OK, OK, it seems fine. Perfect. Recording in progress. So for this last talk, we're going to have Alberto Bietti from NYU. And he's going to tell us about single index models and how to learn them with shallow neural networks. Alberto. OK, thank you so much, Sebastian, for the intro. Thanks for inviting me to this wonderful workshop. So since I'm the last speaker, I have to say this was a really great week. I really enjoyed every talk. And meeting everyone. So thanks to the organizers again. OK, so this is kind of a work that is not yet on archive. Hopefully, it'll be there soon. So it's a little preliminary, but please stop me for any questions. I'm happy to take questions also during the talk. I don't have that many slides. So the idea is single index models. If you've heard of that, that's basically when you have a target function that actually only depends on one direction. So a projection of your data on only one direction. So the goal here is you would like to kind of recover that structure, but also learn potentially the complexities of the function that you define on top of that projection. And I should say this is joint work with Joan Brunet at NYU and two students, one at Columbia. And so Clayton from Columbia, Minjay from NYU. OK, so let's start with some intro about structure in neural net. So neural networks, we know that they work very well in many problems, typically very high-dimensional problems. So on images, on texts, graphs, biology problems, et cetera. So high-dimensionality of the data is usually kind of a crucial aspect of these models. And hopefully it seems like deep learning really is successful on these kind of tasks. So high-dimensionality is a big deal. Another problem, another aspect is that optimization typically works. So we just use simple algorithms like gradient descent. And we can learn models and things work out. So there has to be an important aspect of any theory that tries to explain these models. And finally, often when you have a lot of parameters, these models are very expressive. So typically you have universal approximation aspects so you can fit any data. And even on a training set, you can typically easily fit all your data using neural net. So we're going to try to incorporate these three aspects. And when I talk about structure, here we'll consider one form of structure, which is we have some structure on the target function. So we assume that we have some data x with perhaps a simple distribution, think of it as a Gaussian. And then the labels y in your data are generated from some function f star of x, and then maybe with some noise. And the point is that typically in these high-dimensional problems, you need some structure on this f star in order to learn efficiently. So one question that you might think of is if you have these problems that work in high-dimension, what's the right structure to put on this f star? So there's various ways to put in structure and we'll think about one specific one in this talk. And then once you put these assumptions on the target, what some parts of deep learning theorists have to do is just how do you show that a neural network can actually efficiently learn, so from few samples and with efficient algorithms with such a structure, with such an assumption on the data distribution. Okay, so let me go through a few things that kind of show up in the literature. So the first case is kind of generic assumption on f star. So this is classical sort of non-parametric statistics. So maybe f star is Lipschitz's function or it's a smooth function with a certain number of derivatives, let's say beta. And so in this regime, even kernels, which are kind of a simplified view of models that have existed for a long time, actually give you optimal rates. So we know that typically even, so neural nets are linked to these kernels. Sometimes when you think about neural networks with random weights, it's basically like a kernel. And we know that in this case, if you have these generic assumptions, you can typically learn efficiently. So when I say efficiently though, this is not really a great thing in high dimensions because typically if f star is only Lipschitz, you have a non-smooth function in high dimension that's really hard to learn and you pay exponentially in the dimension. So the number of sample has to be exponentially, you have this cursive dimensionality phenomenon. So we have to go, hopefully we have to go beyond that with some structure. So another simple setting that has been considered a lot and here, I'm just quoting two papers that are relevant to what I'll talk about, but there's really been a lot of work on these kinds of models.  So you have one direction and then you have a specific neuron that matches the one you're using for training. So you're using a value network and your target is a value. And then, so in this case, there's actually some results that tell you, even if you use a single neuron and your model that you're training, you can actually fit this direction theta star and so do inference basically. And you can learn efficiently, the number of samples typically doesn't have to scale with the full dimension. Like, okay, it has to grow with dimension but not exponentially, let's say. So the issue here is that these models, okay, maybe they're nice in some cases, but this phi, you really have to know the activity. Basically you have to assume that your target is some value. Why would it be a value? Why not a more complicated function? That's kind of a limitation of this framework. And also typically you only need one neuron so you don't have this expressivity aspect that allows you to actually fit arbitrary functions. So the next step is the single index model, which is what I'll talk about here. And so here basically we'll consider functions that depend still on only one direction with this sort of planted theta star direction, but then you have a function f star on top of it which could be arbitrary. So maybe it's elliptic function, maybe it's like slightly smooth. But ideally we don't want to depend on the smoothness in the same way as this first class which actually dependent also exponentially in dimension. So here there's been, there's some works here, let's say this back paper here, which shows that some kind of convex ways to define neural networks actually adapt to such structure and can give you the right statistical rates. And there's some ways to study neural networks in these infinite with limits called the mean field regime that actually allows you to potentially learn those models but we don't really know how fast you converge or if you converge at all or what happens if you actually don't have infinite with. So it's kind of a nice theoretical model but we don't really know if the algorithms in practice would actually work. So this is why I put intractable. And then there's some other works that kind of consider models of this form but have kind of very tailored algorithms. Maybe you do something with some specific polynomials at one degree and then another degree. So these kind of can solve these tasks but they're not really something you would use every day. So okay, just some other examples of structure. So multi-index is when instead of depending only on one planted direction, you might depend on a few. And then there's other kinds of assumptions that you might make. Perhaps F star has some symmetries and it's invariant to some kind of translations of your image, let's say. You might have some F star that is actually a composition of functions. So these are all like very interesting models for deep learning but not the focus of the talk. So here we'll talk about single index models and try to use some kind of shallow network architectures gradient descent and can we learn these functions? All right, we'll keep going. Just one extra motivation for single index models that I usually think of when you have CNNs in practice which are convolutional networks very successful in images. If you look at the first layer weights, you typically see these kind of nice filters that look like some Gabor filters. And usually you initialize randomly and yet these neurons kind of align to these nice structured filters. So clearly there is some learning of these features that's happening at least in these models. And so it's kind of a reasonable assumption to make that you might have data generated by some kind of multi-index model. The next layers are kind of more messy but people might interpret similar things. Okay, let me start with some setting here. So I said the data on X will make simple assumptions so we'll just assume here that the data is Gaussian in D dimensions. And the labels are generated by the single index model with some function F star, 1D function F star, some theta star direction of norm one and then perhaps we have some noise, a variant sigma. We use, this is the architecture we use. So it looks like a shallow network. So we have large N, this is the number of neurons that we use. CI are the second layer variables, second layer weights but at the first layer we actually fix this theta. So we only have one neuron. So it's kind of not a standard architecture. We're kind of constraining all these weights to be the same. We're tying them also to be the same theta but we do have some biases B, BI here for each neuron. And the point here is we'll stick to something where the biases are actually random. So we just have random initialization for the biases and we don't train them. And all we train is this weight theta, so the direction and the second layer weights CI. And then for the activation you can stick to the value even though we can potentially adapt this easily to other activations. And so as I said, we'll think about some kind of standard gradient descent algorithm. So we have some empirical loss on our training dataset. We have a regular rise around the second layer weights and we train this with respect to C and theta. We'll also kind of use a projected version where we keep theta on the sphere. So we do like a spherical gradient on theta and then gradient descent on C. So this will be kind of the setup. Nothing too complicated, it's just this trick of kind of fixing the direction theta for the same neurons. Just kind of for simplicity, because we know it's potentially enough to only fit one neuron. Okay, so just as a warm up, I'll talk about something that kind of inspired this analysis, which is this paper on actually teacher student networks where this F star is both, it's the same F star basically, the activation and the neuron. Okay, so here we will make this assumption on the F star, which is, so we have Gaussian data. So typically when you have Gaussian data, you think about decomposition in Hermite polynomials, which are diagonal in that basis. So Hj here are Hermite polynomials of degree J and they're all orthonormal with respect to gamma here, which actually is just a one D Gaussian measure. So we'll assume that the activation and the target actually decompose with these alpha J coefficients. Okay, so if in this case, if you assume this, we can look at this squared loss objective on the population data, so in expectation over X. So we have F star, so we assume that we know F star, so both our activation and the target have this F star. Our training is on theta, but then the target is theta star. So you compute this square, you develop it, the only term that actually ends up depending on theta is this cross term here. And then there's a property of Hermite polynomials, which is when you take these functions that actually depend on two different angles, projection with respect