{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:IVP2OP5IYGB3J6M7T5HJIEPVNS","short_pith_number":"pith:IVP2OP5I","canonical_record":{"source":{"id":"1610.08568","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-10-26T23:05:09Z","cross_cats_sorted":[],"title_canon_sha256":"c52b2f443e3098cae474151cf085bbe4c929a225732358dbeacca867729c9bd0","abstract_canon_sha256":"e8421c6ef71d6e4885dd86e94b0b6d6db9cd38ffe2cbdafdeabcd3ea77731f1d"},"schema_version":"1.0"},"canonical_sha256":"455fa73fa8c183b4f99f9f4e9411f56cb3b0bbeaa20a7f9507ebf114090e4785","source":{"kind":"arxiv","id":"1610.08568","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.08568","created_at":"2026-05-18T01:01:07Z"},{"alias_kind":"arxiv_version","alias_value":"1610.08568v1","created_at":"2026-05-18T01:01:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.08568","created_at":"2026-05-18T01:01:07Z"},{"alias_kind":"pith_short_12","alias_value":"IVP2OP5IYGB3","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"IVP2OP5IYGB3J6M7","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"IVP2OP5I","created_at":"2026-05-18T12:30:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:IVP2OP5IYGB3J6M7T5HJIEPVNS","target":"record","payload":{"canonical_record":{"source":{"id":"1610.08568","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-10-26T23:05:09Z","cross_cats_sorted":[],"title_canon_sha256":"c52b2f443e3098cae474151cf085bbe4c929a225732358dbeacca867729c9bd0","abstract_canon_sha256":"e8421c6ef71d6e4885dd86e94b0b6d6db9cd38ffe2cbdafdeabcd3ea77731f1d"},"schema_version":"1.0"},"canonical_sha256":"455fa73fa8c183b4f99f9f4e9411f56cb3b0bbeaa20a7f9507ebf114090e4785","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:01:07.840836Z","signature_b64":"yX5gefaZtYcYJpdNgsj5G8cf5/EJB10lN0cwJK9IdL7SFPwh6uXLTf/C4a+2iAfwlr0bQnpoJMkRM+OsBpTQBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"455fa73fa8c183b4f99f9f4e9411f56cb3b0bbeaa20a7f9507ebf114090e4785","last_reissued_at":"2026-05-18T01:01:07.840164Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:01:07.840164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.08568","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":"2026-05-18T01:01:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uWr06VXxyVdGfhO7yBj3RAzXrpBkq8UTHXJ22b9w8hQhKPM76GHIbJ4tCZXgoUVUxV23tqJGRP0oVqESTlWWBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T22:19:00.481430Z"},"content_sha256":"8d252c2f343c15e22d7b2062e107cfab569dca582e4bd6daeb69756e03faa6f1","schema_version":"1.0","event_id":"sha256:8d252c2f343c15e22d7b2062e107cfab569dca582e4bd6daeb69756e03faa6f1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:IVP2OP5IYGB3J6M7T5HJIEPVNS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stochastic First-Order Minimization Techniques Using Jensen Surrogates for X-Ray Transmission Tomography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"David G. Politte, Joseph A. O'Sullivan, Soysal Degirmenci","submitted_at":"2016-10-26T23:05:09Z","abstract_excerpt":"Image reconstruction in X-ray transmission tomography has been an important research field for decades. In light of data volume increasing faster than processor speeds, one needs accelerated iterative algorithms to solve the optimization problem in the X-ray CT application. Incremental methods, in which a subset of data is being used at each iteration to accelerate the computations, have been getting more popular lately in the machine learning and mathematical optimization fields. The most popular member of this family of algorithms in the X-ray CT field is ordered-subsets. Even though it perf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.08568","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":"2026-05-18T01:01:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N9Rkxjssh7QG94LjNWZ3wkCqN/mUm1RnloM8teQNPs4AMhVu8kRskSj1/z8LvDHK8xRlFSKq5neNVErXbN5gDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T22:19:00.481765Z"},"content_sha256":"bfca748aec8de6efb031979e778a040a74c98c9978f1d16755f303a104b16cd1","schema_version":"1.0","event_id":"sha256:bfca748aec8de6efb031979e778a040a74c98c9978f1d16755f303a104b16cd1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IVP2OP5IYGB3J6M7T5HJIEPVNS/bundle.json","state_url":"https://pith.science