I'm gonna just give a few introductory comments and then hand most of the time over to Shoda. But I wanted to start out by talking a little bit more about what we do specifically at GX Labs. So we're part of a bank called State Street. It's what's known as a custody bank. So that's not well known to people not in the finance industry, but whenever you do a trade, all the information, the transactions need to be settled and custody banks typically do that. We also have a large arm that a lot of people know about called Global Advisors, which focuses on investment management and they sell a range of products including something called an exchange or an ETF, an exchange traded fund. Now at GX Labs, we are focused at the intersection of data science and portfolio risk. So big institutional investors are trying to look across their whole portfolios of many different types of asset classes and figure out what that risk is. So this helps them in allocation decisions, helps them in risk management. So that's broadly what we tried to do. And the last time I was here, I talked a little bit about the theory and methodology behind what it is that we're trying to get at. Today we're gonna take a slightly different tack and a lot of this is to hopefully spark some interest on the part of some of you in the audience around the technology and some of the approaches that we're taking to get at this risk problem. Now we use Monte Carlo simulation, we use stochastic calculus, we use a number of statistical tools that are common in other disciplines and we found it very fruitful to cross disciplinary discussion. And then a new area which we hope to start, we haven't started this yet, but we'll probably start in the academic arena is around machine learning. So how can we use some of these new techniques to do things like identify systemic risk regimes in the economy generally or to identify the macroeconomic regime that we might be in or even to do things like fraud detection on a trading floor. So there's a number of interesting applications coming out of the machine learning and what's often called deep learning community that we find quite interesting. So that's just a quick introduction. So I'm gonna turn the time over to Shoda, he's gonna present some of our thinking and development specifically in the technology architecture and then hopefully we'll have enough time for some interesting Q and A. So hi, I'm Shoda. So I will be presenting the overall project that we have at GX Labs. So this is a multifaceted project, it really involves putting together machinery that can deal with a lot of the issues that institutional asset managers and people that manage money generally are facing today. And I'm gonna start off with a very quick sort of background and motivation. But the financial motivation or the sort of business motivation behind this is actually quite interesting because with most of the developed economies, having created welfare states after World War II and with the aging population that you see, there's these huge piles of pension money that needs to be somehow managed and that money needs to be matched to the liabilities of the pension that you've promised you're gonna pay out. So this creates a asset liability sort of matching or management problem.  Another motivation is the fact that for the last 30 years, interest rates have been going down and now they might come back up or the Fed started raising them in December of last year. So we are entering into regimes that we haven't really experienced for a long time. And this means that as interest rates go up, and there's a lot of volatility in the world, the world looks very different today than it did 30 years ago. 30 years ago, most of the world economy was these OECD economies, right? They've developed market economies. But with the rise of China with a lot of things happening in the emerging markets, again, not a lot of data where we can sort of rely on past experience to understand how to manage our portfolios. So these types of things are motivating the project overall at GX Labs. So we have clients and we help our clients think about these issues. And instead of just talking about one asset class, so just equities or fixed income, we help clients look at a range of asset classes and put them together in a way that matches what we call their risk appetite. So they're tolerance for taking on certain risks in order to match those pension liabilities in order to give the payouts that's desired in minimizing the risks. But the issue has been that most of quantitative finance has been sort of predicated on a lot of strong assumptions around things such as normality of distributions. Just if you look at these equations, they're just mostly linear regressions. And that's really part of the fact that the machinery available to deal with these things arose from the 1950s through the 1990s. So basically you had... And the other thing is data limitations. So the best data you had was mostly US equity. So most of the papers that you read just mostly based on US equity data, a large portion of it. And so these sort of informed the limitations around the traditional approaches to modeling these types of risks and asset returns. And so we go through this and you realize that back then people had mainframes or worse in the 50s. So if you think about, if you match up when for example, Harry Markowitz was coming up with modern portfolio theory in the 50s or Black Scholes Merton was coming up with stuff in the 70s, basically these are the types of computers that they had to deal with. Very limited data, very limited computing capability. So part of the project that we have here at GX Labs is basically to take advantage of all the new data that's becoming more readily available as well as to take advantage of the computing power that's much cheaper and faster and better these days. And so if you look at things that we characterize as financial assets, so again these could be stocks or bonds, FX, commodities, what have you. So I'm just gonna generally call them assets. So if you look at asset returns, a lot of this literature is predicated on the fact that for simplifying reasons they have normal or log normal return distribution. So the normal distribution is this blue. Now this is just a stylized picture but if you look at just equity returns, you'll find that they often have long tails and you could try to fit some distribution on it that give you those long tails but do we have enough data to really characterize what they look like? Is it really IID? So these are questions that pop up and as you look and look further and further, we get into issues that if we understand better can characterize the asset returns better. So for example, over what periods? So there might be momentum over very short periods of time but over longer periods is it really sort of a Brownian motion or absence of autocorrelations? Do we get heavy tails, conditional heavy tails? So even if we correct for regimes we may get heavy long tails even corrected for that. We might get evidence of things like volatility clustering or intermittency which is just bursts of stuff happening discontinuously. So how do you begin to understand model and incorporate that and the behavior of these asset returns in the construction of the portfolios? And you can't really start to do that until you have the appropriate data. And then coming to our rescue is what I'm generally gonna call high performance computing. So people talk about machine learning, people talk about all sorts of things but high performance computing, I'm just gonna sort of group everything together. So it's all this stuff we love, Spark and just really fast paralyzed computers. And a lot of people talk about Moore's Law. So this is a set of slides I've grabbed for a general presentation but I think Moore's Law everyone knows. So I thought this was a little bit easier to understand. So the cost of computing power equal to an iPad too. So if you buy an iPad too, these are sort of orders and magnitudes but in 2010 dollars it's about say $100 but back in the 1950 it would have cost a trillion dollars for the same computing power. So this is enabling new types of things where we're not forced to stick to those linear regression equations. Now we're still gonna use a lot of linear math, linear algebra to delve into some of these topics because that's the machinery we have readily available. But with this new machinery, we're gonna start to explore other types of relationships that can be a little bit different. And as you probably know, a catalyst for changing approaches, we have just a lot more data.  So everyone's on mobile and that's what they use. That's generating a lot of data. Internet of things. So that's probably not quite here yet but people talk about it a lot. But devices embedded in every single thing that's doing something so that you can get the data. Part of the limitation of asset management and economic modeling has been that basically things like GDP is just a very gross measure of what's going on. It's a lagging indicator and there's huge estimation error in this. What's the total economic activity in Brazil? Anyone's guess. But with these types of things if you can start to measure the granular activity of every single transaction that's occurring in the economy you start to get to a better picture of what's going on. So the data and the new types of databases. So whether it's sort of a graph database or these no SQL databases, there's new types of databases that are much better fit for exploring the types of issues we have. The infrastructure and culture. So cloud-based computing. So basically things from Amazon Web Services, Azure, Google Cloud Platform. So these are things that allow anyone to just sort of pop up in a computer and open an account and start computing very quickly. This is changing a lot of the mindset around how we can explore these issues as well as the culture of open source, these communities that allow us to bootstrap very quickly around very difficult problems. And I can't emphasize that enough because coming from a mainstream financial institution this stuff, everyone's used to this stuff on the right here. It's like I'm an IT guy at XYZ Bank. I know Oracle, I work with Microsoft. And then here are these other people from some alien planet that are working on things like Spark, using mesos and NVIDIA GPUs. So it's a totally different world. And there's a huge gap and the people over here don't know how to even hire the people who have the right expertise. And I'm making a very gross generalization but for the most part it's true. And so this gets you into a sort of technological barrier where how do you get the right human resources in these mainstream large banks to actually appropriate this type of technology?  And not to mention the culture. So open source, everything is actually not very well looked upon in a very compliance heavy, risk excellent environment. And banking is obviously very heavily regulated and we'll even get more so. And so people get very scared of oh, it's open source. You guys are gonna talk to some community. So if you wanna put like, if you wanna use GitHub it's kind of frowned upon, right?  