{"id":"W1636696700","doi":"10.1109/iscas.1999.780103","title":"VLSI concentric partitioning using interior point quadratic programming","year":2003,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quadratic programming; Very-large-scale integration; Quadratic equation; Mathematical optimization; Computer science; Fixed point; Point (geometry); Moment (physics); Boundary (topology); Algorithm; Mathematics; Parallel computing; Embedded system; Geometry; Mathematical analysis","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":[0.0006892354,0.0007505446,0.0008736786,0.0004738374,0.0003515949,0.0006503716,0.001077233,0.0005561752,0.002528539],"category_scores_gemma":[0.001145117,0.0004417361,0.0005611427,0.000629887,0.0005233143,0.0008065615,0.0009196632,0.0007306106,0.0008296991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005709282,"about_ca_system_score_gemma":0.0008443153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001591659,"about_ca_topic_score_gemma":0.002207995,"domain_scores_codex":[0.9994895,0.0001668567,0.00001767524,0.00006325427,0.0002190449,0.00004372254],"domain_scores_gemma":[0.9996597,0.0001649964,0.00003972872,0.00003893299,0.00008107518,0.00001573023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003961785,0.00003013941,0.0001297227,0.00009149937,0.00001934416,0.00004782887,0.00005221065,0.8902133,0.00507839,0.01756624,0.001698262,0.08503348],"study_design_scores_gemma":[0.000008343583,0.00002954462,0.00002862034,0.000004546488,0.000003029394,0.00002459818,0.000006969064,0.9923217,0.0009162825,0.004799194,0.001853866,0.000003381751],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0027489,0.0001039408,0.9949964,0.00004257281,0.00001101923,0.00001829254,0.00001382978,0.0001401083,0.001924817],"genre_scores_gemma":[0.1418177,0.0003053848,0.8527865,0.00008123426,0.00003632487,0.0001652837,0.0001432366,0.0001619366,0.004502418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002528539,"threshold_uncertainty_score":0.008458853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01850059242367785,"score_gpt":0.2361099254195097,"score_spread":0.2176093329958319,"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."}}