Good day fellow investors. Very excited after almost a year of hard work. I have finally finished my book on value investing. It's called The Modern Value Investor. This is just an example. You don't have this gray line here. But the book is finally out. The link is in the description below for Amazon Boat Paperback and Kindle. I think that if you enjoy the channel, if you enjoy what I do, then you will enjoy the book. The book is divided in three parts. The first part is about the value investing mindset and the psychological characteristics you need to have to be a successful value investor. I discuss behavioral finance issues related to value investing. I discuss the theory of reflexivity and then some other Wall Street shenanigans like buybacks or Wall Street fees. Also, what is the key component of value investing? That's risk. So I define risk. I show how to analyze risk. What does risk mean for a value investing perspective? And when you start from a risk perspective, then you are a real value investor. Part two of the book is the core of the book, 25 value investing tools on how to approach analyzing a company, how to put everything into perspective, how to end up with a comparative table so that you can use the 25 tools on different stocks and then see which stock has a better margin of safety, which stock is trading below the intrinsic value, what is the intrinsic value, how to get to the intrinsic value and other 25 tools, how to avoid value traps, how to catch falling knives, how to assess qualitative risks, etc. So 25 tools. I think if you pass those tools on each stock you own or you're thinking of investing, it takes a little bit of time, but you will have a good comprehensive comparable overview to other things and you can limit your risks and increase your returns. That's the point of investing. Number three, the part of the book modernizes value investing a bit and gives you food for thought. To think, okay, should I think about macroeconomics, should I think about owning gold, should I think about hyperbolic discounting, portfolio strategies, all-weather portfolio, rebalancing, selling, buying, managing risk, etc. So in a whole, the book gives you the mindset, the tools and adds value to whatever value investing might be, how value investing might be improved over time. There is also an application of all of the 25 tools on a stock where you see me going through a stock, in this case Daimler, which I found a good example to analyze and then we get to an intrinsic value, see when to buy, how to buy it, how to approach that stock, etc. So I hope you really enjoyed the book. Put a nice review on Amazon if you enjoyed the book. It will mean a lot to me. So thank you for watching, hope you enjoyed the book and I'll see you in the next video.


The Adventures of Frank Race, starring Paul Dubov. The war changed many things, the face of the earth and the people on it. Before the war, Frank Race worked as an attorney. But he traded his law books for the cloak and dagger of the OSS. And when it was over, his former life was over too. Adventure had become his business. The Adventures of Frank Race. Now we join Frank Race for the adventure of the Six Week Cure. Reno, Nevada. A simple city like any other American city. But one made notorious for its gambling clubs and for harboring the sickest people in the world. The temporary home of would-be divorcees taking the Six Week Cure from the ills of marriage. You could see their strained white faces on the streets and in the restaurants and the clubs. The faces of women not quite sure what had happened. Not quite sure why they were here. Not quite sure where they were going tomorrow. You know something, Race. All my life I've been hearing about gay divorces. But the babes around here do not look so gay to me.  For most of them, this is the bitter end of a sweet dream. That's never fun.  You got your business free insurance company all cleared up. Leave us get back to New York. What's your rush? We can take a little vacation. Some great fishing in the mountains here. Oh, no, you don't. No, you don't. Not with me. I remember the last time. I got more hooks into myself when I got into the fish. Besides which, I wind up eating nothing but bread. Because it is the only food we have which don't have bones in it. All right, Mark, because you win. Besides, I'm not so sure that you'd go fishing. What do you mean? There is a dish sitting at that table over in the corner, and she has been making passes with her eyeballs ever since we come in here. And she ain't no trout. Where? It's Olga Petrov.  I most certainly do know her. Olga, Olga, baby. Grace, oh, Grace, I wasn't sure it was you. Olga Petrov. If there's a better ballerina in the world, I've never seen her. She moved with infinite grace, and your heart left your body to walk beside her on stage or off. It's been so long, Grace. Paris. Just before the Germans took over. You left without saying goodbye. Boris was jealous, remember? And with good reason. I was beginning to fall in love with you. That's nice, Grace. That's very sweet. Oh, oh, excuse me, Olga. This is Mark Donovan, my very good friend. Hello, Mr. Donovan. By the way, how is Boris? He's fine. He's here with you? He's here, Grace, but not with me. Don't tell me that you're... Yes. I'm here for what you so quaintly call the six-week cure. Divorce. I'm really very sorry to hear that. Where are you staying, Grace? Well, we're really not staying. We came in on the plane this morning to settle the claim. We're going right back. Grace, please stay for a little while. I am in trouble, desperate trouble. A dude ranch ten miles out on the highway. There are accommodations open. Mark will drive us out. We can talk it over on the way. What went wrong, Olga? I know you didn't take your marriage lightly. I guess I am a war casualty, Grace. Even at this late date. The ballet is ruined temporarily in Europe. They can't support it. Not on the scale Boris demands. They can barely find food. I know. Olga, you know Boris. You know how he likes to live like an emperor. He found somebody to finance a tour. Patron of the arch? I would hardly say that. He was financed by a man named Joe Rockland. Big Rocky the hood? Financing a ballet company? That's right, Grace. Big Rocky, as you call him, is in love with me. And he's forcing you and Boris to split. I wish that were so. If it were, we could run away. But it isn't. She's a woman. Another woman? Boris must be blind. He isn't in love with her. At least I don't think so. But she's very wealthy. That's an attraction Boris cannot resist. Wanna tell me who she is? Fran Maitland. Wilson Maitland's wife. But she's still married. She's here too, Grace. Getting a divorce. We are all what you call She started to cry and I put my arms around her. She wasn't meant for tears, not this baby. Her perfume was taboo, exotic like everything else about her. Even if Fran Maitland had all the money in the world, Boris Petrov was a chump to give this up. I'm sorry, Grace. All I know about dancing or life, I learned from Boris. He has been all things to me. I don't relish the thought of being alone. I know. And you're gonna find it difficult to be alone with big Rocky after you. Very difficult. The bar triple X dude ranch was very much on the sedentary side so far as the guests were concerned. The most strenuous activity seemed to be the lifting of cocktail glasses. But there was nothing sedentary about the owner of the ranch, Ma Wescott. She was pressing 70 by doing it the hard way. This gentle old lady, believe it or not, could sit on a Mustang like Roy Rogers. She could also pilot her own helicopter. Understand you're something of a flyer too, Mr. Ace. I've never been checked out in a helicopter. It's good for keeping the herds inside. Most ranchers think I'm crazy though. It's something new in range riding. They'll come around, Mrs. Wescott. Maybe you'd like to come with me someday and see how big this ranch really is. Any time. Get you away from these crazy females for a while. Women ought to be without their men, folks. It makes you mean. Ma, I've been luckin' all over for you. It's my son, Buzz. What do you want?  She must be lost. Someone ought to go find her. You were supposed to go with her. Well, I had other things to do. Sure. Shinin' up to that Fran Maitland.  Now, don't go yelping at me. Old girl's quite a character, ain't she? He spoke from just behind my left shoulder, but I didn't have to see his face to know him. Joe Rockland, Big Rocky. Hardly the type to be calling anybody else a character. I turned and looked into those small black eyes. He was wearing a bow tie. And if it had been the kind that lit up, it would have spelled murder done cheaply. You gettin' into divorce racing? I'm not married, Rocky. Ordering several kinds of divorces. Sometimes a guy gets his arm broke. That's the kind of divorce from usin' it anymore. And sometimes a guy gets tired of livin' and signs off. You could call it a divorce. What you say and what you mean aren't the same. I got a smart race.  I'm a friend of Olga Petrovs. She's playing a tough hand. I want to stick around until the cards are counted. Boris and me can take care of that. I don't remember inviting you to my wedding. I'm coming anyhow. Just to make sure there's no shotgun pointing at the bride. So you really did come for a divorce. The tough guy. Well, from now on I'm your lawyer. Gonna handle the whole thing for ya. Startin' with a broken arm. Afraid you're even gonna supply the arm, Rocky. Let me call a race. All right, go. Now get outta here. Let's see you again, Philly. I'm sorry, everybody. We're old friends. He gets playful. You think you're pretty good, don't ya? Big man. Tough guy. Just like my husband. Sorry to evoke unpleasant memories, Mrs. Mrs. Towner. Mrs. Robert Towner. But you can call me Sylvia. Because you're just like him. Just like my big, tough husband. What's your name? Race. Frank Race. Share with yourself, aren't ya? Just like Robert. Well, you're just a man. Just another heel. I'll be rid of him in another week. What do you think of that, huh? Congratulations. I'm lonesome. I don't like to be alone. I'm almost 30 and I don't like it. I gave him 10 years. Now I'm alone. You could go back to him, Sylvia. Ever think of that? I don't think of anything else. But I can't. You know why? He doesn't want me back. Because I had him arrested for hitting me. And I should have killed him. You better come outside. People are staring at you. Who do I care? Come on.   Stop it. You're getting hysterical.  Let go. I never want to see another man again as long as I live. Oh, hello, Boris. Olga told me you were here. Olga told me a lot of things, too. What are you setting her up for? We are getting a divorce, that's all. That should not disturb you. You were very fond of Olga. Of course, you may have some rough competition this time.  That girl never looked at anybody but you.  She will find another life. Just as I will find another one. With Fran Maitland, your new life might be more interesting than you think. More interesting and less peaceful. Is that so, Mr. Race? I hadn't seen her come around the end of the ranch house. But suddenly she was there. A striking woman of 35. Everything about her was reckless. Her face, her figure, the way she wore her clothes. I had met her once before investigating the losses and jewelry. And once met, Fran Maitland was not easily forgotten. Let's hear your theories about life with me, Mr. Race. It might brighten my day. I've been bored. That's your trouble, baby. You get bored too easily and it takes a new man to snap you out of it. Are you volunteering, Race? For assignments like this, I never volunteer. I do not like your attitude, Race. I do. I love it. I'm finding you more and more intriguing, Race. Don't try too hard. I don't fit into your collection of oddities. I'm not an artiste like Boris. And I can't match Buzz Wescott's drawl. What do you mean by that, Race? I'll tell you what he means. He means I've been riding with Wescott while you've been chasing after Sylvia Turner. She's younger than I am, Boris, darling. But she doesn't have any money. Her husband won't settle a dime. Well, I can see you two lovebirds have a lot to discuss. Excuse me. Mark and I had only one interest, keeping an eye on Olga Petrov. And we avoided the rest of the guests until night came. It was Mara Wescott who broke up a dull evening for us by taking us for a ride in her helicopter. I do this every once in a while at night. Check up on Russeling. You know, I don't see how he can spot him from up here. You've got a powerful spotlight. Just have to shine it down when I see something suspicious going on. Can see part of the herd up ahead there. You see him in the moonlight? Oh, yeah, yeah, yeah. When we get over him, we'll lie at all.  