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Nightmare on Wall Street: 4 Other Times Our Economy Tanked

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When we think of economic crises in America, two periods come to mind—the Great Depression and whatever it is we're in the middle of right now. But the U.S. stock market has crashed more times than we'd like to admit. Historically, our economy has been brought to its knees by everything from greedy bankers to horse illnesses. So let's take a deep breath and remember that panics are just part of the American way of life.

1. The Panic of 1873: America Stops Horsing Around

During the late 19th century, the American economy relied on horses the way it depends on gas today. Horses unloaded cargo from ports, transported goods from city to city, worked the farms, supported the army, and served as the emergency vehicles of choice. Without them, the American workforce would have ground to a halt.

And that's exactly what happened in 1872, when an estimated 99 percent of all horses in America contracted equine influenza. The highly contagious strain started in Canada and spread through New England to the South in a matter of months, leaving horses across the country too weak to stand and coughing uncontrollably. Street buggies stopped running, paralyzing commerce in the cities. Railroads were stymied because trains run on coal—coal that was hauled out of mines by horses. And as the horse flu spread, U.S. military troops had to go into battle on foot (they were fighting Apache Indians at the time). More tragically, a fire in Boston raged for three days because there were no horses to carry water. The flames destroyed more than 700 buildings, causing an estimated $73.5 million in damages and killing at least 20 people.

The "Great Epizootic," as it was called, spiraled out of control in less than a year. At the height of the panic, as many as 20,000 businesses failed, a third of all railroads went bankrupt, and unemployment spiked to almost 15 percent. The economy took nearly a decade to recover. Ironically, nearly all of the horses recuperated by the following spring.

2. The Winter of 1886: When the Cows Don't Come Home

During the second half of the 19th century, cattle ranches in the American West were thriving. From the Montana grasslands to the Texas prairie, ranches were attracting investors back East and across the pond in Europe. But by 1886, things were getting dicey. Overgrazing, coupled with a hot and dry summer, had left the plains almost bare.

Then came the snow. Known as the "Winter of Death," the following season saw one of the worst cold spells in recorded history. More than half the cattle in the West froze to death, unable to move in the thick snow. Ghoulish firsthand accounts describe the bodies of dead cows stretching for miles across the horizon. When the spring thaw and floods came, thousands of bloated corpses floated into the streams and rivers. Some ranchers quit the business entirely and didn't even bother to round up their surviving cattle.

By the end of 1887, the disaster had wiped out more than half of the United States' western cattle and debilitated the national economy. Most cattle investors went bankrupt, and thousands of cowboys were left unemployed. But more than anything, the winter of 1886 put an end to all those turn-of-the-century, idyllic fantasies of open-range ranching in the Wild West.

3. The Panic of 1907: Captains of Industry to the Rescue!

The Panic of 1907 started the way many panics do, with a greedy capitalist. Multimillionaire Augustus Heinze, who had made his fortune mining in Montana, believed he had enough control over the copper industry to corner the market. With the help of several major banks, he concocted a scheme to buy up all the shares of United Copper. But Heinze had overestimated his prowess, and the scheme failed, bringing down Heinze, United Copper, the banks, and many, many stockholders. The debacle sent ripples of anxiety throughout the market, and investors started pulling their money out of banks altogether. After one of New York City's biggest trusts went under, panic ensued, and the stock market collapsed.

JP-Morgan.jpgAt the time, there were no central banks in place, so the federal government had no means of bailing out businesses or injecting cash into the economy. It just stood by, idly waiting for a hero to save the day. Amazingly, one did.

James Pierpont Morgan, banker extraordinaire, rescued the American economy. He propped up many of the failing banks in New York by twisting the arms of other financiers, and he assuaged investors' fears by backing up the market with his own vast cash reserves. Before long, Wall Street was on the mend.

The government also learned its lesson. With the panic resolved, it created the Federal Reserve, ensuring that it could buttress the economy during hard times. Since then, the government has taken a more active role in financial matters and relied less on the kindness of robber barons.

