{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:2MKHOWSO7NMP5Y3AUU4HUKIP55","short_pith_number":"pith:2MKHOWSO","canonical_record":{"source":{"id":"1909.06658","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2019-09-14T19:25:15Z","cross_cats_sorted":[],"title_canon_sha256":"1471be4fe06c2cf2c5d77ded7896e4e51e23b868c36dd52665924071976475cf","abstract_canon_sha256":"c4594fcfd279fbeae00e36e4838d85e0a5c65229797f62e3b864ae8de8afd70f"},"schema_version":"1.0"},"canonical_sha256":"d314775a4efb58fee360a5387a290fef6b0b14e16323ed2a90bae8425128518d","source":{"kind":"arxiv","id":"1909.06658","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.06658","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"arxiv_version","alias_value":"1909.06658v5","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.06658","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"pith_short_12","alias_value":"2MKHOWSO7NMP","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"pith_short_16","alias_value":"2MKHOWSO7NMP5Y3A","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"pith_short_8","alias_value":"2MKHOWSO","created_at":"2026-07-05T01:31:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:2MKHOWSO7NMP5Y3AUU4HUKIP55","target":"record","payload":{"canonical_record":{"source":{"id":"1909.06658","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2019-09-14T19:25:15Z","cross_cats_sorted":[],"title_canon_sha256":"1471be4fe06c2cf2c5d77ded7896e4e51e23b868c36dd52665924071976475cf","abstract_canon_sha256":"c4594fcfd279fbeae00e36e4838d85e0a5c65229797f62e3b864ae8de8afd70f"},"schema_version":"1.0"},"canonical_sha256":"d314775a4efb58fee360a5387a290fef6b0b14e16323ed2a90bae8425128518d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:31:02.767408Z","signature_b64":"UnYN+Y7Ydp8Xzq95ZHacv/F0rLyLzsrI1wZiWp4eqNQiSn50b64ogkpOBulDLWGZwXHA8fOUyZkwkAhXA4m/AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d314775a4efb58fee360a5387a290fef6b0b14e16323ed2a90bae8425128518d","last_reissued_at":"2026-07-05T01:31:02.767019Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:31:02.767019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.06658","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":"2026-07-05T01:31:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hw1QDm49pBtOp7dEX2TTJLIORXI8nfDt14inZ/QpaAZbTby/ObkAE6ayIQo0CDPyGKdJhHJQMsa9LSb3LsF2Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T06:50:19.182056Z"},"content_sha256":"4f12c1c7da380c1e812f80f6d6721f805f7e4eaf86e3ab9191750e8641262272","schema_version":"1.0","event_id":"sha256:4f12c1c7da380c1e812f80f6d6721f805f7e4eaf86e3ab9191750e8641262272"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:2MKHOWSO7NMP5Y3AUU4HUKIP55","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Committee machines -- a universal method to deal with non-idealities in memristor-based neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.ET","authors_text":"A. J. Kenyon, A. Mehonic, C. Li, D. Joksas, M. Buckwell, P. Freitas, Q. Xia, W. D. Zhang, W. H. Ng, Z. Chai","submitted_at":"2019-09-14T19:25:15Z","abstract_excerpt":"Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artificial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.06658","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/1909.06658/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":"2026-07-05T01:31:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JvcwvMV61ZPy7NoecKAuY5fK+ngik2OSWjnqka3yBS+aj3fTxRFk3J1U06g428HEXnXSfQ6NAK37wEtKC8u7Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T06:50:19.182452Z"},"content_sha256":"861f266ff05b362685d15740d7075d8362a0092cad268f21ac45ca51618304b8","schema_version":"1.0","event_id":"sha256:861f266ff05b362685d15740d7075d8362a0092cad268f21ac45ca51618304b8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2MKHOWSO7NMP5Y3AUU4HUKIP55/bundle.json","state_url":"https://pith.science/pith/2MKHOWSO7NMP5Y3AUU4HUKIP55/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2MKHOWSO7NMP5Y3AUU4HUKIP55/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":"2026-07-12T06:50:19Z","links":{"resolver":"https://pith.science/pith/2MKHOWSO7NMP5Y3AUU4HUKIP55","bundle":"https://pith.science/pith/2MKHOWSO7NMP5Y3AUU4HUKIP55/bundle.json","state":"https://pith.science/pith/2MKHOWSO7NMP5Y3AUU4HUKIP55/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2MKHOWSO7NMP5Y3AUU4HUKIP55/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:2MKHOWSO7NMP5Y3AUU4HUKIP55","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":"c4594fcfd279fbeae00e36e4838d85e0a5c65229797f62e3b864ae8de8afd70f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2019-09-14T19:25:15Z","title_canon_sha256":"1471be4fe06c2cf2c5d77ded7896e4e51e23b868c36dd52665924071976475cf"},"schema_version":"1.0","source":{"id":"1909.06658","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.06658","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"arxiv_version","alias_value":"1909.06658v5","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.06658","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"pith_short_12","alias_value":"2MKHOWSO7NMP","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"pith_short_16","alias_value":"2MKHOWSO7NMP5Y3A","created_at":"2026-07-05T01:31:02Z"},{"alias_kind":"pith_short_8","alias_value":"2MKHOWSO","created_at":"2026-07-05T01:31:02Z"}],"graph_snapshots":[{"event_id":"sha256:861f266ff05b362685d15740d7075d8362a0092cad268f21ac45ca51618304b8","target":"graph","created_at":"2026-07-05T01:31:02Z","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/1909.06658/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artificial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known co","authors_text":"A. J. Kenyon, A. Mehonic, C. Li, D. Joksas, M. Buckwell, P. Freitas, Q. Xia, W. D. Zhang, W. H. Ng, Z. Chai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2019-09-14T19:25:15Z","title":"Committee machines -- a universal method to deal with non-idealities in memristor-based neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.06658","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":"sha256:4f12c1c7da380c1e812f80f6d6721f805f7e4eaf86e3ab9191750e8641262272","target":"record","created_at":"2026-07-05T01:31:02Z","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":"c4594fcfd279fbeae00e36e4838d85e0a5c65229797f62e3b864ae8de8afd70f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2019-09-14T19:25:15Z","title_canon_sha256":"1471be4fe06c2cf2c5d77ded7896e4e51e23b868c36dd52665924071976475cf"},"schema_version":"1.0","source":{"id":"1909.06658","kind":"arxiv","version":5}},"canonical_sha256":"d314775a4efb58fee360a5387a290fef6b0b14e16323ed2a90bae8425128518d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d314775a4efb58fee360a5387a290fef6b0b14e16323ed2a90bae8425128518d","first_computed_at":"2026-07-05T01:31:02.767019Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:31:02.767019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UnYN+Y7Ydp8Xzq95ZHacv/F0rLyLzsrI1wZiWp4eqNQiSn50b64ogkpOBulDLWGZwXHA8fOUyZkwkAhXA4m/AA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:31:02.767408Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.06658","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4f12c1c7da380c1e812f80f6d6721f805f7e4eaf86e3ab9191750e8641262272","sha256:861f266ff05b362685d15740d7075d8362a0092cad268f21ac45ca51618304b8"],"state_sha256":"b34dde38098a88ce991eeeb939b20a51c9a4281852dcd8acfdbc7467754fd671"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d5vsZeCaGh9M5Z/YwDZHHR+Y3KOCVKzglKmkz20TcHRxeeG85VjQxsPWB9ac1xlacCYc8SbJo0tbnZauDCwcCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-12T06:50:19.184482Z","bundle_sha256":"01a030d61627f736c384df068d80b5821abc3b4749211a7c77703691739b22aa"}}