This is before github and now we have oversight committees We have marketing committees and we have technical steering committees and there's a technical steering committee for Fido And it's called the the Fido TSC and I'm one of the representatives on the Fido TSC And we have input into the technical direction of Fido and Fido is And a collection of projects Fido is really three key things Fido is VPP, which you heard about earlier VPP is a is a high-performance network stack Built with the same kind of optimizations as DPDK. So DPDK is a very efficient layer for IO Fido is a net Fido VPP is a series is a network stack is a series of protocol implementations on top of DPDK So that's one part of Fido a second part of Fido is the integrations that gives you so and you talked you heard Ratislav talk about it earlier and Giles talk about it earlier And then you also heard Billy talk about the same thing which are all of these Integrations that sit on top of DPDK. So with DPDK, sorry on top of Fido So with Fido, we don't just flip a network stack out the door and say you guys just kind of take care of yourselves We take care of the integrations into Kubernetes and we have so many integrations They have guys arguing with each other about which is the best way to integrate Fido with Kubernetes And there's I think Billy's presentation called out very well. There's three different ways to achieve it, right? There's integrations with open stack I think Giles talked about integrations with opens and with strong swan was it Giles strong swan And we have net Conf and Yang support. So we have all these integrations on top So that's the second part of Fido is integrations and the third piece of Fido is what I'm going to talk about today Which is benchmarking now usually I'll take a software. How many software engineers in the room? Many software engineers. How many test engineers in the room? One two three four, okay, so that's probably like a probably like it's 22 There's probably like it's probably like a 10 to 1 so Seesit is hugely important in Fido Seesit for Fido is is is how we maintain and we ensure that the Fido data planes Performance does not degrade over time So what and I and I'm not you know, it's no commentary on any other open source project But the danger when you move into open source is that when you get when patches come in the door You need a very very very tight and very elaborate CI and CD Infrastructure to make sure that as you take new patches that you're not trading off performance patch by patch by patch by patch And that's the important role sees it plays in Fido and it's a significant role and that's what I'm going to talk through Today, so what were the tree keeping tree tree parts of Fido? Three question question. I know I'm picking on you. It's like four o'clock and everybody's like we've been to the bar already Ray, you're asking this questions now. What do you do? I said there was three parts of Fido One was the data plane second piece was though up integrations third piece was the benchmarking This is the benchmarking so today. I'm going to talk about why code is not all no longer enough And this is essentially why benchmarking is important I'm going to talk a little bit about Seesit what it is and then we're going to talk dig into three interesting Problem statements with Seesit or three new problems. We're trying to solve with Seesit And then I'm going to summary have a bit of a summary So today you heard about a number of different open source projects. You heard about snab from Luke You heard about open v-switch from Kira and Bruce What else did you heard about Fido from Giles and Rastlav and Billy McFall and you heard about Sillium and who's presenting on Sillium? Yes, gentlemen there and then we have a and then there is a few others that you may not have heard of so there's a galaxy of open source projects and if you also learn more about the galaxy of open source projects I'm giving a talk a 20 to 7 and if you're not already in the pub You can come to the lightning talk room to learn about the open did the The user space networking ecosystem I'll probably just be presenting to myself, but there's a galaxy of open source projects in user space today There's never been more choice Well, how will it perform for me? you know You know, there's a whole bunch of typically vendor reports from the Ross Intel Melnox produces them. There's a lot to arm produces as well. There's a series of academic papers There's blog posters YouTube's but you always come back to the same set of questions. How are the benchmarking are the benchmarks repeatable? Are the tests quite applicable to my use case and if I had a dollar or a euro for every time somebody said Yeah, but is it a real-world use case? I'd be a very rich man You know and the difference between synthetic benchmarks versus real world use. I see Javier smiling there Our tests for platform and vendor neutral and that's kind of an important one to what extent are is vendors You know skewing the skewing the game to their own interests So these are all Important questions and they really all come back to how do you evaluate? Software data planes. How do you test and how do you ensure neutrality? well Without data and without the consistent benchmark, you know without data You're just another pen a person with the pinion and you know, this is from Edward Deming and Myself a match I chose this because I think a kind of illustrates very well our sentiments towards sees it and again That's not a criticism of anybody else But it's this is more of a statement of our intention the intention of Fido sees it is to bring volumes of data That reflect the kind of performance you're going to get from a Fido deployment in real-world use cases So to bring significant to bring that kind of clarity so that when you're asking the question about How will it work for me that you'll have the kind of data that you need to answer those questions? Now we're doing for time.  So if you go to the Fido website and You navigate through there is a whole bunch of sub projects and there's one that's called a sees it Sees it CPL sees it stands for a continuing system test system and integration testing. I think that's right. Yeah That's my first fail of the day So what is Fido sees it Fido sees it uses standard industry benchmarks and tools So we everybody's heard of RFC 2 5 4 4 I think that RFC 2 5 4 4 will be written on my epithet when I die Okay, and the metro ethernet forum standards we test ranges of packets we test 64 by packets we test All the all the packet sizes up to 15 and 18 we test I mix we test Jumbo frames so we test a large Lot just one or two different sizes. We test all the different sizes you might possibly use including jumbo frames We use only open source tools We use only open source tools our traffic generators t-rex, which just happens to be another Fido project We used everything's orchestrated and run by the test robot the robot test framework And then we use Jenkins as the CI so Jenkins takes care of running the nightly bills and running the tests and those kind of things We test multi-core scaling So, you know we test with a single core we test with two cores and we test with four cores And sometimes we test with even more and I'll talk with that about that later on we test There's a typo here. We test NDR. It is non-drak rate non drop rate Sorry when the NDR is when you don't drop packets PGR, which is partial drop rate I think we we have a tolerance of 0.5 percent packet drop and we test MRR Who knows what MRR is? Maximum receive rate is where you throw the kitchen sink and you see how much actually gets true We test the whole range of different network for the whole range of Different network functions things you might be likely to do L2 switching IPv4 IPv6 routing ACL security groups Overlays and we have a slew of unit functional and performance testing Everything's open and fully documented test environment I want to say that again open and fully documented test environment that means that and we have lots of people who do this We have lots of people who are involved in the phyto community who actually go and clone everything and Reproduce it internally because it's all open source all the test cases are open source all the tools are open source You don't need any proprietary tools so you can reproduce the whole thing internally It supports Intel and ARM architecture ARM architectures so What do what does that mean? We actually have an open lab that's hosted by the Linux foundation that gets hardware Contributions from people like Intel from people like Mel Nox from the the ARM ecosystem And we have a very sizable lab that's run inside the Linux foundation That's free for anyone involved in CC2 use where all this stuff gets tested And then that allows us to do lots of different permutations of tests We can do it same test on different generations of Intel hardware I'll talk about that later different same test with different nicks Same test on different platforms same test with an ARM platform same test with an Intel platform. It's the same test Very rigorous. It's the same test run through the same set of permutations the different packet sizes Different numbers of cores different platforms different nicks to give you a very very large Corpus of data about how this thing for info runs in the real world It's multi-platform multi-vendor. So we have it runs today on top of CentOS Suzy Ubuntu, I think I'm not sure about Fedora. I think that might be gone. Anyway, and then we also support cloud native You've heard extensively about cloud. They will open stuff also and cloud native is the hotness at the moment I know but it also supports open stack environments as well And we also have it support for some accelerators like crypto accelerators So we have an open lab Where everything gets tested in the open where patches get tested where nightly bills get tested Where release reports get tested all in an open lab in the phoenix foundation and we own use only open source tools And open source tests So if you don't believe the numbers that are produced by the open lab they can go reproduce the whole damn thing yourself and satisfy yourself that it's completely authentic and That's the idea is that we want to drive good practice and engineering disciplines at the phyto dev community So if you submit a patch To phyto and it causes a performance degradation and we'll come on with show you that later causes the performance degradation And the alarm bell goes off somewhere and somebody sees that and we can take action So the idea is that by having this elaborate CICD environment that we catch those kind of regressions Early on I'll talk more about that more in depth later that we also You know when so much of phyto and so much about user space networking is about performance are You know achieving the best possible performance that you can through software well again Phyto CICD provides you the kind of tool chains that you need to measure the performance And then it also can prevent things like performance aggressions So it's all fully automated is integrated into g it's intermay Integrated into the gerrits so that when you submit a patch at all the tests get kicked off We run functional tests performance tests. We have use case driven test definitions So if you want to do things like test things like vxlan termination those kind of things we have test cases for