{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VBDX5F4LC4F5IRKWXZZHYNEHDB","short_pith_number":"pith:VBDX5F4L","canonical_record":{"source":{"id":"1710.04987","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-10-13T16:22:21Z","cross_cats_sorted":[],"title_canon_sha256":"73da824a5d847234fea59f385c6a408cde0a45f872d97f35745430aff80ae0e1","abstract_canon_sha256":"2ea0ae7cb1ec039832f9896994c003bcc51e19cb34182e5c9e9c841c42ffb1ff"},"schema_version":"1.0"},"canonical_sha256":"a8477e978b170bd44556be727c3487185ae2e1082d8dfaf25b3d45481e3d2555","source":{"kind":"arxiv","id":"1710.04987","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.04987","created_at":"2026-05-18T00:32:56Z"},{"alias_kind":"arxiv_version","alias_value":"1710.04987v1","created_at":"2026-05-18T00:32:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04987","created_at":"2026-05-18T00:32:56Z"},{"alias_kind":"pith_short_12","alias_value":"VBDX5F4LC4F5","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VBDX5F4LC4F5IRKW","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VBDX5F4L","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VBDX5F4LC4F5IRKWXZZHYNEHDB","target":"record","payload":{"canonical_record":{"source":{"id":"1710.04987","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-10-13T16:22:21Z","cross_cats_sorted":[],"title_canon_sha256":"73da824a5d847234fea59f385c6a408cde0a45f872d97f35745430aff80ae0e1","abstract_canon_sha256":"2ea0ae7cb1ec039832f9896994c003bcc51e19cb34182e5c9e9c841c42ffb1ff"},"schema_version":"1.0"},"canonical_sha256":"a8477e978b170bd44556be727c3487185ae2e1082d8dfaf25b3d45481e3d2555","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:56.098810Z","signature_b64":"+I5mMTUa+M+Q7djX7+2WUPW3uDTBckpFSZDO1Au0D36x8lEQfiThlFkQtAcZT/1wMGAUCDuOYhyi60bW8lnVAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8477e978b170bd44556be727c3487185ae2e1082d8dfaf25b3d45481e3d2555","last_reissued_at":"2026-05-18T00:32:56.098106Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:56.098106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.04987","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":"2026-05-18T00:32:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cuqBVaBdqxEpLBBTa/KQVjePhHu+qgpMpKYV/RZx4FW/n2+N6/jpzj+wzAnPyGHwiys8scgle5SAmkZZgu3uCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T19:55:57.874202Z"},"content_sha256":"b82847878ea79a9af174c9e10d4e1ae486884b13af4b19632d837308c9234627","schema_version":"1.0","event_id":"sha256:b82847878ea79a9af174c9e10d4e1ae486884b13af4b19632d837308c9234627"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VBDX5F4LC4F5IRKWXZZHYNEHDB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Simulating the Ising Model with a Deep Convolutional Generative Adversarial Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.dis-nn","authors_text":"Sean P. Rodrigues, Wenshan Cai, Zhaocheng Liu","submitted_at":"2017-10-13T16:22:21Z","abstract_excerpt":"The deep learning framework is witnessing expansive growth into diverse applications such as biological systems, human cognition, robotics, and the social sciences, thanks to its immense ability to extract essential features from complicated systems. In particular, recent developments of the field have revealed the unique faculty of deep learning to accurately approximate complex physical systems in fluid dynamics, condensed matter physics, etc. The convolutional neural network (CNN) is an efficient approach to represent complex systems with large degrees of freedom. On the other hand, the gen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04987","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":"2026-05-18T00:32:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VE9OjnIk9WYvX6DfdRy7/aE7UaOcclq96k/ZHu8WCDm2n2bzAsYjGzvXElsDIHS0xL7fj/+9TrC9eJmczjU+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T19:55:57.874583Z"},"content_sha256":"8c58ca4ee950cb72417464480996fafa9596c1748300b7215cd1cc1e77a1c4d1","schema_version":"1.0","event_id":"sha256:8c58ca4ee950cb72417464480996fafa9596c1748300b7215cd1cc1e77a1c4d1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VBDX5F4LC4F5IRKWXZZHYNEHDB/bundle.json","state_url":"https