Effects of spatial noncommutativity on energy spectrum of a trapped Bose-Einstein condensate

In noncommutative space, we examine the problem of a noninteracting and harmonically trapped Bose-Einstein condensate, and derive a simple analytic expression for the effect of spatial noncommutativity on energy spectrum of the condensate. It indicates that the ground-state energy incorporating the spatial noncommutativity is reduced to a lower level, which depends upon the noncommutativity parameter $\theta$. The appeared gap between the noncommutative space and commutative one for the ground-state level of the condensate should be a signal of spatial noncommutativity.Comment: 7 pages; revtex

Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark

Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people's emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have confirmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks (CNNs), visual emotion analysis has not achieved the same level of success. This may be primarily due to the unavailability of confidently labeled and relatively large image data sets for visual emotion analysis. In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. We hope that this data set encourages further research on visual emotion analysis. We also perform extensive benchmarking analyses on this large data set using the state of the art methods including CNNs.Comment: 7 pages, 7 figures, AAAI 201

Coherent Destruction of Tunneling and Dark Floquet State

We study a system of three coherently coupled states, where one state is shifted periodically against the other two. We discover such a system possesses a dark Floquet state at zero quasienergy and always with negligible population at the intermediate state. This dark Floquet state manifests itself dynamically in terms of the suppression of inter-state tunneling, a phenomenon known as coherent destruction of tunneling. We suggest to call it dark coherent destruction of tunneling (DCDT). At high frequency limit for the periodic driving, this Floquet state reduces to the well-known dark state widely used for STIRAP. Our results can be generalized to systems with more states and can be verified with easily implemented experiments within current technologies.Comment: 5 pages, 3 figure

Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.Comment: 9 pages, 5 figures, AAAI 201

Heavy and light flavor jet quenching at RHIC and LHC energies

The Linear Boltzmann Transport (LBT) model coupled to hydrodynamical background is extended to include transport of both light partons and heavy quarks through the quark-gluon plasma (QGP) in high-energy heavy-ion collisions. The LBT model includes both elastic and inelastic medium-interaction of both primary jet shower partons and thermal recoil partons within perturbative QCD (pQCD). It is shown to simultaneously describe the experimental data on heavy and light flavor hadron suppression in high-energy heavy-ion collisions for different centralities at RHIC and LHC energies. More detailed investigations within the LBT model illustrate the importance of both initial parton spectra and the shapes of fragmentation functions on the difference between the nuclear modifications of light and heavy flavor hadrons. The dependence of the jet quenching parameter $\hat{q}$ on medium temperature and jet flavor is quantitatively extracted.Comment: 6 pages, 6 figure