I'm Shiv Arur. I'm executive editor at India Today Television. Very honored to be receiving this award from the ENBA Awards 2021 for my daily news show, Five Live. It's an extreme honor because this is a show that I put my blood and sweat into on a daily basis as a journalist, as an anchor. It's extremely heartening to see that the jury, the ENBA, the very prestigious panel that has looked at all these entries has chosen Five Live. It means a great deal to me. And one of the reasons why the ENBA Awards are so sought after and so prestigious to us as journalists is because here we are being judged by our peers, people who are within the industry, people who are, you know, who hold a great deal of credibility in an industry and a sector which is considered to be quite cutthroat, very, very competitive and therefore to actually be judged and honored in this manner by ENBA for a news show that provides hard news on a daily basis for a full hour without any compromise, without any biases, is very, very moving. So I'd like to thank the jury. I'd like to thank the panel. I'd like to thank all my peers. You know, I don't know how many people were there on the jury, but I do know that many of them are people who are, you know, like me, they're like journalists. So to be judged by those who are within your industry as well is a great honor for us at India Today Television. We believe that showing no bias, being fair, being centrist at a time when things are so polarized is extremely important and Five Live really does stand for that in its entirety. So thank you once again to ENBA for this honor.


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Two people you have met in your lives, who are both intelligent, intelligent as per you. An intelligent person that you dislike, it could be anyone, it could be your boss. Bosses are always intelligent, many people dislike them. And it could be your colleague, it could be anyone.  Again, it could be anyone, it could be a friend, your spouse or someone you've met. And I would like you to spend a few moments thinking about why this is the case. They were both intelligent but you liked one person and you disliked one person. What were those things because of which you put that person in that box and this person in this box? As you think about this, I'm going to introduce you to Cogbooks. Now Cogbooks is an adaptive e-learning company that makes online courses.  What Cogbooks does is it analyzes how each student is learning and based on that, customizes the course for each student so that every student becomes successful. This is done using machine learning to analyze how the students learn and then recommending the right activities for that student to learn. So if some of you who is a senior person and some of you who is a junior person both take on the same course, both are not given the same activities to do. So that is about Cogbooks and this is what happened at Cogbooks. We built an AI-based product and we thought, okay, Amazon uses AI, YouTube uses AI to recommend videos to people. It works for them. Amazon uses AI to recommend products to people. It works for them. So what we should do? We should use AI to recommend learning activities to students and it will work for them. It didn't work. Nobody liked our product. So when we had a product which shows learning activities and some recommended more learning activities for you because this worked for people like you, nobody visited them and it didn't really help them succeed. So even though those recommendations were there, they were not having any impact and we didn't get any positive feedback. And why was this happening? For two reasons. One, it felt mechanical. A learner is going through a course and we are just saying, okay, these are the recommendations for you. It works for things like YouTube where the content, their attention is on the content and their emotional needs are different. But it doesn't work for an online learning environment. The second part was it lacked empathy. So the learners were going through and we were showing recommendations, but there was nothing we were doing to handle their emotions. That means it lacked empathy. And the reason that happened is because we used the old design process. I'm assuming most of you guys are designers. So I want to go into what personas are. But we defined personas. Then we defined some common scenarios that these personas go through. And then we designed an interface that works for most of these scenarios. That's the usual design process. So we followed it for this also. But then it did not work. And then we got into finding out why it did not work. And it turns out that people think of products as people. And this was a fundamental shift for us. And an extension of that is intelligent products are thought of as intelligent people and not just products with some AI based component in them. So how do we make sure that we design our product that is perceived as an intelligent person, not just as a product with some recommendations in it. And we just saw in the previous slide where you imagined people that just because someone is intelligent doesn't mean they are likeable because of various reasons.  The reasons why you disliked that person and the reasons why you liked that person does anyone want to share any one reason?  So it's not invading your privacy. Anyone wants to share why they disliked an intelligent person in their lives? So probably dislike will be with confrontations. Too many to-and-for discussions. And liking is mutually agreement on the ideas. So an intelligent person, although dislike is a strong word but I usually sort of did not get along very well was someone who would always try to force his way or is always of the notion that either it's my way or the highway. And an intelligent person that I liked is someone who is very open, who is very transparent and who is at least giving you a patient hearing, trying to understand your perspective and comes to a conversation without a prejudice or a bias. Thank you. Both fantastic answers. Thanks a lot. These are exactly the kind of insights that we got when we studied intelligent, likeable people and intelligent people whom we disliked.  That is one of the reasons. And in some cases in which they shared they even make them work against us so it turns into a confrontation. So how do we make sure that since our product is being treated as a person, it is treated like this person. It's perceived like this person and not that person or anywhere in between. And we got proof for this in sci-fi films where AI is almost always shown as a person and in this specific film X Machina, anyone seen X Machina? Okay. It's the same AI person and we've shown how that person can be likeable at some point and evil at another point. So all these things kind of came together and we thought what is the process that we should use to make our product like a likeable person. There were two approaches. The first approach is we can make the product having universally likeable traits. We can make it cute, have some cute drawings and everyone likes cute things but that only works for certain kinds of products and not all kinds of products. If it's a story, we can make it an underdog and we can make innocent attributes. So this was not the path that we wanted to take. The other path is that we can give them a positive personality, a personality that goes with the product. So if it's an online learning environment, the users don't expect it to be cute. What do they expect it to be? What do they expect in that environment? We thought to find out. And second one is about how do we understand the user's situations and give empathy to the artificial intelligence so that it treats each situation with empathy. So we defined a user persona, everyone knows personas and here I'm only showing the aspects of the persona that are relevant for this talk. So our persona was a student in US. It's a US-based company and he's a 19-year-old college student. He's studying biology and he's a low-academic performer and he's at the risk of dropping out of college. So what do we know from this? That we should treat him with care and in an online learning environment, if we treat this guy and say some strong words to him, it's likely that he's going to drop out of college and fail in his career. The next part was defining a brand personality or rather a product personality. So we are all familiar with user persona but the same process can be applied to a product also. That was the new thing here. Now how do we define this? Now there are already books like Nicely Said and Designing for the Digital Age that talk about how we define a personality but both those books are talking for different reasons. Nicely Said is talking for voice and tone, Design for Digital Age is talking about visuals. Now how can we extend that process of finding a product persona to the interactions as well? So for that we held meetings with the CEO, the founders of the company and asked them if this product, if this environment was a person, what kind of a person do you think it would be? Would it be a funny person? Would it be a friendly person? Would it be someone who helps you? Would it be someone who is interested in taking you further? And we collected all these answers and just like how we build a user persona from user research we built a product persona from founders research. So now let's look at how can the AI with this product persona understand Jacob's situation, Jacob is the user and give recommendations with empathy. So like I said, this was the old method where we define persona, we define scenarios and we design an interface that works for all of them. This is the new method. We define the brand persona or the product persona, we define the user persona that we do once and then for every situation that the user faces in his experience, we understand what are the emotions that are experienced by that user and then we imagine a response from the brand persona to the user persona and we translate that response into a user interface. So imagine a situation where Jacob the user is answering an exam or it may not be a final exam of the year, some test and his teacher is there and he is not able to write that exam. What would the brand persona who is a friendly person do in that situation and how can we translate that action, empathetic action into a user interface was the thing we needed to do for each. So let's look at an example, just what I told you. Jacob answered an assessment. He needs to complete the current module by the end of this week, so time is limited and he has, if