Link prediction based on a semi-local similarity index

Missing link prediction provides significant instruction for both analysis of network structure and mining of unknown links in incomplete networks. Recently, many algorithms have been proposed based on various node-similarity measures. Among these measures, the common neighbour index, the resource allocation index, and the local path index, stemming from different source, have been proved to have relatively high accuracy and low computational effort. In this paper, we propose a similarity index by combining the resource allocation index and the local path index. Simulation results on six unweighted networks show that the accuracy of the proposed index is higher than that of the local path one. Based on the same idea of the present index, we develop its corresponding weighted version and test it on several weighted networks. It is found that, except for the USAir network, the weighted variant also performs better than both the weighted resource allocation index and the weighted local path index. Due to the improved accuracy and the still low computational complexity, the indices may be useful for link prediction.     

A Girsanov particle filter in nonlinear engineering dynamics

In this Letter, we propose a novel variant of the particle filter (PF) for state and parameter estimations of nonlinear engineering dynamical systems, modelled through stochastic differential equations (SDEs). The aim is to address a possible loss of accuracy in the estimates due to the discretization errors, which are inevitable during numerical integration of the SDEs. In particular, we adopt an explicit local linearization of the governing nonlinear SDEs and the resulting linearization errors in the estimates are corrected using Girsanov transformation of measures. Indeed, the linearization scheme via transformation of measures provides a weak framework for computing moments and this fits in well with any stochastic filtering strategy wherein estimates are themselves statistical moments. We presently implement the strategy using a bootstrap PF and numerically illustrate its performance for state and parameter estimations of the Duffing oscillator with linear and nonlinear measurement equations.     

Using Copula distributions to support more accurate imaging-based diagnostic classifiers for neuropsychiatric disorders

Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. > 10) relative to the number of participants who provide the MRI data (< 100). Sparse data in a high dimensional space increase the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone.     

Predicting missing links via local information

Missing link prediction in networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely Common Neighbours, has the best overall performance, and the Adamic-Adar index performs second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbours. It is found that many links are assigned the same scores if only the information of the nearest neighbours is used. We therefore design another new measure exploiting information on the next nearest neighbours, which can remarkably enhance the prediction accuracy.     

Entanglement cost in practical scenarios

We quantify the one-shot entanglement cost of an arbitrary bipartite state, that is, the minimum number of singlets needed by two distant parties to create a single copy of the state up to a finite accuracy, by using local operations and classical communication only. This analysis, in contrast to the traditional one, pertains to scenarios of practical relevance, in which resources are finite and transformations can be achieved only approximately. Moreover, it unveils a fundamental relation between two well-known entanglement measures, namely, the Schmidt number and the entanglement of formation. Using this relation, we are able to recover the usual expression of the entanglement cost as a special case.     

Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy

Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.     

Numerical-experimental hybrid method for stress separation in digital gradient sensing method

A numerical-experimental hybrid method for the stress separation in the digital gradient sensing (DGS) method is proposed in this study. In the proposed hybrid method, boundary conditions for a local finite element model, that is, nodal force along boundaries are inversely determined from experimental values obtained by the digital gradient sensing method. The hybrid method follows two stages. In stage 1, the DGS method measures the Cartesian stress gradient components directly and, subsequently, the sum in Cartesian stresses at all interesting points on the surface; stress sum are used to compute the unknown boundary conditions for the local model. In stage 2, the individual stress components are calculated by the direct finite element method using the computed boundary conditions from stage 1. The effectiveness is demonstrated by applying the proposed method to a stress concentration problem involving concentrated load acting on an edge of a large planar sheet. The individual stress components thus determined are summed and compared with analytical stress sum, confirming the effectiveness and accuracy of the proposed technique.     

