{"id":"W2962703591","doi":"10.1016/j.acha.2014.01.006","title":"A null space analysis of the <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.gif\" overflow=\"scroll\"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-synthesis method in dictionary-based compressed sensing","year":2014,"lang":"lv","type":"article","venue":"Applied and Computational Harmonic Analysis","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Defense Threat Reduction Agency; Defense Advanced Research Projects Agency","keywords":"Noise (video); Compressed sensing; SPARK (programming language); Algorithm; Property (philosophy); Computer science; Space (punctuation); Focus (optics); Stability (learning theory); Null (SQL); Mathematics; Artificial intelligence; Data mining; Machine learning; Image (mathematics); Programming language","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001294513,0.0005598788,0.0004389939,0.000883649,0.0008124632,0.000535504,0.0008894959,0.0009213123,0.0711327],"category_scores_gemma":[0.0003372389,0.0009601719,0.001657533,0.002392912,0.0006705242,0.0003528161,0.0006765251,0.0008424618,0.0001413584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001816457,"about_ca_system_score_gemma":0.0004997609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001888162,"about_ca_topic_score_gemma":0.0007860028,"domain_scores_codex":[0.9946491,0.0004123275,0.001243062,0.001134477,0.001541258,0.00101982],"domain_scores_gemma":[0.9945542,0.002435998,0.001194843,0.001271388,0.0001535924,0.0003900104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008241894,0.0002715049,0.00006459919,0.0004859106,0.01065484,0.0002243807,0.001375371,0.544128,0.005549241,0.3824828,0.0483465,0.005592671],"study_design_scores_gemma":[0.000515796,0.0001177785,0.0004773669,0.0002511812,0.004252688,0.00005009123,0.0004069233,0.5443943,0.4485548,0.0001845774,0.000288848,0.0005056888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6700326,0.0009327397,0.1222118,0.0007226847,0.0005363918,0.00004996831,0.0002377485,0.0002708344,0.2050053],"genre_scores_gemma":[0.9797185,0.000282952,0.01766071,0.0007597521,0.0003947926,0.000224626,0.0006621224,0.000256431,0.00004011167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4430056,"threshold_uncertainty_score":0.9992849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01520993801406338,"score_gpt":0.234852896131709,"score_spread":0.2196429581176456,"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."}}