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Meerkat Matriarchs Are Selfish Street Crossers

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Why did the meerkat cross the road? To get to the other side. How did the meerkat cross the road? Like a chicken. 

From flocks of migrating birds to human friends on a road trip, lot of animals travel in groups. When they run into trouble during these trips, they'll often change their formation so they can better deal with the problem and keep themselves safe. When a flock of sheep encounter a predator, for example, they clump together, and each individual sheep tries to move towards the center of the group and away from the vulnerable edges, while the group collectively moves away from the threat. In the same situation, elephants form a defensive circle around the calves while the matriarch of the group inspects the threat and may charge at it. Alpha male baboons likewise take up a position at the edges of their groups when moving through dangerous areas in order to protect more vulnerable individuals.

What if the danger to the traveling party isn’t the same old predator that’s been hunting a species for thousands or millions of years, though? What if it's a relatively new threat, like man-made roadways? To find out, Simon Townsend, who studies animal communication and cognition at the University of Zurich, looked to meerkats. 

These members of the mongoose family are highly social and live in groups of up to 50, led by a dominant mating pair. They deal with predator attacks from both land and air, and deploy subordinate group members as sentries to keep watch while the group forages. In South Africa’s Kalahari Desert, they also have to contend with roads that cut through their territories. 

Meerkat groups are matriarchal, so the alpha female runs the show and leads the group on foraging trips and burrow excavations and in conflicts with other meerkat groups. Given their importance in the group, Townsend figured that these females would be wary of the danger that roadways presented. Specifically, he predicted that even given the novelty and recent appearance of the roads, the matriarchs would position themselves deep within the group to maximize their own safety while crossing. 

After periodically watching different meerkat groups at the road over the course of a year, Townsend found that while the alpha female usually started at the front of the group in the walk to the road, more than half the time they dropped back into the middle of the group and let one or more subordinate females go first as they crossed. When the lower-ranking females were at the front of the group to begin with, they tended to stay there when crossing the road. Using this data, Townsend created computer simulations of meerkat crossings that allowed him to quantify the shuffling of positions that happened at the side of the road, and found that the dominant females were about 40 percent more risk averse than the other females. 

Chimpanzees, Townsend points out, also change their behavior in response to the danger posed by roads. While crossing, the alpha male and other high-ranking males usually take up exposed positions at the front and rear of the group so they can scan the road or keep an eye on all the other group members. In the way they handle a road crossing, both these meerkats and chimps are showing off their mental flexibility, applying and adapting old behaviors to new threats. 

In comparison to the chimps, who take up more vulnerable positions that allow them to protect the group, the meerkat matriarchs’ retreat to the middle of the group seems selfish. It’s still for the good of the group, though: The matriarchs are the core of the meerkat social structure, and when they die, groups have been known to completely break down and disperse, leaving lone animals to fend for themselves. Being a wimp and moving in the middle of the pack reduces both the risks to themselves and to group stability. 

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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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