So thanks to the organizers for inviting me to speak here. I'm going to talk about some work that I'm doing with Andrea Montanari on de-biasing the lasso with an accurate precision matrix where nearing completion of this work. So it should appear on the archive shortly. And, okay, the de-biasing the lasso is a topic that's studied in the statistics literature. The reason I think this talk appears in this session is that we approach this problem using exact asymptotic techniques from the convex Gaussian-Midmax theorem. So the setting here is we have a linear model. This linear model has a bunch of features. So we have P features in this matrix X. And we have one feature in this vector W. And so really this is just a P plus one dimensional linear model. But I sort of separate this special feature W because in this talk I'm only going to care about estimating beta, and I'm only going to be interested in theta and so far as it helps me estimate beta. We're going to make a random design assumption. And the assumption is going to be that all the features, both W and X are jointly Gaussian, but in the spirit of separating out this special feature W, I'm going to write the Gaussian distribution in this way. So first I'm going to assume that these features X, which I'll call the nuisance features are jointly Gaussian with some covariance matrix sigma and that the special feature W depends upon the nuisance features through this linear model that has parameter gamma. And this model up here I'm going to call the outcome model because it's a linear model for the outcome variable Y. And this linear model I'm going to call the precision model. And the reason for that is that it's related to the inverse covariance matrix for all the features jointly. And the inverse covariance matrix is often called the precision matrix. So I call this the precision model and the schema, the sort of precision parameter. And okay, I'm going to make this sparsity assumption on the unknown parameter. And my goal is, like I said, to estimate beta. Our theoretical results are going to establish consistency of a certain estimator, though, what we're motivated by is, is, is, you know, really creating an also approximately unbiased estimator for beta. And, you know, ultimately we would love to be able to show that this estimator is normally distributed asymptotically, we think it probably is, but, but we, you know, that's purely a conjecture at this point. Okay, so let me just describe some, you know, maybe limitations of some other methods. So one way that you could estimate beta is to estimate beta and this nuisance parameter theta simultaneously using the lasso. So this is the lasso here where, again, I'm sort of separating out this special feature w in my notation. And let me just start with a simulation that shows that the lasso estimate of beta can, can be quite bad. So, so here, beta is equal to one. And I have a 400 samples and the dimensionality of, of the linear model is 500. So it's larger than n, though not dramatically so I should say that, you know, what you should have in mind here as in, I think the other talks in this session is that we're thinking about sort of a proportional asymptotic regime. So this is sort of in some sort of proportional high dimensional regime. The nuisance features are just independent standard Gaussians. What I'm going to do is I'm going to set up the outcome and precision parameters in such a way that the sort of estimation of beta is, is substantially degraded when you estimate beta using the lasso. So I'm going to actually take the sparsity of each of these parameters to be the same. In fact, I'm going to take the parameters to be exactly the same. And I'm not going to sort of have time to provide intuition for why this way of determining the parameters is sort of really bad if we're interested in estimating beta. But this new parameter is, is sort of a signal strength parameter. It's the L2 norm of, of these parameters. And what we have in the plot is on the y axis, I have the bias of my estimate of beta computed over 10 simulations. And on the x axis, I have the signal strength parameter mu. And I compute the bias of my estimate of beta for three different sparsity levels. And we see that when there's sort of no signal strength, the estimate of beta is slightly biased. It's biased downward is actually biased sort of on the order of the regularization parameter. But when I sort of increase the signal strength and, and set my nuisance parameters in this way, the bias actually gets inflated and degrades substantially. And the degradation is, is exacerbated by having a more complex model. So the, you know, actually already when sparsity is 10, things are quite bad. And when the sparsity is 100, which is, you know, comparable to this sample size and the dimensionality to the degradation is, is even a bit worse. So, okay, how well can we hope the vanilla last to perform in terms of estimating a single coordinate. And so there's some results on this. So one result involves making an incoherence assumption.  And an ultra sparsity assumption. So you assume the sparsity is, is smaller than a square root of N. And here you say that the, the error in estimating beta is sort of on the order of the regularization parameters, square root of one P over N, but, but these are very strong assumptions. They were violated in the simulation I just showed. And, okay, I don't know what these constants are, but I think the simulation maybe should lead us to believe that, you know, this bound is also not really describing this simulation. Now, another set of results and, okay, really the ultra sparsity term here doesn't apply to this second bullet point. So perhaps I should have given this slide a different title, but you can sort of under weaker assumptions, compatibility or restricted eigenvalue conditions, which are, I think, satisfied in my simulations, show these weaker bounds on the estimation error beta. And these are really quite bad bounds. So if you're familiar with the lasso literature, this is the sort of L one error of the lasso. This is the L two error of the lasso. And so this result would tell you that you can consistently estimate beta if you can consistently estimate the whole parameter. So that's a pretty tall order for if we're only interested in messing with a very low dimensional thing, we have to estimate this very high dimensional thing. Well, so, okay. So now I'm going to move on from just a little last time talking about other approaches to try to target estimation to beta. And I'm going to begin with a success story when we have an accurate knowledge of this parameter precision parameter gamma. And so here's one thing that we can do. It's called the D bias lasso. And it involves starting with the lasso estimate of beta and then adding this correction term. And I don't have time to motivate or describe where this correction term comes from. It shows up in an AP iterations, for example. But what you have here is it's a correction term. It depends upon your data, WX and Y depends upon your lasso estimates for the parameter of interest in the nuisance parameter. And it involves the true precision parameter. So we have accurate knowledge of the precision parameter. And there's lots of theorems about this estimator. I'm going to just present sort of not the strongest versions of them because in this talk about, you know, thinking about asymptotic normality, which is, you know, often discussed in the literature of the device lasso. But, you know, we can, we should, we should expect that the sort of estimation error of this device lasso is a voter one of her squared event. And, you know, there are indeed some results to this effect, which hold any sort of a proportional regime. And so, you know, in this paper by Leo Lian and Maria Montanari and myself, Andrea and Yuting Wei, you know, we can sort of show that this device procedure has sort of for the typical coordinate, this type of behavior. And in this paper by Pierre Bellick and Sun Wuzang, they established that this occurs for a fixed coordinate of interest. Well, if you start with an assumption that the vanilla lasso is consistent, which is a strong assumption, or if you know that n is bigger than p, that doesn't need to be dramatically bigger than p. Okay. So, you know, this thing works in the anticipation of what's to come. I'm going to sort of try to show what these methods do diagrammatically. So this sort of de-biasing lasso procedure with an accurate precision model works as follows.  You have a bunch of data that you can use to estimate beta and theta using the lasso. And then you have some sort of prior knowledge of the true parameter gamma, which doesn't come from your data. You combine them in some fancy way to construct this de-biased estimate data. And indeed, if you do some simulations in exactly the same setting that I showed in the previous slide, you find that the bias of this estimator is, you know, a little bit different from the other two. So these are the two different sparsity regimes. Okay. So now I'm going to show you another method. So now we're going to successfully de-biased the lasso with an accurate precision model. So now we're going to unsuccessfully de-biased the lasso with an inaccurate precision model. Okay. So what if you don't know gamma? So one thing to do if you don't know gamma is to estimate it using your data. So we're going to use the lasso. Okay. So in particular, here's the device lasso. It's exactly the same as what I showed on the previous slide, except now in place of the true gamma, I'm going to put an estimate of gamma that I got from my data. And okay, unfortunately things don't work as well. Here's, here's sort of the, the same, uh, target estimation to beta. We're going to have our labeled data. We're going to construct the last estimate of beta and theta in the same way we did before. But now we're going to get an estimate of gamma also from the same data. And then we're going to have the same, uh, you know, uh, simulation that I've been showing throughout the talk so far. And we see that at least when you have, uh, you know, if you have, you know, very sparse model and it seems to work, but if you have, uh, you know, even rather dramatically large scarcity, it starts to fail. Okay. Now is this just sort of a feature of the procedure I'm using or is this fundamental? And, um, well, there's not a straight straightforward answer to that, but I think there are some results that would lead us to believe that maybe it is fun. Um, but I think it's, it's, it's, it's, you know, it's really hard to get something to work when you don't know gamma. So, you know, uh, uh, uh, uh, a result in this literature on D biasing is, it's from Tony Cayenne and Zidjian Guo and, uh, was refined by Adele Java modern Andrea Montanari. And it says that the, uh, estimation of beta