One Way to Describe What I Did Today

I've thought a bit about how best to describe what I did today. I spent a whole day, knowingly, adding more technical debt rather than two days removing existing debt. Now writing bad code is not linear and it's hard to judge but from what I can tell there is now twice as much debt as yesterday. And this is seen as progress.

Things Get Dark

The Crisis of Credit Visualized. It was quite good except maybe the explanation of leverage was a bit extreme, with 10,000 turning into 990,000.

Haptic Technology for the iPhone

One of the questions that often occurs is how could you improve on the iPhone without changing the form factor too much. One of the things that occurred to me was mentioned was a keyboard. But it would be nice to have a keyboard that worked as well as the current one does (well better) so that it could work in both portrait and landscape mode. Is that crazy talk?

Well the idea would be to use electroheological fluid to create a keyboard dynamically. I came across some discussion on how to use it for braille and it should work equally well for creating a QWERTY keyboard - although power usage maybe a problem. For more technical discussion (and I haven't really read it except for the practical use in virtual reality gloves), Haptic Interfaces Using
Electroheological Fluids.

You Should Read This

Tony's letter to the Medical Board of Queensland.

Some nice programming with Jenabeans

Writing out SIOC triples using Jena + Jenabean "Jenabean’s model connected programming model makes this easy, using interfaces that declare each of the vocabularies as a set of methods." The code example shows how you can piece together (using a fluent interface) different vocabularies:
Thing thing = new Thing(m);
thing.at(uri1).
as(DCTerms.class).
title("Creating connections between discussion clouds with SIOC").
created("2006-09-07T09:33:30Z").
isa(Sioc.Post.class).
has_container(thing.at(uri2)).
...

CloudBurst

This is software that uses MapReduce to handle massive amounts of sequence data and reassemble it. It includes an explanation of the algorithmic side of things. I wonder how it compares with using de Bruijn graphs.

With the passage of time

Who would've thought that RoboCop was overly optimistic.

First, it was assumed that people would still be living there. Second, that they'd still be able to make things like robots. And third, that cars in America would get 8 mpg and not 1-3.5 mpg.

URLs for 10 minutes

Protect Your Site With URL Rewriting seems a little bit of a mad suggestion. They are basically suggesting that you change your application's URIs every 10 minutes to prevent XSS and XSRF (Cross Site Request Forging).

We could mitigate much of the risk of these vulnerabilities by frequently changing our URLs—not once every 200 years but once every 10 minutes. Attackers would no longer be able to exploit application vulnerabilities by mass e-mailing poisoned hyperlinks because the links would be broken and invalid by the time the messages reached their intended victims. With all due respect to Sir Tim, while "cool" URIs may not change, secure ones certainly do.

The negatives seem vast and I wonder what this is really trying to solve, as they say at the end:

An automatically expiring URL can still be exploited by an attacker with access to a Web server of his own. Instead of sending out malicious hyperlinks that point directly to the vulnerable page, he can send out hyperlinks that point to his own site. When his site gets a hit from one of the phished e-mails, it can contact a landing page on the vulnerable site to obtain a valid time stamp and then redirect the user accordingly.

If you are a REST advocate then maybe a quick read over, The Resource-Oriented Architecture in Action, may soothe you (from the excellent book, RESTful Web Services).

A Quick Survey of Bio-Ontologies

The other day I was trying to find a paper that talks about the need for ontologies in biology that dated sometime around the early 90s - way before OWL and the Semantic Web. I couldn't find the paper I was thinking about, but here are some others that are pretty good that seem to follow at least the same themes:

"Ontologies for molecular biology": "Molecular biology has a communication problem. There are many databases using their own labels and categories for storing data objects and some using identical labels and categories but with a different meaning."


"Ontological Foundations for Biology Knowledge Models": This one was good because it talked about processes and transformations which is where the rules and inferencing stuff comes in.

"Toward Principles for the Design of Ontologies Used for Knowledge Sharing": While not specifically about biology, this is probably the most cited paper and it's what I often think about when you're explaining ontologies and the process of improvement that occurs when you make one.

"Bio-ontologies: current trends and future directions": This covers the process and the parts that make a good ontology on the web. I guess the key for the use of the Web for ontologies is a means to share knowledge. It also has a good history of ontologies going back to the 1600s.

And this one is one of the original GO papers, "Gene Ontology: tool for the unification of biology".

Plastic Ocean

"Sailing the Great Pacific Garbage Patch" has some interesting points including that there's no such thing as a free range fish (because the oceans are so polluted) and that the island of plastic is more a concentration of plastic - which sounds far worse as far as the environment is concerned.

Adding Some Herbs to Open Databases

"Harnessing the Crowd to Make Better Drugs: Merck’s Friend Nails Down $5M to Propel New Open Source Era"

Friend, 54, is leaving his high-profile job as Merck’s senior vice president of cancer research, after having nailed down $5 million in anonymous donations to pursue this vision at a nonprofit organization getting started in Seattle called Sage.

Sage is built on the premise that vast networks of genes get perturbed, or thrown off-kilter, in complex diseases like cancer, diabetes, and obesity. Scientists can’t just pick one faulty gene or protein and make a magic bullet to shut it down. But what if researchers around the world capturing genomic profiles on patients could get all of their data to talk to each other through a free, open database? A researcher in Seattle looking at how all 35,000 genes in breast cancer patients are dialed on or off at a certain stage of illness might be able to make critical comparisons by stacking results up against a deeper and broader data pool that integrates clinical, genetic, and other molecular data from peers in, say, San Francisco, New Haven, CT, or anywhere else.

Some big names have signed on for the early incubating phase. Besides the full-time efforts of Friend and Schadt, the Sage board includes Nobel Laureate Lee Hartwell of the Fred Hutchinson Cancer Research Center; Paul Ramsey dean of the School of Medicine at the University of Washington; Richard Lifton, the chairman of genetics at Yale University; and Hans Wigzell, director emeritus of Sweden’s Karolinska Institute. For insight into how to apply lessons from the open-source computing world, the board has brought on John Wilbanks, the vice president of science at the San Francisco-based Creative Commons.

As with any far-out vision, plenty of things can derail it along the way. What if researchers use different gene analysis machines, from Affymetrix, Illumina, or Applied Biosystems? How will Sage reconcile differences in how experiments are designed by different scientists? How will researchers be enticed to let go of their precious data, currently stored on password-protected hard drives and servers? How will Sage manage the intellectual property that arises from the database? Why would companies want to participate and run the risk of putting valuable proprietary data out in public? How will this get financed?

Some of these things Friend can answer, and some still need to be worked out. Software is already making it possible to manage differences between the various instruments scientists use, and deal with the differences in experimental design, Friend says.