So that's a material issue. And then of course the computation, I'm not gonna talk about it, but basically you have new faster GPUs and things like that, as well as the rise of machine learning. So everything from deep neural nets to more sort of traditional statistical approaches to doing machine learning. So and what's interesting is that most of the time people have been using sort of these regressions which have been developed and which is great but there's new ways to learn and perhaps how do we deal with unsupervised data? And these are topics that are very, very ill explored. And one of the interesting things that I noticed is that most of finance and most of the people that know about things like machine learning they wanna look for alpha. Alpha seeking is basically finding extra money that shouldn't be there if the markets are truly efficient. And so that's what hedge funds do. They look for alpha, they make money. And what we're trying to do is slightly different. We're trying to actually put together different asset classes and different securities to change the shape of the profile or the distribution of your future returns. The reason we wanna do that is we wanna help our clients, these institutions that need to pinch in money. If you're a sovereign fund, you have a lot of oil, you need to convert that oil money into money that's a little bit more sustainable and diversified than just oil. So you're gonna invest in different asset classes. So how do you put that all together? In a world that's extremely volatile, unpredictable and very different from say 30 years ago and make sense of it. And so this is really the sort of the problem that we're dealing with. So what we do is we're gonna try and we're trying to put all of these technologies together to do a portfolio analysis. So I'm gonna get into this discussion around how do you simulate a portfolio using high performance computing environment. So a lot of the techniques that we're gonna be talking about are actually pretty old school stuff. It's been around for a while but we're gonna sort of boost it up by putting it on this high performance computing environment. So a lot of the things that you'll see, it'll be familiar and it'll be fairly standard techniques. But we will be using things like Monte Carlo simulations, factor modeling. So if you're in finance, these are sort of standard bread and butter approaches. But let me sort of posit the issue. So here's your normal distribution. I told you equities have these long tail distributions. And then when you put just not equities but also fixed income, these are bonds, commodities, what have you. So different asset classes. When you have a multi-asset class portfolio, which if you're a serious institutional investor, you're gonna have a multi-asset class portfolio. You're not just gonna have a stock portfolio. So when you put these together, you get these skewed long tail distributions. And how do you characterize that? Does that distribution have a name? We don't know. So we're gonna have some sort of generative process that's less parametric in order to understand how these things look like. And to do that, we're gonna use a generative process that involves a factor model. So we're gonna look at everything that's happened in terms of the returns of all the securities we're interested in. And we're gonna do some dimensionality reduction using factor model. And we're gonna use that as a driver to figure out how they move together and then simulate in a generative process a return distribution out in the future. And we're gonna look at that characteristic to derive what the risk is, what the anticipated tail risk is, maybe even the returns. So just to get you familiar with a little bit of the terminology. Sorry, it's a little bit wordy, but just look at the picture. So you have this distribution. So in finance, typically a lot of people worry about risk. Here they had a lot of risk in return. And when most people talk about risk, they're really just referring to the one standard deviation of this distribution. So they'll typically call it just volatility.  And the risk is this volatility. And then the issue is that in, for example, a pension fund again, you're worried that you're gonna suffer some catastrophic loss. That might be very, very rare. So these are what we characterize as tail losses. And what's the tail? Well, the tail is a little bit of a subjective notion. Can I handle a really big hit or really, really, really big hit? So it's a little bit subjective. And you can set the start of the tail to be whatever tolerance. So let's say that I'm willing to take on a loss event that's gonna hit me with some impact of X dollars, right?  And so this yellowish region here is defining your tail loss. And so some of you may have heard of the notion of value at risk. So the value at risk is sort of how much you lose at the tail there. What we tend to focus on is what's the average loss you will suffer conditional on the fact that you've hit the tail.  So that's the expected tail loss or ETL, right? So we're gonna focus on that. And this is actually even, you know, it's often quite common to think about these things in banking, but in asset management, it's actually quite new. People don't think about this because they're so focused around volatility. And also, most of, again, asset management's been predicated on thinking about things in terms of just equity. So in equity, you don't worry so much about these very, very long tails. And so as you get into the multi-asset class context, this becomes much more pertinent. And so in order to generate observations in a simulation where you're hitting the tail, you're hitting states of the world in your simulation, where you're hitting the tail, right? These are rare events. So if I'm looking at this, you know, one in 33 year event, then in order to get, say, 10,000 dots in here in the tail region, I need to run the simulation a million times, right? And that may or may not sound like a lot, but typically if you look at equity simulations, people have run them 1,000 or 10,000 times, right? And those are the tools, yeah. Yeah, so you can do important sampling. So that's a way to speed things up and to get more resolution in the tail. So I'm just talking very broadly here, right? So yeah, so we basically need to do a lot of iterations, more than that have been built into typical risk management tools that you can buy off the shelf. And so, you know, computing speed becomes fairly key, right? So what are we gonna do? What exactly is this process? This portfolio simulation process is actually fairly simple. I characterize it in two big chunks, right? Chunk one is there's this factor model and a factor model's just, you know, basically taking all the market price returns, so you have your equity returns or your bond returns and you break them down into things that are simpler. So instead of looking at, you know, 100,000 by 100,000 securities and looking at how they all move together at once, you're gonna do some dimensionality reduction and describe them in terms of, say, 10 factors, right? And then, so the 10 factors explain most of what we call the systematic drivers or systematic risk factors. And then, whatever you can explain using those is the residual, it's the idiosyncratic component that's specific to the security, right? Oh, I'm not familiar with that, but basically, right now, this particular machinery that we're building is basically looking at straightforward PCA, but we're gonna do a little bit of engineering around the parameters on the edges as well as, once we have a little bit more time and resources, we'll look at different methodologies. And I think, obviously, PCA is not the only way to do this. Lisa's been working on this, she has something to say. Yeah, I think there's a zoo of possibilities of what to do around low-rank matrix approximation. So, I mean, PCA is kind of a starting point and different varieties of that PCA you can do, but obviously, even within PCA you could do sparse PCA, kernel PCA, CUR decomposition, whatever you want. So, I think what the machinery, the advantage of building out the machinery on this technical sort of sandbox is the ability for us to test this and play around with it very quickly, which wasn't really possible before, right? So, we do this and then, we actually, so what we're doing is we're getting the coefficients or what we call the factor sensitivities or loadings. So, if I have a stock, right? The stock returns can be explained by how sensitive I am to the 10 systematic factors plus what Esver's left over. And the coefficients for those 10 systematic factors are my factor loadings or those betas, okay? And that defines my portfolio. I'm gonna stick that into my Monte Carlo simulation and I'm gonna have some random generative process. So, I have a pseudo-random number generator that's drawing random factor outcomes that represent states of the world or states of the factors. And I'm gonna do that to generate a portfolio value conditional on that state of the world, a conditional on those factor outcomes. And then I can repeat that, you know, say a million times to get a portfolio distribution.  That, well, okay, so how do you know this is working? So, you need to test both the factor model, right? And whether the factors are giving you, giving you performance that makes sense and there's a certain number of statistics. So, okay. So, okay, let me, let me, so you're gonna test this and then you're gonna test the whole thing as well. Meaning the output out here. So, first thing first is you're gonna test the factor model.  And then you could sort of come up with a portfolio and look at out of sample.  So, there's certain statistics that we can focus on and such as Q. Well, the other thing is the world, if the world I think we're static forever, meaning that we don't go through regime shifts and things like that, it's easier to see and it's easier to test in the way that you say, right? But the problem is that, you know, if, for example, the rates haven't come up in the last 30 years and suddenly you have this regime where the Fed is raising interest rates. So, no one's ever seen that before in the data that we've seen, for example, right? So, how valid is that in that context? So, you get into very sort of difficult practical issues around back testing this. The other thing is that you wanna look at, you know, you wanna focus a little bit around the tail events and the tail events happen very, very rarely. So, it gets very, very difficult to back test out of sample as well. Right. Yeah, so the, this is just presenting the overall picture. I'll actually get into the mechanics of it a little bit. So, yeah, so typical sort of factor model.  So, you know, things like this single factor model is sort of the canonical version where basically, you know, whatever security you have can be explained by this thing called the market and how sensitive this thing is to the market plus, you know, plus some residual. And then you have sort of multi-factor models that gives you more than just one market, you have different explanatory factors and that's where the factor model comes from. So, for example, in equities, you know, if you go to any financial website, you know, people will talk about, oh, it's, you know, the value or growth or the momentum factor, the size factor, these are the types of things that are purported to explain equity returns, right? And in fixed income, you have other factors such as a level slope curvature. Now, as a practical matter, part of the problem is that, you know, the equity people have their own factor models and then the fixed income people have their own factor models and they run their own portfolios. But at the top of the house, right, these big institutions, there's very few places that bring them all together in a coherent framework so that you can look at equities, fixed income, you know, credit, all sorts of asset classes and all the risks and drivers in a coherent way. So that's one of the things that we're working on is a multi-asset class approach that can explain a lot of these things, equity, fixed income, in multi-asset class context. And part of the problem of these traditional sort of regression models is that you get these, or the fundamental factor models, is that you get issues of collinearity. So there's some, you know, overlap between the factors. You typically have fairly low explanatory power. And this depends on how you construct it, but it's often not reflective of the macroeconomic regime. So we need to sort of adjust