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@temptoetiam @_thegeoff

On shallow enough angles you get more reflection (and maybe even more reflection than scattering). This gets exploited by en.wikipedia.org/wiki/Wolter_t

I actually realised now that I don't understand why the angles need to be shallower for gamma (Fresnel equations imply that reflectivity for a given direction and polarisation of beam depends only on refractive index -- with no reflection at all for equal refractive indices -- and in the around-visible range refractive indices tend to grow with decreasing wavelength).

@eoaiuastwg Does fellow have different connotations or is it not gender-neutral (or both)?

@b0rk

And if you have a directory with a colon in its path (a totally legit character for filenames), you are screwed.

@isomer

There's something simple that works that escapes me this moment. Just one comment: if you get it via a soundcard-like input you get real values, because you shifted it to baseband and IIUC lost the distinction between negative and positive frequencies. If it was shifted s.t. carrier wasn't DC you'd IIRC be able to retrieve the expected complex signal (because carrier-eps and carrier+eps are distinguishable frequencies).

@isomer

Yes. And that isn't affected by components of the signal of sufficiently different frequencies (with the "sufficient" distance being on the order of magnitude of 1/sampling window).

@isomer

Ah, and you need to have these be counterrotating (i.e. flip the sign of time in quadratureXXX definitions).

The reason that works is that if you have a spinny thing that spins significantly more than once during your sampling period, the value of the spinny thing will average to zero. If the spinny thing makes much less than one revolution, the average will be close to something on the unit circle. So, for a signal that's a single sine wave this should work well to detect whether its frequency is close to the given frequency (up until the point when aliasing becomes a problem -- which is when the spinny thing spins at least ~once between two adjacent samples).

It also works for arbitrary signals because everything up to the point where you take the absolute value is linear, and all the uninteresting signals contribute ~0.

@isomer

No, absolute value of the mean rather than mean of the absolute value.

@isomer

Don't you mean "pointwise multiply" when you say convolve? (Then, the _resulting signal_ would average out to 0 if the sine was absent and to a complex number with absolute value prop. to sine's strength in the original signal and arg indicating the phase offset. Note that this is about the average of the signal and not the absolute value of the signal.)

@isomer

Why do you want RMS of a complex signal? What is the physical thing you are trying to model?

ISTM that the transformation that does to bodies of async functions to split them into pieces-between-await-calls requires unsafe blocks (if we hold a ref from one block to another, the ref remains valid only by virtue of !Unpin around its target and so we start relying on things that cannot be expressed in the type/lifetime system for safety).

Is there a macro library/something that would allow me to do something similar _without writing unsafe myself_?

@isomer

Yes, magnitude(mean) != mean(magnitude).

Mean of complex numbers is useful or not in a very contextual way :) (e.g. see why the Fourier basis is linearly independent).

It seems that numpy is slightly silly and your options for computing squared norm is either np.real(x)**2+np.imag(x)**2 or np.abs(x)**2.

@isomer

Isn't `x**2` literally the square, which will be a complex value that just rotates twice as quickly?

@_thegeoff

Do you need a dome, or just a room of any convex shape with high enough ceiling? Parallax shift rates (i.e. angle change per head position linear change) are inversely proportional to distance, so the shifts should be continuous but not necessarily smooth for a non-dome ceiling. I'm not sure what's the threshold for noticeability.

@suricrasia

If you haven't played much any, Dreamhold is a good intro.

ifdb.org is a great resource to look for games

@_thegeoff

I was thinking of giving someone a view of sky with clouds with a very large (at least tens of meters) effective intereye distance, thus effectively scaling distances down.

Technorama in Winterthur has a "distance magnifier" on the roof, which is basically a large pair of binoculars with effective intereye distance of ~2m that point horizontally and can be rotated around the vertical axis. That causes surrounding buildings to appear much less flat, so the obvious idea is doing the same to the view of clouds. I would guess that having 3 such cameras would already allow for some nonterrible interpolation for viewing angles where the line joining the eyes doesn't align with the line joining any two cameras (I suspect that being able to provide roughly correct view for slight angular movements is important for creating a realistic-looking view for humans).

@_thegeoff

Somewhat unrelatedly, have you thought about setting up 1-2 additional cameras for parallax to the clouds?

@koakuma

The abstract made me think that one can assign "worst-case(?) number of CAS per MCAS" to any implementation of MCAS and that they prove that: (a) lower bound for that is k (b) their implementation achieves k+1. That would be a near-optimality claim about their implementation. However, that's not the case, because there are actually two different such values: the one the lower bound (in the Impossibility section) talks about is not the one they show is equal to k+1 in their implementation.

In fact, one can probably show that any such implementation, for some adversarial scheduling and operation sequences, might need to do unboundedly many CASes for some MCAS operations (otherwise it would be waitfree).

@koakuma

But it does make you install semi-permanent per-operation data (the descriptors cannot be cleaned up until way later -- if you wanted to clean them up immediately you'd be back up to 2k CASes -- and even if they could you'd need to let them stay at least dereferencable-but-with-arbitrary-content for nearly as long).

@koakuma

Do you understand what is the thing they're providing bounds on? The lower bound seems to be on number of CASes needed pessimistically under some interleaving of contended MCASes and the upper seems to be on number of CASes needed for _uncontended_ MCAS operations.

@koakuma

Scratch that, I'm reading too inattentively. Still seems slightly fishy in different ways, but I'm probably still reading too inattentively.

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