# Determinism, Indeterminism, and the nature of the laws of physics…

The laws of physics are causal, but this fact does not imply that they can be used to determine each and everything that you feel should be determinable using them, in each and every context in which they apply. What matters is the nature of the laws themselves. The laws of physics are not literally boundless; nothing in the universe is. They are logically bounded by the kind of abstractions they are.

Let’s take a concrete example.

Take a bottle, pour a little water and detergent in it, shake well, and have fun watching the Technicolor wonder which results. Bubbles form; they show resplendent colors. Then, some of them shrink, others grow, one or two of them eventually collapse, and the rest of the network of the similar bubbles adjusts itself. The process continues.

Looking at it in an idle way can be fun: those colorful tendrils of water sliding over those thin little surfaces, those fascinating hues and geometric patterns… That dynamics which unfolds at such a leisurely pace. … Just watching it all can make for a neat time-sink—at least for a while.

But merely having fun watching bubbles collapse is not physics. Physics proper begins with a lawful description of the many different aspects of the visually evident spectacle—be it the explanation as to how those unreal-looking colors come about, or be it an explanation of the mechanisms involved in their shrinkage or growth, and eventual collapse, … Or, a prediction of exactly which bubble is going to collapse next.

For now, consider the problem of determining, given a configuration of some bubbles at a certain time $t_0$, predicting exactly which bubble is going to collapse next, and why… To solve this problem, we have to study many different processes involved in the bubbles dynamics…

Theories do exist to predict various aspects of the bubble collapse process taken individually. Further it should also be possible to combine them together. The explanation involves such theories as: the Navier-Stokes equations, which govern the flow of soap water in the thin films, and of the motion of the air entrapped within each bubble; the phenomenon of film-breakage, which can involves either the particles-based approaches to modeling of fluids, or, if you insist on a continuum theory, then theories of crack initiatiation and growth in thin lamella/shells; the propagation of a film-breakage, and the propagation of the stress-strain waves associated with the process; and also, theories concerning how the collapse process gets preferentially localized to only one (or at most few) bubbles, which involves again, nonlinear theories from mechanics of materials, and material science.

All these are causal theories. It should also be possible to “throw them together” in a multi-physics simulation.

But even then, they still are not very useful in predicting which bubble in your particular setup is going to collapse next, and when, because not the combination of these theories, but even each theory involved is too complex.

The fact of the matter is, we cannot in practice predict precisely which bubble is going to collapse next.

The reason for our inability to predict, in this context, does not have to do just with the precision of the initial conditions. It’s also their vastness.

And, the known, causal, physical laws which tell us how a sensitive dependence on the smallest changes in the initial conditions deterministically leads to such huge changes in the outcomes, that using these laws to actually make a prediction squarely lies outside of our capacity to calculate.

Even simple (first- or second-order) variations to the initial conditions specified over a very small part of the network can have repercussions for the entire evolution, which is ultimately responsible for predicting which bubble is going to collapse next.

I mention this situation because it is amply illustrative of a special kind of problems which we encounter in physics today. The laws governing the system evolution are known. Yet, in practice, they cannot be applied for performing calculations in every given situation which falls under their purview. The reason for this circumstance is that the very paradigm of formulating physical laws falls short. Let me explain what I mean very briefly here.

All physical laws are essentially quantitative in nature, and can be thought of as “functions,” i.e., as mappings from a specific set of inputs to a specific set of outputs. Since the universe is lawful, given a certain set of values for the inputs, and the specific function (the law) which does the mapping, the output is  uniquely determined. Such a nature of the physical laws has come to be known as determinism. (At least that’s what the working physicist understands by the term “determinism.”) The initial conditions together with the governing equation completely determine the final outcome.

However, there are situations in which even if the laws themselves are deterministic, they still cannot practically be put to use in order to determine the outcomes. One such a situation is what we discussed above: the problem of predicting the next bubble which will collapse.

