Group testing (GT) is the art of identifying binary signals and the marketplace for exchanging new ideas for related fields such as unique-element counting, compressed sensing, traitor tracing, and geno-typing. A GT scheme can be nonadaptive or adaptive; the latter is preferred when latency is ess of an issue. To construct adaptive GT schemes, a popular strategy is to spend the majority of tests in the first few rounds to gain as much information as possible, and uses later rounds to refine details. In this paper, we propose a transparent strategy called "isolate and then identify" (I@I). In the first few rounds, I@I divides the population into teams until every team contains at most one sick person. Then, in the last round, I@I identifies the sick person in each team. Performance-wise, I@I is the first GT scheme that achieves the optimal coefficient $1/$capacity$(Z)$ for the $k \log_2 (n/k)$ term in the number of tests when $Z$ is a generic channel corrupting the test outcomes. I@I follows a modular methodology whereby the isolating part and the identification part can be optimized separately.

翻译：群组检测（GT）是一门识别二元信号的艺术，也是交换相关领域新思想的平台，这些领域包括唯一元素计数、压缩感知、叛徒追踪和基因分型。GT方案可分为非自适应与自适应两类；当延迟问题至关重要时，后者更受青睐。构建自适应GT方案的常用策略是在前几轮检测中投入大量测试以获取尽可能多的信息，并在后续轮次中细化结果。本文提出了一种名为“隔离再识别”（I@I）的透明化策略。在前几轮检测中，I@I将总体划分为若干小组，直至每个小组至多包含一名感染者。随后，在最后一轮检测中，I@I识别每个小组中的感染者。在性能方面，I@I是首个在测试次数中实现$k \log_2 (n/k)$项最优系数$1/$容量$(Z)$的GT方案，其中$Z$为干扰检测结果的通用信道。I@I采用模块化方法，其隔离部分与识别部分可分别进行优化。