用金属卟啉络合物引发合成高分子量聚醚

介绍了用四苯基卟啉铝络合物催化体系合成高分子量聚醚的方法。该聚合过程显示出一种持续特性。产物具有均一的链长和极窄的分子量分布，且数均分子量Ｍｎ可通过改变单体与引发剂的摩尔比来控制。     

Robust sparse principal component analysis

The model for improving the robustness of sparse principal component analysis（PCA） is proposed in this paper. Instead of the l2-norm variance utilized in the conventional sparse PCA model,the proposed model maximizes the l1-norm variance,which is less sensitive to noise and outlier. To ensure sparsity,lp-norm（0 p 1） constraint,which is more general and effective than l1-norm,is considered. A simple yet efficient algorithm is developed against the proposed model. The complexity of the algorithm approximately linearly increases with both of the size and the dimensionality of the given data,which is comparable to or better than the current sparse PCA methods. The proposed algorithm is also proved to converge to a reasonable local optimum of the model. The efficiency and robustness of the algorithm is verified by a series of experiments on both synthetic and digit number image data.     

Protein interaction networks: centrality,modularity, dynamics,and applications

In the post-genomic era, proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies. Especially the rapid accumulation of protein-protein interactions (PPIs) provides a foundation for constructing protein interaction networks (PINs), which can furnish a new perspective for understanding cellular organizations, processes, and functions at network level. In this paper, we present a comprehensive survey on three main characteristics of PINs: centrality, modularity, and dynamics. 1) Different centrality measures, which are used to calculate the importance of proteins, are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information; 2) Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced; 3) The dynamics of proteins, PPIs and sub-networks are discussed, respectively. Finally, the main applications of PINs in the complex diseases are reviewed, and the challenges and future research directions are also discussed.     

复杂系统平均寿命的估计和性质

根据已知的基于最小路径或最小割集复杂系统可靠度函数的解析表达式,给出当子系统寿命分布服从指数分布时,系统平均寿命的表达式.利用这一表达形式,在子系统寿命变量服从指数分布条件下,分别就完全数据和截尾数据两种情况,给出了系统平均寿命的估计,并对估计性质进行了研究.并在完全数据下,给出了估计的渐近分布.     

利用金属络合物的电火花加工研究

在电火花加工的工作液中添加络合剂，使之与被蚀除的金属颗粒产生络合化学反应，生成可溶于工作液中的络合物。利用这种原理，可提高电火花加工性能，在小孔、深槽等加工应用中起到促进排屑的作用。     

基于PCA-Logistic模型的围岩稳定性分类研究

为了建立易于理解和应用的围岩稳定性分类模型,采用主成分分析法对围岩样本数据进行降维处理,以Logistic模型来描述围岩稳定性影响因素与围岩稳定性之间的经验关系。选取影响围岩分类的5个主要因素,根据分类标准,采用在每级标准中随机内插的方法,得到100个标准样本,用于建立PCA-Logistic模型。分析结果显示,PCA-Logistic模型拟合效果良好,对工程实例的预测结果同投影寻踪模型和分形-插值模型完全一致,表明该模型在围岩稳定性评价中具有可行性和有效性。     

Toward a Conceptual Emulation Framework for the Preservation of Archaeological 3D Visualizations

Through the Preservation of Complex Objects Symposia (POCOS), leading researchers and practitioners have managed to propose strategies for preserving digital art and computer games. The same is not true for the preservation of archaeological visualizations. This article therefore discusses the following question: “Can emulation be employed to effectively preserve obsolete computer visualizations from the Archaeology domain?” Guidelines and test results coming from this work would be of great benefit to the archaeological community, and would contribute knowledge to other research communities, specifically those interested in similar data types/3D visualizations.     

技术防范电信欠费

电信用户欠费是当今电信业者面临的日益严峻的问题,本文主要阐述如何利用电信企业拥有的数据仓库,依靠技术手段分析追踪和预防用户异常行为,达到防范电信欠费的效果。     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.