# Finding 2016 in pi

2016 appears in π starting at the 7173rd decimal place:

You can confirm this with Mathematica or Wolfram Alpha:

`        Mod[ Floor[10^7177 pi] , 10000]`

I found it using the following Python code:

```        >>> from sympy import pi
>>> digits = str(pi.evalf(10000))[2:]
>>> digits.find('2016')
7173
```

By the way, it’s also true that 2016 = 1 + 2 + 3 + … + 63.

# Most popular posts of 2015

Here are this blog’s most popular technology posts of 2015:

And here are the most popular math posts of 2015:

# Automate to save mental energy, not time

Automation doesn’t always save as much time or effort as we expect.

The xkcd cartoon above is looking at automation as an investment. Does the work I put in now eventually save more work than I put into it? Automation may be well worth it even if the answer is “no.”

Automation can be like a battery as well as an investment. Putting energy into batteries is a bad investment; you’ll never get out as much energy as you put in. But that’s not why you put energy into batteries. You put energy in while you can so you can use some of that energy later when you need it.

Write automation scripts when you have the time, energy, and motivation to do so and when nothing else is more important. (Or nothing is more interesting, if you’re looking for a way to procrastinate without feeling too guilty. This is called “moral compensation.”) You may indeed save more work than you put into writing the scripts. But you also may save mental energy just when you need it.

Suppose it takes you an hour to write a script that only saves you two minutes later. If that two minutes would have derailed your concentration at a critical moment, but it didn’t because you had the script, writing the script may have paid for itself, even though you invested 60 minutes to save 2 minutes.

If your goal is to save mental energy, not time, you have a different strategy for automation. If a script executes faster than a manual process, but it takes a long time to remember where to find the script and how to run it, it may be a net loss. The less often you run a script, the chattier the interface should be.

The same considerations apply to learning third party software. I suspect the time I’ve put into learning some features of Emacs, for example, will not pay for itself in terms of time invested versus time saved. But I’ve invested leisure time to save time when I’m working hard, not to save keystrokes but to save mental energy for the project at hand.

Related post: How much does typing speed matter?

# Restarting @DSP_fact, ending @PerlRegex

I’m making a couple changes to my Twitter accounts.

First, I’m winding down @PerlRegex. I’ll stop tweeting there when my scheduled tweets run out. I suggest that everyone who has been following @PerlRegex start following @RegexTip instead. The latter is more general, but is mostly compatible with Perl.

Second, I’m reviving my @DSP_Fact. I stopped tweeting there a couple years ago, but I’d like to start posting there again. This time it’s going to be a little broader. I intend to include some material on acoustics, Fourier analysis (continuous and discrete), and maybe some other related material.

# Unicode / LaTeX page updated

Almost three years ago I put up a web page to let you go back and forth between Unicode code points and LaTeX commands. Here’s the page and here’s a blog post explaining it.

I’ve expanded the data the page uses by merging in data from the STIX Project. More queries should return successfully now.

* * *

# Alternating sums of factorials

Richard Guy’s Strong Law of Small Numbers says

There aren’t enough small numbers to meet the many demands made of them.

In his article by the same name [1] Guy illustrates his law with several examples of patterns that hold for small numbers but eventually fail. One of these examples is

3! – 2! + 1! = 5

4! – 3! + 2! – 1! = 19

5! – 4! + 3! – 2! + 1! = 101

6! – 5! + 4! – 3! + 2! – 1! = 619

7! – 6! + 5! – 4! + 3! – 2! + 1! = 4421

8! – 7! + 6! – 5! + 4! – 3! + 2! – 1! = 35899

If we let f(n) be the alternating factorial sum starting with nf(n) is prime for n = 3, 4, 5, 6, 7, 8, but not for n = 9. So the alternating sums aren’t all prime. Is f(n) usually prime? f(10) is, so maybe 9 is the odd one. Let’s write a code to find out.

```    from sympy import factorial, isprime

def altfact(n):
sign = 1
sum = 0
while n > 0:
sum += sign*factorial(n)
sign *= -1
n -= 1
return sum

numprimes = 0
for i in range(3, 1000):
if isprime( altfact(i) ):
print(i)
numprimes += 1
print(numprimes)
```

You could speed up this code by noticing that

`    altfact(n+1) = factorial(n+1) - altfact(n)`

and tabulating the values of `altfact`. The code above corresponds directly to the math, though it takes a little while to run.

