So, you have these definition of positive definiteness and I want to connect it to this matrix. I want to sort of claim in some sense that the matrix function x transpose Ax or basically if I just add a t plus 1 does not change much. This is also positive definite function in the sense of you know how we talk about positive definiteness. So, I want both definitions to align. So, I want to claim that x transpose Ax given that A is positive definite is a positive definite function. How do I do that? It is pretty straightforward. I can decompose A in this form because A is a symmetric matrix because I cannot talk about positive definiteness. So, A is symmetric. Therefore, you can decompose it in this form. This m transpose lambda m where lambda contains a diagonal matrix of eigenvalues and m is just the eigenvectors. So, I can do this sort of characterization here and then if I write my, if I write x transpose Ax in this form, basically I am just in a sense doing a similarity transformation if you think in terms of coordinates. So, if I write my x transpose Ax like this and I choose, so I can write this is mx transpose lambda mx and I choose y as mx. This is the new state. This is the similarity transformation. So, y is constructed out of the eigenvectors. Then this quadratic form can be written as this y transpose lambda y. All of this is very simple for symmetric matrices, real eigenvalues diagonalizable naturally and all that. If it is not, then you have more problem, but we are dealing with this nice symmetric matrices. So, this is essentially sum of lambda i yi squared and all the lambda i's are obviously strictly positive and so this has to be greater than equal to some lambda min norm y squared because I take just the smallest lambda i, it is a finite number of lambda i. So, I can take the min, so I can just write this as lambda min sum of yi squared. So, lambda min sum of yi squared is just lambda min times norm of y squared and so basically what I have is a class k function, it is lambda min times class k function of norm y because the norm itself is a class k function, it is like saying x square is a class k function.  So, therefore, this and this is just a scaling, it is a positive scaling, it does not affect the class k nature of the function. So, this is just a positive scaling multiplied by phi norm y square, in fact I can take this with the norm y square and just say that it is, it is a class k function. So, what have we shown? We have just shown that x transpose Ax is, dominates a class k function, in fact it dominates a class k r function. Therefore, x transpose Ax itself is a positive definite function, ok, that is all we need, we need, it has to be 0 at 0 and all that, I mean it is 0 at 0, right, x transpose Ax is obviously 0 at 0, right and it has to be continuous which is also obviously the case, x transpose Ax is continuous in x, no problem, smooth in x in fact, yeah. So, this is a x transpose Ax has been proven to be a class, sorry, a positive definite function, ok and that is what we needed, we wanted to reconcile, yeah. So, even if I take my v tx with a time argument here, multiplied by x transpose Ax in this form, no problem, it is still greater than equal to x transpose Ax for all non-negative t. So, therefore, this is also a positive definite function, ok, does that make sense, all right, ok, great, all right. So, now that we have this sort of characterization for class k function, I will again say that it is not easy to verify this in general, yeah, because I do it all this, I sort of cheated, right, because I first constructed a phi and then constructed a v, in reality for most systems, you have to construct a v first and you do not think about constructing a class k function to dominate and all that, all right, but once you have a v, trying to find a class k function that it dominates is not very easy, all right and so here we have this nice and easier conditions which obviously have been sort of collected years from Vidya Sagar's book, ok and the characterization is very straightforward. If you have a function of only the state, ok, therefore I use a different notation, it is still the same v or whatever you want to call it, I have used wx because there is no time argument, I am using just a different notation to distinguish these cases. So, if you want to discuss positive definiteness of wx which is a function of the state only, then you require to check, you are required to check only two things, one that it is 0 at 0, this was anyway one of the conditions, same condition does not change anything, right and the second one is this guy, yeah, that wx has to be strictly positive for all x which is not 0 in this domain, yeah. So, this domain that is ball of radius r is fixed, we are somehow assuming that r states evolve in this ball, ok. So, you have to only verify two things, one that it is 0 at 0 value of the state and two that it is strictly positive whenever the states are non-zero, ok, again something that should remind you of the norm, right, norm also has such a property, right, it is 0 when the argument is 0 and it is strictly positive when argument is non-zero, ok. You can see that there are these, you know similarities between the two, ok, great. Now, the easy check does not remain so easy when you have a function of both time and state, in this case the only way to check is obviously you want the first condition which we cannot do away with in any case, alright. The only way to verify this is that you have to find a positive definite w to dominate which is only a function of the state norm, ok. So, instead of hunting for a class k function you are hunting for a positive definite wx, ok. So, I would not say this is significantly easier or anything like that, but still it is another characterization, ok. So, what this first characterization, more often than not we are dealing with time invariant or autonomous systems, we hardly talk about or we hardly see a lot of real examples where there is time varying quantities in the system, usually we do not, yeah. And even if we do more often than not the Lyapunov or the Lyapunov function means that we look at do not contain a time argument, ok. We use a time invariant v to analyze even systems that are time varying, at times, ok. So, this second one being not so useful does not impact us in a lot of scenarios, but it can also, yeah. So, these are the easy characterizations you can see all you have to check is that it is 0 for especially for this case where there is only the state, you just have to check that it is 0 at 0 and then it is positive for all non-zero values of the state, very easy, yeah. And the proof of this is obviously in Vidya Sagar's book, yeah. Please take a look at it, it is very interesting. What it basically says is that if you have this kind of a condition, then you can always find a class k function to dominate, ok. If this condition is satisfied you can always find a class k function to dominate, ok. So, this is rather nice, right, rather powerful. It is not a, it is not exactly constructive in the sense that the book is not actually showing you a construction of the class k function, but it just shows that there exists such a class k function, ok. So, this is pretty cool, ok, alright. The only thing is for v to be negative definite minus v needs to be positive definite, yeah, this guy, alright. And the notation we use is just the flipped over here, alright, ok, great. Acha, I do not know why I have repeated this because it is we already looked at this idea, ok. We already looked at this example, yeah. If you take v t x as t plus 1 x transpose Ax, ok, then it is, then in this case I know that it is greater than equal to Wx defined in this form for all non-negative t, right. And once I have Wx equal to this, I just need to verify this positive definiteness of Wx, right. It is not difficult at all, it is 0 at 0, no problem, yeah. And since, in fact, I do not have to even look at a lot of arguments because it is a positive definite matrix in between the quadratic form is always positive, right. By virtue of it being a positive definite matrix, x transpose Ax is always positive for non-zero x, yeah. That is what it means for matrix to be positive definite. So, even without looking at this eigenvalue decomposition, I can directly say this, right, because A is positive definite for all non-zero values of x, x transpose Ax has to be strictly positive. And that is all we need here, strictly positive for all non-zero states, ok, which exactly is satisfied in this case, ok. So, pretty straightforward. So, the idea is positive definite matrices lead to positive definite functions, ok. So, and please do not think of this as a trivial result. The point is we use in a lot of scenarios, we do use quadratically up, even for non-linear systems, ok. Especially when you have systems which are to a large extent feedback linearizable, ok. We have not talked about this obviously, we will come to this in the second half of the course, but a lot of systems can be linearized via feedback, yeah, a lot of aeromechanical system, ok, can be linearized via feedback, ok. And for most of those systems, we can use quadratic Lyapunov function, yeah, because once you have some kind, something linear appearing in your dynamics, then you can use the linear Lyapunov candidate, right, which is x transpose Ax or Px or whatever you want to call, ok, alright, excellent. What about this guy? This is this function, ok. Earlier we tried to verify that everything is a class k function and all that, but I know now that this is greater than equal to this guy, and I know that this is 0 at 0, and I know that for all non-zero values of the state which is norm x is non-zero. If norm x is non-zero, this is positive, right. So, therefore, v, v t, x is dominating a positive definite function, alright. So, I did not have to find any class k function to dominate again, yeah. Of course, in this case, this is also a class k function, but even if it is not the case, we do not have to worry about it, alright, ok. The next more stringent or more or a smaller class of functions is the radially unbounded function, ok. In this case, we can no longer take as argument states in a ball, but it has to take arguments