Skip to main content

Our ability to speak

I've started reading the book 'Guns, Germs and Steel' by Jared Diamond. The author attempts to paint a picture as to why certain regions evolved more prosperously while certain didn't, why technological and cultural advancements occurred in one region while they didn't in another and in general, how our world came to be. It's a grand attempt by a single author in a single book but it's been an impressing journey so far. I'm still in the first chapter of the first book and my brain is already starting to go off on tangential questions.

A brief mention of the fact that the ability to produce sounds to communicate set me off on thinking how such a complex system evolved from rudimentary animal sounds. Human learning is largely biased towards visual and hearing cues. While visual cues such as writing or a sign language can be complex and do impart a large amount of information, even before one learns the ability to write or read one develops the ability to speak and listen. And as i am writing this, I am wondering if muscle memory is how we gain the ability to speak, memory which is reinforced as we speak and listen. This sounds (seems) like a very interesting field to me all of a sudden!

A rudimentary google search led to the a wiki page on the origin of speech, two articles on the human vocal apparatus and a brief summary of a review paper on the human vocal tract. And finally, a paper on the evolution of the human vocal apparatus in the cell journal.

On a different note, these blog posts are turning out to be a good place to tag and leave articles for future reference. There's a lot I need to be explore in science and while I can't read into them right now, the least I can do is mark them hoping for future me to look back at these posts.

Popular posts from this blog

Animation using GNUPlot

Animation using GNUPlotI've been trying to create an animation depicting a quasar spectrum moving across the 5 SDSS pass bands with respect to redshift. It is important to visualise what emission lines are moving in and out of bands to be able to understand the color-redshift plots and the changes in it.
I've tried doing this using the animate function in matplotlib, python but i wasn't able to make it work - meaning i worked on it for a couple of days and then i gave up, not having found solutions for my problems on the internet.
And then i came across this site, where the gunn-peterson trough and the lyman alpha forest have been depicted - in a beautiful manner. And this got me interested in using js and d3 to do the animations and make it dynamic - using sliders etc.
In the meanwhile, i thought i'd look up and see if there was a way to create animations in gnuplot and whoopdedoo, what do i find but nirvana!

In the image, you see 5 static curves and one dynam…

Pandas download statistics, PyPI and Google BigQuery - Daily downloads and downloads by latest version

Inspired by this blog post :, I wanted to play around with Google BigQuery myself. And the blog post is pretty awesome because it has sample queries. I mix and matched the examples mentioned on the blog post, intent on answering two questions - 
1. How many people download the Pandas library on a daily basis? Actually, if you think about it, it's more of a question of how many times was the pandas library downloaded in a single day, because the same person could've downloaded multiple times. Or a bot could've.
This was just a fun first query/question.
2. What is the adoption rate of different versions of the Pandas library? You might have come across similar graphs which show the adoption rate of various versions of Windows.
Answering this question is actually important because the developers should have an idea of what the most popular versions are, see whether or not users are adopting new features/changes they provide…

Adaptive step size Runge-Kutta method

I am still trying to implement an adaptive step size RK routine. So far, I've been able to implement the step-halving method but not the RK-Fehlberg. I am not able to figure out how to increase the step size after reducing it initially.

To give some background on the topic, Runge-Kutta methods are used to solve ordinary differential equations, of any order. For example, in a first order differential equation, it uses the derivative of the function to predict what the function value at the next step should be. Euler's method is a rudimentary implementation of RK. Adaptive step size RK is changing the step size depending on how fastly or slowly the function is changing. If a function is rapidly rising or falling, it is in a region that we should sample carefully and therefore, we reduce the step size and if the rate of change of the function is small, we can increase the step size. I've been able to implement a way to reduce the step size depending on the rate of change of …