Symbolic execution is a static-analysis technique that has been used for applications such as test-input generation and change analysis. Symbolic execution’s path sensitivity makes scaling it difficult. Despite recent advances that reduce the number of paths to explore, the scalability problem remains. Moreover, there are applications that require the analysis of all paths in a program fragment...

This paper studies asymmetric power assignments for various network topologies under the kresilience criterion in static and dynamic geometric settings. We aim to minimize the total energy consumption, which is usually NP-hard for a desired link topology. We develop a general approximation framework for various topology control problems under the k-fault resilience criterion in the plane. We us...

Redox properties of yeast cytochrome c are estimated using molecular dynamics combined with a simple linear response approximation, as well as a hybrid continuum-molecular dynamics (COMD) approach. In both approaches, the free energy associated with an electrostatic perturbation (a redox electron) is separated into its relaxation and static (non-relaxation) components. The static component is c...

The goal of this paper is to relate the capacitance matrix formalism tight-binding approximation. By doing so, we open way use mathematical techniques and tools from condensed matter theory in numerical analysis metamaterials, particular for understanding their topological properties. We first study how formalism, both when material parameters are static modulated, can be posed a Hamiltonian fo...

Deep brain stimulation (DBS) is a surgical treatment for Parkinson’s Disease. Static models based on quasi-static approximation are common approaches for DBS modeling. While this simplification has been validated for bioelectric sources, its application to rapid stimulation pulses, which contain more high-frequency power, may not be appropriate, as DBS therapeutic results depend on stimulus par...

This paper describes a static load balancing scheme for partial differential equation solvers in a distributed computing environment. Though there has been much research on static load balancing for uniform processors, a distributed computing environment is a computationally more difficult target because it usually consists of a variety of processors. Our method considers both computing and com...

A power series approximation for the correlation kernel of time-dependent density-functional theory is presented. Using this approximation in the adiabatic-connection fluctuation-dissipation (ACFD) theorem leads to a new family of Kohn-Sham methods. The new methods yield reaction energies and barriers of unprecedented accuracy and enable a treatment of static (strong) correlation with an accura...

Abstract. Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as p...

In this paper, we study the performance of static solutions in two-stage adjustable robust packing linear optimization problem with uncertain constraint coefficients. Such problems arise in many important applications such as revenue management and resource allocation problems where demand requests have uncertain resource requirements. The goal is to find a two-stage solution that maximizes the...

We extend the notion of multi-resolution spatial data approximation of static datasets to spatio-temporal approximation of time-varying datasets. By including the temporal dimension, we allow a region of one time-step to approximate a congruent region at another time-step. Approximations of static datasets are generated by refining an approximation until a given error-bound is met. To approxima...