In the real world, many problems can be too complex to be solved by a single machine, by a single machine learning model, whether that be predicting sales for each individual store, building a predictive maintenance model for thousands of oil wells, or tailoring and experience to individual users. This leads to a pattern of many models. When should you use this approach and what are some of the best practices or things to avoid if you decide to go down this route? I wonder. Well, to tell us everything they know about it, we have with us Maria Medina and Jose in Alizade from Microsoft. Welcome Maria, Jose in. How are you? Thank you. I'm glad to be here. Lovely to see you. So we're looking forward to all your secrets, to finding out all your secrets. Remember we're in the attic and the attic is the place of the hidden treasure. So all yours, Maria. Okay. Yeah. So let's talk about the many models pattern or how to manage thousands of models at a scale all at once. Yeah. So as Elena said, I'm Maria Medina. I work as a data scientist in Microsoft Consulting Services. I'm based in Madrid in Spain. And together with me, we have Dr. Jose Alizade, who's also a data scientist in Microsoft Consulting Services and he's based in Melbourne, Australia. So he's joined us from the other side of the world today. So in the following 30 minutes, we're going to talk about this many models pattern and why the need of it. We're going to talk a bit also about Azure Machine Learning, which is the tool that we're going to be using for developing all these solutions. We're going to talk about how to train those thousands of models and how to make predictions with them. And then we'll end up explaining how to parallelize everything and a bit of wrap up. So with that, I'll pass it on to Jose, who will start explaining us why many models. Thank you, Maria. And I'm also very glad to be here today, sharing what Maria and I have learned through helping our customers, building many models for their resolution complex problems. So yeah, as some of you might already have seen this slide, organizations can be different in terms of where they are in their data and AI maturity journey. Some might only have some basic analytical capabilities. Some might be embarking on their digitization and some further up in this ladder. The more organizations get further up in this ladder, the more they are matured in this, in their data and AI journey. We see at Microsoft more and more a need for an emerging pattern for many models solution. So instead of just training only one model or inferencing from one model, we see a pattern where there is a need for doing and managing the full life cycle and machine learning models on a scale, including training of thousands of models at the same time in parallel forecasting of that in terms of auto drift detection and also in terms of productionization of this. As an example, one of our customers, which is a hundred year old Australian utility company, they have reached out to us with their problem. They are pretty mature in their AI journey. They already have a sizable center of excellence for advanced analytics. So the requirement they have is also very advanced. What they come up with in terms of the problem, they had a forecasting problem for their solar production and load from the grid for every household among their customers. The solution they required had to be scalable enough to leverage all of their customer base, which is around 400,000 of households. So this is a perfect example of that requirement where you want, where you need to build thousands of models like a scale and create a solution that is scalable and being able to manage the life cycle of the full solution at a scale. But this is not the only example. We have observed a variety of industries and organizations that share similar requirements to train and manage life cycle of thousands of machine learning models, that whether it is a bank which has cash replenishment models for thousands of their ATM machines, or it's a retail business which has thousands or even hundreds of thousands models for their price optimizations and want to manage that whole solution in a proper manner or it's an energy and utility that has demand forecasting problem at a scale. They all again share the same pattern of problem and requirements that requires a solution, a proper solution. What we came up at Microsoft in terms of the solution is what we call it as many models solution accelerator. This solution accelerator is addressing that problem. At Microsoft we open source this solution accelerator and everyone can access that in through MS slash many models. We publicly shared that so that we empower every organization in the planet to achieve more in terms of their AI capabilities. But what is this solution? What are those components or the main components in this solution? How they work and how different bits and pieces in this full solution work to achieve this goal? We are going to, for the rest of this talk we are going to unbox this solution and talk through each components one by one and share how we can actually. But before going through that, first we need to define and to go through some of the key concepts and components in Azure machine learning for those who may not be aware or may not be familiar with that so that we can take it from there. So with that I would like to ask Maria to take us to the introduction of the learning please. Thank you. Thank you. Yes. So Azure machine learning or AML in short as we call it, it's a tool that helps you manage the life cycle of your machine learning applications, the end to end life cycle. So it's not meant to replace any of your machine learning code, instead of that it's meant to assist you during the process of executing the code, storing all the results and metrics that the code produces and also help you put in that code into production. So for example, when you're training a model it can help you by tracking all the experiments that you make, all the metrics that you produce, all the artifacts that you produce and when you have a build some successful models you can also package those models and version them so they're ready for being potential candidates to be put into production, kind of like your favorite model list. It also has some features and capabilities to help you validate those models and understand and interpret what those models are doing on the inside and why they're making the predictions they're making. It has a wide variety of functionalities to help you deploy these models and use them for making predictions in real life, in production, ready environments and also it has features to help you monitor the models and detect if the data that you're using is drifting or if the model is drifting and then you might need to retrain it and start the process all over again, closing the cycle. So when you create an AML service in the Azure cloud where you're creating in the cloud is what is called a workspace, an AML workspace. So that is essentially like a folder for your project where you will store all your artifacts or your data, your models, your experiments and everything like a very strict and with versions so you can keep track on everything you're doing and never lose anything that you're doing in your machine learning system. And for interacting with this workspace, you can use the main two tools are the Python package and the R package, but you also have some other tools depending on the language you're using for building your models. And one very special artifact that you can create with AML are the pipelines. These pipelines are used for defining workflows in your machine learning applications. This is a good way to encapsulate your machine learning logic so it's more repeatable and unreproducible and modular, right? So a pipeline is essentially like a sequence of tasks that you're going to perform. Row data is going to come from one of the sites and the result of a training pipeline, for example, you would have a train model. So inside the pipeline, yes, as we mentioned, we will have a sequence of steps. And again, this is not meant to replace your scikit-learn pipelines, for example. Instead, this is like a layer of abstraction about this to make the code more manageable. So for example, in a training process, you would have a step for data preprocessing at high level, then you would have one step or several steps for doing your training with different algorithms, different configurations, and so on. And you might have a final step for evaluating all those training results and choosing which is the best performing model and registering it into your workspace. And we have very different types of steps. So there are steps for running Python code or R code or specializing hyperparameter tuning, for example. But in our case, we're going to be using a very special type of step, which is the parallel runner step. This is a step that allows you to run a task in parallel many times, as many times as you need. So what you are going to be using is you're going to provide a script, which is going to be defining the task that you want to perform over a set of data, and you're going to input a whole data set. And then parallel run step automatically is going to split that data set in mini batches. You can configure how you want to do that splitting. It's going to split everything, process these mini batches in parallel by executing the task that you define in the script that will produce some output. And that output will be aggregated again automatically. And the output of the parallel run step will be this aggregated data set with the final result of applying this task in parallel to all your mini batches. So this is the structure that we're going to use in the solution accelerator for many models for training and also for casting. And so I'll pass it to Jose, who will explain to you how exactly we're going to do this. Let's have a look further in detail on the training side of the machine learning of the mini model solution. So on the training side, all we need to do is provide to the mini model solution with some scripts for the training of one of those, for one data. Then we have the mini model solution, which actually takes that data and that script and run it in parallel and provide the results, which are basically the models and register them in the model registry. But how it does that? So let's look at the under the hood, what's going on. So if you look at the right hand side, you see that up there, there are data and also a training script provided. So they are provided to the mini models solution. And the core of mini model solution is that parallel run step. So the job of parallel run step is to take the data, break the data down to very small pieces and then pass each one of those pieces along with the training script to the different nodes within a cluster. So you see down there, there is a cluster, each node within that cluster is taking one piece of that data with the training script and run an algorithm on that data and train that and the results will be saved in the Azure machine, Azure model registry. And this is done in parallel on all of the nodes in the cluster. You may want to provide your custom script for training, a custom model or if you don't want to, there is an option in Azure in mini model solution where you can choose to leverage AutoML and that is also included there. So AutoML can automatically test through many of machine learning algorithms and give you the best results out of those experimentations. So this is basically the training side of the solution and when it is done, it provides all the models in the model registry. So you'll see that all of the models when they are finished in the model registry and you can keep track of them, you can see the version, you can see what experiment they have been and results of, and so on. Next please. Yeah. So for our specific use case in the site level forecasting, we have had 1,500 households for the first phase of our solution implementation. Each one of those households had basically three models underneath. Solar Production AC was a time series to be forecasted. Then Solar Production DC was another one and then consumer usage, which is basically the load from the grid. So overall, we had 3 times 1,500. Overall, we had 4,500 models to be trained, to be forecasted and to be productionized. For the training script, we have had a custom script that we're training a neural network-based forecasting model. For implementation, we leveraged many models, solution accelerator and specifically the parallel runner step to train all of those 4,500 models in parallel. There was also a requirement from the customer to leverage the real-time thing with a single API. But how we can do that? Let's have a look further into the foresight. What is the forecasting capability in the many models solution? So generally, we have two types of forecasting. One is batch inferencing or batch forecasting or batch prediction and the other is real-time forecasting. On the batch forecasting, it's pretty much straightforward. It is you have a pipeline and you want to, on a schedule-based, you want to periodically generate the predictions for all of your data and save it in a storage. And so this can happen on every day or every week or every hour, whichever suits the best for the business, you can schedule that. On the real-time inference, however, you want to have some web services that are up and running 24-7 and they are always ready for prediction for whatever data that you provide to them. So they are always ready up on request. So they are basically more resource heavy compared to the batch inference. Let's look at each one of them in more detail. For the batch forecasting, it's pretty much very, very similar to the architecture, to the batch training.  