Hello everyone. Quantum computing is one of the most hyped but maybe one of the most misunderstood technologies that are currently in development and I'm really delighted to have Ling G from Tencent here to talk us through it. I think maybe we should start with the basics, right? So what is quantum computing, Ling? Well for me, I think quantum computing offers us a window to look through the universe. What I mean by that is in the 1970s, 1980s, when Richard Feynman first proposed quantum computing in the modern day age, he mentioned that the nature is quantum. So at the sub-atomic level, we are all made of atoms, atoms are made from electrons or quarks or something even smaller and all of these things are governed by the quirky, superposition, entanglement, decoherence, etc. So all of these are all operating at a quantum mechanical level and that's how the world really works. Yeah, so I guess it's this idea that when you get down to the really small stuff in nature, as you get down to the sub-atomic level, you get these strange interactions because of these quantum properties of the universe and if we're going to simulate those systems, those natural systems, then we need the computer but we'll do that and that's, I guess, the crux of what quantum computing is and what it's for. But what are some of the applications of quantum that you're most excited about? I think I'm most excited about modelling the world at the sub-atomic level. There are some better publicised applications such as optimizations or doing quantum finance as well as quantum biology. But I think at the end of the day, it's how we model all the materials, properties at the sub-atomic level to look at how the electrons move, how do we describe the world in their true nature. I guess one of the other applications that gets talked about a lot is the cybersecurity implications of quantum computing. Do you think that's a real threat or is that something that gets a bit over-talked about? I think it's a double-sword edge. So quantum computing can make things super secure, unbreakable, but on the other hand, it can also break into systems. Everyone knows about the RSA encryption. So in quantum computing, there is a very famous algorithm called SUAR algorithm and that's to factorise very, very large numbers into prime numbers. So things don't sound that complicated, but to factor, say, a number of 10 billion digits might take the age of the universe to crack it. But once quantum computing is here, it takes a split of a second. On the other hand, a lot of research, for example, in the US, next day, they are setting out a competition, trying to set the standard, having various crypto schemes in order to make things unbreakable. So a lot of researchers are doing post-quantum in order to use classical computing to make the crypto super secure. I think that's a pattern that appears a lot in quantum computing. The headline is really shocking and scary. Quantum computing is going to break RSA encryption and open up all of our emails to everyone. But actually, when you dig a bit deeper, you find that, well, yes, quantum computing can break RSA encryption, but people are already working, as you said, on NIST, on quantum secure algorithms that won't be vulnerable to quantum computers. And I think one of the big misconceptions about quantum is that it's going to be the best at everything. And that's not actually true, right? Quantum computers will be very useful, but only for a subset of tasks. It's not going to be like a supercomputer ties a million, it's something slightly different. Yes, that's totally right. So as probably most of the people know, there is two sets of problems. One is the P that stands for polynomial and NP non-polynomial. And the quantum computing only solves a subset of the NP problems. So lots of the things, A, it's not exponentially faster, mainly in factoring into prime numbers. So that's exponentially faster. In most of the other cases, say, optimization problems, people normally use another famous quantum algorithm, the so-called Grover algorithm. So that's like searching a database, you can make it still dramatically faster, but not exponentially faster.  But I mean, there have been a lot of hardware developments in quantum over the last few years. So Google very famously achieved or said it achieved quantum supremacy back in 2018, 2019. And recently IBM said it created a 128 qubit supercomputer. We'll get into what qubits are in them. But how far away are we realistically from something like Shure's algorithm being able to run on a quantum computer at a useful scale? I think while still, yes, all of these developments are super exciting, but I think people should remember building a quantum computer is extremely hard. So I made you mention about how do we build one. So let's go back to the fundamental building block that's a quantum bit, the so-called qubit, which is in analogy to the classical computing, the classical bits, 1 and 0. And in the quantum computer, when we use superposition, so we can have a qubit that's 0 or 1 or 0 and 1 at the same time. And the second important quantum property is entanglement. So that's the two qubits, they can be linked with each other no matter how long apart, how far apart they are. So I can be on the moon and a minute you can be on the earth. Our two qubits can still be entangled together. And then there is decoherence. So when in a quantum system, say when we have entangled qubits, but they are in the real world, they are very messy. They get lots of interference from the environment, from the noise, and the system decoherence. So they are no longer in an entangled state. That's why quantum computing breaks down in a way. So in short, it's still a long way away from a quantum computer that can properly use short algorithms. I think the biggest number of factors so far a while ago was 21. But we are also entering a near-term phase that's called a NISC era, which stands for near-term intermediate-scale quantum computing. So we're already starting to see some of these initial applications, which Google and IBM, they are showing and working with enterprise customers. I think the idea of qubits is quite difficult to get your head around if you don't have a background in physics or a PhD in quantum computing as you do. The analogy that I like, and this is a very dumbed-down analogy, is if a bit is like a coin and it can be the heads or tails, then a qubit is like a spinning coin. And it's either or both, or not at the same time. And that is quite an oversimplification, but it just helped me to kind of understand what I was thinking of. And it also helps to explain why quantum computers are so difficult to build, right? The hardware required to build a qubit is really, really difficult to get right, right? Yes. So most of the times, all these qubits, the fundamental building blocks, they could be electrons, they could be photons in the superconducting approach. Google and IBM are leading the superconducting approach. So they're using something called Josephson junctions. It's like one superconductor, another superconductor connected or separated with a nanoscale gap. So all of these things are very difficult to control. And most of the times, they have to be at extreme temperature, almost absolute zero temperature in a superconducting case. Yeah. And you might have seen those fridges that they have, that kind of cool, cool these tiny little chips down. So if you see these kind of chandeliers of a quantum computer, and I think you can see something like that at the IQM stand where they've kind of created a sort of model of one of these cryostats. And that's all for cooling this quantum chip down to near to zero degrees to keep it cool. So the qubits kind of stay in the state that you want them to stay in.   