which are all from all of R n, ok. So, no more Br ball of radius R n all that, yeah, because we are talking about radial unboundedness, which is a global property in some sense, ok. And again, everything else is the same, time and states maps to some real number, right. You require that the function is 0 at 0. So, the only difference now is that it has to dominate a class k R function, ok. The function has the function v t, x has to dominate a class k R function, ok, for all t in R plus and for all x in R n, ok. Now, slightly different picture, v is allowed to be oscillating, no problem, oscillating v is fine, again I should say, yeah, and oscillating v is fine, but it has to be above this class k R function, ok. Therefore, as you can imagine as x goes to infinity, v t x also goes to infinity, right, because it is always above this guy. So, if this guy is going to infinity, this also has to go to infinity, ok, alright. So, that is the difference. So, the property of going to infinity is inherited by the radially unbounded function also. Therefore, the word radially unbounded, ok. Why radially? Because you can think of somehow norm of x as some kind of, you know, radial direction, ok. So, it basically as norm of x goes unbounded, which is the same as saying state goes unbounded, v also has to go unbounded, ok. And as I mentioned, this is connected to global stability, ok, yeah. We will talk about why in some sense, I mean, may be later, but the idea is not that complicated. If your function sort of, I mean, if you have a function, say, if your function looks like this, yeah, I mean I am at 0, yeah, going to infinity on both sides, this is v and this is x, forget the time argument, ok. If the function looks like this, then great, I mean, if you say that, if you somehow say that in the y argument, that is in, if you somehow can claim that my function lies below this guy, yeah. If I can claim that my function all value always remains below this, right, then I can claim that my x lies within this, yes, no problem, ok. This is a radially unbounded function, ok. This is a radially unbounded function, yeah, it goes all the way to infinity. But now, if I have a different scenario, I have to make a different picture, sorry, I cannot fit it here. If I have a different scenario, which is that you have a function, which now does this, yeah, very much a class, I mean, positive definite function, right, because I am only concerned about, you know, say some domain, whatever, yeah, you can think of this as increasing even there, yeah. I mean, you can sort of imagine that this is also increasing, I mean, not actually flat, ok. But the point is, now if I say that my y, just look at this. Now, if I say that my y is restricted to this level, ok. Again, this level is not, whatever, I mean, this level is close to the upper bound in some sense. I cannot say much about x, right. x can be really large. The bound on x could be really, really, very large. Of course, if I give you smaller bound then, ok. But if I am actually giving you bound right on the boundary, then I cannot say anything much about x. x could be very large, ok. Therefore, you can see this is not the one on the left. This is not a radially unbounded function, ok. Even, let us assume that in both cases, the domain is Rn. Let us not worry about Brn all that. There is no Brn. Let us assume that the domain in both cases is Rn. But this function is this structure and this function is this structure.  For all possible values of v, you can claim some bound on x. Here you cannot. So, this invertibility sort of property is what makes radially unbounded functions amenable to global results, yeah. And here it will only give you local results. Why local? Only until some x you can, some levels you can work with. From this level, ok, this level, ok, this level, ok, here not ok. Beyond that, forget it. You cannot say anything. Beyond this level though, nothing, obviously. You cannot say anything about x. x could be anything, ok. Because we can never reach that level, all right. So, that keep that in mind. That is the idea. We can, we will of course prove some things so that these ideas are not just ideas and you see that it works in the math also. But it all depends. It is always using invertibility property. You will always think of using v inverse. Whenever you look at the proof, you will see. Entire proof goes by using v inverse and so on and that is what this is. This is v inverse, right, ok, all right, great, ok. So, examples. This function obviously class k r, just the norm in fact. This is the Euclidean norm. Yeah, this itself is a class k r function. So, if I take v as that, v has to be radially unbounded, because it is equal to a class k r function, all right, simple. This example, again obviously class k r. Why? Because this dominates this way, right. This dominates this for all non-negative time, right. So, again class k r, sorry, dominates a class k r function or a positive definite or a radially unbounded function, right. So, you are fine. So, this is radially unbounded, yeah. In fact, you can also think of it differently. You can say that this is dominating a class k function and goes to infinity as x goes to infinity in any direction. So, we are fine, ok, all right. I think that goes until like this. Yeah, let us look at this guy. What about this guy? 1 plus sin square t divided by 2 x 1 square plus x 2 square, ok. I am again claiming that this is greater than equal to half x 1 square plus x 2 square for all non-negative t. That means, yes, because sinusoid smallest value is 0 because I took a square deliberately. So, therefore, I get the half, ok. I have deliberately taken this example for one specific reason, ok. Easier conditions, ok. Unfortunately, for all the examples I am taking easier conditions and the normal conditions do not look too different, but I can promise you these easier conditions are what most people use. Yeah, they never try to find a lower bounding class k function and k r function, all right. What are the easier conditions? The first two conditions look exactly the same for radial unboundedness. The only thing additional is this going to infinity condition, right, because obviously this verifies that w is a class k function and radial unboundedness is just a class k function with going, it going to infinity, that is it, all right. Though again remember, it is not that simple. This is being verified for all Rn other than 0, not just x in a ball, ok, not just x in a ball. So, the first two conditions in Vidya Sagar's book especially, they tend to have additional notation, they use local positive definite LPDF and PDF. So, locally positive definite functions and positive definite functions. So, this is actually a PDF, a positive definite function in Vidya Sagar's notation. Why? Because if this is verifying this class k condition for all states, not just states within some ball around the origin, all right. So, that is the difference here. So, that is why I have written global positive definiteness plus the unboundedness condition, ok, great. Counter examples, ok, counter examples are very good because they tend to jog you. If you look at this function, this is positive definite radially unbounded, I have written an explanation. The function is x1 plus x2 whole squared divided by 2, yeah. What happens? There are only two states, x1 and x2 and I am giving you a function x1 plus x2 whole squared divided by 2 is irrelevant, but whatever. Is this class k, class kr, what is it? Is this class kr, ok? So, this is you are talking about class k, ok. So, what are those non-zero points? Right, as simple as that, yeah. This is not even class k. Why? Because if x2 and x1 are opposite signs, x2 is minus x1, all right. For all possible values of x2 equal to minus x1, basically this line, that is what I have drawn here, yeah. Along this line, v is 0. Along this line in state space, this is 0 and this does not satisfy our easier test. Easier test requires for all possible non-zero states, yeah. And these are obviously non-zero states, for all possible non-zero states except for this guy, v has to be strictly positive or w has to be strictly positive, which is not, ok. So, that is a problem, not positive definitely. What about this guy? x1 square plus x14, yes, yes. So, if I take points of the form this, where x2 is 0. So, basically along the, sorry, where x1 is 0, yeah. Basically along the y-axis, I take anything, this function is exactly 0, yeah. Again, 0 for non-zero values, so not positive definite, yeah. State being 0 means every element has to be 0, yeah. So, this is a non-zero state, 0, alpha and this is evaluating to be 0 at that, ok. So, these are counter. So, simplest thing to remember, if all the states do not appear in your w or v, whatever notation you want to use, if all the states do not appear, then it is immediately not positive definite, ok. And it is not a function we can do Lyapunov analysis with, ok. So, all states must appear, simple. This is the first key requirement, alright. Next, but this, this we have already done, right. This function, this is positive definite, right. We have already done that because it is strictly positive, yeah, for non-zero values of norm x, right. But this is not readily unbounded. That should also be very evident, why? It maxes out at 1, right. Maximum value this can take is 1 as x goes to infinity. Therefore, it is not unbounded, ok. So, this is something like a, I mean, I mean, maybe the shape will be different, but it is something like, you get, tapers off, does not go to infinity. So, this is again cannot be used for global stability analysis, only for local results you can use, ok, alright. Similarly, for the easier test for radial unboundedness or not necessarily easier anymore, if it is a function of time and state, you still need the zero condition and you need a Wx which is readily unbounded that you can dominate, ok. So, this is not an easier condition like I said because it is almost the same as trying to find a class Kr function, ok. So, for the time varying cases as you can see the easier conditions are not too easy, but that is about all you have, ok. So, but for the time invariant V or W, you have very easy regards to everything, ok.


