{"id":"W2946173218","doi":"10.1109/sampta45681.2019.9030842","title":"Reconstructing high-dimensional Hilbert-valued functions via compressed sensing","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Oak Ridge National Laboratory; Office of Science; UT-Battelle; Battelle; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Hilbert space; Reproducing kernel Hilbert space; Parameterized complexity; Mathematics; Applied mathematics; Norm (philosophy); Compressed sensing; Algorithm; Pure mathematics","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":["insufficient_payload"],"category_scores_codex":[0.00239313,0.0005062605,0.0009506477,0.0005048274,0.0002897452,0.0004342441,0.0008687751,0.0005490069,0.001790414],"category_scores_gemma":[0.002463449,0.0003821593,0.0003462586,0.0004481866,0.0001649719,0.0001595506,0.001271533,0.001080238,0.001739569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000151436,"about_ca_system_score_gemma":0.0003914794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003196233,"about_ca_topic_score_gemma":0.00001208119,"domain_scores_codex":[0.9948158,0.0003247114,0.001318756,0.001444564,0.001593294,0.0005029158],"domain_scores_gemma":[0.9938331,0.002836755,0.0005381865,0.001800252,0.0007723793,0.0002193169],"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":[0.00003248237,0.00003770435,0.000327776,0.00003975634,0.0001192217,0.00001375561,0.0000700541,0.9529805,0.0007133072,0.00199364,0.02944238,0.01422941],"study_design_scores_gemma":[0.0003720405,0.00002466006,0.0003560363,0.0001558725,0.00006716219,0.00008682783,0.00007454154,0.9529237,0.0002187136,0.0436206,0.001515764,0.0005840798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02160761,0.000120733,0.9575723,0.0004821627,0.01087014,0.0005512546,0.00005127896,0.0003600826,0.00838443],"genre_scores_gemma":[0.7574592,0.000001978625,0.2267834,0.0002594432,0.0005121835,0.000008260267,0.00007623561,0.00005984249,0.01483951],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7358516,"threshold_uncertainty_score":0.999863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08693105610771029,"score_gpt":0.3072872394297375,"score_spread":0.2203561833220272,"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."}}