{"id":"W3036014493","doi":"10.48550/arxiv.2006.10187","title":"TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Point cloud; Autoencoder; Topology (electrical circuits); Computer science; Network topology; Cloud computing; Theoretical computer science; Graph; Artificial intelligence; Point (geometry); Deep learning; Mathematics; Geometry; Computer network; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000103435,0.0003136633,0.0003998398,0.0002809965,0.0001288617,0.00006331789,0.0005694621,0.0002668567,0.000216517],"category_scores_gemma":[0.00005987942,0.0003960155,0.0002850933,0.0005326351,0.0000546192,0.00008451365,0.0006052814,0.0007655883,0.0005346238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001762489,"about_ca_system_score_gemma":0.00005902863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000378816,"about_ca_topic_score_gemma":0.0001168515,"domain_scores_codex":[0.9984589,0.00005843308,0.0002375327,0.0008070921,0.00007935243,0.0003587232],"domain_scores_gemma":[0.9987878,0.00004696592,0.00005930516,0.0007364838,0.00008354921,0.0002858518],"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":[0.00001054334,0.0000174472,0.0004933609,0.0000504266,0.000213474,0.0001224299,0.0004589629,0.9906422,0.00008035822,0.003293694,0.00447349,0.0001435371],"study_design_scores_gemma":[0.0001848665,0.00002821533,0.0002653362,0.00004281334,0.0002161588,0.000002189539,0.0004193792,0.9891106,0.00007795452,0.007536931,0.001675406,0.0004401537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1917671,0.00006561991,0.7937714,0.0008256585,0.0006705353,0.0002103806,0.00004537856,0.0009775563,0.01166639],"genre_scores_gemma":[0.9943522,0.00009793011,0.001719816,0.0001337413,0.0002336318,0.000001972195,0.00004850733,0.0000542859,0.003357929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8025851,"threshold_uncertainty_score":0.9998492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05728296883477053,"score_gpt":0.2000661662235008,"score_spread":0.1427831973887303,"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."}}