{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ZGQAIVIEODQT7DUITBCNPDGET3","short_pith_number":"pith:ZGQAIVIE","canonical_record":{"source":{"id":"2606.12252","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T15:55:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7bec3ee3ba09cfd1bd82e8dd299974b364fc1869961cacd556817e4e6e5fb68f","abstract_canon_sha256":"cc0739b33a090dae4d30d7315d1972ec310dce12cd003c2486faecb27b145208"},"schema_version":"1.0"},"canonical_sha256":"c9a004550470e13f8e889844d78cc49ef659b1578ae71abcdb0c3440e506888a","source":{"kind":"arxiv","id":"2606.12252","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.12252","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"arxiv_version","alias_value":"2606.12252v1","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12252","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"pith_short_12","alias_value":"ZGQAIVIEODQT","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"pith_short_16","alias_value":"ZGQAIVIEODQT7DUI","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"pith_short_8","alias_value":"ZGQAIVIE","created_at":"2026-06-11T01:10:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ZGQAIVIEODQT7DUITBCNPDGET3","target":"record","payload":{"canonical_record":{"source":{"id":"2606.12252","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T15:55:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7bec3ee3ba09cfd1bd82e8dd299974b364fc1869961cacd556817e4e6e5fb68f","abstract_canon_sha256":"cc0739b33a090dae4d30d7315d1972ec310dce12cd003c2486faecb27b145208"},"schema_version":"1.0"},"canonical_sha256":"c9a004550470e13f8e889844d78cc49ef659b1578ae71abcdb0c3440e506888a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:10:57.007277Z","signature_b64":"Dc2nQKegNBvSlPJW2RnPL/i5wrAcjGccG7bzc30CmKvM4MhtxefNv1FY7t4XiTgTl/gwH+Hdqlmb8ykCyTtmDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9a004550470e13f8e889844d78cc49ef659b1578ae71abcdb0c3440e506888a","last_reissued_at":"2026-06-11T01:10:57.006270Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:10:57.006270Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.12252","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":"2026-06-11T01:10:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lZvNfHyRY1EDP03bgHuIozlZoNtIvyRgz9J+enOGUOQQNf6bj+qJ2kDU7D4XchFoNKByQtu4aVJG5aF1tUzADQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:54:24.877230Z"},"content_sha256":"f62387fc191cce8d8a8ebbdde4ebc6b93f665db2bb0b88ede5480a947878a638","schema_version":"1.0","event_id":"sha256:f62387fc191cce8d8a8ebbdde4ebc6b93f665db2bb0b88ede5480a947878a638"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ZGQAIVIEODQT7DUITBCNPDGET3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Shreyank N Gowda, Veerendhra Kumar Dangeti, Xiao Gu, Ying Weng","submitted_at":"2026-06-10T15:55:00Z","abstract_excerpt":"Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment. This challenge is particularly evident in electrocardiogram classification, where large datasets and long training schedules make efficiency practically important. Progressive Data Dropout reduces training cost by excluding samples from gradient updates once they are learned, but it relies on model confidence and may retain samples that are difficult due to noise or ambiguity rather than useful sign"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12252","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/2606.12252/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":"2026-06-11T01:10:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZIJrljnWLQ7wRnUhN5L1pZb3OWsee6AReOei/BHYmzpWeyvp9iYH6aF9vZwG1ZZ969u3qDoTtQN7wArCOxqDBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:54:24.878014Z"},"content_sha256":"7311855c5eecf183d58a9c8023fdc1f82e016f2eca8d2fb2be29d78eec91f37b","schema_version":"1.0","event_id":"sha256:7311855c5eecf183d58a9c8023fdc1f82e016f2eca8d2fb2be29d78eec91f37b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZGQAIVIEODQT7DUITBCNPDGET3/bundle.json","state_url":"https://pith.