Transport Efficiency of Continuous-Time Quantum Walks on Graphs

Continuous-time quantum walk describes the propagation of a quantum particle (or an excitation) evolving continuously in time on a graph. As such, it provides a natural framework for modeling transport processes, e.g., in light-harvesting systems. In particular, the transport properties strongly depend on the initial state and specific features of the graph under investigation. In this paper, we address the role of graph topology, and investigate the transport properties of graphs with different regularity, symmetry, and connectivity. We neglect disorder and decoherence, and assume a single trap vertex that is accountable for the loss processes. In particular, for each graph, we analytically determine the subspace of states having maximum transport efficiency. Our results provide a set of benchmarks for environment-assisted quantum transport, and suggest that connectivity is a poor indicator for transport efficiency. Indeed, we observe some specific correlations between transport efficiency and connectivity for certain graphs, but, in general, they are uncorrelated.     

基于ICA与SVM算法的高光谱遥感影像分类

提出了一种利用独立分量分析(ICA)与支撑向量机(SVM)算法进行高光谱遥感影像分类的新方法。采用ICA算法对高光谱遥感影像(PHI传感器获取,80波段)进行了特征提取,并以提取出的影像数据(光谱维数为20)构建SVM分类器。对SVM算法进行核函数删选与参数寻优后,发现采用RBF核的SVM算法(C=103,γ=0.05)分类结果最佳,分类精度与Kappa系数分别达94.5127%与0.935 1,优于BP-神经网络(分类精度39.4758%,Kappa系数0.315 5)、波谱角分类(分类精度80.282 6,Kappa系数0.770 9)、最小距离分类(分类精度85.462 7%,Kappa系数0.827 7)以及最大似然分类(分类精度86.015 6%,Kappa系数0.835 1)4种方法。针对分类结果常出现的"椒盐"现象,利用形态学算子对SVM(RBF核)分类结果进行了类别集群处理,将分类精度与Kappa系数分别提高至94.758 4%与0.938 0,获得了更接近实况的分类图像。结果表明:ICA结合SVM算法准确率高,是高光谱遥感影像分类的优选方法,且类别集群是优化影像分类的有效方法之一。     

Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models

Latent variable models (LVMs) for neural population spikes have revealed informative low-dimensional dynamics about the neural data and have become powerful tools for analyzing and interpreting neural activity. However, these approaches are unable to determine the neurophysiological meaning of the inferred latent dynamics. On the other hand, emerging evidence suggests that dynamic functional connectivities (DFC) may be responsible for neural activity patterns underlying cognition or behavior. We are interested in studying how DFC are associated with the low-dimensional structure of neural activities. Most existing LVMs are based on a point process and fail to model evolving relationships. In this work, we introduce a dynamic graph as the latent variable and develop a Variational Dynamic Graph Latent Variable Model (VDGLVM), a representation learning model based on the variational information bottleneck framework. VDGLVM utilizes a graph generative model and a graph neural network to capture dynamic communication between nodes that one has no access to from the observed data. The proposed computational model provides guaranteed behavior-decoding performance and improves LVMs by associating the inferred latent dynamics with probable DFC.     

Quantum theory of chiral interactions in cholesteric liquid crystals

The effective chiral interaction between molecules arising from long-range quantum interactions between fluctuating charge moments is analyzed in terms of a simple model of chiral molecules. This model is based on the approximations that (a) the dominant excited states of a molecule form a band whose width is small compared to the average energy of excitation above the ground state and (b) biaxial orientational correlation between adjacent molecules can be neglected. Previous treatments of quantum chiral interactions have been based on a multipole expansion of the effective interaction energy within second-order perturbation theory. We consider a system consisting of elongated molecules and, although we invoke the expansion in terms of coordinates transverse to the long axis of constituent molecules, we treat the longitudinal coordinate exactly. Such an approximation is plausible for molecules in real liquid crystals. The macroscopic cholesteric wave vector Q (Q=2 pi/P, where P is the pitch) is obtained via Q=h/K(2), where K2 is the Frank elastic constant for twist and h is the torque field which we calculate from the effective chiral interaction kappa(IJ)a(I)xa(J) x R(IJ), where the unit vector a(I) specifies the orientation of molecule I and R(IJ) is the displacement of molecule I relative to molecule J. We identify two distinct physical limits depending on whether one or both of the interacting molecules are excited in the virtual state. When both molecules are excited, we regain the R(-8)(IJ) dependence of kappa(IJ) on intermolecular separation found previously by Van der Meer et al. [J. Chem. Phys. 65, 3935 (1976)]. The two-molecule, unlike the one-molecule term, can be interpreted in terms of a superposition of pairwise interactions between individual atoms (or local chiral centers) on the two molecules. Contributions to kappa(IJ) when one molecule is excited in the virtual state are of order R(-7)(IJ) for helical molecules which are assumed not to have a global dipole moment, but whose atoms possess a dipole moment. It is shown that for a helical molecule Q can have either the same or the opposite sign as the chiral pitch of an individual molecule, depending on the details of the anisotropy of the atomic polarizability. The one-molecule mechanism can become important when the local atomic dipoles become sizable, although biaxial correlations (ignored here) should then be taken into account. Our results suggest how the architecture of molecular dipole moments might be adjusted to significantly influence the macroscopic pitch.     

