{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WJZJJRDVR7ZGKA3L3MHS53OCYC","short_pith_number":"pith:WJZJJRDV","schema_version":"1.0","canonical_sha256":"b27294c4758ff265036bdb0f2eedc2c084902ecbcc660a47275c232b257cb9b8","source":{"kind":"arxiv","id":"2607.25147","version":1},"attestation_state":"computed","paper":{"title":"Inferring Missing Trajectory Data with Temporal Convolutional Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Gabriel Turinici, Ilinca Tiriblecea","submitted_at":"2026-07-27T23:46:15Z","abstract_excerpt":"Trajectory data collected in real-world settings is frequently incomplete due to sensor failure, communication loss, or occlusion. We address the task of \\emph{trajectory inpainting}: reconstructing contiguous missing segments from observed context. We propose a Temporal Convolutional Network (TCN) with symmetric dilation that relaxes the standard causality constraint, allowing each time step to draw on both past and future observations, a property that is essential for inpainting, but absent from forecasting-oriented architectures. The model is trained with a composite loss that combines weig"},"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":"2607.25147","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-27T23:46:15Z","cross_cats_sorted":[],"title_canon_sha256":"56ff565a622c61486c33519e86a76f3714d3664105c865bd80dee7701fc20b49","abstract_canon_sha256":"0187c390024b0b3ca5b2c1efbcda98dc758970f5da8ab27599ec74527fb6bef7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T00:25:05.314105Z","signature_b64":"LueEb7WfqPnr4wv1f4Up2t9XP3tsMOX74c0KTMW5HN+EsmZdB4p9I9O+F0AXFNzVslg5+whR4v/QvNR/zJoyDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b27294c4758ff265036bdb0f2eedc2c084902ecbcc660a47275c232b257cb9b8","last_reissued_at":"2026-07-29T00:25:05.313256Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T00:25:05.313256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inferring Missing Trajectory Data with Temporal Convolutional Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Gabriel Turinici, Ilinca Tiriblecea","submitted_at":"2026-07-27T23:46:15Z","abstract_excerpt":"Trajectory data collected in real-world settings is frequently incomplete due to sensor failure, communication loss, or occlusion. We address the task of \\emph{trajectory inpainting}: reconstructing contiguous missing segments from observed context. We propose a Temporal Convolutional Network (TCN) with symmetric dilation that relaxes the standard causality constraint, allowing each time step to draw on both past and future observations, a property that is essential for inpainting, but absent from forecasting-oriented architectures. The model is trained with a composite loss that combines weig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25147","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/2607.25147/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":"2607.25147","created_at":"2026-07-29T00:25:05.313705+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.25147v1","created_at":"2026-07-29T00:25:05.313705+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.25147","created_at":"2026-07-29T00:25:05.313705+00:00"},{"alias_kind":"pith_short_12","alias_value":"WJZJJRDVR7ZG","created_at":"2026-07-29T00:25:05.313705+00:00"},{"alias_kind":"pith_short_16","alias_value":"WJZJJRDVR7ZGKA3L","created_at":"2026-07-29T00:25:05.313705+00:00"},{"alias_kind":"pith_short_8","alias_value":"WJZJJRDV","created_at":"2026-07-29T00:25:05.313705+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/WJZJJRDVR7ZGKA3L3MHS53OCYC","json":"https://pith.science/pith/WJZJJRDVR7ZGKA3L3MHS53OCYC.json","graph_json":"https://pith.science/api/pith-number/WJZJJRDVR7ZGKA3L3MHS53OCYC/graph.json","events_json":"https://pith.science/api/pith-number/WJZJJRDVR7ZGKA3L3MHS53OCYC/events.json","paper":"https://pith.science/paper/WJZJJRDV"},"agent_actions":{"view_html":"https://pith.science/pith/WJZJJRDVR7ZGKA3L3MHS53OCYC","download_json":"https://pith.science/pith/WJZJJRDVR7ZGKA3L3MHS53OCYC.json","view_paper":"https://pith.science/paper/WJZJJRDV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.25147&json=true","fetch_graph":"https://pith.science/api/pith-number/WJZJJRDVR7ZGKA3L3MHS53OCYC/graph.json","fetch_events":"https://pith.science/api/pith-number/WJZJJRDVR7ZGKA3L3MHS53OCYC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WJZJJRDVR7ZGKA3L3MHS53OCYC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WJZJJRDVR7ZGKA3L3MHS53OCYC/action/storage_attestation","attest_author":"https://pith.science/pith/WJZJJRDVR7ZGKA3L3MHS53OCYC/action/author_attestation","sign_citation":"https://pith.science/pith/WJZJJRDVR7ZGKA3L3MHS53OCYC/action/citation_signature","submit_replication":"https://pith.science/pith/WJZJJRDVR7ZGKA3L3MHS53OCYC/action/replication_record"}},"created_at":"2026-07-29T00:25:05.313705+00:00","updated_at":"2026-07-29T00:25:05.313705+00:00"}