Collaboration based Multi-Label Learning

It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels. Besides, label correlations are normally used to regularize the hypothesis space, while the final predictions are not explicitly correlated. In this paper, we suggest that for each individual label, the final prediction involves the collaboration between its own prediction and the predictions of other labels. Based on this assumption, we first propose a novel method to learn the label correlations via sparse reconstruction in the label space. Then, by seamlessly integrating the learned label correlations into model training, we propose a novel multi-label learning approach that aims to explicitly account for the correlated predictions of labels while training the desired model simultaneously. Extensive experimental results show that our approach outperforms the state-of-the-art counterparts.Comment: Accepted by AAAI-1

Edge States and Quantum Hall Effect in Graphene under a Modulated Magnetic Field

Graphene properties can be manipulated by a periodic potential. Based on the tight-binding model, we study graphene under a one-dimensional (1D) modulated magnetic field which contains both a uniform and a staggered component. New chiral current-carrying edge states are generated at the interfaces where the staggered component changes direction. These edge states lead to an unusual integer quantum Hall effect (QHE) in graphene, which can be observed experimentally by a standard four-terminal Hall measurement. When Zeeman spin splitting is considered, a novel state is predicted where the electron edge currents with opposite polarization propagate in the opposite directions at one sample boundary, whereas propagate in the same directions at the other sample boundary.Comment: 5 pages, 4 figure

Child Health and the Income Gradient: Evidence from China

Though the positive income gradient of child health is well documented in developed countries, evidence from developing countries is rare. Few studies attempt to identify a causal link between family income and child health. Utilizing unique longitudinal data from the China Health and Nutrition Survey, we have found a positive, age-enhancing income gradient of child health, measured by height-for-age z scores. The gradient is robust to alternative specifications and a comprehensive set of controls. Using the fact that the rural tax reform implemented since 2000 created an exogenous variation in family income across regions and over time, we explore a causal explanation for the income gradient, and find that it has a very strong independent causal effect on child health.child health, income gradient, rural tax reform