to theta and respect to theta star, then actually the function will end up depending only on this correlation that I'll call m between theta and theta star, okay? So, and then because the coefficients of both F star and of both things here are actually these alpha J's, we got alpha J squared times mj. So if you just kind of develop and use the orthogonality property, this is what you get. So basically we have at least when m here, the correlation is positive, the landscape of this population with respect to m, so you just look at it as a function of the correlation here, it just basically goes down, okay, as m increases between zero and one, this landscape kind of decreases all the way to something, okay? So basically as long as we are on the right side, if you initialize basically with an inner product that a correlation that is positive, then you can think that if you do gradient descent on the population loss, at least you start going down and eventually you get to m equals one. Okay, so this is basically what is shown in that paper. They define this information exponent s which is kind of the first non-zero alpha J. So basically the first terms might be zero which is why you have maybe something flat here, like you have a saddle point, especially, so in this plot I think s equals two, so the first two, like the zero order term, well, zero order term doesn't matter, but the first one is zero, so you actually get this saddle point at zero and you have to somehow escape it in the beginning. And typically, okay, so when you initialize, this is the kind of, if you do random initialization on the sphere, you expect that m initially is something like one over square root d. This is kind of a standard Gaussian, like CLT type thing, and then we assume that we're on the right side, so m is positive with half probability, and then what happens is that you need enough samples to actually capture the saddle well, and what they show in the paper is that, okay, actually it could be something tighter than this. I think it's s minus one when s is large enough, but basically when you have d to the s samples, then that's enough to kind of escape this saddle point in the beginning. Okay, so this is kind of, actually it relates also to the previous talk where you have these things that might shoot up. If this might tell you actually the kind of sample complexity you need when you have tensors of a certain order, perhaps, so these things are actually linked. Okay, so we have this initial saddle. The other terms might be negligible, at least when m is very small, but this is kind of what your landscape looks like, and then with enough samples, you can kind of escape this. Okay, so this is kind of a starting point, and once you escape the saddle point, then things can shoot down pretty quickly. As you can see, the things kind of get easier over time, and you can actually recover perfectly your theta star. So this is the picture. Now what happens if we don't know f star? So now f star has this decomposition with these alpha j coefficients, but phi, which is our activation, has some different coefficients. So maybe this is a relu, that's where we're using to train on neural net, but our target actually has different coefficients. So in this case, you can again rewrite the lost landscape on the population here. It still depends on this correlation m, but now you might have different, so you don't have a square here, so this actually could be negative, which means that you don't necessarily go down, and this is an example where some of the coefficients are negative, and so you actually might get stuck before reaching one. So in this case, you don't get full recover, you don't actually align completely the two neurons, and you get stuck somewhere in between. All right. So if alpha j, beta j is negative, then we may get stuck, because you have some critical point in between, but yeah, so at least if the very first term, so what will matter is basically the first term that is positive, so that you do go down a little bit. So if the information, S here is the information exponent, the first non-zero term, if that coefficient alpha S, beta S is actually positive, then you can go down a little bit until you get stuck at some first zero gradient point. Okay. So what basically what we hope to do here in neural nets is just to kind of learn these beta j's. Beta j is the activation, but if instead of using an activation, you use something more complicated, then maybe hopefully we can learn these beta j's, and if these beta j's can align with the alpha j's, then this thing should be, all these terms should be non-negative, and so then we can actually go down all the way and keep aligning the neurons until the end. So that's kind of the idea of this work, and the point will be using these random biases to somehow try to learn something about this outer function, and so then this should allow us to also learn theta perfectly. Okay, so this was kind of the full idea, and I'll give a bit more details just so that you get something out of it hopefully. And the starting point is we can again write the population landscape. So here F star is the same target function with these coefficients alpha j, and our network is this FC theta that depends both on output layer C, and on these, so here big phi is kind of a feature map. You can, if you know kernels, you could think of this as a kernel, but it also, this kernel depends on this projection with respect to theta, so the kernel is actually learned in the sense that theta is being shifted. And so if you develop this, this is basically what the network looks like. So we have this parameter theta that we're optimizing as well as these parameters C of the second layer, and you can again write the population loss. Here we'll add this regularization term, but basically you have another term that pops out which comes from the square, the expectation of the square of our learning network. That's this first term. And then the second term again, depends on this M correlation. So M is again the correlation. We have this alpha j, but then we have something that actually depends on C, our weights. So this can potentially be learned. So we can tune it, we can learn it. If C is very high dimensional, if you had let's say an infinite dimensional feature vector, then perhaps all of these can be completely learned. We can learn all of them for any j. That's kind of the hope. So okay, we have this operator T. For those who are curious, basically T is a standard operator that takes any function in L2 and projects it down towards all these random features that we're using. So it's a standard quantity that it shows up when you study random feature kernels. And when you project the Hermite polynomials, this is what this Tj vector ends up being. And if you take T times the adjuvant of T, that's basically an N by N matrix, which corresponds to your covariance matrix. So this is what this Q is here. So we have a covariance. Now, because we're looking at the population loss and everything is rotational invariant, that doesn't actually depend, this doesn't depend on theta. It only depends, it just depends on the data, but not on the specific direction. So basically theta doesn't really come up in this term, it would just have this quadratic form. Question, so this is kind of the landscape that we get, if you have any questions. All right. And the first, okay, so the first point, as I said, the idea is that initially, if you tune even without, basically even without tuning these second layer weights, if the very first coefficient here is of the same time of alpha, then we can actually at least get some weak kind of alignment. So M can go somewhat beyond zero. So we can get to some constant level. And then the idea will be, what can we learn beyond that? And hopefully what we can show is that there's no bad critical points. All the critical points should be either at M equals zero or at the very end. And this is kind of the first result that I'll talk about here. And we have to assume a lambda that is small enough, so regularization that is small enough, and a certain number of features here, random features that scales with one over lambda. So once you do assume this, so having a small lambda basically tells us we're close enough to approximating any function with our RKHS, especially if you have infinite data, you can actually approximate anything. And so if you have these two conditions, then basically the only critical points of this population objective are, one is okay, this degenerate thing, the saddle at the beginning, where M equals zero and the second layer is also zero. And the other critical points are either you aligned completely theta star or minus theta star, and then C is uniquely determined by this population loss. Okay, so we get basically, these are the critical points that you would need. So what we know is that there's nothing, nothing's gonna be stuck with M, but it's like strictly between zero and one, and we're gonna, hopefully if we get to a critical point, either it means that we got stuck in the initialization or that we actually recovered. So just for some details, the way this works is we use, this is where we actually use kernels. So with random biases, basically what we're defining is this expected random feature kernel, where the bias is random, and we have the same value and we have xx prime. This is what this kernel looks like. And you can actually study the approximation properties of this kernel, see how hard it is to approximate some function f here. And this p hat lambda here is the projection, when you have a certain number of random features and you have this lambda parameter, then this tells us how, this is the projection onto the random feature space, and you can control how close the projection is to the actual function, and this will scale with lambda. So if lambda is very small, this will actually go very closely, very close to zero, and it will depend on the second derivative. So typically we'll make this assumption that all these functions are twice, have a second derivative in the Gaussian norm. So that will be kind of an assumption that we'll make. And to have this as approximation, we will need that you have enough random features, and particularly as is scale with whenever lambda. And so basically it turns out that when, I think, yeah, when lambda is small enough, then you can look at the equation for the critical point and there's some term, there's some kind of approximation term that will be so small that it actually won't matter. And as long as M is strictly less than one, basically this approximation term has to be canceled. And so in the end, you end up with only the right critical points. And this is kind of the conditional lambda that you need. It has to depend on this alpha S, which is, I guess, the signal that you get at this information exponent, which was the first non-zero alpha, alpha J. This tells us basically, if we use a second layer, it can actually help us cancel out those critical points which might be an intermediate correlation. So hopefully if we train all the way, we can actually get a perfect alignment. And just some intuition on what the algorithm actually looks like. So remember, this is the population loss. One key thing is initialization. This will actually, this is what's relevant to capture sample complexity because you will need these things to not be, so you will need, you will have something that's very small because you have the saddle, so you want to capture enough of signal so by using enough samples. And okay, we don't actually use