{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:6BVJPITBQ52OI7QGLNWV424Y2V","short_pith_number":"pith:6BVJPITB","canonical_record":{"source":{"id":"2403.19243","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-28T08:58:20Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"ba02841eac701a317a4a99b63be9a64fe87631aa1ba01abe8915b56d22e2ccb1","abstract_canon_sha256":"b4364b6cf0e13f57a47f0f347295acef5ca45d2c2a50514215a7d8c6a79c8118"},"schema_version":"1.0"},"canonical_sha256":"f06a97a2618774e47e065b6d5e6b98d5722f54e57fcd82dbc73bf405151e31a1","source":{"kind":"arxiv","id":"2403.19243","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19243","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19243v5","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19243","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"pith_short_12","alias_value":"6BVJPITBQ52O","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"6BVJPITBQ52OI7QG","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"6BVJPITB","created_at":"2026-07-05T10:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:6BVJPITBQ52OI7QGLNWV424Y2V","target":"record","payload":{"canonical_record":{"source":{"id":"2403.19243","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-28T08:58:20Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"ba02841eac701a317a4a99b63be9a64fe87631aa1ba01abe8915b56d22e2ccb1","abstract_canon_sha256":"b4364b6cf0e13f57a47f0f347295acef5ca45d2c2a50514215a7d8c6a79c8118"},"schema_version":"1.0"},"canonical_sha256":"f06a97a2618774e47e065b6d5e6b98d5722f54e57fcd82dbc73bf405151e31a1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:25.823746Z","signature_b64":"TscrIi9P1BurM+Z7OOucu7URoKiymWcuoTD+hQ0LaDWV5dFLMZxfhLT3a35lsXJCPlKK02llsCyatuYSOW4mCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f06a97a2618774e47e065b6d5e6b98d5722f54e57fcd82dbc73bf405151e31a1","last_reissued_at":"2026-07-05T10:32:25.822792Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:25.822792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.19243","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:32:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zXokOSI0eTqyA5Gq05wqUPwwDTBg6sGLpwITR/IyXSvNKVGTA289yMfs6E9h2U44S6Es+5RVavoJlqIyLZb8Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:29:15.923945Z"},"content_sha256":"00a9181fa78bba0d399687db88a9cc98f89db3fc9e5ccce3c3989a03af582fd7","schema_version":"1.0","event_id":"sha256:00a9181fa78bba0d399687db88a9cc98f89db3fc9e5ccce3c3989a03af582fd7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:6BVJPITBQ52OI7QGLNWV424Y2V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Learning With Sine-Activated Low-rank Matrices","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Cameron Gordon, Hemanth Saratchandran, Simon Lucey, Yiping Ji, Zeyu Zhang","submitted_at":"2024-03-28T08:58:20Z","abstract_excerpt":"Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning. These techniques significantly lower the number of parameters, striking a balance between compactness and performance. However, a common challenge has been the compromise between parameter efficiency and the accuracy of the model, where reduced parameters often lead to diminished accuracy compared to their full-rank counterparts. In this work, we propose a novel theoretical framework that integrates a sinusoidal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19243","kind":"arxiv","version":5},"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/2403.19243/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":"2026-07-05T10:32:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wgjoqH6Nb1xvUzv5G8ELd82oVWEq5y/kyHTYfAugwScqG1Aabxs/4WTLHgaV4GSD9nODTHv9j5Y9uvIbGvbBBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:29:15.924435Z"},"content_sha256":"3f1fc86bbfed8528d6216c72914057b97da25adc1bdd65cbddc7657453904484","schema_version":"1.0","event_id":"sha256:3f1fc86bbfed8528d6216c72914057b97da25adc1bdd65cbddc7657453904484"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6BVJPITBQ52OI7QGLNWV424Y2V/bundle.json","state_url":"https://pith.science/pith/6BVJPITBQ52OI7QGLNWV424Y2V