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hi, i'm matthew

The world of embedded hardware and firmware is confusing. If you’re already confused, “firmware” just means “software that runs very close to the hardware.” And “very close to the hardware” means that you have no operating system to work with. The code you run is the only code there is. Nothing’s magic, and that’s important to remember, probably in general. I’d guess the culprit is the variation among manufacturers which becomes accentuated with embedded platforms since you are working so close…

Back in April I pre-ordered the new reMarkable 2 e-paper tablet. Shipping took several months, but I finally received my tablet back in November. In the days leading up to receiving my unit, I heard mixed, but overall positive reviews for the new device. After working with the device for the last month, I wanted to add my thoughts to the mix. The basics are there: palm rejection is phenomenal since the pen uses different technology than the touchscreen, writing is fluid with no noticeable delay…

In his 1981 and 1983 Baseball Abstracts, pioneering sabermetrician Bill James proposed the log5 method for mixing event probabilities, which is similar to metrics used in other fields. Here are two motivating scenarios: Team A has winning percentage . Team B has winning percentage . What is the expected winning percentage of Team A against Team B? A pitcher strikes out 20% of batters he faces. A batter strikes out 10% of the time. What is the expected strikeout rate in this matchup? Winning…

I’ve recently been reading about the JointVAE model proposed by Emilien Dupont in the paper Learning Disentangled Joint Continuous and Discrete Representations. The paper builds on the development of variational autoencoders (VAEs). As a quick overview, autoencoders are neural networks that take an input, generate some “secret” representation of it, then try to use that “secret” code to reconstruct the input. If it can do that well, then we have found a secret code that summarizes the important…

One of my courses recently introduced Markov Chain Monte Carlo (MCMC) sampling, which has a lot of applications. I’d like to dive into those applications in a future post, but for now let’s take a quick look at Metropolis-Hastings MCMC. A Brief Prologue Let’s say we have a probability distribution function (within a mulplicative constant) that is very complex. We have an equation, but maybe it is impossible to integrate. Somehow, we’d like to draw samples from this distribution to estimate…

Let’s say that you and I are roommates, and I notice you’ve been gone the last two Friday nights. This is not necessarily unusual, and sometimes your Friday night excursion is a date. However, you don’t communicate well, so I have no idea if you had a date or not. As you prepare to go out for the third Friday in a row, I wonder if this Friday you have a date. You won’t spill the beans, but I have a mind-reading superpower — *pause for dramatic effect* — math 🔥. I make an educated guess that if…

How AI recognizes things it has only seen a handful of times, once, or not at all

Numpy defaults to OpenBLAS, but conda has automatic MKL support. How can we tell Numpy to skip OpenBLAS and use MKL?

You could use the Cloudera Quickstart VM, but then you wouldn't be able to use Kotlin.

As competition season comes upon us, several suggestions come to mind for students eager to learn the methods and madness of science fair competitions.

Recently, I was attempting to setup a Raspberry Pi Zero W with the latest Raspbian Buster Lite image and ran into an ... uh ... issue.

(UPDATE: I didn't win.) Hackster recently (read: September) announced a LEGO MINDSTORMS EV3 and Alexa challenge.

I've considered starting a blog more than once over the past few years. Also, markdown is the best.