Good morning. Let's see if this is working. Can you hear me guys? I have five amazing women with me this morning. My name is Yana.  Actually, yes it is. We will be talking about data-driven innovation across different industries. So we assembled these ladies here who are working in different industries. We have Maggie, who is a head of data at Citi. We have Celine, who is a head of data science and innovation at AXA. Then we have Judeon, who is... I'm now leading the data analysis team in Shopee. And then we have Jen, who is working at Facebook, and she is... Jen, where do I have you? Partner at Marketing Science. And then we have Sigrid, who is a lead data scientist at Stock Exchange. What we will do today, when I hear the subject when Anne came to me, and she said, let's talk about data-driven innovation. And I said, oh, this is great. Data-driven is kind of everywhere. So let's share the experiences that you ladies are using the data in your day-to-day job. And maybe we should start about the data-related kind of things and procedures you run in your profession and in your job. So maybe introduce yourself a little bit more how you landed in this particular position and what it means for you data-driven and data. Maggie? Hello, everyone. So I work at Citibank for the Consumer Bank, and I look after all data technology across all our APAC and Amir countries. So in that technology, I basically provide all the technology that data scientists need, all the tools that the data scientists actually need. I look after... So I own the data lake, our real-time event hub. I build the real-time use cases that says what's the best next action for our customers at this point in time, what are the best offers to give our customers at this point in time, our risk analytics, all of the real-time and old times. I own both the old and the new technology. And how I got into data... Because Yana always wants me to talk about how I got into data. This is very interesting because Maggie used to be ahead of infrastructure. So how do you go from infrastructure to data? That's an interesting transition. Right. So basically I only started my job in July this year. Prior to that, I've always been managing infrastructure for the same consumer business. Last year sometime I decided actually I was really, really interested in data, and that was the area and the field that I wanted to get into. So the first thing I did was actually make sure I was relevant enough to say I want to get into that field. So I got involved in lots of external things including trying to get to know Yana and kneel in the back, most data professionals know. I ran without even being in data. With the Singapore Computer Society I ran a woman in data analytics conference where I then met lots more data professionals. I went and read things up and I ran and registered for a course in NUS. Having done all of that and feel like actually I can reasonably talk about why I'm passionate in getting into data, then the next thing I needed to do was convince my company that I was the best person for the job. This is interesting as well, right?  Yeah, so I first had to actually convince my business stakeholders, in fact the guy who needs the business analytics, and he had only just joined the company a year ago coming from eBay. So I knew actually one of his frustrations joining Citibank was, he didn't know how to execute at the same speed as eBay at Citibank. So I actually went and put myself in front of him. I then told him that I know exactly all the issues you are having and I am the very person that can actually help you solve them. I said, I actually said to him, I'm not going to pretend I'm a data expert because I'm not, but because you are a data expert, why do you want me to be a data expert? That's my advice. Then I had to do the influencing because I had been managing infrastructure for the consumer business four years prior to that. I actually went to talk to all my previous customers who I had built a very good brand and reputation with and basically got their sponsorship, telling them that the same way that I succeeded in infrastructure, I was succeeded in data. Then my new stakeholder went and checked with his team peers and they all basically said, yes, maybe it's your person. That's fantastic. Thank you so much for sharing that. Now we have Celine and Celine as well as coming from slightly different background. She's coming from marketing and we know that marketing, it's all about data today and technologies. So it's not such a kind of strange change, but it's interesting coming from automotive into insurance industry. So what was this transition for you about? So my focus is, so we're running some of the same use cases as what you said internally, but for me data and I think the topic is data driven innovation across industry. There's the innovation part and there is the across industry that is very important. Today the business model are changing. So I come from the automotive industry and my previous job was, oh my God, autonomous vehicle is coming. How will we survive? And this is where all the car makers are moving into mobility. So a new type of business model, you know, with the arrival of Uber or Grab in the market. How could those guys be born? They were born because they had access to your real-time geolocalization data. And Uber created Uber and Grab created Grab. It's not Apple or Samsung or whoever. It's not the hardware manufacturer who is actually creating all the services around it. And so they were able to create Grab because Apple and Samsung and Google were sharing the geolocalization data, instant geolocalization data. So for me, I've been working on new business model and how the word is changing with the arrival of the sharing economy because you do have access to people data. And you are able now through platform to propose new type of services. We are going to an ownership word to a usage word. We don't want to own anything, especially the young generation. I used to work in car manufacturing for like eight years in an R&D center. I never owned a car. Really. I don't see the point. It's an object that is like very expensive, very like you need to maintain, you need to pay parking, you need to pay fuel. And then you have the chance of driving in the traffic when it's raining. Did you share this opinion while you were still working? Yes, I did. This is why I was in charge of mobility and new business model because I'm sure that a lot of people feel the same way I do. I do prefer to use a Grab or a DD in China for that matter rather than owning my home car and having the pleasure to spend time in traffic, wasting time, my hands on the wheel. That's why I was really good. Someone enjoys it. But let's talk about your transition from automotive and obviously R&D and very innovative industry from this perspective. Going to quite traditional insurance business. Very traditional insurance business. I think it's about curiosity because there was this thinking that my job was in monetizing data, so creating new business model with partner. I was working with DD in China and Alibaba Tongxun in sharing data to create new type of services for the car. And the industry thinks that 80% of your revenue of monetizing car data will come from insurance. They say, yeah, pay as you drive, pay how you drive. And I was not seeing the business model going on. I was like, how will it work? And all the car manufacturer will put all the data into big data dump and all the insurer will come and then pick it up. How is it working? So I wanted to understand insurance. What's about insurance? And I think that no one understands insurance, to be honest. There is lots of statistics and mathematics behind the insurance model. Yes, but today using very non-behavioral type of data. That's the thing. Actories have been using data and statistics for ages already but are not able today to use real-time instant data in order to do their pricing. And so I wanted to understand insurance and how it's working and what would be the value of the car data for an insurance company. And then I go into an insurance company and I'm like, okay, everybody's wrong. Everybody thinks that the insurance will be paid something but that's not how the word of tomorrow is going to work. And so now we've been focusing also on creating new type of product that are more insurance usage-based product but with our partners. For example, we did launch a product with Grab in 2016 called Pay As You Grab that was targeting the part-time driver that wanted to part-time drive for Grab and had an insurance, commercial insurance that they were only paying to the kilometer. So how did we do it? We partner with Grab. We exchange our data with Grab. And thanks to Grab data, we're able to do a product, a usage-based pricing dedicated for the driver without having to install a box, an app, or whatever. We're just already using the existing ecosystem and our partner in order to create new value proposition for the customer. Okay, let's stay in our finance industry and let's go to C-grade because so we do it a little bit more in a structured way. So you work at Stock Exchange and you've been there for two years and Stock Exchange, based on my opinion, because I don't know much about it, but it's all about data, all about technology, all about like fast real-time decisions. So what about your job? What do you do with data specifically? Alright, so when I joined two years ago, there was no data team. So it's actually a fairly recent initiative. We started by building the infrastructure so making sure that all the data is in a centralized location. And then six months later, I got hired. And so I started from scratch. I built the team, I decided the technology, I decided on the use cases. So the type of use cases, so as the data team, we serve the whole company. So we serve our securities and derivatives market, but we also serve operation, technology, and regulation. So we have really a very wide range of problems to solve. So the first thing is trying to understand how people trade on the platform. So if you understand their behavior on the platform, we are able to serve them better and recommend them better products. Then we also have a lot of things around text. So believe it or not, we have a lot of data in text. So all the corporate actions, so when a company declare a new dividend or a new board of directors, they have to send us the data and they usually send it as a PDF. And we have like 1,000 PDF, 1,000 pages PDF long, and we have all this data in there that we have to mine. But we also look at operations. So for example, latency is a really big issue for traders. They want to have their orders in the first, as fast as possible on the trading engine. So we make sure that there is no anomaly on the latency side. So it's really, really a very wide range of problems. And that's why I really like this position because really exposed to a wide range of different issues. Thank you so much. Let's go to Jen. Jen is in advertising and tech, Facebook. And she's a partner in marketing science. So how, and I know that we all know when we say Facebook and data, it's a little bit sensitive. But how is it from your position? I won't be speaking about all those topics today. We don't have enough time clearly. No, what is your, from your position, how do you work with data? Yeah, so we're working with clients, many brands that are represented here on the stage today. And I'm sure many of you in the room on a daily basis to assess advertising effectiveness. So proving that out in various forms on Facebook as a platform alone, Facebook properties, and kind of what we call a cross publisher, cross media as well. So as we look at the relationships of advertising across digital and TV and where we can assess that. For me, as Yana said, I'm a partner in what's called our marketing science department. But there are many different departments where I have colleagues who focus on data and data analysis. So whether it's day-to-day campaign management and assessment, it could be our business integrity team who's working with the platform and with our product teams to assess, you know, bad actors on our various platforms. It's actual operations analysts who are working with product to understand how you all are using our platforms so that we build better products for that very need. So there's many different roles that encompass data. So as Yana said before, kind of, you know, touching on everything. Me, the way I face my role, I actually am similar to Maggie, but a little different. My background is not in data analysis. I come from startup and large advertising agency background. And the way I... Well, the way my role came about was by way of a reorganization. Shortly after I joined Facebook. So I joined Facebook in an advertising technology role. And then, obviously, advertising technology is used to assess advertising effectiveness, right? So it only made sense that we re-ordered into marketing science. And with that, I was asked to move into this role. So my company made that request of me. And at that time, I could have panicked. But when you think about it, whether I was on the startup space before and building out models or on the advertising side before, always assessing my client's data, it wasn't that much of a stretch. Yes, were there things I had to upscale on? Yes, I took the initiative to upscale and kind of go on data camp and work with my colleagues, ask them for help, you know, okay, teach me how to merge these data sets most effectively. And you just have to find your way about it in that respect, I would say. If I were to give one piece of advice there, it would be don't let other people define you. You define what you want out of that yourself. So for me, I come at the role a little bit as a data consultant. I know what advertising agencies, what my clients need, the challenges they face in servicing their clients. And I try to come at it from that angle. And I want to say, Jen is our, I actually run community that is called She Loves Data. Do we have anyone here from She Loves Data? Yay! Yay! This is fantastic. So what we try to, we try to raise awareness about data and we try to say that ladies, data is everywhere in every position, no matter what you do, you don't need to be in tech, you don't need to be in STEM. Make sure, as you said, learn some basics around data because in that way you will be actually future-proofing your job. And Jen reached out to us like few months, one and a half years ago actually, and Facebook is our partner and helping us to reach out to the women and help them to take this education for free. And I know Jen actually brushed up her SQL skills to be able to be one of the instructors. So that was fantastic. Now, thank you, Jen. Let's go to a different industry. Industry that is as well as advertising and marketing full of data. And it's e-commerce.  