he doesn't do that he does not pass in the semester and he fails that exam. Now in this situation, what are the feelings, what are the emotions that Jacob is experiencing? One is the experience of stress. He's feeling bad that he couldn't pass the test. First only he's a low academic performer then he passed and failed a test. Then he's feeling insecure about his studies. Like most people who are bad at studies do. I failed a test, probably I'm not good at biology. I will quit biology altogether. That's the other emotion and then the next emotion is not knowing what to do next. So I failed in this test, now what do I do? And imagine he's going through these emotions and we just show him recommendations for you. That's really un-empathetic, right? So what would David, Dave, our product persona do in this case? He will take this situation coolly. He will say if you are a person interacting with another person he will say okay, you didn't get the test right. There is no big deal. You can learn and you can come back and answer it. And then he will advise him what to study so that you can answer this test well and do well in general. So it seems obvious when it's a human-to-human interaction but we miss out on these things if we design for a common scenario where all interactions are clubbed together in one or two scenarios. So what we realize is that every situation like this has to be listed out in a table. We made a huge Excel sheet on paper first. We had to list down all the emotions that the user will likely feel at this point and imagine the response of our product persona to those emotions. And then how do we translate this into a UI? That's a craft that we are still coming up with. So the first one, how does the user know that the product is taking it coolly? One sentence. Then how do we encourage him to study more? Again, another some UI element that encourages Jacob to study more. And then what should Jacob do next? That is the next steps for him shown in the UI. So once we show these elements in the UI that was nothing but a translation of Dave's empathetic response. So I'll show you an example of the UI. This was a question, again, sorry for the American context. It's about Civil War in USA. And the question is about what was the primary goal following the Civil War. And he was given options. And Jacob did not know the answer to this question. And he answered, he gave a wrong answer to the typical e-learning program. Now you see here, we are not converting the page with all red things. You got it wrong. And that was a wrong answer. We are just giving a sentence that said looks like you didn't get that right. But don't worry, you can answer this again after going through the recommended items. And the recommended items are sequenced in a way that the next item for them is highlighted. Not only that, it's presented in a magazine-like layout where they are told about what they should read next and why they should read it. They should read it because you appear to need help with the post-Civil War goals of Abraham Lincoln. This is what you don't know. So you read this and you come back. So this was one page.  And what ended up is a user experience that is nothing but a translation of a product personality which was empathetic in nature. And again, this was the process that we used and every interaction, every situation was based on an imagined response of the product persona. The AI-based product became human. And after this redesign, the product became phenomenally successful for its target market. The user started giving positive feedback. You can check out the positive feedback on our website, cockbooks.com and how it has helped transform the lives of people. But behind all that is this process. Design the persona and brand once. And for every situation, treat the situation with empathy. Thanks a lot. I would like to take up questions from any of you. We have time for one question because suppose that we can take it offline. Thank you. You wanted to ask one thing. While going through this process, you considered how the interaction would be on chats, on voice. Anything that you considered talking to these guys, when it will be actually implemented, it will be a voice based or something chat based. So you can get the actual emotions. So the product that we were building was not our chat bot. If it were a chat bot, the output would have been sentences. But since the product, that's a limitation set by the industry and other things that are out of the hands of the user experience. Since the product was a user interface, we limited our scope to what are the elements that we can play with within that user interface like images, text, blocks of content, etc. And we translated only those elements into this. So voice and tone is a part of it. Only in form of written sentences. Yes, yes, yes. So those sentences, each of those sentences were made for that situation to address the emotions that the person was feeling for that.


Hi, I am Neil Oliver and I'm speaking also for my colleague Kanan Babu Ramya. We are principal engineers in the network platforms group at Intel. Our topic is green computing as it applies to edge computing platforms. So-called green computing refers to the technologies and design choices that allow computing services to be provided to people and reduce its impact to the environment. Computing can be made greener in many ways. It can use less power by taking advantage of energy efficient hardware and software. It can use ruggedized hardware that doesn't require as much secondary power usage such as for cooling. It can use green power sources such as solar, wind and hydroelectric generated electricity. It can source hardware elements that are less damaging to the environment. Cloud computing is already a voracious consumer of energy resources and as edge networks come online green computing will become very important in these domains. We work on the Intel SmartEdge project which is developing and deploying both open and commercial high-performance edge computing platforms. As part of this project we are developing building blocks for green edge computing and would like to describe our work. We will describe the use cases of principal interest and note the different use cases that appear in the cloud and the edge. Since our building blocks presuppose CPUs that provide sophisticated power management we will review how these work and how we can take advantage of them. We then move up to the software level and talk about how edge use cases lead us to focus on telemetry and scheduling for applications. Finally, since our project is work in progress we will discuss next steps and invite interested partners to collaborate with us in deploying green edge networks. Up to a point cloud and edge computing have common use cases and can use common solutions. We will talk about cloud use cases as it will make it easier to understand edge use cases. Clouds derive their value from economies of scale so incremental reductions in power usage yield large cost benefits. These can accrue to cloud operators, to application providers and to end users. Because of their large and increasing energy demands, clouds are attracting the attention of regulators. Sometimes by mandate and sometimes in the spirit of public spiritedness, large enterprises and clouds are increasingly finding it in their interest to reduce those energy demands. Some use cases, such as selection of renewable energy sources, have consequences for architecture and design. Renewable energy sources may also be volatile sources. For example, no wind, no wind power. A cloud that can select its energy sources may need to use them judiciously to maintain continuity of service. The easiest way for a cloud to reduce power usage is to provision energy efficient infrastructure. This obviously includes servers but also includes accelerators of various kinds such as for media processing or AI inferencing. Because cloud data centers are so large in scale, special purpose hardware need not be a scarce resource and need not have much impact on scheduling the workloads. Another reasonable approach is to optimize workloads for power usage. For many cloud applications, this may not be a good choice because time to deployment may be more important than extreme optimization. However, many network functions such as ran and core are both heavily used and power intensive and optimizing them as advantageous. In a cloud data center, workload scheduling can be done on the basis of CPU utilization as networks inside a data center incur essentially no latency. Scaling up and down of workloads on servers with different power management configurations is often good enough. When we look at edge computing use cases, we can see that some of the same issues apply and so some solutions such as energy efficient hardware selection and app optimization are also appropriate. However, edge platforms have constraints that do not apply to cloud data centers and these drive different use cases. Edge platforms are more constrained in size in clouds and scaling beyond an edge platform typically incurs a latency cost. After all, the point of an edge network is to move compute resources so as to reduce network latency. Since an edge platform can't achieve unlimited compute capacity, it has to get by with high compute density and may have to have special configurations for critical applications. So the scale up, scale down scheduling methods that work well in a cloud will be harder to accomplish in an edge platform. Also, if an edge platform is in a remote location, some power management constraints may be hard constraints. For example, there may be an absolute cap on the available power or the power may be supplied by a battery whose charge must be managed. To deal with these use cases, app optimization is still helpful and scheduling among platforms is still useful but more difficult. Making applications energy aware now looks like a more interesting solution for edge platforms. For example, an application could be notified that it needs to be moved to a lower capacity core in order to save battery power. If it was developed for an edge environment, it might be able to accommodate a relocation by operating in a reduced function mode. Now that we have looked at edge use cases and possible solutions, we will start to look at the resources at our disposal. We will first look at how power management operates in modern CPUs. In general, we will see that there are tradeoffs between power consumption and compute performance, so this is often referred to as power performance management. Hardware based