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4 Places That Will Never See a Club Med

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By Martin Lewis

What exactly is the worst climate in the world? Whether a given climate is good or bad is subjective; to a native of northern Alaska, for instance, 75°F can seem miserably hot. But, in general, what makes for the worst climate depends on what you dread the most: fire or ice. Here are 4 places we're not planning on setting up shop.

1. Jacobabad, Pakistan

Anyone averse to fire should avoid spending a summer in Death Valley, California, where the average July temperature is 101°F, or Marble Bar, Australia, which once recorded 161 days in a row when the mercury topped 100°F. Even hotter—or at least more sticky—times can be had in Jacobabad, Pakistan.

Here the average June high temperature is 114°F, with relative humidity averaging nearly 60% in the morning hours.

Dust storms are also frequent at this time of the year. Add to that the prevalence of Islamic extremism and clan feuds in the area, and Jacobabad might not be the ideal place for resort development.

2. Djibouti, Africa

djibouti.jpg At least Jacobabad, like Death Valley and Marble Bar, has relatively pleasant winters. For year-round heat and general unpleasantness, the best selection is probably Djibouti, in northeastern Africa, where it's always hot, always humid, and hardly ever rains. Djibouti's winters are marginally bearable, with average high temperatures in the mid-80s Fahrenheit and relative humidity at midday hovering at 70%, but the rest of the year is something else. By July expect a temperature range from 87°F at night to 106°F in the afternoon, with early morning relative humidity around 60%. The people of Djibouti are especially inclined to seek shelter during the summer months when the khamsin wind blows in from the desert, compounding the heat with ample quantities of dust and grit.

3. Sakha, Siberia

yakutia_photo1.jpgIce haters should avoid the polar areas, but that's easy enough, since no humans live there. Roughly 1 million people, on the other hand, live in Sakha (or Yakutia) in east-central Siberia. In its capital city of Yakutsk, the average January temperature is -45.4°F. Further north, Verkhoyansk enjoys an average January high temperature of -54°F. Cultural practices exacerbate the discomfort: in the winter, the local people traditionally live with their horses and cattle, subsisting on milk tar—an intriguing blend of fish, berries, bones, and the inner bark of pine trees conveniently dissolved in sour milk. Not surprisingly, Russia's Czarist and Communist authorities used to enjoy exiling troublesome intellectuals to this region. But partially as a result, the people of Sakha are now noted for their intellectual and political sophistication.

4. Kerguelen

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Despite its winter frigidity, Sakha's brief summers are sweet. For incessant unpleasantness, look to maritime locations between 50° and 60° latitude, where raw temperatures; brisk winds; and rain, sleet, and snow predominate year-round. Alaska's Aleutian Islands certainly fit the bill, but the best example is probably Kerguelen, a sizable French-owned archipelago in the southern Indian Ocean. Kerguelen experiences precipitation on more than 300 days a year, and its average temperatures range from 35.6°F in July to 45.5°F in January.

Kerguelen has no flying insects—not too surprising considering its average wind speed of 35 kph, which would quickly send the hapless butterfly far out to sea.

Thus even the ubiquitous Kerguelen cabbage, a former godsend for scurvy-racked whalers, has adapted to being pollinated by wind rather than insects.

Ed Note: This list was pulled from Condensed Knowledge, available here.

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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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Name the Author Based on the Character
May 23, 2017
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