Alcohol is believed to be a factor in a three car crash near Pierce that left one dead and sent five people including two children to the hospital. The crash happened Tuesday a little after five in the Pierce Township on Highway 25 at 133rd Street. According to the Minnesota State Patrol, a semi-trailer was northbound on Highway 25 while an SUV was stopped in the southbound lane, waiting to turn onto 133rd Street. A car driven by 64-year-old Vonda Hydel of Waverly was traveling west when it failed to stop and hit both the semi and the SUV. Hydel died as a result of the crash. The driver of the semi and the driver of the SUV, along with three other passengers including the two children, who were also in the SUV, were sent to St. Gabriel's Hospital in Little Falls to be treated for non-life-threatening injuries. Alcohol was believed to be a factor in the crash on the part of Hydel. All the people involved in the crash were wearing seatbelts.


Hey, everybody. Dr. O here. In this video, I'm going to talk about the U.S. Navy Body Fat Calculator, which to me is the simplest way to estimate or determine your body fat percentage from home. All you need is a tape measure and maybe a friend to help you with a couple parts of this. So the accuracy rate in some studies, the U.S. Navy Body Fat Calculator is accurate within three to four percent, which is really amazing. I mean, DEXA scans, the underwater weighing, you're looking at five or six percent. So even if it's not, even if it's in the same ballpark as the DEXA scan, it's cheap, convenient. You can do it on a regular basis. I really like this. So all you need to do is take proper measurements. Then you have to have a calculator to actually input the numbers. And I'll show you where I like to do that. So the measurement guidelines, that's what I want to go through here first. You can properly measure yourself. So you need to know your height. This should be barefoot. And then I recommend taking your height three times and then averaging that, right? Just to make sure it's right. Have somebody help you with this one if you can, you know, with the kids, we do it on the wall and those kind of things, but find a really good way to measure your height. Get it done at the doctor's office. That's fine. But make sure you're barefoot. Okay. So now the measurements. For a male, you need to take a measurements at the neck and at the waist. And for females, it's neck, waist, and hips. So for the neck, it's the same for both males and females. You can see an actual picture here of a naval instructor doing this. The measurement needs to go around the neck right below the larynx, right below the Adam's apple. So that's where you want to take that measurement. And I always recommend taking two or three measurements and then averaging them. So that's going to be, you want to make sure you don't get a bunch of neck muscles there. So you want to be right at that spot. You want to be too low, but then not at the Adam's apple and definitely not above it. And you shouldn't be crushing someone's windpipe there. So that's the neck, right below the larynx or the Adam's apple. The waist is going to be different for males and females. So it's at the level of the belly button for males, for most males. You put it right there at the level of the belly button, and it's the narrowest part of the abdomen for females because we're going to be comparing the waist and the hip. So that's usually going to be midway between the bottom of the sternum and between the belly button. So it's going to be a little higher like you see here in this picture. So for a male, you take the waist measurement at the navel, at the belly button. For a female, you find the narrowest part of the abdomen. The key for both though is you take it after a normal relaxed exhalation.  So take a few quiet, normal, quiet breaths at the end of a normal exhale is when you want to take your measurement and take a couple of them and average them out. All right. For males only, you use the hip. So this is specifically how it says in the U.S. Navy's information about this. I'll read it. The hip tape measurement consists of the circumference of the waist at the level of the greatest protrusion of the gluteal muscles. So for most people, that's going to be at the widest point of the hip, the greatest protrusion of the buttocks of the gluteal muscles. So you find that widest area there, and then you take those calculations. All right.    So I'm going to show you how to use the calculator that I use from Legion. So I'll take you there now. Okay. Dr. O here. So I'm going to walk you through how I take my calculations for the U.S. Navy Body Fat Calculator and where I input them, which is here on this Legion site. So I have no affiliation with them. I do buy some of their pre-workouts and different products. I like them, but that's where I found the tools. So this is not an advertisement in any way. Just a great tool. So you see I'm at Legionathletics.com slash tools there, slash tools. Yeah. You can see it there. So you see they have a lot of different tools. But the one we're going to go to is the body fat percentage calculator. So I'm going to click on it here. And then here you're going to see where you can read through all this if you want. But you're going to see that I can, let's say it's so I'm a male. I put 40 year old male, the waist 250 pounds. But notice, here's what you have to do. The originally it's the skin fold.  And then you have your, your height. So you choose the height here. I'm just whatever. So six, four, you choose a, your neck measurement. Remember that's going to be right below the larynx. Let's say that it's a, when you see all the numbers are changing. So we'll say it's 22. That's a very, very big neck. And the abdomen is, we'll go to 38. So it is estimated, yeah, this is 8.59% body fat.   So the key there is to make sure you click on the Navy body fat measurement. But now, watch this. As I click to a female, you see an extra category has now been added. So if we have a 250 pound, 40 year old female that says 55 and then you input your different numbers. So I'll just leave them here.  So that's the key. You go to the tools section, you find their body percentage calculator. Make sure you switch right here to the Navy Tape Measure Method.  What else is it going to tell you? It tells you kind of where you would fit from a BMI standpoint. There's a lot of extra information down here if you want to go through that. But I just wanted you to have a home where you could calculate the measurements that you took, OK? I hope that this helps. All right, so hopefully that showed you how to use this calculator. I showed you with both males and females. But while you're there, while you're on their website, and again, this is not an average husband for them. I buy products from them, but I have no affiliation. This is how I found the calculators they have in their tools section. But while you're there, they also have pictures here.  And you can kind of see which one you fit closest to. And then here, we have the same thing with females. So you can use this. It can get you in the right ballpark. It's certainly not going to be perfect. But the numbers you get from the US Navy Body Fat Calculator should be somewhat similar to the pictures that you see here. All right, so why should we care? Why does this matter?  Why does it matter to know your body composition? Why do I do things like get my DEXA scans done and these types of things? General health information, absolutely. But goal setting, right? You're watching this because you probably want to lose some weight. So for goal setting purposes. But the main reason I like it is because once you know your body fat percentage, you can get better information about your basal metabolic rate and your total daily energy expenditure. People are always going online and using these calculators. If you don't know what your body fat percentage is, it has to give you a wild estimate of what it thinks your energy expenditure and your basal metabolic rate are. If you know your body fat percentage or get even close, it can really, really help you. Because two people could be six feet tall and weigh 250 pounds. One could be solid muscle. And one could have 50% body fat. So if you don't know those numbers, how does it give you the right information? So then to me, it's especially important that you know this because if you know how many pounds of fat you have on your body, you can determine how much energy your body can provide on a diet. So it can help you determine what your calorie target should be if you're on a diet. And it should also be able to help you monitor to make sure you're not being too aggressive on your diet and you're not losing too much lean mass on a diet. Because you want to make sure that you're losing fat, not just losing weight.  Like I always say, you're better off losing nothing than losing muscle. So that's why I think this stuff is important. So I hope that this helped you. Have a wonderful day. Be blessed.


