Contribuições da pesquisa linguística sobre interrogativas para melhorias no cotidiano do afásico em seu ambiente familiar um roteiro

A afasiologia linguística, enquanto ramo da Linguística, contribui para a verificação dos pressupostos da Teoria Gerativa e para a classificação e descrição das particularidades linguísticas características dos diferentes tipos de afasias. Neste contexto, os problemas com a produção de estruturas complexas, como é o caso das interrogativas, têm sido tema de muitos estudos em diversas línguas. Entretanto, os benefícios que tais pesquisas podem oferecer no sentido de melhorar o cotidiano dos sujeitos de pesquisa raramente são abordados. Com o intuito de atuar sobre esta lacuna, a presente dissertação tem como objetivo geral fornecer um roteiro de pesquisa com sugestões de intervenção de base linguística no tratamento fonoaudiológico de um indivíduo afásico, proporcionando, desta forma, melhora do cotidiano do afásico em seu ambiente familiar. A hipótese que orienta este trabalho é a de que a aplicação de um roteiro de pesquisa linguística descentralizado e desverticalizado sobre o tratamento da afasia pode proporcionar melhorias no cotidiano familiar do paciente acometido pelo déficit. Para desenvolver o roteiro em questão, foram escolhidas as interrogativas-QU como tópico linguístico. O roteiro, todavia, foi desenvolvido para que qualquer outro tópico linguístico, identificado como problemático e passível de tratamento específico, seja aplicável ao esquema. Como objetivo subsidiário, pretende-se desenvolver um modelo de pesquisa que busque contribuir para a aplicação do princípio da indissociabilidade entre ensino, pesquisa e extensão. A metodologia consiste na utilização de um estudo de caso com testes avaliativos, treinamento de sentenças e entrevistas. O sujeito selecionado para o estudo é um afásico agramático. Há, ainda, a colaboração de membros de sua família e da equipe de fonoaudiologia do Centro de Recuperação do Paciente Afásico (CRPA) da Universidade Veiga de Almeida (UVA). Os resultados coletados nos testes e nas entrevistas apontam para melhora no processamento linguístico de interrogativas-QU, tanto no que se refere à organização dos sintagmas nas sentenças, quanto à seleção de itens lexicais adequados (elementos-QU). Resultados também apontam para melhoras no cotidiano do afásico no que se refere à redução da ansiedade perante erros, presença de mais opções comunicativas e aumento da capacidade de comunicação independente. Através dos resultados, constata-se a viabilidade de aplicação do roteiro proposto para futura aplicação por profissionais envolvidos com o tratamento e o estudo das afasias

PCR-SSCP and Isoelectric Focusing as Screening Methods for Identification of (Sub-) Tropical Fish Species

To ensure the authentication of fishery products lacking biological characters, rapid species identification methods are required. Two DNA- and protein-based methods, PCR-SSCP (polymerase chain reaction - single strand conformation polymorphism) of a 464 bp segment of the cytochrome b – gene and isoelectric focusing (IEF) of water-soluble proteins from fish fillets, were applied to identify fillets of (sub-) tropical fish species available on the European market. Among the samples analysed were two taxonomically identified species from the family Sciaenidae and one from Sphyraenidae. By comparison of DNA- and protein patterns of different samples, information about intra-species variability of patterns, and homogeneity of batches (e.g. fillet blocks or bags) can be obtained. PCR-SSCP and IEF may be useful for pre-checking of a large number of samples by food control laboratories. Zusammenfassung Zur Sicherstellung der Authentizität von Fischerei-Erzeugnissen ohne biologische Merkmale sind schnelle Verfahren zur Speziesidentifizierung hilfreich. Zwei Methoden der DNA- bzw. Protein-Analyse wurden eingesetzt, um Filets (sub-) tropischer Fischarten, die auf dem europäischen Markt angeboten werden, zu identifizieren. Bei diesen Methoden handelt es sich um die PCR-SSCP (Polymerase-Kettenreaktion – Einzelstrang-Konformationspolymorphismus) – Analyse der PCR-Produkte und die IEF (isoelektrische Fokussierung) der wasserlöslichen Fischmuskelproteine. Unter den untersuchten Proben waren zwei taxonomisch bestimmte Arten aus der Familie Sciaenidae und eine Spezies aus der Familie Sphyraenidae. Durch Vergleich der DNA- bzw. Proteinmuster lassen sich Informationen über die intra-spezifische Variabilität solcher Muster und die Einheitlichkeit von Partien (beispielsweise Filetblöcke oder Filetbeutel) gewinnen. PCR-SSCP und IEF können in Laboratorien der Lebensmittelüberwachung als Vortest gerade bei hohen Probenzahlen sinnvoll eingesetzt werden.