has a minimax lower bound of this form. And particularly you have this term, which involves the sparsity of the outcome model and the sparsity of the outcome model. And if you're familiar with the last literature, you know that, uh, when this, uh, term goes to zero, it means we can either estimate gamma or, or theta consistently using the lasso. So what this would sort of suggest is that, uh, you know, maybe in order to sort of successfully target estimation to beta, we need to consistently, uh, estimate at least one of these, uh, parameters and know it accurately. Uh, which is sort of the regime of this session, then, uh, estimating these parameters consistently is not, it's not possible, uh, just information theoretically. So, uh, perhaps we are discouraged that we'll be successfully consistently estimate beta in this session. So in the same, uh, okay. So, uh, now that I've constructed a bit of a strong man, uh, I'm going to tell you about how to, uh, successfully devise the lasso with an inaccurate precision model. Okay. And this is now, now we're finally at the topic of our paper. Um, so, uh, the key idea that we're going to use is that we're going to have an inaccurate estimate of gamma, but it's going to be unbiased estimate of gamma. Okay. And so this, because it's unbiased, we're going to actually be able to do something. So, uh, here's our new data pipeline that we're going to be in a slightly new setting that we're, we're managing that we have sort of a semi-supervised, uh, setting. So we have a bunch of labeled data, but we also have some unlabeled data. We don't have a dramatically large amount of unlabeled data so that we can sort of consistently estimate gamma, but we do have sort of more total samples than P, the dimensionality of the parameter. So we can get an unbiased estimate of gamma. Okay. We're going to combine our unlabeled data and our labeled data to get an OLS estimate of gamma, which is unbiased. And then we're going to get the lasso estimate of data just from the labeled data. And we're going to combine them in now a new way. And then we're going to use the same form as the device lasso to create what I'm going to call the sort of device plus estimate of beta. Okay. And so here's just, uh, you know, explicitly what we're doing. So I'll just remind you that these are linear models. This is the outcome model and this is the precision model. And, um, the, uh, the estimate of beta that we're new uses written here. So we have some sort of empirical correlation. Uh, of, of fitted residuals where we're here, we're using this sort of estimate from both the labeled and unlabeled data of gamma. Here we're using the lasso estimate of theta that only uses the labeled data. And we have this sort of complicated, uh, correction term out front. Okay. So this is the device lasso or the device plus estimate of beta. And, uh, in our simulations and exactly the same setting where now there's just the added caveat that we have some unlabeled data. So we're using a total of 750 unlabeled samples, giving a total of 750 samples, which is larger than 500, but not so large that we would expect to be able to consistently estimate the parameter gamma. And okay, we see a bit more noise in this, in this simulation, but we seem to have corrected the bias across all of the, uh, signal strength and, uh, sparsity regimes. And indeed we, you know, prove this as theorem. So, uh, uh, we're in sort of a personal asymptotic, but we're not making too many or really any assumptions about, you know, empirical distributions converging or, or spectrum of, of covariance matrices converging as, you know, often occurs in this literature. We're just saying take any limit, uh, in which, uh, the number of samples you have is some non-negligible fraction of, of the dimensionality of your parameter. And if Sigma has some bounded significant values, and Theta is sufficiently sparse and some other conditions hold, then this device plus estimate is, is a consistent estimate of Theta. Uh, and you know, I should mark, remark that sufficiently sparse, you know, is permissive of sort of proportional sparsity. And so in my remaining time, and, uh, I think like so, I forgot to take a note of when I started. Uh, so hopefully I have just a couple more minutes. So, uh, these proof techniques, I think are over-justified the presence of this presentation in this session. Uh, so we're going to prove this using some exact asymptotics. Uh, and I'll just briefly describe what, what they are. So it turns out that, uh, this problem was consistently estimated. Beta can be sort of reduced to this, uh, a separate problem in, in linear models. And so, so here's sort of this. We have, uh, two linear models with the same features. Uh, but they have different, uh, underlying parameters and they have correlated noise. Okay. So the noise from these two models is correlated with covariance matrix S. And there's a question of, can we, uh, estimate the sort of covariance matrix for the noise. Yes. And if you can, then actually I can use my construction of this estimate to, uh, consistently estimate beta. And in the original setting I described. And so this is sort of related to the problem. So, uh, in high dimensional or noise variance estimation in high dimensional statistics. Uh, and it's related to a construction of a noise. Variance estimate in the last so that was proposed by Andrea Montanari. And mostly by after 2013. Uh, now what we're going to do to construct an estimate of the covariance matrix S is, uh, we're going to have a regression estimator. In both linear models, so we're going to use the same data set and simultaneously run to, you know, construct an estimator for each parameter using sort of the same data. Okay.  Okay. So a standard result in this, you know, very large literature, which I haven't listed exhaustively on this slide is, uh, you know, providing a prediction for what the limit of, um, say the size of the residuals or the size of the up to estimation error is for one regression at a time. Uh, and what we're going to need, because if you recall, the sort of the biased estimator involves the correlation of residuals, we're going to need some knowledge of, of the limit of a quantity that involves, uh, both regressions simultaneously. So we're going to need to know something about the sort of joint distribution of two regression estimators computed on the same data. Um, and, uh, okay, that's what we provide. I'm not going to go into detail. And the archive will be posted soon. Uh, but, you know, what we do, which I think is sort of a more general interest in justice devising, uh, the lasso question in particular is provide, uh, an exact asymptotic sort of joint distribution of two. Estimators and, and linear models. Uh, and we use Gordon's comparison and equality to, uh, construct this sort of, uh, exact characterization of, of, uh, the joint distribution. Uh, there was a paper by, uh, Marco Modelli, uh, Venkatara and, and Trump leaders, which, you know, also, uh, used exact asymptotics from A and P, uh, to, uh, uh, study the joint distribution of, of, of estimators and, you know, GLM. Uh, here we use Gordon's inequality to come up with this joint characterization. Uh, we're able to show consistency at the device estimate, uh, using, based on the last estimator, but we also study, uh, consistent estimation of data where we estimate the nuisance parameter is using, uh, uh, read regression. And I should remark that, you know, we provide more general results than the one I stated in this, in this, this presentation. Uh, and the results apply both to a semi supervised setting and to a fully supervised setting. Uh, as I've already alluded to, the results are, are non asymptotic. Uh, and we provide, you know, you know, really the basis of the result is, is consistently estimated as per variance matrix, uh, S, which is a general edition of this, uh, result by most inviolate and Andrea Montanara. And, uh, okay. Yeah, I was coming in and I hope, uh, I hope it really is coming very soon. And, uh, I hope you'll take a look when it, when it's supposed to.


So let's think a little bit about genetics as it's related to the human ABO blood type. This is a really interesting situation because it exhibits both co-dominance and multiple alleles. In the situation of co-dominance, when there are two different alleles, both are expressed equally. This is also a situation in which we have multiple alleles for one particular gene locus. In the human ABO group, there are three alleles that may be involved with determining one's blood type. Of course, any individual only has two of each allele, but there are three in the population. One of these we refer to as the IA allele. IA is just a standard convention, but you could use a different system if you wanted to. But the IA allele codes for antigen A, and I've just represented antigen A on this red blood cell. This circle is representing a red blood cell, and the little triangles are just representing the A antigen that's found on the surface of the red blood cells. The IB allele codes for antigen B, and I've represented antigen B as a little semicircle on this circle, which represents a red blood cell. Then we have the little I allele, and the little I allele doesn't code for an antigen like antigen A or antigen B, and so I've drawn a red blood cell here that doesn't have an antigen displayed on its surface. Three different alleles are involved with determining the human ABO blood type. So I've created a little chart here that lists the different human blood types, and it shows the genotypes that are available. Blood type would be the phenotype or the physical characteristic in this situation, and then the genotypes are which combination of alleles the individual might have. So folks with type A blood could be homozygous IAA, or they could be IA little I. In both situations they have the A antigen on their red blood cells. People that have type B blood are either IB, IB, or IB little I. In both situations they have the B antigen on the surface of their red blood cells. Then there are folks that have type AB blood. In order to have type AB blood, one must have both the IA allele and the IB allele. And so type AB blood would look like this. The IA allele would cause the A antigen to be produced and displayed on the surface of the red blood cell membrane, and the IB allele would cause the B antigen to be produced and displayed on the surface of the red blood cell. So they have both antigens on their red blood cells. Then we have the type O blood type, and the only possible way to get type O blood is for one to have a homozygous little I situation. So little I results in type O blood, and this is what a type O blood cell would look like. No antigens displayed on the surface of those membranes. So human ABO blood type is an example of co-dominance and multiple alleles.


