{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:RYXKCGMNLQFJBGT2MGXICWKVNG","short_pith_number":"pith:RYXKCGMN","canonical_record":{"source":{"id":"1904.12413","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T01:12:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e25bb1a88a13573e3e7a34d3ba1ba8ae4229ac36891f8dce13ba35e0e371d68e","abstract_canon_sha256":"1dcf48815f18b0582df51ec03e09d52a4f6c0b90eed802ec1a269cfe6db76c24"},"schema_version":"1.0"},"canonical_sha256":"8e2ea1198d5c0a909a7a61ae81595569b5c8e812cca304538ba269d169b19139","source":{"kind":"arxiv","id":"1904.12413","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.12413","created_at":"2026-05-17T23:47:35Z"},{"alias_kind":"arxiv_version","alias_value":"1904.12413v1","created_at":"2026-05-17T23:47:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12413","created_at":"2026-05-17T23:47:35Z"},{"alias_kind":"pith_short_12","alias_value":"RYXKCGMNLQFJ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RYXKCGMNLQFJBGT2","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RYXKCGMN","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:RYXKCGMNLQFJBGT2MGXICWKVNG","target":"record","payload":{"canonical_record":{"source":{"id":"1904.12413","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T01:12:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e25bb1a88a13573e3e7a34d3ba1ba8ae4229ac36891f8dce13ba35e0e371d68e","abstract_canon_sha256":"1dcf48815f18b0582df51ec03e09d52a4f6c0b90eed802ec1a269cfe6db76c24"},"schema_version":"1.0"},"canonical_sha256":"8e2ea1198d5c0a909a7a61ae81595569b5c8e812cca304538ba269d169b19139","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:35.223106Z","signature_b64":"vOpaQ1hkWy6rQslqFgVHNrRU5YMxLFwYUJJsHTpRJ6Iz9INs58VLHvfbD7P3sWpW/7Mo8GQpG1DbbgFegg6bDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e2ea1198d5c0a909a7a61ae81595569b5c8e812cca304538ba269d169b19139","last_reissued_at":"2026-05-17T23:47:35.222737Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:35.222737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.12413","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:47:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+z9bxFrgejpOlAvu44YBwO9dHzljalhEauKWWhidhMi29oPAOOap2szUPhT0rhjSc/bI/oegDjZd+2VZCKUkBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T05:02:09.310988Z"},"content_sha256":"af27f90566b20fe70e2c6b3f55915ef4c83cde440669a9c804042a39b0fd7d75","schema_version":"1.0","event_id":"sha256:af27f90566b20fe70e2c6b3f55915ef4c83cde440669a9c804042a39b0fd7d75"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:RYXKCGMNLQFJBGT2MGXICWKVNG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A convolution recurrent autoencoder for spatio-temporal missing data imputation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Amelia Regan, Reza Asadi","submitted_at":"2019-04-29T01:12:28Z","abstract_excerpt":"When sensors collect spatio-temporal data in a large geographical area, the existence of missing data cannot be escaped. Missing data negatively impacts the performance of data analysis and machine learning algorithms. In this paper, we study deep autoencoders for missing data imputation in spatio-temporal problems. We propose a convolution bidirectional-LSTM for capturing spatial and temporal patterns. Moreover, we analyze an autoencoder's latent feature representation in spatio-temporal data and illustrate its performance for missing data imputation. Traffic flow data are used for evaluation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12413","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":""},"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":"2026-05-17T23:47:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eEAe7BamuvQbp+/c9Uk9due/3bbPEt3P6o2wKug79lgA09+q7xJpDTyRfp+HR1hU6N03lOuMvw/zo/aFaXQyAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T05:02:09.311713Z"},"content_sha256":"401162f4d95545d35b6e9ca0598158ddde2724c7a2ddbcbfc9619e55f4b5c144","schema_version":"1.0","event_id":"sha256:401162f4d95545d35b6e9ca0598158ddde2724c7a2ddbcbfc9619e55f4b5c144"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RYXKCGMNLQFJBGT2MGXICWKVNG/bundle.json","state_url":"https://pith.science