What is your role and how are you guys using data? You have 50 people in your data science team, right? Yeah, yeah. Or data analytics team. Data analytics team. So my name is Dujuan, and so I'm now working in Shopee, leading a team of around 50 people of data analytics team. So what we do, what is the daily job in Shopee for data analytics team? Basically, we utilize our data to support both product and the business. We know like for a product data-driven lifecycle, it includes like design, development, and analysis. So for data analytics team, we participate a lot in the design and analysis stage. For example, right, since we built our in-house user tracking system, which is something very similar to Google Analytics. So we based on these user behavior data, we did a user journey analysis, and we found like if we simplify the user journeys to reach to our recommendation module, the outcome will become better. So we give these suggestions to our product team, and product team accept then they design the new features and then release. So we finally value the outcome. The outcome is like the order number increase around 5%. So this is what is the impact of data, and in addition, for e-commerce companies, we know for users, for people, somehow it's a little bit hard for us to find something on the platform, especially on some like ladies fashion dress. You see this piece of item is very nice, but I don't know how to describe it. So yeah, we also provide this suggestion to our product.  So this is how our daily life looks like. In addition, from my opinion, another area that becomes more and more popular is about causal inference analysis. The reason is like what we are doing for the analysis right now, most of them are correlation. So correlation somehow is very different from causal. For example, so here is a question, right? Whether attending these coding girls' events can help me to find a better job? This is a question, right? Maybe we can do some studies based on the people who are attending these coding girls' events, and we found out it seems like it can help you to find a better job, but is it really scientific? It might because the ladies, the girls who are attending this event is very talented, it's very smart, so they can find a better job. So causal inference analysis is another very interesting topic, and I think if you want to join the data area, you can start to learn these causal inference theories, and I think the market will need more and more people who can conduct this analysis. This is fantastic. I think you jumped into actually a real example of how data-driven innovation can be run and how companies are doing it. Do you ladies have some example of a data-driven innovation? Let's just simplify it for me and for everyone here. Data-driven innovation is something that we use data to create some results that help innovation in the business, right? Just let's simplify it like that. But how do you measure that? Do you have some examples of projects that your organizations put up out there based on data, data-powered innovation, and then you were able to measure right away the positive impact on the business? Anyone? It's there. So yeah, the way we work with the data is that every time we're scoping a project with the business, we try to actually assess what would be the impact. And then we're running a small-scale pilot where we are actually seeing the impact of the model before deploying it. So we are tracking all the time what is the business impact in order to also transform the business. But because also the team might be a bit tired about evaluating all the time or quantifying all the time the impact, we're working on a what-if tool that will be able to automatically tell us what would be the impact of such a machine learning model being deployed in their organization.  Yeah, I mean, we've been rolling out since we've had real-time life data coming in that every moment. We know what's happening. It's raining out there. You are sitting in Vivo City. You love Chinese food. It's personalization about how we can actually give our customers better offers of what they really need. Since rolling out a couple of the real-time models, we rolled out one where we actually make the merchant offers that are most appropriate at that point in time. And then we rolled out another model where we offer financing when we know our customers just bought a large purchase. And actually, would you like to share with us your interest financing options? And actually, those two, within a couple of months, and we always measure them, it is very important to us in the banking industry to measure what we're getting in return. So just a few months in a couple of markets because consumer has 17, so we tend to pick which markets we go with. Two million additional sales just in a couple of months. Fantastic. And I think one of the things about innovation is there's so much technology that's going coming out. The difficulties in the technology, technology is there, is actually finding the right products and the right customer needs is about actually being customer obsessed. That's actually going to be able to and actually developing those products that actually does add value to your customers. And as well maybe collect all the data that is useful because we collect so much data and it's all out there. But how do we define what type of data is useful for these type of innovation projects? Sigrid, you wanted to add something. Yeah, I think when you are not directly customer facing, it's harder to measure the impact of what you're doing. And so the way I approach it, for someone inside the company, I build a dashboard, I build an analytics, I want to know whether they're using it. So I don't just build my model and that's it. So I go back to my customer, my internal customer and ask them how it has helped them. I also try to find ways to measure how often they have logged into the dashboard or how often they have run the analysis. And I think that's a little bit tricky for us because really we serve internal people and then they serve the customer. So it's a little bit more diluted. So I think that's a little bit of an intermediary measurement to see how the adoption is there. That makes sense. That's actually very true. How do you measure is different for different industries and businesses. Jen, can you share a little bit about how you actually drive the innovation so it gets to everyone within the organization. And I mean, because often what we see, we have a group of data scientists, group of data analysts, they are very aware that we change the adoption process of the entire organization to work with the data in a smart way. Because what I've seen with many of my clients have been working with data for 20 years is that it's easy to put the innovative products out there, but the adoption process of the organization to change the processes and mindset of people is the hardest part. And I know you guys are probably, or I have the impression, you're good at that. There are some key things to change. For us, it's not only the analysis involved, because our analysis is informing our auction every day, so that's just a common practice there. But it's actually using that analysis to educate and educate not only our clients, but the industry and the advertising industry and even broader than that. We're having data-led conversations that are talking about advertising effectiveness and why are we looking at apples-to-apples comparison of ads on Facebook, a video ad on Facebook versus video TVC, right? It's not apples-to-apples. And so, yes, we need to have benchmarks in the industry, but we're looking to kind of educate and bring about, well, this is the way people are using our platform, for example. Think about it. How many of you scroll rapidly like this, your Facebook newsfeed?  It's either thumb or index, usually. Yeah. If we find any other fingers, I think you're anomaly. But, yeah, it's using those insights, like I said, and not only informs advertising effectiveness and how marketers can use that data, but also informs our product teams. How you all are using, that's how we develop products. It's funny when you hear in the trays or just consumer publications sometimes, oh, why did Facebook roll this out? It's all you are behaving. We're following those trends, right? Maybe you're the anomaly, you're the outsider that's not on that trend, but that's kind of a very common way. Makes a definite sense. Because we have not enough time, but can we continue for five more minutes because I have two very important questions based on my opinion. Innovation and diversity. Because we are all women here and majority of women out there. Does it play any role on innovation, the diversity? You have 50 people in your team. You have 200 plus people in your team. How does it look like, actually, in your industries? Do you have enough women? Do you have enough diversity? Not only gender diversity, but all the types of diversity. And does it have impact on innovation? For my 50 people, most of them are girls, right? Mostly half of them are women. Half of women. Because we are doing data analysis, you must be very careful. Because sometimes maybe not fair to the men, but sometimes girls are more careful generating the numbers. That's a good summary. I think it's very interesting for a team to contain a certain portion of ladies inside the team. Because the way of thinking is very different. Especially in e-commerce company. Why? Because women like shopping. They can provide more creative ideas about how to design these e-commerce products, how to utilize the data to provide suggestions. I think especially in e-commerce industry, I'm not quite sure about other industries. We want ladies to join us to do fancy data analysis stuff. This is a call out to you guys. We are hiring. Let's do this promo because I know Maggie is hiring as well. I'm going to shamelessly say I am always hiring. Hiring women in data. I've always been very, very passionate about women in technology. I actually run this women's network with the computer society on women in technology. Since coming into data, I've become more and more passionate about women in technology and women in data. It's more than just actually having the women's mindset when we are designing for customers. And it's very, very important for us to actually create in the data community enough women with the empathy and the attention to details and the sense of community to actually make sure that we don't go down the route of multiplying biases. And especially when people now are talking about in data a lot of machine learning. And again, machine learning becomes very unpredictable sometimes, unintended results. It's very, very important to have more women just taken over my team, not enough women. Please come to me if you are interested in joining data. This is the ethical approach to machine learning and intelligence is a big topic right now. So I think what you said plays a super important role. Jenny, you wanted to add on? Yeah, I will. I think we at Facebook come from a little bit of a juxtaposition with it because Yana mentioned when I reached out to her over a year and a half ago it was on the basis of I attended our annual conference in Silicon Valley and panel like this up on stage of our senior leadership in marketing science globally. And we all pointed out the fact even the head of the organization that only one of seven of those people on stage was a female. And so I decided to kind of take that away and put it into action when I came back here and so hence reached out to Yana and we we've been involved in many CSR initiatives together in that way upskilling, educating, partnering together and we're very fortunate at Facebook on top of that we actually have what we call a diverse slate initiative. So