{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/min-max-scale","entry":"min_max_scale","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":6,"n_papers_ran":2,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":6,"n_samples_ran":2,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":4},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2410.24075","paper":"/paper/identifying-spatio-temporal-drivers-of","title":"Identifying Spatio-Temporal Drivers of Extreme Events","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HakamShams/Synthetic_Multivariate_Anomalies","path":"src/utils.py","file_url":"https://github.com/HakamShams/Synthetic_Multivariate_Anomalies/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53bd006f17418469","mcp_get_code":{"code_sha256":"53bd006f17418469"}},{"arxiv_id":"2410.09290","paper":"/paper/ranking-over-regression-for-bayesian","title":"Ranking over Regression for Bayesian Optimization and Molecule Selection","date":"2024-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gkwt/rbo","path":"rbbo/utils.py","file_url":"https://github.com/gkwt/rbo/blob/HEAD/rbbo/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89278db10e6e955a","mcp_get_code":{"code_sha256":"89278db10e6e955a"}},{"arxiv_id":"2404.13736","paper":"/paper/interval-abstractions-for-robust","title":"Interval Abstractions for Robust Counterfactual Explanations","date":"2024-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junqi-jiang/interval-abstractions","path":"intabs_multi/dataset.py","file_url":"https://github.com/junqi-jiang/interval-abstractions/blob/HEAD/intabs_multi/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbdcdeb2d88a985f","mcp_get_code":{"code_sha256":"dbdcdeb2d88a985f"}},{"arxiv_id":"2208.14878","paper":"/paper/formalising-the-robustness-of-counterfactual","title":"Formalising the Robustness of Counterfactual Explanations for Neural Networks","date":"2022-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junqi-jiang/robust-ce-inn","path":"expnns/preprocessor.py","file_url":"https://github.com/junqi-jiang/robust-ce-inn/blob/HEAD/expnns/preprocessor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ddf3e6c7f6112481","mcp_get_code":{"code_sha256":"ddf3e6c7f6112481"}},{"arxiv_id":"1903.07933","paper":"/paper/the-simpler-the-better-constant-velocity-for","title":"What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction","date":"2019-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elbuco1/AttentionMechanismsTrajectoryPrediction","path":"src/features/helpers/helpers.py","file_url":"https://github.com/elbuco1/AttentionMechanismsTrajectoryPrediction/blob/HEAD/src/features/helpers/helpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"093da9385c920cd8","mcp_get_code":{"code_sha256":"093da9385c920cd8"}},{"arxiv_id":"2022.findings-naacl.156","paper":null,"title":"arXiv:2022.findings-naacl.156","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MJ-Jang/beyond-distributional","path":"src/analysis/template_token.py","file_url":"https://github.com/MJ-Jang/beyond-distributional/blob/HEAD/src/analysis/template_token.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"429b38a0cd48ea76","mcp_get_code":{"code_sha256":"429b38a0cd48ea76"}}]}