the old and the new

I’m just back from a 2 day residential Theo Murphy scientific workshop held in the Royal Society’s Kavli Centre at Chicheley Hall.  It’s my first visit there, and I can certainly recommend it as a marvellous venue for a workshop: great facilities, marvelous food, and friendly, efficient staff.  The science was great fun: I learned lots of new things, discovered links between seemingly diverse areas, and had interesting discussions over food and coffee.  I’m buzzing with ideas, which is the whole point!

I did the usual “photograph from my bedroom window” thing, which had a somewhat different from usual view:

first day of the workshop, view due east, into the rising sun

Oh, and then I took a photo from the other window in my bedroom:

first day of the workshop, view due south

Blissful.  I found an amusing view from the window half way down the main stairs:

A lovely formal garden, with lawns, paths, clipped bushes and trees, and, just visible at the vanishing point of the path … a wind farm!  The old and the new collide.

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back from Lyon

More cathedral than train station?

I’m just back from a short trip to Lyon, attending the kick-off meeting of our new EU research project, EvoEvo (“Evolution of Evolution”), selected for the FET Proactive “Evolving Living Technologies” call.  It was an excellent meeting, promising lots of interesting and exciting research over the next three years.  Our task at York is to develop a novel bio-inspired algorithm for tackling open-ended problems, based on the biological experiments and computational modelling being done by the other partners.  It’s going to be great fun.

It was my first trip to Lyon.  A 90 minute EasyJet flight from Stansted arrived at Terminal 3 in Lyon Saint-Exupéry airport, which is a sort of temporary high tech cowshed with metal walls and a plastic roof.

blue sky!

Sights architectural improved rapidly at the adjacent railway station, with its gorgeous swooping roof.  From there is was a 30 minute shuttle-tram ride into Lyon itself, then a shorter tram ride to the hotel.

… amoeba-table

amoeba basin …

The hotel itself, although perfectly comfortable and fine, had clearly embraced some form of crazed design concept. The first clue there was something different about the place was its stealth lifts, entrances cunningly camouflaged with a garish mural.  Then there was the the shape and colour of the hand basin in the en suite, manifesting as some sort of fluorescent green amoeboid entity.  This was clearly deliberate, as its colour and shape were mirrored by the small table in the room proper.

I never actually
tried sitting on this

looks like steel;
feels like lino

The decor continued in a sort of industrial-chic flavour, with a tin chair, and faux riveted brushed steel flooring (actually a very pleasant-feeling warm lino).

This being France, the food was, of course, excellent.  Even a mere sandwich from an airport cafe on the return trip was delicious.  On the Monday we sat outside to eat lunch in glorious sunshine.  It was blustery (probably the edge of St Jude’s storm), but very warm: I was actually rather concerned about the possibility of sunburn!

Another short hop, back to Stansted.  The flight arrived 10 minutes ahead of schedule, due to a strong tail wind (yet more remnants of the storm).  By happy chance I ended up first in the queue at passport control, and was thinking about where I had to go to get the bus to the airport carpark.  I realised the guy looking at my passport had asked me a question. Uhh. Rewind.  Oh, he’d said: “where was your flight from today?”  I looked at him blankly.  I’m in Stansted.  That means I’ve just flown in from somewhere.  Where?  Taormina?  No.  Where then?  Uhh...  Oh.  “Lyon”, I said, after a long, suspicious pause.  He looked more closely at my passport, but then let me through.  Whew.

So, safely back home.  Now to get down to the research…

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TRUCE at ECAL

One of the projects I’m working on is called TRUCE: Training and Research in Unconventional Computation in Europe. As it says on the TRUCE project website:

Unconventional computation (UCOMP) is an important and emerging area of scientific research, which explores new ways of computing that go beyond the traditional model, as well as quantum- and brain inspired computing. Such alternatives may encompass novel substrates (e.g., DNA, living cells, or mixtures of the two) as well as new paradigms which, for example, support combined information processing and material production (as living systems do). UCOMP researchers draw inspiration from a wide and diverse range of sources, from physics, to chemistry, biology and ecology. The field is growing quickly, and has the potential to revolutionize not only our fundamental understanding of the nature of computing, but the way in which we solve problems, design networks, do industrial fabrication, make drugs or construct buildings.

TRUCE provides an ‘umbrella organisation’ to help coordinate, nurture and develop activities within the UCOMP community in Europe and beyond.

We ran a TRUCE workshop this Monday at ECAL, the European Conference on Artificial Life. Workshops at conferences are often run as mini-conferences themselves, with presented papers and posters. But we wanted to do something different, something ... unconventional.

One issue with unconventional computation is that conventional computers have a 60 year engineering head start. Although researchers can do what are actually amazing things with their slime molds, quantum computers, chemical droplets, bacteria, DNA, optical computers, and what-not, it’s early days yet, and the applications aren’t a patch on a smartphone. What we would like to do is think about what things will be like in about 60 years time in terms of unconventional computing.

So we decided that we should get some scientists together with some speculative fiction authors, and tell narratives of possible futures. We made a call for story ideas a few months back, and got a bunch of submissions. I put one in, too, so that the project could have an “inside view” of the process. The authors looked through the submissions, and each picked out an idea they thought they could weave into a story.

