Fault Diagnosis for Wireless Sensor by Twin Support Vector Machine

Various data mining techniques have been applied to fault diagnosis for wireless sensor because of the advantage of discovering useful knowledge from large data sets. In order to improve the diagnosis accuracy of wireless sensor, a novel fault diagnosis for wireless sensor technology by twin support vector machine (TSVM) is proposed in the paper. Twin SVM is a binary classifier that performs classification by using two nonparallel hyperplanes instead of the single hyperplane used in the classical SVM. However, the parameter setting in the TSVM training procedure significantly influences the classification accuracy. Thus, this study introduces PSO as an optimization technique to simultaneously optimize the TSVM training parameter. The experimental results indicate that the diagnosis results for wireless sensor of twin support vector machine are better than those of SVM, ANN

DMIS: Dynamic Mesh-based Importance Sampling for Training Physics-Informed Neural Networks

Modeling dynamics in the form of partial differential equations (PDEs) is an effectual way to understand real-world physics processes. For complex physics systems, analytical solutions are not available and numerical solutions are widely-used. However, traditional numerical algorithms are computationally expensive and challenging in handling multiphysics systems. Recently, using neural networks to solve PDEs has made significant progress, called physics-informed neural networks (PINNs). PINNs encode physical laws into neural networks and learn the continuous solutions of PDEs. For the training of PINNs, existing methods suffer from the problems of inefficiency and unstable convergence, since the PDE residuals require calculating automatic differentiation. In this paper, we propose Dynamic Mesh-based Importance Sampling (DMIS) to tackle these problems. DMIS is a novel sampling scheme based on importance sampling, which constructs a dynamic triangular mesh to estimate sample weights efficiently. DMIS has broad applicability and can be easily integrated into existing methods. The evaluation of DMIS on three widely-used benchmarks shows that DMIS improves the convergence speed and accuracy in the meantime. Especially in solving the highly nonlinear Schr\"odinger Equation, compared with state-of-the-art methods, DMIS shows up to 46% smaller root mean square error and five times faster convergence speed. Code are available at https://github.com/MatrixBrain/DMIS.Comment: Accepted to AAAl-2

Online Social Networks, Media Supervision and Investment Efficiency:An Empirical Examination of Chinese Listed Firms

Prior literature suggests that media reports acting as external supervision improve information transparency and corporate governance leading to increased investment efficiency. This study empirically tests this hypothesis in the context of online social networks by investigating the combined effects of online social networking and media reports on investment efficiency using a sample of Chinese listed firms. Our results show that the interaction of media reports and Tobin's q ratio is negatively related to corporate investment efficiency. However, the introduction of online social networks turns this relationship from a negative to a positive and statistically significant one. The combined factors significantly increase investment efficiency in non-SOEs (State Owned Enterprises) but not in SOEs. We provide evidence that online social networking effectively mitigates the negative effect of media supervision on investment efficiency, further advancing knowledge of the link of external supervision and corporate governance.National Natural Science Foundation of Chin

Distillation of Gaussian Einstein-Podolsky-Rosen steering with noiseless linear amplification

Einstein-Podolsky-Rosen (EPR) steering is one of the most intriguing features of quantum mechanics and an important resource for quantum communication. The inevitable loss and noise in the quantum channel will lead to decrease of the steerability and turn it from two-way to one-way. Despite an extensive research on protecting entanglement from decoherence, it remains a challenge to protect EPR steering due to its intrinsic difference from entanglement. Here, we experimentally demonstrate the distillation of Gaussian EPR steering in lossy and noisy environment using measurement-based noiseless linear amplification. Our scheme recovers the two-way steerability from one-way in certain region of loss and enhances EPR steering for both directions. We also show that the distilled EPR steering allows to extract secret key in one-sided device-independent quantum key distribution. Our work paves the way for quantum communication exploiting EPR steering in practical quantum channels