that and It's all get executed in the open environment. I've said all this and integrated with CI. I've said all this that's fine So what does this look like in practice now clearly? You know I did a count Last week to see how many test cases in total I could I could find and I counted 998 test cases that are currently now. These are these are performance benchmarks That are currently being run in phyto C sit on a regular basis now That's if you go through every permutation of packet size across every platform across every nick of course every microprocessor generation across every use case and you can see here that you know we actually break it out There's 144 layer 2 tests or 216 layer 3 tests 300 You know very very significant number of test cases in each area So when you go through all the different permutations the test cases across you come up with a very very large number I clearly check and show the result of a thousand test cases and it won't a you know at the same time So I just picked one and one or two in particular that I quite like So I quite like the IPv4 test get IPv4 routing test cases So and we have two test cases that we run one's called IPv4 base that runs phyto That runs a performance test of IPv4 routing With one route So you've only got one route in your lookup table and then we have IPv4 scale and IPv4 scale runs with two million routes in your test in your in your in your routing table and you can see this this is a Haswell generation microprocessor, so we have two Intel platforms in the lab We've got has well and we've got Skylake and you can see the IPv4 base with One route gets around 11 million packets per second on has well and then it gets With the two million routes it gets well about nine million packets per second on has well, okay So then we went to phyto vpp 18th. That was with 1804 we then went to 1807 on has well We did some software optimizations Came along and we also introduced the second nick So the software optimizations cause a small performance bump from about 11 million packet seconds to about 12 million packets a Seconds for the million packets a second for the single route test case And there's another small performance bump from about nine to nine and a half or something like that for the two million route test case But then we were also at that was these test cases here where we're a 10 gig nick We're able to do the same test case now with the The x7 10 and the x7 10 again is a 10 gig nick, but it's 4 by 10 gig. So it's a 40 gig nick. That's It's a 40 gig Ethernet controller that has 10 4 by 10 gig physical interfaces And you can see here that you know performance is much the same as it is with the 10 gig As with the 10 gig nick But now we have the satisfaction that we're know that we're getting roughly we're getting pretty much the same performance across a 10 gig nick I'm a 40 gig nick And then we introduced the Intel Skylake micro architecture in Fido 18.7 as well And you can see that performance again is roughly the same on Skylake. So we've also introduced a new microprocessor architecture We've introduced a new introduced a new nick and We have that warm fuzzy feeling that performance is more or less the same and then Fido VP P 1810 came along and Fido VP P 1810 made very liberal use of AVX 512 instructions now AVX 512 instructions, so I presume everybody knows in the room knows what vector instructions are I really know the vector instructions. I see nodding heads and I see hands up. So AVX 512 is the latest Is a is a laces improvement in vector instructions? So before this was AVX 2 which can't think came out and has well was it? A VX 2 is has well AVX 512 is on Skylake Which basically allows you to process even more packets Simultaneously is the idea so vector what vector instructions typically do or how we typically use vector instructions in both DPDK and Fido VP P is we use them to process packets in parallel So if you are using let's say a scalar instructions You typically do a one packet at a time if you use vector instructions You typically do four packets at a time with AVX AVX 2 we typically did four packets at a time with AVX 512 We typically do eight packets at a time if memory serves me correctly, and I hope I'm not wrong Well, what happened when we introduced all the AVX 512 optimizations into Fido VP P on Skylake You can see what happened to the baseline performance there. We went from About what 12 million packets a second on 80 and we're about 12 million packets a second up to something like 19 million packets a second Just simply by using this By using that this power inherent parallelism the microprocessor that AVX gives you Similarly at the scale test case with the 2 million routes went up from something like 9 million packets a second to something like over you know to over To over 16 million to around 16 million packets a second So you can see here the general point and I'm probably labored it now at this stage But the general point is you know you can look at the same test case across a series of nicks and across a series of microprocessor revisions and see what the impact of both of you know What the impact is without optimization the impact is with optimization across a whole range of 99 nearly a thousand test cases So this is how we bring a real breath depth and Predictability what I mean by breath is the breadth of test cases when we adapt the measurement and I mean predictability We do this for every patch. We do this nightly. We do this for every release and we generate a huge corpus of data as a result So I think I've got ten minutes left So I'm gonna talk very briefly about Yeah, there you go Let me draw draw back for a minute any questions. I'm what I've just said go ahead Funny you should ask let me come on to that Any other questions go ahead so not actually nightly Nightly nightly not every patch because that just becomes insane to your point, right? You couldn't do it every path Okay, sorry. I'm just I'm being told so and you're asked the question you're asking is how frequently we do run the entire test case Yes, so I come on to that in a bit of depth. So let me let me come to it so and we talked about this and Previously which is the continuity problem, which is that If you find performance regressions at release time, it's typically very expensive anybody here and there's lots of software engineers in the room If you have a measure performance regression at release time suddenly it's get bisect fun, right? So you're typically really get bisect and you're running the same test case and it's typically very very painful Particularly the rate of velocity of communities like DPDK and FIDO VPP the rate of velocity is huge Means that we have a huge input of patches Which means you really need is a very strong safety net to catch performance regressions So how do you maintain best in class performance and how do you address the danger of creeping normality? And I think the more common name of creeping normality is boiled frog syndrome where you're just things slowly degrade over time and you Don't notice Well, I have a pretty picture for you. So this is our performance trending Timeline so we publish this and all the developers have access to this and you can see this is a whole bunch of L2 test cases here You won't be able to read this but I can show L2 bridge domains with one Mac With one Mac address up to 100,000 Mac addresses I think we actually might do an even a million Mac addresses, you know It doesn't matter but you get the idea that we have a whole series of L2 bridge domain test cases here from a single Scaling the hallway down to a hundred thousand Mac Mac addresses in a single bridge and we can see how that test case Performs over time and not only that we're actually able to run it against what we know Where you know our good numbers and bad numbers so we can say, okay Well, what's in the green and what's in the red and we can make judgments and you can see here the red Circles are regressed where regressions have happened and green circles are where performance improvements have happened And this is a dashboard that the entire community has on a regular basis This is how you generate a warm fuzzy feeling that you're not losing performance over time and Because I said earlier that we have a very very large number of test cases We have a very nice dashboard in which people can just log on and see whether performance is increasing What's the short term percentage change? What's the long term percentage change? What's the trending number of millions packets per second and is there how many regressions you're getting a Outliers and that kind of thing So what are the challenges here and I think the two questions before outlined the challenge is very well We have a lot of tests And we have a limited number of physical platforms And so you very need to be very judicious about when you run so the entire test We definitely gets run every release and we typically run the entire test suite on a nightly basis But that consumes a consumes it consumes a lot of systems When you submit a patch we tend to be more judicious about how many test cases you we run against any individual patch Because that you know consume that can really snarl up number of tests resort sources You know, this is not an unlimited lab. We have we're not an open source project with unlimited funds far from it So we need to be very judicious about what we run and when Yeah, and one of the challenges is how you get how you can ensure you get coverage So how you know that every patch is getting the kind of coverage test case coverage it needs. Okay, I'm going to keep going Deeper diving so moving beyond the symptom so I'm gonna move past this and maybe on to Yeah, probably onto this slide So, you know, I've kind of labored the point at this stage and you know You'd be forgiven for throwing rotten fruit at me at this stage Well, I live with a point at about, you know, how many benchmarks we have But at a certain point you need to move beyond just benchmarking the problem, you know it At a certain point you need more data to try and to try and root cause the problem It's it's one thing telling you know with number of software engineers in this room You know that it's one thing telling me there's a performance Regression or there's a bug somewhere but give me a clue give me some sort of an idea as what the actual underlying problem is and You know, it's well known today that Linux ships with a whole bunch of tools that you can use for next level analysis of where Performance regressions are coming from, you know tools like Linux perf We also have things like PMU tools for okay for pulling PMU stats from the microprocessor That tells you where cycles are being born and we actually also have very good introspection actual instrumentation in phyto VPP itself to tell you where cycles are being for Borned so what we're starting to do now is we're starting to use these tools and again They're all free and open source tools to start to generate the next level of information So when you go in and you see a performance regression now You can actually drill down and get to the next level of detail and look at the graph pipeline in VPP One of the I think Giles there earlier talked about the VPP graph hierarchy And you can see in the next level where cycles are being burned and that interface and that data again is being generated and it's there So when we get a