Colt provides a set of Open Source Libraries for High Performance Scientific and Technical Computing in Java.

Scientific and technical computing, as, for example, carried out at CERN, is characterized by demanding problem sizes and a need for high performance at reasonably small memory footprint. There is a perception by many that the Java language is unsuited for such work. However, recent trends in its evolution suggest that it may soon be a major player in performance sensitive scientific and technical computing. For example, IBM Watson's Ninja project showed that Java can indeed perform BLAS matrix computations up to 90% as fast as optimized Fortran. The Java Grande Forum Numerics Working Group provides a focal point for information on numerical computing in Java. With the performance gap steadily closing, Java has recently found increased adoption in the field. The reasons include ease of use, cross-platform nature, built-in support for multi-threading, network friendly APIs and a healthy pool of available developers. Still, these efforts are to a significant degree hindered by the lack of foundation toolkits broadly available and conveniently accessible in C and Fortran.

http://dsd.lbl.gov/~hoschek/colt/index.html

JAMA : A Java Matrix Package

Background

JAMA is a basic linear algebra package for Java. It provides user-level classes for constructing and manipulating real, dense matrices.  It is meant to provide sufficient functionality for routine problems, packaged in a way that is natural and understandable to non-experts.  It is intended to serve as the standard matrix class for Java, and will be proposed as such to the Java Grande Forum and then to Sun.  A straightforward public-domain reference implementation has been developed by the MathWorks and NIST as a strawman for such a class.  We are releasing this version in order to obtain public comment. There is no guarantee that future versions of JAMA will be compatible with this one.

A sibling matrix package, Jampack, has also been developed at NIST and the University of Maryland. The two packages arose from the need to evaluate alternate designs for the implementation of matrices in Java. JAMA is based on a single matrix class within a strictly object-oriented framework. Jampack uses a more open approach that lends itself to extension by the user. As it turns out, for the casual user the packages differ principally in the syntax of the matrix operations. We hope you will take the time to look at Jampack along with JAMA. There is much to be learned from both packages.

Capabilities. JAMA is comprised of six Java classes: Matrix, CholeskyDecomposition, LUDecomposition, QRDecomposition, SingularValueDecomposition and EigenvalueDecomposition.

The Matrix class provides the fundamental operations of numerical linear algebra.  Various constructors create Matrices from two dimensional arrays of double precision floating point numbers.  Various gets and sets provide access to submatrices and matrix elements.   The basic arithmetic operations include matrix addition and multiplication, matrix norms and selected element-by-element array operations.  A convenient matrix print method is also included.

Five fundamental matrix decompositions, which consist of pairs or triples of matrices, permutation vectors, and the like, produce results in five decomposition classes.  These decompositions are accessed by the Matrix class to compute solutions of simultaneous linear equations, determinants, inverses and other matrix functions.  The five decompositions are

• Cholesky Decomposition of symmetric, positive definite matrices
• LU Decomposition (Gaussian elimination) of rectangular matrices
• QR Decomposition of rectangular matrices
• Eigenvalue Decomposition of both symmetric and nonsymmetric square matrices
• Singular Value Decomposition of rectangular matrices

The current JAMA deals only with real matrices. We expect that future versions will also address complex matrices. This has been deferred since crucial design decisions cannot be made until certain issues regarding the implementation of complex in the Java language are resolved.

The design of JAMA represents a compromise between the need for pure and elegant object-oriented design and the need to enable high performance implementations.  

Summary of JAMA Capabilities

Object Manipulation	constructors  set elements  get elements  copy  clone
Elementary Operations	addition  subtraction  multiplication  scalar multiplication  element-wise multiplication  element-wise division  unary minus  transpose  norm
Decompositions	Cholesky  LU  QR  SVD  symmetric eigenvalue  nonsymmetric eigenvalue
Equation Solution	nonsingular systems  least squares
Derived Quantities	condition number  determinant  rank  inverse  pseudoinverse

 

Example of Use.  The following simple example solves a 3x3 linear system Ax=b and computes the
norm of the residual.

             double[][] array = {{1.,2.,3},{4.,5.,6.},{7.,8.,10.}};
      Matrix A = new Matrix(array);
      Matrix b = Matrix.random(3,1);
      Matrix x = A.solve(b);
      Matrix Residual = A.times(x).minus(b);
      double rnorm = Residual.normInf();

Reference Implementation.  The implementation of JAMA downloadable from this site is meant to be a reference implementation only.   As such, it is pedagogical in nature.  The algorithms employed are similar to those of the classic Wilkinson and Reinsch Handbook, i.e. the same algorithms used in EISPACK, LINPACK and MATLAB.  Matrices are stored internally as native Java arrays (i.e., double[][]).  The coding style is straightforward and readable.  While the reference implementation itself should provide reasonable execution speed for small to moderate size applications, we fully expect software vendors and Java VMs to provide versions which are optimized for particular environments.

Not Covered.  JAMA is by no means a complete linear algebra environment.  For example, there are no provisions for matrices with particular structure (e.g., banded, sparse) or for more specialized decompositions (e.g. Shur, generalized eigenvalue).  Complex matrices are not included.  It is not our intention to ignore these important problems.  We expect that some of these (e.g. complex) will be addressed in future versions.  It is our intent that the design of JAMA not preclude extension to some of these additional areas.

Finally, JAMA is not a general-purpose array class.  Instead, it focuses on the principle mathematical functionality required to do numerical linear algebra.  As a result, there are no methods for array operations such as reshaping or applying elementary functions (e.g. sine, exp, log) elementwise.  Such operations, while quite useful in many applications, are best collected into a separate array class.

---

The Package

Version 1.0.2

• Documentation
• Example
• Source [ zip archive, 105Kb ] [ gzipped tar file, 77Kb ]
• Jar file [ Jama-1.0.2.jar ]
• ChangeLog

Previous version

• Jama-1.0.1 Source: Jama-1.0.1.tar.gz, Jama-1.0.1.zip
• Jama-1.0.0 Source: Jama-1.0.0.tar.gz, Jama-1.0.0.zip

---

Request for Comments

We plan to propose JAMA as the primary linear algebra package for Java.  Such standardization will insure wide availability, improving the portability and performance of Java applications with numeric components. Because of this we are interested in hearing any and all comments of potential users.  While we are cognisant that JAMA will not be suitable for all users, we hope it to be useful to the majority of routine applications.

Discussion Group. A discussion group has been established for such comments. Comments and suggestions sent to jama@nist.gov will automatically be sent to the JAMA authors, as well as to all subscribers.  To subscribe, send email to listproc@nist.gov containing the text subscribe jama your-name in the message body.  A public archive of the discussion can be browsed.

[Note: NIST will not use the email addresses provided for any purpose other than the maintenance of this discussion list. Participants may remove themselves at any time by sending an email message to listproc@nist.gov containing the text unsubscribe jama in the message body. See the NIST Privacy Policy.]

---

Authors

JAMA's initial design, as well as this reference implementation, was developed by

Joe Hicklin Cleve Moler Peter Webb ... from The MathWorks

Ronald F. Boisvert Bruce Miller Roldan Pozo Karin Remington ... from NIST

Copyright Notice This software is a cooperative product of The MathWorks and the National Institute of Standards and Technology (NIST) which has been released to the public domain. Neither The MathWorks nor NIST assumes any responsibility whatsoever for its use by other parties, and makes no guarantees, expressed or implied, about its quality, reliability, or any other characteristic.

---

Related Links

• Java for Computational Science and Engineering
• Java Numerics Working Group
• NIST Mathematical and Computational Sciences Division

---

Identification of commercial products on this page is for information only, and  does not imply recommendation or endorsement by the National Institute of Standards and Technology.

Last change in this page : July 13, 2005. Comments welcome.

Data mining uncovers patterns in data using predictive techniques. These patterns play a critical role in decision making because they reveal areas for process improvement. Using data mining, organizations can increase the profitability of their interactions with customers, detect fraud, and improve risk management. The patterns uncovered using data mining help organizations make better and timelier decisions.

Most analysts separate data mining software into two groups: data mining tools and data mining applications. Data mining tools provide a number of techniques that can be applied to any business problem. Data mining applications, on the other hand, embed techniques inside an application customized to address a specific business problem. Regardless of whether we are aware of it, our daily lives are influenced by data mining applications. For example, almost every financial transaction is processed by a data mining application to detect fraud. Both data mining tools and data mining applications are valuable, however. Increasingly, organizations are using data mining tools and data mining applications together in an integrated environment for predictive analytics.

So what do data mining tools add? Data mining tools are used to ensure flexibility and the greatest accuracy possible. Essentially, data mining tools increase the effectiveness of data mining applications. Since no two organizations or data sets are alike, no single technique delivers the best results for everyone. Not only do data mining tools deliver in-depth techniques, but data mining tools also deliver flexibility to use combinations of techniques to improve predictive accuracy.

