Pi Day in Pune!

Happy Pi Day!

Friend of the Blog and former OU faculty member, Steven Spallone, is now a faculty member at the Indian Institue of Science and Education in Pune, India.  Those who know of Steven’s other life as a world famous YJogi won’t be surprised that not only is he living in India and doing mathematics, but he is the co-organizer (with Neha Prabhu) of a a Pi Day version of “The Life of Pi”.

Juggling and Mathematics? Steven Spallone is afoot!

Lest you think this just some class project, we point your attention to coverage by the Indian Express newspaper.

Go to Steven’s facebook page for more pictures of Steven in India.

Ironically, Steven is an avowed Tauist.  Hey, Steven should write a book called “The Tau of Pi” (trademark! :-)).

Problem of the Month(s)

The POM Author (artist’s rendering)

In the hustle-bustle of finals, you may have missed that the Grand Vizier of monthly problems posted the December Problem of the Month.  You can read it here.

Don’t be alarmed by the fact that December is more than half over, though, because this is also the Problem of the Month for January.  So you have until the end of next month to get your solutions in.

Here’s the problem:

Four (shy) mathematicians are sitting around a table and want to know the average of their salaries. The problem is, none of them wants to reveal their salary to anyone else at the table. Is it possible to develop a scheme by which they can compute this average without spilling the beans on their salary?

Problem 1: Develop a scheme that our mathematicians can use to compute the average salary, without any one person knowing the salary of anyone else.

What if one of these four does not mind divulging his salary so that he is able determine the other three salaries, is it possible to construct a scheme to compute the average without revealing all the salaries?

Problem 2: Develop a scheme that the above mathematicians can use to compute the average of their salaries, without divulging the salary of anyone who wants to keep it secret.

— from the POM webpage

Just the thing to stimulate your mind while pretending to listen to your uncle drone on and on about who knows what during the family dinner!

Math Club @Home: Vi Hart

In case three Math Clubs in two weeks aren’t enough for you (or if you’re reading this outside the greater Norman area), we thought we’d make this a week of math videos.

First up, we’ve mentioned her fantastic videos before, but we thought we should remind you that Vi Hart is still hard at work keeping the Ritalin folks in business.  Apropos to the season, she’s on a Thanksgiving theme these days:

What the Higgs is up with the Higgs Particle?

Dr. Peter Higgs

Unless you’ve been living without power in your basement this week, you know that the folks at the LHC have announced the experimental confirmation of the long theorized Higgs particle.   See here for an article about the announcement, or here for CERN’s actual press release.

In the mid 60’s a number of theoretical physicists (Brout, Englert, Higgs, Hagen, Guralnik, and Kibble) independently predicted that an as-yet undiscovered particle should exist with certain properties.  This particle became known as the Higgs particle (you’d have to be fluent in Klingon to correctly say “the BEHHGK particle”).

It’s a triumph of theoretical physics (aka math) to predict a particle before it’s experimentally discovered.  No doubt Dr. Higgs is delighted to have lived long enough to see his prediction confirmed experimentally.

The more cautious will say that their data indicates a new “Higgs-like” particle at a 5 sigma level.

What does 5-sigma mean?  That’s just physics-speak for 5 standard deviations (sigmas) away from the mean.  That is, there is only a 0.00006% chance the evidence for the Higgs particle is just because of chance fluctuations in the data.

Of course you should be careful! Remember last year when it was announced that the light speed barrier had been broken?  Data showed that neutrinos were traveling faster than light.

It would have been astounding if it were true.

The data which showed that nutrenios travel faster than the speed of light was at the 6-sigma level.  This means that there was only a 0.0000001973% chance that the results were due to chance.

It turns out that nutrenios don’t go faster than light.  What went wrong?  Standard deviation (sigmas) only measures how likely your result is due to random variations in the data.  It doesn’t protect you from other errors in the data*.  In this case, the researchers were getting consistently bad data because of bad cables in their equipment!

A great blog article talking about sigmas can be found here.

Incidentally, this is part of the reason people are so excited about the Higgs particle data.  It’s “only” at the 5-sigma level, but it was independently confirmed by two separate experiments.  This makes bad cables and such much less likely!

A great video about the LHC and the Higgs particle was done (pre-announcement) by PhD Comics:

* Imagine you’re trying to measure the average height of males in India and you only collect data from Dr. Pitale’s family!

Moneyball!

This

Wednesday, October 5th, in PHSC 1105 at 5pm

we’ll have a Math Club talk by Friend of the Math Club, John Paul CookNot only is he a grad student here in the math department, he’s our resident guru of sabermetrics.  What’s that?

That’s the math of baseball (and, more generally, sports).

More precisely, it’s the use of statistics and probability to evaluate players and teams by the numbers, and not just by gut feelings and urban legends.  For decades, people thought a good batter was one who hit lots of home runs.  But if you actually want to win games, it turns out that other things are more important!

The movie Moneyball is about how the (relatively) cash poor Oakland A’s were able to use sabermetrics to find good players with low salaries by identifying the under-appreciated players!

John Paul Cook will be telling us about the math and history behind this story:

What is Moneyball?  Sure, it’s a new film about the Oakland Athletics baseball team and how they found inefficiences in the market to compete with payroll behemouths like the New York Yankees.  It’s also starring Brad Pitt — maybe you’ve heard of him.  But did you know that some of the methods the Oakland A’s used have a solid grounding in mathematics and statistics?  In this talk, we examine some of the statistical tools employed by the frugal Oakland A’s and how they were able to compile one of baseball’s best won-loss records from 2001-2003 as a result.
— John Paul Cook’s abstract

As usual we’ll have The Pizza.  But afterwords we’ll head on over to the movie theater to see the film itself!

Something for anyone who likes math, pizza, baseball, or Brad Pitt!  The movie is getting great reviews.  Here’s the trailer:

Note:  We’ll be carpooling to the movie so if you have a car you can share, please bring it!  And, sadly, you’ll have to buy your own movie ticket; so bring a little cash, too.

…which this post is too small to contain.

In honor of Pierre de Fermat’s  410th (or maybe 403rd or 404th) birthday, Google’s homepage has a homage to Fermat and his famous Last Theorem:

Of course Fermat did much mathematics besides his Last Theorem.

For example, in Calc II you hopefully did the integral of $x$, $x^2$, and $x^3$ using formulas for geometric series.  It turns out that Fermat was the one who first developed this technique!  See here for a nice short history of doing integrals, including Fermat’s method.

P.S.  For a little math humor, be sure to mouse over the Google logo on their webpage.