power management is fundamental to green edge computing. The ability to reduce the frequency of a core and the ability to put a core into a deep sleep state where little or no power is consumed are among the most significant actions that can be taken to reduce power consumption of a platform. Doing this well is difficult. Every additional degree of freedom of changing the frequency or power state of a CPU or of its cores leads to more states at an edge platform can be in. And because there are more states, there are also more state transitions and some of them will cause bugs in programs if they are not managed well. A high level point of view, the basic actions that can be taken are to modify clock frequency on a device. The device can be a CPU, a core, a memory, or storage device, or a network interface, or to change the frequency of a CPU or a core. Some more sophisticated actions are those where an energy based or temperature based pattern action rule can be established in a CPU. This is like doing power management from software but with much better response times because everything is embedded in the hardware. Let's look at some of the options. This slide depicts a variety of hardware modes that modify power consumption of a processor. Of course, power and performance represent a trade-off. First let's consider the C states. These are occasionally called sleep states because they halt various levels of processor activity. C0 represents a fully on state in terms of clock, caches, voltage, and power consumption. Other C states above 0 halt the processor in the sense of turning off the clock but other parts of the device such as caches may still be on and the core still has a certain idle power level. The idle power may be nearly as high as power in active state for state C1. States at flush caches reduce idle power to a much lower level. The cost of low idle power, though, is long wake-up time when it's time to turn the core back on. The amount of wake-up time that can be tolerated is a function of the program being run on the core. A packet processing core, for example, may not be able to tolerate a high C state because of the risk of dropping packets. C states can apply to an entire package or can apply to devices as well as well, so called D states. C states or performance states control the operating frequency and voltage of a core. In principle, this is a trade-off of power for execution time. For real-time or time-sensitive software, it may not be possible to use the P state to power-reduce a core. Further, there are P state dependencies for multi-core CPUs that constrain the ability to independently change the P state of an individual core. This complicates scheduling algorithms. In C states and P states, certain additional technologies are available. Turbo Boost provides a means of trading off frequency against temperature. The core that gets too hot is throttled back by the hardware to prevent damage, but this feature provides for dynamic behavior to obtain higher performance. It is of itself not an obvious means of power reduction as it was designed to optimize the processing speed of the core against the heat. While speed select technology is a complex feature with multiple modes, two of its most interesting modes for green computing are performance profiles and class of service configurations. Performance profiles allow a predetermined profile specifying which cores are active and other parameters and then to select a profile to be active. A typical use of this is to trade off the number of active cores against the maximum core frequencies available on the cores. It is typically used to give a boost to critical workloads by giving them a fast core to run on, but the concept of profiles gives a scheduler a tool to manage the power performance of the CPU. Class of service or CLOS configurations allow configurations to specify maximum and minimum core frequencies to prioritize the configurations and to tie cores to them so that when throttling is required the cores that are impacted can be chosen by the users of the system. Running average power limit, RAPL, is a processor feature added specifically to manage for power consumption at the hardware level. Power limits and averaging windows can be adjusted, RAPL power and thermal data is reported as telemetry and values of this telemetry can be used as triggers for scheduling decisions. At this point we understand some of the tools at our disposal to manage the power consumption of the CPU and understand the performance implications. To deliver green computing to end users we need to integrate it into an edge platform. For us that means the Intel SmartEdge Open platform. Intel SmartEdge Open is a software toolkit for building high performance edge platforms. It exists as part of a family of edge computing solutions under the general name Intel SmartEdge which also includes commercial solutions. SmartEdge Open has a Kubernetes based infrastructure along with additional agents, resources and other components. The infrastructure along with the additional components can be composed into a fully functional edge computing platform. The range of components includes various virtual networking components, virtualized radio access network and core implementations, databases and image stores and reference implementations of applications. The project ships a variety of experience kits which are validated platforms designed for the most important use cases that our customers have brought to us. As a toolkit however a partner or customer with an existing Kubernetes based platform can integrate components into that platform. This talk is not the right place for a deep dive into SmartEdge Open but a brief walkthrough is necessary in order to explain how it supports green computing. In the figure look at the box on the right labeled SmartEdge Edge Cluster Multi-Host. This is an edge platform or cluster with a separate control plane on the top and an edge node on the bottom. In both the control plane and the edge node the infrastructure components are shown in gray. Above the hardware is the host operating system. We currently support Red Hat and Ubuntu. Now that is the containerization layer made up of Docker and Kubernetes. Above the infrastructure are a collection of SmartEdge pods, each containing a wide variety of components used to operate the system. Together they provide the ability for the various components to set up container networks and manage the lifecycle of applications which includes scheduling, descheduling and relocation and observing which includes telemetry. This platform attestation provides the ability to make the platform a trusted platform. The number of SmartEdge components is too numerous to even a list here and different experience kits contain different combinations of components. Take the depiction on this slide as just an example. The box labeled SmartEdge all-in-one single node cluster depicts the same functionality that we just covered but in a different packaging allowing SmartEdge configurations to fit different hardware footprints. Customer apps can cover a wide variety of functionality. Generally speaking if they can run Linux images in a Docker container they can run in a SmartEdge platform. SmartEdge uses Helm to deploy applications as well as the SmartEdge platform itself. A growing number of applications both developed by Intel and by third parties are validated to run on a SmartEdge platform and can be obtained through the Intel Edge software hub. At this point we understand that there is a control plane and edge node and SmartEdge components that interact with each other. There are two major differences between this slide and the previous one. First there are several SmartEdge components that were not shown previously. These components exist to collect telemetry including power telemetry and to act on external commands to modify the scheduling of applications and the power management configurations of the hardware. Second there is an external system, the orchestrator, that is responsible for decisions concerning the scheduling and execution of applications over all of the Edge platforms under its control. An orchestrator may provide a user interface for use by a human system administrator. This is a common mode of operation in fact. The orchestrator receives telemetry information which is visualized for the system administrator. The administrator makes decisions and the orchestrator directs the Edge platform to carry them out. The orchestrator may be able to operate without a human in the loop, at least not directly. The orchestrator can provide a method for defining policies for managing applications and platforms. The policies are created by a system administrator but executed automatically. When a policy is triggered an action is sent to the scheduler which instructs the Edge platform to take action. The orchestrator depicted here however has an analytics component that processes telemetry according to a trained model, makes orchestration decisions for the scheduler which in turn instructs the Edge platform to take action. An orchestrator may provide all three modes of operation, human policy and model based. They are all however dependent on telemetry received from the Edge platform about the applications. This is the critical part. A modern server can provide power and temperature readings from all of its cores, peripheral devices, fans, chassis, and so on. But the orchestrator needs to take action based on the behavior of the applications. Now let's return to the Edge platform and the new components. Telemetry is collected and aggregated by the telemetry agent. This agent in general can collect metrics from any hardware or software component that provides or simulates a sensor. In Smart Edge Open the telemetry agent currently uses an open source package called CollectD but other telemetry collection packages exist and could possibly be used. The telemetry agent correlates power metrics collected from the hardware cores and other devices to hardware on which pods are running. Making the reasonable assumption that an application pod corresponds to an application, this allows power consumption to be attributed to the execution of the application. This is what we refer to as application aware telemetry. It is supplied to the monitoring system Prometheus. Prometheus is an open source system monitoring and alerting package used to receive telemetry from the telemetry agent in a uniform standardized way. Other components such as the orchestrator and the power management controller can receive alerts and query it for data. The power