2,597 research outputs found
Petrov type I Condition and Rindler Fluid in Vacuum Einstein-Gauss-Bonnet Gravity
Recently the Petrov type I condition is introduced to reduce the degrees of
freedom in the extrinsic curvature of a timelike hypersurface to the degrees of
freedom in the dual Rindler fluid in Einstein gravity. In this paper we show
that the Petrov type I condition holds for the solutions of vacuum
Einstein-Gauss-Bonnet gravity up to the second order in the relativistic
hydrodynamic expansion. On the other hand, if imposing the Petrov type I
condition and Hamiltonian constraint on a finite cutoff hypersurface, the
stress tensor of the relativistic Rindler fluid in vacuum Einstein-Gauss-Bonnet
gravity can be recovered with correct first order and second order transport
coefficients.Comment: 25 page
Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Computer-generated graphics (CGs) are images generated by computer software.
The~rapid development of computer graphics technologies has made it easier to
generate photorealistic computer graphics, and these graphics are quite
difficult to distinguish from natural images (NIs) with the naked eye. In this
paper, we propose a method based on sensor pattern noise (SPN) and deep
learning to distinguish CGs from NIs. Before being fed into our convolutional
neural network (CNN)-based model, these images---CGs and NIs---are clipped into
image patches. Furthermore, three high-pass filters (HPFs) are used to remove
low-frequency signals, which represent the image content. These filters are
also used to reveal the residual signal as well as SPN introduced by the
digital camera device. Different from the traditional methods of distinguishing
CGs from NIs, the proposed method utilizes a five-layer CNN to classify the
input image patches. Based on the classification results of the image patches,
we deploy a majority vote scheme to obtain the classification results for the
full-size images. The~experiments have demonstrated that (1) the proposed
method with three HPFs can achieve better results than that with only one HPF
or no HPF and that (2) the proposed method with three HPFs achieves 100\%
accuracy, although the NIs undergo a JPEG compression with a quality factor of
75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296;
Sensors 2018, 18(4), 129
Elastic metamaterials with simultaneously negative effective shear modulus and mass density
We propose a type of elastic metamaterial comprising fluid-solid composite
inclusions which can possess negative shear modulus and negative mass density
over a large frequency region. Such a solid metamaterial has a unique elastic
property that only transverse waves can propagate with a negative dispersion
while longitudinal waves are forbidden. This leads to many interesting
phenomena such as negative refraction, which is demonstrated by using a wedge
sample, and a significant amount of mode conversion from transverse waves to
longitudinal waves that cannot occur on the interface of two natural solids
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