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6BVJPITBQ52OI7QGLNWV424Y2V/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":"2026-08-09T05:29:15Z","links":{"resolver":"https://pith.science/pith/6BVJPITBQ52OI7QGLNWV424Y2V","bundle":"https://pith.science/pith/6BVJPITBQ52OI7QGLNWV424Y2V/bundle.json","state":"https://pith.science/pith/6BVJPITBQ52OI7QGLNWV424Y2V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6BVJPITBQ52OI7QGLNWV424Y2V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:6BVJPITBQ52OI7QGLNWV424Y2V","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":"b4364b6cf0e13f57a47f0f347295acef5ca45d2c2a50514215a7d8c6a79c8118","cross_cats_sorted":["cs.CV","cs.NE"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-28T08:58:20Z","title_canon_sha256":"ba02841eac701a317a4a99b63be9a64fe87631aa1ba01abe8915b56d22e2ccb1"},"schema_version":"1.0","source":{"id":"2403.19243","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19243","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19243v5","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19243","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"pith_short_12","alias_value":"6BVJPITBQ52O","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"6BVJPITBQ52OI7QG","created_at":"2026-07-05T10:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"6BVJPITB","created_at":"2026-07-05T10:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:3f1fc86bbfed8528d6216c72914057b97da25adc1bdd65cbddc7657453904484","target":"graph","created_at":"2026-07-05T10:32:25Z","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/2403.19243/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning. These techniques significantly lower the number of parameters, striking a balance between compactness and performance. However, a common challenge has been the compromise between parameter efficiency and the accuracy of the model, where reduced parameters often lead to diminished accuracy compared to their full-rank counterparts. In this work, we propose a novel theoretical framework that integrates a sinusoidal","authors_text":"Cameron Gordon, Hemanth Saratchandran, Simon Lucey, Yiping Ji, Zeyu Zhang","cross_cats":["cs.CV","cs.NE"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-28T08:58:20Z","title":"Efficient Learning With Sine-Activated Low-rank Matrices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19243","kind":"arxiv","version":5},"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":"sha256:00a9181fa78bba0d399687db88a9cc98f89db3fc9e5ccce3c3989a03af582fd7","target":"record","created_at":"2026-07-05T10:32:25Z","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":"b4364b6cf0e13f57a47f0f347295acef5ca45d2c2a50514215a7d8c6a79c8118","cross_cats_sorted":["cs.CV","cs.NE"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-28T08:58:20Z","title_canon_sha256":"ba02841eac701a317a4a99b63be9a64fe87631aa1ba01abe8915b56d22e2ccb1"},"schema_version":"1.0","source":{"id":"2403.19243","kind":"arxiv","version":5}},"canonical_sha256":"f06a97a2618774e47e065b6d5e6b98d5722f54e57fcd82dbc73bf405151e31a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f06a97a2618774e47e065b6d5e6b98d5722f54e57fcd82dbc73bf405151e31a1","first_computed_at":"2026-07-05T10:32:25.822792Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:32:25.822792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TscrIi9P1BurM+Z7OOucu7URoKiymWcuoTD+hQ0LaDWV5dFLMZxfhLT3a35lsXJCPlKK02llsCyatuYSOW4mCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:32:25.823746Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.19243","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:00a9181fa78bba0d399687db88a9cc98f89db3fc9e5ccce3c3989a03af582fd7","sha256:3f1fc86bbfed8528d6216c72914057b97da25adc1bdd65cbddc7657453904484"],"state_sha256":"9ec80044114aa072987c0ddcf653a4e34aa7f7b408595725d17a8da986142465"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jRpni4c4dHDTHp8TAr7IhOrF9yeVPNUnRx7bd1pniRAHIgS2lb2l9go2wwidIN3lTVcpZzcPgIlFIHKAxUbDBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T05:29:15.927972Z","bundle_sha256":"c24de319669e49ad9d9e16d1266998d6237c082e758d98aed750838df9fbcc51"}}