You think the Texas Rangers would give up their ponies and learn to fly things like these instead, huh? I'll hang right over him now. Turn on that light. Where'd you race? Sure. Look at that. He ain't scared or anything. I've seen a light from way up here. Mark, there used to a thing called the sun. It comes up every morning. Yeah, yeah, you're right. The horse is... Yes, and he saddled. Hey, stand up by something. What is it? I don't know, but we'd better go down and find out. Ma Westcott set the helicopter down and we walked around the herd to where the horse stood. The animal was nuzzling the thing we had seen on the ground. It was the body of a woman. I knelt over her. A pointed branding iron had been stabbed through her throat. It was Sylvia Turner and she was dead. It was Frank Race in just about one minute. Now, back to the adventures of Frank Race. Sylvia Turner had had her last drink and her last heartache. Mark stood guard over the body while Ma Westcott and I flew back to the ranch house to call the authorities. There wasn't much to be done, so I went to the cottage to turn in. Don't go in, Race. It's a nice night. It was Rocky again. But this time he wasn't playing it on the physical side. He was using science, the science of a deadly automatic. It produced the desired chemical reaction. It dispelled my inclination to ignore him. I figured we might take a little walk down the road and I got a car there. We could take a spin in the night air. Who knows, might give you pneumonia. Or lead poisoning. That's a possibility.  Like Sylvia Turner? I got a theory about her race. I think you killed her. I think I can clear myself. No. You and Sylvia had quite a beef today. You were trying to get romantic and broad daylight and they might have scratched you up a bit. Boris told me. You spent quite a yarn. It gets more interesting. Go ahead.  You wasn't around all afternoon, so it figures you followed her. I was in my cottage. Yeah, but the cops won't know that. You blew your top and killed it.  You can't sell that to the police. I ain't finished. I'm coming to the best part. You went for a plane ride, and who spotted the body? You. That is something a guy always does to cover up. Only after the call for the bulls goes up, you get nervous. So you hop into your rented car, which I have down by the road. You drive out to a lonely spot. Then you take this automatic, which also happens to be yours. I just lifted it, and you blow your brains out. Neat? Very neat. But there's one weak point. Yeah? What? Your chin! Give me that gun! You're here! Missed again. Come on, let it go! Into the bushes, Rocky! Grace, what is... Somebody with a long range rifle trying to get into the act. Keep low. Give me the gun back so I can blast. Who's back? Mind, keep low and stay happy. Whatever it is, took a party. It wouldn't stay around. Lights are going on in the other cottages. You're right. Now you can get lost, Rocky, and don't come back.  What's wrong, Marcus? Leave me out in that cactus with a dame who was in no condition to be company for me. And I'm coyote, sir Arlen. Well, thanks for lovely evening. It was fully as interesting here. Rocky had me set up for an obituary that would have blackened the family escutcheon. Talk English. You think Rocky croaked the dame? I don't know. Anybody could have done it.  Even Robert Towner is a possibility. Sylvia's husband, he might be around some place, but he's very unlikely. Why? Because he'd have no reason to be taking rifle shots at me or Rocky. I've got to find out who was behind that rifle, who they were shooting at and why. You know something? We toss this crown into our mixed master. We could whip up a dilly of a cake. Mark, tomorrow we start a mingle with the guests. Sooner or later, one of them will make a mistake. The next morning a tennis match got underway and I found myself teamed in the doubles with Fran Maitland. Our opponents were Olga and Boris Petrov. Nice recovery race. These are tough opponents. Watch it. Got it. Very good, Fran. And I get this race. That was a beautiful shot. That's the set for us. Congratulations. You are still very good, Ray. We had a win. I don't think I could leap that net as easily as you and Boris do it. It seems to me it's time for a little liquid refreshment. I'll go back to ski shooting in the future. It's less strenuous. Who is that on the horse? He comes like the pictures of the Pony Express. It's Buzz Westcott. Looks like there's something wrong. Hold on. You may have better come help me. We've got to get a wagon. What's happened? It's at Mr. Rockland. I found him out on a range. You've been shot. The bullet had gone through Big Rocky's back. And if he had known who fired it, it was knowledge he'd never pass on. He was dead. But his demise filled in one bit of information. He, and not I, had been the target of the long-range rifle of the night before.  I should say, since early this morning, about 5 a.m. Anybody who's conscious of that hour deserves to be shot. How can you say such a thing? Have you no heart? Well, gosh. All right, Sven Golly. This is no place to be choosing upsides. You fought with him yesterday. Did you not, Ray? That I did, Boris. And I may fight with you today if you don't get out of the way and keep quiet. Look at this wound, Westcott. I won't bother it. Entered the back between the shoulder blades and came out through the stomach. It follows a downward course. Does that have a significance, Ray? Yes. It means that the shot might have been fired by somebody mounted. Might have come from the top of that hill there, too. That's possible. Here comes Marl. Must have told her at the ranch. All of you. Get off the ranch and get back to the ranch and stay there. Mrs. Westcott, there's no point in blaming anybody for this until we... I ain't blaming anybody but myself, Mr. Ray's. I never locked this guest business and I'm closing it down today.  Now wait a minute. I reckon I got something to say about that. Well, you just reckon again. This ranch is mine until I die and I ain't close to that yet. You don't like it? You can clear off through the rest. Now load them on the wagon and follow me. I'll go with you, Mrs. Westcott. I don't need an extra hand, Mr. Ray's. You can go back with your friends. I don't want you to put them off the ranch just yet. If you do, the murderer may never be caught. That's none of my affair. I ain't the sheriff.  Mind explaining that? The shot that killed Rockland was fired from a point above and behind him. It could have been fired by a rider or from the top of a hill. It also had been fired from a helicopter. I'd have to have a reason, wouldn't I? Your son might have been the reason. You told him to stay away from the guests. Sylvia Towner fluctuated between loneliness and hysteria. Maybe Buzz was getting involved with her.  It's always easy to tie up a second murder if you have a motive for the first. Sort of got me crowd, haven't you, Mr. Ace? I don't like it, but that's the way it is. All right. I'll meet you halfway. Everybody can stay for 24 hours. Listen, Ray, see you off your rocker. What do you want to go riding the night for? Just setting yourself up like a clay pigeon. It's the only way to play it. We have a much time. You go into the house and just drop a hint that I've discovered something that may force a move. Yeah, like the move Big Rocky got. He rode out of here too when he'd come back very unhealthy. Yes, he did, but... What's the matter? Rode out of here. Did he ride out? There was no horse near him and it was too far for him to walk. I don't get it. He was packed out, Mark. He was killed here and carried out there. Get your flashlight out. Open that corral gate. Yeah, yeah. Look. What are you looking for? There's just a chance. I'll show you if I find it. One side, boy. Come here, fella. Come here. All right, Mark. Hold the light close. Yeah, yeah. No. Try that drone there. Yeah, sure. But if you don't need to tell the... Yeah. Here it is. Look. All right. It's always got a straight good going on him. So what? It isn't dirt, Marcus. It's blood, dried blood on his hide. And now I know I'm a steak. What's that? I was looking for one murderer and there were two, or rather there were two. You mean Rocky was one of them? Yes. He killed Sylvia to keep her away from Boris. He was safe. There were other suspects with good motives. Yeah, yeah, yeah. But who? Bob Rocky? We'll know within the hour. You're still going to spread a little propaganda in the ranch house. All right, sure. Tell him that Rockland wasn't killed on the range. Tell them I'm checking the scene of the murder. Yeah, but, race, you don't know where... Yes, I do. Rocky was killed in his own cottage. That explains the course of the bullet. He was in bed asleep and the murderer fired the rifle from the window above the head of the bed. All right, John, let's go to Redmond. I'll stake myself out in his cottage. Go past the word. Race? I'm here, Fran. Couldn't we have a little light? We don't need it, baby. It's more romantic this way. Besides, I found what I was seeking. What did you find, Race? I dug a rifle slug out of Rocky's mattress. It was clever of you to change the linens. What made you suspect me? Rocky was a rough customer. Your infatuation with Boris was wearing thin. You wanted to dump him. That didn't gel with Rocky's plan, so he made threats, but you beat him to the gun. I'm a determined woman. You should get to know me better. I don't think I'd get to know you for very long, though. My divorce settlement is quite liberal, Race. I might make you forget a lot of things. Money and me. That should be a tempting combination. Money and you are tempting. But I imagine there's a rather forbidden pistol in your hand, isn't there? Yes. I tried to be more clever the last time. The rifle seemed to be a rather unlikely weapon for a woman to use. Not for you, Fran. You made a mistake. You mentioned ski-tuning as one of your favorite pastimes. Why don't you move out of the shadows, Race? They give you a better shot? That might make it quick and painless. I have some bad news for you, Fran. Mark Donovan steered you here deliberately. And right at this moment, he's outside the window drawing a bead on you through the screen. That's right, Sister. Oh, you... Oh! Help me. Take it easy, Fran. I only met the winger. That's all you did. She's fainted. She'll lift a stand trial. Why are you... She'll get that alga-dame out of her pickle. I wonder, Mark. Boris has nobody to run to now. So he'll stick to her for a while. It'll give her a chance to be miserably happy for a little longer. Yeah. Yeah, I know what you mean. But, uh... Race, will you tell me something? I should be delighted, old boy. Look, look, look. Why did a nice thing always marry Heald? That is one of the mysteries of life, Mark. One of the major mysteries of life. The Adventures of Frank Race, starring Paul Dubov with Tony Barrett as Mark Donovan comes to you from Hollywood. Others heard in tonight's cast were Lynn Allen, Charlotte Lawrence, Alice Drake, Michael Ann Barrett, and Tom Holland. This series is written and directed by Buckley Angel and Joel Murcott. The music is composed and played by Ivan Dittmarz. Be sure to be with us again this time next week for another dramatic chapter in The Adventures of Frank Race. Art Gilmore speaking. This is a Brucell's production.


