March 11, 2015 — Brett Champion, Manager, Visualization

A few years ago we created a timeline of the history of systematic data and computable knowledge, which you can look at online. I wrote the code that placed events along the timeline, and then our graphic designers did the real work in deciding where to put the labels, choosing fonts and colors, and doing all the other things that go into creating a production-quality poster.

Printed poster timeline

Fast-forward a bit, and last year we added NumberLinePlot to the Wolfram Language to visualize points, intervals, and inequalities. Once people started seeing the number lines, we began getting requests for similar plots, but with dates and times, so we decided it was time to tackle TimelinePlot.

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March 4, 2015 — Stephen Wolfram

Where should data from the Internet of Things go? We’ve got great technology in the Wolfram Language for interpreting, visualizing, analyzing, querying and otherwise doing interesting things with it. But the question is, how should the data from all those connected devices and everything else actually get to where good things can be done with it? Today we’re launching what I think is a great solution: the Wolfram Data Drop.

Wolfram Data Drop

When I first started thinking about the Data Drop, I viewed it mainly as a convenience—a means to get data from here to there. But now that we’ve built the Data Drop, I’ve realized it’s much more than that. And in fact, it’s a major step in our continuing efforts to integrate computation and the real world.

So what is the Wolfram Data Drop? At a functional level, it’s a universal accumulator of data, set up to get—and organize—data coming from sensors, devices, programs, or for that matter, humans or anything else. And to store this data in the cloud in a way that makes it completely seamless to compute with.

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February 5, 2015 — Emily Suess, Technical Writer, Technical Communications and Strategy Group

As Valentine’s Day approaches, Wolfram is holding a Tweet-a-Program challenge. To help us celebrate the romantic holiday, tweet us your best Valentine-themed Wolfram Language code. As with our other challenges, we’ll pin, retweet, and share your submissions with our followers—and we’ll use the Wolfram Language to randomly select winning tweets, along with one or two of our favorites. If you’re a lucky winner, we’ll send you a Wolfram T-shirt!

Submissions aren’t limited to heart-themed programs, but check out these examples if you need a little inspiration:

Wolfram Tweet-a-Program ContourPlot3D

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January 15, 2015 — Oleksandr Pavlyk, Kernel Technology

January 16, 2015, marks the 360th birthday anniversary of Jacob Bernoulli (also James, or Jacques).

Input 1 through Output 4

Jacob Bernoulli was the first mathematician in the Bernoulli family, which produced many notable mathematicians of the seventeenth and eighteenth centuries.

Jacob Bernoulli’s mathematical legacy is rich. He introduced Bernoulli numbers, solved the Bernoulli differential equation, studied the Bernoulli trials process, proved the Bernoulli inequality, discovered the number e, and demonstrated the weak law of large numbers (Bernoulli’s theorem).

Jacob Bernoulli's mathematical achievements

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January 6, 2015 — Mikael Forsgren, Wolfram MathCore

Mathematical modeling is not just used for understanding and designing new products and drugs; modeling can also be used in health care, and in the future, your doctor might examine your liver with a mathematical model just like the one researchers at AstraZeneca have developed.


The liver is a vital organ, and currently there isn’t really a way to compensate for loss of liver function in the long term. The liver performs a wide range of functions, including detoxification, protein synthesis, and secretion of compounds necessary for digestion, just to mention a few. In the US and Europe, up to 15 % of all acute liver failure cases are due to drug-induced liver injury, and the risk of injuring the liver is of major concern in testing new drug candidates. So in order to safely monitor the impact of a new drug candidate on the liver, researchers at the pharmaceutical company AstraZeneca have recently published a method for evaluating liver function that combines magnetic resonance imaging (MRI) and mathematical modeling—potentially allowing for early identification of any reduced liver function in humans.

Last year, Wolfram MathCore and AstraZeneca worked together on a project where we investigated some modifications of AstraZeneca’s modeling framework. We presented the promising results at the ISMRM-ESMRMB Joint Annual Meeting, which is the major international magnetic resonance conference. In this blog post, I’ll show how the Wolfram Language was used to calculate liver function and how more complex models of liver function can be implemented in Wolfram SystemModeler.

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December 29, 2014 — Tom Sherlock, User Interface Group

As an amateur astronomer, I’m always interested in ways to use Mathematica in my hobby. In earlier blog posts, I’ve written about how Mathematica can be used to process and improve images taken of planets and nebulae. However, I’d like to be able to control my astronomical hardware directly with the Wolfram Language.

