{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:X5SBHY56U3HNRNX5DH53PNDLLY","short_pith_number":"pith:X5SBHY56","canonical_record":{"source":{"id":"1708.05406","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2017-08-17T18:31:40Z","cross_cats_sorted":[],"title_canon_sha256":"43d8f9739bb11d616a40039bd4505d1b2778a141e084207aba994b6bc2577cbd","abstract_canon_sha256":"5d05a120e61fd82231e2bf4e21bad992f8f63105c162d25594aac887744bc230"},"schema_version":"1.0"},"canonical_sha256":"bf6413e3bea6ced8b6fd19fbb7b46b5e3cd13d7c64620bbc5ec39d8bc5755b03","source":{"kind":"arxiv","id":"1708.05406","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.05406","created_at":"2026-05-17T23:58:56Z"},{"alias_kind":"arxiv_version","alias_value":"1708.05406v2","created_at":"2026-05-17T23:58:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.05406","created_at":"2026-05-17T23:58:56Z"},{"alias_kind":"pith_short_12","alias_value":"X5SBHY56U3HN","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"X5SBHY56U3HNRNX5","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"X5SBHY56","created_at":"2026-05-18T12:31:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:X5SBHY56U3HNRNX5DH53PNDLLY","target":"record","payload":{"canonical_record":{"source":{"id":"1708.05406","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2017-08-17T18:31:40Z","cross_cats_sorted":[],"title_canon_sha256":"43d8f9739bb11d616a40039bd4505d1b2778a141e084207aba994b6bc2577cbd","abstract_canon_sha256":"5d05a120e61fd82231e2bf4e21bad992f8f63105c162d25594aac887744bc230"},"schema_version":"1.0"},"canonical_sha256":"bf6413e3bea6ced8b6fd19fbb7b46b5e3cd13d7c64620bbc5ec39d8bc5755b03","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:56.254904Z","signature_b64":"auEVHhn/B8KFBEHUzgn2ZGw/8C68G+tEKMdE7T81R/fcZCBTXd0GaijRnnTMd9rILHaGfMwr/k6rzkLgp7DaDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf6413e3bea6ced8b6fd19fbb7b46b5e3cd13d7c64620bbc5ec39d8bc5755b03","last_reissued_at":"2026-05-17T23:58:56.254432Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:56.254432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.05406","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":"2026-05-17T23:58:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CtD8pTfH96JvTXP/2inPhBKNFkIA1yeqd9eekrL+sIsEAVVi7GrFqC73N0TOJOOcqiw2q3Q/1amXXEykfITqBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:59:16.066929Z"},"content_sha256":"59a186d5e68dbd5eb8cdaed3c9e48be2487e537ea7c900024cc7e0c6eeb166ce","schema_version":"1.0","event_id":"sha256:59a186d5e68dbd5eb8cdaed3c9e48be2487e537ea7c900024cc7e0c6eeb166ce"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:X5SBHY56U3HNRNX5DH53PNDLLY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.data-an","authors_text":"Erik M. Bollt, Felix Dietrich, Felix P. Kemeth, Ioannis G. Kevrekidis, Katharina Krischer, Kevin H\\\"ohlein, Qianxiao Li, Ronen Talmon, Sindre W. Haugland, Tom Bertalan","submitted_at":"2017-08-17T18:31:40Z","abstract_excerpt":"Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here on the case of time series data that can ultimately be modelled as a spatially distributed system (e.g. a partial differential equation, PDE), but where we do not know the space in which this PDE should be formulated. Hence, even the spatial coordinates for the distributed system themselves need to be identified - to emerge from - the data mining process. We "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.05406","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":""},"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":"2026-05-17T23:58:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hHOSKRC1A7aGdXYi7k8nFU9tPbpAa5COBHdHW9hFpT8h6xxtMuvdUDUN6yVgCZYSXxfAc+XFIHhCgRQqUdoHBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:59:16.067277Z"},"content_sha256":"238e65ec59d6cbe61bf5695281168cf4540678a9e66344ba5a999c5f43a01df3","schema_version":"1.0","event_id":"sha256:238e65ec59d6cbe61bf5695281168cf4540678a9e66344ba5a999c5f43a01df3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X5SBHY56U3HNRNX5DH53PNDLLY/bundle.json","state_url":"https://