{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GRXVEXX2OHINNXA2OHDDBF7MBV","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":"95da37552b73695143e18899aed0266402f366749d1426f06b15863c35bc7e99","cross_cats_sorted":["cs.AI","cs.LG","q-bio.TO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-08-21T16:24:24Z","title_canon_sha256":"656d25db6e2a41f31a01c519a1f211f8402e22ae69a3a8cc614dc5d89e213fa3"},"schema_version":"1.0","source":{"id":"2508.15883","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.15883","created_at":"2026-07-05T11:58:36Z"},{"alias_kind":"arxiv_version","alias_value":"2508.15883v2","created_at":"2026-07-05T11:58:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.15883","created_at":"2026-07-05T11:58:36Z"},{"alias_kind":"pith_short_12","alias_value":"GRXVEXX2OHIN","created_at":"2026-07-05T11:58:36Z"},{"alias_kind":"pith_short_16","alias_value":"GRXVEXX2OHINNXA2","created_at":"2026-07-05T11:58:36Z"},{"alias_kind":"pith_short_8","alias_value":"GRXVEXX2","created_at":"2026-07-05T11:58:36Z"}],"graph_snapshots":[{"event_id":"sha256:1dc621eec55bf7a4333e5271d0bc8560e3f2dd4bdb478a99855ca4092bef01c6","target":"graph","created_at":"2026-07-05T11:58:36Z","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/2508.15883/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Understanding the dynamic organization and homeostasis of living tissues requires high-resolution, time-resolved imaging coupled with methods capable of extracting interpretable, predictive insights from complex datasets. Here, we present the Vision Transformer Digital Twin Surrogate Network (VT-DTSN), a deep learning framework for predictive modeling of 3D+T imaging data from biological tissue. By leveraging Vision Transformers pretrained with DINO (Self-Distillation with NO Labels) and employing a multi-view fusion strategy, VT-DTSN learns to reconstruct high-fidelity, time-resolved dynamics","authors_text":"Joaqu\\'in de Navascu\\'es, Kaan Berke Ugurlar, Michael Taynnan Barros","cross_cats":["cs.AI","cs.LG","q-bio.TO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-08-21T16:24:24Z","title":"Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.15883","kind":"arxiv","version":2},"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":"sha256:03c87c995425268e94ee0b9531053fec405562a2ee11967257f15fe30820ae1a","target":"record","created_at":"2026-07-05T11:58:36Z","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":"95da37552b73695143e18899aed0266402f366749d1426f06b15863c35bc7e99","cross_cats_sorted":["cs.AI","cs.LG","q-bio.TO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-08-21T16:24:24Z","title_canon_sha256":"656d25db6e2a41f31a01c519a1f211f8402e22ae69a3a8cc614dc5d89e213fa3"},"schema_version":"1.0","source":{"id":"2508.15883","kind":"arxiv","version":2}},"canonical_sha256":"346f525efa71d0d6dc1a71c63097ec0d4058c740308e5f84d8e87174779e585a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"346f525efa71d0d6dc1a71c63097ec0d4058c740308e5f84d8e87174779e585a","first_computed_at":"2026-07-05T11:58:36.437562Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:58:36.437562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vz8rsIwvffq1gX/Jy5ISt9fQVXfi1FAwIjMRkHQ6H7HP75G/NL6l1NQzF1+PZFqCZk1FkEhdCvuQomFWlLI6Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:58:36.438050Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.15883","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:03c87c995425268e94ee0b9531053fec405562a2ee11967257f15fe30820ae1a","sha256:1dc621eec55bf7a4333e5271d0bc8560e3f2dd4bdb478a99855ca4092bef01c6"],"state_sha256":"7e77f1220e623206310ccc3ee761dfcc2bb3670be85cfcea0cdaf014961063c1"}