{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:J6ELBO7SUWB5ILGKT6UN5S7STJ","short_pith_number":"pith:J6ELBO7S","canonical_record":{"source":{"id":"2103.01904","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-02T18:05:43Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"484a9930a6532981823d958b891970479415a8c6f2758d2674084f2865f6b400","abstract_canon_sha256":"1fe7b339e8e0092530837dd0effee7509fed18b563bd397770fc19301e8fad39"},"schema_version":"1.0"},"canonical_sha256":"4f88b0bbf2a583d42cca9fa8decbf29a6c1d9e0cf05f045184034fbd8adf75a8","source":{"kind":"arxiv","id":"2103.01904","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.01904","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"arxiv_version","alias_value":"2103.01904v1","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.01904","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"pith_short_12","alias_value":"J6ELBO7SUWB5","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"pith_short_16","alias_value":"J6ELBO7SUWB5ILGK","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"pith_short_8","alias_value":"J6ELBO7S","created_at":"2026-07-05T02:19:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:J6ELBO7SUWB5ILGKT6UN5S7STJ","target":"record","payload":{"canonical_record":{"source":{"id":"2103.01904","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-02T18:05:43Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"484a9930a6532981823d958b891970479415a8c6f2758d2674084f2865f6b400","abstract_canon_sha256":"1fe7b339e8e0092530837dd0effee7509fed18b563bd397770fc19301e8fad39"},"schema_version":"1.0"},"canonical_sha256":"4f88b0bbf2a583d42cca9fa8decbf29a6c1d9e0cf05f045184034fbd8adf75a8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:19:51.767081Z","signature_b64":"u3nELe4cVM4u3yWcPBLkQPM0i0nyheOI7IJ9ijvG8SoALpu+uMNsWMWr6iQ+0EfA3X6lvW1/M0EttKf2+ZPCDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f88b0bbf2a583d42cca9fa8decbf29a6c1d9e0cf05f045184034fbd8adf75a8","last_reissued_at":"2026-07-05T02:19:51.766687Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:19:51.766687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.01904","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":"2026-07-05T02:19:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3XTl4RbgeYhGPOgs4J56vzUa3ooNu8FvvgcGGK4cvS1v67vKHdjcp5wPeZSf2sIc1hmlPMvwOmR/b8Fz40ayCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:44:58.972237Z"},"content_sha256":"d4e2c37ea053f3e46be881ba8fa7da821b8ec6484b3e1d3d8e4d1214f752c0d2","schema_version":"1.0","event_id":"sha256:d4e2c37ea053f3e46be881ba8fa7da821b8ec6484b3e1d3d8e4d1214f752c0d2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:J6ELBO7SUWB5ILGKT6UN5S7STJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Spectral Enabled GAN for Time Series Data Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Anthony O. Smith, Kaleb E. Smith","submitted_at":"2021-03-02T18:05:43Z","abstract_excerpt":"Time dependent data is a main source of information in today's data driven world. Generating this type of data though has shown its challenges and made it an interesting research area in the field of generative machine learning. One such approach was that by Smith et al. who developed Time Series Generative Adversarial Network (TSGAN) which showed promising performance in generating time dependent data and the ability of few shot generation though being flawed in certain aspects of training and learning. This paper looks to improve on the results from TSGAN and address those flaws by unifying "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.01904","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/2103.01904/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":"2026-07-05T02:19:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GQwLk1F2VyqczsRN27JZuA4mdTlaKVbDCUpazzVeBSl4Iy0inwl0sSJU1wxj7DT86Bcc9TDc4gnei5/SUMrnAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:44:58.972626Z"},"content_sha256":"74b8da0146849907490ee5f9eb5492b107f84f4307e1130d075fb53f52ab40b9","schema_version":"1.0","event_id":"sha256:74b8da0146849907490ee5f9eb5492b107f84f4307e1130d075fb53f52ab40b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J6ELBO7SUWB5ILGKT6UN5S7STJ/bundle.json","state_url":"https://