论文标题
基于压缩传感的SARS-COV-2池测试
Compressed sensing-based SARS-CoV-2 pool testing
论文作者
论文摘要
我们提出了一种基于压缩感应的测试方法，采用实用的测量设计以及一种用于检测受感染者的无调和噪声算法。压缩感应结果可用于可证明在可能的大量人中发现少数受感染的人。与经典组测试相比，这种方法有几个优点。首先，与适应性方法相比，它的执行速度可能更快，这在指数增长的大流行阶段至关重要。其次，由于测量值的非负性和适当的噪声模型，可以使用非负绝对偏差回归（NNLAD）算法来解决压缩感应问题。此无调调整程序需要与当前的TAR组测试方法相同数量的测试。从经验上讲，它的性能明显优于理论上的保证，因此与其他方法相比，高通量将测试数量减少到分数。此外，数值证据表明我们的方法可以纠正稀疏发生的错误。
We propose a compressed sensing-based testing approach with a practical measurement design and a tuning-free and noise-robust algorithm for detecting infected persons. Compressed sensing results can be used to provably detect a small number of infected persons among a possibly large number of people. There are several advantages of this method compared to classical group testing. Firstly, it is non-adaptive and thus possibly faster to perform than adaptive methods which is crucial in exponentially growing pandemic phases. Secondly, due to nonnegativity of measurements and an appropriate noise model, the compressed sensing problem can be solved with the non-negative least absolute deviation regression (NNLAD) algorithm. This convex tuning-free program requires the same number of tests as current state of the art group testing methods. Empirically it performs significantly better than theoretically guaranteed, and thus the high-throughput, reducing the number of tests to a fraction compared to other methods. Further, numerical evidence suggests that our method can correct sparsely occurring errors.
