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Ben Franklin's 200+ Synonyms for "Drunk"

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Today we're celebrating Ben Franklin's 308th birthday. If you're celebrating at home, perhaps one of these phrases from The Drinkers Dictionary will come in handy. The lengthy list of expressions meaning "inebriated" was first published by Franklin in the Pennsylvania Gazette on January 6, 1737. Feel free to add your own phrases in the comments.

First, a note from Mr. Franklin: "The Phrases in this Dictionary are not (like most of our Terms of Art) borrow'd from Foreign Languages, neither are they collected from the Writings of the Learned in our own, but gather'd wholly from the modern Tavern-Conversation of Tiplers. I do not doubt but that there are many more in use; and I was even tempted to add a new one my self under the Letter B, to wit, Brutify'd: But upon Consideration, I fear'd being guilty of Injustice to the Brute Creation, if I represented Drunkenness as a beastly Vice, since, 'tis well-known, that the Brutes are in general a very sober sort of People."

A
He is Addled,
He's casting up his Accounts,
He's Afflicted,
He's in his Airs.

B
He's Biggy,
Bewitch'd,
Block and Block,
Boozy,
Bowz'd,
Been at Barbadoes,
Piss'd in the Brook,
Drunk as a Wheel-Barrow,
Burdock'd,
Buskey,
Buzzey,
Has Stole a Manchet out of the Brewer's Basket,
His Head is full of Bees,
Has been in the Bibbing Plot,
Has drank more than he has bled,
He's Bungey,
As Drunk as a Beggar,
He sees the Bears,
He's kiss'd black Betty,
He's had a Thump over the Head with Sampson's Jawbone,
He's Bridgey.

C
He's Cat,
Cagrin'd,
Capable,
Cramp'd,
Cherubimical,
Cherry Merry,
Wamble Crop'd,
Crack'd,
Concern'd,
Half Way to Concord,
Has taken a Chirriping-Glass,
Got Corns in his Head,
A Cup to much,
Coguy,
Copey,
He's heat his Copper,
He's Crocus,
Catch'd,
He cuts his Capers,
He's been in the Cellar,
He's in his Cups,
Non Compos,
Cock'd,
Curv'd,
Cut,
Chipper,
Chickery,
Loaded his Cart,
He's been too free with the Creature,
Sir Richard has taken off his Considering Cap,
He's Chap-fallen,

D
He's Disguiz'd,
He's got a Dish,
Kill'd his Dog,
Took his Drops,
It is a Dark Day with him,
He's a Dead Man,
Has Dipp'd his Bill,
He's Dagg'd,
He's seen the Devil,

E
He's Prince Eugene,
Enter'd,
Wet both Eyes,
Cock Ey'd,
Got the Pole Evil,
Got a brass Eye,
Made an Example,
He's Eat a Toad & half for Breakfast.
In his Element,

F
He's Fishey,
Fox'd,
Fuddled,
Sore Footed,
Frozen,
Well in for't,
Owes no Man a Farthing,
Fears no Man,
Crump Footed,
Been to France,
Flush'd,
Froze his Mouth,
Fetter'd,
Been to a Funeral,
His Flag is out,
Fuzl'd,
Spoke with his Friend,
Been at an Indian Feast.

G
He's Glad,
Groatable,
Gold-headed,
Glaiz'd,
Generous,
Booz'd the Gage,
As Dizzy as a Goose,
Been before George,
Got the Gout,
Had a Kick in the Guts,
Been with Sir John Goa,
Been at Geneva,
Globular,
Got the Glanders.

H
Half and Half,
Hardy,
Top Heavy,
Got by the Head,
Hiddey,
Got on his little Hat,
Hammerish,
Loose in the Hilts,
Knows not the way Home,
Got the Hornson,
Haunted with Evil Spirits,
Has Taken Hippocrates grand Elixir,

I
He's Intoxicated,
Jolly,
Jagg'd,
Jambled,
Going to Jerusalem,
Jocular,
Been to Jerico,
Juicy.

K
He's a King,
Clips the King's English,
Seen the French King,
The King is his Cousin,
Got Kib'd Heels,
Knapt,
Het his Kettle.

L
He's in Liquor,
Lordly,
He makes Indentures with his Leggs,
Well to Live,
Light,
Lappy,
Limber,

M
He sees two Moons,
Merry,
Middling,
Moon-Ey'd,
Muddled,
Seen a Flock of Moons,
Maudlin,
Mountous,
Muddy,
Rais'd his Monuments,
Mellow,

N
He's eat the Cocoa Nut,
Nimptopsical,
Got the Night Mare,

O
He's Oil'd,
Eat Opium,
Smelt of an Onion,
Oxycrocium,
Overset,

P
He drank till he gave up his Half-Penny,
Pidgeon Ey'd,
Pungey,
Priddy,
As good conditioned as a Puppy,
Has scalt his Head Pan,
Been among the Philistines,
In his Prosperity,
He's been among the Philippians,
He's contending with Pharaoh,
Wasted his Paunch,
He's Polite,
Eat a Pudding Bagg,

Q
He's Quarrelsome,

R
He's Rocky,
Raddled,
Rich,
Religious,
Lost his Rudder,
Ragged,
Rais'd,
Been too free with Sir Richard,
Like a Rat in Trouble.

S
He's Stitch'd,
Seafaring,
In the Sudds,
Strong,
Been in the Sun,
As Drunk as David's Sow,
Swampt,
His Skin is full,
He's Steady,
He's Stiff,
He's burnt his Shoulder,
He's got his Top Gallant Sails out,
Seen the yellow Star,
As Stiff as a Ring-bolt,
Half Seas over,
His Shoe pinches him,
Staggerish,
It is Star-light with him,
He carries too much Sail,
Stew'd
Stubb'd,
Soak'd,
Soft,
Been too free with Sir John Strawberry,
He's right before the Wind with all his Studding Sails out,
Has Sold his Senses.

T
He's Top'd,
Tongue-ty'd,
Tann'd,
Tipium Grove,
Double Tongu'd,
Topsy Turvey,
Tipsey,
Has Swallow'd a Tavern Token,
He's Thaw'd,
He's in a Trance,
He's Trammel'd,

V
He makes Virginia Fence,
Valiant,
Got the Indian Vapours,

W
The Malt is above the Water,
He's Wise,
He's Wet,
He's been to the Salt Water,
He's Water-soaken,
He's very Weary,
Out of the Way.

See Also:

10 of Ben Franklin's Lesser-Known Feats of Awesomeness

[We first posted this list back on Franklin's 304th. Thanks to HistoryCarper.com for their Ben Franklin archives.]

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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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