{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FO3BZJUETNQ6TFURCER7XCFEQ5","short_pith_number":"pith:FO3BZJUE","canonical_record":{"source":{"id":"2504.05024","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T12:49:20Z","cross_cats_sorted":[],"title_canon_sha256":"a8becbfb050420de7496f34a8c8aac4edb418437bce357bf82c5ec88fe52a208","abstract_canon_sha256":"bfea6a2166fb6ab48640d212d07ef9e8f3b5f3bc993bbcfbe1f82f0d74e75e85"},"schema_version":"1.0"},"canonical_sha256":"2bb61ca6849b61e996911123fb88a4874ce1855eae2118b58f4ab79a12b58e98","source":{"kind":"arxiv","id":"2504.05024","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.05024","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"arxiv_version","alias_value":"2504.05024v1","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.05024","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"pith_short_12","alias_value":"FO3BZJUETNQ6","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"pith_short_16","alias_value":"FO3BZJUETNQ6TFUR","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"pith_short_8","alias_value":"FO3BZJUE","created_at":"2026-07-05T10:45:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FO3BZJUETNQ6TFURCER7XCFEQ5","target":"record","payload":{"canonical_record":{"source":{"id":"2504.05024","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T12:49:20Z","cross_cats_sorted":[],"title_canon_sha256":"a8becbfb050420de7496f34a8c8aac4edb418437bce357bf82c5ec88fe52a208","abstract_canon_sha256":"bfea6a2166fb6ab48640d212d07ef9e8f3b5f3bc993bbcfbe1f82f0d74e75e85"},"schema_version":"1.0"},"canonical_sha256":"2bb61ca6849b61e996911123fb88a4874ce1855eae2118b58f4ab79a12b58e98","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:45:32.778666Z","signature_b64":"jGMVBXZcBHZ7IXAe1YSLf55+PghkwBLUZUFaiNOby9PtHepLY7ARcRLOmi7YM4gb7XVnzZhN2VF0PpLo+HM+AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bb61ca6849b61e996911123fb88a4874ce1855eae2118b58f4ab79a12b58e98","last_reissued_at":"2026-07-05T10:45:32.778200Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:45:32.778200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.05024","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":"2026-07-05T10:45:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"he494D15VKgKa6uN8Q1LabmHmbvtEzHplev6GnMbjuevIn0QWVNYL/ov+7PRyb8piR8nKQFWqIXVUjoOQ8OECw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T18:45:06.954026Z"},"content_sha256":"ddf9f1109276b8d1ec0577beeb5000f093620064c961739fa82f248cf22cf5cf","schema_version":"1.0","event_id":"sha256:ddf9f1109276b8d1ec0577beeb5000f093620064c961739fa82f248cf22cf5cf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FO3BZJUETNQ6TFURCER7XCFEQ5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Concept Extraction for Time Series with ECLAD-ts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andres Felipe Posada-Moreno, Antonia Holzapfel, Sebastian Trimpe","submitted_at":"2025-04-07T12:49:20Z","abstract_excerpt":"Convolutional neural networks (CNNs) for time series classification (TSC) are being increasingly used in applications ranging from quality prediction to medical diagnosis. The black box nature of these models makes understanding their prediction process difficult. This issue is crucial because CNNs are prone to learning shortcuts and biases, compromising their robustness and alignment with human expectations. To assess whether such mechanisms are being used and the associated risk, it is essential to provide model explanations that reflect the inner workings of the model. Concept Extraction (C"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.05024","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/2504.05024/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":"2026-07-05T10:45:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hBflem39ehU7UoKkPufQ8OLHjpn0mddpoKucNJY7DRSVeiOqLFIztJpv7oH+XlLlxCz7kFm79pkir2ZbPGHQAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T18:45:06.954524Z"},"content_sha256":"0dc2c40d44d8f650ac7d75f3e98b288f48aa1c4d75382cb8d19263ca4bb3c4da","schema_version":"1.0","event_id":"sha256:0dc2c40d44d8f650ac7d75f3e98b288f48aa1c4d75382cb8d19263ca4bb3c4da"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FO3BZJUETNQ6TFURCER7XCFEQ5/bundle.json","state_url":"https://