37,553 research outputs found
Dynamical chiral symmetry breaking in QED 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
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 is beyond a threshold
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
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