Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Experimental Model of Arthritis Induced by Paracoccidioides brasiliensis in Rats

Paracoccidioidomycosis (PCM), a disease caused by the fungus Paracoccidioides brasiliensis (Pb), is highly prevalent in Brazil, where it is the principal cause of death by systemic mycoses. The disease primarily affects men aged 30-50 year old and usually starts as a pulmonary focus and then may spread to other organs and systems, including the joints. The present study aimed to develop an experimental model of paracoccidioidomycotic arthritis. Two-month-old male Wistar rats (n = 48) were used, divided in 6 groups: test groups EG/15 and EG/45 (received one dose of 100 mu l of saline containing 10(5) Pb viable yeasts in the knee); heat killed Pb-group HK/15 and HK/45 (received a suspension of 10(5) Pb nonviable yeasts in the knee) and control groups CG/15 and CG/45 (received only sterile saline in the knee). The rats were killed 15 and 45 days postinoculation. In contrast with the control rats, the histopathology of the joints of rats of the test groups (EG/15 and EG/45) revealed a picture of well-established PCM arthritis characterized by extensive sclerosing granulomatous inflammation with numerous multiple budding fungal cells. The X-ray examination revealed joint alterations in these groups. Only metabolic active fungi evoked inflammation. The experimental model was able to induce fungal arthritis in the knees of the rats infected with metabolic active P. brasiliensis. The disease tended to be regressive and restrained by the immune system. No evidence of fungal dissemination to the lungs was observed.

Evaluation of the Inductive Coupling between Equivalent Emission Sources of Components

The electromagnetic interference between electronic systems or between their components influences the overall performance. It is important thus to model these interferences in order to optimize the position of the components of an electronic system. In this paper, a methodology to construct the equivalent model of magnetic field sources is proposed. It is based on the multipole expansion, and it represents the radiated emission of generic structures in a spherical reference frame. Experimental results for different kinds of sources are presented illustrating our method.

Structure and anomalous solubility for hard spheres in an associating lattice gas model

In this paper we investigate the solubility of a hard-sphere gas in a solvent modeled as an associating lattice gas. The solution phase diagram for solute at 5% is compared with the phase diagram of the original solute free model. Model properties are investigated both through Monte Carlo simulations and a cluster approximation. The model solubility is computed via simulations and is shown to exhibit a minimum as a function of temperature. The line of minimum solubility (TmS) coincides with the line of maximum density (TMD) for different solvent chemical potentials, in accordance with the literature on continuous realistic models and on the "cavity" picture. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4743635]