Gaussian Noises

Well Hello There

2010-09-16T13:33:00.000-07:00

In a possibly ill-advised move, I have decided to re-activate this blog in the middle of what may well be the busiest period of my life (also known as the third year of my PhD). We shall see how long this good intention lasts. In the meantime, look forward to more dispatches from the life of a PhD student.

Warning: May include occasional desperation-fuelled late-night ramblings.

On Learning When To Shut Up

2008-11-07T08:59:00.000-08:00

You know what phrase I'm beginning to dread? It's "Oh, so you're saying that...". Nine times out of ten, I'm not actually saying that. Or if I was saying that, I was merely speculating.

Example:

Me: This is not giving the result I expected. I observed effect Y, so maybe it's due to Z.
Person X: Oh, so you're saying that [slight rephrasing of Z] is causing the problem.

No, I'm not stating an absolute, as indicated by my use of the word "maybe". I'm putting forward a hypothesis, which is what you do in science. But I don't appreciate you nailing me down on that hypothesis before I have even investigated it.

Maybe (just a hypothesis!) the problem is with me. These are (for the most part) busy people I'm talking to, and maybe they can't afford to spend that much time speculating anymore. So when I'm coming up with a hypothesis, they just assume I wouldn't be telling them if I hadn't already thought about it for some time.

In other words, do I need to learn when to shut up?

A Plea

2008-10-13T08:54:00.000-07:00

Please, please, please, please... document your data.

It's not fun to get a matrix without any column or row labels. Like my old physics teacher used to say when somebody proudly proclaimed that the answer to an exercise was 5.029, "5.029 what? Elephants?". In physics, a quantity is not meaningful without a unit, and in data management, a matrix is not useful without labels.

It's not fun to know nothing about any preprocessing of the data. Has it been normalised? I guess I can check the mean and standard deviation, but what if it's only close to 0 and 1, but not exactly so? Was that some special normalisation method? Hell if I know.

It's not fun to know nothing about the experimental setup. Maybe you told me every second data point is a wildtype. Does that mean that these were results from two-colour arrays? Or two singe-colour arrays? Are the wildtypes from the same time point as the mutants? Come to think of it, are these even time-course data?

It's not fun to know nothing about what the biologists* want you to find. Are they looking for similarly expressed genes or for regulators? Would they rather have a network of the knocked-out genes or of all genes? Is it worse if I give them false positives or false negatives?

So please, please, please, document your data. Maybe some time in the future I will tell you about fun things called wikis and databases, but for now even a text file would do.

*Or insert other applied science here.

The View from the Foothills

2008-09-29T08:39:00.000-07:00

I'm finally there. Five years of hard work, first as an undergraduate and then as a Masters student, have paid off. Last Monday, I was granted my rightful place in academia... on the lowest rung of the ladder.

Yes, PhD students are a dime a dozen, even in my small institute, and despite the excitement of starting research in earnest, I can't help but feel slightly apprehensive. This may just be the result of reading too many PhD comics, but a tinge of anxiety is setting in. What if my advisor turns out to be a workaholic? What if I can't finish in the required 3 1/2 years before my funding runs out? What if my office mates are insane (they're not, I think) or my experiments all fail?

Then I remember that a million students have survived their PhD just fine before me and a million will again. I may not have the prettiest office (in fact, drab is not an inaccurate description), but at least I'm not sharing with 14 other people like my flatmate. My supervisor has only been nice to me, despite the bollocking that he gave his other PhD student last week. And my project, even though it looks daunting from here, will rest on the foundations of my Masters project, meaning that I have a reasonable idea of where to start.

So, the base camp has been established in the foothills of Mt. PhD. Only the future will show if I scale the summit triumphantly, or freeze to death in a crevice somewhere. Boy, that metaphor took a bleak turn, didn't it?

Research is Easier if You Make It Up

2008-08-30T08:13:00.000-07:00

Yes, I know I haven't written in a good few months. In my defence, I have been kept quite busy by the research for my MSc project. Now that it is done, however, I'd like to share a few thoughts on my first real experience with research.

For this first post, I want to talk about what was perhaps the most humbling experience, and that was how tempting it was to cheat.

Like many research projects, my research was beset with problems. There were contradictory results, vague results, results that were the opposite of what we expected, without any indication why this happened. And often, when I got these results, I would think: "Gee, wouldn't it be nice if I could make up the results I wanted instead."

Now before you cast the first stone, let me be very clear: I did not fake any results, nor will I hopefully ever do so. But it got me thinking. How easy would it really be to fake results? For my MSc project, it would have been really easy. We do not have to hand in the code (although it is possible that the markers may ask for the code if they smell a rat, but let's assume for the sake of the argument that the faked results are completely convincing), so I would not even have to write the programs. I knew how the different experiments were supposed to work, so generating some convincing results would have been easy. The only people to see the results are my supervisor and a second marker. Of those two, only my supervisor could possibly spot fake results, because the second marker is not an expert in the field. If I had gotten any fake results past my supervisor, I would basically be home free.

You might be thinking that that's all very well for a Masters project, but surely in real research faked data would be spotted. But would it really? I agree that you would probably have difficulty faking a whole project: You'd be hard-pressed to answer questions from reviewers of your paper, and anyone trying to repeat the experiments would obviously get very different results. But what about just tweaking that one experiment that's poking a hole in your theory? That would again be very easy and would probably not be spotted unless somebody decides to repeat that exact experiment. If somebody later disproves your theory, well, you got a paper out of it, and nobody can really blame you for not spotting the flaw when all of your experiments were confirming the hypothesis.

Cheating can get even more subtle (choosing your experiments, skimping on controls, omitting results) and harder to spot. So the question is, given how easy it would be to cheat, what, other than personal integrity, is keeping scientists honest?

I believe curiosity and ambition are big factors. If you get results that contradict your hypothesis, you don't just say "Aw, crud", you get excited, because there's another problem to solve. Maybe this new problem will lead to an even bigger discovery than the one you were hoping to make. If you just fake the result, you'll probably never do really ground-breaking science. Worse yet, you might set back other scientists who will not pursue their theories because your "results" seem to have disproved them.

