On the one hand, this development is a milestone triumph of digital efficiency and convenience, something I greatly appreciate. On the other hand, I — and it seems many others — can’t seem to muster the schadenfreude over the demise of a bookstore. Doesn’t seem right, but why?

A digression. What exactly do retail stores sell? They sell objects of value. Some objects have value only in their physical form — food, clothes, utensils, machines, wood, whatever. Then there are objects that derive value primarily from the intellectual property embodied, the physical form being but a representation — books, newspapers, magazines, music, movies, even toys. Retail stores that sell the first class of objects haven’t gone out of business even now. There is some convenience to shopping online but there is still need to “check out” the object in question and take delivery of it, so the retail store isn’t superseded; some stores just made the retail locations a bona fide delivery point; over all the transition was incremental. Initially, retail stores that sell physical embodiments of intellectual property followed the same path. But then, purely digital devices happened — mp3 players, phones, e-readers, tablets — and the physical embodiment got stripped from the latter class of objects like chaff. I think it was this step that made the huge difference. Now what is sold is a digital object — in other words, the object sold got swapped out; the physical store became superfluous if you were only after the intellectual property, because it sold something else, and the differences in cost of delivery made physical stores untenable.

Back to why bookstores closing doesn’t feel great. Could it be nostalgia for the old fashioned paper form-factor? I’ve always felt that e-readers just aren’t that great yet, not a replacement of a book from many perspectives, except on the question of weight and volume. But this is minor. Then what about the next question, does a physical bookstore mean anything more than a warehouse of paper? The answer is affirmative. A bookstore, like a library, takes on the role of a commons.

If not enough people buy paper books, bookstores will close down, and that’s a shared loss. Whereas before, people pooled for a common good by paying more for paper books, now, for each person’s convenience, there will be something less for everyone. I don’t think this is the desired outcome. A lot of the conversion to electronic and digital forms is really a conversion to a more personal living style. Nowadays there is still the supermarket and coffee shops where you must get your objects in physical form, but imagine a day when none of these are necessary, when all objects have been digitized, Second Life style. Then there would be no physical stores for anything, and no need to physically be anywhere or interact. This social withdrawal is a profound yet perverse aspect of digitization, long predicted to happen, but the first steps of which we are seeing now. Is Google+ or Facebook going to be adequate replacement to glue people together? (If so, they had better get their “entire social experience” model right.)

Bateson and Wright tested their bees with a type of experiment designed to show whether animals are, like humans, capable of experiencing cognitive states in which ambiguous information is interpreted in negative fashion.

and from the referenced paper itself:

We show for the first time that agitated bees are more likely to classify ambiguous stimuli as predicting punishment. Shaken bees also have lower levels of hemolymph dopamine, octopamine, and serotonin.

If an “ambiguous” stimulus is one that is unassociated with anything but is interpreted as a mixture of two known stimuli (or if it is explicitly some mixture-state stimulus… is it in this case?), then isn’t all this simply risk-aversion? If so, then basically bees have concave utility like probably all evolved organisms naturally should — to stay alive. Indeed, the fact that the actual chemical signaling mechanism for risk-aversion is associated with “pessimism” or “depression” in higher organisms is the more intriguing (but on second thought, obvious) result, suggesting that emotions are just internal (psychological) reward adjustments that drive external (behavioral) risk adjustments. Emotions bend the utility “function” into shape by applying compensatory rewards to the actual external rewards: a risk and reward add-on circuit.

In my limited understanding, Penrose suggests that the universe goes through these cycles of what can be interpreted as infinite expansions “followed by” big bangs, where the cycle renewal “happens” in a mathematical sense: in the way spacetime is metrized. He says that in the infinite future, when all massive particles will have evaporated, we will be returned to a situation without a notion of space or time (since all things are lightlike, I suppose). From this, the very large scale of the given final universe can be reinterpreted as the very small beginning of the next universe. It’s an interesting thought.

Our synthetic genomic approach stands in sharp contrast to a variety of other approaches to genome engineering that modify natural genomes by introducing multiple insertions, substitutions, or deletions (18–22). This work provides a proof of principle for producing cells based upon genome sequences designed in the computer. DNA sequencing of a cellular genome allows storage of the genetic instructions for life as a digital file.

This seems significant, equivalent to booting up the first stored-program computer.

Scientists who were not involved in the study are cautioning that the new species is not a truly synthetic life form because its genome was put into an existing cell.

