Kinetically Constrained

Just hurry up and sit down!

2012-01-21T11:36:00.000Z

As a semi frequent flyer, and incredibly impatient stand-behinderer I couldn't resist linking to this - Time needed to board an airplane: A power law and the structure behind it from a Norwegian group, Vidar Frette and Per Hemmer.

Boarding strategy is of great importance to airlines, where the turn around time of planes – especially short haul – can make a real dent in profits. For the authors of this paper, however, it seems they just think it's a neat model to test out 1D problems where the particles are distinguishable, rather than the more common indistinguishable particles. In a traffic model the cars are usually identical, whereas here the passengers have a specific seat booking. Statistically this makes a difference.

Of course many people do look at specific strategies. For example here, it seems that it's difficult to think up a strategy that beats random loading. One would think that loading back-to-front would be better but this is not the case. A quick google search finds this nice page from Menkes van den Briel. There you can see videos of all the different strategies.

Unfortunately the best strategy seems to involve seating people in order of window/middle/aisle. Not great if you're sitting next to your kids.

All of which did remind me that it is much quicker boarding when you don't have seat bookings. When I fly to England using a certain orange-themed airline, that doesn't book seats, there's an initial mêlée followed by reasonably rapid sitting down. On a certain royal blue-themed airline it takes forever for a plane half the size to get sat down.

My suggestion is that I should be allowed to starting poking, with increasing frequency and verbal abuse, anyone that I deem to be taking too long to put their bag away.

Clustering in sea-ice floes

2012-01-15T18:26:00.001Z

I started writing this post as a long winded account of the difference between equilibrium and non-equilibrium statistical mechanics. It turns out that that is hard to discuss without waffling on, so instead I will just talk about an interesting paper from the world out of equilibrium - which is most of the real world.

I've been walking around with this interesting paper, "Molecular-dynamics simulation of clustering processes in sea-ice floes" by Agnieszka Herman, in my bag since November. It was picked up in the spotlight section, in Phys. Rev. E (loosely the stat-mech/complexity section). My attention was grabbed by the idea that simple ideas in granular gases could hold sway in the icy seas of the Arctic.

Marginal ice zone

Roughly speaking, it's always icy at the top of the earth and then as you go south it turns into ocean. Around the transition between icy and not icy (only the best technical explanations for my readers) is the so called marginal ice zone (MIZ). This is where bits of ice break away from the main ice pack and float around in the sea. Understanding how this ice moves around, and the effect of external forcing, is important if we're to best understand the impact of global climate change.

The ice-floes studied in this paper are in an intermediate region between densely packed and very low density. The sizes of the ice fragments are roughly distributed with a power-law tail and they float about and hit each other inelastically. It is here that one can make the link to something closer to my own field, it is a 2D granular gas.

Granular gases

In the world of the small everything is constantly being battered by random thermal noise. It's so random that it, in fact, becomes rather predictable and Boltzmann distributed. In the world of a bit bigger, this thermal noise doesn't really affect the individual particles any more and we're now dealing with grains. I've talked about this before in the context of colloids – the last bastion of thermodynamics before everything goes granular.

In a granular context the ice fragments are particles that move ballistically in between collisions, and when they collide energy is lost. This system, of dissipative colliding grains is known to have interesting dynamics including the clustering of particles and other complicated correlations.

The really nice thing about this paper is that what Agnieszka Herman has done is to simulate such a granular gas, but adding in realistic numbers for all sorts of effects such as friction, wind, currents, restitution coefficient (how inelastic it is) and to see if it can reproduce what is observed in the oceans. This can not have been easy to set up!

Comparing to real life

The image below is the sort of sea ice clustering that is seen in the MIZ. One sees that the smaller floes tend to accumulate on one side of the larger floes.

This is also seen in the simulations results. This is because, as well as losing energy in collisions, the floes are being driven by wind and currents. The larger floes catch up with the smaller ones pushing them along for a while until they fall off. The colour bar shows the velocities of the different floes.

At higher densities – more collisions – you can still see the gaps behind the large floes, although the distribution of velocities is now narrower.

I don't know how rigid this system is, it'd be interesting to know if there's a breakout point where the ice floes can suddenly escape. It's really neat to think that you can connect such different systems, not to mention such different scales, and still be able to say something sensible.

Big thanks to Agnieszka for providing the colour images. Images, copyright APS, are reproduced with permission from the paper Phys. Rev. E 84, 056104 (2011).

Networks in Nature Physics

2011-12-22T16:25:00.003Z

For those with access, looks like Nature Physics has a complexity issue. With articles by Barabási and Newman and the likes, it looks like it has a solid networks bent.

There's a paper on community structure by my favourite physicist, Mark Newman, that I'm looking forward to reading.

Enjoy!

We all do economics

2011-11-15T09:32:00.001Z

The very interesting blog, Mind Hacks, has a post on a theory of a bipolar economy.

A 1935 Psychological Review article proposed a ‘manic-depressive psychoses’ theory of economic highs and lows based on the idea that the market has a form of monetary bipolar disorder.

I find it quite interesting how people like to reframe the problem of economic crashes in their own subject. In psychology it seems perfectly natural to ascribe the behaviour to individual human behaviour. As a physicist I'm completely convinced that it's a collective effect that arises from many relatively simple individuals, trying to win a game, interacting in a highly complex system. Of course one could possibly say the same about the brain itself.

I wonder if biochemists have some hormone explanation and neuroscientists some neurotransmitter reason. Perhaps all these perspectives are equally right (or wrong) – I guess the only thing for sure is that economists have no clue!

A phase diagram in a jar

2011-11-07T14:25:00.001Z

One of the things I love about colloids is just how visual they are. Be it watching them jiggling around under a confocal microscope, or the beautiful TEM images of crystal structures, I always find them quite inspirational, or at least instructional, for better understanding statistical mechanics.

Sedimentation

Just to prove I'm on the cutting edge of science, I recently discovered another neat example from 1993. At the liquid matter conference in Vienna Roberto Piazza gave a talk titled "The unbearable heaviness of colloids". As a side note there was a distinct lack of playful titles, maybe people were too nervous at such a big meeting. Anyway, the talk was about sedimentation of colloids.

Sedimentation is something I don't usually like to think about because gravity, as any particle physicist will agree, is a massive pain in the arse. Never-the-less, my experimental colleagues are somewhat stuck with it (well, most of them). As is often the way it turns out you can turn this into a big advantage. What Piazza did, and then others later, was to use the sedimentation profile of a colloidal suspension to get the full equation of state, in fact the full phase diagram, from a single sample.

The nicest example is from Paul Chaikin's lab (now in NYU, then in Princeton), where they used a colloidal suspension that was really close to hard spheres. They mixed a bunch of these tiny snooker balls in suspension, and then let it settle for three months. What they got is this lovely sample, with crystal at the bottom (hence the strange scattering of the light), and then a dense liquid which eventually becomes a low density gas at the top. It's as though the whole phase diagram is laid out before you.

Equation of State

This is a very beautiful illustration, but it's not the best bit. In the same way that atmospheric pressure is due to the weight of the air above you, if you can weigh the colloids above a particular point in the sample then you can calculate the pressure at that point. This is exactly what they did. There are many different ways to measure the density of colloids at a particular height, if you can do it accurately enough (which was the big breakthrough in Piazza's 1993 paper) then you can calculate the density as a function of pressure. In a system where temperature plays no role such as this, this is exactly the equation of state (EoS).

When compared with theoretical calculations for hard spheres the experimental data lies perfectly on the theory curves, complete with first order phase transition where it crystallises. This is really a lovely thing. EoSs are very sensitive to exact details, so in the same way that in my group we compare our simulation of the EoS to check our code, this showed very accurately that their colloids really were hard spheres.

So I think this is all very nice. I nicked the above images from Paul Chaikin's website, I recommend having a poke around, there's loads of great stuff (you really need to see the m&ms).

Back from the dead

2011-11-04T19:04:00.001Z

Can't remember the number of times I've said I've been away because I've been busy, but this time it'll be different. Well it probably won't be different, it looks like I'm destined to be an inconsistent blogger!

It's now been three months since I arrived in the Netherlands for my new job and I'm enjoying it a lot here. The pace is much faster in the group than I'm used to but I'm enjoying the buzz of lots of interesting things getting done. Now I'm more settled I'm hoping for a spectacular return to blogging - there's certainly enough to talk about here!

The Dutch are good at science

In general the Netherlands has a fantastic history in the sciences. I was watching Carl Sagan's Cosmos the other day (best telly ever made), he loved the Netherlands it would seem. There's a whole episode where people dress up in pointy hats and reenact bits from Dutch scientific history.

I'm no historian so there's no point making a huge list. Some notable greats though include Cristiaan Huygens, famous for the wave theory of light, he worked on telescopes and even the pendulum clock. The microscope was invented in the Netherlands, allowing the Antonie van Leeuwenhoek to discover "a universe in a drop of water".

What about statistical mechanics?

Closer to the focus of this blog, the name Johannes van der Waals is never far away. His theories allowed us to begin to understand why matter undergoes phase transitions.Two names that are important for us here in Utrecht are Peter Debye and Leonard Ornstein.

Peter Debye is another one of those names that just seems to pop up all the time. It's littered through my thesis because of his work on phonons. Debye was professor at the university of Utrecht for a very short time. I believe the university didn't deliver on his startup money so he left. The picture is from our coffee room in the Debye Institute.

As well as working in the Debye Institute I also work in the Ornstein Lab, after Leonard Ornstein. For me his name is most famous from the Ornstein-Zernike relation in liquid state theory, however, I think he did a lot of varied stuff. He followed on from Debye at Utrecht in 1914 where he remained until 1940. Ornstein was Jewish and at the beginning of the war was dismissed from his position at the university. Only six months later he died. Seems to me it should be the Ornstein Institute, anyway, we also have his picture up.

Enough history

So the Dutch weren't too bad at science. The living ones aren't too shabby either. So hopefully lots of interesting things to be posted in the coming weeks.

Universality at the critical point

2011-07-09T00:26:00.000+01:00

Time for more critical phenomena.

Another critical intro

I've talked about this a lot before so I will only very quickly go back over it. The phase transitions you're probably used to are water boiling to steam or freezing to ice. Now water is, symmetrically, very different from ice. So to go from one to the other you need to start building an interface and then slowly grow your new phase (crystal growth). This is called a first order phase transition and it's the only way to make ice.

Now water and steam are, symmetrically, the same. At most pressures the transition still goes the same way – build an interface and grow. However, if you crank up the pressure enough there comes a special point where the distinction between the two phases becomes a bit fuzzy. The cost of building an interface goes to zero so there's no need to grow anything. You just smoothly change between the two. This is a second order, or continuous, phase transition and it's what I mean by a critical point.

As I've demonstrated before, one of the consequences of criticality is a loss of a sense of scale. This is why, for instance, a critical fluid looks cloudy. Light is being scattered by structure at every scale. This insight is embodied in the theory of the renormalisation group, and it got lots of people prizes.

Universality

A second feature of critical phenomena is universality. Close to the critical point it turns out that the physics of a system doesn't depend on the exact details of what the little pieces are doing, but only on broad characteristics such as dimension, symmetry or whether the interaction is long or short ranged. Two systems that share these properties are in the same universality class and will behave identically around the critical point.

At this stage you may not have a good picture in your head of what I mean, it does sound a bit funny. So I've made a movie to demonstrate the point. The movie shows two systems at criticality. On the left will be an Ising model for a magnet. Each site can be up or down (north or south) and neighbouring sites like to line up. The two phases at the critical point are the opposite magnetisations represented here by black and white squares.

On the right will be a Lennard-Jones fluid. This is a model for how simple atoms like Argon interact. Atoms are attracted to one another at close enough range but a strong repulsion prevents overlap. The two phases in this case are a dense liquid and a sparse gas.

One of these systems lives on a lattice, the other is particles in a continuous space that are free to move around. Very different as you can see from the pictures. However, what happens when we look on a slightly bigger length scale? Role the tape!

At the end of the movie (which you can view HD) the scale is about a thousand particle diameters across containing about 350,000 particles and similar for the magnet. At this distance you just can't tell which is which. This demands an important point: These pictures I've been making don't just show a critical Ising model, they pretty much show you what any two-dimensional critical system looks like (isotropic, short range...). Even something complicated from outside of theory land. And this is why the theory of critical phenomena is so powerful, something that works for the simplest model we can think of applies exactly - not approximately - to real life atoms and molecules, or whatever's around the kitchen.

Meeting is good

2011-06-15T16:28:00.001+01:00

Once again I find myself making some excuse as to why it's been over a month since my last post. My first reason is I'm finishing up my current postdoc. My other reason is I've been doing lots of travelling. This is much more exciting as I've been finding out more about all the cool soft matter / stat-mech work that is going on in the UK. Some of which I will blog about in time. I've also learned that half the people in soft matter in the UK have worked at some point in the Netherlands, which is handy because I'm moving to the Netherlands!

Getting to the point

All this travelling is related to the topic I wanted to get to today - the value of meeting. I was started off thinking about this thanks to Alice Bell's article in the THE on the value of the seminar. Here Alice calls for seminars to be posted online, something I agree with very much, as a way to reach more people (and to improve the standard a bit). From my experience I've had to use hundreds of pounds of grant money touring the country giving the same seminar. While I value that experience - meeting the people in the groups, direct interaction and so on - it's a shame that people at other universities can't see the talk as well.

Of course if people knew it was online they may not turn up, but hopefully not. I might start sticking mine up here.

The more efficient way of way of reaching many like-minded academics is of course the conference. A good conference can do wonders for your creativity and enthusiasm, it can give you an instant snapshot of the state-of-art and you can meet future employers/collaborators.

But they can be a bit stuffy and long. And expensive. So I'd like to fly the flag for a third kind of academic interaction, the informal science "retreat". Not long ago we had our annual Cornish Soft Matter weekend. A small group of physicists and chemists from a couple of universities got together for a more relaxed meeting. Talks were projected onto a sheet, we were sitting on sofas or the floor, and the start of a talk would be delayed due to people making a last minute cup of tea (usually this was me). All this in a really nice setting.