Where is the catch? It is in here:

When you say that a physical law performs a mapping from a set of input to the set of outputs, this description is actually vastly more general than what appears on the first sight.

Consider another example, the law of Newtonian gravity.

If you have only two bodies interacting gravitationally, i.e., if all other bodies in the universe can be ignored (because their influence on the two bodies is negligibly small in the problem as posed), then the set of the required input data is indeed very small. The system itself is simple because there is only one interaction going on—that between two bodies. The simplicity of the problem design lends a certain simplicity to the system behaviour: If you vary the set of input conditions slightly, then the output changes proportionately. In other words, the change in the output is proportionately small. The system configuration itself is simple enough to ensure that such a linear relation exists between the variations in the input, and the variations in the output. Therefore, in practice, even if you specify the input conditions somewhat loosely, your prediction does err, but not too much. Its error too remains bounded well enough that we can say that the description is deterministic. In other words, we can say that the system is deterministic, only because the input–output mapping is robust under minor changes to the input.

However, if you consider the N-body problem in all its generality, then the very size of the input set itself becomes big. Any two bodies from the N-bodies form a simple interacting pair. But the number of pairs is large, and worse, they all are coupled to each other through the positions of the bodies. Further, the nonlinearities involved in such a problem statement work to take away the robustness in the solution procedure. Not only is the size of the input set big, the end-solution too varies wildly with even a small variation in the input set. If you failed to specify even a single part of the input set to an adequate precision, then the predicted end-state can deterministically become very wildly different. The input–output mapping is deterministic—but it is not robust under minor changes to the input. A small change in the initial angle can lead to an object ending up either on this side of the Sun or that. Small changes produce big variations in predictions.

So, even if the mapping is known and is known to work (deterministically), you still cannot use this “knowledge” to actually perform the mapping from the input to the output, because the mapping is not robust to small variations in the input.

Ditto, for the soap bubbles collapse problem. If you change the initial configuration ever so slightly—e.g., if there was just a small air current in one setup and a more perfect stillness in another setup, it can lead to wildly different predictions as to which bubble will collapse next.

What holds for the N-body problem also holds for the bubble collapse process. The similarity is that these are complex systems. Their parts may be simple, and the physical laws governing such simple parts may be completely deterministic. Yet, there are a great many parts, and they all are coupled together such that a small change in one part—one interaction—gets multiplied and felt in all other parts, making the overall system fragile to small changes in the input specifications.

Let me add: What holds for the N-body problem or the bubble-collapse problems also holds for quantum-mechanical measurement processes. The latter too involves a large number of parts that are nonlinearly coupled to each other, and hence, forms a complex system. It is as futile to expect that you would be able to predict the exact time of the next atomic decay as it is to expect that you will be able to predict which bubble collapses next.

But all the above still does not mean that the laws themselves are indeterministic, or that, therefore, physical theories must be regarded as indeterministic. The complex systems may not be robust. But they still are composed from deterministically operating parts. It’s just that the configuration of these parts is far too complex.

It would be far too naive to think that it should be possible to make exact (non-probabilistic) predictions even in the context of systems that are nonlinear, and whose parts are coupled together in complex manner. It smacks of harboring irresponsible attitudes to take this naive expectation as the standard by which to judge physical theories, and since they don’t come up to your expectations, to jump to the conclusion that physical theories are indeterministic in nature. That’s what has happened to QM.

It should have been clear to the critic of the science that the truth-hood of an assertion (or a law, or a theory) is not subject to whether every complex manner in which it can be recombined with other theoretical elements leads to robust formulations or not. The truth-hood of an assertion is subject only to whether it by itself and in its own context corresponds to reality or not.

The error involved here is similar, in many ways, to expecting that if a substance is good for your health in a certain quantity, then it must be good in every quantity, or that if two medicines are without side-effects when taken individually, they must remain without any harmful effects even when taken in any combination—that there should be no interaction effects. It’s the same error, albeit couched in physicists’ and philosopher’s terms, that’s all.

