{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:F6NJBIFGGC7NN3OA2MTESB2AVV","short_pith_number":"pith:F6NJBIFG","canonical_record":{"source":{"id":"1507.08254","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-07-29T18:38:36Z","cross_cats_sorted":["math.IT","math.NA","math.OC","math.ST","stat.TH"],"title_canon_sha256":"859acde697d677bf111018409d6cf29e8d9083423d1f0df4dad7603af7f49f86","abstract_canon_sha256":"904f9fab1ad1f23117d11a2a9f90ff03e16139c4de151283a000d89905ffbf0d"},"schema_version":"1.0"},"canonical_sha256":"2f9a90a0a630bed6edc0d326490740ad51fd8dd22e0fafa42e7182acc9e0c61a","source":{"kind":"arxiv","id":"1507.08254","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.08254","created_at":"2026-05-18T01:29:13Z"},{"alias_kind":"arxiv_version","alias_value":"1507.08254v2","created_at":"2026-05-18T01:29:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.08254","created_at":"2026-05-18T01:29:13Z"},{"alias_kind":"pith_short_12","alias_value":"F6NJBIFGGC7N","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"F6NJBIFGGC7NN3OA","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"F6NJBIFG","created_at":"2026-05-18T12:29:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:F6NJBIFGGC7NN3OA2MTESB2AVV","target":"record","payload":{"canonical_record":{"source":{"id":"1507.08254","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-07-29T18:38:36Z","cross_cats_sorted":["math.IT","math.NA","math.OC","math.ST","stat.TH"],"title_canon_sha256":"859acde697d677bf111018409d6cf29e8d9083423d1f0df4dad7603af7f49f86","abstract_canon_sha256":"904f9fab1ad1f23117d11a2a9f90ff03e16139c4de151283a000d89905ffbf0d"},"schema_version":"1.0"},"canonical_sha256":"2f9a90a0a630bed6edc0d326490740ad51fd8dd22e0fafa42e7182acc9e0c61a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:29:13.199990Z","signature_b64":"K63P4jKVZWn1X0AAY4w3gouOOVcJ34V2O9Ozaz38n/pgdJzITJuKRDk00AyKuTPppD4MDmlC10R0gyieSw2qDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f9a90a0a630bed6edc0d326490740ad51fd8dd22e0fafa42e7182acc9e0c61a","last_reissued_at":"2026-05-18T01:29:13.199259Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:29:13.199259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1507.08254","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":"2026-05-18T01:29:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sseYFdzMcrspGWKhaUSZ+A/QQ1FdvSJ2KkTu2sYz6O6PiK68ST6u9lzfRDucsovXBMvVIKAUOwgDdXZIATeOCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T14:39:43.011213Z"},"content_sha256":"3b94bb22159b107cca1c4aaaaf201e6271124ba15fc7ab4b189b1f7acbfc6330","schema_version":"1.0","event_id":"sha256:3b94bb22159b107cca1c4aaaaf201e6271124ba15fc7ab4b189b1f7acbfc6330"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:F6NJBIFGGC7NN3OA2MTESB2AVV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Compressive Phase Retrieval with Constrained Sensing Vectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","math.NA","math.OC","math.ST","stat.TH"],"primary_cat":"cs.IT","authors_text":"Justin Romberg, Sohail Bahmani","submitted_at":"2015-07-29T18:38:36Z","abstract_excerpt":"We propose a robust and efficient approach to the problem of compressive phase retrieval in which the goal is to reconstruct a sparse vector from the magnitude of a number of its linear measurements. The proposed framework relies on constrained sensing vectors and a two-stage reconstruction method that consists of two standard convex programs that are solved sequentially.\n  In recent years, various methods are proposed for compressive phase retrieval, but they have suboptimal sample complexity or lack robustness guarantees. The main obstacle has been that there is no straightforward convex rel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.08254","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":"2026-05-18T01:29:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UcRRcV4VheZr/w2CNL9mN+JJsv95+K/GOz72O5XWUWAwijd8lqryP6D8Bz59SqJxFJwW7yn2Jm9dDHoyWcwQCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T14:39:43.011933Z"},"content_sha256":"5415a2ba63dea1b79cbde39265710985906b5f750e933f2b3d825912a2fca3b0","schema_version":"1.0","event_id":"sha256:5415a2ba63dea1b79cbde39265710985906b5f750e933f2b3d825912a2fca3b0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F6NJBIFGGC7NN3OA2MTESB2AVV/bundle.json","state_url":"https://pith