{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:UWUU7V2JAVJHXFYQJ7TJFAYE7Y","short_pith_number":"pith:UWUU7V2J","canonical_record":{"source":{"id":"2008.03961","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-10T08:34:20Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a78518f7db63842075b3d1f004f78e6938ebbb6eb9ff91e09463122709b5c975","abstract_canon_sha256":"ddf37fd529323fe3cc79140c64af00fa61d547f1d456cf72080598a7d5ac9cc9"},"schema_version":"1.0"},"canonical_sha256":"a5a94fd74905527b97104fe6928304fe36db72f3f766e591631627e2db23e04f","source":{"kind":"arxiv","id":"2008.03961","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.03961","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"arxiv_version","alias_value":"2008.03961v1","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.03961","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"pith_short_12","alias_value":"UWUU7V2JAVJH","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"pith_short_16","alias_value":"UWUU7V2JAVJHXFYQ","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"pith_short_8","alias_value":"UWUU7V2J","created_at":"2026-07-05T01:25:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:UWUU7V2JAVJHXFYQJ7TJFAYE7Y","target":"record","payload":{"canonical_record":{"source":{"id":"2008.03961","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-10T08:34:20Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a78518f7db63842075b3d1f004f78e6938ebbb6eb9ff91e09463122709b5c975","abstract_canon_sha256":"ddf37fd529323fe3cc79140c64af00fa61d547f1d456cf72080598a7d5ac9cc9"},"schema_version":"1.0"},"canonical_sha256":"a5a94fd74905527b97104fe6928304fe36db72f3f766e591631627e2db23e04f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:25:58.290626Z","signature_b64":"8oBeF6JLG5gfLWvI7hfXkPzA+o/n/Prw/i+omIxilo0TNEEOz/FsneTuqqC++VcDIDIOGABxhNZz9RS3z+DkDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5a94fd74905527b97104fe6928304fe36db72f3f766e591631627e2db23e04f","last_reissued_at":"2026-07-05T01:25:58.290207Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:25:58.290207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2008.03961","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":"2026-07-05T01:25:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XGRW6gcF3cke5ySZEde9y4PiAkrzf5cKcQC5/HaDIu+lPA3R5xn/Cl2AOZh5+RhvhPW038y47U+bPlZKhlPFBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T07:46:03.232862Z"},"content_sha256":"e154809f2b90906c6798a8872e55e61c87377863bd5a825c872fdeef753206fa","schema_version":"1.0","event_id":"sha256:e154809f2b90906c6798a8872e55e61c87377863bd5a825c872fdeef753206fa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:UWUU7V2JAVJHXFYQJ7TJFAYE7Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Michael Beigl, Michael Hefenbrock, Till Riedel, Yexu Zhou, Yiran Huang, Yuting Gao","submitted_at":"2020-08-10T08:34:20Z","abstract_excerpt":"An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series. Using the sliding window method, Convolutional Neural Network (CNN) and conventional Recurrent Neural Network (RNN) approaches have produced impressive results on this matter, due to their ability to learn optimized features. However, sequence information is only partially modeled by CNN approaches. Due to the flatten mechanism in conventional RNNs, like Long Short Term Memories (LSTM), the temporal information within the window is not fully preserv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.03961","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/2008.03961/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":"2026-07-05T01:25:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4G3FP0IxmQs5hnwqIVK+0iCqCZy1czdzJQ/HXpjKSHOajYZ4XRhIFvYrzpCHaEkxHBleO6wWpsOo8mgn1fSsDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T07:46:03.233403Z"},"content_sha256":"6f3257882b9d7e1cf7afadc171b86b04d6abc0816035248a332e334d1d5a15c1","schema_version":"1.0","event_id":"sha256:6f3257882b9d7e1cf7afadc171b86b04d6abc0816035248a332e334d1d5a15c1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UWUU7V2JAVJHXFYQJ7TJFAYE7Y/bundle.json","state_url":"https://