Hall time is the best time. I think I've said that before, but it's still true Hey everyone welcome back to another installment to the very first haul edition of a We Can Geek them in 2019 it is a very exciting time. I love me doing some good old-fashioned hauls talking about comic books manga figures Statues if you will What else do we have over there blu-rays 4k blu-rays dvds a little bit of everything it was an Insane month and if you want me to review a particular title Please let me know in the comment section down below and I will happily do so now a little Clarification one of the books that I'm gonna feature here. There was supposed to be a review on it but I Completely missed the deadline. I wanted it by January and I completely forgot and when I did realize that I was out of time It was you know was a little bit too late, but look forward to it. Anyways, let's start with Comic books and start with trade paperback first because they're skinny and pretty easy to hold. This was part of December 2018 and it never arrived in time This was part of the aquatic haul that I was gonna feature because there are a lot of Aquaman Items in this haul, but this is the first one. This is volume six Kingslayer of the rebirth Series next up from Marvel. It is Donny Kate's death of the inhumans I love the inhumans some of you may be logging into this channel and be thinking I'm just an aquatic fanboy But no, I do love the inhumans and there are some of my favorite stories and all that stuff So the title is grim and makes me sad But the story in this is really good and eventually I'll talk about it But yeah death of the inhumans from dark horse. We've got a black hammer age of doom part one Or you know like hammer volume three if you will I'm not getting the library editions I know those things are beautiful, but for the time being I don't I don't need them right now So yeah, I'm comfortable with the smaller trades and now we move on to hard covers It is VBRD hell on earth volume four word one hardcover away to finishing hell on earth So I cannot wait to read volume five and see how it all ends This is not necessarily a comic book per se But I did do a video on this if you have been checking out the channel It was actually the very first video of 2019. It is from inside editions inside editions and Mike Avila The art and making of Aquaman. I rarely collect art books and making ofs I love those type of books, but I just don't have room for them. But this was an exception This is a beautifully crafted book if you want to check out the video. It's on this channel We have a bunch of omnibus is here starting with Super Sons from DC This I was supposed to review this in the month of January, but I totally forgot So yeah, look forward to this look forward to this really really soon Super Sons from Rebirth at Superman from Gleason and Tomasi. This is the oversized hardcover Or rebirth deluxe edition volume three one more to go and we'll be done with this Ten year of Superman. I'm very much looking forward to owning the whole Gleason and Tomasi Material this I upgraded from my hardcovers because I really wanted the omnibus It is the Grant Morrison seven soldiers omnibus Love this fine. I love this story So a friend of mine got a cool Christmas gift as a result so I could get this Instead I love this story and of course if you watched the op-nies over at the omnibus collectors network and omnibus live you might have Heard my picks. Unfortunately. I couldn't be on because of the whole internet thing But I voted this as the best release of 2018 is it it should come as no surprise that this would be my favorite release In my opinion, it is the best release the best omnibus of 2018 It is of course Aquaman by Jeff John's omnibus this my friends is Monumental one this book is amazing to the art in it is superb and three It's just overall Extremely fun man. I love this run so much and this I was a fan before this started before the new 52 This cemented my status and my Love for the characters so for this I will be forever thankful And I am just happy that the character finally after decades of published material finally has an omnibus That's awesome. All right, ladies and gentlemen. Let's move on to manga 2019 is gonna be a very interesting year one a lot of manga are going to be read and reviewed and To I helped launched I helped launch The manga bros as we're calling it as of January 2019 It doesn't really have an official name But I think that's what we're gonna be sticking with for the time being I helped launched the idea of manga bros eventually I will be on it, but I do work behind the scenes creating that livestream and Yeah, it's gonna be a Manga-filled year. So if you don't like manga, I I don't know. Sorry But I do try and balance things out with comic books and other type of content on this channel So don't worry.  But speaking of manga I finally went ahead and grabbed all of this. Good night. Boom-boom series volumes 3 all the way up to 7 I had the first two and I couldn't wait. I was getting them at a slow pace But I went up and got all of it reddit. It was life-changing and I cannot wait to talk about it really soon on this channel another series that I have been picking up is Spirit circle here. We have the first four volumes. I've heard nothing but great things about it they're all the covers and stuff and It was going for cheap. I think it was going for like out of 13 bucks It was going for like eight. So I went ahead and picked up the first four out of six I like the short running series, which will be ironic in a couple of minutes I also went ahead and finished getting all of wolf's mod. So here is volumes 4 all the way to volume 8 the Finale so let's take a quick look at the covers which are really impressive I love the covers and the art in this series. Here's volume 5 Here is volume 6 volume 7 and volume 8 Last episode last haul I should say the final haul of 2018. I mentioned that these books were missing they did arrive Safe and sound it took a while But here are the final volumes that I was missing for Berserk right there volumes 30 32 36 37 And of course the latest one that to be released locally volume 39 as well I have read everything and I cannot wait to talk about it because this is easily one of my favorite things of all time It was that great steps some Shown any books if you will I did get Dr. Stone volume 3 I hear academia vigilantes volume 3 fire force volumes 2 all the way up to Volume 5 so yeah, I will finally be continuing this series. It's been a long wait I did the review on volume 1 a long time ago. So yeah volumes the next four volumes. I am very much looking forward to Talking about this because it's a fun series as well, and I said I liked short series Okay, this should give you a hint. This is old and I don't think I showed them on the on the previous on the previous haul but We have two volumes of one piece The omni editions the omnibus editions all the way up to volume 9 those were old I forgot to mention or show them on previous hauls or whatever alright, so Last time I do believe I highlighted up to book 15 I don't I don't know Up to volume 10 or something. Yeah, I know it was volume 24 look at all these loofies Omnibus editions up to 15 which conclude the water seven stuff and Features up to volume. What is this volume 45? So yeah, that is a lot a lot of One piece I'm currently as of this video going through all the volumes and doing a read-a-thon and Eventually on this channel. I am going to be discussing all the arts or the major plot Stories or whatever you want to call them, but yeah one piece. It's an excellent manga. Next we're gonna talk about Blu-rays. There's a lot of them as well These are regular movies. I did get on 4k the steel book edition for Jurassic World collection It's basically all five movies in 4k. They look awesome by the way, especially the older ones They look really good. I it was like watching them for the first time in a long while Because of glass I went ahead and picked up split. I had already seen this a couple times But I didn't own it and now I do This this little thing the black sheep of the Marvel family the incredible Hulk on 4k This is the best transfer available for this movie. I don't think it can get better than this But even then I was a little bit Underwhelmed by the quality like certain scenes look great others especially the whole CG thing You know it's been ten years and it's a little bit dated in my opinion, but still it's fun I genuinely enjoy this movie a lot the plot in this is really good the effects like I said They've aged a little bit, but I like it and I hate the fact that it's the black sheep and rarely gets mentioned Officially, so yeah the incredible Hulk. I never bothered picking it up on Blu-ray. I only had like the DVD from back in the day if you can believe it So I said, you know what it was going on sale for like nine bucks or something. I'll pick it up What's the worst that could happen and finally an Oscar-nominated movie? Black Clansman on Blu-ray 4k. I do believe this was on sale for God how much but this was on sale So I got it for cheap as well moving on to anime This is a very bizarre portion of this haul video It is of course, I did a little quick little unboxing on it Jojo's Bizarre Adventure Set one limited edition, whatever. This is an amazing presentation I love this Blu-ray box and I am very much looking forward to getting the other two that are released This is awesome. I've watched a little bit and I love the series. I get I get the hype Don't worry. I get it next up if you watched my haul You will remember my aniplex rant It got worse from there. You see I am a huge fan of the fate stay night series I have the first one and I had I picked up the heavens feel movie I watched unlimited blade works and fate zero on I think it was Netflix or something like that or a streaming service I watched them or maybe crunchy roll So now for the holidays. I said, you know what I want to collect them at least Unlimited blade works because fate zero had gone out of print. So I picked up unlimited blade works This is what I have here. Unfortunately, I picked them up. They were both in stock both volumes Volume 2 shipped and then volume 1 went out of stock mysteriously. It stayed that way. I call them They're like, oh, yeah, don't worry about it. We got it. But let me check just in case they go Talk to a supervisor, I guess. I don't know. I don't know how that works. Turns out it was a mistake Everything's out of print. So they canceled everybody's orders and I was left without part one and aniplex in their infinite wisdom They don't really care. They're not gonna say anything. I'll probably get a loyalist hitting the dislike button. Maybe I don't know but Regardless, here's what I ended up getting. This is Fade Stay Night Unlimited blade works set 2 with the later half of the series. This is a great box It has a soundtrack and some art cards or whatever art prints Booklet, bunch of cool stuff. So for part one the DVDs are still in stock I know DVDs, but whatever 2019 and here I am getting DVDs, but at least I got it This is the complete first season, if you will, or the first 13 episodes Oh, aniplex Whatever. Here are some other anime blu-rays that I got. It is Mobile Suit Gundam Thunderbolt Bandit Flower This is set in the Universal Century timeline. I also got Death Note the complete series. I already had it I gave away my DVDs to a friend as a gift so I could get this version instead and you know save myself some shelf space I got the Essentials editions of Myriad Colors Phantom World. I'm starting to watch this for the first time It is Overlord. I had already actually I had already seen the first half of it So I went up and picked up this version so I could watch the whole thing and then move on to the other two seasons I also got One Punch Man, which I hadn't I have not seen actually. I know everybody's seen this and their grandmother But yeah, here I am. One Punch Man And last but not least one of my favorite shows I finally got it is Toradora the two volumes It's a DVD case, but it's actually blu-ray and DVD as well. Finally to finish things off This is probably actually no 2019. We will see overwatch pops So those will probably be the last ones for now, but I had to get it It is the deluxe DC collectibles Jim Lee Aquaman pop This is my favorite version of Aquaman if you've been wondering. I love Peter David. I love Rebirth I love Silver Age Golden Age. I love all versions of the character in the comic books But if you were to ask me was my what's my favorite version of Arthur in the comics It has to be this it has the toughness and grittiness of the 90s with the sensibilities of the old-school stuff With a new twist and stuff He's got the scruffy beard and of course that iconic scene where he is killing a parody man Here in the back you can see the cover recreated. This was Justice League issue This was Justice League issue number four of the new 52 stuff And last but not least technically they're more like figurines, but people like to label them as statues to each zone I got the Fujikomine Ban presto Master Stars piece Fujiko. This is the second version. I do believe this is from part five of the anime So yeah, this is a really cool Statue or figurine if you will and from a good friend who gifted this to me Uh, this is the general Frankie from one piece Looking pretty awesome as well. I haven't opened them yet, but they're pretty awesome Whoo. All right. So that in a nutshell is the first haul of 2019 From now on. I don't know. I'm gonna slow down on a few things But I am very excited to tackle some of these long-running series like One piece and bring you reviews on or discussions on berserk and all that stuff Guys, thank you so very much.  I love every single one of you Thank you for liking commenting subscribing and sharing the content sharing the love you You make my day with that stuff. I wish you nothing but the best in this new year, I guess and I will catch all of you On our next review slash unboxing slash discussion. I don't know the next video. I'll catch you then Bye


