From the Archives: Smooth Criminals

Today's second archival tidbit comes from our current issue, on stands now. Shame on you if you don't already have a copy.

The Original Ocean's 11: The 2003 Antwerp Diamond Heist

If you think George Clooney's "Ocean's 11" character was smooth, check out the velvet finish on criminal mastermind Antonino Finotto. In February 2003, Finotto and his gang of thieves, known as the School of Turin, pulled off one of the stealthiest heists in history. Daring to break into the famous Antwerp Diamond Center—a building that holds 80 percent of the world's diamonds—the group made out with $120 million in jewels.

How They Did It: Not ones to rush into something this big, the Turin boys began laying the groundwork for the project three years prior. Posing as a company owner, gang member Leonardo Notarbartolo rented an office in the Diamond Center in 2000 and proceeded to obtain copies of master keys and learn how the alarm system worked. Then, the group waited for the perfect distraction—the Diamond Games tennis tournament on February 16, 2003. As Venus Williams wowed throngs of spectators (many of them Diamond Center employees and security guards), Finotto's crew used their duplicate keys to sneak into 123 of the building's underground vaults. Simply riding the elevator down to the basement, they deactivated a motion sensor and taped over light detectors. Then, instead of just covering the lens of the CCTV (closed circuit television) security cameras, they avoided suspicion by replacing the tapes with previously recorded footage.

Of course, the biggest hurdle was getting past the vault's 12-inch thick doors. Knowing the doors were equipped with internal magnets that would set off alarms if they detached, the robbers drilled holes into them, carefully taped the magnets together, and moved them out of the way so that they wouldn't separate. After that, all they had to do was break the locks to the safety deposit boxes, rake in the diamonds, and then quietly flee the scene. To escape undetected, they memorized the surveillance patterns of the 24-hour police patrols outside the building. (Hey, they didn't have nicknames like King of Thieves and The Magician with the Keys for nothing.) Amazingly, even though the heist took place on a Saturday night, authorities didn't discover anything suspicious until Monday morning.

How They Got Caught: Here's a tip for would-be thieves: If you leave the crime scene with a bag full of diamonds and dispose of the bags on the road leading out of the city, make sure you don't leave your half-eaten sandwich in one of them. Inspectors used the DNA evidence on the food to nab Notarbartolo, who currently awaits prosecution. Investigators also found DNA evidence in one of the vaults that linked Finotto to the Belgian heist, but he was already back in Turin, Italy, safe from extradition. Meanwhile, none of the diamonds have been recovered. Some have microscopic inscriptions on them that would reveal their identity, but only if the thieves ever decide to sell them legally.

And with that, we're going dark for the next week. Hey, give us a break! We'll be back bright and early on January 1. Okay, January 2.

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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]