The group testing problem concerns discovering a small number of defective items within a
large population by performing tests on pools of items. A test is positive if the pool contains …

In this paper, motivated by network inference and tomography applications, we study the
problem of compressive sensing for sparse signal vectors over graphs. In particular, we are …

In this paper, we propose algorithms that leverage a known community structure to make
group testing more efficient. We consider a population organized in disjoint communities …

Since the inception of the group testing problem in World War II, one of the prevailing
assumptions in the probabilistic variant of the problem has been that individuals in the …

In many signal processing applications, the aim is to reconstruct a signal that has a simple
representation with respect to a certain basis or frame. Fundamental elements of the basis …

An information theoretic perspective on group testing problems has recently been proposed
by Atia and Saligrama, in order to characterise the optimal number of tests. Their results hold …

We investigate the capability of localizing node failures in communication networks from
binary states (normal/failed) of end-to-end paths. Given a set of nodes of interest, uniquely …

Group testing is a technique that can reduce the number of tests needed to identify infected
members in a population, by pooling together multiple diagnostic samples. Despite the …

In the long-studied problem of combinatorial group testing, one is asked to detect a set of k
defective items out of a population of size n, using m≪ n disjunctive measurements. In the …

One of the main challenges in containing the Coronoavirus disease 2019 (COVID-19)
pandemic stems from the difficulty in carrying out efficient mass diagnosis over large …