Dr Administrator

Author's posts

Oct 30 2009

Who Dr.?

After the big and shiny experience as an undergraduate I went off to do a PhD., to make me into a Dr. This was something I’d intended to do since a visit to the Campden and Chorleywood Food Research Centre as a school student; there we were shown around the labs and I was convinced that a career in science without a PhD. was going to be a serious uphill struggle involving the cleaning of much lab glassware.

The exact nature of a PhD. varies from country to country and from subject to subject. In the UK a PhD. in physical chemistry is typically 3 years long and the supervisor will usually have a big say in what the student does.

I did my PhD. at Durham University in the Interdisciplinary Research Centre for Polymer Science, supervised by Prof. Randal Richards. Prime motivation for this particular PhD. was the cash, it was funded by Courtaulds Plc and paid a research assistant salary. It also got me back to more big and shiny science, in the form of the neutron source at the Rutherford-Appleton Laboratory  (RAL) and with the added benefit that a very skilled technician made my polymers for me. This was good because I’ve never been “at one” with synthetic chemistry, the untidiness of the process didn’t suit my temperament. Apocryphally the start of polymer science was a bit slow because the early polymer synthesisers couldn’t crystallise their material, this led to much derision from other synthetic chemists who made lovely crystals from their materials, rather than black sludge that polymer scientists made. The molecular nature of polymers wasn’t appreciated until the 1920’s which is really rather recent.

So for 3 years I slaved away: I prepared samples – spinning thin films onto lumps of shiny flat silicon, I went down to the RAL for 48 hour experimental runs, I wrote FORTRAN programs do do data analysis, I read journal articles, I attended conferences, made posters and gave presentations. I observed, from a small distance, the activities of synthetic chemists.

The chap over the desk from me was a historical re-enacter, I watched as he made his own chain-mail.

It was whilst I was writing my thesis, entitled “Surface composition profiles in some polymer mixtures”, that I first met with the elephant of despair. The elephant of despair lived in the library, he was made of a transparent material so you could scarcely see him and he was only about 6 inches tall. He stood in the gaps between the journals, waiting for when I would arrive to find an article and discover on the way a paper published 10 years ago which captured most of what I’d slaved over for the last three years. His plaintive trumpeting has haunted me on and off through the years.

I think the day I decided I wasn’t going to make an effort to get “Dr.” onto all my paperwork was the day I was in the bank the man in front of me was having a lengthy discussion with the cashier because the printed numbers in his saving book did not line up with the ruled lines. After he’d left the cashier turned to her colleague and said: “He had to complain, he was a doctor”. As it stands the only people who call me “Dr Hopkinson” are my parents, one of my credit cards and the odd polite student.

For reasons I don’t understand medical doctors appear to refer to PhD’s as “proper doctors”, whilst I’ve always considered myself a bit of fraud since I was not a “proper doctor” – who could potentially save your life. Perhaps they’re just being polite.

And now I’m nearly a PhD. grandfather, I supervised three PhD. students of my own and one of these has a student who is about to do her viva. I don’t have children, but I feel very ‘parental’ about my students – I’m immensely proud of them and their achievements.

Oct 28 2009

Wordless Wednesday

Oct 22 2009

Talkin’ about my generation

My generation have all been wallowing in nostalgia at the Electronic Revolution strand on BBC4, in particular Electric Dreams – the 80’s and Micro Men – the story of Sinclair and Acorn computers. We grew up in a golden age for programming, the generation before us had no hardware and the generation after us had no need to write their own software. We programmed because we had to.

I had a Commodore VIC20, cheaper than the BBC Micro, more classy and substantial looking than the Sinclair ZX81, available slightly before the ZX Spectrum. All of these lovely old machines available for your viewing pleasure at Centre for Computing History, along with many others. Look around the internet and you can also find all manner of emulators and manuals for these early machines. We wrote our own programs, or we typed in games from magazines – this was often a rather lengthy process and a bit prone to error.

I found the “VIC20 Programmers Reference Guide” here re-typed by Asbjorn Djupdal. Here’s snippet: a program which allows you to enter the scores in each quarter for an American football game and then prints them out on screen in a table:

100 DIM S(1,5), T$(1)
110 INPUT “TEAM NAMES”;T$(0),T$(1)
120 FOR Q = 1 TO 5
130 FOR T = 0 TO 1
140 PRINT T$(T),”SCORE IN QUARTER” Q
150 INPUT S(T,Q)
160 S(T,Q) = S(T,0) + S(T,Q)
170 NEXT T,Q
180 PRINT CHR$(147) “SCOREBOARD”
190 PRINT “QUARTER”;
200 FOR Q = 1 TO 5
210 PRINT TAB(Q*2 + 9)Q;
220 NEXT
230 PRINT TAB(15)”TOTAL”
240 FOR T = 0 TO 1
250 PRINT T$(T)
260 FOR Q = 1 TO 5
270 PRINT TAB(Q*2 + 9) S(T,Q);
280 NEXT
290 PRINT TAB(15) S(T,0)
300 NEXT

Oh, this brings back memories!

