{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ZK7TBKKXEADA7F4WBW7MGYXNZK","short_pith_number":"pith:ZK7TBKKX","canonical_record":{"source":{"id":"2308.12508","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-08-24T02:28:18Z","cross_cats_sorted":["cs.CV","cs.GR"],"title_canon_sha256":"5241476a3123fad6478119940eecb93fd2e6f4b729cd5605c95456ff4e4953c5","abstract_canon_sha256":"e0114cb607ea09ea4f6b41c183947545d8dca136d0d35dfb3df46568c3d3e5e8"},"schema_version":"1.0"},"canonical_sha256":"cabf30a95720060f97960dbec362edca877b17a356a482905d0cef501a5b3194","source":{"kind":"arxiv","id":"2308.12508","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.12508","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"arxiv_version","alias_value":"2308.12508v2","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.12508","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"pith_short_12","alias_value":"ZK7TBKKXEADA","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"pith_short_16","alias_value":"ZK7TBKKXEADA7F4W","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"pith_short_8","alias_value":"ZK7TBKKX","created_at":"2026-07-05T06:44:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ZK7TBKKXEADA7F4WBW7MGYXNZK","target":"record","payload":{"canonical_record":{"source":{"id":"2308.12508","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-08-24T02:28:18Z","cross_cats_sorted":["cs.CV","cs.GR"],"title_canon_sha256":"5241476a3123fad6478119940eecb93fd2e6f4b729cd5605c95456ff4e4953c5","abstract_canon_sha256":"e0114cb607ea09ea4f6b41c183947545d8dca136d0d35dfb3df46568c3d3e5e8"},"schema_version":"1.0"},"canonical_sha256":"cabf30a95720060f97960dbec362edca877b17a356a482905d0cef501a5b3194","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:44:51.651289Z","signature_b64":"Cw0zCUFJnB6sOo8Q9ynO3PvmfQKBZIpD0c7a4nIh6Y2dY7T2KrO+dBa7qAj9/grbnvFu1FsqbXx/R/jI2hDeAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cabf30a95720060f97960dbec362edca877b17a356a482905d0cef501a5b3194","last_reissued_at":"2026-07-05T06:44:51.650856Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:44:51.650856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.12508","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":"2026-07-05T06:44:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QWzJ9yfmab1CzxZEMokohTeW1kgoD6OSSaRwxkNNeQI8PFyQC9Aw9vQRBZBb7HlVyBZE8F3ubiHq7/ltZN8kAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T04:51:20.744514Z"},"content_sha256":"4967ab45cce92e5dc2b1580685fb2959374563185f93b3cbe8e89d866b9b4ed1","schema_version":"1.0","event_id":"sha256:4967ab45cce92e5dc2b1580685fb2959374563185f93b3cbe8e89d866b9b4ed1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ZK7TBKKXEADA7F4WBW7MGYXNZK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.GR"],"primary_cat":"eess.IV","authors_text":"Chenyue Jiao, Chongke Bi, Lu Yang","submitted_at":"2023-08-24T02:28:18Z","abstract_excerpt":"Large-scale numerical simulations are capable of generating data up to terabytes or even petabytes. As a promising method of data reduction, super-resolution (SR) has been widely studied in the scientific visualization community. However, most of them are based on deep convolutional neural networks (CNNs) or generative adversarial networks (GANs) and the scale factor needs to be determined before constructing the network. As a result, a single training session only supports a fixed factor and has poor generalization ability. To address these problems, this paper proposes a Feature-Enhanced Imp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.12508","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/2308.12508/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":"2026-07-05T06:44:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VbasxFHWBruYE1Osd3fO68VQ1JvCGI/c0dknC3upt05DvjuYO0EKuxqjKQLh4AeTo3wqmD3zis0L4R8GuzDOBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T04:51:20.745155Z"},"content_sha256":"ff0ed35424d96bb7e7f81bba864637bf91d6bed2e5de6de07a10fde012451b2f","schema_version":"1.0","event_id":"sha256:ff0ed35424d96bb7e7f81bba864637bf91d6bed2e5de6de07a10fde012451b2f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZK7TBKKXEADA7F4WBW7MGYXNZK/bundle.json","state_url":"https://pith.science/pith/ZK7TBKKXEADA7F4WBW7MGYXNZK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZK7TBKKXEADA7F4WBW7MGYXNZK/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":"2026-08-20T04:51:20Z","links":{"resolver":"https://pith.science/pith/ZK7TBKKXEADA7F4WBW7MGYXNZK","bundle":"https://pith.science/pith/ZK7TBKKXEADA7F4WBW7MGYXNZK/bundle.json","state":"https://pith.science/pith/ZK7TBKKXEADA7F4WBW7MGYXNZK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZK7TBKKXEADA7F4WBW7MGYXNZK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ZK7TBKKXEADA7F4WBW7MGYXNZK","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":"e0114cb607ea09ea4f6b41c183947545d8dca136d0d35dfb3df46568c3d3e5e8","cross_cats_sorted":["cs.CV","cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-08-24T02:28:18Z","title_canon_sha256":"5241476a3123fad6478119940eecb93fd2e6f4b729cd5605c95456ff4e4953c5"},"schema_version":"1.0","source":{"id":"2308.12508","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.12508","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"arxiv_version","alias_value":"2308.12508v2","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.12508","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"pith_short_12","alias_value":"ZK7TBKKXEADA","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"pith_short_16","alias_value":"ZK7TBKKXEADA7F4W","created_at":"2026-07-05T06:44:51Z"},{"alias_kind":"pith_short_8","alias_value":"ZK7TBKKX","created_at":"2026-07-05T06:44:51Z"}],"graph_snapshots":[{"event_id":"sha256:ff0ed35424d96bb7e7f81bba864637bf91d6bed2e5de6de07a10fde012451b2f","target":"graph","created_at":"2026-07-05T06:44:51Z","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/2308.12508/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale numerical simulations are capable of generating data up to terabytes or even petabytes. As a promising method of data reduction, super-resolution (SR) has been widely studied in the scientific visualization community. However, most of them are based on deep convolutional neural networks (CNNs) or generative adversarial networks (GANs) and the scale factor needs to be determined before constructing the network. As a result, a single training session only supports a fixed factor and has poor generalization ability. To address these problems, this paper proposes a Feature-Enhanced Imp","authors_text":"Chenyue Jiao, Chongke Bi, Lu Yang","cross_cats":["cs.CV","cs.GR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-08-24T02:28:18Z","title":"FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.12508","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":"sha256:4967ab45cce92e5dc2b1580685fb2959374563185f93b3cbe8e89d866b9b4ed1","target":"record","created_at":"2026-07-05T06:44:51Z","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":"e0114cb607ea09ea4f6b41c183947545d8dca136d0d35dfb3df46568c3d3e5e8","cross_cats_sorted":["cs.CV","cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-08-24T02:28:18Z","title_canon_sha256":"5241476a3123fad6478119940eecb93fd2e6f4b729cd5605c95456ff4e4953c5"},"schema_version":"1.0","source":{"id":"2308.12508","kind":"arxiv","version":2}},"canonical_sha256":"cabf30a95720060f97960dbec362edca877b17a356a482905d0cef501a5b3194","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cabf30a95720060f97960dbec362edca877b17a356a482905d0cef501a5b3194","first_computed_at":"2026-07-05T06:44:51.650856Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:44:51.650856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Cw0zCUFJnB6sOo8Q9ynO3PvmfQKBZIpD0c7a4nIh6Y2dY7T2KrO+dBa7qAj9/grbnvFu1FsqbXx/R/jI2hDeAA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:44:51.651289Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.12508","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4967ab45cce92e5dc2b1580685fb2959374563185f93b3cbe8e89d866b9b4ed1","sha256:ff0ed35424d96bb7e7f81bba864637bf91d6bed2e5de6de07a10fde012451b2f"],"state_sha256":"0d117970986e5056885252adbaef63a2a0713cfbd1971e16fc90ea46b01df525"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G9h2rTZ7kMUzhhITTQTOCM6ltjnZECGv6lS2ULR5biXMifeKg1Chnqw1H4GJwjYOrRV1tFR4rZ7dUfHl0NPLAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T04:51:20.750953Z","bundle_sha256":"8e485b644f0ea59d0251fa4f48304e2ff3b3386b6848e262c6b871eb08268b90"}}