Sound classification can be a hard task, especially when sound samples have small variations that can be imperceptible to the human ear. The use of machines, and recently machine learning models, has been shown to be an effective approach to solving the problem of classifying sounds. These applications can help improve diagnoses and have been a topic of research in areas such as cardiology and pulmonology. Recent innovations such as a convolutional neural network identifying COVID-19 coughs and the MIT AI model detecting asymptomatic COVID-19 infections using cough recordings show some promising results for identifying COVID-19 patients just by the sound of their coughs. Looking at these references, this task may look quite challenging and like something that can be done only by top-notch researchers. In this post, we will discuss how you can get very promising results using the machine learning and audio functionalities in the Wolfram Language.
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.
This year has been Wolfram Media’s most productive yet, with five new titles and another published in partnership. While 2020's state and global shutdowns created unprecedented logistical challenges for the Wolfram Media team, I'm really proud of how we pulled together this year's list, with several more books already in production for release next year.
Halloween this year had a surprise up its sleeve. In rare celestial serendipity, the night of costume metamorphosis also featured a full moon, which helped to conjure the spooky mood. Because it might have been the first and last full-moon Halloween that some people witnessed in their lifetime (cue ominous music), I think it was significantly underrated. Moreover, it was the day of a blue moon (the second full moon within a month), but that is not a surprise, as any Halloween’s full moon is always a blue moon. The Moon’s color did not change, though, at least for those away from the smoke of volcanos and forest fires that are capable of turning it visibly blue. To appreciate the science and uniqueness of a full moon this Halloween, I built this visualization that tells the whole story in one picture. This is how I did it.
A few months before I accepted a Wolfram Research internship—around March—I was very fearful, and so was the majority of the world. We knew very little about the novel coronavirus, and the data was just not robust. In addition to the limited data we had, the scientific process necessarily takes time, so even that was not used to its full extent. In a world where not enough data can quickly become data overload, the question didn’t seem to be finding more data, but rather how can one extract useful and meaningful information from the available data?
A worldwide pandemic is definitely stressful, but a worldwide pandemic without accessible and computable information is much more so. Using Wolfram technologies in coordination with several internal teams, I created a Wolfram U course called COVID-19 Data Analysis and Visualization to try and cut through the informational fog and find some clarity. I saw this course as one that gives power to everyone to be able to look at data and gain insight. After all, data is knowledge, and knowledge is power.
In March, my work life changed dramatically, as it did for many around the world. After working in an office environment for almost 25 years, I was told that I needed to work from home for the first time. So I took my laptop, power cord and extra battery home with the expectation that I’d be there for one to two weeks. The first couple of days were a mad dash with our IT department to ensure my VPN, softphone and main tools were set up correctly for remote work. They were great, and for the most part, I was working at 90% right away. The experience opened my eyes, as many of my sales employees work remotely, so I learned a lot about the positives (and, of course, the negatives) they often experience that I may not have appreciated in the past.
How did the Department of Health and Social Care (DHSC) come up with their multi-phase response to tackle COVID-19? In this post, I investigate how the UK government's original plan against the coronavirus aligns with the four-step computational thinking process. Teachers are welcome to use this post as a free resource.
Please note: where possible, I have taken data from before the DHSC's plan was published.
The Computational Thinking Process
What is the computational thinking process? Simply put, it is a sequence of four steps that you can take in order to solve a problem. The aim is not just to obtain a solution, but to ensure that the right choices were made, the right tools were used and the right outcomes were achieved along the way. The steps are as follows: you define explicitly the problem you wish to solve, abstract it to a computational form, compute an answer, then interpret the result:
When the world is in distress, Wolfram users turn to computation! Even in the midst of this global pandemic, Wolfram staff, friends and colleagues continue to show the power of computational curiosity. We’ve provided a centralized COVID-19 data and resources page, with ways to get free licenses for Wolfram technology through August, livestreamed multiparadigm explorations into the science and data behind the virus, computational explorations from Wolfram users and more. This resource will be continually updated, so make sure to check back often!Our community of staff and users have been incredibly active, creating their own innovative resources and exploring available data from many different angles. Wolfram Community gathers talented and experienced data scientists, biologists, chemists, supply chain experts, epidemiologists, mathematicians, physicists and more. In recent weeks, we’ve seen a flurry of activity and exploration, a willingness to share ideas and information, and mutual encouragement from industry professionals and high-school students alike.
With many schools transitioning to remote learning for the remainder of the school year, educators face the challenge of maintaining the same quality of education as in-person lessons. Here's a collection of the resources offered by Wolfram Research and others to help educators in an e‑learning environment.