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To the South Pole! ... Er, make that Greenland.

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We have quite a few readers in England, and I am intensely jealous of them, partly because of their access to ready-made scotch eggs and partly because they've had the chance over the last month to watch Blizzard: Race to the Pole, a documentary in which two small groups of lunatics brave explorers recreated the famous British/Norwegian scramble for the South Pole in 1911-12. (U.S. readers not blessed with BBC2 can check out the accompanying book.) The teams traveled over 1,500 miles using only the cold-weather technology their predecessors had: fuzzy hats, wills of steel, and a sparse list of basics you'll find after the jump. Thankfully, there were three rather important differences in the modern day expedition:

1. The leader of the original Norwegian team, short on food, ended up killing and eating his sled dogs as he approached the Pole. The modern dogs suffered no such indignity; they were flown out by helicopter, and the teams chowed down on beef and seal meat instead.

2. Dogs aren't allowed on Antarctica anymore, so the race to the South Pole became a race across Greenland. Hey, cold is cold.

3. Unlike poor Robert Scott, the leader of the Brits' original team, everyone on the modern teams came back alive.

Disclaimer: One of these nutcases the British team doctor is a dear friend.


British Team
Scott's team relied heavily on thick, woollen garments. This is just a sample of the clothing the British team had to protect them:
woollen hats;
balaclava helmets;
wolsey thermal shirts;
woollen jerkins;
woollen scarves;
thick jumpers;
woollen/tweed trousers.

Norwegian Team
Amundsen on the other hand, relied on animal skins:
reindeer skin mittens;
reindeer skin Finneskoe boots;
weal skin anoraks with hood.


British team:
curry powder;
spices, eg ginger;
fresh beef.

Norwegian team:
milk powder;
seal meat.
And lots of dog food"¦

Cooking equipment

stove plus spare;
big pot and lid;
wooden spoons;
tin openers;
one-litre nalgene bottle;
containers and lids, large and medium sized;
lighters and matches;
stainless steel Thermos flasks;
cutting knives;
tea spoons;
tea towels;
small plastic bottle for detergent;
pan scrubbers;
industrial hand cleaner;
travel wipes;
engine wipes;
kitchen roll;
bin liners;
poly bags;
fire extinguisher;
fire blanket;
meths can;
20-litre jerry cans;
MSR fuel bottles and MSR cookset;
large and small funnels;
drum spanner;
drum tap.

And toilet roll"¦

Medical kit

medical books;
burns kit;
neck brace;
head immobiliser;
sutures and needles;
triangular bandages;
disposable gloves;
cough sweets;
pencil and paper;
hand and feet warmers.

Navigational equipment
waterproof map cases;
pens, pencils and paper;
wind gauge;

unleaded gasoline;
two-stroke oil;

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iStock // Ekaterina Minaeva
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
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]