… Too much emphasis on “math,” and too little an appreciation of the qualitative features, only helps in compounding the error.

A preliminary version of this post appeared as a comment on Roger Schlafly’s blog, here [^]. Schlafly has often wondered about the determinism vs. indeterminism issue on his blog, and often, seems to have taken positions similar to what I expressed here in this post.

The posting of this entry was motivated out of noticing certain remarks in Lee Smolin’s response to The Edge Question, 2013 edition [^], which I recently mentioned at my own blog, here [^].

A song I like:
(Marathi) “kaa re duraavaa, kaa re abolaa…”
Singer: Asha Bhosale

[In the interests of providing better clarity, this post shall undergo further unannounced changes/updates over the due course of time.

Revision history:
2019.04.24 23:05: First published
2019.04.25 14:41: Posted a fully revised and enlarged version.
]

/

# General update: Will be away from blogging for a while

I won’t come back for some 2–3 weeks or more. The reason is this.

As you know, I had started writing some notes on FVM. I would then convert my earlier, simple, CFD code snippets, from FDM to FVM. Then, I would pursue modeling Schrodinger’s equation using FVM. That was the plan.

But before getting to the nitty-gritties of FVM itself, I thought of jotting down a note, once and for all, putting in writing my thoughts thus far on the concept of flux.

If you remember, it was several years ago that I had mentioned on this blog that I had sort of succeeded in deriving the Navier-Stokes equation in the Eulerian but differential form (d + E for short).

… Not an achievement by any stretch of imagination—there are tomes written on say, differentiable manifolds and whatnot. I feel sure that deriving the NS equations in the (d + E) form would be less than peanuts for them.

Yet, the fact of the matter is: They actually don’t do that!

Show me a single textbook or a paper that does that. If not at the UG level, then at least at the PG level, but one that is written using the language of only plain calculus, as used by engineers—not that of advanced analysis.

And as to the UG/PG books from engineering:

What people normally do is to derive these equations in its integral form, whether using the Lagrangian or the Eulerian approach. That is, they adopt either the (i + L) approach or the (i + D) approach.

At some rare times, if they at all begin fluid dynamics with a differential form of the NS equations, then they invariably follow the Lagrangian approach, never the Eulerian. That is, they invariably begin with only (d + L)—even in those cases when their objective is to obtain (d + E). Then, after having derived (d +L) , they simply invoke some arbitrary-looking vector calculus identities to “transform” those equations from (d + L) to (d +E).

And, worse:

They never discuss the context, meaning, or proofs of those identities. None from fluid dynamics or CFD side does that. And neither do the books on maths written for scientists and engineers.

The physical bases of the “transformation” process must remain a mystery.

When I started working through it a few years ago, I realized that the one probable reason why they don’t use the (d +E) form right from the beginning is because: forget the NS equations, no one understands even the much simpler idea of the flux—if it is to be couched entirely in the settings of (d+E). You see, the idea of the flux too always remains couched in the integral form, never the differential. For example, see Narasimhan [^]. Or, any other continuum mechanics books that impresses you.

It’s no accident that the Wiki article on Flux [^] says that it

needs attention from an expert in Physics.

And then, more important for us, the text of the article itself admits that the formula it notes, for a definition of flux in differential terms, is

an abuse of notation

See the section here [^].

Also, ask yourself, why is a formula that is free of the abuse of notation not being made available? In spite of all those tomes having been written on higher mathematics?

Further, there were also other related things I wanted to write about, like an easy pathway to the idea of tensors in general, and to that of the stress tensor in particular.

So, I thought of writing it down it for once and for all, in one note. I possibly could convert some parts of it into a paper later on, perhaps. For the time being though, the note would be more in the nature of a tutorial.

I started writing down the note, I guess, from 17 August 2018. However, it kept on growing, and with growth came reorganization of material for a better hierarchy or presentation. It has already gone through some 4–5 thorough re-orgs (meaning: discarding the earlier LaTeX file entirely and starting completely afresh), and it has already become more than 10 LaTeX pages. Even then, I am nowhere near finishing it. I may be just about half-way through—even though I have been working on it for some 7–8 hours every day for the past fortnight.