Thank you, Nathan. Thank you very much for the invitation. So I switch gears a little bit back from a lot of phonology to a lot of morphology. Here's some preliminaries concerning morphology and subgrouping, basically my talk here. So the major advances of Indo-European historical comparative linguistics in the last 50 years were made in the morphological internal reconstruction of the pre or proto-Indo-European nominal and verbal system. Maybe not accidentally due to progress in and in cooperation of the understanding of the two major branches discovered 100 years ago, namely Anatolian and Tocharian, which will figure more prominently in a minute, and I'm a Tochariologist and always strive for making Tocharian great again. So this will of course figure here prominently. And maybe not accidentally due to progress in incorporation of general linguistics and understanding of historical linguistics concerning these advances in the last 50 years. Compared to other traditions and other historical linguistic family traditions like Semitic historical comparative linguistics, Indo-European linguistics is a bit of a late bloomer concerning higher order subgrouping that is subgrouping above the level of the tested daughter languages and basically between this level and the proto-Indo-European level. Today I think that's kind of fair to say only proto-Indo-Ranian as a higher note is completely uncontroversial. Though this is also fair to say a systematic reconstruction of proto-Indo-Ranian is still in its early stages and I think Martin Kümmel is working on a book on the reconstruction of proto-Indo-Ranian and this is another caveat concerning Indo-Ranian. There are of course issues with the status of the Nouristani languages and the exact affiliation of sub branches within the Iranian part of the family and we will hear about Nouristani at this conference. So if you looked or look at current handbooks you basically you kind of get this big bang tree, everything out of Indo-European all at once which of course cannot be possible and which is kind of strange and we are one of the historical fields that still get away with presenting our tree like this and we should not get away with it. The underdevelopment of subgrouping in Indo-European I should really be able to pronounce that is due to several factors. One is a long-term focus on archaeasms rather than on shared innovations, a focus on lexical items rather than on shared non-trivial innovations in the morphology and a negligence toward the reconstruction of the immediate ancestors of the tested daughter languages of a branch. We already talked a little bit about Indo-Ranian but for example in my specialty we have not even really started to systematically reconstruct the morphology of Proto-Tukerian. We're basically in the process of doing this. But this negligence of course is not negligence because some people don't want to do it but partly also some fields like Anatolian and Tukerian saw rapid development in their understanding the philology in the linguistics and these new discoveries are just in need of incorporation into our reconstructions. Okay so the sub-branching that has been proposed of course figured very prominently or Anatolian figured very prominently in this because it is very different from the other languages and it has become widely accepted that this otherness of Anatolian otherness where in verbal morphology so rarity of so-called simple thematic presence these are basically presence that are formed with an uploading OA, lack of perfect as we see in in Indian and Greek for example then in nominal morphology lack of feminine gender so there's no feminine gender in Anatolian and a very important lack of participles with fixed diethesis built on the tense spectral stems that at least Greek and the oldest Vedic have and so this otherness as we just examined in this sub-groups of linguistics of Anatolian is due to its Anatolian branching of first. In this scenario the other languages share a common ancestor so basically what we get is Prolindropian and Anatolian splitting of first and then whatever comes next and the different different people call this stage with different names and I will also have one later so we'll come to it. Yes now growing camp over the last 20 years or so that takes Taukarian the other language that was kind of recently discovered and almost at the same time as the Anatolian languages also well split of early and split of second after Anatolian and the arguments for this are quite similar to the ones found in Anatolian so lexical archaeisms so we did not have that and these will go eventually. Bubble morphology again rarity of simple thematic presence lack of perfect in the nominal morphology otherness of the feminine morphology and lack of participles with fixed diethesis built on tense aspect stamps so basically what these people who proposed that Taukarian split the first thing is we have Prolindropian and Anatolian whatever you call this then Taukarian and then what I think is now almost established in in the European with sub branching that has not been figured out yet but with the other languages coming after Taukarian has split off. Okay so what to do I follow or try to follow Mark Hale here since the reconstruction of in the European was done without much in the way of higher order subgroups and since the precise characterization of the inflectional properties of individual nouns and verbs at the in the European rather than the Prolindropian stage and of classes of nouns and verbs and of the system as a whole is a task which has to be which has not been seriously undertaken at this point I think it's necessary to start to systematically trace the evolution of individual morphemes and the morphosyntactic features and systems of morphemes and their morphosyntactic features regarding shared non-trivial morphological innovations and in the following the focus will be on the participial system of in the European and how Anatolian and Taukarian fit into this. Participants as we saw already have been in the discussion and Anatolian does not have anything like the other languages and for Taukarian this was also proposed. Then the association of nominal morphemes of participial morphology with the verbal system can be considered I think non-trivial innovation and therefore potentially lead to new insights in the understanding of the evolution of both nominal and verbal systems in the European languages generally and regarding of course subgrouping. Okay so these are our participles in what I term here uh brookmanian in the european uh brookman wrote the great grondries of the european languages so the system of reconstruction before Anatolian and Taukarian was discovered. My former teacher JJ Asimov in a recent paper wrote and I like this expression the ink on the grondries was not yet dry and then suddenly they discovered Anatolian and Taukarian and basically the whole grondries would have had to be remodeled but it wasn't. Anyway so we start with the brookmanian system here here are morphemes these are participles in a strict sense and this year especially the Tau is the verbal adjective. The ont is associated with morphologically active verbs it shows upload and the morphosyntax is basically the same morphosyntax than the associated verb has. Mkhno is the mediopassive participle non-uploading and again morphosyntax associated with the verb and mediopassive means that it is associated with finite verbs that are mediopassive and then we have the was-us-perfect which is uploading it is associated with finite perfect and again the morphosyntax depends or is exactly the same as with the finite verbs they're associated with and then Tau basically also non-uploading of the adjective that kind of has stative semantics. So if we look here well this distinction is not really important but I just wanted to to give it anyways so that a thematic thematic distinction is not super important for what is to come but anyway a thematic just means no uploading or eval and thematic means an uploading or eval in the finite verbs and no no upload in the presence. So I give you Vedic in greek as the classical group manian in european languages and these are always a cognate right so Dharant is a direct correspondent of Tidus, Barant is a direct correspondent of Pheron, I have a pointer, Jant is a direct correspondent of Ion, Sravant is a direct correspondent of Rion. So they are super nicely cognate okay. Then let's look at Muchno and here I just took the mediopassive versions of the cited verbs before so Dadana is secretly the manna but with some sound changes but anyway direct cognate with the dominoes, Baramana direct cognate with Pheromenos and here these are just blank because they are not attested but Rio has a mediopassive Rion and we saw Rion as the normal active participle and here we see Rio Menos as the middle participle. Then here are wass suffix or perfect participle, here are just the third singular corresponding perfect forms so cagara from the root gar, egregora from the verb egero and we get participle, cigarvas and greek egregoras okay and which I somehow humoristically translated as walk. Okay and now we turn to the top participle and here a distinction of actions art of lexical aspect becomes important why that you will see in a minute in Anatolian so non-stative here are lexical aspect and the differentiation so this differentiation will become important in a minute but anyway we get Peter from Pa, Potos from Pino, Stita from Starr, Statos from Histimi and the completely nicely cognate, Gata from Ga, Batos from Baino completely cognate okay good if we look at the evidence of all the bookmanian-indrupian languages we basically see some that behave very nicely but the fact is are what I tried to show that in the Iranian greek they're very nice cognates for anti-muchno was Tor and actually also Balthoslavic has anti-muchno was the Tor becomes in most of the languages kind of a new specialized perfect participle passive participle like we know in latin and we don't have to go over all that but I mean some languages don't behave nicely like Armenian we have relics of the system but the system as such is gone and of course same is true for Albanian and Celtic but this is just to give you an impression that are of course our nicest languages are in Iranian and Greek here but for all the other we have to assume the same systems okay good yeah now let's look at Anatolian first impressionistically uh muchno is not there was is not there Tor is not there and anti is kind of there but it is a verbal adjective exactly like the Tor in the languages okay to to show you this again here so lexical aspect so if we have Quincy to kill Cunand means killed exactly like Potos from Pino if we have a stative Arta stand then we get Arant standing and if we have a non-stative Archi die we get Arkand died okay so it's exactly the way we have exactly like the Tor participles in the in the in the European languages and if you want to look for the gory philological details consult the excellent dissertation of michael frotcher who basically tracked every single anti at the station in he thought okay Laura geistnergen I and she will speak tomorrow we try to reconcile the anti in Anatolian with the anti in the rest of the languages because kind of before we did the people started to think well anti is so different in Anatolian that this is just a thing that looks like the anti of the other languages but it's something completely different but I think it's easier to come up with the way of bringing all of these together and we did that in assuming that basically anti was original denominal theoretical possessive which in in european jargon means adjectival suffix like we actually see so burgh height in in burres or burgh's gothic castle and bergond having height high like in western burasant and burghant or so this was the original function and that we see also in detail so we have nada read and we get nada and having a read really so this is the first step the second is in Anatolian this was reanalyzed as a result in state so we have our denominal here and then they transferred this kind of concept on the verbs and we get kind of what we saw before so we have a result in state interpretation of this anti and this is how we get the verbal adjective in Anatolian so what happened in the post Anatolian in European languages well we also have a reanalyzed analysis as a result in state but this is then further interpreted as a processual so we we saw our denominal here so burgh is anti to burgh's height and this is actually a very nice example because here we get in Vedic jivan we basically get all the steps that we need for our explanation this was originally denominal and meant having life and from having life it got this result in state interpretation being alive and being alive of course is living and with this processual interpretation this one expanded to the other verbs to to non-state different transitives like yant going and then further to transitives and here we go we have baroned kering so this is what we assume now let's look good to kering we find the anti like in prokmanian in the european we find mychno like in prokmanian in european and we find was us like in prokmanian in european and we're not sure about the status of toh stay tuned i've argued against well yeah against one of my own teachers melanie markzahn that all these participles in in to kering and to muchno behave exactly like like anti muchno participles in the prokmanian languages before they were kind of thought to be like ancient nouns of some sort but they're real nice participles and directly correspond to their final verbs so we get brancha brand this of course directly corresponds to baron and ferron kering kering we get cliancha cliant standing to stand and we get yannisha and yand must exist but we just accidentally don't have it going we just have not so much to kering a as to kering b and the difference is like we have 80 percent more to kering b than we have to kering a but yand must have existed then let's look at the muchno guys here so trinkmane trinkmane from trink say for oneself being said premane purmane caring for oneself being carried yannemane yaman because we also have finite passive or medial inflection of the verb we have going and then kat kemane kat kmane being led from the root of the word good and then finally our was and they come in reduplicated and non reduplicated forms so our or so this is the so-called oblique our or or or they all get go back to us with different vowel things happening but believe me they all go back to us and here we have a cursor so and here we have a reduplicated one a kekamu kak mu and of course this is the same root that we have here the gram root to go and and actually this is a directly cognate right kekamu is directly coordinated with jaganvas and beba also here i gave you just the finite forms the only difference is that the reduplication vowel into kering is an all but this is just the copy of the root vowel into the reduplicant which is kind of trivial innovation okay so let's take stock again and now we're talking about our suffixes here in the participial sense and atolian does not have the nt in the participial way does not have mukno and does not have was us but to carrying and basically behaves exactly like the prokmanian european languages so here is the development we start out with the nominal possessive nt in and so we get the verbal adjective and prokmanian european which now includes to kering has the innovation of associating nt with active verbs mukno with middle verbs and was with perfect and here's a slight twist here namely the origin of the perfect participle here i follow also one of my teachers Jeremy Rao who assumed that originally the perfect participle started out as a verbal adjectival used them derived from the perfect or directly from the root so we get basically our perfect and then we get which is this derived used them and we kind of have this mean they're close enough related so chakra is our final perfect and then we get chaga awake as the participle or for a theoretical derivation we have weight and we get way to we dev this is one of our accent upload classes and this is also directly attested so this is the first step to go to the perfect participle the next step is this adjectival used them created a neuter as them abstract so we start here with this you adjective and we just add an s i mean we not the prokman europeans added an s and made this into an abstract and there are there are parallels in the living languages for that like tapu hot and you can make an abstract but directly adding an s and you get tapu in very heat okay and here's the third step and then we are at the perfect participle origin and that is this neuter abstract made internally derived that means by changing the accent upload pattern basically acquiring here an emphykinetic so-called emphykinetic animate as them with this kind of upload behavior was us that we see in the perfect and this emphykinetic derivative from state of being awake was one who is awake or as I translated it woke and widows and one who knows knowing okay and parallel for getting an animate emphykinetic noun from a neuter abstract is for example greek pr which means fat substantive and then we get animate peon fatty as an adjective okay but this means if we believe Jeremy and I do this means that in order for to carrying to have this particular was that is derived from and use them you have to have had the perfect and this means to carrying inherited the perfect and then we can look at to carrying again what is the evidence for having for it having split of second lexical archisms in the world morphology relative simple thematic present like a perfect otherness of the family morphology lack of participle with fixed the thesis bill intense as factual stems lexical archisms have to go because lexical changes are unpredictable and should not be used for subgrouping arguments in the world morphology sorry nominal morphology otherness of the family morphology I showed that to carrying just got normally inherited both the ih2 feminine and the ih2 feminine and we can talk about that but this was not part of of this talk today lack of participle with fixed the thesis bill intense is spectral stems has to go that's what we saw a lack of perfect has to go because we have the perfect participle and the perfect participle says at some point the language at a perfect well it changed the perfect but so did other in European languages Latin made a new patriot category it is composed of perfect and iris and something similar must have happened to carry and we have to figure out exactly how this worked out so we're left with rarity of simple thematic presence and accompanying things like a thematic optative that is interesting to Karen does not have a lot of thematic presence does not have a thematic optative but anyway but we see not so many arguments for to Karen having split off second okay here's just a little preaching because to carry in subgrouping is very often reinforced by arguments that are not from linguistic so artifacts without inscriptions are not linguistic evidence mummies are not linguistic evidence genes are not linguistic evidence and this is just an aside and to kind of kindle your interest the earliest linguistic evidence for to Karen's because near Xinjiang where the texts were found and where they are attested over a period of 600 years between 400 CE and 1000 CE are furnished by long words and this is ongoing work with Bill Baxter and here are just some good long long words but this is basically just to kindle your interest and and stay tuned in this respect okay so to conclude participles with fixed stethesis build intense spectral stems are shared innovation that happened after an atollian split off the to Karen part to Karen branch was part of this major innovation to Karen inherited the classical book money in european perfect to Karen inherited all participles in their classical book money in european value and later of course changing bits and pieces like almost everyone else except our preciously friends greek and indoreanian what's the perspective the perspective is tracing the evolution of individual morphemes and demorphos and tactic features and systems of morphemes and just morphos and tactic features regarding non-trivial innovations hopefully at some point soon reasonably computer aided and this will lead to modifications in the subgrouping and here's Mark Hale again and since any modification in subgrouping changes the value of individual data points for the reconstruction of the proto language we can anticipate many modifications to reconstruction as we finally do the work necessary to confidently establish subgrouping relations and what i presented here today was just a bit of how we have to change individual data points here and here is a good in the european seromon in order to do this the subgrouping the focus of the field i.e. should be on building on an advancing the great achievements of the historical comparative method in the morphophonology and morphos syntax of the european languages instead of shady evidence purely based on lexical items or worse combined with evidence of shady evidence of population genetics and instead of deconstructing the system that are kind of established in favor of of having membranous yector and projecting every t-stem that looks funny in an individual language directly back to the proto language where we then get seven different t-stems instead of one t-stem which i termed i hope to be not to root here and a lot for me a lot for me like infamy um anyway um as to carrying these speakers would say yes park school christian yes so thank you for your attention


Hello and welcome to the session. In this session we discussed the following question which says, a die is thrown two times and the sum of the observed numbers is 6. What is the conditional probability that the number 3 is observed at least once? Given any two events E and F, the probability of occurrence of the event E under the condition that event F has already occurred is equal to probability of E intersection F upon probability of F where we have probability of F is not equal to 0. Or you can also say this is equal to number of elements in E intersection F upon the number of elements in F. So thus we can also say probability of occurrence of the event E under the condition that event F has already occurred is equal to number of elements in E intersection F upon number of elements in F. So this is the key idea that we use for this question. Let us move on to the solution now. We take let A be an event of getting the sum 6 and let B be the event that 3 appears at least once. Then we get A is equal to, we set with elements 1, 5, 2, 4, 3, 3, 4, 2, 5, 1. So these are the elements of set A and we have B equal to we set with elements 1, 3, 2, 3, 3, 3, 4, 5, 3, 6, 3, 3, 1, 3, 2, 3, 4, 3, 5, 3, 6. You can see that 3 appears at least once. You find the conditional probability that the number 3 is observed at least once. This means as we have taken B to be the event that 3 appears at least once. So we need to find the conditional probability of occurrence of event B under the condition that event A has already occurred. Now from the formula given for conditional probability in key idea we get this would be equal to probability of B intersection A upon the probability of A or you can say this is equal to number of elements of B intersection A upon number of elements of A. Now from the sets A and B let's find out what is B intersection A this is equal to the set with single term element 3, 3 because as you can see that 3, 3 occurs in set A also and in such B also. So B intersection A is equal to 3, 3. That means number of elements of B intersection A is equal to 1 since we have just one element in this set. Now the number of elements of set A is equal to 1, 2, 3, 4, 5. Since we have 5 elements in the set A so number of elements in set A is equal to 5 thus the conditional probability of the event B given that event A has already occurred is equal to the number of elements of B intersection A which is 1 upon the number of elements of A which is 5. So the required conditional probability is equal to 1 upon 5. So this is our final answer. This completes the session. Hope you have understood the solution of this question.


