University of the Aegean

About: University of the Aegean is a education organization based out in Mytilene, Greece. It is known for research contribution in the topics: Population & Context (language use). The organization has 2818 authors who have published 8100 publications receiving 179275 citations. The organization is also known as: UAEG.

Exploring the relationship of emotional intelligence with physical and psychological health functioning

Abstract: This study investigates the relationship of emotional intelligence (EI) characteristics, such as perception, control, use and understanding of emotions, with physical and psychological health. In the first study, 365 individuals filled in measures of EI and general health. It was hypothesized that EI would be negatively associated with poor general health. In the second study, 212 working adults completed the same measure of EI and another measure, which apart from the standard information regarding physical and psychological health, provided also information about other health related behaviours, such as smoking, drinking, and exercising. It was also hypothesized that EI would negatively correlate with smoking and drinking and positively correlate with exercising. The findings confirmed both hypotheses and provided further support on the claims that there is a relationship between EI and health functioning. Additionally, in a series of hierarchical regression analyses the unique contribution of each of the EI scales on the overall health score were investigated. The findings are discussed in the context of the importance of emotional competences on health and personal lifestyle, while implications for practice and directions for future research are proposed. Copyright © 2005 John Wiley & Sons, Ltd.

Teleparallel equivalent of Gauss-Bonnet gravity and its modifications

Abstract: Inspired by the teleparallel formulation of general relativity, whose Lagrangian is the torsion invariant T , we have constructed the teleparallel equivalent of Gauss-Bonnet gravity in arbitrary dimensions. Without imposing the Weitzenbock connection, we have extracted the torsion invariant T G , equivalent (up to boundary terms) to the Gauss-Bonnet term G . T G is constructed by the vielbein and the connection, it contains quartic powers of the torsion tensor, it is diffeomorphism and Lorentz invariant, and in four dimensions it reduces to a topological invariant as expected. Imposing the Weitzenbock connection, T G depends only on the vielbein, and this allows us to consider a novel class of modified gravity theories based on F ( T , T G ) , which is not spanned by the class of F ( T ) theories, nor by the F ( R , G ) class of curvature modified gravity. Finally, varying the action we extract the equations of motion for F ( T , T G ) gravity.

Syntactic N-grams as machine learning features for natural language processing

Abstract: In this paper we introduce and discuss a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner how we construct them, i.e., what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking words as they appear in a text, i.e., sn-grams are constructed by following paths in syntactic trees. In this manner, sn-grams allow bringing syntactic knowledge into machine learning methods; still, previous parsing is necessary for their construction. Sn-grams can be applied in any natural language processing (NLP) task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. We used as baseline traditional n-grams of words, part of speech (POS) tags and characters; three classifiers were applied: support vector machines (SVM), naive Bayes (NB), and tree classifier J48. Sn-grams give better results with SVM classifier.