That Time When I Discovered The Earth Was Round

The recent flare up between rapper B.o.B and Neil DeGrasse Tyson over earth curvature has been interesting to watch. A nice summary can be found here (NPR link). B.o.B asserts not only that the earth is flat but that there is also a NASA coverup. It’s not clear why there would be a coverup.

So lets unpack this a bit. The tweet below is what set off the whole thing:

First, lets explain why our perceptions can be mistaken. B.o.B asks, “Where is the curve?”. Well it is right in front of his eyes but over a distance of 16 miles it is not perceptible. Sixteen miles is only about 0.064 % of the earths circumference (if you believe it has a circumference) which means there are over 1500 more of these distances in the great circle that includes these two cities. Now, imagine you cut a pie or an orange 1500 times, pull out a single slice and looked at its edge. How much evidence of the original curve do you think you will see? This is a classic mistake where we think we are seeing everything we need to see. And ‘seeing is believing’. ‘Our eyes are never mistaken’. This is wrong. And there is a litany of optical illusions on line that can demonstrate this. A classic is the straight lines that look bent.


It’s a different phenomenon but it illustrates the problem.

Another tweet B.o.B put out appears to be a from a list of reasons why the earth is flat. No doubt from a very trusted online source.

This contains a lie and demonstrates that the earth is curved, all in one poorly thought out argument. To begin, you can’t see Polaris 20 degrees South. But thanks for using a curved metric (what does 20 degrees mean if the earth is flat?) to prove the earth is not curved. Polaris is about 2 degrees off the Northern Pole so Polaris is visible from the Southern Hemisphere so long as you are very close to the equator and not too far south into the Southern Hemisphere. Any further south than Nairobi in Kenya and it does indeed never rise above the horizon.

So I guess Barak Obama never saw it either until he moved to the US. Yes, that is how dumb this all sounds.

Now lets get personal. I know all this about Polaris because I was born and grew up in the Southern Hemisphere. I never saw Polaris until one day I moved to the US. One of the first things I did was go out at night and look up at the night sky and look for all the constellations I had been told about but could never see. Heh, maybe I had been lied to. Think of it from my perspective. Time and again, we of the Southern Hemisphere are told how to find north by using the Pole Star and how to find it using the ‘Big Dipper’ (North American media is strong – it’s not called the big dipper anywhere else). What utter crap! There is no Big Dipper, no Pole Star! From my 33 Degrees South location, none of this is visible. But I could see the Southern Cross and the Magellanic Clouds, neither of which can be seen from most of the Northern Hemisphere and certainly not Atlanta (just saying).

I exist, Northern Hemisphere bitches! “Magellanic Clouds ― Irregular Dwarf Galaxies” by ESO/S. BrunierESO. Licensed under CC BY 4.0 via Commons.

These things are real, but you have probably never seen them for two reasons. 1) You probably have never heard about them and 2) You are probably from the Northern Hemisphere (see first reason) and the earth beneath you makes you point in the wrong direction to see it… because the earth is round. Just like it is impossible to see the Pole Star from the Southern Hemisphere. This is how I knew for sure the earth was curved – not that I ever doubted it but it was amazing to have it demonstrated. It was mind blowing and humbling. The Pole star was there, but my Southern Cross was gone. It hadn’t just been moved further down in the sky (like if the earth of flat)… it was gone!

So this is the fundamental problem. Our perspective can be so limiting some times its hard to think outside it. It’s easier to accept our small world view and assume a conspiracy of lies:

For what purpose? Doesn’t matter. We just assume we can’t be mistaken.

And that is so damn arrogant. The realm of twitter feeds.


Hiatus is over…

<stream of consciouness>

My last blog post was way back in February 2014. Thinking back now its obvious to me why I stopped blogging here then. My life took a serious detour. Truth be known I have been blogging elsewhere – a far more personal (and anonymous) blog. The problem with anonymity is the posts are disconnected from a person. I don’t own them the way I would like. And while I have my reasons for having a anonymous period, that writing can’t be fully integrated into who I am. Here (this blog) I am who I am. Science weirdo. Sometimes obsessive. Mostly completely ignoring this place.

So this is why I’m back here. Well other reasons too. Reason the first) I think I have something to say and I think I should communicate that. Reason the second) I still have lots of technical things I like to explore and discuss. But from time to time I will incorporate less technical topics and perhaps more personal topics into this blog. Well, we’ll see how that goes.

