Monthly Archives: July 2007

Reading List Updated

I have made a few adjustments to my “Reading List” page (see tab above).

ADDED to future reads:

Fiction

Non-Fiction

* – I already own the books with the asterisk.

I haven’t been reading as much for leisure this year. Most of my time has been committed to textbooks and papers in bioinformatics. I did manage to finish Freakonomics and you can check out my review, if you are interested.

Freakonomics: A Review

(cross posted on Zone Defense)

freakonomicsI just finished reading the book by Steven D. Levitt and Stephen J. Dubner.  It had been recommended to me by a couple of different folks but the most recent recommendation came from listening to the TWIT podcast where Leo Laporte recommended it as an Audio Book.  The TWIT podcast, in fact all TWIT podcasts are now recommending a different Audible book.  I had only had a cursory introduction to the topic of this book but when Leo mentioned it, he gave a very good overview that convinced me to pick it up the last time I was at the library with my kids. I no longer pay for a subscription with Audible, so I figured I might as well read it the old fashion way.

Steven D. Levitt is an economist who has a tendency to look with an economist’s perspective at everyday issues in society.  For example:

  • What do schoolteachers and sumo wrestler’s have in common? (Answer: Cheating, evidenced by the numbers)
  • How is the KKK like a group of real estate agents? (Answer: Thrive off of misinformation)
  • Why do drug dealers still live with their moms? (Answer: Because drug dealing is not nearly as profitable as conventional wisdom tells us)

Stephen Dubner helps to paint the answers to these questions through data collected and analyzed by Levitt in a very understandable way.  I enjoyed the book and would recommend it because it helps to provide a mindset to answering the tough questions a society faces. Of course, as a mathematician, I like the idea of looking at correlations between data sets and I especially like the care with which the authors explain the difference between a correlation between two factors and a cause-effect relationship between those two factors.

Like I said, I would recommend this book as a very interesting read to anyone who likes to question conventional wisdom.

I have two favorite sections in the book.  The first is the chapter on parenting (Do we really need parents?). In this chapter, the authors show the correlation between things like reading with your child and the performance in school.  In fact, they give a list of several things, of which half are strongly correlated with school performance (such as having a lot of books in the home). The other half are not correlated with school performance (such as reading with the child). It paints a very interesting picture, suggesting that it is not what you do as a parent as much as it is who you are.

In another section, Dubner gives an overview of Levitt’s research in explaining the surprising drop in crime rate over the last decade.  As recently as the late 80s to early 90s crime was on the rise with nothing but doom and gloom in the forecast but suddenly, without any explanation at all, crime began to decrease.  Levitt reviewed many of the theories that popped up trying to explain this change in trend but in looking at available data, none of those explanations could account for the sizeable change.  Suggestions like changes in policing strategies and the “bursting of the crack bubble” could not account for the dramatic reduction that was taking place.  Levitt proposes an idea that is intriguing but a little bit disturbing.  He suggests that it is abortion. Roe v. Wade allowed an enormous percentage of unwanted pregnancies to end in abortion instead of children being raised in lower income families with a mother who sees that child as an inconvenience.  Both of these circumstances can be strongly correlated to criminal behavior later in life.  Plus, it can be shown that a majority of abortions occur in these settings.  Thus, thousands, if not millions of potential criminals are no longer being born.  The authors are careful not to suggest that morality of abortion can be determined by this unpredictable, yet beneficial, result for society.  In fact, the rightness or wrongness of abortion has nothing to do with this result.  It simply happened and the data seems to support their claim.  Of course, there are all sorts of immoral actions that can lead to beneficial results for society.  For example, if the death penalty became the consequence for all criminal behavior even down to shoplifting, wouldn’t crime also decrease? Still, it is a very interesting point to be made and one I had never heard before.

Overall, I was pleased with the read.  It is fairly short, taking me only a few days to finish.  I look at difficult issues a little differently after having read the book, often asking myself about what the data would suggest, or simply wandering about the relationships between certain actions and the consequences.  Intuition might tell me one thing, but the actual results may be completely different.

