Hello and welcome. This is the tutorial about the R package per uniform. My name is Roby Van Aert, and I am the author and also the developer of the R package per uniform. So over here you see the links to the package on CRAN and also on GitHub. And what I will do in this tutorial is that I will focus on two of the methods that are in the package. And I will focus on the methods per uniform, per uniform star. At the end of this tutorial, I will tell you a bit more about all the methods that are also included in the package. OK, so what the methods per uniform, per uniform star do is they are able to correct for publication bias. And publication bias is a selective publication of studies with a statistically significant outcome. So in its most extreme case, this means that studies with a statistically significant effect size get published, and studies with a statistically no significant effect size do not get published. And there was actually quite some evidence for publication bias in the literature. So on the right hand side of this slide, you see a figure that indicates the percentage of papers that report support for the tested main hypothesis of papers, or the main hypothesis that is reported in a paper. And what you see is that in the last row, the psychiatry and psychology, you see that it's about 90% of the papers report support for the main hypothesis. And what we also know, based on the literature, is that the average statistical power is in psychology between, let's say, 20% and 50% percent. So these two, this large percentage of support that's hypothesis on one hand and on the other hand, the low statistical power on average, 20% and 50%, these two are just not in line with each other. And a possible reason for this is publication bias. And the consequence of publication bias are really severe also for meta-analysis, because first of all, it yields overestimated effect signs in the individual studies and also in the meta-analysis if you are going to combine these studies. And it also yields a false impression of whether our effect exists or not, because imagine a Jewish reader interested in a particular relationship between two variables. And all the time you see in the literature that there is statistically significant effect, but it's only due to publication bias. And you get a false impression about the existence of such an effect. I'm going to apply the beautiful, beautiful style method to correct for publication bias and to show you how these methods work. And I do this by using an example. And this is an example about the efficacy of cognitive behavior therapy for treating pathological and problem general. And this is data for meta-analysis by a colleague and colleagues. And there are two groups in these studies that were synthesized in this meta-analysis. One group, the experimental group, achieved the CBT, the cognitive behavior therapy, and there was no treatment given to the participatical problem. And this meta-analysis consists of seven studies, seven standardized mean differences where a positive effect size indicates a smaller financial loss for the experimental group. Okay, if we first take a look at the individual studies and if you just apply, also apply the random effects model by using the metaphor package and the restricted maximum logic estimate for the estimating between study pairings, then what we see if we first focus on the individual studies, we see that two studies overlap with this dotted line indicating that there is no statistically significant effect for these two studies. All the other studies do not overlap with this vertical line and zero indicating that there is statistically significant finding. And also if you fit the random effects meta-analysis model, we also observe a statistically significant result because it does not overlap with zero and we observe as estimate of the average effect size, port 52. So this indicates an effect which is of about medium size as we use these thresholds for interpreting effect size. And if we look at the between study variants, this is estimated as zero and if we test the normal hypothesis of no heterogeneity, then the PVL is port 69, that's also no evidence for heterogeneity. We cannot reject the normal hypothesis of no heterogeneity. I'm going to try to explain how the pre-uniform method works. And I will first do it by using an example, an example where I simulate data of studies and I do this repeatedly. So I do this for multiple studies and then every time I compute a right tilt PVL and the true effect size is equal to zero, which means that I'm computing PVLs over the neural outputs, generating data under the neural outputs. So let's do this and let's generate some studies. Then starts like this. So let's now pause for a moment. And then what you see is that if I have to say something by the distribution of these generated project PVLs, I would say, well, they seem to be, they seem to follow a uniform distribution. It's not both uniform, but they seem to start following a uniform distribution. So let's add some extra studies and then you see the more studies are generated, and they simulate the data, the more this distribution looks like a uniform distribution. And this is used for the PVL method. So the PVL method makes use of the property that the PVLs are uniformly distributed under the neural outputs. The PVL distribution for other effects size. So if the effect size is, for instance, 0.2, then we get a right skew distribution. If the effect size is 0.5, then we get also a right skew distribution that's more extreme than the one for 0.2. And the opposite is actually true for negative effect sizes. So some left skew distribution for minus 0.2 and the distribution becomes more left skewed if you have an effect size of minus 0.5. And the PVL method makes use of this property for estimating the effect size. And more detail or some more technical details are in these two papers. Let's do this Gamble example. And remember that the effect size estimate, the effect size estimate in the Gamble example was about port 52. And then what we need to do, and you probably noticed, we first need to install the package. So like this with this line of code, we need to load the package. And then we can use the PVL form function in order to apply the method. And now what is needed, we need to provide some input for the argument yi. So these are the effect sizes. And these are now in the data frame dot. And we also need to provide the sampling variances, the vi, and these are also in the data frame dot. And finally, we need to specify the side and the side can be either right or left and indicates whether significant effect sizes are in the right or the left tail of the distribution. Let's have a look at the results. What we see is that the effect size estimate of the method is about port 22, which is substantially smaller than the port 52 that we obtained with the random effects model. So the random effects model, we have let's say an effect size of medium size and now it is an effect size of small size. Over here, we get a confidence interval. This is a test for the null hypothesis of no effect. So in this case, we cannot reject the null hypothesis of no effect, p-value of port 25, and this indicates the number of statistically significant times. You see that you can also get a publication bias test based on the pu-reform method. We're over here a test statistic and this is a p-value to test whether there is publication bias and over here, we cannot reject the null hypothesis of no publication bias. And in this case, we have specified in the pu-reform function, we have specified the effect size and so the sampling variance, but you could also specify, for example, the correlations together with the sample sizes, or you can specify the means of two groups together with the standard deviations of two sample sizes, and then the function itself computes for you the effect size. So drawbacks of the method, that's what we realized when we worked on this. And drawbacks of the method, the pu-reform method is as it deals over estimation of effect size, in case of heterogeneity in the true effect size. And it's also non-efficient method because not all available information is used because the method makes only use of the statistically significant effect size. And for that reason, we improved the method and we call this method the beautiful star method. And it's an improvement because first of all, it enables estimating and testing of heterogeneity in true effect size. So that's an advantage. And because of that, it also does not overestimate the effect size anymore in case of heterogeneity. And the second advantage is that it takes into account significant and non-significant effect size and is therefore a more efficient method that uses all the available information. And you can apply the method by using the code pu and i underscore star. That's also in the pu-reform package. If you use this function, then you need again, you need to provide the function with the effect sizes, the sampling variances of the effect sizes. And also you need to specify whether the significant effect sizes are on the right or the right or the right or the left tail of the distribution. The output looks like this of the pu-reform star method. And what we now see over here, seven studies included. K is equal to seven, still five significant studies, of course. And we now get an effect size estimate of about port four, which is still quite a bit lower than the port 52 of the random effects one. We still get a confidence interval and we also get a test of the null hypothesis of no effect that is now still rejected, p-value of port two. And I also estimate the tween study variance using the methods where the between study variance being from star is now equal to zero. This is a confidence interval of the between study variance estimate. And this is a test of the null hypothesis of no heterogeneity. And remember that also in the random effects model, the between study variance was estimated as equal to zero. It's the same as with the pu-reform star method. So to conclude regarding this example, the effect size and the average effect size is considerably smaller when we use the pu-reform pu-reform star method. So when we correct it for publication bias, what's the null hypothesis of null effect was rejected with pu-reform star but not with the pu-reform method. So there seems to be evidence of publication bias over here with quite a bit lower effect sizes. Also developed some web applications for the pu-reform pu-reform star methods. And the reason for this is that some users might not be familiar with R and therefore it would be more straightforward for them to use these web applications. So that's why we also have implemented the methods in these web applications. And for future developments, we have in mind to extend the methods such that the pu-reform star method, for example, does not only draw distinction between the one hand significant studies and on the other hand, non-significant studies because currently the method only draws distastation and treats only significant and non-significant studies differently, but to extend this to more intervals. So you can, for example, imagine that also the probability of publishing a study that this is different for, for example, a study that's positive compared to a study that's negative. This is something that could also be included in the method. And we are already working on the inclusion of moderators in the model. So to also correct for publication bias if you're interested in the effects of moderators in the meta-analysis. So this was only a very small part of the functionality of the package. There are also other methods included. So for example, two methods included to meta-analyze an original study and a replication study. And what the methods do is they take it to accounts that is likely bias in the original study but not in a replication study. And then they combine these by means of a meta-analysis. So there is a prequitous methods which is called the hybrid method of meta-analysis. That's the hybrid function in the package. And we have a Bayesian method that computes posterior model probabilities for different effect sizes. And this method is called a snapshot Bayesian hybrid meta-analysis method. And you can use this method using the snapshot function. Then we've also created a plot. Plot that's called the meta-plot. And this plot is actually can be used instead of a funnel plot and it's based on cumulative meta-analysis. And finally, we have also some functions in the package to correct for outcome reporting bias using this corp method. So that's it. That's a small illustration of what you can do with the package. So I would like to thank you for your attention. Over here are the links to the package, Sira over here and over here on Githam. And here is an overview of the references that I used during the presentation. And most of this work on especially on a beautiful, beautiful style method is together with my colleague, Marcel van Aas also from Tilburg University. Well, thank you for your attention.


