{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3MVVBLJZEEAMGS4ZFVBWZLWI6X","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":"c7ae4fe0b3ae91d87f9642dae88aef556ae460a73762e631d7b98d7c9f00ef49","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-27T08:08:26Z","title_canon_sha256":"aa90af48488847c1cc9a65afe74bafc1b3b252ec59730b0ca0a473a7f43c4fa5"},"schema_version":"1.0","source":{"id":"2210.15247","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.15247","created_at":"2026-07-05T05:11:04Z"},{"alias_kind":"arxiv_version","alias_value":"2210.15247v1","created_at":"2026-07-05T05:11:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.15247","created_at":"2026-07-05T05:11:04Z"},{"alias_kind":"pith_short_12","alias_value":"3MVVBLJZEEAM","created_at":"2026-07-05T05:11:04Z"},{"alias_kind":"pith_short_16","alias_value":"3MVVBLJZEEAMGS4Z","created_at":"2026-07-05T05:11:04Z"},{"alias_kind":"pith_short_8","alias_value":"3MVVBLJZ","created_at":"2026-07-05T05:11:04Z"}],"graph_snapshots":[{"event_id":"sha256:b5deb8694513a7a33185e2093c238986aa92220d67d25a31a87a14c260388279","target":"graph","created_at":"2026-07-05T05:11:04Z","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/2210.15247/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We design a metric learning approach that aims to address computational challenges that yield from modeling human outcomes from ambulatory real-life data. The proposed metric learning is based on a Siamese neural network (SNN) that learns the relative difference between pairs of samples from a target user and non-target users, thus being able to address the scarcity of labelled data from the target. The SNN further minimizes the Wasserstein distance of the learned embeddings between target and non-target users, thus mitigating the distribution mismatch between the two. Finally, given the fact ","authors_text":"Adela C. Timmons, Gayla Margolin, Jacqueline B. Duong, Kayla E. Carta, Kexin Feng, Sierra Walters, Theodora Chaspari","cross_cats":["cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-27T08:08:26Z","title":"A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationships"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.15247","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:e1c69056009f00edf821ba2b6e0230188a922744edf96f7e8650f83265cbf0c8","target":"record","created_at":"2026-07-05T05:11:04Z","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":"c7ae4fe0b3ae91d87f9642dae88aef556ae460a73762e631d7b98d7c9f00ef49","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-27T08:08:26Z","title_canon_sha256":"aa90af48488847c1cc9a65afe74bafc1b3b252ec59730b0ca0a473a7f43c4fa5"},"schema_version":"1.0","source":{"id":"2210.15247","kind":"arxiv","version":1}},"canonical_sha256":"db2b50ad392100c34b992d436caec8f5d60e8f3fc8037e614abe83faf1882bb9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db2b50ad392100c34b992d436caec8f5d60e8f3fc8037e614abe83faf1882bb9","first_computed_at":"2026-07-05T05:11:04.241581Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:11:04.241581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fjGGNMXdcLm4VDyfiem2UZ5w0M013Mw3CtkhTfFo+2220Iko8fbL/lhrNog/u/ZrPhL9hN8Alsla1kYz2XHxCw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:11:04.242038Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.15247","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e1c69056009f00edf821ba2b6e0230188a922744edf96f7e8650f83265cbf0c8","sha256:b5deb8694513a7a33185e2093c238986aa92220d67d25a31a87a14c260388279"],"state_sha256":"6089a8c53374a031560f5f7aa71b26e309b41c8d320ebb457e664a32ddfb823a"}