A Movie I Like(d)

It was:

“A Few Good Men”

Too sorry, it was an American movie, and to subsidiarily note, that it was in English.


For obvious reasons, there will not be the “A Song I Like” section, as usually has been the case for some few years.


I have had something to write about the occupants of the car on the 15th August 2017, i.e. 15th August, popularly aka Independence Day (and in my mind, the Freedom Day), having the Government of India Registration Number: 16 B 1212AE.

Like my enemies [^], almost all of them Indian Army Brats, these Government of India-Granted Authority Idiots had had an association with the Indian Army, or at least English-medium schooling, I suspect. Also with Psychology.

I only wonder what effect the Indian Army produces on the children of its own Personnel (aka Human Resources).

Do they [I mean the Indian Army assholes writing for, say, a Pune daily, whether in English or in Marathi or better still, in Hindi] encourage physically attacking someone while at all times legally protecting themselves?

What prompts an Indian Army Officer to play the game with an ordinary Indian citizen? What prompts his assistants physically to attack an ordinary Indian citizen? and for BOTH (or ALL) of them to get away with the hassle? The Indian Government Subsidized “Raashan” i.e.  ration, esp. of Alcoholic Drinks, always available in the Camp Area?

Do they really digest the fragments of the glass bottle as were the stories told me by my (not so trustworthy, by now I find out) friend who was an Army Brat produced by the Indian Army?

Yours sincerely (and not physically attacking (not of his own volition, and not without provocation, unlike the Indian Army and its Brats (and the culturally associated cohorts like the “convent-educated” easily attacking its own, anyway))),

 

–Ajit R. Jadhav

 

Advertisements

An Assault Spurred by an Indian Army Officer (?) on a Civilian Professor

I was assaulted by (what looked like) the assistants of (what looked like) an Indian Army Officer on 15th August 2017 at around 6:35 PM, following a minor argument as to who had the priority for withdrawing money at an ATM. The attack was spurred on and encouraged by the Officer.

The actual assaulters were the assistants, but their boss did first physically blocked my way to the ATM machine and then, after I was done with withdrawing money and thus was out of the ATM cubicle, also shoved his stomach against me. There were threats, followed by some 5–6 kicks and slaps by the driver of his vehicle and his assistant.

The argument prior to the assault might have been captured by the ATM CCTV camera, though I am not sure the words were.

The matter degenerated because I refused to allow me the satisfaction that they actually did have the priority—and the boss didn’t, anyway.

I am not going to register a police complaint, because being an intelligent middle-class Indian, I know what happens to cases such as these.

The grounds for my suspicion that he must have been an Indian Army Officer are:

  1. His car was a black Ambassador, with the registration number [available on request] written in white letters against a black background, with a black cap placed on the central indicator on the bonnet.
  2. All were clean-shaven and sporting short hair.
  3. The boss was wearing white shirt and black slacks, was tall and well-built, must be in his late 30s or early 40s, though I felt for sure that he had his moustache and hair dyed with a black dye
  4. The assistant was wearing a pink shirt; the driver was short and stout and also had a pouch, and was in not the whitest-shade but well-ironed white uniform, also with moustache
  5. The boss was good in English, and showed typical prejudices of the Indian Army (or Indian Government Employee) people. For instance, when I enquired something like: “Now why don’t you finish your business fast so I can withdraw my money,” the first thing he noticed (and indirectly argued about) was “business.” He also said I was using “unparliamentary” words at a time I had said none. Upon his numerous promptings like this, finally, the worst thing I said—not to him, but including in my general comment—was a suppressed: “idiots.”

The incident is past me. It must be. I am an Indian citizen, that’s why. The law of the land is not always objective, and the execution and implementation is even worse. And, Army is powerful. I cannot seek justice.

But I can, and will, talk about it, at an opportune time.

For obvious reasons, there will be no section on a song I like.

