24,969 research outputs found
Quantum effect on luminosity-redshift relation
There are many different proposals for a theory of quantum gravity. Even
leaving aside the fundamental difference among theories such as the string
theory and the non-perturbative quantum gravity, we are still left with many
ambiguities (and/or parameters to be determined) with regard to the choice of
variables, the choice of related groups, etc. Loop quantum gravity is also in
such a state. It is interesting to search for experimental observables to
distinguish these quantum schemes. This paper investigates the loop quantum
gravity effect on luminosity-redshift relation. The quantum bounce behavior of
loop quantum cosmology is found to result in multivalued correspondence in
luminosity-redshift relation. And the detail multivalued behavior can tell the
difference of different quantum parameters. The inverse volume quantum
correction does not result in bounce behavior in this model, but affects
luminosity-redshift relation also significantly.Comment: 11 pages, 3 figures; revised versio
Dynamical Horizon Entropy Bound Conjecture in Loop Quantum Cosmology
The covariant entropy bound conjecture is an important hint for the quantum
gravity, with several versions available in the literature. For cosmology,
Ashtekar and Wilson-Ewing ever show the consistence between the loop gravity
theory and one version of this conjecture. Recently, S. He and H. Zhang
proposed a version for the dynamical horizon of the universe, which validates
the entropy bound conjecture for the cosmology filled with perfect fluid in the
classical scenario when the universe is far away from the big bang singularity.
However, their conjecture breaks down near big bang region. We examine this
conjecture in the context of the loop quantum cosmology. With the example of
photon gas, this conjecture is protected by the quantum geometry effects as
expected.Comment: 4 pages, 2 figures, revised versio
Stochastic resonance with matched filtering
Along with the development of interferometric gravitational wave detector, we
enter into an epoch of gravitational wave astronomy, which will open a brand
new window for astrophysics to observe our universe. Almost all of the data
analysis methods in gravitational wave detection are based on matched
filtering. Gravitational wave detection is a typical example of weak signal
detection, and this weak signal is buried in strong instrument noise. So it
seems attractable if we can take advantage of stochastic resonance. But
unfortunately, almost all of the stochastic resonance theory is based on
Fourier transformation and has no relation to matched filtering. In this paper
we try to relate stochastic resonance to matched filtering. Our results show
that stochastic resonance can indeed be combined with matched filtering for
both periodic and non-periodic input signal. This encouraging result will be
the first step to apply stochastic resonance to matched filtering in
gravitational wave detection. In addition, based on matched filtering, we
firstly proposed a novel measurement method for stochastic resonance which is
valid for both periodic and non-periodic driven signal.Comment: 5 pages, 3 figure
SpecWatch: A Framework for Adversarial Spectrum Monitoring with Unknown Statistics
In cognitive radio networks (CRNs), dynamic spectrum access has been proposed
to improve the spectrum utilization, but it also generates spectrum misuse
problems. One common solution to these problems is to deploy monitors to detect
misbehaviors on certain channel. However, in multi-channel CRNs, it is very
costly to deploy monitors on every channel. With a limited number of monitors,
we have to decide which channels to monitor. In addition, we need to determine
how long to monitor each channel and in which order to monitor, because
switching channels incurs costs. Moreover, the information about the misuse
behavior is not available a priori. To answer those questions, we model the
spectrum monitoring problem as an adversarial multi-armed bandit problem with
switching costs (MAB-SC), propose an effective framework, and design two online
algorithms, SpecWatch-II and SpecWatch-III, based on the same framework. To
evaluate the algorithms, we use weak regret, i.e., the performance difference
between the solution of our algorithm and optimal (fixed) solution in
hindsight, as the metric. We prove that the expected weak regret of
SpecWatch-II is O(T^{2/3}), where T is the time horizon. Whereas, the actual
weak regret of SpecWatch-III is O(T^{2/3}) with probability 1 - {\delta}, for
any {\delta} in (0, 1). Both algorithms guarantee the upper bounds matching the
lower bound of the general adversarial MAB- SC problem. Therefore, they are all
asymptotically optimal
Effects of the temperature and magnetic-field dependent coupling on the properties of QCD matter
To reflect the asymptotic freedom in the thermal direction, a
temperature-dependent coupling was proposed in the literature. We investigate
its effect on QCD matter with and without strong magnetic fields. Compared with
the fixed coupling constant, the running coupling leads to a drastic change in
the dynamical quark mass, entropy density, sound velocity, and specific heat.
The crossover transition of QCD matter at finite temperature is characterized
by the pseudocritical temperature , which is generally
determined by the peak of the derivative of the quark condensate with respect
to the temperature , or equivalently, by the derivative of the quark
dynamical mass . In a strong magnetic field, the temperature- and
magnetic-field-dependent coupling was recently introduced to account
for inverse magnetic catalysis. We propose an analytical relation between the
two criteria and and show a discrepancy between them in
finding the pseudocritical temperature. The magnitude of the discrepancy
depends on the behavior of .Comment: 7 pages, 7 figures, version accepted for publication in Phys. Rev.
