{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:AJCFDVFDM3N2NXX52UP2RYOV5C","short_pith_number":"pith:AJCFDVFD","canonical_record":{"source":{"id":"2301.13733","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T16:12:26Z","cross_cats_sorted":[],"title_canon_sha256":"42738b83ce25c5ba0fbe92e63f34ee190aa3f027b8fc51d13ebb70920c49eee4","abstract_canon_sha256":"ea1fcaf7b90618f02b1447e33f31d1d27dfee51063d61831072b2b5862a55d51"},"schema_version":"1.0"},"canonical_sha256":"024451d4a366dba6defdd51fa8e1d5e886f4bb559d30927276476601916ddfaa","source":{"kind":"arxiv","id":"2301.13733","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.13733","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"arxiv_version","alias_value":"2301.13733v1","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13733","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"pith_short_12","alias_value":"AJCFDVFDM3N2","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"pith_short_16","alias_value":"AJCFDVFDM3N2NXX5","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"pith_short_8","alias_value":"AJCFDVFD","created_at":"2026-07-05T05:37:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:AJCFDVFDM3N2NXX52UP2RYOV5C","target":"record","payload":{"canonical_record":{"source":{"id":"2301.13733","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T16:12:26Z","cross_cats_sorted":[],"title_canon_sha256":"42738b83ce25c5ba0fbe92e63f34ee190aa3f027b8fc51d13ebb70920c49eee4","abstract_canon_sha256":"ea1fcaf7b90618f02b1447e33f31d1d27dfee51063d61831072b2b5862a55d51"},"schema_version":"1.0"},"canonical_sha256":"024451d4a366dba6defdd51fa8e1d5e886f4bb559d30927276476601916ddfaa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:37:18.038594Z","signature_b64":"gX6WmUKXpVfglHOr8i+1HM2OWFMgIYTuDiQDUqRyL6u4cevfY0BhqtNynGZjk9cy07y1B8N5epqSt84O5uDSBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"024451d4a366dba6defdd51fa8e1d5e886f4bb559d30927276476601916ddfaa","last_reissued_at":"2026-07-05T05:37:18.038103Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:37:18.038103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.13733","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":"2026-07-05T05:37:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B+4AVbUxQ00hDQQr675Xjd7v72QnwjTSv16nHmh8kCfwzWvQ5CBTDmg4hgndVgnNnpkVoJB5+wfYEIDkDPVRCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:14:04.835115Z"},"content_sha256":"dae904b042079d9c4c8a620d9dcfb92469c2b118224efff5ebfd0b7296a347f3","schema_version":"1.0","event_id":"sha256:dae904b042079d9c4c8a620d9dcfb92469c2b118224efff5ebfd0b7296a347f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:AJCFDVFDM3N2NXX52UP2RYOV5C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ali Haghighi, Alireza Koochali, Amin E. Bakhshipour, Andreas Dengel, Sheraz Ahmad, Ulrich Dittmer","submitted_at":"2023-01-31T16:12:26Z","abstract_excerpt":"Despite various breakthroughs in machine learning and data analysis techniques for improving smart operation and management of urban water infrastructures, some key limitations obstruct this progress. Among these shortcomings, the absence of freely available data due to data privacy or high costs of data gathering and the nonexistence of adequate rare or extreme events in the available data plays a crucial role. Here, Generative Adversarial Networks (GANs) can help overcome these challenges. In machine learning, generative models are a class of methods capable of learning data distribution to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13733","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/2301.13733/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":"2026-07-05T05:37:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qni6fe/KalcN69pn53mSSuA1B8Obt0NPLSu0JWAqg4n0sfPfmJJg/pN/6h3Xek1tBV1IsnOclZKKqMpfZFcmBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:14:04.836012Z"},"content_sha256":"98089d19d4464d16bf21e8dfbf1bfdc7213f603b00a16783ee680d9984619e94","schema_version":"1.0","event_id":"sha256:98089d19d4464d16bf21e8dfbf1bfdc7213f603b00a16783ee680d9984619e94"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AJCFDVFDM3N2NXX52UP2RYOV5C/bundle.json","state_url":"https://pith.science/pith/AJCFDVFDM3N