Because data mining tools are so flexible, a set of data mining guidelines and a data mining methodology have been developed to help guide the process. The Cross-Industry Standard Process for Data Mining (CRISP-DM) ensures your organization's results with data mining tools are timely and reliable. This methodology was created in conjunction with practitioners and vendors to supply data mining practitioners with checklists, guidelines, tasks, and objectives for every stage of the data mining process.

以前习惯了用Java开发，现在用Flex写的东西感觉自动化测试的资源有些匮乏（可能是刚接触Flex的缘故吧，还要不断学习），看到有个名为QTP的软件可以结合Adobe提供的扩展来做自动化测试&功能测试&回归测试，甚是兴奋，下载一个来试用一下，如果好用的话就不用自己写框架和工具了，以后就设法用它了。呵呵，等待软件下载的间隙，转一段关于QTP软件的介绍。

Mercury QuickTest Professional™是一款先进的自动化测试解决方案，用于创建功能和回归测试。它自动捕获、验证和重放用户的交互行为。

Mercury QuickTest Professional为每一个重要软件应用和环境提供功能和回归测试自动化的行业最佳解决方案。

QuickTest Professional是新一代自动化测试解决方案，采用了关键词驱动（Keyword-Driven）测试的理念，能完全简化测试的创建和维护工作。QuickTest关键词驱动方式独有之处在于，测试自动化专家可以通过一个整合的脚本和纠错环境，拥有对基础测试脚本和对象属性的完全访问权限，这些脚本和纠错环境与关键词视图（Keyword View）可以互为同步。

QuickTest Professional同时满足了技术型和非技术型用户的需求，让各个公司有能力部署更高质量的应用，同时部署的速度更快，费用更低，风险也更小。QuickTest Professional和我们新的测试自动化系统Mercury Business Process Testing™的紧密结合，可以将非技术型的业务专家（SME, Subject-Matter Experts）引入质量流程，这一意义重大的引入可以将IT和业务更好地融合，最终建立起更出色的应用。

有了该产品，您的QA机构可以获取多方面的优势：
        用最少的培训赋予整个小组创建成熟测试方案的能力。
        确保跨所有环境、数据包和业务流程的正确功能点。
        为开发人员全面记录和复制缺陷，使他们能更快地修复缺陷，满足最后上线期限。
        对不断变化的应用和环境展开便捷的回归测试。
        成为帮助整个机构实现高质量产品和服务、提高总收入和收益率的关键角色。

QuickTest Professional是如何工作的
QuickTest Professional易于操作，即使是初级的测试人员也能在短时间内对其驾轻就熟。您可以使用无需脚本的关键词视图来表现测试的每个步骤，仅由此就可创建一个测试。您还可以通过QuickTest Professional所集成的录制能力来捕获测试步骤。该产品用简单的英语以文档形式记录每个步骤，并通过活动屏幕将文档与一个集成截屏相结合。传统的脚本记录工具所生产的脚本不易修改，与此不同的是，QuickTest Professional的关键词驱动方式能让您便捷地插入、修改、数据驱动（data-drive）和移除测试步骤。

QuickTest Professional可以自动引入检查点来验证应用的属性和功能点，比如确认输出量或检查链接的有效性。在关键词视图的每一步骤中，活动屏幕可显示被测应用在该步骤中的确切状态。您还可以为任意对象加入几种检查点，仅仅在活动屏幕中点击该对象，就可以验证该组件行为是否达到了期望值。

然后您可以将测试数据输入数据表（Data Table），它拥有和Excel同样完善的功能特性，是一个集成的电子数据表格。您可以使用数据集并创建多种重复测试，无需编程就可以扩展测试案例的覆盖面。数据可以通过键入的方式输入或从数据库、数据表格或文本文档中导出。

高级测试人员可以在专家视图（Expert View）中查看和修改他们的测试，在专家视图中显示了由QuickTest Professional自动生成的基于行业标准的基本VBScript语言。在专家视图中所做的任何改动将自动与关键词视图同步。

一旦测试人员运行了一个脚本，TestFusion报告将显示测试运行各方面的信息，包括：高水平的结果纵览；一个可扩展的测试脚本树状视图（Tree View），其明确指出了应用错误的发生位置；被使用的测试数据；每个步骤的应用截屏，其中并标明了所有的差异；以及通过或未通过每个检查点的详细解释。您可以将TestFusion报告和QuickTest Professional结合，从而与整个QA和开发小组分享这些报告。