Humans are naturally social beings, and it is pretty intuitive for us to find somebody to lean on when life gets tough, when we need a job to get done, when we want to learn and acquire more knowledge, or when everything does not make sense. Whether you believe life has no inherent meaning and purpose, or a hidden metaphysical reality governs our mere existence, I think we can all agree that we can find some meaning and purpose and friendship. And aside from the most prominent questions philosophers have dealt with for millennia like what's the nature of reality, of our minds, why are we here? Friendship is one of the focal points of human existence. One of the most influential philosophers of all time, who had several things to say about friendship, is no other than Aristotle. I will have to discuss his thoughts in a bit. But first, I'd like to share some thoughts about two pivotal moments that had me thinking more about friendship. One was after I graduated from college, and the second was during the pandemic. If there is one major event that drastically changed our lives in the past few years, it would be the pandemic. It's one thing that many people have suffered financially and physically, but more than that is its undeniable effect on our mental health. Just last year, I've seen that some people felt they had lost their friends, making them feel more alone and unvalued. Some people thought they lost half of their meaningful events in life because they can't go out with friends. Some felt like they're just buried in the house watching the news and stuff, which is understandable. As for myself, it's a win-win situation because I'm used to being alone because I'm an only child. I'm not that extroverted. I mean, I can be loud sometimes, especially to my closest friends. But hanging out with people for too long is kind of mentally draining for me. So the pandemic setup was ideal for me, but not for everybody, of course. Another moment that got me thinking deeply about friendship was after I graduated college. For a moment, I saw that my circle of friends got smaller and smaller from time to time. Not that I have too many friends anyway, but that's just fine for me, to be honest. But to those who are asking, are they real friends if they're not in our lives anymore? I had a great understanding of why it happens, and I want to share with you the thoughts of Aristotle in one of the famous books he wrote more than 2,000 years ago, the Nicomachean Ethics. But first, a little appreciation for the man behind the book. Aristotle was a Greek philosopher during the classical period in ancient Greece. His writings had contributed to the areas of metaphysics, mathematics, science, psychology, politics, logic, aesthetics, music, and ethics. He was also the founder of Delicium, a school where he taught the principles of Aristotelianism and was considered the first formal teacher during his time. For 20 years, he had been the student of Plato and a good friend. He was also the teacher of Alexander the Great. For almost three years, he shaped young Alexander's thinking on everything from leadership to natural science, philosophy, and mathematics. But today, let's focus on one of his famous writings, the Nicomachean Ethics. It consists of 10 books about happiness, virtue, morals, and justice, in light of his philosophical understanding of the nature of reality, a rich two out of 10 he dedicated to friendship. Aristotle thought that it is one of the true joys in life, and a life well lived needed to be built on it. He's also famous for asserting that without friends, no one would want to live. Even if he had all other goods. But why did Aristotle think friendship is essential if he knows that we are responsible for our own happiness? In poverty, as well as in other misfortunes, people suppose that friends are their only refuge. And friendship is a help to the young and saving them from error, just as it is also to the old, with the view to the care they require and their diminished capacity for action stemming from their weakness. It is a help also to those in their prime in performing noble actions. For two going together are better able to think and to act. Does it seem to be true? Going back to the question about friendship, on why we encounter short-lived friendships throughout our lives, some were not so meaningful and were just a spur of the moment. Aristotle made three important distinctions to the different types of friendships we encounter in life. The friendship of utility, the friendship of pleasure, and the friendship of virtue. The friendship of utility is a type of bond in which two or more people receives a benefit in exchange. Whenever the service ends, so does the relationship that brought them together. It's not permanent in nature and it's based on the usefulness of the fellowship. This type of friendship is very common in business, work, and even school. The sense of friendship is there. You both help and enjoy the times you've been together. But once the situation changes, like you move to another workplace or transfer it to a different school, so does the nature of the bond changes. Another accidental form of friendship is the friendship of pleasure. The source of friendship is often more emotional. It may not be necessarily helpful or useful, but it does feel good. It doesn't have to be sexual, although it can be. It may also be something like those people who can make you laugh and forget all your problems, probably with a few shots of alcohol, a long ride, and fun nightlife. The friendship is there as long as the parties gain fun and enjoyment through mutual interest. But as soon as the similarity of the preferences changes, so does the level of the friendship. These two are the most common types of friendships we get mostly. The people we meet out of utility and pleasure tend to change over time, as the phases they're in change the course and their purpose for many reasons. But the most important of all is the friendship of virtue. Unlike the two mentioned earlier, this is very hard to find. You may or may not even see them for the rest of your life. Probably that's why some people think negatively about the concept of friendship. Aristotle, however, didn't find the accidental friendships as bad. He was just describing the very nature of friendship as it is. And he even thought these unexpected friendships were just normal in life. But he saw that the friendship of virtue was the best among the three. This relationship is based on a mutual understanding of morals and virtues that the other person believes. You see this friend for who they are, not just because of utility and pleasure. There's a level of goodness attached to this form of relationship as to why it is the most preferable and often long-lasting. Both people care for each other's welfare and growth and wish for their best version in life. Aristotle in Book 8 Chapter 3 said, Now these reasons differ from each other in kind. So therefore, do the corresponding forms of love in friendship. There are therefore three kinds of friendship, equal in number to the things that are lovable. For with respect to each other is a mutual and recognized love, and those who love each other wish well to each other in that respect in which they love one another. In short, the friendship of virtue is what we can call true friendship. I think nowadays it is essential to distinguish which of your friends falls into the category of these three to know our limitations and expectations of everyone in our life. By understanding the nature of friendship, we may also refrain from being disappointed whenever we feel like our circle is getting smaller. To be honest, I think the smaller the circle, the lesser the problem. Aristotle also believed that we should find virtuous friendships throughout our lives. Not only it is beneficial for our mental health, but a world that knows the value of friendship built through virtue can make life worth living for.


