{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:725KUJRRBMGMAPN6DN4KT6H5ZU","short_pith_number":"pith:725KUJRR","canonical_record":{"source":{"id":"2303.06544","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2023-03-12T02:49:19Z","cross_cats_sorted":["cs.AI","cs.LG","math.OC"],"title_canon_sha256":"f46fb31f30849840021f466c5861257a447563bc5331a4a778043dae967ebb71","abstract_canon_sha256":"544629154f8440e20ea72cf876170b59160d8eb52b46c3b17b409317bd9a3e20"},"schema_version":"1.0"},"canonical_sha256":"febaaa26310b0cc03dbe1b78a9f8fdcd255fcbec4ceb17ab2fa4f28003a9e054","source":{"kind":"arxiv","id":"2303.06544","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.06544","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"arxiv_version","alias_value":"2303.06544v1","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.06544","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"pith_short_12","alias_value":"725KUJRRBMGM","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"pith_short_16","alias_value":"725KUJRRBMGMAPN6","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"pith_short_8","alias_value":"725KUJRR","created_at":"2026-07-05T05:50:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:725KUJRRBMGMAPN6DN4KT6H5ZU","target":"record","payload":{"canonical_record":{"source":{"id":"2303.06544","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2023-03-12T02:49:19Z","cross_cats_sorted":["cs.AI","cs.LG","math.OC"],"title_canon_sha256":"f46fb31f30849840021f466c5861257a447563bc5331a4a778043dae967ebb71","abstract_canon_sha256":"544629154f8440e20ea72cf876170b59160d8eb52b46c3b17b409317bd9a3e20"},"schema_version":"1.0"},"canonical_sha256":"febaaa26310b0cc03dbe1b78a9f8fdcd255fcbec4ceb17ab2fa4f28003a9e054","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:50:20.187943Z","signature_b64":"LZ3rwMxeMZm9bkdRC0LHIEwVWMOwoSCEgt2ZcQqqAYrRilzA4+CXj8eHo1FUflzF/zJBPwyzAlsUlWaOWxZuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"febaaa26310b0cc03dbe1b78a9f8fdcd255fcbec4ceb17ab2fa4f28003a9e054","last_reissued_at":"2026-07-05T05:50:20.187563Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:50:20.187563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.06544","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":"2026-07-05T05:50:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2+9ifCzb/VtPK6Tzoo/wZ8Ee68HzBCqQ9mlGtPgLN6zjuyd2Wo73P28y/i/Dx7j87JLTlLonk+ub57n6oOkGCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T16:15:19.671923Z"},"content_sha256":"bbe43ccbf4152a44983012ec82fea1e210ee5ddd2f93144ff38ca5dce0eb4d6e","schema_version":"1.0","event_id":"sha256:bbe43ccbf4152a44983012ec82fea1e210ee5ddd2f93144ff38ca5dce0eb4d6e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:725KUJRRBMGMAPN6DN4KT6H5ZU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","math.OC"],"primary_cat":"astro-ph.IM","authors_text":"Domingo Mery, Francisco P\\'erez-Galarce, Karim Pichara, M\\'arcio Catelan, Pablo Huijse","submitted_at":"2023-03-12T02:49:19Z","abstract_excerpt":"In recent decades, machine learning has provided valuable models and algorithms for processing and extracting knowledge from time-series surveys. Different classifiers have been proposed and performed to an excellent standard. Nevertheless, few papers have tackled the data shift problem in labeled training sets, which occurs when there is a mismatch between the data distribution in the training set and the testing set. This drawback can damage the prediction performance in unseen data. Consequently, we propose a scalable and easily adaptable approach based on an informative regularization and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.06544","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/2303.06544/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":"2026-07-05T05:50:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ux63PIIxq2qu+isTinXsunBcdwmdUaISNO6BxdqUYpEYQHdSB3LJCmrBf2dk22Za3cqrDWHJLA2MjQzAemZdCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T16:15:19.672547Z"},"content_sha256":"44eed5ec9ad8cbb8ca9cc8910e083d92c4a5ff137e90ea07dc4c0f6d21b8df0a","schema_version":"1.0","event_id":"sha256:44eed5ec9ad8cbb8ca9cc8910e083d92c4a5ff137e90ea07dc4c0f6d21b8df0a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/725KUJRRBMGMAPN6DN4KT6H5ZU/bundle.json","state_url":"https://pith.science/pith/725KUJRRBMGMAPN6DN4KT