Neural networks (NNs) are widely used to create parametric models of S-parameters for
various components in electronic systems. The focus of deriving these models has so far …

Differential privacy mechanism design has traditionally been tailored for a scalar-valued
query function. Although many mechanisms such as the Laplace and Gaussian mechanisms …

Detection of entanglement through partial knowledge of the quantum state is a challenge to
implement efficiently. Here we propose a separability criterion for detecting bipartite …

This paper presents a perturbation analysis for the estimate of epipolar matrices using the 8-
Point Algorithm (8-PA). Our approach explores existing bounds for singular subspaces and …

We examine an 1880 theorem of Laguerre [50] concerning polynomials with all real roots
and a 1968 inequality of Sanmelson [117] for the maximum and minimum deviation from the …

High-quality passive devices are becoming increasingly important for the development of
mobile devices and telecommunications, but obtaining such devices through simulation and …

In this letter, we analyze the performance of a zero forcing (ZF) crosstalk canceler/precoder
in vectored VDSL2. We show that there is gap between the theoretical capacity and the …

We derive an increasing sequence of lower bounds for the spectral radius of a matrix with
real spectrum and progressively improved bounds for the largest singular value of a …

We study the few-shot learning (FSL) problem, where a model learns to recognize new
objects with extremely few labeled training data per category. Most of previous FSL …

This paper proposes a method to ensure that S-Parameters generated using neural network
(NN) models are physically consistent and can be safely used in subsequent time-domain …