{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TEXFHD7U5GXW4MUCXZ4RQQL6UD","short_pith_number":"pith:TEXFHD7U","canonical_record":{"source":{"id":"2605.19861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T13:53:02Z","cross_cats_sorted":[],"title_canon_sha256":"9102ab5b38fd23248f70625bc7b44a713e3403482b8a31116f785619e7bb8109","abstract_canon_sha256":"7255b1ff3bbee8ae913042a3ae4668ce4a6949e7a7eefc4aa150e72cfdfe68e4"},"schema_version":"1.0"},"canonical_sha256":"992e538ff4e9af6e3282be7918417ea0db9070ac5c903cb762e7abb4f215a8db","source":{"kind":"arxiv","id":"2605.19861","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19861","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19861v1","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19861","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"pith_short_12","alias_value":"TEXFHD7U5GXW","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"pith_short_16","alias_value":"TEXFHD7U5GXW4MUC","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"pith_short_8","alias_value":"TEXFHD7U","created_at":"2026-05-20T01:06:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TEXFHD7U5GXW4MUCXZ4RQQL6UD","target":"record","payload":{"canonical_record":{"source":{"id":"2605.19861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T13:53:02Z","cross_cats_sorted":[],"title_canon_sha256":"9102ab5b38fd23248f70625bc7b44a713e3403482b8a31116f785619e7bb8109","abstract_canon_sha256":"7255b1ff3bbee8ae913042a3ae4668ce4a6949e7a7eefc4aa150e72cfdfe68e4"},"schema_version":"1.0"},"canonical_sha256":"992e538ff4e9af6e3282be7918417ea0db9070ac5c903cb762e7abb4f215a8db","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:06:18.068632Z","signature_b64":"YxR/RWongcXSNSu3Eqt3ogS9CJbu0QK8XGtCdhiuQV5pA+bwhlEG/0zCsyS4pLoFV17Zrfix9z4zdC0JR4nZBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"992e538ff4e9af6e3282be7918417ea0db9070ac5c903cb762e7abb4f215a8db","last_reissued_at":"2026-05-20T01:06:18.067918Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:06:18.067918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.19861","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":"2026-05-20T01:06:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nWdsLHjmUlIPe/XRGHMBK58f5CvpD7FpbTtWHanfROGmtkVOLiflEtc7aqZD3HDuBHuDw6kXt5ORv3rTBHVpAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T23:50:32.545691Z"},"content_sha256":"6da607a8886884473ee3935c6aa415c926a55abbae48edebd47f8e52e843aa04","schema_version":"1.0","event_id":"sha256:6da607a8886884473ee3935c6aa415c926a55abbae48edebd47f8e52e843aa04"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TEXFHD7U5GXW4MUCXZ4RQQL6UD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stationary subspace analysis for spatial data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Anne M. Ruiz, Jaakko Pere, Klaus Nordhausen, Perttu Saarela","submitted_at":"2026-05-19T13:53:02Z","abstract_excerpt":"Stationary subspace analysis (SSA) is a blind source separation framework that decomposes linearly mixed multivariate data into stationary and nonstationary components. We extend SSA to spatially indexed data by introducing spatial stationary subspace analysis (spSSA), which explicitly accounts for spatial dependence. We propose three estimation procedures for the unmixing matrix based on first- and second-order spatial statistics. Each procedure targets a different type of nonstationarity and can be formulated as the solution to a generalized eigenvalue problem. To address situations where mu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19861","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.19861/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":"2026-05-20T01:06:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"euqwt6tIJZY7tP70l4iDv1Wo+LU9G91dmrp5z8/HcOkpS6yq4ByLf3KW8wIRwXFQKEueq6VTlNBizsFDj1WnAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T23:50:32.546266Z"},"content_sha256":"d422eb56d2514fdd04da5da8890ced74b0afdd330da72278399bfef32c578f40","schema_version":"1.0","event_id":"sha256:d422eb56d2514fdd04da5da8890ced74b0afdd330da72278399bfef32c578f40"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TEXFHD7U5GXW4MUCXZ4RQQL6UD/bundle.json","state_url":"https://pith.science