The Sustainability, Preservation and Accessibility of Internal and External Communities by Universities

4th International Conference on Open RepositoriesThis presentation was part of the session : DSpace User Group PresentationsDate: 2009-05-20 03:30 PM – 05:00 PMThis paper will provide three different cases or examples of how a mid-size University is able to implement DSpace across diverse groups of users. Additionally, one of the cases will show how the DSpace software has been 'repurposed' to serve as the university library's Electronic Reserve and how it has been linked the library's ILS. The paper will show how the university has obtained a consistent level of sustainability, preservation and accessibility to using DSpace with limited resources

CORRELATIONS BETWEEN OIL AND STOCK MARKETS: A WAVELET-BASED APPROACH

In a global economy, shocks occurring in one market can spill over to other markets. This paper investigates the impact of oil shocks and stock markets crashes on correlations between stock and oil markets. We test changes in correlations at different scales with non-overlapping confidence intervals based on estimated wavelet correlations. Contrary to other approaches, this method does not need adjustment for heteroskedasticity biases on the correlation coefficients. Our results show that oil shocks affect the correlation between both markets. The evidence on the change of correlation between stock markets after an oil shock is weaker; except in some specific cases during the Kuwait war and the OPEC cutback period. Conversely, we only find weak evidence that stock market crashes change the correlation between oil and stock markets. Overall, the evidence gives support to including oil as an asset class in asset allocation strategies.he authors acknowledge financial support from Financial Research Center–UNIDE and from the Spanish Ministry of Education and Science, research projects MTM2010-17323, ECO2011-25706, ECO2012-32401 and MTM2012-36163-C06-03

Binarized support vector machines

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals which are critical for the classification. The method involves the optimization of a Linear Programming problem, with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard Column-Generation strategy leads to a classification method which, in terms of classification ability, is competitive against the standard linear SVM and Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler, still competitive, classifiers