{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:O65HNHR75PD4CF22L2COGLTACA","short_pith_number":"pith:O65HNHR7","canonical_record":{"source":{"id":"2308.00685","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-01T17:42:35Z","cross_cats_sorted":["cs.ET"],"title_canon_sha256":"91b19736fe4051b467dccb7f756a937e84dfce525cdb9447397c2bfd42f1417f","abstract_canon_sha256":"d1546d4c5d644665f7fe68030e6d55a299c58915679580e59d0e0b91db924f8b"},"schema_version":"1.0"},"canonical_sha256":"77ba769e3febc7c1175a5e84e32e60100a7aefc0eca042f5c872801d8c0b8f26","source":{"kind":"arxiv","id":"2308.00685","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.00685","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"arxiv_version","alias_value":"2308.00685v1","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.00685","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"pith_short_12","alias_value":"O65HNHR75PD4","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"pith_short_16","alias_value":"O65HNHR75PD4CF22","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"pith_short_8","alias_value":"O65HNHR7","created_at":"2026-07-05T06:36:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:O65HNHR75PD4CF22L2COGLTACA","target":"record","payload":{"canonical_record":{"source":{"id":"2308.00685","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-01T17:42:35Z","cross_cats_sorted":["cs.ET"],"title_canon_sha256":"91b19736fe4051b467dccb7f756a937e84dfce525cdb9447397c2bfd42f1417f","abstract_canon_sha256":"d1546d4c5d644665f7fe68030e6d55a299c58915679580e59d0e0b91db924f8b"},"schema_version":"1.0"},"canonical_sha256":"77ba769e3febc7c1175a5e84e32e60100a7aefc0eca042f5c872801d8c0b8f26","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:36:50.556500Z","signature_b64":"JnJ26+uft11onlnG+z0c1O565/pucJoqHXD7oB6g2PLpfOE/JLod70k+YsIWgcz+HM7KkwpLaRHleYs4gFgXBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77ba769e3febc7c1175a5e84e32e60100a7aefc0eca042f5c872801d8c0b8f26","last_reissued_at":"2026-07-05T06:36:50.555966Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:36:50.555966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.00685","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":"2026-07-05T06:36:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"np0kgiRfQTMGQ8mat7M8sAQBohp4b5zFSXzGVLntNX9Rw8JKmCpaEIip8BdH0XEbMSIdkMiwK3ttAiSY1dKnAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T18:01:59.285528Z"},"content_sha256":"aafce5167db01f521a936f0faa6c3acc185a14fe46b69d4e1a06fe048be96e24","schema_version":"1.0","event_id":"sha256:aafce5167db01f521a936f0faa6c3acc185a14fe46b69d4e1a06fe048be96e24"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:O65HNHR75PD4CF22L2COGLTACA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning from Hypervectors: A Survey on Hypervector Encoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.ET"],"primary_cat":"cs.LG","authors_text":"Mehran Shoushtari Moghadam, M. Hassan Najafi, Mohsen Imani, Sercan Aygun","submitted_at":"2023-08-01T17:42:35Z","abstract_excerpt":"Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the brain's structure to offer a powerful and efficient processing and learning model. In HDC, the data are encoded with long vectors, called hypervectors, typically with a length of 1K to 10K. The literature provides several encoding techniques to generate orthogonal or correlated hypervectors, depending on the intended application. The existing surveys in the literature often focus on the overall aspects of HDC systems, including system inputs, primary computations, and final outputs. However, this study takes a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.00685","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/2308.00685/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":"2026-07-05T06:36:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZnB2mmv0R/klFjpgm9IrSJ26ZhJ+lEK0qYECtb57edHfxa/I/8emwM7UGkNgltlT3vfFhqP/iEL8I/0e9vwIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T18:01:59.285904Z"},"content_sha256":"45f02e1f29553d123902371f095188535b7bbe716e9f8e3a1290106e9ea1df45","schema_version":"1.0","event_id":"sha256:45f02e1f29553d123902371f095188535b7bbe716e9f8e3a1290106e9ea1df45"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O65HNHR75PD4CF22L2COGLTACA/bundle.json","state_url