基于高光谱图像多光谱参数的草莓成熟度识别

为了建立多光谱参数用于草莓成熟度的自动识别,采用高光谱图像技术,通过提取草莓样本ROI的平均光谱,计算已有的八个成熟度参数Ind1,Ind2,Ind3,IAD,I1,I2,I3,I4的参数值,并结合Fisher线性判别法判断八个参数对于三种成熟度(成熟、接近成熟、未成熟)草莓样本的分类识别效果,发现基于I4参数的线性判别分析模型的识别效果最佳,建模集和预测集识别准确率分别为90%和91.67%;基于草莓样本的光谱特征,提取与草莓成熟度相关的三个波长535,675和980 nm,并基于这三个波长和已有的参数形式,构建了四个用于草莓成熟度检测的新参数：i1,i2,i3,i4,通过Fisher线性判别法判断四个参数的分类识别效果,发现基于参数i1,i2和i4的线性判别分析模型的识别效果均比参数I4好,建模集和预测集识别准确率为95.83%,95.83%,95.83%和95%,95%,96.67%。结果表明新建立的多光谱参数i1,i2和i4可以用于草莓成熟度的自动分类识别,为草莓成熟度的在线检测提供了理论依据。     

The Effect of a Hidden Source on the Estimation of Connectivity Networks from Multivariate Time Series

Many methods of Granger causality, or broadly termed connectivity, have been developed to assess the causal relationships between the system variables based only on the information extracted from the time series. The power of these methods to capture the true underlying connectivity structure has been assessed using simulated dynamical systems where the ground truth is known. Here, we consider the presence of an unobserved variable that acts as a hidden source for the observed high-dimensional dynamical system and study the effect of the hidden source on the estimation of the connectivity structure. In particular, the focus is on estimating the direct causality effects in high-dimensional time series (not including the hidden source) of relatively short length. We examine the performance of a linear and a nonlinear connectivity measure using dimension reduction and compare them to a linear measure designed for latent variables. For the simulations, four systems are considered, the coupled Hénon maps system, the coupled Mackey–Glass system, the neural mass model and the vector autoregressive (VAR) process, each comprising 25 subsystems (variables for VAR) at close chain coupling structure and another subsystem (variable for VAR) driving all others acting as the hidden source. The results show that the direct causality measures estimate, in general terms, correctly the existing connectivity in the absence of the source when its driving is zero or weak, yet fail to detect the actual relationships when the driving is strong, with the nonlinear measure of dimension reduction performing best. An example from finance including and excluding the USA index in the global market indices highlights the different performance of the connectivity measures in the presence of hidden source.     

应用多种近红外建模方法分析梨的坚实度

近红外光谱(near infrared spectroscopy,NIRS)技术是一种快速、无损的仪器分析方法,在农产品品质检测方面引起了广泛的关注,在近红外光谱信息和品质指标之间建立一个稳健的模型是近红外光谱分析中十分重要且有一定难度的过程,常见的多元校正方法有偏最小二乘回归(PLSR)、主成分回归(PCR)和逐步多元线性回归(SMLR)等,该研究中除了常用的线性方法外,还采用了一种结合非线性方法的组合算法[结合了SMLR和径向基神经网络(RBFN)]用于梨坚实度的近红外光谱检测。比较常用的线性建模方法,原始光谱的PLSR模型的得到了较好的结果：校正集相关系数r=0.87, 校正均方根误差RMSEC=3.88 N,预测集r=0.84, 预测均方根误差RMSEP=4.26 N;组合算法的建模结果比SMLR和PCR的结果好,但比PLSR的结果稍差：校正集r=0.85, RMSEC=4.15 N,预测集r=0.82, RMSEP=4.67 N。结果表明：NIRS可用于梨的坚实度检测,但是建模方法的选择值得进一步研究以提高预测的精度。     

