{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:5XNYKXC2GCU7WROICDVRHNE3ZT","short_pith_number":"pith:5XNYKXC2","canonical_record":{"source":{"id":"2206.06243","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-13T15:23:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d3035e9d50b23e92fea8c2b70a11c3fc16d91979a7cd5a243867900189df0667","abstract_canon_sha256":"0d3e97803e7c8b979424d940999b1503a155e10ff5244f473ea33246e587e9c2"},"schema_version":"1.0"},"canonical_sha256":"eddb855c5a30a9fb45c810eb13b49bccc4763e272611f21a160cb1625917cc3b","source":{"kind":"arxiv","id":"2206.06243","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.06243","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"arxiv_version","alias_value":"2206.06243v4","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.06243","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"pith_short_12","alias_value":"5XNYKXC2GCU7","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"pith_short_16","alias_value":"5XNYKXC2GCU7WROI","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"pith_short_8","alias_value":"5XNYKXC2","created_at":"2026-07-05T05:39:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:5XNYKXC2GCU7WROICDVRHNE3ZT","target":"record","payload":{"canonical_record":{"source":{"id":"2206.06243","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-13T15:23:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d3035e9d50b23e92fea8c2b70a11c3fc16d91979a7cd5a243867900189df0667","abstract_canon_sha256":"0d3e97803e7c8b979424d940999b1503a155e10ff5244f473ea33246e587e9c2"},"schema_version":"1.0"},"canonical_sha256":"eddb855c5a30a9fb45c810eb13b49bccc4763e272611f21a160cb1625917cc3b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:14.991133Z","signature_b64":"+6seUnKuPyhk3wVUTGa/TQyakbRVjsMfegzEgqYayI+A7migqffcjjVeSXycvlGWFPvLq7G9EKD0Oz4S1gHdCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eddb855c5a30a9fb45c810eb13b49bccc4763e272611f21a160cb1625917cc3b","last_reissued_at":"2026-07-05T05:39:14.990605Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:14.990605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.06243","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:39:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5EemnElfXJRJ7fB+P3X3qRf8VqcagX03Fl5Z2lZDANNbdZWXn335SBrrq1Fx+w8RQoNzlzyJIPSG3ICCmnLbCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:20:01.229321Z"},"content_sha256":"6d473d8a4afd9520dffe4b2ac4f932f4061d1196a594a825bbd761671ff1f496","schema_version":"1.0","event_id":"sha256:6d473d8a4afd9520dffe4b2ac4f932f4061d1196a594a825bbd761671ff1f496"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:5XNYKXC2GCU7WROICDVRHNE3ZT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Contrastive Learning for Unsupervised Domain Adaptation of Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ce Zhang, Stefan Feuerriegel, Yilmazcan Ozyurt","submitted_at":"2022-06-13T15:23:31Z","abstract_excerpt":"Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain. UDA is important in many applications such as medicine, where it is used to adapt risk scores across different patient cohorts. In this paper, we develop a novel framework for UDA of time series data, called CLUDA. Specifically, we propose a contrastive learning framework to learn contextual representations in multivariate time series, so that these preserve label information for the prediction task. In our framewor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.06243","kind":"arxiv","version":4},"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/2206.06243/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":"2026-07-05T05:39:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oiv9iONftZYVdk5TjCeUWcRgaVcdm9H5a2UnVGbfbCXiKbYbqi26hdPFf22Xugk6zCQ2KA85AHIFH+3tK1vGDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:20:01.230110Z"},"content_sha256":"adc59c0934319d50fd070a82ebf5009f7794eeb30134e2c95c5e1bbf72c41ac6","schema_version":"1.0","event_id":"sha256:adc59c0934319d50fd070a82ebf5009f7794eeb30134e2c95c5e1bbf72c41ac6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5XNYKXC2GCU7WROICDVRHNE3ZT/bundle.json","state_url":"https