The demographic was largely PhD students and postdocs, and everyone had to give a short talk. If it overran, fine, if people had questions they'd asked them right away. Students were encouraged to ask as many questions as possible and academics resisted the urge to tear anyone to bits with their sharpened critical skills.

Scientifically it's great. I got to hear from the people who make all these synthetic colloids that I always cite. Their concerns weren't always about phase diagrams or dynamic arrest, sometimes it was simply how much stabiliser or chemical X do I need to get the polydispersity down. These are problems I don't usually get to hear about and it's particularly nice to get it from the people at the coal face.

Because the atmosphere is more relaxed you can give a different kind of talk. In a conference you're so worried about being jumped on that you tend to take out all the personality from a talk, all the wild speculation and, well, then fun side of science. Here we could kind of let rip. If we wanted.

Socially it is also a good thing. It's easy to get a little isolated with your own little problem, especially when your doing a PhD, so it's nice to mix a bit. Science, like most jobs, requires a degree of networking. While I hate this word and all that it implies, these informal gatherings are a much better way to get to know people than conferences. People at conferences are always trying to look smart and generally suck up to the established professors. Makes me shiver just thinking about it.

A snappy conclusion

The main thing that made this meeting nice was the atmosphere. I highly recommend anyone to organise something similar if it's possible. Sure, it was no Copenhagen, but the science was good and it helped create that sense of being in a scientific community.

While it's not free it's a lot cheaper than a conference. I guess you don't need to go all the way to Cornwall but it is nice to get out the department for a couple of days - especially when you usually sit at a desk all the time.

An early look at simulation

2011-04-27T17:43:00.001+01:00

While I was putting together the post on 2D disks I came across a lovely paper from 1962 on 2D melting by Alder and Wainwright. From there I found this paper from 1959: Studies in Molecular Dynamics. I. General Method by the same authors.

They describe the "event driven" molecular dynamics (MD) algorithm. Normally, with MD you calculate forces, and thus accelerations, and update this way. Hard disks or spheres behave more like snooker balls, the forces are more or less instantaneous impulses that conserve momentum so it's better to deal with collision events and leave out the acceleration part.

The paper gives a fascinating insight into the early days of computer simulation (they still refer to them as "automatic computers"), what their limitations were and what details were worth worrying about. To give you an idea, in 1959 they say:

With the best presently available computers, it has been possible to treat up to five hundred molecules. With five hundred molecules it requires about a half-hour to achieve an average of one collision per molecule.

So in their case it was CPU speed that was the problem, they get about thousand collisions per hour. To put that in perspective, a modern event-driven simulation of a similar system will maybe hit about a billion collisions per hour on a reasonable desktop [source]. I don't say this to mock their efforts, these are the giants on whom's shoulders we stand. I'll come back to why that number is so comparatively big these days, first I want to look at visualisation.

Visualistion

In 1959 there were no jpegs or postscripts and certainly no Povray or VMD, I'm not sure they even had printers. So how do you visualise your simulation? Well they had a rather elegant answer to that. They could output the current state of the system to a cathode-ray tube as a bunch of dots in the positions of the particles. Then they pointed a camera at the screen and left the shutter open while they ran a simulation. What you get is these beautiful images below showing the particle trajectories. Firstly in a crystal phase you can see the particles rattling around their lattice sites

This is a projection of the FCC lattice (the squares confused me at first). In the fluid phase they do a little bit of cage rattling and then start to wander off.

[Figures reprinted with permission Alder and Wainwright, J. Chem. Phys. 31, 459 (1959). Copyright 1959, American Institute of Physics].

I honestly couldn't show it better today. Some people dismiss visualisations as pretty pictures that only exist to attract attention. Perhaps this is sometimes true but it only takes one look at this to see how they can stir the imagination and shape the intuition – and that's what creates new ideas.

Algorithms

I'd quickly like to come back to the speed difference between 1959 and today. A lot of the difference can be put down to Moore's law. After an annoying amount of Googling I can't really say how much faster modern CPUs are. A lot probably. However, I'd like to focus on an often overlooked factor – the development of algorithms.

A general event driven algorithm calculates the collision time for each pair of particles and, if it is under a cutoff, stores it in an event queue. It then fast forwards to the shortest time whereupon it will need to update the queue with new events that appear after the collision. Initially this requires checking all pairs, Alder and Wainwright call this the "long cycle", and this has complexity of order N-squared, O(N^2). This means that if you double the number of particles, N, then you have four times as many calculations to perform.

After a collision you only need to update events involving the particles that collided so you can get away with doing N updates. This is the "short cycle" and is O(N). It's not mentioned in this paper but I think there's an issue with sorting the event queue so this is probably still O(N^2). Either way, for their early simulations the total number of collisions per hour tanked as N was increased.

And this is where algorithms come in. You can use all sorts of tricks. In dense systems you can use a cell structure to rule out collisions between pairs far away. Modern algorithms focus largely on keeping the event queue properly sorted. A binary tree will sort with O(log(N)) and here they claim to have it O(1). Of course the complexity is not the only important factor, there may be other more important overheads, but it gives an idea of the limitations.

In equilibrium statistical mechanics specialised computer algorithms have made a spectacular impact. Techniques such as the Wolff algorithm, Umbrella sampling, and many many more, have outstripped any speed up by Moore's law by many orders of magnitude. I could go on about algorithms for hours (maybe a post brewing), instead I'll just make the point that it doesn't always pay to just sit and wait for a faster computer.

We've come a long way

These early simulation studies weren't just important for developing methods, they were able to answer some serious questions that were hopelessly out of reach at the time. Since then simulation has firmly established itself in the dance between theory and experiment, testing ideas and generating new ones. And it shows no sign of giving up that position.

Lipid membranes on the arXiv

2011-04-15T09:02:00.003+01:00

A while ago I discussed lipid membranes and how they could exhibit critical behaviour. There were some lovely pictures on criticality on giant unilamellar vesicles (GUVs) which are sort of model cell walls. That work was done by Sarah Keller and friends in Seattle.

This morning on the arXiv I saw this new paper, also by Sarah:

Dynamic critical exponent in a 2D lipid membrane with conserved order parameter

They look at the critical dynamics of the GUV's surface. Being embedded in a 3D fluid does have its consequences so they've attempted to account for the effect of hydrodynamic interactions. I haven't poured over their model but the paper looks really nice.

Paper review: Hexatic phases in 2D

2011-04-13T17:40:00.002+01:00

I'm doing my journal club on this paper by Etienne Bernard and Werner Krauth at ENS in Paris:

First-order liquid-hexatic phase transition in hard disks

So I thought that instead of making pen-and-paper notes I'd make them here so that you, my huge following, can join in. If you want we can do it proper journal club style in the comments. For now, here's my piece.

Phase transitions in 2D

Dimension two is the lowest dimension we see phase transitions. In one dimension there just aren't enough connections between the different particles – or spins, or whatever we have – to build up the necessary correlations to beat temperature. In three dimensions there are loads of paths between A and B and the correlations really get going. We get crisp phase transitions and materials will readily gain long range order. Interestingly, while it should be easier and easier to form crystals in higher dimensions there do exist pesky glass transitions that get worse with increasing dimension. But I digress.

In two dimensions slightly strange things can happen. For one thing, while we can build nice crystals they are never quite as good as the ones you can get in 3D. What do I mean by this? Well in 3D I can give you the position of one particle and then the direction of the lattice vectors and you can predict exactly where every particle in the box will sit (save a bit of thermal wiggling). In 2D we get close, if I give you the position and lattice vectors then that defines the relative position and orientation for a long way – but not everywhere.

By "a long way" I mean correlations decay algebraically (distance to the power something) rather than exponentially (something to the power distance), which would be short ranged. We can call it quasi-long ranged.

Never-the-less, this defines a solid phase and this solid can melt into a liquid (no long range order of any kind). What is very interesting in two dimensions is that this appears to happen in two stages. First the solid loses its positional order, then it loses it's orientational order as well. This is vividly demonstrated in Fig 3. of the paper. The phase in the middle, with quasi-long range orientational order but short range positional order, is known as the hexatic phase.

When the lattice is shifted a bit the orientation can be maintained but the positions become disordered.

A brief XY interlude

Before we get on to hard disks it might help to understand a slightly simpler model. The XY model is similar to the Ising model, it's on a lattice but the spins are now continuous in the XY plane. This is basically enough to kill the phase transition we see in the Ising model (separation of up and down spins) because the XY spins can gradually rotate from up to down getting rid of a sharp interface.

So we lose any long range order, however, at low temperatures the XY model can hold quasi-long range order – just like the hexatic. Most importantly there is a phase transition from disordered to quasi-ordered. This transition, known as the Kosterlitz–Thouless (KT) transition, is a bit weird and it is related to topological defects in the vector field. Chapter 9 of Chaikin and Lubensky will blow your head off if you want to learn all there is to know about these things. To get a rough idea here is what these defects, or "vortices" can look like (nicked from here).

These vortices cost free energy but at high enough temperature we can afford them. In turn they have affect of suppressing correlations in the director field. When the temperature drops such that we can't afford these defects, we develop quasi-long ranged order. The KT transition is continuous in nature (ie, you don't get interfaces) although it's not strictly second order.

Back to disks

Going back to particles, this hexatic picture appears to be fairly ubiquitous in 2D systems. To get something more concrete we now focus on one model, the simplest of all the off-lattice models, hard disks. Hard disks, like hard spheres, are very interesting because they are the basis of most liquid theories and are the simplest approximation to an atom we can think of. So how do they melt?

No one pretends (or pretended) to know for sure. Most people agree that there should be a (quasi) solid that melts to a liquid via a hexatic but the nature of the transition is hotly contested. One prediction is via KTHNY theory. This horribly named theory (I now pronounce either "kuthny" or I cough and wave my hands) can give two continuous, XY-esque, transitions: solid-[continuous]->hexatic-[continuous]-> liquid.

So we finally arrive at the paper. What Etienne and Werner are now saying is that the transition from liquid to hexatic is actually first order. The reason it is so difficult to say for sure is that you need a very big system to see it and, because it's so dense, you need a long time to equilibrate it. In this paper they have a huge system (10^6 particles) and they use a special Monte Carlo algorithm, the Event Chain algorithm, that is very efficient for hard disks. These together allow them to really see what the transition looks like.

To verify what they're seeing they first study the two phases in isolation, just above and below the coexistence region. By monitoring the pressure as they scan the coexistence region (in an infinite system it would be constant) they can see how the peak-to-peak pressure difference scales. The scaling is quite clean and consistent with a first order transition. The most vivid demonstration of the first order nature is the picture in Fig 1. that shows the interface between the liquid and the hexatic phase.

First order then?

So the journal club part of this is to ask how convinced you are? My brain is naturally attracted to pictures and that interface is pretty striking. I happen to know they've done simulations with 4x the area and it looks even better there. As it's shown here there's maybe a bit of ambiguity just by eye.

If you want to remember what a second order transition looks like there's always the super huge Ising model (now in HD!).

The pressure measurements are probably the most convincing piece of evidence for me. It's certainly an impressive achievement, I definitely look forward to any follow-ups.

Six degrees - the documentary you can't see

2011-03-17T17:28:00.001Z

A while back the BBC put on an documentary about networks called "Six Degrees". Normally when you see a documentary about a field that you're vaguely related to you feel a bit sick because they did it all wrong.

Well I have worked in networks a bit and I thought Six Degrees was excellent. It got a great balance of the historical study of networks and then it ran its own version of the Milgram experiment which was mostly used as a plot device to keep driving the story forwards. The people involved (Watts, Strogatz, Barabasi) were all very entertaining and successfully transmitted the excitement of scientific discovery.

Suffice to say it was great. I had planned to link to it and then discuss it a little bit. Annoyingly the BBC have switched off the iPlayer version of the programme and they now appear to have shutdown the version at top documentaries.

I know the beeb don't want to give away content for free but it strikes me that a resource this useful (I'd even recommend it to scientists new to the field) should be kept live. Instead it's buried away where it's now useless. Scientists are always told about public engagement, well unblock this film - engage!

I'm going to write to them an encourage them to let it free, then perhaps instead of a rant about the BBC we can talk about some science.

UPDATE:
As you can see from the comments, the BBC didn't make the film so they can't keep it online. I can't work out how to get a DVD yet but when I find out I'll put up a link and then can get on talking about networks. In the mean time, this book, "Small World" by Mark Buchanan is well worth a read.

Thoughts of a first-time peer reviewer

2011-03-10T18:08:00.000Z

Most of my time is spent tirelessly chipping away at the scientific rock face, probably bogged down fixing a bug in my code or staring at some noisy looking data. Every now and then it all comes together and I want to tell people about it. So I write up my results as best I can, spend hours tinkering with figures, another few hours getting the fonts right on the axes, and after drafts and re-drafts, eventually I'll send it away to a journal to be published. This is where I become caught up in the process of peer review.

Usually it goes like this: The editor of the journal will check that the paper is basically interesting and then send it out to two reviewers who are chosen for their expertise in your area. These reviewers, or referees, will then read the paper, check it for basic errors and then comment on its originality and its pertinence to the field. This is sent back to the editor who will decide whether or not to publish. Usually the referees make you fix something, sometimes nothing, sometimes you can have a right old ding dong.

The main point is that the process is anonymous and behind closed doors. This is good and bad. Better blogs than this one discuss different options. It's not really my intention to criticise or support peer review, just to share my experiences.

Recently I was sent my first ever article to review. I can't say anything about the details, but it has been strange crossing over to the other side. I've had to ask questions that I have never thought much about before. So I wanted to put it down before I forget what all the fuss is about.

Reviewed

Up to now my only experience has been on the reviewed side of peer review. I've certainly had mixed experiences here. The first paper I had reviewed went through after lots of useful comments by the reviewers. It gave us more work but it made the paper better. Good experience. Another time a reviewer spotted a small error in our equations - also a good experience.

