On q-deformed infinite-dimensional n-algebra

The $q$-deformation of the infinite-dimensional $n$-algebra is investigated. Based on the structure of the $q$-deformed Virasoro-Witt algebra, we derive a nontrivial $q$-deformed Virasoro-Witt $n$-algebra which is nothing but a sh-$n$-Lie algebra. Furthermore in terms of the pseud-differential operators on the quantum plane, we construct the (co)sine $n$-algebra and the $q$-deformed $SDiff(T^2)$ $n$-algebra. We prove that they are the sh-$n$-Lie algebras for the case of even $n$. An explicit physical realization of the (co)sine $n$-algebra is given.Comment: 22 page

Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201

Effect of near-fault ground motions with long-period pulses on the tunnel

Investigations from recent strong earthquakes indicate most of the tunnels severely damaged are located near the causative faults. First, the dynamic response of the tunnel to the near-fault and far-field ground motions was investigated. The results show that the near-fault motions with long-period pulses especially the forward directivity pulses are more damaging than the typical far-field records, which should be reflected in the seismic design guideline for tunnels near causative faults. Furthermore, the effects of the key parameters for the simplified pulse on the dynamic response of the tunnel were also studied. Generally, the pulse with larger amplitude brings more energy and leads to larger strains in rock. Consequently, it becomes more damaging to the tunnel. The period of the pulse can remarkably influence the response of the tunnel. When the period of the pulse is less than 3.0 s, the pulse becomes less damaging to the tunnel with the increase of the period. Once the period exceeds 3.0 s, the pulse has little effect on the dynamic response of the tunnel. Thus, the earthquake with lower magnitude, which is likely to leads to lower period, may be more damaging to the tunnel. Besides, as the number of significant cycles increases, the damage potential of the ground motions increases accordingly. For the sake of security, two significant cycles in velocity-time history are recommended for the seismic design of tunnels close to ruptured faults