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Michael Habib, Pterosaur Flight Expert

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AMNH

The American Museum of Natural History's latest exhibition, Pterosaurs: Flight in the Age of Dinosaurs, opens today (check out just a few of the things we learned at the exhibit here). At the media preview, we spoke with pterosaur flight expert Dr. Michael Habib about figuring out how these reptiles flew.

When you and other scientists are trying to figure out how pterosaurs flew, do you start with the fossils? Or do you start with an animal alive today because we know the flight mechanics and work backward?

We do a little bit of both. Mostly, you start with fossils. Then you go to the principles of physics—things that are fundamentals that you know are true, because physical laws make them true and they're going to be true for everything. Then you build models from that and validate them using the living things: Does the model make good predictions in birds? Does it make good predictions in bats? If it does, I'm relatively confident that it will make good predictions in pterosaurs.

The trick, of course, is the predictions all have to do with anatomy, and make predictions about the anatomy. To say, "Alright, if this hypothesis is true, then it would look like this and if it was false, it would not look like this." And then you test to see if that is what the anatomy looks like in the animals you have. That's kinda tricky, and part of what you do to make it reasonable, is you pick the tractable questions and tractable approaches. An intractable question for pterosaur flight is "Exactly how fast does a Quetzalcoatlus fly?" And a tractable question is, "Would quetzalcoatlus fly faster or slower than a living large bird?" Comparable questions are more tractable than absolutes.

That question is actually not completely intractable. I can get a good idea of how fast it could probably fly. But I couldn't give you an absolute answer because we don't know exactly what the wing shape was on the animal. So actually the answer would be—it would vary. Flying animals change their speed based on how much fat they've burned on that long trip, for example. They start off as a big fat bird and show up as a little skinny bird. So there is no single answer to that anyway. But I can give you an idea of range. What I can say with more confidence is how pterosaurs would fly relative to certain first principle models and relative living animals.

Pterosaurs came in a huge array of sizes. How would you say the 10 inch little guy, Nemicolopterus cryptus, flies compared to something bigger like Quetzalcoatlus?

Small things tend to be more maneuverable. They fly slowly in terms of mass of speed, but they tend to be more maneuverable. And landing and takeoff is less energetic for them. In this particular case, the animal is not just smaller, it also has other wing characteristics that are associated with highly maneuverable flights, so it would be less efficient but more maneuverable. Quetzalcoatlus would be a faster flyer, overall, because it's so much larger. It would probably be a flat glider that would flap in bursts and they would glide for long periods; it would probably be a soaring animal. Soaring is what we call it when you have an external source of lift—you're gliding, but you're not dropping relative to the ground because you've got some rising air, which is your source of external lift. Quetzalcoatlus probably hunted on the ground and flew between places to eat or escape predators or things like that. 

Would there be differences in take off and landing, too, depending on the animal's size?

We have some good ideas. Take off is sort of my specialty. It turns out that in all fliers—including unpowerfliers like gliding snakes, for example, even flying squirrels, things like that—in all the ones we've measured, launch is effectively ballistic. So the launch is not initiated with the wings. You don't flap yourself into the air, you jump yourself into the air. And then you engage your wings. Now, we don't see that. It's so fast. What it looks like to us is that a pigeon is pulling himself into the air with his wings, but he's actually pushing his feet and then pulling himself higher with his wings. Which might seem like a nitpick, but in terms of physics, it is fundamentally different.

Some animals run into the jump—especially on water; that's mostly where you see them running—some just jump. For pterosaurs, we're fairly certain they would leap as well. Since they walked on both feet and hands, the expectation is that they would probably leap with all four limbs—we call it a quadripedic launch. I have not run the test for all known pterosaurs by any stretch of the imagination. For all the ones I have done analysis on, it appears that that is true, so I would expect small and large ones use quadripedic launch.

That said, a little guy has a much larger room for error than a big one in the sense of, it doesn't have to put as much "oomf" into it. It could, from a power perspective, maybe launch bipedally, but there's no reason to think that it would. A small pterosaur wouldn't have to leap nearly as hard [as a big one] before it could engage its wings. It would probably get relatively much higher, launch more vertically, if it wanted, when it took off.

Big guys would have to launch at a much more shallow angle. That means they need some clearing in front of them in order to take off, which limits their habitat a little bit, and they're going to have to devote a lot of their muscle power to launch, which means you would expect those animals—as per the predictions I’ve mentioned before—if this model were true, you'd expect that there'd be certain anatomical features related to launch that would be exaggerated in big pterosaurs that wouldn't be in small ones. And that seems to hold true. Big pterosaurs are devoting more of their anatomy to that initial takeoff phase because it's a more rigorous phase for them.

What kind of computer programs are you using to model pterosaur flight?

For me personally, I do a lot of my stuff on a Matlab. It's the big bruiser on the market, but it's flexible. The equations it collapses through are surprisingly simple structures. The best expressions are the ones that are as simple as possible. I spend most of my time on a white board, quite frankly.

There's another Jurassic Park movie coming out. What would you want the director to get right about the pterosaurs in that movies if they include them?

Take off is my personal bias. Might as well see if they get that right. And it would actually be kind of embarrassing if they didn't, because they’ve done TV shows and gotten it right. So if Jurassic Park 4 didn't get it right, that would be embarrassing.

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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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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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