Welcome to Teens On Topic, I'm your host Emma Arnston and today we're talking about animal testing. But first, let's hear from some people around Davis. Do you think that testing on animals should be allowed? I guess from a scientific standpoint, you know, there's some validity to that. Sometimes I feel like, you know, it might not be ethical to try to.  So I mean, I sort of do, you know, sort of agree with it. Like, it can be acceptable at some points, I think. Yes, I do. It depends on what sort of testing they're doing. If it's for things like the betterment of human health, things like that, without necessarily the, like, the detriment to the animal, then I kind of, I'm for it, sure. No, I don't think that animal testing should be legal. I think that it's very harmful for not only the animals, but the people involved. I feel like maybe on small animals like mice, but nothing bigger than that. Yeah, I kind of agree. I don't really care what the animal is. Basically, if we're going to solve for diseases and a lot of the problems that humanity is facing, we obviously have to test that out and experiment with that. And doing it on humans is probably not a great idea. So animals are the next best option. I think animal testing should absolutely be legal. To expand on that, you know, animals, we have dominion over animals. You know, it was said in the Good Book that Sweet Baby Jesus said that we should be able to test animals and make them suffer if we like. No, not at all. I think there are better ways to determine if a product's safe, especially if they have the test from previous animal testing, they can just recycle that without testing on more animals. So it's like completely, it doesn't make sense. Yeah, basically what she said, I mean, she wrapped it up pretty well. I mean, I don't really know everything about what goes into animal testing, but I think there are a lot of, you know, health problems or like, you know, different dangers for the animal specifically. And I don't think that's really necessary. Thank you guys. Awesome. Well, that was some interesting things that people had to say. Most of them had to say that they agreed with animal testing, but what did you guys think about their opinions? I just learned that Sweet Baby Jesus went for animal testing and for animals to suffer. I did not know that. Yeah, I didn't remember reading about that one. I was learning new things all the time. One of the things that I thought was interesting is how, like, people talked about animals, like testing on animals to help prevent any diseases, but one thing that I feel like people didn't touch on was, like, how harmful it is when they test on animals for, like, makeup companies and, like, hair products and stuff. And so, like, I feel like I try to steer away from companies that are known for, like, harming animals with animal testing just because I feel like if you're buying your makeup from, or, like, hair products from someone that's more, like, ethical, you're getting a better product. I think one of the people there brought up a good point. There's a different, there's a wide variety of ways you can test animals, cosmetic, medical, and, you know, food applications. And it just really depends on if you're hurting the animal, if you should do so. I believe that if you are hurting the animal, you probably shouldn't, but if you're simply, I don't know, rinsing the animal with a product that is designed for humans and won't hurt the animal when I feel that it should be okay and should be allowed. The only important part of animal testing is that, like, it's hard to tell whether you are hurting the animal until you do it, which is kind of why they do testing. I want to go back to the theological implications of this. I think it's, it is interesting the idea that mankind is, I mean, he said he's the word Dominion, that we have Dominion over animals, and the idea that we're allowed to make them suffer. I think it's interesting when you think about how far is too far. You know, I don't know. But it's really tough, and who should be making these pronouncements about what's too far in animal testing? Well, I mean, I feel like about how far is too far, you kind of have to look at the individual case of something. Like, I mean, I don't know really anything about how the process of cosmetics being made and tested works. I feel like that's probably not something where you really need to cause a lot of pain to create them well. So I feel like you should, you know, try to, you know, based on what kind of test you're doing, see, okay, is it really necessary to cause pain? Is there some way we can be more careful about this? But I know, I mean, I feel like in terms of experimenting with medical treatments that could potentially go wrong and harm animals, you know, if that's going to, in the future, help us to prevent the deaths of a lot of humans or the sickness of a lot of humans, I think it's okay that we test that out on animals. And I mean, I feel like the worst pain is the pain that kind of comes with being able to process things more, you know, that kind of comes with self-awareness. So I feel like testing things on animals that aren't self-aware, the pain that they feel, I don't feel like just on a, just instinctually I don't feel like is as bad as the pain that self-aware animals can feel. So I think maybe we should ban testing on, like, octopi and dolphins. For sure. I definitely agree with that. Yeah. And as far as, like, medical treatments and stuff go, like, obviously, if it's going to save lives, like, that is a whole other different story. But it's hard to determine, like, how to, like, what part of that testing is helpful and harmful or unnecessary. But I definitely agree that when it comes to medical stuff, it's a whole other story than just cosmetics. What do you all think of when laboratories breed animals with specific diseases so that they can find a cure? Do you think that's also... Yeah, the point that the guy was bringing up about lab rats, I feel like doesn't make a lot of sense.  I feel like on some level that is even worse. I mean, raising them to have diseases, at least when you don't look at the, like, external consequences of that, that seems pretty immoral. I don't really feel like there are any problems in and of themselves with, like, genetic modification of animals. Maybe things where there's not really an end goal. Like, people, like, scientists have genetically altered some bunnies, I believe, to make it so they glow in the dark. They're obviously not going to try to make glow in the dark humans. So I feel like if you can kind of have some expected consequential, like, impact, you know, some expected thing that is going to help humanity, that it's justified, but I don't think if you are really... I think if you're just doing it for the idea of pure science, then you shouldn't cause suffering. Yeah, I'm just going to take the opposite view of Adam. I think that it's totally fine to have lab rats. I wouldn't want to ever work in that kind of environment. I wouldn't want to be around them. That's kind of creepy to me. But I think it's totally fine to have animals that have little to no significance be there for the betterment of science. But what does scare me is the genetic influx. You see all kinds of scientists around the world coming up with chimeric animals where they're human genes and brain cells put into these animals. And I think it has very, very scary implications for our rapidly changing genetic engineering landscape. And then, yeah. I think another variable we should take into consideration is that no matter how closely related we may be to a certain animal, you know, because we're mammals or whatever, it's not a human. In the end you also got to think that these animals, you know, they cannot 100% predict what the outcome will be on a human. Yeah, exactly. Do you guys think we'll have animal testing forever or do you think this is something like an intermediate step before we come up with something better? I think that we're going to have animal testing for a really long time. I mean, obviously the idea that it would be indefinite is kind of hard to predict, but I think that as of right now, it's kind of just the way to test things without testing them on humans, especially like lab rats and bunnies and stuff like that. So, yeah, I think it's not going to go away anytime soon. It's just about regulations and stuff. I feel like before any experience it is really, really difficult to predict the consequences of some kind of new medication.  I don't think that's really possible to do before looking at the impact on some animal. Yeah, I definitely think it's going to be here for the long haul. I think as we mentioned before, it's going to change significantly towards genetically modified animals towards these, you know, it's not just going to be lame bunnies, it's going to be a lot of different stuff. It's kind of scary, but I think it's also going to be, I mean, science is always moving us forward and helping us in a lot of ways. So I think despite the possible dystopian aspects that we kind of worry about, there's a lot of good for the human race out of this. What were the bad aspects that you were bringing up? Well, just genetic modifying and chimeric animals and then just the abuse of animals when you look at testing them nowadays. So what do you think is like the bad, where is the bad in like splicing animal DNA? I think when you're mixing human DNA into animals, that's incredibly scary. Just as a reader of comic books. Sure. It's also another thing. What if a human volunteer is to be tested on? What would the case be then? Would you guys know? I don't know. I mean, nowadays we do have clinical trials, right? Yeah. Why don't we just stick to that in that case? I'm not questioning you. I think it's just like starting on a smaller scale, like with lab rats or something like that, like that is like they start there so then they can move on to clinical trials and stuff like that. So just to ensure that there is going to be less harm done to people by the time that they're coming around to clinical trials because a lot of the times like if they're making a medicine like they wouldn't know what exactly the side effects were going to be so they don't just want to kill a whole group of people who signed up for a trial. Right. And I feel like sometimes it's not entirely your choice either.  I mean, there are probably going to be a lot of people who are really poor who are going to take that chance and I feel like that will result probably in a lot of kind of horrifying genetic modification of people because of their economic status. Yeah, I don't necessarily think a lot of it is because of money. I think a lot of it is when people start running out of established methods so I think a lot of people when chemotherapy doesn't work in cancer that's when people start moving on to experimental methods. So I do think it will be a lot more to be a lot of medical desperation than economic situations. That being said, I still think that it has the potential for good and bad. Yeah, definitely. Does anyone have any last thoughts they'd like to share? No. Well, thank you all for joining me and us here at the studio. It's been a great time as always. Tune in next time when we have a very interesting topic that pertains to a lot of teens in America.


