Processing of hierarchical syntactic strusture in music

Hierarchical structure with nested nonlocal dependencies is a key feature of human language and can be identified theoretically in most pieces of tonal music. However, previous studies have argued against the perception of such structures in music. Here, we show processing of nonlocal dependencies in music. We presented chorales by J. S. Bach and modified versions inwhich the hierarchical structure was rendered irregular whereas the local structure was kept intact. Brain electric responses differed between regular and irregular hierarchical structures, in both musicians and nonmusicians. This finding indicates that, when listening to music, humans apply cognitive processes that are capable of dealing with longdistance dependencies resulting from hierarchically organized syntactic structures. Our results reveal that a brain mechanism fundamental for syntactic processing is engaged during the perception of music, indicating that processing of hierarchical structure with nested nonlocal dependencies is not just a key component of human language, but a multidomain capacity of human cognition.

Processing of highly porous titanium parts by metal injection moulding in combination with innovative plasma treatment

A combinação da Moldagem por Injeção de pós Metálicos (Metal Injection Moulding MIM) e o Método do Retentor Espacial (Space Holder Method - SHM) é uma técnica promissora para fabricação de peças porosas de titânio com porosidade bem definida como implantes biomédicos, uma vez que permite um alto grau de automatização e redução dos custos de produção em larga escala quando comparado a técnica tradicional (SHM e usinagem a verde). Contudo a aplicação desta técnica é limitada pelo fato que há o fechamento parcial da porosidade na superfície das amostras, levando ao deterioramento da fixação do implante ao osso. E além disso, até o presente momento não foi possível atingir condições de processamento estáveis quando a quantidade de retentor espacial excede 50 vol. %. Entretanto, a literatura descreve que a melhor faixa de porosidade para implantes de titânio para coluna vertebral está entre 60 - 65 vol. %. Portanto, no presente estudo, duas abordagens foram conduzidas visando a produção de amostras altamente porosas através da combinação de MIM e SHM com o valor constante de retentor espacial de 70 vol. % e uma porosidade aberta na superfície. Na primeira abordagem, a quantidade ótima de retentor espacial foi investigada, para tal foram melhorados a homogeneização do feedstock e os parâmetros de processo com o propósito de permitir a injeção do feedstock. Na segunda abordagem, tratamento por plasma foi aplicado nas amostras antes da etapa final de sinterização. Ambas rotas resultaram na melhoria da estabilidade dimensional das amostras durante a extração térmica do ligante e sinterização, permitindo a sinterização de amostras de titânio altamente porosas sem deformação da estrutura.

Processing of hierarchical syntactic strusture in music

Hierarchical structure with nested nonlocal dependencies is a key feature of human language and can be identified theoretically in most pieces of tonal music. However, previous studies have argued against the perception of such structures in music. Here, we show processing of nonlocal dependencies in music. We presented chorales by J. S. Bach and modified versions inwhich the hierarchical structure was rendered irregular whereas the local structure was kept intact. Brain electric responses differed between regular and irregular hierarchical structures, in both musicians and nonmusicians. This finding indicates that, when listening to music, humans apply cognitive processes that are capable of dealing with longdistance dependencies resulting from hierarchically organized syntactic structures. Our results reveal that a brain mechanism fundamental for syntactic processing is engaged during the perception of music, indicating that processing of hierarchical structure with nested nonlocal dependencies is not just a key component of human language, but a multidomain capacity of human cognition.

Análise e classificação de imagens de lesões da pele por atributos de cor, forma e textura utilizando máquina de vetor de suporte

The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries

Técnicas neuronais de agrupamentos aplicadas à detecção de anomalias em mamogramas digitais

This work proposes the development of an intelligent system for analysis of digital mammograms, capable to detect and to classify masses and microcalcifications. The digital mammograms will be pre-processed through techniques of digital processing of images with the purpose of adapting the image to the detection system and automatic classification of the existent calcifications in the suckles. The model adopted for the detection and classification of the mammograms uses the neural network of Kohonen by the algorithm Self Organization Map - SOM. The algorithm of Vector quantization, Kmeans it is also used with the same purpose of the SOM. An analysis of the performance of the two algorithms in the automatic classification of digital mammograms is developed. The developed system will aid the radiologist in the diagnosis and accompaniment of the development of abnormalities