{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CVXJ45233KPNQADPUBHAEPK3I2","short_pith_number":"pith:CVXJ4523","canonical_record":{"source":{"id":"1811.08069","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T04:28:29Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6e7acf6fd69fbf92dc29e2ff2d18cbb9813317724263f139b115465396c0b645","abstract_canon_sha256":"be6c1a478fafc13f16c6e94b614c4d95ddb5e785aecddb6e1acd5a5077f7fc9e"},"schema_version":"1.0"},"canonical_sha256":"156e9e775bda9ed8006fa04e023d5b46bd1e566f662159535bd27432e6fcdaaa","source":{"kind":"arxiv","id":"1811.08069","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.08069","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"arxiv_version","alias_value":"1811.08069v1","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.08069","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"pith_short_12","alias_value":"CVXJ45233KPN","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"CVXJ45233KPNQADP","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"CVXJ4523","created_at":"2026-05-18T12:32:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CVXJ45233KPNQADPUBHAEPK3I2","target":"record","payload":{"canonical_record":{"source":{"id":"1811.08069","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T04:28:29Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6e7acf6fd69fbf92dc29e2ff2d18cbb9813317724263f139b115465396c0b645","abstract_canon_sha256":"be6c1a478fafc13f16c6e94b614c4d95ddb5e785aecddb6e1acd5a5077f7fc9e"},"schema_version":"1.0"},"canonical_sha256":"156e9e775bda9ed8006fa04e023d5b46bd1e566f662159535bd27432e6fcdaaa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:16.962735Z","signature_b64":"EOEs4+XPYVUIP2faeZQDgB6dnZcACqdnoTtcdieCxwqMKbTKnUBDMrba8gR6Od9o2cxymNK+FoEDegNJ7VViAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"156e9e775bda9ed8006fa04e023d5b46bd1e566f662159535bd27432e6fcdaaa","last_reissued_at":"2026-05-18T00:00:16.962017Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:16.962017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.08069","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":"2026-05-18T00:00:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EZ0hD6uYobUvWdKWGowYzk37qsAx/5zYUH/0DXB8/damnKE/Hm9OlAEh6JcHGqGIPHkgLLkaq2EMuYxEKDOODA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T04:51:23.548017Z"},"content_sha256":"95b1652f7f7e90afed43f216f57d17ad3f72e58fe3fd19b1a5e6e47ca1769cd3","schema_version":"1.0","event_id":"sha256:95b1652f7f7e90afed43f216f57d17ad3f72e58fe3fd19b1a5e6e47ca1769cd3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CVXJ45233KPNQADPUBHAEPK3I2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Representation Learning of Pedestrian Trajectories Using Actor-Critic Sequence-to-Sequence Autoencoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Anish Hiranandani, Ka-Ho Chow, S.-H. Gary Chan, Yifeng Zhang","submitted_at":"2018-11-20T04:28:29Z","abstract_excerpt":"Representation learning of pedestrian trajectories transforms variable-length timestamp-coordinate tuples of a trajectory into a fixed-length vector representation that summarizes spatiotemporal characteristics. It is a crucial technique to connect feature-based data mining with trajectory data. Trajectory representation is a challenging problem, because both environmental constraints (e.g., wall partitions) and temporal user dynamics should be meticulously considered and accounted for. Furthermore, traditional sequence-to-sequence autoencoders using maximum log-likelihood often require datase"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.08069","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":"2026-05-18T00:00:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ainwc4QRFx42e86RWXRpFvNUMHsJb1qOpgAOo3P1eGLryEcGe3r+FMZrVQDmYWpQja/BeU7btVor8dkoOcWGBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T04:51:23.548400Z"},"content_sha256":"cd00e8d15723f70aa70825975d95be61dd3e995cf6c3f4f7e3d21dc7a0276f07","schema_version":"1.0","event_id":"sha256:cd00e8d15723f70aa70825975d95be61dd3e995cf6c3f4f7e3d21dc7a0276f07"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CVXJ45233KPNQADPUBHAEPK3I2/bundle.json","state_url":"https://