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 $\Delta p_T \propto \alpha \, p_T$, where $\alpha$ 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 $p_T$ 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