Solving the generalized Higgs model from the generalized CRS model

In this paper, we reveal a direct relation between the generalized one-dimensional Carinena-Ranada-Santander (CRS) model and the radial part of two-dimensional generalized Higgs model. By this relation, we construct a series of quasi-exactly solutions for the two-dimensional Higgs model from a solved generalized CRS model.Comment: 10 page

An Effective Two-component Entanglement in Double-well Condensation

We propose a spin-half approximation method for two-component condensation in double wells to discuss the quantum entanglement of two components. This approximation is presented to be valid under stationary tunneling effect for odd particle number of each component. The evolution of the entanglement is found to be affected by the particle number both quantitatively and qualitatively. In detail, the maximal entanglement are shown to be hyperbolic like with respect to tunneling rate and time. To successively obtain large and long time sustained entanglement, the particle number should not be large.Comment: 5 pages,7 figure

Local field modulated entanglment among three distant atoms

We extend the scheme for that proposed by S. Mancini and S. Bose (Phys. Rev. A \QTR{bf}{70}, 022307(2004)) to the case of triple-atom. Under mean field approximation, we obtain an effective Hamiltonian of triple-body Ising-model interaction. Furthermore, we stress on discussing the influence of the existence of a third-atom on the two-atom entanglement and testing the modulation effects of locally applied optical fields and fiber on the entanglement properties of our system.Comment: 10 pages, 4 figure

The Quasi-exact models in two-dimensional curved space based on the generalized CRS Harmonic Oscillator

In this paper, by searching the relation between the radial part of Higgs harmonic oscillator in the two-dimensional curved space and the generalized CRS harmonic oscillator model, we can find a series of quasi-exact models in two-dimensional curved space based on this relation.Comment: 7 page

Background Subtraction using Compressed Low-resolution Images

Image processing and recognition are an important part of the modern society, with applications in fields such as advanced artificial intelligence, smart assistants, and security surveillance. The essential first step involved in almost all the visual tasks is background subtraction with a static camera. Ensuring that this critical step is performed in the most efficient manner would therefore improve all aspects related to objects recognition and tracking, behavior comprehension, etc.. Although background subtraction method has been applied for many years, its application suffers from real-time requirement. In this letter, we present a novel approach in implementing the background subtraction. The proposed method uses compressed, low-resolution grayscale image for the background subtraction. These low-resolution grayscale images were found to preserve the salient information very well. To verify the feasibility of our methodology, two prevalent methods, ViBe and GMM, are used in the experiment. The results of the proposed methodology confirm the effectiveness of our approach.Comment: 4 pages,36 figure

ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images

Building footprint extraction from high-resolution aerial images is always an essential part of urban dynamic monitoring, planning and management. It has also been a challenging task in remote sensing research. In recent years, deep neural networks have made great achievement in improving accuracy of building extraction from remote sensing imagery. However, most of existing approaches usually require large amount of parameters and floating point operations for high accuracy, it leads to high memory consumption and low inference speed which are harmful to research. In this paper, we proposed a novel efficient network named ESFNet which employs separable factorized residual block and utilizes the dilated convolutions, aiming to preserve slight accuracy loss with low computational cost and memory consumption. Our ESFNet obtains a better trade-off between accuracy and efficiency, it can run at over 100 FPS on single Tesla V100, requires 6x fewer FLOPs and has 18x fewer parameters than state-of-the-art real-time architecture ERFNet while preserving similar accuracy without any additional context module, post-processing and pre-trained scheme. We evaluated our networks on WHU Building Dataset and compared it with other state-of-the-art architectures. The result and comprehensive analysis show that our networks are benefit for efficient remote sensing researches, and the idea can be further extended to other areas. The code is public available at: https://github.com/mrluin/ESFNet-PytorchComment: 10 pages, 3 figures, 4 tables. Accepted for IEEE Acces

A simple and robust single-pixel computational ghost imaging

A simple and robust experiment demonstrating computational ghost imaging with structured illumination and a single-pixel detector has been performed. Our experimental setup utilizes a general computer for generating pseudo-randomly patterns on the liquid crystal display screen to illuminate a partially-transmissive object. With an incoherent light source, this object is imaged. The effects of light source, light path, and the number of measurements on the reconstruction quality of the object are discussed both theoretically and experimentally. The realization of computational ghost imaging with computer liquid crystal display is a further setup toward the practical application of ghost imaging with ordinary incoherent light.Comment: 5 pages, 5 figure

Unraveling nonadiabatic ionization and Coulomb potential effects in strong-field photoelectron holography

Strong field photoelectron holography has been proposed as a means for interrogating the spatial and temporal information of electrons and ions in a dynamic system. After ionization, part of the electron wave packet may directly go to the detector (the reference wave), while another part may be driven back to the ion where it scatters off (the signal wave). The interference hologram of the two waves may be used to retrieve the target information. However, unlike conventional optical holography, the propagations of electron wave packets are affected by the Coulomb potential as well as by the laser field. In addition, electrons are emitted over the whole laser pulse duration, thus multiple interferences may occur. In this work, we used a generalized quantum-trajectory Monte Carlo method to investigate the effect of Coulomb potential and the nonadiabatic subcycle ionization on the photoelectron hologram. We showed that photoelectron hologram can be well described only when the nonadiabatic effect in ionization is accounted for, and Coulomb potential can be neglected only in the tunnel ionization regime. Our results help establishing photoelectron holography for probing spatial and dynamic properties of atoms and molecules.Comment: 8 pages, 6 figure

Observation of inverse Edelstein effect in Rashba-split 2DEG between SrTiO3 and LaAlO3 at room temperature.

The Rashba physics has been intensively studied in the field of spin orbitronics for the purpose of searching novel physical properties and the ferromagnetic (FM) magnetization switching for technological applications. We report our observation of the inverse Edelstein effect up to room temperature in the Rashba-split two-dimensional electron gas (2DEG) between two insulating oxides, SrTiO3 and LaAlO3, with the LaAlO3 layer thickness from 3 to 40 unit cells (UC). We further demonstrate that the spin voltage could be markedly manipulated by electric field effect for the 2DEG between SrTiO3 and 3-UC LaAlO3. These results demonstrate that the Rashba-split 2DEG at the complex oxide interface can be used for efficient charge-and-spin conversion at room temperature for the generation and detection of spin current

Statistical properties of random clique networks

In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein interaction networks, is introduced by combining the random selection rule of the Erd\"os and R\'enyi (ER) model and the concept of cliques. We find that random clique networks having a small average degree differ from the ER network in that they have a large clustering coefficient and a power law clustering spectrum, while networks having a high average degree have similar properties as the ER model. In addition, we find that the relation between the clustering coefficient and the average degree shows a non-monotonic behavior and that the degree distributions can be fit by multiple Poisson curves; we explain the origin of such novel behaviors and degree distributions.Comment: 7 pages,10 figure