Let me know when you want to. Ready. Ready? Yeah. OK, so good evening, everyone, or good afternoon, depending on what you are doing. Today we have Pablo Rancho from the Institute for Theoretical Biology in Cuba University in Berlin. He's going to talk to us about collective information processing in collective biological systems. So whenever you want, Pablo, you can take the lead. Thank you very much, by the way, for accepting the invitation. You're welcome. Thank you for inviting me to give this talk. So welcome, everyone, first of all. So yeah, as my name is Pablo Rancho, I'm located in Berlin and head of an independent so-called Immunoterm Research Group. And the title of the group is actually Collective Information Processing. And this will be also the main talk or the main content of my talk. So my background is actually physics. But I moved over the years more and more into biology. And one reason for this was this beautiful examples of collective behavior that we see in the natural world. Starting from the smallest scales, if you think of bacteria coordinating the motion already at the micrometer scale, and moving up the scales to meters up to larger swarms that we have actually in the natural world, which are local swarms can consist of millions of individuals and span of tens of kilometers in size and travel over continental length scales. And right now, some of you probably have heard that we have actually over the past months quite a problem with locals in eastern Africa, which is something which probably doesn't really enter the news that much compared to other current developments. But it's actually a huge problem for large parts of the world. What fascinates me in particular is, first of all, if you come from physics, you might be interested about this complexity of the system and the self-organizing nature of the system. And you can ask whether there are any universal principles or laws that govern the macroscopic behavior of the systems despite the biological differences. So what could be the common features of the self-organizing systems? However, if you're coming from the biological side, what I find particularly interesting or even more maybe interesting about the systems is that they just don't come together by simple minimization of free energy, like, for example, fluid droplets or something like that, but they actually evolved over long time skills to be collective.  And one, there are a lot of theories in biology why being in the group might be beneficial to individuals and why it might be evolutionary advantages to be in the group. I like to summarize a lot of these benefits actually as the collective information processing. And in particular, if you think of predator avoidance, for example, for a bird in the flock, it's actually might be a single bird doesn't have to detect a predator. It may actually pay attention to the social information provided by others and if they try to escape, it might be a good hint that this focal bird should escape as well. But what I'm particularly interested in my research is actually this interplay between the self-organizing nature of the systems basically through simple local interactions and also this biological function. How do these two interplay, how they constrain each other and how they might sometimes even synergize to really work together. Let me, before I jump to an actual two topics that I wanted to talk about in more detail, give a brief overview of what we're doing in general now on that. So there are plenty of models of collective behavior, of collective movement in literature. Maybe some of you have heard of the famous Vitchick model or the three zone models. So these models are typically referred to as social force models and they typically assume that you have something like binary force like interaction between agents which could be repulsion, attraction, alignment so that you coordinate your directions of motion. And however, what these simple models typically neglect is the actual perception at the level of individuals. This is something that bugged me for a long time and this is one main activity of our lab to actually develop new models of collective movement which take the potential limitations of perception and maybe even cognition into account but still with the bottom up approach really taking the physics approach to this but really thinking about how can we bring perception into a physical way of thinking. And one very recent paper that I'm pretty excited about in this direction is with Renaud Bastien which just appeared in the beginning of this year where we really take a bunch of agents, self-proclaimed agents that move in a, let's for the beginning in a two-dimensional plane and they have only interact through vision. So basically they can only see what is protected in the retina. And then the simplest case it's shown here depicted on this graph is you can imagine that they have the simplest possible vision that it's basically black and white. So if there's something it's black if there's nothing it's white and there's might be no object recognition. So even you cannot distinguish whether it's a one individual close by or two or three individual overlapping because of conclusions. The question is, is this enough to generate for example, coordinated movement? And in this paper we actually started from bottom up writing down like a mathematical framework how this vision-based model could work and how agents can adjust the movement based only on the pure minimal visual input by this visual field as depicted below in a simple sketch. And just to give you some examples of this paper, here this is just one example how such bunch of agents, this is I think 40 agents here can effectively just paying attention to this intermediate or this immediate visual input how they can effectively coordinate and eventually start to move in a very coherent coordinated fashion. So you should remember that each agent just pays attention to the basically black-white input on the retina on its kind of what it perceives. So there's no concept of distance. There's no concept of other object or other agents. It just sees this black-white swirling pattern on its retina and tries to respond to that. And eventually if all agents do the same they can effectively coordinate the motion. And here we actually looked for very, very simple or the simplest possible rules that allow for such a visual flocking. Another just parameter regime gives you a different dynamic like for example this kind of disordered swarm dynamics that might remind you maybe of mosquito swarms and it still remains cohesive. Maybe it's not as on the first glance not as impressive as this coordinated movement but when you pay attention to this and I hope kind of visible in this movie already is that there are basically no collisions despite this very chaotic nature of the swarming motion but the agents still with this very limited visual perception are almost perfect at avoiding collisions. And this is something if you work with social force models not trivial at all if you want to think if you really want to avoid collisions that agents really don't come to close. And here it kind of seems to work at least for this visual, pure visual flocking or a swarming dynamics. So this is a huge part of our research actually goes into trying to develop new models or models that of collective behavior that take into account individual perception but another big field of the research that I'm gonna talk today a little bit more is actually behavioral contagion and collective decision-making in particular fish. And as I said, especially in the second part of my talk I will talk more about this. Then there's also a bit less prominent in recent years but also a huge interest of mine understanding new phases of biological active matter what type of microscopic patterns and microscopic dynamics you can obtain. And last but not least what I'm also interested in is coupling this complex collective spatial temporal models with evolution dynamics and really see what is the consequence of potential spatial structure or spatial temporal dynamics on the evolutionary outcomes in such evolutionary games. All right, but let's me after this brief overview let me jump to the first topic that I want to talk about which is flocking in the complex environments and in particular something which I refer to as coordination versus environmental responsiveness trade-off. And this work has been done in together with visiting red student, Parisa Ramani in collaboration with Fernando Perrani who is at the University of Nice and was just in April published in cross computation biology. So that typically when you think of collective movement models most of them happen or are assumed to happen in bare or empty environments. Oh, I think, sorry. So I think I don't know if it was me or your connection was lost for a while. Oh, I think it was my connection. I apologize. No, no, it's fine. So it was cut after you were introduced in the visiting red student, Parisa Ramani. Okay, so yeah, it was done together with Parisa Ramani and Fernando Perrani from the University of Nice and this paper just got published or this work just got published a few months ago. And one motivation for this study was that if you think of modeling collective behavior but even experimental collective behavior a lot of this is done in simple, bare, empty environments. You think for example of fish being just an empty tank and seeing how they swim together and coordinate motion. In simulations you even assume oftentimes periodic boundary condition just to get completely rid of any boundaries and just look at the bulk behavior in an empty environment. But this is not the situation that most animals that try to coordinate motion actually encounter in real nature and real ecological setting. And this was our motivation. We wanted to see how can actually agents coordinate and collectively process information if they're in a complex environment where on the one hand they want to pay attention to social cues, coordinate for example with other fish but on the other hand there might be other environmental cues which might also provide some important information. For example, signal that there might be a threat or a danger so that the agent should avoid areas for example around this structures in the environment. So this is our basic motivation and we developed like a simple generic model which tries to really see how agents can do that. In particular, when we assume that they have a finite attention capacity. So they cannot pay attention to all features whether it's environmental or social at the same time. So the essential model set up looks like this. We have agents, let's think of fish but it could be basically any agents in the complex environment and any agent can only pay attention to cane nearest objects. Irrespective whether it's another agent or whether it's a structure or a cue from the environment. All agents in our case try to coordinate the motion with the neighbors that they detect in order to move together with them and flock together. They also want to avoid avoidance zones around a point like a distraction