pith.science/pith/J6ELBO7SUWB5ILGKT6UN5S7STJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J6ELBO7SUWB5ILGKT6UN5S7STJ/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":"2026-07-07T13:44:58Z","links":{"resolver":"https://pith.science/pith/J6ELBO7SUWB5ILGKT6UN5S7STJ","bundle":"https://pith.science/pith/J6ELBO7SUWB5ILGKT6UN5S7STJ/bundle.json","state":"https://pith.science/pith/J6ELBO7SUWB5ILGKT6UN5S7STJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J6ELBO7SUWB5ILGKT6UN5S7STJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:J6ELBO7SUWB5ILGKT6UN5S7STJ","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":"1fe7b339e8e0092530837dd0effee7509fed18b563bd397770fc19301e8fad39","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-02T18:05:43Z","title_canon_sha256":"484a9930a6532981823d958b891970479415a8c6f2758d2674084f2865f6b400"},"schema_version":"1.0","source":{"id":"2103.01904","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.01904","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"arxiv_version","alias_value":"2103.01904v1","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.01904","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"pith_short_12","alias_value":"J6ELBO7SUWB5","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"pith_short_16","alias_value":"J6ELBO7SUWB5ILGK","created_at":"2026-07-05T02:19:51Z"},{"alias_kind":"pith_short_8","alias_value":"J6ELBO7S","created_at":"2026-07-05T02:19:51Z"}],"graph_snapshots":[{"event_id":"sha256:74b8da0146849907490ee5f9eb5492b107f84f4307e1130d075fb53f52ab40b9","target":"graph","created_at":"2026-07-05T02:19:51Z","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/2103.01904/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time dependent data is a main source of information in today's data driven world. Generating this type of data though has shown its challenges and made it an interesting research area in the field of generative machine learning. One such approach was that by Smith et al. who developed Time Series Generative Adversarial Network (TSGAN) which showed promising performance in generating time dependent data and the ability of few shot generation though being flawed in certain aspects of training and learning. This paper looks to improve on the results from TSGAN and address those flaws by unifying ","authors_text":"Anthony O. Smith, Kaleb E. Smith","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-02T18:05:43Z","title":"A Spectral Enabled GAN for Time Series Data Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.01904","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:d4e2c37ea053f3e46be881ba8fa7da821b8ec6484b3e1d3d8e4d1214f752c0d2","target":"record","created_at":"2026-07-05T02:19:51Z","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":"1fe7b339e8e0092530837dd0effee7509fed18b563bd397770fc19301e8fad39","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-02T18:05:43Z","title_canon_sha256":"484a9930a6532981823d958b891970479415a8c6f2758d2674084f2865f6b400"},"schema_version":"1.0","source":{"id":"2103.01904","kind":"arxiv","version":1}},"canonical_sha256":"4f88b0bbf2a583d42cca9fa8decbf29a6c1d9e0cf05f045184034fbd8adf75a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4f88b0bbf2a583d42cca9fa8decbf29a6c1d9e0cf05f045184034fbd8adf75a8","first_computed_at":"2026-07-05T02:19:51.766687Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:19:51.766687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u3nELe4cVM4u3yWcPBLkQPM0i0nyheOI7IJ9ijvG8SoALpu+uMNsWMWr6iQ+0EfA3X6lvW1/M0EttKf2+ZPCDg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:19:51.767081Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.01904","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d4e2c37ea053f3e46be881ba8fa7da821b8ec6484b3e1d3d8e4d1214f752c0d2","sha256:74b8da0146849907490ee5f9eb5492b107f84f4307e1130d075fb53f52ab40b9"],"state_sha256":"ac9cfcff3e6ae4c5c39afd75a559cfd204d526d087c672792135e960471bd912"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"596f97bnPY3QSUbbmVbAUPuJRoOm3j63veJLoG6900Dy2BSN794S+HVJz2MqY8/wj0WQ60svFFyxVuizSifTBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:44:58.974563Z","bundle_sha256":"e2c7bb171df53484ad17c19cc36d2d19c5f4a6cb903339acdb51e0f11e3da4b8"}}