{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7HUQMZRHRBALSHDQXGALIVHVKZ","short_pith_number":"pith:7HUQMZRH","canonical_record":{"source":{"id":"2412.07080","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-10T00:42:54Z","cross_cats_sorted":["cs.AI","cs.MM"],"title_canon_sha256":"5f9c9772002ab6a0e8408ae29c393433c52581b7e479dda6815e17070e1a8b50","abstract_canon_sha256":"657761d463309060f0e2e429a354b5b7cedd1308759181e31cfb1454e728f194"},"schema_version":"1.0"},"canonical_sha256":"f9e90666278840b91c70b980b454f5566a477d995b17d13dd790f7ea45665818","source":{"kind":"arxiv","id":"2412.07080","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.07080","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"arxiv_version","alias_value":"2412.07080v1","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.07080","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"pith_short_12","alias_value":"7HUQMZRHRBAL","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"pith_short_16","alias_value":"7HUQMZRHRBALSHDQ","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"pith_short_8","alias_value":"7HUQMZRH","created_at":"2026-07-05T09:47:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7HUQMZRHRBALSHDQXGALIVHVKZ","target":"record","payload":{"canonical_record":{"source":{"id":"2412.07080","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-10T00:42:54Z","cross_cats_sorted":["cs.AI","cs.MM"],"title_canon_sha256":"5f9c9772002ab6a0e8408ae29c393433c52581b7e479dda6815e17070e1a8b50","abstract_canon_sha256":"657761d463309060f0e2e429a354b5b7cedd1308759181e31cfb1454e728f194"},"schema_version":"1.0"},"canonical_sha256":"f9e90666278840b91c70b980b454f5566a477d995b17d13dd790f7ea45665818","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:47:03.220285Z","signature_b64":"BeOl/KAgPiwk8eiVvF/QUEBKhccfRDiCOrXbJ7p5nSBQ+J3jgkQAoiKUcRe60qu24Tn2BuOvaQU0absmAt5XAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9e90666278840b91c70b980b454f5566a477d995b17d13dd790f7ea45665818","last_reissued_at":"2026-07-05T09:47:03.219867Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:47:03.219867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.07080","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":"2026-07-05T09:47:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7s/5u85c0nbanBxTf/QdomSAvmb9s1GzHIbFtT6WHugz26MaE+076GAuCYcquXiBqi9Eo8BqWuJtowL5VahsAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T21:38:24.830227Z"},"content_sha256":"c7564044ca733898e91d625ff7ef368f387b03e4f957f22789dcebaa1661e801","schema_version":"1.0","event_id":"sha256:c7564044ca733898e91d625ff7ef368f387b03e4f957f22789dcebaa1661e801"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7HUQMZRHRBALSHDQXGALIVHVKZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CV","authors_text":"Qiang Qu, Xiaoming Chen, Yiran Shen, Yuk Ying Chung","submitted_at":"2024-12-10T00:42:54Z","abstract_excerpt":"Event-stream representation is the first step for many computer vision tasks using event cameras. It converts the asynchronous event-streams into a formatted structure so that conventional machine learning models can be applied easily. However, most of the state-of-the-art event-stream representations are manually designed and the quality of these representations cannot be guaranteed due to the noisy nature of event-streams. In this paper, we introduce a data-driven approach aiming at enhancing the quality of event-stream representations. Our approach commences with the introduction of a new e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.07080","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/2412.07080/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":"2026-07-05T09:47:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iGfwFH1Htz47WlRVCGbmjwcQUyab2wSxmM00C5FQM5MgTEo5uIa6wdKoV6QTp4hyBTBGsVebLJb37g16MNrnBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T21:38:24.831389Z"},"content_sha256":"fbce01fc2c6c5572f7aa3df21babf41ec3b1a14c6d830b4be972c2cda217e0cb","schema_version":"1.0","event_id":"sha256:fbce01fc2c6c5572f7aa3df21babf41ec3b1a14c6d830b4be972c2cda217e0cb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7HUQMZRHRBALSHDQXGALIVHVKZ/bundle.json","state_url":"https://pith.science/pith