for that. So again, what we do is a fairly straightforward PCA. This is still research in progress. I think we're working on, you know, we're starting with a very basic PCA approach, but one of the things we're starting with is with this high performance computing and we can actually run this very quickly so we can adjust the parameters and do backtesting and all sorts of fun things to get comfort around both the dataset that we're looking at and the results. So we can change sort of the cutoff for the number of principal components that we consider to be systematic. We can change the sampling frequency. So do you take the sampling weekly, daily, monthly? We can change the sort of look back window, the estimation period. We can change the temporal weighting. So, you know, do you have a half-life? That's about 50% of this window, or is it gonna be 80%? And then you have some cross-sectional weighting. So, you know, maybe instead of just equally weighting all of the securities before you run the PCA, you may want to weight it such that maybe the more liquid, whatever you consider to be the more liquid names are counted more. You know, so we seem to be converging on the idea that for many of our client's portfolios, you know, the square root of market cap, for example, might be an appropriate cross-sectional weighting. And we also might want to do some regime conditioning. So like I said, the world changes fairly quickly, and you go through these different regimes, volatility clustering. So we may want to identify regimes where the rules change and condition the behavior based on the regimes that you can identify. So some of the desirable model features, you want, you know, I'm just gonna read this, accurate, stable, transparent, factor-based risk forecast. So this is actually just a slide from our colleague, Lisa. Who just spoke. And we want it to be multi-acid class, consistent treatment at different time horizons, both short-term and long-term. So there's sort of a difference between people that are managing money every day, and they want to make money a quick buck every day, or every week, versus pension funds that are looking at 10, 20, 30 years out. Consistent treatment of acid liabilities, more focused on the generation of an accurate tail risk picture, because that's what will kill you. So these are types of things that we want to build in to the model. So, pardon? When you say attribution problem. Oh, I see. Right. Attribution is you, so attribution here, and the way we've used it here, is sort of a not-afford-looking idea, but a backward-looking idea. So I've realized some performance over some period. Okay, and given that performance, and given the assets that I've invested in, can I attribute this to, say, the factors that I've identified? So I can say most of the return was caused by these 10 factors, and then there's some portion that I can't explain with those 10 factors. So I'm looking for a causal effect. So that's a sort of backward look. It's slightly different problem than what I've been talking about, but it does enter into the picture, because you have to create reports for shareholders, or whoever it is, stakeholders, to explain why you've done what you've done. Yeah. Yeah, yeah. Exactly. So in fact, this is a point where I find it's even difficult to explain, even inside our company, because people mix up this attribution problem with this forward-looking problem, and I think that there's a lot of, I don't wanna say confusion, but there's a lot of explaining to do to a lot of people to explain the nuance there. Yes. So again, just a rehash. So in our latent factor estimation, so we have some temporal weighting, some look-back window, and this can be weighted in different ways across time and cross-sectionally as well, and then we'll do some dimensionality reduction. So right now using PCA, we can infuse it with some regime information. So for example, in equities, there's a thing called the VIX. The VIX is just a forward-looking implied volatility, and it's implied from the market. So the markets are telling you, if that's the correct level of, say, the S&P, then over the next three months, the implied volatility in the markets is gonna be this level, called the VIX, and so we can condition it on the VIX, and then maybe the returns look a little bit more normal than if it weren't conditioned, and then we get the latent factor. So right now we're starting off just very simply trying to characterize equities, fixed income, credit, which is a slightly different animal. They're often packaged together because often a bond has both a rate component, which is basically the discount factor, so the time value of money, which is a rate issue, and then the fact that someone cannot pay you back as they default, which is a credit problem. So those are packaged in bonds, but we need to address both, and then there's the notion of foreign exchange movements. So if I'm a dollar investor, I'm sitting here investing in a dollar in say a Japanese bond, and the relative movements between the dollar and the yen are gonna impact my returns. So I need to think about that. There's a number of other things that we'd wanna cover, but in order to create a multi-acid class portfolio, you'd wanna address these in a fairly coherent manner. So I don't think we have an empirical answer for that yet. I think the regimes can shift very, very quickly, and so it makes, so for example, you can use the VIX as almost like a state variable, and then say, okay, if the VIX is, or if the change in VIX is above a certain point and just condition it on that, and that's one way to do it. But you also find sort of broader themes and broader changes, which can be a little bit slower in changing, and so that's where I think some of the machine learning can come into play in detecting some of these regimes, because part of the problem is that it's hard for me as a human that has limited experience to identify and pick the right indicator for what the regime is. So can we let the data tell us how these things cluster better? And I think that's an open question. These are Bayesian, these can be me, right? And then, so at this point I've sort of generated what we call the factor returns, right? And these are telling you how the factors move around. And then I go back and I have a second process where I regress all the securities that I'm interested in back onto the factor returns to see their sensitivities to the factor returns, right? So now we get into the simulation bit where basically, so this little representation I have here is just imagine one position, right? One stock or one bond, and I'm trying to value it, but it's a conditional valuation dependent on a state of the world, and the state of the world is represented by these random factor draws. And the blue bars there are the factor loadings or the sensitivities of this particular security to those factors. So I'm gonna do a random factor draw, right? And that represents a state of the world. And based on my sensitivity to those factors, there's some transformation that happens to do a valuation and convert that into say a price or value or something for that particular stock or bond, right? So that's what we call the conditional valuation, okay? And then, and so that's for one position, but I need to do a valuation for all my different positions. So I'm gonna have different pricing models or different valuation models for every single type of asset I have. So stocks are gonna have their own valuation models, bonds are gonna have different ones, et cetera, et cetera. And we can, part of the challenge for us as a business is actually to cover all the different types of things there are. So there can be derivatives, which are relatively simple, but you go into things like real estate and more complex securitization. So we have to create valuation models for each one of these. And we do it for the entire portfolio to get one portfolio value conditional on one state of the world.  So you're gonna repeat this many, many times. So say a million times in order to generate this overall portfolio distribution. And again, depending on how we condition it or not, it's gonna give you different shapes. I think the point is that we really don't know what the right answer is. We need to do a lot of back testing. It's hard to do back testing, but having again the machinery in the sandbox that allows us to change the parameters very quickly and run the simulations more quickly allows us to have a very empirical approach, which really hasn't been common enough, I think, in finance. So a lot of people just write equations on the board and then make assumptions and then sort of follow that logic through. So this machinery allows us to sort of look at the data and then transform it into this distribution in a way that we can explore without waiting months. And so, but you still get into issues where you have to deal with a lot of numbers, right? So if you have a portfolio that's fairly large, so let's say 100,000 positions, right? And you have an order of 10 factors that are explaining. And then you're doing a million simulation. You get into manipulating fairly large number of things. So how do you do that? How do you get that efficiently in and out of memory? How do you store that efficiently? Do we store this for every single state of every single security? So these are the types of practical issues we get into in the implementation. And so once you have the distribution, like I said, there's a number of typical risk metrics. Volatility, I just talked about before, the value at risk, which is the loss at the tail or the expected tail loss, which is just the the the expectation value of the portion in the tail. So you can look at these types of things. And there's a lot of education that needs to happen around what the appropriate risk measure is, depending on what your goal is. So how fast can we speed this up? Just a proof of concept. So we just did something very, very simple, but we took I think 100,000 bonds. Yeah, so 100,000 bonds and we did a million simulations. And we just took a sort of one GPU or one CPU and GPU, right? Single threaded and we saw how long it took. And so on the CPU, you know, it would have taken 900 hours. And then on the GPU, so this Tesla here, you know, it took 3.6 hours. So a 250 times speed up relative to the CPU. So this is just the type of thing that allows us to have some confidence that we could do these things much more quickly. You know, and we're right now in the process of building out an infrastructure that allows us to do this. So this is actually just a diagram of the architecture. But you know, we have our usual data ETL. So all the market fields, Bloomberg, Reuters, all of these types of things coming in on the left. And then we have a distributed data storage. So I think we're building out four Cassandra nodes. Was it four? Three, yeah, to start with. To house all of this. And then we're gonna sort of stream or put that into Spark. And that will be sort of our main computation machinery. We're gonna have the simulator, the factor modeling, all of that on Spark. And we're gonna hopefully have some GPU acceleration. And that GPU will provide us with a facility also to do more exploration around machine learning and deep learning if we want to and things like that. So this is the type of thing that we're building out today. We're also creating a user interface.  