基于均值距离测度的医学图像配准

针对互信息测度在配准医学图像时易陷入局部极值、速度慢的缺点,提出了基于均值不等式的均值距离测度.首先根据均值不等式推导出5种均值距离测度：方根-算术均值距离(SAM)、方根-几何均值距离(SGM)、方根-调和均值距离(SHM)、算术-几何均值距离(AGM)、算术-调和均值距离(AHM).然后通过人体脑部CT/MR和MR-T1/PD图像的刚体配准实验,从函数曲线、配准精度、计算时间和收敛性能方面,对互信息与5种均值距离信息测度进行了比较与分析.实验结果表明,在不损失配准精度的前提下,AHM和SAM测度可以获得更快的配准速度,对噪声有很强的鲁棒性.     

Rescaling multipartite entanglement measures for mixed states

A relevant problem regarding entanglement measures is the following: Given an arbitrary mixed state, how does a measure for multipartite entanglement change if general local operations are applied to the state? This question is nontrivial as the normalization of the states has to be taken into account. Here we answer it for pure-state entanglement measures which are invariant under determinant-one local operations and homogeneous in the state coefficients, and their convex-roof extension which quantifies mixed-state entanglement. Our analysis allows us to enlarge the set of mixed states for which these important measures can be calculated exactly. In particular, our results hint at a distinguished role of entanglement measures which have homogeneous degree 2 in the state coefficients.     

Continuity of Information Transport in Surjective Cellular Automata

We introduce a local version of the Shannon entropy in order to describe information transport in spatially extended dynamical systems, and to explore to what extent information can be viewed as a local quantity. Using an appropriately defined information current, this quantity is shown to obey a local conservation law in the case of one-dimensional reversible cellular automata with arbitrary initial measures. The result is also shown to apply to one-dimensional surjective cellular automata in the case of shift-invariant measures. Bounds on the information flow are also shown.     

轨迹成型法加工球面光学零件面形精度分析

分析了轨迹成型法加工球面时影响零面形精度的多种因素，其中包括：工件回转轴与摆动轴不共面误差，工件回转轴与砂轮轴不共面误差，工件回转轴径向跳动、摆动误差，轴向窜动工件摆动轴的径向跳动以及砂轮的振动等因素对工件面形精度的影响。为了提高工件面形精度，首先应提高各回转轴和移动件的制造精度和减少配合误差，使其静刚度和动刚度提高，这是保证面形精度的最重要和基本的条件。     

Bending effect on light propagation in an optical fiber: Application to a temperature sensor

Curvature of a multimode optical fiber reduces the number of modes propagating through the fiber and thus the light power at the fiber output. In this paper we study this phenomenon and use it to make a new intrinsic temperature sensor, which operates by a local refractive index variation of the constituent material as a function of both temperature and curvature. The sensing effect comes from an organized competition between a predetermined strain obtained by bending the fiber with a known bending radius and a temperature-indiced strain that will modulate the waveguide. This sensor measures to an accuracy of 0°1C.     

Image segmentation based on hierarchical belief propagation with variable weighting parameters

The belief propagation (BP) algorithm is an efficient way to minimize the MRF energy for image segmentation. This paper proposes a hierarchical BP algorithm with variable weighting parameters (HBP-VW) to improve the segmentation accuracy of the BP-based algorithms. In the HBP-VW, two variable weighting parameters are introduced, the global parameter and the local parameter. The global parameter is used to overall adjust the influence of each part in the message update rule. The local parameter is designed to describe the local texture pattern for each site. Texture, remote sensing, and nature images are employed to test the proposed algorithm. Experimental results illustrate a better segmentation accuracy compared with other BP-based algorithms.     

Damage detection in plates structures based on frequency shift surface curvature

This paper presents a novel damage detection method for plate structures based on the curvature of frequency shift surface (FSS). Unlike other commonly used vibration properties like mode shapes which have low accuracy in practice; this method uses the FSS curvature to improve the accuracy because the measurement of frequency gives better accuracy. Furthermore, it is found that the local damage will only cause local change on the FSS curvature which can be considered as abnormality because the FSS curvature of an intact plate is smooth according to the assumption that intact plate structures are often homogenous and smooth. To avoid the usage of prior knowledge of the health structure, the curve fitting technique based on local regression is adopted to simulate the FSS curvature for the intact state so that only the data from the damaged plate structure is required. Compared with traditional methods, this method is more sensitive and accurate.     