/pith/IVP2OP5IYGB3J6M7T5HJIEPVNS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IVP2OP5IYGB3J6M7T5HJIEPVNS/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":"2026-06-24T22:19:00Z","links":{"resolver":"https://pith.science/pith/IVP2OP5IYGB3J6M7T5HJIEPVNS","bundle":"https://pith.science/pith/IVP2OP5IYGB3J6M7T5HJIEPVNS/bundle.json","state":"https://pith.science/pith/IVP2OP5IYGB3J6M7T5HJIEPVNS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IVP2OP5IYGB3J6M7T5HJIEPVNS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:IVP2OP5IYGB3J6M7T5HJIEPVNS","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":"e8421c6ef71d6e4885dd86e94b0b6d6db9cd38ffe2cbdafdeabcd3ea77731f1d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-10-26T23:05:09Z","title_canon_sha256":"c52b2f443e3098cae474151cf085bbe4c929a225732358dbeacca867729c9bd0"},"schema_version":"1.0","source":{"id":"1610.08568","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.08568","created_at":"2026-05-18T01:01:07Z"},{"alias_kind":"arxiv_version","alias_value":"1610.08568v1","created_at":"2026-05-18T01:01:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.08568","created_at":"2026-05-18T01:01:07Z"},{"alias_kind":"pith_short_12","alias_value":"IVP2OP5IYGB3","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"IVP2OP5IYGB3J6M7","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"IVP2OP5I","created_at":"2026-05-18T12:30:22Z"}],"graph_snapshots":[{"event_id":"sha256:bfca748aec8de6efb031979e778a040a74c98c9978f1d16755f303a104b16cd1","target":"graph","created_at":"2026-05-18T01:01:07Z","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":"Image reconstruction in X-ray transmission tomography has been an important research field for decades. In light of data volume increasing faster than processor speeds, one needs accelerated iterative algorithms to solve the optimization problem in the X-ray CT application. Incremental methods, in which a subset of data is being used at each iteration to accelerate the computations, have been getting more popular lately in the machine learning and mathematical optimization fields. The most popular member of this family of algorithms in the X-ray CT field is ordered-subsets. Even though it perf","authors_text":"David G. Politte, Joseph A. O'Sullivan, Soysal Degirmenci","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-10-26T23:05:09Z","title":"Stochastic First-Order Minimization Techniques Using Jensen Surrogates for X-Ray Transmission Tomography"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.08568","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":"sha256:8d252c2f343c15e22d7b2062e107cfab569dca582e4bd6daeb69756e03faa6f1","target":"record","created_at":"2026-05-18T01:01:07Z","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":"e8421c6ef71d6e4885dd86e94b0b6d6db9cd38ffe2cbdafdeabcd3ea77731f1d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-10-26T23:05:09Z","title_canon_sha256":"c52b2f443e3098cae474151cf085bbe4c929a225732358dbeacca867729c9bd0"},"schema_version":"1.0","source":{"id":"1610.08568","kind":"arxiv","version":1}},"canonical_sha256":"455fa73fa8c183b4f99f9f4e9411f56cb3b0bbeaa20a7f9507ebf114090e4785","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"455fa73fa8c183b4f99f9f4e9411f56cb3b0bbeaa20a7f9507ebf114090e4785","first_computed_at":"2026-05-18T01:01:07.840164Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:01:07.840164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yX5gefaZtYcYJpdNgsj5G8cf5/EJB10lN0cwJK9IdL7SFPwh6uXLTf/C4a+2iAfwlr0bQnpoJMkRM+OsBpTQBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:01:07.840836Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.08568","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8d252c2f343c15e22d7b2062e107cfab569dca582e4bd6daeb69756e03faa6f1","sha256:bfca748aec8de6efb031979e778a040a74c98c9978f1d16755f303a104b16cd1"],"state_sha256":"f99d94924fadab68ebafac0e119a1cf0a6001d353460011e23eb736c0f55f65a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0G2umG8iJTU5zQCsnqUsxJlNlxb1jAwMbugSe9if5W3D7w8sWHXx9ULoCkIsNRijCiDwGiObZre3x3BgtK+6CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T22:19:00.483623Z","bundle_sha256":"d2d5b9576c3a21af16d18b0b59a021903f30fbd7665248a7c7453f1b420f22e0"}}