{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:C7QYCEKHNAP6BGH3R6KAGT6AFA","short_pith_number":"pith:C7QYCEKH","canonical_record":{"source":{"id":"2601.15779","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-22T09:12:05Z","cross_cats_sorted":[],"title_canon_sha256":"ee532de297b87b1e250f1eecb1ada67c81483517d8f17b6f429921710a756425","abstract_canon_sha256":"6e96a7775c3c1344f95392234ca65bf789a8de30eb6c5c8ed1c414018a22a23f"},"schema_version":"1.0"},"canonical_sha256":"17e1811147681fe098fb8f94034fc02825970a86a56df765ceabec0b4122fe78","source":{"kind":"arxiv","id":"2601.15779","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.15779","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"arxiv_version","alias_value":"2601.15779v1","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.15779","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"pith_short_12","alias_value":"C7QYCEKHNAP6","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"pith_short_16","alias_value":"C7QYCEKHNAP6BGH3","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"pith_short_8","alias_value":"C7QYCEKH","created_at":"2026-07-09T01:19:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:C7QYCEKHNAP6BGH3R6KAGT6AFA","target":"record","payload":{"canonical_record":{"source":{"id":"2601.15779","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-22T09:12:05Z","cross_cats_sorted":[],"title_canon_sha256":"ee532de297b87b1e250f1eecb1ada67c81483517d8f17b6f429921710a756425","abstract_canon_sha256":"6e96a7775c3c1344f95392234ca65bf789a8de30eb6c5c8ed1c414018a22a23f"},"schema_version":"1.0"},"canonical_sha256":"17e1811147681fe098fb8f94034fc02825970a86a56df765ceabec0b4122fe78","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-09T01:19:51.096029Z","signature_b64":"PK/u0nIdwtsmttd8hOzeBO49JDrlu0f/9ek+u+uSMZNvYbcfnoxW4yV2N3fw0iySks08qYT4NawIdedN741eDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17e1811147681fe098fb8f94034fc02825970a86a56df765ceabec0b4122fe78","last_reissued_at":"2026-07-09T01:19:51.095520Z","signature_status":"signed_v1","first_computed_at":"2026-07-09T01:19:51.095520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.15779","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-09T01:19:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rlr61W2Y5FAt/7lzxpK34wlD3CKpWznoSyVOJfIc+wpWahRb4MkxqST4r+28FxL/mkthj3/zqK4V/j+duTAACg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T17:33:31.348007Z"},"content_sha256":"5df2118260756140b3406bf2b7228093d79da3f1e664564552d52d43f6ea5d47","schema_version":"1.0","event_id":"sha256:5df2118260756140b3406bf2b7228093d79da3f1e664564552d52d43f6ea5d47"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:C7QYCEKHNAP6BGH3R6KAGT6AFA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Diffusion Model-Based Data Augmentation for Enhanced Neuron Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hua Han, Jinyue Guo, Liuyun Jiang, Ruining Zhou, Yanchao Zhang, Yizhuo Lu","submitted_at":"2026-01-22T09:12:05Z","abstract_excerpt":"Neuron segmentation in electron microscopy (EM) aims to reconstruct the complete neuronal connectome; however, current deep learning-based methods are limited by their reliance on large-scale training data and extensive, time-consuming manual annotations. Traditional methods augment the training set through geometric and photometric transformations; however, the generated samples remain highly correlated with the original images and lack structural diversity. To address this limitation, we propose a diffusion-based data augmentation framework capable of generating diverse and structurally plau"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.15779","kind":"arxiv","version":1},"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/2601.15779/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-09T01:19:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qbMIQlliQTb6EcNWm5IgET0nUD33wB3PAZjER2HhXD2d2x40tWRZFAGOShCYVoEin72whBYpiQL/iUncNxUdBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T17:33:31.348640Z"},"content_sha256":"46ddccb0d9661a2391f70432d79f58b23a642c65bd1c7146539dd4f27538acff","schema_version":"1.0","event_id":"sha256:46ddccb0d9661a2391f70432d79f58b23a642c65bd1c7146539dd4f27538acff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C7QYCEKHNAP6BGH3R6KAGT6AFA/bundle