Anomalous spin-charge separation in a driven Hubbard system

Spin-charge separation (SCS) is a striking manifestation of strong correlations in low-dimensional quantum systems, whereby a fermion splits into separate spin and charge excitations that travel at different speeds. Here, we demonstrate that periodic driving enables control over SCS in a Hubbard system near half-filling. In one dimension, we predict analytically an exotic regime where charge travels slower than spin and can even become 'frozen', in agreement with numerical calculations. In two dimensions, the driving slows both charge and spin, and leads to complex interferences between single-particle and pair-hopping processes.Comment: arXiv admin note: text overlap with arXiv:2002.0231

Controlling magnetic correlations in a driven Hubbard system far from half-filling

We propose using ultracold fermionic atoms trapped in a periodically shaken optical lattice as a quantum simulator of the t-J Hamiltonian, which describes the dynamics in doped antiferromagnets and is thought to be relevant to the problem of high-temperature superconductivity in the cuprates. We show analytically that the effective Hamiltonian describing this system for off-resonant driving is the t-J model with additional pair hopping terms, whose parameters can all be controlled by the drive. We then demonstrate numerically using tensor network methods for a 1D lattice that a slow modification of the driving strength allows near-adiabatic transfer of the system from the ground state of the underlying Hubbard model to the ground state of the effective t-J Hamiltonian. Finally, we report exact diagonalization calculations illustrating the control achievable on the dynamics of spin-singlet pairs in 2D lattices utilising this technique with current cold-atom quantum-simulation technology. These results open new routes to explore the interplay between density and spin in strongly-correlated fermionic systems through their out-of-equilibrium dynamics

A Super-resolution Reconstruction Method of Remotely Sensed Image Based on Sparse Representation

The traditional method of image super-resolution reconstruction uses the sub-pixel displacement information between multi-frame low-resolution images to reconstruct a high-resolution image. Image super-resolution reconstruction is a typical mathematical inverse problem, and it is ill-posed problem [1]. To solve this problem, prior knowledge of data or question should be added. As the latest development achievements of signal priori or modeling, sparse representation of the signal has been studied in depth in the field of image processing. Super-resolution reconstruction based on sparse representation can improve the image quality and get richer image details [8]. Due to the sparse representation of image reconstruction has strong priority, this paper focuses on super-resolution reconstruction of the single frame remotely sensed image based on sparse representation. Compared with other algorithms, it is proved that the super-resolution reconstruction algorithm based on sparse representation has advantages in remotely sensed image reconstruction

An Improved Antenna Group Delay Measurement Method Using a Three-antenna Extrapolation Technique

In order to minimize the error due to multiple reflections between antennas in the conventional group delay (GD) measurement, an improved antenna GD measurement method is proposed. In this method, antenna group delay is measured as a function of distances using a three-antenna extrapolation method. The GD is determined by averaging a set of measured GD values according to a derived multiple-reflection error model. Measurement in frequency band of (1575.42±16) MHz for a circularly polarised helical antenna is presented, which gives the detail measurement procedures and validates the method. The uncertainty evaluation for this measurement was carried out as well, and an expanded uncertainty of 0.20 ns (k = 2) has been achieved. One more measurement example in frequency band of (4000±10) MHz for a standard gain horn antenna with an expanded uncertainty of 0.12 ns (k = 2) is also presented briefly in this paper

Few-shot remote sensing scene classification based on multi subband deep feature fusion

Recently, convolutional neural networks (CNNs) have performed well in object classification and object recognition. However, due to the particularity of geographic data, the labeled samples are seriously insufficient, which limits the practical application of CNN methods in remote sensing (RS) image processing. To address the problem of small sample RS image classification, a discrete wavelet-based multi-level deep feature fusion method is proposed. First, the deep features are extracted from the RS images using pre-trained deep CNNs and discrete wavelet transform (DWT) methods. Next, a modified discriminant correlation analysis (DCA) approach is proposed to distinguish easily confused categories effectively, which is based on the distance coefficient of between-class. The proposed approach can effectively integrate the deep feature information of various frequency bands. Thereby, the proposed method obtains the low-dimensional features with good discrimination, which is demonstrated through experiments on four benchmark datasets. Compared with several state-of-the-art methods, the proposed method achieves outstanding performance under limited training samples, especially one or two training samples per class

