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Dog Naming Trends Through the Ages

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ThinkStock

2012: Bye Bye, Max. Hello, Bella!

According to the yearly roundup of popular pet names in the database of Veterinary Pet Insurance, the 10 most popular dog names for 2012 were Bella, Bailey, Max, Lucy, Molly, Buddy, Daisy, Maggie, Charlie, and Sophie. It was the third straight year Bella came in at number 1, after unseating Max in 2009. A company spokesman thought the ascendancy of Bella might have had to do with "the name of the heroine in a certain vampire book/film series that’s pretty popular these days."

2008: Sorry, Jake and Rocky—Here come Chloe and Sophie.

The year before Max lost the top spot, Jake and Rocky dropped of the top 10, replaced by newcomers Chloe and Sophie. Is there an "end of men" situation happening in the canine world too? Someone get the trend piece writers on that!

1985: Nipper is now George.

In 1985, New York Times columnist William Safire asked readers to submit stories of how they named their dogs, and in return got a list of over 12,000 dog names from all over the country. He noted a few trends. People tended to name their dogs after food (Cookie, Candy, Taffy, Peaches), disposition (Rascal, Bandit, Crab), color (Blackie, Amber, Midnight), and owner occupation ("Lawyers like Shyster and Escrow; doctors prefer Bones.") But the most noticeable trend was that people were using human names for their dogs more than they used to: "Instead of turning verbs and adjectives into proper nouns (for example, by calling a puppy that likes to nip your finger Nipper), we are using proper nouns directly, calling the little nipper George, Daisy or Charley."

1960s-1980s: Getting gender specific.

Anthropologist Stanley Brandes published a 2009 study of pet name trends as revealed by the gravestones at Hartsdale, America's first pet cemetery. He noticed the trend toward human names for pets develop slowly from the 1960s to the 1980s when names like Riko, Ginny, Francois, Samantha, Daniel and Venus started to pop up among names like Freckles, Snowy, Clover, Spaghetti, Champ, Happy, Rusty and Taka. One consequence of this shift was that names started to entail information about the sex of the animal. This was not merely a consequence of a switch to human naming, though. Even non-human names started to show sex distinctions. Note, for example, the graves of Cha Cha Man, Candy Girl, Mr. Cat, and Dot-Z-Girl.

1896-WWII: Hobo, Jaba, Boogles.

Hartsdale Pet Cemetery, just outside of New York City, was established in 1896. Brandes notes that in the earliest monuments, the names of the pets might not even appear at all. Many of the early graves leave it at "Pets" or "My Pet." The family name of the owner is sometimes the only identifier. A well-known dancer of the time, Irene Castle, buried five dogs and a pet monkey under a monument engraved simply "Castle." Most of the graves do show pet names, but before WWII, they are almost never human names. The first half century at Hartsdale is represented by the likes of Brownie, Laddie, Hobo, Trixie, Rags, Jaba, Bunty, Boogles, Teko, Dicksie, Snap, Punch, Bébé and Pippy.

1800s: Semper Fido.

Abraham Lincoln had a dog named Fido, and this is often cited as the reason the name became the quintessential dog's name, but Fido was popular before Lincoln even became president. A favorite children's book of 1845 was called "Fido or the Faithful Friend," and told of the quintessential adventures of the quintessential boy and his dog. It's rather too bad presidents' dogs aren't the source of lasting naming fashions. We could be calling our dogs Sweetlips, Scentwell, Vulcan, Drunkard, Taster, Tipler and Tipsy like George Washington did!

Medieval: Mopsus and Mopsulus

Kathleen Walker-Meikle's book Medieval Pets shows that people gave a wide range of creative names to their pets then, despite the general objection that indulging pets was "an extravagance and a distraction from one's duties and obligations, in particular charity to the poor." Just as in Safire's 1985 survey, dogs were named for characteristics (Sturdy, Whitefoot, Hardy) and owner occupation – Stosel (Pestle) for an apothecary, Hemmerli (Little Hammer) for a locksmith, Speichli (Little Spoke) for a wagoner. They could even have human names like Jakke and Parceval. However, the most popular human names given to dogs were not the same as the most popular names given to babies, as they are today. For dog owners looking to buck the trends (or for that matter, baby-havers looking to buck the trends), here's a list of awesome medieval dog names: Blawnche, Nosewise, Smylfeste, Bragge, Holdfast, Zaphyro, Zalbot, Mopsus and Mopsulus.

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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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Stephen Missal
crime
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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]

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