June 29, 2017 — Swede White, Media & Communications Specialist

Revolutionary social networks lead image

As the Fourth of July approaches, many in America will celebrate 241 years since the founders of the United States of America signed the Declaration of Independence, their very own disruptive, revolutionary startup. Prior to independence, colonists would celebrate the birth of the king. However, after the Revolutionary War broke out in April of 1775, some colonists began holding mock funerals of King George III. Additionally, bonfires, celebratory cannon and musket fire and parades were common, along with public readings of the Declaration of Independence. There was also rum.

Today, we often celebrate with BBQ, fireworks and a host of other festivities. As an aspiring data nerd and a sociologist, I thought I would use the Wolfram Language to explore the Declaration of Independence using some basic natural language processing.

Using metadata, I’ll also explore a political network of colonists with particular attention paid to Paul Revere, using built-in Wolfram Language functions and network science to uncover some hidden truths about colonial Boston and its key players leading up to the signing of the Declaration of Independence.

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June 22, 2017 — Andrew Steinacher, Wolfram|Alpha Developer, Wolfram|Alpha Scientific Content

Completed reportPlot 3D animation

When I first started driving in high school, I had to pay for my own gas. Since I was also saving for college, I had to be careful about my spending, so I started manually tracking how much I was paying for gas in a spreadsheet and calculating how much gas I was using. Whenever I filled my tank, I kept the receipts and wrote down how many miles I’d traveled and how many gallons I’d used. Every few weeks, I would manually enter all of this information into the spreadsheet and plot out the costs and the amount of fuel I had used. This process helped me both visualize how much money I was spending on fuel and manage my budget.

Once I got to college, however, I got a more fuel-efficient car and my schedule got a lot busier, so I didn’t have the time to track my fuel consumption like this anymore. Now I work at Wolfram Research and I’m still really busy, but the cool thing is that I can use our company technology to more easily accomplish my automotive assessments.

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June 6, 2017 — Keiko Hirayama, Wolfram|Alpha Developer, Wolfram|Alpha Scientific Content

Brain image and brain flow graph

As the next phase of Wolfram Research’s endeavor to make biology computable, we are happy to announce the recent release of neuroscience-related content.

The most central part of the human nervous system is the brain. It contains roughly 100 billion neurons that act together to process information, subdivided functionally and structurally into areas specialized for certain tasks. The brain’s anatomy, the characteristics of neurons and cognitive maps are used to represent some key aspects of the functional organization and processing abilities of our nervous system. Our new neuroscience content will give you a sneak peek into the amazing world of neuroscience with some facts about brains, neurons and cognition.

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June 2, 2017 — Michael Gammon, Blog Coordinator, Document and Media Systems

We’re always excited to see new books that illustrate applications of Wolfram technology in a wide range of fields. Below is another set of recently published books using the Wolfram Language to explore computational thinking. From André Dauphiné’s outstanding geographical studies of our planet to Romano and Caveliere’s work on the geometric optics that help us study the stars, we find a variety of fields served by Wolfram technology.

Application Books Set 1

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April 26, 2017 — Lois Jamieson, Marketing Projects Coordinator

Data Science Conference

With the world of data science developing at a rapid pace and companies increasingly aware of its importance, Wolfram is pleased to bring together a range of data science experts at the Computation Meets Data Science Conference on 11 May, in partnership with the Satellite Applications Catapult and Digital Catapult.

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March 2, 2017 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)

Until now, it has been difficult for the average engineer to perform simple vibration analysis. The initial cost for simple equipment, including software, may be several thousand dollars—and it is not unusual for advanced equipment and software to cost ten times as much. Normally, a vibration specialist starts an investigation with a hammer impact test. An accelerometer is mounted on a structure, and a special impact hammer is used to excite the structure at several locations in the simplest and most common form of hammer impact testing. The accelerometer and hammer-force signals are recorded. Modal analysis is then used to get a preliminary understanding of the behavior of the system. The minimum equipment requirements for such a test are an accelerometer, an impact hammer, amplifiers, a signal recorder and analysis software.

