So, welcome everyone to our session in machine learning and data management. Today we have a great line of speakers that are raising from topics about automatic machine learning, model agnostic measures, and so forth and more. But before I really get into and start our first speaker and introduce our first speaker, I'd like to first highlight our sponsors for the day is RStudio, and the sponsor for the session is Membridge. If you have a chance to thank them, please do so. Okay, so with that, I'll get started. And so our first speaker is Alex, and he's a data scientist in Germany, and he studies mathematics and he has a master's degree in data science. And today he will talk about automatic machine learning in R using this package called MLR3 AutoML. And with that, please welcome Alex. Yeah, hello everyone. Welcome to my presentation on MLR3 AutoML. This is a package for automated machine learning now. And this work was done during my master's thesis, which was supervised by Ben Bischel and Martin Binder. And you can find the code along with all the documentation and examples on this link. I also shared it in Slack, so yeah, please feel free to check it out. Yeah, the name is quite long. I know MLR3 AutoML. So let me break it down. The first part is MLR3. And maybe some of you are already familiar with this. This is a powerful object oriented and extensible framework for machine learning. Hence the name machine learning in R, and it provides a really nice and rich package ecosystem, which has a lot of tools for machine learning that you can use to build all kinds of applications. For example, an AutoML framework. Here in the bottom of slide one we have a picture of all the different components that are included in MLR3. You can go into detail and all of them, but just to give some brief overview, you have tools for tuning different machine learning algorithms, you have all kinds of utilities. You can build machine learning pipelines, and so on. So it's really nice. And yeah, I encourage everyone to check out MLR3. The first part of the name MLR3 AutoML is AutoML. So few words on what is AutoML and what is it good for. In general, it deals with automating machine learning workflows. This could be, for example, pre-processing, model selection, hyperparameter tuning, anything that you need to go from a raw data set to a tuned machine learning model. You can serve different purposes. I see two very nice use cases for this. On one hand, if you're an experienced machine learning practitioner, can give you a very nice baseline model with almost no effort. So all you do is plug in your data set and you get some reasonable baseline, more or less a lower bound for the performance of machine learning on your data set. Or maybe if you're starting your machine learning journey, it can also help you build your first model because the AutoML tool will avoid a lot of the common pitfalls. There are many different approaches, how to solve the AutoML task, and we decided to use the combined algorithm selection, hyperparameter optimization paradigm, which is explained in the image on slide two. So on the left hand side, we see a machine learning pipeline with some different pre-processing operators, perfector encoding, also PCA. And we also have different machine learning algorithms like GLMNet, support vector machine and gradient boosting. Now finding the optimal model has basically two parts. First is which operators should be in this pipeline. And second, what should the hyperparameters be? And we see this encoded in the search space here on the right hand side. So for all the machine learning operators, we have categorical parameters. For example, encoding is a categorical parameter, either one-hot or impact or learning algorithms. We have three different options, GLMNet, support vector machine and boosting. This is just an example. And we also see associated to the boosting different hyperparameters for this as well. For example, the number of boosting iterations. So in general, we have a large search space, which is also nested. And the AutoML problem boils down to optimizing or tuning over this large, large search space. I will spend a bit more time later explaining how we do this. Okay, so MLR3 AutoML, now we're bringing it together, is an AutoML package for regression and classification. And it uses different components from MLR3. So we have first some automatic preprocessing using MLR3 pipelines. We also include some very well-tested learning algorithms. I will explain in more detail what those are later. And the pipeline and model hyperparameters are optimized jointly, just as I explained on the previous slide, using the hyperband algorithm. We additionally include a portfolio of known good pipelines. So these are hyperparameter configurations where we've seen in our benchmarks, good performance previously. But to go into a bit more detail on how the tuning works in MLR3 AutoML, we have two steps. First, we start by evaluating the hyperparameter configurations from the fixed portfolio. So these are eight simple models that are evaluated sequentially, just to give a very rough baseline on even the most challenging datasets where you cannot train a very complex model. So in terms of tuning this portfolio, if there's still budget or time left to do some further evaluations, we continue the tuning with the hyperband algorithm. Hyperband is a multi-fidelity approach that speeds up random search and it's based around budget parameter. So this controls how long the evaluation of a single hyperparameter configuration takes. In this case, we use sub-sampling rate here. So in the beginning, we train lots of different models on small subsets of the data, which is reasonably fast. And then as the tuning progresses, we move towards larger subsets of the training data, but training fewer algorithms. This tuning procedure is depicted in this bottom illustration on slide four. So on the left-hand side, we see a graph where we have the cost function, depending on a hyperparameter configuration on the y-axis and on the x-axis, we have the training budget. And in this example, we see eight different hyperparameter configurations that are trained. And at the start of the tuning, we start with a low budget. So let's say the sub-sampling rate of 12.5% of the full data set, and we evaluate all eight hyperparameter configurations and get some associated cost measure for those. And the general procedure now is to discard the worst-performing ones. So these four hyperparameter configurations with the highest cost are discarded, and the remaining half are evaluated again on the larger subset of the training data, which we see here. This procedure is now iterated. We discard the worst half of the hyperparameter configurations. Continue training the better half on a larger budget, until only one hyperparameter configuration is left. This whole procedure is then iterated over different starting configurations. And this is shown here on the right-hand table. So we have different brackets where we have a different number of starting configurations and slightly different setups for the budget. And general hyperband is not only for this AutoML project. You can use MLR3 hyperband as a plugin for basically any tuning scenario that you have in MLR3. So this is all the time I have today for the background. Let's look at some examples. Our first example is how to use MLR3 AutoML in general. So we have this AutoML interface function, which has one required argument, training task. So if you're familiar with MLR3, this is basically a wrapper around a data set and could be a regression or classification task. So that's all you have to do is train your model. And once this is finished, you can predict on some unseen data. The AutoML interface function comes with different customization options, which I will explain in more detail. So you have, for example, a runtime. You can set a custom performance measure to optimize for. You can select different learning algorithms. You can influence the type of preprocessing that is happening and also add some additional parameters. So, yeah, let's look at another example where we add some custom learners and the runtime budget. In this example, I use the empty cast data set, which is for regression. And I use some regression learners. So a random forest from the Ranger package and some linear models. We can set the learner time out to 10 seconds. So this means every single hyper parameter configuration. The training is stopped after 10 seconds. And overall, the runtime is kept at 300 seconds. So after 300 seconds, tuning is stopped. And just as before, then we can train predict and so on. The default ML3 automations with the Ranger implementation of a random forest. We have a gradient boosting from the XGBoost package and logistic and support vector regression from the linear package. And these algorithms showed very stable performance for us on a variety of data sets also provided good coverage. So, more or less, most data sets should be should be handled well. But if you're not happy with those three, you can input any learner from MLR three or the extension packages, which should give you access to most common machine learning algorithms. Yeah, as I explained, you can set timeouts. And one other nice aspect is that the learners are encapsulated in separate our sessions, which means that if you train 20 different models, if one of them fails this won't affect the other 19, which is very important for the stability of the system. Second example, I will skip for the sake of time, but just briefly, you can add your own parameters to the search space and also transform them to the arbitrary functions. And all the learners that are included in MLR three AutoML also come with a predefined parameter space. And the last example that I have for today is how to influence the pre processing. So one of the blocks MLR three AutoML comes with three predefined pre processing settings. You have no pre processing if your data set is already sufficiently pre processed. You have the stability option, which deals with missing data with numerical columns high cardinality features, basically anything that could lead to a failure in the learning algorithms. You have a full pre processing option, which adds tunable imputation methods, tunable factor encoding methods, and a PCA for dimensionality reduction. As an alternative, you can also provide your own custom graph object. So basically write your own pre processing pipeline. And this is shown in the code sample here in slide eight. This is the MLR three pipelines package. I'm creating a pipeline here for fictional imbalanced classification task. So we use a pipe operator for imputation, then add a pipe operator for minority over sampling using this mode algorithm, and also some class weights. Now you are, you can basically create any pre processing pipeline that you can imagine you and pass this in as the pre processing argument to our auto interface. Okay, yeah, that's all the examples that I brought for today. And last question I want to highlight is how well does it work in practice. We created this on the auto ML benchmark, which consists of 39 very challenging classification tasks, and in terms of the time budget we chose a very constrained time budget of 10 minutes for the small tasks, and one hour otherwise everything larger than 10,000 observations. But a more extensive benchmark is also currently underway, and we're curious to see what the results will be. So we have the auto ML frameworks that we benchmarked against for auto glue on tabular, auto escalone and H2O auto ML, as well as teapot. And in this small benchmark winning framework was auto glue on tabular. The performance of MLR three autumn was slightly worse. So we see, on average, the AUC on binary classification tasks is 1.1% less. So for multi class tasks, we have mean accuracy, which is 2.8% below the winning framework auto glue on tabular. But I want to highlight that MLR three autumn was also the only library, other than auto glue on tabular could finish all the tasks on time without failures. Because that while the performance might not quite be at state of the art levels, it's still a very nice and stable and can handle any data