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 W(a,b)\mathcal{W}(a,b)

In this paper, under some natural condition, a complete classification of compatible left-symmetric conformal algebraic structures on the Lie conformal algebra $\mathcal{W}(a,b)$ is presented. Moreover, applying this result, we obtain a class of compatible left-symmetric algebraic structures on the coefficient algebra of $\mathcal{W}(a,b)$.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 $pp \to gg \to t\bar{c}(\bar{t}c)$ 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 $\sim 750$ 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 $2$ 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 $\sim 60$ 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 $\Gamma/M\approx \mathcal{O}(10^{-2})$ of the resonance. The upper limit on the dijet search at LHC Run-1 leads to a $lower$ limit on the predicted cross section for DM annihilating into $\gamma\gamma$ 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