{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:AKJD52OKT6QQTKQ3LFI3SIB5DI","short_pith_number":"pith:AKJD52OK","canonical_record":{"source":{"id":"1701.07767","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T16:38:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ea00e4a277bb3bf365f2116e19770ae73c6606138e77771c20814b0e65a4ef13","abstract_canon_sha256":"76dbfd157fb9829b699fbfed3498ff4fc724ee8889e4c6d2561d9e2cd43e11fe"},"schema_version":"1.0"},"canonical_sha256":"02923ee9ca9fa109aa1b5951b9203d1a15607afe419eaae047a3ca91ef826d90","source":{"kind":"arxiv","id":"1701.07767","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07767","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07767v1","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07767","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"pith_short_12","alias_value":"AKJD52OKT6QQ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"AKJD52OKT6QQTKQ3","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"AKJD52OK","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:AKJD52OKT6QQTKQ3LFI3SIB5DI","target":"record","payload":{"canonical_record":{"source":{"id":"1701.07767","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T16:38:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ea00e4a277bb3bf365f2116e19770ae73c6606138e77771c20814b0e65a4ef13","abstract_canon_sha256":"76dbfd157fb9829b699fbfed3498ff4fc724ee8889e4c6d2561d9e2cd43e11fe"},"schema_version":"1.0"},"canonical_sha256":"02923ee9ca9fa109aa1b5951b9203d1a15607afe419eaae047a3ca91ef826d90","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:02.621643Z","signature_b64":"4Q+b/LZGLeDhKuntTMknRr4FSZIK1Zg3S59Exd+R03pnYo0H2bcjVWToLD8y8DMZymbYje2CkoZ6PpQIIQ4HDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02923ee9ca9fa109aa1b5951b9203d1a15607afe419eaae047a3ca91ef826d90","last_reissued_at":"2026-05-18T00:52:02.621064Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:02.621064Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.07767","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":"2026-05-18T00:52:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ml+Vl+ePl451ihMZdYZ8BvZwcx/SYNGua9vRQ9Y/I6NCBZxa/V2iPn/7Qn1L818qWPNwFZeCsKNKqwPkcUXOAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:41:11.940446Z"},"content_sha256":"905e636b34c0edccd1a2b8d442c10fbae65719c55a81e172575919cc9bc4d5aa","schema_version":"1.0","event_id":"sha256:905e636b34c0edccd1a2b8d442c10fbae65719c55a81e172575919cc9bc4d5aa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:AKJD52OKT6QQTKQ3LFI3SIB5DI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"David S. Wack, Henry E. Baidoo-Williams, Jean M. Vettel, Konstantinos Slavakis, Matthew Cieslak, Sarah F. Muldoon, Scott T. Grafton, Shiva Salsabilian","submitted_at":"2017-01-26T16:38:00Z","abstract_excerpt":"This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis. Time-series data, collected sequentially over time and across a network, yield features which are viewed as points in or close to a union of multiple submanifolds of a Riemannian manifold, and distinguishing disparate time series amounts to clustering multiple Riemannian submanifolds. To support the claim that exploiting the latent Riemannian geometry behind many statistical features of time series is beneficial to learning from network data, this paper focuses on brain n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07767","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":"2026-05-18T00:52:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AtAqFV0Qry+Zq3eHJZN6Bnd3J2XYfZ2JBc3YdkWq4pD0PkKwTrtMKiFdNmCQUArpD+B+lhiolPT2x4ThYZlmCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:41:11.940864Z"},"content_sha256":"8699e6d467028b2bea56e3fea016ef8a06262b01166794d417d411e0f3e76161","schema_version":"1.0","event_id":"sha256:8699e6d467028b2bea56e3fea016ef8a06262b01166794d417d411e0f3e76161"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AKJD52OKT6QQTKQ3LFI3SIB5DI/bundle.json","state_url":"https://pith.science/pith/AKJD52OKT6QQTKQ3LFI3SIB