No, my agenda is not pulling up, but it doesn't really matter to be able to handle it. So we'll call the Thursday, May 6th, special select board meeting to order. We've got Flo Smith, Brad Town, myself, Justin Lawrence along with Vince Conti. Any additions or changes to the agenda? No. There's two items on the agenda since you don't have yours in front of you, Justin. The two items are the liquor licenses for Twin City Lanes and the working in right away permit for Mr. Picard on Addison Drive. Yep. Any public comment? Hearing none. Liquor licenses for Twin City Lanes. Move to approve liquor licenses for Twin City Lanes. I second that motion. Any discussion? All in favor? Aye. All right, motion carries. All right, John Picard working in the right of way, Addison Lane. John, have you had talked with the road foreman? Yes, I have. No, I haven't, not the road foreman, no. Yes. We can hear you. You haven't had a chance. So you haven't had the opportunity to speak to Tim yet. No, not because I haven't got to go ahead on it yet. Okay, Brad, typically how we, it looked like a larger working in the right of way job compared to a lot of what we see. Yep. I can't pull up the application. I don't know if you guys, if the board has any questions on the scope of the work or the timeframe. John, how are you going to take a deal with traffic control? Well, because it's off to the side, we're not going to be really in the road at all until it comes a time when we have to cross the road and then we're going to close the road for one day. But I've got a, I filled out a form and everything. You don't have it there. That shows where the line's going and everything. Well, I can't get it. I'm like, Justin, I don't think my phone is, it's feeling friendly tonight. Because I've got a, I've filled out a plan and a diagram because we can't place the line where it is now because then you'd have to go all the way down through the center of the highway. Yeah. And by staying off to the side, we don't, you know, we don't have to get involved with the traffic too much until it comes a time when we have to cross the road. And what we're trying to do is stay off the black top. And then when we get ready to cross over, we're going to be on dirt road instead of black top. Does that help any? Yeah, I just brought it up on my phone here. That's what I'm going to say, I have it up as well. So let's see here. So you're going to take the old, the old line was going down the middle of the road. Pretty much, it goes down the middle of the road and then it kind of crosses over to the two, the driveways going into the apartment houses, you're familiar with those? Yeah. So that would mean we'd have to block those off too. But see what we want to do is put it on the opposite side of the road and stay alongside that side of the road and run it on the opposite side and then cross it over. Yeah. If I'm not mistaken, John, this is Vince, you're going to cross over below the paving. So you'll actually be crossing over dirt. We won't be ripping up any pavement. Yes. Excellent. That's the way it looked to me too. Thanks for clarifying that, Vince. Sure. Okay. Plus, we're trying to keep the cost down for the elderly people that, you know, because they don't have an awful lot of money in aways and it's quite a long line to go for, you know. Yeah. 400 feet from intersection. Yeah, that's what I saw was the total project costs looked higher than most that we see. So that's why I was curious. So you're going to have the road closed for a short period of time. Yes. When we dig the first half of it, which is, I think it's around 300 and something feet, it's going to be beside the road. Right. And then when we cross over, we need to shut the road down for, I don't see any reason. I think we got it down for two days, but I think it can do it all in one day. And so we're going to make sure, we're going to want to make sure that, I mean, we notify the fire safe. Sorry about that. Let's be somebody's here. We're going to want to make sure we notify fires, you know, the fire department. And all, if we get approved, that's that everybody's well aware that the road's going to be shut down. Yeah. What did you have? Yeah, go ahead. Sorry, it's Vince. I asked Mr. Picard to give me at least, at least a two day notice when he expected to be crossing the road so that we could get it out to the public. And the emergency services and such that that section of the road could be closed for a day or possibly two, but when he gets within 48 hours of knowing that, so we can get at least a 48 hour notice out to everyone. All right. And if it happens to be eight where it's exposed and open, what are we going to do for safety precautions or how are we going to divert traffic? Just, I want to know what the plan is there, probably. Well, John, are you going to be able to take in a habit so that Addison Drive is at least one lane if you have to do it for two days? Sure. I guess we could. But like I say, we're going to try to pull it off in one day. I can't believe you take you all that long and dig across the road. Right. That's just it.  So I would imagine you'd want to get it in one day. We're shooting for one day, anyways. And like you say, I don't unless. But see, there's some issues that there's some ledge comes up across here. So we're just trying to cover ourselves for that a little bit. Yeah. So Brad, did you get the answer to traffic control? Yeah. OK. See, we're trying to stay away from hiring traffic control just because it costs. Absolutely. So no, I mean, we obviously would like to see improvements. So just curious, does the board have any additional questions? I have none. Mr. Beard, this is Flo Smith. I was just curious, do you have an estimation of date of when you want to begin? No, not right now because the folks that I'm working for, they're trying to get their finance ready. So so so they could give me a deposit on the on the job. And then once I get that, then I can, you know, I can I can clarify where you guys afterwards what the date's going to be understandable. Excellent. Thank you. OK. Well, I would move to approve the permit for working in the right away for this project. And I second that motion. All right. Any discussion? All those in favor? Aye. Aye. Motion carries. Thank you, John. Oh, thank you guys, muchly. All right, we'll see you later, John. I'm sure that the folks up there will be very glad about hearing this. OK. Appreciate your time. Yeah. Thank you. Nothing to adjourn. What's that? Move to adjourn. Second. All in favor? Aye. Aye. Motion carries. OK. Justin. Thank you. Have a good evening. Thanks, everyone. Have a nice evening.


