{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4U55RRMQEZ2C4432SAXZQM2TX4","short_pith_number":"pith:4U55RRMQ","schema_version":"1.0","canonical_sha256":"e53bd8c59026742e737a902f983353bf0c53b51b8c2dfa795037374f90882858","source":{"kind":"arxiv","id":"2505.15363","version":2},"attestation_state":"computed","paper":{"title":"Robust extrapolation using physics-related activation functions in neural networks for nuclear masses","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"nucl-th","authors_text":"C. H. Kim, K. Y. Chae, M. S. Smith","submitted_at":"2025-05-21T10:50:02Z","abstract_excerpt":"Given the importance of nuclear mass predictions, numerous models have been developed to extrapolate the measured data into unknown regions. While neural networks -- the core of modern artificial intelligence -- have been recently suggested as powerful methods, showcasing high predictive power in the measured region, their ability to extrapolate remains questionable. This limitation stems from their `black box' nature and large number of parameters entangled with nonlinear functions designed in the context of computer science. In this study, we demonstrate that replacing such nonlinear functio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.15363","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"nucl-th","submitted_at":"2025-05-21T10:50:02Z","cross_cats_sorted":[],"title_canon_sha256":"455238f7081c8d0c9eb1496d459069c490a7697f14f489497adc1cde556546a2","abstract_canon_sha256":"e34b0410e0f22f1ce2080299cd376b8c15be9cd334b46bdb6928213994672046"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:51.340051Z","signature_b64":"bGOY7A7KXilJAJS7OYdsJFy4bv87wjR51ikxQ8x0Yj9ftymIhVrAdXP4+Glf7j2wqspKxQKLjHBeUoAAcq/+BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e53bd8c59026742e737a902f983353bf0c53b51b8c2dfa795037374f90882858","last_reissued_at":"2026-07-05T11:34:51.339405Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:51.339405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust extrapolation using physics-related activation functions in neural networks for nuclear masses","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"nucl-th","authors_text":"C. H. Kim, K. Y. Chae, M. S. Smith","submitted_at":"2025-05-21T10:50:02Z","abstract_excerpt":"Given the importance of nuclear mass predictions, numerous models have been developed to extrapolate the measured data into unknown regions. While neural networks -- the core of modern artificial intelligence -- have been recently suggested as powerful methods, showcasing high predictive power in the measured region, their ability to extrapolate remains questionable. This limitation stems from their `black box' nature and large number of parameters entangled with nonlinear functions designed in the context of computer science. In this study, we demonstrate that replacing such nonlinear functio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15363","kind":"arxiv","version":2},"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/2505.15363/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.15363","created_at":"2026-07-05T11:34:51.339474+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.15363v2","created_at":"2026-07-05T11:34:51.339474+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15363","created_at":"2026-07-05T11:34:51.339474+00:00"},{"alias_kind":"pith_short_12","alias_value":"4U55RRMQEZ2C","created_at":"2026-07-05T11:34:51.339474+00:00"},{"alias_kind":"pith_short_16","alias_value":"4U55RRMQEZ2C4432","created_at":"2026-07-05T11:34:51.339474+00:00"},{"alias_kind":"pith_short_8","alias_value":"4U55RRMQ","created_at":"2026-07-05T11:34:51.339474+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4U55RRMQEZ2C4432SAXZQM2TX4","json":"https://pith.science/pith/4U55RRMQEZ2C4432SAXZQM2TX4.json","graph_json":"https://pith.science/api/pith-number/4U55RRMQEZ2C4432SAXZQM2TX4/graph.json","events_json":"https://pith.science/api/pith-number/4U55RRMQEZ2C4432SAXZQM2TX4/events.json","paper":"https://pith.science/paper/4U55RRMQ"},"agent_actions":{"view_html":"https://pith.science/pith/4U55RRMQEZ2C4432SAXZQM2TX4","download_json":"https://pith.science/pith/4U55RRMQEZ2C4432SAXZQM2TX4.json","view_paper":"https://pith.science/paper/4U55RRMQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.15363&json=true","fetch_graph":"https://pith.science/api/pith-number/4U55RRMQEZ2C4432SAXZQM2TX4/graph.json","fetch_events":"https://pith.science/api/pith-number/4U55RRMQEZ2C4432SAXZQM2TX4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4U55RRMQEZ2C4432SAXZQM2TX4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4U55RRMQEZ2C4432SAXZQM2TX4/action/storage_attestation","attest_author":"https://pith.science/pith/4U55RRMQEZ2C4432SAXZQM2TX4/action/author_attestation","sign_citation":"https://pith.science/pith/4U55RRMQEZ2C4432SAXZQM2TX4/action/citation_signature","submit_replication":"https://pith.science/pith/4U55RRMQEZ2C4432SAXZQM2TX4/action/replication_record"}},"created_at":"2026-07-05T11:34:51.339474+00:00","updated_at":"2026-07-05T11:34:51.339474+00:00"}