pith.science/pith/UWUU7V2JAVJHXFYQJ7TJFAYE7Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UWUU7V2JAVJHXFYQJ7TJFAYE7Y/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":"2026-08-16T07:46:03Z","links":{"resolver":"https://pith.science/pith/UWUU7V2JAVJHXFYQJ7TJFAYE7Y","bundle":"https://pith.science/pith/UWUU7V2JAVJHXFYQJ7TJFAYE7Y/bundle.json","state":"https://pith.science/pith/UWUU7V2JAVJHXFYQJ7TJFAYE7Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UWUU7V2JAVJHXFYQJ7TJFAYE7Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:UWUU7V2JAVJHXFYQJ7TJFAYE7Y","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":"ddf37fd529323fe3cc79140c64af00fa61d547f1d456cf72080598a7d5ac9cc9","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-10T08:34:20Z","title_canon_sha256":"a78518f7db63842075b3d1f004f78e6938ebbb6eb9ff91e09463122709b5c975"},"schema_version":"1.0","source":{"id":"2008.03961","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.03961","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"arxiv_version","alias_value":"2008.03961v1","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.03961","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"pith_short_12","alias_value":"UWUU7V2JAVJH","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"pith_short_16","alias_value":"UWUU7V2JAVJHXFYQ","created_at":"2026-07-05T01:25:58Z"},{"alias_kind":"pith_short_8","alias_value":"UWUU7V2J","created_at":"2026-07-05T01:25:58Z"}],"graph_snapshots":[{"event_id":"sha256:6f3257882b9d7e1cf7afadc171b86b04d6abc0816035248a332e334d1d5a15c1","target":"graph","created_at":"2026-07-05T01:25:58Z","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/2008.03961/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series. Using the sliding window method, Convolutional Neural Network (CNN) and conventional Recurrent Neural Network (RNN) approaches have produced impressive results on this matter, due to their ability to learn optimized features. However, sequence information is only partially modeled by CNN approaches. Due to the flatten mechanism in conventional RNNs, like Long Short Term Memories (LSTM), the temporal information within the window is not fully preserv","authors_text":"Michael Beigl, Michael Hefenbrock, Till Riedel, Yexu Zhou, Yiran Huang, Yuting Gao","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-10T08:34:20Z","title":"Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.03961","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:e154809f2b90906c6798a8872e55e61c87377863bd5a825c872fdeef753206fa","target":"record","created_at":"2026-07-05T01:25:58Z","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":"ddf37fd529323fe3cc79140c64af00fa61d547f1d456cf72080598a7d5ac9cc9","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-10T08:34:20Z","title_canon_sha256":"a78518f7db63842075b3d1f004f78e6938ebbb6eb9ff91e09463122709b5c975"},"schema_version":"1.0","source":{"id":"2008.03961","kind":"arxiv","version":1}},"canonical_sha256":"a5a94fd74905527b97104fe6928304fe36db72f3f766e591631627e2db23e04f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5a94fd74905527b97104fe6928304fe36db72f3f766e591631627e2db23e04f","first_computed_at":"2026-07-05T01:25:58.290207Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:25:58.290207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8oBeF6JLG5gfLWvI7hfXkPzA+o/n/Prw/i+omIxilo0TNEEOz/FsneTuqqC++VcDIDIOGABxhNZz9RS3z+DkDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:25:58.290626Z","signed_message":"canonical_sha256_bytes"},"source_id":"2008.03961","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e154809f2b90906c6798a8872e55e61c87377863bd5a825c872fdeef753206fa","sha256:6f3257882b9d7e1cf7afadc171b86b04d6abc0816035248a332e334d1d5a15c1"],"state_sha256":"9047a7a3e9a16fbb16ab16ba9cec79173fe16177b72c19fd2427f5d3c08d2e02"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"53VIM4ZX9XjQFs4NnY/5viiMijllTLVMap2ENCT+gBOA6Mj2Sa2MkKwcWReu1cNUTZTy9Tqerqe6ZXX6c1wnCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T07:46:03.237895Z","bundle_sha256":"d43b5baf434ecee654cb678a697f5271b6668ab8b44a171c549c283c065018f9"}}