Project TANIFA is a project started from Auckland Bioengineering Institute to compete in international submarine race where all the submarines from international universities and companies and school compete for the performance. I first came across this project in the library and people were just walking past but I found it really interesting and very cool how the small submarine body can travel so fast underwater. Luckily for me the project was open for any students to join and I emailed supervisor Professor Ian Anderson and he welcomed me and showed me around the project. We have challenges and problems that occur during the year we like to try and find a solution to so we were wanting to know how we can improve our TANIFA submarine and that's where the project the men worked on has come from. My role is mostly a computational fluid dynamics analyst analyzing the shape of the submarine body computationally and optimizing the shape calculating the drags and force to do with the submarine body. I'm trying to find out how much drag coefficient changes when the submarine brake flap is attached compared to when there is nothing there to stop the submarine. He's given us some solid calculations we can base our sub-design and improvements on analysis of the hydrodynamics of the submarine. If you look at the map here this year's European international submarine race we are driving straightforward for the first half of the section you have to dodge around the poles. There is a safety net 30 meters distance apart from the finish line and if the submarine hits the net it will be automatically eight volts to your submarine team. Once I solve the simulation I get a result from the software and it tells me how much force it receives from which part of the body. From the result it appears that by having even a tiny area of the submarine brake next to the body it will be able to stop half the distance it used to stop without the brakes on. We originally built the prototype back in 2014 but there has been a lot of improvements ever since and I am very confident that we're gonna have a very good result in this year's submarine race.


Mary asks, does anyone know anything about the catch up function? Not catch up that you put on a hamburger, but catch up function. It's in files top right. For all the docs I've tried, it appears to not work. I see on help pages that it's supposed to have like a feed of updates to the doc, but mine always says all caught up. This is an important feature for my org because we're not doing anything inside SharePoint, so no looking at version history. The only other way I see to be able to see what changes others have made on a doc is to track changes, which is not ideal to have to remember to turn on every time. Look, I found that in terms of some of the what's happening in the show changes, you can do that and if we're talking about files, you need to be like it's an actually an office functionality of the app, like the browser or through Teams. On the review tab, you've got show changes. That's where you're needing to go. It's not a SharePoint functionality. It's an Office app function, not the application on the desktop, the online version. With the catch up in the top right hand corner, it always comes down to who's been in and out, and it'll come up with the show changes. You go review show changes because the catch up button in the top right might say you haven't done anything recently. You've got the version history you can go and have a look at so you can go back to the past, but it's only been recent things. It's more when you're all in there together. The moment you get out, it's not going to have that there anymore. When you come back in again, show changes isn't going to come up. It's going to be in the version history. I think it's a timing thing because the web versions as Christie is talking about, that's where you see this catch up. It knows the last time you were in that file. If you've opened it in the desktop version, then go to the web.  It's time-sensitive, I think, if they're showing you're all caught up because you've looked at it in another mode, maybe. It's actually when you go in and out. Because with version history, it's around the functionality of someone comes into the document, someone comes into the document, you're working together. If you sat there for 30 minutes and you both got out, you've only got one version. It's just you, the owner usually. But if you come in and you worked on it, and someone else comes in and works on it, you leave, it creates a version. If they do anything after that point, then they leave and it leaves a version. So when you look at the version history and in the catch up, the way the catch up functionality is working on it, when you're in there and working together, it goes, who's done what whilst you're in there? And the moment you get out, so once you're out and you're coming back in, depending on how you're coming back in, there's gonna be, there aren't any changes. Because ultimately it's created a version history which would show you some of those changes, but it's not like you're all in here. When you're all in and working together, like if I've had it and I'm doing co-collaboration and especially when I'm doing all those demos and training, you've got 20 people in there, it will show you the whole list so that you could catch up on what's going on whilst you're in there and working and who's done what. But when you get out, it's gone. So it's not gonna be there because it goes into the version history then. That's the way I'm saying it in the nature that it works. And I'd have to agree with you. And if keeping track of the changes is that mission critical for a document, then just turn on track changes. That's the best way to make sure that you capture all of the changes and who made them and at what time. And the nice thing about track changes versus version history, if you restore a version, you lose all the work that happened after that. The track changes is kind of like spell check. You can go through and accept this, decline that one and cherry pick which of the changes you want to keep and which ones you don't and want to reject. So it just all depends on your documentation process and how critical that is to your business process to turn and manage. The only way you could go around that, Shari, is if you're restoring, you could go back to the previous one because it becomes just the latest version. You could go back to the previous one and do a compare documents. I mean, if it was Word, you could do that compare document so you can kind of see, well, what was the difference and it would then come up and do a track changes effectively for you with doing a compare document. But then that's only gonna be really that sort of Word feature functionality. It's like, well, which app are you even in when you're talking files too? So it comes down to which one. And even then you wouldn't see who did what if there's multiple people in it. That's why your best bet is the track changes if you really need to capture that information. Yeah, yeah, absolutely. Which by the way, it's something that I just noticed today again. I mean, I've seen it before, but it was reminded of it because I just used it was I went to share a link with the document for review to a team of people. And it is part of the options, making sure the permissions were correct and allowing people to edit that version. It also has a tagged out or a little toggle to turn on track changes within that sharing function now, which didn't used to be there. I love that it's there now. And so I find myself, what I'm sharing out with the group, I will turn that on then send it out. Yeah, it's a great reminder for those that aren't even thinking that way. It just kind of put it right in their face. It's awesome. Yeah. And then the version history too, if again, if you're really wanting to manage the versions of your documents, make sure people check them out and check them back in because then it hard stamps them as specific versions. I'll let you add comments as to what has changed between that version and the previous version. So it's not just version 2.2 that happened on Tuesday. It's like, this is where we added this section or this clause or updated whatever it was. You can actually identify what was different. So if you have to revert back, it's not some weird date and time. You actually know which version you're reverting.


Hey guys shave your 1,000 here today. We're going to be doing a review of the Sony handy cam It is the hdr-cx 240 9.2 megapixel, so it is the Cx 240 stay tuned. Okay guys Here we go. Let's get a light on here. That helps a little bit I Got this camera and I got it for a steal Okay, I got it off eBay. I didn't steal it. No not per se, but I Yeah, I got it cheap. I gave like 40 bucks for this thing. It is new And I've got you know the cables for it and everything but We're going to review on this a little Sony. I love sonys The camera I'm using right now, of course is not a sony, but it has a sony lens But anyway, this is this little camera here It's That's a clear image zoom 50 Excuse me 54 times 54 times clear image view I know or zoom So I know you can't read that because of this camera. I don't have the macro lens on So what I'm going to do here is if the battery don't go dead I'm just going to go over some things with you on it. It's just it's a plane Jane Let's get it opened up here Just a plane Jane you open it up and turn it on Let's cover here we go and I know like I said, you want you're not gonna see it with the camera. I'm using even with the macro lens Now I'm not cutting down the camera. I'm using I love it and I really love our new one with the night vision I may have to resort to using it but see these like I was talking about the other Day on one of my videos. They just don't there's no autofocus Well, there's no focus at all really So I really can't go into much detail, but there's your menu options and It's got all kinds of things you can do with it Um As far as that goes the uh Like our new camera has has more features And this does but this has more settings on it So without any further ado, I'm going to talk about this camera While I'm using this camera because I got a battery light flashing on this one that I'm using So I'll just kind of go over some of the things with you and besides I wanted to show you You know how good the sound is for Uh, just for a versatile little camera. It's great for Like a beginner youtuber or whatever. It's very light and it's very easy to use. You just point shoot really and Another thing that I like is the photo button You can Take a still image while You're recording the camera. I'm using you can't do that with So I'll go over some pros and cons with it what I like about it and what I wish it had because there's really nothing I don't like about it. Either one of these cameras. I love all my cameras Equally, but there's just some things I like more than Okay guys, we're back. We are now using the uh, the new camera that we got Excuse me The sony So this is what I'm filming with now. This is the one I was just filming with This is our first new camera that we bought what a year and a half ago the one that got wet When we was on the ship and it had some Issues it seems to be working all right now these different memory cards and stuff like that. It seems to be working Okay now, but remember it kept getting corrupted files on here The files would just be missing when I went to import them And they wouldn't be on there, but they'd be on here. So I had no way to And this that hot sheen mount I was talking about here on top that you can Put your light on just this light without having to have this one, but that's okay. You know Like uh This one, you know has a feature on top just like the one I'm using now You know, there's your zoom and there's your photo Your photo button there, but you cannot Record video and take a still image at the same time like you can on this one So there's there there's pros and cons to everything, you know, but You know, like I said the menu on the camera we're using right now is a lot Is a lot more there's a lot more stuff like on this one Here we'll go to menu on here. This is touch screen the one I'm using is not no big deal See just pretty this is really all you got you've got resolution your awb Which is your automatic