I'd like to welcome everybody here to our data capture briefing today. We are very pleased to have representatives of so many places here today. And indeed, it's a good idea perhaps before we proceed any further to do some introductions. We have a lot of Ann's staff here, people who are speaking, such as myself, I'm Margaret Henty. I come from the Canberra office of Ann's. But we also, people who are speaking and also those who are associated with data capture briefing, project proposals and so on, the Ann's contact people here at Monash. So if I can just get people to stand up Sally, Sally good enough from the Canberra office. Myself, as I've said, Margaret Henty, Monica from the Canberra office who's up the back. Nick Nicholas will be talking to us, Nick. He's hiding outside. He's shy. Shabin up in the back row. Andrew Trelaw in the front. Steve Bennett, who I whom I can't see. Jeff Christensen in the third row there. Stephanie Kethers, who has been asking you to sign our video release forms. Angela Lange over on the side there. And Susan Leeper, who is again an Ann's contact person. And if you want to know why people are listed there in that particular order, I think it is totally random. We have here representatives of La Trobe University. Can you identify yourselves? Oh, in the front row. Monash University, Anthony Wright, oh, quite a number of people. RMIT, Wright. The Synctitron up the back. The University of Melbourne. Oh, nice group of people. And people from Versi. And the Versi people I understand are here because they are actually associated with data capture projects. Is that right? It is. Good. Excellent. Now, what we're going to be looking at today, and you will all have a program in the folders which you've been given. Up till the break, we're going to be taking a look at what Ann's is about. Looking at some institutional case studies, which I suspect you will all find very interesting because this is where you will get an opportunity to find out what other people are doing and see whether there are any things around what they're doing that you might like to tap into. We're going to hear about the Australian Research Data Commons and its various relations. We're going to have a break and then we'll work through the program as it's set out there. A break for morning tea, a break for lunch, a break in the afternoon. We've designed the last session of the day so that it should allow plenty of time for questions. So to some extent you may like to keep your questions till then, as I'm fairly sure that along the way some of your questions are going to be answered. However, that is not to stop your eyes asking questions as we go through, but just to let you know that there will be, we hope, adequate time for questions. On the subject of housekeeping, Stephanie Kettis has been handing out video permission forms and if anybody has one which they haven't given back to Stephanie if they could do so, these forms are really quite important. As you can see, the proceedings are being videoed and we do want to make these videos available for sharing later on. They will not be put up on YouTube. You'll be pleased to know. So I think that's all in the housekeeping. Yes, Andrew? Mobile phones. Yes, please. I was at a function once at the Sydney Opera House where they got up and made an announcement that all the staff at the Sydney Opera House had been given special permission to throw people's phones if they rang into the Sydney Harbour. We don't have Sydney Harbour here, but I'm sure we could make a hole in a window and throw them out. I would like to talk a little bit about the ANS partners. How many people here are members already of the ANS partners Google group? A number. Okay. ANS has set up this particular Google group to allow for communications between those people who are working on ANS funded projects. The idea here is to give you a space where you can perhaps ask questions, provide solutions, discuss issues that you might be having. And I will be probably tomorrow for those of you who aren't already members of ANS partners you will probably receive an invitation from me to join up the Google group. In association with the Google group we have our ANS community bulletin board which you will find at community.ans.org.au and this is where we are encouraging people to put up documents which they may wish to share. The bulletin board is already proving to be quite active and this and indeed access to the videos from today will be provided via the bulletin board. There is a website listed there ans.org.au slash partners which will tell you a bit more about the partners and the way that it operates. But we would certainly encourage anybody associated with a data capture project or indeed any other kind of an ANS funded project to join up to this and to take advantage of the opportunity which it provides to communicate with your peers. Now I'm just going to talk a little bit about ANS. I'm presuming that most of the people here do know quite a lot about ANS already. Although perhaps as a source of funding rather than as a source of bringing goodness to the world which is the way we see ourselves. As it says there ANS is an initiative the Australian government being conducted as part of a national collaborative research infrastructure strategy which most of you all know as ENCRS and the Super Science Initiative through EAF the Education Infrastructure Fund. ANS is a collaboration. There are three partners within this collaboration. Monash University who is the lead partner the Australian National University and CSRO. We operate at their offices at Monash. There is another office at ANU where the CSRO people in fact join the ANU people. And ANS now has representatives in I think every other capital city with the exception of Darwin. So ANS is indeed spreading its tentacles quite far and wide and we have here in fact today the ANS representative from Hobart which is nice. Well we do get to see each other quite often although perhaps not often enough. The part of the initiative for ANS or the sorts of things that ANS wanting to do is represented in this statement from Venturis Australia which came out in August two years ago. We should aim to create an open scientific culture where as much information as possible is moved out of people's heads and labs onto the network and into tools which can help us structure and filter the information. I think within ANS we don't actually see ourselves as being limited to scientific information however that particular statement is very apt in terms of where we do see ourselves progressing. But what we are trying to do is to encourage people to share data, to make their data more broadly available. And for anybody who watched the Gruen Transfer last night I was quite intrigued when they talked about data in an advertising context and I thought hmm I'm not too sure about this one. I suspect that the reasons for the kinds of things we are doing are becoming quite apparent to people. The intensity, the amount of research data is increasing and I'm sure you're being data capture project people you are all very much aware of this because you are collecting data from instruments not just one bit at a time. The problems that people are working on are increasing in scale. We are trying to solve problems around massive issues such as climate water and so on. Part of INCRS investment is trying to enable people to work on some of these huge problems through the platforms of collaboration and there are other organisations which have been set up alongside ANS to help with some of this. Computation is the responsibility of NCI, the National Computational Initiative which is located at the Australian National University. In terms of collaboration there are two groups there receiving funding, the Australian Research Collaboration Yes, what's the S for? Service of course, thank you. And AAF which is dealing with identity issues when people are signing in and out of things. ANS is looking after the data side of things and ANS of course works with institutions and indeed an association with all of these other bodies which have been set up. Early last year the government the government came up with powering ideas, an innovation agenda for the 21st century. The government is very keen, very supportive of the kinds of things we are doing which in fact is precisely why we are doing them. This was released on budget night 2009 in response to the review of the national innovation system and it included recommendations regarding funding of research and research infrastructure. This provided a very significant injection of funding into this system that we're talking about and in particular to ANS but others certainly received additional funding as well. Within that budget we got a $1.1 billion boost for critical areas of scientific endeavour and this slide outlines some of the ways in which that money is being spent and some of the particular discipline areas to which it is going. In particular there's $97 million being put into data storage and collaboration infrastructure and additional $48 million into the Australian data commons which is being implemented by ANS and which Andrew will be talking about very soon and other funds given to other organisations. The development of the Australian research data commons is the particular focus of ANS and as I said Andrew will be talking about that so I don't think I will talk about that in any in any further detail and we are certainly keen to assist with the construction of all of those infrastructure with the development of those infrastructure things which are underway. I'm not going to talk about the research data commons Andrew can talk about that but what does ANS provide well it doesn't just talk about the Australian research data commons it talks about all of those things that go with it. We provide online services, we provide expertise and information, we provide resourcing and most of you here today are recipients of our resourcing initiatives and we also provide policy advocacy which is something I could talk about at length but I won't we're certainly in touch with organisations such as the Australian Research Council the National Health and Medical Research Council with the department itself to look at the frameworks within which all of us are operating and to ensure that there is policy backing for what is being done. This provides you with an opportunity, an opportunity to enable your researchers to publish their data to publicise your research and together to build the data commons so that people do have access to Australia's research data and with that I'd like to pass over to representatives of the organisations here today who will indeed talk to us a little bit about their projects.


