{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:RICPWUKGZ6CJLAVLCBF6IE6BKM","short_pith_number":"pith:RICPWUKG","canonical_record":{"source":{"id":"1709.02597","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-09-08T09:04:03Z","cross_cats_sorted":[],"title_canon_sha256":"297cd7561364ca618fe0df38ddbb2cab80c00eb0afaae208f0b394634540fa14","abstract_canon_sha256":"38da43a1833b8426a22bf10f68d4c14b8b03e97d9121640105dff31744244fc6"},"schema_version":"1.0"},"canonical_sha256":"8a04fb5146cf849582ab104be413c1530448118dd7654bbf63bcd6abd943fc53","source":{"kind":"arxiv","id":"1709.02597","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.02597","created_at":"2026-05-18T00:19:41Z"},{"alias_kind":"arxiv_version","alias_value":"1709.02597v2","created_at":"2026-05-18T00:19:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.02597","created_at":"2026-05-18T00:19:41Z"},{"alias_kind":"pith_short_12","alias_value":"RICPWUKGZ6CJ","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RICPWUKGZ6CJLAVL","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RICPWUKG","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:RICPWUKGZ6CJLAVLCBF6IE6BKM","target":"record","payload":{"canonical_record":{"source":{"id":"1709.02597","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-09-08T09:04:03Z","cross_cats_sorted":[],"title_canon_sha256":"297cd7561364ca618fe0df38ddbb2cab80c00eb0afaae208f0b394634540fa14","abstract_canon_sha256":"38da43a1833b8426a22bf10f68d4c14b8b03e97d9121640105dff31744244fc6"},"schema_version":"1.0"},"canonical_sha256":"8a04fb5146cf849582ab104be413c1530448118dd7654bbf63bcd6abd943fc53","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:41.890299Z","signature_b64":"sllNo9Rt05sfQ4U932dW98frOKznGkwIVkAbAKmmxRnuffU4+R7LNLzINmzCMZXmocbrIJZ1GZJ2e6vnU6GzAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a04fb5146cf849582ab104be413c1530448118dd7654bbf63bcd6abd943fc53","last_reissued_at":"2026-05-18T00:19:41.889547Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:41.889547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.02597","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":"2026-05-18T00:19:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Mh4rvo9DOfue7MUEoH+BQC7+1dFOqIY+7Fp1LS9wUh6HaX10Qoc7Zi0a3BX5P+6nAguz7P8ofHQvoct1DRkBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T05:04:35.057752Z"},"content_sha256":"b627dcd03598d96a67bd96ec8804004372ec7863fbafb9883298ec5aac574dd8","schema_version":"1.0","event_id":"sha256:b627dcd03598d96a67bd96ec8804004372ec7863fbafb9883298ec5aac574dd8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:RICPWUKGZ6CJLAVLCBF6IE6BKM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Identifying Product Order with Restricted Boltzmann Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.dis-nn","authors_text":"Mingxing Luo, Qiong Zhu, Wen-Jia Rao, Xin Wan, Zhenyu Li","submitted_at":"2017-09-08T09:04:03Z","abstract_excerpt":"Unsupervised machine learning via a restricted Boltzmann machine is an useful tool in distinguishing an ordered phase from a disordered phase. Here we study its application on the two-dimensional Ashkin-Teller model, which features a partially ordered product phase. We train the neural network with spin configuration data generated by Monte Carlo simulations and show that distinct features of the product phase can be learned from non-ergodic samples resulting from symmetry breaking. Careful analysis of the weight matrices inspires us to define a nontrivial machine-learning motivated quantity o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.02597","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":""},"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":"2026-05-18T00:19:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"smN3medY4gxxqNdmalckAD3yZc4i2aQTKhGC6gieM4olDdPao17iMz9QzO36lUxpIfkIXcT5ClxIYmcCdVIwCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T05:04:35.058470Z"},"content_sha256":"36c7e138ece36bef367fb43082fc852cfeeaecb58d0c2f085a67c0eefe42d950","schema_version":"1.0","event_id":"sha256:36c7e138ece36bef367fb43082fc852cfeeaecb58d0c2f085a67c0eefe42d950"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RICPWUKGZ6CJLAVLCBF6IE6BKM/bundle.json","state_url":"https://pith.science/pith/RICPWUKGZ6CJLAVLCBF6IE6BKM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RICPWUKGZ6CJLAVLCBF6IE6BKM/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":"2026-05-22T05:04:35Z","links":{"resolver":"https://pith.science/pith/RICPWUKGZ6CJLAVLCBF6IE6BKM","bundle":"https://pith.science/pith/RICPWUKGZ6CJLAVLCBF6IE6BKM/bundle.json","state":"https://pith.science/pith/RICPWUKGZ6CJLAVLCBF6IE6BKM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RICPWUKGZ6CJLAVLCBF6IE6BKM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:RICPWUKGZ6CJLAVLCBF6IE6BKM","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":"38da43a1833b8426a22bf10f68d4c14b8b03e97d9121640105dff31744244fc6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-09-08T09:04:03Z","title_canon_sha256":"297cd7561364ca618fe0df38ddbb2cab80c00eb0afaae208f0b394634540fa14"},"schema_version":"1.0","source":{"id":"1709.02597","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.02597","created_at":"2026-05-18T00:19:41Z"},{"alias_kind":"arxiv_version","alias_value":"1709.02597v2","created_at":"2026-05-18T00:19:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.02597","created_at":"2026-05-18T00:19:41Z"},{"alias_kind":"pith_short_12","alias_value":"RICPWUKGZ6CJ","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RICPWUKGZ6CJLAVL","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RICPWUKG","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:36c7e138ece36bef367fb43082fc852cfeeaecb58d0c2f085a67c0eefe42d950","target":"graph","created_at":"2026-05-18T00:19:41Z","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":"Unsupervised machine learning via a restricted Boltzmann machine is an useful tool in distinguishing an ordered phase from a disordered phase. Here we study its application on the two-dimensional Ashkin-Teller model, which features a partially ordered product phase. We train the neural network with spin configuration data generated by Monte Carlo simulations and show that distinct features of the product phase can be learned from non-ergodic samples resulting from symmetry breaking. Careful analysis of the weight matrices inspires us to define a nontrivial machine-learning motivated quantity o","authors_text":"Mingxing Luo, Qiong Zhu, Wen-Jia Rao, Xin Wan, Zhenyu Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-09-08T09:04:03Z","title":"Identifying Product Order with Restricted Boltzmann Machines"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.02597","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:b627dcd03598d96a67bd96ec8804004372ec7863fbafb9883298ec5aac574dd8","target":"record","created_at":"2026-05-18T00:19:41Z","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":"38da43a1833b8426a22bf10f68d4c14b8b03e97d9121640105dff31744244fc6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2017-09-08T09:04:03Z","title_canon_sha256":"297cd7561364ca618fe0df38ddbb2cab80c00eb0afaae208f0b394634540fa14"},"schema_version":"1.0","source":{"id":"1709.02597","kind":"arxiv","version":2}},"canonical_sha256":"8a04fb5146cf849582ab104be413c1530448118dd7654bbf63bcd6abd943fc53","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a04fb5146cf849582ab104be413c1530448118dd7654bbf63bcd6abd943fc53","first_computed_at":"2026-05-18T00:19:41.889547Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:41.889547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sllNo9Rt05sfQ4U932dW98frOKznGkwIVkAbAKmmxRnuffU4+R7LNLzINmzCMZXmocbrIJZ1GZJ2e6vnU6GzAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:41.890299Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.02597","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b627dcd03598d96a67bd96ec8804004372ec7863fbafb9883298ec5aac574dd8","sha256:36c7e138ece36bef367fb43082fc852cfeeaecb58d0c2f085a67c0eefe42d950"],"state_sha256":"869cf741c882177fcc374d2d028611943b8877b5ba88033ef7f135dd4affd76e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zyd+VBSLTM5SPLUpTaSm8sLQI4NoyZO89wS8ssEZQQVMXylf819vOSWSW243vK8vnLscm9cUQT5CvLZpksPDBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T05:04:35.062674Z","bundle_sha256":"bba792c322be25ba1b4408da61439839b6ca9cfb50677ee267737e4bbdd7de1f"}}