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11 Names for Alphabetical Antics and Other Word Games

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If you love word play, you probably know that a word—or longer piece of writing—that reads the same forward and backward is called a palindrome. But what do you call a word that spells another word backwards, or a word that looks the same upside down? When terms for these orthographic puzzlers didn’t exist, logolologists (such as the authors of the books listed below) were happy to invent some. Here are a few.

1. Isogram

A word in which no letter of the alphabet occurs more than once.

Dimitri Borgmann’s longest example: dermatoglyphics, the study of skin markings or patterns on fingers, hands, and feet, and its application, especially in criminology.

2. Pangram

A phrase or sentence containing all 26 letter of the alphabet (ideally repeating as few letters as possible).

You may remember this one from typing class: “The quick brown fox jumped over the lazy sleeping dog,” but Willard Espy came up with a shorter and more interesting one: “Bawds jog, flick quartz, vex nymphs.” An abundance of pangrams, using some very obscure words or initials can be found here.

3. Palindrome

A word, sentence, or longer written work that reads the same backwards.

Example: A declaration facetiously attributed to Napoleon, “Able was I ere I saw Elba.” Weird Al Yankovic’s song “Bob” spoofs Bob Dylan’s “Subterranean Homesick Blues” using a slew of palindromes. Need more palindromes? Find a huge stash here.

4. Semordnilap

A word or name that spells a different word backwards (notice what semordnilap spells backwards).

Semordnilaps (coined by Martin Gardner in 1961) are also known as backronyms, volvograms, heteropalindromes, semi-palindromes, half-palindromes, reversgrams, mynoretehs, recurrent palindromes, reversible anagrams, word reversals, or anadromes. (Do you get the feeling that fans of word play love to make up words?)

Here’s a semordnilap dieters can relate to: Stressed is desserts backwards.

5. Kangaroo word or marsupial

This refers to a word carrying another word within it (without transposing any letters).

Example: encourage contains courage, cog, cur, urge, core, cure, nag, rag, age, nor, rage and enrage. Ouch! That mama roo is going to need a pouchlift after carrying around that brood!

6. Lipogram

A written work composed of words chosen to avoid the use of one or more letters.

You may hail F. Scott Fitzgerald’s Gatsby as great, but in 1939 Ernest Vincent Wright produced the phenomenal Gadsby: A Story of Over 50,000 Words Without Using the letter “E,” a scarcely believable achievement considering that “E” is the most common letter in English. Imagine an entire novel without he, she, the, or the past tense marker –ed.

7. Rebus

A representation of words with pictures, letter names, or symbols that suggest the sound of the words.

Rebus has been used in English since 1605, when William Camden wrote, “They which lackt wit to expresse their conceit in speech, did vse to depaint it out … in pictures, which they called Rebus.” Popular in autograph books and on vanity license plates, rebuses include such classics as:

YYUR; YYUB. ICUR YY4me
NE1410S
4 A _ I 8 0

(The solutions are below.)

8. Tautonym

David Grambs uses this term for a word or name made up of two identical parts, such as so-so, tom-tom or Pago Pago.

9. Anagram

A word or phrase formed by rearranging the letters of another word or phrase.

The English word anagram goes back to 1589. Grambs uses the word transposal in this general sense, and anagram more narrowly to mean a transposal of letters resulting in synonymous term. Others call these particularly apt anagrams “aptigrams.” For example: Villainousness is an anagram of “an evil soul’s sin.”

10. Antigram

The opposite of an aptigram, these words or phrases form antonyms when rearranged.

Examples: violence — nice, love; funeral — real fun.

11. Ambigram

A term coined by John Langdon for words made to look the same when inverted with the help of calligraphy.

Willard Espy calls a word that looks the same upside down an invertogram and Schaaf calls a number like that strobogrammatic. Examples: NOON, SWIMS, SIS; 1881, 1961, 91016.

Rebus solutions:
Too wise you are; too wise you be. I see you are too wise for me.
Anyone for tennis?
For a long period I ate next to nothing.

Sources: Borgmann, Dmitri A. Language on Vacation: An Olio of Orthographic Oddities, 1965. Espy, Willard. The Word’s Gotten Out, 1989. Grambs, David. Words About Words, 1984. Langdon, John. Wordplay: Reflections on the Art of Ambigrams, 1992. Schaaf, William Leonard. A Bibliography of Recreational Mathematics, v. 4, 1978.

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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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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]

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