There's also training and your research environment. Never underestimate social conformity, which in this case is a good thing. If everybody around you is excited about research, as most scientists will be, you'll find it very hard to be the cheater, even if you're the only one who knows that your results weren't real. You'll want to be just as good as the rest, and if they can deal with contradictory results, then so can you.

Of course, this only applies if people the people in your research environment let you know about the problems they were having. They may be competitive people who feel that talking about struggling with research is equivalent to showing weakness. If that is the case, I recommend reading some of the many excellent blogs from scientists who are not afraid to talk about their research issues.

One thing that is clear is that you cannot just assume that every result that is published is automatically set in stone. If you think you have a better theory, test it, and if necessary repeat an experiment that has already been done. If enough people do that there might actually be a chance of demasking the cheaters. And that would be another great incentive not to cheat in the first place.

Conference Noises

2008-07-05T07:43:00.000-07:00

Would you say that a scientists first conference is like his first kiss*, a unique experience, never forgotten despite the fumbling and nervousness? Or is it more like the first time you went to a McDonald's: Sure, it's exciting and colourful, but after you've been a few dozen times you notice that they're all the same.

I couldn't say yet which of these is a better description, since I've only just experienced my first conference. Conference might be saying a bit much: It was a one-day symposium, and I didn't even have to leave the city.

Still, there were some memorable experiences to be made. Some were of the mundane variety: It seems that even in Britain, coffee break means coffee break, and not tea break. And don't even dare ask for water. Also, pinning your badge to your shirt is a fashion faux-pas; the correct place is discreetly on your belt.

The poster session was different from what I expected, because there were really only posters. Somehow, I always expected the poster creators to be standing next to them with proud smiles, eager to explain their science to anyone passing by. Not so here: There were posters, there were people reading the posters, and that was it.

The talks ranged from the fascinating to the mystifying. I've always been better at learning things from papers than at picking them up in lectures, so it's no surprise that I couldn't follow some of the more complicated topics. Listing to those lectures was not a waste of time, though, since at least now I know those topics exist and I can find out more about them (by reading papers!) if I want to.

The quality of the speakers varied (doesn't it always?) but some of them were very good, even inspirational. There are so many unsolved problems in bioinformatics, but these speakers were pointing the way to solving many of them.

Now for the more disappointing part of the symposium. No, not the food, that was alright. This is something that I'm willing to be not many attendees even noticed, but it's actually a huge statistical fluke if it was random: Out of 15 speakers, not a single one was female. I'm used to gender bias in my field, especially on the informatics side, but 0 out of 15? Seriously? You're telling me that there's not a single female professor that you could have invited to talk about her research?

At least many of the people in the audience were female, but jeez!

*With the first conference occasionally preceding the first kiss by a while.

I Can't Remember Where I Found This

2008-06-01T06:31:00.001-07:00

...but it's really neat. A science motivational poster:

Fun with Proteins

2008-05-22T04:15:00.000-07:00

I think almost anyone who has studied protein 3D structures would agree that it is a hard problem.

For the uninitiated, proteins are made out of chains of amino acids. Each amino-acid consists of a backbone and side-chains. The properties of the side-chains determine the structure that the chain will take on in 3D. For example, polar side-chains may repell each other, and hence tend not to be close. Another example is hydrophobic ("water-fearing") side-chains which need to be on the inside of the protein, away from the water molecules that surround a protein inside the cell.

There's more than one way to determine protein 3D structure. You can take the actual protein, crystallise it, shoot x-rays at it and work out the structure from seeing how the x-rays diverge. Or you can take all of the contraints mentioned above, encode them in a computational model and get a computer to crunch the numbers for you until it finds the optimal structure.

Or, well, you could just get people to do it by hand. For free. And have fun while they're doing it.

That's the principle behind FoldIt, a new game based on, yes, you guessed it, protein structures. The idea is that protein folding is much like a puzzle, and people love doing puzzles. So we let them fold virtual proteins, and evaluate the structures based on the constraints that we know about. Add some fun sounds when you're tugging and dragging proteins, a bonus for reducing the number of moves starting from the initial configuration, and an element of competitiveness in the form of an online ladder, and you've got a fun little game that people will actually want to play.

FoldIt is currently in open Beta and completely free to play. I've tried it out, and it really is a lot of fun. The online option allows you to chat with fellow folders while you're playing, and the interface is simple and intuitive. You don't even have to know anything about proteins; there's a very easy tutorial to get you up to speed, and you'll figure out soon enough what works and what doesn't.

It will be interesting to see if FoldIt players can come up with better protein foldings than a computer could. FoldIt is not the first instance of a "useful" game I've come across. I first heard about the concept, called human computation, in the context of a game called ESP that gets its users to label images with text. It makes you wonder what other arduous bioinformatics tasks we could turn into games (gene-finding anyone?).

A Cautionary Tale

2008-04-18T09:51:00.000-07:00

Remember the asteroid that's coming close to hitting the earth in 2036? Most of us probably heard about it at some point or other, made a quick calculation to see if we would be alive then, and forgot about it. If you looked into it a bit further, you found out that NASA only gave it a 1 in 45000 chance of hitting the earth; not nearly enough to worry about.

But then, shock! horror! what if NASA were wrong? People make mistakes. It's not as if they double-checked these results.* And it's not as if the smartest minds of the planet were working for NASA.** Who could we rely on to find out if there are any problems with NASAs calculations? Oh, I know. Let's ask a 13-year old schoolboy. It's good enough for The Times. And what do you know, the whiz kid places the risk at 1 in 450. Definitely in the "we should worry about this" category.

Except NASA never confirmed it, as stated in that article, and has in fact since denied that the boy's calculations are correct. Mark over at Good Math, Bad Math has a simple explanation why his assumptions are flawed.

I don't mean to put down Nico Marquardt, the boy who made the calculations. He obviously put a lot of effort into this, and it must have been pretty good work to convince so many people. Who I do want to put down is the many many papers who simply picked up this story, based on its "newsworthiness", seemingly with no fact checking at all. One phone call to NASA would have cleared it all up. Is this all we can expect from Old Media?

