첫 번째는 백그라운드입니다. 이 부분은 멀티코러와 트렌드에 대해 이야기합니다. 요즘 멀티코러 프로세서는 인테리어 파트와 인월드 디베이스와 인테리어 시스템이 되어있습니다. 또한, 글자들의 글자들과 많은 글자들이 계속 increasing. 이 글자는 트렌드의 멀티코러 프로세서를 보여주었습니다. 그리고 글자들의 서버 데스크타피스 CPU, 그리고 애플이예요. 이 글자들은 인월드 디베이스 마켓 포켓을 사용했습니다. 이 글자의 멀티코러 프로세서는 인월드 디베이스 마켓의 멀티코러 프로세서를 보여주었습니다. 이 글자들은 AP 마켓의 멀티코러 프로세서를 보여주었습니다. 이 글자의 멀티코러 프로세서는 인월드 디베이스 마켓의 멀티코러 프로세서를 보여주었습니다. 이 글자의 멀티코러 프로세서를 보여주었습니다. 이 글자의 멀티코러 프로세서는 강력한 적용을 제공할 수 있습니다. 이 글자의 멀티코러 프로세서는 개인 컴퓨터를 사용할 수 있습니다. 이 글자의 멀티코러 프로세서는 태그라-3의 오브 앰비디아에서 만든 글자의 멀티코러 프로세서입니다. 이 아키텍션의 새로운 모습은 4코어에는 A9의 코트엑스, 그리고 1코어의 코트엑스, 그는 A9의 코트엑스 같은 아키텍스에 designs, 그런데 이 코트엑스는 500MHz가 제작되어 있습니다. 탱글아3의 코트엑스에 이 아키텍처에 의한 어떤 점이 있을까요? 이 아키텍처에 의한 어떤 점이 있을까요? 태그라스 3는 더 많은 공간을 제공할 수 있습니다. 멀티코어 프로세서는 공간을 제공할 수 있습니다. 하지만 대부분의 테스코 스마트 디바이스는 오디오프레이, 심부 웹설팅, 액티브 스탠바이 모드에 필요합니다. 확실한 속도가 필요하지 않습니다. 그러나 저의 공간이 더 많은 공간을 제공할 수 있습니다. 태그라스 3는 더 많은 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 다른 공간이 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 암티브 스탠바이 모드에 필요합니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 이 아키텍처는 전체 공간을 제공할 수 있습니다. 런웨이블 테스트 매니지멘트는 론드로빈 스케줄인 파티시에서 스케줄 wasn't complex, but was simple and fast at that time. 리누스커널 버전 2.2에 아이디어로 스케줄인 클래쓰, permitting 스케줄인 파티시, for real-time tasks, non-preemptive tasks, and non-real-time tasks. The 2.2 스케줄이 시멘트링 멀티프로세싱 프로세서에 심플파티시와 SNP를 설정합니다. 그리고 리누스커널 버전 2.2, 스케줄 락과 싱글 런큐를 사용하고 이 버전에 스케줄이 싱글 론드로빈 스케줄을 설정합니다. 스케줄이 시스템에 적용되지 않습니다. 이 스케줄은 심플파티시와 싱글 론드로빈 스케줄을 설정합니다. 론드로빈 스케줄을 설정합니다. 2.6 스케줄이 싱글 론드로빈 스케줄을 설정합니다. 2.6 스케줄을 설정합니다. 스케줄이 1컴플렉시리에 적용되지 않습니다. 우원 스케줄을 설정합니다. 우원 스케줄을 설정합니다. 2.4 스케줄을 설정합니다. 이 버전에 스케줄을 설정하지 않았습니다. 스케줄이 싱글 론드로빈 스케줄을 설정합니다. 우원 스케줄을 설정합니다. 런큐를 설정합니다. 스케줄이 싱글 론드로빈 스케줄을 설정합니다. 스케줄은 스케줄이 싱글 론드로빈 스케줄을 설정합니다. 이 버전은 우원 스케줄을 설정합니다. 우원 스케줄을 설정하고 우원 스케줄이 싱글 론드로빈 스케줄을 설정합니다. 우원 스케줄은 싱글 론드로빈 스케줄을 설정합니다. 스케줄이 싱글 론드로빈 스케줄을 설정합니다. 마지막으로 2.6.23 버전을 설정합니다. 스케줄이 싱글 론드로빈 스케줄이 싱글 론드로빈 스케줄을 설정합니다.ивалabbutin policy, lealabu, lealabu, lealabu, lealabu, lealabu, 9 didn't. lealabu, lealabu, CFS는 3개의 설치와 인서션을 사용할 수 있습니다. CFS는 2.6.23%를 사용하고 있습니다. CFS는 최고의 스케줄인 파티시입니다. 이 means implicitity, the fairness of CFS is most superior than others in a single core. So then how is the CFS in multi-core environment? Of course, since CFS has many good features for a CNP processor, CFPRO is still considered smart but despite such a advantage the Linux can for short of expectation when it comes to fair share multi-core scheduling. So in the next part, I'll talk about the details of CFPRO's multi-core environment. To analyze CFPRO's in the multi-core environment, 먼저는 롯바랜싱 기술을 생각해 봅시다. 이 슬라이는 CFS의 롯바랜싱을 보여줍니다. CFS에서 모든 코를 배우는 롯블랙3의 Q입니다. 이 컨디션에서 롯바랜싱이 시작됩니다. 롯바랜싱의 시작뿐만 아니라, 첫 번째 컨디션은 택sqpq의 시작때문입니다. 두 번째 컨디션은 롯블랙3의 Q입니다. 두 번째 컨디션은 롯블랙3의 Q입니다. 두 번째 컨디션은 롯바랜싱 기술을 생각해 봅시다. 첫 번째 컨디션은 bgstq의 시작때문입니다. 택sqpq의 bgstrongq이 롯바랜싱 기술을 맞춘 롯바랜싱 기술입니다. CFS는 공항되어 질언이 vacuum으로 보관하지 않습니다. 하지만 롯바랜싱 기술을 생각할 수 debris가 필요합니다. 여부 개선을 estimating의 기술을 맞춘 CFS를 유지할 수 있었으며, 육수도와 조금씩 물을 유지할 수 있습니다. Therefore, this scenario may relate to some overhead. If the chas period is shorter, the overhead can be larger. But actually in CFS, overall performance is more important than global fairness. That is the CFS does not migrate the task in order to reduce migration cost as possible according to these conditions. 예를 들어, QK의 론은 QK의 론이 1번의 론을 제거하고, SK는 QK의 론을 제거하고, 2번의 론은 QK의 론을 제거하고, 3번의 론은 QK의 론은 QK의 론을 제거하고, QK의 론을 제거하고, QK의 론을 제거하고, 이것은 CFS로드 밸런스의 예방입니다. 이 예방은 T1-T5에서 5개의 테스트가 있습니다. T1의 weight is 1024kg, T2-T5의 weight is 335kg. T1의 weight is three times bigger than others. T1의 weight is running on Core 1, and the others are running on Core 2. According to previous formulas, scheduler will decide whether to perform road balance or not. But in this example, scheduler decides not to perform road balance. As a result, although T1's weight is three times bigger than others' weight, runtime of T1 is four times longer than other tasks. Therefore, in this case, CFS is forced to achieve fairness, and even if exactly the same amount of road is given to each core, it will happen again. Thus, our team has researched to complement this situation, and we think carefully that it's time to rethink the scheduler for embedded Linux system. But we face the problem that is the global fairness, the most important factor in multi-core. It is hard to answer easily, so we performed some experiment to find the answer. Literally, perfect road balancing ensures that all cores have same amount of work, and the execution time of each cores is almost same. In our world, superior road balancing scheduler has outstanding global fairness. Therefore, the first requirement of efficient multi-core embedded Linux scheduler is of course the global fairness. But as you know, task migration may cost some overhead cost, and it may also cost to increase the cache miss rate. So the second consideration of multi-core scheduler is maximize cache effectiveness. However, the expectation of cache effectiveness is very difficult because they are influenced by their workload, CPU architecture, and so on. But in case of embedded device like smart phone, we can find out some common feature like this. So we think it is possible to design the new scheduler to support efficiently for multi-core embedded device. But as you know, it takes sufficient time to develop, and it's so much hard work. So among those requirements, we decided to research to improve the global fairness above all. From this slide, I'll talk about our research result last year. In last year, our team tried to analyze current scheduler and to improve the global fairness. So we adapted previous research called distributed weighted round robin, shortly called DWLR algorithm to CFS scheduler. And compare CFS and DWLR as conducting various experiments. So in this part, first I explained the DWLR algorithm, and next part I'll show the comparison experiment result. DWLR is the most intuitive algorithm, and its main goal is simple and obviously to achieve global fairness in multi-core. DWLR algorithm is proposed by Lee as a scalable multi-core processor fair share scheduling algorithm. It schedules tasks via weighted round robin on each core. Of course, it performs road balancing to ensure that all tasks go through the same number of rounds. This is the main architecture of DWLR. As you can see, each core has two run queues. One is active, and the other is expired run queue, like O1 scheduler. And each run queue is also read with three. There are three important definitions in the DWLR. First, round slice is defined to be small w and large b, small w multiply large b. Where w is the stress weight, and b is the system-wide constant, which is round slice unit. Second concept is round. Round is the shortest time period during which every thread in the system completes at least one of its round slices. The round slice of thread determines the total CPU time that the thread is allowed to receive in each round. When a thread uses each round slice, we said that this thread has finished the round. The DWLR removed it from the CPU run queue to prevent it from running again, and the thread is moved to round expired run queue. This operation can be maintained, can be maintained local values. Sorry. When all threads on this CPU have finished the current DWLR round, DWLR such as other CPUs for threads that have not and moved them over. This action is round balancing. If none is found on the CPU, CPU increments its round number and allows all local threads to advance to the next round with the full round slice. This operation can be maintained global values. In the next slide, I'll show you an example of DWLR operation. This shows the DWLR operation. First, assume two CPUs and three threads A, B, and C, each with weight one and round slice of one time unit. And one important assumption is that task migration question is ignored. At time 0, A and B in round active of CPU 0 and C in round active of CPU 1. At time 1, both A and B have run half a time unit and C has run one time unit because of DWLR excuse the round slice. Does C move to round expired on CPU 1 since its round active becomes empty? CPU 1 performs round balancing and moves B to round active, not A because it is currently running. At time 1.5, both A and B have run one time unit so they move to round expired of their CPUs. Both CPUs then perform round balancing but find no thread to move. First they switch round active and round expired and advance to round one. Therefore with DWLR we can finish the job at time 1.5. But how about CFS road balancing? In case of CFS algorithm, road balancing will not be performed B, Cs which is same and thus it is not conditioned to perform road balance. So A, B are performed only in CPU 0 and C is performed in CPU 1 thus total time is needed at time 2. Therefore DWLR is able to solve the previous CFS problem. Basically it moves B over to CPU Wait so it moves B over to CPU B After round 0 it goes off it looks like it balanced B to C we'll confuse why the first line from A and B are on same CPU correct and C is on CPU 1 and then you switch then you migrate B over to CPU 1 If this continued B actually bounced back to CPU 0 after this whole slide if A is running now and you have C and B then it's possible you could be ping-ponging B between CPU 0 and CPU 1 I'm sorry Maybe I don't understand what you're talking about Second figure Second figure Where is the figure? Let's say where the arrow is starting A and B are sharing C finishes its round or whatever but C is still in the running state correct? Yeah But you're saying it just finished a round and you're doing a load balancing and you say oh I can move B on the CPU Yeah So A gets more CPU and A finishes C and B Now you finish off A on its own CPU and on the CPU 1 you have C and B Now if we were to continue the round A would finish its round and then you have C and B using each of the CPU What if that also if you continued here load balance B again back to CPU 0 So basically at the end of the slide you can actually replace A, B with C and B and C with kind of A and what if that causes a ping-pong Maybe I don't understand what's going on But we assumed that A, B and C just wasted it 1x1 Right I understand you're trying to give everyone more fairness or whatever But the problem is it's what caused a lot more ping-pong tasks and you're going to hit cash problems so you may actually slow things down What you're trying to ask is the cause of context of switching from one CPU to the other Yes Are you