And let me welcome our guest today, who is Adrian Gaiden. He studied mathematics and computer science in Grenoble and then who gained his PhD from INRIA and Microsoft Research in Paris in 2012. Today, he is the head of machine learning research at the Toyota Research Institute, an institution dedicated to exploring the potential of artificial intelligence for autonomous driving and robotics. His work revolves around applying machine learning to robot autonomy, which, among other things, involves building a robust understanding of the robot's environment. Today, Adrian will talk about exactly that problem, extracting the maximum amount of information from sensor data and then using these data to understand the world around the robot, which at the end of the day is not that different to what we try to do in science. And with that, Adrian, please, the stage is yours. Awesome. Thank you, Philip. Thank you, Peter, for the invitation. It's my pleasure to be there. I have a soft spot for Oxford because I remember during my PhD, I was invited by Andrew Zisserman to spend a couple of weeks there and it was just an amazing experience. Really helped me a lot during my PhD. So it's a pleasure to give back a little bit and share the progress we've made at TRI. We do a lot of stuff across the board, as Philip mentioned, on robotics, autonomous driving. But I thought I would talk mostly about the computer vision work we've done. At the end, if you have questions about the rest or about this, just feel free to just ask. So the main, I would say the main research thrust we have in the team beyond other areas like prediction, planning, et cetera, has been computer vision and in particular, self-supervised 3D vision. And so we've done a lot of research covering this big problem that Philip mentioned about understanding the world and making sense of it for a robot. So that covers seen understanding but that covers also behavior, modeling and prediction, learning for planning and control. And one of the key uniqueness of our approach at TRI is we're not going for the very cost-intensive manual supervision because we don't think that's just gonna work at the scale. We're looking at self-supervised learning and other ways to scalably supervise robots and self-driving cars, including simulation and auto-labeling. And we've been quite productive at this with a lot of papers in recent years and actually some of this technology that you're seeing today used in production. So one provocative way of thinking about our research agenda is and in the trend of machine learning papers that end with is all you need is one eye is all you need. Of course, that's not true, you need more but it's an interesting scientific question. Can we learn robot perception from raw videos only? Can we get robust 3D depestimation from a single monocular camera in particular is something that has been exciting us scientifically and has had some positive impact in applications of the line. So just as a reminder, you've here, you've maybe heard sometimes the term pseudolidar. It's the same thing as monocular depestimation which is about taking a single RGB image and outputting a depth map per pixel range estimates. How far is the object that was captured in that pixel? So again, naturally the question in the age of rich sensor suite, stereo, lidar, you name it, why a monocular depth and in particular from the angle of driving and robotics. So first and foremost, there's something obvious which is the camera is the lingua franca, if you pardon my French. It's the universal sensor, it's everywhere. It's the cheapest sensor you can buy. It's common in robots and cars but also in your mobile phones. The second is that in complex sensor suites like the ones you'll see today that is common in soldering cars for instance, you often have like for cost minimization reasons you have often a wide baseline. We don't litter the car with the bazillion number of cameras. We try to minimize and optimize the sensor placement. And again, in the order of reducing costs so that you can commercialize products at a very large scale. As a reminder Toyota sells 10 million cars every year. So at this scale, you're thinking about costs. So the other important thing in robotics is redundancy, obviously. And so no sensor is perfect, even cameras, even LiDAR. And so having multiple different sensors that are capable of the same features, the same functionalities enables you to get robustness from redundancy. And that's related to the Byzantine general's problem and et cetera. And now, why depth in particular? I mean, it's a legitimate question because we care about 3D objects, right? Like detecting where the objects are. So do we care about every pixel, every range of underlying every pixel? And the answer is yes, because interestingly, if you want, even if you just want to detect objects in 3D from vision, the main bottleneck today is monocular depth. If we had good depth estimates, we would have much, much better 3D detection. So it's the scientific bottleneck right now. And so the overarching question in our research is, is robust 3D vision possible thanks to large-scale self-supervision? That's a specific scientific question that me and my team, a part of my team, have been studying extensively in this past few years. So as just a quick refresher, supervised learning, you get raw data, which is easy to acquire, right? Just record the sensor stream. You feed it into a model and out of it you get predictions. And you pay a loss to basically train your model, right? By gradient descent and back propagation in the usual way. And that loss is computed by computing an error with respect to ground truth, right? Target values, right? Or labels. And that is the real dirty secret that everybody in machine learning knows, which is that is what drives performance, is how much labeled data you can get. And the quality of that labeled data. So in self-supervised learning, it's different. You go from raw data to model to predictions, same. But the loss is not paid by comparing to the ground truth. It's paid by comparing to the raw data itself. And so how do you do that? Well, obviously, you can't just have a reconstruction objective. You need to inject more inductive priors. So help learning the right things. And that's where prior knowledge really comes into play. The reason we're doing this is because we have large volumes of unlabeled but structured data. So we can make some assumptions about the road, other objects, or about geometry. And there's also multiple sensors. So you can make assumptions about the relative calibration of the different sensors and things like that. One thing that is different in our research when we talk about self-supervised learning is that it's different than image classification self-supervised learning that you might be familiar with. Because here, we're not talking just about the pre-training task. We're not just trying to learn a good representation and then use that representation in a fine-tuning way and a transfer learning way for a downstream task for which we have labels. Here, the task itself, which is predicting that, is self-supervised. So after the self-supervised learning, the model is ready to be used. So with this brief intro, I'll basically dive right in into the three big topics that I wanted to cover and the research that we've done in the team around 3D self-supervision, how to go beyond self-supervision because that's not a panacea either. And more importantly, how do we use monocular depth? So it all started actually with a really famous paper from Clemangadar et al. about self-supervised stereo training, where they've shown that basically you can use some simple geometric constraints when you have a stereo pair, so two time synchronized frontal parallel cameras, where you can feed in the left image from your stereo pair into a monotef network, get a depth prediction or a disparity prediction. And then you can use the equations of projective geometry to warp the pixels from the left image onto the right image because you know the baseline. And that can enable you to reconstruct the right image from the left image. And then you can compare the pixels, which is called photometric loss. And if your pixels are not aligned in color space, then it means, not geometry is wrong, obviously, but it means that your depth network is wrong. And that gives you a useful signal to backpropagate and then learn this network. And so that's why you see that in the learning objective for self-supervised learning. You're trying to learn this theta hat parameters, and you're minimizing an empirical risk, which is a sum over losses over the different image pairs. And it has multiple different terms. The photometric loss, which is the loss that you obtain you have use synthesis by comparing pixels, as I just explained, plus a sum of other losses to try to add a bit more structure to your depth maps, for instance, by regularizing using edge-aware depth smoothing, or by also regularizing for occlusion. And so that work really inspired us. And we made a paper called SuperDepth, where one of the things that we noticed is that, obviously, the more pixels you have or the more the higher your resolution, the easier the problem is. And that's because most papers in research deal with limited resolution. So they haven't really necessarily seen that fact before. But as soon as we started to use our data and starting to use it in the cars, we realized that if you want to improve your performance, just increase your resolution mechanically. For cell data, it's very important because you want to know how far things are for far away things so that you have the time to react to them, so you need a high resolution. And if you train these networks at high resolution, which is not trivial either because it requires a lot of memory. It requires maybe some different optimization techniques. But if you are able to do that, that improves performance. And so one natural extension of that thought process was to say, well, in a typical depth network, you do an encoder-decoder where you're compressing, spatially, the information into the encoder. And then you're trying to re-interpolate missed details, basically, by decoding at progressively high resolution back towards the input resolution. And again, this is typically done for memory constraints. Also, to some extent, to prevent overfitting and things like that. But here, we basically, in the super-depth network, just looked at the neighboring field of super resolution and looked at how to recover lost details from images, or in this case, feature maps. And there's a set of new interesting operators that had come out at the time called sub-pixel convolutions, which we've used and extended, basically, the encoder-decoder architecture to try to learn to recover lost details by using these super-resolution techniques for the intermediate disparity maps. That turned out to work really