3D ShapeNets: A Deep Representation for Volumetric Shapes

3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet -- a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.Comment: to be appeared in CVPR 201

SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks

Spiking neural networks are efficient computation models for low-power environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful techniques for SNN training. Nevertheless, the spike-base BP training is slow and requires large memory costs. Though ANN2NN provides a low-cost way to train SNNs, it requires many inference steps to mimic the well-trained ANN for good performance. In this paper, we propose a SNN-to-ANN (SNN2ANN) framework to train the SNN in a fast and memory-efficient way. The SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN and SNN and b) spiking mapping units. Firstly, the architecture trains the weight-sharing parameters on the ANN branch, resulting in fast training and low memory costs for SNN. Secondly, the spiking mapping units ensure that the activation values of the ANN are the spiking features. As a result, the classification error of the SNN can be optimized by training the ANN branch. Besides, we design an adaptive threshold adjustment (ATA) algorithm to address the noisy spike problem. Experiment results show that our SNN2ANN-based models perform well on the benchmark datasets (CIFAR10, CIFAR100, and Tiny-ImageNet). Moreover, the SNN2ANN can achieve comparable accuracy under 0.625x time steps, 0.377x training time, 0.27x GPU memory costs, and 0.33x spike activities of the Spike-based BP model

Spatial-temporal clustering of an outbreak of SARS-CoV-2 Delta VOC in Guangzhou, China in 2021

BackgroundIn May 2021, the SARS-CoV-2 Delta variant led to the first local outbreak in China in Guangzhou City. We explored the epidemiological characteristics and spatial-temporal clustering of this outbreak.MethodsBased on the 153 cases in the SARS-CoV-2 Delta variant outbreak, the Knox test was used to analyze the spatial-temporal clustering of the outbreak. We further explored the spatial-temporal clustering by gender and age groups, as well as compared the changes of clustering strength (S) value between the two outbreaks in Guangzhou.ResultsThe result of the Knox analysis showed that the areas at short distances and brief periods presented a relatively high risk. The strength of clustering of male-male pairs was higher. Age groups showed that clustering was concentrated in cases aged ≤ 18 years matched to 18–59 years and cases aged 60+ years. The strength of clustering of the outbreak declined after the implementation of public health measures. The change of strength of clustering at time intervals of 1–5 days decreased greater in 2021 (S = 129.19, change rate 38.87%) than that in 2020 (S = 83.81, change rate 30.02%).ConclusionsThe outbreak of SARS-CoV-2 Delta VOC in Guangzhou has obvious spatial-temporal clustering. The timely intervention measures are essential role to contain this outbreak of high transmission

Optimal Control for a Class of Chaotic Systems

This paper proposes the optimal control methods for a class of chaotic systems via state feedback. By converting the chaotic systems to the form of uncertain piecewise linear systems, we can obtain the optimal controller minimizing the upper bound on cost function by virtue of the robust optimal control method of piecewise linear systems, which is cast as an optimization problem under constraints of bilinear matrix inequalities (BMIs). In addition, the lower bound on cost function can be achieved by solving a semidefinite programming (SDP). Finally, numerical examples are given to illustrate the results

Stabilization Strategies of Supply Networks with Stochastic Switched Topology

In this paper, a dynamical supply networks model with stochastic switched topology is presented, in which the stochastic switched topology is dependent on a continuous time Markov process. The goal is to design the state-feedback control strategies to stabilize the dynamical supply networks. Based on Lyapunov stability theory, sufficient conditions for the existence of state feedback control strategies are given in terms of matrix inequalities, which ensure the robust stability of the supply networks at the stationary states and a prescribed ∞ disturbance attenuation level with respect to the uncertain demand. A numerical example is given to illustrate the effectiveness of the proposed method

A dynamic advertising model with reference price effect

This paper develops an advertising model in which goodwill affected by advertising effort has a positive effect on reference price and market demand. In a finite planning horizon, the optimal advertising strategy is provided by solving the optimization problem on the basis of Pontryagin’s maximum principle, then the optimal sales price is obtained through one time pricing strategy. Furthermore, we extend this problem to an infinite planning horizon and present the corresponding optimal strategies. In addition, the relationships between system parameters and optimal solutions are analyzed. Numerical examples are employed to illustrate the effectiveness of the theoretical results, and to assess the sensitivity analysis of system parameters on the optimal strategies

Spatial Effects of Digital Economy on Tourism Development: Empirical Research Based on 284 Cities at the Prefecture and Higher Levels in China

The digital economy, an essential engine for the high-quality development of China's economy, has the potential to become a breakthrough in promoting the rapid recovery of tourism. From a spatial perspective, this study used panel data from 284 prefecture-level and higher cities in China from 2011 to 2019 and constructed a spatial Durbin model (SDM) to empirically test the spatial effect and mechanism of the digital economy on tourism development. (1) Digital economy and tourism development showed significant positive global spatial autocorrelation during the study period. Hotspots of the digital economy have long been located in southeastern coastal areas, and cold spots in central and western China have shrunk significantly. Tourism development hotspots are mainly distributed in the Yangtze River Delta urban agglomerations and in Yunnan, Guangxi, Guizhou, and Chongqing. Cold spots were distributed in the central and western cities of the Shandong Peninsula and gradually expanded southward. (2) In China, the digital economy has a significant direct effect and positive spatial spillover effect, which was confirmed by a series of robustness tests were conducted. From the perspective of different regions, although the direct effect was significantly positive in all regions, the influence coefficient in the eastern region was significantly larger than that in the central, western, and northeastern regions. The spatial spillover effect is entirely significant in the eastern region, partly significant in the central and northeastern regions, and not significant in the western region, indicating that "digital segregation" exists in the western region. (3) The positive spatial spillover effect of the digital economy on tourism development is optimal at 300 km. Subsequently, the spatial spillover effect followed the law of geographical distance attenuation. The spatial spillover effect reaches the critical point of the practical effect at 800 km and almost disappears at 1500 km. (4) Among the digital economy components, digital infrastructure, digital industry development, and digital inclusive finance can significantly promote local tourism development. However, only digitally inclusive finance has a significant positive spatial spillover effect, and the effects of the remaining components are insignificant. This study constructs an analytical framework for the spatial effects of the digital economy on tourism development and conducts rigorous empirical research to compensate for the limitations of current research from a local perspective. This study also examined the spatial effects of various components of the digital economy, which helped identify the source of the impact of the digital economy on tourism development more accurately. In addition, the regional heterogeneity and distance attenuation law of the spatial effect of the digital economy on tourism development were analyzed, and customized policy implications were proposed based on the research conclusions. Overall, this study has essential reference value for achieving high-quality tourism development and expanding the scope of digital economy application