So, I’m teaching a new course, Math 2033, Mathematical Thought, and it’s going great! I’d like to take a moment to write about it!

(This is one reason the MF has been kinda slow lately; another is that I’m chair) When it’s fully up and running, we’ll have about 150 students in one large section each semester (we’re starting with about 100). In a nutshell, it’s the Math Factor, as a course.

So, the list of topics is pretty familiar; from the podcast you are pretty well acquainted with the kinds of things I like to share: game theory, encryption, a little number theory, theory of computation & godel’s theorem, cardinality/infinity; plus more visual hands on things like topology, graph theory, symmetry, four-dimensional geometry, and so forth, and some baby programming in a playground IDE (scratch.mit.edu)

The real thing though is that my co-teacher and I have taken a kind of radical approach to the structure of the course—and our crazy ideas are working out great! In a nutshell, the students are guaranteed a C just for showing up and doing what they’re told (more on that in a sec), but to get an A, they have to become active collaborators in the building of the course, adding to the long-term infrastructure. I didn’t expect to give out very many A’s at all, but a surprising number of students seem up to the challenge. Part of the point is that this reward structure aligns the interests of the course directly with the interest of the students. Another nice thing is that it is much more like the reward structure of Real Life, far more so than most academic experiences: you can coast and do ok, but to really succeed, initiative and imagination are required. Interestingly, 20% of the class can’t even rise to the minimal standard of showing up, and will fail. 

Another nice thing is that students can bring to bear any of their own interests and abilities; we need such a wide range of things done—photography, writing, editing, leaders on our discussion board, organizers, all kinds of stuff. It is in fact possible to get an A by dragooning other students into harvesting, trimming and delivering a huge load of bamboo for some math sculptures the class will be making soon. The fact is, I have large ambitions for this, and no way to do more than a fraction of the work; students that help bring this off will be the ones that get an A.

SO, how does it actually work? The basic daily rhythm is that we give a lecture, usually with some sort of hands-on fun and games component. We then post a prompt or two on the class discussion board (hidden to the outside world). The students have 24 hours to post, and then another 24 hours to comment on each others ideas. This is the real heart of the course and the activity has steadily grown, reaching 1700 posts a couple of weeks ago. Wow!  (I shouldn’t exaggerate this though: some are really into it, many are trying to get by with as little as possible. I am aiming for a culture where slacking is gently disgraced, and we’re on track to get there)

As you can guess, this has completely lifted out of my ability to monitor; we have a number of ways this is digested and managed. For example, about half a dozen of the more thoughtful students are responsible for reading all of posts and trying to raise the level of discourse, and for creating useful summaries of the best ideas.

BUT that’s just the “internal” part of the course. Externally, open to the world, is a wiki, math2033.uark.edu which is going pretty well. I view this as a multi-year project, so this is a pretty good start. Most of what you see there is the product of about twenty students, and a few really do A LOT of work, including having developed the basic organizational framework. (So they get A’s for sure)

I’ve been spending a lot of time developing solid materials for use in the course, such as this sample handout, on the Halting Problem.

The students seem pretty pumped. It’s working!!! 

(We passed an important milestone last week; several students told me they ended up fooling around way too much with one of the optional assignments, messing them up in other classes! Perfect!)

   Three Points are taken at random on an infinite Plane.  Find the chance of their being the vertices of an obtuse-angled Triangle.

Note: An obtuse-angled triangle is one that has an angle measuring more than 90 degrees.

You are going to take a drive from City A to City B and back, but in a rather unusual car.  When travelling uphill, the car always moves at exactly 56 miles per hour.  On level ground, it travels at 63 miles per hour and finally when travelling downhill it travels at 72 miles per hour.  Assume that it transitions from one speed to another instantaneously and all of those other “mathematically perfect” qualities that make questions like this answerable.

You find that travelling from City A to City B takes exactly 4 hours of travel time.  On the return trip, driving time sums to 4 hours and 40 minutes.

How far apart are Cities A and B?

Imagine that you have immortal rabbits, Bugs Bunny’s version of Olympus perhaps.  Even if they are immortal however rabbits are famous for one thing.  They breed like, well rabbits.  Some of the rabbits are children and some adults and are divided into pairs.  Each month any child pairs become adults and any adult pairs breed to produce a new child pair.  They are immortal so no pair ever dies.  These rabbits are also a little odd.  They live on a line (don’t complain, this is no more ludicrous than that they are immortal!), but can shuffle along.  Also if you are worried about inbreeding, the male rabbit leave the family hutch and shuffle along the line past others until they find a suitably unrelated mate.  Why we would be worried about inbreeding in immortal rabbits living on a line escapes me!

