{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:HDXMUBNRHMGWH3FWN6Y5GBVXW2","short_pith_number":"pith:HDXMUBNR","canonical_record":{"source":{"id":"2211.12324","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-22T15:14:20Z","cross_cats_sorted":[],"title_canon_sha256":"e62a412b5cf818d12d201c0e539601b823a1448f3f025552c2a9596c95825c34","abstract_canon_sha256":"96372dac13c84e4ba258c1f1295f73054b07a85e0228e6fbc266eecf66cf9d75"},"schema_version":"1.0"},"canonical_sha256":"38eeca05b13b0d63ecb66fb1d306b7b6bdc504f0e9856e6d7d0c69cac3af5cd6","source":{"kind":"arxiv","id":"2211.12324","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.12324","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"arxiv_version","alias_value":"2211.12324v1","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.12324","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"pith_short_12","alias_value":"HDXMUBNRHMGW","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"pith_short_16","alias_value":"HDXMUBNRHMGWH3FW","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"pith_short_8","alias_value":"HDXMUBNR","created_at":"2026-07-05T05:18:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:HDXMUBNRHMGWH3FWN6Y5GBVXW2","target":"record","payload":{"canonical_record":{"source":{"id":"2211.12324","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-22T15:14:20Z","cross_cats_sorted":[],"title_canon_sha256":"e62a412b5cf818d12d201c0e539601b823a1448f3f025552c2a9596c95825c34","abstract_canon_sha256":"96372dac13c84e4ba258c1f1295f73054b07a85e0228e6fbc266eecf66cf9d75"},"schema_version":"1.0"},"canonical_sha256":"38eeca05b13b0d63ecb66fb1d306b7b6bdc504f0e9856e6d7d0c69cac3af5cd6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:18:15.784911Z","signature_b64":"F7dcRHC8jUnh+YrQVwWAqBCGk3jut5BkxQHy6skFnnto366bhOmws2BeKCr8392l8IjRzvXC+keawArbDoS0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38eeca05b13b0d63ecb66fb1d306b7b6bdc504f0e9856e6d7d0c69cac3af5cd6","last_reissued_at":"2026-07-05T05:18:15.784447Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:18:15.784447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.12324","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":"2026-07-05T05:18:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fG+M3HdyQBRKCLqdJ4hreNbLGcNqHjeRXyCayiA++fAHWxDdo2peT4w+f0USNt3T26JgZT+trWYpZU0NVSvkCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T09:19:57.316924Z"},"content_sha256":"6bfe1ec8202e6c21f31ac89624b1d10ef25683cb77641744dd49160f0684b6e5","schema_version":"1.0","event_id":"sha256:6bfe1ec8202e6c21f31ac89624b1d10ef25683cb77641744dd49160f0684b6e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:HDXMUBNRHMGWH3FWN6Y5GBVXW2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daniel Gehrig, Davide Scaramuzza","submitted_at":"2022-11-22T15:14:20Z","abstract_excerpt":"State-of-the-art machine-learning methods for event cameras treat events as dense representations and process them with conventional deep neural networks. Thus, they fail to maintain the sparsity and asynchronous nature of event data, thereby imposing significant computation and latency constraints on downstream systems. A recent line of work tackles this issue by modeling events as spatiotemporally evolving graphs that can be efficiently and asynchronously processed using graph neural networks. These works showed impressive computation reductions, yet their accuracy is still limited by the sm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.12324","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/2211.12324/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":"2026-07-05T05:18:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nS2uk9nUKvd4vcQBOuEPQ3ltyjxtbJ39jWeaEqr9EP1WSaC3qmVOQuWfBEx9DZrJkgONOBZ20PmAP2a80aCbAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T09:19:57.317347Z"},"content_sha256":"83ebb46b639653cd56156ebd22c668edbfecd3393c8118772bfd10d3e3dc8df0","schema_version":"1.0","event_id":"sha256:83ebb46b639653cd56156ebd22c668edbfecd3393c8118772bfd10d3e3dc8df0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HDXMUBNRHMGWH3FWN6Y5GBVXW2/bundle.json","state_url":"https://pith.science/pith/HDXMUBNRHMGWH3FWN