{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:2MT6B3VRSB6T2EQ7I7ZVYPRZE7","short_pith_number":"pith:2MT6B3VR","canonical_record":{"source":{"id":"2205.04886","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-07T22:23:21Z","cross_cats_sorted":[],"title_canon_sha256":"72a89860fc6820b0179b4895ccbe4043e04a161e14c84a7510e830b6610ef281","abstract_canon_sha256":"d3f4081daada6ef698dd6ca1487504f94a151dfcde493307c02c9d0c666d18f2"},"schema_version":"1.0"},"canonical_sha256":"d327e0eeb1907d3d121f47f35c3e3927ea9ebe21d930146f86d67141ccfea43f","source":{"kind":"arxiv","id":"2205.04886","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.04886","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"arxiv_version","alias_value":"2205.04886v1","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.04886","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"pith_short_12","alias_value":"2MT6B3VRSB6T","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"pith_short_16","alias_value":"2MT6B3VRSB6T2EQ7","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"pith_short_8","alias_value":"2MT6B3VR","created_at":"2026-07-05T04:22:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:2MT6B3VRSB6T2EQ7I7ZVYPRZE7","target":"record","payload":{"canonical_record":{"source":{"id":"2205.04886","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-07T22:23:21Z","cross_cats_sorted":[],"title_canon_sha256":"72a89860fc6820b0179b4895ccbe4043e04a161e14c84a7510e830b6610ef281","abstract_canon_sha256":"d3f4081daada6ef698dd6ca1487504f94a151dfcde493307c02c9d0c666d18f2"},"schema_version":"1.0"},"canonical_sha256":"d327e0eeb1907d3d121f47f35c3e3927ea9ebe21d930146f86d67141ccfea43f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:22:11.602858Z","signature_b64":"lrj4r/HJouMgQr34bD/FSJ/kiylbdFhSKSz+surDkMhbawHlCAogBmJdT4Y1Aei4guSMglxUEyjheuf0ISihBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d327e0eeb1907d3d121f47f35c3e3927ea9ebe21d930146f86d67141ccfea43f","last_reissued_at":"2026-07-05T04:22:11.602332Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:22:11.602332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.04886","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":"2026-07-05T04:22:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JIBqDTqYPErLE7OMZmoSeP7OAR/n3UnfuWNmKKJu4jzuZG0g/6ZcbLJj2aP4b1VnSiGczC8Y3qT4tb0rzcBJAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T17:22:53.409393Z"},"content_sha256":"6affc9df1408217b00266d4b344679a2339ada35345263247f77a81abb95cc6c","schema_version":"1.0","event_id":"sha256:6affc9df1408217b00266d4b344679a2339ada35345263247f77a81abb95cc6c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:2MT6B3VRSB6T2EQ7I7ZVYPRZE7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Lijun Qian, Omobayode Fagbohungbe","submitted_at":"2022-05-07T22:23:21Z","abstract_excerpt":"Analog hardware has become a popular choice for machine learning on resource-constrained devices recently due to its fast execution and energy efficiency. However, the inherent presence of noise in analog hardware and the negative impact of the noise on deployed deep neural network (DNN) models limit their usage. The degradation in performance due to the noise calls for the novel design of DNN models that have excellent noiseresistant property, leveraging the properties of the fundamental building block of DNN models. In this work, the use of L1 or TopK BatchNorm type, a fundamental DNN model "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.04886","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/2205.04886/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":"2026-07-05T04:22:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"71zBaYFtCzwRdTrjJMInMh/zXRGpXMGhW1SNNIOJENFHiWGQZm6wYNemH/adTKDbAf+Os9GAoHvcDvFSYNiDCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T17:22:53.409923Z"},"content_sha256":"d849fb972a316d3df9e2522f4c25aa08d481da5305247e4bf647c4817c144170","schema_version":"1.0","event_id":"sha256:d849fb972a316d3df9e2522f4c25aa08d481da5305247e4bf647c4817c144170"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2MT6B3VRSB6T2EQ7I7ZVYPRZE7/bundle.json","state_url":"https://pith.science/pith/2MT6B3VRSB6T2EQ7I7ZVYPRZE7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2MT6B3VRSB6T2EQ7I7ZVYPRZE7/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":"2026-08-20T17:22:53Z","links":{"resolver":"https://pith.science/pith/2MT6B3VRSB6T2EQ7I7ZVYPRZE7","bundle":"https://pith.science/pith/2MT6B3VRSB6T2EQ7I7ZVYPRZE7/bundle.json","state":"https://pith.science/pith/2MT6B3VRSB6T2EQ7I7ZVYPRZE7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2MT6B3VRSB6T2EQ7I7ZVYPRZE7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:2MT6B3VRSB6T2EQ7I7ZVYPRZE7","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":"d3f4081daada6ef698dd6ca1487504f94a151dfcde493307c02c9d0c666d18f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-07T22:23:21Z","title_canon_sha256":"72a89860fc6820b0179b4895ccbe4043e04a161e14c84a7510e830b6610ef281"},"schema_version":"1.0","source":{"id":"2205.04886","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.04886","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"arxiv_version","alias_value":"2205.04886v1","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.04886","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"pith_short_12","alias_value":"2MT6B3VRSB6T","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"pith_short_16","alias_value":"2MT6B3VRSB6T2EQ7","created_at":"2026-07-05T04:22:11Z"},{"alias_kind":"pith_short_8","alias_value":"2MT6B3VR","created_at":"2026-07-05T04:22:11Z"}],"graph_snapshots":[{"event_id":"sha256:d849fb972a316d3df9e2522f4c25aa08d481da5305247e4bf647c4817c144170","target":"graph","created_at":"2026-07-05T04:22:11Z","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/2205.04886/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Analog hardware has become a popular choice for machine learning on resource-constrained devices recently due to its fast execution and energy efficiency. However, the inherent presence of noise in analog hardware and the negative impact of the noise on deployed deep neural network (DNN) models limit their usage. The degradation in performance due to the noise calls for the novel design of DNN models that have excellent noiseresistant property, leveraging the properties of the fundamental building block of DNN models. In this work, the use of L1 or TopK BatchNorm type, a fundamental DNN model ","authors_text":"Lijun Qian, Omobayode Fagbohungbe","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-07T22:23:21Z","title":"Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.04886","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":"sha256:6affc9df1408217b00266d4b344679a2339ada35345263247f77a81abb95cc6c","target":"record","created_at":"2026-07-05T04:22:11Z","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":"d3f4081daada6ef698dd6ca1487504f94a151dfcde493307c02c9d0c666d18f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-07T22:23:21Z","title_canon_sha256":"72a89860fc6820b0179b4895ccbe4043e04a161e14c84a7510e830b6610ef281"},"schema_version":"1.0","source":{"id":"2205.04886","kind":"arxiv","version":1}},"canonical_sha256":"d327e0eeb1907d3d121f47f35c3e3927ea9ebe21d930146f86d67141ccfea43f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d327e0eeb1907d3d121f47f35c3e3927ea9ebe21d930146f86d67141ccfea43f","first_computed_at":"2026-07-05T04:22:11.602332Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:22:11.602332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lrj4r/HJouMgQr34bD/FSJ/kiylbdFhSKSz+surDkMhbawHlCAogBmJdT4Y1Aei4guSMglxUEyjheuf0ISihBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:22:11.602858Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.04886","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6affc9df1408217b00266d4b344679a2339ada35345263247f77a81abb95cc6c","sha256:d849fb972a316d3df9e2522f4c25aa08d481da5305247e4bf647c4817c144170"],"state_sha256":"1d81f99d231b9313939edef3082fc0ae490338d3b2d0fc59d24d2b77470a6287"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZJQ+fMIw1uuw6li4yEFeSaOVKMJ5tXTNvV88lcffuHnmg0ukv2K1XNmwvOylN0NLih70sp3VYZ+bH1BUJ4/LAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T17:22:53.415349Z","bundle_sha256":"7a95962c573f48312b565b53a03f276149c3e05b1d77b58c005bd626874feb9a"}}