Comparison of several equivalent local potentials for microscopic nonlocal Nα potentials

The phase shifts obtained in the Perey-Saxon, Horiuchi and Peierls-Vinh Mau approximations for the microscopic nonlocal Nα interaction of Lassaut and Vinh Mau have been compared at 10 and 20 MeV to the exact results, without the customary approximation of Perey and Buck, which neglects part of the angular dependence of the nonlocality and is inadequate. We have also compared the equivalent local potentials with the corresponding local wave functions and damping factors to their exact wronskian counterparts and obtained the corresponding nonlocal wave functions as a product of the approximate local wave function and the damping factor in all cases. The results show that of all the approximations the Peierls-Vinh Mau approximation is the most accurate one for s-waves and that the Perey-Saxon approximation is inadequate for Nα scattering. The accuracy of all the approximations is dependent on the degree of repulsiveness of the effective nuclear force. For the exact wronskian equivalent local potential and damping factor however, we reproduce the exact nonlocal wave function to high accuracy in all cases, confirming the accuracy of our numerical methods. The implications for nuclear reactions where the off-shell behaviour of the Nα interaction is important are discussed.     

ITER PF֧�Žṹ���Ӽ���

The manufacturing process and welding technology of PF coil support are introduced, and the taken technical measures to control the deformation stress of welding, to improve low temperature impact toughness and to control the weld ferrite content are explained. The machine accuracy and the quality were improved through post weld stress relief and anti deformation measures.     

ITER PF支撑结构焊接技术

介绍了ITER PF 支撑结构制造工艺及焊接技术方案,阐述了对控制焊接变形应力、提高低温冲击韧性、控制焊缝铁素体含量等采取的技术措施。焊后应力消除方案以及防变形措施使得加工精度和成品质量得到了提高。     

国家同步辐射实验室反射率计误差分析

中国科技大学国家同步辐射实验室"光谱辐射标准与计量实验站"上的反射率计主要用于测量各种光学元器件在X射线、真空紫外波段的反射率.为保证测试结果的精确性和可靠性,在大量实验数据的基础上,结合必要的理论推导,对反射率计测试误差来源及影响程度进行了分析,确定影响反射率计测试精度的主要因素包括光源、探测器、样品安装等.总结了光源波动、探测器损坏的几种典型形式,定性、定量地分析了这些因素和样品安装、光斑尺寸对测试精度的影响,针对性地提出了应对办法,把测试误差控制在2%以内,从而有效地保证了测试精度.     

Wave vector,local momentum and local coordinate from the perspective of information theory

In the context of informational analysis of atomic and molecular systems, the connection between local quantum observables and information measures is of interest. In this paper, analytical relationships for the imaginary part of the total local momentum (coordinate) in terms of information theoretic measures have been established. Moreover, on the basis of another scheme in which the relationship among densities of information energy, Shannon entropy and Fisher information has been proposed [M. Alipour and A. Mohajeri, Mol. Phys. 110, 403 (2012)], the general formulae for the imaginary part of the total local momentum and the corresponding variance are expressed. The presented proof may be viewed in light of the relation between the local wave vector and the information energy density. Through this study, another noteworthy application of information theory is highlighted.     

Combined-method for designing broadband dispersion mirror based on local optimization approach and needle optimization technique

A combined-method based on local optimization and needle optimization is proposed to design broadband dispersion mirrors. The broadband dispersion mirrors are designed by using needle optimization combined with cross application of local optimization and changing high refractive index materials. We combine high efficiency of the local optimization and accuracy of needle optimization. This method can efficiently prevent optimizing process from being captured in a local minimum and obtain higher reflectivity and more smooth group delay dispersion (GDD) curves. The designed dispersion mirrors have a few numbers of layers, which do not have the thin layers that are not easily deposited.