taking the cost of the migration itself? Yeah obviously you're right but we did not did not consider because this research is the first step of our project so we just improved the global fairness So the cash mist rate was some other things Because there's a reason why CFS does not try to be truly balanced There's actually a reason for it That's why I'm saying you can make it truly balanced but you might actually perfectly expect So DWRI is the best solution for a multi-core system We think the DWRI obviously have pros but because of their architecture like 01 assumptions are too critical to be neglected You're right DWRI have also obvious weak points like the non-consideration migration higher cash mist rate and so on So we have to obtain its pros, global fairness and we improve its limitation and our team have tried to do so continuously So far I explained DWRI algorithm key ideas and its limitation Now I'll show you the comparison result between CFS and DWRI The test environment is like this Our target post consists of quad-core CPU Quotex A9 and we used Rinaural Ubuntu 12.04 and the kernel version is 3.1.41 The comparison schedule are of course CFS and CFS with DWRI This is the practical experiment result of comparison of CFS and DWRI We ran 5 tasks on 4 cores and the tasks are just performed in infinite loop and we use top util to monitor CPU allocation This figure shows the snapshots of top for each schedule As you can see CPU utilization of tasks in DWRI achieved nearly perfect fairness This is a natural result on reflection of DWRI operation So we need to perform more experiment This benchmark is performed to show the performance comparison when the CPU cores are busy In this case we use the video play and the running time of video is 150 seconds The video decoding time of each case are these As you can see in this case DWRI has also best performance Another test is to compare the performance with database benchmark Recently most smart device has their own database engine in their platform and users of database is growing So we tested the database benchmark using sysbenchy the test mode or tp which has a lot of file IOR and IOR pattern is render models and size data I think this pattern is common case in smart devices As you can see in this case DWRI's transactions per second is second larger among them Interesting point of this CFS with scared granularities at 1.25 millisecond which is equal to DWRI's round slice unit show the best performance Consequently in case of database which have a lot of smaller size render model show lower time slice has better performance But overhead with DWRI slice slide I think Final benchmark is javascript benchmark Maybe web application are in the spotlight today because of its independability for any platform the powerful HTML5 based on javascript function So it's important to benchmark the HTML5 especially javascript engine and we tested the javascript benchmark using Sunspider which is famous benchmark suit In this benchmark DWRI has good performance compare with CFS But I think this result actually not meaningful because javascript engine does not use multi-thread Anyway in this case DWRI is quite good I think Q. Are you also measuring the fairness of the different schedulers? No It's very hard to detect fairly So actually we test the first benchmark we show the DWRI has the best fairness other experiment we just know is the DWRI is really good in common case or practical case So Okay Conclusion As previous mentioned the weighted based algorithm in multi-core like CFS is able to achieve fairness in practical even if the exactly same amount of round amount of road is given to each core Yes DWRI can be new trial to improve the schedule in multi-core system but DWRI has also have several problems so it is not the best solution So consequently our conclusion is that thinking the multi-core of our schedule is worse enough now so I hope to be develop the new schedule which can fulfill the requirements embedded multi-core systems and this is our team's future work Okay, thank you Any question? Yeah I just want to be sure sounds like you're just trying one big task and spreading it's work across the processors and of course your schedule is going to work better for that one big task but what about the problem where a typical system is doing 20 things at once and some of them have different priorities and whatever how do you solve that problem with this model Okay Actually our organization is concerned our government so our project is is just focused on the special case so we just try like this scenario but we have to do so many experiment and so many yeah think so many environment or some case so when we we will get some some result we will sounds like it needs to be more of a policy that could be adjusted in the kernel rather than changing the core of the schedule okay okay another question yeah thank you for listening


Alrighty, I have a construction update for you guys today and a really bold statement to make. I am here to make amusement insiders official 2025 prediction on where the coast is going to go and I guess like what kind or what kinds. I'm going to stick to two, maybe three, but I'm going to rule out the third one. It's just not possible. But today was a really eventful day.  So I flew and I'm going to tell you there's a lot going on at the park. And if you pay attention, I almost wonder if I just upload this video without my voice. If the average person would be able to figure out what's going on, but I'm going to describe it anyways. Don't worry. So here you are. I'll go over the basic stuff first. This is moose horn falls location. All the plumbing is going in. They got two water mains installed. And that's a lot of work for one water slide. So again, at the ace event, they teased that there was going to be a bigger water park project down the road. And moose horn was just a part of that. Based off of what I'm seeing there, that is pretty evident. Here we are over by Leviathan. You can see a cement truck or concrete truck. And yeah, so they are pouring all new garbage disposal units around the park. So you're going to see a lot of that. Here you're going to start seeing this is where I almost wonder if you guys would clue in if I just didn't do a voiceover, but you're going to see a lot of stuff being moved into Kingswood. So Kingswood, if you look around, if you see my drone footage over the last month, there's a lot of new stuff. And in today's video, you're going to see a lot of action here. Okay, I got a lot of it on camera. I don't know if I'm just going to stop my voice over at one point and then you guys can just continue watching. But I stayed up in the air for 30 minutes. I'm going to be late for work. So please drop a like for the fact that I rushed home after flying. Normally, I just upload this tonight, but I was like, you know, I want to get this up. This is really important news. I can make a bold statement. So I'm going to do it. So I'm going to be late for work and drop a like just for me being late for work. Or you can write me up in the comments section down below. This manager is light to his own store. But nonetheless, construction at front gate is moving along really nicely. You can actually see where the metal detectors, those new scanners are going to go. And the new front gate store, the foundation has been dug out to the exact width and diameter of the other store. Sorry, I'm sick, by the way, if you can't tell. Here you can see some things moved from the mountain into Kingswood. So you're going to see that that's a big theme of today's video. So Kingswood looks like the average person might jump to the conclusion that Kingswood looks like it's getting prepped for construction. Well, it's quite the opposite. Kingswood is being turned into a storage facility. So everything in that mountain inside the mountain is going in the garbage or going to Kingswood. You're going to see a lot of that in this video again. I keep saying that, but I'm here to make my bold statement. So it is with 100% certainty on amusement insiders part that Wonderland has chosen to go forward with the blast coaster. So I've heard through my birdies that construction is going to start in January. And based off of what I'm seeing today, three weeks leading up to January, they are clearing up the mountain, both the haunt maze, the old haunt maze is being gutted. And any old belongings in the mountain are being moved to Kingswood. So this was seen on the drone. This to me is solid evidence that they're about to do a major construction project inside the mountain, exactly located where the image showed the blast coaster in their little survey to their guests. So that leads me to believe that the blast coaster is 100% going to happen. Now, which two coaster models do I think it's going to be? I think it's greatly obviously weighed towards a wing coaster just because the survey said that. But a lot of people pointed out that it looks like an invert in the actual renderings. And that is true. It does look like an invert in the actual renderings. I actually have a whole video coming out soon. I found some of the artwork online. So I'll give you guys my true opinion on that in a different video. But I do believe it's between a wing coaster and an invert that are going to be launching through here. I think the invert would be more practical and obviously enthusiasts would like that a lot more than a wing coaster. Inverts lose a lot less speed than wing coasters do. And digging that layout for a wing coaster to go through that mountain, that's going to take up a lot of width. So I think an invert would be a lot more easier and practical for Wonderland construction wise as well. So it'll be really interesting to see what happens. But as you see here, this is the old haunt maze that they are throwing out. And I saw them hauling out steel columns, old Hanna-Barbera theming. All of it is getting moved to Kingswood. So definitely in my opinion, confirming that Wonderland is moving forward with the blast coaster. So hopefully you find that exciting. Comment down below what you think. I know that's a really bold statement to make, but with the information that I do know with the construction starting, and so happens three weeks before the construction is supposed to start, that they're clearing out the mountain, it's pretty easy to predict that the blast coaster is what Wonderland has moved forward with. So exciting. I honestly, a launch coaster is exactly what Wonderland needed. So I'm truthfully excited. Anyways, I'm going to end my voice over here. Stay tuned for a lot more construction updates as it ramps up. I will be at the park tomorrow. And yeah, thanks so much, guys. Have a good one. Bye.


