{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:35XLCZJVLJPEYQ3FKMR2I4E7U3","short_pith_number":"pith:35XLCZJV","canonical_record":{"source":{"id":"1803.03503","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-09T13:28:01Z","cross_cats_sorted":[],"title_canon_sha256":"49d1a4b7e765a892945ff106a9e1a18205bcd82613098fbd40e10a0d6a22003a","abstract_canon_sha256":"b0ec54e12700cc92a147139de4b763d8da9968a5b05a0cbdf0d749295e098d5f"},"schema_version":"1.0"},"canonical_sha256":"df6eb165355a5e4c43655323a4709fa6cf0beef30432131227c5e872cba5cb32","source":{"kind":"arxiv","id":"1803.03503","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.03503","created_at":"2026-05-18T00:21:39Z"},{"alias_kind":"arxiv_version","alias_value":"1803.03503v1","created_at":"2026-05-18T00:21:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.03503","created_at":"2026-05-18T00:21:39Z"},{"alias_kind":"pith_short_12","alias_value":"35XLCZJVLJPE","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"35XLCZJVLJPEYQ3F","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"35XLCZJV","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:35XLCZJVLJPEYQ3FKMR2I4E7U3","target":"record","payload":{"canonical_record":{"source":{"id":"1803.03503","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-09T13:28:01Z","cross_cats_sorted":[],"title_canon_sha256":"49d1a4b7e765a892945ff106a9e1a18205bcd82613098fbd40e10a0d6a22003a","abstract_canon_sha256":"b0ec54e12700cc92a147139de4b763d8da9968a5b05a0cbdf0d749295e098d5f"},"schema_version":"1.0"},"canonical_sha256":"df6eb165355a5e4c43655323a4709fa6cf0beef30432131227c5e872cba5cb32","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:39.342647Z","signature_b64":"l2G6hoh2MGyN7L0I2tyXksvJpAviI5AxV0lPzk23aKl1PjVY7SpcrbSns0jU03EU15CgtwEslhsycm8Tz3VRAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df6eb165355a5e4c43655323a4709fa6cf0beef30432131227c5e872cba5cb32","last_reissued_at":"2026-05-18T00:21:39.342007Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:39.342007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.03503","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":"2026-05-18T00:21:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gT6N7vKQC35RQ57APjoRNXzHiP6ObHqgY80gDmwn+oTaaelMp9xnwBm/x7v8+KmuycYyOPbQHkeYGHlGhQnMBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T08:32:36.786884Z"},"content_sha256":"b7ec4d5195bad4c60f5ad86b53b3187a79f049f2ee8d5494591e283494401a42","schema_version":"1.0","event_id":"sha256:b7ec4d5195bad4c60f5ad86b53b3187a79f049f2ee8d5494591e283494401a42"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:35XLCZJVLJPEYQ3FKMR2I4E7U3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Construction of neural networks for realization of localized deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Charles K. Chui, Ding-Xuan Zhou, Shao-Bo Lin","submitted_at":"2018-03-09T13:28:01Z","abstract_excerpt":"The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition, computer vision and natural language processing, time series forecasting, and search engines. However, theoretical development of deep learning is still at its infancy. The objective of this paper is to introduce a deep neural network (also called deep-net) approach to localized manifold learning, with each hidden layer endowed with a specific learning task"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.03503","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":""},"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":"2026-05-18T00:21:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SJrCwNOADse2Vu0mAgfgVBNG6pyghO3NumRg85BplYsKdVpveKJlIY+rxkVmUUR6QjjCb7HWHF84JWfzqTUOCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T08:32:36.787266Z"},"content_sha256":"b50f3797249d76b8e63a11513327d5af1d4606429685d37f247f67cbc0945089","schema_version":"1.0","event_id":"sha256:b50f3797249d76b8e63a11513327d5af1d4606429685d37f247f67cbc0945089"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/35XLCZJVLJPEYQ3FKMR2I4E7U3/bundle.json","