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Education & Academic

Creating Escher-Inspired Art with Mathematica

Kenzo Nakamura uses Mathematica to create Escher-inspired mathematical art. His trademark piece, Three-Circle Mandala, depicts a large circle covered by three smaller, repeating circles that form a Sierpinksi gasket. When Nakamura began using Mathematica, he didn’t originally intend to use it for his artistic endeavors. He found the program by chance at a seminar while looking for the right tool to help him write his master’s thesis. Now, in addition to using Mathematica for technical and operations research, Nakamura uses it to create Mathematica-derived visual illusions. Although his works are static drawings, their infinite properties create the illusion of movement. Watch Nakamura discuss using Mathematica to create his drawings, and see a few of his creations. (YouTube in Japanese)
Wolfram Blog Team
Products

Announcing Wolfram SystemModeler 4

Explore the contents of this article with a free Wolfram SystemModeler trial. Today we are proud to announce the release of Wolfram SystemModeler 4. For SystemModeler 4, we have expanded the supported model libraries to cover many new areas. We've also improved workflows for everything from learning the software to developing models to analyzing and deploying them. People have been using SystemModeler in an astonishing variety of areas. Many of those have been well supported by built-in libraries, but many are totally new domains where models typically need to be built from scratch. For most applications, using existing model libraries gives a real boost to productivity, but developing a good library takes a lot of effort. There are many aspects to think of: the best structure for easy modeling, the right level of detail, the interfaces to other components, which components to include, documentation, etc. And you may very well have to refactor the library more than once before you're done. Reusing components and interfaces from already tested and documented libraries not only speeds up development and learning, but also improves quality. So we've made SystemModeler's already broad collection of built-in libraries even larger. For instance, we've added Digital, for digital electronics following the VHDL multivalued logic standard; QuasiStationary, for efficient approximate modeling of large analog circuits; and FundamentalWave, for modeling multiphase electrical machines. There are also many improvements to existing libraries, such as support for thermal ports in the Rotational and Translational mechanics libraries so that heat losses can be captured.
Roger Germundsson, Director of Research & Development Jan Brugård, CEO, Wolfram MathCore
Computation & Analysis

How Citizen Computation Changes Democracy: Conrad Wolfram at TEDxHousesofParliament

Photography by Tracy Howl and Paul Clarke Has our newfound massive availability of data improved decisions and lead to better democracy around the world? Most would say, "It's highly questionable." Conrad Wolfram's TEDx UK Parliament talk poses this question and explains how computation can be key to the answer, bridging the divide between availability and practical accessibility of data, individualized answers, and the democratization of new knowledge generation. This transformation will be critical not only to government efficiency and business effectiveness---but will fundamentally affect education, society, and democracy as a whole. Wolfram|Alpha and Mathematica 10 demos feature throughout---including a live Wolfram Language generated tweet. More about Wolfram's solutions for your organization's data »
Wolfram Blog Team
Announcements & Events

Launching Mathematica 10—
with 700+ New Functions and a Crazy Amount of R&D

We’ve got an incredible amount of new technology coming out this summer. Two weeks ago we launched Wolfram Programming Cloud. Today I’m pleased to announce the release of a major new version of Mathematica: Mathematica 10. We released Mathematica 1 just over 26 years ago—on June 23, 1988. And ever since we’ve been systematically making […]

Stephen Wolfram
Products

World Cup Follow-Up: Update of Winning Probabilities and Betting Results

Find out Etienne’s initial predictions by visiting last week’s World Cup blog post. The World Cup is half-way through: the group phase is over, and the knockout phase is beginning. Let's update the winning probabilities for the remaining teams, and analyze how our classifier performed on the group-phase matches. From the 32 initial teams, 16 are qualified for the knockout phase:
Etienne Bernard, Lead Architect, Machine Learning
Products

Wolfram Programming Cloud Is Live!

Twenty-six years ago today we launched Mathematica 1.0. And I am excited that today we have what I think is another historic moment: the launch of Wolfram Programming Cloud—the first in a sequence of products based on the new Wolfram Language. My goal with the Wolfram Language in general—and Wolfram Programming Cloud in particular—is to […]

Stephen Wolfram
Best of Blog

Predicting Who Will Win the World Cup with Wolfram Language

Check out Etienne’s updated predictions from Thursday, June 26 here. The FIFA World Cup is underway. From June 12 to July 13, 32 national football teams play against each other to determine the FIFA world champion for the next four years. Who will succeed? Experts and fans all have their opinions, but is it possible to answer this question in a more scientific way? Football is an unpredictable sport: few goals are scored, the supposedly weaker team often manages to win, and referees make mistakes. Nevertheless, by investigating the data of past matches and using the new machine learning functions of the Wolfram Language Predict and Classify, we can attempt to predict the outcome of matches. The first step is to gather data. FIFA results will soon be accessible from Wolfram|Alpha, but for now we have to do it the hard way: scrape the data from the web. Fortunately, many websites gather historical data (www.espn.co.uk, www.rsssf.com, www.11v11.com, etc.) and all the scraping and parsing can be done with Wolfram Language functions. We first stored web pages locally using URLSave and then imported these pages using Import[myfile,"XMLObject"] (and Import[myfile,"Hyperlinks"] for the links). Using XML objects allows us to keep the structure of the page, and the content can be parsed using Part and pattern-matching functions such as Cases. After the scraping, we cleaned and interpreted the data: for example, we had to infer the country from a large number of cities and used Interpreter to do so: From scraping various websites, we obtained a dataset of about 30,000 international matches of 203 teams from 1950 to 2014 and 75,000 players. Loaded into the Wolfram Language, its size is about 200MB of data. Here is a match and a player example stored in a Dataset:
Etienne Bernard, Lead Architect, Machine Learning
Announcements & Events

Wolfram Technology Conference 2014: Register Now!

It’s been a productive 2014 already here at Wolfram with tons of new technology being released and a whole new world of possibilities opening up. One great way to learn more about these accomplishments is to join us at the 2014 Wolfram Technology Conference. The conference takes place Wednesday, October 22 through Friday, October 24, in Champaign, Illinois (our headquarters). This year's talks will highlight the Wolfram Language and the thriving ecosystem growing around it, including the new Wolfram Programming Cloud, Mathematica, Wolfram|Alpha, SystemModeler, and more. At the conference, you’ll hear from Stephen Wolfram himself. Plus our top Wolfram developers will cover exciting new features in-depth, while industry experts will show you how you can use Wolfram technologies in your everyday work to accomplish more--and do so more efficiently.
Wolfram Blog Team
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