{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:UPDXOEKIFUFDT7CHUH2C7LYINM","short_pith_number":"pith:UPDXOEKI","canonical_record":{"source":{"id":"2302.06564","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-13T18:06:59Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f674c39dac3bb0b94fbc8ab4afed6041af99bd3f7bffe7894d44424ce1d3c4f2","abstract_canon_sha256":"f0979db0007bc3748ad97c18ce019c37f85dbb81b44b6af6cd227425738e99b1"},"schema_version":"1.0"},"canonical_sha256":"a3c77711482d0a39fc47a1f42faf086b142569bf8982910362db10739e8b987a","source":{"kind":"arxiv","id":"2302.06564","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.06564","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"arxiv_version","alias_value":"2302.06564v2","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.06564","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"pith_short_12","alias_value":"UPDXOEKIFUFD","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"pith_short_16","alias_value":"UPDXOEKIFUFDT7CH","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"pith_short_8","alias_value":"UPDXOEKI","created_at":"2026-07-05T08:38:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:UPDXOEKIFUFDT7CHUH2C7LYINM","target":"record","payload":{"canonical_record":{"source":{"id":"2302.06564","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-13T18:06:59Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f674c39dac3bb0b94fbc8ab4afed6041af99bd3f7bffe7894d44424ce1d3c4f2","abstract_canon_sha256":"f0979db0007bc3748ad97c18ce019c37f85dbb81b44b6af6cd227425738e99b1"},"schema_version":"1.0"},"canonical_sha256":"a3c77711482d0a39fc47a1f42faf086b142569bf8982910362db10739e8b987a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:51.287406Z","signature_b64":"u+hFnDIWHw3zfcZcjA8UETa3MRSMbiLS8Jkl3FqpIBcFs4Aox4gRpqciordzeL8wU8dMESlPF3hBHsmg48zuAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3c77711482d0a39fc47a1f42faf086b142569bf8982910362db10739e8b987a","last_reissued_at":"2026-07-05T08:38:51.286979Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:51.286979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.06564","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:38:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UhhI/zZv14xZkmI32wGJjHdk5gn9+MzkeIEWZbmaIVUN4HzuTY+baV2SnfihTfTJ3vuqzyAXd6+wKbTAPZBzAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T12:34:56.463645Z"},"content_sha256":"9f1b3d92bc646317849d21ef55e209f066e6311a7fad836bf2f9eb2b89c6f9be","schema_version":"1.0","event_id":"sha256:9f1b3d92bc646317849d21ef55e209f066e6311a7fad836bf2f9eb2b89c6f9be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:UPDXOEKIFUFDT7CHUH2C7LYINM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Domain Decomposition-Based CNN-DNN Architecture for Model Parallel Training Applied to Image Recognition Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Axel Klawonn, Janine Weber, Martin Lanser","submitted_at":"2023-02-13T18:06:59Z","abstract_excerpt":"Deep neural networks (DNNs) and, in particular, convolutional neural networks (CNNs) have brought significant advances in a wide range of modern computer application problems. However, the increasing availability of large amounts of datasets as well as the increasing available computational power of modern computers lead to a steady growth in the complexity and size of DNN and CNN models, respectively, and thus, to longer training times. Hence, various methods and attempts have been developed to accelerate and parallelize the training of complex network architectures. In this work, a novel CNN"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.06564","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/2302.06564/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":"2026-07-05T08:38:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mHBrMsGL67z9NoBXTlbYErW3b60hCWwpE1n9DjJr3ULRtOilVi0ZgSFyYqJn/xZm4yOLPVuDwuLVymWmEIAiDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T12:34:56.464205Z"},"content_sha256":"2fa1ada2d1d745ee4379148a0fc92dd29a93d7969184f73abf61930437a12676","schema_version":"1.0","event_id":"sha256:2fa1ada2d1d745ee4379148a0fc92dd29a93d7969184f73abf61930437a12676"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UPDXOEKIFUFDT7CHUH2C7LYINM/bundle.json","state_url":"https://pith.science/pith/