{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VXZ6YZTFBKCPGRRZVGF32BHIDY","short_pith_number":"pith:VXZ6YZTF","schema_version":"1.0","canonical_sha256":"adf3ec66650a84f34639a98bbd04e81e01b051df0b6ef283146a586556e51700","source":{"kind":"arxiv","id":"2410.24028","version":1},"attestation_state":"computed","paper":{"title":"AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.LG","authors_text":"Bin Guo, Fenmin Wu, Hongkai Wen, Kehao Zhu, Lehao Wang, Sicong Liu, XiangRui Xu, Xiangyu Liu, Xiaochen LI, Zhiwen Yu","submitted_at":"2024-10-31T15:28:22Z","abstract_excerpt":"The rise of mobile devices equipped with numerous sensors, such as LiDAR and cameras, has spurred the adoption of multi-modal deep intelligence for distributed sensing tasks, such as smart cabins and driving assistance. However, the arrival times of mobile sensory data vary due to modality size and network dynamics, which can lead to delays (if waiting for slower data) or accuracy decline (if inference proceeds without waiting). Moreover, the diversity and dynamic nature of mobile systems exacerbate this challenge. In response, we present a shift to \\textit{opportunistic} inference for asynchr"},"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":"2410.24028","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-31T15:28:22Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"d3d14894c8dce8d6ba2285bc958cb13a7cfdc80bda176f302b0fd699dc8d27e3","abstract_canon_sha256":"fc897090659e82863c546e5cee50239f99a37227bf014c1d5de7e23139e3557d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:29:18.187271Z","signature_b64":"bGqk5RQjBJbGYM7tsGTsZH4dX8KCdbMOk6bGr5OsefHi2xDa3s232FOUraLgtzpe/vwMw/FsTSug0KLLprHYBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"adf3ec66650a84f34639a98bbd04e81e01b051df0b6ef283146a586556e51700","last_reissued_at":"2026-07-05T09:29:18.186786Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:29:18.186786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.LG","authors_text":"Bin Guo, Fenmin Wu, Hongkai Wen, Kehao Zhu, Lehao Wang, Sicong Liu, XiangRui Xu, Xiangyu Liu, Xiaochen LI, Zhiwen Yu","submitted_at":"2024-10-31T15:28:22Z","abstract_excerpt":"The rise of mobile devices equipped with numerous sensors, such as LiDAR and cameras, has spurred the adoption of multi-modal deep intelligence for distributed sensing tasks, such as smart cabins and driving assistance. However, the arrival times of mobile sensory data vary due to modality size and network dynamics, which can lead to delays (if waiting for slower data) or accuracy decline (if inference proceeds without waiting). Moreover, the diversity and dynamic nature of mobile systems exacerbate this challenge. In response, we present a shift to \\textit{opportunistic} inference for asynchr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.24028","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/2410.24028/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":"2410.24028","created_at":"2026-07-05T09:29:18.186843+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.24028v1","created_at":"2026-07-05T09:29:18.186843+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.24028","created_at":"2026-07-05T09:29:18.186843+00:00"},{"alias_kind":"pith_short_12","alias_value":"VXZ6YZTFBKCP","created_at":"2026-07-05T09:29:18.186843+00:00"},{"alias_kind":"pith_short_16","alias_value":"VXZ6YZTFBKCPGRRZ","created_at":"2026-07-05T09:29:18.186843+00:00"},{"alias_kind":"pith_short_8","alias_value":"VXZ6YZTF","created_at":"2026-07-05T09:29:18.186843+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VXZ6YZTFBKCPGRRZVGF32BHIDY","json":"https://pith.science/pith/VXZ6YZTFBKCPGRRZVGF32BHIDY.json","graph_json":"https://pith.science/api/pith-number/VXZ6YZTFBKCPGRRZVGF32BHIDY/graph.json","events_json":"https://pith.science/api/pith-number/VXZ6YZTFBKCPGRRZVGF32BHIDY/events.json","paper":"https://pith.science/paper/VXZ6YZTF"},"agent_actions":{"view_html":"https://pith.science/pith/VXZ6YZTFBKCPGRRZVGF32BHIDY","download_json":"https://pith.science/pith/VXZ6YZTFBKCPGRRZVGF32BHIDY.json","view_paper":"https://pith.science/paper/VXZ6YZTF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.24028&json=true","fetch_graph":"https://pith.science/api/pith-number/VXZ6YZTFBKCPGRRZVGF32BHIDY/graph.json","fetch_events":"https://pith.science/api/pith-number/VXZ6YZTFBKCPGRRZVGF32BHIDY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VXZ6YZTFBKCPGRRZVGF32BHIDY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VXZ6YZTFBKCPGRRZVGF32BHIDY/action/storage_attestation","attest_author":"https://pith.science/pith/VXZ6YZTFBKCPGRRZVGF32BHIDY/action/author_attestation","sign_citation":"https://pith.science/pith/VXZ6YZTFBKCPGRRZVGF32BHIDY/action/citation_signature","submit_replication":"https://pith.science/pith/VXZ6YZTFBKCPGRRZVGF32BHIDY/action/replication_record"}},"created_at":"2026-07-05T09:29:18.186843+00:00","updated_at":"2026-07-05T09:29:18.186843+00:00"}