Yes, writing something in original is a lot of hard work. I mean “original” not in the sense of discovery, but in the sense of a lack of any directly citable material whatsoever, on the topic. Forget copy-pasting. You can’t even just gather a gist of the issue so that you could cite it.

And, the trouble here is, this topic is otherwise so very mature. (It is some 150+ years old.) So, you know that if you go even partly wrong, the whole world is going to pile on you.

And that way, in my experience, when you write originally, there is at least 5–10 pages of material you typically end up throwing away for every page that makes it to the final, published, version. Yes, the garbage thrown out is some 5–10 times the material retained in—no matter how “simple” and “straightforward” the published material might look.

Indeed, I could even make a case that the simpler and the more straight-forward the published material looks, if it also happens to be original, then the more strenuous it has been, on the part of the author.

Few come to grasp this simple an observation, ever, in their entire life.

As a case in point, I wish to recall here my conference paper on diffusion. [To be added here soon enough.]

I have many times silently watched people as they were going through this paper for the first time.

Typically, when engineers read it, they invariably come out with a mild expression which suggests that they probably were thinking of something like: “isn’t it all so simple and straight-forward?” Sometimes they even explicitly ask: “And, what do you say was the new contribution here?” [Even after having gone through both the abstract and the conclusion part of it, that is.]

On the other hand, on the four-five rare occasions when I have had the opportunity to watch professional mathematicians go through this paper of mine, in each case, the expression they invariably gave at the end of finishing it was as if they still were very intently absorbed in it. In particular, they never do ask me what was new about it—they just remain deeply engaged in what looks like an exercise in “fault-finding”, i.e., in checking if any proof, theorem or lemma they had ever had come across could be used in order to demolish the new idea that has been presented. Invariably, they give the same argument by way of an objection. Invariably, I explain why their argument does not address the issue I have raised in the paper. Invariably they chuckle and then go back to the paper and to their intent thinking mode, to see if there is any other weakness to my basic argument…

Till date (even after more than a decade), they haven’t come back.

But in all cases, they were very ready to admit that they were coming across this argument for the first time. I didn’t have to explain it to them that though the language and the tone of the paper looked simple enough, the argument itself was not easy to derive originally.

No, the notes which I am currently working on are nowhere near as original as that. [But yes, original, these are.]

Yet, let me confess, even as I keep prodding through it for the better part of the day the way I have done over the past fortnight or so, I find myself dealing with a certain doubt: wouldn’t they just dismiss it all as being too obvious? as if all the time and effort I spent on it was, more or less, ill spent? that it was all meaningless to begin with?

Anyway, I want to finish this task before resuming blogging—simply because I’ve got a groove about it by now… I am in a complete and pure state of anti-procrastination.

… Well, as they say: Make the hay while the Sun shines…

A Song I Like:
(Marathi) “dnyaandev baaL maajhaa…”
Singer: Asha Bhosale
Lyrics: P. Savalaram
Music: Vasant Prabhu

# Where are those other equations?

Multiple header images, and the problem with them:

As noted in my last post, I have made quite a few changes to the layout of this blog, including adding a “Less transient” page [^].

Another important change was that now, there were header images too, at the top.

Actually, initially, there was only one image. For the record, it was this: [^] However, there weren’t enough equations in it. So, I made another image. It was this [^]. But as I had already noted in the last post, this image was already crowded, and even then, it left out some other equations that I wanted to include.

Then, knowing that WordPress allows multiple images that can be shown at random, I created three images, and uploaded them. These are what is being displayed currently.

However, randomizing means that even after re-loading a page a couple of times, there still is a good chance that you will miss some or the other image, out of those three.

Ummm… OK.

