Learn More
—The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (ℓ2 penalty) or scattered (ℓ1 penalty) predictive pattern with a very limited relevance. A penalty like Total(More)
BACKGROUND A widespread and fundamental assumption in the health sciences is that muscle functions are related to a wide variety of conditions, for example pain, ischemic and neurological disorder, exercise and injury. It is therefore highly desirable to study musculoskeletal contributions in clinical applications such as the treatment of muscle injuries,(More)
Reactive oxygen species (ROS) are involved in the regulation of diverse physiological processes in plants, including various biotic and abiotic stress responses. Thus, oxidative stress tolerance mechanisms in plants are complex, and diverse responses at multiple levels need to be characterized in order to understand them. Here we present system responses to(More)
BACKGROUND Muscle functions are generally assumed to affect a wide variety of conditions and activities, including pain, ischemic and neurological disorders, exercise and injury. It is therefore very desirable to obtain more information on musculoskeletal contributions to and activity during clinical processes such as the treatment of muscle injuries,(More)
High-dimensional regression or classification models are increasingly used to analyze biological data such as neuroimaging of genetic data sets. However, classical penalized algorithms produce dense solutions that are difficult to interpret without arbitrary thresholding. Alternatives based on sparsity-inducing penalties suffer from coefficient instability.(More)
—Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA's inter-pretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful(More)
In the 18th and 19th centuries the branch of mathematics that would later be known as fractal geometry was developed. It was the ideas of Benoˆıt Mandelbrot that made the area expand so rapidly as it has done recently, and since the publication of his works there have for fractals, and most commonly the estimation of the fractal dimension, been found uses(More)
The ideas of Benoˆıt Mandelbrot, about the geometrical properties of sets which he called fractal, was published as late as 1975. Since then, there have for fractals, and most commonly the estimation of the fractal dimension, been found uses in the most diverse applications. Fractal geometry has been used in information theory, economics, flow dynamics and(More)
  • 1