My worst experience involved two bad scenarios. Our first reviewer had not understood the paper, nor taken the time to follow the references that would allow him/her to do so. Instead of passing on to someone more qualified they just said it didn't make sense and was not interesting. The second referee had some interesting points but appeared to block it mainly on the basis that it didn't agree with other (presumably their) results. As you can see, I'm still bitter about this paper! It took 18 months to eventually get it through by which time it was thoroughly buried.

Of course, I'm biased, our paper could have been crap. Either way, the experience was bad enough that I was close to leaving science because of it. Receiving sneering anonymous reviews is a crushing blow to your ego - even if they're right.

Reviewer

So now I've reviewed my first paper. I won't say what I did, most of the questions I found myself asking would apply to any paper.

I'm quite used to reading other people's work, occasionally making a scoffing remark, or more likely not fully understanding it. The prospect of checking a paper for errors and assessing its quality filled me with dread. The only way I could deal with it was telling myself that it doesn't matter if I don't understand absolutely everything. The main thing is to check that they haven't done anything completely stupid.

This part of peer review I think is not too bad. There is an element of trust that someone has collected their data properly, but checking that it's not completely upside down is not too difficult or controversial.

Where it starts to get subtle is questioning the interpretation. Pulling someone up on their conclusion requires quite a bit of guts. Or I suppose an over inflated ego - of which there are many in science. This is related to another question, when should a scientific argument happen before publication and when should it happen afterwards? If the signal to noise ratio is to kept reasonably high then some things will need to be filtered out before hitting public view. I have not worked out an answer to this.

The final problem I had was with the question, "is this work of sufficient quality to be published in journal X?". Again this is really tricky, scientists can be real bitches when deciding what is or isn't interesting. On the other hand some scientists can try and get away with putting out any old crap just to lift their publication count. I found being asked to be the arbiter of quality quite stressful. Most results need to be on the scientific record somewhere, but should something be blocked for being too "incremental"? I suppose this is the journal's decision.

Is it worth it?

Apart from some initial stress I found the whole experience quite enjoyable. It makes you feel part of the scientific collective and it really tunes your critical skills. It will be interesting to see what becomes of peer review in the web 2.0 era, I would quite like to see it open up a little. I worry that unregulated, open anonymous comments, could be unhelpful. People are arseholes when they're anonymous, just ask a peer reviewer.

New domain

2011-02-22T15:30:00.000Z

I've taken the domain name, kineticallyconstrained.com. For now don't change anything as the exact address might move about a bit. I haven't quite worked out what sub domains to use blah blah blah.

Eventually I plan to put some permanent content and develop the site a bit. For now, to be honest, I'm mostly testing that the RSS feed is still working!

Colloids are just right

2011-02-03T19:18:00.000Z

All being good it looks like I've secured employment for a tiny while longer. Hooray!

The place I'm moving to is a big place for synthetic colloids, so it seems like a good time to go through what I know about colloids. If nothing else it'll be interesting to compare this to what I'll know in a year's time! So, here is a theorists perspective on colloid science.

I'll spare the usual introduction about how colloids are ubiquitous in nature, you can go to Wikipedia for that. The type of colloids I'm interested in here are synthetic colloids made in the lab. They're usually made from silica or PMMA (perspex), you can make a lot of them, they can be made so they're roughly the same size and you'll have them floating around in a solution. By playing with the solution you can have them density matched (no gravity) or you can have them sinking/floating depending what you want to study.

The colloids that people make sit nicely in a sweet spot of size and density that make them perfect for testing our fundamental understanding of why matter arranges itself in the way it does. Colloids can undergo most of the same phase transitions that we get in molecular systems, but here we can actually see them. Take for example this beautiful electron microscope image of a colloidal crystal from the Pine group at NYU.

1. They're big enough to image

Colloids are usually of the order a micron across. At this size it is still possible to use confocal microscopy to image the particles. While nothing like the resolution of the electron microscope, the confocal can actually track the positions of individual particles in real time, in solution. It's almost like a simulation without the periodic boundary conditions! A confocal can take lots of 2D slices through the sample, such as below from the Weeks group. The scale bar is 5 microns.

If you do it quick enough then you can keep track of the particle moving before it loses its identity. The Weeks group did some very famous work visualising dynamic heterogeneity in liquids near the glass transition. (see their science paper if you can).

If we want to think about colloids as model atoms, which we do, then there's another property apart from just their size that we need to be able to control.

2. You can control their interactions

Being the size they are, if we didn't do anything to our colloids after making the spheres they would stick together quite strongly due to van de Waals forces - this is the attraction between any smooth surface to another, as used by clingfilm. To counteract this the clever experimentalists are able to graft a layer of polymers around onto the surface of the colloid.

It's like covering it with little hairs. When the hairs from two particles come into contact they repel, overcoming the van de Waals attraction. The particles are "stabilised". In this way it's possible to make colloids that interact pretty much like hard spheres. So not only can we use them as model atoms, but we can use them to test theoretical models as well!

Further to this the colloids can be charged and by adding salt to the solvent one can control the screening length for attraction or repulsion to other colloids. Finally there's the depletion interaction. I want to come back to this so for now I'll just say that by adding coiled up polymers into the soup we can create, and tightly control, attractions between the colloids. With this experimentalists can tune their particles to create a zoo of different behaviours.

3. They're thermal

If the colloids are not too small to be imaged, why not make them bigger? If we made them, say 1cm, then we could just sit and watch them, right? Well not really. If you filled a bucket with ball bearings and solution, density matched them so they don't sink or float and then waited, you'd be there a long time. The only way to move them in a realistic amount of time is to shake them - this is granular physics.

Granular physics is great but it's not what we're doing here. Real atoms are subject to random thermal motions and they seek to fit the Boltzmann distribution. For this to work with colloids they need to be sensitive to temperature.

When a colloid is immersed in a fluid it subject to a number of forces. If it's moving then there will be viscous forces, and on an atomistic level it is constantly being bombarded by the molecules that comprise the fluid. In the interests of keeping this post to a respectable size I can't go through the detail, but suffice it to say that this is an old problem in physics - Brownian motion.

Under Brownian motion the large particle will perform a random walk that is characterised by its diffusion constant. The bigger this number the quicker it moves around. A more intuitive number is the time it takes for a particle to move a distance of one particle diameter. When you solve the equation of motion for a large particle in a Stokesian fluid you find that this time is given by

where is viscosity, a is the particle diameter, and k_B T is Boltzmann's constant and temperature. Now this does get more complicated in dense systems and the properties of the fluid matter, but this is a good start. This could be a topic for another post.

For a typical colloidal particle, around a micron in size, you have to wait about a second for it to move its own diameter. For something only as big as a grain of sand you can be waiting hours or days. Even by 10 microns it's getting a bit too slow. But close to 1 micron, not only does it move about in an acceptable time frame, we can easily track it with our confocal microscope. If it's diffusing around then we can hope that it will be properly sampling the Boltzmann distribution - or at the very least be heading there. So once again, that micron size sweet spot is cropping up.

So what else?

Hopefully this serves as a good starting point to colloids. Obviously there's a lot more to it. An area that I'm very interested in at the moment is what happens when the colloids are not spheres but some other shape. I'll be posting more about it in the coming months.

If you don't remember anything else just remember that colloids are the perfect size to test statistical mechanics and to be visible.

So well done colloids, you're just right size.

It's been a while

2011-01-19T17:46:00.001Z

But I'm planning a dramatic comeback - just as soon as I've sorted my next job!

I've got some more critical-scaling stuff in the pipeline, some nice crystallisation videos and it may be time for some chat about self-assembly seeing as I'm now officially a self-assembler (self-assemblist?).

So don't delete Kinetically Constrained from your RSS reader just yet!

Comment Spam Filter

2010-08-12T13:40:00.001+01:00

If this is true:

Blogger Buzz: New Comments System on Blogger

Then it will halt my planned relocation to Wordpress. It's just in the nick of time. It's not enabled for me yet but as soon as it is I'll be switching off the moderation in favour of a spam filter.

UPDATE: In true Blogger fashion they've managed to arse this up. The spam filter only comes on when you enable full comment moderation. The whole point of a spam filter is that I don't want to moderate comments! I want them to go straight on the blog unless they look like obvious spam.

I'm beginning to wonder if any of these people actually run their own blog. Wordpress migration preparation continues...

UPDATE Nov 2011: As far as I'm concerned comment moderation is fixed, blogger has been humming along nicely for me for a while now.

Statistical mechanics of tetris

2010-07-25T01:59:00.000+01:00

I'm finding that I'm becoming increasingly fascinated by shape. It seems such a simple thing yet scratch the surface only a little and the complexity comes pouring out. Take simple tiling problems; I can tile my floor with squares or regular hexagons, but not regular octagons - they'll always leave annoying gaps. From a statistical mechanics point of view those gaps are very important, little sources of entropy that you can't get rid of. In three dimensions understanding the packing of tetrahedra has proved no simple task. But that's a story for another day.

So it came as no surprise that I was very taken with Lev Gelb's talk on polyominoes at the Brno conference. Polyominoes are connected shapes on a two dimensional lattice. A monomino is a square, a domino you know. Tetrominoes are made of four squares and are exactly like the pieces from Tetris. Assuming that they're stuck in the plane (so you can't flip them over) there are 7 tetrominoes.

All of these can individually tile space so it is possible to have no gaps. Shapes such as S and Z, L and J are chiral opposites. There are some subtle differences in the rotational entropy as well. O has a single rotational state. I, S and Z have 2, the rest have 4.

Many have studied these sorts of shapes before. Lev's group are looking at it in the context of nanoparticles adsorbing to a surface. What they've done is to run really efficient simulations in an open system (grand canonical). They fix a chemical potential - this is kind of like fixing the pressure - and then measure what how many particles are on the surface.

At low pressure the density you get is the same for all shapes, it's too low to notice the difference. At high pressures you start to see the difference. As you may expect, with their simpler shape, the squares are the easiest to pack in. For the same pressure you get the highest density of all the shapes. The pictures below (from the paper) show what the high density configurations look like.

You can see there's quite a bit of ordering. But is it long range ordering, i.e. a phase transition? The quick answer is no. The correlations build up, and in interesting ways unique to each shape, but there are no discontinuities that could be considered a proper phase transition. It would seem you need more than four monomers for that. How many more? Well just one apparently! With pentominoes you do see phase transitions with some shapes.

On a related note you can ask, how long must the rods be before they will form a liquid crystal? For that they must be seven squares long (according to JCP 128, 214902).

Finally they move on to mixtures. There a lots of combinations you can do. A nice shot is the full mixture with all the shapes in.

It seems that the different shapes mix quite happily, they don't split into domains of each different type. What does all this mean for tetris? Um, well it looks to me that if they mix pretty well then it should be possible to play a perfect game! OK, I'm clutching at straws a bit there.

I could geek out about shapes all day. See the detailed paper (open access) at Langmuir, 2009, 25 (12), pp 6702–6716. Or Lev's group pages for some nice animations.

Tree diagrams solve everything

2010-07-01T13:21:00.000+01:00

Just a quick one. I saw this post, When intuition and math probably look wrong, via Ben Goldacre's mini blog. The problem is set as follows:

I have two children, one of whom is a son born on a Tuesday. What is the probability that I have two boys?

Intuition tells you the answer is 1/2, mathematicians tell you it's something else. I'll leave the answer until the end of the post in case you want to run off and solve it first. It's essentially a fancier version of the Monty Hall problem.
The Science News article deals with this just fine so I don't really want to expand on it. The only thing I wasn't too impressed with was their grid visualisation. It didn't really make anything any clearer for me. No, the only way to go is a good old fashioned tree diagram. So I thought I'd just show how I solve pretty much all probability problems, as techniques go it's slow but you always get the right answer - no intuition required.

The basic approach is to follow all possible scenarios and split the probabilities of all the events. From the information here we split three events. A child is born, it is either a girl (1/2) or a boy (1/2). We care if the boy was born on a Tuesday or not so we'll add this as a possibility. I like to put everything over a common denominator so we have: girl (7/14), boy not Tuesday (6/14), boy Tuesday (1/14). On the diagram this goes

Now we have another child, everything's independent so we have the same possibilities on each branch. We just multiply through the probabilities and keep the common denominator to get

We can use this to solve all girl, boy, boy on Tuesday related questions. Given one child is a boy born on a Tuesday we select all final states that satisfy this (highlighted in red). If you add up their numbers this is a total of 27 out of the 196. Of these 27 where there is one boy born on a Tuesday we count the ones where the other child is a boy (b or t). This comes to a total 13 of the final states.

And that's it. Of the 27 possibilities where one child is a boy born on Tuesday, 13 have another boy, so the answer is 13/27.

Note, if the question had been phrased "My first child was a boy born on Tuesday..." then you see we follow down the bottom branch and the problem returns to the intuitive answer. If this post seems a bit too elementary then I apologise. It just seems like no one ever mentions tree diagrams as a technique, preferring instead to make the problem seem really hard by banging on about Bayes' theorem and conditional probabilities and so on. They're all in there, just hidden out of view.

I like tree diagrams because setting one up is pretty mechanical and solving the problem just becomes a question of event counting. You certainly don't need to worry about Bayes' theorem or anything fancy sounding. And once again, no intuition required.

Which is good because my intuition is crap.

Comment spam

2010-06-15T16:11:00.002+01:00

I'm getting killed by comment spam at the moment. You'd really think Google would be better at stopping it but I don't want to get into that. Anyway, given I don't have as much internet access at the moment I can't mop up the spam quick enough so for a little while I'm going to have either moderation or require openid signing in. It's a shame because I really want to keep comments open.

Update: login didn't work so for now it's bloody moderation.

Update 2: Constantly catching comment spam. Wordpress has a facility to block comments with more than one hyperlink. I'm seriously looking at moving the site over.