But the real challenge is how you scale up from dozens of qubits to thousands or millions. We might need millions of qubits to actually run Shaw's algorithm because of the error correction that's required and things like that. Yes. And also hardware isn't the only hurdle, right? There are other hurdles that need to be overcome for this bit to become a reality. Yes. So the longer term goal is to build a fault tolerant quantum computer. So Amit, you just mentioned we need millions of qubits. So all of these are physical qubits. And then when you read academic papers, most of the times they are talking about this concept of logical qubit, which is fault tolerant in a quantum computing scheme. You can think about a bulletproof qubit in a way. But to get to that stage, there is still a long way to go. So say almost if we want to achieve one logical qubit, we might need thousands of messy physical qubits to surround it. So the 127 IBM qubit quantum computer that's referring to 127 physical qubits. And the world so far doesn't even have one single logical qubit achieved. But a month and a half ago, in Chris Monroe's group, they have achieved some milestones in this space. So I think there will be some exciting progress coming. That's why the qubit number can be a bit misleading. And I think why you need to take stories about we've achieved an X number quantum, we've built a quantum computer with X number of qubits. You have to take that with a bit of a pinch of salt because the number of qubits in itself doesn't really tell you a whole lot about how useful that quantum device is. And I think some companies have developed their own terms, you know, like quantum volume or things like that, quantum advantage to tell you how useful the quantum computer is. Because it's not just the qubits. It's the way they're wired up. It's how isolated they are. It's how fault-corrected they are that really adds up to whether or not this hardware is going to do what it needs to do. But hardware, there's also kind of the software that needs to be written, right? So you mentioned Shure's algorithm and Grover's algorithm, but there's a whole kind of missing layer to this that we're going to need if we're going to, these machines are going to be useful, and that's the software and the skills, right? Yes. So in a software space, there is some exciting development recently. For example, Cambridge Quantum Computing and Honeywell, they just announced yesterday they have formed world's largest quantum unit. And then there's a lot of quantum operating system, the new quantum languages, all the IBM, Google, Microsoft, they all have a new set of, so not C++ language, there is a whole new set of new quantum languages to learn. And my Oxford classmate, he's starting a very interesting startup in the software space in Singapore. And that can translate any type of classical computing programs into quantum computers directly, which can be then run on quantum computers. So in the near term, I would see a hybrid model, so say we can have some of the quantum computers embedded in the high-performance computing centers, and then we can have a combination when we run a whole suite of classical computing programs, but with certain parts where quantum computing can bring the most acceleration, now we just use that particular part in quantum, the rest of the computing programs can be still in classical. And I think that can still bring a lot of near-term advantage. What does the future look like, do you think? I mean, because I guess we're talking about these machines as if it's something that I'm going to have in my study, but that's not the case, right? These are going to be in research labs, and if I as a founder or as a programmer access them, it's going to be remotely via the internet. So is that kind of how you see it? Is that how companies will access these machines in the future? I think most of the providers these days, yeah, so in short, I think it will be accessible via cloud. So for example, IBM probably, they started offering these services a few years ago. Google, Amazon, Web Services, Rigeti, IonQ, they are all offering these kind of services via some of the cloud providers. IQM in Finland here, they have a unique selling point which is kind of over at on-premises, but I guess in summary, one thing I can guarantee you, we wouldn't have a quantum computer in our study or bedroom like how we play mobile phone or tablet. Yeah, it's more like a supercomputer, right? It's something you access when you've got a specific requirement. I guess you might not even know that you're using a quantum computer. I think the way if you talk to Microsoft, if you talk to IBM, the way they're selling it is that a company will come to them with a problem that needs solving, an optimization problem, say a delivery company needs to find the best route for them to send its driver around to deliver thousands of deliveries, right? So they might approach a company like IBM or Microsoft or something like that with that problem and then that company will then go away and decide which combination of supercomputer, quantum computer, traditional computing is the best approach to tackle that problem and then they'll give the answer at the end of it. And actually, do I need to know as a startup founder whether that's been done on a quantum computer or a supercomputer? Maybe I don't. Yeah, I think you are totally right. I don't think you need to. So all of these things can be orchestrated at the back end. So quantum computer, hardware, software providers, they can work in tandem and then provide a whole solution and then choose which bit needs to use quantum. The rest of the parts can be classical. So you mentioned a bunch of different startups and there's a lot of the ecosystem is growing. Do you think from your position at Tencent, do you think this is a good time to invest in the quantum ecosystem? Is this something that you'd be recommending to other people in the space that they pay a lot of attention to this? Or is it too soon? Yeah, I think it depends on your risk appetite. Yeah, so we can see there are so many quantum startups in recent years and lots and lots more government and private investment going into all these companies which is supporting great research and advancement for the whole human race in a way, which is fantastic. But how do we, how do investors know quantum computers in this black box? How do you know the startup you are looking at is truly at the forefront of quantum innovation? It's just very difficult to know. And at Tencent we have, we call this investment challenge black box paradox. And ourselves we have developed three lines of defense to look at at relevant stages, say from seed stage to series A. We are looking at certain set of achievements or criteria or milestones and series B to C, that's the second stage, and the series D to pre-IPO, then there are some other criteria we're looking at. But out of all the quantum startups there at the moment, even if it's the most advanced one with the current technology, it's still not possible to tell if it's quantum computer who's actually calculating behind the black box or if it's classical computing because at the moment with all the classical computing power, we can more or less simulate around 50 qubits or so. I guess once people go beyond, say, 200 qubits, then that's well at a stage where you can use techniques such as blind quantum computing or other quantum crypto methods to evaluate if the claim is true quantum or if it's just classical computing pretending as a quantum computer. I'm interested in your thoughts on what the exit strategy for these kind of early stage hardware and software startups looks like, considering the fact that we could be 10 or 20 or maybe even 50 years away from practical quantum computing at scale. I think we've already seen some good exits in the field. For example, INQ has just become the first publicly listed quantum computing company. I think the market will tell how the world is responding to this inspiring deep tech company, but perhaps very early stage commercialization and how the market will respond to it. I think we see more and more IPOs of even the very early commercial stage with really solid technology, but they IPO'd successfully. I'd love to know a bit more about Tencent's own investments in the space you mentioned. Can you tell us a bit about IQM and what made them attractive to you at the time with the investment? I met the IQM founders about five years ago. I came here a few months before I came here for Slash for the first time. What struck me best is I think you have visited the Google quantum computer and we know how difficult to call the system to the almost absolute zero, to be you wrote in your quantum book, it took two days to cool it down and one week to bring it back up to the temperature, room temperature. IQM has developed this really innovative on-chip cooling technology, so that would really help with the scalability. Some of the other advantages are they have very fast, for example, gate reset time and operation time, so those are the advantages. They also have a new concept that's called co-design, so say if you are enterprise customer, like leading OEM, they can work with the customer and develop a quantum chip that's especially