site or danger sites that we distribute in environment but they can only detect those danger sites if this danger site is within its cane nearest objects. So again, this is the kind of limited attention capacity that we implemented. And in addition, there are few informed agents that actually know what is the right direction of motion the right direction for example to go to food or to migrate and this will be throughout the talk always along the X axis to the right. And the question is now given this set up what is the role of this limited attention to actually for the agents to coordinate to avoid the danger zones but also to find the right direction of motion provided by the few informed individuals. And in order to address this question the first and obvious thing would be just to first look at the interaction networks. So how do the agents actually interact with each other depending on this attention capacity how many objects they can pay attention to. And these are the social interaction networks for the agents. The agents are shown in black or in red. Black are just agents socially interacting and red are agents which are within a danger zone but also close enough to the center of the danger zone that they can actually detect the distraction site and respond to this. So these are the agents actually responding to the sites and I should mention that once you respond to a danger site it overrides all the social interaction so you're not coordinating anywhere any more with your neighbors you're just trying to get away from directly away from the center of this distraction site. So this is basically what is shown here for two different attention capacities. One very low attention fragmented networks with low degree and you get this disconnected clusters basically which are connected within each other but there's no global connection between the different clusters. On the right hand side if you have eye attention capacity on the other hand what you basically see is a single giant component within the interaction network and there are of course some isolated individuals which respond directly to the distraction sites and this is why they're non-responding not linked to the interaction network. I should mention that this is in fact an actually directed network here for simplicity we just show an undirected version but basically a link means that there's either connection from one agent or the other it's not necessarily bidirectional connection. And another thing that you should notice that this is of course dynamical so this is just a snapshot but in reality and we will see examples of this these networks are dynamically involved over time. So the question is now what would you predict where the system can better coordinate and better collectively process information? And from a simple network perspective you would argue well for the better connected network on the right hand side for example the information provided by few informed agents can diffuse much more effectively to the entire population therefore you would expect that for K12 this is the network that should coordinate better or be better at collective information processing. And in fact this is what you also see for empty environments if there are no distraction sites so completely bare empty environments and this is as expected with increasing attention capacity with increasing parameter K for all ratios of informed individuals starting from very low ratios to a fully informed system you always get better accuracy of following the few informed agents to moving to the right in a stationary state with a higher attention capacity. So the larger the K the better the network the interaction network is connected on average and the higher the accuracy collective accuracy of the system. But surprisingly if we now move to a situation with a complex environment or heterogeneous environment with a high density of the distraction side the situation completely reverses. So here actually a better connected network leads always to a lower collective accuracy. And surprisingly if you have a look at this graph even a fully connected, sorry even a fully informed system with K24 this is the yellow line this is actually as accurate as a flocking system at K1 at the minimum attention capacity at only a few percent of informed individuals. And the question is why is that? I think it's much easier to actually develop and do that by looking at actual what happens in the real simulations. And here this is now a simulation movie of a high attention case. So for K24 each agent can pay attention to 24 other objects. And this is a high density scenario randomly distributed distraction side with the danger zones depicted as this bright bluish areas around the point like distraction side. And then we have naive and informed individuals.  And the red ones are actually socially interacting whereas the red ones are responding to the distraction side. And in this example there's always or in the examples that I'm going to show there are always 10% of informed individuals so only one 10% of the systems knows that one should go along the X axis to the right. And if you look at this high attention capacity what you see if you start from random initial condition that the system very quickly starts to converge and you see this development of local flocks or swarms that move together in a coordinated fashion. However, they are very good at responding to the environmental cues. So trying to find the free paths within the environment but they just basically move randomly and there's no global coordination or no global order for the agents moving to the right. So now if you look at the collective accuracy how well they are actually able to follow the informed direction of motion. This is shown in the screen curve accuracy it's basically oscillating around zero. So they're basically randomly moving just through the free paths in the environment. So this is the why the accuracy is actually so low at high attention capacity. The agents are very much responding to the environmental cues. On the other hand, if we go to the low attention capacity case, K1, this is actually the smallest attention capacity, the picture changes dramatically. So here initially it's also random initial conditions but you see the locally agents start to coordinate again and move together. However, they are not forming this large clusters but rather moving middle clusters of three, four agents maximum together. But eventually if you wait a long time you seem to realize that more and more agents start to move towards the right. Actually towards the right direction of motion. And what you actually see is that the accuracy goes almost to 0.9, 0.8 in this particular example. So the whole system globally is very good about following the information provided by the few informed agents. And they all in the end basically move almost perfectly along the access to the route. However, as you can see is the agents seem not to care that much about the danger sites and corresponding environmental structure. And the reason for this is essentially that there's low attention capacity. The attention of individual agents gets almost completely saturated by social cues because of the self-organized nature. So if you start to move with someone else you actually end up much closer to them. You form this local dense clusters and therefore for a typical agent social interacting its attention capacity is already saturated at this low attention capacity at this low K by the social cues. And it becomes blind to the environmental feature to the distraction side. Basically the system self isolates from the environment but it's very good at coordinating at this low K. This effect gets even more or even more clear if you look at structured environments. And here we have a single circular path. The right direction of motion provided by the informed individuals still go to the right. But for this high attention capacity again the agents are pretty good about coordinating the motion but also trying to find the free path which is circular here. And if we wait long enough eventually most of the agents will end up moving together in this free path in the environment and only few by accident enter this completely dangerous kind of region filled with distraction side and danger zones. So this is for the high attention capacity. So they basically are able to follow the structure and environment completely and ignore the information provided by the informed individuals which is moved to the right. For K1, again this looks very different. The agents just don't seem to care about the environment at all and are more or less able to work through this high density distraction side field but they are able to follow the information provided by the informed agents. So essentially because of this dynamical networks even if you have always small clusters but this clusters break up fuse together and therefore the on a long time scale the information provided by the informed agents is able actually to percolate through the whole system and you see very high collective accrues. So now you can actually quantify this and really look at the collective accurses versus attention capacity and this is now basically quantified what we saw before in assimilations. You get the maximum accuracy at low attention capacity typically the attention capacities of one or two and then if you increase the attention capacity further then a collective accuracy goes down and the different curses here are for different densities of obstacles. So the blue one, the top one is for a lowest density of obstacles and if you increase the density of obstacles this maximum becomes even more pronounced but this high collective accuracy at low attention capacity comes at the price. You basically become unresponsive to the environment if you have this low attention capacity and this is shown now here on the right hand side this is a measure of the distraction side avoidance and here we normalize it actually by a control group of solitary agents which do not socially interact only respond to the environment and this is the black line. So essentially collective system is much for low attention capacity is actually much worse as responding to the environment that solitary agents. So being collective does not provide any benefits in this situation but rather actually gives you provides only costs because if it's really important for you to pay attention to the environment and only above a critical attention capacity and in this example it's around 10 you become more responsive to the environment than solitary agents. So there you can only see benefits of being collective with respect to the response to the local environment cues. And the question is, you know we believe that this coordination response in this trade-off is actually maybe a general potentially a general feature of collective information processing system where you basically use the same, let's say cognitive machinery to do two different tasks. So you either can coordinate with others or you can respond to the environment. You cannot do the same if you use the same sensory machinery if you use