* They did.
** They are.

Open Reviews?

2008-03-29T05:38:00.000-07:00

The PLoS Computational Biology journal has an editorial about open access biology papers. Most of it is the usual Web 2.0 aspirations: We need more structured data, more semantics, more killer apps taking advantage of it.

Not that I disagree, necessarily, but I've heard it all so often that it's not really registering anymore. Maybe the killer apps will come along, and maybe they won't. Web 2.0 thinking has potential for science publications, but not everything that has potential gets realised.

But towards the end of the article, there was something that caught my attention: Open review.

Certainly rating a paper would seem reasonable when done by the Faculty of 1000 (http://www.f1000biology.com), but it is not a generally accepted practice. We challenge you to rate this Editorial too. In some ways the reluctance to rate a scientific paper is strange since we suspect the same person may well rate a book on amazon.com. Another option would be to add a Digg or del.icio.us button (http://digg.com or http://del.icio.us) to incorporate conventional media ranking tools into an academic journal Web site. If one finds an interesting article, one could immediately flag it with these tools.

Now this is interesting, because peer-review is a nearly sacred notion in science. Your paper has not proven merit until it has passed the peer-reviewing process and been published. It basically says, "other scientists thought this was worth reading".

So what happens if the reviewing suddenly become open to all? Well, it essentially become a popularity contest. Digg is a perfect example: The stories that end up on the front page are the ones that a lot of people liked. Very democratic, isn't it? Only it means that today the stories included "Pranks to pull on your Co-workers" and "The 10 Most Mismatched Movie Couples".

Oh, there were plenty of interesting stories as well, but my point is that what's popular is not necessarily what's best. By all means, let people comment and review papers, but make sure we know which reviews come from scientists, and which come from your average schmoe. I know it sounds elitists, but it's not much more elitist than demanding that the person who takes out your appendix have a medical degree.

Some Reading for your Easter Weekend

2008-03-21T07:18:00.000-07:00

I can't seem to find any interesting science stories today. (Maybe everybody's taking an extended weekend off?) So instead, I decided that it was time to round up some of the blogs that I read, but don't cite here so often.

FemaleScienceProfessor

Reading FSP makes me wish that I'd had somebody like her as a lecturer in my undergraduate years, rather than a series of boring white guys. (Not you Dr. S! Nor you Professor W!) She clearly cares about her students and loves her job, always a winning combination. Plus, I find her stories about careless misogyny in academia endlessly fascinating (in a horrifying, hope-I-never-act-like-that way).

Bioinformatics Zen

Not updated very often (kind of like this blog, huh?), but when it is, the articles are always worth reading if you're interested in the nitty-gritty of bioinformatics. Anything related to the field can come up here, whether it's the intricacies of programming, tips on how to get a PhD or humorous characterisations of stereotypical bioinformatics people.

Minor Revisions

This blog is a more recent addition to my reading schedule, but a charming one. Katie gives us a glimpse into the life of a biomedical engineering postgrad, and a very personal glimpse at that. I'm always impressed with people who are willing to share their ups and downs on their blog; it's a (skill? trait? strength?) I don't seem to possess. Katie likes having lots of subscribers to her feed, so go subscribe!

Talking the Talk

2008-03-15T09:14:00.000-07:00

And here's another post that I'm stealing from Of Two Minds: How to Give a Bad Science Presentation.

Of course, the advice they give applies to presentations given to fellow scientists, with the objective of introducing your work to them. And in that particular scenario, I probably agree with everything they say.

However, what if the aim of the presentation is not to inform, but to educate? In other words, what if you're giving a lecture? This is very topical for me, as I've just finished a course where students were giving presentations on papers, and I've had to do one of the presentations myself. We disregarded most of the rule they came up with. Were we right to do so? Well, let's look at the rules:

- Be able to give the presentation without support of the slides.

That one's a tricky one, because we were explaining a technique. In my part, I was heavily relying on examples to explain what was happening, and those examples were all on the slides. Could I have done it on the blackboard? Probably, but not without taking considerably more time. Still, we did rehearse a few times, so I think we could have brought the point across even without the slides. Overall, this rule holds.

- No outlines on the slides

Now this I can't completely agree with. Sure, giving an outline is slightly superfluous when you're repeating what it says on the slides. But if you're trying to get an unfamiliar topic across to an audience, reinforcement helps. During the presentations by other groups, I often found myself referring back to the slides when I hadn't caught what they were saying. I think outlines have their place in lecture slides.

- The less text the better

Two problems with this one: The first is the point that I just raised that it helps to refer back to the slide if you missed or were confused by what the speaker was saying. The second is that sometimes, the slides are made available to the audience as a study help before or after the talk. They effectively double as lecture notes, and so it is helpful if they contain enough detail so that you can understand them without the help of the speaker.

On the other hand, too much text can indeed be distracting during the presentation. So I'd advocate a compromise solution here: Keep the slides sparse, but provide detailed lecture notes at the end. Unless you're confident that your speaking ability is good enough to allow your audience to follow along easily and take notes while they do.

- Let us see the data

No argument there: Figures should be clear and big enough so that the audience can get a sense of what it is you're trying to show.

And Now For Something Completely Silly

2008-03-03T14:44:00.000-08:00

For his inaugural post at the new Of Two Minds blog, Steve Higgins chose one the most important scientific issues of our times: Could Superman's x-ray vision really exist?

There are three basic conditions that a superman x-ray system must meet to be plausible.

1. Transparency:
The rays must be such that all objects but lead are entirely or almost entirely transparent to them. Lead is always entirely opaque to the rays.

2. Color:
The rays and processor must result in Superman perceiving the same colors as would an Earthling viewing the scene in ordinary sunlight.

3. Exclusivity:
The rays must permit Superman, but not Earthling standing in line with the reflected rays, to see through normally opaque surfaces.

Steve wrote his article tongue-in-cheek, of course, but he bases it on a very real article that appeared in 1985 (!) in the journal Perception: "On the plausibility of superman's x-ray vision" by J.B. Pittenger.

And they wonder why everybody thinks scientists are a bunch of nerds...