well. So working at a high resolution and using sub-pixel convolutions to super-resolve intermediate disparity maps. And as you can see at the top, depth maps. One of the benefits is also because it's a stereo pair for which you know the baseline in meters. You can actually also reconstruct point clouds, metrically-scaled point clouds with the camera calibration information, the stereo rigging calibration information. This is what you see at the bottom. That's great, but stereo is not really ubiquitous. Also has some challenges with respect to calibration and maintaining the calibration over time, if you're in a pothole or something like that. And so one of the major source of information for us is actually monocular videos. And so the question then naturally came for, can we do the same thing with a generalized type of stereo setup where instead of having two time synchronized front-to-parallel cameras, we have one single camera, but we're using the frame T minus one and frame T. And the answer is yes. You can use a similar idea, but there's some differences. Namely, the fact that now you also need to predict how the camera has moved from frame T minus one to frame T. And you can use very expensive and precise sensors to do that, as in self-driving cars. But turns out you really don't need to do that. You can actually just feed the camera pairs, frame T minus one and frame T, into a convolutional network that tries to regress the rotation and translation of the camera, so also called the ego motion, and use that for view synthesis. And the equation then is this one, where you're trying to basically synthesize a target frame, say frame T, from a context frame or a set of context frames. And the way you do that is you, again, predict the depth from the image, predict the pose from the two pairs of images, and then projective geometry tells you if you have the camera calibration, the Intran 6K, and the ego motion T hat, and the depth D hat, you can basically exactly reconstruct the frame T from frame T minus one, using this equation. That is a reprojection equation of the pixels using the pinhole camera model. And so we've proposed basically this network called PACnet in our CPR paper from 2020 called 3D Packing for Self-Supervised Molecular Depth Estimation. And this network basically built upon the insight of super depth, which is, same thing, resolution matters. And to quote a very famous speaker from Andrew Zisserman, the devil is in the details, and especially for the photometric loss, where if you remember, we're warping the pixels from frame T to frame T minus one and comparing the pixels in this photometric loss. So the higher your resolution, the more detail you capture and the less ambiguous your photometric loss is going to be. For instance, two pixels on my vest right now would look black. So if you are at a low resolution, they will all look black the same.  And so here, as I mentioned, we took this idea of going further into the details. And instead of doing the traditional encoder decoder where you do pooling operations in the encoder, so max pooling, which is destructive, you lose information, we looked at learning this compression. So we are learning to compress spatial details, and then later learning to decompress them. So it's a lossy compression to compression of intermediate features that is done through these packing and unpacking operations that replace max pooling and interpolation that you find in traditional deep networks. Now, how do these packing and unpacking layers look like? It's a bit maybe too technical for this talk, but at a high level, you're basically taking your 2D tensor, h by w, and you're shuffling the pixels to pack it along the channel dimension so that you're turning it into, you see here, instead of a CHW tensor, you get a 4C times h by 2 w by 2 tensor. So you're packing along the channel dimension or along the depth. This is what's called a space-to-depth operation. And then what you have is you basically have a weird kind of tensor because it has like pixels and colors along the channel axis. And so one way we found to actually make sense of that feature space of that tensor shape is to use 3D convolutions to basically learn to pack this into compact 2D tensor representation of features. The resolution is still divided by two, but these features are basically learned with some spatial details that are preserved. And then unpacking is basically the reverse operation. One of the, just proof of like toy examples that you can see whether it makes sense is if you take an input image and you do a kind of two-layer like encoder-decoder where you use the traditional operation of max pooling and bilinear upsampling and you're trying to learn to reconstruct the details. As max pooling is destructive and bilinear upsampling is just simple interpolation, you see that the reconstructed image is very blurry. It's like a very poor encoding, lossy, lossy encoding. But if you use a single packing and unpacking operation, you can get almost lossless reconstruction. I won't go into the details of this monster table, but this is a very active field of research. We've had a lot of people working on this very interesting challenge. One of the interesting things is not just that Pac-Net actually was the first state-of-the-art method, but also it was the first method where self-supervised network outperformed a supervised state-of-the-art, meaning methods that take an image as input and trying to regress depth, from ground truth, depth measurements from LIDAR. And in particular, other strengths of Pac-Net was the fact that it scaled much better, which is, again, for us, I think that's something very important. We're very much thinking at very large scale applications. And so we found that it scaled in two ways. One is it scaled better. If you look at, so the x-axis is, the y-axis is absolute relative error, so lower is better. On the y-axis, it's number of parameters. And so what we found is that ResNet's type of architectures, they scaled better with parameters, but they kind of like paper off, like plateau, whereas our models, they really scale very well with the number of parameters. So depending on your computational budget. And the other thing is that with resolution, the higher your resolution, the better your performance, as I mentioned. ResNet has improved a little bit from the orange curve to the yellow curve, but Pac-Net has improved a lot from the blue curve to the green curve, because that's what it was made to do, to learn, to preserve these details. So, and again, the reason it doesn't over-fix there, in spite of the number of signatures, because of the strong inductive biases in addition, especially in the 3D convolutions. By the way, if you are scared about like this size of parameter spaces in the hundreds of millions, that's normal. But one of the cool things is that this can run in real time on GPUs, and this actually runs in the car. We did a lot of different ablations. I'll skip over that. And one interesting thing also is, I mentioned like over-fitting risks and with big networks like this, but it turns out that, and as often the case in deep learning, over-parameterization is a good thing. And we found out that our network also generalizes much better to other domains. So for instance, train on TT evaluated on UCS. Okay, enough of the blah, blah. Now some results. So here what you see is upper left image inputs, lower left depth map prediction color coded. And on the right, what you see is a point cloud that we reconstruct from that, because again, we have the intrinsic parameters. So we can basically reconstruct a point cloud. And so you see this is data from our fleet in Japan in an area of Tokyo, close to Tokyo called Odaiba. Here you see it works on the pedestrians, on the cones, on the cars, on the buses, on the road, quite far away to previous, you see it works really well on wet surfaces where LIDAR struggles quite a bit, including very wet surfaces such as this one. So there's here some just more qualitative results to show that it captures a lot of details. And in particular, if you see this one on the lower left, you can see through the fence, which I find very cool to be able to be that precise. All the code for that model that I talked about, PAKNET SFM is available on our Github. The dataset that I also talked about, which is called DDAD for dense depth for autonomous driving is also available publicly. And we're actually organizing a competition at a workshop that we're organizing at CVPR, which is called Frontiers of Monocular 3D Perception. That competition is ongoing, and we encourage you to participate. I'm still having to have one month to participate. So if you want to start from our code and try to use your own ideas build upon that, or if you have your own models that you wanna try, feel free to give it a go. Cool, one thing you might have noticed is that it's only front camera and obviously in driving, we need 360 3D perception, also for certain robotics applications. And so to minimize the costs, as I mentioned, you typically don't have like 50 cameras all around the car. So instead you have like a minimal sensor suite, like indeed that case, for instance, six cameras with minimal overlap. So how do you reconstruct a full 360 point cloud from these six cameras? That's a problem that we call full surround monodepth. And we've just recently put a paper on archive called full surround monodepth for multiple cameras. So FSM is a play on word on structure for motion, SFM. And so the problem and the result looks like something like that. So this is actually from the new scenes dataset where you see that for new scenes, it's even worse than for DDAD in terms of the overlap is extremely small. You see these blue points here, which represents the field of view overlap between cameras. So most of the visual field is actually single camera. So another reason to work on monodepth. And this is on the right, you see this is what we are able to obtain in terms of the 3D point cloud reconstruction from a single model. And the way we do this is by leveraging again, like a geometry and prior knowledge, but in this case, we have even more that we can leverage, which is this relationship across cameras in space, but also in time. And so that's really the key to make this system work is to leverage spatial temple transformation matrices between cameras. So we don't just leverage the relationship between cameras at the same time, but also across time. And these constraints, and obviously even if you don't overlap, two cameras don't overlap a lot in their field of view at a given time, because the platform moves, the overlap is increased, right? Different cameras can see the same thing over different time steps. And if you leverage those constraints together with a couple of other important design factors like masking, like photometric loss masking and things like that, we managed to get a really