Hello and welcome, everyone. Thanks for joining us today. We're excited to have you on. During today's webcast, we'll be going over our latest release, 11.3, where we have lots of exciting new features. My name is Agnes and I work on the marketing programs team here at GitLab. I'm joining you from Sunny San Jose, California today. Also joining us this morning is our presenter, Brandi, from marketing. Daniel Grisso and Joshua Lambert from our product team and Chen Jay from our support team. We're going to give people just a couple more minutes to get locked on. While we're waiting, I'm going to launch a poll you can take part in if you'd like. The graphic on this slide may be useful as you think through your answer for the first poll question. Thank you to everyone who participated in the poll. Before we get started, I'm going to cover a couple of housekeeping items. First, feel free to ask questions throughout the presentation. You can use the Q&A function at the bottom of your screen for that. We'll have dedicated time for questions at the end of the presentation and demo, but you can go ahead and send in your questions as you think of them and we'll make sure to get to them at the end. If you are experiencing any technical difficulties, you can use the chat function to get in touch with a moderator, me for help. Now I'm going to turn it over to Brandi to talk about the results. All right, helps if I unmute myself. Agnes, as you can see GitLab is more than just an issue board, source code management and CI, which are the areas that we were focused on in the past. Over the last year, GitLab has moved towards being a single application for the entire DevOps slice cycle. So let's take a look at the results. It seems like we have it right between create and manage, which is great. Some people are also using to release package verify. So it's a good kind of mix, it seems. One of the areas that GitLab has invested in this year is around the capabilities for auto DevOps. So I just want to take a minute to discuss what auto DevOps is, oops, sorry. Auto DevOps is a subset of the phases that we saw in the previous slide. And rightfully so, the seven phases shown here are the ones that can be automated within the pipeline itself. GitLab auto DevOps eliminates the complexities of automating software delivery. It will automatically set up your pipeline and necessary integrations. Auto DevOps simplifies and accelerates delivery with a complete delivery pipeline out of the box. Simply commit code and we do the rest. Auto DevOps detects the language of the code, automatically builds tests and measures code quality, scans the security and licensing issues, packages things up, sets up instruments for monitoring, and deploys the application. So basically you create and auto DevOps does the rest. Now let's talk about the latest and greatest. What are the new GitLab 11.3 features? Our main theme this month was around compliance, specifically features that help automate controls around environments and code while providing further efficiencies for Java developers. For relevant sake, let's pause here and take a poll to see how many of you are currently using Maven repositories. Well, everyone has a chance to vote. There's two things to note. As always, if you need further details on this release, you can find it at the link at the bottom. And we are proud to say that this is our 87th consecutive monthly release. So 60% of you are using Maven repositories. 40% are not. So basically if you know a Java developer who might be interested, you can pass along the information that you get from the next bit. And the rest of you, well, this will be interesting for you. In this release, we have expanded our support for Java projects and developers by building Maven repositories directly into GitLab. And staying true with our GitLab values around MVC, minimum viable change, you can expect more functionality in upcoming releases. Daniel and Joshua will be demoing these features for you today, so I won't spoil their big reveal. I will just say that this really helps GitLab to provide Java developers with a secure standardized way to share version control in Maven libraries and save time by reusing these libraries across their projects. As organizations move toward more automated practices across their SDLC and increasing their velocity of delivery, compliant standards and assurance of separation of duties becomes a very obvious area where automation can ensure governance standards are being met. With our new protected environments features, operators can now set permissions to determine who can deploy the code to production environments. This significantly reduces the risk of the wrong person committing something that they shouldn't and increases overall security of the environment. I'm sure you can all understand why that's important. Very similar to our new protected environments feature, we now support the assignment of code owners to files to indicate the appropriate team members responsible for the code. Again, in GitLab's MVC, by adding these definitions now, it prepares us for future releases that will enforce internal controls at the code level. The last 11.3 highlight is around our project and portfolio management capabilities, specifically around Epic forecasting with integrated milestones. If you're using our PPM capabilities, you know that you have always been able to establish plan start and end dates at the Epic level. With 11.3, you will be able to gain insights into potential slippage and Epic delivery by analyzing the work that is scheduled through milestones. You will be able to understand the Delta difference between when you thought you could deliver the Epic versus when the milestone work is scheduled to deliver. This will enable faster, better decisions on what can be delivered and when the plans need to be adjusted. I'd like to highlight very quickly some noteworthy enhancements, some of which will be included in our demo. Remember early in the presentation when we discussed auto DevOps. In this release, we have enabled that use of auto DevOps pipeline by default. So please look at how that works in the system. Additionally, we have included interactive web terminals for Shell and Kubernetes runners. This is a pretty technical feature and Daniel and Joshua will highlight this in the demo. Also, if you're using our web IDE, which by the way, I absolutely love, you'll be happy to hear that we are supporting five file templates in the web ID that you can take advantage of. And lastly, 11.3 now has SAS support for Groovy. But I've talked enough. Let's see what's new in the demo. Joshua, over to you. Thanks. So let me go ahead and share my screen and we can take a bit of a deeper look at our new Maven repository features that are now in GitLab 11.3. All right, so hopefully everyone can see my screen. If not, please do let us know by posting an item in our chat. But we'll go ahead and talk a little bit about what we have here and what we're looking at. So first off, this is a project in our examples repository. This is a repository that includes a number of examples and we'll be able to build it over time. We have another examples project repository as well where users can go ahead and essentially find some getting started or some hello world type tutorials that they can use to essentially better leverage our features and get a quick start. So in here we have our Maven example and this one comes with a pretty simple Java project here as well as some of the Maven settings and files you might need to configure it. So let's go ahead and take a look at what's in this repository. Again, so we have our pretty simple Maven or Java application here, very straightforward. Again, complexity application itself isn't the goal but the integration with how to publish to our packages service is. We also have the, again, the required Maven configuration files and so here is our settings in XML and you can see here we're authenticating with the CI job token. That is the job token that gets provisioned for every single job, it's unique to every single job, automatically when the CI runs and our Maven repository could automatically understand that and authenticate it securely. The greeting with the job token again is it rotates every job, it's how to secure and if it gets essentially lost or misplaced or somehow leaked, again, it's only valid for that single job's lifetime which typically is just a few minutes. We also have our pond at XML as well and then here you can see that we're defining a couple items here on dependencies. We're also gonna go ahead and leverage our API endpoint for Maven here. So it's essentially the projects URL and you can see here we have parameterized the project ID so it's really easy to reuse this with other repositories and you can see we have packages and then Maven as far as the rest of the URL here. Down below we've again utilized our variables as well to configure the repository URL to get repository, our project URL and also again the get repository URL as well and so you can see here essentially all of the required configuration and settings have already been configured for us simply by leveraging the environment variables that GitLab already generates automatically for you as part of your project and so essentially from here what I can do is I can simply fork this. I'll use my private name space here and since all the configuration, all the settings are all again available with default CI options, we can simply go ahead and actually run the pipeline to go ahead and deploy our first Maven package to my project's Maven repository. So let's go ahead and take a look at this now. We have our packages UI and this is where you'll see the Maven projects show up here. Maven packages rather and we're also working to improve the screen here as well. We mentioned kind of the minimal NVC and iterating towards a larger or more fully featured Maven support which we're absolutely doing and what I was trying to get to here soon is improving this kind of screen here so folks know what's there and know what's happening and how to use it. Let's go ahead and publish our first package.  