So it turns out the alternating factorial sum is only prime for 15 values less than 1000. In addition to the values of n mentioned above, the other values are 15, 19, 41, 59, 61, 105, 160, and 601.

* * *

[1] The Strong Law of Small Numbers, Richard K. Guy, The American Mathematical Monthly, Vol. 95, No. 8 (Oct., 1988), pp. 697-712.

For daily tips on Python and scientific computing, follow @SciPyTip on Twitter.

Here are some of the non-technical books I’ve mentioned in blog posts this year. I posted the technical list a couple days ago.

Maybe I should say “less technical” rather than “non-technical.” For example, Surely You’re Joking, Mr. Feynman is a book about a physicist, but it’s at least as much a human interest book as a science book.

# Four ways to find hidden RSS feeds

Blogs used to display an icon linking to the site’s RSS feed, and any still do. Blogging software still creates RSS feeds, though links to these feeds have become harder to find.

There are several ways to find the RSS feed of a site that does not make this feed obvious.

2. You can try adding `/feed` or `/rss` to the end of the domain.
3. You may be able to find the RSS feed by looking at the HTML source of the front page.
4. There are browser plug-ins that will show when a page has RSS feeds.

## Example

Digg Reader was able to find the RSS feed from just the top level URL `http://www.gatesnotes.com/`.

Tacking /feed on the end doesn’t work, but `http://www.gatesnotes.com/rss` does.

If you want to subscribe to this blog via RSS, there’s a big blue button on the right side that says Subscribe by RSS. You can also subscribe to individual categories of posts in case you’d like to subscribe to my math posts,  for example, but not my software development posts, or vise versa.

There are also RSS feeds to my Twitter accounts, thanks to BazQux.

Here are some of the books I’ve mentioned in blog posts this year. One of these books may be just the present for a geek in your life.

This post looks at technical books: math, science, engineering, programming. I’ll have a follow-up post with non-technical books I’ve written about. (Update: here’s the non-technical list.)

# Colors of noise

The term white noise is fairly common. People unfamiliar with its technical meaning will describe some sort of background noise, like a fan, as white noise. Less common are terms like pink noise, red noise, etc.

The colors of noise are defined various ways, but they’re all based on an analogy between the power spectrum of the noisy signal and the spectrum of visible light. This post gives the motivations and intuitive definitions. I may give rigorous definitions in some future post.

White noise has a flat power spectrum, analogous to white light containing all other colors (frequencies) of light.

Pink noise has a power spectrum inversely proportional to its frequency f (or in some definitions, inversely proportional to fα for some exponent α near 1). Visible light with such a spectrum appears pink because there is more power toward the low (red) end of the spectrum, but a substantial amount of power at higher frequencies since the power drops off slowly.

The spectrum of red noise is more heavily weighted toward low frequencies, dropping off like  1/f2, analogous to light with more red and less white. Confusingly, red noise is also called Brown noise, not after the color brown but after the person Robert Brown, discoverer of Brownian motion.

Blue noise is the opposite of red, with power increasing in proportion to frequency, analogous to light with more power toward the high (blue) frequencies.

Grey noise is a sort of psychologically white noise. Instead of all frequencies having equal power, all frequencies have equal perceived power, with lower actual power in the middle and higher actual power on the high and low end.

# R lists and XML

Hadley Wickham posted a photo on Twitter back in September illustrating R list indices with pepper:

Then a few days ago, Jenny Bryan posted on Twitter her follow up, an analogous photo for XML:

Related posts:

Here are wordclouds for some of my most popular Twitter accounts. Thanks to Mike Croucher for creating these images. He explains on his blog how to create your own Twitter wordclouds using R.