Start right now. We're so money geeks Mr. V here. Welcome to another video. So into this video guys, I wanna talk to you guys about starting your investment journey. If this is 2020, again, new year, people are setting a ton of new year resolutions. Some people wanna lose weight, some wanna be debt-free, some wanna get married, wanna have kids, all these good things that everybody's talking about. And some people just wanna start saving either the emergency fund or even you start learning how to invest or start the process of investing. But there's certain things that have been holding you back. Maybe it's just fear of you thinking, oh, I don't have a degree in finance so I don't think I can invest or I'm not making a ton of money. How can I invest when I'm only making X amount of dollars a month? All those things are myth and they're holding you back because you haven't talked to the right people or you haven't asked the right questions. So in today's video, I kinda wanna walk you guys just to show you that even if you can spare $50 or $100 a month, you can definitely start your investment journey. So the first place where you would start is that I want you to go to vanguard.com, open an account. I did a video and talked about how to open a Vanguard investment account, a brokerage account with Vanguard. I'm gonna hook it up here so you can definitely go check it out. I walk you through the steps of actually opening the account and not transferring funds into the account. So that will get you started. So you have $50 a month you have or $100 a month. In Vanguard, you have two options as far as passive investment. And when I say passive, I mean, the one where you don't have to worry about is looking at the stock market every single day. So you can either invest in ETFs which are exchange traded funds or you can invest in index funds. And Vanguard actually started this whole concept of index funds saying I'm not gonna go into the details of that. You can look it up here on YouTube. There's people have talked a lot about how the index funds came about. You can go look it up. But so these are the two options. ETFs are just the trade like stocks. And the difference is that there's no minimum investment required. Whereas if you wanna invest in, let's say index funds, there is like a minimum investment amount. In most cases with Vanguard, it's about $3,000 that's required before you can start. Again, for you starting out, you don't have $3,000 sitting around to get you started, right? You have $50 or maybe have $100. So what you're gonna do is once you create your brokerage account with Vanguard, you can transfer your $50 or your $100 into that account and you can invest in ETFs. The good thing again is that with Vanguard ETFs, once you buy the ETFs, it's commission free. You don't have to pay any commission. So you only pay for the ETF that you buy. So you can start very simple. And then you can set it so that every month, maybe $50 or $100 leaves your bank account into your brokerage account and buy more of that ETFs. So I love like the technology ETF, which is VGT. That's a little bit expensive, it's over $200. But there's other ETFs like VYM that you can buy for less than $100. And so once you buy your first one and it gets in the market and then every month, you just continually transfer money into that account and buy more ETFs as you go. So again, the goal is not for you to start with $1,000, $10,000 or $20,000. The goal is for you to start with your $50 and gradually grow from there. And then once you buy that ETF, you set it up so that when they pay dividends on those ETFs, it reinvests into the account. So let's say the ETF pay dividends of, I don't know, 2% or whatever they pay, don't pull that dividend out, just let it reinvest. That's a compound effect. So you're bringing in money from your bank account, you're investing it, they pay your dividends, they reinvest that dividends. And then so next time that you, they wanna pay dividends, they're paying dividends for the compound amount, not just the amount that you have in there. Do you see how that whole thing works? It's kind of like a snowball. Once you get it started, it starts moving down the hill as it goes down the hill, it gets bigger and bigger and bigger because it just keeps picking more along the way. The only thing that you have to make this happen is that you have to have snow in my analogy. Because if a snowball rolls down the hill, there's no snow, it's gonna lose snow, right? But in this case, you rolling the snowball down the hill, the snow that you have to keep putting there is you putting that money constantly every month. So that's how you can get started. And so for people that are really worrying about starting to invest and they have a low budget, this is a strategy that would definitely help you on your journey to investing for retirement. Again, let's recap here, guys. The myth about you having a financial degree before you can start investing is not true. Another myth is that you need thousands of dollars to start investing, that's not true. You can start investing with as low as $50 a month. Most importantly is for you to get started. Open your Vanguard account, your brokerage account. Transfer money from your Vanguard account into your brokerage account. Even if it's just $50, like we talked about, or $100, once that money gets in there, buy an ETF. Do some research and look for ETFs that are within that range, that price range of $50. There's a lot of ETFs on Vanguard that you can find. And start small. So buy your first ETF. Make sure that you set it so that the dividends from that ETF gets reinvested. And that's how your compound interest is gonna come into play and that amount's gonna grow big. And every month you keep transferring that money over. So don't take your foot off the gas. Once you get it started, stick to that $50 at least. And if you make more money, you increase that number to maybe $100 to $200 to $300 based on how much extra money you make. But for the minimum, make sure that you're putting in that $50. So this is a strategy, again, for people that are starting out, people that have no idea. If you are struggling and don't understand the concept of investing, you think it's complex, these videos should definitely help you get started. ETFs are your friends. Index funds are even better. And so once you've gotten to the point where you have enough money, let's say you have $3,000, and then now you can now maybe sell some of those ETFs and then looking at buying some index funds based on their performance. If you find some really good index funds like VTSAX, that is a really excellent ETF index fund that I use. So you can sell some of the ETF and buy that index fund and just continue to grow your portfolio just like that. So that is a strategy that would help you get started in 2020 and you're not gonna regret it. So definitely let me know if you have any questions about stuff like this. Most importantly, get started. Like, share, subscribe, and as always, stay motivated.


our JTF capable headquarters in all of the AFRICOM area of responsibility. We now have full administrative control and training readiness authority over the 173rd Airborne Brigade and we ensure that the 173rd is trained and ready for crisis response and contingency operations to include the ability to support the North and West African response force that we call the NARF. And finally, we're responsible for the oversight of all Army activities and personnel in Italy who is a critical NATO ally. So that's a big mission with a big footprint and we rely on the work of our teammates in the intelligence enterprise to make sense of this environment and to make better decisions faster than our competitors and our adversaries. Today you will hear from one of our operational subordinate commands, the 207th Theater Military Intelligence Brigade. The 207th conducts military intelligence analysis, collection and exploitation in support of CTAP, VAP and AFRICOM. It's one of 17 brigades belonging to US Army Intelligence and Security Command, INSCOM. And we're really proud to have a close relationship with INSCOM and we leverage the intelligence capabilities that the 207th brings to the fight to accomplish our mission in Africa. What's remarkable is that the 207th is the smallest theater MI Brigade in all of INSCOM but it has the widest coverage. A full spectrum of intelligence support that connects and delivers the intelligence enterprise to the AFRICOM Theater. That's an area about three and a half times as big as the continental United States with 53 sovereign nations in five regions, each with a unique history, culture and political system. It's a challenging environment for Intel collection. It's an area where our near pair of competitors are actively seeking to influence events on the ground through diplomacy, disinformation, economic leverage and military presence, much of it malign. An area where violent extremist organizations continue to be a persistent threat, destabilizing fragile governments and expanding their safe havens while opening space for our competitors. The 207th helps CETAF, AFRICOM, understand these activities and the range of threats to US interests and those of our African partners. Today you will hear about one of our unique capabilities. The Africa Data Science Center or ADSC is a pilot program that leverages data science to help analysts quickly understand and exploit the massive amount of data that is currently available to the intelligence community. This capability is a pilot as a game changer for us. The ADSC enables traditional intelligence collection methods in Africa's resource constrained environment where we can't always rely on ISR assets. This program supports one of the Secretary of the Army's primary objectives to ensure that the Army becomes more data-centric and can conduct operations in contested environments. What is really exciting is this approach is exportable to the rest of the Army in the DOD enterprise. And in particular to other areas of responsibility where the US military anticipates and in fact is operating in contested environments every day. We see a lot of opportunity for this program and I know that INSCOM is looking at opportunities to expand its reach. I now want to turn the floor over to the real expert, Colonel Mark Denton. He'll provide a more detailed view on how the ADSC program actually works and then we'll have an opportunity for a few questions and answers and we look forward to that dialogue. Thanks very much for your attention and please welcome Colonel Denton. Thank you, sir. Good afternoon, ladies and gentlemen. As General Wassman pointed out, I'm Colonel Mark Denton, commander of the 207th Military Intelligence Brigade. And today I'll be talking to you about the Africa Data Science Center. But before we get started, one of the individuals I also have here with me is Mr. JT Gill. JT is my senior civilian. And he's been around since we started with the Africa Data Science Center. Let me get this tablet up and then we will continue. Well, it's a good thing that I have a backup because the tablet is down right now. So JT, we'll go to the notes. So as General Wassman said, I am Colonel Mark Denton and I am the commander of the 207th Military Intelligence Brigade. Today we're going to be talking about the Africa Data Science Center. And as I talk about that, one of the things I do want to let you know is that we are headquartered in Vicenza, Italy as the 207th Military Intelligence Brigade. Not a bad assignment there in Vicenza, Italy. And one of the things I'll also make sure that you understand is it is late in the day here, but it's even later in Italy. So jet lag is kicking in right now. So bear with me this evening and we'll get started. So again, as General Wassman mentioned, we are one of 17 of INSCOM, the Intelligence Security Command's brigades that's out there. And we are the Theater Intelligence Brigade that is assigned to the Africa Command Area of Responsibility and further operationally command by the Southern European Task Force, as you just heard from General Wassman. Our Warrior Corner, again, is focused on using data science as an intelligence multiplier. Our Africa Data Science Center, or the ADSC, as we will call it, is not on our EMTO, Modified Table of Organization and Equipment. So that is an out-of-hide activity that we've had to do. And it's currently a three-person team within our analysis and control element, our ACE, which is an element within our operations battalion. Go to the next slide, please. It's the situation. So as you can see on this slide, it shows the chart of our ADSC. The ADSC, again, is led by a captain and a warrant officer and supported by three contract data science engineers. But before we dive into that, what I do want to do is I do want to play this video real quick for you. So go ahead real quick and roll the video, please. And turn it up as live as you can. What's the situation? Historic first. And in response to ruffles, unprovoked, it was a federal government, a dissent, and it was our nation, a US nation, a government, a city women of the United States, a federal government, a state, a state, a state. But it's a non-competitive task force from Africa. But it was done by an ADSC. Hey, I can help you narrow this down. We are the Africa Data Science Center, transforming data into decision advantage. The Army G2's relevant deep sensing at the edge warriors corner. You heard Alex Miller as he artfully and eloquently articulated that we don't have a data problem is what he said, we have, you know, what do we do with the data problem, which is much different. Our intelligence analyst has a data overload problem, as you saw there inside the video. So therefore, the Africa Data