Archive for Nature

sex, lies, & brain scans [not a book review]

Posted in Statistics with tags , , , , , , , on February 11, 2017 by xi'an

“Sahakian and Gottwald discuss the problem of “reverse inference” regrettably late in the book.”

In the book review section of Nature [Jan 12, 2017 issue], there was a long coverage of the book sex. lies, & brain scans: How fMRI Reveals What Really Goes on in our Minds, by Barbara J. Sahakian and Julia Gottwald. While I have not read the book (which is not even yet out on amazon), I found some mentions of associating brain patterns with criminal behaviour quite puzzling: “neuroimaging will probably be an imperfect predictor of criminal behaviour”. Actually, much more than puzzling, both frightening with its Minority Report prospects [once again quoted as a movie rather than Philip K. Dick’s novel!], and bordering the irrational, for associating breaking rules with a brain pattern. Of course this is just an impression from reading a book review and the attempts may be restricted to psychological diseases rather than attempt at social engineering and brain policing, but if this is the case, as suggested by the review, it is downright scary!

lords of the rings

Posted in Books, pictures, Statistics, University life with tags , , , , , , on February 9, 2017 by xi'an

In the 19 Jan 2017 issue of Nature [that I received two weeks later], a paper by Tarnita et al discusses regular vegetation patterns like fairy patterns. While this would seem like an ideal setting for point process modelling, the article does not seem to get into that direction, debating instead between ecological models. Which combines vegetal self-organisation, with subterranean insect competition. Since the paper seems to derive validation of a model by simulation means without producing a single equation, I went and checked the supplementary material attached to this paper. What I gathered from this material is that the system of differential equations used to build this model seems to be extrapolated by seeking parameter values consistent with what is known” rather than estimated as in a statistical model. Given the extreme complexity of the resulting five page model, I am surprised at the low level of validation of the construct, with no visible proof of stationarity of the (stochastic) model thus constructed, and no model assessment in a statistical sense. Of course, a major disclaimer applies: (a) this area does not even border my domains of (relative) expertise and (b) I have not spent much time perusing over the published paper and the attached supplementary material. (Note: This issue of Nature also contains a fascinating review paper by Nielsen et al. on a detailed scenario of human evolutionary history, based on the sequencing of genomes of extinct hominids.)

Elsevier in the frontline

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , on January 27, 2017 by xi'an

“Viewed this way, the logo represents, in classical symbolism, the symbiotic relationship between publisher and scholar. The addition of the Non Solus inscription reinforces the message that publishers, like the elm tree, are needed to provide sturdy support for scholars, just as surely as scholars, the vine, are needed to produce fruit. Publishers and scholars cannot do it alone. They need each other. This remains as apt a representation of the relationship between Elsevier and its authors today – neither dependent, nor independent, but interdependent.”

There were two items of news related with the publishark Elsevier in the latest issue of Nature I read. One was that Germany, Peru, and Taiwan had no longer access to Elsevier journals, after negotiations or funding stopped. Meaning the scientists there have to find alternative ways to procure the papers, from the authors’ webpage [I do not get why authors fail to provide their papers through their publication webpage!] to peer-to-peer platforms like Sci-Hub. Beyond this short term solution, I hope this pushes for the development of arXiv-based journals, like Gower’s Discrete Analysis. Actually, we [statisticians] should start planing a Statistics version of it!

The second item is about  Elsevier developing its own impact factor index, CiteScore. While I do not deem the competition any more relevant for assessing research “worth”, seeing a publishark developing its own metrics sounds about as appropriate as Breithart News starting an ethical index for fake news. I checked the assessment of Series B on that platform, which returns the journal as ranking third, with the surprising inclusion of the Annual Review of Statistics and its Application [sic], a review journal that only started two years ago, of Annals of Mathematics, which does not seem to pertain to the category of Statistics, Probability, and Uncertainty, and of Statistics Surveys, an IMS review journal that started in 2009 (of which I was blissfully unaware). And the article in Nature points out that, “scientists at the Eigenfactor project, a research group at the University of Washington, published a preliminary calculation finding that Elsevier’s portfolio of journals gains a 25% boost relative to others if CiteScore is used instead of the JIF“. Not particularly surprising, eh?!