.json","state_url":"https://pith.science/pith/C7QYCEKHNAP6BGH3R6KAGT6AFA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C7QYCEKHNAP6BGH3R6KAGT6AFA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T17:33:31Z","links":{"resolver":"https://pith.science/pith/C7QYCEKHNAP6BGH3R6KAGT6AFA","bundle":"https://pith.science/pith/C7QYCEKHNAP6BGH3R6KAGT6AFA/bundle.json","state":"https://pith.science/pith/C7QYCEKHNAP6BGH3R6KAGT6AFA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C7QYCEKHNAP6BGH3R6KAGT6AFA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:C7QYCEKHNAP6BGH3R6KAGT6AFA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6e96a7775c3c1344f95392234ca65bf789a8de30eb6c5c8ed1c414018a22a23f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-22T09:12:05Z","title_canon_sha256":"ee532de297b87b1e250f1eecb1ada67c81483517d8f17b6f429921710a756425"},"schema_version":"1.0","source":{"id":"2601.15779","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.15779","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"arxiv_version","alias_value":"2601.15779v1","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.15779","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"pith_short_12","alias_value":"C7QYCEKHNAP6","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"pith_short_16","alias_value":"C7QYCEKHNAP6BGH3","created_at":"2026-07-09T01:19:51Z"},{"alias_kind":"pith_short_8","alias_value":"C7QYCEKH","created_at":"2026-07-09T01:19:51Z"}],"graph_snapshots":[{"event_id":"sha256:46ddccb0d9661a2391f70432d79f58b23a642c65bd1c7146539dd4f27538acff","target":"graph","created_at":"2026-07-09T01:19:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2601.15779/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neuron segmentation in electron microscopy (EM) aims to reconstruct the complete neuronal connectome; however, current deep learning-based methods are limited by their reliance on large-scale training data and extensive, time-consuming manual annotations. Traditional methods augment the training set through geometric and photometric transformations; however, the generated samples remain highly correlated with the original images and lack structural diversity. To address this limitation, we propose a diffusion-based data augmentation framework capable of generating diverse and structurally plau","authors_text":"Hua Han, Jinyue Guo, Liuyun Jiang, Ruining Zhou, Yanchao Zhang, Yizhuo Lu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-22T09:12:05Z","title":"Diffusion Model-Based Data Augmentation for Enhanced Neuron Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.15779","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5df2118260756140b3406bf2b7228093d79da3f1e664564552d52d43f6ea5d47","target":"record","created_at":"2026-07-09T01:19:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6e96a7775c3c1344f95392234ca65bf789a8de30eb6c5c8ed1c414018a22a23f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-22T09:12:05Z","title_canon_sha256":"ee532de297b87b1e250f1eecb1ada67c81483517d8f17b6f429921710a756425"},"schema_version":"1.0","source":{"id":"2601.15779","kind":"arxiv","version":1}},"canonical_sha256":"17e1811147681fe098fb8f94034fc02825970a86a56df765ceabec0b4122fe78","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"17e1811147681fe098fb8f94034fc02825970a86a56df765ceabec0b4122fe78","first_computed_at":"2026-07-09T01:19:51.095520Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-09T01:19:51.095520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PK/u0nIdwtsmttd8hOzeBO49JDrlu0f/9ek+u+uSMZNvYbcfnoxW4yV2N3fw0iySks08qYT4NawIdedN741eDw==","signature_status":"signed_v1","signed_at":"2026-07-09T01:19:51.096029Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.15779","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5df2118260756140b3406bf2b7228093d79da3f1e664564552d52d43f6ea5d47","sha256:46ddccb0d9661a2391f70432d79f58b23a642c65bd1c7146539dd4f27538acff"],"state_sha256":"cd305779a2b1e72feacc1f92421a2804611de6351e6255d190aa5ba4ab6b307a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4uQCmR4T6g9Bh4HeNIyTA0w3F+BEQKNaf2kes+03Jnt294SFK+4SKDLm4FJdphxfiK9CLI4s4weOAe+pItTADg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T17:33:31.371993Z","bundle_sha256":"8c4b5f4f11d66fb2a5debcf726269362ea10712b91d72653eac6cf7c4169ddb3"}}