移植、再移植

怎么形容这样一种游戏呢?它最初在一个机种上登场，然后被移植到另外的机种，几年以后这个游戏又被移植了回来。呵呵，大家看懂没?     

话说移植

随着电脑软件和硬件的不断发展,电脑游戏也在日益更新,好游戏更是层出不穷。《仙剑奇侠传》、《三国志》系列等旧日佳作早已深入人心,《DIABLO》、《FIFA 97》、《雷神之锤》、《C＆C》等一批又一批新登场的电脑游戏大作更是脍炙人口。可当我们看到《VR战士》、《真·侍魂》等其它游戏机种(世嘉16位、土星、PLAY STATION等)上更加丰富、刺激的游戏节目时,     

移植日产

作为一家成立仅一年多的合资企业,有着东风集团和日产公司背景的东风有限并不缺少如何做信息化的经验,而是要为如何恰当的运用这些经验动些脑筋     

移植记忆

从FOTO社区论坛作品及《数码摄影》杂志月赛获奖作品中,我们看到了摄影师杨洋由传统生态到创意摄影的转型过程和创意作品日渐成熟的作品风格。为什么喜欢上创意？为什么水平提高得这么快？本期我们把杨洋请进了“创意聊吧”……     

移植奇观

在高度现代化的城市中，如何创建出一方花与树、山与水的自然新天地？     

移植工具软件

你刚刚花了好多时间将你的PC机配置成理想的样子,就买了一台新计算机,你不得不重新再来一遍配置的过程。移植工具软件可以帮助用户将应用程序或者系统设置移至新的机器上,即使系统的版本并不一致,这样用户在新的机器上操作会感觉跟原来的系统一样。有些移植工具软件甚至还可以为用户只移动某些特定的程序。     

移植安全

网络领域正掀起一场安全运动。不过,这场运动的主导者并不像以往那样是安全公司,而是网络设备公司。几个星期前,华为3Com公司联手瑞星、江民和金山等防病毒公司,共同构建起一个"安全网络"。"安全必须走与网络紧密融合的道路,实现从'网络安全'向'安全的网络'演变。'安全渗透网络'就是这个演变过程中的一个必由的阶段。"华为3Com公司副总裁吴敬传说道。而这也是华为3Com公司成立以来最重要的计划之一。     

Linux移植指南

Linux在嵌入式系统方面的声誉正在日趋上升。许多销售商非常擅长于将Linux移植到嵌入式系统。本文不但诠释了Linux移植到某种特定嵌入式系统这个概念,而且还详细介绍了如何在嵌入式系统中具体实施。     

另类的移植

提起魔兽争霸3大家都不会陌生，这款由暴雪推出的即时战斗类游戏用其出色的画面和出众的平衡性征服了全世界的即时战斗玩家。我们这次推荐给大家的移植类作品就是各路高手利用魔兽地图编辑器制作的经典游戏的移植。     

轻松移植应用

Borland 公司推出了一套编程工具,据该公司称这些工具将帮助 Windows 程序员轻松地将程序移植到Linux 上.这套名为 Kylix 的软件不仅可用于字处理等桌面应用,而且还可用于运行数据库的服务器软件和Apache Web 服务器.Borland 公司 CEO 在 Linux World 大会暨贸易展览会的新闻发布会上说,Kylix 使许多为在 Windows 上运行而编写的程序可以通过使用 Borland 的 Delphi 编程软件也能够在 Linux 上运行.而通过新版本 Delphi,使用Kylix 为 Linux 编写的程序也将能在 Windows 机器上运行。Kylix 可与 Red Hat、SuSE 和 MandrakeSoft 的多种Linux 一同工作.在与数据库的关系方面,该软件可与     

A new multineuron spike train metric

The Victor-Purpura spike train metric has recently been extended to a family of multineuron metrics and used to analyze spike trains recorded simultaneously from pairs of proximate neurons. The metric is one of the two metrics commonly used for quantifying the distance between two spike trains; the other is the van Rossum metric. Here, we suggest an extension of the van Rossum metric to a multineuron metric. We believe this gives a metric that is both natural and easy to calculate. Both types of multineuron metric are applied to simulated data and are compared.     

A bounded distance metric for comparing tree structure

Comparing tree-structured data for structural similarity is a recurring theme and one on which much effort has been spent. Most approaches so far are grounded, implicitly or explicitly, in algorithmic information theory, being approximations to an information distance derived from Kolmogorov complexity. In this paper we propose a novel complexity metric, also grounded in information theory, but calculated via Shannon's entropy equations. This is used to formulate a directly and efficiently computable metric for the structural difference between unordered trees. The paper explains the derivation of the metric in terms of information theory, and proves the essential property that it is a distance metric. The property of boundedness means that the metric can be used in contexts such as clustering, where second-order comparisons are required. The distance metric property means that the metric can be used in the context of similarity search and metric spaces in general, allowing trees to be indexed and stored within this domain. We are not aware of any other tree similarity metric with these properties.     

