{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:Q34XZHVVZQBWRH5CRRSR7OAKTN","short_pith_number":"pith:Q34XZHVV","canonical_record":{"source":{"id":"1111.7248","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-11-30T17:39:22Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"5646a2085665526ecc01c410c3c5c3927d45575f698821d5d6083f65d938ba02","abstract_canon_sha256":"240ca6bfb255d5cc6bf72d6e30d72bb7a2c8460054103c628cb08af7c924fac4"},"schema_version":"1.0"},"canonical_sha256":"86f97c9eb5cc03689fa28c651fb80a9b44973b1f116c5624eaa1308949ff0529","source":{"kind":"arxiv","id":"1111.7248","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1111.7248","created_at":"2026-05-18T04:07:18Z"},{"alias_kind":"arxiv_version","alias_value":"1111.7248v1","created_at":"2026-05-18T04:07:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1111.7248","created_at":"2026-05-18T04:07:18Z"},{"alias_kind":"pith_short_12","alias_value":"Q34XZHVVZQBW","created_at":"2026-05-18T12:26:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q34XZHVVZQBWRH5C","created_at":"2026-05-18T12:26:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q34XZHVV","created_at":"2026-05-18T12:26:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:Q34XZHVVZQBWRH5CRRSR7OAKTN","target":"record","payload":{"canonical_record":{"source":{"id":"1111.7248","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-11-30T17:39:22Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"5646a2085665526ecc01c410c3c5c3927d45575f698821d5d6083f65d938ba02","abstract_canon_sha256":"240ca6bfb255d5cc6bf72d6e30d72bb7a2c8460054103c628cb08af7c924fac4"},"schema_version":"1.0"},"canonical_sha256":"86f97c9eb5cc03689fa28c651fb80a9b44973b1f116c5624eaa1308949ff0529","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:07:18.322485Z","signature_b64":"AL14qfgfaf8cgtdqcMJSxgGc6/Un2gfU12TySA8cnevvckRUbyL48/+4MjUxfH1RiywRIoC9svEQTvZ+Y48cDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86f97c9eb5cc03689fa28c651fb80a9b44973b1f116c5624eaa1308949ff0529","last_reissued_at":"2026-05-18T04:07:18.321682Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:07:18.321682Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1111.7248","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":"2026-05-18T04:07:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9+ZpKFLU+G6UOCUMlbVG+jYrtTj+h0MTjaKPbMsY1nQs14Q7kFx6/6UmpYszRlp7yzqsQl3zWwDsbY+6aM6dCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T15:14:13.353214Z"},"content_sha256":"b1f8990861c7cee5d2d85cf317d1270e5e48d74466838b30968f0c4740efd639","schema_version":"1.0","event_id":"sha256:b1f8990861c7cee5d2d85cf317d1270e5e48d74466838b30968f0c4740efd639"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:Q34XZHVVZQBWRH5CRRSR7OAKTN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Blind calibration for compressed sensing by convex optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Gilles Chardon, Laurent Daudet, R\\'emi Gribonval","submitted_at":"2011-11-30T17:39:22Z","abstract_excerpt":"We consider the problem of calibrating a compressed sensing measurement system under the assumption that the decalibration consists in unknown gains on each measure. We focus on {\\em blind} calibration, using measures performed on a few unknown (but sparse) signals. A naive formulation of this blind calibration problem, using $\\ell_{1}$ minimization, is reminiscent of blind source separation and dictionary learning, which are known to be highly non-convex and riddled with local minima. In the considered context, we show that in fact this formulation can be exactly expressed as a convex optimiz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1111.7248","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":"2026-05-18T04:07:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R/ZfAFFr/hnUPohSq+dUIqq4Ex9tFBzDnJwWG6pHct7ZMc0KG9fKiHrOGSYWoHOMyX314ch+owwQ0ZH4G5OYAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T15:14:13.353572Z"},"content_sha256":"34009f59a5bcc3821df0b0c2c218b5229cda90c7ad9b90ccda100988391ccf4b","schema_version":"1.0","event_id":"sha256:34009f59a5bcc3821df0b0c2c218b5229cda90c7ad9b90ccda100988391ccf4b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q34XZHVVZQBWRH5CRRSR7OAKTN/bundle.json","state_url":"https://pith.science