A quick question:

Here is the problem statement:

There are three different header images for this blog. The server shows you only one of them during a single visit. Refreshing the page in the browser also counts as a separate visit. In each visit, the server will once again select an image completely at random.

Assume also that the PDF for the random sequence is uniform. That is to say, there is no greater probability for any of the three images during any visit. Cookies, e.g., play no role.

Now, suppose you make only three visits to this blog. For instance, suppose you visit some page on this blog, and then refresh the same page twice in the browser. The problem is to estimate the chances that you will get to see:

• all of the three different images, but in only three visits
• one and the same image, each time, during exactly three visits
• exactly two different images, during exactly three visits

Don’t read further until you solve this problem, right now: right on-the-fly and right in your head (i.e. without using paper and pencil).

(Hint [LOL!]: There are three balls of different colors (say Red, Green, and Blue) in a box, and $\dots$.)

…No, really!

Ummm… Still with me?

OK. That tells me that you are now qualified to read further.

Just in case you were wondering what was there in the “other” header images, here is a little document I am uploading for you. Go, see it (.PDF [^]), but also note the caveat below.

Caveats: It is a work in progress. If you spot a mistake or even just a typo, then please do let me know. Also, don’t rely on this work.

For example, the definition of stress given in the document is what I have not so far read in any book. So, take it with a pinch of the salt—even if I feel confident that it is correct. Similarly, there might be some other changes, especially those related to the definition of the flux and its usage in the generic equation. Also, I am not sure if the product ansatz for the separation of variables technique began with d’Alembert or not. I vaguely remember its invention being attributed to him, but it was a long time ago, and I am no longer sure. May be it was before him. May be it was much later, at the hands of Fourier, or, even still later, by Lame. … Anyway let it be…

…BTW, the equations in the images currently being shown are slightly different—the PDF document is the latest thing there is.

Also, let me have your suggestions for any further inclusions, too, if any. (As to me: Yes, I would like to add a bit on the finite volume method, too.)

As usual, I may change the PDF document at any time in future. However, the document will always carry the date of compilation as the “version number”.

General update:

These days, I am also busy converting my already posted CFD snippets [^] into an FVM-based code.

The earlier posted code was done using FDM, not FVM, but it was not my choice—SPPU (Pune University) had thrust it upon me.

Writing an illustrative code for teaching purposes is fairly simple and straight-forward, esp. in Python—and especially if you treat the numpy arrays exactly as if they were Python arrays!! (That is, very inefficiently.) But I also thought of writing some notes on at least some initial parts of FVM (in a PDF document) to go with the code. That’s why, it is going to take a bit of time.

Once all this work is over, I will also try to model the Schrodinger equation using FVM. … Let’s see how it all goes…

…Alright, time to sign off, already! So, OK, take care and bye for now. …

A Song I Like:
(Hindi) “baharon, mera jeevan bhee savaron…”
Music: Khayyam
Singer: Lata Mangeshkar
Lyrics: Kaifi Aazmi

[The obligatory PS: In all probability, I won’t make any changes to the text of this post. However, the linked PDF document is bound to undergo changes, including addition of new material, reorganization, etc. When I do revise that document, I will note the updates in the post, too.]

# Changes at this blog…

The changes at this blog:

In case you haven’t noticed it already, notice [what else?] that the layout of this blog has undergone a change. Hopefully for the better!

In particular, I’ve made the following changes:

1. This blog is now concerned not only with the more transient writings of mine, but also with the less transient ones! … Accordingly, I have made a new page which holds links to my less transient writings, too, whether the write-ups were published here or elsewhere. See that page here [^].
2. The tagline too now reflects the change in the purpose of this blog.
3. I have added a header image, too. As of now, it holds some of the equations that have come to grab my attention for a long while. This may change in future. (See the separate section below.)
4. A more minor change is the one made to the font.

A note for reading on the mobile:

In case you read this blog on a mobile phone, then to see the “less transient” page, you will have to press the menu button appearing at the top to get to the new page. On a desktop, however, the menu is by default seen as expanded.