基于LIBS和遗传算法的畜禽粪便中钙含量研究

为实现畜禽粪便高效、安全资源化利用,快速、准确定量表征分析畜禽粪便中钙(Ca)元素含量具有重要意义。以我国畜禽养殖比较集中地区的代表性畜禽粪便样本为对象,探讨了LIBS快速、定量分析其Ca元素含量模型构建和通过GA筛选变量优化模型的可行性。采集光谱的仪器参数设置如下：采集点数、激光能量、压制样压力、光斑大小和延迟时间分别为80个,15%,20 T,400 μm和1 μs。建模结果表明：采用基于Ca元素特征波长所建三种线性模型精度较低;基于全谱的PLS所建模型中,R2_v和RPD分别为0.85和2.13;选用化学计量学方法-遗传算法进行变量筛选可显著减少建模变量信息,提高建模效率,经GA优选所得12个变量所建PLS模型中R2_v和RPD为0.90和3.04,具有较高精度和实际应用价值。研究表明,将激光诱导击穿光谱技术和遗传算法相结合可定量分析畜禽粪便中Ca元素含量。     

Particle chaos in the Earth's magnetotail

Nonlinear particle dynamics is studied both in current sheets and near neutral lines. The parameter governing particle chaos in a current sheet with a constant normal component, B(n), is kappa=(R(min)/rho(max))(1/2), where R(min) is the minimum field line radius of curvature and rho(max) is the maximum gyroradius. In such a current sheet, motion can be viewed as a combination of a component normal to the current sheet and a tangential component. The parameter kappa represents the ratio of the characteristic time scale of the normal component to the tangential, and thus, particle chaos is maximized for kappa approximately 1. For kappa<1, the slow motion preserves the action integral of the fast motion, J(z), except near the separatrix, the phase space boundary separating motion that crosses the current sheet midplane from that which does not. Near a linear neutral line, it is found that the parameter b(n), which is the ratio of the characteristic vertical and horizontal field strengths, rather than kappa governs particle chaos. In the limit b(n)<1, the slow motion again preserves J(z), and J(z) has the same analytic form as in a constant B(n) current sheet. In the limit of b(n)<1, the structure of x-p(x) phase space is controlled by the stable and unstable manifolds associated with the unstable fixed point orbit at (x,p(x))=(0,0), and this structure lies along a contour of constant J(z).     

Testing the chiral magnetic and chiral vortical effects in heavy ion collisions

We devise a test of the chiral magnetic and chiral vortical effects (CME and CVE) in relativistic heavy ion collisions that relies only on the general properties of triangle anomalies. We show that the ratio R(EB) = J(E)/J(B) of charge J(E) and baryon J(B) currents for CME is R(EB)(CME) → ∞ for three light flavors of quarks (N(f) = 3), and R(EB)(CME) = 5 for N(f) = 2, whereas for CVE it is R(EB)(CVE) = 0 for N(f) = 3 and R(EB)(CME) = 1/2 for N(f) = 2. The physical world with light u,d quarks and a heavier s quark is in between the N(f) = 2 and N(f) = 3 cases; therefore, the ratios R(EB) for CME and CVE should differ by over an order of magnitude providing a possibility to separate clearly the CME and CVE contributions. In both cases, there has to be a positive correlation between the charge and baryon number asymmetries that can be tested on the event-by-event basis.     

