{"id":"W4386128311","doi":"10.1109/pn58661.2023.10222941","title":"Compressive real-time and ultrahigh-speed single-pixel imaging by swept aggregate patterns","year":2023,"lang":"en","type":"article","venue":"","topic":"Random lasers and scattering media","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Nature; Canadian Cancer Society; Canada Foundation for Innovation","keywords":"Computer science; Frame rate; Pixel; Computer vision; Artificial intelligence; Compressed sensing; Computer graphics (images); Computation; Frame (networking); Projection (relational algebra); Speedup; Iterative reconstruction; Computer hardware; Algorithm; Telecommunications","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":[0.0001398214,0.0002432841,0.0002238291,0.0002402163,0.0001451828,0.0004639514,0.0004085869,0.0002231962,0.0008568252],"category_scores_gemma":[0.0003542639,0.0001842569,0.0001199551,0.0002565132,0.0003731611,0.0006872719,0.0005209848,0.0004312114,0.0002762938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001687424,"about_ca_system_score_gemma":0.0001842717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000210214,"about_ca_topic_score_gemma":0.000457217,"domain_scores_codex":[0.999845,0.00001109582,0.000005332965,0.00002753323,0.00009936073,0.00001153616],"domain_scores_gemma":[0.9997869,0.00005707935,0.00005603499,0.00004886154,0.00003249707,0.00001859387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005405504,0.00002238944,0.0002654,0.00005124935,0.000005061627,0.00009703774,0.00007467125,0.002336591,0.9608888,0.001831584,0.0003279384,0.03404526],"study_design_scores_gemma":[0.00001076872,0.000119788,0.0007750925,0.000006520468,0.000006925438,0.0004565187,0.00002868128,0.0585272,0.934674,0.0007077672,0.004671654,0.00001501346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4499225,0.0006952282,0.5411741,0.0003463142,0.00009063754,0.0000725824,0.0001969903,0.001355818,0.00614588],"genre_scores_gemma":[0.7389808,0.0004521039,0.2570756,0.00006940956,0.000057786,0.00006752692,0.0001394743,0.000105682,0.003051675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008568252,"threshold_uncertainty_score":0.002866328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007060938387316426,"score_gpt":0.2147850398077716,"score_spread":0.2077241014204552,"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."}}