</stream of consciouness>

Life goes on. You still get to score a goal from time to time. And you never ever stop learning more about yourself.



Lotteries and “Getting what you are looking for”

I came across a review/excerpt in Scientific American for a new book called “The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day”. This article has an excellent description of how seemingly impossible events can and do take place all the time.

The major point of the excerpt was that some events like finding people with matching birthdays can be a rare event or a common event depending on what you mean by a matching event. In a room of 23 people there is about a 6% chance one of them will share a birthday with you, but a greater than 50% chance that two of the 23 people in the room will share a birthday (see a description of the birthday problem here). An intuitive explanation for this is when you’re looking for someone who shares your birthday you have limited the possibilities – they must match your birthday – but when we allow anyone to match any other person, the possible number of combinations of people matching birthdays goes up a lot. So its a lot more likely. The maths for this is at the above link. I don’t need to repeat it here. The point is, a subtle change in the question can have big consequences for the answer.

The excerpt then goes on to talk about the Bulgarian Lottery coincident that occurred in 2009. In one draw, the winning numbers were 4, 15, 23, 24, 35, 42. “Amazingly” the next draw, the same numbers again. There was outrage with people calling the draw rigged and there was even a full government investigation. As an aside, if you could rig the draw why would you repeat the numbers? Seems silly to do that. Why not just rig the draw with some other numbers? Anyway…. the question is, how surprised should we be that this happens at all?

The Bulgarian lottery is a 6 number draw from 49 numbers. So the probability of getting the right numbers is 1/(49!/(6!*43!)) or one in 13,983,816. Once you have a particular set of numbers the chance that any other specific draw will have the same numbers is one in 13,983,816 – its the same as picking the numbers in the first place. This seems like a pretty unlikely event. But this is like finding someone who has the same birthday as you. You are trying to match one specific draw to another. What happens when you have many draws and you are trying to find a match between any of those two draws? The Bulgarian lottery has been going on for a while, so there have been many draws and so the chances that a match would happen among all these draws is much less amazing. For example, among 50 draws there are 1,225 ways we could match 2 of the draws. Quickly, the number of ways to find a match goes up and as a result the probability of a match happening goes up as well.

Now some skeptics might say, “But this was two draws in a row!!! Not a draw this week matching a draw from 5 years ago. This is very special because of the serial way in which it happened!”. I felt this objection was glossed over in the SciAm excerpt. Thinking that the consecutive nature of the draws is special in this case is thinking about it all wrong. Actually its thinking yourself into the special circumstance. You are saying that the two draws in a row is a special thing. But this is the same as finding that one person who matches your birthday.

Another way to think about this sort of thing is to consider the following. Instead of drawing 4, 15, 23, 24, 35 and 42, imagine the lottery drew 1, 2, 3, 4, 5 and 6. Would you think this was amazing? Rigged? Would it be all over the internet and in all the papers? Yes it would. But is it any more unlikely than 4, 15, 23, 24, 35 and 42? No, its not. They have the same chance of happening… one in 13,983,816. The only reason it would be considered special is because the numbers are consecutive. By why is that any more special than starting at 4, adding 11, adding 8, adding 1, adding 11 again and then adding 7? Its only because of the value placed on consecutive numbers by our minds. We humans would say consecutive draws has more meaning or is more special than one draw matching a draw from 5, 10 or 15 years ago. In terms of random events neither is more special than the other.

Your perspective influences your expectation, but not reality. Uncommon things will still happen all the time, just not the sort of uncommon things you might expect to happen.

When Maths Gets Weird – and Maybe Derailed

\sum_{i=1}^{\infty}i = 1+2+3+4... obviously doesn’t converge. Right? This sum doesn’t actually evaluate to a number, does it? Well I wouldn’t think so – no part of my intuition suggests this series is limited. So imagine how surprised I was (and perhaps you too) when I learned this Maths conundrum has been spreading around the internet early this year due to a few videos from the Numberphile guys. Their answer?