Hi everyone, welcome to the basic science series by Dr. Lokendra Kumar. I have created this program to promote scientific knowledge among students and young researchers. In the previous episode, we discussed about the major histocompatibility complex in general. We also identified that there are three classes of major histocompatibility complex that is class 1, class 2 and class 3. In this presentation, we will focus on the structure and function of major histocompatibility complex class 1 molecule in detail. We will also focus on the interaction of CD8 coreceptor and the function of various domains of MHC class 1 molecule. So, please watch the full presentation to understand the structure and function of MHC class 1 molecule. The first question is where MHC class 1 molecules are found. They are present in almost all the cells of our body. They are present on the cell surface of all nucleated cells. It is very important to note down that they are present on platelets but not on the red blood cells. Let's know about the structure of MHC class 1 molecule. MHC class 1 molecules are heterodimers. That means they are consist of two polypeptide chains, alpha chain and beta 2 micro globulin chain. Alpha chain contains three domain, alpha 1, alpha 2 and alpha 3. Alpha and beta micro globulin chains are linked non-covalently via interaction of beta 2 micro globulin and the alpha 3 domain. The alpha chain is encoded by a HLA gene while the beta 2 micro globulin unit is encoded by the beta 2 micro globulin gene. MHC class 1 molecule complex interacts with CD8 coreceptor and T cell receptor to recognize foreign molecules. The alpha 1 and alpha 2 domains fold to make up a groove for peptides to bind. It is very important to note down that the alpha 3 domain interacts with the CD8 coreceptor. The alpha 3 CD8 interaction holds the MHC 1 molecule in place while the T cell receptor on the surface of cytotoxic T cells bind with the alpha 1, alpha 2 chains and checks the coupled peptide for its antigenicity. MHC class 1 molecule binds with peptides that are predominately 8 to 10 amino acid in length but the binding of longer peptides have also been reported. When a foreign protein enters in our body the antigen presenting cells generate the peptides using proteosomes. Proteosomes are protein complexes which degrade unneeded or damaged protein by proteolysis. The MHC class 1 peptide complex is then inserted via endoplasmic reticulum into the external plasma membrane of the cell. The epitope is bound on extracellular part of the class 1 MHC molecule. Thus the function of class 1 MHC is to display intracellular protein to cytotoxic T cells. A normal cell will display peptides from normal cellular protein via class 1 MHC molecule. And cytotoxic T cells will not be activated in response to them due to the central and peripheral tolerance mechanisms. When a cell expresses foreign proteins such as after viral infection a fraction of class 1 MHC will display these peptides on the cell surface. Consequently cytotoxic T cells specific for the MHC peptide complex will recognize and kill the infected cell. In the next episode I will be covering exciting topics of immune systems so please stay tuned and don't forget to subscribe the YouTube channel. With this note I conclude this presentation. I hope you understand what is MHC class 1 molecule and its role in human health. Please subscribe the channel and share the presentation with students and young researchers. Please forgive my mistakes. Thank you and Namaste.


So, today we want to talk about an application of AI, which is computer vision, and we talk about that because we want to talk about two methods that we have to use before we apply any AI technique. They could be considered theoretically AI techniques, but they come from computer vision, which is a mixture of AI and non-AI techniques. So it could be a lot that we have to talk about today, but we will do our best. And so today we start by saying that AI is vision, so if you understand images, that requires some intelligence, which is saying intelligence is to recognize people, scenes, objects, patterns. The applications are familiar for everybody, face recognition, object recognition, auto-captioning of images, actually a recent development, robot navigation, and many more. So the question is how do we do this when this happens to be one of the most impressive exciting examples for AI? That's the reason that we are talking about. What we talk about is because we want to mention two methods, that if you have visual data, you have to apply those methods as one of the non-AI techniques. And then hopefully down the road, three weeks from now, we will see how AI itself does it, and then we have a better feeling for the differences. So given a digital image, we have basically two possible ways for recognition. So you want to do something impressive. If an image is given and you want to recognize the face, or you want to say that's a cat, that's a car, that's a cat, a dog playing in the park, whatever that is. So first we have the AI approach. Well, it's not first, I just grabbed it as first. So which is, you get the image, you give it to some sort of AI black box, and there it comes out classes or text. So that's basically it. I give an image to some sort of AI technology, and the other side, it tells me either a label, or a phrase, or a sentence, if I'm lucky. The second approach is computer vision slash AI approach. I'm not sure we can make that distinction, but there are still techniques that are part of computer vision and per se they don't exhibit any intelligence. So we have to keep them separate. So you get still the image. You give it to the black box, that's it called computer vision.  It's usually not a black box, it's a white box. And then it goes to the black box of AI. And then, again, you get classes and text. Why did I say that for the computer vision is not a black box? Because we can verify every step. There is no hidden things in computer vision algorithm. Everything is deterministic. One, two, three, four, you can follow it. Usually there's a loop, there is some recursion. You understand what's going on. So this is usually a white box, this is a black box. So who says which one I should use? Well, most of the time it's not up to us. Most of the time the nature of the data will dictate to us which technique you use. Do you, can you go pure AI or you need a combination of computer vision and AI? Whereas, again, some of the colleagues may just get upset and say, well, you are separating computer vision from AI because the most recent success is because of AI, so people may not separate that anymore. But we have 2% of techniques that right now are impressing us coming from AI. 98% of techniques in computer vision have no intelligence, but they're good. So we have to be clear about this. So what is it that we talk about? So it's about feature extraction. So what is feature extraction? So feature is, give me some numbers that the computer understand the data. Not the data itself, not the raw data. The raw data is difficult to understand. Extract some descriptive rich numbers that describe the data. So that's what we call features. So in computer vision, we have basically two approaches. One is key point oriented and there are techniques for that like SIFT, SERF, and others. We will not talk about them. Again, computer vision algorithms, we can look them up, SIFT features, SERF features, you will find them. So it's not our focus, how they work. But they will give you several hundred, several hundreds or even thousands of features. So the way that they work, you give an image of a class and those algorithms find key points. They say this point, that point, that point, that point are important. So they are key points. They calculate it somehow. Because if I look at that wall, that wall has no key point. It's just surface, it's homogenous. There is no discontinuity. Nothing is happening. The derivative is zero everywhere, it's constant. If I look at this wall, okay, something is happening. If I look at the class, a lot is happening. So no key points, some key points, a lot of key points. So we don't need to go in detail. Second approach in computer vision is histogram oriented. Histogram oriented. Here we have techniques that call LBP, HOG or ELP and many others. So here what you get is just one histogram, one histogram. Which is just a vector of some numbers. Usually it's a short vector, 128, 256, 500 is a short vector. So, okay, if we take a look at an example, I cannot draw an image on the board, but something simple. So if I get an image and this image is a scene, there's a house, there is mountains, and so on. So one of the methods is, for example, Harris corner detection. Which is key points. So this is one of these techniques, similar to sift and surf. It finds key points. So what does that mean when we talk about key points? Basically, these methods will go in. I want to understand how non-intelligent technique do it, before we go into intelligent techniques. So it will find corners. So say this is a corner, this is a corner, corner, corner, corner, corner, corner, corner, corner, corner. The obvious ones are clear. But you may do that, apply it on some image, and you see also it finds a corner here, and you say, wow, there is no corner. Why if you zoom in, there is a digital corner? So not maybe for us. So I say that because the concept of corner may be counter-intuitive for us as human experts. Because we really want to see a corner. Wow, there is no corner in the digital world. The question is, what is your threshold for defining the sharp edges that build a corner? So you apply corner detection, and you will find some corners. So then around each corner, you get that out. For example, you see that here, you have something like this. So if I get a small sub-image around that point, around one of the key points, let's say, I don't know, this is 16 by 16 pixels. So usually don't see it unless in Photoshop you zoom on it and say, oh, this is this. And maybe this is slight curvature has caused the algorithm to say that's a corner, but we don't see it when we zoom out. Or the gap between, that's the semantic gap. That's the gap between computers and people. And when we find this, then we calculate some numbers for this here. For example, 22, whatever, 31, 53, 111, 165. So after you find a key point, then around that key point, you construct a small window, and then for that small window, you calculate some numbers. So these are features. Of course, let's say if you have, the length of these features has to be smaller than 16 by 16, then