And we've focused a little bit around how to create better visualizations. These are screenshots, but I can show you just a little bit. So these are the types of tools that finance people use, except typically they're a lot uglier, I think. But we focused a little bit around the aesthetic component. So you have different portfolio sets. I can choose, let's say, the short-term portfolio. And so this portfolio is a little bit stale. It's a December, 2014. It's about a billion dollar portfolio with 17,000 positions. I can look at it. Okay, so this is just a cross-sectional exploration. So it looks like it's about 60% fixed income, 40% equities, mostly a US portfolio. This is by dollar value. But if I change the access, so I make bubble size, for example, the volatility. So back then, Greece pops up as a risky name. So these are some of the visualizations I can go into looking at return distributions. And I've run a portfolio. This is the baseline here. And compared to a situation where, let's say there's a 30% drop in equities. How does the distribution move? Or if there's a 2% rise in interest rates, what happens? So we can start to explore these things very quickly. And then you can take a lot of data and dense data and represent them fairly efficiently. So things like, this is a risk return graph. So all of these dots are individual positions. And here I've actually only represented the top 5,000. So I could just look at the top 250, right? Focus on those, and then I can go to the top 1,000, top 5,000, right? And I can look at, say, just the equities. What does that look like? Right, and then I can color in by, say, volatility on a contribution to the portfolio basis, right? So who is this name here? It's Hewlett Packard. This big red dot, who is that? It's Apple, right? So Apple's probably a good name, but I just have too much of it. So these are things that are allowing us to do sort of data exploration on the fly. In ways that are hopefully a little bit more compelling than just Excel reports. And this is actually quite a big, I think, problem in finance, is that you sit in a risk committee, there's a bunch of guys, typically they're guys, and they come with these reports, and no one really knows how to understand the 30 charts that are on it. And so this is much more interactive, and you could say, okay, what if oil price drops? Right, so I think, it's kind of fun, but it's actually quite important, and germane to making better risk management practice in finance. So, sorry for the interlude. So just to give you an example. So we have, so we've constructed a proxy portfolio, so these are sort of toy portfolios. I can give you sort of some more details, but just at a high level, we've created five portfolios that get bigger and bigger. So the smallest one, portfolio one, is just the equities minus all financial firms and utilities. So financials and utilities have their own characteristics. They're not typical to, they're not typical. So we've sort of exploited them for the first portfolio. Then you add those financials and utilities, so all equities. The third one is all equities and OECD bonds. So OECD bonds means basically, develop market government bonds. And then four is all equities and all sovereign bonds. And then five is all equities, all sovereign bonds, and corporate credit. So you put in the default risk.   This is sort of what I wanna focus on is the distribution. So this is the portfolio one. This is as of June 2008. And if you remember June, or if you remember 2008, that was sort of a marquee year in finance because everything stopped working and people lost a lot of money. Okay. But this is sort of before the collapse of Lehman Brothers and things like that and getting into sort of the bad zone. But this is the equities, X utilities, X financials, right, return distribution. If I add in just all the equities, so that's the green line. Now if I add in bonds, so just OECD bonds, that's the purple line. If I add in all equities and all sovereign bonds, that's the blue line. And then if I add in corporate credit, it's that red line. Okay, so what do I see? I see the distribution just sort of tightening up, right? So it looks like diversification is working, right? And again, it's created out of this generative process from the simulation, but I get to sort of see in a multi-asset class context for the first time, it's coherent across all the different asset classes and I see them together and I see that somehow diversification is helping my portfolio out. And then what happens, this is June as I get into say December, right? You'll notice that, boom, the expected difference goes down, the distributions are much wider, you've probably lost a lot of money. Because in September, Lehman went broke and here we are. But if you had had a more diversified portfolio, you would have had a tighter distribution, lower risk and less expected losses. So we're running a one-year time horizon. Okay. Pardon? Yeah, yeah, if you were confident that this worked. Yeah. But again, it's kind of a, and you'll see it's a zigzaggy so we haven't run as many simulations as we would have liked. So probably the confidence intervals aren't as great as we want, but once we have the spark up and running, we could run millions of simulations, change all the parameterizations on the factor model, rerun them. I mean that's really what we're going for. So we're not ideological about sort of the particular settings where we just want to have this playground where we can test this out and then use that to explore the type of portfolio that we're managing. So that's really sort of the project so far, but there's a number of sort of open questions. So in terms of how do you measure the performance, what are the right, how much data do we need to actually characterize these things to a level that we're confident or comfortable with, how do we add in more asset classes and things like that. And then that's enough to sort of do a business around this, but we want to go even further, right? So what are the other possibilities? So this is, I mean, big data and HPC and finance, so high performance computing and finance, so this is sort of a marketing slide, but people like it because if you think about the possibilities, so like I said, going back to the notion of getting granular economic transactions, right, you can track oil tanker. So there's these hedge funds out there that are measuring the water displacement of oil tankers as it leaves the Gulf of Hormuz, you know, the Persian Gulf and then like passes through the Malacca Straits. And of course the displacement is less as it passes through the Malacca Straits because it's sold a lot of its oil. And so if you could track all these oil tankers in real time, it's actually fairly good proxy for how much economic activity there is, right? So fairly clever.  Yeah. That's right. Yeah, so, you know, there's a lot of these different sources of data.  And now you can source these different types of data. And hopefully, you know, it has information content that's quite different from orthogonal to everything else that you've been looking at. So, you know, you can look at that smart grid. So the reason I had all those windmills in the very first slide was because, you know, I was thinking of windmills and smart grids and things like that. But if you can get, if you can harness all that data together and somehow, you know, sort of coherently put that together, there's already certain advances we've made. So you've mentioned the sort of the Bloomberg's already selling this data. At State Street, there's this effort called the... It's in conjunction with MIT. And it's something called price stats. And what it does is it scrapes the web. I think there's about 22 countries. And it scrapes the web for prices, online prices. So you go to Alibaba or eBay or what have you. And then that gives you a daily high frequency inflation index. So the normal inflation numbers are announced monthly or quarterly or whatever by the central banks typically. So that's this sort of blue line, the CPI there. And then you see that price stats is sometimes a leading indicator, right? So you can use these types of things. If you managed to strike partnerships with providers, you can get transaction information from all sorts of vendors and transaction partners. So this is just another example. But the point is that there's just all sorts of data that's becoming available or sometimes not available. But most of mainstream finance is not yet looking at these things. They're talking about it. But there's a lot of questions about how to actually integrate this into a coherent implementation to help your own portfolios out or risk management out. And again, part of the divide is the fact that a lot of the people that have this, the knowledge of how to deal with this. So people with expertise in Spark and things like that, they're not hired into the mainstream finance IT departments or the risk management departments. So using such data, we can potentially construct granular high-frequency macroeconomic indicators. And once you have that sort of data, then it becomes interesting to throw some, say machine learning, deep learning, whatever pattern recognition algorithms may make sense to do things like regime detection, right? How do these things cluster? And you can take the dimensionality out much higher and do it in finite time. So I think that's quite interesting. And what we ultimately wanna do as a business, not the only thing, but just imagine that you could do all this very quickly. You can then take this simulation out. So you have some macroeconomic scenario that you're forecasting, and then you could do a path dependent, multi-period Monte Carlo simulation, right? So then you could sort of take it out further. And remember, this again gets into a huge computational problem because I'm keeping track of, say, a trillion numbers in just one period of simulation, right? But from each one of these points, I'm generating yet another million states of the world or another million points in the distribution. And so I get this explosion of data that I need to keep track of, so just even just simulated data. So this is where some of the high-performance computing infrastructure gets very interesting. And then on top of that, imagine that I'm running a pension fund, so it's not just the portfolio values that I need to keep track of. I'm gonna have two distributions that I need to keep track of. So what's the distribution of my portfolio values and also my liabilities? And the liabilities are often driven by the same macroeconomic factors as the portfolio. So how do I keep track of that? And how much money do I need to keep so that I can retire safely when I'm like whatever age, 80 probably, when they pay out my pension? So these are the types of problems that we would like to approach. Another interesting potential application of machine learning or some of the new techniques that we're exploring is something that we're calling reverse stress testing. So the Fed always are pushing the banks to do things called stress testing where if the world goes down, what happens to your portfolio? We can actually run the process in reverse. So starting with today's distribution, the current portfolio distribution, and you pick your tail interval, what are the factor realizations that can potentially land you in a bad state of the world? And then so you sort of search through this grid of factor realization space. And then maybe you can apply some clustering and figure out that statistically when a lot of these red things are clustering together, it looks like, boom, on the macroeconomic variable side, oil prices come down. So this is actually not a trivial problem. There's a lot of issues that are slippery around trying to map these macro variables to the factors, the PCA factors that we've derived. And those relationships are non-linear and you don't know if they're stable. So there's a lot of issues. We'd love your help in thinking about this. But overall, the point is that there's a huge opportunity in finance, both afforded by the explosion in data, the availability of computing power, and some of the new techniques that we're developing to actually sort of solve these multi-asset class, long period problems with a much more of an empirical approach than was possible before. So, so that's it.