suited with that particular customer. I think on-chip cooling is a really interesting example of the kinds of technology that we're going to need if we are going to scale these things up, and it might not necessarily be but super conducting qubits with on-chip cooling are the end technology that ends up being. Because there's a bunch of different competing technologies, do you think that we've reached a winner in super conducting qubits and those are going to be the ones that are going to see us through to full-scale quantum or do you think there's room for another kind of paradigm shift? Because 10 or 20 years ago, super conducting qubits were like a fringe idea that no one really thought was viable and then Google came along and built their quantum computer and now it's like the leading thing and it's used by Google and IBM and to a certain extent by Microsoft as well. What do you think is going to be the kind of winner in this race? I think it's too early to tell. So we normally, when we look at investments so early stage, we would use a portfolio approach. There are just different approaches to have that individual advantages and disadvantages. Superconducting approach, it's the most far ahead at this point, but some might go this curve and some might go slightly more steady at the beginning and very sharp growth towards the middle. So iron traps is another. So iron cue is using iron traps. That's also the qubit fidelity is probably the best amongst all the approaches and they can operate at much more friendly temperature, but it's very hard to miniaturize the device, but maybe that's not so much a problem. And there is topological qubit. Microsoft has been pursuing theoretically it would be much better than iron traps or superconducting, but it's the fundamental problem is the particle itself is so elusive no one has observed it properly in the experiments yet. And there is also Psi quantum in California. They were originally leading professors in the UK from Bristol and the Imperial College and they have silicon photonics approach. The advantage is they can use traditional silicon fab and they can operate at room temperature and they're already targeting millions of qubits, so one million qubits in the next few years. So given the dozens of competing technologies at vastly different scales with different potentials to either saw or completely crash and burn, how can businesses prepare right now and investors, how can they, what should they do? What advice would you give them at this point? I think maybe two things. One is to study the field quite carefully, not just use cases, but especially for investments. So at the first stage maybe even look at the, find the main experts and to look at scientific publications, no publishing, nature science or physical reviews and to look at if the theoretical method is solid. And then within each company I think find out what kind of use cases are there. I know Goldman Sachs, they have a research team looking at how quantum computing can link with black shores and speed up the financial or risk calculations and some of the logistics companies are already starting to use the optimization part and to make, yes, so only the traveling salesman problem solved more easily. So and I'm sure there are lots of other use cases in making communication super secure. Yeah, yeah, absolutely. It's going to be one of those really interesting fields to keep an eye on over the next 10 or 20 years as we see whether or not quantum computing actually comes to fruition and delivers all these potential applications that have been talked about across everything from banking to chemistry to health to cyber security. We are just about out of time, so thank you so much, Link, for that. That was fascinating and thank you for watching, guys. Cheers. Thank you.


I'm Sergeant Arianna Villanoza. I'm from Indio, California, and I'm with HHB15FA. So yesterday and today we were able to take the FIS teams out to OP's to give them some training on their individual tasks as well as team tasks for fire support teams. So the fire support teams are tasked with being able to conduct their fire missions and support of the maneuver battalions. So the purpose of this training event that we conducted out here was to give the the FIS teams the ability to test out and train on the tasks that they've worked hard on these last seven months being out here on this rotation. They actually got the ability to receive an OP order and go out and conduct their operations in support of maneuver battalions. The thing I love the most about being an officer is being able to provide that comfort for the maneuver battalions, that peace of mind that they know that they have long range guns in support of them by the use of us. We direct that maneuver commander to ease his soldiers in the comfort of knowing that if anything were to happen they have support of artillery to get them out of a bad situation. I was really excited to put together this training event to be able to give the teams a well organized in detailed event that they can really test their knowledge and their ability to work it together as a team. I was excited to be given the opportunity to lead this and make sure that these soldiers got everything they could out of their training event.


What's up guys, welcome to today's video. Today I've got something really fun for you guys. I just finished editing the video. I know you're gonna like this cut. Make sure you subscribe to the channel, like this video and share it with all of your friends. Let's get started, here we go. All right guys, so quick sectioning breakdown for this cut, real simple parietal ridge back through mid crown and then wrap it around again. You get that U shape separating top and bottom and then on the top I just separate four different triangular sections through the top and now we're gonna start working on the side. My goal here on the side guys is to create a balanced line. When we talk about balanced open, close, that's where do I wanna push the weight horizontally. So when I'm talking about a balanced line, I want that horizontal line to have a balanced feel, to have a straight across feel. So as I bring those sections out, I make sure that I cut my guideline first and then when I move to section two, I bring section one halfway to section two and bring section two halfway to section one meet in the middle and I cut that line. Then I drop off section one, I bring two halfway to three and three back to two and then I just continue that pattern all the way through the side of the head and that will give me a really nice balanced horizontal line. So my scissor choice for this cut is the FSE Precision Pro scissor at five inches. I love a five inch scissor for precision cutting. Anytime I'm cutting a haircut like this and I want those nice crisp lines, a five inch scissor is my go-to because the shorter the blade, the stronger the blade. You also don't need to cut a lot of hair at once when you're cutting a precision cut. That's why they say don't cut past your second knuckle because basically you wanna work little bits at a time and not try to stretch out your tension too much or lose tension in a cut because it'll affect the line. So as I'm going through, I'm using the FSE Precision Pro. We have it in all different lengths. We have five, five and a half, six inch as well if you love scissor over comb barbering work. We just launched this scissor line and I'm super proud about it, psyched about it. Can't wait for all of you guys to try it. We made all of the scissors affordable for every hairdresser out there. So this scissor is only 199 bucks and we have deals going on on our website as well. So definitely check that out, freesaloneducation.com or shopfse.com. So as I round the corner in this cut, you'll see that I'm pulling the hair all the way from the one side to the other side to continue that straight balanced horizontal line in the back. Then once I get to the center back, then I switch and start working the other side. Now, when you start the other side of a haircut, sometimes it's hard to figure out the length that you're looking for or to match up both sides. So what I like to do and what you'll see me do here is I start my cut and then I check the sides just to make sure that they're matching up well. And then once I know that both sides match up, then I continue on through the rest of the section and we're gonna mimic everything we did on the right hand side on the left side right now. You'll notice sometimes when I cut, I do a little reverse