the same basically brain to... So let's see, yeah. So Pavel, we lost, sorry. Yes. We lost you again. Okay, I'm sorry for that. It seems to be an internet issue although I'm using Ethernet cable. When did you lose me? So we lost you when you were like one minute ago it was a chain of a long you were explaining this accuracy trade-off. Okay, right. So did I already explain the right-hand side or only the left-hand side? No, no, the right-hand side was already explained on the normalization and everything, it was like very short break. Okay, so yeah. So we are just basically wondering whether this is like a fundamental feature of collective information processing systems and whether this trade-off, coordination response in this trade-off could be even as fundamental to the system as a speed accuracy trade-off that is well-established. Okay, so this is maybe the first part of my talk and before I move on to the next one maybe there are questions to this first part. If not, I'm happy to move forwards. So is there any question? I have a very quick question that maybe when you were showing these composite plots with the video and the accuracy and the avoidance as a function of time, I think the accuracy was, at some point it was getting negative, it was fluctuating around zero but it was becoming negative. Yes, so this is because we, as a accuracy, we calculated the scalar product of the individual velocities of all agents with the right direction of motion which is just basically a unit vector along x. And if the average velocity of the agent actually moves sometimes in the negative x direction then your accuracy can become negative. This is different, for example, if you think rather like of an order parameter which can go from zero to one, here for this, if there is actually a selected direction of motion, it can also become negative. But this is just a stochastic effect and it typically fluctuates around zero. So is there any more questions before we move? Yes, I have a question. Yes, I was gonna ask how is the information actually implemented in the simulation, the information flow? Yeah. So the information flow is just implemented in a way that we have these different agents, they all obey to stochastic differential equations and they're different force term and the informed agents which is, as I said, 10% ratio of 0.1 in these examples, informed agents have an additional term which tries to align the velocity with the x axis. So they bias the motion slightly, not very strong but slightly towards the x direction. So this is like an additional force that they feel. And then the only way that other agents notice or pay attention to this information is that they try to align with the neighbors and some of them are informed which tends to move towards x direction and therefore they get this through social interaction as bias to move along the x direction. And so this is all basically social interactions and basically copying of the movement directions. So how is the danger, for example, the danger area modeled? So the danger area, so we try to be as simple as possible so we assume point like distraction sites and when the distraction side which was like at the center of these blue zones was within the canary's object, then you detected this and then it had a certain, the danger zone around this had a certain size which was set to unity just for simplicity. And if the agent detected the distraction side and was within this danger zone, then it moved directly away so it ignored all other influences and just experienced a force, a repulsive force away from the distraction side. So it was basically once you detected the environmental queue, you ignored all other forces. Actually... Let's see if we recover him. I have a certain chance to detect this. Yeah, sorry, Pavel. After you said that once you detect the danger zone, you start paying attention to all the other forces, we lost you again. Okay, so yeah, and then you just move away. But we actually did some additional variations of the model just to test for robustness of our results. So for example, we increased the saliency of the distraction sides by saying if an agent is within a danger zone, so within a distance one to this distraction side, and it actually doesn't detect it because it's not within its canary's objects, it still has a finite probability of detecting it because it might be just very important for them to pay attention to the environment. And as long as this probability is not one where they always perfectly detect the environmental queues, you always get this coordination response in the trade-off. So this is really robust with respect to the response to the distraction sides, as long as there's some trade-off in terms of the perception of social and environmental queues. Okay, thanks. All right. So I think we've had one more question before, I don't know. Yeah, I'm about to ask a question, but it was answered in this, so thank you. Okay. All right, yeah, if there are any, maybe I should mention if there are any questions which go more in deep, I'm actually happy also to reply to them by email or maybe in another private chat if someone is interested. But of course, there's much more in the paper that I can discuss here within the talk. But if you have some urgent questions, you can always contact me after the talk at any point. Okay, but then let's move to the second part, which is rather changing the topic from this very theoretical collective information processing in terms of movement coordination to collective escape response and behavioral contagion in animal groups. And in particular, what I'm going to talk about is actually a work that we published end of last year together with Ian Kazan and his students, in particular, Matt Sosner who did the experiments on individual versus collective encoding on brisk. And the basic dynamics of biological system that we're interested in are so-called starting cascade in fish schools. So here's a fish school and sometimes individuals just get scared and they perform this very fast so-called sea start or startle where they just basically jump suddenly to the side. And this startle response has been studied in neuroscience for fish for a long time and it's very interesting behavior which is known to be governed by a single neuronal structure. But what is really interesting about this behavior that can also spread socially through a group. So basically, if spontaneously one fish just startles because it got scared maybe by a light reflection, then other fish may copy this behavior and also startle and then you see this cascade spreading through the system. And actually in this Rosenthal paper, the first one, this is where the group of Ian Kazan actually, Ian Kazan for the first time, showed this social contagion dynamics in the startle cascades. The nice thing about the startling cascades is that they are very, this is very fast acceleration and you can nicely quantify this. It's more like neurons firing one or zero and in the tracking data you can actually clearly, more or less clearly say, oh, this individual was startling and this individual started a few milliseconds after that. And using this information in this clear quantification, who was the first startler, spontaneous startler, who was the first responder, you can actually build interaction networks which is actually in general very challenging for animal groups. And this is an example of such an interaction network from the paper by Brendan Rosenthal and Colin Toomey where they just overlaid on top of the, of free swimming schools, the interaction networks they gained that they deduced by analyzing many of such startles. So each link is actually a probability of an individual startling given that the corresponding other agent that it's connected to startles at a given time. And you can see it's a very complex network which depends on the local structure, but it's also very much dependent on distance. And you can summarize it in general as the closer you are, the more likely you are to startle if your agents, if your neighbor starts. So if you're interested more in the details of how to determine this interaction networks, I definitely refer you to this nice paper by Ian Cousins group. What Matt Sosner did, which was a grad student in Ian Cousins lab at the time when I was a postdoc in this lab, was he moved a step further. He wanted to ask what happens if you change the context, the school, the fish are actually swimming in. So what happens if you can change, for example, the risk perception? How does the system respond to this change in risk? And the great thing about fish is that there is actually a chemical agent, so-called trek stop or alarm stuff, and that can be put in the water which can be obtained from the scales of the fish and to other fish, the signal danger. Because maybe another fish was injured by a predator, so if they notice the trek stop in the water, they respond very, very strongly and they get much more afraid and perceive this as strong subjective increase of risk. And what Matt did was doing control experiment where he actually added the trek stop after a certain time and looked how did the structure, how did the school respond in terms, for example, of nearest neighbor distance, but also what happened to the spontaneous starting cascades that we observed just in these videos. And what can be seen is that if you add the trek stop, after you add the trek stop, the, in general, the collector response increases because you get larger cascades of a larger size as shown in this plot where you see the cascade size distribution. So for the forearm, when the fish are kind of relaxed, you see only very few or mostly very small cascades with one, two fish most of the time only responding. But if you add the trek stop, then in this case, you see much larger cascades with almost half of the fish school oftentimes responding. So the total fish school size was here, I think 14 individuals. So there's a strongly significant effect and the question is, what is the mechanism that governs this collective increase in perceived risk? So when you think of how animal can adapt to changes in perceived risk, if you have to think of solitary individuals, they have only one possibility. They can increase the individual sensitivity so they can set down their threshold and pay more attention to the environment and just be more responsive on their own. However, if you're in a group, you can have another possibility to adjust the collective sensitivity by modifying the network structure. So for example, that you move closer together and then a spontaneous startle cascade could actually, given that the links become stronger, propagate much farther through the system. And the question now is, where is the fish system actually located? And this experimental setup allows us actually to really disinvigorate this two dimensions. So really to ask, where is this natural fish system located? So is, do the fish only modify their own sensitivity or really do they change the group structure or is it a combination of both effects? And what you can immediately notice from analyzing the tracking data and watching