Saturday excuse my voice I'm still getting over this cold but today is actually the first day of the agriculture fair and I'm not gonna lie I feel a little sad and disappointed because I worked so hard to accomplish everything just for this weekend and I have to sit it out I know I probably mentioned in our last clip or video that I would still try and set up and have one of my sisters work at my table but it just wasn't working out like everybody in my fun is really busy right now my younger sister has a boot selling food my parents has a boot selling food and then my two other sisters are actually helping me with the boys who are also sick and to try and get them to do that plus then for me would be a lot not only a lot to ask for but it still wouldn't have come together how it needed to from being sick on Wednesday and then giving birth Thursday I didn't even get to accomplish the finishing touches such as putting on my labels and so forth so I would have literally had to rush and do all of that yesterday which I had intentions on doing but as y'all could see like my body has had it I can't do anything else and I had to listen to my body and also relying on anyone else to do it for me would just be putting more added pressure on them so I just decided that instead of trying to stress myself out or anybody else I'll just sit this one out and I know it was the best decision but it doesn't change the fact that like you was doing all of this just for this weekend and and then like yesterday when my mother went down to the Agfair to check out their boot she passed by my tent assignment and they assigned me like not not just the worst spot but like the worst tent like the outside of the tent was all rested inside was rested like it looked so horrible like someone that was on a waiting list to get a spot in the Agfair was assigned a way better tent than me and my tent was paid for since February so all of that into consideration I was just like this is just a sign to sit your ass down not literally but yeah literally so I was just like I'm not I'm not gonna do it like I can't even it's not even like I could go down there and put up some drapes and some sides and some flowers and like just to make it look nice and presentable despite the rust but I can't do any of that and everybody else is too busy to do it for me so I'm not gonna put my stuff out there and present it in a tacky way so I just decided to sit it out but I am still sad about it because like Agfair is once a year like this was just like the perfect relaunch the perfect time to get exposure for my business and let me not say perfect I have to really watch the words that I use because words sound and power but it was a great opportunity and I'm just a little sad about it it could be postpartum hormones I don't know but I am sad when you put so much effort and time into something and then you just have to sit down and just let it go sometimes it's hard you know and I don't want nobody to think that I'm being ungrateful because I'm grateful because my baby is healthy and I'm healthy and you know given the circumstances things could have been a lot worse so I am grateful but as my therapist said I should also acknowledge the feelings that I have instead of trying to push them to the side and bury them so I'm acknowledging that I feel sad right now and it won't last but that's just how I feel right now I didn't come down here to cry trust me I don't do that but it's just a lot I'm processing mentally I think I'm just gonna have to stay off of social media for the next three days because seeing everybody's beautiful setup and beautiful tent even a friend of mine who also makes products her setup is so beautiful her tent is so beautiful this is not shade this is actually admiration and you know it was just seeing my vision come to light like I was really looking forward to that and I can't do that right now I'm just gonna end this video and I'm just thankful for the woman that has been surrounding me right now during this time because they always put so much positive energy in my way and I'm actually waiting for one to come over soon and I know when she comes I'll feel better because she'll know exactly what to say but I'm just home alone with the baby right now and I am grateful yeah I'm grateful don't think that I'm not I'm grateful I'm just sad as well for now but I'll be better anyways I don't even know what I'm gonna do with this video I don't know I don't know if I'm gonna post it I don't know I'm just if I just need some time to vent but I hope everyone's having a great day and I hope that if you did or are going out to the Agriculture Fair that you support the local farmers please please support yes please support the local farmers emphasis on local local okay please support the local farmers and all the local businesses and especially people who do this work all on their own like those small business owners stop by even if you're not buying anything like stop by check out what they have take their business card you know take a picture of this stuff post it share it like there's other ways to support even if you can't maybe afford to buy anything in the moment so support your local business owners your local farmers to all my melanated women protect your room by any means necessary and to everyone watching protect your peace by any means necessary I wish you all love and prosperity and you'll see me in my next video


Welcome back to tennis talk. My name's cam Williams next week. We have the Monte Carlo Masters Which is one of the biggest tournaments on the clay court season It's an ATP 1000 event and with some big names that are playing in that event as we'd expect some big returns But we're also missing some big names as well So let's go have a look at the withdrawal this so far So these are some of the players that have pulled out over the last couple of weeks through injury the tower baritini He's pulled out coppa has also pulled out of this one Daniel Medvedev has pulled out We all know that he got the hernia during the Miami open, so he'll be out for a while We don't know when he's coming back, but not playing in Monte Carlo and Rafa Nadal with that rib injury He's pulled out of Monte Carlo. We don't know when he's coming back But he could be out for six weeks, which means a majority of the clay court season It's not too many players at the moment, but it's not about how many players are pulled out It's about who's pulled out we've lost three of the biggest players in the world three top tenors in Baratini Medvedev and Nadal but for guys like Medvedev and Rafa especially we're looking at guys who are playing very well this season and Medvedev obviously has been number one for a couple weeks this year He would have been playing against Djokovic for that number one ranking and Rafa would have maybe played Djokovic for the first time in 2022 We haven't seen them cross paths yet But let me know down the comments below out of those guys who are you gonna miss the most? I know a lot of people gonna miss Rafa, but he might be back sooner than we think Medvedev we don't know when he's coming back Baratini not sure about him either So we might not see these guys in Monte Carlo Hopefully we'll see them sooner rather than later But let me know down the comments below who are you gonna miss the most out of the ones who have withdrawn so far?


This study shows that assisted reproduction techniques can be made more accessible by developing a simplified culture system, SCS, which has been shown to be effective in reducing preterm births and low birth weight in singletons born after fertilization. The SCS has been shown to reduce the cost of IVF treatments, making them more affordable for those living in low and middle income countries. This article was authored by Willem Ombelit, Jonathan Van Blurcombe, Lisbeth Brockers, and others.


