Analysis of the Chinese provincial air transportation network

The air transportation system is of a great impact on the economy and globalization of a country. In this paper, we analyze the Chinese air transportation network (ATN) from a provincial perspective via the complex network framework, where all airports located in one province are abstracted as a single node and flights between two provinces are denoted by a link. The results show that the network exhibits small-world property, homogeneous structure and disassortative mixing. The variation of the flight flow within 24 h is investigated and an obvious tide phenomenon is found in the dynamics of Chinese provincial ATN for high output level of tertiary industry. Our work will offer a novel approach for understanding the characteristic of the Chinese air transportation network.Peer ReviewedPostprint (author's final draft

Identifying vital edges in Chinese air route network via memetic algorithm

Due to its rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of highest topological importance, for which we provide an extensive explanation from the microscope of view. Our findings also offer new insights to understanding and optimizing other real-world network systems

Efficient calculation of the robustness measure R for complex networks

In a recent work, Schneider et al. (2011) proposed a new measure R for network robustness, where the value of R is calculated within the entire process of malicious node attacks. In this paper, we present an approach to improve the calculation efficiency of R, in which a computationally efficient robustness measure R' is introduced when the fraction of failed nodes reaches to a critical threshold qc. Simulation results on three different types of network models and three real networks show that these networks all exhibit a computationally efficient robustness measure R'. The relationships between R' and the network size N and the network average degree are also explored. It is found that the value of R' decreases with N while increases with . Our results would be useful for improving the calculation efficiency of network robustness measure R for complex networks.Peer ReviewedPostprint (author's final draft

Nesterov smoothing for sampling without smoothness

We study the problem of sampling from a target distribution in $\mathbb{R}^d$ whose potential is not smooth. Compared with the sampling problem with smooth potentials, this problem is much less well-understood due to the lack of smoothness. In this paper, we propose a novel sampling algorithm for a class of non-smooth potentials by first approximating them by smooth potentials using a technique that is akin to Nesterov smoothing. We then utilize sampling algorithms on the smooth potentials to generate approximate samples from the original non-smooth potentials. We select an appropriate smoothing intensity to ensure that the distance between the smoothed and un-smoothed distributions is minimal, thereby guaranteeing the algorithm's accuracy. Hence we obtain non-asymptotic convergence results based on existing analysis of smooth sampling. We verify our convergence result on a synthetic example and apply our method to improve the worst-case performance of Bayesian inference on a real-world example

On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks

Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated in both industry and academia. Specific forms of binary neural networks (BNNs) and stochastic computing based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations. Despite the obvious advantages in hardware implementation, these approximate computing techniques are questioned by researchers in terms of accuracy and universal applicability. Also it is important to understand the relative pros and cons of SCNNs and BNNs in theory and in actual hardware implementations. In order to address these concerns, in this paper we prove that the "ideal" SCNNs and BNNs satisfy the universal approximation property with probability 1 (due to the stochastic behavior). The proof is conducted by first proving the property for SCNNs from the strong law of large numbers, and then using SCNNs as a "bridge" to prove for BNNs. Based on the universal approximation property, we further prove that SCNNs and BNNs exhibit the same energy complexity. In other words, they have the same asymptotic energy consumption with the growing of network size. We also provide a detailed analysis of the pros and cons of SCNNs and BNNs for hardware implementations and conclude that SCNNs are more suitable for hardware.Comment: 9 pages, 3 figure

Optimization of extraction of total flavonoids from Drynaria rhizome, and its effect on osteoclast differentiation

Purpose: To optimize the extraction parameters for total flavonoids of Drynaria rhizome, and determine their effect on osteoclast differentiation and bone resorption in vitro. Methods: A 5-level 3-factor central composite design was applied to the optimization of extract yield of total flavonoids from Drynaria rhizome. Response Surface Methodology (RSM) design was used to optimize total flavonoids extraction from Drynaria rhizome. The independent factors included extraction temperature (A), extraction time (B) and liquid: solid ratio (C). Moreover, NFATc1, DC-STAMP, cathepsin K and MMP-9 mRNA expressions were determined. Results: Values obtained were fitted into a second-order polynomial equation with multiple regression analysis using a statistical method. Analysis of variance results indicate that the independent variables influenced total flavonoid extraction from Drynaria rhizome. The optimal conditions for extraction yield were extraction temperature of 75 oC, extraction time of 100 min, and liquid: solid ratio of 107:1. The yield of 5.38 ± 0.62 % was consistent with these optimized conditions, which was an indication of the accuracy of the model. Experiments revealed that total flavonoids from Drynaria rhizome regulated the expression levels of NFATc1, DC-STAMP, cathepsin K and MMP-9 mRNA Conclusion: This study has successfully optimized the extraction yield of total flavonoids from Drynaria rhizome. The total flavonoids inhibit osteoclast differentiation and bone resorption. Thus, they may be beneficial in the treatment of bone diseases