this SGD analysis of the paper that I talked about before. We hear what we do is just concentration of empirical and population landscape. And so those two need to be close enough so that you don't get lost in the initialization. But so we can have these anti-concentration results so that the initialization of both M and this other factor C transpose TS are somewhat like away from zero. We'll assume that things are kind of aligned so that we can go down. So that's probability one half event. Then we'll have two phases. So the first phase will only do what I said earlier which is kind of try to get to a non-zero level of M. So here we only train theta. C, we leave it at this random initialization and we'll actually try to, we'll tune the initialization norm so that eventually when we get to this level gamma I will actually have escaped the initial saddle. And then so in this case, because M is small enough, basically this is what the population loss looks like. So if you think about optimizing the population loss, it basically looks like this M to the S until you escape something. And then after that, because we've chosen things so that that was enough to escape the bad critical points near the saddle, then we can just kind of trust that there's no stationary points until we get to the end. So we've basically escaped some level set. So we get below zero and some, if you look at these two terms here, basically the bad critical point with M equals zero gives you zero lower bound. And what we get instead, it was get a little bit below zero so that we're sure that we're not at that bad point and we can keep optimizing until the end. That's the second phase. So you must get to something close to one. So we must kind of recover the direction. And finally to get right sort of non-parametric rates with the kernel, we have this final fine tuning phase in the theory which is we retrain the second layer weights with some fresh samples and we can kind of optimize this new lambda to get the right rates. Okay, so just the final generalization guarantee, this is what it looks like. We have a certain number of samples for this first gradient descent phase and then we have some new samples for the final fine tuning where we fix the direction and we only optimize the second layer. And this second part is just to get basically the first part we have to assume N is larger than this D to the S over something. That's to escape the saddle point. And the second part, we have some number and prime of samples and we get this kind of non-parametric rate here. So the first part depends on dimension and that's the high dimensional part. The second part, we're basically doing regression in one dimension. So that's a very easy problem that people did in the 70s, 80s. So this is what we get and we have some rate that depends also on the kernel and its approximation properties. A few comments. Okay, so as I said, we talked about the intuition on the population landscape. What happens in practice, actually we do it on the empirical landscape. So we rely on some landscape concentration results. Recovery is near optimal. So we have this N larger than D to the S, which is similar to that paper even though they actually get potentially better exponents in some regimes. Fine tuning, that's the final phase where we optimize the second rate, gives us something, basically we can exactly inherit what the kernel method would do. So you get the right rates for this 1D problem. And one comment is that if you don't do fine tuning, so if you only look at gradient descent until the end, you can still get good excess risk vanishing, but you'll get something worse. So we don't need the second sample, but we'll have something that depends on D and with a slower rate. Some ugly, not very nice looking experiments here just to see, so this is without the fine tuning phase, but we get, okay, this is S equals one. So we have a, this is a piecewise linear teacher. So it starts going down immediately, whereas if you kind of get rid of the first few harmonics, first few hermite coefficients, S equals three, then you get stuck in the beginning, but when you have enough sample, you start going down. And in these cases, you can actually get full recovery. So afterwards, what I have to still do is kind of do some experiments where you actually do fine tuning and hopefully that also improves the excess risk further. Okay, so I'll just conclude efficient learning of single index models. We'll see how we have this tied direction. So kind of a weird architecture where the neurons are tied between all the directions are tied between all neurons and the biases are random. And that's enough to basically do feature learning of one direction, plus we can do some nonparametric regression of a 1D function. So we combine these two things together and we get the right kind of reasonably right statistical guarantees. Further questions, okay, lots of them here. What if we train jointly from the start? So here we have this two phase thing where we first optimize data and then we jointly optimize. Maybe we can immediately optimize data as long as the step size is large enough. As GD on the population loss. So here we've used this argument of concentrating empirical to population, but maybe we can directly study as GD on population. Untied neuron direction. Okay, what happens if we actually have different weights on each neuron? What happens if we also train biases? What happens if we have multiple index, like different directions that we want to fit? Do we need fine tuning, et cetera? These are all interesting questions. And I'll stop here. Thank you for your attention. Thank you very much. We have time for a couple of questions. So Mark wants to start. Thank you very much for the very nice talk. Can you make any guesses of how the landscape will look like, say for a two index model? Right. I mean, I think there's actually some papers that start studying that I think the initial saddle point might be similar. What I don't know is like when you have different neurons they might move in strange ways. And I know, actually, maybe Sebastian has done some work on tracking these different correlations between different neurons. So things might behave sometimes nicely but maybe sometimes in complicated ways. So anyway, I feel like that's an interesting question and characterizing provable guarantees on when you can align. For example, if there are a thousand, would you expect to have a similar picture? Yeah, I think. Even if you have just two training neurons, I think they might align to the right things. Thanks. Maybe if I can follow up with a question. So when you do the fine tuning, like what kind of solutions do you find? Like, is the network going to do something very sparse where it basically turns off all the neurons but one with the second layer weights? Do you take some kind of effective average at the second layer? Maybe you can use that. I think it's, so basically if you don't do fine tuning you already get kind of rich like L2 solution. Cause we have, so the point is we have this L2 regularizer. So you get kind of a Hilbert norm, so you don't get sparse C. You get kind of a dense C and the point of fine tuning is also we can, so the lambda, it turns out that this lambda regularization parameter, if you make it too small then you'll suffer in the first recovery phase. So that's also why it's nice to have a separate phase where you can take a lambda that's potentially smaller and get the right rates for the non-parametric part. But basically you'll get a kernel method, so kind of an L2 Euclidean. Okay, cool. Thanks. That's another question, right there. In case in which you were to train the betas of the activation function and so train your activation function, do you expect to get better rates? I think that would be, so training the bias, right? Oh, beta, what is beta? The one you had on the activation function when you decompose the activation function on your harmonics. Oh, yeah. Oh, these beta. And of a hyperbarometer that you could optimize as well, right? Yeah, but you have an infinite number of them. Okay, yes, but you could truncate that some. Yeah, so that's actually what this other paper that I mentioned by Daniel Su, they kind of optimize one polynomial at a time until you get a non-zero one and they get the direction. They don't talk about the non-parametric rates, so that's potentially to be studied, but you should be able to get the right things. So that would mean you basically train with specific hermite polynomials as your activations. Thank you for that question. Any other questions? Any last question for Alberto? Anything on the chat, maybe, Marco? No, okay. Well, in that case, let's thank Alberto again and all the speakers of this morning's sessions.


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Live lobster? You know what she tells me? It's out of season. Like a robot. She kept on saying it's out of season. It's out of season. They're lying. They're lying. There's no way that ground mashed potatoes... Yesterday being Saturday. Do you want me to do it? This is the one question. Yeah, yeah, yeah. You do it. Yesterday being Saturday. My friend or acquaintance, Andrew McDonnells, I leave the girl here at McDonnells. Does she leave shortly after Ray and hook up with Ray? That's pretty disgusting when you think about it. You think? Well, the both of them are kind of the wacko. Look at the big yes. Are you kidding me? Am I accurate when I say that Eileen and Ray are the best walking advertisements for birth control? There is. They left together yesterday. Yes. It says yes. They are the best walking advertisements. He left without saying a word. She didn't even say a word to us. At 30 seconds or less, she jumped up and ran out. The best walking advertisements for birth control. Yes. Mysticism says yes. At what out of doubt? She loves her my guy. Oh, I told... I told my mother's caregiver, Stephanie, about the my guy.  She's known him for eight years and she hasn't been to his house. Think about that, Eileen. I mean, Jesus, I'm sorry. I'm sorry to insult you. Eight years and he's never been to his place down south. Nothing good is going to happen until you put him up with it. Just like having an essential air conditioning unit that works every day. That's not going to happen either. He said to her, he'd marry her when he turned 60. Well, Karen, he's 63 now. I'm sick of these people. I've lost all faces. I'd be there. The heat's all in here, Karen. This is good. Karen, I had to give Santa Grievance to all the market chain. I couldn't get it. I can't get a human being on the phone. I had to send a by e-message on the Internet.  They're so cheap about hiring human beings. This is our acquaintance from here at McDonald's, Eileen. Oh my God. She's still with the other guy, Ray, today. You know, this should be a law against wackos reproducing. Like, could you imagine Ray and Eileen reproducing? They should put a heavy tax on weird and ugly people to have babies. I'm sorry. I'm sorry. I mean, to bring more... Listen, Billy, to bring more... This is insane. This is how the kid... When the doctor slaps the baby, the baby goes like this. No, you know what Eileen would say? Are you sure it's mine? After she gets birth to it. Are you sure it's mine? Are you sure it's mine, kid?  But you know what?  It's always not working. That's the thing. I know. Yeah, I mean, it's not working. Wouldn't it be great if this franchise owner came in right now? You know why he'd walk out? Say I don't mean this. He'd walk out. He doesn't want to hear it. He doesn't want to hear it. People are spending their money here. He doesn't care.  