优化光谱指数建立水稻叶片SPAD的高光谱反演模型

利用高光谱反射率光谱的特征波段构建光谱指数,建立叶绿素含量反演模型是实现水稻生产精准调控和科学管理的必要手段之一。为了建立适用于拔节孕穗期水稻叶片叶绿素相对含量（SPAD）的高光谱反演模型,分别获取了拔节孕穗期水稻叶片的高光谱和SPAD数据,利用小波分析法对原始光谱反射率曲线进行降噪处理,并对基于积分运算的光谱指数NAOC进行简化,获得了基于双波段简化运算的优化光谱指数。利用相关分析法计算由原始反射率光谱R和数学变换光谱LgR、1/R和R构建的优化光谱和变换光谱指数与水稻叶片SPAD的相关系数,获得了以积分限（a,b）为横、纵坐标的相关系数二维矩阵,并绘制相关性等势图,得到相关系数最高的3个波段组合：R（641,790）（0.872 6）,R（653,767）（0.871 7）和R（644,774）（0.871 6）,计算出20个原始样本中3个积分波段组合所对应的60个优化光谱指数值,按照2∶1的比例划分为建模集和验证集,建立了三种水稻叶片SPAD反演模型：偏最小二乘回归（PLSR）、支持向量机（SVM）和BP神经网络模型。结果显示：利用优化光谱和变换光谱指数建立的3种水稻叶片SPAD反演模型决定系数R2均大于0.79,归一化均方根误差NRMSE则小于5.4%。其中BP神经网络相对于其他两种模型具有较高的拟合度,预测精度也相对较高,建模集R2=0.842 6,NRMSE=5.152 7%;验证集R2=0.857,NRMSE=4.829 9%。总体来看,基于双波段简化运算后的优化光谱和变换光谱指数建立拔节孕穗期水稻叶片SPAD反演模型是可行的;对比分析3种模型反演结果发现,BP神经网络对水稻叶片SPAD的反演效果较好。该工作对提高拔节孕穗期水稻精准调控技术和建立水稻生产的科学管理体系具有一定的参考价值。     

Investigating the neural basis for fMRI-based functional connectivity in a blocked design: application to interregional correlations and psycho-physiological interactions

This paper investigates how well different kinds of fMRI functional connectivity analysis reflect the underlying interregional neural interactions. This is hard to evaluate using real experimental data where such relationships are unknown. Rather, we use a biologically realistic neural model to simulate both neuronal activities and multiregional fMRI data from a blocked design. Because we know how every element in the model is related to every other element, we can compare functional connectivity measurements across different spatial and temporal scales. We focus on (1) psycho-physiological interaction (PPI) analysis, which is a simple brain connectivity method that characterizes the activity in one brain region by the interaction between another region's activity and a psychological factor, and (2) interregional correlation analysis. We investigated the neurobiological underpinnings of PPI using simulated neural activities and fMRI signals generated by a large-scale neural model that performs a visual delayed match-to-sample task. Simulated fMRI data are generated by convolving integrated synaptic activities (ISAs) with a hemodynamic response function. The simulation was done under three task conditions: high-attention, low-attention and a control task ('passive viewing'). We investigated how biological and scanning parameters affect PPI and compared these with functional connectivity measures obtained using correlation analysis. We performed correlational and PPI analyses with three types of time-series data: ISA, fMRI and deconvolved fMRI (which yields estimated neural signals) obtained using a deconvolution algorithm. The simulated ISA can be considered as the 'gold standard' because it represents the underlying neural activity. Our main findings show (1) that evaluating the change in an interregional functional connection using the difference in regression coefficients (as is essentially done in the PPI method) produces results that better reflect the underlying changes in neural interrelationships than does evaluating the functional connectivity difference as a change in correlation coefficient; (2) that using fMRI and deconvolved fMRI data led to similar conclusions in the PPI-based functional connectivity results, and these generally agreed with the nature of the underlying neural interactions; and (3) the functional connectivity correlation measures often led to different conclusions regarding significance for different scanning and hemodynamic parameters, but the significances of the PPI regression parameters were relatively robust. These results highlight the way in which neural modeling can be used to help validate the inferences one can make about functional connectivity based on fMRI data.     

基于新的整数型分子图论指数M系统探索烷烃核磁共振碳谱(13C NMR)化学位移和CSS规律

系统研究了核磁共振碳谱与化学位移和规律,以及拓扑图论方法在定量结构波谱关系(QSSR)中的应用.本文基于路径长度矢量p=p1,p2,p3,…,pm)组合研究提出了一种新的整数型拓扑图论指数M=(p1+p2)+(p1-p3),并发现它与烷烃¹³C NMR化学位移和有良好线性相关性:回归方程及其统计参数为CSS=+14.329^* M-21.089;n=65,R=0.991,SD=7.684,F=3648.021,U=215381.656,Q=3719.563;交互校验CV结果为:R=0.990,R²(01)=0.981,SD(01)=8.086,F(01)=3288.113,U(01)=214982.172,Q(01)=4119.042.     

