{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LFMGYTUT543W6Z7LYF35OMOSF2","short_pith_number":"pith:LFMGYTUT","canonical_record":{"source":{"id":"2502.02924","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-05T06:37:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"83187b4453df74efdcdefcb7f3bf41a2040a861a2fd8071f060e83ddb393df02","abstract_canon_sha256":"f8f67f8483d22ed13b34e99a473119205330a372235bca7de007f2d97959d4e7"},"schema_version":"1.0"},"canonical_sha256":"59586c4e93ef376f67ebc177d731d22eb04202fa13225fb7e703b89b3486305f","source":{"kind":"arxiv","id":"2502.02924","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.02924","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"arxiv_version","alias_value":"2502.02924v1","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02924","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"pith_short_12","alias_value":"LFMGYTUT543W","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"pith_short_16","alias_value":"LFMGYTUT543W6Z7L","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"pith_short_8","alias_value":"LFMGYTUT","created_at":"2026-07-05T10:09:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LFMGYTUT543W6Z7LYF35OMOSF2","target":"record","payload":{"canonical_record":{"source":{"id":"2502.02924","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-05T06:37:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"83187b4453df74efdcdefcb7f3bf41a2040a861a2fd8071f060e83ddb393df02","abstract_canon_sha256":"f8f67f8483d22ed13b34e99a473119205330a372235bca7de007f2d97959d4e7"},"schema_version":"1.0"},"canonical_sha256":"59586c4e93ef376f67ebc177d731d22eb04202fa13225fb7e703b89b3486305f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:56.936544Z","signature_b64":"Ur7V25hKlVo+0Wdq+G9u9HSj5latadvp9M5w9knfjYepbRQXDLUnWoFIzW85zkL2YYyjXegvze533QnK8qGyDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59586c4e93ef376f67ebc177d731d22eb04202fa13225fb7e703b89b3486305f","last_reissued_at":"2026-07-05T10:09:56.936151Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:56.936151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.02924","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":"2026-07-05T10:09:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"chFPWdfGkPTkgCriwFTgLb3kuNYU9B1EDn0X6GGbOWeW7mOWoKZ2pnbosYbAZksEsGnOGMhkeo4/Rwr9vJn/Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T01:06:28.036396Z"},"content_sha256":"fe53a314e32605aecaedd15acf09835db02fea24ce43f741d1ff3ca6cbef0f33","schema_version":"1.0","event_id":"sha256:fe53a314e32605aecaedd15acf09835db02fea24ce43f741d1ff3ca6cbef0f33"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LFMGYTUT543W6Z7LYF35OMOSF2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TopoCL: Topological Contrastive Learning for Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hyungryul Baik, Namwoo Kim, Yoonjin Yoon","submitted_at":"2025-02-05T06:37:35Z","abstract_excerpt":"Universal time series representation learning is challenging but valuable in real-world applications such as classification, anomaly detection, and forecasting. Recently, contrastive learning (CL) has been actively explored to tackle time series representation. However, a key challenge is that the data augmentation process in CL can distort seasonal patterns or temporal dependencies, inevitably leading to a loss of semantic information. To address this challenge, we propose Topological Contrastive Learning for time series (TopoCL). TopoCL mitigates such information loss by incorporating persis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02924","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/2502.02924/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":"2026-07-05T10:09:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IxiqJ3Y43TU16GA3xQgDWRFbqLgiH1177+Cm0qycP8BGA64rJ3wtsVxEJIFUSUF73bn02kkp9+9MrNTXbNgrBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T01:06:28.037079Z"},"content_sha256":"7757ec127e7ccfad4d82b67508d02c08ac776cc2a6954bc77df705381f363563","schema_version":"1.0","event_id":"sha256:7757ec127e7ccfad4d82b67508d02c08ac776cc2a6954bc77df705381f363563"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LFMGYTUT543W6Z7LYF35OMOSF2/bundle.json","state_url":"https://pith.science