To me programming and science (or at least physics) are intimately linked, almost the first programming I ever did was to visualise beat frequencies. To this day, if I want to really understand a scientific paper I’ll implement the equations in a program, as often as not a few typos in the equations are revealed in this way and I’ll have learnt exactly what the paper was on about. Teaching a student is a fantastic why to learn something, teaching a computer is almost as good.

Most the programming I do is of a workmanlike nature, it drives machines for measurements; it processes data; it analyses results; it computes equations, but there is scope in programming for a deep elegance, a pared down beauty which is difficult to describe – it’s like finding the answer to a cryptic crossword clue – perhaps for an artist it’s like finding just the right line to give a character personality. It’s an algorithm that does what it has to do with the least effort required. I still program a lot for my work (relatively small stuff that only I will use), and it’s not unknown for me to waste an hour doing something elegantly rather use the quick, dirty and obvious approach.

Programming is in my genes, in two ways really – my parents were both programmers from the sixties. We once found a leaflet advertising the Elliot 503 in our loft, 400sq ft of ’60s computer with substantially less processor power than the most lowly of today’s devices – this is the computer on which my mum learnt to program. Dad started on an early Ferranti of some description in the late 50’s.

Earlier programming for me pretty much amounted to shouting verbs at things, possibly because I used FORTRAN which at the time was ALL IN CAPITALS. Programming today feels very different, it’s more like visiting a library to get a book of spells to cast or the singing of a choir. I still enjoy doing it, in fact I’m writing a twitter client in C# just so see how to do it.

You might get the impression from all of this that programming is for the mathematically minded, but it isn’t – it’s really for the logically minded, for some mathematical applications maths is required but otherwise it isn’t.

I taught the basics of programming to first year physics students a few years ago, and the thing that really shocked me was that, out of a class of fifty, only one had any real programming experience. There is hope though, I suspect programming still holds a fascination – my single data point: father and son sitting down to program the BBC Micro on Electric Dreams.

Oct 21 2009

Wordless Wednesday

Oct 20 2009

Twitter, rumours and physics

The twittersphere avoided making a bit of a mistake this morning. Wikileaks had obtained a new version of the BNP membership list, which they released (the BNP claim this list is a fake). Prior to release it was claimed that a peer of the realm was on the list and immediately post release that peer was named. Only it turns out it wasn’t him, someone who styled himself Lord with a very similar name was the man on the list. Fortunately the released list was detailed enough that this could be checked, someone had the wit to check before blindly repeating the name. Once they’d done this they started correcting the false rumour (in what looks like quite a vigorous manual effort). It’s worth noting here that the fact-checker appears to be a trained journalist.

But it could so easily have been very different. It could have been very difficult to establish the rumour was false, it could have been that the diligent fact checker stopped to finish his cup of tea before tweeting his correction, the rumour could have been re-tweeted by someone with many followers. All of these things could have happened but didn’t, will this be true the next time?

On the plus side, twitter rumours do appear to be traceable back to source and it’s very easy to find the individual rumour-mongers and put them right. This is certainly true for non-malicious rumourmongering (that’s to say where people have not made a special effort to propagate a rumour, nor hide their tracks).

There is a scientific link here, modelling of all sorts of networks has long been a respectable scientific field. For example, there’s Per Bak’s forest fire model and work that follows on from there. More recently there’s been work focussing more explicitly on computer networks and social networks. To a physicist Twitter represents an example of a simple system which has nodes (with ingoing and outgoing links) and messages that are propagated between the nodes. The nodes could be trees in a forest and the thing passed could be fire, or the nodes could be computers in a network with the message being network traffic; the nodes could be scientific papers with the messages citations of other papers. The physics doesn’t care about the detail of these things, it cares about a small number of parameters in the system: how many links in and out of a node? What’s the probability of a message being transmitted from one node to the next?

So there’s an interesting bit of network analysis to do here. How fast can a rumour propagate on Twitter? What fraction of people refrain from tweeting a false rumour to stop it propagating? What’s the best way to squash a false rumour?

Having watched the no doubt frenzied activities involved in squashing today’s rumour. One useful tool would be an automated rumour-quashing robot. It would search for tweets containing the rumour (probably based on a manually selected keyword) and tweet the originator with a rebuttal.

Think before you tweet!