management controller is an executive function that has the ability to invoke power management changes on the infrastructure. It is designed to be a Kubernetes component. It implements custom resource definitions or CRDs so that other parts of a Kubernetes system can invoke APIs on it. This means that when the orchestrator human policy trigger or AI decides that an edge platform needs to change the power mode of the core it invokes an API on the power management controller in the standard Kubernetes manner and the controller takes a requested action. The power agent corresponds to the power management controller. It is analogous to a device driver and translates power management commands from the controller into actions on the hardware infrastructure. This was a really long explanation so let's summarize. The orchestrator makes decisions about the functioning of the edge platform in the applications. These actions can be to start or stop applications or to change the power management settings of the hardware. These actions are scheduling actions and are communicated by the scheduler. The scheduler has familiar Kubernetes resources that it uses to do this. The Kubernetes scheduler in the platform and the power management controller. The orchestrator provides an automation loop which receives telemetry from the platform, analyzes it in its various ways and responds by telling the scheduler to take action. The components in the edge platform provide power telemetry in a standardized fashion and provide agents that control the power management to the hardware. In the previous slide when we described the telemetry agent we indicated that it uses a collectee open source package for metrics collection. Collectee is a very flexible package which uses a plugin architecture to collect different types of metrics. It currently implements dozens of plugins for many different applications. The metrics of interest for power management are highlighted in the slide. More information on them can be found in the URL at the bottom. Earlier we noted that the orchestrator has a scheduler that could take actions affecting application scheduling and hardware power management. We will look at a couple of quick examples to show how this works. In this example we look at pod scheduling. The automation in this loop is policy-based. It has a policy rule that forces rescheduling of an application pod. The actual mechanics of this are carried out by Kubernetes. Kubernetes has the ability to de-schedule and reschedule pods as part of its normal operation. By composing appropriate de-schedule reschedule operations a pod can be relocated to a different edge platform. This is another example. It depicts an automation loop that uses a model to forecast utilization. In terms of number of applications it will increase or decrease and decides to expand or consolidate applications based on the information. Movement of the actual application pod from a currently active node operates as shown in the previous slide. However, relocation of the pod may require that a node be activated and moved into an active C-state or possibly in the other direction after de-scheduling a core may be put into a sleep state. The scheduler sequences these actions appropriately and interacts with the reschedule de-scheduler and with the power management controller to take the actions. In discussing the orchestrator we pointed out that orchestration decisions could be done in various modes including a human in the loop mode. For that to happen the human needs to be able to visualize the telemetry data. This is an example of a telemetry dashboard depicting power telemetry. It is implemented using the Grafana open source package which supports a variety of widgets for time series visualization and the ability to customize dashboards. In this dashboard we see two widgets that display power consumption data for the pods that are active in the edge platform which are obviously correlated with applications and the power consumed by each socket which is strictly a hardware measure. For a system administrator a dashboard visualization can be used to orchestrate a system. For a policy-based orchestrator data sets collected in this manner are analyzed manually resulting in policy pattern action rules to be executed automatically by the orchestrator. For a model-based orchestrator data sets collected in this manner are inputs for offline model training or for reinforcement learning models. As we explained earlier when the orchestrator chooses an action to take that action is in terms of scheduling pods and changing the power management configuration of the hardware. The actions that can be taken such as setting C states and P states must be defined in terms of the orchestrator can select and the power management controller and power agent can carry out. This table describes at a high level what those actions look like because all these technologies work differently the actions cover a lot of ground. In many cases bios settings must be changed for example with turbo boost depending on whether the scheduler wants direct control the feature must be disabled in bios. Speed select technology is a more interesting case because it has many sub features and to work properly the scheduler needs to know what features are available. Smart Edge Open has a component node feature discovery or NFD that inventories an edge platform to determine what hardware is available together with the hardware's capabilities. An additional component called telemetry aware scheduling or TAS works together with the Kubernetes CPU manager to select landing zones for pods based on that inventory of capabilities. Well here we are at the end of the talk. The work we described is work in progress. Our in-house experiments have demonstrated the feasibility of application aware telemetry and orchestration to implement the fundamental procedures of greenedge computing namely orchestration of applications and power management to the underlying hardware. Our next steps as Smart Edge Open is deployed are to package this work as Smart Edge components and get it into the hands of users. Telemetry collection and monitoring visualization for the human in the loop mode of operation will be first followed by policy and model based orchestrators. Back in the section on use cases we mentioned the need for building energy aware applications. This is rather forward looking work because most cloud and edge applications are not designed to consume telemetry. We see a need to work with app developers and standards of organizations to develop frameworks for telemetry for a variety of reasons beyond energy awareness. We would look forward to conversations with anybody in the audience who is interested in collaboration on this. Well thank you very much for me and my colleague Kanan for attending our talk. We hope you have a fun conference. Goodbye.


OK, so thank you for the invitation. It's very nice to be here again in Montevideo. And my talk will be about something which is not directly related to dynamics, but I will try to make the relation to a kind of dynamical system that appears in the middle. So the main object of my talk will be this kind of things. This is the famous Penrose tiling, which is tiling of the plane by using two pieces, two polygons. And the special feature of this tiling is that it is a periodic. So there is no translation of the plane that sends the tiling into itself. This was not the first examples of such a thing. But I think this is the most beautiful one because it uses only two pieces. And well, it is still an open problem whether you can build something similar using just one piece. This is the so-called Einstein problem. And well, this is an open problem. There have been some announcements in both directions. So in the recent years, but it is still an open problem. The only thing that it is known is that it is not very hard to prove that if you have a polygon that tiles the plane only in a periodic way, then this polygon has to be non-convex. So convex polygons, if they tile, then there is a periodic tile. And well, there are many special features of this tiling. One of these is that it has somehow a self-similarity property, so you can build this tiling by iterating some procedure. So you start with the so-called, this is the magic triangle. It is called the golden triangle. And you take the division of this triangle using following the golden proportions. And then you get some configurations that if you rescale, then at the limit what you get in one of these rescaling is the pendulum tiling. This is one way to produce the pendulum tiling. But actually, there are many others. And as I said before, well, this has been very interesting because it was discovered that there are real quasi-periodic tiling.  So the discovery of quasi-crystal in the 80s showed that this kind of geometric configuration actually appeared in nature. But the pendulum tiling was not the first in history. And actually, this is somehow an equivalent form of the pendulum tiling. And this was discovered only, I think, five years ago in Iran, in a city which is called Isfahan. This is the temple Dar Hifam, which is from the, I think this is from the 11th century, if I guess. And this is, believe me, this is really equivalent to the pendulum tiling in a certain way. But this kind of knowledge disappeared, actually. And nobody knows how they produced such a thing. But actually, there are many, many copies of this kind of tiling in Isfahan. Now, the mathematical object that I want to concentrate on is what I call the delon sets, or delone sets. So what is the delon set? So keep in mind the pendulum tiling and just keep in mind the vertices of the polygons that appear in the pendulum tiling. And the set of vertices with the Spanish accent, sorry, is what is called a delon set, which is uniformly separated and coarsely dense set. Uniformly separated means that if you take any two points, then there is a minimum distance or a positive infimum for the distance that separates any two points in the set. And coarsely dense set means that almost all the plane is covered by this set, which means that there exists a row, a positive row such that any point in the plane is a distance at most row of a point of this delon set. So this is the definition. This is a kind of discretization of the plane. And of course, you can define the very same thing for any metric space. So a delon set is uniformly separated coarsely dense subset of the metric space you are dealing with. It's a kind of discretization of the surface. And of course, there are obvious examples. You