一维自旋轨道模型中自旋-轨道能隙产生机理分析

讨论考虑洪特耦合的两带赫伯德模型得到的一维自旋轨道模型中自旋-轨道能隙的产生.运用SU(4)赝费米子表象下的平均场理论,计算求得价键序参数、准粒子激发谱能隙和自旋、轨道密度-密度关联函数随系统耦合参数变化的结果.随洪特耦合相互作用由零开始增强,系统激发谱能隙逐渐打开,并且系统在参数取值为J₁/J₂=1/3处由具有阻错的无序状态相变到自旋铁磁有序和轨道反铁磁有序的状态.     

一维铁磁/有机系统的基态与自旋分布研究

文中用一维紧束缚模型描述铁磁金属,用一维非简并的Su-Schrieffer -Heeger (SSH)模型描述共轭聚合物,研究了在一维铁磁/共轭聚合物系统和一维CMR材料/ 聚合物系统中的电子转移和自旋转移.发现在聚合物部分没有自旋的双极化子比有自旋的极化子具有较低的能量而容易产生.然而在铁磁CMR材料/聚合物系统中极化子的产生能低于聚合物中极化子的产生能,增加了有机物中自旋极化输运的可能性.     

一种等效于反铁磁海森伯混合自旋链的铁磁-反铁磁交替自旋链

利用严格对角化方法研究了一种自旋为1/2的铁磁-反铁磁交替自旋链的物理性质.计算了自旋关联函数、自旋z方向分量、自旋激发谱和磁化曲线等基本物理量,并将这些物理量和自旋为(1,1/2)的混合反铁磁自旋链对应的物理性质进行了比较.结果表明,当铁磁作用居于主导地位时,该模型的物理行为等效于混合自旋模型.因此,可以通过分析铁磁-反铁磁交替自旋链的物理性质研究混合自旋链的物理行为.     

一维量子子自自旋链中拓扑有序态的物理描述

一维量子多体系统是凝聚态物理学中的重要研究方向之一,其中的新奇量子物态则是重要的研究课题。本文我们首先简要回顾一维量子整数自旋链体系的相关研究背景,然后提出一类SO(n)对称的严格可解量子自旋链模型及其矩阵乘积基态。当奇数n≥3时,体系的基态为Haldane相。利用这类态中隐藏的稀薄反铁磁序,我们找到了刻画这类态的非局域弦序参量,并在隐藏拓扑对称性的统一框架下解释了稀薄反铁磁序以及边缘态等奇特现象的起源。当偶数n≥4时,体系的基态为二聚化态。这些态属于破缺平移对称性的非Haldane相,但同样具有隐藏的反铁磁序。通过这些严格解的研究,我们还得到了一维SO(n)对称的双线性–双二次模型的基态相图,并发现在n≥5时,一维SO(n)对称的反铁磁海森堡模型的基态处于二聚化相中。基于以上这些结果,我们推广构造了一维平移不变且包含李群G对称性的Valence BondState(VBS)态,并利用其矩阵乘积表示讨论了对应哈密顿量的构造方法。对于自旋为S的量子整数自旋链,我们研究了两类具有不同拓扑属性的VBS类,前一类VBS态的边缘态处于SU(2)自旋J的不可约表示,后一类VBS态的边缘态为SO(2S+1)旋量。在前一类态中,我们以自旋为1的费米型VBS态为例构造了对应的哈密顿量。对后一类态,我们证明了它们等价于SO(2S+1)矩阵乘积态,从而揭示了呈展对称性的起源和边缘态的性质。我们还推广了SO(5)对称的玻色型和费米型VBS态,并探讨了它们的拓扑刻画方式。     

