{"id":"W3163146462","doi":"10.1109/tgrs.2021.3073159","title":"1-Bit Radar Imaging Based on Adversarial Samples","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Quantization (signal processing); Computer science; Radar imaging; Algorithm; Radar; Iterative reconstruction; Computer vision; Artificial intelligence; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0000889636,0.0001644287,0.0001468666,0.0001521804,0.0003126166,0.00009372299,0.00005989526,0.00005517967,0.000009744524],"category_scores_gemma":[0.000007571205,0.0001634419,0.00005996672,0.0002918108,0.0001074261,0.0001077818,0.000001286835,0.0002274192,0.000006062236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004281088,"about_ca_system_score_gemma":0.00004202715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001193664,"about_ca_topic_score_gemma":0.00003794774,"domain_scores_codex":[0.9990528,0.00003351307,0.0001398714,0.0003010267,0.0002086937,0.0002640939],"domain_scores_gemma":[0.9994916,0.00009643841,0.00001886632,0.0002621141,0.00005157398,0.00007943209],"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":[0.00001592505,0.00001660254,5.939146e-7,0.000009816171,0.000009144149,0.0001065937,0.0001006705,0.02383844,0.1011947,0.000006463111,0.0001003122,0.8746007],"study_design_scores_gemma":[0.0001743294,0.00002321899,0.00004246362,0.0001670168,0.00001671618,0.00006222358,0.00006736511,0.6996436,0.298332,0.0001582112,0.00113879,0.00017405],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02229225,0.00006512134,0.9747099,0.0002433036,0.0009117166,0.00006475444,0.000006751746,0.000440116,0.001266091],"genre_scores_gemma":[0.9005972,0.0000785468,0.09874675,0.0004286188,0.00006287431,6.343397e-8,0.000001316389,0.00002202945,0.00006262212],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.878305,"threshold_uncertainty_score":0.6664968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358950716816,"score_gpt":0.2217909647006742,"score_spread":0.2082014575325142,"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."}}