{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:UKSKMU5Q3BRX6IPJZUJYNU7GYX","short_pith_number":"pith:UKSKMU5Q","canonical_record":{"source":{"id":"1508.02489","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2015-08-11T05:08:25Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"7fcc7838a42e6fb3fe3592092702c1287fadbb913999c940e92ccca08f9798dd","abstract_canon_sha256":"ee0551b8d7b1a11f4a8d48ffec92ec936a966c0de182d3b1f8f57fef09ded0f4"},"schema_version":"1.0"},"canonical_sha256":"a2a4a653b0d8637f21e9cd1386d3e6c5e862fb364096b84cc0670f4e4681b924","source":{"kind":"arxiv","id":"1508.02489","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.02489","created_at":"2026-05-18T01:35:29Z"},{"alias_kind":"arxiv_version","alias_value":"1508.02489v1","created_at":"2026-05-18T01:35:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.02489","created_at":"2026-05-18T01:35:29Z"},{"alias_kind":"pith_short_12","alias_value":"UKSKMU5Q3BRX","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_16","alias_value":"UKSKMU5Q3BRX6IPJ","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_8","alias_value":"UKSKMU5Q","created_at":"2026-05-18T12:29:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:UKSKMU5Q3BRX6IPJZUJYNU7GYX","target":"record","payload":{"canonical_record":{"source":{"id":"1508.02489","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2015-08-11T05:08:25Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"7fcc7838a42e6fb3fe3592092702c1287fadbb913999c940e92ccca08f9798dd","abstract_canon_sha256":"ee0551b8d7b1a11f4a8d48ffec92ec936a966c0de182d3b1f8f57fef09ded0f4"},"schema_version":"1.0"},"canonical_sha256":"a2a4a653b0d8637f21e9cd1386d3e6c5e862fb364096b84cc0670f4e4681b924","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:35:29.058194Z","signature_b64":"9Jn0IICIV1ngqKhSxiYOPPGJOEIC42y5ihWORvYIIP5t32PWm2h3h+zqbI0FiBKLZueg5eT0jGj6YgDdVEk1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2a4a653b0d8637f21e9cd1386d3e6c5e862fb364096b84cc0670f4e4681b924","last_reissued_at":"2026-05-18T01:35:29.057480Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:35:29.057480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1508.02489","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":"2026-05-18T01:35:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5T/vuQ+hY26VUb0GFP/OQS16pO35SxqFgZw7/NR2eQa3rnt9EvyazHas0EDYetnNMNA5OERqdBuF+3cLYBylBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T06:42:24.342005Z"},"content_sha256":"45a5df8a94c38ff6117e99ca51379df640d580c90b23e8211f25514c3d93f394","schema_version":"1.0","event_id":"sha256:45a5df8a94c38ff6117e99ca51379df640d580c90b23e8211f25514c3d93f394"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:UKSKMU5Q3BRX6IPJZUJYNU7GYX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Probabilistic Power Flow Computation via Low-Rank and Sparse Tensor Recovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"cs.CE","authors_text":"Hung Dinh Nguyen, Konstantin Turitsyn, Luca Daniel, Zheng Zhang","submitted_at":"2015-08-11T05:08:25Z","abstract_excerpt":"This paper presents a tensor-recovery method to solve probabilistic power flow problems. Our approach generates a high-dimensional and sparse generalized polynomial-chaos expansion that provides useful statistical information. The result can also speed up other essential routines in power systems (e.g., stochastic planning, operations and controls).\n  Instead of simulating a power flow equation at all quadrature points, our approach only simulates an extremely small subset of samples. We suggest a model to exploit the underlying low-rank and sparse structure of high-dimensional simulation data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.02489","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":"2026-05-18T01:35:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lOBo6iCeGp5GTbapbKxrWGixdz8VD/3LVBoGKYkdHyUV7YJGYrG15W0a8jibh4QOUff5g74ssDR5rqRQDjh1Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T06:42:24.342788Z"},"content_sha256":"eadc7adcda272c6e440c3f1dcf96d659c756bba5d07aff2bd00a0013b365a3ef","schema_version":"1.0","event_id":"sha256:eadc7adcda272c6e440c3f1dcf96d659c756bba5d07aff2bd00a0013b365a3ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UKSKMU5Q3BRX6IPJZUJYNU7GYX/bundle.json","state_url":"https://pith