The aim is that each author will write their story set in their chosen scientific background, then the scientist will write an afterword, explaining the science in a bit more detail (or possibly explaining where the author ignored the science to make the story work!) These will all be published by Comma Press some time next year.

There was some preliminary contact between authors and scientists via Skype, then we all met up at the workshop for in-depth discussions. The author-scientist pairs scattered around the conference venue for most of the day, sitting in little coffee-fueled huddles, talking through the story ideas, and the underlying science. One attendee said “that’s the longest I’ve ever talked to someone who isn’t my wife continuously on a single subject!”

In the late afternoon, we all got back together and presented the current status of our ideas. Not all of them turned out to be dystopias, fortunately, but authors tend to want an interesting plot, which usually means things going wrong! But that’s fine: we can give warnings of futures that we don’t want to happen.

So, the authors get some futuristic scientific ideas for their stories. What do the scientists get out of the process? Well, some of my work is with biologists, building simulations of complex systems, which requires me to understand quite a bit of their science, which means I ask a lot of questions. One thing they say is “I like working with computer scientists, because they ask such different questions.” Different questions are good: they make you think about things from different angles, giving a different view on the problem.

And I can now say, I like working with authors, for exactly the same reason.

We’ll be having a second call for ideas soon. So, if you are researching some aspect of unconventional computation, and would like some unconventional discussion of your work, why not submit an idea? The second round won’t get to go to Sicily – sorry! – but you will get all the other spin off benefits.

And for everyone else: look out for the book next year.

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a new piling system

I write academic papers, and read a lot of other papers. What with hard copy scattered through filing cabinets, and soft copies on a wide variety of websites, it can be hard to keep track of what I’ve read, made notes on, made BiBTeX citation data, and referenced.

Nearly two years ago I had a go at using Mendeley, designed to help with this very task. But for some reason, it never gelled for me, and I gave up using it. Then, when I became an Evernote convert, I fiddled about with using it to store data on the papers: author, title, etc, notes, BibTeX, and links to the papers themselves. But it was clunky work-flow, requiring a lot of hand-effort. Only the note keeping part of it worked well.

Then a colleague raved about Paperpile on his G+ stream. I had a look. It has a similar concept to Mendeley and other such systems, keeping references, supporting tags, good searching, associated notes, and citation output in multiple formats (although I only care about BibTeX). Instead of being a stand-alone application, however, it’s Chrome-based, and uses your Google Drive as a repository to store your papers. It’s currently in Beta, and I thought I’d give it a try. I emailed off for an account, and about a week later I got one.

The first thing I did was import my dusty Mendeley data (with a single button click and a log on), so I started off with a well-populated, if slightly out-of-date, database. I decided to trial it on the paper I had just started writing.

I defined a tag for the new paper. As I was writing, each time I included a reference in the text, I checked if that reference was already in my Paperpile database; if not, I added it. (Since there is usually a fair amount of overlap between bibliographies of papers in a similar domain, this should get faster as the db gets more populated.) Then I tagged it with the paper’s tag, and any other relevant tags.

My current Paperpile db, with 321 entries

To generate the BibTeX, all I needed to do was select the tag, which selected all the references, and then export the whole BibTeX file in one go.

The real benefit showed up when I got comments back on a draft of the paper. One was about a short quotation I had included, and I needed to find the quotation in its original context in order to address the comment. Click the Paperpile tab in Chrome. Type the author’s name into the search box; the paper’s details appear. Click the “view PDF” button. Voila! The paper is on my screen! I can’t think of a faster way to achieve that, without using telepathy.

You can upload in various ways, including PubMed, arXiv, doi, navigating to a website, or just giving it a PDF.  When you upload a PDF directly, the system searches the web to find the right data to populated the various fields. It gets this mostly right, but there have been a couple of odd peculiarities. Still, the system is in Beta. I reported the couple of weird results, and got very fast and helpful responses from the team. (I like Beta testing. I can whinge about software, and feel virtuous about doing so!)

One thing I particularly like is that the uploaded PDFs are stored in my Google Drive in a very sensible way. The files have obvious names: <Author> <Year> - <Title>.pdf. So if Paperpile were to disappear for any reason, I still have all my PDFs in a very usable form. (I’m not expecting it to disappear; I’m just paranoid: I have my Google Drive synched to my hard drive at home, in case Google disappears!)

I haven’t used all the facilities, in particular, I haven’t exploited the Google Docs integration. The Paperpile team report that some people are using it to write their papers collaboratively in Google Docs. That looks potentially useful, but I don’t use this plain format: I write my papers in LaTeX/BibTeX, using TeXnicCenter.

One part of the system I probably won’t use that much is the notes field. It’s perfectly fine, but I like to keep more substantial notes, with formatting, sketches, figures, and the like. So I’ll continue using my Evernote “papers” notebook, but now just for the notes, not the entire paper and other detials. For a paper with notes, I add a link from the Paperile entry to the relevant Evernote note, using the URL field which gives a clickable link (and I add an Evernote tag, to make it clear to me that there is a note). Similarly, I have a lot of already existing book reviews on my website. So when I include a book that I own, I add a link to the review on my site.