Yes. What'd you call that like? Chicken Parmesan. Oh yeah, you went to that pizzeria. I spoke to my sister about... What did they reopen after the food poisoning?  My sister says, try tiramisu. And I think it's on 46 in Emmerich Park. It's excellent. It's an excellent Italian restaurant. She been going there for years. Oh my god, it's really humid in here.   He broke, yeah. He broke, he broke. There's nothing coming out whatsoever.  He broke. That's where the AC is working back there. Why is that? Why does it work in the bathroom? I don't get this. Yesterday when I was here, you're not here at 6.30.  I said, you don't need the air now. Now it's pouring out. It is ice cold over there. And now today... My friend that does... My friend from New Orleans, from New Orleans, J. Terry, that does the booze shows, the live booze shows. He's like Jacqueline. He gets up, he goes to bed. Kind of like you and Howard Stern by 8, 9 o'clock. And he gets up at Crack-a-Dorn, basically. I'm up at around 4 o'clock. Yeah. That Jacqueline used to do that. I'm over at the Holiday Inn. Yeah, he used to do that. I'm at the Holiday Inn every morning getting my paper at 20, 26. Like the chickens. Get up with the sun and go to bed with the sun. I bring my chickens. Why is this woman taking a photo of the... the sign of McDonald's? It's... It must be heat stroke, man. I mean, if that... I wish I had never met her. Put Ray on, please. That'd be funny. Oh, my God. He's a whack job. I wish I knew where she's at. He's a whack on. You know, it'd be great if we had a loud speaker and we opened the window. Now hear this. What do you think about football this season? I said it hasn't started yet. Oh, Ray asked you this? Yeah. I said, what do you... It hasn't started. We're trying to... We're trying to ask the pendulum questions, right? He comes in and he goes... But his big mouth... Remember the guy who talks real loud when he set the big guts, Ray? Trying to talk like a big cowboy when he's not a cowboy. No, you know when he starts yelling? You guys having a pow wow over there? Yes, we are. I'm trying to ask... Yeah, like the disco song. We'll have a pow wow wow, smoke to peace pipe. Remember that song? Smoke to peace pipe. You know who you mean, right? I gotta find that song.  He just goes on and on. I'm like, please. Oh, my goodness.  But he's annoying. Yeah. It's not a normal conversation. When I was in grammar school, we had this genius high IQ geek, named Eugene Hoyas. That was me. I changed my name. You don't look like a Eugene. I felt like one back. No, but this kid used to like insult... He used to insult the football jocks and even in high school, they used to pick them up by the ankles and throw them in the dumpster.  I had the highest IQ in this whole school. Maybe you did. You don't make fun of that. But you don't put down everybody to make yourself look wonderful. No. I didn't feel sorry for him getting thrown in the dumpster by the football jocks.  It's like revenge on the nerds. That was good. I remember revenge on the nerds, too. Did you see it, Karen? No, I don't like people. That's not like horse-shack. The pretty boy frat tried to trick the nerds and gave them this beautiful gorgeous suite in this hotel, this resort complex. These little nerds were up there with the pretty boys. And Robert Karen goes, this is perfect, isn't it? This is just perfect. Isn't this perfect booger? Well, it's not perfect. They said, Well, what's wrong? They said, it would be perfect if I had a boner. Sounds like porkies. Oh, good. Hey, did you see the great CNN special about the 1970s? Wasn't that great? Well, I've seen them all. Now they're going to do the 2000s. No, last night was the 90s. It was great. Oh, that brought back memories. They went through Motown, everything. They went right up to the end. That's all this stuff. Classic rock, heavy metal rock. These people today, the kids today, don't know what they missed. They don't know what they missed. The TV shows, Bob Newhart, Mary Tyler Moore. Oh, Hill Street Blues New 80s. Shortly after. St. Elsewhere.  Look at the writers. The writers must be like freaking horrible compared to I don't know. They had great writers. I don't call them shitcoms today. I call them shitcoms. They're just awful.  I said, what are you, for real?  Yes and no, it's overused. Some points maybe a little bit. But boy, when they do it for every time they do it for every force. Yeah. Yeah. Oh, could you imagine hiring? You know, that'll be the perfect career for Eileen. A stand-up comic. Iron Hurt is sitting the front row. You know what? I told her honestly, too. Eileen, you've got severe mental issues. You've got problems. You've got to get something, some help. You need help. Yeah, help is like jumping into Mount Kilauea Volcano. That'll be helpful. Well, he runs off with Ray. That's disgusting. That's worse than her talking. Just the thought of them together is worse than her... And it's funny, I didn't even say goodbye. He jumps up and runs out. Not even 30 seconds. She jumps up. Oh, that's a catch, man. Look at this. That was desperation. I should have run right outside. Whoa, where are you going? What are you doing? Come back here. You're not going with Ray, are you? Come back here, Eileen. I mean, just think about that. To run off without saying goodbye because you picked up a really winner of a dude, man. But I love my guy. Oh, yeah. You know what? We have her number a long time ago. Number two. Yeah, hey, that's exactly what they are. Number two. Next time she's here we'll just go along with what I say. Jimmy checked it out. He tracked down Jerry Gull's phone number. He's going to call Jerry about this.  I'm going to say, how was everything with Ray? Ray to retard. Hey, R.R. Ray to retard. What do you think about the football? It's not here yet. What's wrong with everybody? They can't make sense. Football hasn't started. Ray. He did this a couple of times in other years. I said, I don't... I said, Ray, I've got to be honest. I don't have a clue what you're talking about.   Amulet.  Should they be taxed heavily if they happen to get pregnant? I would.  The son of a mother. I will. Actoon. I think you should be taxed. Me. Are you kidding me? I'm the guiding light for the 21st century. Dementia.  How are you doing? Dementia. Are you kidding me? Hey. At least... If you wanted to, you could get a foldable camping car for bringing me. How'd you get off on that now? Because it bugs me that you got the money to buy one and you don't get one. Yeah, but your knees. I'm thinking about your knees, man. Not for my knees. When you get off the floor, you must have a special technique for getting up. Well, I guess you... But when do your knees hurt? Just general walking? Always. I can't go up or down if you go to the bathroom. The same as I do every time. I go to the bathroom. So what did the doctor say? Your treatments are officially for now. Which treatments? The immuno. Oh, no, I started immuno. I quit chemo. I thought you said you completed something the other day. No, I quit chemo. I gave up. I quit chemo. Because I know you had an appointment recently. I had one Thursday. I had one. And that's an appointment to... I had an infusion of the immunotherapy. So we'll see how it works. So you did start it? It's my second one. Oh, okay. You said before you haven't... The gun yet? That's brutal, man. No, seriously. That's often worse than cancer. Really? Everybody said you didn't lose any hair that we can see. You didn't get scrawled? Well, you lost a little weight. I lost quite a bit of weight. Yeah. It's more of a sex matter.  Everybody said get off the chemo and destroy it. Now what about the drugs that we're making and you feel exhausted? They're still on those as well. Basics? I've got to keep my bladder at the blood pressure. Right. Cholesterol. Leprosol is for... Oh, you're on like a statin, like lipotourism? Yes, a torvus statin once a night. A torvus statin. I started taking garlic. Larry King used to advertise for the LDL and hypertension. I'm like... They said it was sort of borderline. Borderline what? Hypertension. They said I had high LDL. But lipotour has so many horrible negative side effects that I went on garlic and my blood pressure is not bad now. It's within range. Larry King, it's a green box. Larry King used to advertise it. He used to say garlic is man-ya-feek. Of course, Billy would sound much better doing that commercial than Larry King. Nobody wears suspenders like Larry King. Oh, Flood, what about when he pulls his pants all the way up? That's when I get excited. Oh, me when you... With the suspenders, he pulls his pants. Larry would make me a... With brain. Good thing I'm not eating. Look at this, Jimmy. Oh, Jimmy. Oh, yeah. Oh, that's... I wish we knew where they live. I hope they don't have a baby that becomes like them. I really hope not. You want to raise Mary? Yeah, I do. So she's the other woman? She's the mistress? No, not woman. I take that back, I take that back. She's... She's the other one. Yeah, like cousin it. Like I said earlier, to you too. Can you picture those two naked? Oh, my God. You know, sometimes people post elderly couples cartoons, though, of course. You know, naked in a bedroom. That's pretty... I mean, that's probably how they look in clothes. Yeah. Like, give us the oil cured olives.  That's basically how they... That's probably what they both look like in the oil cured olives. That's probably what kind of brain they have, Ray and Eileen. Think about that.  Oh. Oh. I'm trying to think of what's worse. No central air or the thought of them dating. I can't make up my mind. What's worse? I am, but next time she's here, I'm going to say... So, you and Ray, huh? Oh. Well, we need a new... Oh, no, no. No, no, no. We need new material. Because I can't do swamp-ass for her. Oh. And listen, and I bet Ray has swamp-ass. Oh, jeez. I think the both of them have swamp-ass. Oh, yeah. Well, the fly's probably joined in. Oh. Oh, gosh. We're going to... We're going to really bust them the next time they're both here at the same... No, forget it. Even when Eileen's here without Ray, we're going to bust on her. Oh, we're going to have fun. Talk about maniacal madness. That's going to be a blast.  Gullah? Yeah, it was that short for gullible. Remember that? You said C-gullah. C-gullah. And she's gullible. But I'm going to tell her that. Jimmy tracked down on the computer and got her phone number. Yeah, you start it. You get the ball rolling. And I go with you. No, I didn't. Don't just disagree. No, no, no. I'll go with you. Because we need to rile her up. And then eventually we'll follow up with the amulet questions and we'll watch her have a meltdown. Oh, she gets nervous with the amulet. She self-destructs. Now, look at that dock shine. That dock shine is overweight. Dock shine? Docks? Docks wound. That means badger now. Docks with short legs. Anthony Rita. That looks like Carlos Danger, right? No, people make the mistake of giving your dogs table food. Carbohydrates. Big mistake. No. Give your pets nothing but tables. Nothing but. Give them scraps. That's nothing of fat. Kills them. Carnivorous creature has a short digestive tract. We're carnivorous creatures. I like to eat carnivorous. Vegans. Oh, I enjoy a good rib-eye steak. Oh, I want a burger. Remember, we have the meat. We got the brisket. A smoked brisket. That's if it's real brisket. What if it's steak? What if it's steak? That's great.  