Attending #CIDU2012 in Boulder Colorado

I am currently attending Conference on Intelligent Data Understanding (CIDU) here in Boulder.
The conference theme for this year is "Bringing Data and Models Together". The presentations consist of scientists from a wide variety of fields: Space Science, Earth and Environment Systems, and Aerospace and Engineering Systems. This is a great conference bringing researchers practicing data mining, machine learning or computational intelligence.
I am enjoying all the talks. The final agenda for CIDU 2012 can be found  here.

This is the first time that the CIDU is being held in NCAR, Boulder, away from its "home".

I presented yesterday. First day first slot: nice!!
It was about "Estimation and Bias Correction of  Aerosol Abundance using  Data driven Machine Learning and Remote Sensing ". Basically this paper discusses a general framework to choosing the optimal set of variables for machine learning/bias correction. Neural network was used, however one can insert his/her favorite Machine learning tool (SVM, DT, RF, GP etc). This involves massive number crunching for brute force search among all possible combination of variables. For 15 variable case, it has more than 32 thousands of combinations to try. I wonder if Bayes Net can help me to intelligently reduce the search.

Forgot my SD card, and it is cloudy+started to snow. While driving down the road, I saw nice mountains!! However, no pictures on this post!
(Happy Dashain!!)
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Hack Kinect to automate map making

In the video Maurice Fallon, an MIT researcher, describe a wearable sensor system that automatically creates a digital map of the environment through which the wearer is moving.
Using LIDAR, MS Kinect, IMU (battery), the user gathers the data which is processed on the fly on a base (computer) and a 2D map is built in real time.
This could be very useful in disaster response zone.

--> Read more:
http://web.mit.edu/newsoffice/2012/automatic-building-mapping-0924.html

#WinkAtTheMoon for Neil Armstrong!

There is a Blue Moon today, coinciding with a private family memorial service for Neil Armstrong, the first man to walk on the moon. In honor of Neil, NASA’s asking that you share publicly your photos of the moon tonight on Google+ and tag them with the hashtag #WinkAtTheMoon .
NASA will repost a gallery on the +NASA page of some of our favorite photos.

After Neil’s passing, his family stated: “For those who may ask what they can do to honor Neil, we have a simple request. Honor his example of service, accomplishment and modesty, and the next time you walk outside on a clear night and see the moon smiling down at you, think of Neil Armstrong and give him a wink.”

From... https://plus.google.com/u/0/102371865054310418159/posts/MkFPThjWsiP

Curiosity @ Mars #msl

Curiosity will be landing on Mars this week.
You can follow it @MarsCuriosity
We wish the curiosity rover a nice journey and (√) a smooth landing.


The mission will take about 8 months to reach Mars.
Lets start with a minute video.



Here is the animation of landing to the red planet.


The process on the ground


For updates follow http://twitter.com/#!/MarsCuriosity
http://www.nasa.gov/mission_pages/msl/index.html

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Statistical Physics of Human Mobility: Paper

Statistical physics help understand relating the microscopic properties of atoms and molecules to the macroscopic properties of materials that can be observed in everyday life. As a result, it is able to explain thermodynamics as a natural result of statistics, classical mechanics, and quantum mechanics at the microscopic level. [1]

By looking into the GPS information, from vehicles (collected) in Italy, Gallotti et al have performed a study to apply ideas of statistical physics to describe the properties of human mobility.

The human mobility is an interesting research question. Understanding of human mobility can be useful in urban planning, and to understand spread of epidemic. In addition, the authors suggest that such studies may also be useful to discover possible "laws" that can be related to the dynamical cognitive features of individuals.

The average speed variance (on the left), the distribution (on the right) can be decomposed as a mixture of Gaussian. Two Gaussians with mean speed of around 20 Km/hr and 45 Km/hr emerges. This indicates the distinct behavior of drivers. I find this to be an interesting decomposition.

The left figure shows the statistical distribution of the activity time. The presence of straight line indicates Benford's law. Figure on the right shows "total activity time". With the help of the "down time" i.e. the period for which the GPS is turned off, the authors suggest that at least three distinct peaks for full-time (~9 hrs), part-time (~4 hrs) jobs and night rest (~13 hrs). However, there is also one more peak around 1hr downtime. I guess the down-time for one hour peak shows short-term activities such as shopping behavior.

In the paper, using the travel time as a cost function, the authors show that the distribution between successive trips are indeed driven by an underlying Benford's law. The ranking of the the distribution of the average visitaion frequency may also help to understand how people organize their daily agenda. An interesting feature comes out when the average speed distribution for the recorded trip is decomposed as a mixture of two Gaussians: one with ≤ 5km. I think such characteristics distribution indicate the local constraint on the movements. Obviously, the motion is not free of constraints. The mobility data is strictly constrained by the road structures.
It would be interesting to see if there are such statistical phenomena as "phase transition" in such statistical law of human mobility.
This is an interesting paper. See [2].


At last, Why do we move from one place to another?
If we assume some aggregate effect on social scale; are we different than the gas molecules contained in a box? Moreover, it seems someone has to drive an extra mile since the system demands it!

References:
(Special thanks to Prof. Armando Bazzani for allowing me to use the figures.)
[1] http://en.wikipedia.org/wiki/Statistical_physics
[2] Towards a Statistical Physics of Human Mobility
Riccardo Gallotti, Armando Bazzani, Sandro Rambaldi
http://arxiv.org/abs/1207.5698