white balance your color exposure And your metering Okay, which is all right, but the one I'm using also it it has a It's 1080 i 1080p and you can Set in each one of those 1080 1080 i and 1080p you can set Very high quality high quality medium quality low quality, you know In each one of those and then it's got automatic, you know, and then plus it's it's just got so much more And yes, you can on on the one i'm using you can Change the battery and get an extra battery and you can change it out You know just it goes dead or it's going dead pop it out from another battery into it And you're good to go The one i'm using opens up turns on shuts shut it and it turns off this one you can shut it will not turn off Unless it goes to the time So but it does have a power on it So The power button the one i'm using does not just find something i'm going to get monkey one of these she really likes it and She says it's light. It's easy to use See all these buttons confuse her, you know to turn on light. She don't need any of that display What's the display? Button as you can see If I can hit the display button here Maybe you can see Let's see takes it off like that just like the one i'm using You know so There you go Um, the one i'm using does it still has the battery light or the battery in see this one just went dead So we got done just in time The one i'm using says record menu and has the batteries and which which is fine Okay, but now something else i was really liking now I showed you on here Excuse me You you can't you know Even with the macro lens you can't get really close up. Okay Now i want to show you something i get this little thing. They always give you in your ebay packages or whatnot Let me show you this Look at that. I'll zoom in a little bit Like I said, it's 54 times clear image zoom or it's got 350 times Opti optical zoom or digital zoom won't it? Yeah, but look at that. That's You know, you can you can read it, you know I mean you can get right down to the nitty gritty with it and let it focus it does autofocus Whereas that one does not autofocus. It doesn't focus at all And as you can see so that's pretty pretty little Letters there or you know, they're fairly little letters that you can see You know To how big the camera is and how little the we all know how little These letters are on the bottom of something Okay, so let's see how well it will Yeah, you can you can read it You know just fine And we all know when you turn the bottom of a camera phone over how little them letters Them letters are to see, you know, you got glasses on blah blah blah, you know, so Yeah, I mean you can really Zoom in on this placement right here You know It will start going out of zoom, but now I'm halfway zoomed up, you know, so just back it off And it'll focus, you know If you can hold it still enough it would probably focus on its own See it's trying to right now, but I'm moving if I had it on tripod. I guarantee you I could focus in on that But I mean, you know, that's pretty close right there because I'll see what I'm looking at. There we go This is the placement Uh, like I said, it does not have an external mic Port where this one does But it does have a charging port. Well, it's got the charging cable. I showed you goes right into the to the handle um And of course, like I said, it doesn't have a light. It's not night vision. But hey, you know, what do you want? You know, I mean for You know less than 200 dollars But like I said, I got this one Like for 45, I think it was because that's some Earn points and stuff like that. So I'm going to get monkey one. She she seems to really like it She's been using her phone. I have her a movie maker I put on her computer, which I found out where to get it So I got my movie maker back on my pc So I love it. It's just so much faster now I'm going to keep on the other editing software because I I will use it You know for other things, but see this is And of course, this is not wi-fi It's okay. But yeah, the the one we're filming does but the one we're filming with does not So this is right there hot shoe. This does not have a hot shoe mode That's okay. And we know how little these oops These letters are I'm holding still there good See that's 24 megapixels the one we're using is like 9.2. It's okay. I mean it's it's good for for youtube videos So I thought I'd do a quick review on it and let you guys Uh, it'll know how much I like it if you get the chance, you know, I mean this camera It's it's it's phenomenal.  It's progressive. You can just take a still image You know while you are recording So I really like that about these things because sometimes I want to do You know some I've been having you to do screenshots if if I want to you know Do a custom thumbnail I've been having to use do screenshots because when I'm filming I don't want to stop filming Take a picture and then start filming again you know so Like right now, I just took a still image picture You know and I'm still recording which I will throw the still image up as well now the the Camera quality as far as for the pictures ain't as great as or I shouldn't say ain't It's not as great As the other cameras, but it's very impressive for what this camera is. I really like it. It reminds me of the one From a few years ago when I was living in Ohio that I used all the time You know, I probably got 250 videos I made with that camera just for youtube I lost it. I don't know what I did with it. You know, I don't know if I left it in a motel I don't know if it got lost when I moved down here But I know I I packed it and then it was gone. So I don't know whatever happened to it But it was a sony. I was a higher end. You know, it was more heavy and it was more You know a little bigger like these But uh, I'm not complaining this one here Does almost what the other one would do only it had a light on it It wasn't night vision, but it did have a light on it in the hot shoe mountain all that But this just reminds me of that that camera. So right now I'm really happy with this I think I'm gonna get monkey one here in a week or two and And let her go from there and let her start makes pretty good videos And you know, like I said, I wish it did have a little light on it, but You know, if it wasn't for this light here, you know, these little lights on these things Like it's right here on the top of the lens the little They don't do a whole lot get helped in close-up when we was filming this camera It kind of helped that.  I got the finger out a little bit. I'll shut it off and You'll see the difference here So yeah, so anyway guys again There you go, there's so much I wish I could show you all the functions in the menu that this thing is loaded with But like I said with this camera here, even with the macro lens, you wouldn't even you wouldn't be able to see it um, all I can say is um If you're wanting a good cheap camera, absolutely get it You you won't be uh, you You know You'll be glad you did You won't be disappointed by any means so I'm gonna say I'm monkey wanted me to do this so Uh, I'm gonna go ahead and go for now see See, I mean it just It's amazing, but I'm gonna go ahead and go for now guys and stay tuned for more videos of our of our New trike. Yeah, that trike is rough but uh Yeah, got it cheap, you know help my nephew out and I got it cheap So and he helped me out give me something to make some videos with all right guys So again, thanks for watching. Remember check out monkey 1000 Check her channel out. Oh shout out to Nancy jones I'm giving you a shout out because the shout out you gave for me for monkey and I So I appreciate it and I hope the train works well for you.  Thanks for watching. I'm she bear the myth the man a legend.


You had a good question about how to find the initial condition x hat, I don't know if you looked at it some more or thought about it some more. Okay, so I looked at the original paper, it's a Garamani and Hinton paper and what they do in that paper is to use a form of common filter that runs both forwards and backwards and basically the way we run the common filter is to make predictions about the next state. So we use the state update equation to say that if we know x hat at time n, we can make predictions at time n plus 1 and then we compare it to our observation and we change it. We can also write that equation so that given n of plus 1, we can make predictions about n because basically what we have is that, so we can simply write this equation as x of n is equal to a minus 1 times x of n plus 1 minus b u of n plus the noise. And in a scenario where you are making predictions about things that are going forward, it's just a common filter and then if you take the common filter observations and then run it backwards, it's called smoothing. And it was an extension that was made to the common algorithm about 10 years after that paper was published in the 70s. So what Garmani and Hinton do in their paper is they say that get your x hat measurement both first through the common approach and then run it backwards so now you take into account the observations that are you making in the future. So you get a little bit better estimate of what x hat is. But this way if you have x hat of 1, which we assume we do, we also get x hat of 0 because we just run it backwards basically. We say that if we have an observation made at time point, we can make this estimate x of 0 if we know x of 1, which in our assumption is that we have that. And so the mean of the, we form the log likelihood, log of the complete likelihood. And then we said that if you look at the log of the complete likelihood, it has a Gaussian that has a center associated with our x hat of time 0. Well, what is x hat of time 0? Well, obviously the best estimate of that, whatever it's going to be, it's going to be such that, you know, you set it so that basically becomes like this, the common filter. So the x hat of time 0 is going to be a minus 1 x hat of time 1. That's the expected value of it, right? So that's the approach. And then to find its variance, you multiply both sides of the equation by the top equation by x transpose. So you just get expected value of x times x transpose. That means it's called the backwards prediction. Yeah. No, it's smoothing in the sense that in the common filter, what you're doing is you're making a prediction about the future given all the past observation. When you run it backwards, you're making a prediction about now given all the future observations. Does that make sense? So it's smoothing in the sense that your initial prediction depended on all the things in the past. And in engineering, you assume you have all the data. And if you have all the data, then what you do is you just run the time backwards. And that's just called smoothing. Okay. Thank you. So, so far what we've been doing has been using the state estimation approach and the system identification approach to make estimates of state and make predictions about how our inputs to the world is going to change those states. If we extend an input, it's going to change the state of the system that we're interested in. But all we're doing is making predictions, right? So we're not actually doing anything in the sense that we're not generating a command to do anything. We're just saying that if such a command was generated, here's what's going to happen. But how do you generate good commands? How do you generate commands that get you things? In the last, I would say, 10, 12 years, there's been thought that, well, in control theory, there's been approaches made to understand how does one control things in the sense that if we want to move, you know, a tractor from here to there, if we want to move a rocket from where it's sitting on the platform to the moon, is there a way for us to generate commands to it in order to achieve something? And in the last 12 years or so, the question has been does that framework help us understand how the brain controls our movements? And this is the idea that there are states that we want to achieve because those are where the rewards or the valuable things are. And then there are actions that you're going to produce that are going to carry certain costs for you. And the question is, what's the optimum behavior? And in a sense, in biology, the concept of optimum behavior is something natural because you would sort of imagine that evolutionary things that have