You provide that to the many models. You provide that to the, specifically the parallel runner step. Parallel runner step, again, distribute that against the cluster. And every node within that cluster takes one subset of that data with the predict script and then apply the model, retrieve the model from model registry, apply that model on this part of the data and provide the forecast and return the prediction. It may save it in a storage or wherever it is appropriate. But on the real-time, they are a bit different. You want to deploy them on web services. You want to deploy your models on web services. So let's assume that you have 1,000 models in your model registry. So you want to take each model with the predict script and deploy that on a web service. Therefore, you will have 1,000 web services for taking the 1,000 models deployed. And each one of them will provide you an API or a link where the user, the end user, can call that model through that API. With this approach, you will have, you will deploy 1,000 web services and provide 1,000 end points to end user. But that is not an ideal approach because it is not using the resources well. The 1,000 web services are very costly. And most of them will be idle most of the time. Therefore, we want to come up with a solution that optimize the resourcing and the resource management. Therefore, if we go to the next slide, therefore, we have packaged three groups of models into a web service that's possible. Let's assume that our web services are big enough to contain 100 models there. If so, we go with groups of 100. So we package all of the 100 models along with the training script to deploy them into one web service. And then we do another 100 and another 100. So in this approach, you will have only 10 web services for your 1,000 models. And each web service provides you with an API. So you will end up with 10 points. And the end user need to call them. That's much better than the first approach. However, there is still an issue. And that is the end user does not want the headache to go through the understanding of which model is deployed in which web service. They just want one API so that they can just call that API all the time. To address that requirement, we came up with a two-layer structure where, on the right-hand side, you see the routing web service. So we have here in this web service, we have deployed a router. What is this router? This router is nothing more than a dictionary where it's served, it saves. And the model 1 and model 2 up to model 100, they are saved or they are deployed in web service number 1 and so on, like model 901 and 902, they are deployed in web service number 10. So this router knows where each model is deployed. So this router also, because it is a web service, it also has an API. So we provide that API to the end user. So end user provide the data and call this single endpoint. And this routing web service, its job is only to call the associated web service that has the relevant model inside. Apply that, get the results back, and provide the results back to the end user. So with this approach, you will deploy 11 web services and provide only one endpoint, which is the best case for our end user and solve that single API issue. This is the same approach that we have leveraged for our use case as well. And with that, we are going to see how we are going to productionize this solution. So Maria, can you please take us through that productionization phase? Sure, thank you. So yeah, you probably have seen this figure before. This shows how little our actual machine learning code is compared to the other many parts of our machine learning system. So in our particular case, the machine learning code, this little black box here, it's only the training code that we're going to provide, the training script with these modules or whatever, and the predicting script that we were using either for batch forecasting or for real-time forecasting. All the rest, the Azure machine learning pipelines and all the infrastructure needed to connect everything, to do the data cleaning, all of that is part of our machine learning system, but it's not machine learning code. But still, of course, we need to make sure that we follow the best practices for productionizing this for in development and also operations. So we're talking, of course, about DevOps practices. This is a very broad area. So we'll just focus on six practices to summarize. We're going to see, yeah, of course, we're going to use version control. We're going to be using continuous integration pipelines and continuous delivery pipelines. On the infrastructure side, we're going to leverage infrastructure as code and use microservices to make everything more interconnected and easy to use. And of course, we're going to be monitoring and logging everything that happens in the system to make sure that everything is working properly and if not, to get an alarm and be able to fix it quickly. But in our case, we're not talking about traditional software systems. We're talking about machine learning systems. And we need to adapt this. So now we're talking about MLOps practices, not DevOps. And what are the differences in this? Well, first, about versioning. So version control exists because we need to find a way to control everything that is going to influence the behavior of our system. And in this case, in machine learning systems, it's not only the code that determines the behavior of the system. It's also the models that have been trained and give predictions. And ultimately, the data that has been used to train those models. So all of that needs to be controlled. The versions need to be controlled. So of course, we'll just get for controlling the version of the code. And we can use the tools such as Azure Machine Learning to versioning the data and the models and make sure that everything is tracked. About continuous integration. So continuous integration is having the capability to, from our code in our version control system, build the code and make it ready to be used whenever we need to use it. So in our case, having a machine learning system ready to be used means having a model trained and ready to give predictions whenever we need to, right? So our continuous integration pipeline is going to consist of the training of the model. Then we have continuous delivery. In this case, where we are delivering our model predictions. So what we're going to do in continuous delivery is deploying these models. There's not much change needed on the infrastructure side, but of course, we're going to be leveraging infrastructure as code, for example, when running trainings remotely. So we have the nature of the course that with the clusters we're going to use remotely for training. We have that configured as code. So we can replicate all these nodes and with all the environment that we need as many times as we want to run this training in parallel. And then of course, we're using services in the cloud with these microservices approved. And then on the monitoring and logging side, we also have a big difference here because our system, the code might be working perfectly and the model might be giving predictions. So everything looks fine on that side, but model predictions might be not accurate enough. And that also means that the system is failing. So we don't need just to monitor the code, but we also need to monitor the data that is coming to the system and how the model is working. And we need to place this drift monitoring in place to be able to, for example, retrain and we see that the model is not performing as we expected. And for building this continuous integration continuous deployment pipelines that we mentioned, we normally use Azure DevOps which has many capabilities for handling all these practices. So a continuous integration pipeline would look like something like this from changes on the code would trigger the pipeline which would essentially, as we mentioned before, launch the training, evaluate all the models, register the best one. And then that best model that is registered will be published as a result of the pipeline as an artifact. And then that artifact will trigger the continuous deployment pipeline which will essentially put this model into production. So the typical way of doing this is deploying the model in different stages. So we start with the development environment and we might be wanting to try deploying the model in successive environments until we eventually deploy it in production. And what we normally do is we set a manual approval gate before deploying it to production because data science processes are also very influenced by business. So it's not just a technical, a quantitative decision whether to deploy a model or not, you also have to take into consideration some qualitative things that will come from business from, for example. So this is like the whole architecture of how everything would look like. We would first have a setup pipeline, then we have the train pipeline, then we have the deploy pipeline and then we have this monitoring in place that eventually can make us retrain the whole process again. And this is how these pipelines look in our use case in many models. So we have this setup pipeline to deploy the infrastructure and do the setup of the data and environment. Then we have the modeling for simplicity. We have combined these two. So first we update the data that we're going to use for training. We run the model training and then right away we deploy these models into different HR resources for deploying web services. And this is optional, of course. You can skip it if you don't want to do real-time deployment. And in that case, you will run the batch inferences pipeline. So for example, you might run training pipeline once a month to train your models, but you might want to run the batch inference pipeline once a day to make daily predictions. And this is essentially the same update in the data and then running the pipeline to issue the forecasts. But in a real-life use case, apart from having all these pipelines, we start from the beginning with experimentation. So data science is the end of the day, it's a science, right? So it has a lot of components of trial and error and experimentation. So you need to start with that part. Then eventually you will move on to developing the components that are going to be part of your DevOps and MLOPS pipelines. And then you will have, of course, your monitoring system in place that could retrigger some trainings to launch the CI pipeline if something is not going well, or you might need to even go back to the experimentation phase. For example, try with different new features that will make your model perform better. But this is an ongoing cycle that never ends because at the end, we're talking about a product here. We're not longer talking