History in our Genes

2008-02-23T05:05:00.000-08:00

Via the Wired Science Blog, I found this story about a study of variations in the human genome among different populations. While this has been done before, the new study shows how much data we can get just by looking at people's genes.

"The novel finding is the depth of the resolution we've gone to," said National Institutes of Health neurogeneticist Andrew Singleton, co-author of one of the papers in Nature. "This really lets you start moving towards locating individuals geographically. Previously, we've been able to look at the genome and say, 'This part is from Africa, this is from Asia. Now we can look past that and say, 'It's from this part of Africa or Eurasia.'"

Continued Singleton, "We can use these data to look at other areas of the genome that might have been under particular pressure for survival, and go from there to figuring out what the pressure is. One area that was highlighted was the genes responsible for digesting lactose. In countries where there's milk consumption, that one particular haplotype that allows more efficient lactose digestion has arisen."

They've not only been able to identify populations based on the genome alone, but they've also managed to model how humanity spread around the globe. Our history is encoded in our genes. How cool is that?

And all this was done using only the genomes of about a thousand people. Imagine what will be possible once we have even more data. And on the biology side, we will be able to repeat the same analysis for animals or plants. These are truly exciting times.

The full article that appeared in Science can be read here.

Evolving Evolution

2008-02-14T06:38:00.000-08:00

Okay, that title was too easy a pun. I apologise. But Wired blog had a couple of posts about where evolution (as in, "the theory of...") may be headed. The name for this extension of evolution is complexity theory. I bet that's going to be confused with chaos theory a lot.

What is complexity theory? Let's let Wired explain:

Not a religiously inspired revision -- intelligent designers need not apply. Nobody suggests that genetic mutation and natural selection aren't responsible for the evolution of birds from reptiles or humans from tree-swingers.

But a growing number of scientists do say that neo-Darwinian evolution doesn't explain certain jumps in biological complexity: from single-celled to multicellular organisms, from single organisms to entire communities.

The jumps -- saltations, in complexity parlance -- appear to be non-linear emergent phenomena, the result of networked interactions that produce self-organization at ever higher levels. From this perspective, Darwinian evolution is a mechanism of a higher universal law, perhaps even a variant on the second law of thermodynamics.

There's something that will strike fear into the hearts of mathematicians and computer scientists everyhwere: non-linear emergent phenomena. Everybody knows that that is the last thing you want to be modelling.

Personal fears aside, this seems like a reasonable extension. It is important to find out how certain networked properties emerge, whether they be multi-cellular organisms, or human societies.

Now, some people, including me before I read this article, might think that classic evolution might be all we need to explain these jumps. Just because we haven't figured it out yet, doesn't necessarily mean that we need a new framework, right?

Maybe, but the second Wired article that caught my eye gives more evidence that our current view of evolution may limit our ability to explain certain phenomena.

When Guy Hoelzer runs computer simulations of organisms living in the modeling equivalent of a featureless plain, he sees them break into different species -- even though there's no reason for natural selection to take place.
That preliminary but tantalizing finding hints at some larger phenomenon driving the mechanisms of neo-Darwinian evolution. Hoelzer thinks the phenomenon is self-organization: combine energy with complex networked interaction and order will emerge.

As with all experiments based on simulation, you have to take this with a grain of salt, but it's certainly more fodder for complexity theory.

One thing I don't quite agree with is that they try to relate it back to the second law of thermodynamics. ("The entropy of an isolated system not in equilibrium will tend to increase over time, approaching a maximum value at equilibrium.") That seems doubtful to me (speciation is entropy?) and also a bit premature. Save the unification with physics for when you've worked out the details of your theory.

It's Started Already

2008-02-07T03:42:00.000-08:00

Remember the "telepathic" DNA story? Well, what'd I tell you:

DNA Found to Have "Impossible" Telepathic Properties

[...]No one knows how individual DNA strands could possibly be communicating in this way, yet somehow they do. The “telepathic” effect is a source of wonder and amazement for scientists.

So we've moved from a "telepathy-like quality", that can be explained, to "impossible telepathic properties". I predict that we'll reach alien influences in two more steps.

I love Bad Science

2008-02-05T05:12:00.001-08:00

Let me clarify: I hate bad science, but I love Bad Science. Ben Goldacre cuts right through the bull and exposes charlatans, cranks and clueless journalists. In his latest column, he dissects a collection of recent articles that are full of bad science.

I know I’m wrong to care. On the BBC news site “crews were hopeful the 20m cubic litres of water could be held back and not breach the dam wall”. And that’ll be a struggle, since “cubic litres” are a nine-dimensional measuring system, so the hyperdimensional water could breach the dam in almost any one of the five other dimensions you haven’t noticed yet.

Seriously, go read it.

Interesting Article, Bad Title

2008-01-27T06:46:00.000-08:00

LiveScience had an article about a recent discovery in genetics made by some researchers in Maryland. They found out that DNA sequences that are the same are more likely to cluster together than those that are different.

Curiously, DNA with identical sequences of bases were roughly twice as likely to gather together as DNA molecules with different sequences.

[...]

The electrically charged chains of sugars and phosphates of double helixes of DNA cause the molecules to repel each other. However, identical DNA double helixes have matching curves, meaning they repel each other the least, Leikin explained.

It's an interesting discovery, although it remains to be seen how useful it's going to be. But that's not the reason I'm mentioning it here. What made me laugh was the title they gave the article:
DNA Molecules Display Telepathy-like Quality.

How long until the first New Age healer picks up on this and claims he can heal your DNA by telepathy?

Woe is PhD

2008-01-23T05:34:00.000-08:00

So I'm looking for a PhD place. Ideally, I'd like it to be at my current university, as the other universities in the UK that are good in my field are mostly located in London, and I don't like that city much. Nor can I really afford to live there.

I'd like it to involve Bioinformatics, but not require any wetlab work that I would have to do myself. There should also be scope for applying Machine Learning techniques. There's two areas of research that interest me. One is work in genetics, such as gene regulation modelling, or protein structure and function prediction. The other is to model biological systems at the macro level and predict how changes in the environment influence animal population size or plant growth.