The image at the top:

Just for the record, the equations in the top image, as of today (13 August 2018, 11:31 hrs), are the following:

• The inner product and the outer product of two vectors, expressed using the more familiar notation of matrices.
• Definitions of the grad of scalars and vectors, and the div of vectors and tensors.
• The Taylor series expansion
• The Fourier series expansion
• The generic conservation equation for a scalar quantity, in the Eulerian form
• The conservation equation for momentum, in the Eulerian form. (NB: The source term is in terms of $\Phi$ i.e. the conserved quantity itself, whereas the rest of the terms have the mass-specific term $\phi$ in them. This is correct.)
• Definition of stress. (See the note for this equation below.)
• Definitions of the displacement gradient tensor, the strain tensor, and the rotation tensor.
• Cauchy’s formula (the relation between stress and the net force)
• The Planck-Einstein relations
• The most general form of the Schrodinger equation
• The time-dependent Schrodinger equation in $1D$
• The inner product defined over a Hilbert space, and expansion of a function in terms of its basis set defined in a Hilbert space

An important note on the definition of stress as given in the header image:

I haven’t yet seen this definition in any solid/fluid/continuum mechanics text. So, please treat it with caution.

Also, please do drop me a line if you find it erroneous, problematic, or simply not general enough.

On the other hand, if you run into this definition anywhere, then please do bring the reference to my attention; thanks in advance. [This definition is a part of my planned paper on stress and strain.]

Some of the equations that got left out:

The equations which I would have liked to have in the header, but which got left out for a lack of space, are the following (in no particular order):

• Newton’s second law defining force
• Definitions of action (as momentum-dot-displacement and energy-times-time); action as an integral; action as a functional
• The general equation for the methods of the weighted residuals, and the particular equations for the commonly used test functions (i.e., the Galerkin, the pseudospectral, the least-squares, the method of moments, and the collocation)
• The Euler identity

Perhaps also, things like:

• The wavefunction normalization principle, and the Born equation for finding probabilities
• Structure of probability: simultaneous vs. subsequent events
• The wave, diffusion and potential equations (juxtaposed with the Schrodinger equation)

On the other hand, some of the equations that are generally of great importance, but which have not come to preoccupy me a lot, are the following:

• The Euler-Lagrange equations for classical mechanics
• The Maxwell equations of electrodynamics, supplemented with the “fifth” (i.e. the Lorentz) force equation
• Boltzmann’s equation, and other equations from statistical mechanics

I must have left out quite a few more in both the lists.

However, I am sure that the three laws of thermodynamics probably would not make it to the header image, despite all their grandeur, their all-encompassing scope.

The reason is this: a computational modeler like me seldom works in a very direct manner with the laws of thermodynamics themselves. These laws do inform his theory; the derivation of the equations he uses indeed are based on them, even if only indirectly. However, the equations he works with happen to be much more detailed (and of far more delimited scope). For instance: the Navier-Stokes system (CFD)—an expression of the first law; the stress-strain fields (FEM)—which makes for merely a part of the internal energy; or the Maxwell system (FDTD)—ditto. Etc.

Further change may be coming:

All in all, I am not quite happy with the top image as it exists right now. … It is too crowded, and speaking from a visual aesthetics point of view, its layout is not well-balanced.

So, on both these counts (too much crowding already, and too many good equations being left out), I am thinking of a further idea: may be I should create a sequence of images, each containing only a few equations, and let the server show you one of them at random. Whaddaya think?

Do check out the “less transient” page:

But yes, if you are interested, check out the “less transient” page too, and let me know if something I wrote in the past should be there or not.

So… does that mean that I’ve gone “mathy”?

Though I exclusively include only equations in the header image—no pictures or visualizations at all, no code, and not much text either—it doesn’t mean that I have gone “mathy”. … Hell, no! Not at all! … Just check out my less transient page [^].

A song I like:

(Hindi) “aankhon aankhon mein hum tum, ho gaye…”
Music: Kalyanji-Anandji
Singers: Kishore Kumar, Asha Bhosale
Lyrics: Anand Bakshi

# Some suggested time-pass (including ideas for Python scripts involving vectors and tensors)

Actually, I am busy writing down some notes on scalars, vectors and tensors, which I will share once they are complete. No, nothing great or very systematic; these are just a few notings here and there taken down mainly for myself. More like a formulae cheat-sheet, but the topic is complicated enough that it was necessary that I have them in one place. Once ready, I will share them. (They may get distributed as extra material on my upcoming FDP (faculty development program) on CFD, too.)

While I remain busy in this activity, and thus stay away from blogging, you can do a few things:

1.

Think about it: You can always build a unique tensor field from any given vector field, say by taking its gradient. (Or, you can build yet another unique tensor field, by taking the Kronecker product of the vector field variable with itself. Or, yet another one by taking the Kronecker product with some other vector field, even just the position field!). And, of course, as you know, you can always build a unique vector field from any scalar field, say by taking its gradient.

So, you can write a Python script to load a B&W image file (or load a color .PNG/.BMP/even .JPEG, and convert it into a gray-scale image). You can then interpret the gray-scale intensities of the individual pixels as the local scalar field values existing at the centers of cells of a structured (squares) mesh, and numerically compute the corresponding gradient vector and tensor fields.

Alternatively, you can also interpret the RGB (or HSL/HSV) values of a color image as the x-, y-, and z-components of a vector field, and then proceed to calculate the corresponding gradient tensor field.

Write the output in XML format.

2.

Think about it: You can always build a unique vector field from a given tensor field, say by taking its divergence. Similarly, you can always build a unique scalar field from a vector field, say by taking its divergence.

So, you can write a Python script to load a color image, and interpret the RGB (or HSL/HSV) values now as the $xx$-, $xy$-, and $yy$-components of a symmetrical 2D tensor, and go on to write the code to produce the corresponding vector and scalar fields.

Yes, as my resume shows, I was going to write a paper on a simple, interactive, pedagogical, software tool called “ToyDNS” (from Toy + Displacements, Strains, Stresses). I had written an extended abstract, and it had even got accepted in a renowned international conference. However, at that time, I was in an industrial job, and didn’t get the time to write the software or the paper. Even later on, the matter kept slipping.

I now plan to surely take this up on priority, as soon as I am done with (i) the notes currently in progress, and immediately thereafter, (ii) my upcoming stress-definition paper (see my last couple of posts here and the related discussion at iMechanica).

Anyway, the ideas in the points 1. and 2. above were, originally, a part of my planned “ToyDNS” paper.

3.

You can induce a “zen-like” state in you, or if not that, then at least a “TV-watching” state (actually, something better than that), simply by pursuing this URL [^], and pouring in all your valuable hours into it. … Or who knows, you might also turn into a closet meteorologist, just like me. [And don’t tell anyone, but what they show here is actually a vector field.]

4.

You can listen to this song in the next section…. It’s one of those flowy things which have come to us from that great old Grand-Master, viz., SD Burman himself! … Other songs falling in this same sub-sub-genre include, “yeh kisine geet chheDaa,” and “ThanDi hawaaein,” both of which I have run before. So, now, you go enjoy yet another one of the same kind—and quality. …

A Song I Like:

[It’s impossible to figure out whose contribution is greater here: SD’s, Sahir’s, or Lata’s. So, this is one of those happy circumstances in which the order of the listing of the credits is purely incidental … Also recommended is the video of this song. Mona Singh (aka Kalpana Kartik (i.e. Dev Anand’s wife, for the new generation)) is sooooo magical here, simply because she is so… natural here…]

(Hindi) “phailee huyi hai sapanon ki baahen”
Music: S. D. Burman
Lyrics: Sahir
Singer: Lata Mangeshkar

But don’t forget to write those Python scripts….

Take care, and bye for now…