it doesn't mean that we're going to hire you because you are a female and you get the edge that way but at least the interview process starts with that diversity right? So we have some rules around that in our interview loops if you have four in an interview loop at least one are depending on the role there's certain criteria 25 to 50 percent should be of equal gender representation. I also just came from an event this week where we have women at Facebook a women leadership day and we not this year but in the past we've had Cheryl we've had lots of senior representation come out and we're very lucky to work at a company that drives that at the same time and we know in tech that that's a real challenge and we know that we're still underrepresented and to me that's where the long tail comes in because you can't just say go out and hire women on the spot right? So long tail initiatives like Yana and others are involved in like this thank you coding girls thank you very much really matter. So I just want to add something because like Elina said earlier that we are 60 something women in AXA and that's true but most of the women are today doing a job that will disappear tomorrow they're in operation they're like sending claims or you know like more traditional kind of career path and yeah and today most of those women job will be automated tomorrow with the RPA coming along with the data science and stuff so this is why for me we'll launch the super user initiative in the company to be able to up skill the people that had no data skill whatsoever so they can be empowered by the data and maybe tomorrow become data analysts because otherwise those women would potentially lose their job in the next five to ten years so we need to take action now to prepare the women whoever they are or whatever the background to the word of tomorrow so let's close this discussion by you ladies saying one sentence okay because the time is ticking what would you advise to the audience here one take away for them to get your jobs to get data related jobs because you are the leaders in the industries across industries so what would you advise them stay curious the field is moving so fast and find that passion curiosity ladies I would say be bold everyone can learn about that be bold I would say number itself means nothing just combine the numbers with business with use cases be impactful and actually bridge the data and the business right the technology and the business be the bridge be the translator of that I'll build on Maggie's a little bit develop a growth mindset it's about resilience and not necessarily endurance resilience and last but not least to me would be get mentors but get both men and women so you will have both sides of the picture so have a diverse mentors thank you so much ladies it's been pleasure thank you can we give them a round of applause


Hello, everyone, my name is Liang and I'm from the infrastructure team of DD2C. Let's get started. With the rapid development of artificial intelligence in these years, TPU plays a very important role and is widely used. It is usually provided as a form of virtual machine instance by cloud service providers. For some technical reasons, creating virtual machine with a pass-through TPU card is very slow, which is a common issue faced by all cloud service providers. Today, I will share you with the practice that how did you solve this issue. The agenda of my presentation contains the following parts. The first part is the background, and the second part will list the main issues slowed down the creation of a virtual machine with a pass-through TPU card. The third part will give you some details of the solutions, and then the fact of the optimization will be showed. The last part is the conclusion. In this topic, I will call a virtual machine without a pass-through PCIe device as a CPU virtual machine, and a virtual machine with one or more pass-through TPU cards as a TPU virtual machine. Creating a CPU virtual machine instance is fast. It usually takes several seconds. While creating a GPU virtual machine instance with the same resource configuration is slow. It may take several minutes if the virtual machine has a large RAM. In most cases, virtual machine creation time is not critical. But in some interactivity scenario, a long virtual machine instance creation time means poor user experience and computing resource waste. Virtual machine creation time is defined as a time interval between queuing process start to execute and guest color start to run. It's divided into two parts. The first part is virtual machine initialization time, which is defined as a time interval between queuing process start to execute and the VCPO start to run. The second part is BIOS execution time, which is defined as a time interval between VCPO start to run and the first guest color log is printed. The chart in the right shows the creation time of different virtual machine instance before optimization. Some factors like virtual machine RAM size, the type of GPU card, and the count of GPU cards will affect virtual machine creation time. You will be curious about the reason for the long time it takes when creating a GPU VR virtual machine instance. What is slowed down the creation process? To find out the reason, we can use proof to get the hotspot functions of queuing process. The frame graph in this page shows the hotspot is in the core chain or core function, VFIO pin pages remote. This page lists the main factors slowed down the virtual machine creation. So key factors is the function VFIO pin pages remote is slow. By the way, you will find some repeated VFIO DMA map and unmapped for the same IOVA area, which makes sense worse. Besides that, PCI device reset, and measurement metadata initialization in KOM, and other is seniors configuration will slow down virtual machine instance creation. I will describe these factors in detail. For VFIO pin pages remote, there are two main issues make it slow. The first one is third auto operation, which is required when allocating pages for user space. Third auto page content will make sure sensitive information invisible to user space. Because page allocated may have been used by other process of the kernel and has sensitive information retained. As a solution, we introduce a new feature called pre-zero auto free page to speed up page allocation. The idea is simple. Third auto free page in advance. When allocating page, zero auto can be skipped. The second issue is the VFIO DMA map pin memory in a page by page way. This result in too many page type entries access. As a solution, pin memory embark is used to reduce the cost. For pre-zero auto free page, it is based on free page reporting. Page-zero auto operation is done in a codework slab. After page is zeroed out, set the page zero flag in corresponding page structure. When page allocated leads to be zeroed out, check page zero flag first. If it is set, zero-out operation can be skipped. When pages are free, page zero flag is cleared and the zero-out worker will be woken up. I have sent the RFC patch set to upstream. You can find the implementation details with the link in this page. I will not talk more about it. To make VFIO DMA map pin memory embark, we add two functions in kernel. They are getUserCityPace and getUserCityPace long-term. That corresponds to getUserPace and getUserPace long-term. City here means physical continuous. The new function will try to pin memory and get the information about embark or physical continuous memory. The chart on the right shows the different behavior of the new function and the original one. The new function will be friendlier to function with or draw, getPFN, and make its life easier to pin memory embark. To take more benefits, huge page should be used. VFIO DMA map is inefficient. There are two reasons for this. The first one is the same IOVA map experienced as a map, unmapped, and then remapped procedure. It's unreasonable. To serve this issue, the mapped IOVA area information are retained in QMU to avoid unnecessary VFIO DMA map and unmapped operation. For the whole sense to work, VFIO IOMU unmapped DMA outcontrol is old-cored when mapped or conflict IOVA area. Conflict here means a new added IOVA area, which is intersected with an already mapped IOVA area, but not equal to it. The second issue is inefficient IOVA area mapping update. For example, to change or tribute our subregion of an IOVA area which is already mapped, the whole IOVA area needs to be unmapped first and then be mapped again. This will happen for an IOVA area containing the address of 1M. As a solution, the IOVA area is split into two parts. One part is below 1M and the last part is above 1M. It can prevent high part IOVA area from being unmapped for modifications to slow part. For PCIe device reset, one reset takes about one second, so it's a slow operation. There are two issues in QMU. The first one, a device was reset twice during virtual machine creation. One is in function QMU system reset and the other one in VFIO group get device fd IO control. Reset twice is redundant. One of them can be removed. The second issue is PCIe device reset operation are specified, are serialized. If a virtual machine has more than one GPU card, it will take more time to reset the device. Do the PCIe device reset in parallel will be more scalable. And first of all, make the PCIe device reset in parallel with VFIO DMA map can maximize the benefits. In the QMU color model, there are some time consuming operations which can be optimized. DERPAID DMA initialization is one of them. Virtual machine with pass-through GPU does not support live migration currently. So DERPAID login for PCIe bars specified MMI range is the usage. Which can be skipped. When PML is enabled, set EPT Entrance Debit is the last time consuming operation. It uses reverse map to get the EPT Entrance for setting the Debit. Trosing all the reverse map entries are time consuming if the virtual machine has a large RAM. During the virtual machine creation, most of the EPT Entrance are empty. So, processing all the reverse map to find a few or effective EPT Entrance is not worth. This can be improved by making the reverse map coercer more efficient. For example, we can introduce a sparse bitmap mechanism. Like HPF map used in QMU to skip the empty reverse map entries. In our environment, we use a lot of simple way to serve this issue by counting the effective reverse map entries. The reverse map entries for memory slot can be skipped. If the effective reverse map entries count is zero. This is not an ideal solution. But it is a balance between the benefits and the development complexity. There are some configurations which will affect the virtual machine creation time. They are listed above. C-Bow's boot manual has 2.5 seconds of time out by default. If the boot manual is unnecessary, please disable it. If Linux guest is used, the graph may config a timeout for selecting different color. If it is unnecessary, please change it to zero. NUMA memory puzzle will affect the speed or page allocation when some of the loads have memory pressure. So be careful with it. This page shows the QMU process flame graph after optimization. You can find the hotspot we saw before has disappeared. And this page shows the accumulated time takes by some way QMU functions. As you see, the time is reduced greatly after optimization. This page shows the GPU virtual machine instance creation time when different optimization method is used. Compared with CPU virtual machine instance, it takes a little longer. And this page shows the creation time of a GPU virtual machine instance with one GPU card and 48GB RAM. Before optimization, it takes about 38 seconds. After optimization, it takes 3.3 seconds. The creation time is reduced by more than 90%. 4GPU virtual machine instance with 4GPU cards and 192GB RAM. It is reduced from about 2 minutes to 4.4 seconds, which is reduced by more than 95%. The concluding part. I have to point out, worse optimizations are not limited to GPU virtual machine instance. They apply to virtual machine instance with other PCI pass through device. And some of them apply to CPU virtual machine instance. About pre-serial or free-page, it has some limitations. Its current implementation is not friendly to huge TRB file systems. Extra work is needed for this case. And there is a court case. If the free-page are not served out in time, it may lose the effect or boost the sense up. So it's far from perfect. All solutions have some pros. The first point is transparent to guest. Nothing needs to be changed for guest. So it's more appropriate for public cloud environment. The second point, DMA operation in BIOS stage can be handled correctly. Other solutions based on parallelization lead some work around to forbid DMA operations in BIOS execution stage. About GPU virtual machine creation time, there is some room for further improvement. We find Linux memory management seems inefficient in device pass through scenario. For example, all the features for page migration and memory or commits are useless in this case. So it's possible to make sense simpler and more efficient. It's in our future play. One more thing, we will contribute our work to upstream. That's all for my presentation. And this is my email. If you have a question which I can't answer online, you can send an email to me. Thanks. Bye bye.