I could also see myself doing a straight Machine Learning project without any Bioinformatics involved, but with slightly less enthusiasm.

I'd like to have a supportive supervisor, who I can talk to before I start my PhD and who will advise me on how to write up my project proposal. He or she doesn't need to be an academic superstar, but a fair number of puplications and at least some amount of recognition in either Bioinformatics or Machine Learning would not go amiss.

I'd like to get the chance to teach during my PhD, either tutorials or even lectures.

A scholarship would be helpful. If I don't get one, my parents could help, but I'd like to be able to support myself for once.

And tomorrow, I will meet with a potential supervisor who might be able to offer the place that has most of these characteristics. (I'm not sure about the teaching yet.) Fingers crossed!

Google's Newest Invasion

2007-12-21T05:09:00.000-08:00

Having cornered the search market, taken over Youtube and struck fear into the hearts of publishers, Google is licking its lips and looking for new targets. Next stop, Wikipedia.

The potential Wikipedia killer app that Google is developing is called knol, and it has of course sparked much discussion in the blogosphere, with bloggers outbidding each other in who can come up with the wittiest pun. (My favourite: Google Sets its Guns on the Grassy Knol). But puns aside, should Wikipedia be worried? Should we?

Yes and no. You have to remember that Google does not always suceed. Remember Google Video? That didn't take off. Google News? I don't know any News junky who uses that. And I don't see people rushing to pick up Google Talk.

Knol operates on a completely different model from Wikipedia. Instead of having a page for a topic that everybody can edit if they think they know better than the original author, in Knol, the author controls the content, and other users can only suggest changes. Also, you could have more than one "knol" on each topic. Google assures us that more popular (and hence, presumably, more accurate) knols will float to the top, but will they really?

Of course, you have a similar problem in Wikipedia, but it's mitigated because you can correct information: In knols, it seems you can only decide if you like the information or not.

Then there is the problem of orphaned topics: What happens if a "good" knol is abandoned by the author? Can we reuse it to start a new knol? Or is that information frozen in time forever, neither to be reused nor updated?

There are tons of problems that could bring knols to its knees. Of course, there are also reasons why it could succeed: There's less risk of vandalism like Wikipedia has seen, and there are more incentives for people to contribute (Google has agreed to share ad revenue if knol owners let them place ads on their pages).

Whether or not knol succeeds, I believe that the two models are different enough that they could even exist side-by-side. After all Encarta and the Encyclopedia Britannica both sold copies, didn't they?

Pay Per Use Bioinformatics Software

2007-12-13T07:38:00.000-08:00

Equinox, a company based in London and started by the Imperial College, has started offering access to some of its bioinformatics tools on a pay per use basis. Basically, they host the software on their servers, and you pay them each time you want to run a query. Their flagship product has to do with protein structure prediction:

The first product available will be Equinox's leading Phyre(TM) homology modelling and fold-recognition software. User research has shown that proteomics is an ideal target market with positive feedback from research, biotech and pharma audiences.

Yadda, yadda, the press release goes on to state how great this all is. Of course my knee-jerk reaction was: "Pay? Nevah!" But then I realised that you'd have to pay anyway. Previously, this product had only been available via a software licence. Now, that's fine for big companies and universities, who use it frequently. I suspect most of them will stick with the licence as well.

But for smaller companies, or an isolated researcher who may only need to use the software once in his career, the Pay Per Use model may actually have an advantage. Of course, it would be preferable if they'd offer the tool for free, but what kind of business model is that?

Unfortunately, I couldn't find out what the actual price per use was, or how it compared to the cost of a licence. How many uses before a licence would be cheaper? They need to get the balance right, otherwise people will opt for licences every time, and the PPU idea might well die a premature death, as far as bioinformatics is concerned.

Meanwhile, if you want to play around with a bare-bones version of Phyre, you can still go here. Just put on your academic hat first.

Open Source Genetics?

2007-12-06T06:27:00.000-08:00

Here's an interesting premise: Wired Blog has an article on "The Open Organism: Genetic Engineering in the Open Source Era". What would happen if you applied the principles of Open Source Software to genetic engineering?

Modularity in computer science has helped unleash crazy amounts of creativity, and new business models derived from user-generated content. Take Google Maps open-API. Or even HTML itself, which allowed users to create graphically sophisticated pages with no real programming knowledge. By putting the hard stuff into a black box and just letting you access what you need to know, user/producers have been able to focus on creating interesting content quickly and easily. What if, in the next decade, the same group of elite users/coders could do the same thing with corn?

They might be a little too optimistic, in my opinion. The allure of Open Source (or indeed any kind of hacking) is that anyone can do it. You don't need much initial investment, beyond the computer which you likely already have. Install Linux, get a GNU compiler of your choice, fire up the text editor and you're in business.

Genetic engineering is not like that. Or maybe it's exactly like that, but at a far grander scale. Instead of a computer*, you need a lab: You need pipettes, petri dishes, microscopes, solutions, PCR machines, microarrays, maybe even a gene sequencer. These things don't come cheap.

But let's assume for a moment that you have all of that already. Then you'll still need the things every programmer takes for granted, the libraries or APIs containing shortcuts to all the common tasks that you don't want to design from the ground up. As a genetic engineer, you'll need promoters, restriction enzymes and specialised vectors, each different depending on what you started with.

It is always possible that in the future, genetics labs and components will become as ubiquitous as computers and code libraries. I'm sure that when we were putting punch-cards into basement-sized supercomputers, open source software development seemed as far away as open source genetic engineering seems today. But the transition still took thirty years. I don't think we have to worry about it just yet.

*Or rather, in addition to.

Reviewing Fun

2007-11-15T06:13:00.000-08:00

One of the required courses for my Masters is a literature review on a topic which we can choose ourselves. So I've been reading lots and lots of papers (on Bayesian networks for modelling gene regulation, in case you want to know), and the more I read, the more I can see certain common themes emerge. Not common themes about the topic, mind you, but just about papers in general.

First of all, most papers can be summarised pretty easily. However, the summary I would come up with almost never matches with the abstract that the authors wrote. I realise that this is a function of their desire to show every aspect of the paper in their abstract, while I would summarise the most important ones (which might be subjective), but I'm still left with the feeling that most abstracts are not reflective of the gyst of the paper.

