{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:PBSRLQU5DJEKW2SPMQRMQJYVUX","short_pith_number":"pith:PBSRLQU5","canonical_record":{"source":{"id":"2409.04977","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-08T05:13:58Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"05f220bd734bdd41965245bc53be03041ded54e205593cde503d42c98438be5f","abstract_canon_sha256":"643ab9be3ad3db9606041bb9e6b580ea0825c0a5545bb77541b996dd453e0d5c"},"schema_version":"1.0"},"canonical_sha256":"786515c29d1a48ab6a4f6422c82715a5c48e284b736565d82154e24de96416c5","source":{"kind":"arxiv","id":"2409.04977","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.04977","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"arxiv_version","alias_value":"2409.04977v1","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.04977","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"pith_short_12","alias_value":"PBSRLQU5DJEK","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"pith_short_16","alias_value":"PBSRLQU5DJEKW2SP","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"pith_short_8","alias_value":"PBSRLQU5","created_at":"2026-07-05T09:04:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:PBSRLQU5DJEKW2SPMQRMQJYVUX","target":"record","payload":{"canonical_record":{"source":{"id":"2409.04977","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-08T05:13:58Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"05f220bd734bdd41965245bc53be03041ded54e205593cde503d42c98438be5f","abstract_canon_sha256":"643ab9be3ad3db9606041bb9e6b580ea0825c0a5545bb77541b996dd453e0d5c"},"schema_version":"1.0"},"canonical_sha256":"786515c29d1a48ab6a4f6422c82715a5c48e284b736565d82154e24de96416c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:04:42.425858Z","signature_b64":"CTe1s8aNZMgFPUwYBLF40Ln/2yOs2Uw8VK5zujiWF4XLENe6tBkvHGi2tqCpyIiPhYAA+IwsLlvhrPSxsh0oCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"786515c29d1a48ab6a4f6422c82715a5c48e284b736565d82154e24de96416c5","last_reissued_at":"2026-07-05T09:04:42.425486Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:04:42.425486Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.04977","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":"2026-07-05T09:04:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2/XdZJSHEpMyzHdt5lxtIaNG7HYIlEgb2x59z6TbOHs54lgJzJV6knVAUa7VtDTgo4iRiFkcRZM7XIqAMVqrAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-14T22:26:34.312609Z"},"content_sha256":"79612aa3334bd0977aad04331656532cc70a7e38f8a558c1b0133a8e8c760cd0","schema_version":"1.0","event_id":"sha256:79612aa3334bd0977aad04331656532cc70a7e38f8a558c1b0133a8e8c760cd0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:PBSRLQU5DJEKW2SPMQRMQJYVUX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Iris Li, Mingxiu Sui, Qi Wang, Taiyuan Mei, Xiaohan Cheng, Zijun Gao","submitted_at":"2024-09-08T05:13:58Z","abstract_excerpt":"With the rise of deep learning technology in practical applications, Convolutional Neural Networks (CNNs) have been able to assist humans in solving many real-world problems. To enhance the performance of CNNs, numerous network architectures have been explored. Some of these architectures are designed based on the accumulated experience of researchers over time, while others are designed through neural architecture search methods. The improvements made to CNNs by the aforementioned methods are quite significant, but most of the improvement methods are limited in reality by model size and envir"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.04977","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/2409.04977/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":"2026-07-05T09:04:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HCvOm+SJu05rdxD310vvvE/kqKCkNGtPE/MkYbCSUJkovpgIM5kjtnk/zfNRA3lh7zY37D0mPpSWAJpzuUbcDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-14T22:26:34.312983Z"},"content_sha256":"b8502abf9d0db5a27cdd1c28bbc813c12baf58d96d433284ab63a4c8eb34607b","schema_version":"1.0","event_id":"sha256:b8502abf9d0db5a27cdd1c28bbc813c12baf58d96d433284ab63a4c8eb34607b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PBSRLQU5DJEKW2SPMQRMQJYVUX/bundle.json","state_url":"https://pith.science/pith/PBSRLQU5DJEKW2SPMQRMQJYVUX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PBSRLQU5DJEKW2SPMQRMQJYVUX/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":"2026-07-14T22:26:34Z","links":{"resolver":"https://pith.science/pith/PBSRLQU5DJEKW2SPMQRMQJYVUX","bundle":"https://pith.science/pith/PBSRLQU5DJEKW2SPMQRMQJYVUX/bundle.json","state":"https://pith.science/pith/PBSRLQU5DJEKW2SPMQRMQJYVUX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PBSRLQU5DJEKW2SPMQRMQJYVUX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:PBSRLQU5DJEKW2SPMQRMQJYVUX","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":"643ab9be3ad3db9606041bb9e6b580ea0825c0a5545bb77541b996dd453e0d5c","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-08T05:13:58Z","title_canon_sha256":"05f220bd734bdd41965245bc53be03041ded54e205593cde503d42c98438be5f"},"schema_version":"1.0","source":{"id":"2409.04977","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.04977","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"arxiv_version","alias_value":"2409.04977v1","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.04977","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"pith_short_12","alias_value":"PBSRLQU5DJEK","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"pith_short_16","alias_value":"PBSRLQU5DJEKW2SP","created_at":"2026-07-05T09:04:42Z"},{"alias_kind":"pith_short_8","alias_value":"PBSRLQU5","created_at":"2026-07-05T09:04:42Z"}],"graph_snapshots":[{"event_id":"sha256:b8502abf9d0db5a27cdd1c28bbc813c12baf58d96d433284ab63a4c8eb34607b","target":"graph","created_at":"2026-07-05T09:04:42Z","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/2409.04977/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the rise of deep learning technology in practical applications, Convolutional Neural Networks (CNNs) have been able to assist humans in solving many real-world problems. To enhance the performance of CNNs, numerous network architectures have been explored. Some of these architectures are designed based on the accumulated experience of researchers over time, while others are designed through neural architecture search methods. The improvements made to CNNs by the aforementioned methods are quite significant, but most of the improvement methods are limited in reality by model size and envir","authors_text":"Iris Li, Mingxiu Sui, Qi Wang, Taiyuan Mei, Xiaohan Cheng, Zijun Gao","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-08T05:13:58Z","title":"Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.04977","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":"sha256:79612aa3334bd0977aad04331656532cc70a7e38f8a558c1b0133a8e8c760cd0","target":"record","created_at":"2026-07-05T09:04:42Z","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":"643ab9be3ad3db9606041bb9e6b580ea0825c0a5545bb77541b996dd453e0d5c","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-08T05:13:58Z","title_canon_sha256":"05f220bd734bdd41965245bc53be03041ded54e205593cde503d42c98438be5f"},"schema_version":"1.0","source":{"id":"2409.04977","kind":"arxiv","version":1}},"canonical_sha256":"786515c29d1a48ab6a4f6422c82715a5c48e284b736565d82154e24de96416c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"786515c29d1a48ab6a4f6422c82715a5c48e284b736565d82154e24de96416c5","first_computed_at":"2026-07-05T09:04:42.425486Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:04:42.425486Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CTe1s8aNZMgFPUwYBLF40Ln/2yOs2Uw8VK5zujiWF4XLENe6tBkvHGi2tqCpyIiPhYAA+IwsLlvhrPSxsh0oCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:04:42.425858Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.04977","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79612aa3334bd0977aad04331656532cc70a7e38f8a558c1b0133a8e8c760cd0","sha256:b8502abf9d0db5a27cdd1c28bbc813c12baf58d96d433284ab63a4c8eb34607b"],"state_sha256":"1ab681dae079f595333edbc6c27e1ecef9be701c0c7ee460b62d27614e2c809d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vy3LvjHIoi7Ze5smH1kEG69WMwlbk7k8TJ1Nda2uBv+8ObVe6nsfneG2YenzTFSSOBOD4wzcul/XV20xrnDlBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-14T22:26:34.315153Z","bundle_sha256":"ad7d6c89b65e53dadf163e2e7db15a124fcc0deeb7f979de0cc6c1c9cb30dce3"}}