QuickTest Professional处理一些应用的新版本问题。当一个被测应用发生变化时，比如把一个”Login”按钮被改名为”Sign in”，您可以在共享对象容器（Shared Object Repository）中做一次更新，接着此次更新将扩展到所有涉及这个对象的脚本。您可以将测试脚本公布给Mercury Quality Management，使其它的QA小组成员也可以使用您的测试脚本，从而减少了重复工作。

通过与Business Process Testing的整合，在一个基于Web的系统中，QuickTest Professional被用于实现自动化操作，使非技术型用户可以便捷地在一个完全的无脚本环境中也能够建立起测试。

QuickTest Professional支持多种企业环境的功能测试，包括Windows、Web、.NET、 Java/J2EE、SAP、Siebel、Oracle、PeopleSoft、Visual Basic、ActiveX、Mainframe terminal emulators和Web services。

Mercury功能测试
那些在Mercury WinRunner®测试工具上投入大量资金，并想转入Mercury QuickTest Professional™的用户，可以使用Mercury Functional Testing™来实现这种转变。Mercury Functional Testing将QuickTest Professional和WinRunner结合成一种集成产品，它不仅可以使用WinRunner脚本，也可以使用QuickTest Professional脚本，使测试资源得到极大地利用。质量工程师可以使用Mercury Functional Testing来创建“复合脚本”测试，这些脚本是在WinRunner和QuickTest Professional中建立的。Mercury Functional Testing是WinRunner和QuickTest Professional的集成，产品间可以相互调用脚本，测试结果可以在一个共有的报告界面上呈现。

Mercury质量中心的组成部分之一
Mercury QuickTest Professional是Mercury质量中心（Mercury Quality Center™）的组成部分之一，Mercury质量中心集成了一整套软件、服务和最佳实践，用于自动化关键质量活动，包括需求管理、测试管理、缺陷管理、功能测试和业务流程测试。

特点和优势
        具有行业 Mercury QuickTest Professional™是一款先进的自动化测试解决方案，用于创建功能和回归测试。它自动捕获、验证和重放用户的交互行为。

Mercury QuickTest Professional为每一个重要软件应用和环境提供功能和回归测试自动化的行业最佳解决方案。

QuickTest Professional是新一代自动化测试解决方案，采用了关键词驱动（Keyword-Driven）测试的理念，能完全简化测试的创建和维护工作。QuickTest关键词驱动方式独有之处在于，测试自动化专家可以通过一个整合的脚本和纠错环境，拥有对基础测试脚本和对象属性的完全访问权限，这些脚本和纠错环境与关键词视图（Keyword View）可以互为同步。

QuickTest Professional同时满足了技术型和非技术型用户的需求，让各个公司有能力部署更高质量的应用，同时部署的速度更快，费用更低，风险也更小。QuickTest Professional和我们新的测试自动化系统Mercury Business Process Testing™的紧密结合，可以将非技术型的业务专家（SME, Subject-Matter Experts）引入质量流程，这一意义重大的引入可以将IT和业务更好地融合，最终建立起更出色的应用。

有了该产品，您的QA机构可以获取多方面的优势：
        用最少的培训赋予整个小组创建成熟测试方案的能力。
        确保跨所有环境、数据包和业务流程的正确功能点。
        为开发人员全面记录和复制缺陷，使他们能更快地修复缺陷，满足最后上线期限。
        对不断变化的应用和环境展开便捷的回归测试。
        成为帮助整个机构实现高质量产品和服务、提高总收入和收益率的关键角色。

QuickTest Professional是如何工作的
QuickTest Professional易于操作，即使是初级的测试人员也能在短时间内对其驾轻就熟。您可以使用无需脚本的关键词视图来表现测试的每个步骤，仅由此就可创建一个测试。您还可以通过QuickTest Professional所集成的录制能力来捕获测试步骤。该产品用简单的英语以文档形式记录每个步骤，并通过活动屏幕将文档与一个集成截屏相结合。传统的脚本记录工具所生产的脚本不易修改，与此不同的是，QuickTest Professional的关键词驱动方式能让您便捷地插入、修改、数据驱动（data-drive）和移除测试步骤。