2NXX52UP2RYOV5C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AJCFDVFDM3N2NXX52UP2RYOV5C/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":"2026-08-15T00:14:04Z","links":{"resolver":"https://pith.science/pith/AJCFDVFDM3N2NXX52UP2RYOV5C","bundle":"https://pith.science/pith/AJCFDVFDM3N2NXX52UP2RYOV5C/bundle.json","state":"https://pith.science/pith/AJCFDVFDM3N2NXX52UP2RYOV5C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AJCFDVFDM3N2NXX52UP2RYOV5C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:AJCFDVFDM3N2NXX52UP2RYOV5C","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":"ea1fcaf7b90618f02b1447e33f31d1d27dfee51063d61831072b2b5862a55d51","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T16:12:26Z","title_canon_sha256":"42738b83ce25c5ba0fbe92e63f34ee190aa3f027b8fc51d13ebb70920c49eee4"},"schema_version":"1.0","source":{"id":"2301.13733","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.13733","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"arxiv_version","alias_value":"2301.13733v1","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13733","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"pith_short_12","alias_value":"AJCFDVFDM3N2","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"pith_short_16","alias_value":"AJCFDVFDM3N2NXX5","created_at":"2026-07-05T05:37:18Z"},{"alias_kind":"pith_short_8","alias_value":"AJCFDVFD","created_at":"2026-07-05T05:37:18Z"}],"graph_snapshots":[{"event_id":"sha256:98089d19d4464d16bf21e8dfbf1bfdc7213f603b00a16783ee680d9984619e94","target":"graph","created_at":"2026-07-05T05:37:18Z","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/2301.13733/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite various breakthroughs in machine learning and data analysis techniques for improving smart operation and management of urban water infrastructures, some key limitations obstruct this progress. Among these shortcomings, the absence of freely available data due to data privacy or high costs of data gathering and the nonexistence of adequate rare or extreme events in the available data plays a crucial role. Here, Generative Adversarial Networks (GANs) can help overcome these challenges. In machine learning, generative models are a class of methods capable of learning data distribution to ","authors_text":"Ali Haghighi, Alireza Koochali, Amin E. Bakhshipour, Andreas Dengel, Sheraz Ahmad, Ulrich Dittmer","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T16:12:26Z","title":"A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13733","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:dae904b042079d9c4c8a620d9dcfb92469c2b118224efff5ebfd0b7296a347f3","target":"record","created_at":"2026-07-05T05:37:18Z","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":"ea1fcaf7b90618f02b1447e33f31d1d27dfee51063d61831072b2b5862a55d51","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T16:12:26Z","title_canon_sha256":"42738b83ce25c5ba0fbe92e63f34ee190aa3f027b8fc51d13ebb70920c49eee4"},"schema_version":"1.0","source":{"id":"2301.13733","kind":"arxiv","version":1}},"canonical_sha256":"024451d4a366dba6defdd51fa8e1d5e886f4bb559d30927276476601916ddfaa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"024451d4a366dba6defdd51fa8e1d5e886f4bb559d30927276476601916ddfaa","first_computed_at":"2026-07-05T05:37:18.038103Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:37:18.038103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gX6WmUKXpVfglHOr8i+1HM2OWFMgIYTuDiQDUqRyL6u4cevfY0BhqtNynGZjk9cy07y1B8N5epqSt84O5uDSBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:37:18.038594Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.13733","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dae904b042079d9c4c8a620d9dcfb92469c2b118224efff5ebfd0b7296a347f3","sha256:98089d19d4464d16bf21e8dfbf1bfdc7213f603b00a16783ee680d9984619e94"],"state_sha256":"b77ce14c49c4cdc100cf7d29100346ffecda7480b8a0c9631f23da6112c055ee"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jj+LBUozbI/ct4Lu4wZDN6qy1vvn51fuAQPytNib4fvvQ+DfnrY2g541Q53X7H+TvKnp2NsxkAFvGDijQNdtDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T00:14:04.846244Z","bundle_sha256":"4102ac11b3f6c211a5bfaa82c21691e62a4e185fb09f6736cec42621b80ae133"}}