What's going on Navigation Nation? Welcome to the web class where we're gonna be talking about the new up-and-coming small exchange We've got special guest with us today Tom Sosnoff From Tasty Trade Tom. How you doing today? I'm good. How are you? I'm great. Good day great Well to start with to give our listeners some context about your background I want to go through a couple of different phases of where you've been where you've been throughout your career And and get to the point and then we're I want to end talking about the small exchange and really dive into that to get some Some questions answered on that and and tell us more about it So let's start with when you first started you were on the CBOE.  You are running around Chicago with with your buddies as a floor trader making a ton of money Just with as retail traders today I think we all kind of look at that with a little bit of fascination so tell us about your time as a floor trader Well, it was actually almost to I did it for almost two decades and so I think 19 years total was my time on the floor So it was you know It was kind of a I think I was in the right place at the right time. Just lucky, you know Sometimes you just you're in the you're born in the right era and and the the trading floor was Like this last crazy frontier of just Really brutal Capitalism, I mean it was it was it was almost as pure as form And and I was just a I grew up in New York and went to state school in New York and when I got a school I got a job at a brokerage firm in New York and and Met some guys out there and they said if you move to Chicago and I didn't even know honestly I was one of those New Yorkers that thought, you know The whole country ended at the end of the East River just outside of Manhattan And so I'd never been west of Pennsylvania so so I figured that Chicago was I didn't know where Chicago was and flew out to Chicago Met this guy who was gonna give me a tour of the trading floor This is I walked on the trading floor. It was the old trading floor before they had really built up any of It's kind of like an old annex to the border trade and I walked on the trading floor and I hung out for probably I don't know about half hour to an hour and everybody was yelling and screaming and I didn't have any idea What's going on, but I thought it was the coolest place in the world It looked like to me like a like it looked like the end of a race but it went on for hours like a like a like a harness race or a horse race or something like that and Everybody's just yelling and screaming and papers were flying and I was like, oh my god This is the coolest place I've ever seen so I took the job which wasn't really a job was just working for a couple of guys and and Move to Chicago and and and decided to learn the business. I lasted for you know, I lasted for 20 years and And I built a nice, you know, we built a nice prop business in a 20-year period Not everybody made it, but you know, I was one of the few and that was good You know, it was a good life And was and was there a was there a specific time or point in your floor trading career when you kind of when it clicked And you really felt like okay. I've made it Yeah, a couple of years The first year was really hard like I didn't know if I was gonna make it because I didn't I Didn't really understand the terminology and I wasn't as strong I was quick, but I wasn't as strong in math as other people were So and everything was in fractions and that was completely new to me You know adding and subtracting all these because remember options are strategic so everything comes in combinations of spreads and combinations of orders and so there was a lot of fractions and and these guys were You know all these other guys were really fast I mean they were doing it like they were like they were like mini computers way before there were computers and And my brain didn't work the same way with fractions. So it took me a little while, but by the second year I was just as fast as everybody so and So I so I made it, you know I don't know why but sometimes some people make it some people don't I just was lucky and I turned the corner probably in the Second year and and then it was pretty much, you know, I Had a nice run for 20 years and and you guys are young you're in your 20s to begin with and you run around you I've heard you tell a story about how you know you guys have you have seats at the Chicago bowls right behind the bench During the games you guys are making bets with Michael Jordan during the game Tell us tell us a little Michael Jordan story. Well, okay, this this is so So so, you know Michael is drafted By the Bulls and it's 19. It's it's just after the Olympics 1984 he's a rookie and they The Bulls were like one of the worst teams, I don't know if you remember how old are you I'm 40 40 Okay, yeah, this is this way before your time. So the Bulls are the one the Chicago stadiums are really cool place the old Chicago stadium, but the Bulls are a horrible horrible team and there's about three or four thousand people at night at the game and they They draft Michael and all of a sudden, you know, the Bulls get a little got a little You know, they got a little extra Jump in their step and they went around they couldn't sell these new floor seats So they came to all that they came to the trading offices They not just started cold calling and knocking on doors to see who would buy seats at seventy five dollars a seat when you think about it These are, you know floor seats at seventy five dollars, which for 1984 1984 was was kind of a lot of money And but we were the only crazy people making, you know crazy money back in 1984 So they offered us the two center. Oh, sorry fork four seats on center court And we were like, well, we should try them for a year and then one of my buddies kicks me and goes Hey, the Bulls stink. Let's just take the seats next to Michael Jordan So we took the four seats next to Michael instead of the four seats at center court And then we just became, you know, he was only a kid We were 25 and and he was 20, you know, 22 or something whatever the age was, you know at the time Or we were 20. I was let's see the 1984 I was I was 27 and and he was 22 or 23 and and So we all just became friends and you know, we'd go out after the gameplay poker like at my house and you know Or his at one of the guys houses and and it was just a stupid relationship that lasted for like, you know 15 years and it was kind of fun, but then he started making too much money and then and The first night we ever played poker is a good story the first night we played poker with Michael Jordan He brought a lot of money with them and he he lost it to all these traders that were there And he was so embarrassed and he ran out of money, but we made his credit good But he wouldn't take any credit from us and then and he went out and found some money came back and then the game lasted for the next 15 years and He got his money back a couple times over What a great story Isn't kind of crazy, you know, the funniest thing about Michael was that he's the only guy in the whole world That you could because we were all married and stuff You know, he's the only guy the whole world that you could have come over in the middle of week on a Wednesday or Thursday night And play cards until three or four in the morning and smoke cigars in your house and your wife would be like Can I get you guys something? They've been thrown out They're really fun, you know, it's fun I have I've lost contact with him like 15 years ago, you know, he got too big for us Gotcha forgot about the small guys, huh?  Yeah, okay, so fast forward you leave the floor Yeah, and because you see the writing on the wall, right? You see that everything's getting computerized the the life of as a floor trader is not gonna be the same and and you and a partner Scott Sheridan correct me if I'm wrong decide to start a retail trading platform. Yes, this is true and Back when I was trading back then and I was using trade station and interactive brokers and those were those were names I mean you say that name you think of a trading platform Yeah, you guys have started something called think or swim.  And what was that all about? Oh, well actually we we ran it We had a pretty successful business and we had saved We ran a prop business, you know We had a bunch of traders and they worked for us and we traded ourselves So we were pretty successful, but we at that point in 1999 I just thought like I gotta I want to do something different, you know it's time to do something different and so We took all the money we had made over the past, you know, Scott and I have been partners for like 10 15 years to that point 15 years almost and we we decided we just rolled the dice and and I came up with the name just fooling around at home, you know kind of talking to my dog and And I ran the name by Scott and he's like he didn't really care about names He's like he's like we can always change the name later, you know, because it's kind of a stupid name we'll always change it later and People like the name and we ended up, you know Wasn't our original idea to build just a brooch from there was a lot more to it But it kind of consumed us and so, you know, we got consumed in the project and we just went crazy over it for the next You know 10 years and it became our lives and That's really it. I mean it wasn't you know, we didn't do anything. We didn't do anything like Special except hire really smart people and and work really hard.  So now you're one of us. Yeah Right and so, you know, I so we have a community of of traders and I just presented a question a few weeks ago And I and I kind of knew where it was going to go as far as what kind of answers I was going to get And I was correct and almost every single the the question I asked was what's your number one challenge as a trader And the the answers that I got were all about You know discipline emotion Trading too big fear of missing out getting into early getting out too late those kind of things As a if you can think back, I mean as a new trader What kind of what kind of suggestions do you have to help people with help newer traders? Get over those emotions or psychological issues when it comes to trading well, you know There's a I can I can I can kind of read you, you know, I kind of I'm not read you but I kind of list off, you know a series of Mechanics and steps that we that that I follow or or different tenants to kind of foundationally how I think but you know as a new trader or even Anybody that's trying to you know, turn the corner go to the next level or even get better at what they do And and by the way, let's be fair steve I think that you know over the last couple of years like I've been doing this now since 1981 But I think over the last I'm going to say two and a half or three years, you know I I've gotten to be a much better trader than I was just three or four years ago And compared to what I was 10 or 20 years ago. It's not even close like what you know What we are today just the way we think and you know how our brains process everything that we do but the key to success is And and and a lot of them are just you know, I can easily say things like just you know Like discipline that but I'm not going to the key to success is is Being able to make Decisions and being confident in your decisions and I believe that that carries over to For whatever you do So like if you're a trader great and if you're a trader who also has a business great If you're a trader that also works in another business That's great too. If you're an investor that likes trading or your investor that works somewhere else that works If you own your own business that works People that can make decisions Decision making is a skill and trading is nothing nothing, but you know decision making and so so I'm I focus most of my life now trying to engage people I call it, you know Teaching them to finance and the and the concept of teaching people to finance is