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25 years of education data


It comes from this article:

It’s absolutely mind boggling to me that the number of computer science majors in 1985 was greater than the number of computer science majors today.







Today, I fell in love. With an omelette

.mmmmmm fluffy omelette

I’m a huge fan of omelettes. In typical Berkeley fashion, my CSA delivers a dozen eggs to me every 2 weeks, which allows me to make a four egg omelette at least a couple times a month. Usually, it’s either a quickie meal, or an intense craving to eat breakfast for dinner which causes me to make omelettes, but today, I woke up wrapped around a fluffy blanket, and the thought “fluffy omelette” popped into my head. After a bit of googling about, I discovered a list of apocryphal things I could do to fluff up my omelette. Here’s how I did it:


  • 4 eggs
  • 2-3 tbsp of milk
  • 1 tbsp butter
  • 1 tsp salt
  • Copious amounts of pepper
How to do it
  • Separate the egg yolks from the egg whites. I always saw them doing this on The Food Channel and thought it looked difficult, but it’s actually really easy. Get a bowl and break an egg roughly in half. Pour the yolk from one half of the shell to the other — the lighter egg whites will pour out into the bowl while the heavier yolks will stay in the shell.
  • Add the milk and salt to the egg whites. The purpose of these is basically to introduce air into your egg whites to make them fluffy. When the milk hits the pan, it will evaporate somewhat, leaving behind air bubbles. The salt somehow reacts with the whites and makes them fluffy through a process that I will, for the moment, call “black magick”.
  • Immersion blend the crap out of the egg whites. I started off slow at a low setting and then cranked it up to medium. It’s actually quite amazing — if you do it right, you’ll increase the volume of your egg whites by 30-40%, and there will be a nice little foam layer at the top. From what I understand, if you don’t have an immersion blender, you can also beat your egg whites into submission with good ol’ American effort and a fork.
  • Beat the yolks too, just for good measure. Break ’em up and make sure you have a yolky liquid that is somewhat consistent.
  • Fold the whites into the yolks. Folding was a term that was unknown to me before today — basically, you want to pour a small amount of the white into the yolks, then take a spoon and spoon parts of the heavier yolk from the bottom of the bowl over the top of the fluffy egg whites. The reasoning is that it’s a lighter form of blending so that when you’re done, in theory, you’ll have little layers of yolk in between layers of white.
  • Melt the butter in an omelette pan. I did this at an 8 setting (my stove goes up to 10). The internet indicates that you want your eggs to be at 160 degrees Fahrenheit so that the milk parts will evaporate and bubble up through your omelette, but this very specific number did not cause me to act in a scientific manner at all (I pretty much make everything on the stove at about an 8).
  • Pour the egg mixture in the pan
  • You’ll notice that the foamy bits on top might start cooking and sticking to the sides of your pan. Gently use the corner of a spatula and sort of peel them away and move them towards the gooey center of the omelette.
  • When the whole thing started to solidify, but the top was still moist (something around the consistency of Jello or tofu), I managed to flip it over with a spatula. If you’re good, I think you can do this with just the pan alone, but I really don’t understand how with a four egg omelette. I’m also conflicted about this step in general, because I kinda like my omelettes a little runny on top — it finishes cooking by the time you get to eating it, but it gives it a texture I like. I might try just covering it next time so the top cooks a little more.
  • Side note: At this point, the back of my omelette (after I had flipped it) was a beautiful golden brown color. It looked kinda flaky too.
  • After about 30 seconds, put the omelette on a plate and serve. I put lots of pepper on mine, since I love pepper.

the most interesting blog in the world


Thanks Abe ūüôā

the most interesting blog in the world

I know that reading huge blocks of text isn’t for everybody, so I’ve included what I think is one of the best visual representations of information constructed by man. Below (click on it to see it in its¬†unadulterated, full sized glory) is a picture of a map for the London underground. At a glance, knowing nothing about London, I can see that to get from, say, Northwood to Kingsbury, I have to transfer at Wembley Park. It’s clear, simplifies a great deal of complexity, wastes no space at all, and packs information about connections between locations, routes, transfers, times of service, and transit agencies into a single image. It’s beautiful.

I learned about two interesting, if mostly unrelated concepts lately. I’m sure that they’ll be useful to me at some point in the future.

The first one is called a Wilson score. Wilson scores are useful to sort a set of reviews or ratings in a meaningful way. Let’s say you run some sort of e-commerce site (we’ll call it Omazan) which lets buyers leave either thumbs up or thumbs down ratings for any given product. There are two common ways of sorting these that are totally wrong.