performance regression, we don't just leave you hanging and say hey guys There's a performance regression. Sorry, we give you the next level of data They actually does a performance regression in this graph node as opposed to a performance a system-wide performance regression Okay, and then what lastly we'll talk about service density and this is you know when like we talk about real-world use cases So, you know, there's a lot been a lot of discussion today about The cloud and cloud night cloud native deployments, but there isn't really a whole lot of data around how Data planes react to cloud native deployments or certainly not enough data around how cloud How did and deployments react to cloud native environments? So we've been doing some work to try and understand that better and we've been Comparing service densities for virtual machines as compared to containers There's a lot of industry discussion at the moment a lot of ops discussion in the community around Hey containers are more efficient than virtual machines hands who proves her that statement containers are more efficient Virtual machines. Okay, lots of hands But then that's well, there is that as well But there is all that's a lot of talk around that but that in my experience There isn't I haven't seen a whole lot of data to substantiate that So what we've been doing in Fido again is trying to put data behind that to understand What that looks like and then where the inflection point is So we're we have what we're benchmarking today is we're benchmarking a VNF service chain where you have a whole bunch Sorry a whole bunch of virtual machines in a chain all connected by a v-switch And then we also have the same setup for as a container service chain again a whole bunch of containers Connected by a v-switch and then we also have a container pipeline where the container where you you inject Packet into the first container and then they just pass it to each other also to understand what a point-to-point performance looks like So and in that way what we do again as we test with one Sorry one virtual machine on the switch we test with two virtual machines four virtual machines eight virtual machines We test with one container two containers four containers a containers and then we you know We typically test again You get the idea of labor the point across all the packet sizes and we produce this kind of data that you can see here that again illustrates very well where the inflection point is for you know where When you start to get a switch over cost in performance from containers versus virtual machines, I've got one minute left to go So three problem statements that we're working on ensuring that you don't lose your performance As your net as your data plane evolves also understanding where performance regressions are coming from at the next level of information And then also how cloud how data planes perform in a cloud native environment So that's the Fido sees a project. It's an open and welcoming project and Same as every other open source project There's always there's always plenty to do so we'd love to love to see you turn up any questions Okay. Thank you very much


Some minutes after 4 a.m. this morning my wife worked me up to inform me about our 13 month old daughter's high temperature The next thing was to rush her to the hospital after spending over an hour in the hospital running tests and taking her medications We returned home returning home I knew that my morning had been interrupted from what it used to be I needed to lie down and reset my mind as I was trying to do that What I heard next to me was the video my brother-in-law was watching It was just around 6 a.m. in the morning and my brother-in-law was busy watching comedy videos I felt like slapping him But in my culture if you do that nobody comes to your home again in today's video I'll be sharing with you the one wrong thing every poor person does in the morning if you're new here Consider subscribing so that you won't miss other interesting videos like this Minding your own business the one wrong thing almost every poor person I know does wrong is that they don't usually mind their own business This is even worse because most people I know wake up every morning to another man's business You can never be rich if you wake up every morning to prioritize other people's lives or businesses Over yours if there's anything you need to be extremely selfish about that's your morning And I will explain why you see when you wake up in the morning everything in you is alive And you can spend the first hour to set the tone for the day Unfortunately your mind can hardly get the nourishment it needs to build a solid foundation for the day by consuming entertainment or news First thing in the morning instead Something like staying alone Meditation thinking reading or listening to motivational speeches may be the best thing to have a positive spirit You need to conquer your day unprepared soldiers Life is a battle. You don't need anyone to tell you about that What I think most of us need to be told is that you have to prepare for these battles Every morning or else you will lose to your enemies What are the battles I'm talking about when I was a struggling entrepreneur? I knew that each day when I woke up there was a big battle to be fought one day It could mean that I'll go and negotiate with a potential investor another day It might be that I'll need to convince someone to work for my company without pay until we are profitable The next day it might be that I'll launch a new product All these are very heavy loads and nobody could carry them except you are motivated to do so And that's the reason why I had to wake up each morning to cool down my head Read a few pages in a motivational book or listen to motivational music Today I don't have as much heavy load to carry as I did a few years ago Yet I can't afford to attack my day with a weak spirit Don't start your day with a weak spirit and don't start your day with other people's business Life is tough and the battle is daily. You can't afford to attack your day Unprepared for the ups and downs inside it and that's why you don't want to start your day with entertainment The busy employee There are a million things I hate about being an employee And one of them is the fact that you are just too busy doing your job Most people don't think that is a bad thing Some people are proud of being busy doing a job or their business Well, I can be happy being busy as long as I can have an hour or two each day to nourish my mind When I advise that people should spend their first hour in the day to mind their own business Many people will think well, I have to rush to my job Yeah, that's not necessarily a bad thing But as long as you don't have a few minutes in the morning to set your mind on the right motion And an hour in the day to learn and develop your mind You'll be where you will be in the next 10 years Exactly where you are today. This is simple arithmetic But most people don't understand it your mind is the only asset that matters But it's like a tree if you nourish it your mind will grow and produce fruits if you neglect it Your mind will bring you no fruit and you'll have no progress in life To be successful in life You have to spend the first half hour of your day for yourself to program your mind for a great day If you're too busy to do this, you're too busy to grow or succeed Aside from using your morning to set a good foundation for the day Every living thing should have at least half an hour every day to nourish their minds You should have some time in the day to learn a new thing Read a book or watch some educative videos.  I read a few books that made me hate the tv Don't get me wrong. There are some very educative programs on tv The only problem I have with the tv is that it is very interesting And so is the social media and every other entertainment box The problem with the tv and social media being interesting is that they are not your business When something is so interesting that people wake up in the morning to consume it And such thing is either their business or helping their business Then that's the invitation to poverty As a matter of principle, I make sure I derive the most pleasure from my work And whenever something else seems to be super pleasant, I look for the door that leads me out of such things That's the reason why I don't have a tv or use facebook instagram And so on these things are not bad for people who have self control But for people like me who don't have self control tv and social media should be your suspects How do you know whether you have self control? Well, by the time your first duty in the morning is to check your social media profile the post of photos Start spending the first 30 minutes of your day watching a comedy video or a tv show Then you've lost your mind to the world around you and you need to retrace your feet Let me summarize this video by reminding you of the most important advice from the richest man in Babylon And that's pay yourself first While this advice has a lot to do with your money It is as well important for your time when you wake up each day. Remember this pay yourself first Spend the first half hour for yourself to nourish and develop your mind Don't spend your first hour in the day to consume other people's products or creativity Except such products videos or creativity helps you to become a better individual Thank you very much for watching our videos We'd like to give you another interesting video for you to enjoy next But before then our team will be very happy if you can like this video and share it with your friends on social media If you're new here, don't forget to subscribe so that you won't miss other interesting videos like this Look at your screen now to see two other videos. We handpicked for you to enjoy next if you like this channel We're happy to announce to you that our team has launched three other interesting channels One of our new channels is called hunt tv where we publish Inspirational videos to inspire and encourage you as you go through the harsh journey of life Our second channel is called fame tv where we share with you the interesting and inspiring stories and biographies Of the successful people who have achieved greatness with their lives Our third channel is called money tv Which is a channel designed to use more dramatic video clips to teach you about money Look at the description box to join our three new channels. We love you


Pre-commissioning unit Zoomwall is the Navy's first automated stealth destroyer. The first in its class ship contains highly advanced technology and many unique features including staterooms for all sailors. This is my first ship and I feel pretty lucky because this is a four-man stateroom as opposed to traditional birthing utilized in our laptops that we were issued. We can just connect to the network drive that we have and just accomplish any work that we need to straight out of our own staterooms. We have our own private head for the four of us to share and we don't have really assigned cleaning duties and responsibilities. We just kind of take it upon ourselves to keep each other in check. To maximize efficiency, sailors are also equipped with cell phones. All of us E5 and above were issued little yellow cell phones to be able to communicate with one another. Stay tuned to all hands update for future coverage on PCU Zoomwall.


