April 4, 2019 — Dan McDonald, Lead Developer, Synthetic Geometry Project

Version 12 of the Wolfram Language introduces the functions `GeometricScene`, `RandomInstance` and `FindGeometricConjectures` for representing, drawing and reasoning about problems in plane geometry. In particular, abstract scene descriptions can be automatically supplied with coordinate values to produce diagrams satisfying the conditions of the scene. Let’s apply this functionality to some of the articles and problems about geometry appearing in the issues of *The American Mathematical Monthly* from February and March of 2019.

April 2, 2019 — Jon McLoone, Director, Technical Communication & Strategy

Over the years, I have been asked many times about my opinions on free and open-source software. Sometimes the questions are driven by comparison to some promising or newly fashionable open-source project, sometimes by comparison to a stagnating open-source project and sometimes by the belief that Wolfram technology would be better if it were open source.

At the risk of provoking the fundamentalist end of the open-source community, I thought I would share some of my views in this blog. While there are counterexamples to most of what I have to say, not every point applies to every project, and I am somewhat glossing over the different kinds of “free” and “open,” I hope I have crystallized some key points.

March 29, 2019 — Michael Trott, Chief Scientist

## Introduction

In the so-called “new SI,” the updated version of the International System of Units that will define the seven base units (second, meter, kilogram, ampere, kelvin, mole and candela) and that goes into effect May 20 of 2019, all SI units will be definitionally based on exact values of fundamental constants of physics. And as a result, all the named units of the SI (newton, volt, ohm, pascal, etc.) will ultimately be expressible through fundamental constants. (Finally, fundamental physics will be literally ruling our daily life 😁.)

Here is how things will change from the evening of Monday, May 20, to the morning of Tuesday, May 21, of this year.

March 7, 2019 — Ed Pegg Jr, Editor, Wolfram Demonstrations Project

## Similar Triangle Dissections

Version 12 of the Wolfram Language introduces solvers for geometry problems. The documentation for the new function `GeometricScene` has a neat example showing the following piece of code, with `GeometricAssertion` calling for seven similar triangles:

✕
o=Sequence[Opacity[.9],EdgeForm[Black]];plasticDissection=RandomInstance[GeometricScene[{a,b,c,d,e,f,g},{ a=={1,0},e=={0,0},Line[{a,e,d,c}], p0==Polygon[{a,b,c}], p1==Style[Polygon[{b,d,c}],Orange,o], p2==Style[Polygon[{d,f,e}],Yellow,o], p3==Style[Polygon[{b,f,d}],Blue,o], p4==Style[Polygon[{g,f,b}],Green,o], p5==Style[Polygon[{e,g,f}],Magenta,o], p6==Style[Polygon[{a,e,g}],Purple,o], GeometricAssertion[{p0,p1,p2,p3,p4,p5,p6},"Similar"]}],RandomSeeding->28] |

February 1, 2019 — Andrew Steinacher, Lead Developer, Wolfram|Alpha Scientific Content

## New Archive Conversion Utility in Version 12

Soon there will be 100,000 questions on MathOverflow.net, a question-and-answer site for professional mathematicians! To celebrate this event, I have been working on a Wolfram Language utility package to convert archives of Stack Exchange network websites into Wolfram Language entity stores.

The archives are hosted on the Internet Archive and are updated every few months. The package, although not yet publicly available, will be released in the coming weeks as part of Version 12 of the Wolfram Language—so keep watching this space for more news about the release!

January 10, 2019 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems

So far in this series, I’ve covered the process of extracting, cleaning and structuring data from a website. So what does one do with a structured dataset? Continuing with the Election Atlas data from the previous post, this final entry will talk about how to store your scraped data permanently and deploy results to the web for universal access and sharing.

September 6, 2018 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems

In my previous post, I demonstrated the first step of a multiparadigm data science workflow: extracting data. Now it’s time to take a closer look at how the Wolfram Language can help make sense of that data by cleaning it, sorting it and structuring it for your workflow. I’ll discuss key Wolfram Language functions for making imported data easier to browse, query and compute with, as well as share some strategies for automating the process of importing and structuring data. Throughout this post, I’ll refer to the US Election Atlas website, which contains tables of US presidential election results for given years:

July 26, 2018 — Itai Seggev, Senior Kernel Developer, Algorithms R&D

One of the many beautiful aspects of mathematics is that often, things that look radically different are in fact the same—or at least share a common core. On their faces, algorithm analysis, function approximation and number theory seem radically different. After all, the first is about computer programs, the second is about smooth functions and the third is about whole numbers. However, they share a common toolset: asymptotic relations and the important concept of asymptotic scale.

By comparing the “important parts” of two functions—a common trick in mathematics—asymptotic analysis classifies functions based on the relative size of their absolute values near a particular point. Depending on the application, this comparison provides quantitative answers to questions such as “Which of these algorithms is fastest?” or “Is function a good approximation to function *g*?”. Version 11.3 of the Wolfram Language introduces six of these relations, summarized in the following table.

July 19, 2018 — Devendra Kapadia, Kernel Developer, Algorithms R&D

Asymptotic expansions have played a key role in the development of fields such as aerodynamics, quantum physics and mathematical analysis, as they allow us to bridge the gap between intricate theories and practical calculations. Indeed, the leading term in such an expansion often gives more insight into the solution of a problem than a long and complicated exact solution. Version 11.3 of the Wolfram Language introduces two new functions, `AsymptoticDSolveValue` and `AsymptoticIntegrate`, which compute asymptotic expansions for differential equations and integrals, respectively. Here, I would like to give you an introduction to asymptotic expansions using these new functions.

May 31, 2018 — Sjoerd Smit, Technical Consultant

Neural networks are very well known for their uses in machine learning, but can be used as well in other, more specialized topics, like regression. Many people would probably first associate regression with statistics, but let me show you the ways in which neural networks can be helpful in this field. They are especially useful if the data you’re interested in doesn’t follow an obvious underlying trend you can exploit, like in polynomial regression.

In a sense, you can view neural network regression as a kind of intermediary solution between true regression (where you have a fixed probabilistic model with some underlying parameters you need to find) and interpolation (where your goal is mostly to draw an eye-pleasing line between your data points). Neural networks can get you something from both worlds: the flexibility of interpolation and the ability to produce predictions with error bars like when you do regression.