wch

That page says that the affected versions are ">=0". Does that seem right? That page also says:

> Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be rotated immediately from a different computer. The package should be removed, but as full control of the computer may have been given to an outside entity, there is no guarantee that removing the package will remove all malicious software resulting from installing it.

Is this information accurate?

The irony is that he's doing it, just in the opposite direction of those he disagrees with.

Some things _are_ economically inevitable, and it's wishful thinking to ignore them. For example, it is inevitable that solar will be the primary form of electricity generation in the coming decades (and I am thankful that this is the case). For those that were watching closely, this was clear several years ago, and it is even more clear today.

People whose fortunes are built on the fossil fuel industry might wish it were otherwise, and they might dismiss "solar inevitabilists" with the same technique as the author. But those people would be wrong.

If someone says that something is inevitable, they could be wrong. But they could also be right.

https://github.com/posit-dev/positron

With the patches to LLVM Flang, WebR can be built with a real Fortran compiler.

I think George didn't want to say it directly in the blog post, but he has said that he's hoping that Flang would take his patches or implement better ones. That would be a win-win -- these patches wouldn't need to be maintained separately, and since unmodified Flang would be able to compile to wasm, it would benefit other projects out there that use Fortran.

https://docs.r-wasm.org/webr/latest/

> One catch: you have to invest your life savings in Twitter and it has been in the fast lane to bankruptcy since May.

https://twitter.com/elonmusk/status/1604626103326253056

This is really interesting, and it made me wonder how to convert between space and time. I mean, one meter up is equivalent in magnitude to one meter forward, is equivalent to one meter to the right. Is _c_ the conversion between space and time? In other words, is 300 million meters equivalent in magnitude to one second of time?

> These results imply that, after correcting for confounding variables (see notes under Table 3), those who were deficient at time of birth had on average 0.06 point higher gender-weighted SRS score, which corresponds to an increase of approximately one fourth of the s.d. (s.d. of the SRS in the total sample=0.23). When treating SRS as a dichotomous outcome, that is, comparing children with SRS scores above the suggested cutoff with children below the suggested cutoff, those who were vitamin D deficient at mid-gestation were 3.8 times more likely to be screened positive than those who were vitamin D sufficient at mid-gestation (see also Supplementary Table 4).

(SRS is the Social Responsiveness Scale)

- Child care (necessary if both parents work or there's just one parent)

- Larger apartment/house, in an area with good schools

My wife and I live in a large midwestern city. Prior to having kids, we had more than enough space in a 2-bedroom apartment. We had in the past (as students) lived comfortably on less than $20k each a year.

Putting an infant in a day care costs between $1200 and $1600 here. It drops a few hundred when the baby moves into the toddler room, and a few hundred more when the toddler moves into the older kids' room. With two kids in day care (an infant and an older kid), that still costs in the ballpark of $2000-2500 per month.

The 2-bedroom apartment is too small for 2 adults plus 2 children, plus visiting family members who help with kids, and moving to a bigger place in an area with good schools also increases monthly costs substantially.

Even if you're not spending money frivolously, having kids is expensive compared to not having them.

Today, much of lens correction are done in hardware, by adding more lens elements, which makes the hardware larger, heavier and more expensive. Some corrections can be done in software these days (barrel/pincushion distortion, chromatic aberration), but not all.

What they've been able to do with this camera is move a lot of those corrections into software, allowing them to use a simpler, cheaper lens with a very large aperture. The constant F/2 aperture is unheard of in a lens with such a large zoom range.

Besides aberrations, there's another issue with using traditional large-aperture lenses: focusing takes time, and it's not always accurate. With the Lytro, I imagine that you can take pictures instantly without needing to focus, and you can get perfect focus in post-processing.

From what I've seen in the sample gallery, image quality isn't great compared to regular camera/lens combinations in the same price range. This camera might not be for everyone, but I really hope the technology continues to improve and eventually become mainstream.

And I hope these aren't developers they were hiring and firing. The average salary of these bad hires was about $57,000 per year. (The total was $70,000, divided by 6 people who stayed an average of 75 days.)

First, the statistical tests used for these experiments don't make use of Bayesian stats, so the prior 50%-loaded probability simply isn't factored in. The standard is to use null-hypothesis testing, which says roughly, that if the null hypothesis is true -- that is, if there is no actual difference between the populations (experimental groups A and B, for example) -- what is the probability that you'd see a pattern like the one observed in the data. And the tests take sample size into account in calculating this probability.

If you throw the die once, the test that you'd use here (Chi-square) would _never_ give you a false positive, that is a p-value of <.05. With small samples, there is too little power to get a the requisite p-value. (And I'll note that Chi-square is one of the tests used in these papers.)

There's a whole other debate about whether p-values and null hypothesis tests are the right thing to use, whether the standard 0.05 threshold p-value is small enough, whether Bayesian stats should be used, etc. These are legitimate issues. But they're separate from the claim that small samples will increase the likelihood of a false positive.

We've already agreed that a small sample size doesn't make it any more likely to find a false positive for a given hypothesis. This is true for H1, H2, H3, etc., where each of these is a hypothesis. Therefore the aggregate effect of testing N different hypotheses is that you're no more likely to find a false positive with a small sample size vs a large sample size. You are more likely to have false negatives with small samples, though.

That's a good goal - but how do you know in advance whether it's a fad (like Zynga) or the real deal (like Facebook)?

On the other hand, selling this stuff with claims like, "Soylent is perfectly balanced and optimized for your body and lifestyle, meaning it automatically puts you at an optimal weight, makes you feel full, and improves your focus and cognition," is another thing completely. It sounds like a total scam, especially since they don't even say what's in it.

    # Memoize read.csv so each data set only needs to be downloaded once
    require(memoise)
    readcsv <- memoise(read.csv)

    fluPlot2 <- function(country="ca", regions="Nova.Scotia") {
      require(ggplot2)
      require(reshape2)

      url <- sprintf("http://www.google.org/flutrends/intl/en_us/%s/data.txt", country)
      d <- readcsv(url, skip=11, header=TRUE)
      d$Date <- as.Date(d$Date)

      # Convert to long format
      dl <- melt(d, id.vars = "Date", variable.name = "region")

      # Select regions of interest
      dlsub <- subset(dl, region %in% regions)

      ggplot(dlsub, aes(x=Date, y=value, colour=region)) + geom_line() +
        theme_bw()
    }

    fluPlot2("us", c("Minnesota", "California"))