That'll take just a few moments to get pulled down by the runner and then we'll go ahead and execute our CI script inside. While we're doing that we can go ahead and take a quick look at the CI script itself. So you can see here just how simple it is. We're gonna go ahead and take that CI settings to XML and copy it into the place that Maven expects it. If you wanted to you could also pass a CLI option to tell Maven where to use a file instead of looking in this directory but in the case that you might have an existing pipeline already set up it can be easier to just copy the new one there. But you can see here again how small and minimal of a required CI script is really required to utilize this feature. Let's go back to our job here. You can see we've actually already started so let's zip back up the top of our job here and we can take a look at what happened. All right, so you can see we did download the image that's great when we went ahead and we copied the settings of XML over to the place that Maven expects it and the user's M2 SAS settings directory. And you can now see we're gonna go ahead and download the required upstream plugins. As you can see we're picking up some Maven items. We're also gonna pick up Plexus as well. So you can see here some plugins are picking up and that's gonna go ahead and download a few of these here based on the requirements. And we're doubting them and then we'll go ahead and once we're done picking these up we'll go ahead and actually start to package things and then post them back up here. So we can zip down to the bottom. You can see we're just wrapping up picking up Plexus here and down below you can see we're now gonna go ahead and actually upload our package to our Maven endpoint. You can see that right here. So you can see you're posting our snapshot and you can see down below we've finished posting the relevant details if we go back to our packages UI. You'll now see we have a brand new 1.5 snapshot version which was configured in our files there. And you can see we've got our metadata and our Pondlin.jar files. So that's really how easy it is to go ahead and get started with Maven integration. Again, the advantage of this being integrated within GitLab is that it's really easy to use. There's not a lot of setup required. You can see here we just basically just forked our example repository and we're able to go ahead and get started. And so hopefully it's easy to use for everyone out there who wants to use it. I'll watch all the developers on this call here. If not, if you have feedback and other improvements you can make, please do let us know. Create an issue and we'd love to engage with you and learn how to improve this and make it better. So with that, I think we can wrap up the quick demo there of Maven and pushing a package to Maven. I can hand it off to Daniel to go ahead and talk about protected environments. Thank you, Josh. Okay, let me go ahead and show my screen here. Okay. Hopefully here you would be able to see my screen. Please let me know if not. Let me go ahead and move this out of the way. Okay, so as you know, GitLab is made so that everyone can collaborate in getting the software value delivered to customers. This effort, operators are every bit as important as developers. So we've been paying extra attention to that side of the partnership to make sure that their needs are met as well. So basically we want operators to be first class citizens in GitLab. And that's the reason we added protected environments. It's important to be able to lock down environments like production to make sure that any changes are well controlled. So just because a developer writes code, it doesn't necessarily mean that every org wants broad access to make changes to the production environment whenever they want. So in fact, many IT organizations are required to keep separation of duty so that the people writing the code are not the same people pushing out the code into production. So when an environment is set to be protected, then the only approved users, groups, or roles are going to be able to execute deployments into this environment. Okay, so one second here. I'm going to share my second screen with my browser just to quickly go over the demo for protected environments. And here is my pipeline screen. Okay, great. All right, so here we see a simple three stage CI setup where the deployment to production has been configured as a manual step. So currently I'm logged in as admin. So let's go ahead and switch over to developer view. And that way I'll show kind of what the developer is seeing here. So here in developer view, I kind of have the same setup. Here you see, I can see the deployment button for this particular CI run. So now let me show you how easy it is to set an environment to be protected. So here when I'm logged in as admin, I simply go to the project settings and under CI CD, I can expand the protected environments section. And here what we're going to do is on the first dropdown, I can select which environment I want to protect. Let's go ahead and choose a production environment to be protected. And on the second dropdown, I can define who will be able to deploy to that environment. So I can choose here for three options. So the first one is roles. You want everybody with a particular role to be able to deploy. The second one is groups. And the last one is users. So basically which specific users are able to deploy. So this last one is specifically functional where if you have a certain group of operators that you want to be able to deploy, but then you want to add one extra person that may be a one of, you can do that through the users here. All right, so let's go ahead and set up the administrator to be able to deploy for this particular environment. Now we'll go ahead and click protect. Okay, so here I'm going to go ahead and go back to my pipeline. Now that this environment is protected, I see that the pipeline is blocked because of that manual step to deploy. And now let's go ahead and switch back to my developer view. And now that the environment is protected and only the admin is able to deploy. Go ahead and refresh the screen. You will see that the action to deploy is no longer available to the developer. So for this particular role that we're logged in as a developer. Likewise here, if the deployment action was said to be automated, and so not a manual step, then your job would fail. And it would basically tell you that the environment is job is the point to is protected. And that basically does it for the protected environments demo. We look forward to operators getting great value from this feature. And we'll look forward to your feedback. And with that, I'll turn it back to you, Raddy. Thank you so much. I think now that's it for our highlights. So why don't we do some Q&A? All right, we have not seen any questions come in. But of course, feel free to continue submitting. But while we're waiting, perhaps the audience be interested in some questions that we've seen often come up. So for example, maybe, you know, Chen Jie, you can answer this or Daniel or Josh. First of all, can we protect that environment or just production? So with protected environments, any environment that you'd have defined for your CI, CD pipeline can be protected. As you saw in the demo, Daniel went and selected production, but there was an option to select staging as well. So any environment that's defined for your pipeline can be protected. Thank you, Chen Jie. I think we've also seen something that we've also seen come in quite often. As far as questions goes, people are often interested in finding out kind of Maven repository stored to S3. So perhaps you'd like to share. Okay. Yes, this is possible to use object storage for this. And I'll post the link to the documentation for this with the steps that one would need to take to get it to work with S3 storage. So I'll just put that in the chat for everyone to see. Thank you, Josh. Still we don't have any questions yet, so it's quiet and drip this morning. I guess if there's no questions, I will basically wrap up with the final question that we often see come up, which is, does the Maven repository support proxy? Yeah, I can take this one. So we actually do want to have the support within GitLab. We do hear quite a bit from our users that they would like to have a solution where they can have essentially a single point on their network that can act as sort of both the network gateway to access these internal or internet-based resources, but also to act as essentially a what-isting agent so they can control essentially what artifacts and upstream resources can be utilized. So we're absolutely looking to build this in, but with this being our first iteration, it's not there just yet. So we do have an issue open for this along with a broader set of improvements to our product. And again, definitely please pass this feedback. Please let us know what you'd love to see. That helps us to ensure we're doing the right things. And of course, an example of this is the proxy support, which we don't have today, but definitely are trying to get in there pretty soon here. All right, thanks again, Josh. And if since we don't have any questions, I think we will move on to perhaps, Brandy, you can share our product vision. Of course. Our head of product here at GitLab has recently given a presentation on our product vision for 2019 and beyond. I would really encourage you to take some time to read his blog or view the recording of our own map and where we plan to go. You've seen in the first slide that I presented how far we've come since we've started to now where we're getting into auto DevOps. So it's kind of, we're growing, we're changing and where we are becoming the one single application for all of DevOps. And on that note, I would like to thank you so much for your time today and back over to you Agnes to close things up for us. Thank you, Brandy. We'd love to hear your thoughts on today's webinar. So please fill out our webinar survey, which I'll drop in the chat. And also we'd like to invite you to sign up for a free trial of GitLab Ultimate. We hope you're excited to see what your team can do with it. I will also chat that link. And finally, if you have any other questions, please don't hesitate to reach us via our sales contact page about.gitlab.com slash sales. That's all for today. Thank you so much for joining us.