My iPhone is an evil vindictive bastard

2010-06-15T09:15:00.001+01:00

Yes I know you're supposed to say phone and not iPhone, but I think it's relevant here.

Being a clever smarty pants, when I landed in the Czech Republic I switched my phone to Prague time and it took care of everything. Oo, isn't it clever! No. For some bizarre reason overnight last night it decided that I couldn't possibly still be in Czech Republic and I must be back in the UK. So this morning I managed to miss the one talk I really wanted to see (Sharon Glotzers tetrahedra talk) because I was on bloody London time.

So you can keep your retina display and your megapixels, Mr Jobs - get the bloody time right! Just about ready to throw this thing at the wall. Oh, but it is so pretty...

Brno - best poster spot ever

2010-06-14T23:01:00.001+01:00

It's 10pm and I'm writing this during a talk on modelling water. In case you didn't know water is about as far from a simple liquid as you can get. It's very interesting, although from the baffling number of water models you'd think it's been solved by now, but they do keep going. And going...

Anyway, I'll talk more about the science later, in general I had a couple of thoughts today. First, and most certainly without naming names, there is a huge gulf in standard between the good talks and the bad talks. Experience seems to be a factor, the two best talks by a long way were the invited speakers. The best talk introduced a new subject to me, which is on coarse graining, and I felt like I had a good idea how it all worked when he finished. The worst talks I lost attention within a minute.

It surprised me that people can do this for years and still be no good at it, I guess they don't care. But it's so so important that you can tell people what you're doing! Not to be negative though, the good ones were good and they really make it worth being here.

Second thought is that this poster fiasco is heading to the ridiculous. I was lucky enough to get the board at the back of the room, facing a wall two feet away! Not exactly a prime spot as you can see from the photos. At A0 I doubt they'll be able to focus on it from that distance! C'est la vie.

I'll let you know how many punters I get.

Conferences

2010-06-10T11:16:00.001+01:00

Seems like it's been a very long time since I've posted anything. This is mostly because things have been a bit of a blur recently preparing a paper, a talk and a poster for some up coming conferences. As soon as conference season is over I'll be back on the regular posts.

The science poster is a bizarre and demoralising ritual. You know that hardly anyone will see it (at one of my conferences there will be something like 500 posters), but you daren't not do it properly just in case. So you spend days putting this thing together, £30 getting it printed, only to have hundreds of people walk straight past it. Who knows, maybe you can catch one or two people who will write down a reference.

Anyway, this is what I'm going to be standing in front of next week, I think it's quite pretty:

It occurred to me that I haven't really blogged about my own work (is it not done?) but I'll start doing so when I return. In the meantime, it's all on the poster!

On the off chance anyone is going to Brno next week then come and find me and say hello.

Using a blog as a Logbook

2010-04-25T16:50:00.000+01:00

Last productivity post for a while I promise, then back to proper physics.

I'm trying out using a private blog (secured and unlisted etc) as a logbook. Logbooks are so important, the first time you realise you need one it's too late. My reasoning for going online goes:

I can access from anywhere, including emailing in posts from my phone. For example I could take a photo of a whiteboard discussion and send it to the blog so I won't forget about it.
It's safely backed up on the servers of whoever's hosting it.
I'm more likely to actually make entries because it's easy.
A blog is much like a logbook anyway so it's naturally suited.

I can see some downsides but they're all pretty minor. Security could be an issue but how sensitive is the information you put in your logbook? Well mine isn't much, I don't want to accidentally broadcast my latest idea but it wouldn't cause a new climategate or anything. Besides, I think it's pretty secure.

I've chosen to use WordPress for my logbook for one simple reason. The LaTeX integration is fantastic. It's so good I'd consider moving this blog if it wasn't such a pain (for the record I otherwise like blogger). In wordpress you do this:

I will now insert an equation here, $latex E=mc^2$, inline with the text.

which would look like

although the superscripts do appear to have messed up the alignment... Otherwise it does a brilliant job at interpreting the tex and inserting the image. If you need a lot of LaTeX then there are programmes that convert between regular .tex files and the wordpress format.

There are similar things available for blogger but I think you lose your source code in a more drastic way. Anyway, I'm going to see how it goes.

Pipes and Python

2010-04-21T10:31:00.001+01:00

I spent ages writing a post about some tricks I use to do quick analysis of data but it got incredibly bloated and started waffling about work flows and so on. Anyway, I woke up from that nightmare so I thought I'd just bash out a couple of my top tips.

This is a pretty nerdy post, you may want to back away slowly.

Pipes
Pipes are, in my opinion, why the command line will reign for many years to come. Using the pipe I can quickly process my data by passing it between different programmes gradually refining it as it goes. Here's an example that makes a histogram (from a Bash terminal):

> cat myfile.data | awk 'NR>100 {print $5}' | histogram | xmgrace -pipe

The first command prints the data file. The | is the pipe, this redirects the output to the next programme, Awk, which here we are simply using to pick out the 5th column for all rows over 100 and print the result. Our pruned data is piped down the line to a programme I made called histogram which does the histogram and outputs the final result to my favourite plotting programme to have a look at it.

So we've used three programmes with a single "one liner" (some of my one-liners become ginormous). Once you start getting the hang of this sort of daisy chaining it can speed things up incredibly. One bit that took me a while the first time was the histogram programme. This took an annoying amount of time to set up because I used C.

This is where Python now comes in.

Python

I won't even try to give a Python tutorial. I'm a decade late to the party and have barely scratched the surface. However, I've found that for relatively little effort you can get access to thousands of functions, libraries and even graphics. Most importantly you can quickly write a programme, pipe in some data, and do sophisticated analysis on it.

With the scipy and numpy libraries I've done root-finding and integration. The pylab module seems to provide many of the functions you'd get in MatLab. Python is a bit of a missing link for me, it's much lighter than huge programmes like Mathematica or MatLab and I just get things done quickly. Here's that histogram programme, Python style.


#! /usr/bin/env python
import sys
import pylab
import numpy

# Check the inputs from the command line
if len(sys.argv)!=3:
   print "Must provide file name and number of bins"
   sys.exit(1)

# Read in the data file
f = open(sys.argv[1],'r')
histo=[]
for line in f.readlines():
   histo.append(map(float, line.split()))

dimension = len(histo[0])

if dimension == 1:
   pylab.hist(histo, bins=int(sys.argv[2]))

   pylab.xlabel("x")
   pylab.ylabel("N(x)")
   pylab.show()

elif dimension == 2:
   # Need to chop up the histo list into two 1D lists
   x=[]
   y=[]
   for val in histo:
      x.append(val[0])
      y.append(val[1])

   # This function is apparently straight out of MatLab
   # I killed most of the options
   pylab.hexbin(x, y, gridsize = int(sys.argv[2]))

   pylab.show()

Which conveniently detects how many dimensions we're histogramming in so you don't need two programmes. This is pretty short for a programme that does what it does.

I hate wasting my time trying to do something that my brain imagined hours ago. I wouldn't say that these techniques are super easy, but once you've learned the tools they are quick to reuse. I'd say they're as important to my work now as knowing C. Got any good tricks? Leave a comment.

Something less nerdy next week I promise.

Bootstrapping: errors for dummies

2010-04-07T14:21:00.004+01:00

The trouble with science is that you need to do things properly. I'm working on a paper at the moment where we measured some phase diagrams. We've known what the results are for ages now, but because we have to do it properly we have to quantify how certain we are. Yes, that's right. ERRORS!

I've come on a long way with statistics, I've learned to love them, I defy anyone to truly love errors. However, I took a step closer this month after discovering bootstrapping. It's a name that has long confused me, I seem to see it everywhere. It comes from the phrase "to pull yourself up by your boot straps". My old friend says it's "a self-sustaining process that proceeds without external help". We'll see why that's relevant in a moment.

Doing errors "properly"
Calculating errors properly is often a daunting task. You can spend thousands on the software, many people make careers out of it. This will often involve creating a statistical model and all sorts of clever stuff. I really don't have much of a clue about this and, to be honest, I just want a reasonable error bar that doesn't undersell, or oversell, my data. Also, in my case, I have to do quite a bit of arithmetic gymnastics to convert my raw data into a final number so knowing where to start with models is beyond me.

Bootstrapping
I think this is best introduced with an example. Suppose we have measured the heights of ten women and we want to make an estimate of the average height of the population. For the sake of argument our numbers are:

135.8	145.0	160.2	160.9	145.6
156.3	170.5	192.7	174.3	138.2

in cm

The mean is 157.95cm, the standard deviation is 16.88cm. Suppose we don't have anything except these numbers. We don't necessarily want to assume a particular model (Normal distribution in this case), we just want to do the best with what we have.

The key step with bootstrapping is to make a new "fake" data set by randomly selecting from the original (allowing duplicates). If the measurements are all independent and randomly distributed etc, then the fake data set can be thought of as an alternate version of the data. It is a data set that you could have taken the first time if you'd happened to get a different sample of people. Each fake set is thought equally likely. So let's make a fake set:

156.3	192.7	160.9	135.8	135.8
156.3	156.3	170.5	156.3	192.7

Mean=161.36cm, standard deviation = 18.5935

As you can see, there's quite a bit of replication of data. For larger sets it doesn't look quite so weird. On average you keep about 60% of the original data and the rest is replicated. Now let's do this again lots and lots of times (say 10000) using different fake data sets each time, generating different means and standard deviations. We can make a histogram

From this distribution we can estimate the error on the mean to whatever confidence interval we like. If it's 67% (+/- sigma) then we can say that the error on the mean is +/-5.2cm. Incidentally that's nearly what we'd get if we'd assumed a normal distribution and done 16.88/sqrt(10). Strangely the mean of the means is not 157.95 as the input data was, but 160.2. This is interesting because I drew the example data from a normal distribution centred at 160cm.

We can also plot the bootstrapped standard deviation.

What's interesting about this is that the average is std=15.2 whereas the actual standard deviation that I used for the data was 19.5. I guess this is an artefact of the tiny data set. That said 19.5 looks within "error".

So, without making any assumptions about the model we've got a way of getting an uncertainty in measurements where all we have is the raw data. This is where the term bootstrap comes in; the error calculation was a completely internal process. If it all seems a bit too good to be true then you're not alone. It took statisticians a while to accept bootstrapping and I'm sure it's not always appropriate. For me it's all I've got and it's relatively easy.

To make these figures I used a python code that you can get here. Data here.

Update: It's been pointed out to me that working out the error on the standard deviation is a bit dodgy. I think that the distribution is interesting - "what standard deviations could I have measured in a sample of 10?" - but perhaps one should be a little careful extrapolating to the population values. Like I said, I'm not a statistician!

Even colder still

2010-03-24T00:58:00.002Z

In a previous post I was talking about how you can use a laser to cool atoms. By tuning the laser to just below the energy of an atomic transition you can selectively kick atoms that are moving towards the laser. If you fire six lasers in (one for each side of the cube) you can selectively kick any atom that is trying to leave the centre. So we've made a trap!

There is a hitch unfortunately. There is a minimum to which one can cool the atoms, once the atoms have an energy that is comparable to the photons coming from the laser then that's about as low as they can go. After all, there's only so much you can cool something by kicking it. We're already pretty cold - around 100 micro Kelvin - we'd like to go a bit colder if we can. Now we're into magnetic traps.

Magnetic Traps

Up to now we've been acting quite aggressively towards the atoms - kicking anything that's moving too quickly. To do better we're going try and round them up where we can control things better. Fortunately there's a neat way to do this. We can make use of an inhomogeneous magnetic field and the Zeeman effect.

If you apply a magnetic field to our gas of atoms then the magnetic dipoles of the atoms tend to line up with the field. Being quantum physics they can only do so in a discrete number of ways. What happens is that the transition that used to be a line splits and shifts into a number of different lines.

If we use a stronger field then the shift is larger. We can finely tune the energy at which our laser will interact with the atoms. So now we do this; if we put a magnetic field that is zero in the middle of the trap and gets bigger as you move away from the centre (you can do this) then we can control how hard we kick the atoms depending where they are. If we do it right then inside the trap we hardly kick them at all and outside trap we kick them back in.

Evaporation

We've managed to confine the atoms in our trap, the final step is to switch off the lasers (to stop all that noisy kicking and recoiling) and to try and use evaporation to get rid of as much energy as possible. It is understandably quite complicated to stop them all flying out once you've switched off the lasers and unfortunately it's at this point I start getting lost! The actual cooling mechanism is nothing more complicated than why your cup of tea goes cold.

After all this we're down the micro Kelvin level - a millionth of a degree above absolute zero! At these sort of temperatures the atoms can undergo a quantum phase transition and become a Bose-Einstein Condensate (BEC). This is a new state of matter, predicted by theory and finally observed in the nineties. As far as I know this is as cold as it gets anywhere in the universe.

Well I think I'm done with cooling things now. It starts off beautifully simple and then gets a bit harder! Needless to say I salute anyone that can actually do this - it's back to simulations for me.

EDIT: I over-link to wikipedia but this is a good page on Magneto-optical traps

Ghost Jams

2010-03-17T14:48:00.000Z

via Lester, a nice video showing ghost jams in action

See New Scientist for more.

The drivers were asked to drive around at a constant speed. For a while this works OK, eventually a ghost jam develops and propagates at the same speed that they're observed in real traffic. I don't know if they tried to apply any external stimulus to see if they could guide it better.

Simulating a molecule with a quantum computer

2010-02-22T11:26:00.004Z

Simulating a molecule

There's a fairly nifty paper out in PRL on simulating a molecule with a quantum computer. In principle doing calculations on quantum systems will be much faster with quantum computers (when they become a reality) thanks to being able to hold the computer in a superposition of states. These guys have had a bash using an NMR based "computer" - it's pretty fun.

Help with twitter name

2010-02-16T01:16:00.000Z

What do you think this blog's twitter feed should be called?

KineticallyConstrained is a bit long (will hurt the retweets)
KineticCon?
KConstrained?
Kinetically?
KinCon (taken)
TwittersPointlessDontBother?

