A Machine Learning Approach for Disease Genes Signatures
In the context of network medicine, disease genes, i.e. genes that have been experimentally associated to the onset or progression of a pathology, show a complex set of features that are not easily reduced to, and grasped by a simple network approach (e.g., studying centrality measures or clustering characteristics of the gene network).
Less Is Enough: Assessment of the Random Sampling Method for the Analysis of Magnetoencephalography (MEG) Data
Magnetoencephalography (MEG) aims at reconstructing the unknown neuroelectric activity in the brain from non-invasive measurements of the magnetic field induced by neural sources. The solution of this ill-posed, ill-conditioned inverse problem is usually dealt with using regularization techniques that are often time-consuming, and computationally and memory storage demanding. In this paper we analyze how a slimmer procedure, random sampling, affects the estimation of the brain activity generated by both synthetic and real sources.
Uniform weighted approximation by multivariate filtered polynomials
The paper concerns the weighted uniform approximation of a real function on the d-cube [-1, 1]^d, with d > 1, by means of some multivariate filtered polynomials. These polynomials have been deduced, via tensor product, from certain de la Vallée Poussin type means on [-1, 1], which generalize classical delayed arithmetic means of Fourier partial sums. They are based on arbitrary sequences of filter coefficients, not necessarily connected with a smooth filter function.
Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices
Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease.
Some Remarks about the Hilbert Transform
In this paper we present som eremarks about the Hilbert transform on the real line and its numerical approximation, in connection with its application in signal processing.
Benchmarking multi-GPU applications on modern multi-GPU integrated systems
GPUs are very powerful computing accelerators that are often employed in single-device configuration. However, there is a steadily growing interest in using multiple GPUs in a concurrent way both to overcome the memory limitations of the single device and to further reduce execution times. Until recently, communication among GPUs had been carried out mainly by using networking technologies originally devised for standard CPUs with the CPU playing an active role in the communication.