/pith/LFMGYTUT543W6Z7LYF35OMOSF2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LFMGYTUT543W6Z7LYF35OMOSF2/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":"2026-08-11T01:06:28Z","links":{"resolver":"https://pith.science/pith/LFMGYTUT543W6Z7LYF35OMOSF2","bundle":"https://pith.science/pith/LFMGYTUT543W6Z7LYF35OMOSF2/bundle.json","state":"https://pith.science/pith/LFMGYTUT543W6Z7LYF35OMOSF2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LFMGYTUT543W6Z7LYF35OMOSF2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LFMGYTUT543W6Z7LYF35OMOSF2","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":"f8f67f8483d22ed13b34e99a473119205330a372235bca7de007f2d97959d4e7","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-05T06:37:35Z","title_canon_sha256":"83187b4453df74efdcdefcb7f3bf41a2040a861a2fd8071f060e83ddb393df02"},"schema_version":"1.0","source":{"id":"2502.02924","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.02924","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"arxiv_version","alias_value":"2502.02924v1","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02924","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"pith_short_12","alias_value":"LFMGYTUT543W","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"pith_short_16","alias_value":"LFMGYTUT543W6Z7L","created_at":"2026-07-05T10:09:56Z"},{"alias_kind":"pith_short_8","alias_value":"LFMGYTUT","created_at":"2026-07-05T10:09:56Z"}],"graph_snapshots":[{"event_id":"sha256:7757ec127e7ccfad4d82b67508d02c08ac776cc2a6954bc77df705381f363563","target":"graph","created_at":"2026-07-05T10:09:56Z","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/2502.02924/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Universal time series representation learning is challenging but valuable in real-world applications such as classification, anomaly detection, and forecasting. Recently, contrastive learning (CL) has been actively explored to tackle time series representation. However, a key challenge is that the data augmentation process in CL can distort seasonal patterns or temporal dependencies, inevitably leading to a loss of semantic information. To address this challenge, we propose Topological Contrastive Learning for time series (TopoCL). TopoCL mitigates such information loss by incorporating persis","authors_text":"Hyungryul Baik, Namwoo Kim, Yoonjin Yoon","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-05T06:37:35Z","title":"TopoCL: Topological Contrastive Learning for Time Series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02924","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:fe53a314e32605aecaedd15acf09835db02fea24ce43f741d1ff3ca6cbef0f33","target":"record","created_at":"2026-07-05T10:09:56Z","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":"f8f67f8483d22ed13b34e99a473119205330a372235bca7de007f2d97959d4e7","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-05T06:37:35Z","title_canon_sha256":"83187b4453df74efdcdefcb7f3bf41a2040a861a2fd8071f060e83ddb393df02"},"schema_version":"1.0","source":{"id":"2502.02924","kind":"arxiv","version":1}},"canonical_sha256":"59586c4e93ef376f67ebc177d731d22eb04202fa13225fb7e703b89b3486305f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"59586c4e93ef376f67ebc177d731d22eb04202fa13225fb7e703b89b3486305f","first_computed_at":"2026-07-05T10:09:56.936151Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:09:56.936151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ur7V25hKlVo+0Wdq+G9u9HSj5latadvp9M5w9knfjYepbRQXDLUnWoFIzW85zkL2YYyjXegvze533QnK8qGyDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:09:56.936544Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.02924","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fe53a314e32605aecaedd15acf09835db02fea24ce43f741d1ff3ca6cbef0f33","sha256:7757ec127e7ccfad4d82b67508d02c08ac776cc2a6954bc77df705381f363563"],"state_sha256":"b413f9560e42c0628236b23d378c4406246aedcee0a42f8549cac2e42ca78b01"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wtWopZvsAp7pAmgpOsl8ZqwvIYMISpfHrjwmm1OF0P7YI6V/2SZeSx4IsJYT9PH7LjnEcVsikQMPoiblUObYBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T01:06:28.041120Z","bundle_sha256":"cbd22c3c9884812f02abc498e495ec313272581dc242500d8dadd8d44f6be82b"}}