with a twist and strain. Very much looking forward to your talk. Okay, so good morning and thanks the organizers for the invitation. The ability to isolate atomically thin layers has made it possible for us to engineer band structures to change the Fermi energy in the system. And it has opened an enormous vista of possibilities for creating correlated states, by for example, creating flat bands as we discussed here and bringing the Fermi energy into the flat band and then watch new the emergence of correlated states that have possibly interesting topology. So what I'm gonna tell you about is I'm gonna start with the experimentalist understanding of flat bands. And I'm gonna put that in a context called Landau levels. And then I'm gonna give a very quick introduction to graphene. And then I'm gonna discuss two cases of engineering flat bands. One is in twisted by layer graphene. And there I will ask a question. We know that we have superconductivity and twisted by layer graphene at the magic angle. But I'm gonna ask the question is the flat band also topological and can we say, can we observe that experimentally? And then the second part of the talk, I'm gonna introduce another way of creating flat band by using strain if I get there. Okay, so let's get started. Okay, so let's get started with the most important people here. So the pictures here are of postdocs and students or former postdocs who've done all the work at Rutgers in red here. We have our theoretical collaborators. This is the team of Andre Geim who provide graphene on an open-dice cell night where we did the work on strain and the Japanese group that provided the boron nitride. So let me get started. Of course, the reason that we are also interested in flat bands is that you have the kinetic energy is quenched and when you're able to bring your Fermi energy into the flat band, you have enhanced correlation effects and you give rise to a whole slew of possible correlated states. So the most interesting one for this discussion here is superconductivity. So we all know very well that conventional superconductor, the BCS theory, the pairing of the paired electrons live in a very thin sliver on the Fermi C and the TC as we've seen already many times is exponentially suppressed here in the pairing energy and density of states. So the traditional way of increasing TC is to increase the density of state. For example, usually by chemical doping for in graphite, you do your calcium intercalation and you can bring TC up to a few Kelvin. Sorry about this. And in transition metal dichalconegide, you can use ionic liquid doping and people have observed the TC, superconductivity with TC as high as 15 Kelvin. So is there another, an alternative to that? And that was proposed in the 90s by this mostly Finnish group. And what they show that alternative approach to increasing TC is to create flat bands and then because of diverging, you get a diverging density of state of the Fermi energy. And what is very interesting about it that the pairing spans the entire Fermi C in this case. And what Volovic showed is that and his collaborator that in this case, TC is not exponentially suppressed, but it's linear in the pairing energy times the area of the flat band or volume of the flat band within the Brillouin zone. And these are pretty very interesting and intriguing predictions. In 2007, in fact, Volovic predicted that you could get a room temperature superconductivity in graphite due to in the presence of screw dislocations. And of course, there's a big question whether that is possible. So however, the caveat about all this, and we've heard it just now from Deban John and yesterday from Andre Bernovic, is that even though the pairing can happen at very high temperatures, unless the superfluid stiffness is finite, it's completely useless. And if you have a single band, isolated band in the Ginsburg-Landau sense, of course, the superfluid stiffness is going to go to zero because the mass diverges. So in 2015, this Finnish group found the way around this and they pointed out that if you have a non-trivial geometry of the wave function, then you actually have a lower band on the, which is determined a geometrical bound of the superfluid stiffness. And they found that this geometrical bound was a lower bound, which is proportional to the churn number here. However, subsequent words show first that, in fact, you don't need a finite churn number, all you need is a finite bearing curvature. And more recently, all you need is the non-trivial, you need a quantum geometry. So this condition has been relaxed quite dramatically. So in fact, so the bottom line is not only do we need flat bands, but we want some kind of non-trivial topology. Experimentally, what we can measure is the churn number. We cannot measure the metric or anything else. So we're going to be looking to whether those flat bands have a finite churn number. So now let me do a little bit of a detour for the students on graphene. And you've heard the lecture on Monday in the tutorial. So this is the crystal structure of graphene. It has two sublattices, A and B. The ingredients for the band structure are very simple here. Two-dimensional honeycomb structure and identical atoms on the two lattices. This gives rise at the low energy to this very simple Hamiltonian here. You have two Dirac cones. The low energy excitation are described by two Dirac cones, which are related, which each of them is chiral. And they're correlated by time reversal symmetry. And that gives a density of state, which is linear and vanishes here at the Dirac point. Experimentally, the fact, and this is where charge neutrality sits. So experimentally, the fact the density of state vanishes makes it possible with relatively small gate voltage to walk around here and to change the Fermi energy almost as well in this kind of a geometry about one volt in gate voltage by 10 to the 11th carrier per centimeter square, or it changes the Fermi energy by 30 millivolts. Now, if you add a magnetic field, you had to have a gauge field here. And that gives rise to lambda levels, which in this case go like the square root of the magnetic field times the lambda level index. And what's interesting here, and most interesting for the discussion today, is the n equals 0 lambda level, which actually, if you look here, it's made up of both electrons and holes. If you park your Fermi energy in between the zeros and an equal one level, you find that there's no quantum hole effect, your number is 0, and it's going to stay 0 unless you remove this degeneracy either by spin or by value. OK, now experimentally, what we do with we measure, in this case, a scanning tunneling microscopy. In scanning tunneling microscopy, we measure the tunneling current, which here, this integral here goes from the Fermi energy to the bias voltage that we apply. Rho here is the density of state, and this exponential factor comes from the matrix element. And so that allows us to do two kinds of measurements. One is what we call topography. So we keep the current constant, the bias constant, and we raster the sample so we can see where the atoms are sitting. Another measurement that we can do with STM is take the derivative of the density, dI dV, and that extract the density of state here as a function of energy, which is controlled with the bias voltage. So this is what you see, what it looks like in graphene. This is graphene on graphite. And when you apply magnetic field, you see the Landau levels pop up, and there's a lot of information that you can extract, in particular the Fermi velocity from this sequence here, quasi-particle lifetimes from the line width, coupling to the substrate, et cetera. So there's a lot of information that you can extract from here. And so basically, Landau levels are bequins-decentral flat band with non-trivial topology. So for example, if you park your Fermi energy here, you can see the quantum whole effect, which basically measured directly the chair number. Basically, this is the number of edge states that you have in your system. If you park your Fermi energy in one of these Landau levels, you can see fractional quantum whole effect. That is the emergence of interactive state. However, the problem is that, you know, we have to apply magnetic field here. So the question is, can we create flat bands without breaking time reversal symmetry? And there are, I know, of three ways of doing it. One is the magic angle twisted graphene. So you take two graphene layers, superpose them, and you have a certain angle where the band flattens here. That is kind of a resonance effect. That is when the interlayer and intralayer hopping are comparable to each other. This is not a trivial flat band that you would get by just putting the system in a periodic potential. So I'm gonna be discussing this. The other way of doing it is apply periodic strain that creates a pseudo magnetic field, which doesn't break time reversal symmetry, but act exactly like a magnetic field in the sense that it gives you these pseudo-lander levels, flat bands, and by bringing the Fermi energy into one of them, you can hope to create non-privile correlated states. The third way, which I'm not gonna discuss, but Andre Bernovich discussed it yesterday, is by partite lapis and their line graph partners. And these are a few examples. And these have naturally symmetry protected spiral flat bands. In this case here, for example, the LibBand. And these are very interesting systems that have not yet been really fully explored experimentally. Okay, so let me go to Twisted by Lea Graphene. So you've seen this probably many, many times. You bring the green layer on top of the red and twist it and you're gonna create a super period here, which is inversely proportional to the twist angle. But something that not many people may be realized is that when you shift it, when you shift the two layers, this is what happens. You see the pattern remains the same. The only thing that happens, you change the origin. Okay, so the pattern, the translation, the pattern shift, but nothing very interesting happening. You will see that you have more nice right present. The story is totally different. So if the two lattices have different lattice mismatch, of course, this period is a little bit more complicated and then there is a lower bound on the size of your Moiré cell. So Moiré patterns have been, macroscopic Moiré patterns have been known for hundreds of years in the textile industry. At the nano level, microscope atomic Moiré patterns have only been discovered with the invention of the STM. So that's when they were first observed, when people, the first thing that people looked at to when they build the first STM was to look at graphite and they saw these Moiré patterns. But what they did not realize, and then you see here, as you decrease the twist angle, the period gets larger and larger. But what people didn't realize for quite a long time is that not only that it generate pretty patterns, but this periodic Moiré also changes the band structure. So as you decrease the twist angle, you see the band structure changes. You see the appearance of van Hof singularities that get closer and closer together as you decrease the twist angle. And this is here 1.8 degrees and then you go to 1.1 degrees and the two merge. Another thing that you see that at the Fermi energy, you open a small gap here, which is kind of a hint that you might have correlated states. So here is the gap between the van Hof singularities.  