The next important part is to select the appropriate journal. It's very important that you select a journal that matches your topic and your intended audience. And this is a common mistake that authors make, is they pick the wrong journal for their paper. Or that journal is targeted for an audience that doesn't match what your intended audience may be. For example, I'm the editor of an educationally focused journal. It's intended to be read by faculty and the topics are all about education, curriculum, faculty issues. When I receive a paper that's clinically focused, that paper is immediately rejected even though it could be the best paper on that topic. For example, I've received papers on care of the patient with gangrene. I receive the summary of a basic science research paper about ischemia in rats. So these papers will be rejected before they're ever even sent for review because they don't match the purpose of the journal. They don't match the intended audience for the journal. So that step is absolutely critical, is finding the right journal for your topic and for your intended audience. Next, you want to know if your journal is peer reviewed. We often use the term juried or we use the term refereed. All three terms mean the same thing. Juried, peer reviewed and refereed all mean that your paper is sent out to your peers for a critique. And then you will receive those critiques back to help you revise and help guide and making a better paper. In academic settings, faculty are expected to publish in journals that are peer reviewed. So I would encourage you for your paper to determine is your target journal one that is going to be peer reviewed. You might also want to use directories and websites to help you learn about your targeted journal. And if you go on the website of Nurse Author and Editor or go on to the website of it's called INAIN, the International Academy of Nurse Editors. On both of those websites they link you to the same directory of all nursing journals in the world. And there's a link to each journal to the journal's editor and to the journal's author guidelines. So I would encourage you if you're searching, trying to find the right match for your paper, use that website, use that link to help guide you. Another consideration you may want to make in picking your journal is what's called the impact factor. And that is a formula that's calculated based on how often the targeted journal is referenced in other journals. And so some are very critical of this measure and say it really isn't that meaningful. Others follow impact factors very closely. So it's something you might want to consider and your journal website should be able to tell you the impact factor for your targeted journal. Another factor you might want to consider in selecting your journal is the time that it takes for the peer review process. As we mentioned earlier in our module about the process of publishing a journal, the papers go out to peer reviewers. We ask peer reviewers to return the paper in about three weeks. And people have great intention and mean to do that, but they often get busy and they can't do it. So although we like to get our papers back quickly and return the paper to the author quickly to give you feedback in a decision, it will take time, probably more time than you'd like to wait as an author. But that is the reality because all our peer reviewers are busy people who have full-time jobs. They do peer reviewing. For no honorarium, they just do it as a professional service. So be patient, but you might want to inquire with the editor, what's the typical time for the peer review process to occur? What about how long should you expect to wait? And if you wait beyond that time, is it appropriate to contact the editor and ask about the status of your paper? You might also want to inquire about acceptance rates of papers, but be careful of how you interpret that fact. For example, as I mentioned, the journal I edit is educationally focused. So all the papers that are clinical papers are rejected. So if I gave you my percentage of rejection, it might be falsely interpreted. What you would really want to know of how many papers that were appropriate for the journal were rejected. So be careful when you look at rejection rates because many rejections may simply mean it was the best paper ever on that topic. It was simply sent to the wrong journal. I would encourage you also then to pick up your targeted journal and sit down and examine it more closely. Look at the style of writing in a way that maybe you've never looked at when you were simply a reader of that journal. Look at the type of formats that are in that journal. How do they package their articles? Are there a lot of tables and figures and headings? What's the typical length of the article? Does that journal break itself into varying departments? Does your paper fit in a certain department? And sometimes instead of the word department, they'll use the term columns. But maybe your paper fits into one of those. And when you submit it, you can indicate to the editor that you're writing for that department or column. Next, you want to search the index of your journal, your targeted journal, to see how your topic has been covered in the past. It doesn't mean you can't write on the same topic if it's been addressed recently, but you just may need to take a different slant on that topic. So journals often will put a topic index in their last issue of the year or their first issue of the following year, or you might be able to find topics online, or you might be able to find a list of desired topics on the journal's website. So I would encourage you to check that as well. It will really help you know how to shape your topic. You want to decide is your targeted journal one that is a thematic journal. In other words, each issue is devoted to only one topic. If that's the case, I'll warn you that those topics are selected a year or two in advance, and those papers must be in almost a year before they'll be in print in order for the whole review process, the revision process, the editing and printing process to occur. So if you want to write for a journal that is topical, you really need to contact the editor and be sure you submit in a timely way. At one point in my career, I was the editor of a topical journal and we published in the front of the journal the upcoming topics for a minimum of a year, sometimes for two years. And it was hard if our very next month's topic was published and an author contacted us and said, can I write a paper for that topic? It's really too late if you wait to a month before it's to be released. All those papers at that point are really at the printing press, so it wouldn't work if you waited to the last minute. So decide is your paper best for a topical journal or a journal that is mixed. It doesn't use a thematic. In those journals, it doesn't matter exactly when you submit your paper, but if your paper has a seasonal aspect to it, for instance, if you're going to write on care of the patient with hypothermia, it probably isn't going to get published in July, just as if you want to care for the patient with sunburn or heat stroke. That's probably not going to get published in January. So there are some nuances to topics and when and how the journal wants to publish those.


All right, so in the meantime, ready to get back to work? It's, summer is, is it pretty sad? Summer is almost over? Holy cow, I can't believe it. Just literally, got about another, what, three more weeks and we're pretty much done over there. I mean, it's gonna, yeah, hopefully everybody's great to just start enjoying their rest of their summer, have done a couple of great activities. Time flies, kids are ready to go back, some kids are already starting to go back to college next week or two. So, let's kind of make the best of it. In the meantime, everyone, last week, we had a couple of good things that happened in the market. First of all, you see what's happening in the market today. Let me tell you, it's gonna be, I got out of all my swing trades. I told you that last week.  It says, you know what, we're capping out, you got this whole, once President Trump came out, we're raising the tariffs on China and like, you know, this doesn't look pretty good. And then it was like a perfect storm and then the Fed didn't really lower rates as much as they wanted to. So I'm like, you know what, not really making anything on these swings. Let me get out and good thing I got out of it because they all pretty much tanked. And when you look at a lot of the long-term, when you look at some of the stocks, like, you know, some big leaders, for example, like, let's talk about Amazon. I mean, like, stock was 2,000, you know, now it's down to 1,800, Microsoft, you know, when you're looking at some of these stocks, there we go, and we look at some of these stocks on where they're at and where they're trading at. I mean, they had some really, really nice runs. So you're going to get some type of a correction out there. Sorry about that, guys. You're going to get some corrections that are in the market that go into effect. And you just got to like, more or less, just take your time and, you know, it's always nice to be in cash because, you know, there are a lot of stocks I wish I could have bought earlier and could have made some and held certain ones.  So we all know what happened over the weekend. You know, one thing you learn being in the market, you learn exactly how economics do work. And you know that you heard what happened with China, devaluing their currencies. And, you know, it's so funny, but if you talk to a lot of people, they just don't understand that. I don't even think the news people when you hear President Trump talk about it goes, China keeps devaluing their currency. And honestly, I've been hearing that for the past two, three years. And you know what, you really, until you keep hearing it over and over and over again, then you like it sinks in like, and then you have people put in layman terms explaining how they devalue their currency and how the terrorists won't affect them. They just totally crush their currency. They make up the difference.  Cold war, I basically would call it again. Cold trade war. And it could happen only to the election. So, you know, I think we're going to have a little rockiness going on. Now, unless the feds keep blowing rates, and then obviously there's going to be more of a trade war going on from there on in. But you know what, how does this all help us? Volatility, that's what we want, right? That's what we need. Now there were a couple of great stocks that did move. And one of the big ones that you have to keep an eye on is a lot of the ETFs. I mean, ETFs have not stopped. I mean, you're talking about like Nugget. I mean, you would bring up Nugget here. I mean, I was looking at these things, holy cow. I mean, these things from like 14 to 31, that actually wasn't even the big one. I think it was JNug, right? JNug I think was probably the best. Pretty damn volatile, JNug, yeah, that stock. I mean, just in June was $30, already up to 90. I mean, this is like, yeah, Grant must be super excited, Grant.  