Can you hear me now? Yes? Good. First of all, thank you very much to the organizers for having me here, and thank you for coming to listen to me. I will start with the most important parts. This work was not just done by me. There is at least four people who have contributed substantially to this work that I will talk about. One is Brian Daniels, who is an assistant professor in Arizona, Will Roo is my long-term collaborator in Toronto, C. elegance experimentalist. Sam Sobor is an experimentalist who works on birds, studying how birds learn to sing. And Damian Hernandez, my former postdoc, who is now a faculty in Burialochia in Argentina. So what I promised I will talk about is learning dynamics of biological systems, but then I realized that this is actually a very heterogeneous conference, meaning that I don't usually get to speak to these people about many other events. And so I decided to sort of do the thing which I always advise my students not to do, which is to cramp too much into the stock and actually talk about two different, completely independent things, because that's probably the only time when I can talk to all of you about them. And the only thing that connects them is that we're using more or less machine learning methods to try to understand what's going on in different biological systems. And so the first example, we'll talk about what is the dynamics of the in a susceptive pain, escape response in C elegans, in the tiny worm which is about a millimeter long. And there what we are going to be using is essentially recurrent neural networks, but in a way that allows us to deliver interpretable model, dynamical model of what goes on in the worm, rather than sort of the usual big black box that we get when applying machine learning methods. And then the second part we're going to talk about how the bird controls its own muscles and there, even though we have substantial amount of data, this data is just not enough to use, at least in our hands, to use sort of plain vanilla black box machine learning approaches. And we have to go beyond those and I will try to sort of point out if you think that we're able to do. So this is the animal that we are going to talk about in the first part. The worm is about a millimeter long. It's the best-studied organism, multi-cell organism in the world. We know every single neuron. We know all of the connections between those neurons. We know every single cell. They're all the same in all the worms modular, the sex of the worm. And yet we have no idea how this worm works, meaning that we have known the connectivity patterns of the brain for about 30 years now and we still cannot predict how worm is going to respond to a simple stimulus. And the stimulus that you've seen here is this big red dot which hits the worm's head. This is an infrared laser. Goes on very quickly, increases the temperature around the worm very quickly and then goes off and the worm doesn't like being hot and it sort of stops, looks around, retreats, then crosses over its own body, makes a random term in some random direction and tries to escape that, right? And that's a behavior that we will try to model. So how are we gonna model this? I'm always tempted to read this thing aloud. This is a short story by Borges and that's sort of the philosophy which I have about modeling biological systems. And the title of this short story is of exacting science. In that empire, the craft of cartography attained such perfection that the map of a single province covered the space of the entire city and the map of the empire itself and entire province. In the course of time, this extensive maps were found somehow wanting and so the College of Cartographers evolved the map of the empire that was of the same scale as the empire that coincided with it point for point. Less attentive to the study of cartography, succeeding generations came to judge a map of such mangy cumbersome and not without irreverence. They abandoned it to the rigors of sun and rain. In the western desert, started fragments of the map are still to be found, sheltered in occasional beast to beggar and in the whole nation, there is no other leg is left of the discipline of geography, right? So the reason I'm putting it together is that again, talking, putting the slide together to show to you is that again, in this warm, we know a lot. We know there is a lot of details about the mechanical organization of the world, cell biology organization of the warm, neural organization of the warm and there are attempts, very large attempts to try to model this warm on a scale of one to one, meaning on this molecular scale where we will try to explain what the warm is doing by counting every single RNA inside this warm, every single myocyte and so on and so forth. And that's not what we're interested in doing. So my own, what's going on? Biasis are such that, I think I'm just far away from the laptop, that's an issue. My own biases are that if I want to understand something, if I want to build a model of something, I should not be building a model of everything that goes on in the system, but rather a model that is going to explain the specific question that I'm interested in. In this case, escape response, I don't care about the locomotion, I don't care about its other behaviors, like feeding behaviors, mating behavior and so on and so forth. There's just one question, input output relationship, let's build it. And then in that case, I am not interested in trying to build the most detailed model first and then try to course grade and throw details out, which are unnecessary because then I'm solving a harder problem on the root to the simpler problem and that's sort of the quote that I remember the most from reading Wabnik's book back in the late 90s that you never want to solve a harder problem in the root to the easier one. And so instead what we will try to do is to build a model which is as simple as possible to explain the behavior that we're interested in and as more details emerge experimentally, we will refine this model rather than course grade in that model. So in this case, we're interested in dynamics, input output relation, right? The temporal dynamics of the stimulus goes in, temperature is a function of time, and then on the back end, we need to get the velocity of the worm as a function of time. So how are we going to do this? So what we will try to do is that the dynamics is temporarily local, right? We'll assume the dynamics is temporarily local, which means that we should be able to explain this dynamics to model it in terms of some kind of differential equations. We don't need to go to time delayed systems or integrated differential systems and so on and so forth. That's the first assumption we're going to make. We're not going to fit curves. I'm not going to try to predict a trajectory, right? You know, having a trajectory and then what's the future of the trajectory? Rather, I will try to learn the dynamics itself and then from the dynamics, I can generate as many trajectories as I want. I will neglect the stochasticity at this point and my current students are trying to relax these assumptions. And so effectively what we're interested in is we're trying to look at the time series, x as a function of time. In this case, it's a one-dimensional x, velocity as a function of time. In principle, we can do it more dimensions and we're trying to basically write down systems that look like this, right? The set of first order ordinary nonlinear differential equations that describe this data. And of course, this is not an easy thing to do. I need to be closer to the laptop. This is just not working. And so it's not an easy thing to do, especially not an easy thing to do automatically, right? The question is how do we enumerate all possible systems of differential equations? How do we search through that space? How do we not overfit all the usual questions that emerge when we do statistical inference as still going to be there? In this case, there is an additional complication. We know going back to even late last century, sort of to the structural risk minimization of FAPNIC or to Bayesian model selection and so on, is that we are generally able to find a consistent estimator, consistent, probably consistent model from the data if the models that we're fitting form some kind of a nested class, right? In this case, forming a nested class of differential equations is rather hard. I could imagine myself starting from a linear system, DX by DT is AX, where X is some matrix of observation that we're making, but then I can expand it along multiple different