稀疏识别用于自动大规模筛查：一种具有超快解码算法的约束感知框架
Sparse Identification for Automatic Large-Scale Screening: A Constraint-Aware Framework with Ultra Fast Decoding Algorithm
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中文总结 AI 辅助
针对大流行早期大规模筛查中现有分组检测方法在硬约束下计算复杂或精度不足的问题，本文提出LoSc框架，其基于逻辑运算的超快解码算法仅需O(klogn)次检测即可识别全部阳性，并纳入稀释与样本使用约束，兼具理论保证与高效可扩展性。
中文摘要 AI 辅助
在大流行病的早期阶段，通过大规模筛查识别少量感染者对于疫情控制至关重要，但在试剂和检测能力有限的情况下仍然具有挑战性。现有的分组检测方法要么计算复杂度高，要么识别准确率低。更糟糕的是，在样本使用约束和稀释效应（在实际应用中普遍存在）所导致的硬约束下，没有现有方法能为稀疏识别提供理论严谨的分析。在本文中，我们提出了逻辑筛查方法（LoSc），一种用于大规模筛查的超快、准确且具有理论基础的框架。LoSc引入了一种具有非常简单的选择策略的新型解码算法，仅需O(klogn)次合并检测即可实现对所有阳性个体的识别。该解码仅依赖于逻辑运算，使得硬件直接实现成为可能，并产生超快的计算实现。此外，LoSc明确地将稀释和样本使用约束纳入合并设计，并建立了理论保证以指导最优合并配置。大量模拟证实了其优越的有效性、效率和可扩展性。我们相信LoSc为自动大规模筛查提供了一种快速可靠的解决方案。
英文摘要
In the early stages of a pandemic, identification of a small number of infected individuals through large-scale screening is critical for pandemic control, yet remains challenging under limited reagents and testing capacity. Existing group testing methods suffer from either high computational complexity or low identification accuracy. Even worse, no available methods provide theoretically rigorous analysis for sparse identification with hard constraints caused by the sample usage constraint and the dilution effect existing ubiquitously in practical applications. In this article, we propose the Logic Screening method (LoSc), an ultra fast, accurate, and theoretically grounded framework for large-scale screening. LoSc introduces a novel decoding algorithm with a very simple selection strategy, achieving identification of all positives with only O(klogn) pooled tests. The decoding relies only on logical operations, enabling direct hardware implementation and yielding ultra fast computational implementation. Moreover, LoSc explicitly incorporates dilution and sample usage constraints into pooling designs, and establishes theoretical guarantees to guide optimal pooling configurations. Extensive simulations confirm the superior effectiveness, efficiency, and scalability. We believe LoSc offers a fast and reliable solution for automatic large-scale screening.
发表机构
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
