31,926 research outputs found
First-Principles calculation of atomic hydrogen adsorption on Be(10\={1}0) thin films
We present a first-principles study of the atomic hydrogen adsorption onto
the Be(10\={1}0) thin film. There are two types of Be(10\={1}0) surfaces
according to the interlayer spacing between the surface and its
nearest-neighbor layer. We show that the H adsorption features on these two
kinds of surfaces are remarkably different. The work function, averaged
electrostatic potential, and the local charge density consistently show that
the charge is transferred from H to Be for L-type (see the text below)
surfaces, while the transfer process is inverted for S-type surfaces.Comment: 7 figure
Convolutional Neural Network-Based Image Representation for Visual Loop Closure Detection
Deep convolutional neural networks (CNN) have recently been shown in many
computer vision and pattern recog- nition applications to outperform by a
significant margin state- of-the-art solutions that use traditional
hand-crafted features. However, this impressive performance is yet to be fully
exploited in robotics. In this paper, we focus one specific problem that can
benefit from the recent development of the CNN technology, i.e., we focus on
using a pre-trained CNN model as a method of generating an image representation
appropriate for visual loop closure detection in SLAM (simultaneous
localization and mapping). We perform a comprehensive evaluation of the outputs
at the intermediate layers of a CNN as image descriptors, in comparison with
state-of-the-art image descriptors, in terms of their ability to match images
for detecting loop closures. The main conclusions of our study include: (a)
CNN-based image representations perform comparably to state-of-the-art hand-
crafted competitors in environments without significant lighting change, (b)
they outperform state-of-the-art competitors when lighting changes
significantly, and (c) they are also significantly faster to extract than the
state-of-the-art hand-crafted features even on a conventional CPU and are two
orders of magnitude faster on an entry-level GPU.Comment: 8 pages, 4 figure
Solving the Jaynes-Cummings Model with Shift Operators Constructed by Means of the Matrix-Diagonalizing Technique
The Jaynes-Cummings model is solved with the raising and lowering (shift)
operators by using the matrix-diagonalizing technique. Bell nonlocality is also
found present ubiquitously in the excitations states of the model.Comment: 5 page
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss
As a basic task in computer vision, semantic segmentation can provide
fundamental information for object detection and instance segmentation to help
the artificial intelligence better understand real world. Since the proposal of
fully convolutional neural network (FCNN), it has been widely used in semantic
segmentation because of its high accuracy of pixel-wise classification as well
as high precision of localization. In this paper, we apply several famous FCNN
to brain tumor segmentation, making comparisons and adjusting network
architectures to achieve better performance measured by metrics such as
precision, recall, mean of intersection of union (mIoU) and dice score
coefficient (DSC). The adjustments to the classic FCNN include adding more
connections between convolutional layers, enlarging decoders after up sample
layers and changing the way shallower layers' information is reused. Besides
the structure modification, we also propose a new classifier with a
hierarchical dice loss. Inspired by the containing relationship between
classes, the loss function converts multiple classification to multiple binary
classification in order to counteract the negative effect caused by imbalance
data set. Massive experiments have been done on the training set and testing
set in order to assess our refined fully convolutional neural networks and new
types of loss function. Competitive figures prove they are more effective than
their predecessors.Comment: 14 pages, 7 figures, 6 table
Classification of compatible left-symmetric conformal algebraic structures on the Lie conformal algebra
In this paper, under some natural condition, a complete classification of
compatible left-symmetric conformal algebraic structures on the Lie conformal
algebra is presented. Moreover, applying this result, we
obtain a class of compatible left-symmetric algebraic structures on the
coefficient algebra of .Comment: 21 pages, to appear in Communications in Algebr
An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network
In this paper, we present a new automatic diagnosis method of facial acne
vulgaris based on convolutional neural network. This method is proposed to
