What is discussion all about?

I find intriguing, and to some degree disappointing, that so many people I know have such a hard time understanding what is a discussion about. The confrontation of ideas is more often than not perceived as a personal attack, directed at one's personal belief. People tend to misunderstand the meaning of Ad Hominen attacks, while their ideas are being confronted, in the two possible ways: they either think the confrontation is personal, or they use the ad hominen subterfuge to gain leverage in the discussion. This usually only leads to frustration (which leads to the dark side).

In this sense, I found this TEDTalk enlightening. Philosopher Daniel Cohen, explains what exactly is the kind of argument I'm ranting about, the conceptual metaphor called "Argument as War" (more on this here). In this metaphor, the debate is perceived as a combat between points of view (represented, of course, by the debaters).

To win the debate, is to reduce the opponent to either agreeing with your point of view, or making him/her doubt himself/herself enough to give up arguing against you. The alternative, of course, is losing the debate, in which case you are forced either to change your point of view, or agree that your argumentation is just not good enough to keep discussing.

The most valid point he makes is: what do we gain, cognitively, by winning? Nothing. Except, of course, for a short ego massage. The person that actually gains something is the one that loses the argument, in the sense that he/she learns enough of the subject to change his/her mind.

My only addition to this interpretation is that we also learn things about the subject during the debate, no matter what the outcome. Whenever we confront our ideas, perspectives and points of view with another's, we have the chance of learning whatever facts or impressions they have on the subject that led to their different conclusion.

I like to think I always learn something in a good discussion, no matter if someone wins, or it ties. This is what drives me, and why I love a good debate.

Positive negation, negative affirmation, or else?

It is easy to notice that our cognitive abilities have much less problem processing a positive than a negative assertion. That is, it is just plain easier to understand that something is right, than saying something is not wrong, specially in long discourses like a mathematical demonstration, a lecture, thesis or paper.

I think this is the reasoning behind most lists on how to fail at something: instead of saying "don't these otherwise you you fail", one says that "do these in order to fail", often in a satirical mood. Interesting examples include a book called How to Fail: The Self-Hurt Guide, and this quick guide to fail in biology fields.

Scientific fields don't fall short of this trend, though. For example, an article was published last year entitled How Not to be a Bioinformatician. Rob Hyndman, in his blog (which I recently discovered looking for some LaTeX examples), has the post How to fail a PhD.

To contradict this trend, he also compiled the straightforward guide called How to avoid annoying a referee (which anybody in the scientific business should read and follow), expanding from this post in stats.stackexchange.com (which, in turn, follows the mindset described so far).

Of course one does not necessarily need to write guidelines in a jocular manner. One such (must-read) example is 1990's article from Gopen and Swan called The Science of Scientific Writing, in which they convey that most of the effort in communicating a result lies on the writer, and should not be deferred to the reader.

I find myself amused by the positive "how to fail" guides, though. They can make the point they want to address, while using a lighter tone. Maybe these are better ways of planting the seed of understanding something important, without taking the change of being perceived as boring.

The prodigal wannabe returns

Pheew! It's been a while, and by that I mean my last post drafts are from 2009. The PhD wasn't a walk in the park, but hey... nobody said it would be easy, right?

Since I last tried to create a posting habit, I lost count of how many books and articles I've read (and for some of those articles, I lost count of how many times I read them) switched jobs, and started a Post-Doc Fellowship in another research focus, completely different from my doctorate focus. I guess changing is what I do best.

Logging modules for perl

I recently decided not to create a logging class of my own, since there must already be a lot out there. Not surprisingly this proved to be a correct decision.

I started looking for packages already available for Ubuntu, and found Log::Handler, Log::Log4perl, and Log::LogLite. I started drafting prototypes for them.

F: {alignments} -> {tree topologies} is continuous

If the title is not clear enough, it means that the function that maps alignments of genetic information to phylogenetic tree topologies is continuous. Well, kind of. It's a scientific proof, not a mathematical one, so it's based on evidence. But as far as evidence goes, it makes a compelling argument in favor of the thesis.

The paper is Wong, et al, 2008 (Science) "Alignment Uncertainty and Genomic Analysis". DOI: 10.1126/science.1151532 .

The relevance IMHO of this paper is not to estabilish that 'common sense' is usually correct (although it is important in its own matter to know when it's possible to infer things solely from ituition). The authors make the case for interpreting alignments themselves as random variables, and in doing so, they conclude that (in a very precise way) small variations in alignments produce small variations in tree topologies inferred from those alignments. More so, they indicate this result is robust in respect to methods of inferring phylogenetic trees.

For the mathematically inclined, they defined a metric in the set of alignments, a metric in tree topologies, generated alignments (via MCMC) that were near the reference alignment, and observed that the resulting trees all had topologies near the reference topology in the given metric.

This CPU intensive work was done for alignments of whole genomes, with several alignment techniques and tree inferring algorithms, with similar results for most cases. Definitely worth a read.

