基于改进图划分的异构并行计算模型设计

为了实现大规模计算机集群上的高效分布式并行计算,设计了一种基于改进图划分和量子遗传算法的异构节点并行计算模型;首先,介绍了传统图划分模型并分析了其不足,然后从图的有向性、通信开销计算和负载均衡度等方面对传统的图划分模型进行了改进,从而得到一个改进的图划分模型;最后,以最小化通信开销和优化资源负载均衡为目标,通过设计编码方案,在改进的图划分模型上提出了采用量子遗传算法获取最优任务划分方案的最优解;仿真实验表明:文中方法能有效实现任务的并行计算,与其它方法相比,具有较小的通信开销和较好的负载均衡度,具有很强的可行性。     

Detection of community structure in networks based on community coefficients

Determining community structure in networks is fundamental to the analysis of the structural and functional properties of those networks, including social networks, computer networks, and biological networks. Modularity function Q$Q$, which was proposed by Newman and Girvan, was once the most widely used criterion for evaluating the partition of a network into communities. However, modularity Q$Q$ is subject to a serious resolution limit. In this paper, we propose a new function for evaluating the partition of a network into communities. This is called community coefficient C$C$. Using community coefficient C$C$, we can automatically identify the ideal number of communities in the network, without any prior knowledge. We demonstrate that community coefficient C$C$ is superior to the modularity Q$Q$ and does not have a resolution limit. We also compared the two widely used community structure partitioning methods, the hierarchical partitioning algorithm and the normalized cuts (Ncut) spectral partitioning algorithm. We tested these methods on computer-generated networks and real-world networks whose community structures were already known. The Ncut algorithm and community coefficient C$C$ were found to produce better results than hierarchical algorithms. Unlike several other community detection methods, the proposed method effectively partitioned the networks into different community structures and indicated the correct number of communities.     

The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies

In the Home Care Crew Scheduling Problem a staff of home carers has to be assigned a number of visits to patients’ homes, such that the overall service level is maximised. The problem is a generalisation of the vehicle routing problem with time windows. Required travel time between visits and time windows of the visits must be respected. The challenge when assigning visits to home carers lies in the existence of soft preference constraints and in temporal dependencies between the start times of visits.     

Degradative encryption: An efficient way to protect SPIHT compressed images

Degradative encryption, a new selective image encryption paradigm, is proposed to encrypt only a small part of image data to make the detail blurred but keep the skeleton discernible. The efficiency is further optimized by combining compression and encryption. A format-compliant degradative encryption algorithm based on set partitioning in hierarchical trees (SPIHT) is then proposed, and the scheme is designed to work in progressive mode for gaining a tradeoff between efficiency and security. Extensive experiments are conducted to evaluate the strength and efficiency of the scheme, and it is found that less than 10% data need to be encrypted for a secure degradation. In security analysis, the scheme is verified to be immune to cryptographic attacks as well as those adversaries utilizing image processing techniques. The scheme can find its wide applications in online try-and-buy service on mobile devices, searchable multimedia encryption in cloud computing, etc.     

块循环矩阵方程组的新算法

1　基本概念形如 A=a1 a2 … a Na N a1 … a N- 1?彙?廰2 a3 … a1的矩阵称为由 a1 ,a2 ,… ,a N 生成的循环矩阵 .力学和工程中的轴对称结构的计算产生上述循环矩阵 [2 - 3] .以循环矩阵A为系数矩阵的方程组 ,称为循环矩阵方程组 .已有的求解循环矩阵方程组的办法主要是各种迭代法 ,如递推法及 SOR,SSOR,SAOR超松弛迭代法[2 - 6] 等 .定义 1　形如A =A1 A2 … ANAN A1… AN- 1?彙?廇2 A3… A1　 (Ai,i =1 ,2 ,… ,N为 m阶矩阵 )的矩阵称为由 A1 ,A2 ,… ,AN 生成的块循环矩阵 .定义 2　系数矩阵 A为块循环矩阵的方程组AX …     

Strong Universal Pointwise Consistency of Recursive Regression Estimates

For semi-recursive and recursive kernel estimates of a regression of Y on X (d-dimensional random vector X, integrable real random variable Y), introduced by Devroye and Wagner and by Révész, respectively, strong universal pointwise consistency is shown, i.e. strong consistency P _X -almost everywhere for general distribution of (X, Y). Similar results are shown for the corresponding partitioning estimates.     

SoC软硬件自动划分系统设计

设计了一种将IP核复用和多目标优化相结合的SoC软硬件自动划分系统，详细探讨了系统各个模块的功能，重点论述了系统实现的关键技术，包括基于遗传算法的软硬件划分方法和基于表调度的系统性能评估方法，实验结果表明，该系统满足了SoC软硬件协同设计中对软硬件自动划分的要求，为开发SoC系统级仿真验证平台奠定了基础．     

Random sampling and greedy sparsification for matroid optimization problems

Random sampling is a powerful tool for gathering information about a group by considering only a small part of it. We discuss some broadly applicable paradigms for using random sampling in combinatorial optimization, and demonstrate the effectiveness of these paradigms for two optimization problems on matroids: finding an optimum matroid basis and packing disjoint matroid bases. Application of these ideas to the graphic matroid led to fast algorithms for minimum spanning trees and minimum cuts. An optimum matroid basis is typically found by agreedy algorithm that grows an independent set into an optimum basis one element at a time. This continuous change in the independent set can make it hard to perform the independence tests needed by the greedy algorithm. We simplify matters by using sampling to reduce the problem of finding an optimum matroid basis to the problem of verifying that a givenfixed basis is optimum, showing that the two problems can be solved in roughly the same time. Another application of sampling is to packing matroid bases, also known as matroid partitioning. Sampling reduces the number of bases that must be packed. We combine sampling with a greedy packing strategy that reduces the size of the matroid. Together, these techniques give accelerated packing algorithms. We give particular attention to the problem of packing spanning trees in graphs, which has applications in network reliability analysis. Our results can be seen as generalizing certain results from random graph theory. The techniques have also been effective for other packing problems. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.Some of this work done at Stanford University, supported by National Science Foundation and Hertz Foundation Graduate Fellowships, and NSF Young Investigator Award CCR-9357849, with matching funds from IBM, Schlumberger Foundation, Shell Foundation and Xerox Corporation. Also supported by NSF award 962-4239.     

求解带状线性方程组的并行分割算法

讨论了求解带状线性方程组的并行分割算法．对影响算法并行效率的约化方程组采取了在多台处理机上重复求解的方法，减少了数据的通信次数，提高了算法的并行效率．算法在一些新的并行计算机系统上使用，达到高并行效率．