This is all you, buddy. This is payday for you. Hopefully you're doing great. Congrats, you know. Listen, sometimes you keep triggering, triggering away and eventually you start taking off. So just got to basically, yeah. What, that's right. What you're gonna have a heart attack or anything like that. It's seen exciting, but it just, what do they say? What else is up pretty big too? Bitcoin's up pretty big too. Up 25% from where it bottomed out on. You know, it's funny. I was looking at, you know, I was looking at blockchain, like a RIOT. Shockingly, that really didn't move though.  I don't know if it has anything to do with it, but you know, some of them are moving, some of them aren't. So I mean, we know that overstocked it a little bit. That made a big move. But as anyway, before we start going through the watch list and see what's going on, it is Monday, guys. So let's be very careful what's going on. But talking about what happened last week, we had the PHAH, wanted to talk about some of these stocks that did pretty well on Friday. You made a little money on this one. Stock went from five, gapped up to about 610, went to about 730. This one was pretty good on earnings. Pins, Pinterest. That one some of you guys did really well too on pins. Pins, there we go.  Can't take broke taking a profit, but it did exactly what it did when it opened up. Hit it high and just came back right off. So that one did pretty well on that earnings. Always got to take the profit square. Also came out earnings. A lot of stocks earnings are coming out. And also there's a lot of them coming out this week too. I mean, you got Hertz that's coming out. You got Weight Watchers. Tuesday, you got Disney that's coming out. So you're getting a lot of earnings in it come out and hopefully a lot of it will make up for the bad news that's coming out that just came out recently. All this trade wars and gun violence, all this stuff. You know, it's funny. I was looking at some of the gun stocks. I really didn't see them moving too much. I hate to bring this up, but sometimes when you hear some of these catastrophes of what's going on in the market regarding about these mass shootings, sometimes you'll think that it would affect the gun market. Well actually, Ruger took a big hit right here. You can see that one went from, literally went from 56 and just tanked all the way down to about 45.  So, I mean, Ruger could be a watch on the bounce. You know, I would keep an eye on that one on Ruger. Keep an eye on that was Smith and Wesson, right? Which was, was AO, AOBC is another one. You know, that one basically trading alongside with the two. It's coming to some support levels. That one also could hold up pretty strong. You can see it's got support levels. Going to June, July took a little bit of a hit. Ruger took obviously took the most, but I want to keep an eye on some of those stocks on the dead cat bounce.  They were good shorts.  You know, us as traders, listen, the rest of Wall Street does the same thing, unfortunately. So, you know, as much as you have, you got to look at one way. You got to look at the other way as a trader because you got to remember something very important. What happens if you owned one of these stocks, right? And you're in that position. And so, and now it affects you personally and you had nothing to do with it. So what are you supposed to do? I mean, as much as you're looking at it in one, in one set of eyes, but in the other set of eyes, you know, it's gonna kill you in the other way. So in the pocket. So you got to know how to trade it too.  I'll never forget back in the day when those planes crashed in 9-11. And I was doing a coaching class with a student of mine from Las Vegas and he just told me what happened and the markets were still open. And let me tell you, I didn't get a chance to short my shorter position. I got a chance to get out of all my swing positions and IRA and the good thing I got out of it because the market opened up 3000 points after that. And I just bought it back 3000 points. What am I supposed to do?  So people are like, wait, I got a hundred, I got a lot of money in there. I don't want that to burn down with the house. That's all I got and I lose that, I lose everything. So sometimes you got to look at it that way and, you know, fortunately it hurts. By the way, great movie you guys got to watch. I say it all the time, watch the biggest short. It's a great movie. Kind of teaches so much about economics. All right, so let's go through the watch list because we do got a bunch of stocks that are moving. The stock ALLK has been pretty damn volatile. We saw it early this morning. I saw it around eight o'clock this morning at 50. It ran to 65. Stock has gapped up huge. It came right up to the major resistance levels right around here. You can see it right around down December and now it's starting to back off. The only problem with this stock right here, it's just too damn volatile. The spread is just humongous. It's a level five stock. So unless you got, unless you're a level five trader, stay away from it. All right, TVIX is another one that's right now. Like, listen, all the ETFs are moving. What's going on with the news? The market is getting crushed right now. So all these ETFs are moving. So be very, very careful. By the way, Ken, regarding what the book map that you guys are talking about, I know you guys, listen, is that thing incredible platform? Just to let you know, I know we have the onsite that's coming up next month. Book map is coming to our onsite and they're going to be doing an event for us here. So that's gonna be one of the bonuses that you guys, one of the guest speakers that we have going on. So it's gonna be a great, great, great opportunity for you guys to meet the owners of book map if you come into onsite, all right? So then you're gonna have the time to chat with them away too because they're gonna be joining us for the blue carpet dinner.  I mean, what's going on, Bitcoin, the bookmarks, it's worth every penny and it's even helps you when you meet them. Now, regarding about blockchain, I don't know, I'm gonna throw it out there just to see what's happening, what's going on with the cryptocurrency. So I'll keep an eye on it. I'll make this second protocol. GL, YC came up on my radar also. This stock is getting crushed. I mean, could you talk about getting decimated? Stock was $13, absolutely zero mercy. Went from 13, finally built some support levels at nine. You wake up this morning at four. I'm telling you, I tell you about this all the time, guys. You know what? Sometimes getting out at nine because you didn't get out at 13 is not the worst thing in the world because guess what, now it opens up at four. How much lower could the stock go can go to zero. And you know what? Look at the trend of this thing. So, you know what? A lot of the students that become students at Cybertree University, not because about the winners, it's about because they can't control losers. And this is some of the things that we spent a lot of time talking about in the phase two and three, phase one, two and three on the last phase of the courses. All right, so be careful of these stocks right here. I don't know if she's still on the short list but she's obviously not looking too well. We got some ETFs in here. UVXY is one of them. Just guys, be very careful about all these ETFs. They're very, very brand name. If you look on the level three and you look at the level four platform, it's the orders are everywhere. They're very, very hard to trade. This is not by far any beginner stock. We know about Jnug, Nugget. A couple of the stocks getting crushed. Look at cars. Cars getting destroyed. Now, the difference between cars and a pharmaceutical, they do come back, all right? So cars, it looks like it's starting getting a little bit of a bounce, but the stock is obviously not doing too well. Maybe you get a little bit of a dead cap bounce. You can see, you don't see too many orders out there on the level three, but when I brought it up on the level four platform, I started getting some data. Not a whole lot, but it did trade 157,000 shares out there. I got a little bit of data over here. No, not too crazy. You could see it right here. Couple of orders hovering right around here, but not too major. It was this big buyer right here. It got executed right here at 1190 back around 830 this morning. So I'll keep an eye on it.  All right, Ben, you went along the GLYC. Bring that up. Well, there are some orders coming in there, right? It's all about seeing those orders getting executed. Look at the time it sales. You see it has 11,000 shares, just got executed at four bucks. So it looks like four is really kind of holding some support levels right here. So it definitely, you know, it looks like a goodbye where you got it. So listen, everything gets, you know, eventually somethings do come back. Just, he's not all of a sudden think like, oh, well now maybe it's a swing trade. Remember, it takes a couple of days before, you know, it happens over the course of the day, which trickles down to a swing. All right, guys. All right, so there's the watch list. Listen, today is Monday. Got a lot more earnings that are coming out this week. You got a lot of news that came out. Market's gonna be down. Just be very careful. I would probably recommend don't do anything for the first five minutes of the open. Let the smoke clear. Let all the bots do what they have to do. And then we'll usually see something that will open up when the market opens up.  But I do like some of these stocks that are getting crushed though on the day cap bounce. All right, guys. Good luck today. Happy trading. And we'll see you back at 2.30 for the afternoon meeting. Good luck, everyone.