Magnetization reversal in spin patterns with complex geometry

We study field-driven dynamics of spins with antiferromagnetic interactions along the links of a complex substrate geometry, which is modeled by graphs of a controlled connectivity distribution. The magnetization reversal occurs in avalanches of spin flips, which are pinned by the topological constraints of the underlying graph. The hysteresis loop and avalanche sizes are analyzed and classified in terms of the graph's connectivity and clustering. The results are relevant for magnets with a hierarchical spatial inhomogeneity and for design of nanoscale magnetic devices.     

Further studies on the Schroeder-Hall hair-cell model.

The Schroeder-Hall hair-cell model [M.R. Shroeder and J.L. Hall, "Model for mechanical to neural transduction in the auditory receptor," J. Acoust. Soc. Am. 55, 1055-1060 (1974] was further explored using additional stimulus waveforms and analysis techniques. The model is shown to have other interesting properties such as the ability to generate realistic two-tone interactions. Amended to limit the growth of transmitter release, the model also produces realistic adaptation and incremental response data. Other amendments improved high-intensity period histogram waveshapes. No one amendment, however, allowed the model to produce both realistic adaptation curves as well as period histogram waveforms that faithfully mimicked physiological data.     

Neural field dynamics under variation of local and global connectivity and finite transmission speed

Spatially continuous networks with heterogeneous connections are ubiquitous in biological systems, in particular neural systems. To understand the mutual effects of locally homogeneous and globally heterogeneous connectivity, we investigate the stability of the steady state activity of a neural field as a function of its connectivity. The variation of the connectivity is implemented through manipulation of a heterogeneous two-point connection embedded into the otherwise homogeneous connectivity matrix and by variation of the connectivity strength and transmission speed. Detailed examples including the Ginzburg–Landau equation and various other local architectures are discussed. Our analysis shows that developmental changes such as the myelination of the cortical large-scale fiber system generally result in the stabilization of steady state activity independent of the local connectivity. Non-oscillatory instabilities are shown to be independent of any influences of time delay.     

Percolation in living neural networks

We study living neural networks by measuring the neurons' response to a global electrical stimulation. Neural connectivity is lowered by reducing the synaptic strength, chemically blocking neurotransmitter receptors. We use a graph-theoretic approach to show that the connectivity undergoes a percolation transition. This occurs as the giant component disintegrates, characterized by a power law with an exponent beta approximately or = 0.65. Beta is independent of the balance between excitatory and inhibitory neurons and indicates that the degree distribution is Gaussian rather than scale free.     

Google matrix analysis of C.elegans neural network

We study the structural properties of the neural network of the C.elegans (worm) from a directed graph point of view. The Google matrix analysis is used to characterize the neuron connectivity structure and node classifications are discussed and compared with physiological properties of the cells. Our results are obtained by a proper definition of neural directed network and subsequent eigenvector analysis which recovers some results of previous studies. Our analysis highlights particular sets of important neurons constituting the core of the neural system. The applications of PageRank, CheiRank and ImpactRank to characterization of interdependency of neurons are discussed.     

分子拓扑学方法估算多环芳香烃类化合物的电离能

在构建多环芳烃类化合物分子的邻接矩阵主对角元时,结合烷基极化效应指数考虑分子图顶点的性质,以分子碎片C、CH、CH2、CH3等的相对能量作主元,用分子拓扑学方法构建了多环芳香烃类化合物新的邻接矩阵.研究结果发现,新的邻接矩阵特征根与多环芳香烃类化合物的电离能有良好的相关性Ipi=4.756+2.870OMOi,R=0.9853,s=0.1765,n=446.用这种新方法估算多环芳香烃类化合物的电离能,所用参数少且为分子结构性参数,稳定可靠,计算简便,结果较满意.     

Neural field dynamics with heterogeneous connection topology

Neural fields receive inputs from local and nonlocal sources. Notably in a biologically realistic architecture the latter vary under spatial translations (heterogeneous), the former do not (homogeneous). To understand the mutual effects of homogeneous and heterogeneous connectivity, we study the stability of the steady state activity of a neural field as a function of its connectivity and transmission speed. We show that myelination, a developmentally relevant change of the heterogeneous connectivity, always results in the stabilization of the steady state via oscillatory instabilities, independent of the local connectivity. Nonoscillatory instabilities are shown to be independent of any influences of time delay.