{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:3SYMACA5PQY3B2KUGMR65PXP27","short_pith_number":"pith:3SYMACA5","schema_version":"1.0","canonical_sha256":"dcb0c0081d7c31b0e9543323eebeefd7d50ddd2fc57fd0a8b2b45eb0bc0c4208","source":{"kind":"arxiv","id":"2111.05973","version":1},"attestation_state":"computed","paper":{"title":"Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andrey Rzhetsky, Chao Zhang, Jaswanth Yella, Sergei Petrov, Sthitie Bom, Xiaoye Qian, Yu Huang","submitted_at":"2021-11-10T22:31:32Z","abstract_excerpt":"With the rapid development of AI technology in recent years, there have been many studies with deep learning models in soft sensing area. However, the models have become more complex, yet, the data sets remain limited: researchers are fitting million-parameter models with hundreds of data samples, which is insufficient to exercise the effectiveness of their models and thus often fail to perform when implemented in industrial applications. To solve this long-lasting problem, we are providing large scale, high dimensional time series manufacturing sensor data from Seagate Technology to the publi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2111.05973","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-10T22:31:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d2e612f69fe8c369ddc2da152d53b35c2dfd37ab6b6d708da36587b4d0355d3f","abstract_canon_sha256":"7ca62f7a51c57a0429208b87f7d592ffebe46f4581e108ca43e1d1538f7520a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:50:36.987483Z","signature_b64":"9CjuQxL+T9ejntQVTeXELkSfnrUnzZ8Su4q7XlBV5MPpClbkNHstEXCi60/KZ5VeIQbHjRGj39XWqwibM2HADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcb0c0081d7c31b0e9543323eebeefd7d50ddd2fc57fd0a8b2b45eb0bc0c4208","last_reissued_at":"2026-07-05T03:50:36.986989Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:50:36.986989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andrey Rzhetsky, Chao Zhang, Jaswanth Yella, Sergei Petrov, Sthitie Bom, Xiaoye Qian, Yu Huang","submitted_at":"2021-11-10T22:31:32Z","abstract_excerpt":"With the rapid development of AI technology in recent years, there have been many studies with deep learning models in soft sensing area. However, the models have become more complex, yet, the data sets remain limited: researchers are fitting million-parameter models with hundreds of data samples, which is insufficient to exercise the effectiveness of their models and thus often fail to perform when implemented in industrial applications. To solve this long-lasting problem, we are providing large scale, high dimensional time series manufacturing sensor data from Seagate Technology to the publi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.05973","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/2111.05973/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2111.05973","created_at":"2026-07-05T03:50:36.987046+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.05973v1","created_at":"2026-07-05T03:50:36.987046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.05973","created_at":"2026-07-05T03:50:36.987046+00:00"},{"alias_kind":"pith_short_12","alias_value":"3SYMACA5PQY3","created_at":"2026-07-05T03:50:36.987046+00:00"},{"alias_kind":"pith_short_16","alias_value":"3SYMACA5PQY3B2KU","created_at":"2026-07-05T03:50:36.987046+00:00"},{"alias_kind":"pith_short_8","alias_value":"3SYMACA5","created_at":"2026-07-05T03:50:36.987046+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3SYMACA5PQY3B2KUGMR65PXP27","json":"https://pith.science/pith/3SYMACA5PQY3B2KUGMR65PXP27.json","graph_json":"https://pith.science/api/pith-number/3SYMACA5PQY3B2KUGMR65PXP27/graph.json","events_json":"https://pith.science/api/pith-number/3SYMACA5PQY3B2KUGMR65PXP27/events.json","paper":"https://pith.science/paper/3SYMACA5"},"agent_actions":{"view_html":"https://pith.science/pith/3SYMACA5PQY3B2KUGMR65PXP27","download_json":"https://pith.science/pith/3SYMACA5PQY3B2KUGMR65PXP27.json","view_paper":"https://pith.science/paper/3SYMACA5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.05973&json=true","fetch_graph":"https://pith.science/api/pith-number/3SYMACA5PQY3B2KUGMR65PXP27/graph.json","fetch_events":"https://pith.science/api/pith-number/3SYMACA5PQY3B2KUGMR65PXP27/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3SYMACA5PQY3B2KUGMR65PXP27/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3SYMACA5PQY3B2KUGMR65PXP27/action/storage_attestation","attest_author":"https://pith.science/pith/3SYMACA5PQY3B2KUGMR65PXP27/action/author_attestation","sign_citation":"https://pith.science/pith/3SYMACA5PQY3B2KUGMR65PXP27/action/citation_signature","submit_replication":"https://pith.science/pith/3SYMACA5PQY3B2KUGMR65PXP27/action/replication_record"}},"created_at":"2026-07-05T03:50:36.987046+00:00","updated_at":"2026-07-05T03:50:36.987046+00:00"}