Revisit of directed flow in relativistic heavy-ion collisions from a multiphase transport model

We have revisited several interesting questions on how the rapidity-odd directed flow is developed in relativistic $^{197}$Au+$^{197}$Au collisions at $\sqrt{s_{NN}}$ = 200 and 39 GeV based on a multiphase transport model. As the partonic phase evolves with time, the slope of the parton directed flow at midrapidity region changes from negative to positive as a result of the later dynamics at 200 GeV, while it remains negative at 39 GeV due to the shorter life time of the partonic phase. The directed flow splitting for various quark species due to their different initial eccentricities is observed at 39 GeV, while the splitting is very small at 200 GeV. From a dynamical coalescence algorithm with Wigner functions, we found that the directed flow of hadrons is a result of competition between the coalescence in momentum and coordinate space as well as further modifications by the hadronic rescatterings.Comment: 8 pages, 8 figures, version after major revisio

ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

Deep neural networks are vulnerable to adversarial attacks. The literature is rich with algorithms that can easily craft successful adversarial examples. In contrast, the performance of defense techniques still lags behind. This paper proposes ME-Net, a defense method that leverages matrix estimation (ME). In ME-Net, images are preprocessed using two steps: first pixels are randomly dropped from the image; then, the image is reconstructed using ME. We show that this process destroys the adversarial structure of the noise, while re-enforcing the global structure in the original image. Since humans typically rely on such global structures in classifying images, the process makes the network mode compatible with human perception. We conduct comprehensive experiments on prevailing benchmarks such as MNIST, CIFAR-10, SVHN, and Tiny-ImageNet. Comparing ME-Net with state-of-the-art defense mechanisms shows that ME-Net consistently outperforms prior techniques, improving robustness against both black-box and white-box attacks.Comment: ICML 201