.science/pith/UKSKMU5Q3BRX6IPJZUJYNU7GYX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UKSKMU5Q3BRX6IPJZUJYNU7GYX/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":"2026-06-10T06:42:24Z","links":{"resolver":"https://pith.science/pith/UKSKMU5Q3BRX6IPJZUJYNU7GYX","bundle":"https://pith.science/pith/UKSKMU5Q3BRX6IPJZUJYNU7GYX/bundle.json","state":"https://pith.science/pith/UKSKMU5Q3BRX6IPJZUJYNU7GYX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UKSKMU5Q3BRX6IPJZUJYNU7GYX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:UKSKMU5Q3BRX6IPJZUJYNU7GYX","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":"ee0551b8d7b1a11f4a8d48ffec92ec936a966c0de182d3b1f8f57fef09ded0f4","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2015-08-11T05:08:25Z","title_canon_sha256":"7fcc7838a42e6fb3fe3592092702c1287fadbb913999c940e92ccca08f9798dd"},"schema_version":"1.0","source":{"id":"1508.02489","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.02489","created_at":"2026-05-18T01:35:29Z"},{"alias_kind":"arxiv_version","alias_value":"1508.02489v1","created_at":"2026-05-18T01:35:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.02489","created_at":"2026-05-18T01:35:29Z"},{"alias_kind":"pith_short_12","alias_value":"UKSKMU5Q3BRX","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_16","alias_value":"UKSKMU5Q3BRX6IPJ","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_8","alias_value":"UKSKMU5Q","created_at":"2026-05-18T12:29:44Z"}],"graph_snapshots":[{"event_id":"sha256:eadc7adcda272c6e440c3f1dcf96d659c756bba5d07aff2bd00a0013b365a3ef","target":"graph","created_at":"2026-05-18T01:35:29Z","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 presents a tensor-recovery method to solve probabilistic power flow problems. Our approach generates a high-dimensional and sparse generalized polynomial-chaos expansion that provides useful statistical information. The result can also speed up other essential routines in power systems (e.g., stochastic planning, operations and controls).\n  Instead of simulating a power flow equation at all quadrature points, our approach only simulates an extremely small subset of samples. We suggest a model to exploit the underlying low-rank and sparse structure of high-dimensional simulation data","authors_text":"Hung Dinh Nguyen, Konstantin Turitsyn, Luca Daniel, Zheng Zhang","cross_cats":["math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2015-08-11T05:08:25Z","title":"Probabilistic Power Flow Computation via Low-Rank and Sparse Tensor Recovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.02489","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":"sha256:45a5df8a94c38ff6117e99ca51379df640d580c90b23e8211f25514c3d93f394","target":"record","created_at":"2026-05-18T01:35:29Z","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":"ee0551b8d7b1a11f4a8d48ffec92ec936a966c0de182d3b1f8f57fef09ded0f4","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2015-08-11T05:08:25Z","title_canon_sha256":"7fcc7838a42e6fb3fe3592092702c1287fadbb913999c940e92ccca08f9798dd"},"schema_version":"1.0","source":{"id":"1508.02489","kind":"arxiv","version":1}},"canonical_sha256":"a2a4a653b0d8637f21e9cd1386d3e6c5e862fb364096b84cc0670f4e4681b924","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a2a4a653b0d8637f21e9cd1386d3e6c5e862fb364096b84cc0670f4e4681b924","first_computed_at":"2026-05-18T01:35:29.057480Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:35:29.057480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9Jn0IICIV1ngqKhSxiYOPPGJOEIC42y5ihWORvYIIP5t32PWm2h3h+zqbI0FiBKLZueg5eT0jGj6YgDdVEk1Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:35:29.058194Z","signed_message":"canonical_sha256_bytes"},"source_id":"1508.02489","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:45a5df8a94c38ff6117e99ca51379df640d580c90b23e8211f25514c3d93f394","sha256:eadc7adcda272c6e440c3f1dcf96d659c756bba5d07aff2bd00a0013b365a3ef"],"state_sha256":"a6c9e7ea46575aaf3e059cc4066999a3f51affb3ead1797675c89958e12deebe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"npk4Z5lL85fblchgha3eqQsJ0jzaaPciKIIUMC8CBxB8asybeJES5AQhVzkBMu3Rc4uMnQKysX48R3oPt8RIAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T06:42:24.346880Z","bundle_sha256":"08cf701c70b9df8a198d2522add6882c16e87dbc9098ca6fddec78c19fe4a63f"}}