good reconstruction, 3D point cloud. And in particular, we're much stronger than like approaches that are trying to reason explicitly in terms of matching like things like cold map, for instance. So how does it look like in practice? So here you see the six images on the left, the six depth maps, the color code of the border of the images correspond to the color code of the cameras that you see on the right. And then you see the point cloud. So obviously it's not perfect. I wouldn't drive off of this, but it's actually fairly decent and a close range. What you see is some bleeding artifacts around the boundaries of objects. That means we should be going even higher resolution and try to be even more precise. But we get one scale consistent point cloud from all these cameras with a single model. And that's again, only self supervised. That's great. If you remember one important little thing that I mentioned earlier, which is this projection equation, which is assuming a pinhole camera model. Now in the standard pinhole camera model and projection is a simple matrix vector product, right? As you see here, but it's just an approximation when you have distortion such as these examples here, like percussion or barrel distortion. And it's not a very good model at all when you have wide angle cameras, like fisheye cameras or catabiotic cameras, when you have like panoramas, when you have like a dash cam behind the windshield because the windshield creates some distortions, even worse if you have rain. And also even crazier application would be when you have a camera underwater because the water acts like as a very complicated type of lens. So we can't use geometry, the projector geometry in the pinhole camera model in those instances. And if we, I mean, we can, but the results are gonna be really bad. And then remember when I said self supervised learning is you compare the pixels after reprojection and if the reprojection is wrong, it's not because geometry is wrong, it's because the depth is wrong. Well, in this case, it might be geometry that is wrong or your assumption about geometry. So to go beyond pinhole cameras, we proposed something called neural ray surfaces. So we are leveraging a generalized camera model, which is inspired by Grossberg and Nair, which is just saying that instead of having a global linear projection operator, we're just gonna assume per pixel viewing rays. And so this neural ray surface is something we're trying to predict from a single image where we use a deep network to predict these per pixel rays. And that's what we did in our paper mentioned here. So here you see pinhole model, right? That's a classical picture of the pinhole camera model, where again, your equation, simple linear equation, but in the case of this generic camera model where you have neural ray surfaces, you have a much more complicated model. So for the proposed rays, like each 3D point Pj, basically, we must find the corresponding pixel, PI that belongs to IC, the image, with ray surface vector qi. So q are, these q are the ray surfaces, right? And the set of qs is this neural ray surface. And we have to find for each pixel and each 3D point. So for each 3D point, the pixel whose ray surface is the closest to the direction between Pj, that 3D point, and the camera center, SC, right? So here you have, for instance, like three, four qs, and, or three qs, q1, q2, qi, and you're trying to find the q that's closest when your space would be q1. So the problem is that here, you see you're trying to find the best ray surface, the closest ray surface, and you have an argmax over there. And so we want to basically find a neural network that can predict those qs and optimize that your back propagation. So for gradient-based optimization. So we approximate this non-differentiable argmax with a softmax over patch-based residual surface to have some also regularization, local regularization, and that enables us to get the really fast end-to-end differentiable model. So the picture overall looks almost the same as before. You have an encoder, an image encoder, predicts depth, yeah, depth decoder. You have also smoothness loss and other losses, regularizers. You take two frames, you feed into the pose network, you get the ego motion estimation, again, a rotation translation that doesn't change. But what changes is you have this ray surface decoder that is trying to predict this per pixel ray, which you see here, color-coded arrows. And then you use these equations of the generic camera model to do view synthesis, to warp the pixels from one frame to the previous frame and then pay the photometric loss. So does it work? The answer is yes. And actually, surprisingly, the same model, the same approach works really well for crazy different configurations. Like here you see a catapulted camera from the Omnicam data sets. We are able to get a really good depth map and a good accurate point cloud over a short range because Omnicam is basically, like the catapulted cameras don't have high resolution for faraway objects. This is for fisheye, same thing. We get like very heavy distortion and we're able to predict the depth really well on the poles, on everything and reconstruct really good point clouds. And this also worked in practice for the dash cam setup that I mentioned before. Same thing, code is available online if you wanna try it out. And yeah, of course it also works in panel setups. It can approximate the panel camera really well too. Last, but not least, I really briefly mentioned ego motion estimation and this pose net part of the architecture. We've also done quite a bit of work on that, whether end to end with deep networks, but also more structured approaches. For instance, using 3D key points and a bit more geometric approach than just predicting rotation translation from pairs of images. And again, this is available online including the code. All right, so I hope I convinced you that there's significant progress being made in depth estimation, but there's still some challenges. And in particular, you have some issues with self-supervised learning, but hopefully in practice you very often have some supervision. And this is where research institutes at the company, so practicality beats purity some of you know the Python Zen. So we're trying to leverage whatever supervision we have, which is common in practice, which would be some supervision from cheap range sensors. So for instance, more and more cars are getting light ours, but not the light ours that you're thinking about for self-driving cars, just more like low number of beams, like very sparse, cheap range sensors, like with four beams that are there just mainly for frontal collision avoidance for safety applications. But they still return you some 3D points, maybe no easy. So we could use that. Also very often, as I mentioned, you don't just care about predicting depth, you want to know what are the objects, where they are, et cetera.  And also another big source of information and supervision is synthetic data from simulation. So what we found in all these cases is that we should leverage supervision as much as possible, but what also motivated us really to work on self-supervised learning is that we found that even when you have this little bit of supervision, what is key to unlock its use is self-supervised learning. Because when you have insufficient supervision, you need to compensate with this prior knowledge, this inductive priors that are encoded in the self-supervised learning approaches. I won't go into too much details about the semi-supervised learning because it's exactly what you think it is. In addition to your self-supervised loss, if you know some ground truth 3D points in the world, you can re-project them onto the camera plane and pay an additional loss for those pixels in terms of a supervised loss, just a pure regression loss, depth regression loss. We made a paper on that because what we found is that that loss is very different. The photometric loss is lost between colors and RGB in the 3D plane. And the 3D loss is typically lost in meters in 3D. And so you're comparing basically pixels to 3D points and mixing them into the same loss, which does not work really well. So for optimization purposes, you actually need to re-project the 3D loss into a 2D plane using again some basic geometry that actually turns out to be not done in practice by most people, but actually turned out to be really important. And so that improves performance a lot and in particular that can improve performance even when you have very few points, like a hundred points per scene. And that's interesting. And because you can assume you have a few points that are available to you during training time, but what about inference time? And actually, if you're assuming that the cars, some cars have some forebeam lidars, for instance, well, you should be able to use them, not just for training, but you should be able to use them for inference. And this is exactly what we've done in our most recent CEPR paper called sparse auxiliary networks for unified molecular depth prediction and completion. And the idea is the following is you in the bottom, what you see is, so at the top, sorry, what you see is the typical setup of depth prediction where you have an image, feed it to a network like backnet and you get a predicted depth. But below what you see is you see that if you have a very few points in the environment, like not enough to really drive, but you want to still leverage them to improve your depth estimates. Well, that problem is called depth completion going from a few points to the completed depth map. And so here, what we do is we use the same network, the depth prediction network, but we are basically imbuing the decoder features with some information about the depth, which we, about the sparse depth, which we encode via this sparse auxiliary networks. Now the sparse auxiliary networks, they're basically sparse convolution with a bunch of convolutional layers that then gets transformed and mixed with the decoder features of the depth decoder. And the beauty with that type of approach is that when you have some points, you can just dump them in your decoder via this way. But when you don't have these points, you can just still use straight up the encoder decoder and predict the depth map. So this doesn't just provide you with the flexibility to use different networks for different cars or different robots that might or might not have this sparse range sensor, but it also works at runtime where if, for some reason, drop packets or sensor failure, you don't get the depth readings at any given point in time, you can still predict a depth map. And this provides you with this redundancy and robustness that we need. I won't go into the details of the sparse residual block, which are these blocks over there, but essentially relies on sparse convolutions, which are really important for efficiency. How does it look? So