{"id":"W2949995719","doi":"10.48550/arxiv.1408.0828","title":"Pre-Reduction Graph Products: Hardnesses of Properly Learning DFAs and Approximating EDP on DAGs","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Combinatorics; Discrete mathematics; Mathematics; Disjoint sets; Deterministic finite automaton; Graph; Algorithm; Finite-state machine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003779232,0.001566326,0.002306346,0.0008370577,0.001127031,0.002867675,0.003834472,0.003319838,0.006494592],"category_scores_gemma":[0.02937998,0.001261813,0.003407708,0.001376805,0.003119997,0.01009214,0.003293765,0.007759817,0.0006896053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003245809,"about_ca_system_score_gemma":0.002600573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004843511,"about_ca_topic_score_gemma":0.005578502,"domain_scores_codex":[0.9956915,0.001235216,0.0002813796,0.001495718,0.0007698601,0.0005263438],"domain_scores_gemma":[0.9698384,0.02375177,0.0009845442,0.003865908,0.0007761489,0.000783148],"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":[0.001207095,0.0008383908,0.009187288,0.0009034046,0.0002832127,0.0003331668,0.0009517148,0.6649309,0.003895472,0.1940327,0.0159535,0.1074832],"study_design_scores_gemma":[0.00008712222,0.0001432143,0.0008090626,0.00003680343,0.00005431128,0.0001406531,0.0001651931,0.6544087,0.002290544,0.3397483,0.002091321,0.00002482675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2943638,0.001482418,0.6807216,0.007133777,0.0001574041,0.0002341607,0.002894341,0.002307825,0.01070479],"genre_scores_gemma":[0.8427605,0.000765602,0.1449363,0.001087215,0.0002421731,0.0003482195,0.003311868,0.0006324088,0.005915671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006494592,"threshold_uncertainty_score":0.02355015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06393270973561822,"score_gpt":0.1876688978864426,"score_spread":0.1237361881508244,"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."}}