this paper introduces a new method for accelerating current sluggish fem and improving memory demand in fem problems with high node resolution or bulky structures. like most of the numerical methods, fem results to a matrix equation which normally has huge dimension. breaking the main matrix equation into several smaller size matrices, the solving procedure can be accelerated. for implementing ...

Random finite sets (RFSs) has been a fruitful area of research in recent years, yielding new approximate filters such as the probability hypothesis density (PHD), cardinalised PHD (CPHD), and multiple target multiBernoulli (MeMBer). These new methods have largely been based on approximations that side-step the need for measurement-to-track association. Comparably, RFS methods that incorporate d...

In this paper, we show a general way to interpret the infrastructure of a global field of arbitrary unit rank. This interpretation generalizes the prior concepts of the giant-step operation and f -representations, and makes it possible to relate the infrastructure to the (Arakelov) divisor class group of the global field. In the case of global function fields, we present results that establish ...

We discuss the nonabelian world-volume action which governs the dynamics of N coincident Dp-branes. In this theory, the branes’ transverse displacements are described by matrix-valued scalar fields, and so this is a natural physical framework for the appearance of noncommutative geometry. One example is the dielectric effect by which Dp-branes may be polarized into a noncommutative geometry by ...

This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamic programming-based reinforcement learning method, with the TD() return estimation process, which is typically used in actor-critic learning, another well-known dynamic programming-based reinforcement learning method. The parameter is used to distribute credit throughout sequences of actions, leading ...