{"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/save-hdf5","entry":"save_hdf5","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":5,"n_papers_ran":1,"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":4,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":3},"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":"2602.02282","paper":"/paper/arxiv-2602-02282","title":"MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"susuhu/MoLF","path":"utils/hest_utils.py","file_url":"https://github.com/susuhu/MoLF/blob/HEAD/utils/hest_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ea9107de0867ddb5","mcp_get_code":{"code_sha256":"ea9107de0867ddb5"}},{"arxiv_id":"2602.01951","paper":"/paper/arxiv-2602-01951","title":"Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"olgarithmics/ICLR_CAMIL","path":"feature_extractor/compute_feats.py","file_url":"https://github.com/olgarithmics/ICLR_CAMIL/blob/HEAD/feature_extractor/compute_feats.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c6bbc9c9564d7227","mcp_get_code":{"code_sha256":"c6bbc9c9564d7227"}},{"arxiv_id":"2408.02859","paper":"/paper/2408-02859","title":"Multistain Pretraining for Slide Representation Learning in Pathology","date":"2024-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mahmoodlab/madeleine","path":"madeleine/preprocessing/conch_patch_embedder.py","file_url":"https://github.com/mahmoodlab/madeleine/blob/HEAD/madeleine/preprocessing/conch_patch_embedder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"385cba0794c85a7e","mcp_get_code":{"code_sha256":"385cba0794c85a7e"}},{"arxiv_id":"2406.05205","paper":"/paper/cplip-zero-shot-learning-for-histopathology","title":"CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iyyakuttiiyappan/CPLIP","path":"extract_embeddings.py","file_url":"https://github.com/iyyakuttiiyappan/CPLIP/blob/HEAD/extract_embeddings.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c6bbc9c9564d7227","mcp_get_code":{"code_sha256":"c6bbc9c9564d7227"}},{"arxiv_id":"2025.findings-acl.352","paper":null,"title":"arXiv:2025.findings-acl.352","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HKU-MedAI/CTPD","path":"src/cmehr/utils/file_utils.py","file_url":"https://github.com/HKU-MedAI/CTPD/blob/HEAD/src/cmehr/utils/file_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"78d1eb8c49c0ec4a","mcp_get_code":{"code_sha256":"78d1eb8c49c0ec4a"}}]}