set you throw at it. Yeah, and lastly, I just want to give a shout out to everyone in the open source community. So thanks to team open ML to the R foundation and to everyone who's contributing to MLR or MLR three. If you want to try out the project. Here's a link to the give top. And again, it's also in the slack. If you want to keep in touch. It's easiest to reach me on LinkedIn. Thank you very much. Okay, all right. Thanks, Alex for this nice presentation. A couple of questions that we'd like to share. The first question is, can this package run on multiple cores or on like a cluster system. So in general, MLR three supports parallelization with the future back end. So you can do this. There are some limitations, though, because of the hyper band algorithm is in its nature sequential. So you might start with nine hyper parameter configurations to begin with which I evaluated in parallel. But if one of them takes very long, you might have to wait until this one finishes before the next stage starts. So there's some limitations with this but in general we support parallelization. Awesome. The next question was is the rational behind the timeouts. Could you explain a bit more about how it's how the this framework decides when to time out and finish on time. So I think that's one of the key advantage of this packet. So, if you don't provide any settings, this timeout or this timeout is basically infinite. So you will have the hyper band decide when to end the tuning. But you can set of course the timeout for some overall time if you say for example my system should run for five hours or something like this you can put this in. And you have the individual learner timeouts. Because sometimes you might have the situation where one of the learning algorithms is not super well behaved and might take forever for example and we want to avoid this. This is why we have timeouts for individual learners as well where you can set, let's say 10% of your overall time or something like this. And after this stop the evaluation of any single learner. This is so we can cancel learners that that take too long. Do you think it's possible like with a follow up question do you think it's possible to predict a good timeout. Right, because there's there's always going to be a trade off between like waiting a little bit more to find the best hyper parameter for example. And there will be a certain point where there's the expectation of reaching that decreases right. So do you think what are your thoughts on that. I know that there are some auto ML frameworks that do things like this. So for example they perform some polynomial regression based on some very simple features just predict some order of magnitude for the runtime. This is possible. But yeah, currently not implemented but yeah would be would be nice to have. Okay, awesome. Okay, so with that we're going to move on to our next speaker so please think Alex for his presentation. There's also Alex while you're there's also some questions in the Q&A box so whenever you have time. Please do and you may feel free to answer the questions there. Thank you. Okay, with that we'll have our next speaker. Hello, my name is Kasia Penkala and today with my colleague Kasia Voznica, we would like to share with you the results of our work. We are part of MI Square Data Lab at Warsaw University of Technology. Today we'd like to present new our package TripLot. TripLot offers more diagnostic approach to compute and visualize variable importance in predictive modeling. This approach takes into account correlation structure between variables in input data. In general, TripLot is developed as a response to the challenges faced by existing variable importance techniques. As the package name suggests, this chart is composed of three components. Together they summarize correlation structure and variable importance. What is worth highlighting that TripLot is not a related tool but is the part of Dalek Universe.  Let's start with a brief reminder of what problem we are addressing. Principle of predictive modeling is simple. On the base on input data we would like to predict expect output. This output might be different depending on the application. For instance, it may be probability of customer's default in predict scoring price of apartment in considered location or many different application. On the other hand, input are variables connected with expected output providing supplementary information. This may be information about applicant age, his income or credit history in the case of credit scoring. In classical predictive modeling input feature are stored in tabular data. The relationship between descriptive variables and expected output may be very complex and difficult to capture for human perception. So we use machine learning models. Black box tools. They are very effective in finding this relationship but it is hard to summarize how model works and why make this decision.  X-I-Y methods provide insight into machine learning models and try to summarize the dependency between exploratory variables and model output. The examples of these techniques are partial dependency profile and or variable importance. But very important information which these techniques ignore is internal structure of input data correlation between columns. In real world variables are not independent entities. Often substance of them describe the same phenomenon from different perspective. For example, how rich customer is. So it is natural that these variables are correlated. This dependency influence model process and X-I-Y methods try to eliminate bias caused by correlation. In triplet package we would like to reinforce X-I-Y interference by adding information about correlation structure as input to explanation methods. In this presentation we will focus on variable importance. For every single variable we want to attribute how much model performance depends on this feature. To assess this we perturb input data and create new observation in which we try to imitate excluding the impact of considered feature. After perturbation we check how model performance has changed. The most common perturbation is permutation. We break dependency between variable and target variable. So we permute one selected column and check how much model performance change. We repeat this many times to estimate expected change of model performance. In a result we can assess the importance for every feature and summarize of which feature caused the biggest disturbances in model predictions. This method is model agnostic and straight forward, but there are some challenges. This approach doesn't work in the case of correlation between explanatory variables. If we treat every variable independently and permute them separately column by column we may create very unlikely new observation out of input data distribution. It is easy to understand on the example of two skill of soccer player, dribbling and ball control. Lehmann would say that they are highly correlated and if player is good at dribbling he is also able to control ball. And we can observe this in real data. If we permute ball control column independently we could create strange player with high value of dribbling but low value of ball control. In this region of data model may be unstable and provide out of range predictions because it has no observation during training model. We can mitigate the problem caused by correlated variables by using variable importance for groups. Let's look again at the football player's example. If we permute variables dribbling and ball control together we can rest assured that we won't receive a pair of data points that will be out of the distribution. Permuting correlated features together and assessing the importance of the whole groups of variables may provide more concise explanations and more truthful picture. But what if we don't know the internal data structure that well? Well, let's look at it this way. It's relatively easy to use variable importance on correlated variables when there is only pair or two pairs of them. But what if the internal structure is much more complex? If strongly correlated features create multiple interrelated groups? In that case defining such groups is not trivial. For the task we can use a dendogram that is a diagram that shows hierarchical clustering of correlated variables. This facilitates making decisions about the number and composition of groups that will be used in variable importance calculations. But then the question still remains. At what cutoff point should we calculate the variable importance? How the variable importance looks like when calculated for five groups of strongly correlated variables? And how does it look like when it is calculated for only three groups of little less correlated variables? We can try to do it by trial and error. And for different cutoff points calculate variable importance. But well, that's created a lot of overhead. So the triplet does it for us. It takes every cutoff point from the dendogram that is defined by the successive nodes and it calculates the variable importance for newly created groups. And to make the picture complete, on the left triplets show single variable importance. In that way in one picture we get the information about the simple variable importance. Then on the right we can see how the data is structured. That is how variables are correlated. And in the middle we can find out how different groups of more or less correlated variables influence the model. This is how real example of triplet looks like for a simple model. Again, single variable importance on left, correlation structure on the right and hierarchical variable importance in the middle. For the purpose of this presentation we are using a simple model built on Boston dataset, included in MLBench package. This model is built on only a few features out of the dataset, namely six variables. Tax rate, accessibility to highways, probability to ratio, percentage of lower status of the population, average number of rooms and ratio proportions. Okay, let's understand how the triplet works by building it. On the left we see single variable importance for every feature. Now let's make the rest of the chart step by step. We start by looking at the variables tax and rad. Thanks to the dendrogram we can see that the correlation between tax and rad is the strongest. Importance of this group is equal to almost four. Second part of correlated features is LSAT and RM. Importance of group that consists of this feature is equal to almost eleven. Next we see that when we add PT ratio to tax and rad we get a correlation equal to 0.3. And again thanks to the chart in the middle we see that importance for this three elements group is equal to five. Finally correlation between group LSAT plus RM and group PT ratio tax rad is 0.11. And importance of this five elements group is equal to 12. And at the end correlation between LSAT plus RM plus PT ratio plus tax plus rad group and B is almost negligible. So finally the baseline for the model is 12. Finally we can notice or highlight the following. Even if we don't see many correlated features we can already observe that feature of importance is not additive. We can see it by looking for the most important variables LSAT and RM. Their single variable importance values are about eight and six respectively. But importance of this group is equal to less than eleven. And we can clearly see it thanks to the triplet. Triplet package also provides a possibility to make a chart for local variable importance. It addresses a similar problem. How single variables influence the prediction of a given observation. What is the correlation between variables and how groups of variables impact the prediction. It is based on an experimental method for local variable importance called predict aspects. This method is also implemented in the triplet package. At the end we would like to show you the code that produces the triplet. You can see that after building the model we create a Dalek explainer. A useful adapter for the machine learning explanation. Afterwards triplet object is built on the explainer and then plotted. To summarize we would like to highlight the necessity of taking into account the input data correlation structure when we are building machine learning explanations. To mitigate the problem correlation making variable importance results misleading we can use triplet. Triplet package is available on CRUN. And more information about methods described here can be found in our pre-printed paper as well as on the github. Thank you very much for your attention. Alright, thanks KataZerla for a nice presentation. Oh that's... Okay. So with that we'll take some questions. So KataZerla are you there? Yes, hello. Hi, awesome. I have a couple questions about this triplet method. So it's very interesting how you're addressing this question about a variable importance. But instead of using a more, I guess, a more traditional way of looking at it, in terms of a more data science perspective there are many variables that are correlated with each other. Taking account of that correlation structure, how can we visualize and assess the importance of variable importance? And so I was wondering, the first question I had was related to scalability. So this is using permutation for each variable but in a very grouping manner. So if there's like five variables then you choose two variables that have high correlation and then you permute it while preserving that correlation structure.  