6Y5GBVXW2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HDXMUBNRHMGWH3FWN6Y5GBVXW2/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":"2026-07-05T09:19:57Z","links":{"resolver":"https://pith.science/pith/HDXMUBNRHMGWH3FWN6Y5GBVXW2","bundle":"https://pith.science/pith/HDXMUBNRHMGWH3FWN6Y5GBVXW2/bundle.json","state":"https://pith.science/pith/HDXMUBNRHMGWH3FWN6Y5GBVXW2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HDXMUBNRHMGWH3FWN6Y5GBVXW2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:HDXMUBNRHMGWH3FWN6Y5GBVXW2","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":"96372dac13c84e4ba258c1f1295f73054b07a85e0228e6fbc266eecf66cf9d75","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-22T15:14:20Z","title_canon_sha256":"e62a412b5cf818d12d201c0e539601b823a1448f3f025552c2a9596c95825c34"},"schema_version":"1.0","source":{"id":"2211.12324","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.12324","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"arxiv_version","alias_value":"2211.12324v1","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.12324","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"pith_short_12","alias_value":"HDXMUBNRHMGW","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"pith_short_16","alias_value":"HDXMUBNRHMGWH3FW","created_at":"2026-07-05T05:18:15Z"},{"alias_kind":"pith_short_8","alias_value":"HDXMUBNR","created_at":"2026-07-05T05:18:15Z"}],"graph_snapshots":[{"event_id":"sha256:83ebb46b639653cd56156ebd22c668edbfecd3393c8118772bfd10d3e3dc8df0","target":"graph","created_at":"2026-07-05T05:18:15Z","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/2211.12324/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"State-of-the-art machine-learning methods for event cameras treat events as dense representations and process them with conventional deep neural networks. Thus, they fail to maintain the sparsity and asynchronous nature of event data, thereby imposing significant computation and latency constraints on downstream systems. A recent line of work tackles this issue by modeling events as spatiotemporally evolving graphs that can be efficiently and asynchronously processed using graph neural networks. These works showed impressive computation reductions, yet their accuracy is still limited by the sm","authors_text":"Daniel Gehrig, Davide Scaramuzza","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-22T15:14:20Z","title":"Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.12324","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":"sha256:6bfe1ec8202e6c21f31ac89624b1d10ef25683cb77641744dd49160f0684b6e5","target":"record","created_at":"2026-07-05T05:18:15Z","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":"96372dac13c84e4ba258c1f1295f73054b07a85e0228e6fbc266eecf66cf9d75","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-22T15:14:20Z","title_canon_sha256":"e62a412b5cf818d12d201c0e539601b823a1448f3f025552c2a9596c95825c34"},"schema_version":"1.0","source":{"id":"2211.12324","kind":"arxiv","version":1}},"canonical_sha256":"38eeca05b13b0d63ecb66fb1d306b7b6bdc504f0e9856e6d7d0c69cac3af5cd6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38eeca05b13b0d63ecb66fb1d306b7b6bdc504f0e9856e6d7d0c69cac3af5cd6","first_computed_at":"2026-07-05T05:18:15.784447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:18:15.784447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F7dcRHC8jUnh+YrQVwWAqBCGk3jut5BkxQHy6skFnnto366bhOmws2BeKCr8392l8IjRzvXC+keawArbDoS0BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:18:15.784911Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.12324","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6bfe1ec8202e6c21f31ac89624b1d10ef25683cb77641744dd49160f0684b6e5","sha256:83ebb46b639653cd56156ebd22c668edbfecd3393c8118772bfd10d3e3dc8df0"],"state_sha256":"d89165ed5c0b25379925a2dda5027c7536daf1ffba9e117178e3c462d9604402"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o/kSht1/f/pGEKSmGu4TKyEPmRFqW1jqKJwSf7m7bLnXk17e1uaxxojEBwVDXUlVTCKBXjff/D2FVHJi2FQmAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T09:19:57.319473Z","bundle_sha256":"9461bc237fde813f3d1a4aba6ce6809c281c79085d45168465f685951dd2b8de"}}