happened have made it so that perhaps the way we walk, the way we swim, the way we run, the way we behave is in some way making our fitness best, whatever is a measure of fitness. Now in Darwinian terms, fitness is your ability to live long enough so you can do biology. Whether that's the only cost, it's unclear. In engineering, what has taken place is the concept of describing behavior and control in terms of a cost and a cost cost. We're going to assume that when you generate an action, it's because you have a goal. You have some goal in mind. You want to make the state of whatever it is that's going to be controlled by your actions so that it reaches some place or some region where things are good. So you come to class today, you sit here because it's better than being in the rain. This state is better than that state. And maybe you also will learn something. So you made a value judgment about a state. And you said, if I go to the learning theory class today, that's a more valuable state than me going to whatever else was there. Okay. But now the question is, all right, how do I achieve that state? You have to do something to your body, get out of chair, get out of bed, whatever was the state before for you to come to here. So you carry a cost for that. So here's something and then there is some effort that you have to spend to get to that notion of good. So at the end of the day, when you sit in the class and you've achieved the state after spending certain amount of energy to get here, the question is, did the way you perform that action wasn't as good as it could have been? And this framework of thinking about actions in terms of costs and rewards is the basis for the way we think about some of the ways individuals make movements and the way they make decisions. And it's a broad framework because it has a potential for us to understand not just how disease affects the way you move, but also is there a link between the diseases and its effect on your movement and also the effect of the diseases and how you make decisions? So for example, you may decide that you're going to go to, say, the pharmacy and you're going to look at the line, waiting for people to get good prescription, and when you look at that line, you're going to say to yourself, I think it's going to take me 20 minutes, and you're going to make a decision. Is the passage of time worth it for you to wait, or is there something better to do during that time? So that means that if you have to wait 20 minutes, you're going to miss the beginning of a test in some class. Well, you're probably not going to wait. If on the other hand, nothing's happening today. You are bored as could be. You might as well wait in line to get the prescription. So you made a decision about this or that, and our life is full of such decisions. You make decisions about what time to get up, whether you should invest your money in this stock or that stock, whether you should react patiently or impulsively to stimuli around you, and all of this is decisions that consider costs and rewards.  So in the framework of applying mathematics, what economists, what psychologists, and what control theorists have done is considered control and describing things in terms of mathematical costs. So we're going to do the same, and we're going to show that by thinking about control of simple things, the simplest of all movements perhaps in terms of these costs, we can understand why the nervous system moves the way it does, and then ask, well, you know, what does this say about the representation of these costs in our brain? So the typical formulation goes like this. We have a state that is going to be controlled by us. So we have an object here, we have something, maybe on body or something like that. And then it has a state, let's say x is a state, to give it some interview, this changes a state.  And when you make that observation, you're going to have to now imagine something where it's going to say, all right, this was my observation. And here, this was my model, this was my model. You're going to combine these, and this is going to give you a posterior, y of n plus 1, given n plus 1. And this is going to go into something that's going to be our control. That's going to say, all right, this is the motor command that I'm going to produce next. So, so far, what we've been talking about is essentially, we've been talking about this, we have a model of the world, and there's going to be a real world out there. And then you're going to combine these things, and you're going to do something like that. And that's going to give you a posterior. This is the estimate that has happened. This is the estimate of the world now. But the objective remains to generate actions, to do something. And who cares if you can be a root predictor? That just makes you a soothsayer, right? People will come to you and be able to pay you for you to read the tarot cards, the tea leaves, and you can predict the future for them, right? But you can't actually do anything until you generate the commands you. What we're going to be talking about is this thing and this view. And the idea is as follows. So we're still going to have a system. No, we'll make it more complicated, but that's the basic system. But in addition to that, it's going to be such that it's going to depend on our actions. So there's going to be some function.  So for example, you know, when I'm reaching, well, it makes sense for my hand to get to the cup of coffee out here. So if it gets out here, it's not as good as if it gets to the cup of coffee. So there's some sense of spatial representation of goodness of state. So this genobax is going to be something good, something good to be at that location. This f here has some function of effort. And it's, you know, as you generate your commands, it just makes sense to be as lazy as possible. So this function f of u is going to be such that it's going to grow in some way, maybe quadratically, maybe in some other way. It's going to grow as a function of u. So it's good to be as lazy as possible. Okay, but we're not quite done with this because we also have to have some concept of time. Why is that? Why is it that it's not enough to arrive at a good state with minimum effort? Why is it that we also have some kind of measure of time? Yeah, because if I were to say, I'm going to give you ten dollars today and I'm going to ask you how about if you were to consider ten dollars today versus a thousand dollars at the end of the year, which one would you prefer? That's better. Well, there's a question. Ten dollars today or a thousand dollars in the end of the year? Which one do you want? Yeah, most people would take a thousand because a thousand dollars to them is somehow valued more despite the fact that you have to wait a year. So what I'm asking you to do is to think about a thousand dollars at the end of the year discounted to today. How much does it get discounted? Is it more than ten? If it is, then you would pick that. So let me just show you what I just asked you to do. So the reward that I'm trying to get to, this state is going to have value, right? And so this value isn't constant. It depends on when you get it. So this is time. This is a subjective value. So when you reach to this cup that has coffee in it, there's a cup at that location. So I have to get to that, right? And there's some effort that I'm going to spend to get there. But if that cup has value for me, that value is higher if I get there sooner than later. So this state gets discounted the longer it takes for me to get to it. Okay? So these are the costs that we're going to be talking about. You perform actions to maximize success. You want to achieve success. But it turns out that that success is better if it's better than later. That success is not as valuable if you have to bust your back to get it than if you could just walk to it. So effort that you spend is going to discount the amount of success time is going to discount your success. What you want is to generate the command U. So what we want to do is to minimize some kind of a cost. Based on this order, we have to end up with what's called a feedback component. So what we want is a policy. And so where does this estimate of state come from? Well, so what you mentioned what I want to get is as follows. So I'm going to give you a cost. It says here's where the good stuff is. And I want you to produce an action that allows you to get to the good stuff given a cost that has a cost of time and a cost of effort. You're going to find a policy U. And by those, the strain equation is the state update equation because that's the thing you control. You're generating actions, it changes the state. So that's the machine that I want you to control. You want to minimize the cost given a constraint. The constraint is the state update equation. The cost is the cost. And at the end, what you're going to end up with is a feedback controller. So what do I mean by feedback controller? A feedback controller is something that doesn't compute all the commands from now until forever. What it does is it says what's your best estimate of where you are? What's your estimate of X? Where are you? And given this cost that you have, I'm going to now generate the best command that I possibly can to get you there. And then, on the next iteration, it's going to ask you where are you now? I'm going to generate the best command that you can so you can get you there. So you have a terminal thing that you want to get to. This idea that, you know, you want to drive to Las Vegas. There you go. Start out in Baltimore. You don't plan out the entire trajectory because who knows, the road may be closed. There may be a bad weather. You may see a call from a friend in Cleveland. Ask you to stay. So you have a policy that has a cost. It says, you know, I need to be there at this time. I'm willing to spend this much money. And here's my current plan of how to get there. As you actually drive, you evaluate where you are with respect to where you want to go. And then you change your actions. That's a feedback control policy. It's similar to, you know, life in the sense that you get up in the morning. You ask, okay, what's happening in the near term? What sort of things are happening today? I'm going to do my actions based on this stuff. But I have this long-term goal of, you know, graduating, getting a job, those kinds of things. So your today's actions are sort of vaguely related to this eventual goal. But they're mostly related to the current state that you're in, things that are around you. That's a feedback control.  