.1510957,0.1093446,0.01339881,0.7042417],"study_design_scores_gemma":[0.0001629206,0.0008909355,0.001301953,0.0001363881,0.0002373671,0.00199791,0.0001354386,0.5081855,0.2566015,0.07463698,0.1556207,0.00009249595],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1118898,0.01287986,0.8219115,0.0009069756,0.001077351,0.0001382958,0.0002649473,0.006057904,0.04487346],"genre_scores_gemma":[0.7504672,0.004649117,0.2156962,0.0009619578,0.0003972588,0.0001614327,0.0004994569,0.0001026625,0.02706467],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005423161,"threshold_uncertainty_score":0.01814228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02592593717819626,"score_gpt":0.2777082642219807,"score_spread":0.2517823270437845,"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."}}