{"id":"W2949955083","doi":"10.48550/arxiv.1703.10530","title":"Efficient optimization for Hierarchically-structured Interacting Segments (HINTS)","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Computer science; Maxima and minima; Segmentation; Margin (machine learning); Hierarchy; Tree (set theory); Path (computing); Algorithm; Artificial intelligence; Mathematical optimization; Mathematics; Machine learning; Combinatorics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009613053,0.001136664,0.000971965,0.0006448919,0.000484895,0.0007903098,0.001728418,0.002493767,0.004555975],"category_scores_gemma":[0.002398515,0.0007667808,0.0009157783,0.0007006163,0.001055168,0.001733497,0.001506104,0.001515709,0.001035831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062128,"about_ca_system_score_gemma":0.001236158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00320079,"about_ca_topic_score_gemma":0.003945261,"domain_scores_codex":[0.9996343,0.00009888382,0.0000145111,0.0001151037,0.00008614253,0.00005099928],"domain_scores_gemma":[0.9992595,0.0004656301,0.00007870363,0.0000650587,0.0000827137,0.00004836703],"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":[0.00009575555,0.0000453476,0.000354142,0.0001359893,0.00003472828,0.0001070396,0.0001181488,0.8647462,0.00788122,0.04563476,0.003760026,0.07708657],"study_design_scores_gemma":[0.000005206149,0.00001314386,0.00003241386,0.000004827903,0.000003160219,0.00001184041,0.000007357554,0.9896175,0.0005507133,0.00922738,0.0005222721,0.00000418574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006883708,0.00008983469,0.9915643,0.0001374262,0.00001179417,0.00002337819,0.00005033059,0.0004038989,0.0008353976],"genre_scores_gemma":[0.1711851,0.0001171108,0.8239334,0.0001894979,0.00002536522,0.0001830233,0.0003131985,0.0005232049,0.003530053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004555975,"threshold_uncertainty_score":0.01524127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03112255837815225,"score_gpt":0.2271573634862976,"score_spread":0.1960348051081454,"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."}}