Okay welcome back everybody Next up really interesting talk here from Karen beer. It's going to talk about bringing Zen to Centos 6 Which has been really one of the more interesting things that's transpired in this past year Karen beer How many if you guys have used Centos I'm guessing a few people have right okay. How many of you started using Zen because it was in Centos Okay In 2007 Something interesting happened Centos 5 was was made available Thanks to the work done upstream and some of the work that we did as well But what it really did was that this is the first time there was a complete open-source solution that he could use and abuse And do whatever you want to do in whichever way you want to do you could brand it sell it retell it You could use it internally you could use it with you know commodity storage on commodity machines To achieve stuff which otherwise in the past you'd have to pay a lot of money to a company called VM Where for this is really the first time there was a stable virtualized platform that you could just use it and worked out of the box With the best part about the whole about the whole you know ecosystem that came up around Zen and Centos 5 Was the fact that the community Created itself screaming shouting whether they wanted to whether they didn't want to it didn't matter the community happened on its own Because then was integrated in with the system There was there was no external component that you had to get in from somewhere It was all a part of the distribution you could install it and install it was smart enough to work out Hey, I'm being installed into a Zen environment I'm being I'm being installed into a PV Zen environment or a HVM Zen environment and it would make decisions for you It would help you do things like you know get the right Dry via modules up and things like that so it would do it would do some really cool smart stuff which meant a lot of people started using it and Then these people started blogging about it and then these people started talking about it And then started writing how to is about it and they would go to these Linux user group meetups and tell other people about the fact that hey I've got four computers on my computer I've got one machine and I can reboot any of those four at any point I want and I can do whatever I want It doesn't make a difference to the other machines. I Guess the point that I'm trying to get to is that these are people who weren't buying into virtualization because of something They're read on a you know in a white paper and an academic setup. These are people who are really finding benefits of virtualization and You know small abstract examples of this was a bump into somebody who's a who's a doctor in and So how many of you guys have been to India? So it's a couple of people have been to India and survived the experience I'm guessing the empty chairs are the ones who didn't quite survive the experience There's a couple of things that that are quite sort of you know back to basics in India and the medical setup You know the whole social government supported medical infrastructure is one of them And I was talking to this young doctor who's what I say young. I'm not really that old myself But youngish guy Who's using Zen on centos 5 to and what they were doing was they were building these VMs Which would contain medical records for statistics and for models for medicines that have been proved by their state department And they were shipping these VMs out On USB keys by post to all of the hospitals and all of the doctors who needed this information And all those guys would do is they would get a dot CFG file They would drop it into 80 C's end and do XM minus E create and Bob's your uncle and that's it If anything failed they had yesterday's or they had last week's image. They could fall back to this is not really Classic virtualization solution, right? This is not really the kind of problem domain that the guys that Citrix were really working towards right or at that point Zen source, but it's you know ingenious stuff that people came up with And that sent us six that equation changed a bit as in you could still do all of this stuff But you had to use something else Which created a bit of a problem and my top kind of addresses more of you know what we did how we did it Why we did it kind of a thing rather than the bits and bobs about it And I hope and I hope you guys sort of you know get some value from that So a bit about me. I'm based out of the UK. I'm based out of London. I Love the rain. I don't know why people have a problem with it It was terrible, yeah, I mean I had to shut my machines down in the in the garage and it was getting so warm I don't know what's up with that.  How do you cool your servers down? I've been involved with the centros project for about seven years and again because I Was doing something for my employer at that point which didn't quite work on any other platform And and the amount of money that red hat wanted to support us was was way they were like, I mean There were about four or five zeros more than we were ready to pay for it And centos seemed to be like a good place to be so, you know I kind of joined the community got involved with the thing and and seven years down the road and still around I love bullet points as you'll see through my talk and I and I love Trappist beer. I Haven't quite gotten around the whole IPA infatuation in the US. What is that all about? Everybody here seems to be wanting to drink IPA And really harsh IP as well. I think you guys should all try Trappist Belgian beers. It's like this is a whole new world up there So I've given you a bit of the background already that you know, sometimes five came out a lot of people invested in Virtualization around it to do some really interesting things I know there was there's a school project in in Brazil, which which does some stuff with virtualization as well where they deliver classroom content using Zen PV domains and They ship them out the ship or the examination content on a Luxe encrypted LV volume Which we can put on and the day off the test the teachers get a text message Which has the decryption password? So and then they kind of know that hey nobody else can get to this test and the test is delivered as a as a web app From a Zen Dom you Which is so it's like I think going back to the point It was a lot of really creative crazy stuff that people were doing around You know virtualization on five which wasn't possible earlier or maybe it was possible It just wasn't that main line. It wasn't that mainstream people weren't really doing like crazy stuff with it Red Hat had an agenda had a Policy had a principle had a marketing goal of pushing intellectual property that they own right and another point when six came out KVM was what they were doing KVM was like the in-house kind of thing So the message that they were pushing out was that hey, you know, we've just shipped six and that's fine You know six is great, and then there was you know lots of people think well hang on I can't run a dom zero on center six or rel six and And the feedback or what you know redhead marketing kind of was pushing out was that's fine You know so you can still run a dom zero on five and you can run your doms used on on six So you can still have the goodness of six you can still have you know a batchy two-two and you can still have PHP 53 or whatever and you know what's wrong with running your dom zero on on five you carry on running it There are a few differences between the Linux kernel two six eight and Two six thirty two right One of them one of the really really beneficial ones is Okay, let me not let me not let me not that slack off a bunch of guys A lot of people worked on this code right between two six eighteen two six eight two two six thirty two and they all really good features to have One really cool thing is power management about two six thirty Had a had a massive rewrite And when we did some tests and this was within our own centers build systems We've got like six or seven machines. We did a few tests and we found The guy who's giving us space to host those machines said that you can't use more than eight amps of electricity And then we upgraded those machines from five to six We suddenly found hey, we've got enough electricity to squeeze in another machine, which we didn't have earlier So what's going on with that? And on Dell gen 11 gen 12 machines We found that the power utilization of the center six kernel was between eight to twelve percent lower than power utilization of the same workload of The center's five kernel, which is a massive benefit to have Perhaps not so much if it's just me, you know on the laptop But if you've got a hundred machines, you can you know ten percent of electricity bill is a good way to have What is also happening was that a lot of tooling that was coming up a lot of the management stuff that is coming up now is coming up around six And five isn't really supported that much like a lot of the really cool stuff with puppet and chef and foreman and Kettle all of those things are being driven towards six and you want to be able to manage your base layers with the same tooling Right, so you want six on that So people are trying some interesting things to work around the solutions right to to work around this thing the number one was moved to Debian Right. Hey Debian's got Zen To seven years ago when I got involved in the center's project This is what we try to do at my workplace as well. We said, hey, you know, we've got 150 machines running my sequel shorted setup We're doing some really cool stuff. It doesn't matter what we use under it, right? Let's move to Debian It took us six hours to find the Apache configs Because this was a group of five guys who had basically used red hat technologies since I mean my first red hat install was back in 96 right, I think 41 or something And it's just you know, it had kind of worked and then you know Stuff kind of worked and we knew where things were we knew that whole 86 is conflict kind of stuff So what three or four days down the road the boss was you know So how's the Debian migration plan going on and you were like not very well This is this is going to need like, you know a lot more resources than we had planned for But the biggest challenge was that none of the tooling that we had in place None of the provisioning tools that we had in place none of the PXE configs We had in place none of the kick starts that we had in place none of the my sequel tuning stuff that we had in Place none of our PHP front layers we had in place and okay I admit I have used visual basic in the past and at that point we did have a very large visual basic 6 application That used to talk to this my sequel cluster of 150 machines even the drivers for that didn't work with Debian and We were like, you know, this could be a massive rabbit hole We have no idea, you know, what's at the other end of this and we don't even know when we might come out of it And I think a lot of people who tried to do this including some of the really large hosting companies Who tried to go the Debian root found that hey with the investment that they already had with Zen on five on cent five It wasn't really going to work out moving to Debian. They would have to either rethink the entire architecture or Move to KVM and they're like look, we've done the Zen thing for a while. We see the wins At this point Zen was also about seven years mature six or seven years into the majority cycle KVM was about maybe two and a half years To a point I think where Red Hat had only started supporting it in production about a year and a half before this point A lot of the things that you could do with Zen a lot of things that people already doing with then you couldn't really do with KVM So you had to come up with workarounds so that plan didn't work out very well, and you still needed new tools because when Zen shipped with five everybody invested on XM everybody invested in scraping XM Everybody invested in writing their own little API layers because all developers love doing that, right? If you get five developers into a