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TRUE CRIMES: Don't Mess with Texas (Banks)

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Most bank robberies follow a similar pattern: robber enters bank, approaches teller while brandishing a weapon, demands money, and (he hopes) makes his escape. So when a robbery call came in Saturday morning, September 21, 1991, for the Texas Commerce Bank in San Antonio, Texas, law enforcement officials had to double-check the address. It was a motor bank, that is, a bank that provided drive-thru service only by tellers locked inside a small building behind layers of bullet-proof glass. How could such a fortress get robbed?

The Crime

Kelly McGinnis, 21, and Lisa Silvas, 19, were the tellers assigned to work the 9AM-1PM shift that Saturday. The two girls were certainly young to be working alone in a low-security facility (no security guards, no CCTV equipment), but they were outgoing and pretty (both were former cheerleaders) and they were popular with the bank's clientele, so management didn't hesitate to pair them on shifts together. When they arrived for work that day, Kelly unlocked the first of two doors in the building and walked with Lisa down a corridor toward the second door. As she opened that door, a figure in coveralls and a mask topped by a ski cap seemingly appeared from nowhere and, in an obviously disguised voice, ordered her to cut off the alarm. Pointing a pistol at his captives, the robber restrained Kelly with a pair of plastic handcuffs and ordered Lisa to open the vault. He warned her not to trip any silent alarms: "I have a police scanner," he said indicating his pocket, "and if I hear any calls about the bank on the radio, I'll kill both of you." Inside the vault, he waited by Lisa nervously opened both safes and then dumped the money inside a plastic trash bag. He then ordered her to empty the two teller drawers into the bag. He marched both girls back into the vault and told them to stay there. Seconds later, Kelly wanted to hit the alarm, but Lisa wasn't sure the robber had left yet, so she persuaded her to wait a few minutes.

When the police did arrive, they were puzzled. How had the perp known where the vault and teller drawers were located? Why didn't he scout around the rest of the building? Why did he bother to disguise his voice?

The Perpetrators

All the indicators pointed to an inside job, and suspicion then fell on Lisa Silvas (the teller who hadn't been handcuffed) and her husband, Jack Nealy. Nealy had served as a Marine for eight years and was now in his second year working as a San Antonio police officer. At 28, he was almost ten years older than Lisa, but he had been smitten since they first met in September 1990. His adoration of her bordered on obsession: Jack called her several times each day and would drive by the bank to make sure she was at work. He regularly pulled over Lisa's ex-boyfriend for any minor traffic infraction and left him with both a citation and a warning to stay away from Lisa. Lisa loved the attention and bragged to friends how Jack spoiled her. Lisa also loved nice clothes and designer handbags, and Jack was having trouble supporting her shopping habit on a police officer's salary (especially since he was paying alimony to an ex-wife plus child support). Lisa had mentioned the lax security at her bank branch to Jack many times, and eventually the pair hatched the scheme that would net them just under a quarter of a million dollars. Nine days before the robbery, they got married in a quick ceremony at the county courthouse (authorities later surmised that they'd done so to avoid being forced to testify against one another should they get caught).

Stepfather knows Best

Picture 77.pngAll of the FBI's evidence was circumstantial, however, and Lisa and Jack may well have made their planned getaway to Grand Cayman had Jack's stepfather not noticed a patch of freshly disturbed earth on his property. He got a shovel and began digging and found a canvas tote bag filled with money, which he turned over to the FBI. The money included 30 different "bait bills" "“ hundred dollar bills whose serial numbers had been recorded by the bank so they could be traced in the event of a robbery. Even though Jack and Lisa maintained their innocence throughout the subsequent trial, they were found guilty and sentenced to 15 and 12 years in prison respectively.

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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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8 Common Dog Behaviors, Decoded
May 25, 2017
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Dogs are a lot more complicated than we give them credit for. As a result, sometimes things get lost in translation. We’ve yet to invent a dog-to-English translator, but there are certain behaviors you can learn to read in order to better understand what your dog is trying to tell you. The more tuned-in you are to your dog’s emotions, the better you’ll be able to respond—whether that means giving her some space or welcoming a wet, slobbery kiss. 