I don't care. The other day he pitched about. Velveeta. I just bought them both earlier. Billy, tell me it's not so. No, I love that stuff. But that's plastic. I don't care. Why can't you get months or a provolone or Swiss? I like it all. It's ugly, right? Mr. Priss here with his... No, listen, I... Real food. You tell her about the seafood salad thing. Years ago I went to buy the seafood salad. To stop it. It's plastic. You know that's not real lobster. I said, I don't care. It tastes good. Well, there I have to agree with you. It does taste good. I said, I don't care. No, I read the ingredients. Listen, Billy, I read the ingredients. I give it my stamp of approval. I don't care. I want certified, organic. I want... Who you do? I don't. I want what tastes good. I want my cheese with it. I want my Velveeta and that. I want the junk. Don't you care about extending not only your life span, but the quality of your life span. No, I want to pay something that's good. That's all I want. It's quality of life. Quality of your life span. I like cheese with it. I like... I like Velveeta. I had a huge falling out with somebody on the internet over Velveeta and cheese with it. Eat the stuff. Mr. Frisk here. I would never eat this stuff. I do all the time and I don't care. You know what kind of one-third pound hamburgers I got? Grass fed certified, organic beef burgers. I don't know what else to say except... It's a lot better than what Ray would say. I don't care. I like it. It's good. Eat it. Listen, no matter what I say, Billy's going to eat whatever he wants anyway. Hey, what are you looking at? Excuse me. Throw a big slab of Velveeta on that burger too. Okay, thank you.  No, he's getting an education. Oh, you have a look into you? No, by listening to me. Okay, let's see him respond. Go ahead, ask him a question. No, actually, honestly, Doc, is German for badger? No, it's not. They were bred to hunt badgers. They would be doctor. They changed it. Did you notice Carlos Danger, Anthony Wiener, looks like a Doc Chauncey? They are cute dogs. The whole Ham Dog family. Oh, that was great. Karen and I were in that one. Elvis Presley wrote a song about a Ham Dog.   No, that's not it. What was the other one? Crying all the time. Vivo Las Vegas. I also... Oh, that's a great... That's one of his best songs. What's this? What's that song? Falling in a trap, I can't get out? Suspicious Eyes. That was it. He's always the girls in the background. He must have been awfully hot in that jumpsuit in Vegas with that heat. My friend was with him for five hours on his private plane. But he'll treat you like gold if you're a good guitarist. He said, Billy, what? I'm down at the Earth place. He said to this day, before he passed, he could not accept but I can't understand why I'm having all this success. You know, Colonel Parker overworked him. He's got a bad... Like Michael Jackson's father. He just died recently. Joe, Joe Jackson. Mr. Warm's eyes. Colonel Parker, that greedy bastard, he pushed Elvis with his schedule. He didn't care. I don't think he really cared about that. His name, I forget. His name was no clear. He was close to Colonel Tom Parker. Colonel. He was like Hans Zimmerman or something like that. So that was his station. Oh yeah. It was a fake. That was like a shtick. But that's also Elvis who's hanging around behind him.    He took advantage of a very nice man. Decent man. But nobody told him, did they? Yeah, right. That's it. I don't know. Hello. What's up? Yeah. Now that was a very good point you made. Why didn't people who cared about Elvis and these other very nice stars entertainers? I couldn't to see him be abused and overbooked.  This is a scumbag.  He's not helping you. He's out to help himself at your expense. Like the way Don King treated the boxes. Until Larry Holmes was the first to officially take him to court, right? Larry Holmes? I think, I don't know. But anyway, in other words, they exploit people for money. That's it. Why didn't somebody take Elvis aside? He destroyed his life. And I think Joe Jackson contributed to the downfall of Michael Jackson. Oh, he was another one? No, he was. Now, the Jackie when did the Jackie Cougan go off? That's a 30. Now, where the child has money went into a trust fund. And now, there has to go into a trust fund. Until you're 18, you know. I don't worry. I don't worry.   There's a lot of dogmas right now.  You've always heard me tell you that. Open your mouth. I mean, look at poor Erin Moran of Happy Days. She was molested. Well, nobody believes her either.  That's your legal system too, Jimmy.   She died young. She died young. Overdose, I think or something. No, no, she cares herself. Poor kid.   Scott Bay. Oh, I doubt. I think he's a rapist. I'm not sure. No, but he talks a lot of shit on The other girl from Charles and Charged and this and that.   You know who posts a lot of great articles? Ralph Nader is still around. And, you know, articles about one guy that they used to be around helping people. He had one of my favorite movies, The Outsiders. A karate kid. Ralph Machio. He is. I saw, you know, he has the same face. He looks the same. He doesn't age. He was actually older as the karate kid than people thought he was. He was in his 30s, I think, when he was the karate kid. Where is he today? He doesn't get that many parts. Isn't amazing your star, but after the movie, you're not. That's why it's important to learn how to invest your money. It's more important with athletes.  Your career will be over. To all these people that was supposed to be the next great star, I have one hit movie you'll never hear again. And you'll never hear from them. Hey, look at Britney Spears. She's a... Nowadays, you don't really... She's huge. She had that long couple of years running of weight. Yes. Speaking of long running show, I can never understand Wayne Newton. God. He was there like forever in Vegas. You see him? Oh, he looks horrible.  He's probably done it 10,000, 20,000 times. All the old time is like well, the Lawrence Welkshow remember the Bubbles Lawrence Welkshow? Thank you, thank you. Thank you, thank you, thank you. Listen, when I was a kid... What is it? The King family. He was big on polka, right? And you already had the guy with the accordion come out. Oh, the concertina. Roll out the barrel. We're gonna have...  Remember that polka song? Yeah. Oh, remember Mitch Miller, sing along with Mitch? He had to... He looked like Satan, he had to go to tea. When I was a kid, you know... At the American Patriotic Show, sing along with Johnny? Oh, God, I don't remember that. I don't remember that. Probably took it off of Mitch. Oh, some other stuff. But the Lawrence Welk, it's got to be comedy today for somebody to watch it now. That's one of the best shows I think you could ever watch if you're stoned. You know when you got...  What's his name again? That discovered the Osmonds. And... To Andy Williams. And he bring... When he brought the Osmonds on, he discovered it, and that's when Marie had buck teeth and everything. You know what's great? I saw a thing for him yesterday. Now she does NutriSystem. And I think one of the greatest singers in history, nobody will disagree. Johnny Mathis. You know what, he had... I like Nat King Cole. And his daughter died, I heard. Natalie Cole. She had a hit in the 70s during disco. And she sang with her dad when they brought back the archives. And with computers. That's where you are. Unforgettable. They were very, very good. Yeah, I remember Jerry Vail. But I remember... They were like lounge... crooner type singers. Yeah. Well, Bing Crosby was one of the original croos. He was big. He was big.  But you know what? You could be mega-talented and still be a real Jericho in real life. Your father was a horrible father was Henry Fonda. Really? Maybe that's why Jane Fonda rebelled so much. Henry Fonda. Bing Crosby. Well, Joe Crawford. Mommy Dearest. Why are hangers? I buy you beautiful clothes. Yeah, no child. Rock Hudson got married so it looked good.  He was gay, but he would never think it. What did he call that, a bear? That's why they had him get married. He wouldn't think it. Merv Griffin, only when he went, ooh, on a show.  Merv, otherwise you... No, I don't care. Back then it was a different era. Of course. Liberace, Richard Simmons, and Rip Taylor. Richard Simmons is gay? No.  He's funny looking though. He still was an afro.  He had one or two hits and that was it. He had this big afro. Those were the days we all did back then. Those were the days when white guys actually got a perm with the bell bottom polyester slacks. I mean, that was hilarious. That was hilarious. Certain people are they last forever like Barbara Streisand. Yeah, she never got a nose job. Yeah, Carly Simon. She was great. Grace Slick? Grace Slick? Well, not as big as them, because they last in 40, 50. Linda Rodstadt? She can't sing anymore. Something's wrong with her vocal. Oh, wow. Karen Carpenter, very sad story. Very sad. Very sad, sad story. My parents knew that because they went with Acapulco with IVM and McCronkites. And mom and dad got to meet them and hang out with mom. I will never forget mom and dad so they were the nicest two kids they ever met. They suggest phenomenal. What a voice. What a voice. Never replicate. Never replicate. It's like, you know, we're country western. Conway Twitty was Conway Twitty. Well, Johnny Cash. George Strait. Incredible. Yeah, George Strait and Conway Twitty Hello, darling. Remember that song? You've got one, two in your eyes. Something there. The one who's still going on for 15 years or so now, Willie Nelson. He's like the Rolling Stones. He just keeps on going on and on. Willie Nelson, boy. Oh, Roy Orbison. One of a kind. Roy Orbison. But he wasn't country or rock. He was like... Only the lonely pretty woman. All of them. He banged out a lot of hits. A lot of rock groups, especially Bad Hale, did his stuff. Rock style. Really? Do you still have ACDC? Yeah. Led Zeppelin. Beach Boys. They were on the 70's show. The Led Zeppelin. They call them the Led Zeppelin. I think when they first... Stones. They sound like a Brian. What's his face? A guy with no neck. No, I was trying to say...  What did he do with Slides though? You weren't explaining that to me where that makes sense? I said, that's insanity. Did they do everyday people Slides of Family Stone? Three Dog Night. Did they do Joy to the World? Jeremiah was a bullfrog. Was a good friend of mine. Steppenwolf. The first one to be wild. Magic Carpet Ride. Steppenwolf. The Sugar Hill gang was the first one to do, I think, a rap. Well, they weren't that big. No. They can't be included in what we're doing. No, no, no, but they just came. They started with the first so called rap. No, I didn't know that. Yeah. Who's from Tenefly or Tendak is the Geisling Brothers. Now the Tramps, they're the ones that sang Burn Baby Burn, Disco Infernal. I think it was the Tramps. You had so many of the Tramps. Tavares. The Heaven Must Have Been An Angel. The other one I liked was all about all the different cop shows. Who done it? Who stole my baby? Who done it? Remember that? You know, everyone in the book looks crazy. But I like Earth Wind and Fire. Oh, well, there you go. 50 years. And you notice Casey in the Sunshine Band had the horns too. He had horns in his group too. They're still playing. Shit. My boogie shoes. No, the one that they did was the one I loved. What's your favorite boogie? Their first hit, the biggest one, was the best. Jesus Christ. Talking about Casey? Yeah. Their first hit, their biggest was their biggest. And then the other one from Australia I think it was, they had a number of the number ones. The average white band was very good. Oh, I never heard of them. Sure you did. You'd know them. You'd know them.