room, they'll come up with 14 APIs and the next day They would have re-engineered three of them And all of those APIs were using XM we did a very little spike thing where I went around and spoke to a lot of people at hosting conferences So you know what virtualization do you use on Santas like Zen? Okay, so how do you how do you actually you know interface with Zen? Yeah XM, but do you really use XM? No, I've got these bash scripts which call XM and then we scrape what comes out and we do stuff here So again KVM was going to be completely new tooling even if you were using live, but it was going to mean complete New tooling and then the way you provision and what your provision was going to change Right, so that both of the steps weren't really working out very well So what we did in the within the project was we said hey, okay? Let's pull in our resources. We have a bunch of smart people ash people What can we do and then the first thing that a lot of people within the center's project as you everybody knows And a lot of people don't really realize is that the center's project is an independent entity from the center's Linux distribution As in the center's project are the bunch of guys who create the center's Linux Distribution they're also the people who do things like forum support mailing lists IRC Help out in bugs help out with code extend the system all that kind of stuff But the core values the core principles of what we do within sent us is that we don't promote sent us as a platform We promote what people do with sent us Right, so you so it's a user focused stuff. It's a completely user focused Ecosystem also, we're not developer and feature led the entire platform the entire sent us project moves based on user expectations and problem We're not looking to build new features So if you've got if you've got a little app that needs yesterday's version of lip curl and tomorrow's version of Libyama and you know The libexml 42 that's coming out in six days time Then sent us is really not the platform that you want to be using, you know There's there's a lot of other people who try and solve that problem But if you want something you can deploy today, that's still going to be here three years from now Then hey think about sent us. It's a it's quite a viable platform for that kind of a workload Which is why it also makes fantastic? hypervisor platforms and Our entire cloud strategy from the project side has been that hey, you know, you want to run the cloud Excellent everybody wants to run a cloud you want to run VMs open to windows whatever you want to do You know instances fine, but when it comes down to considering hypervisor platform consider sent us Install it today five years from now. It's still doing what it's supposed to be doing So the problem we had was that we don't really have the developer depth that was required to make this happen so we started talking to Lars and and David Nellie and seeing hey, you know, what what can we kind of do? So we had a couple of initial meetings with the Citrix guys We had a couple of meetings with people we thought represented the best user base And we brought in a bunch of people we said, you know, who are the ideal candidates that we would want to target Who are the people who represent the workloads that we really want to go for? And obviously right up on top of the list was me.  I have a niche ever, you know, like my own corner case and I want to bring that to the table So we kind of spoke about that a little bit. We did, you know, make make installs We tried to build a few things see how the patches could work We did something like take 41 at the time Zen for one and try to apply it to the last 15 cent or six kernels I was like, yeah, this is not going to work out So we did we did a few things and we came up with a set of core targets that we wanted to hit We said it has to be Zen for based Because that's what everybody really wants to be on right now. It has to have a stable kernel We can't get into a situation where we building yesterday's LTS or we building, you know, yesterday's mainline And at this point, we've also said that 2632 isn't really going to work out Especially with the red hat patch set it was going to be too much of an effort getting Zen for back in so that has to go And then from the user side, we wanted to preserve its existing infrastructure So we wanted kick starts that worked on five to still work We wanted like spacewalk satellite installs to still be able to consume it We wanted people to be able to install centers the way they install centers and then consume Zen on top of it And you wanted to target the needs of the existing Zen users. You're like, hey, you know Zen is doing great stuff in feature terms in terms of, you know, so I think to this particular audience I don't have to sell Zen, right? I think everybody's kind of bought into it But what we really wanted to do was not get into a point where it was feature led We didn't want to get into a point where, you know, we were building and releasing every Zen release all the time And then expecting all of the users to hit the upgrade Treadmill as it were we wanted to get something which is kind of you know going to stay stable And the process we adopted was the thing that we started doing in CentOS now and it has it has it has it's a double-edged sword I know some people in the communities aren't very keen on on us doing the sort of thing and why we do it But what we really do is we try and get people who represent the user base people who represent the problem domain on a private mailing list We try and find the people who can help them solve those problems on the same private mailing list And then we try and build sent us infrastructure around it because what happens is that there's an expectation In the user base around sent us branded code going out And even though you know you can have like blink flashing red lights around it that you know This is dev code do not use this people will still use it Right and doing this basically meant that we could build the process we could build the code We could get to a point where we knew we could actually ship something Before getting into a situation where you know 500 people are using it And we have to then sort of trying to call them up by saying look you're gonna have to uninstall this Or your machine is going to get hacked because we're not supporting this anymore Right and if 500 people are using it three people will have ever get in touch with you the 497 other silent majority You'll never have any contact with so we started using this process and I think it worked out really well For for what you're trying to do here. So we said okay two six thirty isn't going to work So if you're going to branched on to three point four LTS The main line LTS colonel for for the time being we'll try and get to a two-year production cycle Which is what you know based on user feedback that that was good enough That's what people are really looking for people didn't want the 511 year cycle for for this particular solution They were like to use good enough ensure that during those two years all upgrades and in place so we test for those updates And we'll build every even generally so we'll do we did for to we'll do 4-4 We're not actually doing 4-3 unless somebody extra tricks, you know picks up the phone and says you know Whoa, we've got a massive security issue. We're going to have to do this So the aim is that you're only going to build even updates and and work with that The colonel is basically the bog standard LTS 3 4 with a few zen specific patches including the block tap 2.5 How many people know block tap 2.5? How many people know that how many people I guess I didn't know there was something called blocked back to 5 I knew there was one there was two and there was going to be a three And then turns out there was a 2.5 as well Which some of the people said it's critical for them to be able to move to this platform and and the Citrix guys Actually put in the effort to get this going on that colonel From the Zen side we wanted to make sure We wanted to make sure that we don't fragment the communities Whatever we were shipping we wanted those guys to be able to go back to the Zen org or the Zen project or dog Infrastructure and actually talk to people there unlike Let's just Samba for example like the Samba shipped and sent us is about you know We two and a half three years adrift from from in line So you go to the Samba communities and say I've got a problem with this The first response you get is you need to install the latest release And we wanted to not get into that situation with Zen So what we've tried to do is we try and promote as much as possible The communities to come up around the Zen project rather than the Zen communities coming up around sent us project and then fragmenting it The ongoing plan I think we've touched on this already is you know deliver Zen for even releases do a two-year thing And and deliver and maintain the ecosystem around Zen sent us rather than around sent us with a bunch of people who use We use then Our primary user testing was a hosting company you may have heard of Is anybody here who hasn't heard of that? No somebody here, okay? Our second big user case was the guys who were helping us doing the trench work was another hosting company that some of you may have heard of and Which was which is the two large hosting services companies with about 180,000 clients one of those two without going into names had a 30,000 hypervisor cluster that they actually deployed this on and tested as well. So we knew that it actually works We had and there were a couple of small vendors as well There were a couple of people who build solutions around Zen and they kind of came along and said, you know How can we get involved and I think when we released we had about 80 odd people Representing different interest groups who were involved in the in a test release process Our biggest challenge has been engaging the users and This is a there's a bit of a numbers thing at the end Which I think people will find amusing as well But what what we've kind of realized is that we're hitting about a 1.8 to 1.1 1.9 percent hit rate in engaging users. So for every hundred users out there There's a one and a half person who actually talks to us Which is a bit of an interesting or an awkward position to be in because what's happening now is that the people who work with us within the Keywear release process Even a lot of them have now gone away and done a little bit of work around the edges Which which isn't really being fed back in and that's a massive challenge And we're using what people would normally do in the community to address that if anybody has bright ideas if anybody has you know Comments on how we can fix that please come and find me The the other problem has been engaging Wenders and providers a lot of people have started building and developing solutions around the Zen for Center stack It would be nice if some of them actually came and spoke to us because some of them Including small you know one-man two-man three-man companies all over the world are building stuff Which is otherwise open source and we've actually got a process in place that allows them to release the code into the centos ecosystem Giving them access to a massive user base But how do we actually reach out and how do we get them in through that door has proven to be a massive issue? So again if anybody has ideas if anybody has views on how we can help solve that please come and find me I would love I would love to have you know a better ecosystem that helps people succeed around around Zen for Centos In terms of future plans we're looking to expand scope We've got Seth and cluster FS coming in by the end of this month and that's specific to The functionality needed who within the Zen for stack as used by cloud stack and open stack So this is not so this is not Seth and cluster SFs going into genetic Centos repositories. This is Seth and cluster FS going into Targeting so at one of the law at the previous the dojo before last David Scott from Citrix did a talk on Open stack running