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Desolation Vacation: Mina, Nevada

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There are few landscapes in the United States lonelier than that of western Nevada. Towns -- remote outposts connected by endless, thin ribbons of highway -- are named for what miners used to pull out of the ground: Coaldale, Silverpeak, Goldfield. But the mining industry in places like Mineral County has largely disappeared, and with it, the towns it gave birth to. Those that aren't ghost towns already cling precariously to life, burned-out and abandoned structures at their margins creeping inexorably toward the center like some scabrous and fatal disease. For many, it's just a matter of time; even those hamlets that still have a few hundred people living in them are sometimes left off of state road maps. For someone who's attracted to desolate places and question marks on big, empty-looking maps -- someone like myself -- this was a part of the country I had to see for myself.

There are many ghost and near-ghost towns in Mineral County -- a county that boasts just 5,071 residents, or about one per square mile. 261 of those people live in Mina, a town named for a railroad executive's daughter 100 years ago, which in Spanish means "ore." The railroad and mining operations are long gone, and from the looks of things, at least half the town sits abandoned. Best known for a 1921 murder scandal that resulted in the world's first execution by lethal gas, today Mina is a perfect example of a desert town on its way out.

Above: inside the first house I saw in Mina. (Yep, that's a tumbleweed.) Below: its charming exterior.


What's left of a trailer:

I didn't see a working gas station, and the motel, obviously, is closed.

There is, however, one restaurant. It's called the Desert Lobster, and it's inside a boat. I have no problem clambering around inside abandoned and possibly unsafe buildings, but I was not man enough to eat at the Desert Lobster.

A charming little peaked-roofed number on the other side of town.

Written with a stick in wet cement 65 years ago, just two weeks after the Nazis finally surrendered.

The quintessential shack.

Inside, I learned that LaMona loves Tony.

I also liked this vantage, through the backdoorless back door.

For the life of me, I couldn't figure out what the few people who still lived in Mina did. How do people live in a town without an economy, 35 miles from the nearest gas station? To make matters even more confusing, I noticed there was an airstrip on the outskirts of town, and while it wasn't bustling, it definitely wasn't abandoned. Why would people need to fly in and out of this place -- and who in this post-apocalyptic town could even afford to? Another mile down the road outside of town, and I had my answer: the Playmate Ranch.


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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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Name the Author Based on the Character
May 23, 2017
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