Anisotropic thermal expansion and thermomechanic properties of monolayer β\beta-Te

Recently, $\beta$-Te (atomically 2D tellurium) with rectangular crystal structure has been synthesized successfully on highly oriented pyrolytic graphite substrates by using molecular beam epitaxy. It has been found possessing remarkable properties such as ultralow lattice thermal conductivity and high thermoelectric efficiency. Based on the first-principles calculations, we study the thermal expansion and thermomechanic properties of the experimental phase monolayer $\beta$-Te, using quasiharmonic approach. It is found $\beta$-Te shows large positive thermal expansion at elevated temperature, while the linear thermal expansion coefficient is negative along a direction at very low temperature. The linear thermal expansion coefficient along b direction is 4.9*10$^{-5}$ K$^{-1}$ at 500 K, which is considerably large in 2D materials. $\beta$-Te exhibits strong in-plane anisotropy, including thermal expansion, 2D elastic moduli and Poisson's ratios. However, the elastic moduli, Poisson's ratios and the in-plane anisotropy are weakened with increasing temperature, and the variations are dominated by the generalized mode Gr\"{u}neisen parameters.Comment: 25 pages, 7 figures, 14 formula

Synthesizing and Controlling Helical Indirect Exchange Interactions at Nonequilibrium

We study the nonequilibrium effects of spin and/or electric currents on the helical indirect exchange interactions of local spins that embedded in general open electronic systems. Especially, besides the synthesized anisotropic Heisenberg interactions, we find that the synthetic helical indirect exchange interactions possess two parts: antisymmetric (Dzyaloshinskii-Moriya interaction) and symmetric (Kaplan-Shekhtman-Entin-Wohlman-Aharony interaction), which are all formulated in terms of Keldysh nonequilibrium Green's functions. The presence of either spin-orbit coupling or spin polarized currents alone is able to synthesize and control the antisymmetric Dzyaloshinskii-Moriya exchange interactions, as the same direction as spin splitting. However, the appearance of symmetric Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions requires both, i.e., the spin-orbit coupling and spin polarized currents with different splitting directions. Our results show the detailed scheme of controlling the sign, magnitude, and direction of indirect Dzyaloshinskii-Moriya vectors and Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions at nonequilibrium in open quantum devices.Comment: 8 pages, 7 figure

Next-to-leading order QCD corrections to HZW±HZW^{\pm} production at 14 TeV LHC

Since the precise study of Higgs gauge couplings is important to test the Standard Model (SM), we calculate the complete next-to-leading order QCD(NLO QCD) correction to the $pp \to HZW^{\pm}$ production in the SM at 14 TeV LHC. Our results show that the NLO QCD correction can enhance the leading-order cross section of $pp \to HZW^{\pm}$ by 45%, when $m_H$ = 125.3 GeV. We also study the dependence of the LO and NLO corrected cross sections on the renormalization and factorization scale $\mu$. Besides, due to the unbalance of parton distribution functions, we investigate the charge asymmetry of $W^{\pm}$ in the production of $pp\to HZW^{\pm}$, which can reach 32.94% for $\mu=(m_H+m_Z+m_W)/2$ at 14 TeV LHC.Comment: discussions added, accepted by Physics Letters

Entanglement entropy and fidelity susceptibility in the one-dimensional spin-1 XXZ chains with alternating single-site anisotropy

We study the fidelity susceptibility in an antiferromagnetic spin-1 XXZ chain numerically. By using the density-matrix renormalization group method, the effects of the alternating single-site anisotropy $D$ on fidelity susceptibility are investigated. Its relation with the quantum phase transition is analyzed. It is found that the quantum phase transition from the Haldane spin liquid to periodic N\'{e}el spin solid can be well characterized by the fidelity. Finite size scaling of fidelity susceptibility shows a power-law divergence at criticality, which indicates the quantum phase transition is of second order. The results are confirmed by the second derivative of the ground-state energy. We also study the relationship between the entanglement entropy, the Schmidt gap and quantum phase transitions. Conclusions drawn from these quantum information observables agree well with each other.Comment: 5 pages, 6 figures, accepted by J. Phys.: Condens. Matte

Revisiting Associated Production of 125 GeV Higgs Boson with a Photon at a Higgs Factory

Considering the constraints from the flavor physics, precision electroweak measurements, Higgs data and dark matter detections, we scan over the parameter space of the MSSM and calculate the cross section of $e^+e^- \to h \gamma$ in the allowed parameter space. Since the loop-induced gauge couplings $h\gamma\gamma$ and $hZ\gamma$ can simultaneously contribute to the process $e^+e^- \to h \gamma$, we find the cross section can be sizably enhanced by a light stau, maximally 1.47(1.38) times larger than the SM prediction at $\sqrt{s}=240(350)$ GeV. So with the high luminosity, the measurement of $e^+e^- \to h \gamma$ may be used to test the anomalous gauge couplings $h\gamma\gamma$ and $hZ\gamma$ in the MSSM at a Higgs Factory.Comment: discussions and references added, version accepted by J. Phys.

Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection

Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, robustly detecting pedestrians with a large variant on sizes and with occlusions remains a challenging problem. In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions. The proposed gated feature extraction framework consists of squeeze units, gate units and a concatenation layer which perform feature dimension squeezing, feature elements manipulation and convolutional features combination from multiple CNN layers, respectively. We proposed two different gate models which can manipulate the regional feature maps in a channel-wise selection manner and a spatial-wise selection manner, respectively. Experiments on the challenging CityPersons dataset demonstrate the effectiveness of the proposed method, especially on detecting those small-size and occluded pedestrians

Physical States and BRST Operators for Higher-spin WW Strings

In this paper, we mainly investigate the $W_{2,s}^{M}\otimes W_{2,s}^{L}$ system, in which the matter and the Liouville subsystems generate $W_{2,s}^{M}$ and $W_{2,s}^L$ algebras respectively. We first give a brief discussion of the physical states for corresponding $W$ stings. The lower states are given by freezing the spin-2 and spin-$s$ currents. Then, introducing two pairs of ghost-like fields, we give the realizations of $W_{1,2,s}$ algebras. Based on these linear realizations, BRST operators for $W_{2,s}$ algebras are obtained. Finally, we construct new BRST charges of Liouville system for $W_{2,s}^{L}$ strings at the specific values of central charges $c$: $c=-{22/5}$ for $W_{2,3}^{L}$ algebra, $c=-24$ for $W_{2,4}^{L}$ algebra and $c=-2,-{286/3}$ for $W_{2,6}^{L}$ algebra, at which the corresponding $W_{2,s}^L$ algebras are singular.Comment: 18 pages, 2 tables, no figure

An End-to-End Compression Framework Based on Convolutional Neural Networks

Deep learning, e.g., convolutional neural networks (CNNs), has achieved great success in image processing and computer vision especially in high level vision applications such as recognition and understanding. However, it is rarely used to solve low-level vision problems such as image compression studied in this paper. Here, we move forward a step and propose a novel compression framework based on CNNs. To achieve high-quality image compression at low bit rates, two CNNs are seamlessly integrated into an end-to-end compression framework. The first CNN, named compact convolutional neural network (ComCNN), learns an optimal compact representation from an input image, which preserves the structural information and is then encoded using an image codec (e.g., JPEG, JPEG2000 or BPG). The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end. To make two CNNs effectively collaborate, we develop a unified end-to-end learning algorithm to simultaneously learn ComCNN and RecCNN, which facilitates the accurate reconstruction of the decoded image using RecCNN. Such a design also makes the proposed compression framework compatible with existing image coding standards. Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with state-of-the-art deblocking or denoising post-processing methods.Comment: Submitted to IEEE Transactions on Circuits and Systems for Video Technolog

Real-Time Robot Localization, Vision, and Speech Recognition on Nvidia Jetson TX1

Robotics systems are complex, often consisted of basic services including SLAM for localization and mapping, Convolution Neural Networks for scene understanding, and Speech Recognition for user interaction, etc. Meanwhile, robots are mobile and usually have tight energy constraints, integrating these services onto an embedded platform with around 10 W of power consumption is critical to the proliferation of mobile robots. In this paper, we present a case study on integrating real-time localization, vision, and speech recognition services on a mobile SoC, Nvidia Jetson TX1, within about 10 W of power envelope. In addition, we explore whether offloading some of the services to cloud platform can lead to further energy efficiency while meeting the real-time requirementsComment: 12 pages, 8 figure

High Thermoelectric Performance in Two-Dimensional Tellurium: An Ab Initio Study

In 2016, bulk tellurium was experimentally observed as a remarkable thermoelectric material. Recently, two-dimensional (2D) tellurium, called tellurene, has been synthesized and has exhibited unexpected electronic properties compared with the 2D MoS$_2$. They have also been fabricated into air-stable and high efficient field-effect transistors. There are two stable 2D tellurene phases. One ($\beta$-Te) has been confirmed with an ultralow lattice thermal conductivity ($\kappa_L$). However, the study of the transport properties of the other more stable phase, $\alpha$-Te, is still lacking. Here, we report the thermoelectric performance and phonon properties of $\alpha$-Te using Boltzmann transport theory and first principle calculations. A maximum ZT value of 0.83 is achieved under reasonable hole concentration, suggesting that the monolayer $\alpha$-Te is a potential competitor in the thermoelectric field.Comment: 14 pages, 5 figure