{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:HF73AKVPDZAZXXPA47TDVJ2JNZ","short_pith_number":"pith:HF73AKVP","canonical_record":{"source":{"id":"2406.16959","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-21T03:21:22Z","cross_cats_sorted":[],"title_canon_sha256":"d0b6ec1b2dce4023de5a77ea4b0675f07c9eb9965bce24dd746775b58eaa2ce6","abstract_canon_sha256":"daac32b8c0044d1864f676935b747cdba77f53d18110475ee0093f2f1d9bc352"},"schema_version":"1.0"},"canonical_sha256":"397fb02aaf1e419bdde0e7e63aa7496e7dba47a8a532db89e400e101da27aefc","source":{"kind":"arxiv","id":"2406.16959","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.16959","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"arxiv_version","alias_value":"2406.16959v3","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.16959","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"pith_short_12","alias_value":"HF73AKVPDZAZ","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"pith_short_16","alias_value":"HF73AKVPDZAZXXPA","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"pith_short_8","alias_value":"HF73AKVP","created_at":"2026-07-05T10:43:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:HF73AKVPDZAZXXPA47TDVJ2JNZ","target":"record","payload":{"canonical_record":{"source":{"id":"2406.16959","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-21T03:21:22Z","cross_cats_sorted":[],"title_canon_sha256":"d0b6ec1b2dce4023de5a77ea4b0675f07c9eb9965bce24dd746775b58eaa2ce6","abstract_canon_sha256":"daac32b8c0044d1864f676935b747cdba77f53d18110475ee0093f2f1d9bc352"},"schema_version":"1.0"},"canonical_sha256":"397fb02aaf1e419bdde0e7e63aa7496e7dba47a8a532db89e400e101da27aefc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:00.041850Z","signature_b64":"S82hShXTjrq8crfBNclQucK1fnNN6O40AUMk7g0lGR7BZH6FB8ZXC6FopQWtHnrofUWazhq+IkWGbXHLPA4kAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"397fb02aaf1e419bdde0e7e63aa7496e7dba47a8a532db89e400e101da27aefc","last_reissued_at":"2026-07-05T10:43:00.041426Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:00.041426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.16959","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:43:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H7Km6zFt/Rsw2nV1XNq5pca0kXkXT778D4mM9Mi9D9fpzQe281Xtfijt7IPqADOkEUW9kRmnN5LzatACRR0vBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:17:50.375576Z"},"content_sha256":"45a8843e62e8138ae924f2be68b31d3f3703627a7fc274e254429b44312ad16a","schema_version":"1.0","event_id":"sha256:45a8843e62e8138ae924f2be68b31d3f3703627a7fc274e254429b44312ad16a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:HF73AKVPDZAZXXPA47TDVJ2JNZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recurrent Stochastic Configuration Networks for Temporal Data Analytics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dianhui Wang, Gang Dang","submitted_at":"2024-06-21T03:21:22Z","abstract_excerpt":"Temporal data modelling techniques with neural networks are useful in many domain applications, including time-series forecasting and control engineering. This paper aims at developing a recurrent version of stochastic configuration networks (RSCNs) for problem solving, where we have no underlying assumption on the dynamic orders of the input variables. Given a collection of historical data, we first build an initial RSCN model in the light of a supervisory mechanism, followed by an online update of the output weights by using a projection algorithm. Some theoretical results are established, i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.16959","kind":"arxiv","version":3},"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/2406.16959/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":"2026-07-05T10:43:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9Cq7ty8NhKYadbViQwf/ZFXHP8EJOJbfC9791RYHt40pwRl354xFDtXTb/qdYmWzie0qyrhfTBkSDUxr84lSBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:17:50.376469Z"},"content_sha256":"1c0c4761342aa411aee941af83ba93f9ee833f2ae7f03a27c347139c8f33f146","schema_version":"1.0","event_id":"sha256:1c0c4761342aa411aee941af83ba93f9ee833f2ae7f03a27c347139c8f33f146"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HF73AKVPDZAZXXPA47TDVJ2JNZ/bundle.json","state_url":"https://pith.science/pith/