{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:6PBI5FWLIYTMSR4ETQOCSEVTSK","short_pith_number":"pith:6PBI5FWL","canonical_record":{"source":{"id":"2508.00141","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-31T20:00:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7942e3661c345de490498c9dc96d966d2c3ba9a641e78c1be4a48d5cb13dff57","abstract_canon_sha256":"f2dcf65a3c2377e6d88d47a9463efd5ed4a795758d5b3619660a0455d8fb7dec"},"schema_version":"1.0"},"canonical_sha256":"f3c28e96cb4626c947849c1c2912b392b72e8c5be7624d384a022ababe8de142","source":{"kind":"arxiv","id":"2508.00141","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.00141","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"arxiv_version","alias_value":"2508.00141v1","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.00141","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"pith_short_12","alias_value":"6PBI5FWLIYTM","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"pith_short_16","alias_value":"6PBI5FWLIYTMSR4E","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"pith_short_8","alias_value":"6PBI5FWL","created_at":"2026-07-05T11:46:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:6PBI5FWLIYTMSR4ETQOCSEVTSK","target":"record","payload":{"canonical_record":{"source":{"id":"2508.00141","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-31T20:00:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7942e3661c345de490498c9dc96d966d2c3ba9a641e78c1be4a48d5cb13dff57","abstract_canon_sha256":"f2dcf65a3c2377e6d88d47a9463efd5ed4a795758d5b3619660a0455d8fb7dec"},"schema_version":"1.0"},"canonical_sha256":"f3c28e96cb4626c947849c1c2912b392b72e8c5be7624d384a022ababe8de142","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:46:49.537527Z","signature_b64":"BqqcRhq17EYLqyKWbTZPnVoUvDNP8KEoBou53+VyM2ph1lKM12Eym9K/DrGdQp+k6VHS4XkHCDtvyxhRN/UMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f3c28e96cb4626c947849c1c2912b392b72e8c5be7624d384a022ababe8de142","last_reissued_at":"2026-07-05T11:46:49.536986Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:46:49.536986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.00141","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":"2026-07-05T11:46:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YhjlcoXMFlt8zJvnsd8rtOJM/CIHf6W8Vi3OFdt+7rW3Rs9jOmlwA+pD9r9fZwyiILQr1vsDLky/njU4BEFxCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:49:02.477443Z"},"content_sha256":"0fe5950ac33c070d78d7ded21acfa70da80a5373f4d202bd178de7f05094d5c8","schema_version":"1.0","event_id":"sha256:0fe5950ac33c070d78d7ded21acfa70da80a5373f4d202bd178de7f05094d5c8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:6PBI5FWLIYTMSR4ETQOCSEVTSK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ben Beck, Debjit Bhowmick, Meead Saberi, Mohit Gupta, Rhys Newbury, Shirui Pan","submitted_at":"2025-07-31T20:00:35Z","abstract_excerpt":"Accurate link-level bicycling volume estimation is essential for sustainable urban transportation planning. However, many cities face significant challenges of high data sparsity due to limited bicycling count sensor coverage. To address this issue, we propose INSPIRE-GNN, a novel Reinforcement Learning (RL)-boosted hybrid Graph Neural Network (GNN) framework designed to optimize sensor placement and improve link-level bicycling volume estimation in data-sparse environments. INSPIRE-GNN integrates Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) with a Deep Q-Network (DQN)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.00141","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/2508.00141/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:46:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HKgrVoRxs4nNz2Gz20s8FDebkXilSa5oSRE5nssPumWrst9bs9QGwZc4B5O/pbI75P86xmAwlpFFdXdwt6WiBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:49:02.477936Z"},"content_sha256":"57f89d34ec2c17aa3ccff58df34422a4a30279e0cbae8a4e445e3f5738355899","schema_version":"1.0","event_id":"sha256:57f89d34ec2c17aa3ccff58df34422a4a30279e0cbae8a4e445e3f5738355899"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6PBI5FWLIYTMSR4ETQOCSEVTSK/bundle.json","state_url":"https://pith.science/pith/6PBI5FWLIYTMSR4ETQOCSEVTSK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6PBI5FWLIYTMSR4ETQOCSEVTSK/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":"2026-08-08T11:49:02Z","links":{"resolver":"https://pith.science/pith/6PBI5FWLIYTMSR4ETQOCSEVTSK","bundle":"https://pith.science/pith/6PBI5FWLIYTMSR4ETQOCSEVTSK/bundle.json","state":"https://pith.science/pith/6PBI5FWLIYTMSR4ETQOCSEVTSK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6PBI5FWLIYTMSR4ETQOCSEVTSK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6PBI5FWLIYTMSR4ETQOCSEVTSK","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":"f2dcf65a3c2377e6d88d47a9463efd5ed4a795758d5b3619660a0455d8fb7dec","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-31T20:00:35Z","title_canon_sha256":"7942e3661c345de490498c9dc96d966d2c3ba9a641e78c1be4a48d5cb13dff57"},"schema_version":"1.0","source":{"id":"2508.00141","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.00141","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"arxiv_version","alias_value":"2508.00141v1","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.00141","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"pith_short_12","alias_value":"6PBI5FWLIYTM","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"pith_short_16","alias_value":"6PBI5FWLIYTMSR4E","created_at":"2026-07-05T11:46:49Z"},{"alias_kind":"pith_short_8","alias_value":"6PBI5FWL","created_at":"2026-07-05T11:46:49Z"}],"graph_snapshots":[{"event_id":"sha256:57f89d34ec2c17aa3ccff58df34422a4a30279e0cbae8a4e445e3f5738355899","target":"graph","created_at":"2026-07-05T11:46:49Z","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/2508.00141/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate link-level bicycling volume estimation is essential for sustainable urban transportation planning. However, many cities face significant challenges of high data sparsity due to limited bicycling count sensor coverage. To address this issue, we propose INSPIRE-GNN, a novel Reinforcement Learning (RL)-boosted hybrid Graph Neural Network (GNN) framework designed to optimize sensor placement and improve link-level bicycling volume estimation in data-sparse environments. INSPIRE-GNN integrates Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) with a Deep Q-Network (DQN)","authors_text":"Ben Beck, Debjit Bhowmick, Meead Saberi, Mohit Gupta, Rhys Newbury, Shirui Pan","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-31T20:00:35Z","title":"INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.00141","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":"sha256:0fe5950ac33c070d78d7ded21acfa70da80a5373f4d202bd178de7f05094d5c8","target":"record","created_at":"2026-07-05T11:46:49Z","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":"f2dcf65a3c2377e6d88d47a9463efd5ed4a795758d5b3619660a0455d8fb7dec","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-31T20:00:35Z","title_canon_sha256":"7942e3661c345de490498c9dc96d966d2c3ba9a641e78c1be4a48d5cb13dff57"},"schema_version":"1.0","source":{"id":"2508.00141","kind":"arxiv","version":1}},"canonical_sha256":"f3c28e96cb4626c947849c1c2912b392b72e8c5be7624d384a022ababe8de142","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f3c28e96cb4626c947849c1c2912b392b72e8c5be7624d384a022ababe8de142","first_computed_at":"2026-07-05T11:46:49.536986Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:46:49.536986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BqqcRhq17EYLqyKWbTZPnVoUvDNP8KEoBou53+VyM2ph1lKM12Eym9K/DrGdQp+k6VHS4XkHCDtvyxhRN/UMAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:46:49.537527Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.00141","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0fe5950ac33c070d78d7ded21acfa70da80a5373f4d202bd178de7f05094d5c8","sha256:57f89d34ec2c17aa3ccff58df34422a4a30279e0cbae8a4e445e3f5738355899"],"state_sha256":"677ff4b92eafc070b81b14008dec8bccd3dc43b0fa5c08b797296d483d09e3c7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IO+/3z34M7ak2N7NFIlum9rIlkP+6c4tIkUOIR1mABqG2LKvE6WFE4w4X5cc75LE3kaN4HSquCf0q6EIHFijBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T11:49:02.481864Z","bundle_sha256":"d85999ef21c7470d3a634a40dcfffad5114d61ec173e4ef0389fcb9842f56afd"}}