again, here we can, with the same network, we can basically compare the outputs of prediction, meaning no sparse input points or end completion. And what you see is that obviously when the results are pretty good with prediction just like before, but for completion, we get a lot more details like, for instance, the bars on this truck. This also works, so as I mentioned, talk a lot about driving because that's one of the major applications that we're thinking about at Toyota, but robotics is also a very big application for us. And so we've also validated that this approach works in home indoor scenarios, which typically are a bit easier because it's a bit cluttered, more cluttered, less structured, but has less range. So it works also pretty well there. We have much more results in the paper where we show that we can improve the state of the art on depth prediction and get quite competitive with specialized completion approaches. And we also showed that you can use this depth for 3D detection and it works quite well, actually better than the traditional supervised depth. And I'll talk a bit more about this later. So how does it look? Same thing, bottom left input image, bottom right depth map, upper section, it's the predicted depth. And when you see some color points that are flashing in and out, that's when we're switching on and off the sparse input depth. So this is basically, now we're doing completion, now we're turning off completion, we're resorting, we're falling back to just prediction. And so that enables you to be very robust to this kind of like maybe sense for failures. So I mentioned like to go beyond self-supervised learning, you can use some supervision, right? Like semi-supervised learning or even like completion, what we call dialable perception, you can dial between the image input or the image plus depth input. Semantics is a big one. And if you remember your basics of computer vision or even human vision, it should be that in theory, geometrically it's impossible to invert the image, the depth, right? You get the 3D world projected into the 2D plane and there's actually many un-projections that are valid, for instance, up to a scale parameter. And so it's an ill-posed, what we called an ill-posed inverse problem to do depth prediction from a single image. So why does it even work? Why is it even possible for us to do it? And that's simply because, and why can you do it when you look at a photograph or when you look with just one eye? And it's because there's obviously patterns that relate appearance to category level geometry. We all have faces that are different, but are roughly the same dimensions. And so they re-project into the 2D plane in a way that is equivalent with how far we are from the camera. And so obviously, because it works based on appearance and relating appearance to geometry, one natural question is to wonder whether Monodeph can benefit from pre-trained semantic features. And the answer is yes. And we showed that in a paper called Semantically Guided Representation Learning for Self-Survived Monocular Depth. I won't go into the details, but same idea. You have a pre-trained semantic network, you have a depth network, and same idea as a pac-net sun. You can actually guide the representation learning of the decoder to be more semantic aware. In this case, we use pixel adaptive convolutions. And so the results are actually nicer and we improved upon the state of the art again. What you can see here in particular is that there are certain structures that are really hardly like this pole here is really dark and it's hard to tell what depth and whether it's a pole or not. But because we're having some more semantic aware features, we can actually recover the depth much better. Same for other type of fin structures that have a weird geometric structure, but that is very consistent across categories like signs, people, trucks, things like that. And I mentioned we get a pre-trained semantic network to help us guide the learning of depth representation, but where do we get this pre-trained network? And you typically get it from manual labels, but that's very expensive. So one question that we've been exploring is can we use simulation as a source of supervision? And so the answer again is yes to those questions. Can we get synthetic data that is useful? Can synthetic semantic segmentation benefit depth and can depth help seem to real of some seg? So it's the other way. It's the two ways, right? Is that can semantic help depth and can depth help semantic? Can they work together in a multitask framework? And the answer is yes. In this very recent paper we put on archive called Guda, which is about geometric unsupervised domain adaptation for semantic segmentation. And we've shown across multiple data sets how this can work. In a nutshell, you have real data, which is just a video as usual. And same thing as before goes to a pose encoder to get the ego motion, goes to an image encoder and a depth decoder to get a depth map. Now this time in addition goes to a semantic decoder to predict the semantic segmentation information. And on the simulation side, you have the video, but you also have ground truth, semantic segmentation that comes for free because it's in simulation. You know everything that's happening in the world because you created depth ground truth and ego motion ground truth. And then what you can do is you can basically feed that through the same network architecture and pay the supervised loss in addition to the self-supervised losses. Whereas in the real world we only pay the self-supervised losses, which is the view synthesis loss. And in addition, there's a couple of different regularizers, regularization terms that you can compute in simulation and that helps stabilize the training. Again, it works really well. So what you're seeing here are examples of predictions of our network. Again, that predicts independently semantic segmentation and I'm sorry, jointly semantic segmentation and depth. And we're just superimposing the semantic labels onto the depth point cloud that we're getting. And all of these predictions are obtained without ever having seen any real world semantic label. So it transfers really well in this form called seem to real transfer without any supervision in the real world. I'll skip over this. We have plenty of results in the paper. Interesting stuff we're showing is also that how does it scale? If you feed more stimulated data, if you feed higher quality simulation does it get better and better? And the answer is again, yes. Lots of results in the paper. That's all right, I encourage you to check it out. Because I'm running a bit out of time, I'll go quickly over this, but same thing. There's all these works are available online and feel free to just ping me if you have any questions. So as I mentioned before, we can predict depth. We can predict better depth thanks to like going a bit beyond self-surprise learning. One natural question is to say, what is it good for? I briefly mentioned 3D detection. So here is another example of work where we've done 3D detection, molecular 3D detection, a paper called Royton D. And where really you get as input in RGB image, you predict the depth. And then from the depth plus the image features, you can get 3D bounding boxes. And this is actually like an old paper, like in deep learning days. So if you are 2019, since then results have improved quite a lot, but actually the approach remains very similar. You predict depth as an intermediate step. So one of the cool things about that work is that we didn't just predict 3D boxes, we also reconstructed 3D shapes. And the way we did that is by learning some priors over CAD models of the 3D shapes. And then we just regress, in addition to the 3D bounding box, we regress the latent shape encodings to get shape reconstructions as you see here on the bottom right. And one of the cool things you can do then is you can start to do data augmentation where you do augmented reality style where you're adding, you're pasting into the scene fake instances of cars that you reconstructed from other sequences and enables you to like improve your training data with some 3D data augmentation technique. And we took that actually further and we looked at a fully auto labeling scene. So because the previous approach still needs 3D bounding box labels, the ground truth, it's supervised. So we've looked at auto labeling, the differential rendering. So the same idea as before, which would be you are trying to predict parts of the scene, but instead of comparing like parts of the scene like object shapes, et cetera, to a ground truth, you're instead using a self-supervised approach based on differentiable rendering where your image, if you look at the bottom, the image is fed through a neural network that predicts intermediate scene parameters, objects, their shapes, et cetera. And then you are re-rendering the scene by saying, okay, if I'm right, I'm able to regenerate the pixel values of my input image, right? Because I can re-render the objects in the way that I think they're placed. And then if I'm right, my pixels of my re-rendered image should be close to my input image. And so this is very similar to the idea of view sensors as before. Instead of copying pixels from one image to the other, we're actively deconstructing the image in this inverse rendering process and reconstructing it with the rendering process. Now this de-rendering process because it goes through a neural network is obviously differentiable. So the hard part is the renderer that needs to be differentiable itself. So you can back propagate through, again, this photometric loss. And luckily there's a lot of research in differentiable rendering. And so we've been able to leverage recent differentiable renders and actually came up with our own 3D differentiable renderer. And this is what you're seeing here in this little animation, which is the whole inverse rendering, re-rendering and optimization throughout this whole process for the three car shapes. And now the cool thing about that is that now you can basically this de-rendering of the scene is actually labels you can use for like downstream training of 3D detection networks. And what we found is that these labels are almost just as good as manual labels you would get from LiDAR. We did the follow-up work at ECCV. So that previous works called SDF label was neural at CPR and we did follow-up work at ECCV where we didn't use any LiDAR. We really just did it this time from images. So to conclude, I've talked about self-supervised 3D vision and our research in that area. I've explained why we are doing it. It's to get robust 3D perception from a vision. How do we do it? We use geometry for scalable 3D self-supervision. I talked about SuperDef, which was in the stereo case, PacNet, which was in the monocular structure for motion case. I showed that it works for one camera, multiple cameras, like