Secondly, too many papers overuse references. I've read papers where there's two pages of text and three pages of references. What especially ticks me off is when the mentions a topic and then gives five references for it. We don't need five references, we need one good reference. Maybe two if there are two particularly good papers and you can't decide. Five is just overkill.

Thirdly, and finally, I've noticed a distinct lack of detail in some explanations. Now this is something I can understand if you're trying to boil down a paper to two or three pages for publication. But if you're going to gloss over something, at least say that you're doing so. Also, since this is the 21st century, how about providing a link to your webpage where more detailed information can be found?

Undergrad Proves Important Theorem

2007-11-05T03:40:00.000-08:00

I read on the Wired blog that a 20-year old engineering student from the University of Birmingham has formally proved that a certain Turing machine model invented by Stephen Wolfram* is the simplest possible model.

Turing machines, for those not up on their theoretical computer science, are simple computing machines that Alan Turing conjectured were capable of calculating any computable function (he didn't say anything about efficency).

The usual model is that of a machine with a ticker tape with a sequence of symbols and a number of states. The machine looks at the tape one symbol at a time, and, depending on the symbol and its current state, decides what to do: Go forward one character, go back one character, overwrite the current character, or change state.

What makes this minimal Turing machine special is that it only has two states and three different symbols (sometimes called colours). The student proved that if you take away a colour or a state, it wouldn't be a Turing machine anymore.

Meanwhile, what important result did I discover during my time as an undergraduate? Oh, that's right, I discovered that you can live on nothing but pizza for a week...

*Yes, the same one who created Mathematica.

2007-10-24T02:53:00.000-07:00

Sandra Porter over at Discovering Biology in a Digital World has some computer woes from her Bioinformatics class to relate. All I can say is, I'm glad our Bioinformatics course is run by the Informatics department. Although so far, computer use for that course has been minimal.

No, I'm not Dead

2007-10-16T08:33:00.000-07:00

Just busy. It appear that while I was trying to catch up to uni work (imagine a hamster in a wheel, running as fast as he can, always convinced that he will get there eventually), Al Gore won the Nobel Peace Prize.

Now, some people are wondering why he won. There's two responses for that.

First, there's nothing more important to peace than stopping global warming. Pandagon has a more elaborate explanation of this point than I'm willing to give.

Second, get some perspective. The Nobel Peace Prize has gone to Yasser Arafat before, and you're complaining about Al Gore getting it? Now, if they were giving it to George Bush, then you'd have something to complain about...

Quantum Confusion

2007-10-08T05:44:00.000-07:00

For every computer scientist waiting for the first working quantum computer, there are two that are hoping that that day will never come, because it means that they will have to start developing quantum algorithms. Why is that a problem? Well, have a good look at Shor's quantum algorithm for integer factorisation. Yep, it's not pretty, in fact, I'm not sure I could find out how it works from that article.

Fortunately for me, and other lazy computer scientists, Scott Aaron has provided an easy-to-understand guide to Shor's algorithm. In the process of explaining it, he also debunks some of the most common myths about quantum computing:

Look: if you think about quantum computing in terms of “parallel universes” (and whether you do or don’t is up to you), there’s no feasible way to detect a single universe that’s different from all the rest. Such a lone voice in the wilderness would be drowned out by the vast number of suburb-dwelling, Dockers-wearing conformist universes. What one can hope to detect, however, is a joint property of all the parallel universes together — a property that can only be revealed by a computation to which all the universes contribute.

(Note: For safety reasons, please don’t explain the above to popular writers of the “quantum computing = exponential parallelism” school. They might shrivel up like vampires exposed to sunlight.)

On Participation During Lectures

2007-10-04T05:56:00.000-07:00

I don't speak up much in lectures. That's not because I'm shy (well, not mainly). Nor is it because I have nothing to say. No, I'd say that the main reason is lack of opportunity.

I should start off by explaining that the British lecture system does not encourage participation in lectures. Unlike the American system (I'm told), you're being lectured at, and 90% of the lecturers expect nothing more than polite attention from you. Very rarely are you actively being asked to participate.

That leaves only two opportunities for speaking up: Answering questions that the lecturer asks and asking your own. And to be fair, a lot of people do answer and ask questions. But I have a problem with both.

When it comes to answering questions, the problem is the sort of questions that get asked. Some of them are too hard: I've never been very good at thinking on my feet, so I seldom work out the answer to a hard question in a lecture. Most of the others are too easy: If the answer can be found one line further down on the slide, then I'm not going to bother to tell it to him.

Asking questions presents a different problem. I've found out over the years that I can find out by myself most of the questions that I would ask in the lecture if I look over the lecture notes later on. That knowledge, and a bit of pride, prevent me from asking them during the lecture.

I realise that all of these problems could be solved by changing my attitude, but it doesn't seem worth the effort. I don't get the feeling that more participation will give me a deeper understanding of the material. It just seems like so much wasted effort. And maybe that's one of the flaws of the British system.

Do WHAT for the Forest?

2007-10-03T08:15:00.000-07:00

This doesn't have anything to do with machine learning, but I just couldn't pass it up. Fuck for Forest* is a porn site for and by environmentalists. Yep, there really is porn for every niche on the internet. Here's how they describe themselves:

FFF are concerned humans, exploring the power of sexuality, to save nature and liberate life. We know a lot of people are interested in sexuality, including us. We want to have fun with sex, show natural people and collect money for saving nature! We think it is time to pay respect to nature, and give back with love!

I guess the end justifies the, er, porn?

* If you believe that this is safe for work, you probably don't deserve your job anyway.

Skynet can't be far off...

2007-10-02T08:12:00.000-07:00

From the scary side of AI comes this report from Wired: The US government is sponsoring research into tracking terrorists on the Web. Now that might sound innocuous, but according to the article:

The University of Arizona's ultra-ambitious "Dark Web" project "aims to systematically collect and analyze all terrorist-generated content on the Web," the National Science Foundation notes. And that analysis, according to the Arizona Star, includes a program which "identif[ies] and track[s] individual authors by their writing styles."