grip on the handle of the scissor. We actually designed this handle custom to cater to that type of cutting because that's something that I really like to do. So I wanted to make sure that I had a handle that didn't just work in the one hand position. It feels really comfortable in every hand position. Notice how I'm scooping the hair and pulling it towards my body. That's just to make sure that I don't lose that corner. Now we're gonna go up just to remove that corner that we created. So when you over-direct the hair on both sides to the furthest point, you're gonna get the heaviest point in the center back. So I just go in, do a little point cutting just on that edge. I keep the length in the very bottom nape area but I texturize it and add a little bit of shortness to that crown. Now we're just gonna do a pivot section, kind of working pie-shaped sections throughout that first triangle and cutting a rounded line on the head shape. So really following the head shape, cutting it in a layered form so no weight is being built up in this part of the cut and that's gonna create a ton of texture movement in that crown area. So notice I'm still pushing because our guide is traveling. So we wanna travel the guide towards that new section. Now we'll do the same thing on the left hand side. All right, so now we finished up the crown area. Now we're just gonna move into that fringe. I wanna keep this pretty basic and what I'm gonna do is I'm gonna over direct it over top of what I just cut to really just get a little bit of extra length towards the front. But you're gonna notice in the dry cut, I'm gonna do a ton of techniques to create a really cool fringe with this cut. So I just wanted to pause the video for one second and let you guys know that this video is sponsored by the new FSC Pro and Elite Series Scissors. You heard it right, super excited. We launched this brand just recently and it's now available shipping worldwide. We got the Precision Pro. We've got the Dual 20 Pro. We've also got a full Elite Series as well. You can check it out on the website freesaloneducation.com. You guys are gonna see all the tricks I've done. So the Precision Cut that I've done so far, I did with the Precision Pro five inch. That's my favorite scissor in that lineup and then in my favorite length and then also the Dual 20 Pro, I'm about to get into that. When we texturize and we really make this haircut come to life.  So if you're looking to upgrade your scissor collection, then check out these scissors, check out the Elite Series. My goal with this scissor line guys was to make it affordable for every hairdresser out there. So I think that you're gonna like the prices of it. You can actually get this two scissor set for $349. It's insane, right? So go check it out, freesaloneducation.com. Let's get back to the haircut. You guys are gonna love what we do here. All right, so just to prep the hair for the dry cut, I go in to a little blow drying and now hit it with the air wrap just to get the hair set and ready for this style. We're gonna use the Dual 20 Pro. This is my favorite new little gadget tool. It's a dual texture and blending scissor. So if you flip it one way, it's gonna texturize. You flip it the other way, it's gonna blend. Right now I'm doing a blending technique. You can see the blade is down, the teeth are up. That gives me that blend effect. And even when I wanna work this fringe, you can see I'm going through there and I'm moving that blade really quick. That allows me to actually cut the hair off but gives me the most soft line ever. So that's the biggest difference between using a scissor like this and using the dual texture. So you're gonna get 20% removal of the hair weight. So if you just keep moving that nice and fast, you will actually get a cut through. But it's a very safe way to go about cutting into a fringe and not cutting too much hair. Little more blending on top, got that blade down. And now I'm gonna go in and do some point cutting with it. So you can do point cutting. You can see how easy it goes in, cuts the hair and slides out. And now we're gonna do texturizing.  Again, this is much easier to do with this scissor as opposed to a regular scissor. And now I use my Velcro clip and I go in and soften the nape. This is a great technique for going in.  Now I go back in with the Precision Pro and just detail the outer perimeter line. And then I let that down. You can see how much softer that looks. More texturizing, just lifting up the hair, slowly letting it drop out, texturizing as I go. That creates that airy feel to the haircut. This works great on curly hair, straight hair, any hair texture as you go through it. More texturizing, more pinching. It's just really fun scissor to work with, guys. All right, and so now I'll take the Precision Pro. I wanted to add a little bit more precision lines in it. So one of my favorite things to do is when you have a really textured haircut is to have some of that precise lines in there. So you can really see the work that went into it. It's not just a shattered haircut. So now I use the Airwrap with no attachment on it to just blow dry it. Then the Airwrap to really bring out that texture. You can see how it wraps itself around the barrel and gives it that just modern feel. I love the shagginess of this haircut. Hope you guys like it. Let me know in the comments below which scissor is your favorite. Do you love this haircut? Are you subscribed to the channel? Let me know all of these things in the comments below. I appreciate all of you guys. Thank you so much. Make sure you go to shopfse.com or freeslawandeducation.com for these tools and more. We also have an elite series of scissors as well. You'll see on future videos. So thank you guys. I'll see you soon.


Go ahead Meredith. Overall how would you evaluate yourself tonight? Pretty good. Would like to finish a little stronger but just kind of try and take as many positives out as I can and try and work on the the negatives I thought throughout the outing to get better next time. You mentioned fatiguing a little bit. Is that just a matter of building yourself up not being used to going out there against haters? Yeah I mean I was a little fatigued and kind of lost the zone a little bit and when you're pitching to best lineup and baseball 2-0-3-0 you're going to get hit around a little bit. So just got to be in the zone a little more build better counts and execute some better pitches in those bad counts. It seems like you were trying to throw your fastball in a lot to right-handed batters. Did you feel good about the way you were able to execute that? Was that part of the plan? Yeah I've been going four steamers-cutters in. I was pretty happy with it this time. Last two outings ago it got me in some bad counts also but really just kind of thinking middle and kind of being aggressive is my thought process. Middle up. You mentioned the positives. What positives did you take away? I mean I struck out six and three innings so there's all some really good hitters in there. So obviously my stuff plays when I'm in control and kind of around the zone. I just had a lot of non-competitive pitches at last inning that kind of got me in a bind and I couldn't get myself out. Eric Boland you have the next question go right ahead. Hey Jordan I know it probably is not going to impact you at any point this year but a couple of your relievers that were just in there talking about it. I'm just curious as a pitcher what do you think of the man on second to start extra inning? I think it's horrible. You think a lot of teams that miss the playoffs by a game or they just barely make it by a game. I don't know I mean that's just kind of making whoever can bunt and put the ball in the air wins a game sometimes so I don't think that's really telling the best team is going to win a game. So I think it's not great but what do I say? Anybody else with a question for Mani Marley go ahead. Jordan how much are you looking forward to seeing I mean obviously your own hitters give you a lot of feedback but how much are you looking forward to these exhibition games in order to get ready? Yeah these feel pretty game like to me just because I'm I don't know that's how I'm wired once the guys in there I want to beat them so it's like they're my friends but also I don't want them to be able to talk junk to me. So I'm excited to face some guys from other teams and just get this season going with this team. I think I think we're gonna be really good and I'm excited to get it going. Thank you Jordan.