Anyway we start with one pair of children.  Lets put a c.  After a month they become adults, a.  Another month passes and they now have a pair of children, but are still there themselves.  We therfore have the original pair and a pair of children: ac.  Next month the adults have another pair of children and the children become adults: aca.  Can you see how this will work?  Each month the children become adults so we replace every c with and a, each pair of adults has a new pair of children but stays as adults, so we replace every a with ac.  We can continue to get longer and longer sequences of rabbits on this line:

aca  to acaac to acaacaca to acaacacaacaac….

Now some puzzles.  Given a line with 21 adult pairs and 13 child pairs, how many pairs of adults and children would there be after one month?

Given p adults and q children how many adults and children will there be after one month?

Finally a more difficult one.  How will the ratio of adults to children behave month on month?  Will it

a) Get closer and closer to a particular number?

b) Keep on changing without pattern?

In either case can you say more?

A man leaves at exactly 6:00 AM to climb a mountain.  He may not climb at a constant rate.  In fact, he may stop to rest, or even backtrack a bit to see something interesting.  He arrives at the summit at exactly 6:00 PM and camps for the night.

The next day, he starts down at exactly 6:00 AM, again meandering unevenly, but following exactly the same path and reaching the bottom at exactly 6:00 PM.

The question is, was there some point on the path he was sure to have visited at exactly the same time on the two trips?

It’s really quite amusing to repeatedly apply a series of effects to an image, say using Photoshop filters. I didn’t think about it until yesterday, but this is a great way to model various kinds of things, particularly certain partial differential equations.

To take a really simple example, we all know that heat diffuses, and that in the absence of any energy being pumped into a system, temperatures will tend to even out over time. Similarly, if we apply a blur to an image over and over again, soon we will have a smeared out gray. 

Heat is modeled in a continuous realm: time is continuous, space is continuous. But while blurring, time occurs in discrete steps, and space is modeled in discrete pixels. Nonetheless, the two phenomena are closely related.

The famous heat equation is very simple:

du/dt   ∝ Δu  

All this means is that the amount of a quantity ‘u’ in a given location changes over time, and this change is proportional to how much net  variation there is nearby– the more variation the more u will change. (For those with more than Cal III under your belt, no need to explain; if you’ve had Cal I, if we measure temperature along a rod, then this Δu works out to be just d²u/dt² and sure– the more concave up the function is, say, the faster the heat will increase at that spot, in an attempt to smooth out the temperature; if you haven’t had any calculus, or even if you have, it’s interesting to consider a discrete version of this:

Suppose we have a bunch of graph paper, with a temperature written in every cell. Then the new temperature at each new time can be modeled by taking a weighted average of the neighbors. For example, if we’re at X :

a  b  c

d  X  f       

g  h  i

we might take an average using none of a,c,g and i, 1/8 of b,d,f,h and 1/2 of our original value X. (Generally we count closer spots more)

i.e. new value = X/2 + (b+d+f+h)/8

The change from the old value to the new value is —X/2 + (b+d+f+h)/8, and sure enough this is a discrete form of the laplacian.

We used photoshop to make the image at left , at the top of this post: we’ve applied a gaussian blur (rad = 1 px) and then three sharpens, and then repeated this over and over again.

And look!! Unmistakably, the spots and stripes that are the hallmarks of reaction diffusion types of equations have popped up!!

Reaction diffusion patterns pop up in many places– not least of which in the patterns of spots or stripes on many living things. The idea in reaction diffusion is that one or more quantities (like the amount of black in a pixel, or the amounts of various hormones or chemicals) simultaneously diffuse (blur) and react (changing the concentrations). These two actions are in a kind of tug-of-war, and when they are well-matched all kinds of interesting things can occur.

Here’s my question (for experts only): that doesn’t make sense– aren’t both a gaussian blur and sharpening modeled by adding / subtracting a laplacian? And where is the reactive term?

Incidentally, here’s another example that works beautifully:

If we repeatedly apply a gaussian blur and heighten the contrast, we obtain a sequence of images as below

This is exactly what we should expect: this is a discrete form of a well-known reaction diffusion equation in which the reactive force drives the interface between black and white towards having less and less total curvature. 

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Incidentally, the discoverer of this class of  equations was none other than the great Alan Turing, father of the theory of computation (and, as it happens, leader of the effort to crack the Nazi enigma code).  I  must take the opportunity to remind us all again that this great man was hounded to his suicide for his homosexuality by the very government he’d worked to save— a clear a martyr to the cause of universal human rights as there can be.

Imagine a tight band wrapped around the Earth (a perfectly spherical Earth!). If one foot is added to the band, it will be possible to lift it uniformly up, away from the surface of the Earth. Will the resulting gap be enough to pass a baseball card under? A baseball? A baseball player? 

Another variation, which is really quite amazing, is what if a foot is added and the band is lifted up in just one spot? How high will the band lift up? Higher than a seball? A baseball player? A baseball stadium?