PASCAL过程蓝图到抽象逻辑结构图的逆向映射

给出PASCAL过程蓝图逻辑结点到抽象逻辑结构图概念结点的逆向映射规则，并通过构造与双向映射规则集表示等价的双向映射关系图,揭示了两个映射规则集之间的关系和特性。在定义概念层表示与PASCAL逻辑层表示之间双向映射函数的有效性概念基础上，进一步给出双向映射函数的有效性定理。     

抽象逻辑结构图到VB过程蓝图的导出与映射

本文给出独立于程序实现言的抽象逻辑结构图到VB过程蓝图的平滑过渡方法概念结点逻辑结点的映射规则，给出了抽象逻辑结构图程序表示的有效性定义，导出VB抽象逻辑结构图的有效性定理及其证明     

PASCAL过程蓝图到抽象逻辑结构的逆向映射

给出PASCAL过程蓝逻辑结点到抽象逻辑结构图概念结点的逆向映射规则，并通过构造与双向映射规则集表示等价的双向映射关系图，揭示了两映射规则集之间的关系和特性。在定义概念层表示与PASCAL逻辑辑层表示之间双向映射函数的有效性概念基础上，进一步给出向映射函数的有效性定理。     

抽象逻辑结构图C++逻辑程序到概念程序的逆向映射

文章给出C++过程蓝图逻辑程序表示到抽象逻辑结构图概念程序表示的逆向映射规则。在定义概念结点与C++逻辑结点之间双向映射函数的有效性概念基础上,进一步给出双向映射函数的有效性定理及其证明。     

抽象逻辑结构图到VFP过程蓝图的过渡与映射

给出独立于程序实现语言的抽象逻辑结构图到VFP过程蓝图的平滑过渡方法及概念结点到逻辑结点的映射规则;给出了抽象逻辑结构图程序表示的有效性定义、导出VFP抽象逻辑结构图的有效性定理及其证明。     

抽象逻辑结构图导出PASCAL过程蓝图的方法

概念层抽象逻辑结构图是一种不依赖于程序实现语言的概念算法描述工具,文中给出概念抽象逻辑结构图到PASCAL过程蓝图的平滑过渡方法及概念结点到逻辑结点的映射规则,给出了抽象逻辑结构图程序表示的有效性定义,导出PASCAL抽象逻辑结构图的有效性定理及其证明。     

从概念程序表示到C++过程蓝图的映射

文章给出了与程序实现语言无关的概念层抽象逻辑结构图程序表示到C＋＋过程蓝图的平滑过渡方法和映射规则,给出了概念层和逻辑层抽象逻辑结构图的有效性定义、导出C＋＋逻辑层抽象逻辑结构图的有效性定理及其证明。     

VFP过程蓝图到抽象逻辑结构图的逆向映射规则与同步更新方法

当对导出VFP抽象逻辑结构图逻辑层表示进行编辑操作时，解决解决抽象逻辑结构图概念层表示的一致性同步更新问题。给了VFP逻辑结点到概念结点的逆向映射规则及其一致性同步更新方法。     

元数据驱动的双向映射ETL模型研究

论文重点研究异构数据模型间的映射关系,并参考ETL框架思想,设计了针对异构数据模型间数据映射、转换和加载于一身的双向映射策略,分析并定义了数据模型间的映射关系与映射规则.基于元数据驱动理论方法设计了数据映射元模型,并将映射关系按照复杂程度分类,分为简单映射与复合映射关系,明确给出了分类原则.论文还对双向映射关系进行了描述,并分析了双向映射中遇到的问题.     

XML DTD到关系模式的映射研究

本文首先分析了XML DTD中元素、属性和操作符,其次给出了XML DTD简化规划,最后讨论了XML DTD到关系模式的映射规则和映射步骤。给出了应用实例,在应用中证明了算法的有效性。     

