Challenges and opportunities of Sino-Japan FTA

Die Masterarbeit behandelt die positiven und negativen Auswirkungen eines FTA (Freihandelsabkommen) zwischen China und Japan auf die wichtigsten Industrien der beiden Länder, und erläutert wie man die negativen Auswirkungen verringern und die wirtschaftliche Zusammenarbeit zwischen den beiden Ländern, sowie in der ganzen Region fördern kann.The present paper deals with the positive and negative impacts of a Sino-Japan FTA on both countries‘ major industries, and elaborates on how to reduce the negative impacts and promote economic cooperation between the two countries, and even cooperation in the entire region

Underwater target recognition method based on t-SNE and stacked nonnegative constrained denoising autoencoder

1822-1832Underwater targets recognition is a difficult task due to the specific attributes of underwater target radiated noises, low signal to noise ratio and so on. In this paper, the input data optimization method and recognition model were researched. The underwater target radiated noise spectrum was chosen as the original feature. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was used to reduce the dimensionality of the original spectrum segments divided by frequency. The optimal features can be obtained by analyzing the separability. Then the stacked nonnegative constrained denoising autoencoder (SNDAE) model was established to recognize the optimal features. The experimental signal spectra were processed by above methods. The results show that the recognition accuracy of SNDAE is higher than that of other contrastive methods. And the frequency of input band with the highest recognition accuracy is approximately the same as that with the best separability based on t-SNE, indicating that the above method can improve the recognition accuracy and efficiency

Improved bounds on the size of the smallest representation of relation algebra 326532_{65} with the aid of a SAT solver

In this paper, we shed new light on the spectrum of relation algebra $32_{65}$. We show that 1024 is in the spectrum, and no number smaller than 20 is in the spectrum. In addition, we derive upper and lower bounds on the smallest member of the spectra of an infinite class of algebras derived from $32_{65}$ via splitting.Comment: 14 page

Adaptive absorbing boundary conditions for Schrodinger-type equations: application to nonlinear and multi-dimensional problems

We propose an adaptive approach in picking the wave-number parameter of absorbing boundary conditions for Schr\"{o}dinger-type equations. Based on the Gabor transform which captures local frequency information in the vicinity of artificial boundaries, the parameter is determined by an energy-weighted method and yields a quasi-optimal absorbing boundary conditions. It is shown that this approach can minimize reflected waves even when the wave function is composed of waves with different group velocities. We also extend the split local absorbing boundary (SLAB) method [Z. Xu and H. Han, {\it Phys. Rev. E}, 74(2006), pp. 037704] to problems in multidimensional nonlinear cases by coupling the adaptive approach. Numerical examples of nonlinear Schr\"{o}dinger equations in one- and two dimensions are presented to demonstrate the properties of the discussed absorbing boundary conditions.Comment: 18 pages; 12 figures. A short movie for the 2D NLS equation with absorbing boundary conditions can be downloaded at http://home.ustc.edu.cn/~xuzl/movie.avi. To appear in Journal of Computational Physic

Dialogue State Distillation Network with Inter-Slot Contrastive Learning for Dialogue State Tracking

In task-oriented dialogue systems, Dialogue State Tracking (DST) aims to extract users' intentions from the dialogue history. Currently, most existing approaches suffer from error propagation and are unable to dynamically select relevant information when utilizing previous dialogue states. Moreover, the relations between the updates of different slots provide vital clues for DST. However, the existing approaches rely only on predefined graphs to indirectly capture the relations. In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing. Thus, it can dynamically exploit previous dialogue states and avoid introducing error propagation simultaneously. Further, we propose an inter-slot contrastive learning loss to effectively capture the slot co-update relations from dialogue context. Experiments are conducted on the widely used MultiWOZ 2.0 and MultiWOZ 2.1 datasets. The experimental results show that our proposed model achieves the state-of-the-art performance for DST.Comment: Accepted by AAAI 202