Archive for July, 2009

I would try to get more systematic about my posts from now on. For every two non-technical posts I would keep two technical posts.

This post would also be the first in a series of posts that in which I intend to write about some Visual Illusions only.

Before getting into subject of this post, it would be helpful to have a quick recap of the background.


The Blind Spot:

Consider a horizontal cross section of the human eye as shown below.


As seen in the above, the innermost membrane is the Retina, and it lines the walls of the posterior portion of the eye. When the eye is focused, light from the focused object is imaged onto the Retina. It thus acts as a screen. Pattern vision is caused by the distribution of discrete light receptors called rods and cones over the retinal surface.

Each eye has about 6-7 million cones, located primarily in the central portion of the Retina and they are highly sensitive to color. Humans can resolve fine details with cones as each cone is connected to its own nerve end. The vision due to cones is called Photopic or bright-light vision.

The number of rods is about 75-150 million andare distributed throughout the retina. The amount of details that can be resolved by rods is lesser as several of them are connected to the same nerve unlike in the cones. Vision due to rods is simply to give an overall picture of the field of view. Objects that seen in bright day light appear as color-less forms in moonlight as only the rods are stimulated. This type of vision is called Scotopic or dim-light vision.

As seen in the figure there is a portion on the retina which has no receptors (rods or cones), thus will not cause any sensation. This is called the blind spot.

Now because of the blind spot a certain field of vision is not perceived. We however do not notice it as the brain fills it with details from the surroundings or using information from the other eye.

The blind spots in both the eyes are arranged symmetrically so that the loss in field of vision in one eye will compensate for the other. This is shown by the figure below.

illustration-blind-spot[Image Source]

If the brain would not fill the lost field of vision with surrounding details and information from the other eye, then the blind spot would appear something like the black dot on the image below.

Blind Spot view

[Image Source]


Now that means, if you close one eye then you can indeed detect the presence of the blind spot as the brain would not have sufficient information about the lost field of vision (though it would be good enough for us to not notice it normally). The presence of the blind spot can be demonstrated by the simple figure below.

Demo of Blind Spot

Click on the above image to enlarge

Now enlarge the above image and close your right eye and focus your left eye on the X only. Don’t try to look at the O on the left. You’d just notice it at the periphery. The object of interest should only be X.

Now move towards the screen, at a certain point you will not see O in the periphery. If you go ahead of this point or behind it you’ll see O again, this specific point (a range actually) where you can not see O indicates the presence of the blind spot.


The Vanishing Head Illusion:

This leads to some interesting illusions, one of the most interesting being the so called vanishing head illusion.

As in the above figure. If the O is replaced by a head, the person would appear headless if the head falls on the blind spot.

Check the video below in full screen for best results.

View in Full Screen

We notice that Richard Wiseman on the left indeed appears headless and that field of view is filled up by the orange background when the blind spot falls.  Then he does something even more interesting. He uses a black bar and moves it up and down in front of his face.  Now instead of seeing the bar as discontinuous, the brain manages to show the bar as a continuous entity!


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A week ago I observed that there was a wonderful new documentary on you-tube, put-up by none other than author and documentary film-maker Christopher Sykes. This post is about this documentary and some thoughts related to it. Before I talk again about the documentary, I’ll digress for a moment and come back to it in a while.

With the exception of the Feynman Lectures in Physics Volume III, Six not so easy pieces (both of which I don’t intend to read in the conceivable future) there is no book with which Feynman was involved (he never wrote himself) that I have not had the opportunity to read. The last that I read was “Don’t You Have Time to Think“, a collection of delightful letters by Feynman written over the years (Note that “Don’t you have time to think” is the same as “Perfectly Reasonable Deviations”).

Don't You Have Time To Think

A number of people including many of Feynman’s close friends were surprised to learn that Feynman wrote letters and so many of them. He didn’t seem to be the kinds who would write the kind of letters that he did.  These give a very different picture of the man than a conventional biography would. Usually, collections of letters tend to be boring and drab, but I think these are an exception.  They reveal him to be a genius with a human touch. I have written about Feynman before, like I have covered points in an earlier post which now seems to me to be overtly enthusiastic. ;-)

Sean Caroll aptly writes that Feynman worship is often overdone, I think he is right. Let me make my own opinion on the matter.

I don’t consider Feynman god or anywhere close to that (but definitely one of my idols and one man I admire greatly), I actually consider him to be very human and some one who was unashamed of admitting to his weaknesses and who had a certain love for life that’s rare. I only am attracted to Feynman for one reason : People like Feynman are a breath of fresh air in the bunch of supercilious pseudo-intellectual snobs that are abound in academia and industry. A breath of fresh air especially for the lesser mortals like me. That’s why I like that man. Why is he so famous? I have tried writing on it before. And I won’t do so anymore.