Mapping computations

Let Z be a set of integers and Z ^n×n be a ring for any integer n. We define as a latter point. Hom(Z ⁿ ,Z ^m ) denotes as a homomorphism of Z ⁿ into Z ^m . For any element in Z ⁿ , we define S+T:Z ⁿ →Z ^m as . As a result, S+T become a homomorphism of Z ⁿ into Z ^m . We also define kU:Z ⁿ →Z ^m as . Consequently, kU become a homomorphism of Z ⁿ into Z ^m . Moreover, Hom (Z ⁿ ,Z ^m ) is isomorphic to Z ^n×m . A novel class of the structured matrices which is a set of elements of Hom (Z ⁿ ,Z ⁿ ) over a ring of integers with a displacement structure, referred to as a C-Cauchy-like matrix, will be formulated and presented. Using the displacement approach, which was originally discovered by Kailath, Kung, and Morf (J. Math. Anal. Appl. 68:395–407, 1979), a new superfast algorithm for the multiplication of a C-Cauchy-like matrix of the size n×n over a field with a vector will be designed. The memory space for storing a C-Cauchy-like matrix of the size n×n over a field is O(n) versus O(n ²) for a general matrix of the size n×n over a field. The arithmetic operations of a product of a C-Cauchy-like matrix and a vector is reduced dramatically to O(n) from O(n ²), which can be used to transform a latter point to another latter point such that . Moreover, the displacement structure can also be extended to a Kronecker matrix W ⊗ Z. A new class of the Kronecker-like matrices with the displacement rank r, r<n will be also discovered. The memory space for storing a Kronecker-like matrix of the size (n×1)⊗(1×n) over a field is decreased to O(rn). The arithmetic operations for a product of a Kronecker-like matrix with the displacement rank r and a vector is also accelerated to O(rn).     

Mapping objects

We present a technique that is based on volatile mapping objects and enables wrappers-based mediation architectures to describe bi-directional (read–write) interschema mappings of multiple, disparate data sources. We describe the structure of these mapping objects, explain how they work, and compare them to other traditional techniques used for describing schema mappings in data-mediation systems.     

Mapping the image

The Brainstorm feature introduced in Adobe After Effects CS3 (2007) allows users to automate parts of the process of generating design variations for the purposes of comparison and selection. The paper begins with a brief discussion of current discursive formations around software and software-based practice among digital design practitioners and educators. Next, the paper draws upon critical concepts drawn from multimodal discourse analysis, media theory and sociology to analyse Brainstorm in terms of the interplay of software structure and design agency. The key concepts used are modality, articulation and interpretation (Kress and van Leeuwen 1996 Kress, G. and van Leeuwen, T. 1996. Reading images: the grammar of visual design, London: Routledge.  [Google Scholar], 2001), the database as cultural form and the logic of selection (Manovich 2001 Manovich, L. 2001. The language of new media, Cambridge, MA: MIT Press.  [Google Scholar]), habitus and practical logic (Bourdieu 1977 Bourdieu, P. 1977. Outline of a theory of practice, New York: Cambridge University Press. [Crossref] , [Google Scholar]) and the radius of creativity (Toynbee 2000 Toynbee, J. 2000. Making popular music: musicians, creativity and institutions, London: Arnold.  [Google Scholar]). Throughout, the paper addresses specific structural features of the software, thus developing an overview of the affordances and constraints of Brainstorm as a creative tool.     

Manifold Mapping Machine

Nonlinear classification has been a non-trivial task in machine learning for the past decades. In recent years, kernel machines have successfully generalized the inner-product based linear classifiers to nonlinear ones by transforming data into some high or infinite dimensional feature space. However, due to their implicit space transformation and unobservable latent feature space, it is hard to have an intuitive understanding of their working mechanism. In this paper, we propose a comprehensible framework for nonlinear classifier design, called Manifold Mapping Machine (M³). M³ can generalize any linear classifier to nonlinear by transforming data into some low-dimensional feature space explicitly. To demonstrate the effectiveness of M³ framework, we further present an algorithmic implementation of M³ named Supervised Spectral Space Classifier (S³C). Compared with the kernel classifiers, S³C can achieve similar or even better data separation by mapping data into the low-dimensional spectral space, allowing both of its mapped data and new feature space to be examined directly. Moreover, with the discriminative information integrated into the spectral space transformation, the classification performance of S³C is more robust than that of the kernel classifiers. Experimental results show that S³C is superior to other state-of-the-art nonlinear classifiers on both synthetic and real-world data sets.     

Mapping Filtering Streaming Applications

In this paper, we explore the complexity of mapping filtering streaming applications on large-scale homogeneous and heterogeneous platforms, with a particular emphasis on communication models and their impact. Filtering applications are streaming applications where each node also has a selectivity which either increases or decreases the size of its input data set. This selectivity makes the problem of scheduling these applications more challenging than the more studied problem of scheduling “non-filtering” streaming workflows. We address the complexity of the following two problems:

Evaluation: Given a mapping of nodes to processors, how can one compute the period and latency?

