After watching Blair White's Trisha Paytas video, I finally subscribed. And some might ask, why is a cis man subscribing to a trans woman? But there might actually be a psychological explanation for this. And it might also explain why so many people start dating their co-workers. What is up everybody? This is Chris from the Rewired Soul where we talk about the problem, but focus on the solution. And if you're new to my channel, what I try to do is take different topics going on in the YouTube community and try to see what we can learn from them. And I like to use a little blend of philosophy and psychology and kind of get our wheels turning a little bit. So if you're into that stuff, make sure you subscribe and ring that notification bell. And if you're not yet, follow me over on Instagram and Twitter at the Rewired Soul. And so this video, we will be donating 20% of all of the ad revenue to the Trevor Project. The Trevor Project is a great, great, great organization that is dedicated to helping the LGBTQ youth, all right? So please share this video. And if you would like to donate to the Trevor Project directly, you can find a link down in the description and in the pinned comment below. All right. So here's my history with Blair White. So obviously I've heard of Blair White over the years. My girlfriend, my beautiful girlfriend Tristan watches Blair White. And yeah, I've heard about some of Blair White's opinions and views and everything like that, but I never really watched her. And I don't know what it was. It was like a month, two months ago, something like that, where YouTube was just recommending the hell out of Blair White to me, all right? I'm a YouTube creator and I'm really nerdy about the algorithm and all that stuff works. And it was really confusing to me because I don't watch content really related to Blair White either, but she just kept popping up on my recommended. So finally, after a while, I'm like, OK, Blair White keeps popping up on my recommended. Let's see what's going on with Blair White. And I clicked on it and I watched. And yeah, she is a very, very smart young woman. I do enjoy how she's like intelligent, but she's also a little sassy and stuff. And I don't agree with all of her opinions, but I respect that she has those opinions. And it's interesting to me always getting a different perspective on things. But still, I didn't subscribe. And after that, YouTube's really just pushing Blair White on me like, yo, you want to watch some more Blair White? I'm like, all right, all right. So I watched some of her other videos. I watched some videos that she did on Jessica Yaniv. I think I did. I watched the yeah, I watched the interview that Blair White did with that young woman who was all mixed up with Onision and everything like that. And then finally, because of this tritipatus situation, obviously, I've been, you know, covering that from different angles and everything. I was recommended Blair White's video about tritipatus. And finally, I subscribed to Blair White. Now, why is that? So while many people might think, well, Chris, you just watched her.  But it might actually be a little bit more complicated than that. All right. So something that I don't think we take into consideration enough is why do we like the things that we like, right? We all have preferences, whether it's food, whether it's colors, all these different things, you know what I mean? And why does this happen? So it turns out this may have something to do with something called the mere exposure effect. All right. So we're going to turn to Professor Jonah Berger, who is a bestselling author of books such as Contagious and Invisible Influence. And Invisible Influence was the first book I read by Jonah Berger. I just finished his other book, Contagious, which actually came out first. But anyways, I cannot recommend Invisible Influence enough. Like when you read that book, it kind of just debunks this myth of free will that we all have in our mind just through all the different studies that he talks about, about why we have certain preferences and everything like that. But I do want to focus on one part of the book where he discusses the mere exposure effect. All right. So he discusses a study that they did where they showed people Chinese calligraphy. All right. And basically what they did was they said, OK, we're going to show you some shapes and on a scale of one to 100, how satisfying do you find these shapes? All right. This is stage one of the experiment. All right. Now here's the kicker. When they showed them these shapes, they showed them for five milliseconds. All right. If you don't know how long five milliseconds is, it's about how long it takes for a bee to flap its wings. All right. It's not even enough for you to really even comprehend it. OK. So that is how quick they flash these shapes in front of people. All right. So they would flash that shape and they would show them like a solid color, kind of cleanse their visual palette a little bit. And then they brought them over to stage two of the experiment. Same thing on a scale of one to 100, how satisfying do you find these shapes? And they showed them three different types of shapes. One of them was the Chinese calligraphy that they already showed them before. All right. In those five milliseconds where nobody can even comprehend them. All right. The second shapes they showed them was different types of Chinese calligraphy. All right. So Chinese calligraphy, but some they've never seen before. And then the third set of shapes they showed them was just random polygons. All right. So what they ended up finding out was even though they flashed those shapes in front of the participants before they could even comprehend what they were saying when they showed them those same shapes for the second time, they found them more satisfying compared to the other shape. All right. So this is where they started to develop the idea of the mere exposure effect. The more you see something, the more satisfying it becomes. All right. So they took this a step further. All right. There this study is actually discussed in the book as well. But I found an article about this where I'll link it down in the description below. All right. So this article discusses another experiment where it says one of my favorite demonstrations of this effect comes from a 1992 study. The researchers arranged for four different women of similar appearance to attend a college class a certain number of times throughout the semester. One of these women didn't actually attend at all. One attended five times, one attended 10 times, and the last woman attended 15 times. These women didn't interact with the students at all. They just sat in on the lecture. At the end of the semester, the students in the class saw pictures of each of the women and rated them on several scales like physical attractiveness. Despite never having interacted with these women, the students showed a clear mere exposure effect. That is, they evaluated the woman who they had seen 15 times much more positively than the woman they hadn't seen at all. So this actually explains why so many relationships begin at the workplace or begin with people that you meet as school. What's interesting about this is it kind of screws up your whole idea of, you know, soulmates and everything like that. So am I in love with Blair White? No, but this is just a really fascinating subject to me. Like when I learned about this and something I'm really fascinated with is the parasocial relationships between the viewers and the YouTubers. And I think this kind of explains those parasocial relationships where you not only like this person even more because you keep watching their content, but this kind of like relationship. And then obviously on the severe end of the spectrum, there are people who developed obsessive relationships with, you know, celebrities and people in the public spotlight. Right. So the other thing that I find interesting is when it comes to YouTube as a whole, we have no control over the algorithm. There are certain things that we as creators can do.  Like I'm a viewer as well. And I discussed how Blair White was recommended to me just constantly, even though I wasn't subscribed to her. Like this is something that I think is just really interesting when it comes to the psychology of YouTube, how subscribers work and everything like that, because the more something is recommended to you, the more you see somebody in the thumbnails, the more you see a channel, you're going to like it based on the mere exposure effect. Right. Now, don't get me wrong. I don't want to make it seem like, oh, this is the only reason like this, this subliminal thing going on is the only reason I subscribe to Blair White. Like Blair White makes really awesome content. And obviously she knows a lot more about the trans community, the trans experience than I ever will. And I really, I really think it's important for all of us to watch other creators who are in different niches and everything like that. But also in our regular life, too, right, we need to learn from other people who have different experiences than us so we can broaden our mind. So many of us are just stuck in our own bubbles. And the mere exposure effect explains why, like why we stay in our bubbles, right, why we don't hang out with other groups, why we don't talk with other groups, you know what I mean? But just know the more you hang out with other people, the more you watch content from other people, the more you'll start to not only like their content, which you may never like their content, but it helps give us a different perspective. And like I said, like with my channel, I really try to just look at these from different angles and try to get our wheels turning. And I feel that maybe if we can start understanding how our minds work, you know, on a psychological level, like it might explain like why we're so close minded about certain things, but not others. Why we have biases towards certain things and not others. Why we don't have the well balanced opinions that we believe we do. Why we hold so tight onto our beliefs, even when we're presented with new evidence. There's so many reasons behind that. And I think part of just making this world a little bit better of a place is acknowledging what's going on and working towards, you know, just being more mindful that these things are actually going on. All right. But anyways, I will link Blair White's channel down below. Make sure you go check out her stuff. Go subscribe. And like I said, even if you I know she could be a little polarizing, but even if you don't agree with her, like you don't have to agree with everybody, like just get new perspectives. All right. And don't forget, share this video. Twenty percent of all the ad revenue will be going towards the Trevor Project. All right. But anyways, that's all I got for this video. If you like this video, please give it a thumbs up. If you're new, make sure you subscribe and ring that notification bell and a huge, huge thank you to everybody who supports the channel or on Patreon, as well as everybody who supports the channel in other ways like buying my books and merch and everything like that. You're awesome. All right. Thanks again for watching. I'll see you next time.


