Troca de cuidados: estudo sobre as negociações dos casais adeptos do swing acerca da prevenção de DSTs/AIDS.

A presente pesquisa tem como principal objetivo investigar as características do casal heterossexual moderno praticante de swing. Em especial, busca-se compreender quais fatores influenciam as negociações dos adeptos acerca da prevenção de DSTs/Aids. O swing, também conhecido como troca de casais, é considerado uma das experiências possíveis de não exclusividade sexual dentro da relação conjugal, o que significa dizer que os parceiros que o praticam, em comum acordo, permitem a ocorrência de intercursos sexuais envolvendo terceiros e preferencialmente em ambientes compartilhados. O estabelecimento do swing enquanto estilo de vida é a principal premissa dos praticantes. A partir das observações etnográficas de festas swingers realizadas em uma boate na Zona Oeste da cidade do Rio de Janeiro, da análise dos discursos de casais informantes e do levantamento das pesquisas sobre swing realizadas no Brasil, Europa e Estados Unidos, foi possível refletir a respeito das particularidades socioculturais deste grupo, bem como apreender o conjunto de valores que o orientam. As trajetórias dos sujeitos, desde o descobrimento do swing até o envolvimento real com o universo em questão, também são abordados neste trabalho. Finalmente, procura-se descrever e analisar os principais aspectos em torno das condutas sexuais dos swingers e a relação destas com o uso ou desuso de estratégias preventivas a fim de suscitar reflexões contributivas às discussões sobre prevenção de DSTs/Aids entre swingers.

A Theory of Networks for Appxoimation and Learning

Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multi-dimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. This paper considers the problems of an exact representation and, in more detail, of the approximation of linear and nolinear mappings in terms of simpler functions of fewer variables. Kolmogorov's theorem concerning the representation of functions of several variables in terms of functions of one variable turns out to be almost irrelevant in the context of networks for learning. We develop a theoretical framework for approximation based on regularization techniques that leads to a class of three-layer networks that we call Generalized Radial Basis Functions (GRBF), since they are mathematically related to the well-known Radial Basis Functions, mainly used for strict interpolation tasks. GRBF networks are not only equivalent to generalized splines, but are also closely related to pattern recognition methods such as Parzen windows and potential functions and to several neural network algorithms, such as Kanerva's associative memory, backpropagation and Kohonen's topology preserving map. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage. The paper introduces several extensions and applications of the technique and discusses intriguing analogies with neurobiological data.

Extensions of a Theory of Networks for Approximation and Learning: Outliers and Negative Examples

Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multi-dimensional function. From this point of view, this form of learning is closely related to regularization theory. In this note, we extend the theory by introducing ways of dealing with two aspects of learning: learning in the presence of unreliable examples and learning from positive and negative examples. The first extension corresponds to dealing with outliers among the sparse data. The second one corresponds to exploiting information about points or regions in the range of the function that are forbidden.

A Nondeterministic Minimization Algorithm

The problem of minimizing a multivariate function is recurrent in many disciplines as Physics, Mathematics, Engeneering and, of course, Computer Science. In this paper we describe a simple nondeterministic algorithm which is based on the idea of adaptive noise, and that proved to be particularly effective in the minimization of a class of multivariate, continuous valued, smooth functions, associated with some recent extension of regularization theory by Poggio and Girosi (1990). Results obtained by using this method and a more traditional gradient descent technique are also compared.

Models of Noise and Robust Estimates

Given n noisy observations g; of the same quantity f, it is common use to give an estimate of f by minimizing the function Eni=1(gi-f)2. From a statistical point of view this corresponds to computing the Maximum likelihood estimate, under the assumption of Gaussian noise. However, it is well known that this choice leads to results that are very sensitive to the presence of outliers in the data. For this reason it has been proposed to minimize the functions of the form Eni=1V(gi-f), where V is a function that increases less rapidly than the square. Several choices for V have been proposed and successfully used to obtain "robust" estimates. In this paper we show that, for a class of functions V, using these robust estimators corresponds to assuming that data are corrupted by Gaussian noise whose variance fluctuates according to some given probability distribution, that uniquely determines the shape of V.

A Connection Between GRBF and MLP

Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approximation properties, theoretically and experimentally. Are they related? The main point of this paper is to show that for normalized inputs, multilayer perceptron networks are radial function networks (albeit with a non-standard radial function). This provides an interpretation of the weights w as centers t of the radial function network, and therefore as equivalent to templates. This insight may be useful for practical applications, including better initialization procedures for MLP. In the remainder of the paper, we discuss the relation between the radial functions that correspond to the sigmoid for normalized inputs and well-behaved radial basis functions, such as the Gaussian. In particular, we observe that the radial function associated with the sigmoid is an activation function that is good approximation to Gaussian basis functions for a range of values of the bias parameter. The implication is that a MLP network can always simulate a Gaussian GRBF network (with the same number of units but less parameters); the converse is true only for certain values of the bias parameter. Numerical experiments indicate that this constraint is not always satisfied in practice by MLP networks trained with backpropagation. Multiscale GRBF networks, on the other hand, can approximate MLP networks with a similar number of parameters.

Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers fo the lower dimensional maniforlds that define the boundaries between classes, for clouds of multi-dimensional, mult-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the applicatin of these extensions are also given.

On the Relationship Between Generalization Error, Hypothesis Complexity, and Sample Complexity for Radial Basis Functions

In this paper, we bound the generalization error of a class of Radial Basis Function networks, for certain well defined function learning tasks, in terms of the number of parameters and number of examples. We show that the total generalization error is partly due to the insufficient representational capacity of the network (because of its finite size) and partly due to insufficient information about the target function (because of finite number of samples). We make several observations about generalization error which are valid irrespective of the approximation scheme. Our result also sheds light on ways to choose an appropriate network architecture for a particular problem.

Características espermáticas de caprinos Moxotó de acordo com a morfologia escrotal.

1983

Toward a comprehensive care of HIV patients: Finding a strategy to detect depression in a Spanish HIV cohort

Depression is a common but frequently undiagnosed feature in individuals with HIV infection. To find a strategy to detect depression in a non-specialized clinical setting, the overall performance of the Hospital Anxiety and Depression Scale (HADS) and the depression identification questions proposed by the European AIDS Clinical Society (EACS) guidelines were assessed in a descriptive cross-sectional study of 113 patients with HIV infection. The clinician asked the two screening questions that were proposed under the EACS guidelines and requested patients to complete the HADS. A psychiatrist or psychologist administered semi-structured clinical interviews to yield psychiatric diagnoses of depression (gold standard). A receiver operating characteristic (ROC) analysis for the HADS-Depression (HADS-D) subscale indicated that the best sensitivity and specificity were obtained between the cut-off points of 5 and 8, and the ROC curve for the HADS-Total (HADS-T) indicated that the best cut-off points were between 12 and 14. There were no statistically significant differences in the correlations of the EACS (considering positive responses to one [A] or both questions [B]), the HADS-D ≥ 8 or the HADS-T ≥ 12 with the gold standard. The study concludes that both approaches (the two EACS questions and the HADS-D subscale) are appropriate depression-screening methods in HIV population. We believe that using the EACS-B and the HADS-D subscale in a two-step approach allows for rapid, assumable and accurate clinical diagnosis in non-psychiatric hospital settings.

Exgerentes de Artesanías de Colombia.

Compendio de fotografías que ilustran actividades de promoción social para el desarrollo de la artesanía, suscripción de convenios interinstitucionales, eventos feriales, visitas a municipios artesanales tradicionales, entrega de condecoraciones y reconocimientos a las destrezas de los artesanos, inauguración de ferias artesanales que en colaboración con funcionarios de la entidad fueron llevados a cabo por parte de los exgerentes de Artesanías de Colombia: Federico Echeberría Olarte (1968-1972), Graciela Samper de Bermúdez (1972-1984), María Cristina Palau de Angulo (1985-1990), Cecilia Duque Dique (01990-2005) y Paola Andrea Munñoz Jurado (2005-2009). (Herrera Rubio, Neve Enrique)

DSpace Basic Tutorial

PDF of powerpoint slides presented at DSUG 2007 Roma

Guidelines on artificial nutrition versus hydration in terminal cancer patients

Whether a terminally ill cancer patient should be actively fed or simply hydrated through subcutaneous or intravenous infusion of isotonic fluids is a matter of ongoing controversy among clinicians involved in the care of these patients. Under the auspices of the European Association for Palliative Care, a committee of experts developed guidelines to help clinicians make a reasonable decision on what type of nutritional support should be provided on a case-by-case basis. It was acknowledged that part of the controversy related to the definition of the terminal cancer patient, since this is a heterogeneous group of patients with different needs, expectations, and potential for a medical intervention. A major difficulty is the prediction of life expectancy and the patient's likely response to vigorous nutritional support. In an attempt to reach a decision on the type of treatment support (artificial nutrition vs. hydration) which would best meet the needs and expectations of the patient, we propose a three-step process: Step I: define the eight key elements necessary to reach a decision: Step II: make the decision; and Step III: reevaluate the patient and the proposed treatment at specified intervals. Step I involves assessing the patient concerning the following: 1) oncological/clinical condition; 2) symptoms; 3) expected length of survival; 4) hydration and nutritional status; 5) spontaneous or voluntary nutrient intake; 6) psychological profile; 7) gut function and potential route of administration; and 8) need for special services based on type of nutritional support prescribed. Step II involves the overall assessment of pros and cons, based on information determined in Step I, in order to reach an appropriate decision based on a well-defined end point (i.e. improvement of quality of life; maintaining patient survival; attaining rehydration). Step III involves the periodic reevaluation of the decision made in Step II based on the proposed goal and the attained result.