Method of Reservoir Optimal Operation Based on Improved Simulated Annealing Genetic Algorithm

According to the specific circumstances of Wanjiazhai Reservoir, establish a reservoir optimal scheduling nonlinear mathematical model with a maximum generation capacity target, this paper uses an improved simulated annealing genetic algorithm to solve the model. The algorithm is in view of the defects of the traditional simulated annealing genetic algorithm to improve the algorithm from three aspects: introducing the niche technology, using adaptive crossover and mutation strategy, using the elitist strategy during the selection. Through examples are calculated and compared with the traditional simulated annealing genetic algorithm, the improved algorithm effectively overcomes the stagnation phenomenon, to enhance the global search ability. Its optimization performance is better than that of the traditional simulated annealing genetic algorithm

Method of Optimal Scheduling of Cascade Reservoirs based on Improved Chaotic Ant Colony Algorithm

On the basis of the analysis of the basic information of the river basin reservoirs and application of chaotic ant swarm algorithm, the medium-and long-term optimization operation model is established, which regards the maximum annual generation capacity of the cascade hydropower stations as the main purpose. The simulation result shows the algorithm improves the total annual power generation of the cascade reservoirs, and is better than the basic chaotic ant colony solving method of reservoir operation model, finally provides an effective solution to solve the cascade reservoirs optimization operation problem

Object-Oriented Classification of Hyperspectral Remote Sensing Images Based on Genetic Algorithm and Support Vector Machine

This paper proposes a method of reducing dimensions based on genetic algorithm and object-oriented classification based on support vector machine (SVM). The basic idea is subspace decomposition of hyperspectral images at first, then selecting suitable bands in each subspace by using genetic algorithm and putting all selected bands of each subspace together. Furthermore, the hyperspectral image is segmented into a series of objects and then the spectral features and spatial features of objects in the selected bands are extracted. Finally, SVM classification is used according to features of the objects. The algorithm proposed is more effective and superior in dimension reduction and classification of hyperspectral image

Method of Optimal Scheduling of Cascade Reservoirs based on Improved Chaotic Ant Colony Algorithm

Abstract: On the basis of the analysis of the basic information of the river basin reservoirs and application of chaotic ant swarm algorithm, the medium-and long-term optimization operation model is established, which regards the maximum annual generation capacity of the cascade hydropower stations as the main purpose. The simulation result shows the algorithm improves the total annual power generation of the cascade reservoirs, and is better than the basic chaotic ant colony solving method of reservoir operation model, finally provides an effective solution to solve the cascade reservoirs optimization operation problem

Microbial profiling identifies potential key drivers in gastric cancer patients

Gastric cancer (GC) is the fifth most commonly diagnosed cancer and the third leading cause of cancer-related death in the world. Microbiota is believed to be associated with GC. Growing evidences showed Helicobacter pylori played a key role in GC development. However, little was known about the microbiota in gastric juices and tissues in GC patients, and thus it was difficult to understand other potential microbial causation for GC. Here, we collected the gastric juice and surgically removed gastric tissues from GC patients to give insight into GC microbiota. Most microbes identified in the gastric samples were opportunistic pathogens or resident flora of the human microbiota. Further network analyses identified five opportunistic pathogens as keystone species. H. pylori is the direct cause of GC, but other opportunistic microbes might also function in GC development. The microbiota in the gastric juice and gastric tissue of the GC patients were complex, and some dominant opportunistic pathogens contributed to the GC development. This study introduces microbiota in gastric juice, gastric normal tissue and gastric cancer tissue of GC patients, and highlights the potential keystone microbes functioned during GC development