Hi everyone, Joe for jaspyscasebreaks.com, coming at you with a pack of 2021 Panini Chronicles Football, hangar pack number 37, and this is our last filler, last one we got to do to unlock that Chronicles case, which is coming up in the next video. But first, let's do the pack break itself, big thanks to this group for making it happen. There are the teams right there, let's roll it, let's randomize names and teams six into two, eight times each, one, two, three, four, seven, and eighth in final time, and then six into two, eight times for the teams, one, two, three, four, six, seven, and eighth in final time. After eight, we've got Baltimore down to Denver. All right, so there's the first half of the list right there, and then we've got the second half right over here. No trade window, let's sort by team and print this out, let's rip open this pack. All right, and here is the same as the screen you saw before, now it's officially printed out hot off the presses. Thanks everybody. So this is what Chronicles look like in 2021, we're going to find out in the next video what it looks like in 2022. I've got a couple Justin Fields here for Lance and the Bears. Chronicles Justin Fields, the score Justin Fields, rookie cards, I've got some Jalen Waddle, I'm going to show a Bateman right there, the Jalen Waddle, that green foil I think is exclusive to this set here. That'll be for Miami and that's going to go to Greg. All right, now the important part of this break, who's going to get into that Chronicles break? We can advance the next round. So one through 32, let's gather those names, new dice, new list, and it'll be the top eight after three. Two and a one, three times, good luck. One, two, and three. After three, now it's going to be from nine through 32 sad times. Chris, I appreciate you getting trying. Your names might still be there, might still be there, I hope it is. But for now, sad times for nine through 32, happy times for the top eight. Lance is happy. So is Chris, there we go. Adam is happy. So is Greg, Adam again, and again, Nathan, and Lance. There you go, top eight after three. We'll see you in the next video for the break itself, jazby'scasebreaks.com.


Welcome to the course on dealing with materials data. In today's session, we are going to talk about certain special random variables. Let us review what we have done in the past. We introduced formally the definition of probability through the definition of sample space and events. We said that any random experiments, the collection of all its possible outcomes is called a sample space and any subset of a sample space is called an event. Then we introduced three postulates of probability which basically says that for any event probability is positive, for the complete event that is for the sample space probability is 1 and if there are mutually exclusive events finite or infinite of them, the probability of union of all of them is the sum of all the probability of sum of probability of all the events. Then we also introduced what is known as conditional probability that instead of sample space, if you have a knowledge of one particular event and in the light of having the knowledge of that event, if you look at the probability of other events, it is called conditional probability and from that we introduced Bayes theorem and we briefly talked about the importance of Bayes theorem in today's life. Then we went further and we also introduced what is known as random variable which is a function, a real function defined from any probability space which is a sample space and a probability measure from probability space to a real line and then we said that it could be discrete or continuous depending on the kind of values it takes. If it takes a countable values finite or infinite, it is called discrete and if it takes different kinds of continuous values, continuous numbers, it is called continuous random variable. With every random variable, we introduced that there is a cumulative distribution function attached which is probability of that random variable taking a value given then a specified value x. Now depending on the discrete random variable, you have a probability mass function attached to it and if it is a continuous random variable, you have probability density functions attached to it. This time what we want to do is, we want to introduce certain special discrete random variables or discrete distributions which we come across more frequently in our studies of materials data. Similarly, there will be some distribution, some random variables which are discrete. So we will have discrete distributions, some random variables will be continuous. So we will have some continuous distribution. While covering this what we would like to do is, we would like to see very briefly what is the attached sample space, very important to know where it is defined you must know. So we defined a sample space, we give mean and variance formulae for the given distribution, we also give standard deviation because it these are the two very basic parameters that one would like to know to describe the data. And if there are any other special features, we will talk about it as the time comes, wherever possible we will give an example or we will discuss those examples. So let us move on. The first distribution that we would like to talk about is the first discrete uniform distribution, this discrete uniform distribution is the simplest of the distribution. So if you are rolling a die, then we all know that there are 6 possible outcomes and if the die is fair, then probability of any outcome occurring that is whether it will turn of phase 2, phase 3 or phase 4 whatever, it has the probability of 1 over 6. So this also comes from the postulate, the total number of say if you are looking for probability that the phase, the die phase turns up 2, then the event can occur 2 is only 1, the number of times it can come is 1 and the total number of such phases can turn up are 6. So the probability is 1 over 6 is the example we have given here. In general it can be defined that if the sample space is consists of n equally likely outcomes, then the probability of f of x that is the probability mass function f of x is 1 over n for each x is equal to x1, x2, x3, xn. The next we would like to talk about is Bernoulli trials, this is one of the very important trials, it is any experiment is called a Bernoulli trial if it lands up with exactly 2 outcomes, 2 possible outcomes, it could be success or failure, it could be defective or non-defective or it could be answer yes or no. So whenever you have an experiment which throws up only 2 answers it is called a Bernoulli trial. So if you are tossing a coin it is also a Bernoulli trial and if you are talking rolling a die, but saying that your outcome is success if you get a number between 1 and 3 and if you it is a failure if you get number 4, 5, 6 then also it is a Bernoulli trial. So in that case the sample space is only 0 and 1 because there are only 2 outcomes. Generally we say success is 1 and failure is 0 but this is a convention. Let the probability of success be P, the probability that a Bernoulli trial is successful is P then the PMF, the probability mass function f of x is defined as P to the power x multiplied by 1 minus P to the power 1 minus x where x will take value 0 or 1 and the in that case the mean value mu is P for the distribution and the variance of the distribution is given by P times 1 minus P. I am going to leave the proof to the students to try it out for themselves. Here is a very simple example of a tossing a coin. Next there are distributions which come out of Bernoulli trials. How you conduct the Bernoulli trials, how you conduct your experiment counting the Bernoulli trials gives you raise, gives rise to different distributions. So the first one that we would like to consider is binomial distribution in which you throw n identical Bernoulli trials with n you count you want to know the probability that there will be exactly x successes. Another way to look at is that you throw n trials before you encounter the first success. This is called a geometric distribution and then you have n Bernoulli trials until you get k successes here there is a spelling mistake it should be k successes and this is called a negative binomial why the adjective negative and why still binomial we will look when we look into the distribution. So first let us go to binomial distribution. Consider n identical Bernoulli trials you have conducted and exactly x have resulted in a success. The probability of success is p then your sample space is there can be no success or there can be exactly n successes here it should be it should not be dot dot it should be n please correct it. Probability of success is p then the probability mass function p of x is out of n choose out of n you choose x successes because it can occur