Taiwan is the country which relies on foreign trade and the value of import and export markets accounts for eighty percent of the Gross Net Product¡]GNP¡^. It is obvious that the feature of economic system in island highly depends on the existence of foreign trade. Therefore, exchange rate is considered as one of the major indexes for Taiwan¡¦s economic activities. Federal Reserve System¡]FED¡^has constantly begun to lower the interest rate for thirteen times since 2001, and this would influence the trends of the interest rate of the whole world. Also, it seems that reducing the interest rate promotes the low interest which leads to a more prosperous economy in Taiwan society than before. It is clear, thus, that the interest is regarded as a major variable in economic system. The stock market of Taiwan has shifted from bear market to bullish one since 2002 and it would have developed the bullish market for almost ten years. According to this phenomenon, the issue of how to evaluate the trend of the stock index has been becoming important for Taiwanese investors to explore the stock market. This study is based on the observation of the relationships between the stock index and the two rates, exchange and interest rates. It is hoping, by doing so, that investors can obtain sufficient information and successfully estimate different aspects of investing trends in the stock market in Taiwan.

<p>Modular decompositions of protein interaction networks can be used to identify modules of cooperating proteins. The biological plausibility off these modules might be questioned though. This report describes how a modular decomposition can be completed with semantic information in the visual representation. Possible methods for creating modules of functionally related proteins are also proposed in this work. The results show that such modules, with advantage can be combined with modules from a graph decomposition, to find proteins that are likely to cooperate to perform certain functions in organisms</p>

Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. / Title from PDF title page (viewed Jan. 28, 2010). Additional advisors: Derrick R. Dean, Wiliam K. Nonidez, Andrei Stanishevsky, Charles L. Watkins. Includes bibliographical references.

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 18, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Thesis advisor: Dr. Stephen Lombardo. Includes bibliographical references.

Keepaway is a simpler subtask of robot soccer where three `keepers' attempt to keep possession of the ball while a `taker' tries to steal it from them. This is a less complex task than full robot soccer, and lends itself well as a testbed for multi-agent systems. This thesis does a comprehensive evaluation of various learning methods using neuroevolution with Enforced Sub-Populations (ESP) with the robocup soccer simulator. Both single and multi-component ESP are evaluated using various learning methods on homogeneous and heterogeneous teams of agents. In particular, the effectiveness of modularity and task decomposition for evolving keepaway teams is evaluated. It is shown that in the robocup soccer simulator, homogeneous agents controlled by monolithic networks perform the best. More complex learning approaches like layered learning, concurrent layered learning and co-evolution decrease the performance as does making the agents heterogeneous. The results are also compared with previous results in the keepaway domain. / text

Bicycle sharing systems (BSS) have emerged as a powerful stimulus to non- motorized travel, especially for short-distance trips. However, the imbalances in the distribution of bicycles in BSS are widely observed. It is thus necessary to reposition bicycles to reduce the unmet demand due to such imbalances as much as possible. This paper formulates a new mixed-integer linear programming model considering the dynamic nature of the demand to solve the repositioning problem, which is later validated by an illustrative example. Due to the NP-Hard nature of this problem, we seek for two heuristics (greedy algorithm and rolling horizon approach) and one exact solution method (Benders’ decomposition) to get an acceptable solution for problems with large instances within a reasonable computation time. We create four datasets based on real world data with 12, 24, 36, and 48 stations respectively. Computational results show that our model and solution methods performed well. Finally, this paper gives some suggestions on extensions or modifications that might be added to our work in the future. / text

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Quantum chemistry is plagued by the presence of high-rank quantities, stemming from the N-body nature of the electronic Schrödinger equation. These high-rank quantities present a significant mathematical and computational barrier to the computation of chemical observables, and also drastically complicate the pedagogical understanding of important interactions between particles in a molecular system. The application of physically-motivated rank reduction approaches can help address these to problems. This thesis details recent efforts to apply rank reduction techniques in both of these arenas. With regards to computational tractability, the representation of the 1/r Coulomb repulsion between electrons is a critical stage in the solution of the electronic Schrödinger equation. Typically, this interaction is encapsulated via the order-4 electron repulsion integral (ERI) tensor, which is a major bottleneck in terms of generation, manipulation, and storage. Many rank reduction techniques for the ERI tensor have been proposed to ameliorate this bottleneck, most notably including the order-3 density fitting (DF) and pseudospectral (PS) representations. Here we detail a new and uniquely powerful factorization - tensor hypercontraction (THC). THC decomposes the ERI tensor as a product of five order-2 matrices (the first wholly order-2 compression proposed for the ERI) and offers great flexibility for low-scaling algorithms for the manipulations of the ERI tensor underlying electronic structure theory. THC is shown to be physically-motivated, markedly accurate, and uniquely efficient for some of the most difficult operations encountered in modern quantum chemistry. On the front of chemical understanding of electronic structure theory, we present our recent work in developing robust two-body partitions for ab initio computations of intermolecular interactions. Noncovalent interactions are the critical and delicate forces which govern such important processes as drug-protein docking, enzyme function, crystal packing, and zeolite adsorption. These forces arise as weak residual interactions leftover after the binding of electrons and nuclei into molecule, and, as such, are extremely difficult to accurately quantify or systematically understand. Symmetry-adapted perturbation theory (SAPT) provides an excellent approach to rigorously compute the interaction energy in terms of the physically-motivated components of electrostatics, exchange, induction, and dispersion. For small intermolecular dimers, this breakdown provides great insight into the nature of noncovalent interactions. However, SAPT abstracts away considerable details about the N-body interactions between particles on the two monomers which give rise to the interaction energy components. In the work presented herein, we step back slightly and extract an effective 2-body interaction for each of the N-body SAPT terms, rather than immediately tracing all the way down to the order-0 interaction energy. This effective order-2 representation of the order-N SAPT interaction allows for the robust assignment of interaction energy contributions to pairs of atoms or functional groups (the A-SAPT or F-SAPT partitions), allowing one to discuss the interaction in terms of atom- or functional-group-pairwise interactions. These A-SAPT and F-SAPT partitions can provide deep insight into the origins of complicated noncovalent interactions, e.g., by clearly shedding light on the long-contested question of the nature of the substituent effect in substituted sandwich benzene dimers.

This article seeks to enhance multi criteria decision making by providing a scientic approach for decomposing and structuring decision problems. We propose a process, based on concept mapping, which integrates group creativity techniques, card sorting procedures, quantitative data analysis and algorithmic automatization to construct meaningful and complete hierarchies of criteria. The algorithmic aspect is covered by a newly proposed recursive cluster algorithm, which automatically generates hierarchies from card sorting data. Based on comparison with another basic algorithm and empirical engineered and real-case test data, we validate that our process efficiently produces reasonable hierarchies of descriptive elements like goal- or problem-criteria. (authors' abstract)

published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy