Partitioned Shadow Volumes

Real‐time shadows remain a challenging problem in computer graphics. In this context, shadow algorithms generally rely either on shadow mapping or shadow volumes. This paper rediscovers an old class of algorithms that build a binary space partition over the shadow volumes. For almost 20 years, such methods have received little attention as they have been considered lacking of both robustness and efficiency. We show that these issues can be overcome, leading to a simple and robust shadow algorithm. Hence we demonstrate that this kind of approach can reach a high level of performance. Our algorithm uses a new partitioning strategy which avoids any polygon clipping. It relies on a Ternary Object Partitioning tree, a new data structure used to find if an image point is shadowed. Our method works on a triangle soup and its memory footprint is fixed. Our experiments show that it is efficient and robust, including for finely tessellated models.     

面向航空电子的分区操作系统

伴随新一代航空电子的综合化趋势,机载软件对机载操作系统提出新的要求.通过管理系统的软硬件资源,包括内存、CPU、通信等,机载操作系统将各类机载软件融合在同一个CPU上,实现综合化.该文给出一种面向航空电子的分区操作系统的实现框架,支持二级调度,对于各类机载软件实施分区调度,并且分区的运行互不干扰,保证了分区的安全性.     

Partitioned storage for temporal databases

Efficiently maintaining history data on line together with current data is difficult. This paper discusses one promising approach, the temporally partitioned store. The current store contains current data and possibly some history data, whil the history store holds the rest of the data. The two stores can utilize different storage formats, and even different storage media, depending on the individual data characteristics. We discuss various issues on the temporally partitioned store, investigate several formats for the history store, and evaluate their performance on a set of sample queries.     

Partitioned optimization of complex queries

Performing complex analysis on top of massive data stores is essential to most modern enterprises and organizations and requires significant aggregation over different attribute sets (dimensions) of the participating relations. Such queries may take hours or days, a time period unacceptable in most cases. As a result, it is important to study these queries and identify special frequent cases that can be evaluated with specialized algorithms. Understanding complex aggregate queries leads to better execution plans and, consequently, performance.     

Partitioned Simulation of Fluid-Structure Interaction

In this review article, the focus is on partitioned simulation techniques for strongly coupled fluid-structure interaction problems, especially on techniques which use at least one of the solvers as a black box. First, a number of analyses are reviewed to explain why Gauss–Seidel coupling iterations converge slowly or not at all for fluid-structure interaction problems with strong coupling. This provides the theoretical basis for the fast convergence of quasi-Newton and multi-level techniques. Second, several partitioned techniques that couple two black-box solvers are compared with respect to implementation and performance. Furthermore, performance comparisons between partitioned and monolithic techniques are examined. Subsequently, two similar techniques to couple a black-box solver with an accessible solver are analyzed. In addition, several other techniques for fluid-structure interaction simulations are studied and various methods to take into account deforming fluid domains are discussed.     

HPAM: Hirshfeld Partitioned Atomic Multipoles

An implementation of the Hirshfeld (HD) and Hirshfeld-Iterated (HD-I) atomic charge density partitioning schemes is described. Atomic charges and atomic multipoles are calculated from the HD and HD-I atomic charge densities for arbitrary atomic multipole rank l(max) on molecules of arbitrary shape and size. The HD and HD-I atomic charges/multipoles are tested by comparing molecular multipole moments and the electrostatic potential (ESP) surrounding a molecule with their reference ab initio values. In general, the HD-I atomic charges/multipoles are found to better reproduce ab initio electrostatic properties over HD atomic charges/multipoles. A systematic increase in precision for reproducing ab initio electrostatic properties is demonstrated by increasing the atomic multipole rank from l(max) = 0 (atomic charges) to l(max) = 4 (atomic hexadecapoles). Both HD and HD-I atomic multipoles up to rank l(max) are shown to exactly reproduce ab initio molecular multipole moments of rank L for L ≤ l(max). In addition, molecular dipole moments calculated by HD, HD-I, and ChelpG atomic charges only (l(max) = 0) are compared with reference ab initio values. Significant errors in reproducing ab initio molecular dipole moments are found if only HD or HD-I atomic charges used.     