And another thing that you see, you can measure the Fermi velocity as a function of twist angle by measuring from Landau level spectroscopy. And you see that it tends to decrease and maybe tends to zero right at the magic angle. So that is another signature of the flat man. The illustration there. Yes. For activity, is it possible to measure the... On the previous slide, how accurately is it possible to measure near one degree? How accurately? It's very accurately because when you look at the Fourier transform, we can measure it within a fraction of a degree. No, I meant... That you mean the twist angle, right? Very accurately, right? So you go to the Fourier transform, you compare the Bragg peaks from the atomic lattice to the Bragg peaks of the twisted of the Moray pattern, and you can see exactly that. And then it's going to depend on how large the area that you look at, but we are pretty accurate and it's much more accurate than we can actually control the twist. Andrei? What's the meaning of this? You can add in one whole piece to your experiment. This k-o in your work is in electric control. So they repeat for... We see them merging, but at this point here, you see the Fermi energy is here and there's a little gap right at the Fermi energy. So the minimum distance is our resolution, which in this case was not very great. It was like five millivolts. But I'm looking at the lower plot. You want to use two millivolts, what? I'm looking at the lower plot when you put... I guess this is the distance between one whole points as a function of the angle. Right, right, right. So I'm asking about the lowest points that you have because the vertical scale is in electrical... The lowest point that... Okay, the arrow bars are pretty large. This gives you an idea of the arrow bars. They go down as you go here. I took them out, but it is... I don't know, 0.1 degree or so. You mean... It's an energy. It's about five millivolts. Okay. Any other question? Okay. So in order to understand what's going on, let's go to reciprocal space. And here we have the diracons of the two layers, red and green. And you see that they're the distance between adjacent red and green diracons in one of the cave valleys is proportional to twist angle that gives rise to this. This spans the mini-brillow and zone here. And because each one of them is descended from a particular cave valley, they have opposite chiralities. And now we have too many brillow and zone with opposite chiral, eight orbitals per more so two from valley, two from layer, two from spin. So now let's go back and look at this Hamiltonian. This is Hamiltonian in one particular cave valley. And we're looking at these two diracons. And you see that if there is tunneling between the two layers, which means that you have off diagonal term here, then the intersection point between the two cones goes down and creates a saddle point. And by keep perp here is the interlayer tunneling. And it creates, so as we know, a saddle point in the band structure gives rise to one of singularities in density of state, a logarithmic divergence basically in the density of states. And at the magic angle, and you see as you decrease the twist angle, eventually you get to an angle, which is the ratio of the interlayer to intra-layer tunneling, which is about at one degree. And this is just an illustration of how the flat band is created. This is work from the Cretaceous group. And you see as you decrease the twist angle, the band closes here. And eventually when you go up to 1.1, you get along a perfectly flat band. The problem is that if you look at this, there's no gap to the remote bands. So there's nothing interesting gonna happen. You need a gap to the remote bands for something interesting to happen. And here is where a nature is kind to us. And let me show you what happens to open the gap there. So here is again, the Moiré cell, the bright regions here correspond to what we call AA stacking, where every atom in the top layer eclipses an atom in the bottom layer. The darker regions correspond to AB or BA stacking, where in one case, the atoms in the A sub lattice eclipse atoms in the B sub lattice in the bottom layer and vice versa for BA. Now there is something, we can plot something that we call registry index, which is the extent of which the top layer obstructs, the top layer atom obstructs the bottom layer atom. And if you have a rigid lattice, that the AA regions and AB regions have roughly the same size. However, if we look at the energetics of stacking, you see that the AA stacking is much, much more expensive. It's like 12, it's about 20 millivolt per atom, more expensive than AB stacking. So the system really doesn't like to be in the AA stacking. And because this is an atomically thin layer, it can really readjust to reduce the size of the AA layers. So this is how, for example, at 0.3 degrees, the registry index looks like this. So the AA regions have shrunk to basically 10% of the Mora cell. Experimentally, we see that as well. This is 0.28 degree. The AA regions are the bright ones here, ABBA, and they're separated by domain walls. Now in order for this to happen, you need a lot of built-in strain in your system. And how does that happen? It happens actually in a very interesting way. Every atom here, I don't know if you can see the arrows, is kind of shifted a little bit in the Mora cell. And they form kind of vortices that are, in one sense, you have a vortex lattice that is sitting on this Morae. It actually has opposite vorticity in the top and bottom layer. Very interesting phenomenon. Okay, and this reconstruction, this is what gives rise to a gap. So now, because of the reconstruction, we have a nice gap between the flat band here and the remote bands. And that's what allows us to do all this, interesting flat band physics. So bottom line in magic angle twisted by layer graphene, we have an isolated flat band. It's eight-fold degeneracy. The degeneracy protected by C2P symmetry inversion times time reversal. And the band filling is controlled by the electrostatic gating. So by gating the sample, we can move the Fermi energy within this system. And there's a way of, for us, this is the way we counted. Other people count some were different, but I think this is a good way of counting the filling. So N naught is one carrier per Morae cell. So N equals to zero is charge neutrality. Electron sector is positive N over N naught and holes is negative N over N naught. And minus four here would be an empty band, plus four would be a full band. Okay, so since, you know, back in 2010 when we did our experiment on suspended graphene layers, we didn't have much control over the twist angle. What we did have was CVD graphene that had all the possible twist angle in the sample. So you could just walk on our sample and look at different angles. But there were several important technical developments in the intervening years. First, Coridine et al and Jim Hones group discovered that boron nitride is a very good substrate. If you don't align it with that, they didn't know that with the graphene and this enabled the observational fraction of quantum whole effect without actually suspending the sample, which we had to do. The second development was the ability to perfectly control the twist angle. This was developed by Emmanuel Tutu and using these techniques, a group of Pablo Jariah-Hilaero and MIT were able to create this device graph twisted by layer graphene sitting here. They had a whole bar here and that, you know, they observed superconductivity but it created an avalanche of both theoretical and experimental paper. And this is, I'm listing that just the highlights, churn insulator, superconductivity, pseudo gap phase, nematic charge order, orbital magnetism, blanket dissipation, fractional churn insulator, all this observed in this one system. And this is a subset of the many, many papers that have done that. So I'm gonna ask now the question, are these, is this flat band also has some non-trivial topology? I can't measure the quantum metric but I can measure churn number. Okay, for that, there's one more theoretical thing here. Let's go back to the Hamiltonian here. So this is the Hamiltonian in the layer base. So top, in red we have layer one, in green we have layer two and there's something very interesting that happens when you change basis, when you go from layer basis to sublattice basis. And if you do that, what emerges is a kind of, it's a gauge field and you see here A and plus A, this is only in the K valley. So on the A sublattice, you have the gauge field has a positive side and the B sublattice is a negative side. Bottom line, it looks like a magnetic field except you haven't broken time reversal symmetry, right? So it looks like a magnetic field, it should act like a magnetic field. If you take the curl of that thing, you can estimate, it's kind of 120 Tesla, this is a rough estimate. And if you have a magnetic field and of course the first thing that you know, you're gonna get sudo-landau level. These are sudo-landau levels because you haven't broken time reversal symmetry. And this is in the K valley. So you see sublattice A in red, sublattice B in blue. And if you look in the moiré cell, you're gonna have a magnetic field