about projects that have an initial phase and an end. We're talking about an ongoing cycle that never ends and always improves. And with that, I will pass it on to Hussein to do a bit of wrap up of what we learned in this session. Thank you, Maria. So we have so far discussed and unboxed basically the many models solution accelerator, the different components and parts within that, especially the training prediction and prediction side of it. We also saw how we have leveraged the many models to solve and to address the business problem that we had with our customer, with Microsoft's customers. So, please. So we also leveraged the productionization and how we have a productionized that solution into the customer's environment. But that was basically mainly around the technical side of the operationalization. What is still remaining and is also very important to note is that the business always on the operationalization side cares about the orchestration of the capabilities and the tools and processes to successfully drive business outcome. They like productionization from business point of view is all about adopting change which drives the organization's success. So, we at Microsoft, we are very glad that the solution that we helped our customer to implement has been delivered and received with great success. There has been great testimonies from the customer on that and you can read more on this use case with aka.ms many models use case. So, that solution is already in their live production and they are using that on their day-to-day basis. So, with that, I would like to thank you, everyone who stayed with us in the last 35 minutes, 40 minutes and we are happy to take on any question. Well, thank you both, Maria and Hossein, especially Hossein, because I didn't realize that obviously he's in Australia, so it's what, four o'clock in the morning for you? It is four or nine. So, it doesn't look, you look fresh as a lettuce. Lovely, and so nobody could tell, but it is true. Poor thing is four o'clock in the morning, so I'm not sure if you want to take the question so we'll let Maria do it. We have time for a couple of quick questions if you're okay with that. They're asking you about security and can AML access possible sensitive data on premises and how to tackle this? Yeah, so what we normally do with Azure Machine Learning, it's that we have some connections, so for the data scientists that is using Azure Machine Learning is transparent, but what it does is setting up some like virtual connections to some resources that are in the cloud. So normally all this data is located in the Azure cloud of the customer and you can set some, of course, a lot of permissions in place to make sure that anyone and nobody that isn't allowed to access can access. So what we normally do, in fact, is with these pipelines organization, we have like a pipelines user, which is not a real user, and the pipeline user is the only one that has access to the production data. And then the data scientists, for example, are testing with a subset of data that might be masked, but they don't have access to production data, they don't need to. Okay, so everything has been thought about. Of course, I was sure about it, but they ask we have to double check. Just a quick one, if you can, Jose, now that you're awake, I don't know if... It's a terrible time for o'clock in the morning because you don't know if you should go back to bed or there's no point, but it's too early, so you're gonna hate us just for 24 hours. Tomorrow you'll be fine. Just quickly on AML as well, what are the prerequisites of the solution and the automation of the solution? I guess this is a long answer, but if you could just quickly, yeah. Sure, so I guess from the prerequisites of leveraging many models solution, you basically requires the access to Azure machine learning environment and a workspace. So it's not really... So if you think about that, that you may need to be very advanced to be able to leverage this solution, I think that is already taken care of and this solution accelerator is pretty much ready and easy to use for a person if they want to leverage this. For the productionization, also we have many components already built in within the system. Maria has worked herself, she has worked extensively on the productionization, pipelines of this solution, so it is very well ready for everyone who wants to use that. As you said, there's been a successful solution so far, your customers are happy, so I guess you just encourage our viewers and the audience to use it and if they have any questions, just to let you know. It's always room for improvement, yeah. But we'll have no time for more, but it was lovely having you both, Maria and Joseen. This is one of the great things about technology that we didn't have to fly you in, but you had to wake up at four or five or, sorry, at three, probably or two, I don't know, but it was lovely to have you all the way from Australia and obviously Maria as well, she's closer to us and we hope to see you very soon, so next year maybe in Madrid. Joseen, Maria, from Microsoft, thank you both so much for this fantastic in-depth explanation and congratulations for such a great job. It looks fantastic. Thank you very much. Stay around in the attic for our next keynote speakers, our keynote which will come in just a few minutes, so stay around, go in the platform, remember we have a hashtag, bigth20, go in the platform and don't forget to check on your next keynote in order to ask questions to our coming speakers. See you in a minute, bye-bye.


So now you have decided about your domain name and you have decided what you want to do with your website Now it's about creating your website developing your website so What should be the options before you? You would like to go for a designer or you would like to do it yourself and Of course there are pros and cons of all of these Now if you are a self-starter You may like to do it yourself and if you have a certain skill set and you can do some concentration You can do it yourself But of course there are number of factors about five factors which you need to consider for example, what would be the cost of developing that super website and What would be the complexity of that website how much time it would take and of course what would be the cost of customization and Finally about maintenance and updating that website So you need to consider all of these factors when you have to decide about different routes, which you can take of developing that website and What are those roots? Roots not as in our OOTS, but as our OU TES That route could be you do it yourself and of course there are tools available Which can help you do this or? You can use a content management system or Of course, you can hire or engage a professional Now remember one thing that the five aspects which I have just described to you You need to consider and prioritize those five aspects, which are important to you and you have to assign weights to those Aspects and then you decide what is the right path for you? So let's dive into more details Now this this is one question here if this survey was done and you can see the the the link At the bottom the survey was when you as a customer Go for a local purchase locally web presence company Then what is the importance you associate with that local website of the website of the local business? So now you are sitting at the other side of the table So there are four responses you can see over here and as you can see over here is that about 36 percent Which is quite a large number 36 percent of the people they say that they are of course influenced by a good website and There's only a five percent which you can read over there They are not significantly influenced if it is if it's not a good website The point I'm trying to make over here is that having a business presence on the internet does counts and of course The further analysis which which is available over here Was that a website which attracts more people in a certain age group? It has to be smart and clean and For a certain age group young generation 18 to 34 they go for a website which has aesthetics and women go for a website Which is there for business in their neighborhood? The point is that it is important and critical that you have Website for your small business and it should be drawing the customers So what are your choices? So one choice is the side builder Side builder. It's a simple like like for example, Vix like Vix so you can go there you can drag and drop and You can select certain themes and you can have your website running and it's not very expensive and It doesn't require any any special skills The other option is wordpress now wordpress is kind of a global leader In this domain of the websites and it is free you can download web press for free and you can customize it and there are many many themes and There are many many add-ons and It offers a lot of flexibility and of course You don't have to have too much knowledge of HTML to Make that excellent website and then is the professional designer Now remember one thing I'll come to see panel that If you don't have the time but you have the resources then you can engage a professional designer and Unless that professional designer owes you something is your friend That is going to cost money and that money can be from few hundred dollars to tens of thousands of US dollars but then Sometimes it is worth the investment because they can create a professional looking website for you Which has all the ingredients which has all the facilities which has all the features which you were looking for Okay, and then is a cPanel cPanel It is it is a dashboard on your hosting server And it provides a lot of features and flexibility which I will briefly go through at the last slide So this is the comparison of the site builder versus the wordpress and versus the Professional website designer and you can see over here that the hosting is often included over here There is certain price associated with it and For example, if you look at the website design, it is pre-built over here. You can have custom design Of course advanced features are not available there You see it's it's a choice between what is critical to you what is important to you and How much time you are going to spend your willingness to spend how much time you have and how much resources you have and of course Customer support varies it is it is for this professional design So see if you have these these features and you have these facilities and not just a brief overview of cPanel So in in my personal experience, I have found certain features of cPanel, which are good And for example over here that integrated webmail you can create your own email addresses specific to your domain name support at data2ba.com help at data2ba.com and And the list goes on and of course there is the backup facility also you can take a backup of your website In case the application crashes you can bring it up. There is database connectivity also and But the the disadvantages and the downsize is that cPanel is not free unlike Webpress, it's not free. There are certain fees associated with it. It is commercial And the other thing is that you need to have a fair amount of HTML standing understanding so that you can customize the default web pages which are there and It is not recommended cPanel is not recommended for very large websites So these are some of the advantages and disadvantages of using the cPanel So it's up to you as I stated in the in the beginning that you have to decide which which option suits you So that's all I have in this module


Hello families and friends of the 31st Mu. Behind me is Peralta Hall, our headquarters here on Camp Hanson, Okinawa, Japan. I'm excited to announce our latest initiative. We're developing a video series that's designed to take you behind the scenes of what it's like to be the only forward deployed Mu in the Marine Corps. We want to take you on a journey with us, so that you feel what it's like to be side by side as we train in and around Okinawa. We'll put you in the back of our amphibious vehicles. We'll put you in the pilot seat of our aircraft and at the helm of our combat rubber rating craft as we prepare for our next appointment. Leave us a comment and let us know what you think about this project. Semper Fi.