(UPDATE: fixed title typo)

Migrating mail filters from MUA to MDA

I like freedom of choice. A lot. I even like to be able to choose different alternatives for common tasks during the course of the day. Call it a whim, but when it comes to mail programs, I can never decide, really, what's the best for me. Maybe this is because I never found one that suits all my needs.

Nevertheless, I used to use KDE before I became an Ubuntu user, and from that time I kept my whole PIM suite in the KDE stack from Dapper 6.06 to Hardy 8.04. Enough is enough, and if I'm not using KDE I wasting precious RAM and cycles keeping all those libraries loaded just for kmail and amarok. I will surely miss amarok, and kmail was great but it's time to move on.

I wanted my PIM to sync both from my Palm PDA and my home desktop, laptop, and workstation at work. I'm not a big fan of third party cloud services, if I can't be sure who has access to my data, so I decided to go back in time and migrate from a fully-featured-MUA centered setup to a home cloud-like setup. Provided I can access my PIM data from whatever MUA I choose, I can use pretty much anything that talks IMAP (which is anything these days). I'll be truly free, then, to use a great GUI app for the daily routine, a light GUI app if I need RAM to do more resource intensive work, and good old pine (ressurected as alpine) if I need to access my mail remotely, via ssh.

The project is basically to store everything in the desktop an imap server in my desktop machine, POP3 accounts fetched by fetchmail , and fed to an MDA (procmail or maildrop).

I already had kmail store my mails in maildir format, so installing an imapd server was a matter of installing the package. After a brief poll in ubuntu-users mailing list I installed dovecot, and kept using kmail for a year or so later to pull my gmail mail via POP3. Now, I'm only accesssing my mail via IMAP since the upgrade to Jaunty, so my home cloud project is kind of stalled.

I'm in the process of testing gnome-pilot and opensync to keep my PIM and Palm PDA synced.

All that remains is to migrate my mail filters from kmail to an MDA (be it procmail or maildrop), and then use fetchmail to get mail via POP. This is where I'm stomped. So far, the only think I've found that resembles a solution is this perl script, except it's the oposite of what I want. I'm not even sure I can use it to create the reverse solution I need.

I've drafted a little perl script, and I'll probably have to do it from scratch since my needs are pretty modest. I have 100+ filters (too many to convert manually), but most or all are the kind of 'match and move to a folder' simple filters. I'm just trying to scratch my own itch, not create a general purpose application, so it's probabl y a matter of simple Perl, once I decide what exactly will I output to.

Since time is always in short supply, if anyone knows a way to convert filters to either procmail or maildrop, I'll be glad to hear it. I also welcome suggestions on which of these MDAs to use.

Stochasticity x Determinism in elementary math

Most of the problems I'm having in studying theoretical topics nowadays stem from the fact that I've had null measure content of Statistics as a math student. I've seen interesting topics as Analysis, and Differential Geometry in undergrad, and useful stuff like Linear Algebra and Computational Linea Algebra in the masters course, but absolutely no Probability and Inference.

Nothing related to Data (not the android) at all. And this is mostly my fault. I knew before the Masters Course I wouldn't be pursuing a PhD in Math, and I knew my Math teachers wouldn't deal with topics I would most likely need in the near future (the one I'm living now).

I'm chasing the lost time, with books in Bayesian Data Analysis and Inference, but I although most of the times I understand what I read, I never seem to grok it. I will, certainly, in time, but time is a commodity a grad student doesn't have - I need Statistics now. As well as Biology, Ecology, Evolutionary Biology, and (why not?) a little Computer Science. So it's fair to say I'm chasing the lost time, and losing.

Prior to leaving I've seen the creation of a new undergrad course, Applied Math, in my former University; it started with concentrations in Finances, Mathematical Biology and Scientific Computing, and it soon became obvious to the faculty that some basic knowledge in Probability and Inference were a must, so it's been introduced as obligatory courses for all concentrations. My former advisor there told me he thought it was overkill at first but soon (in the first year or so) realized how suitable it was.

This is why I think it's a terrific idea (definitely worth spreading) this nice Arthur Benjamin fella presented on this talk on TED.



Obviously I don't think you should rip out everything related to Calculus (I like it, after all :) ). If you are really to grok Probability, you need a strong base in Calculus (from integrals, to maximizing Likelihood functions). But a change in paradigm is definitely well deserved. Our modern western societies are still studying according to old rules. Rules that fit well to the time and reality where they were idealized, but probably are just outdated now. We are a new society on overdrive, with a new (still changing) set of moral rules, new problems and challenges, new perspectives, new age limits. Why stick with the 19th century education philosophies? At least let's realize it's about time to discuss if it's worth changing it. See also another TED talk on this subject.

This all also brings an old question I've always had: is it a good thing that education curricula should be centralized? It's good to know beforehand what people must (might? should?) have learned looking at their curriculum. I'ts useful for the teacher/professor to know what to expect the student to know, and this also applies to the student. I've been bitten before, when the pre-requisites for a course weren't clear. I also have first hand experience of how good and dynamic an improvised class can be, when given by a motivated (and skilled) professor. OTOH, I also have firsthand experience in improvised classes that sucked.

Which is the lesser evil?