\sum\limits_{i=1}^{\infty}i = 1+2+3+4... = -\dfrac{1}{12}
What? Really? -\frac{1}{12}? There is something odd here, clearly. How can this be true? This is even beyond breaking intuition, this is just plain wrong. So why is this even taken seriously? The ‘proof’ comes from a field of Maths called Mathematical Analysis and infinite series. I wont go over the proof, there are examples here, here and here. However,  they all depend on the following equality:

1-1+1-1+1-1.... = \dfrac{1}{2}

This is true for a Cesàro summation. That is, its true because of a definition of how to sum a series that doesn’t actually converge to a value. This is fairly well accepted and I suppose if you define the process of Cesàro summation to be a method of summing non-converging sequences then that is just fine… by definition. For me, however, this doesn’t sit right. This is not a sum that comes from the axioms that lead to arithmetic, the so called Peano axioms (I don’t have problems with the Peano axioms – I doubt you do/would too). But I do have a problem with Cesàro summation as a definition. Since Cesàro summations have results like 1-1+1-1… = 0.5 that contradict other forms of the sum like…

(1-1) + (1-1) + (1-1)... = 0 + 0 + 0... = 0


1 - (1+1) - (1+1) - (1+ 1)... =1 - 0 - 0 - 0... = 1

the only way the sum could be evaluated to 0.5 is by a specific definition that excludes the above sums. But why should I select this definition? Do I accept it as an axiom? If I do, how do I account for the fact that this axiom has two other intuitive sums? Obviously it is a bad axiom and therefore a bad definition. I really don’t understand why anyone would accept the Cesàro summation as anything useful at all. It immediately leads to inconsistencies. Its useless.

More to come….

NMR, Compressed Sensing and sampling randomness Part II

Using Compressed Sensing: Selecting which points to collect.

NMR data is collected as a vector or matrix of points (depending how many dimensions the experiment has). Compressed sensing permits us to collect a subset of the actual points that are usually required. The question is, how do you decide which points to collect.

TL;DR Summary: Compressed sensing (CS) allows us to sample a subset of the points normally required for an NMR spectrum. CS theory suggests we should sample these points randomly. Random sampling leads to problems with the distribution of the gaps between samples. There is a better solution for NMR data. 

The problem of Random Sampling.

Formal Compressed Sensing (CS) theory says we should collect a random spread of points between 1 and the final point (N). We could make a schedule of points by constructing a “dart-throwing” algorithm that randomly selects a point between 1 and N. If the algorithm selects a point twice, we just ask it to select another time. We run this until we have N/5 points. Surprisingly, it turns out this does not work well. Lets see why…

Below is an example of what happens when you select randomly 64 points out of 256 points. First of all, to make sure I selected points in an unbiased way, I took the numbers 1 through 256 into a vector and did a Fisher-Yates shuffle. Then I simply select the first 64 points in the vector after the shuffle. It has been shown that the Fisher-Yates shuffle is unbiased and I think it is a better solution to sampling purely randomly than a dart-throwing algorithm. Since I have 64 random points out of 256, you would expect that the average distance between numbers should be:

(256 – 64)/65 = 2.95385

This is because we need to eliminate 64 out of 256 (256 – 64) because they are selected and not part of gaps, then we must divide by 65 because by selecting 64 points we actually create 65 gaps. This means that the average gap size (that is the gap between selected numbers) is 2.95385.

I did 45 simulations like this, generating 64 * 45 = 2880 gaps. The average gap was 2.96319 so this basically looks good. Now, the Standard Deviation was 3.35334 which shows something funky is happening right away. This is a very large standard deviation for a mean value of 2.95385 and permits negative numbers to appear within one standard deviation. This can’t even happen for a gap as  gaps can’t be negative and there are no negative gap values in my numbers. How can this standard deviation be? Clearly the gaps are not normally distributed. Lets look at a histogram of the gap sizes.


Yikes, its an exponential distribution. This has been noted before for random gap sizes, but I was amazed when I first saw this – in hind sight I’m not, but at first I was a little shocked. To confirm that this really does happen for random sampling I went looking for another example of a subset of random numbers selected from a pool of numbers. Lottery results of course are a good example.

I acquired the complete set of Megamillions lottery numbers from about June 2005 to October 2013 (the nature of the pool of numbers did not change in that time) and calculated the gaps between these random numbers. In this game you had to pick 5 numbers from 1-56 (the rules have since changed). So the average distance between numbers is

(56 -5) / 6 = 8.5

My calculated gap distance is 8.42009 and the histogram of the gap sizes is below.