take C2 inside the plane. This is the delon set. And again in the plane, if you take the set of vertices of a tiling that uses only finitely many polygons, finitely many pieces, then, of course, the infimum of the, say, of the systoles of the polygon is going to be the row. And it's going to be the sigma. And the row is going to be the diameter of the maximum of the diameters of the polygons. And let me go very, very quickly to the main question of this. This is very interesting because it was formulated in an independent way by Forsenberg and Gromov with very different motivations. The motivation by Forsenberg was a motivation coming from dynamics. It was related to some recurrence times for some flows on the torus. And the motivation of Gromov was mostly geometric. And it came from geometric group theory. In geometric group theory, there is a very nice theorem which is not totally easy to prove, is that if you have a group which is, say, by ellipse equivalent to C2, then actually it is C2 up to some finite index of group. And so the question of Gromov in this view was very natural is whether every delon subset of the plane is by ellipse equivalent to C2. There is a by elliptic map, the finite on the delon set, which sends this set of points into C2. This question was answered by Burago and Kleiner, and again independently by MacMillan. And the answer is no. There are strange delon subsets of the plane. There are delon subsets of the plane that are not by ellipse equivalent to C2. And actually this is very recent work by Dmon, Calusa, and Copeca. They managed to prove that this is still true for elliptic equivalent, which is something very strong. They managed to prove this dealing with a problem coming from this discrete geometry, from pure discrete geometry. And a way to restate this theorem is saying that there is no uniform bound. So let me write it. So there exists no C such that for every subset of C2 with finite cardinality, there exists a elliptic map into, say, let me take a subset of n square points. So there is no uniform constant for which there is a elliptic map, C elliptic map from S into the square of length n. And this is very, very impressive because you can think that this cannot be true because if you have a set S, then this is a very sparse set on the plane. But in order to make this set coming into the square, you have to do many things to wrap around on the plane so that the elliptic constant has to go to infinity. So this is a very interesting work. And actually, I will try to say a few words about the proof because the proof is not very different from the one of Burago-Klein and Agma-Mulen. It's a kind of technical improvement but which is very, very clear. Anyway, so let me give some concrete features about the loan set. So this is an exercise. If you take the loan subset of a plane, then it is by elliptic equivalent to a subset of C2. This is very easy to see because if you have a loan set, then you make each every point on the set to correspond to the closest point in C2. Maybe you have to rescale in order that this operation makes sense. And doing so, you get something which is by elliptic equivalent. The second exercise is not that easy. It is not tautological. One may think that this is just a consequence of the definition, but it is not. So the image of the loan set by a elliptic of the plane is at the loan set. This is not obvious because maybe by taking a elliptic map, you can create kind of holes in the set. But actually, this is impossible. But it doesn't follow from the definition, I mean. You have to work a little bit too. Yeah, so OK. OK, thank you. So this is for C2. So actually, it's correct. Because you could start creating holes in the image. And at the end, the set you get is not coarsely dense. This cannot happen. But you need some. I have some argument somehow tautological argument to do that. But thank you for the question, because all the things that I so I forgot to say something. So the question of first seminal gromo makes sense in C2 because in the one-dimensional case, it is an exercise to show you have the loan subset of the real line. Then it is equivalent to C. This is just moving points along the line. So this is quite elementary. So it makes sense on the planar. Actually, it makes sense on CD for every D larger or equal than 2. But the proofs are mostly the same starting from dimension 2. So I will only concentrate in the case of dimension 2. And actually, there is a nice open problem for coming from analysis. So there are many partial results on this problem. Is whether there is every by-lipschitz map defined on, say, on C2 or any other, the loan set. But actually, it is already open for C2. Whether these maps can be extended into by-lipschitz homomorphism of the plane. So whether you can, starting with something which is defined on a discrete seed, to extend it into a homomorphism of the plane. This is an open question. This is a question coming from analysis. It is known that the answer is yes, if the by-lipschitz constant is very close to 1. But in general, it is open. So here I will only concentrate on by-lipschitz maps defining on discrete sets. And I will say a few words about this general problem for particular cases. Well, what happens with the penrose tining is that it is not only at the loan set, which is a periodic, but it has a complementary property, which is very nice, which is almost periodic. Which means that somehow every pattern in the tiling can be appeared. So it is copied in almost every part of the tiling. And so the precise, the technical notion here is what is called repetitive. So the definition is there. You take the loan set, and you say that this repetitive is for every small r, there is a capital R, such that every pattern of diameter small r appears in every ball of diameter capital R. And this is what happens with the penrose tiling, and this is what in general is referred to as being repetitive. And this is a theorem that we proved some years ago with Maria Isabel Cortez. Is that there are many loan sets that are not by-lipschitz equivalent to C2 in the plane, but which are repetitive. So they are something like the penrose tiling, but they are not equivalent to C2. And the point is that the penrose tiling, it is equivalent to C2. And this is a theorem by, so I forgot the name of the guy who proved for the first time. But this follows from a more general theorem by Alistair Coronel and Gambodot, which says that if you have a loan set which is linearly repetitive, then it is by-lipschitz equivalent to C2. And this is the k for the penrose tiling because of this self-similarity property. And linearly repetitive means that in this definition above, the capital R depends linearly on the small r. If you have this property, then you know immediately that the loan set is by-lipschitz equivalent to the standard lattice. And the proof is very nice because it is a mixture of many, many arguments coming from different fields. There are even partial differential equations which are solved in the middle of the proof. This is really very nice. And the point is that if you have points, then you have to transform this point into weight, into somehow into density functions on the plane. And then you have to move a density function into another one. And this is encoded by a partial differential equation. And it is proved that in some cases, these partial differential equations have solutions. And then you put all the things together and you get the theorem of Alistair, Coronet, and above. A small r and capital R. No, linearly it's bounded above by a linear. So it's something like a small r is something like this. But if you change the condition by, say, 1 plus epsilon, so the theorem is no longer true. Actually, the theorem of Gambo-Doc, Coronet, and Alistair can be improved. And for the case of, you take a linearly repetitive the long set of the plane. And actually, there is a binitism amorphous of the plane that sends the long set into the standard lattice. So the open question, which is in general open for the long set, can be solved in this case. And the proof is somehow the same as the one of ACG. Except for that, you have to use all the things that are already proven and point to remark something which is quite technical in the middle of the proof. There is no new idea in the proof of the last theorem. But the point is that we wanted to add the repetitivity to the construction of Vurago-Klein and McMullen. And we managed to do that. And actually, we managed to do that in many different ways. It is here where dynamics appear. The point is that you can restate the property of being repetitive in dynamical terms. First, you take the family of the long sets of the plane. And it is not really a metric space or a compact space, if you wish. And not in general compact space, but there is a natural metric, a natural distance in the set of the long sets. So you say that, or a natural topology, you say that two long sets are closed. If when you restrict to a very big ball, then what you see are finite sets of point which are very close, one from the other, in the house of distance. And then if you want this set to be closer and closer, then you pick larger balls and you select an even smaller house of distance for the trace of the long sets on the ball. This is a classical construction that I think goes back to Chaboti. It's in general referred to as the Grom of Distance, but actually this is a much older idea. And the point is that if you take in this, this family is very large, it doesn't give a compact space. But if you remember that in the definition of the long set, there were two parameters, the sigma and the rho, which are corresponding to the final properties of the long set. And if you fix this constant, so at least you make them to move in compact sets, then what you get are compact sets of the long sets. In particular, if you take a single long set and you translate this long set, then you get new long sets. But all these long sets move in a compact family of the long sets. And the property of being repetitive is equivalent to that the action by translations, by R2 translations on this set of the long sets is minimal. This is exactly the same. It's really tautological. You just take the definition and you will see it's really the same. So the point of our work with Maria Isabel was trying to relate the original question, the question by Gromov, the geometric question, to dynamical properties for this action. But actually what we prove is that there is no relation at all because you can manage to do almost whatever you like. So you can construct repetitive long sets that are not by literally equivalent to the standard set and for which the action is minimal because they are repetitive and for which the action is, for instance, uniquely ergodic. And even more than that, you can prescribe any set of invariant measures. So any Chaget Simplex can be realized as the set of invariant measure of repetitive, non-rectifiable the long set. So you can do whatever you like. And somehow there is no relation between the geometry of the long set and the dynamical properties of the translation action on the closure of this set. This is the point. OK. But what is nice here is that you can formulate this question in another way. So remind that if you have the long set on the plane, then you can move this plane. You can promote this set into a subset of C2. So that the question makes sense only for subsets of C2. And the question is whether a subset of C2, which is coarsely dense, is by which is equivalent to C2. And the answer is no, because of Burago-Kleiner theorem and McMullen theorem. But this question, C2 is a group. So this question can be asked for any group. So the question is, what are the groups that contain a coarsely dense subset?  