Sizer – Windows App

I just stumbled across a little application that looks like it will come in handy, both for just screen managements on my widescreen laptop and for designing webpages for different sized screens.

Sizer is a freeware utility that allows you to resize any window to an exact, predefined size. This is extremely useful when designing web pages, as it allows you to see how the page will look when viewed at a smaller size. The utility is also handy when compiling screen-shots for documentation, using Sizer allows you to easily maintain the same window size across screen grabs.

Check it our here: http://www.brianapps.net/sizer.html

Beauty in Mathematics

Symmetries are nice. I like seeing interesting patterns arrive even if they are not natural features of mathematics, but contrivances of our chosen numerical notation.

Here’s a good example. I wouldn’t call it beauty in mathematics as much as a beauty of numbers, or even elegant numerical relationships:

The Beauty of Mathematics

1 x 8 + 1 = 9
12 x 8 + 2 = 98
123 x 8 + 3 = 987
1234 x 8 + 4 = 9876
12345 x 8 + 5 = 98765
123456 x 8 + 6 = 987654
1234567 x 8 + 7 = 9876543
12345678 x 8 + 8 = 98765432
123456789 x 8 + 9 = 987654321

1 x 9 + 2 = 11
12 x 9 + 3 = 111
123 x 9 + 4 = 1111
1234 x 9 + 5 = 11111
12345 x 9 + 6 = 111111
123456 x 9 + 7 = 1111111
1234567 x 9 + 8 = 11111111
12345678 x 9 + 9 = 111111111
123456789 x 9 +10= 1111111111

9 x 9 + 7 = 88
98 x 9 + 6 = 888
987 x 9 + 5 = 8888
9876 x 9 + 4 = 88888
98765 x 9 + 3 = 888888
987654 x 9 + 2 = 8888888
9876543 x 9 + 1 = 88888888
98765432 x 9 + 0 = 888888888

1 x 1 = 1
11 x 11 = 121
111 x 111 = 12321
1111 x 1111 = 1234321
11111 x 11111 = 123454321
111111 x 111111 = 12345654321
1111111 x 111111! 1 = 1234567654321
11111111 x 11111111 = 123456787654321
111111111 x 111111111=12345678987654321

HT: O’ Grandad

Science, Open or Closed

803099_lab_work I am being introduced to a side of research that is making me more and more uncomfortable.  I know it’s probably hard to believe but I’ve made it all the way through a Ph.D. program and didn’t see a lick of the hoarding of information, especially like I see it now in the field of biology (and bioinformatics).

I’ve seen a number of blog entries covering “Open Science” on the bioinformatics blogs that I keep up with. However, I’ve not been reading them in depth, only just skimming them.  I had the naive notion that all the people I work with would be wide open with their work, especially within the same lab.  Such is not the case.

On one side, I can completely understand.  Say, I have a colleague who is working on a particular project and puts in a significant amount of work to produce a certain conclusion.  There are many steps along the way for which that colleague developed the analysis to reach the conclusion.  However, this same analysis can be repeated on other work. Whether it is code or simply a methodology/protocol, that work is significant and can make a name for that colleague. Does he share this with me or others in the lab so that it can further my own projects? Or, do I give him my data and let him do his analysis. Surely, even if he handed over all his information, I would give him credit.  In a perfect world, we all receive credit for the work we do.  However, what if I don’t plan on giving him credit.  What if I want to make a name for myself and take his techniques produce work and give him no credit at all?

Deep down in my heart I am not a open source kind of guy.  I am full-blooded capitalist, through and through.  I believe in competition.  Of course, there are ethics that provide boundaries for such competition, but I believe that healthy competition can produce quality results.  I am not saying that competition and “open science” must be mutually exclusive, as it is possible to race to a result or conclusion with your information being fully available to the other party.  Ultimately, the ideal goal is the same for all scientists: true understanding of the physical world. Nevertheless, human nature will prevent scientists from a selfless pursuit of truth.

By entering the realm of full-time research I need to come to some decisions on just what those boundaries are.  There is a line somewhere between 100 percent open science and proprietary, commercial science that must be drawn and supported. So, now I’m going back to read those blog posts on open science and to decide just where I stand.