science/pith/ZGQAIVIEODQT7DUITBCNPDGET3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZGQAIVIEODQT7DUITBCNPDGET3/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":"2026-06-11T22:54:24Z","links":{"resolver":"https://pith.science/pith/ZGQAIVIEODQT7DUITBCNPDGET3","bundle":"https://pith.science/pith/ZGQAIVIEODQT7DUITBCNPDGET3/bundle.json","state":"https://pith.science/pith/ZGQAIVIEODQT7DUITBCNPDGET3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZGQAIVIEODQT7DUITBCNPDGET3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZGQAIVIEODQT7DUITBCNPDGET3","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":"cc0739b33a090dae4d30d7315d1972ec310dce12cd003c2486faecb27b145208","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T15:55:00Z","title_canon_sha256":"7bec3ee3ba09cfd1bd82e8dd299974b364fc1869961cacd556817e4e6e5fb68f"},"schema_version":"1.0","source":{"id":"2606.12252","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.12252","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"arxiv_version","alias_value":"2606.12252v1","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12252","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"pith_short_12","alias_value":"ZGQAIVIEODQT","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"pith_short_16","alias_value":"ZGQAIVIEODQT7DUI","created_at":"2026-06-11T01:10:57Z"},{"alias_kind":"pith_short_8","alias_value":"ZGQAIVIE","created_at":"2026-06-11T01:10:57Z"}],"graph_snapshots":[{"event_id":"sha256:7311855c5eecf183d58a9c8023fdc1f82e016f2eca8d2fb2be29d78eec91f37b","target":"graph","created_at":"2026-06-11T01:10:57Z","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/2606.12252/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment. This challenge is particularly evident in electrocardiogram classification, where large datasets and long training schedules make efficiency practically important. Progressive Data Dropout reduces training cost by excluding samples from gradient updates once they are learned, but it relies on model confidence and may retain samples that are difficult due to noise or ambiguity rather than useful sign","authors_text":"Shreyank N Gowda, Veerendhra Kumar Dangeti, Xiao Gu, Ying Weng","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T15:55:00Z","title":"Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12252","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:f62387fc191cce8d8a8ebbdde4ebc6b93f665db2bb0b88ede5480a947878a638","target":"record","created_at":"2026-06-11T01:10:57Z","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":"cc0739b33a090dae4d30d7315d1972ec310dce12cd003c2486faecb27b145208","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T15:55:00Z","title_canon_sha256":"7bec3ee3ba09cfd1bd82e8dd299974b364fc1869961cacd556817e4e6e5fb68f"},"schema_version":"1.0","source":{"id":"2606.12252","kind":"arxiv","version":1}},"canonical_sha256":"c9a004550470e13f8e889844d78cc49ef659b1578ae71abcdb0c3440e506888a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c9a004550470e13f8e889844d78cc49ef659b1578ae71abcdb0c3440e506888a","first_computed_at":"2026-06-11T01:10:57.006270Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:10:57.006270Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Dc2nQKegNBvSlPJW2RnPL/i5wrAcjGccG7bzc30CmKvM4MhtxefNv1FY7t4XiTgTl/gwH+Hdqlmb8ykCyTtmDw==","signature_status":"signed_v1","signed_at":"2026-06-11T01:10:57.007277Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.12252","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f62387fc191cce8d8a8ebbdde4ebc6b93f665db2bb0b88ede5480a947878a638","sha256:7311855c5eecf183d58a9c8023fdc1f82e016f2eca8d2fb2be29d78eec91f37b"],"state_sha256":"13045990499779e2b0a43a4d391213d40cc768293514d21358f9a6f951fd662e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"weHdlXUpbKkJCsmbMAc+tuHouOtDS8R10r3hPHQ/A+mg5Wcg/dP9s44C4BC67AFTiiCahJHRjBLLkPtkQGsoAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T22:54:24.882120Z","bundle_sha256":"ab5693254f3ebdb3079b0ef44d42e9ff62f409ae6219f646160cc65989fb98f8"}}