And you take a subset, which is coarsely dense, which means that every ball of radius, say, 11, contains a point in the subset, whether such a subset can exist, which is not by which is equivalent to the whole group. And this is the case for C2 and for CD, because of Burago-Kleiner theorem. And actually, this is the case also for some solvable groups, like the Bauller solitar and sol, and some nilpotent groups. And actually, the conjecture is true forever in a potent group, which is not essentially Z. And this is mainly the word by Tulia D. Martz and collaborators. And for the case of solvable groups, it's not that easy to conjecture anything, because for the case of the potent group, what happens is that they are mostly realized as lattices in the group. So somehow, you can repeat the arguments of Burago-Kleiner. But for the case of solvable groups, there are many groups that are not lattices. And then it is not clear what happens in general. But what is interesting is that these were examples of very small groups. And this is a necessary condition, because there is a very nice theorem in the thesis of Kevin White, which it says much more than this. But if you translate into this framework, the theorem of Kevin White says that if you have a group with coarsely dense subset, which is not by this equivalent to the group, then the group is non-aminable. No, sorry. Then the group is amenable. Then the group is amenable. And the reason, well, this is somehow natural, because in non-aminable groups, what happens is that if you move from one ball to the next ball, then there are many other points that appear. So you have a lot of, somehow, volume that is being created. And moving very strictly the points, then you can manage to actually achieve this by this equivalent, just by translations, in a bounded concept. This is an illustration of the theorem of Kevin White. So remember that in the plane, you take C2. And you take, somehow, randomly points in C2. And then you get a point, a set. You prescribe a condition so that it is coarsely dense. And then you get something which is not by this equivalent to the plane. And here, if you take this picture by, this is by Escher, you can do more or less the same. So there are some, these are the angels and devils. How it is called this? Yeah? So you pick, for every devil, you select one or the two ices, and you get at the long subset of the hyperbolic plane, which is coarsely dense because you are selecting one or the two. And for most choices, you will get something, we will get sets. For every choice that you make, you will get a set that is by each equivalent to any other shoes. For any choice that you make, you get something which is, which comes from a universal model. So, for instance, the universal model could be that you pick the two ices of every devil, okay? Which is something that doesn't happen in the plane. So there is really a difference here. Okay, and this is, this is another side of the history. And this is the key, the key side, at least in the view of Browclain and MacMillan, because this is not a theorem on geometry, this is a theorem on analysis, okay? And I will try to explain why. So there is a classical theorem by Radimacher, which says that if you take a elliptic map, elliptic function actually, then it is almost everywhere differentiable. So this is a famous and classic theorem on harmonic analysis, which is somehow, again, this is easier in the case of dimension one, but in higher dimension is more difficult. But what they prove Browclain and MacMillan is a theorem on analysis that translates into the theorem on the, on these, the long sets. And the theorem is that on the plane, there are positives, sorry, there is a mistake there, positive and continuous function, which are actually bounded away from zero, okay? There are not the Jacobian of a bilithismomorphism, okay? And this is the theorem. And this is actually equivalent to the existence of the long subset of the plane, which are not bilithism equivalent to C2. And the reason is that when you have a delon set, what you, what you can create is somehow a density function of this delon set. And then you rescale, you rescale the set and you rescale the density function. And at the end, you get some function which is continuous because it's a limit of density functions. And this function is not the Jacobian of a bilithismomorphism, and then you come back and you get, as a conclusion that the original delon set is not bilithism equivalent to C2. This is the way how they prove the theorem. So the proof is not constructed. At the end, they don't get, at the long set, what they get is a density function which is not realizable as the Jacobian of a bilithismomorphism. And this is why they prove. If you go into the paper, what you will see is lots of lemmas about analysis, okay? And this is a very, very nice theorem. And actually it's a very technical theorem because if you, for instance, if you, instead of having a continuous function, you get, you have a holder function, then this is no longer true. This is a theorem by Moser. If you have a holder function which is bounded away from zero and positive, then it is the Jacobian of a bilithismomorphism, okay? Yes, this is somehow related to this, okay? When you do the rescaling, you see, you translate the holder condition and density function into a condition like this one, okay? So this is a very technical theorem, but what is nice again, this is a theorem by Rodolfo Vieira, is that this is, when bad things happen, they happen generically, and actually this is a generic property of continuous function. If you take a positive continuous function, in general it is not the Jacobian of a bilithismomorphism. And actually because of the Calusa and the other guys, recent theorem, this, you can replace bilithism, by ellipses and you still get a theorem here, okay? Well, the point of comparison is the theorem of the fundamental theorem of calculus. If you have a positive function on the interval, then you integrate this function, you get a diffimorphism, and the derivative of this diffimorphism is the original function, okay? So what this theorem says is that there is no analog of the fundamental theorem of calculus in dimension two. This is one way to say, to restate the theorem, okay? But the point is that it is this theorem that is proved by Burago and Klein and also by MacMillan. The proofs are somehow similar, but they, there are some tricky differences, but I will retain the proof of Burago and Klein because it is the proof that is closer to our view of this thing. Well, the proof is very technical. It's really very technical, and what we did with Maria Isabel was just to restate the proof of Burago and Klein, which is a theorem on analysis into a statement on discrete subsets of the plane. So we are trying to restate at least the key lemmas of the proof into lemmas about functions defined on discrete sets and doing some course differentiation and things like that. These are things that are well known in geometric group theory, but it becomes technical, and this is an example of a lemma that you can find in our paper. I just took a picture of the lemma and this is the statement, and I know it's very hard to read, but I will try to explain a little bit what is in this statement without any notation here. So there are many proofs of Radimacher theorem. Radimacher theorem is the theorem that says that you have something which is Lipschitz, then it is almost very well-differentiated, okay? And but one of the proofs that I like a lot, which appears in the appendix of Gromov's book, I think it's approved by Stephen Cimes, says that if you have something which is not differentiable, then when you take the, I think this has a name, the increment, what is the name for this expression? The increment, it has a name. The increment quotient. So the point is that when something is differentiable, the increment quotient converts to the same limit at every scale. So you can take here T, you can take one over T, one over N, or you can take one over E to the E to the E to the N, and you will convert to the same limit because it is differentiable, okay? And the key point in the proof of Radimacher theorem is that if there is no derivative, then there are some sequences of scales where if you pass from one scale to another one, then these increments will explode, okay? This is a classical idea for Radimacher theorem. And this is somehow what is happening here without saying what is exactly this condition, so you can see that there are some, let me see. So here you can see that you are adding some M over P, and here we are adding some M, which means that there is a change of a scale, okay? And here we are saying that if moving from this scale to this other one, from one scale to another one, it doesn't change too much the increments, then there is an almost derivative, okay? This is the statement in this theorem. So let me draw a picture to explain a little bit what are these sets here. So here you have a rectangle, you have many squares, and you will compare the increments between two points here and two points here, which are at different scales. And what I am saying here is that if this is somehow M over P and this is somehow the M in the statement of the lemma, and if the increments, so what is written here is the increment at this scale, and what is saying this is that if the increments