1. What you’ll see: Your dog is standing with his legs and body relaxed and tail low. His ears are up, but not pointed forward. His mouth is slightly open, he’s panting lightly, and his tongue is loose. His eyes? Soft or maybe slightly squinty from getting his smile on.

What it means: “Hey there, friend!” Your pup is in a calm, relaxed state. He’s open to mingling, which means you can feel comfortable letting friends say hi.

2. What you’ll see: Your dog is standing with her body leaning forward. Her ears are erect and angled forward—or have at least perked up if they’re floppy—and her mouth is closed. Her tail might be sticking out horizontally or sticking straight up and wagging slightly.

What it means: “Hark! Who goes there?!” Something caught your pup’s attention and now she’s on high alert, trying to discern whether or not the person, animal, or situation is a threat. She’ll likely stay on guard until she feels safe or becomes distracted.

3. What you’ll see: Your dog is standing, leaning slightly forward. His body and legs are tense, and his hackles—those hairs along his back and neck—are raised. His tail is stiff and twitching, not swooping playfully. His mouth is open, teeth are exposed, and he may be snarling, snapping, or barking excessively.

What it means: “Don’t mess with me!” This dog is asserting his social dominance and letting others know that he might attack if they don’t defer accordingly. A dog in this stance could be either offensively aggressive or defensively aggressive. If you encounter a dog in this state, play it safe and back away slowly without making eye contact.

4. What you’ll see: As another dog approaches, your dog lies down on his back with his tail tucked in between his legs. His paws are tucked in too, his ears are flat, and he isn’t making direct eye contact with the other dog standing over him.

What it means: “I come in peace!” Your pooch is displaying signs of submission to a more dominant dog, conveying total surrender to avoid physical confrontation. Other, less obvious, signs of submission include ears that are flattened back against the head, an avoidance of eye contact, a tongue flick, and bared teeth. Yup—a dog might bare his teeth while still being submissive, but they’ll likely be clenched together, the lips opened horizontally rather than curled up to show the front canines. A submissive dog will also slink backward or inward rather than forward, which would indicate more aggressive behavior.

5. What you’ll see: Your dog is crouching with her back hunched, tail tucked, and the corner of her mouth pulled back with lips slightly curled. Her shoulders, or hackles, are raised and her ears are flattened. She’s avoiding eye contact.

What it means: “I’m scared, but will fight you if I have to.” This dog’s fight or flight instincts have been activated. It’s best to keep your distance from a dog in this emotional state because she could attack if she feels cornered.

6. What you’ll see: You’re staring at your dog, holding eye contact. Your dog looks away from you, tentatively looks back, then looks away again. After some time, he licks his chops and yawns.

What it means: “I don’t know what’s going on and it’s weirding me out.” Your dog doesn’t know what to make of the situation, but rather than nipping or barking, he’ll stick to behaviors he knows are OK, like yawning, licking his chops, or shaking as if he’s wet. You’ll want to intervene by removing whatever it is causing him discomfort—such as an overly grabby child—and giving him some space to relax.

7. What you’ll see: Your dog has her front paws bent and lowered onto the ground with her rear in the air. Her body is relaxed, loose, and wiggly, and her tail is up and wagging from side to side. She might also let out a high-pitched or impatient bark.

What it means: “What’s the hold up? Let’s play!” This classic stance, known to dog trainers and behaviorists as “the play bow,” is a sign she’s ready to let the good times roll. Get ready for a round of fetch or tug of war, or for a good long outing at the dog park.

8. What you’ll see: You’ve just gotten home from work and your dog rushes over. He can’t stop wiggling his backside, and he may even lower himself into a giant stretch, like he’s doing yoga.

What it means: “OhmygoshImsohappytoseeyou I love you so much you’re my best friend foreverandeverandever!!!!” This one’s easy: Your pup is overjoyed his BFF is back. That big stretch is something dogs don’t pull out for just anyone; they save that for the people they truly love. Show him you feel the same way with a good belly rub and a handful of his favorite treats.

The best way to say “I love you” in dog? A monthly subscription to BarkBox. Your favorite pup will get a package filled with treats, toys, and other good stuff (and in return, you’ll probably get lots of sloppy kisses). Visit BarkBox to learn more.

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