{"id":"W2735138796","doi":"10.1145/3186564","title":"Natural Boundary Conditions for Smoothing in Geometry Processing","year":2018,"lang":"en","type":"preprint","venue":"ACM Transactions on Graphics","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Connaught Fund; National Science Foundation","keywords":"Hessian matrix; Smoothing; Boundary (topology); Mathematics; Laplace operator; Interpolation (computer graphics); Laplacian matrix; Geometry processing; Geometry; Polygon mesh; Boundary value problem; Shape optimization; Mathematical analysis; Smoothness; Norm (philosophy); Applied mathematics; Mathematical optimization; Computer science; Finite element method; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002137512,0.0009315938,0.0007380866,0.001285843,0.0009881817,0.002322953,0.001212426,0.002223224,0.007589213],"category_scores_gemma":[0.01351729,0.0005761731,0.0008328434,0.000851323,0.002679947,0.003493773,0.002526327,0.002503146,0.002019918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008452872,"about_ca_system_score_gemma":0.000878357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001639897,"about_ca_topic_score_gemma":0.001580319,"domain_scores_codex":[0.998427,0.0004409667,0.00009365284,0.0002818483,0.0006547131,0.0001017163],"domain_scores_gemma":[0.9967546,0.001658807,0.0002558451,0.000553227,0.000631893,0.000145669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009846154,0.00005235859,0.0005353131,0.0002597153,0.00002253123,0.0001497964,0.0002844964,0.1001327,0.01828948,0.8081605,0.005914223,0.06610048],"study_design_scores_gemma":[0.00002169489,0.00005215139,0.0003840603,0.00007071807,0.00001025871,0.0001770271,0.00007069763,0.5707345,0.008972231,0.4022002,0.01726062,0.00004582298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006314049,0.0003457798,0.9869397,0.0004237879,0.0001087811,0.0000364548,0.00007366887,0.0003233579,0.005434479],"genre_scores_gemma":[0.2656016,0.001020974,0.717999,0.0006554913,0.0003260965,0.0003102283,0.0005182811,0.001556253,0.01201205],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007589213,"threshold_uncertainty_score":0.02538848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01708905340003384,"score_gpt":0.2965298540734621,"score_spread":0.2794408006734282,"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."}}