Crystalline color superconductors

Inhomogeneous superconductors and inhomogeneous superfluids appear in a variety of contexts including quark matter at extreme densities, fermionic systems of cold atoms, type-II cuprates, and organic superconductors. In the present review the focus is on properties of quark matter at high baryonic density, which may exist in the interior of compact stars. The conditions realized in these stellar objects tend to disfavor standard symmetric BCS pairing and may favor an inhomogeneous color superconducting phase. The properties of inhomogeneous color superconductors are discussed in detail and in particular of crystalline color superconductors. The possible astrophysical signatures associated with the presence of crystalline color superconducting phases within the core of compact stars are also reviewed.Comment: Added 3 figures, added section II F, added section with conclusions. Several references added. Improved the quality of the presentation and removed various typos. Almost matches the version accepted for publication of Reviews of Modern Physic

Neutrino emission from compact stars and inhomogeneous color superconductivity

We discuss specific heat and neutrino emissivity due to direct Urca processes for quark matter in the color superconductive Larkin-Ovchinnikov-Fulde-Ferrell (LOFF) phase of Quantum-Chromodynamics. We assume that the three light quarks $u, d, s$ are in a color and electrically neutral state and interact by a four fermion Nambu-Jona Lasinio coupling. We study a LOFF state characterized by a single plane wave for each pairing. From the evaluation of neutrino emissivity and fermionic specific heat, the cooling rate of simplified models of compact stars with a quark core in the LOFF state is estimated.Comment: 16 pages, 5 figures, revtex4 style. Version accepted for publication in Phys. Rev.

INTEGERS: ELECTRONIC JOURNAL OF COMBINATORIAL NUMBER THEORY 5 (2005), #A18 TWO VERY SHORT PROOFS OF A COMBINATORIAL IDENTITY

We give two quick elementary proofs of a well-known additive formula for the factorial which arises from the calculus of finite differences. The first one is purely analytical, the second one purely combinatorial. 1

Robust scheduling of parallel machines with sequence-dependent set-up costs

In this paper we propose a robust approach for solving the scheduling problem of parallel machines with sequence-dependent set-up costs. In the literature, several mathematical models and solution methods have been proposed to solve such scheduling problems, but most of which are based on the strong assumption that input data are known in a deterministic way. In this paper, a fuzzy mathematical programming model is formulated by taking into account the uncertainty in processing times to provide the optimal solution as a trade-off between total set-up cost and robustness in demand satisfaction. The proposed approach requires the solution of a non-linear mixed integer programming (NLMIP), that can be formulated as an equivalent mixed integer linear programming (MILP) model. The resulting MILP model in real applications could be intractable due to its NP-hardness. Therefore, we propose a solution method technique, based on the solution of an approximated model, whose dimension is remarkably reduced with respect to the original counterpart. Numerical experiments conducted on the basis of data taken from a real application show that the average deviation of the reduced model solution over the optimum is less than 1.5%

Evolving and explainable clinical risk assessment at the edge

The progress of the Internet of Medical Things (IoMT) and mobile technologies is a crucial driver for the evolution of healthcare systems in the path of prevention, early diagnosis and care, consequently unleashing the full potential of medical devices. Especially in intensive care, several vital signs can be monitored to provide an Early Warning Score (EWS) useful to detect the onset of pathological events or severe conditions. However, under these conditions, it would be beneficial to design a system that can provide a risk assessment even in the presence of a reduced number of vital signs. In this work, we propose an on-edge system, connected to one or more wearable medical devices, that is able to collect, analyze and interpret real-time clinical parameters and to provide an EWS-like clinical risk measurement. The system shows an evolutionary behavior by dividing the learning problem in two simpler ones, in order to correctly distinguish between low-urgency and emergency scenarios, with the possibility of selecting the most convenient configuration able to choose the most appropriate classifier even when the feature set does not allow a robust model selection. In particular, we focus on a comparative analysis of machine learning (ML) methods in different conditions of available vital parameter sets, evolving therefore to an adaptive ML approach. Moreover, since from the integration of artificial intelligence tools and IoMT, emerging ethical issues may arise about lack of transparency, a semantic-based explanation is associated to enrich the predictions along with the health data by means of Semantic Web technologies

An Ensemble Learning Approach Based on Diffusion Tensor Imaging Measures for Alzheimer’s Disease Classification

Recent advances in neuroimaging techniques, such as diffusion tensor imaging (DTI), represent a crucial resource for structural brain analysis and allow the identification of alterations related to severe neurodegenerative disorders, such as Alzheimer’s disease (AD). At the same time, machine-learning-based computational tools for early diagnosis and decision support systems are adopted to uncover hidden patterns in data for phenotype stratification and to identify pathological scenarios. In this landscape, ensemble learning approaches, conceived to simulate human behavior in making decisions, are suitable methods in healthcare prediction tasks, generally improving classification performances. In this work, we propose a novel technique for the automatic discrimination between healthy controls and AD patients, using DTI measures as predicting features and a soft-voting ensemble approach for the classification. We show that this approach, efficiently combining single classifiers trained on specific groups of features, is able to improve classification performances with respect to the comprehensive approach of the concatenation of global features (with an increase of up to 9% on average) and the use of individual groups of features (with a notable enhancement in sensitivity of up to 11%). Ultimately, the feature selection phase in similar classification tasks can take advantage of this kind of strategy, allowing one to exploit the information content of data and at the same time reducing the dimensionality of the feature space, and in turn the computational effort