量子自旋液体候选材料的缪子自旋弛豫研究

量子自旋液体是一种新奇的磁性物态。由于极强的量子涨落,直至零温都不会出现长程序。量子自旋液体的基态不能用序参量描述,并且缺少对称性破缺,因此该物态的实现打破朗道理论的范式。对于量子自旋液体的研究有助于理解高温超导的机理,并且可以被应用在量子计算和量子信息中。目前,尽管理论上有了长足的发展,但仍旧没有任何一个材料被证实为量子自旋液体。因此,探测和确认一个真正的量子自旋液体材料是当前的研究重点。缪子自旋弛豫是一个对磁场极为敏感的实验技术,被广泛应用于量子自旋液体候选材料的研究中。该技术可以观测基态中是否存在磁有序,测量系统中的涨落频率,这两点都是表征量子自旋液体的重要性质。本文简要介绍了量子自旋液体态和缪子自旋弛豫技术,回顾了近期在不同体系的量子自旋液体候选材料中的实验结果,特别是缪子自旋弛豫的成果。这些体系包括一维反铁磁海森堡链（苯甲酸铜）,三角格子（YbMgGaO4,NaYbO2 和TbInO3）,笼目格[ZnCu3(OH)6Cl2 和 m3Sb3Zn2O14],蜂窝状格子（Na2IrO3 和 α-RuCl3）,以及烧绿石结构（Tb2Ti2O7,Pr2Ir2O7 和Ce2Zr2O7）。     

瞬态自旋光栅系统的建设及其在自旋输运研究中的应用

简要介绍了瞬态光栅系统原理及光路的建设,包括瞬态光栅的产生与探测.采用了外差探测法（heterodyne detection）,大大提高了信噪比.利用瞬态自旋光栅系统,研究了(110)方向生长的本征GaAs/AlGaAs单量子阱中自旋输运特性,测得室温下电子自旋的扩散常数D_s=551 cm/s. 关键词： 瞬态自旋光栅 自旋扩散 自旋输运 自旋弛豫     

阻挫诱导的亚铁磁性Heisenberg系统中的量子相变

利用密度矩阵重整化群(DMRG)方法研究磁性阻挫对一种S=1/2准一维反铁磁自旋链但却具有亚铁磁性的Heisenberg系统基态的影响.计算了单个晶胞的基态能、自旋关联函数以及自旋能隙.研究表明这种Heisenberg自旋系统的基态随着阻挫α的增强将从磁有序相变化到自旋无序相,并且伴随着自旋能隙的出现,量子相变点为α≈0.412.同时线形链上格点间自旋长程关联值的计算结果表明在磁有序区间体系的磁有序性质随着α的增强而减弱,阻挫在0≤α< 关键词： 准一维反铁磁自旋链 亚铁磁性 密度矩阵重整化群 自旋能隙     

阻挫对准一维非对称子格反铁磁链自旋波激发的影响

子格对称性破缺使得s=1/2准一维海森伯自旋链具有三支自旋波激发谱(其中一支属于声学模，两支属于光学模). 计算表明：阻挫导致两支光学模能隙简并解除；阻挫引起声学模自旋波激发谱软化，但并不导致光学模激发谱软化；阻挫导致两支光学模激发谱中的一支明显下移，这意味着阻挫使光学模激发变得重要且容易实现；阻挫引起的自旋偏离对于属于不同子格的自旋是不同的；阻挫对于系统基态磁性长程序的削弱随着阻挫增强变得越来越明显. 通过与严格对角化方法和DMRG方法的数值结果比较，还分析了自旋波近似的合理性和不足之处. 关键词： 准一维反铁磁自旋系统 自旋波激发 光学模 能隙简并     

反铁磁链的自旋Peierls相变

通过费米-玻色变换将一维自旋1/2反铁磁链映射为连续玻色场形式,讨论了模型基态能量和激发态能量,证明了此系统存在自旋Peierls相变.并给出了模型基态能量密度与晶格畸变大小的关系.数值计算的结果验证了上述理论的正确性. 关键词：     

自旋轨道耦合和磁场对准一维铁磁/半导体/铁磁系统中电子输运性质的影响

利用传递矩阵方法，我们计算了自旋轨道耦合和磁场对准一维铁磁/半导体/铁磁系统中电子输运性质的影响。计算结果发现，透射系数的振幅随磁场增加而增大。在反铁磁排列时，即使在磁场作用下，上、下自旋电子具有相同的透射系数。与不加磁场时的情况相反，在一定的磁场和耦合强度时，铁磁排列中，上自旋电子的透射系数大于下自旋电子的，而且出现了自旋反转。     

