4,866 research outputs found
Massive vector particles tunneling from Kerr and Kerr-Newman black holes
In this paper, we investigate the Hawking radiation of massive spin-1
particles from 4-dimensional Kerr and Kerr-Newman black holes. By applying the
Hamilton-Jacobi ansatz and the WKB approximation to the field equations of the
massive bosons in Kerr and Kerr-Newman space-time, the quantum tunneling method
is successfully implemented. As a result, we obtain the tunneling rate of the
emitted vector particles and recover the standard Hawking temperature of both
the two black holes.Comment: 14 pages, Acknowledgements added and typos corrected, Version
accepted for publication in Physics Letters
Reliable Energy-Efficient Routing Algorithm for Vehicle-Assisted Wireless Ad-Hoc Networks
We investigate the design of the optimal routing path in a moving vehicles
involved the internet of things (IoT). In our model, jammers exist that may
interfere with the information exchange between wireless nodes, leading to
worsened quality of service (QoS) in communications. In addition, the transmit
power of each battery-equipped node is constrained to save energy. We propose a
three-step optimal routing path algorithm for reliable and energy-efficient
communications. Moreover, results show that with the assistance of moving
vehicles, the total energy consumed can be reduced to a large extend. We also
study the impact on the optimal routing path design and energy consumption
which is caused by path loss, maximum transmit power constrain, QoS
requirement, etc.Comment: 6 pages, 5 figures, rejected by IEEE Globecom 2017,resubmit to IEEE
WCNC 201
Learning Two-layer Neural Networks with Symmetric Inputs
We give a new algorithm for learning a two-layer neural network under a
general class of input distributions. Assuming there is a ground-truth
two-layer network where are weight
matrices, represents noise, and the number of neurons in the hidden layer
is no larger than the input or output, our algorithm is guaranteed to recover
the parameters of the ground-truth network. The only requirement on the
input is that it is symmetric, which still allows highly complicated and
structured input.
Our algorithm is based on the method-of-moments framework and extends several
results in tensor decompositions. We use spectral algorithms to avoid the
complicated non-convex optimization in learning neural networks. Experiments
show that our algorithm can robustly learn the ground-truth neural network with
a small number of samples for many symmetric input distributions
Downlink Small-cell Base Station Cooperation Strategy in Fractal Small-cell Networks
Coordinated multipoint (CoMP) communications are considered for the
fifth-generation (5G) small-cell networks as a tool to improve the high data
rates and the cell-edge throughput. The average achievable rates of the
small-cell base stations (SBS) cooperation strategies with distance and
received signal power constraints are respectively derived for the fractal
small-cell networks based on the anisotropic path loss model. Simulation
results are presented to show that the average achievable rate with the
received signal power constraint is larger than the rate with a distance
constraint considering the same number of cooperative SBSs. The average
achievable rate with distance constraint decreases with the increase of the
intensity of SBSs when the anisotropic path loss model is considered. What's
more, the network energy efficiency of fractal smallcell networks adopting the
SBS cooperation strategy with the received signal power constraint is analyzed.
The network energy efficiency decreases with the increase of the intensity of
SBSs which indicates a challenge on the deployment design for fractal
small-cell networks.Comment: 5 figures. Accepted by Globecom 201
Molecular Dynamics Simulation of Macromolecules Using Graphics Processing Unit
Molecular dynamics (MD) simulation is a powerful computational tool to study
the behavior of macromolecular systems. But many simulations of this field are
limited in spatial or temporal scale by the available computational resource.
In recent years, graphics processing unit (GPU) provides unprecedented
computational power for scientific applications. Many MD algorithms suit with
the multithread nature of GPU. In this paper, MD algorithms for macromolecular
systems that run entirely on GPU are presented. Compared to the MD simulation
with free software GROMACS on a single CPU core, our codes achieve about 10
times speed-up on a single GPU. For validation, we have performed MD
simulations of polymer crystallization on GPU, and the results observed
perfectly agree with computations on CPU. Therefore, our single GPU codes have
already provided an inexpensive alternative for macromolecular simulations on
traditional CPU clusters and they can also be used as a basis to develop
parallel GPU programs to further speedup the computations.Comment: 21 pages, 16 figure
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