Quantum coherence of the molecular states and their corresponding currents in nanoscale Aharonov-Bohm interferometers

By considering a nanoscale Aharonov-Bohm (AB) interferometer containing a parrallel-coupled double dot coupled to the source and drain electrodes, we investigate the AB phase oscillations of transport current via the bonding and antibonding state channels. The results we obtained justify the experimental analysis given in [Phys. Rev. Lett. \textbf{106}, 076801 (2011)] that bonding state currents in different energy configurations are almost the same. On the other hand, we extend the analysis to the transient transport current components flowing through different channels, to explore the effect of the parity of bonding and antibonding states on the AB phase dependence of the corresponding current components in the transient regime. The relations of the AB phase dependence between the quantum states and the associated current components are analyzed in details, which provides useful information for the reconstruction of quantum states through the measurement of the transport current in such systems. With the coherent properties in the quantum dot states as well as in the transport currents, we also provide a way to manipulate the bonding and antibonding states by the AB magnetic flux.Comment: 10 pages, 7 figure

Gravity Localization and Effective Newtonian Potential for Bent Thick Branes

In this letter, we first investigate the gravity localization and mass spectrum of gravity KK modes on de Sitter and Anti-de Sitter thick branes. Then, the effective Newtonian gravitational potentials for these bent branes are discussed by the two typical examples. The corrections of the Newtonian potential turns out to be $\Delta U(r)\sim 1/r^{2}$ at small $r$ for both cases. These corrections are very different from that of the Randall-Sundrum brane model $\Delta U(r)\sim 1/r^{3}$.Comment: 6 pages, 2 figure

A Dependency-Based Neural Network for Relation Classification

Previous research on relation classification has verified the effectiveness of using dependency shortest paths or subtrees. In this paper, we further explore how to make full use of the combination of these dependency information. We first propose a new structure, termed augmented dependency path (ADP), which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path. To exploit the semantic representation behind the ADP structure, we develop dependency-based neural networks (DepNN): a recursive neural network designed to model the subtrees, and a convolutional neural network to capture the most important features on the shortest path. Experiments on the SemEval-2010 dataset show that our proposed method achieves state-of-art results.Comment: This preprint is the full version of a short paper accepted in the annual meeting of the Association for Computational Linguistics (ACL) 2015 (Beijing, China

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge.Comment: ijca