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If you need a quick reference guide for the UML notation, check one of the following, IMHO, gret UML cheat sheets (in no particular order):

Erlang allows for massively scalable concurrency, often with millions of lightweight, thread-like components known as actors. Unfortunately, using Erlang requires rewriting all of your legacy code into a rather esoteric language. But there are other options, such as the little known CCR platform that was developed by .NET's robotics department. Actor based languages such as Erlang are able to achieve high degrees of parallelism by using the Actor model. Under this model the fundamental unit of concurrency is not a thread or fiber, but rather something much smaller. Known as a "process" in Erlang, each unit of concurrency has a base overhead of about 1200 bytes on a 32-bit system. By comparison, a thread on the Windows operating system defaults to 1 MB just for the stack, additional space is also needed for bookkeeping and thread local storage. Because they are so lightweight, an application can spawn literally millions of processes simultaneously.

Complex computer models can involve thousands of variables. But paradoxically, adding more variables can sometimes make them easier to work with.

In Stack Machines, Expression Evaluation, and the Magic of Reverse Polish, I showed how expressions can be evaluated by rewriting them in reverse Polish and translating this into machine-code instructions for a stack machine. I demonstrated with a stack-machine interpreter that I'd written as part of a working model of a Pascal compiler. But as well as expressions, the compiler needs to compile assignments and jumps, so — in my progress towards explaining the compiler — I'm going to extend the machine code so it can handle these. I'll demonstrate by interpreting a program that calculates five factorial.

What made me fall of my chair is the methodology/architecture part of this statement. It’s hard enough (but doable) to use RDF to map philosophically similar APIs. It’s a non-starter to use it to bridge architectural and methodological differences. I have spent a fair amount of time looking at Semantic Web technologies in the context of modeling IT systems (see the “semantic tech” category of this blog). While I think they would be a great foundation I don’t see them ever coming anywhere near what Reuven describes.

Microsoft Technical Fellow and C# creator Anders Hejlsberg explains the new C# and VB.NET asynchronous programming model, available as Async CTP now, which makes async programming much easier for .NET developers.

ScalaModules is an open source project aimed at providing fluent support for OSGi to Scala developers. It takes advantage of Scala's infix operator notation, higher order functions, and implicit conversions. ScalaModules transparently uses the Scala compiler to wrap an OSGi BundleContext with its own RichBundleContext model. This general technique is not unusual for creating DSLs in mainstream languages. Sean McDirmid uses similar tricks for his C# Bling library for WPF, except that Bling must overcome the lack of C# offering comparable extensions to Scala.

Verification of algorithm-intensive systems is a long, costly process. Studies show that the majority of flaws in embedded systems are introduced at the specification stage, but are not detected until late in the development process. These flaws are the dominant cause of project delays and a major contributor to engineering costs. For algorithm-intensive systems —including systems with communications, audio, video, imaging, and navigation functions— these delays and costs are exploding as system complexity increases. It doesn't have to be this way. Many designers of algorithm-intensive systems already have the tools they need to get verification under control. Engineers can use these same tools to build system models that help them find and correct problems earlier in the development process. This can not only reduce verification time, but also improves the performance of their designs. In this article, we'll explain three practical approaches to early verification that make this possible. First, let's examine why the current algorithm verification process is inefficient and error-prone. In a typical workflow, designs start with algorithm developers, who pass the design to hardware and software teams using specification documents.

Andrew Phillips holds the title of Scientist with Microsoft Research Cambridge, and he's working on a method of programming that compiles into DNA. Part of this involves a visual programming language called Stochastic Pi Machine, or SPiM. This system models biological processes to help give researchers feedback on how organisms will react to modifications. The hope is that this can be used to help scientists program for large biological systems using modular components compiled to DNA. Yes, I’m in way over my head here, but I do my best to ask Andrew about the role this will play in medical treatment going forward, what it means to DNA computing, and the ability of back-engineering the genetic code we don’t use now

Andrew Phillips holds the title of Scientist with Microsoft Research Cambridge, and he's working on a method of programming that compiles into DNA. Part of this involves a visual programming language called Stochastic Pi Machine, or SPiM. This system models biological processes to help give researchers feedback on how organisms will react to modifications. The hope is that this can be used to help scientists program for large biological systems using modular components compiled to DNA. Yes, I’m in way over my head here, but I do my best to ask Andrew about the role this will play in medical treatment going forward, what it means to DNA computing, and the ability of back-engineering the genetic code we don’t use now.

You would be forgiven if you thought the "F" in F# -- which made its debut as part of Visual Studio 2010 -- stands for "functional." After all, F# (pronounced "F sharp") is a functional programming language for the .NET Framework that combines the succinct, expressive, and compositional style of functional programming with the runtime, libraries, interoperability, and object model of .NET. But Don Syme, inventor of F# and leader of the team that incubated the language, has a different, truncated, and entirely whimsical definition. "In the F# team," says Syme, a principal researcher at Microsoft Research Cambridge, "We say, 'F is for Fun.' F# enables users to write simple code to solve complex problems. Programming with F# really does make many programming tasks simpler, and our users have consistently reported that they've found using the language enjoyable." Indeed, F#, which has been developed in a partnership between Microsoft Research and the Microsoft Developer Division, is already popular with the .NET developer community. The language is widely known in the academic community and among thought leaders, and the list of admirers will only increase as Visual F#, the result of a partnership between Microsoft Research Cambridge and Microsoft's Developer Division, becomes a first-class language in Visual Studio 2010.