118,075 research outputs found
A Mean-field Calculation for the Three-Dimensional Holstein Model
A path integral representation appropriate for further Monte Carlo
simulations is derived for the electron-phonon Holstein model in three spatial
dimensions. The model is studied within mean-field theory. Charge density wave
and superconducting phase transitions are discussed.Comment: Latex file typeset using elsart.cls, 16 pages, 2 figures, submitted
to Journal of Physics: Condensed Matte
The Weight Distributions of a Class of Cyclic Codes with Three Nonzeros over F3
Cyclic codes have efficient encoding and decoding algorithms. The decoding
error probability and the undetected error probability are usually bounded by
or given from the weight distributions of the codes. Most researches are about
the determination of the weight distributions of cyclic codes with few
nonzeros, by using quadratic form and exponential sum but limited to low
moments. In this paper, we focus on the application of higher moments of the
exponential sum to determine the weight distributions of a class of ternary
cyclic codes with three nonzeros, combining with not only quadratic form but
also MacWilliams' identities. Another application of this paper is to emphasize
the computer algebra system Magma for the investigation of the higher moments.
In the end, the result is verified by one example using Matlab.Comment: 10 pages, 3 table
Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks
Taking a photo outside, can we predict the immediate future, e.g., how would
the cloud move in the sky? We address this problem by presenting a generative
adversarial network (GAN) based two-stage approach to generating realistic
time-lapse videos of high resolution. Given the first frame, our model learns
to generate long-term future frames. The first stage generates videos of
realistic contents for each frame. The second stage refines the generated video
from the first stage by enforcing it to be closer to real videos with regard to
motion dynamics. To further encourage vivid motion in the final generated
video, Gram matrix is employed to model the motion more precisely. We build a
large scale time-lapse dataset, and test our approach on this new dataset.
Using our model, we are able to generate realistic videos of up to resolution for 32 frames. Quantitative and qualitative experiment results
have demonstrated the superiority of our model over the state-of-the-art
models.Comment: To appear in Proceedings of CVPR 201
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