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13 Sonorous Terms for Snoring from Across the U.S.

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According to the National Sleep Foundation, about 90 million Americans experience some kind of snoring activity, from simple snoring to sleep apnea. That’s a lot of people blowing Zs (as they might say in Pennsylvania), which requires a lot of ways to say "snore." Here are 13 from across the U.S., brought to you thanks to our friends at the Dictionary of American Regional English (DARE), in honor of Stop Snoring Day.

1. SAW LOGS

To saw or cut logs is a snoring expression that’s widespread except in the Northeast. You could also say you’re sawing big logs or sawing logs and stacking them.

2. SAW WOOD

Variations include chop wood, cut timber, cut wood, and buzz wood. The use of these terms is widespread but less frequent in the South Atlantic, Inland South, and Lower Mississippi Valley.

3. SAW (OR CUT) GOURDS

Like sawing logs or wood, except with gourds. Chiefly used in the South and South Midland.

4. HIT A KNOT

Knots in wood are dense. Hence, all the sawing noise comparable to snoring. Hit a knot is lumberjack lingo that might be heard in New England and the Great Lakes region, as well as California and Colorado.

5. TAKE TWO ROWS AT A TIME

This South Midland idiom meaning to sleep very soundly or to snore might refer to working two rows at a time in a field with some type of farming machinery, resulting in double the commotion.

6. MOW HAY

We’d say some somnolent sounds are definitely as loud as a hay mower. This term might be used in California.

7. CALL HOGS

This term chiefly used in the South and South Midland is attested in both Scotland and England, according to DARE, where call comes from the Scots word meaning “to drive” and hog actually refers to a yearling sheep. (The English hog refers to, well, a hog.) Variations include call pigs, cows, or dogs, and drive pigs.

8. AND 9. PULL CORN AND CRACK CORN

You might hear pull corn—meaning to pick or gather in corn—in Florida and Virginia, while crack corn might be used in Indiana. To crack corn means to crush it into small pieces.

10. RAKE UP THE COALS

Snoozing up a storm in Massachusetts? You’re raking up the coals.

11. KNOCK OR RATTLE THE SHINGLES

Refers to any clamorous activity, especially snoring, and includes variations such as rip or tear. “You sure ripped off a heap of shingles last night!” you might tell a vociferous slumberer, as per a quote in DARE. Or of a boisterous party: “They certainly tore off the shingles last night.”

12. GRIND GRAVEL

This saying for rowdy repose might be used in Wisconsin.

13. COOK TURNIPS

Named for the turbulent activity of boiling root vegetables, cook turnips is a chiefly Pennsylvanian term. Variations include cook coffee and cook (or boil) cabbage.

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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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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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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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