/pith/Q34XZHVVZQBWRH5CRRSR7OAKTN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q34XZHVVZQBWRH5CRRSR7OAKTN/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":"2026-06-25T15:14:13Z","links":{"resolver":"https://pith.science/pith/Q34XZHVVZQBWRH5CRRSR7OAKTN","bundle":"https://pith.science/pith/Q34XZHVVZQBWRH5CRRSR7OAKTN/bundle.json","state":"https://pith.science/pith/Q34XZHVVZQBWRH5CRRSR7OAKTN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q34XZHVVZQBWRH5CRRSR7OAKTN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:Q34XZHVVZQBWRH5CRRSR7OAKTN","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":"240ca6bfb255d5cc6bf72d6e30d72bb7a2c8460054103c628cb08af7c924fac4","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-11-30T17:39:22Z","title_canon_sha256":"5646a2085665526ecc01c410c3c5c3927d45575f698821d5d6083f65d938ba02"},"schema_version":"1.0","source":{"id":"1111.7248","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1111.7248","created_at":"2026-05-18T04:07:18Z"},{"alias_kind":"arxiv_version","alias_value":"1111.7248v1","created_at":"2026-05-18T04:07:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1111.7248","created_at":"2026-05-18T04:07:18Z"},{"alias_kind":"pith_short_12","alias_value":"Q34XZHVVZQBW","created_at":"2026-05-18T12:26:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q34XZHVVZQBWRH5C","created_at":"2026-05-18T12:26:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q34XZHVV","created_at":"2026-05-18T12:26:39Z"}],"graph_snapshots":[{"event_id":"sha256:34009f59a5bcc3821df0b0c2c218b5229cda90c7ad9b90ccda100988391ccf4b","target":"graph","created_at":"2026-05-18T04:07:18Z","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":"We consider the problem of calibrating a compressed sensing measurement system under the assumption that the decalibration consists in unknown gains on each measure. We focus on {\\em blind} calibration, using measures performed on a few unknown (but sparse) signals. A naive formulation of this blind calibration problem, using $\\ell_{1}$ minimization, is reminiscent of blind source separation and dictionary learning, which are known to be highly non-convex and riddled with local minima. In the considered context, we show that in fact this formulation can be exactly expressed as a convex optimiz","authors_text":"Gilles Chardon, Laurent Daudet, R\\'emi Gribonval","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-11-30T17:39:22Z","title":"Blind calibration for compressed sensing by convex optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1111.7248","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:b1f8990861c7cee5d2d85cf317d1270e5e48d74466838b30968f0c4740efd639","target":"record","created_at":"2026-05-18T04:07:18Z","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":"240ca6bfb255d5cc6bf72d6e30d72bb7a2c8460054103c628cb08af7c924fac4","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-11-30T17:39:22Z","title_canon_sha256":"5646a2085665526ecc01c410c3c5c3927d45575f698821d5d6083f65d938ba02"},"schema_version":"1.0","source":{"id":"1111.7248","kind":"arxiv","version":1}},"canonical_sha256":"86f97c9eb5cc03689fa28c651fb80a9b44973b1f116c5624eaa1308949ff0529","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86f97c9eb5cc03689fa28c651fb80a9b44973b1f116c5624eaa1308949ff0529","first_computed_at":"2026-05-18T04:07:18.321682Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:07:18.321682Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AL14qfgfaf8cgtdqcMJSxgGc6/Un2gfU12TySA8cnevvckRUbyL48/+4MjUxfH1RiywRIoC9svEQTvZ+Y48cDA==","signature_status":"signed_v1","signed_at":"2026-05-18T04:07:18.322485Z","signed_message":"canonical_sha256_bytes"},"source_id":"1111.7248","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b1f8990861c7cee5d2d85cf317d1270e5e48d74466838b30968f0c4740efd639","sha256:34009f59a5bcc3821df0b0c2c218b5229cda90c7ad9b90ccda100988391ccf4b"],"state_sha256":"2534d7d30d9cb1ad60b89fcf36b834d053a2a0cb70c9120486a91c2d36af2eb3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+rY15z9zOgDvrl6vtAU3s7WPwTdDOEEvzsRgboUX16ouqOiRmdBYL4b6np3o1RE4/g6MwW1k+6XChZehOh/tCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-25T15:14:13.355543Z","bundle_sha256":"14fb9aea3bab3365f03a73865ac5f8d890a47f10e5bdd5eab1a10da633a61a60"}}