The beer that made Milwaukee famous presents The Halls of Ivy, starring Mr. and Mrs. Ronald Coleman. Good evening, this is Ronald Coleman. And Benita Coleman. Ladies and gentlemen, this is the 100th performance of The Halls of Ivy. And this broadcast is dedicated to a college which was founded in 1852 and is observing its 100th anniversary. We salute Tufts College, Medford, Massachusetts, and its president, Leonard Carmichael. The taste of Schlitz. The taste so many people prefer has made Schlitz beer first in sales in the USA. If you like good beer, those millions of people are doing all over the nation. Ask for Schlitz, the most popular beer in history. Let's surround the sphere today. Welcome again to Ivy, Ivy College that is in the town of Ivy, USA. Sadly enough, there are people who make a dubious living from trumped up lawsuits, professional litigants. There are also professional witnesses who for $15 or so will swear to the sun rising in the west. Professional bondsmen and other full-time legal fringe practitioners. But professional defendants are rare and among them are college presidents. They must be constantly defending their policies, their plans, and their day-to-day actions. As President William Todd Hunter Hall of Ivy College tells his lovely wife, Victoria... And so tomorrow I must go before the Board of Governors and make the best defense I can against the charge of extravagance. It seems that I ordered Emerson Hall painted for the third time in 20 years. Furthermore, excuse me darling. Dr. Hall speaking. Oh yes, Miss Haywood. He is? Well, certainly I'll be happy to. What seems to be the difficulty? I see. Well, I'll do anything I can, of course. Yes, Miss Haywood. Goodbye. That was Miss Haywood, Vicki. She's a prime mover in the Ivy players. Yes, I know. As someone said about someone else, she thinks she's... Mary's all rolled into one. She's a fine little stage manager when there's nobody on the stage. What did she want? Well, it seems that Sidney Mullins is on his way over here on some desperate mission. Oh, that usually means they need some money for something. Do you know Sidney? No, no, I'm afraid that pleasure has been denied me so far. I take it he is the promoter, big exploitation expert, and general manager of the Ivy players. Yes, and a good one. His family were all Vaudville people. Comedy acrobat. The four flying Mullins. Four flying Mullins. He had two sets of parents. In his show business, darling, lots of families are made up of complete strangers. Yet three girls get together from Peoria, Yonkers, and Death Valley, and if they can harmonize well enough, boom, they're the McAndrew sisters. Ah, fascinating profession. Producing fascinating people. And if you think there is anything personal in that observation, my love, you are quite right. Tell me more about Sidney. Well, he talks like variety magazine. He says he was born in a trunk backstage and his first words were not, Mama and Papa, they were, get a load of that audience.  Ah, precocious child, I don't suppose. I let him in, Luisa's at the market, being skeptical about the meat and making nasty remarks about the oranges. Well, the market people have the retort perfect on the first of the month. And then, Sidney, we were expecting you. Well, hello, Dr. Hall. Hello, Sidney. It is Sidney Mullins, isn't it? Yes, sir. Later before flying Mullins? Artists of equilibrium, feats of strength and agility, clean entertainment for the entire family. Drum roll, cordon G, and take a look at the balcony. Think of being up there without food or water. Well, from what I have gathered in several years of being married to an actress, Mr. Mullins. To Sid, sir. Thank you. I have the impression that to perform as the number and quality of the people in an auditorium are of paramount importance. And not only paramount, also the palace. That impression is good for a three-year run, doctor. It's the old story. The manager says, OK, we'll redecorate the theater. How do you want the seats covered? And the producer says, with people.  They don't have to come back tomorrow. My folks were the c- Oh, I'm sorry. I guess I was vaccinated with a long-playing needle. No, no, don't be sorry. But I'd like to hear about whatever seems to be distressing your Miss Haywood. Oh, she's not sitting is Miss Haywood, William. He's trying his best to get her adopted by a family in Pakistan. Oh, she isn't so bad. It says here in fine print. Look, doctor, the Ivy players have been nominated by the ISPP. The ISPP? The Indicategiate Society for Play Presentations, dear. Yes, they nominated us to compete with seven other colleges. Nationwide contest, best dramatic organization. For student theatricals, this is the Kentucky Derby, with ham instead of horse. I- I take it the finger of fate has tapped you on a sore shoulder. At the sawn is being financial. Sir, I can see why you're president of this culture camp.  Well, it's experience, Sidney. Most problems he encounters can be divided into two groups. One concerning money and the other concerning not enough of it. Money is an absentee landlord. It's tyranny is most evident when it is not present. But as Duke Gerald of Tuscany so well said, the precious metals are neither gold nor silver. They are the warm bronze of health and the cold steel of curry. So that's very good. Thank you. Just how desperate are your straights, Sidney? Well, if somebody gave us $14, we'd have $9 because we owe five. Here's how it is, sir. We'd like to get into this competition because we think we could win against those other jokers in a dark theater on a rainy day during Holy Week with a pit-band of six ocarinas playing the deadmarch from Saul. But here's the gimmick. Travel and production expenses are to be paid by the competing colleges. Well, we're so broke if I shoot myself tonight, they can put a penny on only one of my eyes. The Ivy players just use up the treasury buying new scenery and costumes, William. All the money they made on their spring tour. I see. Well, inasmuch as the president of a college usually holds his job by an instinct for raising funds from obscure sources by devious devices for peculiar contingencies, I shall see what I can do to alleviate your distress, Sidney. Dr. Hall, I know this isn't like asking you to pull a rabbit out of a hat. It's more like pulling a base drum out of a flute. If you can do anything to get the show on the road, we'll put up a life-size statue made of ground-up ticket stubs mixed with tears. A statue of me? Well, that's... No, no, sir. No, sir.  We'll put up a memorial to the wisest, wise guy who ever lived. The one who first said, go hire a hall. Well, if that's the case, Mr. Pearson, I can't argue with it. Thank you very much for the information. Goodbye. Mr. Pearson, are our financial officer? Yes, Vicki, and if you think Mr. Clarence Wellman is frugal in the name of Ivy, you must meet our Mr. Pearson sometime. They say he is fiscally, so devout, he makes an annual pilgrimage to the Denver Mint. Oh, well, being a financial officer is hardly the job for a spendthrift. Oh, I agree with you. However, with that telephone call, I have exhausted the last possibilities of borrowing money for the Ivy players. Yes, we must apparently raise it ourselves in some way. It's a peculiar situation, isn't it? The best way to raise funds is put on a show. But the best people to put on the show are the ones who need the money. Maybe they shouldn't have spent it all on scenery and costumes. All dressed up and no place to go. Well, today is necessity. Today is necessity. It is tomorrow's folly. And if we all had the faculty of planning for the... Toddy! That's it! What is it, my darling? You just said it! You just said it! Folly is faculty! The faculty folly is what you put on a show! It's sensational! You mean that we should... should I mean the faculty? But it is for ratio, not Professor Dillard. That's magic. I'll sing. And Toddy, Toddy, you can do your piano log, you know? The one about the Hawaiian dancer and the impresario. Oh, no, no, I shouldn't... Oh, really, it's unthinkable. I wouldn't dream of it. I couldn't even... Well, all right. It entertains me, anyway. This is a marvellous idea. Let's start calling people. Oh, I knew I'd get you into the theatre sometime. It's really a promotion for me, isn't it? Promotion from what to what? From president to acting president. Classes were in session as I dropped into the Ivy bookstore the other day to see Marty Fossil who operates the place. So it was pretty quiet. So quiet, in fact, that I didn't think anyone was there as I opened the door and walked in. Hello! Hello in here! Anybody home? I say, anybody home? Shh! Nobody here but just us books, carpenter. And I must say we like it that way. Now, if you don't mind, I'm very busy. Gosh, Marty, I didn't think you were here. From the tone of your voice, I guess you wish I weren't. Oh, you gathered that, did you? Yes. Well, at least I'm sending at the right frequency, but I'm not receiving today. My antenna's down. Marty, it sounds to me like you've been sitting in here reading the books you sell instead of concentrating on selling them. Well, is that different? You don't confine yourself merely to selling Schlitz beer to other people. You drink a bottle too now, and then don't you? I certainly. Anything I tell people about Schlitz beer, I mean. When I say that Schlitz tastes so good to so many people it's America's best-like beer, I'm giving my personal testimony about Schlitz. When I say that... Okay, you've made your point, Ken. And that part about your personal testimony intrigues me. Why don't you write a book? Me? Write a book? What about for goodness' sake? Well, you might title it Anomalous of Celebrated Schlitz Beer Commercials Compiled by Dr. Kenneth Carpenter. How's that? Gosh, you think I could, Marty? I sure would like to get the word about Schlitz around to as many people as possible about its light, bright taste, sparkling quality, bottle after bottle. It's magnificent. Here, here, let's save that for the paying customers. Hmm. We could probably get about $6 of volume. Prices being what they are. Oh, I couldn't let you sell my book, Marty. I want people to learn about Schlitz from me, free of charge. Such is the fact that sales figures prove that... Okay, you win, Ken. But as long as we're giving things away, let me contribute this. I like Schlitz myself. It's the best brew I ever browsed through. And I've lived through a lot of editions of the beer that made Milwaukee famous. Ah, fine, Marty. What are you saying we step out and sample my favorite best-seller right now? Don't mind if I do. Lead the way, Ken. As we return to the halls of Ivy, we find Dr. and Mrs. Hall eagerly discussing plans for the newly conceived Faculty Follies, an upper echelon production designed to raise money for the student dramatic club, The Ivy Players. Dr. Hall is saying... And you don't think we should call in any outside talent? Any professional entertainers? Oh, no, Toddy. Let's just keep it the Faculty Follies. Just professors, administrative people, like you. And believe me, they all do. They all do what, darling? Like you. Oh, thank you. But I'm not an entertainer. Oh, didn't you tell me as an undergraduate you were in everything? Glee clubs, orchestras, dramatics? Well, good heavens, my sweet. That was a long time ago. I mean, and even then, I wasn't very much of a... That is, I wasn't too bad. In fact, I had some very good reviews. Some fine reviews, really. One critic said, Young William T. Hall, who portrays the character of Carter Gaylord, a riverboat gambler, does so with such a determination to underplay that he is practically cataleptic. Cataleptic? Yes, I treasured that clipping for weeks until I looked up the word cataleptic and found that it meant a condition of peculiar muscular rigidity. Oh, it could have been worse. He might have said catastrophic. Yes. Anyway, he went on to say that I bore a startling resemblance to the late Ronald Coleman. What do you mean late? He's still around. He is? So, getting back to me, I'm sure I don't know what I could contri... Vicki. You thought of something. Did I ever give you, in a moment of exuberance, of course, my rendition of three blind mice as Kipling would have written it? No. Sounds wonderful. Now, do it. Go on. Do it. Do it. Now, please. Don't please, darling. Do it. I'll have to brush up a bit. But how about you? You have such a repertoire. It'll just be a matter of selection. Yeah, but that's the trouble with a big repertoire. You never know what to do.  