do not increase too much from passing this from this small scale to this larger scale, then at the end what you get is an almost derivative, okay? And when you get an almost derivative in a discrete sitting, what you are saying is that somehow your map is like an affine map, okay? And since the map is somehow not an affine map, then you do the last trick, which is the following one. Now you will select your delon set and you will put many points here and a quantity of points, which is much smaller here and many points here. For instance, you select all the points of Z2 in this square and this square, but only a half here and so on. But what you get is that in some set, the map is almost affine. So when you look at the image, the image of this has to be very similar to the image of this, but this is impossible because here you have a quantity of points which is much larger than the quantity of points here, okay? So this is another point which is very important in the proof is that at the end what you are doing is to do a very large variation of the density of the function. And this is why at the end the density function cannot be holder, for instance, but it is allowed to be continuous, okay? So this is somehow the idea. This is the translation into a discrete setting. One of our main jobs here was, first of all, trying to understand but I would like a paper because the proofs are very short. At the very beginning we were thinking that it was wrong, but actually everything is correct. Except for one sentence which is in the paper, they claim that everything can be done in the Lipsy setting, but actually that way it cannot be done. And this is the recent work by Calusa and his co-authors. They managed to do that, but there is a very clever remark in the middle and the clever remark is that the density function you are getting at the end is bounded away from zero and when you integrate such a thing, what you get is not only a Lipsy function, but it is a Lipsy function with some supplementary conditions that are well studied in harmonic analysis and then you can make the matching of Bravo-Glenus work again and they managed to prove the theorem for Lipsy maps. But actually it's an 80 page paper in GAFA which has been published this year, but anyway. Now the point with Maria Isabel is having this lemma which is the discrete analog of a lemma in Bravo-Glenus paper. We start with these two patterns and then we put these patterns together along this rectangle here which is somehow the analog of this rectangle and then you make the variation of densities here, but then you have to do this again and again. So using this you have this rectangle and then you build a square which is going to be the analog of this square but in the larger scale. And then you do the same, but you put a square with this density in a larger square and then you repeat the construction. But the point is that again, the proportion of this length to this length is going to be unbounded in the limit because otherwise you will get something which is linearly repetitive for which the Coronel Gambodou Alice theorem applies and so it would be rectifiable. Okay, as I said before, we proved this some years ago, but actually what we would like to do is something more interesting, but this is still an open question and this is the natural open question. Okay, but it is still open and somehow we think that we have the ingredients to prove that it is such a thing, such a set exists. So there is a classical way to produce the long sets which are called the Catam project in the long sets. So for instance, well, this is a picture in dimension one, so in dimension two, but actually it projects into something in dimension one. So you take C2 and then you put a line such that the slope of this line is irrational and then you project the points that are, let's say at this and at most one over two to this line into the line here and doing so you get the long set. Of course, here we are in dimension one, so this is the long set by which it is equivalent to C, but you can do the same in any dimension, okay? And the natural thing to do in dimension two is you take a plane in dimension three, which is totally irrational, so that which means that the angles do not have any kind of linearly dependence among them and then you project into the plane, you get at the long set of the plane and the question is whether this subset of the plane can be a non-byelift is equivalent to C2, okay? So this is a very concrete open question, whether for every alpha and beta, which are linearly independent, well linearly independence is a condition which means essentially that the long set that you get is not has no periodicity, so it is a periodic, okay? It is a quite natural condition and this question is open, but what is very interesting is that it seems that the answer to this question is yes, I would like to say it is yes, but it is a very, when both are rational, you have something, yeah, yeah, when one of these is right, yeah, yeah, of course, of course, of course. So, yeah, because if one of them is rational, then you have in the other direction something in dimension one, then it is violated. And, but the point is that you have rational independence to get a periodicity, okay? Exactly, exactly, exactly. Okay, so for this problem, there is a theorem by, again, by Bouddhaug and Kleiner, which says that if the angles are geofantine, actually if one of the angles is geofantine, then the long set what you get is by which is equivalent to C2, okay? And this is a very nice result. And actually, you need less than geofantine, but you need some kind of arithmetic conditions on the angle to make this proof work. But it is totally open for newville angles. And the key, the idea would be trying to produce such a long set using somehow an approximation method, like a Katoch, a method, something like this. So you start with the, with sets which are actually periodic, associated to rational angles, which are very bad, which converge to very, which converge in the limit to newville angles. And then you will produce this, the long set as, as a limit of the long set which are actually periodic, but for which the periodicity is being broken in the limit, okay? And in the middle, one would be to be able to reproduce the Burawan-Kleinert estimates in order to show that this is not by which it's equivalent, but this is not that easy. And the point is that repetitivity comes for free, okay? This is an exercise. You have something which is, if the angles are rational and independent, then the long set what you get is non-periodic. So the only thing that remains to do is to select the good angles and to re-prove the estimates of Burawan-Kleinert to produce such a thing, okay? We are somehow optimistic, but we don't know. Maybe we are wrong on this, but anyway. So I will stop here. Thank you very much. You still be projecting on the assumption that it's aligned over the plane to rational reproducers, right? You project on. So instead of projecting on planes which are linking objects, you project on the surface, right? And maybe it's not being one of your... Ah, let me try to remind. I think we tried to do that, but it didn't work. But it is not equivalent. No. It is not an equivalent problem. And we tried to do that. Because after projecting into this surface, then you could project this surface onto the plane. But it is not exactly the same, but we don't know, okay?


What's going on and welcome to the FanDuel. Hurry up, my name is Tom Vecchio. Week three of the NFL is here and I am set to bring you three value plays for this week's Sunday main slate. Starting off, let's go to Saquon Barkley at $6,000. That's right, a value play Saquon, not something we've been able to say too often, but he hasn't quite looked like himself yet. He hasn't put up big performances yet, but that could certainly be coming this week. Going up against the Atlanta Falcons, they're allowing 21.5 fan-to-points-per-game to opposing Renovacs. That's the 12th worst in the league. We've seen Saquon play on 67% of the snaps so far this season. He's seen good volume in the Russian game. He has six targets in the passing game through the first two weeks. Yes, not the biggest performances from him, but this match against the Falcons who are just so, so weak on defense is really setting up to be a good spot and hopefully his breakout performance of this season. Moving on, a two-wide receiver, DJ Chark of the Jacksonville Jaguars comes in at $5,800 this week. He's going up against the Arizona Cardinals. We do see the Cardinals as seven-and-a-half-point favorites. This does set up for a positive passing game script for the Jaguars next should benefit DJ Chark, specifically in this matchup, combined now with his 16.31 ADOT that's averaged at the target. Him getting down the field is something he's always been able to do. Going up against the Cardinals, they're allowing 36.5 fan-to-points-per-game to opposing wide receivers. That's the 11th worst in the league. He has an 18% target share so far this season. Marvin Jones taking a big portion from the Jaguars, but we did see the Cardinals get burned last week deep by some of the Vikings' receivers and then certainly something DJ Chark can do, especially if they're playing from behind and they have to pass the ball more. So $5,800 for DJ Chark. He's one of my favorite value plays at wide receiver this week. And then moving on, finally at tight end, David and Joku, $5,000 for the Cleveland Browns. Jarvis Landry is out. Otto Beckham is expected to play this week, but really the question is how much will he be playing? Will he be able to play 80, 90, 100% of the offensive snaps? That's something we certainly don't know yet. So their receiving core is certainly very thin and that could elevate David and Joku, who already has a 16.5% market share of targets this year, elevate him just a bit more. He's played on 58% of the snaps so far this season. He's running around on 47% of those passing plays. And of course those are modest numbers overall, we have to account for the fact that the first two weeks we did see Jarvis Landry play. So this is a good spot for in Joku to take a larger role in the Browns passing offense, do the injuries that they have with Landry and due to the uncertainty that they have with Otto Beckham. So $5,000 for David and Joku, a role that could certainly be expanding moving forward. All right, so that does it for this week's value plays here on The Fandal. Hurry up, my name is Tom Vecchio. I'll be back next week to bring you three more value plays for week four of the NFL season.


