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Harriet Tubman's Perfect Record

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How did Harriet Tubman lead so many slaves to freedom on the Underground Railroad? With careful planning, plenty of luck, and a little opium.

“I never run my train off the track, and I never lost a passenger”—so boasted Harriet Tubman, the most successful conductor on the Underground Railroad. Tubman ran up her unblemished record while leading groups of runaways on a 650-mile odyssey from eastern Maryland to St. Catharines, Ontario. Starting in 1850, Tubman made a total of 19 journeys, personally freeing more than 300 slaves. The rewards offered for her capture totaled an astronomical $40,000 (just over $1 million in today’s money), but the bounties went unpaid.

So how exactly did she score that perfect record? Here are some tips based on her harrowing adventures—call it the Tubman Technique.

KNOW THE TERRAIN; MOVE BY NIGHT: Many slaves had never ventured far from their owners’ property. Slave owners deliberately kept them close so they wouldn’t know how to escape. As a result, runaways needed Tubman to do the navigating. She led groups along dirt roads and paths by night. If no safe house was available during the day, Tubman hid her passengers in dense forests, swamps, or other places no one would think to look. When it was safer to split up—a decision she sometimes made when she knew the group was being hunted—Tubman gave simple, easy-to-follow advice for reaching a meeting point, like “follow the drinking gourd” (the Big Dipper, which points north).

MAKE SURE EVERYONE KNOWS WHO’S IN CHARGE: With runaway slaves facing draconian punishments if they were caught, it’s no surprise that Tubman’s passengers occasionally changed their minds and wanted to return to servitude. But Tubman would have none of it—letting fugitives go back to their old homes risked exposing her entire network. When faced with timorous souls, Tubman would brandish her gun and offer them a simple choice: “You’ll be free or die a slave!”

KNOW YOUR LIMITS: Although there were thousands of slaves waiting to be freed, Tubman never bit off more than she could chew. Since large numbers would inevitably attract more attention, she usually conducted runaways in groups of 12 to 15—the most that could safely take cover in an out-of-the-way barn, cellar, or ditch.

DRUG THE KIDS: Since Tubman always tried to keep families together, her traveling parties often included small children who could slow the group down or, worse, give it away by crying at the wrong moment. To curb these problems, Tubman always carried paregoric, an opium tincture that could knock out tots for hours at a time.

WORK THE NEWS CYCLE: Slave owners often ran newspaper ads to alert bounty hunters and law enforcement about substantial rewards for capturing runaway slaves. So Tubman timed her rescues to begin on Saturdays—giving her passengers a 48-hour head start before masters could run ads in the Monday papers.

GET GOOD INTEL: During the Civil War Tubman ramped up her activities through a partnership with the Union Army, which freed slaves to weaken the Confederate economy. On June 1, 1863, Union officers provided 150 black soldiers for a Tubman-masterminded raid on rice plantations along the Combahee River in South Carolina. Tubman used an elaborate network of spies among the slave population to gather detailed intelligence about Confederate defenses, including the location of floating mines in the river. The raid freed around 750 slaves.

WHEN ALL ELSE FAILS, TRY BRIBERY: Underground Railroad conductors were no strangers to “greasing the wheels” by paying off corrupt officials and ordinary citizens. Tubman found bribes especially effective at the Canadian border, where officials could be persuaded to turn a blind eye to “visitors” who clearly weren’t tourists. The bankroll for bribes came from supporters, both white and black, called “stockholders” in railroad lingo.

DON’T BE AFRAID TO USE LIVESTOCK: Tubman’s greatest strength was her ability to think on her feet—but her use of strategic poultry didn’t hurt, either. When a route forced her to pass through her own former master’s hometown, Tubman disguised herself as an old woman and bought two chickens, carrying one under each arm to complete the disguise of a domestic slave fetching dinner. When she spotted her former master approaching in the street, Tubman “lost” the chickens and went scrambling after them, to the amusement of her master and the other white townsfolk—thus allowing her to make a quick escape.

This article originally appeared in mental_floss magazine, available wherever brilliant/lots of magazines are sold. Get a free issue here!

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iStock // Ekaterina Minaeva
technology
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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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Scientists Think They Know How Whales Got So Big
May 24, 2017
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iStock

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]

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