In fact, I think that having a variety of tools that each does their own thing well, with links between the relevant parts, works better than trying to shoehorn all capability into every application. So in Evernote, for example, for a meeting note, I don’t copy the agenda from the email notification, I just store a link to it. Evernote Webclipper allows you to clip the whole email into Evernote, but that just seems to cause unnecessary duplication. It’s good practice to keep only one copy of data (except for backups, of course!), otherwise there is the potential for copies to get out of synch. When that agenda is inevitably later updated, and a new copy emailed out, I don’t have to do anything: provided it is sent out with the same subject line, the Gmail link pulls up the entire conversation, and I can select the most recent agenda. (One day, the agenda setters may start using Google Docs, and so everyone will always have the most up to date version – provided they haven't clipped it into Evernote!) With all these web-based tools, the URL can link everything together.

In summary: first impressions of Paperpile are very favourable. I’ve successfully and productively used it for writing a paper, and it being web/Google based means it’s available at work, at home, and elsewhere, without me having to do anything special. I’ll report on progress if and when it gets more deeply integrated into my web-life.

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the scientific process

There have been two well-publicised mathematical and scientific breakthroughs in the news recently, that have got the community all a-buzz, and that illustrate two quite different approaches to advancing understanding.

Yitang Zhang

In the first, a "virtually unknown" mathematician has made a breakthrough in the twin prime conjecture, a deep question in number theory.  A beautifully clear write-up from the Simons Foundation explains both the mathematical problem, and the process by which Zhang's contribution was handled by the mathematical community.  Zhang wrote a paper and submitted it to the Annals of Mathematics, a highly respected journal.  There is was refereed, and swiftly accepted, since, not only was it correct, it was "written with crystalline clarity and a total command of the topic’s current state of the art".  Being right is crucial, but being comprehensible to the community is also important: it is hard to check someone's work if they have written it in a language all their own.  So, kudos to Zhang for his achievement, and to the mathematical community for recognising it.

Eric Weinstein

The second well-publicised "breakthrough" is Eric Weinstein's "Geometric Unity", a contribution to particle physics.  Or is it?  Here we have another unknown worker, but this time his idea has been launched on the world through a public lecture and a newspaper article written by his sponsor and friend, mathematician Marcus du Soutoy. No publication, no journal submission, not even an unrefereed pre-print, and so no peer scrutiny is possible. Several commentators, including writers at New Scientist and Scientific American, and many science bloggers, have been highly critical of this process that amounts to "science via press conference".

Andrew Wiles

So, two announced breakthroughs by virtual unknowns, given two very different receptions.  Why the difference?  Well, because the conduct of science and mathematics is a process, not (just) a result. It's not some bizarre club, where you have to go through the right channels, jump through the community's hoops, in some arbitrary hazing process.  Those hoops are there for a reason.  It is so very easy to make a mistake, and the hoops are part of the process to help ensure that any mistakes are caught.  Remember the story of Andrew Wiles, slaving away in secrecy for seven years, proving Fermat's last theorem. Well, his first proof had a flaw. Fortunately, the error was correctable, after a lot of effort, and his proof stands. But other equally eminent mathematicians have thought they had made breakthroughs, only for peer review to determine otherwise.

Peer review is an essential part of the scientific process.  It's not there to keep out "outsiders". It's there to keep out errors.  Put up or shut up.  And show your working.

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sequestering carbon, several books at a time II

This is the catch from Amazon et al over the last fortnight, patiently waiting to be databased:

I'm already about a third of the way through Redshirts, and enjoying it immensely   It seems obvious where it's going, so I'm assuming it will instead go somewhere completely different.

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double, double

So there I was, sitting in my study, listening to the rain hammer down, when light suddenly poured in through the window next to me, as the setting sun broke through from under the clouds. Aha!  I grabbed my phone, and dashed to the other side of the house.  And yes, there it was:

A beautiful double rainbow.  (As ever, reality was better than the phone camera could capture.)

And amusingly, the filename of this photo is 20130513_201305.jpg, because the photo was snapped at 13 minutes and 5 seconds past 8pm, on the 13th of May 2013.

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ODE to a Petri net

Writing ordinary differential equations (ODEs) to model various natural world processes comes more readily to some than to others.  And once written, it can take some effort to pick apart their real-world meaning.  It would be nice to have a more visual form.  Petri nets are one such approach.

Epidemics

For example, consider the simple SIR model of epidemic infection.

• +++S+++ : those uninfected, but susceptible -- their number is reduced as they become infected, at a rate proportional to the number of susceptibles and the number of infected
• +++I+++ : those infected -- their number is increased by susceptibles who become infected;  it is also reduced as those infected recover, at a rate proportional to the number of infected
• +++R+++ : those recovered -- their number is increased by those who were infected recovering

For those happy with ODEs, it is straightforward to write down a set of coupled equations to model this:

$$\dot{S} = - i S I $$

$$\dot{I} = i S I - r I$$

$$\dot{R} = r I$$
In addition to natural language (the bullet list) and maths (the ODEs), there is another language useful to explain and understand models: diagrams.

For example, we could draw a simple state transition diagram to show the movement from susceptible to infected to recovered:

This captures some of the information, but not all of it.  A (continuous) Petri net can do better:

The circles are called "places", and represent the "things" involved: here the susceptibles, the infected, and the recovered.  The rectangles (the colours aren't significant) are called "transitions", and represent how the things in "input" places get transformed into things in "output" places.

