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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.

I happened to write this code sometime back that took Excel data and converted it to specific xml format. It was a simple Linq statement and here is the code

Dave Megginson (who drove the development of the SAX API that will be familiar to many XML developers who use Java) recently wrote Java is dead. Java stood out as a programming language (though not as a platform) in that Sun had refused to standardize it through an independent and reputable standards organization (a lot of the hard work had been done in one attempt to put it through ECMA and one to put it through ISO, both times Sun pulled out and eventually made their highly unsatisfactory JCP Java Community Process system.) Without the ability to alter Java significantly in ways that might go against their druthers, Java suffered two major forks (Microsoft's J++ then its C#, and IBM's SWT) where significant players disagreed with a major component (the graphics library). Java succeeded in middleware, and but failed to take advantage of the rise of browsers on the deskop: their HTML parser was great for the middle 1990s but was deliberately neglected to the point of being unusable: it is hard not to see this as a deliberate attempt by Sun to leave the browser market to its friends and enemies. I really liked Java, and bet my company on it (in a sense): I would not do that today.

The latest episode of the Robots Podcast looks at the following scenario: Imagine being able to throw a hand-full of smart matter in a tank full of liquid and then pulling out a ready-to-use wrench once the matter has assembled. This is the vision of this episode's guests Michael Tolley and Jonas Neubert from the Computational Synthesis Laboratory run by Hod Lipson at Cornell University, NY. Tolley and Neubert give an introduction into Programmable Matter and then present their research on stochastic assembly of matter in fluid, including both simulation (see video above) and real-world implementation. Read on or tune in!

In functional programming, monadic characterizations of computational effects are normally understood denotationally: they describe how an effectful program can be systematically expanded or translated into a larger, pure program, which can then be evaluated according to an effect-free semantics. Any effect-specific operations expressible in the monad are also given purely functional definitions, but these definitions are only directly executable in the context of an already translated program. This approach thus takes an inherently Church-style view of effects: the nominal meaning of every effectful term in the program depends crucially on its type. We present here a complementary, operational view of monadic effects, in which an effect definition directly induces an imperative behavior of the new operations expressible in the monad. This behavior is formalized as additional operational rules for only the new constructs; it does not require any structural changes to the evaluation judgment. Specifically, we give a small-step operational semantics of a prototypical functional language supporting programmer-definable, layered effects, and show how this semantics naturally supports reasoning by familiar syntactic techniques, such as showing soundness of a Curry-style effect-type system by the progress+preservation method.

This paper develops a family of multi-pass image resampling algorithms that use one-dimensional filtering stages to achieve high-quality results at low computational cost. Our key insight is to perform a frequency-domain analysis to ensure that very little aliasing occurs at each stage in the multi-pass transform and to insert additional stages where necessary to ensure this. Using one-dimensional resampling enables the use of small resampling kernels, thus producing highly efficient algorithms. We compare our results with other state of the art software and hardware resampling algorithms.

When Sven Groot was in town a while ago we dropped by Erik Meijer's world and got a look at what he and team have been and still are working on (thus there is no out-of-date property of this fun and insightful interview that is off-the-cuff as it gets: deep Channel 9 ). It's great that we were able to put a real live Niner into fire in one of Erik's team meetings. There is a great deal to learn here. Thank you, Sven, for being a real sport! Great stuff in here. Tune in!

From Nic:   We're piloting a new show idea on Wednesday called Help Desk where we put together a group of Windows trouble shooting gurus and get them to answer your Windows PC issues live on the air...   Any Niners out there have Windows PC issues/questions they need some help with?   Send to ch9live(AT)microsoft(DOT)com or tweet us @ch9live and make sure to watch the show live or check out the broadcast afterwards on demand.

Welcome to the first episode of Cloud Cover!  Join Ryan and Steve as they cover the Windows Azure platform, digging into features, discussing the latest news and announcements, and sharing tips and tricks. Follow and interact with us at @cloudcovershow In this episode: Learn about the Service Management API and how to use PowerShell cmdlets to manage your cloud services. Find out how to get started quickly on the Windows Azure platform. Other topics include: SQL Azure updates! Windows Azure Drives (XDrive).  Hear about some cool new Windows Azure storage management tools. Azure Reader architecture.

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.

Dr. Cormac Herley spends most of his time thinking about why and how computer users reject security advice (originating from both fellow humans and software security warning prompts). Recently, his paper So Long, and No Thanks for the Externalities: the Rational Rejection of Security Advice by Users, received a fair amount of attention from the general media (Boston Globe, Tech Republic, NPR, etc). The paper also prompted our favorite software renegade, Dr. Erik Meijer, to send me an email simply asking "please set up and E2E with Cormac Herley". I did just that and the following conversation is what happened...

The VMKit project is an implementation of a JVM and CLI virtual machine (.Net is an implementation of the CLI). It translates Java bytecode and MSIL in the LLVM IR and uses the LLVM framework for optimizations and compilation. For garbage collection, it uses MMTk. You can get and build the source today.

Ralph Johnson presents a pattern language that he and his colleagues are working on in an attempt to solve the hard issues of parallel programming through a set of design patterns: Structural Patterns, Computational Patterns, Parallel Algorithm Strategy Patterns, Implementation Strategy Patterns, and Concurrent Execution Patterns.

When C# 4.0 was released we C# developers were given a new toy. to play with, and that's Parallel Programming. This is done with the System.Threading.Tasks.Parallel Namespace. This allows for parallel loops & regions (to be discussed at a later date). What is parallel programming you ask, well in todays age most, if not all, household computers have multi-core processors, and parallel programming allows us to take davantage of this new found power, well not actually new as multi-core processors have been around a while now, but software has not kept up with the changes in hardware.

With the GA release of Scala 2.8 getting very close, David Pollak, the creator of the Scala-based web framework: Lift, has announced a Scala community initiative that  will have an equally large impact on Scala developers.  The Fresh Scala Initiative aims to address the issue of version fragility in the ecosystem.  You may have heard that Scala 2.8 is not binary compatible with the 2.7 branch.  Therefore, some community members have banded together to maintain a repository and provide nightly builds of popular Scala library collections to build against Scala 2.8.  

Scala is a general purpose programming language principally targeting the Java Virtual Machine. Designed to express common programming patterns in a concise, elegant, and type-safe way, it fuses both imperative and functional programming styles. Its key features are: statically typed; advanced type-system with type inference; function types; pattern-matching; implicit parameters and conversions; operator overloading; full interop with Java

cala is a general purpose programming language principally targeting the Java Virtual Machine. Designed to express common programming patterns in a concise, elegant, and type-safe way, it fuses both imperative and functional programming styles. Its key features are: statically typed; advanced type-system with type inference; function types; pattern-matching; implicit parameters and conversions; operator overloading; full interop with Java

Introducing Functional Programming Server-Side Functional Programming Developing Client-Side Applications Numerical Computing Working with Data Visualizing Data with .NET Charts