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How Did the States in the USA Get Their Names? (Part V)

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Reader Adam from Fairfax, Virginia, wrote in to ask, “How did the US states get their names?” This week, we’re tackling the origins and meaning of the names 10 states at a time. Here’s South Dakota through Wyoming.

(Be sure to also check out Monday’s post on Alabama through Georgia, Tuesday’s post on Hawaii through Maryland, Wednesday’s post on Massachusetts through New Jersey, and Thursday's post on New Mexico through South Carolina.)

South Dakota

North and South Dakota both take their names from the Dakota, a tribe of Siouan people who lived in the region. No detailed etymology of Dakota is widely accepted, but the most common explanation is that it means “friend” or “ally” in the language of the Sioux.

Tennessee

While traveling inland from South Carolina in 1567, Spanish explorer Juan Pardo passed through a Native American village in modern-day Tennessee named Tanasqui. Almost two centuries later, British traders came upon a Cherokee village called Tanasi (in present-day Monroe County, Tennessee). No one knows whether Tanasi and Tanasqui were actually the same village, though it is known that Tanasi was located on the Little Tennessee River and recent research suggests that Tanasqui was close to the confluence of the Pigeon River and the French Broad River (near modern-day Newport). Tennessee could have come from either one of these village names, but the meanings of both words have since been lost.

Texas

Texas comes from teysha (sometimes spelled tejas, tayshas, texias, thecas, techan, teysas, or techas), a word widely used by the natives of the eastern Texas region before the arrival of the Spanish. The tribes had various spellings and interpretations of the word, but the usual meaning was “friends” or “allies.” Some tribes, like the Hasinais and the Caddo, used it as a greeting, “hello, friend.” This is the usage that Spanish explorers picked up and used to greet friendly tribes throughout Texas and Oklahoma. The explorers also applied the word as a name for the Caddo people and the area around their East Texas settlement.

Utah

Derived from the name of the native tribe known as the Nuutsiu or Utes (which itself may come from the Apache yudah, yiuta or yuttahih, meaning “they who are higher up”), whom the Spanish first encountered in modern-day Utah in the late 1500s. In the tribe’s language, ute means “Land of the Sun.” (The tribe referred to themselves as the “Nuciu” or “Noochew,” which simply means “The People.”)

Vermont

Derived from the French words vert (“green”) and mont (“mountain”). Samuel Peters claimed that he christened the land with that name in 1763 while standing on top of a mountain, saying, “The new name is Vert-Mont, in token that her mountains and hills shall be ever green and shall never die." Most historians would disagree, as would Thomas Young, the Pennsylvania statesman who suggested that his state’s constitution be used as the basis for Vermont's and is generally credited with suggesting the name to maintain the memory of the Green Mountain Boys, the militia organization formed to resist New York’s attempted take-over of the area.

Virginia

Named for Queen Elizabeth I of England (known as the Virgin Queen), who granted Walter Raleigh the charter to form a colony north of Spanish Florida.

Washington

Named in honor of the first president of the United States, George Washington. In the eastern US, the state is referred to as Washington State or the state of Washington to distinguish it from the District of Columbia, which they usually just call “Washington”, "D.C." or, if they're very local, "the District."  Washingtonians and other Pacific Northwesterners simply call the state “Washington” and refer to the national capital as “Washington, D.C.” or just “D.C.”

West Virginia

West Virginia, formed from 39 Virginia counties whose residents voted to form a new state rather than join the Confederacy, was named after the same queen as the state it split from, though the new state was originally to be called Kanawha.

Wisconsin

Derived from Meskousing, the name applied to the Wisconsin River by the Algonquian-speaking tribes in the region. The French explorer Jacques Marquette recorded the name in 1673, and the word was eventually corrupted into Ouisconsin, anglicized to its modern form during the early 19th century, and its current spelling made official by the territorial legislature in 1845. Modern linguists had been unable to find any word in an Algonquian language similar to the one Marquette recorded, and now believe that the tribes borrowed the name from the Miami meskonsing, or “it lies red,” a reference to the reddish sandstone of the Wisconsin Dells.

Wyoming

Derived from the Delaware (Lenape) Indian word mecheweami-ing (“at/on the big plains”), which the tribe used to refer their home region in Pennsylvania (which was eventually named the Wyoming Valley [Wilkes-Barre represent!]). Other names considered for the new territory were Cheyenne, Shoshoni, Arapaho, Sioux, Platte, Big Horn, Yellowstone and Sweetwater, but Wyoming was chosen because it was already in common use by the territory’s settlers.

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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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