{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:YHYLRBCFAV7DS57LG6MZK2VVVH","short_pith_number":"pith:YHYLRBCF","canonical_record":{"source":{"id":"1608.07739","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-27T19:59:29Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"da5f73e8ac9c6c84c1179e86741a78658d80cc9e9edf507765ebd45aadb4564f","abstract_canon_sha256":"03576ef3235bfab76dbf42b6502129c33e982d1bfae6b353690ec4bd910b79d0"},"schema_version":"1.0"},"canonical_sha256":"c1f0b88445057e3977eb3799956ab5a9ee399ba8ded9c4ff66b2a5915b1e3b79","source":{"kind":"arxiv","id":"1608.07739","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.07739","created_at":"2026-05-18T00:33:23Z"},{"alias_kind":"arxiv_version","alias_value":"1608.07739v2","created_at":"2026-05-18T00:33:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07739","created_at":"2026-05-18T00:33:23Z"},{"alias_kind":"pith_short_12","alias_value":"YHYLRBCFAV7D","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YHYLRBCFAV7DS57L","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YHYLRBCF","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:YHYLRBCFAV7DS57LG6MZK2VVVH","target":"record","payload":{"canonical_record":{"source":{"id":"1608.07739","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-27T19:59:29Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"da5f73e8ac9c6c84c1179e86741a78658d80cc9e9edf507765ebd45aadb4564f","abstract_canon_sha256":"03576ef3235bfab76dbf42b6502129c33e982d1bfae6b353690ec4bd910b79d0"},"schema_version":"1.0"},"canonical_sha256":"c1f0b88445057e3977eb3799956ab5a9ee399ba8ded9c4ff66b2a5915b1e3b79","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:23.164944Z","signature_b64":"S9TjYNfDaLt/ANKjj3dXN78MV3jLAUQdL3hSyZB6hBaIU7I1+Gxi7zD98W4CgMGl8nbryccIYq4nKZru6Hr2DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1f0b88445057e3977eb3799956ab5a9ee399ba8ded9c4ff66b2a5915b1e3b79","last_reissued_at":"2026-05-18T00:33:23.164162Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:23.164162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.07739","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:33:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2/lVf4YG+ohS8H5ZNMyC1hNcOW8xbXPHl/K4Vtdxmt8TNMXyrnj52KO+sz+UuoUU4uT6yI7AkYph0p3MRtNfBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T22:32:21.460045Z"},"content_sha256":"b85a49018505de82fe3fb98622676aec282a866b4d1612117da24385395ae389","schema_version":"1.0","event_id":"sha256:b85a49018505de82fe3fb98622676aec282a866b4d1612117da24385395ae389"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:YHYLRBCFAV7DS57LG6MZK2VVVH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Herwig Wendt, Jordan Frecon, Nelly Pustelnik, Nicolas Dobigeon, Patrice Abry","submitted_at":"2016-08-27T19:59:29Z","abstract_excerpt":"Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low computational time but require the selection of a regularization parameter, whose value significantly impacts the achieved solution, and whose automated selection remains an involved and challenging problem. Conversely, fully Bayesian formalisms encapsulate the regularization parameter selection into hierarchical models, at the price of high computational costs. This contribution proposes an operational strategy that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07739","kind":"arxiv","version":2},"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":"2026-05-18T00:33:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gFILTln7khue44nVV2akLNhJX3zfjtXns5NohdDNF1nFpwM7U+BlRkUB3obgzvMWyVZscRGNJ2vfx9SvNcLZBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T22:32:21.460748Z"},"content_sha256":"cc652c67f18bcbfbe140a8bdd90b79cfdbdc1ddd29cc4d12c75871901c233d4d","schema_version":"1.0","event_id":"sha256:cc652c67f18bcbfbe140a8bdd90b79cfdbdc1ddd29cc4d12c75871901c233d4d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YHYLRBCFAV7DS57LG6MZK2VVVH/bundle.json","state_url":"https://pith.science/pith/YHYLRBCFAV7DS57LG6MZK