  1. # thumbs up – # thumbs down: Consider the case where you have an item that has 20 thumbs up and 10 thumbs down. This means that 2/3 of people like it. Suppose there is another product that has 500 thumbs up and 490 thumbs down. This means that only about half of people like it. However, both of these products are rated equally given this heuristic.
  2. # thumbs up / # total ratings: A simple average works well in many cases, but not very well for small numbers. Say you have a product which has received no thumbs down, but 1 thumb up, and a product which has received 500 thumbs up and 1 thumbs down. The first product, which has much fewer ratings, will be rated higher than the second product, and it doesn’t make sense to order them this way.
How does the Wilson score work? Essentially, you plug the reviews you have into the formula, as well as a confidence score. You will get out a confidence interval for that score (in layman’s terms, you have a confidence interval at 95% of what the actual distribution of ratings is). If you take the lower bound of this confidence interval, it’s a pretty good way of ordering things. Here’s some psuedocode to calculate it (assuming a magic function that can look up a Z score for a confidence interval):
def wilson_score(num_positive, num_negative, conf):
    num_total = num_positive + num_negative
    if  num_total == 0:
        return 0
    z = lookup_z(conf)
    p_hat = num_positive / num_total
    return (p_hat + z*z/(2*num_total) Рz*sqrt((p_hat*(1-p_hat)+z*z/(4*num_total))/num_total))/(1+z*z/num_total)
Useful trick.
The second thing is something called an H-index. H-indices are used to calculate how awesome a scholar is. An H-index is a value H which is the highest number for which H or more publications have received at least H citations. So for example, if I have published 8 papers, and one of them had been cited 20 times, but the other 7 had been cited only 7 times, I would have an H score of 7. If each of those seven had been cited 6 times each, I would have an H score of 6. This serves to be a valuable measure of widespread impact, rather than blockbuster ability; sort of a way to weed out the one hit wonders.

Persimmons in Seoul, courtesy of the Boston Big Picture

An incomplete and unsorted list of pieces of classical music I associate with certain emotions:

  • American Quartet, Antonin Dvorak: longing
  • An American in Paris, George Gershwin: playfulness
  • Quiet City, Aaron Copland: loneliness
  • String Quartet No. 2, Alexander Borodin: nostalgia
  • La Puerto del Vino, Claude Debussy: mystery
  • Adagio for Strings, Samuel Barber: mourning
  • Piano Concert no. 3, Sergei Rachmaninoff: weariness

Ecclesiastes is a book of poetry in the Old Testament of the Bible. It was inferred that the author of the book is King Solomon, the ruler of Israel at its peak power in ancient times. Whether it truly is written by the greatest king of Israel or not, it’s a beautiful piece of writing by any standard, and it’s brought me a lot of comfort to read the words of an author 2000 years ago who experienced the same human feelings of doubt and uncertainty about the role of mankind in the universe.

There is a time for everything, and a season for every activity under the heavens:

a time to be born and a time to die,
a time to plant and a time to uproot,
a time to kill and a time to heal,
a time to tear down and a time to build,
a time to weep and a time to laugh,
a time to mourn and a time to dance,
a time to scatter stones and a time to gather them,
a time to embrace and a time to refrain from embracing,
a time to search and a time to give up,
a time to keep and a time to throw away,
a time to tear and a time to mend,
a time to be silent and a time to speak,
a time to love and a time to hate,
a time for war and a time for peace.

This is an absolutely fascinating read about the current state of leadership in the CCP, and provides a lot of insight into chinese politics.

Over the past few months, several people have written asking me to offer a short ‚Äúprimer‚ÄĚ on China‚Äôs upcoming leadership transition, which begins next year.¬† The handover to a new president and premier has generated plenty of speculation in the press, about who the leaders are and what is will all mean, but sometimes it‚Äôs useful to go back and fill in the very basics, since China has a unique and in some ways¬†quite confusing political system.

Hideaki Akaiwa

This guy is named Hideaki Akaiwa. He is 43 years old, and has an office job¬†just outside the port city of Ishinomaki in Japan’s Miyagi Prefecture. He also has one of the most inspirational stories I’ve read about in my life.

It’s Friday, March 11, and Hideaki is doing work at his desk. It’s 2:45, and he has no idea that in about a minute, one of the top five earthquakes in recorded human history is about to devastate Japan, sending tsunami waves of 40.5 meters crashing into Ishinomaki. Torrents of water will sweep the city, pushing cars around, flooding buildings, and causing widespread panic. In a matter of minutes, Ishinomaki goes from being a city to a lake, 10 foot deep in water. Hideaki’s wife is somewhere in the middle of that lake. They’ve been married for just over 20 years.

Instead of running away and waiting for assistance from the army and other social services, Hideaki dons a wetsuit and scuba gear and rushes into the torrential currents, swimming around cars, chunks of wood, and twisted shards of metal that used to be houses. At this point, it’s pitch black, but he somehow manages to navigate underwater to his house. He finds his wife trapped in an upstairs room of their house, panicking, with only a small amount of air left, and manages to pull her to safety.

He’s not done though. After rescuing his wife, he finds out that his elderly mother is unaccounted for in any rescue shelter, so he puts on the scuba gear and goes out to look for her. Four days later, he finds her in the upper levels of a house and manages to rescue her also.

For a week after the tsunami hit, he continued to help out with the relief effort, looking for survivors and assisting in the relief effort.

What a badass.