Science Center has been important to the workflow and production for our analysts, as you heard General Wassman also artfully articulate earlier. A little bit of background on the Africa Data Science Center. The ADSC is an INSCOM pilot program which started in 2018 and continues today with INSCOM, AFRICOM, and CTAF funding that important capability. So General Wassman mentioned a little bit, but let me just tell you Africa lies at a global crossroads. It holds tremendous geo-strategic significance while being shaped by competing forces of prosperity, poverty, and also conflict that we see on a daily basis. Across the continent, all 54 countries, CTAF and the 207th MI Brigade Enterprise provide cutting edge information that regularly impacts national level decisions on a daily basis. The 207th MI Brigade Africa Data Science Center uses machine learning techniques to quickly scrutinize large sums of data, eliminate redundancies, and provide relevant information in a usable format. The ADSC leverages best practices from across the intelligence community and applies leading edge data science techniques to fulfill operational intelligence requirements to operational commanders like General Wassman within our theater. As an out of high data science support cell, as previously mentioned, with a startup mentality, this unique program received recognition by the House Armed Services Committee in the FY22 National Defense Authorization Act. The NDAA lauded the ADSC for serving as a model of innovation and providing invaluable clarity on adversary activities across Africa. As General Wassman mentioned, this capability is not specific just to Intel or specific just to CTAF. This capability can be exported to other environments and other theaters as well. Yesterday, in the opening ceremony, we heard the Secretary of the Army lay out her priorities. The ADSC is one small but innovative example of how the 207th is meeting the Secretary of the Army's objective number two to become more data-centric at echelon. We accomplished this by using machine learning, advanced analytics, data visualization tools, and most importantly, intelligence domain expertise to build products that enable analysts to sort through vast amounts of data, spot trends easier, and ask more informed questions to get to the so-what side of the equation a lot quicker. As you saw in the video, our analysts can be overwhelmed by the volume and variety of data available to them on a daily basis. One way the ADSC has tackled this problem is by building software categories and curate tens of thousands of reports for analysts in just a few minutes. This would normally take analysts days or weeks to organize that amount of information before analyzing it. As an example, the ADSC can easily consolidate over 3,000 reports in a matter of seconds. Go to the next slide, please. As we talk about transforming data into the intelligence process, the ADSC is not a stand-alone. The ADSC is embedded and incorporated into the intelligence cycle and everything that we do on a daily basis as it integrates with the analysts and not separated from the analysts. So ultimately, the ADSC is focused on using modern tools to solve complex intelligence process. To do this, we had to adapt the traditional software development practices to fit the Army intelligence cycle, as you see there depicted on the slide. By integrating data science into the intelligence process at various echelons of command, the ADSC supports both analysis and operations through tight integration with the CTAF commander's requirements or whatever your entity is, their requirements and the objectives within our analysis and control element in our battle rhythm events. So this is scalable and taleable to your specific requirements and environment and to your commander's needs. For instance, the ADSC helps senior leaders in the Africa community of interest better understand complex problems such as the evolving violent extremist organization threat and near peer adversary Russia and China and their activities within the Africa area of responsibility. One of the most critical parts of integrating advanced analytics with intelligence is understanding the problem. Without deep domain expertise, i.e., the analysts, we're going to develop things that end up off the mark. So the analysts have to be involved in what you develop as a data science or data scientist. The analysts have the expertise, while the data scientists have programming expertise. So they're not one and the same. So you do need that expertise on both sides. Before we start using machine learning, coding, and big data techniques, we have to understand our customer's requirements or our commander's requirements and needs. Consideration for handling sensitive data permeate every step of data science integration from how we get the data to what happens after we transform, disseminate, and store that data. While many of the Africa data science tools are used on TS systems or JWICS, the information often used that is often used at the collateral level and can be made appropriate at any other level, again, based on the needs and requirements of the commander. The ADSC uses existing platforms where feasible and we're not feasible. They also can create their own versions of a platform. The Africa data science is an enabler first and foremost. Our team integrates our work with the IC. So along the intelligence community-wide platforms and partners, we will utilize those first. So whether it's FADEMIS, Bode, Ejema, Safehouse, Getz, ArcGIS, Chrome, some of the more familiar crate, et cetera, some of those more familiar tools and platforms, that's what we tend to use on a routine and daily basis to be able to integrate with the rest of the IC. The ADSC pushes and pulls data in various formats while transforming it en route to meet the needs of the production requirement of our consumer. Our soldiers are not equipped or trained to do that task. So the Intel professional is not trained at Fort Wachuka to be able to do the work of the engineers or the scientists. So I don't want you to confuse that we're now turning our soldiers, our 35 foxes, that's trained at Fort Wachuka into data scientists and we can talk about that a little bit more. In addition, although the African Data Science Center isn't a traditional int, again it's not an int by itself and we can have that debate about open source also, but we take all that information that's available out there and it's not a traditional int by itself. However, and it doesn't produce and disseminate traditional intelligence products, but they do build tools to support and enable the analysts in ways you can't buy off the shelf. The ADSC does not replace the analysts again, rather they work with the analysts who contribute deep regional expertise which cannot be replaced by a scientist. Go to the next slide please. Here's a vignette and again as you heard in the video we talk about going from chaos to clarity on the right hand side there you see all that purple stuff, that's all the stuff that comes in that an analyst needs to, that's all the chaos and an analyst needs to make sense of that so we can inform folks like General Wassman. So another major tenet of the Africa Data Science Center is work to simplify and organize the tremendous amount of data available to our organization. The idea is to get our analysts to the starting line a lot quicker, allowing them to focus on analytical part of intelligence work and let computers do the manual repetitive task. So we'll do that through machine learning. So time and time again we heard from analysts that it can be difficult to accurately assess trends with so much data available out there to them on a daily basis. You saw the analysts again in the video with all that information coming in and he's got it, folks just asking him what's the answer, what's the answer? Well he can't begin to start doing the analytical work because he's just sifting through data and oftentimes a lot of that data is the same repetitive reports that are coming in but they're coming in different forms from different entities. The graphic on the slide shows the chaos that too much data can bring. The ADSC uses a series of machine learning models to automatically characterize thousands of reports for analysts, excuse me, allowing them to focus on analytical task at hand and get out of the blocks a lot faster. This machine learning system uses natural language processing which is essentially pulling in elementary terms, turning words into numbers and then doing the math immediately after that to put intelligence reports into categories based on mathematical similarities to a baseline. That's the easiest way to explain that. We then take those curated reports and let the analysts interact with the data in a way that makes sense to them, either through a dashboard or an Excel spreadsheet, whatever format they prefer. This goes back to where I started with the comments about sense, making sense and then acting. So you sense the data, you try to make sense of it by binning it or categorizing it and then you're able to give it to commanders to be able to act on that data based on the predictive analysis that comes from that. This program was developed at CTAPF by the ADSC with the direction from analysts and the theater leadership and is designed to help our folks at the big picture of what's happening on the continent within the context of army doctrine and also our intelligence trade calf. So trying to nest all those things together and this can be done in any theater, whether you're in the Pacific or whether you're in UCOM or other theaters, you can do the same exact thing. So as I get on short final here and try to wrap up, let me just recap what I really just said. If you took nothing else away in the last 10 minutes or so, what I'm telling you is in the last 10 minutes, the U.S. AFRICOM AOR is a vast and complicated environment that possesses significant challenges to U.S. interests and requires unique processing methods to gain the information advantage. When we talk about spread across such a large array of environment over 54 countries, sometimes 55 depending on who you ask on what day, it is challenging to be able to get all that information in a usable form to our commanders on a daily basis. And the Africa Data Science Center is a force multiplier in CTAPF's arsenal to exploit and maximize tangible insights from the massive amount of data currently available to the intelligence community. Emerging technologies continue to increase the amount of data the joint force has at its disposal making previously inaccessible insights a daily occurrence. The rapidly developing field of data science will provide decision advantage to commanders at the unified command and theater echelons. Theater embedded teams ensure technical expertise is co-located with regional subject matter experts, allowing for quick prototyping of unique data science solutions to meet the commander's evolving information requirements. As I said, in closing, the Africa Data Science Center has a bright future. With the increased intelligence production, it allows we've only begun to scratch the surface of the ADSC's capabilities. In the future, we would like to see data sharing through technical reach back to a hub and tie in with other ADSCs across the enterprise in more of a hub and spoke model. Right now, we are essentially a silo and there aren't other ADSCs out there where we can tie into a hub and spoke model. But right now, I'll tell you though, the INSCOM G7 under the direction of INSCOM is working through that. And I know some folks at Fort Wachuk are also working this where they're exploring concepts for an enterprise approach to Africa Data Science Center and more of a data science hub across the entire community. So the capability to process so much data independently through machine learning is the way ahead that will allow analysts to spot emerging trends and support commander's intelligence requirements much quicker. In the lessons learned since its inception in 2018, the Africa Data Science Center program can be applied to other national security and regional challenges. So let me just say one final comment and then we'll open up for questions. And I'll also turn it over to my senior civilian see if he's got any comments he wants to make. But I do know that folks out of Fort Wachuka, General Hale is making a lot of progress on this in terms of making our individuals more data literate. And that's really important. So the leaders out there, the lieutenants, captains, even the soldiers as they go through training, they're getting a lot of this information on understanding data and how to exploit and utilize that data and becoming more data literate. So there are a lot of folks that's making a lot of efforts on this and thank you for your time. I really appreciate it. JT, do you have anything before we close? Go to the next slide, please. Nothing additional at this time, sir. Okay. We'll go ahead and open up for any questions that anyone has. Can we get a microphone? Hi, Todd, South Army Times. I have two questions real quick. One, you mentioned that this is exportable. You also mentioned it was siloed because it's not a hub necessarily. So I guess first off, do these exist in other combat commands yet they're just not connected or is this solely an enterprise only for Africa and not replicated elsewhere at all? So for us right now, and I'll have JT also comment because JT's been doing this. He's been part of it since 2019. But for us, we are not connected to other data science center. We're connected to other individuals like folks in the soft community. They have a similar capability, but there isn't a hub at the top that has the ability like a hub and spoke to be