June 11, 2013 — Mikael Forsgren, Wolfram MathCore
Wolfram SystemModeler ships with model libraries for a large selection of domains such as electronics, mechanics, and biochemistry. Now I am pleased to present a new library in the family, the SystemDynamics library by François E. Cellier and Stefan Fabricius. System dynamics, a methodology developed by Jay Forrester in the ’60s and ’70s, is well suited for understanding the dynamics of large-scale systems with diverse components. It has been famously applied by the Club of Rome to investigate the limits of human growth; other applications include production management, life sciences, and economics (some showcases of the methodology can be found here).
June 6, 2013 — Stephen Wolfram
In a few weeks it’ll be 25 years ago: June 23, 1988—the day Mathematica was launched.
Late the night before we were still duplicating floppy disks and stuffing product boxes. But at noon on June 23 there I was at a conference center in Santa Clara starting up Mathematica in public for the first time:
(Yes, that was the original startup screen, and yes, Mathematica 1.0 ran on Macs and various Unix workstation computers; PCs weren’t yet powerful enough.)
People were pretty excited to see what Mathematica could do. And there were pretty nice speeches about the promise of Mathematica from a spectrum of computer industry leaders, including Steve Jobs (then at NeXT), who was kind enough to come even though he hadn’t appeared in public for a while. And someone at the event had the foresight to get all the speakers to sign a copy of the book, which had just gone on sale that day at bookstores all over the country:
So much has happened with Mathematica in the quarter century since then. What began with Mathematica 1.0 has turned into the vast system that is Mathematica today. And as I look at the 25th Anniversary Scrapbook, it makes me proud to see how many contributions Mathematica has made to invention, discovery and education:
But to me what’s perhaps most satisfying is how the fundamental principles on which I built Mathematica have stood the test of time. And how the core ideas and language that were in Mathematica 1.0 persist today (and yes, most Mathematica 1.0 code will still run unchanged today).
June 3, 2013 — Oleksandr Pavlyk, Kernel Technology
I am a junkie for a good math problem. Over the weekend, I encountered such a good problem on a favorite subject of mine–probability. It’s the last problem from the article “A Mathematical Trivium” by V. I. Arnol’d, Russian Mathematical Surveys 46(1), 1991, 271–278.
It’s short enough to reproduce in its entirety: “Find the mathematical expectation of the area of the projection of a cube with edge of length 1 onto a plane with an isotropically distributed random direction of projection.” In other words, what is the average area of a cube’s shadow over all possible orientations?
This blog post explores the use of Mathematica to understand and ultimately solve the problem. It recreates how I approached the problem.
May 30, 2013 — Wolfram Blog Team
Using Mathematica, Wolfgang Schmidt, a scientist at the Jülich Centre for Neutron Science, designed new neutron optical components to improve the efficiency of one of the most powerful spectrometers available for neutron scattering research.
Mathematica‘s flexible programming language allowed Schmidt to quickly write new programs and verify lengthy calculations for simulations he needed to investigate for spectrometer upgrades, which included a neutron polarizer. With Mathematica, he could test and visualize various parameters that helped him design the polarizer and optimize its performance.
May 22, 2013 — Jon McLoone, International Business & Strategic Development
The benefits of linking from Mathematica to other languages and tools differ from case to case. But unusually, in the case of the new RLink in Mathematica 9, I think the benefits have very little to do with R, the language. The real benefit, I believe, is in the connection it makes to the R community.
When we first added the MathLink libraries for C, there were real benefits in farming out intensive numerical work (though Mathematica performance improvements over the years and development of the compiler have greatly reduced the occasions where that would be worth the effort). Creating an Excel link added an alternative interface paradigm to Mathematica that wasn’t available in the Mathematica front end. But in the case of R, it isn’t immediately obvious that it does many things that you can’t already do in Mathematica or many that it does significantly better.
However, with RLink I now have immediate access to the work of the R community through the add-on libraries that they have created to extend R into their field. A great zoo of these free libraries fill out thousands of niches–sometimes popular, sometimes obscure–but lots of them. There are over 4,000 right here and more elsewhere. At a stroke, all of them are made immediately available to the Mathematica environment, interpreted through the R language runtime.