at the movies of the experiment is that the network structure changes a lot in this experiment. So this is the fish school before Shrekstorf. So it's pretty relaxed and the fish are spread out through the tank and swim more or less in a coordinated fashion but still more or less also on a global scale, random, but they seem very much relaxed and the nearest neighbor distance is pretty large given what the fish could do in principle. If you add Shrekstorf, the situation changes dramatically, the fish move much closer together. So they basically form this much more cohesive show and stay much closer together and the nearest neighbor distance just drops massively within few seconds after they smell the Shrekstorf in the water. So we know now that the structure changes a lot but how can we actually look into the brain, so to say, of the fish and ask, well, maybe in addition, they also change internal threshold because they get so scared. And in order to address this question, we combined the experimental data together with a mathematical model and here we used a generalized contagion model which was actually inspired by the dots in what's paper for generalized contagion model proposed actually for more social human systems and we just took this model and adjusted it for continuous time dynamics and probability rates instead of the discrete time as they did in original paper in 2004. So essentially we have a network, we have a focal agent here and the red agents are now the active agents, so through their behavior, through their escape behavior, they send activation signals with a certain rate represented by the strength of the links to the focal agent. The focal agent receives this activation signals over time and what it does, it just integrates with the moving window, it receives signals so that at some point the cumulative dose increases and may hit its own threshold and if it hits its own threshold, then the agent becomes close from a susceptible state infected or active state with also response with the escape response. So it's now basically pure network model that we map the system to and after a while after the agent becomes active, it can go back to a refractory state and for us, for simplicity, we assume that this refractory state is an absorbing state just to be sure that we have only one cascade and that we can in this way clearly analyze the size distribution of these cascades but given that the cascades in the real system are rather rare and it's also a good approximation of the real biological situation. So now we have this model and we can actually parametrize this model by the experiment data. So what we can do is we can take the networks that we have that we can obtain from the experiment from the tracking data for before and after structure. So we can actually count for the change in network structure due to the change in perceived risk. And now what we can ask is what happens if we now change the average individual threshold, we can actually eliminate all the other parameters and we end up with this individual threshold to be the single free parameter in our model. And now we can simulate this contagion process on the experimentally obtained network for before and after. This is one example of a real fish network and if we have now a high average threshold, you see what you expect. Basically, after spontaneous target, you have only a small cascade appearing. If you have a low average threshold, then the situation, sorry, somehow my presentation crashed. I will try to open it again. Okay. Maybe there's time for questions if someone wants to ask in between. Is there any question? I have a quick one, but I don't know if you will answer it like in two seconds. In this container process that you are showing now in a network, the network is stationary. It's going to also, the links are going to quibble because I guess these fish networks are dynamic, no? Yes. It's a very good question and I can answer it maybe not in two, but in five seconds. Okay. So starting a scale up very, very quick. They happen within less than a second. And so you can assume it as a time scale separation. So even if these networks are dynamic on a larger time scale, you can assume that in the beginning of the starting cascade it's essentially almost like a fixed network. And this is the fundamental assumption that we do that basically what determines the size of the cascade is the network at the onset of the cascade. It's not true for very large cascades, but it's a reasonable approximation which seems to work pretty well in terms of predictions. Okay. Thank you. And let me share now again the talk, I hope it works. Okay. Yeah, it's perfect. So now for a low average threshold, which when each individual gets much more responsive, then again, you see what you expect is that instead of this localized cascades, you get basically a whole system firing up. So you basically get a global cascade and basically every individual in the network responds. And now we can ask, given that we know what the experimental cascade size distribution is, we can do some maximum likelihood fitting and see which threshold actually explains the cascades before and which threshold explains the cascades for the after networks. So in a way to account, we know how the networks change from extrovert data. We don't know what changes in the brain, but now through this combination of the modeling work can freely change the threshold parameter, we can see which values of the threshold parameter for the before and after condition match best the observed experimental cascades. And to our surprise, what came out is basically that the individual threshold don't have to change at all. Effectively, this is shown here on the top one, the average dose threshold is the same predicted by the model from the maximum likelihood for the before and after conditions. And these are just two different experimental exposures. So it really doesn't matter, but it seems to be really robust. And you can actually see what effects can contribute to this. So the baseline is the first graph, the first bar on the bottom. And then if you only change the thresholds and keep the networks constant, you cannot explain the increased collective response by the increased cascade size. Only if you change the structure of the network alone, then you get almost the, or you get basically the effects that you observe in experiments. And if you do two modulations, so you change both the responsiveness slightly and the spatial positioning, you get some synergy of both modulation, but it's actually much smaller than the adjustment of the network structure itself. So now given our two-dimensional diagram, what seems to be the case for this fish, which are the golden China's, is that they essentially seem to, at least in this experiment, to only modify the network structure and not really modify the internal thresholds. And the question is, well, this is true for this fish, but is it true maybe for other natural collectives or maybe other natural collectives do this differently? What about artificial collectives that we think of, for example, swarm robotic applications? Can we maybe exploit this two different dimensions in a different way? And one important question is, of course, what about real-world behavior? Because again, this was something that happened in lab and our artificial lab condition maybe is different for the fish in the water. And this brings me to the very last part, the few last slides, which is more like an outlook of ongoing research where we actually look at this behavioral contagion processes in the wild. And in particular, we look at so-called giant escape cascades in Sulphamollis, which is a species of fish that lives in the very special conditions in central Mexico. And they live in Los Azufres, which is a special basso or special ecological setting in sulfuric river system, which contains high concentration of sulfur hydrogen in the water, which is typically toxic to most or to all living beings, but this fish actually developed a specific evolutionary, the specific capability to survive in this toxic sulfur containing water. And the reason they want to live in this environment because they can feed on sulfuric bacteria that thrive in this environment, so they have plenty of food. And so that's the reason why they adapted to live in this environment and they can live at very, very high densities. However, they pay a price for that. Because of the sulfur in the water, basically there's no oxygen in the water column because it immediately reacts with the sulfur hydrogen. And therefore, if they want to breathe, they have to stay, most of the time, this fish have to stay at the surface to do so-called surface respiration. And this is some snapshots from the field side where we do our field research. So this is in the bottom left picture, just taking from underwater, showing upwards. And all this fish that you see here nicely aligned in the stream, I actually at the surface. And this is another close up. So this is a really natural setting and this is the fish are very small, but being in the surface, that makes them very vulnerable to bird predators. So there are no underwater predators because no other fish can survive in this environment. But of course, being in the surface, make this fish a very prime target for bird predators.  And what is interesting is that this fish seem to develop a very peculiar collective response to this bird attacks, which typically happened that for example, a kingfisher, this black-white birdie at the top flies in, basically dives down, picks one fish up and then moves off. And you can simulate this by shooting projectiles into the water and they basically very much resemble, the response that you observe very much resembles their bird attack. And if you do this, and this is now a perspective corrected video of such projectile that we shoot in into the water, then you see this collective response of the fish. So here, when we start the movie, you see the impact and there are little ripple waves spreading out of this. But then you see these additional waves, surface perturbation spreading through the system. And this is actually a startling response of thousands of fish in this open field system. The length scale here is 10 meters. So you see actually tens of thousands or even hundreds of thousands of fish responding in this collective manner. So it seems to be boiling a little bit immediately after impact, it seems to come down.  