Hey there Chameleons! It's Chamele and today I'm bringing you a video that you probably did not expect from me, but it's a back to school video since I am actually going back to school after my gap year. This video includes quick makeup that's perfect for school, several outfit ideas, and some home decor ideas.    It only takes a few seconds to do this so I recommend using that sponge. I'm setting that using my huge fluffy brush so I can get all over my face just very softly and evenly and quickly. Now you're just gonna fill in your brows however you want and I know brows can take a while so if you can figure out the easiest and best way for you to do this I would definitely recommend that before school starts.  I'm using this beautiful bronze color from NYX just all over my lids and that's all you need if you use a shimmery color it's enough. Now I'm gonna put on some highlight and of course I'm using my favorite highlighter as usual and just highlighting everywhere on my face with this because that way you only have to use one product and since it's something that I love and I know will give me some results and reflection that's all I need in my life. I'm skipping eyeliner because that takes too long and I'm just throwing on some mascara. Whatever mascara you're currently loving and gives you that volume that you crave will be perfect for this especially since we're not adding any eyeliner you want your mascara to be nice and thickening and voluminous so you get your eyes that nice pop without having to go in with that liquid and taking like five hours to get the perfect wing.  So here are three simple outfit ideas you can use on any day when you want to look good but you don't really know what to wear and you don't have time to spend on your outfit. I like to start with three simple pairs of shoes including one pair of sneakers for walking around in comfy days and then I like to have a pair of flats that are cute but also good for walking around and then when those days you feel like stunting on people of course you can have your slightly heeled Chelsea boots still comfortable to wear but they look really good. I'd recommend that everyone has a C LBD. I threw the C in there for casual so a casual little black dress that you can wear to school. It doesn't really require any time picking it out and you can pair it with those cute Chelsea boots and then you can look like you actually tried that day. Similarly I love to have patterned clothes because it gives it way more detail without you having to figure out different layers for an outfit and since this is a romper it's just one single piece and then you can throw on those flats and you're good to go.  This one's from Redbubble and it's wide enough at the top that you can do off one shoulder off two shoulders whatever and it makes the outfit a little bit cuter. Now packing my backpack here are some of my back to school essentials. I'm starting with my handy dandy planner which was given to me by Leah and that's where I track everything that I need to do that month that day and event and everything so that's a must have. Next I have this adorable portable mug from Redbubble and it's perfect for carrying my green tea because as I transition away from coffee into tea I want some way to carry it with me to class. Next I have my rosewater spray so whenever I just need to hydrate myself or brush it or awaken myself I have something to spray my face with. I just love this I don't know it's just fun. Of course I have my phone in my charger no explanation needed there. Next I have this sunflower printed notebook also from Redbubble partially because it's adorable and it makes me happy looking at it and partially because I need to take notes and write stuff while I'm in school. They have a whole back to school collection right now and you can check it out on their site which is linked down below as well as all of the products that I featured in this video and then of course you gotta have some pens and a pencil. I have decided to spruce up my living space a little bit before school starts. I got a few items from Redbubble including the succulent clock that matches with my portable mug and I got a few posters because there was a lot of white space on the wall and it just feels a little bit more at home and decorated when you fill in that white space. These are actually all made by different artists and if you check out the Redbubble site you'll see that they have artists in 190 different countries so you can always find something cool on there. This one's my favorite piece because it promotes diversity and that's really what the whole world is all about. Can I get an amen? Amen! So I hope you guys liked my tips on back to school makeup fashion and home decor. If you guys are in school comment down below and let me know what your major is and if you're not in college yet let me know what your grade is and what you're interested in. So thank you chameleons for watching this video I hope you all enjoyed it. If you did don't forget to subscribe and check out my other social channels linked down below. I wish you all a happy healthy life and I will see you in the next video. Kisses!


Greetings and welcome to the Introduction to Astronomy and Physical Science. The purpose of this video is to walk you through the way the courses are set up. So I will walk you through a sample course of one of my classes and they are all very similar. So when you log in through Brightspace you will find something like this on the home page and you will show your courses there. There are a number of different ways to organize your courses if you like. They are already set up by semesters so you can see the semester that you may be taking the class. And when you're in that one this will show you all classes available for that semester. And that is one way to organize them. You can also of course look at all of your classes which isn't very useful especially if you've taken a lot of classes here. And you can look at the classes for which you are enrolled. And you can also pin classes using the little pin icon within it. And that is useful in that it will show you the classes. When you click on this little icon up at the top which has the nine little squares which will show you all of your classes. But anything that you've pinned will be shown up at the top. So you can see those classes here that I happen to have pinned right now that you can then access. So it's a couple of different ways that you can use to be able to access your classes. So semester is a good way to do it but you can also pin certain classes if you want to be able to have quick access to them. Now we're going to take a look at one course in particular. So some of the things may be different for the specific class that you happen to be taking. But when you log into one of my classes what you will see is the information at the top which tells you about the class. You will see the navigation bar here that you can use to navigate the class. Some icons for things like mail and announcements up at the top. And as I mentioned this is a way to quickly access to go from one class to another that if you're in one of your classes and want to quickly switch to another if you have them pinned then you can just click on this and you can quickly jump to another one of your pinned classes or other classes as you scroll through those. Now within the class the navigation bar is what you need to really get around in my class. There are a couple of buttons course home will take you back here where you start out. The content button contains all of the information you need for the class with the exception of the grades. So your grades are accessed through a separate icon and then things like the communications will open up other links that you can see for things like chat and email. Now you may note that when I show you these, there's a few things that won't show up in yours. Some of these are specifically for the instructor. So you won't see everything. For example, when you click on general links, there's a lot of things for course builder and editing the course that won't show up for you. But you will be able to see links to all of the discussions, drop boxes, and rubrics, etc. that you can use as well. So as we go through these are the main things that you really need to use for the class. I also give a banner at the top of the class here, which shows what lesson and topic we're currently studying. If there's more than one lesson for the current week, it will show that for you. So that is updated constantly. And then you'll want to watch the news. That's where I post any important announcements for the class. And the updates will tell you about upcoming quizzes, upcoming assignments that are due. And you'll want to look at, you can keep an eye on that. It will tell you if there are unread discussion board postings. And you can go through and set that up how you like. In this case, I have no updates for this one, but you may see things in there, which will give you reminders of what's coming due. And then of course, there's the calendar showing you the current date and any upcoming events that would be important there. So when we go through this, what you really need to do to access the course is to use the content button, the content button will take you through the content for the course. And this will take you to the content navigator. And when that loads here, what you'll get is a table of contents here. Now when you first log into your course, you won't see all of this. And in fact, when you first log in, all you're going to see are the first two items here. There's an introduction and syllabus information and a practice lesson. The lessons will not appear until later until they actually start. So when you first log in, you won't necessarily be able to see all of those. But this is the way you can navigate through all of the content. And within each of these modules, it contains all of the information that you need. So if we look, for example, at the syllabus and introduction and syllabus module, when we click on that and open that up, then we will see all the information there. You will see the course, syllabus, other contacts, anything, any other information that you need that would be useful for the class. So there's different information on different types of assignments that you may want to look at. At the end of this, you will see the syllabus checklist. That just verifies course policies. So once you go through and have read the syllabus and reviewed this and reviewed the checklist, you do need to go into that checklist and mark off all of the different policies. Once you do that, the first lesson will become available for you. So you'll then be able to see lesson one and be able to access the materials within it. So I'm going to go through and show lesson one as just an example of what would be covered in the class in a given lesson and how to navigate that. So when you click on lesson one, you'll see that I break it down into three parts. There are lecture materials, discussions, and assignments. So lecture materials include just that. There's a folder here with the textbook readings, which links directly to the textbooks that you need. There's a set of the lecture slides that you can use that will link to the lecture slides if you want to follow along. There's a folder with links to my video lectures and some review materials and review games that you can use to help reinforce your learning of that chapter. So this will cover any chapter or chapters that are covered within that lesson. And this is the same from lesson to lesson. If you go into lesson two, it will have exactly the same things we see here except for lesson two. So it'll be covering the next chapter or chapters. Now then I have a set for discussions. So if we look at the discussion section down here, I break the discussions down into