It is a pleasure to welcome you all once again to MSP lecture series on interpretive spectroscopy. In my last lecture I showed you how very nicely we can use phosphorous NMR to monitor a reaction and also we can gauge when the reaction is going to end and also whether any products are formed or not other than the expected product. So now let us continue looking into more such examples of course using our laser NMR active nuclei as well. Now let us look into 19 F NMR and of course about the abundance, gyromagnetic ratio all those things I have given in the beginning. So now let us focus our attention to the chemical shift values as far as 19 F NMR is concerned. 19 F is 100 percent abundant and I equals half. Then it is as simple as 1 H R 31 P to measure and understand. So here if you just look into CF here when we have something like this on a alkane chain reference we are using is CF Cl3. So relative to trifluorocytic acid so it will show around minus 131 or with respect to fluorobenzene, monofluorobenzene it comes around minus 96 but with respect to CF Cl3 it comes at minus 210 different reference are used that is the reason I have given the value relative to different standards. And then in case of 2 fluorogrups present on same carbon we see a peak at minus 140 in case of CF Cl3 and minus 69 with trifluorocytic acid and minus 26 here in fluororomatic compounds the range is minus 140 or minus 60 or minus 26 and then in case of fluorine atoms present next to carbonyl group the ranges are minus 125 or minus 46 or minus 11 and again we have a CF3 group next to a CH minus 75, 4 or 39 and then a CF3 group next to carbonyl group would appear at minus 81 or minus 2 or 33 and if you have a sulfonate group it comes at minus 50 or 129 or 164 these are some of the important ones besides that we have numerous compounds. So, for example, if somebody asks you to sketch the 19 F NMR spectrum of Cl3 simply by looking into valence shell electron peripersion theory you should be able to arrive at the right kind of geometry for ClF3 and then again let us recall your basic understanding of VSEPR theory. So, valence shell electron peripersion theory the abbreviation is self-explanatory and now if you just look into the valence electrons present on chlorine and fluorine atoms we are talking about 7 plus 3 here the 10 electrons are there 10 electrons means steric number equal to 5 are there and in case of 5 here 3 bonded pairs are there and 2 lone pairs are there. So, the preferred geometry for steric number 5 is trigonal bipyramidal and then we may have trigonal bipyramidal with 3 bonded pairs and 2 lone pairs all these possibilities are there and then we have to look into lone pair, lone pair, lone pair, bond pair and bond pair, bond pair repulsion and from this basic knowledge we will arrive at the right structure where we have 2 lone pairs in the plane and then 1 fluorine atom also in the plane equatorial and other 2 fluorine atoms being axial. So, this is the right structure and also we say that it has a T shape and then we have to sketch the 19 FNMR spectrum of this one here and assuming this molecule is static we can anticipate 2 type of fluorine signals one is due to axial one other one is due to equatorial one equatorial one will be coupled with 2 axial ones to give a triplet and then 2 axial will be coupled with equatorial one to give a doublet the spectrum should be a doublet and a triplet something like this. So, this is how the 19 FNMR spectrum of CLF3 looks like. So, now let us look into another interesting molecule again some of these molecules are really meant for explaining VACPR theory SF4 very interesting to look into the shapes of the molecules are species and also the geometry and you should remember the difference between the shape of a molecule and geometry of a molecule when we define the geometry of a molecule like this we have to consider both lone pairs and bonded pairs that means the entire steric number has to be considered while defining the geometry, but while defining the shape we can ignore the lone pairs that one should remember sketch the 19 FNMR spectrum of SF4 of course SF4 also same thing if you look into SF4 we have 6 electrons are here on sulfur S2P4 and we have 4 electrons coming. So, we have 10 electrons are there and steric number is 5 and here 4 bonded pairs are there and 1 lone pair is there. So, that means again trigonal bipyramidal geometry with trigonal bipyramidal geometry what basically happens is one something like this if S is here what we have is one clue in here one clue in here and lone pair is there. So, lone pair will be occupying and then this is a trigonal bipyramidal molecule, but the shape is CSA shape and also to minimize these repulsions what happens they are further bent little bit in this fashion. So, it is this fashion. So, that means here we have just by simply looking into the molecule we can say that there are 2 types of fluorine environments are there and 2 axial ones and 2 equatorial ones. So, when we look into the signals we anticipate 2 type of signals and each signal will be a triplet. So, that means we can see presence of 2 triplets for SF4 here. So, it should something like this this is a 2 triplets are there one for so axial one for equatorial and then this is FF coupling the spacing what we see here or here it is FF coupling. So, this is how we can sketch the 19 FMR spectrum of SF4 let us look into another molecule here. Now, we have taken SCF4 you know that the 77 selenium is NMR active with I equals half and it is only about 7.6 percent present rest 78 is NMR in active. So, when we look into 19 FMR we can see again very similar to SF4 it has a CSA type structure. So, with color I can distinguish them if you look into 19 FMR again we are seeing a triplet in each case of course, in each case triplet is there and again because of selenium being NMR active that is present is 7.6 percent or so what happens we can see satellite peaks that means basically if you see a triplet something like this. So, let me make it little bigger now we can see this coupling will also have this will give you 1 j FSC coupling and then what you see here is this spacing is j FF coupling 1 2 j coupling. So, same thing happens in both the cases both the cases we get a triplet of a triplet and also a triplet of doublets that would be here. So, this happens in both the cases, but when we look into 77 selenium NMR 7 different selenium NMR what basically happens is you get a triplet first this is coupled with axial ones and then each one will be split further into triplet into equatorial ones. So, if I plot this one it should be 1 is to 2 is to 1. So, this how we can sketch both 19 FMR spectrum as well as 77 NMR spectrum for SCF4. So, you can see here triplet of a triplet it is pretty easy. So, now let us look into another example here SCF4 SCF4 is a tetrahedral molecule and if you look into fluorine NMR this is fluorine NMR fluorine NMR would not show much difference because all fluorine are in a single environment and if it is tetrahedral molecule here, but you should remember the fact that silicon has 3 isotopes 28 silicon 29 silicon and 30 silicon and then 28 silicon is 92.23 percent 29 is 4.67 and this is 3.10 30's and now this is i equals 0 this i equals half and then again this i equals 0. So, that means basically we should think of a signal coming here without involving any coupling in case of 19 F this corresponds to 92 means 95.33 which is NMR in active i equals 0 and this one whatever we are seeing is 1 j 29 to silicon coupling that is about 178 hertz and if you look into intensity this corresponds to 2.32 or 2.32 or something like that. So, this is how this again satellite peaks, but if you look into 29 silicon NMR. So, you can see here then it looks all fluorine atoms being identical all 4 fluorine atoms would split silicon signal into a pentate a quintate we do not use the term pentate for 5 we use the term quintate. So, we have 1 is to 4 is to 6 is to 4 is to 1 we are getting something like this and here again if you look into the coupling the magnitude of this one should be same as this one. So, again double verification we can do. So, silicon NMR looks like this 29 silicon NMR for Si of 4 would be a quintate you see now how simple it is to look into various other NMR active nuclei other than 1813 C and also 31 P. Let us focus our attention to two more NMR active nuclei that is nitrogen 14 N and 15 N. The 14 N NMR experiment is much less sensitive than one edge, but has a much larger chemical shift range and signals are broadened due to quadrupolar interaction of 14 N having I equals 1. So, the larger the molecule and the more symmetric the nitrogen environment the broader the signal this one should remember if running 14 N or 15 N is inevitable 15 N there should be any problem, but if you have when you are running 14 N what we should remember is the larger the molecule and the more symmetric the nitrogen environment the broader the signal. The small and highly symmetric aqueous ammonium ion gives a very sharp line less than 1 hertz wide. So, that means if the molecules larger and more asymmetric around the nitrogen environment the broader will be the signal and small and highly symmetric aqueous ammonium ion gives a sharp line with less than 1 hertz width line width. So, on the other hand if you look into liquid ammonia being less symmetric here the width is 16 hertz on a 400 megahertz spectrometer at room temperature and urii is larger and asymmetric. So, the line width is approximately 1 kilo hertz you see very broad signal. So, molecules that are significantly larger than urii yield signals too broad to be observed with a higher resolution NMR spectrometer even with a 600 it is very difficult to observe. Since the nitrogen chemical shift range is wide may be readily used for distinguishing nitrogen species in very small molecules and if you want to run 15 N it has to be preferably enriched otherwise the percentage is so low it is very difficult you can see how much percentage is there. So, for example, natural abundance of 14 N is 99.63 and chemical shift range is 900 ppm from 0 to 900 ppm and frequency ratio is also given for ammonia and reference compound is nitromethane in CdCl3 and line width of reference is 19 hertz and T1 black station time is also given and then all this vital information is given and one should keep distance in mind especially when you want to run 14 N and of course 15 N is very very important from biological point of view because we have lot of nitrogen compounds. Now, as I mentioned if the molecule is more asymmetric the line width will be more you can see here the spectrum of uria it is very huge and then if you see here NH3 in liquid very small and less symmetric. So, 16 hertz is there and then if you see NH4 plus very small and highly symmetric you can see the line width is 0.8 it looks like 13 CNMR or phosphorous CNMR very simple. So, that means if you have a smaller molecules and symmetric looking into 14 N NMR would be rather easy. So, this plot essentially to show how the line width varies with the molecule symmetry and also the size. Now, this is 1H NOS spectrum of ammonium chloride this is a tetrahedral molecule you can see NH4 plus and then this 4 protons are identical and they are coupled with nitrogen I equals 1. So, you see a triplet of equal intensity 1 is 2 1 is 2 1 here and then the coupling is 1J 14 N to hydrogen coupling is 52.4 hertz. So, what is interesting is in this one we are also seeing very very tiny peaks here this is due to 15 N less than 0.4 percent or something this is about 1J 15 N H coupling is 73.5 hertz probably they have taken much longer time to identify these two peaks here this is the 15 N satellites here this was observed in case of ammonium chloride.  