Some aspects of global Lambda polarization in heavy-ion collisions

Large orbital angular momentum can be generated in non-central heavy-ion collisions, and part of it is expected to be converted into final particle's polarization due to the spin-orbit coupling. Within the framework of A Multi-Phase Transport (AMPT) model, we studied the vorticity-induced polarization of $\Lambda$ hyperons at the midrapidity region $|\eta|<1$ in Au-Au collisions at energies $\sqrt{s_{NN}}=7.7\sim200$ GeV. Our results show that the global polarization decreases with the collisional energies and is consistent with the recent STAR measurements. This behavior can be understood by less asymmetry of participant matter in the midrapidity region due to faster expansion of fireball at higher energies. As another evidence, we discuss how much the angular momentum is deposited in different rapidity region. The result supports our asymmetry argument.Comment: 6 pages, 4 figures, CPOD 2017 proceedin

Robust Stabilization and H

This paper is concerned with the problem of robust stabilization and H∞ control for a class of uncertain neural networks. For the robust stabilization problem, sufficient conditions are derived based on the quadratic convex combination property together with Lyapunov stability theory. The feedback controller we design ensures the robust stability of uncertain neural networks with mixed time delays. We further design a robust H∞ controller which guarantees the robust stability of the uncertain neural networks with a given H∞ performance level. The delay-dependent criteria are derived in terms of LMI (linear matrix inequality). Finally, numerical examples are provided to show the effectiveness of the obtained results

A multi-task learning CNN for image steganalysis

Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN