基于张量分解融合RGB-D图像的物体识别

为了充分利用RGB-D图像的深度图像信息，提出了基于张量分解的物体识别方法。首先将RGB-D图像构造成一个四阶张量，然后将该四阶张量分解为一个核心张量和四个因子矩阵，再利用相应的因子矩阵将原张量进行投影，获得融合后的RGB-D数据，最后输入到卷积神经网络中进行识别。RGB-D数据集中三组相似物体的识别结果表明，利用张量分解融合RGB-D图像的物体识别准确率高于未采用张量分解的物体识别准确率，并且单一错分实例的准确率最高可提升99%。     

基于小波变换和Tucker分解的彩色图像数字水印

提出了一种通过小波变换和Tucker分解将水印信息同时嵌入彩色图像R、G、B通道的水印算法。首先对彩色图像R、G、B三通道进行小波变换,然后将获得的三个低频系数构成三阶张量,再经过Tucker分解后获得核心张量,通过在核心张量中嵌入水印信息使得水印信息扩散到原彩色图像的R、G、B三个分量中。通过实验表明,本算法对于压缩、噪声、滤波、缩放等攻击具有一定的鲁棒性,并与在同样嵌入规则下,直接将Tucker分解应用于彩色图像的算法比较,该算法具有更好的稳定性。     

基于张量鲁棒主成分分析的视网膜眼底图像序列变化检测

在计算机辅助诊断系统中,视网膜眼底图像序列的变化检测是一项重要且具有挑战性的任务。针对视网膜眼底图像序列采样帧少、光照干扰大、难以获得稳健的背景模型,提出了一种基于张量鲁棒主成分分析(tensor robust principal component analysis, TRPCA)的变化检测方法。该方法以TRPCA为模型,通过对序列背景扩充,再利用张量分解而获得变化区域：首先,先选择出序列中最接近正常状态的一张图像作为背景模型;然后,通过预处理将单帧背景模型扩张成多帧背景使得背景模型包含更丰富的光照变化;接着,将整个序列建模为一个3维张量体;最后,利用总变分约束背景模型和变化区域的时空连续性,通过Tucker分解分离出背景模型,获得变化区域。实验结果表明,与基于矩阵的鲁棒主成分分析(matrix robust principal component analysis, Matrix RPCA)方法,Masked-RPCA方法以及不加总变分约束的TRPCA方法相比,基于总变分约束的TRPCA方法能够更准确地分离出变化区域,对血管和光照干扰更具有鲁棒性。     

Sampling period scheduling of networked control systems with multiple-control loops

This paper addresses the sampling period scheduling of Networked Control Systems (NCSs) with multiple control loops. The generalized exponential function is employed to describe Integral Absolute Error (IAE) performance versus sampling period by Truetime toolbox under Matlab environment, and the sampling periods are scheduled to obtain the optimal integrated performance based on Kuhn–Tucker Theorem, which are subject to the stability of every control loop and the bandwidth on available network resource. Numerical examples are given to show the effectiveness of our method.     

Prevalence and personality correlates of Facebook bullying among university undergraduates

The purpose of the present study was to examine the prevalence of cyber-bullying through Facebook in a sample of 226 Greek university undergraduates, and to explore whether big five personality characteristics, narcissism, as well as attitudes toward Facebook, technological knowledge and skills were predictive of such behavior. Participants completed a self-report questionnaire measuring the above constructs. Results indicated that almost one third of the sample reported Facebook bullying engagement at least once during the past month, with male students reporting more frequent involvement than females. Bullying through Facebook was predicted by low Agreeableness and more time spent on Facebook only for males, whereas for females none of the studied variables predicted engagement in Facebook bullying. Findings are discussed in terms of prevention and intervention strategies.     

Use of Graduate Study in Psychology as a data source.

Comments on an article by C. M. Stoup and L. T. Benjamin, Jr. (see record 1983-24200-001), in which they used various editions of Graduate Study in Psychology, published by the American Psychological Association (APA), to report the GPAs and Graduate Record Examination scores of master's and doctoral students across various areas of psychology. It is cautioned that the use of Graduate Study in Psychology as a data source calls for skepticism, as there is sufficient reason to question some of the data that are submitted to the APA by psychology departments. (3 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Hyperspectral image compression based on lapped transform and Tucker decomposition

In this paper, we present a hyperspectral image compression system based on the lapped transform and Tucker decomposition (LT-TD). In the proposed method, each band of a hyperspectral image is first decorrelated by a lapped transform. The transformed coefficients of different frequencies are rearranged into three-dimensional (3D) wavelet sub-band structures. The 3D sub-bands are viewed as third-order tensors. Then they are decomposed by Tucker decomposition into a core tensor and three factor matrices. The core tensor preserves most of the energy of the original tensor, and it is encoded using a bit-plane coding algorithm into bit-streams. Comparison experiments have been performed and provided, as well as an analysis regarding the contributing factors for the compression performance, such as the rank of the core tensor and quantization of the factor matrices.     

Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification

Analysis of high dimensional data in modern applications, such as neuroscience, text mining, spectral analysis, chemometrices naturally requires tensor decomposition methods. The Tucker decompositions allow us to extract hidden factors (component matrices) with different dimension in each mode, and investigate interactions among various modalities. The alternating least squares (ALS) algorithms have been confirmed effective and efficient in most of tensor decompositions, especially Tucker with orthogonality constraints. However, for nonnegative Tucker decomposition (NTD), standard ALS algorithms suffer from unstable convergence properties, demand high computational cost for large scale problems due to matrix inverse, and often return suboptimal solutions. Moreover they are quite sensitive with respect to noise, and can be relatively slow in the special case when data are nearly collinear. In this paper, we propose a new algorithm for nonnegative Tucker decomposition based on constrained minimization of a set of local cost functions and hierarchical alternating least squares (HALS). The developed NTD-HALS algorithm sequentially updates components, hence avoids matrix inverse, and is suitable for large-scale problems. The proposed algorithm is also regularized with additional constraint terms such as sparseness, orthogonality, smoothness, and especially discriminant. Extensive experiments confirm the validity and higher performance of the developed algorithm in comparison with other existing algorithms.     

Actualising (Overlooked) Material Capacities

An emphasis on material synthesis is not without its pitfalls and challenges. Here Branko Kolarevic , Professor and Chair in Integrated Design at the University of Calgary Faculty of Environmental Design, alerts us to the dangers of overlooking materials' shifting capacities, which are so often scale dependent, while also disregarding existing materials in favour of the new or novel. As the potential grows for material systems in buildings to become more adaptive and to harness automation, Kolarevic describes some exciting developments that are taking place in dynamic facade design, providing the much-heralded promise of an efficient use of ambient energy.