/pith/RYXKCGMNLQFJBGT2MGXICWKVNG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RYXKCGMNLQFJBGT2MGXICWKVNG/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":"2026-06-11T05:02:09Z","links":{"resolver":"https://pith.science/pith/RYXKCGMNLQFJBGT2MGXICWKVNG","bundle":"https://pith.science/pith/RYXKCGMNLQFJBGT2MGXICWKVNG/bundle.json","state":"https://pith.science/pith/RYXKCGMNLQFJBGT2MGXICWKVNG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RYXKCGMNLQFJBGT2MGXICWKVNG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:RYXKCGMNLQFJBGT2MGXICWKVNG","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":"1dcf48815f18b0582df51ec03e09d52a4f6c0b90eed802ec1a269cfe6db76c24","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T01:12:28Z","title_canon_sha256":"e25bb1a88a13573e3e7a34d3ba1ba8ae4229ac36891f8dce13ba35e0e371d68e"},"schema_version":"1.0","source":{"id":"1904.12413","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.12413","created_at":"2026-05-17T23:47:35Z"},{"alias_kind":"arxiv_version","alias_value":"1904.12413v1","created_at":"2026-05-17T23:47:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12413","created_at":"2026-05-17T23:47:35Z"},{"alias_kind":"pith_short_12","alias_value":"RYXKCGMNLQFJ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RYXKCGMNLQFJBGT2","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RYXKCGMN","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:401162f4d95545d35b6e9ca0598158ddde2724c7a2ddbcbfc9619e55f4b5c144","target":"graph","created_at":"2026-05-17T23:47:35Z","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"},"paper":{"abstract_excerpt":"When sensors collect spatio-temporal data in a large geographical area, the existence of missing data cannot be escaped. Missing data negatively impacts the performance of data analysis and machine learning algorithms. In this paper, we study deep autoencoders for missing data imputation in spatio-temporal problems. We propose a convolution bidirectional-LSTM for capturing spatial and temporal patterns. Moreover, we analyze an autoencoder's latent feature representation in spatio-temporal data and illustrate its performance for missing data imputation. Traffic flow data are used for evaluation","authors_text":"Amelia Regan, Reza Asadi","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T01:12:28Z","title":"A convolution recurrent autoencoder for spatio-temporal missing data imputation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12413","kind":"arxiv","version":1},"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:af27f90566b20fe70e2c6b3f55915ef4c83cde440669a9c804042a39b0fd7d75","target":"record","created_at":"2026-05-17T23:47:35Z","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":"1dcf48815f18b0582df51ec03e09d52a4f6c0b90eed802ec1a269cfe6db76c24","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T01:12:28Z","title_canon_sha256":"e25bb1a88a13573e3e7a34d3ba1ba8ae4229ac36891f8dce13ba35e0e371d68e"},"schema_version":"1.0","source":{"id":"1904.12413","kind":"arxiv","version":1}},"canonical_sha256":"8e2ea1198d5c0a909a7a61ae81595569b5c8e812cca304538ba269d169b19139","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8e2ea1198d5c0a909a7a61ae81595569b5c8e812cca304538ba269d169b19139","first_computed_at":"2026-05-17T23:47:35.222737Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:35.222737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vOpaQ1hkWy6rQslqFgVHNrRU5YMxLFwYUJJsHTpRJ6Iz9INs58VLHvfbD7P3sWpW/7Mo8GQpG1DbbgFegg6bDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:35.223106Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.12413","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:af27f90566b20fe70e2c6b3f55915ef4c83cde440669a9c804042a39b0fd7d75","sha256:401162f4d95545d35b6e9ca0598158ddde2724c7a2ddbcbfc9619e55f4b5c144"],"state_sha256":"3854ca545200d3931f9ee5f75040c066a733590ce865829a45737ac798edf25c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BUBzhz+NFeX8xyLWI0Q+vNN3nLLlKS7x2rWi46jX/Emiqz/zp09wFD6Jnl3Ov1TFtJcKBzuuEIPgkTCIKeLVCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T05:02:09.315903Z","bundle_sha256":"f5c1fea6c2aed16d878193606e255067eb0fdac60f695a3f9fcfac6820bb8aab"}}