Nonsymmetric Riccati equations: Partitioned algorithms

Generalized partitioned solutions (GPS) of nonsymmetric matric Riccati equations are presented in terms of forward and backward time differential equations that are of theoretical interest and also are computationally powerful. The GPS are the natural framework for the effective change of initial conditions, and the transformation of backward Riccati equation to forward Riccati equation and vice versa.Based on the GPS, computationally effective algorithms are obtained for the numerical solution of Riccati equations. These partitioned numerical algorithms have a decomposed or “partitioned” structure. They are given exactly in terms of a set of elemental solutions which are completely decoupled, and as such computable in either a parallel or serial processing mode. The overall solution is given exactly in terms of a simple recursive operation on the elemental solutions. Except for a subinterval of the total computation interval, the partitioned numerical algorithms are integration-free for the Riccati equation with constant or periodic matrices.Most importantly based on the GPS, a computationally attractive numerical algorithm is obtained for the computation of the steady-state solution of time-invariant Riccati equations. By making use of the GPS and some simple iterative operations, the Riccati solution is obtained in an interval which is twice as long as the previous interval requiring integration only in the initial subinterval.     

Partitioned data objects in distributed databases

In many distributed databases locality of reference is crucial to achieve acceptable performance. However, the purpose of data distribution is to spread the data among several remote sites. One way to solve this contradiction is to use partitioned data techniques. Instead of accessing the entire data, a site works on a fraction that is made locally available, thereby increasing the site's autonomy. We present a theory of partitioned data that formalizes the concept and establishes the basis to develop a correctness criterion and a concurrency control protocol for partitioned databases. Set-serializability is proposed as a correctness criterion and we suggest an implementation that integrates partitioned and non-partitioned data. To complete this study, the policies required in a real implementation are also analyzed. Recommended by: Hector Garcia-Molina     

划分交互式箱粒子概率假设密度滤波法

针对现有的多机动目标追踪问题,将交互式多模型(interacting multiple model,IMM)思想与箱粒子概率假设密度滤波器(box probability hypothesis density filter,Box-PHD)相结合,并针对箱粒子在区间密集杂波等复杂环境下箱体偏大,所导致的箱粒子冗余和目标...     

Hadoop-MapReduce下的PageRank矩阵分块算法

PageRank是Web结构挖掘的经典算法,已在Google搜索引擎中取得了巨大成功.但其迭代次数多,时空消耗大,执行速度和收敛速度都还较慢.文中详细讨论了Hadoop-MapReduce的执行流程及其内部实现机制后,提出了一种并行MapReduce实现矩阵分块的PageRank算法,其实质是减少MapReduce框架结构中Map阶段和Reduce阶段的迭代次数,从而减少时空开销.最后搭建Hadoop-MapReduce开源平台,模拟Web结构爬取,比较了传统算法和改进算法的性能.结果表明,改进后的算法迭代次数低,并行效率较高,在模拟环境中PgeRank标识网页等级显示其优越性.     

多方向分区网络结构的行人再识别

将全局特征与局部特征相结合是提高行人再识别(re-identification)任务识别能力的重要解决方案.以往主要借助姿态估计等外部信息来定位有相应语义的区域,从而挖掘局部信息,这种方法大多是非端到端的,训练过程复杂且缺乏鲁棒性.针对该问题,文中提出了一种能有效挖掘局部信息并且能结合全局信息与局部信息进行端到端特征学习的方法,即多方向分区网络(Multi-orientation Partitioned Network,MOPN),该网络有3个分支,一个用于提取全局特征,两个用于提取局部特征.该算法不依靠外部信息,而是在不同的局部分支分别将图像按水平方向和竖直方向切分为若干横条纹和竖条纹,从而得到不同的局部特征表示.在Market-1501、DukeMTMC-reID、CUHK03和跨模态素描数据集SketchRe-ID上的综合实验表明,该算法的整体性能优于其他对比算法,具备有效性和鲁棒性.     

Partitioned EDF scheduling: a closer look

The partitioned EDF scheduling of implicit-deadline sporadic task systems upon identical multiprocessor platforms is considered. The problem is known to be intractable, but many different polynomial-time algorithms have been proposed for solving it approximately. These different approximation algorithms have previously been compared using utilization bounds; they are compared here using a different metric—the speedup factor. It is shown that from the perspective of their speedup factors, the best partitioning algorithms are those that (i) assign the tasks in decreasing order of utilization; and (ii) are “reasonable” in the sense that they will assign a task if there is capacity available on some processor—such algorithms include the widely-used First-Fit Decreasing, Best-Fit Decreasing, and Worst-Fit Decreasing partitioning heuristics.     

Symplectic Partitioned Runge-Kutta methods with minimal phase-lag

Symplectic Partitioned Runge-Kutta (SPRK) methods with minimal phase-lag are derived. Specifically two new symplectic methods are constructed of second and third order with fifth phase-lag order. The methods are tested on the numerical integration of Hamiltonian problems and the Schrödinger equation.     