You just have some pseudo-goal of where you want to go, and then you react to things around you. Okay. So, like everything else that we've done in this class, what we're going to focus on are various things that we can write mathematically. So, you know, we're not going to talk about how to retire from Bahamas. What we're going to talk about is how to move simple systems so that they minimize the cost. But, you know, the principle is that simple mathematical equations that we think can be understood in terms of minimizing these costs. All right. Okay. So that's what we're going toward figuring this out. And the way we're going to figure it out is using what's called the Bellman equation. Bellman equation is fundamental ideas in this class. One is the Coleman equation, the second is the Bellman equation. The Bellman equation is how we're going to minimize this cost function. And it allows us to solve the feedback control problem. But, you know, you guys can go take optimal control class from mechanical engineering and then we'll teach you that. So, why are you in this class? Because this class is concerned about biology. So, we're not going to be talking about just the Bellman equation. What I'm interested in is how would the brain solve this thing? You know, what is the example that it can actually be implemented in anything associated with biology? And so, that's the notion. Any questions? All right. So, things are pretty vague. And that's the way they are at the beginning. But hopefully, we'll fill in the parts. So, today, what we're going to do is begin the path by talking about how to find the optimum view, but in an open loop control. I spent too much time talking and I'm not writing much. In a loop scenario, what happens is that we're going to have to cost function in a single step. So, we're just going to find the minimum condition that the u that minimizes j, but this is going to be done not for some passage of time, but for a single condition. And so, the j that we're considering, and we'll see how to do that. So, it's going to be very simple. There's only going to be one command that will minimize it. Now, to do this, what I want to show you is data from the humans as well as monkeys were asked to move a joystick. What I'm talking about today is a lecture on one chapter. So, let me show you the results. And then you can just find an optimum set of commands to produce the resulting action. So, if you were to take a muscle, there's a scalar quantity, another activation, and there's going to be a pulling direction associated with the force that's going to be produced. So, if you were to take a circle to represent the various muscles that act on a satyr wrist, there's some muscle that if you want to activate it and pull here, it would be another muscle maybe that would be activated and pulled here, maybe one here, and then maybe one here, and maybe one here. So, these are the muscles that you have, and these are the pulling directions that you have. So, the experiment that I want to show you establishes something interesting, and that's a follow-up, that if you look at how humans, monkeys, and other animals produce activation of their muscles, what you find is that there is a scenario where if you plot activation as a function of the direction of which force is being generated, so you can imagine how the goal, so maybe my goal is to produce force here, this is my goal, fg, and if I change this goal all around, and I measure the activation of this muscle here, this muscle that has a pulling direction there, what I find is that if the goal is here, this muscle is activated a lot. If the goal gets closer to this, it gets activated maybe a little bit more, and then as the goal force gets farther away from the pulling direction of this muscle, it gets activated less. So, what you see is a tuning function. So, if you plot fg, the angle of fg, this is theta, some angle of x-axis, and then you plot ui for muscle i here, what you find is that there's some direction by which it's activated a lot, and then it falls off. That's some width to it. So, the experiment that I want to show you, by using a measure of cost, helps us understand why this thing has that width the way it does. Why isn't it like this? Why isn't it like this? To help you understand this, I want to give you a sense of what it's like to have many actors that can contribute to an end result. So, there are many, many muscles here, and you could make it so that only one of those muscles is producing all the force you need, or you could spread the force across the various muscles. So, the idea is if you have two people that can pull on the rope, you could make it so that all the force is produced by one of those people, or you could make it so that half the force is produced by A and half the force is produced by B. In the two conditions, what's the difference between these two conditions? Well, if I were to say there's a cost, the cost is the sum of the squared force being produced by each person, then if I have two people that are each producing half of the force, the square root of 5 times 2 is smaller than the square root of 1. Let me write this down. So, suppose I have two muscles, f1 and f2. That's equal to 1. You can choose f1 and f2 any way you like. You could make it so that, well, I'm going to say f1 is equal to 1, f2 is equal to 0. Now, let's say I had a cost, and that cost was f1 squared plus f2 squared. So in this case, my cost is 1. I could also have a scenario where f1 is 0.5, f2 is 0.5. This is fine, right? I could produce a force of 1. Now, in this case, the cost is 0.5 squared plus 0.5 squared, which is what? What is this? So this policy costs less than this policy. So what do they do? I share my activity among the various actors. So a turning function we're going to see is sharing. And when we have a cost function that is squared like this, sum of fi to the m, or m is equal to 2, it generates policies that are saying share things between people because it's going to cost you a lot to have a win or take off. And the larger this m is, the more sharing is going to take place. As m approaches 1, then you can later take off. So in this case, m is equal to 1. If it doesn't matter to me, so if my cost is this, and m is equal to 1, so j is equal to sum of fi to the power of 1, then this cost is the same here as this here. It makes no difference. This policy is not better than this if m is equal to 1. But this policy is better than this if m is equal to 2. So by penalizing effort to the power of 2, you are imposing a sharing policy so that there's not going to be a win or take off. Everybody's going to pitch it. So let me show you a couple of questions. So is m the parameter we're trying to find? Yeah, I'm going to show you that with actual data, if we just have a model that says we have a gold force to produce, we can biologically measure the various pointing directions, the pulling directions of the muscles, how does the nervous system allocate activity to the muscles? Let's find the m. Are we assuming those two new functions are symmetric? Well, we make no assumptions about their shapes. We're going to say that for the m that you choose, what is the tuning function? In fact, we're going to see they're asymmetric because it really depends on this distribution here. So if this distribution is weird like this and most of the things over to the left, it will not be symmetric. Any other questions? So we have force in muscle i is ui pi. So the force that's going to produce is the activation times this vector that's going to tell me the pulling direction. So this is just a 2 by 1. This is a 2 by 1 and this is a scalar. Now what we have is that we have a set of activations u which is just u1 to un, so how many muscles we have. And then we have matrix p which is p1 pn. And so we can write the total force that the system is producing f as p times u. So now what we're going to do is we're going to say the cost and that cost is the difference between the goal force minus what you did analyzing the simple way. So you want to generate your forces so that you arrive at this goal that you want and you want to do it with least effort as possible. To generate the commands so that you produce the goal and activate these muscles so that they generate the total force that you want, but do it in such a way that we're going to analyze the amount of commands that you produce by the cost that depends on u and the power that depends on that. And all I want to show you is that, well, how do we minimize this cost? 1 and 2, what does the result look like depending on the shape of this m function? It turns out that if you look at what m is doing, as I've described for you here, is that it is basically imposing a cost that if u becomes large for one particular muscle, it's going to cost you a lot. So what this does is that it makes it so that when m is equal to 2, what we get is a scenario where the preferred direction, p i, the angle of p i, is going to be at some location. We're going to see the activity for that muscle tends to be maximum in that direction in fall when m is equal to 2, when m is equal to something less than 2, it doesn't look like this. It's going to become narrower.  As m gets larger than 2, it becomes large.  Write the equation for you. You just simply have to search it in the space to find a view that minimizes that. But the effect of parameter, parametrically penalizing the command is that you get greater sharing when the penalty is large and you get less sharing when the penalty is smaller. I should have... This is not quite right. The peak becomes larger as the... So it's like an distribution. The peak grows larger as the width becomes smaller. In other words... Okay. So this is really the beginning of the idea that by looking at real behavior, measuring activity, we can potentially arrive at these cost functions. Now, when this paper was published, this paper was, I think, 2004 or something like that, where they measured this activity and they said that no one can describe the cost function that seems to be predicted. The problem with it was as follows. I want to describe it to you because it is not the case that we can write such an equation. So really what we have is we have force in the muscle is equal to ui times pi. But there is also noise associated with this force where pi is a random variable with mean, zero, and variance 1. So what did I write? I wrote that the force that the muscle produces depends on its activation but also depends on a random variable that is being multiplied by something that has a gain of ki. And what that means is that the variance of this force so what's the mean of this? So the expected value of fi is just ui pi but variance of fi is equal to ui squared ki squared times pi pi transpose. So what this says is that the variance of my force depends on the input u. But what this means is that as u increases the variance of force is going to quadratically increase. Some function that's going to increase. Because you notice that the relationship between variance and u depends on the squared u. So why is that important? Because if force depends on not just the input but also the noise associated with the way that input generates force then our cost function is not a deterministic value. It's a random value. So what we need to do is minimize, not j what we need to do is minimize the expected value of j. So what I want to show you is that if you have a system that has this kind of noise signal dependent noise arises in biological systems and what that means is that if you look at force time and you ask someone to produce some amount of force like this you get a little bit of a cold. As the force increases you get more of a cold then you get more of a cold. So you notice that the variance of the signal is changing from a critical idea to a biological system because it's very important to know what the noise structure is because as you remember when we were doing the Coleman filter we assumed the noise structure was Gaussian a mean that was independent of its variance. Here we're dealing with systems that have a mean but a variance that depends on it that's the way most realistic systems work. So what's the consequence of this? Well, the consequence of this is that when we see that in our cost function when we find the expected value of the cost we are naturally squaring the U as a component of our cost meaning that just by trying to be at the target produce the force that you want me to do represent the expected value of that force I am penalizing the squared force why I'm doing that is because when I want to find the expected value of something that says be accurate if I have commands that increase the variance of my force the larger these commands are the less accurate I'm going to be so effectively I'm going to have an equation that's going to have be associated with my more so let me show you so as a preliminary what I need to do is to show you how to find expected value of squared random variables because our J is going to have squared random variables so if we have X as our random variable and we want to find the expected value of X squared how do we do that? well we have variance of X which is equal to the the value of X squared minus the expected value of X squared so therefore the expected value of X squared is going to be the variance of X the mean of X squared so what if we have the expected value of the scalar quantity X transpose X so this is the scalar quantity and the reason why it's important for us to consider is because this quantity here J is also a scalar quantity and it's going to have random variables to U that are going to be multiplied by themselves so this is going to be a useful thing for us to consider so alright how do we approach this the variance of X is the expected value of X X transpose minus the expected value of X X transpose so this quantity here I'll take one more step here so this quantity expected value of X transpose X X transpose X is a scalar quantity which is also the same as the trace of X X transpose I think the trace of X X transpose and finding the expected value is the