Text Summarization

With the overwhelming amount of textual information available in electronic formats on the web, there is a need for an efficient text summarizer capable of condensing large bodies of text into shorter versions while keeping the relevant information intact. Such a technology would allow users to get their information in a shortened form, saving valuable time. Since 1997, Microsoft Word has included a summarizer for documents, and currently there are companies that summarize breaking news and send SMS for mobile phones. I wish to create a text summarizer to provide condensed versions of original documents. My focus is on blogs, because people are increasingly using this mode of communication to express their opinions on a variety of topics. Consequently, it will be very useful for a reader to be able to employ a concise summary, tailored to his or her own interests to quickly browse through volumes of opinions relevant to any number of topics. Although many summarization methods exist, my approach involves employing the Lanczos algorithm to compute eigenvalues and eigenvectors of a large sparse matrix and SVD (Singular Value Decomposition) as a means of identifying latent topics hidden in contexts; and the next phase of the process involves taking a high-dimensional set of data and reducing it to a lower-dimensional set. This procedure makes it possible to identify the best approximation of the original text. Since SQL makes it possible to allow analyzing data sets and take advantage of the parallel processing available today, in most database management systems, SQL is employed in my project. The utilization of SQL without external math libraries, however, adds to challenge in the computation of the SVD and the Lanczos algorithm

Correlation Structures Corresponding to Forward Rates

In finance, there is a constant effort to model future prices of stocks, bonds, and commodities; the ability to predict future behaviour provides important information about the underlying structure of these securities. While it has become common to model a single stock using the Black-Scholes formulation, the modelling of bond prices requires one to simulate the change of interest rates as a function of their maturity, which requires one to model the movement of an entire yield curve. If one studies the spectral decomposition of the correlation matrix corresponding to the spot rates from this curve, then one finds that the top three components can explain nearly all of the data; in addition, this same structure is observed for any bond or commodity. In his 2000 paper, Ilias Lekkos [4] proposes that such results are an artefact due to the implicit correlation between spot rates, and that the analysis should instead be performed using forward rates. In this paper, we discuss the results obtained for the spectral structure of the correlation matrices of forward rates, and investigate a model for this associated structure. The paper is divided into four parts, covering forward rates background material, principal components analysis, yield curve modelling, and conclusions and research extensions

Power Spectrum Analysis of the OMC1 Image at 1.1 mm Wavelength

We present a 1.1mm emission map of the OMC1 region observed with AzTEC, a new large-format array composed of 144 silicon-nitride micromesh bolometers, that was in use at the James Clerk Maxwell Telescope (JCMT). These AzTEC observations reveal dozens of cloud cores and a tail of filaments in a manner that is almost identical to the submillimeter continuum emission of the entire OMC1 region at 450 and 850 micronm. We perform Fourier analysis of the image with a modified periodogram and the density power spectrum, which provides the distribution of the length scale of the structures, is determined. The expected value of the periodogram converges to the resulting power spectrum in the mean squared sense. The present analysis reveals that the power spectrum steepens at relatively smaller scales. At larger scales, the spectrum flattens and the power law becomes shallower. The power spectra of the 1.1mm emission show clear deviations from a single power law. We find that at least three components of power law might be fitted to the calculated power spectrum of the 1.1mm emission. The slope of the best fit power law, \gamma ~ -2.7 is similar to those values found in numerical simulations. The effects of beam size and the noise spectrum on the shape and slope of the power spectrum are also included in the present analysis. The slope of the power law changes significantly at higher spatial frequency as the beam size increases.Comment: 7 pages, 2 figures, Journal of the Korean Astronomical Society, vol. 45, pp.93-99; For Figure 1, please refer to http://jkas.kas.org/journals/2012v45n4/v45n4p093_skim.pd

A Model of Political Judgment: An Agent-Based Simulation of Candidate Evaluation

This paper advances Kim, Taber, and Lodge's work (2010). Specifically, it is shown here that the psychological model of political judgment proposed by Kim et al (2010) is consistent with a set of well-known empirical regularities repeatedly found in electoral and psychological researches, that the model in general implies motivated reasoning - discounting contradictory information to the prior while accepting consistent information more or less at its face value - under general conditions, and that (prior) evaluative affect towards candidates plays a fundamental role in this process. It is also discussed the implication of motivated reasoning in accounting for the responsiveness, persistence, and polarization of candidate evaluation often observed in elections.Candidate Evaluation, Election, Cognition and Affect, Political Judgment, ACT-r

Sequence Space Localization in the Immune System Response to Vaccination and Disease

We introduce a model of protein evolution to explain limitations in the immune system response to vaccination and disease. The phenomenon of original antigenic sin, wherein vaccination creates memory sequences that can \emph{increase} susceptibility to future exposures to the same disease, is explained as stemming from localization of the immune system response in antibody sequence space. This localization is a result of the roughness in sequence space of the evolved antibody affinity constant for antigen and is observed for diseases with high year-to-year mutation rates, such as influenza.Comment: 5 pages, 2 figure