OK, but how about distributing the LaTeX *source*, and having each device compile it for the screen size? As a bonus, cross-referencing would would work: "see page 35" could become "see page 65" on small screens without further magic.

Of course authors would have to agree on standard LaTeX libraries, otherwise you'd get errors about using your package incorrectly - could be embarrassing.

For anyone else who's curious, from http://www.etymonline.com/index.php?allowed_in_frame=0&search=nice&searchmode=none:

nice:

late 13c., "foolish, stupid, senseless," from O.Fr. nice "silly, foolish," from L. nescius "ignorant," lit. "not-knowing," from ne- "not" (see un-) + stem of scire "to know." "The sense development has been extraordinary, even for an adj." [Weekley] -- from "timid" (pre-1300); to "fussy, fastidious" (late 14c.); to "dainty, delicate" (c.1400); to "precise, careful" (1500s, preserved in such terms as a nice distinction and nice and early); to "agreeable, delightful" (1769); to "kind, thoughtful" (1830). In 16c.-17c. it is often difficult to determine exactly what is meant when a writer uses this word. By 1926, it was pronounced "too great a favorite with the ladies, who have charmed out of it all its individuality and converted it into a mere diffuser of vague and mild agreeableness." [Fowler]

        "I am sure," cried Catherine, "I did not mean to say anything wrong; but it is a nice book, and why should I not call it so?" "Very true," said Henry, "and this is a very nice day, and we are taking a very nice walk; and you are two very nice young ladies. Oh! It is a very nice word indeed! It does for everything." [Jane Austen, "Northanger Abbey"]

What did "nice" used to mean?

I agree with you 100%, but my application of this sentiment differs from yours. It's a shame when only a few dozen bits are discovered when the same effort could have lead to enormous gains in other fields. That's why I encourage scientists away from particle physics and into other areas like biophysics, nonlinear physics, fluid dynamics, bioengineering, neuroscience, machine intelligence and applied mathematics.

The only way the LHC makes sense is if you believe one bit of particle physics knowledge is worth millions of times more than one bit of neuroscience knowledge, since there might be a million to one ratio of understanding per effort spent. By your signature, it looks like you might still think that particle physics knowledge really is a million times more important than knowledge in any other field. Different strokes for different folks, I guess.

From TFS:

'Either would be a major advance in our exploration of nature, bringing us closer to understanding how the fundamental particles acquire their mass, and marking the beginning of a new chapter in particle physics.'

I can't help but point out that knowing if the Higgs exists will increase our information about the universe by a maximum of 1 bit. (Knowing its mass and decay modes probably would give us more like a dozen or two bits of information - more than 1 bit but still not much.)

Particle physics is great, and doing it carefully *does* increase our knowledge of the world, but only by the tiniest of margins. Imagine if all those thousands of super-bright minds had been focused on some other task for decades? What kind of magic tech would we have by now? The ratio of opportunity cost to benefit is sky high.

Let me chime in with a zoom-out perspective.

Physics is using math to predict what matter will do in certain circumstances. (I find that pretty mind-blowing - that you can *calculate* what will happen to *stuff* if the system is simple enough. Too bad the calculation approach didn't work out for me so well in the girlfriend department in high school - another story.)

Anyway, the math behind how positive and negative charges attract is the same as the math behind how masses attract: they're both "inverse square laws." Three times the distance, one-ninth the force, since 3 squared is nine.

That means that the motion of a charged water droplet around the needle will be the same type of motion as orbiting, which is why it looks just like gravity. The math is the same, so the motion is the same.

There's been some really promising work in the direction of OCR-like problems lately. Here's an algorithm that can efficiently learn a small dictionary of symbols (like letters) and decompose a signal into elements that fit within this "low-rank" dictionary plus sparse noise (bugs squashed on the text?) plus Gaussian noise: https://sites.google.com/site/godecomposition/.

It's not literally magical, but it's super-duper awesome (an no, I'm not an author of this one) and it should contribute to the minor revolution in signal processing (compressed sensing & low-rank matrix completion) that's been gaining momentum since about 2005. If our machines can learn features efficiently and robustly from natural images, many industries are in for a wild shake-up. More on this minor revolution is available at http://nuit-blanche.blogspot.com/.

These algorithms are part of the reason why self-driving cars are starting to work, and I have the excited feeling like we're on the cusp (read, next ten years or so) of a sea change in our ability to have machines able to understand and interact with the physical world with a dash of common sense.

Dear afabbro,

You are largely correct. Most software has not sped up much since the 1970s, and it could even be argued that developers write such sloppy code these days that even our improved compilers can't compensate, especially in applications where performance is no longer critical.

On the other hand, since about 2006 there have been some tremendous advances in algorithms. One optimization problem I work on, Basis Pursuit Denoising http://en.wikipedia.org/wiki/Basis_pursuit_denoising, has had on the order of a 10-fold increase in real-world speed on constant hardware every year for the past 5 years (see http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5940245 for my contribution).

These advances are not just academic games; they are actually worth doing. They could eventually lead to computers with sensory processing routines that have a mote of common sense to them, able to perform some real-world tasks we currently need humans for.

While I agree that by and large, most software is getting fat and lazy, there are a few problems where today's algorithms on 2002 hardware mop the floor with 2002 algorithms on today's hardware.

Best,

LeDopore

There's a lot of talk as to what you should do while an after the prof is speaking, but so far very little has been said about what to do *before* the professor speaks it. During my Physics undergrad, I would challenge myself to try to derive results and formulas before the prof finished. I was often wrong, and I usually had to have my notes at least nudged along at least a few times per lecture, but trying to derive on the fly is an awesome way to learn something. There's nothing quite like figuring out a problem by yourself to have it really gel with your overall understanding.

That's my advice: rather than just trying to learn, as much as possible *do your own thinking* in class and you'll be amazed at how little you have to work later to recall it.

I'm with you on this one, Russ1642.