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Bats Follow Musical Rules When Writing Love Songs

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Making a mix of love songs for your special someone? Looking for something lively to slip in between Sonny and Cher's "I Got You Babe" and The Temptations' "My Girl"? I highly recommend a little number called "Chirp-Buzz-Buzz" by...a group of Brazilian free-tailed bats.

Turns out that bats are quite the romantic crooners, using "love song" vocalizations to attract females (and in some cases, to scare away intruding males). According to a new study*, their love songs are more complex than previously thought and have a number of musical rules. The researchers—from the Department of Biology at Texas A&M University, the Section of Neurobiology at the University of Texas at Austin and Bat World, a bat sanctuary and rehabilitation center in Mineral Wells, Texas—spent close to four years recording and analyzing the songs of two populations of Brazilian free-tailed bats (also known as Mexican free-tailed, scientific name Tadarida brasiliensis). The first group was a captive colony of about 60 bats in Austin, maintained by one of the study's authors. The second group was a wild colony of approximately 100,000 to 250,000 bats within Texas A&M's athletic complex in College Station.

After examining a total of 412 songs from 33 bats and comparing song variation within and across individuals and between the two different colonies, the researchers determined the male bats use several types of syllables with  individual sounds to create three easily recognizable phrases:

Chirps are complex phrases composed of "A" and "B" syllables**.
*
Trills are composed of short (mean = 3.4 ms) downward FM syllables that can be connected or are separated by short silent intervals.
*
Buzzes are composed of short (3 ms) downward FM syllables that are never connected.

These phrases, in turn, are used in different combinations to produce songs. The researchers found that particular phrase sequences kept coming up and identified several rules governing phrase order:

1) Songs begin almost exclusively with chirps.

2) Trills do not follow buzzes, but instead always follow chirps or another trill.

3) The majority of buzzes (90%) are followed by another buzz or occur at the end of the song (songs containing a buzz ended in a buzz 84 % of the time).

This may not seem like very impressive music theory, but complex songs and specific structural "language rules" are rare among mammals; previous mammalian research hasn't gone much further than determining that song elements are used in a non-random order. These bats' songs and the rules that govern them, though, may be "more analogous to those of some birds than to other mammals," say they researchers. Birds and their songs have long been the basis for understanding vocal production and the evolution of vocal complexity as well as the physiology of vocal production. With this new study, there's a foundation for future research into mammalian vocals, "a model not only to study communication similarities in other animals, but also human speech," says lead author Kirsten M. Bohn.

Here's a video featuring the vocal stylings of Sid the bat, with commentary by researcher Dr. George Pollak:

* Bohn KM, Schmidt-French B, Schwartz C, Smotherman M, Pollak GD. (2009). Versatility and Stereotypy of Free-Tailed Bat Songs. PLoS ONE 4(8):e6746. doi:10.1371/journal.pone.0006746

** "A" syllables are short (5 ms) downward frequency modulated (FM) sweep syllables. "B" syllables are longer (17ms) and more complex, often beginning with an upward FM followed by a longer downward FM and sometimes ending with another upward FM.

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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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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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