All right, welcome. This presentation will be about merging in quantum and k-xO and k-sum algorithms, and this is going to work with Maya and Aya Plizinten. So we're going to be interested in merging algorithms in a very broad range of applications. First I'm going to introduce the merging technique when there are many solutions for the problem, and then we're going to see that in the quantum setting, and then we're going to extend that to a single solution case. So all of the main topic is quantum algorithms. We're going to remain at a very high level, so please, you don't need to be a quantum expert. So originally the main idea was to solve these problems, the generalized birthday problem. We're going to consider that, we're going to consider oracle versions of these problems, where we are given oracle access from the n-bit to n-bit function h. And the first one is whether there are many solutions. We're just looking for k elements such that their image x0 to 0. And the second problem for us is the problem where there is a unique solution. So you restrict the inputs of h to n of our k bits and you're going to find a single solution table. And the xor that we're going to take can be replaced by modular additions everywhere, but it's just a bit more technical. So originally the problem was defined by using only, by querying only lists, and we're going to consider an oracle here. So there are many applications for this problem. Sometimes there is a direct reduction to the k-sum problem. So even if this doesn't give the best, necessarily the best algorithm, this, because there are dedicated ones, sometimes we have good time memory traders, for example. So for direct reduction we have subsetsum problem, parity check problem, hpn as well. And the multiple encryption problem is not directly reducible to k-sum, but there are similar algorithms that will apply to it. So there has been many works on this topic, but we're going to focus on the time complexity and make it to focus on the exponents. So we're going to forget about all the logarithmic improvements. So we actually need roughly two results. The first one is that the optimal query complexity is 2 to the power k, because if you make 2 to the power k queries to h, then you can build 2 to the n k-tuples and have k0 to 0 with high probability. And the second is the time complexity for general k in this many solution case, which is given by Wagner's algorithm on the next slide. And we're going to focus on that. So Wagner's algorithm works by using this idea of merging. In the paper of Wagner, this is computing the join operator. If you have two lists that contains elements, let's say outputs of the function h, then it's really easy and it's really efficient to compute the join of these lists. The set of pairs, that's, so of pairs from A1 time cell 2, is that partially collides on the first two bits. It's really efficient because you can just assume that both lists are sorted and go through both at the same time. And the time is necessary is basically the time to go through the input lists and the time to produce the output list. Wagner's algorithm uses this and applies this recursively. So it's a tree of merges. And it depends on log 2 of k because 2 to the log 2 of k is the biggest number, it's the biggest number of lists that we can merge in such a binary structure. I'm going to give an example. So we're going to take four lists and we're going to keep four lists right under the end of this presentation. Okay, so we start by building these four lists and querying to the other three elements. When I say elements, it's simply x and h of x. And then we're going to compute two joins. A1 join L2 and A3 join L4. So we merge into these two intermediate lists. And we merge on n over 3 bits. So we obtain lists which are of size 2 to the n over 3 as well on expectation. And then we merge another time. But in the end, we only want a single solution. And we're going to merge on 2 n over 3 bits. And that would be a single solution because we have two lists which are of size 2 to the n over 3. And we have 2 n over 3 bits that remain to be put to 0. So in terms of all the time complexity is 2 to the n over 3. If we had eight lists, it would be 2 to the n over 4. If we had 16 lists, it would be 2 to the n over 5 and so on. In the quantum setting, there is a similar query complexity. In that case, we're given quantum recall access to h to the function that produces elements. This is optimal query complexity which is 2 to the n over k plus 1 instead of n over k, which was given by Williamson's Panic. And for any k, there are previous algorithms that give you time complexities, such as n over 3 for 4 conditions instead of n over 2. And an exponent 1 over 2 plus log 2 of k instead of 1 over 1 plus log 2 of k for any k. And all these complexities, so these are algorithms that use quantum accessible memory. I'm going to focus on that, but there are also results without. But these are more simple to analyze. Okay. So in this previous work, we had a very similar curve as classically. And actually, the algorithms that were used were very similar. And the curve was decreasing step by step. And in this new framework that I'm going to present, we can reach better than that. And we can do something that doesn't happen classically, that is decreasing the time complexity at each new value of k, strictly, and having a much more smooth curve for these complexity exponents. So now I'm going to present a framework that enables to do that and give a few examples. Okay. So in order to perform these quantum merging steps, we're going to use quantum search. In order to present quantum search, we can start by presenting classical search. So classical search is where classical exhaustive search is where we have a search space. And we have good elements in this search space. And we know how to recognize the good elements. And we know how to sample an element from a search space at random. So if we're able to do that, then we can turn this sample from a search space into a sample from the good subspace only. And we just need to sample repeatedly from the search space and test all the time if we have a good element or not. And we need to repeat that depending on the number of good elements t among n, we need to repeat that n over t times. And so we're really going to see that as a function, like an operator that transforms a sampling from the search space into a sampling for the solution space. Because in the quantum setting, things are really similar, except that everything is quantum. If we have a quantum algorithm that samples from x and a quantum algorithm that tests if an element is good, and I don't even need to go into the details, then we can obtain a quantum algorithm that samples from the good subspace. And it does only need to repeat this sampling and testing operation, square root of n over t times instead of n over t. This is where these famous square root speedup for quantum search arrives. And we need to implement these functions as quantum algorithms, but that's all. And once we have done that, we'll also have a quantum algorithm that if we measure the results, then we can obtain one element from j. But we can also use this sampling inside another quantum search and we can pile up quantum searches the same way we would do for classical searches. This is an interesting feature. Okay, classical merging can be seen as a sampling procedure. Here is how. We want to create this list l from these two lists l1 and l2. But what we're going to do actually is to sample from the list l. In order to sample from the list l, we suppose that l2 has been created. It is stored somewhere. And we're going to sample elements from l1. And when we sample from l1, we try to match these elements against l2 and to find someone that has a matching prefix, so that we can put a new element in l. So there is a time to sample from l that depends on the time to sample from l1 and on the number of new zeros we want to obtain. And if we wanted to compute the full join, the full list l, then we simply can sample from it repeatedly. If we put that this idea in Wagner's algorithm, this means that we don't want to build the list l0, we want to sample from it. Okay, this doesn't change a lot because l0 has only one element anyway. So we want to sample from l0 once, hooray. But then what we're going to do is to suppose that l3 join l4 has been built and is stored somewhere. It is given. And we're going to sample from l0 by sampling from l1 join l2. And we need to sample from l1 join l2 to the number three times before we find someone in l0. And if we suppose again that l2 has been stored in memory and is somewhere, then we can do that by sampling from l1. So there is a pile of searches. And in the algorithm, we're going to sample from l1. And then this will enable us to sample from l1 join l2. And then this will enable us to sample from the final list. This doesn't change anything to the classical time complexity of Wagner's algorithm. This merely changes the amount of memory because now we're doing everything here by only storing two lists, l2 and like the two blue ones. But it's really important in the quantum setting. Because in the quantum setting, this whole right branch here is an exhaustive search that we can do quantumly. So this is the idea of quantum merging. The list that we put in blue here is going to remain exactly the same. It's a list that is built and stored in memory and is going to help us. And then we're going to sample from the list l1 and to use that in order to sample from the list l. And we just quantum search everywhere. So the time to sample from l has been reduced by a square root factor compared to the previous formula. So on this tree, this means that the time to compute the right branch, the time to sample this element of l0, is reduced by a square root factor. So instead of needing to sample the classical l1 to do the number three times, we need to sample it quantumly to do the number six times. So this is great, but it doesn't change anything to its time complexity because we still have these two blue intermediates which are of size to do the number three and which cost the same time to create. So what we need to do now is to adapt to this quantum capacities. We need to rebalance the tree. And in order to reoptimize, we're going to take smaller lists l2 and l3, join l4 and we're going to take a much bigger list l1, much bigger space to span using Grover's algorithm and quantum search at a much smaller space in the intermediate lists. So we're going to take l1 of size to do the number two and we're going to take the others of size to do the number four and then everything is balanced with the complexity to do the number four. In general, a k-exor problem can be decomposed in many ways because you can consider it as another problem which involves smaller k-primes or problems and so on. And we try to optimize the strategy over all the possible decompositions using the fact that at each time, a new list is going to be sampled using quantum searches that use intermediate lists, the blue ones in the example. And if you write down everything, then there is this space of possible emerging strategies to span and the exponents in the time complexities optimizing them is a linear problem. So you can find the best strategies using a mixed integer linear programming framework which is what we did. And this enabled us to find these emerging strategies that led to the complexities we saw in the graph. And then then we moved on to actual proofs for it. So we got this close formula for the complexity here in the case where there are many solutions. So among all the examples that are presented at the beginning, these examples were actually all for the single solution case. And sometimes we also have intermediate examples where there are not, there are a few solutions only. If there is a single solution to find, then we can try to merge as well. And we can try to do similarly. But we're going to run into troubles. For example, in the fourth list example that I'm going to keep, all merges are going to become trivial. Because if we don't do that, if we put a non-trivial prefix in the join operators here, then we were going to miss the solution. So in fact, we can't really merge. We're forced to do something trivial. Well, however, we're going to use another idea. We still want to merge because merging is a very efficient operation. Why not? And we still want to merge and use a non-empty prefix. But since we're going to miss the solution, we have to repeat the computation for all values of this prefix. And this is Schruppel and Chamier's four-list algorithm. And