Professional and Personal Updates

I like using the qTip library to handle dynamic tooltips in my sites. I also use WordPress as my CMS-of-choice. Unfortunately, I found that they don’t, by default, play very nicely together.

I’ve attached my modified version of the qTip library (based on v.1.0.0-rc3 — current at the time of writing) which solves these issues.

Changes

Here’s what I had to fix in order to make this compatible:

1. There’s a bug in qTip that is still lingering. It only becomes an issue when using a recent version of jQuery. I had to make the modifications described in the link mentioned previously.
2. WordPress can’t give jQuery exclusive usage of the “$” variable, so all “$” references must be changed to “jQuery.” This script has been updated to reflect those changes.

Download

As promised, the modified Javascript file is available here: Wordpress-Friendly qTip Library (442).

Usage

To use this (or any) Javascript file in WordPress, you’ll need to add a few lines to the “functions.php” file of your active theme.

//make sure we've loaded jQuery
wp_enqueue_script('jQuery');

/*
* Adds the qTip javascript library.
*/
function addQtip() {
 wp_enqueue_script('qtip',get_bloginfo('template_directory') . '/js/jquery.qtip-1.0.0-rc3.min.js');
}
add_action('init', 'addQtip');

This script assumes that you’ve uploaded the attached Javascript file in the “js” directory of your active theme. If you have it elsewhere, you’ll need to adjust the reference in the “wp_enque_script” line.

And that’s it! You should now be able to use qTip within WordPress.

The Model-Based Background Correction (MBCB) R package is now included in “under-development” Bioconductor version (currently v. 2.7). There are two ways to use the version of MBCB included on Bioconductor:

1. Use R 2.12 (under development, at the time of writing) and execute the following two commands:

source("http://bioconductor.org/biocLite.R")
biocLite("MBCB")

2. Manually install the packages into older R version available here.

Sample data files are still available in the previous announcement.

As a part of my thesis in Keystroke Dynamics, I collected quite a bit of data on users typing a handful of passwords. I’ve decided to make this data available publicly for two main reasons:

1. It could lower the entry barrier to the field. Individuals interested in developing an algorithm but without the means to collect a significant amount of data will be able to use this data to experiment. Also, most researchers don’t have access to pressure-sensitive keyboards when developing their algorithms. This data will allow researchers to test their algorithms and consider this extra dimension of typing data.
2. I hope that this dataset could become a standard for the field of keystroke dynamics. One thing which I felt was substantially lacking in my literature review was any basis of comparison when analyzing different approaches. My hope is that researchers can, after developing an algorithm, measure their results on this dataset as a way to objectively compare performance.

Read more

Our Model-Based Background Correction (MBCB) R package (publication previously discussed here) has finally been submitted to Bioconductor.

MBCB is a software package aimed at background-correcting Illumina microarrays using model-based statistics. These methods include a Bayesian Method, MLE, Non-Parametric, Robust Multi-Array Average, and GMLE. Various normalization methods are also offered for post-processing.

Hopefully it will be available within the coming weeks. In the mean time, the current versions of the software are available here:

Update: MBCB is now included in Bioconductor 2.7. More detailed instructions are available here.

Sample data files: Signal.txt (375), Negative Control (475)

I worked on algorithms related to large-scale wireless sensor networks. Researchers are now envisioning sensor networks scaling countries, oceans, the planet, and even further. To be able to study and analyze networks of that scale, efficient algorithms have to be developed in order to process that level of complexity.

By using graph coloring algorithms on a Random Geometric Graph, I was able to develop an algorithm which can efficiently segment a graph into independent, dominating sets which can be used as “backbones” in a network. The idea is that, at any given time, only one backbone would need to be active which would allow for significant power savings without a significant decrease in network coverage.

A more complete description of the algorithms involved including an interactive simulation is available here. A write-up describing the paper and my solution can be found here: Wireless Sensor Networks (796)