The parallel complexity of growth models

This paper investigates the parallel complexity of several nonequilibrium growth models.Invasion percolation, Eden growth, ballistic deposition, andsolid-on-solid growth are all seemingly highly sequential processes that yield self-similar or self-affine random clusters. Nonetheless, we present fast parallel randomized algorithms for generating these clusters. The running times of the algorithms scale asO(log² N), whereN is the system size, and the number of processors required scales as a polynomial inN. The algorithms are based on fast parallel procedures for finding minimum-weight paths; they illuminate the close connection between growth models and self-avoiding paths in random environments. In addition to their potential practical value, our algorithms serve to classify these growth models as less complex than other growth models, such asdiffusion-limited aggregation, for which fast parallel algorithms probably do not exist.     

小麦黑胚病识别模型优选和多分类识别分析

为探讨利用可见/近红外光谱进行小麦黑胚病快速无损检测的可行性,以及基于主流机器学习算法,寻找面向生产的小麦黑胚病优化识别模型,利用自行研发的近红外光谱采集平台采集了579～1 099 nm波段23个品种共2760个小麦单籽粒的吸光度光谱数据,采用标准正态变量变换(SNV)进行预处理之后分别经过SPA(successive projections algorithm), PCA(principal component analysis)等两种数据降维方法,结合ELM(extreme learning machine), SVM(support vector machine), RF(random forest)和AdaBoost等四种分类方法,分别构建SPA-SVM, SPA-ELM, SPA-RF, SPA-AdaBoost, PCA-SVM, PCA-ELM, PCA-RF, PCA-AdaBoost八种小麦黑胚病识别模型; 结果表明小麦黑胚籽粒的识别准确率达到93.3%～98.6%,识别效果优于前人文献中利用近红外波段的识别效果;其中SPA-SVM模型具有最高的识别率,PCA-AdaBoost模型具有更好的普适性。将SPA-SVM模型和PCA-AdaBoost模型作为优选模型,从生产实际出发,分别对未感病+轻感病、中感病+重感病籽粒进行了二分类识别,对未感病,轻感病+中感病、重感病籽粒进行了三分类识别,以及对未感病、轻感病、中感病、重感病籽粒进行了四分类识别,并深入分析了识别效果和产生原因。总体来说,小麦黑胚粒的识别准确率随分类程度的细化而下降,二分类的识别模型可直接用于生产,尽管三分类和四分类的感病粒识别效果较差,但是对未感病粒的检出率则不受分类程度的影响,识别率在87.2%以上,符合生产需求。综合来看,SPA-SVM模型分类效果优于PCA-AdaBoost模型,可作为首选识别模型,该研究为小麦籽粒黑胚病的在线批量快速检测提供了技术依据。     

NEURAL MODELS FOR THE BROADSIDE-COUPLED V-SHAPED MICROSHIELD COPLANAR WAVEGUIDES

This article presents a new approach based on multilayered perceptron neural networks (MLPNNs) to calculate the odd-and even-mode characteristic impedances and effective permittivities of the broadside-coupled V-shaped microshield coplanar waveguides (BC-VSMCPWs). Six learning algorithms, bayesian regulation (BR), Levenberg-Marquardt (LM), quasi-Newton (QN), scaled conjugate gradient (SCG), resilient propagation (RP), and conjugate gradient of Fletcher-Powell (CGF), are used to train the MLPNNs. The neural results are in very good agreement with the results reported elsewhere. When the performances of neural models are compared with each other, the best and worst results are obtained from the MLPNNs trained by the BR and CGF algorithms, respectively.     

PADE'逼近在FEL数值模拟中的应用

应用PADE'逼近方法构造出高阶格式,对一类简单方程组,该方法可导出一种快速递推算法,它能满足这类简单方程自身存在的守恒要求。     

Algorithms for suppressing ultrasonic backscattering from material structure

In pulse-echo ultrasonic inspection the backscattering from the material structure appears in the received ultrasonic images as clutter, often referred to as grain noise, which impairs the inspection results. A toolbox including algorithms for suppressing ultrasonic clutter is presented in the paper. Several processing algorithms capable of suppressing grain noise have been proposed, of which the split spectrum processing (SSP) probably is the most renowned. The classical SSP technique applies a filter bank to some frequency band that has to be precisely known in advance, to obtain a set of narrow-band signals that are tested for mutual correlation using some statistical operation. A number of SSP algorithms with different statistical operations are included in the toolbox. A completely different approach is to use explicit statistical models of grain noise and defects and to design an optimal filter based on those models. A simple such algorithm, based on noncoherent detection (NCD) known from communications, is also included in the toolbox. The toolbox, implemented in Matlab, is provided with a user-friendly graphical interface facilitating comparison of the algorithms.     

