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How Did You Know Natt Supab?

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I'm happy to announce a winner to our last How Did You Know? trivia hunt. First-timer Natt Supab was the first of dozens who had all the correct answers. Congrats Natt! I'll post her pic and bio below, and her answers after the jump. Our Random Winner this month is Meaghin Burke, who turned in the answers a full two hours and 20 minutes after the deadline. So it always pays to play whether you're the first or the 50th!
See everyone back for another round on the 24th of March when Natt will be looking to defend the title and we'll be giving away even bigger prizes.

Wow, really?! No joke?! I am thrilled and astonished, in equal parts, to emerge victorious in my first attempt at HDYK? I am a recent graduate of Harvey Mudd College (Biology '08, baby!) and an even more recent mental_floss devotee. I wish I had discovered the site much earlier in my aimless (and fruitless) job-searching days -- it brings me much joy (Thanks!).

It must be said, I couldn't have figured out Day 2 without the help of some clever friends (credit given below) and I think I'll collaborate with somebody for the next puzzle. After all, no (wo)man is an island.

Cheers!

Final Answer

I'm a Paleontologist (David Schwimmer plays Ross Gellar on Friends, who is a paleontologist) who wrote a book called Dinosaurs (generic name for dimorphodon and gorgosaurus) Heresies (plural of Heresy). I've also penned a novel called Raptor (from "prorate," "rap" is a music genre) Red. My Name is Robert T. Bakker.

Day 1

1. NewsRadio (imdb'd Andy Dick, perused the page until I found NewsRadio, with Dave Foley, who I recognized as the other guy in the clip)
2. Friends (knew)
3. MacGyver (knew)
4. Bewitched (knew)
5. Sports Night (googled the quote "Isaac has a highly developed sense of right and wrong and he is hip to my battle plan")
6. Emily's Reasons Why Not (imdb'd Heather Graham, looked for TV series, made best guess)
7. Parker Lewis Can't Lose (googled "nun when she misplaced his lunch" and got this article: http://query.nytimes.com/gst/fullpage.html?res=9C0CE1DD1530F93BA35753C1A966958260&sec=&spon=&pagewanted=all)

Day 2

1. Lion (from saLutatION)
2. Mule (from voLUME)
3. Raptor (from PRORATe) [thanks, Darren!]
4. Snail (from SaLutAtIoN)
5. Ant (from sAluTatioN)
6. Parrot (from PRORATe) [thanks, Christina!]
7. Otter (from TORTE)
8. Mole (from vOLuME)
9. Tuna (from salUTAtioN)
10. Rat (from proRATe)

I struggled on this one quite a bit, but with "a little help from my friends" I prevailed! Special thanks to Sonia for the extra inspiration to keep going!

Day 3

1. Scrantonicity (asked a friend [thanks, Tricia!] who watches "The Office")
2. The Four Peters (SETH GREEN ROCKS. ahem. I googled "Robot Chicken fake band" and "Family Guy fake band" and got to this page: http://www.fakebands.com/fakeband_f.html ...was very disappointed that it was NOT Dingos Ate My Baby or DuJour!)
3. Stillwater (recognized Billy Crudup in Almost Famous, Wiki'ed it)
4. Spinal Tap (vaguely associated the image with the movie, This is Spinal Tap, and found the name within the camouflage)
5. The Way-Outs (lots and lots of googling until I googled "Flintstones fake band," which should've been an obvious choice -_- and got to this forum: http://digg.com/music/20_Real_Cool_Fake_Bands_from_TV_and_Movies_with_PICS)
6. Global Heresy (recognized Alicia Silverstone, imdb's "aka Rock my World" caught my eye, and viola!)

Day 4

1. Dodo bird (knew. Quick google image search confirmed.)
2. Toolache Wallaby (thought it looked like a kangaroo, so googled "extinct marsupials" and found this wiki http://en.wikipedia.org/wiki/File:Macropus_greyi_-_Gould.jpg and double-checked by looking at images on google; got this page http://museumvictoria.com.au/bioinformatics/mammals/images/thumbgkan.htm)
3. Pig-Footed Bandicoot (once again, Wiki's "extinct marsupials" page proved helpful http://en.wikipedia.org/wiki/Category:Extinct_marsupials)
4. Dimorphodon (got desperate enough to use a reverse image search engine [my secret weapon!] which yielded http://www.search4dinosaurs.com/jcjcDimorphodon.html)
5. Gorgosaurus libratus (same method as for the dimorphodon; got to http://vnexpress.net/Vietnam/Khoa-hoc/2007/07/3B9F799D/)

Day 5

I'm a Paleontologist (David Schwimmer plays Ross Gellar on Friends, who is a paleontologist) who wrote a book called Dinosaurs (generic name for dimorphodon and gorgosaurus) Heresies (plural of Heresy). I've also penned a novel called Raptor (from "prorate," "rap" is a music genre) Red. My Name is Robert T. Bakker.

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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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iStock
Animals
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Scientists Think They Know How Whales Got So Big
May 24, 2017
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iStock

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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