{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:DMVJ7X7VCIG4IHGFDKDNO3G2DC","short_pith_number":"pith:DMVJ7X7V","canonical_record":{"source":{"id":"2405.03185","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-06T06:23:06Z","cross_cats_sorted":[],"title_canon_sha256":"ec7863d5a4132422c0764e7a54b1735120d3db7ecd8c3394b2f087ee6c57eaf2","abstract_canon_sha256":"13e92ed84f97b525bc11634975d06387022c79bcc4fbef00f8f5bd1ee1c0ac12"},"schema_version":"1.0"},"canonical_sha256":"1b2a9fdff5120dc41cc51a86d76cda1880c0b9b79274c229bd0e64f26daced27","source":{"kind":"arxiv","id":"2405.03185","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.03185","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"arxiv_version","alias_value":"2405.03185v2","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.03185","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"pith_short_12","alias_value":"DMVJ7X7VCIG4","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"pith_short_16","alias_value":"DMVJ7X7VCIG4IHGF","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"pith_short_8","alias_value":"DMVJ7X7V","created_at":"2026-07-05T09:24:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:DMVJ7X7VCIG4IHGFDKDNO3G2DC","target":"record","payload":{"canonical_record":{"source":{"id":"2405.03185","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-06T06:23:06Z","cross_cats_sorted":[],"title_canon_sha256":"ec7863d5a4132422c0764e7a54b1735120d3db7ecd8c3394b2f087ee6c57eaf2","abstract_canon_sha256":"13e92ed84f97b525bc11634975d06387022c79bcc4fbef00f8f5bd1ee1c0ac12"},"schema_version":"1.0"},"canonical_sha256":"1b2a9fdff5120dc41cc51a86d76cda1880c0b9b79274c229bd0e64f26daced27","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:24:57.891889Z","signature_b64":"+eeE3svs67Wmbh6FaMiWmoc72SfS7e0brkTtyxnSqWNH/xXfksCE+auTvzpc2qlH9sNMrsgzmWsTC+9U7DwEBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b2a9fdff5120dc41cc51a86d76cda1880c0b9b79274c229bd0e64f26daced27","last_reissued_at":"2026-07-05T09:24:57.891403Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:24:57.891403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.03185","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:24:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yltlRFdDxk1zCn4WDFCB66bgJSqhBmNmi6D9sg/pg5Vl5lsSTSMgbu03TGFzPaZmO0sPcWLbjGvJFHFDZ6l+BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:29:48.726896Z"},"content_sha256":"674332ab5920070e9c54223c798a3ecce65a2c78f30abbc0fc5ad22c7a102efc","schema_version":"1.0","event_id":"sha256:674332ab5920070e9c54223c798a3ecce65a2c78f30abbc0fc5ad22c7a102efc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:DMVJ7X7VCIG4IHGFDKDNO3G2DC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Guoyang Qin, Jian Sun, Tong Nie, Wei Ma","submitted_at":"2024-05-06T06:23:06Z","abstract_excerpt":"Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation. To discern the underlying dynamics in low-dimensional regimes, coordinate-based neural networks that can encode high-frequency structures are employ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.03185","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/2405.03185/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":"2026-07-05T09:24:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2lAPEDccvbhTBSC1oeCxKVESw0CrHM+NkU+gfJWTpdJWN+6d5lf8HmQxIa0pCKhFhdKsse3okRURH2kJkoMUBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:29:48.727401Z"},"content_sha256":"32765fad4cf598c3fab625c729ff59661777c42126294955a774f0c60570a5cd","schema_version":"1.0","event_id":"sha256:32765fad4cf598c3fab625c729ff59661777c42126294955a774f0c60570a5cd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DMVJ7X7VCIG4IHGFDKDNO3G2DC/bundle.json","state_url":"https://pith.science/pith/DMVJ7X7VCIG4IHGFDKDNO3G2DC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DMVJ7X7VCIG4IHGFDKDNO3G2DC/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":"2026-08-06T18:29:48Z","links":{"resolver":"https://pith.science/pith/DMVJ7X7VCIG4IHGFDKDNO3G2DC","bundle":"https://pith.science/pith/DMVJ7X7VCIG4IHGFDKDNO3G2DC/bundle.json","state":"https://pith.science/pith/DMVJ7X7VCIG4IHGFDKDNO3G2DC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DMVJ7X7VCIG4IHGFDKDNO3G2DC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DMVJ7X7VCIG4IHGFDKDNO3G2DC","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":"13e92ed84f97b525bc11634975d06387022c79bcc4fbef00f8f5bd1ee1c0ac12","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-06T06:23:06Z","title_canon_sha256":"ec7863d5a4132422c0764e7a54b1735120d3db7ecd8c3394b2f087ee6c57eaf2"},"schema_version":"1.0","source":{"id":"2405.03185","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.03185","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"arxiv_version","alias_value":"2405.03185v2","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.03185","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"pith_short_12","alias_value":"DMVJ7X7VCIG4","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"pith_short_16","alias_value":"DMVJ7X7VCIG4IHGF","created_at":"2026-07-05T09:24:57Z"},{"alias_kind":"pith_short_8","alias_value":"DMVJ7X7V","created_at":"2026-07-05T09:24:57Z"}],"graph_snapshots":[{"event_id":"sha256:32765fad4cf598c3fab625c729ff59661777c42126294955a774f0c60570a5cd","target":"graph","created_at":"2026-07-05T09:24:57Z","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/2405.03185/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation. To discern the underlying dynamics in low-dimensional regimes, coordinate-based neural networks that can encode high-frequency structures are employ","authors_text":"Guoyang Qin, Jian Sun, Tong Nie, Wei Ma","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-06T06:23:06Z","title":"Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.03185","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":"sha256:674332ab5920070e9c54223c798a3ecce65a2c78f30abbc0fc5ad22c7a102efc","target":"record","created_at":"2026-07-05T09:24:57Z","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":"13e92ed84f97b525bc11634975d06387022c79bcc4fbef00f8f5bd1ee1c0ac12","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-06T06:23:06Z","title_canon_sha256":"ec7863d5a4132422c0764e7a54b1735120d3db7ecd8c3394b2f087ee6c57eaf2"},"schema_version":"1.0","source":{"id":"2405.03185","kind":"arxiv","version":2}},"canonical_sha256":"1b2a9fdff5120dc41cc51a86d76cda1880c0b9b79274c229bd0e64f26daced27","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1b2a9fdff5120dc41cc51a86d76cda1880c0b9b79274c229bd0e64f26daced27","first_computed_at":"2026-07-05T09:24:57.891403Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:24:57.891403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+eeE3svs67Wmbh6FaMiWmoc72SfS7e0brkTtyxnSqWNH/xXfksCE+auTvzpc2qlH9sNMrsgzmWsTC+9U7DwEBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:24:57.891889Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.03185","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:674332ab5920070e9c54223c798a3ecce65a2c78f30abbc0fc5ad22c7a102efc","sha256:32765fad4cf598c3fab625c729ff59661777c42126294955a774f0c60570a5cd"],"state_sha256":"c626284ee679757644cc977bc7596f0bb0eca014dd0f35b6b42a062bc0e1dc2d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4wMn8ACA1y5GCcEEu3ptXiMVGZ4L46jak5ILkNERnQUflsZECAaRV00a59RnmLyxss2f63GwZsQ3H0oQpVoOCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T18:29:48.731773Z","bundle_sha256":"d28c9714f809abf8d4a861fe957403b813584a7df1f4b079ab04e3b5c632900e"}}