n't seen any major sex dependent effects in the gut experiments we've done. Perfect. We have two questions from Rajane Rua. The first one is, you mentioned sensory fibers but are sympathetic and parasympathetic mediators also affecting goblet cell and mucous production? Yes. So actually, you know, actually classically the parasympathetic system does induce goblet cell release. So acetylcholine is one of the mediators that cause goblet cell release. So this kind of neuron goblet cell communication we're just adding to that picture with the CGRP. Okay. And Rajane also wondered about the sex difference but also interesting question. Is there a differential activation of sensory fibers in day versus night? Is there a circadian? That's very interesting. Well, we know there's definitely connection between sleep and pain in different ways. If you don't have sleep, there's an impact on pain. I haven't, we haven't looked into this in terms of our phenotypes. So that would be important. Okay. And Catherine Figarela is wondering whether these effects that you describe of the gut brain axis are generalized in the body. So are nociceptors a kind of bridge between the brain and the periphery in general? Yes, yes. So every barrier site is innervated by pain fibers and also our deep tissues like, you know, the joints and the visceral organs. So I think it is one of the key, you know, nerve ways of that the body is connected to the brain. Perfect. Okay. So from Juan Inclan Rico also thanking you for a great seminar. I'd like to ask whether activation of the TRPV1A positive neurons induces a behavioral nociceptive intestinal response. And it says, not sure how that one can be evaluated, but basically whether it uses a behavioral nociceptive intestinal response. Yeah. So the way to study visceral pain is we use a visceral motor reflex. I didn't show the data, but there you put a balloon in the gut and it extends the gut and then it causes a painful reflex, right? So when you have colitis, this increases. So we did show that at least a nav 1.8 neurons, the last part that if we target them that visceral motor response goes away. So they are indeed the neurons that mediate the pain in the colon. There are many different assays for pain for different aspects. Like we study for the skin, there are many ways of testing pain, heat pain, you know, mechanical or cold related pain. Wonderful. You showed us the ramp 1 expression, right? In the goblet cells and Leslie science is saying, is the ramp 1 receptor highly expressed in an enteroendocrine cells as well? Do you know? Yeah, that's a good question. So it is expressed in enteroendocrine cells, but much, much lower. So I think the goblet cells are, if you look at the data is really, really high. And that was one of the inspirations for our study. Wonderful. Okay, I think we are reaching the end of our symposium. So thank you so much, Azsak. That was wonderful. I will now, yes, thanks to everybody and I will now let Beth say some words on final remarks. Okay, great. Thank you so much. So thanks to all the speakers really for sharing their exciting talks and their exciting data, much of which was unpublished and new that really captured the breadth of neuroimmunology that ranged from development to homeostasis and in the context of disease, aging, pain, infection. This has really also been a fantastic opportunity to bring together scientists from different fields, immunologists, neuroscientists who are really interested in understanding mechanisms of neuroimmune and also brain body access and communication. So there's much to do as it was illustrated by all of the amazing questions that we couldn't even get to. Huge thanks also to the participants and the trainees for all their excellent and thought provoking questions. And huge thanks to Eli for supporting and hosting this open symposium, especially Emma Smith, Maria Guerrero and Anya Starris who all made it happen and run so smoothly. So I'm gonna turn it over now to Carla for any additional comments and closing remarks. Yeah, well, thank you. Thanks everybody. It was a pleasure to join all of you across the world and looking forward to other Eli's symposiums. Thanks everybody. Thank you. Bye. Thanks. See you soon.


What's going on guys got Kyle here relating sports cards, and we are ripping one box of 2022 tops chrome platinum anniversary and this one is for Joseph s Good luck Joseph. Let's see what we got We'll do the same thing we're doing for the group breaks Joseph I'll do these stack. I'll stack some piles here, and then once we got all the cards out. We'll start going through them Give me a mini here to get all the stacks ready to go Then we'll start getting through them here all the cards out here first Can you go see if there's an mls renaissance up front? Pretty sure there is Yeah We're ready. Good luck Joseph.  We have Matt coming in at five and he'll be ripping until midnight Donald W and John a gonna be the first ones with Matt tomorrow not a big cue at the moment guys You want to get something order? You'll be one of the first ones up tomorrow with Matt Definitely not a big cue at the moment. Hope everyone's enjoying the stream tonight. That's some fun man We hit some huge cards and NT man. That was really fun Everyone enjoyed the stream like I said we got Sam starting at 10 a.m. Tomorrow got force coming in at five and Matt coming in at five So we'll have plenty of streams going tomorrow Appreciate everybody grabbing spots today. Let's do this last one for Joseph, and then we got Edward Got Cal Muehler rookie. There's a refractor Gary Sheffield Francisco Lindore Gary Carter Jake Cousins There's a Jake Myers on the Rose gold mini diamond number to 75 rookie for the Astros not a bad one there 275 Jake Myers got catfish hunter Refractor is Hermon Marquez Rockies Got Lee Smith and a mower Vera. So Jake Myers rookie base Behind it. We got Larry Walker nice one there on the aqua auto for the Rockies Sick throwback there that one is a hundred of 150 Larry Walker on the on-card Rockies auto.  This is Cabrion Hayes on the blue pattern That is to 199 cabrion Hayes pirates Haven Smith Cody Bellinger. There's a refractor rookie Marcos Diplom Fractor rookie see like it this one. I don't have to turn anything around but the all the ones for the brakes earlier Yeah, they're all over the place. This one is all facing one direction They're not all but Dang near. Yeah, there's a Max Kepler. That one's gonna be fuchsia pattern on the atomic Um, that one is to 100 max Kepler for the twins Refractor is Craig B. Geo Strokes Nelly crooks. There's gold Cooper Hummel number do a 50 Cooper Hummel gold wave D-backs rookie there 32 of 50 On the gold wave there's a refractor Robbie Ray and rookie Griffin Jax So handful. Good luck Joseph. There's a extractor Bryce Harper Sick card there Bryce Harper extractor This one is Bernie Williams on the mini diamond for the Yankees there Bernie the 150 mini diamond refractor rookie is Mike Balman Oh Connor Wong rookie Red Sox Maneuvera and this one is yellow lava. It's gonna be Tyler Stevenson or yellow ray wave to 75 Reds and Grandal on the back Alrighty there, mr. Joseph that'll do it for your one box of platinum anniversary. Appreciate you sir. We'll get these cards right out