Well, there's a song I did once called A Day with a Knight of the Garter. Hmm. I, uh, I don't think it would be quite... Well, then there was a ballad entitled Send Out for a Handful of Sawdust They've Broken My Dolly's Heart. It sounds just a trifle. You're absolutely right. It was. I know Vicki. Vicki, the one about Alice. Yes. Remember my favorite. Alice. I don't...  Alice's house is like a palace. Is Alice's house. Oh, yes. That's the one. Now, let's, let's see what we have lined up. And there's Professor Heaslip in Something Shakespearean. Oh, must we?  Yeah, well, we must at least consider all the available talent we... Talent? Yeah, well, that we can muster up. And discard what we can later. Yeah, all right. Then we have Professor Heaslip doing the first grave digger. And the way he loves to throw the dirt around, he ought to be great. Um, we can get Mr. Dillard in chemistry to do his magic act. Good. He won't lack for assistance, and a number of his students would like to saw him in half. Then you'll do your bit, and I'll sing. Oh, yeah. Wait a minute. We need some dancing. Have you any hoofers on the staff, Doctor? Hoofers? Oh, yes, yes, yes. No, no, no, dear. Well, I don't think so unless I can qualify myself. They tell me that some of my sidestepping around the Board of Governors is worthy of Fred Astaire. But I'll inquire around. Oh, that must be Sid Mullins checking back. Are you writing these down, Toddie? And I'll go to the door. Oh, hurry back, my love. I miss you dreadfully. Now, let me put this down. Heaslip, Shakespearean bit. And short one. Dillard, Legidemain. Vicky, Alice's house. W-T-H, three blind mice and a little kip. Fined, fuffing, croff, if pass. Oh, Dillard, look who dropped in. It is I, Doc, the last of the tobacco-tuned professors. Ah, Professor Warren. Yep, the superannuated, over-aged fire horse. Nice to see you, Joe. You've neglected us. Yes, and you're busy writing another historical novel, Professor. One with a pretty lady on the cover Please, my dear, will you never forgive me for being a successful author who got filthy rich writing comparatively clean literature? No, we've never blamed you for it, Joe. After all, some people cheat at solitaire, some beat their wives, some put buttons in the church collection, and some write historical novels. But what are you carrying there, Professor, in the little black case? This, Doc, contains a gold-plated coronet. Here. A coronet. Then you've heard about it. Oh, this is wonderful. Professor, consider yourself engaged for the run of the play. Think of you rushing over here in this hour of experiment. No, no, no. Just a minute. Hold the phone. What's this all about? Well, you haven't heard about the faculty, Follies? Well, I've heard about a lot of them, but I'm no gossip. No, no, no. Getting off a show, produced, acted, and directed by the faculty members to help out the ID players.  And I'll go out with it, too, lady. I haven't played this cornet since the Coolidge administration. My motto was keep cool with Coolidge. Get hot with Joe Warren. Yes, but how did you happen to... No, don't go thinking this is a Charlie horse in the long arm of Coincidence, Doc. Burgess, my housekeeper, was spring cleaning and said she was tired of dusting this old cornet. And why didn't I give it to the Ivy Band where it'd be useful? So on the way to the music department, I stop in here to say hello, and I get falsely accused of trying to crash your burlesque show. Oh, what burlesque? We need you, really. A cornet solo would be wonderful. Victoria, a cornet solo, the way I play it, hasn't been wonderful since the curse of a naking heart led the hit parade. I also did glow worm in the Washington Post-March. End of repertoire. Well, Professor, you're being modest. I'm sure that with a little practice, you could... All the things are moving, gentlemen. Excuse me. Well, we've really missed you, Professor. How long since you returned from your lecture talk? Oh, just been back a few days, Doc, and none too soon. I'm just ham enough to enjoy lecturing, but I get tired of suitcases. To me, there is no gladness about a Gladstone bag. Excuse me, Professor Warren. Do you know Sidney Mullins, manager of the Ivy Pairs? I don't think so. Hello, Sidney. Hello, Professor Warren. You signing up for the faculty follies? A little legomania, songs and patter with a grass mat and a bamboo cane? I know more, see, Professor Warren with a bamboo cane during a soft show than I can see myself as a baggy pants comedian with a nose that lights up. Oh, no, sir. You haven't got the right family history for a comic, sir. Comedians have got to have baggy backgrounds and a resentment that lights up. What do you mean by that, Sidney? Well, with comedy, you're fighting something and privileged kids don't have to fight. Look, I'm no psychologist, sir, but I was brought up with fright wigs in the Flugel Street bit and I know comics. With a few exceptions, a comedian was the kid with no dough and a homemade sled who got laughed off the hill by the other kids. So he grew up with the idea of making people laugh with him before they could laugh at him. It's very simple. He just beats him to the punch with a punchline. End of lecture. There will be a special performance in five minutes for men only in the rear ten. Sixth Oriental Dancing Girls will see how... Hey! Well, what's the matter? Look, look, the Bugle. But that's Professor Warren's cornet, Sidney. Well, this is great. A little bugle called Ragon One with a blue spot between the Hamlet and the other... Look, how's your lip, Professor? Well, it's kind of you to inquire, my boy, and I doubt if I have one anymore. Never did I have much of one. Two hours' practice and I'd have lips like a bee-stung U-Bangie. Well, I had to have a special mouthpiece made. Look, without that mouthpiece I couldn't play as good as I did, which was pretty bad. Well, you people want to get on with your plans, so I'll just... No, please, please, Professor. You can't walk out on it. Oh, come on, Joe. Audition. We'll be kind. Yeah! Just play anything, Professor. Well... All right. I'll try one called the Untutored Tutor because I can talk between choruses and rest malipses. Good. You want a downbeat, sir? You want to stamp it off yourself? I'll just sneak up on it. Just give me a little vamp on the piano. Crazy. Ready? When I was a boy in Indiana, my folks wanted me to learn piano. But I'd run and hide till the sun had set, because all I wanted was a brass cornet. I saved up my money in a sold mahog, and they ordered a horn from the catalog. It was a joyous occasion when it came one day with six easy lessons on how to play. I have a tutor, but I learned to toot some fancy arpeggios and stuff to boot. I could play any lorry most all the way through, and the folks were proud of how their little boy blew. Then a medicine show came out our way, and the doc said he needed a man to play. He said I need some music while I hand out the cures, play the billboard march, and the job is yours. I was neat grinding. That was a cornet solo. I don't think I ever heard anything quite like it. Thanks, everybody. When do we start rehearsals? At will. Let me see. We haven't set him up yet, professor. Matter of fact, we haven't even routineed the show yet. We might have you opening, closing, in the middle, or out entirely.  Well, just let me know, son. I realize I'm a little rusty, but I'll practice. Where's my cornet case? Oh, let me do it, Mrs. Hall. I'll pack it away for him. Here you are, sir. Now, don't call us. We'll call you. Good day, Joe. Hope we'll see you soon. At the first rehearsal, doc. Bye, everybody. Goodbye. A little rusty, he says. That wasn't rust. That was corrosion. It was dreadful. The question is, how to gracefully prevent his participation without hurting his feelings? Oh, well, I think I've taken care of that, sir. You see this? Oh, good heavens. He forgot to take the mouthpiece for his cornet. Oh, no, sir. He didn't forget it. I palmed it. He'll think it's mislaid, and after the follies, you can say you found it under the piano. Well, Sidney, that was fast-thinking. Fast-thinking. It was a kind act. In this production, we'll need all the kind acts we can find. Excuse me, folks. I forgot something. Here's the cornet to go with that mouthpiece. Give it to someone who can blow it. I'm too old a chicken to lay an egg. Isn't he a cupcake? Yes, our only little trouble disposed of so quickly. Isn't this wonderful, William? A great big project like this with no trouble? No problems? No? I hope nobody whistled in the dressing room. Dr. Hall speaking. Who? Oh, yes, Mr. Wellman. I don't agree. But we're going on with it, nevertheless, Mr. Wellman. Goodbye. Don't tell me, Toddy. I know. He says the show is undignified, degrading, subversive, ridiculous. And he says he has the way to positively prevent us going on with it. What's the way? Well, he didn't specify, but it seemed quite confident. Well, let him try. To quote Ulysses, Come, my friends, push off. And sitting well in order smite the sounding furrows. It may be that the gulfs will wash us down. It may be we shall touch the happy isles. But will the gulfs wash our heroes down? Or will they touch the happy isles? Or will Dr. Hall, that red-blooded fighter for the right, succeeded foiling the villainous Mr. Wellman? Will the lovely Victoria and the ingenious Sidney Mullins win on to victory and lift the mortgage on the ivy players? Tune in next week for the conclusion of this stark thrilling drama of right against might. Mr. and Mrs. Ronald Coleman has been presented by Schlitz, the beer that made Milwaukee famous. The taste of Schlitz. The taste so many people prefer has made Schlitz beer first in sales in the USA. Why don't you two enjoy the most popular beer in history? Next time, every time. Ask for Schlitz beer. Now, here again, our Mr. and Mrs. Ronald Coleman. Good night, everybody. Good night from all of us. And from our sponsor, the Joseph Schlitz Brewing Company of Milwaukee, Wisconsin, and its thousands of friendly dealers throughout the nation. Bye, good night. At the same time at the Halls of Ivy, starring Mr. and Mrs. Ronald Coleman. Professor Warren is played by Arthur Q. Bryan and Sidney Mullins was Sidney Miller. Tonight's script was written by Don Quinn and Charles Henry. Music was composed and conducted by Henry Russell. The Halls of Ivy was created by Don Quinn, directed by Milton Merlin, and presented for the Joseph Schlitz Brewing Company of Milwaukee, Wisconsin. To invite you to enjoy on television, the Schlitz Playhouse of Stars with Helen Hayes, Margaret Sullivan, Lillian Gish, and more of the brightest names of Hollywood and Broadway. Say you're a newspaper for time and channel. Ken Carpenter speaking. Oh, we love that surrounding spirit. The NBC Radio Network for the very finest in radio listening. At this spot on your dial, you can be sure...


