Inhibition of respiratory syncytial virus by nasally administered siRNA modified with F-ANA

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

El humor judío femenino argentino contemporáneo en la narrativa de Alicia Steimberg, Silvia Plager y Ana María Shúa

En la creación literaria latinoamericana del siglo XX, la narrativa de los autores judíos ha proseguido un trayecto aparte, casi silencioso, en relación con la escritura canónica del continente. No obstante, hacia los años setenta, surge una producción literaria judía femenina que hoy ha alcanzado un renombre internacional. Esta tesis estudia la peculiaridad literaria del humor judío femenino argentino contemporáneo en torno a la definición de la identidad y rol de género, para comprender cómo se resignifica la hibridez cultural en el texto literario. El humor como catarsis de conflictos problematiza la relación entre lo propio y lo ajeno mediante una puesta en tela de juicio de la realidad para manifestarse como una preocupación, una responsabilidad y un compromiso crítico. A través del análisis de las obras de Alicia Steimberg, Silvia Plager y Ana María Shúa, la investigación reconfigura la vida judía diaspórica y el sentido de ser mujer respecto a la importancia del ámbito culinario en cuanto al influjo de la modernidad argentina en la tradición judía. En este estudio, se analizarán las novelas Músicos y relojeros, Como papas para varenikes y Risas y emociones de la cocina judía donde las diferentes reflexiones de Steimberg, Plager y Shúa en relación con la historia judía elaboran discursos con singulares diferencias en la intención humorística. En la primera parte de la tesis, se hará mención de diversas perspectivas teóricas que destacan las implicaciones psicológicas, sociales y psicoanalíticas respecto al humor en general, el humor de la minoría, el humor femenino y el humor judío. En los tres capítulos subsiguientes, se procederá a una lectura de los textos antes mencionados para interpretar las preocupaciones de nuestro tema frente a los dictámenes que la religión y sociedad desean imponer. Finalmente, la conclusión aborda la evolución de una visión entre humor negro, humor como crítica social y humor como lúdico juego en paralelo con una búsqueda de la identidad híbrida actualizada mediante una crítica de lo tradicional.

Modified Large Margin Nearest Neighbor Metric Learning for Regression

The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.

Regression models for bivariate survival data

Multivariate lifetime data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated lifetime when an individual is followed for the occurrence of two or more types of events, or when distinct individuals have dependent event times. In most studies there are covariates such as treatments, group indicators, individual characteristics, or environmental conditions, whose relationship to lifetime is of interest. This leads to a consideration of regression models.The well known Cox proportional hazards model and its variations, using the marginal hazard functions employed for the analysis of multivariate survival data in literature are not sufficient to explain the complete dependence structure of pair of lifetimes on the covariate vector. Motivated by this, in Chapter 2, we introduced a bivariate proportional hazards model using vector hazard function of Johnson and Kotz (1975), in which the covariates under study have different effect on two components of the vector hazard function. The proposed model is useful in real life situations to study the dependence structure of pair of lifetimes on the covariate vector . The well known partial likelihood approach is used for the estimation of parameter vectors. We then introduced a bivariate proportional hazards model for gap times of recurrent events in Chapter 3. The model incorporates both marginal and joint dependence of the distribution of gap times on the covariate vector . In many fields of application, mean residual life function is considered superior concept than the hazard function. Motivated by this, in Chapter 4, we considered a new semi-parametric model, bivariate proportional mean residual life time model, to assess the relationship between mean residual life and covariates for gap time of recurrent events. The counting process approach is used for the inference procedures of the gap time of recurrent events. In many survival studies, the distribution of lifetime may depend on the distribution of censoring time. In Chapter 5, we introduced a proportional hazards model for duration times and developed inference procedures under dependent (informative) censoring. In Chapter 6, we introduced a bivariate proportional hazards model for competing risks data under right censoring. The asymptotic properties of the estimators of the parameters of different models developed in previous chapters, were studied. The proposed models were applied to various real life situations.

An Improved Color Video Super-Resolution Using Kernel Regression and Fuzzy Enhancement

An improved color video super-resolution technique using kernel regression and fuzzy enhancement is presented in this paper. A high resolution frame is computed from a set of low resolution video frames by kernel regression using an adaptive Gaussian kernel. A fuzzy smoothing filter is proposed to enhance the regression output. The proposed technique is a low cost software solution to resolution enhancement of color video in multimedia applications. The performance of the proposed technique is evaluated using several color videos and it is found to be better than other techniques in producing high quality high resolution color videos

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Certamen literario para el alumnado 'Ana María Aparicio Pardo' : relatos premiados 2002-2006.

