Basketball Prodigy Brings Hoop Dreams to India | Playbook With the NBA looking to make inroads in untapped markets around the world, Satnam Singh could hold the key to bringing full-blown basketball fever to the 1.3 billion people of the Indian subcontinent. Singh possesses decent agility for a hulking 7-footer, and his 250-pound frame (though on the light side) gives him a decent foundation for which to dominate down low in the post. However, Singh has plenty of time to fill out his frame with extra muscle. That’s because Satnam Singh is 14 years old. Singh was India’s best-kept hoops secret until he recently spent six weeks training at the IMG Basketball Academy in Bradenton, Florida, working with former college coaches to hone a raw skill set that already has some people calling him India’s answer to Yao Ming. But if Singh keeps working on his development and practices against legitimate, near-NBA competition, it’s hard to overstate how good this kid can be. Image: Basketball Federation of India
Has Canvas F Sharp Programming/Values and Functions - Wikibooks, collection of open-content textbooks Compared to other .NET languages such as C# and VB.Net, F# has a somewhat terse and minimalistic syntax. To follow along in this tutorial, open F# Interactive (fsi) or Visual Studio and run the examples. Declaring Variables[edit] The most ubiquitous, familiar keyword in F# is the let keyword, which allows programmers to declare functions and variables in their applications. For example: This declares a variable called x and assigns it the value 5. let x = 5let y = 10let z = x + y z now holds the value 15. A complete program looks like this: let x = 5let y = 10let z = x + y printfn "x: %i" x printfn "y: %i" y printfn "z: %i" z The statement printfn prints text out to the console window. Note to F# Interactive users: all statements in F# Interactive are terminated by ;; (two semicolons). Values, Not Variables[edit] In F#, "variable" is a misnomer. Declaring Functions[edit] There is little distinction between functions and values in F#. This program outputs: num1: 5 num2: 17 num3: 12 int -> string
open processing ocaml.janestreet.com As anyone who has looked into functional reactive programming (FRP) knows, there are lots of competing approaches to it, and not a lot of conceptual clarity about how they relate to each other. In this post, I'll try to shed some light, and in particular give you some guide posts for understanding how the different versions of FRP relate to each other. Plus, I'll show some connections to a similar technique called self-adjusting computation (SAC). The analysis here should mostly be credited to Evan Czaplicki, who gave a talk at Jane Street a week ago. Any confusions and mistakes are, of course, my own. Also, thanks to Jake McArthur for filling me in on a few key details. In all of this I'm basically going to talk only about discrete FRP. First, some basics. Now, time to oversimplify. An FRP program effectively ties signals together in a dependency graph, where each signal is either an external input or a derived signal that feeds off of other signals that have already been defined.
joa ebert's as3 image processing Library The Imageprocessing Library is a set of ActionScript 3 classes designed to work simple and performant. It comes with more than 50 filters to manipulate images and synthesize textures. Examples External: SoundMixer with Papervision3d & ImageProcessing Description From a technical point of view the core image objects use a multi-channel model. BinaryGrayscaleRGBRGBA A core image object uses one layer. More than 50 filters to manipulate or create imagesUtils to publish your images in an easy wayUtils to capture live dataSerialization into common file formats License The Imageprocessing Library is licensed under a Creative Commons License (Attribution-Noncommercial-No Derivative Works 2.5 Generic). Do not hesitate to contact me if you have any suggestions or questions.
THE FOURTH QUADRANT: A MAP OF THE LIMITS OF STATISTICS By Nassim Nicholas Taleb Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the "logic of science"; it is the instrument of risk-taking; it is the applied tools of epistemology; you can't be a modern intellectual and not think probabilistically—but... let's not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. The current subprime crisis has been doing wonders for the reception of any ideas about probability-driven claims in science, particularly in social science, economics, and "econometrics" (quantitative economics). It appears that financial institutions earn money on transactions (say fees on your mother-in-law's checking account) and lose everything taking risks they don't understand. Are we using models of uncertainty to produce certainties?
State of the Union State of the Union (SOTU) provides access to the corpus of all the State of the Union addresses from 1790 to 2013. SOTU allows you to explore how specific words gain and lose prominence over time, and to link to information on the historical context for their use. SOTU focuses on the relationship between individual addresses as compared to the entire collection of addresses, highlighting what is different about the selected document. You are invited to try and understand from this information the connection between politics and language–between the state we are in, and the language which names it and calls it into being. The Words SOTU maps the significant content of each State of the Union address so that users can appreciate its key terms and their relative importance. The horizontal axis shows the average position of a word in the document. The Data The data underneath the map of significant words shows trends in the language of the State of the Union addresses.
Michael Steele's Academic Misadventure - Raw Fisher Michael Steele's Academic Misadventure He hasn't exactly held high office, and he's neither a policy leader nor a brilliant campaigner, but former Maryland lieutenant governor and Republican National Committee Chairman Michael Steele is a hugely charming storyteller, and in a video to appear this weekend on C-SPAN, Steele keeps an audience of high school students spellbound with the scary yet inspirational tale of the time he was booted out of Johns Hopkins University. The heroine of Steele's story--as is often the case--is his mother, Maebell Turner, who managed to scare him onto the right course without ever deigning to look at her son or to stop stirring the grits. Steele told the story to students at Woodson Senior High School in the District, as part of C-SPAN's "Students and Leaders" program, which brings big-name politicians, journalists and others to five D.C. public schools. "My mother never spoke about that issue again," Steele tells the students.
Does the U.S. Produce Too Many Scientists? Editor's Note: Beryl Lieff Benderly, a fellow of the American Associaton for the Advancement of Science, writes about scientific labor force and early career issues in the Science Careers section of Science. In this rough-draft article, she argues that the scientific labor market is broken, that the U.S. educational system actually produces too many qualified researchers for too few positions, and that a perverse funding structure perpetuates the problem, among other points. We'd like your views on this topic and suggestions on ways to further develop the article. Please use the Comments section at the bottom of the page. For years, Americans have heard blue-ribbon commissions and major industrialists bemoan a shortage of scientists caused by an inadequate education system. But many less publicized Americans, including prominent labor economists, disagree. Many Applicants, Few Academic Posts Still Tapping Other Countries Is Education Really to Blame? So why all the talk of a shortage?