is about teaching people to make decisions And I don't really care the more decisions you make The easier it becomes to make decisions and the more successful you ultimately are so when people talk about trading I mean really what is it? There's there's there's definitely a set of mechanics You can't just make decisions, you know You can't blindly make decisions But but it's the process of making as many decisions as you can That really changes the way you think about taking risk and about assessing risk about taking risk about About everything from taking losses to taking profits and everything else decisions that come Quickly to you that don't have this huge emotional hold back or or or pull back You know those are the most successful people he he or or or her who can make the quickest decisions Um is usually the most successful That's great. That's great information and and kind of along those lines and we had you on last time We had you on was in december of 2017 Wow, and And at that time I I asked you about bitcoin because it was all it was all the rage right bitcoin was over $16,000 at that point And I just asked you what what your thoughts were as a currency as you know where it was going that kind of thing and your comments What your comment was You kind of looked at it from an expected move standpoint and you said well it could be it could go as high as 30 It could go, you know under 5 and you said My bet is it's going under 5 which obviously that was a that was a great call But what what was your main for something like that? What was your main assessment of why you would make that call? Well, first of all, I'm a freak about map. So So and over the years, you know And you you give me an efficient marketplace like bitcoin and I would argue that bitcoin was in a bubble But it was also efficient and the implied volatility of bitcoin at the time You know was probably when it was 16 or 18,000 was probably approaching 150 to 200 percent So the math just suggested the expected range. That's all that was But for me, you know anytime I I've witnessed a lot of bubbles in my career And I felt like that was just a massive bubble, you know, there's a lot of ways There's no such thing as Mean reversion with respect to price. So just because something goes up doesn't mean it's going to go down There is such a thing as mean reversion with respect to volatility So so I knew that the the range of bitcoin was going to contract dramatically but So so mean reversion applies as a statistical as a statistical or math model applies to volatility But it doesn't apply to price But with respect to price, you know, there's there's kind of sometimes there's price extreme that that Seems like it's a derivative of common sense and in the case of bitcoin, you know The easiest measurement for me is Is kind of when my mom who was 85 years old at the time, you know calls me up and says, how come we're not long bitcoin? I'm pretty sure I'm pretty sure You know that my short position. I was short actually.   It was not it was to acquire It was to grow our Customer base so we acquired invest tools, which was an investor education company and They had a lot of customers and And they were a public company. So it was like we we We became a public company through the acquisition of investors And you're the you're the ceo of this now public company Yeah, and so you're the one doing the quarterly conference calls giving the earnings announcements giving the earnings projections all that stuff that public companies do and To kind of drive the point home that nobody knows what's going to happen Especially from a price standpoint You knew the numbers, right? You knew the numbers before the public did but yet Even in that situation, you still can't predict what price is going to do after after you announce the earnings Absolutely impossible. In fact, so so i've been part of the public company environment when we were public our symbol was swim swim And then when we were brought out by TD Ameritrade TD Ameritrade was public and I was a party to you know, I was It was a member of their, you know Senior member so of their management committee So so I also knew the non-public, you know the non-public numbers and we would sit around and and You know just like, you know talk to each other and say, you know, do you think this is good or bad? And and you know somebody would say well, I think these numbers are great We beat by two cents and I'm like that doesn't mean, you know That doesn't mean a damn thing like I don't think people that that That run public companies have any idea what their stock is going to do When they you know when they put the numbers out. I just don't think it's possible I I I was I was easily the only trader On the senior, you know on the senior management committee at both Well, not and Scott was too at our firm But there were other board members and people that you know had had access to you know Not the material non-public information and I don't think anybody You know would have any clue what the stock would do and we for sure as even as traders Didn't even know how we would have played it if we could have played it You know, I was much more fearful of what the market would do Like when you're pushing out an earnings report in 2000 end of 2008 or early 2009 when the market's in freefall I was much more worried about oh man We have to launch an earnings report tomorrow morning when the you know when the s&p's are down 6% You know, there's no way you're gonna you're you're gonna come out smelling good in that move Yeah, so You know the answer is I don't believe that any public company I don't believe if you had the numbers on any earnings report on any company in the world You'd have any idea what to do. I don't believe if you knew what the fed was gonna do Based on interest rates that would have any you could make money knowing what the fed was about to do If they were making interest rate announcements. I just don't think it's possible I don't think it's easy. So safe to say nobody knows anything That's that's it's it's yes, that is You can just write that on the tombstone Very good. So okay, so then you sell toss to td for a cool 750 mil Yeah, and you Complete your contractual obligations with them. You don't really want to be part of of that going forward And so the next thing you do is you start and this was I think about eight years ago. You start tasty trade Yeah, that's right. And this is to provide Retail traders access to content And what was the thought process initially behind starting tasty trade? well when we sold when we sold um, I think or something to td could because um At that point, you know, I had to stay on for two years and and I did I was supposed to down for three years, but I asked them if they would let me leave and they said yes I td was a good firm and they had good people there, but um, I couldn't work in that environment. It wasn't I had been I never got a paycheck in my life I've been self-employed since I was you know, 21 22 years old and so I didn't want to live in that environment and So I asked if I could leave and I they said what are you gonna do and I said I'm gonna go start a I'm gonna go start. I'm gonna try to disrupt financial media because I think that we build really cool technology, but I don't think people Have the right content yet and the content that's being disseminated outside of brokerage platforms You know the content that's being disseminated over, you know, cnbc and bloomberg and And Reuters and and all these other places. It's not that they're disingenuous And not that they're trying to do something that that's that they know is wrong It's just they don't really understand what people, you know, what people need to learn They they don't get the math part of it. There's no strategic angle to it So we decided to disrupt try to disrupt financial media and build this crazy company called tasty trade and actually TD Ameritrade was our first investor And we built this company and we were just hey We're hoping we can pull this off and it works and all this kind of stuff and and it was pretty wildly successful and and It it just kept growing and growing and growing until it became the largest, you know, digital financial digital network in the world and In two years ago we launched tasty work. So That that's pretty much, you know, how it worked. It was fun. It's a men amazing run like I I'm so happy we did it because I I would never trade this experience for anything. It's been incredible And so you you're providing this content not only as entertaining, but it's also very Math and research driven. You build an internal think tank research team within tasty trade What what would you say is maybe the top three pieces of research that has really taken your trading to another level?  And and so I was I it was just absolutely shocking to me, you know, how big we used to trade and how How small and active we trade right now. It's completely Different. So I would say number one. It's the trade small trade often It's it's a thousand times better way to approach, you know, this business We couldn't do that until we were effective until we were able to effectively build our own rate schedule And until we were able to build the technology that supports that but that's number one number two would be managing trades early I I don't think that I ever I think we did it by default, but we didn't do it because we knew what we were doing and After we don't research for now for eight years and this is using, you know, I mean we do 50 to 60 hours of research every single day with, you know, eight data scientists and all this other kind of stuff And the whole concept of managing early is It makes you it makes everything we do so mechanical So so there's the trade small trade often there's managing early and then I think the final pieces is we've we've become almost addicted to slash dedicated to Implied volatility rank which is which is using Which is building a measure of how we rank implied volatility on a scale of one to a hundred So that we can easily make decisions based on IV rank as opposed to making decisions based on something subjective And it's really helped us, you know, narrow It's it's it's narrowed us into using just, you know, quantitative stuff to figure out trades and nothing else So so those would be the three it's it's the dependence on implied volatility rank and to simplify the context around implied volatility It's trade small trade often And it's managing trades early and those would be the, you know, three things that that really stand out after eight years of doing research excellent and then At this point You are you're still kind of you've got it's called a marketing arrangement with td think or swim But you you don't have control over the costs and so You guys disrupt the industry again, and you start tasty works And And one of the biggest, you know disruptors was was the commission schedule Which which you initiated with zero commissions to close trades Right. Why did you why did you do zero to close trades? Why not just do a cheaper in and out? Um just because we wanted to do something different. So we decided to do cheaper We we made our rates in way cheaper than anybody else and we included out in that because we wanted to really um We felt like if we did something incredibly aggressive That it would change Not only the rest of the industry, but it would change the way people It would allow people to trade small trade often because you close a trade costs nothing and so um tasty works was Revolutionary in the sense that we could you know change the the way commissions were structured in the industry But you know it was also really close. We built technology It's very underappreciated technology because at this point it's it's it's all high frequency which nobody else has And it's it's all built to let you you know roll trades Roll trades up and down adjust trades and do everything that no other platform can do and it's all in front of you on one