That component, called Writeprint, helps combat the Web's anonymity by studying thousands of lingual, structural and semantic features in online postings. With 95 percent certainty, it can attribute multiple postings to a single author.

From there, Dark Web has the ability to track a single person over time as his views become radicalized.

The project analyzes which types of individuals might be more susceptible to recruitment by extremist groups, and which messages or rhetoric are more effective in radicalizing people.

You can probably imagine what would happen if Writeprint were used to track down real terrorists and made a mistake. Better not type to heatedly in those flame wars. And stay away from "they set us up the bomb" jokes!

I'm not condemning the research as such: The idea of being able to tell who a certain text was written by is fascinating. But the application is worrisome. If this ever becomes a viable tool for counter-terrorism, it should be very strictly controlled.

The Wired article focuses on Writeprint, but a quick look at the website for the Darkweb project shows some more interesting projects:

The Terrorism Knowledge Portal is a search engine created specifically for the domain of terrorism research. [..] It aims to explore governmental, social, technical, and educational issues relevant to supporting intelligent Web searching in terrorism-related research. The portal supports searching of a customized terrorism research database with over 360,000 quality pages. In addition, it provides access to terrorism research institutes, government Web sites, news and presses, and a collection of useful Web resources for researchers.

A terrorism search engine? When is Google getting in on this?

A computer-driven natural language chatterbot that will respond to queries about the terrorism domain and provide real-time data on terrorism activities, prevention, and preparation strategies.

Real-time data on terrorism activities? Good luck with that.

Finally, to close on a lighter note, the Wired article quotes the National Science Foundations on some of the risks of the project:

"They [terrorists] can put booby-traps in their Web forums," Chen explains, "and the spider can bring back viruses to our machines." This online cat-and-mouse game means Dark Web must be constantly vigilant against these and other counter-measures deployed by the terrorists.

Right. Because obviously you instruct your spider to download any file it finds to your servers, execute it and maybe display a skull logo on your monitor as well while the virus deletes your files. I understand your desire to make your work sound glamorous by phrasing it in terms of a battle, but please don't pretend you don't know about basic security procedures.

2007-10-01T06:38:00.000-07:00

Three-Toed Sloth has a thoughtful examination of heritability in IQ, complete with disclaimer:

Attention conservation notice: It's long, and it's about something which makes eyes glaze over even as tempers flare up, and it's not funny at all. Worse yet, more is on the way. You could always read it later, but time spent now is gone forever.

The conclusion? IQ may depend much less on your genetic heritage than previously assumed. I find some of his arguments quite convincing.

Injustice in Open Source

2007-09-30T08:37:00.000-07:00

Rob Knop of Galactic Interactions has an article about misogyny in open source development.

Whereas the number of women in biology and chemistry has improved a lot in recent decades, the number of women in Physics creeps up much more slowly. Meanwhile, in computer science, the number of women has actually be declining. As for the absolute values of those numbers, one need only look at a picture of a Linux Kernel Developers' Summit to realize that within statistical uncertainty, the number of Y chromosomes is the same as the number of people in the picture.

Knop attributes this to the misguided belief of men in these fields that they are somehow smarter than the rest of the populace, and hence also smarter than women. It must be that way, because you don't see many women computer scientists, do you? It never occurs so them that their attitude might be the reason that many women don't try for a career in computer science.

While I've never worked in open source, I tend to agree with Knop's assessment. Now here's the million-dollar (or million-women working in computer science) question: How change this state of affairs?

Sure, it would be nice if open source developers would be more welcoming to women, but let's just assume that they're not that cooperative for a moment. There's certainly no way to force them, after all, these projects are their own to do with as they please. So how can women fight back?

What I would like to see is more open source projects started by women, with a quota of 50% or more women developers. Maybe a little community could spring up around it. Think of it as Sourceforge with more X chromosomes. There could be discussion forums for female software developers, and maybe a blog collecting instances of misogyny and of female successes. All it takes is a few women developers getting together, buying a couple of servers, coming up with a light-weight content management system, and you're off. No males required.

What's So Special about the Humanities?

2007-09-28T05:12:00.000-07:00

The school finally showed some mercy and moved us to a new lecture theatre for the Probabilistic Modelling class. Not only does this mean that we no longer have to suffocate in a small room, but the new venue is also in one of the old buildings of the University.

I've very seldom been in the old buildings, with the exception of special occasions like exams and graduations. Mostly, these buildings only house the Humanities as well as the School of Law and the School of Medicine.

It's a complete change of scenery. Where we get ugly buildings from the seventies and eighties, with sparsely furnished, functional lecture theatres, the other schools are housed in huge stone buildings with marble arches, balconies and skylights. I mean, I get it, they have been around longer than Science and Engineering, but seriously, would it kill the University to at least give us some lectures in nice surroundings?

Newsflash: Earth Simulator Exists, Written in Fortran

2007-09-27T06:41:00.000-07:00

This is hilarious: A professor from the University of Texas claims to have written a complete simulation of the planet Earth. By himself. In Fortran. Oh, and it runs in a matter of minutes. There are so many unbelievable, nay, impossible claims in his paper that it's hard to decide where to start in dissecting it. Fortunately, Marc Chu-Carroll over at Good Math, Bad Math has already done it better than I could.

I think that accomplishing that [incorporating quantum mechanics] would easily win him another Nobel prize, in addition to the Nobel for the non-quantum simulator. All he'd need to do is publish the data. Wouldn't that be a coup? A creationist professor from a diddly little school in Texas showing up all of the best and brightest physicists in the world, with something he did on a lark with one of his friends? Gosh, why do you suppose that he hasn't published this? Hasn't shown anyone the simulation? You think that maybe, just maybe, there's a reason for that?

I suppose that Granville, modest gentleman that he is, might not like the spotlight that these awards would generate. That must be why he only mentions this astonishing feat of brilliance in a piece of sloppy apologetics.

Assignment Time

2007-09-27T05:47:00.000-07:00

I just heard that the first assignment for this year will be about spam detection. Ironically, spam detection is exactly the topic that I was looking at late last year when choosing a project for the Google Summer of Code. Now if I had actually got off my behind, put together a proposal and done that project, this first assignment might be a breeze. Oh well.

