{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:H2WMEPIVTZN2QUB22RCPUGNCFT","short_pith_number":"pith:H2WMEPIV","canonical_record":{"source":{"id":"2506.05942","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T10:09:40Z","cross_cats_sorted":[],"title_canon_sha256":"47bc2151fff95a35b9edcbd76643b45d559baeda836cfb5a3b1181013e6e8f80","abstract_canon_sha256":"f3c843762ead8f5c6b24ce3be48e79dd52a7651f687fb2faee09de759ed7760a"},"schema_version":"1.0"},"canonical_sha256":"3eacc23d159e5ba8503ad444fa19a22cf49d6581494a24b5d57d48f87ddca370","source":{"kind":"arxiv","id":"2506.05942","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.05942","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"arxiv_version","alias_value":"2506.05942v1","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05942","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"pith_short_12","alias_value":"H2WMEPIVTZN2","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"pith_short_16","alias_value":"H2WMEPIVTZN2QUB2","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"pith_short_8","alias_value":"H2WMEPIV","created_at":"2026-07-05T11:17:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:H2WMEPIVTZN2QUB22RCPUGNCFT","target":"record","payload":{"canonical_record":{"source":{"id":"2506.05942","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T10:09:40Z","cross_cats_sorted":[],"title_canon_sha256":"47bc2151fff95a35b9edcbd76643b45d559baeda836cfb5a3b1181013e6e8f80","abstract_canon_sha256":"f3c843762ead8f5c6b24ce3be48e79dd52a7651f687fb2faee09de759ed7760a"},"schema_version":"1.0"},"canonical_sha256":"3eacc23d159e5ba8503ad444fa19a22cf49d6581494a24b5d57d48f87ddca370","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:13.420237Z","signature_b64":"uNN6ARv/BFbwr5ZjMYuvEKQjsLGXwsLPb+DauvDU8HVM/2StTuXIg7FR3iSDkRQl9BFZcuFrQHJk7yhZ/MCrCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3eacc23d159e5ba8503ad444fa19a22cf49d6581494a24b5d57d48f87ddca370","last_reissued_at":"2026-07-05T11:17:13.419742Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:13.419742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.05942","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":"2026-07-05T11:17:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3kEW/wyIlTawE886I49nD3UkWYBg+/hz0mhW1nsdu178SsVzemzqcvevUI0aCy8/9JagZXFrYknGlTdq+Y64Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T21:00:03.199392Z"},"content_sha256":"dfe267b60b3581952190f8cae1c7018e0b3542725a3bcb1db595e86f2cc1ca59","schema_version":"1.0","event_id":"sha256:dfe267b60b3581952190f8cae1c7018e0b3542725a3bcb1db595e86f2cc1ca59"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:H2WMEPIVTZN2QUB22RCPUGNCFT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Additive decomposition of one-dimensional signals using Transformers","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alessandro Lanza, Andrea Pinto, Samuele Salti, Serena Morigi","submitted_at":"2025-06-06T10:09:40Z","abstract_excerpt":"One-dimensional signal decomposition is a well-established and widely used technique across various scientific fields. It serves as a highly valuable pre-processing step for data analysis. While traditional decomposition techniques often rely on mathematical models, recent research suggests that applying the latest deep learning models to this problem presents an exciting, unexplored area with promising potential. This work presents a novel method for the additive decomposition of one-dimensional signals. We leverage the Transformer architecture to decompose signals into their constituent comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05942","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/2506.05942/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":"2026-07-05T11:17:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IUR++dXIKvngXlYcCWIxAlmI+UZcAX+0StzcJWa1hbh/Iw7Q5L4tOoMDioAB+S3CnvVpCCJXXqWMy6UiEXSvCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T21:00:03.199954Z"},"content_sha256":"ec207e446604113834f556d8e890daf9f5cd5dcd07622aa14820007b9f1e415a","schema_version":"1.0","event_id":"sha256:ec207e446604113834f556d8e890daf9f5cd5dcd07622aa14820007b9f1e415a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H2WMEPIVTZN2QUB22RCPUGNCFT/bundle.json","state_url