with Zen for Centos Zappy and Seth and there's a video of this online on the centos project YouTube channel I think about a 1500 600 people have seen that video I highly recommend everybody goes and see it in about 20 minutes He got the entire cloud stack stuff up and in a stage where he could scale it with the whole stack Which is which is which is a really good place to be so Seth and cluster F has actually targeted that particular platform What we're also trying to do is eucalyptus and open nebula are both kind of community driven Cloud controllers, I'm sure most people would have heard of at least one of the two But both of them dropped the ball a little bit on Zen support and we're trying to engage with both of those Communities or both of the developer communities to try and bring support for Zen for Centos back in I think the open Nebula guys showed up at the What's it that Google hosted Zen hackathon? I think they've actually put in the code has gone in and the new release has support for Zen for Centos I'm working quite hard on the eucalyptus guys to try and get them to do this as well because they've kind of bridged over to only supporting KVM And we're trying to facilitate we're trying to kind of get resources going so they can start that checking this as well We've got an ISO which is ready to go. We didn't release it with six forward We're going to release it with six five which is pre booted pre loaded with Zen So it's a Centos installer which has the Zen code built in plug it in put up your machine And it'll give you a Zen first install on it I think James touched on the fact that we're trying to get zappy and most of the work has been done but we hit a roadblock in I think People change environment changed a lot of other stuff happened So we quite I'm quite keen on sort of rebooting that effort and getting it in before six five comes along And the other thing the final point in here is that we ship a custom version of LibWord into the Zen for Centos repositories, which is a different version of LibWord from the generic Centos six KVM LibWord And that's because we're supporting XM and XL and we also have the patches in for getting zappy and through LibWord in the Version shipped with Zen for Centos And Red Hat hasn't been hasn't accepted those patches in yet So the the LibWord that's in there is better than the LibWord that you can get in Fedora or Debian at the moment in that everything works including Somebody's going to probably kill me for saying this but I do know that spice kind of works as well Sometimes on Mondays when your machine is facing north, but it does it So I mean the support for the whole thing is there and XM and XL work out of the box And you can do remote management and you can integrate it with any LibWord script and stuff Okay, I've Lost something maybe hang on. Let's see this works. So a Little bit of a story of numbers, right? Centos 5 was released quarter one 2007. A lot of people enjoyed it much festivities around it You know Zen works people came up with some interesting things hosting, VoIP, HPC and in embedded Areas were what what I think based on what you know digging around I've been able to do with the biggest adopters for Zen on Centos And all industries which were traditionally Either not looking at virtualization at all or we're looking at massive, you know, window lock-in kind of solutions So it was it went well for many many years and I think I don't have the number up there But off the back of my head. I remember that at one point We were greater than 1.6 to 1.7 million installs where people were using Centos 5 with Zen at various points But in the first week of Jan this year I did I actually went through an exercise for a couple of days and try to come up with a number that hey Are we actually solving this now this problem for me myself or is it really somebody else out there? Just like me who's got exactly the problems that I do and it was kind of important that if you're going to commit resources You know, we do it to something which is worthwhile Because there's so many things you could do Let's find something which is worthwhile and the worthwhile was that we came up with a number which we can extrapolate a little bit Around and we said 750,000 estimated installs out there in January this year We're running Zen 3 on Centos 5, which is a big number Which was like that that'd be really good problem to solve for these guys and June 20th we released it and Kind of quietly there hasn't been a lot of fanfare around it because we've let it grow organically And I think the plan was that by the time we get around to having more support from the Zen projects out of things when Things like you know Zen server becomes a young install and things then we'll start making a lot more noise about it You know and things like that but September 10th I did I went through the same exercise that I had gone through in early Jan this year and the number of unique IPs that were tracking those repositories So this is not drive-by hits these are people who are actually hitting yum against the Zen for Centos repositories was about 87,000 I'm not sure how many people will be surprised by this But it was 98.3 or something like that IPv4 and there was like in the four guys who use IPv6 All of them were using this repository So I'm going to kind of finish off. I don't know how we're doing for time During the fun There's a there's a wiki page that gives you all of the details. I'll make sure this presentation thing gets posted as well I know a lot of people are going to be looking at the numbers thing to tweet around which should be fun The the effort is completely run off the Centos work list Join in the fun. The the git repositories are public the git repositories are available to anybody and everybody who wants to use them We're going to try and push them out to GitHub so that people can fork and do their own thing the Workflow that we're trying to set up around the zappy rpms is going to be on github. So good github.com slash centos slash Whatever the zappy stuff is going to be it's not there yet, but that's what we're trying to get to build system access Community access all of that stuff is available. It's in place look at the wiki page You should get you should get basically all of the bootstrap information you need Thanks, of course to everybody including a big thanks to the Citrix guys who at that point weren't really sold on what They were doing with open source or maybe they were they just weren't admitting what they were doing This was last year in November December a Big thanks to them for actually reaching out because the big problem We had a roadblock that we had was we did not have to develop a depth to do things like hey Can I get blocked at 2.5 over into the kernel? I actually had a go at it for about three days later I was like look this is going to need to be somebody who's looked at this code before Who knows what the hell is going on so and it was it was it was really good of them to kind of reach out And help us out with that sort of stuff also one of the things that is key And I haven't put it up on this on the slides there is the kernel that we ship the 3 4 kernel that we ship is actually curated by a Bunch of people and in that bunch of people include kernel engineers at this is in a project So if you look at the change log, you'll see guys at Citrix comma doing commits as well So should you trust that kernel? I think you should you should trust it because it's a main line It's an LTS main line So you know what's happening if the security fixes happening upstream they're coming through if the Zen specific stuff The Citrix guys are pushing that through I am kind of hopeful I don't know if this is going to happen or not I'm kind of hopeful that in futures and several products They actually end up using the same kernel which makes our life a lot easier because we can just use those sources then so the The the plan and I know Johnny's working on this He's done a spike as well is that we want to start rebasing three four to three ten sort of March next year Because there's a couple of one of the big reasons why I'm caught keen on still pushing Zen Even though a lot of people keep saying hey, you know when KVM works. Why are you kind of wasting your time? It is something like what Larry touched on that's then because of the way it is and because of what it does And because the way that it runs kind of above the Linux kernel It lends itself better to the whole disruptive hardware situation that we ran at the moment So even on the Intel platform people are engineering around what is sort of established conventional engineering practices And in many of those situations or many of those problem domains then just fits in as a better hypervisor than KVM does so for example, you've got a MIPS based Network switch you want to drop OBS on it. You don't want you know, not all software on it But it's fine. How about you know something from Bay Networks from like 1932 So, you know, you have the option of actually dropping in as an hypervisor and being able to do a bunch of this sort of stuff I'm not sure if this was NDA or not, but I haven't signed the NDA so I'm fine with that Arm of course is big and that's coming through and we trying to bootstrap arm on For sent us purely because we want to deliver Zen with the center solution Because we think the Fedora guys redhead guys are going to do whatever they do with arm anyway But what we want to be able to do is deliver that hypervisor there because we think when you get to situations like, you know 288 cores in a single box But each core has limited processing capability each core can only hit 1.4 gigahertz and can't do you know Context switching becomes a bit of a challenge. It's nice to have the Zen wins on some of those platforms as well A couple of links if anybody's interested My email address is on that if anybody wants this t-shirt, which I think has got the Vintage logo. Let's not say the old logo the vintage logo of the Zen project Send me an email send me an email with your address and I'll make sure and your size And yeah, so American sizes please and I'll make sure I send something out to you So the last slide I've got before I wind up is I I Started the whole talk with you know that the primary focus of what we were trying to do was solve the problem That whole bunch of people out there whole bunch of hosting companies out there were running Zen on five Right, so one of the key issues that you know, we wanted to hit was hey How can we just you know get to a point where the guy puts in a USB key and reboots the machine and Six minutes later. He's running Santa six and all of his VMs are still there all of his storage is still there It was a good dream to have And we tried quite hard to make that happen But it is tricky because because of the number of changes and because of the way So one of the wins that people had with the XEP platform was that because the kernel is different and because access to that Domain is controlled by a Citrix very few people go in there to install for example Oracle DB right Whereas on centers five because it's native because it's already there So the so we had three guys who said they would really love to help out with this testing stuff One guy had an Oracle database running on his Dom zero and he like well hang on. What are you? You know? Is that is that a good idea? And he did set up a dev instance of his platform and he didn't go through a couple of iterations of trying to do the migration Script it wasn't going to happen. It wasn't pretty it wasn't going to happen One of the other guys had this high performance my sequel stack on there and and somebody else was running his DRBD stack on the Dom zero which is which is not really a Corner case it's it's you know Lot of people would want to run like cluster LVM on the DRBD or something and use that as the storage that they exposed to the Dom use and we found that you know just the migration from DRBD 8 1 to 8 4 Was was painful and you had to synchronize it in a way where you would have to bring the cluster down anyway So then it was a case of