Localization and delocalization of a one-dimensional system coupled with the environment

We investigate several models of a one-dimensional chain coupling with surrounding atoms to elucidate disorder- induced delocalization in quantum wires, a peculiar behaviour against common wisdom. We show that the localization length is enhanced by disorder of side sites in the case of strong disorder, but in the case of weak disorder there is a plateau in this dependence. The above behaviour is the conjunct influence of the coupling to the surrounding atoms and the antiresonant effect. We also discuss different effects and their physical origin of different types of disorder in such systems. The numerical results show that coupling with the surrounding atoms can induce either the localization or delocalization effect depending on the values of parameters.

Characteristics of local photonic state density in an infinite two-dimensional photonic crystal

The local density of photonic states （LDPS） of an infinite two-dimensional （2D） photonic crystal （PC） composed of rotated square-pillars in a 2D square lattice is calculated in terms of the plane-wave expansion method in a combination with the point group theory. The calculation results show that the LDPS strongly depends on the spatial positions. The variations of the LDPS as functions of the radial coordinate and frequency exhibit “mountain chain” structures with sharp peaks. The LDPS with large value spans a finite area and falls abruptly down to small value at the position corresponding to the interfaces between two different refractive index materials. The larger/lower LDPS occurs inward the lower/larger dielectric-constant medium. This feature can be well interpreted by the continuity of electricdisplacement vector at the interface. In the frequency range of the pseudo-PBG （photonic band gap）, the LDPS keeps very low value over the whole Wiger-Seitz cell. It indicates that the spontaneous emission in 2D PCs cannot be prohibited completely, but it can be inhibited intensively when the resonate frequency falls into the pseudo-PBG.

An efficient locally one-dimensional finite-difference time-domain method based on the conformal scheme

An efficient conformal locally one-dimensional finite-difference time-domain（LOD-CFDTD） method is presented for solving two-dimensional（2D） electromagnetic（EM） scattering problems. The formulation for the 2D transverse-electric（TE） case is presented and its stability property and numerical dispersion relationship are theoretically investigated. It is shown that the introduction of irregular grids will not damage the numerical stability. Instead of the staircasing approximation, the conformal scheme is only employed to model the curve boundaries, whereas the standard Yee grids are used for the remaining regions. As the irregular grids account for a very small percentage of the total space grids, the conformal scheme has little effect on the numerical dispersion. Moreover, the proposed method, which requires fewer arithmetic operations than the alternating-direction-implicit（ADI） CFDTD method, leads to a further reduction of the CPU time. With the total-field/scattered-field（TF/SF） boundary and the perfectly matched layer（PML）, the radar cross section（RCS） of two2 D structures is calculated. The numerical examples verify the accuracy and efficiency of the proposed method.

High-precision two-dimensional atom localization via probe absorption in an M-scheme atomic system

In the present paper, we investigate the behavior of two-dimensional atom localization in a five-level M-scheme atomic system driven by two orthogonal standing-wave fields. We find that the precision and resolution of the atom localization depends on the probe field detuning significantly. And because of the effect of the microwave field, an atom can be located at a particular position via adjusting the system parameters.

Local Similarity Relationships of Non-Dimensional Wind and Temperature Gradient in the Tower-Layer Atmosphere over Beijing City

Micrometeorological data for wind and temperature from a 325 m high tower in Beijing City are analyzed by use of local similarity theory. Non-dimensional wind and temperature gradients, Phi(m) and Phi(h), are determined by three techniques called, respectively, eddy-correlation, mean profiles and inertia-subrange cospectra (ISC) method for a wide range of atmospheric stratification from unstable to stable conditions. Average dissipation rate Phi(e) of turbulent kinetic energy (TKE) is evaluated from u-spectrum, as a quantity required in the last technique. Ratio of the eddy transfer coefficients, alpha(= K-h / K-m), is calculated from Phi(m) and Phi(h) estimations. The results from various techniques are compared with each other and with some available empirical results in the tower-layer, It is shown that the empirical relationships determined by mean profiles and ISC methods in the lower-layer turbulence ore in agreement with each other and with some other results.

Two-dimensional atom localization via probe absorption in a four-level atomic system

We have investigated the two-dimensional （2D） atom localization via probe absorption in a coherently driven four-level atomic system by means of a radio-frequency field driving a hyperfine transition. It is found that the detecting probability and precision of 2D atom localization can be significantly improved via adjusting the system parameters. As a result, our scheme may be helpful in laser cooling or the atom nano-lithography via atom localization.

Outlier detection based on multi-dimensional clustering and local density

Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.