various different areas. So there will always be a set of miscellaneous discussions for various things that you may use. There are class help discussions where you can ask questions of me or your classmates if you're having difficulty with an assignment. And there are graded discussions. And we'll look at this in a little bit more detail. These actually contain the graded discussions that are assigned. And for my astronomy classes, there is a photo of the day discussion where we look at a specific astronomy website and talk about a picture that is shown on it each week. Now when we look at the graded discussions, let's go in that in a little bit more detail. And when you click on that, there is a graded discussion and there will be a prompt here for you to see. Now you'll see in my case, there's actually three prompts because I use various different discussions for each discussion, graded discussions. So they may change from year to year. When you go in, you're only gonna see one of them. So I hide the other two so as not to confuse you. So you know which one to do. The discussion topic itself gives you the specific requirements for all discussions. And the prompt that you need and any links that you need are given in the discussion prompt. Just click on that. It will open up and you can then review what you need to talk about or do for this specific discussion. In my astronomy classes, we also have a semester long observing discussion which is here and is broken up just the same. This one is visible for the entire semester. So you will see it in each lesson if you're in one of my astronomy classes and you will see the specific discussion prompt that applies to the term when you are taking that. So if you're taking it in winter, you'll just see winter. If you're taking it in fall, you'll see fall. And if you're taking it in summer, you will see summer. So it just depends on when you happen to be taking the class and that explains the more details as to what to get you started on the observation. And again, the requirements are within the discussion posting itself. Now for assignments, when we look at the assignment section, that is where any other assignments that are not discussions will be present. So when we look at that, in this case for assignments, there is a lab for this lesson and the information for that lab is given to you here. It may be broken down into different parts. So in this case, there's a video explaining the lab for you which you may find useful to go through and watch before doing the lab. There's a drop box where you will submit the lab when you're done. This is the assignment sheet with printed instructions for the lab and this is the answer sheet which you will fill out and then return. So you'll take the answer sheet, fill that in and submit it to the drop box by the date that it is due. Now you'll notice that any drop box has three dates. There's a start date which is the first day you can access the materials there. There is a due date when the assignment is due for full credit and there is an end date after which I no longer accept submissions at all. So the due date is the one you really want to watch because that is when you have to have the assignment in for full credit. In this case, there also happens to be a discussion board associated with this lab that you'll need to post to and an answer sheet that will appear after the lab has been due and you have submitted the lab. So you can actually have access to the answers to review your answers and I may refer it back to some of those when we're grading this. Now also, you may see some other things here and there will be an extra credit section of each lesson. So there should be extra credit, a little bit of an example and in that case, we'll see the review quizzes which are partially extra credit as we talk about in the syllabus and there may be another extra credit assignment or assignments that you can access as well and you go through those just as you would any other assignment. So you can click on the folder there which will open it up and it will give you the assignment sheet here that you need to read with the instructions and then a place to submit your submission. And again, the due dates and dates are exactly the same. There's a start date before which you cannot submit it. There's a date that it is due and there is an end date after which you cannot receive any credit for it and at that point, the drop box is gone and will no longer be accessible to you. So it will no longer take submissions after that date and time. Now, this structure is the same for every single lesson. So if you go into lesson two, for example, you will see the same structure in my classes and lesson two will have again the same lecture materials, links to the discussions and links to any assignments for that unit. You'll note that everything that you need for the class is here. The whole idea is I don't want you having to search around to find where to do anything. All you need to do is use that content button and that will then take you to everything that you possibly could need for the lesson. So this is the key for the class and the key for the navigation of the class. So you will do that, you will need that button the most. And then of course, if you want to review your grades, you would click on the grades button and that will take you to the grade book and will show you all of your current grades for the class. And if we take a look at that here, again, it will just show, you'll see something similar to this and of course in this case there's no class here so I don't have anything to show directly but your grades would then show up there for any of the assignments that you have completed. So really those are the two key buttons that you need to use. The other things that are useful would be of course the email if you wish to email me from within the class, the email link up there and this notification for announcements that you can follow as well. So between those, those will guide you through the entire class and each lesson will be exactly the same. So if you want to finish lesson one, when lesson two is available, you'll be able to access the materials for lesson two and continue to move on with the class. So each lesson, again, will be exactly the same so you don't need to get used to anything different. Everything will stay exactly the same from lesson one through lesson 14. The structures will be exactly the same. Just make sure you're using that content button and at the end there is a checklist for each lesson that will allow you to review any materials that we'll do so it'll give you a reminder as an example of what was due here and this says what chapters you should have read, what videos you should have watched, what graded assignments you should have been done and what ungraded assignments, what things maybe you should be working on over the course of the semester so what you should have been doing this week and it's a good checklist, a good way to remind yourself did I get everything done for this week? Did I miss anything? Well, this will tell you did you do your review quiz? So if you didn't do the review quiz then it's a reminder to go back and take a look at that and make sure you do that before the assignments are due. So that finishes up this video walkthrough showing you how to access the course. As always, feel free to email me if there are any questions or concerns as you get started with the class. I am more than happy to assist you and I will also be more than happy to set up, for example, a Zoom session where we can sit down and go through the class and help you get started if you are having any difficulty. So until next time, have a great day everyone and I will see you in class.


Sugar boy is back because today we are making a vegan bread pudding all by coincidence guys a lot of these recipes that we've done over the past two years are really just designed to be minimally inflammatory Someone like me with the liver damage compromised digestive function has to be really really really careful and specific about What ingredients I put into my diet if I want to feel good if I want to sleep, right? So this really fits in with the protocol. I've been doing is like a snack or a treat food And there are quite a few people that I know that are following that type of stuff So let me show you guys what those ingredients are so we have a lot of nostalgic Holiday-esque flavors here, of course everything highest quality and organic that most people will have access to Starting with the bread. This is organic bread alone sourdough bread I think it's in the northeastern area This is really the best bread that is in the supermarket and it's excellent excellent excellent because the only ingredients are flour water and salt taste very good too as long as it's fresh But you know for recipe like this ideally you want some leftover or stale bread. I Guess the next largest component of this is going to be the coconut milk and we're not using oil We're not using butter. We're not using coconut cream. We're not going to really add any of that We're just using coconut milk which does have a good amount of fat But this is not going to be nearly as high and fat as a typical bread pudding recipe, which is why This is going to be a little bit better for our liver health The sweetener we have a lot of options guys Classically you would go with the cane sugar. You can use maple syrup. You can use honey There's even this cool product sweetened condensed coconut milk, which has like a nice caramelized delicious flavor You could also add that to that And then for our kind of flavorings textural components, we're doing some walnuts and some raisins These are organic raw walnuts. I roasted some up with a little bit of salt Ideally you would candied these and they would taste a lot better and These Hunza golden raisins very very delicious very sweet pretty expensive But super high quality as far as I know these are going to be excellent in this bread pudding It's going to give us this like a cinnamon raisin bread-esque thing going on and of course the classic vanilla Maybe we'll put a little cinnamon. Maybe we won't But that's really it guy. It's really simple from an ingredient perspective because it's one sweetener coconut milk nuts raisins Bread and then spices. It's really five six ingredients max. So first we're going to combine all of our wet ingredients and spices We have the three cups of coconut milk which is two of these cans and This is one of the better brands out there Can be kind of expensive, but the only thing in there should be organic coconut You want three quarters of a cup of sweetener. So we're going to do some maple syrup And we're going to do some cane sugar Into that we're going to add one teaspoon of vanilla extract and about half a teaspoon of cinnamon And we're just going to mix this up a little bit mainly make sure that the sugar is dissolved into the coconut milk We need a pound of bread and this is a bit more than that. So We'll leave a few slices in the bag All right, so this is pretty basic. We're just going to cut this bread into chunks and if you have a sharp enough knife It should be very easy and fast. So I'm just trying to hold it together. We did a couple slices Vertically and now we're just cubing it horizontally Okay, now we're just going to take all that bread and put it in the wet mixture Before I mix it, we're going to add half a cup of walnuts and half a cup of the raisins Okay, so now we're just going to Get the bread to absorb all of that liquid. So I'm just going to flatten this out and press it So our oven has been set to 375 This is going to take between 45 minutes to an hour All right, let's see. I mean this looks amazing But we're gonna have to wait at least a few hours for this to cool off So I'll check in with you guys in a little bit to taste. All right guys, we just had dinner and we're gonna try some of the bread pudding I Mean reminds me a lot of French toast you'd make with cinnamon raisin bread Now this is delicious what I will say is Definitely Candy the walnuts or do something to them now. This is really really good. I like it a lot It's kind of light compared to regular bread pudding even though you know, it's still pretty heavy and It's nice clean and I don't really want to use the word plain because there's plenty of flavor here But it almost tastes like fresh like there's not too much stuff added to it. It's honestly Not something like you're gonna pass out on the couch after eating unless you eat half the tray, but I think this is something that I can enjoy at the end of my meals and Not worry too much about have any allergic reactions or any issues from consuming too much fat. So Thank you guys for joining me Hopefully you enjoy this recipe as much as I do whether it's because of your dietary restrictions Or you just want to try something new you can go to Frank dash to follow calm to support me through all of my businesses Outside of that guys, please drop a like on the video. Leave a comment down below Subscribe to that YouTube can unsubscribe you next week and be sure to check that notification bell So they don't notify you of my videos. We have just restocked the brown rice crisp bars As well as the Pemkin on Frankie's syringe foods and hopefully we have time to make some cookies this week Which are also And with a very similar hot by allergenic high quality organic recipe. Thanks again guys. See you soon You


