{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HJVGDACSWFS27R6H3F3K6T6UJM","short_pith_number":"pith:HJVGDACS","schema_version":"1.0","canonical_sha256":"3a6a618052b165afc7c7d976af4fd44b1e0622485bf5ce814a907721ea07d0fd","source":{"kind":"arxiv","id":"2410.11373","version":2},"attestation_state":"computed","paper":{"title":"DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Haizhao Dai, Jiakai Zhang, Jingyi Yu, Qihe Chen, Yan Zeng, Yingjun Shen, Yuan Pei","submitted_at":"2024-10-15T08:12:11Z","abstract_excerpt":"Foundation models in computer vision have demonstrated exceptional performance in zero-shot and few-shot tasks by extracting multi-purpose features from large-scale datasets through self-supervised pre-training methods. However, these models often overlook the severe corruption in cryogenic electron microscopy (cryo-EM) images by high-level noises. We introduce DRACO, a Denoising-Reconstruction Autoencoder for CryO-EM, inspired by the Noise2Noise (N2N) approach. By processing cryo-EM movies into odd and even images and treating them as independent noisy observations, we apply a denoising-recon"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.11373","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-15T08:12:11Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"f662dffb0147c76710fe7e3b4433405b63be875f1eaf186c2ef961dec8e5159c","abstract_canon_sha256":"cb72369833ea11bdb9160e6563fc6224768bdbd048b07023aa9ec538adb24f2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:27:06.202351Z","signature_b64":"J4Js63leT8NQNZMSmEOpt5CiZ1cjwVaeh2tvUJTXkKRwNFkWztyH7daJQ+kt6JLlWdlRI9wbcfAmEMPbCftgCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a6a618052b165afc7c7d976af4fd44b1e0622485bf5ce814a907721ea07d0fd","last_reissued_at":"2026-07-05T09:27:06.201743Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:27:06.201743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Haizhao Dai, Jiakai Zhang, Jingyi Yu, Qihe Chen, Yan Zeng, Yingjun Shen, Yuan Pei","submitted_at":"2024-10-15T08:12:11Z","abstract_excerpt":"Foundation models in computer vision have demonstrated exceptional performance in zero-shot and few-shot tasks by extracting multi-purpose features from large-scale datasets through self-supervised pre-training methods. However, these models often overlook the severe corruption in cryogenic electron microscopy (cryo-EM) images by high-level noises. We introduce DRACO, a Denoising-Reconstruction Autoencoder for CryO-EM, inspired by the Noise2Noise (N2N) approach. By processing cryo-EM movies into odd and even images and treating them as independent noisy observations, we apply a denoising-recon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.11373","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.11373/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.11373","created_at":"2026-07-05T09:27:06.201809+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.11373v2","created_at":"2026-07-05T09:27:06.201809+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.11373","created_at":"2026-07-05T09:27:06.201809+00:00"},{"alias_kind":"pith_short_12","alias_value":"HJVGDACSWFS2","created_at":"2026-07-05T09:27:06.201809+00:00"},{"alias_kind":"pith_short_16","alias_value":"HJVGDACSWFS27R6H","created_at":"2026-07-05T09:27:06.201809+00:00"},{"alias_kind":"pith_short_8","alias_value":"HJVGDACS","created_at":"2026-07-05T09:27:06.201809+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HJVGDACSWFS27R6H3F3K6T6UJM","json":"https://pith.science/pith/HJVGDACSWFS27R6H3F3K6T6UJM.json","graph_json":"https://pith.science/api/pith-number/HJVGDACSWFS27R6H3F3K6T6UJM/graph.json","events_json":"https://pith.science/api/pith-number/HJVGDACSWFS27R6H3F3K6T6UJM/events.json","paper":"https://pith.science/paper/HJVGDACS"},"agent_actions":{"view_html":"https://pith.science/pith/HJVGDACSWFS27R6H3F3K6T6UJM","download_json":"https://pith.science/pith/HJVGDACSWFS27R6H3F3K6T6UJM.json","view_paper":"https://pith.science/paper/HJVGDACS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.11373&json=true","fetch_graph":"https://pith.science/api/pith-number/HJVGDACSWFS27R6H3F3K6T6UJM/graph.json","fetch_events":"https://pith.science/api/pith-number/HJVGDACSWFS27R6H3F3K6T6UJM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HJVGDACSWFS27R6H3F3K6T6UJM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HJVGDACSWFS27R6H3F3K6T6UJM/action/storage_attestation","attest_author":"https://pith.science/pith/HJVGDACSWFS27R6H3F3K6T6UJM/action/author_attestation","sign_citation":"https://pith.science/pith/HJVGDACSWFS27R6H3F3K6T6UJM/action/citation_signature","submit_replication":"https://pith.science/pith/HJVGDACSWFS27R6H3F3K6T6UJM/action/replication_record"}},"created_at":"2026-07-05T09:27:06.201809+00:00","updated_at":"2026-07-05T09:27:06.201809+00:00"}