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2 Odd Things I Just Learned About Fish

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When I'm not blogging for mental_floss, I can usually be found wearing bright orange rubber pants and gutting, cutting and selling fish at the local Whole Foods. Sometimes, these two worlds collide and I find some scientific research involving my finned friends that needs blogging about. Recently I learned two things about fish that may seem like throwaway trivia at first, but provide us with some useful information once you get past the "gee-whiz" stage. (Note: That's not me in the photo. But it is Charlize Theron. Picture courtesy of Hollywood Back Wash.)


1. Odd thing I learned #1:
Vulnerability to being caught by fishermen is a heritable trait in largemouth bass

After tracking catches during four years of experimental fishing, researchers at the University of Illinois segregated largemouth bass in a lake into groups of high and low vulnerability (HV and LV groups, respectively) to being caught. The offspring of these two groups were reared, tracked and segregated into HV and LV groups. Their offspring, the third generation of fish in the experiment, went through the same process (there's a slightly more detailed explanation of the study at my website). The researchers recently published the results of the 20-year experiment* and conclude that, one, vulnerability to being caught by fishermen is a heritable trait in the bass, and two, recreational fishing can cause evolutionary changes the same way commercial fishing can.

The takeaway:

Male largemouth bass are single dads, staying with the eggs and guarding their offspring for the first month after they hatch (the females leave the eggs after laying them). Aggressive HV males have more success mating and are great dads, protecting their fry from predators, but they're also more likely to go after fishermen's lures, get caught and leave their offspring vulnerable to predators. The combination of the bass' reproductive strategies and the pressure of recreational fishing during spawning season may affect the bass' reproductive success and continuation, so the researchers recommend that wildlife management agencies create bass spawning sanctuaries and limit or restrict bass fishing during spawning season.

Odd thing I learned #2:
Fish get seasick

German zoologist Reinhold Hilbig put an aquarium containing 49 fish on a plane, which, during its flight, went into a steep dive to simulating the loss of gravity. Eight of the fish began turning around in circles, and exhibited other signs of having lost their orientation and sense of balance. Hilbig told the Telegraph that the fish behaved "like humans who get seasick," and "looked as if they were about to vomit." (What that looks like I don't know.)

The takeaway: This was an experiment on the effects of weightlessness in water as part of Hilbig's research on how humans are affected in space. The eight seasick fish were later killed and their brains were examined, revealing that many of them had asymmetric inner ears. Some fish have an inner ear system that helps them stay upright, like the inner ear balance system in humans. Asymmetric inner ears in humans, and apparently, fish, make one more susceptible to motion sickness. Hilbig hopes to use the results of the experiment to draw conclusions about how humans might react in similar situations. (That's sort of vague, I know. I wish there were more, but the news stories on this are all short on info and no amount of Googling seems to turn up the name of a paper associated with the study or even background info on Hilbig. Alas.)

*Philipp, David P., Cooke, Steven J., Claussen, Julie E., Koppelman, Jeffrey B., Suski, Cory D., Burkett, Dale P. Selection for Vulnerability to Angling in Largemouth Bass. Transactions of the American Fisheries Society 2009;138:189"“199. DOI: 10.1577/T06-243.1

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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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May 23, 2017
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