{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:74ONWJLU4FWCOJB7AAXBDVNZPW","short_pith_number":"pith:74ONWJLU","schema_version":"1.0","canonical_sha256":"ff1cdb2574e16c27243f002e11d5b97d9e9c1d2aae0a2b03af49aba03b8b38f1","source":{"kind":"arxiv","id":"2404.06483","version":2},"attestation_state":"computed","paper":{"title":"RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bochao Zou, Huimin Ma, Xiaocheng Hu, Zizheng Guo","submitted_at":"2024-04-09T17:34:19Z","abstract_excerpt":"Remote photoplethysmography (rPPG) is a method for non-contact measurement of physiological signals from facial videos, holding great potential in various applications such as healthcare, affective computing, and anti-spoofing. Existing deep learning methods struggle to address two core issues of rPPG simultaneously: understanding the periodic pattern of rPPG among long contexts and addressing large spatiotemporal redundancy in video segments. These represent a trade-off between computational complexity and the ability to capture long-range dependencies. In this paper, we introduce RhythmMamba"},"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":"2404.06483","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-04-09T17:34:19Z","cross_cats_sorted":[],"title_canon_sha256":"eca0ad5737d53fec1cd2ad6b6acf57d8c060357aa982bcafff40e1dd68a22f26","abstract_canon_sha256":"9aeebc2b6ef63e8fe4fcbc262149159becc4d54ab197f10af4f5514830c7c28f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:18:36.094739Z","signature_b64":"8Uniho/9JgY13YU+LtdPSPQ2AJzGYp4Ly7u/VYNHYFIkqU3twaDJekKifg9mlQ5lsLUVTscKkj08ynOOlnxGDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff1cdb2574e16c27243f002e11d5b97d9e9c1d2aae0a2b03af49aba03b8b38f1","last_reissued_at":"2026-07-05T10:18:36.094219Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:18:36.094219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bochao Zou, Huimin Ma, Xiaocheng Hu, Zizheng Guo","submitted_at":"2024-04-09T17:34:19Z","abstract_excerpt":"Remote photoplethysmography (rPPG) is a method for non-contact measurement of physiological signals from facial videos, holding great potential in various applications such as healthcare, affective computing, and anti-spoofing. Existing deep learning methods struggle to address two core issues of rPPG simultaneously: understanding the periodic pattern of rPPG among long contexts and addressing large spatiotemporal redundancy in video segments. These represent a trade-off between computational complexity and the ability to capture long-range dependencies. In this paper, we introduce RhythmMamba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.06483","kind":"arxiv","version":2},"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/2404.06483/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":"2404.06483","created_at":"2026-07-05T10:18:36.094281+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.06483v2","created_at":"2026-07-05T10:18:36.094281+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.06483","created_at":"2026-07-05T10:18:36.094281+00:00"},{"alias_kind":"pith_short_12","alias_value":"74ONWJLU4FWC","created_at":"2026-07-05T10:18:36.094281+00:00"},{"alias_kind":"pith_short_16","alias_value":"74ONWJLU4FWCOJB7","created_at":"2026-07-05T10:18:36.094281+00:00"},{"alias_kind":"pith_short_8","alias_value":"74ONWJLU","created_at":"2026-07-05T10:18:36.094281+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2408.01129","citing_title":"A Survey of Mamba","ref_index":247,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/74ONWJLU4FWCOJB7AAXBDVNZPW","json":"https://pith.science/pith/74ONWJLU4FWCOJB7AAXBDVNZPW.json","graph_json":"https://pith.science/api/pith-number/74ONWJLU4FWCOJB7AAXBDVNZPW/graph.json","events_json":"https://pith.science/api/pith-number/74ONWJLU4FWCOJB7AAXBDVNZPW/events.json","paper":"https://pith.science/paper/74ONWJLU"},"agent_actions":{"view_html":"https://pith.science/pith/74ONWJLU4FWCOJB7AAXBDVNZPW","download_json":"https://pith.science/pith/74ONWJLU4FWCOJB7AAXBDVNZPW.json","view_paper":"https://pith.science/paper/74ONWJLU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.06483&json=true","fetch_graph":"https://pith.science/api/pith-number/74ONWJLU4FWCOJB7AAXBDVNZPW/graph.json","fetch_events":"https://pith.science/api/pith-number/74ONWJLU4FWCOJB7AAXBDVNZPW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/74ONWJLU4FWCOJB7AAXBDVNZPW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/74ONWJLU4FWCOJB7AAXBDVNZPW/action/storage_attestation","attest_author":"https://pith.science/pith/74ONWJLU4FWCOJB7AAXBDVNZPW/action/author_attestation","sign_citation":"https://pith.science/pith/74ONWJLU4FWCOJB7AAXBDVNZPW/action/citation_signature","submit_replication":"https://pith.science/pith/74ONWJLU4FWCOJB7AAXBDVNZPW/action/replication_record"}},"created_at":"2026-07-05T10:18:36.094281+00:00","updated_at":"2026-07-05T10:18:36.094281+00:00"}