Your two team keep it clean the 740 a.m. And I just had to come in check in with y'all Just say what's up and real quick just say thank you again for all the messages that y'all sent to Carter In the video yesterday told y'all when when he's woken up today. I'm gonna show it to him I'm gonna show it to him, but he's doing good Yesterday for a while. He was just tired. He's just super tired tired like real sleepy and whatnot He was kind of out of it, but all of that wolf and he went back to being his normal self He didn't really open his eyes too much because they were still in a little bit uncomfortable but He's back to normal Carter back to normal Carter He even yesterday while he was laying down I was talking to him and I hadn't eaten anything all day So keep that in mind for this next part, but I was talking to him and I hadn't eaten anything and I'm talking to Hey Carter, how you doing? He's talking back to me. I'm good. He's a dad Can you brush your teeth? Okay, thanks, but anyway, so yeah, he's back to his slick self. So yeah, that's that's normal Carter But anyway team keep it clean The Ravens they have restructured the deal of MP juice man Marcus Peters They restructured his deal and they opened up 3.5 mil in cap space for this year Now that's a good and not necessarily a bad thing But a good thing and a another thing because while it opens up 3.5 mil in cap space this year Next year, they will have 3.5 mil less than cap space due to his deal, but I'm assuming with the whole C 19 With them with the NFL knowing how it's going and knowing how they're gonna approach it this year and this year should be a lot more Prosperous or more more profitable than last year was Then the salary cap I would expect it to go up slowly, but I would expect it to go up So teams should be in much better shape But anyway, Ravens are gonna have to deal with that next season, but it's all good. So MP juice man Because he signed through this year and next year so I Guess they said they'll cross that road when they get there and it's crazy because right now in the time Where a lot of teams are ziggin Ravens are zagging and for now.  They're getting rid of Good play great players.  Well, I bet this is awkward for those guys They're gonna have that conversation and right trust me, right? I'm gonna have to have those conversations too But Ravens they they do this every year. They always get rid of guys every year even the guys who Fans may love teams may like even mark England But see mark England they didn't get it. They got rid of him way before this whole salary cap thing was a thing They they been got rid of him Rg3 was another one. They got rid of them. Well, that ain't have anything to do with salary cap They had to do it. Hey, well Rg3 was because he was on injured reserve and they had to cut him But with Mark Ingram They had to get they got rid of them. They have to but they did and now he's a Texan But with the Ravens they right now. They're like in the middle of the pack I think a little bit higher than the middle of the pack when it comes to salary cap space So they walking around like some big dogs on campus like oh, man.  Oh, I don't know how that feels But Ravens, they don't have the most money now, but they got a good amount They're in a lot better shape than a lot of teams are in a lot better shape Now I think the top team that has the most salary cap space would be the Jacksonville Jaguars But again a lot of times usually like eight times out of ten the teams that have the most salary cap space Usually were terrible the year before and have been terrible for a while Because they have so much turnover on the roster. They usually don't have those cornerstone players They don't usually have a competitive team That's how it works. Usually like I said eight times out of ten I know Colts are somewhere higher in cap space, but they had a good year last year bad one the year before now But last year they did So Colts are up and coming team that did well last year so they're gonna be able to do some things So they're gonna be able to really build that roster for real And Browns to Browns got a significant amount of cap space I think they got like five mil more than the Ravens not a big difference, but a big difference But Brown Browns had been bad for a while But then last year boom they they turn that corner So it just you just you just never know you just never know but with the Ravens While they're watching all these other teams get rid of all these really good players not saying that they're not gonna have to do it, too because I mean By no choice of their own a lot of good players are gonna have to leave anyway, they're gonna lose a lot of guys They're gonna lose a significant amount of guys like it just when you look at the the past rusher and edge guys alone You got you got so many guys, man Your top guys obviously you need King Gakway Matt Judo Bowser. You could lose all three of them Then you got Jihad Ward. You got a pernil make fee.  Let's have a cup group conversation Hey, everybody join the group chat. Oh, y'all boys are getting re-signed. No, that's not happening You're gonna lose most of them so It's just right Raven's got some tough decisions to make too But when Marcus Peters restructuring this deal, it does give them some more wiggle room And I would expect them to do some more of restructuring I know a name that's come up a lot Matt Marcus Peters name his name had come up a lot too when people thought about who could the Ravens restructure the Admiral Capspace His name was always on those lists But another name who's always on those lists is Brandon Williams So with him, I would think that um, I don't even think they would do a restructuring I think they may do a re-sign like an extension with Brandon Williams.  They could do that. I don't see them doing that. That's like old-school Raven Yeah, I remember when they would do that a lot.  See ya That Ravens player will be just like at the end of these videos. I'm out. He be gone But with Brandon Williams, I don't see them doing that because he's definitely on command He will command some attention He will command some teens teams will be like a brand new way. Hey, what's up big dog? We couldn't really run against you when you on your Ravens, but hey come join us We want that to happen with us But I would expect something to happen with Brandon Williams very soon, too. I mean, it's Monday Monday is the date Monday it starts free agency. Well, yeah Monday starts the the legal tampering period which they need to get rid of They just do away with that But we'll see how things go team keep it clean man. I love y'all so much. I appreciate y'all so much Y'all I y'all just don't know y'all really don't know So thank you. Like I said, I'm gonna tell Carter about all every message that we saw in the comment section of that video Every DM all of that stuff. I'm gonna show him and he's gonna read and he is where he gonna see his name Oh, that's for me. I'm gonna say yeah, that's that's team keep it clean, man You know, they got love for you, man So but again, like I said, he's doing really good. He's chilling. He's vibing. He's he's he's good, man So I love y'all it is 748 now So I got to get ready and start waking him up So we can go to our appointment at 9 a.m Y'all know, I don't like waking up early in the morning for nothing, but it is what it is Love you all team. Keep it clean. Whenever you see this video. I hope you're having and have a great great day I love you. I appreciate you and Tell somebody you love them today, please whether it's family friends neighbor What a what tell somebody you love them and tell somebody that you appreciate what they do I love y'all and I appreciate what y'all do. So I'm starting it off Y'all go ahead and finish it with somebody else, man. I appreciate it and we out