HF73AKVPDZAZXXPA47TDVJ2JNZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HF73AKVPDZAZXXPA47TDVJ2JNZ/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":"2026-08-18T12:17:50Z","links":{"resolver":"https://pith.science/pith/HF73AKVPDZAZXXPA47TDVJ2JNZ","bundle":"https://pith.science/pith/HF73AKVPDZAZXXPA47TDVJ2JNZ/bundle.json","state":"https://pith.science/pith/HF73AKVPDZAZXXPA47TDVJ2JNZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HF73AKVPDZAZXXPA47TDVJ2JNZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HF73AKVPDZAZXXPA47TDVJ2JNZ","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":"daac32b8c0044d1864f676935b747cdba77f53d18110475ee0093f2f1d9bc352","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-21T03:21:22Z","title_canon_sha256":"d0b6ec1b2dce4023de5a77ea4b0675f07c9eb9965bce24dd746775b58eaa2ce6"},"schema_version":"1.0","source":{"id":"2406.16959","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.16959","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"arxiv_version","alias_value":"2406.16959v3","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.16959","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"pith_short_12","alias_value":"HF73AKVPDZAZ","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"pith_short_16","alias_value":"HF73AKVPDZAZXXPA","created_at":"2026-07-05T10:43:00Z"},{"alias_kind":"pith_short_8","alias_value":"HF73AKVP","created_at":"2026-07-05T10:43:00Z"}],"graph_snapshots":[{"event_id":"sha256:1c0c4761342aa411aee941af83ba93f9ee833f2ae7f03a27c347139c8f33f146","target":"graph","created_at":"2026-07-05T10:43:00Z","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/2406.16959/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Temporal data modelling techniques with neural networks are useful in many domain applications, including time-series forecasting and control engineering. This paper aims at developing a recurrent version of stochastic configuration networks (RSCNs) for problem solving, where we have no underlying assumption on the dynamic orders of the input variables. Given a collection of historical data, we first build an initial RSCN model in the light of a supervisory mechanism, followed by an online update of the output weights by using a projection algorithm. Some theoretical results are established, i","authors_text":"Dianhui Wang, Gang Dang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-21T03:21:22Z","title":"Recurrent Stochastic Configuration Networks for Temporal Data Analytics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.16959","kind":"arxiv","version":3},"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":"sha256:45a8843e62e8138ae924f2be68b31d3f3703627a7fc274e254429b44312ad16a","target":"record","created_at":"2026-07-05T10:43:00Z","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":"daac32b8c0044d1864f676935b747cdba77f53d18110475ee0093f2f1d9bc352","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-21T03:21:22Z","title_canon_sha256":"d0b6ec1b2dce4023de5a77ea4b0675f07c9eb9965bce24dd746775b58eaa2ce6"},"schema_version":"1.0","source":{"id":"2406.16959","kind":"arxiv","version":3}},"canonical_sha256":"397fb02aaf1e419bdde0e7e63aa7496e7dba47a8a532db89e400e101da27aefc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"397fb02aaf1e419bdde0e7e63aa7496e7dba47a8a532db89e400e101da27aefc","first_computed_at":"2026-07-05T10:43:00.041426Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:43:00.041426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S82hShXTjrq8crfBNclQucK1fnNN6O40AUMk7g0lGR7BZH6FB8ZXC6FopQWtHnrofUWazhq+IkWGbXHLPA4kAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:43:00.041850Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.16959","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:45a8843e62e8138ae924f2be68b31d3f3703627a7fc274e254429b44312ad16a","sha256:1c0c4761342aa411aee941af83ba93f9ee833f2ae7f03a27c347139c8f33f146"],"state_sha256":"924f2964183773715178810ced28a12601ab5f0048f8bbf1479840a40ddf85b0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LpbwMiuNFkKEDQTaksmOe9P/ahlv0bnCAQrTr51yQElBx86M84QtHUCEU4uAyTAGBZiZy3vuoALhEm7qhokgDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T12:17:50.456477Z","bundle_sha256":"061784593d17898d0f7c8da733cc09381ec77319703e37ee5757e5166bf38d34"}}