{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:4XLFO6CWYVF5CE33L5RNIU55A6","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":"f4ff14d6e36af5c894aa11a3de682b75558ef37fde0889a9f1a8ae0a00f4c4d1","cross_cats_sorted":["cs.CV","cs.IR","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-01-26T12:41:58Z","title_canon_sha256":"33cfe314524a65edbd8548a3294a62db7a586e2a77c1b7424e4f493e8cef4e40"},"schema_version":"1.0","source":{"id":"1701.07675","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07675","created_at":"2026-05-18T00:45:42Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07675v2","created_at":"2026-05-18T00:45:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07675","created_at":"2026-05-18T00:45:42Z"},{"alias_kind":"pith_short_12","alias_value":"4XLFO6CWYVF5","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"4XLFO6CWYVF5CE33","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"4XLFO6CW","created_at":"2026-05-18T12:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:0397ffb65631f0c9a707e3af409428c27b69f3e053db7f0c4340489be0358a22","target":"graph","created_at":"2026-05-18T00:45:42Z","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":"This paper addresses the problem of Approximate Nearest Neighbor (ANN) search in pattern recognition where feature vectors in a database are encoded as compact codes in order to speed-up the similarity search in large-scale databases. Considering the ANN problem from an information-theoretic perspective, we interpret it as an encoding, which maps the original feature vectors to a less entropic sparse representation while requiring them to be as informative as possible. We then define the coding gain for ANN search using information-theoretic measures. We next show that the classical approach t","authors_text":"Dimche Kostadinov, Slava Voloshynovskiy, Sohrab Ferdowsi, Taras Holotyak","cross_cats":["cs.CV","cs.IR","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-01-26T12:41:58Z","title":"Sparse Ternary Codes for similarity search have higher coding gain than dense binary codes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07675","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:c1f2f4920f73a6d7ea6a12df729757fc335018dd9aa8690a78c456e8be44146e","target":"record","created_at":"2026-05-18T00:45:42Z","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":"f4ff14d6e36af5c894aa11a3de682b75558ef37fde0889a9f1a8ae0a00f4c4d1","cross_cats_sorted":["cs.CV","cs.IR","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-01-26T12:41:58Z","title_canon_sha256":"33cfe314524a65edbd8548a3294a62db7a586e2a77c1b7424e4f493e8cef4e40"},"schema_version":"1.0","source":{"id":"1701.07675","kind":"arxiv","version":2}},"canonical_sha256":"e5d6577856c54bd1137b5f62d453bd0792ec094a5aea3c697b8f7c48b1bda65c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e5d6577856c54bd1137b5f62d453bd0792ec094a5aea3c697b8f7c48b1bda65c","first_computed_at":"2026-05-18T00:45:42.735873Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:42.735873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"benmMOh/wcpYf7wlXqCJ8vIZGZLv01VIFbxWFS/0HYkyoAaczVGKAGtbX8hkhg22AqgSvD+0f4qm6ed6ckHBCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:42.736468Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.07675","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c1f2f4920f73a6d7ea6a12df729757fc335018dd9aa8690a78c456e8be44146e","sha256:0397ffb65631f0c9a707e3af409428c27b69f3e053db7f0c4340489be0358a22"],"state_sha256":"cfeadc7a09856747fcf83184488dde2f04bad8eb33e91269efc8f4190fa612a5"}