The Impacts Of Regulatory Regimes And Science & Technology Policy On Innovation Performance：Based On China’s National Hi-Tech Industry Development Zone

Along with the internal environment of China’s National Hi-tech industry Development Zone becoming more complicated, it’s difficult to show the advantages of their original resources. And internal institutional environment has gradually become more importance on innovation performance. Based on the existing studies, this paper tries to do a regression analysis of Hi-tech zone’s regulatory regimes, policy, and innovation performance, aiming to find out the key institutional factors which influenced the High-tech zone’s innovation performance. The results showed that: (1) the more Municipal administrative privileges Hi-tech zone has, the better its performance will be. (2) The national level policy has a significant positive correlation with innovation performance; but the policy from provincial and municipal governments has a significant negative correlation. (3) The nature of management agency has negative regulation in the relationship between the power of provincial and municipal policies and the innovation performance. This research tries to provide a new revelation for the hi-tech zones, which will help them get more scientific management operations and development policy. Keywords : National Hi-tech Industry Development Zone, regulatory Regimes, Science & Technology policy, innovation Performanc

Impact of high-frequency pumping on anomalous finite-size effects in three-dimensional topological insulators

Lowering of the thickness of a thin-film three-dimensional topological insulator down to a few nanometers results in the gap opening in the spectrum of topologically protected two-dimensional surface states. This phenomenon, which is referred to as the anomalous finite-size effect, originates from hybridization between the states propagating along the opposite boundaries. In this work, we consider a bismuth-based topological insulator and show how the coupling to an intense high-frequency linearly polarized pumping can further be used to manipulate the value of a gap. We address this effect within recently proposed Brillouin-Wigner perturbation theory that allows us to map a time-dependent problem into a stationary one. Our analysis reveals that both the gap and the components of the group velocity of the surface states can be tuned in a controllable fashion by adjusting the intensity of the driving field within an experimentally accessible range and demonstrate the effect of light-induced band inversion in the spectrum of the surface states for high enough values of the pump.Comment: 6 pages, 3 figure

Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification

To better address challenging issues of the irregularity and inhomogeneity inherently present in 3D point clouds, researchers have been shifting their focus from the design of hand-craft point feature towards the learning of 3D point signatures using deep neural networks for 3D point cloud classification. Recent proposed deep learning based point cloud classification methods either apply 2D CNN on projected feature images or apply 1D convolutional layers directly on raw point sets. These methods cannot adequately recognize fine-grained local structures caused by the uneven density distribution of the point cloud data. In this paper, to address this challenging issue, we introduced a density-aware convolution module which uses the point-wise density to re-weight the learnable weights of convolution kernels. The proposed convolution module is able to fully approximate the 3D continuous convolution on unevenly distributed 3D point sets. Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning. In addition, to regularize the global semantic context, we implemented a context encoding module to predict a global context encoding and formulated a context encoding regularizer to enforce the predicted context encoding to be aligned with the ground truth one. The overall network can be trained in an end-to-end fashion with the raw 3D coordinates as well as the height above ground as inputs. Experiments on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark demonstrated the superiority of the proposed method for point cloud classification. Our model achieved a new state-of-the-art performance with an average F1 score of 71.2% and improved the performance by a large margin on several categories