.science/pith/F6NJBIFGGC7NN3OA2MTESB2AVV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F6NJBIFGGC7NN3OA2MTESB2AVV/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":"2026-05-27T14:39:43Z","links":{"resolver":"https://pith.science/pith/F6NJBIFGGC7NN3OA2MTESB2AVV","bundle":"https://pith.science/pith/F6NJBIFGGC7NN3OA2MTESB2AVV/bundle.json","state":"https://pith.science/pith/F6NJBIFGGC7NN3OA2MTESB2AVV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F6NJBIFGGC7NN3OA2MTESB2AVV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:F6NJBIFGGC7NN3OA2MTESB2AVV","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":"904f9fab1ad1f23117d11a2a9f90ff03e16139c4de151283a000d89905ffbf0d","cross_cats_sorted":["math.IT","math.NA","math.OC","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-07-29T18:38:36Z","title_canon_sha256":"859acde697d677bf111018409d6cf29e8d9083423d1f0df4dad7603af7f49f86"},"schema_version":"1.0","source":{"id":"1507.08254","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.08254","created_at":"2026-05-18T01:29:13Z"},{"alias_kind":"arxiv_version","alias_value":"1507.08254v2","created_at":"2026-05-18T01:29:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.08254","created_at":"2026-05-18T01:29:13Z"},{"alias_kind":"pith_short_12","alias_value":"F6NJBIFGGC7N","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"F6NJBIFGGC7NN3OA","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"F6NJBIFG","created_at":"2026-05-18T12:29:19Z"}],"graph_snapshots":[{"event_id":"sha256:5415a2ba63dea1b79cbde39265710985906b5f750e933f2b3d825912a2fca3b0","target":"graph","created_at":"2026-05-18T01:29:13Z","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 propose a robust and efficient approach to the problem of compressive phase retrieval in which the goal is to reconstruct a sparse vector from the magnitude of a number of its linear measurements. The proposed framework relies on constrained sensing vectors and a two-stage reconstruction method that consists of two standard convex programs that are solved sequentially.\n  In recent years, various methods are proposed for compressive phase retrieval, but they have suboptimal sample complexity or lack robustness guarantees. The main obstacle has been that there is no straightforward convex rel","authors_text":"Justin Romberg, Sohail Bahmani","cross_cats":["math.IT","math.NA","math.OC","math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-07-29T18:38:36Z","title":"Efficient Compressive Phase Retrieval with Constrained Sensing Vectors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.08254","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":"sha256:3b94bb22159b107cca1c4aaaaf201e6271124ba15fc7ab4b189b1f7acbfc6330","target":"record","created_at":"2026-05-18T01:29:13Z","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":"904f9fab1ad1f23117d11a2a9f90ff03e16139c4de151283a000d89905ffbf0d","cross_cats_sorted":["math.IT","math.NA","math.OC","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-07-29T18:38:36Z","title_canon_sha256":"859acde697d677bf111018409d6cf29e8d9083423d1f0df4dad7603af7f49f86"},"schema_version":"1.0","source":{"id":"1507.08254","kind":"arxiv","version":2}},"canonical_sha256":"2f9a90a0a630bed6edc0d326490740ad51fd8dd22e0fafa42e7182acc9e0c61a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f9a90a0a630bed6edc0d326490740ad51fd8dd22e0fafa42e7182acc9e0c61a","first_computed_at":"2026-05-18T01:29:13.199259Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:29:13.199259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"K63P4jKVZWn1X0AAY4w3gouOOVcJ34V2O9Ozaz38n/pgdJzITJuKRDk00AyKuTPppD4MDmlC10R0gyieSw2qDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:29:13.199990Z","signed_message":"canonical_sha256_bytes"},"source_id":"1507.08254","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3b94bb22159b107cca1c4aaaaf201e6271124ba15fc7ab4b189b1f7acbfc6330","sha256:5415a2ba63dea1b79cbde39265710985906b5f750e933f2b3d825912a2fca3b0"],"state_sha256":"122648fa0327b76db15da594c180684dc5696bf74621cb7bff2155145cca6376"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G9T90u7PTBwxqTP3hbNioswhxZXyjfDjU2VkIeAppZd85Me8ZdVhJ52Xauf5JYue/8k0qxZAwWV0p3KogMk4Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T14:39:43.015724Z","bundle_sha256":"baab7d60d1582a800f5fd413e31675254e732b4fa3499916a33bd7a297af3089"}}