5DI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AKJD52OKT6QQTKQ3LFI3SIB5DI/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":"2026-05-25T19:41:11Z","links":{"resolver":"https://pith.science/pith/AKJD52OKT6QQTKQ3LFI3SIB5DI","bundle":"https://pith.science/pith/AKJD52OKT6QQTKQ3LFI3SIB5DI/bundle.json","state":"https://pith.science/pith/AKJD52OKT6QQTKQ3LFI3SIB5DI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AKJD52OKT6QQTKQ3LFI3SIB5DI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:AKJD52OKT6QQTKQ3LFI3SIB5DI","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":"76dbfd157fb9829b699fbfed3498ff4fc724ee8889e4c6d2561d9e2cd43e11fe","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T16:38:00Z","title_canon_sha256":"ea00e4a277bb3bf365f2116e19770ae73c6606138e77771c20814b0e65a4ef13"},"schema_version":"1.0","source":{"id":"1701.07767","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07767","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07767v1","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07767","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"pith_short_12","alias_value":"AKJD52OKT6QQ","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"AKJD52OKT6QQTKQ3","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"AKJD52OK","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:8699e6d467028b2bea56e3fea016ef8a06262b01166794d417d411e0f3e76161","target":"graph","created_at":"2026-05-18T00:52:02Z","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":"This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis. Time-series data, collected sequentially over time and across a network, yield features which are viewed as points in or close to a union of multiple submanifolds of a Riemannian manifold, and distinguishing disparate time series amounts to clustering multiple Riemannian submanifolds. To support the claim that exploiting the latent Riemannian geometry behind many statistical features of time series is beneficial to learning from network data, this paper focuses on brain n","authors_text":"David S. Wack, Henry E. Baidoo-Williams, Jean M. Vettel, Konstantinos Slavakis, Matthew Cieslak, Sarah F. Muldoon, Scott T. Grafton, Shiva Salsabilian","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T16:38:00Z","title":"Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07767","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":"sha256:905e636b34c0edccd1a2b8d442c10fbae65719c55a81e172575919cc9bc4d5aa","target":"record","created_at":"2026-05-18T00:52:02Z","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":"76dbfd157fb9829b699fbfed3498ff4fc724ee8889e4c6d2561d9e2cd43e11fe","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T16:38:00Z","title_canon_sha256":"ea00e4a277bb3bf365f2116e19770ae73c6606138e77771c20814b0e65a4ef13"},"schema_version":"1.0","source":{"id":"1701.07767","kind":"arxiv","version":1}},"canonical_sha256":"02923ee9ca9fa109aa1b5951b9203d1a15607afe419eaae047a3ca91ef826d90","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"02923ee9ca9fa109aa1b5951b9203d1a15607afe419eaae047a3ca91ef826d90","first_computed_at":"2026-05-18T00:52:02.621064Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:52:02.621064Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Q+b/LZGLeDhKuntTMknRr4FSZIK1Zg3S59Exd+R03pnYo0H2bcjVWToLD8y8DMZymbYje2CkoZ6PpQIIQ4HDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:52:02.621643Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.07767","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:905e636b34c0edccd1a2b8d442c10fbae65719c55a81e172575919cc9bc4d5aa","sha256:8699e6d467028b2bea56e3fea016ef8a06262b01166794d417d411e0f3e76161"],"state_sha256":"77166a0793c6f923bc71d94481a8e3710f2f4ee6c65f2f5a825b8ad68b9eb6a3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2yO5KYxjA72xu3brC37358eQeE2v6PABkVc+azjLg2T+090Rz5lWdYXfDmc65soFdd8Ccbrg1nHHOTbsgcO1Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T19:41:11.944398Z","bundle_sha256":"433b1be31752c6e57100bf77aa4555d071796ab1b62248cdfd8aa36de7b98c35"}}