what can we do and what is the best way that we can make this happen? So we have a migration script Which will take a seven plus five machine and you will end up with a seven plus six machine And it does some crazy things like non-district content will get our synced away to slash opt slash deprecated So if you've installed with stuff that make make install it'll find all of the bits that are not managed that didn't come from RBMs Todd ball all of that stuff up and leave it for you to handle later Which effectively means your machine may not boot if you're running for example if you've been so-called PKI certified by Certain large companies whose names don't need to be brought up and they've done make make install open SSH installs Because that hits the right version numbers. You may end up with a machine that doesn't boot Hopefully not a lot of people have done that kind of thing but It's something to look at it's something to consider it goes to three reboots because it does it changes stuff at different points And during that during those three reboots everything on your machine will will be down the General line that the project toes is that if you can't find that script on your own Then you shouldn't be using that script Because it will cause damage, you know, and it's not the kind of thing like you know C panel Hey, I can just go in there type in a command and I end up with C panel. That's not how this works And because of the fact that it's intrusive and because the fact we say that you know There's a good chance your machine is not going to come back up We don't link to that script anyway We don't talk about that script anyway like if you can find the script which means There's a good chance. It's a it's in a git repository and not a very hard place to find But if this is a problem that you guys want to look at then then there's an option But what we say is hey migrate your VMs instead Just if you've got ten machines take one out install sense six on it migrate your VMs and then cascade down and just do that loop And I think most people would be familiar with that process, right? We haven't tested live migration from let me okay Let me rephrase that we have tested Live migration from three to four, but because this is not something that Zen project supports We usually say that we haven't tested it In certain cases if you're just using some very basic stuff if using fireback stuff and just raw storage or VHD files It does kind of work But I believe that official line is that you know don't do live migration is not probably going to work So just offline it and it should go through So is anything anybody wants to talk about any questions that anybody has or would anybody like to know anything about Zen on CentOS or about CentOS or About Zen I did have a really nice demo where I had a I have I actually have hopefully I still have nobody's run away with that machine I have a machine which has about half a terabyte of RAM and One of the things that that's really cool, and I've done this in the past is bringing up a thousand VMs on Zen in about it takes about 18 minutes for all of them to come up because you know Malik takes time when you've got that much of memory and then DHCP takes time when you've got that much of memory and So Because I believe you didn't have time I haven't done that also when I was sitting here in the morning and I was looking around it Who's here? It's like maybe bringing up VMs isn't going to be a very interesting thing to see right? So I think most people have done that already So kind of cycle that out. But is there anything else anybody wants to talk? Yeah, I was curious if you had like a Best practices for optimal performance like You know how much memory Per VM or what to put on your Dom you at Dom zero You know how many CPUs to put on each In I must be really growing old I can't remember the day but in March this year Roger Bow who works with Citrix did a talk at the CentOS dojo in Antwerp Which is recorded is a video of that available on the CentOS project YouTube channel and the slides are available Where he did 45 minutes on how to performance tunes and for CentOS and he went into exactly all of these things He went into things like you know, how do you how do you solve the numer problem? Yes, how do you get your storage stuff sorted out? How can you boot in various places? What you know? So even things like everybody seems to have this real big infatuation for QCOW and especially the breast QCOW But it's really interesting to see under real workloads. What happens to the back end with that even things like if you're running PV stuff on Zen4 you still need QEMU instances running in your Dom zero for some of the some of the backing stores So what impact those have and how to manage memory in your Dom zero discovered all of that stuff really well And I won't even start going into some other things. Okay. I that's exactly what I would like to get If you drop me an email, I'm happy to send you a link to his talk. The video is there And his slides are available as well And it is a fantastic talk. I just I don't even want to kind of go in there And if you really want to kind of get the whole stick experience Two sessions after him was Jaime mellis from the open ebola project doing best practices of KVM based VMs in a cloud environment Which is also quite interesting because a lot of stuff they were talking about was was common And a lot of the things that one guy brought up is are also relevant to the other setup And what the other setup brought up was also relevant to the other setup I think both of those talks are worth looking at In sync and it's about two hours of your life. I think I highly recommend it Thank you. All right. Well, let's thank Karen Bair for this wonderful talk


Well, first of all, thanks for coming. Last year, I was here talking about how to use actor-critic reinforcement learning algorithms to make malware undetectable against anti-virus software. And this year, I have been lucky enough to be a speaker again, so thank you. Well, I'm going to introduce myself. I'm Ruben Martinez, and I've worked as a security pentester as an ethical hacker for more than 10 years. But I've been interested in artificial intelligence since I was at Polytechnic University. And I'm currently working at Datahack, specifically in the area of labs, developing a research project called DIAFORA. And what are we doing in this project in DIAFORA? Well, we have to give intelligence to a humanoid robot. It's a paper robot. You may have seen it in the Datahack booth, or in TV, or in Masterchef maybe. So it can assist people with Alzheimer's disease. And some of the skills that a robot must have are autonomous navigation, speech-to-text, text-to-speech, emotions detection, combining sources of voice tone, message meaning, and micro-facial expressions, and facial recognition capabilities. And this last feature, the facial recognition capability, is what has motivated this talk. And a robot must be able to recognize a known patient and must have a quick method to learn the identity of a new patient using only one picture of that new patient. This is a very important point. And let's start. It's time to see the menu for this talk. Well, let's start by talking about how we have faced the problem of recognizing faces. Next, we are going to see how convolutional neural network works. These algorithms will be the bricks to achieve our goal of identifying faces. And at this point, we are going to see a very important thing, some pitfalls of convolutional neural networks. CNNs have become in the standard way to develop artificial vision projects. Because of that, it's good to know what limitations we are going to find if we work with these kind of algorithms. And in the third point, we are going to see how to make more robust and generally stable convolutional neural networks. And one of the hardening techniques that we have used to reach our goal is known as a spatial transformer networks. And once we have seen all the basic tools with which you are going to work, I'm going to present what kind of neural network architecture we have used to develop this facial recognition model. And what kind of loss function we have tried to minimize to carry out the training of this model. And once we have seen all this theory, I'm going to show you a video of the robot using this facial recognition pipeline. And in this video, the robot will have to recognize a person from whom the robot will only have one picture of his face in its knowledge base. But that person won't have a period in the training set of the facial recognition model. OK, this is the key. And you could ask, what is this knowledge base of the robot? It's only a folder in which we are going to place one picture of the face of each person that the robot will have to recognize. And we're going to label its image with the name of the owner of that face. And I would like to finish the talk by showing a new path that we are going to explore, spiking neural networks. Is there anyone who has ever heard about spiking neural networks? No? Yes, one, two, three. OK, that's right. I'm going to start in, I'm going to be in your first time. So a good point. Well, this new path is where we believe that artificial intelligence will advance if we really want to achieve what is known as artificial general intelligence or a strong AI. Well, what we are doing now using deep learning models are known as weak AI. We only can solve very small problems in very limited environments. But spiking neural networks are more inspired by biological models based on neuroscience than deep learning models. Well, so let's start. Well, how have we faced the problem of recognizing a person's identity? Well, we have followed that pipeline. The robot will capture using its frontal camera, an Asus X-Tion Pro with capture depth, an image of the scene in front of it. And we are going to use a machine learning model to detect its face present in that image. And now we are going to make a crop of this area. And we are going to pass this area to the convolutional facial recognition model, which will be in charge of assigning numerical representation to that face. And this numerical representation will be known as embedding. Well, our model will have to compute a distance between the embedding of that face and the embedding of each face present in the knowledge base of the robot. And once we have done that, the network will have to determine which embedding of the knowledge base of the robot is the closest to the embedding of the face captured by the camera of the robot. And if that distance is less than a certain threshold, then the model will return the identity associated with the embedding of the knowledge base that is the closest to the embedding of the face captured by the camera of the robot. Otherwise, the model will return the value of unknown identity. This is the pipeline. In this point, I would like to talk about one thing. All of this that we are doing here is to ensure that the thing that we have to use a lot of methods to make our more robust convolutional neural networks, such as data augmentation techniques or, for example, transfer learning, which is a must if you work with convolutional neural networks or the spatial transformer networks or modification in the convolutional operator to add it more transformational operations. All of this is to ensure that the data distribution of the training set or the facial recognition model is as similar as possible than the data distribution that the model will find in real environments when it has to make predictions. And you will think that these kind of algorithms have poor generalization capacity. And you will be right. Because of that, we still need big data. If you want to work with supervised learning using convolutional neural networks. Well, and to develop the detection of each face present in an image, we didn't want to train a deep learning model. But we decided to use a supervised machine learning algorithm that is quite robust in this task. And its name is hard cascades. Well, this model receives an image. And for each face present