{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:GDSZD6WU4NA557MQPQ4HEMJGFM","short_pith_number":"pith:GDSZD6WU","schema_version":"1.0","canonical_sha256":"30e591fad4e341defd907c387231262b030d19e85fa0521304f3f2d1cfb1397d","source":{"kind":"arxiv","id":"2606.05700","version":1},"attestation_state":"computed","paper":{"title":"T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Abem Woldesenbet, Kerod Woldesenbet","submitted_at":"2026-06-04T04:41:08Z","abstract_excerpt":"We present T-SAR-JEPA, a self-supervised framework for temporal anomaly detection in SAR amplitude stacks via latent prediction. A ViT-Base/16 encoder from SAR-JEPA is domain-adapted on 39,300 Capella patches using local masked reconstruction with gradient feature prediction. A temporal transformer with sinusoidal time encoding forecasts future latent states from K=7 acquisitions, with progressive unfreezing substantially reducing validation loss. The model operates on amplitude alone; InSAR coherence serves exclusively as independent pseudo-ground-truth. On the DFC 2026 dataset (300 time-seri"},"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":"2606.05700","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-04T04:41:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8f8b468d7f113c73700e16f4121f33c894dad191ecbf4c5f5c88563f2c92141d","abstract_canon_sha256":"f10e495a0ab5c700007d3e091ea48a1bd22066f92a2888f0e590a204c9c3c9ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:14:59.705804Z","signature_b64":"PM/9bWHshO9tOgaKFtz5khaaB9cNNU0R81hAcm6Dki54SjSnOks2WBAW8t4lB2nd3KQQ+czNq/qpjMgsb/mJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30e591fad4e341defd907c387231262b030d19e85fa0521304f3f2d1cfb1397d","last_reissued_at":"2026-06-05T01:14:59.705273Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:14:59.705273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Abem Woldesenbet, Kerod Woldesenbet","submitted_at":"2026-06-04T04:41:08Z","abstract_excerpt":"We present T-SAR-JEPA, a self-supervised framework for temporal anomaly detection in SAR amplitude stacks via latent prediction. A ViT-Base/16 encoder from SAR-JEPA is domain-adapted on 39,300 Capella patches using local masked reconstruction with gradient feature prediction. A temporal transformer with sinusoidal time encoding forecasts future latent states from K=7 acquisitions, with progressive unfreezing substantially reducing validation loss. The model operates on amplitude alone; InSAR coherence serves exclusively as independent pseudo-ground-truth. On the DFC 2026 dataset (300 time-seri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05700","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/2606.05700/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":"2606.05700","created_at":"2026-06-05T01:14:59.705372+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05700v1","created_at":"2026-06-05T01:14:59.705372+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05700","created_at":"2026-06-05T01:14:59.705372+00:00"},{"alias_kind":"pith_short_12","alias_value":"GDSZD6WU4NA5","created_at":"2026-06-05T01:14:59.705372+00:00"},{"alias_kind":"pith_short_16","alias_value":"GDSZD6WU4NA557MQ","created_at":"2026-06-05T01:14:59.705372+00:00"},{"alias_kind":"pith_short_8","alias_value":"GDSZD6WU","created_at":"2026-06-05T01:14:59.705372+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/GDSZD6WU4NA557MQPQ4HEMJGFM","json":"https://pith.science/pith/GDSZD6WU4NA557MQPQ4HEMJGFM.json","graph_json":"https://pith.science/api/pith-number/GDSZD6WU4NA557MQPQ4HEMJGFM/graph.json","events_json":"https://pith.science/api/pith-number/GDSZD6WU4NA557MQPQ4HEMJGFM/events.json","paper":"https://pith.science/paper/GDSZD6WU"},"agent_actions":{"view_html":"https://pith.science/pith/GDSZD6WU4NA557MQPQ4HEMJGFM","download_json":"https://pith.science/pith/GDSZD6WU4NA557MQPQ4HEMJGFM.json","view_paper":"https://pith.science/paper/GDSZD6WU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05700&json=true","fetch_graph":"https://pith.science/api/pith-number/GDSZD6WU4NA557MQPQ4HEMJGFM/graph.json","fetch_events":"https://pith.science/api/pith-number/GDSZD6WU4NA557MQPQ4HEMJGFM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GDSZD6WU4NA557MQPQ4HEMJGFM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GDSZD6WU4NA557MQPQ4HEMJGFM/action/storage_attestation","attest_author":"https://pith.science/pith/GDSZD6WU4NA557MQPQ4HEMJGFM/action/author_attestation","sign_citation":"https://pith.science/pith/GDSZD6WU4NA557MQPQ4HEMJGFM/action/citation_signature","submit_replication":"https://pith.science/pith/GDSZD6WU4NA557MQPQ4HEMJGFM/action/replication_record"}},"created_at":"2026-06-05T01:14:59.705372+00:00","updated_at":"2026-06-05T01:14:59.705372+00:00"}