All right, let's pull our chain link texture into Unity and into Unreal and just give them a quick little test Okay, so I've gone ahead and opened up the location of the texture here And what I want to do is I want to try to move this guy, but the mouse isn't letting me there you go I want to drag and drop this into my texture folder here that I have inside of Unity All right, so I just set up a Unity project here, and I'm gonna go and drag and drop that into the scene and Actually before I go and do that I'm gonna go and create a material that I can drag and drop into the scene onto a grid So let's just call this chain link met And we're gonna assign our albedo to it. I'm gonna set this guy to fade and We are going to tile this a couple times there All right, so then I'm gonna go and create a plane in the scene and just drag and drop my texture on there And look at that Perfect. That is awesome Very very cool All right, so with that we now have a tiling texture Awesome, so what I'm gonna do is jump over into Unreal and do the same quick test And now we're gonna close out this section and move on to getting this set up on to an HDA that creates a chain fence for us. Which is why not? We have a texture now Might as well automate the creation of a chain link fence as well All right, so let's walk through the unreal side of things It's pretty much the same. We just have to do a little bit of material setup But for overall, I mean, it's really basic process So what I'm gonna do is I'm going to drag and drop the texture into my textures folder No, I just went and created that all right, and I'm gonna go and save this guy Just to make sure we commit it to the project All right, so what I need to do is first let's come into the scene up here and let's just put in a plane like so and Let's scale it So we can see it. I'm gonna hit G on the keyboard to get rid of all those gizmos. Cool Let's go and create a new material now. All right, so I just right click down here and create new material I'm gonna call this chain link Matt there we go And what I want to do now is just pop this guy open and we're gonna get some stuff set up here So I'm gonna select the texture and then inside of the Material editor here. I'm just gonna hit T on the keyboard or hold down T and then left-click What that'll do is it'll create a texture sample for me and automatically hook up the texture that I had selected in the content browser Cool. So right off the bat We can just come in here and connect that into the base color All right, so we take the RGB colors and put it into that base color and now I need this to be Translucent or transparent. All right, so to do that here Inside the material editor, I'm going to select the base node here and switch the blend mode over to Translucent Like so what that does is it exposes the opacity input right here So we can drag our alpha value into that opacity And after it's done compiling here, I'm gonna hit apply and save we should see the chain link texture on the sphere here All right, and there we go. So let me switch this over to a Quad and let's go and create a texture coordinate node so we can modify the coordinates So I'm just going to type in coordinate and then select the texture coordinate node And then the next thing I want to do is I need to multiply this value because coming out of here We have a vector 2 value for the x and y tiling. All right So you could just go and plug that straight in like that But what I want to do is I want to multiply it so we have control All right, so I'm going to hold down M on the keyboard and create a multiply node I'm gonna hold down one on the keyboard and create a constant. All right, I'm gonna set this constant to Value of two just for now All right, let's go put that guy in there and put that guy in there Cool, and I'm gonna pump that into the UV's There we go Hope I keep missing it There we go Cool. So now I've titled it twice All right, cool That's looking pretty good. It looks like if what it's really zoomed out like that We're getting a little bit of a seam in there But I think that's just because of the ambient occlusion that we multiply it over there So I have to move that around but for the pattern itself. We are tiling perfect. Awesome So I'm just going to close that down and let's just drag and drop our material onto our plane there Now it's gonna be kind of hard to see here, but you can see We have chain fence pattern all created In Houdini with a bunch of nodes Very cool. All right, so I'm gonna leave you guys there And we are going to close out this section and talk about the concepts that we covered throughout the section


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It's a high polishing toothpaste that acts like a jeweler's polish in removing tarnish from silver. Quickly but also gently colonel's erases stubborn surface film from your teeth revealing all their glorious natural brilliance.  Now Mr. Keen Tracer of Lost Persons. This time our story opens about a hundred miles from New York in a chemical plant in Connecticut. There's a hum of motors and a hiss of compressed air as Mr. Keen and his assistant Mike Clancy are guided across the floor amid great bats and retorts. Halfway across the floor their guide a thin near sighted man pauses to answer a question. Oh yes Mr. Keen our production right now is entirely for the war effort. And I gather Mr. Wallace this plant supplies chemicals for various types of explosives. Oh yes shells bombs mines. Mine mine mine look at the size of them big bats over there. It wouldn't like to think of all the shooting that's gonna come out of them a little bit. Quite right Mr. Clancy quite right and that may have a lot to do with the reason that our manager Mr. Morley has sent for you two gentlemen. Well why did he Mr. Wallace all I know is that he phoned my office yesterday in New York and asked me to come here the first thing this morning. What's happened? Well I'm only in the personnel division I'll have to let Mr. Morley talk for himself. If you come along to his office over there on the right. No sir must be on the left. The left. Yes there's the sign manager. Oh yes yes forgive me for not knowing the way around my own plan but I've broken my glasses you see I'm very near sighted. Never mind Mr. Wallace we'll guide you. In fact here we are right now. One moment while I knock. Come in. Good morning Mr. Morley. Good morning you must be Mr. Keen. That's right this is my assistant Mike Clancy. Glad to meet you. How do you do Mr. Clancy. Won't you come inside please gentlemen. You too Wallace. Yes. Right now if you just sit down and make yourselves comfortable. Thank you. Now then Mr. Morley what's it all about. I'll come right to the point Mr. Keen. As you probably noticed this is a war plant and a very busy one. Right now we're well we've just received a very serious threat to our production. How. By the disappearance of a member of our staff. In fact our best chemical engineer. That is serious. There is another man on the planet who has his know how. We must get him back as soon as possible. What's this man's name. Henry Trevor. And how old. Forty two or so. When did he disappear. Three days ago. Under what circumstances. Well I'll let Wallace here tell you. Well sir it was late Monday afternoon. I went to Mr. Travis office to check a personnel matter but he didn't have time to talk to me. He already had his hat and coat on was leaving the plant and not that he was a clock watcher. Oh no indeed. Continue please. Well Mr. Keen there isn't much more to tell. Mr. Trevor left the plant and just never returned. You've checked at his home. Well he has lodgings but he never slept in his bed that night. Was he in any sort of private difficulties. None at all. Unless Wallace here has an idea. What is it Mr. Wallace. You heard how valuable Mr. Trevor is to us. Obviously if anybody stands to profit from his disappearance. Are you hinting that his disappearance may be the work of enemy agents. Yes Mr. Keen. Glory be. I don't know whether I'm permitted to tell you but perhaps Mr. Morley. I think I can refer to it in a general way. Mr. Keen this plant has been working on an important new device. Really. And Trevor was handling all the experiments. Well that is important. And what's more Mr. Keen. Yes Mr. Wallace. When I went to his lodgings to make a check I found that several strangers had taken rooms there recently. Really. Well we'll have to look into that. But first I'd like a few more facts on Trevor's professional background. Wallace can give you all that Mr. Keen. Yes in fact I've dug up his folder from my files. If you don't mind reading it to me. Oh dear me my classes. I'm half blind until they finish making me a new pair. I'll read it myself then. Notice Mr. Trevor came here only six months ago. Yes that's correct. Before that. Why all his previous experience was as a school teacher. Yes quite so. Six months as chemistry instructor in the Tappingworth school for boys. Before that six months at the Jones Walton school. Then sick for a year in Nevada. Long trouble he said. But he was completely cured. And before his illness. Fifteen years at the Morton Latin school. This strikes me as very odd Mr. Morley. What does. Here is a man who was content for fifteen years to stay in one place. Then he becomes ill. When he recovers he shows an extreme restlessness. He rushes from one job to another. Well he told me that after he recovered his health he started looking for better jobs. And here's another point. You say he was your best chemical engineer. Absolutely. But all his past experience was as a teacher. Something very much different. Well Mr. Keen he had a college degree in chemistry so. We were mighty glad to have him. What with the shortage of technicians since the war. I still think that the only explanation is enemy agents. Well possibly. Meanwhile thanks for the information Mr. Wallace. I'll pay a visit to his lodgings. Whatever you wish Mr. Keen but just find Trevor we need him. I'll do my best. Come along Mike. Okay boss. If you'd like me to show you out it won't be necessary thank you Mr. Wallace. Good I have some other matters to discuss with Mr. Morley. Good day. I'll let you know just as soon as anything develops. Thank you Mr. Keen. Well boss what do you think. First let's get some facts Mike and then we'll think. Oh you may have Mr. Keen. My boss somebody's calling. Mr. Keen. Yes that girl over there by the machine. Yes young woman. Mr. Keen I heard Wallace mentioning your name when you came by before. You're the famous investigator aren't you. Yes. Well I guess you're up here to look for Mr. Trevor. You know about his disappearance. Why sure everybody knows. Well do you have some information that might help me. Boss I think she means. What do I mean. Somebody stuffed him into one of them big bats. What I wanted to say is if you could get interested in the women's hats women's hats you might find out a whole lot more than you know right now. What do you mean by that. Stop that machine for a moment. I got work to do please this is important. I told you. Stop it. Please young woman listen to me.  Women's hats what does she mean by that. Mike I have half an idea. On our way through the town we stopped once to ask directions. Sure in a cigar store did you notice what was next door. Oh yes a hat store a very smart looking little place unusual for a town like this. You think that's what the chick meant let's find out. This is the place and there's the owner's name on the window Mrs. Lydia Groves. This is only a hunch but I'm going to play it for all it's worth OK. Nobody here as she comes now from the back room a brunette and done good looking. Mrs. Groves. Yes that's my name. You haven't come to buy hats. No this groves to ask a question. I don't understand. When did you last see Henry Trevor Henry Trevor. Yes the engineer from the chemical plant. I don't know him. This is a small community you must know. I mind my own business which is to make and sell hats but Mrs. All my hats are for women. Good day gentlemen but lady this is important. You've made a mistake. Good day. Very well. Good day. Come along Mike. Boss do you think she was telling the truth. No Mike I don't. Enemy agent Mike in my long life I found out that a beautiful woman can cause plenty of trouble without being anybody's agent. But this fellow Wallace said we still need more information. The only way to get it how so let's circulate around town and dig up every bit of gossip that we can. You hang around the lunch wagons the gas stations as for me I'm going to get a haircut.  