anywhere p to the power x multiplied by 1 minus p to the power n minus x please recall that p to the power x tells you the probability of successes in x successful trials this is probability of n minus x unsuccessful trials and this is the different ways the successes can occur in a sequence of n trials. I will again leave it to you it can be shown that the mean value for such a PMF for a such a distribution is np and the variance is n times p times 1 minus p sometimes 1 minus p is also denoted by q so it is also called sigma square is npq few things we need to notice number 1 why it is called a binomial distribution because these are binomial coefficients please recall your expansion of a plus b to the power n then this is a binomial coefficient and therefore this is called binomial distribution. Number 2 the mu being np and sigma square being np multiplied by 1 minus np it very clearly says that whenever you see that the variance is directly proportional to the mean value you have a chance that you are actually playing with a Bernoulli trial. So, if you are coming up with some data in which you see this is a property very specific to the Bernoulli sorry n Bernoulli trials or binomial distribution the notation is when we say that random variable x is following binomial distribution with two parameters n and p remember x is the small x is the value that a random variable x takes. Here is an example we will work out suppose a production unit has a probability of producing a defective unit is 0.1 and a batch of 100 unit is chosen randomly what is the probability that exactly 6 defective units will be found. So, this becomes a case of Bernoulli trial with a probability of defective 1 you have taken 100 independent Bernoulli trials and then once you have the 100 independent and identical Bernoulli trials the probability that x takes a value 6 is 100 choose 6 times the probability of p to the power 6 1 minus p to the power n minus 6 and therefore it comes to this you can work out using simple calculator. So, the earth please remember the probabilities 0.059 because we are going to work out the same problem using Poisson distribution by taking an approximation, but that will be in future let us continue with some of the Bernoulli trials. Now, we go to the next distribution which we called a geometric distribution what we are going to do there we consider x Bernoulli trials are performed until the first success has happened on x set trial. So, it means that the first success can happen on the first trial or second trial or third trial or any trial in future. So, s is an infinite set probability of success again is p as in Bernoulli trials and in that case the probability under geometric distribution probability of x is actually p the time x set time when you have got the success multiplied by x minus 1 unsuccessful trials. So, it is 1 minus p to the power x minus 1 and in this case the mean value is 1 over p and the sigma square is 1 minus p divided by p square this also you are welcome to work out by yourself. Now, we come to negative binomial trials or negative binomial distribution. So, now we are considering a sequence of independent Bernoulli trials and the random variable x will denote the number of trials when the nth success will occur. Please remember in geometric distribution we said on the x set trial the first success occurs here we say that on the x set trial nth success will occur n is a fixed number. So, right in the light we said that first trial first success occurs nth is a fixed number it is the nth trial which occurs then we say that x follows a negative binomial distribution. Therefore, your s that is the values that x can take is n, n plus 1, n plus 2, etc, etc probability of success it is a Bernoulli trial. So, we still consider it as a probability p and in that case the probability of x is the number of trials you have to make to reach the nth success it means that you have variety of successes and unsuccessful trials in the previous n minus 1 successes have come in the x minus 1 trials. So, this have been chosen it is x minus 1 how many ways it can occur is x minus 1 choose n minus 1. The n successes have occur so it is p to the power n and then you have x minus n unsuccessful trials and therefore it is 1 minus p to the power x minus n why it is called negative binomial is reasoned out here. If instead of looking at the successful trial you look at the unsuccessful trials then the total number of unsuccessful trials have occurred when you have taken x trials is y is equal to x minus n and in that case you can show that p of probability of y unsuccessful trials is n plus y minus 1 choose y because you have taken so many trials of which y trials are unsuccessful and therefore the n successful trials multiplied by 1 minus p to the power y basically you have to replace x minus n by y and you get this formula straight from this formula. Now this binomial coefficient n plus y minus 1 choose y if you simplify it it turns out to be it is minus 1 to the power y minus n choose y. These are also binomial coefficients with negative integers and therefore this distribution is called negative binomial distribution. Please do not worry about going into so many details if you can understand what exactly is the phenomena rest of it will follow by itself. So, if as far as we understand that there are sequence of independent Bernoulli trials in which a random variable x will denote the number of number of trials we had to go through until we arrive at the nth successful trial n is a fixed number then we say that x follows a negative binomial distribution. So, if this is understood rest of the mathematics will follow by itself. In this case the it is easier to calculate the expected value of y and expected value and variance of y from which you can of course work out the expected value of x minus x that is y being x minus n you know the expected value of x and variance of x. So, it turns out that very expected value of y is n times 1 minus p to y of p and variance of y is n times 1 minus p multiplied by p square divided by 1 over p square. Now, we move to another distribution very commonly used to distribution which is called Poisson distribution. This is a random variable which takes a value 0, 1, 2, 3 onwards and has a mass probability mass function defined in such a way where lambda is its parameter which is a positive value which takes on only positive value. So, px is e to the power lambda lambda to the power x divided by x factorial is said to follow Poisson distribution and is denoted by x distributed as Poisson lambda. I think I have forgotten to mention the notation for distribution. So, let me do it here for your information. If x follows a geometric distribution, we are going to say that x follows geometric and it has only one parameter which is p. It is called a geometric distribution. If it follows a negative binomial distribution, the notation is x follows negative binomial and remember the parameters are same as in binomial. So, it is n and p. Please note this. Now, we move on. So, Poisson distribution you denoted by x following Poisson with lambda. The mean value is lambda and the variance is also lambda. This is the specialty of this distribution where the mean value and the variance are exactly the same. This can be one of the characteristics of this distribution when you are trying to identify a distribution. Where does this distribution occur? You see we started with a Bernoulli trial uniform distribution, Bernoulli trial and the distribution derived from Bernoulli trial. We had some genesis. Poisson distributions genesis is such that when you are talking about a very occurrence of a very rare event, that is an event which occurs very after a long period of time it occurs and it occurs with a very small probability, it is generally tends to follow Poisson distribution. And therefore, for example, the number of if you have a good edited book, number of typographical error in a page can be, can follow a Poisson distribution. A number of defective parts produced in a day if your system is very well defined and it follows in a very quality assured way, then the possibility of getting a defective part is very, very, very low and therefore, it tends to follow Poisson distribution. A number of accident that can take place on a particular crossroad in a day is a very rare event fortunately and therefore, it is called a Poisson distribution. The Poisson distribution is also derived with this philosophy and from binomial. So, there is a relationship between binomial and Poisson distribution. This relationship is this. If you assume that x follows a binomial distribution with parameter n and p, it means that you have conducted the probability of success is p and you have conducted n independent Bernoulli trials, then as n tends to infinity, the number of trials tend to