May 17, 2013 — Michael Trott, Chief Scientist
Here at Wolfram Research and at Wolfram|Alpha we love mathematics and computations. Our favorite topics are algorithms, followed by formulas and equations. For instance, Mathematica can calculate millions of (more precisely, for all practical purposes, infinitely many) integrals, and Wolfram|Alpha knows hundreds of thousands of mathematical formulas (from Euler’s formula and BBP-type formulas for pi to complicated definite integrals containing sin(x)) and plenty of physics formulas (e.g from Poiseuille’s law to the classical mechanics solutions of a point particle in a rectangle to the inverse-distance potential in 4D in hyperspherical coordinates), as well as lesser-known formulas, such as formulas for the shaking frequency of a wet dog, the maximal height of a sandcastle, or the cooking time of a turkey.
Recently we added formulas for a variety of shapes and forms, and the Wolfram|Alpha Blog showed some examples of shapes that were represented through mathematical equations and inequalities. These included fictional character curves:
May 13, 2013 — Wolfram Blog Team
Thank you to all who made the Wolfram Virtual Conference Spring 2013 a great success. The free event featured two tracks of talks covering applications of Wolfram technologies in industry, education, and research as well as a Q&A with our experts and access to virtual networking.
Attendees of all experience levels joined the event to gain new insights on how to get the most out of our technologies, including Mathematica‘s Predictive Interface, CDF and EnterpriseCDF, Wolfram SystemModeler, and more.
May 9, 2013 — Matthias Odisio, Software Technology
Detecting skin in images can be quite useful: it is one of the primary steps for various sophisticated systems aimed at detecting people, recognizing gestures, detecting faces, content-based filtering, and more. In spite of this host of applications, when I decided to develop a skin detector, my main motivation lay elsewhere. The research and development department I work in at Wolfram Research just underwent a gentle reorganization. With my colleagues who work on probability and statistics becoming closer neighbors, I felt like developing a small application that would make use of both Mathematica‘s image processing and statistics features; skin detection just came to my mind.
Skin tones and appearances vary, and so do flavors of skin detectors. The detector I wanted to develop is based on probabilistic models of pixel colors. For each pixel of an image given as input, the skin detector provides a probability that the pixel color belongs to a skin region.
Last year we released Wolfram Finance Platform, beginning a new chapter in the way the financial world uses Wolfram technologies. Today we’re pleased to announce Wolfram Finance Platform 2, which expands and improves the groundwork begun by our first version.
One set of new capabilities that Finance Platform 2 introduces is a major enhancement to the way financial analysis is deployed: automated report generation.
Report Generation allows you to create documents quickly and easily using Wolfram Finance Platform documents. Since Report Generation is built on Finance Platform‘s Computable Document Format interface, it’s easy to add it into your normal workflow.
Data for the report can come from a variety of sources, such as the result of a computation, a database query, or Finance Platform‘s integrated computation data source or integrated market data streams. Portfolio performance, risk analyses, and market/economic outlook are just a few of the applications that can take advantage of Report Generation.
A century ago, Srinivasa Ramanujan and G. H. Hardy started a famous correspondence about mathematics so amazing that Hardy described it as “scarcely possible to believe.” On May 1, 1913, Ramanujan was given a permanent position at the University of Cambridge. Five years and a day later, he became a Fellow of the Royal Society, then the most prestigious scientific group in the world at that time. In 1919 Ramanujan was deathly ill while on a long ride back to India, from February 27 to March 13 on the steamship Nagoya. All he had was a pen and pad of paper (no Mathematica at that time), and he wanted to write down his equations before he died. He claimed to have solutions for a particular function, but only had time to write down a few before moving on to other areas of mathematics. He wrote the following incomplete equation with 14 others, only 3 of them solved.
Within months, he passed away, probably from hepatic amoebiasis. His final notebook was sent by the University of Madras to G. H. Hardy, who in turn gave it to mathematician G. N. Watson. When Watson died in 1965, the college chancellor found the notebook in his office while looking through papers scheduled to be incinerated. George Andrews rediscovered the notebook in 1976, and it was finally published in 1987. Bruce Berndt and Andrews wrote about Ramanujan’s Lost Notebook in a series of books (Part 1, Part 2, and Part 3). Berndt said, “The discovery of this ‘Lost Notebook’ caused roughly as much stir in the mathematical world as the discovery of Beethoven’s tenth symphony would cause in the musical world.”