/7HUQMZRHRBALSHDQXGALIVHVKZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7HUQMZRHRBALSHDQXGALIVHVKZ/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":"2026-08-13T21:38:24Z","links":{"resolver":"https://pith.science/pith/7HUQMZRHRBALSHDQXGALIVHVKZ","bundle":"https://pith.science/pith/7HUQMZRHRBALSHDQXGALIVHVKZ/bundle.json","state":"https://pith.science/pith/7HUQMZRHRBALSHDQXGALIVHVKZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7HUQMZRHRBALSHDQXGALIVHVKZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7HUQMZRHRBALSHDQXGALIVHVKZ","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":"657761d463309060f0e2e429a354b5b7cedd1308759181e31cfb1454e728f194","cross_cats_sorted":["cs.AI","cs.MM"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-10T00:42:54Z","title_canon_sha256":"5f9c9772002ab6a0e8408ae29c393433c52581b7e479dda6815e17070e1a8b50"},"schema_version":"1.0","source":{"id":"2412.07080","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.07080","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"arxiv_version","alias_value":"2412.07080v1","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.07080","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"pith_short_12","alias_value":"7HUQMZRHRBAL","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"pith_short_16","alias_value":"7HUQMZRHRBALSHDQ","created_at":"2026-07-05T09:47:03Z"},{"alias_kind":"pith_short_8","alias_value":"7HUQMZRH","created_at":"2026-07-05T09:47:03Z"}],"graph_snapshots":[{"event_id":"sha256:fbce01fc2c6c5572f7aa3df21babf41ec3b1a14c6d830b4be972c2cda217e0cb","target":"graph","created_at":"2026-07-05T09:47:03Z","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/2412.07080/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Event-stream representation is the first step for many computer vision tasks using event cameras. It converts the asynchronous event-streams into a formatted structure so that conventional machine learning models can be applied easily. However, most of the state-of-the-art event-stream representations are manually designed and the quality of these representations cannot be guaranteed due to the noisy nature of event-streams. In this paper, we introduce a data-driven approach aiming at enhancing the quality of event-stream representations. Our approach commences with the introduction of a new e","authors_text":"Qiang Qu, Xiaoming Chen, Yiran Shen, Yuk Ying Chung","cross_cats":["cs.AI","cs.MM"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.07080","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:c7564044ca733898e91d625ff7ef368f387b03e4f957f22789dcebaa1661e801","target":"record","created_at":"2026-07-05T09:47:03Z","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":"657761d463309060f0e2e429a354b5b7cedd1308759181e31cfb1454e728f194","cross_cats_sorted":["cs.AI","cs.MM"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-10T00:42:54Z","title_canon_sha256":"5f9c9772002ab6a0e8408ae29c393433c52581b7e479dda6815e17070e1a8b50"},"schema_version":"1.0","source":{"id":"2412.07080","kind":"arxiv","version":1}},"canonical_sha256":"f9e90666278840b91c70b980b454f5566a477d995b17d13dd790f7ea45665818","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f9e90666278840b91c70b980b454f5566a477d995b17d13dd790f7ea45665818","first_computed_at":"2026-07-05T09:47:03.219867Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:47:03.219867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BeOl/KAgPiwk8eiVvF/QUEBKhccfRDiCOrXbJ7p5nSBQ+J3jgkQAoiKUcRe60qu24Tn2BuOvaQU0absmAt5XAA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:47:03.220285Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.07080","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c7564044ca733898e91d625ff7ef368f387b03e4f957f22789dcebaa1661e801","sha256:fbce01fc2c6c5572f7aa3df21babf41ec3b1a14c6d830b4be972c2cda217e0cb"],"state_sha256":"ecc52cd4f404ef75cbb0ba8a2213c78f84c0d39a9b569b99901590e29d364de3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1+kiiIx6RFY05prqGkhCy6uvcVCsm53RuAjmaiW4bFXQDWoxRh/s2ooYhh2IdT+3c5RczlnqenKhOnH7MC/sAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T21:38:24.841596Z","bundle_sha256":"f384be0469dfd1a16218ee238b65e04c575cca1afe708047b4245a60a1f8c334"}}