11,072 research outputs found
Effects of quarks on the formation and evolution of Z(3) walls and strings in relativistic heavy-ion collisions
We investigate the effects of explicit breaking of Z(3) symmetry due to the
presence of dynamical quarks on the formation and evolution of Z(3) walls and
associated QGP strings within Polyakov loop model. We carry out numerical
simulations of the first order quark-hadron phase transition via bubble
nucleation (which may be appropriate, for example, at finite baryon chemical
potential) in the context of relativistic heavy-ion collision experiments.
Using appropriate shifting of the order parameter in the Polyakov loop
effective potential, we calculate the bubble profiles using bounce technique,
for the true vacuum as well as for the metastable Z(3) vacua, and estimate the
associated nucleation probabilities. These different bubbles are then nucleated
and evolved and resulting formation and dynamics of Z(3) walls and QGP strings
is studied. We discuss various implications of the existence of these Z(3)
interfaces and the QGP strings, especially in view of the effects of the
explicit breaking of the Z(3) symmetry on the formation and dynamical evolution
of these objects.Comment: 17 pages, 9 figures, PDFLate
Adversarial training and dilated convolutions for brain MRI segmentation
Convolutional neural networks (CNNs) have been applied to various automatic
image segmentation tasks in medical image analysis, including brain MRI
segmentation. Generative adversarial networks have recently gained popularity
because of their power in generating images that are difficult to distinguish
from real images.
In this study we use an adversarial training approach to improve CNN-based
brain MRI segmentation. To this end, we include an additional loss function
that motivates the network to generate segmentations that are difficult to
distinguish from manual segmentations. During training, this loss function is
optimised together with the conventional average per-voxel cross entropy loss.
The results show improved segmentation performance using this adversarial
training procedure for segmentation of two different sets of images and using
two different network architectures, both visually and in terms of Dice
coefficients.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi
Empirical determination of charm quark energy loss and its consequences for azimuthal anisotropy
We propose an empirical model to determine the form of energy loss of charm
quarks due to multiple scatterings in quark gluon plasma by demanding a good
description of production of D mesons and non-photonic electrons in
relativistic collision of heavy nuclei at RHIC and LHC energies. Best results
are obtained when we approximate the momentum loss per collision , where is a constant depending on the
centrality and the centre of mass energy. Comparing our results with those
obtained earlier for drag coefficients estimated using Langevin equation for
heavy quarks we find that up to half of the energy loss of charm quarks at top
RHIC energy could be due to collisions while that at LHC energy at 2760 GeV/A
the collisional energy loss could be about one third of the total. Estimates
are obtained for azimuthal anisotropy in momentum spectra of heavy mesons, due
to this energy loss. We further suggest that energy loss of charm quarks may
lead to an enhanced production of D-mesons and single electrons at low in
AA collisions.Comment: 11 pages, 3 figures, Typographical errors corrected, Key-words and
PACS indices added, sequence of figures corrected, references added in
section 3, discussions expande
Numerical Simulations of Magnetoacoustic-Gravity Waves in the Solar Atmosphere
We investigate the excitation of magnetoacoustic-gravity waves generated from
localized pulses in the gas pressure as well as in vertical component of
velocity. These pulses are initially launched at the top of the solar
photosphere that is permeated by a weak magnetic field. We investigate three
different configurations of the background magnetic field lines: horizontal,
vertical and oblique to the gravitational force. We numerically model
magnetoacoustic-gravity waves by implementing a realistic (VAL-C) model of
solar temperature. We solve two-dimensional ideal magnetohydrodynamic equations
numerically with the use of the FLASH code to simulate the dynamics of the
lower solar atmosphere. The initial pulses result in shocks at higher
altitudes. Our numerical simulations reveal that a small-amplitude initial
pulse can produce magnetoacoustic-gravity waves, which are later reflected from
the transition region due to the large temperature gradient. The atmospheric
cavities in the lower solar atmosphere are found to be the ideal places that
may act as a resonator for various oscillations, including their trapping and
leakage into the higher atmosphere. Our numerical simulations successfully
model the excitation of such wave modes, their reflection and trapping, as well
as the associated plasma dynamics
Deep Bilevel Learning
We present a novel regularization approach to train neural networks that
enjoys better generalization and test error than standard stochastic gradient
descent. Our approach is based on the principles of cross-validation, where a
validation set is used to limit the model overfitting. We formulate such
principles as a bilevel optimization problem. This formulation allows us to
define the optimization of a cost on the validation set subject to another
optimization on the training set. The overfitting is controlled by introducing
weights on each mini-batch in the training set and by choosing their values so
that they minimize the error on the validation set. In practice, these weights
define mini-batch learning rates in a gradient descent update equation that
favor gradients with better generalization capabilities. Because of its
simplicity, this approach can be integrated with other regularization methods
and training schemes. We evaluate extensively our proposed algorithm on several
neural network architectures and datasets, and find that it consistently
improves the generalization of the model, especially when labels are noisy.Comment: ECCV 201
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