{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CM344RCXMWK4R7RSBGX2LCRFGV","short_pith_number":"pith:CM344RCX","canonical_record":{"source":{"id":"2401.09703","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2024-01-18T03:21:36Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"c41d6ed0e20f606353a7eb9fc13efd1f9bd69bec1b055ecbbfef376f3d4c6117","abstract_canon_sha256":"d39de3af611dee8b44d82625f13e02786c4cc457d3e6cbd5fd564495ade63357"},"schema_version":"1.0"},"canonical_sha256":"1337ce44576595c8fe3209afa58a25357702914438cee8fef90b3771c206e098","source":{"kind":"arxiv","id":"2401.09703","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.09703","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"arxiv_version","alias_value":"2401.09703v1","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.09703","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"pith_short_12","alias_value":"CM344RCXMWK4","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"pith_short_16","alias_value":"CM344RCXMWK4R7RS","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"pith_short_8","alias_value":"CM344RCX","created_at":"2026-07-05T07:35:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CM344RCXMWK4R7RSBGX2LCRFGV","target":"record","payload":{"canonical_record":{"source":{"id":"2401.09703","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2024-01-18T03:21:36Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"c41d6ed0e20f606353a7eb9fc13efd1f9bd69bec1b055ecbbfef376f3d4c6117","abstract_canon_sha256":"d39de3af611dee8b44d82625f13e02786c4cc457d3e6cbd5fd564495ade63357"},"schema_version":"1.0"},"canonical_sha256":"1337ce44576595c8fe3209afa58a25357702914438cee8fef90b3771c206e098","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:35:01.040644Z","signature_b64":"uqyY0rlGMH+PnfZBPj8C07n7PMAvhmpS+vqZ5nzObhCQwx2mxIqrr3BznGIFPKXV4CZKDiGI+DUJTHGR8Lh3Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1337ce44576595c8fe3209afa58a25357702914438cee8fef90b3771c206e098","last_reissued_at":"2026-07-05T07:35:01.040233Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:35:01.040233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.09703","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":"2026-07-05T07:35:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RXl5J56k4WhwlFksmFRnefozuKUjeex9V35ARoBLGfDR4gyUdvqH76EfkxmDa4jOavnwpCwzFkvbvUtcBa4+Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:21:23.686453Z"},"content_sha256":"ca09c28a83dbfc497ea99152f4c6d80239992ee2f9ae050dfab48e8f4a8ee6a8","schema_version":"1.0","event_id":"sha256:ca09c28a83dbfc497ea99152f4c6d80239992ee2f9ae050dfab48e8f4a8ee6a8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CM344RCXMWK4R7RSBGX2LCRFGV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Updating Truncated SVD for Representation Learning with Sparse Matrices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Cheng Chen, Haoran Deng, Jiahe Li, Shiliang Pu, Weihao Jiang, Yang Yang","submitted_at":"2024-01-18T03:21:36Z","abstract_excerpt":"Updating a truncated Singular Value Decomposition (SVD) is crucial in representation learning, especially when dealing with large-scale data matrices that continuously evolve in practical scenarios. Aligning SVD-based models with fast-paced updates becomes increasingly important. Existing methods for updating truncated SVDs employ Rayleigh-Ritz projection procedures, where projection matrices are augmented based on original singular vectors. However, these methods suffer from inefficiency due to the densification of the update matrix and the application of the projection to all singular vector"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.09703","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.09703/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T07:35:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"juottFuiBt50KzB52Y6Uz3iiLMa5ao4R/ch/p7asFIZdcKz4WiBSk3KSVwoHpKzOfxxtyG43HQaezwryPHCmBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:21:23.686961Z"},"content_sha256":"f542c044d2a3ee947c0dfa9b4abf2b86c35119edd61ad66894d0502a5687d688","schema_version":"1.0","event_id":"sha256:f542c044d2a3ee947c0dfa9b4abf2b86c35119edd61ad66894d0502a5687d688"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CM344RCXMWK4R7RSBGX2LCRFGV/bundle.json","state_url":"https://pith.science/pith/CM344