I’d like to cite two quotes that would give my point of view on the celebrity-fication of scientists, in this case Feynman. Dave Brooks writes in the Telegraph in an article titled “Physicist still leaves some all shook up” February 5, 2003:

Feynman is the person every geek would want to be: very smart, honored  by the establishment even as he won’t play by his rules, admired by people of both sexes, arrogant without being envied and humble without being pitied. In other words, he’s young Elvis, with the Earth  shaking talent transferred from larynx to brain cells and enough sense to have avoided the fat Las Vegas phase. Is such celebrity-fication of scientists good? I think so, even if people do have a tendency to go overboard. Anything that gets us thinking about science is something to be admired, whether it comes in the form of an algorithm or an anecdote.

I remember reading an essay by the legendary Freeman Dyson that said:

Science too needs its share of super heroes to bring in new talent.

These rest my case I suppose.


The only other book of Feynman that I have not read and that I have wanted to read for a LONG time is Tuva or Bust! Richard Feyman’s Last Journey. Unfortunately I have never been able to find it.

Tuva or Bust! Richard Feyman's Last Journey

There was a BBC Horizon documentary on the same. And thankfully Christopher J. Sykes has uploaded that documentary on you-tube.

This is a rare documentary and was the last in which Feynman appeared. It was infact shot just some days before his death. This documents the obsession of Richard Feynman and his friend Ralph Leighton with visiting an obscure place in central Asia called Tannu Tuva. During a discussion on geography and in a teasing mood Feynman was reminded of a long forgotten memory and quipped at Leighton, “Whatever happened to Tannu Tuva”. Leighton thought it was a joke and confidently said that there was no such country at all. After some searching they found out that Tannu Tuva was once a country and now a soviet satellite. It’s capital was “Kyzyl”, the name was so interesting to Feynman that he though he just had to go to this place. The book and the documentary covers Feynman’s and Leighton’s adventure of scheming of getting to go to Tannu Tuva and to get around Soviet bureaucracy. It is an extremely entertaining film to say the least. The end for it is a little sad though. Feynman passed away three days before he got a letter from the Soviets about permission to visit Tannu Tuva and Leighton appears to be on the verge of tears.

The introduction to the documentary reads as:

The story of physicist Richard Feynman’s fascination with the remote Asian country of Tannu Tuva, and his efforts to go there with his great friend and drumming partner Ralph Leighton (co-author of the classic ‘Surely You’re Joking, Mr Feynman’). Feynman was dying of cancer when this was filmed, and died a few weeks after the filming. Originally shown in the BBC TV science series ‘Horizon’ in 1987, and also shown in the USA on PBS ‘Nova’ under the title ‘Last Journey of a Genius’

Find the five parts to the documentary below:

“I’m an explorer okay? I get curious about everything and I want to investigate all kinds of stuff”

Part 1

tatu1Click on the above image to watch


Part 2

tatu2Click on the above image to watch


Part 3

tatu3-2Click on the above image to watch


Part 4

tatu4Click on the above image to watch


Part 5

tatu5-2Click on the above image to watch


After I got done with the documentary did I realize that the PBS version of the above documentary was available on google video for quite some time.

Find the video here.


Michelle Feynman

Michelle Feynman

As an aside :  though Feynman could not manage to go to Tuva in his lifetime. His daughter Michelle did visit Tuva last month!


One of the things that has me in awe after the documentary over the last week is Tuvan throat singing. It is one of the most remarkable things that I have seen in the past month or two. I am strongly attracted to Tibetan chants too, but these are very different and fascinating. The remarkable thing about them being that the singer can produce two pitches as if being sung by two separate singers. Have a look!


Project Tuva : Character of Physical Law Lectures

On the same day I came across 7 lectures which were given by Feynman at Cornell in 1964 and were put into a book later by the name “The Character of Physical Law”.  These have been made freely available by Microsoft Research. Though some of these lectures have already been on youtube for a while, the ones that were not needless to say were a joy to watch. I had linked to the lectures on Gravitation and Arrow of Time previously.

Project TuvaClick on the above image to be directed to the lectures

I came to know of these lectures on Prof Terence Tao’s page, who I find very inspiring too!


Quick Links:

1. Christopher J. Sykes’ Youtube channel.

2. Tuva or Bust

3. Project Tuva at Microsoft Research


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I picked up these images at Wired two days ago and just could not fit in the time to put them up earlier.

There is a remarkable quote by Einstein

“Two things are infinite: the universe and human stupidity; and I’m not sure about the the universe.”

