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Feline Physics: Why Cats Can Survive Falls From Great Heights

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Cat image via Shutterstock

The other night, I watched a YouTube video featuring a woman standing on her bed, holding a cat upside down by its feet, then repeatedly dropping the cat onto the mattress. Amazingly, every time the cat was released, it immediately righted itself and landed on its feet.

The woman was performing the same basic experiment that French scientist Etienne Jules Marey did back in 1890. Marey, famous for investigations in which his chronophotographic camera was able to capture up to 60 consecutive frames a second, dropped a cat and filmed it. And yes, there’s a clip on YouTube:

The purpose of both of these videos was to demonstrate the cat’s unique innate ability to reorient its body during a fall. There’s even a name for this phenomenon: the “righting reflex.” Animal experts say that the righting reflex is observable in kittens as early as three to four weeks, and is fully developed at seven weeks.

How does the righting reflex work?

First, cats have supersensitive sense organs. A vestibular apparatus in their inner ear acts as a balance and orientation compass. They always know right side up. Second, cats have a unique skeletal structure - an unusually flexible backbone and the absence of a collarbone. So when a cat falls, its senses respond with lightning speed, and it is able to reorient its body and twist its head around so it can see where it’s going to land.

Beyond their amazing aerial spins, cats also have what could be called a built-in parachute. Like many small animals, they have a low body-volume-to-weight ratio, which when falling, allows them to slow their velocity by spreading out and becoming their own parachute. It’s the same kind of maneuver that flying squirrels do in mid-air.

But as amazing as their gravity-defying abilities are, cats are not invincible.

In 1987, veterinarians at New York City’s Animal Medical Center did a study of felines that had fallen from tall buildings. 90% of them survived, though most sustained serious injuries. Of those, more than one-third needed life-saving treatment, while just under a third required no treatment. What’s remarkable is that the study found that cats that fell from heights of 7 to 32 stories were less likely to die than those that fell from 2 to 6 stories.

Why? One theory is that after a certain distance, a cat reaches maximum speed and that vestibular mechanism in its ear shuts off. As a result, the cat relaxes. As any stuntman can tell you, relaxed limbs are less likely to break than unrelaxed ones. Another is that the greater height gives the cat time to adopt its parachute pose.

For those of you who enjoy physics, the “falling cat problem,” as it’s called, has been parsed in diagrams and technical language in online dissertations such as “Gauge Theory of the Falling Cat” and the Monty Python-ish sounding “Aerial Righting Reflexes in Flightless Animals.”

Then, of course, there's The Buttered Cat Paradox, which Miss Cellania discussed in great detail last year.

So over to you, cat owners. Any amazing stories of your kitty taking daredevil falls and landing on its feet?

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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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May 23, 2017
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