Is that right? Yes, that's correct. So in that case then, in the example that we showed, I'm sure this example I showed was just to illustrate the point, but if we're looking at more than five variables, like let's say we're looking at 100 variables, or I think a lot of this correlation structure is very high in image data. And so if we apply this method for image data, then I imagine that the computational costs would be quite demanding. And so I was wondering what are your thoughts in terms of extending this approach to data sets to have more variables? Yes, that's correct. Actually, we focus on the tabular data. And so making it work on different kind of data would be computational heavy. And because it, as you said, it calculates the feature importance for every newly created group of features. So at some point when we are getting the bigger and bigger data set, it does take some time to calculate the whole triplet. So at some point we need to decide how many, because this is calculated by using the subset of the data. So at some point we need to very carefully consider how to choose the parameters for those computations. Kashi, would you like to add something to that? I totally agree with you, because if we consider more variables, we create more groups and it will be, I think that this may be computational heavy because of this new created aspects. So we may think about creating some penalization during the creating group split. We need to think about it. There's a related question here that asks, again, for large data sets, hyperclustering can be unstable. I think this means in terms of this very greedy algorithm manner that you're just grouping two variables that have high correlation and so forth. In terms of large data sets that might not lead to the optimal solution in terms of grouping variables. Do you have any thoughts on elaborating different approaches in terms of grouping variables? Yes, so in general we are thinking about extending our approach and not using only the hierarchical clustering, but also some other methods. And what we would also like to approach is to give the possibility for the user to use the user defined methods. So because what we are really looking here, we are looking here for the order in which the variables are joined into the groups and at what points they are joined. So our idea for next experiments with this package is to provide the way for the user to define it, to define the grouping methods or the measure that would be used for building such a tree. I think it's a very interesting point that the tripod is addressing and I think in some sense the tripod is a very extensive approach. It looks at based on the grouping algorithm, it looks at every single subgroup structure of the data. But in reality, depending on what you are projecting or what your objective of the machine learning algorithm, I think there might be some groups that are more informed than others and maybe by developing some algorithm that strategically chooses which group to focus more on and I think that would also be interesting to see in the future. In terms of scalability, that would definitely help. Yes, that would help. Exactly. That would support the calculations of the triplet on the bigger data sets for sure. Okay. All right, so with that, we'll move on to the next viewer. Please thank Katarzyna, both Katarzynas for a nice presentation. Thank you very much. Thank you. Okay, so we are moving on to our third talk. Hello, everybody. My name is Jonathan Bourne. I'm a PhD student at UCL, studying complex networks and their relationship to cascading failures on the power grid. During my PhD, I developed the strain elevation tension spring embedding algorithm, also known as SETC and the author and maintainer of our SETC on Cran. So what is an embedding? I like to think of embeddings as a way of representing data, which is more useful for a specific purpose than the original representation. And so to give you an example of that, we've got this MNIST data set, which are the numbers 0 to 9 handwritten by a lot of different people and two different ways of embedding data. MNIST is a matrix of I think 6428 points and it can be embedded using any kind of tabular method. So we've got principle component analysis, a common statistical data compression and an embedding method and TSNE, which is T distributors to casting neural embeddings, which is a nonlinear approach. And what, you know, the task in this case we're saying is, okay, we want to be able to move all the similar numbers together. So all the 0s plus together or 1s plus together or 2s plus together. And we want to do that in a two-dimensional space. And what you can see is that the nonlinear approach of TSNE, although unsupervised, really does manage to do this itself and group those similar numbers together in an unsupervised way. And so for this purposes, this embedding is more useful because we want to be able to see things that are similar close together than the original representation. And so graph embeddings are the same but for the irregular structures of graphs. And there are two different sort of main families here. There's those embedding methods which are good for visualization but not very useful for analytical insight. And those which are good for analytical insight but less valuable for visualization. And Sexy takes techniques employed by the visualization methods but manages to use this for analytical insight. So it's kind of a hybrid of the two. And what is our Sexy? Well, our Sexy is a physics-based deterministic graph embedding algorithm that represents a network as a system of springs where the edges are springs and the node features are forces and it tries to position the nodes such in embedded space that the forces on the nodes are cancelled by the forces on the springs and the system is in a state of equilibrium. And so how does it do this? So the easiest way to think about this is looking at the image on the left which is a one-dimensional embedding. So you could consider that a network which only has one variable on the node so that might be age or amount of money or something like this or it could be a binary variable such as you are a member of Ruby or you are a member of Ruby. And so Sexy lays out the graph in some sort of plane, graph space and the nodes are free to move in the one-dimensional case up and down. So effectively they act like beads on a rod and the nodes, two nodes connected to each other are separated by some distance d and the equilibrium state is found when the elevation of the node hence Sexy the elevation of the node is such that you get this equilibrium. So in higher dimensions like this two-dimensional example on the right the nodes become discs that move on parallel planes and then you can keep adding dimensions as you measure. So this is a very simple example. It's a network of two groups group A and group B where group B has a positive force and group A has a negative force so group A will move down into the screen and group B will move back out of the screen and attempt to anyway the sum of the forces in each group both add to 1 and minus 1 respectively so group B adds up to 1 meaning it's a force of 1 third on each node and group A adds up to minus 1 so it's a group force of minus 0.25 on each node. So what then happens is although each node is free to move on its own any displacement from the initial plane causes the springs to stretch in accordance with Hook's law this generates a force and when the sum of the forces attached to each node are equal and opposite to the force exerted by the node itself and this is the case for all nodes in the network then you have an equilibrium so you move the nodes to those certain position each node has an elevation the springs are extended this creates a strain and a tension elevation tension strain and this gives you your final embedded values one thing that's quite interesting to look at is in this case the movement of the nodes as they move up and down heading towards convergence in this case where you can see the image on the right has the movement patterns of the elevation patterns across time for each of the nodes and what we can see is that nodes A, B and C have the same convergence pattern and nodes G and F have the same convergence pattern and this is because these nodes have the same relationship to each other as they do to the rest of the network so they're structurally identical within the network so they act in the same way we can also see that the group A nodes which is A, B, C and D all have their final value below the initial plane so they have negative embedding and the group B nodes are above that and this is expected because one has negative force and one has positive force so what can you use set C4? well you can use it for quite a lot of different things but the original purpose was for understanding more about the robustness of networks under targeted attack but here we can see five networks which represent PilsQuintet and PilsQuintet is a family of five different networks which are structurally clearly quite different but have identical statistical properties and so they're in many ways quite similar to Anscombe's quartet which I'm sure you're aware of is a collection of four different figures which look very different but have identical correlation mean number of summer and so the task with PilsQuintet is whether a graph embedding algorithm can distinguish between these different families so what I did in my first paper was I generated 100 examples of each of the PilsQuintet types and embedded them into two dimensional space using various different embedding methods and as you can see set C in the lower right hand corner was very very good at separating out these families so we embedded them at network level took averages and then saw what we got out and set C has almost perfect separation there's a little bit of mixing up going on between groups A and B but overall very very effective but set C works not only at network level like the previous slide but also at node level and so one of the things about PilsQuintet is it's got these two groups group A and group B but it also has subgroups A1 and A2 and so embedding the networks at group level A and B sets it automatically distinguishes or separates in the embedded space out of families and you can see these patterns emerging quite clearly at node level so this image is actually one of the original uses of set C and one of the reasons I created it which is to look at the robustness of power grids under random attack and what we can see in the lower right hand corner is that the tension element of set C so that is the spring tension when the network is embedded is a very good predictor of the robustness of a network during when it's under attack and we can see that other network embedding methods were not quite as effective so set C can also analyze social relations and social networks in this image the historical conflict in which