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Rosetta snaps a selfie. Courtesy ESA/Rosetta/Philae/CIVA.

Why Did We Land on a Comet?

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Rosetta snaps a selfie. Courtesy ESA/Rosetta/Philae/CIVA.

Earlier today, as part of its Rosetta mission, the European Space Agency landed its Philae probe on Comet 67P/Churyumov-Gerasimenko—the first time in history this feat has ever been achieved. But why land a probe on a comet?

The Rosetta spacecraft launched in March 2004 and began its 10-year journey across the solar system into deep space—more than five times the distance between the Earth and the Sun—to rendezvous with Comet 67P/Churyumov-Gerasimenko, which occurred on August 6, 2014. The lander deployed today contains 10 scientific instruments; its batteries, which will drain after 64 hours, will be recharged by solar panels, allowing for one hour of exploration every two days. While the probe is exploring, the Rosetta spacecraft will continue to orbit the comet’s nucleus, following the celestial body on its path around the Sun (another historical first). The mission concludes in December 2015.

One of Rosetta’s goals is simply to observe a comet up close. “We have only observed comets from afar,” Joel W. Parker, a planetary scientist at the Southwest Research Institute in Boulder, Colorado, and the deputy principal investigator for an ultraviolet spectrograph instrument on the Rosetta spacecraft, told the New York Times. “Even the previous spacecraft flybys have been brief and could only study the comet by what they saw remotely. It is like the difference between what you can learn taking pictures from an airplane versus a geologist digging directly into the ground.” Scientists will also look at what happens to a frozen comet when it encounters the warmth of the Sun.

But the mission’s name holds some clues to its primary purpose: The Rosetta Stone, which helped us decipher hieroglyphics, and thereby understand the civilization in Ancient Egypt. The Rosetta spacecraft will help scientists understand comets, the oldest and most primitive bodies in the Solar System (at least, as far as we know). And by understanding comets, scientists hope to find out a little bit more about how our Solar System and its planets came to be.  From the website:

Rosetta's prime objective is to help understand the origin and evolution of the Solar System. The comet’s composition reflects the composition of the pre-solar nebula out of which the Sun and the planets of the Solar System formed, more than 4.6 billion years ago. Therefore, an in-depth analysis of comet 67P/Churyumov-Gerasimenko by Rosetta and its lander will provide essential information to understand how the Solar System formed.

There is convincing evidence that comets played a key role in the evolution of the planets, because cometary impacts are known to have been much more common in the early Solar System than today. ... Previous studies by ESA’s Giotto spacecraft and ground-based observatories have shown that comets contain complex organic molecules. These are compounds that are rich in carbon, hydrogen, oxygen, and nitrogen. Intriguingly, these are the elements that make up nucleic acids and amino acids, essential ingredients for life as we know it.

With this mission, scientists hope to determine whether life on Earth began with the help of a comet impact (or “comet seeding”). And even if Rosetta doesn't provide an answer, it will still provide scientists with a lot to study. You can follow the progress of the mission here, and keep up with the Philae probe's activities on the comet's surface 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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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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