infinity and the probability of success, the probability of occurrence tends to 0 in such a way that the np that is the, you remember the average of the binomial distribution remains constant, then x as n tends to infinity tends to Poisson distribution with a parameter lambda which is the average value of binomial distribution. Now, you can see why we do not want to call probability p as a probability of success here because we do not want to have the success tending to 0. So, therefore, number of times we simply say the probability and it is a probability of occurrence of an error or defective or something. So, here there is a slight change in the definition of p because otherwise it sounds very funny that you have so many trials and you have no success, that is not what we are trying to say. This relationship is used to approximate binomial distribution with Poisson distribution, when n is large and np remains constant, when n is large I have forgotten to write, when n is large and p is getting smaller and np remains constant. I will show you how it can be proved mathematically. So, here we have x distributed as binomial np, np is a constant say lambda, then we start with the probability of x which is a binomial distribution. So, it is n choose x p to the power x 1 minus p to the power n minus x, we replace p by lambda by n. So, it simplifies into this format and this is nothing but the simplification of this and this together is sitting here and this is also the simplification of this. So, this simplifies into this and you will find that as n tends to infinity this tends to 1, this comes out as it is, this tends to 1 the denominator and this tends to e to the power lambda. So, as n tends to infinity this is how it turns out to be a Poisson density and therefore it is proved, therefore it is proved that when you are doing the binomial trials this is what really happens. When you have a binomial trials and n tends to infinity p becomes tends to 0, the distribution the random variable tends to Poisson distribution with np which is a constant as its average value. Here is an example of a Poisson distribution. So, we consider an example for, we consider the same example for binomial that is we had 100 randomly chosen items where the probability of having defective is 0.1 and we want to find out exactly 6 items being defective. So, here for np you know np is lambda which is 100 times 0.1 which is 10 using Poisson approximation to binomial probability that x is equal to 6 is e to the power minus 10 which is lambda 10 to the power 6 divided by 6 factorial and that is 0.063. Please recall that the same value in the past sorry the same value in the past had been 0.059 instead we got approximated value as 0.063. See this approximation was 0.059 and 0.063 which are almost the same. So, this is how the Poisson approximation in number of problems that you solve it is much easier to solve it through Poisson method than finding the 100 to 6 kind of parameter it is much easier to calculate e to the power 10, 10 to the power 6 divided by 6 factorial. The next distribution that I would like to talk about is a hyper geometric distribution. This is not coming out of Bernoulli trials it is a very different one, but remember that when we are considering the binomial distribution we are considering the case of choosing n items with replacement while here you are choosing the n items without replacement this is the main difference. So, here we consider a case of total n items that you have of which m are defective. n small n items are chosen without replacement with the probability that x of n are defective gives rise to hyper geometric distribution. So, your sample space is 0, 1, 2, 3, k where does the k come from? I think I have taken a mistake it should not be k it should be m. Let me correct it it should be m. So, you have x is equal s your sample space is 0, 1, 2, 3 up to m. Probability that you have chosen exactly x defective item is m choose x multiplied by remaining item n minus m you have chosen n minus x items divided by the total number. This is coming straight from the probability theory these are the total number of events you how many times you can choose the n items small n items out of the n and in this circumstances the mean value is given as n multiplied by it is n p. So, you can see that it is n multiplied by m divided by n and sigma square is this. This comes as I said this is because we are considering without replacement binomial distribution comes with replacement. Now where is the application for all this distribution in material science? We say that dealing with material science data right. So, here it comes the all answers I would like you to find out, but here is a case of three dimensional atom probe field ion microscopy. This is the machine and the purpose is that you take any specimen any metallic or any material specimen which has two kinds of atoms in it and you take a very thin sort of a thin cylinder like a you know in a pencil what you have you put it in the microscope and then it uses the it probes and it finally takes out the atoms from the specimen and it detects is over the detector. So, the atoms are field evaporated from the surface and the detector identifies the chemical nature of the detected ion by the time of flight in the mass spectroscopy. So, how much time it takes to fly to reach depending on that it actually detects the atom. You can refer to this paper, but it is very beautifully described that if this is your whole specimen the probed volume will be a piece of that. So, suppose you assume that you are looking for a atom A which is the dark one and you are looking for proportion P of atoms A this is your main purpose. What you are getting out is a probed volume which has a total number of say small m atoms and J are the A type atoms and the proportion of P of A atoms is you are looking for. Now, what really comes is through detector is you see only detected atoms in which you see small n number of total atoms of which I is the A type of atoms and then you only know the proportion P 0 of atoms A which has comes out in the detected atoms and this paper actually describes how to estimate the variation in this P value. This P is the best estimator for your capital P here and this are the questions that you have if you answer you have all the knowledge that you need in this particular area in this for the discrete distribution. So, let us take one after the other n is known a priory and i is observed value of A atoms, i is a random variable observed on n total n atoms detected from the probed volume containing total of m atoms and with J atoms of type A. So, this distribution will be hyper geometric distribution. If m atoms evaporated before n atoms got detected with detection probability of Q then what will be the distribution of m? It is like coming up to the nth success you conduct the Bernoulli trial till you come up to the nth success. So, this is going to lead to negative binomial distribution and then assuming that capital P is the proportion in the specimen given that small m atoms in the probed volume and of which J are the atoms what is the distribution of J? I leave it to you and finally given all that what is the probability of i? See we have already said one distribution of random variable i. I am asking the distribution of random variable i again. It is a good question for conditional probability, negative binomial distribution and hyper geometric distribution. You are welcome to refer to this particular paper. It has the solution, but it will give you the idea that all distributions have some role or the other to play in the field of material science data analytics. So, let us summarize quickly. We introduced special discrete distributions in today's lecture. We introduced discrete uniform distribution which is a very basic kind of a distribution. Then we introduced Bernoulli trials. I have missed out a few things. I will note them down here. We introduced Bernoulli trials and then we introduced three distributions arising out of Bernoulli trial. Binomial distribution, geometric distribution and negative binomial distribution. Then we introduced Poisson distribution and its relationship with the binomial that is in some sense as the number of trial increases to very large quantity. It tends to become a Poisson distribution provided the average of binomial distribution tends to constant value lambda as p also tends to 0. Hyper geometric distribution, binomial distribution occurs without replacement. This occurs with replacement, I am sorry this occurs with replacement, this hyper geometric distribution occurs without replacement. We took an example of 3D AP FIM and raised a few questions and gave you the reference of the paper to understand that all these distributions do occur in the world of materials science and materials engineering. Thank you.


