News, Views & Insights
Today we celebrate the arrival of the 36th (x.x) version of the Wolfram Language and Mathematica: Version 14.1. We’ve been doing this since 1986: continually inventing new ideas and implementing them in our larger and larger tower of technology. And it’s always very satisfying to be able to deliver our latest achievements to the world.
Today we celebrate a new waypoint on our journey of nearly four decades with the release of Version 14.0 of Wolfram Language and Mathematica. Over the two years since we released Version 13.0 we’ve been steadily delivering the fruits of our research and development in .1 releases every six months. Today we’re aggregating these—and more—into Version 14.0.
心臓病学のような医療分野でも，Wolfram言語は新たな知見獲得や臨床現場における予測に役立っています．最近，金沢大学先進予防医学研究科の中嶋憲一教授がリードする，Wolfram言語の機械学習機能を用いた心不全による死亡リスク予測の研究に参加し，論文が出版されましたのでその概略を紹介します．そこでは，末期心不全(HFD)と重度の不整脈/突然死(ArE)によって引き起こされる心臓死の確率を区別できる分類器を構築することを目指しました.
The past year of learning ushered in a variety of new experiences for instructors and students alike, and the United States Military Academy at West Point was no exception. In addition to masks in the classroom, reduced class sizes to allow for social distancing, rigorous testing and tracing efforts, and precautionary remote video classes, we have also needed to adjust aspects of our teaching styles. While such adjustments were voluntary, to enhance the discussion I chose to teach several lessons outside under large white tents and even in stadium bleachers to safely enable larger conversations with my cadets. Sometimes this meant carrying a large whiteboard with a tripod out to the stadium. At other times it meant putting quiz-style questions on a website so that students could submit answers via forms that were easier to grade while allowing everyone to work at a safe distance on individual devices.
Today’s handheld devices are powerful enough to run neural networks locally without the need for a cloud server connection, which can be a great convenience when you’re on the go. Deploying and running a custom neural network on your phone or tablet is not straightforward, though, and the process depends on the operating system of the machine. In this post, I will focus on iOS devices and walk you through all the necessary steps to train a custom image classifier neural network model using the Wolfram Language, export it through ONNX (new in Version 12.2), convert it to Core ML (Apple’s machine learning framework for iOS apps) and finally deploy it to your iPhone or iPad.
Cars are getting smarter and more connected, yet how much have you explored the technology that helps run our vehicles? I was curious to see how I could connect to my vehicle’s communication center and what kind of interface I could create in Wolfram Notebooks to report on the data gathered.
I’ll begin this blog post by admitting that I personally have forgotten many passwords in my lifetime. If you’re like me (which I’m sure you are in this regard), you use many online tools and websites that require a login and password. We also know it’s wise practice to use a variety of passwords and to change them frequently. We hope a new feature of Mathematica Online has made this a little less daunting for you in your educational settings.
On December 21, 2020, a visual astronomical spectacle will occur. The planets Jupiter and Saturn will pass so close to each other in the sky that, to the unaided eye, they will be difficult to separate. This is the closest the two planets have come in 397 years; the last time they were this close was July 16, 1623. When Jupiter and Saturn come close to each other in the sky as seen from Earth, this is known as a “great conjunction” and happens about every 20 years or so. But not all great conjunctions are as close as this one. The next great conjunction will be on November 5, 2040, and again on April 10, 2060, but the planets will be a bit over a degree apart, so not as close as the 2020 event. The next comparable event will be on March 15, 2080.
Yet Bigger than Ever Before When we released Version 12.1 in March of this year, I was pleased to be able to say that with its 182 new functions it was the biggest .1 release we’d ever had. But just nine months later, we’ve got an even bigger .1 release! Version 12.2, launching today, has […]
Explore the contents of this article with a free Wolfram System Modeler trial. Wolfram System Modeler 12.2 was just released, featuring things such as personalization of plots, new model libraries and extended GUI support for advanced modeling. One of the other additions is a new workflow for generating 3D models from 3D shapes. We will use this feature to illustrate some bizarre and counterintuitive physics.
Ever since Thomas Robert Malthus’s book An Essay on the Principle of Population, scientists have sought to determine the limit to the growth of human population due to finite resources. One such resource is groundwater. About 40% of global food production ultimately depends on irrigation and, increasingly, the source of irrigation water is groundwater aquifers. Groundwater irrigation allows farmers to increase crop yields, maintain them in dry spells and overcome temporal mismatches between growing seasons and seasonal rain. In many parts of the world, groundwater withdrawal (or pumping from wells) exceeds recharge, leading to groundwater depletion. In these regions, the “lifespan” of groundwater aquifers is limited, putting a bound on the amount of irrigation per year and the sustainability of groundwater-based agriculture. The goal of this study was to propose a dynamical systems framework capable of explaining past trends in groundwater-based irrigation and providing forecasts of food production.