New periodic wave solutions, localized excitations and their interaction for (2+1)-dimensional Burgers equation

Based on the B/icklund method and the multilinear variable separation approach （MLVSA）, this paper finds a general solution including two arbitrary functions for the （2＋1）-dimensional Burgers equations. Then a class of new doubly periodic wave solutions for （2＋l）-dimensional Burgers equations is obtained by introducing appropriate Jacobi elliptic functions, Weierstrass elliptic functions and their combination in the general solutions （which contains two arbitrary functions）. Two types of limit cases are considered. Firstly, taking one of the moduli to be unity and the other zero, it obtains particular wave （called semi-localized） patterns, which is periodic in one direction, but localized in the other direction. Secondly, if both moduli are tending to 1 as a limit, it derives some novel localized excitations （two-dromion solution）.

Automated measurement of three-dimensional cerebral cortical thickness in Alzheimer’s patients using localized gradient vector trajectory in fuzzy membership maps

Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.

Local Solution of Three-Dimensional Axisymmetric Supersonic Flow in a Nozzle

In this paper, we construct a local supersonic flow in a 3-dimensional axis-symmetry nozzle when a uniform supersonic flow inserts the throat. We apply the local existence theory of boundary value problem for quasilinear hyperbolic system to solve this problem. The boundary value condition is set in particular to guarantee the character number condition. By this trick, the theory in quasilinear hyperbolic system can be employed to a large range of the boundary value problem.

LOCAL NONLINEAR CHARACTERISTICS OF GAS LIQUID SOLID THREE PHASE SELF ASPIRATED REVERSED FLOW JET LOOP REACTOR

Hursts rescaled range (R/S) analysis and Wolfs attractor reconstruction technique have been adopted to estimate the local fractal dimensions and the local largest Lyapunov exponents in terms of the time series pressure fluctuations obtained from a gas liquid solid three phase self aspirated reversed flow jet loop reactor,respectively.The results indicate that the local fractal dimensions and the local largest Lyapunov exponents in both the jet region and the tubular region inside the draft tube increase with the increase in the jet liquid flowrates and the solid loadings,the local fractal dimension profiles are similar to those of the largest Lyapunov exponent,the local largest lyapunov exponents are positive for all cases,and the flow behavior of such a reactor is chaotic.The local nonlinear characteristic parameters such as the local fractal dimension and the local largest Lyapunov exponent could be applied to further study the flow properties such as the flow regime transitions and flow structures of this three phase jet loop reactor.

Bounds on Fractional-Based Metric Dimension of Petersen Networks

The problem of investigating the minimum set of landmarks consisting of auto-machines(Robots)in a connected network is studied with the concept of location number ormetric dimension of this network.In this paper,we study the latest type of metric dimension called as local fractional metric dimension(LFMD)and find its upper bounds for generalized Petersen networks GP(n,3),where n≥7.For n≥9.The limiting values of LFMD for GP(n,3)are also obtained as 1(bounded)if n approaches to infinity.

Two linear subpattern dimensionality reduction algorithms

This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA). The modified SpC2DLDPCA and SpC2DLPPCA algorithm over their non-subpattern version and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefit greatly in the following four points: (1) SpC2DLDPCA and SpC2DLPPCA can avoid the failure that the larger dimension matrix may bring about more consuming time on computing their eigenvalues and eigenvectors. (2) SpC2DLDPCA and SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Linear Discriminant Analysis (2DLDA). SpC2DLDPCA combines a discriminant analysis and a compression technique with low energy loss. (4) The idea is also introduced into 2DPCA and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact feature expressions. Finally, the experiments on the CASIA(B) gait database show that SpC2DLDPCA and SpC2DLPPCA have higher recognition accuracies than their non-subpattern versions and SpC2DPCA.

3D Target Localization Based on FrFT from Spaceborne Curve SAR

Synthetic aperture radar(SAR)three-dimensional(3D)imaging technology can reconstruct the complete structure of observed targets and has been a hot topic.Compared with tomographic SAR,array interferometric SAR,and circular SAR,curve SAR can use less data to achieve 3D positioning of targets.Most existing algorithms for estimating Doppler frequency modulation(FM)rate are based on sub aperture partitioning,resulting in low computational efficiency.To address this,this article establishes a target height estimation model,which reflects the relation-ship between the height and the residual Doppler FM rate for spaceborne curve SAR.Then,a fast SAR 3D localization processing flow based on fractional Fourier transform(FrFT)is proposed.Experimental verification demonstrates that this method can estimate the Doppler FM of the target column by column,and the 3D position error for non-overlapping targets is controlled within 1 m.For overlapping points with an intensity ratio greater than 1.5,the root mean square error(RMSE)of the estimation results is around 5 m.If the separation between overlapping points is greater than 35 m,the RMSE decreases to approximately 2 m.

