Instituto Superior Técnico

About: Instituto Superior Técnico is a based out in . It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 10085 authors who have published 30226 publications receiving 667524 citations. The organization is also known as: IST & Instituto Superior Tecnico.

Structuring theories on consequence

Abstract: Building on the work of Goguen and Burstall on institutions and on Tarski's notion of deductive system, a categorial framework for manipulating theories in an arbitrary logic is presented. Its main contribution is the formalisation of the semantics of theory-building operations on top of a consequence relation. For that purpose, the notion of π-institution is proposed as an alternative to the notion of institution, replacing the notions of model and satisfaction by a primitive consequence operator in the definition of a logic. The resulting approach to the semantics of specification languages is intrinsically different from the original one in the sense that the ultimate denotation of a specification is taken herein to be a class of theories (sets of formulae closed for the consequence relation) and not a class of models of that logic. Adopting this point of view, the semantics of Clear-like specification building operations is analysed.

On the choice of source points in the method of fundamental solutions

Abstract: The method of fundamental solutions (MFS) may be seen as one of the simplest methods for solving boundary value problems for some linear partial differential equations (PDEs). It is a meshfree method that may present remarkable results with a small computational effort. The meshfree feature is particularly attractive when we need to change the shape of the domain, which occurs, for instance, in shape optimization and inverse problems. The MFS may be viewed as a Trefftz method, where the approximations have the advantage of verifying the linear PDE, and therefore we may bound the inner error from the boundary error, in well-posed problems. A main counterpart for these global numerical methods, that avoid meshes, are the associated linear systems with dense and ill conditioned matrices. In these methods a sort of uncertainty principle occurs—we cannot get both accurate results and good conditioning—one of the two is lost. A specific feature of the MFS is some freedom in choosing the source points. This might lead to excellent results, but it may also lead to poor results, or even to impossible approximations. In this work we will discuss the choice of source points and propose a choice along the discrete normal direction (following [Alves CJS, Antunes PRS. The method of fundamental solutions applied to the calculation of eigenfrequencies and eigenmodes of 2D simply connected shapes. Comput Mater Continua 2005;2(4):251–66]), with a possible local criterion to define the distance to the boundary. We will also address some extensions that connect the asymptotic MFS to other methods by choosing the sources on a circle/sphere far from the boundary. We also present a direct connection between the approximation based on radial basis functions (RBF) and the MFS approximation in a higher dimension. This increase in dimension was somehow already present in a previous work [Alves CJS, Chen CS. A new method of fundamental solutions applied to non-homogeneous elliptic problems. Adv Comput Math 2005;23:125–42], where the frequency was used as the extra dimension. The free parameters in RBF inverse multiquadrics 2D approximation correspond in fact to the source point distance to the boundary plane in a Laplace 3D setting. Some numerical simulations are presented to illustrate theoretical issues.

Automatic quality assessment of content created collaboratively by web communities: a case study of wikipedia

Abstract: The old dream of a universal repository containing all the human knowledge and culture is becoming possible through the Internet and the Web. Moreover, this is happening with the direct collaborative, participation of people. Wikipedia is a great example. It is an enormous repository of information with free access and edition, created by the community in a collaborative manner. However, this large amount of information, made available democratically and virtually without any control, raises questions about its relative quality. In this work we explore a significant number of quality indicators, some of them proposed by us and used here for the first time, and study their capability to assess the quality of Wikipedia articles. Furthermore, we explore machine learning techniques to combine these quality indicators into one single assessment judgment. Through experiments, we show that the most important quality indicators are the easiest ones to extract, namely, textual features related to length, structure and style. We were also able to determine which indicators did not contribute significantly to the quality assessment. These were, coincidentally, the most complex features, such as those based on link analysis. Finally, we compare our combination method with state-of-the-art solution and show significant improvements in terms of effective quality prediction.