• transition +++r+++: an infected comes in, and a recovered comes out
• transition +++i+++: a susceptible and an infected come in; two infected come out (the two infected outputs are the newly infected, and the original infecter)

This diagram has enough information in it to reproduce the original ODEs.  We have one ODE per place, with the terms being given by the transitions feeding that place.

• place +++R+++. +++R+++ has only one transition feeding it: transition +++r+++.  It is feeding into +++R+++ at a rate proportional to all the inputs to +++r+++: here just +++I+++.  Hence +++\dot{R} \propto I+++.  If we call the constant of proportionality (the rate constant) +++r+++, we get +++\dot{R} = r I+++
• place +++S+++. +++S+++ has only one transition, +++i+++, removing stuff from +++S+++.  It is removing at a rate proportional to all the inputs to +++i+++: here +++S+++ and +++I+++.  Calling the rate constant +++i+++, we get  +++\dot{S} = - i S I+++ (the minus sign is there because we a removing from +++S+++, and it is conventional to keep the rate constants positive)
• place +++I+++.  +++I+++ has two transitions: +++i+++ both feeding it (two input arrows) and removing from it (one output arrow), and +++r+++ removing from it.  We get +++\dot{I} = i S I - r I+++

Thus we have recovered the original equations.

We can write this focussing on the transitions, in a more algorithmic way (algorithms, or pseudo-code, are yet a further language we can use to explain, describe, and define processes).

• initialise the rates of change to each place to be +++0+++.  +++\dot{S}, \dot{I}, \dot{R} := 0+++
• transition +++r+++.  This is removing stuff from place +++I+++ and adding it to +++R+++ at a rate +++rI+++. Update the output place +++R+++ and input place +++I+++ appropriately: +++\dot{I} {-}{=}\ rI+++, +++\dot{R} {+}{=}\ rI+++
• transition +++i+++.  This is removing stuff from place +++I+++ and from place +++S+++ at a rate +++iSI+++ and adding it to +++I+++ at a rate +++2iSI+++ (from the two input arrows).  Hence there is a net input to place +++I+++ at a rate +++iSI+++.  Update the output place +++S+++ and net input place +++I+++ appropriately: +++\dot{I} {+}{=}\ iSI+++, +++\dot{S} {-}{=}\ iSI+++

We can write this as a general algorithm:

  for each place Pi
    Pi_dot := 0
  for each transition Ti
    let Pin = < Pin_1, ... , Pin_n > = list of n places,
                                       one for each input arrow of Ti;
        Pout = list of m places, one for each output arrow of Ti;
        t = Ti x Pin_1 x ... x Pin_n
    for each Pi in Pin
      Pi_dot -= t
    for each Pi in Pout
      Pi_dot += t

So now we have a diagrammatic form, and an ODE form, that are equivalent, and an algorithm to translate on to the other.  This is useful, because we can use them interchangeably, without risk of losing information.  In particular, notice how explanation accompanying the Petri net focusses on what is happening in the transitions, whilst that for the ODE form focusses on what is happening to the places.  Having different forms of explanation can be useful in different circumstances (modelling, communication, modification, validation, calculation, etc).

Catalysis

Although that all works well, the handling of the infecter in the +++i+++ transition seems a bit unnatural: infecter goes in, infecter comes out, resulting in an addition and subtraction of this rate.  The infecter is needed for the transition, and affects the rate of the transition, but is not itself changed by the transition.  In chemistry, this is called a catalyst, and there is some special Petri net syntax for it.  We can draw the SIR Petri net above equivalently as:

Here the dashed arrow means that +++I+++ is a catalyst: it is needed for the transition, but is not consumed by the transition. Hence there is now only one arrow out to +++I+++: since the catalyst wasn't consumed, it doesn't need to be replaced; the remaining single arrow represents the newly infected.

The algorithm needs a little  modification:

  for each place Pi
    Pi_dot := 0
  for each transition Ti
    let Pin = < Pin_1, ... , Pin_n > = list of n places, 
                                       one for each input arrow of Ti;
        Pcat (sublist of Pin) = list of catalytic input arrow of Ti;
        Pout = list of m places, one for each output arrow of Ti
    let t = Ti x Pin_1 x ... x Pin_n
    for each Pi in Pin \ Pcat
      Pi_dot -= t
    for each Pi in Pout
      Pi_dot += t

So the catalytic arrows still contribute to the functional form of the overall rate +++t+++, but not to the changes to the specific places.

Logistic equation

Possibly the simplest bounded growth model in biology is the logistic equation:
$$ \dot{N} = rN(1-N/K)$$where +++r+++ is the growth rate, and +++K+++ is the carrying capacity (so when +++K=N+++, +++\dot{N}=0+++).