RCXMWK4R7RSBGX2LCRFGV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CM344RCXMWK4R7RSBGX2LCRFGV/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":"2026-08-08T19:21:23Z","links":{"resolver":"https://pith.science/pith/CM344RCXMWK4R7RSBGX2LCRFGV","bundle":"https://pith.science/pith/CM344RCXMWK4R7RSBGX2LCRFGV/bundle.json","state":"https://pith.science/pith/CM344RCXMWK4R7RSBGX2LCRFGV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CM344RCXMWK4R7RSBGX2LCRFGV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CM344RCXMWK4R7RSBGX2LCRFGV","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":"d39de3af611dee8b44d82625f13e02786c4cc457d3e6cbd5fd564495ade63357","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2024-01-18T03:21:36Z","title_canon_sha256":"c41d6ed0e20f606353a7eb9fc13efd1f9bd69bec1b055ecbbfef376f3d4c6117"},"schema_version":"1.0","source":{"id":"2401.09703","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.09703","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"arxiv_version","alias_value":"2401.09703v1","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.09703","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"pith_short_12","alias_value":"CM344RCXMWK4","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"pith_short_16","alias_value":"CM344RCXMWK4R7RS","created_at":"2026-07-05T07:35:01Z"},{"alias_kind":"pith_short_8","alias_value":"CM344RCX","created_at":"2026-07-05T07:35:01Z"}],"graph_snapshots":[{"event_id":"sha256:f542c044d2a3ee947c0dfa9b4abf2b86c35119edd61ad66894d0502a5687d688","target":"graph","created_at":"2026-07-05T07:35:01Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2401.09703/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Updating a truncated Singular Value Decomposition (SVD) is crucial in representation learning, especially when dealing with large-scale data matrices that continuously evolve in practical scenarios. Aligning SVD-based models with fast-paced updates becomes increasingly important. Existing methods for updating truncated SVDs employ Rayleigh-Ritz projection procedures, where projection matrices are augmented based on original singular vectors. However, these methods suffer from inefficiency due to the densification of the update matrix and the application of the projection to all singular vector","authors_text":"Cheng Chen, Haoran Deng, Jiahe Li, Shiliang Pu, Weihao Jiang, Yang Yang","cross_cats":["cs.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2024-01-18T03:21:36Z","title":"Fast Updating Truncated SVD for Representation Learning with Sparse Matrices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.09703","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:ca09c28a83dbfc497ea99152f4c6d80239992ee2f9ae050dfab48e8f4a8ee6a8","target":"record","created_at":"2026-07-05T07:35:01Z","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":"d39de3af611dee8b44d82625f13e02786c4cc457d3e6cbd5fd564495ade63357","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2024-01-18T03:21:36Z","title_canon_sha256":"c41d6ed0e20f606353a7eb9fc13efd1f9bd69bec1b055ecbbfef376f3d4c6117"},"schema_version":"1.0","source":{"id":"2401.09703","kind":"arxiv","version":1}},"canonical_sha256":"1337ce44576595c8fe3209afa58a25357702914438cee8fef90b3771c206e098","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1337ce44576595c8fe3209afa58a25357702914438cee8fef90b3771c206e098","first_computed_at":"2026-07-05T07:35:01.040233Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:35:01.040233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uqyY0rlGMH+PnfZBPj8C07n7PMAvhmpS+vqZ5nzObhCQwx2mxIqrr3BznGIFPKXV4CZKDiGI+DUJTHGR8Lh3Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T07:35:01.040644Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.09703","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ca09c28a83dbfc497ea99152f4c6d80239992ee2f9ae050dfab48e8f4a8ee6a8","sha256:f542c044d2a3ee947c0dfa9b4abf2b86c35119edd61ad66894d0502a5687d688"],"state_sha256":"4138a834883608e1bbde8c83701db912b1120f666c2f8c98bda2d55a2e7456bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FksNefYzA4oH0ll4awm/rPtkqWWqhhQKVjz3U1SpgJB/UT3tr9rfvzNxjQkZy/Y8kNoBwQ8+53cDWECmc1WkBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T19:21:23.692194Z","bundle_sha256":"0dc7e03fe9e540c0a060b6186e119b59bcb54820c56f93ec0391201b7b16728e"}}