it doesn't make sense if that's not the case. You cannot need more features to describe the data than the data itself. Part of intelligence is for the features to be compact, short length. So for example, for SIFT, you get many features of length 128. So by default, that algorithm that is called SIFT, if you apply it on an image, usually if I capture a photo from this side of class, I would guess and I give it to SIFT, let's say 2,000 by 3,000 pixel, I would get four, 5,000 key points. So that's overwhelming. You cannot manually verify them. So four, 5,000 keywords and each one of them is described with 128 numbers. Well, that's massive. Well, but the image is 2,000 by 3,000, it's six million pixels. We are reducing it to 5,000 times 128, that's a lot. So what is it that you wanna do? If AI is vision and we as humans acquire and process 90% of information that we have and we work with visually, you cannot have AI without being able to process digital images, it cannot be. So that's why we are talking about it. So, which means then, your image is the union of those feature vectors Vi when I goes one to the number of key points. Now it's too small. So that's the sign of union, that's not a U. Well, that's a U, but the union of all feature vectors, like this, whereas I get N key points. So N key points, I get N key points and this is my V sub I. So for the computer, if you give me 5,000 key points and for each one of them I have a feature vector, the union of these feature vectors is what the computer understand under this image. So everything else is ignored. So that's what the computer sees this image. Okay. So this is, the problem is this, challenge is first, data is too large. And second, data may not be descriptive enough. So solution or solutions, well, it could use embedding, it could use pooling, which is a type of embedding, or it could use encoding. So one concern is if you give me 5,000 key points and each one of them is basically a vector of 128, that's still a lot of information. Well, we talked about PCA, that's good. We talked about PCA because I could apply PCA on 5,000 feature vectors and say some of them are garbage. Get rid of it, great, fantastic, we do that. But okay, I'm not, I'm before PCA. PCA is an AI technique. I wanna make the life as easy as I can. That's a philosophical concept.  We wanna clean it up, we wanna make the best we can, and then we give it to AI and say you do the rest. You do actually the difficult part. So solution, which is one is feature vector, or feature vectors, and one is the so-called Vlad. I wanna see whether today we can talk about all this. We'll see because the two of them, one Vlad is relatively simple and straightforward. Fisher vector, it requires a lot of statistics and equations. We will try to scratch the surface just for the feature vector to get some idea what statistics are used, and then we will see. So feature vector is fundamentally embedding and pooling. So Vlad is encoding and increasing through that, increasing discrimination, discrimination. Different things, but one could regard Vlad as a simplification of feature vector. So we start with a complex one. Hopefully we get some sense what it does and then we move to the simple one. Okay, so let's say we have our data set X which is the set of XT and T goes from one to, up to capital T, and then we have mu lambda being a probability density function, probability density function, which models the generative process of elements of X. Many newcomers get confused with the terminology because then suddenly we leave the words that we know features and scenes and objects and faces and cats and dogs, and then we start talking about probability distribution, generative process, what is that? Why? X is your features. There is a generative process that generates those features, right? So for example, SIFT, that's a process that generates those numbers, okay? Like anything else, that's a distribution. Everything and anything is a distribution. Hopefully it's a Gaussian one. We can assume it's a Gaussian one.  If it's not a Gaussian one, then we will resort to other things. So, but I have a distribution for my features and that distribution has some parameters lambda that I'm not talking about it yet. So this distribution models the generative process. If I can get a sense of the distribution, I can understand how features are generated. If I can understand how features are generated, I can pull features. The important ones, not the crappy ones.  We still don't know neural networks, so we don't know that black box, what it does with the millions of weights. So we wanna do it in a different way. And then we have lambda which is lambda one, lambda two, up to lambda M, capital M transposed, which is a vector in the M dimensional space, parameters of U lambda. So that distribution has some parameters. So I grab any parameter, the standard deviation, the position of the mean, the location, the spread, all that could be able, how can I define that distribution? Okay, good, I understand up so far. So now we need some statistics. Well, why we are saying this? Look, so if I have a function, if I have this and I have a lambda O, or, this cannot be distributed, let's call it a function L that we don't know at the moment, or I have this. So I may get two different distributions that will contribute to a function that will be of some use for me. And then I see this, of course, the standard deviation is small, so I can really map this to my lambda O with a lot less error than I can do it here. So fits the data more accurately. So if that helps you to come with me, to why is that important? What is it that we talk about distribution? Doesn't matter who does it.  The distribution of the data, the distribution of the future is always that. There's nothing else. So the question that we are trying to just formalize is okay, there are some parameters that give me a nice fit distribution to the data. There are some parameters that are crappy. So I have to find the one that gives me a fit, good distribution of the data, the generative process. The generative process, how are those features generated? We don't know because that process is highly unlinear because if I get a sense of it, I can then do a better job in selecting good features. Okay, so now we have to go in the difficult part and talk about the statistics. So in the statistics, there is the concept of the score function. The score function is the gradient which is the partial derivative, the partial derivative with respect to WRT, parameter lambda of the natural log logarithm of the likelihood function, of the likelihood function. So that score function is given as, so the score function is given as the partial derivative with respect to lambda log of mu lambda X. When you estimate a distribution, any distribution has a parameter. Usually, if you are looking for, let me see, I can do it without making it too complicated. So let's say, keep it here. Side note, because otherwise, you don't understand why it's a log likelihood. So let's say A and B, completely different terminology, are independent and identically distributed random variables. They are IIDs, independent identically distributed. So then the function AB, given a distribution mu, is given as F of A given U times F of B given U is the likelihood. We should know that also, if you have heard about Bayesian theorem, that we will talk about it after the reading week. So if I understand F as a probability density function, what is the probability and A and B are correct, are good, are together, given a distribution? If they are independent, that's the probability of A given U times the probability of F be given mu, if they are independent. If they are not independent, that's not that easy. Okay, but we cannot really work with this because that's multiplication that's really difficult to do. So people, what they do, they use the log of F of AB given U, which is of course the log of F of A given U plus the log of FB given U. So what we did, we fundamentally, and we should not have done this, but I'm just doing it just to be safe. So we converted this multiplication in an addition by using log. So now we have a log likelihood. Mathematical convenience. There's no magic or Y log. If you could work with this, that would give us the same result, but we can't because things get really crazily complex. And working with the product of probabilities is really tough. So addition is nice, is easier. Okay, so if I accept that, that was a side note. I just didn't want to, that we get into two complicated things. So that's being a side note. So now we want to go back, see how much we have to do to just recognize that the cat is a cat. So when we do this, when we do this torture, then we will appreciate the deep networks. Just give it to the network, it says that's a cat. But you get to understand how it's done, otherwise you are not a good engineer. So the score function, again, the score function was equal the gradient with respect to lambda log of U lambda of X and this is equal the gradient of lambda with respect to log of the probability of X given U lambda. So what's the probability? That's what you wrote. I wrote F to keep it general. That's a density function, that's a probability. So let X be the set of D dimensional local descriptors. I want to connect it to what you're talking about. So these are local descriptors. That's a global image. Any of this is a local descriptor. Any of this feature vectors, we didn't talk about how we calculate these numbers. We are not interested. It's not our business at the moment. Somebody gives me 5,000 vectors each 128 numbers and says this is a cat. Can you recognize it? I want to process that. So let X be the set of D dimensional local descriptors extracted from an image. Extracted from an image. It could come, for example, it could come from SIFT, it could come from SURF, it could come from many computer vision techniques that do not have intelligence. Forgive me, 80% of researchers do not have intelligence in the common sense in the AI community. That means they don't learn something. They have some deterministic steps. They do not have an iteration to adjust. Then we can say that my score function as a function of lambda operating on X, the G, so G lambda of X is equal to sum of L, a new function L as a function of lambda times the gradient with respect to lambda of log of U lambda of X, of XT, sorry. Whereas T goes from one to T. Do I still have it? So now I'm back to this. So I'm back here. If you have difficulty, maybe I stick, do we have, when I write here, do you guys have difficulty to read it and write? What should we do? Stick in the center? Yeah. Okay, at least I'm being fair. But so the only problem is then I have to stick to the center. Should I stick to the center? Okay. So let me, let me finish it. Sorry again. Independent and identical distribution, yes. So now we are introducing something new. So that's a function that has a parameter lambda and is operating on X. We still have not named it anything because that's our Fisher vector. Whatever it does, we call it Fisher vector. So that's our Fisher vector. Okay, what is that? So I will finish this and then I stick to the center. So Fisher vector is a sum of normalized gradient statistics, gradient statistics computed for each descriptor. And when we say descriptor, we mean feature vector. That's the same thing. So that's a descriptor. So the idea of Fisher vector is give me a normalized gradient statistics. So if you bring in the statistics, we have to become better. So what is the simplest statistic you can use? Instead of working with the data, work with the average of the data. Substract the average. Do something with the statistic. Why? Why? Why statistics? Any idea? Why statistics? Why not calculus? Sorry? Yeah, but it doesn't match with us. AI is about data. You get data. When you get data, you have samples and population. The only field of mathematics that can help you out is statistics. There is no other way. We cannot use algebra right there, where you wanna find out what is important. After we put things in matrix format, then we can use other stuff. So but that's the reason that we talk about the statistics. So and then this operation that you map X of t to some sort of function that uses an Fisher kernel, fk, for each data point, Xt, and give us, of course, that function L of lambda times the gradient of log of u, lambda of Xt is an embedding. So this operation, if I add them up, that's your Fisher vector. If you add all of them up. So imagine you have many of this underneath of each other. First, you embed them, then you add them. So is an embedding of local descriptors Xt in a higher dimensional space in a higher dimensional space, which is easier for a linear classifier, which is easier for a linear classifier. Okay, so we are not done yet, but okay, so you're getting some crucial information. So first of all, you're talking about normalized gradients that are based on some statistics. And then we map this with some sort of function that we still don't know how we get that function to put the Fisher vector together. I know it could be frustrating, stick with me. So around reading, we have a better picture why we are doing this. Is an embedding, that's a keyboard. Embedding is I read the data, put it in another space. I read the data, put it in another space. A better space, a more compact space, a more descriptive space. Otherwise why I'm doing this, just use the data. For a local descriptor in a higher dimensional space, which is easier for a linear classifier. Oh, I see. So we wanna recognize that this is a house, this is a mountain, this is a river. You wanna recognize that. You wanna recognize the scene. That's a highly non-linear problem. So to make it easy for a class, after this it has to be a classifier.  