Principal component analysis (PCA) is used to reduce the dimensionalities of high-
dimensional datasets in a variety of research areas. For example, biological …

We give an overview of random matrix theory (RMT) with the objective of highlighting the
results and concepts that have a growing impact in the formulation and inference of …

This book is an invitation to the world of free probability theory. Free probability is a quite
young mathematical theory with many avatars. It owes its existence to the visions of one …

This paper considers the application of multiple-input multiple-output (MIMO) techniques to
nonorthogonal multiple access (NOMA) systems. A new design of precoding and detection …

Single-cell RNA sequencing (RNA-Seq) provides rich information about cell types and
states. However, it is difficult to capture rare dynamic processes, such as adult …

In their preface, the authors state that 'this is a book for absolute beginners'. Well, that
depends on the definition of a beginner! Here,(s) he should be thoroughly at home in …

Background As a recently discovered member of the DPANN superphylum,
Woesearchaeota account for a wide diversity of 16S rRNA gene sequences, but their …

This concise and self-contained introduction builds up the spectral theory of graphs from
scratch, with linear algebra and the theory of polynomials developed in the later parts. The …

Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep Neural
Networks (DNNs), including both production quality, pre-trained models such as AlexNet …

Free Probability Theory studies a special class of'noncommutative'random variables, which
appear in the context of operators on Hilbert spaces and in one of the large random …