quote-unquote dimensions, right? I can add nonlinearities to the system, or I can add hidden variables to the system. This worm has 300 neurons in it and probably 30 or more are actually responsible for different parts of this locomotory escape behavior, right? I'm just recording a single variable X, but clearly there will be hidden variables, latent variables in the system. And so any two-dimensional set cannot be uniquely ordered. And as a result of that, I cannot find a unique, sort of structure, unique nested set of families of models that I can just look one at a time and see how they fit the data. And as a result of that, I have to start again making assumptions. And so what we are going to try to do is to look at this sort of two-dimensional set of more hidden variables, more nonlinearities, and chart just an arbitrary path through that space starting from the simplest model, the fewest degrees of freedom, the fewest nonlinearities, and ending eventually at infinity and infinity, just putting a line and exploring model after model and after model on this line, using Bayesian model selection to choose the models that are good enough but not too good to overfit the data. And then if we have chosen a correct hierarchy of models if we sort of go this way, and it turns out that we can fit very easily and the hierarchy is good, if it turns out that we've chosen hierarchy sort of goes maybe like this in some weird way, it will take us a lot of terms in this model until we actually will start fitting, right? And then hopefully by seeing which hierarchy provides the simplest fit, the easiest fit to the data, we will actually be able to learn something about the underlying structure of the model. Maybe this model is actually not nonlinear, right? Or something along those lines. So, let's skip this. There are two different model families that we are going to explore. One seems pretty familiar, right? This is almost recurrent neural networks except that we change the order of nonlinearity and the summation here is called sigmoidal networks. You can map if the time scales, the stars over here are constants across all variables that you are dealing with and they are absolutely equivalent, mathematically equivalent to recurrent neural nets. The reason why we chose this is because this seems to be a bit easier to map on biochemical kinetics which is what we originally developed this model for. And another set of higher, and so the idea here is that we start with the axis, the observed variables, they interact with themselves, interact with other variables, interact with input signals, which are eyes in this case, and that just grows this model by adding additional axis in a line, right? Sort of in a column. And so the number of units increases, they all start interacting with each other. At some point I say, I have finally the simplest possible network that can explain my data, I'm gonna stop here, right? Another network that we are dealing with is another type of model that we are going to be dealing with, something called the S systems. This again, non-linear recurrent systems, they don't look like neural networks and they aren't, but that's just a different type of activation function, right? The idea here is that axis activate each other as power loss, and then they deactivate each other as power loss, and this comes again from biochemistry was developed by Mike Savojo some 40 years ago. The idea is that in a logarithmic space, if I take log of axis, this to the first order looks like a linear response model, right? And so then you start adding additional terms to that. And so in both of these cases, we know that any dynamics in principle can be modeled by this type of equations. The nestedness of the structure is pretty clear. I just keep on adding additional variables and the system grows. And so all requirements are satisfied. So I'm guaranteed by using Bayesian model selection to find, to not overfeed and to eventually, as amount of data goes to infinity to actually have a consistent, statistically consistent result. So before going to try to model the worm, let's try to see if we can with this very simple approach learn something that we already know exists.  And in fact, not actually whole trajectories, but just two points from a trajectory, right? The planet starts at a certain distance away from the sun. And then some random time later, the planet ends at a certain distance away around the sun. And we have a couple of hundred of such trajectories or such pairs, right? Beginning initial conditions, final conditions. Can we learn that there is an underlying gravitational, universal gravitational law, one of our squared that explains that? And let's look at these figures over here. What I'm doing is showing a face portrait of the true system, right? There's two dynamical variables in the true system. There is radius and the velocity, dr, dr, dt and r. And this is how the true system is going to look like. This is a planet moving in a circle, radius constant, velocity constant. This is an ellipse. This is how it looks in this coordinates. This is a hyperbola. Our algorithm applied to the system learns something that looks like this, which of course is not exactly something that is on the left, but of course we don't know that the hidden variable should be a velocity rather than velocity cubed or some other function of velocity, right? The important part is that we have discovered that the equations of motion are second order equations that you need two dynamical variables to explain the dynamics. And if you look deeper, you will see it's more small complex of both of the systems is exactly the same. So we have actually discovered the correct dynamics, right? Subject to some small statistical fluctuations. And then looking on the left, what you see is the dotted lines are now planets which started, which we have not trained on, right? We release them with some random positions, velocities, and we ask where they will go. And the solid lines is what our algorithm predicts. And it's pretty good, right? From about 200 sort of input-output pairs, we have discovered the underlying Newton's laws, right? And so we like that. And so that's why we called the algorithm Sir Isaac. And so you can download it on GitHub if you'd like. There's one point that I wanna make here before going further to the worms. And that is, this fit is done with the S systems, with the power law activation functions, right? If I try to do the same with the sigmoidals, this is what I get. So we always train on the first 100 units and we try to predict what's going to happen for the next 100 units of time. And so what you see here is that the, effectively the neural network is very good at interpolating, right? We are quite good even on the systems that we have not trained on for the first 100 units of time, which is the range of which we're trained on, but from different initial conditions, we are doing pretty good. But then extrapolation becomes pretty bad. And the reason here is that the actual forces in one over R squared diverge near zero, near R equal to zero, and it takes a whole lot of sigmoids to create an infinity, right? So, yes, better. Yes, and the score that we get for the, I mean, they, I'm a defied likelihood or whatever, it is better for this system than for that. But I think that what's even more interesting is that if I look at these two cases, I clearly see that I've learned something about the underlying dynamics, and that is that that dynamics is not saturating, right? At infinities or at zeros, that it has divergences at the infinities and zeros. And that, to me, is maybe even more important than learning the actual equations, right? Because I've actually understood something about the structure of the forces. Equations depend on parameterization, divergences don't. You had a question. You learn conservation of momentum or conservation of the energy or conservation laws, I mean. Okay, in this system, we did not, right? So I skipped those slides because I wanted to scram again too much stuff into it, but we have applied, of course, this message to other dynamical system. So one specifically that we started with is the glycolytic oscillations in an east. And so that's a seven-dimensional dynamical system, but in fact, it has a conservation law. And what we were surprised to find out, I mean, maybe it's a coincidence, maybe we got lucky, but what we were surprised to see is that our engine, in that case, this one didn't work well because biological dynamic doesn't have infinities, doesn't have infinities. Sigmoids are a lot better for describing