pointing up on sublattice A, magnetic field pointing down on sublattice B. This looks like almost a Haldane Hamiltonian. Should the one hand still come from three or? It comes from going, it's inherent in this structure here. You just do a change of basis to the sublattice basis. No, the strain is not included here. No, there's a U here. This is the, of course the interlayer tunneling, that is when you do the transformation, it's buried in here. And the zero here is kind of because it's tunneling between the A B sublattice and that can be neglected for the N equals zero level. Yes? Do you think it's gonna come back to this thing? No, no, not at all. You're gonna see in a moment what it looks like. You know, it's kind of, okay. Now, if I add to that the K prime valley, of course it's time reversal symmetric, then it's gonna look like this. So you have the K and K prime valley. And now we have this flat bed, which is just the equivalent of the N equals zero lambda level, N equals zero pseudo lambda level, eight-fold degeneracy protected by C2T symmetry. So unless you break one of these symmetries, you're not gonna see anything that has to do with topology. Okay, so here we have again, the pseudo magnetic field induced by more A potential. We can describe these, the wave vector in two ways. One is what I call the churn basis. So you put on the left everything that have churn number plus one or field up on the right, everything that has churn number minus one. And then on the left we have K valley on sub lattice A, K prime valley sub lattice V and vice versa. You can also, now, whether you use this basis or, so for example, when I'm having a filling of three, I have all these states filled except for one, with minus one here, for example. And, but this is not gonna make any difference unless you really break the symmetry because you're gonna fill these seven states in different regions of the sample, they're gonna be all over. So you're not gonna recover the symmetry unless you break some of the symmetry. And I'm gonna get into that into a moment. The other possibility is look at a sub lattice basis. So you separate A on the left and B on the right. So in A we have K and K prime valley, churn number plus one, minus one, and vice versa here. In a moment you see why I'm talking about this. Now, if you look in real space, in the real more ASL, and that goes back to your question here, that this pseudo magnetic field creates this chiral current that counter propagating the diamagnetic current loops on the AB and BA regions alternating here with opposite sign pseudo magnetic field. So depending on how you fill your states, you might discover this orbital magnetism. Okay, so let's see how we can break the symmetry. So first let's see how we can break the C to Z symmetry, the inversion symmetry. We put the sample on a staggered potential. In this case, we have to perfectly align it with the boron nitride substrate. And let's fill it to a filling of, so now what happened, the A sub lattice is lower in energy than the B sub lattice. So when I start throwing in electrons, they're gonna first go in here and only once this is filled, they're gonna go up here. So for example, when I'm at the filling of three, this is what it's gonna look like. The last electron that say goes here, we have a hole that has, it has both, well, it's both valley polarized, spin polarized and sub lattice polarized. Okay, yes? The other thing of the one of the sub lattice. Yes. The alignment with the boron nitride is the whole thing is aligned. So which depending on, okay, boron nitride has two atoms, right? So the boron, depending on the sub lattice on the boron is gonna stay lower than the sub lattice on the nitrogen. So they have different energies. So that's how I break it. So if you go back to the graphene Hamiltonian, the reason we had this, the rock cones with no gap that the gap was the, was that we had exactly the same atoms on the sub lattice, the same potential on the sub lattice. The moment you break that, you open a gap. My question is really how do you use to go only at the one of the layer of the... You only alight a lower one. The higher one is already automatically aligned. I'm gonna get to that in detail if I get there. Okay. Okay, so experimentally people did this, two groups, Goldabert-Gorbans group and Andrea Young's group and Andrea Young's groups by training the sample with a magnetic field.  Now, the other way to break the symmetry is to break the time reversal symmetry by applying an external magnetic field. This time you're not breaking the sub lattice symmetry, just the time reversal symmetry. And again, for C equal to three, this is gonna look like this. Now I'm looking in the churn basis, not in the sub lattice basis. And again, I have one hole here. So the system for the case of three is gonna be valley polarized. And sub lattice polarized, it should show an anomalous quantum hole effect, which it does. So here is the straight up formula. So filling of two, we go to precisely quantized state at about three Tesla, filling of three, it's precisely quantized at seven Tesla or so. So both depending on how you break your symmetry, you can reveal the underlying topology. It's not there unless you break some symmetry. It's not visible unless you break some symmetry. Okay, so let me now focus on this part here, breaking the symmetry with the boron nitride. Now, aligning two graphene layers. Experimentally, we know how to do it very, very precisely. However, we have no idea how to align the boron nitride. So you have a magic angle to survive that graphene, you put it on your boron nitride, you cannot control the angle, very, very difficult. And why am I saying that? In order to achieve this state, you had to be aligned to within a fraction of a percent of the angles. And let me show you why. So the conditions for that, you have to have commensurability between the boron nitride moire pattern and the twist TBG moire pattern, okay? And here is the condition for commensurability. We want to have the TBG at the magic angle. And so NPQ are three co-prime numbers. So the anomalous quantum whole expected in a non-trivial case is graphene, graphene is 1.1, graphene boron nitride is 0.55, or you can have graphene, graphene 1.1, graphene boron nitride is 0, and that's it. And there's the very tight bounds on that. So let me show you why. So this is my first layer of graphene, I'm bringing up the second layer of graphene and twisting it to 1.1 degree, okay? And now I'm going to add the boron nitride underneath and I'm going to twist it to 0.55 degrees. And you see, you create a pattern. Now, no matter how hard I try, I can't hit the exact twist angle. And I'm going to a shift. Let's say experimentally, I have no control over the shift. And you've seen that in TBG, the shift doesn't do much. But in TBG on HBN, it completely, it's not, it's completely different. Every shift is a little bit different. It's very critical to have both the twist angle right and the alignment right, unless you're at the perfect angle. But what does perfect angle mean? So this is a calculation that was done by this group here. Two groups here, this was Centiel's group and Allen McDonald's group. And Allen McDonald's show that if you are perfectly aligned, you have a sure number of either plus one or minus one that spans the entire sample.  And this is, red is sure number plus one, green is minus one. Blue is minus one, green is zero. And this is the displacement from perfect alignment. And you see that to create regions that are not, you're not uniform here. And unless this red percolates throughout the sample, you're not going to be able to see anomalous quantum whole effect. So let's see how tight those bounds are. Yes? Yes. So here we have that you have a lattice mismatch with the boron nitride. Now when there are, I don't haven't tried that. At what point? I haven't tried that. I don't know. But certainly when you have a lattice mismatch, it's a mess. Okay, so how tight is it? Now that Andrea Young's sample was here. And you see, this is the region. You know, P graphene graphene versus P graphene boron nitride. This is a region that we have to be in order to see anomalous quantum whole effect. You have to align it within the precision of a fraction of a percent, impossible to do it. So we were wondering what's going on. Either these guys are just unbelievably lucky or something else may be going on, okay? So it turns out that something else is going on. And this is what's going on. So we prepared a whole bunch of samples and we looked at them with the STM. This is 0.46 for TBG, 0.45 for GBN and so on and so forth. This is the Fourier transform. And you see immediately that we have two groups here. On the left, we have commensurate because you see that the black peaks, the vectors are beautifully aligned. And in this case here, they're perfectly aligned. On the left, on the right-hand side, they're much further from being commensurate and indeed they are not commensurate, okay? And you can measure the built-in strain and it is not negligible because the lattices do readjust to each other. And if I'm looking at 1.03 TBG, 0.3 boronitride, this is what the topography looks like, perfectly aligned in the reciprocal lattice here, the Fourier transform. You see you have beautiful second order peaks and the black vectors here are perfectly aligned. So we have its commensurate. However, if you look at a number, it's really very, very far from the alignment that was predicted for rigid lattices. It's really like 50% away. And still we get perfect commensuration. So we get relaxation and Volodya Falco talked about this. There is a lot of, when you have these 2D layers, there's a lot of relaxation going on. You cannot assume rigid lattices. And then you have relaxation but you also have strain buildup. Now, what does that do? So the lattice is gonna try to align, it's gonna try. But as it aligns, it's gonna build up strain and that's gonna grow and grow. Eventually it's gonna give because it competes with the Van der Waals energy. So that will create domains. And here is an example of this sample here. And here we have three commensurate domains. I'm gonna focus on one. It depends on how far you are from the twist angle. This one is about 300 nanometers. So one can model this by a Franko Cantorova model. I mean, we roughly estimate that we need to do better. We would be good to have some theoretical help on that. Okay, so this is our commensurate domain. And here we have a tensile domain wall that it gives and then we have a hole here. It becomes incommensurate and then the organizing starts over again. So now we are looking at then we're taking density of state maps across this domain and we're taking them at different energy, zero millivolts, 40 millivolts, 60 millivolts. This is the domain.  