able to manipulate and push and pull data from each of the elements and then tie it together at the top. So no, there isn't others that are doing the exact same thing. There are others that do some elements of it, but not to the level that we're more efficient where everybody is tied in and we're interwoven where we're actually able to exploit all the data across the entire community. So no. Thank you. And just one separate question. As far as the vignette you shared, what is the audit process to ensure that what you discovered was accurate after the fact? So a couple of things. So the audit process is really reaching out to other entities, other data scientists within the community, such as folks within the GOINT community. I've got one of the doctors back there. He can tell you and utilize them as the experts in each of their different disciplines to be able to help us make sure that we're doing that correctly. So they help us with that audit process. And there's a community of folks out there that are doing data science. They're just not all collectively together where it's all integrated, which I think about two weeks ago, maybe a month ago, they had a symposium on data science where they brought all these individuals together and they talked about best practices. This Africa Data Science Center was one of those that was briefed at that symposium. I don't know if Gus Wright is here. He may want to elaborate a little bit more. Can we get Gus a mic? Because I do know that Gus was in that symposium also with my data scientists at the same time. Gus, anything? Yeah. I got some, sir. Can you guys hear me okay? To answer your question more, that audit after the fact that here's how machine learning works. A machine learning model is essentially a computer-coded model of the human brain, right? And it has neurons and synapses, but it's written in ones and zeros. So whenever someone makes a discernment and say, hey, here's the answer that this data is telling us to, after the fact you can then go back and correct the model based on the accuracy, then in the future the model becomes a better model, if that makes sense. So that audit and process is circular in so many words. And to answer the colonel's question, tons of collaboration because that deep domain knowledge is such an important asset, you know, it's almost easier to teach when in terms of data literacy, someone who's been doing something like what I do, geospatial sciences or geospatial engineering, it's almost easier to teach me to code than it is to teach me to science. So that's a little bit of correction that we'll have to do in the future to ensure that as we train our soldiers and as we cultivate a paradigm shift and the approach to answering intelligence questions in the future, we'll have to cultivate this skill set as an essential body of knowledge going forward while leaning on experts that have been doing data engineering for the time being, if that makes sense. No, thanks, Gus. That was absolutely spot on. And that's why we need to collaborate with folks like Gus and others within the data science arena. It's a collaborative effort and it's not just one individual. But yes, that's why I say that ours is a silo, but we do collaborate across the IC. JT, you had something? Can you hear me? There we go. Hi, I'm JT Gill, senior civilian at the 207th. So getting to your question, provenance of data, especially when you're talking about unclassified data, like the data that you saw there that's generally drawn from ACLID. And when you use machine learning and natural language, what you're naturally going to get is circular reporting, right? And it may be incorrect right from the start. But the beauty of where it's located within the A's is that we also ever integrate a GWENT detachment, right?  And so what we're able to do then is to establish the provenance and the actual progeny of the data to actually get some true ground truth. And ultimately you have to have an analyst to be able to do that. So it's not just putting in data science and generating a computer code because all the computer code's going to do is say this one's like all the rest, right? And if it's like all the rest and it's circular or incorrect from the beginning, you get an incorrect answer. And so really it's about having the analyst there with the data science, scientists to be able to understand the data, be data literate on the analyst side of the house. And then for the data scientists to be able to understand the correct question because ultimately at the end of the day when they write a computer code to go do something, they're asking questions of the data, but if you ask the wrong question, you get the wrong answer. Hey, that's an important point that JT made. Bad data in means bad data out. And so it goes back to what we talked about, what I heard yesterday from Alex Miller, what problem are you trying to solve? And it's not that we've got a data problem and we just have to make sure that we're able to solve it. And this is one of the ways that we get after. But you've got to have the right individuals and the data scientists, data engineers, we absolutely need them. Now earlier I was talking to someone and they asked, well, why don't we just have a bunch of data scientists and start making Intel analysts data scientists? No. I heard you say somebody say yes. No, absolutely not. We need Intel analysts to be experts in the region and understand the region. We don't need more data scientists. You need maybe one or two of those for us. We've got three of them and that suffices. And if we had a hub, we could probably go down to two. But what we need them to do is sit side by side with our analysts and then the analysts who are data literate understand what they're talking about to be able to tell them, this is the outcome that I want to achieve. And then they do the coding and the prototyping and says, OK, this is what I can spit out for you. This is what we can do.  Well, about 6,000 of those reports were duplicate. We were able to get rid of those. Those are reports that the analyst does not have to read or deal with at that point.  We can't go back to that and then have the analysts in the back just slopping through reports manually or using their own Excel spreadsheet where they're just manually going through or creating their own pretend code. We've got to have experts who are trained to do that and able to do that in a matter of minutes and sometimes seconds. OK, next question, please. Sir. Hey, sir. This is Chief Chowick. I'll come at you from that side. There are 75th Innovation Command. I used to be supporting 513th MI Brigade. So I feel a lot of this. And I think you guys are on the right track. I have one question for you, though. Who is your data product owners? Is it that you've worn officers in there? No. So the data product owners really are data scientists. They are the ones that curate and they are the ones that are in charge of all the data. Now, it's under the purview of the ace chief who runs the ace. But in terms of, is it the senior worn officer within the ace? No, not at this time. But that's something I'll go back and take a look at. Thanks for bringing that up. Questions, please. Question over here on the right side. Thanks a lot. Sorry, this is a question that's a bit broader than some of the ones that have been asked already. I'm just wondering how you integrate this with other countries such as Australia, especially under the AUKUS agreement. And what sort of challenges you face in doing that? Some of them just bureaucratic. OK, JT, since we talked about that one earlier, I'll let you take that one. OK. That's a fantastic question, right? Because ultimately, when we go to look at a problem, our analysts are asked to look at all the data, right? Not just the data that fits what's releasable to our partners or whatever it may be. That being said, right, depending upon the environment they're in and depending upon our product, we're going to write it to be releasable. So whether or not to be able to put it on StoneGhost or any of the other systems that could be accessible by our partners. One of the ways in which it really gets, especially for our African partners in collaborating with them, is that one of the ways that this translates down is that when our analysts are actually teaching them analysis, probably without even thinking about it, one of the things that they're asking about is, what is the data sources that you have? And then they're already thinking data science type things, like what could you do with this data if I had this data? And because they may not be in our systems and maybe in their systems. And so we're actually teaching our partners to actually be data literate and to think about the possibilities of it. A lot of times, they do not have the advanced tools that our analysts have access to. So for any one given problem of these, there's probably between 20 to 30 different data sets and tools that they're going to use for any specific problem. But those are tools that are already developed. In some of the partner nations where we're actually working, they're not going to have those tools. And so really it's going to be about, OK, how do you think about this data? And then do you have your own integral data scientists to be able to do that? On the larger scale with our foreign partners, that's already happening in terms of our intelligence sharing agreements that already exist. So on the second side of the house, on the geo-inside of the house, some of that is already happening at the 5Is level. At the data science level, that would be more of a mill to mill type arrangement. Does that answer the question? OK. AT, thank you. Hey, I want to thank everybody for your attention. I want to remind you also that what this does, it transforms chaos into clarity. It really is helpful. With a theater as big as we have, a theater as complex as we have, the ability to gain information advantage over our adversaries to think, to have clarity faster than our adversaries can achieve it. This is the secret sauce of the 207th MI Brigade. It's a pilot. And we're looking to learn about it. We're looking to export this around the other MI brigades around the Army. So I just want to thank JT and Mark for sharing this with us. We appreciate your questions and help us learn about it. We're excited about it. We hope you are. Hope you have a great afternoon and enjoy the rest of the AUSA. Thank you.


Tansinita Temtek, Dallas Hunt, Nitsugasan, Nia Omanehiel, Eguawapasasipi Otsinia. So hi everyone, my name is Dallas Hunt and I'm the author of The Wasis and The World Famous Panic. And I just wanted to give you a brief introduction to the book. It is a Kree revitalization project. So a revitalization project for indigenous languages. And it's a picture book and I think it's quite whimsical and cute. And Amanda Strong's illustrations are quite beautiful and I would encourage you to check it out. I would also like to let you know that the book will be available at the San Francisco Public Library, as well as potentially some of your favorite independent bookstores. So thanks so much for looking into the book and I hope you enjoy it. So hi hi, thanks everyone.


your bubbles, make sure your levels, see how it's a little to the left, go ahead and lock it. Bubbles good, bubbles good, it's all locked down.


Really, since the beginning of the time, financiers have not only been viewed as not productive, but also as evil, as bad, as immoral, because they're making money from money. They're making a profit, and we've always, in our culture, viewed profit with suspicion as something inherently immoral. But what is profit? Profit is what we make from providing services, from creating values for customers, for other people. It's the reward for having a new idea. It's the reward for being more productive, for creating something new or something better or something cheaper. Profit is a virtuous thing. When people make a profit, it means that they've created something. They've built something. They've made something. What could be more virtuous and good than that, if your standard of value is human flourishing, is the well-being of human beings? When somebody else makes a profit, it means that they produce the good or service that I will probably benefit from. My life is better off from their profit. I actually celebrate when I hear that companies are very profitable. In a free market, profit represents virtue. It represents productive activity. In the book, In Pursuit of Wealth, The Mallcase for Finance, Don Watkins and I make the case that not only is finance a productive endeavor, but by enhancing human flourishing, by making it possible for us to live better, more productive, more enjoyable lives, finance as a noble, virtuous profession, and the financier who is using his mind to figure out who to invest with, who not to invest with, which products are going to be viable products, which are not, who is taking vast amounts of risks, he is acting to enhance his own life by providing us with the values we need in order to enhance our lives. I can think of very few professions, if any, more noble than what financiers do. So all of us are better off. Our lives are better off because of what financiers do. They are responsible for the allocation of capital throughout the entire economy. They make possible the industries and the products and the services that we all consume that enhance all of our lives. And the financier does this by applying his own mind to the problem of human existence, to the problem of how to create the products and services that we all need now if we value human flourishing, if you value human success, if you value human life, then one has to recognize the nobility of the field of finance.