Hello, everyone, welcome to this CUBE Conversation. I'm John Furrier, host of theCUBE. We're here in Palo Alto, California. You've got two great guests coming in from London, England, talking about business transformation, persistent systems here. Raj Sugumar, senior vice president, head of Europe with persistent and Simon Inger senior vice president of technology and inclusive. Great transaction happening here. Great technology transformation, business transformation, extension. Gentlemen, thank you for coming on this CUBE Conversation. Thank you. Thank you for having us, John. So Raj, congratulations on your recent news at Avis London Summit. Saw the big news there, a lot of action in the cloud. One of the biggest accomplishments we're having in the industry today is how to continue and be agile and scale and also continue the business growth under any circumstances, from growth to repositioning and growth. Your partner here, Inclusive, has a great use case. It really kind of illustrates the agility challenge. Simon, take a minute to explain what you guys do and the transaction you're involved in and what that's caused from an opportunity standpoint. Yeah, thanks, John. So the business is both a carve out and a roll up, originally incepted out of Cantor, which in turn was part of WPP conglomerate. And on 1st of Jan 22, the business was sold and we were really into a complete new standup situation and then we no longer had, well, we had transitional service agreements with the parent company, 18 different agreements for different services, but we really had to stand on our own feet. It's like a divorce where you have to move out of the house, you have to get your own new friends, your new suppliers and obviously one of our great friends here were persistent who were the golden thread partner in helping us stand up the business post the transaction. This is a classic case of they need help, they're flying this airplane at 35,000 feet, they got to make transform, they got to make all these changes without disruption and it's a challenge. How do you get involved? How does the engagement work? Take us through your role and what are some of the key things that you guys are working on as they come to you to make this transaction, you know, simple, easy, smooth, yet positioned for growth. Yeah, absolutely, John. So, you know, businesses like Unclusive are really at the forefront of driving disruptive change in their domains, right? And in order to do that, they really have to keep focus on their core objectives and business processes. There are a lot of complexities involved in a car boat, setting up a new company, there's so many moving parts and it's also a private equity landscape. So it's important to force the right partnerships, bring in partners like us with the right cultural fit, to be able to navigate the waters, right? There are several things involved here. And we have a good foundation in digital engineering and that's our primary focus that really allows us to be, you know, ahead of the technology trends and stay closer to our customers. And that's what we bring to the table, right? Sometimes you need somebody to orchestrate all the moving parts, you know, and bring harmony into the whole ecosystem, right? That's how we got involved. It's a great partnership, super proud of it. And the real value here is 18 different service agreements. You know, you need to manage all of them. You can't do it alone. You need somebody to dance with you and that's what we really bring to the table. Son, what's the psychology on your side? And then the tactics that you have to move into place immediately, once the ball starts rolling, what's the playbook? What do you do? And just call them up to say, hey, look, we got to get going. It's got to let the structure, I mean, there's so much you got to get on. What's the, what do you attack first? What's the low-hanging fruit you go after? And how do you tackle the problem? Yeah, I mean, that's, we only have 15 minutes, right?  Number one was established new brand, you know, and we had to move five offices in the first six months in different parts of Europe. We had to create, you know, separate networks and isolate ourselves security-wise from the previous company. So it's, the challenges are relentless. They come thick and fast and they come at you from all angles. You're also dealing with, you know, big changes for our staff and our other suppliers. You know, change is always disruptive to a business. So you have to manage the way through that. And you need a supplier who is, you know, in it for the long game. They're not a, you know, some of our suppliers are very transactional.  It's a completely different sort of relationship with persistent because, yes, there's certain stand-up activities, but that then leads into, you know, running 24 hour a day, seven days a week service desks for us that can support, you know, we're a follow the sun business now with offices from California through to Singapore.  You know, the runbook is things are moving so quickly and you have to have guiding principles that you work to. And of course, you know, you try and work to sort of standard processes and methodologies, but, you know, we were rolling up, you know, six or seven different businesses with different cultures, different ways of working, different go-to markets, different supplier stacks, and you can have a target operating model in mind, but it's the journey to the target operating model where we really need persistence. It's the hardest problem to have. You've got people, process technology in all areas are completely radically changing. You know, it's not like a venture capital is giving a startup some capital. Oh yeah, go build a company. The private equity ownership piece, you know, they move fast. There's a lot going on at super speed here, private equities, they're not going to sit around and wait for you guys. Oh yeah, it'll take 12 months. No, they want action. So, so, Roz, what does this mean for the CTO office? Because, you know, you got speed, you got, they want to make money, they got to make some changes and move fast. It's a, it's a, it's be agile, move fast, but you have a lot to work with. What's the challenge for the CTO office? So, the way we see it, you know, we need to be a true extension of inclusive CTO office, right? And there are three broad challenges or complexities involved in this. The first one is really the complexity of transition. So, we talked about the TSA, there's a car route, it's time bound, significant risks on cost and operations and the private equity activity which never stops. So, you need a well-defined playbook and significant prior experience to be able to execute this at scale and at base. The second one is the complexity of choice. You have so many platforms and tools and technologies together and when you bring in multiple companies together, you bring people together as well from different backgrounds, different cultures. So, the choice is unlimited there. So, how do you bring it together? How do you set up the right model? What's your IT strategy? For example, multi-cloud with AWS Azure, how does it work? And then finally, it is the complexity of orchestration. So, how do you bring the whole dance together, right? It's about people, the platforms, the required customization for the business. So, this really needs a specialist skill sets and a very, very strong partner ecosystem to be able to bring it together. So, the single most important thing for the CTO office here is to forge the right partnerships. That's the important thing here. Now, with partnerships in the right place, then you would be able to navigate this together rather than doing it on your own and which significantly, if you were to do it, it'll distract you from your core objectives. So, that's really the idea here. Simon, talk about the KPIs that you're under right now because you've got a business you're running.  You're bolting on more companies. But your business model though, you also have partnerships for the business side. I'm sure there's some connected tissue there. So, you've got a lot going on. What are some of the KPIs that you're operating under on the success plan for this? Because you're, again, you're moving a lot of stuff fast in real time while the business is running. Yeah, look, the KPIs, first and foremost KPIs are around time, time and quality. So, you know, it's about getting progress made.  It's the sort of mantra I have in my head from the conversations I have with the private equity partners. Now, of course, that comes with responsibilities and, you know, you have to really respect the heritage of the business and transform in an orderly way. But, you know, it's so important that you know, you're hitting regular milestones and demonstrating that change is happening. Because again, coming back to the sort of carve out and roll up and, you know, we're talking, you know, all of the major European countries with different cultures and languages.  And if you get stuck, you have a problem. So, you know, that's really where I'm looking at.  