{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EMSZBRWRDOC62A6VRRX5REHCKK","short_pith_number":"pith:EMSZBRWR","schema_version":"1.0","canonical_sha256":"232590c6d11b85ed03d58c6fd890e252824be05aa6d328c81b5c49d82087ab90","source":{"kind":"arxiv","id":"2506.05375","version":1},"attestation_state":"computed","paper":{"title":"State Estimation and Control of Dynamic Systems from High-Dimensional Image Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ashik E Rasul, Hyung-Jin Yoon","submitted_at":"2025-05-30T17:40:06Z","abstract_excerpt":"Accurate state estimation is critical for optimal policy design in dynamic systems. However, obtaining true system states is often impractical or infeasible, complicating the policy learning process. This paper introduces a novel neural architecture that integrates spatial feature extraction using convolutional neural networks (CNNs) and temporal modeling through gated recurrent units (GRUs), enabling effective state representation from sequences of images and corresponding actions. These learned state representations are used to train a reinforcement learning agent with a Deep Q-Network (DQN)"},"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":"2506.05375","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-30T17:40:06Z","cross_cats_sorted":[],"title_canon_sha256":"abec6dcf1a7458ca7010461825f03e9c9910e6d434a0429e6e5ba8128146b3d0","abstract_canon_sha256":"f9c0b7956790ffc7df90eac450bc89cbead2dcd75e3af466bea2339a261ead17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:01.469008Z","signature_b64":"T7ug6wAJ2mfoB9OdG077O48ThoEHzza559nKWRG9DTtaMMDIvR18abtKZa6LS39WwNUTx5DhDPJ+E5DUYUaLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"232590c6d11b85ed03d58c6fd890e252824be05aa6d328c81b5c49d82087ab90","last_reissued_at":"2026-07-05T11:17:01.468540Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:01.468540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"State Estimation and Control of Dynamic Systems from High-Dimensional Image Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ashik E Rasul, Hyung-Jin Yoon","submitted_at":"2025-05-30T17:40:06Z","abstract_excerpt":"Accurate state estimation is critical for optimal policy design in dynamic systems. However, obtaining true system states is often impractical or infeasible, complicating the policy learning process. This paper introduces a novel neural architecture that integrates spatial feature extraction using convolutional neural networks (CNNs) and temporal modeling through gated recurrent units (GRUs), enabling effective state representation from sequences of images and corresponding actions. These learned state representations are used to train a reinforcement learning agent with a Deep Q-Network (DQN)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05375","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.05375/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":"2506.05375","created_at":"2026-07-05T11:17:01.468596+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.05375v1","created_at":"2026-07-05T11:17:01.468596+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05375","created_at":"2026-07-05T11:17:01.468596+00:00"},{"alias_kind":"pith_short_12","alias_value":"EMSZBRWRDOC6","created_at":"2026-07-05T11:17:01.468596+00:00"},{"alias_kind":"pith_short_16","alias_value":"EMSZBRWRDOC62A6V","created_at":"2026-07-05T11:17:01.468596+00:00"},{"alias_kind":"pith_short_8","alias_value":"EMSZBRWR","created_at":"2026-07-05T11:17:01.468596+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EMSZBRWRDOC62A6VRRX5REHCKK","json":"https://pith.science/pith/EMSZBRWRDOC62A6VRRX5REHCKK.json","graph_json":"https://pith.science/api/pith-number/EMSZBRWRDOC62A6VRRX5REHCKK/graph.json","events_json":"https://pith.science/api/pith-number/EMSZBRWRDOC62A6VRRX5REHCKK/events.json","paper":"https://pith.science/paper/EMSZBRWR"},"agent_actions":{"view_html":"https://pith.science/pith/EMSZBRWRDOC62A6VRRX5REHCKK","download_json":"https://pith.science/pith/EMSZBRWRDOC62A6VRRX5REHCKK.json","view_paper":"https://pith.science/paper/EMSZBRWR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.05375&json=true","fetch_graph":"https://pith.science/api/pith-number/EMSZBRWRDOC62A6VRRX5REHCKK/graph.json","fetch_events":"https://pith.science/api/pith-number/EMSZBRWRDOC62A6VRRX5REHCKK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EMSZBRWRDOC62A6VRRX5REHCKK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EMSZBRWRDOC62A6VRRX5REHCKK/action/storage_attestation","attest_author":"https://pith.science/pith/EMSZBRWRDOC62A6VRRX5REHCKK/action/author_attestation","sign_citation":"https://pith.science/pith/EMSZBRWRDOC62A6VRRX5REHCKK/action/citation_signature","submit_replication":"https://pith.science/pith/EMSZBRWRDOC62A6VRRX5REHCKK/action/replication_record"}},"created_at":"2026-07-05T11:17:01.468596+00:00","updated_at":"2026-07-05T11:17:01.468596+00:00"}