Cooking for Engineers

2007-09-25T10:09:00.000-07:00

Ever been frustrated with imprecise recipes? A pinch of salt. What exactly is a pinch of salt? And does "whisk" mean whisking until solid, or until combined? Well, Cooking for Engineers promises to change all that with recipes made for engineers, by engineers.

Of course, we'd better hope they're not software engineers, otherwise we'd have to start cooking before we get the recipe, change ingredients after putting them in, and finish up by testing the meal on the dog.

Predicting Antibodies

2007-09-24T12:20:00.000-07:00

Here's some interesting news from ScienceDaily: Researchers at MIT have developed a computational model that can predict how given changes to an antibody can influence its effects.

Traditionally, researchers have developed antibody-based drugs using an evolutionary approach. They remove antibodies from mice and further evolve them in the laboratory, screening for improved efficacy. This can lead to improved binding affinities but the process is time-consuming, and it restricts the control that researchers have over the design of antibodies.
In contrast, the MIT computational approach can quickly calculate a huge number of possible antibody variants and conformations, and predict the molecules' binding affinity for their targets based on the interactions that occur between atoms.

The interesting bit is the prediction. It's presumably easy enough to model changes an antibody (essentially a protein), but predicting what the new version of the antibody will do is much harder. I can't wait to read the paper.

Breasts on Facebook?

2007-09-23T10:01:00.000-07:00

Brace yourselves. What I say next may shock you. Ready? Okay:

There were breasts on Facebook.

Don't panic! Breathe. Stay with me. The good news is, Facebook acted decisively and removed ALL of the...

Wait, hang on.

Okay, I'm being told they didn't remove all of the pictures of breasts. Apparently, they removed pictures of a woman breast-feeding and banned the woman in question from Facebook.

Now, even most prudes would find it hard to condemn breast-feeding as obscene. See, contrary to popular (read, male) opinion, breasts actually serve another purpose besides being sexually arousing. In deleting the pictures, Facebook was focusing on the nudity of said breasts, and many commentators are making the same mistake. Instead of saying "Facebook deletes pictures of woman's breasts", how about "Facebook deletes pictures of woman feeding her baby". That's all that that picture was about, a baby being fed. How much more innocuous can you get?

How Quaint

2007-09-22T06:09:00.000-07:00

I don't know about you, but I barely remember web searching without Google. So it's kind of quaint to see the humble beginnings of Google in this paper from around 1997, The Anatomy of a Large-Scale Hypertextual Web Search Engine:

Search engine technology has had to scale dramatically to keep up with the growth of the web. In 1994, one of the first web search engines, the World Wide Web Worm (WWWW) [McBryan 94] had an index of 110,000 web pages and web accessible documents. As of November, 1997, the top search engines claim to index from 2 million (WebCrawler) to 100 million web documents (from Search Engine Watch). It is foreseeable that by the year 2000, a comprehensive index of the Web will contain over a billion documents. At the same time, the number of queries search engines handle has grown incredibly too. In March and April 1994, the World Wide Web Worm received an average of about 1500 queries per day. In November 1997, Altavista claimed it handled roughly 20 million queries per day. With the increasing number of users on the web, and automated systems which query search engines, it is likely that top search engines will handle hundreds of millions of queries per day by the year 2000. The goal of our system is to address many of the problems, both in quality and scalability, introduced by scaling search engine technology to such extraordinary numbers.

What is this "Altavista" thing they keep talking about?

Some Thoughts About Recaps

2007-09-21T05:34:00.000-07:00

So today's course on Probabilistic Modelling started, not surprisingly, with a recap of basic probability theory. I'm not objecting to that, and there were clearly people in the class who have not done probability before. However, I had the same recap last year for a Modelling and Simulation course, and the year before that for an Artificial Intelligence course.

The repetitiveness of it got me thinking: Why waste time on all these separate recaps? Wouldn't a much more elegant solution be to organise one class of one or two hours a year which recaps probability theory, and everybody who needs it could go there?

Sure, there might be scheduling problems, but it still seems better than to subject everyone to the same recap, and force three different lecturers to teach the same material at roughly the same time. And I'm sure probability theory is not the only subject that recurs frequently in recaps.

First Impressions

2007-09-20T06:43:00.000-07:00

The view of the city is lovely from the 7th floor of that skyscraper where our first lecture was housed. On a clear day like today, you can see all the way to the hills in the distance. I know this because I've had lectures there during my undergraduate career. I certainly didn't notice any of this today, because I was crammed into a corner of the room, trying desperately not to suffocate as more and more people piled into the room.

There are three kinds of lectures in Informatics; those that are contain so much Maths that only joint CS/Maths students take them, those that focus on cramming as much knowledge as possible into our heads, and those that don't require much Maths or knowledge, but teach us how to actually solve problems. You can probably guess which is the most popular kind.

This lecture was one of those, and despite having a certain fondness for the mathematical courses, I'm not complaining. I merely wish that, at the next lecture, there will be fewer people and more space in the room for other things. Like, for a start, oxygen.

Obligatory Introduction

2007-09-19T06:36:00.000-07:00

If you're reading this, I can only assume that you took a wrong turn somewhere in the network of internet tubes. You misclicked, your mouse shattered on the ground, and you've ended up on my blog. Now you're staring at your screen in horror, desperately trying to remember the keyboard shortcut for the Back button.

Fear not, gentle reader. You're quite safe with me. I do not use this blog to present my Hentai collection to the world or introduce you to the latest in body modification (don't do a Google Image search!). I won't even post images of my cat, mainly because I don't have a cat. I promise that nothing on these pages will make you want to scoop your eyes out with a spork. Unless I link to Warren Ellis.

So what can you expect from this blog? Well, I am currently starting an MSc. programme with its main focus on Machine Learning at a wonderful British university, and I expect that will colour the Gaussian Noises that this blog emits. Look forward to reading about my take on current research, old ideas, and any weirdness that I might come across. Of course, I reserve the right to post off-topic once in a while. But I promise: No cats!