So many important decisions...

Do you like my new header?

2010-02-10T00:18:00.000Z

OK, so I'm no Banksy, but I do like green. I'll probably be playing around with themes a bit over the next few weeks.

Hopefully the header captures how "kinetically constrained" can apply to complex statistical systems and that sort of stuck feeling that I can never quite shake off. Look at me full of bollocks, maybe I can get on Newsnight review or something.

How should we teach Maths

2010-02-09T01:01:00.001Z

I came across this new feature in the NYT via Science Blogs by Steven Strogatz. You may remember him from his paper with Duncan Watts on small-worlds that arguably kick started modern network theory. It looks like it's going to be a regular series so I highly recommend adding the feed to your rss reader.

The article that first caught my eye was called Rock Groups. It starts by differentiating between the serious side of arithmetic and the playful side. This is something I've long gone on about but never quite had the nice way of putting it like these guys do. Maths teaching for kids is like torture. I was having a discussion a while ago where I questioned whether we really needed to recite endless times tables aged 10 years old. A suggestion that drew scorn from my opposite number. But really, why?

The book that is heavily quoted in the article, "A Mathematician's Lament" by Paul Lockhart. It starts with a musician having a nightmare that children are not allowed to touch an instrument until they have mastered the theory of music and how to read a score. Only after many painful years are they allowed to lay their hands on an instrument.

This is a powerful analogy. You don't have to learn all the nuts and bolts of mathematics before you can start playing with numbers. Back in the Strogatz article he shows how much you can discover without being able to do any addition at all, just by grouping rocks. I wish I could quickly multiply two large numbers in my head but it wouldn't make me a better mathematician. It's like arguing that the best playwright should be able to spell every word in the dictionary.

The beautiful thing about the rocks is that it shows how much you can learn about number by pushing things around with your hands and being creative. Perhaps all those people who complain to me that "oo I could never do maths me" would have enjoyed it more if it was based on this rather than being expected to master "a complex set of algorithms for manipulating Hindi symbols".

Make sure you keep up with the Strogatz series. I found a pdf of the essay that inspired the Lockhart book. If I ever get through my Christmas backlog I might get around the getting the book.

Laser Cooling

2010-01-28T00:32:00.002Z

Last semester I was helping out teaching a bit of quantum and atomic physics. It was quite fun going back to stuff I was a little hazy on the first time. I finally understand the periodic table for one thing. Another thing that I knew about but never really got the detail is laser cooling. This is really nice, I'll blast through it here. Watch out for the stat-mech bit, blink and you miss it.

In an atom electrons are not free to sit anywhere they want (more or less), they inhabit precisely defined quantum states that have well defined energies, angular momenta etc. Therefore if you give an atom a kick then it will release the energy you give it in precisely defined packets of energy. So if you take the light emitted by the atoms and put it through a spectrometer (could just be a prism) you'd see something like this, from here, for sodium.

You'll recognise the orange line from the street lamps that are slowly on their way out. I did a version of this experiment when I was an undergrad where we did the opposite, we shone white light through sodium gas and while most of it goes through the frequencies that match the right transition frequencies get absorbed and are missing from the final spectrum. Might look like this, ish

Notice that the lines aren't all that sharp whereas I said they should be precise lines. This is for a number of reasons. One is that the uncertainty principle doesn't like precise energies. There's an uncertainty attached to the lifetime of atomic transitions or collisions. Another, more important effect is Doppler shifting due to the temperature of the gas. We can assume that the atoms in the gas have a distribution of velocities that comes from the famous Boltzmann distribution

Light emitted from a moving atom will be Doppler shifted which will take our precise emission line and spread it out around the average. This property turns out to be very useful and what we'll use. First a mention about the laser.

Lasers are brilliant. With a laser you can send in a beam of photons with a highly tuned narrow band frequency. When a photon hits with a frequency that matches the absorption frequency of the atom, they collide and scatter. When it's too much or too little it will most likely just go straight through.

So finally we get to how you cool the gas. If you send in a laser pulse into a warm gas of atoms then different atoms will see different things. Thanks to the Doppler shift, an atom moving with speed, v, will see the laser frequency, f_0, Doppler shifted to (c = speed of light)

Atoms moving away from the laser see it red shifted (lower frequency), atoms moving toward the laser see it blue shifted (higher frequency). If we tune the laser to just below the absorption frequency of the atom then the only atoms that collide with the beam are those moving towards it (the ones that see the blue shift).

Were it not for the precision of the transition level the laser would equally kick atoms moving towards it and atoms moving away - adding no net energy into the system. However, if we only collide with atoms moving towards the beam then we can actually remove energy. What's even more staggering is that this actually works!

Laser cooling can make things seriously cold. You may have seen the headlines that the LHC is colder than space. Impressive given the size of the thing, but space is about 2 Kelvin. This is peanuts compared to laser cooling. This can get a gas down around 1 mK - that's a factor of a thousand. You can get even colder with new techniques but somehow laser cooling pleases me the most.

So that's laser cooling. It's beautifully simple, uses basic ideas from quantum mechanics, relativity, statistical mechanics and then makes something brilliant thanks to a laser.

LA's big lake of colloids

2009-12-17T11:53:00.002Z

The New York Times is running a piece about tap water and the regulation thereof called "That Tap Water Is Legal but May Be Unhealthy". One particular contaminant becomes dangerous on exposure to sunlight so, at a lake in Los Angeles, they've tipped 400,000 plastic balls into the lake to block out the sunlight.

Perhaps this shows I've been in stat-mech too long. All I could think about upon seeing this picture was - "cool, a massive 2D elastic disc simulation!".

It's quite interesting where the crystal structure is interrupted - each one of those interfaces costs a lot of free energy. You can also see it's not truly 2D as along certain stress lines the particles have gone up and over to reduce the energy.

I wonder if it's in equilibrium or whether it'll age with time...

This is what science can do to you :-s

Don't know what fair use would be for stealing this photo but hopefully if I link to the NYT enough they won't mind - go and click on one of their ads of something...

Backup news

2009-12-09T15:48:00.001Z

Anyone that's been here from the start will know I have a slightly unhealthy obsession with backups. A couple of things have changed since I last blogged about this.

Time Machine
Firstly, I now have a mac at home and I've started using Time Machine. I don't want to pat Apple on the back too much because that really gets of my nerves, but Time Machine is absolutely fantastic.

It's exactly how personal backup software should work. You buy an external hard disk, tell Time Machine to backup there, and then you're done. You never need to worry about it again. Most of the time when I need my backup it's because I've accidentally deleted something I shouldn't. Time Machine allows you to, as the name suggests, just go back in time and find it before you made the mistake. Works like a dream.

After a botched attempt to upgrade to Snow Leopard I recently had my first call to do a complete system restore. All I can say is that it seemed to work perfectly for me - it didn't even take that long.

Rsync + windows
At work we backup to an external file server. Until recently that was Linux based and so I had no trouble using Rsync. Now we've been moved to a Windows server which creates all kinds of problems. Rsync just doesn't get on with Windows. Anyway, after a bit of poking around I finally have a script that does the job. This is my basic rsync call now:

rsync -rptgoDhpP --modify-window=1 --delete --log-file=RSYNCLOG --exclude-from=./exclude /home/username/ username

I'm pretty sure most of those options could be replaced with the -a but honestly, now it's working I don't want to touch it! The key command is the modify-window. This accounts for the different way that Windows and Unix file systems time stamp modified files.

SVN - Subversion
For programming and writing papers (in LaTeX) I've started using subversion to take care of version control. I'm also using a shared repository to co-write a paper at the moment - it handles simultaneous editing quite well. There is a start up cost in getting your head around how it works, I found this page very helpful, but once you're there it works very nicely.

I mention it here because the version control works a bit like a backup. You can step back through committed versions very easily. If you use OS X then it's installed along with XCode so you probably have it. With Linux it'll be in the standard repositories.

Well that's enough backup geekery for this year. Anyone using anything that they're particularly happy with? I've kind of given up on backing up over the internet for now but would be interested if there's been any developments.

An unintuitive probability problem

2009-11-29T12:53:00.027Z

Probability can do strange things to your mind. This week I had a probability problem where every time I tried to use intuition to solve it I ended up going completely wrong. I thought I'd share it as I think it's interesting.

Consider a one dimensional random walk. At each time step my walker will go left with probability , and right with probability . It stays where it is with probability . Furthermore these probabilities are dependent on the walker's position in space, so it's really and . I'm imagining I'm on a finite line of length, L, although it doesn't matter too much.

Now if , then we just have a normal random walker. In my problem I have the following setup: but . What does this mean? At any given point, x, my walker is more likely to go left than right. If it does go left it will come back with the same rate (although it's more likely to go left again).

So here's the question: If I leave this for a really long time, what is the equilibrium probability distribution for the walkers position, ?

Intuitive answer (1)
At any given site the chances are you are more likely to go left than right. Therefore, this introduces a bias to go left. The walker is more likely to be on the left hand side of the line after a long time.

Intuitive answer (2)
Because the transition rates are much faster on the left than the right, if the walker happens to go to the left it will much more quickly return. If the walker happens to go right then it's going to take a long time to return. Therefore you're more likely to find it on the right.

The correct answer
Well it's a little of both. The correct answer is that there is equal probability of finding the walker absolutely anywhere. The reason for this does not come from intuition. At equilibrium we have the condition that there must be no probability "current". That is to say that the probability of being found at a given site must not change in time. This can be expressed mathematically by the master equation (if you don't want to do the maths we'll meet back at the end):

You have to think of the ps as rates for this to work. In our case we have that the rate for going left or right is the same as making the reverse move. The master equation then reduces to

and the solution is that . So how does this sit with our intuitive ideas above? Well perhaps it's best to look at a trace of such a random walk. In the below example we have that . What happens is that the walker does indeed move quickly at small x and slowly at large x (as in intuitive answer 2), but the slowness at large x seems to reflect the walker away. When it does manage to penetrate the barrier it takes a long time to come back. The average behaviour is that every site, x, is occupied for the same amount of time.

So there it is. Never trust your instincts when it comes to probability, well not mine at least.

Oh, and on a separate note, putting maths into a blog post is a bloody nightmare - any ideas?
EDIT: Some maths fixed...

Great LHC animation

2009-11-20T10:18:00.002Z

The purpose of this blog was to showcase other types of physics other than the LHC. But I can't resist, this is a really nice animated video showing the stages of getting stationary protons up to 7TeV

http://cdsweb.cern.ch/record/1125472

(via @CERN)

Speed limit for computer processors - serial vs parallel

2009-10-29T14:48:00.006Z

This news item from Nature about this PRL talks about how computer processors are going to hit a speed limit due to the speed at which a system can make transitions between quantum states.

There are two independent bounds on this minimum time — one based on the average energy of the quantum system, the other based on the uncertainty in the system's energy. In their calculations, Levitin and Toffoli unify the bounds and show there is an absolute limit to the number of operations that can be achieved per second by a computer system of a given energy.

I'm not an expert in quantum information so all I can say is that it looks interesting. There are implications for myself because most of my work is pretty intensive computer simulation. Some of what I do simply needs fast processors, there are sections of my Monte Carlo simulations that cannot be parallelised (fancy cluster algorithms being one). So for these, in principle, it limits what could ever be done.

However, mostly my limit is on what statistics I can collect and that can be solved by using more and more processors. The move from single core being standard, to eight these days, has been a revolution in terms of what I can now get done in a reasonable time scale.

In fact one very interesting development is using standard computer graphics cards to perform molecular dynamics (MD) simulations. I've only read the abstract of this paper I'm afraid but they've apparently done this. Graphics cards designed for games have many little processors on them (GPUs) and they can all work on the problem more efficiently than one super powered CPU trying to do it on its own.

So next time you say that computer games are a waste of time think of this...

There's more to the LHC than bloody black holes

2009-10-19T23:22:00.011+01:00

The LHC is cold again. This is very exciting, and also it can't come soon enough. In the absence of any actual science going on an endless stream of bollocks seems to have been coming out about the collider. The latest being this drivel about things coming from the future to... oh God I can't be bothered. It rather upsets me that the only things people really know about the LHC are that it might make a black hole and maybe something is coming through time to sabotage it. So I thought I'd talk about why this machine is ridiculously fantastic and complicated (the more likely cause of breakage).

One of the features of synchrotrons that I've always thought is amazing is the way they cool the beams. By cool I'm not talking about temperature around the beam pipe (although that's bloody cold too so that the magnets work). I'll quickly describe what it is and how people solve it, although I'm still not 100% sure how they've solved it at the LHC.

Our general collider accelerates particles around a ring using strong electric fields. The particles are bent into a circle by bending magnets and they are kept in a beam by the focussing quadropole magnets. The effect of these magnets is that if a particle is heading sideways out of the beam then they push it back in in the opposite direction. In this way the particles kind of snake around the course never straying too far out of line. The task of cooling the beam is to reduce this snaking as much as possible so that we have a really dense, straight running beam.

Electron Cooling

You can very much think of this random side-to-side motion of our particles as temperature. Our aim is to get rid of as much of this motion as possible, to cool the beam. One way that this problem has been tackled is called electron cooling. Because they're so light compared to protons (well, anti-protons in this case, negative charge) it's not so bad to send in a beam of electrons through a section of the collider at the same speed as the anti-protons but much cooler. The two beams are then left to effectively equilibrate - taking transverse energy out of the main beam.

That said, the LHC uses two proton beams (positive charge) so I'm not sure they use this. They'd have to use positrons I suppose - anyone knows please do let me know. Electron cooling is very clever either way.

Stochastic Cooling

This is the one that surprised me the most. Stochastic cooling takes a more direct approach to cooling the beam and it makes use of the fact that we're sending our particles in a circle. What you do here is you try to measure small fluctuations from the beam. If a particle has too much transverse momentum then a sensor can send a message to the other side of the ring that can (staggeringly) give the particle a sideways kick to correct the fluctuation.