Thank you very much. Can you hear me? So it's always a great pleasure to be in Trieste. I'm very grateful for this invitation. So I'm going to be the first speaker on exit and polaritons. So I will give some short introduction on this subject which then will be addressed by several speakers in this meeting. So I will present a theoretical work. I'm a theorist. And this work has been done in collaboration with Aleksandr Sheremet from Russian Quantum Center in Moscow. Yura Ruboku is here from Yunam, Kurnavaka, Mexico and Ivan Shilik, who is now working in Iceland and Singapore. So this will be about some fundamental properties of exit and polaritons, superposition, light, metaclasm particles. And I will start from the overview of recent experiments which attracted a lot of attention to this field. And then I will try to analyze the intrinsic structure of this interesting quasi-particle, unusual quasi-particle exit and polariton and will try to link it with some philosophical questions of quantum mechanics. So the recent really avalanche of publications was devoted to the realization of this device which is called polariton laser. The concept was proposed by Imamoglu and Yamamoto in 1995. And it is quite simple. So imagine you have an optical cavity. So these are dielectric black mirrors that confine light within this green layer. And this green layer is a semiconductor where you can inject either optically or electronically through contacts. Electrons and holes. These electrons and holes can form excitons. Now what happens? That exciton, the dispersion of exciton is shown on this scheme by the horizontal line. And exciton has a parabolic dispersion but on this scale you would not see this parabola. It is like a straight line. So exciton can be coupled, even strongly coupled to light which means that it can emit photon but then photon comes back, absorbs and what exactly happens there is actually the subject of my talk. Just the consequence of this interaction which is not a subject of my talk but something you can find in textbooks now is the modification of dispersion of eigenmores of this system. So instead of the exciton dispersion which is this one and the photon dispersion which is parabolic because photon is confined in this direction. So the wave vector of light is fixed in this direction and you can only vary wave vector in plane. Now you obtain this parabolic dispersion with very light effectiveness. So as a result of the scalping you have two new dispersion branches which are called exciton polaritons. From the point of view of quantum mechanics these are superposition exciton photon states and what it means I am going to discuss today. So far just let us assume that it is so and let us inject this exciton polaritons in the system. Then due to the excitonic component they can interact with photons for example or between each other or with free carriers if there are any free carriers in this system and due to these interactions they can relax their energy down to the bottom of the low polariton branch. So this is the low polariton branch this is the upper polariton branch. As a result in the bottom of this low polariton branch you can form what is what can be called boson's time condensate of exciton polaritons. Here I must have mentioned that excitons are of course bosons because they are like cooper pairs they are composed by two fermions an electron is a fermion, a hole is a fermion so put two fermions together and you have a boson and photons are bosons of course. So this superposition quasi particle which is part exciton part photon is necessarily boson it follows bosonic statistics even though we are out of equilibrium usually there is pumping, there is decay in this system because light can go away tunneling through the mirror nevertheless one can hope to be able to create a macroscopically populated single quantum state somewhere here near the bottom of the low polariton branch. Now this state would of course also be characterized by some decay some radiative decay because light can go away from the structure by tunneling through these mirrors there is no ideal mirrors in the world and even if you have the best quality cavity your reflection coefficient is something like 99.9% that means that after just maybe 25 picoseconds your photon will go away from the cavity and then you can detect it you can see light going out from the cavity so the idea of imomoglu and imomotor was that this light which is emitted spontaneously there is no stimulated emission here it is spontaneous emission by the condensate but still this light will have all properties of laser light it will be monochromatic it will be characterized by a fixed wave vector which is perpendicular to the surface of the cavity and it is expected to be coherent because this light is generated by a coherent quantum state many body quantum states which is the condensate of exotin polarity so formally it will be laser but laser without stimulated emission of radiation the laser without inversion of population so kind of unusual laser, bosonic laser and after some discussion the term polaritone laser was accepted I believe by the major part of the community so now this device is referred to as polaritone laser so these polaritone lasers have been realized and the most famous paper is by the next speaker Peter Littlewood and co-authors where the boson-stank condensation of exotin polaritones was demonstrated at 5 Kelvin in cadmium telluride micro cavity but I should say that for polaritone laser you do not necessarily need strictly speaking boson-stank condensation because you do not care of thermodynamics you do not really need a thermal equilibrium in the system the only thing you need for the laser is to have this condensate so to put many identical exotin polaritones in the same quantum state what happens in other quantum states other their population is described by some equilibrium equilibrium distribution function or not is not so important so there were many papers where polaritone laser was reported not necessarily boson-stank condensation and there is still debate on what should be called boson-stank condensation what should be called polaritone laser so this is beyond the scope of my talk I just want to emphasize that now polaritone lasers are realized in many systems and they are realized also at room temperature in wideband gap semiconductors so there is an overwhelming I would say evidence that one can create condensates of this half light half matter quasi-particles called exotin polaritones now I come to actually to the topic of this artwork which is kind of rhetoric question what is an exotin polaritone so what are in detail how this superposition quasi-particles are organized what really happens inside the micro cavity and superposition means that you are for example half exotone half photon of course there are many superposition states in nature and it is not surprising to have a superposition state even circular polarized light can be considered a superposition of two linearly polarized lights but here the peculiarity is that it is a superposition of two quite different objects because one of them is a matter quasi-particle exotone which has an effective mass which has some dimension bore radius can interact with other particles etc can be decomposed after all it can be decomposed into an electron hole and another part of this state is a photon so what could be the microscopic image of this exotone polaritone so let us first of all consider not a micro cavity but just a crystal slab let us say this is some semiconductor crystal and we have a single photon source and we send a photon to this semiconductor slab so with some probability this photon can just go through and ignore I mean it can be absorbed but it can be not absorbed just propagates through and you will detect photon at the exit but of course there is a probability that it is absorbed and it creates an exotone so one image of exotone polaritone is a naive image I would say is the following that your photon enters to the crystal propagates for some time then at some stage it creates an exotone exotone is a very heavy compared to photon it almost does not move so it moves very very slowly then it recombines and creates another photon which keeps propagating and then creates again exotone et cetera et cetera so it is a chain of absorption and reemission acts it looks like an incoherent picture but why not? I mean there are situations and of course absorption and reemission are very well known phenomena and maybe observed in different systems so at least this scheme is not self-contradictive but the most generally accepted is a different scheme the most generally accepted is this one that you arrive and immediately inside this crystal you do not have a photon, you do not have exotone but you have this exotone polaritone so from the first works of the pioneers of this area Agranovic and Hofelt this is the generally accepted point of view and if we speak in terms of classical optics we describe this exotone polaritone by modifying the dielectric response function of this crystal so here light propagates according to its dispersion law in vacuum and here it propagates according to some different dispersion law which is obtained taken into account like metacoupling in a semiconductor so there is no exotone, no photons here but there is a new mode exotone polaritone which exists from this point to this point and then at the back edge it converts again to photon so it reminds this famous discussion of Bore and Einstein on the nature of wave function so I will come back to this thing again but let us remember these two images one is what I would call a statistical model where we have real exotones, real photons and real acts of absorption and emission maybe stochastic so we do not know where photon will convert to exotone and where exotone will convert to photon but we know that something happens here and we have exotones and photons and they really exist in the structure and the other image is that there is no exotones and photons there are completely new quasi particles which are the superposition particles exotone polaritones so no emission, no absorption acts just this conversion of photon to exotone polaritone and back on the surface now if we look at the microcavity