biochemistry. And so, fitting with sigmoids, we produced the feed there and the system said that six variables is enough, right? And turns out that there is a conservation law, total number of carbon atoms is conserved, even though there are seven chemical species. There's the geometry, which means that only six are independent. And so in that sense, we have discovered the conservation law. We do not discover it in terms of energy is equal to a constant. In this case, over here, we're only looking at radius, not x, y, z, but rather just the radius, the distance away from the sun. And so in this case, the conservation law goes, the momentum conservation goes into the fact that you have effectively repulsive force near zero, one over r cubed repulsive force near zero. So there is no more conservation law left. It's already in the effect of energy, in the effect of force. So now let's go back to the worm. This is what the worm behaves. Looks like this is the same worm that I showed you before. If I apply very small current, the worm, which usually goes forward blue, causes, which is black, just stops more or less. And then it either can start going forward or it may make a turn or it can reverse. And as I increase the strength of the pain that the worm experiences, its behavior becomes more and more deterministic. It goes through this very specific epochs where you are going forward, you are reversing, you are turning, and then you are going forward back again. And so what are the questions that we're asking here? Again, I don't really care much about fitting the dynamics. I'm interested in trying to understand what are the general properties of the dynamics. So what are the questions? So the interesting question in the field of animal behavior is how do behaviors get generated? Are there multiple distinct behaviors, sort of the reverse behavior, reverse motion and the forward motion that the worm can select? And so you can view them as two different attractors and the worm, due to perturbation, goes from one attractor to another attractor. Or is there always a single dominant attractor, let's say going forward, right? And then as you apply the stimulus, it's not that you are switched from one attractor to another, but the whole attractor itself moves, right? So the idea is when you apply a stimulus, does it change the landscape of all possible behaviors or does it just move a worm from one point on that landscape to another point on that landscape? And so that's sort of a common question in the field of studying behavior of small animals and that's what we try to answer. This is the type of data that we get. We apply the current, the worm goes forward, we apply the current here, worm immediately recoils back, starts slowing down, eventually at about four, five seconds it's gonna stop and cross over itself and start going forward again. The solid lines with ranges around them is the mean of a set of a bunch of worms plus standard error of the mean plus minus. And then the dotted lines as a fits. By all measures, the fits are very good. We get chi-squared per degree of freedom of less than one, which is even worrisome that we're overfitting. It turns out we are not. And so for this system, the reason why we started working on this is because my previous graduate students spent three, four years on trying to build a detailed, biologically plausible model of the system accounting for reasonable sort of knowledge of the physiology, right? And in that case, so this is a curated model, we did quite well. We got an R-squared between our model and the data of about 0.8, 0.85, right? So we explained 85% of data that was available for explanation, right? And then we ran this inference engine on the same data and we got something that looks like that where the unexplainable variance almost kisses zero here, right? So we on average explain about 0.92, I think, R-squared of 0.92, which for biological data is sort of unheard of. You don't usually model behavior of whole organisms with the R-squared of larger than 0.9. So that's interesting, right? So we fit the data quite well. Can we make predictions? Why should you believe our fits, right? And so in that data set, we only fitted the data sets to the first two seconds of the activity after the warmest heat with the laser. And then after about four seconds, five seconds, the warm usually stops, crosses over, as I already said, multiple time and goes forward. Our data doesn't know that this is what's going to happen, right? We do not use that data. We only use first couple of seconds of behavior. But we can ask our model at which point will the warm stop, right? And this is a prediction of the model. There is no three parameters here anymore. So depending on the amount of the laser current that you provide, when will the warm stop and stop going backwards and start going forwards again? And again, this is not a fit. This is now a prediction. This is our curve for prediction. And this is a means of different warms that experience the same as current. This is a similar prediction where we now are trying to predict when will the warm stop based on its fastest backwards speed, right? The peak reverse speed. And again, there's no three parameters in the model. This is a prediction. This is the data, right? So it seems like we are not just interpolating the data, not just fitting the data, but we are making predictions based on the first two seconds of behavior about what's going to happen at about four to five seconds after the laser has applied, which is interesting. And this is sort of my test usually for whether the model that somebody builds is in some sense a pure statistical model, which are always good for interpolation, but are rarely good for extrapolation, versus if it's a model that has recovered underlined physical mechanisms, whatever they are, because if you got the physical mechanisms correctly, if you got the actual system correctly, then you can not just interpolate, but you can extrapolate at least for a while. Okay, I keep on moving away from my laptop and that's not good. So this is how the dynamics looks like, right? Coming back to the question, is this a single attractor or multiple attractors? So if the worm doesn't have a laser hitting it, it has just a single attractor according to our model. Here it is. By the way, the system is two dimensional, even though the velocity is just a single variable that we're trying to feed. Our engine again tells us that you cannot explain this data unless you have an additional hidden unit in the system. And so that hidden unit is at zero in at rest. The worm is going at 12 certain pixels forward and then you apply laser current and what happens is in our model, the attractor moves and the worm starts chasing its own attractor. So it seems like the external stimulus rearranges the landscape of possible behavior and then the worm evolves on an evolving landscape. But there is always just a single attractor at least within our model, which is sort of an interesting observation whether we will fully believe it or not we need to test additional more interesting stimulation patterns, but this is where at least we stand at right now. And now this is finally the model that we have derived, right? And it's of the sigmoidal form that I have promised you it will be. dv by dt relaxes to zero is driven by the heat that the worm experiences and has sigmoidal interactions with itself and sigmoidal interactions with a hidden unit. And this is a hidden unit X2. And what you will notice is that this hidden unit is just a linear, it's a low pass filter of the velocity. The interest of the heat, the interesting part here is that for each one of these terms, I can tell you, or rather not I, but will remind experimental collaborator, tell you which neuron is doing this. We probably got incredibly lucky, but we can assign a particular neuron or a particular known feature of a behavior of certain neurons to each one of these terms, right? We know that there is a forward speed generator in this form. We know that the heat when it applies to the worm just shuts it off. We know that when this forward speed generate, velocity forward motion generator pacer stops, it immediately turns on the backward pacer. We know that when the backward pacer runs for a bit, it turns itself off and turns on the forward pacer and so on. And so each one of this term, if you look well enough, you can assign which of the neurons are responsible for this behavior, W11, for W12 and so on. The model is extremely interpretable. There is one thing that we didn't find in the literature. And