{"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/get-sensitivity","entry":"get_sensitivity","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":3,"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":8,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":11,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":0,"unverified":5},"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":"2405.01719","paper":"/paper/inherent-trade-offs-between-diversity-and","title":"Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks","date":"2024-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"socialfoundations/benchbench","path":"benchbench/measures/cardinal.py","file_url":"https://github.com/socialfoundations/benchbench/blob/HEAD/benchbench/measures/cardinal.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a977e4f402ff472","mcp_get_code":{"code_sha256":"2a977e4f402ff472"}},{"arxiv_id":"2405.01719","paper":"/paper/inherent-trade-offs-between-diversity-and","title":"Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks","date":"2024-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"socialfoundations/benchbench","path":"benchbench/measures/ordinal.py","file_url":"https://github.com/socialfoundations/benchbench/blob/HEAD/benchbench/measures/ordinal.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7a936e8599af8beb","mcp_get_code":{"code_sha256":"7a936e8599af8beb"}},{"arxiv_id":"2402.15853","paper":"/paper/rauca-a-novel-physical-adversarial-attack-on","title":"RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation","date":"2024-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SeRAlab/Robust-and-Accurate-UV-map-based-Camouflage-Attack","path":"src/Image_Segmentation/evaluation.py","file_url":"https://github.com/SeRAlab/Robust-and-Accurate-UV-map-based-Camouflage-Attack/blob/HEAD/src/Image_Segmentation/evaluation.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3fdc610e589e29bd","mcp_get_code":{"code_sha256":"3fdc610e589e29bd"}},{"arxiv_id":"2210.13012","paper":"/paper/cmu-net-a-strong-convmixer-based-medical","title":"CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network","date":"2022-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fenghetan9/cmu-net","path":"src/metrics.py","file_url":"https://github.com/fenghetan9/cmu-net/blob/HEAD/src/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eeb3b3a1000e9560","mcp_get_code":{"code_sha256":"eeb3b3a1000e9560"}},{"arxiv_id":"2110.13029","paper":"/paper/fair-enough-searching-for-sufficient-measures","title":"Fair Enough: Searching for Sufficient Measures of Fairness","date":"2021-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"repoanonymous/fairness_metrics","path":"src/get_sensitivity.py","file_url":"https://github.com/repoanonymous/fairness_metrics/blob/HEAD/src/get_sensitivity.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a43f64d93bc3450","mcp_get_code":{"code_sha256":"2a43f64d93bc3450"}},{"arxiv_id":"1804.03999","paper":"/paper/attention-u-net-learning-where-to-look-for","title":"Attention U-Net: Learning Where to Look for the Pancreas","date":"2018-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wjcheon/MedicalImageSegmentation_Pytorch","path":"evaluation.py","file_url":"https://github.com/wjcheon/MedicalImageSegmentation_Pytorch/blob/HEAD/evaluation.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dd0ff9a848c9079a","mcp_get_code":{"code_sha256":"dd0ff9a848c9079a"}},{"arxiv_id":"1804.03999","paper":"/paper/attention-u-net-learning-where-to-look-for","title":"Attention U-Net: Learning Where to Look for the Pancreas","date":"2018-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lbareiro/Image_Segmentation-master","path":"evaluation.py","file_url":"https://github.com/lbareiro/Image_Segmentation-master/blob/HEAD/evaluation.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6a33f9a26b1965ca","mcp_get_code":{"code_sha256":"6a33f9a26b1965ca"}},{"arxiv_id":"1804.03999","paper":"/paper/attention-u-net-learning-where-to-look-for","title":"Attention U-Net: Learning Where to Look for the Pancreas","date":"2018-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeeJunHyun/Image_Segmentation","path":"evaluation.py","file_url":"https://github.com/LeeJunHyun/Image_Segmentation/blob/HEAD/evaluation.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3fdc610e589e29bd","mcp_get_code":{"code_sha256":"3fdc610e589e29bd"}},{"arxiv_id":"1804.03999","paper":"/paper/attention-u-net-learning-where-to-look-for","title":"Attention U-Net: Learning Where to Look for the Pancreas","date":"2018-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Spider-scnu/Instance-Segmentation-For-Cancer","path":"code/evaluation.py","file_url":"https://github.com/Spider-scnu/Instance-Segmentation-For-Cancer/blob/HEAD/code/evaluation.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":"3162b492d7ffa807","mcp_get_code":{"code_sha256":"3162b492d7ffa807"}},{"arxiv_id":"1802.06955","paper":"/paper/recurrent-residual-convolutional-neural","title":"Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation","date":"2018-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"dd0ff9a848c9079a","mcp_get_code":{"code_sha256":"dd0ff9a848c9079a"}},{"arxiv_id":"1802.06955","paper":"/paper/recurrent-residual-convolutional-neural","title":"Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation","date":"2018-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"3fdc610e589e29bd","mcp_get_code":{"code_sha256":"3fdc610e589e29bd"}}]}