{"id":"W2534741016","doi":"10.4230/lipics.ccc.2017.23","title":"Augmented Index and Quantum Streaming Algorithms for DYCK(2)","year":2016,"lang":"en","type":"preprint","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Industry Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Leverage (statistics); Upper and lower bounds; Quantum; Computer science; Generalization; Algorithm; Quantum algorithm; Theoretical computer science; Mathematics; Quantum mechanics; Physics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009564435,0.001066572,0.001126098,0.000595783,0.0006933997,0.001275625,0.002487931,0.0007501302,0.000005593605],"category_scores_gemma":[0.0001564299,0.0008543585,0.0005562365,0.0002484501,0.0002173794,0.0008337005,0.003718835,0.001091666,0.00001754757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00019222,"about_ca_system_score_gemma":0.0002715511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000283036,"about_ca_topic_score_gemma":0.000008036192,"domain_scores_codex":[0.9950205,0.00006717373,0.001718912,0.00101478,0.0007265607,0.001452036],"domain_scores_gemma":[0.9956764,0.000541634,0.001184949,0.001659282,0.0004984245,0.0004393],"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":[0.0004282446,0.001031329,0.003649056,0.009588051,0.002104484,0.00004217479,0.02457559,0.00457517,0.0001380642,0.0565239,0.01432973,0.8830142],"study_design_scores_gemma":[0.003460576,0.0003577515,0.0004740143,0.001139053,0.00005959905,0.00007974124,0.0002001662,0.9464044,0.000223457,0.02245258,0.02397283,0.001175835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05680278,0.0002078388,0.9332176,0.001099659,0.003341944,0.002592694,0.001846682,0.000577456,0.0003132685],"genre_scores_gemma":[0.620129,0.0002057669,0.3722619,0.001810686,0.002014577,0.001124607,0.001413816,0.0003069572,0.000732692],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9418292,"threshold_uncertainty_score":0.9997612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01704133513518973,"score_gpt":0.2674432466132265,"score_spread":0.2504019114780368,"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."}}