4. Whale of a Crisis: The Collapse of America's First Oil Industry

During the early 19th century, America was one of the top oil-producing countries in the world. But it wasn't petroleum the nation was exporting; it was whale oil. By the mid-1800s, the high-risk, high-profit business was the fifth-largest industry in the United States. At its height, the American whaling industry produced more than 10 million gallons of oil a year and sold it for $1.77 a gallon (about $35 per gallon today). Better still, an American fleet of 1,000 ships had exclusive access to the North Atlantic territories, which ensured profits.

What could have stopped such a juggernaut of an industry? For one thing, other sources of oil. In 1846, Canadian geologist Abraham Gesner developed a technique for distilling kerosene from petroleum, and within a few decades, kerosene had replaced whale oil as the most popular fuel for lamps. Another reason for the decline was that the whales were dying off. The enthusiastic slaughter throughout the 1800s drove some whale species to extinction and put others on the brink. With so few left to hunt, the cost of whaling became prohibitively expensive. The final blow to whalers came during the harsh winter of 1871, when the North Atlantic ice trapped and crushed the bulk of the American fleet.

Although American consumers didn't suffer as the country switched from whale oil to petroleum, coastal towns in New England and the Mid-Atlantic languished, and shipbuilders and fishermen found themselves out of work. By the time of the Civil War, whaling ships had become so worthless that Union soldiers loaded a fleet of them with stones and sank them into Charleston harbor. The hope was to blockade the South from the port, but when the plan didn't work, the ships were no great loss. America's first oil industry had been tapped out.

This article originally appeared in the January-February issue of mental_floss magazine "“ available wherever brilliant/lots of magazines are sold. You can learn more about mental_floss here.

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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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Stephen Missal
New Evidence Emerges in Norway’s Most Famous Unsolved Murder Case
May 22, 2017
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A 2016 sketch by a forensic artist of the Isdal Woman
Stephen Missal

For almost 50 years, Norwegian investigators have been baffled by the case of the “Isdal Woman,” whose burned corpse was found in a valley outside the city of Bergen in 1970. Most of her face and hair had been burned off and the labels in her clothes had been removed. The police investigation eventually led to a pair of suitcases stuffed with wigs and the discovery that the woman had stayed at numerous hotels around Norway under different aliases. Still, the police eventually ruled it a suicide.

Almost five decades later, the Norwegian public broadcaster NRK has launched a new investigation into the case, working with police to help track down her identity. And it is already yielding results. The BBC reports that forensic analysis of the woman’s teeth show that she was from a region along the French-German border.

In 1970, hikers discovered the Isdal Woman’s body, burned and lying on a remote slope surrounded by an umbrella, melted plastic bottles, what may have been a passport cover, and more. Her clothes and possessions were scraped clean of any kind of identifying marks or labels. Later, the police found that she left two suitcases at the Bergen train station, containing sunglasses with her fingerprints on the lenses, a hairbrush, a prescription bottle of eczema cream, several wigs, and glasses with clear lenses. Again, all labels and other identifying marks had been removed, even from the prescription cream. A notepad found inside was filled with handwritten letters that looked like a code. A shopping bag led police to a shoe store, where, finally, an employee remembered selling rubber boots just like the ones found on the woman’s body.

Eventually, the police discovered that she had stayed in different hotels all over the country under different names, which would have required passports under several different aliases. This strongly suggests that she was a spy. Though she was both burned alive and had a stomach full of undigested sleeping pills, the police eventually ruled the death a suicide, unable to track down any evidence that they could tie to her murder.

But some of the forensic data that can help solve her case still exists. The Isdal Woman’s jaw was preserved in a forensic archive, allowing researchers from the University of Canberra in Australia to use isotopic analysis to figure out where she came from, based on the chemical traces left on her teeth while she was growing up. It’s the first time this technique has been used in a Norwegian criminal investigation.

The isotopic analysis was so effective that the researchers can tell that she probably grew up in eastern or central Europe, then moved west toward France during her adolescence, possibly just before or during World War II. Previous studies of her handwriting have indicated that she learned to write in France or in another French-speaking country.

Narrowing down the woman’s origins to such a specific region could help find someone who knew her, or reports of missing women who matched her description. The case is still a long way from solved, but the search is now much narrower than it had been in the mystery's long history.

[h/t BBC]