{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:RBWCP5W2ZW6ONZVBW2M7VVNH6L","short_pith_number":"pith:RBWCP5W2","canonical_record":{"source":{"id":"1409.5009","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-17T14:42:28Z","cross_cats_sorted":["math.ST","stat.ME","stat.TH"],"title_canon_sha256":"c34228e16ab844a6fef2f0e7310801c6c4a272665329bb3e6e2bd62f177d27c2","abstract_canon_sha256":"3fa61be10cce987dafcd11ffa5562b74c46bb5fe3eca4f164c8dc7abf04e701b"},"schema_version":"1.0"},"canonical_sha256":"886c27f6dacdbce6e6a1b699fad5a7f2c6b49a6315d55f45b2b273a56820b74f","source":{"kind":"arxiv","id":"1409.5009","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.5009","created_at":"2026-05-18T02:42:38Z"},{"alias_kind":"arxiv_version","alias_value":"1409.5009v1","created_at":"2026-05-18T02:42:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.5009","created_at":"2026-05-18T02:42:38Z"},{"alias_kind":"pith_short_12","alias_value":"RBWCP5W2ZW6O","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_16","alias_value":"RBWCP5W2ZW6ONZVB","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_8","alias_value":"RBWCP5W2","created_at":"2026-05-18T12:28:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:RBWCP5W2ZW6ONZVBW2M7VVNH6L","target":"record","payload":{"canonical_record":{"source":{"id":"1409.5009","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-17T14:42:28Z","cross_cats_sorted":["math.ST","stat.ME","stat.TH"],"title_canon_sha256":"c34228e16ab844a6fef2f0e7310801c6c4a272665329bb3e6e2bd62f177d27c2","abstract_canon_sha256":"3fa61be10cce987dafcd11ffa5562b74c46bb5fe3eca4f164c8dc7abf04e701b"},"schema_version":"1.0"},"canonical_sha256":"886c27f6dacdbce6e6a1b699fad5a7f2c6b49a6315d55f45b2b273a56820b74f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:42:38.095775Z","signature_b64":"7MPfmGxzz+qxg78ycifA3sGBlCrftqT2zMN/oocb5TnF1NbfLCh/TLoQUN8f7zY9wBfkbCR493Q4gVKhaRHhAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"886c27f6dacdbce6e6a1b699fad5a7f2c6b49a6315d55f45b2b273a56820b74f","last_reissued_at":"2026-05-18T02:42:38.095050Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:42:38.095050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.5009","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":"2026-05-18T02:42:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pcvqdPMUG15z3Y5nkUkE1u9xc5c3/oP+AZ1hfro0Q/FdAHSVO7SlCbcmc5QyvtF2HKvQUVkO6pgaopjsfeCPDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T14:36:03.879237Z"},"content_sha256":"4dc0443123114882523ec43f75501490392fd110d6c42dd710e8369d1e9311f6","schema_version":"1.0","event_id":"sha256:4dc0443123114882523ec43f75501490392fd110d6c42dd710e8369d1e9311f6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:RBWCP5W2ZW6ONZVBW2M7VVNH6L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distance Shrinkage and Euclidean Embedding via Regularized Kernel Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.ME","stat.TH"],"primary_cat":"stat.ML","authors_text":"Grace Wahba, Luwan Zhang, Ming Yuan","submitted_at":"2014-09-17T14:42:28Z","abstract_excerpt":"Although recovering an Euclidean distance matrix from noisy observations is a common problem in practice, how well this could be done remains largely unknown. To fill in this void, we study a simple distance matrix estimate based upon the so-called regularized kernel estimate. We show that such an estimate can be characterized as simply applying a constant amount of shrinkage to all observed pairwise distances. This fact allows us to establish risk bounds for the estimate implying that the true distances can be estimated consistently in an average sense as the number of objects increases. In