pith.science/pith/X5SBHY56U3HNRNX5DH53PNDLLY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X5SBHY56U3HNRNX5DH53PNDLLY/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":"2026-06-01T21:59:16Z","links":{"resolver":"https://pith.science/pith/X5SBHY56U3HNRNX5DH53PNDLLY","bundle":"https://pith.science/pith/X5SBHY56U3HNRNX5DH53PNDLLY/bundle.json","state":"https://pith.science/pith/X5SBHY56U3HNRNX5DH53PNDLLY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X5SBHY56U3HNRNX5DH53PNDLLY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:X5SBHY56U3HNRNX5DH53PNDLLY","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":"5d05a120e61fd82231e2bf4e21bad992f8f63105c162d25594aac887744bc230","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2017-08-17T18:31:40Z","title_canon_sha256":"43d8f9739bb11d616a40039bd4505d1b2778a141e084207aba994b6bc2577cbd"},"schema_version":"1.0","source":{"id":"1708.05406","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.05406","created_at":"2026-05-17T23:58:56Z"},{"alias_kind":"arxiv_version","alias_value":"1708.05406v2","created_at":"2026-05-17T23:58:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.05406","created_at":"2026-05-17T23:58:56Z"},{"alias_kind":"pith_short_12","alias_value":"X5SBHY56U3HN","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"X5SBHY56U3HNRNX5","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"X5SBHY56","created_at":"2026-05-18T12:31:53Z"}],"graph_snapshots":[{"event_id":"sha256:238e65ec59d6cbe61bf5695281168cf4540678a9e66344ba5a999c5f43a01df3","target":"graph","created_at":"2026-05-17T23:58:56Z","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":"Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here on the case of time series data that can ultimately be modelled as a spatially distributed system (e.g. a partial differential equation, PDE), but where we do not know the space in which this PDE should be formulated. Hence, even the spatial coordinates for the distributed system themselves need to be identified - to emerge from - the data mining process. We ","authors_text":"Erik M. Bollt, Felix Dietrich, Felix P. Kemeth, Ioannis G. Kevrekidis, Katharina Krischer, Kevin H\\\"ohlein, Qianxiao Li, Ronen Talmon, Sindre W. Haugland, Tom Bertalan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2017-08-17T18:31:40Z","title":"An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.05406","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":"sha256:59a186d5e68dbd5eb8cdaed3c9e48be2487e537ea7c900024cc7e0c6eeb166ce","target":"record","created_at":"2026-05-17T23:58:56Z","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":"5d05a120e61fd82231e2bf4e21bad992f8f63105c162d25594aac887744bc230","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2017-08-17T18:31:40Z","title_canon_sha256":"43d8f9739bb11d616a40039bd4505d1b2778a141e084207aba994b6bc2577cbd"},"schema_version":"1.0","source":{"id":"1708.05406","kind":"arxiv","version":2}},"canonical_sha256":"bf6413e3bea6ced8b6fd19fbb7b46b5e3cd13d7c64620bbc5ec39d8bc5755b03","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bf6413e3bea6ced8b6fd19fbb7b46b5e3cd13d7c64620bbc5ec39d8bc5755b03","first_computed_at":"2026-05-17T23:58:56.254432Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:56.254432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"auEVHhn/B8KFBEHUzgn2ZGw/8C68G+tEKMdE7T81R/fcZCBTXd0GaijRnnTMd9rILHaGfMwr/k6rzkLgp7DaDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:56.254904Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.05406","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:59a186d5e68dbd5eb8cdaed3c9e48be2487e537ea7c900024cc7e0c6eeb166ce","sha256:238e65ec59d6cbe61bf5695281168cf4540678a9e66344ba5a999c5f43a01df3"],"state_sha256":"4ae08f482e9d484a0c4623f2c647fe9a09802281b1900f4fe0e614a7adede9c9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zsI3z4LJcIEP/kq1tcMcSULKVguO0bnTy4F0/795MX+ELBFZ1HSstkQaayaokqI3OvET1Y2tOVoKbAhaQk9TCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T21:59:16.069170Z","bundle_sha256":"30598ddd598e3c7368302a9b2e8aeaa76bdd220b1ce21924046c1f52c51e067c"}}