pith.science/pith/FO3BZJUETNQ6TFURCER7XCFEQ5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FO3BZJUETNQ6TFURCER7XCFEQ5/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":"2026-08-19T18:45:06Z","links":{"resolver":"https://pith.science/pith/FO3BZJUETNQ6TFURCER7XCFEQ5","bundle":"https://pith.science/pith/FO3BZJUETNQ6TFURCER7XCFEQ5/bundle.json","state":"https://pith.science/pith/FO3BZJUETNQ6TFURCER7XCFEQ5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FO3BZJUETNQ6TFURCER7XCFEQ5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FO3BZJUETNQ6TFURCER7XCFEQ5","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":"bfea6a2166fb6ab48640d212d07ef9e8f3b5f3bc993bbcfbe1f82f0d74e75e85","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T12:49:20Z","title_canon_sha256":"a8becbfb050420de7496f34a8c8aac4edb418437bce357bf82c5ec88fe52a208"},"schema_version":"1.0","source":{"id":"2504.05024","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.05024","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"arxiv_version","alias_value":"2504.05024v1","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.05024","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"pith_short_12","alias_value":"FO3BZJUETNQ6","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"pith_short_16","alias_value":"FO3BZJUETNQ6TFUR","created_at":"2026-07-05T10:45:32Z"},{"alias_kind":"pith_short_8","alias_value":"FO3BZJUE","created_at":"2026-07-05T10:45:32Z"}],"graph_snapshots":[{"event_id":"sha256:0dc2c40d44d8f650ac7d75f3e98b288f48aa1c4d75382cb8d19263ca4bb3c4da","target":"graph","created_at":"2026-07-05T10:45:32Z","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/2504.05024/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Convolutional neural networks (CNNs) for time series classification (TSC) are being increasingly used in applications ranging from quality prediction to medical diagnosis. The black box nature of these models makes understanding their prediction process difficult. This issue is crucial because CNNs are prone to learning shortcuts and biases, compromising their robustness and alignment with human expectations. To assess whether such mechanisms are being used and the associated risk, it is essential to provide model explanations that reflect the inner workings of the model. Concept Extraction (C","authors_text":"Andres Felipe Posada-Moreno, Antonia Holzapfel, Sebastian Trimpe","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T12:49:20Z","title":"Concept Extraction for Time Series with ECLAD-ts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.05024","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:ddf9f1109276b8d1ec0577beeb5000f093620064c961739fa82f248cf22cf5cf","target":"record","created_at":"2026-07-05T10:45:32Z","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":"bfea6a2166fb6ab48640d212d07ef9e8f3b5f3bc993bbcfbe1f82f0d74e75e85","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T12:49:20Z","title_canon_sha256":"a8becbfb050420de7496f34a8c8aac4edb418437bce357bf82c5ec88fe52a208"},"schema_version":"1.0","source":{"id":"2504.05024","kind":"arxiv","version":1}},"canonical_sha256":"2bb61ca6849b61e996911123fb88a4874ce1855eae2118b58f4ab79a12b58e98","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2bb61ca6849b61e996911123fb88a4874ce1855eae2118b58f4ab79a12b58e98","first_computed_at":"2026-07-05T10:45:32.778200Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:45:32.778200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jGMVBXZcBHZ7IXAe1YSLf55+PghkwBLUZUFaiNOby9PtHepLY7ARcRLOmi7YM4gb7XVnzZhN2VF0PpLo+HM+AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:45:32.778666Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.05024","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ddf9f1109276b8d1ec0577beeb5000f093620064c961739fa82f248cf22cf5cf","sha256:0dc2c40d44d8f650ac7d75f3e98b288f48aa1c4d75382cb8d19263ca4bb3c4da"],"state_sha256":"fd77ee84d80d40d3f31b31e486c0e8716c05ecda4842809c170d5ea6df490c18"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HbYqpP5XN0S1C4tWl7Sz/fVyItqlmQMYm4iVpaD2YGb3NOfzAh2oxX9PjaaI5rXpk+jO8dSYrmxZNo72CSKXBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T18:45:06.959080Z","bundle_sha256":"c0246ff4f038b288692fa8cde92d6568685227f826656e3cd9c32b12f3b16ceb"}}