Uma amostra do tratamento concedido às classes de palavras na tradição gramatical do português

Within a broader study of the apparatus governing the establishment of word classes in grammatical tradition, this study is centered on examining the treatment of the (sub)class of pronouns in two grammar guide groups from different periods. The collation was especially oriented by the existing general notion of a very strong link of the first grammarians with logic and, as a counterpart, of an insertion of the most recent works in the universe of a linguistic science and, at the same time, in the universe of a parameterization limiting actions and decisions.

Pareto Frontier for the time-energy cost vector to an Earth-Moon transfer orbit using the patched-conic approximation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A radial basis function network (RBFN) for function approximation

A radial basis function network (RBFN) circuit for function approximation is presented. Simulation and experimental results show that the network has good approximation capabilities. The RBFN was a squared hyperbolic secant with three adjustable parameters amplitude, width and center. To test the network a sinusoidal and sine function,vas approximated.

Comparative study between powers of sigmoid functions, MLP-backpropagation and polynomials in function approximation problems

Function approximation is a very important task in environments where the computation has to be based on extracting information from data samples in real world processes. So, the development of new mathematical model is a very important activity to guarantee the evolution of the function approximation area. In this sense, we will present the Polynomials Powers of Sigmoid (PPS) as a linear neural network. In this paper, we will introduce one series of practical results for the Polynomials Powers of Sigmoid, where we will show some advantages of the use of the powers of sigmiod functions in relationship the traditional MLP-Backpropagation and Polynomials in functions approximation problems.

Aprendizagem após via exclusão e formação de classes de equivalência em crianças com deficiência auditiva e implante coclear (aceito condicionalmente)

Studies on learning by exclusion have shown that participants tend to select a new object or a new figure when a new word is dictated, rejecting the objects and figures they already know or that were associated with other words. This study aimed at training conditional relations between dictated word-picture and between picture-printed word, by exclusion, and verify whether this training would be a condition for the emergence of relations between dictated word-printed word, printed word-figure, picture naming and reading. We also investigated whether responding to the words dictated with a female voice generalized to other frequencies such as male and child voices. Participants were five children between five and nine years old, with acute neurosensorial bilateral hearing impairment, users of cochlear implant Nucleus 24k®. They were exposed, individually, to tasks that consisted in selecting a comparison stimulus (either picture or printed word) related to the sample (either dictated word or picture). Words with lowest scores on a pre-test were used. The relations between dictated word-figure (AB) and figure-printed word (BC) were taught by exclusion. We assessed the emergence of the relations between dictated and printed words (AC), printed word and picture (CB), male and child voices generalization (A’C and A’’C), naming (BD) and reading (CD). All the children responded by exclusion and learned relations AB and BC, showing receptive vocabulary; AC and CB relations also were learned, consistent with class formation. Responding generalized to male and child voices, but data on naming were not systematic. Learning by exclusion was similar to that of children with typical hearing and these results describe some conditions that can improve receptive verbal repertoire.

Discriminação de classes de cobertura vegetal utilizando técnicas de classificação digital de imagens de sensoriamento remoto

The aim of this work is to discriminate vegetation classes throught remote sensing images from the satellite CBERS-2, related to winter and summer seasons in the Campos Gerais region Paraná State, Brazil. The vegetation cover of the region presents different kinds of vegetations: summer and winter cultures, reforestation areas, natural areas and pasture. Supervised classification techniques like Maximum Likelihood Classifier (MLC) and Decision Tree were evaluated, considering a set of attributes from images, composed by bands of the CCD sensor (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture models (soil, shadow, vegetation) and the two first main components. The evaluation of the classifications accuracy was made using the classification error matrix and the kappa coefficient. It was defined a high discriminatory level during the classes definition, in order to allow separation of different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and the kappa coefficient was 0.9389 for the scene 157/128. For the scene 158/127, the values were 88% and 0.8667, respectively. The classification accuracy by MLC was 84.86% and the kappa coefficient was 0.8099 for the scene 157/128. For the scene 158/127, the values were 77.90% and 0.7476, respectively. The results showed a better performance of the Decision Tree classifier than MLC, especially to the classes related to cultivated crops, indicating the use of the Decision Tree classifier to the vegetation cover mapping including different kinds of crops.

A dinâmica das classes sociais no pensamento de Florestan Fernandes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)