- 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.
- 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.
- In the US, if I know your zip code, your gender and your birthdate, I can nearly uniquely identify you.
- In wealthy countries, happier people are more likely to have children.
- 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.
- It seems that very large mammals co-existed with the dinosaurs.
8 thoughts on “Science and Technology links (December 8th 2018)”
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 â€œmale-end,â€ â€œfemale-end,â€ 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 â€œmaleâ€ and â€œfemaleâ€ brain.”
His finding is stated as such:
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.
The reply by Joel et al. addresses that 80% result directly.
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?
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 ‘0â€“8.2% internally consistent brains and 23â€“53% substantially variable brains’.
For #3, I think there are some serious issues with the paper that Cook’s simulation is based on (https://dataprivacylab.org/projects/identifiability/paper1.pdf).
Cook does a simulation using fixed population per zipcode and uniform probability of any dob for 0-78 years, and gets an 84% probability uniquely identifiability. But the paper, which supposedly accounts for the actual distribution of population per zipcodes and actual clustered age brackets gets 87%. The problem is that Cook’s approach should be an upper bound, and any clustering should lower the probability.
So I read the paper, and found that rather than doing a simulation, the author just used a simple binary “yes/no” for all residents in a zipcode depending on the number of people in their age bracket. On the bright side, this is clearly described in Section 4.3.1 (apart from what I’m hoping is a crucial typo). On the dark side, this means the 87% number doesn’t bear any relation to the simulation that Cook ran, or the actual number of people that are identifiable.
To get a better idea of what the real number would be, I rewrote a modified version of his simulation to use the actual zipcode populations (https://blog.splitwise.com/2013/09/18/the-2010-us-census-population-by-zip-code-totally-free/). Then I ran it (on Power9!) and got 64% identifiability. If you were add in the age specific information (which I didn’t find in my quick searching), this number would drop further, although I don’t know by how much.
So while I think the paper is right that (zip, dob, sex) does uniquely identify some large percentage of Americans, I’m disappointed that the exact number being touted turns out to be so flimsy. Maybe I’m wrong, but it feels like none of the people currently promoting the paper did any verification on whether it was actually right. When Cook talks about the 20 rejections for the paper, it makes me wonder if maybe peer review was actually doing a good job.
I did figure out how to download the age bracketed zipcode population data from the census.gov website and massaged it into a form I could work with. The age clustering didn’t have much further effect on the percentage identifiable, only a couple percent more.
It’s a little hard to compare the numbers directly, as the five-year age brackets go through age 90 and the previous assumption was for a max age of 78, but my final conclusion is that 63% is a good final estimate. That is, if we use the 5-year-age-brackets from the 2010 census, and actual populations for zipcodes, a little under 63% of Americans are uniquely identifiable by (zip, sex, dob).
I’ve always heard that heart and brain cells don’t regenerate and once gone are gone, etc.
But this has always made little common sense to me. Speaking of heart cells, athletic training in the highest heart rate zone builds denser heart muscle that contracts with more strength, and the zone just below increases the volume of blood pumped per beat. And top athletes are known to have enlarged hearts for these reasons.
Thus… clearly change is afoot?
And there are similar arguments about brain plasticity.
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