We have proposed and fabricated dual self-growing polymer microtips, acting as parallel Fabry-Perot interferometers (FPIs), at the end-facet of a multicore fiber (MCF). With the significantly diffe…
We investigate the effectiveness of convex relaxation and nonconvex optimization in solving bilinear systems of equations under two different designs (i.e., a sort of random Fourier design and Gaus…
We propose, analyze and demonstrate a side-coupled Fabry-Perot (F-P) resonator filter composed of two dissimilar waveguides with π phase-shifted Bragg grating. The resonance circuit is formed by t…
Digital-to-analog converters (DACs) are widely used in bandwidth-limited visible-light communication (VLC) systems for high spectral-efficiency multi-carrier signal generation. To reduce the implem…
Policy optimization, which learns the policy of interest by maximizing the value function via large-scale optimization techniques, lies at the heart of modern reinforcement learning (RL). In additi…
We investigate how historical price information (e.g., accessed through price trackers) influences consumers’ purchase decisions and thus affects a firm’s dynamic pricing strategy. We first sho…
We investigate how historical price information (e.g., accessed through price trackers) influences consumers’ purchase decisions and thus affects a firm’s dynamic pricing strategy. We first sho…
Natural policy gradient (NPG) methods are among the most widely used policy optimization algorithms in contemporary reinforcement learning. This class of methods is often applied in conjunction wit…
Machine learning models have been shown to be vulnerable to adversarial examples. While most of the existing methods for adversarial attack and defense work on the 2D image domain, a few recent att…