in general, this is the dissection technique of Tinier, Dugan, Makinner and Chamier of Kipto-12. Classically, this is really interesting to save memory, but we're going to see that quantum matrix also reduces the time complexity, which is a nice feature. So how does it work for Schruppel and Chamier's algorithm? We're taking a prefix of size n over 4 so that when we merge, we obtain intermediate lists of size 2 to the n over 4 as well. And then, well, there is only a solution with probability 2 to the minus n over 4, which means we have to repeat this for every value of s. The whole tree here costs only 2 to the n over 4 time. So the time complexity in total when we repeat for every s is 2 to the n over 2. But the memory complexity has decreased. It's not 2 to the n over 4. In a quantum setting, we're going to do the same thing. It's just going to be a little more complicated because the intermediate list, which was of size 2 to the n over 4, is a bit too large for us. But we're still going to keep this prefix s of size n over 4. And we're simply adding a new parameter i, which defines a choice of sublist of l3. So if we have taken some s and some i, we have a sublist here of size 2 to the n over 8, an arbitrary sublist. And we compute a join here which is of size 2 to the n over 8 as well. And here we have l2 of size 2 to the n over 4. And now we do a Grover search for l1 find an element here. And we try to find an element here. And this only happens with probability to the minus 3 n over 8. So we have to repeat all of this in a loop. If we look at the complexities that this gives, then while computing the join operator in time to the n over 8, we're doing the Grover search in time to the n over 8. And we are looping over all choices of sublist 4 by 3 and of prefix s. And this gives a complexity which is 5 n over 16, little below 2 to the n over 3. Why below 2 to the n over 3? Because n over 3 is what we would have obtained if we only tried to merge two lists of size 2 to the n over 2 basically. Quantumly. And this is really interesting. This means there is really an improvement in the time complexity to do that. Actually if you have a problem that can be cut in any number of lists. So if it's a K-list problem with a single solution, but for any K, and this is the case for the subset sum problem for example, the best time complexity that we could obtain with these methods is 2 to the 0.3 n, which is also smaller than n over 3. And it happens for K equals 5 or multiples of 5. Actually it turns out that in this case the complexity is really well balanced between computing this intermediate join and doing the Grover search here and doing a loop on the value of s. This advantage in time comes from the fact that in general the quantum advantage in merging is not quadratic. It's less than quadratic. But the advantage in loops is quadratic because we are using Grover's algorithm in the loops. Which means as we put more work into the loops, we do better in time than if we were only merging. And this is something that really doesn't happen. So if you want to compare with other algorithms that solve the same problem, there is the algorithm by Ambienis that solves the problem for two lists, basically the element distinctness problem, or the algorithm of Bershine, Jeffrey, Lange and Meurer solving the four list problem. And this algorithm actually obtained a time to the 0.3 n. It obtains this, but only for of course four lists or a multiple of four. And for every K, which is not a multiple of four, if we optimize with our exponents, we get a better exponent if optimized with our method. It's not a anymore, only for K, when K is a multiple of four, it's not the best. So to go back to the problems that I briefly mentioned at the beginning of the talk, we could obtain improved algorithms for basically all of them for the priority check problem, because this is a K list problem as well for the K encryption problem, because this is similar to a K list problem for the subset sum. If we use simply a K list algorithm to solve it, we can obtain the best time memory product, not the best time, because there are better dedicated algorithms, but these algorithms use much more memory than we do. And we could also put that in the system BKW algorithm of SLHAT at crypto 18, which is an algorithm that uses less memory than the BKW algorithm for LPN and LWE. And this improved all the quantum time memory trade-offs. So normal details about the framework and about the applications and full version of the paper, and also some code that is available that computes the best emerging strategies. And with that, thank you for your attention.


Hello everyone. So in this video, I will talk to you about the using the Firebase cloud of firestore attached to a recycler view, which is kind of the dominant way of showing data in Android apps these days showing it attached to a recycler view. It gives you a lot of flexibility in how you present the contents of your data. So this video was recorded a while back. Everything is still relevant. The code looks the same. The lessons are the same. You may see some cosmetic differences in Android Studio and in the Firebase console. One big difference you will notice is I work within the Firebase example project, which is linked both in the lab that's attached to this video and in the video description on YouTube down below. It looks a little bit different. Let me bring up the emulator so you can see. So if you have checked out the Firebase example project, which you should have and you're playing with the code in there. So again, the code is going to be the same as you will see in the video, but the screen is going to look a little bit different. I have some old buttons, old styled buttons in the video you're about to see, but we're going to be working in this area here in the firestore plus recycler view. Okay, so that's it. Everything else is still relevant. And mainly this is an introductory video explores the code a little bit explores what the code does a little bit. But the bulk of the learning here is for you to spend time in the code and kind of playing with it and see how it compares with recycler views that you were asked to implement in the past. All right, that's it for now. And I'll switch it over to my younger self. All right, let me switch over to some code here. So I want to show you just briefly what's going on. Okay, so here we've got the Firebase example project. So in your lab for this this week, you will have a link to this Firebase example project. I encourage you to check it out after this just follow along for now. And then in your lab, you can check out this project and see the kind of code that we're working with. In the Firebase example project, I've opened up my firestore activity. Okay, so here's my emulator that's running over here. Here's the authentication example from last time. This still works if you want to try it, but I'm going to click on launch firestore activity. And so I get this example activity that comes up and it's pretty simple. All it does is you pick from a spinner, a first name and a last name. And when you click add person, it's going to save some information to the database and then display it right here. Okay, so let me let me just add Alan Turing. Okay, so what happened when I click this and let me add someone else Donald. Okay, and let me click add person. Okay, adding Donald Knuth. So what is happening here is when I click add person in my Java code, I create a person object, one of these things. And I tell the firestore to save it. And I also have a recycler view down here that is attached to my cloud firestore collection that stores people. And anytime it detects a change in the people collection, it automatically updates the recycler view. So that's pretty cool. So as I click add person, it actually is going up to the cloud firestore in the cloud, storing it in the database. And then coming back and pushing out that change here. Okay, so this recycler view is actually listening to the cloud. It's not doing anything directly in response to me clicking add person. It's listening to the cloud for changes. The other thing that I can do here is when you click on one of these cards, it will delete them. So I can delete these people and now they're all gone. All right, but let's look in this activity for just a moment. So if we look at the top, kind of here right here, I'm initializing my firebase firestore firebasefirestore.getinstance. And then down here, you can kind of see I'm hinting that I'm setting up a recycler view with the recycler adapter. We'll talk more about that in a second. But let me scroll down to the add user method. This is actually attached to add person. I probably need to rename this, but this method gets fired when you click add person. These two spinners are not attached to anything in particular, but you can see here person gets new person. First name from the first spinner, last name from the second spinner. User ID I create by taking their first name plus their last name and doing some transformation. And then getting the current date. And if we look at the person, the person is just a data object. No, it's just a plain old Java class that's holding person information. Nothing to it. Let's go back over here to this code. Here, MDB is my instance that lets me talk to the firestore. And I say for the people collection, I want to add to that collection new person. Where a person is a Java object. And that's it. That does it. I have a success listener here that logs whenever the user ID was successfully created and a failure listener if there was some error. But that's it. Firebase instance, the collection I want to talk to collection dot people. People is just a string. Let's see in a second. And that puts it up in the cloud. Let's go over to the cloud for a second. Here's my Firebase console. And click on database. All right. So here's the cloud firestore. I have a collection up here already called plants. We'll see that in the next video. There's no users here currently or no people currently here. Now watch me. Let me add grace hopper back. Okay, I need to refresh this page. Hmm. And now I have a people collection. Right. People collection is what I specified. That's where I want people to go to when I create them. So here she is. Right. She's in the people collection. There's a document. This is a unique up document identifier generated by the firestore. And then here are her fields. Created time, which is a date time stamp string first string last string user ID. Right. Let me add somebody else. Ada hopper. You may have noticed that the firestore was actually clever enough to show me that this person was added in real time. Watch over here again. Let me add another person. Okay. Watch over here. Actually updates in real time. That's pretty cool. Let me delete someone. Let me delete grace hopper over here by clicking on her. She goes away. Right. Here real time in the cloud firestore database. So that's pretty nifty. Let me go back to my code for just a second. So the the recycler view down here. This is a recycler view. Oh boy. Time to get them working. The recycler view is actually kind of set up a little bit differently than the recycler views that we studied in earlier labs. It is using a library called the firebase UI that makes it really easy to kind of connect and talk to the firestore. Right. So the thing about it that's neat is that this firebase recycler recycler options and then the person recycler adapter. They use library methods that are effectively listening to this for real time updates. So you don't have to do any real time listening. This does it for you. That's really awesome. All right. So the best way to learn about this and understand what it's doing is to kind of get in here and play around with this lab. So that's what I'm going to encourage you to do. Let me switch back over here. So let me bring myself back to you. All right. So what I want you to do is to go check out the lab called firebase cloud firestore. It links to the sample project that I showed. And yes, you will be using the firestore in your term project. Once you get into it and start using it a little bit, you'll see it's not too bad.  Did it fail? What are you going to do? You've got that event handling stuff. But other than that, it's really not too bad. Certainly the most complicated thing about it is that recycler view. And hopefully I've given you a useful template to draw from there. All right. So next time we will talk about cloud storage, which is how you can store files like pictures in the cloud and display them in your application. Until then, have a good rest of your day.