Now, if we look into 14 N NMR spectrum of ammonium chloride now 14 N this nitrogen is equally coupled to 4 equivalent hydrogen atoms as a result what happens it shows a quintet of this type. So, 14 NMR spectrum of ammonium chloride consists of quintet whereas, 1H NMR spectrum of ammonium chloride consists of a triplet of equal intensity 1 is 2 1 is 2 1 line and of course, this also you can say it is a EX4 spin system. Look into this molecule here 15 N enriched cyclosporine A in CdCl3 this spectrum was acquired in 1 hour very interesting we got a beautiful spectrum for this 15 N because it is a 15 N enriched molecule here still it took 1 hour for acquiring the entire data to identify all this 1 2 11 nitrogen atoms here to obtain similar sensibility without enriching would take about 10 years that means basically we have to take this sample put into NMR spectrometer be 600 megahertz or something run continuously for 10 years run 15 N NMR for 10 years continuously without stopping to see something like this. So, you can imagine the presence of 15 N so low and then how much sample is needed and how much time it takes but on the other hand when you enrich this one with 15 N it appears like any other simple molecules and measuring 1 H NMR beautiful spectrum you there you can see all 15 N are here 1 2 3 4 5 6 7 8 9 10 11 in biological studies 15 N plays a very very major role. So, now this one is for sodium azide sodium azide of course you have 2 different type of nitrogen atoms are there one is cationic and other 2 are anionic and of sodium you say so sodium plus so it is neutral and then the middle one is appearing here and these 2 anionic are appearing here and the ratio is 1 is to 2 here. So, this is a 15 N enriched sodium azide taken in T2. So, let me show you another very interesting example and this is about 13 C NMR the structure of tertiary butyl ethium is very similar to the top methyl ethium we know that methyl ethium has a Cruban structure where alternate kernels are occupied by methyl group and lithium with 4 lithium atoms having tetrahedral relationship but with each hydrogen atom replaced by a methyl group in case of methyl you get tertiary butyl ethium that is it. So, in methyl we have 3 hydrogen atoms are there if you replace each one with a methyl group we end up with tertiary butyl ethium that also has very similar structure to that of methyl ethium Cruban structure. The 75 megahertz 13 C NMR spectrum of a sample of T butyl ethium prepared from 6 lithium metal consists of 2 signals one for the methyl carbons and one for the quaternary carbon atom. That means if you look into 13 C NMR spectrum we get 2 signal sets one is for quaternary carbon directly interacting with lithium other one is methyl groups. So, now if you focus our attention to the quaternary carbon the spectrum look like what I have shown here at 185 Kelvin that is at low temperature it shows 7 lines here a typical septet on the other hand at 299 Kelvin that is at room temperature it shows 9 lines. So, now we have to analyze and explain. So, why at low temperature 13 C NMR spectrum for quaternary carbon shows a septet whereas at room temperature or 299 Kelvin it shows a multiplied having 9 lines. Now, let us look into the Cuban structure this is how you can show the relationship of 4 lithium atoms in methyl ethium or T butyl ethium this heteromeric and then in each phase we have a carbon is sitting like this it is a methyl or T butyl group and that means at 185 Kelvin what happens and this the molecule is very static. When it is very static this carbon present in one of the triangular phases would see only 3 lithium atoms it is seen only 3 lithium atoms because the whole molecule has no flexibility and it is very static and it is sitting something like this overlapping with orbitals of 2 S orbitals of 3 lithium atoms and it is a 4 centered to electron bond system. So, in this case what happens it identifies all equally that means the carbon is split by 3 equivalent lithium atoms if we use 2 Ni plus 1 rule at low temperature we have 3 and i equals 1 plus 1 it shows 7 lines. So, 7 lines so this is carbon but when you look into room temperature what happens it is no longer static. So, all lithium atoms are exchanging as a result what happens carbon sees 4 equivalent lithium atoms on 4 they will be exchanging as a result what happens you will see this carbon see observes are in contact with 4 equivalent lithium atoms now. So, 4 equivalent lithium atoms are there at 299 Kelvin. So, what would happen is if you use again 2 Ni plus 1 rule we have 4 identical 1 i equals 1 so this is 9 lines. So, we will see 9 lines here. So, that this how even the dynamic process can be understood readily using NMR spectroscopy. So, let me continue in my next lecture more details and more examples thank you. you


Okay, welcome to the bookmap platform details webinar. This is Bruce at bookmap risk disclaimer trading equities and futures involved Substantial risk of loss, and it's not suitable for all investors past performance. It's not necessarily indicative of future results For more information go to bookmap.com. There is a 14-day free trial of the platform and it comes with education So you'll get an educational course And then you'll also get these Access to these advanced order flow webinars that support the educational course Okay, it just does it in live markets in real time so that you're able to make that transition between Concept in education and live market. Okay, so that's what you get as well as the bookmap platform If you have any questions, you can always reach us at support at bookmap.com and And let's go to the website here and just briefly go through it here quickly So just click on explore. It just drops you down here. It's a scrolling page Drops you down into the intro video that you see here It's a minute long or so it goes over bookmap very basic You know general Advantages that book map displays for you and then we continue on down here just about book map a little bit about the platform, etc Some testimonials book map for equities. Okay, so you can it's not just for futures any longer This has been for a number of months now. This is an excellent data feed with the NASDAQ total view We're again, we're not data providers. You will need one. It's just that one was dev experts data fee or Yeah, dev experts will allow you connection to NASDAQ total view. Okay, here are all of the other connectivity Platforms or data providers I should say so and we can see here the There are I don't know a dozen or so so you will need one of these to connect to your bookmap to the live markets and Let's scroll down a bit further.  Well, you can also connect book map either to CQG directly if you want or you can Connect book map as an indicator or through the API of ninja trader. All right, so that's that's all I Wanted to make that point there a bit further down. This is where you can find the free trial. It's 14 days The pricing there's book map basic advanced and quant $49 per month for book map basic and 99 for the advanced The difference between the two are the add-ons. Okay, so there's really one version of book map There's just different features. All right, so a train being able to trade from the book map chart Nice advantage here because you have the liquidity in front of you So you can manage your trades and optimize your trading By a number of ticks with each each trade basically Being able to front run large liquidity or hide stops behind areas of large liquidity And then these proprietary indicators that we have put together Reading order book and balances reading larger players large lot tracker as well as iceberg detector, etc All right, quants, you'll have specific needs like connecting to your own data or your own proprietary indicators You can reach out to us here Scroll down a bit further some of our partners. So if you already have some of these brokers or Relationships we we do as well. All right, and then you can contact us here Okay Social media you can follow us on Twitter It's underscore or at book map underscore pro and then the YouTube page you can subscribe to and We have the intro videos here that if you're new here I would recommend watching some of those and then features and components Just to get get an understanding of what book map is the user interface what some of the features are And get familiar with it Okay, that's what this webinar is all about is to understand what book map is and then we do look at some of the order flow but The advanced order flow is much more like these order flow video snippets Okay, so now we're now we're looking here at content and market phenomena and how book map Uncovers that and how it gives you advantages by by allowing you that transparency into the marketplace All right, so this is what we go through in in detail in the Advanced order flow webinars Okay. All right. Well, let's take a look at the market and Step back and take a bigger picture look. All right, so we're looking at the Nasdaq again here We saw them move up the sideways move yesterday and then the move down today It looks like some buyer found some buyers down below this area and we're right back into the middle of the range Let's look at book map. All right, so here's that move down below All right and area a large liquidity here. Okay, so no no no surprise there, but This for those of you who are new here, this may look a little daunting Okay, let's close up the indicator panel that we have and I'm just going to go through the basics here of what book map is showing you Okay, because it looks like there's a lot of data. It looks very confusing and overwhelming It's actually not it's actually there's only three things that we're showing here and Looking at the indicator based strategies and candlesticks and other other bar types Those those are conceptually much more advanced than what we're actually showing here I mean, we're really showing a very basic view of the marketplace and let me explain so I'm going to turn on candlesticks and we're going to start that direction and I'm going to Turn off everything else here and show you that what we're just showing you basically in book map is Very transparent objective view of the market. All right. It's showing you historical best bid and offer We're showing you the volume that traded on the historical best bid and offer and we're showing you the Historical auction of the dome the limit order book. Okay It's just recorded and transposed onto the chart. So if we look at a candlestick chart All right Lot of unanswered questions here. Where did the volume trade? Okay? We will usually have a volume sub chart on our candlestick chart But that gives us no insight to exactly where the volume traded on the candlestick We have no clue to that. We have no clue to what type of volume. What was the aggressor more buyers or more sellers? We also don't understand. I have an understanding of How much traded exactly where? Okay? These are really important questions to answer and it's completely unanswered here in this candlestick You're just looking at an aggregate period of open high low close of five minutes All right, and that does not give you a lot of insight here. Okay, so We want to know those details. There's going to be microstructural areas within this candlestick here That we