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10 Fun Photos of the 2014 Winter Classic

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Every New Year's Day since 2008, hockey fans have watched the NHL Winter Classic, which pits two teams against each other in a regular-season game—but instead of hitting the ice in an enclosed arena, the teams play hockey as it was intended to be played: outside. Yesterday, the Toronto Maple Leafs and the Detroit Red Wings faced off in Ann Arbor's Michigan Stadium, usually home to the University of Michigan's football team. You can see how the stadium was transformed in the time-lapse video below.

During the game, it snowed a lot, with wind chill causing temperatures to drop to -1 degrees Fahrenheit. The Leafs eventually won the game in a 3-2 shootout victory. Here are a few fun photos from the game.

1. Throwback Sweaters

It's Winter Classic tradition for teams to wear throwback jerseys. Detroit's sweaters—seen here on Captain Henrik Zetterberg (#40)—were a combination of the 1920s-era Detroit Lions uniforms, the winged wheel from the 1930s Red Wings, and the font and number system from the 1980s. Toronto's jerseys—as seen on Jake Gardiner (#51)—got their stripes from the Leafs 1930s uniforms with the inaugural Leafs logo from 1924; the neckline comes from the team's 1960s jerseys and the stitch on the numbers is inspired by its 1950s uniforms.  "Each NHL Winter Classic uniform is an embodiment of that team's core brand values and represents memorable milestones in that team's history," Brian Jennings, NHL Executive Vice President of Marketing, said in a press release.

2. A little something extra

Maple Leafs goaltender Jonathan Bernier donned a tuque (or knit hat) over his helmet to keep heat from escaping. The leg pads and blockers he and Red Wings goalie Jimmy Howard wore are reminiscent of old-school leather pads.

3. Taking a Fall

During the first period, the Red Wings' Brendan Smith (#2) lost an edge and fell to the ice.

4. Where's the puck?

Bernier scrambles to make a save.

5. Let it Snow

According to Weather.com, it snowed 5.2 inches in Ann Arbor yesterday. During breaks in the action, workers removed the snow from the ice with shovels.

6. Brrrr

The snow made playing pretty tough. "Sometimes you're skating with the puck and then the puck was behind you, because it hit a pile of snow or something," Detroit's Brendan Smith told Sports Illustrated. It was so cold that the tape on the players' sticks lost its stickiness, making puck control difficult. 

7. Fansanity

Cold temperatures couldn't dampen the enthusiasm of the crowd: More than 105,000 people came out to see the game, which also delivered record ratings for NBC.

8. Goal!

The Red Wings' Justin Abdelkader (#8) celebrates his third-period goal, which tied the game 2-2. 

9. On the Bench

Leafs players watch the game from the bench. (Players on both sides wore eye black to cut down on glare and improve contrast sensitivity.) Head coach Randy Carlyle (standing) told the New York Times that “the snow coming down brought back a lot of memories from a childhood of playing outside. Growing up in northern Ontario, I never really played indoors until I was 14 years old."

10. For the Win

After 60 minutes of regulation time and 5 minutes of sudden-death overtime, the game went to a shootout. In the third round, Toronto's Tyler Bozak (#42) got the puck past the Red Wings' Howard. Despite his team's loss, Red Wings coach Mike Babcock told the New York Times, “Today was spectacular. It was a home run for hockey.”

All photos courtesy of Getty Images.

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iStock // Ekaterina Minaeva
technology
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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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Scientists Think They Know How Whales Got So Big
May 24, 2017
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It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]

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