Hello everyone, am I audible? Am I audible dear? Yes, sir. We have discussed till here, no? Have we discussed here? Yes, sir, we have discussed. Year war completed in the previous class? Mechanism of hearing? Yes, sir. If you have any doubt and CRT and if you have any doubt, you can ask me, okay? Now, chemical control and coordination. Chemical control and coordination. Let's sit here. What happens in chemical control and coordination? What is coordination actually? What is coordination actually? coordination is the process C process by which one or more organ or organ system work together to perform functions like you are running, you are running which system is used a skeletal system is used, but along with that only heartbeat increases, respiratory rate increases. Getting my point means once a skeletal system starts working, getting my point, you can say, you can say along with that the circulatory system as well as respiratory system also started working, yes or no? And everything comes to normal once we stop performing work. Agree, dear? Tell me that first. Yes, sir. Yes, that is known as coordination. Coordination is of two types. Okay, you people can write down this thing. Done, everyone? Done, dear? Sir, just a second, sir. No issues. Done, sir. Done. Let's see one more thing. Coordination in human body is brought about by two system. One is the nervous system. Okay, and one is endocrine system. What's brought about by nervous system is known as neural control and coordination and by endocrine system chemical coordination, yes or no? Agreed? Yes. Agreed? Here are the differences. You people can note down. I think that is too simple. You will understand. You can say without any interference only. Done, dear? Neural coordination is fast. Neural is point to point. It is diffuse and long lasting. It is brought about by nervous system and it is brought about by endocrine system, yes or no? Everyone, yes. Endocrine system in human consists of endocrine glands and hormones. Let's see the endocrine glands. These are the endocrine glands of human. There are three in the brain. Pneumothalamus, pineal, and pituitary. Two are there. Thyroid and parathyroid. Parathyroid is situated on the backside of thyroid only. Here is the thymus, pancreas, and adrenal gland. And in females, ovaries found in males, testis is found. Together they are going to be known as gonads. Together they are going to be known as gonads. Yes or no? Everyone, yes. See over here. So this is there. You can write down. In the brain, three are there. Remember it like that only. Done. Let's see. I will be asking about hypothalamus. You can say telling you people about hypothalamus and pituitary at the same time. Getting my point. First thing is development of pituitary gland. Actually pituitary gland is ectodermal in origin. What is the origin of pituitary gland? It is ectodermal. Agreed? It is ectodermal. Getting my point. How it develops? This is the diencephalon part of the brain. And this is the primitive gut. Primitive gut. From the gut, here is developing a retchage pouch. What? Retchage pouch. Agreed here? Yes. Let's see. What happens? This is the imagination found from hypothalamus. The diencephalon only. This blue color. And this retchage pouch got broken and get itself, you can say, surrounded around this part. Okay. The part that is developing from the diencephalon is known as neurohypophysis. What? Neurohypophysis. This is the blue color part. And the part which is developing from retchage pouch that is known as adenohypophysis. What? Adenohypophysis. Adenohypophysis consists of pars tubarellis, pars distalis or anterior lobe and pars intermedia or middle lobe of pituitary gland. No. Everyone. Everyone. So it is developing from the two places. And it is having two components, adenohypophysis and neurohypophysis. Adenohypophysis having three components, pars tubarellis. This is the part which is surrounded around the infundiblum. Infundiblum is the stalk that connect hypothalamus to pituitary gland. Is it anterior? Everyone? Yes. So pars tubarellis, pars intermedia and pars distalis are the three component of adenohypophysis. Agreed? Everyone? Right on. Excuse me, sir. Yes. So what is the primitive gut again? Primitive gut is your GIT gastrointestinal system. And the primitive is when it is in the earliest stage of development. Yes or no? Sir, could you scroll down a little bit? Yes. Sir, could you scroll to the adenohypophysis part? Done, everyone. So this is the component of pituitary. Getting my point? Now, this diagram. What this diagram is showing? Let's see. Actually, what happens? These are the nuclei. These are the nuclei. Nuclei. Do you know what is the meaning of nucleus in central nervous system? What is nucleus, dear? Cell bodies. Collection of cell bodies. In CNS. Agreed, dear? Collection of cell bodies in CNS is the nucleus. Okay. So these are the archway supra optic and para ventricular nucleus. Yes or no, dear? Yes. You see how it is secreting over here. This is superior hypofacial artery. It is breaking into capillaries over here and it is coming down to the pituitary. Okay. It means anything that is coming to hypothalamus to anterior pituitary or adenohypophysis, that comes through this. This is hypofacial portal vein. That comes through hypofacial portal vein. Yes or no? Agreed? Agreed, dear? Hypofacial portal vein only. Okay. Okay. But this part posterior pituitary is connected to hypothalamus through exones of these nuclei, means there is direct exonal connection between hypothalamus and posterior pituitary and anterior pituitary or rather adenohypophysis is connected to hypothalamus through. Connected to hypothalamus through hypofacial portal vein. Getting my point. So things are, secretion of posterior pituitary is there. It is synthesized in these nuclei transported through exones and only released from what? From posterior pituitary. So if somebody will ask you, secretion of posterior pituitary is synthesized in, what will you say? What will you say? Nuclear. It is there in the hypothalamus only. Secretion of posterior pituitary is synthesized in hypothalamus. Agreed? Transported externally to posterior pituitary and is stored and released from the posterior pituitary. Yes or no? Tell me. Yes, sir. Secretion from hypothalamus reaches adenohypophysis through hypofacial portal vein. Agreed, dear? Everyone? Yes, sir. Getting my point. So secretion of anterior pituitary is synthesized in anterior pituitary only. Only controlled by hypothalamus. But secretion of posterior pituitary is synthesized in hypothalamus. Only released and stored from posterior pituitary. Yes or no? Yes. So what is the site of synthesis of hormones of posterior pituitary? The nuclei in the hypothalamus. Yeah. Please write down. Sir, are the axons specialized to carry chemicals or are they normal? Yes. Yes, it will be transported. Anything that is synthesized in the cell body that come to the exon terminal. Usually. So, sir, the chemicals are not in the synaptic knobs. They're just transported through the axon. Yes, sir. It is transported like that only. Actually, they are modified neurons. They are neurosecretary cells. So can they also transmit action potential or only carry hormones? No, not only carry hormones. They are not going to, they are not specialized for conduction. They got modified for secretion. Yes, sir. Great, dear. Done. Everyone? Just a minute, sir. Could you write the name of the nuclei like on the side or something? It's not visible. Is it visible now? Arqueate supraoptic paraventricular. Done, dear. These two lines are very important. Pay attention to them. Now, hormones secreted by hypothalamus. Actually, hypothalamus is the floor of diencephalon. Already we have discussed that. It controls the pituitary. In the old terminology, pituitary is known as master gland. So hypothalamus at times is known as master or master gland because it is going to control the pituitaries or not. So hypothalamus secretes two groups of hormone. One is the releasing hormone. Another is the other is inhibiting hormone. If releasing hormone is going to be released, it stimulates the secretion of anterior pituitary. And if inhibiting hormone is going to be released, it is going to stop or decrease the secretion of anterior pituitary. GNRH is growth hormone. Releasing hormone released from hypothalamus. And once it is released, it increased secretion of growth hormone. Yes or no? And inhibiting hormone is it is going to inhibit the secretion of anterior pituitary. And once it is released, it leads to decrease in the secretion of growth hormone. Agreed, dear? Everyone? Yes. Done. Now, there are few cells which is there in the pituitary gland and hypothalamus is going to control that. Like human growth hormone is going to be