the Medici family took over Renaissance Florence is reenacted with different levels of importance laid on inter-family marriage and business relationships. The figure shows how the Medici skillful network positioning allowed them and use of marriage allowed them to take over Renaissance Florence as a final example we can see here set C used in mapping and this is very nice because it shows really what set C does so although set C embeds a network on high dimensional manifold it's really a kind of network smoothing mechanism so it smooths the node features of the regular structure of a network and this allows you to create quite nice maps and these maps if you look on the elevation and tension facets they actually take quite a lot about you know population in this case power generation where people are living where power is being generated and can actually provide quite useful qualitative insight into what a network is doing when it's embed in a geographical space so all set C has been implemented in R on surprisingly that's why I'm here and it's got a whole load of documentation for you to check out and it's available on cram which makes it nice and easy to do with your computer. When you look at our set C when you install it there's five different sets of functions it can seem a bit daunting but really three of them do the same thing they automatically choose your hyper parameters to ensure a very quick and effective embedding so these are the auto functions auto auto high dimensional or the bi-connected components which does a kind of tricky way which can be faster in certain circumstances so what you do with those is it selects hyper parameters which gives you this very smooth very fast embedding you can see on the right in the line in purple and prevents divergence which is the line in red or the slower types of convergence. So the package is fully documented so you can get to grips with simple examples of sets and get your head around what sets it means and then focus on embedding your own data and understanding what your own data can tell you instead of worrying about confusing code so in summary sets is a physics based deterministic graph embedding algorithm it can predict a loading network level it's implemented in R in the R setsi package available for CRAM and there's detailed documentation and vignettes available for you. So if you want to find out more about setsi look at my paper the spring bounces back which is available in applied networks or I have a pre-print on high-tech called high-tension lines which is available on archive we've also got the website the documentation website we'll just install using install packages R setsi and have a play around so I'd like to thank the EPRSC International Doctoral Scholars IDS grant for funding my PhD the UCL Myriad High Performance Computing Facility for making a lot of the calculations possible and the UCL R user group who funded me to come to this conference so thank you very much to everyone and thank you very much for your time. Goodbye Okay awesome thank you for your nice talk we're going to set up so we have a couple questions here the first question is how does setsi scale with the size of the network? Yeah that's a really good question and there's a bit more detail in the spring bounces back paper but roughly in terms of time complexity it's quadratic it scales quadratically with the number of nodes although this is a bit like dependent on the actual structure of the network so one of the functions I mentioned was the setsi bicomp and what this does is where possible it breaks the network up into small independent subnetworks solve those and then puts it back together and so depending on your network structure generally it's faster but it can depending on your network structure be dramatically faster because essentially you could break down say a network of 10,000, 20,000 nodes into 5,000 networks of 4 nodes or something like that which should be extraordinarily fast relatively and in terms of memory complexity it's linear and it uses very very little memory to run so you can actually quite large networks on quite a small computer I can't remember now but I think when I was solving a network with about 40,000 nodes and one and a half million edges I think that took about four hours which is not hugely fast four hours and but only used like 300 megs of RAM so you can run things quite parallel if you've got a lot of networks to solve I follow a question with that does that mean that the algorithm will depend on how setsi breaks down the network so are you identifying the community structures of it and so itself could also be a quite time consuming part as well yeah so I mean setsi doesn't look for the community setsi in a way can be used as a community detection algorithm like you saw when I was embedding peels quintet at node level effectively it discovered these hidden communities within the network but what a by connected network is essentially if you have two clusters of nodes or a network but the network can be cut into two by slicing through a single node that node is a by connecting point and so where the network can be cut into separate pieces with a single cut that's what setsi looks for so that's a standard algorithm that you can use and you can just separate networks into their sub by connected components and when a network has that structure which most networks do you can solve them you can solve the sort of by piecemeal approach you solve all these smaller parts of a network and then you reassemble it into the overall embedded structure so it's only looking at these single single-tent work these by connecting nodes when you use the by connected network version but that you may not want to I mean generally speaking for one dimensional or binary embeddings yeah use that but you may not want to so there's a couple of different versions available depending on what the use case is the other question that I had was so I've also been using a lot of these network in my analysis and often I found that using R like since we're in this R conference I mean there are packages that help network analysis but was there a reason why you chose to implement your method using R mostly because that's what I'm programming right but the iGraph package a lot and I think iGraph is a really really fast generally speaking because it's as closely possible wrapped around C++ so it's as lightweight as it can be and then tries to do all the heavy lifting in C++ it's also got a Python implementation and so that's really why and I actually really the big package on Python I think is network X and obviously I'm more you know in R person but I prefer the way iGraph works to the way network X works and that that could be subjective and so that's really why R sets it was implemented in in R I mean I should mention actually this is a bit of an aside I should mention that although this is the machine learning section it's kind of controversial where the set to you is actually machine learning right because it's deterministic algorithm right you know and it actually doesn't use statistical inference you know it uses physical laws so is it machine learning is difficult but really the reason I'm here is because every single other algorithm that it's comparable to is a machine learning algorithm so the ones I compare against are like Notavec which is a kind of word to variant for graphs or you've got DGI which is you know a deep learning approach and all of the techniques to do with graph embedding and really all of the modern embedding techniques are machine learning techniques and I think one of the things that's interesting about sets is that even though it's technically dumb right how it does it's not an intelligent system actually it can find really quite nuanced and complex structures within the algorithm within these networks in very very low dimensional space and so I think even if it isn't machine learning algorithm itself it can act like one and it can also act as the sort of input to other machine learning systems. Yeah so I have a little bit different take on it so I think a lot of complex networks I don't know how many people in this audience knows which are complex networks but I think after Jonathan I think I know a little bit more about it but it's it's mostly based on its statistical physics so that's I think in that perspective of course this approach that you take in is somewhat unique but the whole the field in itself is sort of grown in a statistical physics type of area and in that sense you know you could say that it's related to statistics maybe not traditional statistics that we know with like p-value and so forth. The other thing is that I think what's nice about a lot of these machine learning algorithm is not necessarily about the pedigree of where it's originated but more about the goal of the problem and here I think what Setzi is doing is doing something very similar to a lot of dimension reduction algorithms and applying that here so in that sense I think you know I think you fit perfectly fine here but I'm pretty sure in the audience there will be audiences who are unfamiliar with this graph structure but yeah I think I really enjoyed your talk and I look forward to your next projects and so forth. Okay we were a little bit over time so I'm going to so please thank Jonathan for his nice talk in complex networks and graph embeddings. Alright so that's the end of the session in machine learning and data management I think one thing for sure I think there's many different areas that maybe you might be unfamiliar with some of the people in the audience but what's very clear is that there's many things you could do with R. You could do auto ML, you could do feature selection. You could use network analysis and then also if you're interested in studying economics then there's also work that is being done here and so I think if there's one take home message I think it's very clear that the R community is growing and thank you all for joining us Bye


It was going on fellow traders. This is Brandon Wellington aka be wells and in my seat chat I just want to take a moment to talk about my experience So one of the things that prompted me to join in my seat was the community aspect. I am an actor entertainer You see I'm on set today so I'm in commercial But I like to meet people in person. It's a big thing to me and in my seat. They have as a trading community They have meetups. They had a tab program which stands for trading accountability buddy and for me It was really big to get a chance to meet people and fill energy especially people that are telling you what to do with your heart earned money and so I Ended up joining my seat about a year ago, and it took me about two months to get profitable I was coming from a different trading service and a lot of that I had a lot of bad habits that I had to get rid of and So I was in the process of trading the strategy also in process of Doing what I learned from other things and so I had an inconsistent approach Which also led to inconsistent results and so I kind of locked in cut out all the other things that I learned It's just really focused in on the things they teach at MIT and From there I got profitable around month two month three and just start sticking to the process And then I got a trading accountability buddy. We talked to each other pretty much every morning. We go over What the plan is going to be for the day We we know what to look for they teach us all these things in MIT and then we pretty much trade that plan and stay Discipline now if you're coming in and you don't have that much experience Probably be even better for you because you don't have bad habits to unlearn whereas I did so it really took it took me Too much to get green But again, I've been to four four or five different meet-ups across three or four different states And it's just a chance for you to actually connect with people on a journey. That's Essentially very lonely. You're trading by yourself, but in my seat offers that community aspect you'll get a tab you get a chance to meet up with people who are interested in the same thing as you and You know, I think it makes a world of a difference I gotta go I'll catch you guys a little bit later and see you in chat