in that image, it will return the coordinates of the bounding box that surrounds each face. I'm not going to explain these algorithms because the time of the talk is limited. And I prefer to delve into the part of the pipeline in which we have used deep learning models. So as I said before, next we are going to make a crop of the area of the bounding box that surrounds its face. And we are going to pass this area to the facial recognition model. And this facial recognition model is based on a convolutional neural network. So let's see a brief introduction. Well, convolutional neural networks combines different types of layers. And among them, we can find, for example, the convolutional layer and the pooling layer. So how does the convolutional layer work? Well, first of all, we are going to see how to represent an image. We are going to use a matrix of h pixel of height and w pixel of width and c channel conos. For example, grayscale image will only have one color channel. And color image will be represented using red, green, and blue color channels. And in the convolutional layer, we are going to slide these orange matrix over original image, the green matrix, by one pixel. And this value is monadid using a hyperparameter called sestrite. And for its position, we are going to compute the element-wise multiplication between the two matrices. And we are going to add the multiplication outputs to get the final, the interior, that interior, which will be a single element in the output matrix. And in that terminology, the orange matrix is known as a filter or kernel or feature detector. And the output matrix is known as a convoluted feature or feature map or activation map. And in practice, a convolutional neural network learns the values of these filters during its training process using back propagation. And the point is that we still need to provide some hyperparameters before of the training process. Hyperparameters, like for example, the number of filters, the filter size, the sestrite, the padding, which consists of filling the edges of an image with zeros. And here, you can see the equation to compute the feature map size using the values of that hyperparameters. And summarizing, the more filters you have, the more image features get extracted, and the better your convolutional neural networks become at recognizing patterns in unseen images. Well, now in this picture, we can see an image of our entire convolutional neural networks, which is hierarchical learning. I mean, the filters of the fields layers are going to detect patterns or simple patterns like lines, borders, coordinates, and something like that. And the filters of the final layers are going to detect complex patterns based on the knowledge of the previous layers. This is the key point of this slide. Well, now it's time to talk about the pooling layer. Well, the pooling layer reduces the dimensionality of each feature map, but retains the most important information. It's like a generalization technique that has a convolutional neural network. We can find different types of spatial poolings. For example, max pooling, average pooling, zoom pooling. In case of max pooling, we are going to define a spatial neighborhood, for example, a 2 times 2 window. And we are going to take the largest value of the feature map within that window. Or instead of taking the largest value, we could have taken, for example, the zoom or the average or full elements within that window. And we'll have used it in these cases, the average pooling or the zoom pooling. Next, we are going to slide or 2 times 2 window. And we are going to take the maximum value for each region. OK? And in this point, I'm going to introduce the concept of a receptive field, which is present also in neuroscience in our visual cortex. OK? What is the receptive field of our neuron? Well, it's only the region of the stimulus space that causes the foreign activation of that neuron. And here, you can see the equation to compute the size of the receptive field in layer k, assuming that the size of the receptive field in the first layer is 1. But coming back to the topic of the pooling layer, there are a few downsize, which make it an undesirable operator, we can say that. For example, the pooling layer is destructive. It discards a lot of feature activations. Because of that, we are going to lose exact positional information. And the point is, the positional information is invaluable when we are working with visual recognition tasks. And another limitation of the pooling layer is that with a small receptive field, the effects of the pooling operator will be felt only towards deeper layers of the network, where the size of the feature maps will be usually small. And because of that, intermediate layers may suffer from large input distortions. And we cannot increase the size of the receptive field arbitrarily, because it will don't sample our feature maps too aggressively, and we will lose a lot of positional information. So here is where spatial transformers networks come into play to make more robust and generally stable convolutional neural networks. Well, spatial transformer networks are a different stable module that learns how to apply different types of affine transformation to the feature map where they are inserted. And hold this to ensure or to remove in this case a spatial variance. This is the key point of this technique. Well, we could define a spatial transformer network using its three characteristics. For example, a spatial transformer network is modular. I mean, it could be inserted anywhere in your network with relatively small tweaking. And also, a spatial transformer network is dynamic. I mean, a spatial transformer network applies different transformation on the feature map where it is injected in for its input example, as compared to the pooling layer that acts identically for all the input examples. Another characteristic and a very important point is that a spatial transformer network is a differential one. And because of that, it can be trainable using back propagation and offer us the ability to perform an end-to-end training of the main network when the spatial transformer network is injected in. In our case, the main network will be the facial recognition network. And we can say that a spatial transformer network has three components, a localization network, a generator, and a sampler. Well, the objective of the localization network is to return the parameters theta of the affine transformation that will be applied to the feature map where this spatial transformer network is injected in. And what is an affine transformation? Well, it's only any kind of transformation that preserves collinearity and that preserves the ratio of distances. For example, the midpoint of a line segment initially will be the midpoint of that line segment after the transformation. And we can find different types of affine transformations. For example, rotations, translations, scalings, transvections. And the thing is rotations, scalings, and transvections are linear transformations. And linear transformations can be applied to a point P of coordinate x and y using the matrix multiplication. But the translation is not a linear transformation because it doesn't leave the origin fixed. And if we want to apply a translation to a point P of coordinates x and y, we will have to use the zoom operator. And in this example, we can see how we have applied linear transformation given by these four parameters, a, b, d, and e, to a point P of coordinates x and y. And this linear transformation could be a rotation, or a scaling, or a transvection. And after that, we have applied a translation given by these four parameters, c and f. And summarizing, we can represent all these affine transformations using six parameters. This is the main important thing of this part. Well, and coming back to the localization network, the input of the localization network will be the input feature map of shape h, w, and c. And the output will be the transfer method output of six parameters, the six parameters of the affine transformation. And the architecture that we can use in this place could be a multilayer perceptron or a convolutional neural network, ending with a regression layer of six neurons. And here, we can see how to go to the tertiary flow of this localization network using a multilayer perceptron, in this case of two layers. And we can see that the final layer, the second layer, has six neurons. And the bias of this final layer is initialized using the identity matrix. Well, we are going to see the next component. Well, the grid generator. Well, the job of the grid generator is to return a parameterized sampling grid, which is a set of points, x of s, and y super s in this case, which will be the points where the input math should be sampled to perform the desired output matrix. Well, to obtain that parameterized sampling grid, first, the grid generator creates a normalized set mess grid with the same shape as the input feature map. The input feature map is called u, in this case, in the image. And after that, we are going to, in this case, we are going to apply some, this operation, we are going to take the output of the localization network. And we are going to multiply this output of localization of the localization network times this normalized mess grid, which will have values between minus, equally spaced, between minus 1 and 1 in each axis. And it will be represented using this notation, x super t and y super t. And the point is the values of the parameterized sampling grid, I mean, x super s and y super s, may be fractional. And because of that, we need something more. And this something more is known as sampler. Well, in this case, in this slide, we can see how to code this normalized mess grid, which will have equally spaced values between minus 1 and 1 in each axis. And here, we can see the sampler. The objective of the sampler is to take the input feature map and the parameterized sampling grid, which will have the coordinates where the input feature map should be sampled to produce the output feature map, to produce the output feature map called B, phi, in this case. And the sampler don't have using a bilinear interpolation. The interpolation step is necessary, as I said before, because the values, the output values of the parameterized sampling grid will be usually fractional. And because of that, these values won't exist in the input feature map. And we need to perform this kernel interpolation. And in this formula, we can see how to compute the output values of the pixel y, which will be in the position x of i super t and y sub i super t in the channel c. And the u value will be the input value of the pixel of the feature map at the position nm and in the input channel color t. And next, we are going to apply the interpolation kernel k to the values of the parameterized sampling grid coordinates where phi sub x and phi sub y will be parameters of the kernel interpolation. As I said before, we usually use bilinear interpolation in this case. Well, and combining these three components, the spatial transformer networks learns how to apply a different transformation on the feature map where it is injected for each input example. This is the main important thing. And the main network, I mean the facial recognition network, learns that some object, for example, a face is the same regardless whether it's rotated or scaled or so on. OK? Well, in this slide, you can see an image of the neural network architecture that we have used to develop the facial recognition model. And you can see what kind of loss function we have tried to minimize in this case, which is known as triplet loss. The triplet loss is a way to learn good embeddings for each face. And in the embedded space, all the faces of some person should be close together and form where separated clusters. And the objective of the triplet loss will be to ensure that two examples with the same label have their embeddings close together in the embedding space. And two examples with different labels have their embeddings far away. However, we don't want to push the train