Bunch of another vector, it says house. Bunch of another vector, it says mountain. But that classification, that you have multiple classes for which you should have trained. You cannot recognize the entire planet. You have recognized for river, landscape, hills, mountains, houses, buildings. And if anything else comes, you say I don't know. So you may have one class I don't know. Other than that, everything that you do is highly non-linear. That approximation, that we wanna find what function maps X to Y. And X here is our features and Y is the class, river, house, mountain. It's very non-linear. So with all this stuff that I'm still skipping a lot of it, we wanna make it easier for the classifier. So if it is non-linear to 85%, I wanna make it non-linear to 25%. So to make the problem easier. Okay, so let's stick to the center. So what is this mysterious L of lambda? So this could be, for example, the Holesky decomposition. This is where it gets really interesting and complicated in terms of how we use the statistics. So the inverse of F of lambda can be given of L lambda transposed times L lambda. And the kernel, we come back to kernels. If I use the Fisher kernel, and I'm looking at X and Y, we will not say more, but we will come back to this. So a kernel is a function with certain properties that takes two numbers and tells you how similar it is in a higher dimensional space. We call a kernel trick. When we get to SVM support vector machines, we talk about it. At the moment, we don't wanna talk about it. We just take, there is a kernel that uses Fisher, that we call Fisher kernel. So that Fisher kernel is the matrix for X transposed as a function of lambda times the inverse Fisher information matrix times the G, the Fisher vector that we calculated here. So I'm putting it in a matrix format. So I need to do this. I need to decompose the Fisher vectors that I put. I put it in a matrix and I need to decompose it. And after I decompose it, I can write it this way. So and then I do this, the magic of kernels. Give me some numbers. So this is the embedding part, actually. So this magic will give me better numbers. Okay, whatever you say. So this is the Fisher kernel. I don't wanna go more than this because if you don't talk about how we decompose and how we do this, okay, this is not the statistics course. So we are just taking a little bit of it. Okay, so it's still a lot of steps to do this. But I don't have to do it. If you go, there is a website, especially for the two techniques that we talk about it and you can just download the algorithm and use it. What do you wanna have some minimal understanding? So I wanna give you a simpler one, which is much simpler because people said, ah, that's fantastic, that's great, that's a lot of, that's 120 years of statistics behind it, but I didn't take a statistic course, so what should I do? Okay, so we have other stuff. But if you stick with AI and computer vision, learn this, learn Fisher information matrix. So the next one, which is very simple, actually is VLAB, which stands for the vector for locally aggregated, for locally aggregated descriptors. Again, we still are looking at that image and I'm getting many, many descriptors and I wanna aggregate them. So I did that really sophisticated with Fisher vector, with a lot of statistics behind it, with a decomposition and embedding and all that. We went back to the origin of distribution, this is distribution, so you can reconstruct it by multiplying the individual probabilities, but that's too difficult. Take the log, it becomes plus, it becomes easier. Bring in the Fisher information matrix, all that. If you don't understand all of it, it's not your fault because you're just scratching the surface. So I have not taught you Fisher vector. I have just mentioned Fisher vector to you. There's a lot of additional information necessary for that. Hence, let's talk about something simpler. So why we do this then? Well, because again, we wanna build the foundation for why we do things with neural networks and not with this type of stuff, which are very, very useful and very good, but we don't. Maybe other people. So again, the question is how to recognize images. The question comes up again. So the Fisher vector was fundamentally suitable if you have key points, well, at least I would do it. If you have key points. So if you have an image of 1,000 pixels, you have 1 million pixels. You cannot process all of that. So, but if a method like SIFT gives me 5,000 key points, so 5,000 out of a million. Okay, that's not bad. But then each of one is vector, then I use this, then I embed it, hopefully I can do a easier classification. So how do we recognize it? One of the very established method is the so-called bag of visual words. It was inspired by bag of visual words. It was inspired by bag of words, which is a method for text document classification, website classification, spam recognition, all that. We work a bag of features or bag of words. The words that are important describe the content of a website, for example. But okay, what is a word in an image? So what is a visual word? So if I look at that, it could be that any of those is a visual word. So if you give me an image, if I draw my kindergarten picture again, so then let's say, so I grab a point here, bring it out, that's a visual word. Usually we have a fixed size, most commonly 16 by 16. So if I grab one here, because I have some key points, and bring it out, that's a visual word. So now, there is not infinite number of visual words that can construct an image. That's an interesting thing. So if you have some small way of getting enough visual words, let's say edges, this direction, this direction, this direction, maybe a corner this way, a corner this way, maybe some curves, then you could paint any image with those words. Like a dictionary. How many of these words do I need to paint an image? A very interesting question. Is there such a thing as a visual dictionary that they could come up with? And then using this tiny 16 by 16 pixel, if I knew how to use which word here, which word here, which word here, I could construct the image of a cat. Is it possible? People do it. Because the bag of visual words is a very, very competitive technique. To this day, for certain application, the bag of visual words can beat deep networks for image recognition. So okay, so given an image, given an image, given an image I, divided into small cells or windows of size N by N, usually, in general, is 16 by 16. And nobody knows why. It just worked nicely with 16 by 16. If you make it 14 by 14, it collapses. If you make it 18 by 18, for most images, it doesn't converge. One of those things that we know empirically, but we cannot theorize it. Big part of AI has become empirical. So okay, which means what? So you get this image, whatever it contains, and you split it in many, many cells or windows of this N by N, which is generally 16 by 16. And then, for example, this one is an edge like this. This one is a curve like this. This one is a corner like this. So you collect visual words. It has to be small enough to count as a word. It cannot be a big part of the image because then it gets too complicated. It has to be something that you can describe with one or two words. That's a horizontal edge. That's a slight horizontal curve. That's a perpendicular corner, so I can describe it. But if you give me a big portion of the image, I cannot describe it easily. I have to write half a page. So every cell is a visual word. So now the challenge is I have to go back and delete here. Okay, so we usually vectorize visual words for convenient calculation. For convenient calculations, calculations. You see, this is the type of things that we have to do when we do computer vision algorithm. There's no learning involved. We have to think about every step. I wanna recognize that this is a river. That's a house, yes. Sorry, again, louder. To a certain extent, yes, yeah. So you mean thresholding for what is what? It has its limits. It has its limits with respect to the resolution. But again, if the data is well prepared, you can use it basically for every application. So the alternative is, of course, you can also use, you don't need to do this. You still can use key points. But visual dictionary usually do this. So if you collect all of them, you get many, many of this. So you get many, many edges that are horizontal and with slight differences. And you get many, many curves that are almost the same. So can I keep all of them? That's not smart. This is not intelligence. If I have to keep all visual words. Well, I need a representative for them. So you need to encode them. You mean, yeah, how do I encode them? Well, I could find an average. I could take an average of this and I would be fine. As a matter of fact, we sort of do. We apply the simplest techniques, simplest clustering technique that we have. Hopefully we talk about it next week. K-means, which is give me an average, calculate the similarity of every one of them from the average and put similar things in the same bucket. Okay. But still, this is not encoding because this is the raw data. We cannot work with raw data. Raw data is brutal. We need to encode it, which means what? Grasp the essence of the data and give it to me in a form that is easier to process, like the intelligent Mr. Fisher. But that's too complicated. Can you do it simpler for me? What's the question? What is the difference between embedding and encoding? So embedding usually has also pulling in it. So you may reduce the dimensionality in some way, but encoding may not touch the dimensionality, just encodes. So makes the classification easier, such that whatever that is, you still after that, you still may need PCA or LDA. You may still need that, depending on how high dimensional the problem is. Okay. So what is the theory? What is the theory? The theory says, you get an image, you apply the bag of visual words, you get many, many visual words. You get millions of them. And you give that to a classifier and the classifier says, that's a cat. That's a house. That's a bicycle. So that's the theory. The theory says, find a bag of visual words, a bunch of visual words, give it to a good classifier. So this is computer vision. It cannot solve the problem. It gives it to AI. I say, okay, I get the visual words. That was hard enough. Now you do the rest. Use any classifier is within the domain of AI because it has some iterations in it. It minimizes or maximizes something. So what is the practice of it? The theory says, do this. The practice is, first of all, you have way too many vectors, especially if you go with this, which we call dense sampling. So you sample everything. That one is a sparse sampling. You only sample the key points, the significant locations in the image. Sparse sampling, dense sampling. You may have redundancy. Well, good