But we're moving into the next stage, which is much more of a transformational stage for the business. And... Go ahead, continue. Sorry, I interrupted you. No, I was just going to cover the earlier question about how inclusive and persistent came together. Because although we had worked with persistent, you know, prior to this within Cantor, they were one of probably five or six viable companies we could have worked with. We ran a, you know, a fast but rigorous selection process. And I think where persistence stood out was just, they were walking towards the problem. They were very engaged in understanding the problem and explaining, you know, how they would approach it and tackle it. So, I just wanted to... Raj, I was going to ask you that question. Same thing, you guys, this is what you guys do. The balance between disruption because they want to operate their business but also transformations happening at the same time. This is like, this is a very key part of how you guys do business. What are some of the KPIs you guys are focused on? What are some of the milestones? Take us through your side of the street here. What's going on there? No, I think it really comes down to aligning to, inclusive objectives at the end of the day, right? So, you know, it's about understanding that at the get go. And once you understand that and make it part of your problem, when you work out models which are more risk sharing and that gives comfort on both sides to operate, right? Walk the thin line. So, I think the metrics are fairly simple. I think it's, in this case, it's all about time and it's all about, you know, how you get to that goal faster and allow a company like Inclusive to trade as one separate entity outside of all the service agreements and the day that happens, everybody can go to bed peacefully. But at the same time, you know, the transformation piece which you're referring to, that's not going to stop. You know, businesses don't wait for something to get sorted out to start transforming. It has to, you know, it has to run in parallel. So, we have a very large practice around working with private equity firms and their portfolio companies and their journey of value creation. So, this is something that we've done at scale for several companies as well. And yes, we work for a lot of large global enterprises, but at the same time, we also work with new modern technology companies like Inclusive who really try to be at the forefront of their space which is PR and communications, right? So, that combined capability really gives us a unique knowledge of how to just navigate that. So, it's usually almost always a factor of time and our ability to get them over the line in that timeframe, but at the same time, put the right people in place, the solutions, the accelerators for driving transformation in parallel. So, that comes with alignment with the CTO office from the get go. I like the partner strategy as well. That adds a lot to it, there's a lot going on there. The infrastructure piece is interesting to me. I want to ask you guys, you mentioned multi-cloud. What's the unification strategy of the infrastructure? I can almost imagine that as we move into this next-gen cloud, the conversations that you guys might be having around platforms versus tools. What's the strategy? How do you guys look at the foundational elements of the infrastructure? You got some legacy, maybe put some modernization wrappers around that and containers and Kubernetes around the corner. You got cloud-native applications. What's the strategy? What are you guys thinking around the infrastructure piece of it? Yeah, look, and coming into this, you know, we had a hybrid model, as a lot of companies do. We were running AWS cloud solutions. We were also running Azure cloud solutions and private data center hosting as well. So, you know, I'm not being controversial to say, obviously, we want to tidy that all up, you know, make it much simpler to support and much more scalable by consolidating. But we have to pick our battles. You know, you've got a business that its heart is over sort of 20 years old. There were, I think when we did the audit, there was over 400 applications in the business. Now, clearly, that's happened over time. And, you know, part of this transformation journey is the future-facing part. It's the bit everybody's excited about, where you rationalize the things that are, you know, kind of outliers, not really needed, not really essential. And you really focus the future investments on, you know, core platforms. But at the same time, making sure that you're not building platforms that fulfill the processes and practices of yesterday, they are the forward-facing side of it is the most important part there. Raj, what's your strategy? You mentioned earlier complexity of choice. I think you mentioned complexity of choice, complexity of transition first and then choice partners. There's a lot of partner strategies that you could add into here. They have a partnership model on their side. Talk about that further, because that adds into it because with APIs and cloud and multi-cloud, you've got a lot of opportunities to integrate. Yeah, absolutely.  That's, you know, it is what it is. You know, simply because business is a global today, they operate in different geographies, different markets, different nuances. The requirements are different. So you need to, you know, have a cloud strategy, you know, which works for you. And in many cases, it'll be multi-cloud. So, and to add to that, it's also about, you know, remediating while you're transforming because cloud as a strategy could be really great, but it could also go out of control. So how do you put the right checks and balances in place to be able to operate effectively in that multi-cloud environment? So now for somebody like us, we are a partner. We are a service provider. So we have to work extremely close with our extended technology partner ecosystem, you know, AWS or Microsoft Azure and any other technology provider, Salesforce, for example.  So we have to be at the forefront of our mind share and we need to invest into that ecosystem proactively as well to be able to get that back from them. And once you bring those partnerships to bear, then it's about how you stitch that together in a meaningful way for the customers, right? So you can't obviously throw everything at the problem, but the ability to work with them, work alongside them, to choose the right platforms and tools, and also be agile and nimble enough to, you know, correct the course if something doesn't work, you know, plug it out, plug it in, to be able to have that sort of a microservices led architecture in place really helps the business to stay lean and agile, right? So yeah, so us, our alignment with partners like technology vendors is very, very important to drive in transformation like this one for our inclusive. It's key to have that balance. You've got to take care of business and set up for the future and get that next trajectory on the project. Simon, I want to wrap up here the next couple of minutes we have left on inclusive and what's next for you guys? Where are you right now in the journey? It's a big project, got to move fast, a lot online, it's a heavy lift as they say, but it's big, it's a big business. Where are you now, where are you in the journey and what's next? Yeah, so we're through the sort of separation and stand-up phase of this. And you know, we're private equity owned, it's about the growth now. You know, it's about the scaling of the business. Now that can come through inorganic or organic growth. We have a big investment internally in our innovation team, our engineering team who are producing the next-gen core platform which is our intellectual property. That's the bit that will differentiate us from our competitors. And we are bringing that to market in starting in Q3, which is what, three weeks away, two weeks away. But it's, you have a big rollout plan around that affecting 15,000 clients. So this is going to be the main part of the business now is just getting all of the platforms harmonized and getting the support models harmonized. And the vision is borderless company, you can provide services anywhere in the world and have a support model that flexes and works anywhere in the world. So that's the next phase of the journey for us. Scaling up the business, that's the next step, the private equities, they want to see the dollars, get that growth, Raj, persistent, you guys have great experience with PE. Talk about what makes this different than a traditional deployment or engagement. What's different about the PE private equity space and how we help these guys move fast? Because I'm sure the power dynamics are a little bit different than some other classic enterprises. Or maybe it's the same, explain and talk about the relationship there. Well, it's same in some ways, but very different in many ways. So yeah, the rigor that comes within a private equity setup is very different. And in a lot of ways, it gives you a lot of discipline to work in an engagement. So what's unique about us in this space is that we saw this trend very early on about how private equity companies invest new dollars in the market, in new markets, and how they go about trying to create value for businesses that they're trying to acquire and at the same time for the shareholders and the broader, you know, surround around it, right? So we caught that trend very early on. So we did set up a practice years ago and we've seen good success in there. So the magic source is really, you know, understanding the nuances of the whole life cycle, right from when a PE firm is trying to acquire an asset, the due diligence process, and what's the journey around that? And once the transaction happens, how do you put the blueprint in place to execute, transform, drive operational efficiencies, do a car boat, and then all the way down to the exit and you help them through the journey. So once you do this journey end to end several times over, then it just becomes a repeatable template that we can leverage every now and then. So that's what we have done effectively. And we've seen some great success. It's a really exciting time for us at the system. We've just hit a billion dollars in annual revenues as well. So, and a lot of the recent goals that we've seen is specifically in the private expertise and we'll continue to invest in that. Great, you guys totally understand the complexities and then you get the partners network and the ecosystem and the capabilities that gets great stuff, congratulations. Simon, I'll give you the final word for the folks watching who are going to go through a process as complex and heavy lifting as yours and maintain that operation integrity. What advice would you have for them? What are your learnings? Can you share, take a minute to explain? Your learnings, advice. I'll do my best, but I think the first thing I'd say is that every situation is going to be unique. But from my case, it's pick your battles, decide what really matters, give clear direction, be clear on your guiding principles and get the right flexible partner who can ride the waves with you because it won't be a smooth journey. It's fun, it's a good ride, but you need the right partners. A lot of speed, a lot of energy there. Sounds super exciting. What a great project. Can only imagine how fun it must be. And a little stressful I can imagine, but great transformation and big project. Congratulations, Simon, on your new path and looking forward to seeing what happens when scale and following up. And Raj, great to chat with you and congratulations on your success there too. Thanks a lot, John. Thanks very much. Thank you very much. Okay, this is theCUBE Conversation here. I'm John Furrier, host of theCUBE. Business transformation, private equity, startups, enterprises all require the same thing. Speed, agility, foundational services, building insecurity from day one, a lot more cloud native applications coming in the future, stabilize and scale. That's the theme here in theCUBE. Thanks for watching.


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All right, folks, we are back for all of us here live inside cyber group Norman. No, no audio right right at 2 30 Here we go folks here We are right here for our afternoon meeting this Friday December 10th great to see Leda Norman as I said before fill Leda Deppert W Mark Ari and all of us otherwise as I said inside cyber group But of course if you're joining us back on social media Facebook live or YouTube welcome on back as well So with that in mind well this end you trade is pushing back up for us a little bit I'm in on this trade right now. I've been in on this trade practically from this big giant red volume spike here So, you know for more or less. We'll kind of just go through this quick It's just to say that pretty much here at 1080. There was a 2.2 million share Transaction a print that came in on the time in sales program. You know who bought or sold at that price What's their intent? I don't have that answer. I never have that answer right out the gate folks But it's a matter of seeing the reaction afterwards and if this stock wasn't going to completely tank right away after seeing that Big print come in I figured with the news out on this stock the hype around it I figured there's one more chance to see this try and make a push up Just a matter of following the big money right how often do we see like a 2.2 million share print come in on the Tape for this stock let alone really any other stock. So, you know for that reason I'm actually still in on this trade right now and half of my original entry I doubled up on my shares off that 1080 level. I closed out the first half of that position right under 1125 here and With that I'm still in on the second half So for right now this end you trade definitely gonna put it back on our main watch list not because I'm in it right? There are plenty of stocks I show them in during this meeting that I decide not to put on the list this one still looks pretty strong, right? You know not only from the 1080 level but off of 11 It just popped off 11 here it pulled back and where the volume looked like it was beginning to kind of die off again Well, it ended up holding support beautifully broke under and over from 11 here and for right now pushing back up So this end you trade definitely a great start so far for us heading into this afternoon All right, we'll go right from top to bottom. Otherwise, I don't have class this afternoon I do have a coaching session at 3 but we also have to talk a bit about Bitcoin So we'll talk a bit about this SGMA trade quick at first then a couple of the other stocks Otherwise, and then we'll kind of just see what else we have So SGMA just still near the tops near the highs so far heading us for us heading into the afternoon So for that reason more than anything, I'll put it on our watch list You know pretty liquid order book all be at the spreads couldn't you jump up and down a bit? I'm more so looking at the ask and seeing that there is a bunch of volume that's out there at 1280 912 90 Of course up top at 13. So yeah, this SGMA trade will put on our list here, too Want to take a look on the daily chart for this stock if anything. I don't think this is all-time highs. Oh Maybe it is maybe a broke all-time highs just earlier. Perhaps let's go back on the on the monthly chart even All right, so not all-time highs clearly not all-time highs But it's pretty much right there at historical resistance even dating back to like 2007 right around like 2013 ish give or take 1337 perhaps this high back from October of 2007 No, that could be a pretty strong historical level up top here So yeah for right now the SGMA definitely gonna put on our main watch list. We did really well on this stock earlier today. I Know a couple of us were talking about the CELZ Stock creative medical tech ended up making a nice pop going into the early afternoon But it was off of like kind of light volume So when you see this big spike at first or not so big spike when you see the spike you're not fully confident of it making this move you're hopeful but You know just to say this stock ended up breaking higher and once we started to see more buying coming into the trade right here That's where you start feeling a little bit more confident about this So for right now definitely go goes on the afternoon watch list here the CELZ trade Looks like just back from like go back on the monthly even Back from 2019 is that 2018 19 the stock got crushed It wasn't like a 20 30 $50,000 you know symbol back then my guess is that the stock's been reversed split to eternity And I guess right now getting a little bit of a push. So for right now sure CELZ All right, we keep the train going here folks AVC T a little disappointed on this trade a little disappointed I mean we did really well in this stock from the morning ended up holding above $2 and 38 cents key level pretty nicely even for The afternoon heading for us into the early afternoon It just came back down and you know broke under and back over that 238 level and for the meantime though Well now at this point if it's not gonna run off the level I think it's just gonna re break it and crash So, you know not a good stock for us here heading into the afternoon at least in my opinion at least not yet But from the morning nice push-up earlier where it broke above the pre-market resistance and then took off pretty nicely Alright, we'll see what else we got for us here folks BLRX familiar name here PTP I perhaps this one had a nasty shape just took a huge dive You know made a new high the day earlier right off of 330 340 I think that even is and then from there came back down pretty quick But man ugly shake once it re broke below 3 Norman says game over on PTP I I'd say so I'd say probably It's only trading about like a thousand shares per minute.  What's this e-pay stock? It's also really expensive You can certainly say the same but this year All right, see if there's anything more within our price range right now folks are RSLS familiar name at least from this morning's pre-market It's actually trying to push up to the regular session high of the day right now If this isn't exactly it then it's actually at the HOD here So 2 13 2 12, you know, it's got a liquid order book pretty cheap I don't know how confident I am of this stock making another move back up to 250 if so then man That would be the home run, but I'll take even 15 20 cents out of this trade right to get close up to 230 230 ish You know, we'll see what happens here. All right, so we'll kind of just keep keep skimming down this list See what else we got court Familiar name. Oh, I didn't I didn't even notice this stock had the gap down back from Wednesday I was actually going to say more so than anything. We ended up trading this court stock back in November off the gap up But after that point it didn't really offer us too much so interesting at least this is trying to Recover perhaps even fill the gap from the daily chart back from earlier this week Just not really trading a whole lot of volume per minute for us right now 23,000 shares per minute right now at most All right, where is it? I just saw it here a second ago. There we go Ford All right, so no brainer for right now Ford will put on our watch list with just how well it's done today It's near the highs very similar to the SGMA stack Although this is a lot less volatile compared to the SGMA. I just broke over 21 pretty effortlessly here, too Might I say I ended up breaking above this like 870 plus thousand share iceberg here, you know for the most part it ran up Since it's got to really begin to pop soon in my opinion though And if it doesn't pop right away, then you know, maybe if it ends up coming back down to 21 If it could break under 21 and slingshot from there and pop right back up and run Then that would be the other play, but you know for right now. We'll keep a good eye in the stock again Ford. Oh Really quick. I had someone ask is this all-time highs on Ford. It is not all-time highs I want to just you know mention that go back on the monthly chart.  I have to you know make the setting change But just so you folks know the all-time high was back from 1999 or 2000 I think the price there was like 33 a little over 33 if not mistaken. I wrote it in the chat board earlier All right, so we'll kind of just cherry-pick otherwise see it see what else we have markets Just print been pretty stagnant across the last hour and a half hour or so CTI see that ended up making a nice run earlier heading into the early afternoon pop from like 185 as you could see on my 5-minute chart took off pretty nicely, but wheels fell off fast. Oh 4245 bill really I got to go back even a little bit more. I reckon maybe I was thinking 42 instead of 32 or whatever All right, we'll hop over to the loser's list though here We'll check out EVBG down about 45 percent pretty expensive stock to be down 45 percent Yeah, not a whole lot happening in terms of direction. It's pretty range-bound. It seems I Shouldn't even be clicking on these two here. Just you know treating a very little volume across the whole day Then otherwise, let's see a NGN a little close to the ground floor here, but Yes, little too slow for us right now. Otherwise MESA short of on trade station platform right now as good order book. It's trending down a bit It's not as close to the lows as I would want it to be You know just for these stocks on the gainers list that are near the highs You want to see a stock on the losers list? That's you know pretty close to the lows and that's not necessarily the case here AMC how about this AMC down over 10% right now? This one's a little bit closer to the lows, right? Why not? We'll give it a look AMC perhaps more is a short heading into the afternoon We'll see if the markets continue to drop off a little bit more All right, now I'll tell you what folks just looking quickly on the losers side here There's not a whole lot out there that looks more like a familiar name So for all of us live inside cyber group all of us on social media Facebook live YouTube if you have any other stocks for me Type it in the chat board. Let me know From Mitch. I'm just gonna look at YouTube chat first here today for Mitch She's asking about the BFRI bit of an ugly spread that was from like a three four minute ago Mmm Yeah, I already made such a big move earlier today.  