What's a fun thing to do when you learn a less than intuitive concept? Searching the web to find another person's opinion of it! I recently learned about Grassman numbers and my search turned up a blog post by a professor named Luboš Motl who makes some pretty debatable claims. After reading the post, I found out that he is actually quite famous. So yes, he probably knows much more than me about the subject, but I must still object to how complacent he is with using an object and not defining it.

First of all, happy holidays to anyone reading this! You didn't think I was going to let Christmas / Grav-mass go by without a post did you? Well I absolutely would have if I didn't have this post ready in time. Spreading the spirit of the season can be done in a small number of words - and all of the posts I write have to be long. No exceptions!

Anyway, like many people who have a piano at home, I sometimes hear a great piece of orchestral music in a movie and try to play an approximation to it on the piano. Usually what I try to play is full of mistakes and I lose interest after half an hour. However, my approximations to two Star Wars songs have evolved into fairly well defined pieces that I can play from start to finish. Want to guess which ones?

Both pieces are ending themes so they have to merge into this music that plays during the black and blue credits of every Star Wars movie. That should narrow it down significantly. Anyway, in the rest of this post you can find audio files and sheet music. Learning to play this was satisfying enough. But then I realized that this was a perfect opportunity to learn the music TeX packages so it's a win-win situation.

There are plenty of cases where a proof written down by a physicist is worse than a proof written down by a mathematician, but this is a particularly bad one. In one of my courses, we got to derive the Dirac matrices, which are instrumental in describing spin 1/2 particles. These four matrices are written as with an index. One definition of them says that they should satisfy the anti-commutation relations of the Clifford algebra:

where is the Minkowski metric from special relativity.

How big do our matrices have to be in order to satisfy this? They obviously cannot be 1x1 matrices because these are just numbers that commute. It turns out that they have to be at least 4x4 but all published sources I have seen fail at explaining why. I will go through the physics proof that is often given and then set the record straight by writing a real proof. If it appears nowhere else, let it appear here!

Normally when I see an article about numerology, astrology or homoeopathy, I don't give it the time of day. But this one is interesting because it sounds like the author actually made an honest effort to read up on the science related to the fine structure constant and just got it horribly wrong.

The article is The Mystery of 137 and it lives on a site dedicated to the new age philosopher Ken Wilber. Who would've guessed that a site like that would actually have a correct equation that comes up all the time in quantum electrodynamics?

Awhile ago, my friend showed me Pimp My Gun. This site has a Flash driven app that lets you assemble the weapon of your dreams. It is basically a drawing program that has a library of hundreds of firearm components. There is so much room for customization. I tried it out and came up with the following guns:

I never turn down a chance to be a smart-ass. One of the best things higher mathematics can teach you is how to go back and correct almost everyone who claimed to be teaching you math. It's almost impossible to a cover a decent amount of material in a math course without sacrificing correctness. This is true in grade school when you learn tons of stuff that isn't real math and it is true in grad school when writing one proof that is perfectly rigorous takes two weeks. Here are some common questions that need to be rephrased before they make any sense. The links point to where I found the questions but they could've come from anywhere. If they look like they were taken straight out of your high school calculus textbook, they probably were.

As some of you may know, the programming languages I use the most are C and Python. One reason for this is popularity - I want to learn something that will help me edit the programs I use. I also think it's good to know at least one compiled language and one interpreted language. Interpreted languages or "scripting languages" are more convenient in most respects but they take longer to run. I already knew Python would be slower than C but I wanted to see how much slower.

To make the above plot, I used C and Python codes to diagonalize an n by n matrix and kept track of their execution times. Once you get past the small matrices, the trend that begins to emerge is that Python is ~30 times slower than C.

As part of a summer internship, I got to put together several electronic components, and for the first time, use something more permanent than a breadboard. I found my first circuit very frustrating because my solder connections kept coming loose and I was told to make it as small as possible. But seriously... am I so used to learning about algebraic varieties and Feynman diagrams that I have become allergic to learning a real transferable skill?

The need for my first circuit arose because the photodiode that we used to measure the power in various lasers had a proportionality constant that was too small. For every Watt of power, the diode was calibrated to put only across two pins. We wanted to amplify this to a larger value. The component typically used for these applications that you can buy off the shelf is the operational amplifier.

As those of you who read my third most recent post will know, I recently became excited about methods for predicting the spread of diseases mathematically. When I learned about compartmental models, I began searching for tips on how they could best be applied to real data. I stumbled upon a solution on Abraham Flaxman's blog, Healthy Algorithms.

In Abraham's post, he presents some code that will estimate the parameters in a dynamical system using *Bayesian Inference* - the most elegant thing to come out of statistics since the Central Limit Theorem. Also present is an exercise challenging the reader to estimate the parameters of a 1967 smallpox outbreak in Nigeria.

If you want to do this exercise without a spoiler then stop! Otherwise, keep reading and I will tell you how I approached the problem while making some random remarks on the strengths and weaknesses of this particular fitting routine.

In a recent post, we used trigonometry to derive the length of a day on the Earth as a function of the observer's latitude and the time of year. As promised, I want to continue modelling the Earth's orbit to see what it can tell us about temperature. The simplest explanation for the seasonal variation of temperature comes from the concept of solar flux. To see what this means, think about taking a ray of sunlight shining on the Earth and decomposing it into two components - one parallel to the Earth's surface and one perpendicular. Most of the sunlight going *into* the ground means this location will be hot, whereas most of the sunlight going *along* the ground means this location will be cold. The temperature due to direct sunlight is therefore proportional to the cosine of the angle between the ray and the outward normal to the Earth. To actually solve for a temperature, we would have to multiply the intensity of the light by and integrate this over a region of interest. Our main concern will be solving for , allowing us to express the temperature at one time relative to the temperature at another time without knowing the absolute intensity.