It isn’t definite to me if I liked this quotation earlier. But I am NOW wholly convinced that I love it. And this new found unequivocalness for it is due to the following:

As a kid, I used to have a large collection of encyclopedias. I remember reading about the Aral Sea in the picture atlas, and that it mentioned that there was increasing salination of the sea water and that it would disappear in some decades.

Over the years that time, we were fed with doomsday scenarios all the while. Like all the coastal cities would be soon under sea due to rising ocean levels, and that the Himalayas would soon be ice free etc etc. Over a period of time you get fed up with such idle talk and since you don’t see anyone giving convincing answers, you tend to believe that nothing like that is true. Secondly, the eternal optimist that I am, I just probably wished that what that encyclopedia said about the Aral was some “minor” problem.

I last read about the problem many years ago and after that never came across anything on it. And just a couple of days back was shocked by these images. I have only one word for them : Tragic!

The images are from 1973, 1987, 1999, 2006 and 2009. The two recent images were released by the European Space Agency, the earlier ones were taken by the United States Geological Survey.

Aral Sea - 1973

Aral Sea - 1973

Aral Sea - 1987

Aral Sea - 1987

Aral Sea - 1999

Aral Sea - 1999

Aral Sea - 2006

Aral Sea - 2006

Aral Sea - 2009

Aral Sea - 2009

[Image(s) Source : Wired Science]

The South Aral Sea, the remnant of the original lake that you can see to your left on the above image is also expected to vanish by 2020, thankfully the North Aral sea (the part on the right) has been saved due to a world bank funded dam project.

The Aral sea, once the world’s fourth largest lake at roughly around 68,000 sq kms is now just about one-tenth that size. The trouble started when it was decided by the Soviets in 1918 that the two rivers that drained into the Aral – The Amu Darya and Syr Darya would be largely diverted to the deserts to develop them into cotton growing lands. The Soviet plan worked and cotton became one of the most important exports from that area. By the 1960s massive amount of water was being diverted and the sea began to shrink steadily. And how that happened is spoken out loud by the pictures.

The death of the Aral is extremely sad. It’s death has left it’s once thriving fishing industry destroyed, the diverting of the rivers has mostly reduced the two rivers to a shadow of their former selves. The Aral served as a climate moderator in the largely arid lands there, it’s death might herald major environmental catastrophe in the region.

This is a prime example of what human stupidity could lead to and leaves me short of words to describe my anguish at the same.

It has a number of things to say:

Ignoring warnings which have clear proof is just plain stupidity. There is ample proof for example of climate change and its bad impact. For example, I have been visiting the Himalayas once every few years since 1991. And the change there is apparent, as compared to the 80s the glaciers that make up the Ganges have shrunk by several kilometers. I don’t know what the solutions are, nor am I comparing the Aral problem with it. I understand that the Aral was a different kind of a problem. Different because it was known to the Soviets that the lake would dry up from the start. Climate change can not be compared to it as we do not yet fully understand a number of things about it, so how effective the correctives would be is debatable. It would be for our good if that debate is settled soon with good and incisive scientific evidence.

It also is a comment on how totalitarian regimes can be dangerous. In such regimes, since a decision taken can not be opposed, such a decision could either lead to major dividends/progress as it would be implemented very rapidly or major catastrophe as was in the above case.  Soviet officials were aware that the Aral would sooner or later evaporate. In 1964 Aleksandr Asarin noted that :

“It was part of the five-year plans, approved by the council of ministers and the Politburo. Nobody on a lower level would dare to say a word contradicting those plans, even if it was the fate of the Aral Sea.”

Ofcourse he was right, there is rarely any way to convince or reason with or oppose supercilious totalitarian regimes even if their decisions are clearly suicidal. I am tempted to make a political comment on two present day countries (one a totalitarian state and one a liberal democracy) here, but would avoid the temptation.

Anyhow, the images above disturbed me enough to lose sleep.


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Here are a number of interesting courses, two of which I am looking at for the past two weeks and that i would hopefully finish by the end of August-September.

Introduction to Neural Networks (MIT):

These days, amongst the other things that I have at hand including a project on content based image retrieval. I have been making it a point to look at a MIT course on Neural Networks. And needless to say, I am getting to learn loads.


I would like to emphasize that though I have implemented a signature verification system using Neural Nets, I am by no means good with them. I can be classified a beginner. The tool that I am more comfortable with are Support Vector Machines.

I have been wanting to know more about them for some years now, but I never really got the time or you can say the opportunity. Now that I can invest some time, I am glad I came across this course. So far I have been able to look at 7 lectures and I should say that I am MORE than very happy with the course. I think it is very detailed and extremely well suited for the beginner as well as the expert.

The instructor is H. Sebastian Seung who is the professor of computational neuroscience at the MIT.