Optimal robustness in the gap metric

The application of the gap metric to robust stabilization of feedback systems is considered. In particular, a solution to the problem of robustness optimization in the gap metric is presented. The problem of robust stabilization under simultaneous plant-controller perturbations is addressed, and the least amount of combined plant-controller uncertainty, measured by the gap metric, that can cause instability of a nominally stable feedback system is determined. Included are a detailed summary of the main properties of the gap metric and the introduction of a dual metric called the T-gap metric. A key contribution of this study is to show that the problem of robustness optimization in the gap metric is equivalent to robustness optimization for normalized coprime factor perturbations. This settles the question as to whether maximizing allowable coprime factor uncertainty corresponds to tolerating the largest ball of uncertainty in a well-defined metric     

Two-dimensional Delaunay-based anisotropic mesh adaptation

Science and engineering applications often have anisotropic physics and therefore require anisotropic mesh adaptation. In common with previous researchers on this topic, we use metrics to specify the desired mesh. Where previous approaches are typically heuristic and sometimes require expensive optimization steps, our approach is an extension of isotropic Delaunay meshing methods and requires only occasional, relatively inexpensive optimization operations. We use a discrete metric formulation, with the metric defined at vertices. To map a local sub-mesh to the metric space, we compute metric lengths for edges, and use those lengths to construct a triangulation in the metric space. Based on the metric edge lengths, we define a quality measure in the metric space similar to the well-known shortest-edge to circumradius ratio for isotropic meshes. We extend the common mesh swapping, Delaunay insertion, and vertex removal primitives for use in the metric space. We give examples demonstrating our scheme’s ability to produce a mesh consistent with a discontinuous, anisotropic mesh metric and the use of our scheme in solution adaptive refinement.     

行人重识别中度量学习方法研究进展

行人重识别是计算机视觉领域极具挑战的研究课题.近年来,伴随大规模行人数据集推出和深度学习发展,针对行人特征提取与描述、距离度量学习两大关键技术的研究取得众多成果.已有综述文献主要对特征提取与描述方法开展了归纳总结,尚缺乏对度量学习方法的全面分析.同时,鉴于度量学习在提升重识别性能中的关键作用,有必要对行人重识别中度量学习研究现状进行系统梳理.基于此,从距离度量方式、度量学习算法和重排序3方面系统总结了行人重识别度量学习方法,比较了部分典型方法的实验效果,并对未来可能的研究方向作了展望.     

Frequency domain uncertainty and the graph topology

A new metric on linear, time-invariant systems is defined. This metric is no greater than the gap metric, and is in fact the smallest metric for which a certain robust stabilization result holds. Unlike other known metrics which induce the graph topology, it has a clear frequency response interpretation. This allows questions regarding robustness in the face of parametric uncertainty to be considered in terms of this metric     

Metric learning for text documents

Many algorithms in machine learning rely on being given a good distance metric over the input space. Rather than using a default metric such as the Euclidean metric, it is desirable to obtain a metric based on the provided data. We consider the problem of learning a Riemannian metric associated with a given differentiable manifold and a set of points. Our approach to the problem involves choosing a metric from a parametric family that is based on maximizing the inverse volume of a given data set of points. From a statistical perspective, it is related to maximum likelihood under a model that assigns probabilities inversely proportional to the Riemannian volume element. We discuss in detail learning a metric on the multinomial simplex where the metric candidates are pull-back metrics of the Fisher information under a Lie group of transformations. When applied to text document classification the resulting geodesic distance resemble, but outperform, the tfidf cosine similarity measure.     

高维数据的增量式聚类算法的距离度量选择研究

合适的距离度量函数对于聚类结果有重要的影响。针对大规模高维数据集,使用增量式聚类算法进行距离度量的选择分析。SpFCM算法是将大规模数据集分成小样本进行增量分批聚类,可在有限的计算机内存中获得较好的聚类结果。在传统的SpFCM算法的基础上,使用不同的距离度量函数来衡量样本之间的相似性,以得出不同的距离度量对SpFCM算法的影响。在不同的大规模高维数据集中,使用欧氏距离、余弦距离、相关系数距离和扩展的杰卡德距离来计算距离。实验结果表明,后3个距离度量相对于欧氏距离可以很大程度地提高聚类效果,其中相关系数距离可以得到较好的结果,余弦距离和扩展的杰卡德距离效果比较一般。     

软件质量度量研究分析

随着计算机技术的飞速发展,对软件质量的要求也更高了,软件质量度量就是衡量软件品质的一种手段。本文分析了软件质量度量模型,建立了软件质量度量框架,并给出了常用度量方法。     

On the similarity metric and the distance metric

Similarity and dissimilarity measures are widely used in many research areas and applications. When a dissimilarity measure is used, it is normally required to be a distance metric. However, when a similarity measure is used, there is no formal requirement. In this article, we have three contributions. First, we give a formal definition of similarity metric. Second, we show the relationship between similarity metric and distance metric. Third, we present general solutions to normalize a given similarity metric or distance metric.