{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:CZFHO7A6CTLFWHCXLN4HY26NPV","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":"e3fd9b25fddbc4b5d66437f7cece8b893b14d571f8b99a60edbb5f2c4830dac5","cross_cats_sorted":["cs.LG","cs.MA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2023-10-29T15:07:08Z","title_canon_sha256":"adb53caaa570cd94c9ac03b447f88dd4eaa7fc005cbc8317686438877b770367"},"schema_version":"1.0","source":{"id":"2310.19039","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.19039","created_at":"2026-07-05T08:44:48Z"},{"alias_kind":"arxiv_version","alias_value":"2310.19039v2","created_at":"2026-07-05T08:44:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.19039","created_at":"2026-07-05T08:44:48Z"},{"alias_kind":"pith_short_12","alias_value":"CZFHO7A6CTLF","created_at":"2026-07-05T08:44:48Z"},{"alias_kind":"pith_short_16","alias_value":"CZFHO7A6CTLFWHCX","created_at":"2026-07-05T08:44:48Z"},{"alias_kind":"pith_short_8","alias_value":"CZFHO7A6","created_at":"2026-07-05T08:44:48Z"}],"graph_snapshots":[{"event_id":"sha256:25ce255da1ab50915cc36aad7a480f52fc29e0a3d24744d0e33250d3ee0ed41c","target":"graph","created_at":"2026-07-05T08:44:48Z","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/2310.19039/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM- the Desai-Zwanzig model in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and show","authors_text":"Dimitrios G. Giovanis, George A. Kevrekidis, Grigorios A. Pavliotis, Ioannis G. Kevrekidis, Nikolaos Evangelou","cross_cats":["cs.LG","cs.MA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2023-10-29T15:07:08Z","title":"Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig example"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.19039","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":"sha256:9d00b628864ea1d0c0e1f7adce04416e35c7d412c357519c3ae962214af67b6e","target":"record","created_at":"2026-07-05T08:44:48Z","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":"e3fd9b25fddbc4b5d66437f7cece8b893b14d571f8b99a60edbb5f2c4830dac5","cross_cats_sorted":["cs.LG","cs.MA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2023-10-29T15:07:08Z","title_canon_sha256":"adb53caaa570cd94c9ac03b447f88dd4eaa7fc005cbc8317686438877b770367"},"schema_version":"1.0","source":{"id":"2310.19039","kind":"arxiv","version":2}},"canonical_sha256":"164a777c1e14d65b1c575b787c6bcd7d5117469324e840ff7f6d706027ea642a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"164a777c1e14d65b1c575b787c6bcd7d5117469324e840ff7f6d706027ea642a","first_computed_at":"2026-07-05T08:44:48.215800Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:44:48.215800Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fSEMX30qYKQmGB/3P2x7P9I7B5vAlYF2S0qQYHpJN/HifJAFEPPSa+dcf3t023od0ePilF7OEBTKjy1CUL39DA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:44:48.216225Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.19039","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d00b628864ea1d0c0e1f7adce04416e35c7d412c357519c3ae962214af67b6e","sha256:25ce255da1ab50915cc36aad7a480f52fc29e0a3d24744d0e33250d3ee0ed41c"],"state_sha256":"67bb09d930a75758d06c08fb0059b1f2beea1a5d7cf9b39e3b21aa6e18a2de19"}