{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:7LKJNL2T64I7HTDXZEL3VRXZWX","short_pith_number":"pith:7LKJNL2T","canonical_record":{"source":{"id":"1910.08842","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-19T20:47:13Z","cross_cats_sorted":["eess.SP","stat.ML"],"title_canon_sha256":"303884ad4ab1d68facff485a61d55480ec807791e719cca7f135e4434b21a8df","abstract_canon_sha256":"ed3305664de63997475b08c6e0debd6e0552a02c663cd5f84456d4ef6d50ddc8"},"schema_version":"1.0"},"canonical_sha256":"fad496af53f711f3cc77c917bac6f9b5d020bcf9372a32994b0e0e0d8668f1ee","source":{"kind":"arxiv","id":"1910.08842","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.08842","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"arxiv_version","alias_value":"1910.08842v1","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.08842","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"pith_short_12","alias_value":"7LKJNL2T64I7","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"pith_short_16","alias_value":"7LKJNL2T64I7HTDX","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"pith_short_8","alias_value":"7LKJNL2T","created_at":"2026-07-05T00:13:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:7LKJNL2T64I7HTDXZEL3VRXZWX","target":"record","payload":{"canonical_record":{"source":{"id":"1910.08842","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-19T20:47:13Z","cross_cats_sorted":["eess.SP","stat.ML"],"title_canon_sha256":"303884ad4ab1d68facff485a61d55480ec807791e719cca7f135e4434b21a8df","abstract_canon_sha256":"ed3305664de63997475b08c6e0debd6e0552a02c663cd5f84456d4ef6d50ddc8"},"schema_version":"1.0"},"canonical_sha256":"fad496af53f711f3cc77c917bac6f9b5d020bcf9372a32994b0e0e0d8668f1ee","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:13:31.335199Z","signature_b64":"u6sRrKfpvJvWDRE4Z+gsDxtCg3xqO2jw+3rvpFCx3yJ+vh4TJWixkN8j4TY9J0omh4GKiMzlSLwZDUWJlEWTCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fad496af53f711f3cc77c917bac6f9b5d020bcf9372a32994b0e0e0d8668f1ee","last_reissued_at":"2026-07-05T00:13:31.334803Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:13:31.334803Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.08842","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":"2026-07-05T00:13:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UroyakdRmbgvePztWRRfxP1OCSpYxf+BxqDlbUZ5oDz8f93W3rXULYzUzGPqzufkQTnnY5u5JbbnibXRNSNRCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:13:59.385152Z"},"content_sha256":"84031107f92930267a77723399bf3bb99e16dd2d5b53495a692db9db95eb052e","schema_version":"1.0","event_id":"sha256:84031107f92930267a77723399bf3bb99e16dd2d5b53495a692db9db95eb052e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:7LKJNL2T64I7HTDXZEL3VRXZWX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine Learning for AC Optimal Power Flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arun Majumdar, Matt Wytock, Neel Guha, Zhecheng Wang","submitted_at":"2019-10-19T20:47:13Z","abstract_excerpt":"We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.08842","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/1910.08842/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":"2026-07-05T00:13:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VScFVv1YoJLioIoq3SGj5aNQBou5FAJN08HYGJmE9nG/CbUFWiLRWQwQGwXf4mrzRPu2G+DPi8fKbmibOPQSCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:13:59.385648Z"},"content_sha256":"e0553163c41ee517cd76be2c61aa094c60430ab14f9cc051388f4d0c8fa758d6","schema_version":"1.0","event_id":"sha256:e0553163c41ee517cd76be2c61aa094c60430ab14f9cc051388f4d0c8fa758d6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7LKJNL2T64I7HTDXZEL3VRXZWX/bundle.json","state_url":"https://pith.science/pith/7LKJNL2T64I7HTDXZEL3VRXZWX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7LKJNL2T64I7HTDXZEL3VRXZWX/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":"2026-08-10T18:13:59Z","links":{"resolver":"https://pith.science/pith/7LKJNL2T64I7HTDXZEL3VRXZWX","bundle":"https://pith.science/pith/7LKJNL2T64I7HTDXZEL3VRXZWX/bundle.json","state":"https://pith.science/pith/7LKJNL2T64I7HTDXZEL3VRXZWX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7LKJNL2T64I7HTDXZEL3VRXZWX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:7LKJNL2T64I7HTDXZEL3VRXZWX","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":"ed3305664de63997475b08c6e0debd6e0552a02c663cd5f84456d4ef6d50ddc8","cross_cats_sorted":["eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-19T20:47:13Z","title_canon_sha256":"303884ad4ab1d68facff485a61d55480ec807791e719cca7f135e4434b21a8df"},"schema_version":"1.0","source":{"id":"1910.08842","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.08842","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"arxiv_version","alias_value":"1910.08842v1","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.08842","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"pith_short_12","alias_value":"7LKJNL2T64I7","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"pith_short_16","alias_value":"7LKJNL2T64I7HTDX","created_at":"2026-07-05T00:13:31Z"},{"alias_kind":"pith_short_8","alias_value":"7LKJNL2T","created_at":"2026-07-05T00:13:31Z"}],"graph_snapshots":[{"event_id":"sha256:e0553163c41ee517cd76be2c61aa094c60430ab14f9cc051388f4d0c8fa758d6","target":"graph","created_at":"2026-07-05T00:13:31Z","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/1910.08842/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids.","authors_text":"Arun Majumdar, Matt Wytock, Neel Guha, Zhecheng Wang","cross_cats":["eess.SP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-19T20:47:13Z","title":"Machine Learning for AC Optimal Power Flow"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.08842","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":"sha256:84031107f92930267a77723399bf3bb99e16dd2d5b53495a692db9db95eb052e","target":"record","created_at":"2026-07-05T00:13:31Z","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":"ed3305664de63997475b08c6e0debd6e0552a02c663cd5f84456d4ef6d50ddc8","cross_cats_sorted":["eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-19T20:47:13Z","title_canon_sha256":"303884ad4ab1d68facff485a61d55480ec807791e719cca7f135e4434b21a8df"},"schema_version":"1.0","source":{"id":"1910.08842","kind":"arxiv","version":1}},"canonical_sha256":"fad496af53f711f3cc77c917bac6f9b5d020bcf9372a32994b0e0e0d8668f1ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fad496af53f711f3cc77c917bac6f9b5d020bcf9372a32994b0e0e0d8668f1ee","first_computed_at":"2026-07-05T00:13:31.334803Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:13:31.334803Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u6sRrKfpvJvWDRE4Z+gsDxtCg3xqO2jw+3rvpFCx3yJ+vh4TJWixkN8j4TY9J0omh4GKiMzlSLwZDUWJlEWTCw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:13:31.335199Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.08842","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:84031107f92930267a77723399bf3bb99e16dd2d5b53495a692db9db95eb052e","sha256:e0553163c41ee517cd76be2c61aa094c60430ab14f9cc051388f4d0c8fa758d6"],"state_sha256":"b836f1ab587acbc169908960447ee23c6b7fe9978664679fb5adf1e2276ff66b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FLl2to2a7AsZEJSPzR9t5q80PWoCYTC0QcO7F4NMoskRtmnCw40NAy9iVRywwd0lLy0/pjZhkrP2mecU5kCZBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T18:13:59.390172Z","bundle_sha256":"2d831efca7e4b0e471bae5450e28a99493ed062d5a60b948b2b919e4a31de124"}}