6H5ZU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/725KUJRRBMGMAPN6DN4KT6H5ZU/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":"2026-08-16T16:15:19Z","links":{"resolver":"https://pith.science/pith/725KUJRRBMGMAPN6DN4KT6H5ZU","bundle":"https://pith.science/pith/725KUJRRBMGMAPN6DN4KT6H5ZU/bundle.json","state":"https://pith.science/pith/725KUJRRBMGMAPN6DN4KT6H5ZU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/725KUJRRBMGMAPN6DN4KT6H5ZU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:725KUJRRBMGMAPN6DN4KT6H5ZU","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":"544629154f8440e20ea72cf876170b59160d8eb52b46c3b17b409317bd9a3e20","cross_cats_sorted":["cs.AI","cs.LG","math.OC"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2023-03-12T02:49:19Z","title_canon_sha256":"f46fb31f30849840021f466c5861257a447563bc5331a4a778043dae967ebb71"},"schema_version":"1.0","source":{"id":"2303.06544","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.06544","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"arxiv_version","alias_value":"2303.06544v1","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.06544","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"pith_short_12","alias_value":"725KUJRRBMGM","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"pith_short_16","alias_value":"725KUJRRBMGMAPN6","created_at":"2026-07-05T05:50:20Z"},{"alias_kind":"pith_short_8","alias_value":"725KUJRR","created_at":"2026-07-05T05:50:20Z"}],"graph_snapshots":[{"event_id":"sha256:44eed5ec9ad8cbb8ca9cc8910e083d92c4a5ff137e90ea07dc4c0f6d21b8df0a","target":"graph","created_at":"2026-07-05T05:50:20Z","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/2303.06544/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent decades, machine learning has provided valuable models and algorithms for processing and extracting knowledge from time-series surveys. Different classifiers have been proposed and performed to an excellent standard. Nevertheless, few papers have tackled the data shift problem in labeled training sets, which occurs when there is a mismatch between the data distribution in the training set and the testing set. This drawback can damage the prediction performance in unseen data. Consequently, we propose a scalable and easily adaptable approach based on an informative regularization and ","authors_text":"Domingo Mery, Francisco P\\'erez-Galarce, Karim Pichara, M\\'arcio Catelan, Pablo Huijse","cross_cats":["cs.AI","cs.LG","math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2023-03-12T02:49:19Z","title":"Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.06544","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:bbe43ccbf4152a44983012ec82fea1e210ee5ddd2f93144ff38ca5dce0eb4d6e","target":"record","created_at":"2026-07-05T05:50:20Z","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":"544629154f8440e20ea72cf876170b59160d8eb52b46c3b17b409317bd9a3e20","cross_cats_sorted":["cs.AI","cs.LG","math.OC"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2023-03-12T02:49:19Z","title_canon_sha256":"f46fb31f30849840021f466c5861257a447563bc5331a4a778043dae967ebb71"},"schema_version":"1.0","source":{"id":"2303.06544","kind":"arxiv","version":1}},"canonical_sha256":"febaaa26310b0cc03dbe1b78a9f8fdcd255fcbec4ceb17ab2fa4f28003a9e054","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"febaaa26310b0cc03dbe1b78a9f8fdcd255fcbec4ceb17ab2fa4f28003a9e054","first_computed_at":"2026-07-05T05:50:20.187563Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:50:20.187563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LZ3rwMxeMZm9bkdRC0LHIEwVWMOwoSCEgt2ZcQqqAYrRilzA4+CXj8eHo1FUflzF/zJBPwyzAlsUlWaOWxZuDw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:50:20.187943Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.06544","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bbe43ccbf4152a44983012ec82fea1e210ee5ddd2f93144ff38ca5dce0eb4d6e","sha256:44eed5ec9ad8cbb8ca9cc8910e083d92c4a5ff137e90ea07dc4c0f6d21b8df0a"],"state_sha256":"721cae4d972ef94c4fbf5185a7859ce6067b0cf21ce86c063282921e384a66a0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xgfBoUmixL6VHv0hQAYKeYRpMWoNPGCqJaozmzC3rExckwC7/iihS1MRfpIDU+ZUx96Dz3jm87rvExN8d+/CBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T16:15:19.677256Z","bundle_sha256":"1d66ce73ce7c27393675209e035891dfa9d4ca93b120c366b4d0615deca7778c"}}