867 resultados para Teaching-learning of Portuguese language


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A 77-year-old man with 8 year progressive language deterioration in the face of grossly intact memory was followed. No acute or chronic physiological or psychological event was associated with symptom onset. CT revealed small left basal ganglia infarct. Mild atrophy, no lacunar infarcts, mild diffuse periventricular changes registered on MRI. Gait normal but slow. Speech hesitant and sparse. Affect euthymic; neurobehavioral disturbance absent. MMSE 26/30; clock incorrect, concrete. Neuropsychological testing revealed simple attention intact; complex attention, processing speed impaired. Visuospatial copying and delayed recall of copy average with some perseveration. Apraxia absent. Recall mildly impaired. Mild deficits in planning, organization apparent. Patient severely aphasic, dysarthric without paraphasias. Repetition of automatic speech, recitation moderately impaired; prosody intact. Understanding of written language, nonverbal communication abilities, intact. Frontal release signs developed over last 12 months. Repeated cognitive testing revealed mild deterioration across all domains with significant further decrease in expressive, receptive language. Neurobehavioral changes remain absent to date; he remains interested, engaged and independent in basic ADLs. Speech completely deteriorated; gait and movements appreciably slowed. Although signs of frontal/executive dysfunction present, lack of behavioral abnormalities, psychiatric disturbance, personality change argue against focal or progressive frontal impairment or dementia. Relative intactness of memory and comprehension argue against Alzheimer’s disease. Lack of findings on neuroimaging argue against CVA or tumor. It is possible that the small basal ganglia infarct has resulted in a mild lateral prefrontal syndrome. However, the absence of depression as well as the relatively circumscribed language problem suggests otherwise. The progressive, severe nature of language impairments, with relatively minor impairments in attention and memory, argues for a possible diagnosis of primary progressive aphasia.

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Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

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An adaptive back-propagation algorithm is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, both numerical studies and a rigorous analysis show that the adaptive back-propagation method results in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.

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We complement recent advances in thermodynamic limit analyses of mean on-line gradient descent learning dynamics in multi-layer networks by calculating fluctuations possessed by finite dimensional systems. Fluctuations from the mean dynamics are largest at the onset of specialisation as student hidden unit weight vectors begin to imitate specific teacher vectors, increasing with the degree of symmetry of the initial conditions. In light of this, we include a term to stimulate asymmetry in the learning process, which typically also leads to a significant decrease in training time.

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Gibt es eine Traditionslinie extremistischer Poetiken in der deutschsprachigen Literatur? Uwe Schütte untersucht anhand literarischer Texte ab dem späten 18. Jahrhundert den Konnex zwischen historischen Phänomenen wie Revolution, Krieg oder Terrorismus und extremen biografischen Umständen wie Schizophrenie für die Herausbildung radikaler Schreibweisen. Die Spannbreite der behandelten Autoren reicht dabei von der Klassikertrias Kleist, Hölderlin und Büchner über Schriftsteller des 20. Jahrhunderts wie Ernst Jünger oder Hans Henny Jahnn bis zu den Gegenwartsautoren Ernst Herbeck und Rainald Goetz. The study investigates aesthetic representations of extremism in German-language literature from around 1800 to the present. Its aim is to examine the interplay between three different areas: historical circumstances, (auto)biographical issues, and literary texts. Discussed are texts by both major and marginal writers from various genres, ranging from classics such as Heinrich von Kleist or Friedrich Hölderlin to the marginalised poet Ernst Herbeck or the contemporary writer Rainald Goetz. Subjects and factors considered include extremist phenomena in modern history (such as revolutions, wars, terrorism) and extreme individual experiences (such as suicide or schizophrenia) on the aesthetic domain(s) with regard to the production of literary discourses that could be considered as extremist. These manifest themselves in the development of what can be viewed as ‘radical poetics’, decidedly innovative styles of writing and moral or political transgression in fiction. Being the first critical attempt to trace the history of radical discourses in German literature, the study explores the validity of creating an aesthetic category of 'literary extremism'.

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The influence of biases on the learning dynamics of a two-layer neural network, a normalized soft-committee machine, is studied for on-line gradient descent learning. Within a statistical mechanics framework, numerical studies show that the inclusion of adjustable biases dramatically alters the learning dynamics found previously. The symmetric phase which has often been predominant in the original model all but disappears for a non-degenerate bias task. The extended model furthermore exhibits a much richer dynamical behavior, e.g. attractive suboptimal symmetric phases even for realizable cases and noiseless data.

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The dynamics of on-line learning is investigated for structurally unrealizable tasks in the context of two-layer neural networks with an arbitrary number of hidden neurons. Within a statistical mechanics framework, a closed set of differential equations describing the learning dynamics can be derived, for the general case of unrealizable isotropic tasks. In the asymptotic regime one can solve the dynamics analytically in the limit of large number of hidden neurons, providing an analytical expression for the residual generalization error, the optimal and critical asymptotic training parameters, and the corresponding prefactor of the generalization error decay.

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An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteHGTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKabanpami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteHGTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

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The advent of Internet banking and phone banking is changing the role of bank branches from a predominantly transaction-based one to a sales-oriented role. This paper reports on an assessment of the branches of a Portuguese bank in terms of their performance in their new roles in three different areas: Their efficiency in fostering the use of new transaction channels, their efficiency in increasing sales and their customer base, and their efficiency in generating profits. Service quality is also a major issue in service organisations like bank branches, and therefore we analyse the way this dimension of performance has been accounted for in the literature and take it into account in our empirical application. We have used data envelopment analysis (DEA) for the different performance assessments, but we depart from traditional DEA models in some cases. Performance comparisons on each dimension allowed us to identify benchmark bank branches and also problematic bank branches. In addition, we found positive links between operational and profit efficiency and also between transactional and operational efficiency. Service quality is positively related with operational and profit efficiency. © 2006 Elsevier B.V. All rights reserved.