/pith/TEXFHD7U5GXW4MUCXZ4RQQL6UD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TEXFHD7U5GXW4MUCXZ4RQQL6UD/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":"2026-05-21T23:50:32Z","links":{"resolver":"https://pith.science/pith/TEXFHD7U5GXW4MUCXZ4RQQL6UD","bundle":"https://pith.science/pith/TEXFHD7U5GXW4MUCXZ4RQQL6UD/bundle.json","state":"https://pith.science/pith/TEXFHD7U5GXW4MUCXZ4RQQL6UD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TEXFHD7U5GXW4MUCXZ4RQQL6UD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TEXFHD7U5GXW4MUCXZ4RQQL6UD","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":"7255b1ff3bbee8ae913042a3ae4668ce4a6949e7a7eefc4aa150e72cfdfe68e4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T13:53:02Z","title_canon_sha256":"9102ab5b38fd23248f70625bc7b44a713e3403482b8a31116f785619e7bb8109"},"schema_version":"1.0","source":{"id":"2605.19861","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19861","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19861v1","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19861","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"pith_short_12","alias_value":"TEXFHD7U5GXW","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"pith_short_16","alias_value":"TEXFHD7U5GXW4MUC","created_at":"2026-05-20T01:06:18Z"},{"alias_kind":"pith_short_8","alias_value":"TEXFHD7U","created_at":"2026-05-20T01:06:18Z"}],"graph_snapshots":[{"event_id":"sha256:d422eb56d2514fdd04da5da8890ced74b0afdd330da72278399bfef32c578f40","target":"graph","created_at":"2026-05-20T01:06:18Z","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/2605.19861/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Stationary subspace analysis (SSA) is a blind source separation framework that decomposes linearly mixed multivariate data into stationary and nonstationary components. We extend SSA to spatially indexed data by introducing spatial stationary subspace analysis (spSSA), which explicitly accounts for spatial dependence. We propose three estimation procedures for the unmixing matrix based on first- and second-order spatial statistics. Each procedure targets a different type of nonstationarity and can be formulated as the solution to a generalized eigenvalue problem. To address situations where mu","authors_text":"Anne M. Ruiz, Jaakko Pere, Klaus Nordhausen, Perttu Saarela","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T13:53:02Z","title":"Stationary subspace analysis for spatial data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19861","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":"sha256:6da607a8886884473ee3935c6aa415c926a55abbae48edebd47f8e52e843aa04","target":"record","created_at":"2026-05-20T01:06:18Z","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":"7255b1ff3bbee8ae913042a3ae4668ce4a6949e7a7eefc4aa150e72cfdfe68e4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T13:53:02Z","title_canon_sha256":"9102ab5b38fd23248f70625bc7b44a713e3403482b8a31116f785619e7bb8109"},"schema_version":"1.0","source":{"id":"2605.19861","kind":"arxiv","version":1}},"canonical_sha256":"992e538ff4e9af6e3282be7918417ea0db9070ac5c903cb762e7abb4f215a8db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"992e538ff4e9af6e3282be7918417ea0db9070ac5c903cb762e7abb4f215a8db","first_computed_at":"2026-05-20T01:06:18.067918Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:06:18.067918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YxR/RWongcXSNSu3Eqt3ogS9CJbu0QK8XGtCdhiuQV5pA+bwhlEG/0zCsyS4pLoFV17Zrfix9z4zdC0JR4nZBw==","signature_status":"signed_v1","signed_at":"2026-05-20T01:06:18.068632Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.19861","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6da607a8886884473ee3935c6aa415c926a55abbae48edebd47f8e52e843aa04","sha256:d422eb56d2514fdd04da5da8890ced74b0afdd330da72278399bfef32c578f40"],"state_sha256":"fa911be04a627fbe7c66e6f11b1dfb3b965e362e57ab9452534fe6e3b5bf5b32"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QZ04NMpKHpoTxf0fFR/nvNwSH82KdeJARtAkfD4cEr7udD1Zg4vCUfSkxIAPV9y2lR+7/ZJo7LaH7s51i8kHCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T23:50:32.548303Z","bundle_sha256":"1a221e5d34bb58b83e0dabb2a4a7544015cf2585c09c230c209b63eb50d4c0b0"}}