Abstruct -A digital architecture which uses stochastic logic for simulating the behavior of Hopfield neural networks is described. This stochastic architecture provides mussiw paruf/e/ism (since stochastic logic is very space efficient), reprogrammability (since synaptic weights are stored in digital shift registers), large dynumic runge (by using either fixed or floating-point weights), unneul...

We consider the e cient solution of partial di↵erential equations for strongly elliptic operators with constant coe cients and stochastic Dirichlet data by the boundary integral equation method. The computation of the solution’s twopoint correlation is well understood if the two-point correlation of the Dirichlet data is known and su ciently smooth. Unfortunately, the problem becomes much more ...

Assume r̂ is not larger than . The goal of the present paper is to study meager, empty, pseudo-Germain monodromies. We show that every Euclid morphism equipped with a stochastic, contra-arithmetic, Hardy polytope is embedded. Is it possible to compute Déscartes subalegebras? So a useful survey of the subject can be found in [28].

This paper considers the problem of pricing options with early-exercise features whose payoff depends on several sources of uncertainty. We propose a stochastic grid method for estimating the upper and lower bound values of high-dimensional American options. The method is a hybrid of the least squares method of Longstaff and Schwartz (2001) [22], the stochastic mesh method of Broadie and Glasse...

Numerical verification of industrial codes, such as those developed at Électricit e de France (EDF) R&D, requires estimating the precision and the quality of computed results, which is even more challenging for codes running in HPC environments where billions of instructions are performed each second, usually using external libraries (e.g., MPI, BLACS, BLAS, LAPACK). In this context, one needs ...

A type of optimal investment problem can be regarded as an optimal stopping problem in the field of applied stochastic analysis. This study derives the existence conditions of the optimal stopping time when the stochastic process is a geometric Brownian motion or an arithmetic Brownian motion. The conditions concern the intrinsic value function and are natural extensions of the certainty case. ...

We present two novel culling tests for rasterization of simultaneous depth of field and motion blur. These tests efficiently reduce the set of xyuvt samples that need to be coverage tested within a screen space tile. The first test finds linear bounds in utand vt-space using a separating line algorithm. We also derive a hyperplane in xyuvtspace for each triangle edge, and all samples outside of...

Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network’s behavior during training. Our results s...

The researchers aimed to study the nonlinear fractional order model of malaria infection based on Caputo-Fabrizio derivative. homotopy analysis transform method (HATM) is applied floating-point arithmetic (FPA) and discrete stochastic (DSA). In FPA, show accuracy we use absolute error which depends exact solution a positive value ?. Because in real life problems do not have optimal ?, need intr...

Spiking neural P systems (in short, SN P systems) have been introduced as computing devices inspired by the structure and functioning of neural cells. The presence of unreliable components in SN P systems can be considered in many different aspects. In this paper we focus on two types of unreliability: the stochastic delays of the spiking rules and the stochastic loss of spikes. We propose the ...