Optimization: Given a filtering workflow, how can one compute the mapping and schedule that minimize the period or latency? A?solution to this problem requires generating both the mapping and the associated operation list—the order in which each processor executes its assigned tasks.

We address this general problem in two steps. First, we address the simplified model without communication cost. In this case, the evaluation problems are easy, and the optimization problems have polynomial complexity on homogeneous platforms. However, we show that the optimization problems become NP-hard on heterogeneous platforms. Second, we consider platforms with communication costs. Clearly, due to the previous results, the optimization problems on heterogeneous platforms are still NP-hard. Therefore we come back to homogeneous platforms and extend the framework with three significant realistic communication models. Now even evaluation problems become difficult, because the mapping must now be enriched with an operation list that provides the time-steps at which each computation and each communication occurs in the system: determining the best operation list has a combinatorial nature. Not too surprisingly, optimization problems are NP-hard too. Altogether, this paper provides a comprehensive overview of the additional difficulties induced by heterogeneity and communication costs.     

Revectorization-Based Soft Shadow Mapping

In this paper, we present revectorization-based soft shadow mapping, an algorithm that enables the rendering of visually plausible anti-aliased soft shadows in real time. In revectorization-based shadow mapping, shadow silhouettes are anti-aliased and filtered on the basis of a discontinuity space. By replacing the filtering step of the theoretical framework of the percentage-closer soft shadow algorithm by a revectorization-based filtering algorithm, we are able to provide anti-aliasing mainly for near contact shadows or small penumbra sizes generated from low-resolution shadow maps. Moreover, we present a screen-space variant of our technique that generates visually plausible soft shadows with an overhead of only in processing time, when compared to the fastest soft shadow algorithms proposed in the literature, but that introduces shadow overestimation artefacts in the final rendering.     

Variance Soft Shadow Mapping

We present variance soft shadow mapping (VSSM) for rendering plausible soft shadow in real‐time. VSSM is based on the theoretical framework of percentage‐closer soft shadows (PCSS) and exploits recent advances in variance shadow mapping (VSM). Our new formulation allows for the efficient computation of (average) blocker distances, a common bottleneck in PCSS‐based methods. Furthermore, we avoid incorrectly lit pixels commonly encountered in VSM‐based methods by appropriately subdividing the filter kernel. We demonstrate that VSSM renders high‐quality soft shadows efficiently (usually over 100 fps) for complex scene settings. Its speed is at least one order of magnitude faster than PCSS for large penumbra.     

QoS映射的区间映射模型

提出一种新的QoS映射模型一区间映射，该映射模型可以将应用层参数映射为传输层参数的一个区间范围，解决了传统映射方法不能适应不同用户的使用需求，或不能适应网络动态变化的缺点，为在开放网络中实施自适应QoS保障打下了基础。     

Diffusion Based Photon Mapping

Density estimation employed in multi‐pass global illumination algorithms give cause to a trade‐off problem between bias and noise. The problem is seen most evident as blurring of strong illumination features. In particular, this blurring erodes fine structures and sharp lines prominent in caustics. To address this problem, we introduce a photon mapping algorithm based on nonlinear anisotropic diffusion. Our algorithm adapts according to the structure of the photon map such that smoothing occurs along edges and structures and not across. In this way, we preserve important illumination features, while eliminating noise. We demonstrate the applicability of our algorithm through a series of tests. In the tests, we evaluate the visual and computational performance of our algorithm comparing it to existing popular algorithms.     

Mapping classifiers and datasets

Given the posterior probability estimates of 14 classifiers on 38 datasets, we plot two-dimensional maps of classifiers and datasets using principal component analysis (PCA) and Isomap. The similarity between classifiers indicate correlation (or diversity) between them and can be used in deciding whether to include both in an ensemble. Similarly, datasets which are too similar need not both be used in a general comparison experiment. The results show that (i) most of the datasets (approximately two third) we used are similar to each other, (ii) multilayer perceptrons and k-nearest neighbor variants are more similar to each other than support vector machine and decision tree variants, (iii) the number of classes and the sample size has an effect on similarity.