 Let's talk about all these preferences series. What we have been talking about so far is WebOm something. This means that I have a random number generator, each generally samples, and this is the sample that I'm going to use. And many net addition for thinking that we could perhaps do much better than that. Because what I would be looking for is the hemisphere, and I would be shooting samples on the surface on this hemisphere. And I can do this deterministically. What if I have an algorithm that doesn't generate random numbers, but it will make sure that this hemisphere, if I have 100 samples, the samples are well distributed on this hemisphere. And if you do this, you may get much better convergence and much better looking results. So below, you can see a random number generator generated samples in 2D. And up there, the halter sequence, which is called below this frequency series, what this means is it's not completely random, but it tries to fill the space reasonably. Now this is not fibio. If you read after it, what you would think is that you would just get a grid. It would be very simple to get a grid, and just put points in the grid. And then you would have samples that are really well distributed. And this you can do it 2D. This you can do in one line, just given the space of this work step. But there are mathematical clues that tell you that this is absolutely terrible in higher dimensional. So if you have higher dimensional spaces, then this is not well specified. So the most commonly used sequences are the halter sequence. So the most commonly used sequences are the halter series, the soil series and water corpid series. You can do this low discrepancy sampling in many different ways. And this matter of years ago, an even distribution of the noise, because you are sampling these halus spheres reasonably, in a reasonably stratified way. So it cannot really be that one side of the hemisphere is sampled almost exhaustedly and the other one is completely neglected. So you would get images with a noise distribution that's better for you. So that's a plus. But what is even more important is that this is deterministic. So if you're entering an animation, imagine completely random sampling. If you have frame number one, you distribute your samples. Then comes frame number two. And then you distribute your samples in a completely different way. So the noise could look like this from frame one and that can be completely different. So until you have converged perfectly, you will have these issues that you call temporal flickering or temporal hearings issues. Because the noise looks like this on frame one, looks like that on frame two. And if you take 25 of these frames every second or you leave a door, then you will have really is probably incredibly different. You have computer and different things in every frame. And the solo series and all low discrepancy series help you with that. So they love to use the same thing as screen because of this reason. In the subsequent frames, you will compute the very same thing. And advantages. OK? Disadvantages. Well, these advantages are also huge. It's often not trivial to implement such a thing. If you take a look at this image, these walls are not textured. These are one color green. This is a one color green. This is a one color red, if you want. This should be rendered like this at all. This is a body image. And I have implemented the halter subplot. And the problem that I encountered was called correlating dimensions. And this is a serious problem that you can encounter. I will not go into the details. But you just messed up with one small detail. And you can get an image like that. Well, this is actually a delightful way of failing. I don't know about you, but most of my primary errors and these calculations are quite like this. They tend to look like this. So I usually read send-onation problems, or like that. So if you make a mistake in global innovation, rendering even your errors, it's better than in other fields.