Design e Marketing: uma relação de sinergia para a Sustentabilidade

Esta dissertação aborda os princípios do Design, os fundamentos do Marketing e as condições que ambas as atividades podem, em parceria, gerar ambientes criativos para a Sustentabilidade. Acredita-se que tanto Design quanto Marketing são campos de saber parceiros, complementares e transversais, tendo deste modo um natural potencial de sinergia para a construção de projetos consistentes e duráveis. A insistência nesta ideia se deve, em parte, à vivência profissional nesses setores de gestão de projeto, e também ao desenvolvimento de exames, através da pesquisa bibliográfica, sobre os sentidos de termos, conceitos e objetivos para a organização de dados, a análise e alinhamento conceitual e a verificação de tangências entre esses campos. Este trabalho objetiva contribuir para a interação e a integração de saberes, em cursos de nível superior, também com a sugestão de ferramenta criativo-analítica e criativo-gerativa para o desenvolvimento de soluções mais sustentáveis. A crescente necessidade de atuações adequadas e eficazes dos campos estudados nos setores produtivos, a necessidade de profissionais capazes de se adequarem estas demandas, e a oferta deficitária de pesquisas com esta abordagem reforçam a crença na utilidade desta investigação. Os saberes em foco têm em comum relações multidisciplinares, sistêmicas e interdependentes.

Intonation modelling and adaptation for emotional prosody generation

This paper proposes an HMM-based approach to generating emotional intonation patterns. A set of models were built to represent syllable-length intonation units. In a classification framework, the models were able to detect a sequence of intonation units from raw fundamental frequency values. Using the models in a generative framework, we were able to synthesize smooth and natural sounding pitch contours. As a case study for emotional intonation generation, Maximum Likelihood Linear Regression (MLLR) adaptation was used to transform the neutral model parameters with a small amount of happy and sad speech data. Perceptual tests showed that listeners could identify the speech with the sad intonation 80% of the time. On the other hand, listeners formed a bimodal distribution in their ability to detect the system generated happy intontation and on average listeners were able to detect happy intonation only 46% of the time. © Springer-Verlag Berlin Heidelberg 2005.

Pitman-Yor Diffusion Trees

We introduce the Pitman Yor Diffusion Tree (PYDT) for hierarchical clustering, a generalization of the Dirichlet Diffusion Tree (Neal, 2001) which removes the restriction to binary branching structure. The generative process is described and shown to result in an exchangeable distribution over data points. We prove some theoretical properties of the model and then present two inference methods: a collapsed MCMC sampler which allows us to model uncertainty over tree structures, and a computationally efficient greedy Bayesian EM search algorithm. Both algorithms use message passing on the tree structure. The utility of the model and algorithms is demonstrated on synthetic and real world data, both continuous and binary.

Theoretical performance of a coiled coil piezoelectric bimorph

The electromechanical coupling behaviour of a novel, highly coiled piezoelectric strip structure is developed in full, in order to expound its performance and efficiency. The strip is doubly coiled for compactness and, compared to a standard straight actuator of the same cross-section, it is shown that the actuator here offers better generative forces and energy conversion, and substantial actuated displacements, however, at the expense of a much lower stiffness. The device is therefore proposed for high-displacement, quasi-static applications. © 2006 Elsevier B.V. All rights reserved.

Structured discriminative models for noise robust continuous speech recognition

Recently there has been interest in structured discriminative models for speech recognition. In these models sentence posteriors are directly modelled, given a set of features extracted from the observation sequence, and hypothesised word sequence. In previous work these discriminative models have been combined with features derived from generative models for noise-robust speech recognition for continuous digits. This paper extends this work to medium to large vocabulary tasks. The form of the score-space extracted using the generative models, and parameter tying of the discriminative model, are both discussed. Update formulae for both conditional maximum likelihood and minimum Bayes' risk training are described. Experimental results are presented on small and medium to large vocabulary noise-corrupted speech recognition tasks: AURORA 2 and 4. © 2011 IEEE.

