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Spam e-mail inundates inboxes. Little is known about consumer responses to spam e-mail advertising computer software products. We conducted a study among 200 college students to determine variables associated both with opening/reading spam e mail about computer software products and with purchasing these items. With regard to opening/reading, we found that increasing age (OR:1.22, 95% CI:1.02, 1.47; p=0.03), previously responding to fraudulent e-mail (OR:2.91, 95% CI:1.31, 6.46; p=0.01), and wanting to learn more information online about computer software (OR:1.72, 95% CI:1.12, 2.64; p=0.01) had significant associations. With regard to purchasing, we found that wanting to learn more information online about computer software had a significant association (OR:2.47, 95% CI:1.32, 4.60; p=0.01) and previously responding to fraudulent e-mail approached significance (OR:2.55, 95% CI:0.99, 6.58; p=0.052). Ethical e-mail advertisers would benefit when advertising computer software products to include in the e-mail relevant information about learning more information online about computer software. This can encourage the recipient to click-through and purchase the advertised computer software product. [PUBLICATION ABSTRACT]

In this paper we discuss mathematical programming methods for insurance companies, mutual fund managers, and banks to match cash flow and liabilities. We focus on mortgage-backed securities, and methods for using them for asset allocation. Given the recent and ongoing sub-prime mortgage debacle, it is imperative that the conscientious and conservative investor use robust mathematical models to decide whether to hold or sell their current investments, or to invest in other portfolios, instead of making such decisions without careful consideration. We first discuss the difference between a deterministic and stochastic instrument, then describe a linear programming model for allocating bonds deterministically, and then, as in the case of mortgage-backed securities, stochastically. [PUBLICATION ABSTRACT]

In this paper we present and expand upon procedures for obtaining large d digit prime number to an arbitrary probability. We use a layered approach. The first step is to limit the pool of random number to exclude numbers that are obviously composite. We first remove any number ending in 1,3,7 or 9. We then exclude numbers whose digital root is not 3, 6, or 9. This sharply reduces the probability of the random number being composite. We then use the Prime Number Theorem to find the probability that the selected number n is prime and use primality tests to increase the probability to an arbitrarily high degree that n is prime. We apply primality tests including Euler's test based on Fermat Little theorem and the Miller-Rabin test. We computed these conditional probabilities and implemented it using the GNU GMP library.

The Simplex Method, the most popular method for solving Linear Programs (LPs), has two major variants. They are the revised method and the standard, or full tableau method. Today, virtually all serious implementations are of the revised method because it is more efficient for sparse LPs which are the most common. However, the full tableau method has advantages as well. First, the full tableau can be very effective for dense problems. Second, a full tableau method can easily and effectively be extended to a coarse grained distributed algorithm. While dense problems are uncommon in general, they occur frequently in some important applications such as digital filter design, text categorization, image processing and relaxations of scheduling problems. We implement two full tableau algorithms. The first, a serial implementation, is effective for small to moderately sized dense problems. The second, a simple extension of the first, is a distributed algorithm, which is effective for large problems of all densities. We developed performance models that predict running times per iteration for the serial version of our method, the parallel version of our method and the revised method for problems of different sizes, aspect ratios and densities. We also developed methods for choosing the number of processors to optimize the tradeoff between computation and communication in distributed computations. We tested our algorithms on practical (Netlib) and synthetic problems.

One important tool is the optimal clustering of data into useful categories. Dividing similar objects into a smaller number of clusters is of importance in many applications. These include search engines, monitoring of academic performance, biology and wireless networks. We first discuss a number of clustering methods. We present a parallel algorithm for the efficient clustering of objects into groups based on their similarity to each other. The input consists of an n by n distance matrix. This matrix would have a distance ranking for each pair of objects. The smaller the number, the more similar the two objects are to each other. We utilize parallel processors to calculate a hierarchal cluster of these n items based on this matrix. Another advantage of our method is distribution of the large n by n matrix. We have implemented our algorithm and have found it to be scalable both in terms of processing speed and storage.

An important aspect of teaching is enabling students to analyze and solve problems in a manner most suitable for them. Some people can quickly visualize the effects of a formula. Others may first solve problems with a hands-on model approach, a diagram, or a wordy description. In this paper, we illustrate a variety of representational choices through two familiar problems / methods: the Towers of Hanoi problem and the Bucket Sort. In a broad sense, some representations of solutions may be viewed as \"extensional,\" while others are \"intensional.\" Depending on the predilections of the problem-solver, either approach, in general, may be preferred. We emphasize the importance of making good representational choices when attempting to solve problems. Hand in hand with the understanding of a problem, developing an algorithm for its solution, and the actual solution process and its testing, is awareness of the importance of representational choice. Such awareness is certain to enhance development of students' problem-solving skills. [PUBLICATION ABSTRACT]