So, why does this matter? Well what is happening is the gaps are distributed in a way which can cause problems when we try and fill in the data we skipped in the gaps. To begin with look at the above histograms. When we randomly select points we end up making gaps such that the most common gap size is actually no gap at all. Thats pretty remarkable when you think about it. Then the second most common gap is the smallest gap, a gap of 1. By the time we have reach the average gap (around 8 for Megamillions, or a little less than 3 for selecting 64 points from 256) one half of our gaps have been accounted for, by definition. The reason so many gaps fall here is because there is a lower limit to the size of the gap. They can’t be less than zero. Now, all these small gaps are great, because they should be easy to fill in. The problem is the upper limit on the gap size is the size of the sampling space minus the number of samples. This number is always going to be several times larger than the average gap size. Simply put, this will always give the exponential distribution seen in the histograms, resulting in some gaps that are much larger than the average gap size. These large gaps present problems when trying to fill in data with predictive methods.

So how do we do better than this when ‘randomly’ selecting which samples to take? First of all, we don’t use a straight random algorithm – it makes gaps that are too large. Instead we introduced some randomness around the average gap size by using a poisson distribution. We call this Poisson Gap sampling, a method developed by Sven Hyberts in my lab.

Details will be in the next post.

NMR, Compressed Sensing and sampling randomness Part I

One of the things I spend my time thinking about is the problem of shortening the time it takes to acquire Nuclear Magnetic Resonance data on biomolecular macromolecules like proteins and nucleic acids. Below is an attempt to describe the problem non-technically and how we can speed up data acquisition by ‘randomly’ sampling a subset of the data normally required.

TL;DR Summary: Acquiring nuclear magnetic resonance data takes a long time. This is because to get good resolution traditionally a certain number of data points must be sampled.  Can we skip some of these points? The answer is yes, the next question is which points?

The problem: NMR takes a long time.

NMR spectra have great power in describing macromolecules at the atomic level when collected in 2, 3 or even 4 dimensions. Each dimension represents a different kind of information (say, nucleus type, location in a repeating unit of the polymer, distance from another nucleus) – so multiple-dimension spectra are data-heavy but help isolate specific atomic groups really well. The information in each dimension is actually frequency information – it is the frequency of each atom in the molecule. The down-side is these spectra take a long time to acquire. In fact, to acquire 3 and 4 dimensional data, experiments are usually shortened by not acquiring an ideal number of samples. That is, most of the dimensions are truncated in time which leads to poor frequency discrimination in that dimension.

Now, each dimension beyond the first dimension is acquired slowly for a number of technical reasons I wont discuss here. However, lets say that in one of these dimensions we would ideally like to acquire N points, but we really only have time to acquire N/4 points. This means our frequency resolution will drop 4 times. For further technical reasons, our frequency resolution is not just a function of N but also a function of some time delays between the sampled points. These time delays (we call them evolution delays) are actually very fast, its just that the time between when we can collect these points is slow (blah blah blah – further technical reason). This means we don’t have to wait a long time to actually collect the Nth point above, it just takes a long time to get to N because we must collect 1, 2, 3… N-2, N-1, N points along the way. This is a window of opportunity here if we can actually skip points and quickly get to the Nth point and maintain the same high frequency resolution expected when collecting all N points.

This can be done and in NMR and is called non-uniform sampling. It is also a type of compressed sensing.

Compressed Sensing: Collecting some data and discarding most.

Several techniques have been developed to allow collection of data out to distance points without having to collect all the points in between. Programmatically it is fairly easy to get an NMR spectrometer to do this. The problem lies in processing the data into a spectrum that contains few, if any, significant artifacts. The regular FFT (Fast Fourier Transform), for example, can be used by simply setting the non-collected data points to zero. This however results in significant artifacts. The problem is how to reconstruct the missing data and minimize the artifacts. Compressed sensing (CS) is a theory that describes a way to do this. Originally CS was developed for image processing but it has successfully been applied to NMR data. Assuming signals are sparse (true for NMR data) and that noise is not significant (mostly true for NMR data), compressed sensing algorithms can reconstruct the skipped data.

How do we decide which points to skip?

I will talk more about this in the next post… stay tuned.