same as finding the expected value of X transpose because the trace of this is just the diagonal terms of X X transpose so the trace of the expected value of X X transpose this is just the expected value of X transpose of the expected value so this is our solution of x transpose x, our scale of quantity is plus the trace of the variance, scale of quantity. Okay, so if we have a term of expected value of x transpose A x here, some matrix in between, then that's going to be equal to the trace of A of the variance plus the expected value of x transpose A. So these are the very important equations for us, because we're going to be using this a lot, because what we're going to be doing is find the expected value of that j, and to do so, we're going to have to find the derivatives with respect to u, which means we're going to have to be able to expand it to our variance root. All right, so let's go back to our basic problem and minimize our equation. So if I have the following cost that I want to minimize, that says minimize force difference. We've got a goal and the individual forces that are produced by the various constants. Suppose we have a very simple cost like this, and individual forces at pi are equal to ui, pi. So what we're going to do is find ui, and so we have a goal, we want to find, I'm sorry, pi, well ui is our activation in each muscle, so let me minimize this cost. So to do that, we want to find the expected value of j, so the expected value of j is kind of a scalar quantity. It's going to be the expected value times plus the variance, plus the trace of the variance, the expected value. This term here, the trace of the variance, the variance of fg minus sum of f i is going to be the variance of f i, the sum of f i. The trace of the sum, the variance of the equation is ui squared, ai squared, pi, which this trace here is just going to be the sum of ui squared, ai squared, pi times what we've got. So the objective here is for me to show you that I started with this cost function, that all it says is that find the commands that sum together to produce the goal, and the closer you are to the goal the better, but if the forces that I generate are single dependent, then the commands ui are going to be such that they minimize the cost in which I generate forces that I get as close as possible to the goal and I am minimizing another cost that's associated with the square. How do we, inside the trace, go from the variance of fg minus sum of f i to this, that sets up? Trace the sum, yeah. Well, the only random variable is f i, the sum of f i. And they're independent. Yeah. Okay. Because the noises here are fi's, but they're independent. So, if you have a single dependent noise in your system by trying to measure, by trying to reduce the cost associated with bringing your, you know, the sum of action to the goal, you are implicitly minimizing the cost that says, activate your things as small as possible. It's a square cost that you have to do. You see, the expected value of j includes accuracy plus, that, does that make sense? So, one of my former students, Jordan Dietrichson, after he got his faculty position in the University of College London, did a neat experiment to test whether it wanted to come up with this j in the way individuals control the force of the instrument. He said, take two fingers, left hand and right hand, and push on a force transducer. So, you're going to have a force transducer on the left, a force transducer on the right. You're going to generate some force on the left. You're going to generate some force on the right.  So, your goal is to displace this cursor, and you can do it by pushing all the way with your left hand, pushing all the way with your right hand over the sum of the two, however you want. And you just solve this problem. How much force do you use with your left and the right hand? And what's nice is that, you know, some finger, like our index finger, is going to have probably low noise, whereas you know, this little finger may have a lot of experience, but we're not so good at controlling it. And so, k is going to differ for different fingers. That signal-dependent noise term on the f i there. And you wanted to know, and maybe also with your right hand, that maybe the noise would be different. So, you wanted to know, when you are splitting this force between the right and the left hand, what's the policy that you're using? How are you solving this problem? So, let me show you the approach. So, what we have is x i, the cursor displacement that's produced by input u left or right, is going to be a normal with mean u i.  So, displacement, cursor, u l is the force, and this is the variance of that force. It looks like this. The variance of the force depends on input u squared and the noise associated with that, which is k l. Okay. So, just to give you a sense of what this means, what this means is that if you were to look at the standard deviation of force as a function of force, you would have a slope vk. So, the standard deviation of the noise grows as a function of the beam of vector value of the force. And the slope is k. So, the variance is quadratic, the standard deviation is linear. So, the optimal control problem is as follows. What we want to do is minimize the difference between the displacement and some gold. So, the expected value of this is what we want to minimize. Why did u that minimizes this u star is what we want, and we want to minimize this expected value? So, just before I do it, it's going to be very simple, and I just want to step back a little bit so that we get a sense of what's going on now. So, these control problems that we're considering have very simple relationships between input u and some force. So, there is no dynamics in the sense of what we're going to be going to want. So, right now, we just have this very simple relationship. Action, consequence. In the real system, action has dynamics. When you generate u, there's a dynamical system that has a hidden state x that gets translated in time. So, we're not considering that. We're just considering u generates a force, and that force is going to have some variance. So, it's an open loop, one single time step scenario. So, you want to minimize this cost. What is this cost? The cost and the difference between what our cursor does and where the goal is x left plus x right. So, the displacement cost on the cursor by the left and right forces that we're producing should be such that we get as close to the target as possible. So, do that well. You want to find the expected value of that random variable. So, the expected value of j is going to be equal to the expected value of xl plus xr minus g squared plus the trace of the variance xl plus xr. What is xl and xr? These are our random variables. So, the expected value of this is going to be ul plus ur minus squared. So, this term here is going to trace variance of this. Variance of xl is going to be gl squared, ul squared. Variance of xr is going to be r squared. Trace here is meaningless because it's a scalar variable. So, I'm just going to get rid of these. So, now I find the optimum command. So, let's find the derivative of this with respect to ul. So, we get 2sg plus 2kl squared. I said that equal to zero. Then similarly, we can find the ur. The command for the left hand, command for the right hand is going to be g minus ul. What we just did is that we said you have a goal forced to produce. You should produce it in such a way that it depends on the noise. And now what I can do is I can take ur. Here's the equation for ur. This is the optimum view of the star on it. And I can take this and put it in here and solve for ul. And what I get is the following.  So, this is mean. The larger the noise associated with your right finger, the larger should be the force produced by the left hand. So, you know, if you have two fingers, one is really noisy, you should give more command to the other one. That's what the other equation says. So, if we have such a cost function where all that matters is to generate a force so we get as close as possible to the target and we want to minimize the expected value, then the activation, the amount of force we produce with each finger should be related to the noise in each finger. That was the theoretical prediction. So, you're measured the noise in the right finger. Each asset of the subject to produce force one, force two, force three, force four at different levels. So, you measure pain with different amounts of force. You ask them to produce different amounts of force. You measure the standard deviation of force. You fit it as k and you found the noise with the left finger for the right finger. Does that make sense? Then what he did is then I said, okay, so now you've given a chance to activate each muscle, however you want, each finger however you want. And do you produce force on your left hand and right hand in such a way that minimizes the cost that depends on these noises? Do you allocate each finger that's out? And you found the answer was no. It wasn't as simple as that. So, then he said, all right, what else is wrong? It's not just these noises that matter. It's not just that cost. So, he said, all right, what if I add to this another kind of cost? So, this is the cost that he started with. Let's say a cost that has a weight of lambda one. Then he said, let's add another one, lambda two, in which we explicitly are going to penalize effort. So, what we're going to have is that we have these U's in there.  So, this penalizes effort in a particular way. See that? It's sorry. It penalizes effort by the amount of noise in each one. Right? So, what if we just penalize effort independent of noise? That's what the second term does. And then he added the third term that said, well, really, these U's, what they really are, matter isn't just by themselves. What they matter is with respect to the maximum voluntary U, the maximum voluntary U R. So, force by itself, maybe it doesn't matter so much. Maybe what matters is how much force that finger can produce maximally. So, not an absolute value of force, but we'd rather think of its maximum voluntary force. So, he fitted a function that looked like this. And then he was able to explain the results very well. So, he seemed like really what mattered was not just noise, but also the effort that is being spent, not in terms of absolute value of the force, but in terms of how did that force compare to the maximum force. And then the things were worked out. I don't feel like the noise doesn't matter at all. Yeah. In fact, in this case, we look like the noise didn't matter much. What really mattered was the effort, the U, the U term, normalized to maximum voluntary force. Is the maximum voluntary force proportional to the noise? Like, I think about my pinky, which is noise is also weakest. Right. No. I don't know if there was any relationship between K and MPF. Usually, noise has not to do with the size of the muscle. It has to do with the neural control of that muscle, meaning how much brain space do you allocate into that. So, you may have a very delicate muscle that you can really control well, or you can have an opposite. It just depends on what you use it for. So, I think our thumb, for example, may produce a huge amount of force, but I don't know if we could just associate it with that. Yeah. That's because our vocal cords are very precise. The U is very small. Very small. Yeah. So that has to do with use, right, and how well we can practice it. So, he measured these, and he came up with some estimates of these lambdas, and with a scenario where force was normalized and maximum volume through force, he got a good fit. Any questions? So, just to summarize the basic ideas, what we're moving toward is thinking about control of action in terms of having a cost function. And this cost function, so far we've been describing it in terms of having a effort cost and something that says the state that you want to arrive at has some distance away from where you are. So, it's good to be at a particular state. And you have to find commands that provide you with an invention that will add a cost to time to instead of being able to get there as quickly as possible while minimizing the December. And to do so, first we're going to describe this spelling equation and then we'll be realizing when it's like, okay, see you Wednesday.