overcome the shortcoming of classification types in previous methods. The core
of our method is to extract features of images based on convolutional neural
network and achieve classification by classifier. We design a binary classifier
of skin-and-non-skin to detect skin area and a seven-classifier to achieve the
classification of facial acne vulgaris and healthy skin. In the experiment, we
compared the effectiveness of our convolutional neural network and the
pre-trained VGG16 neural network on the ImageNet dataset. And we use the ROC
curve and normal confusion matrix to evaluate the performance of the binary
classifier and the seven-classifier. The results of our experiment show that
the pre-trained VGG16 neural network is more effective in extracting image
features. The classifiers based on the pre-trained VGG16 neural network achieve
the skin detection and acne classification and have good robustness.Comment: 12 pages, 7 figures, 5 table
Top-charm associated production at hadron colliders in the standard model with large extra dimensions
The precise calculations are carried out on the flavor changing neutral
current couplings in the process at the
large hadron collider(LHC) and very large hadron collider(VLHC) in both
frameworks of the minimal standard model(MSM) and its extension with extra
dimensions. We find that the effects from the large extra dimensions can
enhance the total cross section up to about several hundred times as that in
the MSM, quantitatively.Comment: 5 pages, 8 figure
Stochastic symplectic Runge-Kutta methods for the strong approximation of Hamiltonian systems with additive noise
In this paper, we construct stochastic symplectic Runge--Kutta (SSRK) methods
of high strong order for Hamiltonian systems with additive noise. By means of
colored rooted tree theory, we combine conditions of mean-square order 1.5 and
symplectic conditions to get totally derivative-free schemes. We also achieve
mean-square order 2.0 symplectic schemes for a class of second-order
Hamiltonian systems with additive noise by similar analysis. Finally, linear
and non-linear systems are solved numerically, which verifies the theoretical
analysis on convergence order. Especially for the stochastic harmonic
oscillator with additive noise, the linear growth property can be preserved
exactly over long-time simulation.Comment: 23 pages, 5 figure
A 750 GeV dark matter messenger at the Galactic Center
The first data from the LHC Run-2 have shown a possible excess in diphoton
events with invariant mass GeV, suggesting the existence of a new
resonance which may decay dominantly into dark matter (DM) particles. We show
in a simple model that the reported diphoton excess at the LHC is consistent
with another photon excess, the GeV excess in cosmic gamma-ray fluxes
towards the Galactic Center observed by the Fermi-LAT. Both the excesses can be
simultaneously explained by a GeV scalar DM particle annihilating
dominantly into two gluons with a typical thermal annihilation cross section,
which leads to the prediction of a large width to mass ratio of the resonance. The upper limit on the dijet search at
LHC Run-1 leads to a limit on the predicted cross section for DM
annihilating into final states \langle\sigma
v\rangle_{\gamma\gamma}
\gtrsim\mathcal{O}(10^{-30})~\mbox{cm}^{3}\mbox{s}^{-1}. Both the predictions
can be tested by the LHC, Fermi-LAT and future experiments.Comment: 7 pages, 2 figures, version to appear in PR
Super sub-wavelength patterns in photon coincidence detection
High-precision measurements implemented by means of light is desired in all
fields of science. However, light is a wave and Rayleigh criterion gives us a
diffraction limitation in classical optics which restricts to get arbitrary
high resolution. Sub-wavelength interference has a potential application in
lithography to beat the classical Rayleigh limit of resolution. We carefully
study the second-order correlation theory to get the physics behind
sub-wavelength interference in photon coincidence detection. A Young's
double-slit experiment with pseudo-thermal light is carried out to test the
second-order correlation pattern. The result shows that when different scanning
ways of two point detectors are chosen, one can get super sub-wavelength
interference patterns. We then give a theoretical explanation to this
surprising result, and find this explanation is also suitable for the result by
using entangled light. Furthermore, we discuss the limitation of this kind of
super sub-wavelength interference patterns in quantum lithography.Comment: 5 pages, 5 figures, comments are welcom
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