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The Late Movies: Jump Blues

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Jump blues is a type of up-tempo blues that first gained popularity in the 1940s and experienced a renewal in interest during the (dreaded) 1990s swing revival. Billboard has described jump tunes as having a “bright bounce in the medium tempo and a steady drive maintained.” I usually think of it as Big Band pared down to a few horns and a rhythm section, rolled around in the dirt a little bit and then given some uppers.

Shake, Rattle and Roll

You’re probably more familiar with the Bill Haley & His Comets version of this song, and for that I’ll forgive you, but Big Joe Turner's original is the real deal. Recorded the day after Valentine’s Day in 1954, the original featured Turner, songwriter Jesse Stone, and record-company execs Jerry Wexler and Ahmet Ertegün doing the shout chorus, as well as a number of double entendres and sexual innuendos (some of which aren’t in this video). “I've been holdin' it in, way down underneath / You make me roll my eyes, baby, make me grit my teeth,” and “I'm like a one-eyed cat peepin' in a seafood store,” are both sort of self-explanatory, but subtle enough that you might not have noticed them on the first listen.

Rocket 88

Jackie Brenston was learned to play saxophone after coming home from the army in 1947, and hooked up with Ike Turner’s band a few years later. B.B. King liked the band and recommended them to Sam Phillips, who owned a studio in Memphis. There, the band recorded a few songs, including this one, on which Brenston sang lead and was credited with writing. The recordings found their way to Chess Records which released the song under "Jackie Brenston and his Delta Cats", rather than Turner's name. The song went to #1 on the Billboard R&B chart and Philips used the success of the tune to jumpstart Sun Records.

Hoy Hoy

Little Johnny Jones mantra may as well have been “have instrument, will travel.” Beginning in 1945, he played piano in Tampa Red's band, harmonica in Muddy Waters’ band, and played and recorded piano and vocals for Elmore James, Howling Wolf, Billy Boy Arnold and Magic Sam. among others. This one-off release, put out under his own name, features a role reversal for Jones and James, with sideman Jones taking over vocals and usual band leader James handling slide guitar.

Voo Doo

Delores LaVern Baker had the blues in her blood. She was related to both Merline Johnson and Memphis Minnie. She also had a great sense of humor. When Georgia Gibbs had the bigger hit with her cover of Baker’s “Tweedle Dee” Baker took out flight insurance at the airport and sent it to Gibbs with a note reading “You need this more than I do because if anything happens to me, you're out of business.”

Jump Jive and Wail

Louis Prima was, like David Bowie, a musical chameleon. He led, at one time or another in his career, a New Orleans style jazz band, a swing combo, a big band a Vegas lounge act and a pop-rock band. You’re likely familiar with Brian Setzer’s version of this song, which gets points for a flashier video but lacks the late Prima’s legendary exuberance.

Good Rockin’ Tonight

Written by Roy Brown in the late 40’s, this song was originally offered to Wynonie Harris, who turned it down and only decided to cover it later after the Brown had some success with his own recording of it. Brown's original recording hit #13 on the the Billboard R&B chart, but Harris' went all the way to #1.

Juicy Fruit

Rudolph Spencer Greene was neither prolific nor famous, and today most people, myself included, only find out about him from compilations of blues, R&B and early rock songs and there is only one known photo of him (which depicts him playing the guitar behind his head). He is, in fact, so un-famous that I can’t find any sort of video for this song. Even though this is “The Late Movies,” I can’t pass up the chance to share Greene’s fantastic, surreal “Juicy Fruit,” wherein he brags about his $50 flattop, cashmere clothes and a car so long that he is forced to park it in the air. Listen to it here.

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iStock // Ekaterina Minaeva
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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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]