Hello everyone, welcome to the next lecture in the course remote sensing principles and applications, we are discussing the topic of active microwave remote sensing with imaging radar. In the last lectures we got introduced to the concept of like how radar image is acquired, what sort of like image distortions will occur, the concept of resolution in a radar image all those things. In this lecture we are going to see the actual signal contained within a radar image like what characterizes it and what are the terrain properties that will influences the reflection of objects in the radar signal. First we will get introduced to what is known as the radar equation. So, we are not going to discuss everything in detail or derive anything here, but we are going to understand what is actually contained in a radar image. So, when I introduced the concept of radar image I told you that it will collect the power that is being reflected back from the object. So, here the term reflection mean the signal which is reflected in the same direction as that of the receiver, because we know that that radar will have an antenna, it will transmit microwave radiation towards the object and whatever is reflected towards the antenna again alone will be collected. So, whatever reflection of scattering happens in direction anything other than this antenna direction will not be collected by this particular sensor. So, we call this as back scattering. So, essentially the radar antenna measures what is known as back scattering that is how much energy is being scattered back by the object towards the sensor itself. We call it as back scattering. So, normally when you look at like any scientific literature related to radar or retro radar remote sensing we will have this term back scattering commonly being referred to. Now, anyway like we are going to sense the power. So, the power received by the antenna depends upon the power transmitted multiplied by the gain of the antenna. So, here gain you can think it off like how well the antenna is focusing the power in a given direction. You can think it in terms of like that we are not going to elaborate on that. But you can think some sort of power is going to be transmitted from the antenna initially. So, how much is like kind of focused on a given direction. So, once this power is transmitted let us say this is the antenna. So, once this power is transmitted like a pulse is being released it will start spreading in all the three dimensions. It is not going to travel in kind of like a straight line microwave pulses they are going to spread across the power is going to spread across in all the directions. We know this because of this inverse square law whatever is the power that is transmitted it is going to spread across. So, this 1 by 4 by r square is to account for this spreading of power where this r is the distance between the radar antenna and the object antenna and object distance. So, that is if this is considered like antenna is kind of like a point source. So, this is like a target if p power is like transmitted from here it will slowly start spreading in the entire sphere surrounding it. So, we are going to use this inverse square law function in order to calculate what is the power received by this particular object with a given area. So, now these three terms will define the power reaching the object of interest from the radar antenna the initial power that was transmitted multiplied by the gain into accounting for the spreading loss. Now, whatever is the power reflected back will depend on the effective scattering area of the object or here we denote it as sigma where sigma is known as the radar cross-section. So, what radar cross-section is maybe like we will see it like little bit later, but consider it is terms of like it is kind of like reflectance think it similar to in terms of reflectance not actual reflectance I will describe it in later, but just for like simplicity sake for introducing this particular equation I am telling it like this. So, this is like think it radar cross-section think it in terms of like reflectance. So, some amount of power is reflected back by this particular object and it has to again travel back after reflection also it will start spreading again right from this particular small area it will start spreading all over again. So, this is to account for that spreading loss in the return direction this particular term 1 by 4 pi square and AR is the area of the antenna that is receiving the signal. So, if you look at this if you divide this particular equation into different components like sensor and object characteristics we can know that that power received depends on some sensor factors or sensor characteristics that is the term PT, GT, AR, area of the antenna are all sensor factors. The sigma what we referred as the radar cross-section actually defines the property of the object on the earth surface that controls this power that is reaching the sensor. So, essentially that single particular term sigma the radar cross-section will influence how much power will be returned back to the radar antenna and that is entirely controlled by the object of interest itself. Rest all the factors are all system properties. So, the power received if we think like ok system properties are all fixed once the system is launched if it is kind of fixed means then the only variable in the equation every time when an image is acquired is the sigma term the radar cross-section term also like distance anyway it is like a natural phenomena distance is always going to vary. So, the major factor that controls the signal received in the radar image or major factor of the object the earth surface feature is radar cross-section. What exactly radar cross-section is? So, here we would not I told you like think it in terms of like reflectance but it is not like it is not defined very anyway similar to reflectance at all. So, what radar cross-section is let us say there is some object now some power is falling on it some radar power is falling on it microwave signal. The object is sending back certain amount of that particular instant energy towards the radar antenna. So, that object can be anything in order to calculate the power that was like scattered back by the object of interest we take some reference that reference is known as like a perfect isotropic reflector or like a isotropic scatterer. What it means? We can think it in terms of like a polished spherical metallic surface which has good amount of scattering in the microwave wavelength and it will scatter the microwave wavelengths equally in all the directions like a spherical metallic surface we can think of. So, that is kind of taken as a reference. Why that is taken as a reference means say whatever be the microwave power that is falling over it that power will be scattered equally in all directions by that particular object like that spherical object. So, this is kind of taken as a reference. Now, let us say we have some object of interest that is scattering the energy back towards the antenna any feature cropland water body building whatever let the power that was like scattered back towards the antenna be X. So, now what we have to calculate is we have to estimate the area of this isotropic reflector that should be there in order to reflect back or scatter back the same power X towards the antenna under identical illumination conditions. I repeat it again for sake of clarity like let there be any object of interest on the earth's surface. Let us say it is scattering some power X towards the antenna in response to the incoming microwave signals. Radar cross section here means the area of a perfect isotropic scattering object like example I told you this metallic sphere right. So, the area like the surface area of the object that is required in order to scatter back the same amount of power X under identical illumination conditions by the microwave. So, here we are not calculating the reflectance of object, but here we are trying to calculate the area of the isotropic reflector that is required to produce the same amount of power that has been reflected back. So, it is kind of like may appear little bit confusing in the first go, but the concept is it is like very simple. Let us say there is like a very good reflector in the microwave band. Let us say take an example kind of like a very healthy crop plant or like a vegetation over like a standing water or something okay. So, what will happen is later we will see those kind of objects will produce like a very bright response like they will appear very bright in radar images. They will send back almost most of the energy that was transmitted from the radar. They have like very high scattering backscattering towards the radar itself. So, what it means in order to produce such a bright backscattering towards the antenna that particular object should be replaced by like a isotropic reflector with a bigger area. Let us say there is some other object which is like very poor which is a poor backscatter like a calm still water body. The backscattering will be kind of like really poor towards the antenna. Under that circumstances the area of the isotropic reflector to produce the same backscattering will be smaller. So, essentially based on the backscattering capacity of the actual terrain features we can calculate the surface area of the perfect isotropic reflector that will be needed to produce the same amount of power okay. So, this is known as radar backscattering. So, radar cross section. Here we are not calculating any ratios normally in optical remote sensing we will calculate the ratio right reflectance. We will calculate reflectance that reflectance is nothing but a ratio ratio of the energy that was reflected back to the energy that was incident. Here we are not doing that. Here we are trying to do some sort of like hypothetical calculations like where we try to calculate the area of the perfect isotropic reflector that is required to produce the same amount of backscattering produced by any natural feature. Higher the backscattering capacity of this natural feature larger will be the area of this particular isotropic reflector will be required. Hence, the radar backscattering will be larger. If the object has a lower backscattering capacity then even like a small area of this isotropic reflector can produce the same reflect same back scattering and hence the radar cross section will be smaller. So, the radar cross section essentially tells the backscattering capacity of the object. So, here we told like the radar cross section essentially determines the power that is reaching back the sensor. But this radar cross section has to be kind of normalized with respect to something like normalized with respect to something is what I mean is like normally a single pixel will have like many number of features like if you take like a 10 meter by 10 meter pixel it will have a large amount of features present and all. So, that can be many objects. So, we will get kind of like a combined radar cross section. But in order to normalize this in order to bring it to some sort of standard we will convert that to what is known as a radar backscattering coefficient. So, this is like denoted here as sigma naught the radar cross section is kind of normalized further to backscattering coefficient. So, what exactly this backscattering coefficient means? Now, we know this is like it has like ratio where in the numerator we have the radar cross section and in the denominator we have the area on the ground that produces or that requires this much radar cross section that is this is also an area sigma is nothing but a area area of the perfect isotropic backscatterer that is needed in order to produce the same backscattering received at the antenna under identical illumination conditions divided by area of the ground that actually produced that radar or that actually contained that radar cross section. So, here we are now dividing now we are converting into a ratio. Okay, this is the area of the object or this is the area of like the isotropic reflector that is required somehow we calculated once we calculated we normalize that with respect to the area of the ground that produced or that requires that much radar cross section. This is needed because like radar pixel size is not like a constant right we have seen that the pixel size of radar images will vary across the range direction and azimuth direction in case of real aperture radars. If that is the scenario as the pixel size changes like some pixel may have large number of objects some pixel may have the some pixel size itself may be small dimensions it may contain like very few objects that has like poor backscatter capacity all these things will happen and the radar cross section sigma itself will depend on the area actual ground area that is covered by the radar pulse. In order to avoid this we do this conversion we convert this radar cross section to radar backscattering coefficients so that the power received in the antenna will be normalized okay per unit meter of ground area say for a pixel what is the radar cross section per unit meter of ground area that is what we will calculate okay. Say let us say there is like a pixel size 10 meter by 10 meter. So, whatever be the radar cross section we will divide this by this 100 meter square in order to calculate this backscattering coefficient. So, essentially this is what we will calculate in radar image preprocessing. So, whenever like a radar image comes we will do some sort of like preprocessing steps before we use it for like further applications. So, one of the important step is to like convert the slant range distance to ground range distance, remove the distortions using what is known as terrain correction procedure after that like we will convert everything to radar backscattering coefficient. So, the backscattering coefficient will tell at each pixel what is the radar cross section required per unit meter square of area that will be there. So, again like I repeat for the purpose of clarity why we need to convert this radar cross section to backscattering coefficient just for deeper understanding okay. So, we will take like two examples one pixel 10 meter by 10 meter, another pixel let us say it is like 10 meter by 20 meter. It is natural that radar resolution will vary across the image in both the range direction Nazimut direction. If that is the case let us say both the pixels had like same radar cross section like it had like lot of bright objects this 10 meter by 10 pixel whereas this 10 meter by 20 meter pixel had like less number of objects kind of thing okay. Let us assume both of them has the same radar cross section but the same radar cross section has been produced by this pixels. One of the major factor that produced