have no understanding of and that's going to be really insightful It's going to give us understanding of where Some of these bigger moves start to take place because we'll see it in the microstructure to begin with so let's turn on the microstructure and just look at historical best bid and offer and That's it. That's all we're looking at. All right, and this already gives us a tremendous amount of detail here and For example, let's just zoom in to this bottom area here Okay, and we can see a little structure here already. Okay. There's a double bottom right here Okay, and we can see that it was broken right here Okay, we see the swing above and we see that actually price accepted above this area In fact, it's not here's the pullback coming back to it and we see buyers step in okay, or we see that there's a lack of sellers Right. This is showing us strength already. Okay, and we don't see that in this candlestick whatsoever All right, you just see that you know came down here and we see maybe it went to about halfway of the of this down candle here Okay, this is already giving us a transparency and insight here. We understand the structure. We understand what's going on and That's going to allow us much more informed trading decisions and that's because the historical best bid and offer does not aggregate Okay, if you look at a footprint chart that is showing you the volume Where it traded on the candlestick, but it's still aggregating within a time period or bar rotation Okay, so let's turn on the volume and let's see exactly what occurred here Okay, so the volume dots here as you can see are on that they transact on the historical best bid and offer and we're getting insight to You know the type of trader by the color here We're looking at the aggressor classification of volume. So a red dot Is a market sell someone hit the market sell button? Okay a green dot is a market buy So let me zoom into this area to just show that distinction Okay, and we'll just zoom in here and really get into the nitty-gritty details here of what Book map is showing you and it's a simplified version here. Here's your historical best bid and offer Okay, let's go into this area here. It's just a little bit But it's a lot of stuff going on here. Actually, let's go to the simpler area, perhaps Yeah Hold on a minute. Yeah, okay. This looks pretty good. Okay So historical best offer is this red line Historical best bid is this green line. Okay, that's where the market is being made now these these dots here are transactions Okay, so this green dot for example, we can use the data tip tool hover over here And it says the date the time what was on the ask at this price level and it gives me the volume to Okay, so I know exactly what traded here You know two contracts traded here So this is an aggressive market buy someone hit the market buy button and they took liquidity off of the best offer Aggressive sell is right here. Okay, it's the opposite. So That's what we're showing here, but notice all these little structural areas. Okay, we can really get into the details here In fact, we can zoom into Levels here like in and to see what really occurred here. Okay And we can start to zoom in and see everything that took place And in fact, you know, you can see we're down at microsecond level Well, we can continue to zoom in and look at nanosecond level. We're looking at billions of seconds here Okay, that kind of transparency is is handled with our complex event processor and that is Ah Giving you a very very objective transparent view of the market all of this all of this data is recorded. Okay, all we have very very Accurate portrayal of what occurred here in the market very objective Okay, so Now we're not trading at these levels unless you're trading algorithmically Okay, but that doesn't matter you're gonna note here as I zoom out although we've recorded every single event as I zoom out We're gonna give you the overall shape of that with the aggregate an aggregate view just a visual graphical Aggregation okay, and as I zoom out and let me bring up the dot size of it So just to make this point As I zoom out these dots turn into Like a pie display Okay, so you can see that pie display in several of these areas here Okay, so I zoom out more and you'll see these pie displays It's just that there's so many transactions that took place that the size of the dot and if I hover over here It says 279 contracts. Okay, we know we know the overall shape of the transactions here There was a lot of transactions and the majority of it and a slight majority was selling Okay, there were buyers down here too though. All right, so we're getting a feel for large transactions the type of transaction you know buying or selling the overall Delta of it and Where exactly it took place that's giving us tremendous insight. Okay, so for this candlestick for for this wick down here Well, we know that there's sellers down here. Okay, and we know that they're gonna be trapped Okay, look at the microstructure that we were looking at earlier And in this little area here and this is all trapped volume at this point because look at them hit or lift the offer here Okay, they've they're charging up out of that area with aggressive buying and Anyone who sold down here is probably going to be covering at some of these little spots up above Here as they maybe move their stop down or just have their stop up here. Okay All right, let's bring that dot size down a bit now Point made so that looks pretty good. Okay, so we have an understanding of what's occurring at some of these levels. All right Here's our double bottom Okay, and So we can we can see really what on a bigger scale now. Okay zoomed out. We see another structural area Here's our microstructural area. We see the aggressive buyers buyers are now in charge here Okay, we need to see follow-through on that though All right, we get another retest down here and in this little retest It gives us some some insight in the microstructure. Okay, look at the selling down in these areas here Do we see big? Large red dots here any longer. No, we don't Okay, and in fact we get it out of the bigger double bottom here We get a micro double bottom here. It's exhausting out on cell on the cell side. Okay We start to kind of rotate back up and buyers take control all right and So Offering the volume within the microstructure Gives you a lot of insight here. Okay, but that's just half the picture We want to also understand the other side of order flow and that's the limit order book Okay, where are they auctioning for this product? Usually that occurs in the current market understanding the The limit order book. All right, so let me get to a few questions here that Want to answer questions that you guys have and interrupt myself interrupt me anytime here I want to go through your guys questions about the You know anything you may have about this but That's what this webinar is is is for all right See Brian your book map is locked up scrolling but nothing Hmm, but not showing any price changes Okay, you probably lost connectivity Brian Maybe try another symbol See if see if you just see like a you know You're basically what you're going to see is your best bid and offers is going to go straight across Right. I imagine that's probably what happened. You might maybe lost connection to your data provider Yeah, that probably that probably is the case So well I Wouldn't refresh it if if you see that scrolling line going across here It's it's not connected, you know check. I think you're going through Ninja trader And if you are see if see if another chart is doing that that's connected, okay transact All right. Do you have another platform that is connected to transact data? Okay, and it's working fine Okay, not not sure what to tell you there but Yeah, I mean, yeah, you can you can You can try to refresh I guess but no, I mean it if It sounds like a connectivity issue So I'm not really sure how to how to help you at this point.  That's the that's the issue. Okay Jesus you want to look at Big orders only for example orders more than a hundred contracts. Well, what do you mean by orders? Do you want to see? Transactions that occurred for a hundred contracts or greater, okay Okay, all right. Well, let's we'll take a look at that Let me let me just a quickly finish up here with the limit order book Okay, so here's our liquidity that we see here in the limit order book This is the current snapshot of the market You see where the traders are lined up down here at like sixty two ninety three eighty five contracts down at ninety There's a hundred and forty three contracts We're we understand the auction here where where traders are lining up to deal in this auction It gives a lot of insight here. Okay, we're at sixty three hundred the figure Okay, there's always going to be some interest at this area here But the problem is with the dome It gives a great snapshot of current market, but no historical market and that's a that's an issue Right because we want to understand how they be how they previously behaved in the in the market So look at the interest up here at sixty three hundred. Okay, and now they're starting to pull and add higher We can see it very clearly in this window here. Okay, you see the heat map changing Well, it's reflecting the equity that's added and pulled in the dome okay, so areas of high liquidity are bright white and Now we can see Historically how they're transposed onto the chart. So now we have a complete picture of the market Okay, we have and let's just take off the candles now. Okay, we have the Historical best bid and offer that shows microstructure We're seeing the volume that takes place on the historical best bid and offer and now We also have the historical auction. Okay, where they're where they were previously bid in an offering. It's all it's all been Recorded here. All right, so Jesus, let's get to your question here. You want to look at larger transactions. Absolutely. Yep real easy to do click on studies configuration click on volume dots here and then Minimum accountable trade size. This is going to look at trade events that took place for a specific size. Okay, your block orders All right, so a NASDAQ. Let's let's input 10 and let's see what we get Okay, well, you don't see a lot. We don't see we see very few. Okay, so, you know, that's That's the NASDAQ market if I input five. Okay, any transaction that now occurs It's going to be for five Contracts or greater. All right, and now we start to see some of these transactions. Okay And that's how you do it Okay, there are all sorts of ways of filtering for the volume. That is one way The I usually just keep things on default and I I like the smart dot clustering Okay, this is an algorithm that starts to put together the clustering of the dots. Okay, understand where they're going Okay. Yeah, you're welcome Jesus Brian, yeah, sorry Are you are you reconnected at all anything show back up? Okay? Okay, great. All right You had to disconnect and reconnect. That's that's unfortunate.  