Hey guys, how are you today? So I just wanted to pop on really quick and tell you about some new stuff I've got listed in the Etsy shop. Now years ago, I did a lot of embroidery. I still do occasionally I like to embroider when the mood strikes me, but I I also used to sell at a shop and I just like art supplies so I have way too much hordes of everything and one of the last few things I'm Decluttering and clearing out and getting rid of in my horde is some of my embroidery supplies specifically Some of these cocktail napkins and I also have some tea towels. I'll show you these are from Sir Latab There are sets of six cocktail napkins. They're 10 by 10 cloth napkins Brand new in the box just like you see this. I have quite a few of them There's going to be listing for them in the Etsy shop. The original price was $14. Now. I didn't pay that I know I didn't because I got them on clearance, but I don't remember how much I did pay because you know It's been that long But they're not going to be anywhere near that price They're going to be super affordable because I just want to make them go away that being said these are great still to do little embroidered Designs on and you can give them as Christmas gifts Christmas I know is coming you could also paint a little design on with fabric paint if you wanted you could use them for Rags in your art room once you own them you can do whatever you want with them I took a bunch of them and I have a lot of them and I've put little let me adjust the camera thing so I can see what you're Saying there we go. So I did different I have all different theme designs I have some with just these flourishes on them some that have different vegetables on them I have a whole series of them. I did one whole set that has birds I just honestly just grabbed a few off of the top of my stack over by where I have them sitting Yes, we do use cloth cocktail and regular napkins in my house We don't often use paper towels or paper napkins to be honest with you so they as you can see they're just simple straight running stitch and So this one has some French knots on it, but real simple clean nothing complicated, right real easy designs so if you want to make something like this or Maybe you want to paint on it. Maybe you want to use fabric pens on it to doodle on it. Any of those things will work Here's a brand new set of plain six napkins. They are for sale in my Etsy shop super cheap. All right Yeah, I also have a Bunch of these These are tea towels now these are old I Got these from some another artist who retired and stopped crafting. I'm sure they were white at one time I'm not positive. They may have always been off white to be honest with you I do believe each one of these bundles is a dozen. I won't be selling them that way I'll be selling them by individual towels They measure After they're washed about 36 by 27 I do recommend with the napkins and the towels that you wash them before you do anything with them and This is one that I actually washed last night I took one of my stencils from my design line and I stenciled a roll of the citrus across the bottom Isn't that super cute with some random fabric paint? I happen to have laying around in my art room You could use art paint Artist quality paint or craft paint and add textile medium to the paint that'll make it adhere to the fabric and also make it washable So you could do that if you have some textile medium or you can get it in the craft paint department in a small bottle So and once you do that you can paint on the towels or the Napkins you also of course like with the napkins you could use these as rags in your art room These are all cotton. They wash really nicely. This is like I said, this is what I just did and I just stuck in an ink But that's okay Anyway, so here you go here you have it the listings for these are in the description below Plus here's one. It's wet or I would I Would you know fold it and I wouldn't have it laying out flat, but it's what I literally just did it So anyway, here you have it. This is something you can do with some stuff I have laying around make some great Christmas gifts with it I have both of these blank not decorated so they're blank the ones in my Etsy shop are not going to be decorated But I have the blank ones listed in my shop. You can get some and then decorate them However, you like and give them out as Christmas gifts or use them in your own house Check out the video description for the listing and a bunch of other information Including my happy mail address my tip jar link and all that stuff Don't forget the most important thing and that is to go out and have a great day and do something nice for yourself Because you deserve it and I'll see you later. Bye guys


Suspense and the producer of radio's outstanding theatre of thrills, William and Robson. We pause now in this gayest of all seasons for a little murder. We interrupt the pleasing flow of hot warming greetings and ageless hymns and carols for a slight case of oxoricide. We turn our attention from tinsel and gay ornaments and strings of colored lights to blood. As Herbert Marshall stars in Back for Christmas, a tale well calculated to keep you in suspense. Happy, Mrs. Carpenter? Deliriously, Professor Carpenter. Then how about another glass of champagne? Nothing goes better with a happy bride than a glass of bubbly. Nothing? How about a happy husband? That you have. To us? To us. Forever. Oh, Herbert, I'm afraid it's all a dream and I'll wake up any minute to find myself back in that drab little bookshop in Luster Street, searching for obscure volumes for absent-minded professors. Oh, so that's what you thought of me. Oh, not you, Herbert. You were different. I felt it the first time you came into the place. Premarition, second sight.  Well, not exactly, but I'm awfully glad you did. Do you think you like America? I love it. I thought perhaps after this lecture tour is over, I might try to find a connection at some college or other, and just stay on. What would you say to that? I say wherever you are is where I want to be. Now, who in the world? Who is it? Bellboy. Just a minute. Yes, what is it? A letter, sir, or Mrs. Carpenter. Mrs? Let me have it. Here. Thank you, sir. A letter for Mrs. Carpenter? Herbert, you were a widow, weren't you? I mean, Hermione isn't still alive. Why would he heavens know? But that letter, no one back in England knows that I'm married. Let's forget it, shall we? Never do the glass of wine. I've often wondered what she was like. Who? Hermione. You never told me anything about her. I just as soon forget her, but she's not an easy woman to forget. Dominating, always managing things, the house, my wardrobe, my friends. You might say she managed herself to death. What are you doing down here in the cellar? Putting the finishing touches on this wine bin. Have you taken leave of your senses? Yes, I dare say you have.  Oh, I wouldn't say that. Well, I would. Whatever would become of you if I weren't here to look after you. I wonder. And well, you might. Have you any idea what today is? Why, Tuesday, I believe. Indeed. And what is going to happen today? Well, according to your plans, we are flying to America on the nine o'clock plane. Precisely. And the good noses are coming in for tea in half an hour. And here you are digging a hole in the cellar. It is not a hole. It's a wine bin. Well, it looks more like a grave to me. Yes, Hermione. Now upstairs with you now and get into some clean things. Yes, Hermione. I've already dismissed the cook. You'll have to get the tea things ready while I'm taking my bath. Yes, Hermione.   Back for Christmas. Back for Christmas. You keep saying that. Oh, why not? We are coming back for Christmas, aren't we? Well, supposing I will offer the position in an American university. Oh, no. Well, they did ask me to come to lecture. That means something. Well, not very much. Americans would pay to hear any foreigner deliver a lecture once. But there's no use building dream castles, Herbert. Nobody's going to offer you a fortune in America. Of course, the extra money you will get will be very handy when we arrive back for Christmas. Precisely. Miss, no good you're making a joke of it. Oh, but Heaven knows where you'd be today if I hadn't got a sense of time. Yes, Hermione.  The second best, the good knows will understand. Yes, Hermione. Now, I'm going up to have my bath now. Call me when they arrive. Yes, Hermione. Yes, Hermione. Yes, Hermione. Twenty years of it. Twenty years of Hermione being right. Twenty years of personal management. Down to the smallest detail of which necktie to wear and how many minutes one's eggs should be cooked.  Which will only have ended in dull death if it had not been for Marion. At least Marion was one detail that Hermione could not manage, could not even imagine. The mousy proprietor of a bookstore, proper, ever so intellectual with her heavy horn-rimmed spectacles and the absent-minded professor. But such things do happen sometimes as they did that afternoon last summer. Good afternoon. Oh, good afternoon is Miss Markham about you. All right. Oh, bless me. Is it you, Miss Markham? Yes, Professor Carpenter. I say I didn't recognize you without short glasses. Why, I thought you couldn't see a thing without them. I can't. But you're not wearing them. Oh, yes I am. Contact lenses. Well, I must say. Must save what, Professor? Oh, I never realized what lovely eyes you have. Thank you, Professor. And that frock, it is so becoming. Well, once I decided to get rid of the horn-rimmed glasses, it wasn't too difficult to get out of tweeds. It's a delightful transformation from cocoon to butterfly. Yes, butterflies are not nearly as lonely as cocoons. You're lonely? I'm completely alone since my father died. Did you never think of marrying? My father was a very remarkable man. I never found anyone who could measure up to him, so... Oh, what pity. How long have you been alone, Professor? Alone? Well, I knew you were a widower, of course, the first time I saw you. A widower? Oh, I can always tell. It was the same with my father. There's a certain sadness in a man's eyes. A sweet sadness, I think, when he's been married and then... A widower. I never thought of it quite that way. Oh, I have your books. Books? Yes, the ones you ordered last week. That's what you came by for, isn't it? Yes, I suppose it is. I've quite forgotten. The Fatatomy of Falloid Gamma Phytites and coniferous shrubs of North America. Those are the ones you ordered, aren't they? Yes, thank you. You're very kind, Miss Malcolm. Why kind, Professor Carpenter? Well, not many young ladies in bookshops who go out of their way to look up rare books for an old professor of botany. Well, you're not old, Professor Carpenter. Really, you look... Besides, I adore botany. It's my particular hobby. You never told me that before, Miss Malcolm. I was afraid to. You always intimidated me, you seemed so preoccupied. Yes, I suppose I did. Well, you might be interested in some specimens of alpine polliances that were just sent to me by a friend in Switzerland. Switzerland? Why, I used to go there always for my holidays before the war. You like Switzerland? Oh, every part of it. The lakes, the mountains, the beautiful spring flowers, especially the flowers. It seems that we have quite a lot in common, Miss Malcolm. I'm sorry we haven't put before. I am too, but perhaps we can make up for it in the future. Indeed we shall. So had it begun, and one thing led to another, as they will with two lonely people. The invitation to lecture America made a happy ending possible. While Hermione proceeded with her plans for the trip, I made my own plans, quite different plans. Now they were all but complete. Everything was proceeding according to schedule, and my schedule. Even Hermione, drawing her bath upstairs, gave me the opportunity to phone Mariam. Hello. Hello, dear. Darling. Are you all packed? Almost. Not much time left. I know. Excited? Friendless. Heathrow Aerodrome, half past eight. I'll be there. I love you, Mariam. And? I'm sorry I've got to wing off now, darling. I'll see you at the Aerodrome. Tonight and for... Yes, Hermione. Freddie. Freddie Sinclair. Did I hear you say something about seeing somebody at the Aerodrome? Why, yes.  