In particular, I’ve been curious about using the Wolfram Language as a way to drive my telescope mount, for the purpose of automating an observing session. There is precedent for this because some amateurs use their computerized telescopes to hunt down transient phenomena like supernovas. Software already exists for performing many of the tasks that astronomers engage in—locating objects, managing data, and performing image processing. However, it would be quite cool to automate all the different tasks associated with an observing session from one notebook.

Mathematica is highly useful because it can perform many of these operations in a unified manner. For example, Mathematica incorporates a vast amount of useful astronomical data, including the celestial coordinates of hundreds of thousands of stars, nebula, galaxies, asteroids, and planets. In addition to this, Mathematica‘s image processing and data handling functionality are extremely useful when processing astronomical data.

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December 15, 2014 — Wolfram Blog Team

It’s the holiday season, and Wolfram is gearing up for bright lights and winter weather by holding a new Tweet-a-Program challenge. To help us celebrate the holidays, tweet your best holiday ornament-themed lines of Wolfram Language code. As with our other challenges, we’ll use the Wolfram Language to randomly select winning tweets (along with a few of our favorites) to pin, retweet, and share with our followers. If you’re a lucky winner, we’ll send you a free Wolfram T-shirt!

If you need some help getting into the holiday spirit, check out these examples:


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December 3, 2014 — Adriana O'Brien, Business Development, Partnerships

Get ready, get set… code! It’s the time of year to get thinking about programming with the Hour of Code.

For many years, Wolfram Research has promoted and supported initiatives that encourage computation, programming, and STEM education, and we are always thrilled when efforts are taken by others to do the same., in conjunction with Computer Science Education Week, is sponsoring an event to encourage educators and organizations across the country to dedicate a single hour to coding. This hour gives kids (and adults, too!) a taste of what it means to study computer science—and how it can actually be a creative, fun, and fulfilling process. Millions of students participated in the Hour of Code in past years, and instructors are looking for more engaging activities for their students to try. Enter the Wolfram Language.

Built into the Wolfram Language is the technology from Wolfram|Alpha that enables natural language input—and lets students create code just by writing English.

W|A using natural language

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November 26, 2014 — Rita Crook, Marketing Projects Manager

The Imitation Game, a movie portraying Alan Turing’s life (who would have celebrated his 100th birthday on Mathematica‘s 23rd birthday—read our blog post), was released this week, which we’ve been looking forward to. Turing machines were one of the focal points of the movie, and we launched a prize in 2007 to determine whether the 2,3 Turing machine was universal.

So of course, Cumberbatch’s promotional video where he impersonates other beloved actors reached us as well, which got me wondering, could Mathematica‘s machine learning capabilities recognize his voice, or could he fool a computer too?

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November 21, 2014
Matthias Odisio, Mathematica Algorithm R&D
Giulio Alessandrini, Mathematica Algorithm R&D

For most of us, taking bad pictures is incredibly easy. Band-Aid or remedy, digital post-processing can involve altering the photographed scene itself. Say you’re trekking through the mountains taking photos of the horizon, or you’re walking down the street and catch a beautiful perspective of the city, or it’s finally the right time to put the new, expensive phone camera to good use and capture the magic of this riverside… Just why do all the pictures look so bad? They’re all foggy! It’s not that you’re a bad photographer—OK, maybe you are—but that you’ve stumbled on a characteristic problem in outdoor photography: haze.

What is haze? Technically, haze is scattered light, photons bumped around by the molecules in the air and deprived of their original color, which they got by bouncing off the objects you are trying to see. The problem gets worse with distance: the more the light has to travel, the more it gets scattered around, and the more the scene takes that foggy appearance.

What can we do? What can possibly help our poor photographer? Science, of course.

Wolfram recently attended and sponsored the 2014 IEEE International Conference on Image Processing (ICIP), which ended October 30 in Paris. It was a good occasion to review the previous years’ best papers at the conference, and we noticed an interesting take on the haze problem proposed by Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, and Sabine Süsstrunk [1]. Let’s give their method a try and implement their “dehazing” algorithm.

The core idea behind the paper is to leverage the different susceptibilities of the light being scattered, which depend on the wavelength of the light. Light with a larger wavelength, such as red light, is more likely to travel around the dust, the smog, and all the other particles present in the air than shorter wavelength colors, like green or blue. Therefore, the red channel in an image carries better information about the non-hazy content of the scene.

But what if we could go even further? What prevents us from using the part of the spectrum slightly beyond the visible light? Nothing really—save for the fact we need an infrared camera.

Provided we are well equipped, we can then use the four channels of data (near infrared, red, green, and blue) to estimate the haze color and distribution and proceed to remove it from our image.

RGB, IR removal

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