://pith.science/pith/VBDX5F4LC4F5IRKWXZZHYNEHDB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VBDX5F4LC4F5IRKWXZZHYNEHDB/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":"2026-05-27T19:55:57Z","links":{"resolver":"https://pith.science/pith/VBDX5F4LC4F5IRKWXZZHYNEHDB","bundle":"https://pith.science/pith/VBDX5F4LC4F5IRKWXZZHYNEHDB/bundle.json","state":"https://pith.science/pith/VBDX5F4LC4F5IRKWXZZHYNEHDB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VBDX5F4LC4F5IRKWXZZHYNEHDB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VBDX5F4LC4F5IRKWXZZHYNEHDB","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":"2ea0ae7cb1ec039832f9896994c003bcc51e19cb34182e5c9e9c841c42ffb1ff","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-10-13T16:22:21Z","title_canon_sha256":"73da824a5d847234fea59f385c6a408cde0a45f872d97f35745430aff80ae0e1"},"schema_version":"1.0","source":{"id":"1710.04987","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.04987","created_at":"2026-05-18T00:32:56Z"},{"alias_kind":"arxiv_version","alias_value":"1710.04987v1","created_at":"2026-05-18T00:32:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04987","created_at":"2026-05-18T00:32:56Z"},{"alias_kind":"pith_short_12","alias_value":"VBDX5F4LC4F5","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VBDX5F4LC4F5IRKW","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VBDX5F4L","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:8c58ca4ee950cb72417464480996fafa9596c1748300b7215cd1cc1e77a1c4d1","target":"graph","created_at":"2026-05-18T00:32:56Z","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":"The deep learning framework is witnessing expansive growth into diverse applications such as biological systems, human cognition, robotics, and the social sciences, thanks to its immense ability to extract essential features from complicated systems. In particular, recent developments of the field have revealed the unique faculty of deep learning to accurately approximate complex physical systems in fluid dynamics, condensed matter physics, etc. The convolutional neural network (CNN) is an efficient approach to represent complex systems with large degrees of freedom. On the other hand, the gen","authors_text":"Sean P. Rodrigues, Wenshan Cai, Zhaocheng Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-10-13T16:22:21Z","title":"Simulating the Ising Model with a Deep Convolutional Generative Adversarial Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04987","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:b82847878ea79a9af174c9e10d4e1ae486884b13af4b19632d837308c9234627","target":"record","created_at":"2026-05-18T00:32:56Z","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":"2ea0ae7cb1ec039832f9896994c003bcc51e19cb34182e5c9e9c841c42ffb1ff","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-10-13T16:22:21Z","title_canon_sha256":"73da824a5d847234fea59f385c6a408cde0a45f872d97f35745430aff80ae0e1"},"schema_version":"1.0","source":{"id":"1710.04987","kind":"arxiv","version":1}},"canonical_sha256":"a8477e978b170bd44556be727c3487185ae2e1082d8dfaf25b3d45481e3d2555","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a8477e978b170bd44556be727c3487185ae2e1082d8dfaf25b3d45481e3d2555","first_computed_at":"2026-05-18T00:32:56.098106Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:56.098106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+I5mMTUa+M+Q7djX7+2WUPW3uDTBckpFSZDO1Au0D36x8lEQfiThlFkQtAcZT/1wMGAUCDuOYhyi60bW8lnVAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:56.098810Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.04987","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b82847878ea79a9af174c9e10d4e1ae486884b13af4b19632d837308c9234627","sha256:8c58ca4ee950cb72417464480996fafa9596c1748300b7215cd1cc1e77a1c4d1"],"state_sha256":"ca1a1b433fdb0b5ed7f28f84a13cddde66af6f34096fb525b90231c2fffa084e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bwuw3ZhAenM/wisGC4uE683zJ4x6DBxZlyMqMuTV47MUXEaMIlnVo3i8nn1xlcyXSoafcOV7JwtSMzu8fdrBAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T19:55:57.876954Z","bundle_sha256":"87cfdf8c9ef0dd0e9024d5aa0cf183616d31685655c145d5490054e64fcbb3f5"}}