Machine “Learning”—An Entertainment [Industry] Edition

Yes, “Machine ‘Learning’,” too, has been one of my “research” interests for some time by now. … Machine learning, esp. ANN (Artificial Neural Networks), esp. Deep Learning. …

Yesterday, I wrote a comment about it at iMechanica. Though it was made in a certain technical context, today I thought that the comment could, perhaps, make sense to many of my general readers, too, if I supply a bit of context to it. So, let me report it here (after a bit of editing). But before coming to my comment, let me first give you the context in which it was made:


Context for my iMechanica comment:

It all began with a fellow iMechanician, one Mingchuan Wang, writing a post of the title “Is machine learning a research priority now in mechanics?” at iMechanica [^]. Biswajit Banerjee responded by pointing out that

“Machine learning includes a large set of techniques that can be summarized as curve fitting in high dimensional spaces. [snip] The usefulness of the new techniques [in machine learning] should not be underestimated.” [Emphasis mine.]

Then Biswajit had pointed out an arXiv paper [^] in which machine learning was reported as having produced some good DFT-like simulations for quantum mechanical simulations, too.

A word about DFT for those who (still) don’t know about it:

DFT, i.e. Density Functional Theory, is “formally exact description of a many-body quantum system through the density alone. In practice, approximations are necessary” [^]. DFT thus is a computational technique; it is used for simulating the electronic structure in quantum mechanical systems involving several hundreds of electrons (i.e. hundreds of atoms). Here is the obligatory link to the Wiki [^], though a better introduction perhaps appears here [(.PDF) ^]. Here is a StackExchange on its limitations [^].

Trivia: Kohn and Sham received a Physics Nobel for inventing DFT. It was a very, very rare instance of a Physics Nobel being awarded for an invention—not a discovery. But the Nobel committee, once again, turned out to have put old Nobel’s money in the right place. Even if the work itself was only an invention, it did directly led to a lot of discoveries in condensed matter physics! That was because DFT was fast—it was fast enough that it could bring the physics of the larger quantum systems within the scope of (any) study at all!

And now, it seems, Machine Learning has advanced enough to be able to produce results that are similar to DFT, but without using any QM theory at all! The computer does have to “learn” its “art” (i.e. “skill”), but it does so from the results of previous DFT-based simulations, not from the theory at the base of DFT. But once the computer does that—“learning”—and the paper shows that it is possible for computer to do that—it is able to compute very similar-looking simulations much, much faster than even the rather fast technique of DFT itself.

OK. Context over. Now here in the next section is my yesterday’s comment at iMechanica. (Also note that the previous exchange on this thread at iMechanica had occurred almost a year ago.) Since it has been edited quite a bit, I will not format it using a quotation block.


[An edited version of my comment begins]

A very late comment, but still, just because something struck me only this late… May as well share it….

I think that, as Biswajit points out, it’s a question of matching a technique to an application area where it is likely to be of “good enough” a fit.

I mean to say, consider fluid dynamics, and contrast it to QM.

In (C)FD, the nonlinearity present in the advective term is a major headache. As far as I can gather, this nonlinearity has all but been “proved” as the basic cause behind the phenomenon of turbulence. If so, using machine learning in CFD would be, by the simple-minded “analysis”, a basically hopeless endeavour. The very idea of using a potential presupposes differential linearity. Therefore, machine learning may be thought as viable in computational Quantum Mechanics (viz. DFT), but not in the more mundane, classical mechanical, CFD.

But then, consider the role of the BCs and the ICs in any simulation. It is true that if you don’t handle nonlinearities right, then as the simulation time progresses, errors are soon enough going to multiply (sort of), and lead to a blowup—or at least a dramatic departure from a realistic simulation.

But then, also notice that there still is some small but nonzero interval of time which has to pass before a really bad amplification of the errors actually begins to occur. Now what if a new “BC-IC” gets imposed right within that time-interval—the one which does show “good enough” an accuracy? In this case, you can expect the simulation to remain “sufficiently” realistic-looking for a long, very long time!