That’s sour grapes, because the original cell cytoplasm decays to zero exponentially fast in the number of replications, a point well made in the paper. It’s only needed for booting. What’s more useful to know is how much of the 1.08Mbp genome consists of existing genes. The paper says it’s a close copy of M. mycoides:

The synthetic genome described in this paper has only limited modifications from the naturally occurring M. mycoides genome. However, the approach we have developed should be applicable to the synthesis and transplantation of more.

The next step will be a basic cell with a minimal genome, a barebones cell OS, if you will. Then, on to synthetic functions. Pretty soon we’ll have cell API’s, fancy-pants programming frameworks, and bugs and viruses. I mean real ones.

LED light bulbs coming, but incandescents being phased out by mandate in January, 2012? What?! Time to stockpile bulbs. I like my black-body radiation.

Speaking of black-body radiation, suppose I have an enclosed system with a single aperture for light and only light to pass through. Do I now have a system for converting heat to light, and therefore to electricity via bandgaps? Doesn’t that violate some law of thermodynamics?

Okay, I guess the temperature of the photovoltaic converter (the heat sink) matters also, since photons incident on it need to find a site where the electron is in a low-energy state, so unless the converter is at absolute zero, there is some probability the photon will not be captured, which becomes waste heat.

Now what if we enclose the photovoltaic converter, too, so photons cannot escape? That won’t work, either, since at thermal equilbrium, the converter radiates as many photons as it captures, so no voltage develops. The converter just becomes hotter.

But wait, if the converter’s temperature rises above that of the radiating source, doesn’t that imply that the enclosed converter receives some kind of energy, even if it isn’t converted to electricity? Doesn’t that also violate a law of thermodynamics? Ah, but the radiating source and the converter are in optical contact. That must mean that no passive one-way optical material can exist, in which one side is totally opaque and absorptive and another side is totally transparent and reflective. Sounds like Kirchhoff’s law…

Yet, there are transparent optical interfaces that use total internal reflection on one side to achieve exactly this one-way effect, is there not? Okay, the optics also radiate, so no free lunch here.

This undirected graphical model stating conditional independence relationships of senate voting behavior was shown.

If two nodes A and B are connected only through a set of nodes C, then A and B are independent, conditioned on C. Basically it says if you want to predict anything about B from A and C, then C is enough, because A won’t tell you anything more. As pretty as the graph looks, this is a rather odd visualization. Without seeing the (Ising) model parameters, especially where the edge weights are positive or negative, this graph is hard to interpret, and the conclusions in the paper are especially questionable to me. In particular, being in the middle of this graph does not necessarily imply “moderation” or “independence”, (unlike in let’s say this graph). We would expect moderates to exhibit weak dependency to either party’s large cliques. But if, for example, the edge weight between Allen and B. Nelson is a strongly negative one (which it very well may be, since the two parties are not otherwise connected via negatively weighted edges), then the graph seems to imply that how the two parties vote can largely be predicted from the votes of the likes of Allen or B. Nelson; in that sense, they are the indicators for their parties, disagreeing on exactly those party-disambiguating issues.

There is some additional funny stuff going on. According to the paper, a missing vote counts as a “no” because they only solved the problem for binary and Gaussian distributions. I also count only about 80 nodes in there, while there are 100 senators. The graph structure also seems a bit too sparse, but this may be intentional, in order to drop weak dependencies from the graph. One does wonder though, whether the results weren’t really that good without manual fudging.

Unrelatedly, this reminds me of another famous academic paper graph, the high school dating graph:

If you look carefully, there is some oddball stuff going on here, too.

Here’s a guy attempting to read Tang Dynasty poetry in Middle Chinese.

Here’s another guy attempting to read some of the oldest written poetry from the Zhou era in Old Chinese.

Generally I find these unbelievable and unnatural, probaby due to a combination of reconstruction error and bad or exaggerated intonation. Nobody can speak like this. For Middle Chinese it has been argued that the rhyme book scheme does not give one speech pattern and in any case the reconstruction has severe survivor bias toward modern southern dialects. For Old Chinese, I’m just speechless… I don’t think this guy even knows how to read Zheng Zhang Shang Fang’s phonetic notation, so he produces hilariously impractical initial and ending clusters with bizarre tonal variations to boot. Anyway, it’s a start.

For five years, Li’s formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.

And anyway, here is the paper referenced in the article.