I'm a Toffi TV it's three talking points now that milk not a shitty tour of Goodersons today you know they're woeful game of football They're ever going to be involved in real disappointments so let's get into it talking point one leadership or lack of leadership at Everton football club particularly on the pitch and Just a real poor You know show'em from the team and nobody look around that team nobody is ond mae'n gwybwch i gael yma. Yn y bwrdd gan ymdeg yn gondol, ond mae'n gynnais beth sydd yn gwybwch yn y rhanau. Rwy'n gwneud o gymryd oes horsiwr, dwi fwy yr wybr oedd o adeilad o'r ffordd o'r weld, dwi gyd yn ddechrau cofnod, o'r ymddiw ymddangos o'r ddiogel, o'r ysgolion o'r rhaedd yma. Felly mae'n gofio. Wel, yr ystyried fel cael deithas ball tŷ oedd yn geisio'r ddedig. Felly mae gennym ni i gael, a chael gwybod yn gweithio rhai. Felly, rhai oherwydd ei fod yn dweud i'r ffredinol yn holl yn hwnnod i chi, rhai oherwydd na'r rhai oherwydd, ond weithio'n gan leol, efallai i wneud hynny, rwy'n lle i ddim yn ffordd ac yn cyfryd. Mae'r ffordd yma, wedi fofynu'n ddim yn eu sgwm, a'r ffordd, gwnaeth bod y sgwm yn newid y maedgaid, yn y port yn ffordd, a mae'r ffordd yma yna, mae'r popeth yn ffordd a'r ffordd o'r ffordd yma. Ond y mhysgr yma, mae'n deimlo ar y dyfodol gyda'r sgwm o'r ffordd, Ie dwi'n gallu gynnwys heelion, yn hyn yn rhaid i gynnwysio'i gynnwysio'r ddefnyddio – fwy'n gynnwysio'r cyflwyco信 sy'n gweld i'r edrych. Roedd hynny'n gynnwysio'r gynnwysio – yw'n gofg o'i gynnwysio gyda'r séf antym na'r Honasu theoretically a'r hyn yn y rodd ynddo, a'r hyffordd hynny eich gyff transfolwyr yn y maen nhw yw'n gynnwysio'r cyflwyco He's a technically a good footballer. He's a little bit too slow for my liking, but technically he's a good footballer. He's not a captain. He really isn't a bad Luke a Dean, the captain. Give it a day, give it to someone and go yw'r captain. Guilty figures and shouldn't be the captain. And that to me, just reflecting the lack of leadership in that team. Second thing I want to talk about today is the substitutions. One in particular, you know, fair enough. He wanted to bring on Alex Oboby, who should have started as far as I was concerned, and we feel fair enough. And obviously the natural fifth of that would have been Snidlin, but sorry, would have been Sigarton off. But obviously Snidlin was very, very lucky. Should have been sent off. No question about it. He's on a booking. He drags Pucky for some reason. Ridiculously, the referee sees it because knowledge on the break into our box, he lets play go on. Now, I don't know what the rules are with that. I thought when it went out, he was going to send them off. I don't know whether you can. So that's me and my ignorance there. But Snidlin was very lucky to stay on the pitch. He should have been sent off. But a man had to tuck them off, which is fair enough. He brought Oboby on. And he brought Dominic Alvertley on. But then the next sub should have been Moise Keane. Because we are 1-0 down at home to the team bottom of the league who struggle to keep clean sheets. And he brings on Seamus Coleman for Siddi B. Now Siddi B wasn't injured. He shook his head. He was fuming when he ran off. Coleman come on. I don't understand what that was for. We needed Moise Keane. We took Walcott off. We took, you know, you've got an attack on playing for Walcott and we're taking him off. Alex Oboby's better through the middle of the park. If you wanted to take Snidlin off, it should have been Oboby, Davies and Sigarton. If we were getting beat anyway, 1-0, for trying to create things. It should have been Moise Keane, Dominic Alvertley and Alex. And they should have been the front three.  Not Siddi B for me. And that to me was really poor, really poor substitutions and didn't help us at all. Coleman didn't offer anything really. That Siddi B wouldn't have been offering so. I don't know. It was just to me. You might disagree but I just couldn't believe it. 1-0, and we need forward players on what we had up there with doing nothing. Jeng Tolson, bless him. He's just not quick enough for the Premier League. I don't know, you know. Just poor for my opinion, really, really poor. And the third thing, listen, I spoke to someone after the game, Blue knows after the game. You know you're at the concourse and he said to me he could pick 1-11. Tolson points to me, couldn't you? You know, with that start and line up and the manager. And we're at the stage now where we go, we have to ask us. And listen, a lot of people have been asking this for a while. And I've batted it off on being fully behind the manager. But as a football club, we need to look now and go where do we go from here. We've just, I've just seen a tweet about the six games we've lost this, sorry, the seven defeats we've had this season. And six of them are to Aston Villa, Bournemouth, Sheffield United, Brighton, Burnley and Norwich. We've lost those six games. That to me should be 18 points without a shadow, without even having to think about it. They should be 18 points if you're trying to get to the top four, top six, whatever. And you've spent the money ever now. Most of those games should be victories. We've lost all six. And in four of those six, or four of the seven games this season, we haven't scored a goal in. We've the three promoter teams have beaten us to now this season, which is embarrassing in itself. Two of them have won a Goodersham Park to now. You know, so where do we go from here? The manager's in place. We've spent money. We're changing the team every couple of weeks. There's no style of play. And that to me is a fairly frustrating thing. We played West Ham the other week and he picked a team that's fluid midfield. Now I know he's lost Bernard and Andre Gomez. I know that. But we could still play the same style, the same way. So that players know roughly what we're doing. But we change it. We go back to defence of midfield and Guilffy comes back in and it's slow. And then Toulson's in order to call the loom in order to charles him through the middle. No one could get into any rhythm. And this is because we're not playing the way he wants us to play. So he's trying to throw everything at it. It just lacks a plan to me. That's how it seems anyway. And I've backed him and said, no, you know, he's been a lucky. He's lost Gabarman. He's lost Gomez. He hasn't dealt with him being fit. He has been unfortunate and Bernard. But I've said it before. Sometimes it feels as though we're starting again with him. And it shouldn't be the case 18 months down the line. It should be following a plan. The club has to decide, I suppose, where we go from here. Away from the manager.  Do they question each other off the pitch? Are they sitting together going, what is going on? How are we losing games to squads less talented than us? What are they doing that way now? Are they working harder than us? Are they more together as a team? Are they better creatively than we are? Because we can blame the manager. And that's fine. I've got no issue with people having to go with the manager. He was shocking today. But the players have to ask themselves the same question.  Did our players give everything today? Some did. Some didn't. I don't think they did. It doesn't look like it hurts people enough. And that is a problem. It's an issue. And when you spent a lot of money, we seemed to, as a football club, have stepped away from the values that we had that made us difficult to beat. And I know it's changed. It started going like that since David Moyer's left. Martin has softened us up a bit. Cumin was different. He had less said about him the better. And I thought with silver, the hunger, the desire, the energy that he would bring would transmit even this season. But it just isn't. Seven defeats in 13. When we haven't played anyone, it's very worrying. We're going in now to a period where we could easily lose another six games. Quite easily in this next month or so. And that would be 13 defeats at Christmas. So it's terrifying when you look at it like that. I'm confident, listen, we're at five points off Europe. But we're going into a run of games that are very difficult to see if we'll win them now. I always say I'm confident we can beat anyone at home. And I am, but we've just been beaten by Norwich City. At home and that, that's terrifying. Where they had him at the bottom of the league, you know, had them on their way again. She scored one goal away and they've come and beat us two in a line. I go home and go, wasn't that tough again really. So I think the club needs to ask itself where's it going tonight and is there a plan? Is there enough signs of improvement and enough evidence that this is moving forward? And that's the question it has to ask itself. Fans, the fans made their feelings pretty clear in that stadium tonight. Let's see what the club do. Make sure you check out the other maths reaction videos and Peds player ratings should be entertaining. Give us a subscribe if you haven't already. Check out the Duncan Ferguson interview if you haven't, it's brilliant. Take forget about football, current football. I'm just going to watch that one. It might take your mind off it. Otherwise, have a good weekend, see you later.


Welcome back ladies and gentlemen to the greatest show on the internet where one of the few authorized meme-writers goes ahead and rate some of the best NBA 2k memes. Y'all been on your game. Just gonna be really honest here, and I've really appreciated it It's been making me laugh This video is brought to you by Squarespace from websites to online stores to marketing tools Squarespace is the number one spot to start building your online presence more on that later If you guys are new here, you can subscribe, but you don't have to you're probably gonna pop up on your home page anyway But if you don't want to do that drop a like but you don't have to it doesn't really affect the video whatsoever But if you don't want to do that subscribe to my new channel podcast first episode dropping tomorrow on YouTube But if you don't want to do that then let's just get straight into it We don't have to do none of that g-man sent this first one me when agent and Davis collab Huh You something you want to tell us g-man it's going on wait you sniffing your phone like that It'd be kind of cool though phones could emit smells like if you're watching Animal Planet and it's smelled like dog shit that enhanced experience. I think so but this this is weird I wouldn't even consider this a meme. This is just a fetish a Man followed up saying I'm sorry for anyone who sees this and we're sorry that we ever did two out of ten NBA2k20 Intel says mr. Rani is there a new gig glitch in town?  Is he nervous and? It's a six nine a.k.a. Chalk meme ladies and gentlemen But to fill y'all in, in case you guys haven't been watching my videos in the last week, bro, what are you doing, man?  Um, Chalk decided to tweet at NBA2K when there was an unlimited VC red-ball-blue-ball glitch, and everyone is labeling him a snitch. We know he didn't really have nothing to do with it, but it's funny to say anyway, so people are saying it regardless. And you don't need me to explain the 6-9 situation. Come on, guys.  I have to explain everything to you? Josh responded, funny, yeah, accurate, I guess, but I will not rate this meme. I do not have that authority. I have the authority, and I'll give it... Hmm, let me call my board of directors. Give me one moment, guys. Hey, hey, hey, hey. Yeah, so, um, what would you rate that as like a board of director authorized meme-rater? Isn't Chalk the snitch to everybody? Exactly, so that's the point of it. So what would you rate that meme as an authorized meme-rater? 11 out of 10. 11 out of 10? All right, man, you guys heard it. It wasn't for me, but it was for my authorized writer, so it counts. By the way, I see it there are three possibilities. One, you stole it. Two, you stole it.  You didn't give me a caption, dude.  This was a fantastic video, but if you captioned it something creative and funny, this could have been a magnificent 10 out of 10 meme. But you're lazy, and I will have time for lazy people in these videos, so I'm giving you a 5 out of 10. Um, Suwante just dropped a video.  