Hey everybody, welcome to my kitchen. I know that we usually go live on Friday for Freezer Meal Friday, but actually today we are all together as sisters and we are planning out the next 12 months of all the things that are gonna be happening on Six Sisters Stuff. And so we are prerecording this so that we can have our planning meeting. But if there's something that you want to see on Six Sisters Stuff, whether it's recipes or what else can we do to help you? Like leave us a comment, let us know because as we're planning out all this stuff we would love your feedback. So if there's something that you want to see on Six Sisters Stuff or if there's a recipe that you're dying for us to try, we would love to try it out and see if we can make it work. But leave a comment, let us know. But other than that, we are gonna jump right in and get started on today's Freezer Meal. So we or I am going to be making Instant Pot Shredded Beef and Geladas. But really all that it is is Mexican shredded beef. You can use it for tacos. You can use it for nachos. You can use it for a salad. It's just a super versatile, flavorful meat that you can use for anything. So let's jump right in. I have a two pound roast. Now in the recipe it calls for like a three to four pound roast, which is awesome if you want the leftovers or if you have a huge family you're trying to feed. But really you can use this for any size roast. So I'm gonna go with a two pound roast because that's what I need to feed my family. And I wrote on the outside shredded beef and geladas. So I remember what I'm going to use this for. Now the other thing when you are doing a freezer meal of a beef roast, you want to write on the outside how many pounds the beef roast is. Because that's gonna determine how long it cooks in your Instant Pot. Now if you're cooking in your soul cooker, it's pretty much one time for all the roasts. Like even for a two pound roast, I'd probably cook it for seven to nine hours. If it's like a three to four pound roast, eight to 10 hours. In the Instant Pot though, you do it by weight. And so a two pound roast I'm gonna cook for 60 minutes from frozen. That's 30 minutes per pound from frozen. If it's thawed, you're gonna do 20 to 25 minutes per pound just depending on how your Instant Pot cooks. I tend to go a little bit higher, like 25 minutes. So that's how with the roast, make sure you write on the outside how much it weighs if you're using your Instant Pot. Okay, so let's jump into all the ingredients. We're gonna start with just one onion. And I diced it all up. So go ahead, dump that in. And then we're going to add in one cup of salsa. Any kind of salsa will work. When it's a 16 ounce container like this, I know I just need half. So I'm not even gonna measure it out because I've got half of the container left. So dump that in there. And then we're gonna add one can or eight to 10 ounces of your favorite enchilada sauce. Now I recently found this brand. I think it's called Frontera. And they don't do cans. They actually do these little bags, but I love their sauce. It's super fiber-full. This isn't sponsored. This is just what I'm using for my family. Any enchilada sauce will do though. Okay, so add that in. Now let's do some seasonings. So we've got four different seasonings we're gonna add. We're gonna start with a teaspoon of cumin or cumin, however you wanna say it. Just go ahead and dump that in. And then we're gonna do a teaspoon of chili powder. I don't feel like this adds heat. It just adds some really good flavor. So don't be afraid. This isn't, this is not a hot dish at all. Then we're gonna do a teaspoon of garlic powder and a teaspoon of onion powder. Now, if you are super short on time and you want to make this as easy as possible, you could forego all these seasonings and just add a packet of taco seasoning. But I do like just the flavor that this adds by doing all these different seasonings. So do whatever is best for you. So then you are all dead. But let me tell you something. Because with our recipes, really they're very basic recipes. You can add whatever you want to make it your own. But I have been cleaning out my fridge and just trying to use up all the vegetables we had. So I had a red bell pepper and a yellow bell pepper. I diced it up and I am actually going to add it in with this freezer mill with this meat. The, in the Instant Pot or in the slow cooker, these are just going to become so soft and flavorful. And I'm just gonna, when I shred the meat up, it's so easy to just include those peppers. So if you've got some extra vegetables you need to throw in or that you need to use up, this is a great recipe to throw them into. So you can see I just added those peppers on top and we are good to go. So this is going to stay good in your freezer for up to 90 days, which is so nice. Because now I can pull it out and be like, okay, tonight we'll have enchiladas, I'll cook the meat and we'll roll them up and we'll be good to go. So now that's what I'm gonna do. I actually cooked the meat earlier today. So it's in the Instant Pot ready to go. And I'm gonna roll it up and show you how I make these. So I'll set these aside, kind of move all this stuff out of the way and we'll get rolling. If you've tried this recipe before, I would love to hear how you liked it. You can leave a comment below while I kind of just clear this face a little bit. We will dive right in. Okay, so like I said, I cooked this earlier today in my Instant Pot. And it has actually been sitting on low for almost seven hours. That is probably my number one tip when it comes to cooking a roast. I like it to cook on, like cook for its cooking time. I put it in in the morning and then I just let it sit on low for a couple of hours because that just makes the roast so fall apart tender and just so good. So I'll take that lid off. I don't know, you can't see in there. Let's see if I can show you. There's the roast. And I actually added a couple peppers to that one too. I had a lot of peppers I had to get rid of and use up. So using two forks or whatever you want, you just need to shred the meat. I'm actually gonna pull it out. I think that will be easier if I can pull out this roast and shred it up. So real time, this is how it is. So there is our roast. Awesome, it is so tender. And then there's tons of sauce in there too, which is where there's so much of the flavor. So if you want to, you can add the roast back in after you shred it up, or you can just drizzle some of that sauce on top, which is probably what I will do. Okay, so you can see with these two forks, I'm gonna move up a little bit closer so you can see. Hopefully that's good. It's just kind of falling apart. All that I'm kind of doing is just pulling gently with my two forks and it just shreds right apart. And I feel like that's because it's sat in the Instant Pot on that to keep warm setting for a couple of hours. Plus the flavor is so good because it's been sitting in those juices for so long. Okay, once you kind of get it all shredded like this, got one more piece that I'm working through, almost there.  And what I do is I usually get like a measuring cup or just some type of scoop. And I can use that to pour it on. Okay, got that all shredded just like that. We'll set these forks aside. I'm gonna grab a measuring cup really quick. I don't even want to use you. You can use a big spoon even. I just kind of go in and just use it to kind of just moisten that meat a little bit, add a little bit more flavor. And it's easier as I roll them up into enchiladas. It's not super juicy. Okay, it's good to go. I'm gonna move this instant pot and then we'll roll up the enchiladas. Okay, so I have a nine by 13 pan here. I'm just gonna spray the inside with some non-stick cooking spray. Because I feel like that just helps clean up so much easier. My kids do dishes. So any way I can make it easier for them is good in their book. I'm gonna grab one plate. This is where I'm gonna roll my meat. And so you can use any type of tortilla that you want. I'm just using a regular just flour tortilla. Some people prefer corn tortillas. Whatever you like for enchiladas, go ahead and use those. And then I am actually going to use some refried beans. So just as a way to stretch my enchilada meat a little bit further, we have six people in our family. So I wanna be sure that I'm making enough enchiladas to feed all of them. If the meat is not enough, I like to usually add a little bit of beans. So tonight I'm just have some canned refried beans that I warmed up in the microwave. So they're all soft and ready to go. And I usually add a scoop of refried beans to the bottom, just to the tortilla. And then I scoop a little bit of the meat on top. So I'm using tongs to get the enchilada meat because it is so juicy and good. So I kind of squeeze out the moisture a little bit. You absolutely don't have to. That's just what I prefer. And then we're gonna roll up. So there you go. There's your enchiladas. Now, you know that we love freezer meals. We love quick and easy meals that take just a few minutes to prepare. And so if you aren't part of our freezer meal membership, you might wanna check it out. You are looking for quick and easy recipes, especially as we head into the back to school season and just all the craziness that comes with fall. Our freezer meal membership gives you 10 easy freezer meal recipes every single month. But what's great about them is that even if you don't wanna make freezer meals with them, they're just great easy recipes to have for those busy days when you need a quick and easy dinner. I say easy a lot. We are all about easy. With all of us being moms and we have so many kids between all of us, between all six sisters, there are 22 kids. So we get it. We understand how busy mom life is. So any way that we can help you and make it a little bit easier, that's what we're all about. So let me roll this up. Okay. So we'll just keep rolling, but if you ever have questions and I'll leave a link to the freezer meal membership if you want those 10 freezer meal recipes every single month that are exclusive to our membership community, plus we're gonna send you our most favorite shredded chicken recipe that's like super versatile. You can use it for anything and use it in as a freezer meal. And then you pull out that frozen chicken and we're gonna give you also 30 extra recipes that you can make with just that frozen chicken. You can also use a rotisserie chicken if you don't wanna use, make your own frozen chicken. But so that's included in the freezer meal membership. That's a super versatile chicken recipe. And then also the 30 recipes of what you can make with that. So we just try that membership is just to help make your life a little bit easier when it comes to getting dinner on the table. Okay, I'm almost done rolling all these enchiladas. If you ever have a question though, feel free to leave a comment. We'll be sure to be watching our comments and getting to them. But hopefully you'll have to let me know if you try this.  And then once you get these all rolled, we do cook them in your oven. So a lot of people will be like, wait a minute, that's not an instant pot recipe.  And it's just mostly to melt the cheese and to kind of make your enchiladas all come together.  Because let's be honest, the star of the show is this yummy roast, this Mexican shredded roast that's so good. So this is where all the magic happens and that's done in your silk cooker or your instant pot. Okay, I've got one more to roll just like that. It's much more fun to have people here watching and helping while I'm pulling enchiladas. Usually I'll go live when I am cooking dinners and it's so much more fun to have people to talk to. I mean, when you're cooking by yourself, it's not that. It's just not this fun. So I'm glad that you are here. Thanks for joining me today. Okay, so we got all our enchiladas finished. What I'm gonna do, let me just wipe off my hands, grab a paper towel. So I'm gonna take one more can of enchilada sauce or mines in a bag and I'm gonna pour that on the top. Some people also like to put cheese inside their enchiladas. I forgot to mention that. You do whatever feels best for you. Okay, so just pour that can on top, try to cover all the enchiladas like I did here and I'm gonna top it with some cheese. I should have opened this before. Grab some scissors really quick. Okay, and I'm just using Mexican, like a shredded Mexican cheese. You just use what you have on hand. Like cheddar cheese would be awesome and that Monterey Jack would be super good. Just whatever you have is going to be perfect. This one is like a four cheese, Mexican cheese. It's got all kinds mixed together, but I just like the dual color, like the yellow and the light. Whatever you have is gonna be perfect. So cover it in cheese. You can go as cheesy as you want. I've added about a cup here. And then I just like to top it with some sliced tomatoes and some cilantro. But you can add, if you want toppings, great. If you don't, that's fine too. You just do whatever is going to work for you. Okay, so we got that added and then I'm gonna throw just some fresh cilantro on top that I already chopped up. And there we go, dinner is done. So I'm gonna put this in the oven. I usually, if I have time, I'll cook it at 350 for about 20 minutes. But if you are short on time, sometimes I will throw this in at like 450 and cook it for 10 minutes. And that seems to do the trick too. The goal is to just melt the cheese and kind of let all those flavors melt together. But thank you so much for joining me. I hope that you can see how easy it is to use a freezer meal because I didn't have to do any work this morning as I was putting it into my Instant Pot. Took it from the freezer, put it in my Instant Pot. Dinner cooked all day long. And then all I had to do was take a couple of minutes, roll these enchiladas when the meat was done cooking. You saw how fast and easy it was. And that's how dinner should be. Quick, easy, but still tastes so good for your family. So if you want this recipe, I'll put a link in the description to it. It's our Instant Pot Shredded Beef Enchiladas. But like I said, you can make it in the slow cooker too. And then if you ever want to join our freezer meal membership, just go to freezermills.sixsistersstuff.com forward slash membership. And we will get you those 10 recipes every single month, plus our shredded chicken recipe that we love and use for everything. And the 30 recipes that go with that shredded chicken recipe that you can use when you need a quick dinner in a hurry. So by the end of the year, there'll be 150 easy go-to recipes. You'll want to check it out. But thanks for joining me today, guys. I hope you have a great rest of your week. I'm excited to be back with you live next time. Sorry we had to pre-record this as we are planning and strategizing all the fun things that are gonna be coming your way from Sixsisters Stuff. Until next time, we'll see you later. Bye everybody.