embeddings of some label to collapse into very small clusters. The only requirement is that given two positive examples of the same clays, also the same clays, and one negative example, the negative should be farther away than the positive by some margin. And to formalize these requirements, we can say that the triplet loss will work with triplets of embeddings. Well, we have used a ResNet convolutional neural network. And we have cloned this network two times where the input of the first network will be the anchor that will be an example of some class. And the input of the second network will be known as positive, which will be a different example of the same class as the anchor. And the input of the third network will be the negative that will be a different example of a different label than the positive and the anchor. And you can say that in this picture, I have chosen a picture of my face as the anchor and a different picture of my face as the positive. And I have to apologize for choosing this picture of my partner and friend Alejandro as the negative. OK. And here, we can see how to compute the value of the triplet loss, which will be the maximum between 0 and the value of that expression. That will be the distance between the embedding of the anchor and the embedding of the positive plus, minus in this case, the distance between the embedding of the anchor and the embedding of the negative plus some margin. And the most important part here is that as we minimize the triplet loss, the distance between the embedding of the anchor and the embedding of the positive will be closer to 0. And the distance between the embedding of the anchor and the embedding of the negative will be greater than the distance between the embedding of the anchor and the embedding of the positive plus some margin. Well, perfect. And in this point, we are going to see all this theory working in a robot with real data. As I said before, the robot will have a knowledge base in which we are going to have one picture of the face of each person that the robot will have to recognize. And additionally, we are going to train our facial recognition model with a training set composed by approximately 5,000 pictures of each identity. Well, let's play the video. This is the recording of the RE-FR-002 test in which we are going to show the face recognition model here I'm going to explain the environment to the person who is present in the training set. What it has done is to train a neural network that generates an embedding function. So when an image reaches that neural network, the embedding function will assign it a value. That neural network has been trained in a supervised way through three types of images. Let's see here what they are. We have three folders. In the folder number 0, there are my images. In the folder number 1, there are images of my colleague Alejandro. And in the folder number 2, there are images of my colleague Javier. Additionally, the model has a database in which we will place a single image for each person we want the model to recognize. In our case, we have an image of Alejandro who is present in the training set, another image of me who is also present in the training set, and finally an image of our colleague Rosa who is not in the training set. So let's describe the test. What we are going to do is that our colleague Rosa is going to place in front of the robot, it will say the command verbally that will activate the face recognition model and then the robot will begin to take images through its camera. Those images will turn into a topic of view and they will arrive at the model. For each image that arrives at the model, it will verify in the first place if there is a face in this image and if there is, it will take care of predicting that a person is present in that image. And finally, a model of text speech that will reportedly reproduce the name of the person predicted by the visual recognition model. Very well, we are going to start with the test with our colleague Rosa. If you can say the word Rosa. Recognize. Right now the model has already accepted the command and it is starting to capture images in short, it will say the name of the preface. As you have been able to check, it has already said the name of the preface. I will say it again, we are going to stop it to check. Rosa. In this case, we are going to continue to stop it to see if we can say it. I say it's coming out, but I don't have a clue. It's an example. Here we are now going to check the output of the model. Well, here it has been able to check how the model has saved for each image that it has received the name of the preface person in that image, which is Rosa. Additionally, what we do is publish that information on a topic of Rosa, called, in this case, Peperutils Guion Bajo Facename, with the purpose that another application can make use of this information. Until here, this test. Okay. This is a demo of this facial recognition model with all of its limitations. And I would like to finish the talk by showing that we should move towards models that combine concepts like quantum computing and neuroscience. One example, a first approach based on neuroscience could be spiking neural networks. Well, spiking neural networks work with spikes, which are discrete events that take place at points in time rather than continuous values. Well, essentially, when a neuron reaches a certain potential, it spikes, and after that, the potential of that neuron will be reset. At first glance, this may seem like a step backwards, because we have moved from continuous outputs in deep learning models to binary outputs in spiking neural network models. But in the end, the spike trines offer us the ability to process spatial temporal data, or in other words, real-world sensory data. The spatial aspect refers to the fact that neurons are only connected to neurons local to them. And the temporal aspect refers to the fact that spikes occur over time. And what we are losing with the binary encoding, we gain with the temporal information of the spikes. OK? And one common model used in spiking neural networks is known as the Leakey Integrator and Fire Model, which is a simple description of a neuron. Well, real neurons have a membrane and have a family of ion channels that control the flow of current across that membrane. Which modulates the action potentials, the membrane action potentials, including the firing of these action potentials. Well, the Leakey Integrator and Fire Model implements four features of a real neuron. Well, the entire cell has a single voltage called v sub m. And it has a membrane with a capacitance called c sub m, which its units are in farads or columns over volts. And it has a leaked channel that allows the current to flow across the membrane with a resistance of r sub m. Or inversely, with conductance g equals to 1 over r sub m. Well, and without the application of an external current, the charge carriers traveling across the membrane will be driven by an equilibrium voltage called v sub equilibrium. And the action potentials are simulated when the voltage of the neuron reaches a certain value, called v sub threshold. And when this neuron spikes, then the voltage of that neuron will be artificially reset to a reset value called v sub air. And in this picture, you can see a simulation of the structure of a neuron. And the equation that describes how the voltage of a neuron changes over time in the phase of an externally applied current, y sub m, assuming that tau is equal to the capacitance time the resistance r sub m, is that equation. The derivative of voltage with respect to the time is equal to that equation. And if assuming that we have a discrete time step, we could evolve that equation to work with discrete time steps. And after that, we should define some kind of learning rule. For example, STDP, which is the acronym of Spike Time Independent Plasticity. And this learning rules modifies the strength of the synaptic, based on the post-synaptic spikes. This is only a brief introduction of a spiking neural networks. We deem that some of you are attracted to this topic. As the time of my talk has finished, I would like to encourage you to keep researching in alternative ways of artificial intelligence to test more robust models. That's all. You have any questions? Thank you. If you want, we'll control later. I'll be in the Data Hub booth, OK?


Hey everyone, welcome to theCUBE's coverage of Women in Data Science 2022. I'm Lisa Martin, and I'm here with one of the key featured keynotes for this year's WID events, Cecilia Aragon, the professor, the Department of Human-Centered Design and Engineering at the University of Washington. Cecilia, it's a pleasure to have you on theCUBE. Thank you so much, Lisa. It's a pleasure to be here as well. You have an amazing background that I want to share with the audience.  That's quite the background. Well, thank you so much. It's all very interesting and fun. And as a professor, you study how people make sense of vast data sets, including a combination of computer science and art, which I love. And as an author, you write about interesting things. You write about how to overcome fear, which is something that everybody can benefit from, and how to expand your life until it becomes amazing. I need to take a page out of your book. You were also honored by President Obama a few years back. My goodness. Thank you so much. Yes, I've had quite a journey to come here, but I feel really fortunate to be here today. Let's talk about that journey. I'd love to understand if you were always interested in STEM, if it was something that you got into later. I know that you are the co-founder of Latinas in Computing, a passionate advocate for girls and women in STEM. Were you always interested in STEM, or was it something that you got into in a kind of a non-linear path? I was always interested in it. When I was a young girl, I grew up in a small Midwestern town, and my parents are both immigrants. And I was one of the few Latinas in a mostly white community. And I loved math, but I also wanted to be an astronaut. And I remember when we were asked, I think it was in second grade, what would you like to be when you grow up?  And my teacher said, oh, you can't do that.  Pick something else. And so I picked math, and she was like, okay. So I always wanted to, well, maybe it would be better to say I never really quite lost my love of being up in the air and potentially space, but I ended up working in math and science. And I loved it because one of the great advantages of math is that it's kind of like a magic trick for young people, especially if you're a girl or if you are from an underrepresented group. Because if you get the answers right on a math test, no one can mark you wrong. It doesn't matter what the color of your skin is or what your gender is. Math is powerful that way.  I love that. I never thought about math as power before, but it clearly is. But also, and I wish we had more time because I would love to get to how you overcame that fear.  