{"id":"W2762128374","doi":"10.1109/infocom.2017.8057149","title":"Single restart with time stamps for computational offloading in a semi-online setting","year":2017,"lang":"en","type":"article","venue":"","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Job shop scheduling; Competitive analysis; Computer science; Scheduling (production processes); Heuristic; Task (project management); Computational complexity theory; Parallel computing; Server; Constant (computer programming); A priori and a posteriori; Asymptotically optimal algorithm; Online algorithm; Execution time; Time complexity; Distributed computing; Mathematical optimization; Algorithm; Upper and lower bounds; Mathematics; Artificial intelligence; Embedded system; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001641248,0.001106592,0.001551064,0.0004207593,0.0006136728,0.00124008,0.001687011,0.001131006,0.00212317],"category_scores_gemma":[0.005973774,0.0006717642,0.0007894303,0.0008054421,0.001198589,0.001848982,0.0009191933,0.001140285,0.0003383224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028304,"about_ca_system_score_gemma":0.001844881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005154122,"about_ca_topic_score_gemma":0.003394571,"domain_scores_codex":[0.9987798,0.0004707979,0.00005137013,0.0002579838,0.0002174444,0.0002226012],"domain_scores_gemma":[0.9958779,0.002860641,0.0004942834,0.0003566494,0.000175119,0.0002354386],"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":[0.0003271568,0.0001248255,0.0005021648,0.0001184222,0.00003653865,0.0001259027,0.0000562175,0.9675305,0.002551388,0.01075127,0.0006880445,0.01718744],"study_design_scores_gemma":[0.00001171757,0.00003310814,0.00006993764,0.000002508151,0.000004417759,0.00001677132,0.00000601596,0.995652,0.0003346112,0.003723503,0.0001422605,0.000003126744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1063007,0.0006569079,0.8880572,0.0004347033,0.00008825624,0.0001098209,0.0001096799,0.0005601507,0.003682585],"genre_scores_gemma":[0.9093331,0.0003306025,0.08790256,0.0001219783,0.00007270411,0.0001415551,0.0001263194,0.0001185452,0.001852719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005154122,"threshold_uncertainty_score":0.01024824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03434765795284483,"score_gpt":0.293111656975736,"score_spread":0.2587639990228912,"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."}}