This can be drawn as an equivalent Petri net:

• transition +++r+++ (birth): one in, two out
• transition +++rK+++ (competition): two in, one out

These two transitions can also be shown in a simpler catalytic form (if maybe not with the same intuition as before):

• transition +++r+++ (birth): one "catalyses" the birth of the other
• transition +++rK+++ (competition): one "catalyses" the death of the other

Lotka-Volterra predator-prey

The simple predator-prey model, usually cast as rabbits and foxes, has rabbits being born, predated on by foxes to produce more foxes, who then die.  A simplistic version of this might be:

• transition +++b+++ (birth): one rabbit in, two rabbits out
• transition +++d+++ (death): one fox dies
• transition +++p+++ (predation): one fox and one rabbit in, two foxes out

This however has a problem: it has a new fox produced every time a rabbit is eaten.  Real foxes need more food than this to reproduce.  We can't solve the problem by changing the rate +++p+++, as this affects the consumption of rabbits and production of foxes equally.  What we really need is for the consumption of a rabbit to produce a bit of a fox.  We can do this by adding a separate rate to the arrow:

• transition +++p+++ (predation): one fox and one rabbit in, one plus +++\epsilon+++ foxes out

The algorithm needs to be updated to multiply the rate by the weight of the arrow before adding/subtracting  as appropriate.  This then yields the familiar equations:

$$\dot{R} = R(b-pF)$$

$$\dot{F} = F(-d+p\epsilon R)$$

If we use the catalytic form, the diagram simplifies to:

• transition +++p+++ (predation): one fox "catalyses" the transformation of a rabbit into +++\epsilon+++ of a new fox

Lotka-Volterra competition

The simple competition model, usually cast as rabbits and sheep, has rabbits and sheep being born and dying following their own logistic equation, and also competing with each other for resources.

• transition +++rb,rs+++ (birth): one +++x+++ "catalyses" the birth of the next +++x+++
• transition +++rc,sc+++ (competition): one +++x+++ "catalyses" the death of another +++x+++
• transition +++rs+++ (rabbit/sheep competition): one sheep and one rabbit in, a proportion of each out

$$\dot{R} = R(rb-rcS) + (pr-1)rs RS$$

$$\dot{S} = S(sb-scS) + (ps-1)rs RS$$

Combining predator-prey and competition

The competition example has two logistic "subnets", showing how these diagrammatic forms can be readily combined.  So, for example, we could easily add some foxes to the brew:

$$\dot{S} = S(sb-scS) + (ps-1)rs RS$$

$$\dot{R} = R(rb-rcS) + (pr-1)rs RS -pRF$$

$$\dot{F} = F(-d+p\epsilon R)$$

If the foxes also worried the sheep, the diagram would get messier, but would still visually represent the relationships between the different components.

Diagrams v cartoons

Pictures can be very helpful at getting across ideas, but they have their problems if they are ambiguous, incomplete, or otherwise open to misinterpretation.

The (continuous) Petri nets shown here have a formal meaning: they can be translated into equivalent ODEs. They are not informal "cartoons", merely sketching some part of the meaning.  There is an algorithm from diagrams to equations, and it is possible to build a tools allowing the manipulation of diagrams and equations as two different "concrete syntaxes" of the same underlying model.  Which syntax to use depends on what you are doing: it truly is the best of both worlds.

Acknowledgments

• I first came across the formal link between continuous Petri nets and ODEs on Alexi Sharov's web site
• I was reminded of using Petri nets to model population dynamics on reading David Tanzer's guest post on the Azimuth site
• I drew the diagrams in graphviz
• The maths is formatted with LaTeX and displayed using MathJax

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gamified research

My day job includes working with people who research on flocking, social networks, and other complex interactions of groups of agents.  A while back, some of our researchers decided to see if their work could be brought to the public through "gamification".  Over the summer, the University has funded a couple of interns to work on this, and now ComplexCityApps have just released the first game for the Android: Floqua.

As the game's website says:

Find all of the lost fish in each level and guide them to safety in the castle. The tricky part is keeping them in your flock. Swim too fast and you'll leave your fish behind. Watch out for the predators: they're hungry!

The game is now available through Google play.  I confess, I've spent more time than I should herding fish.  It's easier than herding cats, which is part of my day job...

In addition to being a fun game (we hope!), it's educational (we also hope!).  You can simply play the game.  But, if you want to know more, there are pointers to the science behind the app.

And the developers have bravely been keeping a blog, so you can also dig down into some of the game design process, including effort to make sure the colour-coding of fish types is accessible to colour-blind players.

Okay, I'm off to try the next level, which has two rather hungry predators trying to eat my fish...

hiding from predators...

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unconventional identity

I'm just back from a wonderful week in Orleans, at the Unconventional Computation and Natural Computation conference, and associated specialist workshops. Interesting people, fascinating science, good weather, great food: what more could you want?

Here's the semi-obligatory photo from my hotel window, with the cathedral visible in the distance (oh, the bells! the bells!):

As well as the packed science programme, there was the customary conference event.  The conference organisers spoiled us with a choice: a visit to Chambord Castle, or kayaking on the Loire.  Unsurprisingly, I chose the castle.  Very impressive, with an excellent guided tour giving us all the history, from its original use as a hunting lodge, to the bullet holes from WWII.

It's difficult to know how much to believe on these tours.  We were told that eating with a fork was introduced, because it was needed to get past the ruff worn around the neck. It sounds no sillier than the reasons behind any other piece of etiquette, but I can find no evidence for the claim.  The web seems quite happy to credit Catherine de Medici with introducing both the table fork and the ruff to France, but doesn't connect the two, in fact claiming that the fork didn't catch on for a while. Ah well, it's a nice story.