And when I saw this immediately, I thought, well, that looks like excitable media or statistically driven excitable media. And it's very much resemble some things that you might even see in normal cultures or certain chemical reactions that are noise-driven. So I was very much excited by this. How are these waves produced? Again, this is a startling response. However, it's slightly different than in the lab. They don't do this in the surface, but what they do is a collective diving response. So essentially the fish, when they respond like this, they collectively dive down, and what you see in the surface videos is actually the splash that they're generating by suddenly diving down with the startling response. And we can actually use this type of surface videos to quantify the macroscopic activity. And this is just one example where we can track the wave fronts developing and in this way actually obtain statistics on the stochastic behavior of these waves. And we have actually evidence. So let's see if we recover him again. The fish in the spawns. So we know we can make... Sorry, Pavel, sorry to interrupt you. We lost you when you were saying that you can characterize the wave front of the stochastically driven waves. Yes, okay. So yeah, we can, I can maybe restart the video again. So sorry, Hades. Now my computer is not responding. So yeah, we can quantify this. We have evidence that this is actually a excitable, noise-driven system based on this analysis. We can quantify the size and the speed of these waves. And for example, we can show that the largest waves can span up to 30 square meters in area. And this means that there are, you know, even up to 100,000 of fish participating in such a way. And we can also quantify some effects. How long does this waving effect act before the system goes back to the resting state? And here this seems to be that there's actually something like collective memory of the system for several minutes. So this enhanced activity of the system after bird attack or a fake bird attack acts up to two or three minutes after it happens. But we can do more in the system. We can actually do even close-ups. And I apologize for the low quality, but this is actually a real field recording. So on the left-hand side, this is a real recording of such a wave with a close-up. And if you would look very closely, you can see actually individual fish in there. And if I let it run, you can see in slow motion a wave spreading through the system, through this surface perturbation. And what we can do now, we can actually take these videos, use for example, convolutional neural networks to obtain the position of the fish just before the wave spreads through. So what we can actually do, we can get the position and the networks in a similar way we did this in the lab for the starting scales. And we can here, this is now such a network that we get reconstructed on real positions and we can run our complex contagion model on this real networks to ask the question, for example, how does the speed of the wave or the size of the wave or the probability that the wave propagates through certain regions depend, for example, on the density of the fish and compare this to the actual empirical observations. And this is something that we are currently working on. So this is work in progress, but we are very confident that we may get some really interesting results for this behavior, for this behavioral contagion in real system in the lab. And this brings me to the summary of my talk. So basically I gave you two parts. The first part was, sorry, somehow my slide doesn't change. It's stuck on the video. I hope you enjoy this. So yeah, basically the summary would be, I showed you two examples of our recent work. One was about this coordination, responsiveness trade-off in collective information processing. Sorry, I stopped this here and restart. And especially in the role of the limited attention capacity and how it may change actually in unexpected way the collective information processing capabilities. And the second part was about this behavioral contagion in fish and in particular this special type of escape cascades. And what we could show in the lab is that the collective response to increased risk seems to happen almost exclusively or at least in our experiment exclusively through the adaptation of the collective structure, through the network structure and not through the adaptation of individual sensitivity. And the question is how general is this really for other biological systems? And you can imagine maybe other biological systems where agents cannot easily change their relative positions and therefore such a system to collectively adapt to a changing environment, it would make much more sense to change the behavior of individual nodes instead of changing the network. So we are really happy and looking forward to see some other examples or learn of some other examples in the nature where this could be the case and where we can test this hypothesis. And last but not least, I gave you a little bit of an outlook of what we are doing currently with our feed research when we look at this large scale escape waves which we can now study in the wild. And there I hope we will get some first publication rather soon. So if you're interested, stay tuned. And with this, thank you very much for, well, I should thank all my colleagues. So while we wait for Pablo to come back, if I mean now we're going to open it for questions, you can either ask yourself or if you want to write in the chat. So Pablo, we lost you when you were acknowledging the collaborators. Okay, so not much missed there. I think I acknowledge also the funding agencies. So you see their logos. Thank you very much, Pablo. And that was an amazing talk. Thanks a lot. Thank you. So questions, yeah, time for questions. Yeah, can I ask something, please? Of course. Sure. So, I seem to understand from the first part of the talk that when you have limited attention capacity, then clusters are more likely to be formed, you know, and then you have this kind of directional swimming. So is this something more, let's say generic in statistical mechanics? I mean, like when you have nearest neighbor interaction or short range interaction, let's say you're more likely to see clusters or is it just like something that you observe in your system? Have you seen it before anywhere else? So, I think it's pretty generic for this type of flocking models with nearest neighbor interaction. So especially if you have this low nearest neighbor interaction, surprisingly, so we have actually a follow-up paper on this project, but surprisingly not many people look at this from a statistical point of view and we are currently finalizing a preprint that we hope to publish within a few weeks that where we look at the same dynamics, not for a kind of collective information processing point of view, but rather from an active meta statistical physics point of view. And so, surprisingly, people didn't look at this that much in terms, you know, what is the role of K for the spatial structure that evolved on a larger scale? So the clustering seems to be generic feature of the skein nearest neighbor interactions. If you have another topological interaction, topological means that it doesn't really depend on distance but only on the who is your ranked nearest neighbor, which could be a Voronoi interaction where you interact with the first shell of neighbors. This one is typically spatially balanced so that you have all, you know, to all direction neighbors and there you don't see such a strong clustering, but one reason for this also that for Voronoi you always interact with six nearest neighbors which is typically the first shell of neighbors in 2D. There has been another work by the group of, I think, Kavanya where they looked at spatially balanced K and M and I think for this type of model which is much closer to Voronoi, you also would see less of clustering, but still as long as you interact with only very few agents or alternatively very short range interactions, if you have metric interaction, you will see strong clustering. So for example, for a Vitchick model which is classically has a metric interaction, so you have a certain interaction range and you interact with all neighbors within this interaction range. The smaller your interaction range is and you have small noise, the stronger clustering you get essentially. So yeah, I think it's a generic feature and people have a look at this a little bit in the context of the Vitchick model, not that much in the context of topological models like we used here, like K and M for example. Okay, thanks. So more questions? I do have a question. In the summary, and maybe I missed when you were explaining it, do you talk about some tragedy of the commons in the sense of evolutionary, tragedy of common information in an evolutionary sense? What did you really mean by that? Yes, so very well spotted. This is actually a mistake because I had a couple of slides extra of some very, which was not part of the publication. So essentially when you think of this collective accuracy or collective obstacle avoidance, this is a kind of a group's fitness or you can build some kind of a group fitness from that. If you say, for example, coordinating or moving in the right direction is that beneficial for agents, but being too close to the danger zones is that costly for the agents you can build from this like a fitness function and that would be like a group fitness function. And there you can actually see, you can vary the parameters, the costs and benefits and you can see this two distinct maxima. So you can either be good at coordinating or you can be good at avoiding obstacles. But this is still like a group selection or group fitness setting. And from a biology point of view, it would be always a glacial well, does it really hold if you have the evolution adaptation at the level of individual agents? This is what we did in the follow-up project with the master student of mine where she actually looked at each agent can evolve its own attention capacity and then see what is the attention capacity that it gets evolved. And we test a different fitness function but we chose one fitness function where the optimal attention capacity at the group level would be four. So something which is like a little bit in between but it's definitely not one or two which would be the one which is typically optimal for a coordination. And that would be the group optimal. And if you do the individual level adaptation evolution what you realize is that this is not the evolutionary stable strategy but the evolutionary stable strategy is again a minimal attention capacity of K equals one. So there's actually a strategy of common information. I call it strategy of common information but it's basically the same as strategy of the commons. So as long as there are some guys providing some information about the environmental cues because they have a higher attention capacity it always pays for individuals to decrease their attention capacity evolutionary. And therefore they have always benefited that the free riders and eventually the evolutionary stable strategy is where everyone is a free rider and everyone has only minimal attention capacity. And so this is what we observe in the system. However, this is not part of the paper and it's not published yet. I see, thank you. More questions for Pavel? Yeah, I guess if there are more questions which maybe someone wants to ask but not in the setting I'm as I said, I'm very happy to take them by email but even also if possible via online chat if someone wants to talk more. But yeah, just feel free to contact me. Thank you. So if there's no more questions, thank you again Pavel. That was again, I'm very, very agree. Well, we all really enjoy your talk. Thanks a lot.


