7,717 research outputs found
Quantum particle confined to a thin-layer volume: Non-uniform convergence toward the curved surface
We clearly refine the fundamental framework of the thin-layer quantization
procedure, and further develop the procedure by taking the proper terms of
degree one in ( denotes the curvilinear coordinate variable
perpendicular to curved surface) back into the surface quantum equation. The
well-known geometric potential and kinetic term are modified by the surface
thickness. Applying the developed formalism to a toroidal system obtains the
modification for the kinetic term and the modified geometric potential
including the influence of the surface thickness.Comment: 9 pages, 3 figure
Dynamics of domain wall in charged AdS dilaton black hole spacetime
For the dimensional FRW domain wall universe induced by dimensional
charged dilaton black hole, its movement formula in the bulk can be rewrite as
the expansion or collapsing of domain wall. By analysing, we found that in this
static AdS space, the cosmologic behaviour of domain wall is particularly
single. Even more surprising, it exists an anomaly that the domain wall has a
motion area outside of horizon, in which it cannot be explained by our
classical theory.Comment: 6 pages, 10figure
A Pyramid Scheme Model Based on "Consumption Rebate" Frauds
There are various types of pyramid schemes which have inflicted or are
inflicting losses on many people in the world. We propose a pyramid scheme
model which has the principal characters of many pyramid schemes appeared in
recent years: promising high returns, rewarding the participants recruiting the
next generation of participants, and the organizer will take all the money away
when he finds the money from the new participants is not enough to pay the
previous participants interest and rewards. We assume the pyramid scheme
carries on in the tree network, ER random network, SW small-world network or BA
scale-free network respectively, then give the analytical results of how many
generations the pyramid scheme can last in these cases. We also use our model
to analyse a pyramid scheme in the real world and we find the connections
between participants in the pyramid scheme may constitute a SW small-world
network.Comment: 17 pages, 10 figure
Sparse-View X-Ray CT Reconstruction Using Prior with Learned Transform
A major challenge in X-ray computed tomography (CT) is reducing radiation
dose while maintaining high quality of reconstructed images. To reduce the
radiation dose, one can reduce the number of projection views (sparse-view CT);
however, it becomes difficult to achieve high-quality image reconstruction as
the number of projection views decreases. Researchers have applied the concept
of learning sparse representations from (high-quality) CT image dataset to the
sparse-view CT reconstruction. We propose a new statistical CT reconstruction
model that combines penalized weighted-least squares (PWLS) and prior
with learned sparsifying transform (PWLS-ST-), and a corresponding
efficient algorithm based on Alternating Direction Method of Multipliers
(ADMM). To moderate the difficulty of tuning ADMM parameters, we propose a new
ADMM parameter selection scheme based on approximated condition numbers. We
interpret the proposed model by analyzing the minimum mean square error of its
(-norm relaxed) image update estimator. Our results with the extended
cardiac-torso (XCAT) phantom data and clinical chest data show that, for
sparse-view 2D fan-beam CT and 3D axial cone-beam CT, PWLS-ST- improves
the quality of reconstructed images compared to the CT reconstruction methods
using edge-preserving regularizer and prior with learned ST. These
results also show that, for sparse-view 2D fan-beam CT, PWLS-ST-
achieves comparable or better image quality and requires much shorter runtime
than PWLS-DL using a learned overcomplete dictionary. Our results with clinical
chest data show that, methods using the unsupervised learned prior generalize
better than a state-of-the-art deep "denoising" neural network that does not
use a physical imaging model.Comment: The first two authors contributed equally to this wor
Color-Magnitude Distribution of Face-on Nearby Galaxies in SDSS DR7
We have analyzed the distributions in the color-magnitude diagram (CMD) of a
large sample of face-on galaxies to minimize the effect of dust extinctions on
galaxy color. About 300 thousand galaxies with 0.2 and redshift
are selected from the SDSS DR7 catalog. Two methods are employed to
investigate the distributions of galaxies in the CMD including 1-D Gaussian
fitting to the distributions in individual magnitude bins and 2-D Gaussian
mixture model (GMM) fitting to galaxies as a whole. We find that in the 1-D
fitting only two Gaussians are not enough to fit galaxies with the excess
present between the blue cloud and the red sequence. The fitting to this excess
defines the centre of the green-valley in the local universe to be . The fraction of blue cloud and red sequence galaxies
turns over around mag, corresponding to stellar mass of
. For the 2-D GMM fitting, a total of four Gaussians are
required, one for the blue cloud, one for the red sequence and the additional
two for the green valley. The fact that two Gaussians are needed to describe
the distributions of galaxies in the green valley is consistent with some
models that argue for two different evolutionary paths from the blue cloud to
the red sequence.Comment: Accepted by ApJ, 9 pages, 8 figures, 1 tabl
A Scalable Limited Feedback Design for Network MIMO using Per-Cell Product Codebook
In network MIMO systems, channel state information is required at the
transmitter side to multiplex users in the spatial domain. Since perfect
channel knowledge is difficult to obtain in practice, \emph{limited feedback}
is a widely accepted solution. The {\em dynamic number of cooperating BSs} and
{\em heterogeneous path loss effects} of network MIMO systems pose new
