The Theory of Bottleneck Structures is a recently developed framework for studying the performance of data networks. It describes how…

Large data sets can contain patterns at multiple scales (spatial, temporal, etc.). In practice, it is useful for data exploration…

In the post-Moore’s Law era, the quest for exascale computing has resulted in diverse hardware architecture trends, including novel custom…

CANDECOMP/PARAFAC (CP) tensor decomposition is a popular unsupervised machine learning method with numerous applications. This process involves modeling a high–dimensional,…

As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms…

Cryptographic techniques have the potential to enable distrusting parties to collaborate in fundamentally new ways, but their practical implementation poses…

In this paper, we introduce the Theory of Bottleneck Ordering, a mathematical framework that reveals the bottleneck structure of data…

We present a computational technique for low-frequency electromagnetic imaging in inhomogeneous media that provides superior three-dimensional resolution over existing techniques.…

We propose a method for generating uniform samples among a domain of integer points defined by a polyhedron in a…