screen. So I there's We we we got the technology that we wanted and we got the fee structure that we wanted And we were able to embed the content that we wanted and so we feel like we give people You know, we don't charge for anything by the way, you know, nothing everything is free except the the commission So so we feel like we gave a really good offer. You know, it was a really good Um just a really good offer to people to to come on over and it's worked. It's it's fastest growing brokerage firm Um in the u.s. Fastest growing kind of traditional brokerage firm in the u.s Is there any new features or anything new coming as as it relates to the brokerage platform that that's coming that you can talk about Yeah, so well, we'll be launching portfolio margin in a couple weeks. We will be adding cash bit cash, uh digital currencies to the platform We're about to go into test mode for that. We are going to be launching something called Um a test drive platform which will allow people to actually test out our soft Software for a day or so and if they if they make a little money, you know I'm trading stocks on it. We'll let them keep it. Just it's kind of a fun little Um marketing thing we're going to be doing We're going to be launching something called option tracker option chains, which is an option tracker Which allows you to track individual positions Um from start to finish regardless of the number of adjustments and and anything like that. That's huge huge It's huge. We've been building it for two years. So I know how huge it is It's just it's a massive amount of work. We're deploying a brand new mobile a simplified mobile app And then we have a couple of We have a couple of kind of really cool little treats that we I can't talk about yet, but that are also coming that are that are That are things that don't currently exist in the industry. So we're playing around with some with some really neat little Gadgets and stuff and and you know, we also do Live shows around the country all year long. We do we're doing 27 live events this year all over the country. So we're pretty aggressive I was in Omaha for the he said she said last year. That was a really cool event Yeah, we're gonna do it this year. This year. It's fun. We're in like that was the small show We've ever done that show in Omaha last year And we wanted to try a city with 600,000 people This year we're back to a little bit bigger shows, but yeah, we loved it. That was fun Cool. All right, so let's get into what we're really here today. Thanks. Thanks for that background I think that really gives our listeners some good context of where you've been where you're going And I want to talk about this small exchange and go ahead and take over my screen whenever you get a chance but the first question I have is What what is the role of an exchange? What what do they do? You know, they create the products. They clear the orders They they are kind of the market makers at this point. Tell us what is an exchange? What what's the role? The the role of an exchange exchange is is a facilitator Yeah, don't think of the role. Um Exchanges actually don't clear the trades Um, the trades are cleared by, you know, some clearing group. It could be like occ It could be could be cme, but they're but they're not exchanges Um, and the exchanges don't fill the orders There's a market making groups all over that actually are members of the exchange that are counter party firms liquidity providers hedge funds They make markets exchanges just provide the technology and the oversight to allow for the meeting of a customer and the facilitator Or two customers to to um so they can interact and and make a trade Basically, it's kind of something so instead of person a trading with anonymous person b And that trade living, you know anonymously on on in some ledger or that trade living somewhere else This is just centrally cleared stuff That's usually that could be fungible if the products are in different places But essentially this exchange role is as a facilitator Person a meets person b and person a doesn't really care Person b is because the exchange is guaranteeing the trade and that's all it is And they and they create the product So they create the symbols and that sort of thing they create the products Or they can license the product from somebody else or they create the products and they create everything They create the symbols. They you know, they market the products everything. There's there's not that many exchanges so it's very hard to build an exchange and So why are you guys building an exchange? well one of my so i'm getting a little older and I I one of the things i've always wanted to do Is you know, I feel like we've we've really done some cool things on the broker side We've done some amazing things on the media side but one of my lifelong dreams was to disrupt the futures exchange business because I there's only two major futures exchange the cme and the ic And I feel like they're always been they've always been Designed to take care of the professional hedger or the institutional customer And they've never been supportive of the individual investor and it drives me crazy And their products are too big their fees are too high And their technology is too old and they don't have any standardization And they don't support what individual investor needs And so about 15 years ago. I started to design an exchange And I marketed it to the existing exchanges Suggest because I was busy and I had other things to do and everybody loved it But they were like, you know what? We just don't want to upset our model And so I didn't I pulled it back. I tabled it for a while and I tabled it for 10 years and then And essentially now, you know, I brought it back out, but it's very different today than I was thinking about it 15 years ago And we have the capital to do it and we have the technology to do it today And we have the desire to do it So we started a separate company Called the small exchange. We're building products for Individual self-directed investors The average Futures product right now is about a hundred thousand dollars in notional size Some are 150 like bonds and s&p's things like that and some are like, you know, 50 like Like crude oil but the average is out to over a hundred thousand dollars for one contract That's way too big when the average customer count is let's say 40 or 50 thousand dollars for traders and so We need to have products that are significantly smaller But when you have products that are significantly smaller, they need to have High enough implied volatility so they can move around a little bit So we developed products for the small exchange that are standardized Meaning they don't they don't like it's not fractions on one and Decimals on another and each ticks the same and each expiration is the same And everything's cash settled and it's so much easier to understand And the products are much smaller like the equivalent of a hundred shares of stock So an individual investor that's always been scared of futures because it's a hundred thousand dollar product Can look at these products go. It's the same as a hundred shares of you know the cues that kind of thing size-wise I can handle that with slightly higher volatility, but but But you know perfect size for me And there's a lot of other really cool features Of the small exchange and it's just it's it's got a product design That that we've been thinking about for a long time. It's got products that we build ourselves We're not using anybody else's products and most most importantly The small exchange is has a matching engine, which is the engine that drives all exchanges That we have built ourselves We've spent the last almost two years building a matching engine And so we didn't want to license the existing ones that right now most people that try to build exchange License an existing matching engine So if you go to like for example, brazil or mexico or somewhere or even in the us If somebody wants to start an exchange they just license a matching engine from an existing exchange The problem with that is you can't do anything special technology-wise We built something really special that's going to do stuff that no other exchange technology has ever done before And it's going to allow customers to trade futures the way they trade options the way they trade stocks It's going to be very very different from any futures product currently and and all that said It's a customer exchange. So we're even letting customers buy subscriptions for ridiculously low price That will eventually turn into seats when this exchange goes live And so they'll have a lifetime of half price exchange fees We're being extremely generous in giving away half of our future revenue just to get enough people on board So the exchange is successful when it launches And you know back in the day or when you had a seat on an exchange I mean first of all they were Being sold for hundreds of thousands of dollars and then later millions of dollars. I mean Should this be looked at as as somewhere you're buying a seat and you're going to be able to sell it later for a lot more No, actually we should not look at it that way because what you're buying is actually a subscription That will turn into a seat But it's not designed to be an investment because there's there's going to be a lot more of these What it's designed to do is save you a lot of money over time if you use these products Can it go up in value? Yes. Are we selling it as if it's an investment and it can go up in value? The answer is no so theoretically it can go up in value and it's going to be open for It's going to be open in an auction marketplace a cup once or twice a year But we cannot sell it that way and we won't because because our goal is not to create seats that are I'm sorry to create subscriptions that will be ultimately worth a lot of money Our goal is to create subscriptions that will allow people to be engaged in the product Like we're not interested in building, you know an equity marketplace where people are just investing so they can make money It'd be cool to do but the crowdfunding rules don't appreciate don't allow that and the And the regulatory rules do not allow for that either But they do allow for like, you know subscriptions cooperatives where you can reduce On the back end you can reduce fees And that's what we're doing and so we're playing it, you know Totally kosher. We're making sure that we we're making sure that we give Individual investors an opportunity, you know, imagine if you had bought a cosco membership that was good for life Before cosco ever launched and they said for a hundred dollars.  