{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZR6CLWEYQA7OYPP4MQF5VMVU5X","short_pith_number":"pith:ZR6CLWEY","schema_version":"1.0","canonical_sha256":"cc7c25d898803eec3dfc640bdab2b4edeb92892c949fcbc27627ec8691a00a66","source":{"kind":"arxiv","id":"2301.11934","version":3},"attestation_state":"computed","paper":{"title":"Compression theory for inhomogeneous systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.dis-nn"],"primary_cat":"cond-mat.stat-mech","authors_text":"Doruk Efe G\\\"okmen, Felix Flicker, Maciej Koch-Janusz, Sebastian D. Huber, Sounak Biswas, Zohar Ringel","submitted_at":"2023-01-27T19:00:00Z","abstract_excerpt":"The physics of complex systems stands to greatly benefit from the qualitative changes in data availability and advances in data-driven computational methods. Many of these systems can be represented by interacting degrees of freedom on inhomogeneous graphs. However, the lack of translational invariance presents a fundamental challenge to theoretical tools, such as the renormalization group, which were so successful in characterizing the universal physical behaviour in critical phenomena. Here we show that compression theory allows the extraction of relevant degrees of freedom in arbitrary geom"},"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":"2301.11934","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2023-01-27T19:00:00Z","cross_cats_sorted":["cond-mat.dis-nn"],"title_canon_sha256":"caff48a89fa14a2311bc6947817d1c91a036f4a3fad732f0702a80d4c779f24c","abstract_canon_sha256":"4ba90b8deaaefd42aed8989aed7f0a2f6c97fe98439d3e7b9e6079d8226e3aa0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:13.784498Z","signature_b64":"nfe6S2zqzVy8pi+T2cy6UmzuPen8Muhm35Wg/j7Ih84y4yzjeEa5Mj7456DtiUOmfcjGCUfzOc6ztLMYW3v3Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc7c25d898803eec3dfc640bdab2b4edeb92892c949fcbc27627ec8691a00a66","last_reissued_at":"2026-07-05T09:40:13.783982Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:13.783982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compression theory for inhomogeneous systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.dis-nn"],"primary_cat":"cond-mat.stat-mech","authors_text":"Doruk Efe G\\\"okmen, Felix Flicker, Maciej Koch-Janusz, Sebastian D. Huber, Sounak Biswas, Zohar Ringel","submitted_at":"2023-01-27T19:00:00Z","abstract_excerpt":"The physics of complex systems stands to greatly benefit from the qualitative changes in data availability and advances in data-driven computational methods. Many of these systems can be represented by interacting degrees of freedom on inhomogeneous graphs. However, the lack of translational invariance presents a fundamental challenge to theoretical tools, such as the renormalization group, which were so successful in characterizing the universal physical behaviour in critical phenomena. Here we show that compression theory allows the extraction of relevant degrees of freedom in arbitrary geom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.11934","kind":"arxiv","version":3},"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/2301.11934/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":"2301.11934","created_at":"2026-07-05T09:40:13.784039+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.11934v3","created_at":"2026-07-05T09:40:13.784039+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.11934","created_at":"2026-07-05T09:40:13.784039+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZR6CLWEYQA7O","created_at":"2026-07-05T09:40:13.784039+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZR6CLWEYQA7OYPP4","created_at":"2026-07-05T09:40:13.784039+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZR6CLWEY","created_at":"2026-07-05T09:40:13.784039+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.28929","citing_title":"Improving CFT Operators Using Machine Learning","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZR6CLWEYQA7OYPP4MQF5VMVU5X","json":"https://pith.science/pith/ZR6CLWEYQA7OYPP4MQF5VMVU5X.json","graph_json":"https://pith.science/api/pith-number/ZR6CLWEYQA7OYPP4MQF5VMVU5X/graph.json","events_json":"https://pith.science/api/pith-number/ZR6CLWEYQA7OYPP4MQF5VMVU5X/events.json","paper":"https://pith.science/paper/ZR6CLWEY"},"agent_actions":{"view_html":"https://pith.science/pith/ZR6CLWEYQA7OYPP4MQF5VMVU5X","download_json":"https://pith.science/pith/ZR6CLWEYQA7OYPP4MQF5VMVU5X.json","view_paper":"https://pith.science/paper/ZR6CLWEY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.11934&json=true","fetch_graph":"https://pith.science/api/pith-number/ZR6CLWEYQA7OYPP4MQF5VMVU5X/graph.json","fetch_events":"https://pith.science/api/pith-number/ZR6CLWEYQA7OYPP4MQF5VMVU5X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZR6CLWEYQA7OYPP4MQF5VMVU5X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZR6CLWEYQA7OYPP4MQF5VMVU5X/action/storage_attestation","attest_author":"https://pith.science/pith/ZR6CLWEYQA7OYPP4MQF5VMVU5X/action/author_attestation","sign_citation":"https://pith.science/pith/ZR6CLWEYQA7OYPP4MQF5VMVU5X/action/citation_signature","submit_replication":"https://pith.science/pith/ZR6CLWEYQA7OYPP4MQF5VMVU5X/action/replication_record"}},"created_at":"2026-07-05T09:40:13.784039+00:00","updated_at":"2026-07-05T09:40:13.784039+00:00"}