Artimate: an articulatory animation framework for audiovisual speech synthesis

We present a modular framework for articulatory animation synthesis using speech motion capture data obtained with electromagnetic articulography (EMA). Adapting a skeletal animation approach, the articulatory motion data is applied to a three-dimensional (3D) model of the vocal tract, creating a portable resource that can be integrated in an audiovisual (AV) speech synthesis platform to provide realistic animation of the tongue and teeth for a virtual character. The framework also provides an interface to articulatory animation synthesis, as well as an example application to illustrate its use with a 3D game engine. We rely on cross-platform, open-source software and open standards to provide a lightweight, accessible, and portable workflow.Comment: Workshop on Innovation and Applications in Speech Technology (2012

Singular limits for 4-dimensional semilinear elliptic problems with exponential nonlinearity

Using some nonlinear domain decomposition method, we prove the existence of singular limits for solution of semilinear elliptic problems with exponential nonlinearity.Comment: 29 page

Characterization of ligninolytic enzymes and metabolic profile of Cryphonectria parasitica and the isogenic converted strains by CHV1 hypovirus

Dupla diplomação com a Université Libre de TunisCryphonectria parasitica, the causal agent of Chestnut Blight, causes necrotic lesions (so-called cankers) on the bark of stems and branches of susceptible host trees. Cryphonectria Hypovirus 1 (CHV1) infects C. parasitica and reduces the fungus virulence (hypovirulence) and alters the fungus morphology in culture (pigmentation and sporulation capacity). By these characteristics the mycovirus CHV1 is used in Europe as a biological control agent of Chestnut Blight. The aim of this project is to better understand the effect of the mycovirus on the fungi pathogenicity by comparing the production of some lignin degrading enzymes and the metabolic profiles of some virulent and hypovirulent (converted and original) strains. For qualitative evaluation, several different compounds have been used as indicators for ligninolytic enzymes production. For quantitative evaluation, among nine strains five were chosen for biological tests and cultivation in minimal liquid media and the amount of enzyme produced were analyzed. Virulent strains were found to cause more damage in chestnut branches and to produce more lignin degrading enzymes. In apple fruits, some CHV1 infected strains produced bigger rot lesions than wild type strains. In parallel, Biolog FF MicroPlates have been used for the first time with C. parasitica strains to assess their metabolic profiles with concurrent reads of utilization of 95 different carbon sources. Moreover, carbohydrates, amino acids, amines/amides, miscellaneous and polymers were found to be more consumed by hypovirulent strains; therefore, this may suggest a novel adaptation mechanism in fungal ecology and fitness.Cryphonectria parasitica, o agente causal do cancro do castanheiro, provoca lesões necróticas (cancros) na casca do tronco e ramos de hospedeiros suscetíveis. O micovírus Cryphonectria hipovírus 1 (CHV1) infecta C. parasitica e reduz a virulência do fungo (hipovirulência) e altera a morfologia do fungo em cultura (capacidade de pigmentação e esporulação). Dadas essas características, o micovírus CHV1 é usado na Europa como agente de controlo biológico para o tratamento do cancro do castanheiro. O objetivo deste trabalho é entender melhor o efeito do micovírus na patogenicidade do fungo, comparando a produção de algumas enzimas que degradam a lenhina entre estirpes virulentas e hipovirulentas (convertidas e originais), assim como os perfis metabólicos. Para a avaliação qualitativa, vários compostos diferentes foram utilizados como indicadores para a produção de enzimas lenhinolíticas. Para a avaliação quantitativa, foram escolhidas cinco estirpes para testes biológicos e cultura em meio líquido mínimo, e a quantidade de enzima produzida analisada. Verificou-se que estirpes virulentas causam maior dimensão da lesãonos ramos do castanheiro e produzem mais enzimas lenhinolíticas. Em maçãs, algumas das estirpes hipovirulentas produziram lesões maiores do que as de tipo selvagem. Paralelamente, foram usadas pela primeira vez microplacas Biolog FF com estirpes de C. parasitica, para avaliar perfis metabólicos com leituras simultâneas de 95 fontes de carbono diferentes. Hidratos de carbono, aminoácidos, aminas / amidas, compostos diversos e polímeros foram mais consumidos pelas estirpes hipovirulentas; o que pode sugerir um mecanismo de adaptação ecológica do fungo

L'effet de la privatisation sur la croissance économique

Cette étude s'inscrit dans un cadre de mise en évidence la relation entre la privatisation et la croissance économique. En effet, Plane (1997) et Bernett (2000) ont dégagé une corrélation alors que Cook et Uchida (2001) ont montré qu'il s ‘agit d'une corrélation négative. Notre étude s'inscrit dans le même cadre, à partir d'un échantillon de 47 pays en développement durant la période 1990-99, nous avons testé un modèle à la croissance (EBA). Les résultats suggèrent que la privatisation n'est pas toujours synonyme à la performance macroéconomique. Elle doit être accompagnée par d'autres politiques telles que la libéralisation de l'activité économique ainsi qu'un environnement concurrentiel pour avoir un effet positif.

Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images

Detecting COVID-19 patients using Computed Tomography (CT) images of the lungs is an active area of research. Datasets of CT images from COVID-19 patients are becoming available. Deep learning (DL) solutions and in particular Convolutional Neural Networks (CNN) have achieved impressive results for the classification of COVID-19 CT images, but only when the training and testing take place within the same dataset. Work on the cross-dataset problem is still limited and the achieved results are low. Our work tackles the cross-dataset problem through a Domain Adaptation (DA) technique with deep learning. Our proposed solution, COVID19-DANet, is based on pre-trained CNN backbone for feature extraction. For this task, we select the pre-trained Efficientnet-B3 CNN because it has achieved impressive classification accuracy in previous work. The backbone CNN is followed by a prototypical layer which is a concept borrowed from prototypical networks in few-shot learning (FSL). It computes a cosine distance between given samples and the class prototypes and then converts them to class probabilities using the Softmax function. To train the COVID19-DANet model, we propose a combined loss function that is composed of the standard cross-entropy loss for class discrimination and another entropy loss computed over the unlabelled target set only. This so-called unlabelled target entropy loss is minimized and maximized in an alternative fashion, to reach the two objectives of class discrimination and domain invariance. COVID19-DANet is tested under four cross-dataset scenarios using the SARS-CoV-2-CT and COVID19-CT datasets and has achieved encouraging results compared to recent work in the literature.Comment: 31 pages, 15 figure