You had a free membership to use cosco That's worth something You know, um, that's essentially what this is. It's not something that that's that's going to be worth A lot of money, but it's something that can save you, you know, a lot of money Like my son, I'll give you an example. Steve my son when he was in college. He bought amazon prime membership for life for 30 dollars And he can't trade he can't sell it He's not he got it under some student deal where he cannot sell it He can't trade anybody. He owns it for life for 30 dollars like that's worth something to him And that's how we view this except you will be able to sell this. That's the only difference. I see And and so starting out an exchange like this I mean, where is the where's the initial liquidity going to come from to to start These new products getting volume open interest to trade between all the traders. Well, it's it's it's a hell of a challenge That's for sure. That's why that's why we're going to have between 25 and 35 000 subscribers before we even launched the exchange um, but the liquidity side has to come from from very strong partners and We recently we haven't announced it yet, but we will Hopefully within the next month announce a list of our partners Who are going to be providing liquidity to this exchange and it's going to be at least I hope it's going to be a very impressive list Of all the major players now we have an advantage over other people trying to start an exchange in that We already have a massive amount of liquidity that goes through our platform And so we already have a lot of liquidity providers that that work very closely with us and that currently partner with us So we're not like a brand new exchange coming in and saying hey, you know what please make market for us We're going to be we're promised. We're going to send you business. We already send them a ton of business and when you say So you got your liquidity providers When you say other major players, is that other brokerage firms that will be routing orders to you? Well that too, but that's not what I'm talking about. Okay. I'm saying I'm saying liquidity providers are firms that Are the firms that that currently make markets for stocks and options The exchanges all the exchanges out there today, you know, they're just facilitators So behind the exchanges are all these firms that make markets the biggest being Citadel the second biggest being burr 2 and sasca hana and and You know peak 6 and The just dash and imc and there's just there's a million firms out there Um, we route orders to all those firms And they facilitate the other side of our customer of our customer business Those firms will be also supporting the small exchange because we have partnerships already in place with with With many of them and and you know, they want You know, this is how they make money and this is so we think they're going to make pretty pretty good markets The small exchange will be offering futures options too So it's just a it's a futures exchange for launch and then it's futures options after launch, you know Maybe six months or eight months after launch. We'll start launching futures options So this exchange is going to be, you know, we're going to compete with the cme. We're going to compete with the ic They're both 70 75 billion dollar companies and here we are this little pissant, you know startup exchange and um You know, we're we're going to scare the children And and so what what's kind of the range of products that are going to be offered? Is it going to be something that gives you exposure in the Financial markets and the commodities and what was kind of a range of the products that will be offered? So um, initially we're not discussing all the products that we're going to offer but initially The range will be um, and again, we've designed all our own products, but the range of products will be You know, there'll be equity products. There'll be energy products. There'll be currency products, there'll be Bond products, debt products, treasury products, I mean There will be precious metal products. There'll be volatility products. There'll be, you know, ultimately there'll be Hopefully there'll be digital currency. There'll be cannabis They'll be, you know We're hoping in the end Initially, there'll be five or six and then followed by another five Followed relatively quickly by another five or six I see And then and you think would you say about eight or nine months afterwards options should be Coming on board on those different Products on the most liquid products. Yes. So we have to see, you know, which products that we launch Have the liquidity necessary to support an option marketplace, but um, assuming that we um Assuming that our guests is, you know, that that assuming that our hunches are right about, you know The amount of business that we can drive and and how good our products are We're hoping that there's there's a, you know, relatively vibrant marketplace for the options Within six or eight months at least on the top, you know, four or five names Cool And so for our listeners who want to get involved and everybody better be getting involved I mean, there's no reason not to it's a hundred dollar subscription fee and Is correct me? Keep uh, correct me if i'm wrong. You hit you need to be you need to have a tasty works account to get involved No, no, no. No, no if you open a tasty works account We give you the small exchange membership for free. It's our giveaway this year We pick up a hundred dollars based on what it is. Got it. You don't if you'd like to do it yourself You can just go right to the small exchange.com and sign up. There's a you have to You have to fill out a small form that you have to sign a document Which just says that this is what you want to do and then we're not even collecting any money now I think in march We'll be sending out emails to collect the money This just reserves your spot and make sure that you you know because we're going to cut this off in a We're within a few months We're going to cut this off because we're going to be oversubscribed And then when more people find out about this and so and we're not touching the money until the exchange is approved by the CFTC So it's going to be held separately. So for any reason we don't get approved or anything like that We will return all the money. We're not using it until after we're approved The exchange is fully funded. Just so you know, so we put up money. We've raised money and At this point the exchange is fully funded and we will do we're guessing we'll do a series a round on the exchange sometime later this year or early next year, but for the time being all the All the startup capital was provided by Tasty trade and peak six peak six is a very large prop firm and investment firm in in Chicago and We'll be announcing other investors very soon We have no individual investors, but we do have individual subscribers But at least but at least initially to actually trade the products that you're creating on the exchange It'll be done through Tasty works platform Tasty works and there'll be other brokers signed up. We just don't know who yet So far so far only a few brokers have already committed But we think eventually we'll get it everybody But if we don't then customers will just if they want to trade the product they'll come over to Tasty works But we think the other firms will you know We'll come over once they once they see, you know, how cool the products are and you know And how people are trading it and and listen if somebody leaves one firm to come to us to open an account They'll eventually come over You know, right Okay, I mean I don't have any other questions. I'm looking at the questions from our members I know about the about the fees You're looking at this At cutting exchange fees basically approximately in half talk a little bit more about that So we were trying to decide, you know, what's the best benefit we can give to you know to The only way the exchange makes money is on exchange fees for the product So we said If you sign up for this and as you're an early subscriber before we even you know launch Before you can get approval. We're going to give you lifetime of no membership fees No one half off exchange fees and reduced Reduced data fees and we're doing data fees very different than anybody else has ever done them before So it's something that we're trying to make this Cheaper than trading, you know anything else And so it's a lifetime of half price fees. It's a lifetime of discounted data fees And it's a lifetime of no subscription or seek fees or anything like that. It's not going to stay this way once we get approval Um, once we get exchange approval, you know, the All the all these rules for the next group in the the price is going to go up to 500 the There'll be annual fees things like that. So we have a very different, you know, this is just kind of the initial offer There's already We're approaching. I believe we're Getting close to 8,000 people are already signed up for this small exchange and it's only been announced a month ago. So You know, we're cutting it off at 25,000 And and well these with these reduced exchange fees. Does that apply just to the futures or options on futures as well? Once those are available everything everything. Yeah, sure And and this is in a this is in an approval process. You guys have applied to open this exchange And what was kind of the timeline that you're hoping or thinking this is going to come to fruition? Well, the government shutdown didn't help us last month Because these are all government cftc is a government governmental agency, but um, uh, we are expecting approval in the third quarter Third quarter. Yep q3 And our our our technology is built and our company. It's a separate company And like I said, there's there's two Fully announced partners now tasty trade and peak six But there'll be other announcements coming over the course of the next Couple of weeks and months and you'll see who else is involved and it's it's you know what steve? This is this is I would be it wouldn't be fair if I said this is anything other than a long shot because that's what it is, but you know, we've done a lot of long shots and We're we're we're very strong technologists and we're very strong disruptors in the financial space And the small exchange is a really cool Concept it is just it's simple. It's standardized and it's going to be fun That's great. And another question coming in If you have a let's let's just say you've got a tasty works account outside of the us Or you've got a brokerage account outside of the us as long as you Are approved on that broker in other countries, whatever products are available They'll be able to trade so if you're in australia and you've got a tasty works You're going to be able to trade these well tasty works is tasty works. Australia should be launching in a matter of days So australia is already covered If you're if you're an international customer listening to this listening to this webinar today You can buy this membership even if you're in canada. We the the small exchange It's individuals only and you can only buy one. It's not like you can't buy five or ten or anything like that it's for individuals only you can only buy one and And you can buy from anywhere anywhere in the world anywhere and ultimately, you know tasty works takes customers from 65 different countries But we are not yet approved in canada, but we are going through the process Australia we are we we I can say we're approved because we're 95 there We're we're just waiting for the final margin, but we will be approved and then else we're in the world It just depends what country but for most countries. We're pretty good right now What about singapore? We have one person in here in singapore sure singapore china Yes, yes, we cannot take customers from hong kong, but singapore absolutely china absolutely Everywhere in europe absolutely, you know, there's there's a couple the next country. We're proving hopefully within two weeks is colombia, but Countries that have an issue with you know money laundering just you know And it's not us. It's just the regulators. There are some countries out there that are difficult, but Like we have viewers from 168 countries, but we only can take 65 countries right now for accounts, but what about india india I need to check on india india is an issue Sometimes we have customers I think india is a yes, but I need to just you just need to send a letter to support for us I'm pretty sure india is a yes okay Well tom you have been super gracious with your time as always we really appreciate you being here Thank you so much if anybody has any doubts You know, there's there's really nothing to lose to sign up for the small exchange I would encourage everybody to do so. I've already done it and we really look forward to Hearing more about this tom. Thanks for your time. I'm available for you anytime. That was a great u.s. Great questions I appreciate it. Thanks tom. Take care everybody. Thanks everyone


