34,623 research outputs found
Holographic p-wave superconductor models with Weyl corrections
We study the effect of the Weyl corrections on the holographic p-wave dual
models in the backgrounds of AdS soliton and AdS black hole via a Maxwell
complex vector field model by using the numerical and analytical methods. We
find that, in the soliton background, the Weyl corrections do not influence the
properties of the holographic p-wave insulator/superconductor phase transition,
which is different from that of the Yang-Mills theory. However, in the black
hole background, we observe that similar to the Weyl correction effects in the
Yang-Mills theory, the higher Weyl corrections make it easier for the p-wave
metal/superconductor phase transition to be triggered, which shows that these
two p-wave models with Weyl corrections share some similar features for the
condensation of the vector operator.Comment: 17 pages, 3 figures, 3 tables, accepted for publication in Phys.
Lett.
Some one-sided estimates for oscillatory singular integrals
The purpose of this paper is to establish some one-sided estimates for
oscillatory singular integrals. The boundedness of certain oscillatory singular
integral on weighted Hardy spaces is proved. It is here also
show that the theory of oscillatory singular integrals above
cannot be extended to the case of when and , a wider weight class than the classical Muckenhoupt class.
Furthermore, a criterion on the weighted -boundednesss of the
oscillatory singular integral is given.Comment: 24 pages, Nonlinear Anal. 201
Virtual to Real Reinforcement Learning for Autonomous Driving
Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real environment. In this paper, we propose a novel
realistic translation network to make model trained in virtual environment be
workable in real world. The proposed network can convert non-realistic virtual
image input into a realistic one with similar scene structure. Given realistic
frames as input, driving policy trained by reinforcement learning can nicely
adapt to real world driving. Experiments show that our proposed virtual to real
(VR) reinforcement learning (RL) works pretty well. To our knowledge, this is
the first successful case of driving policy trained by reinforcement learning
that can adapt to real world driving data
A simple entanglement measure for multipartite pure states
A simple entanglement measure for multipartite pure states is formulated
based on the partial entropy of a series of reduced density matrices. Use of
the proposed new measure to distinguish disentangled, partially entangled, and
maximally entangled multipartite pure states is illustrated.Comment: 8 pages LaTe
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