the full surround monodep for even weird cameras, like the neural, where for which you need to use the neural ray surfaces. In the second part, I talked about beyond self-supervised learning using semi-supervision from maybe partial point clouds, but you can also use them not just at training, but also at inference time. Semantic guidance, where you can use pre-trained networks for semantic segmentation, but also you can use simulation and this is the good artwork that we talked about. And finally, I very briefly brushed on how we use molecular depth, mainly for 3D detection, but also for autolabeling thanks to differential rendering. That's a lot of work. These are basically, like this is the bibliography. I'll share the slides so people can have all the references. And just as a reminder, code is available, data sets are available. We're organizing this workshop and competition at CEPR, which I encourage you to participate in. You can find more information on Terai Global website or on my website. Obviously, that's a lot of work. Takes a village to do that. So thanks to my many collaborators and thank you for your attention. And yeah, if you have any questions, feel free to ask. Cool, fantastic. Thanks a lot, Adrian. That was really interesting. I actually do have a couple of questions, but maybe let me ask first if somebody else wants to go. Can I ask if... Of course. In this very beautiful sketch you've given here of the depth assessment, does it make sense to use 3D information? So as opposed to working from an image, could you gain a lot of extra learning capability from saying this is a moving scene with other objects that are maybe stationary or have some limited speed? Is there other opportunities in that direction? Yeah, absolutely. That's a great question. So it's a fairly deep question. So we're doing some of that, but not enough of that. So in a sense of the semi-supervised learning, when you have this multi-sensor setup, we are reasoning in 3D, but we're just for optimization purposes reprojecting it into the 2D image plane. So for the neural ray surface also, we also reason in 3D. But I think the case where we used 3D information the most in this 3D composition of the scene is really this auto-labeling work. Because in the auto-labeling work, the scene parameters are 3D scene parameters, right? It's the full 3D scene that we're trying to reconstruct. So here we're focusing in this work or focusing just on objects, but one interesting research direction that we're actively working on is reconstructing everything in the scene in 3D and to be able to do the reasoning exactly in the way you describe Peter. Thanks. Yeah, maybe to continue along that direction. I mean, in principle, if I understand this correctly, using this differentiable renderer, you can turn this essentially into a totally generic unfolding problem, right? It doesn't need to be, you can replace the renderer, which of course does some ray tracing through the atmosphere and stuff, or something that hasn't even have optics, right? In principle, you can have some arbitrary unfolding of some arbitrary physical system, as long as you know, somehow how to go from the scene parameters to the output which you have accessible, right? With your sensors. Exactly, exactly. And this is actually what we've done because if you look at these optimization, so the second work that I mentioned is using only images for self-supervised differentiable rendering, but here we're using LIDAR point cloud. So going back to Peter's question also, here we're reasoning in 3D because we have a 2D loss, but we also have a 3D loss, which you see is being optimized at the bottom of the animation. And so we actually made a differentiable renderer, which is a 3D differentiable renderer of sine distance fields of SDFs. And so here we leverage the properties of that SDF space to basically optimize the auto labels and the shapes. So it's not a renderer, it's not a renderer per like in a game engine sense. It's actually a renderer of the output we're trying to optimize over. Yeah, I see, I see, nice. And maybe you hinted a bit at the generalization, but I also think, correct me if I'm wrong, but I think with autonomous driving, would you really need to get correct is somehow the generalization to be very extreme tales of the events that can occur on a road, right? That usually accidents happen because of a reason, right? That's just a long chain of unfortunate coincidences or whatever. How stable is all of this? Or some of these techniques to these very strange unexpected situations or unexpected inputs. Yes, so that's just a very good question. And that's like what animates us. And that's why when we're looking at examples like the one I had shown before, we're looking at the very little details to see if it works, right? So, yes, so how does it generalize to the long tail? So for us in terms of computer vision first, like the long tail can be like very fine-grained details. So if we look at things like this, for instance, right? Like construction cones, right? You don't see them very often, you don't see a lot of them, right? Or things like thin structures, like here you don't care too much about that, that's okay. But there's other instances like child, like a child has a weird size distribution compared to most of your pedestrians. So I think that the challenge is that machine learning is pattern recognition. And the long tail means you might not sample, have enough samples to generalize to those rare events. And there's really no way around that besides two things. One is, well, you need to replace the lack of evidence, right, of experience for that long tail by prior knowledge, right? You need enough information to be able to make predictions. So if you don't observe it, then you need to inject it, and so using stronger priors, using more structure, et cetera. The issue with that is that you need to be right. As a human, when you decide to implement those priors, because if those priors turned out to be wrong or bias, as we know in machine learning, this is why there were racist computer vision algorithms, right? Not because people were necessarily racist, but because people didn't think about darker skin colors, for instance, right? Because they were themselves white. And so that's very dangerous to not be careful when you design these priors. And the other solution being, for instance, data. So, okay, I have the long tail, I have these rare events. I have only 10 examples of that. I need 100 more, right? So you need to go and find those examples. And there's ways to do that, but it has the same issues as what I mentioned before about the human bias, which is you need to be aware of what long tail events you want to boost in your signal. And so that's also why I like self-supervised learning, because self-supervised learning is taking kind of an extreme stance on this, which is, if you know the Hard Rock Cafe moto, love all, serve all, you know? Well, this is what self-supervised learning does. It tries to learn from all the data. And hopefully you have enough of everything if you collected enough data. And because you can train from all the data, maybe that's gonna work. Now the problem is that's not also like a perfect picture, because all the biases in your data, like the dominant modes, for instance, will be dominating your learning process too. So you have to also account for that, but it's got more complex and self-supervised learning because self-supervised means less human oversight. So this is really hard. I mean, these things problems, I don't have a solution, right? This is why it's a research challenge. So the long tail is like, as you said yourself, it's a long tail problem in itself because if you start to kind of unroll the issues, it's like it's never ending. So I think we need some fundamental scientific progress to tackle that issue. It's not easy. Yeah, that's a very nice answer. Thanks a lot. It's a long answer because it's a very, it's a core problem for us. Yeah, right, as you say, right? Exactly, yeah. And also one that affects many other people, I suppose. I mean, this is not specific to that specific problem, right? I mean- Yeah, right, right. And when the stakes are high, of course it becomes important to get right or to try to, you know- Yep, absolutely, absolutely. Here's one more question, which I apologize because it's very vague. Having thought a lot about this, with your colleagues and so on for a long time, is there anything to be said about why evolution didn't come up with this? So why do we have two eyes? Why do most species on this planet have two eyes instead of a single one? I mean, we have two eyes, but actually there is, we have very strong monocular depth cues. Right? I think I was rereading recently, the Wikipedia article on depth perception is really good. And it covers a lot about this, including the monocular depth cues. And it's quite fun article to read because you can like close one eye and do these experiments and check for yourself, like experiments on yourself, you know, which every scientist should do, you know, responsibly. But like here it's easy. You know, don't have to poke your eye out. Just hide your eye and just try a couple of different things. And you'll see that we, even though stereo and products and all these kinds of things are what like binocular vision is what enables precise depth perception, you can do pretty good depth perception with just one eye. Right. Evolution did come up. To do so, right? Yeah, without training, evolution did come up with it.  Isn't that just because your visual cortex already has learned how to deal with the information from two eyes and then interpolate back if one of them goes missing? Like if the only thing you ever had from your birth was just a single eye. Yeah, I think that would, I don't know if there was ever an experiment or someone that actually was in this situation. So I don't know. But from first principles, it would seem like it would still be okay because you again have motion. Right, exactly. So you can get relax from motion and et cetera. Right. Okay, nice. Thanks so much, Peter, if anything else? Because you're on mute. I think we're good. Yeah. Okay, wonderful. Thank you very much. Thank you. It was a wonderful talk. Yeah. Thank you for having me. Bye-bye. Bye-bye.


This paper discusses the development of a novel imaging based in vitro assay to measure the potency of lipid nanoparticle, LPN, encapsulated messenger RNA, mRNA, vaccine candidates. The assay was designed to provide a wide dose response curve and dynamic range, while also being sensitive to the various properties of the LPNs, such as polyethylene glycolipid, PG lipid, composition, mRNA mass, and LPN size. Several biologically relevant cell lines were screened and optimized to ensure the assay's suitability for measuring mRNA vaccine potency. This article was authored by Nisurg Patel, Zack Davis, Carl Hoffman, and others.