My favourite bit of the castle was the four storey double helix staircase, allegedly designed by Leonardo da Vinci:

This trip was followed by a walking tour of Orleans itself, which seemed to involve a lot of different statues of Joan of Arc.

several statues, of varying size

Our guide told us that all these statues depict Joan of Arc wearing a dress, because it used to be thought wrong for a woman to wear "boys clothes", as she actually did.  Why revere someone for saving you, yet deny what they actually did?

One of the things on the tour that was not a statue of Joan of Arc was Joan of Arc's house.  Except that it wasn't actually her house, she only stayed there; also, the original was destroyed in WWII, and later reconstructed, but at a different size.  It reminded me of the story of "my grandfather's axe". Identity is a slippery concept.

The availability of cheap and plentiful smart-phone photography changes what I snap, too.  As well as the obvious statues, vases, furniture, stair cases, carvings, and other typical touristy shots, I also photographed the timetable at the tram stop:

I didn't snap it (only) because it's a nice example of a stem-and-leaf plot, but because I actually wanted a copy of the timetable, for my trips back-and-forth between the conference venue and the city centre. I was accused by my colleagues of taking the most boring tourist photograph ever.  And then one of them photographed our restaurant receipt, for their expenses claim...

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citation patterns

An academic's contribution to the community is made through publications.  And so we obsessively keep good records of what we have published, and neurotically check out if anyone has noticed.

To this end, I’ve been playing around with Google Scholar, tidying up my auto-generated publications list there. Although I still maintain my own publications page, this separate one is a useful resource, mainly because it includes citation counts and links.

Citation links are good for more than justegoboo and feeding neuroses, honestly! Citations indicate other authors who find my work relevant to them, so it's very likely that their work is interesting and relevant to my own research. It makes sense for me to check them out, and then also check out the other papers they reference, and also their citations, to see if there’s something interesting and relevant to me. Although it is a bit easy to get sucked into a never-ending death-spiral of link following: there’s just too much interesting stuff.

That's the beauty of Google Scholar.  it doesn't just have a list of my papers (a task I can do myself with relatively little effort); it also auto-generates links to citing papers (a task that would be ridiculously time-consuming to do myself).

More than that, it also provides neat little graphs of citations per year. These are quite interesting for publications that are several years old, where patterns have had time to emerge.

The typical pattern shows a peak a few years after publication, then a tail off into obscurity.

Slightly more gratifying is the pattern of a peak, followed by a drop, but then carrying on bumping along at about the same rate.

But the weirdest citation pattern must be to a paper written during my PhD, published nearly 30 years ago. It caused barely a ripple when published (I comfort myself that there was no Web in those days, and hence restricted access to it). But then there was a second (modest) peak of citations after about 15 years, followed by a drop-off. And then there was a third peak, starting more than two decades after publication.

Clearly, a paper ahead of its time!

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narrating complexity

Our new interdisciplinary network on Narrative and Complex Systems is having its first workshop, in York.  Yesterday we had a great time explaining to each other what we did, from narrative in fiction, in films, in computer games, to the narrative of complexity and emergence, and trying to discover common concepts.  We finished the day by brainstorming a mindmap:

This process was amazingly helpful in distilling our ideas.  The result, however, is pretty incomprehensible to anyone who wasn't there!

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it's gravity, Jim, but not as we know it

I’ve just finished Angry Birds * Space (which everyone seems to call Angry Birds in Spaaaace, of course), released March 2012.  So that didn’t last long, either.

This time, the birds and pigs are fighting it out in space, and the birds have rocket drives.  There’s gravity in some places (near planets), and not in other places (not near planets). Only it’s not as simple as that. The existence of a gravitational field is shown by a red disc centred on the planet. Inside the disc there’s gravity, with the force directed towards the centre (I can’t tell if it’s 1/r^2 or constant on my little tiny phone screen); outside there’s no gravity. Gravity just switches on when an object enters the disc. Real gravity in space doesn’t work like that, of course.

Outside the disc, in the no-gravity regions, things travel in straight lines. So far, so good, maybe. But they gradually drift to a halt. No gravity, okay, but friction?? (Which is why the birds' rocket drives are on all the time.)  Real gravity in space doesn’t work like that, of course.

Also, there are some “black holes”: nothing visible at the centre of the gravity field, but stuff does get broken up if it goes there. There are also, confusingly, sometimes large planets with no gravity field at all, that act as hard (but massless!) obstacles. (Cases with invisible gravitating black holes plus visible gravity-free planets are particularly … interesting.)

There are two new birds: a light blue "ice cube" bird which freezes stuff, making it easier to break later, and a purple ("Lazer") bird that boosts in the direction of your tap (instead of the yellow Lazer bird that boosts along its tangent when you tap).

It's a Small World

The planets are very small – Clanger-esque, or even “Little Prince”-like.  In order to erect buildings on these small spheres, there are often large flat platforms. Sometimes this can lead to an almost square planet. Even with these platforms, gravity still points to the centre of the planet, so there is a small resultant force along the direction of the platform. I was delighted to notice, when an ice block was at the end of a platform, it slid towards the centre, overshot, then slid back: simple harmonic motion in action. Yay!

