On shallow enough angles you get more reflection (and maybe even more reflection than scattering). This gets exploited by https://en.wikipedia.org/wiki/Wolter_telescope
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)?
And if you have a directory with a colon in its path (a totally legit character for filenames), you are screwed.
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).
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).
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.
No, absolute value of the mean rather than mean of the absolute value.
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.)
Why do you want RMS of a complex signal? What is the physical thing you are trying to model?
ISTM that the transformation that #rust 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_?
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.
Isn't `x**2` literally the square, which will be a complex value that just rotates twice as quickly?
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.
If you haven't played much any, Dreamhold is a good intro.
ifdb.org is a great resource to look for games
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).
Somewhat unrelatedly, have you thought about setting up 1-2 additional cameras for parallax to the clouds?
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).
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).
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.
Scratch that, I'm reading too inattentively. Still seems slightly fishy in different ways, but I'm probably still reading too inattentively.
I enjoy things around information theory (and data compression), complexity theory (and cryptography), read hard scifi, currently work on weird ML (we'll see how it goes), am somewhat literal minded and have approximate knowledge of random things. I like when statements have truth values, and when things can be described simply (which is not exactly the same as shortly) and yet have interesting properties.
I live in the largest city of Switzerland (and yet have cow and sheep pastures and a swimmable lake within a few hundred meters of my place :)). I speak Polish, English, German, and can understand simple Swiss German and French.
If in doubt, please err on the side of being direct with me. I very much appreciate when people tell me that I'm being inaccurate. I think that satisfying people's curiosity is the most important thing I could be doing (and usually enjoy doing it). I am normally terse in my writing and would appreciate requests to verbosify.
I appreciate it if my grammar or style is corrected (in any of the languages I use here).