that's this thing, right? The integration. It turns out that nobody saw that the worm is integrating its temperature. This was not a known function of any of the neurons in the literature on the worm. But since the model works so well, we are sort of willing to claim that this is, in fact, what's going on. And again, the beauty of working with a good experiment is that they can go and check. And they did. And it turns out that the AFD neuron is, in fact, essentially a linear filter. At least one of its functions is effectively, in fact, a linear filter of the temperature that the worm experiences with a timescale of about one second just happens to be. So not only we are able to predict, but we are able to relate things to the known biology and to predict the existence of yet unknown biology in this system. And so that's about the end of this story. And I think I have about 15 minutes to move to the second part. And so what I want to finish with here is that what I've been trying to do here is not to build the true model, right, the interactions of every molecule with every molecule, every neuron with every neuron, but the simplest possible model, which recovers the behavior that I'm interested in, we do this by effectively building a set of recurrent neural networks and trying to find the smallest possible network that is able to explain the data that I have. It's a totally opposite direction from what the modern machine learning sort of literature would tell us to do, build the biggest thing and it's gonna be, and then try to make sure that it doesn't overfit, right? We're going from a different direction. And the interesting part is that because of some coincidence or maybe because of some deep profound reasons that we don't understand yet, the model turns out to be predictive and interpretable. And that's very surprising. So I'm gonna go, mm-hmm, yeah. So I'm curious about the fact that you started from this model to try to capture the dynamics, but then you eventually say that you landed on finding, I mean, even predicting the existence of a specific neurons performing some specific functions, right? So I wonder why then the way around would not work? Why if you actually start from the biology, from these neurons that you know exist, you put them together, you wired them together, then you're not able to model the behavior of the world. I think it's a great question, right? So there's two reasons. The first one, I have no idea what, which part of the known biology to actually include in the model. There is some 30 different neurons which are known to affect locomotion. And so, but not all of them apparently are really important for this type of behavior, but maybe even a deeper question. I mean, we know that AFD is a thermosensory neuron and it affects, you know, works in thermotaxis, works in the susceptive taxes and so on. Nobody, this neuron is very well characterized. Nobody has saw that this neuron is a low pass filter with a, you know, linear low pass filter with one second time scale, right? So people have never applied fast enough stimuli to this worm to see that it has, that that neuron has this function. And so even if I tried to build a model of this escape circuitry, there was just not that bit of knowledge in all of the available literature that I do have that behavior. So I'll really fly over the other part, but here what we're interested in is sort of solving this old question that was posed, you know, by Chuck Stevens some many, many years ago, decoding the neural code, you know, the neural code is the enigma of the brain. How do we figure out, can we understand which specific spike patterns carry information? In our case, we're interested in carry information between the brain and the muscles. How do the brains control their own muscles? And this is a problem which we have already seen here in this conference. Simona Koka was talking about essentially the same problem in a different disguise, right? The problem looks like this, where you have this huge matrix, and maybe not huge, but large matrix, where you're recording from multiple different units for multiple different times. In her case, this were protein families, and then the values of amino acids. And what you're interested in is to understand which combinations are over-represented in this case, right? In our case, we're going to be looking at time slices over here. And then the number of different motions that the bird does in this case, the bird is trying to sing. And so every rendition of a song we're going to record at which point before that song, the bird has, the certain neuron in the brain has either spiked or not spiked, right? So this is my, this axis over here, zero, one, spiked, not spiked. And then many, many different renditions of the same song. And so what we're interested in is trying to understand which of the spike patterns are over or under-represented there. And those things which are over-represented are possibly the keywords in some sense, right? The code words, which is what the animal is using to control its muscles, right? And if they are over-represented jointly with a certain feature of the behavior, which in our case is going to be the fundamental frequency, the pitch of this behavior, then they are at least correlatively code words that are encoding the subsequent behavior, right? And so exactly the same problem is showing up in predicting structure of proteins and trying to understand 3D structure of genes and trying to, of chromosomes, trying to predict gene expression, you name it, right? There is a whole big field of very, very related problems across all domains of biophysics. The problem with this is that at least in our hands, for the type of data that we're interested in, we cannot use the standard approximators for distributions like restricted Boltzmann machines or anything like that to find those over-represented patterns. And the problem is, the reason why this is the case is that we just have too few data points. 40 or 50 units that we're recording from, there are two to the 40 multiple possible combinations that could happen, multiple different code words. We have, if we're lucky, 1,000 recordings and two to the 40 is much larger than 1,000, almost no signals repeat more than just a handful of times. The signal to noise ratio is tiny. You don't have a situation where a certain code word is just massively over-represented. They are all just a bit above the expected noise level, right? And on top of that, there are no such things as sort of symmetries that you can use like convolutional symmetries, translational symmetries, rotational symmetries. Those things don't exist in this type of systems. And so you are in the regime where you are massively over-sampled, under-sampled and at least in our hands, none of the black box methods work. And so what we are trying to do is basically write down this joint probability distribution as a standard sort of energy-based style model and then try to identify which of these coefficients are non-zero with some kind of reasonable probability, right? We're not even going to ask which of these coefficients, oh, what these coefficients are. We're gonna be very happy if we can say that this coefficient is non-zero with probability 80%. And so it's quite likely that this word actually contributes to the neural code. And this is enough for us because then I can go to my collaborator and tell him, look, what you can do now is you can stimulate this word, this neuron at this particular moment in time. And we can see is the animal going to respond in a certain way, is it gonna sing the song that you wanted to sing, right? We don't need to do everything with machine. We just need to have putative things that later could be tested. And so again, as I mentioned, the big problem here is that every neuron in this animal's brain is different from every other neuron. Every brain, every animal, is different from every other animal. We cannot pull together data sets across neurons, across animals, and we're horribly, horribly under-sampled, right? So how do we solve this problem? And our idea is that, again, we do not want to actually fit the distribution. We don't want to build a model of the probability distribution. We just want to understand which terms in that distribution are likely non-zero, right? What are the code words? Not exactly what they mean or how strongly they contribute to describing the distribution. And the way that we do it is the following. So first of all, we discretize a spike train. Zero, there's no spike one. There is a spike at a certain time point. Behavior, we're also going to discretize if you're singing above median picture, below median picture at zero or one. So we are now trying to fit this probability distribution, but instead, what we are going to do, we are going to multiply each term in this probability distribution by a binary variable S, an indicator variable. If this variable is zero, it means that that term doesn't contribute. If the variable is one, it means that that term contributes, right? And what we will try to do is estimate a posteriori given the data that we have, which of this Ss are zero so once and really don't care what the status are, what the actual coupling constants are, right? So I can put some kind of a regularizing prior, say that status are all very small, which is reasonable for this data because none of the patterns happened at very, very large frequencies. They're all barely above chance. So there is no strong terms there. Every contribution is very, very weak. And so I will put this small, very, it shouldn't be weak, it should be very strong regularizing prior on status, right? So all status are very small there. And this gives me a small parameter. What is the typical scale or feature of the terms that I expect there to be? I'm going to call it epsilon. I can integrate out all of the status in this probability distribution to the couple of leading orders in epsilon. And this is what I'm going to get, the posterior distribution of this indicator variable. Should I or should I not include a particular term given the data I observed? It's an Ising model. It's actually now an Ising model not on 20 or 40 or 50 spins, but it's an Ising model on two to the 20 spins, right? Because there are, each particular pattern has its own indicator variable associated with this, right? So pattern 0, 0, 0, 0 has one indicator. 0, 0, 1 has another indicator and so on. So it's normally a much harder Ising model than I had before except that the couplings, the epsilon's here are small. And so it becomes a problem which I can solve perturbatively. And we have a code for this which you can read and we can of course calculate the values of this Hs and Js and they make sense, right? The Hs is basically how over or under represented a particular pattern is given, you know, subject compared to a null model and Js are actually kind of interesting. They tell us how patterns compete with each other. So if, for example, I have a pattern of three spikes which can explain a certain over-represented structure in the data, then maybe this pattern is also over-represented or maybe this pattern with four spikes is also over-represented, right? Because they're all correlated to each other. And if I'm trying to assign some feature in the data to a certain pattern, maybe I should assign it to just one, right? To the most irreducible one, to the strongest one. And so it turns out that the interactions between the patterns have a negative minus, minus times the strength of the correlation between those patterns. So patterns that could co-occur are suppressing each other. Patterns that cannot co-occur are actually co-activating each other. And so this is, you know, I'm just flying over this very quickly. It was just finished piece of paper, piece of work a couple of weeks ago. And so this is a type of thing that we observe if we do the analysis in this bird. So this, for example, a neuron that's a single neuron and we try to understand what this neuron codes, what are the patterns that predict high pitch or low pitch? And so the blue ones are one predicting high pitch. The red ones are predicting the low pitch. This is the same for neuron number two. This is from the same neuron. Can this neuron predict, let's say, the volume, the amplitude of how the bird sings? Or what is the complexity of the song, the spectral structure of the song? And what you will see is that, which is kind of interesting biologically, is that these neurons are tuned. Some of the neurons predict pitch but do not predict the amplitude, but maybe predict the spectral complexity. Some other neurons do the opposite things. Another interesting story is that none or very few of the patterns here have just a single spike in them. Most of the patterns are combinatorial patterns having a spike here, a spike here, and spike here. And that means something while an individual spike sitting here actually has no predictive power. So it turns out that this code that we're dealing with is not a simple code where you just linearly add spikes, but rather a precise spike pattern down to about two millisecond resolution. Spikes happening at exactly the right point is what is the language, the artographies that these neurons use to communicate with the brain. And we can take it further. I'm gonna skip this part. I'm just gonna focus on this thing, just one last slide. So we can do the same analysis in the young bird and in an older bird. And it turns out that in the younger bird, all of the keywords that we observe have individual spikes in them, while in the old bird, they're all multi-spike patterns. So it turns out that the young birds, when they learn, when they just learn the song, they're first trying to figure out how many spikes to put at an appropriate moment in time, but when they become older, they start figuring out knowing the number of spikes, where should I put them, right? And that's a very different mechanism for learning. It's the, you know, when they're young, they're figuring out what the rate of firing is. When they're older, they're figuring out when exactly to fire. And coming back to sort of machine learning, it would be maybe nice to introduce this idea of timing in artificial neural networks and try to see if we actually, if we can take them beyond learning rates, right, to learning timing of action potentials and trying to compute with timing, maybe that's going to make them as, you know, somewhat more powerful, just like birds are still a lot more powerful than in controlling their vocal chords and any of our machines that we can create. And I'm ending right here. So timing is the right keyword. Thank you very much.


Vicks presents dangerously yours a half-hour of romance and adventure starring Victor Jauré in Monsieur Boccair You know more and more millions of people are using Vicks Vaternal nose drops to relieve distress of head colds Benefit by their experience and now Dangerously yours I am adventure in my name men of Travis the highways the byways the skyways of the world I am the fire that burns in the heart of youth that makes men dream and dare and conquer. I am dangerously yours They follow me to the English court in the early 18th century Meet that daring impudent Frenchman who risk his neck many times over to win a rose and to win the heart of his lady Monsieur Boccair Your highness his Majesty is in the throne room. He would like to see you immediately very well, honoree. I'll go at once Yes, father with a leap I have been thinking about you.  You are the Prince of the realm and this time you had a wife I have been duly considering all the available princesses and I have decided that you are to marry your cousin the princess Henriette Well now father it brings me deeply to have to go against you in anything But I don't happen to want to marry my cousin Henriette. I wasn't aware so that I'd asked you what you wanted Don't you think you should ask?  