connectome...

now in nyt, connectome is getting more attention... :)
http://www.nytimes.com/2010/12/28/science/28brain.html

in addition to the questions stated in the article, i got some more... 

1. structure at one point to functions? 

2. embodiment? each brain is connected to its own body... so probably we might

have to slice all the body... otherwise, the difference from different brain structure 

may be from different sensory input or different environment... 

3. overfitting? each brain has too many millions neurons and we can have "some" data 

(like a few data points in a very high dimensional space)...

consciousness meter.

http://www.nytimes.com/2010/09/21/science/21consciousness.html

this is awesome if we could measure the degree of consciousness like temperature...
they try to measure it based on the information amount in the subject's brain.
if lots of information flows, the brain is very conscious, otherwise, not so much.
i guess this idea or approach is closely related to Dr. Olaf Sporns' research,
which is to make a map of the brain network based on information flow.

anyway, recently, i see a lot of application of information theory to brain science
or even psychology...

maybe i am on the right track... ^^

Autonomous Vehicle Driving from Italy to China

Autonomous Vehicle Driving from Italy to China
http://spectrum.ieee.org/automaton/robotics/robotics-software/autonomous-vehicle-driving-from-italy-to-china

a very interesting article about an autonomous vehicle testing...

but, do you believe that we are going to have a real autonomous vehicle in, say, 10 or 20 or years?
or even in your life time? or even in your grandchildren's life?

i am sorry but without a big break through from a super genius, i respectfully doubt that....

Philosophy of Science.

"Philosophy of Science: a very short introduction"
by Samir Okasha

i learned a lot from this book, a really interesting and well written book.
if you consider yourself as a scientist (or to-be) and have never read any book
on 'philosophy of science', try this one. it's easy and worth....

In Search of Memory

"In Search of Memory" by Eric R. Kandel, winner of the Nobel prize.

a well written summary of neuroscience... with focus on memory.
and his life experience and wisdom.

i should have read this book earlier...
then it could have changed my research and even my life.

this is a must read to engineering or science students...

Henri Poincaré

http://en.wikipedia.org/wiki/Henri_Poincaré

"creativity and invention consist of two mental stages, 
first random combinations of possible solutions to a problem, followed by a critical evaluation."
according to Poincaré's two stages.

prosthetic fingers...

http://www.nytimes.com/2010/04/11/business/11novel.html
artificial hand... (prosthetic fingers). pretty cool!

Paul Erdos...

the followings are copied from http://en.wikipedia.org/wiki/Paul_Erdős

Other idiosyncratic elements of Erdős' vocabulary include:

• children were referred to as "epsilons";
• women were "bosses";
• men were "slaves";
• people who stopped doing math had "died";
• people who physically died had "left";
• alcoholic drinks were "poison";
• music was "noise";
• people who had married were "captured";
• people who had divorced were "liberated";
• to give a mathematical lecture was "to preach" and
• to give an oral exam to a student was "to torture" him/her.

Animation about Searle's Chinese Room Argument

http://www.mind.ilstu.edu/curriculum/searle_chinese_room/searle_chinese_room.php

one nice animation is way much better than 100 pages' text... ^^

R.I.P. Sam Roweis

http://www.huffingtonpost.com/2010/01/14/sam-roweis-nyu-professor-_n_421500.html

What a tragic loss to us all in machine learning including manifold learning.

Rest In Peace... Dr. Roweis....

Bees can recognize faces?

http://www.nytimes.com/2010/02/02/science/02bees.html?emc=tnt&tntemail1=y

They say bees can recognize human faces... Can you believe it? :)

I am not sure about it, but as Dr. Forsyth said, we definitely have to have animal studies

on face recognition research. That's for sure.

- H. Choi

HMAX

M. Riesenhuber and T. Poggio,

"Hierarchical Models of Object Recognition in Cortex,"

Nature Neurosciece, Vol. 2, No. 11, November 1999. pp 1019-1025.

They say simple cells and complex cells and

complex cells are responsible for invariant properties.

Invariance can be implemented as a pooling mechanism

where there are view-invariant units and view-tuned units.

Hierarchical feedforward network is considered and

the network is based on MAX rather than linear summation (SUM).

MAX is proved to be more robust and invariant than SUM.

That is, Hierarchical MAX has view-invariant object recognition ability

and is supported by biological (physiological) facts.

Anyway, I say, this is a very similar concept to ISA, but a little more flexible.

But this paper doesn't say much of implementation such as

how to get the simple cells or complex cells,

and how to construct the network structure.

See [1] for a specific implementation and examples of HMAX.

In [1], interestingly, simple and complex cells are not learned from data

but designed by second derivative of Gaussians.

[1] T. Serre and M. Riesenhuber,

"Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model,

and Implications for Invariant Object Recognition in Cortex,"

AI Memo 2004-017, CBCL Memo 239, MIT, July 2004.

- H. Choi

"models of object recognition"

M. Riesenhuber and T. Poggio,

"Models of Object Recognition,"

Nature Neurosciece Supplement, Vol. 3, November 2000. pp 1199-1204

It's a review paper about object recognition models.

Here is what I got from the paper.

"the distinction between identification and categorization is mostly semantic."

There are two kinds of models for object recognition.

1. view-based model: "objects are represented as collections of view- specific features"

It is something like ICA.

2. object-centered model: there is 3-D model of the object.

It's something like Geon theory.

They took "view-based model" in this paper.

Considering the speed of processing in the brain,

feedback model cannot be the prime model for object recognition.

It should be more like feedforward processing

where invariant properties can be obtained by hierarchical structure.

Making the connections between input image to higher level units as in Fig. 3,

the different tasks (categorization and identification) can be achieved by learning.

- H. Choi

Defense!!!

DISSERTATION DEFENSE OF

HEEYOUL "HENRY" CHOI

TITLE: Manifold Integration: Data Integration on Multiple Manifolds

Advisor: Dr. Yoonsuck Choe

2:00 p.m. Room 307 HRBB

- H. Choi