Dynamical chiral symmetry breaking in QED3_{3} at finite density and impurity potential

We study the effects of finite chemical potential and impurity scattering on dynamical fermion mass generation in (2+1)-dimensional quantum electrodynamics. In any realistic systems, these effects usually can not be neglected. The longitudinal component of gauge field develops a finite static length produced by chemical potential and impurity scattering, while the transverse component remains long-ranged because of the gauge invariance. Another important consequence of impurity scattering is that the fermions have a finite damping rate, which reduces their lifetime staying in a definite quantum state. By solving the Dyson-Schwinger equation for fermion mass function, it is found that these effects lead to strong suppression of the critical fermion flavor $N_c$ and the dynamical fermion mass in the symmetry broken phase.Comment: 8 pages, 4 figure

Interaction and excitonic insulating transition in graphene

The strong long-range Coulomb interaction between massless Dirac fermions in graphene can drive a semimetal-insulator transition. We show that this transition is strongly suppressed when the Coulomb interaction is screened by such effects as disorder, thermal fluctuation, doping, and finite volume. It is completely suppressed once the screening factor $\mu$ is beyond a threshold $\mu_{c}$ even for infinitely strong coupling. However, such transition is still possible if there is an additional strong contact four-fermion interaction. The differences between screened and contact interactions are also discussed.Comment: 7 pages, 4 figures, to appear in Phys. Rev.

Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes

In this paper, we present a label transfer model from texts to images for image classification tasks. The problem of image classification is often much more challenging than text classification. On one hand, labeled text data is more widely available than the labeled images for classification tasks. On the other hand, text data tends to have natural semantic interpretability, and they are often more directly related to class labels. On the contrary, the image features are not directly related to concepts inherent in class labels. One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images. This is implemented by learning a transfer function as a bridge to propagate the labels between two multimodal spaces. However, the intermodal label transfers could be undermined by blindly transferring the labels of noisy texts to annotate images. To mitigate this problem, we present an intramodal label transfer process, which complements the intermodal label transfer by transferring the image labels instead when relevant text is absent from the source corpus. In addition, we generalize the inter-modal label transfer to zero-shot learning scenario where there are only text examples available to label unseen classes of images without any positive image examples. We evaluate our algorithm on an image classification task and show the effectiveness with respect to the other compared algorithms.Comment: The paper has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence. It will apear in a future issu