QuickTest Professional可以自动引入检查点来验证应用的属性和功能点，比如确认输出量或检查链接的有效性。在关键词视图的每一步骤中，活动屏幕可显示被测应用在该步骤中的确切状态。您还可以为任意对象加入几种检查点，仅仅在活动屏幕中点击该对象，就可以验证该组件行为是否达到了期望值。

然后您可以将测试数据输入数据表（Data Table），它拥有和Excel同样完善的功能特性，是一个集成的电子数据表格。您可以使用数据集并创建多种重复测试，无需编程就可以扩展测试案例的覆盖面。数据可以通过键入的方式输入或从数据库、数据表格或文本文档中导出。

高级测试人员可以在专家视图（Expert View）中查看和修改他们的测试，在专家视图中显示了由QuickTest Professional自动生成的基于行业标准的基本VBScript语言。在专家视图中所做的任何改动将自动与关键词视图同步。

一旦测试人员运行了一个脚本，TestFusion报告将显示测试运行各方面的信息，包括：高水平的结果纵览；一个可扩展的测试脚本树状视图（Tree View），其明确指出了应用错误的发生位置；被使用的测试数据；每个步骤的应用截屏，其中并标明了所有的差异；以及通过或未通过每个检查点的详细解释。您可以将TestFusion报告和QuickTest Professional结合，从而与整个QA和开发小组分享这些报告。

QuickTest Professional处理一些应用的新版本问题。当一个被测应用发生变化时，比如把一个”Login”按钮被改名为”Sign in”，您可以在共享对象容器（Shared Object Repository）中做一次更新，接着此次更新将扩展到所有涉及这个对象的脚本。您可以将测试脚本公布给Mercury Quality Management，使其它的QA小组成员也可以使用您的测试脚本，从而减少了重复工作。

通过与Business Process Testing的整合，在一个基于Web的系统中，QuickTest Professional被用于实现自动化操作，使非技术型用户可以便捷地在一个完全的无脚本环境中也能够建立起测试。

QuickTest Professional支持多种企业环境的功能测试，包括Windows、Web、.NET、 Java/J2EE、SAP、Siebel、Oracle、PeopleSoft、Visual Basic、ActiveX、Mainframe terminal emulators和Web services。

Mercury功能测试
那些在Mercury WinRunner®测试工具上投入大量资金，并想转入Mercury QuickTest Professional™的用户，可以使用Mercury Functional Testing™来实现这种转变。Mercury Functional Testing将QuickTest Professional和WinRunner结合成一种集成产品，它不仅可以使用WinRunner脚本，也可以使用QuickTest Professional脚本，使测试资源得到极大地利用。质量工程师可以使用Mercury Functional Testing来创建“复合脚本”测试，这些脚本是在WinRunner和QuickTest Professional中建立的。Mercury Functional Testing是WinRunner和QuickTest Professional的集成，产品间可以相互调用脚本，测试结果可以在一个共有的报告界面上呈现。

Mercury质量中心的组成部分之一
Mercury QuickTest Professional是Mercury质量中心（Mercury Quality Center™）的组成部分之一，Mercury质量中心集成了一整套软件、服务和最佳实践，用于自动化关键质量活动，包括需求管理、测试管理、缺陷管理、功能测试和业务流程测试。

特点和优势
        具有行业领先的便于使用的特性，以及支持提前配置环境的功能，确保了快速的投资回报。
        可独立运行，也可以同Mercury Business Process Testing和Mercury质量中心集成。
        引进了QuickTest Professional 8.0中新一代的“零配置”关键词驱动测试技术，从而实现了快速建立测试、测试脚本更易维护，和更强大的数据驱动能力。
        使用独特智能对象识别（Unique Smart Object Recognition）来发现对象，即使对象创建不断在改变，但仍可保证无监控方式脚本执行的可靠性。
        恢复管理器（Recovery Manager）可处理不可预知的应用意外事件，实现24x7的不间断测试，赶上测试项目的最后期限。
        自动文档技术把测试文档的建立与测试脚本的建立同步。
        通过集成的数据表，可数据驱动任意对象、方式、检查点和输出值。
        为QA工程师提供全面的集成开发环境。
        通过使用QuickTest Professional和WinRunner集成的TSL资源，使您在Mercury WinRunner测试脚本上的投资得以保值。
        TestFusion报告可快速隔离和诊断缺陷。
        通过完善检查点，实现应用的全面验证。
领先的便于使用的特性，以及支持提前配置环境的功能，确保了快速的投资回报。
        可独立运行，也可以同Mercury Business Process Testing和Mercury质量中心集成。
        引进了QuickTest Professional 8.0中新一代的“零配置”关键词驱动测试技术，从而实现了快速建立测试、测试脚本更易维护，和更强大的数据驱动能力。
        使用独特智能对象识别（Unique Smart Object Recognition）来发现对象，即使对象创建不断在改变，但仍可保证无监控方式脚本执行的可靠性。
        恢复管理器（Recovery Manager）可处理不可预知的应用意外事件，实现24x7的不间断测试，赶上测试项目的最后期限。
        自动文档技术把测试文档的建立与测试脚本的建立同步。
        通过集成的数据表，可数据驱动任意对象、方式、检查点和输出值。
        为QA工程师提供全面的集成开发环境。
        通过使用QuickTest Professional和WinRunner集成的TSL资源，使您在Mercury WinRunner测试脚本上的投资得以保值。
        TestFusion报告可快速隔离和诊断缺陷。
        通过完善检查点，实现应用的全面验证。
QuickTest Professional 9.2 下载