Everybody forced here ripping three boxes for Harper B Harper's got two boxes of twenty twenty one one football and one box of twenty twenty one one and one basketball Here we go Harper. Good luck man. Let's do some football first Yeah, I got to get a sous vide. I need a lot of things right now. I don't even have a food processor, man How do I living right now? Oh? That's pretty blue precision RPA Raimandre Stevenson Four color patch there 44 of 75 solid hit.  That's sick Seahawks legend Jerry Rice and One box of one-on-one basketball. Come on Anthony Edwards. Let's see it Luca or something or downtown will be cool, too, or all of that. That'd be cool. All right. No downtown Got a shy gildus Alexander out of 99. Oh, it's a timeless moments Horizontal horizontal timeless moments. It's green Come on It is Gary Payton heat Gary Payton two of five the glove That is awesome nice hit there The the Seattle Supersonic Sgt. Gary Payton all the Seattle legends for you Harper Gary the glove Jerry Rice That's a sick Peyton getting that shot went in the championship with the 07 or 06 heat 06 heat Thanks again, man. We'll get that right out to you


What's the problem? Oh, doctor. My daughter has an stomach ache. Oh, let me see. What part of your stomach hurts?


Mystery House. Mystery House. That strange publishing firm owned by Dan and Barbara Glenn, where each new novel is acted out by the Mystery House staff before it is accepted for publication. Mystery House. Hey, Barb, you said the novel we're acting out for Mystery House tonight has a detective who never even sees the scene of the crime. That's technically correct, Dan. At least she never gets on the premises. She? Yes. The girls in England. Oh, that sounds impossible. Now, don't use that word, Mr. Glenn. Nothing's impossible. No. Well, I can think of a good many things that nobody's ever been able to do. Well, I can remember when people said it would be impossible to make the radio commercials as interesting as the show itself. But listen to this. Okay, places, everybody. Set the scene, will you, Tom? Death past my window. A story about a young girl who lives in that section of an industrial city where the tenements sit in squalid rows side by side with only a few feet between. Hope grave. I've been a hopeless invalid for eight years ever since the night a hidden run driver struck me down. I lie in my bed now in a dingy room of a crowded, faded tenement. My one window stares at a window in another tenement, almost directly opposite. That window and the girl who lives behind it have become friends of mine. Morning, Hope. How's the back today? Much better, thanks, Jenny. If you were telling the truth, you'd have the finest back in the world. Always better, every morning. Did Eddie bring you home last night, Jenny? Yes, as per usual.  Hey, not so fast, John. Nice girls don't bring their boyfriends to their room. Even when the girl happens to be a nightclub dancer and the boy's a sax player in the dance band. I'll bet he's handsome. No, I wouldn't say he's handsome. But he's kind of a sweet kid. He can sit in a crowded, noisy, smoky nightclub all night long, night after night, and still not be there at all. Know what I mean? Mm-hmm. The way I go sailing out on the lake in a little racing sloop, like I shop the beautiful stars on the avenue. Poor kid.  But I do have one window. You know, Jenny, I sometimes wonder if people realize what wonderful inventions windows are.  Well, I got a lot to do this morning. I had a son with some costumes. So long. Jenny's days and nights are all mixed up, like mine. It isn't really morning, but four o'clock in the afternoon. Being in bed 24 hours of the day, year after year, you forget about day and night. Time is just something to fill the best you can. It goes on, on, and on. The hours from ten to three. Those are the hardest. Nothing but blackness outside my window. The complete blackness of night. Sometimes I wish Jenny wasn't a dancer, but then I'd miss the funny little squeak one of the floorboards always makes when she practices her dance steps. Her visits mean so much to me. Morning, hopeful. What you reading? Hi, Jenny. It's a new mystery and a honey. Oh, I'll bet it's good. I'll just bet everything in the whole world is good. Well, what are you so happy about? Take a gander. See? I know it's small, but you can see it from there, can't you? The weightless. Oh, it's a diamond ring. It wouldn't exactly blind you, but it's real. Eddie? Yeah. We're going to be married. Oh, wonderful. It sounds simply terrific. Oh, he isn't really cute, but he's nice and a square guy. Oh, Jenny, you've got to let me see him.  I'll bring him over to your room some night. Promise? If it's a promise, I'll cross my heart. In hope to die? No, just cross my heart. The way I feel now, I think I'd like to live forever. It's from a pattern, not all alike, but each like some remembered one from the past. One I know I've lived before, and one I'll live again. Ready for me to turn out your light, dear? Yes. Thank you, Mother. I've brought you something for tomorrow, honey. Oh, what? It's one of the Flamon stories. The case of the Insulin Dinn. I got it from the rental library. Oh, gee. It's a big book. Oh, Mother, you're wonderful. Good night, dear. Night again. The hour's from ten to three. Nothing but black night. What? Light. A light in Ginny's room. Anyhow, come your home so early. She must be opening a wooden box. Maybe it's something she doesn't want me to see. Ginny. And I wish she wouldn't. A lighted cigarette stopped being thrown out of the window. And Ginny doesn't smoke. Why didn't Ginny answer me? I guess she brought Eddie up to fix the squeaky board. She didn't want me to know he was there. But I didn't mean to be nosy. She has a right to a little privacy. I just won't say anything to her about it. I'm hopeful. Hello, Ginny. When are you gonna bring Eddie around? Gosh, honey, I've been so dug on busy hours. I see you finally got around to fixing the squeaky board in your floor. You could hear that squeaky board way over your plate? Of course. He's lost to fix itself, then. I didn't do anything. I guess lots of things do fix themselves at that. Hey, work hard, Nyssa. No harder than usual. But we have a late show tonight. Gotta practice a little. We see you, hopeful. Twelve o'clock. And Ginny's light just went on again. Oh, this is wrong. Before the pattern lost its form, it was never like this. Never. It's not right, Eddie.  Your imagination's working overtime. I don't imagine things, Eddie. I wouldn't have to get off early tonight and come back here just for imagination. I'm afraid. Oh, Ginny. Eddie, let's get married tonight. Tonight? At this hour? Well, nobody's around. Oh, we could find someone. When you and I get married, we'll do it right, for keeps. Come here. Oh. Is that it? I... I guess so. Well, I better be going now. I didn't have any right up here. Oh, just a minute before you go. Hope. Hope you're awake. Oh, sure, Ginny. I just woke up. You been home long? No.  Hello.  Hello, Eddie. Let's hope great. You know the girl I was telling you about. Glad to know you hope. Well, I have to be running along now. Will you... Will you do something for me before you go, Eddie? Sure, kid. Anything you say.  Sure. How was that, Hope? Like, it was beautiful. Four o'clock in the afternoon. It's time for Ginny to pull up her shade and start her day. That's really nice. I've never been jealous of Ginny's dancing feet, but today I am. She has a prince charming, and I'll never have one. I'll go on getting excitement from books, but I'll think of Ginny and her Eddie. I'll think of the misty sparkle in her eyes when Eddie kissed her last night. I'll think of it because it was so wonderful. And because I... I can't have it... ever. Funny that Ginny hasn't raised her window shade yet. So, probably dreaming of Eddie. But she should be up now. Ginny? Ginny?  It's time for you to get up. Oh, Ginny! Is something wrong? Answer me, please. Right, open up, miss. You've got your landlady worried. Exempt at Brian, Virginia. You know the cop on the corner. Come on now. Open the door like a good girl. Out of the house, ordinarily, she is, but it's time off as a Brian. It's cold and cold and it don't do no good. Virginia, are you opening the door now? Answer me. You got a best key, Mrs. Johnson? No. These doors all got good spring locks. And the key to this one is locked. All right, stand back then, Mrs. Johnson. There's nothing for it but the bust in the door. Hey! See? Wait. The saint's had mercy on her soul.  It looks like a long, heavy, black-sept necklace. Yeah, but it's got a snap hook on it. No, it ain't a necklace, Mrs. Scholes. What is it then? I think maybe it's something that's gonna hang a man. No, Officer Brian, no. Oh, I am, am I? What is it then? Yeah, that's what I thought it was. Oh, Eddie, huh? So Virginia had a boyfriend named Eddie and he left a saxophone part in her room. You know when he left it? No. You don't know or you just won't tell me? I said I didn't know. I heard you, but I've been around this neighborhood too long to be fooled all the time. Now, if you do know anything, kid, you should tell it fast. Why? If you know something that had pinned a murder on somebody, that somebody would try another murder to keep me from getting the answer. And you wouldn't be able to defend yourself very well. Is Officer Brian right? Will the murderer strike again? Act two. I'll death past my window. Opens at the battered door of the murdered girl's apartment. Somebody is knocking. Ginny, you're late for work. What's the matter, honey? Ginny! Ginny! What? What's the devil? A cop. What are you doing here? Where's Ginny? Oh, no. She hasn't done anything. Take me to her right away. It ain't a bad act, kid. You almost convinced me, almost. What are you talking about? Sit down. I want to see Ginny. She's late for work right now. She ain't going to work, kid, ever. What? What are you trying to say? You already know. I don't know a thing. What torturing me? You've got to tell me. Something's happened. What is it? Well, Virginia's had an accident, Eddie. A bad accident. Early this morning. Oh, no. No, you're lying. No. No. It isn't true. It isn't true. It isn't true. This death I've met, it passed my window, not 20 feet away. And yet, invisible. In detective stories, death is always exciting. This was Ginny, my friend. And it isn't exciting at all. Horrible. Where were you at four o'clock this morning, Eddie? I... I don't know. Did you bring Virginia home? Yes. Yes, I did. How did you get into her room? I didn't get into her room. Eddie, no. Brian knows you were there. He found your saxophone court. Hope. Tell him.  