All right. Well, let's say let's get into some of the order flow now. All right, so You know, we were looking at The Nasdaq here. Okay, we were looking way back here at this double bottom and What do we look at? Let's just go through current market. What do we see right now? Okay? All right. Well, they're here at 6300 the figure really important number We know that we see the high liquidity just above it here. Okay, so Sellers are up here. We know that look at them layer in up here Okay, we know that the larger larger players are up in these areas Okay, we even have the large lot tracker here that shows them holding a specific amount of liquidity up here Hey, where are they on the bid? Well, they're starting to show some interest here around 97 But they're not this is see how we're able to make the distinction here in bookmark because they're pulling they're adding and pulling So it answers the question are these traders in this auction? Do they really mean to trade here and the answer is no because they're pulling right so they're in any just think of an auction environment if You know, there's someone in that auction and price comes down to a specific Specific level and he's like yeah. Yeah, I want to buy there. I want to buy there but then As soon as price comes really close to him. He goes no, you know what I changed my mind. I don't want to buy there Well, that's what's occurring here. Okay, so this guy does not have intent to trade So we're able to determine that here by looking at this historically Okay, and it looks like these guys they do have the intent at the moment But we will see once price gets up into these areas. Okay, so now on the bid They're you know dropping it down to these areas here at 95 and a half and 94 and a half around this area Okay, and note that they just pulled as well at 95 and a half. Okay, and now they're a little lower All right So anyway now we can start to put all of this together and this is where it gets really insightful is We we can put all of this together In your trading methodology. So let's say for example your volume profile guy Well, here we are going sideways Where are we finding responsive buyers? Well, we can see them in the limit order book. They're down here at these areas here Responsive sellers they're up here Okay, and we're just channeling between those two right now Okay, and we're getting a real good feel for Where the you know, we have a volume profile here as well for this range. I'm looking at just this range It's a chart range volume profile And and now I'm starting to understand if there's a if let's say I want to be a buyer Let's say I'm looking for I'm bullish. I'm looking for a break of this Range and for it to come back into the range and trade to the other side here Okay, let's say that's your your your strategy here. All right. Well, you want to see if You know this liquidity here how it's behaving if it pulls and if it gets filled or if You know the if we exhaust out We trade through this area and then exhaust out. Okay, and we found we find lack of sellers Look for the aggressive buyers to start coming in Okay, look for them to start pulling their liquidity and You know, we can see them up here now at 63 Okay, so looks like we want to test this area at 94. Okay. Well, just as I say that it drops a point point and a half and So yeah, here we are now. Let's see if we go down to 93 Okay, so we just traded through Okay, okay, and now did these did these sellers are these buyers transact? Okay, we can zoom in here and we can see them hitting the bid into these limit orders So we know that These this was real liquidity. Okay. A lot of it was it was real liquidity. They stayed in the book and they traded Making that distinction right So now we know that there's there's interested, you know buyers at some of these levels All right Okay, let's look at this what just transacted down here Yeah, they're they're buying as well. All right So we know that they're starting to absorb some of the price I'm looking for the aggressive buyer to step in right now Okay, I'm looking forward to come up at least to this swing here the prior around 97 And then let's see if we can get up above the the point of control or this most traded price level at 98 and Let's see if we can hit the the other side of the range now up here around 6300 the figure or 6301 All right. Anyway, I'll leave you with that. I've got a I got a jump for those of you registered We'll see you in the next webinar. Okay. Bye


Hi and welcome to the session. Today we will learn how to solve an equation. Let us start with taking an equality and an equation. So 9 minus 4 is equal to 4 plus 1 is an equality. As 9 minus 4 is equal to 5 and 4 plus 1 is also equal to 5. So that means these both are equal. Now x plus 5 is equal to 9 is an equation. Now to solve an equation or for an equality we should keep certain points in our mind. First is if we add the same number to both sides of an equality or equation. The same number of equality, equality and equation by the same non-zero number it still holds. Thus if we fail to do the same mathematical operation on both the sides of an equality or an equation then the equality or equation does not hold. Now let us see how to solve an equation. For this we will take an example. We need to solve the equation 2x plus 6 is equal to 12 where x is the variable and we need to find the value of x. So the given equation is 2x plus 6 is equal to 12. Now we will go stepwise to separate the variable x on the left hand side of the equation. Now left hand side is 2x plus 6. So first of all we will subtract 6 from both the sides to get 2x. So let us subtract 6 we get 2x plus 6 minus 6 is equal to 12 minus 6. Now plus 6 minus 6 will get cancelled so this gives us 2x is equal to 12 minus 6 is equal to 6. So we have 2x is equal to 6 that is left hand side by. So we will divide both the sides by 2 and we get 2x upon 2 is equal to 6 upon 2 which gives us x is equal to thus the value of x is 3 we substitute the value of x in the given equation. We have 2x plus 6 is equal to 12. Now we will take LHS let us substitute the value of x we have 2x plus 6. So this will be equal to 3 plus 6 that is we get LHS is equal to RHS thus our answer x equal to 3 is correct using another method that is the number instead of adding or subtracting it from both the sides of the equation. The equation that is 2x plus 6 is equal to 12 we have 2x plus 6. So first of all we will transpose is equal to 12 here we will have minus 6 as the sign of plus 6 will change. So this gives us 2x is equal to 6 now we will divide both the sides by 2 upon 2 is equal to 6 upon 2. And this gives us x is equal to 2 we can use any of these methods that is adding or subtracting on both the sides. Now let us solve one more equation which involves bracket we have 3 into n minus 5 is equal to minus 21. Here n is the variable the value of n. So here the given equation is 3 into n minus 5 is equal to minus 21. Bracket from LHS we will divide both the sides by 3. So we get 3 into n minus 5 upon 3 is equal to minus 21 upon 3 this gives us n minus 5 is equal to minus 7. Here let us use the method of transposing a number. So let us transpose minus 5 from LHS to RHS and thus the sign of minus 5 will change. So we get n is equal to minus 7 so this gives us n need to check our hands. Let us take the equation that is 3 into n minus 5 is equal to minus 21. Let us substitute the value of n in LHS. LHS is equal to 3 into n minus 5. So this will be equal to 3 into minus 2 minus 5 which is equal to 3 into minus 7 that is minus 21. And RHS is also equal to minus 21 thus LHS is equal to RHS so our answer n is equal to minus 2 is correct. So we have learnt how to solve an equation. With this we have finished this session. Hope you must have understood all the concepts.


What's up everyone? It's your girl, Kenty here from Leighton Sports Cards and I'm ripping brand new release today of 21 22 Panini prison basketball hobby six box break number three. Pick your team You've got Alex T heat M. O. D. Spurs Andrew a calves Eden R. Nicks Eric W magic Gordon P with the Celtics and Sixers whoation W Clippers Harper B with the Bucks and T Wolves and Mavs Hong J Rockets HPC with the Thunder Gillesi D with the Lakers Jeffrey H. Pistons Rafters Justin R Blazers Kuhn Paul P Pacers Kuhn K. Pallikins Koi K. O. Suke and Wizards Lungpo Ball K with the Warriors Michael C. Kings Paul E. Hornets Ryan S. Bowles Ryan M. Nets Saralak J with the Hawks Steve W. Jazz Tianqui H. Grizzlies Ting Z. Y. Nuggets and Victor F. Suns All right, here we go Get everyone next break still has five teams left Celtics Lakers Sixers Jazz and Wizards. Let me get you the link here There you go. All right. Here we go number three our first rookie coming out on Evan Mowley Okay, go cats Shreya Ma'am Boston, Jr. Denia via silver. We've got Serge and Baca to 49 Clippers Tatum fireworks Corey Kisbert rookie Quentin Grimes rookie Obi-Top in on the Pulsar for the Knicks We have Bruce Brown red Nets to 2.99 Bones, Highland rookie from Lone silver Devin Booker Hyper Aaron Gordon charismatic Jalen Brown Austin Rivas rookie Duarte rookie Josh Christopher rookie we've got Jama rant on the hyper fearless You go Grizzlies nice one there Pistons Sensational signatures rookie auto Luca Garza second garza that we fold today You go Pistons Luca Garza rookie Bruce Brown silver KD fireworks silver They've got Lakers rookie auto of Joel I I Lakers Daniel Feel like there's there's an I I and NFL Keita rookie Wade, man, we pull this guy all day. How about it's easy red wave Tyrese maxi on the hyper a D prismatic Santiago Dama rookie Johnson rookie we've got a feel mild on on the red wave deer and Fox on the cracked ice red to 75 on the anniversary card Love the look of those there you go Kings prismatic Steph Curry Dust hitting your nose to Jaden Springer rookie bunch of dust on these Malachi Flynn silver Franz Wagner on the hyper Magic nice Pretty well-centered on tad tad high There you go magic nice one there. We'll mag that some of Simon Ovech rookie Bassie rookie Scotty Lewis rookie Aaron Wiggins rookie blue ice to 125 of Avery Bradley Rockets He on Johnson rookie. We've got silver rookie of David Johnson for the rafters silver fearless Steph Curry Come on a little curry streak here, huh? Jaden Springer rookie Charles Bassie rookie. We've got red wave Carmelo Anthony Lakers Yeah, Josh Richardson hyper mindset Tatum Scotty Lewis rookie. I've got silver Kauai Leonard red Justin Holiday for the Pacers 75 years of Corey Kispert wizards Fox to let's see where we are on the next break here Lakers Celtic Sixers and Jazz everyone still five to go breaks next Let's get those teams filled up. Most of them are on sale Silver rookie Quentin Grimes Knicks. We've got fireworks silver Lamello ball. Nice one there Hornets Kessler Edwards rookie Simon Ovech rookie. We've got red wave Bradley Beal Hyper rookie of Kessler Edwards to go nuts Silver Chris Paul all-star Josh Getty rookie and Jalen Suggs rookie magic Silver Tim Duncan blue Larry Nance jr. Blazers 75 years Walt Frazier Kai Jones rookie and JT Thor rookie got red wave Moses Brown red Bruce Brown Karen Silver silver Jaren Jackson jr. Hyper. We've got prismatic Julius Randall Miles McBride rookie kid and a Zorowski rookie Austin Rivas rookie got purple Dylan Brooks Grizzlies Zyra Williams rookie Jason Williams Silver Vince Carter Raptors silver prismatic Russell Westbrook wizards We've got jazz sensational signatures of Elias Sova You go jazz Elias Sova Keter rookie the Wade the D Wade auto out of the first Caps just pretty sick But again everyone five to go in the next break five teams still left. Let's get it filled up Got red wave Tyler hero PJ Washington jr. Hyper Anthony Edwards rookie fearless Evan Mobley rookie for the cast there you go Shrave man and Boston jr. Nice rookie down at that time Xavier Tillman silver got Marcus smarts on the purple ice for the Celtics out of 149 Cory Kisbert rookie Quentin Grimes rookie next Pulsar Collins sextant for the Cavs red Justin Holliday Pacers Mindset