We have a gap in the bulk, the domain lights up. There is a state within the mid gap state in the domain. So we walk away from the domain into the domain and plot on the vertical, these are the spectra as a functional position and you see zero millivolts here, 40, 60. We are filling of three and you see that we have a mid gap state in the domain wall. And no, this is just preliminary work and we trained a system and we look at, we trained a system with 500 millitas law and we observed that there is a well-defined chirality here and that chirality turn is change a sign when we train it with an opposite magnetic field. Okay, five minutes, okay. Okay. So, right. So it looks like first of all, nature helps align these systems and perhaps it's these domains that give rise to the anomalous quantum whole effect. We don't know yet, but it looks pretty promising and we still have to do a lot of work to understand this. Okay, I have three more slides and I'm gonna summarize. So it's scorecard for magic angle twisted by layer graphene. It turns out that in our group, only one third of the samples are superconducting. Pablo tells me that they're now better than that, but the point is that it's not so easy to get the samples to be superconducting. And one of the reasons, probably the main reasons is that there's twist angle disorder. No matter what you do, the twist angle changes across your sample and you can see that with various technique and unless the magic angle percolates throughout the whole sample, a transport measurement is not gonna give you superconductivity. You of course can see locally, but not globally. The other problem is the PC is never larger than four Kelvin. So unlike what, you know, what Volovic predicted, it looks like we're never gonna get above four Kelvin in this system. And one can speculate why. So going back to Volovic's very simple formula here, you see what enters here is the volume of the flat band in the Brillouin zone. And for magic angle graphene, it's only 0.04%. It's tiny. So this factor here kills DC. So unless we can maybe increase this, we can probably not get go much higher. Now, if you look at the formula here, there is a recipe for increasing it, the volume in reciprocal space, because if you make the twist angle larger, the magic twist angle larger, then the volume in reciprocal space is gonna increase. How do you make it larger by increasing the hopping between the layer? And that can be done with pressure, right? So that was done by the Columbia group. So they applied pressure and gigapascal, huge amount of pressure, and indeed they increased the magic angle from 1.1 to 1.27, and they increased DC from an initial, I think two Kelvin to three Kelvin. Not there yet. So it looks like this route is not that promising. So one has to find other systems of flat bands where perhaps the volume, first of all, that are less finicky and where the volume is larger. And since I'm at the end of my talk, I'm not gonna tell you what the system is, I'm just gonna give you a hint. And the hint is twisted by layer graphene, we have a periodically modulated interlayer hopping. So what we thought about, okay, instead of a periodically modulated interlayer hopping, you have a periodically modulator intra-layer hopping. And in this case, what happens, so that you can do that by periodic strain, and that gives you pseudo-lander level, which I won't get into, but we can discuss it later. So here we can control the strain period, no big deal. So that controls the mini-brillouin zone area, controls the volume of the brillouin zone in reciprocal of the flat band reciprocal space. And you can also control its strength by the amount of strain that you put in there, and that allows you to control the flatness of the band. And since I'm at the end of my time, let me go to the end, jump here to the end. Okay, let me jump to the summary. Okay, so summary of what I told you. So basically I only had time to talk about this part. So if you have a partially filled flat band, you can get a whole bunch of different correlated phases, TBG on HBN aligned, the lattice relaxation favors commensurate moray pattern to a certain extent, unless you're too far away from the commensurate angle, you form domains of commensurate lattices, you have the main walls that host mid-gap states, which may be responsible for the quantum anomalous whole effect. And thank you for your attention. Thanks for the last talk, you have time for questions? Just a simple question, since you are not sure whether domain walls host mid-gap states, the quantum anomalous solid effects, have you been able to between the samples, like at the play with the tweening such that somehow you remove these domain walls and see whether transport change. So the domain walls are created by the self-alignment. And unless you are at the perfect commensurate angle, which is 1.1 for TBG 0.55 for TBG HBN, at that point you should be able to create, get rid of the domain walls. You don't wanna get rid of the domain walls, but well, I mean.   So the edge is defined either by domain wall or just by the physical edge of the sample, wherever, whichever is smaller, my opinion. Okay, go ahead. How is the domain wall distributed in the sample? Is it uniformly distributed? I know that, oh, that's a whole different story. That's very, very interesting. So the domain walls, they like to be aligned across some crystallographic axis now, but if they're forced to be along another axis, they go like this, they go along the right axis and then they jump and then, and they become much wider and we have both tensile and shear domains. And we are working on writing this up, but this is, you know, a whole different talk, yeah. Thanks. So yeah, about the last part of the string effect, two questions. One is, what is the energy scale in the experiment they didn't get a chance to talk about compared to matriarchal graphene? The energy scale of what? For example, the flatband and the next band, remote band, what does the energy count? Well, I can show you that. So it will depend on, you know, pseudo-lander levels, right? If you have a periodic, pseudo-lander levels, so it's gonna depend on the pseudo-magnetic field, which depends on the strain, you can tune that. So, you know, we have about 100 millivolts between what, in this particular sample, if I remember correctly, 100 millivolts, the pseudo-magnetic field here's about 120 volt. I mean, here we have pseudo-magnetic field which is about eight of all. So this is graphene on pillars, eight test lines. Another quick question is, have you tried a one-dimensional periodic strain field? You mean tensile strain? One-dimensional, so you can create a pseudo-magnetic field which is periodic varying, but it's in one direction, not in the other direction. I see, so just have one array of pillars. Yeah, yeah. We have done that and we looked at it. There's very interesting quantum whole physics there and there's very, very interesting things going on there. We couldn't understand, we couldn't get anybody to help us understand it, so we never published it, but I'll be happy to talk to you about this. I think the end-of-scale maybe even more favorable in that case. I see, I see, okay. Yeah, if I remember correctly, they see the anomalous quantum hole in the plus three filling, but they don't see it at the minus three filling if I remember correctly. That's right, they only see it at plus three, but now somebody, they only see it so at plus one. Now, there is a strong electron hole asymmetry here, like for example, superconductivity is strongest in the whole side. It looks like this anomalous quantum hole effect, if you break the sublattice symmetry, then the samples don't show superconductivity. There's some evidence that they do, but it's probably different domains. So it looks, and that might help rule out certain models, but it looks like the breaking inversion symmetry is inimical to superconductivity. Yeah, my question was related to that. Do you have this domain wall kind of a picture in the whole side where they don't see the phenomenon? Does it help explain that as well? You mean no domain walls? Yeah, I'd like to see that in the other side. Nobody's looked at this before, so a lot more work needs to be done to correlate those things. So for example, we haven't done transport on these. Okay, and one last question is that, do you have some quantitative measures on the twist angle disorder thing? Like you say that there are disorders. The twist angle disorder is huge. I showed you like in STM, like you have maybe the best sample is at best, you have five or six more cells that are the right angle. Really, the disorder is huge. And there's no way of controlling that, because of the strain and because of how you put, by the way, if you do CVD graphing, they're much, much more ordered. If you can, the problem with CVD graphing that you can't control the twist angle, but those are the samples that we looked at back in 2010, they were much, much more ordered. Good, so maybe one final question. Okay, thanks. There's an online question. What is the interplay between the domain walls and the stability of the superconducting phase? Well, so as I just said, there's no, if you have a, if you're aligned with boronitri, there's no superconducting phase. It's somehow that the two don't like each other. Now, we don't know why, but that's what stimulates, the whole idea of this, the pairing happening different valleys came from that. So if they happen in different valleys, if you break the valley symmetry, then you don't have superconducting pairing, but we don't know. I mean, and there's no superconductivity in these samples. Good. So I would like to thank you again for your burners talk into discussion. I would say that we have been at 11.15 for about an hour today. Yeah, the quick question. So when you resolve different shunt then, like you have the flood rate of the sea density.