2,209 research outputs found
Performance of the Micromegas detector in the CAST experiment
The gaseous Micromegas detector designed for the CERN Axion search experiment
CAST, operated smoothly during Phase-I, which included the 2003 and 2004
running periods. It exhibited linear response in the energy range of interest
(1-10keV), good spatial sensitivity and energy resolution (15-19% FWHM at
5.9keV)as well as remarkable stability. The detector's upgrade for the 2004
run, supported by the development of advanced offline analysis tools, improved
the background rejection capability, leading to an average rate 5x10^-5
counts/sec/cm^2/keV with 94% cut efficiency. Also, the origin of the detected
background was studied with a Monte Carlo simulation, using the GEANT4 package.Comment: Prepared for PSD7: The Seventh International Conference on Position
Sensitive Detectors, Liverpool, United Kingdom, 12-16 Sep. 200
Bioinformatics Solutions for Image Data Processing
In recent years, the increasing use of medical devices has led to the generation of large amounts of data, including image data. Bioinformatics solutions provide an effective approach for image data processing in order to retrieve information of interest and to integrate several data sources for knowledge extraction; furthermore, images processing techniques support scientists and physicians in diagnosis and therapies. In addition, bioinformatics image analysis may be extended to support several scenarios, for instance, in cyber-security the biometric recognition systems are applied to unlock devices and restricted areas, as well as to access sensitive data. In medicine, computational platforms generate high amount of data from medical devices such as Computed Tomography (CT), and Magnetic Resonance Imaging (MRI); this chapter will survey on bioinformatics solutions and toolkits for medical imaging in order to suggest an overview of techniques and methods that can be applied for the imaging analysis in medicine
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
We consider a discrete optimization formulation for learning sparse
classifiers, where the outcome depends upon a linear combination of a small
subset of features. Recent work has shown that mixed integer programming (MIP)
can be used to solve (to optimality) -regularized regression problems
at scales much larger than what was conventionally considered possible. Despite
their usefulness, MIP-based global optimization approaches are significantly
slower compared to the relatively mature algorithms for -regularization
and heuristics for nonconvex regularized problems. We aim to bridge this gap in
computation times by developing new MIP-based algorithms for
-regularized classification. We propose two classes of scalable
algorithms: an exact algorithm that can handle features in a
few minutes, and approximate algorithms that can address instances with
in times comparable to the fast -based algorithms. Our
exact algorithm is based on the novel idea of \textsl{integrality generation},
which solves the original problem (with binary variables) via a sequence of
mixed integer programs that involve a small number of binary variables. Our
approximate algorithms are based on coordinate descent and local combinatorial
search. In addition, we present new estimation error bounds for a class of
-regularized estimators. Experiments on real and synthetic data
demonstrate that our approach leads to models with considerably improved
statistical performance (especially, variable selection) when compared to
competing methods.Comment: To appear in JML
HEP Community White Paper on Software trigger and event reconstruction
Realizing the physics programs of the planned and upgraded high-energy
physics (HEP) experiments over the next 10 years will require the HEP community
to address a number of challenges in the area of software and computing. For
this reason, the HEP software community has engaged in a planning process over
the past two years, with the objective of identifying and prioritizing the
research and development required to enable the next generation of HEP
detectors to fulfill their full physics potential. The aim is to produce a
Community White Paper which will describe the community strategy and a roadmap
for software and computing research and development in HEP for the 2020s. The
topics of event reconstruction and software triggers were considered by a joint
working group and are summarized together in this document.Comment: Editors Vladimir Vava Gligorov and David Lang
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Service provision and barriers to care for homeless people with mental health problems across 14 European capital cities
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