I don't love her now That's just about the silly kind of answer I would expect from you. What on earth do you want? I want to choose my wife I want someone who is beautiful and witty and charming and clever I want someone who is honest and courageous and straightforward and trustworthy I want someone who will love me and only me and who will love me without knowing who I am or what I am My boy such a woman never lived You might as well face that fact and marry your cousin Henriette. No, sir. I won't do it I positively will not marry Henriette. Parblur you will do as I say. Parblur I won't. Well then sir You will pay a little visit to our prison at Vincent and you will stay in prison until you make up your mind to marry Henriette I'll never marry Henriette. You will start for Vincent tonight. I'll never marry Henriette Let's rest our mouths for a moment You know gentlemen, I don't have much wish to go to prison. So I think I'll leave you here. Good evening gentlemen Well, come on after him. We can just let him ride away Oh, he's a much better horseman than any of us and a much better swordsman Why get all hot and steaming with a chase and a fight? It's much simpler to go back and tell the king he escaped Well Pierre, will you do it? Will you let me travel to England with you? No, your highness. You put me in a very unhappy spot I've got to get out of France at once if I don't my father's going to put me in prison But to suggest that you accompany me as my barber my barber. It's well, I just don't like it What do you know about barbering in the first place? I've been observing barbers for some time now. There's nothing to it at all. Nothing to it at all You'll see that I will bring about some changes in that beard of yours Now you see, I'm very fond of that beard and I'm not going to have you tampering with it You forget yourself, Mollinier Well, suppose I refuse to take you with me to England Then I will order you to take me I am not a happy man Why do you want to go to England anyhow? I'm just going to take the sun at Bath. You'll find it very dull Bath dull Why there's gambling there and dancing and beautiful ladies And there I may find that which I seek may I take the liberty of asking just what it is that you do seek your highness I seek a lady as beautiful as the morning as beautiful a lady What lady or what's her name? Does she have a title? I don't know her name, but her title might be Cinderella Ha, ha, Mollinier, Mollinier, Mollinier, you wonderful handsome Mollinier Don't shout so. You'll have everyone up here to see what's wrong. I what is wrong? I found her. I knew at the moment I saw her. She had a red rose in her hand And she was riding by in her carriage and she dropped the rose when she saw me and when I returned it I touched her hand for a moment Oh, Mollinier, she's so lovely, so beautiful, so charming, hand me those scissors I will trim your beard while I tell you about it You trimmed my beard yesterday and it really doesn't need it Doesn't need it, Mollinier I don't want you to have a hair out of place.  I order you to sit down. Yes, your highness I am not a happy man. This is supposed to be a vacation for me. I found out her name already It is lady Mary Carlisle.  Yes, your highness Now then Mollinier How can you fix it for me to meet lady Mary? I cannot you just don't introduce your barber to highborn ladies You don't huh? Well, I'm not going to make a fellow countryman commit a social error I'll tell you what you do Suppose you invite the duke of winter set to your rooms tonight for a game of card I don't like the duke. Why should I invite him in for a game? Well, I've heard the duke is very intimate with lady Mary Carlisle So I've just decided that he shall have the honor of introducing me Oh, my beard Too vain about that beard, Mollinier. If you're not good, I may cut it off altogether Yes, the duke shall have the honor of introducing us Duke can't seriously think he'll introduce a barber to his friends Well, I'm told the duke cheats at cards if he cheats me He will introduce me but not as a barber I shall become a duke for the purpose the duke of um Chateaurean Winter set is no child your highness You're flirting with danger. I like to flirt with danger. I love it. It's the wine and the sauce the salt and the stew The soft spot in the head That's enough trimming for this afternoon.   Thank you Cut no, thank you What's happened to your beard, Mollinier? It's different shape every day It changes with the mood of my barber, monsieur la duke Pardon, but one of the cards seems to be missing one of the cards missing Well, that's impossible.  Money does not interest me. I want something that will not hurt you as much And if you will not do it for me murder. Oh, no, monsieur only disgrace Why you scurvy foot boy you greasy barber you stable boy born in a stable Is it not an honor to be born where the duke of winter set must have been bred? Do you think a soul in bath would take your word that I attempted to cheat you? They would take the word of my master molliner. You'd pass that word about molliner Yes What do you want of me? I want to be presented to society as the duke of chaturian And I also want to meet the lovely lady mary carlyle Lady mary may I present his grace the duke of chaturian Your grace Lady mary carlyle I kneel at the feet of such beauty. You're very kind Would you grant me the honor of this dance? The pleasure is mine your grace I've dreamed of this moment for so long a time And now that it's here and you're in my arms I don't even know what to say And there's so much that I should say What do you want to say?  I'd say you couldn't possibly know We english are practical people your grace surely not about love Who can be practical about love if a lady were not practical about you mr. The duke she might very easily get her heart broken. Oh don't be practical about me I'll not break your heart But I'll try very hard to take it away from you I'm not sure you can You see I'm waiting for someone very special what kind of a someone Oh someone who is straightforward Who never says anything he does not mean Someone who can be trusted And of course He must be very handsome And he must love me very much I think I know just the man for you Is his name monsieur le dupe de chateau rien? No not his real name And what might his real name be monsieur le dupe? His real name might be prince charming You must tell me about him sometime Sometime I will you are leaving the ball now? Yes, it is late and I'm a little tired My carriage is waiting Look at that moon lady mary By tomorrow night it'll be in its prime Will you go driving with me? I'm supposed to attend a dinner And in any case it would not be proper You're English and I'm French What is proper in my country may not be in yours In my country if you refused to come that would mean there was no hope for me at all And I would not try to see you again I will try to change my engagement If you would care to call about eight I will be there My lady Yes your grace I ask a favor May I have that small red rose you wear in your hair What would that mean in your country? The same as it means in yours Will you give it to me? It is yours And now good night Thank you my lady Be careful of my heart won't you You see you're taking it home with you tonight Please don't break it Good night your grace Goodbye until tomorrow I see you have a rose monsieur barba Yes I have a rose The rose will be dead before morning and so may be the barba Are you threatening me? I have intended for some time to make lady mary carlyle my wife I don't intend to change my plans I have the same intention and I don't have any idea of changing my plan Do you think you can marry her and keep her from finding out that you are a barba? No Do you think her ladyship would marry a barba? I think she would if she loved him I mean to find out At the risk of personal disgrace At any risk I will keep you from seeing her again At any risk I will see her again Well then monsieur I'm afraid I shall have to kill you Sorry winter set I don't have time right now I must get some rest I have an engagement with lady mary tomorrow night And I want to be at my best I'm warning you No do your worst winter set Let's see how much of a blaggard you really can be Step carefully getting into the carriage my lady Here permit me to help you Thank you your grace Now then we're on our way All right men we'll follow them into the country We'll let them get about 10 miles out of town And then we'll take care of monsieur la barba We must keep out of sight until we're ready for the kill Come out to them You know folks how your head sometimes stuffs up at night And makes breathing so difficult it's mighty hard to get to sleep Now the next time you're bothered with this Trenching congestion at bedtime Just put a few drops of vix vatronol in each nostril Then feel this specialized medication Start to open up the clogged nasal passages And make breathing easier What grand relief it brings Vatronol is so effective because it's expressly designed Not only to help relieve local congestion But reduce irritating grindness too In this way it invites restful sleep Try vatronol tonight if you're troubled with nasal stuffiness And notice how fast breathing becomes easier Remember too that vatronol is equally effective To relieve distress of head colds So if you're snippling and sneezing Just use a few drops of vatronol Follow directions in the folder Vix vatronol nose drops And now the second act of misceable care Starring Victor Jury We'll stop for a moment And look at the ocean and the moonlight No Very well But you aren't looking at the moon or the ocean Ah, you are more beautiful Compliments roll so smoothly from your tongue, Mr. LaDuke One could wish you did not say them quite so glibly Compliments are the result of honest emotion What I feel I can express strongly and quickly Because