thing we know PCA. I can't get rid of that. That's not an issue. You may have noise. So you may have to filter stuff. And in image, we have all type of noise. We have void Gaussian noise. We have impulsive noise. We have speckle noise. All type of noise. So in the theory says, grab an image, give me a bag of visual words, which are basically tiny part of the image that are similar. And then give it to a good classifier. And the classifier tells me what it is. So we wanna follow how is the intelligence manifested in this type of techniques? You can do this in many different ways. There are 5,000 papers on how to do this differently. It's not about detail. It's about the big picture. So what is the general approach? Is that a design problem that you have such a room and you have to do this? Should be just in the center and a little bit brighter. Okay, whatever. Not an intelligent design. So general approach. What is the general approach? Build a code book C with element C1, C2 up to Cn. From M, much larger than N, feature vectors. Feature vectors, which are for us vectorized visual words. So take your visual words that we just had some examples with, vectorize it, make it a vector, consider it as your feature vector. It has that feature vector, you have five millions of them. So there are many, many, but we want just a small number of them. So if you have a million feature vector, I wanna construct a code book, which is my dictionary. So the code book, which is the dictionary, usually we set the size of dictionary. I said, I want a dictionary with a thousand words. You cannot have 6,000 words, 600,000 words like Oxford or Webster, you can't. We cannot afford that. Usually we start with 600, 1,000, 1,200, 1,600, maybe 2,000. Because if you go 2,000 and cross more than 2,000 visual words, oh my God, you need massive computational power. You have to extract 2,000 visual words. You know how much it takes. So N is very small, and M, the number of feature vectors is much larger. You give me five million visual words that you extract and I wanna make it 1,500, wow. And of course I don't wanna lose anything because if I lose stuff, so what would happen if I lose the ears of the cat? So not physically, so. In the image, what would happen? I cannot recognize the cat. So what is the idea? What is the idea? Use a clustering algorithm like K-Means. Wow, this is something we don't know. So if you use a clustering algorithm like K-Means, which at the moment I would say we don't even know what clustering means, officially we don't, and I have no idea what K-Means is. Apparently this is one of those simple AI techniques that you give the bunch of number and it will group them in some categories. Okay, why is that important? Because as I drew that you get many, many edges or corners or curves that are similar, all of them are the same board. So that super algorithm has to put all of them that are similar in the same category and say this is a horizontal line, this is a vertical line, this is a corner like this, this is a corner like this, all of them should be in the same class. But we don't know what that is, it doesn't matter, we come back to it. So then if you do this, the CI in my code book is the center of N classes found in the data.  Maybe we go with the cluster center with the prototype of each category, with the most typical horizontal edge, most typical vertical edge, most typical corner. Okay, so what is the core idea of lab? Accumulate for each visual word CI, I didn't say anything here because I don't wanna go too much into it. So if this is my classes and I have no idea what is what, I guess I have to draw at least something. So this could be my horizontal line. So each one of them is a horizontal line. This could be my vertical lines, each one of them is one of those 16 by 16 in the image. This could be corners that look like this. This could be parts that are a 45 degree curve. This could be something else. So a corner, let's say, looking like this. So this would be the cluster. And if I cluster for each one of them, each category has a center, the blue cross here. So the blue cross is the class center, which is the most typical representative of that category. It's the most prototypical horizontal edge. So we need a technique that does that for us.  Why images is the wrong word? Neighborhoods, windows, 16 by 16 is hardly qualified to be an image. Okay, we don't know how to do this. I know some of you do, but as a class we don't. We don't know how to do that. We come back to it hopefully next week. So we wanna understand what is the, if Vlad is promising us to do the sort of the same thing easier than Fisher vector. So I don't need that much statistics. Okay, how is it gonna work? Maybe I use Vlad. So accumulate for each visual world CI. So these are visual world CI. You see, we have broken down the image that we see as human and we understand at a glance. We have broken it down in tiny parts to make the computer understand the same thing that they understand. So accumulate for each visual world CI, the difference of the vectors x assigned to CI. So if each one of them is a CI, there are many, many, so the blue one is the CI, but there are many, many, many, many, right? Which are other x's. So there are other, many, many other vectors in the same class assigned to that class center, to that prototype. So accumulate for each visual world CI, the difference of the vectors x assigned to a CI, which is x minus CI. In general, distribution of data with respect to WRC, the class centers. So we accumulate the difference to the class center. So the so-called Vlad vector is Vij is equal, the Vlad vector is the sum of xj minus CIj. Whereas xj is coming from the nearest neighbor and n of x, sorry, and n of x is CI. So what does it say? Look at just the x's that belong to the same class and build the difference of every x with the class center and add them up, okay? What else? That's it. You've got to be kidding me. So I can use this instead of visual vector? Yes, that's a simplification of that. Why is that a simplification of visual vector? We have, again, we have a scratch to surface of visual vector, but we saw enough to guess why this is a simplification of visual vector. Or first, tell me why this should be better? Why this should be better? We are just subtracting the class average, yes. Okay, I had the question first. Okay, what is the question? n, n is the nearest neighbor. So find the nearest neighbor of x such that the class center is CI for every given x. So for every given x, I find that, oh, this is very close to this one. This is very close to this one. This is very close to this one. This is very close to this one. This is very close to this one. I find the nearest neighbor. The concept of nearest neighbor sticks with us. It's a very important concept. Things that are close to each other, again, Disney, are similar, happen to be similar. So why do we subtract mean? This is the second time that we are subtracting mean. But this time has a different purpose, yes. But why? What is the effect of filtering? Anybody else? Yes. The intra-variability inside the class, if you just use the data, you don't see it. You don't see, where can I draw it? Okay, so let me draw something here. That's a messy, but sorry about that. So let's say this is your class center, and then you have data around it. So now compare this guy, right, with this guy. Both of them are in the same class, but one of them is very close to the average. If you use the data, you don't have that information. If I subtract the mean, the proximity to the prototype is embedded and coded in the features. So I know what is closer to be a horizontal edge. So we arrive that. Okay, so that's the purpose of subtracting mean. That's the purpose of using mean, sorry. What is the similarity of this with Fisher vector? Somebody may say it's not similar at all. You just showed me one equation here, x minus c, come on. And what we had for Fisher vector was 20 equations out of 50. But this is a simplification of that. We started with the gradient of statistics, partial derivatives. What do you think a difference does? It's the same thing. It's a very simple approximation of gradient. So the reason that both of them are good is because both of them use derivatives. They look at the changes of statistics, averages, and other stuff. So this is a first-order statistic. Okay, if that works, so let's use second-order statistics. Well, I take it easy. It becomes more difficult. So let's stick with this and use it. And if things work, we can think about other sort of stuff. So this is, so let me write it again. This is nearest neighbor. This is the jth component of the descriptor x, of the descriptor. And this is, of course, the corresponding cluster center. And the VLAD does not really perform well unless we do also a final step, which is one of those secrets that nobody tells us. That your data has to be normalized. If things are not normalized, it will take a lot more time, even for the deepest network, to figure it out. Not just VLAD with my humble sum of xi minus xi. So not even for that, even for really sophisticated things. So we do an L2 normalization. So these are my vectors that I now use in my dictionary. But before I put it in the dictionary, I normalize it. How should I write it? In terms of vectors or matrices, how do I write it? How do I normalize it? Sorry? No? Okay. Somebody makes my day by telling me how to do it. Vector divided by the magnitude of the vector. Thank you. You made my day. So the vector divided by the length of the vector, or as he said, the magnitude. Length of the vector. So just look it up at home. So what does that mean, this? This is the Euclidean distance. So if I have a vector of 2.1, 1.2, and this is my vector, how do I get the length? I don't wanna go back to high school. So I divide every vector, I normalize it by dividing it by its length. So now I have it normalized. I approximated partial derivatives with a simple derivative. I use first-order statistic, which is the mean of every class. Then I normalize it. Now your data is something that can be given to a simple classifier to say what is a house? What is this? What is that? So what is that any other way for doing this? Because this is way too much. So okay, so what is the final chain? What is the final chain? This is pre-2012. So we are still not, we have not still not arrived at 2012. So you get the image, we give it to something like SIFT. You look it up, shift invariant feature transform. SIFT will give us many, many, many features. Hundreds of thousands of features. So feature vectors, feature vectors. SIFT works with key points. So that's a sparse sampling. If I go with totally getting everything, that's dense sampling. Then I give this to Vlad. Vlad gives me a different representation that I'm representing with this vertical one because I don't know how else should I visualize it. So that's still the dimension. So what Vlad is not much of, is not about dimensionality reduction, is not about pooling, is about making things more easier to discriminate and normalizing it for easier processing. So I may get a different representation but the dimensionality is fundamentally the same thing. You say, okay, good that I know PCA, then I get D prime and D prime is much smaller than D. Then I give it to a classifier. And then the classifier tells me that's a dog. That's a pre-2012 processing chain for image recognition. You have to sweat to get some of it done. It's not like, okay, have you downloaded the TensorFlow? Just run it. Not gonna work that way. You have to put something together. What doesn't mean we don't need that anymore? No, not at all. In some cases, we have to do this. In what cases we have to do this? In what cases we still have to do this? When you don't have enough data, when you don't have labeled data, then you still have to do this. For a dictionary approach, I do not need a lot of, I don't need a million images. Give me 500 images, I'm good to go. We then sample it. Yes. The nearest neighbor classifier is a classifier and K-means is a classifier. But there are different concepts. We will, there are distinct, yes. We will talk about them. We have also approximate nearest neighbor and many other things. So, okay, so hopefully by, on Thursday we can start talking about actual AI techniques. See you then.


