Category: miscellaneous

And from now, you can read the weblog of Tim Berners-Lee, the grandfather of WWW!!!! Welcome Tim!

Read this later: Edwin Land, “GENERATION OF GREATNESS
The Idea of a University in an Age of Science,” 1957

I just find it interesting:
Anat Levin, Dani Lischinski, Yair Weiss, “Colorization Using Optimization,” ACM Transactions on Graphics, Aug 2004.

For a successful technology, reality must take precedence over public relations, for nature cannot be fooled. [*]
You may like to read R. P. Feynman, Personal observations on the reliability of the Shuttle. I think every engineers can get something from this paper.

Happy birthday to me! (:

The long war between Palestinians and Israelians frustrates me. It is not fair to kill many people just for political disagreement between groups. I am against any radical solutions like erasing the Israel from the earth or throwing all Palestinians away from their homeland. I guess Muslims, Jews, and Christian can live with each other without any important and noticable conflicts. I am not a politician so I cannot analyze the politcal state and judge about the current situation very well (however, it seems that I’m not alone in this regard), but I just want to state my current opinion about the overal situation: let’s live with peace!
Aha … You may like to read this short piece of news.

Recently, I’ve become interested in multi-modal observers, e.g. particle filters. I like the idea of tracking different hypotheses simultaneously. In this viewpoint, we consider our estimation of the system’s state as pdf with arbitrary shape. In classical observers (e.g. Kalman filters), there is only a single unimodal hypothesis about the state. But in these particle filter-like methods, we consider different state estimations with different probabilities.
Well! This kind of viewpoint is not what was emphasized in my control theory courses. In those courses, everything were about proving the stability of the system (observer in this case) in a deterministic setting. We made no explicit attention to the probabilistic interpretation of the state estimation except that we assume that the measurment and state transition model are contaminated with a normal noise (so unimodal), and we evaluate the covariance of the state over time.
Now, I wonder what would happen to the control problem: Knowing the pdf of probable current states, what is the best control action? Suppose that one hypothesis says that your state is (10,0) and the other says that it is in (-10,0). If your controller is linear in states, what would you select? -K1*10 or -K1*(-10)?!
I must read more about these things. I am completely new in this field!

Iran’s president told something which made horrible reactions in the world. I don’t know what is going on in Ahmadinejad’s head, but I know that if I like my country I would not talk that way. I guess he intentionally wants USA invade Iran!

I want to propose an adversarial perturbation method for global optimization. I don’t know much about these perturbation methods for global optimization and I haven’t seen any theorem about them yet, but I guess this can be categorized as an adversarial perturbation method! I have not run any simulation yet since this morning that I found out that this method is as ridiculous as any other perturbation method! Anyway, I may do more investigations in the future:

Suppose that you can write your objective function in the additive form: f^k(X,\theta^k) = \sum^N_i(\theta^k_i g_i(X) ). The main objective function has all \theta^k_i equal to 1.
We are in step k and we have done a local search. So X^k is the local minimum of f^k(X,\theta^k).
Now as the perturbation, we can change \theta^k_i. If we change the sign of those \theta^k_i, we would get an “Alternating Trajectory Method” (named after whom?!). The conventional adversarial method tries to find the gradient of f w.r.t. theta and goes it uphill. I propose to change sign of a few \theta^k_i randomly with a probability of \mu in which \mu has the property that \theta^k_i will be 1 at the limit.
This way, we (hopefully) solve the primary objective function in the limit, we can change the minimum to a saddle point instantly (so, goes away from the local min very fast which is considered as an advantage of an adversarial method), and make a link between a trajectory method and a perturbation one.

Well! Just now, I finished writing three reviews for my Teaching and Research Methods course. Well! Those would not be the kind of reviews I like to do if they were real paper (I like to read cited papers too), but they are not that bad. Well! It is too late in the night, not eating any thing, and I should go home and cook something. Wow … a sample of hard working?!