A Study on the Impact of Quantitative Easing Monetary Policy of the United States on China's Economy

This paper studies the impact of US quantitative easing monetary policy on China's economy. In order to cope with the financial crisis in 2008 and walk out of the economic downturn, the United States adopted quantitative easing monetary policy. However, due to the special status of the US dollar as an international currency, although this unconventional monetary policy has injected sufficient liquidity into the world economy, it also has significant negative spillover effects on other countries. On the basis of relevant theories, this paper empirically analyzes the actual impact of the policy on emerging economies represented by China through VAR model. The results show that the policy has a great impact on China's export trade, domestic prices, capital market and monetary policy, among which the impact on China's domestic price level is the largest. But the overall effects are complex, that there is no consistent direction of change, and the volatility is high. Based on the findings, the China government should put more emphasis on the foreign risks when making monetary policies

Possibility of S=1 spin liquids with fermionic spinons on triangular lattices

In this paper we generalize the fermionic representation for $S=1/2$ spins to arbitrary spins. Within a mean field theory we obtain several spin liquid states for spin $S=1$ antiferromagnets on triangular lattices, including gapless f-wave spin liquid and topologically nontrivial $p_x+ip_y$ spin liquid. After considering different competing orders, we construct a phase diagram for the $J_1$-$J_3$-$K$ model. The application to recently discovered material $\mathrm{NiGa_2S_4}$ is discussed.Comment: 5 pages, 3 figure

Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees

This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The SENC problem can be decomposed into three sub problems: detecting emerging new classes, classifying for known classes, and updating models to enable classification of instances of the new class and detection of more emerging new classes. The proposed method employs completely random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. This is the first time, as far as we know, that completely random trees are used as a single common core to solve all three sub problems: unsupervised learning, supervised learning and model update in data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods