libstable: Fast, Parallel, and High-Precision Computation of α-Stable Distributions in R, C/C++, and MATLAB

α-stable distributions are a family of well-known probability distributions. However, the lack of closed analytical expressions hinders their application. Currently, several tools have been developed to numerically evaluate their density and distribution functions or to estimate their parameters, but available solutions either do not reach sufficient precision on their evaluations or are excessively slow for practical purposes. Moreover, they do not take full advantage of the parallel processing capabilities of current multi-core machines. Other solutions work only on a subset of the α-stable parameter space. In this paper we present an R package and a C/C++ library with a MATLAB front-end that permit parallelized, fast and high precision evaluation of density, distribution and quantile functions, as well as random variable generation and parameter estimation of α-stable distributions in their whole parameter space. The described library can be easily integrated into third party developments

Libstable: Fast, Parallel and High-Precision Computation of -Stable Distributions in C/C++ and MATLAB

-stable distributions are a wide family of probability distributions used in many elds where probabilistic approaches are taken. However, the lack of closed analytical expressions is a major drawback for their application. Currently, several tools have been developed to numerically evaluate their density and distribution functions or estimate their parameters, but available solutions either do not reach su cient precision on their evaluations or are too slow for several practical purposes. Moreover, they do not take full advantage of the parallel processing capabilities of current multi-core machines. Other solutions work only on a subset of the -stable parameter space. In this paper we present a C/C++ library and a MATLAB front-end that allows fully parallelized, fast and high precision evaluation of density, distribution and quantile functions (PDF, CDF and CDF1 respectively), random variable generation and parameter estimation of -stable distributions in their whole parameter space. The library provided can be easily integrated on third party developments

Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups

Producción CientíficaAttention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood that often persists into adulthood. Objectively diagnosing ADHD can be challenging due to the reliance on subjective questionnaires in clinical assessment. Fortunately, recent advancements in artificial intelligence (AI) have shown promise in providing objective diagnoses through the analysis of medical images or activity recordings. These AI-based techniques have demonstrated accurate ADHD diagnosis; however, the growing complexity of deep learning models has introduced a lack of interpretability. These models often function as black boxes, unable to offer meaningful insights into the data patterns that characterize ADHD.Agencia Estatal de Investigación (grants PID2020-115339RB-I00, TED2021-130090B-I00 and TED2021-131536B-I00)EU Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement (101008297)Company ESAOTE Ltd (grant 18IQBM

An extended BEPU approach integrating probabilistic assumptions on the availability of safety systems in deterministic safety analyses

[EN] The International Atomic Energy Agency (IAEA) produced guidance on the use of Deterministic Safety Analysis (DSA) for the design and licensing of Nuclear Power Plants (NPPs) in "DSA for NPP Specific Safety Guide, No. SSG-2", which proposes four options for the application of DSA. Option 3 involves the use of Best Estimate codes and data together with an evaluation of the uncertainties, the so called BEPU methodology. Several BEPU approaches have been developed in scopes that are accepted by the regulator authorities nowadays. They normally adopt conservative assumptions on the availability of safety systems. Option 4 goes beyond by pursuing the incorporation of realistic assumption on the availability of safety systems into the DSA. This paper proposes an Extended BEPU (EBEPU) approach that integrates insights from probabilistic Safety Analysis into a typical BEPU approach. There is an aim at combining the use of well-established BEPU methods and realistic ("probabilistic") assumptions on safety system availability. This paper presents the fundamentals of the EBEPU approach and the main results obtained for an example of application that focuses on an accident scenario corresponding to the initiating event "Loss of Feed Water (LOFW)" for a typical three-loops Pressurized Water Reactor (PWR) NPP.This work has been developed partially with the support of Programa de Apoyo a la Investigacion y Desarrollo of the Universitat Politecnica de Valencia (PAID UPV).Martorell Alsina, SS.; Sanchez Saez, F.; Villanueva López, JF.; Carlos Alberola, S. (2017). An extended BEPU approach integrating probabilistic assumptions on the availability of safety systems in deterministic safety analyses. Reliability Engineering & System Safety. 167:474-483. doi:10.1016/j.ress.2017.06.020S47448316

Anisotropic Diffusion Filter with Memory based on Speckle Statistics for Ultrasound Images

Ultrasound imaging exhibits considerable difficulties for medical visual inspection and for the development of automatic analysis methods due to speckle, which negatively affects the perception of tissue boundaries and the performance of automatic segmentation methods. With the aim of alleviating the effect of speckle, many filtering techniques are usually considered as a preprocessing step prior to automatic analysis methods or visual inspection. Most of the state-of-the-art filters try to reduce the speckle effect without considering its relevance for the characterization of tissue nature. However, the speckle phenomenon is the inherent response of echo signals in tissues and can provide important features for clinical purposes. This loss of information is even magnified due to the iterative process of some speckle filters, e.g., diffusion filters, which tend to produce over-filtering because of the progressive loss of relevant information for diagnostic purposes during the diffusion process. In this work, we propose an anisotropic diffusion filter with a probabilistic-driven memory mechanism to overcome the over-filtering problem by following a tissue selective philosophy. Specifically, we formulate the memory mechanism as a delay differential equation for the diffusion tensor whose behavior depends on the statistics of the tissues, by accelerating the diffusion process in meaningless regions and including the memory effect in regions where relevant details should be preserved. Results both in synthetic and real US images support the inclusion of the probabilistic memory mechanism for maintaining clinical relevant structures, which are removed by the state-of-the-art filters