://pith.science/pith/5XNYKXC2GCU7WROICDVRHNE3ZT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5XNYKXC2GCU7WROICDVRHNE3ZT/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":"2026-08-06T06:20:01Z","links":{"resolver":"https://pith.science/pith/5XNYKXC2GCU7WROICDVRHNE3ZT","bundle":"https://pith.science/pith/5XNYKXC2GCU7WROICDVRHNE3ZT/bundle.json","state":"https://pith.science/pith/5XNYKXC2GCU7WROICDVRHNE3ZT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5XNYKXC2GCU7WROICDVRHNE3ZT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:5XNYKXC2GCU7WROICDVRHNE3ZT","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":"0d3e97803e7c8b979424d940999b1503a155e10ff5244f473ea33246e587e9c2","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-13T15:23:31Z","title_canon_sha256":"d3035e9d50b23e92fea8c2b70a11c3fc16d91979a7cd5a243867900189df0667"},"schema_version":"1.0","source":{"id":"2206.06243","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.06243","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"arxiv_version","alias_value":"2206.06243v4","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.06243","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"pith_short_12","alias_value":"5XNYKXC2GCU7","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"pith_short_16","alias_value":"5XNYKXC2GCU7WROI","created_at":"2026-07-05T05:39:14Z"},{"alias_kind":"pith_short_8","alias_value":"5XNYKXC2","created_at":"2026-07-05T05:39:14Z"}],"graph_snapshots":[{"event_id":"sha256:adc59c0934319d50fd070a82ebf5009f7794eeb30134e2c95c5e1bbf72c41ac6","target":"graph","created_at":"2026-07-05T05:39:14Z","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/2206.06243/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain. UDA is important in many applications such as medicine, where it is used to adapt risk scores across different patient cohorts. In this paper, we develop a novel framework for UDA of time series data, called CLUDA. Specifically, we propose a contrastive learning framework to learn contextual representations in multivariate time series, so that these preserve label information for the prediction task. In our framewor","authors_text":"Ce Zhang, Stefan Feuerriegel, Yilmazcan Ozyurt","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-13T15:23:31Z","title":"Contrastive Learning for Unsupervised Domain Adaptation of Time Series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.06243","kind":"arxiv","version":4},"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":"sha256:6d473d8a4afd9520dffe4b2ac4f932f4061d1196a594a825bbd761671ff1f496","target":"record","created_at":"2026-07-05T05:39:14Z","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":"0d3e97803e7c8b979424d940999b1503a155e10ff5244f473ea33246e587e9c2","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-13T15:23:31Z","title_canon_sha256":"d3035e9d50b23e92fea8c2b70a11c3fc16d91979a7cd5a243867900189df0667"},"schema_version":"1.0","source":{"id":"2206.06243","kind":"arxiv","version":4}},"canonical_sha256":"eddb855c5a30a9fb45c810eb13b49bccc4763e272611f21a160cb1625917cc3b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eddb855c5a30a9fb45c810eb13b49bccc4763e272611f21a160cb1625917cc3b","first_computed_at":"2026-07-05T05:39:14.990605Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:39:14.990605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+6seUnKuPyhk3wVUTGa/TQyakbRVjsMfegzEgqYayI+A7migqffcjjVeSXycvlGWFPvLq7G9EKD0Oz4S1gHdCg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:39:14.991133Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.06243","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6d473d8a4afd9520dffe4b2ac4f932f4061d1196a594a825bbd761671ff1f496","sha256:adc59c0934319d50fd070a82ebf5009f7794eeb30134e2c95c5e1bbf72c41ac6"],"state_sha256":"894a2cc9e1bd9071f472c7fa51d40f81baae7cce966ee59a4b574560da3ea432"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3gwwGWHJaqeuY+U2TGfFg9dp4JFuYslWJW/usdryZBeQlRcPRibcRslz7YwEhsErlzi+swuBNiAG+ZtZ/NixAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T06:20:01.236075Z","bundle_sha256":"a1c75a8a3cca97f0ec890f71f8aa6dc7d6bfd2806b5a29c0c8360f2de164cbc0"}}