let's go shampoo linguistic grapefruit cream oh there we go hi everyone this is dr. Wallace today i'm going to ask you what is your favorite word my favorite word is joyful progress marmalade i like that word marmalade i like how it sounds well no i know what my favorite word is but it's weird it's you it's nuggets all right yeah i can't explain why that's just my favorite word pizza spatula is a nice word the color aqua i like the the word i like the word static i don't know why it just like sounds cool my favorite word is gonna have to be yes because good things tend to follow that word no i'd say my favorite word is no i would have to say my favorite word is like patience mainly because like patience is so useful in everyday situations and the more you use it like the more you can build off of it and there's really no limit to it like if that makes sense vein vein but the a i n instead of actual vein i just like the word my favorite word is swag because i'm swagger um y'all because like i can't stop saying it when i'm like with people i just say hey what's up my favorite word is breath because i can use it a lot times uh serendipity serendipitous it's like our meeting today serendipity means um the joy of happenstance things just happening without planning my favorite word is like because i use it a lot and um it's easy to join sentences with like my favorite word definitely is gonna be possible because i feel like uh everything is possible in my life i never see a obstacle in my life i feel like uh everything is can make it and i can make it happen too like temple honestly because it's kind of just like when you think of a temple you think of like a pretty calm place it's not very hectic i really like the word surprise i feel like good things tend to follow like surprise like a surprise party or something i think you guys are gonna hate this word i like the word moist um for a lot of reasons i think love because i think uh it just relates to like happiness joy community building be it with your family you're at work be it with your partner i think like love is just a beautiful word a language altogether that i think i don't think so any word can replace that word you know thing that looks good for me i always choose the word perfect i don't know why i thought i'll change my vocabulary but i couldn't but i ended up inspiring other people use to use the same word yes that was a perfect interview i didn't say this was perfect


Hello and welcome to the session. In this session we are going to discuss the following question and the question says that Samuel got a new phone plan. The following graph shows the cost for several minutes. First, write the point 1R where R is unit rate. Second, what does the point 5, 7.5 represent on the graph? Third, what does other pair 5, 7.5 mean with respect to the point 1R? Let us proceed to the solution. In this question we have given the graph showing the cost of the call for several minutes. On x-axis we are having minutes and on y-axis we are having cost and sense. As we can see the graph is of a straight line passing through the origin that is 00 also ratio of each ordered pair is given by 1.5 upon 1 which is equal to 7.5 upon 5 and is equal to 9 upon 6 and we observe that each ratio is equal to 1.5 which is constant as the graph is of a straight line passing through origin and ratio of each of the ordered pair is constant. So we can say it is a proportional relationship also unit rate will be given by y upon x that is 1.5 by 1 which is equal to 1.5. So here R is the unit rate and cost of call per minute will be 1.5 cents. So we have 1R as 1, 1.5. So we can say the point 1R where R is the unit rate is given by 1, 1.5. Next we shall find out what does the point 5, 7.5 represent on the graph. In ordered pair 5, 7.5 the x coordinate is 5 and the y coordinate is 7.5. Here we see that the x coordinate is given by 5 and the y coordinate is 7.5. It means for a 5 minute call the cost is 7.5 cents. Next we shall see what does ordered pair 5, 7.5 mean with respect to the point 1R. Now we know that the unit rate is 1.5. It means the cost per minute is given by 1.5 cents which means with increase in 1 minute the cost increases by 1.5 cents. Now let us consider the ordered pairs 1R that is 1, 1.5 and 5, 7.5. Here we see change in y by change in x will be equal to change in y means 7.5 minus 1.5 and change in x will be 5 minus 1 which is equal to 6.4. It shows that when we move 4 units horizontally from point 1 to point 5 then we move 6 units vertically from point 1.5 to 7.5. It means when we increase the time by 4 minutes the cost is increased by 6 cents. This completes our session. Hope you enjoyed this session.