secreted by somatotroph. Okay? It is going to be acted by GHRH, which is also known as somatocriene. Once somatocriene will be increased, will be released, secretion of human growth hormone or somatotropin is going to be increased. Yes or no? And if GHIH or somatostatin will be secreted, its secretion is going to decrease. Yes or no? These are the hypothalamic hormone. These are the cells of pituitary. And these are the hormones secreted by anterior pituitary. Yes or no? Yes or no? Yes. You people can write down. Hi everyone. Done here? So just a second sir. You people are going for the exam in school? Yes sir. When? For YPR exam begins next Monday. Tomorrow exam is not there for you people? No sir, but we have practical classes tomorrow and the whole week next week. Okay. Understood? Yes. Prolactin is secreted by which cell of the pituitary? Done sir. Done. Now pituitary gland, its location is inside a bony cavity present in the spinoid bone. It is known as cell atarsica. Another name of pituitary gland is hypofysis. Okay. One is trophic hormone. Trophic hormone is one hormone that is released from one endocrine gland and acting on another. Like TSH, FSH, LH, ACTH. They are released from the pituitary, acting on thyroid. Yes or no? Agreed dear? Yes. So this is there. No. Hormone secreted by pituitary. I will talk about hormone secreted by pituitary. First thing is hormone secreted by adenohypofysis. Adenohypofysis comprises 75% of the pituitary gland and it is having following parts. Stalice, intermediate and tuberculosis. Tuberculosis do not secrete any hormone usually. So hormone secreted by parts distalice. GH, LH, FSH, TSH, PRL and ACTH. Yes or no? GH, LH, FSH, TSH and PRL. Agreed? GH is growth hormone. It is known as somatotrophin or somatotropic hormone. It regulates somatic growth of the tissue like muscle and all. Agreed dear? Yes. If you will talk about exonone, its function, exonone skeletal system. It increases the cell number like C. This is the diphysis of bone. This is the epiphysis of the bone. And in between epiphyseal plate is there. Okay. Already you must be knowing they are the cartilaginous plate. They are known as growth plate also. Growth hormone act on them also. Like if I am going to take growth hormones now, no effect will be there because epiphyseal plate is closed now for me. Yes or no? Yes or no? Yes, sir. So it will increase the cell number. Insulin and thyroxine also have been role for bone formation. Insulin is going to be responsible for cytoplasmic growth. And thyroxine is going to be responsible for DNA replication. Osteogenesis is bone formation. Yes or no? You people can write. Could you scroll up to the hormones? Could you scroll down? Done. Done. Now. Now what do you think? If your muscle has to grow, will you require protein? Will you require protein? Yes, sir. What should be the exon of growth hormone protein synthesis? It should increase, no? So it is having anabolic role. It is having anabolic role. Yes or no? Yes. It is having anabolic role. Yes. Agreed, dear? Now, CA plus 2 absorption from GIT will increase. Why? Why? An excretion of all these things will be decreased from the kidney. Why? Because if bone has to be formed, CA plus 2 will be required. Yes or no? As far as carbohydrate metabolism is concerned, it has to increase blood glucose level also because more energy will be required for the growth. Because more energy will be required for the growth. Yes or no? Yes. So as far as carbohydrate metabolism is concerned, growth hormone is a diabetes hormone. It means it will increase blood glucose. Agreed? Yes. How? It is going to increase hepatic glucose output. Okay? From the liver it will increase the glucose secretion and antagonizing the insulin. It will decrease the function of insulin because insulin having the function of decreasing the blood glucose level. Yes or no? Understood? Yes, sir. You can write. Excuse me, sir. Yes, dear? Sir, what is the gluconeogenesis for? Yes. We are discussing that. Let's see. Yes. It will also increase gluconeogenesis. Neu means new. Synthesis of glucose from a starch is normal or glycogen is normal. Yes or no? Because they are made up of glucose only. So it is synthesis of glucose from a new source and what will be the new source? It can be lactate. It can be fatty acid. It can be amino acid. Yes or no? Yes. So it will, this process will increase the blood glucose. Yes or no? I have written the definition also. Okay. So for gluconeogenesis we are going to require amino acid. Okay. And fatty fats also. That's my point. And principally fat.   Triglycerides have to have to be broken. If fatty acid is required in more amount. Yes. Tell me. Yes sir. So what will happen?  Fatty acid will break. So growth hormone is going to, sorry, fatty acid is going to break metabolized and before that fat is going to be broken. So it will increase the free fatty acid level. Okay. And if fatty acid will be broken, it will lead to formation of ketone bodies. So it is a ketogenic hormone also. So it is increasing fatty acid in the blood. Agree dear? Yes sir. It is also going to increase the milk production in lactating mothers. It is also going to increase the milk production in lactating mothers. Because or you can say development of memory gland also it is required. Agree dear? Okay. Growth hormone also stimulates erythropoiesis, formation of blood. Because in, at any place wherever, at any place wherever you can say growth kind of activity is required. So primary or secondary effect will be there of growth hormone also. Yes or no? We'll talk about disorders. In case of hormones, there are two kind of disorders. One is hyper secretion. One is hyposecretion. One is hyper secretion. Hyposecretion is decreased secretion. And hyper secretion is increased secretion. Yes or no? Yes. Agreed? So due to hyposecretion, it is going to cause two diseases. One is dwarfism. One is acromycra. Okay, one is dwarfism, one is acromycaria. Do you see? What happens due to low secretion of growth hormone before puberty? All the bone will be sought. In this case, all bone will be affected. But if it will happen after puberty, most of the bone has grown already. Only few bones are affected, like bone of hand will be affected. Okay, height will be normal. In this case, hand will be sought. Agreed? Here, height will also be less. Likewise, hypersecretion, chisantism and acromegaly. Chisantism is due to oversecretion of growth hormone before puberty. And it is upper puberty. Yes or no? Yes or no? Done here? Such as these two things. Hyposecretion and hyperspiration. You can write. Done? Yes. Let's see. One is luteinizing hormone. One is luteinizing hormone. It is having effect on male and female and having different effect. Here, between the semi-ferrous tubule, later on in 12th, we are going to study this. Here is the interstitial cell. Okay, outside this lumen. Here is interstitial cell. That is known as lytic cells. So, allege, act on interstitial cell. Okay. Hence also known as interstitial cell stimulating hormone. Yes or no? Yes. Yes. It act on interstitial cell and bring about secretion of testosterone. And that leads to aspermetogenesis. Agreed, dear? In female, allege act on mature graphene follicle and brings about ovulation. Okay, it is an ovulatory hormone in females. Yes or no, dear? Yes. Agreed, everyone? Now, let's see. Once this graphene follicle ruptures, ovum is released and one more structure is formed. That structure is known as corpus luteum. Yes or no? After rupture of graphene follicle, ovum is released and one structure is formed. That is known as corpus luteum. Yes or no? It is having this yellow color pigment, lutein, hence known as corpus luteum. Hence known as corpus luteum. Agreed, dear? Everyone? And corpus luteum secretes progesterone and estrogen. It is situated inside ovary only. It is situated inside ovary only. So if something is getting secreted from corpus luteum, that means it is secreted from ovary only. Yes or no? Yes? Can you scroll up? Yes. Done, everyone? What is going to happen in male and what is going to happen in female, you know? Well, especially remember corpus luteum. Agreed? Done? Yes. Yes. One is follicle-stimulating hormone, FSH, in males and in females. In males and in females. In males, it is going to act on sartoly cells, which cell? Sartoly cells. Agreed, dear? It is going to act on sartoly cells. And in female, it is going to act on developing follicle. See, this is the graphene follicle. It develops through all these stages. Primordial, primary, secondary, tertiary, then graphene follicle is going to be there. And then it will form the corpus luteum in the ovary. Yes or no? Yes or no? So this development, this development is under control of what? This development is under control of what? It is