Derivative Kernels for Noise Robust ASR

Recently there has been interest in combined gen- erative/discriminative classifiers. In these classifiers features for the discriminative models are derived from generative kernels. One advantage of using generative kernels is that systematic approaches exist how to introduce complex dependencies beyond conditional independence assumptions. Furthermore, by using generative kernels model-based compensation/adaptation tech- niques can be applied to make discriminative models robust to noise/speaker conditions. This paper extends previous work with combined generative/discriminative classifiers in several directions. First, it introduces derivative kernels based on context- dependent generative models. Second, it describes how derivative kernels can be incorporated in continuous discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high- dimensional features of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task.

The Gaussian process density sampler

We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We can also infer the hyperparameters of the Gaussian process. We compare this density modeling technique to several existing techniques on a toy problem and a skullreconstruction task.

Derivative kernels for noise robust ASR

Recently there has been interest in combining generative and discriminative classifiers. In these classifiers features for the discriminative models are derived from the generative kernels. One advantage of using generative kernels is that systematic approaches exist to introduce complex dependencies into the feature-space. Furthermore, as the features are based on generative models standard model-based compensation and adaptation techniques can be applied to make discriminative models robust to noise and speaker conditions. This paper extends previous work in this framework in several directions. First, it introduces derivative kernels based on context-dependent generative models. Second, it describes how derivative kernels can be incorporated in structured discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high-dimensional feature-spaces of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task. © 2011 IEEE.

Silhouette-based object phenotype recognition using 3D shape priors

This paper tackles the novel challenging problem of 3D object phenotype recognition from a single 2D silhouette. To bridge the large pose (articulation or deformation) and camera viewpoint changes between the gallery images and query image, we propose a novel probabilistic inference algorithm based on 3D shape priors. Our approach combines both generative and discriminative learning. We use latent probabilistic generative models to capture 3D shape and pose variations from a set of 3D mesh models. Based on these 3D shape priors, we generate a large number of projections for different phenotype classes, poses, and camera viewpoints, and implement Random Forests to efficiently solve the shape and pose inference problems. By model selection in terms of the silhouette coherency between the query and the projections of 3D shapes synthesized using the galleries, we achieve the phenotype recognition result as well as a fast approximate 3D reconstruction of the query. To verify the efficacy of the proposed approach, we present new datasets which contain over 500 images of various human and shark phenotypes and motions. The experimental results clearly show the benefits of using the 3D priors in the proposed method over previous 2D-based methods. © 2011 IEEE.

A Structured Model of Video Reproduces Primary Visual Cortical Organisation

The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.

Occlusive Components Analysis

We study unsupervised learning in a probabilistic generative model for occlusion. The model uses two types of latent variables: one indicates which objects are present in the image, and the other how they are ordered in depth. This depth order then determines how the positions and appearances of the objects present, specified in the model parameters, combine to form the image. We show that the object parameters can be learnt from an unlabelled set of images in which objects occlude one another. Exact maximum-likelihood learning is intractable. However, we show that tractable approximations to Expectation Maximization (EM) can be found if the training images each contain only a small number of objects on average. In numerical experiments it is shown that these approximations recover the correct set of object parameters. Experiments on a novel version of the bars test using colored bars, and experiments on more realistic data, show that the algorithm performs well in extracting the generating causes. Experiments based on the standard bars benchmark test for object learning show that the algorithm performs well in comparison to other recent component extraction approaches. The model and the learning algorithm thus connect research on occlusion with the research field of multiple-causes component extraction methods.

on string languages generated by spiking neural p systems

We continue the study of spiking neural P systems by considering these computing devices as binary string generators: the set of spike trains of halting computations of a given system constitutes the language generated by that system. Although the "direct" generative capacity of spiking neural P systems is rather restricted (some very simple languages cannot be generated in this framework), regular languages are inverse-morphic images of languages of finite spiking neural P systems, and recursively enumerable languages are projections of inverse-morphic images of languages generated by spiking neural P systems.

A Review of Active Appearance Models

Active appearance model (AAM) is a powerful generative method for modeling deformable objects. The model decouples the shape and the texture variations of objects, which is followed by an efficient gradient-based model fitting method. Due to the flexible and simple framework, AAM has been widely applied in the fields of computer vision. However, difficulties are met when it is applied to various practical issues, which lead to a lot of prominent improvements to the model. Nevertheless, these difficulties and improvements have not been studied systematically. This motivates us to review the recent advances of AAM. This paper focuses on the improvements in the literature in turns of the problems suffered by AAM in practical applications. Therefore, these algorithms are summarized from three aspects, i.e., efficiency, discrimination, and robustness. Additionally, some applications and implementations of AAM are also enumerated. The main purpose of this paper is to serve as a guide for further research.