is up Saturday afternoon, May 24th, shortly after 2.30pm Eastern Time. Great to see all of our students and members alike inside our live trading room still with us, perhaps Dave and Brian, Kathleen, Roger, Scott, Mike, Layton, all of us just here joining us right now and all of us back on social media, Facebook Live, LinkedIn, Twitter, YouTube, the works. Great to have all of us here. So we have class on ahead coming up at 305 Eastern Time, actually more on class coming up at the end of this meeting. But with that, the link for class, the phase one stock course will be in the chatboard coming up momentarily, Richard posted that actually a little earlier, but we'll have him do a repost coming up right now for all this just joining us here. So with that coming up at 305 Eastern Time, we'll be wrapping up the phase one stock course talking all about news, how essentially we scan for news, how it's relevant to our trading and you know, really more or less like how news affects the stocks, right? So a lot to digest coming up ahead, but with that let's go right to it here. APLD, I hate to say this because we've been doing so well on this stock. We, as in you and me personally, but we ran up, we ran it up so well from last week. Man, I was just focused on another trade once again, the kind of like I said earlier on a GCT and the morning meeting, my technique for Fausto, but I got caught slipping on this. It started pumping up. It was more so heading into like mid afternoon here early to mid afternoon. And we weren't as focused on it going into class here. So that's fine. But I ended up completely missing out on this pop. I was at my desk. I was just focused on a different trade. But man, this thing still has some more life to give here. Just actually re broke over a price level right here right off of 893. So let's check in right now. This could look to make a nice higher high sooner than later. Actually, this APLD trade, I have it up on my level four right now. See it's on the left hand side. So I had this up since yesterday and the day before I've really been interested in seeing if this could break higher. I was on a coaching called one of our students just before now looking to go over this break over 893 or at the time, we were looking for the drop down to 850. You know, at the time that was used as a pretty big support level between the volume that that got filled there, plus the VWAP price kind of just guiding it right there off 850. So ultimately, I was looking for a pullback lower afterwards. Didn't get that. But now at this stage, if it's not going to break lower, perhaps there's only one other way the stock is going to go. So it's re breaking above big resistance off that 892 ish 893 there. So we'll check back in with this trade definitely at the end of this meeting. But for right now, it makes the list definitely good stock to focus in on for right now is APLD. Now really quick for Roger and then Matt and Chris and all was just here in the chatboard. What stock ended up moving with the APLD last week? We talked about this. We went over this, I think even just yesterday and traders talk perhaps, but you know, what stock from last week really in full ended up moving with this APLD or rather, you know, APLD ended up essentially moving with this stock. Do you folks recall? Just saying the GDC trade not quite. We only traded GDC this morning today, but a stock from last week stock from a little while ago at this point now. So the news with APLD is AI affiliated they ended up getting a contract and AI related contract like $180 million. Well, AI, it's up like 2% today, not up nearly as much, but it's at the highs, right? So we're starting to see that correlation again, at least between AI and the APLD trade. Listen, it's not up as much, but for right now, it's actually still holding over a pretty big level this trade. I think that's right off the closing bell print that we had on this. Let me just double check that. Go back to level four here and check in with this year. So my apologies, it wasn't off the closing print or opening print from today and yesterday, rather it was off this iceberg level. So big iceberg at 2755 about 54, 55,000 shares sitting there. So over time, it was choppy, choppy, and then just heading into the early to mid afternoon, it led to the break above it, flipped it as support, we're getting the push up now up to 28. Let's check in with this APLD again, you know, on our list still, but hasn't broken up higher just yet itself. So, you know, hey, for right now, both make the list, both still could be very interesting heading into the close. Good start so far, even if you're familiar with them, both have relatively good liquidity. Now, what we'll do is go from top to bottom, though, let's build up our list pretty big, markets are kind of slumping off lower heading into the close. I'm actually short on Apple right now, believe it or not, APL. I was calling it out earlier, it goes to show that it's like kind of a bad market otherwise, when you got me calling out like mega cap stocks like Navidian Apple.  So, move on, you know, this is literally the definition of just going to the casino or just trying to play Powerball later on and trying to win. So, yeah, not really for me. I'm going to pass on that one for right now. The ENVB looks done for for the meantime. I mean, it hasn't truly broken lower just yet, right? So, there's still a shot, but it's really slumping back down off the highs pretty nicely. So, great trade from the morning. We did very nicely on this in pre-market as well as after the market had opened up. But for right now, hey, I mean, I'll write this down on the side list, I reckon, but more than likely, we're not going to put this on our list here. Don't want to be a part of this. Pop and drop city here. I didn't even notice these trades really up until right now.  So, you know, I'm more inclined to focus on the stocks I've already been looking at, you know, the PLTR trade from earlier also, Coles want to give a shout out to one of our students, Bell. She crushed that trade herself. You know, there are plenty of others that we took advantage of. You don't need to trade the biggest, scariest, fastest stock to, you know, to make some money. If you're trying to blow up your account, that's what that's the stock that you should be trading.  E-E-I-Q, well, hey, this had some pretty decent liquidity heading into the open, but this one also kind of dud it off. So, maybe it wasn't up as much, but it just goes to show that you're not trying to hold the day trade all throughout the day. This trade's done for right now, too. I was looking for the bounce from $7.90, and I ended up going break even at first. I jumped back in for about $0.30. Could have gotten a lot more afterwards, but it just shook back down and down. Listen, if it's not going to make higher lows into a higher high, then don't try and force it. I tried making the higher high here off the local peaks heading into 11 o'clock, but obviously failed to really hold over that as support for the new leg up. So, that's a cooked trade. We got a lot of cooked trades here at this point, folks.  T-Y-G-O, I didn't even see this until now. Move away from that. A-N-F, all right. So, A-N-F, kind of at the highs right now, all right? So, perhaps we could put more of a further eye on this trade heading into the market close. I hope this becomes a faster moving stock for like a $29 trade, and that's for sure. You could probably say the same on AI, but for right now, this looks like kind of flat though altogether, right? It's not really flat. It's been moving back up heading into the early to mid-afternoon, but let's see a faster trade heading into prime time trading heading into the market close. Comparing Abercrombie and Fitch, A-N-F to Kohl's, this is not looking nearly as good. So, I'm going to pass on Kohl's, pass on Macy's, A-E-O, American Eagle, another kind of a sister company to A-N-F. All right, I'll take it then.  So, you know what? I'll actually add this here. It looks all right. You know, big resistance I would assume up top at 30. You probably have some big, you know, options, you know, strike prices right off 30, some calls coming in there, and probably some people that will look to do puts coming up too, depending on how weak the market is into the close today, but you know, should be pretty volatile there. All right, what else do we have here though for the meantime? We got MVLA, not bad. I mean, this looks pretty darn good to me. Actually, moving back on up to yesterday's highs, 225 is Tuesday's high from yesterday. So, yeah, not bad here. I kind of like it. Good spread, good liquidity for right now. Hopefully, spread doesn't open up more than it is here. RTL, you got AVID.  Wow. Ended up just making a big pop here.  