a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.5009","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":"2026-05-18T02:42:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oi6XvAm5zg90l7QXdcNpA8p49Im/oHR416hnxxR1gWxMh5Pp1Y+muIhObkEAlMslhT8Phezk0E2sXtt0QDp3Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T14:36:03.879614Z"},"content_sha256":"418e8ab832349c7b482f7e937c6d777c3403c08c892792db9f8b816b48e834cf","schema_version":"1.0","event_id":"sha256:418e8ab832349c7b482f7e937c6d777c3403c08c892792db9f8b816b48e834cf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RBWCP5W2ZW6ONZVBW2M7VVNH6L/bundle.json","state_url":"https://pith.science/pith/RBWCP5W2ZW6ONZVBW2M7VVNH6L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RBWCP5W2ZW6ONZVBW2M7VVNH6L/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":"2026-05-30T14:36:03Z","links":{"resolver":"https://pith.science/pith/RBWCP5W2ZW6ONZVBW2M7VVNH6L","bundle":"https://pith.science/pith/RBWCP5W2ZW6ONZVBW2M7VVNH6L/bundle.json","state":"https://pith.science/pith/RBWCP5W2ZW6ONZVBW2M7VVNH6L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RBWCP5W2ZW6ONZVBW2M7VVNH6L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:RBWCP5W2ZW6ONZVBW2M7VVNH6L","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":"3fa61be10cce987dafcd11ffa5562b74c46bb5fe3eca4f164c8dc7abf04e701b","cross_cats_sorted":["math.ST","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-17T14:42:28Z","title_canon_sha256":"c34228e16ab844a6fef2f0e7310801c6c4a272665329bb3e6e2bd62f177d27c2"},"schema_version":"1.0","source":{"id":"1409.5009","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.5009","created_at":"2026-05-18T02:42:38Z"},{"alias_kind":"arxiv_version","alias_value":"1409.5009v1","created_at":"2026-05-18T02:42:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.5009","created_at":"2026-05-18T02:42:38Z"},{"alias_kind":"pith_short_12","alias_value":"RBWCP5W2ZW6O","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_16","alias_value":"RBWCP5W2ZW6ONZVB","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_8","alias_value":"RBWCP5W2","created_at":"2026-05-18T12:28:46Z"}],"graph_snapshots":[{"event_id":"sha256:418e8ab832349c7b482f7e937c6d777c3403c08c892792db9f8b816b48e834cf","target":"graph","created_at":"2026-05-18T02:42:38Z","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":"Although recovering an Euclidean distance matrix from noisy observations is a common problem in practice, how well this could be done remains largely unknown. To fill in this void, we study a simple distance matrix estimate based upon the so-called regularized kernel estimate. We show that such an estimate can be characterized as simply applying a constant amount of shrinkage to all observed pairwise distances. This fact allows us to establish risk bounds for the estimate implying that the true distances can be estimated consistently in an average sense as the number of objects increases. In a","authors_text":"Grace Wahba, Luwan Zhang, Ming Yuan","cross_cats":["math.ST","stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-17T14:42:28Z","title":"Distance Shrinkage and Euclidean Embedding via Regularized Kernel Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.5009","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":"sha256:4dc0443123114882523ec43f75501490392fd110d6c42dd710e8369d1e9311f6","target":"record","created_at":"2026-05-18T02:42:38Z","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":"3fa61be10cce987dafcd11ffa5562b74c46bb5fe3eca4f164c8dc7abf04e701b","cross_cats_sorted":["math.ST","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-17T14:42:28Z","title_canon_sha256":"c34228e16ab844a6fef2f0e7310801c6c4a272665329bb3e6e2bd62f177d27c2"},"schema_version":"1.0","source":{"id":"1409.5009","kind":"arxiv","version":1}},"canonical_sha256":"886c27f6dacdbce6e6a1b699fad5a7f2c6b49a6315d55f45b2b273a56820b74f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"886c27f6dacdbce6e6a1b699fad5a7f2c6b49a6315d55f45b2b273a56820b74f","first_computed_at":"2026-05-18T02:42:38.095050Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:42:38.095050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7MPfmGxzz+qxg78ycifA3sGBlCrftqT2zMN/oocb5TnF1NbfLCh/TLoQUN8f7zY9wBfkbCR493Q4gVKhaRHhAw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:42:38.095775Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.5009","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4dc0443123114882523ec43f75501490392fd110d6c42dd710e8369d1e9311f6","sha256:418e8ab832349c7b482f7e937c6d777c3403c08c892792db9f8b816b48e834cf"],"state_sha256":"85f80eccb8c9b1aa1e18c072efa33ad83fb7243a508c43c31ca48344be0e8b99"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oo3zbB2EIrutGw6ER3AMA6u3C5p+ymvMr1ez9L8TGpTdLzVF+2c9ROFyJVDkQGcKjnMiXnDuK18/xIn4phzLAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T14:36:03.881565Z","bundle_sha256":"6e5fdd35487bb8e6fa2c9adda74d993e6abfa6adc37c35c8b56aec5a99b8303e"}}