26,727 research outputs found
Holographic entanglement entropy in general holographic superconductor models
We study the entanglement entropy of general holographic dual models both in
AdS soliton and AdS black hole backgrounds with full backreaction. We find that
the entanglement entropy is a good probe to explore the properties of the
holographic superconductors and provides richer physics in the phase
transition. We obtain the effects of the scalar mass, model parameter and
backreaction on the entropy, and argue that the jump of the entanglement
entropy may be a quite general feature for the first order phase transition. In
strong contrast to the insulator/superconductor system, we note that the
backreaction coupled with the scalar mass can not be used to trigger the first
order phase transition if the model parameter is below its bottom bound in the
metal/superconductor system.Comment: 14 pages, 6 figures. arXiv admin note: text overlap with
arXiv:1203.6620 by other author
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.Comment: This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 201
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