No caption, Suwante. You just saw this on the internet and posted it. I'm very disappointed. Especially after you were doing so fantastic in ProM the other night. Video coming in a couple days. Yeah, it was really good, guys. You should start watching the ProM videos. I enjoy making them. Alright, here we have a video. Agent when 2K adds Popeyes, okay. Of course, of course the rat has a fedora. Of course. Because why wouldn't the rat have a fedora? No, man. Popeyes memes are dead. I've killed them as an authorized meme rater. So you can't come at me like that because it's no longer funny, buddy. 3 out of 10. Ronnie tweets literally anything. Entire 2K community. Really, really hard. Stop capping. Stop capping. It's not a good look on you. 8 out of 10. Quizzie dropped this meme, 2K taking my VC. And it's a gift of TI snatching the necklace. What? Is it accurate? Well, of course it is. They took everybody's V hard earned VC. Is it funny? Hmm, let me think on that. Kind of. A little, not too much, but just enough for me to give it a 7 out of 10. Someone responded with 2K taking back Agent's logo. Okay, here we got a flight reacts video, so I'm just letting you know. You know the drill. 8, 8, 8. Oh, don't know what the purpose of that was right there. But I guess everybody was just vibing with flight for 10 seconds. Okay, can't be mad at that. iPod, King Carter says, I had a dream that 2K banned everyone that was up all night long chasing that bag. Red, blue, and I'm sure this was dropped on Martin Luther King Day. No. I don't really know if I've ever in my life seen a worse meme. iPod, how is it possible that you're this horrible with memes? I previously thought toasted was the worst, but no, no, no. Here you come swooping and just like that. Just to let me know that you have no meme making ability whatsoever. I want to. I really want to, but I'm not authorized to rate this meme. It's okay. We all know the answer. And the answer is negative 5 out of 10, which is the worst rating I've ever given in the history of this show, guys. Wow, iPod, have a lot of respect for you, man. Get your life together. Davis tries to walk down the street. People trying to, okay. Yep. Zero out of 10. 2K to the people that lost all their VC due to the glitch. Here we go. I stopped by one of my biggest haters funeral today just to make sure.  Now, wow. Is that a real post? Wow. Okay. The meme itself is not funny at all, but the post is so outlandish that it's funny. The post saved you here. I'm giving you a three and a half out of 10. Me handing 2K money for next year's game after 2K20 has been trashed this year. It's just a photo of a guy handing cash but looking promiscuously in the other direction as to not draw any attention. This is the embodiment of what shame would look like personified. Now, if we're talking about the meme, I don't know. I'm going to have to talk to my board of directors. I'm not too sure about this one, guys. Oh, that's so convenient.  Oh, you want to use this one? Yeah, I want to use this one. You're not busy, right? You're not busy? No, I'm busy. So I need your help writing this meme. No, I'm a busy goodness. The relevancy of the meme, because that's like a popular meme right now. For sure, for sure, for sure. As the new official meme reviewer, I will give that a nine. He thought he could take my job. You're an official writer and you're on the board of directors. Can't have none of that, man. I'm giving this a zero out of ten. When you accidentally step on the stairs by the two's court, this little maneuver is going to cost us 51 years. Oh, that's creative, yo. I've never seen a meme about that, but we've all been through it. Running on the neighborhood, you accidentally hit the stairs. Baaah! You're running this way. Slow motion now. This is a quality meme. Good caption. And it's about something that we all experienced, but nobody ever talks about. And that's the essence of comedy. It's to hit those points in our lives that we overlook, but we all share in common. And for that reason, I can't help but give this funny meme a ten out of ten. Agent reviving the 2K community with his content. Aw, look at this cutie here.  I hope that song's not copyrighted, but it probably is, so we'll substitute, like, I don't know, it's my Dr. Seuss song. Thank you. Thank you so much.  Me buying 2K every year! Is that Rod Wave? Huh? I don't know what to believe. You know what? I think Rod Wave was playing 2K when he made that song. I'm pretty sure he was. Incredible. Incredible right there. Great meme. Eight out of ten. When your guard and center are doing all the work and you just stand in the corner and play defense, it ain't much, but it's an honest work. Hey, man. Tell you right now, every team needs a hash sitter. I don't care who you are. So I don't want nobody disrespecting hash sit in the 2K community. That's my people, all right? Me after loading up 2K and checking my VC balance. Oh, no. I'm not having nothing. I'm not having nothing. I've never actually seen this video before. So that's some creativity points right there for showing me something I haven't seen before. As an authorized meme writer, I like to think sometimes that I've seen it all. I don't know what to rate this, so let me sit and think on it.  How 2K feels after stealing everyone's VC. We can't put that song in there at all, because it's the number one song, so it's going to get copyrighted in like two seconds. Not 2K players when they see Ronnie getting slammed in his comments. Man. What did he do to make the nigga stand? Man. He's known me very well, man. It's an appropriately memed caption, boondocks meme, ladies and gentlemen. And there's nothing I like on planet Earth more than a good boondocks meme, ladies and gentlemen. Ten out of ten. Talk D.F. to Ronnie after snitching about the VC glitch. It's a Soluminati meme. Dude.  You know what I'm saying? I'm trying to go crazy. Nine out of ten. Hey, my boy over there thinks you're cute. His boy. His boy. Can you imagine going anywhere with flight? I think that would be the greatest night out ever. When you're giving a presentation in class and the teacher has to quiet the class down and you're just standing there like Clay Thompson. Just awkwardly standing with the awkward smile meme, guys.  I've seen this one a while ago. Your lack of creativity is startling. Very startling. For that reason I'm going to give you a two out of ten but I'll let you know the meme was funny when I first saw it. How people be walking out the ante up after losing all their VC. You be going in there very confident. You leave completely demoralized.  I just get good teammates to carry me while I sit at the hash. Me trying to return 2K20 after getting Vanessa out of $60. I know this meme. Customer service. Get me straight to the- Straight to the- Get me straight to the- More options for how to get help. Customer service. Customer service. Customer service. Why haven't you gotten him straight to the- Oh my God. I hate when customer service doesn't get me directly to the- That's my worst experiences ever when it comes to customer service. Yo, that's one of the funniest memes I've seen so far in the last few months and you accompanied it with a solid caption that we know is accurate and we know is funny. So you're getting a solid one. Zero. Ten out of ten. To everyone thinking 2K17 was the best 2K of all time. Something wrong. Psychologically, something has happened. Yeah. Now I don't know what it is. But Lord, it's bad. That's solid. It's solid, but it's not bad. It's not good, but it's not bad. It's solid, but it's not good or bad. So it's just solid. Six out of ten. Ronnie, when his DMs to Lizzo get leaked. Oh no. Man, I can't even write this over a seven out of ten, regardless of how funny it is now. Because you took a horizontal video in vertical form in a horizontal video.  Just drop the original horizontal video. No. You gave me a song, a meme with copyrighted music with an interesting edgy caption. But you didn't hit the mark. I'm very disappointed in you. Everyone in the 2K community, after 2K, took their VC. I'm growing depressed. I'm growing very, very, very depressed. We've been there, Steven A. We've been there. We've all played 2K18, so we've been there. And we know what that's like, Steven A. You're not alone. All right? Just talk to somebody. You get through it, man. You get through it. I know they took your VC, but you should have used it. You should have known better. Come on, guys. Who's been through this before knows you should have used the VC. But you held onto the VC just in case. And you f**k that. All right? So assume some responsibility here. When Chalk snitches about the VC glitch.  Oh, man. And a doof. And racist. Why is he racist? I don't know why he's racist. When you lag out in a close game and everyone in the party is going crazy because your AI hit the game winner.  That's just so good. That's not even solid. That's a great meme, guys. Hmm. Just a well-put-together meme. Wow. Someone responded too much text, dog. And then he responded saying, I apologize if one sentence is too much for you to comprehend. Yeah. His name is Agent Likes My Memes. Yo, one sentence is fine. Two sentences, we're pushing it. Three, you're out of control, buddy. But one is perfectly fine. This is a 10 out of 10 meme if I ever seen it, fellas. What? Me, when 2K asked if I abused the red ball blue ball glitch. What? What? Me? Never do such a thing. Me, telling someone how many times Davis fell in one video. Oh, I know where this is going. Over and over and over and over and over and over and over and over and over and over and over. And over and over and over and over and over and over and over and over and over and over. Guys, I don't know if you know this, but Marshawn Lynch memes are very much appreciated here on this show, okay? He's my favorite athlete in the NFL. Solid. Is it accurate? Of course it is. Davis falls a lot. Is it funny? Yeah. I'm always recording. I'ma give you a nice nine out of 10. Don't we all love to make money? Well, that's why I love Squarespace. They helped me make money a lot easier. Gotta love technology. Let's say that I have a prolific water-selling business in the downtown Atlanta area. And I'm trying to improve, trying to start selling stuff online. Well, you could do that, ladies and gentlemensquarespace.com. Boom. It's as simple as this. Watch how scary this is. Websites, domains. I'ma create a website. Now I have access to all of these different templates that I can use and customize to help begin building my website. Let's say community and nonprofits. No, cause this is a for-profit business.  Trying to make money over here. I'm really messing with the Barbosa design. Prolific water seller. If you wanna change this picture, it's as simple as uploading your own. Do you know how hard it was to do stuff like this 10 years ago? You had to learn code. No one's trying to learn code. Thankfully, the guys at Squarespace did it for us. So we don't have to do that. So I could just begin telling people about my fantastic prolific water-selling business. There's plenty of things I love about Squarespace. They have in-depth analytics so I could better understand my consumers. There's no plugins. You don't have to download plugins, nothing. Everything is already available. You could purchase a domain through Squarespace. If you're someone who needs to schedule appointments, there's a feature for that too. If you're selling merch and you want something to drop at a specific date, you can schedule that post. So there's no real excuse in 2020, ladies and gentlemen. If you're trying to build your online profile, you're trying to take your stuff to the next level, ladies and gentlemen. Squarespace is the space to do it. Hey, use code AGENT at checkout www.squarespace.com slash agent to get 10% off your order. And a huge thank you to Squarespace for helping sponsor this video. Hey, thank you guys for sending memes, man. You hit the ball on the mark on the head of the top today, guys. And I appreciate it. Remember, there's a link in the description. I started a podcast. Why haven't you clicked it yet? Fine, it's like that. Bet. Who cares? I don't even care. I don't even care about it. I don't even care about that, so.


