An Overview of Kernelization Algorithms for Graph Modification Problems

Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion problems, edge editing problems, edge deletion problems, and edge completion problems. For each type of problem, we outline typical examples together with recent results, analyze the main techniques, and provide some suggestions for future research in this field.     

基于primal RankRLS方法的本体映射算法

本体映射作为实现多本体间相互操作的重要手段,已广泛应用于诸多领域.应用原始RankRLS算法,将多本体图映射到实数轴,由此将本体图中每个顶点都映射成实数.通过比较两概念对应实数间的差值得到本体映射.实验表明,该算法有较高的效率.     

（1-uv）-CONSTACYCLIC CODES OVER Fp＋uFp＋vFp＋uvFp

Constacyclic codes are an important class of linear codes in coding theory.Many optimal linear codes are directly derived from constacyclic codes.In this paper,（1 — uv）-constacyclic codes over the local ring Fp ＋ uFp ＋ vFp ＋ uvFp are studied.It is proved that the image of a（1 — uv）-constacyclic code of length n over Fp ＋ uFp ＋ vFp ＋ uvFp under a Gray map is a distance invariant quasi-cyclic code of index p2 and length p3n over Fp.Several examples of optimal linear codes over Fp from（1 — uv）-constacyclic codes over Fp ＋ uFp ＋ vFp ＋ uvFp are given.     

非连通图3C8m∪C8m-1∪G的优美标号

研究非连通图3C8m∪C8m-1∪G的优美性.证明如下结论：对任意正整数m,若图G是特征为k且缺标号值k＋24m-2的交错图,则非连通图3C8m∪C8m-1∪G存在缺标号值k＋1的优美标号.     

模糊集范畴的性质

定义一种模糊集范畴F,讨论了F具有的范畴性质:F与子集范畴之间的关系;F上的态射用a-截集表示的条件;F上态射和映射的关系:证明了F的乘积、等子、余等子、拉回和极限等存在,并给出它们的具体形式.     

强G-预不变凸函数的性质研究

给出了强G-预不变凸函数的一些新的性质,这些性质在数学规划中有着重要的应用.     

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests’ relationship, we not only consider users’ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model (I-FGM) to incorporate these factors. Experiments on two data sets (bookmarking and music network) demonstrate that this predictive method can achieve better results than the other three methods (ANN, NB, and SVM).     

帐篷映射归宿的完整性探析

应用三进制方法完整地研究了帐篷映射的动力学特性．当初值x0=0，1时，经帐篷映射迭代后的最终归宿为x∞→0；当x0 [0，1]时，x∞→-∞．当x0∈（O，1），x0为有限位小数时，x0→0或-∞；当x0为循环小数时，x∞将处在周期轨道上或趋于-∞；当x0为不循环小数时，x∞将处在混沌轨道上或趋于-∞．     

An Efficient Multidimensional Fusion Algorithm for IoT Data Based on Partitioning

The Internet of Things (IoT) implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions (attributes), i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.     

交通流宏微观参数关系方程组实证分析

本文以James H．Banks关于平均时间间隔的研究为基础，提出交通流宏微观参数关系方程组，理论上较全面地描述两类交通参数的关系；应用实测交通数据对该方程组进行数据实证。分析表明，使用关系方程组产生的偏差不仅与统计时间间隔有关，而且与检测点的位置、交通状态相关。通过分析关系方程组的推导可知，偏差源于两个等式在理论上与实际运用中的不同。