{"id":"W1976603814","doi":"10.1109/acssc.2012.6489374","title":"2D signal compression via parallel compressed sensing with permutations","year":2012,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Permutation (music); Compression ratio; Zigzag; Algorithm; Computer science; Vectorization (mathematics); SIGNAL (programming language); Compression (physics); Data compression; Compressed sensing; Matrix (chemical analysis); Signal-to-noise ratio (imaging); Upper and lower bounds; Block (permutation group theory); Signal compression; Mathematics; Parallel computing; Computer vision; Combinatorics; Image (mathematics); Image processing; Telecommunications; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.00004507656,0.0001766761,0.0001657799,0.00007300245,0.0001094699,0.0000364138,0.00008159171,0.00006463967,0.000112806],"category_scores_gemma":[0.000001741341,0.0001404619,0.00003717645,0.0001114524,0.00004320541,0.0002236629,0.00002775611,0.0001501398,0.00004247855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002461795,"about_ca_system_score_gemma":0.000005615542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003516342,"about_ca_topic_score_gemma":0.000007218456,"domain_scores_codex":[0.9992216,0.00002749385,0.0001469352,0.0001148077,0.0001612982,0.0003279218],"domain_scores_gemma":[0.9995336,0.00005742642,0.00002546292,0.0002143388,0.00005250351,0.0001166545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009834556,0.0002405131,0.004791764,0.00007190138,0.0003114451,0.00005188237,0.002167853,0.3172511,0.6046408,0.002836013,0.0353821,0.03215634],"study_design_scores_gemma":[0.0005215475,0.00005440875,0.004426384,0.0001502086,0.0000587227,0.0001363979,0.000172933,0.7971801,0.1900417,0.0004984842,0.006166612,0.0005925013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09800286,0.0002182765,0.8811567,0.00003237872,0.0001076601,0.0001525741,0.000001709777,0.00158358,0.01874431],"genre_scores_gemma":[0.9335323,0.000007840182,0.06612156,0.0000858777,0.0001153599,0.000003824368,0.00001418541,0.00003963142,0.00007942371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8355294,"threshold_uncertainty_score":0.5727869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695018483292225,"score_gpt":0.2270734114299067,"score_spread":0.2101232265969844,"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."}}