Yeah, exactly. There is, in fact, I hesitated regarding the question mark because at the beginning I was not sure at all. I mean, there are many aspects of toposis probably that could be right now very interesting for what we are doing. But then there are some other aspects. For now, I have no idea of what they could bring to our activity at Huawei and essentially in wireless networks. So, in fact, this talk is essentially a way of trying to identify the topics covered by these wireless networks. The topics in which toposis can bring some value. And, in fact, at the beginning I had very few of them and then by thinking, by looking at the literature many other ones came to my mind and I think that it's just the beginning and probably in the near future we will be able to identify even some other topics very interesting and that could bring some solutions to some of our problems. Okay, so let's start with the problematics that we have in wireless networks and that could be impacted by toposis. Okay, so first what is wireless network? What is the wireless communication in general? In fact, it is something that uses a lot of tools that are very diverse. For example, information theory, so Shannon theory essentially, signal processing because in these wireless communication networks, in fact, we have to transmit bits and the problems are to transmit bits. These bits have to be converted into signals and then, of course, at the receiver these signals have to be processed in a way that we can, for example, minimize the irreprobability or some other measure. We have also optimization. For example, I mean, there are many points where optimization can be useful and I can give you an example. Suppose that you have, in fact, in wireless networks, in fact, the wireless network is a cellular network and the problem is that each cell interferes with the neighboring cells, right? And the problem, of course, is that if everybody starts to talk loudly, the problem is that you will impact the signal-to-noise ratio of the neighbor that will start to speak even louder, et cetera, et cetera, and so, of course, it will diverge and that's why there are some procedure which I'll call the power control in order to avoid these problems and we have to find the optimal solution of power control so it's an optimization problem that can be centralized or decentralized. We have also problem related to data fusion. For example, if we have sensors, we want to use them in order to localize terminals or in order to do something else. And then we have graphs. So graphs can be used to study the cellular network. It can be used also for channel coding.  And finally, maybe I forgot some of them. Sorry for that. And finally, at least in my list, there is channel modeling, which is very important because this is the basis of the wireless network and in order, for example, to be able to transmit or to receive efficiently the signals that have been transmitted, in fact, we need to know the behavior of the channel and we need to model the channel in some sense. Okay, so you see, there are quite a lot of technical areas impacted by wireless. So now, okay, if I just copy the definition of the Grotendig Topos, what can be the relation between this and this? This is not obvious at all and this was the problem that I was faced with that problem. So which of these items can be impacted by this? In fact, it's not easy at all to answer directly. But just using the definition, we can first try to find some relations between some of these topics. Maybe all of them, you will see. The first one, for example, Daniel has shown in his talk the relation that we can find between information theory and the Topos. For the other ones, there can be some relations and in fact, I will start by finding some relations between these points and sheeps. Okay? Moreover, there is another constraint we have. The problem is that we need to implement algorithms. We need to perform computations. And so, we need to use computational and algorithmic mathematics. And that means that, for example, the sites or the topological spaces that we can use will be quite restricted. They cannot be... I mean, we need to find the ones onto which we can compute some interesting quantities like homologes, etc. And also, we need to find, let's say, some... We need to find also sheeps that give rise to computations that make sense for us. For example, what we can do the best is linear algebra in terms of computation. And that's why we will mainly focus on these restrictions. Okay. So, first of all, I needed to analyze why we could need the sheeps or more deeply the toposes In fact, this property of sheeps is the one which we need the more. That means how to... I mean, from local to global, we have local data how to reconstruct the global picture. And in fact, that's why the computations that we will mainly be faced to will be the computation of cohomology groups, etc., etc. And also, sometimes, to derive some criteria for setting to zero some cohomology groups in order to remove the obstructions. Okay. And so, okay. For example, our local data can be local probabilities. There can be LLRs. So, this is log likelihood ratio. So, it is related to probabilities. For example, for coding, for channel coding, there can be local interferences if we consider a cellular network and we want to go from the local interferences picture to something more global. And so, in this case, we need from there to derive the right sheeps. And so, for the last stage, I have to confess that I haven't too much idea for now. Okay. Let's start with one of these applications, which is, in fact, the channel. It is a basis. The channel is the basis for us. Okay. So, first topic is, I would call it learning the channel. I can tell you why. Suppose that we have, for example, this is a cellular network with here cell, with its base station. So, you see the antennas and mobile terminal that communicated with this base station. I consider in this example, the uplink. And so, we have, let's say that, in general, if we use a simple model, we can say that what we receive on all antennas, so this is why there is this word, MIMO, which means multiple input, multiple output. So, multiple antennas at the transmitted, multiple antennas at the receiver. So, in fact, it is this model, which is the simplest and the most commonly used. So, what we receive on all the antennas is modeled as a vector. Why? And it is some matrix H times what is transmitted by the terminal, which is also a vector, and plus some noise, as you see. This noise component, in fact, can incorporate also interferences coming from the neighboring cells. So, as you can see, here the channel is characterized by this matrix H, all right? The problem is that we have to recover the signals that have been transmitted here, so which are embedded in the vector X. And of course, if we don't know X, we don't know X, and if we don't know H, then we are in trouble. So, what... No, no, no, it can... It's a rectangular matrix in general, yeah. So, the idea is that, of course, we have to know in some sense the H matrix. So, what we do in this kind of network is that we transmit what we call pilot sequences, which is, in fact, a sequence of non-symbols, and onto which we will project the coefficients of this channel matrix so that we can estimate these coefficients, all right? And at the end, so our new problem is that we have just to detect what has been transmitted in X when we know, in some sense, or when we have estimated the total coefficients which are embedded in this matrix H. Okay. So, this is what happens in the multi-user case. So, you see, you have exactly the same kind of expression, but then we have the sum of the two components coming from the two terminals. So, now for 5G, 5G, in fact, is using what is called massive MIMO, which means that in this station, we have a lot of antennas, many, many antennas. It can be 64, 128. Okay. And what is called CSI, so it is channel state information, so it is the knowledge of the channel or the knowledge of the H matrices, lives, in this case, in a high dimensional vector space because of massive MIMO. So, the problem is that in order to estimate all these coefficients, we need to send a lot of pilots, all right? And at the end, the problem is that if we send pilots, then we have less space for sending data. And that's why one possibility could be this, because in fact the CSI lives in a very high dimensional vector space. In fact, what is sure is that for a given cell, let's say that the set of all possible CSI doesn't fill the whole space, but rather lives in a manifold which will be of dimension which can be, if we have many, many antennas, it can be much less than the whole space, the whole original space, all right? And so the idea should be here to go from this high dimensional vector that requires a lot of a lot of pilots and so which will let's say prevent the transmission of high rate data instead of using the whole space, just try to use this manifold of lower dimension, all right? In this case, it will reduce signaling and also reduce the feedback, so the feedback can be necessary, for example if you want to transmit then, for example, in downlink from the base station to the terminals the base station has to know the channel which goes from the base station to the terminals and which is accessible only at the terminals, for example, I mean in some cases, let's say let's be not too general and in this case learning this manifold knowing what could be this manifold could be very valuable for us, okay? Then another problem, I mean another application related to the channel is fingerprinting, so there are some people in our lab who have implemented some fingerprinting approach in order to in order to have the localization of the terminals by using machine learning the idea is to use the CSI so the channel knowledge at the base station in order to determine the localization of the terminal, okay? So, what is the idea? The idea is that in fact by using supervised machine learning we train a neural network it can be extreme learning, deep learning I mean, there are many possibilities in order to learn a given let's say function phi which defines a manifold here as well and this function in fact is a function of the CSI and of the position of the terminal, okay? So, we train a neural network in order to learn this function at least partly and then once this stage is finished we can find the localization as a function of the CSI, all right? The problem is that in this case as well as in the other case what happens is that the manifold we have to learn in general is learned with noise it can be noisy observation we can have small variations of the channel because the problem is that the channel behavior depends also on the obstacles that you can find on the path of the the waves and these obstacles may be fixed or moving like cars or human beings I mean, yeah? So, this is another another, let's say, example where we need to learn some manifold which can be impaired by noise by some cluttering by, you know, some small changes of of the channel, all right? So, and for these two cases there in fact we can propose to use persistent homology to solve at least partly these problems. Okay? Another problem, so what is quite amazing is that this morning I have discussed with Daniel about these belief propagation on graphs and I didn't know that there were some solutions I mean, based on on the computation of homologies and, yeah so that's good news for us. Okay, especially we have this problem of making belief propagation work on graphs essentially in channel coding, okay? So for example in 5G, so in the fifth generation that we are developing right now of Wallace networks cellular networks. In fact the channel codes that have been adopted for 5G I mean, for what has been done right now, but maybe there will not be so much changes in the next releases. There are what are called LDPC codes for in order to encode data bits and polar codes in order to encode control bits, control channels so data is the information that is sent and the control is the bits that are useful in order to make the network work, okay? So, and these two families of codes are quite different LDPC codes in fact are decodable by using a belief propagation of a graph which