So hello everyone, and welcome to this seminar series of 30 terms for the machine learning and physics particle and physics seminar series. So our speaker today is Edu Westa, he is the lead for translation on AI at the Arkan National Laboratory with faculty appointments in the department of science and the University of Chicago, and in the department of physics I've at the University of Illinois at Burbank at Champaign. He is a theoretical astrophysicist, a mathematician, and a computer scientist, so his research interests, his research interests are numerous as his positions are, but he tends to focus on the interface of artificial intelligence, theoretical astrophysics, mathematics, extreme skepticism, and scientific visualization. Today he will discuss how to transform new AI approaches into a framework for scientific discovery in his talk titled AI for Science, Let's Talk Business. The talk is recorded and will be shared on YouTube, so if you have any questions regarding how to access it on this day to send it to us. Thank you very much Edu for joining us. All right, great. So I'm going to share my slides and I hope you can see them well. So I hope that this becomes a conversation. If you want to interrupt me any time during the presentation, please feel free to do so. I will not have access to the chat, so maybe if you can unmute and just interrupt me that will be fine. Okay, so I want to start this conversation with a few quotes from Bertrand Russell that are related to how we should be able to size problems and how we try to find solutions for them. And so these two questions I think are quite revealing when we are trying to innovate or to change the state of practice or the state of the art. And as you can see here, first thing that we need to ask is what are the facts? And the other one is what is the truth that the facts bear out? And so in the context of science, what we can see now is that after many years, even decades of effort, we now have large-scale scientific facilities that enables us to study the universe in high fidelity, high precision. And we now have other fingertips, a massive amount of data that we could use to go and answer the bigger questions. The origin of the universe, where the black holes come from, what is the preferred formation channels for them, we can see the universe through many messengers. But the truth is that while scientific facilities are delivering on their promise, now we encounter that we have these large volume, high velocity, multivariate, complex data sets. And we are at the brink of discovery. And we have samples of that in different domains. The discovery of the Higgs boson, the discovery of gravitational waves, that the collision of neutron stars is associated with the emission of gamma rays, sure gamma ray bursts, etc. So all of that is awesome. The problem is that as the amount of data continues to grow, and there is no end to it, what we realize is that the computing approaches that we have been using for many years and the signal processing tools that we use are not up to the task. They really are not keeping up with the pace of discovery that could be possible. And so people have realized that instead of just using machines to accelerate computing, we can use them to boost human intelligence. And we want to talk about that in this presentation. So what are the challenges when you say, when you realize the method I am using to extract knowledge from data from observation is not good enough? Well, the first thing that you realize is that innovation is not easy. Because you need to go against the flow, against programs that have been developed over sometimes decades. And there is like a frame of thought about how you do things. And so here you encounter sociological factors. And the fact that some longer programs have been funded by different agencies want to see some continuity. Disruption sometimes is not well perceived. And if you have been in this AI business for a long time, you do realize that this is an issue. And when we started changing the landscape of AI applications in physics in the early 2010s, well, people were not taking that lightly. And so what are the opportunities here? Here is where diversity is important. Because to change how people perceive things, you need to bring in people that have different perspectives. You cannot have the same group of people and then expect them to innovate. It's hard. You need to bring in new thoughts, new ideas, new perspectives. And critical thinking is important. And sometimes you will realize that people don't like your ideas. And so if you think that they are watertight, then you need to also have some resilience. Because at the end of the day, this is just a human endeavor. And there is not just scientific thought involved in this. There is also the human aspect. And we also need to realize and acknowledge that when you are pursuing knowledge, when you are trying to find answers, there is no established paradigm to accomplish that. It's all about creativity. And so sometimes ideas that have been held for a long time may not be the actual solution to address problems. And if there is something we have learned from the revolution in high performance computing, many people did not like, for example, MPI now, who is not using MPI in supercomputers. Several years ago, people hesitated on the application of deep learning. Who is hesitating that now? So I'm going to drive this conversation now based on a work that I have been doing with my research team in a few institutions. And I'm going to start talking about gravitational wave astrophysics. And let me just reframe, change your mindsets and put it in this context. So what is the challenge in gravitational wave astrophysics? Well, the idea here is that we have scientific facilities like observatories, and they are observing the sky in real time, trying to find gravitational waves. The data sets that contain the gravitational waves are not huge. They are actually lightweight. But the challenge that we have when trying to find these gravitational waves is that they are embedded in very noisy, very large backgrounds. And that the parameters that describe these gravitational waves is highly dimensional. And so in this movie that you see here, the parameters of these astrophysical parameters of these two black holes, they resemble one of the detections that Lago reported.  And again, when you're trying to identify this signal in a noisy background, some people have been using large template bands that describe astrophysically motivated scenarios. But that very fact slows down the process. And so when we had just detected the collision, the first collision of neutron stars, I was working with colleagues across multiple institutions and collaborations to demonstrate how we could harness state-of-the-art scientific computing in the U.S. with a diverse set of tools like containers, the open science grids. And so we connected Lago resources to Blue Waters that at the time was the leadership class supercomputer in the U.S. And we showed that we could very readily, very fast process the data and then confirm that indeed Lago had detected a neutron star collision. But through that exercise, we did realize how computing-intensive these template matching algorithms were, and that we needed really to do something else because Lago was going beyond their own resources and then expanding into other resources like the open science grid, XC, Blue Waters, etc. So while technically appealing the idea of using available resources, you still have the question, is this really the way to go forward? Or can we rethink how we are doing things? And so with my research team, what we started to do was exactly that, to challenge what we were doing and to try to rethink the problem. And so if you step back and you look at the problem, what are the challenges? Well, number one, you have very noisy backgrounds. There are signals embedded here. And it is not just the known Gaussian and non-stationary nature of the noise that complicates the problem, but that the data has a lot of noise anomalies. That could mimic gravitational waves. At the end of the day, a gravitational wave is a noise anomaly, right? But it has some very well-defined physical parameters. So how do you tell apart a real gravitational wave from the zoo of noise anomalies and from these type of very complex noisy backgrounds? So what we did was to break down the key challenges and then start addressing them one by one. We also learned from what had been done already by other scientists. So it is not about discarding what has been done in the past, but to take in the gems of knowledge that had been produced through many years of research and then bring this together with new methodologies. And then we were also very conscious about looking at what was being done elsewhere, what people were doing in the computer science community, in the high performance computing community, and then try to harness these different ideas. You know, this is the value of an interdisciplinary approach. You don't have to recreate the wheel every single time. And so what we did by the end of 2016 is to present a novel approach in which we showed that neural networks can learn the physics of waveforms in a hierarchical manner. What this means is that you no longer need a massive bank of signals, model waveforms, and then filter the data with every single one of these to see whether something in the data resembles these physical models. Rather, what we did was to graph some neural nets, expose them to all these different signals with multiple realizations of nodes. And then we ended up with basically an executable that was just a few megabytes in size and that could process simulated data faster than real time. This was perceived as, you know, a great idea by some, something that was unfeasible, maybe something wrong in the analysis by some others. But the key thing here was that we were trying to address the problems that had been identified by this community, and we were presenting a novel solution to this. So I think the main criticism that we received at the time was that we were using simulated data for this analysis. So we started working on using actual real LIGO data to train the neural networks. And a few months later, we again demonstrated that we could develop these deep learning algorithms and then go and process advanced LIGO data faster than real time and identify real gravitational wave sequences. So there was no doubt anymore these neural networks did provide a novel solution to the challenge of identifying gravitational waves.  