2VVVH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YHYLRBCFAV7DS57LG6MZK2VVVH/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":"2026-05-28T22:32:21Z","links":{"resolver":"https://pith.science/pith/YHYLRBCFAV7DS57LG6MZK2VVVH","bundle":"https://pith.science/pith/YHYLRBCFAV7DS57LG6MZK2VVVH/bundle.json","state":"https://pith.science/pith/YHYLRBCFAV7DS57LG6MZK2VVVH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YHYLRBCFAV7DS57LG6MZK2VVVH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:YHYLRBCFAV7DS57LG6MZK2VVVH","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":"03576ef3235bfab76dbf42b6502129c33e982d1bfae6b353690ec4bd910b79d0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-27T19:59:29Z","title_canon_sha256":"da5f73e8ac9c6c84c1179e86741a78658d80cc9e9edf507765ebd45aadb4564f"},"schema_version":"1.0","source":{"id":"1608.07739","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.07739","created_at":"2026-05-18T00:33:23Z"},{"alias_kind":"arxiv_version","alias_value":"1608.07739v2","created_at":"2026-05-18T00:33:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07739","created_at":"2026-05-18T00:33:23Z"},{"alias_kind":"pith_short_12","alias_value":"YHYLRBCFAV7D","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YHYLRBCFAV7DS57L","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YHYLRBCF","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:cc652c67f18bcbfbe140a8bdd90b79cfdbdc1ddd29cc4d12c75871901c233d4d","target":"graph","created_at":"2026-05-18T00:33:23Z","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":"Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low computational time but require the selection of a regularization parameter, whose value significantly impacts the achieved solution, and whose automated selection remains an involved and challenging problem. Conversely, fully Bayesian formalisms encapsulate the regularization parameter selection into hierarchical models, at the price of high computational costs. This contribution proposes an operational strategy that ","authors_text":"Herwig Wendt, Jordan Frecon, Nelly Pustelnik, Nicolas Dobigeon, Patrice Abry","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-27T19:59:29Z","title":"Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07739","kind":"arxiv","version":2},"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:b85a49018505de82fe3fb98622676aec282a866b4d1612117da24385395ae389","target":"record","created_at":"2026-05-18T00:33:23Z","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":"03576ef3235bfab76dbf42b6502129c33e982d1bfae6b353690ec4bd910b79d0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-27T19:59:29Z","title_canon_sha256":"da5f73e8ac9c6c84c1179e86741a78658d80cc9e9edf507765ebd45aadb4564f"},"schema_version":"1.0","source":{"id":"1608.07739","kind":"arxiv","version":2}},"canonical_sha256":"c1f0b88445057e3977eb3799956ab5a9ee399ba8ded9c4ff66b2a5915b1e3b79","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c1f0b88445057e3977eb3799956ab5a9ee399ba8ded9c4ff66b2a5915b1e3b79","first_computed_at":"2026-05-18T00:33:23.164162Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:33:23.164162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S9TjYNfDaLt/ANKjj3dXN78MV3jLAUQdL3hSyZB6hBaIU7I1+Gxi7zD98W4CgMGl8nbryccIYq4nKZru6Hr2DA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:33:23.164944Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.07739","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b85a49018505de82fe3fb98622676aec282a866b4d1612117da24385395ae389","sha256:cc652c67f18bcbfbe140a8bdd90b79cfdbdc1ddd29cc4d12c75871901c233d4d"],"state_sha256":"386b6ff7d7c957637c56d704a6dfb7a391b89319789faff1b4b9f7c6a340b853"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G7NokUaVy0ZLBfKdBEp1XPZWFMjo4mheJCKocD0wzfB128vHw3uBbKWcYVfyNIBL9nb9+mLiIJ1XAKw7XfJjDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T22:32:21.464301Z","bundle_sha256":"69ab73bfeb4ddda76f85700dbba29461ea58e52fd3eb7317f735953ea1777a42"}}