While moving on now the Nigerian society may experience the worst if eternally or internally rather in displaced persons are not resettled in their communities So says the bishop of the Catholic Darces in Makodi Most rev and bishop will for a cheek, but I'm not we want the president Muhammad The Buhari to as a matter of urgency do the needful and save generations from rotting away on the streets Every day people are dying, the people are going to die You know this has to stop, and the bishop can do it, it's the first day for the foreign public of Nigeria So further clear this and where it will end Anything important than that, but okay If we give the rose and people are dying, who will work on this rose? We need people to wear the case Imagine these young people are not going to school And there are more, from different places What is going to happen in Nigeria in the next 10 years in this place? Who will lead the peace here? And this is what they want to do They want to become an imaginary in this country That is not true and we know how to introduce them And we as a Catholic organization, FJDP Foundation for Justice Development and Peace We are concerned about IDPs, people are displaced internally and the refugees And being that today is one day for the poor We want to mark this day by distributing food and non-food items to IDPs in OTC, informal camp And that's why we are here Basically we have four camps in our mind And of course we can't reach out to everybody So we selected four that we think are most vulnerable to resets And a team of 400 has only 200 here in OTC Being that it's a very large camp And less attention has been given in this camp because it's informal I'm nearly happy Because as a Bishop has recognized Grace Coco is people that has Grace Coco He presented something for them, it's a very great If people would have that mind Don't make those sufferings At least just with peace on the air And that was it, watching more Okay, enjoy


Hello, so good afternoon. We are going to be talking about reviving 19th and 20th century epistolary conversations, but using digitisation. So my name is Helen and my background is in secondary and further education teaching, but I'm now doing a PhD and I've just completed my first year. And for the project I'm working with digital humanities and Graham is going to talk about how they've been helping and really developing this project. So the project is called Hardy's Correspondence and it's a collaborative PhD project that works with the University of Exeter and Dorset County Museum. And we're trying to digitise over 4000 letters that were sent to Thomas Hardy and they're currently held at Dorset County Museum, but we're working towards making them openly accessible online. What we found in doing this is that Hardy's letters, the ones he sent to people, have already been digitised online, but it's through subscription. So in really creating the other side of this correspondence in digital form, we're looking at ways to make it more accessible and more discoverable. So just briefly, some of the key concepts and terms that I guess a lot of people would be familiar with, but maybe not everyone. A lot of the work we're doing in encoding these texts is done using TEI, the Text Encoding Initiative. That's an XML schema instead of guidelines for encoding text. It's very extensive, it covers a very, very wide collection of texts. There are subsets for different types of documents. The markup that you're using in the individual project depends very much on the research focus. Some people will be interested in particular aspects of documents and so they'll approach the project marking up certain features. Let's say that very much depends on the research focus. One of the first things we do as part of the TEI project is sit down, discuss the research focus and agree what elements of the document we're going to, a text we're going to mark up and agree standard ways of doing that because within a single document there could be many different ways of marking up the same features. So obviously a project that's got more than one person working on it, you need to agree on how you're going to approach that. Obviously we're in a linked open data session, so I probably don't need to explain that. ExistDB, it's the web platform that we're using to put this content online. It's an open source, native XML database, and that's what we're using to make this content available. What data are we interested in and why? One of the, I mean obviously the key features is with this and I guess a lot of similar projects is we're interested in preservation. These letters are currently held, as Helen said, in the Dorset County Museum. A lot of them haven't been out of their boxes for a long time, so there's an element of checking the current condition of them. They have been catalogued in the past, but there are documents that have been missed, so we're finding extra documents that perhaps were known about but haven't been catalogued so weren't accessible to researchers, they wouldn't have found them. Obviously accessibility by digitising these, putting them online, putting the transcripts online and the marked up transcripts. We're making them more accessible for people to study and research, we're helping to add to the original source. Obviously it's making it more discoverable. We're aiming to have free open access to that, that's pending kind of a final agreement with the museum, so that's what we're working towards. Just to kind of briefly go through the process that we're following. So we're starting off the first days of projects, obviously digitising them, we've got high resolution cameras using to take photographs of the letters, we're then going through transcribing the text within those letters. That's something at the moment is being done on a fairly small scale within the project for letters of particular interest. We are hoping in future to bring in a crowdsourcing element to this project because as Helen said there's over 4,000 letters in this so there's no way within the project we could get all that transcribed, but obviously there's a lot of big community of interest around Thomas Hardy so we're hoping there's a lot of potential for crowdsourcing there. We then go on and TEI encode that. I don't know if you can see in any detail but this is the same letter with certain features marked up. So we're looking at the, obviously the date, we're marking up place names, people's names, names of sort of works of literature, that sort of thing that are mentioned. And again that's discussed at the start of the project and agreed and that's kind of an involving thing as we go through and discover new things. And then finally this is just an example of how it's looking at the moment. It's still in development, the web output, but that shows you there how the marked up text looks when you've displayed on the web. So obviously you've got the metadata appearing at the top, you've got links appearing within the text to link through to further detail about the people. So just to give you an example of how this technology and the digitisation is facilitating research interests. We started by looking at two correspondence Florence Henneke and Thomas Hardy. Florence Henneke was an English aristocrat, she was a friend to Hardy, but she was also a fellow writer. And we've selected these at the very early stage of the project because both sets of the letters exist in the Hardy archive at Dorset County Museum. But later in the paper we'll talk about how we can link two separate archives as well. And these two people met in 1893, that's when their correspondence also started, and it carried on for well almost 30 years. It only ended with Florence's death in 1922. Amid a kind of interest, renewed interest in the 1970s in biographical approaches to Hardy, one side of the correspondence, Hardy's was published as a single correspondent volume, and it was entitled One Rare Fair Woman, which was a line from his poetry. And for me that creates and shapes a particular narrative of this correspondence. It's the narrative possibly of unloquated love. And when you flick through this published edition, then there is evidence you can look for it and find it in the early stages in the 1890s when they're talking to one another or writing to one another. So for example here Hardy is writing in 1893 and he does seem to be very keen to spend time with Henneke, but he's also at pains to cover that up and say that it's purely for artistic reasons that he wants to go to a play with her. So that's there. But for whatever reason the editors, Evelyn Hardy and Frank Pynion, didn't include Henneke's side, they didn't include her letters. There are fewer, so that may be one of the reasons, but for me I feel that it possibly silenced Henneke and kind of propelled that particular narrative of a male writer deeply in love with somebody who was married and therefore unattainable. And what I'm interested in is saying well letters have lots of different topics in them so surely we can find lots of different narratives. I also want to restore her voice. So they summarised her letters in the book just in two pages and they said that she had this passionate heart and it was drawn towards anti-vivisection and amelioration of animal suffering. But I just think it's so much better for us to understand the intensity of her passion and her interest in something like that by reading her letters, so bringing back her voice and reviving the conversations they were having. So this is one of my favourite quotes basically because she's disgusted with Prince A of Cynought for opening the new physiological laboratory at Cambridge and she's looking for some mad woman to burn it down. She's not going to do that herself but you know a more radical version will hopefully. Hardy is a little bit more kind of muted and controlled in his response and it shows how there's that weighing up of their opinions and their decisions. So I really think there's historical value in looking at what she's saying because at that time the kind of animal rights movement was really closely intertwined with first wave feminism. So that's really kind of focus that I think can come out of this and so what we need to do is think about how we can bring this dialogue out and whereas with more established traditions it would be to publish an entire correspondence of a single author with digital collections we've got the opportunity to recreate lots of different epistolary dialogues and that then for brings out the form and social practice of letter writing. It restores it through creation of a digital open access archive which allows these new interpretations to come out. And therefore I think we will be going to hopefully a live demo of that. So just briefly this slide again showing some of the TEI markup as showing how we've encoded the contextual relationship between one letter and the subsequent and previous letters in the exchange. Within this as Helen said it's quite unusual in that both sides of the conversation are held in the same collection so that makes this a particularly good example for us to start working on to kind of experiment with this idea. But there's no reason at all why if other the other side of the conversation held in separate collections and was marked up similarly in a suitable way that this backwards and forwards kind of correspondence couldn't be reconstructed across separate archives. So just bear with me a second. So this is showing the ExistDB kind of web output as it exists at the moment as I say it's still kind of under development so there might be a few sections that aren't quite as they should be but again you can see here we've got the metadata that's encoded for the letter the letter itself and then we've got this series of links that are following through showing you from one letter to the next and you go backwards and forwards