So this was all very good. But then again, when you are developing this new approach, you need to be, you know, very honest with yourself. And then ask again the questions, well, what are the facts? This is great progress. And then what is it? So that the facts were out. Even though this approach of deep neural networks exhibits great promise, what are the weaknesses and what are the strengths of these approaches? And so the positive thing is that we were able to analyze real LIGO data and find signals. But that was not a production scale framework. It did not have the depth of the state of practice violence used by scientists to find gravitational waves. And the truth of the matter is that these algorithms were making a mistake for every 200 seconds of search data. So, you know, there were remarkable, there was remarkable progress, but there were again, many things to be done. And so what we did was to believe in the promise of these algorithms and to take them to the next level.  So we, at the time in 2017, we had approved concepts. As I said, it was working very well. We were considering just the masses of the black holes. And we could train an neural network to do gravitational wave detection using 40,000 model waveforms. Only one GPU was needed. And the nets were trained within three hours. Now, if we want to take this to a production scale algorithm, we need to consider the same depth of the parameter space that the state of practice computing test pipelines have. And so this would be a four dimensional parameter space, the masses of each of the black holes and the spin of the objects. And now this takes us to a training sets of about 30 million signals. And so we did some benchmarking and we realized that if we continued to use one GPU, then this was going to take us about a month of training. And that is really sad because who's going to be giving you access to, you know, one GPU in a dedicated fashion for one month. And that for researchers and students, you're spending one in one month is basically not acceptable. And so we had to, again, create a lot of infrastructure in terms of algorithms, in terms of physics inspired neural networks. Because if we're going to dive into this challenge, we cannot be doing this blindly and just hope for the best. So the first thing that we did was to, again, start creating these neural networks, but incorporating physics knowledge into them, in the architecture, in the optimization scheme. And now we abandoned the idea of just using a handful of GPUs. We started developing algorithms to scale the training, first to tens of GPUs, as you can see here. And eventually, when I got access to the Summit supercomputer at Operates National Lab, all the way to thousands of GPUs. What is the advantage of this approach? Well, that now we can train our neural networks with a deeper signal manifolds within just a few hours. Again, there are some trade-offs here. You need to realize that when you are training these models at a scale, sometimes the performance is not as good as when you're using just a handful of GPUs. So you need to develop the algorithms to make sure that while you reduce time to insights, you obtain optimal performance when you're training the algorithms. And I think there are questions. So if you want to unmute and ask, that would be great. Can you unmute or someone can read the questions in the chat? Hello. This is not really a question. I think someone has some problems in your slides, but they're working at the moment. Okay. So let me continue. So the next stage is once we are able to train neural networks, incorporating domain knowledge, like for example, the physics behind the spin configuration of black holes, how fast these objects can move and all of that. Once we had that infrastructure and we were able to train algorithms at a scale, then the next question was, okay, let's go back to the detection challenge and let's see what we can learn. So we applied all this knowledge to train a new class of algorithms that now covers these four dimensional signal manifolds and that we can train with hundreds of GPUs in a couple of hours. And what we found is that when you deploy these models on GPUs, then you only need four GPUs to process LIGO data faster than real time. And in this study, what we also realized is that instead of using just one model, what we could try to do was to develop models that are trained with LIGO data that is sampled at different frequencies. So we developed a model that was sampled at 16 kilohertz and a different model that was with data that was sampled at four kilohertz. And we used them in tandem to process LIGO data and we found that they were very good at identifying real events. And now we reduce the number of mistakes from one mistake every 200 seconds to one misclassification for every 2.7 days of search data. And here is where you say, okay, this is great. We are addressing two critical problems with these new methodologies. Number one, we are covering the same signal manifolds of state of practice pipelines. We can still process this data faster than real time. And we have reduced the number of mistakes significantly by orders of magnitude. Okay, so this was again, you know, evidence that we were going in a good direction. And we again took this to the next level. And so what we do now is we realize that using ensembles of AI models is improving the detection capabilities of AI. And so what we did now was to train a suite of models, tens of them in the Summit supercomputer, and then retain the top performers, created an ensemble, and then fed in data in each of these models. As you can see here, each model is processing concurrently Hanoff-Renlinson data. And then we look at the output of the four models. If all of them agree that there is something interesting in the data set, we channel that through post processing. And then we look at the outputs of these analysis. And here, for example, you see that for this particular gravitational wave signal, when there is a real event, there is a sharp response that comes out of these algorithms. And that was what we needed to number one, be able to process data at a scale, and then obtain outputs in real time that is telling us about the existence of an interesting noise trigger in the data. And so this is the first part of the analysis. We use supercomputing to reduce time to insights. Now, that is not enough because, you know, learning from our own experience, we did realize how valuable it is to share knowledge with other researchers. So we realize that not everybody is going to have access to supercomputers. Not everybody is going to have time to go and learn supercomputing and the AI at the same time and combine them to produce these models. So what we did was to release these models and then type of processing analysis through the data learning hub for science. And this was, I think, a great connection between two independent teams. When I was at University of Illinois at Urbana-Champaign, and I had the opportunity to get to know Ben Blasey, Brian Chard, and Ian Foster, and Ergon and Yuchikado. And they told me about these great resources that they were developing in the context of material science. But this is pretty much domain agnostic. So the main idea here is that they take the models, the AI models, and then they containerize them so that they can be used on disparate hardware resources. And so you go to the DL Hub, browse the models, and you get executable to get the model and run it on a compute resource. Now, that is good, but that is not enough. What we want to do is to further lower the barrier for researchers who want to use the models. And what is that approach? Well, the approach is that once you have the models in DL Hub, you can now connect the models with a compute resource. And you can see this computer cluster at the National Center for Supercomputing Applications. That is called the hardware accelerator learning cluster that has 64 GPUs. It is an IBM Power9 machine. And so what we do is we release the models in DL Hub, and then we use FunCakes to connect it to DL Hub and your compute resource. And then FunCakes is going to be the overall workflow manager. It is going to start workers at the cluster, it is going to do the