":"https://pith.science/pith/O65HNHR75PD4CF22L2COGLTACA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O65HNHR75PD4CF22L2COGLTACA/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":"2026-07-08T18:01:59Z","links":{"resolver":"https://pith.science/pith/O65HNHR75PD4CF22L2COGLTACA","bundle":"https://pith.science/pith/O65HNHR75PD4CF22L2COGLTACA/bundle.json","state":"https://pith.science/pith/O65HNHR75PD4CF22L2COGLTACA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O65HNHR75PD4CF22L2COGLTACA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:O65HNHR75PD4CF22L2COGLTACA","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":"d1546d4c5d644665f7fe68030e6d55a299c58915679580e59d0e0b91db924f8b","cross_cats_sorted":["cs.ET"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-01T17:42:35Z","title_canon_sha256":"91b19736fe4051b467dccb7f756a937e84dfce525cdb9447397c2bfd42f1417f"},"schema_version":"1.0","source":{"id":"2308.00685","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.00685","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"arxiv_version","alias_value":"2308.00685v1","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.00685","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"pith_short_12","alias_value":"O65HNHR75PD4","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"pith_short_16","alias_value":"O65HNHR75PD4CF22","created_at":"2026-07-05T06:36:50Z"},{"alias_kind":"pith_short_8","alias_value":"O65HNHR7","created_at":"2026-07-05T06:36:50Z"}],"graph_snapshots":[{"event_id":"sha256:45f02e1f29553d123902371f095188535b7bbe716e9f8e3a1290106e9ea1df45","target":"graph","created_at":"2026-07-05T06:36:50Z","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/2308.00685/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the brain's structure to offer a powerful and efficient processing and learning model. In HDC, the data are encoded with long vectors, called hypervectors, typically with a length of 1K to 10K. The literature provides several encoding techniques to generate orthogonal or correlated hypervectors, depending on the intended application. The existing surveys in the literature often focus on the overall aspects of HDC systems, including system inputs, primary computations, and final outputs. However, this study takes a","authors_text":"Mehran Shoushtari Moghadam, M. Hassan Najafi, Mohsen Imani, Sercan Aygun","cross_cats":["cs.ET"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-01T17:42:35Z","title":"Learning from Hypervectors: A Survey on Hypervector Encoding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.00685","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:aafce5167db01f521a936f0faa6c3acc185a14fe46b69d4e1a06fe048be96e24","target":"record","created_at":"2026-07-05T06:36:50Z","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":"d1546d4c5d644665f7fe68030e6d55a299c58915679580e59d0e0b91db924f8b","cross_cats_sorted":["cs.ET"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-01T17:42:35Z","title_canon_sha256":"91b19736fe4051b467dccb7f756a937e84dfce525cdb9447397c2bfd42f1417f"},"schema_version":"1.0","source":{"id":"2308.00685","kind":"arxiv","version":1}},"canonical_sha256":"77ba769e3febc7c1175a5e84e32e60100a7aefc0eca042f5c872801d8c0b8f26","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77ba769e3febc7c1175a5e84e32e60100a7aefc0eca042f5c872801d8c0b8f26","first_computed_at":"2026-07-05T06:36:50.555966Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:36:50.555966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JnJ26+uft11onlnG+z0c1O565/pucJoqHXD7oB6g2PLpfOE/JLod70k+YsIWgcz+HM7KkwpLaRHleYs4gFgXBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:36:50.556500Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.00685","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aafce5167db01f521a936f0faa6c3acc185a14fe46b69d4e1a06fe048be96e24","sha256:45f02e1f29553d123902371f095188535b7bbe716e9f8e3a1290106e9ea1df45"],"state_sha256":"4e68b1c14b216e88b89c2656ac9b2cf1c407e4f66d551ba1e126aa771f9384f0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/eXq3KxlkbaRBFFOQG9sSs4RWATj8R+H/AJoMTuOZ7LqpFMoAjHpozEUKLBPYJIw0NYHytJkl6E4oCRdKGQACQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T18:01:59.288179Z","bundle_sha256":"92d5149b3a1533f7b6be51d7f667fa92278323b0c55dcdb219f33354a0ac5409"}}