Science and Technology links (December 8th 2018)

  1. The energy density of lithium-ion batteries doubled between 1995 and 2005 but only increased by about 15% between 2005 and 2015. It is estimated that there is relatively little further gains in energy density possible with lithium-ion batteries. However, our mobile devices typically consume far less power than they did only a few years ago while offering faster processing.
  2. In China, 78% of all research institutes focus on science and engineering, and only 12% focus on the humanities. A quarter of the top universities have a science and engineering focus.
  3. In the US, if I know your zip code, your gender and your birthdate, I can nearly uniquely identify you.
  4. In wealthy countries, happier people are more likely to have children.
  5. It is sometimes stated that beyond physical differences, the brains of men and women are identical. Rosenblatt (2016) disagrees: “Brains are indeed typically male or typically female.” Falk and Hermle (2018) further observe that the more that women have equal opportunities, the more they differ from men in their preferences. Zhang et al. (2018) have a related finding:

    On average, women show stronger preferences for mates with good earning capacity than men do, while men show stronger preferences for physically attractive mates than women do (…) we found little evidence that these sex differences were smaller in countries with greater gender equality.

  6. It seems that very large mammals co-existed with the dinosaurs.

Published by

Daniel Lemire

A computer science professor at the Université du Québec (TELUQ).

5 thoughts on “Science and Technology links (December 8th 2018)”

  1. The followup letters to Rosenblatt point make statements like:

    Joel et al. “The high degree of overlap in the form of brain features between females and males combined with the prevalence of mosaicism within brains are at variance with the assumption that sex divides human brains into two separate populations. Moreover, the fact that the large majority of brains consist of unique mosaics of “male-end,” “female-end,” and intermediate (i.e., common in both females and males) features precludes any attempt to predict an individual’s unique brain mosaic on the basis of sex category”

    and Chekroud et al. “Based on these criteria, the authors convincingly establish that there is little evidence for this strict sexually dimorphic view of human brains, counter to the popular lay conception of a “male” and “female” brain.”

    1. His finding is stated as such:

      By fitting a linear support vector machine (2) to the voxel-based morphometry data reported in ref. 1 we achieve a cross-validated misclassification rate of about 80% (depending on the random splits). We thus conclude that, whereas the univariate brain attributes (voxel morphometry) are bad predictors of gender, the multivariate brain morphometry is a very good predictor of gender.

      Thus you can predict gender from brain morphometry with an accuracy of 80%.

      Of course, the result might be wrong but it is a simple classification exercise using available data. One can verify it quickly.

      As far as I can tell, it was never contested. Thus it is reasonable to assume that it is so: if you give me the morphometry of a brain, I can predict the gender well.

      1. The reply by Joel et al. addresses that 80% result directly.

        Rosenblatt (7) correctly identified an individual’s sex category about
        80% of the time … Chekroud et al. (8) correctly identified an
        individual’s sex category about 89.5–95% of the time, but accuracy
        dropped to 65–74% when head-size-related measurements were regressed
        out. This latter finding is in line with previous reports that
        observed sex/gender differences are largely attributed to differences
        in brain size (9, 10) (see also figure S4 in ref. 1). Although the
        different supervised learning methods achieve better accuracy in
        predicting sex category than the simple method described above, they
        have the same conceptual problem, namely, it is unclear what the
        biological meaning of the new space is and in what sense brains that
        seem close in this space are more similar than brains that seem
        distant. Moreover, it is unclear whether the brain variability that is
        represented in the new space is related to sex or rather to
        physiological, psychological, or social variables that correlate with
        sex (e.g., weight, socioeconomic status, or type of education) or to a
        chance difference between the males and females in the sample (2, 4).
        One way to answer this question is by checking whether a model created
        to predict sex category in one dataset can accurately predict sex
        category in another dataset. Using SVM, we found that accuracy may
        drop dramatically (sometimes to less than 50%) when a model created
        using a dataset from one geographical region (Tel-Aviv, Beijing, or
        Cambridge) was tested on the other datasets.

        1. Interesting. I had guessed the brain size was an important variable in this problem, and it appears that I was right, but I am surprised by the strength of the effect. Maybe I shouldn’t have been.

          It does not seem right to reject size-related features, but it is an interesting qualification.

          I am not sure I understand the quote, however. The fact that a model can learn to predict gender based on brain features is a data point… but the fact that one model fails to generalize across different genetics tells you nothing at all.

          Being able to build a model is informative; failing to do so proves nothing.

          Or they mean to imply that a single model cannot cover multiple ethnicities? Why would they think so?

          1. The single model of “male genitalia” and “female genitalia” – strongly bimodal, with “intersex” as a third category – does cover multiple ethnicities, so if you don’t think “male brain”/”female brain” doesn’t do so, then why would you say there are male/female brains?

            Are there male heights and female heights? Someone 157cm high is more likely to be female than male, while someone 188cm high is more likely to be male. Does that make 157cm a “female” height? Clearly no, as there short men, and even subpopulations where most men are under that height.

            I think the argument is that if you try to classify brain features as male and female, then you’ll find out that far more people have “intersex” brains, with some male and some female features, than people with ones which are all male/female. The numbers cited are ‘0–8.2% internally consistent brains and 23–53% substantially variable brains’.

Leave a Reply

Your email address will not be published. Required fields are marked *

To create code blocks or other preformatted text, indent by four spaces:

    This will be displayed in a monospaced font. The first four 
    spaces will be stripped off, but all other whitespace
    will be preserved.
    
    Markdown is turned off in code blocks:
     [This is not a link](http://example.com)

To create not a block, but an inline code span, use backticks:

Here is some inline `code`.

For more help see http://daringfireball.net/projects/markdown/syntax