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How Untangling Headphones Could Stop Epidemics

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Ever pull your iPod out of your pocket only to find your earbuds hopelessly knotted? Physicist Robert Matthews of England’s Aston University has, and he gets as irked as anyone. But every time he examines a tangled cord, it reminds him of something smaller—and more important. “Despite its apparently trivial nature, spontaneous knotting is of great significance in polymer chemistry and molecular biology,” he says.

For instance, each cell in our bodies contains more than three feet of DNA. If those genetic cords tangle, the results can be devastating to the cell’s health. In studying the strands, Matthews found that Mother Nature has a clever solution for keeping them straight: Strings of DNA end in a loop, and loops can’t easily form knots. Naturally, Matthews wondered whether a similar loop solution might also work for everyday cords.

The Loop Conjecture

In 2010, Matthews embarked on the Great British Knot Experiment, enlisting kids in eight schools to put string—some of which was looped (with each end clipped together to form a tiny circle), others unlooped—in boxes, jumble them around, and then report the results. Matthews found that looped string formed knots one tenth as often as unlooped string of the same length. In other words, uniting the ends of your earbud cables—with, say, a paper clip—will keep knotting to a minimum.

This finding is a godsend for anyone who has ever battled tangled Christmas lights or struggled with knotty necklaces. But Matthews’s discovery may have a much bigger impact. If pharmacologists are able to control microscopic loops in DNA and viruses, a breakthrough in fighting cancer and other maladies, like infections, could follow. In the meantime, pick up a paper clip and slip on your blessedly tangle-free headphones.

This story originally appeared in the Think Small issue of mental_floss magazine. Get a free issue here!

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iStock // Ekaterina Minaeva
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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Name the Author Based on the Character
May 23, 2017
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