is the larger dimension of this pixel. So, has the dimension be uniform between these two pixels the radar cross section might have been different right in order to normalize that we convert this. So, sigma 0 of this pixel will be sigma by 100 meters whereas sigma 0 of this pixel will be whatever the sigma divided by 200 meters. So, essentially using the area on the ground will normalize the radar cross section for this variation in pixel size one of the important things that we should do okay. So, we calculate what is known as the sigma 0 and area we can define it in multiple ways normally the general convention that we use is the actual ground area of the pixel okay. What is the actual horizontal dimensions of the ground in the x direction and y direction calculate area out of it and use it that we call it as like sigma 0. We can also calculate the area of the ground in different directions say for example, a schematic is given here. If we use the actual horizontal ground area in order to normalize the radar cross section we get sigma 0 or if we use the slant range area itself like in the slant range let us say this is the and this is the ground like beam will be spreading like this. So, in two dimensions it will like spread and collect all the distance information power information. So, whatever like each pixel here it will have an area projected in the slant range direction right. So, the area of the ground which is used to define the pixel in the slant range direction itself if we define if we use that particular area for normalizing the radar cross section we call it as beta 0. On the other hand if we project this ground area in a direction that is normal to the radar beam or the radar antenna this is like normal that is the case this is like projected. So, that this area is now is normal to this radar beam then if we use that area to normalize this radar cross section we call it as gamma 0. So, the radar backscattering coefficient can be defined in three ways or the area itself can be defined in three ways the actual ground area the area of the pixel that produce the sigma the radar cross section in the slant range or the area of the ground projected in a direction normal to the radar antenna. So, we can defend in three ways but the general convention on the most often used factor is sigma 0, radar cross section sigma divided by the horizontal ground area okay. So, this will be one of the uh obtaining the sigma 0 for each pixel will be one of the important step in radar data processing. Once we get this sigma 0 we can use it for different applications. So, the sigma 0 essentially is the influence of the terrain features on the power that is reaching the radar antenna. So, higher the backscattering coefficient sigma 0 the pixel will have will appear bright like large amount of power would have been backscattered towards the antenna. Lower the sigma 0 the object would have like would not have backscattered same amount of like high amount of power it would have backscattered only like very less amount of power. So, the sigma 0 essentially tells us the characteristics of the object and its ability to backscatter microwave signal. Now, we are going to slowly discuss what all the factors of the terrain features that will influence this sigma 0 like when we discussed the optical remote sensing like visible NAR remote sensing and all we discussed what factors of vegetation controls its spectral reflectance curve what factors of water controls its spectral reflectance curve and so on. Similarly, we will touch upon briefly how different features exhibit this or how different factors of the features on the earth surface controls this sigma 0. So, basically from the radar equation we have inferred that the power reaching the sensor depends both on sensor characteristics and the object characteristics object here I mean the feature present on the earth surface. Some of the important sensor characteristics that control this power reaching the sensor are the frequency in which the microwave signal is transmitted, the polarization of the signal that is transmitted and received and the instance angle at which the angle is incident upon the surface. These three are really important factors from the sensor side that will control the power that is received back by the sensor. So, whether frequency means here I mean whether it is C band, X band, radar, L band, radar and so on. Polarization means whether it is transmitting and horizontal and receiving and horizontal or HP or VV something like that. And incident angle is whether the object is closer to the radar antenna like whether it is near range or whether it is in far range and so on. So, these are all the instrumental parameters. But as I said before like except this instance angle once the system is launched these two are kind of like fixed right these two cannot or not going to vary this is going to instance angle anyway is going to vary based on whether the object is present in near range or far range. But what we are really interested upon is to understand what are all the terrain characteristics that influences this sigma 0. Some of the important terrain characteristics that influences microwave backscatching or the surface roughness that is how rough or how smooth the surface is. Surface geometry basically dealing with the orientation of the surface itself different features that are present on the surface and the dielectric constant of the surface primarily influenced by its moisture content. So, these three factors are like the key controlling factors from the object side or the terrain side that will control the microwave backscattering towards radar antenna. So, essentially by looking at the sigma 0 value we may get some information about any of these either surface roughness property or dielectric constant of the surface and so on. So, first we are going to discuss the importance of surface roughness in the radar backscattering. Actually we have seen this in the earlier classes also when we discuss the interaction of electromagnetic radiation with the terrain normally what we discussed the surface roughness whether a surface that will whether it will appear smooth or rough it depends on the wavelength that is incident on the surface and also the look angle the geometry between the sensor and this object itself that we have like seen here also in radar the same case. But in radar the surface roughness plays a major role that is whenever we get signals from the radar surface roughness will have like a larger influence like when you look like radar images from like a rough surface and smooth surface there can be like drastic variations also. So, surface roughness influence will be quite high. So, what will happen in case of surface roughness just we will do like a quick recall. Say if a surface is like really smooth like we have also defined what is known as like a modified Rayleigh criterion in earlier classes please recall them like if the RMSE height of the surface like say this is like the height variation let us say this is like a soil surface. So, this is like the mean height of this particular there this is the bounding area in which I am interested upon. So, this is like the mean height of the area. So, the variation of the surface measured with respect to mean height if you can calculate kind of like a deviation or like a difference we can land up what is known as the RMSE height this is what we are going to use. If this RMSE height is less than lambda by 25 sin gamma the surface will be smooth for this particular wavelength and that will act as like a specular reflector. So, this we know if a surface is smooth it will act as a specular reflector. If a surface is rough that is its lambda that is the RMSE height h is greater than lambda by 4.4 sin gamma it will act as a rough surface or if the RMSE height is varying between these two limits the surface will act as kind of like an intermediate. So, what exactly will happen for a smooth surface the reflection will be specular and it will be moving away from the radar antenna that is let us take water still calm water is a very good example for a specular reflector. See like this is like the standing water column let us assume microwave signal is instant on this particular radar column since it is like very smooth sorry water column microwave energy is instant on this water column. So, this is like very smooth surface. So, what will happen is radar will be instant on it radar energy and it will be reflected specularly. So, most of the energy is going in a direction away from the radar antenna. So, the radar antenna which is looking for the back scattering is not going to receive may any like a large chunk of energy a large chunk of energy is lost in the direction opposite to that of radar antenna. So, normally specular surfaces will appear dark in microwave or radar images. If the surface is rough like kind of like a let us say a dry agricultural field that is just being plowed ok. So, it is plowed it is left to be kind of like dried or it is watered whatever. So, for such surfaces it will appear rough to the microwave wavelengths. So, it will produce scattering in all directions normally rough surfaces act more like a diffused reflector that we also we have seen. So, what a diffused reflector will do a diffused reflector will reflect the energy or scatter the energy in all possible directions. When that happens a good chunk of energy will be scattered towards the antenna itself like this is like demonstrated here this is a direction which radar energy is coming. So, since this is like kind of like a rough surface it will scatter a large chunk of energy towards the direction of antenna itself. So, a rough scatterer naturally will have higher sigma naught under smooth surface will have a lower sigma naught under the case of all other factors being held constant like if all the factors that controls backscattering remain the same then a rough surface tends to have a higher sigma naught than a smooth surface. See this particular slide demonstrates that the surface roughness is the function of the wavelength and also like the depression angle or instant angle whatever we call normally like depression angle and the look angle are kind of like complementary to each other. So, here we have used the notation of depression angle. So, a same surface may appear rough to one particular wavelength say same surface may appear rough to X X band radar around like 3 centimeter wavelength where that particular surface itself may appear smooth to L band radar with close to 24 centimeter wavelength.  So, for this X band radar this surface will appear kind of like a rough surface or like an intermediate surface whereas for an L band radar this will appear kind of like a smooth surface producing difference in backscattering. So, the band in which we are looking upon that is the wavelength in which we are looking upon and the sensor instant angle are going to play a major role in controlling the backscattering from the object. One more thing what we want to know is this is like the general surface roughness characteristic roughness of the just the top surface be it like a bare soil be it like a dense forest where the roughness will be defined by the canopy. Like if we have like a large cluster of trees like the radar signal will be encountering these tree tops what we call the canopy and the roughness of this canopy like if you closely observe the canopy the canopy cover may not be smooth it may be something like this each tree that can be like small small leaves, trees everything what to say protruding out. So, this will project like kind of like a rough surface. So, this is generally the surface roughness characteristics, but what about like the overall orientation of this particular surface like let us assume there is like a dense forest that is present on a flat horizontal surface and the exactly the same dense forest if it is placed on a sloping surface how that will change the radar signals that kind of like a schematic we will see now. Like here we have kind of like a near perfect specular reflector say the major share of energy is transmitted in forward direction. So, we call it as like kind of like a near perfect specular reflector. Let us say this is present on like a flat horizontal surface this is like the direction in which radar energy is coming in. So, a major fraction of energy will be reflected in a forward direction away from the radar and like a smaller fraction of energy alone will be backscattered. Let us say the same object is present on a slope that is facing the radar antenna. Slope facing the radar antenna means what will happen due to this topography even though the object is reflecting a large fraction of energy in the forward direction because of the slope still a major fraction of energy backscattered energy oriented towards the radar antenna itself thereby increasing the radar cross section. So, the same near perfect specular reflector if it is present on a slope facing the radar antenna because of this terrain characteristic or the sloping nature a relatively higher fraction of energy will be transmitted towards radar antenna. On the other hand if this near perfect specular reflector if it is present on a slope facing away from the radar antenna the amount of energy backscattered towards radar will further decrease like large fraction is going away from the radar antenna because of this slope effect the slope is the slope is kind of like reflecting the energy further away from the radar antenna only it is still like a smaller fraction of energy is going towards the radar antenna. So, not only the surface roughness but the overall orientation of the surface we call it as like macroscopic effect or like the topographic effect. So, the overall topographic effect where the surface roughness element is present that will also play a major role. Say in the example that we have just seen this will have the lowest back sigma naught because large chunk of energy is transmitted away from the radar antenna this will have kind of like an intermediate sigma naught this will have like a high sigma naught relatively. So, everything is in a relative sense with respect to each one of these three things ok. So, as a summary in this lecture we have discussed the factors that will influence the power that is reaching the antenna like we discussed what radar cross section is what radar backscattering coefficient is and we just listed the different properties of the surface that will influence this radar backscattering coefficient and the major factor that influences radar backscattering coefficient is surface roughness and also we have seen how surface roughness will play a role in controlling sigma naught. With this we end this lecture. Thank you very much.