Remember that our particle beam is travelling essentially at the speed of light. To get a signal to the other side of the ring in time you have to run a wire straight across the middle and rely on the fact that the particles must go around the circumference. Amazingly, you can get the signal there and you can correct the beam. I'm still a little hazy on how the sensors work but I think they detect Cherenkov radiation. Hey, I'm not a particle physicist!

What else?

There are many techniques for cooling the beam, many I don't even know about. I'm not even completely sure what they use at the LHC. What I think is amazing is that this is just one tiny detail in how these machines work and it involved a huge amount of research and refinement. I could have picked from a hundred brilliant solutions to a hundred seemingly impossible problems.

If you want to believe aliens came through time to break the LHC then go ahead. Maybe it's just not that easy smashing two beams together with so much accuracy that, to quote cern, it's "akin to firing needles from two positions 10 km apart with such precision that they meet halfway". It's hardly surprising that there have been problems. So please, in this short science interlude can we resist the urge to go crazy?

Oh, and while I'm on a bit of a rant, can everyone immediately stop referring to the Higgs as the "God particle"? Good.

UPDATE: I've been searching around for how the LHC actually cools the beams. I've found this very long document with loads of juicy technical data. From section 4.4 "Transverse damping and feedbacksystem (ADT)" I ended up at this document which is loaded with information that is mostly way over my head. From what I can gather...

It looks like they are using a form of stochastic cooling.

Great.

Quorum decisions in bacteria

2009-09-09T17:49:00.004+01:00

Stumbled across a few nice things related to quorum decision making recently. Remember how sticklebacks make their decisions? Well bacteria do it too, below is a great TED talk by Bonnie Bassler on how they communicate and how they decide to act as an enormous group.

Also came across this article on humans making group decisions in a Kasparov vs The World chess game. It gets the saliva flowing on how you can engineer good decisions.

Addition: Incidentally, I also think this talk is a great example of how to give a science talk. It's a little rushed (probably nerves) but the enthusiasm is fantastic and the use of visual aids is perfect. I'm giving a workshop on presentations so I've been thinking about this stuff a lot recently.

Biological Membranes

2009-08-01T21:20:00.016+01:00

It's been ages since my last post. This is because I've been busy doing lots of interesting physics, met a bunch of interesting physicists, maybe I'll write something about it. For now, something I've been meaning to write about for a while, and for once it's something that's timely.

The journal Soft Matter has an issue out with a membrane biophysics theme. You can read the editorial for yourself if you have access, otherwise make do with my ropey understanding of it. Soft Matter is a relatively new journal that I think is looking really good. Their website needs work but I'll leave that for my science 2.0 rant which is bubbling up.

So why am I interested in membranes (I'm not working on them, I'm just interested)? Well once again I'm interested in them as large system of small parts that make something amazing when they get together - ie statistical physics. So, here's my compressed guide to membranes: please remember I'm not a biologist, I'm very new to this, only barely understand it and I tend to over simplify things.

What does the membrane do?

The cell membrane is what keeps the cell plasma and the outside separated. It has to selectively let in and out things that the cell needs or wants rid of. It also handles communications, controls electrical potential and a host of exciting things. It really is quite a complex beast.

What's it made from?

Your basic building blocks are the lipids. We're interested in the amphiphilic ones. They have hydrophilic heads that like water and hydrophobic tails that don't. See the pretty MS Paint picture below.

If you put a bunch of them in water then they will self assemble a number of different ways depending on the conditions. This on its own is really interesting. They can all go into little balls protecting the tails from the water (micelle). They can also form a bilayer sheet, with all the tails pointing inwards - this is what cell membranes are made of. Picture below from the micelle wikipedia page.

In a real cell it is of course a little more complicated. Membranes are typically made from two kinds of lipid (phospholipids and sphingolipids, the difference seems to be tail length) and on the membrane are scattered proteins that cut through the sheet that perform all manner of functions. On the outside of the membrane the heads are mostly in a two-dimensional liquid kind of organisation (more in moment). Inside the bilayer you get cholesterol floating around and the tails can be ordered or disordered - again, more on this in a moment.

Phases

Within a lipid bilayer we can have two phases that will depend on the temperature. Strictly speaking you have to be careful when you talk about a phase in a small system, but let's not worry about that for now. At higher temperatures we have a state where all the lipid tails are disordered and wiggling around. This is the liquid-disordered phase, lipids have a very high mobility and wander around the surface. If you drop the temperature then the tails all go straight and line up. I think the heads form a crystal structure on the surface but the biologists are a little unclear on this. It's possible the heads arrest in a gel state but I'm not sure. Either way, they stop moving around - this is the solid phase.

Next, if you add cholesterol, then you can get a third phase. The so-called liquid-ordered phase. Here our lipids tails straighten up like the solid but there are lots of cholesterols getting into the gaps between them. This apparently makes a phase that is ordered yet remains a liquid with a high surface mobility. So, we have solid (SO), liquid-disordered (LD) and liquid ordered (LO).

Finally we can have a mixture of species with different melting temperatures. This is what real cells are meant to be like. I have to say I find all these phases a little confusing and the terminology a little ambiguous. Anyway, stick with me, it gets more interesting from here on.

Rafts

The reason I've been going on about phases is because it seems to be important for the function of the cell. It is thought that on real cell membranes there exist "rafts", small domains of liquid-ordered lipids that perform important functions such as "signaling, recruitment of specific proteins and endocytosis". I don't even know what endocytosis is, which is why we'll now leave cells for something a bit simpler.

GUVs

In order to separate what's going on it's possible to grow simplified membranes by using giant unilamellar vesicles (GUVs). Essentially you can make a large sphere with mixtures of lipids and cholesterol but without the proteins and other stuff that's on a real cell. And you can make them seemingly arbitrarily large.

It turns out that with the two species of lipid and the cholesterol you can get very complicated phase behaviour. Depending on the conditions you can get SO-LD coexistence, SO-LO and the most interesting, LO-LD coexistence. As someone that deals with phase coexistence on a daily basis this is very exciting. I really don't want to get into the details so I'll just show some cool pictures from this paper by the Keller group. If my readership ever reaches double figures I might need to think about asking for permission for these things...

Here we have LO-LD coexistence with small domains of LO. You can even get critical points!

There's lots of things I still want to understand. I don't even really know what the interactions are that are driving these phase transitions. There are lots of factors including curvature, surface tensions and all sorts that I haven't thought much about yet. I think this is a really interesting area that any statistical physicist can't fail to be excited by. It's encouraging to see some people who appear to be able to link two such different subjects working on it.

Here are links to the main papers I've been learning from.
Veatch and Keller, great paper on the GUVs.
Bagatolli and Kumar, very nice review in soft matter.
Semrau and Schmidt, another good review. Didn't get to the end sadly.

And complete phase diagrams are in another paper by Veatch and Keller

Hummingbirds are the fastest animals on Earth

2009-06-10T22:58:00.007+01:00

Relative to their body size. Which completely changes everything. According to the Guardian

They can cover more body lengths per second than any other vertebrate and for their size can even outpace fighter jets and the space shuttle

Which is nice, and the high speed photo is beautiful. But it's not really the same is it? In fact the space shuttle statistic sort of makes it seem silly. All the other important numbers, apart from velocity, don't scale with the animal size. The friction, reaction time, not least the speed of sound. It doesn't help me imagine what it feels like to be a hummingbird.

It's somewhat similar to all those statistics you see about insects. Fleas jumping hundreds of times their height and ants carrying many times their body weight. If you had a giant ant I doubt this strength thing continue, the strength of skeletons and legs just don't scale with height.

The dive tops out at 60mph which is pretty impressive, I'd love to know it in perspective with the reaction times of the birds. How does 60mph feel to them? Apparently at the bottom of the dive

the hummingbirds experienced an acceleration force nearly nine times that of gravity, the highest recorded for any vertebrate undergoing a voluntary aerial manoeuvre, with the exception of jet fighter pilots. At 7g, most pilots experience blackouts.

That's definitely cool. So long as by g they don't mean in units of bird length again. Anyway, don't want to be too grouchy, the photo is excellent - enjoy.

Photo by Christopher J. Clark and Teresa Feo/UC Berkeley

Daisy world

2009-06-06T12:25:00.005+01:00

A bit lazy linkage here. I went to a talk a while ago by Graeme Ackland from Edinburgh about Daisy World. It's not new, I think it's been around since the 80s, but it is quite cool. It's a really simple model of a planet where the climate conditions (here just the temperature) and the living organisms on the planet feed back to one another.

On Daisy World there are only daisies, there are a million extensions where they have forests and animals and all sorts. I think the simplest model gives the nicest story. This page gives a nice explanation and it has a java applet that you can play with - this is the best bit.

http://www.ph.ed.ac.uk/nania/nania-projects-Daisy.html

My extremely brief explanation is that there are white daisies and black daisies. White daisies cool down their environment, black daisies heat it up. If they get too hot or cold they die. Then there's a bunch of other parameters: how fast does temperature defuse, rate of daisy mutation, rates for birth and death etc. It's about as simple as it can be, and crucially is simple enough for mathematicians to come up with solutions.

The nice thing is that for reasonable parameters the system pretty much always self regulates. When things are slow to react, mutation rates are low, you get these big mass extinctions followed by regrowth. Really the best way to get a feel for it is to play with the simulations, it's very fun.

A physics of society?

2009-05-19T12:25:00.007+01:00

Is it possible that we're not as in control as we think we are? We spend our entire teenage years convincing ourselves that we're individuals, but when it comes to our collective behaviour is that really true? From governments to economists to red top newspapers, everyone wants to understand why society is how it is. Physicists are no exception.

I recently finished reading Critical Mass by Philip Ball. Philip Ball is a chemist come physicist come science writer. From reading his book he sounds like a physicist at heart, perhaps I'm biased. The book is enormous and contains many of the things I'd like to write about here. There's statistical mechanics, game theory, networks and many other things that I won't review because plenty of other people have done that. I want to focus on the idea that there could be a physics for society. That our complex collective behaviour could be understood in the framework of statistical physics alongside more traditional methods in sociology and economics.

The problem with a physics of society is that it inevitably reduces us to simple units, completely throwing out our little subtleties, hopes and fears etc. This is a thought that is pretty distasteful to most people. It's up there with determinism for unpalatable ideas. But when you think about it it's not so bad. For most of our day to day life our choices are relatively restricted. In Britain and America if I want to vote, European elections aside, I'm realistically going to be choosing between two parties. In a market I'm buying or selling, and when I walk to work my routes are limited by obvious geographic constraints. It's for this reason that, in some circumstances, it is OK to draw a box around us and call us a yes or a no, up or down and so on.

We've already seen through universality that, sometimes, the underlying detail of a system is not the most important thing. As human beings we interact with our neighbours, our colleagues (and perhaps some random internet people). When our choices are limited, our interactions fairly short ranged, is it that ridiculous to think that some of the models we use every day in statistical physics could be applied to us? I think not.

Not too long ago the BBC (I think it was them, I can't find a link) had a programme about the credit crunch. They looked at complicated psychological reasons as to how over competitiveness, and I think something to do with chemicals in the brain, could cause an economic bubble to form. That people willingly fool themselves into believing that everything's okay. The trouble with this approach is it sees the end result of the complex interactions between traders as simply the behaviour of one trader - multiplied up. While I certainly don't want to rubbish the research behind this claim (I don't know where it is), we know that the behaviour of groups is different to the individual.

Physics has a much simpler explanation for bubbles forming in markets. It's simply based on the idea that people tend to follow what people around them are doing. Even if all the external signs are telling you that you should get out, the influence of people around you can often be much stronger. There are lots of models, and I'm going to go through some of them over the next month or so. The point in all of them is that people behave differently when they're around other people. When there's enough of us strange and interesting things can happen.

All of this is not to say that physicists know better than sociologists or psychologists (I suspect we know better than economists :-p ) but it does look like we should be sharing our knowledge better. The basic models of collective behaviour are simple enough for anyone to understand. They're not going to be exact but they can certainly enrich our understanding of the world around us.

I highly recommend Critical Mass. It's very well written and very thoughtful, well worth your time.

Critical Point

2009-05-09T10:47:00.018+01:00

I'm finally getting around to sharing what, for me, is the most beautiful piece of physics we have yet stumbled upon. This is the physics of the critical point. It doesn't involve enormous particle accelerators and it's introduction can border on the mundane. Once the consequences of critical behaviour are understood it becomes truly awe inspiring. First, to get everyone on the same page, I must start with the mundane - please stick with it, there's a really cool movie at the bottom...

Most people are quite familiar with the standard types of phase transition. Water freezes to ice, boils to water vapour and so on. Taking the liquid to gas transition, if you switch on your kettle at atmospheric pressure then when the temperature passes 100 degrees centigrade all the liquid boils. If you did this again at a higher pressure then the boiling point would be at a higher temperature - and the gas produced at a higher density. If you keep pushing up the pressure the boiling point goes higher and higher and the difference in density between the gas and the liquid becomes smaller and smaller. At a certain point, the critical point, that difference goes to zero and for any higher pressure/temperature the distinction between the liquid and gas becomes meaningless, you can only call it a fluid.

The picture below, taken from here, shows the standard phase diagram, with the critical point marked, for water.

Magnets also have a critical point. Above the critical temperature all the little magnetic dipoles inside the material are pointing in different directions and the net magnetisation is zero. Below the critical temperature they can line up all the in the same direction and create a powerful magnet. While the details of this transition are different from the liquid-gas case, it turns out that close to the critical point the details do not matter. The physics of the magnet and the liquid (and many other systems I won't mention) are identical. I'll now try to demonstrate how that can be true.

The pictures below are taken from a computer simulation of an Ising model. The Ising model is a simple model for a magnet. It's been used for so much more than that since its invention but I don't really want to get into it now. For the pictures below squares are coloured white or black. In the Ising model squares can change their shade at any time, white squares like to be next to white squares and black squares like to be next to black squares. Fighting against this is temperature, when there is a high temperature then squares are happier to be next to squares of a different colour. Above the critical temperature, if you could zoom out enough, the picture would just look grey (see T=3 below). Grey, in terms of a magnet, would be zero magnetisation.