Algorithms for the computation of polynomial relationships for the hard hexagon model

We calculate the polynomial relationships P(, z) = 0 and P(, κ) = 0 in both the ordered and disordered regimes for the hard hexagon model. We start with Baxter's exact solution which gives the physical quantities κ, , and z parametrically as a function of a variable τ and exploit the modular properties of Baxter's solution. Using elementary Riemann surface theory, the computation can be reduced to algorithms involving only linear algebra. These algorithms are implemented using a computer algebra system. The method will be applicable to other exactly solvable models in which cusp expansions can be computed.     

The performance comparison and analysis of extended Kalman filters for GPS/DR navigation

This paper proposes several nonlinear filtering algorithms based on the global positioning system (GPS) and the dead reckoning (DR). To achieve high location and velocity accuracy, the first-order extended Kalman filter (FEKF), the second EKF (SEKF) and EKF-Rauch-Tung-Striebel (EKF-RTS) smoother are introduced for GPS/DR integrated navigation system. And the algorithms of the FEKF, SEKF and EKF-RTS are given. Furthermore, the state models and measurement models of GPS/DR are set up. For comparison purpose, the GPS/DR integrated navigation system based on the three algorithms is simulated, and the algorithm performance is analyzed and compared by the simulation results of FEKF, SEKF, FEKF-RTS and SEKF-RTS. Numerical results demonstrate that the EKF-RTS gives clearly better estimates than the FEKF and SEKF.     

Security concerns on machine learning solutions for 6G networks in mmWave beam prediction

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial intelligence (AI) models are typically ignored by the scientific community so far. However, security is also a vital part of AI algorithms because attackers can poison the AI model itself. This paper proposes a mitigation method for adversarial attacks against proposed 6G ML models for the millimeter-wave (mmWave) beam prediction using adversarial training. The main idea behind generating adversarial attacks against ML models is to produce faulty results by manipulating trained DL models for 6G applications for mmWave beam prediction. We also present a proposed adversarial learning mitigation method’s performance for 6G security in mmWave beam prediction application a fast gradient sign method attack. The results show that the defended model under attack’s mean square errors (i.e., the prediction accuracy) are very close to the undefended model without attack.     

校正集选择方法对于积雪草总苷中积雪草苷NIR定量模型的影响

近红外光谱定量分析中,采用合适的校正集选择方法是建立预测性能良好的近红外定量模型的关键技术之一。校正集选择方法有RS法、CS法、KS法和SPXY法等,但是对以上校正集选择方法缺乏系统地比较。本文以积雪草总苷中积雪草苷NIR定量模型为载体,对NIR定量模型的7个评价指标进行分类和筛选,比较了CS法、KS法和SPXY法三种校正集选择方法对NIR定量模型的准确性和稳健性两类评价指标的影响。结果表明,SPXY法与CS法、KS法选择校正集样本后所建近红外模型的RPD和RSEP两个准确性评价指标存在显著性差异,模型的稳健性评价指标RMSECV和|RMSEP-RMSEC|不存在显著性差异。因此,建立积雪草总苷近红外光谱的积雪草苷偏最小二乘定量模型时,SPXY校正集选择方法能显著提高该定量模型的预测准确度,但对模型稳健性的评价指标没有显著影响,以上结论为中药固体体系建立近红外定量模型确定校正集选择方法提供参考。     

可向量计算的块预条件迭代算法

提出一种类似于PE算法的实用并行迭代算法(VPE),可以克服M^-1r^(s)向量或并行化处理的困难.这种算法格式简单明了,收敛速度快.并证明了当矩阵A是M-阵和H-阵时,该算法是收敛的。计算实例显示该算法很有效.