":"https://pith.science/pith/H2WMEPIVTZN2QUB22RCPUGNCFT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H2WMEPIVTZN2QUB22RCPUGNCFT/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":"2026-08-07T21:00:03Z","links":{"resolver":"https://pith.science/pith/H2WMEPIVTZN2QUB22RCPUGNCFT","bundle":"https://pith.science/pith/H2WMEPIVTZN2QUB22RCPUGNCFT/bundle.json","state":"https://pith.science/pith/H2WMEPIVTZN2QUB22RCPUGNCFT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H2WMEPIVTZN2QUB22RCPUGNCFT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:H2WMEPIVTZN2QUB22RCPUGNCFT","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":"f3c843762ead8f5c6b24ce3be48e79dd52a7651f687fb2faee09de759ed7760a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T10:09:40Z","title_canon_sha256":"47bc2151fff95a35b9edcbd76643b45d559baeda836cfb5a3b1181013e6e8f80"},"schema_version":"1.0","source":{"id":"2506.05942","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.05942","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"arxiv_version","alias_value":"2506.05942v1","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05942","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"pith_short_12","alias_value":"H2WMEPIVTZN2","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"pith_short_16","alias_value":"H2WMEPIVTZN2QUB2","created_at":"2026-07-05T11:17:13Z"},{"alias_kind":"pith_short_8","alias_value":"H2WMEPIV","created_at":"2026-07-05T11:17:13Z"}],"graph_snapshots":[{"event_id":"sha256:ec207e446604113834f556d8e890daf9f5cd5dcd07622aa14820007b9f1e415a","target":"graph","created_at":"2026-07-05T11:17:13Z","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/2506.05942/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"One-dimensional signal decomposition is a well-established and widely used technique across various scientific fields. It serves as a highly valuable pre-processing step for data analysis. While traditional decomposition techniques often rely on mathematical models, recent research suggests that applying the latest deep learning models to this problem presents an exciting, unexplored area with promising potential. This work presents a novel method for the additive decomposition of one-dimensional signals. We leverage the Transformer architecture to decompose signals into their constituent comp","authors_text":"Alessandro Lanza, Andrea Pinto, Samuele Salti, Serena Morigi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T10:09:40Z","title":"Additive decomposition of one-dimensional signals using Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05942","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:dfe267b60b3581952190f8cae1c7018e0b3542725a3bcb1db595e86f2cc1ca59","target":"record","created_at":"2026-07-05T11:17:13Z","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":"f3c843762ead8f5c6b24ce3be48e79dd52a7651f687fb2faee09de759ed7760a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T10:09:40Z","title_canon_sha256":"47bc2151fff95a35b9edcbd76643b45d559baeda836cfb5a3b1181013e6e8f80"},"schema_version":"1.0","source":{"id":"2506.05942","kind":"arxiv","version":1}},"canonical_sha256":"3eacc23d159e5ba8503ad444fa19a22cf49d6581494a24b5d57d48f87ddca370","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3eacc23d159e5ba8503ad444fa19a22cf49d6581494a24b5d57d48f87ddca370","first_computed_at":"2026-07-05T11:17:13.419742Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:13.419742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uNN6ARv/BFbwr5ZjMYuvEKQjsLGXwsLPb+DauvDU8HVM/2StTuXIg7FR3iSDkRQl9BFZcuFrQHJk7yhZ/MCrCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:13.420237Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.05942","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dfe267b60b3581952190f8cae1c7018e0b3542725a3bcb1db595e86f2cc1ca59","sha256:ec207e446604113834f556d8e890daf9f5cd5dcd07622aa14820007b9f1e415a"],"state_sha256":"e1ae550e87b60094a69891e2dd532a904403a6d25bb0ab2debb7c545fb30a805"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Je1LG3klU33WPUvoJ+AYzl28c8UZoZykYry+JAUCjPjQw4MKGPrf9OiUJFAyuKHJDqyj4veDo+Jr7QAD376ODQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T21:00:03.204049Z","bundle_sha256":"71d5811e4908f861c17bba76998e07dc814b907ffbf3431fadcc2c299cb77416"}}