Take a look to see what the news is for this one. You don't have Newsman 1110 today, but I'm just going to get the news for this one here. So, AVID shares trading higher following reports suggesting the company is working with Goldman Sachs exploring a sale. So, that's the news on that. What do they say, folks? Buy the rumor? Sell the news? Yeah. So, AVID moving on up right now. Hopefully, it continues to push up higher and to close at this point. But, yeah, I mean, looking at the spread, the liquidity, it's not really your friend here, but a nice trend and off the news, but kind of keep an eye on it. Urban Outfitters also another stock, like Clothing Store, stock up big today. I forgot that one. All right, let's see if there's anything else just on the loser side here. There's actually a lot more that's up big, but a lot of these are kind of low volume, just cheapies. And, you know, hey, if there's anything else looking really good pushing on the way up right now, folks, type it in the chat board. Let me know here. Right now, the APLD is still holding above, you know, past resistance here as support. So, you know, needs to break higher sooner than later, but still not bad. It's in good shape here still. So, top over to the loser's list seeing if there's anything looking just as good. Current time right now is 241. So, we got plenty of time here heading into the class to begin. All right. So, NVTS. I think that we had put this on our morning watch list if I'm not mistaken here, right? So many different stocks that we had called out earlier for ones that we kind of brushed off to the side. Kind of forget about them a bit. Yeah, NVTS. We had on the morning watch list. It was designated as a potential short mind to you. So, you know, led to a little push-up at first, but hey, this actually could have led to a beautiful short afterwards. You know, easy to say that in hindsight, but for the stock trending down heading into the open, you're looking for the short. So, you know, it's a matter of seeing a lower high into a lower low. And, you know, it's just easy to say that worked out beautifully. And so much so, this stock is still looking good into the afternoon. It's right off the low of the day right now. Good spread, good liquidity. Why not? You know, if you're not joining me in class coming up ahead in the phase one, then perhaps you short this into the afternoon there. Not bad, this NVTS. There was a stock yesterday in the PM meeting yesterday 24 hours ago. You know, it just didn't break the level. That's the thing. You know, just tried, but the stupid, this ACHD trade, you know, if you're looking for this, this smash below support at $6, it just held right above it. It was a good setup just to never ended up making that big break, right? So, you know, NVTS pretty similar kind of scenario. This looks stronger in direction on the way down. So, all right, PLCE here, not looking too special for right now. Neo down big, looked like that was a great short from earlier. I've been staying away from that a little too slow for my liking typically, although today was actually pretty good. All right, so let me know if there's anything else looking good on the way up or on the way down right now, folks, heading into the blows. It looks like I'm gonna get stopped at break even soon on my silly little Apple trade. That's not such a tiny share position anyway. That's just a distraction. You know, honestly, who cares about that? I mean, there's better bang for the buck on this AI trade, perhaps more bang for the buck if APLD could push higher. So, we have a really good list already. Let me jump back to ENVB, sideless stock. I said I'd kind of come back to, if anything, still making lower highs, trending back down here for right now. So, not too convinced, not too convinced just yet. All right, there we go from Sam saying, whoof, has been a slow steady up from a big drop yesterday. Yeah, a big drop looks like whether, well, pre-market this morning even, and then it's retraced up really well. So, good call. If it was closer to the lows, obviously, I'd look for it as a short. You know, perhaps you might think it's going to snap back down heading into the afternoon, and it is short of bull. So, perhaps there was a good trade with that, but I wouldn't necessarily expect like a huge move all the way back down to eight. I mean, it's done such a good job squeezing up already. Heck, it's kind of like asking like at this stage, it's like more so near the proverbial highs. So, hey, if this ends up kind of snapping this trend line off the lows here, it makes a lower high. It's short of bull. It could be a very good short heading into the close in that regard. But, you know what, I'll add this to our list. Good one here, Sim. You know, tradable in both directions. It seems good spread, good liquidity. It's moving. It's not like dead. So, you know what, actually a decent call there. I like it. And from Manuel is saying APNG. Oh, maybe a typo there, Manuel. APNG. Maybe APLD, he meant, because APLD, there we go. Anyone in on this trade right now from the $8.93 level? Did anyone jump in right there live during this meeting? Let me know.  You should have or needed to. But you certainly could have. That's a big level. When a stock breaks to a big level, make sure your stocks are set just in case. But hopefully this looks like make a pretty big move on the way up coming up very shortly. So, good start. Let's see if there's a lot more left to it here. From Elizabeth and John both are in right now from that exact price. Beautiful. All right. All right, folks. So, coming up quick, we have the phase one stock course lined up at 305 Eastern Time. We've said that before, but once again, we'll be concluding that class this afternoon. We have the phase two stock course coming up tomorrow. Really quick question. Who here likes pre-market trading? Who here trades the pre-market enjoys pre-market trading? Let me know here in the chat board. In the meantime, Scott asking GCT question mark.  Another pop up for this thing, right? I'm not a believer, Scott. If you're in it, I'm rooting you on, but I am definitely not a believer not just because I personally got teased by it, but it's been so flat there the whole day. So, you know, I feel eventually the winds are going to take this the other way there. So, all right. Who do we got here? Who likes some pre-market trading? We got Elizabeth. We got Ray. We got John, who I know loves it. Absolutely. You and Grant love your pre-market trading, right? Carla, Steve, Sam. There we go. Absolutely. Well, I'll tell you what, folks, and for all of us in social media too, might I say. Hey, be on the lookout on our YouTube channel as well coming up for this. Be on the lookout. Big things coming up, folks, in terms of our education going into Memorial Day weekend and the week after. I can't say too much just yet. Maybe Steve H has a small clue. Maybe Steve H has a small clue inside our chat board right there as to what could be coming ahead. But check your emails going into Friday. That is the end of this week. And check our YouTube channel coming up ahead too. So, we'll make that announcement coming up at the end of this week in full. Little teaser there for you in the meantime. Now, for all of us in social media, otherwise though, I should be tagging in for Fausto throughout the morning meetings during the remainder of this week tomorrow on Friday. So, with that in mind, hey, I appreciate you folks sticking with us each and every day, each and every afternoon, and for the mornings here coming up. So, there is my email address, Josh at C2Trading.com. Any questions or trades that you want to send over? Feel more than free to do so. More than half to help you out in between. But for right now, we've got a couple decent movers still in the meantime. As long as APLD could hold over this 890, 893. We're in good shape there. And we've got a few more here on top, folks. So, for all of us just joining me in class, I look forward to seeing you there. And then, otherwise, for all of us, otherwise, best of luck trading going into the market close today. Take care.


Hello, and welcome back to the craft box. My name is Brenton today. We are doing some more completionist crafting Nearly so so close to finishing off all of these. We've got three more to go today. We are doing the life jewel Really simple as with all of the jewels And as with all of these videos on screen now all the things you'll need all of the requirements all of the items That you need to get all of the reagents that is Yeah, and with all of the other jewels if you haven't seen any of the other completionist craft the videos They can give you completely random jewel from level 15 Or above I believe I think I'm not a hundred percent sure the wiki is your best friend for that But there's a wide selection of jewels that you can get so yeah, let's create this boy boom We got the defense one correct defense Jade 5 So yeah, that is a square So obviously you can get the Correct defense Jade. I think last time when we did the you can also get damages and I think Accuracy I think we got for Yeah, I think we got accuracy for fire as well. I can't remember a hundred percent But yeah, anyway, that's all for this video. Thank you for tuning into like and subscribe if you're already subscribe press The little bells, you know exactly when I post another video and member craft outside the box