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Tips for Keeping your Tenement Tidy (in 1911)

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Mabel Hyde Kittredge, activist and founder of the hot lunch program for public schools in New York, was the Martha Stewart of tenement living. She championed the cause of domestic science for the disadvantaged at her "housekeeping centers"—model apartments where young girls from the crowded tenements could, by observing and doing, learn all the particulars of home management.  Her 1911 book, How to Furnish and Keep House in a Tenement Flat, was organized as a series of lessons to be used at the housekeeping centers in New York or in other cities which had started to establish centers of their own. The young girls who took the courses were meant to see the model apartments as "an illustration of the sanitation and beauty which lie within reach of the laborer's income." But in order to achieve that sanitation and beauty, there was an awful lot of work to be done. 


Kittredge acknowledged that "housework can be very dull," but she emphasized that "when it becomes an art, it is interesting. When a child realizes that she is gradually mastering an art, she has the desire and the ambition to go on." The children were offered motivation in the form of a card with tasks that could be checked off as they mastered them. Here is what the child had to master in order to complete the first course:

The holder of this card has

Made a fire.
Washed dishes.
Washed dish towels.
Cleaned sink.
Prepared soda and cleansed pipes.
Scrubbed floor.
Scrubbed table or tubs.
Cleaned kitchen.
Washed and aired food tins.
Washed windows.
Made bed.
Fought bedbugs.
Cleaned toilet.
Dusted bedroom.
Cleaned drawers.
Scrubbed woodwork.
Dusted down walls.
Boiled out cleaning cloths.

Then they could move on to the card for the second course:

The holder of this card has

Swept and dusted dining-room.
Set table.
Prepared breakfast.
Served breakfast.
Cared for linen and lined drawer.
Cleaned silver.
Cleaned knives.
Cleaned brass.
Cleaned lamps.
Cared (daily) for lamps.
Thoroughly cleaned dining-room.
Made starch. 
Washed and ironed bed linen or towels.
Washed and ironed table linen or curtains.
Covered ironing board.
Prepared meal for sick.
Made and served tea.

Kittredge gave the full details on how each of these tasks was to be done best: Clean the kitchen closets from the top shelf down. Wash bread box with soda and hot water, dry by the stove, and air in the sun. Take apart the kerosene lamps and boil all the parts. If you find bedbugs, wash the bed, alternating soap and water and carbolic acid. Soak the mattress in naphtha (basically, lighter fluid) "but be sure that no fire is near, open all the windows, and after pouring on the naphtha, lock the door of the room and leave it closed for a day to allow the gas to pass off."


How was all of this work to be done? Kittredge stressed the importance of sticking to a strict order of tasks because "confusion is due to lack of order, and running back and forth with no method." The morning routine, for example, has nine steps, from lighting the fire to washing up the breakfast dishes, and by the time it's all done, you are dressed, the family is fed, and the beds and rooms are aired and dusted. It was also important to "see before going to bed that the materials for breakfast are in the house," in order to avoid the inefficiency of the "almost universal tendency to 'run out and buy' before each meal."


Kitteredge gives a complete list of everything a family of little means need to acquire—including furniture, dishes, utensils, and linen—in order to run a proper household. Helpfully, she lists the prices of everything—from the stove ($9) to the pepper shaker (5 cents) to sandpaper for the laundry (1 cent). Other tips include things like how to convert a pickle barrel into a laundry hamper that doubles as a kitchen seat, or how to use a window box to cool food if you don't have an ice box. She also shows why the cheaper option is often the more attractive, as when she notes that not only is kerosene cheaper than gas, but "a low lamplight is better to read by and looks prettier." 


The point of these lessons was not just to make tenement living more sanitary and efficient, but also more beautiful. Some thought given to decorating could go a long way. Kittredge advised yellow paint for all the rooms, "as tenement flats are apt to be dark." For decoration, pictures could be pasted on the walls and then washed over with liquid shellac. That way both pictures and wall could be easily cleaned at the same time. But most important was to take that little extra moment—after all the hard work was done—to see that your work pleased the eye. Because "a room may be clean and yet not attractive. See that the shades are even, the chairs straight, the blotter clean, inkwell clean and filled, plants watered and dead leaves taken off."

Then I suppose it was okay to put your feet up. Until it started all over again the next day.

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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]