The course has 25 lectures each one packed with a great amount of information. Meaning, the lectures might work slow for those who are not very familiar with this stuff.

The video lectures can be accessed over here. I must admit that i am a little disappointed that these lectures are not available on you-tube. That’s because the downloads are rather large in size. But I found them worth it any way.

The lectures cover the following:

Lecture 1: Classical neurodynamics
Lecture 2: Linear threshold neuron
Lecture 3: Multilayer perceptrons
Lecture 4: Convolutional networks and vision
Lecture 5: Amplification and attenuation
Lecture 6: Lateral inhibition in the retina
Lecture 7: Linear recurrent networks
Lecture 8: Nonlinear global inhibition
Lecture 9: Permitted and forbidden sets
Lecture 10: Lateral excitation and inhibition
Lecture 11: Objectives and optimization
Lecture 12: Excitatory-inhibitory networks
Lecture 13: Associative memory I
Lecture 14: Associative memory II
Lecture 15: Vector quantization and competitive learning
Lecture 16: Principal component analysis
Lecture 17: Models of neural development
Lecture 18: Independent component analysis
Lecture 19: Nonnegative matrix factorization. Delta rule.
Lecture 20: Backpropagation I
Lecture 21: Backpropagation II
Lecture 22: Contrastive Hebbian learning
Lecture 23: Reinforcement Learning I
Lecture 24: Reinforcement Learning II
Lecture 25: Review session

The good thing is that I have formally studied most of the stuff after lecture 13 , but going by the quality of lectures so far (first 7), I would not mind seeing them again.

Quick Links:

Course Home Page.

Course Video Lectures.

Prof H. Sebastian Seung’s Homepage.



This is a Harvard course. I don’t know when I’ll get the time to have a look at this course, but it sure looks extremely interesting. And I am sure a number of people would be interested in having a look at it. It looks like a course that be covered up pretty quickly actually.tornado

[Image Source]

The course description says the following:

The amount and complexity of information produced in science, engineering, business, and everyday human activity is increasing at staggering rates. The goal of this course is to expose you to visual representation methods and techniques that increase the understanding of complex data. Good visualizations not only present a visual interpretation of data, but do so by improving comprehension, communication, and decision making.

In this course you will learn how the human visual system processes and perceives images, good design practices for visualization, tools for visualization of data from a variety of fields, collecting data from web sites with Python, and programming of interactive visualization applications using Processing.

The topics covered are:

  • Data and Image Models
  • Visual Perception & Cognitive Principles
  • Color Encoding
  • Design Principles of Effective Visualizations
  • Interaction
  • Graphs & Charts
  • Trees and Networks
  • Maps & Google Earth
  • Higher-dimensional Data
  • Unstructured Text and Document Collections
  • Images and Video
  • Scientific Visualization
  • Medical Visualization
  • Social Visualization
  • Visualization & The Arts

Quick Links:

Course Home Page.

Course Syllabus.

Lectures, Slides and other materials.

Video Lectures


Advanced AI Techniques:

This is one course that I would  be looking at some parts of after I have covered the course on Neural Nets.  I am yet to glance at the first lecture or the materials, so i can not say how they would be like. But I sure am expecting a lot from them going by the topics they are covering.

The topics covered in a broad sense are:

  • Bayesian Networks
  • Statistical NLP
  • Reinforcement Learning
  • Bayes Filtering
  • Distributed AI and Multi-Agent systems
  • An Introduction to Game Theory

Quick Link:

Course Home.


Astrophysical Chemistry:

I don’t know if I would be able to squeeze in time for these. But because of my amateurish interest in chemistry (If I were not an electrical engineer, I would have been into Chemistry), and because I have very high regard for Dr Harry Kroto (who is delivering them) I would try and make it a point to have a look at them. I think I’ll skip gym for some days to have a look at them. ;-)


[Nobel Laureate Harry Kroto with a Bucky-Ball model – Image Source : richarddawkins.net]

Quick Links:

Dr Harold Kroto’s Homepage.

Astrophysical Chemistry Lectures


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In the past month or so I have been looking at a series of lectures on Data Mining that I had long bookmarked. I’ve had a look at the lectures twice and I found them extremely useful, hence I thought it was not a bad idea to share them here, though I am aware that they are pretty old and rather well circulated.

These lectures delivered by Professor David Mease as Google Tech Talks/Stanford Stat202 course lectures, work equally well for beginners as for experts who need to brush up with basic ideas. The course uses R extensively.

data mining icon11Statistical Aspects of Data Mining


Course Video Lectures.

Course website.

Lecture Slides.


I’d end with some Dilbert strips on Data-Mining that I have liked in the past!

Data Mining






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