Something like that seems to have been the line of thought implicit in the results reported by this paper: [(.PDF) ^].

Machine learning seems to work even in CFD, because in an interactive session, a new “modified BC-IC” is every now and then is manually being introduced by none other than the end-user himself! And, the location of the modification is precisely the region from where the flow in the rest of the domain would get most dominantly affected during the subsequent, small, time evolution.

It’s somewhat like an electron rushing through a cloud chamber. By the uncertainty principle, the electron “path” sure begins to get hazy immediately after it is “measured” (i.e. absorbed and re-emitted) by a vapor molecule at a definite point in space. The uncertainty in the position grows quite rapidly. However, what actually happens in a cloud chamber is that, before this cone of haziness becomes too big, comes along another vapor molecule, and “zaps” i.e. “measures” the electron back on to a classical position. … After a rapid succession of such going-hazy-getting-zapped process, the end result turns out to be a very, very classical-looking (line-like) path—as if the electron always were only a particle, never a wave.

Conclusion? Be realistic about how smart the “dumb” “curve-fitting” involved in machine learning can at all get. Yet, at the same time, also remain open to all the application areas where it can be made it work—even including those areas where, “intuitively”, you wouldn’t expect it to have any chance to work!

[An edited version of my comment is over. Original here at iMechanica [^]]


 

“Boy, we seem to have covered a lot of STEM territory here… Mechanics, DFT, QM, CFD, nonlinearity. … But where is either the entertainment or the industry you had promised us in the title?”

You might be saying that….

Well, the CFD paper I cited above was about the entertainment industry. It was, in particular, about the computer games industry. Go check out SoHyeon Jeong’s Web site for more cool videos and graphics [^], all using machine learning.


And, here is another instance connected with entertainment, even though now I am going to make it (mostly) explanation-free.

Check out the following piece of art—a watercolor landscape of a monsoon-time but placid sea-side, in fact. Let me just say that a certain famous artist produced it; in any case, the style is plain unmistakable. … Can you name the artist simply by looking at it? See the picture below:

A sea beach in the monsoons. Watercolor.

If you are unable to name the artist, then check out this story here [^], and a previous story here [^].


A Song I Like:

And finally, to those who have always loved Beatles’ songs…

Here is one song which, I am sure, most of you had never heard before. In any case, it came to be distributed only recently. When and where was it recorded? For both the song and its recording details, check out this site: [^]. Here is another story about it: [^]. And, if you liked what you read (and heard), here is some more stuff of the same kind [^].


Endgame:

I am of the Opinion that 99% of the “modern” “artists” and “music composers” ought to be replaced by computers/robots/machines. Whaddya think?

[Credits: “Endgame” used to be the way Mukul Sharma would end his weekly Mindsport column in the yesteryears’ Sunday Times of India. (The column perhaps also used to appear in The Illustrated Weekly of India before ToI began running it; at least I have a vague recollection of something of that sort, though can’t be quite sure. … I would be a school-boy back then, when the Weekly perhaps ran it.)]

 

A flip, but not a flop…

“Why is it that when you look in the mirror, the left and right directions appear flipped, but not the up and down?”


Stop reading!

Do not read further until you have honestly tried answering that question!


The question was asked at the Physics StackExchange.

As often is the case, using only text is not at all good when it comes to explaining physics [^]; adding figures does help [^]. And then, animations are even better at it than having just “dead” (static) figures. Going further, interactive graphics, which let the user participate in manipulating the presentation of information, of course beats those mere animations. Better than that, if possible, is an actual demonstration in real life, accompanied by an explanation using simple words.

…As far as the above question is concerned, the Physics Girl [^] does a fairly good job [^].

The best mode of teaching-learning, of course, is an actual and immediate interaction with a person, who in turn might use (and allow you to use) any and all of the above options!

And that’s the reason why, regardless of how much technology progresses, the actual person-to-person type of teaching will never go out of business.


A Video I Liked:

A `Thought Leader’ gives a talk that will inspire your thoughts: [^]