Firstly, so much for the sensationalism: so far as I can tell, the paper doesn’t say anything worthy of a Nobel Prize — but still it is mildly interesting. In fact, the whole point of the paper appears to be to introduce to the finance community an already known method for solving the inverse problem of distribution marginalization, that is, (non-uniquely) go from marginal distributions back to the joint distribution, by specifying a mediating copula that captures marginal-invariant joint structure. The technology is very straightforward, and Li didn’t invent it.

That aside, I did wonder, why the heck go through the motion of constructing a Gaussian copula (as in the article) if you assume your marginals and joint are all Gaussian to begin with and all you wanted to capture is the covariance matrix; you can just specify the joint Gaussian explicitly. It seems like a totally pointless exercise. After reading the paper though, I see that wasn’t really Li’s entire suggestion at all. He’s being descriptive rather than prescriptive of what his firm already did by casting it in the language of copulas, an interpretive generalization that allows for potentially more accurate modeling (of non-Gaussian marginals and complicated joint structure if so desired).

Now on to the accusations. The article says that Li tried to “model default correlation” using credit default swaps rather than ratings agency data. It turns out that wasn’t even a problem being solved in this paper. He suggested to use CDS market data to get implied marginal distribution, an established practice. As for how correlation is obtained from limited data, you’d have to blame one Greg Gupton:

Having chosen a copula function, we need to compute the pairwise correlation of survival times. Using the CreditMetrics (Gupton et al. [1997]) asset correlation approach, we can obtain the default correlation of two discrete events over one year period.

However, it is true that there is something funny going on with the concept of using market pricing to price other market instruments, when the only novel input for all of them must be what little information is collected from actual due diligence. A classic case of Garbage In Garbage Out in statistical modeling.

As somebody elsewhere wrote, this sort of thing would not pass muster in “real” engineering design. We’ve seen that dichotomy before between the absolutely error-free stricture of “hardware” design (chips and bridges) vs. the more lax attitude toward “software” design (operating systems and capital market systems). Maybe this dichotomy needs to go away.

However, there is a way to figure this matter of structure from first principles (and perhaps generate a more unique style as a result), albeit with the tradeoff that you cannot be quick, you must be careful.

The first principle for aesthetics is that the character must stand … this is something my old man told me, actually, so I didn’t figure this out myself, but it is very true. If you hold up the piece of paper and look at the strokes as struts of a building, it must look like the character is architecturally sound, i.e. reasonably symmetric if need be, balanced in weight so will not tip over, is not poorly supported with too small a bottom and too big a top, etc. This isn’t too difficult if the character is mechanically drawn, but the trick is to do it even with asymmetric calligraphic strokes and multi-part characters with asymmetric radicals and caps.

The second principle for aesthetics is about spacing, and this is much like optimal typography and typesetting. The strokes should be spread out evenly so that where they appear parallel, they appear to have nearly identical spacing as other such spaces. Otherwise there will be ugly bunching and voids. This is very difficult because the strokes are written in order so there is a pre-commitment issue. Once you commit to a particular stroke, it also commits the spacing requirements for the rest of the character. So one slightly off stroke and you are screwed. This is more a problem for large writing, since bigger mistakes are possible.

Then is the issue of multiple character layout. This wouldn’t be so much of an issue if all characters were the same shape and complexity, but they are not. Some are extremely sparse, and some are very dense. Some are tall and some are fat. They all have to be laid out on paper to look like they take up the same space and also evenly spaced from each other. There is also the compromise of making inter-stroke space appear similar in multiple characters. So one needs to deal with some visual artifacts and vision tricks. As a result, the characters will not all be the same size and will not be spaced evenly, so this is a very tricky thing to get right. You can have perfectly written individual characters but still a terrible collection.

And finally here is a side point: people say Simplified characters are uglier than Traditional characters for calligraphy. In fact this cannot be true. What happens is Simplified characters are sparser and sparser characters writ large are the most difficult to get correctly (not to mention there are no classic master’s character books to trace in Simplified). They are ugly only because (or to the extent that) they are not written well. The bastion of poor practioners (like me) is in small dense characters that distract from scrutiny and generally look pretty good no matter how you write them.

So I understood the point of the fingerprint reader option on this build. Swipe and you can bypass having to type passwords in tablet mode when the keyboard is hidden. But I didn’t get the option, and I believe there are other alternatives.

There are many modes of biometric authentication, fingerprint, face recognition, handwriting, voice, etc., and getting nearly perfect reliability in each case is a difficult problem when used alone. State of the art is just not good enough. But combined into a multifactored authentication protocol, it may just work. Here is something that should work today with existing hardware:

That should do the trick for a quick keyboard-less authentication. Why hasn’t anybody written software to do this?