Hey guys So in this series, I'm gonna start doing a bit of commentary on on videos that I've posted before This one I posted it was taken down a little while ago But I think the message is so powerful it weren't some kind of exclamation. So let me let me play for you here I've to succeed and to conquer the way the all-time greats do the all-time greats have to have a chip on the show Yeah, if you don't have a chip on your shoulder And you're not gonna be ever reach the heights or take the Measures that it takes to become an all-time greats. Yeah, me. I had two chips and robbed me at the Olympics And I had a father that I separated from that I know in My heart didn't really want me to succeed without him So those were my chips and because I had those chips. I Can't lose because if I lose he went You understand me so in my prime I lose he went because he said I can't do it I have in my life the way I was taught where I was bought up all I need is God nothing against my dad All I need is God so my to me God prepared you to get me to a certain level But God took those losers God took a guy named Charlie Campbell And you wouldn't know Charlie Campbell because he was a boxer back in their amateur boxing But God showed me so much through Charlie Campbell that to this day I still speak to Charlie Campbell because without Charlie Campbell. I might have never gotten to be worried. Really Charlie Campbell taught me so much about charisma That my father could never have taught me That it was unbelievable Charlie Campbell beat all my father's fighters as amateurs that were in his weight class Easily almost every time because he had charisma now.  He had confidence and he knew how to Do stuff that made me love watching Yes, Demi and he wasn't the best puncher He really didn't have the best boxing skills, but his emotions he breathed everything he did even though he was slapping He had sharp rough with you know, Demi and To me I was like, whoa, he beating These guys that on my team so they learn the same thing. I'm learning But they can't have that kind of charisma because my dad I want a lot of them to be them people He won't allow them to perform with that kind of confidence. You understand me So I need to learn how to do what he doing because I can airway he doing to what we doing now I got something how did you incorporate it? Well, I start doing the stuff. He did a little bit of it Until I figured out what it really was You understand me? So some of the grunting some of the morning He did why you fighting to show that he had breath control. I start doing so like we had Bruce Lee go whoo Try to come or do stuff like that in boxing match Now his punches were nearly as effective were nearly as effective as the sound was But he kept him on beat and into the sound so it made him look way better than he really was And he won the fights against these guys because these guys don't know how to do that Wow, so I start copying some of it not all of it But I start doing it and it to my stuff when I met him it became hell for all the kids in the area What? Well, a Christmas some my daddy can't teach me and he be my daddy fighters and to me these guys older than me I'm 12. They 17 He beating them So they ought to be better at what we're doing than I am Because they're older and bigger than me, right? He beating him No, I got to get that so I got that so wasn't it also like you obviously had animosity with your father There was a there was these so you were looking for other ways to do it, too No, I had to find other ways God had talked to me other ways and God and speak to me other ways Yeah, here's the real deal when people don't understand. There's nothing against my father's just kind of our destiny, right? We're not lying. This is this why I tell people that Nature is also a Bible Right when the lion get a certain age a day to kick him out He got to go you got to go get your own pride because this is my pride and that's kind of how me my father so it's like At a young age, I knew I was gonna have to go because just the way things shaped up You understand me so me growing up watching even these guys watching before me I watched all of them get to the age, but my dad would never have a lot would never allow them to be as confident and as charismatic as Charlie Campbell was because he didn't allow that He's the boss he was in control But he ain't got to fight nobody right so then after the 76 olympics God gave me a second confirmation Howard Davis junior won the Val Baca cup. I mean he was the best fighter at the Olympics But he probably was the only one of them gold medalists that didn't become world champion Why? Because it's dead and waiting way too long They waited so long that when you finally fall Jim. Why he couldn't be Jim. Why because in Howard Davis The fire had died now in his daddy the fire still was there, but guess what he didn't got a fight. What how it does You understand me? That was my second confirmation that when I get old enough I got to go Yeah, you understand me so it's like I start to understand it Like I said nature is always in my Bible so people always say why you love animals? Why you love chicken when that's where I learned God's Teachings from cause God made those animals and people can't change Hmm, you understand me you can do them But when you knew them you change to a whole nother animal and to me in society sometimes that's what they're trying to do They're trying to neuter us. Yes, so they neuter us. We don't fight back Well, that's a giant thing with men today.  This is what I'm trying to say They try to mentally neuter us. Yes when they neuter it when you neuter a horse. He becomes Landed he again. He don't he don't fight back no more. He don't care no more. You understand me? So it's like if you neuter a man, he becomes Nothing, he don't care no more. He ain't gonna fight me.  You feel me and to me that's Emotionally mentally is what they try to do this without physical doing so That was a problem that me and my father also had and people say well, why you so Against what's goes on well to me. That's what he was trying to do But what I learned is and it's gonna take you down that road too. I used to hunt when I was a kid, right? We will hunt squirrels We'll hunt squirrel.  So this shows me He can't do it physically but this what he's doing. I watch and do it to all these other guys I'll tell you about before me. They can't beat Charlie Campbell because they're getting Charlie Campbell's a stud He ain't better than them. He ain't stronger than them. He ain't a better puncher than them But in his mind, he's a stud In their mind, they're getting because my dad has made them so he's meant to beat them down So know that they have no real fight back in them because they got to be second to my father first. I Can't be that guy So just just some commentary on this You know, he says nature is a Bible That's a very interesting phrase nature is a Bible Because when we think of Bible, we think of holy book. We think of something that's written down You know something that cannot be violated. It's like a bunch of text and this is the sacred text and you shall not see anything Nothing outside the sacred text matters, you know for a lot of a lot of Christians It's like that. It's it's what's written in the Bible matters anything outside the Bible not so much Everything external must be referred to back to the text. Okay, but here Roy Jones is saying Nature is a Bible which means that like, you know Bible in terms of a Guide for moral action a guide for living a guide for what's right and wrong a guide for you know Just a guide in general a guide to live your life That's what a Bible is because we try to you know, Christians live their life trying to emulate Jesus through the Bible And Roy Jones is saying that nature itself is a Bible, you know, so it's very interesting. Basically. He's saying that God The divine order is communicated to human beings Not just through a book or things that are written, but it's around us every single day Seen in all things nature is a Bible everything is nature, you know What Roy Jones had a revelation about the nature of the relationship between him and his father and the other fighters and What he needed to succeed through drawing an analogy with you know, what he learned as a kid His insight into the father and the squirrels and him butting the nights nuts off all his male kids, you know So that pointed the way for him, you know, so it's kind of a it's kind of a lesson for all of us that The the wisdom we need to apply to situations to solve problems to apply to our personal lives Whatever our personal lives are You could find analogy in nature, you know, this is kind of what the each thing is about if you don't read the each End which is the Chinese ancient book of changes. It's a one of the four great Chinese classics It's a divination book. Well, it's actually a book about it's a book of just Insights about life and stuff like that like it's you don't have to really believe in anything The belief more comes in if you toss the coins do they really say something about your question or not? That that aside the book itself is a book of nature analogies Taoism is mostly about nature analogies And I think that's exactly the kind of thing Roy Jones is talking about Looking at nature and seeing parallels in the things that you're facing and the things that you know You see it on nature which unites you you with nature because you're not separate from it at all You are nature the problems you're facing is nature, you know, so nature is a Bible You look to nature to apply to your life But if you do it well, like your life can be like a Bible as well Just like people who think that if they you know live like Jesus did they are fulfilling the biblical Tradition and you know, they will get like a God's reward If Roy Jones is saying it's true. It's like if you Live your life according to your what you see in nature. It's pretty much the same