基于分区的Elias-Fano-Golomb-Rice倒排索引压缩算法

基于分区的Elias-Fano算法被应用于倒排索引压缩,显示出良好的空间压缩性能。本文证明了Golomb-Rice算法的压缩性能优于Elias-Fano算法。结合基于分区的Elias-Fano算法中“分区”思想,提出一种基于分区的Elias-Fano-Golomb-Rice倒排索引压缩算法。实验结果表明,与其他倒排索引压缩算法相比,基于分区的Elias-Fano-Golomb-Rice倒排索引压缩算法有更好的压缩性能。     

Partitioned encryption and achieving simultaneity by partitioning

We present a method called Partitioned Encryption whose main property is its simplicity. It is an extension of Probabilistic Public-Key Encryption, which can be used in designing cryptographic protocols and can be applied to distributed problem solving. We also give a modification of Secret Sharing called Partitioned Secret Sharing. We demonstrate the power of Partitioned Encryption: combining it with the partitioning of the user set gives a solution scheme for ‘Verifiable Secret Sharing’ and ‘Simultaneous Broadcast in the Presence of Faults’, which are important primitives of fault-tolerant distributed computing introduced by Chor, Goldwasser, Micali and Awerbuch (1985). The scheme is fully polynomial, simple, and efficient in terms of communication rounds. The basic partitioning methods are suggested as general tools for distributed computing, which are easy to implement and analyze.     

多处理器混合关键性系统中的划分调度策略

多核处理器正越发广泛地应用到现代嵌入式系统的设计与实现当中,其强大的计算能力为将多个不同关键性级别的功能子系统集成到统一的共享资源平台提供了支持.混合关键性系统的调度问题即便在单处理器平台中都极具挑战性,在多处理器平台则更为困难.将目前资源利用率最高的单处理器混合关键性调度算法EY-VD扩展到多处理器平台中.首先,结合传统的划分调度策略提出了适用于多处理器混合关键性系统的MC-PEDF(mixedcriticality partitioned earliest deadline first)划分调度算法.尽管比之前的算法有更好的可调度性能,但传统的划分策略不能有效地平衡不同关键性级别下的负载,故其不完全适用于混合关键性系统.为了克服传统策略的不足,提出了划分调度策略OCOP(one criticality one partition).OCOP允许系统在关键性模式切换时对实时任务集进行重新划分,进而更好地平衡各个处理器在不同关键性模式中的资源利用率.基于OCOP,提出了第2种划分调度算法MC-MP-EDF(mixed-criticality multi-partitioned EDF).基于随机生成任务集的仿真实验结果表明,与MC-PEDF和已有的算法相比,MC-MP-EDF能够显著地提高系统的可调度性,尤其是在处理器数量较多的系统中.     

An Improved Hierarchy Result for Partitioned BDDs

One of the great challenges of complexity theory is the problem of analyzing the dependence of the complexity of Boolean functions on the resources nondeterminism and randomness. So far this problem could be solved only for very few models of computation. For so-called partitioned binary decision diagrams , which are a restricted variant of nondeterministic read-once branching programs, Bollig and Wegener have proven an astonishing hierarchy result which shows that the smallest possible decrease of the available amount of nondeterminism may incur an exponential blow-up of the branching program size. They have shown that k -partitioned BDDs which may nondeterministically choose between k alternative subprograms may be exponentially larger than (k+1) -partitioned BDDs for the same function if k = o(( log n / log log n) ^1/2 ) , where n is the input size. In this paper an improved hierarchy result is established which still works if the number of nondeterministic decisions is O((n/ log ^1+ε n) ^1/4 ) , where ε > 0 is an arbitrary small constant. Received November 25, 1999.     

基于数据纵向分布的隐私保护逻辑回归

逻辑回归是机器学习的重要算法之一,为解决集中式训练方式不能保护隐私的问题,提出隐私保护的逻辑回归解决方案,该方案适用于数据以特征维度进行划分,纵向分布在两方情况下,两方进行协作式训练学习到共享的模型结构.两方在本地数据集上进行训练,通过交换中间计算结果而不直接暴露私有数据,利用加法同态加密算法在密文下进行运算保证计算安全,保证在交互中不能获取对方的敏感信息.同时,提供隐私保护的预测方法,保证模型部署服务器不能获取询问者的私有数据.经过分析与实验验证,在几乎不损失精度的前提下,该案可以在两方均是半诚实参与者情况下提供隐私保护.