What's up everyone? It's your girl Kenty here from late and sports cards and I'm ripping 2022 tops pristine baseball Hobby four box break number 18 random teams again. Here's a look at the random results You can always find these the late and sports cards calm underneath the results tab link is also in chat for you All right best of luck Yeah, you did six Kunia incoming that would be sick, huh? What six it uh braves? Oh nice How mollard's got rookie autos Smalls glad then I think I've color Kunia's got color obviously. Yeah, very solid And everyone just basketball a couple basketball breaks with spots remaining for tonight Um still room for personal probably close to about halfway filled. I think I haven't really been looking No, yeah Yeah, still plenty of room for personals. I'll be doing those after group breaks tonight. Here we go That's suey Got purple christian yellow Gold hayrod rangers a rod orange of trey turner Kyle moeller. We've got popular uh demand patch auto of zander bogarts Red sox. Yeah Nice one there And a cc sabbatia On the uh slice of the star pulsar there yankees out of 99 Oh, nice.  He pulled me the fast card in the set What hi it's pure power hank erin one of them one what um pokemon product is it? Uh b max uh shielding rain. I want to say. Oh, nice There's a japanese v max. Oh, wow. Yeah, those are sweet pure power. Dave windfield. It's like a full arc blaze again All right skill rookie connor wong borders of hosea brave They're going to pack and then we'll get into our encased here box one Christine borders of rod karu twins. All right. Here we go starting off with the rookie auto tigers matt manning We've got re debtmer's rookie auto for the angels orange pure power edgar martinez mariners That is 24 out of 25 auto popular demands iven rob rigas Nice one there marlin. Do you have marlins on that one a jared kelnic base and six We've got ozzy albis one Oh six said he doesn't want base though, so Okay, so I get to keep it that right There you go congrats on that one six. Nice six. What a box. Yeah, see that one had double the hit. Yeah, right exactly First box Well, let's go baby. Let's see what time that baseball game starts yankees and rays tonight I'm sorry. He's looking forward to watching that one Yeah, what time is that uh baseball game start? Uh 705, I believe So Should be on the rays network On big baseball release tomorrow diamond icons Time and icon super high end baseball products will be doing those random hit style And force will be live starting at 10 a.m. Tomorrow 10 a.m. With that as well as a new release basketball of city in the basketball Oh, yeah, that's gonna be fun. Yeah Both of those are gonna be sweet. Yeah, it's gonna be a fun day tomorrow for sure And then friday we've got optic h2 release for football. So fun release week this week stevens drossford purple Kerala co-ar rookie purple rookie wander franco nice one there for the rays Heck yeah, out of 99 on that and slice of a star of josh peter for the brew crew On the pulsar 19 out of 99 We traded josh here Got borders of uh Yeti melina or double castor rucky ram Auto like snow borders of clain kershaw Got pure power fring thomas laddie borders sammy sosa That's a good one there bradley nah pristine borders shane beaver funny guy bradley are in the chat as always There we go in case hosie al-tube base auto Astros matt veerling for the fillies rookie blue a manual revero rookie royals to 75 Bruhan rookie auto for the rays We've got manual rodriguez cubbies rookie and a hank erin Cool in there for the brace.  Oh wow Border is shane beaver. Yeah, you don't see really crazy patches on me. Yeah, exactly I'll probably look really cool. Yeah Pure power hank erin I gotta say It's packed and encased Jose siri rookie white park A pristine border shohei otani All right encased time We've got uh pink pulsar to 15 one soto gnats blue jose ramirez To 75 Gavin sheets rookie auto white socks Got gold rookie louise gill for the yankees to 50 pure power one gonzalez rangers That's a cool one for texas and iven rodriguez marlins Box here And everyone just a few basketball breaks left for tonight and then we are all sold out Still room on personal as if you'd like to get something ripped i'll be doing those after group breaks tonight Say we'll be doing them tomorrow. Oh, yeah better.  I bought another spot later So i'm buying mood today Hence why I went baby shopping yesterday I spent like almost 700 dollars There you go for the baby Yeah, here we go. Got slice of a star of jose abray white socks Slice slice of a star that is 65 out of 99 purple austin meadows strippy jr. Purple joey gallo for dugo and gold jim carlo stanton for the yankees All right, let's see a big one here Roberto clementi borders Judge pure power one soda She's um pure powers akunya jr. Shamboz ski newt bar borders eudarvish That's five that we'll get into in case didn't have a short recap and moving on to borders clean kerrishaw There's gotta be some fire stuff in the counter tonight. Here we go. We got white socks. Jose abray you popular demand nolan aeronado for the cardinals blue brian reynolds to 99 pirates gold rookie ernie clement for the guardians to 50 jaren diran uh rookie auto boston and a gold jesse winker for the reds All right, let's do a quick recap here. We've got our slices of a star Jose abray you out of 99 white socks aeronola billy's We've got josh hatter on the full star slice of the star to 99 Cc sabatia full star to 99 yankees popular demand zander bogarts red socks not numbered fast for autos. I have a bunch of them in this one jaren diran red socks nolan aeronado cardinals one gonzales rangers Gavin sheets rickey auto white socks. We had one soda to 15 gnats Bruhan rookie auto race jose autu ve astros orange to 25 edgar martinez mariners free depper space auto angels matt manning tigers popular demand iven rodriguez for the marlins andy one of one ozzie albies for the braves Congrats again on that one six. All right, that'll do it for pristine. We've got some definitive coming right up


Ok, dus yw'r gweithio, mae'n gobeithio mewn microvasg, ac mae'n gwybod bod Microvasg ystod yn ymgyrch, CBC, mae'r cyfnwys i'r cyfnwys, yw'r cyfrwyd, yw'r cyfrwyd, a'i'r cyfnwys yw 50-100% phallon o'r cyfrwyd. Mae'n gwybod i'r informatio, dyma'n gwybod eich bod yn y ffianaturiol, mae eich busnes yn y ddechrau, mae'n gwybod i'r busnes yn y cael ei fydd, I'm in terms of the stock market on the expert, I'm failing you into the area. But I just want to share my journey, my research, and show what I think in terms of that. So just move that out of the way. Okay, so let's go through this. So 50-100% what we'll go through is some information on the company, where I see it going, some research, and some of the potential for this, a why it might be a potential buy at the right particular price. So let's get right into it, a respect you time, so let's get into it. So who are they? So essentially they are founded in 2006 and MacroVast is a growing fast growing market leader in design development and manufacturing of ultra fast charging long life batteries, battery power systems with superior safety for electric vehicles. Specifically designed to address the critical constraints currently facing electric vehicle development. Our electric vehicle battery system solutions paves the way for mass adoption of electric vehicles. So this is an area which is growing in terms of it being in the EV market, electric vehicle market. Now there is a lot of hype obviously in this market so you've got to be careful but also at the minute it is fairly cheap the price as the making of this video for the stock is a good price. It might be worth looking at getting into which will go through in a bit. So that's who they are. So the product itself, so in terms of the benefits of the product ultimately it's got fast charging and with an EV battery that is a key component. You need to make sure that the charging is fast for it but also because of that they've put money into research and development they're continuing to do that so they're trying to build a higher quality product. It's really based upon the success of this company is it really based upon the product itself being fast charging and high quality and that's something that they've recognised in their investment sort of information there and they've also got orders, they've probably got contracts lined up in the future most likely you know with the emergence of electric vehicles that they were going to build more contracts as well so that they produce EV batteries. So it's essentially linked with a lot of heavy duty vehicles. So actually in terms of public transports as well so microvastaf of the EV chargers in some of these London buses. So they have been involved in a lot of high, what's the right word? High ticket areas shall we say in terms of credibility working with London buses in the EV market okay in terms of right bus there. So in 2019 microvast actually won the prestigious research and development 100 award so that shows that they are a very innovative company they're creating much better batteries and building more of them. 2018 as of December 2018 the number of vehicles in commercial operation with microvast battery systems has reached 24,000 units worldwide and that is only looking to grow over time and in April 2017 microvast receives 400 million in new funding so they've got some cash on hand there for the research and development and the push to market in terms of the batteries. Actually it was awarded in March a 4.5 million contract there 4.5 million dollar contract that was obviously in March and that essentially the success of this company is predicated on are they going to get loads of these big value contracts and if they do then that's going to be effective for them. Okay another worry is that they're working in terms of a global approach so they're a global network they've got headquarters in Houston Texas USA six locations well wide research and development in China USA and Germany 2500 plus employees well wide so this is a global company with a global approach they want to sell these batteries globally and they have headquarters across the world there and they're already increasing that into other areas in the world and trying to grow a global approach with that so that's very promising to see because you know if a company has a global approach they're increasing the potential market share and the potential market growth in terms of who they can sell to so to try and get their sort of footprint in a range of locations that is only good for business so some of the positives that I want to go through with microvast and why I think that it's it's got a lot of potential so it's within the emerging EV sector supplying the batteries okay so the EV sector in terms of a lot of heavy duty vehicles in terms of batteries for those at the minute is what they do but it is as we say an emerging sector now we have seen a bit of a pullback on some of the stocks recently for EV a bit of a correction but you know we can see the potential in two three four years in terms of electric vehicles and obviously if more of electric vehicles are on the road then there's going to be needing more electric batteries and this is where microvast comes in and there is less hype as the first point mentions there less hype around EV batteries there's a lot of hype around kind of and it's not hype because it's justified you