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Taichung, Taiwan

Providence University is a Catholic co-educational institution in Shalu District, Taichung City, Taiwan. Wikipedia.


Sheu T.F.,Providence University
BMC bioinformatics | Year: 2014

Signatures are short sequences that are unique and not similar to any other sequence in a database that can be used as the basis to identify different species. Even though several signature discovery algorithms have been proposed in the past, these algorithms require the entirety of databases to be loaded in the memory, thus restricting the amount of data that they can process. It makes those algorithms unable to process databases with large amounts of data. Also, those algorithms use sequential models and have slower discovery speeds, meaning that the efficiency can be improved. In this research, we are debuting the utilization of a divide-and-conquer strategy in signature discovery and have proposed a parallel signature discovery algorithm on a computer cluster. The algorithm applies the divide-and-conquer strategy to solve the problem posed to the existing algorithms where they are unable to process large databases and uses a parallel computing mechanism to effectively improve the efficiency of signature discovery. Even when run with just the memory of regular personal computers, the algorithm can still process large databases such as the human whole-genome EST database which were previously unable to be processed by the existing algorithms. The algorithm proposed in this research is not limited by the amount of usable memory and can rapidly find signatures in large databases, making it useful in applications such as Next Generation Sequencing and other large database analysis and processing. The implementation of the proposed algorithm is available at http://www.cs.pu.edu.tw/~fang/DDCSDPrograms/DDCSD.htm. Source


Liu J.-S.,Providence University
International Journal of Innovative Computing, Information and Control | Year: 2012

In this paper, we study the problem of joint routing, scheduling and stream control to maximize the network life, and at the same time, to satisfy end-to-end (ETE) traffic demands in wireless sensor networks (WSNs) with virtual multiple input multiple output (VMIMO) transmission. For this problem, we introduce a cross-layer formulation that can incorporate power and rate adaptation, and seamlessly integrate a SINR constraint at the physical layer to generate feasible sets of links for scheduling at the MAC layer and routing at the network layer. Specifically, we propose a column generation (CG) approach to exactly accommodate the most realistic scenario where power and rate are both discrete. In addition, we develop a fully distributed algorithm using the Lagrangian duality and a subgradient method to allow each node to independently obtain its own lifetime and scheduling parameters for the cross-layer optimization. Finally, we present computational results on different network topologies and discuss the insight to be gained when adopting different parameters on the control method. © 2012 ICIC International. Source


Luh L.-T.,Providence University
Engineering Analysis with Boundary Elements | Year: 2013

This is a continuation of our former study, Luh [1], of the shape parameter β contained in Gaussian e-β[x]2, x∈Rn. Instead of using the error bound presented by Madych and Nelson [2], here we adopt an improved error bound constructed by Luh to evaluate the influence of β on error estimates. This results in a new set of criteria for the optimal choice of β and much sharper error estimates for Gaussian interpolation. What is important is that the notorious ill-conditioning of Gaussian interpolation can be greatly relieved because in this approach the fill distance need not be very small. © 2013 Elsevier Ltd. All rights reserved. Source


Luh L.-T.,Providence University
Engineering Analysis with Boundary Elements | Year: 2012

This is a continuation of our study about shape parameter, based on an approach very different from that of Luh [1,2]. Here we adopt an error bound of convergence order O(dω1 /d) as d→0, where 0<ω<1 is a constant and d denotes essentially the fill-distance. The constant ω is much smaller than the one appears in Luh [1,2] where the error bound is O(ω1 /d) only. Moreover, the constant ω here only mildly depends on the dimension n. It means that for high-dimensional problems the criteria of choosing the shape parameter presented in this paper are much better than those of Luh [1,2]. The drawback is that the distribution of data points must be slightly controlled. © 2012 Elsevier Ltd. All rights reserved. Source


Luh L.-T.,Providence University
Computers and Mathematics with Applications | Year: 2012

This is the fifth of our series of works about the shape parameter. We now explore the parameter β contained in the famous Gaussian function e- β|x|2,x∈ Rn. In the theory of radial basis functions (RBFs), the Gaussian is frequently used in virtue of its good error bound and numerical tractability. However, the optimal choice of β has been unknown. People conversant with RBFs know that β is very influential, but do not have a reliable criterion of its choice. The purpose of this paper is to uncover its mystery. In particular, we have greatly improved the result of Madych (1992) in [15], and we present a concrete function of β which shows the influence of β in the error estimate of Gaussian interpolation and with which the optimal β can always be found. © 2011 Elsevier Ltd. All rights reserved. Source

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