Square planets!

I did this time go for a perfect three stars on all levels.  That’s because the alternate-physics is fun, and three stars often required an interesting solution exploiting the possibilities, especially where planets are so close their gravity-discs overlap. Fortunately there is a helpful little "preview orbit" dotted line, that gives a partial clue to the final trajectory.  (The "Golden Eggsteroid" extra levels are great little spoofs on Space Invaders and the like.)

So, I’m just back from the Science Fiction Eastercon, where I was telling a friend that I’d nearly finished Angry Birds Space. So that’s all three done! (Vanilla, Seasons, Space) There’s also Rio, he said.

AAARGH! WHY DID YOU TELL ME THAT!

I’m now on Rio level four, collecting golden bananas…

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learning from the worst

I'm not the best presenter in the world, but do I try not to make the most obvious of mistakes. Sometimes it's easier to learn what not to do from the worst (since you are not distracted by actually listening to the talk) than learn what to do from the best.

So, don't do this:

Chair: Our next speaker is Alex Umlaut from Olympus Mons University, with a talk on “non-isotropically depolarised orthophasic omicron graticules”.

AU: Good afternoon. My name is Alex Umlaut. I am from Olympus Mons University. My talk is titled non-isotropically depolarised orthophasic omicron graticules.

Here is the outline of my talk.

First, I will give this outline of my talk.

Then I will spend most of the talk on background material that you already know.  There will be lots of detailed equations in a small typeface that I will flash on the screen, but I will tell you that you don’t need to read them.  I will use lots of needless animations that will make you feel seasick.

When I get the five minute warning from the Chair, I will realise that I am running out of time, and will race over all the new and interesting parts, and completely skip 10 slides.

At the one minute warning, I will tell the Chair that I am on my last slide, but it will have 20 different reveals on it, and I will go well over time.

Finally, I will end with my conclusions.  I will mumble off into silence, so that you will not be sure that my talk has actually finished.

Here is the background to my work. You don’t need to read these equations. …

Audience: Zzzz…

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When "organic" is not "good"

The word "organic" is nowadays associated mainly with "environmentally friendly", as in "organic food", "organic agriculture".

Back when I was a student, Chemistry was taught in three main parts: organic chemistry (essentially the chemistry of carbon, and hence the chemistry of molecules in living stuff); inorganic chemistry (essentially the chemistry of everything else); and physical chemistry (focussing on the processes and laws rather than the constituent elements).

And I learned that many organic compounds are toxic and/or carcinogenic. When I was at school, benzene was the organic solvent of choice in practicals. By the time I was at university, benzene was no longer used, because it was realised to be a carcinogen, and toluene was the organic solvent of choice. Thanks, school.

Also, we were gleefully informed (by the physical chemists) that the lifespan of a bench organic chemist was reduced by 10 years on average. I have no idea if this is true (or if it was true then), but it certainly stuck in my mind.

So my strongest association with the word "organic" is "probably a nasty poison!", followed by  "molecules found in living stuff". This makes my reaction to the term "organic food" go something like:

1. Potentially poisonous?  Surely not...
2. Made from living stuff? Isn't all food made from (once) living stuff? If it's not "organic", it's not food. (Well, okay, except for salt.)
3. Oh... food produced without the use of inorganic chemistry

I often have this problem of trying to figure out what people mean when using a technical term I know, but in a non-technical context. My favoutite example is when I first saw a sticker saying "Nuclear power? -- No thanks" with a picture of a smiling sun.  "They want to turn off the sun?  What?  Oh..."

Anyway, if "organic" is the adjective of choice to mean "envinronmentally friendly", what should we call envinronmentally friendly chemistry, given that we can't call it "organic chemistry", because that almost means the opposite?  Well, it uses that other environmentally friendly adjective; it's green chemistry.

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it was 30 years ago today

that Acorn Computers launched the BBC micro.

I got a model B Beeb while I was doing my PhD, and had great fun learning to program.  (I already knew Fortran IV, but until reading Structured Programming with BBC Basic, I hacked, rather than programmed.)  I used my Beeb to do some real science (see the acknowledment section on p544), and even wrote some articles on fractals for Acorn User.  Happy days, and so long ago.

30 years ago, in fact.  That's the reason for the post.  We all know about Moore's law, commonly quoted as "computer power doubles every 18 months" (although of course the reality is more complicated, but let's go with this for now).  One doubling of power doesn't sound much, but what happens after 30 years of Moore's law?

30 years is 20 doubling times. 2^20 ~ 10^6. (Impress you friends with fast mental conversions between powers of two and powers of ten by remembering 2^10 = 1024 ~ 10^3. Caveat: any friend who would be impressed by this feat probably already knows how to do it.)  So that would mean today's PCs are one million times more powerful than the humble Beeb.  Is this true?