inference, and then it's going to allow you to look at the output of these models. And so we share the models with them, the post-processing scripts. And on their own, they were able to rerun the analysis. And what they found is that they could process an entire month of advanced LIGO data, almost 2017. And then by distributing the inference over the entire cluster, the 64 GPUs, we were able to finish the analysis in about seven minutes. And as you can see here, throughout that month, we had no mistakes. So greening here means the two LIGO detectors are up and running, collecting data. These lines mean that there are four binary black hole mergers reported in that month. And this is the ensemble prediction for the existence of gravitational waves. And they coincide. And there are no misclassifications here. So this was the first time that an end-to-end AI framework for gravitational wave detection at scale was introduced. And you see all the pieces, supercomputing to train the algorithms, domain knowledge that goes into the optimization and training, architectural design of the models. Then you harness scientific data infrastructure as the data learning code for science. You connect these two with, again, supercomputing resources through phone cakes to distribute the inference. And then you look at the output of the analysis. Now, think about this. One month of data process in seven minutes. That is really good. But then we wanted to go the extra mile. And the extra mile here is that these models, ones that were released in DL Hub, they could be used by any researchers. And since we released them in July 2021, they had been invoked over a thousand times. And this is good. This means that there is interest in the community to harness this type of resources and to use them. Now, let's see whether we can do something else. Well, this is the outreach that we got out of this analysis. It was published in Nature Astronomy, and I was invited by Nature to write a blog about the human aspect of how this is done. This is obviously a very nonlinear process. And when you're trying to address this challenge, sometimes it seems that it will be really hard to solve some of the problems. But we just kept plowing through and addressing these problems one by one. This is why I mentioned at the beginning that you need a little bit of resilience as well. So the next step was we have learned how to harness disparate resources, supercomputing, data infrastructure, advances in AI, et cetera. Can we do something else? And so what we did here was to start automating this process. We now have infrastructure to train tens of models in summits. We have developed algorithms to identify the top performance using, for example, ROC curves, using long data segments of real LIGO data. Then we know how to optimize these algorithms for inference, for example, through quantization or using TensorFlow RT. And then the final step here is once you accelerate the models for inference, now let's go and use tools like TensorFlow RT that are going to optimize your model using, for example, a different precision. And now we are going to fully utilize the SATA GPU supercomputer at Argo National Lab. And through this approach, we realize that if we go back to that one month of data with 2017, we could process the entire month in just 50 seconds using 160 GPUs. And then the question that arises is, okay, this is pretty good. What about processing longer data segments, not just one month? And so what we did here was to do some time slides to these one month long data sets, shifting one of the LIGO data streams and for Livingston one second with respect to each other, and then continue doing that process until we had five years worth of data. And again, we blazed through that synthetic data set. And we found that on average, this ensemble of AI models was making a mistake for every month of search data. And now compare that to the results we had in the beginning back in 2017, when we were reporting one mistake for every 200 seconds of data. And the additional advantage of using these resources for accelerated inference is that we could process data 53,000 times faster than real time. So think about that. When we first introduced these ideas, people were hesitant to believe that AI models could process data faster than real time. Now, after years of developments and harnessing disparate resources, we are at this level. So I'm going to pause here because I'm going to transition from black holes to neutron star mergers. So if anyone has a question, I think this is the time to do it. So I'm going to go on. So the next source that we have been exploring for the application of these deep learning algorithms is the collision of neutron stars. And they differ from black hole mergers in that they could be observed using multiple messengers, right? Not only gravitational waves, but also electromagnetic waves. And, you know, if these source what was sufficiently close to us, even astroparticles. Now, the beauty of these signals is that they stay in LIGO bands, not just for a fraction of a second or for a couple of seconds, as in the case of black hole mergers, they could be in LIGO band for tens of seconds. And once the LIGO detectors attain the science in CDFT for a long time and even imagine what you could do with third generation gravitational wave detectors. So here there is a great opportunity to do something else than just gravitational wave detection. And what is that? Well, that is forecasting. And the idea of forecasting, you know, this comes from, for example, what people study in the markets. Is it going in the right direction? Is it going to be a crash? What are the elements that may trigger the market to move in one direction or the other? Or when you are speaking, you say a few words and then algorithms predict what is the next word or the next few words that will complete that sentence. And so we adapted these ideas to predict when we would see the merger of one of these type of sources, neutron star signals in advanced LIGO data. And what you can see here in this panel, for example, is that for the first neutron star detection, you can see that the signal becomes noticeable in this spectrogram tens of seconds before the merger. And so we developed this algorithm to process data and then trigger a signal that says, I perceive that there is something of interest and there is going to be a merger that is imminent. And so in these algorithms, what you can see here is that for the real data contained in this event, neural networks could tell us 10 seconds ahead of time, that a neutron star collision is going to happen. What is even more remarkable is that irrespective of the existence of this pretty, pretty loud noise anomaly here, the performance of these neural networks is the same. You can see here that when there is no glitch in the data, we have removed it. You see it is around zero here. And here we stopped the analysis before reaching that noise anomaly. The neural net still predicts that there is going to be an interesting collision 10 seconds ahead of time. So this is really good. This means that even if we have some noise contamination, these algorithms can tell us valuable information about the existence of multi-messenger sources in complex experimental data. And again, you can see here that this is no longer about real-time detection or faster than real-time. This is about looking at the future ahead of time. So we extended this analysis and now we ask the question, okay, this is for neutron star collision.  And we conducted an analysis of this nature and we realized that for different types of configuration signal to noise ratio cases, we could see the sources 10 seconds ahead of time. And in the multi-messenger astrophysics community, they are very soon developing this type of early warning systems as they call it. Again, the beauty of neural networks is that these algorithms are very compact. They are portable and so they don't have to be run on supercomputers. You can just deploy them at the edge in seed to other detectors or on satellites that are observing data in real-time. And you can do the computing there. You can get a trigger that something interesting is going to happen in seconds ahead of time. So it is quite remarkable how you can transform algorithms that were originally developed to study the markets for speech analysis to predict the existence of multi-messenger sources. Now, we took this again to the next level and we started now considering signals that are much more complex to what we have been studying in the literature. So now considering not black holes or neutron stars that follow quasi-circular orbits rather that they follow eccentric ones. And you can see here that there are some beats in the signal and eventually the signal becomes noticeable in the ligament. What we realize is that irrespective of the eccentricity of the