Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

变频调速技术在定量包装机中的应用

介绍一种基于变频调速控制的全自动定量包装机，结构简单，组态灵活，性价比高，可广泛应用于食品饲料等行业，现场运行实践证明其通用性好，准确度和可靠性高、速度快、稳定性好。并论述变频器控制系统的设计及使用中的注意问题。     

电子束曝光机激光工件台系统研制

介绍了nm级电子束曝光机激光定位精密工件台系统的结构组成、各部分技术措施及总体性能指标。该工件台采用HP5527激光干涉测量系统,测量分辨率达0.6nm,结构上成功将导向与承载分离,对承片台、机械手等进行重大创新。无论是整机性能还是关键技术单元均处于国内领先水平,是一台性能优良、高精度的电子束曝光机工件台。     

Performance discrepancies on the California Verbal Learning Test-Second Edition (CVLT-II) in the standardization sample.

The standardization data for the California Verbal Learning Test-Second Edition (CVLT-II; D. C. Delis, J. H. Kramer, E. Kaplan, & B. A. Ober, 2000) were used to evaluate the base rate of 6 specific discrepancies between various key variables. The results indicated that CVLT-II performance discrepancies should equal or exceed 1 or 1.5 z score points (depending on the individual comparison) in the hypothesized direction to be considered potentially unusual. However, because about 1 in 3 persons in the standardization sample displayed at least 1 such large discrepancy, it is concluded that these base rates should be viewed only as a starting point for interpretation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

Implicit Motor Learning in Parkinson's Disease.

Objective: This study examined whether disruption of performance is moderated in Parkinson's disease (PD) patients who acquire their motor behaviors in an implicit manner. Method: Twenty-seven patients with PD learned a hammering task in errorless (implicit) or errorful (explicit) conditions and were tested for robustness of motor performance under a secondary task load, which required them to continuously count backward as they performed the hammering task. Results: Patients in the errorless (implicit) motor learning condition exhibited robustness to secondary task loading, whereas patients in the errorful (explicit) motor learning condition did not. Conclusions: Implicit motor learning techniques should be considered by PD rehabilitation specialists in cases in which existing disruption to movements is exacerbated by conscious control. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

两两NQD列乘积和的Jamison型强稳定性的一个注记

讨论了不同分布两两NQD列的Jamison型加权乘积和的强稳定性。推广了不同分布独立列部分和和同分布NQD列部分和情形相类似的结论，得到了一些新的结果。     

NMDA Receptor Modulation of Incidental Learning in Pavlovian Context Conditioning.

Rats exposed to a footshock show conditional fear when reexposed to the shock context. Immediate presentation of shock after placement in the context significantly reduces this fear. Preexposure to the context in the absence of shock, coupled with a minimum preshock interval during training, overcomes this immediate shock deficit. Because rats learn about the context during preexposure and express that learning after being reinforced, the context preexposure effect is an aversive analogue of latent learning. The authors examined the effect of the N-methyl-D-aspartate (NMDA) receptor antagonist D,L-2-amino-5-phosphovalerate (APV) on the facilitatory effect of context preexposure. Rats were preexposed to a chamber after APV administration. The next day they were placed in the same chamber without drug and received shock 35 s later. APV blocked the facilitatory effect of preexposure. Therefore NMDA receptors are important for contextual latent learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于支持向量机的多类分类研究

现今流行的分类方法的重要基础是传统的统计学，前提是要有足够的样本，当样本数目有限时容易出现过学习的问题，导致分类效果不理想。引入支持向量机方法，它基于统计学习理论，采用了结构风险最小化原则代替经验风险最小化原则，较好的解决了小样本学习的问题；又由于采用了核函数思想，把非线性空间的问题转换到线性空间，降低了算法的复杂度。对其相关内容包括优化算法及多类分类问题的解决进行了研究，最后用一个实例说明了该方法的可行性和有效性。     

一个基于相邻像素兼容度的模糊加权平均滤波器

在文献[1]的基础上，提出几个改进的模糊加权平均滤波器。本文的方案不仅同时考虑了相邻像素对中心像素的兼容性以及中心像素对相邻像素的兼容性，而且还考虑到了权系数对局部环境的适应性。实验结果表明，该方法相比文献[1]中的模糊加权平均滤波器以及标准的均值滤波器、中值滤波器在噪声消除与边缘保留方面具有更好的性能。