I have no doubt about my feelings I'm not a schoolboy stammering over his first love I'm a man looking at my last love How clear it is tonight Yes, for nights like this I used to go out alone and sit by the sea and Dream about a girl like you Makes me a little homesick tonight for France Oh, Lady Mary, you will love my country And you, they'll love you in my country If we were there tonight, we'd find a carnival and ride in a carousel and drink champagne and laugh Later I would take you on the top of the highest hill I could find And I would say that is my kingdom down there And now it's yours If you will but put your hand in mine Mary, look at me Why do you turn away? Because suddenly I want to believe you Suddenly my heart wants to believe you And my mind says no My mind says be cautious He is a stranger Don't let yourself be in his lady Will you marry me? Don't know Do you love me? I think perhaps I do I'm not sure how can I be sure I've known you so short a time You can be sure You can be sure if your heart grows warm when you look at me And you can be sure if time goes singing by when we're together And drags by when we're not And you can be sure you'll love me if there's both peace and fire in your heart when you hear me say I love you Then I think it must be so My dearest My dearest There's something I must tell you There's something I must ask you If I were not what I seem if I were not the Duke de Chattoria Would it make any difference? It seems almost insulting to ask you But I must know You are not the Duke de Chattoria? Then who are you? A man who loves you Listen, I hear horsemen I wonder who they are Yes, I think I know who they are I must get out of the carriage I trust my own men are near Who are they? Who are you? What is wrong? My darling, listen to me If I live tonight, I'll take you to France And you shall be my queen And if I die, I'll count my life well lost for this one hour What is wrong? Who are you?  Why don't you defend him? Defend him? You think that dog of a book care? A common barber? Those are my men that are killing him I led them here You led them here? Why? What is this all about? Step aside, they'll trample you down See how he fights his magnificent Have you ever seen such swordsmanship? Winterseth, why are you not fighting?  He is no Duke, he's a barber A common barber Barber? Oh, no There he is, my lady Barber to Monsieur Molly now Oh, no, I cannot believe it Francois, here Amour, grandpa You were almost too late Monsieur Le Duc, you had better tend to your accomplices Some of them seem to be in pain I'm sorry not to be able to blight you by dying But I have different plans Is it true that you are a barber and nothing more? Would that make a difference to you? You surely could not expect me to marry a barber I thought you would marry whomever you loved I thought you were different from all the other ladies in the world I see now, I was mistaken Forgive me for the assumption Francois, Francois, I need help, Francois Oh, senior, your coat is red with blood Wintercet, will you take me home? With pleasure, my lady We'll meet again, Barber Unless you get out of town immediately I'm not through with you yet Drive on, Wintercet This has all made me a little faint Farewell, my lady Francois, sometimes a red rose comes very high But the rose she gave me seems more beautiful Then it has turned out to be Help me home, Francois Monsieur Mollignure, may I please see Monsieur Bouquere? He's in the garden resting Haven't you done enough to him? Please, let me see him I know he despises me But there is something I must say to him I don't like you, my lady And I'll do nothing to help you see Monsieur Bouquere Ever since he met you, his barbery has gone right out of the window And I've almost lost my entire beard You're ruining his profession You won't take me to him? I will not But I can't help it if you push your way past me And go around to the garden on your own Thank you Monsieur Bouquere Good afternoon, your ladyship I know you don't want to see me But I have to come I won't be able to talk to you long I have some affairs to settle and I'm leaving for France tomorrow A few weeks ago you and I rode out along the sea And you said if I would put my hand in yours You would give me a kingdom Do you remember? But you would not care for a barber's kingdom I think I would I know I wouldn't Why Mary? If I've always thought the titles were important, forgive me I've learned better Just say I had a bad bringing up I've had a lot of weeks to think this over And I know it doesn't matter whether you're a duke or a barber Because the only kingdom I really want is in your arms And the only title I really want is the title Madame Bouquere My dearest, my darling, my sweet I love you That's the only important thing in the whole world to me I love you so much You shall be Madame Bouquere And Monsieur Bouquere is a very lucky man One senior that beast of a winter set his way over here With a whole party of gentlemen from the court Francois just brought word And I shall be ready for them Can't we end this masquerade?  Do you expect me to fight a cutthroat with bare hands? I think one does not expect monsieur to fight anybody Do you think I would fight you? I would gladly fight almost anyone in the world, but I would not soil my hands with a lying, sneaky...   What prince is this? What kind of talk is this? Winter set, you forfeit a dull honor in this place You play cards with me in cheat And then on condition I do not expose you You introduce me as the Chaterion You introduce me at the cost of your honor and then betray me to redeem it You are a liar, a card shark, a coward I'll know the true name of the man that dares bring such a charge Tell him all or no, I think it's time he knows But make it as short as possible Permit me to have the honor of presenting to you His Royal Highness Prince Louis Philippe de Valois Le Duc d'Orliens, Le Duc de Charte Duc de Nemoire, Duc de Mont-Posier, Count de Beaujolais First Prince of the Blood Royal First Prince of France, Lieutenant General of Infantry Governor of Dauphine, Knight of the Golden Fleece Grandmaster of the Order of Notre-Dame of Mont-Carmel End of St. Lazarus in Jerusalem And son of his most Christian Majesty, the King of France Mon Dieu, what a memory The Prince shall hear from me within the hour What gentleman will carry your message, Winterset? Whoever will shall receive a little beating from France war And now may I introduce to this company The lady who has promised to be my wife But I promise to marry Monsieur Bocair I am Bocair in my heart And in my heart you will always be Madame Bocair My Seigneur, my Prince, by your leave I have been a good friend, have I not? That you have, Mollinor? Have I not served you?  You've done all those things Then may I ask one favor in return? Ask, it shall be granted May I be your barber just for a month? I can see room for improvement in your beard Mollinor, if that's what you want, it shall be granted But consider all the trouble masquerading as a barber got me into it My second thought, I withdraw the request On the other hand, look at the joy it brought me I think I'll just take my holiday now Though if you don't mind, goodbye Monsieur Bocair May I wish you great happiness Thank you very much, I have great happiness Now that I've found Madame Bocair Ladies, it's a fact that today many, many women Are throwing away their youthful charm Fading before their time Simply because they fail to live sensibly And get enough of certain indispensable vitamins and iron Now don't you pay that penalty Don't be guilty of throwing away your natural youthfulness Just live sensibly and take vitamins plus once each day You see, vitamins plus gives you full protective amounts of all the certain Indispensable vitamins and iron that you must have To feel and look as young as vital and alive as you should So make it your health charm routine Vitamins plus just once each day I am adventure Next week come with me to the south in the days of Lincoln and Lee When the union almost split into two nations Meet one of the most colorful dashing figures of the war between the states In the ballad of Beauty Stewart Until next week then, I am dangerously yours Our script was written by Jean Holloway From the story Monsieur Bocaire by Booth-Carkington Dangerously yours was directed by Richard Sanville Lady Mary was played by Joan Wetmore Mollignure by Jackson Beck Music for the series is under the direction of Mark Warnell Be sure and listen in again next week When Vicks presents Dangerously yours Starring Victor Jory This is CBS The Columbia Broadcasting System