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NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions,(More)
—Voice over Internet Protocol (VoIP) over a wireless local area network (WLAN) is poised to become an important In-ternet application. However, two major technical problems that stand in the way are: 1) low VoIP capacity in WLAN and 2) unacceptable VoIP performance in the presence of coexisting traffic from other applications. With each VoIP stream(More)
<i>Map-matching</i> is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5(More)
Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference – sometimes prohibitively so in the case of(More)
In this article we develop a novel graph-based approach toward network forensics analysis. Central to our approach is the evidence graph model that facilitates evidence presentation and automated reasoning. Based on the evidence graph, we propose a hierarchical reasoning framework that consists of two levels. Local reasoning aims to infer the functional(More)
We propose a sample average approximation (SAA) method for stochastic programming problems involving an expected value constraint. Such problems arise, for example , in portfolio selection with constraints on conditional value-at-risk (CVaR). Our contributions include an analysis of the convergence rate and a statistical validation scheme for the proposed(More)
Human saccade is a dynamic process of information pursuit. Based on the principle of information maximization, we propose a computational model to simulate human sac-cadic scanpaths on natural images. The model integrates three related factors as driven forces to guide eye movements sequentially — reference sensory responses, fovea-periphery resolution(More)
In this paper, we propose a new computational model for visual saliency derived from the information maximization principle. The model is inspired by a few well acknowledged biological facts. To compute the saliency spots of an image, the model first extracts a number of sub-band feature maps using learned sparse codes. It adopts a fully-connected graph(More)