Well, that seems very peculiar. But then all of your friends are peculiar. Yes, Hermione. I thought I told you to change your clothes before the guests arrive. Yes, Hermione. Never mind. Never mind. Somebody just drove up. Don't let them in. Yes, Hermione. Yes, Hermione. Why, it's... You should have been talking to him on the phone just now when he is coming up for a walk. Yes, isn't it? But then as you say, Hermione, all of my friends are peculiar. We leave for America. Just look at yourself. Yes, Hermione. Yes, Hermione. I think, my dear, I've said it for the last time. You with? Songs Make Christmas. And for this Christmas Eve, you have a very special invitation from CBS Radio to raise your voice in all the old familiar songs in a carol sing led by Bing Crosby. On this festive hour-long holiday show, you'll also hear Rosemary Clooney, Maurice Chevalier, Sarah Churchill, the Paul Weston Orchestra, and the Norman Luboff Choir. There'll be pickups of voices raised in Christmas song from far-off corners of the world. Whatever you're doing this Christmas Eve, wrapping packages, trimming the tree, entertaining friends, add new joy to the evening as all of you sing with Bing. And now, we continue with Back for Christmas starring Herbert Marshall, a tale well calculated to keep you in suspense. Back for Christmas. Hermione was so positive we would be back for Christmas. That afternoon, pouring tea for a few friends who have come in to say last minute farewells, she kept reiterating it.  We absolutely must have you with us for Christmas. Oh, we will be back, I promise you. Oh, it's not absolutely certain, of course. Herbert, what do you mean it's not certain? Of course it's certain. After all, Herbert Elroy, you've contracted the lecture for only two months. Quite right, Freddie, but then, of course, anything may happen. Oh, for Herbert Adore being unpredictable. Now, what other man would decide today, the very day, mind you, before leaving for America to dig a great hole in the floor in the cellar? In the cellar? For a wine bin. He's been talking about putting a wine bin in the cellar for the last two months, but he never got around to working on it until the last few days. A wine bin? Whatever for? It's the only way to store wines, you know, in the cellar, even temperature, all that sort of thing. Indeed. In that damp, they'll all turn. A proper wine bin should be made of concrete. Mine will serve its purpose, I believe. Oh, there you are, my dear men. They're impractical little boys at heart, all of them. I don't know, Hermione. Indeed they are. But not all of them are fortunate enough to have as practical a wife as you, Hermione. Oh, not really. Oh, yes, my dear. I know I could never have done what you've done. I'd just go to pieces. Imagine, Freddie, all the things one has to do before such a journey, and Hermione has done them all, all by herself. Oh, yes, Hermione is quite remarkable. Well, someone had to do them and have it, no help. He's a lucky man to have you, my dear. You realize that, don't you, Herbert? Oh, indeed I do. You have no idea how well I realize it. Well, we very must be going. So? Oh, yes. I'm sure even you have at least a few last-minute things you must attend to. But don't forget, you'll be back for Christmas if you may count on it. Oh, boy, I said my dear Hermione. Goodbye, dear. Goodbye, Hermione. Oh, not yet, Freddie. We'll make our farewells at the airdrome. At the airdrome? Well, yes. Herbert said you phoned to say you'd see us off at Heathrow. I phoned? Yes. Just a few minutes before you arrived. But I couldn't have. I'd been out for a drive all the afternoon. Come, come, Freddie. You must well own up. I'll say, look here, old chap. I don't know what you're trying to... You see? You see? I can't even lie. Well, he's red as a beetroot. Aren't you ashamed of yourself, Herbert, stringing poor Hermione along like that? Well, I suppose the game is up. It, um, it wasn't Freddie I was talking to Hermione. Well, I don't see how he could have been. No. I was, uh... Well, it was about a little surprise I was planning for you. How sweet.  Heavens, I thought they'd never leave. Oh, we still have plenty of time. Well, not if we're to leave the house in proper condition for our return. Now, Herbert, after you get that dust cloth over the dam port, you can help me with the teethings and the scullery. No! Oh, no, no, not that way. Herbert, you're leaving wrinkles. Who's to see it, my dear? Well, I can see it, and I don't like it that way. Yes, sir. Very well. How's this? A little better. Now, let's get at the teethings, as it is. Oh, Herbert. Yes? There's all this nonsense about a surprise for me. Oh, well, if I told you, then it wouldn't be a surprise, would it? No, come now, Herbert. We're too old to play children's games. What is it? Well, if you'll come down to the cellar, I'll give it to you. In the cellar? Why in the cellar? Because that's where it is. Oh, really? But you don't make any sense. Now, help me with the teethings. But you will come down to the cellar. Well, later, Herbert, if this time. Time. There had to be time. But Hermione was meticulous. Had each cup to be washed and dried just so, and put away in the cupboard, and it's a custom paste just so. And the silver had to be carefully wiped and put away in his brushline box just so. And then, once more, all the dust covers had to be straightened and the windows checked to be sure they were locked. And the time was running short. Is that everything? Everything, but your surprise. Will you come down to the cellar now? Why can't you bring it up here? Oh, no, no, that would be quite impossible. Oh, it's so dusty down there, aren't I? Well, I'm all dressed for the trip. I know, but it will only take a minute, and you'll find it quite worthwhile, I'm sure. Oh, very well.  As we reached the bottom, I silently lifted a small crowbar from a nail in the wall. She stood in the middle of the cellar, peering around in the ill-lit gloom. Well, I don't see anything, but that's like a youth dug. That's it? It's in there. Where? Look closer. Step closer to it. Right to the edge. Really, Herbert. Now do you see it? A little large for a wine bin. But just right for a grave. Back for Christmas? No, Hermione. No, Hermione. I'll never be back. You've planned it so well, Hermione. You're so predictable, so practical. Even the typing of your letters and signing them with a neat cryptic H that I have learned to copy. I'll be your Emanuensis. I'll write your friends for you, Hermione. Through me, you will tell them we won't be back for Christmas. And then you will tell them how much you like America and that you persuaded me to accept a teaching position. You will tell them that you may decide to settle there permanently. And then the letters will come less and less frequently until at last they will hear from me and I will break the news to them that you have died in America. Then I will have my solicitors put this empty house with its sad associations up for sale and that will be the end of the matter, Hermione. Back for Christmas? No, Hermione. No, no, no. No. No, no.  Did I leave the front door unlocked? No, no. Keep calm, keep quiet. They won't look down here in the center. Keep calm. They'll go away. They wouldn't leave, Hillstead. All right. Honey, I do want a last drink without her, but you know if he hurts. All right. Let's hurry. We can be back by half past six. Half past six? There's still time. After that it was easy. Put the finishing touches to the wine bin. Dress fast. Get out of the house before 6.30. Take the tube to Waterloo Air Terminal and the airlines bus to Heathrow. The plan is final. But from here on, my plan. Announcing the arrival of BOAC Flight 14 from Rome, Calcutta and Melbourne. Passengers would please report to custody. Darling, right on time, my dear. Shall we check in? Whatever you say. I never felt safer in my life. A new smoking sign just went out. Careful of cigarette, darling. Yes, thanks. Ever been on a plane before? No, you? To enjoy your first experience together. I was worried. About what? Well, you having been married once. Nothing would be new to you. My darling, it will be new. All of it. Is everything all right? Yes, thank you. Would you care for a pillow, Mrs. Carpenter? Were you speaking to me? Yes, Mrs. Carpenter. Would you care for a pillow? No, no thanks. I don't believe so. Well, if you want anything, just ring the bell over your seat. Herbert? Yes, dear? He called me Mrs. Carpenter. That's right. But I'm not yet. Well, you will be tomorrow this time. But how does she know? That's the way our tickets read, Mr. and Mrs. Carpenter. Why? I wanted you to get used to being Mrs. Carpenter. Straight off.  You think of everything. I help you, sir. Yes, please. Professor and Mrs. Carpenter, we have reservations, I believe. Oh, yes, we've been expecting you, sir. Now, boy, take Professor and Mrs. Carpenter's luggage after this week.  Your letter reserving the rooms was so thorough, I was expecting an older, more forbidding sort of person. My letter reserving the rooms? Oh, I wrote the letter, my dear, and I signed it, Mrs. Herbert Carpenter, just as a joke.  Now that I think of it, I am, Robin. Well, Mrs. Carpenter, we might as well kill all the bottles as they say here in America. Darling, you'll make me tipsy. That's what a bride should be, just a little tipsy. Herbert, this letter the bellboy just brought seems to be a bill of some sort from a building contractor in Kensington. Oh, some mistake, no doubt. And address to me? Well, give it here, and we'll get to the bottom of the matter. This is to acknowledge your order, and together with the keys to your house in Lonston Place. Our men had no difficulty in finding the place where your husband had begun the excavation in the cellar. But apparently he changed his mind at the last moment and filled it in again. What is it, Herbert? Our men will begin digging tomorrow, and you may rest assured it will be a professional concrete line job and will be completed in ampoules of your surprise Christmas present to your husband. We are happy to be conspirators with you in this thoughtful gesture and hope that Professor Carpenter will be pleased with our construction of his wine bin. Very truly yours, Paul Holt and son's contractors. What does it mean, Herbert? It means that her mind was right. I will be back for Christmas. Herbert Marshall starred in John Collier's story, Back for Christmas. Listen. Listen again two weeks from now when we return with Russian New Year, a tale well calculated to keep you in suspense. Suspense is produced and directed by William N. Robeson. Heard in tonight's cast were Irene Tedrow, Ellen Morgan, Paula Winslow, Ben Wright and Jack Moyles. The musical score was composed by Lucian Moroek and conducted by Wilbur Hatch. Looking for adventure, excitement, thrills and chills, all without leaving the comforts of your own home? CBS Radio answers your need every Sunday on most of the same CBS radio stations with yours truly, Johnny Daller. Johnny's a freelance insurance investigator in old hand at rapping larsonists and lovely ladies and the author of the most imaginative expense accounts in history. His tales are guaranteed to keep you between chills and chuckles. And you'll find yours truly, Johnny Daller, right on the job when he tackles his next baffling case a little later on in the day. Ladies and gentlemen, don't let death take your holiday. The traffic death toll over these Christmas holidays will be more than twice the average for any other week of the year. Don't allow the holiday spirit, the rush and activity to make you careless. Stay alert. Drive especially carefully. Remember, the best Christmas present is your presence. Stay tuned now for five minutes of CBS news to be followed over most of these same CBS radio stations by indictment.


