4,813 research outputs found
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 spins to
arbitrary spins. Within a mean field theory we obtain several spin liquid
states for spin antiferromagnets on triangular lattices, including
gapless f-wave spin liquid and topologically nontrivial spin liquid.
After considering different competing orders, we construct a phase diagram for
the -- model. The application to recently discovered material
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
- β¦