mkbosmans

If you capture the heat from the burning, e.g. to generate electricity, then that can save burning oil/gas/coal instead.

Furthermore, the landfill will usually also generate gasses like methane, which need to be burned off too.

Nobody says they have no limitations. The question is are those limitation fundamental, i.e. can we expect improvement, say within a year.

No need for radical changes.

  def visit_bf(g):
    n, children = g
    yield n
    if children:
        iterators = [iter(visit_df(c)) for c in children]
        while iterators:
            try:
                yield next(iterators[0])
            except StopIteration:
                iterators.pop(0)
            iterators = iterators[1:] + iterators[:1]

The difference between DFS and BFS is literally just the last line that rotates the list of child trees.

Python is a pretty mainstream language and even though the DFS case can be simplified by using `yield from` and BFS cannot, I consider that just to be syntactic sugar on top of this base implementation.

Well, the article says that the effect of the impact was much larger than the scientists expected. That doesn't really give a lot of confidence in how good we are at predicting these things.

I'm sorry, that second reference was actually for the 3.5ULP variant. The 1 ULP is here: https://github.com/shibatch/sleef/blob/master/src/libm/sleef...

One of the ways that the classics can be improved is not to take the analytic ideal coefficients and approximate them to the closest floating point number, but rather take those ideal coefficients as a starting point for a search of slightly better ones.

The SLEEF Vectorized Math Library [1] does this and therefore can usually provide accuracy guarantees for the whole floating point range with a polynomial order lower than theory would predict.

Its asinf function [2] is accurate to 1 ULP for all single precision floats, and is similar to the `asin_cg` from the article, with the main difference the sqrt is done on the input of the polynomial instead of the output.

[1] https://sleef.org/ [2] https://github.com/shibatch/sleef/blob/master/src/libm/sleef...

That's not just a good prediction—it is literally already happening right now!

Nothing on that list has been named that way by Euler himself of course.

That seems to be due to he microcontroller using its pins in duplex. There is indeed no radiation being emitted in that case, just the lamp and rotation.

https://news.ycombinator.com/item?id=46979936

I saw that you used `float z;` to later use `z` instead of the constant `0.`. You can also apply that to get a zero vector: `vec3 y;` and use `y` in place of `p-p`.

It seems that leaving the obsession behind some more can save another byte.

Another two bytes found (I think)

  (d==0.?K*.01*h:c-c)

could become

  (d>0.?.0:.01)*K*h

Especially in HPC there are lots of workloads that do not benefit from SMT. Such workloads are almost always bottlenecked on either memory bandwidth or vector execution ports. These are exactly the resources that are shared between the sibling threads.

So now you have a choice of either disabling SMT in the bios, or make sure the application correctly interprets the CPU topology and only spawns one thread per physical core. The former is often the easier option, both from software development and system administration perspective.

Sort of niche indeed.

In addition to needing SMT to get full performance, there were a lot of other small details you needed to get right on Xeon Phi to get close to the advertised performance. Think of AVX512 and the HBM.

For practical applications, it never really delivered.

I'm not sure what the OS on the X32 (or the Midas M32 sister model for that matter) is from factory. The higher end Midas Pro consoles do definitely run on Linux though.

It is the performance win for similar looking results that I find improbable. For a box blur to look like gaussian blur, you would need multiple passes. Even though each pass is now O(1) instead of O(n) (with n the blur radius), due to caching effects I think a gaussian kernel would still be faster, especially for the small blur radius as described in the article.

That link is not a box filter, as it still uses weights to approximate a gaussian convolution kernel. It just uses some special hardware to do less texture fetches. But that is a constant 2x improvement over the full 1D convolution, not the box filter O(1) approach that the article suggests that browsers are using.

Do browsers really use a box filter to approximate a gaussian blur? That seems implausible to me, as they produce pretty different looking blurs.

I noticed the blur only "sees" the underlying pixels directly below the glass surface. Any pixels outside that box, but within the blur radius do not get used in the gaussian filter. Probably the edge pixels are just repeated. You can see this when the light of the moon pops in to view when the edge of the rectangle starts to touch the moon. It would look more real if the light pixels from the moon start to through even when the box itself is still just over the dark area.

Would this be possible to achieve in CSS? I presume having a larger box with the blur, but clipping it to a smaller box or something like that.

Why do you say a tritone substitution turns it in a II-bII-I? Can it be as easily said to be II-#I-I? In that case it would be C#.

This looks to me like actual correct usage of the term exponential. Surprisingly correct usage of that term is rare, even in technical writing.

Let's say each dimensions added has a finite set of N possible values. Then for k dimensions there are a total N^k possibilities.

Combinatorical growth would actually be faster still, scaling like k!.

Yes, I think it is valid usage.

Why do you think usage of the term _curse of dimensionality_ is different in ML?

Nothing requires an ESOP to skip on a separate retirement fund for employees and expect them to retire from their share of the investment.

In the contrary, I expect the owners of an ESOP are very much in favor of having a well managed separate employee retirement fund. More so than in a publicly owned company.

But of course you are right that the risks factors of losing your income and losing your investment are pretty much 100% correlated for an ESOP. Some investment diversification is always a good idea.

It is not an unconstrained optimization problem. The constraints in which the company operates might result in a feasible region where even the most profitable point is still a net loss. Those constraints may of course be self-imposed, like e.g. choosing a market to operate in.

> get paid like you should for a job like that

Does that happen very often though?

I do agree about the virtues of a resume that shows that it has been put together with care and attention.

But don't forget that while coding, the IDE, compiler, or whatever will correct your spelling mistakes in a very short feedback loop, with hardly any penalty for your output rate. That might mean that your dyslexic super programmer has never learned the value of carefully going over each text before submission.

I think that is unique to Gitlab though. For example Azure DevOps will rebase the branch for you when completing a pull request.

The big difference between adding the first `== true` to `if (x)` and adding more is that for a bare `x` you need more context to know whether the expression inside the `if` is of boolean type or something that will implicitly be casted to boolean. With `x == true` you know just looking at the `if` statement that it is a boolean expression. Adding more `== true` does not make it more explicit.

That's doable with the Python interface to GnuCash. I hacked together a little script that sucks out invoice data out of a register file and creates PDFs for new ones using a LaTeX template. My wife uses it for her business, mostly without my involvement, so seems robust enough.

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> Unlike using either underflow exceptions or subnormals, the use of flush-to-zero and denormals-are-zero > is acceptable only in the programs where computation errors do not matter at all, such as games.

That is not true. FTZ and DAZ are perfectly reasonable in a lot of scientific computing scenario's, where computation errors in general are closely scrutinized.