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How Spiders Win the Lottery

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ThinkStock

On a cloudy spring day, a little spider scales a tall blade of grass. At the peak, the spider arches up, points its abdomen up to the sky and begins releasing strands of silk from its silk glands. Tens of thousands of strands fill the air, fanning out and then coming back together to form a triangular sheet. A passing breezes catches the silk and suddenly the spider is airborne, riding its homemade parachute into the wild blue yonder.

Spider use these “ballooning” flights to escape from danger and to colonize new habitats. Most times, they only travel a few feet, but the right conditions can carry a spider over vast distances. Sailors have found them landing on ships thousands of miles from shore, and scientists have discovered eight-legged travelers in air samples collected by atmospheric data balloons.

All sorts of tiny arthropods travel this way, and some plants and fungi also use the wind to spread seeds, spores and pollen. Scientists call it passive airborne dispersal and from our perspective, passive is the key word. The tiny flyers seem to be left at the mercy of the elements and there seems to be little opportunity for them to strategize or make the most of their trip. The direction and distance they travel—or whether they travel at all or get stuck waiting to take off—are decided by the movement, direction and speed of the air.

Some researchers dub it the “aerial lottery.” The flyer buys their ticket, catches a breeze and crosses their metaphorical fingers that they land safely in a place they want to be. Whether they’ve won (new habitat, safe from danger, yay) or lost (atmospheric data balloon, boo) isn’t revealed until they’ve landed, and by then their play is over.

The journey appears completely out of their hands, yet many passive dispersers wind up exactly where they should want to be: still sort of close to where they started (where there are reliable, if shrinking, resources), but away on their own with untapped resources and no competition from their fellow spiders/seeds/whatever-they-ares. This winning play is the “shortest unique flight,” similar to the “lowest unique bid” needed to win some auctions and games. Despite all appearances, then, there may be a way to improve one’s odds of winning.

The trick to winning the aerial lottery, scientists think, is all in the take off, the last stage of the game where the “player” still has some control. Plants and fungi have been known to launch their wind-dispersed pollen, spores and seed only in certain conditions. Spiders and other wind-sailing critters, meanwhile, can choose the time and location of their launch.

New research by Andy M. Reynolds from the UK’s Rothamsted agricultural research station suggests that a winning strategy is based on taking flight in specific weather conditions. Warm, gentle breezes on days with some cloud cover are ideal for making the shortest unique flight. In more stable conditions the flight might be unique, but will last longer. In less stable conditions, the flights are shorter but less likely to be unique. The ideal launch seasons, Reynolds suggests, are spring and autumn, exactly when spiders tend to ramp up their “ballooning behavior.”

Whether these creatures win or lose at their lottery is more relevant to us than you might think at first. Spiders are a great help in controlling pests, and knowing where and when they take flight can benefit farmers. “Each day of the growing season around 1,800 spiders land in each hectare of arable farmland after ballooning,” Reynolds said in a statement about the study. “If the farmers can predict the influx of spiders, they can reduce the amount of pesticides accordingly," saving money and hassle. Similarly, being able to predict the spread of problematic fungi can help control them and the diseases they cause, giving us a leg up in this strange game of chance.

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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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