challenges on limited feedback design. In this paper, we propose a scalable
limited feedback design for network MIMO systems with multiple base stations,
multiple users and multiple data streams for each user. We propose a {\em
limited feedback framework using per-cell product codebooks}, along with a {\em
low-complexity feedback indices selection algorithm}. We show that the proposed
per-cell product codebook limited feedback design can asymptotically achieve
the same performance as the joint-cell codebook approach. We also derive an
asymptotic \emph{per-user throughput loss} due to limited feedback with
per-cell product codebooks. Based on that, we show that when the number of
per-user feedback-bits is , the system operates in the \emph{noise-limited} regime in
which the per-user throughput is . On the other hand, when
the number of per-user feedback-bits does not scale with the
\emph{system SNR} , the system operates in the
\emph{interference-limited} regime where the per-user throughput is
. Numerical results
show that the proposed design is very flexible to accommodate dynamic number of
cooperating BSs and achieves much better performance compared with other
baselines (such as the Givens rotation approach).Comment: 11 pages, 5 figures, Accepted to the IEEE transactions on Wireless
Communicatio
A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks
Deep neural networks (DNNs) have been widely used in the fields such as
natural language processing, computer vision and image recognition. But several
studies have been shown that deep neural networks can be easily fooled by
artificial examples with some perturbations, which are widely known as
adversarial examples. Adversarial examples can be used to attack deep neural
networks or to improve the robustness of deep neural networks. A common way of
generating adversarial examples is to first generate some noises and then add
them into original examples. In practice, different examples have different
noise-sensitive. To generate an effective adversarial example, it may be
necessary to add a lot of noise to low noise-sensitive example, which may make
the adversarial example meaningless. In this paper, we propose a
noise-sensitivity-analysis-based test prioritization technique to pick out
examples by their noise sensitivity. We construct an experiment to validate our
approach on four image sets and two DNN models, which shows that examples are
sensitive to noise and our method can effectively pick out examples by their
noise sensitivity
Thermodynamics for Kodama observer in general spherically symmetric spacetimes
By following the spirit of arXiv:1003.5665, we define a new Tolman
temperature of Kodama observer directly related to its acceleration. We give a
generalized integral form of thermodynamics relation on virtual sphere of
constant in non-static spherical symmetric spacetimes. This relation
contains work term contributed by `redshift work density', `pressure density'
and `gravitational work density'. We illustrate it in RN black hole,
Dilaton-Maxwell-Einstein black hole and Vaidya black hole. We argue that the
co-moving observers are not physically related to Kodama observers in FRW
universe unless in the vacuum case. We also find that a generalized
differential form of first law is difficult to be well defined, and it would
not give more information than the integral form.Comment: 18 pages, typos corrected, references adde
Magnetic extraction of energy from accretion disc around a rotating black hole
An analytical expression for the disc power is derived based on an equivalent
circuit in black hole (BH) magnetosphere with a mapping relation between the
radial coordinate of the disc and that of unknown astrophysical load. It turns
out that this disc power is comparable with two other disc powers derived in
the Poynting flux and hydrodynamic regimes, respectively. In addition, the
relative importance of the disc power relative to the BZ power is discussed. It
is shown that the BZ power is generally dominated by the disc power except some
extreme cases. Furthermore, we show that the disc power derived in our model
can be well fitted with the jet power of M87.Comment: 7 pages, 5 figure
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction
The development of computed tomography (CT) image reconstruction methods that
significantly reduce patient radiation exposure while maintaining high image
quality is an important area of research in low-dose CT (LDCT) imaging. We
propose a new penalized weighted least squares (PWLS) reconstruction method
that exploits regularization based on an efficient Union of Learned TRAnsforms
(PWLS-ULTRA). The union of square transforms is pre-learned from numerous image
patches extracted from a dataset of CT images or volumes. The proposed
PWLS-based cost function is optimized by alternating between a CT image
reconstruction step, and a sparse coding and clustering step. The CT image
reconstruction step is accelerated by a relaxed linearized augmented Lagrangian
method with ordered-subsets that reduces the number of forward and back
projections. Simulations with 2-D and 3-D axial CT scans of the extended
cardiac-torso phantom and 3D helical chest and abdomen scans show that for both
normal-dose and low-dose levels, the proposed method significantly improves the
quality of reconstructed images compared to PWLS reconstruction with a
nonadaptive edge-preserving regularizer (PWLS-EP). PWLS with regularization
based on a union of learned transforms leads to better image reconstructions
than using a single learned square transform. We also incorporate patch-based
weights in PWLS-ULTRA that enhance image quality and help improve image
resolution uniformity. The proposed approach achieves comparable or better
image quality compared to learned overcomplete synthesis dictionaries, but
importantly, is much faster (computationally more efficient).Comment: Accepted to IEEE Transaction on Medical Imagin
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