So, we will try to get how many of you have done anything beyond class 12 biology anyone you have done something bb 101 yes you remember anything of bb 101 ok good. So, we will try to recap even if you have not we will try to sort of get familiar with the lingbo as it were of biology. Some common terms we will be using again and again some of which are familiar some of which may not be we will try to get an idea of what are the scales and what are the numbers that we are going to talk about how large is a cell how large is a DNA. Just so we have an idea of what sort of things at what scales we are trying to model stuff ok. It is good to have a physical intuition about the scales of things that we are trying to model. So, that you know what sort of modeling methods are going to be appropriate. So, that is what we will do for the first couple of lectures and then on third lecture onwards hopefully we will start a little bit of actual formal modeling alright. So, let us start. So, before I start I always show this slide how many if you read x k c d ok. So, this is a fields arranged by purity where there are sociologists, psychologists, biologists, chemists and physicists and the psychologist says that sociology is applied psychology, a biologist says that psychology is applied biology and a chemist says that biology is nothing but applied chemistry. And physicists as most of us are look down on all of this and say that well everything is after all applied physics.  So, they do not come into the picture at all right. So, this is in some sense very how do I say it old school way of thinking where you think how fundamental is my subject in some sense you know mathematicians deal with the most purest, most abstract stuff out there, faces we like to think that you know we are dealing with the fundamental forces, the fundamental building blocks of nature and so on and so forth. So, as you go down as you go down on the scale we think that you know you are getting more and more into non-impure stuff maybe you look down a little bit on psychologists and sociologists and so on. But another way of sort of thinking about this is that as you go down the scale from faces down to chemists and down to biologists and so on. What you are doing is that you are dealing with more and more complicated systems. Mathematics in some sense is one of the easiest things to study because you can define exactly the problem that you want it can have no relevance at all to any real world thing, but still you can define it and you can study it and so on. Similarly in physics you might have heard of the spherical cow joke we like to make as many approximations as we can in order to do what mathematics we can right. It may not ultimately map exactly on to the problem that we are studying, but we do not let that stop us right we do as much approximations as we think is right. But as you go into biology and as you go into psychology and so on these are extremely complicated subjects which depend very crucially on the interconnectedness of various parts. You cannot often study one part of the system in isolation and say that let me try to understand this let me try to forget about everything else. That strategy which often works in physics or in mathematics often does not work in biology. We have to look at the whole system you have to look at how different parts influence different other parts and then try to gain a holistic understanding. Does that mean we do not make approximations of course not we do otherwise as physicists will be out of business, but we try to keep in mind that what answers we are getting may not often correspond to what experimentalists are measuring. So these are really really complicated things and of course psychology sociology where you study you know societies as a whole those are immeasurably more complex. We really have no handle on how to mathematically study these things to any degree of accuracy. So the way we like to think of it now the buzz word of course is like interdisciplinary right this whole top this whole course is sort of interdisciplinary and the idea is that we all talk to each other I talk to a chemical engineer I talk to an electrical engineer I talk to a computer scientist I talk to a mathematician and all of us try to come together and try to solve maybe one problem in biology. The things that we learn in physics the things that we learn in computer science the things that we learn in chemistry all of them can we need inputs from all of these fields in order to gain a comprehensive understanding of maybe some problem in biology. So that is the spirit in which we will try to approach this course we will take different things from different places and then try to build models for biological systems. And what makes this course possible although I will not this is going to be mostly a theoretical biology course. So I will mostly be doing theoretical modeling right, but it is very very important to keep in mind that this whole topic of physics of biological systems or mathematical biology whatever you want to call it that is sort of driven by this explosion of data that we have had over the last 20 30 years. When we were in school at least we used to think of biology as somewhat of a descriptive subject you know you had DNA and DNA made proteins and these proteins did stuff and that stuff allowed us to live you had chemical reactions going on and so on it was all very descriptive. But over the last 20 30 years brilliant scientists have made immeasurable progress in devising new experimental techniques that have yielded a wealth of quantitative data. When I say that a gene is activated how much is it activated can I measure it. If I say this much mRNA is produced this much mRNA produces this much proteins all of these questions can now be answered quantitatively. So they can do very high quality measurements which gives us very accurate data numbers basically and these gigabytes and gigabytes of data are being generated by these very high quality experiments. So once you have this sort of big data this sort of wealth of data what you requires of what you when you have this data what you require is sort of quantitative modeling. You can no longer say that well I understand DNA produces proteins and that is it. But can you can you build a model that will predict how much protein is going to be produced at this moment of time or any future instance of time. So what you want to do is you want to build quantitative models and you should use this quantitative models in order to make quantitative predictions right that is the whole point quantitative predictions ok. And then depending on how good your predictions are you go back and you sort of rebuild your model and so on it is an iterative process until you get an explanation of the biological phenomenon that you are happy with at a quantitative level. So that is so again I will try to speak about the experiments as and when I can unfortunately I am not an expert on many of these experiments or on any of these experiments rather to be fair. So I will give I will be able to give only broad hand waving explanations. But still if you are interested you should go back and look up some of these very fascinating experiments as and when we come across them in the course. It is these experiments that sort of make this whole field possible ok. So before I progress here are some books. So one of the problems in teaching physics of biological systems course is that there is no textbook as such. So that is something that you will have to deal with I teach I take stuff from some of these books various stuff from various books various other stuff from papers which I will refer to as and when I use them. But if there is one book that you need to study respective of whether you do this course or not. If you are interested in sort of understanding a little bit about how modeling in biology works how this sort of quantitative modeling works. I would urge you to read this first book over there Rob Phillips physical biology of the cell. It is an extremely thick book over this whole course maybe we will cover 40 percent 50 percent of the topics that is covered in Rob Phillips. The rest I will not even attempt to touch. But it is a very nice book it deals with many many different systems how what sort of models are there in the literature how you should think about these biological systems with the plethora of examples. Next these two the Phil Nelson books are also very nice they are not as comprehensive as the Rob Phillips book. But at least the systems that they talk about it is very nice for a physicist or for a very basically a mathematical biologist to read about how to go about building models of biological systems or living systems. I will again refer to some of the both of these books from time to time. The next book is I will not refer to at all Bill Bialek biophysics searching for principles. But I would urge if any of you are interested in the topic to read. So, it is sort of so one of the things that makes teaching biophysics a little difficult is that you know there is it is not a very well comprehensive linear theory you do not go not study topic A and then topic B and topic C and topic D. If five people were to teach you they would teach you five completely