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The Quick 10: The Ten

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I have to admit, the long weekend has made me a little lazy today. This was my thought process in thinking of my Quick 10 Topic: "Quick 10"¦ Quick 10"¦ 10"¦ Ten. The Ten. A Quick 10 of The Ten. Yes. Brilliant."
The Ten is a group of 10 American Impressionist painters who quit the Society of American Artists in the late 1800s when they felt it had become too commercial. But who were they? I'm happy to oblige"¦

The Quick 10: The Ten

Seated, left to right: Edward Simmons, Willard L. Metcalf, Childe Hassam, J. Alden Weir, Robert Reid
Standing, left to right: William Merritt Chase, Frank W. Benson, Edmund C. Tarbell, Thomas Wilmer Dewing, Joseph Rodefer De Camp

1. Childe Hassam. Hassam's most famous works are the series of 22 flag paintings he started in 1916. They show Fifth Avenue, 57th Street and other streets near Hassam's gallery at the time.

2. J. Alden Weir. Weir was the first president of the Association of American Painters and Sculptors but resigned only a year after being named when the society sponsored the modernist Armory show. His brother was a well-known landscape artist.

3. John Henry Twachtman. Twatchman is famous among art historians for his personal style "“ his interpretation of Impressionism was much more experimental than his contemporaries.

4. Robert Reid. Reid was a mostly "decorative" painter "“ many of his works were of young women sitting in the middle of a field of flowers. He was an instructor at the School of the Museum of Fine Arts, Boston, of which he was also an alumnus.

5. Willard Metcalf. Speaking of the School of the Museum of Fine Arts, Boston, Willard Metcalf was a student there as well. He is thought to have been the first American painter to visit Giverny, the location of Claude Monet's home and garden. He ended up marrying one of the models he used in a mural for a New York courthouse - Marguerite Beaufort Hailé, a stage performer 20 years younger than him. She ended up leaving him for one of his students. In 1923, his work Benediction sold for $13,000 "“ at the time, a record selling price for an American artist who was still alive.

6. Frank Weston Benson. Benson wasn't really considered an Impressionist until after he joined The Ten. Prior to that he had been working on decorative murals for the Library of Congress. Late in his career, Benson became famous for his depictions of his wife and daughters exploring nature at their summer home in Maine. After 1920, however, he started painting a plethora of wildlife. In 1995, a Benson oil painting sold for $4.1 million. More recently, a Benson was donated to Goodwill, which put the work up for auction on its site. It started at $10, but once the piece was verified as an authentic Benson, it ended up selling for $165,002.

7. Edmund Charles Tarbell. Tarbell developed quite the following in Boston "“ in fact, his followers were called the Tarbellites. Like Benson, Tarbell used his wife and children as models in much of his work"¦ except, of course, when he was doing portraits. His portraits included U.S. Presidents Woodrow Wilson, Calvin Coolidge and Herbert Hoover and can still be found in the White House.

8. Thomas Wilmer Dewing. Look at Dewing's paintings and you'll probably notice a theme: women. Women playing instruments, women writing letters, women standing, women sitting. Lots of women. In fact, some critics call him sexist, saying he painted empty-eyed women lounging around in pretty dresses doing nothing.

9. Joseph DeCamp. Probably one of the lesser-known of The Ten, but with good reason: in 1904, his Boston studio caught on fire and hundreds of his works were destroyed, including pretty much all of his landscapes.

10. Edward Simmons. Simmons was probably best known for his murals. After graduating from Harvard, he went on to win the first commission of the Municipal Art Society. They had him paint a series inside of the Criminal Courthouse in Manhattan. He also did murals for the Waldorf-Astoria in New York, the Library of Congress and the Capital at Saint Paul, Minn.

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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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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]