Far than of the eight brought to you from out the pages of Edgar Rice Burroughs amazing book Well, the heart is certainly more than I expected to find in this I chose Jane outside of the matter What are you two whispering about? Nothing, my dear. Just a moment. You see Jane, that is in simple language, my dear the previous inhabitants of this hut poor souls Evidently met their end together With the inevitable result that that their bodies are still here. Oh, no, no, no It must have been years ago Then what you are trying to say is that there are two skeletons and three to be precise There's a baby in this grip. Oh, how terrible all the poor things to think of them all and no Well, now don't upset it happened a long time ago. You just sit down outside and only take a few minutes I'm not squeamish not a drug student in the past few days. All right You can put things in order if that is if you don't mind. Just put them where you want I understand. Get ready to sell us housekeeping in our new home That's the idea. As long as you can see the humorous side of things Well, I'll have no fear.  And I See decidedly anthropoidal characteristics Amazing, you don't suppose Coming my dear Same nothing to the other still and we don't want all right. All right I think That's where I'm going to put the camera Well, my dear, I don't think either the time would all the books will stop us if they are pleased to get back Does anyone know where that pressure lamp was put getting dark while we haven't an inexhaustible supply of fuel? Till we might as well use it tonight. I think In fact, I'm sure it's in the same boxes of the can sausage At least that it accounts for the remarkably strange taste of their sausage. Well, apparently you're right. Here it is Oh, that's much better Well, it's definitely going to be a treat to sleep for once in something like a house again and settle I'd feel much safer with a door closed. Oh, well, go Oh Tarzan swings himself leisurely along the upper jungle terrace a Pale moon sends streaks of shimmering silver across the waters of the bay The swaying trees cut fantastic shadows on the spongy moss a hundred feet below Well Tarzan's body moves leisurely his mind races He's watched Clayton and the party unload their boat and move their gear to his Tarzan plot Strangest of all these MEN have not fought Tarzan stops he leans forward listening The sounds are coming from the beach. He glances toward the hut The lamp glows dimly in the distance. He pulls himself higher branch by branch to the question of an acacia Now we can see down on the fringe of the jungle close by the sea a light gleams and a light means MEN Instinct reason Telling that these men are dangerous are from the thing that floats upon the water with lightning speed He drops limb by limb down down into the inky blackness of the denser foliage His body swaying his strong fingers steel like certain of their hold grasping the branches with a flag accuracy His feet touch the leaky carpet and spreading the treacherous Nile branches of a weight a bit born bush He glides through the clump of bamboo Tarzan stops He sees the pirate crew gathered about yonk a big man with heavy deepening eyebrows The big man waves his arms and points angrily at the ground a little man detaches himself from the group Picks up a thing like a club the big man doubles up his fist shakes it in the little man's face The little man swings the big man steps back or too late the club-like thing crushes the big man to the ground You all he could do with all of He couldn't work, but he could navigate one night. I want to walk about styling a blooming ship. Never mind Let's get this treasure up. This place gives me the creeps Nimes look there through the trees. There's a light Strike me pink appetite That bomb me old professor and his gang Now what's the matter a ship see a light Bearing down on our port now king. That's the news of Douth are running like That's that bloom and bruiser what we cited the other day Now you said he does and they're running lights with that lantern of the professor's gang up Sure, they're shining plain as day for them frogs to see you're right king Yeah take so far He'll take your rifle and put out the professor's life. I like your snacks. I need to go I don't care. How did that it? Pushing through the broken twigs stumbling over roots and vines cursing under his breath King starts off through the underbrush ours and follows The eight man doesn't understand the words, but the meaning the meaning is that She is in danger swiftly cars and swings his life body along from tree to tree with scarcely the rippling of a leaf He makes no sound that would betray his presence to the stumbling cursing man below Now he's between King the pirate and the little clearing where she is He waits Hence he clutches on a low-hanging limb from his right hand. He dangles a length of grass rope root at the end King is coming What a contrast the clumsy sailor stumbling flashing through the brush While far as and grateful sleep Lord of the jungle Wait, I read it nearly under the tree what house and cultures ready for instant action Now King is under the tree. Tarzan clothes his grass rope It settles over the man's head and shoulders the man yells with a mighty heave Tarzan against the Newtonian in the air King's arms up into his side. He struggles kicks Sticks him like a carrier ship for that back and forth back and forth. He is terrified.  He winds the rope around the man's body. He cannot move hand or foot Tarzan leans the sailor against the tree King's eyes almost start from his head when he sees the brawny brown figure of the eight man slowly deliberately Tarzan draws his night from its sheath he points to the cabin and threateningly significantly draws the night across his throat King's teeth chatter. He cannot talk but he understands and vigorously shakes his head Tarzan Satisfied that the man will leave the hut alone picks him up for the scruff of the neck and crosses him across his shoulder And without effort rusts of vine and swings to a branch He balances himself a mere fraction of a second then leaps into space To miss either footing or grip means that two bodies will go hurtling down through the crashing branches to certain death a hundred feet below But with a skill surpassing that of the great apes his practice hand and short foot carrying him branch after branch Vine after vine soundlessly swiftly closer and closer to the pirate He stopped. He hears the ring of metal on metal a shovel striking the treasure chest Diving by the max. All right. All righty treasure chest right now a good size, too Get over that end. Don't you know Other calls She won't move which team was bad for a mister, but there are he makes that French cruise is getting closer We better take another role Don't the chest in get aboard and blow before the cruiser leaves to and leave the treasure. Yes Overall, just in fact, it's good by treasure and the yard arm for all of us Then why move the box that cruise is getting too close. Don't forget mighty This professor bloke might have another man from the office


This study demonstrated that 129-XEMI misses can be used to accurately diagnose pre- and post-capillary pulmonary hypertension, pH. It also showed that the combination of 129-XE dynamic misses and gas exchange MI provides unique insight into the simultaneous assessment of both hemodynamic and gas exchange impairments, potentially aiding in the detection of pH. This article was authored by Elayna A. Beer, for us, Alan Easy, John Landlou, and others.


