{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OLYZCPHBUNV3MQ2P5WOHU5I6CB","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":"1b4f2956b07f31db5b380728bb62333803046fc99080dfaec4fe2697b83bcd91","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-17T15:57:54Z","title_canon_sha256":"83c8113174b766d789076634154ffae25d06e9c5f476a1a32df123a78b275d77"},"schema_version":"1.0","source":{"id":"2409.11302","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.11302","created_at":"2026-07-05T09:08:16Z"},{"alias_kind":"arxiv_version","alias_value":"2409.11302v1","created_at":"2026-07-05T09:08:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.11302","created_at":"2026-07-05T09:08:16Z"},{"alias_kind":"pith_short_12","alias_value":"OLYZCPHBUNV3","created_at":"2026-07-05T09:08:16Z"},{"alias_kind":"pith_short_16","alias_value":"OLYZCPHBUNV3MQ2P","created_at":"2026-07-05T09:08:16Z"},{"alias_kind":"pith_short_8","alias_value":"OLYZCPHB","created_at":"2026-07-05T09:08:16Z"}],"graph_snapshots":[{"event_id":"sha256:b19c324d8e638abccf09c983bf3beffbbf801dbe06db24931f3ebea0c7822f56","target":"graph","created_at":"2026-07-05T09:08:16Z","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/2409.11302/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time Series Foundation Models (TSFMs) have recently garnered attention for their ability to model complex, large-scale time series data across domains such as retail, finance, and transportation. However, their application to sensitive, domain-specific fields like healthcare remains challenging, primarily due to the difficulty of fine-tuning these models for specialized, out-of-domain tasks with scarce publicly available datasets. In this work, we explore the use of Parameter-Efficient Fine-Tuning (PEFT) techniques to address these limitations, focusing on healthcare applications, particularly","authors_text":"Anubhav Bhatti, Divij Gupta, Surajsinh Parmar","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-17T15:57:54Z","title":"Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.11302","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:b697bb8851c75c3fc7fd08a449a22250c77b7c9dda5d1ca571379a7cd431f5be","target":"record","created_at":"2026-07-05T09:08:16Z","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":"1b4f2956b07f31db5b380728bb62333803046fc99080dfaec4fe2697b83bcd91","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-17T15:57:54Z","title_canon_sha256":"83c8113174b766d789076634154ffae25d06e9c5f476a1a32df123a78b275d77"},"schema_version":"1.0","source":{"id":"2409.11302","kind":"arxiv","version":1}},"canonical_sha256":"72f1913ce1a36bb6434fed9c7a751e1047675cfaae42e31edeed75a4ffba2f38","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"72f1913ce1a36bb6434fed9c7a751e1047675cfaae42e31edeed75a4ffba2f38","first_computed_at":"2026-07-05T09:08:16.232753Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:08:16.232753Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dqso7du4qPJxxdo5vkKLHpy1mMwl3yTUA+gN1JYaAdsRrTfYCePIQ9sbOsgRzVSAUZITvrJr3GrfrdLe0rcnAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:08:16.233246Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.11302","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b697bb8851c75c3fc7fd08a449a22250c77b7c9dda5d1ca571379a7cd431f5be","sha256:b19c324d8e638abccf09c983bf3beffbbf801dbe06db24931f3ebea0c7822f56"],"state_sha256":"eb98536eb7a98aad0ba6816dd25a98f7a2ab7979cbf4f65730b414f1d3339d55"}