{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:X43WSGSOSITHW4ESWWRFKTCL2X","short_pith_number":"pith:X43WSGSO","canonical_record":{"source":{"id":"1907.11629","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-26T15:29:46Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"d6cc20b68796c3c828d1f2206f5d6b9e7f3ffce50089240c34ba946557cab5cf","abstract_canon_sha256":"f0014888351e141fd37db5feac7c83194ebc109d5d881948b0e4c2a9d2f185d8"},"schema_version":"1.0"},"canonical_sha256":"bf37691a4e92267b7092b5a2554c4bd5d884b1698c0512379bee29279b797688","source":{"kind":"arxiv","id":"1907.11629","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.11629","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"arxiv_version","alias_value":"1907.11629v1","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11629","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"pith_short_12","alias_value":"X43WSGSOSITH","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"X43WSGSOSITHW4ES","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"X43WSGSO","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:X43WSGSOSITHW4ESWWRFKTCL2X","target":"record","payload":{"canonical_record":{"source":{"id":"1907.11629","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-26T15:29:46Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"d6cc20b68796c3c828d1f2206f5d6b9e7f3ffce50089240c34ba946557cab5cf","abstract_canon_sha256":"f0014888351e141fd37db5feac7c83194ebc109d5d881948b0e4c2a9d2f185d8"},"schema_version":"1.0"},"canonical_sha256":"bf37691a4e92267b7092b5a2554c4bd5d884b1698c0512379bee29279b797688","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:28.471801Z","signature_b64":"zBovABaf7UVYWhZ84Aiv/al2FB2JRRbGXXTqFnR4bBAAPaE3TpA96jEFC7aGWsDSSEvn8MVIUBlaVQpFHv24Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf37691a4e92267b7092b5a2554c4bd5d884b1698c0512379bee29279b797688","last_reissued_at":"2026-05-17T23:39:28.471142Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:28.471142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.11629","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":"2026-05-17T23:39:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cY3VoVgATwz7BDLTZymY7CzkuZdevOhTRy9RiRPRcnxKzcThbxz0Dy/QDBlVCIbRW0Zf3Xntg8PU4MHiP9fjDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T00:19:27.647901Z"},"content_sha256":"e6d192704d869739b7e025edb1a90f843bf6f8318139f24c34029daf9a568113","schema_version":"1.0","event_id":"sha256:e6d192704d869739b7e025edb1a90f843bf6f8318139f24c34029daf9a568113"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:X43WSGSOSITHW4ESWWRFKTCL2X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Stage Prediction Networks for Data Harmonization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Can Son Khoo, Chantal M. W. Tax, Daniel C. Alexander, Marco Palombo, Ryutaro Tanno, Stefano B. Blumberg","submitted_at":"2019-07-26T15:29:46Z","abstract_excerpt":"In this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. This allows us to integrate information from multiple acquisitions and improve the predictive performance and learning efficiency of the harmonization model. Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison. The MSP "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11629","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":""},"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":"2026-05-17T23:39:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XBwF8ReLr9Dsmib3rTs7NI2A6JuNPX71sEgHLmgBbo6SN0WjTaOTkNFlTr05sthfeqWXi1KB3mTDTiDPOMyZCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T00:19:27.648416Z"},"content_sha256":"9a36c459703b539801b7ec436bd611b68afefcc42db43ff4a2925a0d9bf0d499","schema_version":"1.0","event_id":"sha256:9a36c459703b539801b7ec436bd611b68afefcc42db43ff4a2925a0d9bf0d499"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X43WSGSOSITHW4ESWWRFKTCL2X/bundle.json","state_url":"https://pith.science/pith/X43WSGSOSITHW4ESWWRFKTCL2X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X43WSGSOSITHW4ESWWRFKTCL2X/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":"2026-06-02T00:19:27Z","links":{"resolver":"https://pith.science/pith/X43WSGSOSITHW4ESWWRFKTCL2X","bundle":"https://pith.science/pith/X43WSGSOSITHW4ESWWRFKTCL2X/bundle.json","state":"https://pith.science/pith/X43WSGSOSITHW4ESWWRFKTCL2X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X43WSGSOSITHW4ESWWRFKTCL2X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:X43WSGSOSITHW4ESWWRFKTCL2X","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":"f0014888351e141fd37db5feac7c83194ebc109d5d881948b0e4c2a9d2f185d8","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-26T15:29:46Z","title_canon_sha256":"d6cc20b68796c3c828d1f2206f5d6b9e7f3ffce50089240c34ba946557cab5cf"},"schema_version":"1.0","source":{"id":"1907.11629","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.11629","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"arxiv_version","alias_value":"1907.11629v1","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11629","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"pith_short_12","alias_value":"X43WSGSOSITH","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"X43WSGSOSITHW4ES","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"X43WSGSO","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:9a36c459703b539801b7ec436bd611b68afefcc42db43ff4a2925a0d9bf0d499","target":"graph","created_at":"2026-05-17T23:39:28Z","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"},"paper":{"abstract_excerpt":"In this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. This allows us to integrate information from multiple acquisitions and improve the predictive performance and learning efficiency of the harmonization model. Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison. The MSP ","authors_text":"Can Son Khoo, Chantal M. W. Tax, Daniel C. Alexander, Marco Palombo, Ryutaro Tanno, Stefano B. Blumberg","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-26T15:29:46Z","title":"Multi-Stage Prediction Networks for Data Harmonization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11629","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:e6d192704d869739b7e025edb1a90f843bf6f8318139f24c34029daf9a568113","target":"record","created_at":"2026-05-17T23:39:28Z","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":"f0014888351e141fd37db5feac7c83194ebc109d5d881948b0e4c2a9d2f185d8","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-26T15:29:46Z","title_canon_sha256":"d6cc20b68796c3c828d1f2206f5d6b9e7f3ffce50089240c34ba946557cab5cf"},"schema_version":"1.0","source":{"id":"1907.11629","kind":"arxiv","version":1}},"canonical_sha256":"bf37691a4e92267b7092b5a2554c4bd5d884b1698c0512379bee29279b797688","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bf37691a4e92267b7092b5a2554c4bd5d884b1698c0512379bee29279b797688","first_computed_at":"2026-05-17T23:39:28.471142Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:28.471142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zBovABaf7UVYWhZ84Aiv/al2FB2JRRbGXXTqFnR4bBAAPaE3TpA96jEFC7aGWsDSSEvn8MVIUBlaVQpFHv24Cw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:28.471801Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.11629","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e6d192704d869739b7e025edb1a90f843bf6f8318139f24c34029daf9a568113","sha256:9a36c459703b539801b7ec436bd611b68afefcc42db43ff4a2925a0d9bf0d499"],"state_sha256":"d9b81cbe9a398e4cb1293fd803d29b8e2a390f66036d4e777ce4830d14a0918d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fX4ni/cqTGCakwV0/96SCbyJSXR/CCZ8yDUkQYFJcVySnOX8mk9i1qS9yExUtp8giQYoQecgQnK6BpxUzCRGBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T00:19:27.651218Z","bundle_sha256":"7365a044ea25e6b4ac9dabf9c7268fd1b0e540d8c439d44f613ab35a2b1f7180"}}