How did you overcome that? And so never mind. I'm doing it anyway. Yeah. Well, that's a, it's a, okay. The short answer is I had incredible imposter syndrome. I didn't believe that I was smart enough to get a phd in math and computer science. But what enabled me to do that was becoming a pilot. And I be, I learned how to fly small airplanes. I learned how to fly them upside down and pointing straight at the ground. And I know this might sound kind of extreme. So this is not what I recommend to everybody. But if you are brought up in a way where everybody thinks little of you, one of the best things you can possibly do is take on a challenge that's scary. I was afraid of everything. But by learning to fly and especially learning to fly loops and rolls, it gave me confidence to do everything else because I thought I appointed the airplane at the ground at 250 miles an hour and waited. Why am I afraid to get a phd in computer science? Wow. How empowering is that? Yeah, it really was. So that's really how I overcame the fear. And I will say that, you know, I encountered situations getting my phd in computer science where I didn't believe that I was good enough to finish the degree. I didn't believe that I was smart enough. And what I've learned later on is that was just my own emotional, you know, residue from my childhood and from people telling me that they, you know, that I couldn't achieve. And I look what you've achieved so far. It's amazing. We're going to be talking about some of the books that you've written. But I want to get into data science and AI and get your thoughts on this. Why is it necessary to think about human issues and data science and AI? What are your thoughts there? So there's been a lot of work in data science recently looking at societal impacts. And if you just address data science as a purely technical field, and you don't think about unintended consequences, you can end up with tremendous injustices and societal harms and harms to individuals. And I think any of us who has dealt with an inflexible algorithm, even if you just call up, you know, customer service and you get told, press five for this, press four for that, and you say, well, I don't fit into any of those categories, you know, or have the system hang up on you after an hour, I think you'll understand that any type of algorithmic approach, especially on very large data sets, has the risk of impacting people, particularly from low income or marginalized groups, but really any of us can be impacted in a negative way. And so as a developer of algorithms that work over very large data sets, I've always found it really important to consider the humans on the other end of the algorithm. And that's why I believe that all data science is truly human centered, or should be human centered. Should be human centered and also involves both technical issues as well as social issues. Absolutely correct. So one example is that many of us who started working in data science, including I have to admit me when I started out, assume that data is unbiased, it's scrubbed of human influence, it is pure in some ways. However, that's really not true. As I started working with data sets, and this is generally known in the field, that data sets are touched by humans everywhere. As a matter of fact, in the recent book that we're coming out with human centered data science, we talk about five important points where humans touch data no matter how scrubbed of human influence it's supposed to be. So the first one is discovery. So when a human encounters a data set and starts to use it, it's a human decision. And then there's capture, which is the process of searching for a data set. So any data set has to be selected and chosen by an individual. Then once that data set is brought in, there's curation. A human will have to select various data sets, they'll have to decide what is what is the proper set to use. And they'll be making judgments on this the entire time. And perhaps one of the most important ways the data is changed and touched by humans is what we call the design of data. And what that means is whenever you bring in a data set, you have to categorize it.  Well, you don't just take whatever the description of the soil data is, you actually may put it into a previously established taxonomy and you're making human judgments on that.  And finally, people will label the data that they have. And this is especially critical when humans are making subjective judgments, such as what race is the person in this data set. And they may judge it based on looking at the individual skin color. They may try to apply an algorithm to it. But you know what? We all have very different skin colors. Categorizing us into race boxes really diminishes us and makes us less than we truly are. So it's very important to realize that humans touch the data. We interpret the data. It is not scrubbed of bias. And when we make algorithmic decisions, even the very fact of having an algorithm that makes a judgment say on whether a prisoner is likely to offend again, the judge just by having an algorithm, even if the algorithm makes a recommended statement, they are impacted by that algorithm's recommendation.  So we consider all of this. So you just given five solid reasons why data science and AI are inevitably human-centric should be. But in the past, what's led to the separation between data science and humans? Well, I think a lot of it simply has to do with incorrect mental models. So many of us grew up thinking that, oh, humans have biases, but computers don't. And so if we just take decision-making out of people's hands and put it into the hands of an algorithm, we will be having less biased results. However, recent work in the field of data science and artificial intelligence has shown that that's simply not true, that algorithms reinforce human biases, they amplify them. So algorithmic biases can be much worse than human biases and can have greater impacts. So how do we pull ethics into all of this data science and AI and that ethical component, which seems to me that it needs to be foundational? It absolutely has to be foundational. And this is why we believe and what we teach at the University of Washington in our data science courses is that ethical and human-centered approaches and ideas have to be brought in at the very beginning of the algorithm. It's not something you slap on at the end or say, well, I'll wait for the ethicists to weigh in on this. No, we are all human. We can all make human decisions. We can all think about the unintended consequences of our algorithms as we develop them. And we should do that at the very beginning. And all algorithm designers really need to spend some time thinking about the impacts that their algorithm may have. Right. Do you find that people are still in need of convincing of that? Or is it generally moving in that direction of understanding? We need to bring ethics in from the beginning. It's moving in that direction, but there are still people who haven't modified their mental models yet. So we're working on it. And we hope that with the publication of our book that it will be used as a supplemental textbook in many data science courses that are focused exclusively on the algorithms and that they can open up the idea that considering the human-centered approaches at the beginning of learning about algorithms and data science and the mathematical and statistical techniques, that the next generation of data scientists and artificial intelligence developers will be able to mitigate some of the potentially harmful effects. And we're very excited about this. This is why I'm a professor because I want to teach the next generation of data scientists and artificial intelligence experts how to make sure that their work really achieves what they intended it to, which is to make the world a better place, not a worse place, but to enable humans to do better and to mitigate biases and really to lead us into this century in a positive way. So the book Human-Centered Data Science, you can see it there over Cecilia's right shoulder. When does this come out and how can folks get a copy of it? So it came out March 1st and it's available in bookstores everywhere. It was published by MIT Press and you can go online or you can go to your local independent bookstore or you can order it from your university bookstore as well. Excellent. Got to get a copy of, get my hands on that, got to get a copy and dig into that because it sounds so interesting but also so thoughtful and clear in the way that you described that and also all the opportunities that that AI, data science and humans are going to unlock for the world and humans and jobs and great things like that. So I'm sure there's lots of great information there. Last question I mentioned, you are keynoting at this year's WIDS conference. Talk to me about like the top three takeaways that the audience is going to get from your keynote. So I'm very excited to have been invited to WIDS this year which of course is a wonderful conference to support women in data science and I've been a big fan of the conference since it was first developed here at Stanford. The three top takeaways I would say is to really consider the data science can be rigorous and mathematical and human-centered and ethical. It's not a trade-off. It's both at the same time and that's really the number one that I'm hoping the keynote will bring to the entire audience and secondly I hope that it will encourage women or people who've been told that maybe you're not a science person or this isn't for you or you're not good at math. I hope it will encourage them to disbelieve those views and to realize that if you as a member of any type of underrepresented group have ever felt oh I'm not good enough for this, I'm not smart enough, it's not for me, that you will reconsider because I firmly believe that everyone can be good at math and it's a matter of having the information presented to you in a way that honors the background you had. So when I started out my high school didn't have AP classes and I needed to learn in a somewhat different way than other people around me and it's really it's really something that what I tell young people today is if you are struggling in a class don't think it's because you're not good enough. It might just be that the teacher is not presenting it in a way that is best for someone with your particular background. So it doesn't mean they're a bad teacher, it doesn't mean you're unintelligent, it just means that maybe you need to find someone else that can explain it to you in a simple and clear way or maybe you need to get some scaffolding that is take learn extra take extra classes that will help you not necessarily remedial classes. I believe very strongly as a teacher in giving students very challenging classes but then giving them the scaffolding so that they can learn that difficult material and I have longer stories on that but I think I've already talked a bit too long. I love that the scaffolding. I think the one one of the high-level takeaways that we're all going to get from your keynote is inspiration. Thank you so much for sharing your path to STEM, how you got here, why humans data science and AI are have to be foundationally human centered. Looking forward to the keynote and again Cecilia Aragon, thank you so much for spending time with me today. Thank you so much Lisa, it's been a pleasure. Likewise, for Cecilia Aragon, I'm Lisa Martin. You're watching the CUBE's coverage of Women in Data Science 2022.


Today we went over five techniques two of them being knife and the other three being counter the chokes and holds those techniques are Counting to the rear beehive counting to the rear choke and counting to the rear headlock Before they even graduate Paris island they have to be 10 belt qualified They initially start off with no belt just a regular web belt And then we have test days of which they have to come over here to L&S We'll go ahead and we'll test them out for their belts The purpose of the map here in Paris island was just given on the basics But over time it's to develop that ethical warrior through the three disciplines of physical mental and character discipline The disciplines right now that I see recruits struggle with the most It's right now is the mental discipline a lot of them are kind of in their heads because you know This is probably the most strenuous environment that they ever been in so it's like you know that their character is being tested The physicality is being tested as well in regards to intensity endurance strength all those things The map is important for these recruits because especially if you see them for the first time a lot of them don't know how to defend themselves physically and Also, some of them don't understand the principle of self-defense is not only just from a physical aspect There's a mental game that comes with it as well


The addition of inorganic spherical nanoparticles to polymers allows for modification of physical properties and implementation of new features in the polymer matrix, while also providing potential applications in energy storage systems. This article was authored by Thomas Hanaman and Dora Thieving De Sabo.