Search results for "NETWORKS"
showing 10 items of 3260 documents
A neural network clustering algorithm for the ATLAS silicon pixel detector
2014
A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. …
How neutron scattering experiments can target the structure and dynamics of milk proteins?
2016
International audience; The powerful of neutron scattering techniques to characterize structure and dynamics of milk proteins is illustrated. Small angle neutron scattering and reflectivity are used to determine the structure and the interactions between milk proteins in solution, during gelation processes, or the protein structure at different interfaces. Experiments performed by inelastic and quasielastic neutron scattering allow one to observe the dynamics of water and proteins showing the major role of hydration on the dynamics of milk proteins.
Condensation and thermalization of classsical optical waves in a waveguide
2011
http://pra.aps.org/; International audience; We consider the long-term evolution of a random nonlinear wave that propagates in a multimode optical waveguide. The optical wave exhibits a thermalization process characterized by an irreversible evolution toward an equilibrium state. The tails of the equilibrium distribution satisfy the property of energy equipartition among the modes of the waveguide. As a consequence of this thermalization, the optical field undergoes a process of classical wave condensation, which is characterized by a macroscopic occupation of the fundamental mode of the waveguide. Considering the nonlinear Schrödinger equation with a confining potential, we formulate a wav…
Models and solution methods for the uncapacitatedr-allocationp-hub equitable center problem
2017
Hub networks are commonly used in telecommunications and logistics to connect origins to destinations in situations where a direct connection between each origin–destination (o-d) pair is impractical or too costly. Hubs serve as switching points to consolidate and route traffic in order to realize economies of scale. The main decisions associated with hub-network problems include (1) determining the number of hubs (p), (2) selecting the p-nodes in the network that will serve as hubs, (3) allocating non-hub nodes (terminals) to up to r-hubs, and (4) routing the pairwise o-d traffic. Typically, hub location problems include all four decisions while hub allocation problems assume that the valu…
Mean-Field Game Modeling the Bandwagon Effect with Activation Costs
2015
This paper provides a mean-field game theoretic model of the bandwagon effect in social networks. This effect can be observed whenever individuals tend to align their own opinions to a mainstream opinion. The contribution is threefold. First, we describe the opinion propagation as a mean-field game with local interactions. Second, we establish mean-field equilibrium strategies in the case where the mainstream opinion is constant. Such strategies are shown to have a threshold structure. Third, we extend the use of threshold strategies to the case of time-varying mainstream opinion and study the evolution of the macroscopic system.
Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis
2021
Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this st…
Research on the internal pressure behavior of metal gas distribution pipelines with different types of tubing defects
2017
The paper aims to approach an important subject related to natural gas distribution networks which, depending on the expansion of the localities, are composed of intercommunicating pipes, pressure reducing stations and branch connections fittings. The urban networks are the most complex ones and the rural areas networks are the simplest. However, irrespective of their installation, they must meet the safety operating requirements as much as possible. According to standards, all these components must be tight and pressure resistant. In this regard, we intend to approach a very important issue related to the behavior of the tubular steel material showing corrosion and/or material defects, and…
Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR
2021
In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it into the loss function t…
The impact of grain size on the efficiency of embedded SIMD image processing architectures
2004
Pixel-per-processing element (PPE) ratio-the amount of image data directly mapped to each processing element-has a significant impact on the area and energy efficiency of embedded SIMD architectures for image processing applications. This paper quantitatively evaluates the impact of PPE ratio on system performance and efficiency for focal-plane SIMD image processing architectures by comparing throughput, area efficiency, and energy efficiency for a range of common application kernels using architectural and workload simulation. While the impact of grain size is affected by the mix of executed instructions within an application program, the most efficient PPE ratio often does not occur at PE…
TPEN: A Triple-foil differential Plunger for lifetime measurements of excited states in Exotic Nuclei
2019
Abstract A Triple-foil differential Plunger for Exotic Nuclei (TPEN) has been developed to measure the lifetimes of excited states in nuclei with small production cross-sections. TPEN utilises one target foil and two degrader foils to make differential lifetime measurements: directly determining the decay function and its derivative at a single plunger distance setting. The direct measurement of the decay function and its derivative removes the requirement to measure γ -ray intensities at several target-to-degrader distances, thereby reducing the beam-time required relative to a conventional plunger with a single-degrader foil. This paper describes the commissioning of TPEN in the lifetime …