through that conversation and as I say this particular example we have both sides of the conversation within the same archive but there's no reason why it couldn't be set up to work across different collections. Obviously this is at the early stages and when you could see the encoding I've been learning the very basics of that so when you could see little red bits that's because I'm still in the process of picking up TEI and everything but I've found that there have been some successes and also some challenges so I'll start with the successes. In terms of restoring conversations it involves more than just the two sides of the conversation there's more to the context so many of the letters have enclosures it might be newspaper cuttings photographs in one case there's a hundred-year-old leaf from Shelley's grave that one has stayed with the original letter and it's quite lovely to be able to see that but in many cases they become separated and so if we're looking at digitisation we can actually start to bring back some of that context fill the gaps and make some of those original connections and we can do that with TEI so a success has been that it is coming up to quite small but Hardy said to Henneker in one of his letters that he was enclosing two little poems that she might like to see he doesn't say what they are he doesn't say the names of them but reading Henneker's reply she describes them and she describes one in detail where she talks about a couple who walked in wind and rain and from that we can kind of deduce that it's a poem called beyond the last lamp. For the online reader then we can actually link it to a copy of the poem that's on a reliable website it's one that kind of gives you contextual information as well so in that way we can restore that context. Where it's been harder to do that is where Hardy obviously is a very public figure he's had a lot published in newspapers and he wrote an open letter for performing animals and he was saying you know about the cruelties of that and that was published in the Times. For Henneker he cut that out and put it in an envelope with a letter so she was able to see it. If we're going to look for it then we actually have to use university or institutional login then it becomes quite straightforward you can go on to the Times archive but if we're really in the spirit of kind of open access and making this like a street corner university where anybody can find out about it then we've got to think about ways do we link to the Times or is it that we can't restore that part of the context so that's kind of how the letter would appear if we had access to that archive. The other challenge which is really quite engaging for me is the heterogeneity of the letter form so actually we're going to get lots of different topics coming up in one letter and we have to be able to kind of highlight that as a real characteristic of the genre whereas in the past you might get kind of a catalogue and it'll have one entry for each letter saying what it's about we're looking at ways that you can actually use TEI to tease out different narrative threads so we can encode names of places of people and of hardies literary works we can include places and geo locations and these fictional places as well and we can other we can include other points of reference within the texts we're also looking for a greater semantic encoding which would actually help a researcher to understand a letter on a topic by topic basis so that's what we're working towards and then we'll just talk about how that's going to feed into guidelines so as we're working on a collection of correspondence I mean as I mentioned before that the TEI guidelines are very extensive they're still being developed and it's kind of an evolution the special interest group around correspondence so what we're hoping to be able to do is feed into that and feed into the TEI guidelines on marking up correspondence based on the challenges and experiences that we've discovered as part of this project so one of the challenges that we've encountered with this is this feature is quite common getting to the end of a letter and then running out of space and then adding a little bit more at the top often written vertically like that that's apparently I mean I'm not the expert on the correspondence but I understand that's quite common practice in the 19th century and 20th century letter writing so in order to encode that in such a way that the letter continues to make sense in terms of the structure of the letter that hasn't been coded so that bit of text actually appears at the foot of the letter there in the underlying XML that is marked up so to indicate that that bit of text was actually written as part of page one or on page one of the letter so it's things like that within the project we've had to kind of make decisions on how best to do that and that's the kind of thing that we're hoping will feedback into via the special interest group into the guideline so other people facing similar situations in the in the future have got that as a kind of starting point that they they may wish to use as Helen said before we're looking to obviously link into to other sources of data and information that that are relevant and connect our project with others in another of the letters they make reference to the Browning letters which are already online as a collection in this particular case there's no reference to a specific letter it's it's the collection as a whole so by including a link to that as a as a sort of separate data source it enables the reader of our website to see those letters the same as Hardy and Henicka would have been able to. Other ways we're using link open data obviously within within the site we've got various people and places mentioned we're linking through to authority lists to provide data on on those people and places at the moment as I say it's still in development we're just linking out to those sources but we are also eventually intending to kind of draw that data in obviously since we've credited so we're we're building on and linking to existing sources of data and archives. The intention with the project as I mentioned earlier is for it to be openly accessible we're we're looking to make the data that we're kind of collating for this project available as linked open data the idea being that future projects that are looking at other collections of letters from other archives will be able to build links with this that's obviously enhancing the discoverability of our project and and allowing it to be used as a building block for future projects. Where do we need to finish so we'll just flick through this slide and just say that this is kind of one of the future projects hypothetically where we could link up with a very separate collection so we have Lady Hall's letters and they and in her library in Starhead there's Hardee's so kind of we can achieve that hopefully and that's what we're looking towards so just to kind of finish there is education application we're getting students involved in this as well it's helping them to take on researcher roles and we can discuss that in more detail if anybody would like to know more about what we're doing that. You can also contact us so if you'd like to find out about the project that's the first link and the other one is if you just want to talk more about digital humanities. Thank you.


Our next presentation is from Peter Sadausen, the Assistant Professor of Computer Science at the University of Hawaii, Vietnam, who is presenting fishnet, species classification and size regression using deep learning. Now deep learning, hopefully you'll hear a bit more about that from Peter and why that's different. And the label datasets of one million fish. Welcome Peter and welcome again, Peter. And Ricardo, if you would please play the fishnet video. Hi, I'm Peter Sadausen, an Assistant Professor of Computer Science at the University of Hawaii, Manila, and I'm presenting fishnet, species classification and size regression using artificial intelligence and a dataset of one million fish. Our team is a collaboration of the University of Hawaii with the Nature Conservancy affiliate in Indonesia and the Ministry of Marine Affairs and Fisheries in Indonesia. We also have funding from the Walton Foundation. So fish stock in Indonesia is a challenging task and we wanna use artificial intelligence to help them achieve marine stewards ship council certification, which requires monitoring 5% of the snapper feet for about 10,000 boats. This is challenging because the boats are very diverse with different types of fishers. There's an extreme amount of biodiversity spread out over 5,000 kilometers in the Indonesian archipelago.  So we have hope to be able to do this because of three key technological developments. Digital cameras have made it cheap and easy to collect photos of what fishers catch. These photos contain metadata such as time and location and we can use that to verify classification. Computer vision and artificial intelligence have really taken off over the last 10 years. These algorithms are systematic, reproducible and automated so we can redo the processing if we need to. And there have been efforts to collect large databases of images that can be used for training these artificial intelligence systems. And this requires big collaborations and crowdsourcing of annotations. So the Nature Conservancy has worked with Indonesian fishers to create a very large dataset of snapper and grouper catches mainly. So this dataset consists of 200 million digital images collected from over 500,000 vessels. Basically the team gave fisher captains digital cameras and asked them to take photos of everything they caught after placing the fish on this colored board here on the right. These images, many of them were annotated by a team of biologists at the Ministry of Marina Farrison Fisheries with species and size information for each individual fish. And over one million of these fish have been annotated by that team. This data was then processed by computer scientists at the University of Hawaii. And the processing includes cleaning up the data and detecting incorrect annotations and such. So using this dataset, we've built a AI fish stock estimation system that consists of three steps. So the first is to segment each image to detect individual fish from images that might contain multiple fish. And then each individual fish is classified with species information and the length is estimated. So step one is segmentation. To do this, we used a big neural network model known as Facebook's Detectron 2 region CNN. We actually segmented a number of these images ourselves in order to fine tune the model to detect fish and these fiduciary markers on the board, these little colored boxes. These are used for estimating the length of the fish. And the results are very good. So images that only contain one fish, we get over 99% correct segmentation. And for images that contain multiple fish where the fish might be overlapping each other, we get over 93% detection of individual fish. We also measured the intersection over union, basically the overlap in the segmentation and we get very good performance on that. So once the fish have been, the individual fish have been extracted, we can do classification. And we use another big neural network for this which takes in an image of a single fish and outputs probabilities over a set of 163 different species in the dataset. This is a pre-trained ResNet 50, fine tuned on 100,000 labeled training images from our data. We get 94% top one accuracy, which is very good considering that the dominant class is only 30% and 99% top five accuracy. So of the 99% of the time the correct fish species is in the top five list of the output of the bus far. Step three is length regression. For this, we're extracting some engineered features, the length of the color boxes and the