pith.science/pith/CVXJ45233KPNQADPUBHAEPK3I2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CVXJ45233KPNQADPUBHAEPK3I2/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":"2026-06-05T04:51:23Z","links":{"resolver":"https://pith.science/pith/CVXJ45233KPNQADPUBHAEPK3I2","bundle":"https://pith.science/pith/CVXJ45233KPNQADPUBHAEPK3I2/bundle.json","state":"https://pith.science/pith/CVXJ45233KPNQADPUBHAEPK3I2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CVXJ45233KPNQADPUBHAEPK3I2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CVXJ45233KPNQADPUBHAEPK3I2","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":"be6c1a478fafc13f16c6e94b614c4d95ddb5e785aecddb6e1acd5a5077f7fc9e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T04:28:29Z","title_canon_sha256":"6e7acf6fd69fbf92dc29e2ff2d18cbb9813317724263f139b115465396c0b645"},"schema_version":"1.0","source":{"id":"1811.08069","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.08069","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"arxiv_version","alias_value":"1811.08069v1","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.08069","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"pith_short_12","alias_value":"CVXJ45233KPN","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"CVXJ45233KPNQADP","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"CVXJ4523","created_at":"2026-05-18T12:32:19Z"}],"graph_snapshots":[{"event_id":"sha256:cd00e8d15723f70aa70825975d95be61dd3e995cf6c3f4f7e3d21dc7a0276f07","target":"graph","created_at":"2026-05-18T00:00:16Z","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":"Representation learning of pedestrian trajectories transforms variable-length timestamp-coordinate tuples of a trajectory into a fixed-length vector representation that summarizes spatiotemporal characteristics. It is a crucial technique to connect feature-based data mining with trajectory data. Trajectory representation is a challenging problem, because both environmental constraints (e.g., wall partitions) and temporal user dynamics should be meticulously considered and accounted for. Furthermore, traditional sequence-to-sequence autoencoders using maximum log-likelihood often require datase","authors_text":"Anish Hiranandani, Ka-Ho Chow, S.-H. Gary Chan, Yifeng Zhang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T04:28:29Z","title":"Representation Learning of Pedestrian Trajectories Using Actor-Critic Sequence-to-Sequence Autoencoder"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.08069","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":"sha256:95b1652f7f7e90afed43f216f57d17ad3f72e58fe3fd19b1a5e6e47ca1769cd3","target":"record","created_at":"2026-05-18T00:00:16Z","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":"be6c1a478fafc13f16c6e94b614c4d95ddb5e785aecddb6e1acd5a5077f7fc9e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T04:28:29Z","title_canon_sha256":"6e7acf6fd69fbf92dc29e2ff2d18cbb9813317724263f139b115465396c0b645"},"schema_version":"1.0","source":{"id":"1811.08069","kind":"arxiv","version":1}},"canonical_sha256":"156e9e775bda9ed8006fa04e023d5b46bd1e566f662159535bd27432e6fcdaaa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"156e9e775bda9ed8006fa04e023d5b46bd1e566f662159535bd27432e6fcdaaa","first_computed_at":"2026-05-18T00:00:16.962017Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:16.962017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EOEs4+XPYVUIP2faeZQDgB6dnZcACqdnoTtcdieCxwqMKbTKnUBDMrba8gR6Od9o2cxymNK+FoEDegNJ7VViAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:16.962735Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.08069","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:95b1652f7f7e90afed43f216f57d17ad3f72e58fe3fd19b1a5e6e47ca1769cd3","sha256:cd00e8d15723f70aa70825975d95be61dd3e995cf6c3f4f7e3d21dc7a0276f07"],"state_sha256":"ea7f7712d3ae1fd7156a87dfa9a3e5e286fd45c372709bec989e80b7fcefa3f9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"npnBwHZ2dDPNRD9zCmO4cu47I2Q0aUzAsfqBNWgSYmbXwG8O0iE7Ccp+L6R0gM7DsMqExcq6el1EQlf9Sy/DDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T04:51:23.550870Z","bundle_sha256":"c1a6296053ba14fdf685011ae2f55c72f0ad59dbbd369500f09ab212c893b204"}}