I’ve figured out how to use the Wolfram Language on my smartphone to sample and analyze machine vibration and noise, and to perform surprisingly good vibration analysis. I’ll show you how, and give you some simple Wolfram Language code to get you started.

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February 23, 2017 — Michael Trott, Chief Scientist

And How Many Animals, Animal Heads, Human Faces, Aliens and Ghosts in Their 2D Projections?

Introduction

In my recent Wolfram Community post, “How many animals can one find in a random image?,” I looked into the pareidolia phenomenon from the viewpoints of pixel clusters in random (2D) black-and-white images. Here are some of the shapes I found, extracted, rotated, smoothed and colored from the connected black pixel clusters of a single 800×800 image of randomly chosen, uncorrelated black-and-white pixels.

arpimals

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January 31, 2017 — Michael Gammon, Blog Coordinator, Document and Media Systems

Black and white logogram

If aliens actually visited Earth, world leaders would bring in a scientist to develop a process for understanding their language. So when director Denis Villeneuve began working on the science fiction movie Arrival, he and his team turned to real-life computer scientists Stephen and Christopher Wolfram to bring authentic science to the big screen. Christopher specifically was tasked with analyzing and writing code for a fictional nonlinear visual language. On January 31, he demonstrated the development process he went through in a livecoding event broadcast on LiveEdu.tv.

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January 17, 2017 — Jofre Espigule-Pons, Consultant, Technical Communications and Strategy Group

Muhammad Ali (born Cassius Marcellus Clay Jr.; January 17, 1942–June 3, 2016) is considered one of the greatest heavyweight boxers in history, with a record of 56 wins and 5 losses. He remains the only three-time lineal heavyweight champion, so there’s no doubt why he is nicknamed “The Greatest.”

I used the Wolfram Language to create several visualizations to celebrate his work and gain some new insights into his life. Last June, I wrote a Wolfram Community post about Ali’s career. On what would have been The Greatest’s 75th birthday, I wanted to take a minute to explore the larger context of Ali’s career, from late-career boxing stats to poetry.

First, I created a PieChart showing Ali’s record:

bouts = <|"TKO" -> 21, "KO" -> 11, "UD" -> 18, "RTD" -> 5, "SD" -> 1,     "LUD" -> 2, "LSD" -> 2, "LRTD" -> 1|>; PieChart[bouts, ChartStyle -> 24,   ChartLabels ->    Placed[{Map[Style[#, Bold, FontSize -> 14] &, Values[bouts]],      Map[Style[#, FontFamily -> "Helvetica Neue", Bold,         FontSize -> 16] &, Keys[bouts]]}, {"RadialCenter",      "RadialCallout"}], PlotRange -> All,   SectorOrigin -> {Automatic, 1},  ChartLegends -> {"Technical Knockout", "Knockout",     "Unanimous Decision", "Retired", "Split-Decision",     "Lost - Unanimous Decision", "Lost - Split-Decision",     "Lost - Retired"},   PlotLabel ->    Style["Ali's Record", Bold, FontFamily -> "Helvetica Neue",     FontSize -> 22], ImageSize -> 410]
Ali's Record

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January 13, 2017 — Nick Lariviere, Kernel Developer, Core Mathematica Engineering

This post originally appeared on Wolfram Community, where the conversation about reliable cars continues. Be sure to check out that conversation and more—we can’t wait to see what you come up with!

For the past couple of years, I’ve been playing with, collecting and analyzing data from used car auctions in my free time with an automotive journalist named Steve Lang to try and get an idea of what the used car market looks like in terms of long-term vehicle reliability. I figured it was about time that I showed off some of the ways that the Wolfram Language has allowed us to parse through information on over one million vehicles (and counting).

Vehicle Class Quality Index Rating

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