Directional fast neutron detection using a time projection chamber and plastic scintillation detectors
A new method for directional fast neutron detection is proposed based on a
neutron time projection chamber (TPC) and position-sensitive plastic
scintillation detectors. The detection system can efficiently locate the
approximate location of a hot spot with 4{\pi} field-of-view using only the
neutron TPC. Then, the system generates a high-resolution image of the hot spot
using selected coincidence events in the TPC and the scintillation detectors. A
prototype was built and tested using a Cf-252 source. An efficiency of 7.1x10-3
was achieved for fast searching. The angular resolution was 7.8{\deg} (full
width at half maximum, FWHM) for high-resolution imaging using the simple back
projection method.Comment: 12 pages, 11 figures, SORMA XVI
Information transport in multiplex networks
In this paper, we study information transport in multiplex networks comprised
of two coupled subnetworks. The upper subnetwork, called the logical layer,
employs the shortest paths protocol to determine the logical paths for packets
transmission, while the lower subnetwork acts as the physical layer, in which
packets are delivered by the biased random walk mechanism characterized with a
parameter . Through simulation, we obtain the optimal
corresponding to the maximum network lifetime and the maximum number of the
arrival packets. Assortative coupling is better than the random coupling and
the disassortative coupling, since it achieves much better transmission
performances. Generally, the more homogeneous the lower subnetwork, the better
the transmission performances are, which is opposite for the upper subnetwork.
Finally, we propose an attack centrality for nodes based on the topological
information of both subnetworks, and further investigate the transmission
performances under targeted attacks. Our work helps to understand the spreading
and robustness issues of multiplex networks and provides some clues about the
designing of more efficient and robust routing architectures in communication
systems.Comment: 7figure
Traffic-driven SIR epidemic model on networks
We propose a novel SIR epidemic model which is driven by the transmission of
infection packets in networks. Specifically, infected nodes generate and
deliver infection packets causing the spread of the epidemic, while recovered
nodes block the delivery of infection packets, and this inhibits the epidemic
spreading. The efficient routing protocol governed by a control parameter
is used in the packet transmission. We obtain the maximum
instantaneous population of infected nodes, the maximum population of ever
infected nodes, as well as the corresponding optimal through
simulation. We find that generally more balanced load distribution leads to
more intense and wide spread of an epidemic in networks. Increasing either
average node degree or homogeneity of degree distribution will facilitate
epidemic spreading. When packet generation rate is small, increasing
favors epidemic spreading. However, when is large enough, traffic
congestion appears which inhibits epidemic spreading.Comment: 12 pages, 11 figure
Triple Attention Mixed Link Network for Single Image Super Resolution
Single image super resolution is of great importance as a low-level computer
vision task. Recent approaches with deep convolutional neural networks have
achieved im-pressive performance. However, existing architectures have
limitations due to the less sophisticated structure along with less strong
representational power. In this work, to significantly enhance the feature
representation, we proposed Triple Attention mixed link Network (TAN) which
consists of 1) three different aspects (i.e., kernel, spatial and channel) of
attention mechanisms and 2) fu-sion of both powerful residual and dense
connections (i.e., mixed link). Specifically, the network with multi kernel
learns multi hierarchical representations under different receptive fields. The
output features are recalibrated by the effective kernel and channel attentions
and feed into next layer partly residual and partly dense, which filters the
information and enable the network to learn more powerful representations. The
features finally pass through the spatial attention in the reconstruction
network which generates a fusion of local and global information, let the
network restore more details and improves the quality of reconstructed images.
Thanks to the diverse feature recalibrations and the advanced information flow
topology, our proposed model is strong enough to per-form against the
state-of-the-art methods on the bench-mark evaluations
Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks
The Convolutional Neural Networks (CNNs) generate the feature representation
of complex objects by collecting hierarchical and different parts of semantic
sub-features. These sub-features can usually be distributed in grouped form in
the feature vector of each layer, representing various semantic entities.
However, the activation of these sub-features is often spatially affected by
similar patterns and noisy backgrounds, resulting in erroneous localization and
identification. We propose a Spatial Group-wise Enhance (SGE) module that can
adjust the importance of each sub-feature by generating an attention factor for
each spatial location in each semantic group, so that every individual group
can autonomously enhance its learnt expression and suppress possible noise. The
attention factors are only guided by the similarities between the global and
local feature descriptors inside each group, thus the design of SGE module is
extremely lightweight with \emph{almost no extra parameters and calculations}.
Despite being trained with only category supervisions, the SGE component is
extremely effective in highlighting multiple active areas with various
high-order semantics (such as the dog's eyes, nose, etc.). When integrated with
popular CNN backbones, SGE can significantly boost the performance of image
recognition tasks. Specifically, based on ResNet50 backbones, SGE achieves
1.2\% Top-1 accuracy improvement on the ImageNet benchmark and 1.02.0\%
AP gain on the COCO benchmark across a wide range of detectors
(Faster/Mask/Cascade RCNN and RetinaNet). Codes and pretrained models are
available at https://github.com/implus/PytorchInsight.Comment: Code available at: https://github.com/implus/PytorchInsigh
- …