Recopilación de los cuentos ganadores de las cinco primeras ediciones del certamen literario Ana María Aparicio Pardo creado y organizado por dos institutos de secundaria de la provincia de Murcia. Está dirigido a alumnos de enseñanza secundaria y universitaria, con edades comprendidas entre los 12 y 21 años. La temática en estos cuentos es muy variada: violencia de género, amores y desamores, misterio, emigración ilegal, aventuras, explotación laboral o fantasía. .

From Regression to Classification in Support Vector Machines

We study the relation between support vector machines (SVMs) for regression (SVMR) and SVM for classification (SVMC). We show that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain choice of the parameters. In particular our result is that for $epsilon$ sufficiently close to one, the optimal hyperplane and threshold for the SVMC problem with regularization parameter C_c are equal to (1-epsilon)^{- 1} times the optimal hyperplane and threshold for SVMR with regularization parameter C_r = (1-epsilon)C_c. A direct consequence of this result is that SVMC can be seen as a special case of SVMR.

On the Noise Model of Support Vector Machine Regression

Support Vector Machines Regression (SVMR) is a regression technique which has been recently introduced by V. Vapnik and his collaborators (Vapnik, 1995; Vapnik, Golowich and Smola, 1996). In SVMR the goodness of fit is measured not by the usual quadratic loss function (the mean square error), but by a different loss function called Vapnik"s $epsilon$- insensitive loss function, which is similar to the "robust" loss functions introduced by Huber (Huber, 1981). The quadratic loss function is well justified under the assumption of Gaussian additive noise. However, the noise model underlying the choice of Vapnik's loss function is less clear. In this paper the use of Vapnik's loss function is shown to be equivalent to a model of additive and Gaussian noise, where the variance and mean of the Gaussian are random variables. The probability distributions for the variance and mean will be stated explicitly. While this work is presented in the framework of SVMR, it can be extended to justify non-quadratic loss functions in any Maximum Likelihood or Maximum A Posteriori approach. It applies not only to Vapnik's loss function, but to a much broader class of loss functions.

On the V(subscript gamma) Dimension for Regression in Reproducing Kernel Hilbert Spaces

This paper presents a computation of the $V_gamma$ dimension for regression in bounded subspaces of Reproducing Kernel Hilbert Spaces (RKHS) for the Support Vector Machine (SVM) regression $epsilon$-insensitive loss function, and general $L_p$ loss functions. Finiteness of the RV_gamma$ dimension is shown, which also proves uniform convergence in probability for regression machines in RKHS subspaces that use the $L_epsilon$ or general $L_p$ loss functions. This paper presenta a novel proof of this result also for the case that a bias is added to the functions in the RKHS.

Time-series regression models to study the short-term effects of environmental factors on health

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants

Regression analysis of compositional data when both the dependent variable and independent variable are components

It is well known that regression analyses involving compositional data need special attention because the data are not of full rank. For a regression analysis where both the dependent and independent variable are components we propose a transformation of the components emphasizing their role as dependent and independent variables. A simple linear regression can be performed on the transformed components. The regression line can be depicted in a ternary diagram facilitating the interpretation of the analysis in terms of components. An exemple with time-budgets illustrates the method and the graphical features

An alternative method for dating unknown tephras based on a segmented regression model

In CoDaWork’05, we presented an application of discriminant function analysis (DFA) to 4 different compositional datasets and modelled the first canonical variable using a segmented regression model solely based on an observation about the scatter plots. In this paper, multiple linear regressions are applied to different datasets to confirm the validity of our proposed model. In addition to dating the unknown tephras by calibration as discussed previously, another method of mapping the unknown tephras into samples of the reference set or missing samples in between consecutive reference samples is proposed. The application of these methodologies is demonstrated with both simulated and real datasets. This new proposed methodology provides an alternative, more acceptable approach for geologists as their focus is on mapping the unknown tephra with relevant eruptive events rather than estimating the age of unknown tephra. Kew words: Tephrochronology; Segmented regression

Los cuentos de Ana.

Resumen basado en la publicación

Ana Alcolea : bajo el león de San Marcos.

El autor del artículo realiza una reseña sobre el libro 'Bajo el león de San Marcos' de Ana Alcolea. Se trata de una novela de misterio y sentimientos en la que la protagonista se ve envuelta en un asesinato. Se detalla una pequeña biografía de la autora.