{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:CPRFYVEGPB3LHU3ZINFO7VGHES","short_pith_number":"pith:CPRFYVEG","canonical_record":{"source":{"id":"1609.07228","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-23T04:34:54Z","cross_cats_sorted":[],"title_canon_sha256":"16082c7612f28940396e3c8ce0a73df2a6d36ef23239b00c96c892f9d5587e23","abstract_canon_sha256":"4dc73d336b45caa439dce97eb91be045c2a0a7f733bb9a807ec7ad2dd1fa3178"},"schema_version":"1.0"},"canonical_sha256":"13e25c54867876b3d379434aefd4c724833e903edbefb5548a14641f808a0364","source":{"kind":"arxiv","id":"1609.07228","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.07228","created_at":"2026-05-18T00:55:57Z"},{"alias_kind":"arxiv_version","alias_value":"1609.07228v3","created_at":"2026-05-18T00:55:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.07228","created_at":"2026-05-18T00:55:57Z"},{"alias_kind":"pith_short_12","alias_value":"CPRFYVEGPB3L","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"CPRFYVEGPB3LHU3Z","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"CPRFYVEG","created_at":"2026-05-18T12:30:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:CPRFYVEGPB3LHU3ZINFO7VGHES","target":"record","payload":{"canonical_record":{"source":{"id":"1609.07228","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-23T04:34:54Z","cross_cats_sorted":[],"title_canon_sha256":"16082c7612f28940396e3c8ce0a73df2a6d36ef23239b00c96c892f9d5587e23","abstract_canon_sha256":"4dc73d336b45caa439dce97eb91be045c2a0a7f733bb9a807ec7ad2dd1fa3178"},"schema_version":"1.0"},"canonical_sha256":"13e25c54867876b3d379434aefd4c724833e903edbefb5548a14641f808a0364","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:55:57.590208Z","signature_b64":"LIWgH0g+PmrkLJ3utaFQpJXk6NlG0hBsyg7Nsk2NrpLaHFBcepdgbcc7SgqaJm5RyZSS6Q9cKZigmKgla5vJAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13e25c54867876b3d379434aefd4c724833e903edbefb5548a14641f808a0364","last_reissued_at":"2026-05-18T00:55:57.589816Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:55:57.589816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1609.07228","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:55:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pH/7ukJNd3nyvlXRz4s2ew5mHL8TMwYAQ1JMa1mQQHvU8CebTtWvE3SF3CnRlBrbVZxavJPk5O9fR3VGkaiYBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T00:45:50.002404Z"},"content_sha256":"ae2e0bc07de3abc926ab5d4c001dbcddafbf9759dab3250b8d413b697a05bdc7","schema_version":"1.0","event_id":"sha256:ae2e0bc07de3abc926ab5d4c001dbcddafbf9759dab3250b8d413b697a05bdc7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:CPRFYVEGPB3LHU3ZINFO7VGHES","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cong Fu, Deng Cai","submitted_at":"2016-09-23T04:34:54Z","abstract_excerpt":"Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of data mining, machine learning and computer vision. The performance of traditional hierarchical structure (tree) based methods decreases as the dimensionality of data grows, while hashing based methods usually lack efficiency in practice. Recently, the graph based methods have drawn considerable attention. The main idea is that \\emph{a neighbor of a neighbor is also likely to be a neighbor}, which we refer as \\emph{NN-expansion}. These methods construct a $k$-nearest neighbor ($k$NN) graph offline. And at online"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.07228","kind":"arxiv","version":3},"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":"2026-05-18T00:55:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ACbxrkHQ2ChJk8UpSsYkKTUHbG5Xn2QzDH/wqDQnbpn4LPyVYFc48RLGms2oz66oUB6H10uIrJNHqaxwsfQoDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T00:45:50.002761Z"},"content_sha256":"481c1ea20fe022f67a513a573f688e75359d8b649de9db1df50f02217a643eec","schema_version":"1.0","event_id":"sha256:481c1ea20fe022f67a513a573f688e75359d8b649de9db1df50f02217a643eec"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CPRFYVEGPB3LHU3ZINFO7VGHES/bundle.json","state_url":"https://pith.science/pith/CPRFYVEGPB3LHU3ZINFO7VGHES/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CPRFYVEGPB3LHU3ZINFO7VGHES/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":"2026-05-21T00:45:50Z","links":{"resolver":"https://pith.science/pith/CPRFYVEGPB3LHU3ZINFO7VGHES","bundle":"https://pith.science/pith/CPRFYVEGPB3LHU3ZINFO7VGHES/bundle.json","state":"https://pith.science/pith/CPRFYVEGPB3LHU3ZINFO7VGHES/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CPRFYVEGPB3LHU3ZINFO7VGHES/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:CPRFYVEGPB3LHU3ZINFO7VGHES","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":"4dc73d336b45caa439dce97eb91be045c2a0a7f733bb9a807ec7ad2dd1fa3178","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-23T04:34:54Z","title_canon_sha256":"16082c7612f28940396e3c8ce0a73df2a6d36ef23239b00c96c892f9d5587e23"},"schema_version":"1.0","source":{"id":"1609.07228","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.07228","created_at":"2026-05-18T00:55:57Z"},{"alias_kind":"arxiv_version","alias_value":"1609.07228v3","created_at":"2026-05-18T00:55:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.07228","created_at":"2026-05-18T00:55:57Z"},{"alias_kind":"pith_short_12","alias_value":"CPRFYVEGPB3L","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"CPRFYVEGPB3LHU3Z","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"CPRFYVEG","created_at":"2026-05-18T12:30:09Z"}],"graph_snapshots":[{"event_id":"sha256:481c1ea20fe022f67a513a573f688e75359d8b649de9db1df50f02217a643eec","target":"graph","created_at":"2026-05-18T00:55:57Z","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":"Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of data mining, machine learning and computer vision. The performance of traditional hierarchical structure (tree) based methods decreases as the dimensionality of data grows, while hashing based methods usually lack efficiency in practice. Recently, the graph based methods have drawn considerable attention. The main idea is that \\emph{a neighbor of a neighbor is also likely to be a neighbor}, which we refer as \\emph{NN-expansion}. These methods construct a $k$-nearest neighbor ($k$NN) graph offline. And at online","authors_text":"Cong Fu, Deng Cai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-23T04:34:54Z","title":"EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.07228","kind":"arxiv","version":3},"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:ae2e0bc07de3abc926ab5d4c001dbcddafbf9759dab3250b8d413b697a05bdc7","target":"record","created_at":"2026-05-18T00:55:57Z","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":"4dc73d336b45caa439dce97eb91be045c2a0a7f733bb9a807ec7ad2dd1fa3178","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-23T04:34:54Z","title_canon_sha256":"16082c7612f28940396e3c8ce0a73df2a6d36ef23239b00c96c892f9d5587e23"},"schema_version":"1.0","source":{"id":"1609.07228","kind":"arxiv","version":3}},"canonical_sha256":"13e25c54867876b3d379434aefd4c724833e903edbefb5548a14641f808a0364","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"13e25c54867876b3d379434aefd4c724833e903edbefb5548a14641f808a0364","first_computed_at":"2026-05-18T00:55:57.589816Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:55:57.589816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LIWgH0g+PmrkLJ3utaFQpJXk6NlG0hBsyg7Nsk2NrpLaHFBcepdgbcc7SgqaJm5RyZSS6Q9cKZigmKgla5vJAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:55:57.590208Z","signed_message":"canonical_sha256_bytes"},"source_id":"1609.07228","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae2e0bc07de3abc926ab5d4c001dbcddafbf9759dab3250b8d413b697a05bdc7","sha256:481c1ea20fe022f67a513a573f688e75359d8b649de9db1df50f02217a643eec"],"state_sha256":"8bc8491cc5dfad3309fc017d04bfc6c518d5deeb511109b96b7436ebf28c9fbd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c/DkEA2btUYHVVf6yTV3N8S41L07t3dulc0zKrEYVtRRhiGctIfWeYT8rN65YSzKb4TvBpIBrioEVW5+DZ4IAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T00:45:50.005335Z","bundle_sha256":"534b9572bf170559465981245e88fc6ad2a0cacf994becd1cbcac20751feaf78"}}