{"id":"W4300767623","doi":"10.48550/arxiv.1507.02250","title":"Privacy-Preserving Nonlinear Observer Design Using Contraction Analysis","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centers for Disease Control and Prevention","keywords":"Differential privacy; Computer science; Nonlinear system; Estimator; Population; Information privacy; Observer (physics); Noise (video); Data mining; Computer security; Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003681401,0.001840957,0.001529076,0.0005988699,0.0005202204,0.001586484,0.001318052,0.001687185,0.001916043],"category_scores_gemma":[0.01154385,0.0006674388,0.001214828,0.000355522,0.002241795,0.00144284,0.003567301,0.002281217,0.0003994105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118619,"about_ca_system_score_gemma":0.001123527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00145399,"about_ca_topic_score_gemma":0.0008808958,"domain_scores_codex":[0.9979969,0.001006071,0.0000847556,0.0004751034,0.000310313,0.0001269046],"domain_scores_gemma":[0.9939868,0.00397174,0.0005881927,0.0004238084,0.000817432,0.0002119061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002598733,0.00007978531,0.001136491,0.0002334763,0.0001069005,0.0002444517,0.0004115232,0.8388123,0.009947006,0.1172963,0.0009886894,0.03048327],"study_design_scores_gemma":[0.00001647106,0.00007050269,0.00005088842,0.000009858239,0.000008787864,0.00001839827,0.00001500939,0.9842802,0.0006803531,0.01447903,0.0003602971,0.00001019578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004699736,0.00006237035,0.993669,0.0001386143,0.00001930224,0.00003494835,0.00002014783,0.00004718317,0.001308745],"genre_scores_gemma":[0.8282776,0.0005004947,0.1629105,0.0002398637,0.00009912692,0.0007071116,0.0001893694,0.0001019922,0.006973925],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003681401,"threshold_uncertainty_score":0.01946938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1796860680922141,"score_gpt":0.2515936752735463,"score_spread":0.07190760718133216,"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."}}