{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5PXBVKSEINIDZHFG67EWWRX4KO","short_pith_number":"pith:5PXBVKSE","schema_version":"1.0","canonical_sha256":"ebee1aaa4443503c9ca6f7c96b46fc53844a882c4cd73e57fc2044b3110c648c","source":{"kind":"arxiv","id":"2402.01393","version":3},"attestation_state":"computed","paper":{"title":"ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Carmen Martin-Turrero, Manuel Breitenstein, Maxence Bouvier, Pietro Zanuttigh, Vincent Parret","submitted_at":"2024-02-02T13:17:19Z","abstract_excerpt":"We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input spar"},"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":"2402.01393","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-02T13:17:19Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"24bc3a8093fa28ac277efc974608ed1cfc4dda032f488be6dbba22f4127886d7","abstract_canon_sha256":"b34b67606e53a1212071e55db83cea13a25ed94789e6b1e5fb748caab8fd8d3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:50:09.485731Z","signature_b64":"zn78u2afmZhCutqYiu1GYQb5IzWmSZFf0qCpeDlsPY57R26R1PcXmXcA1bS5d76kPBm8Wct5d4wg8uRDqZJwDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebee1aaa4443503c9ca6f7c96b46fc53844a882c4cd73e57fc2044b3110c648c","last_reissued_at":"2026-07-05T08:50:09.485263Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:50:09.485263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Carmen Martin-Turrero, Manuel Breitenstein, Maxence Bouvier, Pietro Zanuttigh, Vincent Parret","submitted_at":"2024-02-02T13:17:19Z","abstract_excerpt":"We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input spar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.01393","kind":"arxiv","version":3},"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/2402.01393/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":"2402.01393","created_at":"2026-07-05T08:50:09.485320+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.01393v3","created_at":"2026-07-05T08:50:09.485320+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.01393","created_at":"2026-07-05T08:50:09.485320+00:00"},{"alias_kind":"pith_short_12","alias_value":"5PXBVKSEINID","created_at":"2026-07-05T08:50:09.485320+00:00"},{"alias_kind":"pith_short_16","alias_value":"5PXBVKSEINIDZHFG","created_at":"2026-07-05T08:50:09.485320+00:00"},{"alias_kind":"pith_short_8","alias_value":"5PXBVKSE","created_at":"2026-07-05T08:50:09.485320+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.11075","citing_title":"Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues","ref_index":203,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5PXBVKSEINIDZHFG67EWWRX4KO","json":"https://pith.science/pith/5PXBVKSEINIDZHFG67EWWRX4KO.json","graph_json":"https://pith.science/api/pith-number/5PXBVKSEINIDZHFG67EWWRX4KO/graph.json","events_json":"https://pith.science/api/pith-number/5PXBVKSEINIDZHFG67EWWRX4KO/events.json","paper":"https://pith.science/paper/5PXBVKSE"},"agent_actions":{"view_html":"https://pith.science/pith/5PXBVKSEINIDZHFG67EWWRX4KO","download_json":"https://pith.science/pith/5PXBVKSEINIDZHFG67EWWRX4KO.json","view_paper":"https://pith.science/paper/5PXBVKSE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.01393&json=true","fetch_graph":"https://pith.science/api/pith-number/5PXBVKSEINIDZHFG67EWWRX4KO/graph.json","fetch_events":"https://pith.science/api/pith-number/5PXBVKSEINIDZHFG67EWWRX4KO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5PXBVKSEINIDZHFG67EWWRX4KO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5PXBVKSEINIDZHFG67EWWRX4KO/action/storage_attestation","attest_author":"https://pith.science/pith/5PXBVKSEINIDZHFG67EWWRX4KO/action/author_attestation","sign_citation":"https://pith.science/pith/5PXBVKSEINIDZHFG67EWWRX4KO/action/citation_signature","submit_replication":"https://pith.science/pith/5PXBVKSEINIDZHFG67EWWRX4KO/action/replication_record"}},"created_at":"2026-07-05T08:50:09.485320+00:00","updated_at":"2026-07-05T08:50:09.485320+00:00"}