2010/10/03 by Mahdi Cheraghchi, Cheraghchi, Mahdi
Biochemistry, Genetics and Molecular Biology · Medicine · #Advanced biosensing and bioanalysis techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #SARS-CoV-2 detection and testing
paper · pdf · doi:10.48550/arxiv.1010.0433
openalex publication_date 2010/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The rapid development of derandomization theory, which is a fundamental area in theoretical computer science, has recently led to many surprising applications outside its initial intention. We will review some recent such developments related to combinatorial group testing. In its most basic setting, the aim of group testing is to identify a set of "positive" individuals in a population of items by taking groups of items and asking whether there is a positive in each group. In particular, we will discuss explicit constructions of optimal or nearly-optimal group testing schemes using "randomness-conducting" functions. Among such developments are constructions of error-correcting group testing schemes using randomness extractors and condensers, as well as threshold group testing schemes from lossless condensers.