My most popular account is CompSciFact, tweets about computer science and related topics.

AlgebraFact is for algebra, number theory, and miscellaneous pure math. (Miscellaneous applied math is more likely to end up on AnalysisFact.)

ProbFact is for probability.

DataSciFact is for data science: statistics, machine learning, visualization, etc.

You can find a full list of my various Twitter accounts here.

# Mathematical alchemy and wrestling

David Mumford wrote a blog post a few weeks ago in which he identified four tribes of mathematicians. Here’s a summary of his description of the four tribes.

• Explorers are people who ask — are there objects with such and such properties and if so, how many? …
• Alchemists … are those whose greatest excitement comes from finding connections between two areas of math that no one had previously seen as having anything to do with each other.
• Wrestlers … thrive not on equalities between numbers but on inequalities, what quantity can be estimated or bounded by what other quantity, and on asymptotic estimates of size or rate of growth. This tribe consists chiefly of analysts …
• Detectives … doggedly pursue the most difficult, deep questions, seeking clues here and there, sure there is a trail somewhere, often searching for years or decades. …

I’m some combination of alchemist and wrestler. I suppose most applied mathematicians are. Applications usually require taking ideas developed in one context and using them in another. They take often complex things then estimate and bound them by things easier to understand.

One of my favorite proofs is Bernstein’s proof of the Weierstrass approximation theorem. It appeals to both alchemists and wrestlers. It takes an inequality from probability and uses it in an entirely different context, one with no randomness in sight, and uses it to explicitly construct an approximation satisfying the theorem.

I thought of David Mumford’s tribes when I got an email a couple days ago from someone who wrote to tell me he found in one of my tech reports a function that he’d studied in his own research. My tech report was motivated by a problem in biostatistics, while he was looking at material structural fatigue. The connection between remote fields was a bit of alchemy, while the content of the tech report, an upper bound on an integral, was a bit of wrestling.

# You do not want to be an edge case

Hilary Mason made an important observation on Twitter a few days ago:

You do not want to be an edge case in this future we are building.

Systems run by algorithms can be more efficient on average, but make life harder on the edge cases, people who are exceptions to the system developers’ expectations.

Algorithms, whether encoded in software or in rigid bureaucratic processes, can unwittingly discriminate against minorities. The problem isn’t recognized minorities, such as racial minorities or the disabled, but unrecognized minorities, people who were overlooked.

For example, two twins were recently prevented from getting their drivers licenses because DMV software couldn’t tell their photos apart. Surely the people who wrote the software harbored no malice toward twins. They just didn’t anticipate that two drivers licence applicants could have indistinguishable photos.

I imagine most people reading this have had difficulty with software (or bureaucratic procedures) that didn’t anticipate something about them; everyone is an edge case in some context. Maybe you don’t have a middle name, but a form insists you cannot leave the middle name field blank. Maybe there are more letters in your name or more children in your family than a programmer anticipated. Maybe you choose not to use some technology that “everybody” uses. Maybe you happen to have a social security number that hashes to a value that causes a program to crash.

When software routinely fails, there obviously has to have a human override. But as software improves for most people, there’s less apparent need to make provision for the exceptional cases. So things could get harder for edge cases as they get better for more people.

Related posts:

# Bastrop State Park, four years later

Four years ago I wrote about the wildfires in Bastrop, Texas. Here’s a photo from the time by Kerri West, used by permission.

Today I visited Bastrop State Park on the way home from Austin. Some trees, particularly oaks, survived the fires. Pines have come back on their own in parts of the park. A volunteer working in the park told me that some of these new trees are 10 feet tall, though I didn’t see these myself. In other parts volunteers have planted pines. Here’s a photo I took this morning.

Most of the new growth in the forest is underbrush, in some places thicker than in the photo above. The same volunteer mentioned above said that the park is already planning prescribed burning in some areas to clear the underbrush and protect the viable trees.