The next item of business is consideration of business motion 13536, in the name of Jofits Patrick, on behalf of the parliamentary bureau, setting it a revision to the business programme for this week. Any member who wishes to speak against the motion should press the request-to-speak button now. I call on Jofits Patrick to move motion number 13536. Firmly moved. No member has asked to speak against the motion, therefore I now put the question to the chamber. The question is that motion number 13536, in the name of Jofits Patrick, be agreed to. Are we all agreed? The motion is therefore agreed to. The next item of business is the topic of question is question 1, Liam McArthur. To ask the Scottish Government what its response is to figures suggesting that there are no head or deputy head teachers from black or minority ethnic backgrounds. The Government is committed to equality. We want to see a diverse education workforce that reflects Scottish society. Statistics indicate that around 2 per cent of the teaching workforce and around 1 per cent of individuals in promoted posts are from a black or minority ethnic background. As employers, it is the responsibility of local authorities to ensure that their recruitment practices are fair and inclusive and that the Scottish Government is committed to work with local authorities on that matter. Liam McArthur. I thank the minister for that response. The Scottish Government's comment last week was that it was ensuring that the master's qualification for headship due in 2018-19 is fully impact assessed. I think that the minister would agree that that is not good enough. The Government is, rightly, I think, proud of its record on the number of women holding cabinet position and on its work generally in relation to gender equality. Does the minister not agree that it is time to widen the Government's equality work to give greater opportunity to those from BAME backgrounds? If so, what specific further steps does the Scottish Government propose to take to remove barriers for those from BAME backgrounds to reaching the top of the teaching profession? The member is right to point to the fact that we need to promote equalities and diversity in all aspects of the teaching profession. The new master's qualification that he refers to the master's qualification for headship, which will be mandatory from 2018-19, will be fully equality impact assessed by the Scottish College for Educational Leadership so that it is fair and accessible for all. We have, as the member has alluded to, put some effort into ensuring that there is more of a balance between the genders, particularly in primary school, in the future and encouraging men to come forward for the teaching profession. We equally need to—the member is right to point to it—to ensure that our teaching workforce is reflective of and representative of Scotland's population as a whole. There are one or two points that I could point to in the statistics that are referred to in the question, but I think that it is suffice to say that we, all of us, want to improve the situation and make it clear that people from all ethnic minority backgrounds are welcome in the teaching profession and are equally likely to be promoted. Liam Kerr, thank you for that further response. Teaching unions have also raised concerns about the overall number of teachers from black and minority ethnic backgrounds in the profession. In order to establish a way of progressing that and making the progress that Liam Kerr has alluded to, has the Scottish Government undertaken any analytical work on the reasons for the lack of diversity in our school's workforce, and particularly among those in senior management positions within schools that might inform future decisions about how to remove those obstacles? I am very willing to work with education Scotland and others to establish some of the reasons. There are anecdotally many possible reasons for that. It may be that, for instance, other professions and other parts of our public life are, for whatever reason, more diverse, more representative than a teaching profession. It may be to do with the progression of teachers in their careers. In terms of the figures, it is worth saying that the data is slightly complex and is capable of being interpreted in a couple of different ways. I say that not to get away from the member's central point, but, for instance, some people who would define themselves as minority ethnic would fall within the white other category. Across all sectors that are publicly funded, the number of individuals in promoted posts—principle teacher, deputy head, headteacher—who fall within non-white ethnic groups is 102. I say none of that to take away from the central point, which is that we should, of course, be encouraging much more diversity within our schools. I think that one of the first steps in increasing the number of black and minority ethnic members from that community who hold senior management posts in schools would be to increase the pool of available candidates in the general teaching workforce. I ask the minister whether he has had any discussions with the Scottish funding council or teacher training institutions as to the intake and percentages of students from black and minority ethnic backgrounds. We will not withstand the point that the member makes. Obviously, the Government is not in a position to dictate to general teaching councils such as an independent body or others around some of those questions. We do not operate the system of quotas, but we need to get to the bottom of why the teaching profession may not be as attractive to people from black and ethnic minority backgrounds as perhaps other professions are. We need to work with the general teaching council for Scotland and, indeed, with the universities that provide the initial teacher education to examine those issues. That is something that I intend to do. To ask the Scottish Government how it is responding to the concerns of the CalMac workforce who has been recently been balloted for strike action. Derek Mackay CalMac's ferry services play a crucial role in the daily lives of our island communities and the Scottish Government has made clear its commitment to the continued delivery of a safe and reliable ferry network. We want to protect CalMac employees by ensuring a fair, affordable and sustainable pension scheme is written into the next ferry service operating contract. I encourage CalMac and the unions to continue with the current process of engagement on proposed changes to the pension scheme and to work together towards an outcome that avoids the need for industrial action. I have had two constructive meetings with the CalMac unions and the STUC in recent weeks and have offered to continue that dialogue. The minister will be well aware that more than 90 per cent of our team members in CalMac voted for industrial action, as they have genuine and heartfelt concerns over job security conditions and particularly pensions. The Scottish Government owns CalMac. What specific actions is the minister going to carry out to resolve those very unsatisfactory and worldly poor climate of industrial relations? I can advise the member that we will ensure that the processes that we are responsible for are carried out competently. I would say again that we want to give the assurance to employees that we continue to support the services by investing in them and that we will conduct a procurement exercise that is in keeping with the same legislation that the previous Labour administration would have to have complied with to come at an outcome in terms of the procurement of services and our guarantees around the pension as well. To put the issue into context, we are aware that there is an identified deficit that is up to £59 million in terms of the revaluation of the pension, and the trade unions are not resisting reform. I am very mindful about what their issues are, and that is why dialogue, continued meetings in a constructive and positive fashion, is necessary and is right. I will do everything that I can to support those discussions, but our support for the public service is absolutely resolute. Does the minister share the views of the RMT general secretary who said, and I quote, that the members in CalMac feel that they are caught in the crossfire of unnecessary and damaging tendering battle that leaves jobs, conditions and pensions hanging by a thread? Surely the Scottish Government has learned the lessons of the fiasco of the Northern Isles contract award when circle axed a crossing and sacked staff? I certainly do understand why employees would feel nervous when their jobs are subject to the same procurement exercise that the Labour Administration would have been undertaking as well, one that we cannot escape in terms of legislation. That is why we set very robust specification around the services that we want. We are investing in the ferry network, with new vessel provision as well, and we will support employees through this process. Can I be clear in terms of what is being procured? Regardless of the ownership status of the successful bidder, those ferry services are not being privatised. What is being tendered is a public service contract to operate lifeline services on behalf of Scottish ministers. The operator will have to comply with the service specification that is defined by Scottish ministers and, as now, will be subject to stringent contract management conditions. All of the vessels and ports currently in public ownership will remain in public ownership and, together with Clyde and Hebride, services remain under public control by Scottish ministers throughout the contract. That is not privatisation as some elements of the Labour Party are suggesting and much to the regret of the Conservatives that I can hear to my right both politically and literary. However, we will protect our public services and conduct this procurement exercise in keeping with European and all necessary legislation and arrive at the right decision in which we protect the lifeline services and also support employees through what I do respect as a difficult process. Thank you very much. Can I ask the minister if he will give a guarantee that, in reacting to the action of the trade unions in relation to CalMac, he will also put his highest priority through this tendering process—value for money for the taxpayer and a quality service for the fair-paying passenger? Of course, those issues are about balance. It is a matter of fact that there was a deficit in the pension fund previously and the Scottish Government has been supporting that. We will look at the assessments and the revaluation once again and, through the employer's dialogue with the trade unions, continue that discussion. However, we will do it in a culture of positivity, a constructive approach in which we encourage CalMac and the unions to talk and to work together so that we can all avoid any industrial action. I am sure that that climate will lead to a positive outcome, as I say, in keeping with all the necessary considerations. Minister, let me get this right. A service currently run by the public sector may be run by the private sector, and that is not privatisation. The reality is that the northern ferries have gone to Cerco, the sleeper has gone to Cerco, and ScotRail has gone to the Dutch company Abelio. Is it any wonder that the RMT and its members have no confidence in the minister's handling of what is going on at the moment? I have to say to Neil Findlay that I recall what privatisation looks like as delivered by the Tory Government, and it is ripping apart of public services and leaving it to the private sector. That is not what is being proposed. We are talking about public services, we are talking about the provision of services as specified by ministers, and they will remain in the ownership and direction of Scottish ministers. Neil Findlay, as I should say not unexpected, uses the intemporate language. I think that he would do well not to try and stoke up grievance and try and create a toxic situation for the employees. We should be looking after the employees of CalMac and working in partnership with the trade unions to arrive at a positive outcome so that we can continue to provide those public services in the interests of the communities that they serve, and we will continue to do so in keeping with the law. Does Neil Findlay suggest that we should break the law? Does he think that that would leave us in a satisfactory position? No, it would lead to a challenge, and the people who would suffer would be the communities and the staff who would suffer if we did not deliver this procurement exercise in keeping with the necessary procurement legislation. I ask the next item of business, which is a statement by Paul Wheelhouse on the Scottish Government's report on the operation of the offensive behaviour at football and threatening communication of Scotland.