different things that is because we do not really have a very fundamental understanding of what makes life tick. We understand bits and pieces we understand how genes work how DNA produces proteins and so on. But is there any overarching principle that is not yet clear and that is something that Bialek's book he argues for his own pet theories of course, but it is provocative to read the book if you are interested the searching for principles as to how should I think about biology in a comprehensive way how what sort of approach should I take to thinking about biophysical problems. These two books I will maybe when I do a little bit about molecular motors I will refer to the next two mathematical biology by Murray is will come completely to a send of the course when I talk about patent formation and this John Howard's book is a very nice book on motor proteins which I will deal with again I think I will come to motor proteins post mid-sem ok. So, there is not going to be any one book it is going to be a combination of different topics from different books, but still if you are looking for any one book I would urge you to read Rock Phillips ok alright. So, that is the idea I will of course, upload these notes. So, there is no need to write down all of this you can just check on more alright. So, here is the lecture plan I just want to show you this. So, that you are not under any illusions as to what we will or will not study. Biology is like I said extremely complicated you can approach biophysics in different ways what I will do is I will take the physical approach what is called physical biology in that I look at how physics forces or physics concepts can be used to study biological problems. So, I will talk about energy I will talk about entropy I will talk about forces and so on. This is not the only way that you can look at biology you can for example, look at it from an information theoretic point of view. So, you can look at things like bioinformatics you can talk about informational entropy and so on which is not something I will cover in the course at all or you could look at systems level biology which is again something not I will that I will not cover too much in the course. So, I will only do I will limit myself to a subset of how to approach biophysical problems which is a physical biology approach and even then I will only look at a subset of topics which I hope will be illustrative. So, if you look at this list may be many of your favorite topics will not be on the list that is partially my shortcomings I will teach what I know also I will teach what I find interesting personally and you will have to suffer through it. So, I will start the first couple of lectures as I said is going to be introduction then I will talk about diffusion problems how diffusion is in important in biology I will talk a little bit about experiments such as this FRAP which is a fluorescence recovery experiments I will talk about the cell signalling process from there on I will move to fluid flows which is a very important because the environment the biological environment the environment in which a cell lives is very different from the environment in which we live. So, one needs to understand low Reynolds number of worlds in order to understand how biological stuff moves. So, that is what I will do for the next couple of lectures and then I will go to equilibrium statistical mechanics and its applications and there are various applications. So, I will pick up a few examples where you can use the concepts energy entropy free energy and so on and see what that predicts of biological systems. Then I will move on to polymer models. So, DNA is for example, is a polymer proteins are polymers and so on. So, look a little bit about how polymer physics how to describe polymers from a mathematical point of view I will talk a little bit about protein folding and little bit about chromatin packing that will sort of take care until the mid-sem hopefully post mid-sem will look at crowding. So, pre mid-sem it will be sort of ideal biology in that will make various approximations post mid-sem I will try to relax those approximations. So, one of them is the dilution approximation. So, when I talk about ideal gases and so on you know the ideal gas law is valid p v equal to n r t is valid for dilute gases right when things do not see each other. Similarly, for a lot of these pre mid-sem stuff I will talk about things in the dilute limit where proteins do not see each other by different constituents not see each other. So, one way to relax that or one way that is wrong is that biological systems are extremely crowded and I will talk about what are the implications of that crowding on diffusion on binding basically on things if you have done pre mid-sem. I will talk a little bit about non-equilibrium systems and how to deal with non-equilibrium systems in a biological context both for cytoskeletal and again if you are not familiar with these terms hopefully this class and the next and so on you will become familiar, but you please stop me and ask if you. So, molecular motors and cytoskeleton are examples of non-equilibrium systems and I will try to show you how to sort of write down the equations that govern these systems and how to make some predictions for them. And finally, I will end with pattern formation how patterns form in biology which are basically examples of reaction diffusion systems and then I will apply that to or in very model way I will try to apply that to the relevant systems such as Drosophila embryo and so on. So, that is the basic plan it is a very limited it is again a very very limited subset of what plausibly could be covered in a biophysics course. Generally what happens is that after I finish this I have two three lectures depending on what is the holiday schedule for this semester free. So, in those two three lectures you are free to suggest if you want me to cover any special topic I will try my best to do that alright. Also before I proceed let us just get a little bit of logistics out of the way. So, generally I have two quizzes so, quiz one and two I will have assignments which I will upload on Moodle there will be a mid-sem. Because this is an elective course and I assume that all of you are here or whoever will stay will continue because you are interested in biophysics there is also a project component there is also a project component and finally, there is the ensign. So, let us say 10 marks for projects 10 marks for assignments 15 into 2 for the mid-sem so, that is what 30, 40, 50 does that work 30, 40, 50, 6, 40, 6 to 100 yes ok. So, that is going to be the basic breaker breaker we will have two quizzes I will upload assignments. So, if that is roughly the number then each of you can do an individual project So, you can pick up any topic that you are interested in since I assume you are interested in biophysics as a topic hopefully there are some topics in biophysics that interest you. So, you pick up a topic that you would want to do. So, a project can be either a reading it can be a reading project where you read a couple of papers on the topic, but ideally I would also like you to do some calculations either from the papers or if you want to extend any small extension.  So, together and then towards the end of the semester we will find a slot where you can present whatever you have done throughout the semester. So, that is why I want to sort of fix the topics a little early so, that you get some time to read some time to code or some time to do some calculations. So, after the first couple of weeks are over please come and tell me what topic you would like to do your project on. If you have a broad idea of the topic, but you are not sure of where you should start from what papers you should start from you are welcome to drop into my office. I am happy to help you at least find out some introductory papers that you can start reading off with. But at least in two three weeks you should I should have a complete list of who is doing what project and then towards the end of the semester you can present whatever you have done clear. The assignments of course I leave up to your own integrity try to do them on your own I have no way of ensuring that, but I hope you know because again because this is an elective course and hopefully you are doing it because you are interested please try to solve the assignments on your own or at least understand if you are copying. So, introduction right. So, let us start with the basic sort of building block of biology which is DNA right. And what is DNA made up of? DNA is made up of these four nucleotides which are over here, adenine, thymine, cytosine and guanine, adenine, thymine, guanine and cytosine A, T, G and C. These make up the four alphabets that make up a genetic code. So, I have these four nucleotides and one picture of DNA is that it is nothing, but repeating string of these alphabets A, T, G and C. So, it is a very simple language in that sense there are only four alphabets and all the information about just scares me sometimes too. All the information that is contained in a genetic code is contained in this alphabets of A, T, G and C. These are arranged as you know in a double helix the famous DNA double helix and this double helix sort of encodes