state_url":"https://pith.science/pith/35XLCZJVLJPEYQ3FKMR2I4E7U3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/35XLCZJVLJPEYQ3FKMR2I4E7U3/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":"2026-06-11T08:32:36Z","links":{"resolver":"https://pith.science/pith/35XLCZJVLJPEYQ3FKMR2I4E7U3","bundle":"https://pith.science/pith/35XLCZJVLJPEYQ3FKMR2I4E7U3/bundle.json","state":"https://pith.science/pith/35XLCZJVLJPEYQ3FKMR2I4E7U3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/35XLCZJVLJPEYQ3FKMR2I4E7U3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:35XLCZJVLJPEYQ3FKMR2I4E7U3","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":"b0ec54e12700cc92a147139de4b763d8da9968a5b05a0cbdf0d749295e098d5f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-09T13:28:01Z","title_canon_sha256":"49d1a4b7e765a892945ff106a9e1a18205bcd82613098fbd40e10a0d6a22003a"},"schema_version":"1.0","source":{"id":"1803.03503","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.03503","created_at":"2026-05-18T00:21:39Z"},{"alias_kind":"arxiv_version","alias_value":"1803.03503v1","created_at":"2026-05-18T00:21:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.03503","created_at":"2026-05-18T00:21:39Z"},{"alias_kind":"pith_short_12","alias_value":"35XLCZJVLJPE","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"35XLCZJVLJPEYQ3F","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"35XLCZJV","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:b50f3797249d76b8e63a11513327d5af1d4606429685d37f247f67cbc0945089","target":"graph","created_at":"2026-05-18T00:21:39Z","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":"The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition, computer vision and natural language processing, time series forecasting, and search engines. However, theoretical development of deep learning is still at its infancy. The objective of this paper is to introduce a deep neural network (also called deep-net) approach to localized manifold learning, with each hidden layer endowed with a specific learning task","authors_text":"Charles K. Chui, Ding-Xuan Zhou, Shao-Bo Lin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-09T13:28:01Z","title":"Construction of neural networks for realization of localized deep learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.03503","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:b7ec4d5195bad4c60f5ad86b53b3187a79f049f2ee8d5494591e283494401a42","target":"record","created_at":"2026-05-18T00:21:39Z","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":"b0ec54e12700cc92a147139de4b763d8da9968a5b05a0cbdf0d749295e098d5f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-09T13:28:01Z","title_canon_sha256":"49d1a4b7e765a892945ff106a9e1a18205bcd82613098fbd40e10a0d6a22003a"},"schema_version":"1.0","source":{"id":"1803.03503","kind":"arxiv","version":1}},"canonical_sha256":"df6eb165355a5e4c43655323a4709fa6cf0beef30432131227c5e872cba5cb32","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df6eb165355a5e4c43655323a4709fa6cf0beef30432131227c5e872cba5cb32","first_computed_at":"2026-05-18T00:21:39.342007Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:21:39.342007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"l2G6hoh2MGyN7L0I2tyXksvJpAviI5AxV0lPzk23aKl1PjVY7SpcrbSns0jU03EU15CgtwEslhsycm8Tz3VRAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:21:39.342647Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.03503","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b7ec4d5195bad4c60f5ad86b53b3187a79f049f2ee8d5494591e283494401a42","sha256:b50f3797249d76b8e63a11513327d5af1d4606429685d37f247f67cbc0945089"],"state_sha256":"023bf038868bcfae0a7ae8ac98fd6782741b1617872b340bcad7c0ee103a3552"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UJONKW2AvwYGKhDDvtCgovNgwXE4/ShAyWbPDOZV112vGSpZ6+hr8HyiVaoQa4LVh2L97hZq0CBbCdoi4qyxBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T08:32:36.791014Z","bundle_sha256":"e0c268f4c931a1f183e00d3c1ba6501c8a54c04a3f11d6aab9320d12bb5ac36e"}}