UPDXOEKIFUFDT7CHUH2C7LYINM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UPDXOEKIFUFDT7CHUH2C7LYINM/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":"2026-08-21T12:34:56Z","links":{"resolver":"https://pith.science/pith/UPDXOEKIFUFDT7CHUH2C7LYINM","bundle":"https://pith.science/pith/UPDXOEKIFUFDT7CHUH2C7LYINM/bundle.json","state":"https://pith.science/pith/UPDXOEKIFUFDT7CHUH2C7LYINM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UPDXOEKIFUFDT7CHUH2C7LYINM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:UPDXOEKIFUFDT7CHUH2C7LYINM","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":"f0979db0007bc3748ad97c18ce019c37f85dbb81b44b6af6cd227425738e99b1","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-13T18:06:59Z","title_canon_sha256":"f674c39dac3bb0b94fbc8ab4afed6041af99bd3f7bffe7894d44424ce1d3c4f2"},"schema_version":"1.0","source":{"id":"2302.06564","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.06564","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"arxiv_version","alias_value":"2302.06564v2","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.06564","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"pith_short_12","alias_value":"UPDXOEKIFUFD","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"pith_short_16","alias_value":"UPDXOEKIFUFDT7CH","created_at":"2026-07-05T08:38:51Z"},{"alias_kind":"pith_short_8","alias_value":"UPDXOEKI","created_at":"2026-07-05T08:38:51Z"}],"graph_snapshots":[{"event_id":"sha256:2fa1ada2d1d745ee4379148a0fc92dd29a93d7969184f73abf61930437a12676","target":"graph","created_at":"2026-07-05T08:38:51Z","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/2302.06564/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks (DNNs) and, in particular, convolutional neural networks (CNNs) have brought significant advances in a wide range of modern computer application problems. However, the increasing availability of large amounts of datasets as well as the increasing available computational power of modern computers lead to a steady growth in the complexity and size of DNN and CNN models, respectively, and thus, to longer training times. Hence, various methods and attempts have been developed to accelerate and parallelize the training of complex network architectures. In this work, a novel CNN","authors_text":"Axel Klawonn, Janine Weber, Martin Lanser","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-13T18:06:59Z","title":"A Domain Decomposition-Based CNN-DNN Architecture for Model Parallel Training Applied to Image Recognition Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.06564","kind":"arxiv","version":2},"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":"sha256:9f1b3d92bc646317849d21ef55e209f066e6311a7fad836bf2f9eb2b89c6f9be","target":"record","created_at":"2026-07-05T08:38:51Z","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":"f0979db0007bc3748ad97c18ce019c37f85dbb81b44b6af6cd227425738e99b1","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-13T18:06:59Z","title_canon_sha256":"f674c39dac3bb0b94fbc8ab4afed6041af99bd3f7bffe7894d44424ce1d3c4f2"},"schema_version":"1.0","source":{"id":"2302.06564","kind":"arxiv","version":2}},"canonical_sha256":"a3c77711482d0a39fc47a1f42faf086b142569bf8982910362db10739e8b987a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a3c77711482d0a39fc47a1f42faf086b142569bf8982910362db10739e8b987a","first_computed_at":"2026-07-05T08:38:51.286979Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:38:51.286979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u+hFnDIWHw3zfcZcjA8UETa3MRSMbiLS8Jkl3FqpIBcFs4Aox4gRpqciordzeL8wU8dMESlPF3hBHsmg48zuAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:38:51.287406Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.06564","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9f1b3d92bc646317849d21ef55e209f066e6311a7fad836bf2f9eb2b89c6f9be","sha256:2fa1ada2d1d745ee4379148a0fc92dd29a93d7969184f73abf61930437a12676"],"state_sha256":"1b253c7e95b0b90576dc19bf125d57d57b3d41f321e9da853b5a34bc538804f7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6LWjAj3mbPmicuYhTngEu4rQ7h44X0CuXgt1QlXX0X/ebCdJx7NSsF1fFb9M3/LN374/Vm8OTqfQjNVWVKrMCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T12:34:56.469205Z","bundle_sha256":"4b5c42ff0dffd02c298c2da5b8186dc8b24dffdc244423286a360bec4e16d72b"}}