signals or how complex they are, the neural net can tame that complexity in the signal and still predict the existence of these mergers ahead of time. We also conducted another analysis which is, okay, the neural network is telling you that something interesting is going to happen in seconds ahead of time. But how certain is the neural network about its own prediction? And so you can see that analysis here. And in addition, we also studied, if you, for example, make a prediction 15 seconds ahead of time, that there is going to be a collision of this type of neutron stars or black hole neutron stars. The area in the sky that is going to be covered at the time is shown here. And so you can see that it is going to be a very broad area in the sky. Of course, only using two detectors. This is going to be further reduced when you use more detectors across the planet. You can see how the area in the sky is shrinking. So, you know, this would be a beautiful exercise to actually use the neural nets and all these different analysis concurrently. There is a tree here and then you see how the area in the sky is reducing and you're eventually pinpointing to the location where the signal is going to be detected. So that is related to neutron star collisions and forecasting. If you have any questions here before I transition to another topic, this is the time. Anyone? So when we are thinking about signal processing algorithms, and detection or regression, we often don't think about how you produce the model signals with which you are going to train the algorithms. You take them for granted, right? You build upon what numerical relativists have done and then data analysts have done part of their job, which is to produce efficient waveform predictors. But sometimes, you know, these assumptions are not so good. And what happens is that you need to start thinking about how to produce signals faster to train these algorithms. So when we were thinking about this, we had this idea. If we were to train a neural network, is it possible, you know, still in the context of forecast, is it possible for the neural network to learn the complex physics of black hole mergers? And just to contextualize this, if you look at the signal that we present here, this early part is known, you know, in the literature as the inspiral evolution. And you can model this with pretty good precision with post-neutronian theory, which are semi analytical descriptions of the dynamics of black hole mergers or neutron star mergers, if you want. But you don't get into the highly dynamical evolution of the binary. And then when people try to model, you know, the late part of evolution, like what you see here, well, they realize that they could use black hole perturbation theory. But things that are here, which are later inspired all the way to merger, they are very difficult to model because you need to solve Einstein's equations. And for that, you need supercomputers. Okay. And so here we have a very natural question. If we are able to use neural networks to forecast the collision of neutron stars, is it possible for neural networks to learn this complex physics that is here? You know, this is at the heart of Einstein's equations, the evolution of black holes, and then to see how they eventually merge and then form stationary black hole. And so we developed something that is called a transformer model that has a few advances in AI like attention. And we realized that when you consider this signal manifold that I show here just one part of it because we consider a mass ratio sort of way to eight, when you train this transformer model, it is able to output all these dots that you see here that resemble very well the dynamics of these mergers. And here I show three different scenarios for different spins for the black holes when they are anti-aligned. And what this means is that the merger happens very fast when they are not spinning and then when they are spinning very fast. And you can see here the number of cycles that are completed before merger. As I said before, negative spins means that the merger happens really fast. Here you have the non-spinning case. And when you have rapidly spinning black holes and the two of them are aligned with angular momentum, then now the merger is extended. And what we found is that the neural net is able of predicting these highly nonlinear dynamics. In addition to that, we also wanted to understand when we are feeding the early part of the signal into the algorithm to predict the final part. What are the bits and pieces of the input data that are responsible for a good prediction? And what you see here in these diagrams is that, for example, to predict this part of the signal, the neural net is paying close attention to the early and spiral evolution. And when it is predicting this part, the transition between latest spiral into merger, the neural net is paying close attention to the final part of the signal. And this makes sense, right? Because what the neural net is doing is it takes one input, makes the prediction, takes the previous one, makes the prediction of the other one. And you can see that here, right? It is a causal prediction. You take the input and make a prediction of this one, look at the previous output and make the prediction for the next step, etc. So this all makes sense, right? You cannot predict something that happens in the future if you don't have a previous time step. And so these interpretable AI studies are available. You can go and take a look at them and they are interactive. You can select a portion of the signal and then look at the attention heads and see how that information is being used to predict a given part of the output signal. So when we were at this stage, now we were thinking, okay, this is pretty exciting. What we are seeing is that we can predict the highly nonlinear dynamics of black hole mergers. Can we now go and try to do something else in the context of partial differential equations that are now used not to study black hole mergers, but neutron star mergers? And this is a very complex problem because now you need to combine general relativity with microphysics, with neutrinophysics. So it is a very complicated problem. And yes, it is true that over decades, people have developed PDE solvers. But when you need to combine different grids because you need a lot of resolution, for example, to study the amplification of magnetic fields. And you don't need a very detailed grid when you are studying the dynamics of the neutron stars when they are separated. Now you start scratching your head because honestly, how do we combine these so that we complete one of these simulations in a reasonable amount of time. And so researchers, compelled by this, have started developing neural nets that solve PDEs very rapidly while keeping the error under control. And we have also started looking at this. One of my students, Sean Rossofsky, started looking at this in the context of magnetic hydrodynamics and the type of studies that you can do with turbulence for this type of systems. And he realized that even using not very complicated neural nets, you could capture the real physics of these systems and outperform all the other methods that you have in the literature right now. And so, you know, the big goal here is to eventually replace some of the modules of these numerical relativity codes to accelerate the modeling of neutron star collisions. And the next step for him has to be, has been to look at a more complex PDEs, you know, gearing towards a magneto hydrodynamics. And so one of the recent PDEs that he has solved using physics-informed neural apparatus is the shallow waters equation. And this equation that you see here is used to model coastal dynamics and tsunamis. And it is the first time that three systems of PDEs coupled, as you can see here, are solved using PINOS. And just a few words about PINOS. These differ from physics-informed neural networks. PINOS have been superseded very rapidly by a variety of techniques. And these new type of algorithms, PINOS, really show great promise to solve complex systems. And what they are doing is they are learning the operators that you see here. And they incorporate a lot of knowledge about physics when you're optimizing them. So they are very powerful algorithms. And you can see here, for example, for some of the parameters that describe this shallow waters equation, like the velocity, you can see here the predictions of the neural network, the actual solution with a traditional PDE solver, and the error that they have. And you can see that it is tiny. So it is very promising. Same for another parameter, which is the height of the perturbation. You can see that the prediction that we have with PINOS are pretty good. And all the codes, all the code that has been used to solve these equations may be found if you go to this archive link. All right. So