 

BusinessObjects 博奥杰

SAS 赛仕

Cognos 优信佳

MicroStrategy 金捷冠

Hyperion 海波龙

名字都很有个性。

 

Data Binding Tip

Here's a simple way to enable/disable controls based on selection. For example, suppose you have a DataGrid and a number of buttons which operate on the data. Some buttons can only be used when a row is selected. Other buttons can only be used when there are no selections. Still others can only be used on certain selections.

The Problem

To handle this you may be tempted to code a number of if-statements or to use data binding to set up a variable and then set the variable at various times in your code. Something like this:

[Bindable] private var somethingSelected:Boolean = false;

<mx:Button label="Publish" click="publishItem()" enabled="{somethingSelected}" />

This is a good attempt. All the controls which depend on the selection state of the DataGrid are bound to this one variable. Changing the variable changes the controls' enabled state. However, you still have to decide when to change the variable. For example:

private function publishItem() : void {
     // get the selected item
     // publish it
     grid.selectedItem = -1; // clear the selection
     somethingSelected = false;
}

To complicate things, suppose another button should be enabled if the selection contains a specific value. You not only have to worry about the somethingSelected variable, you also have to worry about this other test. In other words, in the publishItem() function you have to include setting the other variable. This gets more complex the more conditions you have.

An Easier Way

There is an easier way. Right now the Publish button has a binding with the somethingSelected variable. What is needed is a binding between the somethingSelected variable and the selection state of the DataGrid. You can set that up with the <mx:Binding> tag:

<mx:Binding source="grid.selectedIndex >= 0" destination="somethingSelected" />

Now the value of somethingSelected is tied to the selection state of the DataGrid. Select a row and somethingSelected turns true and all of the buttons with enabled="{somethingSelected}" are enabled and all the buttons with enabled="{!somethingSelected}" are disabled.

The source of the Binding does not have to be a variable. It can be an expression as shown here. The source is simply the condition of the grid's selectedIndex being greater than or equal to zero.
Here is a slightly more complex example:

<mx:Binding source="grid.selectedItem.code == 1" destination="codeOnePicked" />

Now a record in the DataGrid while a field whose value is 1 will turn the variable codeOnePicked true.

<mx:CheckBox label="Code One?" selected="{codeOnePicked}" />
<mx:Button label="Publish" enabled="{somethingSelected && !codeOnePicked}" />

Here, the CheckBox is selected whenever any record from the DataGrid is selected that as a code field of 1; the Publish button is disabled when that happens.

Conclusion

If you have controls that depend on selections or other conditions in the UI, see if the <mx:Binding> tag can clean up your code and make it easier to read and extend.

(Reference from http://weblogs.macromedia.com/pent/archives/2007/02/data_binding_ti.cfm)

Some special days of mine.

Nov.2, 2004

Nov. 24, 2004

Dec. 18

just a log. Do not forget them.

■Flex Themes

ScaleNine 这个站点有不少站长本人创作的Flex皮肤，看起来都很不错，而且站长还提供了一些有关皮肤制作的资源，包括文章和图库等。这个站点很值得一看。

1)主题和皮肤：http://www.scalenine.com/
2)创作资源：http://www.scalenine.com/resources.html

Finished a lot of designing and coding work, I should write my idea in the paper.

one paper in a month.