know in terms of that so there's in terms of EV companies in terms of the vehicles themselves there's a lot of hype around but there's not a lot of hype around the batteries so I think that's what's starting to happen already and it's going to and that's going to be the next sort of phase in terms of okay wait a minute we've got the electric vehicles okay brilliant okay well what what is what is what do we need what what are the stocks are involved with electric vehicles oh we've got electric vehicle batteries we've got materials that are needed for electric vehicles you know there's a lot of stocks interrelated with electric vehicles as a whole as an industry so I think that this is going slightly underlugged still so and this is a potential area for a buying opportunity a good price whilst it is underlugged so so that microvast in terms of supplying the batteries and the next point is the potential growth due to a wide range of potential contracts so because they're operating globally because they're involved in a variety of markets you know the potential for contracts is is is huge and very lucrative contracts as well with business to business well put more EV cars but obviously it's in terms of more EV it's of heavy duty vehicles as well in terms of buses etc but ultimately that will mean more batteries that are needed very very kind of straightforward there don't need to explain that any further high levels of R&D which is you know research and development so if they're building constantly and innovating constantly their batteries if they're building better batteries and being the top high quality battery on the market in terms of charge speed in terms of that the quality of it that is giving the company a competitive advantage over its competition because they're going to get more contracts because they just have the better quality battery and that's what the you know what people will be looking for so that is good it's a competitive advantage potentially government backed industry as well you know electric vehicle industry is government backed in the in that sense so so with that we can we can see that the potential growth there more people switch into electric vehicles but also because it's a government backed industry it's becoming more affordable in terms of there's grants available there's subsidies available for going electric and as the affordability of electric happens for all all sectors then that is just going to increase the the need for batteries and it's got a good history and brand overall so as I say it's won awards in the past it's got a good history leading up to the point that it's currently at and there's a strong brand for for the company and it also has a strong strategic direction as well in terms of where it wants to go into the future it's got its product it wants to obviously develop that product further but also start to reach more customers there so strong strategic direction it's a global marketplace as well okay so so I've mentioned that so if we look at the stock at the minute it's at 12 91 Tuscan Holdings so it's a very quite cheap price it has gone down slightly because of the slight pull back with the EV markets but it's also up but probably in the longer term I would say so this is at a good price at 12 91 it might be a good time to get into this maybe around 12 50 if you can get it around there maybe 12 12 20 12 50 and then that again I'm not a financial advisor make your own research and decisions but I think that this stock has a lot of potential I'm personally holding this stock and I'm hoping to see some some great gains on this stock so certainly look into this as I've mentioned a lot of potential with all those positives emerging industry spack merger cheap price at the minute you know with the hype I think you've got two options you can swing trade it and sell it just before the merger or you can you can hold it long term if you think that the company is going to do a good long term so there's two strategies with it but both of them can lead to potential gains so thanks for watching this video don't forget to give the video a like and also subscribe to the channel I'm new to making stock market videos on YouTube so any support that you can give whether it's just a like or that it's just a subscribe that is that is much appreciated and I appreciate that a lot as I say I'm not an expert I'm a business teacher but I'm not an expert in the stock market at all so so obviously do your own research as well which I'm sure is you know something that you're also doing as well but it's you know another perspective to consider anyways I'll see you all hopefully in my next video if you do feel like subscribing see you later


Hello everybody. E here. Welcome back to Top 5 Friday. And we're in front of the King's Shells, so you know it's a Stephen King video. Today we are talking about a viewer request, the Top 5 Least Scariest Stephen King Books. Now when I went into this, I tried to judge whether or not a book was supposed to be scary, whether or not his aim was to scare people. The only book I don't feel like his aim was to scare anybody, we'll get to that in a second. There's only one on the list that I don't think that was his intention, but it's also one of the only books I've ever seen him write that doesn't have anything intentionally scary in it. Even in Leasy's story, there was some stuff that might have leaned a little more toward the horror aspect. But jumping right into it, Christine. I'm going to get a lot of flack for this one. I know this is a lot of people's favorite book from Stephen King. I know a lot of people think it's scary. I have never found the killer car trope scary. I don't see it, it's just, it's like, I don't understand, it's not like, all you got to do is stand in the blind spot and you're safe. The car is just going to spin around you, either that or get somewhere that the car can't go. And I know this car was super natural, it could crush through everything and then rebuild itself. At no point in time did I find it funny, I didn't even find the book all that fun. So that's my number five. Next up is the one that I don't think he was trying to be scary at all with. Maybe with the future connotations of what happened. Maybe that was some horror there. But 11, 22, 63. I found this to be, especially the middle of the book, just really, really, really boring and frustrating. And there's a love story in here I didn't care anything about. But the biggest issue I had with the non, there's not enough drama in this book. There's no race against time. And part of that problem is the fact that he has to wait five years before anything will happen. And he always has the option to reset everything. And that's going to be a problem coming up in the number one spot. Number four, definitely 11, 22, 63. Nothing scary in it whatsoever. Next up, we have one. And this one does have some intentional scary stuff, I think, anyways, which is under the dome. This does have horror aspects. But I think some of the horror aspects that he used, like the meth addict or the cook or whatever you want to call him. And then the aliens at the end, it's just not that good.  The rest of the book was thrilling. It was a fun adventure up until the end. And then we got the throwaway, you know, crapshoot that I feel that it was a super cop out. But then again, it's what ties it into the world. So maybe it wasn't a cop out. Who knows? A lot of people call anything supernatural a cop out. And that's not what I'm getting at. It just felt like he got to a certain point and he's like, this is the only thing it could be. So let's use that. So that one Ed didn't scare me at all whatsoever. None of the creepy hallway watching that the cook does. None of that stuff really scared me. The next two are what I would consider horror novels. Novels that their main intention is to scare you that Stephen King completely dropped the ball on. The first one is, of course, Dreamcatcher. We all probably knew it was going to be on the list.  There's nothing scary about anything in this book. I don't think. I know Landon will probably disagree. I know several other people will probably disagree, but everything that tried to be scary in here failed because he went also the comedic route with it. Mr. Gray with his English accent, he was never scary. He was never sinister. He was more like one of those old cartoon, I'm gonna get you, she's that kind of thing. There was never anything. And I know that wasn't the right voice, but there wasn't anything that ever scared me. I didn't care about any of the characters, so I couldn't have cared less if something bad happened to them. So that one, but I think intentionally, it was supposed to be a horror novel. There's some blood, Gus and Gore and whatnot. That was the main intention was to try and get back on track after his accident, trying to rekindle some of the stuff. And I wish he wouldn't have went strictly literary after his accident. We might have gotten more like Duma Key or Leasy Story, that kind of thing. We might have got more books like that instead of him trying to recapture it and fix the Tommy Knockers and all the other stuff that he went into that trying. Now, the number one book on this list, the number one least scary Stephen King book has a huge problem with it. If in case you haven't, I don't know why you're watching this video if you don't like spoilers, because of course we're gonna be talking about spoilers. But Doctor Sleep, I honestly have no idea what possessed him to make one of the main characters so overpowered. Abra in here is, I never had any concern for Danny or her or anybody in the book. I had no concern whatsoever because it is established very early on that Abra Stone is amazing. That there is no stopping her, that she can do whatever she wants to. She is the pinnacle of what Danny could have been. She has all these super human abilities and then you put her up against these knockoff vampires, true not. And it was no suspense.  Now I did give this book three stars because it does have some great scenes in it, especially Danny's recovery. But as far as the ending, it was silly.   It's that kind of overpoweredness that ruined the book for me. And when there's no tension, there's no drama of, now before Abra comes into the story, it's great. It's terrific because you have this, you have this very human story and Abra can't do anything about Danny's alcoholism. He can't do anything about that. And had it been just a story about him in this convalescent home, I probably would have liked that a lot more than what we ended up getting with these vampires roomy. Anyways, these psychic vampires, whatever they are.  There's nothing scary. The scariest thing about Rose is her picture on the cover of this book. So what do you think? Did I get any of these right? I am sure you will think I got some of them wrong. So please leave your comments down below. Do you have a top five least scary Stephen King books? I think I have a top five scary Stephen King books. So compare that one to this one and see if you can pick out some of the reasons, you know, some of my own fears, what might have made those books scary, what made me laugh these books off. Who knows, but leave all the comments down there in the doobly-doo. Until next time, I have been E, you have been U. This has been another top five Friday. I'll talk to you guys later. Bye bye.