What's going on everybody? Got Kyle here with Layton Sports Cards and we are ripping this one as 2022-23 upper deck SP Game Use Hockey, 9 box half case number 6, Random Team Style, we did a randomize for the break, had Jonathan B with the stars, down to Furon A with the blue jackets. You can always find those randoms on our website guys, LaytonSportsCards.com, underneath the personal staff, or the random staff. Results tab. Results tab, one of those things. As you see in the chat there, Sean did drop the link 2 to go in 1 in 1 basketball, we'll get that going next to your random style. Good luck here, box one. Really Shane? That is kind of wild. There's a nice one, Stephen Stamco's number 2 in the jersey number, they're 91, nice one, they're lightning. I need one kill out of you. Stammer, got to 9.99, gonna be Noah Cates, got Otto Elvis, Merzlingkis, Merzlingkis, or the blue jackets there, the goalie there on the blue auto, Merzlingkis, and Alexander Barkoff on the jersey there for the Panthers. And Jumbo jersey is gonna be Callan Addison for the while, nice one there on the winner classic jersey. Went to classic, and a Sean Coutierre on the jersey. Box 2, basically Carl. Y'all trying to go out tonight, like what's the plan? That's what the coach is saying in the locker room. What are we doing after this? What's dinner? Going to go to Hell's Kitchen. Waffle House. Waffle House would work for this game. New Grooze is going to be Wyatt Johnson at 199, and it'll deserve much more than Waffle House. There's a game used to receive elastic to 9.99. And to five, wow, it is Mark Messier on the auto. Legends auto to five, that'll go to the Rangers. That was unexpected, there we go, Rangers. That is gonna be John H. John H. John H is in the chat, one of five on the Messier. We will mag that for you, sir. That is sick, that'll go to the Rangers. Mr. John H. Mesta 5. There we go, we'll get that in a mag right away there. Authentic rookie, got Shane right behind it. That is a Fabrics Erasmus Dahlin. And that is gonna be a Fabrics Gabe Velarde. He has a five, I can only imagine. Man, we saw Legends in the last one. Did not see a numbered one. There we go, John. Here we go, John. Box 3. Let's go, John. Let's go, John. Great start, still got seven more boxes. Keep it going here, guys. Mattie Baneer, new grooves to 1.99. There is some 99 gonna be new grooves for the Kings. A Tyler Tucker to 9.99. It's a Ranger, he's on the Eastern Conference. He's got a Rangers glove, it's a Rangers guard. I'm pretty sure. All Rangers on there, it does not say anything else. I can check the checklist after or two. There's a Otto, it's a 99. Jonathan Bergeron, Bergeron there for the Red Wings. Jersey Otto, got a new grooves for the Blue Jackets. That one's Kent Johnson rookie, got Barzol. An authentic rookie of Lucas Dostal. Yeah, yeah, that's what I figured. I saw his gloves too, they had the Rangers gloves on and it had the years he played for the Rangers on the back. So that's why I went with the Rangers on it. I can still check the checklist, but I am pretty confident it's gonna be a Rangers card on that one. A Ray Bork, Legend of 77. Nice one there, Boston. Little throwback Raymond Bork, got some 9.99. Nick Burr, Abaruzi, Abaruzi for the Maple Leafs. Otto is Lucas Johansson, authentic rookies. That's fair, Shane, that's fair. David Jiriak on the rookie outburst. Jersey there, got a Seth Jarvis jersey. And Dicut Purity is gonna be Cole Coffield. Nice rookie there for the Canadians. Nice one, Habs on the Dicut Cole Coffield. Going here. Alrighty guys, still two to go on the next one. Gonna be one in one basketball. Get tiered random teams going. Yeah, I'm giving my gal, you know how it goes, Taylor. Giving it a go. I've never heard most of these names in my life, so not too easy to just take a guess at it. There's a Joe Pavelski to 103. Some interesting numbering on these for sure. Joe Pavelski got some 9.99. Jacob Perriot, Perrialt came used the addition to 9.99. And nice, this is Vitek Vanacheck to 49. Only Purity Otto for the Devils. Vitek Vanacheck for the Devils there. Nice one there to 49 on the Dicut Otto. And mascot marks to 35 is gonna be Howler for the Coyotes. Got a Banner year, Corey Perry. And Purity is gonna be Chase Pearson. Or the Red Wings. Exactly, Sticky. Josh Allen, I know. Josh Allen, I got. Keep it going here. Florida boys, not too much hockey. No, no. I'm up here. Pavelro, okay. Okay, okay. I knew the AULT was the, oh, I was just messing on the last little alt. Oh, man. Got a Dylan Hall way to 120. That's Authentic Rookie's Oilers. Got a really thick one coming. Ronnie Attard to 199. Authentic Rookie's Philly. And that one is gonna be a, wow, a net cord. That is gonna be Roman Yossi for Nashville there to 35 on the All-Star Game Cord. Part of the goal there. That is sick. It is part of the goal he scored in that game to win for the Central Division, 8-3. That is a pretty sick one there on the Roman Yossi. And it's pretty sick, Nashville. The game used, game used goal there from the All-Star Game. That's sick. Cool. Got a Tage Thompson, Authentic Rookie's Jersey. Got a Jacob Move Rare. And a purity of Matthew Kachuk. Matthew Kachuk. Yeah, those are sick. I like that. Those relics are cool. Thank you, Michael. Taking a spot in Bowman Drafts. Taking a spot in Bowman Drafts. Alrighty, guys. Just two to go next. Two to go, one-on-one basketball. Yeah, it is a big one. All right, guys. Two teams per spot. Game used dodgeball relic. What? Been on that. Yeah, I got you, John. I'll hold it up on the recap. I'll hold it up for you so you can grab one. Yes, sir. I'll probably take a picture as well, might see it on Instagram. It's gonna be Jonathan Taves to 234. Another thick one. This is a 10-9-99 Jacob Perot. Wait, wait, wait. It was... Where was it? Perot. Perot. Perot. Perot. It's a 9-99. And Jersey is gonna be a Pawel Buczanevich on the Supreme Jersey to 15. That's a sick one there for the Blues. Steven, I would love to see that. Pawel Buczanevich. Game used tooth. It's a game used tooth. So sick. It's game used Jersey there for the Blues. So sick. It's gonna be a fabrics. That's another Stamco's from the All-Star Game. Got Jersey Banners. That one is Jordan Cairo for the Blues. Another one nice for the Blues. And a purity of Ron Hextal for Philly. Box number eight. Oh, does he really? Was it actually a dodgeball relic? That is very interesting. Have you seen a few of the mascot name plates so far? Got a Super Prospects to 999. That one's gonna be Kent Johnson. Here's the 115, Eric Calgren. And Otto is gonna be Nick Purbex to 149 for the lightning. Lightning on the authentic rookie RPA. That's pretty wild. That's a nice patch there. That is gonna be a random, I believe, to 35. It's fabrics of Zach Wyrinski and Roman Yossi. Check out that blue jackets patch. That is sweet. 27 out of 35. We'll do a random at the end for this one, guys. Nashville in the blue jackets. Let me make sure that's not a... One person doesn't have both. Predators is Steven P. Blue jackets is Ferran. So we'll do a random at the end for this one. Very nice patch there on the Zach Wyrinski and Roman Yossi duel in NHL fabrics. Got eight banners. Brady Kachuk. All-star game. Got a banner here. Claude Giroux. And on the back is gonna be a purity of Mitch Marner. A wrench instead of a ball. You dodge a wrench. You dodge a ball. That's right. Box nine. The last one here on the hockey. Good luck, here, guys. Still just two to go on one-on-one basketball. Patches of Hulahann. Patches of Hulahann. The luck of the Irish. Well, what's Spencer saying you're wrong for? I don't know. I don't know. Whoa, whoa, whoa, whoa. Say Shane Wright game used to 9.99. Do not crack it. Cack it. The 2.24, Cole Kepke, for the lightning. And Nick Purbix, another one there, authentic jersey for the lightning. Back to back. And another one this is gonna be Johnny Goudreau on the premium jersey another sick patch another one for the blue jackets Nice one there Johnny Goudreau Is sick on the patch their game used? Jonathan Kovacevich on the rookie jerseys red got a purity of Joe Pavelski and purity on the back is Josh Bailey for the Islanders necessary All right, we'll do a random real quick guys for this one. I'll be between the Predators and the blue jackets Predators and the blue jackets give me one second to set it up Keep Patrick by one and one and one basketball one to go guys. Come on one to go We'll get it going right away here got typing some names and we'll get going two Two teams per spot on that one as well one from the top tier teams one from the bottom all right, so we got predators predators And blue jackets blue jackets. Let me roll two dice. Give me times. We'll go Didn't get a good roll. I'll go set it. I think that's the same number set four and three seven times Yeah, sorry about that Brandon. All right. Good luck here on the dual patch again It is predators and blue jackets in this one predators and blue jackets on the dual We will go seven times Good luck. Good luck It's one two three four five six seven blue jackets blue jackets We'll get that one. I believe that was for Ron for on with the blue jackets do a quick recap guys And we'll be on to one and one Extends daily. There we go. There's Spencer There's Spencer Alrighty guys one to go in one and one last spot mojo guys, let's get it filled up. We'll get it going next Kovacavich Good Chuck Jiru That one was Stamco's got Kairu Tage Thompson. There's a mover mover rare That one's gonna be Corey Perry Seth Jarvis Jurek, there's Kent Johnston had Barzol Dostal and Shane Wright Rasmus Dahlin have Gabe Valardi that one's gonna be Addison Kutier Barkov, there's the howler for the coyotes mascot. That's a sick one there for Roman Yossi for the predators net cord relic To 15 Pablo Buccinevich and to 10 Johnny Goudreau Yeah, it might Nick. This might be one of their worst games. They've had as a franchise. There's a perfect space auto There's a perfect Jersey out of 149 got Vitek Vannacheck Lucas Johansson got Bergeron and Merz Lincoln's and this one was for John H one of five. We had a Mark Messier on the All-star game auto legends auto very very nice one there John Grab a picture there on that one that is sick nice messy Grab a quick picture of that one guys and we'll be on to the next one one and one basketball. Thank you guys


Hello and welcome to the session. In this session we discuss the following question it says, in the figure m is parallel to n, if pr is perpendicular to m, qs is perpendicular to m and pr is equal to 3 centimeters, find qs. This is the figure given to us in which we have m is parallel to n, then pr is perpendicular to m, qs is also perpendicular to m and we have given that pr is equal to 3 centimeters, we have to find the length of qs. Before we move on to the solution let's recall one fact which says that the perpendicular distance between two parallel lines which is also called the distance between two parallel lines is the same throughout. This is the key idea to be used in this question. Now let's see the solution. We are given this figure in which we have line m is parallel to the line n, then pr is perpendicular to line m, qs is perpendicular to line m, we are given pr equal to 3 centimeters and we have to find qs. Now since we are given that m is parallel to n, so the perpendicular distance between is same throughout since we know that the perpendicular distance between two parallel lines is the same throughout and pr is the perpendicular distance between the two parallel lines m and n and we are given that pr is equal to 3 centimeters. Also this qs is the perpendicular distance between the parallel lines m and n as pr is also the perpendicular distance between the parallel lines m and n. So qs would be the same as pr which is 3 centimeters. So we get qs is equal to 3 centimeters. So this is our final answer. This completes the session. Hope you have understood the solution of this question.