length of the segmented fish. And we plug those into a random forest model. And on a held up test set, we get a root mean squared error of 2.2 centimeters, which is very good. And for fish stock estimation, we don't actually care about the individual fish error, but we care about the distribution of the predicted error. And so on the bottom right here, we show that the predicted distribution of the length and the ground truth as annotated by humans is in agreement. So lastly, this has huge potential for transfer learning because most AI computer vision systems these days use models pre-trained on this ImageNet dataset, which consists of cars, cats, and landscapes. But this is really suboptimal for specialized tasks like medical imaging or even classifying fish species because the features are just different. But with this dataset, it's about the same size as the ImageNet dataset, we could actually pre-train big image classifiers on this dataset and then use that for other fish species and other fisheries. So we have a lot of hope for being able to do transfer learning from this dataset to other datasets. The other thing we're working on developing is pushing this technology out into the field to make it more usable. Thank you. Thanks for sharing. That was eye-opening for many of the listeners. And I think for most of us that aren't very tech savvy, the difference between machine learning and deep learning is something that we're only starting to understand. I'd love you to give us some idea on your future vision for how your work is going from when you started with rulers and sitting them in the right spots. I remember when we started trying to record sizes of abalone in very, very choppy waters in Australia, the only way we could really do this with any efficiency was to actually put a bar in front of the video cameras and just tap the abalone as we swam underwater. And that gave us a fixed distance between us and what we were recording, which allowed us to know what the size would be on that plane. And I've seen that you've taken a different approach where you've got a board with segmentation. And I imagine that in a few years from today, they'll both be looking at our techniques and thinking, wow, those guys were in the early stages of development. But I see that with your deep learning, you potentially are going to let the computer decide if a species fits a model or not. In other words, build a model around a species rather than you having to actually annotate all pictures. And potentially in the future, we won't need boards and such like or distance from camera measures or double cameras to allow us to get these. Can you give us some feeling about where this is going in the research world and what we can potentially look forward to in the future? Thank you. Yeah, thanks, Kim. So we definitely need some information to extract the size. So we were lucky enough that Peter Mohs at the Nature Conservancy planned, he thought ahead by having the fishers put the fish on this board. Because really, the machine learning algorithms can only do so much, right? They're not gonna magically be able to estimate the distance to the fish and estimate the size. So having some sort of fiduciary marker is definitely important. I think the part at the end where I talked about the transfer learning, everyone here doing computer vision is using this idea of transfer learning where you download this data set or you download a model that's really good at detecting images of cats and landscapes. And then you only use a hundred or some hundreds or thousands of labeled training examples in order to classify different fish species. So everybody is using transfer learning. That's what 95% of AI applications are these days. But I just wanna stress that you can often do better if you train your own specialized model without relying on some downloaded model. And so that's kind of what we're excited about with this really big data set. But yeah, there's definitely opportunities for a collaboration. The original ImageNet data set was the biggest user of mechanical Turk, this like crowdsourced annotation system 10 years ago. And it really drove AI forward just having this giant data set of a million labeled images. And so doing fish stock estimation, I think we could do a similar thing where we push for getting lots of different species and some big data set. And then you could build models and everybody could use these models for their own specialized projects. Just before I hand over to Matt for his question, can you give us some insights in the same question I gave to Sherry? Any particular tips for people who are working on collecting imagery of the types of tips for new players about how to line up your images or what was the thing that through some of the models that maybe you could get on the front end of? Thank you. Yeah, that's a good point. You know, I think having some sort of markers that allow you to estimate the size, I think is important. But beyond that, as long as you have something, I think you're good. If it's totally white background, you're kind of stuck. Yeah, I'll just, yeah, great presentation, Peter. Thanks so much for the assembly and your efforts putting it together and the whole team as well for their work. I just have to add to this conundrum of accurate size and biometrics from two-dimensional images from my own work, which stretches back into the 90s doing photogrammetry, but manually matching points as opposed to using, you know, sift or slown methods to match points between photographs.  It's not a real whale shark. And there's no way we can tell what size it is, unless either we have markers in the background or multiple images of the same objects. Mathematically, from my opinion anyway, I don't see it as a feasible thing to train the models without multiple images or some distance from camera mathematics in there. But I think your dataset is absolutely critical for building accurately matched verifiable data for species and, you know, making that accessible globally, I think is really, really important about work that you've done, if it could be possible. Could it be possible? Yeah, so the dataset we have is owned by the Nature Conservancy and we have a data sharing agreement with them. So we're able to publish our model but we're not sure if they're willing to share the dataset yet. But that's something, you know, we see that as something that would be hugely valuable. So we're definitely interested in doing that. But, you know, there's a lot of effort for those biologists in Indonesia who actually annotated a million fish. So we see that as, you know, their data and we're just using it to build these models.


So by now I'm sure that most of you have heard that billionaire Elon Musk is trying to back out of his contract to purchase Twitter Now in the event he's successful He's still maybe on the hook for a billion dollars either way I don't necessarily care too much about this story if I had to choose my preference would be that Elon Musk did not buy Twitter because I have No confidence that he would run it in a competent manner, but either way I don't really care that much about Twitter So it's not that big of a deal to me But people are reacting to the story and conservatives really care about this because they believe that Elon Musk Cares about free speech when demonstrably that's not true But Trump in particular had an interest in Elon Musk buying Twitter because Elon Musk had signaled that in the event He successfully purchased Twitter he would unbend Donald Trump but now that's Seemingly off the table so Donald Trump like all of us reacted to the news and he decided to take a shot at Elon Musk as well Or should I say Leon's?  Where did you hear that before from me? From me fake account. She says fake a lot of them. No, he's got himself a mess You know, he said the other day. Oh, I've never voted for a Republican.  I looked at his contract. Not a good contract, but he's not sign up for truth We love the truth So he called Elon Musk ie Leon's a bullshit artist and claimed that He told Trump that he voted for him Even if Elon Musk recently stated that he never voted for a Republican before now interestingly enough Leon's decided to respond with some insults of his own He claims he never told Donald Trump that he voted for him and he goes on to make more tweets about Trump saying I don't hate the man But it's time for Trump to hang up his hat and sail into the sunset them should also call off the attack Don't make it so Trump's only way to survive is to regain the presidency I say no to that because the president is not about the law But that's a different story for a different day He also claims that Trump is just too much trauma and adds Do we really want a bull in a china shop situation every single day also? I think the legal maximum age for start of presidential term should be 69 Lol nice one Elon He adds Trump would be 82 at end of term which is too old to be chief executive of anything Let alone the president of the United States of America if DeSantis runs against Biden in 2024 Then DeSantis will easily win. He doesn't even need to campaign now I'm not one to agree with Leon's on anything But I do think that he's correct to assume that in the event DeSantis were to run against Joe Biden He'd have an easier time of beating him than Donald Trump So Elon Musk is he's using kid gloves to insult Donald Trump, right? But by stating that DeSantis is his preferred fascist He is pushing Donald Trump's buttons make no mistake about it because Trump currently views DeSantis as his biggest rival in 2024 and DeSantis has not shown enough Deference to Donald Trump and Donald Trump has been noticing so DeSantis is taking money from Trump's donors He's also holding meetings with Trump's donors as political reports and because DeSantis is refusing to kiss Trump's ring There are reports that Trump is literally considering announcing a presidential run again in front of DeSantis's house I kid you not so Donald Trump is truly the king of petty and let's be clear I don't want him to run again But in the event he were to run would I prefer that he make that announcement in front of DeSantis's house? Yes, I think that would be Amazing to watch So look as Cameron Kasky put it on Twitter Love it when the girls are fighting now Trump needs to go after DeSantis to confuse their shit-brained stands Trump versus DeSantis would rule and help the Republican Party be as scrambled and disjointed as the Democratic Party right now Level the playing field exactly. I love to see them fight I hope that Elon Musk and Donald Trump continue to exchange insults I hope that this escalates into a feud between Donald Trump and Ron DeSantis There's already been kind of a cold war between Trump and DeSantis But I hope that this Elon Musk or Leon's feud with Donald Trump Escalates and kind of fans the flames of this fight between Trump and DeSantis It is really nice to see these signs that the GOP base conservatives in general are fracturing because When you see the rise of fascism Any out that you can find is Caused for optimism any splintering that you can see is Caused for celebration and we need to make sure that we foster the development of this feud in any way shape or form So if I have to temporarily jump on team Leon's in order to root against Donald Trump and root for his downfall then I'm out for it I'm down. So I think that they shouldn't continue to argue I anxiously await Donald Trump's response if Trump doesn't respond to Elon Then this is kind of proof that he's a cuck right? So we should probably issue a statement on truth social immediately and call out Elon Musk for a Variety of reasons. I mean there's no shortage of reasons to insult Elon Musk But the same is true for Donald Trump So, you know this feud could go on for years potentially. I just hope that it continues to Vester, I'd love to see it. Let them fight