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If you don’t finish then you’re just busy, not productive

by Jack Simpson
written by Jack Simpson

One of the biggest realisations I’ve come to as part of my PhD, is how little people care about how I spend my time, they only care about what I am able to deliver. Yet in order to deliver, you need to finish. I was recently reading an article on this topic, and I think the advice can be best summed up as:

If you’re always starting interesting projects and not finishing, then no matter how hard you work, you’re just busy, not productive.

I find this message extremely compelling, because I frequently find myself starting new programming projects in my spare time. In a lot of ways they are not a waste – I definitely learn a lot from these projects and gain a new skill. Yet at the same time, because I move on to something else that interests me before I can finish my current side-projects, when I tell people about these projects, all I can say is:

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ResearchThoughts

Why I post my writing to Reddit

by Jack Simpson
written by Jack Simpson

Recently, I helped establish BioSky – a website dedicated to making medical and health research accessible to people without a scientific background. As we’ve been adding content to the site, I’ve been posting the links to multiple social networks including Facebook, Twitter, LinkedIn and Reddit.

If I had to pick only one platform to use, it would undoubtedly be Reddit. I’ve had this view for a while, but a recent experience in the /r/Biology subreddit, where I posted an article explaining prions really solidified my opinion.

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Thoughts

Why I refused a job offer to teach corporate programming workshops

by Jack Simpson
written by Jack Simpson

A couple of months ago I was approached by an organisation that provided programming training to staff at companies. They asked me if I was interested in becoming a trainer for them based on my experience running Software Carpentry workshops.

After seeking clarification and looking through the teaching materials, I refused.

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Hover Fly Mistaken For Bee
Data ScienceMachine LearningThoughts

Mistakes in machine learning datasets

by Jack Simpson
written by Jack Simpson

One of the things you realise once you start learning about machine learning is just how important a well-annotated dataset is that you can use for training. Your predictive model will only ever be as good as the labelled data you originally gave it.

A little while back when I was trying to learn how to use the deep learning library Caffe, I started watching a series of educational webinars that Nvidia had released for free. I can’t recommend these videos enough if you want to learn more about deep learning – I learned a huge amount from watching them. However, during this course, I noticed how one of the images used as an example of a “bee category” in the data looked like this:

Hover Fly Mistaken For Bee

Despite the black and yellow stripes, this insect is not a bee, it is actually a hover fly. I quickly found that images of these flies are misclassified all over the internet as bees – I only realised apparently because I’m one of the few bee researchers who was interested in machine learning.

I think that this misclassification is polluting a lot of the images labelled as containing bees in the major datasets that people use for training. I would urge any computer scientist who was interested in any project involving insect identification to be extremely careful and collaborate closely with entomologists before selecting images from a dataset to use for training.

This also raises the question: how do you test the accuracy of your model if you don’t know if the original labels have any basis in reality? I wonder how many other major categories in these datasets are mistakenly labelled because the people putting the data together don’t have the domain knowledge to make these kinds of subtle distinctions?

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ProgrammingThoughts

First impression of Swift (Apple’s new programming language)

by Jack Simpson
written by Jack Simpson

Swift is Apple’s new programming language, which promises to allow you to write iOS and OS X apps more easily than with Objective-C. Apple have released a free eBook titled “The Swift Programming Language” which provides an overview of the language. This is where I gained my main impressions on Swift. To give you a little background on myself: while I have had an interest in getting into app development for a while, at this point I haven’t touched any Objective-C – I am currently programming mainly with C, C++ and Python.

The first thing I thought when I started looking at the Swift code examples, is how much it reminds me of Python. Consider the first Python script most of us learned:

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Image ProcessingOpenCVThoughtsTips/Bugs

Using C++ or Python for OpenCV Programs

by Jack Simpson
written by Jack Simpson

When I first started learning OpenCV, I was working exclusively with Python. While I am still a huge fan of the language, today all of my OpenCV programs are written in C++. Why?

  1. Some of the deeper functionality of OpenCV has not been completely ported to Python (although hopefully the release of OpenCV 3.0 will fix most of these issues).
  2. Most in-depth textbooks on image processing and computer vision that cover OpenCV use C++ as their primary language. It was therefore easier to learn from these resources by adopting the language.
  3. A lot of the computer vision techniques I use (SIFT, machine learning, etc), are better documented in C++.
  4. Passing images back and forth between NumPy arrays has overhead that C++ doesn’t have to worry about.
  5. As has been suggested to me by Carl Bell, Python struggles to perform well with overloaded functions.
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Thoughts

Are university lectures still relevant?

by Jack Simpson
written by Jack Simpson

Lately, there’s been quite a few articles (such as here and here) discussing the use of the university lecture and its perceived shortcomings. The question is asked: is the lecture an unengaging relic from before the digital age? Are lectures really the best way to learn? I attended many lectures throughout my undergraduate studies, and have also tried out massive open online courses (MOOCs) from providers such as Coursera. To be perfectly honest, I don’t mind lectures at all. While they could be dull at times, they had the advantage of sitting you in a room where information was being fed to you whilst you were (mostly) shielded from the distractions of the outside world. Having a timetable of lectures was a powerful way to organise your learning (especially as you were paying for this education). It takes a lot of motivation to sit through a fraction of the lectures each week from online courses.

