Algorithms for unimodal segmentation with applications to unimodality detection

We study the problem of segmenting a sequence into k pieces so that the resulting segmentation satisfies monotonicity or unimodality constraints. Unimodal functions can be used to model phenomena in which a measured variable first increases to a certain level and then decreases. We combine a well-known unimodal regression algorithm with a simple dynamic-programming approach to obtain an optimal quadratic-time algorithm for the problem of unimodal k-segmentation. In addition, we describe a more efficient greedy-merging heuristic that is experimentally shown to give solutions very close to the optimal. As a concrete application of our algorithms, we describe methods for testing if a sequence behaves unimodally or not. The methods include segmentation error comparisons, permutation testing, and a BIC-based scoring scheme. Our experimental evaluation shows that our algorithms and the proposed unimodality tests give very intuitive results, for both real-valued and binary data. Niina Haiminen received the M.Sc. degree from the University of Helsinki in 2004. She is currently a Graduate Student at the Department of Computer Science of University of Helsinki, and a Researcher at the Basic Research Unit of Helsinki Institute for Information Technology. Her research interests include algorithms, bioinformatics, and data mining. Aristides Gionis received the Ph.D. degree from Stanford University in 2003, and he is currently a Senior Researcher at the Basic Research Unit of Helsinki Institute for Information Technology. His research experience includes summer internship positions at Bell Labs, AT&T Labs, and Microsoft Research. His research areas are data mining, algorithms, and databases. Kari Laasonen received the M.Sc. degree in Theoretical Physics in 1995 from the University of Helsinki. He is currently a Graduate Student in Computer Science at the University of Helsinki and a Researcher at the Basic Research Unit of Helsinki Institute for Information Technology. His research is focused on algorithms and data analysis methods for pervasive computing.     

Computing all efficient solutions of the biobjective minimum spanning tree problem

A common way of computing all efficient (Pareto optimal) solutions for a biobjective combinatorial optimisation problem is to compute first the extreme efficient solutions and then the remaining, non-extreme solutions. The second phase, the computation of non-extreme solutions, can be based on a “k-best” algorithm for the single-objective version of the problem or on the branch-and-bound method. A k-best algorithm computes the k-best solutions in order of their objective values. We compare the performance of these two approaches applied to the biobjective minimum spanning tree problem. Our extensive computational experiments indicate the overwhelming superiority of the k-best approach. We propose heuristic enhancements to this approach which further improve its performance.     

Evaluation of neural networks-based controllers in batch polymerisation of methyl methacrylate

The importance of batch reactors in today's process industries cannot be overstated. Thus said, it is important to optimise their operation in order to consistently achieve products of high quality while minimising the production of undesirables. In processes like polymerisation, these reactors are responsible for a greater number of products than other reactor types and the need for optimal operation is therefore greater.

An approach based on an offline dynamic optimisation and online control strategy is used in this work to generate optimal set point profiles for the batch polymerisation of methyl methacrylate. Dynamic optimisation is carried out from which controller set points to attain desired polymer molecular end point characteristics are achieved. Temperature is the main variable to be controlled, and this is done over finite discrete intervals of time.

For on-line control, we evaluate the performance of neural networks in two controllers used to track the derived optimal set points for the system. The controllers are generic model control (GMC), ([P.L. Lee, G.R. Sullivan, Generic model control, Comput. Chem. Eng. 12(6) (1998) 573–580]) and the neural network-based inverse model-based control (IMBC), ([M.A. Hussain, L.S. Kershenbaum, Implementation of an inverse model based control strategy using neural networks on a partially simulated exothermic reactor, Trans. IchemE 78(A) (2000) 299–311]). Although the GMC is a model-based controller, neural networks are used to estimate the heat release within its framework for on-line control. Despite the application of these two controllers to general batch reactors, no published work exists on their application to batch polymerisation in the literature. In this work, the performance of the neural networks within each controller's algorithm for tracking and setpoint regulation of the optimal trajectory and in robustness tests on the system is evaluated.     

基于二进制的频繁项集挖掘新算法

频繁项集挖掘是数据挖掘中的一个经典的问题。然而,大部分算法需要扫描数据库多次,算法效率比较低。该文提出了一个效率比较好的挖掘频繁项集的新算法,在这个算法中,所有的事务都是以二进制的形式表示,所以挖掘极大频繁项集的任务就变成了从二进制集中发现频繁模式。而且,这种算法只需要扫描原始数据库一次。最后,利用试验来证明这种算法的效率和优势。     

基于二进制的频繁项集挖掘新算法

频繁项集挖掘是数据挖掘中的一个经典的问题。然而,大部分算法需要扫描数据库多次,算法效率比较低。该文提出了一个效率比较好的挖掘频繁项集的新算法,在这个算法中,所有的事务都是以二进制的形式表示,所以挖掘极大频繁项集的任务就变成了从二进制集中发现频繁模式。而且,这种算法只需要扫描原始数据库一次。最后,利用试验来证明这种算法的效率和优势。     

二值probit回归模型的坍缩变分贝叶斯推断算法

给出了二值probit回归模型的坍缩变分贝叶斯推断算法.此算法比变分贝叶斯推断算法能更逼近对数边缘似然,得到更精确的模型参数后验期望值.如果两个算法得到的分类错误一致,则该算法的迭代次数较变分法明显减少.仿真实验结果验证了所提出算法的有效性.     

基于整数小波变换的指纹信息嵌入方案研究

提出量化指纹编码的思想.将指纹数据转化为二值图像嵌入文件,使攻击者难以发现指纹信息,避免了通过比对编码进行的共谋攻击;设计了整数小波变换嵌入算法,并在实验中得到较好的效果.     

数据库中存取图像

本文介绍了一种把图片等二进制文件在数据库中存取的方法,分析了这种方法和一般方法的不同,给出了此方法的基本原理,并通过实例进行方法的说明及实现。     

二元关系的复合与近似算子的合成

定义了各种类型的经典二元关系和模糊二元关系,讨论了二元关系的合成及其性质.给出了两个近似空间合成的概念,并讨论合成前的近似空间所导出的近似算子与合成后的近似空间所导出的近似算子之间的关系.证明了合成后的近似空间所导出的近似算子恰好是两个近似空间所导出的近似算子的合成.     

二进制程序安全缺陷静态分析方法的研究综述

对现有二进制程序安全缺陷静态分析方法进行了综述和分析,提出了整个程序分析过程中的关键问题以及二进制程序安全分析的主要研究方向.通过对二进制程序缺陷静态分析流程的总结,发现二进制程序信息恢复是整个分析过程的关键,构造内容丰富的、通用的中间表示是二进制程序缺陷分析的重要研究方向.