{"id":"W2224568587","doi":"10.48550/arxiv.1601.01988","title":"Compressed sensing with local structure: uniform recovery guarantees for the sparsity in levels class","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; Alfred P. Sloan Foundation","keywords":"Compressed sensing; Restricted isometry property; Corollary; Signal recovery; Property (philosophy); Isometry (Riemannian geometry); Wavelet; Class (philosophy); Algorithm; Mathematics; Inverse; Inverse problem; Computer science; Focus (optics); Current (fluid); Applied mathematics; Mathematical optimization; Pure mathematics; Mathematical analysis; Artificial intelligence; Physics; Geometry","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":[0.002338492,0.0009306321,0.0008006899,0.000830606,0.0004216876,0.001314632,0.001192797,0.001119595,0.003563295],"category_scores_gemma":[0.0206855,0.0003732067,0.0006319823,0.001035652,0.002242004,0.004533208,0.003374601,0.003394277,0.0009015184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006583795,"about_ca_system_score_gemma":0.0006843533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005668595,"about_ca_topic_score_gemma":0.0004743128,"domain_scores_codex":[0.9982783,0.0005013455,0.00006610207,0.0002750397,0.0007197383,0.0001595728],"domain_scores_gemma":[0.9903467,0.006122258,0.0008094277,0.001712664,0.000715869,0.0002929968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003894523,0.0001011335,0.001715983,0.0004118939,0.00006757434,0.0002912456,0.0003434757,0.1309117,0.01666423,0.7169591,0.01037304,0.1217711],"study_design_scores_gemma":[0.00003270071,0.000154019,0.0005940936,0.00006244723,0.0000203234,0.0003708626,0.00009772839,0.6218883,0.007319884,0.3642972,0.005131447,0.00003092804],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01803565,0.000627305,0.973868,0.000761967,0.00005935991,0.00002999325,0.00018293,0.0002685562,0.006166226],"genre_scores_gemma":[0.7890851,0.002366161,0.1994209,0.001166693,0.0007600223,0.0001925379,0.000713543,0.000473839,0.005821236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003563295,"threshold_uncertainty_score":0.01236731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05838869275282034,"score_gpt":0.1788648547722384,"score_spread":0.1204761620194181,"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."}}