let me just give you the big vision, the 40,000-foot vision for the activities that we are conducting at a moment. I only talked about gravitational waves and multi-scale and multi-piece simulations, but the work that I am doing with my team goes well beyond this and enters into cosmology, agriculture, medicine, molecular dynamics, and some other applications. And what we find is that obviously the devil is in the details, but a general framework that is going to work for multiple communities. And how we can share knowledge, learn from models, and move faster is the following. If you have the skills to train, to develop these models using supercomputing, that is amazing. You can continue to do that and share your knowledge with others. And then use the data learning hub for science as a central nexus to share these models. They are ready to use. And then you can connect VL Hub with any computer source that you have available, for example, with Funkex. And you can harness other methods like TensorFlow RT to optimize your models for accelerated inference. But once you have a model that is working and, you know, time goes by and maybe the model is not capturing any more the statistics of your data, there is no need to go back to a supercomputer and stay in queue for days or hours to find in your models. What you can do is to connect your models to a cloud computing resource. And then you can perform active transfer reinforcement learning to find in the performance of your models and then continue here in discovery modes.  And this is a virtue cycle if you wish, because we are using computational resources in an optimal fashion. You only go back to supercomputing when you really need to train the model at a scale and you use age computing resources to find in the performance of your model to continue doing your research.  And we are participating in the development of it. There are a lot of groups across the planet that are working very actively in the traditional way of astrophysics. I did not include many of them in the presentation, but this site I think has a comprehensive list of the important contributions that different groups are making to this endeavor. And so in a few words, what we are now trying to do is work with experimentalists so that they develop AI-related data sets, meaning that they can be readily used to train models or to find new things in these data sets. We can combine that with innovative computing, not only GPUs or accelerators, but we could use different systems like Cerebras, Samba Nova, GROC, etc. And then when you do this, the idea is that when you create fair, which is an acronym for findable, accessible, interoperable, and reusable data sets, and you incorporate your physics knowledge into it, then you can create models that will enable you to accelerate discovery. And at the end of the day, you know, these tools are not a replacement for humans, but to boost human intuition, to optimally use our resources. And quite frankly, if we are able to develop models that understand nature as it is, we will be able to go beyond the approximate theories that we have right now for a great number of different processes that we don't understand fully. So, you know, this is a great opportunity to go from the limited knowledge that we have with simulations and incomplete models and to allow AI to take us to the level that will allow us to understand very complex natural processes. And I end here acknowledging my funding sources. Thank you. Yeah, thank you very much, Elio. That was a very interesting talk. And let me pause recording.


Hello everyone, welcome back to another Photoshop tutorial. This one, gonna show you a quick fix. If you see here, like I've got here, I've got a document open and whenever I click on one of the layers, it's showing up in red and it's acting weird and it just doesn't look right. And I was trying to figure out what happened and what ended up happening is I was trying to command A and command C, command V, but I actually hit the Q and this is the issue. Q means quick mask mode. If I hover over this, it means it's set in quick mask mode. So if you see this red, chances are you're in quick mask mode. Hit Q, it'll take out that. This will go back to being gray or light whiteish color and that is all you gotta do to fix that. Thanks for watching, time for stuff coming up. Stay tuned.


Welcome to The Core Report. I'm Lance Corporal Remington Hall. And I'm Corporal Arya Herrera. Beginning this week, the Marine Corps will host the sixth annual Warrior Games at Marine Corps-based Quantico, Virginia. The Warrior Games is a competition where wounded, ill, and injured service members compete in a variety of sports. 270 athletes from all military branches and the British Armed Forces will compete in the events from June 19th to the 28th. All events are free and open to the public. For more information, click the link in the video description. June 4th marked the 73rd anniversary of the Battle of Midway. Gaining control of Midway was a crucial turning point in World War II for the United States. Marines, sailors, and other service members around the world held ceremonies to remember those who fought and or lost their lives during the battle. Special purpose Marine Air Ground Task Force Southcom deployed to Central America this month to support joint training and theater security cooperation. The Marines will be based out of Soto-Kono Air Base Honduras and the temporary deployment will last the hurricane season into November. I mean, we've been deploying to Afghanistan and Iraq over the last, you know, 10 to 15 years. So now, with Central America, we have an opportunity to take a Marine that hasn't deployed before and demonstrate that he is capable of deploying, operating in a foreign country with cultural savvy and success. While deployed, the Marines will conduct training and security cooperation engagements in Belize, El Salvador, Guatemala, and Honduras to see more used to link in the video description. On an admin note, PCS season is still in full swing. If you have questions about moving entitlements or any related material, check out our blog linked in the video description. Remember, you can catch up on everything else going on around the Corps by logging on to our official social media sites. Thanks for watching, everyone. We'll see you next payday. Right here on the Corps Report.


Hi, this is Jimin Wen. Welcome to my video abstract on my recent paper. My paper is on protein claustrophic, specifically on X-ray radiation induced action addition to the protein residues. I had a great pleasure writing this paper, making this video abstract. I hope you will enjoy reading my paper, watching this video. Some 30 years ago, I first observed enzyme recover from dissolved crystal after exposure to X-ray radiation for data collection was completely broken down into small pieces, with no intact molecule left. Many examples follow in the last 30 years. I present in this paper, high quality electron density maps. My map was rediscovered three years after I initially obtained because they made no sense to me at that time. Moreover, this map are generally the new direct methods. A leap of faith was needed to trust those maps. I sent two students who gave inspiration for careful re-examination of my map. One is a very bright, inquisitive undergraduate student, Carissa Lettichoff, who asked me to explain phantom reaction through a mechanism of free electrical chain reaction for her chemistry lab work. The other is Jessica Wuzha, a PhD student in chemistry department, Yale University, who asked me to exam some features in the enzyme that require high quality electron density maps. My map show, oxygen atom has been added to many protein residues, for example, alanine residue convert the serine residues, which is completely unexpected. A take-home message is that, sometimes, we have to think outside the confined box of acknowledges. Trust your data, trust your instinct. At the last, I thank Professor Peter Moore for insightful discussion on the subject for his help during dissemination of my results. Finally, thank you for watching the videos. Bye.


More tag areas in Moodle 3.3 It's now possible to tag forum posts and glossary entries, and teachers may also tag book chapters thanks to Moodle community member Andrew Hancox. From the Manage Tags screen, when tags are enabled, administrators can either force standard tags, suggest them or allow users a free choice. In this course on Healthy Living, our student tags a forum post. She's given two options to choose from. And then, clicking the tag itself, or clicking from the Search Tags site page, she sees everything with this tag. In this case, book chapters, forum posts and glossary entries. She can filter to specific areas and add the tag to her own user interests. To summarise, forum posts, glossary entries and book chapters may now be tagged.