A couple sits on a park bench at night. A caption, romantic music, appears in front of them. The puzzled man reaches out to touch the caption. A man begins signing in ASL. Nothing mysterious there. These are captions. Open captions are the on-screen display of a movie's dialogue, speaker's names, and sounds. Theaters in New York City are now required to show some of their movies with open captions. To learn more, visit nyc.gov forward slash captions.


Welcome once again. President Mahmudubwari has stated that he would like or rather he would consider granting a state pardon to Ken Saruwa and eight other Oguni leaders executed by the Sania Bacha led military administration on the 10th of November in 1995. After the being accused of murdering four Oguni chiefs at a pro-government meeting, Saruwa and the others were sentenced to death by hanging by a special military tribunal. However, several Nigerians believed Saruwa and the others were framed for the murder because of their very impactful but non-violent campaign against oil extraction and the continuous degradation of the Oguni land by the government-backed multinational oil companies, especially Royal Dutch Shell. Joining us to discuss this is Celestine Nakbubwari, the national coordinator Oguni Solidarity Forum. Nigeria, good evening. Thanks for joining us, Mr. Nakbubwari. Thank you for having me, good evening Nigerians. Great to see you. So I want you to first of all share your thoughts on the discussion about a state pardon. I've seen a response by the Ken Saruwa Foundation that I'll share right after your response now. But let's hear from you first. What are your thoughts? Do you think this is perfect timing, better late than never, or very unimportant? Well, incidentally, I was at the presidential bill on Friday. I was among the Oguni leadership that met with the president. I was there when he made the statement. But the Oguni people and the civil society groups in Nigeria are not pleased with the word pardon because you grant pardon to one who committed an offence. We all know that Ken Saruwa did not commit that offence and the Oguni people and civil society have wished that Mr. President apologised to the Oguni people on behalf of the Nigerian government and exonerate Ken Saruwa and those that died with him because they did not commit that crime. That crime, we are not happy with the word pardon. And if you go through Oguni from that Friday to today, everybody has been almost mad on the street, you know, rejecting the word pardon. We know out there Ken is so hard. In fact, you would have seen a statement from the International Congress, I&C, they released a statement before the Ken Saruwa Foundation. So when the Nigerian government returned to Siboru in 1999, there were two issues plaguing Nigeria. The June 12th and the Oguni issue. June 12th have been vertically settled. Government apologised to the people of the family and even given so many awards and honours. We want the Oguni because we did not commit any offence. Nicely put, you spoke about the response from the Ken Saruwa Foundation. I hope we can put that on screen to show exactly what they are saying. They say exoneration not pardon for Ken Saruwa. Yesterday you have it. It says the Board of Directors for the Ken Saruwa Foundation has received and read the statement from Mr. Adishinal. It says, I'm just going to skip a few paragraphs to say Ken Saruwa and the other Oguni were not criminals. They were innocent activists and justly murdered for fighting for just cause on behalf of the oppressed community. The path to troop peace in the region begins with justice. The cleaning up of the environment which the campaign and died for is first a good step. I'll stop there. Of course there you have it on your screen. Let's go further now. Before we might come back to the exoneration or pardon discussion. But what would you say, and if you were to speak on behalf of the Oguni people, which would you say is more important? An exoneration, a pardon or that the government actually does the things that led to the agitation of the Oguni people in the first place? Cleaning up of Oguni land, cleaning up their farms and giving them of course better benefits for the natural resource that has been sourced from their land for decades. Which would you say is more important? Well in this case, Oguni people were successful fishermen and farmers. We never asked the government and the other companies to come and pollute our land. They owe us a lot. They destroy our means of livelihood and destroy everything. We shouldn't beg them to come and clear up their mess. They are the people who owe enough. And so it's not a year, not a year. They did everything. They are the goat. They are the knife. They polluted the land. They destroyed our livelihood. They devastated the environment. They key our leadership. So all the blames are on the head of the government. So what about the way they want to go about filtering and cleaning up their own mess in their own business? We are the victim here. So why I'm asking this is because like you mentioned the June 12th and of course the conferment of the GCFR to MK Wabiola in 2018. That was somehow and some people interpreted that as somehow some way to appease the Southwest. Same way they've also said that making from President Olisha Goh Basonjo President in 1999 was to appease the Southwest as it is described in some quarters. But you said the government has the yam and the knife. But would you say that the cause that you cancer we want the other eight Oguni leaders were fighting for? So that should be what the government should be focusing on instead of simply talking about a pardon.  Well as part of the process of healing our homes. The issue of a generation is very important. Even if it was not among the demands but I think it's a far way of ways you know the other demands in the Oguni Bureau of Rights as far as I'm concerned.  Not a life such as that of cancer we were. Over 2000 persons were murdered in Koblon in Oguni. You're only talking about the Oguni night that we're hung. Over 2000 people were innocently murdered by poor Kuntuma and their group. And they are alive. Kuntuma is the late but Komu. The other Musa Komu that was the then governor, the minister is there. How many of them that collaborated to kill Oguni people? They are there. So I think they should do all the issue of political marginalization. Up to now, no Oguni have been governor. No Oguni have been deputy governor. No Oguni have been speaker. No Oguni have been chief judge. It's part of the fact that Oguni is a majority in reverse state. Even if we are minority in Nigeria. These are some of the issues that Ken was talking about including cleaning up of the environment and cultural issues that you don't send Oguni people to the university to go and study outside Yoruba or Igbo. When we have our distinct cultural language. So these are some of the things that we will be talking about. Well, would you also say that these conversations and the thoughts of a pardon or exoneration is really only just opening up wounds that we are trying to heal over time? And do you expect that Oguni people would once again bring back these conversations and make these demands louder? Of course it's a continual struggle. We are on a journey. We are on a journey and that's why we are asking that first and foremost you exonerate this man. He's innocent man. In fact, a memorial at work that was brought in from the UK in 2015, the name of Ken was seized by the custom. Today it sees with the custom because the current aid of custom, the current custom boss was the only military officer at Amid Ali. But the only military officer on that people, not a sentence, Ken sir, were to death. So he didn't want to see anything about Ken. The bitterness of 1999 against Oguni is still very high. And the only way you can begin to address these issues is to gradually move one after the other. And November 10 is not far from today. We are expecting that the government will exonerate Ken and Oguni and apologize to the Oguni people and we take it up from there. And what happens if none of that takes place? If none of that takes place, you would have heard that Mr. President said that he has given a license to MPDC or NMPC. I can assure you that one bed will not fly into Oguni in the name of oil if those things are not taken care of. Can you repeat that? What do you mean by that? I read the statement from the president. Because if you like me, you will like my dog.  You said that you have given a license for people to go and explore oil in Oguni. But you don't want to talk about the issues that paint us.  