If you drop the temperature then gradually larger and larger regions start to become the same colour. At a certain point, the critical point, the size of these regions diverges. Any colder and the system will become mostly white, or mostly black (as above, T=2). Precisely at the critical point (T=2.69 in these units), however, a rather beautiful thing happens. As the size of the cooperative regions diverge, so too do fluctuations. In fact at the critical point there is no sense of a length scale. If you are struggling to understand what this means then look at the four pictures below. They are snapshots of the Ising model, around the critical point, at four very different scales - see if you can guess which one is which.

Now watch this movie for the answer (recommend switching to HD and going full screen).

The full picture has 2^34 sites (little squares), that's about 17 billion. This kind of scale invariance is a bit like the fractals you get in mathematics (Mandelbrot set etc) except that this is not deterministic, it is a statistical distribution.

How does it demonstrate that the details of our system (particles, magnetic spins, voting intentions - whatever) are not important? In all these cases the interactions are short ranged and the symmetry and dimension are the same. Now imagine that you have a picture of your system (like above) at the critical point and you just keep zooming out. After a while you'll be so far away that you can't tell if it's particles or zebras interacting at the bottom as that level of detail has been coarse grained out and all the pictures look the same. This is not a rigorous proof, I just want to convey that it's sensible.

Of course the details will come into play at some point, the exact transition temperature is system dependent for example, but the important physics is identical. This is what's known as universality, and it's discovery, in my opinion, is one of the landmarks in modern physics. It means I can take information from a magnet and make sensible comments about a neural network or a complex colloidal liquid. It means that simple models like the Ising model can make exact predictions for real materials.

So there it is. If you don't get it then leave a comment. If you're a physics lecturer and you want to use any of these pictures then feel free. I'd only ask that you let me know as, well, I'd like to know if people think it's useful for teaching. For now you'd have to leave a comment as I haven't sorted out a spam-free email address.

What should we know?

2009-03-02T15:17:00.007Z

I decided a while ago that I didn't want this blog to be a bad science blog. There are plenty of those, I really like them but as the market's a little swamped I thought I'd just talk about stat-mech and hope that someone thinks it's interesting as well. Last weekend, however, I went to a talk by Ben Goldacre in Bath and so these things were brought to mind.

The thrust of the talk was that we, the public, are being misled and lied to by the media when it comes to science. He has compelling examples whereby the media would print unpublished stories from a discredited scientist but ignore several published articles that say the opposite. These examples are clear cut, the media are willing to lie for a good story. Even a well educated member of the public has no chance if information is being withheld.

What if it's less clean cut? Could the blame be shared in some cases? Take this story, the Durham fish oil trial (also mentioned in the talk, I don't have anything new). Uncritically reported by the media this "trial" had no control group, no predefined measure of success and more than a whiff that they knew what the outcome would be before it started. I need go no further describing it. The reasons why this "trial" was of zero scientific value are laid bare for anyone to see. The problem when one accepts what the article is saying (trial will prove fish oil works) without asking the huge question "where the hell's the control group?".

Anyone can ask this question. I expect people to ask this question. The concept of a control group is not difficult and everyone should understand it. In fact a full double blind trial is also easy to understand even if you didn't expect it to be necessary. There are certain things that I believe we should all just know about. Some good starting ones would be

Double blind trials. For me I wouldn't have guessed they need to be double blinded, it's great that scientists don't exclude themselves from ruining their own experiments.
Statistical significance. Small scale experiments can be good, but you need to be able to say when things could have been chance.
Pattern recognition. Related to significance. People are pattern recognition machines, we see things where they are not.

If you ask questions about these things then it'll be a lot harder to slip things past you. If not, you can be taken for a ride. There are few other areas of our lives where we leave ourselves so open to abuse. None of these things are too difficult to understand. It's certainly easier than buying a car...

Anyway, back to physics next time. There's lots I want people to know about physics but that's another fight for another time.

Entropy

2009-02-20T23:45:00.004Z

I've been meaning to post something interesting about stat-mech about once a fortnight and so far I'm not doing so well. For today I thought I'd share my perspective on entropy.

If you ask the (educated) person in the street what entropy is they might say something like "it's a measure of disorder". This is not a bad description, although it's not exactly how I think about it. As a statistical mechanition I tend to think of entropy in a slightly different way to say, my Dad. He's an engineer and as such he thinks of entropy more in terms of the second law of thermodynamics. This is also a good way of thinking about it, but here's mine.

Consider two pictures, I can't be bothered making them (EDIT: see this post, the T=2,3 pictures) so you can just imagine them. First imagine a frozen image of the static on your television, and secondly imagine a white screen. On the basis of the disorder description you might say that the static, looking more disordered, has a higher entropy. However, this is not the case. These are just pictures, and there is one of each, so who is to say which is more disordered?

Entropy does not apply to single pictures, it applies to 'states'. A state, in the thermodynamic sense, is a group of pictures that share some property. So for the static we'll say that the property is that there are roughly as many white pixels as black pixels with no significant correlations and for the white screen we'll say it's all pixels the same colour. The entropy of a state is the number of pictures (strictly it's proportional to the logarithm of this) that fit its description.

For our blank screen it's easy, there are only two pictures, all black or all white. For the static there are a bewildering number of pictures that fit the description. So many that you'll never see the same screen of static twice, for a standard 720x480 screen it'll be something like 10 to the power 100,000*.

So it's the disordered state, all those pictures of static that look roughly the same, that has the high entropy. If we assume that each pixel at any time is randomly (and independently) black or white, then it's clear why you never see a white screen in the static - it's simply out gunned by the stupidly large number of jumbled up screens.

In a similar way a liquid has a higher entropy than a crystal (most of the time, there is one exception), there are more ways for a load of jumbled up particles to look like a liquid than the structured, ordered crystal. So why then does water freeze? This, as you might guess, comes down to energy.

Water molecules like to line up in a particular way that lowers their energy. When temperature is low then energy is the most important thing and the particles will align on a macroscopic scale to make ice. When temperature is high entropy becomes more important, those nice crystalline configurations are washed out by the shear number of liquid configurations.

And this is essentially why matter exists in different phases, it's a constant battle between entropy and energy and depending which wins we will see very different results.

I'll try and update with some links to better descriptions soon.

*this number is only as accurate as my bad definition of the disordered state.

Busy Bees

2009-01-12T22:54:00.006Z

The second installment of Swarm was on BBC 1 last night, I missed the first one but I highly recommend catching this before it goes off iPlayer.

The best bit was the fire ants making an ant raft to escape flooding. Ants are ridiculous. They also had bees trying to decide where to make a new home. The scouter bees come back with reports on possible locations, conveying the message with a dance. All the scouters sell their location and the others decide who to follow. When one of them gets enough support then they all up sticks and move - pretty smart.

On the same theme, I was at a talk recently about consensus decisions in sticklebacks. Apparently they're very reproducible in experimental terms. Again, they have to make a decision, this time about which way to swim. On their own they make the good decision the majority of the time (say 60%) but when they're in a group the almost always get it right. Each fish is pretty stupid, the group is less stupid.

I love problems like this because, while it is a biology problem, it's simple units (fish, ants, bees) that can interact with their peers in some measurable way (well, if you're really clever and patient it's measurable). From this emerges surprising a complex behaviour that didn't exist with the individual - that's what statistical mechanics is all about.

Critical-point post is still delayed, when you're debugging code at work all day it's hard to feel motivated to come home and do the same thing. It's coming though.

UPDATE: Just seen part one, those starlings are badass. They look like drops of liquid, just wait until I get my MD code working and I'm going to be simulating me some birds! (not in the weird science sense, although that would be cool as well).

Out of place

2008-12-14T01:29:00.005Z

This was in Bath Physics Department, seems a bit out of place...

In other news there's a bug in the Ising model code which means I'm getting rather beautiful squares appearing across the system. I can't remember the last time I coded something that wasn't riddled with bugs.

Scientists are possibly a little obsessive

2008-11-23T02:46:00.002Z

I'm feeling pretty smug tonight because I finally managed to implement a new way of simulating the Ising model that I'd been thinking about off and on for years. At the same time I'm also feeling a bit sad that I've stayed up until 3am on a Saturday night doing it. I think it's the element of obsessive scientist that comes out occasionally where you have a tea/coffee fueled binge until you've solved the problem - it's probably not limited to scientists.

The reason I wanted to do this will become clear soon (as in I'll post on it). I wanted to simulate a lattice with as many sites as possible so that I can do some visualisation of renormalisation group ideas (again more on this soon). Instead of storing each site inside a byte of memory I store 8 sites in each byte and then use a bit of binary operating to get the bit I need. This means each site now only takes one bit of memory.

That was the easy bit. The hard bit was simulating this setup around the critical point. Critical points in statistical mechanics are incredibly interesting but real buggers to simulate. Fortunately there have been some very clever people who have worked out how to do this. I've not done anything that clever, but I have worked out how to implement a Wolff cluster algorithm using ~ N/4 bytes of memory which I think is good going.

I will post on this again when I've managed to run the programme on our fancy new computer (it's got 8GB of RAM) and have a nice picture to show. I will also explain what the Ising model is and what a critical point is...

Viva and Backup update

2008-11-16T15:51:00.001Z

I thought I'd return to backups briefly and say how I was getting on with some of the web based ones. Initially I was using Mozy but after some problems with the software I uninstalled it. I'm too impatient to fix problems with this sort of programme so I ditched it. Live mesh, on the other hand, just worked. I have to say I'm getting on well with it. I don't really have a lot to say about it except that it's relatively unobtrusive and I don't think it's slowing the computer down too much.

In other news I passed my viva! Hopefully this means I can start talking about statistical physics more which is sort of what I wanted to do with this blog in the first place.

Backups

2008-11-06T23:29:00.000Z

I'm pretty much backup obsessed. Of all the obessions you can have this is probably quite healthy. For example I wish I was a bit more exercise obsessed but what can you do? It's staggering how many people still don't think about backup despite the fact that they switched all their photos and music over to their PC years ago. One hard disk failure and you're buggered - and they fail depressingly often.

At work I've been using this rsync system for years. It's great, I never have to think about it but every day any new work is saved and you can track changes for as long as you like. Unfortunately I could never get this to work in Windows (through cygwin), I think it's a file system thing. Apple introduced Time Machine with their latest version of OS X. This is exactly the same system but packaged up in the neat Apple way as you'd expect. I haven't got this working on remote servers yet but I'll update if I do.

At home I'm still stuck with Windows XP (see earlier posts on why I won't switch to Linux) and I have an external hard drive. I'd really like something like Time Machine but at the moment I have to copy everything each time I backup - this is far from ideal. The simplest, and most effective, long term backup is still to post a DVD to your parents or a friend. It's cheap and it's off-site. The trouble with these last two (my current home setup) is that if my hard drive goes down I'll still lose 2-4 weeks worth of data as I can only be bothered backing up about once a month. Fortunately there is a new (new to me) solution brewing in the clouds.

It seems that storage is becoming sufficiently cheap that companies can now offer large amounts of online space for free. After reading this page I checked out a few of their recommendations. Initially Mozy looked really good but I didn't get on too well with the software so I moved to Microsoft's "Live Mesh". I have to say Microsoft appear to have knocked it out of the park with this one. I already had a hotmail account so setting it up was a breeze. Once it's installed you can just drag whatever folders you want to synchronise with the mesh. After that you just leave it in the background and it quietly makes sure you're files are synchronised. Seems to work well, I'll let you know how it goes.

This is more meant for synching files than it is backup but it obviously serves both purposes. I'm looking to use these kinds of service to fill in the month-6 month backup gap. I'll still use more traditional methods for older stuff. These systems all seem a bit early days and no doubt will improve with time, it's definitely a move in the right direction though.

If I could afford it I'd get a new mac £950 and a time capsule £200. But I can't. Damn expensive Apples. There are other network hard drive options though so that's worth keeping an eye on. Oh, and don't forget the ultimate. Despite being backed up on three computers I still emailed my thesis to my gmail account as soon as it was finished!

Vegetarian

2008-10-29T23:54:00.000Z

I grew up vegetarian and while I use the term a little more loosely these days it's still been years since I ate any meat at home and I mostly manage to avoid it when I'm out. You'd think it'd be easy but if you're sick of eating cheese all the time then it gets pretty hard being vegetarian.

At the weekend I wandered into a vegetarian shop just to have a look and I picked up some tartex because it's great. It occured to me while I was in there that 90% of the shop was filled with bullshit. I mean real crap. Half of it was vitamin pills and another chunk was homeopathy. I wouldn't be surprised if the guy tried to sell me an organic pencil. Is this what it is to be vegetarian? I don't eat meat because it's expensive and I love vegetables. Vegetables. Not shelf after shelf of pills.

What I want in a vegetarian shop is stuff I'd find hard to get elsewhere. And probably some sodding vegetables. Vegetables are seriously tasty and each one needs appreciating in its own right. I don't know when being veggie became about hippy crap but I think it's time to take it back. I'm a scientist, I'm not afraid of machines, I'm not totally opposed to GM (not really for it yet but that's another post), I like organic in principle but I'm not afraid of chemicals, I eat almost exclusively vegetarian food because I love it. Meat is a massive waste of resources and unless it's really good quality and cooked well then it's a total waste of time. You really miss some amazing food if you're only concentrating on meat.

If vegetarianism is still somehow linked to ludicrous things like homeopathy then it's no wonder that it instills such unprovoked hostility from our meat eating friends. It should really about all the great food. I'd actually prefer it if the veggie label wasn't there. One of my favourite places in Nottingham (Alley Café) is totally vegetarian but they don't make a fuss about it. It's just food, good food. And rather expensive beer...

So, to try and reign this rather chaotic rant in, in summary: more veg, less bullshit.

