基于参数化运动合成的运动图构建及其应用

虚拟人运动的可控性和逼真性是虚拟现实应用中追求的重要目标,为了实现对虚拟人的灵活控制,合成出逼真的运动序列,提出了基于参数化运动合成的运动图方法.运动图的结点中存储了具有明确含义的控制参数,通过改变控制参数合成不同的运动片段,可以实现对虚拟人的灵活控制;提出了一种改进的运动融合方法,运动图的边将不同运动片段进行融合,有效的避免了脚步滑动和根关节朝向抖动的产生.根据用户对交互控制和路径轨迹的不同应用需求设计实验,实验结果表明,方法不仅具有较高的控制精度,而且合成的运动序列逼真自然.     

POM:一个MPI程序的进程优化映射工具

现代超级计算机具有越来越多的计算结点,同时结点内具有多个处理器核。由于互联带宽的差异,结点间与结点内构成两个通信性能不同的通信层次,后者的通信性能好于前者。但是,目前MPI程序的默认进程映射未考虑该通信层次差异,无法利用结点内较好的通信带宽,严重束缚了超级计算机的性能发挥。针对该问题,本文设计实现了能利用层次通信差异的MPI程序自动进程优化映射工具POM,提供了高效、低开销获取MPI程序通信信息的方法,最终通过优化通信在通信层次上的分布提高了程序的通信效率,从而提高了应用程序的性能。本文解决了硬件平台通信层次的抽象、MPI程序通信信息的低开销获取与映射方案的计算三个问题。首先,按照通信能力差异将超级计算机结构抽象为高速互联的不同计算结点与相同结点上的多个处理器核两层。其次,提出了将集合通信转化成点到点通信的简单实现方法。最后,利用无向加权边图来表示MPI程序的进程间通信关系,将MPI程序的进程映射问题转化为图划分问题。在曙光5000A和曙光4000A上的实验结果表明,利用POM工具能够显著提高MPI程序的性能。     

视觉传达设计艺术中的怪诞图形

怪诞图形是西方现代艺术现代视觉传达的一个重要表现形式,它为现代视觉传达设计注入了新的血液,使现代视觉传达设计在形式上发生了根本改变。     

一种恢复状态模式的状态转换图的方法

现有研究不能从状态模式的代码中恢复状态转换图这一状态模式的核心语义信息。针对状态驱动的转换这种典型实现方式,通过分析类型传播图确定状态模式的状态变量在各次赋值前后所属的具体状态类,并由此恢复该模式的状态转换图。给出了具体算法,通过实例分析验证了方法的可行性。     

以图频繁集为基础的核心节点发现

结合基于图的关联规则挖掘和双向搜索的策略,产生最大频繁项集,从而提出基于图的最大频繁项集(graph based maximum frequen tset,GBMFS)生成算法。运用此算法,结合社会网络的动态特征,发现社会网络中所存在的团伙的核心成员。最后,在实际系统中对相关的算法进行了验证。     

A computational trust model for access control in P2P

Trust brings a new method for building scalable and fine-grained access control mechanism in P2P systems. The quantificational expression of trust and the calculation of trust in a trust network are the basis of trust degree based access control. In this paper, the properties of trust is analyzed by referring to the fruits from social science; the semantics of trust in the context of access control is described, and a trust degree based access control model named TDBAC is introduced. Basing on the propertie...     

Deadline division-based heuristic for cost optimization in workflow scheduling

Cost optimization for workflow applications described by Directed Acyclic Graph (DAG) with deadline constraints is a fundamental and intractable problem on Grids. In this paper, an effective and efficient heuristic called DET (Deadline Early Tree) is proposed. An early feasible schedule for a workflow application is defined as an Early Tree. According to the Early Tree, all tasks are grouped and the Critical Path is given. For critical activities, the optimal cost solution under the deadline constraint can be obtained by a dynamic programming strategy, and the whole deadline is segmented into time windows according to the slack time float. For non-critical activities, an iterative procedure is proposed to maximize time windows while maintaining the precedence constraints among activities. In terms of the time window allocations, a local optimization method is developed to minimize execution costs. The two local cost optimization methods can lead to a global near-optimal solution. Experimental results show that DET outperforms two other recent leveling algorithms. Moreover, the deadline division strategy adopted by DET can be applied to all feasible deadlines.     

On the diameter of geometric path graphs of points in convex position

For a set S of n points in convex position in the plane, let P(S) denote the set of all plane spanning paths of S. The geometric path graph of S, denoted by Gn, is the graph with P(S) as its vertex set and two vertices P,Q∈P(S) are adjacent if and only if P and Q can be transformed to each other by means of a single edge replacement. Recently, Akl et al. [S.G. Akl, K. Islam, H. Meijer, On planar path transformation, Inform. Process. Lett. 104 (2007) 59-64] showed that the diameter of Gn is at most 2n−5. In this note, we derive the exact diameter of Gn for n?3.     

A stochastic graph grammar for compositional object representation and recognition

This paper illustrates a hierarchical generative model for representing and recognizing compositional object categories with large intra-category variance. In this model, objects are broken into their constituent parts and the variability of configurations and relationships between these parts are modeled by stochastic attribute graph grammars, which are embedded in an And-Or graph for each compositional object category. It combines the power of a stochastic context free grammar (SCFG) to express the variability of part configurations, and a Markov random field (MRF) to represent the pictorial spatial relationships between these parts. As a generative model, different object instances of a category can be realized as a traversal through the And-Or graph to arrive at a valid configuration (like a valid sentence in language, by analogy). The inference/recognition procedure is intimately tied to the structure of the model and follows a probabilistic formulation consisting of bottom-up detection steps for the parts, which in turn recursively activate the grammar rules for top-down verification and searches for missing parts. We present experiments comparing our results to state of art methods and demonstrate the potential of our proposed framework on compositional objects with cluttered backgrounds using training and testing data from the public Lotus Hill and Caltech datasets.