Not bad, we fly into Oguni land until they begin to address these issues that concern us. We are Nigerians too. Do you also have the same feelings towards state governments in the Niger Delta? And the reason I'm asking that is, yes, I understand the Nigerian government's failures here and there. But I think you might also notice that there's still some level of failure on the state level, on the local government level. I agree completely with you that at the state, I've cheated a majority of Oguni people. There's no doubt about that, but it wasn't in reverse state that he can serve. It was the federal government, which we represent today. Government is a continuum. You take the liability and also take the assets. So why do you apologize to the Ebola family for a crime you did not commit? You should also apologize to the Oguni people for also a crime you did not commit. Do you think that the Niger Delta representatives, Pandev, do you think that the Oguni representatives also are doing enough lobbying to ensure that these things happen? Because someone might argue that the Southwest has its lobbyists in check and they've done well enough to ensure that the Ebola family got what it eventually got for June 12. So do you think that the Ogunis have proper representatives to speak on their behalf? Well, this afternoon I addressed Pandev. Pandev had a general assembly today and I addressed Pandev and I presented this matter. And I hope it will be part of the final statement that will come out. I also want to inform you that Ken Sarawuba's son, the late Ken, Jr., accepted an appointment to work with Abbas and Jonathan as senior special assistant just to achieve this same aim. So what is more lobbying than accepting an appointment as the son of Ken? Ensuring that the government that he worked for, exonerated his father, he was so disappointed and it may be part of the thing that led to his early death. So I think the earlier government begins to address this issue, the better for everybody. Can you shed more light on what the family of Ken Sarawuba currently is like? Where are they? Are they still in the Niger Delta? Do these things still of course hurt them when we have these discussions? Ken was not a local champion. Ken was an international personality and so also as a family. One of the younger brother was the director of military intelligence during the Jonathan administration. We have Dr. Omo and a scientist based in Canada sometime here. So he came familiar with the children, all of them, even when he was alive. So he had not been a local champion. He only came home to help his people. And if coming home to help your people have become a client, then I don't know who else has become a client. The family are in pains. We are also all in pains. Because this was a jolly good fellow that committed no offense. He had to ask that these young people get a better deal and get a chunk of what they produced to service Nigeria. Look at what oil money has done in a budget of a short time. A budget that does not have one oil wealth. A budget that does not suffer one negative consequence of our production. 99% of the oil wealth in Niger Delta belongs to people who have never visited Niger Delta before.   That teacher of people will beat a lesson on how not to stop us. That means that kind of a thing. On November 10, 1995, the Nigerian government chose between oil and blood.  And that's what they did. And that's why they have to apologize and exonerate those who were innocently murdered because of oil. Like you said, according to you, if this isn't done, no bird would fly. Across Oguni land, from the 10th of November. I also got to, you know, of course, reading back at some of the things concerning this discussion today. I watched some of the testimony from the Oputa panel and got to, of course, learn more about the 10th, the 9th actually of November when the execution notice was served. And the 10th morning of November 1995. Really, really sad story. So that's it. I enjoy speaking with you, looking forward to talking with you again, maybe on the 10th or after the 10th of November. Have a great evening and thanks for joining us. Thank you very much. Nice talking to you. Thank you for staying with us. We'll take a short break. And when we come back, of course, we'll get to listen to Nigeria's reaction to the article written by the economist. And when we return then, I'll be giving my take. This is Nigeria. Anything can happen. A country where most of our ministry officers, police and all of them are, you know, not doing much as expected from them. So anything can happen. It's a painful one. It's a shameful thing, really, that this has happened around us. Ministry officers in Lagos, most of them are doing bus drivers, it's a shame. But that's what we find ourselves, it's possible. Most of the attacks we've been observing, in particular most of the ones that happen in the northern state, we notice there is a kind of insider, a kind of betrayer insider in some of the attacks. Like the one that happens in the military school. So some of them, by the way it's happened, you know that definitely there must be some element of betrayal. Some of the militaries are also a kind of those that are helping to support these terrorists, or maybe give them information either which way. It may not really be supplying the weapon. So people are talking based on what is going on in the country. But for me to believe that Nigeria are selling weapons, Nigeria are selling weapons, I don't believe that, I don't believe that. I feel that matter is a delicate matter. When Nigeria is up to date, and with the types of leaders that we have, and with what is going on, even the security challenges in the country, there is nothing that is not possible. Politics, and just before we go, my take. It came as a huge shock. It felt like a sting at the soul of every Nigerian, the news of the execution of the Oguni-9. From testimonies, of course from the Oputa panel and beyond, the execution order was sent at 10 p.m. on the 9th of November 1995. Between 12 p.m. and 1.30 p.m. on the 10th of November, the Oguni-9 were killed. The world was shocked, but it close to nothing. And all of this was for championing the call for a better life for the Oguni people. 26 years later, their waters are still polluted, and their farms are still damaged. In 2018, President Mohamed Abuari conferred the GCFR title on late MKO Abuyola. There have also been calls for a memorial and honour for those who lost their lives during the NSAS protests just last year. But the key issues here are, just like the reason behind the NSAS protests still being evident one year later, the Oguni people still have similar complaints 26 years after. The Nigerian government needs to do better with actually answering the pleas of its people instead of stay pardons and titles being conferred. And that's my take. Thank you for joining us on Plos Politics. Of course, I'll return the same time tomorrow. See you then. And remember, this is Plos TV Africa. Big stories live here.


One argument you get is, well if you put the spine in that position, the muscles around the spine will eventually get strong enough, right? The only problem is the layers of muscles around the spine are much smaller than the glutes and the quads. And if you notice, they're also very short. They don't cover as much of a distance as the glutes and the quads cover. So their role is to hold the curve in the spine. So you can easily overpower those muscles with a normal squatting load, for instance. And then as you get more superficial, the muscles get a bit longer, okay?  And given the relative size of them, that's where you run the chance of them straining, going into a spasm, leading to whatever other complications come out of it. So very common guidelines for the spine, right? Don't load inflection. In other words, when you pick something up, don't slouch into it. Turn don't twist, right? You never see a baseball player lock his legs in place and try to hit this way. It's a turn. The hips turn, the body follows. A boxer, a tennis player. The reason why you turn, you don't twist is the disc is between the vertebrae. If you twist the vertebrae, it's like ringing out a dish towel. And again, it allows it, but it's not the best thing for it over time. And again, especially say in sports, you're trying to apply force, speed, repetitions. So turn don't twist is another one of these cliches.  These aren't just, this isn't just my idea of how the spine works. Every institution that puts out safe lifting guidelines, whether it's universities, Department of Defense, agricultural groups, industrial groups, they all say more or less the same thing. They all advise about the discs. They all talk about lifting with a stiff back, letting your legs do the lifting. They all have the same breakdown of the anatomy. But somehow when we walk into a gym, we touch a kettlebell, a machine, a barbell, doesn't matter. This stuff is going to make me invulnerable. The best manuals and exercises are written. So please let me welcome to the stage Bill de Simone.  And methodology aside and whatever the quibbles people had aside, a lot of people said yes. So that type of thinking, it's not just CrossFit, it's been around for a while. A boxer, a tennis player. The reason why you don't twist is the disc is between the vertebrae. If you twist the vertebrae, it's like wringing out a dish towel. And again, it allows it, but it's not the best thing for it over time.  You can pretty much do it with all hip and quad action and not make it more complicated. Also from the 2000 NSCA textbook. Same exercise, different parts of the book, okay? And neither part of the book identified one of these as the wrong way to do it, or a risky way to do it. Perfect, but if you aim for perfect, then the natural... If you fall short, you still have a lot of margin of error.


QuickBooks Online. Bank feeds and your accounting system. Get ready to start moving on up with QuickBooks Online. Here we are in our browser. We're going to be opening up the free QuickBooks Online test drive by typing into our browser QuickBooks Online test drive searching within the results. I'm looking for the one that has Intuit.com within it because QuickBooks is owned by Intuit. Then I'm going to choose the United States version. So I'm going to go to the United States version and verify that we are not a robot and continue. Remember that we are in currently the accounting view. We will be toggling back and forth with the cog up top between the business view and the accounting view. So I'm going to go back on over but first I'm going to open up two reports in Nuke.