Brain shrinkage

2008-09-27T14:40:00.000+01:00

My mum sent me a link to a press release from the Vegetarian and Vegan Foundation. They're a bit annoyed because there's a study from Oxford University that has found a link between lack of vitamin B12 and a loss of brain volume in older people. Some, but for once not very many, newspapers have jumped to the conclusion that being vegetarian causes your brain to shrink. This really isn't what the study says. In fact I've had a look at the abstract (can't seem to get remote access to the paper) and it doesn't look like they looked at diet at all. The Oxford press release, however, does go on about diet with a disclaimer that they haven't done a clinical trial.

I'm not an expert in anything related to health and I don't want this blog be about that so hopefully I'll only make this point once: What use is this sort of press release? I can't do anything with this information. The implication is that I should eat more meat, but there was no clinical trial so this is not justified. It reminds me of the research that said a glass of red wine was good for you based on a chemical that was in it. Trouble is there are other things in it as well and the evidence actually says that this isn't true (BS about this here but I warn there are some 'orrible pictures of tumours for no apparent reason).

EDIT: Here's the Oxford press release so you don't have to click the link...

Vitamin B12, a nutrient found in meat, fish and milk, may protect against brain volume loss in older people, according to a University of Oxford study.
For the study, 107 people between the ages of 61 and 87 underwent brain scans, memory testing and physical exams. The researchers from the Oxford Project to Investigate Memory and Ageing (OPTIMA) also collected blood samples to check vitamin B12 levels. Brain scans and memory tests were also performed again five years later.
The study, published in the journal Neurology, found that people who had higher vitamin B12 levels were six times less likely to experience brain shrinkage compared with those who had lower levels of the vitamin in their blood. None of the people in the study had vitamin B12 deficiency.
Many factors that affect brain health are thought to be out of our control, but this study suggests that simply adjusting our diets to consume more vitamin B12 through eating meat, fish, fortified cereals or milk may be something we can easily adjust to prevent brain shrinkage and so perhaps save our memory,” says Anna Vogiatzoglou of the Department of Physiology, Anatomy and Genetics at Oxford University. “Research shows that vitamin B12 deficiency is a public health problem, especially among the elderly, so more vitamin B12 intake could help reverse this problem. Without carrying out a clinical trial, we acknowledge that it is still not known whether B12 supplementation would actually make a difference in elderly persons at risk for brain shrinkage.”
Previous research on the vitamin has had mixed results and few studies have been done specifically with brain scans in elderly populations. We tested for vitamin B12 levels in a unique, more accurate way by looking at two certain markers for it in the blood,” adds Ms Vogiatzoglou.
Ms Vogiatzoglou says the study did not look at whether taking vitamin B12 supplements would have the same effect on memory.
The study was supported by the UK Alzheimer’s Research Trust, the Medical Research Council, the Charles Wolfson Charitable Trust, the Norwegian Foundation for Health and Rehabilitation through the Norwegian Health Association, Axis-Shield plc and the Johan Throne Holst Foundation for Nutrition Research.

Thesis is inches away from completion, I can smell the freedom already.

Mac vs. PC vs. Linux

2008-08-31T13:40:00.000+01:00

When I started my new job I was given the option of having an iMac instead of the standard issue Linux PC. They're really nice looking machines and I'd seen the impressive iSight in action so I went for it. I'm a seasoned windows user (from home), Linux user (from the PhD) and after a month getting all my work up and running on the mac I'd say I'm approaching blanket coverage of the major operating systems. Most importantly I'm now in a position to tell those people who get on their high horse about their particular OS to shut up.

Because that's what's always got on my nerves the most. Linux is a lot better than windows in many respects, but a lot worse in many others. The same goes for OS X. It depends what you're doing. Take my work for example: I'm a theoretical physicist and these days the majority of my work is numerical which means getting rather large computers to perform simulations of things rattling about, over and over and over again. All these big computers use Linux and so it's easier if your systems match. More than that though, Linux has a ton of software that is designed exactly for my needs, X-forwarding is great and its heavy use of the command line is much more efficient.

If you got into computers after Windows 95 and you don't like to fiddle under the bonnet then the chances are you won't ever use the command line. This isn't a shame in itself, it's up to you how you use your computer, but it's a shame that your choice is removed because the windows command prompt is so useless. Once you get going with a good command line interface it can make a real difference to your productivity (see for example Imagemagick).

So for work I'll (hopefully) never use Windows again. At home it's a different story. At home I use the web, edit photos and play music. Linux does all this but Windows has a better look and feel. Windows works better with my laptop's hardware (and peripharels) and there is a ton of good general software made for it. In my opinion its best bit of software is Windows Media Player. This is easily the best music player available (codec problems ruin the video playing). It's great for organising music, automatically finding artwork, creating playlists, quick search, seamless playback - the list goes on. It is infinitely better than the terrible iTunes. iTunes feels like it's actively trying to make me angry. I think it's designed to annoy you into only getting your music from the iTunes store. I'm constantly searching the net on how to get it to do this or that, WMP just does it.

Security is the top thing usually thrown at Windows. I go with the usual argument that it's just because there are more Windows PCs than anything else. Get complacent with your mac or Linux box and you'll wind up the same.

So what about the mac? Well Apple have been clever and maintained support for all things unixy (it is based on unix). This means that most of the good Linux features are supported, it does X11 so I can use most Linux programs, I can SSH into it (remote login) and so on. On top of this is has the better look and feel, it runs proprietary stuff like MS Office and it's got the best video chat facility by a mile. Apple (or maybe it's mac users themselves) always seem to be chipping away at improving the user interface. Things like exposé or Quicksilver are good examples. I'm very impressed with my iMac, it's a very neat piece of kit indeed.

Bad things about the mac are well documented. Hardware is expensive and exclusive, in many ways you're buying into a much worse monopoly than Microsoft. You won't be as compatible with everyone else (not a big deal these days), oh, and did I mention how much I hate iTunes? This shouldn't matter but Apple devotees tend to get on my nerves. I think there's a lot of fashion about owning a mac. They're always banging on the MS steal this or that. Honestly, get over yourselves. Everyone's iterating towards a better thing and Apple have reused plenty of ideas themselves (eg. spaces).

If I was buying a new laptop tomorrow I'd go for a mac. For me it covers the maximum amount of my daily stuff and I do like the interface. In general I would only recommend it if you know why you want to spend that bit more. I don't think Linux is ready to be my only OS just yet but I'll always run it on the side. The bottom line is they all do the job, they all crash (despite what people say), but most importantly of all they all run firefox so you won't really notice the difference most of the time.

Glass in the New York Times

2008-07-30T00:24:00.001+01:00

When people think of physics they tend to think of particle accelerators, string theory, E=mc² and so on, so when I tell them I'm studying glass they always look a little disappointed. Anyway, a couple of weeks ago we got a New York Times article from a guy called Kenneth Chang so we're all quite pleased about it. I had written a long post about it but I ended up just repeating what's in the article, so I've decided to list some main points and provide a few extra links.

He managed to give a good sense as to how much debate there is in the field. One thing everyone agrees on however, and where the article begins, is that cathedral windows do not sag because the glass has flowed.

"Medieval stained glass makers were simply unable to make perfectly flat panes, and the windows were just as unevenly thick when new."

If you want something that does do that then let me point you in the direction of pitch, which drips about once a decade but shatters when hit with a hammer. So what is glass then? Is it a liquid or what?

Glass has the same structure as a liquid. If you take a photo you couldn't really tell the difference. A liquid that's on its way to being a glass, a supercool liquid, is the same as well. If instead of a photo you look at a video you'll see that it's actually really different. Weeks and company have actually done this and you can see regions really close to one another, some with lots of motion, some hardly moving at all. This is the dynamic heterogeneity, mentioned in the article, that goes along with the hugely increasing viscosity. Their website has loads of great stuff, including movies and a link to a freely available version of the Science paper, I recommend taking a look.

The region that I'm roughly poking about in at the moment is to do with vibrations and rigidity. This is touched on in the article a couple of times. Matthieu Wyart and others have spent a lot of time developing the idea of a glass as a marginally rigid solid (the introduction to Wyart's thesis is actually quite readable and freely accessible). It's looking at how the random liquid structure affects things at low temperatures.

Anyway, I'll leave it there. If I've missed any important links just stick them in a comment. Been a bit too busy writing my thesis to do this properly. Dear God let it end soon!

Moving adverts

2008-07-23T18:50:00.000+01:00

A couple of weeks ago I was in London and passing through Liverpool Street tube station. Something that I've been dreading for years has finally started; they're now projecting moving adverts on to the wall. They've had the LCD screens on the escalators for ages now, they're not great but they only fill a small part of your peripheral vision - they're after the rest.

TFL try and sell this as something great for the commuters

We believe that this technology will enhance passengers' journeys

Really? Are you sure it won't get on their nerves? Would you stop it if it did? The eventual aim is to have every pixel of your periphery flashing and shouting so that your brain is so confused with the unmanageable amount of information that you'll do anything they tell you. Or stumble in front of a train.

It's an inevitability, an arms race. If you cover every square inch in posters then, per square inch, they're all worth less. So to make your milliacre more valuable it must now move. Soon they will all move. Then they'll start shouting at you, then they'll get linked to your oyster card so they'll be calling your name. Worse, linked to your nectar card so they can chase you down the street offering you carefully selected products you might like.

Not everyone agrees with me: this advertising blogger thinks it's great and shows how hi-tech we are. TFL clearly like it. Anyone else? I'd really like to know.

I have the attention span of a pea. It takes nothing for me to lose track of what the hell I was thinking, what someone just said or even why I got on this train in the first place. Please, I'm begging, leave my poor brain alone!

Plausible theories from experts

2008-07-19T11:29:00.000+01:00

This posting, from the rather excellent Mind Hacks, got me all worked up again (this is quite easy to do). It just struck me how easy it is to say something plausible, for example "increasing violence is caused by computer games", and then make no attempt to check whether it's true.

In this case the plausible statement is on the use of facebook, the internet, other such things. It even managed to be press released by the Royal College of Psychiatrists. This starts off

A generation of Internet users who have never known a world where you can't surf on-line may be growing up with a different and potentially dangerous view of the world and their own identity, according to a warning delivered to the Annual Meeting of the Royal College of Psychiatrists.

Could be true. I wouldn't like to say. Things start to smell a little funny when they say

This is the age group involved with the Bridgend suicides and what many of these young people had in common was their use of Internet to communicate.

OK stop there. Now I'm suspicious, don't all young people use the internet? By the way, the Bridgend suicides are also being blamed on mobile phone masts and, for all I know, computer games. In fact it feels like there is a rather sinister trend for untested/untestable claims to be applied to these tragic events, and why? Because it will get press attention. Without a doubt.

It seems that a horrible statistical fluctuation in the all-too-large distribution of teenage suicides is not a satisfying reason for the media or the public. And this leaves the door wide open for "experts" to fill the gap.

It's just too easy to say you think something is true and then press release to an unquestioning media. A classic example is the evolutionary psychology stuff (badscience has lots on this). These are the claims that we will split in to two distinct races or that we will evolve big willies. The papers just say that "Experts say..." washing themselves of responsibility. But who are these experts? Many of the proposals are plausible but that's not enough.

I could spend all day coming up with things that could be true. Unless it is testable then what use is it? Physicists come up with plausible theories all the time, but no one will get the nobel prize until it can be tested. The famous Feynman quote goes

"It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong."

I appreciate that physics experiments are much easier (and by that I mean controlled) than social experiments, but that's no excuse for claiming you have the answer when all you have is a plausible explanation. It's a massively important distinction.

To anyone claiming to know the cause of the Bridgend suicides I beg you to think carefully; teenage suicide is a serious problem and they deserve much much better.

Edit: Here's the BBC coverage

Supercooling

2008-07-05T15:11:00.000+01:00

Ok, a bit of proper science to try and establish some balance. This is a bit of a lazy post but I did mention I'm busy writing my thesis. On supercooled liquids...

Supercooled water is just water that is below its freezing point but for one reason or another didn't crystalise. It's a form of metastable equilibrium. If you give it a big enough kick then it can escape and this happens

And you can do it with beer

If you keep cooling it further then you eventually get glass, and that's all glass is. Now my stuff is more interested on the supercooled goes to glass bit rather than freezing beer but you get the idea.

Jeremy Clarkson and the prius

2008-06-27T21:44:00.000+01:00

Jeremy Clarkson was just on top gear reproducing the old myths about the toyota prius. There were two strong statements in the program, one I can get on board with, and one where Jeremy Clarkson gave in to lazy temptation of quoting something because you want it to be true, rather than checking it actually is.

Firstly they did a track test comparing the prius against a BMW and found the BMW did better for mpg. This is something I've seen around and I'm certainly open to the fact that hybrids are beatable. They are, after all, just an attempt to make a petrol powered vehicle more efficient. I can't vouch for these tests though, from what I can gather they mostly seem to rely on motorway travel to beat the prius (which is best in the city).

The second thing though is completely contemptible of Clarkson. It's an old piece of bullshit that has been blogged about loads (badscience usually a good place to start) that the Prius is worse over its lifetime than a land rover (last time it was a hummer). I can't do a better job than has been done already, but it comes down to a dodgy report done by a marketing group. Now it's been broadcast on such a huge platform I doubt we'll ever here the end of it.

UPDATE: I think the dodge claim is about 45s in. http://youtube.com/watch?v=PP6fe6i1vaY

First Post

2008-06-24T18:50:00.000+01:00

Welcome to my new blog. I haven't quite worked out what to do with it yet but I think it's good to have a blog, the internet's already full of useless crap so this isn't going to make it any worse.

Right now I'm writing my thesis so I doubt I'll be putting much on here but when I do I suspect it'll mainly be based on the two things: physicsy stuff and statistics stuff. The former should be mainly positive (things I think are really interesting) and the latter will be mostly negative (ranting about stats-abuse). I guess we'll see how it goes.