{"id":"W4283805190","doi":"10.1609/aaai.v36i4.20280","title":"Undercover Boolean Matrix Factorization with MaxSAT","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université du Québec à Montréal","funders":"","keywords":"Maximum satisfiability problem; Logical matrix; Factorization; Matrix (chemical analysis); Mathematics; Implicant; Combinatorics; Discrete mathematics; Block (permutation group theory); Product (mathematics); Algorithm; Boolean function; Computer science; Boolean expression; Group (periodic table); Physics; Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.000335573,0.0002068437,0.0001911254,0.0001553987,0.0003511238,0.0005482562,0.002524733,0.00003793529,0.00007123298],"category_scores_gemma":[0.0001176096,0.0001545715,0.0000683855,0.0009933867,0.0001945219,0.001017587,0.0009333263,0.0003228002,0.0000228047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001109113,"about_ca_system_score_gemma":0.0001493319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002129503,"about_ca_topic_score_gemma":0.000002484785,"domain_scores_codex":[0.9980568,0.00001677827,0.0003620978,0.0004833233,0.0007867766,0.0002942365],"domain_scores_gemma":[0.998744,0.00004385837,0.00037832,0.0003288921,0.0004429965,0.00006197208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005364965,0.0001572111,0.000247134,0.00002878086,0.00001020684,9.995272e-7,0.000742768,0.0001090378,0.01750096,0.943445,0.0003004715,0.03740378],"study_design_scores_gemma":[0.00002631637,0.0004735898,0.00007685618,0.00009444712,0.000009786992,0.00001806777,0.0004874226,0.02211955,0.52895,0.4469073,0.0005396799,0.0002969238],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0533891,0.00003925814,0.8866123,0.006867917,0.0005340345,0.0009564958,0.00001910464,0.000871609,0.0507102],"genre_scores_gemma":[0.9877453,0.000005638802,0.01117319,0.0002391641,0.00002184847,0.00004988529,8.671546e-7,0.00001800869,0.0007460434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9343563,"threshold_uncertainty_score":0.6303244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05503056819948492,"score_gpt":0.2889159486216709,"score_spread":0.233885380422186,"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."}}