follicle-stimulating hormone. Follicle-stimulating hormone in female. So once follicle will develop under the effect of FSH and then it will burst to release ovum under the influence of LH. Yes or no, dear? Yes or no? No. Yes. See, testosterone is released over here. Okay. And it is required inside the lumen. What is the factor that is going to concentrate the testosterone inside the lumen? This is androsine binding protein. Androsine binding protein. Androsine is the group. Testosterone is one of the member. Agreed, dear? Androsine is the group. Testosterone is one of the member. Yes or no? Everyone? Yes. Done? Done? Right on, please. Oh, sorry. I was on mute and I keep on telling you people this and that. Let's see. This is hypothalamus. This is anterior pituitary. This is thyroid gland. What is the role of TSH? Two roles are there. One is growth of thyroid gland. And another is synthesis of thyroxine inside thyroid gland. Yes or no? One is growth of thyroid gland. Another is synthesis of, synthesis of thyroxine inside the T3 and T4 inside the thyroid gland. Yes or no? Everyone? Yes. Yes. Let's see here. What happens? Hypothalamus is going to release TSH. This will act on anterior pituitary gland. Anterior pituitary will release TSH. It will go on thyroid gland and will bring about growth of thyroid gland and synthesis of T3 and T4 into it. When the blood level of T3 and T4 increases, it will send negative feedback. To decrease secretion of TSH and TRH. Agreed? And when blood level is going to decrease, it will increase the synthesis of TSH and TRH. Yes or no? Yes or no? Yes. Everyone? Yes or no? It is like that only. Please write down. Then we will discuss the disorder. Done. Done. Done. Now see what happens in simple guider? What is the problem that is going to be there in simple guider? Iodine deficiency is there. What is there? Iodine deficiency is there. And for synthesis of T3 and T4, iodine is required. So will the level of T3 and T4 will decrease or not? Will the level of T3 and T4 will decrease or not? Yes sir. It will decrease. Surely it will decrease. What will happen? It will send positive feedback and release of TSH will increase. It will act on thyroid gland and will bring about growth of thyroid gland. But will the level of T3 or T4 will increase? Will the level of T3 and T4 increase? No. Why? Because iodine is not there. It will send more and more positive feedback. TSH will keep on increasing, but what will happen? It will not increase. So growth of thyroid gland will keep on taking place, but level of T3 and T4 will not increase. So in simple guider, level of T3 and T4 will remain low. Okay. Level of T3 and T4 will remain low, but level of TSH will increase. Yes or no dear? Yes. Everyone. Everyone. Done? Just a little bit. You can write down. Yeah. Please think about this. Done dear? Done. Now see what happens in exopthalmic guider? Exopthalmic guider is autoimmune disease. Which kind of disease? What happens in this? You can say there is autoimmune disease. What happens in that there is this. What happens in this is what happens in this? Exopthalmic guider is an autoimmune disease. What happens in this? You can say there is some problem with your immune system. And immune system is not able to, you can say differentiate between your own body cell and the foreign cell. Yes or no? Yes. Autoimmune disease. What happens in that? There is an antibody formed. antibody formed which is having TSS like activity. So what it will do? It will increase the size of thyroid gland and as iodine level is normal at this time. So T3 and T4 level will increase. Yes or no? Yes or no? Yes sir. And increased level will send negative feedback. TSS level will decrease. Agreed? TSS level will decrease. Yes or no? Yes sir. TSS level will decrease. And now see here what will happen. But this TSS is not the culprit. Culprit is this one. So it will keep on increasing the size of thyroid gland and keep on increasing the level of T3 and T4. And this will keep on decreasing. So in exhaust tell me goiter what will happen? T3 or T4 will increase. And TSS will decrease. Yes or no? Yes sir. Done? Done? Yes sir. Okay. What happens? In this case the eyeball bulges out of orbit. And this condition is known as exopthalmos. Okay. Exopthalmos. Agreed everyone? Hence this is known as exopthalmic goiter. Yes or no? So you understood both the disease? Done sir. Done everyone? Now next hormone is prolactin. Prolactin is hormone that is responsible for growth of memory gland and also responsible for synthesis of milk into memory gland. So it is milk synthesizing. Yes or no? Agreed dear? Sorry. Done dear? Everyone? Adrenal corticotropic let's see ACTH. It act on adrenal cortex. Okay. And adrenal cortex having three layers. One is glomerulosa. One is aciculata and one is reticulitis. Three names are there. Okay. Glomerulosa, fasciculata and reticulitis. Okay. Glomerulosa secret aldosterone or mineralocorticoid. It is under the control of RAS that we have discussed in kidney. Renin angiotensin aldosterone system. Okay. Agreed? Yes. And secretion of jonofasciculata and reticulitis. Two layers are there. It is under control of glucocorticoid. You can say under control of ACTH. Okay. Fasciculata secretes glucocorticoid. Corticoid means a hormone that is secreted by adrenal cortex. Agreed dear? Okay. You can do this question. Done. Everyone? Just a minute. Done. I hope you people have understood also. Yes. One is melanocyte stimulating hormone. It is secreted by usually past intermediate. Okay. Act on melanocyte and pigment melanin is synthesized in the skin. It causes dark complexion. Dark color skin is there. It is due to MSH only. Done. Done. Everyone? Just a second. Yes. Done. Done. One is next is neurohypophysis. Okay. It is going to secret a hormone oxytocin. It is secreted by paraventricular nuclei of hypothalamus. Paraventricular nuclei of hypothalamus. Okay. And released through pituitary posterior pituitary. Yes or no? What it do? What it do? It is going to act on a smooth muscle. Okay. It act on the myoepithelial cells of the memory gland and will bring about milk release. Hence, it is milk releasing hormone. Prolectin is milk synthesizing hormone. Yes or no? Yes. It causes vigorous contraction of uterus during parturition. It is the birth hormone also. At the time of ejaculation, oxytocin facilitates the transport of sperm towards the urethra. Yes or no? Everyone? So, so the primary function of oxytocin is always muscle contraction. A smooth muscle contraction. Yes, sir. Got it. Yes. Vesopration is loner, anti diuretic hormone. I have told you people the diagram of nephron. Do you people remember? Yes, sir. What do, what does it do? It act on collecting duct and distal DCT brings about absorption of water, of water, reabsorption of water rather. Now you see excess loss of water through urine is known as diuresis. It is preventing what? It is preventing what? Diuresis. Hence, it is known as anti diuretic. Yes or no? Yes, sir. Done. So, diabetes in and if and if hyposecretion of ADH will be there, it will lead to diabetes in C pedus. Agree, dear? Write down. Done, everyone? Done, everyone? Sir, just a second. Done, sir. Done, everyone? Volaveta? Sir, just a second. Oh, just a minute. Done, sir. Done. We will be meeting you people in the next class, okay? Whenever it is. Read out in CRT clearly. Okay, if you have any doubt or the remaining portion that you can ask anytime and let's see when it will be arranged. Bye. Have a good day.


Hi everyone, Joe for Jaspyscasebreaks.com doing our final random number block randomized to unlock NT World Cup pick your team 14 That's coming up in the next video. So this number block features Different countries from the other one we did a few moments ago, Netherlands, Germany, South Korea, Norway, Poland, Croatia and Sweden That's how it works if you're not familiar with it Big thanks to this group Travis Ryan H to Ryan H is Ryan Heights and Ryan Harold and Mark Thanks, everyone for getting into it. There's all the numbers right there Let's roll it randomized names and numbers one and a six seven times one two three four five Six and seventh and final time after seven we got marked down to mark One and a six seven times for the numbers one two three four five Six and seventh and final time after seven got two down to eight all right So mark with a bunch of numbers you got two three zero and seven Number zero gets you any and all redemptions including one of one redemptions But Ryan Harold you have one so you'll still get any live one of ones that are there plus the number four Travis with six Ryan Harold with nine Ryan Heights with five and Mark with eight Let's order these numerically and Coming up in the next video will be the break itself. We'll see you for that Jaspyscasebreaks.com