That's right Baba. Nice day. Oh it's a beautiful day. I think it's going to be warm after all. Definitely. Yes. Don't think I ever saw you around here before. I'm doing a job for the chemical plant. Chemical plant. I believe our Mr. Trevor is one of your customers. Oh sure. Every two weeks. Interesting about him and Mrs. Groves isn't it. Him and Mrs. You know the whole plant knows. Funny thing he took to her from the first day she come to this town opened her shop. Thick as thieves also. When did she first come to town. About the time my wisdom tooth was raised in King last September. More off the side. Yes please. He hung around her shop all the time is still to folk start talking so then they took to cover. You know. They've been meeting there every night. Thrush Lane. All. That goes on there. And just where is thrush Lane. Half miles off the town right by the factory. What are you doing. You can't get up in the chair. Yes your money I'll see you later but I only done one side of your head. Come back. Crazy fellow running off with only half a haircut. No boss there's nothing in the underbrush over here well come over to this side or Mr. Keane so sure we've been out of the three hours already I know Mike this is our only real lead. Remember what the barbers said they met here every night.  The strangest haircut I ever did see and you such a proper gentleman here help me put these branches aside. And you're still hoping to find Trevor's body out here in thrush Lane not hoping I'll be glad if I'm wrong well there's nothing here all right let's get back on the path Mike so far we can be sure of only one thing what's that Trevor and Mrs. Groves knew each other long before they ever came here that I'm certain you see I found they arrived here actually within a week or two of each other. Trevor to take the job at the plant and Mrs. Groves to open the shop then promptly they became friends then promptly. Yes Mr. Keane what are you staring at something there on the ground just yard ahead of us I don't see it look Mike from over here where the sun strikes it sure but what of it just those few little chunks come over here Mike bend down help me pick them up okay so okay. Judging from the thickness of these fragments I think we'd better have another talk with our friend Mr. Wallace. Now for a moment while Mr. Keane and Mike examine their surprising find next time you meet the most successful man you know or the most popular girl take a good look at their teeth chances are they'll be sparkling and beautiful with all the magnetism your own smile should have then examine your own teeth critically if they're not every bit as brilliant and gleaming as they should be if they show signs of being discolored by surface film just do as thousands do try the new colonos a high polishing toothpaste safely speedily colonos helps brush away masking surface film revealing the natural luster and brightness of your teeth your drug is to have an ample supply of colonos on hand so get a tube tonight and see what wonders it made you and helping you to add to the charm and appeal of your smile remember the name colonos KOL why and OS colonos a high polishing toothpaste and now back to the chemical plant where Mr. Keane has a quiet talk with a helpful Mr. Wallace yes Mr. Keane is there something more you wanted to know one or two little details Mr. Wallace fire away Mr. Keane fire away were you aware Mr. Wallace that Henry Trevor was very friendly with a Mrs. Groves I wouldn't know about that well did you ever notice what root Trevor took when he left the factory at night oh back to his lodgings I suppose by way of fresh lane didn't you ever notice whether he made a practice of going through fresh lane did he I never had any occasion for walking there myself no Wallace when did you break your glasses my glasses why several days ago where right here in my office pretty thick lenses I gather oh yes yes sir that's why I'm having trouble getting them replaced Wallace take a look at these I mean these fragments in my hand well how can I without my glasses take them into your hand feel them it's a broken glass very thick bits the kind of glass that might be used in the spectacle of a very near sighted man I don't know what you're driving at sir I found these 20 minutes ago in fresh lane well the world's full of near sighted man are you sure one of them didn't follow Henry Trevor into the woods last Monday night and over here his conversation with Mrs. Groves I'm sure that this one did not are you quite finished sir one thing more do you have any photographs of Henry Trevor in your files you must have yes I'd like to have one at once well boss what are you up to now I'm going on a little trip Mike where to back into Henry Trevor's past dr. Grange you're the headmaster of the Tappingworth school oh yes for 20 years do you recognize this photograph well yes this is Henry Trevor he taught chemistry here last year excellent grasp of his subject too I wish he'd stayed what can you tell me of his background why he came here from the Jones Walton school with the usual references that's all you know well yes Mr. King thank you dr. Grange oh yes yes mr. Trevor taught here for one term at the Jones Walton school he got along well miss Mott our girls simply adored him where had he been before he'd been sick I believe for a year before that the Morton Latin school look at this photograph please is that a good resemblance perfect well mr. King we had mr. Trevor with us here at the Morton school for 15 years until it became ill and then he went to Nevada for his health dr. Heinz quite so you never heard from him again only when other schools asked us about his references I wonder if if you would have a photograph of him we should any one of the yearbooks let me see on this shelf up here yearbook of 1940 that will do very well there we are you say that's Henry Trevor course well let me show you a photograph dr. Heinz this one hmm how good a resemblance is this one mr. King this is the strangest thing do you have time for a story all the time in the world go right ahead I began to wonder what had happened to you I've been away mr. Morley for two days any luck any trace of Trevor yes no what do you mean by that he's alive all right and well I'm sure of that well but where he is and whether he wants to be found that's something else mr. King all I know is that I've got to have him back at the plant he's done fine and important work here we need him desperately all right mr. Morley that makes it a whole lot easier mr. King it's you again yes mr. Groves mr. King if you've come to plague me with questions again no no more questions because I know the answers I I'm not feeling well please come back some other time now is the best time you see Mrs. Groves I've just finished tracing back the history of Henry Trevor or rather the man who called himself Henry Trevor I don't understand the real Henry Trevor died two years ago in Nevada but I told you that I know nothing about it and the moment he died his identity was borrowed by someone else in fact by his brother John Trevor oh yes there were two trevors John and Henry both were trained in chemistry Henry became a teacher John became an engineer John the engineer was very successful made heaps of money but his wife spent it even faster than he made it I know nothing about it in the end this led John to embezzle $5,000 from his company he and his wife ran away wandered all over the country used up every cent they had because there was no job that John could apply for without giving himself away interesting but I don't see how finally two years ago the real Henry Trevor the quiet schoolmaster died immediately John saw a chance to borrow his brother's identity make a new start and so John became Henry how how did you find all this out by going back to the Morton school the headmaster remembered the scandal about the embezzlement very well and how much sorrow it caused Henry at the time all that I had to do was to show him the photograph the man who claimed to be Henry quite simple really Mrs. John Trevor what did you call me you are the wife of John Trevor the man I've been asked to find I I don't suppose there's any use now to deny it none yes it was my extravagance that caused the trouble I wanted things John couldn't afford believe me I regret the whole thing we we've struggled so hard to live it down but Wallace found out the man at the chemical plant yes by by following us to thrush Lane several times and overhearing our conversation then he tried to blackmail your husband yes and last Monday night after John left me Wallace came along again to ask for money five hundred dollars otherwise he would expose John but your husband refused yes he he began to scuffle with Wallace but then he became frightened and decided it was no use he decided he have to run away once again where is he now don't ask me please don't you must tell me no he's my husband and this was all my fault in the first place mr. Keane it was my foolish vanity that caused John to embezzle the money oh please go now but Mrs. Trevor I beg you please the telephone Mrs. Trevor the reason that you want me out of here would it be because your husband is right now calling you on the telephone I have nothing more to say pick up the phone Mrs. Trevor answer it but mr. Keane tell your husband I want to see him in my office in New York any time tomorrow if he can make it I I don't know please do as I say you won't regret it well boss do you think he'll come I have every hope Mike what it's almost five in the afternoon already no he won't come not a guy who embezzled five grand and stole his brother's name there's one thing you've overlooked what's that come in good afternoon Mrs. Trevor you came alone no not alone oh your John Trevor yes come right in both of you this is my Clancy my assistant how do you do how are you sit down now mr. Keane my wife says you insisted on my coming to see you I wouldn't have done it except I was sick of hiding Trevor I want to ask a question if you had the chance to make restitution for that $5,000 would you do it mr. Keane why do you think we've been slaving for the past two years I had any job I could get Lydia in one hat shop after another to save enough money to pay back well why didn't you get in touch with your old firm because we've only got $2,000 so far because I was afraid of what attitude they might take well Trevor there are two things that impressed me about this case first that you and your wife have worked so hard ever since you made that mistake second that you prove yourself so valuable to the war effort it's nice of you to say that mr. Keane but no buts I'll be glad to accept the $2,000 you've saved so far let you pay off the rest in installments what did you say I got a telegram this morning from your old firm what let me read it in view of circumstances as explained by you we are delighted to accept your check and drop all charges against John Trevor why mr.  Keane certainly is but there's still mr. Morley I've already made your excuses to him what did you tell him the truth the best excuse of all and he still wants me back be very much obliged if you'll take the next train out of here for Connecticut mr. Keane you'll find one little change though at the factory what do you mean mr. Wallace has suddenly resigned mr. Keane how can we ever thank you what kindness what understanding never mind the speeches that you'll miss your train along with you there's a war on you know this the next week at the same time as mr. Keane brings us the case of murder in the air few tragedies strike deeper than the loneliness of a girl a woman who is unpopular a girl who dresses smartly looks pretty is gay and charming but whose teeth when seen at close range rob her of the very charm she strives for and the same thing is true of the man who must sell himself socially and in business and whose teeth create an unpleasant effect on others if you have the slightest suspicion that your teeth are holding you back from social or business success do as hundreds of thousands have recently done and that is start cleaning your teeth with the new colonos a high polishing toothpaste its action is like that of a jeweler's polish removing tarnish from a piece of silver try it just once and then you'll know why people everywhere are changing to this new high polishing toothpaste colonos and learning the meaning of teeth that can gleam and shine that give your smile new personality and charm you can get the new colonos in any drug store tonight it's spelled KOLY NOS colonos toothpaste you've been listening to mr. Keane tracer of lost persons now on the air at a new time every thursday night 7 30 to 8 eastern wartime over this network don't miss mr. Keane next thursday night when the kindly old tracer turns to the case of murder in the air this is larry alley at saying goodnight for the makers of colonos toothpaste and inviting you to listen to friday on broadway at 7 30 p.m. eastern wartime over most of these stations tomorrow night there's a wonderful new way to make floors sparkle like new in six to nine minutes flat use aero wax the self-polishing wax that goes on in a jiffy dries without rubbing its marvelous high luster adds beauty to your rooms saves countless scrubbing yet it costs only 25 cents a full pipe get aero wax aero wax this is cbs the columbia broadcasting system


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