is called a tannograph and polar codes are decodable using another kind of algorithm which is called serial cancellation and apparently it's not related to belief propagation not yet, let's see so what we know about the decoding of LDPC codes is that the tannograph of LDPC codes on which we have to run belief propagation is loopy we have cycles and so in this case we know that belief propagation doesn't work I mean we cannot make it work well or it's something that is very painful very complex and so okay? oh sorry, yeah sure, sorry for that so you have a graph and the idea is that you have some probabilities on vertices of this graph right? and the idea is that for example you have some constraints some vertices can correspond to some constraints that in fact will change the probability you start with unconstraints probabilities and then you take into account these constraints right? and you have the idea is to to do what is called also message passing on the edges of the graph so that you take into account locally all these constraints right? and at the end the idea is to have a global understanding that means at the end let's say the the optimal thing that you have to do is to let's say to take into account let's say all these constraints and if you have a tree then it's quite easy to do it but if you have cycles then we get some issues and it's very harder to have the optimal solution in this case right? so and but for LDPC codes in fact what we use to decode them is to use belief propagation as if it were a tree because what we know is that when the length of the LDPC codes goes to infinity then the tannograph tends to be a tree but of course we are not using infinite length codes and the idea is that we use belief propagation as if it were a tree but so it's so optimal but we design the code in such a way that the gap to optimality is kept small alright? okay so in this case let's say that we don't really need to run belief propagation on a loopy graph but there is another case which is polar codes so very quickly so polar codes is based on what is called the kernel so suppose that you have the simplest kernel which is the original one is called the Arikans kernel so Arikans is the inventor of polar codes and in fact it works in this way u1 and u2 are two bits alright? and then x2 equals u2 x1 equals u1 plus u2 okay? then x1 and x2 are signed into the given channel that can introduce errors erasures or can add Gaussian noise for example and so in fact x1 and x2 will see exactly the same channel right? and the output of the channel will be y1 and y2 but then in this case what u1 and u2 which are the information bits what these bits will see I mean which channels will they see because of course x1 and x2 will see the same channel but u1 and u2 will not see the same channel and so here is how it works so okay this is matrix form and let's do a capacity analysis of what happens here okay suppose that i of x and y is the mutual information between x and y so it can be an index because it's exactly the same channel we have here so if we consider this vector channel now we have the mutual information is the sum because these two channels are independent and so this is i of x1, x2 and the vector y y1, y2 then I replace in fact what I want to know is what happens if you instead of having x and x1, x2 you have u1, u2 so in fact because u1 is x1 plus sorry because in this case you can recover u1 by doing the addition of x1 and x2 and then once you have recovered u1 you can say that u2 is just can be recovered by the observation y and the knowledge of u1 then you get this kind of equality right and in fact what happens is that this first term is smaller than the symmetric mutual information right which is the capacity in this case of the symmetric channels and this will be larger ok and now polar codes are formed by considering the end time the Kronecker power of this kernel of the Arican kernel ok and in this case what happens is that for a given ratio of the bits that we have the first term will tend to 0 and for the rest it will tend to 1 and the ratio of bits for which the mutual information here tends to 1 will exactly be the capacity of the channel if n goes to infinity alright and this phenomenon is called the polarization the channel polarization and this is why these codes are called polar codes alright but then the problem is that this Arican channel is polarizing but too slowly so too slowly with respect to the length of the polar code so the idea is that we want to find some better polarizing channels of course their size will be bigger than 2 which is the case for the Arican the problem is that if we have a general kernel so a general kernel will be let's say a square matrix of size L which is invertible in the finite field of size 2 ok and ok let's forget it so this and what we know is that channels with better scaling exponent exist if the length is more than 8 and there is another exponent which is the error exponent and these two exponents in fact characterize the way the channel polarizes with respect to the length of the code and for example there are kernels ok for example this kernel of length 16 is known to be the best kernel in length 16 in terms of error exponent ok so now what we have to do is that if we want to find the equations of decoding of a big kernel then in fact for each bit that is decoded it corresponds to a tanner graph and this tanner graph may have cycles this is for example for length 8 and for decoding the third bit corresponding graph as you can see there are cycles and this is a very simple one if you have for example the kernel that I showed you the ebch kernel of length 16 for decoding for example bits in the middle then the tanner graph will be really will be terrible it's something very very hard to decode so and how to decode it is by running belief propagation on this kind of graph on the tanner graph and we cannot do the same as for ldpc codes we cannot change the code because this is not a code this is the kernel that is fixed and so we have to find a way and what I was thinking even before talking with Daniel was that belief propagation is exactly this local to global so there should be some corpology and so and I was happy to learn that it's true so I will be happy to read what you have done on this topic alright now we have identified a couple of problems I have also identified some publication which applies sheeps on the domains on the areas that I have listed at the beginning of my talk and I can show you a couple of examples first one sorry before starting we have in fact as I told you to use topological spaces on which we can compute things like corpology groups and in this case one good choice is to use simplificial complexes so for those who don't know what it is so what is a case simplex let's say so this is the concrete point of view of simplificial complex so this is for example zero simplex is vertex one simplex is an edge a triangle for two simplex tetrahedron for three simplex etc and a simplification complex is a finite set of simplices that satisfy these two properties so ok now in order to apply these to apply this for example to be able to compute corpology on these simplificial complexes in a way that will be efficient for our applications in fact this is a very restricted way of defining pre-sheaf on this on this complex but it is the one which will be useful for us ok so pre-sheaf on the category of vector spaces or it can be modules and so we define of course restriction which we will call this way f of a a ruby and a b are two phases and of course we have consistency of the composition and we have also to define between pre-sheafs or sheafs and so this is it assigns for each phase a linear map which satisfy this equality ok and then we define a quantity which is so b a and this quantity equals 0 if a is not a phase of b otherwise it equals plus or minus 1 depending on the orientation and this quantity will be useful to compute with what follows and essentially the co-boundaries etc. so this is our co-boundary which is defined this way and so this is the former sum over all case simplex and you see this appears in the prediction map and sigma of a sorry ok let me go on so this we have the classical we have the classical relations between the the kernel of decay and the so the cycles of the boundaries and because of that of course we have we have bk which is a subgroup of zk and in this case the homology group the case homology group will be defined in this way as the quotient group ok and in this case we get a sequence of linear maps and and of course this the idea is that in fact the idea of this homology group is that only the elements of zk that are not already consistent are worth mentioning ok and now let's apply it to sampling theory so this example comes from this reference from Michael Robinson and he proposes a shift interpretation of the sampling theorem so in fact he starts with a shift of vector spaces on an abstract simple complex and then he uses a shift morphism between two shifts so f which is a shift on this simple complex and s is in fact what is called a sampling shift and which correspond to the operation of sampling and it is associated to the sampling morphism from f to s and then he defines an ambiguity shift a in which simply the the stock in fact a of small a where small a is a face is just the kernel of this map and is that the shift theoretic sampling theorem that he derives it is that the global sections of f so of identical with the global section of s which means that we can reconstruct in fact we can reconstruct the we can we can reconstruct this just from this if and only if in fact the case homology homology group related to the ambiguity shift is 0 for k equals 0 and k equals 1 so and from there it can recover the classical Shannon Hartley sampling theorem of of band limited functions for example or some other results coming from graph theory or from quantum graph if I remember okay so in fact we can have in signal processing we can by using shifts we can generalize the results that we had at the origin second example network coding and it has been developed in this paper from Greece and Iraq and in fact so very quickly they have shift homologies in which case H0 is equivalent to the information flows that we have in the network and many practical problems may be solved by using these homologies such as the max flow bounds network robustness and some other ones right and the final example persistent homology which I think will be very valuable in our case if we want to learn the channel to denoise what we measure so to denoise it to remove the small variations in order to keep only what is persistent and so it's still simplices complex okay so in this case now we define an elementary key chain in this way and of course we have two different orientations which are here represented by the sign plus or minus and this is a key chain so in the case of persistent homology what is used is essentially coefficients in Z but it can be an abelian group and yeah and then so we have we define these the boundary operator in this way so now of course we are in the reverse direction compared to homology so we go from k to k-1 and the same way as for homology so we have the kth homology group now which is the quotient group of the kernel so the cycles by the boundaries and we have also Betty numbers which are useful in persistent homology which defines being the rank of these homology groups okay so now coming from there what is persistent homology so suppose we have a cloud of points in a space and in no case it will be the measurements that we will do and from there we need to find structures, we need to remove noise we need to remove the small variations the clutters etc and we need to find some structure inside this cloud of points okay so how to do it so what would be theoretically the most acceptable would be to build a church complex in this way so you draw some balls around of given radius around