A fiery horse with a speed of light, a cloud of dust, and a hockey-high old silver! The Lone Ranger! General Mills, makers of Cheerios, the ready-to-eat oat cereal that gives you go-power, and Wheaties, the breakfast of champions, present by special recording, The Lone Ranger! Diving Doris is 13, and she is the diving queen. She can do a flip because she knows she's got go-power from Cheerios! Yes, she's got go-power! There she goes! She's feeling her Cheerios! Cheerios! Cheerios! That's a mighty good idea for you! Just make sure you eat a big bowl of Cheerios and milk every breakfast, and you'll get go-power too! Because a Cheerios breakfast is loaded with proteins, vitamins, and minerals. The very things that help build healthy bodies, strong bones, good red blood, and muscles. Why they'd be the sort of breakfast you'd go for, even if they didn't taste so good, and they do taste delicious.  So that's for you, swell-tasting Cheerios and milk for go-power. Eat them every morning, and you'll hear... She's feeling her Cheerios! With his faithful Indian companion, Tonto, the daring and resourceful mass writer of the plains led the fight for law and order in the early western United States. Nowhere in the pages of history can one find a greater champion of justice. Return with us now to those thrilling days of yesteryear. From out of the past come the thundering hoofbeats of the great horse, Silver! The lone ranger rides again! Come on, Silver! What's going on? I'm Silver! When outlaw Jim Gatlin felt the law closing in on him, he assembled his gang of desperados, including co-leader Frank Harrison. Boys, we gotta face facts. Texas is getting too hot for us, especially for me. Yeah, Jim. They have posters with your picture on them, and every time we go to. Frank's right. With a reward of $10,000, someone's liable to turn you in. That's why I'm going up to Kansas and work with my old pal Gus Farron again. Oh, I remember Gus.  I sure know that. Gus has run the cafe in a place called Trailville, 40 miles south of Dodd City. He wrote in the city, he'd line up some jobs. Riding by night and resting by day, the outlaws reached the outskirts of Trailville, Kansas, where they separated and entered town at intervals, singly and in pairs. Jim Gatlin and Frank Harrison went to Gus Farron's cafe. Gus, a troubled look on his face, led them into his private office and lifted a poster from the top of his desk. Oh, Jim, look at this. Look at what they plastered all over their town last week. Yeah, my picture. The same poster they used in Tix.  You don't have to tell me. I've read that lots of times. You know, Frank here is the one who led the boys when they broke me out of the jail in El Paso. Yeah. The law wants him and the boys too. Yeah, but they don't have their pictures spread around. They can get by. But Jim, you can't. The law's tough here. Yeah, where'll I hide out? I don't know this territory. In Garrett. That's a town not far from here. It's off the main trail, and there's no post office, no telegraph office, no stagecoach station, no nothing. What about law? They even know Geese they call a marshal, another one who acts as a deputy. But they're alive. They just wear the badges so they'll feel younger. There aren't enough people in their town for them to protect. Well, I'm going into the cafe for a while. Frank, you come with me. All right. I think I'll ride over the gears in the mild evening. Look to place over. On the following evening, the always rash Jim Gatlin slipped out of Baron's cafe office and rose to garrison. He entered a cafe. He kept his hat low over his eyes and stood at the deserted end of the not too crowded bar. A small elderly man eyed Gatlin for a few minutes and then quietly left the cafe. The man was deputy sheriff Alec Bastum who ran rubbery leg to the office of Marshal Tom Searle. Tom, Tom, get your gun. He's in the cafe. That arm be on a poster we got last week. He's here in garrison in the cafe. Jim Gatlin, yeah! Jim Gatlin first was greater than his caution. He saw the two elderly men at the bar but paid them scant attention. Not until they moved next to him did he recall Gosfaren's description of the lawman in garrison. Before Gatlin could move one of them pressed a gun against him. You're covered, Gatlin. From this side too. All right, man. I respect two guns more than I do one. Now put your hand down. Alec, snap the cuffs on him. Put your wrist together. There!  You think not, Gatlin? You'll see difference once they have you in the lock-up. Alec, he'll ride over to the telegraph office in Trailville and get in touch with Texas authorities. And I'll tell him to bring that $10,000 reward. Oh, oh, there, oh! It was after midnight when Deputy Marshal Alec Braskham reached Trailville. The telegraph office was closed. In the infirmary he found the operator in Gosfaren's café. Well, say there. Tell me you're the one who sends messages over the wire. You heard right. After office hours. I'm Deputy Marshal Braskham of Garrison. And I want to send an important message right away. What have you got to say that's so important, Marshal? We've just caught Jim Gatlin. The biggest outlaw in the entire west. You, Captain Gatlin, get Gatlin the Hooskower over in Garrison now. So you've got to open up that office and wire the authorities in Texas about it. Come on, follow me. Gosfaren and Frank Harrison, both in the café, have heard Alec Braskham's blatant announcement. They hurried into Farron's office.  Gos, we warned him not to move around where he could be recognized and now he gets himself in that jail. Ah, they probably picked him up in some café. That's where he always heads. We've got to get him out of that jail. You have a heavy guard regime. You can bet on that. Gos, can you get me a deputy sheriff's badge somewhere? What? Well, I'm not sure. Sure, there are ways of getting everything. Why? Give those hombres over in Garrison a day to get used to having Jim Gatlin in their jailhouse. Then send him a telegram. We can't. There's no office over there. But wires are delivered from here, aren't they? Yeah. Well, listen, one make-believe telegram and a sheriff's badge will get Jim Gatlin out of that jail. I'll put on the badge later tomorrow night. The message of Jim Gatlin's capture reached Texas. It also reached every other telegraph office in surrounding states and territories. Soon, the word was being passed in streets and stores everywhere. In nearby Dodge City, among those who heard the news the next afternoon was an Indian. Oscar, help me! When Tuttle learned of the outlaws' capture, he sped back to where the Lone Ranger waited in camp. There he told the mask man about Jim Gatlin. The Lone Ranger's reaction was immediate. Hello, we'll ride to Garrison at once. Here's over. Ah, it's not Clark, he must have been. We'll make it by 10 or 11 o'clock tonight. Let me wonder why Jim Gatlin go to Garrison. We may learn when we get there. Easy, steady, big shot. Easy, big shot. In Trailville, Gus Farron secured a telegraph blank and prepared a fake message. One of the outlaw gang rode to Garrison that evening and delivered the message to Marshall Serles, who was seated at his desk. As soon as the crook left the office, the Marshall opened the door to the next room where Deputy Alex Braskham sat before a cell guarding Gatlin. And Alex, come here a minute. And he just crook unguarded? He's not going to have time to break out. It'll only take a minute. All right.  That's enough of you, Gatlin. What is it, Tom? Yeah. Look at this. A telegram, huh? Is it natural for the one I sent last night? Not exactly. It's from Sheriff Williams of M. Dorada County, Texas. It says, have word. You captured outlaw Jim Gatlin. Deputy Rance Phillips has been trailing Gatlin for months and is now in Indian territory. Have advised Phillips to proceed to your office at once and claim prisoner who is wanted for murder in his county. It is Science Sheriff Williams. What are you going to do, Tom? Just wait till the deputy shows up. Then we'll go to the judge in Trailville and find out what's legal in a case like this. Now you'd better go back to guarding our $10,000 prisoner. All right. We'll continue our lone ranger adventure in just a moment. Because champions are made not for... Yes, sir. Get on your way. Get on your way. Get on your way with Wheaties. Don't ever doubt it. Champions are made, not born. You can get there. For example, take the story of Wheaties champion Stan Musial of the St. Louis Cardinals. Young Stan was willed no claim to fame, no magic way to learn the game. He had to sweat and give his all, learning to field and hit that ball. Sure, Wheaties was his breakfast call. Today they call him Stan the Man, still and always a Wheaties fan. Stan Musial has been powering up with Wheaties right along, 19 years. Good for Stan, good for you. There's a whole kernel of wheat in every Wheaties flake. Now watch Stan built that ball. Hey, hey, hey. He's on his way, on his way. He's on his way, on his way. Get on your way with Wheaties. Because champions are made not for... Yes, sir. Get on your way. Get on your way. Get on your way with Wheaties. Breakfast of champions. Now to continue. Later in the evening, Toddled called on Marshal Thurls, told of the Lowranger, and escorted the old lawman to the mask man's camp. After an exchange of questions and answers about Jim Gatlin's capture, Marshal showed the Lowranger the telegram he had received and said, Then I got this message from Sheriff Williams of Endorada County, Texas. Marshal Chuck McCullough, Sheriff of Endorado. He has been for years. Ah, we see him last month, when we in Texas. Must be a new man then. Deliver this message to you. Deliver it from the telegraph office in Chaleville. This isn't a regulation message for him. Huh? How do you know it isn't? The telegraph company uses two forms, one which the public uses. The other, for receiving messages, is used only by company personnel. This is a public form. I'd suggest you check on this wire. I'll do that. I wish you'd ride back to town with me and advise me how to handle this, I'm Ray Gatlin. I don't know how to both go with you, Marshal. As the Lowranger, Tutto and Marshal Searle set out on the short journey to Garrison, a man entered the Marshal's office. He wore a shining silver badge on the lapel of his coat and a wide grin on his face. Frank Harrison, in his role as Sheriff, had purposely timed his entrance for this late hour. He saluted the surprised Alec Baskham, who stood near a door at the side of the room. Howdy. Am I talking to the famous Marshal Searle? No. Marshal's not here. I'm Alec Baskham, your deputy. Glad to know you Alec. I'm a deputy myself. I'm from M. Dorado County, Texas. Hey, you must be that fellow Lance Phillips. Yeah. Where's Gatlin? Locked up in the cell, right behind this door. But you can't claim him, Phillips. You've got to make arrangements with the court. I haven't come here to try and take him away illegally. You'll get your reward all right. Well, that's what you were thinking about. Well, I just wanted to tell you. No offense, man. I know that. I suppose you have a big guard looking after Gatlin. You don't need a big guard. Two of us are enough to guard that hombre. Where's the other man? In the cell room, keeping an eye on him. Do you mind if I take a look at Gatlin? Just to make sure there's no mistake. There's no mistake, Phillips. He's Gatlin. No doubt about it. He admitted it. Still want to see him? If you don't mind. Oh, you sure be surprised to see me. I bet. Follow me, Phillips. I want you to meet Deputy Sheriff Phillips from M. Dorado County, Texas. Ellie, is this the gent you were telling me about? Yeah, Louis. He's the one in telegram. All right, you two, get your hands up for it. Gun! Good work. Nice, Frank. I knew you'd get me out. You're not a sheriff, you're a... Oh! Good shot, Ellie. You shouldn't have reached for a gun.  