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ResearchThoughts

Essential Free Tools for Research

by Jack Simpson
written by Jack Simpson

I commenced my PhD in bioinformatics at the Australian National University about a month ago and thought I’d share some of the tools I’ve found absolutely essential. All of these tools are free to use (some do have paid plans, however at this point I haven’t had the need to sign up for them).

  1. Evernote + Evernote Web Clipper: Once I started to really get into using this tool, I couldn’t understand how I’d lived without it before. Now I can clip snippets from web pages for later reference, write notes from a seminar I attended or upload a Python script and have this all in one easily searchable (and tag-able) location. There’s also a plethora of phone/tablet apps which work really well.
  2. Wunderlist: While I tried to use Evernote for keeping to-do lists, I found that this simple yet powerful program (with support for PC, Mac, phones and tablets) worked extremely well for keeping track of my tasks.
  3. Dropbox: I do all my work within the Dropbox folder on my computer. This way, when I move between computers everything is synced and backed-up. While I have tried Google Drive before, the lack of support for Linux as well as issues with syncing have led me back to using Dropbox.
  4. Zotero: Zotero is a referencing manager with a browser plug-in which is fantastic at importing the bibliographic details and pdf of the paper from the web. It can also sync between computers (although you only get 300 mb of free sync space). While I have used the alternative referencing manager Mendeley before (which has 2gb free sync space and a brilliant program which enables you to annotate your pdfs), the Mendeley web import tool was so bad at importing the bibliographic details and pdfs of papers that I went over to Zotero.
  5. VirtualBox: If you find yourself having to use a lot of Linux-only tools (or you like the fact that you can install entire programs with multiple dependencies merely with the command ‘sudo apt-get’) but don’t really want to set up anything permanent , then VirtualBox could be the way to go. Its a free program that allows you to run a whole operating system on your computer within a window just like any other program.
  6. Coursera/EdX/Udacity: These websites offer free courses from great unis in everything from computer science to genomics.
  7. Stack Overflow/Biostars: Great websites for asking questions relating to programming or bioinformatics. If you can’t find anything after searching Google, then this is the place to post your question.
  8. Twitter: Although this can be a bit of a time-waster, I find that by following other researchers in my field and research organisations I can hear about new papers or tutorials.
  9. Google Plus: G+ may not be as popular as Facebook, but I find most of my programming friends tend to be quite active on it and if you join one of the programming communities it can be a great place to learn new things or ask questions.
  10. Google Scholar Alerts: Sign up to receive emails whenever the keywords you select are published in a paper.
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ProgrammingThoughts

Explicit is better than implicit

by Jack Simpson
written by Jack Simpson

Line two of the zen of Python reads “explicit is better than implicit” and until relatively recently I never truly appreciated the wisdom of those words. My change of heart stems from a series of Python scripts, where a large portion of my code dealt with automating and retrieving the results of a BLAST search using the fantastic BioPython toolkit. I was filtering my results based on an expect value of 0.04, which during my initial testing worked perfectly. However, as I wanted to make this value variable, I rewrote it into the program as a command-line argument. What I had not considered (but definitely should have) was how Python implicitly processes a command-line argument – as a string! I was never thrown an error – the program continued to work, so I assumed it was still doing the job just like in my tests. However, behind the scenes the filtering of my results had completely ceased to function.

The second (and in my opinion less obvious) issue of I have had with implicit design decisions relates to the qblast method from BioPython. As far as I could see, I was retrieving plenty of sequences, therefore the program must be working. However, my PI was rather suspicious of how so few sequences were being retrieved compared to the hundreds that were coming up when she would BLAST our sequence with the online interface. I searched numerous sites and went through the BioPython documentation but could find no mention of a sequence retrieval cut-off. Finally in desparation I went through the source code itself from the module that I was using and found this:

qblast code

A default limit of 50 unless overruled! A few extra characters and the problem was fixed, but until this discovery I was having serious problems later in the pipeline that I could not understand.

[Edit] After a Twitter conversation with Peter from the BioPython Project, I’d like to add that this issue is due to the default settings of the online BLAST tool I was calling, as well as potentially the settings of the BioPython wrapper. A good lesson in understanding the defaults of the tools (BLAST) your tools (BioPython) are calling!

These are two of the most recent examples that I have seen of how aware developers need to be about the implicit default values and methods that are present in the language and library they are calling. I hope my mistakes and learning experience will be useful to others who may come across similar issues.

This was originally posted by myself on the Australian Bioinformatics Network.

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