When looking for an illustration of this post, I came upon the hilarious quote given at the top: I particularly enjoy the newspeak reversal between the tree and the vine,  the parasite publishark becoming the support and the academics the (invasive) vine… Just brilliant! (As a last note, the same issue of Nature mentions New Zealand aiming at getting rid of all invasive predators: I wonder if publishing predators are also included!)

quantic random generators

Posted in Books, Statistics with tags , , , , , on January 5, 2017 by xi'an

“…the random numbers should be unpredictable by any physical observer, that is, any observer whose actions are constrained by the laws of physics.”

A review paper in Nature by Acin and Masanes is the first paper I ever read there about random number generation! The central debate in the paper is about the notion of randomness, which the authors qualify as above. This seems to exclude the use of “our” traditional random number generators, although I do not see why they could not be used with an unpredictable initialisation, which does not have to be done according to a specific probability distribution. The only thing that matters is unpredictability.

“…the standard method for certifying randomness consists of running statistical tests1 on sequences generated by the device. However, it is unclear what passing these tests means and, in fact, it is impossible to certify with finite computational power that a given sequence is random.”

The paper supports instead physical and quantum devices. Justified or certified by [violations of] the Bell inequality, which separates classic from quantum. Not that I know anything about this. Or that I can make sense of the notations in the paper, like

nature20119-m1which is supposed to translate that the bits are iid Uniform and independent of the environment. Actually, I understood very little of the entire review paper, which is quite frustrating since this may well be the only paper ever published in Nature about random number generation!

“…a generation rate of 42 random bits after approximately one month of measurements, was performed using two entangled ions in two traps at 1-m distance.”

It is also hard to tell whether or not this approach to quantum random number generation has foreseeable practical consequences. There already exist QRNGs, as shown by this example from ANU. And this much more readable review.

statistics snapshots from Nature

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , on November 6, 2016 by xi'an

Two snapshots from the October 27 issue of Nature, one reporting on David Cox receiving the first “Nobel in Statistics” Prize and one about the @ScientistTrump parody site, where being Bayesian sounds like a slur..!

Nature highlights

Posted in Books, Kids, pictures, Statistics with tags , , , , , , , on November 1, 2016 by xi'an

A mostly genetics issue of Nature this week (of October 13), as the journal contains an article on the genomes of 300 individuals from 142 diverse populations across the globe, and another one on the genetic history of Australia Aborigines, plus a third one of 483 individuals from 125 populations drawing genetic space barriers, leading to diverging opinions on the single versus multiple out-of-Africa scenario. As some of these papers are based on likelihood-based techniques, I wish I had more time to explore the statistics behind. Another paper builds a phylogeny of violence in mammals, rising as one nears the primates. I find the paper most interesting but I am not convinced by the genetic explanation of violence, in particular because it seems hard to believe that data about Palaeolithic, Mesolithic, and Neolithic periods can be that informative about the death rate due to intra-species violence. And to conclude on a “pessimistic” note, the paper that argues there is a maximum lifespan for humans, meaning that the 122 years enjoyed (?) by Jeanne Calment from France may remain a limit. However, the argument seems to be that the observed largest, second largest, &tc., ages at death reached a peak in 1997, the year Jeanne Calment died, and is declining since then. That does not sound super-convincing when considering extreme value theory, since 1997 is the extreme event and thus another extreme event of a similar magnitude is not going to happen immediately after.

machines learning but not teaching…

Posted in Books, pictures with tags , , , , , , , on October 28, 2016 by xi'an

A few weeks after the editorial “Algorithms and Blues“, Nature offers another (general public) entry on AIs and their impact on society, entitled “The Black Box of AI“. The call is less on open source AIs and more on accountability, namely the fact that decisions produced by AIS and impacting people one way or another should be accountable. Rather than excused by the way out “the computer said so”. What the article exposes is how (close to) impossible this is when the algorithms are based on black-box structures like neural networks and other deep-learning algorithms. While optimised to predict as accurately as possible one outcome given a vector of inputs, hence learning in that way how the inputs impact this output [in the same range of values], these methods do not learn in a more profound way in that they very rarely explain why the output occurs given the inputs. Hence, given a neural network that predicts go moves or operates a self-driving car, there is a priori no knowledge to be gathered from this network about the general rules of how humans play go or drive cars. This rather obvious feature means that algorithms that determine the severity of a sentence cannot be argued as being rational and hence should not be used per se (or that the judicial system exploiting them should be sued). The article is not particularly deep (learning), but it mentions a few machine-learning players like Pierre Baldi, Zoubin Ghahramani and Stéphane Mallat, who comments on the distance existing between those networks and true (and transparent) explanations. And on the fact that the human brain itself goes mostly unexplained. [I did not know I could include such dynamic images on WordPress!]