{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:YXA7ZDAWT7VTVDZSHG7TDCUDLD","short_pith_number":"pith:YXA7ZDAW","canonical_record":{"source":{"id":"1803.05391","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-14T16:40:42Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fdde23b835af79e6bd2306a816f7f0b8084dd830f10207c1ab521ae3b133189f","abstract_canon_sha256":"9ec9702712b240c5877fc10a698ded4a1f0f38de65befb1f68eaaf77ad679be7"},"schema_version":"1.0"},"canonical_sha256":"c5c1fc8c169feb3a8f3239bf318a8358fe3b04ac36348af4aea54b11c9fc4f16","source":{"kind":"arxiv","id":"1803.05391","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.05391","created_at":"2026-05-18T00:13:44Z"},{"alias_kind":"arxiv_version","alias_value":"1803.05391v2","created_at":"2026-05-18T00:13:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.05391","created_at":"2026-05-18T00:13:44Z"},{"alias_kind":"pith_short_12","alias_value":"YXA7ZDAWT7VT","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YXA7ZDAWT7VTVDZS","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YXA7ZDAW","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:YXA7ZDAWT7VTVDZSHG7TDCUDLD","target":"record","payload":{"canonical_record":{"source":{"id":"1803.05391","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-14T16:40:42Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fdde23b835af79e6bd2306a816f7f0b8084dd830f10207c1ab521ae3b133189f","abstract_canon_sha256":"9ec9702712b240c5877fc10a698ded4a1f0f38de65befb1f68eaaf77ad679be7"},"schema_version":"1.0"},"canonical_sha256":"c5c1fc8c169feb3a8f3239bf318a8358fe3b04ac36348af4aea54b11c9fc4f16","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:44.613482Z","signature_b64":"ozxPdfDLM6JdPEZGGijwbixV0Hd8SnT8RpOMUKBZ/K0g7MLueDYuJVwPwgMq+wleJb2AQ5oB2Cji0r8wf1GWBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5c1fc8c169feb3a8f3239bf318a8358fe3b04ac36348af4aea54b11c9fc4f16","last_reissued_at":"2026-05-18T00:13:44.612834Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:44.612834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.05391","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:13:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NEY/ILI6Zpu+SyceyDrEEVzjqpbdfy//CNIN6WTRDiMVRQ5c//V03Picmr+rb5YdofYlQOmWci7Li1kL+ulECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:04:29.059869Z"},"content_sha256":"3d8f67117a73931be3f1380565db2d5ed8c2a09caac17c6ef84e44642603bbb4","schema_version":"1.0","event_id":"sha256:3d8f67117a73931be3f1380565db2d5ed8c2a09caac17c6ef84e44642603bbb4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:YXA7ZDAWT7VTVDZSHG7TDCUDLD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo Yuan, Jian Tang, Jiayu Li, Liang Zhao, Siyue Wang, Wujie Wen, Xue Lin, Yanzhi Wang, Zheng Zhan","submitted_at":"2018-03-14T16:40:42Z","abstract_excerpt":"Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated in both industry and academia. Specific forms of binary neural networks (BNNs) and stochastic computing based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations. Despite the obvious advantages in hardware implementation, these approximate computing techniques are quest"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.05391","kind":"arxiv","version":2},"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":"2026-05-18T00:13:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H0f2tvxC8miPzY0M9bvFsNbIgJmurDwKbccITHequNTjrR2NFl0mc3CKkejqWTHc+mpPWH4Ql7sAg+F730ZgAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:04:29.060430Z"},"content_sha256":"992aa57000dc03c4dcf8fbdfee79453d132e0ba33d2edac69f6e91841f0d81bd","schema_version":"1.0","event_id":"sha256:992aa57000dc03c4dcf8fbdfee79453d132e0ba33d2edac69f6e91841f0d81bd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YXA7ZDAWT7VTVDZSHG7TDCUDLD/bundle.json","state_url":"https://pith.science/pith/YXA7ZDAWT7VTVDZSHG7TDCUDLD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YXA7ZDAWT7VTVDZSHG7TDCUDLD/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":"2026-05-25T15:04:29Z","links":{"resolver":"https://pith.science/pith/YXA7ZDAWT7VTVDZSHG7TDCUDLD","bundle":"https://pith.science/pith/YXA7ZDAWT7VTVDZSHG7TDCUDLD/bundle.json","state":"https://pith.science/pith/YXA7ZDAWT7VTVDZSHG7TDCUDLD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YXA7ZDAWT7VTVDZSHG7TDCUDLD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:YXA7ZDAWT7VTVDZSHG7TDCUDLD","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":"9ec9702712b240c5877fc10a698ded4a1f0f38de65befb1f68eaaf77ad679be7","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-14T16:40:42Z","title_canon_sha256":"fdde23b835af79e6bd2306a816f7f0b8084dd830f10207c1ab521ae3b133189f"},"schema_version":"1.0","source":{"id":"1803.05391","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.05391","created_at":"2026-05-18T00:13:44Z"},{"alias_kind":"arxiv_version","alias_value":"1803.05391v2","created_at":"2026-05-18T00:13:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.05391","created_at":"2026-05-18T00:13:44Z"},{"alias_kind":"pith_short_12","alias_value":"YXA7ZDAWT7VT","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YXA7ZDAWT7VTVDZS","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YXA7ZDAW","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:992aa57000dc03c4dcf8fbdfee79453d132e0ba33d2edac69f6e91841f0d81bd","target":"graph","created_at":"2026-05-18T00:13:44Z","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":"Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated in both industry and academia. Specific forms of binary neural networks (BNNs) and stochastic computing based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations. Despite the obvious advantages in hardware implementation, these approximate computing techniques are quest","authors_text":"Bo Yuan, Jian Tang, Jiayu Li, Liang Zhao, Siyue Wang, Wujie Wen, Xue Lin, Yanzhi Wang, Zheng Zhan","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-14T16:40:42Z","title":"On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.05391","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":"sha256:3d8f67117a73931be3f1380565db2d5ed8c2a09caac17c6ef84e44642603bbb4","target":"record","created_at":"2026-05-18T00:13:44Z","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":"9ec9702712b240c5877fc10a698ded4a1f0f38de65befb1f68eaaf77ad679be7","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-14T16:40:42Z","title_canon_sha256":"fdde23b835af79e6bd2306a816f7f0b8084dd830f10207c1ab521ae3b133189f"},"schema_version":"1.0","source":{"id":"1803.05391","kind":"arxiv","version":2}},"canonical_sha256":"c5c1fc8c169feb3a8f3239bf318a8358fe3b04ac36348af4aea54b11c9fc4f16","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c5c1fc8c169feb3a8f3239bf318a8358fe3b04ac36348af4aea54b11c9fc4f16","first_computed_at":"2026-05-18T00:13:44.612834Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:44.612834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ozxPdfDLM6JdPEZGGijwbixV0Hd8SnT8RpOMUKBZ/K0g7MLueDYuJVwPwgMq+wleJb2AQ5oB2Cji0r8wf1GWBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:44.613482Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.05391","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d8f67117a73931be3f1380565db2d5ed8c2a09caac17c6ef84e44642603bbb4","sha256:992aa57000dc03c4dcf8fbdfee79453d132e0ba33d2edac69f6e91841f0d81bd"],"state_sha256":"63d03a3bc24131c6de41d3375dea11eb4b1048d6291ff9d4e3d20a44ff63acd6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LVoa/lq9ujMHTetXAHwrnCZRv3OZS7yj02IMJO6JcBceuuKL3j+hfwJAHmYNZR4KkinTc+7wflRXnUCqOWI1CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T15:04:29.063754Z","bundle_sha256":"d41777014f511531d848e000583933b50ec5c2488b7ebdbf0ba38c3dd2bc2347"}}