Kawai bones Highland rookie carl Malone Silver Gafford hyper of Tyler Horton Tucker Horton here's a who look at Don shit's prismatic Christopher say rookie Josh Christopher rookie Last pack here All right, we got blue ice to 125 jail in the well T-wolves All right. What do we got y'all? Rookie Otto Cade Cunninghans First one of the day Detroit Jeffrey H is killing it hit the Steph Flashback mojo out of the last six box and now hits a cave your honor roll Jeffrey congrats Very nice Heck, yeah We'll mad that one for you Let's go Herbert Jones rookie and Toscano Anderson rookie last pack of box to Jeffrey is killing it Box three here who mango rookie warriors Butler Wendell Carter, Jr. Fearless silver Devin Booker, but again everyone big Magic the Gathering release with Caleb tonight over on late gaming He said it is available up on the site and the strongest Set for magic in 2022. Let's get that filled up for him. He's got some group breaks and I'll be doing Personals later. Josh Hart on the red wave Joe hyper we've got prismatic LeBron James Donovitch. I was just backwards Caleb you start at six Rookie card to have in three to five years. Oh That's a tough one livers rookie Banton rookie John Stockton silver. We've got Aaron Wiggins on the red ice anniversary the 75 warriors I Use the NBA James book nights So personal tonight and group breaks all weekend of the new Magic the Gathering Robinson Earl rookie Jalen Green rookie Red wave a nobie Raptors Laurie Markinon on the purple cracked ice to 149 calves Got silver Randall hyper rookie Scotty Barnes Raptors nice hit Serrano Jeffrey H. Wow Jeffrey continuing to kill it nice Barnes hyper will mad that one for you Shaq fearless. I Don't know. I think Franz Wagner is a sleeper like everyone knows he's good But like he's scoring those type of points With the magic, you know, like he's not on a very good team Like if he was on a superstar team like they were in sharp rookie It's on white side silver Kevin love hyper I don't think people give him the credit I mean, even though like people are saying oh if you were to do like a new NBA draft This class where you know, who would it be? You know, how would that draft look and they I think they took them like four or five overall Primo rookie book night rookie We've got blue Bruce Brown for the Nets Bulls have a little Elton brand auto Chicago Got going well in these breaks. Usually one of the last teams taken two Yeah, Scotty's up there to kill for sure For sure, I don't know. Maybe I'm a little biased because I'm a magic fan He would score. He would score Simonovich bulls We've got Billy rookie silver Simonovich bulls Silver prismatic deer and Fox That's redemption Yeah, Randy, right? That's what people are saying. Good luck everyone. We've got look at dodge it signature prism Silver Dallas Harper B. There you go Harper on the Luca silver Halfcase Said that about the last last half case. We're heating up here y'all. We are heating up Max on that one.  We've got silver Jalen Suggs. There you go magic Very nice. I'm really hoping that they'll they'll turn things around this next year Capella red or at least maybe go closer to 5050 This is number three break number three Harris that's just hooked upside down Isaiah Jackson. We've got pulsar of Cam Johnson for the Suns Got Tyler here out of 49 heats prismatic wise men Just a little bit behind on the schedule. Maybe about 10 minutes or so Nothing crazy. We've got David Johnson rookie Let me look after this box of who we saw up to the next one Franz Wagner magic blue Carol Lewis Jr. Devon Booker prismatic Base let's see. Let's see. Last time I checked there were still five teams remaining Celtic sixers jazz and wizards someone just scooped up the Lakers so four to go now. Let's get those filled up Get those filled up Number four Celtics on sale sixes on sale jazz on sale and so are the wizards Three out of the four teams are under 150 bucks and Celtics are 279 on sale. Get that filled up Again that breaks right after this one. So let's make sure we're ready to go After we finish here Again, everyone force will be live later tonight starting at five. We've got silver Brandon Boston jr. Rookie Clippers fearless silver KD They've got spurs rookie Otto Joe wise camp All of his random teams are sold out for the nights only pick your teams left for the uh For the daytime and then I believe he's got about two or three pick your teams later tonight as well No optic. No, I haven't is that a thing is a livers rookie banton rookie That's kind of crazy red wave of toscano anderson warriors hyper tyler hero I've seen the uh mellow and ant-man fireworks rookie cards Ron that shouldn't be in here Robinson earl, but that's it. I haven't seen any optic cards. Jalen green rookie for the Rockets There you go, silver Duncan robinson jaden mcdaniel on the purple lights to 149 tea wolves Shaq What's that happen to somebody dale? Dorts David johnson rookie. We've got pulsar ray allen for the bucks Red tony kukoch on the color match for the bowls nice red there to 299 prismatic booker Even shenzo I have dosumu for the bowls silver lamello ball Nice silver second year of mellow hornets by donovitch hyper Jason Preston rookie Shangoon rookie We've got hyper prismatic of kawaii Leonard only second one of those we've seen today clippers We've got net silver rookie sensational auto of cameron thomas You go nets Oh, that's crazy I don't know how that happens, but yeah, we haven't seen any optic cards come out of prism, thankfully joshua primo rookie James book night silver dirt silver fireworks of yannis prismatic joellen bead That's crazy luzmongeruva rookie moses moody red yannis onto the kumpo hyper carmelo anthony Looking forward to chronicles football next week One of my favorite products Uh gray brown the third rookie Silver divan shenzo red diallo We've got 75 years of uh mutombo I still find that hilarious That mutombo reminds me of the uh the jimmy kimmel uh the mean tweets Someone tweeted adam and was like uh, we've got the syrief cooper rookie If the cookie man the voice of the cookie monster uh cameron thomas rookie ever died I would have faith in um mutombo to uh be the new cookie monster Simmons redway. We've got ben wallis on the 75th anniversary crack dice is 75 So funny people can be so ruthless. That's for sure scottie barns rookie. There you go raptors See some color of him silver of my guy colanthony uh dorian finney smith hyper uh kominga rookie chariot butler rookie blue ice to 125 uh batham clippers lebron prismatic Base is that Let's see where we are in this next break Still four to go everybody. Let's get it filled up break soon Again Celtic sixers jazz and wizards My bad.  So ready to go after this one db on michael rookie Elijah one then a schroder on the pulsar celtics orange wave to 60 pete maravich for the jazz mindset of luka is a jackson Butler silver carousel avert We've got a villy on the rookie hyper for the bucks Get it filled up almost there y'all four to go They were in sharp rookie dave robinson thomas Bryant blue for the wizards We have nicks sensational signatures rookie of miles mcbride Go nicks Ibaka Joe wisecamp rookie god villy rookie silver rookie corrie kisbert for the wizards silver prismatic paul george They've got clippers rookie auto of jason preston jason preston posh heart Wagner rookie We've got red wave of herbert jones for the pelicans hyper terry rozier fearless luka donchich kid conningham rookie base pistons A couple kada bases and auto of this break is a tawd for thon silver Uh robert williams the third celtics purple prismatic lilor miles mcbride rookie silver gallinari hyper kpj prismatic zaklavine siroski rookie xire williams rookie. We've got red alexander walker pervert jones rookie scionna anderson rookie silver luka donchich she's a mouse she's a harper that'll look nice next to the silver auto hand-wink hold earlier mindset lilor kessler edwards rookie viewing josh kitty rookie red wave of bogdanovich I'm gonna miss these cards for the jazz hyper derris basley got jaylen suggs rookie kai jones rookie silver alexander walker vince carter on the purple ice to 149 rafters 75 years of drew holiday jt thore rookie got damar derosen on the red wave blue carl malone jazz And last box here Gavin everyone still four to go We gotta get this next break filled up so we can rip right afterwards again celtics sixers jazz and wizards Breaks next let's get it filled up celtics sixers jazz wizards We go gotta get that one filled up before we can rip it Breaks right after this all of them are on sale three out of the 14 under 150 bucks Jason Preston rookie shingoon rookie we've got nice jonathan kominga on the pulsar There you go warriors very nice hit We'll mag that we've got purple cracked ice that is young to 121 49 prismatic james harden Keon johnson rookie Silver kuzma got hyper jaylen brown train murphy the third rookie Santi al dama rookie of clint capella. Let's see. There's like a Guess it's like glue or or something down this car. We'll send a box at the hawks First card we've seen like that all day Sorry hawks on that one Got auto up next warriors moses moody sensational signatures rookie boom Here you go dubs Lungbow paul Nice moody Mag that one for you as well Nice I went on a little rookie street there at the warriors robinson earl rookie Jaylen green rookie for the Rockets silver quentin grimes rookie nicks We've got silver prismatic chris paul We've got rafters silver rookie auto david johnson David johnson on this silver rookie auto Hayes Get everyone let's see Still four to go. We got to get this next one filled up seltzix sixers jazz wizards All under 250 three out of the four under 150. Let's get it filled up red wave josh christopher rockets hyper rookie wise camp Let's get it filled up breaks right after this one. So once that fills we'll get it ripped here for you David johnson rookie rego bear. I don't sue me rookie Silver lawni walker the fourth purple drew holiday for the bucks Get it filled up four to go Celtics sixers jazz wizards jared butler rookie Silver mantras herald hyper emanual quickly fearless zion Isaiah livers rookie banton rookie Got red cure lewis jr pelicans prismatic donovan mitchell great brawn the third Silver rego bear silver widescreen lebron and go lakers balacunas charif cooper rookie Cameron thomas rookie red wave kpj rockets Jalen noelle on the hyfer prismatic ben simmonds scotty barns rookie for the rafters silver drumman orange wave to 60 jason tatum you know celtics 75 years david robinson And last pack again everyone four to go on this next break breaks right after this one kumango rookie Let's get it filled up so we can keep on rocking and rolling draymont green red wave Jalen smith blue for the suns to 199 And that'll do it Looks like celtics were just taken so sixers jazz and wizards left everyone sixers jazz and wizards Do a recap here for you? All right, again that breaks as soon as it's filled since we're done with this one. Let's get it filled up All right. We had this silver david johnson rafters Jason Preston clipper his mouth Mcbride nicks camera tom and silver auto nets Joe weisskamp spurs elton brand foals ilia sova jazz joe lii lakers luka garza pistons Our max cards. We had the moses moody for the warriors a jonathan kominga on the um pulsar base rookie wizards um a luka donchitch mafs redemption there Scotty barns on the hyper raptors Tate cunningham rookie auto for the pistons and a franz Wagner hyper magic All right, that'll do it for number three. Uh, once number four is filled up. We'll get it ripped for you