No, sir. Keep your hands high and go close to the cell door. That's it. Jim, can you reach his holster? Yeah. Yeah, I have his gun, Frank. Good. Now, Louis, take your keys and open the cell door. Yeah. I'm open for a minute. Thanks again, Frank. Is the gang with you? No, they're still in trail of him. You, Louis, get into that cell door. I'll keep you covered while Jim tires and gags you. Never mind the cover, and I'll take care of him. You've got to sell other operas. At that moment, Marshal Searle abandoned his office followed by the lone ranger and toddler. That office is empty. Alec must be inside with Louis. I'll go and see. Alec, I came back to get this. Frank, good thing you heard it from in. I didn't. He's out like a light. He'll be cold for hours. This is the Marshal. Let's pick him up and put him into the cell with the other two. Yeah. He's heavy. And I'll place him on the floor. Open the cell again. This will take care of the entire law department at Garrison. But it doesn't take care of the law department's friends.  I'll bail, Frank. Cut him away, Jim. Let him shoot. Cut him off, man.  An Indian tool. He's the one who shot me. Yes.  I'll keep them covered while you lock them up and try to revive Marshal Searle's. Let me do it. Alec and Louis were released and a doctor was called to attend the wounded deputy and the two crooks. In the morning, every able-bodied man in Garrison was recruited into a special posse and all promised to keep the outlaws under constant guard until proper authorities appeared to take Jim Gatlin to Texas. Then Louis repeated Frank Harrison's conversation adding, He told Gatlin that the rest of the gang is in Trailville. If that's the case, we'd better advise the sheriff there at once. Yeah, you're right, mister. We'll go to Trailville anyway, even though we're sure now that telegram was a fake. We'll still check on it, Marshal. Let's find out if possible who wrote the message and send it to you. All right, Tunnel, let's go. In Trailville, the Western Union operator read the telegram which Marshal Searle's handed him. Then snorted. Such Marshal I never wrote this. What's more, no such message ever came over these wires. I know that handwriting, though. Do you do? Yes. I just happened to be reading the telegram I sent yesterday, written in the exact same script. Well, it's right here, just a minute.  See, that is the same writing, isn't it? Sure is. And the fellow who wrote it is Gus Fan. He owns the cafe up to stay here. He said the man who wrote this fake telegram was a fellow named Gus Farron. Gus Farron? We know that man came study. Of course we do, Tunnel. Marshal, that ties up. Gus Farron was a Desperado in Texas. He and Jim Gatson wrote together at one time. They must have joined again recently. If that's the case, now listen, fill the sheriff here. A short time later, while the mask man waited outside in the shadows, Marshal Searle's told Sheriff Luke Gray of Trailville what the Lone Ranger had in mind. And the mask man says he thinks he might be able to trick Farron into leading him to where Gatlin's gang is hiding. Marshal, if you view what you told me, I'll go along with the idea. May save us time and bloodshed. Tell the mask man to get into Farron's office. No one will interfere. Tell him I'll have a big posse ready to follow in case he and Farron run off together. All right, Sheriff! That afternoon, assured that Gus Farron was in his office. The Lone Ranger pounded loudly on the door at the rear of the cafe. Farron himself opened it. What the... Who are you? Wait, don't refer to your gun. Get back inside and listen to me. Hey, what is this? Who are you?  I came to tell you that Jim and Frank are bottled up in Garrison. Bottled up? What do you mean? A telegram, you faked, messed things up a bit. What? How do you know? I came from Garrison this morning. I was with Jim and Frank. They're still back in that town. And Gus, I'm here to get the gang together to get Jim and Frank out. I should never have told that I'm brave to come out here. All right, I don't know how you're mixed up in this outfit, but... and I don't know what Jim does anymore. And take me to the gang and I'll explain the plan. I'll take you, but it'll mean riding across countryways. It's just as well with me wearing this mask. Yeah, come on. Now I'll get my horse and we'll ride to the high down. Oh, ho, ho, ho, ho. Down now. Thanks. That's all I wanted to know. Hey, what are you with? What are you rattling? You're truly rattling. That Sheriff Gray in this posse, you had him follow it. Come here, you duck. In that instance, the lone ranger leaned across his saddle, grabbed the man roughly and pulled him onto the ground. Oh! Tonto at the head of the posse with Sheriff Gray and Marshal Searle. Yeah. Really? Yeah. Yeah. Yeah. Yeah. Yeah. And the old girls reached the struggling men just as the lone rangers blow, knocked out Gus Brown. All right, take it. Oh! Oh, hey, hey, hey! Oh! Sheriff, the gang's met cabin down in the hollow. Have your men right around the top and surround those crooks. They'll never get out. Sheriff! Sheriff, the outlaw's heard his troops there, coming out of the cattle. And you heard what the masked man said? Surrend them! Don't let him get past us.  All right! Right around them, keep firing down at them. Yeah, yeah, yeah! Yeah, yeah, yeah! Yeah, yeah, yeah! He finds his home for his parents. See that he doesn't escape, Tutto. I'll help the posse. They're giving up. See? One of them's waving, a white horse.  End hold your fire! What's the wonder time? Deputy, search them a time. Well, it worked out, Sheriff. You have Gettlin's gang. You turn them over to the Texas lawmen when they come here to get Gettlin and Harrison. Yes, and I'll drive Fahrenheit or Trailville at the same time. You'll get the reward for Gettlin, Mark. Well, I'll see that Alec collects that, but the Masked Man's the one who did everything, seems even down to... Marshall, he ain't right no way. The Masked Man's yielding something to it. All yours. All yours. What do you know? Adios! Adios, stranger! Stranger, he's our best friend, Sheriff. He's the lone ranger. The signature of the Lone Ranger Incorporated is produced by Tendall Campbell Muir Incorporated. The part of the Lone Ranger is played by Brace Beamer, your announcer, Fred Boy. Listen to the Lone Ranger brought to you by special recording Mondays through Fridays at this same time.


Australian war propaganda keeps getting crazier. 60 Minutes Australia has churned out yet another fear-mongering war propaganda piece on China. This one's so ham-fisted and it's called to beef up ADF military spending that it goes so far as to run a brazen advertisement for an actual Australian weapons manufacturer disguised as news reporting. This round of psychological conformity-making features Australian former Major General Jim The Butcher of Fallujah Molan saying that in three to ten years a war will be fought against China over Taiwan and that Australians are going to have to fight in that war to prevent a future Chinese invasion of the land down under. He argues Australia will need to greatly increase its military spending in order to accomplish this because it can't be certain the United States will protect it from Chinese aggression. Australia is monstrously vulnerable at the moment. We have this naive faith that American military power is infinite and it's not, says Molan, who is a contributor to government and arms industry-funded think tanks, Laoi Institute and Australian Strategic Policy Institute. Decrying what he calls panda-huggers, meaning people who aren't China hawks, Molan claims that the Chinese Communist Party's aim is to be dominant in this region and perhaps dominant in the world. Asked when war might break out, he claims, given the power that they have in their military, they could act anytime from now on and that's what frightens me more than anything. The next war is not going to be ten or twenty years away, it's going to be in the next three to ten years, Molan asserts. My estimate is that in a serious fight, the Australian Defence Force only has enough missiles for days. This is not going to be resolved in days, and of course we're not big enough. We should expand the Defence Force significantly. We should fund defence now based on our assessment of the national security strategy which is based on the war we want to win. In short, do you think Australia needs to prepare for war tomorrow? The interviewer asks Molan. Absolutely, he replies. Molan makes the ridiculous argument that if Australia does not commit to defending Taiwan from the mainland, then it won't be long before they can expect a Chinese invasion at home, as though there's any line that could be drawn between the resolution to a decades old Chinese Civil War and China deciding to invade a random continent full of white foreigners thousands of miles away. Suppose we said, OK, Taiwan, you're on your own up there and the Chinese snapped it up and the Chinese started looking around the world and they might snap up other liberal democracies like Australia, Molan argues. And we might then turn to America and say, America, well, could you give us a bit of a hand here? And the Americans might say what we said to Taiwan. Where do you draw the line? This situation that is developing now is an existential threat to Australia as a liberal democracy. Incredibly, the 60-minute segment then plunges into several minutes of blatant advertising for Australian defense technology company DefendTex, which manufactures weaponized drones designed to be used in clusters, saying such systems could be handily used to defeat China militarily in a cost-effective manner. The segment also promotes bare-faced lies which have been commonplace in anti-China propaganda, repeating the false claim that Chinese fighter planes have been breaching Taiwanese airspace and repeating a mistranslation of comments by Xi Jinping, which it uses in previous anti-China segments made to sound more aggressive than they actually were.  That 60-minute segment was preceded by an equally crazy one in May, which branded New Zealand, New Zealand, for refusing to perfectly align with US dictates on one small foreign policy issue.   In the unfortunate event that tensions between Beijing and Taipei cannot be resolved peacefully in the future, there is no justification whatsoever for the US and its allies to enter into a world war between nuclear powers to determine who governs Taiwan. The cost-to-benefit ratio in a conflict which would easily kill tens of millions could lead to the deaths of billions if it goes nuclear, which would make such a war very, very, very far from being worth entering into, especially since there's no actual evidence that Beijing has any interest in attacking nations it doesn't see as Chinese territory. There's so much propaganda going toward generating China hysteria in Westerners generally and Australians in particular, and it's been depressingly successful toward that end. Watching these mass-scale Psyops take control of people's minds one after another has been like watching a zombie outbreak in real time. People's critical thinking faculties just fall right out their ears, and then all of a sudden they're all about cranking up military spending and sending other people's kids off to die defending US interests on some island. Please don't become a zombie. Keep your brain. Stay conscious.


The Kenyan police say the number of soldiers killed in the helicopter crash near Nairobi is 17. Six people on the board are seriously injured. The victims were on the training exercise when the helicopter came down in Kajiadeh County.