{"id":"W2071710078","doi":"10.1109/icosp.2014.7015181","title":"Video reconstruction using inductive three dimensional sparsity measure","year":2014,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Compressed sensing; Computer science; Measure (data warehouse); Artificial intelligence; Projection (relational algebra); Sparse matrix; Domain (mathematical analysis); Iterative reconstruction; Computer vision; Property (philosophy); Sequence (biology); Pattern recognition (psychology); Algorithm; Mathematics; Data mining","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":[0.0006986993,0.000471434,0.0005104175,0.0009203114,0.0002386277,0.0006290633,0.0006825754,0.0005105248,0.0007404802],"category_scores_gemma":[0.00240014,0.0001474523,0.0004774004,0.000656886,0.000696846,0.001142433,0.001661145,0.0007023849,0.0001818329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004294737,"about_ca_system_score_gemma":0.0004197923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000659378,"about_ca_topic_score_gemma":0.0005541148,"domain_scores_codex":[0.9992736,0.0001888073,0.0000398481,0.0001132986,0.0003354444,0.00004891892],"domain_scores_gemma":[0.9988457,0.0004470708,0.0002450316,0.0001485279,0.0002340584,0.00007955998],"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":[0.0005881863,0.0002389373,0.004574904,0.0002702664,0.000117304,0.000326672,0.000222028,0.3379883,0.1062884,0.07872856,0.003777341,0.466879],"study_design_scores_gemma":[0.00001435682,0.0001759548,0.0006178887,0.00001321832,0.00001026575,0.0001963816,0.00003109357,0.9730676,0.01878359,0.00582456,0.001239438,0.0000255859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02903311,0.0001558068,0.9690353,0.0001231691,0.0000228652,0.00002573964,0.00005316,0.0001630798,0.001387773],"genre_scores_gemma":[0.6046926,0.0003884136,0.3930849,0.0001693975,0.00007708774,0.0001022064,0.000330987,0.00004047721,0.00111388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009203114,"threshold_uncertainty_score":0.00369513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02945187916653105,"score_gpt":0.2130621076192702,"score_spread":0.1836102284527392,"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."}}