I've already told him, Eddie. You mean you told me you didn't see him do it. But that doesn't mean he couldn't have. All right, come on, Eddie. Where? Down to the police station. Well, you're making a mistake, officer Brian. Won't come out all right? Worry? I'm through worry. There's nothing to worry about or hope for or dream about anymore. And everything won't come out all right. It's no good, kid. Goodbye, Eddie. I'm sorry. I'm sorry, Hope. Hope. Good one. That is. What the murderer took into the station? Ah, such goings on. What are you doing in Ginny's room, Mrs. Schultz? Brian told me I could move her things to the basement storage. Mrs. Schultz, will you call Officer Brian for me? What for? Tell him I want to talk to him. That I have some important information. But you have already told him all you know. No. No, there's a great deal more. There is an old proverb, Hope. Murdered though it have no tongue will speak. No. People who look for trouble usually find it. Officer Brian, will you do what I asked then? Well, now that all depends. No, that won't do. You'll have to promise. I'm making no promises. You wouldn't want to convict an innocent man, would you? No. Promise? All right. It'll involve some irregular procedure, maybe illegal. Here now, that's not fair. You promised? Yes. All right. All right. What is it you want me to do? I want you to go over next door and rip loose one of the moldings on a window in every apartment on the fourth floor, except genious. What? Does a Brian go back on his word? Well, I know one thing. After this, one Brian will learn to say no. To get them all, Officer Brian? Yeah, fake phone calls to get people out, breaking an entrance, destruction of property, and me a policeman, too. I got every apartment on the fourth floor, and may Captain Ohulihann never hear of it. Well, thanks, Brian. Now, all you have to do is wait. Wait? Sit over here in this room all by myself and wait? Wait for what? Just wait. And don't worry about being alone. We can visit. Hey, listen. Some of my destruction's being repaired. Yes. That's apartment 4-D, isn't it? It sounds like it. Mr. and Mrs. Butovsky, he's a section hand, and he's not the one. Not the one? What are you talking about? Never mind. Just wait. Try to have patience. Young lady, did you have me tear loose all those moldings just so you could hear a hammer serenade? Now, that's three been fixed so far, and all you say is, that's not the one. That's not it either. Oh, that's not it either. That's not it either. How long do you expect me to sit in this room listening to people pounding nails? That's four moldings fixed. Only two to go. Oh, sure. I replace the window moldings and the people fix them. That's supposed to prove that the saxophone player didn't murder the girl. Maybe I'm nuts. That's it. What apartment is it coming from? Four C across the hall. Get him, Brian, quick. Get him before he runs away. He killed Ginny. It'll be tough upon it. What do you want? I want you to step across the hall. The room where the bank got mudded? No thanks, pal, not me. Come on. Step over here by this window, mister. What floor? Hope. Is this the man? Yes. Yes, it has to be him. Say, what kind of a gag is this? I've never seen this girl before in my life. How about that Hope? Oh, he's quite right. I've never seen him before either. See, Copper? But he killed Ginny, and I can prove it.  Officer Brian, there's a wide pine floor in that room. Isn't there? I've never seen it. Yeah, that's right. Wide pine. Lie down on the bed, Brian, and then get up. What? Oh, all right. You okay? Your foot, Brian. Keep it right there on the board it touched when you got up. Mark the board with your pencil. What goes, sister?  Getting interested, mister? All right, Brian. Get a hammer and pry up that board. Well, it still sounds crazy to me, but I might as well, I guess. Oh, no, you don't. Copper, stay right where you are. Put down that gun, you.  I'll keep your hands in the air while I take your gun. And don't get any ideas. What are you gonna do? First, I'm gonna do what the bright girl suggested. I'm gonna pry up that board. Then I'm gonna nick you enough so you won't follow me. And I'm taking on the land. Money. I've never seen so much money in my life. About 30 grand, copper. Your nice, crisp, folding dough. Yeah, but the checks, all them checks. What about them?  That's a racket. The checks are hot. Lifting out of mailboxes. I got kids shoving hot checks all over town. I rented the room across the hall because I knew the dancer was in home nights when I'd be collecting dough and passing out the stolen check to my shelves. I needed a sure, safe hiding place for the stuff. Yeah, and Virginia was working with you. She didn't even know there was such a guy as me. Until she came home early with Eddie. Yeah, yeah, that's right.   I couldn't have her asking questions. You'll never get away with this, lad. Get back, copper. I'll out-shoot. I mean business. Hey, my eyes, my eyes. I can't see. Right, you shoot me, will you? All right. Lay off me, copper. You don't have to be so rough upon it. I'll be rough, all right. I can't see you, Brian. Is everything all right? Yeah, honey. Thanks to your place in that mirror in this guy's eyes, he'd have shot me sure if you hadn't blinded him. I won't forget that either, sister. Ah, well, what you remember or forget ain't going to be of much importance, lad. I've got a feeling that you ain't going to be around long. My name is still Hope Graves. I'm still a bedridden invalid. Yet somehow I don't feel at all like the same person who started to tell this story. The pattern has shifted back to its usual form. But for me, things will never be the same. I'll always know it can change. That my routine isn't set in a drab, colorless schedule that will never vary. Life, even for me, can be exciting.


What's up everybody? J.C. here for Jaspierscasebrakes.com 2017 pinning heat elite draft picks football one-pack break random teams number six Just sold out and this is to help fill up a flawless Sierra number break and you can win five spots just for buying a spot on this break So again the way that works out randomized customer names and teams Open up the pack Distribute the hits then at the end of the break will randomize customer names again in a separate dice roll in randomizer And the top five names at the end of the randomizer win a spot each in the 110 football serum flawless football serial number briefcase break So here you go guys. Good luck Let's roll it Six and a one seven times. This is for the names and teams one two three four Was it seven five six Seven Brian Croft, then Anthony seven times here one two three four five six seven Patriots down to the Denver Broncos Brian Jared Brian with the Patriots Jared He had the Browns and the other 49ers John with my Eagles down with the Saints down with the Cardinals and through the Steelers Down with the Ravens out with the Cowboys Quinn with the Jaguars Brian with the Vikings Down with the Panthers Chris you have the Chiefs John with the Packers Chris with the Varys Queen with the Bucks Jason with the Rams Zach with the Titans Steve with the Raiders Quinn with the Seahawks Brian with the Bengals Kevin with the Dolphins Chad with the Colts Chris with the Jets John with the Giants followed the Lions Brian with the Redskins as well as the Falcons Zach with the Chargers Jared with the Texans Quinn with the Bills and Anthony with the Broncos So there you go guys If you guys want to make a quick trade if not 20 30 seconds I'm closing it People don't care about this right once you go to the Chiefs Since Mitchell Trubiski's option wasn't picked up Although, I mean, I guess if he balls out this year he gets to be the starter He couldn't have an early payday which decline doesn't always mean that it's uh They don't want you anymore. It's just more like Prove it to us And then we'll see if we want to pay you or not Speaking of Mitchell There you go. He's right at the top Molly Hooker Marlon Mack Cameron Sutton Jomo Adams Leonard Fornette Jarell Patti Mahomes Ricky there you go Dow Joe Mixon Tawan Taylor The Orange Dyke Cup Base cards All right guys, so there you go Now let's switch scenes and see who's gonna win a spot in the mixer Or sorry not mixer the flawless briefcase zero number break. Remember top five win one spot each So here you go. Good luck New dice roll. It's a six and a four ten times So again, Anthony, Donna, Brian one two three four five six And it was a 10 right seven eight nine 10 10 times Dan Brian Chad Anthony and John W. There you go. Congratulations Uh after 10 times six and a four Top five win a spot each Congratulations coming up next as follows zero number break. Appreciate it


How's everybody doing welcome back to my channel First thing is first we're gonna go grab some breakfast with the crew to reconnect gossip spill the tea. Oh, sir Are you doing? Look at this fall. Oh my god. Oh my god Look at that. That looks juicy Let me just show you how big this tonsil Let me use you as a model. So this is how big it is I'm not sure if I'm still gonna use this size, but We'll give it a try So last week I introduced a new segment to my videos where I asked artists to tell me a funny or unique story That happened to them during that tattoo session. So this week. I'm featuring Xavier price. Tell us your story Roll the clip. What's up guys? My name is Xavier price I'm a professional tattoo artist at Dark Horse tattoo company in Gilbert Arizona and I specialize in black and gray realism So I'm gonna tell you guys a tattoo chair. So I was like my third or fourth year tattooing I was tattooing in Tempe Arizona at the time. She brings her friend. She brings her husband She leaves her husband outside of the tattoo room. She brings her friend back there We start tattooing her her friend starts pulling out a camcorder starts recording the tattoo process Or whatever and I'm assuming, you know, most clients they want to do that I want to keep something memorable or whatever. So I'm cool with that She's filming but all of a sudden I'm working and I start hearing little noises or whatever She starts moaning out of nowhere and I'm like Okay, so what's going on here cuz I'm not tattooing any of Roger's zones or anything like that I was tattooing her arm and sure enough.  She takes it. She puts it in my back pocket She squeezes my and she grabs my and then walks out of the room to where her husband's waiting for her on the other side I'm pretty sure she was like a cam girl or something like that and She probably did it for views or whatever.  