{"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/normalization-2","entry":"Normalization","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":11,"n_papers_ran":9,"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":10,"n_samples_ran":8,"n_samples_fingerprinted":4,"n_places":11,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":1,"ran_fixture":0,"ran":6,"unverified":2},"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":"2608.24597","paper":"/paper/arxiv-2608-24597","title":"Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"jingyingma01/CodeBrain","path":"Models/SSSM.py","file_url":"https://github.com/jingyingma01/CodeBrain/blob/HEAD/Models/SSSM.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6e10551ad32d731d","mcp_get_code":{"code_sha256":"6e10551ad32d731d"}},{"arxiv_id":"2607.03299","paper":"/paper/arxiv-2607-03299","title":"Piecewise Dynamic Diffusion Regularization for Reconstruction of Cardiac Cine MRI","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"MLI-lab/pddr","path":"baselines/fmlp.py","file_url":"https://github.com/MLI-lab/pddr/blob/HEAD/baselines/fmlp.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"0ff9abe83599db9c","mcp_get_code":{"code_sha256":"0ff9abe83599db9c"}},{"arxiv_id":"2603.24366","paper":"/paper/arxiv-2603-24366","title":"CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"marmotlab/CoordLight","path":"Models/CoordLightModel.py","file_url":"https://github.com/marmotlab/CoordLight/blob/HEAD/Models/CoordLightModel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c6fcba4acde220a","mcp_get_code":{"code_sha256":"0c6fcba4acde220a"}},{"arxiv_id":"2404.11677","paper":"/paper/cross-problem-learning-for-solving-vehicle","title":"Cross-Problem Learning for Solving Vehicle Routing Problems","date":"2024-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuoyi-lin/cross_problem_learning","path":"nets/attention_model.py","file_url":"https://github.com/zhuoyi-lin/cross_problem_learning/blob/HEAD/nets/attention_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d7486b45b2dfeb02","mcp_get_code":{"code_sha256":"d7486b45b2dfeb02"}},{"arxiv_id":"2312.17183","paper":"/paper/one-model-to-rule-them-all-towards-universal","title":"One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text Prompts","date":"2023-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhaoziheng/sat-ds","path":"loader.py","file_url":"https://github.com/zhaoziheng/sat-ds/blob/HEAD/loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a6bc7496b6b250f9","mcp_get_code":{"code_sha256":"a6bc7496b6b250f9"}},{"arxiv_id":"2307.08875","paper":"/paper/natural-actor-critic-for-robust-reinforcement","title":"Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tliu1997/rnac","path":"train_rnac.py","file_url":"https://github.com/tliu1997/rnac/blob/HEAD/train_rnac.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"609d32bfa5631924","mcp_get_code":{"code_sha256":"609d32bfa5631924"}},{"arxiv_id":"2305.06822","paper":"/paper/implicit-neural-networks-with-fourier-feature","title":"Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction","date":"2023-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mli-lab/cinemri","path":"src/models/fmlp.py","file_url":"https://github.com/mli-lab/cinemri/blob/HEAD/src/models/fmlp.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0ff9abe83599db9c","mcp_get_code":{"code_sha256":"0ff9abe83599db9c"}},{"arxiv_id":"2110.02544","paper":"/paper/learning-to-iteratively-solve-routing","title":"Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer","date":"2021-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yining043/VRP-DACT","path":"nets/actor_network.py","file_url":"https://github.com/yining043/VRP-DACT/blob/HEAD/nets/actor_network.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08bc67177889a2b3","mcp_get_code":{"code_sha256":"08bc67177889a2b3"}},{"arxiv_id":"2108.06805","paper":"/paper/ssh-a-self-supervised-framework-for-image","title":"SSH: A Self-Supervised Framework for Image Harmonization","date":"2021-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vita-group/ssharmonization","path":"unet_tile_se_norm.py","file_url":"https://github.com/vita-group/ssharmonization/blob/HEAD/unet_tile_se_norm.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"86cd90fd8e12a1f3","mcp_get_code":{"code_sha256":"86cd90fd8e12a1f3"}},{"arxiv_id":"2108.04562","paper":"/paper/deep-metric-learning-for-open-world-semantic","title":"Deep Metric Learning for Open World Semantic Segmentation","date":"2021-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jun-CEN/Open-World-Semantic-Segmentation","path":"DeepLabV3Plus-Pytorch/main_distillation.py","file_url":"https://github.com/Jun-CEN/Open-World-Semantic-Segmentation/blob/HEAD/DeepLabV3Plus-Pytorch/main_distillation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"345e436021e6eb22","mcp_get_code":{"code_sha256":"345e436021e6eb22"}},{"arxiv_id":"Zhang_Towards_Adversarial_Robustness_via_Debiased_High-Confidence_Logit_Alignment_ICCV_2025_paper","paper":null,"title":"arXiv:Zhang_Towards_Adversarial_Robustness_via_Debiased_High-Confidence_Logit_Alignment_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KejiaZhang-Robust/DHAT","path":"models/utils.py","file_url":"https://github.com/KejiaZhang-Robust/DHAT/blob/HEAD/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f09136a784ff386","mcp_get_code":{"code_sha256":"6f09136a784ff386"}}]}