{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003168024,0.0002805303,0.0004618738,0.0002073436,0.000192394,0.0001282228,0.0006488165,0.0002083894,0.0002021055],"category_scores_gemma":[0.00001875124,0.0003441453,0.0004176356,0.0006836124,0.00006695731,0.000282085,0.0008476241,0.0005139332,0.00002390706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002327206,"about_ca_system_score_gemma":0.0003168692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003424329,"about_ca_topic_score_gemma":0.00002773427,"domain_scores_codex":[0.9985451,0.0001829439,0.0002345438,0.0006437457,0.00009583368,0.0002978194],"domain_scores_gemma":[0.9982767,0.00007606714,0.0003864224,0.0007140771,0.0003677348,0.0001790077],"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":[0.00002544749,0.00007640432,0.06202564,0.00001278145,0.0009442514,0.00001168199,0.0001312997,0.9301919,0.00002883531,0.006425743,0.00005634653,0.00006971179],"study_design_scores_gemma":[0.0003034456,0.00001111926,0.002651195,0.00003314765,0.0009308844,8.590003e-8,0.0002430224,0.9706705,0.00002129206,0.02460169,0.0001667857,0.0003668595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.618547,0.00001517939,0.3800907,0.00001483738,0.0002454132,0.0001914542,0.00007967665,0.00003822707,0.0007774283],"genre_scores_gemma":[0.9964529,0.00001333027,0.002506074,0.00001563925,0.0002637054,6.953196e-7,0.0001436894,0.000023225,0.000580792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3779058,"threshold_uncertainty_score":0.9999011,"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."}}