structure then it becomes even more tricky because usually in this microcavity exotones can only be created in a quantum well so away from the quantum well you really cannot have any exotone you just have photon and then it is, well it looks like photon can go through the quantum well and be absorbed in a quantum well sorry, and how to do it, yeah and convert to this red tomato looking exotone and this is a stochastic process but on the other hand one can say that maybe this is the case in what is called weak coupling regime where people speak independently about exotone and photon but it would not apply to the strong coupling regime which is the most interesting for polaritone laser where due to the coupling between exotone and photon we have two new eigenstates which are the superposition states so maybe the answer is that in weak coupling we do really have exotones and photons but this cannot no more be applied to the strong coupling regime and sorry again for this let us go here and this is reminiscent to again the discussion of godfathers of quantum mechanics on the nature of wave function so suppose that we can capture this particle we can do the measurement and of course when we are doing the measurement we can see either photon for example like in a photo luminescence experiment or an exotone for example we can split an exotone into the couple and hole and it contributes to the electric current so when we do measurements necessarily we kill exotone polaritone we have either exotone or photon now the difference between two views I would say on this process is that what happens between acts of measurement either we have really exotones and photons we just do not know whether our polaritone is exotone or photon in this particular moment in this particular place in the space and this would be a materialistic point of view which was advocated by Schrodinger and Einstein or as more or less everybody believes in this community actually there is no exotone no photon until you do the measurement you cannot say what it is and so the matter does not exist if you do not observe it when you do measurement your wave function to either photonic or exotonic state and then you can speak about the outcome but between acts of measurements you are neither photon nor exotone so this is all philosophical and all very old so why I am coming back to this discussion because this discovery of bosonic condensates of exotone polaritones offers a new tool of studying this interesting problem so if we would have just one exotone polaritone in a given quantum state then any kind of measurement would not help us we can capture it as exotone we can capture it as photon but it would not give us any supplementary knowledge on what has been before but if we have a bosonic condensate this changes the situation so let us look at this scheme suppose that we have a condensate with six quasi-particles, six polaritones in the condensate so two possibilities are either some of them are exotones and some of them are photons for example exotones are white cats and photons are black cats this is one possibility and another possibility is that they are all identical and they are all exotone polaritones in this case when we do measurement the result would be different so let us see in the first case statistical interpretation of polaritone condensate we have a total number of polaritones at each particular moment of time which is equal to the number of exotones white cats plus number of photons black cats we do not know how many white cats how many black cats we have but this condition holds we can admit that they possibly convert to each other in the case of this exotone polaritone picture let's say Copenhagen school view we have this identical particles which are superpositions of exotones and photons and if we want to speak about exotone fraction and photon fraction which we can then detect then they are linked they are both proportional to the total number of exotone polaritone and the coefficients of proportionality are so-called Hopfield coefficients which were introduced by Hopfield in the beginning of 1960s they just tell us what is the weight of the exotonic component in this superposition state and what is the weight of the photonic component so these are some constants they do not look to it they are not stochastic these are just numbers and they satisfy this normalization condition so clearly these two situations are not equivalent and if we do this Gedanken experiment, thought experiment we will see some difference so the thought experiment is that we excite condensate in a micro cavity with a pulse of light so we create some exotone polaritones no matter what is the quantum state here but at least there are more than one in average and then we do two measurements we have here the photon counter which counts photons emitted by the system and we also apply electric field which destroys exotones so an exotone is decomposed into an electron whole pair and these electrons and holes give contribution to the photocurrent which is measured by a meter so we compare let us assume that we can do this experiment which is not trivial with a very good accuracy so we have some noise in this photocurrent some noise in this photoluminescence signal let us compare these two noises and see if there are any correlations between them now what I would expect if we are within the statistical model then of course I would expect some correlations because let us say the number of black cats is correlated with the number of white cats if I have more white cats I have less black cats and the other way around so signal of photoluminescence which is proportional to the number for example black cats is correlated with the signal of photocurrent which is proportional to the number of white cats on the other hand in case of this picture we would expect relatively no correlations because in each case we kill one cat and all cats are identical no matter we kill it converting it to photon or we kill it converting it to exciton we just kill it and it does not affect any how the statistics of the remaining state it depends on the detuning between your exciton and photon and the resonance so in this point you would have half exciton half photon state here half exciton half photon state there so here it is okay here it is written for the bulk crystal so if you go of course to this point you are entirely photonic but in a micro cavity the advantage is that you have confinement of light in this direction so you can have closed energy state in this system which is not at zero energy but which is at some finite energy so if you would move this parabola to this point so that you have the exact resonance you will be half exciton half photon then you can have any other proportionality depending on the detuning okay okay so let me let me go forward so these are all naive qualitative hand-waving arguments but let us try to calculate something quantitatively what we can calculate we can calculate some correlators suppose we can really do this exciton photon counting measurements then what can we measure we can measure the exciton photon correlator which is just the average product of exciton number to photon number divided by multiple by the product of averages we can do a simple G2 measurement for photons like in quantum optics this G2 second order coherence measurement we can do the same for excitons which would be very tricky from the experimental point of view and finally from these three correlators we can build a big correlator big exciton photon correlator G big which is squared exciton photon correlated normalized by exciton G2 and photon G2 so this quantity is interesting I introduced it on purpose because then it will give some some nice some nice it will be helpful for the following story so let us see what would be these correlators in the case of the Copenhagen let's say interpretation of exciton polaritons where we have polaritons not excitons not photons and in this case our exciton photon operators can be represented just as linear combinations of polariton operators for upper and lower polariton branches in this way so low is the annihilation operator of a polariton on the lower polariton branch upper you see you is the annihilation operator of a polariton on the upper polariton branch so this is a simplest linear linear combinations they allow us to express our correlators through polariton operators and we take advantage of the fact that our condensate sits on the lower polariton branch so the upper polariton branch is empty which is why the expectation value of this upper operator or the number of particles on the upper state is zero and this allows after a very simple algebra to reduce our exciton photon correlator to something very similar to just G2 in quantum physics so this combination and of course the answers would depend on the statistics of our polaritons in the condensate but this will be all textbook answers for the coherent state this correlator will be one for the thermal state it would be two for the number state it would be one minus one over the number of particles and so on so no surprises interestingly the big correlator this G big SC will be equal to one for any statistics so for all these states this big correlator is exactly one it is independent on the statistics this is a formal formal algebraic result so if it is one we can be sure that everything works correctly we have polaritons there is no any independent excitons and photons in the system and our vision of polaritonics as it was formulated by Hoplut and Agranovic 50 years ago is perfectly correct now of course the most interesting thing would be how to deviate from one how can I deviate from one so let us put ourselves in a different logic and let us allow for stochastic conversion of exciton to photon and photon to exciton so let us work in the exciton-photon basis and describe our system by some density matrix which has diagonal elements this p of an a and b which depends also on time an a is let us say number of excitons and b is number of photons so the other way around so the probability to have an a excitons and b photons in the system may be found from this master Boltzmann equation which can be derived from the general UV equation for the density matrix but it can be also written from the common sense so it allows for radiative decay where we destroy one photon in the system and it is characterized by some photonic lifetime tau photon there is also a possibility to destroy excitons for example by electric field which is characterized by time constant tau exciton and then we have to take into account this our stochastic conversion so we introduce a new parameter tau xc which is a rate of stochastic exciton to photon and photon to exciton conversion and accounting for the buzonic nature of our excitons and photons we can write the corresponding term into this master Boltzmann equation in this form so again it is a simple algebra and then we can work with this equation we can solve it assuming some initial state for example coherent initial state or number state or anything so let us see what it gives so here we have these probabilities probability to find an exciton and a excitons and b photons in our system as a function of time in the absence and in the presence of this stochastic conversion so in the absence means tau xc equal to infinity and ok we have some dynamics and clearly the dynamics of these probabilities changes in the presence of this stochastic conversion it changes in a very clear way actually the probabilities of all configurations corresponding to the same total number of polaritons total number of quasi-particles in the system become very close to each other and this is very natural if your excitons can randomly convert to photons and photons to excitons then eventually all configurations all possible configurations with a fixed