I was living with my mom. I couldn't find a job, so trying to go back to school, I couldn't afford it. I called my uncle, just distraught, and he's like, why do you rethink the Air Force? Come to find out. If you go into security forces, go through school, everything like that, go to your first base. You can actually volunteer and get the Kennel Masters approval to go to K&I school, so that's exactly what I did. What was hard for me was trying to find the time to come and volunteer. Because once you're done working, normal schedule, you're exhausted. There was days where I was off. I would force myself to come here because this is really what I wanted to do. Go out with the trainers and the other handlers and see what kind of problems that they would do. So that way I can understand the basic concepts of detection work and patrol work. Knowing my grandpa was in the Air Force before me and he was a K9 handler at Myring, it kept me wanting to keep going because I wanted to make myself proud, but I also wanted to make him proud because I was following his footsteps.


that's agreed upon by all the parties but the more important thing that I want to stress it's it's now too late to ask this question but by now you should be knowing what a blockchain is because all of your competitors already have consortium the fangs are have a blockchain consortium they're already adopting they're ready deploying the transportation logistics community to have their own so that if the post don't get a blockchain consortium soon you will actually have a harder time surviving the fourth revolution okay so really briefly I'm just gonna pass this up and talk about so what I'm gonna talk about is that you can't just do blockchain the goal here is to leapfrog it's to actually look better than everyone else and to do it really quickly and to do so what you need to do is pop use the power of your collaboration so we're suggesting it you can't just pop blockchain now you actually have to understand what AI and quantum computing are in order to look ahead of the competition and we have experts in AI and quantum optimization that will be working on that our team and helping the post quantum optimize your telematics data so that you could actually have 10 to the 6th improvement in up in delivery time right one million fold improvement so I'll pass that up for now so the these three things added together this is going to create a revolution that's bigger than the web and this is actually the full expression of the information revolution and it's time for the post offices to catch up and to leapfrog everyone else so what I want to talk about are the things that you this industry needs to do the most important thing here is this one let's let's hope it doesn't keep advancing on me can you make sure it doesn't advance past the slide okay so there are four things we believe that the upu and the post need to do number one is you need to fast track a number of killer app pilots there what we do is we partner with not just the best blockchain company in your country but in the world so what we've done is we brought together consensus which is the leading Ethereum developer we've brought together the executive director of the Center for Blockchain Technologies at UCL who used to be the head digital economist for the German Central Bank we've brought together the leading decentralized ID leadership to work with the upu and that's already happening to discuss how to get s42 and 68 to embed into the the worldwide web's emerging decentralized that's standard actually when it it's ratified and when it's adopted in 6 to 12 months it will be exposed in 1 billion browsers that means you could get s48 installed in a billion browsers if we actually move quickly enough so that's the first thing we want to do is propose a number of very powerful pilots using absolute best of breed developers that you could share through all the posts splitting the cost of it so that it's quite affordable and having a uniform platform the second thing is it's really important to understand not just blockchain identity but the standards that are emerging there around the W3C DID movement the the most important thing is for the post to realize that you know it may look like you need blockchain but the reality is the blockchain industry needs the post in order to fully solve the last mile problem to your customers and the fact that you have how many retail operation unless does the post have like 600,000 right it's an enormous retail outlet so if you look at it the the posts are like battleships you're very big you're very powerful but you're very slow you can't turn what you need to do is partner with speedboats but the last thing you should tell the speedboat is don't move faster than us right and that's what most posts do is that we have compliance you can't move faster than us you have to get all these approval you have to allow speedboat to move very quickly if you could get that done you could win this game and leap from Google and Facebook so what I want to propose is that if all the posts could work together so this last episode I think was actually an opportunity when one country starts making a lot of aggressive moves and demanding that you know you change the dues termino dues rates it's actually an opportunity to move away from the old business models and move faster towards innovative digital models so that you can actually take care you can actually dominate in that area so what I want to recommend is that if the posts work together you'll look like and not just a single battleship but an armada and actually I've been speaking to Spanish posts I was hoping they would are you here somewhere but Spanish Carreros no so I was hoping they would join so we could call it the Spanish Armada but anyway so the other thing is I was also the lead facilitator for HTC's blockchain phone so they're very interested in selling blockchain phones into Africa and there's no distribution channel and so they're building a $200 low-cost smartphone you know and I could tell all about it because we help lead the design effort but they're looking for partners to deploy technology into Africa and there are ways that you can make these lightweight phones which are actually partially notes that can make money when you plug them in you can actually make money off these phones while distributing them into Africa and then finally I think what we have to do is look for quick wins and something quantum competing that really gets the industry to go wow I didn't expect that to happen because what we want to do is create this thing we're calling the universal postal blockchain consortium which would be and if you have any questions about this all you have to do is look for one of my sojits wearing one of these and just a few quick words about the pilots the first one which was actually the idea of Sergei Dukulski at the UPU is to take on Facebook how does the entire postal you know Armada take on Facebook which is deploying Libra which is a global stablecoin we're saying you could actually do your own global stablecoin it would have faster adoption than than Facebook would because you have actually more customers than them in the key areas where everybody's trying to acquire new customers there'll be a billion new customers in the next 10 to 20 years coming out of Africa and it's it's a sweet spot to go and make a significant amount of money and have an impact on the world this financial inclusion engine would using a crypto would probably reduce the cost of the money order system well about an order of magnitude so what we did is we partnered with Consensus which is the leading developer of Ethereum and they actually had a project that's running in the country of the Philippines called the ITI pilot and it's ready to deploy so we said let's take that we don't want to take any risk take that technology install it in the UPU have a few posts and trading partners for remittance pilots here and then let's test how you would deploy a cryptocurrency stablecoin in the marketplace which is very interesting and so that's like one project the second one is smart addressing we actually have already gotten the UPU leadership around postal standards to talk to the worldwide web consortium members all we would need to do is formalize that and push through we've written the papers how to do that so uh that's the second one and the third one is we have a fellow named Dr. Fred Lover who won the won nomen prize in optimization so we have stuff that's running that is 15,000 times faster than anything ever and we think we can actually apply it to the telematics data that the ptc has and actually find immediate improvements and we're just looking for a way to work with uh your organization to deliver that once we do we can take these findings and flow through the entire network so that smaller posts can actually be more optimal to give you an idea of what a quantum optimization could do uh google just leaked a paper where they declared that they've achieved something called quantum supremacy have how many of you have heard of quantum computing is that okay that's good so uh it's been predicted that quantum supremacy which is a quantum computer that actually runs faster than a classical one wouldn't be done for a decade so google this let's see if we can you know push that a little so they uh sneak the paper showing that they've already achieved it so that's the starting that that's the sputnik of this movement and if you don't look at quantum computing you you're going to be behind uh what i can tell you is that my team has already been invited by the chief scientist for the supply chains at amazon to sit down for a day with them on friday okay so this stuff is happening and it's happening now i would love the postal industry to work on that and so anyway uh this pilot already has many partners uh and there's many more things we do but the important thing is this if you'd like to find out more there's going to be a workshop tomorrow at 4 10 where the post expo was kind of have to give us 90 minutes to drill down and talk about this uh if you attend we'll give you a free copy of our book you know blockchain design sprints for the postal industry uh and uh and if you have any questions i my associates will be outside and you can just ask us and we'll talk about all of this so i think that if you're looking for a unified uh platform that the upu can help propel this is it uh we've been working with the upu quietly about it and what we'd like to do is now that you don't have to worry about the extraordinary congress it's time to think about the future okay so um the last thing i have is a favor if you are interested at all i need to show uh that there is interest so take your business card and write the word yes i'm interested yes yes and give it to me if i can show a stack of cards to bishar this would actually get a significant amount of attraction in the organization all right it's like the kind of thing you need after silicon valley is like show me the progress okay oh i'll just say one more thing uh there's a a movie called spare parts i don't know have you ever heard of this movie it's from america it's about a little high school in california that was latino and it was like at the bottom of the list and they decided that they wanted to compete in the robot robotics contest so they went up against mit right which they never thought they'd win but they actually ended up winning the contest against mit which was uh unbelievable so what they did is exactly what the postal industry can do and we're going to talk about what they did in tomorrow's workshop so we can talk about a strategy that will work for the upu to leapfrog facebook and amazon and google okay thank you very much moses for those introductory remarks um now if we can move back to what we were talking about before break and christian perhaps you can build on your introduction remarks and talk about your reflections on things like