Non-redundant data clustering

Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice, this discovery process should avoid redundancies with existing knowledge about class structures or groupings, and reveal novel, previously unknown aspects of the data. In order to deal with this problem, we present an extension of the information bottleneck framework, called coordinated conditional information bottleneck, which takes negative relevance information into account by maximizing a conditional mutual information score subject to constraints. Algorithmically, one can apply an alternating optimization scheme that can be used in conjunction with different types of numeric and non-numeric attributes. We discuss extensions of the technique to the tasks of semi-supervised classification and enumeration of successive non-redundant clusterings. We present experimental results for applications in text mining and computer vision.     

软测量的工程化设计

将软测量应用到工业生产之前必须解决其工程化设计的问题.为此,在软测量技术理论研究和应用经验的基础上,对软测量工程化设计中应解决的关键问题和解决方法进行了研究.软测量的工程化设计包括机理分析、辅助变量选择、数据采集和预处理、建立软测量模型、设计校正模块等步骤.其关键技术在于软测量过程的数据处理、模型的建立和校正.并以杭州龙山化工厂碳酸化塔中C02浓度软测量的工程化设计为例,对工程化设计中的一些关键问题处理进行介绍.实践表明,软测量的工程化设计在实际工业生产中具有很好的应用性和指导性,但也有待更进一步的研究.     

多处理器系统中的数据局部性及其优化技术研究

数据局部性是多处理器系统中的重要研究方向之一。结合该领域目前国内外研究现状和我们近一阶段的研究进展，讨论了多处理器系统中的数据局部性及其优化问题。针对现有局部性度量模型存在的不足，提出了一种增强的可用于层次式并行计算机体系结构的局部性度量模型。在静态和动态局部性优化技术方面，分别探讨了基于投影分层的数据变换框架和基于瞬时访问信息的动态页迁移策略，并展开了系列相关的讨论。另外，针对利用数据局部性时必须解决的一个关键问题--存储一致性问题，进行了深入的研究，提出了以操作系统为中心的线程存储一致性模型。     

基于CORBA和WEB的产品数据管理体系结构研究

 产品数据管理(product data management,PDM)作为产品信息管理集成框架,已成为当前研究开发型企业提高其核心竞争力的一种重要竞争策略.介绍了PDM技术的起源与发展,阐述了PDM的应用功能框架.结合当今软件技术的发展,分析了基于CORBA和WEB技术的PDM/CPC代表性产品Windchill平台的体系结构,指出了传统PDM过渡到CPC的必然发展趋势.     

扇形束投影域重建—再投影迭代法

本文所提出的投影域重建——再投影迭代算法可以恢复不完整的扇形束投影数据,从而实现从有限的投影数据重建图象。这种算法把滤波——反投影图象重建过程和再投影的过程纳入一个公式,仅在投影域上估算未知的投影数据,因此避免了重建过程和反投影过程中的插值运算,改善了收敛性,提高了计算精度,缩短了运算时间。模拟实验表明:即使有70％的投影数据丢失,经过两次迭代就能以令人满意的精度恢复丢失的投影数据。     

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

This paper introduces and evaluates a new class of knowledge model, the recursive Bayesian multinet (RBMN), which encodes the joint probability distribution of a given database. RBMNs extend Bayesian networks (BNs) as well as partitional clustering systems. Briefly, a RBMN is a decision tree with component BNs at the leaves. A RBMN is learnt using a greedy, heuristic approach akin to that used by many supervised decision tree learners, but where BNs are learnt at leaves using constructive induction. A key idea is to treat expected data as real data. This allows us to complete the database and to take advantage of a closed form for the marginal likelihood of the expected complete data that factorizes into separate marginal likelihoods for each family (a node and its parents). Our approach is evaluated on synthetic and real-world databases.     

p,V, T and derived thermodynamic data for toluene,trichloromethane, dichloromethane,acetonitrile, aniline,and n-dodecane

Experimentally determined p,V,T data are reported for toluene, trichloromethane, dichloromethane, acetonitrile, aniline, and n-dodecane at 278, 288, 298, 313, and 323 K, except for dichloromethane, for which the highest temperature was 298 K. At each temperature, measurements were done at pressures up to about 280 MPa or (for aniline and n-dodecane) at a lower pressure slightly below the freezing pressure at the temperature of measurement. Values of the isobaric expansivity isothermal compressibility and (for toluene, trichloromethane, dichloromethane, and acetonitrile) internal pressure, derived from the p,V,T data, are presented.     

快堆伪裂变产物数据的计算

从微观评价核数据库出发 ,计算给出了快堆谱的伪裂变产物数据 ,包括一群的截面数据和多群的全套中子数据 ,裂变核有2 3 5U和2 3 9Pu。截面数据有全截面、弹性散射、非弹散射、(n ,γ)、(n ,2n)截面 ,多群全套中子数据还包括次级中子角分布和能谱。数据以ENDF B -6格式给出 ,同时还分析讨论了数据误差和可靠性 ,数据可用于实际的快堆计算     

Income, Time Effects and Direct Preferences in a Multimodal Choice Context: Application of Mixed RP/SP Models with Non-Linear Utilities

Transport problems typically involve at least two types of constraints, on income and on time. Therefore, the indirect utility function depends either on the income available after having subtracted the cost of the discrete alternative and on the free time left after having worked and travelled by each competing option. In the typical linear-in-the-attributes and in-the-parameters specification, that represents the first grade approximation of the indirect utility function, the effect of income and time constraints cancel out and only the cost and time of the alternatives matter in the comparison between them. From a microeconomic point of view this is equivalent to assume that income and time effects could be disregarded; which is not always the case. To account for these effects the utility function should include second order attributes; however, in non-linear utility functions it may not be easy to distinguish among several effects that could be relevant: direct preferences for good and leisure, and simple interactions between attributes other than income and time effects. This paper analyses these effects from a theoretical point of view focusing on the possible confounding problem in detecting income and time effects. We use a dataset collected for a modal choice context and containing both revealed and stated preference data, and estimate several NL models examining the effect of the different second-order terms on detecting income and time effects. We compared specifications including square cost and time attributes, interactions between time and cost, cost divided by the income available to be spent on free time, and time multiplied by free time. Our results confirm the strong effect of direct preferences for goods and leisure time on choice, and the potential confounding effect between quadratic attributes and other non-linear omitted terms. Finally, we also found that care should be taken in highlighting income and time effects using mixed data sources, since confounding effects can occur when non-linearities are accounted for in both data sets.