Scaling of the chiral magnetic effect in quantum diffusive Weyl semimetals

We investigate the effect of short-range spin-independent disorder on the chiral magnetic effect (CME) in Weyl semimetals. Based on a minimum two-band model, the disorder effect is examined in the quantum diffusion limit by including the Drude correction and the correction due to the Cooperon channel. It is shown that the Drude correction renormalizes the CME coefficient by a factor to a finite value that is independent of the system size. Furthemore, due to an additional momentum expansion involved in deriving the CME coefficient, the contribution of Cooperon to the CME coefficient is governed by the quartic momentum term. As a result, in contrast to the weak localization and weak anti-localization effects observed in the measurement of conductivity of Dirac fermions, we find that in the limit of zero magnetic field, the CME coefficients of finite systems manifest the same scaling of localization even in three dimension. Our results indicate that while the chiral magnetic current due to slowly oscillating magnetic fields can exist in clean systems, its observability will be limited by suppression due to short-range disorder in condensed matters.Comment: 13 pages, 4 figure, to appear in Phys. Rev.

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS