The Central Bank of Nigeria recently sent a presentation to Nigerian banks about the E-Nirah project, revealing more details about its design and operational model. The presentation described how the new currency would be designed and operated. In fact, the report states that the E-Nirah is a legal tender for the entire country. The report also mentions that it will have non-interest-bearing CBBC status, a transaction limit for customers, and a value-based transaction. That's our main focus on the show today. Welcome to Business Insight and Plus TV Africa. I am Justin Acadone. And now to some economic news. The Monetary Policy Committee of the Central Bank of Nigeria has retained a monetary policy rate at 11.5%. The CBN governor, Gadwin Emefile, disclosed this after the committee's two-day meeting in Abuja on Thursday. It also retained the cash reserve ratio and liquidity ratio at 27.5% and 30%, respectively. Now, Nigeria's fall in inflation rate, the 14th annual conference of the Chartered Institute of Bankers of Nigeria, e-commerce and taxation, running up major headlines in business Nigeria this week. Take a look. The federal government has said that it is targeting an increase in e-commerce trading from the current market value of $13 billion to about $75 billion by 2025. The Permanent Secretary, Ministry of Industry, Trade and Investment, Dr Evelyn Ngige, who disclosed this on Tuesday in Abuja at a second national e-commerce roundtable organized by the ministry noted that e-commerce had grown from 14% in 2019 to 17% in 2020. Tax, banks and financial institutions in the country under need to focus on transformative initiatives that will impact positively on the economy. The vice president said this on Tuesday at the 14th annual banking and finance conference organized by Chartered Institute of Bankers on Nigeria in Abuja. The National Bureau of Statistics says Nigeria's inflation rate in the month of August 2021 dropped to 17.01% from 17.38% recorded in July 2021. These represent 15 consecutive decline in the rate of inflation recorded in Nigeria. This is according to the National Bureau of Statistics, MBS. The latest figure is 0.37% points lower than what it was recorded in the previous month. Meanwhile, on month-on-month basis, the headline index increased by 1.02% in August 2021. This is 0.909% higher than the rate recorded in July 2021, 0.93%. Data from the National Bureau of Statistics shows that within a three-year period covering December 2017 to December 2020, Nigeria regenerated a total sum of $6.5 trillion from non-oil exports. This sum is $5.8 trillion lower than its $12.3 trillion, $30 billion target set by the federal government under its zero-oil plan. In the fourth quarter of 2016, Nigeria Promotion Council conceived zero-oil plan to increase the contribution of non-oil exports to Nigeria's gross domestic product by 20%. The Attorney General of the Federation and Minister of Justice, Abubakar Malami, has written 36 state governors over compliance with audits and recovery of back years of stamp duty. The minister's special assistant and media and public relations, Jibrilu Gundu, made this known in a press statement titled, Stamped Duty, No Recovery Yet, as Malami writes 36 governors issued on Wednesday. According to the statement, recoveries have been conducted for the federal ministries, departments, agencies and financial institutions. And those were the render for this week. Now, the central bank of Nigeria, CBN says, is soon to be launched digital currency in Nigeria will strengthen the stability of the banking system as deposits become more diversified when people are banked. The central bank of Nigeria, digital currency will also make it easier for the banking system to comply with existing laws such as anti-money laundering, customer protection, against fraud and ensuring the safety and stability of their payment system. Joining us here this course of all of this right now is Favo Sunano, top leader, planting corn Africa. Good evening to you, Favo. Many thanks for joining us on Business Insight and Plus TV Africa. Good evening, Justine. Super excited to be here. I'm so excited to be here. All right, let us just talk more right now for people who may not ordinarily understand what the e-naira is. How different is it from crypto currency? Well, a lot of propositions have been going on around what this e-naira is all about and how it could really, really change what we already have currently. Now, according to the CBN, as much as I understand, the e-naira is, does not replace the current naira. It uses the same blockchain technology just like Bitcoin, but they say it's not a cryptocurrency. And the truth is the baseline for all cryptocurrency digital assets is the blockchain. And for the e-naira to be, it needs to have the technology. And I am sure that the e-naira has its own blockchain and that is very, very good news. Now, it doesn't replace the existing currency that we have. It is another form of transaction. I need to have a wallet on my mobile phone and an account with a bank that operates that has the e-naira. Then I can do transactions. It's totally different from what we have today. What we have today is called the Fiat FIAT currency. And the digital e-naira is a digital currency that you can use to make payments for goods and services. And I must say, it's a very laudable creation by the CBN. All right. You said it is very laudable from the central bank of Nigeria. But some people may want to really know what benefit is e-naira. What benefit will it bring into the regular digital banking system in the country? Well, the truth is it allows borderless transactions. And I want to believe the e-naira. In the blockchain space, there are private and public blockchains. And I want to believe the e-naira uses the private blockchain. And so because of that, it's not totally decentralized. They could actually have some level of control. So as far as I'm concerned, whether we like it or not, money has become totally digital. And for the bank, for the CBN, the APS are banking the country to decide to create its own digital currency. It makes the banking system in Nigeria way ahead of many other countries around the world who have not decided to make it, who have not taken the decision regarding the use of blockchain technology. It is a big loss for banks in Nigeria and of course in Africa. Alright, let's try and understand this further, just how operational it will be in practice. You've talked about the blockchain technology, you talked about wallets now. So what are you saying in MS is that the average Nigerian who has a bank account would be required to open some sort of wallet for him or her to be able to operate this. What about the issue about technology, enlightenment and all of that? Would you say that we are ready for this particular technology in the banking sector? Well, I don't know how ready the bank is, but definitely just like the days of transition from analog use of data recording to the transition to the point where we now have computers, all the startups being computerized. At one point it looked like it will never happen, but the transition took place and there must be a starting point. Right now, the next thing that will happen is the installation of infrastructure. I don't know how ready it is, but blockchain is an expensive project. Whether it's a country that is using it, whether it's a banking, whether it's a very expensive project and there will be a lot of education that we go on for the next, I don't know how long, maybe three to five years, but it has to start. Now digital currencies, according to the guidelines created by the central bank, digital currency are divided into two classes. We have the central bank, digital currencies, which is issued and is directed by the central bank and the private digital currencies. The central bank issue currencies are divided into wholesale CBDC and retail CBDC. Now wholesale CBDC will be assessed by institutions that hold accounts with central bank and interbank or large value transfers. Now the retail CBDC which the entire populace, the mass people we adopt is retail CBDC which is assessed by the general public, including households and businesses as a means of payment and store of value. Every person will be required to open a new wallet. It's a wallet-based. Everybody will require to open a new wallet, whether with the bank. I don't know how the CBDC is going to bring it about. I'm sure there will be new entrants into the banking system, new form of banking that we've never seen before. Right now the CBN has said that our house plans that it will be operational come 1st of October. That's barely a month ago. And you've talked about infrastructural and commitment to be put in place, enlightenment and all of that. Do you see these October take of feasible judging by what we have on ground? And would you also say that the central bank has actually carried all the operators along concerning this particular move? Honestly, I don't think it's going to be effective October 1st. I mean, yes, the announcement that we are going to do this is going to come up. If it's truly blockchain, then I don't see that. I'm not sure the banks have already, even the existing banks, they're not ready. Infrastructure is not there. Because even building the regular blockchain, I remember my first train in London, the London Blockchain Week in 2017. I approached the ESPAS, those who design blockchains. The very first thing I was told that I have to start to develop the platform. I need a minimum of a million dollars just to start, just the platform alone. But I don't talk about training, I don't talk about all those stuff. So the... I don't think the infrastructure is in place right now. Okay, so what is it good to start? Is it an announcement we made? I don't know how prepared the bank is or the banks are. Alright, from what we understand concerning digital currency, let's talk about Bitcoin and crypto for a bit right now. And for all we know, not too long ago, the central bank came out of the policy with some sort of regulation or ban on cryptocurrency operation. So far, you operate and plant the coin. Tell us how it has been because some countries are actually using Bitcoins as they are in a legal tender. So how has the environment of cryptocurrency been specifically in Nigeria since the CBN ban and all of that, specifically the one you dealt in? Alright, we lost our favor there again. Okay, so we'll take this... Did you know, per se, on eNira and we'll come back and see if we can re-establish contact with FAVOR? Stay with us. Alright, it's still business in site and plus TV Africa. As we wrap up the segment on eNira, let's conclude with FAVOR and see how cryptocurrency has been doing in Nigeria since the central bank of Nigeria in a world that is big stick. FAVOR. So thank you for having me back. So cryptocurrency and blockchain technology is huge. Now, banning cryptocurrency is not the smartest thing to do by any government right now. What should be done is to look out on the underlying technology which is the blockchain. With the blockchain technology and with a platform like the CBDC a country like Nigeria could develop systems that would help us in budgeting, in population, in like in sensors for example we can use it at best control. We can use it any tier to do with data, land registry. We could use it. So and because we don't have enough infrastructure, human capital, young people are the best bet for the spread of this technology. The old and tired people will not be ready because it's very... Blockchain is very cumbersome. It needs sharp minds. People that we take, that we bring about startups. There are startups that can come with this blockchain technology apart from cryptocurrency. There are startups that can be encouraged that can bring billions of dollars into this country that can empower this country. Now, the CBN needs to encourage startups around blockchain technology. Cryptocurrency is thriving. Definitely it will thrive because it's borderless. It's totally peer to peer. So there have not been any setbacks yet. That's why I'm consigned when it comes to the use of cryptocurrency in Nigeria. Actually, we are top two users of cryptocurrency in the world right now even with the so-called ban of the CBN. I believe CBN needs to find a way to embrace the stakeholders of cryptocurrency and blockchain technology in Nigeria and Africa and when it does for us. If they do that, I'm sure that there will be a lot of progress for the country. Alright, thank you so much. I'm Favo Sonano for joining us and sharing insight on the eNir and of course on cryptocurrency the way forward in Nigeria. We do appreciate your time. Alright, for eNirer project to be successful, a good number of Nigerians including companies and government agencies will have to maintain eWallets. Nevertheless, eNirer will be troubled by some of the current issues of the country including electricity, lack of power, lack of internet coverage and the volatility of the Nigerian currency. As we go on the show, the House of Representatives Committee and Public Accounts recently partnered with the Office of the Accountants General of the Federation and the Lagos Mainland District of the Institute of Chartered Accountants of Nigeria to organize a national workshop on the auditory generous queries and the ABC of responses would leave you with a highlight of that. See you again next time. I am Justin Atadone. Bye for now. In an audit, queries address issues where the Auditor General needs further clarifications to erase doubts and address disagreements. The queries may also be the questions asked by the Auditor General during an investigation to gather information to conclude an audit. The key issues in responding to Auditor General's queries and the fundamentals of technology innovations and compliance initiatives at the major discuss in this workshop. The Auditor General audits all governments pending every year by carrying out interim and annual audits as well as ad hoc examinations when the need arises. Key members from the National Assembly and the Institute of Chartered Accountants of Nigeria speak on the need for the provision of answers to these queries. As of Chief Executive Officers to be alive to their responsibilities in terms of rendition of audited accounts to the Auditor General for the Federation for his comments and subsequent report which is expected to be laid before the flow of the House and of the Senate. What this workshop is meant to do is to look at the work of the auditor and the audit query holistically. Audit queries is a phenomenon in the Nigerian environment, so to say in the sense that every now and then the Auditor General for the Federation encounters issues, sometimes unexpendable issues. Delegates speak under plans from the Nigeria respective organizations after this workshop. Yes, I think it's trying to drive the culture of regular financial reporting reliability of account reporting from the MDAs. A useful one, considering that when we prepare, we are accountable for whatever the government has given to us. The workshop is meant to help use the technical operational gaps brought about by the introduction of emerging technology disruptions and of course the consequences of the COVID-19 pandemic.


I saw it. It's a future. My future. Our future. All four of us will be facing our greatest evil and it's all my fault. And for that, I apologise. Look out! It's the Cybermen! What? That's not in the script. Yes, but it could be a very interesting twist.  It should clearly be sealed. I'm not sure that's a good direction. Davison? Davison! Oh, Deterlo! What on earth were you dreaming about? Heaven. What are we doing? We're doing my Time War audio play. It's going to be quite the lesson. All thanks to me, of course. Thanks to all of our excellent voice acting. Now back to the action. Cybermen! No, no, please, McCoy, stick to the script this time. I've worked very hard on it. Yes, yes, sorry. Does anyone have any suggestions on how to fix this mess that I have created? Perhaps we can set up a protective barrier made out of, um... Oh, this won't do at all! Artron energy. Why don't we change it to something else? Well, what do you have in mind? Penny energy. Absolutely not. It should be called Ace energy. No, Artron energy is simple enough. Right, Davison? Davison! No, not in the mouth. What are you dreaming about now? Still, heaven should be called Davison energy. You know what, let's just forget about the Artron energy. Let's just call it a protective barrier. Never mind what it's made of. That can be up to the listener. How about that? Splendid idea! Thank you. Right, Colin, do continue. I will. Perhaps we should set up a protective barrier to keep any undesirables out. Yes. Like the Cybermen. Why do you insist on shoving the Cybermen into this story? We're not Chris Chibnall. I second that. Because it makes narrative sense, of course. In what way? Because it... It's cool. Say your actual line, please. No more ad-libbing. Fine. What do you propose we make this barrier out of? We're in the middle of nowhere. With no signs of a local Tesco to buy snacks with the meal deal. I'm sorry, what? Sorry, did I not mention the brand deal we've got with Tesco's? I could swap you out with Mel at any time. Okay, I'll be good. I don't believe you. I'm taking your lines. What do you propose we make the barrier out of? Your mother's tears after she birthed you. Jesus! Christ! Jesus!  I knew it! Shall we come again?  You ruinous of art. Well, I have never encountered such rudeness. I will leave of my own accord. I'm going to go find Ace and recreate this audio with... Cybermen? The Cybermen? No, no. The Autons, obviously. I'm still awake. That's the joke. That's fine.


OK, so in what follows, we shall be looking at hierarchical Gaussian filtering. And that will be the application of all the preliminary work we've done, all the conceptual insights we've had to the problem of performing inference in a volatile, quickly changing environment. So we've learned a lot about inference. But there's this question, does inference, as we've described and adequately described the situation of actual biological agents? So we found various ways of trying to infer the underlying distribution of a series of outcomes that we see. Now, if we want to apply this to the real world, not just in the sense of economic data we have, but imagining a biological agent that moves in an environment where there are other animals, or where there's an environment where there's night and day and stuff changes, stuff moves, what did we not consider up to now? Basically, I already said it, but I want to hear it from you again. What did we not consider in the way we did inference up to now? Time, yes. So things change. So in this picture here, we assumed that all these x's, x1, x2, x3, x4, were drawn from a distribution that was static. What about if, as time goes on, observations are more over here, and then suddenly they're over here? How do we deal with that? And this is where this picture comes in again. This is the fundamental way we think about this. We have our agent here. We have the outside world here. The two connections, the two interfaces are the sensory input the agent receives and the actions the agent takes. What the agent needs to do to take appropriate actions is it needs to have a tolerably realistic picture of the true states of the world.  They have to be inferred on the basis of sensory input. There's no way to look at them directly. And on the basis of its sensory input, the agent constructs a picture of the world parameterized by this lambda here. And on the basis of the beliefs parameterized by lambda, it takes action. And then the cycle repeats. So the dynamics were missing here. Now what about dynamics? As I already said, up to now we've only looked at inference on static quantities. Biological agents live in a continually changing world. In that boat example, we didn't consider that the boat's position changes all the time. And with it, the angle to the light taps. So one problem we've just ignored is that older observations are worth less than newer observations. So how can we take into account that old information becomes obsolete? And as we already saw at the very start of this morning is that our learning rate in the mean updating formula becomes smaller and smaller. And also in the Gaussian update formula, because our posterior precision gets higher and higher. So the learning rate becomes smaller and smaller because our equations were derived under the assumption that we're accumulating information about the stable quantity. And this assumption is wrong as soon as you live in a dynamic environment. Now here's this question. What's the simplest way to keep the learning rate from going too low? The absolute simplest way. Yes? Steady? You mean study, OK. Yeah, I thought you said steady. But that would be another word for what I'm looking for. Yes? You could do that, but it wouldn't be the simplest way. Decrease it every time. It would automatically do that. And that's what we want to prevent. We want it not to go too low. Increase. OK, yeah. You can do that, but it wouldn't be the simplest way to do it. A constant. Well, why not just make it constant? I think you can't get simpler than that. Just use a constant learning rate. So nothing sophisticated. Extremely simple. Just keep it constant. Then the only thing you have to worry about is what is the value of that constant going to be? So I've written the mean updating formula again. And I've switched around the n's and n plus 1's and replaced them with n's and n minus 1's. So instead of 1 over n plus 1, we now have 1 over n, but it's still the same equation. So we take our 1 over n, which we don't want to decline from time point to time point, and we just replace it with alpha. And now this amounts to the exponential downweighting of observations as they recede into the past. I said this too early before when it wasn't true. Now it's true. If you use a constant learning rate, then that implicitly amounts to, and it's easy to show, it implicitly amounts to exponentially downweighting your previous observations and the further they go into the past. And the higher your learning rate is, the steeper your exponential curve is that downweights them. So in neuroscience and behavioral science, this is called Riscola-Wartner learning because it derives from a paper by Riscola and Wartner in 1972. They didn't arrive at this by this kind of reasoning, but in some other way. But it's been around for more than 40 years now. And despite its extreme simplicity, it has been quite successful at describing the way animals learn. So these were two rat researchers, and they applied this to the way their rats learned. And it was a very successful description.  That's all you need to describe many experiments very successfully. But does a constant learning rate solve our problems? Let's remember we wanted to have a learning rate that can reflect the way the environment changes. Sometimes the environment changes quickly. Sometimes the environment changes more slowly. So does a constant learning rate solve our problems? Not really, only partly. It implies a certain rate of forgetting. Another way to see it is that it amounts to taking only the n equals 1 over alpha last data points into account. But it doesn't address the fundamental problem that we might have to adapt our learning rate in response to changes in the way the environment changes. So if the learning rate is supposed to reflect uncertainty in Bayesian inference, because a more quickly changing environment creates more uncertainty. So the learning rate takes the role of uncertainty in Bayesian inference. And as we already saw, the learning rate in the Bayesian update was a ratio of precision, so a ratio of inverse uncertainties. The question is, how do we know that alpha reflects the right level of uncertainty at any one time? So that's what we need to know. We want to have an alpha, an adaptive learning rate, that accurately reflects the level of uncertainty at any one time. And we want alpha, and this is basically a corollary of the before, we want alpha to be able to change in response to events. So if it's constant, that's not possible. Again, what we really need is an adaptive learning rate that accurately reflects uncertainty. Now, this forces us to think a bit more about what kinds of uncertainty we're dealing with. What do we want reflected in the positions that go into our learning rate? And there are different kinds of uncertainty that we have to deal with. So one possible taxonomy of uncertainty, going back to, for instance, you and Diane and other famous learning researchers, is that you first have, as your first kind of uncertainty, you have outcome uncertainty. And this means anything that's not accounted for by your model, it's basically the precision of your likelihood. It is where you, as a modeler in some sense, give up where you say, OK, this is what I choose not to model. So you're all physicists. And you understand that we often talk about coin tosses as random events. It's a random event, whether it's head or tail. But as physicists, you know that that's not actually the case. If you knew the initial conditions of the coin flip, you could calculate its outcome. It would be complicated. But you could. I mean, it's basically determined. If you could recreate the initial conditions exactly, you would get the same coin toss again. So it's actually a modeling choice to say that outcomes are random. Physically, they're not random. It's just that if we toss a fair coin 1,000 times, we'll get around 500 heads and about 500 tails. So this outcome uncertainty is what we choose not to model. We say, OK, for the purpose of what we're doing, it's much too complicated to look at the physics of each coin toss. So we're just going to say it's random. And this is the outcome uncertainty. It's often called the irreducible uncertainty. But it's not physically irreducible. It's just in the sense that we've not modeled it irreducible. And in our Bayesian example, it was the likelihood precision. Economists call this risk. Then another kind of uncertainty is informational uncertainty, sometimes called expected uncertainty. So we are uncertain about the value of model parameters. We have to infer these model parameters. Now staying with the coin toss example, the irreducible uncertainty, the outcome uncertainty, the first kind of uncertainty is what is left after you already know exactly what parameter governs your outcome. So if you know your coin is fair, then you know the parameter is 0.5. But you're left with this kind of uncertainty. But let's say you don't know whether you have a fair coin. Well, this is an uncertainty you can do something about. You can actually go and toss your coin 1,000 times and learn about your parameter. If you get 562 heads and the rest of them tails, then you know your parameter is about 0.56. So this is a reducible kind of uncertainty, something you can learn. So this is informational uncertainty or expected uncertainty because you know how uncertain you are about this parameter. And now imagine during your 1,000 coin tosses, you have to go to the loom. Then when you come back, you're not entirely certain whether there wasn't somebody who came in and changed the coin. So this is environmental uncertainty. It is sometimes called unexpected uncertainty, but in some sense it's a slightly problematic way to call it. But it is the uncertainty that comes from not knowing whether the parameters in your model have changed. And these three kinds of uncertainty you have to deal with, the irreducible one, the outcome uncertainty, what the economists call risk, your lack of knowledge about the parameters, and even if they were constant, and your lack of knowledge about the movement of your parameters or states. I'm going to call them states if they move. So several efforts have been made to deal with this problem. These are some examples. The first one is a famous one. This is the so-called Kalman filter. It is sometimes credited with putting men on the moon because it was used in guiding the Apollo rockets. And this Kalman filter works very well with physical systems that are linear. So the Kalman filter is optimal, provably optimal for linear dynamic systems. The problem is that realistic data, and I mean realistic data in the settings we work with where we don't have a somewhat simple object like a rock going to the moon, but where we have a very complex environment with interacting agents. This works non-linearly, and so the Kalman filter breaks down. So what happens with the Kalman filter very briefly is this. It starts out with an acceptably high learning rate. And then as in the mean updating, as it learns about its environment, the learning rate goes down. But then when the environment suddenly changes, it has no mechanism to increase it appropriately. So it doesn't go down infinitely. It doesn't go down to zero as it would if you just continued doing the mean updating. It converges to a certain level. But once it's converged to that level, it effectively works like a risk or a partner learning. It has a constant learning rate. So it decreases at the start, and then it converges to something that remains essentially constant. So it falls on its nose when unexpected stuff starts to happen. And then these others are reinforcement learning efforts by Sutton, and these are more Bayesian efforts, all of these down here. And I'll let you guess which one of these approaches we'll focus on in the rest of the lecture here. We'll focus on that one. So we use a generic nonlinear Bayesian model that allows us to derive update equations that are optimal in the sense that they minimize surprise. That's a feature the other approaches don't have. OK, so what's the big idea? The big idea is actually very, very simple. We have a quantity of interest, x1. This is the quantity we want to track. It moves in time. And sometimes our x1 moves slowly, slowly. It goes up, down, and very slowly. And then it moves more quickly, and then it slows down again. That's basically what we want to model. And on the basis of noisy observations of x1, we want to infer the position of x1, and we want to minimize our surprise at our observations of x1. So what we assume is that x1 evolves in discrete time because we observe it in discrete intervals as a Gaussian random walk. Now the index up here, it's not an exponent. That's why I put it in parenthesis. It's a time index. So the probability of finding x1 at a particular place at time k is distributed as a Gaussian around where it was at the previous time k minus 1. So the mean of the Gaussian distribution describing x1 at time k is x1 at time k minus 1. Very simple and straightforward. This is called the Gaussian random walk. Now the other variable we need to have in our Gaussian random walk is the variance. And this is where the hierarchy comes in. So the variance of the Gaussian random walk in x1 is a function. And at this moment, the only constraint on the function is that it's positive because it's a variance, is a function of a quantity x2. And now the whole game repeats. x2, in turn, moves sometimes slowly and sometimes quickly. And the higher x2 is, because this is a monotonic function, because in order for x1 to move more quickly when x2 is large and more slowly when x2 is small, it is another particle. So x1 and x2 are different ones by construction. Basically, think of them as states of the environment. I wouldn't think of them as physical particles. There is no physical model that I think works like this. So it's a hierarchy of Gaussian random walks coupled via the variance. So always the next high level determines the variance of the level below until we get to the nth level, where we have a constant variance theta. Because at some point, we have to stop.  We build a model to the nth level. Yes, this will arise in practice in the sense that when we update our beliefs about x1, we will have to use the latest state we knew of x2. So we will have to look in the past, where was x2? And then we can infer the new x1. Then on the basis of the new, it's not the new x1. It's actually the new sufficient statistics for x1, so mu1 and pi1. And on the basis of mu1 and pi1, we can infer mu2 and pi2. And so on up through the hierarchy, whenever a new observation comes in. Yes? Yes. Because we can, it's more tractable if it's a Gaussian. Yes, x1 is the lowest level, yes? Yes. So how it moves depends on the higher levels. So at the observation level, so we call our observations u or y, mostly u here, because there's going to be a twist where y is going to be another more proximal observation. So this observation here we're going to call u. Now u is going to be Gaussian distributed around x1. So u at time k, these are k's, yeah? And Gaussian distributed around x1 with a variance of what we call this pi hat u to the minus 1. So this is the observation variance corresponding to the pi epsilon we had in the simple Gaussian model. So this is our observation. And then we say, OK, we have some observation noise. So that's the simple observation model. Basically, this is our likelihood. Yes, yes. They're at the same time. That's the observation model, the likelihood. First thing, it's not as simple as that. But you can interpret it in that way. But it's not a velocity is a vector. It goes in a direction. And this just basically tells you how far it is likely to travel in whatever direction. Yes? Yes. Yes, so I should have put in an index here. Nope. So in the way the model is, we're going to have to distinguish always the generative model, which is this, and the inference process. And in the inference process, so when we have a new U, and from that U we infer on X1, X2, and so on, we will have to look into the past. You will see this when we derive the update equations. Yes? But yes, it is a function. Yes. The variance is a function. It's not an alternative. So we take it at this time. So this is basically we have X2. And we generate a new X1 from this. Well, of course, when you generate from this here, you have to take the last state you had when you generate the new one. As you generate from here, you go down like this. So you have the X2 at time k. So you always have to distinguish between the generative. This is a generative model. This is a generative model of the time series. This is how our time series of U's comes about. And you can write a little program that does this. First, it samples a new Xn. And then it samples a new Xn minus 1. And then it samples a new X2. Then it samples a new X1. And then it samples the observation U. Do you agree? I think this is where your apprehension comes from.  Then you will have to work the other way around from the bottom up. And in inferring the new X1, you will have to use your last state of knowledge on X2. And that will be from k minus 1. This process here is down here. This is the so-called generative process. This is the process that we assume generates the time series. And our little agent who wants to learn about its world performs an inferential process. This inferential process proceeds in the other direction from the bottom up. Because all our little agent has is the observations. Yes. Absolutely. Yes, I totally understand that. And that's why we're going to look at this in detail. So in a nutshell, what we're going to do is we're going to take our model. We're going to do a mean field approximation. And then we're going to do something close to a Laplace approximation, but better. And this will give us update equations for the sufficient statistics. So I will have an update equation on the basis of u for mu 1 and pi 1. And then I will have an update equation for mu 2 and pi 2. And for mu 3, pi 3, up to mu n, pi n, on the basis that my belief about all the x's is Gaussian.  So at each level, I need to have an update equation for my mean and for my precision. No, they're not. But we're going to radically simplify them. So we're going to fill them. This is the first thing we're going to do. We're going to look at these f's. And we're going to restrict them. Now they're very unrestricted. All I said was that they're positive and monotonic. But we're going to narrow that down to a much narrow class of functions. OK. Now the promise of this is that it provides a generic solution to the problem of adapting one's learning rate in a volatile environment. And what is more, it does this in an efficient way. So variational inversion leads to update equations that are precision weighted prediction errors, as always. Now turning to the f's. The f's have to be positive. And what do we do when we want to simplify something? We do a Taylor expansion. So the thing with f is it has to be positive. So we cannot directly expand it. Because if f is a polynomial, it will turn negative at some point in general. Yes? Yes? Yes. So the only reason I want that is that that's a requirement I put into the model. This is not mathematics doesn't force me to put it in. I just want to have a situation where x1 moves more quickly when x2 rises. That's just me as the constructor of this model that demands this monotonous. So what we do is we take the logarithm of f, which has to exist. And so for all f that are positive everywhere, there is a function g where the exponential of g is f. So basically, we just take the logarithm of f and we expand it here. We rearrange a bit. And then this is our definition of the parameter kappa and of our parameter omega. So this is the class of f's that we allow. We say our f's are always the exponentials of a linear function, kappa x plus omega. And then we take the exponential. And this is positive everywhere and monotonically increased. It is like a? Yes? Yeah? That's a nice way to interpret it. Yes? Yeah? Yeah? These are all the f's that we allow. So now we will do this on the blackboard, just a little preview of how it will work. As we saw, we can do a mean field approximation. We can separate our levels. We're going to partition our model. So there we partitioned the thetas. And now I said the x's are a special case of thetas. States. So we're going to partition our x. Each x has its own partition. Then we're going to do a mean field approximation. And what the mean field approximation is, it gives us, is a variational energy at each level. So we get a variational energy for x1. We get a variational energy for x2. We get a variational energy for x3, and so on. And now in blue, imagine that this is your true variational energy. And it is not exactly Gaussian. We will see this. We will write it down. You can look at it. And you will see it is not Gaussian or not quadratic in log space. It's not quadratic. But we want to have something quadratic because we want our posterior to be Gaussian. We're enforcing that. We're going to say our q will have to be Gaussian. So what we do, if we did the Laplace approximation, we would go in search for the maximum of i. And we would do our quadratic expansion at the maximum of i. But we don't do that. We do something cleverer. We take our expectation. So the previous mean of our belief on the x, we take the mu at time k minus 1. And we use this as our expansion point. And then we have what we call i tilde of x, which is a quadratic approximation to the true variational energy, and which is different from what Laplace doing Laplace would have given us. So in green, we have the true variational energy in blue. In green, we have what Laplace would have given us. And in red, we have what we are actually doing. We're expanding around the previous mean. And this gives us update equations for mu. And here, I wrote it for sigma, but for pi, it's the same. It gives us these update equations. And the ingredients of these update equations are, because they are update equations, the previous mu at time k minus 1. And here, you have this variational energy, the i tilde here. It's at this point, because we're expanding here. This is the big trick. Because we're expanding here, i and i tilde will be the same at this point. So we can evaluate i at this point. And we can evaluate, sorry, we can evaluate i tilde at this point, and it will be equivalent to i. And this gives us the update equation for mu and for sigma. We'll do this in detail. This is a basic overview preview. So mean field approximation, quadratic approximation. And this leads to simple one step update equation. And what is absolutely remarkable is that these have the same structure as the value updates in Riscola-Wacken learning. And in Bayesian updates everywhere, when you do them exactly even, this is only an approximate update, of course, because we have two approximations in there. We have a mean field approximation in there. And we have a quadratic approximation.  So delta mu i, your update to mu at the i-th level is a precision weighted prediction error. And now, because this is a hierarchical model, it is the prediction error on the level below driving your update on the level above. So it is the prediction error on the i minus first level that drives the update on the i-th level. And this is how we update our beliefs. So there were several people asking themselves, how do we do that? It is because the prediction error at the level below is used for the update at the level above. So precision determines learning rate. This is exactly what we want to have. Yeah, so I mean, this drops out of the equations. This is not because we put it in by hand, but we have exactly the precision of the prediction onto the lower level. So hats from now on here always mean predictions. So pi hat is the precision of a prediction. Pi without a hat is a posterior precision. Prediction, posterior. And in the numerator, we have the precision of the prediction onto the level below. So the i-th level is predicting the i minus first level. And this is the precision of that prediction. And this is the posterior precision at the current level, at the i-th level. So again, we have this precision weighting in the prediction error. And this is the full update equation that you get in the high level of the HDF. It looks a bit more complicated. And we'll see what all these terms mean. But again, here you have the precision ratio, the one you saw on the slide before already. You have the prediction error. And this was our update in the simple Gaussian update. The structure is the same, the exact same structure, precision weight prediction error. And now if we look at the outcome level here at the bottom, here we have exactly what I wrote down here. This is our observation model. So this is how we observe x1. We observe it with a certain level of precision or noise if you take the inverse of the precision. And now we look at how, at this outcome level, the update for x1 looks. The update you saw on the slide before is a higher level update. This is at the outcome level. So very similar to the simple Gaussian model, you have the precision, the pi1 update, is the precision of the prediction plus the precision of the observation. This is the precision with which we believe to know x1 at the time when we're making the observation. And this is the precision with which we observe you. Together they give us the posterior precision on x1. The update of the mean is precision weight prediction error with observation precision in the numerator and posterior precision in the denominator, as usual. And this is the prediction error here. Now the interesting part will be the learning rate here. Let's take that apart. Let's see what is inside it. Yes, apt variables are always predictions. They always refer to predictions. So we always have a prediction step and then an update step, posterior. So we go from time point to time point in two steps. At first, we start with our posterior from the previous update. And then, basically, time evolves before we make the next observation. And we have to adjust our parameters for that. In these simple models, we don't have any drift. We don't have states pushing each other around. But in more complicated models, I mean, they may now look complicated still. But once you've dealt with them for a while, this is the simplest case. So in more complicated models, these states are actually pushing each other around. So before you make your next observation, stuff will have happened. So you update your posteriors to the predictions. So you have new predictions based on the time that has elapsed between your last observation and the next. And that's what these hats refer to. So first, from the posteriors, you go to the hats to the predictions. And then, you make your next observation. And based on that observation, you go from the hats again to the posteriors to the next posteriors, and they don't have hats again. So that's how it goes, yes? Yes. Well, these are precision. So the observation goes into the update to the mean. And the observation is a simple scale. And in other cases, we will have observations that are the squares. As we had when we were trying to infer the width of the Gaussian, there will be the squares of changes at the level below. And then you will have a much more complicated update to the precision here. But because you're just making one scalar observation, this is how your update takes place. So if we take the learning rate here apart, so this is just copied over from the slide before, we just take this as the part that we're interested in, the learning rate, yes? So necessarily, the precisions at the lower levels have to be updated before we update the higher levels. So you will see that the higher levels here will influence the precision of your prediction on x1. So just on the next slide, just one step further, you will see that the higher level is in here. It's hidden in here. So f is part of the generative process, and pi is part of the inference process. So f comes into play when you go from the top down, when you generate new data. And pi is in play when you do inference.  You have x1 here. And this is x1k, and this is x1k minus 1, this is f of x2k.  This is how we model the world that's working outside. Now, in our heads, we get new observations, u, and we update our beliefs on x1. And x1 is described by new1 and pi1. This is what's in our head. Outside in the world, there is x1. That's the reality. And that's how reality evolves. However, we do not have direct access to reality. We only have access to u. That's our interface to the outside world. And on the basis of u, we have to form an opinion, a prediction about x1. And this belief about x1, and belief in the formal sense of probability distribution, our probability distribution on x1, is characterized by the two sufficient statistics, mu1 and pi1. Everything relates. So this is part of the generative process, and this is part of the inference process. And now we're going to take part the learning rate here. What we see is this. The posterior, as we saw on the previous slide, is the precision of the prediction on x1 and the precision of the observation. And now we take this here part, the precision of the prediction. And the precision of the prediction is this here. You have to derive this. So if you derive it, you see it is this. And now let's look at what this means for our uncertainty. So this is the outcome uncertainty. This is our observation precision. And this is the irreducible part of our uncertainty. This is, again, what the economists refer to as risk. This is what we, where we, as modellers, say, we're not going to bother with the details of that. We're just going to say, our observations are going to be a bit noisy. So we have this kind of uncertainty in our learning rate. Then informational uncertainty. Sigma 1, that's simply the inverse of pi 1. This is how uncertain we are about x1. k is the time index at the previous time point. k minus 1. So our uncertainty about x1, this is the informational uncertainty. This is what we can learn. In principle, if we make lots and lots of observations on x1, sigma 1 will decrease. And the third kind of uncertainty is also represented is the environmental uncertainty. This is the states of our environment changing. And here, you have the higher level. Here, you have mu2. And because this is the inference process going from bottom up, before you've done this update that this is the learning rate for, you cannot update your higher level. So you have to take the value, the last value you had in here. So this is k minus 1, and this is also k minus 1. And you can see that the learning rate will be larger. The larger mu2 is. The larger we believe x2 to be, the larger we believe x1 to move around. The more quickly we believe x1 to move around. Because this is in the denominator over denominator, this learning rate here increases with increasing mu2. So all kinds of uncertainty that we said we had to deal with are dealt with in this learning rate. And this is not because we put them in by hand. This is what falls out of the analytic derivation of this update equation. Now, how does this work in practice? Yes, the environmental uncertainty is like an entropy. So I actually think, yeah, that is a good way to think about it. Because in the higher the entropy is in your system, the less predictable it is. This is exactly what's happening here, yes. That's a good way to interpret it. So what we do here is we simply apply the HGF to a financial time series. This is the exchange rate of the US dollar against the Swiss franc during 2010 and much of 2011. And it's just the closing rate at each day. So the US dollar and the Swiss franc are usually about the same value. So it starts close to 1. And then it sort of wanders along here. But there are two really interesting points. The first one is here. The second one is here. Now, what happens here? This is in about April 2010. And suddenly, the markets realize that Greece is effectively broke and that the eurozone has a problem. This leads to people buying lots and lots of dollars and the value of the dollar rising also against the Swiss franc. So you see the spike in the dollar. And you see the learning curve in red for a while lacks behind the green exchange rate curve because the system wasn't expecting this. So what the system is expecting is what you see here, little fluctuations. And the curve smoothly just goes on, unimpressed by these fluctuations, as it should. But at first, it's confused by this because it, of course, tries to see this as another one of these fluctuations that will go away. But no, there's something systematic behind it. And where you see this is here at the next high level of the HGF. So the belief about X2 shoots up because the system realizes, oh no, something has changed. I am now in a regime where X1 moves much more quickly than X2. So this shoots up, the learning rate increases, and the learning curve catches up with reality. And as the markets settle down, so does the volatility estimate. But then, this is around, what was it, September 2011, the euro crisis deepens and deepens and deepens. And suddenly, the markets realize that, oh, the Swiss franc isn't the euro, and they start buying Swiss francs and selling euros. So the value of the Swiss franc starts to rise and rise and rise and rise, also against the US dollar. So the value of the US dollar starts plunging against the franc. And it plunges more and more and more and more. And at this point, and this is bad for the Swiss economy because foreign tourists have more expensive holidays in Switzerland than it's bad for the Swiss export industry because all Swiss products cost more abroad and so on. So the Swiss central bank intervenes and says, we're putting a floor under how far the euro can fall against the franc simply by printing a new franc for every franc that is sold in exchange for a euro. And this leads to an immediate turnaround in the exchange rate between the franc and the dollar. So the dollar rises back again. And again, this is reflected here. And if you even do a third level where you have the volatility of the volatility, this time series realize, oh, x2 is now moving much faster. So x3 increases, but only mildly. And again, here, you have the same. And what you can also see is if you do the same here, but without including a third level, then your second level will jiggle around much more. So the function that higher levels have is that of smoothing lower levels, of making the inference process less erratic in some sense than if you only had fewer levels. Now, what was? x2 is the volatility. So it's exactly the HDF model that I showed. x3 is the volatility of the volatility. So this is the x1 is the exchange rate. We assume we observe sort of the true value of the dollar with respect to the Swiss franc with some noise. So there's noise in the markets. So x1 was sort of the true value of the US dollar expressed in Swiss francs. x2 was the volatility of that. So just the next level by exactly the equations I showed. And then x3 is the volatility of x2. So the volatility of the volatility of x1. So now, yes? Yes. The shaded area, this is the uncertainty. This basically gives you an idea of pi2 and pi3. So what I'm shading here is the 95% interval. Pi inverse. Square root of pi inverse. And then a factor applied, I think. Why is the uncertainty here decreasing? We would have to go through the details of the update equations. The intuition is simply that the world has become more certain again. So our belief about x3 is becoming more precise here. So at this point, our uncertainty about x3 is maximal. And now, our uncertainty about x3 decreases again. It is when you look at the update equations. So this is the magic of these volatility prediction errors. So we're basing these updates on the squares of the updates that we have at the level below. And when we're very surprised by how much our time series moves, we increase our uncertainty. This is something I said in one of the earlier lectures. I said, if you're just doing a kind of information accumulation inference, then your precision will always increase. But if you're learning another thing, and here we are learning another thing, and this is the volatility. So if you have an increase in the volatility, then you can push up the uncertainty of another state. This is exactly what happens here. Yes? Of the second of the third, yeah. So this, again, is because something's happening down there, I don't know exactly why it's increasing here. It may be a reflection of the fact that I know the length of the fluctuations is increasing. So here you have sort of quicker wiggles. And then here, I don't know. It would be an interesting question to look at in more detail. So what this reflects is certainly that the system is getting more uncertain about the level of x3. And here, this is a depiction of the precision weights, so basically of the learning rates at each level. And you can see how these learning rates shoot up when something interesting happens. So the green line is the learning rate of the second level. This shoots up most markedly when interesting stuff happens. This is basically a sign that the system has realized it does no longer understand the world it's trying to predict and has to learn anew. So it increases its learning rate very quickly. And then as prediction errors go down, so does the learning rate. So now it's satisfied, again, that it knows what's going on so it can reduce its learning rate. Because you don't want to have a learning rate that's too high. Because if you have a learning rate that's very high, you're throwing away too much information. You're not learning enough. So in a more or less stable environment, information from long ago is still very informative. So yeah, this is the magic of precision weighting. OK. So I do think it's been a long morning. I'm glad you enjoyed it as much as I did. No, but I did enjoy it. And we'll see each other tomorrow if there aren't any immediate questions. OK.


When I hit the beach we had 335 in dog company. When we walked down from our positions we wound up with 18 with no officers, no sergeants. Just a bunch of PFC corporals and privates and that's how we fought. And we went down and our regimental commander, Harry the horse Colonel Liversedge met us and he said men he said before we go aboard the Higgins and join our transport and go back to Hawaii.  And we lined up and we headed for the gate. It's at the foot of Surabachi it's not there now. But one by one we went through the gate but I noticed ahead of me it took a long time for them to get through the gate. When I finally reached it I looked over on the left pole of the arch and there was a K ration turned inside out and it was tacked. And on that I read fellas when you go home tell the folks we did our best that they may have many more tomorrows. Every Marine that read that broke down. We walked through the cemetery we filed through. I saw three buddies that I went to school with. We all quit. We joined the Corps back in 42. I saw our officers our lieutenants our platoon commanders. I saw our Colonel that took a hundred and five hours a short round and blew the CP up. This all just sort of broke us down. We marched off formed together got joined our USS Missoula APA ship went back to Hawaii and trained to fight Japan.


Valery Zalozhnyi, the commander of Ukrainian armed forces has been dismissed from his position. Ukrainian defense minister Rustam Yumarov wrote about this on his Telegram channel. Highlighting the need for a new approach and strategy in the army, the minister also thanked Valery Zalozhnyi for the armed forces' successes and victories during the war against Russian invasion. A new approach, a new strategy is needed. Today, a decision was made to change the head of the armed forces of Ukraine. We are grateful to Valery Fyodorovich Zalozhnyi for all the successes and victories. President Vladimir Zelensky, in turn, announced the appointment of Alexander Syrskoy as the new commander of the armed forces. Today, a new team under the leadership of the armed forces of Ukraine begins its work. I have appointed Colonel General Syrskoy as the commander of the armed forces, Zelensky said during his evening address. With all this appointment, Alexander Syrskoy was the commander of the ground troops. A little while ago, Syrskoy had a meeting with Zalozhnyi and discussed the change in the new leadership of the armed forces. Zelensky described the meeting as important.


The book of Revelation is not a crystal ball into the future, it is a mythopoetic vision of what is already happening. We face an apocalypse of our own making, with our own weapons and our consumption of the very earth that keeps us alive. The question is not what will happen, what does the Bible say will happen? The question is, are there any wild dreamers who see beyond our empires who call out our own self-destructive evil, who are caught up in a new vision, a new tree of life for our own troubled century, as the book says, come quickly.


Thank you George for introducing me but also for all the incredible work you do as our MP for Skyview. A proud Calgary and a proud voice for Alberta within our party and within our government. It is really, really a pleasure to be back here with you and making this important announcement. It's great to be back in Calgary, great to be back here at SAIT. Thank you so much Sonya for your leadership here in the school but also David thank you for welcoming us as well and thank you to all the teachers and instructors and leaders here that make SAIT such an extraordinary institute where so much happens. Not just building the future and training students to be able to have the impact that they need to have in the economy of tomorrow but also directly serving communities in ways that really demonstrate the values and the ethos that is typical to SAIT but also typical to Albertans being there for each other, visionary about the future and working hard every step of the way. It's why it's so great to be back in Alberta, so great to be having conversations with people about what governments can do to make sure people are having the best possible future because we all know times are tough right now. There's challenges around inflation, challenges around affordability, particularly around housing and groceries, concerns about the impacts of climate change and what that's going to mean for jobs in the future for careers that young people can have. There's a lot of uncertainty in a world that's changing right now and that's why it's a government we've continued to step up and it hasn't been automatic and it hasn't been easy because there's a lot of different perspectives on how to best help Canadians to move forward. I will say that our conversations with the government of Alberta about how to build a strong future have always been very constructive. Our commitment to investing in a better future for all Canadians includes especially Alberta because here is where so much of the economic growth of the powering of a net zero future is going to happen right across the country and around the world. So continuing to be here to invest whether it's investing in housing, investing in other competition and affordability measures around groceries and moving forward with a climate plan that puts more money in people's pockets while we reduce our emissions and prepare for the decarbonization challenges that will continue to have Alberta providing energy in the future. These are all the things that we've been focused on and we're going to continue to focus on and work with Albertans at all levels of government to be able to deliver. Speaking about healthcare is an essential building block of a successful future for Canadians. We moved forward last year with announcements around 200 billion dollars in investments in healthcare right across the country negotiating deals with the provinces to make sure that we're delivering more primary care, that we're delivering better support for frontline healthcare workers, that we're moving forward on mental health and that we're underpinning all of that with better data collection so we can truly see what's happening, what's happening well in different parts of the country where it needs to be improved and so Canadians can actually have a real understanding of the results and the improvements that their tax dollars are investing in in public healthcare systems right across the country. We've announced recently we're moving forward on pharma care particularly around diabetes and contraception because we know those are two areas where people are unfortunately having to make choices between paying for groceries, putting food on the table or their own health well-being and future and that's where stepping up on that way is going to be really important. But today we're here to talk about dental care. This is a way for us to not just go at people's health and improve health outcomes for Canadians across the country. It's also a way of dealing with affordability. Last year almost half a million kids whose families don't have any sort of dental coverage were able to go to the dentist because we stepped up around dental care and this year we've already seen 1.3 million seniors sign up for our Canada dental care plan. This is going to make a huge difference right across the country so far Canadians 70 and older are registering for this program within a month it'll be down to 65 and older who can register and people will start getting services as of later this spring right across the country. It's a really exciting time because we know that that mouth health is body health increasingly and making sure that our seniors and our young people as a first step have access to dental care is about creating both healthier outcomes and less pressures on our on our healthcare systems but also better quality of life while taking away some of the affordability challenges for people. It makes a huge difference and it's something that we're very very pleased to be stepping up on and delivering for seniors right now right across the country. It helps with the cost of life and the affordability with people who are vulnerable and who can't afford to pay for dental visits. It helps with the quality of life with the quality of medical care that we receive. We're investing right now in a stronger future for everyone.


Hi, guys. Welcome to another edition of Yes, We're Here. With me is Michael Grady, Nest's reporter extraordinaire. Hello, my friend. How are you today?  Miss you for sure. It's good to be on with you. Right back at you. We're all a beautiful, big Yes family, and we're all thankful for that. That's for sure. We're going to jump into your basketball career, and, okay, we all know you're from Indiana.  That's stuff we know. But what people may not know is, may not know, rather, is that you're a big Michael Jordan fan. How does that drive? Yeah, you know, it's just one of those things. I think it's funny. Over time, especially as you get older, Nancy, the people that you grew up watching, you just kind of put them on this pedestal. And as these younger guys come up, and these great athletes come up, you know, right after Jordan, it was the Kobe fans who were saying he was as good as Jordan. And all of a sudden, I felt this loyalty to my generation and who I grew up in. So it was, no, no, no. It's not Kobe. It's Jordan.  The Pacers would see the Bulls really often.  There's just nobody else on that level. And so even as the LeBrons and other guys who come along got still very loyal to that generation and that radio background, I've had a lot of opportunities to argue with, too, Nancy. Yeah, I can just imagine because when I first heard that you were an MJ fan, I thought, Michael, you must have taken a lot of grief for that. No, you know, I didn't mention it. You know, I wouldn't mention it, like, publicly or in school or anything. I wanted the Pacers to be down the bull. Yeah. Like, every single time. It's just my respect for Jordan as a, his determination, his will to win, his commitment to excellence, all those things that we've been talking about, you know, Kobe immediately following his passing. You know, Kobe got that blueprint from MJ and it just set him apart from everybody else.  And none of them really got an opportunity other than to keep Elijah on in those that couple year gap where Jordan retired and then came back. There were a lot of great players during that generation couldn't get a championship simply because of number 23. So even though I hated him for stress, Nancy, I couldn't help but expect it. And so all that considered, where do you stand on the LeBron versus MJ debate? Well, I look at it in the same way I look at, you know, boxing. You know, there are going to be other guys that come along who may have a better skill in this area, a better skill in that area. LeBron, great passer, can score with the best of them, has been consistent over the course of his career. 6-8, 250. So the relationship that I put up in it with boxing is pound for pound. You put Jordan 6-8, 250. You put Jordan at 6-8, 250. My goodness. What would that comparison be? And then you get into intangibles and that's where I get back to the whole determination, will to win, all those types of things that, again, LeBron is unbelievable. To say that LeBron is number two all time, that's a heck of a compliment. That's a heck of a compliment. All the guys that have played the game say that one guy's number two all time is a heck of an accomplishment, but I can't put them ahead of Jordan because there are too many killer instincts, whatever you want to call it, intangibles that really separates them. That era, how physical it was, I'm sure LeBron would have adapted, but from Mike at his frame, to go through the bad boy pistons, even those Nick's teams as physical as they were, so many different teams that he had to go through and never saw a game 7 in the finals. You know, never lost to game 7. He went to the end with Pacers at a game 7. There's just so many different things, so many classic moments that came in the postseason. I just can't put anyone in that class with MJ, but I put LeBron at a solid number two. I totally get that, and you also get the feeling that LeBron himself is probably pretty comfortable with that as well, which is pretty cool. And I want to ask you, have to, where does Reggie Miller stand in your consciousness? Well, I had three guys and, you know, superheroes for me growing up, guys who I thought could almost walk on water a little bit. One was Michael Jackson, I must be honest, and I remember the time video I thought he really did turn into the same. Michael Jordan was another one, and then Reggie Miller was another. And it's the clutch moments from Reggie, and we know those great moments against the Knicks at Madison Square Garden. That's why I think it's kind of funny and a little bit poetic that I grew up not necessarily liking the Knicks growing up, Pacers, Knicks, and those playoff rivalries. And then as an adult, I joined the next organization with Yes Network, and it's still okay to not really like the Knicks. So I like that my journey is somewhat gone there, but Reggie's moments in Madison Square Garden, those classic moments and moments against the Bulls, all those types of things. Just, it was like pulling a rabbit out of a hat. That classic game five between the Knicks and the Pacers back in 2002, where Reggie hit that one crazy shot that wouldn't have countered if we had replay today, but that crazy shot sent to overtime. He dunked on four Nets players in that overtime period. It was just a classic game. So those were guys that were superheroes to me. Jordan, clearly the better player, but Reggie just had this feeling in dramatic moments that you just felt like he was just gonna take things over. And I really loved that growing up as a kid.  No love from MJ. No love from you. Right. Exactly. And you know what? As I got older, I appreciate, you know, it's, you know, ripped the Knicks and stuff like that, but I really appreciate that generation and seeing how physical those playoff series were. So the Charles Oakley, you know, my guy Ewing, you know, rooted 40s doing at Georgetown. John Starks, I couldn't stand him, but I just have an appreciation for that generation now. It's just funny how time kind of changes those feelings. So funny. Well, maybe not so funny, but it is interesting because a lot of Jordan fans, et cetera, totally necessary love, Mr. Sparks. Oh, no. Not at all. In this area, in our New York area. Yeah. Okay. I've got one for you, Nancy. Okay. Who's your favorite athlete? Who got you into baseball? That's such an easy one. I had a dad that, you know, my mom wasn't fond of this, but hey, he took me out of school sometimes to take me to sporting events. Okay. So that's how it went. I was the oldest. I have a little baby sister. She was with my mom. Where's daddy's girl? This way it goes. So long story short, I have a photo of myself. I couldn't get out. It's probably at my mom's house, but I have, I'm wearing a Don Mattingly t-shirt and I'm in the bleachers and at Yankee Stadium. And then there's another time where I'm in the upper deck behind home plate. Of course, it's the old Yankee Stadium. I didn't go anywhere without my Mattingly gear. That's just the way it was. Okay. Posters in the room, the whole business. You can imagine it all cut to the early 2000s. He's the bench coach, right? For the Yankees. And I'm ready for it. You know, I'm ready for it. I've interviewed tons of guys. You know how it goes. Oh yeah. Oh yeah. Came around the corner. I wasn't, I wasn't expecting it. I'm inside the clubhouse. I was, I knew I was to interview him that day, but I hadn't seen him enter the room and I turned around and he was right there. And I just stopped for a second and he said, hi, I'm Don Mattingly. Yeah. Okay. All right. I'll just get it out of the way right now. You're my favorite guy. You're my guy. You're the reason I love baseball. All right. Let's get down to business. That's a mystery.  And it's just, it just, you know, a reminder of how cool of a gig this is. And the opportunities that we get to, and I know that with all that's going on in the world, sports can seem somewhat insignificant in some respects, but it also, what we're going through speaks to the significance and to the escapism and how much fun and that camaraderie and people coming together and cheering on a team or cheering against the team or whatever it may be. I know a lot of folks missed it and I know we can't wait to get back, but moments like that, that's one of the reasons I love what we do next. So true. And there's so many lessons in all of it.  And not only do they not just support, but they surpass everything you could have ever hoped that they would be and they teach you things. And when you're young, you don't even realize that lessons are being bestowed, but later you get it. Michael, great to visit with you. Love to the family. Hope everyone's doing well. Right? Yes. Yes. And same to you as well. Same to you as well. Thank you so much. And I share your sentiments. We all do. We can't wait to get back to our new normal and we look forward to it. Michael, really good to see you. Take care. See you soon, my friend. Thank you, Nancy.


Hello, everyone, and welcome to Friday's New Musical Concert Series presented by First Unitarian Society. My name is Ivo Gortrich, and I'm the founder and artistic director of Lunart. For those of you who are not familiar with our work, Lunart is a non-profit arts organization based in Madison, whose mission is to support, promote, and celebrate women's creativity in the arts through performances, workshops, exhibitions, and interdisciplinary collaboration. A huge thank you to Drew Collins for inviting me to serve as the artistic director for the Women's History Month and curate these musical offerings for you during the month of March. Since we cannot meet in person, I decided to create a collage of performances from Lunart's 2018 and 2019 season. Additionally, you will hear some of the pieces straight from the living rooms of Lunart's team. And now relax and enjoy the music. In conjunction with Women's History Month, Lunart has launched a fundraising campaign in her honor. Influential women have shaped us as individuals, from family members to teachers, from artists to activists, from entrepreneurs to scientists. These women served as role models, challenging us to be the best versions of ourselves. With any donation to Lunart, you can now honor the women in your life that have inspired you. For more information, please visit lunartfestival.org slash in her honor. Additionally, if you have been enjoying Friday New Music Hall concert series, please make sure to donate to First Unitarian Society at fussmedicine.org slash giving. Every donation truly makes a difference and with your help, we'll make sure to come out of this pandemic stronger and healthier. Have a lovely day, everyone, and enjoy the rest of the program. Goodbye.


Lab lab perperius, L. Sweet is a legume native to Africa and cultivated throughout the tropics for food and forage. A recent study has produced a high-quality chromosome scale assembly of its genome, which provides insight into the genetics behind the protein-rich seed's nutritious value. Additionally, the study identified two domestication events in the species, as well as genetic and phenotypic diversity in a comprehensive lab lab germplasm collection. This information will help improve the crop's performance and resilience to climate change. Furthermore, this study serves as an example of how researchers can work together with LMICs to sequence indigenous crops and make them available to the global community. This article was authored by Isaac N. J. C. A. C. A., Bernice Waru, Nadia Kamal, and others.


And if we actually take a moment to look at the word dunya as well it relates to something that is lowly And so when we are seeking the material world we need to ask ourselves why are we seeking it هل نحن نحاول أن نتحدث عن بعض المدينة التي سيبقى معنا سوليا في هذا العالم؟ أو هل سيكون لدينا شيء بشكل كبير لأسفل الحياة؟ وإذا كانت محاولة بشكل سوليا بشكل كبير then that's what we know to be as instant gratification and that's basically in short of the social media ads and I'm guilty of this myself but our Twitter, Instagram accounts or Facebook accounts we're constantly seeking validation from others we're constantly posting statuses or pictures so that people can give us lights but we have to ask ourselves what is it really in there for us that we're seeking and that's not going to give obviously the benefits that come from the internet as well if we look at after that though right if we look at this word after the afterlife and everything after that means something that comes after and this verse allows us to kind of look at it it uses the word sa'a which means to strive for something meaning that if you're not striving for the سوليا and if you're actually striving for the afterlife then you're striving for something that is to come which means that role naturally into a difficulty so it's not instant it's not something that will necessarily come immediately towards you but it's something that we need to implicate certain quality within ourselves so that we can achieve that desired goal when the sawlers talk about some of the preconditions or requirements and striving for the afterlife they say that desire alone is not sufficient along with desire or will there needs to be a commitment to faith commitment to faith requires that at times we will not see the wisdom behind why Allah SWT has put us not only in this world but in the circumstances that He has put us in the trials and tribulations that we face right will not necessarily be met with answers but there is an element of patience and there is an element of trust and that Allah SWT has put us in the circumstances that He has in this dunya knowing that we have the capacity and the potential to see and look through it if our desire and if our objective is the loss of khan al-Wazada and that's why the loss of khan al-Wazada and the Quran says that those who are afflicted by trial not necessarily by death we use this phrase a lot when people pass away and it is a sin that you do so but those who are afflicted by hardship say that we are in Allah and we are in Him when one of us is afflicted by a trial or tribulation what is our natural response what should our response be indeed we are from Allah SWT and we are returning to Him and that's faith and then the third thing they mention after will and after faith is action when we are a child or when we are in a state of tribulation we need to ask ourselves what is our response going to be in that moment how are we going to react are we going to act with despair are we going to act having hope in the loss of khan al-Wazada and again trusting that the outcome might not necessarily be something that we want but it might be something that the loss of khan al-Wazada wishes for us and that is something that we should be content and satisfied with at the end of this ayah Allah SWT says فَبُلَا إِيَا كَانَ سَعِيْهُمْ مَشْوُرًا so those who who's objective is the afterlife who's objective is paradise who's objective is a pleasure of Allah SWT and doing so following the footsteps of the Prophet then they are received by Allah SWT as grace or favor now شكر or شكر on these are then Allah SWT doesn't necessarily thank us for something that we do in regards to Allah SWT actually we are a shakur or a shaker the Arabic ghost who is the one who gives abundantly for little action that's done and we know in other traditions that when we extend our hand or when we extend the concept to Allah SWT He runs towards us with hand when we reach out to Allah SWT with little He responds when He gives with tenfold if not more however again we need to constantly remind ourselves not to be not to put ourselves in a situation of despair because when that happens those are moments of what and which we begin to question our faith and our trust in God this is why I've also handed without a sentence another verse thankful in every situation are those amongst the believers that are literal in number so actually being in this city and being in this situation isn't easy all of us have faced some type of difficulty or trial but those who are patient through their trials are not only patient to see what the outcome of the situation is and then to thank Allah SWT that after the outcome of the situation has come because that's usually what has to happen we wait to see what happens if the desired outcome is not met we make excuses and we say that we start to point the finger on the other or if the desired outcome is met then we're in a state of gratitude but the true state of the believer should be that even in that state of trial and tribulation they are thanking Allah SWT before the response comes when bad intentions only seek his displeasure why again because we're not seeing this pleasure per se and in this hadith the Prophet Allah SWT mentions that he dropped as maybe you can say an extreme example because most of us are not traversing across the world we're not crossing Mecca but the deeper meaning behind this is that any situation that we're in we shouldn't forget our desire the outcome which for the believer again shouldn't be anyone but Allah SWT but that we ask Allah SWT to allow us to be people who are patient with as well and we ask Allah SWT الحمد لله بصلاة والسلام وعلى رسول الله وعلى أاله وصحته ومن وعلى ورغاه تبارك وتعالى في اتابه عزيز إن الله وملالكة تدسلونا على النبي يا أيها المهان ومصلي وعليه وصلي متسلينا الله مصلي على سيدنا محمد وعلى أالي سيدنا محمد كما صليت على إبراهيم وعلى أالي الراهيمة وعلى حديث الحكس الله وعليه وسلم يقول ألفى يسو من تان نفسه وععمل يما بقى الموت ولعى يسو من تان نفسه وخواه وزمننا على الله The intelligent person is one who keeps himself in check constantly and seeks and acts according to the afterlife However, the foolish individual is one who follows his desires and places false hope in Allah SWT And again to clarify Allah SWT has created us in a material body We live in the world and we do have desires and we do have pleasures But again what counts is the intention that we have behind each one of those actions When we think about the time that we're spending are the places that we go to or the company that we seek We need to constantly ask ourselves this question at the end of that tabood Where are we actually going Is that desired place or that person our outcome or that goal or is there something deeper behind it Where are we truly trying to seek And What is our mental, spiritual emotional and physical state in those situations Are they truly states Which are pleasing to Allah SWT That would align to what He is content with or would be content with Or Are we meeting those needs Mental, spiritual, emotional and physical Just to make an end for us in this world And there can be specific examples that can be mentioned for each one of these things Especially for those who are going to school who just went back to school I'm going back myself soon Which is that when we go into these spaces When we keep the company that we do When we surround ourselves with our friends What is our state in those moments And are we people who are influencing others Are we people who are easily influenced by others Are we making an impact On those around us through our faith Through our character Or are we giving into the desires That surround us And I'm not saying it's an easy road I'm not saying that high school or college Today I'm necessarily in the easy places to be in And this applies to the workspace as well This applies in any given situation In any given circumstance or relationship But at the heart of Kyle and Tribulation Always remember this question Who and where am I going in this situation And try to develop an inner voice with yourself Try to listen to your heart Develop a relationship with yourself as well Don't neglect yourself In your emotional state In your spiritual state To satisfy somebody else But ask yourself where you truly stand Before trying to stand there for others That comes as well But that comes with a type of mutuality Where when a good company And good people Are an influence to others That naturally the circumstances allow you Around you won't even allow you to talk Your state will be one That will naturally change others Insha'Allah So with that we ask Allah to panel with Allah To grant us sofeek We ask Allah to panel with Allah To make us strong individuals in our faith To make us strong in our circumstances We ask Allah to panel with Allah To give us in this world To make us strong We ask Allah to panel with Allah To make us strong We ask Allah to panel with Allah To make us strong


Well hello and welcome to Understand Men Now. I'm Jonathan Asley of JonathanAsley.com and I'm so excited to be shooting this short video for you today. Our topic this one activity, this one activity is how a man basically falls in love with you or will commit to you. There's one, you know, I will call it that secret activity if you will, but it's not really that secret. It's rather obvious. Really quickly, if you're new to my YouTube channel, please hit the subscribe button, hit the bell so you can be notified of new content. I shoot about three or four videos per week. All right, what is it that's going to take for a man to commit to you? What's that one secret that you haven't really explored? And that's what I want to talk about today because I think a lot of you missed the bus on this one. You know what, you have, you are on the bus in your twenties, actually in your teens in your twenties, but I think you forgot this and I think many of you have forgotten this and we're going to talk about why it's been forgotten. So I was recently watching a show on Netflix called Love on the Spectrum. Love on the Spectrum. By the way, if you've seen the show, please post a comment below. I got to tell you, I watched the show, I was immediately crying right from the get go. This was autistic people and Asperger's such and I don't know if that's the appropriate terminology or not, who were in their twenties trying to find love in their lives and it was so adorable and so cute and the parents were so loving and supportive. It just touched my heart. It's only five episodes. They're 40 minutes each. Go do a binge on it. You know, it's interesting. If people with autism and disabilities can find love, then you can too. But what's unique about them is that they actually operate with not as much ego that most people operate and we, most people operate based on a lot of ego. Like I need all these boxes checked of status and looks and all these things. Whereas autistic people are like, I just want to connect with someone for the most part. I mean, they want to feel chemistry. But what's also interesting is they lack filters. They literally speak their mind. I mean, they're documenting these dates and at the end they're like, I don't want to see you again. Bye. No filter. It's actually kind of cute. Wouldn't it be great if we could just be honest with everyone and just say, I'm not interested after a date? But boy, we're afraid of hurting people's feelings.   And I raise my hand. I am guilty of doing this. I am guilty of saying I would call someone I didn't do it because I'm afraid.  So what did I learn in this video? Well, there was this one young man who was totally into anime, totally into anime. And then there was this other woman who was totally into video games. And she, all she talked about was video games and all he talked about was anime. And there was kind of a disconnect. And while he knew how to play video games, all, I mean, he was a, he was, he drew anime and he was into it. And she, he was into video games and they weren't really connecting. And what I realized was he really wants a woman who's into anime into it. And so I thought to myself, when you look at some of the best relationships, they share this one thing in common and that is they have a common passion, a common passion, a common activity that they share. And I've shared this story before where I met a woman years ago, about a decade ago. And on our second date, she pulled out a deck of playing cards. And, and she said, do you play gin rummy? And I said, yeah, I kind of play and we started to play. And then we ended up going out to a bar for drinks. And we sat at the bar and we played gin rummy for hours. And I got addicted to it. And every time we got together, we played cards together. And I loved it. I was looking forward to it. And even though I was kind of like not really that into her, I was so into that that it kind of kept me a little longer in the relationship. But I, and then as soon as we stopped seeing each other and we would kind of transition to friends, the next woman I met, I'm like, do you play gin rummy? I wanted to play gin rummy with someone I'm in love with kind of thing. And not that gin rummy is my game per se, but I got so into the activity that it became a fun thing to do. And what I see in a lot of relationships today, ladies, you guys are in passive relationships.  You're not really, you don't share this common bond. It might be riding bikes at the beach. I live at the ocean. It might be hiking. You know, my best friend in his relationship, they went hiking every weekend. It was some activity they love to do. Some people, it might be going to museums. Some people, it might be shooting pool together, but it's doing something together. I think of my mom and dad. I grew up, my mom and dad, every night when my dad came home, he, you know, said hi to the kids. He took some time for himself. We all had dinner. And after dinner, the kids went off to do something and my mom and dad went in the backyard and they played backgammon. And they played backgammon every night, every night, every night. And you would think it was World War three when they played. Oh my gosh, they played a dollar a game. And it usually was a dollar back and forth, but they were so passionate about it. They did it for as long as I could remember. My mother and father played backgammon on a regular basis. They had passion for it. They shared this common activity. And I'm going to tell you that a common, not that you, there's the thing about dating. We might share things like, do you like sushi? Me too. Do you like snowboarding? My favorite thing to do. Do you like the Rolling Stones? Oh my god, I've seen them five times. But that's not the same as developing a route in that activity, developing the route in that activity. So my invitation for you is to make sure that this is in your consciousness when you're dating in the early stages. And if you're in a relationship, find that common activity. Because the deeper the route in that activity, the stronger your relationship becomes. And it's much harder to break up with someone, not that it should be about hard about breaking up. But when you, that actually creates the trust for commitment. These are one of the routes to trust for commitment. In fact, in my private coaching, I talk about the five routes of trust to commitment. So schedule a discovery call with me to see if working with the coach is right for you. So you can learn those five routes to commitment that I talk about my private coaching. All right. Hope you found value in this video. Please post a comment below if this resonate with you. Tell me what your favorite activity is. I'd like to hear it post a comment. I'm going to do my best to read all of them and respond. All right, I'm going to wrap up this video as I always do.  I'm going to reach into the camera and give you a hug of love if that's okay. I'm going to ask you to turn to somebody and give them a hug of love because hugs are a great source of love and we can all use more love in our lives.  Bye-bye now.


The triboelectric nanogenerator, Teng, is a new type of energy conversion technology which uses mechanical energy to generate electricity. It has seen rapid advancements since it was invented in 2012, with polymer materials being used as the key component. These polymer materials can capture and transfer charges during friction, allowing them to be used in Tengs. This paper summarizes the use of polymer materials in Tengs, focusing on their use in different types of Tengs and their potential applications. The authors also discuss the challenges facing the development of these materials and how they could be overcome. This article was authored by I-Hua Chen, Qin Zheng, Guang Zhu, and others.