we are on step six of a seven-part series on how to build a brand from scratch. A brilliant question that was asked at the very beginning. I did share the question if you want to go back and see it by Claire in Leeds. So we're on step number six. This is where we're talking about how do you illuminate? How do you put even more exposure on the product or service that you've been going through this process on? Now we have talked a lot about your product or the service all throughout this point of difference and your unique gifts that you're bringing to the world. But now we need to talk about you. Yes you. We need to put the illumination and the spotlight on you and your personal brand because people buy from people that they know, like and trust. If they don't know you or they don't feel like they trust you then they're certainly not going to buy any products from you or any services from you. So this is the step where we need to start really putting that focus on you personally. This is where you need to make sure you've got a sparkling bio otherwise known as a biography. This is where you need to create a hundred word statement about you that first of all starts by demonstrating your gifts in the world and showing off exactly any accolades or accreditations that you have, any awards that you've won, any memberships you're part of, anything that can position you as the expert in your industry. Followed by your greater mission. What is it that you're wanting to do or your vision? You could get your vision and mission in there. What are you building? Who are you building it for and why? Why are you doing it and why do you care so much about what you're doing? It's really important to get behind the person who's building the business, the person behind the product. Years and years ago when I was first building my design agency we learned the hard way that people really do go to your about us section of your website more than you might think. So we launched our new website and we spent so much time on the products and services and making sure everything was ready for those that when it came to actually the about us section of our website we didn't have anything on there and so people were coming to our website they were jumping on the homepage going straight from the homepage to about us and then when they couldn't find us because we said oh we'll sort ourselves out later when they couldn't find that they then were hemorrhaging from the site. So we weren't keeping them there which was a massive problem so you want to make sure that your biography is absolutely sparkling. Now if you're not quite sure what to talk about or how to talk about yourself I would suggest you go back to your earlier days like think about between the ages of 0 to 10 what happened to you that made the experience that happened to you then such a big part of what you're doing now. What was it that happened to you when you were between 10 and 20 years old between 20 and 30 years old 30 and 40, 40 and 50 fill in the gap I don't know how old you are but really sit down with yourself you owe it to yourself and you owe it to your business to really come up with that story and here's the thing right I was filming on a documentary a couple of years ago out in San Diego with that one I've been on a few documentaries this one was in San Diego and at the end of filming this one particular day I think it was day two or three and and we're waiting to go off to the next location for filming and as we're just standing around and chatting and they'd taken 20 change well actually they took a hundred global change makers and divided us across locations they flew us out to those locations put us under an insane amount of pressure to see what does a real leader do under pressure and then they filmed the result which was a process I wouldn't like to repeat at the time I look back on it fondly now when we were going through all these things they were doing to us it was not fun so we're at the end of filming this this day and Catherine Hathaway she was one of the amazing thought leaders on this documentary and Catherine we're just standing there chatting waiting to go to the next location and she says to me Sammy what did they call you when you were younger like what was your nickname when you were younger and there were two other people the the producer of the series Bright Loreano she stood to my left and there was somebody else here I can't remember who that was but so there was four of us stood there and she says this to me and before I even have the opportunity to think about what's about to leap out of my mouth I say fat and she does this and sprite does that and so does this other lady and I do that and I couldn't believe that that came out of my mouth and I said would you just excuse me for a moment and they said sure so I go off and I just have this little moment with myself and think where did that even come from and what came up for me was just unbelievable I couldn't believe just from that one question I mean it was just wow and the next thing I see I'm just I'm thinking oh my god how embarrassing I just said that out loud in front of really important people like that's what was going through my mind and where did it even come from and next thing I see this like this movie in my mind of my first day of school well I was an only child you see till I was 11 years old and so and my grandma used to look after me while my mum went to work and my dad was at work so I grew up with my grandma pretty much and she used to live quite close to the school that I was going to go to of course I like because I had no brothers and sisters I used to play out with the other kids who were living on the same road as my grandma and but because I was a year younger than them they were all going to school before me so there I am every morning looking over the the the wall saying hello to everybody as they go past wishing that I could be going to school with them I couldn't wait to grow up I don't know about you but I couldn't wait to grow up fast enough right so it comes to my first day of school so off we go I've got my little uniform on I'm proud as anything I'm like yes I'm grown up now five years old I'm grown up so off we go and and I'm in there and I'm just just in wonder all the magic and the noise and everything that's going on around me and and the bell goes so it's playtime yes the thing that I've even been waiting for the most has come so we go running out into the playground and playing with some of my friends which is really nice and then I spot this group of girls who are skipping and they've got the playing the skipping rope game you've probably know of it with it one two three one two three and they're skipping over it and if they catch it or if they mess up then they have to go and somebody else's turn and and so I go running over to this group of girls well by that point I haven't experienced any kind of rejection before until that moment and of course like I'm just been with my friends and everybody has been friendly enough so I'm not experienced in this and this was the very first time but I experienced blatant rejection so I go running up to this group and this one girl she puts her hand up right to my face like pretty much in my face and she says go away fat so you're too fat to play here and in that moment not only was I totally crushed hugely embarrassed I mean even at five years old I had an ego I think we all do right when something like that happens and it just totally crushed me and the thing was I just I didn't know what to do with that and so I just took it in and I processed it and you know I carried that around for the rest of my life up until this moment so I remember this moment I go back to Sprite and Catherine and the other lady that was there and I just said to them I'm really sorry I can't believe that came out of my mouth I've gone and had a word with myself and I've actually just had a really strong vision of where that comes from and I've not even thought about that moment in my entire life until now and they said God tell us about it so I told them and Catherine says to me the lady that had originally asked me the question she says Sammy what a wonderful gift I said really is that a gift and is it funny how we can't see those gifts but they can she says yeah what a wonderful gift don't you see what you do for people now I said you're gonna have to enlighten me she said you are on a mission to make sure that people aren't branded or labelled in a way that that isn't them you help people to show up in the world in their authenticity so don't don't care about what other people are saying this is you you make that decision about your label you make that commitment to who you are you own it and she said how amazing that you let that girl brand you all those years ago and ever since then you've been on a mission to brand other people so they don't get that kind of brand on them that isn't right for them like you you're on a real mission don't you realize what a gift that was and it was just like a massive like hammer on the head at that moment I hadn't realized that a that was a gift I hadn't even remembered that that was something as part of my upbringing and I hadn't even thought about it in years but yes I'd let her brand me I let her put her label on me and I carried it around and guess what I also lived up to it so the the label that you give yourself the way you illuminate your brand is to first decide who you are you've got to set that vision for who you are as a personal brand I created a video on this very topic how to build a personal brand it's in this channel go and have a look for it I created a video where I actually I don't talk about this story but I talk about you stepping into your own label you create that label because if you don't somebody else will and guess what when somebody else brands you you do step into that label you do step into it and it's very challenging to even see that you've been that you've made it your story and actually that might not be your story that might not be who you are so I want you in this step to to really think about how you're going to illuminate your real gifts and the real gift is not really the qualifications they're just good things you know in America people love to to know that you're the CEO of the organization in the UK they don't really care about that you could be a trailblazer or a visionary or you know you could be anything so depending on where you're doing your business obviously you know accolades and accreditations and awards they mean something to different people but that might not mean anything to your ideal clients so you know if you haven't got any of those things you're going to work up to getting them I didn't have them at the beginning either you know but I've got all of the those things now but the key thing is that you just start somewhere and then you can just start improving it and add into it as you start to build your brand and build your career so I want you in this step to go back and and just spend some time you owe it to yourself and your business as I already said to spend some time looking back from zero to ten ten to twenty twenty to thirty thirty to forty forty to fifty fifty to sixty whatever age you're at now I want you to go in ten year increments and think about the different experiences and the different stories and the different things that have happened to you during those periods so that you can look back on them and really recap and say that's why I do what I do now that's why I am who I am that's why I'm building what I'm building you know ultimately I'm a brilliant community builder built the brand builders club community that you know brand builders I've got a hundred and about 150 brand builders at the moment from all over the world who I'm working with personally and they're all working with each other that's amazing that's a legacy process you know a much higher end of client going in there they're all building a legacy they're all building something global if that's you if you feel that calling in your heart then drop me an email let's have a conversation it might not be right for you and I'll tell you if it's not then we've got the how to build a brand group there's nearly six thousand members in that group at the point of me making this video it's a much bigger community but I create community because I was so freaking lonely as a child I don't want anybody else to experience the loneliness that I felt so I've been on an all-out mission to do that it's all part of my story it's all part of the fabric and the the that thread that is woven through everything that I do there's a reason why you're so flipping great at what you do there's a reason that you were put on this planet there's a reason and a purpose for which you were created there was a moment that you were born for and if you don't feel like you've had that moment already then let's create it and let's create it together so if you've enjoyed today's show then please do subscribe and let others know how they can as well please share it share it as much as you can let's support as many people in the world as we can to really brand build and grow the business that they want to to be that drop they want to see in the world and to be that drop they want to model somebody like you who's being the drop so go out there and do that do your homework today just to rethink about that bio if you need any help with that go and check out the seven step brand Kickstarter system that's what I created it's the entire positioning and pitch piece around anything that you want to sell so you go through it on your business you go through it on your personal brand and you go through it for every single product or service or program whatever you want to sell you use this system to go through and create it so check out the seven step brand Kickstarter system to help you go through this process a lot easier when you're creating your own positioning story so I have a question for you as always so your question for today is what is your best business affirmation or mantra let's see if we can inspire each other I want to know what your mantras are and maybe there'll be other mantras that are put in the comments that you think oh I'll borrow that I'll have that one as well I do that quite regularly so give me some ideas about what your mantra or your affirmation is when things are really testing you what is it that you say to yourself that gets you out of that that challenging hole that lifts you right back up out of that well and gets you on fire again I'd love to know so take care put your comments below and I'll see you on the next show for step number seven see you there