I'd imagine seem to be set on CNTX. I Was looking earlier RJF on CNTX right off of 584 Area level and I was in it at first but ended up getting stopped out for a minus two right over here It was on a really steady downtrend. So that's you know what hurt me on that trade But at the time it was like practically right off of historical resistance, you know From the last few days then really over here right around 583 ish or so didn't get it So I don't know. I just I don't want to hold my breath waiting for that move here. That's all Grant from cyber group. He says LCID looks done for the day. Also. We'll go to LCID at least that's a good sideless stock from yesterday and You know ever since they ended up getting subpoenaed by the SEC they've been getting killed So for right now. Yeah, this one just looks just yeah, no real trend to it We'd consider it if it had a trend but right now not a whole lot happening like you wrote All right, folks. I'm gonna bring up Bitcoin pretty quick a little bit of a drop off Not a whole lot happened really since we last talked about it from Wednesday If anything the notable part is that with it beginning to slip back down a tiny bit Well, it's beginning to re break under these trend lines that I drew, you know This is back from March of 2020 even back from the pandemic So pretty strong trend line pretty significant trend line there So we really got to begin to see this screwed up pretty soon, you know in the coming week or so Otherwise, I'll be a little nervous, you know, I really haven't been too nervous with this This was pretty scary. This is the severity of the drop but the squeeze back up Kept me pretty confident with seeing this begin to pull back below these trend lines here I'm not nervous yet, but the more it doesn't move back up soon enough Yeah, I'll start to get a little nervous folks. All right, so I haven't bought since last week since pretty much right over here So I got for you, that's all Folks if there's anything else that you would want me to look at here Otherwise just type in the chat board as I said Mark says DTC worth watching No, I wouldn't say so and I appreciate the heads up at least, you know, it's good to ask It's up six and a half percent trading 930,000 shares It's not really a whole lot that we want to look we'd want to see a lot more than this You know for DTC mark, you know, I like the fact it's up six and a half percent But right now not a whole lot happening in terms of liquidity All right, folks. I think that's just about it I mean in total though. Hey for a Friday and then plus next week There is a Fed meeting so it's not just a typical end of the weekday here on Friday next week should be slow It's you know heading more so into the holiday season again. We're going into Christmas. We have the Fed meeting next week It's gonna be slow next week. I'd imagine it for it to be pretty slow here today as well with that in mind So in total we got two, you know four, you got six stocks to work with I feel like that's a pretty decent watch list including the end you trade the CELZ Ford AMC and a couple of others All right, so for all of us live inside cybergroup. I'll post and tweet the pics out in a bit I know that rich will be rich will be back on the mic himself at 3 p.m. Eastern I'll be back on at 3 30 for all of us on social media RJF Mitch Dan and all of us otherwise sticking with us here this Friday afternoon I appreciate you if you have any other questions feel more than free to send an email right to my email address Josh at C2 trading calm if you're ready to join us live inside our trading room continue the audio commentary with us Heading into the market close all you got to do is go right down to the link on the banner below of the stream Just to get yourself started for a small trial. All right for all of us there. I'll talk to you all soon. Take care


Let's talk about another stack that you like this weekend. This time it's a quarterback Drew Brees and Trey Kwan Smith. Tell me why Trey Kwan. Yeah. So Trey Kwan kind of fits with the mentality of getting cheap exposure to a high scoring game. And this Ram Saints game, the total in this game is 60. So there's going to be a lot of points here. And I want to find ways to get exposure, but Michael Thomas is expensive. You've got guys like Todd Gurley, like Brandon cooks, like Robert Woods, Jared Goff. They're all very expensive. So if you want to get exposure to this game, most of the times you got to pay up, but you don't got to pay up for Trey Kwan Smith. He has $5,100 and he's tied to a Drew Brees led offense. And ever since Ted again went down, Trey Kwan's had a really good role. He has 16.5% of their targets in those three games with 38% of the deep targets. And the reason we have not really noticed that is because they have not thrown very much. They've been able to lean on Mark Ingram and Alvin Kamara and kind of take Drew Brees out of the equation, but this week they're hosting the Rams. That's not a matchup where you can just sit back and let the ground game do all the work. I think that Ingram and Kamara should have good games, but I also think that the Saints are going to have to throw the ball a decent amount, which should bump up the target totals for Trey Kwan Smith quite a bit. This Rams defense has not been the same ever since the Akib to Lee Bindre. They allowed big production to Kirk Cousins, Russell Wilson, Aaron Rodgers. So I'm hoping the reason output on Trey Kwan Smith will put a cap on his ownership. We've got guys like Coralyn Sutton, who is a chief wide receiver. We've got DJ Moore. So there are other chief wide receivers to take attention away from Trey Kwan Smith, but they don't get you exposure to a game as high scoring as this one. So I think I am going to have quite a bit of Trey Kwan Smith this weekend. I'm going to use Michael Thomas too for sure. But if I want to get cheap exposure to this game, Trey Kwan Smith me easily the best way for doing so. It's simple. The price is right for Trey Kwan Smith, and you pair him with Drew Brees in a matchup against another potent offense. And it seems like a calculated good risk to take.


Every other breath we take is thanks to the ocean. We need the oxygen from the ocean, we depend on the ocean for food, it drives our weather systems, basically without the oceans, we cannot exist on this planet. Everywhere we look, as scientists, we find plastic. Whether the deep sea, the polar regions, the most remote ocean wilderness areas, we're finding plastic and plastic has come from people. So at the moment, we only recycle 9% of all plastic. That plastic is having an increasing impact on our ecosystem and on marine wildlife. We know it causes more disease in corals, whether it's entangling marine turtles, whether it's strangling seabirds, whether it's in the stomach of whales and dolphins. We now know that plastic is in every level of the food chain and it's having a really concerning impact on our environment and increasingly on us. This thing here is called a polymetallic nodule in the Pacific. There are these large abyssal plains and on the plains are these potato-sized nuggets of minerals. There's cobalt in here and rare earth elements. So these are some of the things that these new ocean miners are beginning to pursue so important to the hardware of renewable energy. That's solar and that's wind and that's also batteries as the storage hardware to capture and bottle up all of that energy. One of the things we think is so important is actually trying to use new technology to monitor the emergence of this industry. We use AI to process out exactly where mining vessels are operating in the oceans and then we share this on an open data platform. So you can see which country actually owns that claim area right next to Hawaii. Which country is doing this mining exploration? These spaces that we're proposing to mine are not empty spaces. They're actually rich with life and incredibly important ecologically. One of the potential impacts is that when you mine in the ocean you do something that you don't do on land, which is on land when you're excavating earth you set that earth aside. It stays put in a mound or a mountain. When you do that in the ocean you kick up these big plumes of sediment which travel with ocean currents which is always flowing like a river and those large plumes could in fact interact negatively with our fisheries that perhaps these plumes could source toxins that are stirred away in the deep ocean up into these food webs. Up until 1950 fishing was a coastal activity. Fishermen stayed close to where they lived. Globally we caught maybe 8 million tons a year. New much more powerful ships, much bigger nets came online and fishers spread across the world. And so by the turn of the century we were taking 95 million tons out of the ocean every year. One of the challenges when fisheries go global is that they go out of sight and so what happens on the water is a mystery to almost everybody and therefore criminals have free reign to take fish in violations of the laws that are meant to manage them. Global Fishing Watch was able to track a fishing vessel cruising across the ocean entering into a marine protected area created by the nation of Kiribati fishing in that protected area and then going home and on the basis of that information they find that fishing vessel $2 million. There's a UN agreement that requires each participating country to control the imports of seafood into its ports and to assure that none of the seafood that's landed in its ports is illegal. 85 countries are now operating under that treaty. If we can broaden that group to encompass all the important port and flag states around the world we can actually begin to close down the market for illegal fish and if illegal fishermen have nowhere to sell their catch they will find another business. How we engage the emergence of mining in the oceans will affect a huge part of the largest ecosystem on our planet. In the next two years the International Seabed Authority is finishing the code to allow this industry to go from exploration to full-scale commercial mining. We should use this data to get ourselves involved in shaping when that future becomes. I'm an ocean optimist. We came up with the hashtag Ocean Optimism to bring more people into ocean conservation and to see that we really can make a difference and there is hope.