total number of particles become will have the same probability so if you have 10 polaritons the probability to have 9 excitons and 1 photons and probability to have 1 excitons would be the same after all if these stochastic conversions are quick so this is what one would expect and let us see what it gives for our correlators so actually for our correlators it is dramatic because the correlators strongly change as a function of time for this stochastic conversion process so even this traditional G2 the photonic G2 which has been started actually in this condensate it strongly deviates from 1 we would expect G2 equal to 1 for the coherent state but here we see that because of this stochastic conversion it would go to something like 1.4 now the exciton photon correlator would go below 1 but more interestingly this big correlator which is always 1 for any statistics of polaritons within the polariton model let's say Copenhagen interpretation of polaritons it will go down to 1.4 so this is a huge difference factor of 4 so if it if we manage to do this experiment and we see that this huge G deviates from 1 goes below 1 then if it decays that something happens that there are these stochastic conversions whatever it means so the limiting case of very strong stochastic conversion so very short how we see maybe understood in terms of a classical model of two coupled oscillators so actually if we have two coupled oscillators with amplitudes a and b and we we impose a condition of energy conservation so that a squared plus b squared is some constant and then in case of fully random distribution of initial phases of these oscillators distribution function p of a and b which is simple delta function which reflects this normalization condition with normalization factor so then we can calculate all expectation values and we can calculate for example our xc correlator which is this quantity and we obtain two thirds which is exactly the limit we have here so this is not unexpected but still quite surprising if we think about it from the traditional point of view now if we look at the spectrum of emission of this condensate we can we can calculate two things we can calculate the energy of the condensate because our stochastic processes they do not conserve energy strictly speaking so the energy can be modified we can calculate the energy variance per particle and this gives the following thing if initially our condensate was in this lower at the lower polyethane branch in this point then due to stochastic conversions it would go up in energy and eventually it would arrive to the middle so this is like weak coupling regime in the weak coupling regime there is no polyethane branches we have just no solution so this is something which we would expect because naively in the weak coupling regime coupling of acetone to photon is so weak that well excitons live for some time without seeing photons photons live for some time without seeing excitons so this stochastic model of acetone to photon conversion would be would make sense and effectively this is what we see so introducing this let's say hidden parameter this tau xc we can describe transition from weak to strong coupling regime now yes now our point is that even in the strong coupling regime possibly these stochastic conversions do take place because we do not see from this reasoning that they immediately disappear that tau xc would go to infinity if we are apart from weak to strong coupling regime it is I would not expect it now of course this all needs to be experimentally verified so next thing we were thinking about was the experiment which would be easy to realize because okay one can count photons but one can hardly count excitons and this splitting of excitons to electron hole pairs and then detection of photons would imply very sophisticated measurements if you really want to have a accuracy on one single electron level so it is not impossible but very hard much easier would be to do a purely optical experiment so the next idea was to put the system in the regime of rhabiosilations in this regime with a short pulse of light you excite both polyethane branches together low and upper branch and then you observe bits between exciton state and photon state initially your system is in the fully photon state then on a frequency given by this splitting it converts to excitons then converts back to photons etc etc so this is a very well studied phenomenon experimentally observed even in 90s by many groups so in this regime what we propose to do we propose to fix at the upper branch and at the lower branch and at this particular selected frequencies put two photon counters and then measure correlations intensity-intensity correlations between upper and lower polyethane branches so this is a perfectly realistic experiment and quickly going through the theory which is similar to what I described earlier we come out with this for example for some given choice of parameters this results for the correlators between low and upper polyethane branches which are similar to what we observed for exciton photon correlators in particular this big correlator again composed from two color intensity-intensity correlator and single color correlators for upper and lower polyethane branches this one it strongly deviates from one it would be one within this Copenhagen model and then it would go to one quarter in the limit of strong stochastic conversions so this brings me to my conclusions so what I wanted to tell you is that stochastic exciton photon correlation processes conversion processes are described maybe described by what we call ambitiously hidden variable stochastic conversion time stochastic conversion time stochastic conversion time which so far was not studied neither experimentally nor theoretically to the best of my knowledge so in the limit of stochastic conversion time we have the traditional let's say quantum mechanical Copenhagen results for all correlators and no surprises but if this tau xc is less than infinity if it is finite then there are interesting things happen in particular if it is less than acted on photon lifetime but more than the inverse Rabi frequency we have very strong deviations of all correlators from the Copenhagen predictions and this big G correlator goes from one to one fourth which is a huge effect then if this tau xc becomes very small we have a transition to the weak coupling regime which is a well known effect and the final result corresponds well to the weak coupling picture so exciton photon conversion mixes actually two polariton branches and changes the energy of the condensate so in a sense it is not an energy concerning process and where the energy goes it depends on the micro model it can be discussed but we just suppose that this process exists but we do not explain where it comes from and finally in the regime of Rabi oscillations the upper low polariton correlator is expected to go below one due to stochastic process so this is something one should be able to check so maybe it goes below one maybe it sticks to one but this is an experiment which can be realized on existing samples within existing experimental facilities thank you very much thank you


You're seeing more than a cannonball serve and a neat light bar to the jaw. You're looking at the spirit of America. This is the way we like it, fast and hard-hitting and clean, instinctively American's love fair play. It's built up from childhood. Give the other guy an even break. Play the game on the level. Don't hit a man when he's down. There's a 15-yard penalty for clipping. The fighter who is below the belt gets tossed out of the range. We like it fast and hard-hitting. And we like it clean. When you're stuck from the gridiron to no man's land, the rule book is ferried and forgot. Here, there are no penalties except the one for losing. It's not measured in yards, it's measured in light and death. War is the law of the jungle, kill or be killed. There are no half-measures, no alibi runs. You've got to twist your instinct inside out to play this game because it's played to win. Anyway, the goal is destruction, pure and simple. Your mind must be tuned to a new pit to go after your enemy all out. No holds far, to hurt, to cripple, to kill. There's the war. Now, that will not for a pot of gold earned in the taking of the Rose Bowl, but for survival. Your stock and trade is the weapon you're armed with. It's your bodyguard and your battering ram. It's your best friend. A Tommy gun, a hundred and five. A mortar, machine gun. Clean, use it properly. It won't let you down. But there are other weapons. It's a long way from this, the symbol of civilized force, the peacetime art of self-defense, to this. But the bludgeon isn't used today as it was thousands of years ago. You won't find it in a squared circle, but you will on the battlefield, handy in a close fight when only one of you's coming out alive. Brass knuckles outlawed in the ring, but legitimate in war. Thought of shotguns, pride of the underworld, reinstated for the duration. Black jacks, simple and hard-hitting. French knives, not a pleasant-looking instrument, but effective, same drawer. Not pretty, but deadly. Yes, there are other weapons. Your enemies use them and so will you. You have two purposes. One is to kill, and the other is to avoid being killed. To that end, you use every method and every weapon you can, but place space in your memory. Use the one that fits the job. With a rifle, you can tattoo a German at 500 yards. You can drill a jab at a quarter of a mile. Killer in safe, which is right, but he's forgotten that full clip in his rifle, which is wrong. How's the time for the bayonet? The time for a bullet. When you've got your enemy trapped in a dugout, don't rush in after them. That's what they're waiting for. Throw us in a grenade and let them divide it. Remember this, no army ever won a battle without getting his hands dirty and blood.  You storm and take a position by sheer physical force, or you don't take it. You'll be in plenty of tight places before this is over all of it. Then your life depends on razor sharp reaction.  Title, how to let them not be safe without improving his look. When you're alive from the lives of those where you depend on self and silence, you do what has to be done. If your instinct says give him a decent stand, remember he wouldn't give you one. The rules of a lifetime may whisper, you can't get a man when he's back turned, but you're deaf to the rules because in war, there are no rules.  A good soldier is a live soldier and this is a good way to stay alive. The past wouldn't kill. You forget the concept of clean cut fighting. There's no such thing as clean cut war. Sometimes the neat right cross to the jaw go right. But there are hundred times when a neat right thumb in the eye is more dependable. This is proof. They ask for it. We can take it and we can take it out. Something besides taking a football. Good soldier is a smart soldier. He must know how to fight. You want to fight with. His reflexes must be split second fast. When they are, this can happen. Good soldier uses the right weapon the right time. When he doesn't, he can look mighty cool. A rifle can kill at a thousand yards. It doesn't take a nose dive until a good soldier when they take advantage of it. This is called locking the stable door after the horse is sold. The prize brings to a bar room roll. But this is what it's going to be like. Alley stuff. Everything goes and there's no referees. And these are the boys who are going up again. A gang of bandit. With as much sense as fair play as a scorpion. Will fight them with their own poison. Bloodins and black jacks. Knives and daggers. Fist and feet. Bullet and bayonet. We'll use the right weapons at the right time in the right way. We'll hit them above the belt and below the belt. We didn't ask for this war, but we know how to fight it and win it.