• Memory: 32kB RAM (yes, 32 kilobytes).  32 GB isn't common today (although it is on a reasonably high end scientific workstation): more like 4 or 8 Gigs typically, so, a quarter of a million times as much memory.
• Processor (6502A) speed: 2 MHz.  More like a thousand times faster today, with a 3GHz processor (but, of course, that's with a 32 or 64 bit processor, not the Beeb's 8 bit one, and then there's multi-core, dual processors, and what not, giving another order of magnitude).
• Screen: max resolution 640x256 (2 colours), or 160x256 (8 colours), using 20 kB of video RAM.  Today, my flat screen is 1920x1080 (4 billion, or 32 bit, colours), so that's about 8MB of video memory, or 400 times as much. 
• Disc storage: a cassette player, or an optional 100 kB 5.25 inch floppy drive once you got fed up with waiting for programs "Loading...". Today, a 500 GB hard drive is more typical: that's 5 million times as much.

Okay, not every feature is a million times more, but combined, today's PCs are easily a million times more powerful than the Beeb.  And for less money!  The BBC Model B cost £399 (which is about £1200 in today's money), but that didn't include the monitor or floppy drive. The Torch dual 800 kB floppy drive cost around £800, although you got a Z80 co-processor thrown in for that.  In March 1987 I bought a 28MB (yes, megabyte) hard drive for £745 (or about £1600 in today's money).

To get a better feel for what this doubling means, think of a slightly older computer, Deep Thought, which took seven and a half million years to calculate the Answer to the Ultimate Question of Life, The Universe, and Everything.  That was in 1978, or 33 years ago, or 22 doubling times, or 4 million times.  If that was a speed improvement, today's equivalent of Deep Though would take only 2 years to calculate the answer!  Not so impressive now.

But wait, you say.  Douglas Adams was a genius.  He would have taken that into account.  The real Deep Thought would have kept upgrading itself, but even so still took 7.5 Myr to calculate the answer.  7.5 Myr is 5 million doubling times, or 10^1,500,00 more powerful.  That would make the Deep Thought who gave the answer 42 to the philosophers' descendents umimaginably more powerful than the original (if such a machine is even possible in the current universe).

Anyway, happy birthday, Beeb!

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On the Road to Mendeley

I decided to evaluate Mendeley as a means to "organise" my scientific paper reading and note-taking. A hard disk full of PDFs from forgotten sources, with separate BibTeX files for each paper I write, is messy.

I decided on Mendeley after reading a few reviews. These mostly said good things, and only very few bad things. The desktop application lets you keep the PDFs of your collected papers (and other materials) together, along with notes, tags, and bibliographic information.

You can search on authors, keywords, tags, text, and so on.

You can keep papers in separate directories.  Having recently started using Gmail, and got the hang of using tags instead of directories to classify my emails, I was pleased to see tags also supported here. I will be tagging papers not only with the bits of their content of interest to me, but also to tell me which of my own papers have referenced them.

You can export reference information in BibTeX format, for ease of writing papers. (Apparently you can also export in MS-Word formats; but why would you write a scientific paper in anything other than LaTeX?)

So I spent a happy few hours importing my PDFs, and various BibTeX files, merging duplicates, cleaning up the entries, and generally populating my account:

You don't have to have a PDF of every paper: you can store just the bibliographic details, or a URL to where it is stored elsewhere.  But if you want to annotate the PDF, you need your own local copy.  (There is a separate "notes" field, so you don't need to do this unless you want to annotate in the exact place.)  You can sync all this information across multiple computers. (I haven't tried this yet: I've only accessed it from home so far). I can see that free 500MB isn't going to go very far...

I'll try using it for a while, and in anger while writing my next paper, to organise the bibliographic information.  So far it looks very useful.  It's the kind of thing that will make writing a review paper a lot easier, I suspect.

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scientific abstract

Scientific and technical writing is a little different from the kind of "creative writing" you are taught at school. There are different conventions. For example, "vary your vocabulary" is a complete no-no if you are striving for precision and lack of ambiguity. If you think using the same word several times may sound a bit clunky, and you decide to make things "read better" by using synonyms instead,  imagine the internal dialogue of your readers going something like this: "You talk about a thingumabob here, a thingamajig there, and a whatchamacallit in your other paper. What subtle distinction are you trying to make? I've been trying to puzzle this out for ages.  Oh, I see.  You are just varying your vocabulary. Come here and say hello to this two-by-four." That should help you decide not to do it.

One important part of a scientific paper is the abstract. It should be short yet comprehensive. It should tell you what the paper is about, the question the paper addresses, and the answer it discovers. It is not a spoiler to put the answer in. You are not writing a blurb for a whodunnit. Don't worry that you are giving away the punchline. The abstract may be the only part of your paper the reader even sees.

So I was delighted to see a paper with an excellent abstract quoted at Ben Goldacre's secondary blog. (Actually, it's only the perfect abstract when combined with the paper's title; techincally, abstracts should be stand-alone. But, hey.)

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spotting the placebo

A lovely letter in New Scientist (22 Jan 2001, p26):

From Phillip Gething

As part of my training to join a medical research ethics committee, I attended lectures on drug trials. We were told of a trial in which a patient spotted when her drugs had been switched, even though great efforts had been made to make the capsules appear identical. When asked by her GP why she thought the drugs had been changed, the patient replied, "I always put them down the loo. Until recently they always floated, now they always sink."

Fleet, Hampshire, UK

Being able to spot the placebo clearly wasn't the main problem here!

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