all not all this double helix sort of encodes the information that makes us what we are that makes us alive that makes each individual species what they are. Just to get a sense of the scales each. So, these are called base pairs. So, these are called base pairs. So, when you have a double helix like this A, G, T, T, G, C and so on and adenine always pairs to a thymine on the complementary base. A guanine always pairs to a cytosine. So, like that. So, you have these two strands and you always have these complementary base pairs on the two strands. So, each base pair the size of each base pair is typically around 0.33 nanometers or what it says here is 0.34 nanometers. So, one third of a nanometer roughly. So, if you took three of these base pairs like this together this thing would maybe make about 1 nanometer. So, that is the scale that we are starting off from and the width of this thing over here is around 2 nanometers. How big would the DNA or let us say let us talk about a particular DNA? Let us talk about the human DNA. How big would the human DNA be if I stretched it out any idea? So, if I took this if I took the DNA that is present in our cells which is inside. So, here is my cell inside my cell is my nucleus inside my nucleus is all my DNA right. If I took this DNA and I took it out and I stretched it out how long would that be? Any idea? Anyone? Extremely big. There is no way that answer can be wrong right. So, correct answer, but so, extremely big.  Thousands of kilometers, thousands kilometers. Anyone else? Anyone else wants to take a wild guess as to what is extremely big? How? Just to get a sense. So, we know that each base is one third of a nanometer right. So, here in this DNA are my base pairs where each base is one third of a nanometer. How big would a cell typically be? Or order of magnitude micrometers. So, this would be of the order of tens of micrometers maybe right. So, 10 to the power of minus 6 meters and therefore, the nucleus is of course, proportionately smaller. So, however long that object is however long that extremely big object is you ultimately have to pack it into something which is of the order of microns. So, that is of course, a challenge. That does not mean that that is wrong of course, the DNA is extremely big. The question is how big is extremely big? Anybody else? Meters. So, this is of the order of kilometers, this is of the order of meters. I think meters is roughly correct. I have at least for human DNA with some 3 meters or something. So, you take the 3 meter long chain which is made up of these 0.33 nanometer building blocks, base pairs and then you package it into the chrome package it into the nucleus. So, as you know over the 90s and so on starting from the 80s and then in the 90s with the human genome project. One of the central challenges of this sort of quantitative approach to biology was to determine the sequence of the sequence of the human genome or the sequence of any other animal genome or any other living species genome. So, one of the central challenges was to sequence the genome which is to say that the idea was that all the information all the genetic information that I am passing on through reproduction that is contained in this sequence in that is in your DNA. So, can I understand what this sequence, can I know what this sequence is? A, G, T, T, G and so on, so on for millions and millions of base pairs. Can I know this complete sequence? So, that was the challenge of the human genome project which is now which was successfully done. So, it was the sort of marvel of science and a marvel of technology that we managed to sort of read out exactly what is the sequence of a the genome of a species including a human species. So, any idea how if you were given with a sort of string. So, basically my I can think of my DNA as a string right this string has these alphabets A, T, G, C and whatever millions and millions and billions of these alphabets in a linear chain. So, if that is all the information that I need in order to understand why a cell behaves as it does I want to I want to first read off this whole sequence of alphabets. How would you go about doing that? Any guesses or any sort of random ideas cut it ok. So, I cut it then do that to multiple DNA strands ok. So, I take multiple DNA strands I cut them that is that is right. That is the first step to actually reading the sequence of a genome is to actually cleave the genome into various very small tiny bits. And then people have found out fancy ways how we can reconstruct them back together. So, if this is strand 1 and this is 2 and this is 3 how do you know where 2 comes and 3 comes and so on. So, that is part 1 which is you break up the genome. So, you break up the break up the sequence break up the sequence what then how do I know which one is A and which one is T and which one is G and so on ok. So, you make a complementary strand is that what you are saying. So, ok. So, that is that is a nice idea I do not know if people have tried it out. The question is so, let us say let me design the perfect complementary strand right T, A, G, C, C right. This would go and stick to this and then you would say that well since I know the sequence of this complementary strand I can tell what is the sequence of the original strand maybe. I just do not know how sensitive it would be given that. So, ok. So, one thing to know is that how big are these strands that they typically cut into they are not very big they are not 5. You typically as far as I know I do not know exactly you can look it up this is about 500 bases. So, in each position you can have 4 letters 4 alphabets. So, you have 4 to the power of 500 possible strands that you need to generate. Secondly the question is how specific would you be. So, if I were to make this one a wrong thing right and everything else correct this would still go and stick together probably right. So, I am not the question is whether you could quantify the stickiness. You can look it up I am not I am not an expert on this and there are various ways actually, but this is an interesting way. So, you design a complementary strand and then try to see whether you can map this original strand to the complementary strand. Ok. So, then you can take this as assignment 1. On Friday you can give me a one page write up as to how to sequence the genome. You can people do different things. So, you can look up what things people do you can choose your favorite method and you can tell me how they do it. So, that I also get to know basically. So, this is your assignment for Friday. One page A4 no need to basically write the central ideas to how people sequence the genomes. I know one way which I can tell you I do not think that is the way people do commercially, but that is something that some of my collaborators used to do. So, I can tell you what they do is that they take a pore they take a very very small nano pore basically or it could be a protein pore. So, this is of the order of nanometers and you thread and you thread a DNA strand through this pore and there are electric. So, there is let us say some current passing from this side to that side and you read off what current passes. So, when so, each base each of these molecules A T G and C are of different sizes right. So, each of them are of different sizes. So, when each of them when a T passes through a let us say A and G's if I remember correctly are the big ones these are called the purines. These are the ones that contain two of these and the T's and the C's are the smaller ones they are called the pyrimidines I hope I am right. So, each of them are of different sizes. So, you can see that A and G are big T and C are small. So, when each of them let us say when a big thing blocks this pore the amount of current that passes through is actually smaller than if a small thing blocks this pore. So, if you have a small thing that blocks this pore the current that passes through this from here to here is larger than if a bigger thing were to block this pore. So, you first send a known strand and you see so, if I send a strand of T's for example, you see what is the current reading is similarly do it with G's and C's and A's and then you take the strand that you actually want to sequence and you measure the current as a function of base pair or as a function of time. So, what you will see is that you will get 4 levels of the current maybe like this. So, you can say that well this level corresponds to maybe G, this level corresponds to a C, this level corresponds to maybe I have drawn 5 levels, this level corresponds to an A and this level corresponds to a T. So, by reading of the current that is passing through the pore you can get a read out of the sequence of this DNA strand that you are passing through the pore. This is called nanopore sequencing this is called nanopore sequencing I am not I do not think this is the method that most commercial sequences now use they probably use some sort of electrophoresis, but I am not sure. So, anyway so, you can find out what are what are the actual sequencing techniques that people are nowadays using it is become quite cheap to actually sequence genomes. So, it has become very fast and very reliable. So, you can find out and tell me on Friday how this sequencing the genome goes.