生活者视角下村民参与生态治理多维机制构建——基于内蒙古B嘎查草原生态补偿实践

目前在北方草原牧区广泛推行实施的生态补偿政策仍延续以往政府主导、自上而下的管理模式。在此政策驱动下,当地居民的主体性、参与性和积极性缺乏有效发挥渠道,客观上影响了基层农牧区经济—社会—环境协调发展。本研究首先立足位于科尔沁沙地西北边缘的西辽河流域环境实践,运用生活环境主义理论,较为全面地剖析生态补偿政策所面临的难题及其社会根源;其次较为深刻地探讨在实现生态修复与乡村振兴双重目标下,通过激活重塑,创建现代共治共享多民族本土知识体系的重要性;最后力求深入探索政府主导、社区主位、村民主体的参与式多维机制构建的内在逻辑及技术路径。

基于non-local先验的贝叶斯变量选择方法及其在高维数据分析中的应用

目的对高维数据进行变量筛选并构建预测模型是组学数据分析的研究热点之一。本研究旨在为结局为二分类变量的高维组学数据筛选自变量并构建预测结局的稀疏统计模型。方法本研究通过模拟研究和实例分析阐释基于non-local先验的贝叶斯变量选择方法--乘积逆矩先验(product inverse moment,piMOM)相较于惩罚类方法ISIS-光滑平切绝对偏差(iterative sure independence screening-smoothly clipped absolute deviation,ISIS-SCAD)和ISIS-最小最大凹惩罚(iterative sure independence screening-minimax concave penalty,ISIS-MCP)在高维数据中变量筛选及其预测效果的性能优劣。结果模拟研究发现:在高维的情况下,经piMOM、ISIS-SCAD和ISIS-MCP方法筛选所得变量的平均真阳性数和受试者工作特征曲线下面积(AUC,area under curve)基本相等,ISIS-SCAD、ISIS-MCP的平均假阳性数、回归系数均方误差以及预测均方误差明显高于基于non-local先验的贝叶斯变量方法所获得的对应值。piMOM方法分析弥漫大B细胞淋巴瘤实例数据共识别5个有意义的基因,AUC为0.996;ISIS-SCAD识别7个基因,AUC为0.975;ISIS-MCP识别7个基因,AUC为0.968。结论在模型选择相合性和预测准确性方面,piMOM方法与ISIS-SCAD和ISIS-MCP相比,具有优势,在一定意义上可有效控制假阳性率。

Fault Detection Based on Incremental Locally Linear Embedding for Satellite TX-I

A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental learning,an incremental LLE method is proposed to acquire low-dimensional feature embedded in high-dimensional space.Then,telemetry data of Satellite TX-I are analyzed.Therefore,fault detection are performed by analyzing feature information extracted from the telemetry data with the statistical indexes T2 and squared prediction error(SPE)and SPE.Simulation results verify the fault detection scheme.

基于改进YOLOX的棉花姿态品级识别及其定位研究

【目的】旨在解决高质量采棉要求下,采棉机对不同姿态和品级的棉花进行精确识别与定位的问题,提出了1种基于改进YOLOX的棉花检测方法YOLOX-Cotton。【方法】YOLOX-Cotton使用YOLOX模型作为主体框架,包含识别模块和定位模块,并引入了CA(coordinate attention)模块和SIoU损失函数,以多种姿态、品级的棉花图片作为数据集,对其进行训练并测试。【结果】YOLOX-Cotton模型的识别模块能够识别不同姿态和品级的棉花,且模型精确率、召回率和平均精度均值达到92.9%、86.8%和92.4%,与原YOLOX模型相比分别提升了5.2、5.5和6.1百分点。该模型的定位模块能够准确获得棉花的位置,测量结果均在田间试验验证结果的阈值范围内,所有样本的标准偏差均小于0.01。【结论】YOLOX-Cotton能够有效解决采棉机在高质量采棉要求下对棉花的识别与定位问题,将为实现高质量采棉提供了有力的技术支撑。

基于降维字典学习的高维数据分类策略

为了解决字典学习中的高维数据与非线性问题,提出一种基于降维字典学习的高维数据分类策略。在降维阶段,利用自编码器学习一种非线性映射,该映射可以降维并保留高维数据的非线性结构;在字典学习阶段,利用标签嵌入进行局部约束;在学习过程中,保留了可分解的非线性局部结构,增强了类的区分能力,同时优化了映射函数和字典。在多个基准数据集上的实验结果表明,提出的方法能够有效解决字典学习中的高维数据与非线性问题。

基于局部信息熵的计算机网络高维数据离群点检测系统

通过离群点检测可以及时发现计算机网络中的异常,从而为风险预警和控制提供重要线索。为此,设计一种基于局部信息熵的计算机网络高维数据离群点检测系统。在高维数据采集模块中,利用Wireshark工具采集计算机网络原始高维数据包;并在高维数据存储模块中建立MySQL数据库、Zooleeper数据库与Redis数据库,用于存储采集的高维数据包。在高维数据离群点检测模块中,通过微聚类划分算法划分存储的高维数据包,得到数个微聚类;然后计算各微聚类的局部信息熵,确定各微聚类内是否存在离群点;再依据偏离度挖掘微聚类内的离群点;最后,利用高维数据可视化模块呈现离群点检测结果。实验证明:所设计系统不仅可以有效采集计算机网络高维数据并划分计算机网络高维数据,还能够有效检测高维数据离群点,且离群点检测效率较快。