{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:RFA5SZFNMK37KHWIPZ5JV5P3LB","short_pith_number":"pith:RFA5SZFN","canonical_record":{"source":{"id":"1606.05113","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-06-16T09:46:19Z","cross_cats_sorted":[],"title_canon_sha256":"ea97126443e64b096086c5537a600b2e3d1e029ce76a8a95d49e1fe406540cd2","abstract_canon_sha256":"51ff372fbcce033ca20560ea8098ca7c9b10de33e02394159cd1b21f6e679570"},"schema_version":"1.0"},"canonical_sha256":"8941d964ad62b7f51ec87e7a9af5fb585e0ebf89f22a344c099b08e08ba1c484","source":{"kind":"arxiv","id":"1606.05113","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.05113","created_at":"2026-05-18T00:41:53Z"},{"alias_kind":"arxiv_version","alias_value":"1606.05113v4","created_at":"2026-05-18T00:41:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.05113","created_at":"2026-05-18T00:41:53Z"},{"alias_kind":"pith_short_12","alias_value":"RFA5SZFNMK37","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RFA5SZFNMK37KHWI","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RFA5SZFN","created_at":"2026-05-18T12:30:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:RFA5SZFNMK37KHWIPZ5JV5P3LB","target":"record","payload":{"canonical_record":{"source":{"id":"1606.05113","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-06-16T09:46:19Z","cross_cats_sorted":[],"title_canon_sha256":"ea97126443e64b096086c5537a600b2e3d1e029ce76a8a95d49e1fe406540cd2","abstract_canon_sha256":"51ff372fbcce033ca20560ea8098ca7c9b10de33e02394159cd1b21f6e679570"},"schema_version":"1.0"},"canonical_sha256":"8941d964ad62b7f51ec87e7a9af5fb585e0ebf89f22a344c099b08e08ba1c484","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:53.604712Z","signature_b64":"xB9OKCmxbCVxqBjXChXSfKQgd7+NXAM6lDuYeaZmk2fLzE3lCnVVfgUjNZzPqVfIS1aCzFG7ftd0xjFWYfS7CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8941d964ad62b7f51ec87e7a9af5fb585e0ebf89f22a344c099b08e08ba1c484","last_reissued_at":"2026-05-18T00:41:53.604050Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:53.604050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.05113","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:41:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uujTml4qd33jSRnNJ0ZDNBCecMH0q6kkr83wOOv/a1EmayrPV5N6Elh2JzIgz/QhRrLAXAazPzxCW0xZ7BjyDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T14:27:46.274185Z"},"content_sha256":"64e7bc5323d19b38769bc4ab8499f87b8e94c39d5558eade9d71994b5a31eea8","schema_version":"1.0","event_id":"sha256:64e7bc5323d19b38769bc4ab8499f87b8e94c39d5558eade9d71994b5a31eea8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:RFA5SZFNMK37KHWIPZ5JV5P3LB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bias-Reduction in Variational Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Camille Sutour, Eva-Maria Brinkmann, Julian Rasch, Martin Burger","submitted_at":"2016-06-16T09:46:19Z","abstract_excerpt":"The aim of this paper is to introduce and study a two-step debiasing method for variational regularization. After solving the standard variational problem, the key idea is to add a consecutive debiasing step minimizing the data fidelity on an appropriate set, the so-called model manifold. The latter is defined by Bregman distances or infimal convolutions thereof, using the (uniquely defined) subgradient appearing in the optimality condition of the variational method. For particular settings, such as anisotropic $\\ell^1$ and TV-type regularization, previously used debiasing techniques are shown"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.05113","kind":"arxiv","version":4},"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":"2026-05-18T00:41:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PptZ/Kcnz/IzG0dPWwNxIVFzqhyB6Ux2MFUy/udv7nnkq4K+J4Y+iLh09QRWgaPbcsRAXEfm/0y3/uBmtqMwCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T14:27:46.274544Z"},"content_sha256":"eb512223f3b7482e0c649f8f13e642d5dc2ccb766d5a41858bfff6dd47214f4b","schema_version":"1.0","event_id":"sha256:eb512223f3b7482e0c649f8f13e642d5dc2ccb766d5a41858bfff6dd47214f4b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RFA5SZFNMK37KHWIPZ5JV5P3LB/bundle.json","state_url":"https://pith.science/pith/RFA5SZFNMK37KHWIPZ5JV5P3LB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RFA5SZFNMK37KHWIPZ5JV5P3LB/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":"2026-06-11T14:27:46Z","links":{"resolver":"https://pith.science/pith/RFA5SZFNMK37KHWIPZ5JV5P3LB","bundle":"https://pith.science/pith/RFA5SZFNMK37KHWIPZ5JV5P3LB/bundle.json","state":"https://pith.science/pith/RFA5SZFNMK37KHWIPZ5JV5P3LB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RFA5SZFNMK37KHWIPZ5JV5P3LB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:RFA5SZFNMK37KHWIPZ5JV5P3LB","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":"51ff372fbcce033ca20560ea8098ca7c9b10de33e02394159cd1b21f6e679570","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-06-16T09:46:19Z","title_canon_sha256":"ea97126443e64b096086c5537a600b2e3d1e029ce76a8a95d49e1fe406540cd2"},"schema_version":"1.0","source":{"id":"1606.05113","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.05113","created_at":"2026-05-18T00:41:53Z"},{"alias_kind":"arxiv_version","alias_value":"1606.05113v4","created_at":"2026-05-18T00:41:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.05113","created_at":"2026-05-18T00:41:53Z"},{"alias_kind":"pith_short_12","alias_value":"RFA5SZFNMK37","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RFA5SZFNMK37KHWI","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RFA5SZFN","created_at":"2026-05-18T12:30:41Z"}],"graph_snapshots":[{"event_id":"sha256:eb512223f3b7482e0c649f8f13e642d5dc2ccb766d5a41858bfff6dd47214f4b","target":"graph","created_at":"2026-05-18T00:41:53Z","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":"The aim of this paper is to introduce and study a two-step debiasing method for variational regularization. After solving the standard variational problem, the key idea is to add a consecutive debiasing step minimizing the data fidelity on an appropriate set, the so-called model manifold. The latter is defined by Bregman distances or infimal convolutions thereof, using the (uniquely defined) subgradient appearing in the optimality condition of the variational method. For particular settings, such as anisotropic $\\ell^1$ and TV-type regularization, previously used debiasing techniques are shown","authors_text":"Camille Sutour, Eva-Maria Brinkmann, Julian Rasch, Martin Burger","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-06-16T09:46:19Z","title":"Bias-Reduction in Variational Regularization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.05113","kind":"arxiv","version":4},"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":"sha256:64e7bc5323d19b38769bc4ab8499f87b8e94c39d5558eade9d71994b5a31eea8","target":"record","created_at":"2026-05-18T00:41:53Z","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":"51ff372fbcce033ca20560ea8098ca7c9b10de33e02394159cd1b21f6e679570","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-06-16T09:46:19Z","title_canon_sha256":"ea97126443e64b096086c5537a600b2e3d1e029ce76a8a95d49e1fe406540cd2"},"schema_version":"1.0","source":{"id":"1606.05113","kind":"arxiv","version":4}},"canonical_sha256":"8941d964ad62b7f51ec87e7a9af5fb585e0ebf89f22a344c099b08e08ba1c484","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8941d964ad62b7f51ec87e7a9af5fb585e0ebf89f22a344c099b08e08ba1c484","first_computed_at":"2026-05-18T00:41:53.604050Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:53.604050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xB9OKCmxbCVxqBjXChXSfKQgd7+NXAM6lDuYeaZmk2fLzE3lCnVVfgUjNZzPqVfIS1aCzFG7ftd0xjFWYfS7CA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:53.604712Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.05113","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:64e7bc5323d19b38769bc4ab8499f87b8e94c39d5558eade9d71994b5a31eea8","sha256:eb512223f3b7482e0c649f8f13e642d5dc2ccb766d5a41858bfff6dd47214f4b"],"state_sha256":"bdfc5cac9774da5a194be43e7073ea857c92798f863aa6057bbb811ba7de073f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bFj85oizJOx+6dWGbIyTMooXcWVKpO46VQmBInSaCai4wq1ZCA0H7bZw6tP1H846BHh0fL77p6RAdhQ+ThPBAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T14:27:46.276580Z","bundle_sha256":"cddc2831fb18799b358673004f8d10b9b331a1bd9505bd0a25517623594f9b94"}}