Does International Trade Synchronize Business Cycles?

This paper studies the relationship between international trade and output fluctuations. The authors find evidence that the business cycles of countries that are more open to international trade are more likely to by synchronized with the business cycles of their major trading partners. A detailed study of the South Korean case shows that while business cycles are related to openness, the diversification of export destinations seems to weaken these links. The authors find no relationship between openness and output volatility.Coherence; Volatility; Business Cycles; Time Series

Hermite-Hadamard type inequalities for operator geometrically convex functions

In this paper, we introduce the concept of operator geometrically convex functions for positive linear operators and prove some Hermite-Hadamard type inequalities for these functions. As applications, we obtain trace inequalities for operators which give some refinements of previous results.Comment: Accepted for publishing in Monatshefte fur Mathemati

Stability Criteria for SIS Epidemiological Models under Switching Policies

We study the spread of disease in an SIS model. The model considered is a time-varying, switched model, in which the parameters of the SIS model are subject to abrupt change. We show that the joint spectral radius can be used as a threshold parameter for this model in the spirit of the basic reproduction number for time-invariant models. We also present conditions for persistence and the existence of periodic orbits for the switched model and results for a stochastic switched model

SEVEN: Deep Semi-supervised Verification Networks

Verification determines whether two samples belong to the same class or not, and has important applications such as face and fingerprint verification, where thousands or millions of categories are present but each category has scarce labeled examples, presenting two major challenges for existing deep learning models. We propose a deep semi-supervised model named SEmi-supervised VErification Network (SEVEN) to address these challenges. The model consists of two complementary components. The generative component addresses the lack of supervision within each category by learning general salient structures from a large amount of data across categories. The discriminative component exploits the learned general features to mitigate the lack of supervision within categories, and also directs the generative component to find more informative structures of the whole data manifold. The two components are tied together in SEVEN to allow an end-to-end training of the two components. Extensive experiments on four verification tasks demonstrate that SEVEN significantly outperforms other state-of-the-art deep semi-supervised techniques when labeled data are in short supply. Furthermore, SEVEN is competitive with fully supervised baselines trained with a larger amount of labeled data. It indicates the importance of the generative component in SEVEN.Comment: 7 pages, 2 figures, accepted to the 2017 International Joint Conference on Artificial Intelligence (IJCAI-17