Cultivating Interdisciplinary Foreign Language Talents in Higher Education in Western China under the Background of the “B&R”

There is an increasing demand for interdisciplinary foreign language talents who master multiple foreign languages and cultures, innovation ability, and management ability, especially under the “Belt and Road” background. However, cultivating interdisciplinary foreign language talents in western China is facing many dilemmas. In order to meet the interdisciplinary foreign language talents demand of the international economic development along the “Belt and Road”, the author puts forward some strategies based on symbiosis theory adopting a comparative method. This article aims to explore methods of cultivating interdisciplinary foreign language talents in higher education in western China and supply high-quality interdisciplinary foreign language talents for the “Belt and Road”

PPGAN: Privacy-preserving Generative Adversarial Network

Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of high-quality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semantic-rich data distribution from a dataset, the density of the generated distribution tends to concentrate on the training data. Due to the gradient parameters of the deep neural network contain the data distribution of the training samples, they can easily remember the training samples. When GAN is applied to private or sensitive data, for instance, patient medical records, as private information may be leakage. To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure. Besides, we introduced the Moments Accountant strategy in the PPGAN training process to improve the stability and compatibility of the model by controlling privacy loss. We also give a mathematical proof of the differential privacy discriminator. Through extensive case studies of the benchmark datasets, we demonstrate that PPGAN can generate high-quality synthetic data while retaining the required data available under a reasonable privacy budget.Comment: This paper was accepted by IEEE ICPADS 2019 Workshop. This paper contains 10 pages, 3 figure

Tunnel splitting and quantum phase interference in biaxial ferrimagnetic particles at excited states

The tunneling splitting in biaxial ferrimagnetic particles at excited states with an explicit calculation of the prefactor of exponent is obtained in terms of periodic instantons which are responsible for tunneling at excited states and is shown as a function of magnetic field applied along an arbitrary direction in the plane of hard and medium axes. Using complex time path-integral we demonstrate the oscillation of tunnel splitting with respect to the magnitude and the direction of the magnetic field due to the quantum phase interference of two tunneling paths of opposite windings . The oscillation is gradually smeared and in the end the tunnel splitting monotonously increases with the magnitude of the magnetic field when the direction of the magnetic field tends to the medium axis. The oscillation behavior is similar to the recent experimental observation with Fe$_8$ molecular clusters. A candidate of possible experiments to observe the effect of quantum phase interference in the ferrimagnetic particles is proposed.Comment: 15 pages, 5 figures, acceptted to be pubblished in Physical Review