the points and then you form a simplex S if there is at least one common point in each ball of S alright the problem is that computationally it's very hard I mean it's something for which it will be hard to compute the homology groups so instead of this what we do in practice is rather to consider the rips complexes and in this case we will form a simplex if there is at least one common point in the balls which are considered now pair wise okay so for example this one is a two simplex for the rips topology but not for the church one okay so and then we consider a nested sequence of topological spaces x0, x1 etc and this is done just by considering very in our case varying radiuses ready from the smallest one to the biggest one and in this case we need for sorry we obtain for each of them homology groups okay and then in fact what happens is that we want to identify when an homology class is born and when it dies alright and so in this case for example this one is born here and dies here and the birth and death of the homology classes will be very important because with this we can construct barcodes and sorry barcodes and in this case long bars will have a long life and it will mean that long bars will be the persistent features alright? so for example if we use the rips complex so this is our cloud of points you see they are more or less in a torus I mean on the torus or donut and so this is the complexes we obtain when the radius increases okay and for the same thing so these are the so on the x-axis you have it's parametrized by epsilon so this is the homology groups H0, H1 and H2 as you can see this one is persistent yeah and so sorry how long does it have to live? this I don't know exactly but it depends probably on the application because it depends mainly on the application okay and I found something related to the topos of persistence and that means that in fact it's a kind of generalization of the topos of sets in which instead of having just sets we have sets indexed by time so these are given by this barcodes that I have shown to you and it corresponds to time index sets and on these time index sets in fact we can construct a persistence-heightening algebra of intervals, ordered by inclusions and the sheaves of this algebra p encode barcodes alright and sorry a very simple example for example here so I time 0 so these 0 correspond to the time and this one to the dimension of the homology so here for example in this case as you can see h0 of 0 is essentially I mean xy quotient by 0 so it is as morphic to z plus z and h1 of 0 is just 0 because you have only 2 vertices so 2 points then at time 1 we have this and we can compute h0 of 1 which is this and in fact the asomorphic to z and h1 of 1 can be computed and we can see that it is asomorphic to z as well etc. we can compute the homology group of all these guys and so by doing gluing in fact we can glue the point wise homology groups together consistently in order to extract global information of course it is a toy example and the global information is this one for t in the interval 0,1 we have h0 equals to this changing to h0 equals to z when t goes from 1 to 5 and for the first homology group for t equals 1 to 2 we have h1 equals z changing to h1 equals 0 when t is in fact the union of these 2 intervals this is a very simple example of one topos which is related to persistent homology and which may be useful for us and that concludes my talk thank you very much in fact there are algorithms for computing of course maybe they have to be simplified because in general it is very heavy but the idea now for example there are some ideas now of combining persistent homology in fact the topological analysis of data with the neural networks and for now I have just seen one paper which is not very I mean it's not very very easy to understand what they are doing so but this will be the next step because apparently using topological data analysis plus machine learning seems to be very efficient are there questions maybe one do this method give an hint on what you told before that's a small dimensional yes the idea is to try to characterize these many faults in fact thanks to the homology group usually it's not really exactly the homology group that comes from the other spectrum it's not really psychological any more comment it was very interesting the fact that some people revisit the sapling theorem of Shannon my question is that do you see some results on compressed sensing in topos which could be also linked to the fact that maybe you want to from a couple of samples try to find a global representation of your information no I haven't seen it right now but I haven't searched really in a different direction ok if there are no other questions we'll take a 15 minutes break and then we'll have a short discussion like half an hour so come back at 4 and we'll do it from 4 to 4.30


Hello there everyone and welcome. This is Melissa Armo with the Stocks Washington Reviewing UPS. This was the Stocks with Show Play of the Day today in the live trading room. And actually, this had a sell-off into the close. People got out in the morning, or you could have held it down. If you did, it paid. So this was the morning move. Called it in here, got the drop. A lot of people got out in here. If you stayed with it, it took all day to get through and break the low. But it did pay. Almost got down to 96 tried. I don't know why this took all day. I really have no idea why this took all day. But it finally broke. Had a high percentage of conviction. This was lower. We did it. A lot of people got out here in the morning. But for the people that stayed with it, it did pay late, late, late in the day. So again, this was a gap down. I teach how to find stock picks that I know we're going to go up or down, whether to go along or short them. In the case of this, this was a short. So I teach my method, my reigning system, and the golden gap course. The class for May is this weekend, May 2nd and 3rd. And this might have some follow through tomorrow. Actually, if the market wants to fall, I did not call any puts in this, but it may follow through. Because this did sell off late, late, late, late, late, late in the day. It almost ran out of time. Late, it broke around three o'clock in the afternoon. Again, I don't know why it took all day, but it did. So I'll be watching this as a continuation, perhaps tomorrow, even though I prefer to look for something new, but this was a nice gap down today. And again, what is the gap down? This closed here the night before, gap down and open lower here this morning. So this closed at one price, open at another price. I rated it, it rated as a gap down to short. We shorted it, I called the trade aggressive in the room and boof, there it is. So when you know what to do, it's very easy to trade. When you don't know what to do, it's difficult to trade. So I'm very good at reading direction of bias, predicting where stock's gonna go and also the entries and targets. So nice, solid day, good start to the week. We'll continue it into tomorrow. If you're interested in the Golden Gap class, it's this weekend, email me and Melissa at thestockswish.com. Have a great day, everyone.


The review provides an overview of modern methods used to diagnose and treat liver echinococcus, key, including the frequency of occurrence, diagnostic techniques, surgical interventions, and preventive measures. Despite advances in surgical techniques, the risk of postoperative complications and relapse remain high. There is currently no consensus on the best approach to access the cyst, with different approaches being used depending on its location. Additionally, the effectiveness of surgical treatment is limited due to unanswered questions about the nature and extent of surgery while processing, drainage, and prevention of biliary fistuli. This article was authored by Shsh Aminov, D.A. Rakhmanov, Zsh Faizev, and others.


NASA officials say some rare and distinct meteorites found on Earth were actually rocks ejected from Mars after a large impact event. One such rock from the red planet made its way to an Army laboratory recently for a special X-ray look inside. Researchers from the U.S. Army Combat Capabilities Development Command's Army Research Laboratory, the Army's corporate laboratory known as ARL, have powerful tools to look deep inside metal and rock. They can see into objects and provide insightful analysis. Recently, NASA asked them to look inside a Mars meteorite named Black Beauty, a sample about the size of a baseball and weighing about half a pound. What's unique about Black Beauty is that it's one of the oldest Martian meteorites that has been discovered on Earth and it has been proven to have some evidence of water. That's what makes it so special. Experts from the NASA Goddard Space Flight Center in nearby Greenbelt, Maryland proposed a collaborative project to look at Apollo 16 lunar samples. The moon project soon led to the Martian meteorite project. I think there's a lot of benefits that the collaboration between NASA and ARL can provide and in the bigger picture we're also, ARL is now also supporting the national space policy. That's helping to advance the mission of going back to the moon and Mars and so in that regard ARL is helping a much bigger picture of advancing science. Army scanning instruments provide high-resolution images at full three-dimensional volume non-destructively. Army researchers use this equipment to study the relationships between the processing of materials, the microstructure and ultimately relate that to mechanical performance. So with the X-ray CT scan we can non-destructively identify voids and defects such as cracks within the material prior to testing. For instance, 3D printed materials are scanned for defects and researchers use this information to create stronger materials for future soldiers. For NASA, the data is a goldmine previously invisible. Science is really intrinsically collaborative because even just the peer review process that allows us to publish our work requires our peers to understand what we're doing not just from a written page or computer screen. And so collaboration I think starts at birth in science and so working together with new measurement techniques that measure the previously unmeasured in things that we're barely understanding is the best way to go. The NASA team sees this collaboration as beneficial. You know space is a big place and there's a lot of work to be done and we were lucky thanks to partnerships between our engineers together with colleagues who they've met through conferences at the U.S. Army Research Lab. We were able to put together the pieces and develop a partnership to start looking at extraterrestrial materials from the moon and Mars in ways that have pushed the limits of resolution. Seton said the entire ARL team was impressed the first time they saw the Martian meteorites. We met in a conference room and they took it out and we were all able to put on gloves and hold it so that was really neat and it's just amazing that this came from another planet and we can hold it in our hand. Scientists from both organizations plan to jointly publish two journal articles later this year about the research into both lunar and Martian X-ray scans. We are definitely looking into opening up the dynamic of this relationship that's newly forming between NASA and ARL. What we all have in common is just curiosity and interest in what we're doing. We're all very passionate about what we do. I'm passionate about technology and new technology and anything that we can do to improve how we do things, how we look at things. The scientists like Dr. Garvin are really interested in what can we learn from this technology from a science perspective and I think those are the same curiosities that are felt at ARL. I never expected that this would happen. I have scanned a lot of interesting materials over the course of my years here at ARL but I have never scanned something so unique and valuable.