Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Le Q-sort, un outil pour la recherche en soins le cas des représentations chez les infirmiers en psychiatrie de l'âge avancé [Q-sort, a useful research method in care in cases of psychiatric nursing of the aged]

Q-sort is a research method which allows defining profiles of attitudes toward a set of statements, ordered in relation to each other. Pertaining to the Q Methodology, the qualitative analysis of the Q-sorts is based on quantitative techniques. This method is of particular interest for research in health professions, a field in which attitudes of patients and professionals are very important. The method is presented in this article, along with an example of application in nursing in old age psychiatry.

I have faith in my thing, you know what I mean? Mixed method elements on individual investments on religious markets.

In contemporary society, religious signification and secular systems mix and influence each other. Holistic conceptions of a world in which man is integrated harmoniously with nature meet representations of a world run by an immanent God. On the market of the various systems, the individual goes from one system to another, following his immediate needs and expectations without necessarily leaving any marks in a meaningful long term system. This article presents the first results of an ongoing research in Switzerland on contemporary religion focusing on (new) paths of socialization of modern that individuals and the various (non-) belief systems that they simultaneously develop

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.

A potential vector of Schistosoma mansoni in Uruguay

Susceptibily experiments were carried out with a Biomphalaria straminea-like planorbid snail (Biomphalaria aff. straminea, species inquirenda) from Espinillar, near Salto (Uruguay), in the area of the Salto Grande reservoir, exposed individually to 5 miracidia of Schistosoma mansoni (SJ2 and BH2 strains). Of 130 snails exposed to the SJ2 strain, originally infective to Biomphalaria tenagophila, 30 became infected (23%). The prepatent (precercaria) period ranged from 35 to 65 days. The cercarial output was irregular, following no definite pattern, varying from 138 to 76,075 per snail (daily average 4.3 to 447.5 and ending up with death. Three specimens that died, without having shed cercarie, on days 69 (2) and 80 after exposure to miracidia, had developing secondary sporocysts in their tissues, justifying the prospect of a longer precercarial period in these cases. In a control group of 120 B. teangophila, exposed to the SJ2 strain, 40 became infected, showing an infection rate (33.3%) not significantly different from that of the Espinillar snail (X [raised to the power of] 2 = 3.26). No cercarie were produced by any of the Espinilar snails exposed to miracidia of the BH2 strain, originally infective to Biomphalaria glabrata. Four specimens showed each a primary sporocyst in one tentacle, which disappeared between 15 and 25 days post-exposure, and two others died with immature, very slender sporocysts in their tissues on days 36 and 54. In a control group of 100 B. glabrata exposed to BH2 miracidia, 94 shed cercariae (94%) and 6 remained negative. Calculation of Frandsen's (1979a, b) TCP/100 index shows that "Espinillar Biomphalaria-SJ2 S. mansoni" is a vector-parasite "compatible" combination. Seeing that tenagophila-borne schistosomiasis is prevalent in Rio de Janeiro and São Paulo states and has recently spread sothwards to Santa Catarina state, and the range of B. tenagophila overlaps taht of the Espinillar Biomphalaria, the possibility of schistosomiais establishing itself in Uruguay, although not imminent, is not to be disregarded.

Bayesian Inference in the Time Varying Cointegration Model

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit cointegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARs when allowing for cointegration. Instead we develop a specification which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving a permanent/transitory variance decomposition for inflation.

Simulación de fluidos inmiscibles con el Particle Finite Element Method (PFEM)

Proyecto de investigación realizado a partir de una estancia en el Centro Internacional de Métodos Computacionales en Ingeniería (CIMEC), Argentina, entre febrero y abril del 2007. La simulación numérica de problemas de mezclas mediante el Particle Finite Element Method (PFEM) es el marco de estudio de una futura tesis doctoral. Éste es un método desarrollado conjuntamente por el CIMEC y el Centre Internacional de Mètodos Numèrics en l'Enginyeria (CIMNE-UPC), basado en la resolución de las ecuaciones de Navier-Stokes en formulación Lagrangiana. El mallador ha sido implementado y desarrollado por Dr. Nestor Calvo, investigador del CIMEC. El desarrollo del módulo de cálculo corresponde al trabajo de tesis de la beneficiaria. La correcta interacción entre ambas partes es fundamental para obtener resultados válidos. En esta memoria se explican los principales aspectos del mallador que fueron modificados (criterios de refinamiento geométrico) y los cambios introducidos en el módulo de cálculo (librería PETSc, algoritmo predictor-corrector) durante la estancia en el CIMEC. Por último, se muestran los resultados obtenidos en un problema de dos fluidos inmiscibles con transferencia de calor.

Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model.

Large Time-Varying Parameter VARs

In this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.

Innovative method for carbon dioxide determination in human postmortem cardiac gas samples using headspace-gas chromatography-mass spectrometry and stable labeled isotope as internal standard.

A novel approach to measure carbon dioxide (CO2) in gaseous samples, based on a precise and accurate quantification by (13)CO2 internal standard generated in situ is presented. The main goal of this study was to provide an innovative headspace-gas chromatography-mass spectrometry (HS-GC-MS) method applicable in the routine determination of CO2. The main drawback of the GC methods discussed in the literature for CO2 measurement is the lack of a specific internal standard necessary to perform quantification. CO2 measurement is still quantified by external calibration without taking into account analytical problems which can often occur considering gaseous samples. To avoid the manipulation of a stable isotope-labeled gas, we have chosen to generate in situ an internal labeled standard gas ((13)CO2) on the basis of the stoichiometric formation of CO2 by the reaction of hydrochloric acid (HCl) with sodium hydrogen carbonate (NaH(13)CO3). This method allows a precise measurement of CO2 concentration and was validated on various human postmortem gas samples in order to study its efficiency.

Modified 'dumbbell' technique: a simple and intuitive method to position balloon-expandable stent valves.

Intraoperative cardiac imaging plays a key role during transcatheter aortic valve replacement. In recent years, new techniques and new tools for improved image quality and virtual navigation have been proposed, in order to simplify and standardize stent valve positioning and implantation. But routine performance of the new techniques may require major economic investments or specific knowledge and skills and, for this reason, they may not be accessible to the majority of cardiac centres involved in transcatheter valve replacement projects. Additionally, they still require injections of contrast medium to obtain computed images. Therefore, we have developed and describe here a very simple and intuitive method of positioning balloon-expandable stent valves, which represents the evolution of the 'dumbbell' technique for echocardiography-guided transcatheter valve replacement without angiography. This method, based on the partial inflation of the balloon catheter during positioning, traps the crimped valve in the aortic valve orifice and, consequently, very near to the ideal landing zone. It does not require specific echocardiographic knowledge; it does not require angiographies that increase the risk of postoperative kidney failure in elderly patients, and it can be also performed in centres not equipped with a hybrid operating room.