{"id":"W4400221954","doi":"10.4230/lipics.esa.2024.100","title":"Fully Dynamic k-Means Coreset in Near-Optimal Update Time","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Agence Nationale de la Recherche; Austrian Science Fund; European Commission; Institute of Science and Technology Austria","keywords":"Computer science; Economics","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":[],"category_scores_codex":[0.0002213477,0.000309899,0.0003037415,0.0003767768,0.00009559531,0.0003020862,0.001477011,0.0003289794,0.0001838632],"category_scores_gemma":[0.00001528666,0.0003451079,0.0001879416,0.0007141568,0.00009934487,0.000319394,0.003279063,0.0009323479,0.004366448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002069478,"about_ca_system_score_gemma":0.0002602055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001069337,"about_ca_topic_score_gemma":0.00006050159,"domain_scores_codex":[0.9979588,0.0001208137,0.0002145359,0.001182386,0.0001155537,0.0004079599],"domain_scores_gemma":[0.9987094,0.00005482124,0.0001153403,0.0008935074,0.00007921125,0.0001476935],"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":[0.0001569577,0.0003397736,0.000771331,0.0004834164,0.0001887818,0.004126341,0.00108309,0.9356913,0.0005651622,0.02838491,0.02190905,0.006299895],"study_design_scores_gemma":[0.0002904149,0.00003408275,0.0003012572,0.0002937391,0.00002824101,0.00000689111,0.00002747533,0.9600655,0.00004643774,0.03713528,0.001380946,0.0003897783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8569095,0.0001294104,0.1364914,0.0007025486,0.001203553,0.0003921668,0.0001076988,0.0005448009,0.003518935],"genre_scores_gemma":[0.9923012,0.0001624863,0.003329322,0.0001273463,0.00003348492,0.000001886317,0.0001106064,0.00002443522,0.003909212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1353917,"threshold_uncertainty_score":0.9999001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02827598535030194,"score_gpt":0.1837184589021733,"score_spread":0.1554424735518714,"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."}}