Oh the classical correlation != causation meme! Read the f***ing paper first and understand the arguments!

You should understand WHICH 4 biomarkers they are testing: VLDL, Albumin, Citrate, and Alpha-1 acid glycoprotein. If these four are high, chances are the metabolism behind these four indicators has been wrong for DECADES and is hardly reversible. It makes sense, therefore, to predict 5-y mortality rate with these 4 biomarkers. Sure the prediction isn't perfect, but boy are they good indicators of someone health just as fasting blood glucose, blood pressures, cholesterols and other measurements!

So, just quit this kneejerk correlation != causation reaction already and understand the science behind it!

> Open articles. Ctrl-F "Controling" No results. Close tab. Nothing of value.

It does. It is abbreviated as "RGSC" on the article. Look at Figure 2 to see the model graphically and you see that RGSC is featured prominently on the top. Also, if you look at Table 2, the authors acknowledge the link between SES of origin AND math / reading abilities. But this paper shows that the math & reading abilities at 7 years old do predict mid-life SES above AND beyond the SES of origin.

Okay, storing is "solved". How about retrieval? Especially random access retrieval that are simple and fast (relatively speaking) that allow such storage medium to be practical? Certainly not DNA sequencing that can take weeks to complete?

The second problem: DNA denature and fragment at room temperature. Certainly a -80C lab freezer for storage wouldn't be practical.

Third problem: DNA secondary and tertiary structure. The coding scheme must also solves the problem of DNA tendency to make secondary structure (like hairpin) or tertiary structure (like super-coil) that can hamper reading / access to the information. I think this is the reason why the storage uses short sequences. But short DNA sequences like the one proposed (~100 bp, from the figure) could still make such structures.

Then, it's not really monkeys. It's more of monkeys with an oracle. That oracle thing made a whole world of difference.

What constitutes a "real" name? Take a look at Sun Yat-Sen, for example. Which one do you think is THE real name? The original name? Baby name? Genealogy name? Courtesy name? School name? Eventually, Sun Yat-Sen was famed in China because of the pseudoname he used in Japan. And Yat-Sen itself is a school name.

The performance of GPU-based codes is highly dependent on the video cards. I highly doubt the dismal performance of NVIDIA cards. I think the authors most likely optimized the kernel code to AMD cards. This is evident when you look at the CL kernel code and you see that there are so many hardwired constants and fixed arrays (aligned to 128 ints or longs). Moreover, the authors GUIMiner don't seem to take advantage of NVIDIA's more local workthreads (compared to AMD's).

I'd say that declaring AMD a victor is premature.

Does "agile" software development allow scrapping 100% of the code and radically change the spec (and thereby everything else) every about 6 months just because of new scientific publication? It may sound extreme, but this often happen in research. If we take time to "structure" our code, before we know it, we have to redo it all over again. We do use libraries like GSL, BLAS, ATLAS, etc. to make our lives easier. These won't change, but whatever we build on top of these often get scrapped at regular basis. So, we really don't have incentives to "beautify" the code.

Not only that most researchers are not proficient in programming language, they shape their codes more like prototypes so that they can modify the codes easily as the science progress. Conventional programmers will be frustrated with this approach since they want every single spec set in stone, which will never happen in research setting since research progresses very rapidly and specs can change dramatically in most cases. If you can set the spec in stone, it is usually a sign that the field has matured and is getting transitioned to engineering-type problems. Once the transition happens, it's no longer research, it's engineering. Then you can "make the code better".

In Ancient China, imperial exam was literally game-changing. The stake is high; it was virtually the only way peasants could become noblemen. Therefore, people did whatever it took to be successful. This system was copied and adapted to some degree in ancient Japan, Korea, or Vietnam. Hence similar attitude also pervades in these countries.