Thanks for coming back to a special yes network presentation Brooklyn next media day live from HSS training center in Brooklyn, New York Ryan Ruko Saracusa can now we are joined by Nick Claxton sporting a new haircut. Yeah, I feel it I feel a little hurt by that. You don't want to be blonde anymore lines have more fun But the blind messed up my hair. So I had to let it go. I like it.  It's a focus for you Yeah, I bulked up a little bit. Just you know, that was the emphasis going into the offseason just continuing to get stronger. So I'm ready to get stuff started You were a free agent. There was a chance that you were not going to be back What were the conversations like to bring you back here to Brooklyn? And what made you feel good about returning? Honestly after the season, you know, I almost was traded towards on the trade deadline, but after the season it was pretty simple just Working everything out with the money and you know, I was excited to come back and continue to build here We got a lot of unfinished business.  I mean honestly, that's kind of how it's been since I've been here though It's always been a roller coaster always something going on, but Once everything was settled, you know, we saw Kyle was coming back first And we saw Katie was coming back and kind of set the roster I'm extremely excited. We're really really really talented on paper So we just got to go out and put the work in and training camp starts tomorrow and get ready How hard or not hard is it Nick to turn the page and have everybody Be bought in cohesive after what you guys went through in the uncertainty of the offseason. Honestly, it's pretty easy Everything in the offseason that happened. You just got to put it in the past and focus on the present and Winning Trump's everything. So, you know, we go out here and We hoop and win games people won't be talking about what happened in the offseason They'll be talking about what's happening now Well, what's your perspective on your growth at the early part of the season because you dealt with injuries that illness you've missed sometimes In terms of your availability What did the end of last season show you the postseason and just things you've been focused on throughout the course of the summer Hadn't into this year. I honestly was a big confidence builder for me Just finishing the season as strong as I did and also showing me, you know The things that I need to work on like, you know, free throws you and continuing to get stronger so I'm able to you know, um hold my hold my own out there and The postseason which is a lot more physical. So it was big for me and this offseason I just been working every single day to Get to where I need to be so I'm able to perform and stay healthy throughout the season What did you do free-throw shooting wise Nick to try and improve upon that just finding a consistent shot last year? I switched up my shot a lot. I had a lot of different Routines that I was using so this summer. I just found something that I was comfortable with and work doing it Everything like a lot a lot got a lot of reps and Just working on that so I have that confidence when I step up to the line When we're looking at this roster the line-ups. There's so much optionality and how You all can play but the one area looking at that five spot looking at you looking at day run potentially Marquise Morris Then could possibly play big you guys can play smaller But there's a lot of expectation or responsibility on the job that you will be able to go in and do What's it like for you? Just understanding the way things look at that position and how you will be relied on this year I mean as a basketball player as a competitor That's what you dream of having a lot on your shoulders having more asked of you every single year And it's my fourth year. I'm ready to take on that role. I miss being a long time coming I feel like you know, we've had a lot of guys come in and out, but I'm I stuck so I'm excited to To just do what I do and go out there and continue to get better every game every practice every day How do you see you and Ben fitting on the floor together Nick?  We both we both Athletic we both defend really well. We can both guard five positions We both get out in transition. So we're both excited to play with play with each other And get out there and who what about your expectations? For how you guys may play just in terms of system wise I know a lot of the players have talked about playing faster and what that could do for you someone who can rim run How do you think things may change with the look of this group? I think like you said, I think we're gonna play a lot faster I think we need to have emphasis on rebounding last year We want the worst rebounding teams in the league and I think that'll be solved with us having a more size out there and Just offensively having a system everybody playing together as one as a cohesive unit Nick how essential was it knowing that you have Kyrie as a full-time player? Not going through anything like what you guys went through last year. Yeah, that's big And you know that took a toll on us last year, but I'm glad that we can put that behind us and having us Available having car available every single game. That'll be big. You know, he's one of our leaders We look up to him. So I'm excited to have him When you think about leadership and leadership of this group and in everything that has occurred in the offseason What will it be like finally getting on the floor together having training camp be able to build together as a group as a team as an Organization After everything that's occurred. It's gonna be great.  So we'll be good. Well, Nick. We appreciate the time We appreciate the insight on to the muscle building and the haircut as well We look forward to watching you out on the floor first preseason game October 3rd on yes And we will be there to call it. Let's get it. All right That's Nick Glaxden plenty more to come here next media day rolling on


So, you don't Unweld, not a problem. Unweld lets you left click a polygroup island and separate it from the rest of the topology. Once you've separated it, you can hold Ctrl and Shift and left click to see what it looks like by itself. If you change target to single face, you can do this one face at a time. Hope that helps and as always I hope you have a fantastic day and I'll see you around.


Did you know this man is skydiving with a nuclear bomb. It is called the Special Atomic Demolition Munition and it is a small man portable atomic bomb. It's 12 inches in diameter, 18 inches long and weighs roughly 58 pounds. Transportable in what looks like a gym bag, it has everything needed to make an estimated yield of between 10 and 1000 tons of TNT including the warhead, a fuse and a firing system with a mechanical timer. Conceived in 1960 and in service between 1964 and the 1980s, this weapon thankfully was never used in anger before its retirement. Officially intended for both offensive and defensive use including flank protection and engineering. Or I personally think it is a bit of a dumb idea.


Good morning, I'm Mary Ellen from the channel Mary Ellen after 60 and today I am here just to chat I'm not going to be talking about any products. I'm just easing back into making videos It has been since I think the last time I uploaded was January 10th But I do want to thank you all who have continued watching my videos and Leaving me comments a lot of you just checked on how I was doing and I'm going to talk about Pat later in the video, but there were just some Things I was just going to talk about today I know I have the best subscribers and anybody does and you are appreciated So so much You know how crazy the year 2020 was well 2021 has been super interesting too on I think it was this Tuesday before the 15th. We had an ice storm and Before the ice could melt and go away We have the big storm that so many of you all have you had that so many of you all had states it seldom see snow and Frigid temperatures Experienced it and and I really hate it for you since I'm from Illinois I'm used to the snow but the snow we just had had us in our home from The 15th until the 23rd so We had electricity the whole time. I was prepared. We have a gas fireplace in a gas stove and We were very fortunate that we didn't lose power in times like this Jim really likes my preparedness a lot of times He doesn't but he really appreciated at this time You all that have watched me for a while know that I have grandsons that play sports Who were in high school? Well in Illinois, we didn't have a football season. My son-in-law is the head coach of football So he didn't have a football season my oldest grandson Didn't and neither did my one that's a sophomore. He goes to another town Well a week or so ago Illinois made the decision that we would have basketball followed by football No matter how big your gym is each there can only be 50 There can only be 50 people in the gymnasium We live in a small town, but our gymnasium holds about 5,000 people So 50 people can go and watch the ball game. So each player gets two tickets That the nice thing is get to watch my grandson that goes to school here. He's a senior I get to watch him It's live-streamed and my grandson who is a sophomore About 40 minutes from here his basketball games are live-streamed and I've really been enjoying that As soon as basketball is over, which is mid-march They'll go straight into football both of them well they have to wear masks at all time playing ball and They're they're happy that they're playing ball. They really are so I'm I'm very happy for them Because I tell you what I can remember when I was a senior in high school I thought to myself if something if I didn't get to do something I almost thought it was the end of the world So I'm thinking all these young people who make it through These hard times are gonna have some really good coping skills that they were that they were forced to use so much Early and earlier in life than we were then I was I Just really have not been that interested at all in makeup today. I have on mascara and lip gloss the Genesequa lip gloss stain that I've loved forever I like all the ones from yet, but it just it's perfect when you're wearing a mask because it doesn't Doesn't stain your mask and you have a little bit of color on your lip But I just haven't been into wearing makeup I did purchase a few things like in December and in January but not much and and I think a little bit this month, but Most of it wasn't make-up, but but I do have some things that I've really enjoyed So I will talk about that in an upcoming video You know, I've always been one that a Really appreciated the ultra reward System how they how they give their rewards out and I've always worked on, you know using my points and Ordering my things when it's like triple points or something. I did not hardly spend any money at all to in 2020 I didn't even get close To the first rung of I think it's platinum I mean, I didn't even get close and the highest level is diamond And I have been diamond in the last couple of years, but not last year, but I did get an email recently I guess a lot of people didn't make it either because they said you will remain diamond Throughout this year, but I haven't bought a thing from Ulta And I know I'll get back into it, but I'm just not in a hurry to I'm just pretty happy with everything that I have I Companies I haven't wanted them to send me anything just because I just haven't everything I have that I use I really like and Maybe something will come out that I'm interested in but you're not gonna learn a whole lot of new things for me Because I'm not trying a lot of new things at all. I Appreciate all your warm and kind comments that you left on my last video when I was talking about losing my friend Pat You all have suffered. There's some there are so many of you that have had huge losses in your lives and It's just so hard to deal with it really is And I'm sorry for your losses But I wanted to give you a little bit more information on Pat and then then then we'll be okay We'll just I just wanted her her January 1st was a normal day. She got up She went to mass. She cooked ham and beans. She just had a normal day and As I said in that video Her husband was going to bed and she was gonna stay up and do some sewing and she didn't come to bed and he checked on her And she was gone Well, I do know that Pat is always gonna be with me the rest of my life and That if I am good, I'll get to meet her in heaven Something I never shared with you Pat had a daughter that she lost to cancer three years ago last October and I was filming during that time and that was such a hard time for me to film because I knew what she'd been going through And her daughter suffered with cancer For quite a few years Well, I get some satisfaction knowing that I know that her daughter was in Heaven waiting for Pat to join her and also joining Pat was her mom and dad and her brother They were all in heaven waiting for her You know, I mentioned we were best friends since birth We even spent Christmas Eve together as families. That's how close our families were We would go to midnight mass after midnight mass. We would go to Pat's We'd stay a little while and then we'd go home and Wake up and Santa Claus had been there, but we were just a real close We were just very very close Pat's oldest child is a son and he is a priest It he started out in the Covington, Kentucky area Well, he was able to preside over Pat's funeral and I was able to watch it live streamed I wasn't I could not attend But both of my children did attend and and he did Pat I know was so proud of him I Plan on starting my every other Sunday routine. I know that the videos that I want to do I have Some things that I purchased. I have some favorites. I'm gonna have some empties Believe it or not. I had like one empties last year. I I'm doing better trying to save the things Would you believe I have now derma rolled my face? For six months all over I've been derma rolling it for oh for years before that But I just started including my cheeks. So I'm gonna do a comparison of What my face looked like six months ago to what it looks like today? And then I have my yearly skin care update video that I'm planning on doing So I've got quite a few things that I want to do But mostly I wanted to say hi today Wanted you to know I am doing fine Jim is doing fine. I'm knocking on wood right now And up and to this point we have remained healthy Thank you so much for watching for spending some time with some of your precious time with me today and it is all good I'll see you in my next video and you take care We are still Be at home But this snow had us in our home that I am I'm just I bet I'm I'm just gonna fill you in on what's going on around here You know, I have don't that are out of school that are out of high school and I have one that are left in in the Force to use early we get to watch it on