That's what happened shout out to the boy mr. Reyes for you know featuring me on the channel today And yeah, I see you guys when I see you That was a story. That was a really unique story But thank you for telling us your story. I really appreciate it. Keep doing your thing Instagram and YouTube follow him So this ball Comes in with the cooler having had a client done that so That was new I'm just everything man, I'm just prepared to have a snag just in case you know you took a break You know some artists are not generous like you an offer to me a meal Today's project we won't be covering up this tattoo right here It was my first tattoo ever and I just really wanted to get something and I wanted to get a virgin Mary because my last name Santa Maria, so The tattoo is a little scar you can feel every single line especially on the hands right here Never My brother got a prison and one of his friends Came out of jail and he had some nice work done on him, but somehow he burnt in my chest So I'm gonna put on this then so just go at it. Just go at it. Don't even think about it I want the nipple to learn right here Right here because we're gonna black it all right. I mean, I'm already there I can't I can't have a partially dotted nipple So even though the tattoo was really scar and dark he got it done about 15 years ago So that means that that ink is weak and it's not gonna overpower my true gray my true my true grace Which works my advantage. I got this piece of tattoo on my chest and it's just like no matter what you got There's like a center of attention as soon as I got to my shirt off For this project, I'm gonna be using two needles My first needle is gonna be my 14 bond liner Which I'm gonna be using throughout the whole entire skull to create texture My second it was gonna be my 27 curve Mac to do the background First I was gonna be scared because we were doing a lot of key points like my ribs center chest nipple collarbone shoulder blade All right, I first I thought there was a 27 I heard more but afterwards I could tell that the 14 round I was just the one that was doing the lines and then I was just like, okay That's the one that gets you right into being a mate. First I'm gonna be applying my solid black ink Then my medium gray tone and finish it off with my lightest great tone to make the shades even brighter It is time to black out this nipple. They made me feel braver.    I was like That hurt way more than the nipple, but it was worth it. It's worth it worth it I know it's gonna be a tough project because I knew there were some thick-ass lines in Every tattoo artist that I've asked and there's a even online on Instagram, whatever No one wants to touch it Everybody tells me to get laser sessions and then a couple laser sessions and then hit them up and see if They'll do it finally I found the salvage to take on this challenge Thank you guys for always commenting and dropping a like on my videos I really appreciate it Make sure to drop a comment because I'm gonna be choosing one of you to sell you some money So you can take your family and friends out to eat breakfast or dinner Last week's winner was Alejandro Avila. Thank you for supporting my channel and I hope you I hope I hope I hope you had a nice meal if you are tattoo artist I hope you found this video useful and if you are tattoo enthusiast, how do you say enthusiast? Enthusiast, I hope you were entertained Thank you again, and I'll see you guys next week. Peace


Welcome to the Bellevue College Library. We're located in D 126 at the main campus. We're excited to have you here. Well, what are you waiting for? Come on in! Hi, my name is Luke. I'm a first-year nursing student at Bellevue College. I am looking for some peer-reviewed articles. Can you help me with that? Hi, Luke. My name is Elena and I'm a librarian, so I can certainly help you find that. I help students with that all the time. I'm really glad that we're able to find those articles on the database earlier. And then here's the nursing section that you were wondering about. So you can check out any of these books downstairs at the circulation desk. This is the one I want to check out is the NCLEX. Great. My name is Luke. I would like to check this book out. Okay. Thanks. Do you have your CTC link ID number or your card? I do. Okay. Here we go. Great. Thank you. I'm just going to look you up by your number here. There you go. And I'll check the book out to you. So this is going to be due back to us in three weeks. But if you'd like to renew it, you can do that online or come visit us again in person. Oh, thank you. You're welcome. Community-acquired pneumonia. So that's perfect. Yeah? Oh, I'm at the library. Do you want to come on down? Because there's a lot of cool things to do here. You could check out books. You can also check out the anatomy here. I'm already right now looking at part of the brain.


Hello everybody, welcome back to another Adobe Photoshop CC 2019 tutorial. In this one, I'm going to show you how to soften the look of skin or even make it look almost like it's glowish. That angelic, cherubic, soft, glowy look that you see, especially in a lot of magazine covers and stuff like that. Well, there is some voodoo going on, but it's not too hard to do once you get the hang of it. So let's dig in and get going. I've got an attractive girl here in the previous video. Pardon me, I showed you how to remove some of the acne and blemishes if that was something that you'd want to do. But in this one, I'm going to make her skin really soft. Come on, Curtis, use your words. All right, rock and roll. Let's go load up an image. Any image will do if it has a higher contrast value. Great, but this is an image that I'm using. And then the first step, you want to take the image. You want to right-click on it and you want to convert it to a smart object. Now, once it becomes a smart object, and you'll know it's a smart object because you'll see the little icon in the bottom right of the image there. You want to head up to the top. We're going to go to Filter. And now, and here's the part where not a lot of people know, is you want to go into Camera Raw Filter. So I'm going to left-click on Camera Raw Filter. And Presto, it opens up. Now, I recommend that you zoom in pretty tight just because you want to see what you're doing. So what do we have? 154 here. So let's go to 200%. Okay, that should do the trick. Even, yeah, you know what? Let's go in. Yeah, maybe that's too much. What do you think? 200? Okay, yeah, 200%. And basically what we want to do is we want to adjust the clarity. This is the magic for soft skin versus the whole high-fi, sharp contrast look. So take the clarity and drop it way down. And I'll show you. Let's go all the way to minus 100. So that is ultra soft. Like, she doesn't even look like a real person, right? And it's also affecting everything, including your eyes and her lips, which is maybe something we don't want. But, and you can go all the way to the top right here to plus 100, and it's, you know, it's way in focus. So let's drop it down to we want to make it soft.  Let's go to like minus 75 in this one. Sure. This is extremely soft. And then you're going to hit OK. And you'll notice that it's created a smart filter. And if we turn it on and off, this is the original image. This is the new image. Now, this is not obviously the final product.  But the eyes don't look right. And maybe I don't like the mouth that way. So what you want to do is you want to click on the smart filter here and we want to fill it with black. And I'm going to show you why. So there are a couple of ways to do it. The way I recommend is command or control delete. That fills it with black command delete. If you're on a Mac control delete, if you're on a PC. So there we filled it with black. OK, we're on our way. The next step is we want to paint on top of this. So we're basically going to paint a mask on her. But we're going to avoid the areas that we don't want it to apply to.  So let's go ahead now and we're going to hit B for brush. And this selects our brush tool. Now, I've got it set to a large brush and a high opacity.  So I'm going to reduce the size of my brush to about this size because I'm going to work on the forehead at the beginning. And I'm also going to reduce the opacity in this case to normally I go down to 20% just and do it multiple times. But because that'll take a little while and I just want to demonstrate the technique. I'm going to drop it down to 50%. And now watch, I'm going to paint over like her forehead and between her eyes. And we're going to see how this works. So slowly, but surely you'll see as I go over top of it, we're sort of starting adding. We're adding in a little bit more and more of that soft look. And you know, you can you can tell that it's working because you'll see over here in the in the smart filters. You're going to see that the white we are painting on this mask right here. So I am slowly went a little too far there. I'm going over top of the parts that I want to to soften up. And again, this is not something that I would release necessarily. This is just to demonstrate the technique. If I was doing this really, really for like a magazine cover or like a high end blog post, I would go way in deep like this. I would have a small brush and you would see me do, you know, nice and slow bit by bit. But again, guys, I will just keep doing this a little bit more so you can see what we got here. We are adding in the soft look that we're going for around the chin. And you'll see it over here again in the mask. So if we were to turn the mask off, this is the original. This is where we are right now. Now, obviously, we still got a little more work to do. There's a little bit of spots in here or on this left cheek. But this technique is how you soften skin inside Adobe Photoshop. And you can do it very, very easily. It's a very, very cool technique. And it also gets rid of a lot of the blemishes as you can see here too. Now, while I got you here, I guess I'll just go quickly here and do one more thing. I'm going to use the spot healing brush. I'm going to get rid of like that little blemish there. And maybe, you know what, that's about it. You know, maybe this one here, I'll just kind of heal it up a bit. But again, that's all there is to it, guys, to softening skin inside Photoshop. I hope you enjoyed this tutorial. I got a lot more stuff coming up. Thanks for watching.